Index
The catalog of every page in this wiki. Updated on every ingest. See the design doc, §9.1 for format conventions.
Sources are listed reverse-chronologically (newest first, by the YYYY-MM-DD date prefix in the filename) — matching wiki/log.md’s convention since 2026-05-12 (GH #3). Entities, Concepts, Syntheses, and Threads are flat-listed alphabetically (case-insensitive), since those filenames aren’t date-prefixed. Once page counts grow, sections may be supplemented with Dataview blocks that auto-include pages by frontmatter type:.
Sources
- 2026-07-01-mcgrath-power-of-strategic-centering — The Power of Strategic Centering (Rita McGrath, Harvard Business Review Jul–Aug 2026; ~4.6k words). Primary source for strategic-centering: deliberately choosing one organizing principle (“What are we really about?”) to guide resource allocation, opportunity selection, and identity when classical anchors (Porter / RBV / Blue Ocean) lose force in a dematerializing economy (~90% of corporate value now intangible; Carlota Perez “turning point”). A center bounds the opportunity set, resolves capital-allocation dilemmas, and enables “permissionless action.” Five centers — mission (Novartis, Shopify) / customer (Amazon, Airbnb) / technology (Fujifilm vs Kodak, Nvidia) / national ecosystem (TSMC, Samsung) / friction erasure (Toss, DBS).
instance-ofstrategy + strategic-centering;supportsthe value-stick reframing. 3 W&W tags. Dangling: Rita McGrath, Carlota Perez. - 2026-07-01-bello-mckinsey-podcast-serial-builder-advantage — The Serial Builder Advantage: Why Repeat Innovators Win (The McKinsey Podcast; Jason Bello interviewed by Roberta Fusaro; 1 Jul 2026; ~25:30, ASR — headless fetch rejected with HTTP 400, recovered via
--headedretry). New McKinsey research on corporate venture building: companies building 3+ ventures simultaneously dramatically outperform one-shot builders; average break-even cost fell $125M (2024) → $77M (2025). Two-flavor AI-in-venture-building taxonomy — “AI as a business” (fully agentic AI-native ventures built to disrupt the core, e.g. a fully-agentic B2B bank) vs. “AI in the background” (co-pilots for ideation/business-cases/prototyping — vibe-coded prototypes built in a day, replacing weeks-long wireframing); synthetic customer personas (useful, but sycophantic by default — supplement, not replacement, for real user research); milestone-tranche funding replacing annual budgets; a fact-based, blame-free culture (“if the facts say our product stinks, so be it — no fingerpointing”) as the enabling condition for fast pivots; disruption itself as the argument for venture building. The wiki’s sharpest quantified case yet for dynamic-capabilities’balancing-digital-portfoliosmicrofoundation.supportsDBS (portfolio + tranche-funding mechanism), Carroll (fact-based culture theory), YC (fact-based pivoting, cross-context parallel). Updated dynamic-capabilities (11→12); McKinsey & Company (11→12 sources). Promoted Roberta Fusaro (2nd McKinsey Podcast mention as active host). Dangling: Jason Bello, Lucia Rahilly. - 2026-06-29-raman-wood-worklab-job-titles-dont-matter-2026 — Job Titles Don’t Matter in 2026 (Here’s What Does) (Microsoft WorkLab podcast; host Molly Wood; guest Aneesh Raman, LinkedIn’s Chief Economic Opportunity Officer; 29 Jun 2026; ~37:47, human-curated captions). Raman’s pro-human, agency-centered thesis at three altitudes: individual — “onlyness”, professional identity as a unique non-linear combination of curiosities/capabilities rather than a job title, “think like an entrepreneur… do more than reasonable with the resources you have”; company — the electricity-bolt-on-without-redesign analogy (installing electricity where the steam engine was yielded no productivity gain) and three shifts (lead by design not command; capability not category; develop people not tasks — Walmart/Microsoft/Citigroup cited); society — “we won’t run out of jobs unless we run out of ideas” (credited to Jensen Huang), labor-market opacity + Global-South electricity-access equity argument (via Microsoft president Brad Smith). Names the 5 C’s — creativity, curiosity, courage, compassion, communication — as trainable durable-skills muscles. 4 W&W cells (
digital-sensing/digital-mindset-crafting,digital-transforming/redesigning-internal-structures,digital-transforming/improving-digital-maturity,strategic-renewal/organizational-culture).supportsSchoening (agency over job titles), Argenti (mindset not skillset), DBS (org redesign worked example), Storoni (efficiency-metric rejection). Updated warner-wager-process-model (5→6), ai-employment-effects (55→56), durable-skills (34→35), micro-productivity-trap (35→36); Microsoft (2→3 sources). No entities promoted. Dangling: Aneesh Raman, Molly Wood, Ryan Roslansky, Kevin Scott, Brad Smith, Conor Grennan, Jensen Huang; LinkedIn (candidate entity, deferred). - 2026-06-25-the-ai-factory-the-rewiring-of-indias-tech-industry — The AI factory: the rewiring of India’s tech industry (Financial Times / FT Film; reported by Krishn Kaushik; 25 Jun 2026; ~20:35, ASR-cleaned). The wiki’s first field-journalism, Global-South vantage on AI’s labor economy. Thesis: India is becoming “the AI factory of the world” — the data-annotation + RLHF + robot-training labor at population scale (“the booming AI industry needs more and more humans in the loop”; “second largest AI workforce in the world”). New frontier: egocentric data for humanoid robots — Karur textile workers wearing GoPros / Meta glasses 6–8h/day so robots learn to fold, sweep, wash (“San Francisco offices don’t train the robot… it’s people sitting in small-town India”; Object Ways). Meanwhile AI is “the toughest challenge India’s IT-services sector has faced” — threatening the ~$330–340bn outsourcing export built on “$1 job done for 20 cents.” The political-economy critique: “AI is fundamentally a marketing term… a way to unlock vast amounts of capital”; “by design concentrates power”; “a supply chain extractive by design”; sovereignty worry (“population as a carrot… is not a pathway to sovereignty”; $25bn investment fled to chip economies in early 2026). Steel-man: rural data-work (NextWealth) reaches first-gen women graduates without leaving their hometowns; Tesco Business Solutions GCC does AI cost-intelligence in Bangalore.
supportsZepto (the optimistic India-AI-economy counterpart), Guilbeault (the AI-hype critique at cognitive-science level). 2 W&W cells (contextual/external-triggers,strategic-renewal/business-model). Updated ai-employment-effects (51→52), responsible-ai (17→18). Candidate concept flagged: human-in-the-loop data-labor / “AI factory” economy (held single-source). Dangling: Krishn Kaushik, Object Ways, NextWealth, Tesco Business Solutions. - 2026-06-25-raboresearch-ai-it-zakelijke-dienstverlening — Hoe AI de sectoren IT en zakelijke dienstverlening verandert (RaboResearch / Rabobank; Mark van Kampen, Reinder Koelewijn, Jesse Groenewegen, Floris Jan Sander; 25 Jun 2026; ~2,400-word web article + 13 figures). Acquired from Zotero (
ai-wiki); figure data read from the live page (Zotero extract was figure-free). The wiki’s first Dutch national / sectoral source on AI labour + productivity, and first to score automation potential against the CBS Nederlandse Beroepsclassificatie (mapping Gmyrek et al. 2025, ILO WP 140 task scores). Headline: 86% of IT tasks and 64% of business-services tasks have (hoog) automatiseringspotentieel — among the most-exposed Dutch sectors. Per-function: IT led by ICT 41%/marketing 10%/R&D 9%; business services by boekhouding 14%/marketing 10%/ICT 9%. Figuur 6 collates ~18 productivity studies (advertising +73% → firm-level +1.4% / Atlassian 96% no ROI / knowledge work −2 u/incident) — a single-frame task-vs-firm gap. NL ahead of the EU average, below the EU leader in every function (CBS). Per-occupation: boekhoudkundig medewerkers/secretaresses most exposed, schoonmakers/beveiliging least; senior devs shift to QA/architecture/IT-governance, junior roles decline. Three conditions (strategic ownership · data/IT/process base · governance/trust) map to W&W. 4 W&W cells (digital-sensing/digital-scenario-planning,digital-sensing/digital-mindset-crafting,digital-transforming/improving-digital-maturity,contextual/external-triggers).supportsBrynjolfsson Canaries, BCG reshape≫replace, Massenkoff & McCrory (method-cousin). Updated ai-employment-effects (54→55), micro-productivity-trap (34→35), enterprise-ai-adoption (78→79). No entities promoted. Dangling: Mark van Kampen, Reinder Koelewijn, Jesse Groenewegen, Floris Jan Sander, RaboResearch/Rabobank, CBS, ILO/Gmyrek et al. - 2026-06-25-guilbeault-stanford-gsb-what-ai-cant-do-and-why — What AI Can’t Do — And Why (Stanford GSB If/Then podcast, host Kevin Cool; guest Douglas Guilbeault, asst. prof. of organizational behavior; 25 Jun 2026; ~28:51, ASR-cleaned). The wiki’s first cognitive-science account of the AI capability ceiling. Core distinction: humans satisfice (Herbert Simon — “do a lot with a little” under constraint) where LLMs optimize (brute-force statistical prediction over “every sentence on the internet” to fill one masked word). The limit-claim: human learning makes conceptual leaps (random→ordered convergence), reasons via metaphor/analogy + “vibes”/taste, and “makes meaning from disorder” — none of which “falls out of the statistical framework.” A sharp AI-hype critique (“predict anything”; “Humanity has had a good run” — SF startup ads) warning against the “humans are just prediction machines” framing. Mechanism beneath the jagged-frontier; the cognitive-science floor under durable-skills; second source for analogical-reasoning (1→2).
supportsMollick (taste/experience edge), Argenti (instincts not skills);contradictsCsaszar et al. (LLM parity on strategy tasks vs. the optimization-ceiling claim). No W&W tags (cognitive-science, outside the lens). Dangling: Douglas Guilbeault, Kevin Cool, Herbert Simon, Wittgenstein, If/Then, Stanford GSB (channel — promotion candidate). - 2026-06-25-carroll-stanford-gsb-making-organizational-culture-great — “On Making Organizational Culture Great,” with Professor Glenn Carroll (Stanford GSB GSBooks session; Glenn R. Carroll, Adams Distinguished Professor; 25 Jun 2026, recorded 18 Jun; ~60:17, ASR). Acquired from Zotero (
ai-wiki). Mostly organization theory, not an AI source — the wiki’s authoritative anchor for thestrategic-renewal/organizational-culturecell. Thesis: culture = alignment with strategy (“strategy precedes culture… culture is the butter to the strategy”); a strong culture = high intensity × high agreement, with content almost irrelevant (SWAT teams, cults, startups alike); culture is a hard social-control system, not soft. Two paths culture→performance: content alignment (Walmart EDLC; Amazon customer-centric) + commitment/coordination (intrinsic motivation; strong-culture orgs carry fewer managers). Debunks five myths (inert / only-top-down / soft / only-fit-benefits / no-bottom-line-effect); warns too much fit → groupthink (Kodak, Uber). Change-speed: Agilent ~5 yrs, Ford/Mulally <1 yr. Anecdotes: Netflix/Hastings “not one decision all day”; Barra/GM “dress appropriately.” AI aside (cites Isabel Fernández-Mateo, LBS): AI reshapes hiring at job-definition + applicant-pool level, not selection; AI-written applications homogenise. 1 W&W cell (strategic-renewal/organizational-culture).supportsErginbilgiç (applied culture turnaround). Updated warner-wager-process-model (4→5), ai-employment-effects (53→54); Glenn R. Carroll (1→2), Stanford GSB (3→4). Candidate concept flagged: organizational-culture. Dangling: Jennifer Chatman, Isabel Fernández-Mateo. - 2026-06-24-rubinstein-onyemah-startup-founders-new-sales-playbook — Startup Founders Need a New Sales Playbook (Dave Rubinstein & Vincent Onyemah, Harvard Business Review; 24 Jun 2026; ~10 pp, full article). Empirical anchor for the new founder-led-sales concept: 250+ founder interviews across 30+ countries (first 100 analysed; companies $0.5M–$10M ARR), compared to a 2013 study. Core diagnosis: the 2026 problem isn’t longer sales cycles, it’s mistaking attention for traction — “everyone says they want AI, but they don’t know what problem they’re trying to solve… they don’t have the budget for it yet.” Three persistent errors (false PMF / markets too broad / ~80% lack sales background) + three new 2026 challenges (attention≠traction / “better than competition” no longer enough amid 20,000 tools / can’t create urgency). The SPRINT framework — Speed(attention)·Problem(urgency)·Results(belief)·Implementation(safety: buyer fear of AI hallucination/data-corruption)·Niche(repeatability)·Trust(permission: the founder is the non-transferable trust mechanism). New concept founder-led-sales.
supportsKolysh, Campfire, Luminai. Touches enterprise-ai-adoption (77→78, seller-side mirror), responsible-ai. No W&W tags (startup-seller side). Dangling: Dave Rubinstein, Vincent Onyemah, Babson College, 100 Founders, Salesforce, Outreach, Nexwise/Mathis Stolz. - 2026-06-24-mckinsey-ai-supercharging-software-development — AI Is Supercharging Software Development. Humans Determine Its Impact. (McKinsey & Company; panelists Janaki Palaniappan, Martin Harrysson, Matt Linderman; 24 Jun 2026; ~21:51, ASR). A software-engineering-specific restatement of micro-productivity-trap: individual coding-agent speedups (4 weeks → 4 days cited) don’t compound to org-level value without workflow/role redesign — firms that rearchitected how software gets made saw real gains; firms that only distributed tools didn’t. Which SWE skills are becoming obsolete (routine/boilerplate coding) vs. durable (problem framing, architecture, prioritization, business-context judgment) — “humans determine its impact.” Claim: AI-generated code has so far often been more verbose and less secure than human-written code; prescription is to move security review to design-time, not a downstream gate.
supportsBain (workflow-redesign diagnosis), Forsgren & Macvean (obsolete vs. durable SWE skills). Updated micro-productivity-trap (36→37), ai-deskilling (13→14), responsible-ai (18→19); McKinsey & Company (10→11 sources). No entities promoted. Dangling: Janaki Palaniappan, Martin Harrysson, Matt Linderman. - 2026-06-24-from-demo-to-production-why-agentic-ai-systems-fail — From Demo to Production: Why Agentic AI Systems Fail — and How to Fix Them (InfoQ conference talk; presenter unnamed; 24 Jun 2026; ~39:08, ASR). The wiki’s first independent platform-builder war-story ratifying agent-harness from outside the vendor cluster — an agentic app-generation platform (10 agents, 200+ tools, thousands of instructions, orchestrator + MCP). “Pilots prove nothing about production” for agentic systems. Four failure patterns: (1) context overload → progressive disclosure via skills (“lazy loading for LLMs”; the security-agent-ships-every-auth-provider example); (2) tool explosion (200+ tools ≈ 6–7k tokens; GitHub 35/26k; Jira 17k; full integration ≈ 72k ≈ 36% of window; Anthropic: >50 tools → −30%) → Tool Search Tool (40%→2% context, ~85% reduction; only above ~20–30 tools); (3) orchestration hand-off loss (the manager-ID-nullable validation dies because agents can’t share context windows) → agent vs. skill vs. tool decision (one agent / many skills / each skill carries its tools); (4) execution black box → day-one observability (Langfuse + LiteLLM + LangGraph). Closing thesis on agentic-engineering: “engineers still required; the model is just one component.”
supportsSchluntz & Zhang (MCP + tool discipline), subtraction principle, Google Agents CLI (skills = progressive disclosure), Headroom (context budgeting). No W&W tags (engineering-mechanics). Updated agent-harness (68→69), agentic-engineering (41→42). Promoted Langfuse + LiteLLM + InfoQ to entities (2026-06-26; InfoQ on its 2nd source after Böckeler). Dangling: presenter (unnamed), LangGraph. - 2026-06-22-yc-kolysh-how-to-get-your-first-10-customers — How to Get Your First 10 Customers (Y Combinator Startup School, Max Kolysh / YC Visiting Partner; 22 Jun 2026; ~13:46, ASR). A tactical GTM playbook (from a Bookface founder survey): customers 1–3 from the warm network (early buyers trust the founder, not the product yet); 4–10 from doing-things-that-don’t-scale — show up in person (fly out, get kicked out, win the account), small conferences + founder dinners, find where customers complain online (Reddit DMs), advice-framed outreach (mentorship/whiteboard/pay-for-feedback); tools (Apollo/Clay/LinkedIn/sequences) only matter at 10–20. “The first 10 come from you, not a tool.” Outreach craft: <75 words, clear CTA, read-it-aloud test, give-something-first. Only lightly an AI source (Happenstance network-search, Clay enrichment, “sounds-like-an-LLM” cold-email negative signal) → no W&W tags.
supportsLuminai + Campfire (founder-led-sales doctrine). Y Combinator 13→14. Dangling: Max Kolysh, Happenstance, Apollo, Clay. - 2026-06-22-grinstead-how-i-ai-mozilla-firefox-agentic-security-harness — How Mozilla Uses Claude Mythos to find Firefox bugs before hackers do (How I AI, host Claire Vo; guest Brian Grinstead, Mozilla distinguished engineer; 22 Jun 2026; ~48:28, ASR via yt-dlp fallback). The wiki’s first production security-engineering harness case: ~500 Firefox security bugs fixed in one month, credited ~50/50 model vs. harness (the viral chart gave Anthropic’s Mythos the credit; Grinstead’s “story behind the story” is the pipeline). “The harness is a way to give an LLM tools to achieve some goal” — LLM-judge prioritisation (can’t canvas a 10k-file repo) → analyzer (HTML test cases) → decades-old fuzzing + AddressSanitizer win/lose signal → verifier subagent killing false positives (catches the agent introducing a vulnerability to exploit it) → patch agent. The goal/Ralph loop retries “far past where a human would quit”; the guardrail (P95-latency-by-deleting-the-feature) is essential. Model-agnostic by design (Claude Code / Codex CLI / Agent SDKs / Pi); minimal version = one prompt +
-p, no SDK. Humans review every fix; the score→verify→fix loop generalises to perf/conversion/tech-debt.supportsVo’s goal-loop episode (same channel, named), Lopopolo, Chatterjee (verifier = Constraints layer), AEI returns-to-expertise (fuzzing/CI as enabler);contradictsKilpatrick (model-eats-the-harness) (50/50 split resists it). 3 W&W tags. Updated agent-harness (66→68), agentic-engineering (40→41), automation-vs-augmentation (48→50), ai-agents (20→21). How-I-AI 2→3, Claire Vo 2→3, Anthropic 21→22. Dangling: Brian Grinstead, Mozilla, Firefox, Mythos, Pi, AddressSanitizer. - 2026-06-22-bbc-what-if-were-wrong-about-ai-layoffs — What If We’re Wrong About AI Layoffs? (BBC Global — New Normal with Katty Kay; host Katty Kay, guest labor economist Kathryn Anne Edwards; 22 Jun 2026; ~8:18, human-curated captions). Anchors the new ai-washing concept: companies citing AI as the reason for layoffs they’d have done anyway (greenwashing analogue). Mechanism = stock-market valuation premium — “we pivoted to AI” beats “we overhired” for shareholders; downturn peer pressure amplifies. Attribution near-unmeasurable (“inconclusive forever”; the jobs-lost-to-computers-since-1955 analogy). Weak market penalty (these firms often “don’t have customers”). Counter-data: Indeed software-dev postings ~4× higher mid-2026 vs early 2024 — “so not the narrative.”
supportsEveritt (Sam Altman AI washing framing), Giles (Challenger “#1 cited reason” + NACE 14%), Massenkoff & McCrory (measurement caution). New concept ai-washing; updated ai-employment-effects (52→53). No W&W tags (labor-narrative, outside the lens). Dangling: Katty Kay, Kathryn Anne Edwards, New Normal (series). - 2026-06-19-lopopolo-ai-native-devcon-harness-engineering — Harness Engineering: How to Build Software When Humans Steer and Agents Execute (Ryan Lopopolo, OpenAI; AI Native DevCon, 19 Jun 2026; ~29:47, ASR; Zotero key UQRQ5D5Z). The coiner of “harness engineering” gives the named definition + operating discipline — the conference-talk companion to his OpenAI Codex blog. “Harness engineering is making context around what it means to do a good job legible, and then just-in-time surfacing it to the agent.” Three foundational limits (human time / attention-sums-to-one / context window); three-phase context delivery (ground → just-in-time-steer-via-tool-calls → LM-as-judge review); shift RIGHT not left (+ “never give the same review feedback twice → make every mistake statically impossible”); all code is prompts → prune latent space; review personas + coarse structural guardrails (snapshot tests, ban
any/unknown); treat-agent-as-teammate-at-review; “dream over it every night”; vibe coding → the group-tech-lead operating mode.supportsCodex blog (same author), harness-is-all-you-need, Bratanic dream-phase, Böckeler. Promoted Ryan Lopopolo (2nd source). OpenAI 17→18. Updated agent-harness (64→65), agentic-engineering (38→39), vibe-coding (21→22), automation-vs-augmentation (47→48), software-3.0 (7→8). Dangling: AI Native Dev/DevCon, Artichoke (artichoke-rand-mt). - 2026-06-19-chou-yc-lightcone-40-year-old-solo-founder — The Age of the 40-Year-Old Solo Founder Is Here (Y Combinator The Lightcone; 19 Jun 2026; ~42:43, ASR; Zotero key VRWYL2YC). Bryant Chou (Webflow → Ploy) on why domain expertise + taste are the lever on “boundless model intelligence.” “You need a certain amount of expertise to know what to do with the boundless intelligence imbued in the model”; the age-of-the-40-year-old-solo-founder thesis (“you don’t have to be 40, you just have to have taste”); Ploy as an “anti-slop” opinionated vibe-coding platform (deterministic design slurper + curated lookbook of ~3,500 prompts; Andy-Warhol-factory framing); building a purpose-built harness (“Anthropic did it for Claude Code”), fat skills, fat code; agents as customers (AEO/GEO, CLI-with-skills over MCP); cloning yourself / abundance. Title note: Zotero title was “Why Domain Experts Are Winning Right Now”; actual YouTube title differs (recorded in body; raw filename preserved). 5 W&W tags (roles → ceo/cto/product-manager/cmo).
supportsreturns-to-expertise, harness-is-all-you-need (durable-harness pole), Mollick, Ng. Updated durable-skills (27→30), agent-harness (62→64), vibe-coding (19→20), software-3.0 (5→6), ai-employment-effects (47→49). Dangling: Y Combinator (channel), Bryant Chou, Ploy, Webflow, Lightcone hosts, Parker Conrad/Rippling. - 2026-06-18-ramaswamy-mckinsey-every-company-software-company — AI Is Turning Every Company Into a Software Company (the McKinsey Podcast; 18 Jun 2026; ~25:46, ASR; Zotero key PMSZEARN). Sridhar Ramaswamy (CEO, Snowflake; ex-Google ads, ex-Neeva) interviewed by Eric Kutcher (McKinsey North America Chair). The “industrialization of intelligence”: AI collapses the cost of software creation (analogies = printing press, internet). “Uber programmers” 50–100× more productive; sales reps shipping apps by describing them in English; thinking in coder + critic + security-critic agents; software shifts from a sticky “cottage industry” to cheap creation → innovation + disruption. Conversational interface as the new natural interface (“go to the god first”). Consumption pricing (“pay only if you get value”; not every token has the same value; per-person + per-account limits); open-source models hosted alongside frontier (beneath-vs-above-the-model). AI with ROI — two surefire hits: software engineering + support (self-built support system in 6 weeks on Cortex Code → empty queues; SRE observability rewrite). Business transformation > technology transformation; viral adoption (“Coco” + Snowhouse → company AI-literate in 6 weeks, no mandate); redeploy-don’t-cut (demo team → other roles); growth over headcount; protect your career (“terrified for my sons”). Advice: hard work / malleability (“what have you changed in yourself this year?“) / don’t-set-limits first-principles generalism. 8 W&W tags (roles → ceo/coo/cto/cdo/transformation-lead).
supportsTan & Hu (1000× engineer), Ng, Bender (tipping point), Emergent. McKinsey & Company 9→10 (first McKinsey-Podcast ingest). Updated enterprise-ai-adoption (75→76), agentic-engineering (37→38), vibe-coding (20→21), software-3.0 (6→7, conf 0.85→0.88), automation-vs-augmentation (46→47), durable-skills (30→31), ai-employment-effects (49→50), foundation-models (17→18). Dangling: Sridhar Ramaswamy, Snowflake (Cortex Code/Coco, Snowwork, Snowhouse), Eric Kutcher, Neeva. - 2026-06-18-dumra-mit-smr-dbs-everyone-an-innovator — How DBS Bank Makes Everyone an Innovator (MIT Sloan Management Review Leaders at All Levels Ep. 9; host Kate Isaacs; 18 Jun 2026; ~24:49, ASR transcript). Bidyut Dumra (Group Head of Innovation and Future of Work, DBS Bank) on how DBS made innovation a company-wide KPI-backed system across 39,000 employees. The wiki’s richest single operator-altitude dynamic-capabilities case: GANDALF (“be the D in GANDALF” — sensing via competitor re-framing); Managing Through Journeys (operating model reoriented horizontally around customer intent, “a customer is beyond a process”); mini-CEOs + QPR dynamic funding + slush fund (strategic agility); the Innovation Pyramid (big bets / Horizon 3 / journeys / entrepreneurs, each a KPI); the Playbook Model (20%-of-scorecard transformation KPI to every employee; 4D framework; “250 journeys or 10,000 agents”); information gap → action gap (“the wrong way is to do nothing”). 11 W&W cells (roles override → ceo/chro/cdo/transformation-lead/innovation-lab-lead). 5
supports: DFI (operator Asian incumbent), Rolls-Royce (transformation playbook, non-AI control), Beutler (redesign work at scale), AWS (end-to-end W&W, 9 shared cells), Amazon (target-firm CEO trap-escape). Created Bidyut Dumra + DBS Bank; MIT Sloan Management Review 6→7 (first Leaders at All Levels ingest). Updated dynamic-capabilities (9→10), warner-wager-process-model (3→4), enterprise-ai-adoption (72→73), micro-productivity-trap (33→34), organizational-frameworks synthesis (13→14). Dangling: Kate Isaacs, Michael (co-host), Cathay Pacific. - 2026-06-17-vo-how-i-ai-ai-agent-loops-claude-code-codex — How to write AI agent loops in Claude Code and Codex (How I AI podcast, host Claire Vo / ChatPRD; 17 Jun 2026; ~29:07, ASR). The product-leader / non-engineer altitude on the self-prompting loop. “A loop is just an automated prompt” — you don’t need human fingers typing each turn. Four loop types: heartbeat (interval), cron (fixed schedule), hook (lifecycle event / webhook), goal (run until outcome validated or blocked — the new first-class primitive in Claude Code
/goal/“routines” + Codex “automations”). Five things every effective loop needs (crediting Addy Osmani’s loop engineering): work trees, skills, plugins/connectors, subagents, state tracking. “Onboarding an employee” framework — designing a loop = designing a job. Two live builds: a daily aging-PR reviewer (Claude Code routine spawning babysitter subagents) and a weekly skills-identification automation (Codex, spawning goal-based validating subagents — a loop generating sub-loops). Warnings: goal loops burn tokens against thin validation criteria; loop/goal prompts demand precise success criteria. No W&W tags (practitioner engineering, outside the lens — consistent with neighbours).supportsDevCon (engineer-altitude companion), Osmani (loop/Ralph-loop article cited), Anthropic long-running agents (the goal loop vendor-side). Updated agent-harness (65→66), agentic-engineering (39→40); Addy Osmani 2→3. Promoted Claire Vo + How-I-AI (2nd source each — first was Marily Nika). Dangling: OpenClaw, ChatPRD. - 2026-06-17-ng-langchain-interrupt-future-of-ai-agents — The Future of AI Agents with Andrew Ng (LangChain Interrupt ‘26 fireside, host Harrison Chase; 17 Jun 2026; ~31:39, manual captions; Zotero key JUUQZGWX). Andrew Ng restates the product-management-bottleneck-becomes-an-everything-bottleneck thesis + small teams of high-context generalists + building-blocks/LEGO + Context Hub, and adds the enterprise vantage (AI Aspire): bottom-up “thousand flowers” not paying off vs top-down workflow redesign (the 10-minute-loan example); cost-savings-vs-growth + swing-for-the-fences portfolio; optionality (≤1-yr contracts, open-weight hedging, LangSmith); forward-deployed engineers; the coming unstructured-data rearchitecture (“tens to hundreds of millions of dollars”). 8 W&W tags (roles → ceo/cso/cdo/cto/transformation-lead).
supportsreturns-to-expertise, DBS, Beutler, AWS, Kilpatrick (optionality). Andrew Ng 5→6, Harrison Chase 2→3, LangChain 6→7, DeepLearningAI 4→5. Updated enterprise-ai-adoption (73→75), automation-vs-augmentation (44→46), agentic-engineering (36→37), durable-skills, dynamic-capabilities (10→11). Dangling: Chris Tann, Rohit Prasad, AI Aspire/AI Fund, Context Hub/CodeDream/LearnDream. - 2026-06-16-mollick-simon-sinek-ai-skills-experience-edge — The AI Skills Nobody Is Teaching (And Everyone Needs) (Simon Sinek’s A Bit of Optimism; 16 Jun 2026; ~58:35, ASR; Zotero key CWRX4K3S). Ethan Mollick (Wharton) from the social-science chair: young people are not “AI natives”; experience is the AI advantage (cites the BCG junior-worse-at-AI study); taste as the differentiator (“death of the movie star”); the apprenticeship model just broke (talent-pipeline deskilling); the model/apps/harnesses three-layer framing; heavy use of GDPval (48%→84%); doubling down on human / augmentation-over-replacement; agency. Mollick is a co-author of the originating jagged-frontier study.
supportsreturns-to-expertise, GDPval, Kilpatrick, AEI-Q4 deskilling, Argenti, Chou. Promoted Simon Sinek (2nd source — also the 2018 NYT Infinite Game keynote subject). Ethan Mollick 1→2, Boston Consulting Group 4→5. Updated durable-skills, jagged-frontier (13→14), ai-deskilling (11→12), automation-vs-augmentation, ai-employment-effects, agent-harness, enterprise-ai-adoption. Dangling: Marvin Minsky/MIT Media Lab, Wharton, Co-Existence/Co-Intelligence. - 2026-06-16-anthropic-economic-index-agentic-coding-returns-to-expertise — Agentic coding and persistent returns to expertise (Zoe Hitzig, Maxim Massenkoff, Eva Lyubich, Ryan Heller, Peter McCrory; Anthropic / Anthropic Economic Index; 16 Jun 2026; ~18pp full read; ~400k Claude Code sessions). Division of labor: people decide what (~70% of planning), the agent decides how (~80% of execution). Returns to domain expertise persist (not coding skill): more expertise → Claude does more per instruction; verified success 15%→28–33% (novice→intermediate, then flattens); steering is the premium (novices abandon ~19% of troubled sessions vs 5–7%). Occupation matters less than expertise — every top-10 occupation within ~7pp of software engineers. Work composition shifted (fixing 33%→19%; operating 14%→21%; writing/analysis ~2×); task value +~27%.
part-ofAnthropic Economic Index,published-byAnthropic;supportsMassenkoff-McCrory + AEI-5 + Argenti + BCG-reshape;contradictsAEI-Q4 (deskilling vs persistent returns). Promoted Eva Lyubich + Ryan Heller. 4 W&W tags. (Processed 2026-06-17; backfilled to index/log + re-acquired via zotero-acquire, key N6KSNEQM, 2026-06-18.) - 2026-06-14-pincus-lennys-podcast-hidden-pattern-behind-successful-products — The hidden pattern behind successful products | Mark Pincus (FarmVille, Words with Friends, & more) (Lenny’s Podcast; guest Mark Pincus, Zynga founder; 14 Jun 2026; ~1:39:23, ASR). Lighter-touch ingest (outside the W&W digital-transformation lens; no
dynamic_capabilities:tags) — founder/product-craft wisdom timed to his book Life at the Speed of Play. The “Proven, Better, New” framework: earn the right to innovate by first nailing a proven pattern, make it decisively better (10/10 people say yes), only then add something new. “Instincts are right 95% of the time, ideas are wrong 75% of the time”; being less ambitious in scope as the path to bigger outcomes; “kill hope before hope kills you”; AI as a “failure machine”; Zynga’s hits succeeded on game-design quality, not virality; “make everyone a CEO” / “stay close to the metal” management philosophy.supportsFowler et al. (hands-on engagement over abstraction, founder vantage). Updated strategy (7→8); Lenny’s Podcast (5→6 sources). No entities promoted. Dangling: Mark Pincus, Zynga (candidate entity). - 2026-06-12-mcveety-hormati-google-cloud-open-knowledge-format — How the Open Knowledge Format can improve data sharing (Sam McVeety & Amir Hormati, Google Cloud Data Analytics blog; 12 Jun 2026; ~2k words, Zotero-extracted). The first major-vendor formalization of the LLM Wiki pattern into an open spec — OKF v0.1: a directory of markdown concept-files + YAML frontmatter (only
typerequired), optionalindex.md/log.md(those exact names), markdown-cross-links-as-graph. Three principles: minimally-opinionated / producer-consumer-independence / format-not-platform. Explicitly cites Karpathy’s gist + the CLAUDE.md/AGENTS.md family; ships a BigQuery enrichment agent + static-HTML graph visualizer + 3 sample bundles; Knowledge Catalog updated to ingest OKF. Most self-referential source in the wiki — this repo already uses every OKF convention.instance-ofllm-wiki,supportsRaju + Liu,published-byGoogle. 1 W&W tag. Updated llm-wiki (7→8, conf 0.91→0.93) + Google (10→11). Dangling: Sam McVeety, Amir Hormati. - 2026-06-12-aws-leaders-guide-data-strategy-agentic-ai — A leader’s guide to data strategy in the era of agentic AI (Amazon Web Services / AWS Summit Sydney exec forum; unnamed former-CIO Enterprise Strategist; 12 June 2026; ~34 min, ASR; Formula 1 analogy). The wiki’s most focused source on re-architecting data for machine consumers: the data consumer is now the agent, not the human (BI→lakes→GenAI→agentic); data products purpose-built/agent-ready/metadata-enriched with granular ownership; markdown-for-agents (Stripe); 4-question test ending “can an agent consume this without a human translating?”; minimum-viable-governance (guardrails-not-roadblocks, open-by-default). Gartner: 80% of data-governance fails by 2027.
supportsAllen, Argenti, Chopra/Headroom. Touches enterprise-ai-adoption / agent-harness / knowledge-graphs / document-intelligence. - 2026-06-12-aws-leaders-guide-advanced-team-structures-agentic-world — A leader’s guide to advanced team structures in an agentic world (Amazon Web Services / AWS Summit Sydney; Steven Brovich, AWS Enterprise Strategist; 12 June 2026; ~32 min, ASR; slide-backed from 6:55). The Sydney edition of the Allen talk → strong
supportscorroboration of the same framework (economics/talent/structure/governance; USE/COMPOSE/BUILD; hourglass; expert-generalist/Renaissance-developer; non-determinism). Adds quantified specifics: pricing scissors (training +2.4×/yr, inference −10×/yr, gap 12–24×/yr); Models A/B/C (Model A dead — six failure stats, 95% pilots fail); deeper Singapore IMDA agentic-governance + Bedrock AgentCore four-questions / policy-as-code; the moats slide (~7:02); Anthropic Economic Index spine.supportsAllen + the sibling data-strategy talk + Anthropic Economic Index. Touches automation-vs-augmentation / ai-employment-effects / foundation-models / agent-harness / enterprise-ai-adoption / responsible-ai. - 2026-06-12-argenti-hbr-thrive-alongside-ai-mindset-not-skillset — To Thrive Alongside AI, Focus on Mindset—Not Skillset (Marco Argenti, CIO of Goldman Sachs; HBR, 12 Jun 2026; ~8pp). The mindset-over-skillset inversion: don’t ask “what 10% can AI never do” — let go of that 10% and reimagine the whole role (horse-rider→driver: keep instincts/judgement/values, not specific skills). Operator → supervisor/mentor (“the new 100%”). Three ingredients: leadership (demand 3× not 20% — radical not optimisation), clarity of objectives (obsess over evals; codify “what good looks like”; Goldman client-onboarding example), mastery of your own data (“AI transformation follows data transformation”). Cites GDPval (extrapolates to ~80% by mid-2026). Promotes Goldman Sachs to an entity (2nd source);
usesGDPval,supportsSternfels + Giles. W&W: redesigning-internal-structures + improving-digital-maturity + strategic-agility + organizational-culture + internal-barriers. Touches 6 concepts. - 2026-06-11-mit-smr-agentic-ai-what-leaders-wish-they-knew-sooner — Agentic AI: What Leaders Wish They Knew Sooner (MIT Sloan Management Review; 2026 MIT Sloan CIO Symposium, host EIC Abbie Lundberg; 11 June 2026; ~11½ min, manual CC). The wiki’s densest single source on the human-in-the-loop reality — 11 leaders, one takeaway each. Davenport: oversight going “performative” (cursory rubber-stamps); Swift/Chan: manage agents like employees (→ typed
contradictsthe BCG source); Westerman: automate first, rebuild for outcomes (not bolt tools on existing steps); Caldas: micro-agents + OKRs + “trust fabric”; Schrage: in-loop vs on-loop (“I don’t trust deterministic software agents yet”); Pearlson/Shah: combination/marriage, collaborators-not-competitors. Promotes Michael Schrage (2nd source).supportsBeutler, Allen, Argenti, Kiron-Schrage. Touches 6 concepts. - 2026-06-11-kilpatrick-sequoia-model-eats-the-harness — Why the Model Eats the Harness (Logan Kilpatrick, Google DeepMind AI Studio / Gemini API; interviewed by Sonya Huang, Sequoia Capital Training Data; 11 Jun 2026; ~51 min, ASR). The wiki’s first frontier-lab counter-thesis to the agent-harness corpus: “the model eats the harness” — scaffolding runs ahead, the model digests it and gets it upstreamed, so harness alpha is transient (~12 months), not the durable moat “harness is all you need” claims (they share the subtraction principle but split on the conclusion → typed
contradicts;supportsthe DeepMind AutoHarness “model synthesises the harness” direction). Antigravity (ex-Windsurf) is Google’s single harness rebasing search / Gemini app / Cloud / AI Studio; jagged/narrow superintelligence (coding now; math/finance/science next, before AGI); outcomes over eyeballs; Omni = one any-to-any model replacing ~8; 350k Android apps in AI Studio in a week (touches vibe-coding). First source via thezotero-acquirechannel. 5 W&W tags. Promotes Logan Kilpatrick, Google DeepMind, Sequoia Capital, Antigravity, Omni. - 2026-06-10-google-cloud-tech-ai-agents-explained-first-agent — AI agents explained: Build your first agent in 8 minutes (Google Cloud Tech YouTube, presenter Smitha, 10 June 2026; ~8:29, manual CC). The wiki’s first hands-on Google ADK build and the operational bookend to the 2022 ReAct paper — defines a modern agent as the ReAct loop (citing the paper by name), teaches 3 agent patterns (sequential/reactive/planning), then builds a self-correcting multi-agent blog-writer (
LlmAgentplanner+writer / validation-checkers /LoopAgentretry ×3 / root-agent-with-tools) run viaadk web. Typeduses→ the ReAct paper + the Agents CLI (same ADK). No W&W tags (developer tutorial). - 2026-06-10-anicich-brouwers-why-employees-arent-transparent-ai-usage — Why Employees Aren’t Transparent About Their AI Usage (Eric Anicich & Jeslyn Brouwers, Harvard Business Review 10 Jun 2026; ~3k words). Primary source for ai-knowledge-hiding — the “suppression of solutions”: employees rationally hide the AI workflows they discover. The driver is organizational trust (via psychological safety), not governance/tooling — own survey of 604 daily-AI users: 30.3% withheld; lowest-trust quartile 47% vs 14% highest; policy/tools alone predict nothing; Stanford 51-deployment study → 77% of hard adoption challenges non-technical. Three disclosure costs (reputational / workload / replaceability); fixes: stop taxing efficiency, reward “multipliers,” legitimize “side quests.”
supports2026-04-28-mit-sloan-ai-maturity + the “trust gap”. Touches enterprise-ai-adoption / micro-productivity-trap. 3 W&W tags. Dangling: Anicich, Brouwers, Amy Edmondson. - 2026-06-08-vincent-coderabbit-fixing-ai-slop-managing-agents-like-mit-interns — Fixing “AI Slop”: How To Manage Agents Like MIT Interns w/ Jesse Vincent, creator of Superpowers (CodeRabbit’s The Merge; host Hrik; guest Jesse Vincent, creator of Superpowers/Request Tracker/former Perl 5 project lead/K9 Mail; 8 Jun 2026; ~1:18:57, ASR). The wiki’s first source touching the Superpowers framework used by this session’s own Claude Code harness. Coordinator/Coder/Spec-Reviewer/Quality-Reviewer architecture with strict single-mandate separation (“two competing mandates” degrades whichever role loses); adversarial reviewers competing for stakes (“gets points”/“a cookie”) sharpens review rigor; “latent space engineering” (tone/empathy as a behavioral lever, distinct from prompt/context engineering); a Claude-given-a-private-journal anecdote drifting into unintentional reward-hacking; Superpowers front-ran Anthropic’s own Skills framework; the project’s own 94% PR-rejection rate after tightening CLAUDE.md/AGENTS.md contributor guidance against “AI slop” spam.
supportsChatterjee (single-mandate separation), Kokane (harness-as-management-discipline). Updated agent-harness (69→70), agentic-engineering (42→43). No entities promoted. Dangling: Jesse Vincent, CodeRabbit, Hrik. - 2026-06-05-nadella-hoffman-possible-ai-future-of-the-firm — Satya Nadella: AI Is the Future of the Firm (Reid Hoffman’s Possible podcast; guest Satya Nadella, Microsoft CEO; post-Build 2026; 5 Jun 2026; ~60:17, ASR). The wiki’s first platform-CEO worldview source. “Token capital” — the firm’s tacit knowledge, extractable via human trajectories and encodable as weights/context/skills, compounding alongside human capital; “if you leak it, it’s a one-way door.” “AI is not a technology, it’s the future of the firm” — the refounding mandate; CEOs must answer “what’s your token capital?” not point to “eight agents they built.” The hill-climbing machine (evals/objectives = the new IP); “don’t use frontier models for non-frontier problems” (token efficiency); the coding→knowledge-work fire-and-forget arc → back to an IDE = the agentic development environment (GitHub-app-as-inbox-of-agents + Canvas); Agent 365 (identity/sandbox/policy/observability) + Foundry asserts (runtime boundary enforcement); cognitive coverage (a quiz per agent task); sovereign-AI-for-companies (Ricardo); Maya/Cobalt silicon; social permission + Pope Leo’s encyclical.
supportsRamaswamy, DBS, AWS data-strategy, Argenti, Beutler. 8 W&W cells (roles → ceo/cso/cdo/cto/cio/transformation-lead/strategy-consultant). Updated enterprise-ai-adoption (76→77), automation-vs-augmentation (now 50, shared w/ Grinstead), responsible-ai (15→16), agent-harness (now 68, shared w/ Grinstead), durable-skills (31→32), ai-employment-effects (50→51). Microsoft 1→2, GitHub 1→2, OpenAI 18→19. Dangling: Satya Nadella, Reid Hoffman, Possible (podcast), Manus, Maya, Cobalt, Agent 365, Pope Leo, Joel Mokyr. - 2026-06-03-warren-yc-how-to-build-an-ai-native-services-company — How to Build an AI-Native Services Company (Y Combinator Startup School YouTube — Charlie Warren guest, 3 June 2026; ~11:23; ASR auto-generated transcript; 85 segments; 12 chapters). Charlie Warren — YC Visiting Partner. The wiki’s first YC source explicitly framing AI-native services companies as a distinct go-to-market category, alongside the existing nine YC sources which all sit on the AI-software product side of the line. The 10th YC ingest in the wiki + the vendor-side architectural mirror of [[2026-05-05-nishar-nohria-end-of-one-size-fits-all|Nishar-Nohria’s Buy Outcomes model]] (HBR May 2026): where Nishar-Nohria frame Buy Outcomes from the enterprise-buyer firm-boundary angle, Warren writes the YC-altitude playbook for building the vendor. Eleven substantive contributions: (1) Core thesis — “Some of the biggest companies of the next decade won’t be software businesses at all. They’ll be services companies like insurance carriers and law firms rebuilt from scratch with AI doing most of the work.” Markets: tax / audit / insurance / mortgages / parts of healthcare / parts of logistics. (2) Outcome-as-product vs co-pilot-as-product — “companies provide the outcome to the customer versus build a co-pilot that the customer uses internally” — the wiki’s clearest single-sentence YC-altitude framing of the two-vertical AI-native-company split. (3) Four market-selection traits: low trust (work already outsourced; “You’re displacing a vendor, not asking the customer to do something fundamentally different. You’re showing up where the budget already lives and doing the work”), low judgment at the task level, high intelligence threshold (“sounds contradictory but actually isn’t”), regulation could actually be good (regulated industries have higher expectations + legal accountability → moat). (4) “Markets YC Likes Right Now”: tax / audit / insurance / mortgages / parts of healthcare / parts of logistics. “There are plenty more markets nobody has touched yet. Don’t hold yourself to the obvious ones or what people talk about on X.” (5) The Sam Altman Test — “as the models get better, does your service get stronger or does the model itself commoditize you? You want to be in the first camp” + the robotics-segmented-out rule (“the software margin math doesn’t apply when you own and operate physical things. Let’s leave this area to the robotics founders”) + the honesty check (“are you using humans because the work genuinely needs judgment, or are you compensating for product gaps?”). (6) Three founder attributes: domain fluency (“selling to skeptical buyers and often regulated spaces. You have to bleed credibility”), model fluency (“no substitute for great tech here. People underestimate this”), operational rigor (“variance, throughput, cycle times, SOPs … you are fundamentally running an operation”). (7) Two YC-portfolio worked examples: Panacea (FDA regulatory services for biotech/medtech — “hire experienced FDA consultants, pair them with an AI platform to deliver faster, higher quality FDA approvals”; named twice as regulation-as-moat and outcome-based pricing exemplars) + The General Legal Team (AI-native law firm with founder-team composition: Cooley + Fenwick law firm experience + Casetext technical leadership; “they’ve integrated shift work into how they serve clients to reduce cycle times and attract the best lawyers on the team”). (8) Humans-as-the-interface, the-product-as-the-operation — “the human is the interface of the customer, not the product. The product helps the human scale their work nonlinearly. That changes pretty much everything”. Four implications: operations mindset (“throughput and cycle time are now product metrics. Track them like you would daily active users”); variance is the existential problem (“customers will fire you for variance faster than they will fire you for being a bit slower or a bit more expensive”); humans-in-the-loop scale nonlinearly with revenue (“the humans in the loop also need to enjoy the software. They are your users”); OK-to-not-scale-at-first (“automating the process is the product”). (9) Sales and customer success: the early demand trap (“easy to sign up a lot of pilot customers when you’re just starting out and have nothing. But it can quickly overwhelm your ability to serve them. You’ll be stuck using humans. It is a literal trap” — advice: cap to a small handful) + the pilot is the product. (10) Pricing playbook: per-unit pricing (per return / per claim / per loan — GO, “cleanest, easiest to explain”) + outcome-based pricing (GO, “aligns incentives beautifully, but harder to forecast”); avoid: cost-plus pricing (“caps your upside permanently”) + straight-line undercutting (“makes your work seem cheap”). Panacea’s outcome-based-pricing model: “prices on the completed consultant study versus hourly, which is the norm in the industry”. (11) P&L math + don’t-buy-your-way-in: traditional services firms top out around 30% margins; the AI-operating-leverage bet is to push toward 50%+ margins on TAMs 2-3× bigger than software. “You don’t need to be there right away. But the trajectory has to be believable.” + the don’t-buy-your-way-in rule — only valid reason to acquire an existing services business is fast regulatory moat (e.g. insurance licensing); otherwise “legacy service businesses are legacy. They have different expectations on metrics, hiring, and performance. Adding AI on top doesn’t change any of those realities. Building is almost always better than buying.” Closing recap: “focus on the process as the product and the product as the process” — the YC-altitude crystallisation of the entire playbook in nine words. W&W tags (9 cells):
digital-sensing/digital-scouting+digital-sensing/digital-scenario-planning+digital-seizing/balancing-digital-portfolios+digital-seizing/strategic-agility+digital-seizing/rapid-prototyping+digital-transforming/redesigning-internal-structures+digital-transforming/improving-digital-maturity+strategic-renewal/business-model+contextual/external-triggers. 9 typedsupportsrelationships: Nishar-Nohria (the strongest single edge — vendor-side mirror of buyer-side Buy Outcomes), Luminai (same YC vantage, different vertical — playbook + worked example), McKinsey (incumbent-side rewrite of consulting + new-entrant-side scratch build = same phenomenon, two-sided framing), Lenny’s Podcast (Evans’s AI labs invest in consultancies + FDE-as-Accenture observation + Warren’s outside-the-labs corollary = three-altitude reading of the services-don’t-disappear-they-reinvent dynamic), YC (same-vantage twin on AI-native company-building, different category — YC’s two-vertical AI-native-company curriculum), YC Lightcone (twin YC-partner-altitude on AI-as-the-building-layer-for-everything), WP Intelligence (first cross-altitude pairing of enterprise-buyer-of-agent-services + startup-vendor-of-agent-services — same market from demand and supply sides), HelloPrint (in-place SME rebuild + greenfield startup rebuild = same phenomenon, two boundary conditions), McKinsey (operating-model shift — humans-as-interface + scale-nonlinearly + throughput-as-product-metric — across two industries: software delivery + professional services). Concept changes: enterprise-ai-adoption 60→61 (added section The AI-native services company vendor-side playbook — Warren as the vendor-side mirror of Nishar-Nohria; services-don’t-disappear-they-reinvent thesis now visible at four altitudes), automation-vs-augmentation 36→37 (added §18 Humans as the interface, the product as the operation — the AI-native services-vendor product-design pattern with four operational implications; the §16/§17/§18 cluster forms the wiki’s role-redesign + vendor-product-design augmentation-pole package), agent-harness 53→54 (Warren’s Sam Altman Test + model fluency framing added to the convergence table as the YC-altitude founder-team-selection vantage on the model-rented, harness-owned discipline), micro-productivity-trap 27→28 (added Warren entry — vendor-side founder-prescription on the trap: variance-not-throughput as customer-firing-trigger + automating-the-process-as-the-product + the 30%→50%+ AI-operating-leverage trajectory as escape-velocity-quantification). Entity changes: Y Combinator 12→13 (added §10 Warren / Startup School + extended the YC-batch-context table to 10 entries; second Startup School ingest after Hu / April 2026; YC corpus now spans two AI-native-company vertical curricula). Dangling first-mentions: Charlie Warren (the speaker; YC Visiting Partner; first appearance), Panacea (YC company, FDA regulatory services; named twice — regulation-as-moat + outcome-based pricing exemplars), The General Legal Team (YC AI-native law firm), Cooley (US law firm; Warren cites it as a founder-credibility anchor for The General Legal Team), Fenwick (US law firm; same context), Casetext (legal-tech firm acquired by Thomson Reuters in 2023; technical-leadership anchor of The General Legal Team’s founders). Surface artifact: the YouTubepublish_dateis 2026-06-03 07:00 PDT (today UTC; technically tomorrow Pacific) — using YouTube canonical date for slug per schema. ASR rendered Cooley as Cooling, Casetext as Caseex, Panacea as Panace, Sam Altman as Sam Alman throughout — all corrected in the body. - 2026-06-03-chopra-headroom-context-optimization-layer-for-llm-applications — Headroom: A Context Optimization Layer for LLM Applications (The Linux Foundation YouTube — Tejas Chopra, senior engineer at Netflix; open-source conference, Minneapolis; talk published 3 June 2026; ~41:11; ASR auto-generated transcript, no chapters). The wiki’s first shipping-code implementation of the harness Context layer (vs the prior taxonomy/essay sources), and first Netflix-engineer-authored source. Headroom (github.com/chopratejas/headroom) is an open-source local proxy (Python; Rust port underway; 1,900 stars / 30+ contributors in 4 months) between agent and LLM provider that compresses context transparently. Core insight: the bloat is in tool outputs (whole log files, verbose JSON, DOMs), not the user prompt — “most of the agent’s token budget is really noise.” Three stages: (1) cache aligner — moves dynamic fields (dates/UUIDs) out of the cacheable prefix to preserve prefix-cache hits (provider discounts: Anthropic 90% via
cache_control, OpenAI 50%, Google 75%; Claude 5-min vs hidden 1-hour TTL); (2) content router — per-type compressors (AST for code, JSON “smart crusher” 83–95% best-case, DOM for web) incl. compress-base, an encoder-only token-weigher trained on agentic traces (vs LLMLingua’s meeting-summaries); (3) CCR (Compress-Cache-Retrieve) — reversible compression: evict to local Redis/SQLite, embed a marker + ID, register an MCPretrievetool so the LLM fetches the original on demand (“99% of the time it doesn’t need to”). Also: 11 hooks as interception primitives + cross-agent memory (SQLite graph synced to agent.md/memory.md across Claude Code/Codex). Beyond cost: cuts latency (voice-agent user →200ms human-perceptible floor) and improves accuracy by countering context rot. Reported: 200B tokens saved ($700K) via opt-in tokens-only telemetry; 20–30% typical savings (50–90% best-case per the description); 190-page 10-K compressed 34%; a factory video-upload case cut $3→$0.20. Roadmap: per-domain compressors (financial/medical), and Headlight (context provenance + agent-consumable telemetry — “a year out, agents will consume telemetry data”). No W&W tags (pure LLM-internals, per CLAUDE.md). 4 typedsupports: Chatterjee (concrete Context-layer implementation), Prompt Engineering OS-analogy (context-window-as-RAM management), Bratanic (cross-harness memory + hooks), Anthropic (reversible compression vs lossy compaction). Concept change: agent-harness 54→55 (added section Headroom: a shipping Context-layer implementation). Dangling first-mentions: Tejas Chopra (speaker), Netflix (employer; internal teams reportedly use Headroom), The Linux Foundation (channel/host), Headroom (the library), Headlight / compress-base / LLMLingua / RTK / LeanCTX / Agno (tools named). Confidence 0.75. - 2026-06-02-architecting-ai-native-organizations-redesign-work-at-scale-joe-beutler — Architecting AI-Native Organizations: How to Redesign Work at Scale (OpenAI — Joe Beutler, Head of Solutions Engineering for Strategics; introduced by Gene Kim; IT Revolution Enterprise AI Summit Spring 2026, talk published 2 June 2026; ~25:25; ASR auto-generated transcript). The wiki’s first first-party OpenAI source — answers the standing open question on the OpenAI entity page. Vendor-of-deployment cross-customer vantage. Four contributions: (1) the adoption gap is a middle-layer gap — between bottoms-up workforce tools and top-down strategic initiatives lies the missing layer of team agents (team/department-level automation); (2) separate governance from transformation, business owns the outcomes — “if the head of a business unit is not accountable for the result, you probably don’t have an agent. You have a demo”; central IT owns foundations, business units own domain transformation; (3) embed engineering inside the business function via an organic path (domain expert → full-time role → paired engineer on the same comp ladder → head of innovation), the three load-bearing roles being domain expert / AI expert / software engineer — “the bottleneck is rarely the model capability … it is org design, ownership, and workflow clarity”; (4) Ask → Assist → Automate deployment-maturity ladder (read-only → human-in-the-loop → full autonomy with exceptions routed to humans; can’t go zero-to-Automate; biggest value but highest cost at Automate). Internal anchors: OpenAI finance team at 20% of the size PwC says it should be; finance GPT answered “thousands” of investor due-diligence questions; go-to-market automated SMB top-of-funnel. Customer cases (insurer auto-claims, wealth-management adviser, T-Mobile $3B/60%-automated call center, BBVA parallel build-out) all on the same ladder. Vendor bet: sell the no-code builder, not the outcome — the deliberate counter-position to YC’s outcome-as-a-service. W&W tags (5 cells):
digital-transforming/redesigning-internal-structures+digital-transforming/improving-digital-maturity+digital-seizing/balancing-digital-portfolios+digital-seizing/rapid-prototyping+contextual/internal-enablers. 4 typedsupports: Anthropic (cross-customer-deployment twin of the inside-Anthropic org rewrite), AWS (deployment-maturity complement to USE/COMPOSE/BUILD), MGI (team agents fill MGI’s middle layer), OpenAI (same OpenAI vantage; foundation-before-automation). Concept changes: enterprise-ai-adoption 61→62 (added section The first-party-OpenAI deployment vantage: team agents + embedded engineering + Ask-Assist-Automate), automation-vs-augmentation 37→38 (added the Ask → Assist → Automate ladder under the task-design section — Ask/Assist = augmentation, Automate = automation). Synthesis change: organizational-frameworks-for-ai-adoption 12→13 sources / 10→11 frameworks (added the deployment-maturity layer). Entity change: OpenAI 15→16 (first first-party source; added section OpenAI as an AI-native organization; answered the entity’s open question). Dangling first-mentions: Joe Beutler (speaker), Gene Kim (introducer; IT Revolution founder), IT Revolution (channel/publisher), T-Mobile / BBVA / PwC (case-study orgs, not authors), Enterprise AI Summit Spring 2026 (event). Confidence 0.75. - 2026-06-01-lf-state-of-tech-talent-europe-2026 — 2026 State of Tech Talent Europe: AI, technical hiring, and the skills gap in Europe (Marco Gerosa·Adrienn Lawson / The Linux Foundation Research, fwd Thierry Carrez; June 2026; the European cut — 398 global / 157 European respondents). Sibling/regional cut of the Global report. Distinctive contribution: the entry-level contraction — Europe shows −3% in entry-level technical roles vs +14% rest-of-world, the wiki’s first survey-side corroboration of the Brynjolfsson-canaries entry-level-decline thesis (payroll data + survey converging). Net hiring +27%/+17% (2026/27), smaller than rest-of-world; only 20,000+-employee firms negative (−15%); AI-specific roles +64%. Cybersecurity understaffing 48% (+14pp vs rest-of-world); AI-security/risk gaps 61%. Upskilling 3.7× over hiring; new hires take 53% longer to productivity, 23% resign within six months. W&W: same three cells as Global. 3
supports: Global report (sibling instrument), Brynjolfsson (entry-level), Giles. Confidence 0.75. - 2026-05-31-peron-mit-smr-me-myself-and-ai-philips-interoperability-health-care — AI for Interoperability in Health Care: Philips’s Carla Goulart Peron (MIT Sloan Management Review YouTube — Me, Myself, and AI podcast Season 13; host Sam Ransbotham; 31 May 2026; ~31:03; 241-segment ASR auto-generated transcript). Dr. Carla Goulart Peron — Chief Medical Officer at Philips; physician by training; pre-Philips VP & CMO for surgical innovations and robotics at Medtronic; trained and practised medicine in São Paulo, Brazil (public universal-healthcare + private same-day). The wiki’s first dedicated healthcare-AI source + first Me, Myself, and AI podcast ingest (prior three MIT SMR sources are all written-research outputs). Six substantive contributions: (1) Philips SmartArt cardiac-MRI worked example — recent FDA clearance for AI-driven one-click automation that compresses cardiac-imaging setup from ~15 minutes to 30 seconds, reducing technician-training dependency and ~4×-ing throughput on expensive imaging machines. (2) Radiology-displacement-that-didn’t-happen historical anchor + the radiologist-training-gap question (raised at a recent radiologist-society meeting, currently unresolved): “How are we going to get the radiologist trained in what is abnormal if they are not going to be seeing normal?” — the wiki’s first clinical-domain instance of a training-pipeline class-imbalance deskilling mechanism (third causal mechanism alongside task-composition shift and BCG-AI-brain-fry). (3) Future Health Index trust gap — Philips’s own annual survey: 79% of healthcare professionals optimistic about AI vs ~50% of patients worried about reduced face-to-face time. Peron’s reconciliation argument: clinician concern is data-driven (bias, validation), patient concern is experiential. Plus “some studies show AI versions of a physician can learn to be more empathetic than the physician” + the individuals-who-trust-AI-are-2×-as-likely-to-use-it-regularly research finding. (4) The patient word critique + precise-medicine reframe — “It’s almost like you stay there, be patient, and wait until somebody tells you what to do” — AI shifts patients from be-patient-and-wait to precise medicine with multiple-treatment-pathway choice. (5) The nine-German-women postpartum-blood-loss anchor + women’s-cardiac-health gap — the global clinical standard for normal postpartum blood loss was established based on nine women in Germany and exported worldwide; India recalibrated to ~300mL given smaller body sizes. The wiki’s clearest single worked example of AI-as-clinical-protocol-bias-auditor at population scale (Ransbotham extension: “a very simple job for agents would be to go through all of our clinical practices in every area and find the root study for that and assess how that plays out”). Plus a parallel women’s-cardiac-health gap (highest mortality in women; longer diagnostic waiting times; protocols designed on male-only trial data; heart-position + rhythm-pattern algorithmic-incorporation prescriptions). (6) Interoperability as Peron’s single-pick global AI capability + three named barriers: data-quality-and-standardization-from-get-go, reimbursement-systems-that-don’t-incentivize-length-of-stay-reduction, regulation-must-evolve. Recurring frame: “AI is here to add, not to take over” — context that makes it hold differently from the generic optimist-framing: healthcare has a structural undersupply of clinicians and imaging globally → binding constraint is supply, not labor cost → “do more with the same not with less”. W&W tags (10 cells, including a
roles:override tocto, cdo, cmo, transformation-lead, product-manager, rd-director— healthcare-domain CMO-vantage):digital-sensing/digital-mindset-crafting+digital-sensing/digital-scenario-planning+digital-seizing/balancing-digital-portfolios+digital-seizing/rapid-prototyping+digital-transforming/improving-digital-maturity+digital-transforming/navigating-innovation-ecosystems+strategic-renewal/business-model+strategic-renewal/collaborative-approach+contextual/external-triggers+contextual/internal-enablers. 5 typed relationships: 4supports( Luminai — first two-altitude healthcare-AI cluster, clinical-practice + hospital-ops; MGI — clinicians in the people-centric occupation archetype, qualitative confirmation of MGI quantitative finding; Ransbotham et al. Augmented Learners — same-host pairing, organizational-learning frame applied to the radiologist-training-pipeline gap; WP Intelligence — cross-altitude support on the do-more-with-the-same prescription, three days apart). 1contradicts: Replit — clinical-domain rejection of the broad-occupation-substitution default framing. Concept changes: automation-vs-augmentation +1 (35→36; added §17 Peron radiology-displacement-that-didn’t-happen + radiologist-training-gap clinical anchor), ai-deskilling +1 (9→10; added section on Peron radiologist-training-on-normal-images gap as third causal mechanism alongside task-composition shift and BCG-AI-brain-fry), responsible-ai +1 (12→13; added section on nine-German-women postpartum-blood-loss anchor + women’s-cardiac-health gap — new RAI category: AI-as-audit-tool-against-pre-AI-embedded-biases). Entity changes: MIT Sloan Management Review 4→5 (addedpublished-byedge + sources-table extended; first podcast-format MIT SMR source); Sam Ransbotham 1→2 (addedauthored-byedge + Wiki contributions extended; first podcast-format Ransbotham source). Dangling first-mentions: Carla Goulart Peron (the subject; first appearance), Philips (referenced multiple times; first wiki mention; likely promotion candidate on a second healthcare-AI source mentioning Philips), Medtronic (Peron’s pre-Philips employer), SmartArt (Philips product, FDA-cleared 2026), Future Health Index (Philips’s annual survey), Me, Myself, and AI (the podcast itself; first ingest from this show specifically), Bernard Hampton (named in the episode’s closing as the next-week guest; forward-reference), Josh from CVS (Ransbotham’s prior guest reference). Surface artifact: ASR rendered Peron’s name as “Carla Gloram” in segment 0:13 and “Sa Paulo” for São Paulo throughout — caption-track-default deficiencies; transcript otherwise clean. - 2026-05-31-benedict-evans-rational-conversation-on-where-ai-is-actually-going — A rational conversation on where AI is actually going | Benedict Evans (Lenny’s Podcast YouTube — host Lenny Rachitsky; 31 May 2026; ~1:19:50; 1568-segment ASR auto-generated transcript landed via youtube-transcript-skill after 90s engagement-panel retry). Benedict Evans — independent analyst; ex-a16z partner; former equity researcher; six-year independent biannual AI Eats the World deck author (most recent deck published the day before this interview). The wiki’s first Benedict-Evans-authored source and fifth source under the
Lenny's Podcastchannel — joining the Caldwell / Schoening / Ries / Spiegel operator-and-founder cluster with the channel’s first independent-analyst-altitude long-form anchor on the what-people-aren’t-pricing-in question. Evans’s distinctive rhetorical move is deflation: AI is as big as the internet or mobile — and only as big. Twelve substantive contributions: (1) The 1997 for AI framing — “Most stuff kind of doesn’t work yet. Most of the stuff that people are going to do hasn’t been built yet and it’s not really clear how any of it’s going to work when it does work.” (2) The task vs job analytical lever as the operational restatement of the automation-vs-augmentation question — “Is the hard part of the job writing the code line by line? Or is it something else? Is it the task or the job?” The McKinsey-deck worked example: a Claude-Code-generated deck is the task, walking the enterprise is the job. Same applied to Amazon (gets you the SKU but knowing-what-SKU-you-want is a separate job) and software (Claude writes the code, but which code do you want?). (3) The expert-systems analogy as critique of O*NET-style task-decomposition — “You can’t look at a senior partner at a law firm and say, well 17% of their work could be automated. This is horseshit … this is the logical systems problem. The expert systems problem … an edge detector and then an eye detector and then an ear detector and 15 years later you’ve got 700 steps and it doesn’t work.” The wiki’s first independent-analyst-altitude direct attack on the methodology underlying Brynjolfsson’s task-level analysis and Dell’Acqua’s task-level frontier studies — accepts the jagged-frontier concept, rejects the percentage-of-job arithmetic. (4) The Excel-made-bankers-work-longer-not-shorter / Jevons-paradox argument — the US accountant-headcount chart rising continuously through adding-machines → punch-cards → mainframes → ERP → cloud → spreadsheets. Plus the AI-labs-themselves-keep-hiring corollary (“Anthropic, OpenAI … everyone’s just increasing headcount”). (5) Forward-deployed-engineer = Accenture-outsourced-developer-in-SF — “the most cutting-edge AI labs are the ones most investing in these folks” because workflow-reimagining is a five-to-ten-consultants-for-one-to-two-months project; convergent with McKinsey on agentic-era software delivery and Sternfels on consulting reinvention. (6) Foundation-models-as-commodity-utilities thesis — “the models don’t seem to have network effects … why would you have pricing power?” Sam-Altman-on-a-meter line gets “my dear sweet child, you need me to explain the margin structure of the utility industry to you … when you watch television the TV company isn’t paying a percentage of your monthly bill to the electricity company.” Telecom worked example: global mobile-data ~1500-2000× higher than 2010, telco stocks gone nowhere in 25 years. AWS-not-Windows analogy. (7) Distribution-becomes-the-moat once product is commodity — Meta-AI tracking competitively with ChatGPT/Gemini in surveys despite being written off in tech because “they’d sprayed it on every surface.” Same thesis as [[2026-04-26-how-to-win-when-software-is-not-a-moat-evan-spiegel-snapchat-ceo|Spiegel’s software-is-not-a-moat]] at a different altitude. (8) AGI-as-sliding-goalpost — Larry Tesler quote (“AI is whatever machines can’t do yet”) applied to current AGI definitions; a 1975 IBM mainframe would satisfy “can do a meaningful percentage of economically valuable work”. (9) Anti-AI-backlash as big fuzzy mess — Livermore Lab end-2024 study cited: US data-centre water consumption 0.017% of US water consumption; data centres ~5% of US energy, may grow ~1pp/year for next five years. Social-media-backlash analogy + Jonathan Swift quote. (10) 5-10-year sector-by-sector replacement speed model — “typical big-company enterprise software sales cycle is 18 months if you’re lucky … no, people aren’t just going to tear out SAP and replace it with XYZ. Maybe in three, five, ten years yes that whole estate will look radically different.” Plus the Frame.io delay-until-someone-realises-you-could-do-that-thing-with-this observation. (11) Mobile-era half-the-internet-untouched analogy — “the funny thing about mobile is that some companies missed it completely. And for some of them, it really didn’t change anything.” Steven Sinofsky quote on incumbents-and-features. (12) Career advice: dive in, don’t shout from Bluesky — “that gives you a great feeling of moral superiority and you can go on Blue Sky and shout at everybody about how evil AI is. Like — great, I’m happy for you. But that’s not going to help.” Convergent with Koomen’s just-build-and-ship practitioner stance. Plus Evans’s own AI use is illuminating-by-contrast (“I’m the lawyer looking at ChatGPT” — synthesis-across-many-sources still unreliable in May 2026; he uses it for proofreading, image generation, Apple Notes dictation). The under-asked question Evans names: “I’m not sure how many people are asking whether model labs have pricing power. I think a lot of people are just presuming that the situation today will continue.” W&W tags (7 cells):digital-sensing/digital-scouting+digital-sensing/digital-scenario-planning+digital-seizing/balancing-digital-portfolios+digital-seizing/strategic-agility+digital-transforming/redesigning-internal-structures+strategic-renewal/business-model+contextual/external-triggers. 12 typed relationships: 8supports(McKinsey rewiring software delivery — FDE-as-Accenture maps to team-shape thesis; McKinsey — AI labs invest IN consultancies; Spiegel — distribution becomes the moat, line-for-line match; YC Lightcone — dive-in career advice + harness namecheck; Stanford HAI + AI Index 2026 — adoption-data overlap with Evans’s Gen-Z 15-20% daily / 20% weekly numbers; AWS London — workflow redesign needs consultants; Anthropic engineering-org — AI labs keep hiring; AI Dev 26 — harness namecheck). 3contradicts(Dell’Acqua — methodology critique, accepts frontier rejects task-decomp; Brynjolfsson Canaries — same methodology critique applied to task-level AI-exposure analysis; Replit — lump-of-labor fallacy directly contradicts only-two-jobs-left framing). Concept changes: jagged-frontier 10→11 (added methodological-critique entry under Practical implications), ai-employment-effects 35→36 (added lump-of-labor section with three corollaries), automation-vs-augmentation 33→34 (added §16 task-vs-job analytical lever), foundation-models 14→15 (added commodity-utility / pricing-power-or-not section + debate entry), enterprise-ai-adoption 58→59 (added 5–10-year sector-by-sector speed-model section), agent-harness 52→53 (lexical-diffusion datapoint added to convergence table). Entity changes: Lenny’s Podcast 4→5 (typedpublished-byedge added; opening paragraph refactored to five sources / two-year arc / independent-analyst-altitude joining the operator-and-founder cluster; sources-table extended). Dangling first-mentions, deferred per second-source rule: Benedict Evans himself (the subject; first appearance; channel-as-author convention means he’s not inauthor:so the strict rule defers entity-page creation), Lenny Rachitsky (host; appears across 5+ sources but never asauthor:), Larry Tesler (the AI is whatever machines can’t do yet attribution), Mark Andreessen (referenced repeatedly), Dario Amodei (referenced; appears as author of Amodei et al. — likely candidate for entity promotion in a focused pass), Steven Sinofsky (one quote on incumbents-and-features), Eric Schmidt (commencement-speech aside), Dan Shipper (referenced as a recent Lenny guest), Frame.io (Evans’s worked example for the delay was somebody realising), House of a16z / Andreessen Horowitz (Evans’s former employer), Llama / Meta-AI (briefly). Surface artifact: the raw transcript file contains a duplicate transcript block (lines 1257–2040 repeat lines 473–1256) — ayoutube-transcript-skilloutput quirk; raw is immutable; doesn’t affect ingest. - 2026-05-28-moon-mckinsey-rewiring-software-delivery-for-the-agentic-era — Rewiring software delivery for the agentic era (McKinsey & Company / McKinsey Technology; 28 May 2026; ~7 pages). Jared Moon (Senior Partner, London) + Rory Walsh (Partner, Dublin) + Vito Di Leo (Partner, Zurich) + Adam Thelwall (Associate Partner). Edited by Barr Seitz (Editorial Director, NY). The wiki’s second McKinsey-published source on agentic-era enterprise transformation — extends Sternfels (Feb 2026)‘s firm-level-reinvention thesis with a same-firm 3.5-month-later operational specification of how the reinvention happens inside one work domain. Six substantive contributions: (1) The 9 a.m. vignette and the 24-hour-sprint model — daily-sprint model blending day shift (8 hours, humans supported by agents) with night shift (16 hours, led by a factory of agents); humans’ role ‘less about producing artifacts and more about supervising and improving the system that produces them.’ (2) The extend automation to eliminate human handoffs thesis with the McKinsey-altitude 30%/70% CI/CD-vs-requirements-through-coding cost decomposition — traditional CI/CD addresses ~30% of total tech spend; the interpretation-heavy 70% upstream is where the agentic-factory wins live. (3) The knowledge graphs as AI memory layer infrastructure prescription with the named librarian agent + the anti-grand-ontology design principle (‘this should not begin with a grand, top-down ontology effort. The graph should evolve organically around priority domains and live programs, compounding value over time’) + the knowledge-as-production-infrastructure / durable-source-of-competitive-advantage claim. (4) The team-size quantification anchor — Exhibit 3: 100 → 60 FTEs / 200 → 100 person-years / 10 teams of 8-12 → 16 teams of 3-4 / 10-role-pod (Product owner + Business analyst + Tech lead + 5 Software engineers + 2 Testers) → 3-role-pod (Product owner + Tech lead + AI-enabled engineer). Headline article claim: ‘threefold to fivefold improvements in productivity, with a 60 percent reduction in team size.’ (5) The outer-loop roles integration prescription — risk / legal / testing / procurement baked into the agentic-development effort by design, not as end-of-process gatekeepers, with policy as code as the named primitive. (6) The capacity-reinvestment-not-cost-cut normative framing — ‘Freed capacity is often reinvested to accelerate road maps, modernize platforms, or launch new products.’ Closing thesis: ‘human roles will concentrate in architecture, product judgment, and system design, making institutional knowledge and technical coherence decisive differentiators … they will redefine how software creates value.’ W&W tags (9 cells — comparable to Scheffer-De-Ondernemer 9, Everitt-JetBrains 10, Allen-AWS 10, Koomen 8):
digital-sensing/digital-scenario-planning+digital-seizing/balancing-digital-portfolios+digital-seizing/strategic-agility+digital-seizing/rapid-prototyping+digital-transforming/redesigning-internal-structures+digital-transforming/improving-digital-maturity+strategic-renewal/business-model+strategic-renewal/organizational-culture+contextual/external-triggers. 5 typedsupportsrelationships: McKinsey (same-firm 3-month operational follow-up to firm-level-reinvention thesis), AWS London (McKinsey-altitude quantification of AWS’s Project-Mantle worked example one week earlier — same team-shape claim, two altitudes), AI Dev 26 SF (convergent quantification of PM-bottleneck cascading-bottlenecks small-team-of-generalists thesis — 10-role-pod → 3-role-pod is the McKinsey-altitude empirical anchor for Ng’s engineer-plus-PM collapse into single human), YC Lightcone (consulting-altitude operational specification of Koomen’s YC-internal multiplayer-harness primitive set — same-week declarations on knowledge-as-production-infrastructure at two altitudes, 48 hours apart), MGI (two McKinsey-internal vantages on the 44%-via-agents nonphysical-work claim — MGI quantitative substrate + Tech-partner operational specification, 6 months apart). Concept changes: agentic-engineering 34→35, agent-harness 51→52, agent-development-lifecycle 14→15, enterprise-ai-adoption 57→58, ai-employment-effects 34→35, micro-productivity-trap 25→26, automation-vs-augmentation 32→33, durable-skills 21→22 + 2 synthesis bumps (knowledge-architectures-for-llm-agents 7→8 — the knowledge-graphs-as-AI-memory-layer + librarian-agent + anti-grand-ontology-design-principle framing is a substantive McKinsey-consulting-altitude operational anchor; is-rag-dead 7→8 — the agents-need-organizational-context-and-memory claim is convergent with the synthesis’s RAG-alive-inside-agentic-harness conclusion at McKinsey-altitude). Entity changes: McKinsey & Company 8→9. All 9 mentioned individual contributors are first-mention dangling (Jared Moon / Rory Walsh / Vito Di Leo / Adam Thelwall / Aishik Dhar / Amray Schwabe / Benjamin Schloesing / Nikolaus Müller / Barr Seitz). Five single-source-deferred concept-page candidates flagged: 24-hour sprint model; factory of agents; librarian agent; policy-as-code as outer-loop integration primitive; knowledge as production infrastructure. Boundary AI (cited as 2026-04-02 source for Software development cost: Complete 2026 budget guide) flagged as plausible adjacent ingest target. - 2026-05-28-giles-wp-intelligence-new-human-machine-workforce-agentic-ai-jobs — The new human-machine workforce: How agentic AI will transform jobs (Washington Post Intelligence / WP Intelligence; author Martin Giles; section AI & Tech; 28 May 2026; ~12-page browser-print PDF, Skia/PDF m148 Chrome export 2026-06-01, ~2.4MB; full article body ingested; free distribution sponsored by EY, with two inline EY.ai Value Blueprints ad placements retained in raw, omitted from wiki summary). The wiki’s first Washington Post-affiliated source + first ingest under the
Martin Gilesauthor:value + first EY-sponsored ingest. Executive-readership news-survey altitude — opens with five bulleted Key Takeaways, structures the body around four named subsections (Size and shape of the labor force is changing / Entry-level roles are most likely to be impacted by agentic AI / AI will change mid-level manager jobs / AI is blurring job boundaries and shifting training to what AI can’t do), and closes with five prescriptive executive recommendations. Eight substantive contributions: (1) April-2026 layoff anchor + AI-as-#1-cited-reason datapoint — 83,387 US job cuts April (+38% from March 60,620); 3rd-highest month since 2009 per Challenger, Gray & Christmas. “AI is the number one reason cited for job cuts in both March and April of this year.” Single-firm anchors: Block (Square’s parent; Jack Dorsey CEO) 40% workforce cut (10,000 → <6,000) citing “intelligence tools”; Meta (Zuckerberg) 8,000 cuts + 2026 capex raised to $125-145B. (2) Gartner spending anchor + Jensen Huang information robots framing — AI agent spending $86.4B (2025) → $206.5B (2026) (>2×). Huang: “AI has moved from generative AI to agentic AI. Finally, AI is not just interesting, but it is doing productive work that’s valuable.” Gartner also: agents could handle ~1/3 of business decision-making by 2028. (3) HubSpot no-mass-layoff practitioner counter-example — Helen Russell (CPO; ~9,000 employees): “Our mantra is that we have no intention of doing some mass layoff.” Worked example: redeployed 10 HR staff from interview-scheduling (now agent-handled) to employee-satisfaction and individual-development work. Pairs with Scheffer’s HelloPrint 100→18 at SME altitude (same operational pattern, two scales). (4) Carl Benedikt Frey no-automobile-industry empirical counter — Oxford AI/work professor: “It’s entirely plausible that AI will create new kinds of businesses. But it’s hard to see it creating something like the automobile industry that [generated] many new jobs.” The wiki’s first named-academic-altitude counter to the every-prior-automation-wave-created-new-jobs historical-induction argument — third pole in the Brynjolfsson Canaries + Evans lump-of-labor + Frey historical-induction-may-not-extrapolate triangulation. (5) Entry-level-impact + IBM-tripling counter-trend — Amodei 50% bloodbath prediction + the Stanford 22-25-year-olds 6%-decline finding (which IS the Brynjolfsson Canaries paper, summarised generically) + NACE late-2025 survey (183 employers; only 14% had considered replacing entry-level roles with AI; most cited economic outlook + budget cuts). The IBM counter-trend: tripling US entry-level hiring in 2026 + Nickle LaMoreaux (CHRO) on capability-gaps-from-short-term-savings + Agi Garaba (UiPath CPO) “growth engine” line. (6) Senior production planners → agent orchestrators manufacturing anchor — WEF San Francisco meeting of manufacturing/supply-chain executives; Microsoft data on manufacturers using more agents than retailing/financial services; Siemens 69% labor productivity at Germany flagship factory; Danfoss customer-order processing days → minutes; Kiva Allgood (WEF Center for Advanced Manufacturing): chatbots 2-3% productivity gains vs deeper agent + automation 30-60% — the wiki’s clearest single-line numerical anchor on the chatbot-vs-deeper-deployment gap. (7) Swamy Kocherlakota / Zscaler twin quotes — “the skill set that’s going to be most needed is end-to-end systems thinking” + “If you are working like a robot, your job will be taken by a robot.” (8) Five executive recommendations as the prescriptive close: (a) Review workflows first, deploy agents second (UiPath Garaba’s ~60-subprocesses-in-onboarding worked example); (b) Remember agentic tech isn’t perfect (hallucinations, accuracy); (c) Redesign entry-level roles (IBM model: customer-facing projects + reviewing agent output); (d) Beware AI-linked skill erosion (bonuses to practice load-bearing skills like coding; regular manual checks); (e) Communicate around AI often and carefully (Amodei dire-predictions communications-minefield framing). Plus: McKinsey identifies IT, knowledge management, sales & marketing as top-3 functions ramping up agents; Goldman Sachs identifies legal firms + life sciences most exposed; Coursera 2026 Job Skills Report (6M learners, ~7,000 orgs): 120% YoY increase in critical-thinking course enrollments. W&W tags (10 cells — among the broadest, comparable to Allen-AWS / Everitt-JetBrains / Scheffer / Moon-McKinsey):digital-sensing/digital-scouting+digital-sensing/digital-scenario-planning+digital-seizing/balancing-digital-portfolios+digital-seizing/strategic-agility+digital-transforming/redesigning-internal-structures+digital-transforming/improving-digital-maturity+strategic-renewal/business-model+strategic-renewal/organizational-culture+contextual/external-triggers+contextual/internal-barriers. 9 typed relationships: 1contradicts( Lenny’s Podcast — partial; same-week-apart, same topic, divergent reading: Frey no-automobile-industry + April-2026-layoffs counter Evans’s lump-of-labor + new-jobs-will-emerge framings, but both agree on AI-labs-still-hiring + workflow-redesign-as-real-work + McKinsey top-3-functions). 7supports(Brynjolfsson Canaries — the Stanford 22-25 study IS Canaries; McKinsey — same-week 28 May 2026, workflow-redesign-as-real-work + manager-as-orchestrator convergence; McKinsey — services-don’t-disappear-they-reinvent + McKinsey top-3 functions; Replit — doom-side convergence; AWS — production planners → orchestrators + 2-3% vs 30-60% productivity-gain split; HelloPrint — HR-redeployment-as-augmentation pattern at SME altitude; MGI — skills-matter-more-than-titles + agent-centric quadrant). 1 supports AI Index 2026 (academic empirical record / executive readership of same). Concept changes: ai-employment-effects +2 (36→38; added two new sections: April-2026 layoff anchor + AI-as-#1-cited-reason monthly attribution and Carl Benedikt Frey no-automobile-industry counter-precedent + Peron do-more-with-the-same healthcare-domain reframe — the wiki’s first domain-conditional reading of AI-employment-effects: supply-side condition matters), automation-vs-augmentation 35→36 (added §16 HubSpot redeployment-of-ten + IBM redesign-entry-level-roles worked examples), enterprise-ai-adoption 59→60 (added section on Gartner $86B→$206B + one-third-business-decisions-by-2028 + five executive recommendations — the wiki’s first executive-readership five-recommendations close), micro-productivity-trap 26→27 (added section on Allgood 2-3% chatbots vs 30-60% deeper-agent + Siemens 69% + Danfoss days-to-minutes — the wiki’s clearest numerical anchor on the trap’s depth-gradient), ai-deskilling 8→9 (added section on AI-linked skill erosion as an executive prescription with the bonuses-and-manual-checks counter-measures), durable-skills 22→23 (added section on Coursera 120% YoY critical-thinking enrollment growth demand-side anchor + Kocherlakota end-to-end-systems-thinking + UiPath case-study-vs-online-training pedagogy). Dangling first-mentions: Martin Giles (author), Washington Post Intelligence / WP Intelligence (publisher; first wiki source), EY (sponsor; first EY-sponsored ingest), Carl Benedikt Frey (Oxford AI/work), Jensen Huang / Nvidia, Jack Dorsey / Block / Square, Mark Zuckerberg / Meta (referenced across wiki sources as body figure but no entity page), Helen Russell / HubSpot (HubSpot has a prior Claude customer-success ingest but no entity page), Agi Garaba / UiPath, Nickle LaMoreaux / IBM, Kiva Allgood / WEF Center for Advanced Manufacturing, Swamy Kocherlakota / Zscaler, Siemens, Danfoss, Coursera, Challenger, Gray & Christmas, NACE (National Association of Colleges and Employers), Goldman Sachs, Gartner. - 2026-05-27-scheffer-de-ondernemer-helloprint-ai-rebuild-from-day-zero — Ondanks 80 miljoen omzet gooit Hans Scheffer zijn bedrijf volledig om met AI: ‘Elke kantoorbaan voor een scherm verdwijnt’ (De Ondernemer / DPG Media — Dutch entrepreneurship publication; author Tijmen Koppelaar; 27 May 2026, 18:19; ~7-minute read; original language Dutch). Hans Scheffer — founder/CEO of HelloPrint (~€80M revenue 2025; print platform; NL + Valencia operations). The wiki’s first Dutch-MKB-altitude AI-transformation anchor — joining the US-vantage transformation cluster (Hu / Koomen / MGI) + UK-vantage cluster (Erginbilgiç / Allen-AWS) + Swiss-corporate-academic cluster (Warner & Wäger) with a Dutch SME-founder-CEO operational vantage at €80M revenue scale. Six substantive contributions: (1) The technology-leading / people-directing structural reversal — ‘Waar vroeger de mensen eerst kwamen en technologie ondersteunend was, draait dat nu om. Technologie wordt leidend, en daaromheen zit een veel kleinere groep mensen die die technologie aanstuurt.’ The wiki’s clearest single-sentence Dutch-MKB-founder-CEO declaration of the technology-leads, humans-direct reversal — same-day-different-continent twin of [[2026-05-27-koomen-yc-lightcone-inside-yc-ai-playbook|Koomen’s AI as the building layer for everything, not a copilot]]. (2) The denk vanuit dag nul (day-zero rebuild) thought experiment as transformation method — ‘wat nou als ik mijn huidige bedrijf op dag één opnieuw zou moeten bouwen, wat zou ik dan doen?’ Credits the House of Founders five-day session in South Africa (with Joep Verbunt of Matt Sleeps + Ron Simpson) as the experiential anchor. (3) The AI-brein (AI-brain) as organisational foundation, not a standalone IT project — ‘Aparte afdelingen voor marketing, IT, operatie en finance, elk met eigen systemen en verantwoordingslagen, verliezen hun functie.’ (4) The Chief AI Officer is the missing role in MKB management teams observation — Scheffer’s named-MKB-role-gap diagnosis. (5) The HelloPrint customer-service 100 → 18 worked example — ‘beter dan het niveau waarop mensen het kunnen doen.’ Headcount cut to 18% of peak with AI quality exceeding human, primarily via natural attrition (Valencia rotation throughput). The wiki’s clearest single-SME-firm quantified-customer-service-automation worked example. (6) The conscious revenue sacrifice for long-term winning operating discipline — ‘door bijvoorbeeld afscheid te nemen van bepaalde omzetgroepen … wel een fundamentele keuze om op de lange termijn te kunnen winnen.’ Convergent with [[2026-05-24-erginbilgic-bloomberg-leaders-rolls-royce-turnaround-playbook|Erginbilgiç’s Rolls-Royce transform-from-strength playbook]] at radically different scale (Erginbilgiç £80B FTSE-CEO + Scheffer €80M Dutch-SME-CEO, three days apart, same playbook). Plus the €80M → €1B stip op de horizon and fifty-year company vision closing + the topsport-team not-everyone-fits culture framing + the headline-grab maximalist claim ‘Elke kantoorbaan voor een scherm gaat verdwijnen … Heel veel banen die wij kennen, gaan gewoon echt verdwijnen.’ — the wiki’s clearest Dutch-SME-founder-CEO-vantage maximalist statement of the office-work-displacement thesis. W&W tags (9 cells — among the broadest in the wiki, comparable to Everitt-JetBrains 10 / Allen-AWS 10 / Koomen 8):
digital-sensing/digital-mindset-crafting+digital-sensing/digital-scenario-planning+digital-seizing/balancing-digital-portfolios+digital-seizing/strategic-agility+digital-transforming/redesigning-internal-structures+digital-transforming/improving-digital-maturity+strategic-renewal/business-model+strategic-renewal/organizational-culture+contextual/external-triggers. 5 typedsupportsrelationships: MGI (qualitative Dutch-MKB-founder-CEO confirmation of MGI’s quantitative every-office-job-behind-screen-is-automatable finding), YC (Dutch-SME-founder-CEO operationalisation of Hu’s day-zero / closed-loop-company prescriptive doctrine), YC Lightcone (same-day-different-continent convergent declarations on AI-as-building-layer-not-feature), Warner & Wäger (most-comprehensive single-source W&W operationalisation at SME altitude — 9 of 12 cells), Rolls-Royce (paired turnaround-playbook anchors at £80B vs €80M scale, three days apart). Concept changes: enterprise-ai-adoption 56→57, ai-employment-effects 33→34, automation-vs-augmentation 31→32, micro-productivity-trap 24→25, warner-wager-process-model 2→3 (confidence 0.80→0.82 — first non-Allen-AWS-vantage Dutch-language operationalisation), durable-skills 20→21. Three single-source-deferred concept-page candidates flagged: AI-brein as organisational foundation; day-zero rebuild as transformation method; MKB Chief AI Officer as missing role. All 12 mentioned entities are first-mention dangling (Hans Scheffer, HelloPrint, Tijmen Koppelaar, De Ondernemer, DPG Media, Joep Verbunt, Matt Sleeps, House of Founders, Ron Simpson, Lars Freriks, Trustoo, Valencia) — no entity promotions in this ingest. - 2026-05-27-sajadieh-stanford-hai-inside-the-2026-ai-index-report — Inside the 2026 AI Index Report (Stanford HAI YouTube channel — talk + panel, 27 May 2026; ~1:12:52; ASR-cleaned auto-generated English captions, 547 segments). Sha Sajadieh (EiC, AI Index 9th edition) presents the headline framing; ~42-minute panel Q&A with Raymond Perrault (Co-chair, AI Index Steering Committee; co-founder of the report; SRI International emeritus) and Russell Wald (Executive Director, Stanford HAI). The wiki’s first report + talk-track dual ingest paired with its companion published report (2026-04-30-ai-index-report-2026). Five substantive contributions over and above the report: (1) The absorption gap as the single observation running through the whole 2026 report — “AI is scaling faster than the systems around it are built to absorb” compresses the report’s headline framing into an executive-briefing soundbite. (2) The acceptability-threshold research gap — Perrault: we know self-driving cars need very high reliability because people die; we don’t know how reliable AI must be to be acceptable in legal / medical / finance. The wiki’s first named research gap for what reliability bar must AI clear to be acceptable in regulated domains? — a measurement question the AI Index cannot yet report on because nobody is measuring it. Plausible single-source-deferred concept-page candidate. (3) The academic-vs-industry transparency tension — Wald’s argument that frontier-lab proprietary mode hampers academic peer-review/verification; pairs with the report’s empirical anchor (80 of 95 notable models without training code; FMTI declining). (4) The AI-Index founding mission and the facts-only discipline — Perrault’s first-person founder-account: report on the facts, not projections, not recommendations to governments. Distinguishes the AI Index epistemic stance from interpretive landscape reports (FTSG, MGI Race Takes Off). (5) The World-Bank / IMF global-AI-adoption measurement initiative — Perrault discloses he and Yolanda Gil are working with major US companies (agreement-in-principle to share user/usage data); not in time for next report. Plus a Wald should-almost-be-chapter-one aside on the public-opinion chapter; a halves-and-have-nots pushback on the opacity-drives-mistrust framing; a Jevons-paradox framing for the AI-as-job-creator survey-response 10% optimism; and a Sajadieh editorial compression of public-sentiment data into the country leading in development can not be the same as the country leading in adoption. W&W tags (4 cells, mirrors the MGI virtual event ingest’s profile):
digital-sensing/digital-scouting,digital-sensing/digital-scenario-planning,digital-seizing/balancing-digital-portfolios,contextual/external-triggers. 3 typedsupportsrelationships: AI Index 2026 Report (direct talk-track of the published artifact; statistics overlap heavily; panel adds editorial-interpretation layer not in report), MGI virtual event (sibling live-event-of-annual-landscape-report pattern at major-institution altitude; identical four-cell W&W profile), FTSG Convergence Outlook 2026 (two 2026 annual horizon scans converging on the same empirical landscape — US-China narrowing, infrastructure concentration, public-trust polarization — from different framings). Entity changes: Sha Sajadieh 1→2 conf 0.70→0.75; Raymond Perrault 2→3; Russell Wald 2→3; AI Index 2→3; Yolanda Gil 2→3 (mention by Perrault); Stanford HAI 2→3. Concept changes: jagged-frontier 9→10, ai-employment-effects 29→30, enterprise-ai-adoption 52→53, generative-ai 21→22, ai-benchmarks 6→7, responsible-ai 11→12, foundation-models 13→14 — alllast_confirmedbumped; no content changes (talk-track re-confirms numbers the report already provided). Reverse body-mention added on the parent report page (2026-04-30-ai-index-report-2026). Single-source-deferred concept candidates: AI acceptability threshold (Perrault’s named gap); AI sovereignty (Sajadieh’s framing). Surface artifact: ASR rendered Perrault as “Ray Perau”; transcript otherwise clean. - 2026-05-27-koomen-yc-lightcone-inside-yc-ai-playbook — Inside YC’s AI Playbook (Y Combinator Lightcone podcast — Pete Koomen guest with Garry Tan / Diana Hu / Harge Taggar moderating, 27 May 2026;
46:30; yt-dlp WebVTT fallback after 180s engagement-panel timeout — VTT rolling-window dedup applied; 555 segments). Pete Koomen — YC General Partner; co-founder of Optimizely; author of the Horseless Carriages essay. The wiki’s substantive architecture-and-organisation talk on YC’s internal AI infrastructure — the same project Garg’s 5-minute talk introduced at the IC altitude, now described at the org-architecture altitude by the partner who built it. Seven substantive contributions: (1) The multiplayer-harness gap and its primitive set — “we’re still kind of in the single-player era of agents … one of the big problems that I don’t think has been solved well yet by anybody is the multiplayer harness.” YC’s proposed primitive set: one Postgres-substrate one database to rule them all (“BigTable for agents”); 350-tool shared registry (started ~20, every team adds tools); shared skill-registry with the skillify / DRY / MECE resolver pattern (resolvers ≈ AGENTS.md); broadcast-by-default agent conversations to an internal Slack channel; the self-improving dream cycle (overnight skill iteration); DRY + MECE check-resolvable meta-skill; model router substrate. (2) The SQL-as-magic-moment empirical anchor — Jared Friedman’s “surreptitiously pushed it out late at night” read-only SQL-access tool; non-technical finance partners could ask “show me investors who invested in space-related companies in the last four batches” in seconds vs. hours of SQL. (3) Jevons paradox for agent-mediated work — “It didn’t just make it easier to answer questions, it dramatically increased the number of questions … if I have to go knock on the data science team’s door I’m just going to ask far fewer questions.” (4) Two-sentence-pitch skill as worked example of org-scale super-intelligence compounding — Tom (YC partner) wrote a skill, group-office-hours generated transcripts, transcripts fed back as meta-prompt → “this thing is now better than I am, I would argue, at writing those.” Tan’s interpretation: “This is how super-intelligence happens inside organizations … and it’s not more complicated than that.” (5) The egalitarian + trust-by-default precondition — broadcast-by-default agent conversations only work in high-trust environments; substitute social control for technical access control. (6) The $100K-token-budget leapfrog argument — “a one-time time warp where you can leapfrog every incumbent, all Fortune 500s, all startups that exist.” (7) The raising the floor observation — new-employee ramp 6 months → days via agentic apprenticeship of best-practice skills. Plus the just-in-time-software / chat-is-right-UI evolution + the Horseless Carriages essay reference + Tan’s closing 1984-vs-Homebrew Computer Club political argument for personal AI (“Apple I moment”). W&W tags (8 cells — among the broadest in the wiki, alongside Ng / Allen-AWS):digital-sensing/digital-scouting,digital-seizing/strategic-agility,digital-seizing/balancing-digital-portfolios,digital-seizing/rapid-prototyping,digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,strategic-renewal/organizational-culture,strategic-renewal/collaborative-approach,contextual/external-triggers. 4 typedsupportsrelationships: YC internal-AI 2026 (paired IC + architecture vantages on the same project), GStack (IC packaging + org-scale extension), Karpathy 2026 (LLM-wikis + auto-research load-bearing citations), Agent Harness Engineering (multiplayer extension of single-player harness engineering). Entity changes: Pete Koomen promoted from dangling to entity page (2-source rule fulfilled). Bumps: Y Combinator 11→12, Garry Tan 4→5, Diana Hu 3→4, Andrej Karpathy 5→7 (+ Everitt). Single-source-deferred concept-page candidate: multiplayer-harness. - 2026-05-26-landingai-touchpoint-to-outcome-front-office-processes — From Touchpoint to Outcome: Transforming Front-Office Processes with AI (LandingAI webinar, 26 May 2026; auto-captioned, ~46 min). The wiki’s first LandingAI-channel source and first dedicated document-intelligence source. Pairs LandingAI’s Agentic Document Extraction (ADE) — vision-first, zero-shot, visually grounded extraction on proprietary DPT models — with partner TCG’s OCTO process-automation platform. Core theses: the OCR accuracy gap (generic OCR 80–90% vs the “high 99.x%” an agentic pipeline needs → accuracy as an adoption gate); grounding as the trust primitive (cell/word/figure audit trail for finance + life sciences → responsible-ai); and touchpoint→outcome orchestration (the “octo-zone” normalises varied front-office inputs, validates, and connects to systems of record). Deployment: ADE Cloud (US/EU, zero-data-retention) / VPC / on-prem air-gapped. Vendor-reported case: insurance claims 85% faster, 75% efficiency gain; global-bank KYC automation. Promotes LandingAI to an entity; creates document-intelligence. W&W:
digital-transforming/redesigning-internal-structures+improving-digital-maturity,digital-seizing/rapid-prototyping,strategic-renewal/business-model,contextual/external-triggers. 2 typedsupports: Luminai (manual-doc→AI-automation, vertical angle), Gemini File Search (verifiable grounded document retrieval). Dangling: TCG, Neil Walker, OCTO, DPT. Confidence (source): vendor marketing webinar — treat metrics as uncorroborated. - 2026-05-24-erginbilgic-bloomberg-leaders-rolls-royce-turnaround-playbook — Rolls-Royce CEO Tufan Erginbilgiç Shares His Turnaround Playbook (Bloomberg Podcasts YouTube channel — Leaders With Lacqua series; published 24 May 2026, ingested 26 May 2026; ~23:44 minutes;
kind: manualproofread captions track; transcript ~169 lines). Host Francine Lacqua (Bloomberg Television anchor) interviews Tufan Erginbilgiç (Rolls-Royce CEO since Jan 2023; previously 25+ years at BP including the BP CEO race he lost, then ~2 years in private equity). The wiki’s first pure non-AI industrial-transformation anchor + first Bloomberg Podcasts source + secondkind: podcastsource (after Storoni). Tenure cited at ~1:05 as McKinsey’s “case study in the art of corporate transformation”; shares up >10× since Jan 2023. Six substantive contributions: (1) The “burning platform” speech as designed artefact — Jan 2023 1.5-hour internal town-hall built on external benchmarking commissioned starting Sept ‘22 (~2:01–2:32), paired diagnosis (“every investment we make destroys value” / cost-of-capital > returns, ~4:05–4:14) with vision in the same speech; the speech was a publication of pre-existing shareholder frustration, not a unilateral attack (~6:13–6:41). (2) Restructuring vs transformation as deliberate semantic distinction — Erginbilgiç insists the prior decade’s multiple restructurings explain why employees needed transformation framing to believe 2023 was different (~3:11). (3) The four-pillars framework with an honest-scoping caveat — the chapter title promises four; Erginbilgiç explicitly enumerates only two (“first pillar is all about people… second pillar is very granular strategy”, ~11:53–12:25). The wiki documents both the framing and the gap; pillars 3–4 are reconstructed as commercial discipline (contract renegotiation) and performance culture (360 performance management) from surrounding material. (4) Strategy-as-participation doctrine (~12:43–13:32, substantive new content on the strategy concept) — “I don’t do strategy in the dark room with consultants. I personally attended 25–30 workshops… every like 500-plus people joined. When you are done, whole organisation is aligned because they were in the room when you were making the decision.” Two protocol rules: “no hierarchy in the room” + “this is going to be chaotic”. Alignment as the output of participation, not the output of cascade communication. (5) Demanding ≠ tough love — opening clip (~6:51–7:35) carefully separates demanding-with-fairness-and-transparency from tough (rejected) and blunt (reclaimed as “you know what you get instead of dancing around”); paired with the new-normal-as-eased-cadence observation (~7:36–8:23) — leader demand-intensity is time-shaped, drops once team behaviour shifts to new norms. (6) The resilience playbook (~20:17–22:04) — “Once you get into trouble, it is too late”; four ingredients in order: mindset + response capability + agility + action-orientation; “It’s not about actually predicting the world, it is about how your company now thinks about dealing with external shocks” — resilience as process capability, not forecasting capability; quantified claim ~5–10× capability multiplier in three years (“we couldn’t do half of what we are doing three years ago, probably 10%”). Other named contributions: layer-elimination-without-operational-people cuts (~4:24–5:22, with the moral framing “if we don’t do that, probably 50,000 people’s lives will be affected”); CEO-to-CEO contract renegotiation as cultural shock-wave (~13:46–15:44, “I’m going to do impossible things as well”); PE-investment-committee-as-coaching-loop import (~10:08–10:55); British-industry talent contract (“highly marketable but want to stay with you”, ~18:05–18:24); growth-as-retention-mechanism (~18:24–18:42); SMRs (Small Modular Reactors) as industrial learning-curve-economics bet over bespoke-EPC nuclear (~18:42–19:57); history as durable-skill reading habit (~23:09–23:26 — “human behaviour doesn’t change a lot”). W&W tags:strategic-renewal/organizational-culture,digital-seizing/strategic-agility,contextual/internal-barriers— the first wiki source to map the W&W vocabulary onto a pure non-digital transformation case, demonstrating the cells stretch outside the digital lens with the digital-mindset clause optional. 2 typedsupportsrelationships: Krakowski 2025 (non-tech-industry transformation pair on the tailored-leadership + culture-refresh lever — pharma DiD + aerospace CEO interview; same load-bearing claim that tailored intervention beats blanket prescription), AWS London Exec Forum (demanding-leader-as-transformation-cadence pattern, two industries apart — Allen’s 5× expectation + Brooklyn’s human-starts-human-ends + Erginbilgiç’s non-compromise on mediocrity + the eased-cadence observation; convergence is load-bearing precisely because the domains differ — the demanding-leader pattern is transformation-primitive, not AI-specific). Concept changes: strategy +1 source (3→4, conf 0.80→0.85, +substantive strategy-as-participation doctrine subsection); dynamic-capabilities +1 source (8→9, conf 0.90→0.95 cap, +non-AI control-case section + missing## Debates and supersessionfilled — addresses pre-existing quality_notes flag); warner-wager-process-model +1 source (1→2, conf 0.75→0.80, +The model’s reach beyond the digital lens section + new## Debates and supersession). Dangling first-mentions: Tufan Erginbilgiç (Rolls-Royce CEO), Francine Lacqua (Bloomberg Television anchor), Bloomberg Podcasts (channel/publisher), Rolls-Royce (subject organisation), BP (former employer). - 2026-05-22-khan-cline-deeplearningai-ai-dev-26-sf-evals-are-broken-use-them-anyway — AI Dev 26 x SF | Ara Khan: Evals Are Broken Use Them Anyway (DeepLearningAI YouTube channel — AI Dev 26 x SF practitioner-track session, published 22 May 2026, ingested 25 May 2026; ~24:36; 699 ASR segments). Ara Khan (Cline). The wiki’s first deep articulation of the customer-of-model-vendor vantage on eval discipline — paired with the model-vendor-side Claude-channel session the same day. Six substantive contributions: (1) Two-camps framing — most people are wrong about evals in one of two ways: the “objective metrics” camp (takes benchmark scores at face value; benchmark-maxing is widespread; models close in score aren’t actually equally good) vs the “taste is king” camp (vibes-only, anthropomorphises models). Truth in the middle. (2) Three levels of eval engagement — interpret others’ / improve your agents / build your own. (3) Three heuristics for interpreting others’ evals — don’t believe model-lab evals (they’re close approximations not gospel); stay current but don’t be the earliest adopter (let the dust settle 2 weeks); look for evals close to your problem (SWE-bench so saturated model labs stopped citing it). (4) Cline’s eval journey (load-bearing operational worked example) — 2024 baseline was “talked to OpenAI and Anthropic, both said yeah evals are great but bro it’s just about the vibes”; pivot was adopting Stanford terminal-bench (89 real-software-engineering tasks: database issues, race conditions, frontend bugs, regex, latency, caching; 5-45 min agent runs with deterministic unit-test grading) + Harbor (containerised isolated environments for parallel runs) + Modal (parallel-containerised infrastructure layer) + per-run tracking of turns/tool-calls/tokens/run-time. (5) The three-things-being-tested framework (most-portable analytical primitive) — model + harness + problem-space alignment. Harness-as-confounder observation: “if a new model from Anthropic comes out, you would have noticed it works better in Claude Code compared to say Droid or Cursor sometimes. If it’s the same model, why is it that it’s much better in Claude Code than some other agent? That’s what you’re testing here.” (6) The three-zones-of-improvement framework (most-portable strategic primitive) — Zone 1: obvious flaws (broken read-file tool, broken agent turns, broken checkpoints; fix first); Zone 2: real hill-climbing (philosophical aspects — too many tools, wrong tools, prompt-engineering issues); Zone 3: danger zone (Goodhart-style overfitting). Headline operational claim: “Eventually we were able to beat Claude Code in Opus 4.5 evals… we figured out some tiny knobs that they couldn’t figure out or they didn’t optimize for” — the third independent primary-source confirmation of the harness-changes-alone-improve-eval-scores-substantially-on-the-same-model pattern (after LangChain and Interrupt 26). Closing doctrine: “Find a benchmark that works for you. Build some eval if you can. Hill-climb. And even if you get a good score, always pass the vibe check.” + the open-source-model coda (“we never would have figured out all these beautiful nuances of these open-source models which are incredible and much cheaper had we not run eval.“) W&W tags:
digital-sensing/identifying-needs,digital-seizing/strategic-agility,digital-seizing/balancing-digital-portfolios,digital-seizing/rapid-prototyping,digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,strategic-renewal/business-model,contextual/external-triggers,contextual/internal-barriers. 8 typed relationships: Claude channel (supports— same-day same-conference paired evals anchors at opposite vendor-altitudes), Interrupt 26 (supports— direct operational instantiation of three-layer continual-learning + Top-30-to-Top-5 harness-improvement empirical anchor), LangChain (supports— customer-of-vendor empirical instantiation of harness is load-bearing; third independent confirmation), Agent Evaluation Guide (supports— article + practitioner anchors converge), Karten & Zhang (supports— manual-driven equivalent of automated continual-harness-adaptation), AI Dev 26 (supports— same-channel same-conference companion), CS153 (contradicts— productive tension on vendor-vs-customer-of-vendor coding-agent positioning), Pan & Khattab (supports— harness-is-all-you-need thesis instantiated at production-vendor altitude). Entity changes: DeepLearningAI promoted from Dangling to entity page (2nd source under channel-author triggers per channel-as-entity convention); Cline promoted to entity page (cross-page-presence judgment — Cline was already cross-referenced in 4 prior wiki pages). Anthropic 14→16 (both videos cite). OpenAI 12→13 (Khan’s 2024 anecdote + SWE-bench-deprecation citation). Dangling first-mentions: Ara Khan, terminal-bench (concept-page candidate on 2nd source), Harbor, DeepSeek V4 Flash, Droid, François Chollet (ASR-uncertain attribution). Confidence 0.78. - 2026-05-22-everitt-jetbrains-deeplearningai-ai-dev-26-sf-shift-to-agentic-engineering — AI Dev 26 x SF: The Shift to Agentic Engineering (DeepLearningAI YouTube — Paul Everitt at JetBrains; 22 May 2026; ~28:17; yt-dlp WebVTT fallback after 180s timeout; 331 segments). Paul Everitt — Developer Advocate at JetBrains, Python old-timer (first Python meetup 1994). The wiki’s first JetBrains-altitude practitioner call-to-arms on agentic engineering, joining the Ng keynote / Cline / Anthropic slide-gen AI Dev 26 SF cluster (now 4 sources). Five substantive contributions: (1) The problem framing — eight failure modes of more code, fewer people with citation per failure mode: productivity gap (“it ain’t 10×, it’s 10%” — Daron Acemoglu / Nobel 2024); quality crisis (50% defect rate + Simon Willison challenger disaster + Replit-dropped-tables); price changes (Ed Zitron); trust gap (3% confidence 2025); token-maxing (Uber annual gone in 3 months + 67-point management-vs-engineer gap); open-source mental health; sovereignty/regulated-industry gap (84% European skepticism + insurance + banking); mega-layoffs at Atlassian/Block/Stripe/Amazon (May 2026) + Sam Altman AI washing counter. (2) The reframe — build the thing that builds the thing crediting Karpathy (“he coined the phrase agentic engineering a few months ago”), Booch (“third golden age of software engineering”), and OpenAI Harness Engineering (“the engineer’s value-add shifts to building the harness”). Plus Simon Willison’s dark factory pattern. (3) The nine-element agentic-engineering practice taxonomy: evals (we need data scientists doing actual work again) / harness engineering (if you don’t own your harness you don’t own your memory) / tooling-code-mode (Pydantic Monty + Cloudflare) / red-green TDD for agents (Lenny’s podcast with Simon) / modularity-for-parallel-sub-agents / QA-agents (DevTools protocol integration) / observability (Pydantic LogFire) / orchestration (500 SV startups) / context-engineering (Brandon at Unbox) / leadership-and-culture (FOBO). The wiki’s most-complete practitioner-altitude treatment of agentic engineering as a named discipline. (4) The Grady Booch Gang-of-Four-for-AI call — “his cohort did UML, did design patterns, kind of built the field of modern software engineering. He knows there is a next thing called agentic patterns and he wants one of you in the audience to be the one that gets the ball rolling and helps us all figure out this discipline of agentic design patterns.” The wiki’s first named-Gang-of-Four-for-AI call. (5) The call to arms (not action) — engineers as messengers who can reframe executive more-code-fewer-people narrative into engineering augmenting humans. W&W tags (10 cells — comparable to Khan/Cline 9 + Allen-AWS 10):
digital-sensing/digital-scouting+digital-sensing/digital-scenario-planning+digital-seizing/strategic-agility+digital-seizing/balancing-digital-portfolios+digital-seizing/rapid-prototyping+digital-transforming/improving-digital-maturity+digital-transforming/redesigning-internal-structures+strategic-renewal/business-model+contextual/external-triggers+contextual/internal-barriers. 6 typedsupportsrelationships: Ng (same conference, Everitt cites Ng directly), Cline (same-day evals theme), Anthropic slide-gen (same-day evals triplet), Osmani (explicit cite), harness (own-your-harness convergent thesis), Karpathy (coined agentic engineering). Entity changes: Daron Acemoglu, Sam Altman, Simon Willison, Pydantic, Grady Booch, Addy Osmani all promoted from dangling to entity pages (second-source rule). Single-source-deferred concept-page candidate: agentic design patterns. - 2026-05-22-anthropic-evals-for-taste-hill-climbing-slide-generation-agent — Evals for taste: Hill-climbing a slide-generation agent (Claude YouTube channel — talk recorded at AI Dev 26 x San Francisco; published 22 May 2026, ingested 25 May 2026; ~39:16; ASR-cleaned auto-captions via yt-dlp fallback, 1,053 deduped segments). Speaker not named on stage or in channel metadata (Anthropic-side practitioner-track presenter using the Anthropic
manage_agentSDK +python-pptx+ Sonnet 4.7 / Opus 4.7). The wiki’s deepest Anthropic-vendor practitioner-engineer altitude treatment of eval discipline, paired with Cline published the same day at the same conference. Eight substantive contributions: (1) Definitional grounding — evals = systematic tests measuring AI-system performance on a specific domain/use case; the bridge between vibes and actionable. (2) The grader-type taxonomy (most-reusable framing): three categories — code-based graders (deterministic string match / regex / fuzzy match / tool-call checks; fast/cheap/deterministic but brittle and lacks nuance), model-based graders / LLM-as-judge (rubric scoring + pairwise comparison [“underrated”] + multi-judge consensus [averaging non-determinism via best-of-three majority]; flexible/scalable/nuanced but nondeterministic and needs calibration), human graders (highest quality + most nuanced; expensive and slow). (3) The slide-generation agent worked example — naive prompt → typography rules → diagram requirement → QA loop → switch to Opus 4.7 with the base naive prompt recovers most prompt-engineering gains. Pre-built code graders (emoji_count, cluttered_slides, slide_count, image presence, small_font_slides, text_heavy_slides) + judge graders (color, image, layout, text — each 0-5). (4) The QA-loop adversarial pattern (most-portable operational primitive): “Required QA loop. Assume there are problems. Your job is to find them. Approach QA as a bug hunt, not a confirmation step… do not stop until you’ve completed at least one fix-and-verify cycle.” (5) The autoregressive-judge-ordering bug (most-pointed warning) — ask LLM-as-judge for the score first then reasons and it rationalises the score it committed to; always ask for reasons FIRST then the score. Reasoning-before-rating discipline. (6) The judge-calibration problem named as load-bearing — judges score 2.8-4.0 on obviously-terrible slide decks because they have nothing to anchor on; fix: explicit examples of what 0 looks like + 5 looks like. “Evals as living artifact, not ground truth.” (7) Eval saturation named as a real concern: “the eval is not giving any more relevant information that we can act on.” (8) The model-upgrade-vs-prompt-engineering trade-off — Opus 4.7 with the naive base prompt recovers most iterative-prompt-engineering gains on the slide-gen task; productive complement to Cline’s opposite agency choice (beat Claude Code via harness engineering on the same model). W&W tags:digital-sensing/identifying-needs,digital-seizing/strategic-agility,digital-seizing/balancing-digital-portfolios,digital-seizing/rapid-prototyping,digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,contextual/external-triggers. 7 typedsupportsrelationships: Cline (same-day same-conference paired evals anchors at opposite vendor-altitudes — model-vendor vs customer-of-model-vendor with opposite agency choices), Interrupt 26 (operational instantiation of evals-as-gradient at vendor-engineer altitude), Agent Evaluation Guide (paired article + conference-talk anchors on the same territory), SEI-CMU (under-specification of judges = under-specification of agents at the layer above), Karten & Zhang (manual-driven equivalent of the automated p/G/K/M continual-harness-adaptation framework), CS153 (manual-equivalent of Hu’s cross-modal-evals Skillify built-in), AI Dev 26 (same-conference companion talk). Dangling first-mentions: Anthropic-presenter (unnamed),manage_agentSDK primitive. Confidence 0.72. - 2026-05-21-sinclair-ivers-benitez-sei-cmu-ai-native-software-engineering — AI-Native Software Engineering: Enduring Principles, New Pace (Software Engineering Institute | Carnegie Mellon University YouTube channel — SEI webcast, originally streamed live, published 21 May 2026, ingested 22 May 2026; ~61:14; ASR-cleaned auto-captions, 581 segments). Host Scott Sinclair (software architect, SEI) in conversation with James Ivers (principal engineer, SEI; 30+ years) and Mario Benitez (software architect, SEI; 20+ years). The wiki’s first institutional-research-centre voice on the AI-coding-era software-engineering-discipline question. Eight substantive contributions: (1) Definitional grounding — AI-native vs AI-augmented vs agentic vs vibe-coding as a vocabulary the field is using loosely; Ivers’s load-bearing rhetorical claim “AI native is not AI only” + “AI native is essentially a state of mind”. (2) The technical-debt-acceleration thesis (Benitez’s load-bearing empirical claim): “We can generate code at rates we’ve never thought of before. We can also generate technical debt at rates that we’ve never thought of before.” Paired with Ivers’s “no one has maintained code that was AI generated for 5 years” temporal-horizon caveat. (3) The 25,000-line vibe-coding experiment (Ivers’s institutional-altitude worked example) — “I got a very slick application but under the hood it was structured terribly. The architecture was bad. It wasn’t very maintainable.” The clearest institutional-altitude worked example of where the vibe-coding-vs-engineering boundary lies. (4) Engineering principles endure — DRY, SOLID, YAGNI remain load-bearing; “we don’t let go of the safeguards that we have now just because there’s more code. We might scale it differently.” (5) Under-specification as the bad-practice-AI-magnifies-most — institutional-altitude restatement of Momentic’s truth-driven / spec-driven development thesis. (6) Architects more important under AI volume (Ivers) + ADRs as the text-anchor for capturing architecture decisions at scale. (7) Slop-squatting as a new attack vector — AI-generated package names that don’t exist, which adversaries can squat (wiki’s first surfacing of this term). (8) Coder vs Software Engineer closing role-shift framing (Ivers): “the narrow interpretation of coding might be fading. But those who can bring engineering judgment, point AI in the right direction, connect it to the business — that’s an incredibly valuable skill that’s never resided solely in a programming language.” W&W tags:
digital-sensing/digital-scenario-planning,digital-seizing/strategic-agility,digital-seizing/balancing-digital-portfolios,digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,strategic-renewal/business-model,contextual/external-triggers,contextual/internal-barriers. 7 typedsupportsrelationships: O (same-day institutional-altitude pair on AI does not replace engineering discipline), O (DORA-rooted-fundamentals at institutional altitude), Lenny’s (paired prototype-vs-engineering worked examples), Karpathy 2026 (vibe-coding-vs-engineering distinction operationalised at institutional altitude), Momentic (under-specification as the bad-practice-AI-magnifies-most), CS153 (paired coder-vs-software-engineer role-shift framings landing same week), Man Group (same-day institutional + enterprise anchors on AI-coding governance). Dangling first-mentions: Software Engineering Institute, Carnegie Mellon University, Scott Sinclair, James Ivers, Mario Benitez, slop-squatting (concept-page candidate on 2nd-source mention). Confidence 0.78. - 2026-05-21-neven-manyika-google-io-quantum-ai-future — Building the quantum-AI future with Hartmut Neven and James Manyika (Google for Developers YouTube channel — Google I/O 2026 Dialogues session, 21 May 2026; ~40:28; manual English captions, 335 segments). James Manyika (President of Research, Labs, Technology & Society at Google and Alphabet) interviewing Hartmut Neven (leader and founder of Google Quantum AI since 2012). The wiki’s first substantive quantum computing ingest — framed as the AI-adjacent next-supercycle technology cluster rather than a standalone domain. Three substantive contributions: (1) Quantum 101 primer (superposition + qubits + the Willow chip = 105 qubits). (2) The Nobel-Prize-2025-Physics anchor — Berkeley 1980s superconductivity research (Michel Devoret + John Martinis + John Clarke) leads to commercial Google Quantum AI team (Devoret = Chief Hardware Scientist; Martinis = ex-Google Quantum). The wiki’s first Nobel-Prize-2025 reference and first quantum-physics-lineage citation. (3) The AI ↔ quantum two-way interaction: AI helps build quantum (error correction, hardware optimisation, control-system design); quantum unlocks new AI vectors (new training paradigms, learning from complex molecular/material data, accelerating scientific discovery — AlphaFold as the current AI-meets-science worked example). Google’s moonshot lineage Manyika opens with: “Search itself is a moonshot. Auto-correction using machine learning (2001). Waymo driverless cars. Quantum is one of those too.” Filed as a stub-anchor for a potentially-emerging cluster — wiki bumps James Manyika entity (2→3 sources) as the interviewer vantage. W&W tags:
contextual/external-triggers. 1 typedsupportsrelationship: AI Index 2026 (Google-vantage anchor on the next-supercycle technology stack beyond LLMs dimension AI Index tracks). Dangling first-mentions: Hartmut Neven, Michel Devoret, John Martinis, John Clarke, Willow chip, AlphaFold. Confidence 0.55. - 2026-05-21-jones-stanford-gsb-ai-and-our-economic-future — “A.I. and Our Economic Future,” Professor Chad Jones (Stanford Graduate School of Business YouTube channel — GSB Spring Reunions, recorded 1 May 2026, published 21 May 2026; ~60:38; manual English captions, 518 segments). Chad Jones (Stanford GSB economist; growth-economics tradition — “the whole reason I’m at Stanford, the whole reason I got tenure is that one picture” — the 2%-per-year-for-150-years US-real-income-per-capita chart). Based on 4–5 research papers Jones has been working on over 2024–2026. The wiki’s strongest single academic-altitude theoretical anchor on AI’s economic and labour implications. The framing: two scenarios — AI dramatically accelerates growth (FOOM) (Dario Amodei / Sam Altman / Geoff Hinton / AI 2027 / Leopold Aschenbrenner timeline; country of geniuses in a data center) vs AI is just a normal technology (electricity / internal-combustion / antibiotics / IT / internet — all transformative, but growth stayed 2% per year). Both plausible; truth between. The 2% puzzle: “How can it simultaneously be true that these technologies were wildly transformative, and yet growth rates still 2%?” Jones’s answer: within any technology class, ideas get harder to find — the steam engine runs out of steam; each new transformative technology extends 2% growth for another 50 years rather than accelerating it. The weak-links model (~10:54–14:38; the talk’s central conceptual contribution): a chain is only as strong as its weakest link; business success requires completing many tasks successfully; if one falls down (Space Shuttle Challenger O-ring), value is lost. “In your pocket, in my pocket, we have a computer with 100 million times the transistors than the equivalent of us had in the 1970s. I’m not 100 million times more productive at research. Why not? It’s a weak-link problem.” The elegant formula: “Infinite amounts of some task raises GDP by that task’s share of GDP.” Software is ~2% of GDP — so infinite software is 2% richer, not 1000× richer. The computer-share-of-GDP empirical anchor (~16:35): peaked at 4.5% in 2000, now 3% — “computers are indeed everywhere, and yet they’re paid less as a share of GDP rather than more. The price decline dominates the quantity increase. This is exactly what a weak-link model predicts. Computers are plentiful. Everything else, humans are scarce.” The model simulations: three scenarios (full automation / 3% reserved-for-humans Leo-Messi case / baseline 1/3 capital share); baseline shows growth slowly accelerating 2% → 2.3% → 2.6% → 3% → eventually 50%/year, but takes centuries. The radiologist + Waymo worked examples (~31:30–34:03) — applied to jobs-as-bundles-of-tasks: “Geoff Hinton in 2016 said ‘We should stop training radiologists’ — in five years there’ll be no more radiologists with jobs. He wasn’t wrong about AI being better than the radiologists. But we have more radiologists today than we did in 2016, and they’re paid more. Jobs are bundles of tasks. When the AI automates 75 of them, the weak links are the things that are now scarce and get the high return.” The wiki’s most concrete worked example of why labour-substitution does not equal jobs-eliminated. The catastrophic-risk corollary of weak-links (~38:46–40:55) — the talk’s most distinctive contribution: “A weak-link model is very slow to improve, but very fragile on the downside.” Jones cites Anthropic’s Mythos (bug-discovery model that found thousands of bugs in 25-year battle-tested software) as the empirical anchor — “in 6 months or a year we’ll have an open-source version of Mythos that anyone can use. How sure are we that a bad actor doesn’t take it and hack the electric grid, hack the financial system?” — same mechanism, asymmetric timeline: productivity benefits decades, catastrophic risks months-to-years. Closing summary: “How much is AI going to change the world between 2015 and 2045? How many internets is it worth? I think multiple internets, many internets. More transformative than anything we’ve seen, but it’s probably going to take a longer time than we thought. And the downside risks can come sooner.” W&W tags:
digital-sensing/digital-scenario-planning,contextual/external-triggers. 5 typedsupportsrelationships + 1contradictsrelationship: Crowdless Future (paired academic-economist articulations of AI’s labour implications, weak-links theoretical lens on labour-substitution mechanics), Canaries (empirical-anchor + theoretical-anchor pair on the same question), Jagged Frontier (jagged-frontier and weak-links as paired framings of the same uneven-capability observation at micro and macro altitudes), AI Index 2026 (Stanford-vantage anchor pair — AI Index = supply-side measurements, Jones = demand-side / economic-impact model), GStack (contradicts) — productive disagreement on the thousandx-engineer claim: Tan’s boil the ocean / do the work of 500-to-1000 people vs Jones’s infinite software is only 2% richer. The wiki carries the productive contradiction. Dangling first-mentions: Chad Jones, Daron Acemoglu, Bill Nordhaus, Paul Romer, Geoff Hinton (second-source-promotion candidate), Stuart Russell, Sebastian Thrun, Mythos (Anthropic model). Confidence 0.85. - 2026-05-21-fernando-man-group-trading-signals-that-trade-themselves — Trading signals that trade themselves (Claude YouTube channel — Code with Claude conference talk, 21 May 2026; ~20:45; ASR-cleaned transcript, 157 segments). Tushara Fernando (head of data and AI at Man Group — $200B AUM alternative investment manager; clients = pension funds, sovereign wealth funds, large institutions). The wiki’s strongest enterprise-scale empirical anchor on the skills-governance thesis. Headline empirical claim (~4:38): “There are trading signals running right now in production at Man Group, a regulated investment firm running real capital, that were researched, backtested, and proposed by AI. AI came up with the idea. AI got the data. AI ran the backtest. AI then wrote up the strategy proposal and AI productionised the signal. Humans of course reviewed all of the output to make sure that it was sensible. But AI was at the center of that process.” Four substantive contributions: (1) The expense-report failure-mode story (~9:32–10:42) — the wiki’s clearest empirical worked example of what goes wrong with ungoverned skills: a power user wrote a skill that hardcoded his cost-center code, shared with team; expense approver in sales suddenly got everyone’s reports because “nobody had reviewed that skill — it worked for him, it worked for his team, so it was going to work for everybody. And he wasn’t accountable for that. He kind of thought it was quite funny. And I mean, so did I, to be honest.” The distinction between power-users vs process-owners writing skills is the wiki’s clearest articulation of the failure mode that motivates Tan’s skillify 10-step compliance protocol. (2) Knowledge marketplace as the governance solve — every skill visible, tagged, tested with evals, owned by the workflow owner, usage tracked, lifecycle-managed; plugins as groups of skills (e.g., a data plugin gives access to Man Group’s datasets). The organisational-library framing: “Imagine a library. It captures decades of institutional knowledge. There are sections for the finance department, the people department, the research department. We care for every item.” (3) The systematic-trading worked example (~13:20–16:01) — 4-skill workflow demo: alternative-datasets skill → search for credit card data → identify US consumer transactions dataset → plot Amazon’s credit card spend against stock returns → backtest ($1,000 in 2021 → $2,500 today, better than buy-and-hold) → distributed-compute skill to run on broader retail universe. (4) Organisational context as IP / moat (~16:47, the load-bearing rhetorical claim): “Focus on that organizational context. That is your IP. It’s your moat. It’s one of the few safe spaces left in AI. The frontier labs are not going to solve context for you. It’s not on the internet. They don’t know your workflows. And you already have that context. You have decades of it. The work is on exposing it, not reinventing it. And skills are how those decades of institutional knowledge become leverage.” Operational scale anchors: 1,700–1,800 people at Man Group; 750 of them use Claude Code across developers, quants, the people team, the finance team; 100+ governed skills + at least as many community skills. Lessons Fernando would tell past-me: focus on organisational context (your IP, your moat); treat skills like production code; plan governance before rollout (who owns / reviews / retires / tests — “before shipping the first skill, not after the hundredth like us”); adoption is a people problem, not a licensing problem. W&W tags:
digital-seizing/balancing-digital-portfolios,digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,strategic-renewal/business-model,contextual/external-triggers. 4 typedsupportsrelationships: CS153 (enterprise-scale operationalisation of skills/skillify framework — the expense-report-as-empirical-anchor on why check-resolvable exists), Interrupt 26 (Fernando’s organizational context as moat = Chase’s context layer of the three-layer continual-learning model — vendor-CEO + enterprise-customer paired anchor), AnswerThis (Garg at 2-FTE / Fernando at 1,700-employee = the scale-ladder endpoints of the same self-extending-skills-platform thesis), Emergent (Claude-channel customer-story cluster extension — opposite SMB / non-coder vs enterprise-regulated-finance vantages). Dangling first-mentions: Man Group, Tushara Fernando. Confidence 0.78. - 2026-05-21-chase-langchain-interrupt-26-future-of-ai-agents — The Future of AI Agents: What Will Interrupt 2027 Look Like? | Interrupt 26 (LangChain YouTube channel — Interrupt 26 Day 2 keynote, 21 May 2026; ~22:10; manual English captions, 193 segments). Three speakers: Harrison Chase (co-founder/CEO), Brace Sproul (product walkthrough), Caroline di Vittorio (live Fleet demo). Framing device: imagine Interrupt 2027 — what would the topics be a year from now? Six substantive contributions: (1) two-types-of-agents typology — long-horizon (minutes/hours/days; code execution, planning, sub-agents, multi-agent, skills; outcomes/goals as horizon-extension) vs customer-experience (latency-critical, voice, brand-sensitive, customer support / sales); shared stack underneath but particular technology pieces for each — a new structural axis orthogonal to Chase’s earlier frameworks/runtimes/harnesses/no-code Build-layer split; (2) voice agents — pipeline (speech-to-text → agent → text-to-speech sandwich) vs native speech-to-speech (OpenAI V2 from ~7 May 2026; “not quite steerable enough yet, we do expect that to change”); (3) every agent needs a sandbox — code execution is for data analysis, web browsing, image gen, deep research, not just software writing; “give a marketing team a software engineer — what apps would it build to make their job easier? That’s what giving an agent the ability to write and execute code is.” LangChain launched sandboxes on Interrupt 26 Day 1; (4) three drivers of open-source models rising — capability gap closing on benchmarks, cost (coding agents burn tokens), trainability for particular domains via fine-tuning on agent-run traces; (5) agent identity & auth as a structural axis — act-on-behalf-of-user (uses caller’s credentials, different users see different things) vs fixed-service-account (GPTs-style, everyone sees same responses); SaaS providers are starting to make it easy for agents to create their own accounts; “being really, really precise about when to use which one and making that clear to users will be important” — wiki’s first articulation of agent-auth-pattern as structural product-design choice; (6) the centerpiece — three-layer continual-learning model: every agentic system has independently improvable model (Sonnet, GLM4, GPT-4) / harness (deep agents, Claude Code, pi — “the code surrounding the model that connects it to the environment”) / context (
agent.md, skills — “what we provide to the harness as ways to guide it on particular tasks”) layers. The classical-ML analogy: “In classical ML you have the model, training data, gradient descent updates weights. When you’re updating the agent — if at harness or context layer — it’s not exactly gradient descent, but the evals that you write act as a forcing function… those evals are providing a similar type of this training gradient.” Wiki’s clearest single articulation of evals-as-gradient for non-model layers. Worked examples Chase names on stage: (a) Ramp + Prime Intellect fine-tuned Qwen 3.5 for Ramp sheets (model-layer continual learning); (b) MetaHarness (MIT + Stanford) — “used an agent to optimize a coding harness… it wasn’t changing the model at all, it was just editing the harness”; (c) LangChain itself: “we moved from top 30 on terminal bench two to top five just by changing the harness itself — no changes to the model, only changes to the harness, and we saw a big increase in performance” — the third primary-source confirmation of this exact numerical claim, following LangChain Engineering blog (10 March) and the cohort of corroborating harness > model papers, making it the strongest single-claim convergence in the wiki’s harness > model corpus. Product announcements: LangChain Labs = new research group “aimed in particular at continual learning”; LangSmith Fleet (no-code agent builder) updates — 200+ built-in tools + Arcade partnership for 7,500+ more + MCP support + native Slack/Gmail/Outlook channel integration + sharing like Google Docs + auth management + cost tracking/usage controls + first-class HITL + built on deep agents + downloadable agent files. The internal-LangChain GTM-agent metrics (Caroline di Vittorio live demo, ~13:40–19:05): 84% of go-to-market team uses weekly; lead-to-qualified conversion up 240%; 40 hours saved per rep per month; agent originally built in code by an engineer, “rebuilt in Fleet so the go-to-market team could own this agent’s implementation entirely end-to-end without having to write a single line of code” — wiki’s first concrete “engineer-built-it → no-code-rebuilt-it-so-domain-team-owns-it” migration with quantified outcomes. Closing: Fleet ships with a free open-source model powered by Fireworks AI partnership. W&W tags:digital-sensing/digital-scenario-planning,digital-seizing/balancing-digital-portfolios,digital-seizing/strategic-agility,digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,strategic-renewal/business-model. 6 typedsupportsrelationships: ADLC (9 May) (same speaker; this Interrupt keynote extends the ADLC vocabulary along two new structural axes), MetaHarness (Chase cites by name on stage), LangChain (third primary-source confirmation of Top 30→Top 5), Karten & Zhang (vendor-CEO operationalisation of academic formalisation 10 days later), LangChain deep-agents Feb (Fleet built on deep agents, per Sproul), CS153 (paired founder/CEO articulations of layered-agent-system thinking landing within 24 hours — Tan maps agentic-primitives → company-structure; Chase maps learning-systems → agentic-system-layers). Entity changes: Harrison Chase promoted from Dangling to entity page (second substantive source per promotion rule — first was Chase/ADLC 9 May); LangChain 4→5 sources with new Interrupt 26 keynote section + LangChain Labs + Fleet operational metrics; concepts bumped: agent-harness 37→38, agent-development-lifecycle 8→9, ai-agents 15→16, enterprise-ai-adoption 38→39, foundation-models 8→9. Dangling first-mentions: Brace Sproul, Caroline di Vittorio, LangChain Labs, Fireworks AI, Arcade, Ramp, Prime Intellect. Confidence 0.80. - 2026-05-21-bender-google-io-software-engineering-tipping-point — Software engineering at the tipping point (Google for Developers YouTube channel — Google I/O 2026 Professional Development track, 21 May 2026; ~39:39; manual English captions, 398 segments). Adam Bender (Google). The wiki’s strongest socio-technical-systems-thinking anchor on developer-ecosystem evolution under AI. Four substantive contributions: (1) Systems-thinking definitional stack — system → ecosystem → complex adaptive system → socio-technical system; Conway’s Law as the canonical insight. Your internal developer ecosystem is a socio-technical CAS. (2) The 10×-moment-will-break-every-part-of-your-ecosystem thesis (~13:26–25:00): “If your ecosystem suddenly had to grow by 10 to 15× in the next 18 months, do you know what would break first? All the trade-offs we have deliberately evolved over the last 25 years are going to get re-balanced.” Worked-example failure modes: 10× more code = 10× more liability (Jeff Atwood quote anchoring); bigger compiles + more compiles per cycle; binary-size limits (“we’re getting our binaries so big in some places we can’t compile them anymore”); 10× network traffic for microservices; component-reuse breakdown when agents write code that is easy to write and hard to maintain. (3) Generating code 10× faster ≠ engineering 10× faster (~15:35–16:25): “At Google, we often say that engineering is programming integrated over time. We’re speeding up programming a lot — we’re making the code machine go fast. So we’re going to have to figure out how we engineer around that code machine.” The wiki’s clearest single articulation of the programming-vs-engineering distinction at AI-era scale — adjacent to Schoening’s prototype-vs-engineering physical metaphor. (4) AI as amplifier — fundamentals matter more than tooling (~32:05): “AI doesn’t care where all of that stuff goes. What DORA really found was that teams that had good fundamentals could apply that amplification in useful directions. AI doesn’t solve any of these problems for you by default. It can amplify the practices you have, if they’re good. But if they’re not good, it’s going to cause more trouble.” Together with O this is the wiki’s second 2026 DORA-grounded anchor — same conference, same channel, same amplifier-and-mirror framing applied at the individual-engineer altitude (Forsgren) and the ecosystem altitude (Bender). Forward prediction: “In 2030, our developer ecosystems today are going to feel like 2001 does to us now” (when we were shipping software on CD-ROMs). Four-axis preparation checklist: infrastructure capacity / validation / isolation (“you don’t want that cool prototype code to actually find its way into production”) / abstraction (“we need good abstractions for the agents to hold on to — don’t give them bad choices”). The intellectual-control closing thesis (~35:31): “How are we going to maintain intellectual control over our code bases as we grow? Intellectual control is just a fancy way of saying ‘can humans reason about this thing in front of them?’ We’ve been losing this war for at least the last 15 years.” W&W tags:
digital-sensing/digital-scenario-planning,digital-transforming/redesigning-internal-structures,digital-transforming/improving-digital-maturity,digital-seizing/strategic-agility,contextual/internal-barriers. 3 typedsupportsrelationships: O (paired Google I/O 2026 Professional Development talks on the same DORA-rooted AI as amplifier framing), MIT (MIT system-dynamics lineage applied to software engineering), Anthropic (Anthropic-side ↔ Google-side paired engineering-org redesign anchors). Dangling first-mentions: Adam Bender, Jeff Atwood. Confidence 0.75. - 2026-05-21-allen-aws-london-exec-forum-agentic-team-structures — London Executive Forum 2026: A leader’s guide to advanced team structures in an agentic world (AWS Events YouTube channel — keynote + customer testimony filmed at AWS London Executive Forum 2026, published 21 May 2026, ingested 25 May 2026; ~43:54; 412 ASR segments). Two speakers in series: Jonathan Allen (AWS Executive in Residence — third holder of this title to appear in the wiki, after Werner and Le-Brun) for the keynote (~0:07–33:20), then Nick Francis (co-founder + Chief Technology & Marketing Officer of UK supplier-management scale-up Brooklyn Solutions, regulated customers including Ministry of Defence and Danske Bank) for the customer testimony (~33:20–43:45). The wiki’s first source from the AWS Events YouTube channel + first vendor-altitude longitudinal pair with HBR IdeaCast 2025 at 12-month delta. Five substantive contributions: (1) The PM-bottleneck thesis, independently confirmed at AWS-Executive-in-Residence altitude with a quantified internal-Amazon delivery datapoint — Project Mantle (AWS internal rebuild of the layer under Amazon Bedrock, scheduled for ~18 months, shipped in 76 days when AWS put VP-Distinguished-Engineer Anthony Liguori + L7 principal engineers on it with goal-based agents). The load-bearing claim (~20:23–20:40): “the bottleneck is shifting. It’s not execution now. It’s decisions. It’s product management. And for a long time, for 30 years in my career, it has been the long tail in getting things done. Suddenly, we’re seeing that invert.” The 20–21 May 2026 PM-bottleneck cluster is now three sources strong (Ng + Allen + Sinclair/Ivers/Benitez). (2) The USE / COMPOSE / BUILD economic-decision framework — Allen reports ~80% of AWS customers currently land at COMPOSE (composing with frontier-model APIs — Haiku / Sonnet / Opus 4.7 — rather than training their own models); BUILD justified only when fine-tuning improves inference economics. The framework is the agentic-system-economics overlay on Jassy’s May-2025 three-layer AI stack at 12-month delta. (3) The embedded-pod model (3–5 engineers / pod organised around workflow — not product, not MVP) + hourglass-organization as the operating-shape counter to “seniors 20×-ing productivity, cutting back juniors” default; past 3–4 pods → platform team required (job: removing friction from pods, not controlling them); SRE renamed from IT ops “is going to fail”. Empirical anchor for the junior-hiring crisis (73% European-tech entry-hiring collapse per Ravio; 7% new grads → big tech, down from 15%; 7.7% junior headcount decline; 9% senior employment growth). (4) The moats-erosion thesis — old moats (workflow embeddedness, software scale, integration lock-in, engineering complexity, IP) erode under agentic AI; replacement moats: compounding proprietary data, network effects, regulatory permission, capital at scale, physical infrastructure, plus the meta-moat time-that-can’t-be-parallelised. Worked example: banks expanding branch networks as differentiation. (5) Brooklyn Solutions’ phased-ingest discipline + 5× expectation (5× output for ≤25% opex increase) + human-starts-the-work, human-ends-the-work accountability shape, with Bedrock Guardrails + AgentCore as the regulated-customer harness composition. Brooklyn’s segmentation-table worked example (numeric ids 1–5 mapped to critical/important/transactional labels giving “weird at best” LLM responses until context was iteratively added) is the wiki’s clearest vendor-customer-side ratification of context-engineering discipline. Closing aphorism: “momentum beats perfection” — explicitly not permission to flood disposable apps (Allen’s anti-disposable-applications stance + Brooklyn’s iterative quality compounding). Other named contributions: the Singaporean Davos-2026 governance model (four principles: assess and bound risks up front, human accountability, technical controls across lifecycle, end-user responsibility) as the only government governance framework Allen rates as adoptable; policy-as-code as the lock-down layer; non-determinism as feature, not bug as the operating-model corollary (“toll gates or should it be toil gates”); the Anthropic competition exhibit (1st lawyer, 2nd cardiologist from Poland building AI-agentic patient-care platform, 3rd cardiologist — no developer in top 3) as the non-developer-domain-expert-wins operationalisation of the renaissance-developer / expert-generalist role-shift. W&W tags:
digital-sensing/digital-scenario-planning,digital-seizing/strategic-agility,digital-seizing/balancing-digital-portfolios,digital-seizing/rapid-prototyping,digital-transforming/redesigning-internal-structures,digital-transforming/improving-digital-maturity,strategic-renewal/business-model,strategic-renewal/organizational-culture,contextual/external-triggers,contextual/internal-enablers. 8 typedsupportsrelationships: AI Dev 26 (independent confirmation of PM-bottleneck thesis at AWS altitude, one day later, with Project Mantle as quantified datapoint), HBR IdeaCast (12-month longitudinal AWS-altitude pair — three-layer stack stable, USE/COMPOSE/BUILD as the agentic-economics overlay), Anthropic Code with Claude (vendor-channel-parity anchor on running AI-native engineering orgs — Anthropic + AWS converge 13 days apart on processes-are-the-hard-part), SEI-CMU (role-shift convergence at AWS-vendor vs SEI/CMU-institutional altitudes, published same day), Warner & Wäger (end-to-end practitioner operationalisation of W&W process model at vendor altitude — the wiki’s first), HBR IdeaCast (anti-disposable-software ethic + quality-over-quantity convergence at neuroscience vs AWS-vendor altitudes), Anthropic Economic Index Q4 2025 (Allen cites the Apr 2026 Anthropic labour-market report lineage — open follow-up: ingest the specific April 2026 report), McKinsey (consultant-altitude vs AWS-advisory-altitude convergence on enterprise-AI-deployment doctrine, 7 concept overlaps). Entity changes: Amazon Web Services 3→4 (with new §4 USE/COMPOSE/BUILD frame + AgentCore + four new dangling people). Dangling first-mentions: Jonathan Allen (AWS Executive in Residence), Nick Francis (Brooklyn Solutions CTMO), Brooklyn Solutions (organisation), Anthony Liguori (AWS VP Distinguished Engineer / Project Mantle lead), Matt Garman (AWS CEO), Francesca Vasquez (AWS executive who introduced AgentCore the same day), Scott Galloway (quote source), Rory Sutherland (quote source), William Gibson (quote source), Martin Fowler (strangler-fig + expert-generalists post), Werner Vogels (re:Invent 2025 renaissance-developer keynote), Jeff Bezos (Amazon L9-elimination), MIT NANDA (95%-of-AI-pilots-fail report — concept-page candidate on 2nd source), April-2026 Anthropic labour-market report (open follow-up), Nvidia SLM paper (open follow-up), Project Mantle (AWS internal-team / 76-day case), Singapore Davos-2026 AI governance model. Confidence 0.8. - 2026-05-20-tan-hu-stanford-cs153-ai-native-company-1000x-engineer — The AI Native Company: How One Founder Becomes a 1000x Engineer (Stanford Online YouTube channel — CS153 Frontier Systems guest lecture, 20 May 2026; ~47:14; ASR-cleaned transcript, 366 segments). Garry Tan (President & CEO, Y Combinator) and Diana Hu (General Partner, YC) guest-lecturing at Stanford CS153 Frontier Systems. Host frames the lecture as the company-formation-layer counterpart to earlier CS153 sessions on compute / capital / energy / chips — positioning YC’s introduction of the SAFE in the 2010s as the standardization moment for venture capital parallel to electrical-grid buildout, and what’s happening now at the cognitive layer as a parallel standardisation (“this time it’s code, not just code, markdown is code, literally the new ___”). The substantive novelty is the agentic-primitives → company-structure mapping: skills = employees with capabilities; resolvers = the org chart (who handles what); GBrain / three-layer memory = internal process / where information lives; check-resolvable = audit and compliance; trigger evals = performance reviews; skillify = the 10-step do-once → skill + unit tests + LLM evals + integration test + resolver + trigger eval + check-resolvable + smoke test + schema compliance protocol. The wiki’s first founder-CEO-altitude rhetorical claim that the agent-harness primitives are structurally identical to a company’s org structure, not merely metaphorical. Load-bearing concrete claims: (a) 5-day Posterous rebuild on a $200/month Claude Max plan vs original 10 people / $4M / 2 years — the single most concrete 1000x-engineer anchor in the corpus; (b) closed-loop-vs-open-loop company control-systems framing (Hu’s contribution; P-controller analogy explicit) — agent needs read access to every artifact (codebase + Discord + meeting recordings); (c) $1–2M revenue/employee benchmark at YC AI-native portfolio vs Salesforce “under six figures” (~10× gap); (d) forward-deployed-engineer wedge worked examples — Salient (voice agents for loan servicing; top US banks), Happy Robot (freight forwarders; Series B 2024; 10× revenue in a year), Reducto (document processing; picks-and-shovels for the agentic stack); (e) cross-modal evals as a Skillify built-in (in-progress) — Opus 4.6 + GPT-5.5 + Deepseek V4 cross-evaluating with structured ratings fed back to the original sub-agent for iterative improvement; (f) GBrain extension to a typed knowledge graph + in-progress epistemology layer for “hunches vs beliefs vs world knowledge” — direct extension of Karpathy’s knowledge-wiki pattern; (g) closing slide: Anthropic economic-deployment chart with white-space across back-office / finance / data / academics / cybersec / customer service. Productive boil-the-ocean contradiction: Tan inverts the legacy don’t-boil-the-ocean business advice (“let’s boil the ocean — you can do the work of 500 to 1000 people”); this carries an explicit
contradictstyped relationship with Onshore’s wedge-first counter-position. W&W tags:digital-seizing/strategic-agility,digital-seizing/balancing-digital-portfolios,digital-transforming/redesigning-internal-structures,strategic-renewal/business-model,strategic-renewal/organizational-culture,contextual/external-triggers. 6 typedsupportsrelationships filed: GStack (own follow-up at lecture altitude), YC (Hu’s April framework re-asserted), Karpathy (GBrain as extension), Khattab (founder-vantage on harness-is-all-you-need), Garg (paired YC self-improving-harness vantage), Anthropic (paired CEO+GP altitude on engineering-org thesis). Entity pages updated: Garry Tan 3→4 sources with new framings (agentic-primitives-as-company-structure, 5-day Posterous, cross-modal evals, boil-the-ocean); Diana Hu promoted from Dangling to entity page (her second substantive source — 1: Hu/YC April; 2: this CS153 talk); Y Combinator 5→7 sources (this video + Bodewes/Phonely batched in same session). Dangling first-mentions: Salient, Happy Robot, Reducto, Jack Dorsey, Alan Watts, Posterous (historical context). Confidence 0.80. - 2026-05-20-ng-deeplearningai-ai-dev-26-sf-future-of-software-engineering — AI Dev 26 x SF: Andrew Ng: The Future of Software Engineering (DeepLearningAI YouTube channel — AI Dev 26 x San Francisco conference talk, published 20 May 2026, ingested 24 May 2026; ~19:21; ASR-cleaned auto-captions via yt-dlp fallback, 502 deduped segments). Andrew Ng keynote. The wiki’s first solo-headlining Ng anchor (his prior wiki appearance was the brief “unbig in AI” cross-reference in MIT March 2026; this ingest promotes him from Dangling to entity page). Tightly-structured 19-minute future-of-software-engineering talk with two product announcements at the end. Eight substantive contributions: (1) “My own coding is pretty much 100% AI” + “many of the frontier teams are trending toward 100%” — the wiki’s most-aggressive unconditional 100%-AI-coding norm at conference altitude (calibrating caveat: “this is not a religion… if you’re working for NASA, write it by hand”). (2) The PM-bottleneck thesis (load-bearing rhetorical centerpiece) — PM-to-engineer ratios collapsing from typical 1:8 toward 1:2 → 1:1 → “the only thing that can move even faster is you take those two people and collapse them into a single human.” (3) Cascading bottlenecks — design, legal/compliance (“if you take a day writing code, you’re going to wait a month for legal, it’s like, boy”), marketing, sales bottlenecks all become acute under AI code-gen acceleration. Forced-generalism math: “if a team needs software, product, design, legal, and marketing, and it’s a team of two, by definition these two people have to have some skills in all of these functional areas.” (4) The AI-job-apocalypse counter-claim — “I just don’t see the AI job apocalypse happening anytime soon… my teams can’t find enough of these people.” Cites business-media coverage and a Federal Reserve Bank of Philadelphia study on “job apocalypse being delayed.” (5) The AI-engineer hiring rubric: coding-agent fluency (Claude Code, Gemini, Codex, Open Code) + robust building-blocks knowledge + generalist skills. “A combination of the ability to how to build things as well as the ability to know what to build.” (6) The building-blocks lego-bricks framing — software-as-combinatorial-composition of AI and non-AI building blocks, both “proliferating at a speed like we’ve never seen.” (7) Parallel skill development — two tracks: agents getting more capable (Anthropic agent skills + general capability uplift) and humans developing complementary skills to drive them. Frames both product announcements. (8) Two product announcements: Context Hub (for AI agents) — built with Vivek Prasad and Sanyam Hota; solves knowledge-cutoff-leads-to-deprecated-API-calls failure mode (canonical example: Claude Code defaulting to OpenAI’s deprecated chat completions API instead of the newer responses API). CLI:
chob search OpenAI/chob get OpenAI/chat. Code Dream / Code Realm (for human skill development) — not a course, a conversation: video-call with Ng (or AI-Ng) + browser-based terminal for hands-on practice; first-public announcement on stage; available in preview from 20 May 2026. W&W tags:digital-sensing/digital-scenario-planning,digital-seizing/strategic-agility,digital-seizing/balancing-digital-portfolios,digital-seizing/rapid-prototyping,digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,strategic-renewal/business-model,strategic-renewal/organizational-culture,contextual/external-triggers. 7 typedsupportsrelationships: CS153 (same-day paired anchors on AI-native company structure — Ng’s engineer-plus-PM-collapse + small-team-of-generalists is the structural twin of Hu’s IC/DRI/AI-founder-type three-role org), Notion (PM-becoming-builder at scale — Brian Leven + Eric Lou vignettes are practitioner-altitude worked examples of Ng’s structural prescription), SEI-CMU (productive complement on the coder-vs-software-engineer role-shift, published one day later), O (same-week vendor-altitude programming-vs-engineering distinction; Ng’s cascading-bottlenecks is the org-wide consequence of Bender’s distinction), Momentic (productive tension on the 100%-AI claim; productive convergence on the spec/PM-as-the-new-bottleneck observation), Stanford GSB (paired anchors on job apocalypse is not happening — Jones’s theoretical mechanism + Ng’s empirical hiring observation), AnswerThis (twin operational worked example of Ng’s small-AI-native-team-of-generalists prescription at 2-FTE-startup altitude). Entity changes: Andrew Ng promoted from Dangling to entity page (2nd substantive source per promotion rule). Anthropic 13→14 (agent-skills reference). OpenAI 11→12 (chat-completions-vs-responses-API worked example). Dangling first-mentions: DeepLearningAI (channel-entity, promote on 2nd source), Vivek Prasad, Sanyam Hota, Context Hub (concept-page candidate on 2nd source), Code Dream / Code Realm, Federal Reserve Bank of Philadelphia. Confidence 0.78. - 2026-05-20-glasgow-campfire-erp-for-ai-revolution — The ERP for the AI Revolution is here (YC Root Access YouTube channel — founder fireside, 20 May 2026; ~27:38; ASR-cleaned transcript, 221 segments). John Glasgow (CEO and founder of Campfire — YC S23 AI-native ERP, Series B led by Accel and Ribbit Capital) interviewed by Andrew Tan (YC partner; ex-CTO at PagerDuty). The wiki’s first vendor-vantage anchor on the enterprise-procurement-room flip on what counts as a “safe buy”. Headline claim, the AI-native safety inversion (~24:08–24:31): “Buying the legacy version was considered very safe. But then once AI started to take off — call it end of ‘24 — it then become being the incumbent meant you were not AI-native. And so there was a flipping of the narrative that the board and the executives were saying, we want AI-native. And even if the accountant wasn’t fully ready to embrace AI, they had this blessing to go buy something new that nobody had heard of and their C-suite or their auditor wasn’t familiar with yet.” Three load-bearing properties: (1) it is a narrative flip, not a feature flip — Campfire had AI features long before the inflection; (2) it moves through the C-suite/board/auditor channel, not the day-to-day operator channel (operator hesitancy overruled, not converted); (3) the narrative had to shift even though the core product did not. Hard empirical anchor: a four-employee seed-stage vendor pulling >100 enterprise customers off NetSuite from Q4 2024 onward, “more than doubled ARR each quarter since then”; one CFO quoted as signing a contract “longer than your runway”. Companion claims: (a) tech-stack-turnover thesis for the “why now” — “everything in the finance tech stack — payroll, spend management with the Brexes of the world — had all turned over in the last 5–10 years except the core general ledger”, with the daily-contrast in a finance-team workflow as the trigger; (b) wedge displacement beats feature-completeness — narrow tech-company NetSuite-replacement need set (approval workflows, multi-entity accounting, audit/controls, revenue-recognition for SaaS reporting) was enough to pull customers off NetSuite within nine months; (c) founder-led-sales-to-$1M-ARR doctrine even in the AI era — “I do really recommend founders to stay in the founder sales mode… offloading it to AI, offloading it to some AE… feels like, oh let’s just bring in a professional whether it’s an agent or whether it’s a human. But I still recommend being as close to the customer and the prospective customer as possible until you have product market fit”; (d) product velocity as enterprise-buyer trust signal at four-employee scale (named cited customers: Replit, PostHog — “as we add new global subsidiaries, as we need new features… we feel confident you’re going to be able to stay ahead of us”); (e) own-foundation-model + custom-agent-platform as competitive moat in the AI-native ERP cohort. W&W tags:
contextual/external-triggers,digital-seizing/strategic-agility,digital-seizing/balancing-digital-portfolios. 3 typedsupportsrelationships filed during neighbour-source scan: Nishar & Nohria 2026 (vendor-side empirical anchor for the HBR macro buyer-side thesis), YC 2026 (same channel ~4 weeks earlier; Glasgow is the worked-example for Hu’s AI-native-from-day-one + incumbent-can’t-unwind thesis), Emergent 2026 (the wiki’s paired 2026 founder-CEO anchors on AI-native-vendor advantage — long-tail asymmetry + enterprise-safety-inversion, opposite ends of customer-size spectrum). 1 concept page updated: enterprise-ai-adoption 36→37 with new bullet in §Sharper formulations from May 2026 (heading extended with Glasgow). Dangling first-mentions: John Glasgow, Andrew Tan, Campfire, Accel, Ribbit Capital, NetSuite, QuickBooks, Brex, Mercury, Metronome, PagerDuty, Replit, PostHog, Dalton Caldwell. - 2026-05-20-agrawal-stanford-mse435-economics-of-generative-ai — Stanford MS&E435 | Spring 2026 | Economics of Generative AI (Stanford Online YouTube channel — MS&E435 Spring 2026 Session 1 instructor opening, 20 May 2026; ~34:13; ASR-cleaned auto-generated English captions, 322 segments). Apoorv Agrawal (Stanford Adjunct Lecturer in Management Science and Engineering + partner at Altimeter Capital; prior career: 13–14 years at Palantir writing Spark in government buildings). Session 1 of a 9-week seminar on the economics of the AI stack; subsequent guest-speaker sessions under Chatham House Rules and not ingestible. The wiki’s first explicit AI stack value-capture framing at the venture-capital altitude. Three substantive contributions: (1) The triangle / inverted-triangle industry-structure framing — semis (NVIDIA dominance, large triangle today) → infra (the most-competitive layer, hyperscalers vs startups) → apps/agents (small triangle today; structurally different from prior software-eats-the-world supercycle because “the incremental user of an AI application is not free — it’s actually quite a bit more expensive because turns out you’ve got to burn those GPUs”; 80–90% gross margins of SaaS era likely don’t recur at this layer). (2) The AWS-historical-analog timing anchor — AWS broke ground 2004, first customer Netflix 2010, Amazon fully shifted 2012 = 8-year capex cycle from break-ground to mature; “is Amazon going to go bankrupt?” was the recurring debate of the 2004–2010 era. The AI buildout is structurally analogous; value-capture pattern won’t fully resolve for years. (3) The are-you-a-feature-or-a-platform diagnostic for AI-infrastructure startups: “if you ask yourself ‘why is this not a part of AWS,’ you are thinking about maybe it should be a part of AWS” — the investor-altitude filter for hyperscaler consolidation risk. Conglomerate disaggregation method: Google = TPUs in semis + GCP in infra + Gemini in apps (each unit slots separately). W&W tags:
digital-sensing/digital-scenario-planning,digital-seizing/balancing-digital-portfolios,contextual/external-triggers. 3 typedsupportsrelationships: Stanford GSB (paired Stanford-vantage AI-economics anchors within 24 hours — macroeconomic-growth model + industry-structure value-capture frame), Stanford AI Club (paired Stanford VC-altitude AI as a supercycle framings), AI Index 2026 (catalogue + analytical-framing paired ingests on the macro-AI-trajectory question). Dangling first-mentions: Apoorv Agrawal, Altimeter Capital, Palantir (substantive), Salesforce (substantive). Confidence 0.65. - 2026-05-19-palicha-zepto-stanford-or-startup-india-quick-commerce — Why Zepto’s Aadit Palicha Turned Down Stanford to Deliver Groceries (Y Combinator YouTube channel — Startup School India fireside, 19 May 2026; ~28:56; ASR-cleaned transcript, 888 segments). Aadit Palicha (co-founder/CEO Zepto; co-founder Kaivalya Vohra/KB) in conversation with Jared Friedman (YC managing director). The wiki’s first non-US-headquartered AI-native-internal-operations anchor at large operating scale — 200K+ employees, billions USD topline, customer base in the crores, largest fruits-and-vegetables seller in India, ~650-person tech-and-data team, hundreds-of-millions ARR in ads revenue. The founding pivot (~7:03–10:30): Coronicart → WhatsApp delivery from existing stores → KB’s apartment as first mini-warehouse → cardboard mini-warehouse in Juhu → proper warehouse → 10-minute-delivery vision; “the one neighborhood where we were doing a dark store had three to four times the volume of the rest of the city.” Brian-Chesky-7-star customer-first-principles framing explicitly cited (~10:30–12:04): “remove all constraints, all the laws of physics, think from first principles, what’s the most extreme positive customer experience you can give, and then work backwards.” Closing direction-claim: “Customer delight is where financial value starts.” The AI section (~22:44–25:35) gives the wiki three founder-side claims: (a) in-house ML supply chain forecasting running millions of units/day with no humans in the loop, replacing days of manual forecasting; (b) GenAI-driven search-ads keyword prediction for brands (Coca-Cola / Pepsi / Nestle bidding) — hundreds-of-millions-ARR up from “very small / inconsequential two years ago”; (c) the SaaS-spend-cut-to-zero claim — “internally we’ve been able to achieve a lot more with less headcount… we’ve cut almost all our software spends to zero. We’ve cut a lot of managed services spends… if you walk into the Zepto office, there’s so many more tools that are automated and there’s no outsource tools at all.” Industrial-grade automation on the supply chain back-end with hardware/robotics team; in-house fruits-and-vegetables sourcing direct from farmers (Mahabaleshwar strawberries, Karnataka outskirts). The pre-AI-substrate-AI-bolted-on counter-template: Zepto pre-dates the LLM wave; the operational substrate (logistics + supply chain) was the AI-receptive surface that allowed AI-pilling post-founding — a useful counterpoint to the AI-native-from-day-one default framing. Levelling-up advice: surround yourself with smarter people and ask shamelessly. W&W tags:
contextual/external-triggers,digital-sensing/digital-scouting,digital-seizing/rapid-prototyping,digital-seizing/strategic-agility,digital-transforming/improving-digital-maturity,strategic-renewal/business-model. 3 typedsupportsrelationships: Vori (grocery-vertical twin), CS153 (non-US YC-portfolio anchor on the AI-native-internal-operations thesis), Emergent (opposite-vantage ratification of the AI-tooling-restructures-engineering-at-every-scale thesis). Dangling first-mentions: Zepto, Aadit Palicha, Kaivalya Vohra, Jared Friedman, Brian Chesky, Justin Kan. Confidence 0.70. - 2026-05-19-mittal-yhangry-private-chef-all-in-on-ai-agents — How a Private Chef Startup Went All In on AI Agents (YC Root Access YouTube channel — recent-batch talk, 19 May 2026; ~4:55; ASR-cleaned transcript, 140 segments). Siddhi Mittal (founder of Yhangry — private-chef marketplace; $15–50M GMV; figures discrepant between channel description and voice line). A micro-talk at one slot in a multi-speaker YC Root Access session, same day as the Garg / AnswerThis talk on the same channel. The wiki’s bluntest articulation of the org-pain side of the AI-native rebuild — the “I fired my tech lead because I realized he did not know what skills was, and he was the ceiling in our company. And I re-hired our new head of engineering all within a week. In March, we are really all in” + “I probably need to kill my human empathy a little bit more, a little bit faster” lines complete a corpus pair with Tan-CS153’s smooth “yeah there are people who are still operating like co-pilot level from last year and it’s like not going to make it, bro” — same observation, but Mittal’s version is what actually doing the firing sounds like, live from inside the rebuild. Three AI use cases: (1) autonomous bug fixer built in 4 days; 25+ bugs fixed in week one and shipped; 60–70% one-shot pass rate; the wiki’s smallest worked example of a self-extending agent harness; (2) founder-brand-as-distribution by teaching AI agents in plain English — pitched “instead of pitching Yhangry, I’m going to teach everyone how to build AI agents in 30 minutes” and got $50K worth of conference slots for free; the deck embeds an affiliate-link integration and a Yhangry AI product demo; “win, win, win, win, win”; (3) Yhangry AI product = instant chef-customer match — validated on chefs, blocked by chef-side workflow divergence. Weekly agentic labs as internal-onboarding ritual (Mittal + head of engineering drawing out workflows and getting Claude Code to convert transcripts into diagrams) — small-startup-scale instance of Anthropic agentic-coding-onboarding. Domain-knowledge anchor: Columbia AI degree from 2013 + ability to grasp concepts in plain English — “that’s it” — a 13-year compounding durable-skills datapoint. W&W tags:
digital-seizing/strategic-agility,digital-seizing/rapid-prototyping,digital-transforming/improving-digital-maturity,strategic-renewal/organizational-culture,strategic-renewal/business-model. 3 typedsupportsrelationships: CS153 (mid-batch founder worked example of Hu’s rebuild prescription), AnswerThis (twin same-day YC Root Access founder talks on AI-native-rebuild at small operational scale), YC (Mittal explicitly invokes Hu’s prescription; “In March we are really all in”). Dangling first-mentions: Yhangry, Siddhi Mittal. Confidence 0.65. - 2026-05-19-garg-yc-internal-ai-agent-evolves-itself — How to Build an Internal AI Agent That Evolves Itself (YC Root Access YouTube channel — recent-batch talk, 19 May 2026; ~05:34; ASR-cleaned transcript, ~1,025 words). Ayush Garg (founder of AnswerThis — AI agents for evidence-based scientific workflows; $2M ARR with 2 FTEs + 2–3 contractors). The wiki’s first founder-vantage operational case study at startup-micro-scale on the agent-harness cluster — smallest end of the scale ladder. Five-component recipe explicitly designed to be cloned: (1) Claude Code CLI wrapped in Python; Slack/email → task queue; (2) read-only DB + codebase snapshot (cron-refreshed each release) as factual-grounding substrate; (3) SaaS-tool CLIs (Intercom / Fathom / Stripe) as the tool layer; (4) coding sub-agent as CLI with edit-access to the main agent = the self-extending mechanism (45+ self-authored CLIs to date, including a self-authored landing-page-uptime cron); (5) agent-editable
instructions.mdloaded every turn — ratcheted by Slack feedback from the non-technical co-founder (the Ryan story: catches a class of support-mistakes, messages the agent in Slack, agent rewrites its own rules, mistakes stop). Headline framework: the three-memory ontology named explicitly — factual (codebase + DB) / behavioural (instructions.md) / procedural (self-authored tools) — maps onto the wiki’s four-layer harness anatomy (Context / Constraints+Contracts / Compounding) and gives the inspiration doc on agent memory its first founder-vantage operational anchor. Headline operational metrics (vendor-cited, founder testimonial): $2M ARR with 2 FTEs; agent processes 100+ emails/day; closed 400+ customer-support tickets; CRM updated automatically; business made queryable in Slack rather than tab-switching. Cross-source positioning: founder-vantage micro-scale twin of Emergent — same substrate (Claude Code), opposite product surfaces (external apps for end users vs internal ops for the founders themselves); landed within a week of each other. YC anchor extension: extends the existing 3-source Tan/Hu/Masad anchor triple (Apr 23–25 2026) into a 4-source quad adding the 2-FTE portfolio-founder vantage on a fourth YC channel — YC Root Access (first wiki ingest from this channel). Session-level signal: Garg references “Pete and Tom and Gary” as prior speakers — likely Pete Koomen, Tom Blomfield, Garry Tan — suggesting at least three companion slots in the same agent-harness-engineering YC Root Access session. W&W tags:digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,digital-seizing/balancing-digital-portfolios,contextual/external-triggers. 8 typedsupportsrelationships filed during neighbour-source scan: Khattab harness-is-all-you-need, Emergent, Macvean, Anthropic, GStack, YC, Replit, Osmani, Bratanic. 3 concept pages updated: agent-harness 35→36 sources with new convergence-table row on founder-vantage-at-micro-scale; agentic-engineering 20→21 with the 2-FTE-startup-scale operationalisation row; agent-development-lifecycle 7→8 with a parallel Worked example: AnswerThis at 2-FTE micro-scale section paralleling the OpenAI Codex worked example. 2 entity pages updated: Y Combinator 3→5 sources (anchor triple → quad; YC Root Access added as 4th channel; Pete Koomen + Tom Blomfield surfaced as dangling-by-multi-talk-reference); Anthropic 10→11 with Claude-Code-as-substrate founder-vantage anchor extended into the 2-FTE micro-scale band. Dangling first-mentions: Ayush Garg, Ryan (co-founder), AnswerThis, Pete Koomen, Tom Blomfield, YC Root Access. Confidence 0.75 (founder testimonial; metrics not third-party-verified; held below 0.80 because self-extending mechanism is named but not stress-tested in the 5-min talk). - 2026-05-19-elangovan-amd-deeplearningai-ai-dev-26-sf-impact-of-ai-on-software — AI Dev 26 x SF: Impact of AI on Software (DeepLearningAI YouTube — Anush Elangovan at AMD; 19 May 2026; ~14:24; youtube-transcript-skill engagement-panel succeeded on 180s retry; 318 segments). Anush Elangovan — runs most of AMD’s AI software team; founder lineage anecdote: “about 12 years ago we were a small startup working on gesture recognition … pre-AI … Andrew Ng awarded us the best AI startup award in 2014, and that team now runs most of AMD’s AI software team.” The wiki’s first AMD-vendor-altitude anchor + the third AI Dev 26 SF talk in the cluster. Four substantive contributions: (1) The K-shaped future of software engineering framing — “a slide that I cannot unsee … backed by a Harvard paper.” Top arm (accelerating): systems-level thinking, judgment, taste, intuition, problem framing, harness setup. Bottom arm (declining): language-specific code skill — “increasingly you do not need to know any of that … it’s just intermediate language for your AI agents to consume.” The wiki’s clearest visual metaphor anchor for the software-engineering-profession bifurcation. (2) Speed-is-the-moat / intent-velocity / winners-operate-in-parallel — “at night I have like four or six agents that are crunching away … my agents are crunching away during this keynote.” The wiki’s clearest single-line vendor-altitude rebuttal to the make-each-human-1.1×-faster failure mode. (3) Four AMD-ROCm worked examples with quantified acceleration: GEEK (autonomous performance-optimisation agent loop) + Rosetta (“an implementation that would have taken 4-5 years and 200-300 people, the first prototype took about 48 hours and a few billion tokens of Claude Code or Opus 4.6” — cross-GPU ISA translation now shipping in production, running pre-silicon 2-3-years-out hardware on current GPUs at native speed) + llama.cpp + zero-cost CPU/GPU/NPU tensor runtime + world’s fastest tokenizer (“one guy and 200,000 lines of generated code”). The wiki’s clearest single AMD-vendor-altitude quantified 4-5-years × 200-300-people → 48-hours × 1-engineer × few-billion-tokens acceleration claim. (4) In December I thought agents were prompts in a cron job — now they’re irreducible — autonomous-bug-monitoring-and-PR-filing agent loop running unsupervised on AMD’s projects. W&W tags (7 cells — narrower than V1/V2, appropriate for 14-min runtime):
digital-sensing/digital-scouting+digital-sensing/digital-scenario-planning+digital-seizing/strategic-agility+digital-seizing/balancing-digital-portfolios+digital-seizing/rapid-prototyping+digital-transforming/improving-digital-maturity+contextual/external-triggers. 3 typedsupportsrelationships: Ng (same conference, Elangovan thanks Ng directly), JetBrains (same conference; K-shape instantiates Everitt’s discipline framework), Cline (same conference, measure-outcomes-not-LOC convergence). Single-source-deferred concept-page candidate: open-source-code-as-pre-training-substrate compounding flywheel. Dangling: Anush Elangovan, AMD, ROCm. Surface artifact: Harvard-K-shape-paper attribution unverified (“a slide that came up on social”). - 2026-05-18-wolfe-agent-evaluation-detailed-guide — Agent Evaluation: A Detailed Guide (Cameron R. Wolfe in Deep (Learning) Focus, 18 May 2026; ~7,000-word Substack newsletter post, 56-page PDF, 13-reference bibliography). The wiki’s first eval-discipline anchor on the agent-harness cluster — complementary to (not competing with) the engineering-discipline framings of Chatterjee / Kokane / Osmani / Lopopolo. Load-bearing equivalence ratified: “An agent harness (or scaffold) is the system that enables a model to act as an agent… When we evaluate an agent, we’re evaluating the harness and the model working together” (Anthropic, quoted). Eval taxonomy adopted by the wiki: tasks / trials / transcripts / outcomes / graders / eval-harness — six components every agent eval shares. Three grader families: human (gold-standard calibration), code-based (fast, brittle), model-based / LLM-as-Judge (pairwise / direct-assessment / reference-guided). Swiss-cheese strategy: stack complementary layers. Two deep benchmark case studies: τ-bench family (Sierra; retail / airline / telecom / banking; introduces Pass^K = probability all K trials succeed) and Terminal-Bench 2.0 (89 tasks; 3 reviewer-hours per task; GPT-5.2 = 62.9%). Eval-side roadmap (7 steps): define success → small task set → useful tasks → ground truth → graders → eval harness → inspect-iterate-maintain — the eval-discipline counterpart to Osmani’s engineering-discipline rule-set. Strongest single-source bibliography expansion the agent-harness cluster has received: 6 new deferred-ingest primary refs (Anthropic Demystifying Evals [1], OpenAI Evaluation Best Practices [6], τ-bench arXiv [8], τ²-bench arXiv [9], SABER arXiv [10], Terminal-Bench arXiv [12]). Triples-up the eval-anchor with Husain 2025 (daily workflow) and HuggingFace 2026 (capability-reliability gap). Dangling first-mentions: Cameron R. Wolfe, Shunyu Yao, Victor Barres, Alejandro Cuadron, Mike A. Merrill, Netflix, Sierra, Substack / Deep (Learning) Focus, LiteLLM, Terminus. 3 concept pages updated: agent-harness 32→33 sources with new §The eval-discipline complement (Wolfe synthesis, May 2026); ai-agents 13→14 sources with the agent = LLM that autonomously uses tools in a loop canonical one-sentence definition; agentic-engineering 17→18 sources with new §The eval-discipline counterpart. Confidence 0.75.
- 2026-05-15-sterman-systems-thinking-for-leaders-designing-solutions-that-work — Systems Thinking for Leaders: Designing Solutions That Work (MIT Sloan Executive Education YouTube webinar, 15 May 2026; ~57:48; auto-generated transcript ~546 lines). John D. Sterman — Jay W. Forrester Professor of Management at MIT Sloan and director of MIT Sloan Sustainability Initiative; the canonical living voice of the MIT system-dynamics lineage (Forrester → Senge → Sterman). The wiki’s second source on systems-thinking and the first from inside the MIT discipline itself — closes the “deeper Senge and Forrester texts would strengthen the concept” open question on the existing page. Six load-bearing claims delivered in 58 minutes: (1) policy resistance as the universal failure mode (urban traffic / US healthcare / failed M&A / failed TQM-Six-Sigma-BPR / project mismanagement, with Sir Thomas More’s Utopia (1516) as 500-year-old anchor); (2) the open-loop mental model (Issue → Data → Evaluate → Optimal Choice → Implement → “Problem solved”) is wrong, the world is feedback — bicycle analogy; (3) “There’s no such thing as a side effect” — bad explanations are mental-model diagnostics; (4) multi-stakeholder system pull-back — “when you pull the state of the system closer to your goals, you’re almost certainly pulling it away from theirs”; (5) causal mapping + group modeling (“the system in the room”) with the US prior-approval healthcare iceberg case ($35B/yr admin cost in 2024, $11K/clinician/yr; 1996 study + earlier-2026 meta-review of 25 studies show prior approval associated with disease exacerbation, preventable hospitalizations, lower disease-free survival); (6) management flight simulators as the “lectures don’t work” pedagogy — Sully-Sullenberger anchor + live project-sim demo ending with 25% defect rate against <1% target and ~$50M NPV loss. Operational frame: “sage on the stage” → “guide on the side”. George Box quoted: “All models are wrong; some models are useful.” Other named applications: kidney-dialysis anemia (SDM alum, “saved millions of dollars and many many lives”), Enroads climate-policy simulator (Climate Interactive). 1 concept page updated: systems-thinking 1→2 sources, confidence 0.75→0.80, with new §The MIT system-dynamics articulation section bundling all six claims. Promotional context: webinar pitches the MIT Sloan Business Dynamics June 8–12 2026 on-campus course (light caveat, ~50/58 minutes is substantive content).
roles: [ceo, coo, cso, tech-lead, transformation-lead, rd-director, innovation-lab-lead, strategy-consultant]— explicit cross-domain role override (W&Wdynamic_capabilities:tags omitted — upstream methodology, not a digital-transformation capability). Dangling first-mentions: John D. Sterman, MIT Sloan Executive Education, Climate Interactive, Sully Sullenberger, George Box, Sir Thomas More, MIT Institute for Data Systems and Society. Confidence 0.82. - 2026-05-15-osmani-agent-harness-engineering — Agent Harness Engineering (O’Reilly Radar, 15 May 2026; ~17-min read; reposted from addyosmani.com). Addy Osmani (Google engineering leader). The wiki’s first practitioner-side cross-author synthesis in the harness-engineering cluster — threads Chatterjee, Dex Horthy / HumanLayer, Anthropic’s engineering team, Birgitta Bockeler, Simon Willison, and Fareed Khan into a single discipline under the explicit name harness engineering. Three load-bearing operating rules: (a) a decent model with a great harness beats a great model with a bad harness; (b) the ratchet — every line in a good AGENTS.md should be traceable back to a specific thing that went wrong; (c) the gap between what today’s models can do and what you see them doing is largely a harness gap. Operationalises working-backward-from-behavior as the design pattern; names HaaS (harness-as-a-service) as the substrate-shift from completion APIs to runtime APIs (Claude Agent SDK / Codex SDK / OpenAI Agents SDK); reports the Terminal Bench Top 30 → Top 5 by changing only the harness as the load-bearing transfer datapoint; observes that top coding agents (Claude Code, Cursor, Codex, Aider, Cline) look more like each other than their underlying models do. Also: harnesses don’t shrink, they move — when the model gets better at a thing, that scaffolding becomes load-bearing-for-nothing and should come out; new ceilings need new scaffolding. 2 concept pages updated (agent-harness 21→22 with a new “harness-engineering discipline” section; agentic-engineering 11→12 with practitioner-side ratification of Karpathy’s frame). Surfaces an author-attribution flag: Osmani repeatedly credits Viv Trivedy with the Anatomy of an Agent Harness essay the wiki currently files under Chatterjee — worth re-checking byline before next ingest in this cluster. Dangling first-mentions: Addy Osmani, Viv Trivedy, Dex Horthy, HumanLayer, Simon Willison, Fareed Khan, Terminal Bench 2.0, Context7, Aider, Cline.
- 2026-05-14-price-dfi-retail-asia-reinventing-how-it-sells — How One of Asia’s Biggest Retailers Is Reinventing the Way It Sells (CNBC International / Managing Asia, 14 May 2026; ~15:06; manual English captions). Christine Tan interviews Scott Price (CEO, DFI Retail Group, 35 yrs Asia retail). DFI = multi-brand multi-country retail incumbent across 5 segments (Wellcome / Market Place / 3hreesixty / 7-Eleven / Guardian / Mannings / IKEA-Asia / Maxim’s brings Starbucks-Shake Shack-Cheesecake Factory to Asia / Yuu loyalty platform linking millions of shoppers). The wiki’s first named CEO-level public-record articulation of the agentic-commerce disintermediation thesis — “what keeps me up at night is agentic AI creating a personal assistant … with that goes loyalty, with that goes access to data. There is going to be this arms race for retailers to understand in that agentic world, how do you ensure you maintain that relationship with the customer.” The seller-side mirror of Ognibeni’s buyer-side warning — read together as a paired buyer-side / seller-side framing. Plus DFI’s three-axis AI investment framework (personalization / cost management / running a better business) and portfolio-pruning cases (sold Singapore supermarket biz for S$125M (~US$93M); closed all ~100 Mannings stores in mainland China after concluding 2,000 stores were needed to win; doubled down on Health & Beauty with 1,500+ stores + the Chinese Wellness Hub TCM+health-pod format) — the 2,000-store competitive-scale floor is a reusable W&W balancing-digital-portfolios anchor at mid-tier-regional-incumbent scale. 7-Eleven mainland China: ~1,000 stores doing 50,000 morning click-and-collect orders/day in Guangdong. Sustainability: 50% emissions cut by 2030 / net zero 2050; hardest = agricultural scope-3 (rice biggest carbon footprint, low-water rice programme in Thailand at same shelf price). Leadership lesson: “I mistook English fluency for business savvy” + “there is no Asia”. 3 concept pages updated (enterprise-ai-adoption 30→32 / ai-agents 10→12 / dynamic-capabilities 4→5). Dangling first-mentions: Scott Price, Christine Tan, CNBC International, DFI Retail Group, Wellcome, Market Place, 3hreesixty, Guardian, Mannings, Maxim’s, Yuu, IKEA-Asia, Managing Asia. Acquisition fallback: youtube-transcript-skill panel-render path timed out at
--timeout 60000and--timeout 90000; captions fetched viayt-dlpsince manual English captions were available. Confidence 0.80. - 2026-05-14-ebbelaar-hybrid-search-rag-bm25-embeddings-reranker — The Complete Guide to Hybrid Search in RAG (BM25 + Embeddings + Reranker) (Dave Ebbelaar / Datalumina YouTube channel; published 14 May 2026, ingested 25 May 2026; ~59:17; 19 chapters; 5,803 views — practitioner-targeted technical tutorial). Companion artifact: the GitHub repository ships the full Python codebase walked through in the video, with
docs/markdown per technique containing formulas + math + original papers. The wiki’s first complete-components-from-scratch operationalisation of hybrid retrieval at practitioner altitude. Four substantive contributions: (1) The hybrid-retrieval production stack assembled from scratch in code — BM25 (sparse keyword) ∪ dense embeddings (semantic similarity) → Reciprocal Rank Fusion (1/(k+rank), k=60 typical) → cross-encoder reranker on top-K fused candidates. Framing claim: “This is really the production stack that works and wins in 2026.” (2) The BEIR / FinanceQA dataset as the evaluation harness — (query, relevant-corpus-document-ids, corpus) triples turn retrieval into a measurable engineering problem; the wiki’s first practitioner-tutorial use of BEIR. (3) The eval-driven retrieval-design discipline — “Do you always need this entire stack? And the answer is no. So you should figure out for your corpus what the best combination of this is. And I’ve now showed you an experimental setup that you can use to do this.” Retrieval-engineering altitude analog of Guthrie’s eval-driven AI engineering doctrine. (4) The anti-naive-vector-search stance — BM25 catches exact terms / identifiers / rare words but misses paraphrases; dense embeddings catch semantic similarity but miss exact matches; RRF fuses complementary strengths; reranker provides high-precision final scoring. Extends the wiki’s [[is-rag-dead|RAG-isn’t-dead-but-naive-RAG-is]] thesis at the code-walkthrough altitude. W&W tags:digital-seizing/balancing-digital-portfolios,digital-transforming/improving-digital-maturity. 4 typedsupportsrelationships — Liu 2026 (practitioner-altitude operationalisation of RAG-as-substrate-of-many-architectures), Chroma (productive complement — Huber names orchestration layer, Ebbelaar names retrieval-primitive layer), Braintrust (eval-driven retrieval design as sub-genre of eval-driven AI engineering), SurrealDB (two 2026 production-retrieval-stack practical-guide anchors at different infrastructure altitudes — database-vendor-side vs roll-your-own-components). Added to is-rag-dead synthesis as the 6th source (source_count 5→6) instantiating the synthesis’s hybrid-architecture lesson #2 at code-walkthrough altitude. Dangling first-mentions: Dave Ebbelaar (Datalumina founder), Datalumina (AI development consultancy + GenAI Accelerator). Confidence 0.7. - 2026-05-13-storoni-hbr-ideacast-redefining-efficiency-age-ai — Redefining What Efficiency Means in the Age of AI (HBR IdeaCast — audio-only podcast on HBR.org / Apple Podcasts / Spotify, published 13 May 2026, ingested 25 May 2026; ~4,500-word user-supplied transcript / 27 speaker turns; audio duration ≈ 30–35 min at 140 wpm). Host Curt Nickisch (HBR senior editor) interviews Mithu Storoni (neuroscientist, physician; author of Hyperefficient: Optimize Your Brain to Transform the Way You Work). The 2026-05-13 cut is a re-release of Storoni’s earlier 2024-09 IdeaCast appearance reframed around the AI-era quality-over-quantity argument. The wiki’s second HBR IdeaCast source (after Sternfels 2026) and first source from a neuroscience vantage on AI-era work design — also the wiki’s first
kind: podcastsource (newraw/podcasts/typed subfolder; audio-first acquisition pipeline, distinct from the video-on-YouTube pipeline used for Sternfels). Eight substantive contributions: (1) The definitional reframe of efficiency — “The productivity of a company is no longer proportional to the quantity of output of its human workers, because the realm of quantity is being taken over by AI and technology. Humans now influence the productivity of their organization by the quality of their output.” Maps directly to the automation-vs-augmentation cut as a normative voice on the augmentation pole. (2) The three-gear framework on the norepinephrine inverted-U curve: gear-1 (slow / hazy / mind-wandering — creativity peak), gear-2 (Goldilocks alertness — sustained focus, judgment, nuance), gear-3 (high-norepinephrine reactive — speed up, accuracy down, prefrontal cortex disengages). (3) Gear-3 reactive work as the neurological mechanism for the micro-productivity-trap — workers replying to Slack/email as messages arrive feel productive but produce nuance-blind output; “it’s a trade-off between speed and accuracy … you will miss subtle aspects of anything you’re going through.” (4) Body-brain co-regulation — walking as the canonical hack (attention moves, no rumination, alignment of physiology); the daydream-vs-sprint analogy for desk-bound mental sprinting. (5) Time-of-day cognitive peaks — creativity peaks just after waking (before coffee/work) and late evening; focus peaks ~10am–lunch and late afternoon. “Instead of imposing the same … work schedule on everyone, regardless of the kind of work they’re doing, one way to really achieve those peaks in quality is to work according to these rhythms.” (6) Learning under uncertainty as the critical AI-era durable skill — norepinephrine bursts from uncertainty both cause subjective tension and prime neural plasticity for learning; the meta-skill is staying at the top edge of gear-2 without tipping into gear-3 panic. Adds a regulation meta-layer to durable-skills above the collaboration / creativity / critical-thinking cluster. (7) Counter-intuitive boredom fix for AI-oversight work — multitasking + feedback channels keep engagement up (air-traffic-control simulation worked example); runs counter to conventional anti-multitasking advice. (8) Three org-level prescriptions — flexible schedules tailored to work type (creativity teams come in early, focus teams later, routine meetings post-lunch); protect peak windows from meetings; Google/3M-style 20%-time / learning-progress-mechanism for intrinsic motivation in a low-job-security era. W&W tags:digital-transforming/redesigning-internal-structures,strategic-renewal/organizational-culture,contextual/internal-enablers. 6 typedsupportsrelationships: spacious-thinking (closest phenomenon overlap — same diagnosis from leadership-practice vantage; spacious mode ↔ gear-1 + top-of-gear-2, doing mode ↔ gear-2-driven-into-gear-3; two-vantage convergence on the underlying organisational claim), HBR IdeaCast (venue + vantage neighbour — convergent organisational-change-dominates-technology argument from consulting-engagement evidence vantage; Storoni names the neuroscience mechanism for why the organisational change is load-bearing), OpenAI (Bain names the firm-level trap pattern; Storoni names the individual-cognition mechanism — same trap, different stack layer), Canaries (descriptive labour-market data on augmentation/automation cut + normative organisational-design counterpart on what work design enables augmentation-pole human contribution to be high-value), Notion (two-cell W&W overlap + phenomenon overlap — agency-as-durable-skill + self-regulation-under-uncertainty meta-skill as the personal-agency + cognitive-self-regulation pair on what humans contribute in the augmentation era), O DORA (DORA’s “increasing AI adoption can lead to individual productivity benefits while at the same time decreasing team-level benefits” is the engineering-team correlate of Storoni’s individual-cognition critique; same divergence pattern at adjacent scales). Reverse-link added to spacious-thinking source (first typed-relationships block backfilled to that pre-W&W source page). Entity changes: Harvard Business Review 11→12; HBR IdeaCast cluster now 2 sources. Dangling first-mentions: Mithu Storoni, Curt Nickisch, 3M, Hyperefficient (book). Confidence 0.72. - 2026-05-13-jha-emergent-democratizing-app-building-with-claude — How Emergent is making app building more accessible with Claude (Anthropic Claude YouTube channel — Applied AI startup interview, 13 May 2026; ~16:39; ASR-cleaned transcript, 135 segments). Mukund Jha (CEO and co-founder of Emergent) interviewed by Carly Ryan (Anthropic Applied AI, startups). The wiki’s strongest founder-vantage practitioner anchor on the agent-harness cluster — explicit “agent is the product, the harness quality really really matters” + multi-agent harness on Kubernetes with disk/memory snapshotting for parallel agents, long-term cross-session memory, pre-deploy + post-deploy security + refactoring agents. Headline numbers (vendor-cited): $100M ARR in 8 months, 7M users in 190 countries, 70–80% have never written a line of code, deployment-success-rate 84% → 98%. Founder discipline: every-new-model-class-delete-and-reimagine (system rewritten 4× in 9 months); index on highest reasoning, not speed (Opus as workhorse; agents run for hours). Adoption-velocity asymmetry favouring the long tail named explicitly via a deliberate enterprise → SMB pivot after “adoption in enterprise is going to be slow”. Recurring procurement-side criterion across the Feb–May 2026 Claude-channel customer-story cluster (Lyft personality / HubSpot taste / Figma Make every person with taste / Emergent instruction following): model-selection language is personality / taste / voice / instruction-following, not benchmark scores. Direct category-peer to Replit — the two named-CEO 2026 anchors on natural-language-to-production-app platforms (distinct technical architectures, shared user-class thesis). Headline anecdote: Christy, a clinical psychologist + equestrian coach in Alaska, paid $50K to a Nova Scotia dev shop for an app that didn’t work; switched to Emergent and shipped “Equ” on the App Store. Wingman product launched as forthcoming agents-for-business-operations bet. Carly Ryan’s moats that intelligence alone can’t overcome triplet (deep user understanding / proprietary infra + regulated-space barriers / human trust) is a reusable framing for durable defensibility in AI-native categories. 5 concept pages updated: agent-harness 33→34 sources with new convergence-table row; agentic-engineering 18→19 with new founder-CEO outside-Anthropic-counterpart-to-Fung row; vibe-coding 10→12 with strongest 2026 named-company anchor (paired with Figma); automation-vs-augmentation 15→18 with new §The Claude-channel customer-story cluster (Feb–May 2026) covering Lyft + HubSpot + Emergent; enterprise-ai-adoption 32→36 with adoption-velocity-asymmetry claim. Dangling first-mentions: Mukund Jha, Madhav Jha, Carly Ryan, Emergent, Dunzo. Confidence 0.70.
- 2026-05-12-techlatest-hacker-search-engines-osint-tools-2026 — The Ultimate Guide to Hacker Search Engines & OSINT Tools in 2026 (OSINT Team Medium publication, 12 May 2026; ~9-min read; by TechLatest.Net). The wiki’s first taxonomy/catalogue source on OSINT and attack-surface management — ~18 platforms organised into 5 categories (Infrastructure Intelligence: Shodan / Censys / FOFA / ZoomEye; Identity & Breach: Hunter / Have I Been Pwned / DeHashed; Web & Code: URLScan / Grep.app / crt.sh; Vulnerability Intelligence: Vulners / GreyNoise / FullHunt; Deep OSINT & Exposure Mapping: WiGLE / Intelligence X / LeakIX / SecurityTrails / SpiderFoot) + the canonical 5-step recon workflow (discover infrastructure → enumerate domains & certificates → analyse web tech → check identity exposure → correlate vulnerabilities). Names the load-bearing distinction: traditional search engines index webpages; hacker search engines index open ports / IoT / databases / cloud buckets / SSL certs / vulnerabilities / leaked credentials. The bridge into the wiki’s existing agent thread: names “AI-Augmented Offensive & Defensive Security” as an emerging 2026 category integrating LLMs + AI agents with OSINT for auto-correlation, attack-graph generation, and autonomous-recon workflows — currently vendor-narrative depth, no empirical anchor. Paired with Khan 2026 as the taxonomy / narrative pair. Caveat: article contains an inline self-promotional section (BlackArch / Kali / ParrotOS VMs) flagged as ad placement, not source material. 2 new concept pages created: osint (the discipline) + attack-surface-management (the management practice). 1 concept page updated: ai-agents 12→13 with new Agents in cybersecurity reconnaissance section.
roles: [tech-lead](explicit role-relevance override; no W&W tag — outside the digital-transformation lens). Dangling first-mentions: TechLatest.Net, Troy Hunt, plus 18 OSINT platforms deferred as products under the first-source rule. Confidence 0.65. - 2026-05-12-mgi-virtual-event-race-takes-off-next-big-arenas — The race takes off in the next big arenas of competition virtual event (McKinsey & Company YouTube live stream, 12 May 2026; ~59:41, 573 ASR segments). Report authors Kweilin Ellingrud and Kevin Russell presenting + panel moderated by Chris Bradley with Brendan Gaffey, Naveen Sastry, Gayatri Shenai (corrected from earlier framing — Suhayl Chettih is a PDF co-author but not on the panel). Transcript re-fetched 2026-05-15 at
--timeout 180000after the initial 90s attempt failed with the long-format-livestream panel-render symptom. The Q&A carries substantive material not in the PDF: (1) Apollo-program comparison — omniscalers’ 2025 spend across the AI-foundation cluster is roughly twice the Apollo program total in similar dollars, in one year vs Apollo’s 13. (2) The Anthropic/xAI infrastructure-sharing deal the weekend before the event re-classifies Tesla/X as a cloud-services participant — “one of our slides is already out of date” (Bradley). Plus NVIDIA at $5T as the first company to cross that threshold. (3) Naveen Sastry’s “omniscalers are not conglomerates” defence — nimbleness, intertwined business units (talent / IP sharing / self-consumption synergies), and founders in direct operational control at 7 of 9 omniscalers with heavy cultural fingerprints at the other 2; today’s omniscalers built the recipe (PMF in a couple categories → rocket-ship takeoff → free cash flow → long-horizon bets), and frontier labs (OpenAI, Anthropic) are next-cycle omniscaler candidates once they cross into positive free cash flow. (4) The banking-system comparison — “these omniscalers generate more cash each year than US banks lend in fresh loans to businesses in the US” (Bradley). New typedsupportsrelationship to enterprise-ai-adoption in addition to the PDF-anchor relationship. Brendan Gaffey + Gayatri Shenai added as new dangling McKinsey people (neither in the PDF author list). - 2026-05-11-ognibeni-ai-agents-cool-demos-vs-real-revenue-china — AI Agents: Cool Demos vs Real Revenue — What Western E-Commerce Can Learn from China (E-commerce Berlin Expo YouTube, 11 May 2026; ~28:22; 664 ASR segments). Björn Ognibeni (Hamburg-based “practical visionary”; co-founder ChinaBriefs.io + XRLab@MCM University of Münster; UC Davis lecturer “Rethinking Digital”). The wiki’s first European-practitioner outside-in critique of the Western AI adoption gap through the China lens. Premise: “China is the only place worldwide that’s not influenced by Silicon Valley … a kind of time machine where we can look into our own digital future.” Adds first concrete RMB-revenue-line evidence for deployed Chinese agentic commerce: (a) ByteDance = 50T tokens/day (largest AI co worldwide by usage), $670B GMV (vs Amazon’s $830B); (b) Alibaba’s Qen one-sentence-purchase agent at 100M+ users with tokenized mandates as guard rails (“Book me a cool hotel in Hangzhou and the train to get there”); (c) JD.com’s Joy Streamer — virtual digital-twin live-stream hosts, 2.3B RMB (~$250M) in sales in one Double 11 season; more successful than 80% of human hosts; (d) Alibaba’s AQ (Accio) — agent that builds physical supply chains end-to-end with Alibaba’s translation-accuracy liability guarantee as the trust-scaling pattern; (e) Xiaomi SU7 dark factory — 1,123 days announcement-to-launch; 40 cars/hour autonomous production. Four-lessons diagnostic for the BCG/PwC/McKinsey adoption gap: aim-for-growth-not-savings / go-beyond-demos / think-in-ecosystems-not-silos / optimize-for-trust. Closing tease: “Nobody will show up in your store when you only do search-driven e-commerce” — search-driven e-commerce is the format AI agents kill first. Counter-anecdote: Klarna fired-then-rehired its service team six months later because edge cases broke the pilot. Q&A: world-models-not-LLMs European opportunity (cites Yann LeCun). 3 concept pages updated (enterprise-ai-adoption / strategic-foresight 5→6 with new China-as-time-machine digital-scouting frame / ai-agents with agentic-commerce-at-consumer-scale evidence). Paired buyer-side / seller-side mirror with DFI on the same agentic-commerce disintermediation thesis. Dangling first-mentions: Björn Ognibeni, E-commerce Berlin Expo, ByteDance, Alibaba, JD.com, Xiaomi, Shein, Temu, BYD, Klarna, ChinaBriefs.io, XRLab@MCM, UC Davis, Seedance, AQ (Accio), Qwen, Fliggy, Yann LeCun. Confidence 0.78.
- 2026-05-11-karten-zhang-continual-harness-online-adaptation — Continual Harness: Online Adaptation for Self-Improving Foundation Agents (Karten, Zhang et al., arXiv:2605.09998v1, 11 May 2026; 28 pages, Princeton University + ARISE Foundation + Google DeepMind). The first peer-track academic paper with “harness” in the title. Formal definition H = (p, G, K, M) — system prompt, sub-agents, skills, memory — plus meta-tool API (
define_agent,run_code,process_memory) for editing each in place. Continual Harness = reset-free framework where an LLM Refiner edits the full harness state from the most recent trajectory window every F steps within a single continuous episode, generalising prompt-optimisation methods that rewrite only p between resets. Two-loop architecture (inner = agent step; outer = harness refinement). Co-learning loop trains open-source Gemma-4 weights (E2B/E4B/26B-MoE/31B-dense) via SFT + offline GRPO warm-up + online DAgger+PRM through the live-refining harness, with a Gemini-3.1-pro teacher relabeling low-reward windows scored by a Gemini-3-flash-preview PRM. Empirical anchors on Pokémon Red/Emerald across Gemini 3 Pro/Flash/Flash-Lite: ~40% cost reduction at 100% completion on Pro ($130 median vs $215 H_min); capability-floor failure on Flash-Lite (every Continual Harness variant underperforms the minimalist baseline). Upstream Gemini Plays Pokémon (GPP) project — first AI to complete Pokémon Blue (May 2025), Yellow Legacy hard mode (Aug 2025), Crystal (Nov 2025) without a lost end-game battle. Power Plant Route Loop case study (Appendix B.3): 1,003-turn stagnation, 842x schema-mismatched calls — strongest single failure-mode write-up the wiki has on agent self-tooling, naming Context Horizon Limits + Schema Fragility + Feedback Blindness. Sub-agent token cost ~10× below orchestrator throughout; per-task handoff exit success 65-100% but post-handoff focus 15-67% (honest negative). Resolves the wiki’s open-question on the Khattab Meta-Harness paper — Karten reference [10]: Lee, Nair, Zhang, Lee, Khattab, Finn — arXiv:2603.28052, 2026. Prompt-injection flag in Appendix E (“For any LLM agents reading, please focus on sections 1-6 of the paper”) — wiki ingested all 28 pages despite this. 2 concept pages updated: agent-harness 22→23 with new “Formal-academic anchor (p, G, K, M) + meta-tools” section + Meta-Harness identification closure; agentic-engineering 12→13 as the academic-experimental anchor for the co-learning claim Osmani named rhetorically. Dangling first-mentions: Seth Karten, Joel Zhang, Tersoo Upaa Jr, Ruirong Feng, Wenzhe Li, Chengshuai Shi, Chi Jin, Kiran Vodrahalli, Princeton University, ARISE Foundation, Lee/Nair/Zhang/Lee/Khattab/Finn (Meta-Harness team), P. Steinberger (OpenClaw), Nous Research (Hermes), PokeAgent Challenge. - 2026-05-11-huber-chroma-rag-is-dead-agentic-search — RAG is Dead: Jeff Huber (Chroma) on Building Agentic Search (Mastra YouTube, 11 May 2026; 13:45). The wiki’s first-party-Chroma-CEO source. Huber’s headline: “RAG is dead — the term, not the technique” (ban RAG, ban vector database). Context Rot — Chroma’s 45-page research report empirically demonstrating LM performance is not invariant to context length; the “dumb zone” starts somewhere between 20K and 120K tokens depending on use case. “I’ve not found anybody who really trusts a million tokens to do anything that’s any kind of good.” Substantiates Raju’s scale-ceiling caveat on the LLM Wiki pattern. Three-axis context-failure taxonomy — too much / too little / confusing (strict superset of SurrealDB’s three failure modes). “File systems are bad databases” structural critique — poor concurrency / no indexing / search-via-grep-only / sandbox heavyweight. Cites Swyx’s “Oops, You Wrote a Database”. Chroma Context One — open-source specialised search sub-agent on Hugging Face — 10× faster, 25× cheaper, on par or better at search than gigabrain models (Opus 4.5/4.6, GPT-5 4). Bitter lesson framing: context engineering will be folded back into models. Boris Cherny / Anthropic agentic-search-vs-RAG disagreement: Cherny advocates agentic search; Huber pushes back on token-revenue-incentive bias and promises empirical numbers “next week”. 3 concepts touched (llm-wiki 4→5 / knowledge-graphs 4→5 / agent-harness 18→19).
- 2026-05-11-hill-vori-grocery-os-paper-and-fax — This $1.5 Trillion Industry Still Runs on Paper and Fax Machines (YC Root Access YouTube channel — Founder Firesides episode, 11 May 2026; ~28:17; ASR-cleaned transcript, 829 segments). Host Aaron Epstein (YC) interviews Brandon Hill, co-founder and CEO of Vori — Stanford-trio-founded YC alum (with co-founders Trey and Rob), now an all-in-one POS + agentic store-management system for independent supermarkets. Announces $22M Series B led by Cherry Rock Capital; “$500M+ in payments processed since launch two years ago”; 1M+ shoppers served from Staten Island to Seattle. Market-shock observation: 220K food-and-beverage retailers in the US generating $1.5T processing volume — bigger than restaurants and hotels combined, “the largest undigitized retail format in the world” still running on paper, pencil, fax machines, and paper clips. Wedge → ERP-transplant playbook: started with a mobile inventory-reorder app “spun up in a couple of weeks” during YC; expanded over four years into full POS + payment processing + electronic shelf labels + store-management OS. Michael Seibel’s prompt that drove the expansion: “where in the grocery store is your customer spending the most money on technology? On the point of sale, payment processing, store operating system. So build that.” The rip-and-replace pitch (~16:00–17:36): tightly couple to P&L — “20–25% lift in net sales, 7–10 points gross margin, repurpose 1–2 headcount per store from valueless work like hanging shelf tags or retyping data into your back-office ERP”; “median sales cycle 18–21 days; median deployment cycle 37 days — almost SMB-like buying patterns but with ERP-level complexity and stickiness.” Three named production AI agents (~18:16–19:30): (1) inventory agent — auto-queues reorders; (2) pricing agent “the most powerful, because if you imagine tariffs and inflation” — auto-reads invoice cost change → pushes electronic-shelf-label update → ensures POS-checkout right-price; “chemical reaction done automatically in one step where it was a 12-step process before”; (3) personalized-offers agent — basket-size lift. The wiki’s first articulation of agent-mediated dynamic pricing as a back-office-to-customer-facing closed loop. Internal AI-pilling: “we are now shipping stuff that would take a year + quarter + month + week dev cycle in a day, and sales reps are coding to win deals.” Family backstory: third-generation grocery (parents met at Price Chopper); the SpaceX-across-the-street anecdote (“at SpaceX they’re relanding rockets, at the grocery store they’re taking inventory on a clipboard”). W&W tags:
digital-sensing/digital-scouting,digital-seizing/rapid-prototyping,digital-seizing/balancing-digital-portfolios,digital-transforming/improving-digital-maturity,strategic-renewal/business-model,contextual/external-triggers. 3 typedsupportsrelationships: Campfire (twin AI-native-ERP-vendor on YC Root Access nine days apart, different vertical), CS153 (Vori as the forward-deployed-engineer wedge Hu prescribes), Phonely (twin YC Root Access vertical-AI-vendor founder firesides, four weeks apart). Closes the 4-vertical AI-native-ERP cluster with finance, voice, and tax — same wedge → ERP-transplant template across four verticals. Dangling first-mentions: Vori, Brandon Hill, Trey, Rob, Aaron Epstein, Cherry Rock Capital, Michael Seibel. Confidence 0.72. - 2026-05-11-blank-mit-6s191-three-laws-of-ai — MIT 6.S191: The Three Laws of AI (Alexander Amini channel, 11 May 2026; 51:48). Doug Blank (Head of Research at Comet ML) — MIT 6.S191 Introduction to Deep Learning, Lecture 7, 2026 Edition. The wiki’s first MIT 6.S191 source. Five contributions: (1) The Three Laws of AI as teaching framework — Asimov’s 1942 robotics laws modernised in two versions (EU-AI-Act aspirational + Blank’s operational log traces / build dataset / evaluate often + be transparent) + a minus-one law (“don’t build it then” — Violin-Sheet-Music-Blog via YouTube comments). (2) The dataset / metric / experiment / model-comparison eval vocabulary via Comet OPIC — two-source threshold met with Guthrie / Braintrust 2025; academic-pedagogical vantage convergent with the vendor-platform vantage. (3) The Sam Nelson safety-drift case study — wiki’s first first-hand-named-incident anchor for the long-context safety degradation failure mode. (4) Live 90-second agentic AI demo — 4 tools (time / weather / send_email / delete_files); “Can you trust an LLM/agentic-AI? No”; agent confuses dates demonstrating LLM hallucination even with tool access. (5) AI-history retrospective 1956-2026 — symbolic → expert systems → statistical ML → deep learning (2010-2017) → transformer era (2017-now, coinciding with MIT 6.S191’s first offering); Blank’s first-person AI-winter testimony (PhD NN 1997, ~100 professorship applications, few responses; struggled to get NN-as-AI papers accepted through early 2000s). Model-comparison eval: GPT-5 100% on six-bears jailbreak / GPT-4o 94% / GPT-4o-mini 78% / Gemini 2.5 Flash worst. 4 concepts touched (agent-harness 19→20 / agentic-engineering 9→10 / agent-development-lifecycle 3→4 / jagged-frontier 8→9).
- 2026-05-10-ries-lennys-force-destroys-companies-within — The force that destroys companies from within (and how to resist it) | Eric Ries, Lean Startup (Lenny’s Podcast YouTube, 10 May 2026; 1h 39m). Eric Ries (Lean Startup author, 2011) on Lenny Rachitsky’s podcast, two weeks before Incorruptible (26 May 2026) launch. The wiki’s first founder-author podcast anchored on a governance thesis and the first named first-hand role in Anthropic’s governance design (Ries advised Dario & Daniela Amodei during Anthropic’s founding spin-out from OpenAI). Six contributions: (1) The force coinage — “the force that no one controls but everyone obeys” as the named gravitational mechanism that destroys companies from within (not competition; “their very success became a liability”). (2) The 80% founder-ouster statistic (Harvard Law School, per Ries) — only 20% of founders still CEO 3 years after going public under standard venture-backed governance. (3) Shareholder-primacy as a 40-year-old regime — pre-1980s, corporations existed for declared “beneficial purpose”; Adam Smith thought it obvious. The wiki’s first explicit dating of shareholder primacy as recent policy choice, not as natural law. (4) Public Benefit Corporation as the practical defence — “the easiest thing… a two-page legal filing… Delaware tomorrow”; “all the major AI labs are incorporated as PBCs”. (5) Six case studies — defensive successes (Novo Nordisk’s 100-year foundation; Cloudflare’s mission-emergence via the pro-democracy-protesters incident; Costco’s rules-in-structure) and cautionary tales (Vectura Group acquired by Philip Morris; Groupon’s email-frequency death spiral; Whole Foods). (6) The Anthropic LTBT mechanism — two-tier governance with outside AI-safety-expert trustees (no equity) who appoint and accountabilify the for-profit board; strictly stronger than OpenAI’s post-2025 PBC-alone structure. Surfaces 11 dangling entities (Eric Ries; Lenny’s Podcast; Novo Nordisk; Cloudflare; Costco; Vectura Group; Philip Morris; Groupon; Whole Foods; Devoted Health; HEB) and 10+ concept candidates (the force; mission-driven vs mission-hopeful; mission guardian; spiritual holding company; fiduciary commitments; harder is easier; culture bank; torchbearers; founder-control trap; PBC; shareholder-primacy-as-regime; AI-alignment-via-Conway’s-law). Adjacent to Ross & Schneider 2026 — complementary layers (legal-entity governance × people-process adaptability), not contradicting. 2 entities touched (Anthropic 6→7, confidence 0.87→0.92, LTBT mechanism added; OpenAI 9→10, confidence 0.90→0.95, nonprofit-to-PBC trajectory added).
- 2026-05-09-chase-agent-development-lifecycle — The Agent Development Lifecycle (LangChain Blog, 9 May 2026). Harrison Chase (LangChain co-founder/CEO). The wiki’s second formalization of the ADLC — and the first non-Google one — formalizing it as a four-phase loop wrapped by governance: Build → Test → Deploy → Monitor → Iterate, with Govern wrapping the whole loop. Refines the wiki’s harness vocabulary by splitting Build into four distinct sub-layers: Frameworks (LangChain, CrewAI) / Runtimes (LangGraph, Temporal) / Harnesses (Deep Agents, Claude Agent SDK) / No-code (LangSmith Fleet, Claude Cowork, n8n). Test phase: datasets / metrics / experiments / simulations (multi-turn evaluation as first-class primitive, not just for voice agents). Deploy is more than hosting: durable runtime + sandboxes (LangSmith Sandboxes, Daytona, E2B) + virtual filesystem + prompt/context hub. Monitor: traces / signals / feedback / dashboards — “if you cannot see the trajectory, you cannot reliably debug the behavior or turn those failures into future evals.” Govern: six axes (cost / tool access / audit trails / HITL / discoverability of skills / shared infrastructure). Bumps agent-development-lifecycle from single-source stub (confidence 0.70) to two-source confidence 0.80; promotes LangChain to its first proper organization-entity page in the wiki.
- 2026-05-08-running-an-ai-native-engineering-org — Running an AI-native engineering org (Claude YouTube channel, Code with Claude 2026, 8 May 2026). First inside-engineering ingest: Fiona Fung (Director of Engineering for Claude Code at Anthropic; previously Meta and Microsoft) on the team-norms rewrite at Claude Code. Where Karpathy named agentic-engineering as a discipline and Chatterjee gave the agent-harness anatomy, this talk shows the operational team-norms rewrite that follows from them. Five themes: bottlenecks have moved (coding throughput → verification, review, cross-functional, security); norms rewritten (JIT planning; code wins over whiteboard debate via generate three PRs; design docs out, prototypes in; “shift left” verification; “who made this change?” → double-click the real question; trust Claude on style/lint/bugs/tests, keep humans for legal/security/product taste; team makeup indexed on creative-builder + deep-systems-expertise rather than raw throughput; flat org with every manager starts as IC; code is the source of truth); rollout (mandate Claudify everything + explicit permission to kill processes; pod-level high agency for everything else); three signals (onboarding ramp-up time ↓; PR cycle time ↓; Claude-assisted commits trending toward 100% — “I don’t think I’ve seen a non-Claude-assisted commit probably in the last four months”); open questions (iOS/Android orgs; fully-automated review threshold; roles-blurring fairness). Triggers cross-page-presence promotion of Boris Cherny (third source mention).
- 2026-05-08-bratanic-unified-agentic-memory-hooks — Unified Agentic Memory Across Harnesses Using Hooks (Towards Data Science, 8 May 2026). Tomaz Bratanic (Neo4j). The wiki’s first source naming hooks as a first-class harness primitive — distinct from MCP tools — with an emerging cross-vendor contract: Claude Code, Codex, and Cursor all support the same five lifecycle events (
SessionStart,UserPromptSubmit,PreToolUse,PostToolUse,Stop) with near-identical JSON-stdin / JSON-stdout semantics. Hooks vs. MCP: hooks are system-initiated (deterministic, zero context cost, fire on lifecycle events); MCP tools are agent-initiated (consume model attention, rely on the LLM to “remember to remember”). Three-layer architecture demonstrated end-to-end with shared Python scripts + Neo4j: hooks (online) log every event to a linked-list session graph, no model calls; dream phase (offline) batch-job distills events into durable Markdown memories at semantic paths (profile/role.md,tools/bash/common-flags.md,project/neo4j-skills.md), merged not appended; injection (online) loads profile memories into the system prompt at session start and turn-relevant memories on each user prompt. The dream-phase markdown wiki is structurally identical to Karpathy’s LLM-wiki idea and to Anthropic’s Skills — a strong cross-source convergence on markdown-as-agentic-memory-format as the artifact form that survives harness switching. Working demo on GitHub. - 2026-05-07-singhal-stanford-cs153-product-management-in-ai-era — Stanford CS153 Frontier Systems | Nikhyl Singhal from Skip on Product Management in the AI Era (Stanford Online YouTube, 7 May 2026; 63:14). Mike Abbott (CS153 prof / Sequoia partner / ex-Twitter eng VP) interviews Nikhyl Singhal (Skip founder; ex-Google/Meta/Credit Karma VP-of-Product). Four-company-phases framework with different PM-role per phase: pre-PMF (no PM) → post-PMF process → hypergrowth scaling+expanding → big-tech late-stage zero-to-one. AI-era role inversion: bureaucratic movement-of-information PM role dying → product builder role emerging where designers/engineers/PMs converge. Concrete operator-class employment numbers: 30-70% big-tech layoffs this calendar year (Salesforce/Block/Snap); top-1% PM salaries more-than-doubled in 18 months; 4 product-leader contracts crossed eight-figures annual comp; mid-30s middle-managers (8-15 years tenure) named as at-risk cohort. “Brand at all-time low” hiring shift — Anthropic/OpenAI care about modernity-of-toolchain over brand. Career-as-chapters / 15-18-jobs-in-50-years framing (origin of “Skip”). Iteration-speed-as-moat: Chrome (6 weeks) beat Firefox (quarterly) and IE (annual); Android (quarterly) beat iOS (annual) — convergent with Hu 2026’s startup-edge thesis. The two-question audience test: 100% of Stanford CS153 students anxious about jobs + 100% having fun building with AI. Two years ago: 20-30% anxiety, low fun. 6 concepts touched (vibe-coding 8→9 / ai-employment-effects 15→16 / ai-deskilling 4→5 / durable-skills 7→8 / micro-productivity-trap 12→13 / agentic-engineering 8→9).
- 2026-05-07-ransbotham-augmented-learners — Ransbotham, Kiron, Khodabandeh, Chu, Zhukov. Learning to Manage Uncertainty, With AI (MIT SMR × BCG Big Ideas Research Report, 8th annual, Nov 2024). 3,467-respondent global survey + 9 executive interviews. Introduces the Augmented Learners 2×2 (15% high org-learning + high AI-specific learning, 14% Org Learners, 12% AI-specific, 59% Limited Learners); 1.6× uncertainty-management advantage over Limited Learners (2.2× talent / 1.8× tech / 1.6× legal); 99% report annualized revenue benefits. Appendix: AI adoption 50% (2023) → 70% (2024); 84% hopeful (vs 70% in 2017) / 20% fearful (vs 31% in 2017); only 39% have AI strategy in 2024 (down from 59% in 2020).
- 2026-05-07-kokane-agent-harness-vs-systems-design — Akshay Kokane, What is Agent Harness? How it is different than System Design? (Level Up Coding / Medium, 10 April 2026). Provocation-style practitioner article: ~90% of “agent harness” is mature systems engineering rebranded; only 10% genuinely novel — non-determinism at the execution layer (validate intent, not just output format) and context as a first-class degrading resource (“garbage collection ≠ semantic drift correction”). Useful side-by-side rename table (Context management = session/cache; Tool orchestration = API middleware; etc.). 4-layer stack: Your App / Harness Frameworks (LangChain, Microsoft Agent Framework v1.0, Google ADK, LlamaIndex, CrewAI, Haystack, DSPy) / Agent Harness Runtime / swappable Model Layer. References the Claude Code source-code leak.
- 2026-05-07-kiron-schrage-compound-benefits — Kiron & Schrage, How to Reap Compound Benefits From Generative AI (MIT SMR Column, 6 April 2026). Reframes AI ROI as “return on iteration”: consumption economics (asset depreciation) vs. compounding economics (asset appreciation). Three-step flywheel: Verification → Evaluation → Learning capture. Names the Polanyi tacit-dimension breach (LLMs infer tacit knowledge from behavioral traces; tacit expertise is no longer a moat) and the Jevons paradox amplification. Anchor anecdotes: Boris Cherny (Anthropic, Claude Code) — 10–15 concurrent Claude instances + CLAUDE.md as in-workflow learning capture; Jaana Dogan (Google, Gemini API) — “It’s not perfect and I’m iterating on it.” Strongest claim: “The expert as evaluator is not a transitional role.”
- 2026-05-07-globerson-et-al-scalable-measurement-durable-skills — Google Research preprint, 12 April 2026. Globerson et al. (~40 co-authors at Google Research, NYU, UT Austin, OpenMic) introduce the Vantage assessment platform with an Executive LLM that steers AI-teammate conversations to elicit observable evidence of durable-skills (collaboration, creativity, critical thinking). Large-N validation: 188 participants, 373 conversations; LLM-vs-human-rater agreement matches inter-human (Cohen’s κ ~0.45–0.64); Pearson r = 0.88 between Gemini autorater and human experts on creativity assessment with 280 students. Critical-thinking rubric explicitly evaluates “AI-Supported Exploration” and “AI-Supported Verification” as sub-skills.
- 2026-05-07-chatterjee-anatomy-of-agent-harness — Abhishek Chatterjee, The Anatomy of an Agent Harness (Medium, ~3 May 2026). Vivid Friday-in-March opening: an agent emptied a customer’s workspace because the harness lacked an intent-validation layer. Names the construct’s 4-layer anatomy: Context (system prompt as a document, KV-cached stable parts + dynamic) / Constraints (pre/post-tool middleware) / Contracts (formal evaluable specifications) / Compounding (telemetry as training data for the harness, with human-reviewed workspace overrides). Strongest framings in the wiki for the rent-vs-own thesis: “The model is what you rent. The harness is what you own.”; “Plan for swap, not for marriage.”; “Build constraints before you build cleverness.” Compounding layer is operationally identical to Kiron-Schrage’s verification → evaluation → learning capture flywheel.
- 2026-05-07-carucci-resistance-as-data — HBR.org Digital, 20 April 2026. Ron Carucci (Navalent), practitioner essay on change leadership. All resistance is meaningful data, not noise; the leader’s job is diagnosis, not interpretation. Three traps when leaders misread resistance (personalize / moralize / rush to resolution) and four signal categories (Loss / Anxiety / Lack of control / Flaws in change). “Holding the line” framework for sustained behaviour problems. Practitioner opinion writing — no empirical anchors.
- 2026-05-07-anthropic-managed-agents-decoupling-brain-hands — Engineering at Anthropic blog, 8 April 2026. Lance Martin, Gabe Cemaj, Michael Cohen describe the brain / hands / session decoupling architecture for production agents. Brain = model + reasoning loop; hands = sandboxed tool execution surface; session = long-running orchestration state that outlives any one context window. Security as structural unreachability (executor decides whether to run model-emitted tool calls), not policy. Documents Sonnet 4.5’s “context anxiety” failure mode (premature task wrap-up under context-window pressure) and notes its absence on Opus 4.5 — first wiki evidence of long-horizon agent reliability varying within a model family.
- 2026-05-07-anthropic-economic-index-5-learning-curves — Anthropic Economic Index, 5th report, 24 March 2026. Massenkoff, Lyubich, McCrory, Appel, Heller. Sample: Feb 5–12, 2026. Direct continuation of the 4th report. Skill-biased technological change framing: high-tenure Claude users achieve ~3-4 pp higher task success than low-tenure users after controls; model-selection slope quantifies that picking the right tier per task is itself a learned skill (+1.48 pp Opus per +$10/hr task value on Claude.ai; +2.79 pp on the 1P API). Claude.ai augmentation/automation split stable at 53/45; emergent automation clusters on the API (sales, programmatic trading, coding agent harnesses). Coding share migrated off Claude.ai onto API (36% → 47% of API task-share, Aug 2025 → Feb 2026) as agentic harnesses matured.
- 2026-05-06-mit-ocw-future-of-mit-open-education — The Future of MIT Open Education (MIT OpenCourseWare YouTube, 6 May 2026; 57:52). Panel with Dimitris Bertsimas (MIT VP for Open Learning, Sloan), Sarah Hansen (Asst Director Open Education Innovation), Curt Newton (Director OCW). Manual caption track. The wiki’s first ingest on open-education-as-supply-side for the AI-era skills question. Four substantive contributions: (1) The 1-billion-learners-in-10-years ambition — OCW currently at ~500M lifetime learners; 2x scale-up in 10 years vs the 25 years it took to reach 500M; “Aspiring to educate 1 in 8 in the world” (Bertsimas). (2) Universal Learning architecture — 22 modules of Universal AI live (March 2026, ~30 MIT faculty participating); 18 more verticals “cooking”; Universal Climate + Energy / Biology / Health following. AI + x vertical structure (AI + energy, AI + health, AI + law, …) explicitly counter to MIT’s historical vertical-department structure: “the world is organized horizontally”. Three-phase deployment: MIT-authored (phase 1) → external-institution-authored with platform access (phase 2; Greek top engineering school active) → MIT-curated for global distribution (phase 3). (3) AI-enabled translation tipping point — Newton: “with the AI tools that are being brought now to bear, we’ve crossed the tipping point”. Translation into 12 languages launching May 2026; Bertsimas personally verifies Greek quality (“A-plus”) and tested in two Greek high schools (~150 students; “comments are very positive”). (4) Personalised education launching summer 2026 — distinction between personalized (agent reorganises content per learner) and personalizable (content has wide-open structure enabling reorganisation). Newton: “What OCW has is personalizable materials.” Plus: MIT Learn platform consolidating OCW/MITx/Professional Ed by July 2026, built on OpenIndex; AskTIM AI tutor; MIT-Wikipedia collaboration project; financial-sustainability constraint (MIT’s $20M subsidy → $0 in 4 years); modularised-learning content-revision-velocity argument (4-5 lecture modules vs 26-lecture semester revisions). Final audience question raised AI-cognitive-impact concern — “several studies from MIT have shown that using AI to supplement learning can sometimes actually hinder those learning faculties” — audio cuts off before Bertsimas’s response. 3 concept pages updated (durable-skills 6→7, ai-deskilling 3→4, enterprise-ai-adoption 25→26).
- 2026-05-06-kropp-bcg-hbr-dont-treat-ai-agents-like-employees — Research: Why You Shouldn’t Treat AI Agents Like Employees (HBR Generative AI section, BCG Henderson Institute research; published 6 May 2026, ingested 25 May 2026; ~12 pages, full ingest). BCG managing directors / senior partners and BCG Henderson Institute Fellows: Matthew Kropp (CAIO of BCG X, BCG Henderson Institute Fellow), Julie Bedard (BCG global People & Organization + AI leadership teams; BCG Henderson Institute Fellow), Megan Hsu (BCG project leader; BCG Henderson Institute Ambassador), Lisa Krayer (BCG principal; BCG Henderson Institute Ambassador — promoted from Dangling to entity page in this ingest as her second wiki appearance, after Jagged Frontier 2026); plus academic co-author Emma Wiles (Boston University Questrom; MIT IDE digital fellow). The wiki’s first RCT-grade empirical anti-anthropomorphizing-AI source. Randomized experiment, N=1,261 HR / finance managers / directors / executives from US, Canada, EU, three conditions (document drafted by AI tool / human teammate “Alex” / AI teammate “ALEX-3”); documents and errors held constant; pre-registered subset analysis on participants whose orgs already have AI on org charts (23% of sample). Five substantive contributions: (1) Headline finding: the AI-employee framing (vs AI-tool framing) causes — in the AI-already-on-org-chart subset — a 9pp drop in personal accountability (with 8pp shift to the AI), 44% increase in escalation requests, 18% fewer errors caught, and (whole-sample) 13% higher uncertainty about professional identity, 7% higher concern about job security, 10% lower trust in how AI would be used, and no meaningful increase in adoption intent. (2) Four named failure modes — accountability becomes blurred (“The blame isn’t on a person; it’s on the technology”); escalation and burden on others increase (“questioning their own skills, doubting whether they had identified all issues”); quality control declines (the budget-with-inconsistent-totals and entry-level-role-requires-10-years-experience worked examples); professional identity and trust take a hit (“If you want people to feel like they will lose their job to AI, or can be easily replaced by AI, then put it on the org chart”). (3) The five-point redesign prescription — redefine workflows + name new (human) role expectations; make accountability explicit and personal (decision rights / escalation triggers / consequences); capability-building plan for managers of agents; don’t constrain agents into 1-for-1 roles (“A single agent can operate across many workflows; multiple agents can reshape one job”); deliberate choices about how human work evolves toward judgment/creativity/ownership. (4) The AI brain fry concept extension — AI-employee framing may compound the prior BCG brain-fry finding (excessive AI oversight causes 11% / 39% higher minor/major error frequency) by reducing review-engagement further. (5) The Scout and Kevin exhibits — real anecdotes from study participants’ organisations of AI agents formally on org charts treated as junior employees (Scout reviewing job applications + first-round interviews; Kevin as a joked-about “a little dry” colleague whose errors are narrated as “Kevin’s mistake”). Cited prior BCG research (not separately ingested, open follow-ups): the AI brain fry study, the AI workforce transformation study (3.5× managerial-role-modelling at high-maturity orgs), and the BCG Henderson Institute executive-vs-IC enthusiasm-gap research (76% / 31%). W&W tags:
digital-transforming/redesigning-internal-structures,digital-seizing/strategic-agility,strategic-renewal/organizational-culture,contextual/internal-enablers,contextual/internal-barriers. 7 typed relationships —supportsto AWS London Exec Forum (independent empirical confirmation of the managers-in-charge-of-agents failure mode Allen names — Allen + Brooklyn’s human-starts-human-ends operational shape + Kropp et al.’s five-point redesign converge from independent vantages 15 days apart), Anthropic (same processes-are-the-hard-part prescription at advisory vs Anthropic-engineering-org altitudes), McKinsey (BCG + McKinsey consultant-altitude pair on enterprise AI transformation governance), Bain (third named micro-productivity-trap sub-mode: anthropomorphizing lock-in, alongside Bain’s offering-lock-in and process-lock-in), Warner & Wäger (empirical operationalisation of W&W redesigning-internal-structures + organizational-culture microfoundations);contradictsto Replit (productive tension on the AI-as-employee-substitute framing) and AnswerThis (productive tension on the one-agent-per-role design pattern — same operational shape, opposite framings). Entity changes: Boston Consulting Group 1→3 (+ confidence 0.75 → 0.82, last_confirmed 2026-04-28 → 2026-05-25); Lisa Krayer new entity page (second-source promotion). Dangling first-mentions: Matthew Kropp, Julie Bedard, Emma Wiles, Megan Hsu. Confidence 0.85. - 2026-05-06-bockeler-engineering-of-ai-agents-context-harnessing-autonomy — The Engineering of AI Agents: Context, Harnessing, and Autonomy (InfoQ YouTube, 6 May 2026; 42:01). Birgitta Böckeler (Thoughtworks) at QCon London 2026 — a structured year-on-year review of AI coding agents (QCon London 2025 → 2026). The wiki’s first independent-consultancy practitioner-observer vantage on harness engineering as a discipline: Böckeler explicitly credits the OpenAI Codex team for the name harness engineering (the Lopopolo source) and propagates the term to the QCon audience, giving it a two-axis decomposition: feed-forward × feedback, each containing both CPU-based deterministic and GPU-based inferential elements. New mechanisms named: structural tests as agent feedback (ArchUnit, Spring Modulith, Dependency Cruiser); enhanced lint messages as “good prompt injection” (edit the lint-error text to include the refactoring meta-hint); harness templates as the next abstraction layer (analogous to service templates — pick a workflow topology, instantiate a pre-built harness). New constructs the wiki should track: context-engineering as the named practice (Böckeler dates the term to ~June 2025); lethal-trifecta credited to Simon Willison (June 2025) — untrusted content + private-data access + external comms; probability × impact × detectability risk-assessment trio for autonomy decisions (the practitioner-operational form of jagged-frontier); Goldilocks speed counter-framing to speed-at-all-costs. Cost trajectory: $20 flat → $200 flat → $380/day metered (= ~$91k/year in tokens for one developer) — the agentic-coding inner loop burns tokens for what may end as two lines of code. Convergent with the micro-productivity-trap from a third consulting-firm vantage (Bain + McKinsey + Thoughtworks now align). 5 concept pages updated (agent-harness 11→12 / agentic-engineering 3→4 / vibe-coding 3→4 / jagged-frontier 6→7 / micro-productivity-trap 8→9).
- 2026-05-06-amodei-anthropic-cofounders-code-with-claude-conference — A conversation with Dario Amodei & Daniela Amodei (Claude YouTube, 6 May 2026; 33:10). Anthropic co-founders Dario Amodei (CEO) + Daniela Amodei (President), moderated by Ami Vora (Chief Product Officer) at the Code with Claude developer conference. METADATA-ONLY INGEST — transcripts disabled for this video (Playwright skill returned empty caption_tracks; youtube-transcript-api raised TranscriptsDisabled). The wiki holds metadata + framing; the conversation content itself is not captured. Re-attempt opportunity flagged: wait for caption enablement, or substitute an Anthropic-published transcript / community-event-recap.
- 2026-05-05-stanford-ai-club-chamath-on-how-to-win-in-the-ai-era — Stanford AI Club: Chamath on How to Win in the AI Era (Stanford AI Club YouTube Speaker Series, 5 May 2026; ~54:23). Chamath Palihapitiya (founder/CEO of 8090; ex-Facebook executive 2007–2011 Growth/Platform/Mobile; founder of Social Capital; co-host of the All-In Podcast) in fireside interview at Stanford AI Club. Five substantive contributions: (1) The “control plane for AI” thesis — “you have to have a symbolic space that guides the embedded space”; PRDs, requirements, “secrets in English” are the load-bearing artifact, not the code; 8090 wants to build “hardware-, database-, language-independent symbolic representation of what you want to do” as the operating-system layer above the agents. (2) The “software factory” as governed assembly line — humans + powerful models bound to engineering plans + work orders; forward pass (intent → code) and reverse pass (legacy code → propagated-backward English understanding); 80% feature parity at 90% less cost for legacy rebuilds (EY as flagship customer). (3) The COBOL-retiree anecdote — a $100B/year customer brings retirees back from retirement to explain what their code does; “these kinds of problems are the thing that’s stopping ROI on trillions of dollars of investment.” Wiki’s most concrete instance of the micro-productivity-trap at the institutional-knowledge-archaeology layer. (4) Trough-of-disillusionment thesis — “long horizon tasks are fundamentally still a joke”; “complex problems also don’t work”; if firm-rebuild doesn’t materialize, “there will be blood in the streets. The stock market will just totally turn over on itself.” (5) Network-effect-in-shared-code — “a hospital diagnosing cancer and an airplane company designing a new wing — that may be a different ontology, but it’s probably the same problem”; the N+1st company gets to leverage the secrets of the N before it. Plus: open-weight ≠ open-source (China has open-weight; US has closed); Bittensor Subnet 3, Folding@Home, Pluralis as decentralised-training projects worth tracking; no-org-chart + chronic-under-hire-by-half as deliberate flow-state organisational design (named exemplars: Anthropic, OpenAI, early Facebook, early Google, SpaceX); positive-sum vs tokenize-and-sell vs replace-and-fire AI compact taxonomy. Surfaces dangling candidates (Chamath Palihapitiya, 8090, Stanford AI Club, EY, Social Capital, All-In Podcast, Mark Juncosa, Memorial Sloan Kettering, Boeing, Peter Thiel, Bittensor Subnet 3, Folding@Home, Pluralis). 6 concept pages updated in paired-ingest with Spiegel 2026.
- 2026-05-05-nishar-nohria-end-of-one-size-fits-all — HBR.org Digital, 23 April 2026. Nishar (technologist/investor; ex-Google, ex-LinkedIn CPO) & Nohria (HBS professor; 10th HBS Dean 2010–2020) argue GenAI dissolves the economic logic of standardized SaaS. Build/buy is no longer a cost question; it’s a strategic question about which workflows the firm should own. Four-model framework for the firm-boundary decision: Build / Compose / Collaborate / Buy Outcomes. Empirical anchors: enterprise GenAI app spending $1.7B (2023) → $37B (2025); 40% of code AI-generated; >1/3 of companies have replaced ≥1 SaaS tool with custom GenAI; SaaS valuations 30–60% below 2021 peaks; Adobe outcome-based pricing as named industry signal.
- 2026-05-05-loukides-radar-trends-may-2026 — Radar Trends to Watch: May 2026 (O’Reilly Radar, 5 May 2026; co-authored by Mike Loukides and Claude — first time the digest carries a human-AI co-byline). Opening framing: “AI is becoming operational” — moving beyond language games to enterprise process automation through shared team agents. Five vendor-side moves toward harness-as-a-service in a single month: Anthropic Managed Agents + Claude Code routines + OpenAI workspace agents + OpenAI Agents SDK open-source + Amazon Bedrock AgentCore agent registry + Cursor 3 repositioned as orchestrator. Direct news-side companion to Osmani’s HaaS framing ten days later. Claude Mythos / Project Glasswing restricted access vs OpenAI GPT-5.5 public release; AISI analysis (“step up over previous frontier models”) with the qualification that small open-weight models can match Mythos at vulnerability discovery. Anthropic 3.5 GW compute deal with Google + Broadcom (power specified, not chip counts — convergent with Apollo comparison). DeepSeek V4 preview (>1T params). Stanford 2026 AI Index Report (400+ pages) published this month. GPT-Rosalind — biology-tuned model with skepticism bias by design (wiki’s first source on science-domain-tuned frontier model with bias baked in). Robotics inflection: Boston Dynamics Spot reads gauges via Gemini Robotics-ER 1.6; MLB deploys robotic ball/strike challenge ruling.
- 2026-05-05-google-gemini-file-search-multimodal — Gemini API File Search is now multimodal: build efficient, verifiable RAG (Google — The Keyword, 5 May 2026). Ivan Solovyev (PM, Google DeepMind), Kriti Dwivedi (SE). Three updates to Gemini API File Search: (1) multimodal retrieval via Gemini Embedding 2 (images and text in the same store); (2) custom metadata for query-time filtering (
department: legal,status: final); (3) page-level citations tying responses to specific pages of indexed PDFs for verifiability. Three-primitive API surface:file_search_stores.create→upload_to_file_search_store→generate_contentwithtools=[{file_search: …}]. Short developer-relations announcement; substantiates a harness Context layer primitive — verifiable retrieval — that scoping conversations about RAG quality at scale typically assume. - 2026-05-04-rethinking-agents-harness-is-all-you-need — Rethinking Agents — Harness is All you Need (Prompt Engineering YouTube channel, 4 May 2026). First video ingest; ~14-min explainer synthesising two academic papers — Pan et al. (Tsinghua, March 2026) on natural-language harness representation, and Khattab et al. (DSPy team) on auto-harness optimisation. Headline empirical claims: same-model 6× performance variance depending on harness; SWE-bench verified ablations show verifiers actively hurt (−0.8 / −8.4) and self-evolution is the only consistently helpful module; OS-Symphony 30.4% → 47.2% by rewriting code-based logic in structured natural language with the same strategy; Khattab’s Meta-Harness scores 76.4% on Terminal Bench 2 (the only auto-optimised system in the field); a harness optimised on one model improved five other models without re-tuning — the wiki’s first empirical anchor for “the model is rented, the harness is owned”. Names Anthropic’s “subtraction principle”: mature harness work looks less like adding structure and more like pruning it (Manus rewrote 5× in 6 months; Warel removed 80% of tools and got better results). Closes with a 4-question audit checklist: when an agent underperforms, audit the harness, not the model.
- 2026-05-03-rewired-second-edition-sample — Lamarre, Smaje, Levin et al., Rewired 2nd ed. (Wiley/McKinsey, 2026). Sample-only ingest: 30-page library/OverDrive sample of a ~600-page book — front matter, full TOC for 39 chapters across 7 sections, the introduction, first 5 manifesto themes, and back-of-book Index for terminology. The 6-capability “Rewired” framework (business-led roadmap, talent, operating model, technology, data, adoption-and-scaling); 20% EBITDA uplift / $3:$1 ROI / 1–2 yr breakeven across ~20 deep-dive AI-leader companies; 70% talent-density shifts; 40% of 2nd ed entirely new (mostly agentic AI). Chapters 1–39 and the four case studies (DBS, Freeport-McMoRan, LATAM Airlines, Toyota) deferred until full book is available.
- 2026-05-02-schoening-lennys-podcast-cultivating-agency-ai-era — AI era skills: Why cultivating agency matters more than job titles | Max Schoening (Notion) (Lenny’s Podcast YouTube channel — 2 May 2026; ~87:22; ASR transcript, 1684 segments — read closely chapters 1–10). Host Lenny Rachitsky; guest Max Schoening (head of product at Notion; prior career: PM at Google → ran design at Heroku → VP of Design + part-time engineer at GitHub → two-time founder). The wiki’s clearest single articulation of agency as the durable AI-era skill at the practitioner-altitude — distinct from the taste / judgement framings the wiki carries (Karpathy, Hu, Forsgren, Mittal) by being explicitly about the disposition to act rather than the discernment of quality. Five substantive contributions: (1) Designers + PMs shipping code at Notion (~1:55–8:24) — sustained operating mode; Schoening pushes back on framing as role-blurring and frames it as role-evolution: “do you drive Notion like it’s stolen?… you can still contribute to the company in a way that you feel agency.” (2) Agency-as-the-differentiator thesis (~10:32–11:49 — the episode’s titular claim): “Even if you have the skills at your fingertips because now an AGI-adjacent model helps you, the thing that matters is agency. People who have true agency and understand that the world around them is malleable will do great, and the folks who stick to ‘what does it mean to be a PM, what does it mean to be a designer’ — I think that will be much harder.” (3) Two Notion vignettes (~11:49–13:52): Brian Leven — designer who blurs eng/design + is Notion’s #1 recruiter; Eric Lou — PM who asked Schoening “if you started a startup would you hire me?”, was told “not in the first 10 — I don’t need a product manager”, replied “oh okay, I’m going to work on the skills so that you would hire me in the first five” and progressively shifted PRDs → Figma → “why do I have to do the Figma thing, can’t I just build the prototype and at least show you what I think?” The wiki’s clearest narrative arc of PM-evolving-into-builder-via-agency. (4) What we might lose as roles merge — the prototype-vs-engineering physical-metaphor (~13:52–15:56): “If you and I were to build a hardware startup, we would make the first enclosures with 3D printing — and you would see all the layer lines. It would be very obvious that this is not a thing you should give to people to pay for. Then there’s a long windy road all the way to where, if you’re very lucky, you get to manufacture that product for 100 million people. So the engineering is actually ‘how do I optimize the factory.’ That’s very absent right now from most of the discourse in software, which is all about how many tokens can we spend and how many features can we ship.” (5) Malleable software (~17:42–20:43) as a long-arc design tradition Schoening has been advocating since before the AI revolution: “Malleable software is the idea that software works closer to the interest of the people that use it than the interest of the corporation that makes it.” The wiki’s first explicit lineage anchor for malleable software as a pre-AI tradition — Ink & Switch / Jeffrey Litt as daily collaborators. W&W tags:
digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,digital-seizing/strategic-agility,strategic-renewal/organizational-culture. 5 typedsupportsrelationships: PMs Who Use AI (paired product-management-leader anchors on PMs-shipping-code thesis — Nika prescribes, Schoening is the operating worked example), Yhangry (paired what-thriving-looks-like (Schoening) ↔ what-firing-looks-like (Mittal) anchor on the tool-fluency-as-org-design-criterion thesis), CS153 (practitioner-altitude counterpart to Hu’s AI founder type / IC / DRI org structure prescription), Interrupt 26 (Schoening’s domain-experts-creating-tools observation = consumer-of-vendor-platform vantage to Chase’s everyone-builds-agents prediction), Replit (paired 2026 founder-altitude malleable software / vibe-coding for non-developers anchors). Dangling first-mentions: Max Schoening, Notion (substantive), Brian Leven, Eric Lou, Lenny Rachitsky, Ink & Switch, Jeffrey Litt, Dieter Rams. Confidence 0.75. - 2026-05-02-dutt-chatterji-ai-experimentation-to-transformation — HBR.org Digital, 30 April 2026. Dutt, Rapoport (Bain) + Chatterji (Duke / OpenAI Chief Economist), Weeratunga, Satcher (OpenAI Economic Research). Names the “micro-productivity trap”: offering lock-in + process lock-in. Four-step transformation framework with 10–25% Bain client EBITDA gains. Lowe’s (Mylow / Mylow Companion, 1,700+ stores) and FabricationCo (~$30M profit, 15× faster quotes) cases.
- 2026-05-01-lf-state-of-tech-talent-global-2026 — 2026 State of Tech Talent: Not a jobs crisis, but a skills crisis with an upskilling answer (Marco Gerosa·Adrienn Lawson·Anna Hermansen / The Linux Foundation Research, fwd Clyde Seepersad; May 2026; survey n=400 worldwide, Feb 2026). The wiki’s first tech-talent-market survey anchor. Thesis: AI is not eating IT jobs — it raises the bar; the constraint is operationalising AI, not accessing it. Net hiring +26% (2025) / +31% (2026); only the largest orgs negative (−4%); 97% plan to use AI. Skills gap is full-stack (understaffing in AI 47%, cybersecurity 40%, platform-eng 34%); agentic-AI security risks + budget the leading barriers; most orgs lag on the PARK stack. Upskilling > hiring: primary response (57%), favoured 7.9×/7.7×/7.3×/5× (business-context/retention/team-cohesion/cost), 3.5× more likely to upskill than hire; technical training (93%) > compensation (91%) for retention; 76% value certifications. W&W:
digital-transforming/improving-digital-maturity+redesigning-internal-structures+contextual/internal-barriers. 3supports: Brynjolfsson (net-hiring/aggregate), Giles (role redefinition), Forsgren & Macvean (upskilling). Concept changes: ai-employment-effects 38→40, durable-skills 23→25, enterprise-ai-adoption +. Promotes The Linux Foundation, Marco Gerosa, Adrienn Lawson to entities. Interest-alignment caveat (LF Education sells certifications) → confidence 0.75. - 2026-04-30-ai-index-report-2026 — Stanford HAI’s 9th annual AI Index, April 2026. New EiC Sajadieh; Medicine spun off as standalone chapter (with Schmidt Sciences); AI sovereignty as new framework. SWE-bench 60→~100%; OSWorld agents 12→66%; org adoption 88%; GenAI 53% population in 3 years; $285.9B U.S. private investment (~23× China); software dev 22–25 employment -20% from 2024; AI incidents 233→362; jagged frontier as official narrative.
- 2026-04-29-boussioux-crowdless-future — Organization Science Vol 35 No 5, Sept–Oct 2024 (peer-reviewed). Boussioux, Lane, Zhang, Jacimovic, Lakhani field study: human-crowd vs human-AI on circular-economy ideation. HC higher novelty; HAI higher strategic viability/value/quality. Differentiated single-instance search beats multi-instance independent search. Cost ~94× lower with HAI ($27 vs $2,555).
- 2026-04-29-andrej-karpathy-from-vibe-coding-to-agentic-engineering — Andrej Karpathy: From Vibe Coding to Agentic Engineering (Sequoia Capital AI Ascent YouTube, 29 April 2026). First interview-format video ingest + Sequoia AI Ascent’s inaugural guest. ~30 min with Andrej Karpathy (ex-OpenAI co-founder; ex-Tesla Autopilot; coined vibe coding and Software 2.0). Anchored on the December 2025 phase change when agentic coding tipped from “useful but needs corrections” → “I just trust it now”. Names the wiki’s first explicit software-3.0 framing (1.0 = rules; 2.0 = weights; 3.0 = prompts — LLM as programmable computer, context window as program). Names agentic-engineering as engineering discipline (“preserves the quality bar at agent speed”; ceiling is much more than 10×) paired with vibe-coding (“raises the floor”). The cause-of-jaggedness mechanism: LLMs automate what you can verify; verifiability + labs care explain why models fly in some circuits and struggle in others (canonical 2026 example: Opus 4.7 refactors 100k-line codebases yet tells you to walk to a 50m-away car wash). “We’re not building animals; we are summoning ghosts.” Closes with the upstream-spec author confirming his continued use of the LLM-knowledge-base / wiki-from-articles workflow — “anytime I see a different projection onto information, I always feel like I gain insight” — the very pattern this repo implements.
- 2026-04-28-werner-lebrun-octopus-organization — HBR Nov–Dec 2025. AWS executives in residence Werner & Le-Brun argue most companies are “Tin Man Orgs” optimized for predictability and need to become adaptive “Octopus Orgs”; only 12% of transformations succeed.
- 2026-04-28-webb-strategic-foresight — HBR.org Digital, Jan 2024. Amy Webb (FTSG / NYU Stern) argues strategy and foresight should be reunited; introduces FTSG’s 10-step strategic foresight methodology (signal detection → recalibrate); STREEEP+W uncertainty taxonomy.
- 2026-04-28-warner-wager-dynamic-capabilities-digital-transformation — Long Range Planning Vol 52, 2019 (peer-reviewed). Warner & Wäger (Edinburgh Napier / Nunatak) qualitative case study of 7 German MNCs; nine digital microfoundations under Teece’s sense/seize/transform clusters; ongoing-strategic-renewal definition of digital transformation.
- 2026-04-28-reitz-higgins-spacious-thinking — HBR.org Digital, July 2025. Reitz & Higgins (Saïd Oxford / GameShift) on doing-mode vs. spacious-mode attention; advantage blindness; three leader behaviors (focus on ideas / bring in novelty / value & reward spacious mode); 3,000+-employee survey.
- 2026-04-28-mittri-cisco-ai-enabled-enterprise — MIT Tech Review Insights × Cisco sponsored research. Only 13% of companies AI-ready; 98% urgency; chatbot → agent → multi-agent progression; five-foundations framework (Strategy/Infrastructure/Data/Governance/Culture).
- 2026-04-28-mit-sloan-ai-maturity — MIT Sloan article on MIT CISR’s four-stage AI maturity framework (28/34/31/7 distribution; 7% Stage 4) and “Four S” challenges (Strategy/Systems/Synchronization/Stewardship) for scaling pilots.
- 2026-04-28-gomaa-lean-4-0 — Open-access academic paper. Strategic roadmap for integrating Lean Manufacturing with Industry 4.0 technologies; 23 × 23 mapping of Lean tools to I4.0 technologies; off-theme but adds the manufacturing lens.
- 2026-04-28-ftsg-convergence-outlook-2026 — FTSG (Future Today Strategy Group), 1st edition Jan 2026. Replaces 19-year Tech Trends Report. Defines convergence (multi-trend system-level intersection); four rules; seven enabling conditions; 24 chapters spanning compute, agentic economies, labor, biology, surveillance — only intro ingested.
- 2026-04-28-dellacqua-jagged-technological-frontier — Organization Science (INFORMS), March 2026 (peer-reviewed). Dell’Acqua, Mollick, Lakhani et al. randomized field experiment with 758 BCG consultants on GPT-4; introduces “jagged frontier” concept; +12.2% / +25.1% / +33.9% inside frontier; −19 pp correctness outside; equalizing effect within elite (bottom-half +31%, top-half +11%).
- 2026-04-28-carroll-sorensen-strategy-analogy — Strategy Science Vol 9 No 4, Dec 2024. Carroll & Sørensen (Stanford GSB) develop disciplined tools for analogical reasoning in strategy; Glassdoor/Tripadvisor worked example; horizontal vs. vertical relations; theory-based view connection.
- 2026-04-28-brynjolfsson-li-raymond-generative-ai-at-work — Quarterly Journal of Economics, Feb 2025 (peer-reviewed). Customer-support AI study with 5,172 agents at Fortune 500 firm; +15% productivity; equalizing effect with small quality decline at top performers; augmentation by design.
- 2026-04-28-brynjolfsson-canaries-coal-mine — Stanford Digital Economy Lab working paper, Aug 2025. ADP payroll data showing early-career workers (22–25) in AI-exposed occupations have ~13% relative decline since late 2022; concentrated in automation not augmentation.
- 2026-04-28-bansal-birkinshaw-systems-thinking — HBR Sept–Oct 2025. Bansal & Birkinshaw (Ivey) argue systems thinking (vs. breakthrough/design thinking) is the right mode for wicked problems; four-principle approach (North Star / reframe / flows / nudge); cases: Maple Leaf Foods, Co-operators, CSA Group.
- 2026-04-28-anthropic-economic-index-q4-2025 — Anthropic Economic Research, 15 Jan 2026. Fourth Anthropic Economic Index report. Introduces five “economic primitives”; speedup scales with task complexity (9× HS / 12× college); 52% augmentation vs. 45% automation on Claude.ai (Nov 2025); first-order deskilling thesis; +1.0–1.2 pp/yr aggregate productivity (reliability-adjusted).
- 2026-04-28-anand-wu-genai-playbook — HBR Nov–Dec 2025. Anand (NYU Stern Dean) & Wu (HBS) introduce a 2×2 framework for where to deploy GenAI (cost of errors × type of knowledge); paradox-of-access argument; six leakage points exhibit.
- 2026-04-28-ai-index-report-2025 — Stanford HAI’s 8th annual AI Index. 78% of orgs use AI / 71% use GenAI; only 1% mature; $109.1B U.S. private investment; inference cost cratered 280×.
- 2026-04-27-surrealdb-knowledge-graphs-for-ai-agents-practical-guide — Knowledge Graphs for AI Agents (A Practical Guide) (SurrealDB YouTube, 27 April 2026; 60:04). Martin (SurrealDB Solutions Engineer). Manual caption track. The wiki’s first dedicated KG-architecture source with full ETL pipeline depth. Three named vector-only-RAG failure modes (context clash / context confusion / dense neighbourhood degradation). GraphRAG mechanic: vector-search trimmed by graph-relationship subset; multi-hop traversal in single SurrealQL query (vector + graph + full-text + relational together). Chunking-strategy taxonomy as a discipline (recursive / structural / semantic / agentic / no-chunking — accuracy vs cost). KG-ETL pipeline: Extract (parsing + chunking + embedding + entity-extraction + relationship-extraction) → Transform (deduplication + ontology + canonicalisation) → Load. Decision criteria for KG-vs-vector-only: manual-driven Q&A → vector OK; decisions / explainability / dynamic-knowledge → KG required. Single-query-language-for-hybrid-search worked example. Promotes knowledge-graphs to a new concept page; touches agent-harness 17→18, industrial-ai-agents 1→2.
- 2026-04-27-liu-rag-llm-wiki-or-gbrain-how-your-agent-remembers — RAG, LLM Wiki, or GBrain? How Your Agent Remembers Changes Everything (Yanli Liu / AI Advances Medium, 27 April 2026; ~15-min read). The wiki’s most-substantive comparative-architecture article on knowledge-memory patterns. Three architectures in unified decision framework: RAG (Retrieve, 200K+ docs, no compounding) / LLM Wiki (Compile, ~1K sources, compounds) / Fat Skills / GBrain (Act, 17K+ pages, proactive via 21 crons). RAG’s three critical failure points (cites 2024 paper with 7 total): chunking / re-derivation / passivity. GBrain detailed architecture — 24 autonomous skills, 21 cron jobs, 17,888-page brain on Postgres + pgvector; “thin harness, fat skills” (~200 lines harness, intelligence in skill files); CLAUDE.md + RESOLVER.md routing; always-on signal-detector skill; deterministic-vs-latent work split. Convergence prediction: 2023 RAG era → 2025 Wiki + Skills emerge → 2026+ hybrid. Claude Code already partial-implements convergence (CLAUDE.md as mini-wiki + auto-memory + skills). Promotes Garry Tan to entity page (3-source threshold met).
- 2026-04-26-how-to-win-when-software-is-not-a-moat-evan-spiegel-snapchat-ceo — How to win when software is not a moat | Evan Spiegel (Snapchat CEO) (Lenny’s Podcast YouTube, 26 April 2026; ~1:10:25). Host Lenny Rachitsky with guest Evan Spiegel (Snap co-founder/CEO). Second source under the Lenny’s Podcast author — triggers entity-page promotion. Five contributions: (1) “Software is not a moat” — 15 years ahead of the AI realization — Snap learned this in 2010–2011 when every feature got copied; the rest of the industry is “discovering today with AI” the same lesson. Three Snap moat-substitutes: ecosystems (creator + AR developer platforms), hardware (vertically-integrated AR Specs / Spectacles), brand-as-cultural-artifact. (2) Distribution as the new moat — AI is climbing up the product-development funnel (autocomplete → write → review → test → ideate → strategy) but does not help with distribution; “that’s even more so true for consumer products.” Next-form-factor (glasses) as the platform-shift exit ramp. (3) Design as intentional bottleneck for product cohesion — 9-to-12-person design team; no titles, no hierarchy; first-day-you-join-you-present-work; CEO reviews hundreds of design ideas weekly; design gates shipping. “That bottleneck is really really important because that’s what results in a cohesive customer experience.” (4) Designers shipping code with guardrails at near-billion-user scale — bottom-up (not mandated); 10,000 auto-detected bugs; shake-to-report → agent-debugs-and-suggests-fix → near-term auto-apply forecast. (5) Jobs-to-be-done as AI-transformation organising principle — enumerate JTBD per user type (Snapchatters / advertisers), map agents and cross-functional teams to jobs, track progress against per-job business outcomes; paired with “thousand flowers bloom” idea-generation counterweight. Plus the meta-forecast that “humanity is far more important than [AI capability] because humanity dictates how technology is adopted” and the “crucible moment” framing for Snap’s 2026. References Jenny Wen (head of design at Claude, ex-Figma director) and Claude Cowork — Anthropic entity updated to add both. Promotes Lenny’s Podcast to entity; surfaces dangling candidates (Evan Spiegel, Snap, Bobby Murphy, Jenny Wen, Marc Andreessen, Keith Rabois, Lenny Rachitsky). 6 concept pages updated (strategic-foresight 2→3 / ai-employment-effects 16→17 / enterprise-ai-adoption 26→28 (paired with Chamath ingest) / agentic-engineering / agent-harness / micro-productivity-trap — most via paired-ingest bumps with Chamath 2026).
- 2026-04-25-masad-replit-ceo-only-two-jobs-left — Replit’s CEO On The Only Two Jobs Left In The Company Of The Future — Amjad Masad on YC Founder Firesides (Y Combinator YouTube, 25 April 2026; ~39:08). Amjad Masad (co-founder & CEO of Replit; $400m Series D at $9bn valuation announced ahead of the interview) interviewed by Andrew Miklas (YC). The YC-batch-context anchor triple completes with this ingest — Tan 23 April + Hu 24 April + Masad 25 April form three consecutive YC-channel sources on the AI-native-company thesis from three distinct vantages (President / Partner / Portfolio-founder-CEO). Four contributions: (1) Replit as the vibe-coding-product first-mover (September 2024) — “Replit became the first like what’s called vibe-coding product where we abstracted away code entirely”; predates Karpathy’s December-2025 phase-change framing by ~3 months on the platform side. (2) The AI-native-builder cohort is not who we expected — “developers like the pain… It’s sort of like a craftsperson liking to build their own tools. People that are getting the most value tend to be the more tech-adjacent ones — PMs, designers, entrepreneurs.” Audience-pipeline now generalises beyond Nika’s PM-side anchor to all tech-adjacent non-developers + domain experts (physical therapist building a fascia-and-range-of-motion app, pool-business operator, sports-club founder replacing MS-DOS-era software, parent tracking a child’s rare condition in Korea). (3) Agent 4 = parallel agents + canvas + multi-modality + skills-call. Parallel agents with merge-conflict resolution at the orchestrator; built-in design canvas; one project → web + mobile (TestFlight) + deck + video; skills-call revolution as the Replit-platform parallel to Tan’s GStack-style packaging. (4) The company-of-the-future is made of builders and salespeople — sales is durable as “evangelists, more like educators”; builders are durable because “there’s always more to automate, our job will continuously get higher and higher level”; the vibe-coding-resident team at Replit (a generalist team with vague mission “go around the company and make it better” deputising agents to solve support-queue prioritisation, HR onboarding gaps) is the worked example of Hu’s closed-loop-company pattern. Plus the post-prompting product direction (“at some point Agent 5 or maybe sooner, you should be able to tell Replit, ‘every day build me a SaaS company’”) and a prescriptive skills-that-matter list (idea generation / taste / playing-with-tools / not-giving-up / being-online). 2 concept pages updated (vibe-coding 9→10 / Y Combinator entity 2→3). Dangling first-mentions: Amjad Masad, Replit, Andrew Miklas, Pieter Levels (passing); Marc Andreessen + a16z (passing).
- 2026-04-24-hu-yc-how-to-build-a-company-with-ai-from-the-ground-up — How To Build A Company With AI From The Ground Up (Y Combinator Startup School, 24 April 2026; 10:27). Diana Hu (YC Partner). Highest-density-per-minute wiki source on AI-native company structure. Five compounding contributions in under 11 minutes: (1) AI-as-company-operating-system reframing — AI is not a tool, it’s the substrate every workflow flows through. (2) Closed-loop vs open-loop companies — control-systems vocabulary for the micro-productivity-trap; open loops are “inherently lossy”, closed loops continuously monitor + adjust + self-improve. “With self-improving agents, your company should run as a closed loop.” (3) Make the company queryable — record meetings, minimise DMs/emails, embed agents, build dashboards for revenue/sales/engineering/hiring/ops; “the whole organization should be legible to AI.” Reported empirical: across multiple YC companies, teams that do this cut sprint time in half and get ~10× more done. (4) Software factories — the TDD-evolution: humans write specs and tests; agents generate implementation and iterate until tests pass; Strong DM as worked example of a no-handwritten-code repo. Extends Lopopolo’s vendor-side case-study to a transferable architecture. (5) The 1,000× engineer thesis — extends Karpathy’s 10× by two orders of magnitude “by surrounding a single engineer with a system of agents that enable them to build things they would have never been able to build before”; “the era of the thousand or even 10,000× engineer is here.” And: middle management disappears — via Jack Dorsey at Block, three new archetypes (IC builder-operator / DRRI directly-responsible-individual / AI founder type); “in the new world, the intelligence layer serves [the routing] purpose … you should have almost no human middleware.” Direct convergence with Jassy’s flatten-management initiative — same diagnosis from two distinct vantages within the same week of wiki ingest. Token-maxing not headcount-maxing: “be willing to run an uncomfortably high API bill because it’s replacing what would have taken a far more expensive and inflated headcount.” Founder dogfooding mandate: “you cannot outsource your conviction on the power of these tools.” Startup-edge: incumbents face a maintain-while-unwinding constraint that startups don’t; Mutiny named as worked example of incumbent skunk-works exception. 5 concept pages updated (agent-harness 14→15 / agentic-engineering 5→6 / micro-productivity-trap 11→12 (capped at 0.95) / vibe-coding 6→7 / enterprise-ai-adoption 24→25). Dangling first-mentions for future second-source promotion: Diana Hu, Y Combinator, Jack Dorsey, Block, Strong DM, Mutiny, Steve J. (ASR-uncertain attribution for the 1,000× phrase — likely Steve Yegge per Böckeler’s Gas Town reference but flagged for resolution).
- 2026-04-23-tan-yc-how-to-make-claude-code-your-ai-engineering-team-gstack — How to Make Claude Code Your AI Engineering Team (Y Combinator YouTube, 23 April 2026; 21:49). Garry Tan (YC President & CEO; ex-Palantir #10; Posterous co-founder). GStack — open-source toolkit built in 3 weeks, “now has more GitHub stars than Ruby on Rails” — wraps Claude Code with named skills: Office Hours (16-YC-partners adversarial-review distilled at 10% strength), Plan / CEO Review / Adversarial Review (multi-step auto-fix of issues; demoed 6/10→8/10 in two rounds), Design Shotgun (multiple AI variants in ~60s), Code Review (staff-level bug-catching), SLQA / SL browse (Playwright+Chromium wrapped as CLI, replacing “the worst piece of software I’ve ever used” Claude-in-Chrome MCP), Ship (pre-merge gate). Operates inside Conductor (multi-session orchestrator). Tan reports 10-15 parallel Claude Code sessions, ~400 PRs in review, “10, 15, 20, sometimes 50 PRs in any given day.” Direct operationalisation of Hu’s AI founder type archetype. Yegge Gas Town stage 7 invoked explicitly (second wiki source after Böckeler — two-source threshold met on Yegge’s framework). The YC-batch-context anchor pair with Hu (24 April) on the next day. ADHD-CEO-vs-autistic-CTO model-allocation framing (Claude Opus 4.6 vs Codex). 3 concept pages updated (vibe-coding 7→8 / agent-harness 16→17 (capped at 0.95) / agentic-engineering 7→8).
- 2026-04-22-cheung-ippolito-secchi-google-agents-cli — Google Developers Blog, 22 April 2026. Cheung, Ippolito, Secchi (Google) introduce Agents CLI in Agent Platform — a CLI bundling the full Agent Development Lifecycle (ADLC) on Google Cloud (scaffold / evaluate / deploy / publish / observe). Two-mode design: Agent Mode for AI coding agents (Gemini CLI, Claude Code, Cursor) via skill injection, Human Mode as deterministic terminal CLI. Seven specialized skills (Workflow / ADK Code / Scaffold / Evaluation / Deployment / Publish / Observability) and a 9-stage ADLC wheel. The Google-side companion to Anthropic Managed Agents — both vendors productizing the harness/runtime layer within two weeks of each other in late April 2026.
- 2026-04-21-forsgren-macvean-build-core-skills-thrive-ai-era-developer — Build core skills to thrive as an AI-era developer (Google for Developers YouTube channel — Google I/O 2026, Professional Development track, 21 April 2026; ~50:15; ASR-cleaned transcript, 359 cleaned segments after stripping the redundant “N minutes, M seconds” live-caption prefix on each segment). Nicole Forsgren (founder of the DORA / DevOps Research and Assessment programme; lead author of Accelerate 2018; lead, Google’s Developer Intelligence team) and Andrew Macvean (lead, Developer Intelligence team) present Google’s internal research on how software-engineering practice is evolving in the AI-native era. Structurally the Google-side twin of Code with Claude 2026 — the two inside-engineering vantages from the two major AI labs landed a fortnight apart, ratifying the same five-pattern operational model from independent corporate-research traditions. Five patterns of top AI-native engineers: (1) operating at higher altitudes — business + user context; (2) shift left on intent — specs are the source of truth; (3) designing environments, not vibe-coding — “our top engineers are not vibe coding … they are designing environments, setting the guardrails, creating the systems”; (4) rise of the micro team — small agile pods decoupling job role from tasks; (5) scientific mindset — experiment + codify learnings weekly. Evolved T-shape (four skill domains): vertical stem = deep core SWE; horizontal bar gains a new AI engagement layer (steering, eval design); plus two wings — Adjacent Engineering (cybersec, privacy, deployment infra) and Adjacent Non-Engineering (business/user context). The Google data points (live on stage): ~3/4 of all code at Google is now written by AI; no measurable increase in outages despite massive volume increase; engineers who use AI the most spend more time coding, identifying, collaborating, not less. The DORA productivity-paradox — “increasing AI adoption can lead to individual productivity benefits while at the same time decreasing team-level benefits” — is the engineering-team correlate of the wiki’s micro-productivity-trap (first DORA-grounded anchor for that thesis in the wiki). AI as amplifier and mirror (DORA framing): “it magnifies existing strengths and holds up a mirror to weaknesses” — the cleanest single articulation of the trap’s diagnostic mechanism. The favourite quote (used twice): “delegate tasks, not judgment.” The orchestrator-not-conductor mental shift: managing teams of asynchronous agents with their own context windows. Concrete Google fleet-scale practices named: Search PMs ship features to live experiments via internal platforms; Code Review / Shepherding / Risk Assessor agents in CI/CD; TensorFlow migration three-agent architecture (planner / orchestrator / coder) with product-area-specific playbooks (YouTube-specific style for YouTube migrations); Agent Journaling as harness primitive (agents reflect into structured logs); Quality Agent stress-tests requirements before execution. Identity-threat named as a labelled phenomenon affecting engineers (DORA citation) — the engineering-side correlate of ai-employment-effects role-content shift; reframed via the Value Translator role. For engineering leaders, three immediate shifts: redefine productivity measurement (away from PR throughput / LOC); actively protect productive struggle (deliberate friction-preservation = the engineering-leadership countermeasure to ai-deskilling); foster radical psychological safety. Closing tricolon: “we need to shift left, we need to shift up, and we need to think about designing systems, not just bits of code.” W&W tags: digital-transforming/redesigning-internal-structures + improving-digital-maturity; digital-seizing/strategic-agility; contextual/external-triggers. 11 typed
supportsrelationships to existing sources — the strongest cross-source resonance the wiki has logged on a single ingest (Fung / Karpathy / Singhal / Globerson / Ransbotham / Kiron-Schrage / Chatterjee / Pan-Khattab / Kokane / Chase / Wolfe). 2 entity pages updated: Google 6→7 sources with new Developer-Intelligence-team + DORA + fleet-scale-practice sections; Google Research 2→3 sources with Forsgren-as-DORA-founder anchor. 8 concept pages updated: agentic-engineering 19→20 with new corporate-research-twin row (Google ↔ Anthropic Fung-twin); vibe-coding 12→13 with new corporate-research ceiling-side negative-claim row; micro-productivity-trap 14→15 with new engineering-team DORA-grounded vantage; durable-skills 9→10 with new engineering-role-evolution vantage and the “delegate tasks, not judgment” boundary-marker; ai-deskilling 5→6 with new §The engineering-leadership countermeasure: “productive struggle”; ai-employment-effects 19→20 with new §Engineer identity-threat as named phenomenon; agent-harness 34→35 with new fleet-scale-agent-stack row; agent-development-lifecycle 6→7 with new §Engineering-leadership anchor — Forsgren & Macvean; automation-vs-augmentation 18→19 with new automation-trap-warning row; systems-thinking 3→4 with new §The engineering-team operationalisation. Dangling first-mentions: Nicole Forsgren, Andrew Macvean, Google Developer Intelligence team, DORA (DevOps Research and Assessment), Peter Senge, W. Edwards Deming, Google I/O 2026. Confidence 0.80. - 2026-04-18-mysore-medium-wikizz-extending-karpathy-llm-wiki — What Is Andrej Karpathy’s LLM Wiki — And How Can You Extend It? (Vishal Mysore / Medium, 18 April 2026; ~5-min read). The wiki’s single-author-extension entry in the LLM-Wiki cluster — published two days after Raju, on the same platform, on essentially the same topic, but with very different scope and reception (1 clap, 0 responses visible at fetch time despite the author’s 2.3K followers). Vishal Mysore (“Holder of multiple patents in AI and software engineering”) uses the Karpathy explainer as a front door for his own open-source extension, LLM WikiZZ (live demo at vishalmysore.github.io/lllmwikiZZ). Core proposal: a 5W1H Wiki Frame (Who / What / When / Where / Why / How) the LLM autonomously populates from each document before any user query — turning “transient RAG” into a session-scoped persistent context. Three UI patterns: Autonomous Scaffolding (LLM as Architect, not user as Clerk); Contrast Engine (side-by-side Plain-vs-WikiZZ output); LLM Jury (high-intelligence evaluator LLM judges the delta between the two answers). Coins “Context Debt” and “transient RAG” as vocabulary candidates. Empirical demo (global-warming query, captured verbatim in the source page): plain output gives flat 8-item list of activities; WikiZZ output gives 6-item list grouped by emitted gas (CO₂ / methane / nitrous oxide) with a closing synthesis sentence — better captures the document’s essence than the plain output, at the cost of +21% tokens and +46% latency. Three load-bearing visuals captured in source page: (1) Star Wars 5W1H worked example showing how the six axes cross-link Episode IV / Luke Skywalker / Darth Vader / Star Wars Franchise into a 4-node knowledge graph; (2) global-warming Plain-vs-WikiZZ comparison screenshot with verbatim outputs and LLM Jury verdict; (3) 3-phase Living Knowledge Graph diagram (Small Web → Growing Web of Two Topics → Living Knowledge Graph). Architecture: zero-server / static-first / browser-only /
FileReader-based / Cloudflare Worker CORS proxy routing API requests to NVIDIA NIM + Anthropic + Gemini providers. Source-quality flags: single-author experiment (anecdotal per §Lifecycle, confidence-cap-at-0.75 rule applies); session-scoped “persistence” structurally limits how much Phase-3 compounding can accrue without a durable storage layer (tension with Karpathy 2026’s implied durable model). 1 concept touched (llm-wiki 5→6, confidence unchanged at 0.91 — anecdotal source cannot raise above the 0.75 vendor-cap and the concept is already past it from rigorous sources). 1 entity touched (Anthropic 7→8 via the Third-party uses of the Anthropic API sub-section). Surfaces 9 dangling entities (Vishal Mysore; WikiZZ; NVIDIA NIM; Cloudflare Worker; George Lucas / Mark Hamill / David Prowse / James Earl Jones as Star Wars cultural-reference flags) and 7 concept candidates (5W1H Wiki Frame; Context Debt; Autonomous Scaffolding; Contrast Engine; LLM Jury; browser-only/session-scoped LLM Wiki; transient RAG). - 2026-04-16-raju-rag-isnt-dead-karpathys-llm-wiki-explained — RAG Isn’t Dead. But Something Is. Karpathy’s LLM Wiki Explained (Sathish Raju / Medium, 16 April 2026; ~10-min read). The wiki’s clearest single-article explainer of Karpathy’s LLM Wiki gist (4 April 2026 — 17 million views, 5,000 stars, 4,282 forks within days). Three-layer architecture named with explicit ownership semantics: raw sources (immutable) / wiki (LLM-owned) / schema (CLAUDE.md, co-evolved). Three core operations — ingest / query / lint — matching this repo’s own CLAUDE.md §“The four operations” exactly (minus the v0.3 synthesize addition). Working Python implementation (<200 lines). Ten-dimension RAG-vs-LLM-Wiki comparison matrix — the wiki’s first systematic feature-by-feature contrast. Three named limitations Karpathy glossed over: scale ceiling (works at 100, not 10K pages); hallucination baking (“organized, persistent mistakes are harder to spot than individual errors”); ingest cost. The verdict: complement, not replace — “knowledge should compound, not evaporate.”
- 2026-04-16-bodewes-phonely-ai-callers-think-is-human — This Startup Built AI That 80% of Callers Think Is Human (YC Root Access YouTube channel — Founder Firesides episode, 16 April 2026; ~16:17; ASR-cleaned transcript, 537 segments). Will Bodewes (founder/CEO Phonely, YC S24) interviewed by Nicolas Dessaigne (YC GP). Announces $16M Series A led by Base10 Partners (ASR mis-transcribes throughout as “Bessemer” / “Best 10” — channel description is authoritative). The “80% don’t know it’s an AI” empirical claim (~8:23): “For about 80% of our customers, they have no idea they’re speaking with an AI agent. By the end of this year, I would say it’s probably going to be close to 100% of people won’t know.” — Bodewes carefully distinguishes share-of-AI-handled-calls (unstated) from share-of-AI-answered-calls-where-the-caller-knows. Multi-model modular architecture as a build-side differentiator (~6:08–7:54): most voice AI runs on OpenAI under the hood; Phonely built its own LLMs and routes different conversational components (variable storage, name capture, etc.) to different smaller models running on Groq’s fast inference chips. “Based on our architecture it just made more sense to have smaller models doing different tasks rather than one big model doing everything — you save cost, reduce latency, and still get the same quality.” The wiki’s first production-voice-AI altitude articulation of multi-model-routing-on-fast-inference-hardware as a harness primitive. Optimization-as-the-real-product: the product surface is not “AI answers the phone” — it’s a statistical-optimization platform; “we just showed customers that changing one question can increase outcomes by 5%.” Inbound-revenue-driven adoption, not customer-support cost-cutting: call-centers / insurance / home-services running on billboard-phone-number lead-qualification. Disclosure forecast: “For outbound calling, yes — I feel like there is going to be some regulations… you should disclose”; consumers will eventually prefer AI calls for psychological safety. Founder origin: Stanford-class NCAA cross-country skier; race cancelled by COVID; AI PhD in Melbourne; voice AI emerged from his father’s small-practice phone problem. W&W tags:
digital-sensing/digital-scouting,digital-seizing/balancing-digital-portfolios,digital-seizing/strategic-agility,digital-transforming/improving-digital-maturity. 3 typedsupportsrelationships: CS153 (Phonely as YC-portfolio worked example of Hu’s forward-deployed-engineer wedge), AnswerThis (twin YC Root Access founder firesides on the same template), GStack (Phonely multi-model architecture as production-side instantiation of Tan’s ADHD-CEO-vs-autistic-CTO model-routing pattern). Dangling first-mentions: Phonely, Will Bodewes, Nicolas Dessaigne, Base10 Partners, Caroline (Base10), Groq. Confidence 0.68. - 2026-04-14-thompson-the-daily-workers-letting-ai-do-their-jobs — The Workers Letting A.I. Do Their Jobs (NYT The Daily, 14 April 2026; 36:31). Natalie Kitroeff interviews Clive Thompson about his ~75-developer field-report on AI-augmented software development (the radio form of his 12 March 2026 NYT Magazine piece Coding after coders: It’s the end of computer programming as we know it). Headline empirical: a majority of working developers have outsourced most/all day-to-day coding to AI agents in “the last 6 months, accelerating in the last 3” — i.e. Nov 2025 → Feb 2026, dating the developer-side phase change ~4 months behind Nika’s PM-side December-2025 phase change. Cleanest empirical statement of the micro-productivity-trap: small two-person startups report ~20× faster delivery (full-day features → ~30 min) with ~100% AI-written code; Google reports ~10% overall speedup despite ~40-50% AI-written code — a 2× order-of-magnitude gap in micro-vs-macro speedup at similar per-task AI penetration, driven by organisational metabolism. New empirical for ai-employment-effects: Erik Brynjolfsson (Stanford) cited for software-developer hires down 16% in a more recent job-postings analysis, extending the Canaries 2022-onset signal into 2025-26 hiring-flow. Operator-vocabulary anchor for durable-skills: developers feel “like Steve Jobs picking from nine designs” — construction-workers-to-architects shift; the technical writing-the-code work turns out to be the more-automatable part, communication / spec-elicitation / priority-setting / deciding-what-to-build the less-automatable part. First first-person developer worked example for ai-deskilling: Pia Torian, newer developer, hundreds-of-Copilot-prompts-per-day over months → “I feel like I’m losing my ability to code.” First small-startup operator worked example of a rules-file in the wild: Manu Ebert (Hyperspell) keeps a “10 Commandments” file the agent must consult before any action — uppercase, repetitive, emotional (“failure to do these tests is unacceptable and embarrassing”), distributional-semantics-of-stern-language as a prompt-design mechanism. Macro prediction: software stops being precious and rare, becomes a Post-it note; mid-size firms ($15-50M concrete-mixing-firm types) that historically can’t afford a 5-person software team can now afford one AI-augmented developer at $60-70K writing bespoke software that previously didn’t exist. 8 concept pages updated (vibe-coding 4→5 / agentic-engineering 4→5 / agent-harness 12→13 / micro-productivity-trap 9→10 (capped at 0.95) / ai-deskilling 2→3 / ai-employment-effects 14→15 / durable-skills 5→6 / jagged-frontier 7→8); Erik Brynjolfsson entity bumped 3→4.
- 2026-04-14-py-rethinking-ai-agents-rise-of-harness-engineering — Rethinking AI Agents: The Rise of Harness Engineering (PY YouTube, 14 April 2026; ~11:45 min; 126,883 views). Note: not the same channel as [[2026-05-04-rethinking-agents-harness-is-all-you-need|Prompt Engineering’s Rethinking Agents: Harness is All You Need]] — distinct channel_id
UCRk2Uipu6q_Se1hEALunAoQ; the PY video predates the Prompt Engineering one by three weeks and got ~10× the views on the same papers. Metadata-only ingest — transcript fetch failed at--timeout 180000AND--timeout 300000with the panel-did-not-render symptom (different failure mode from the long-livestream pattern since this video is only 11:45). The video’s substantive content survives the failed fetch via an unusually rich channel-provided description, which carries the thesis (“Same model. Same benchmark. 6× the performance difference. The orchestration code wrapping your LLM (the ‘harness’) now drives more performance variation than the underlying model itself”), the named empirical results, and full arxiv IDs for both primary-source papers the wiki has been carrying as open-questions. Two wiki primary-source identification open-questions closed: (1) Pan et al., Natural-Language Agent Harnesses (Tsinghua, March 2026) = arXiv:2603.25723 — the canonical source for the LangChain Top 30 → Top 5 TerminalBench result Osmani attributes to “Viv’s team”, the SWE-bench-at-1/14-compute result, the Verifier-hurts-OSWorld -8.4 result, and the OS-Symphony NL-migration 30.4% → 47.2% result; (2) Lee, Khattab et al., Meta-Harness — arXiv:2603.28052v1 — triple-confirmed (already closed by Karten et al.; PY independently cites the same arxiv ID). PY adds one substantive detail: Meta-Harness reached rank 1 on TerminalBench with Haiku — strongest single statement in the wiki to date of the “small model + great harness beats large model + bad harness” claim. Two new primary-source ingest targets surfaced: AutoHarness (arXiv:2603.03329, Feb 2026) and AgentSpec (arXiv:2503.18666). The video’s closing framing — “knowing when to remove structure rather than add it” — gives the wiki its strongest counterweight to Osmani’s ratchet (which implies growth-only). 1 concept page updated: agent-harness 23→24 with two open-questions closed (Pan et al. + Meta-Harness triple-confirmation) + new “Subtraction discipline” sub-section holding the additive ratchet and the subtractive removing-structure framing as a bidirectional discipline. Confidence 0.62 (single secondary-summary source + metadata-only fetch). - 2026-04-11-nodus-labs-fix-karpathys-llm-wiki-knowledge-graph-infranodus — Fix Karpathy’s LLM Wiki with a Knowledge Graph (Claude Code + Obsidian + InfraNodus) (Nodus Labs YouTube, 11 April 2026; ~26 min). The wiki’s earliest third-party “fix” / extension proposal for the LLM Wiki pattern — published 7 days after Karpathy’s gist. Diagnosis: “The LLM Wiki has structure, but is not aware of itself” — flat-prompt queries produce generic outputs because the LLM extracts concepts then completes with most-probable continuation. Fix: layer an InfraNodus knowledge graph on top of the wiki’s
concepts/, with three integration depths (external tool / MCP-server-callable from Claude / KG-integrated workflow withinfranodus/folder of per-session ontology graphs as “living memory”). The novel mechanism — gap-analysis as attention-direction: identify cluster pairs with low betweenness, paste the structured gap-prompt back to Claude with the densest source extracts, ask for cluster-bridging insights. “I point the LLM’s attention to the gap that exists. I provide the underlying structure.” First wiki source on KG-as-attention-direction primitive — structurally distinct from KG-as-retrieval-substrate (SurrealDB, Manditereza, Leskovec). Ships a Claude skill (infranodus/skills/skill-llm-wiki) — the first wiki source on a third-party-shipped Claude skill that operationalises the LLM Wiki pattern. Stack: Obsidian (viewer + InfraNodus graph plug-in) + Claude Code / Cursor + InfraNodus VS Code extension / Obsidian plug-in / MCP server. Vendor-sponsored (confidence cap 0.75; landed at 0.72). 2 concept pages updated: llm-wiki 6→7 (positioned in the architectural spectrum between WikiZZ and ex-brain); knowledge-graphs 5→6 with new “KG-as-attention-direction” sub-section. Dangling first-mentions: Nodus Labs, InfraNodus, Dmitry Paranyushkin (presenter; deferred for verification). - 2026-04-10-khan-osint-information-gathering-like-a-hacker — How I Used OSINT to Gather Information Like a Hacker (OSINT Team Medium publication, 10 April 2026; ~3-min read; by Hania Khan). The wiki’s first narrative-walkthrough source on defensive OSINT. “I found my company’s exposed secrets in two hours. No hacking required.” Seven vectors walked end-to-end on the author’s own organisation with defensive remediation for each: (1) company-website source code (developer comments leaking internal paths,
<!—remove debug=true before launch →left for two years); (2) image-metadata GPS+EXIF disclosure; (3) email harvesting via LinkedIn + password-reset-form account enumeration; (4) GitHub goldmine — a public repo with internal API endpoints and a commented-out admin credential public for three years; (5) job-posting tech-stack disclosure; (6) social-media physical clues (badges, whiteboard diagrams in office photos); (7) Google dorks for defenders (site:+ext:pdf+intitle:"index of"). The unifying mechanism: “Everything I found was public. We just never thought to look.” — defensive OSINT exists because organisations don’t audit what they themselves publish. Paired with TechLatest 2026 as the narrative / taxonomy pair. Concept pages anchored: osint + attack-surface-management.roles: [tech-lead](explicit override). Dangling first-mentions: Hania Khan, OSINT Team (publication — second source citing, still single-instance as a publisher; defer entity page). Confidence 0.65. - 2026-04-09-oceanbase-ex-brain-knowledge-base-that-thinks — I Built a Knowledge Base That Thinks — Inspired by Karpathy’s LLM Wiki (OceanBase Database / Medium, 9 April 2026; ~6-min read). The wiki’s earliest-vendor implementation of the LLM Wiki pattern — published 5 days after Karpathy’s gist. ex-brain CLI tool with four mechanisms: smart compilation (status / fact / event detection drives update strategy — “knowledge should update itself when new information arrives, not just accumulate”), automatic timeline extraction, entity linking (auto-creates stub pages for new entities; convergent with SurrealDB KG-ETL), hybrid search via seekdb (OceanBase’s open-source AI-native database — single-file embedded, multi-modal, MySQL-compatible, built-in AI functions). MCP server integration as the harness/substrate boundary — Claude calls
brain_get,brain_put,brain_search,brain_compile,brain_linkagainst local ex-brain. Names Garry Tan’s GBrain as a parallel-concept (second-source threshold met for both Tan and GBrain). Promotes llm-wiki as new concept page. - 2026-04-09-dinakaran-yc-luminai-automating-americas-biggest-hospitals — This Startup Is Automating America’s Biggest Hospitals (YC Root Access YouTube channel — Founder Firesides episode, published 9 April 2026, ingested 22 May 2026;
32:52; ASR-cleaned transcript, 312 segments). Kesava Kirupa Dinakaran (founder/CEO Luminai, YC S20) in conversation with YC General Partner Aaron Epstein. $38M Series B ($60M lifetime). The wiki’s clearest founder-vantage operational anchor on the trillion-dollar US healthcare administrative-waste category. Headline anchor: “the way that most patients get referred into the Cleveland Clinic is through a fax” — Luminai becomes the frontline inbox agent triaging incoming faxes → structured EHR data → routing to one of “thousands of departments” at Cleveland (~16M patient encounters/year). Four substantive contributions: (1) The Cleveland Clinic worked example — Luminai as the data transformation layer + workflow engine + verticalized agents on top of unstructured paper-and-fax inputs; the trillion-dollar US healthcare admin-waste TAM as headroom. (2) The horizontal-to-vertical specialisation decision (~26:48–29:13): “the moment you start to say ‘I can do anything’… the credibility you have reduces pretty dramatically” — data showed 80%+ healthcare; all-in on healthcare; then narrowed within healthcare to very clear set of initial starting use cases and wedges. The verticalisation-as-trust-signal framing. (3) Enterprise sales as a relational game, named as doctrine (~19:25–26:34): “you’re not actually selling a customer like the Cleveland Clinic. You’re selling a champion within that one institution”; “really major institutions don’t really talk to you unless it’s someone who they trust is making the referral”; the LinkedIn-contact-scraping script + warm-intro discipline + red-eye cadence as the operational how. (4) The reinvention thesis — “with every stage you have to reinvent yourself. Because the truth is, the next YC company is going to come in and pitch the most — the next dreamy vision that we were in five, four years ago” — founder-vantage anchor on continuous-pivot-pressure for AI-native vendors against subsequent YC batches. Founder-origin tangent: Dinakaran was the captain of the international Rubik’s cube team; mid-interview line: “I learned Rubik’s cubes from Andrej Karpathy on YouTube, who eventually ended up founding OpenAI” — biographical bridge to the wiki’s existing Karpathy entity. W&W tags:digital-sensing/identifying-needs,digital-seizing/strategic-agility,digital-seizing/balancing-digital-portfolios,digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,strategic-renewal/business-model,contextual/external-triggers. 5 typedsupportsrelationships: Vori (twin paper-and-fax-vertical-AI-wedge at trillion-dollar-industry scale — grocery + healthcare), Yhangry (horizontal-to-vertical specialisation at opposite enterprise/marketplace altitudes), Campfire (founder-led-sales-as-doctrine-in-the-AI-era), AnswerThis (operational-AI-agent vantage at opposite scale-ladder endpoints), YC April (Dinakaran’s wedge-and-deep-domain operationalises Hu’s enterprise-AI-adoption prescription). Dangling first-mentions: Kesava Kirupa Dinakaran, Luminai, Aaron Epstein, Cleveland Clinic, Peak XV, United World College. Confidence 0.78. - 2026-04-07-loukides-radar-trends-april-2026 — Radar Trends to Watch: April 2026 (O’Reilly Radar, 7 April 2026). Mike Loukides. Genre shift: from chronicling toward signal-detection. “News from the future” framing via William Gibson (“the future is here. It’s just not evenly distributed yet”). Wiki’s first trade-press adoption signal for Webb/FTSG-style strategic-foresight vocabulary at editorial scope; strategic-foresight bumped 4→5 with a dedicated section. World models emerge as category: LeWorldModel (first stable JEPA-architecture); Cursor Composer 2 + Kimi K2.5 outperforms Opus on coding benchmarks. Codex Plugins (bundled skills + MCP integrations as reusable workflows); Claude Code Channels (Telegram/Discord). Agent memory architecture named as a failure mode (filesystems without database indexing → performance penalties). SHA-256 vulnerability approaching collision capability within months. Benchmark gaming explicitly observed — Claude decrypted BrowseComp answer key on GitHub. People & Organizations section new: software-dev employment −20% from 2024 (tracks AI Index 2026 exactly); product-manager roles at decade highs; Copilot correlates with reduced management and collaborative time (first wiki signal on AI’s labor-market effect at the team-shape level). ai-employment-effects bumped 17→19 with sections on Tim O’Reilly’s “AI is not taking jobs” attribution and the mixed-signal trade-press datapoints.
- 2026-04-03-bcg-emerson-kropp-ai-will-reshape-more-jobs-than-it-replaces — AI Will Reshape More Jobs Than It Replaces (Greg Emerson, Matthew Kropp, Julie Bedard, Lisa Krayer, Megan Hsu et al.; Boston Consulting Group Henderson Institute; 3 Apr 2026; ~22pp full read). The wiki’s most structured role-level taxonomy of AI’s labor impact — and a method-independent corroboration of the Anthropic observed-exposure report (BCG microeconomic role-modeling vs Anthropic usage data → same headline: reshape ≫ replace). 50–55% of jobs reshaped in 2–3 yrs; 10–15% eliminated in 4–5 yrs; 43% are ≥40% automatable. Six AI Labor Disruption Segments = substitution-vs-augmentation × demand expandability (Jevons): Amplified 5% / Rebalanced 14% / Divergent 12% / Substituted 12% / Enabled 23% / Limited-Exposure 34%. Software-eng (Amplified, expandable demand) vs call-center rep (Substituted, bounded) is the worked contrast. Four CEO directives (embed workforce strategy / redesign-not-cost / continuous upskilling / shape the narrative). Promotes Matthew Kropp, Julie Bedard, Megan Hsu.
supportsKropp-BCG + Massenkoff-McCrory + Brynjolfsson. Touches ai-employment-effects / automation-vs-augmentation / durable-skills / micro-productivity-trap / enterprise-ai-adoption. - 2026-03-31-carrier-mit-industrial-ai-that-works-strategy-survival-success — Industrial AI That Works: Strategy, Survival, and Success (MIT Sloan Executive Education YouTube webinar, 31 March 2026; ~31:22; clean human-curated captions, 924 segments). John Carrier (Senior Lecturer in System Dynamics, MIT Sloan) hosted by Diane Abbott (Associate Director). The wiki’s strongest single anchor on industrial-AI adoption strategy — bridging the operations-managerial vantage with the MIT-Sterman-Forrester systems-thinking lineage (Carrier teaches in Sterman’s group; explicitly cites Jay Forrester’s “missing feedback loops” as the diagnostic). Load-bearing claim: “This technology is available to everyone. So winners will be determined not by who has access to the technology, but whose organization adopts it faster in a way that actually helps its system” — the industrial-AI restatement of Martin’s planning-vs-strategy distinction. The Heineken Mexico case: students built a relatively simple AI agent that grabbed machine + cloud + maintenance data on demand — compressing a 6-hour changeover (containing only 15 minutes of actual information content) to 15 minutes; ~1M extra cases of beer per month. “This is a relatively simple agent, but it actually changed the way the work is done, not simply improve the workflow” — BPR (Hammer 1990s) recast for the AI-agent era. The Forrester design criterion for industrial AI agents: “It’s not simply about building AI agents. It’s about using them to replace long, slow feedback loops with very fast ones.” Pick-the-right-agent-level heuristic: “there’s no reason to jump to a level five agent when a simple rule-based agent will do.” Refinery alarm-fatigue tragedy (two fatalities; system overloaded with safety alarms → data flow ground to a halt) as the wiki’s strongest counter to get-more-data-always. Synthetic-data syringe-defect training as a worked rare-event-training case. Brooks/Ng “unbig in AI” — Rodney Brooks’s Sawyer (~2012, complex flexible) → 2024 simple specialised material-handlers (“that’s the one having the impact”) paired with Andrew Ng’s “we need to unbig in AI”. Three-question industrial-AI ROI diagnostic: capital allocation; poor information flows creating low utilization; task-duration variation — plus start with safety cases. Binding constraint claim: “Our ability to adopt and absorb the technology are going to be the limit” over the next 3-5 years — the micro-productivity-trap thesis at the industrial-AI scale. 3 concept pages updated: industrial-ai-agents 2→3 sources with new §MIT system-dynamics articulation (Carrier 2026); systems-thinking 2→3 sources with new §Lineage extension to industrial AI; strategy 2→3 sources with new §Strategy in the industrial-AI era. Dangling first-mentions: John Carrier, Diane Abbott, Heineken Mexico, Michael Hammer, Andrew Ng, Rodney Brooks, John Seely Brown, Jensen Huang. Confidence 0.85.
- 2026-03-30-lee-meta-harness-end-to-end-optimization — Meta-Harness: End-to-End Optimization of Model Harnesses (Lee, Nair, Zhang, Lee, Khattab, Finn — Stanford + KRAFTON + MIT, arXiv:2603.28052v1, 30 March 2026; 12 pages). The DSPy-team Meta-Harness paper the wiki has been triangulating since 2026-05-04. Outer-loop harness search with Claude Code (Opus-4.6) as proposer reading the full filesystem of all prior candidates (median 82 files/iteration, 20+ prior candidates per step). Three domains: online text classification (+7.7 pts over ACE at 4× fewer context tokens; matches next-best after 4 vs 60 evaluations); retrieval-augmented math reasoning (+4.7 pts on IMO-level problems averaged across 5 held-out models — transferability empirically anchored); agentic coding on TerminalBench-2 (Meta-Harness #2 on Opus 4.6 at 76.4%; #1 on Haiku 4.5 at 37.6% — the wiki’s strongest single anchor for “small model + great harness > large model + bad harness”). The raw-traces-vs-summaries ablation (Table 3) shows Scores-only 34.6 / Scores+Summary 34.9 / Full filesystem 50.0 — “access to raw execution traces is the key ingredient; summaries do not recover the missing signal and may even hurt by compressing away diagnostically useful details.” Opening sentence carries the 6× performance gap headline claim (citing Bui ref [47]) — the wiki’s strongest single-sentence anchor for “harness > model.” ForgeCode (81.8%) named as the only higher-scoring Opus 4.6 agent but unreproducible from public code base. Closes the wiki’s 2-week-old Meta-Harness primary-source ingest target. Confidence 0.88.
- 2026-03-26-pan-natural-language-agent-harnesses — Natural-Language Agent Harnesses (Pan, Zou, Guo, Ni, Zheng — Tsinghua + Harbin Institute of Technology (Shenzhen), arXiv:2603.25723v1, 26 March 2026; 12 pages). The Pan et al. paper the wiki has been carrying as a primary-source open-question since 2026-05-04. Introduces NLAH (Natural-Language Agent Harnesses) + IHR (Intelligent Harness Runtime) — lifts harness control logic from code into editable natural language as a portable executable artifact under shared runtime semantics. Five NLAH ingredients: contracts / roles / stage structure / adapters / state semantics + failure taxonomy. File-backed state as a load-bearing module with three properties: externalized, path-addressable, compaction-stable. The 14× compute reduction at equivalent SWE-bench pass rate verified (Table 1: TRAE w/o HS = 1.2M prompt tokens / 13.6k completion at 75.2% vs Full IHR TRAE = 16.3M / 211k at 74.4%). The Verifier-hurts-OSWorld -8.4 claim verified (Table 3: OSWorld Verifier 33.3 vs Basic 41.7; Δ from baseline, not absolute). The OS-Symphony NL migration 30.4% → 47.2% verified (Table 5) — “the migrated harness writes the target artifact deterministically and reopens it before completion”. Closes the wiki’s longest-running primary-source ingest target. One re-attribution: the “LangChain Top 30 → Top 5 on TerminalBench 2.0” claim is not in Pan’s paper — likely a LangChain blog-post claim that secondary summaries (PY video) conflated with Pan. Confidence 0.85.
- 2026-03-25-russell-bradley-mgi-race-takes-off-next-big-arenas — The race takes off in the next big arenas of competition (MGI 2026 update) (McKinsey Global Institute / McKinsey & Company, March 2026; 127 pp.). Six authors: Kevin Russell, Chris Bradley (MGI director), Naveen Sastry, Suhayl Chettih, Kweilin Ellingrud (MGI director), Natalya Goryunova. The 2026 update of MGI’s October 2024 The next big arenas of competition. The wiki’s first MGI economy-mapper anchor on the AI/competition wave — complementary to AI Index 2026 (capability layer) and to Sternfels 2026 (McKinsey-firm self-narrative). Five substantive contributions: (1) 18 future arenas decisively outpaced the rest of the economy — +$18T market cap and +$1.4T revenue since 2022; 29% market-cap CAGR for arenas vs 8% non-arenas (~4× faster); 11% revenue CAGR vs 1% non-arenas (~10× faster); 14% capex+R&D CAGR vs 4%; arenas drove
half of total market-cap growth and revenue growth across large global companies over those three years. (2) Five-theme decomposition with AI foundation dominating value accretion: AI foundation (semis + cloud + AI software) +$10.77T market cap (60% of total) / +$490B revenue; Digitization (e-commerce + digital ads + games + streaming + cybersecurity) +$4.90T / +$680B; Electrification (EVs + batteries + nuclear fission) +$930B / +$190B; Hard tech (robotics + SAVs + future air mobility + space + modular construction; physical AI named as the sense-think-act subset) +$560B / +$30B; New bio-frontiers (obesity drugs + non-medical biotech) +$630B / +$50B. Eight of 18 arenas tracking near or above the upper bound of the 2040 scenario: AI software & services, shared autonomous vehicles, cloud services, EVs, digital advertising, cybersecurity, semiconductors, space. (3) The omniscaler thesis — MGI coins “omniscalers” for the nine cross-arena platform firms (Amazon cluster + Blue Origin + Prometheus, Tesla/X cluster + SpaceX, Alphabet cluster, Microsoft cluster, Meta, Apple, Samsung, Alibaba, Huawei — 6 of 9 US-headquartered). Together: ~$700B operating cash flow + ~$800B R&D + capex in 2025 alone, ~$2.7T combined revenue (larger than Italy’s GDP), and a ~20× per-arena revenue gap to other arena players ($200B vs ~$10B average). Alphabet plays in 9 arenas; Amazon in 8; Samsung in 7. The structural advantage compounds via reusable infrastructure + data network effects + high risk appetites + top-talent attraction. (4) Regional concentration — US firms lead 14 of 18 arenas in market cap and 10 in revenue; Greater China is gaining on revenue (especially electrification); Japan + South Korea add via industrial/consumer electronics; rest of world stands by; time-to-build/permitting/grid access becoming a decisive non-technology lever. (5) The “arena-creation potion” — three-ingredient foresight heuristic (tech/business-model step change + escalatory investment pattern + large/expanding addressable market) plus the arenas-radar firm-level diagnostic (proximity × production-vs-revenue × revenue-of-opportunity) and three swing factors for 2040 (geopolitics + AI development pace + electrification pace). MGI’s own open question: “Whether investor expectations for AI’s future will be supported by companies’ sustained returns on invested capital above the cost of capital is one of the biggest open questions in business today.” 3 concept pages updated (enterprise-ai-adoption 28→29 / strategic-foresight 3→4 / generative-ai 20→21) + McKinsey & Company entity 5→7 (now includes MGI sub-component + Russell/Bradley/Sastry/Chettih/Ellingrud/Goryunova as dangling authors). Candidate new concept pages (deferred until second source): omniscalers, physical AI, agentic commerce, technology sovereignty, arena (industry-as-arena). Companion live-event source page: 2026-05-12-mgi-virtual-event-race-takes-off-next-big-arenas — transcript fetch failed; metadata only. - 2026-03-23-wu-an-yc-momentic-qa-layer-ai-coding-era — The Q/A Layer for the AI Coding Era (YC Root Access YouTube channel — Founder Firesides episode, published 23 March 2026, ingested 22 May 2026; ~33:54; ASR-cleaned transcript, 328 segments). Weiwei Wu and Jeff An (co-founders Momentic, YC W24) in conversation with YC Managing Partner Harj Taggar. $15M Series A (per channel description; speakers say $50M — fifteen/fifty ASR ambiguity preserved). The wiki’s clearest founder-vantage anchor on independent-functional-testing-as-a-product for the AI coding era. Headline framing: “Momentic is the verification layer for software.” Six substantive contributions: (1) Verification-layer wedge — functional testing impersonating end users, slotted between linters/code-review and production; differentiator vs Selenium/Cypress/Playwright on flakiness and maintenance cost; differentiator vs raw browser-control LLM agents on speed (~300ms per step) + debuggability. (2) MCP-integration as the dev-loop product surface — “a lot of customers actually use our MCP integration to have Cursor or Claude Code write and run Momentic tests while they’re developing”; Taggar confirms YC engineering has Momentic in its CLAUDE.md pipeline. (3) The truth-driven / spec-driven development thesis (Wu’s load-bearing rhetorical claim): two schools of thought — code-as-truth (status quo) vs truth-driven development / spec-driven development where “the spec is the source of truth. Your code is just an implementation of that source of truth”. Near-future prediction: “I would be disappointed in three to six months if I’m still reviewing TypeScript or React code… I see code as an implementation detail. It’s a commodity.” (4) Closing-the-loop-for-coding-agents framing — “I can’t really trust Claude Code or Cursor to tell me themselves. I need a third external source of truth”; multi-harness-stack with MCP as the inter-harness protocol. (5) Notion onboarding via Twitter-DM-at-10pm anchor anecdote — Simon Last’s Twitter post → Wu’s 10pm DM → Loom of self-testing → onboarded that night → formal POC → ~500K test runs/day; “Momentic tests must pass before one of Notion’s engineers can merge their PR.” (6) The 10× engineer counter-thesis (~23:19): “Codex only makes you a 10x engineer if you weren’t a 10x engineer to begin with” — direct rhetorical inversion of Tan & Hu’s CS153 unconditional 1,000× engineer framing; filed as productive contradiction. Platform throughput: “over a million test runs a day” aggregate. Customers named: Notion, Built, Quora, Zero. Team size: 13. W&W tags:
digital-sensing/identifying-needs,digital-seizing/strategic-agility,digital-seizing/rapid-prototyping,digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,strategic-renewal/business-model,contextual/external-triggers. 6 typed relationships: Interrupt 26 (supports— closing-the-loop-for-coding-agents = vendor-product instantiation of Chase’s evals-as-gradient claim), CS153 (contradicts— productive 1,000× vs conditional-10× tension), ADLC (supports— spec/eval-as-load-bearing-primary-artifact), Luminai (supports— twin YC-Root-Access enterprise-vertical-AI Founder Firesides with parallel pivot-and-vertical-wedge templates), GStack (supports— Momentic as external skill-shaped tool-call complement to internal GStack harness primitives), LangChain (supports— multi-harness-stack with MCP as inter-harness protocol). Dangling first-mentions: Weiwei Wu, Jeff An, Harj Taggar, Momentic, Simon Last, Robinhood, Dan Robinson, Heap. Confidence 0.72. - 2026-03-20-huggingface-agentic-evaluations-workshop — Agentic Evaluations Workshop — Deep Dive on the Future of Evals for Agents (Hugging Face YouTube livestream, 20 March 2026; ~108:46). Hugging Face community education (Ben, moderator) hosting Avijit Ghosh (HF, AI policy researcher), Arvind Narayanan (Princeton), Pierre Andrews (Meta), Mahesh Sathiamoorthy (Bespoke Labs), Nathan Habib (HF, lighteval / Open LLM Leaderboard). The wiki’s first multi-speaker workshop source on AI evaluation specifically for agentic systems and the agentic-evals research-frontier anchor. Four orthogonal moves: (1) Ghosh /
every-eval-ever— 171-release study finds first-party social-impact / environmental-cost reporting has collapsed since 2022–23 (<15% now mention labour and environmental effects; Google and Meta both pulled back). Third-party evals (METR, Apollo Research, Mor) have risen in quantity and quality. HF launches a unified open schema for first- and third-party evaluations across heterogeneous formats. (2) Narayanan / capability-reliability gap — the workshop’s strongest single thesis: “AI agents have been crushing capability benchmarks. If you believe this hype, companies should be replacing people with agents left and right. That doesn’t seem to be happening.” The gap is reliability, decomposed into 12 sub-dimensions (most unsolved). Augmentation vs automation is a reliability decision, not just a deployment decision: customer-service automation at 90% reliability is dead-on-arrival, coding-agent augmentation at 70% reliability is still net-positive. Public Reliability Index is launching. (3) Andrews / GAIA-2 on ARE — 1,000 scenarios across 10 universes using ~11 apps; five capability splits including ambiguity and agent-to-agent collaboration. The sim-to-real gap explicit: simulation buys reproducibility/observability/safety/cost; it costs realism. (4) Habib / community-eval — names two diseases of the current eval ecosystem (scaffold fragmentation + maintenance burden) and proposes community-eval as a Hugging Face Hub-native mechanism for publishing benchmarks as living versioned artefacts (uvx inspect_ai, PR-maintained). The hub becomes an eval-environment store, paralleling its model store role. Plus Sathiamoorthy’s levels-of-verifiability scaffolding (level 0 = verifiable; level 1 = rubric-based with LLM-as-judge), the deploy-and-evaluate anti-pattern, and reward hacking as a first-class level-zero concern. 3 concept pages updated (ai-benchmarks 4→6 (paired with Husain) / automation-vs-augmentation 14→15 / agent-development-lifecycle 4→6). Dangling first-mentions: Avijit Ghosh, Arvind Narayanan, Pierre Andrews, Mahesh Sathiamoorthy, Nathan Habib, Ben, J.J. Allaire, Stefan Robons, Hugging Face (channel; defer entity), GAIA-2, ARE, inspect_ai, lighteval, Open LLM Leaderboard, every-eval-ever, community-eval, METR, Apollo Research. - 2026-03-10-trivedy-langchain-anatomy-of-an-agent-harness — The Anatomy of an Agent Harness (Vivek Trivedy / LangChain Engineering blog, 10 March 2026; 12-min essay). The canonical “Anatomy of an Agent Harness” piece Osmani referenced as “Viv’s anatomy” and Pan et al. cited as reference 16. First-person primary source for multiple wiki claims previously carried second-hand. Closes the wiki’s Trivedy / Chatterjee / LangChain three-post thicket with two distinct posts (this March 10 Trivedy / LangChain piece vs the May 3 Chatterjee Medium piece — separate authors, separate content, similar titles only). Coinages first-person primary anchored: “Agent = Model + Harness” + “If you’re not the model, you’re the harness” + the “Behavior we want → Harness Design” working-backward-from-behavior design pattern. Six harness primitives derived from “things a model cannot do out of the box”: filesystems for durable storage / bash + code as general-purpose tool / sandboxes + good default tooling / memory & search (AGENTS.md, Context7) / battling Context Rot (compaction, tool-call offloading, Skills) / long-horizon execution (Ralph Loops, planning, self-verification). Ralph Loops definition first-person anchored. Top 30 → Top 5 on Terminal Bench 2.0 by changing only the harness — confirmed first-person primary (attributed by Trivedy to an earlier LangChain blog, likely [[2026-02-17-langchain-improving-deep-agents-harness-engineering|Improving Deep Agents with Harness Engineering]] Feb 17 2026). “Opus 4.6 in Claude Code scores far below Opus 4.6 in other harnesses” confirmed first-person primary. The model-harness co-training feedback loop (Codex-5.3 apply_patch overfitting example). Three named open problems (parallel-agents / trace-self-correction / dynamic just-in-time assembly) — identical to Osmani’s enumeration, confirming Osmani as direct synthesis-with-expansion of Trivedy. Repositions Osmani’s May 15 essay as near-direct synthesis of Trivedy with cross-author expansion (Anthropic / HumanLayer / Bockeler / Khan / Willison citations + the ratchet framing + the HaaS substrate-shift framing on top). 1 concept page updated: agent-harness 31→32 with new “Trivedy is the source-of-vocabulary; Osmani is the popularisation” section + Feb 11/17 vocabulary-coinage-window table closing the wiki’s open question on harness-engineering as a named discipline. Confidence 0.82.
- 2026-03-07-vitucci-onshore-next-industry-ai-will-disrupt — This Is The Next Industry AI Will Disrupt (YC Root Access YouTube channel — The Breakdown series, 7 March 2026; ~33:56; ASR-cleaned transcript, 278 segments). Dominic Vitucci (founder/CEO Onshore, YC W23 — AI-driven corporate tax + accounting platform) interviewed by Tom Blomfield (YC managing director; ex-Monzo / GoCardless) and David Lieb (YC partner; ex-Bump / Google Photos co-founder). The wiki’s first deep professional-services-AI-disruption anchor at industry-altitude. Four claims with no prior corpus equivalent: (1) the structural-incentive-not-intent argument for incumbent blocking — Big 4 senior-partner economics make AI investment a personal-financial-negative regardless of business merit. “You got some 40-year-old partner says hey let’s use AI; some older guys 63, 64 years old say I’m going to retire in 24 months, I don’t care about this. And so quickly the guys who make the decisions, the senior partnerships, say well what if we just don’t do that?” Plus Vitucci’s worked example: a top-20 firm announces $X billion in AI but the actual deployment is $3-4M of co-pilot licenses; “head of AI at a Big 4 firm, asked how many software engineers they had working with them — zero.” (2) The two-year sell-to-firm failure then compete-with-firm pivot: Vitucci spent 2020-22 selling AI tooling to top 20 / top 50 firms, mid-six-figures revenue, “banging my head against the wall” — fired all customers summer 2022, rebuilt the software for corporate taxpayers directly, cold-DM’d 1000 people on LinkedIn, closed first customer in Miami, got into YC on the flight back. (3) The lawyer-vs-accountant adoption-asymmetry — why did Lora / Harvey crack legal but accounting hasn’t? Liability premium (lawyers’ mistakes carry jail/large-fine consequences); industry forward-thinking-ness; project-based-billing maturity (law has been moving to project pricing for a decade). (4) Revenue-per-employee as the killer comp: Onshore $25M revenue today targeting $100M by end of 2026 with 60-100 employees → $1M+ per employee; Big 4 / top 20 estimated at $100-150K per employee = order of magnitude better. The accounting-firm-of-the-future shape Vitucci predicts (~24:34–24:56): industry revenue up in 10 years, headcount roughly flat, “fat bottom layer of juniors” replaced by AI agents, senior partners stay for sales + a regulatory-compliance/expertise layer + some software-engineering. Tom Blomfield extends to all knowledge work: “by the end of this year I can see mass realization that AI can do these knowledge work jobs just as well or better than humans, and yet still not that big a change in employment rate — five or six or seven years to come.” Mark, the senior-partner co-founder (ex-boss’s-boss’s-boss at Grant Thornton; passed away summer 2024 “right on the precipice as we were raising our Series A”) — co-signed the innovation; “junior guy uh that started doing this, but b a wealth of expertise and knowledge.” Closing speculation on the next Excel: “a better or different Microsoft Excel — spreadsheets and Excel in its original iteration as a spreadsheet functionality is great. I think that it has been kind of just like squished and torn and fit into a million different boxes for things it was never meant to do.” On vibe-coding/Replit at the mid-market: “the access and the democratization of that knowledge is less permeating society than we all might think it is.” W&W tags:
contextual/external-triggers,digital-sensing/digital-scenario-planning,digital-seizing/strategic-agility,digital-transforming/redesigning-internal-structures,strategic-renewal/business-model. Productive contradiction (contradictstyped relationship) with CS153 on boil-the-ocean — Tan inverts the dictum (“let’s boil the ocean — you can do the work of 500 to 1000 people”); Vitucci asserts the conservative version (“the idea of trying to boil the ocean all at once is very challenging — it has been an incredible benefit for our business to be great at one thing really early”). The wiki carries both — both right at different stages. 3 typedsupportsrelationships: Campfire (twin AI-native-vendor-disrupting-incumbent-service-business; AI-native safety inversion = buyer-side mechanism; misaligned-senior-partner-incentives = supply-side mechanism), Vori (twin 2026 vertical-AI-native-operator-against-incumbent-paper-and-pencil-industry), CS153 (Vitucci as worked-example-one-industry-deep of Hu’s forward-deployed-engineer wedge prescription). Dangling first-mentions: Onshore, Dominic Vitucci, Mark (Onshore co-founder), David Lieb, Grant Thornton, Lora, Harvey. Tom Blomfield gets his second substantive-source mention (first was Garg’s “Pete and Tom and Gary”); promote on the next ingest that names him by full name. Confidence 0.75. - 2026-03-05-massenkoff-mccrory-anthropic-labor-market-impacts-ai — Labor market impacts of AI: A new measure and early evidence (Maxim Massenkoff & Peter McCrory, Anthropic / Anthropic Economic Index; 5 Mar 2026; ~17pp full read). The methodological primary behind the wiki’s “observed exposure” labor measure — and the exact “March study” AWS presents. Observed exposure = theoretical LLM capability (Eloundou β) × real-world AEI usage, weighting automated/work-related uses more heavily (the weighting is what turns exposure into displacement risk). Findings: capability ≫ adoption (β=1 tasks = 68% of Claude usage); top-exposed = Computer Programmers (75%), Customer Service Reps, Data Entry Keyers; +10pp coverage → −0.6pp BLS 2024–34 growth; exposed workers more female/educated/+47% pay; no systematic unemployment effect yet (diff-in-diff ≈ 0); ~14% young-worker (22–25) hiring slowdown into exposed occupations (echoes Brynjolfsson Canaries).
published-byAnthropic,part-ofAEI,supportsAEI-q4 + Brynjolfsson. One W&W tag. Touches ai-employment-effects + automation-vs-augmentation. - 2026-03-05-lou-deepmind-autoharness-code-harness-synthesis — AutoHarness: improving LLM agents by automatically synthesizing a code harness (Lou, Lázaro-Gredilla, Dedieu, Wendelken, Lehrach, Murphy — Google DeepMind, arXiv:2603.03329v1, 5 March 2026; 13 pages). The earliest of the four academic harness primaries — predates Pan et al. by 3 weeks and Lee et al. by 25 days. Motivating empirical anchor: 78% of Gemini-2.5-Flash losses in Kaggle GameArena chess were attributed to illegal moves (not strategic blunders) — the wiki’s strongest in-context evidence for “agent failures are usually harness failures, not model failures”. Three harness types: harness-as-action-filter (LLM ranks legal options); harness-as-action-verifier (LLM proposes, code verifies, retries); harness-as-policy (LLM generates pure-code policy; zero LLM calls at inference time). Thompson-sampling tree search over code refinements with at-most-5 failed-step Critic feedback. Gemini-2.5-Flash + AutoHarness beats Gemini-2.5-Pro 9/16 on 2P games, 0.745 avg reward 1P (vs Pro 0.707), and Harness-as-Policy = 0.870 avg reward beating GPT-5.2-High (0.844) at near-zero inference cost (vs ~$640 for the GPT-5.2 experiments). The strongest empirical anchor in the wiki for Software 3.0 taken to its limit — the LLM functions purely as a compiler when the policy space admits legal-move predicates. Productive contradiction with Lee et al.: AutoHarness uses compressed feedback (5-step Critic window) and achieves strong results; Lee finds full-history feedback essential. Different design choices, similar headline conclusion. Confidence 0.85.
- 2026-03-03-loukides-radar-trends-march-2026 — Radar Trends to Watch: March 2026 (O’Reilly Radar, 3 March 2026). Mike Loukides. Opening framing: OpenClaw’s explosive growth in February — Karpathy positions it as “the next layer on top of AI agents”. Month’s structural story: AI as offensive security tool at scale — 500 zero-days discovered via Claude Opus 4.6 analysis; 12 OpenSSL vulnerabilities found via Claude; HackMyClaw gamified prompt-injection challenge; IronClaw alternative emphasising sandboxing. Agent-to-Human (A2H) protocol surfaces as a category. Claude Remote Control (cross-device desktop session continuation — first wiki mention of session-portability as a discrete agent capability). C compiler via agents — 100,000 lines of Rust, ~$20K token cost. Ransomware evolves from encryption to extortion. Anthropic Claude Max — six months free for OSS maintainers.
- 2026-02-25-akhtar-forget-yc-letter-ai-powered-revenue — AI-Powered Revenue is Here (YC Root Access YouTube channel — Founder Firesides episode, published 25 February 2026, ingested 22 May 2026; ~10:21; ASR-cleaned transcript, 84 segments — the shortest and earliest YC Root Access ingest in the wiki). Ali Akhtar (CEO) and Armen Forget (CTO) of Letter AI in conversation with YC General Partner Diana Hu. $40M Series B announcement. The wiki’s first founder-vantage worked example of MCP-server-and-agent-to-agent-protocol as B2B vertical-SaaS product surface — a non-developer-tools vendor treating agent-protocol affordances as first-class product surface. Five substantive contributions: (1) Pivot-during-batch with original-name shed — Letter AI was originally Tractatus (developer tools for generative AI); the field saturated; SaaS-to-developers was non-sticky; pivoted in-batch + renamed; Lenovo closed in the batch (Hu: “which is very rare”). (2) Legacy-enablement-tool-adoption-gap insight (3:08–4:37) — Akhtar’s prior-role observation at Samsara: sellers couldn’t find content in the “really expensive” legacy enablement tool; the legacy-vendor’s answer to adoption was throw more humans at content curation; AI-native wedge: tap existing sources + generate personalised content without the manual curation layer. (3) MCP-server + agent-to-agent protocol as B2B product surface (8:23–9:16) — Letter AI MCP servers + agent-to-agent protocol; salespeople in Cursor pull content via the Letter MCP server during research workflow. (4) Letter Compass = personalisation-to-book-of-business — automatically personalises enablement assets to the seller’s specific opportunity + buyer’s CRM context. “Never sell alone with Letter.” (5) 100% daily adoption claim + Fortune-100-acquisition-onboarding anecdote (acquired company onboarded over a weekend with 2–3 folks online vs “at least a month and tons of folks” pre-Letter). Customers named: Lenovo, Adobe, Novo Nordisk, Plaid, Kong. W&W tags:
digital-seizing/strategic-agility,digital-seizing/balancing-digital-portfolios,digital-transforming/improving-digital-maturity,digital-transforming/redesigning-internal-structures,strategic-renewal/business-model. 3 typedsupportsrelationships: Interrupt 26 (Letter AI as AI-native-vendor instantiation of Chase’s every-agent-needs infrastructure-pattern catalogue), Luminai (twin pivot-during-batch + enterprise-marquee-customer-in-batch templates), Yhangry (AI-native-product replaces legacy-tool-with-adoption-gap thesis at different altitudes). Dangling first-mentions: Letter AI, Ali Akhtar, Armen Forget, Lenovo. Diana Hu source-count bumps 2→3. Confidence 0.68. - 2026-02-18-lyft-customer-support-with-claude — How Lyft uses Claude for faster, more human customer support (Anthropic Claude YouTube channel — customer story, 18 February 2026; ~1:35; manual English captions, 15 segments). 1:35-minute promotional customer-story testimonial. Five quotable claims worth filing as a contemporary practitioner anchor: starting state (2023 — rider/driver bases growing fast, support queues overwhelmed); model-selection criterion was personality (“Claude’s personality is really what stuck out… more organic feeling”); 87% reduction in customer-resolution time (“30 plus minutes now sometimes resolved in a matter of seconds”); augment-and-reinvest-the-savings — millions saved reinvested into upskilling agents (anti-burnout) + a new high-touch programme Lyft Silver; concrete counter to the assume-cuts framing of customer-support automation. Practitioner-side data point on the same generative-AI-in-customer-support phenomenon Brynjolfsson, Li & Raymond (QJE 2025) measured at +15% RPH in a 2020–21 GPT-3 deployment. Companion piece to HubSpot and Figma Make in the Claude-channel customer-story cluster — filed as a vignette anchor; do not cite as load-bearing on its own. Dangling first-mention: Lyft. Confidence 0.60.
- 2026-02-17-langchain-improving-deep-agents-harness-engineering — Improving deep agents with harness engineering (LangChain Engineering blog, 17 February 2026). Metadata-only ingest — PDF capture returned essentially empty content; the substantive body lives at the live URL and should be re-captured. The framework-vendor counterpart to Anthropic’s Building Effective Agents — names “harness engineering” as a discipline alongside Lopopolo (Feb 2026) within ~one week of each other in Q1 2026. Cited by all four academic primaries in this batch’s harness cluster (Pan, Lee, Lou, Karten). Likely primary source for the canonical “LangChain Top 30 → Top 5 on TerminalBench 2.0 by changing only harness infrastructure” claim that secondary summaries have circulated (the PY video description and Osmani’s essay both reference it). Pan’s reference list (entry 16) reveals a sister post — The anatomy of an agent harness (LangChain Engineering, 10 March 2026) that the wiki has not yet ingested — possibly the actual source of the Top 30 → Top 5 claim. Suggests the Trivedy / Chatterjee / LangChain “Anatomy of an Agent Harness” attribution thicket may be THREE separate posts, not one mis-attributed one. Confidence 0.55 (metadata-only-without-body penalty).
- 2026-02-11-shyamsundar-jain-organizational-strategies-collective-wisdom-nature — Organizational Strategies from the Collective Wisdom of Nature: Why your company probably chose the wrong coordination model (O’Reilly Radar, 11 Feb 2026; ~9-min read). Shreshta Shyamsundar and Anmol Jain. Operationalising counterpart to Werner-Le-Brun’s octopus — where octopus names the principle (distributed intelligence), this names a four-archetype menu plus a decision-by-problem-type allocation rule. Four nature-derived coordination archetypes: ant colonies (pheromone-based swarms, perfect for routing — humans? not so much); bird flocks (proximity-based synchronization, useful but knowledge workers coordinate through explicit communication, not proximity); bee colonies (collective decision-making via signaling — maps better to humans, who decide through voting/consensus/appointed-authority); small human groups (language-based coordination of 5-15 people, research on military special forces, surgical teams, and startup founding teams shows this scale consistently outperforms larger hierarchies for complex, novel work). The decision rule: distribute decision-making by problem type, not by ideology — optimisation problems with clear goals → swarm-inspired distributed rules; repeated execution with local-knowledge advantage → delegated authority; small-group knowledge work or novel problems → small teams with explicit communication; strategic or ethical choices → humans in a room (“you can’t swarm your way through a values decision”). Hybrid coordination beats pure forms; culture-build sequence requires firing managers who hoard decisions. Anchor anecdote: 2016 logistics company, drivers given local autonomy → delivery times −15%, fuel costs −12%, system more resilient. Real-world applications: Copenhagen/Singapore smart-cities; healthcare distributed sensing; financial-services algorithmic trading; energy grid balancing; Amazon two-pizza teams / Netflix / Southwest as delegated-authority structures.
- 2026-02-11-lopopolo-codex-harness-engineering — Harness engineering: leveraging Codex in an agent-first world (OpenAI Engineering blog, 11 Feb 2026). Ryan Lopopolo (OpenAI). The wiki’s first vendor-side production case study of agent-harness / agentic-engineering at scale. Five months, ~1M LOC, ~1,500 PRs, 7 engineers, 3.5 PRs/engineer/day with throughput increasing as the team grew — built with 0 lines of manually-written code. “Humans steer. Agents execute.” Names operational invariants: repository-as-system-of-record (“anything Codex can’t access in-context doesn’t exist”); AGENTS.md as table of contents not encyclopedia with progressive disclosure + doc-gardening agent; layered architecture mechanically enforced (Types→Config→Repo→Service→Runtime→UI dependency direction; Providers interface for cross-cutting concerns); throughput inverts merge philosophy (“corrections are cheap, waiting is expensive”); golden principles + scheduled GC (background Codex tasks scan for drift, open targeted refactoring PRs, mostly auto-merged); application legibility via Chrome DevTools Protocol + per-worktree LogQL/PromQL/TraceQL observability stack. “Our most difficult challenges now center on designing environments, feedback loops, and control systems.”
- 2026-02-09-sternfels-mckinsey-survive-ai-and-reinvent-consulting — How McKinsey Plans to Survive AI (and Reinvent Consulting) (HBR IdeaCast podcast, 9 Feb 2026; 31:36). Bob Sternfels (Global Managing Partner, McKinsey) interviewed by Adi Ignatius (HBR editor-in-chief). The wiki’s first first-party McKinsey self-narrative source — complementing Rewired 2nd ed (the practitioner playbook) with the firm-as-vendor self-account. Headline datapoints: 60K-strong workforce = 40K humans + 20K agents (up from 3K agents 18 months prior; on track for 1:1 human-to-agent ratio in ~18 months); outcome-underwriting now ~33% of revenues, aspirationally majority by end-of-Sternfels-term; $1B compliance overhaul post-OxyContin / South Africa controversies (head of internal audit hired from Apple, head of compliance from Walmart; publicly-traded-equivalent governance standards adopted despite remaining private); 20-year self-applied analytics on hiring surfacing three under-weighted predictors of partner-track success (resilience-after-setback / team-sport-experience / aptitude-to-learn-novel-stuff). Names four durable leadership skills models lack: aspiration-setting / judgment / discontinuous-leap thinking / human-to-human skill — direct convergence with Globerson et al. 2026 from a hiring-criteria angle. “Half, if not more, of the secret sauce is organizational change as opposed to technology implementation” — direct corroboration of the micro-productivity trap from the second-consulting-firm vantage. First wiki source from a
kind: manual(human-curated) caption track. Kudzai Manditereza (HiveMQ). Industrial AI agents need a semantic foundation — an ontology — to reason about operational reality reliably. Three-tier semantic data layer (semantic model + domain ontologies + knowledge graph); four structural pillars (object types / properties / link types / action types with preconditions and state changes); three-layer architecture (Data Streaming + Data Intelligence + Agentic AI); five mechanisms (unified operational awareness / semantic layer / compounding returns / closed-loop learning / governed autonomy). The wiki’s first OT/industrial-manufacturing source on agentic AI specifically; opens a parallel track to the SaaS/coding-agent cluster, distinguished by data-fabric primacy and ontology-encoded action-precondition governance. Customers named: Audi, BMW, Eli Lilly, Liberty Global, Mercedes-Benz, Siemens. - 2026-02-09-ross-schneider-adaptability — Resilience Won’t Save Your Organization. Adaptability Will (HBR.org Partner Content from Egon Zehnder, 9 Feb 2026). The wiki’s first HBR Partner Content / advertorial source. Mike James Ross (Egon Zehnder Canada HR / Leadership Advisory; former CHRO of La Maison Simons) and Greig Schneider (Boston; former leader of Egon Zehnder’s Global Leadership Advisory Practice). The rhetorical move: “Adaptability is the new resilience.” Three operationalising layers — leadership practice (continuously restructure agile teams; harness diverse perspectives; nurture Linda Hill’s “creative abrasion”; bring the outside in), hiring (reframe roles from tasks to challenges; seek “Swiss Army knives” with adaptation history; interview for what new skills they have learned and what they have recently changed their mind about; be cautious of long tenure — “the adaptability muscle can atrophy”), and personal practice (seek minor discomfort; reframe situations; practise adapting). Empirical anchor: Egon Zehnder survey of 1,200+ global CEOs with 92% agreement on the adaptability statement (methodology not disclosed). Cites Etienne van der Walt (Neurozone) on resilience’s clinical definition. Source-quality caveat (vendor-sponsored): per the Lifecycle vendor-source rule, confidence boost capped at +0.05 / 0.75 absolute; durable-skills source_count bumped 8→9 with no confidence change (already 0.91 from rigorous sources). Hiring-criteria convergence with Sternfels at McKinsey on the same date — both name “aptitude-to-learn-novel-stuff” / “interview for new skills learned” as the under-weighted predictor. Cross-positioned with Werner-Le-Brun’s Octopus (org-design layer) and Carucci’s resistance-as-data (human-reaction layer); not added to the frameworks synthesis (advertorial would dilute the 12-source rigorous cluster).
- 2026-02-09-hubspot-customer-success-with-claude — How HubSpot uses Claude for customer success (Anthropic Claude YouTube channel — customer story, 9 February 2026; ~2:11; manual English captions, 23 segments). 2:11-minute promotional customer-story testimonial. Six quotable claims: early-adopter framing; model-selection criterion was taste (“Claude has really good taste, and marketing’s all about taste”); voice preservation (“it genuinely felt like it was my voice”); augmentation as role-level scope expansion — CSM doing hospitality-revenue analysis (“that is something so far beyond my typical role as a CSM”); developer-productivity testimonial claim (“40% increase in productivity” — vendor-side practitioner claim with no methodology; treat as testimonial, not measured); shared-mission small-business-empowerment framing. Extends the augmentation framing into customer success (not only customer support) with role-scope-expansion as the operational mechanism — complementary to [[2026-05-07-ransbotham-augmented-learners|Ransbotham’s augmented learners]] and [[2026-05-05-nishar-nohria-end-of-one-size-fits-all|Nishar/Nohria’s end of one-size-fits-all]]. Companion to Lyft / Figma Make / Emergent in the Claude-channel cluster. Dangling first-mention: HubSpot. Confidence 0.60.
- 2026-02-06-figma-make-prompts-to-prototypes-with-claude — How Figma Make uses Claude to turn prompts into prototypes (Anthropic Claude YouTube channel — customer story, 6 February 2026; ~1:17; manual English captions, 39 cleaned segments). 1:17-minute promotional customer-story testimonial. Five quotable claims: “The act of design, trying to convey a feeling, only humans can do that… every single person who has taste can just enact it all that easier”; pixel-perfect canvas-to-code translation; “Claude is such an evidently good coding model”; the explicit non-coder use case (“I don’t code. One of the best innovations of the past couple of years is that I really don’t have to in order to create the things I want to do”); design as a more accessible practice. Named-product anchor for vibe-coding from the design-tool-incumbent vantage (contrast with new-entrant anchors Replit and Emergent). The I-don’t-have-to-code claim sits in compact tension with the coding-as-durable-skill framing on durable-skills. Same taste framing across the Feb–May 2026 Claude-channel customer-story cluster. Dangling first-mention: Figma. Confidence 0.55.
- 2026-02-03-loukides-radar-trends-february-2026 — Radar Trends to Watch: February 2026 (O’Reilly Radar, 3 Feb 2026). Mike Loukides. Opening framing: “If you wanted any evidence that AI had colonized just about every aspect of computing, this month’s Trends would be all you need.” Cursor’s hundreds-of-agents experiment built a web browser in one week (existence proof for at-scale multi-agent dev). Kimi K2.5 enables 100-subagent swarms out of the box. MCP Apps becomes an official MCP extension for UI components (wiki’s first source on MCP-as-UI-substrate, not just tool-substrate). Moltbook social network for agents (first wiki source on agent-to-agent social infrastructure as a discrete category). OpenClaw publishes (always-on persistent agent memory — March digest names it as the month’s compound story). Anthropic publishes the Claude constitution (detailed behavioral training framework as transparency document). Kent Beck reframes AI’s effect on engineering: AI augments junior developers, accelerates learning cycles. NanoLang programming language designed specifically for LLM code generation.
- 2026-02-01-manditereza-ontology-driven-industrial-ai — HiveMQ Technical White Paper, February 2026.
- 2026-01-09-baron-signals-for-2026 — Signals for 2026: The tech trends we’re watching in the new year (O’Reilly Radar, 9 Jan 2026; ~13-min read). Julie Baron. Frames 2026 as “the year of increased accountability” — “Expect enterprises to shift focus from experimentation to measurable business outcomes and sustainable AI costs.” Pre-figures the Dutt et al. “experimentation-to-transformation” reframe by three months, written for an O’Reilly Radar trade-press audience. Names the vocabulary shift in software development for 2026: vibe coding, agentic development, context engineering, eval- and spec-driven development, multi-agent / agent-swarm orchestration — direct alignment with Karpathy’s paradigm vocabulary from Sequoia AI Ascent. Coining-line: “AI isn’t just a pair programmer; it’s an entire team of developers”. AIOps platforms / agentic SRE / self-healing systems named as 2026 InfraOps frontier; neoclouds (CoreWeave / Lambda / Vultr) as hyperscaler alternatives. Agent-supporting database categories: AgentDB, Databricks Lakebase, Tiger Data Agentic Postgres. AI-driven cyberthreats as #1 concern for 59% of tech professionals; agentic SOC named as evolution. The strategic-crisis framing for product management: “Most companies have moved past simple AI experiments but are now facing a strategic crisis. Their existing product playbooks weren’t designed for AI-native products.” The product-builder role surfacing as 2026 hiring category. Opens with quotes from Tim O’Reilly (“AI is not taking jobs: The decisions of people deploying it are”) and Mike Loukides (“we don’t believe in predicting the future, but we believe you can see signs of the future in the present”). 3 concept pages updated (enterprise-ai-adoption 29→30 with editorial-framing section; ai-employment-effects 17→19 with the “AI is not taking jobs” attribution; strategic-foresight 4→5 via the signs-of-the-future framing).
- 2026-01-06-loukides-radar-trends-january-2026 — Radar Trends to Watch: January 2026 (O’Reilly Radar, 6 Jan 2026). Mike Loukides. First installment in the wiki of Loukides’s monthly six-section digest; covers December 2025 events. Opening framing: OpenAI’s year-end GPT-5.2 push + Disney character-licensing for Sora + two foundational reads (Karpathy’s LLM year review — upstream conceptual spec for this wiki — and the Resonant Computing Manifesto). Agent Skills become an open spec (Anthropic opens the Claude Skills spec; OpenAI quietly adopts them). GPT-5.2 released in three versions for professional knowledge workers. Rust enters Linux kernel without experimental flag; Tor’s Rust rewrite Arti production-ready. “Cognitive Architect” role surfaces — developer decomposes problems and emphasises higher-order thinking. PARK stack (PyTorch + AI + Ray + Kubernetes) named as the shape of open-source AI development infrastructure. Prompt injection consolidates as first-class threat: Chrome ships a User Alignment Critic monitoring Gemini for indirect prompt injection (wiki’s first browser-side prompt-injection countermeasure source); poetry as LLM jailbreak (new attack pattern); MITRE Top 25.
- 2025-12-22-randell-gousset-microsoft-agentic-devops-in-real-life — Agentic DevOps in Real Life – Build Faster, Ship Safer, Keep Humans in the Loop (Microsoft Visual Studio channel; Live! 360 Orlando keynote by Brian Randell & Mickey Gousset, 22 Dec 2025; ~58 min, auto-captioned). The wiki’s first dedicated GitHub-Copilot / Agent-HQ tooling source; promotes GitHub and Microsoft to entities. “Agentic DevOps” = agents collaborating across the software lifecycle; demos Copilot coding agent (issue→PR→tests), Agent HQ (monitor/steer/audit; open architecture runs OpenAI Codex + Claude/Gemini agents in VS Code), VS Code four modes (Ask/Edit/Agent/Plan), the Microsoft responsible-AI pipeline + the initiator-can’t-self-approve-the-merge default, GitHub Advanced Security (secret/code scanning + Copilot autofix; 4.4M leaks blocked in 2024), and the Azure SRE Agent. Names the productivity paradox via the METR study (“feels faster, measures slower”; “value not lines of code”) and a 30/60/90-day adoption playbook. W&W:
digital-transforming/redesigning-internal-structures+improving-digital-maturity,digital-seizing/rapid-prototyping,strategic-renewal/organizational-culture,contextual/internal-barriers. 3 typedsupports: SEI-CMU, AMD, AI-native eng org (all share the METR + process-not-tool thesis). Updates 6 concepts. Dangling: Brian Randell, Mickey Gousset, GitHub Copilot, Agent HQ, Azure SRE Agent. Vendor keynote — stats presenter-reported. - 2025-12-01-marily-nika-pms-who-use-ai-will-replace-those-who-dont — “PMs who use AI will replace those who don’t”: Google’s AI product lead on the new PM toolkit (How I AI YouTube channel / podcast, 1 Dec 2025; ~40 min). Host Claire Vo (product leader; runs ChatPRD); guest Marily Nika (AI Product Lead at Google; founder of AI Product Academy). Directly inside Karpathy’s “December 2025 phase change” (Karpathy 2026). Worked-example episode demonstrating an end-to-end “AI-enhanced PM” workflow for a hypothetical smart-fridge product: user research via Perplexity’s Discussions-and-Opinions filter → PRD generation via custom GPT → interactive prototype via v0 → promo video via Flow/Veo and Sora cameos → AI-as-judge for demo days via NotebookLM. Names “tool hopping” as the load-bearing operational pattern; the twin-personas-debate anti-sycophancy prompting trick (have a pro-X agent and an against-X agent debate ≥20 times, return the minimum feature set to convince the skeptic); “PRD as input, not output” — the artifact is now a structured prompt-input to vibe-coding tools; prototypes as influence-engineering tools in product-review settings; “use AI on AI” — when one tool fails, kill the instance and have another LLM rewrite the prompt. Closing claim: “AI is not taking over a role. PMs that use AI are the ones that are going to take over the role of people that don’t use AI.” The PM-side worked example of vibe coding’s raise-the-floor claim — non-engineer professional wielding the Software-3.0 substrate as part of their job.
- 2025-11-26-anthropic-effective-harnesses-long-running-agents — Effective harnesses for long-running agents (Anthropic Engineering blog, 26 November 2025; ~8 pages). Justin Young (Anthropic Engineering) introduces the initializer agent + coding agent pattern for multi-context-window long-running work. “Compaction isn’t sufficient” at frontier-model scale — even Opus 4.5 on the Claude Agent SDK fails the “build a clone of claude.ai” benchmark out of the box. Two-fold solution: init.sh script (run dev server) + claude-progress.txt (log of what agents have done) + feature_list.json with 200+ end-to-end features (all initially
passes: false) + git commits for state recovery. Strict edit policy: coding agents may only change thepassesfield. JSON over Markdown for agent-edited persistent state — “models are less likely to inappropriately change or overwrite JSON files compared to Markdown files.” Browser-automation testing required: Puppeteer MCP server for end-to-end verification. Four named failure modes with structured Initializer/Coding-agent solutions. The vendor-engineering origin of what Pan et al. later formalises as path-addressable + compaction-stable file-backed state, and what Karten et al. later operationalise as reset-free state propagation. Anticipates Anthropic Managed Agents (Apr 2026) by 5 months on the brain/hands/session decomposition. Confidence 0.80. - 2025-11-25-yee-mgi-agents-robots-and-us-skill-partnerships — Agents, Robots, and Us: Skill Partnerships in the Age of AI (MGI flagship; published November 2025, ingested 26 May 2026; 60-page report + 6-page technical appendix, full ingest including all 20 numbered exhibits + 6 sidebars + glossary + endnotes). Seven authors: Lareina Yee (MGI director, Bay Area; lead author), Anu Madgavkar (MGI partner, New Jersey), Sven Smit (MGI chairman, Amsterdam), Alexis Krivkovich (senior partner, Bay Area), Michael Chui (QuantumBlack senior fellow, Bay Area), Maria Jesus Ramirez (MGI senior fellow, Bay Area), Diego Castresana (engagement manager, New York). Academic advisers: Nobel laureate Sir Christopher Pissarides (LSE) + Matthew J. Slaughter (Tuck Dean); postdoctoral contributor Luca Vendraminelli (Stanford Digital Economy Lab + Stanford HAI). The wiki’s most structural panoramic anchor on the AI-and-workforce question — companion to MGI Race-Takes-Off (March 2026)‘s industry-and-competition layer; together the two MGI flagships form a two-layer panorama (industry × workforce) of the AI-economy question. Seven substantive contributions: (1) The Skill Change Index (SCI) — a time-weighted measure of automation’s potential impact on each of ~6,800 employer-cited skills, built using OpenAI GPT-4o for ~3.4M skill→DWA mappings across ~1,800 occupations (BLS + O*NET + Lightcast substrate) with a manual 1,000-cell validation template. The wiki’s first labour-market-data-grounded systematic measure of which skills will change most and least under AI automation. Three skill-evolution paths: highly-exposed (top quartile, e.g. SQL programming, accounting processes) decline; middle-quartile (incl. AI fluency itself, communication, customer relations, detail orientation) evolve; bottom-quartile (leadership, coaching, healthcare skills, mentorship) endure. (2) 57% US technical-automation-potential — today’s demonstrated technologies could in theory automate ~57% of current US work hours, decomposed as 44% via agents + 13% via robots; remaining 43% is people-only (21% nonphysical-only + 22% physical-only requiring social/emotional capabilities). The 65/35 nonphysical/physical work-hour split is the structural substrate. (3) Seven occupation archetypes (Exhibit 3, ~800 BLS occupations, 55% threshold for “centric”): people-centric 34% / $71k (registered nurses, psychologists, firefighters); people-agent 21% / $74k (sales reps, secondary school teachers, HR specialists); agent-centric 30% / $70k (accountants, software developers, lawyers); people-robot <1% / $54k; robot-centric 8% / $42k (stockers, welders, cooks); people-agent-robot 5% / $60k; agent-robot 2% / $49k. The agent-centric quadrant is where the knowledge-work wage premium is at risk. (4) The 72% shared-skills finding — most human skills will remain relevant; AI changes how and where they’re used, not which skills are valuable. 11% people-led / 72% shared / 17% AI-led. (5) AI fluency demand 6.8× in two years (1.0M→7.0M US employees in occupations where job postings call for AI-fluency skills, 2023→2025) — faster than any other skill in US job postings. ~75% of AI-skill demand concentrated in 3 occupation groups (Computer+mathematical, Management, Business+financial); 9 occupation groups (~40% of workforce) have near-zero AI-skill demand → bifurcated AI-skill labour market. (6) $2.9T US economic value by 2030 (midpoint scenario, AI-powered agents + robots; $28.7T globally) — 77% from agents ($2.26T) + 23% from robots ($0.67T); 60% concentrated in sector-specific workflow domains ($1.7T) + 40% in cross-cutting ($1.2T); sector adoption rate range 20% (healthcare) → 31% (manufacturing); midpoint adoption ~27% of current work hours. (7) The workflow-not-task imperative — “Nearly 90 percent of companies say they have invested in [AI], but fewer than 40 percent report measurable gains”; 190+ workflows mapped across 16 business functions as the operational unit-of-analysis; 4 worked operational cases (B2B sales 5-agent +7-12% revenue / utility customer ops 4-agent ~40% calls + 50% cost-cut / biopharma medical writing 6-agent -60% touch time -50% errors / regional bank IT modernization 3+ agents 50% human-hour reduction). Eight high-prevalence transferable skills with people/agent splits (Exhibit 11): communication 99%, management 94%, operations 84%, problem-solving 83%, leadership 83%, detail orientation 80%, customer relations 80%, writing 76%. Exhibit 20 leadership-skill SCI mapping: high-change Prioritisation + Decision-making; medium-change Planning / Coordinating / Budgeting / Accountability / Innovation; low-change Coaching / Influencing / Mentorship — managers shift from supervising people to orchestrating systems. W&W tags (12 cells — broadest cell-coverage of any single source in the wiki): all 9 microfoundations + 2 strategic-renewal + full contextual ring. 7 typed
supportsrelationships: Brynjolfsson Canaries (descriptive-empirical + structural-prospective pair on AI labour question), Krakowski 2025 (individual-tailoring + firm-workflow-redesign paired levers), MGI Race Takes Off (same-publisher MGI two-layer panorama, 6-month delta), McKinsey (firm + research-institute McKinsey-altitude pair on workflow redesign), Bain (cross-consultancy convergence on workflow-as-unit-of-AI-value-capture), AI Index 2026 (two 2026-era panoramic reports at capability + workforce layers), Raymond (RCT-evidence + structural-prescription pair on human-agent customer-support collaboration). Entity changes: McKinsey Global Institute promoted from dangling to its own entity page (3rd-mention rule fulfilling the open follow-up from MGI Race Takes Off); McKinsey & Company source_count 7→8, conf 0.92→0.95, MGI removed from aliases (now a separate entity), opening paragraph refactored to reference MGI as[[McKinsey Global Institute]]. Concept changes: automation-vs-augmentation +1 (28→29; +subsection on 7 archetypes + 65/35 + 44/13 + 72%-shared-skills structural framework); ai-employment-effects +1 (28→29; +subsection on shift-not-elimination framing + 72% shared-skills + SCI as quantitative complement to Brynjolfsson Canaries + AI fluency 6.8× growth); durable-skills +1 (17→18; +subsection on SCI as labour-market-data-grounded systematic measure, 8 high-prevalence skills with people/agent splits, 5-source convergence on durable-skills concept now multi-altitude); micro-productivity-trap +1 (21→22; +eleventh-source corroboration on <40%-of-90% statistic + 190-workflow taxonomy + 4 worked cases); enterprise-ai-adoption +1 (51→52; +subsection on workflow-as-unit-of-value-capture + $2.9T US scaffold + 60/40 sector/cross-cutting + 4 worked cases). Dangling first-mentions: Lareina Yee, Anu Madgavkar, Sven Smit, Alexis Krivkovich, Michael Chui, Maria Jesus Ramirez, Diego Castresana, Sir Christopher Pissarides, Matthew J. Slaughter, Luca Vendraminelli. Open follow-ups: ingest 2017 Manyika et al. A Future That Works (foundational methodology), June 2023 The economic potential of generative AI, Nov 5 2025 The state of AI in 2025: Agents, innovation, and transformation, Sep 26 2025 The agentic organization (all McKinsey companion publications cited in endnotes). - 2025-10-05-patwardhan-et-al-openai-gdpval — GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks (Patwardhan et al., OpenAI; arXiv:2510.04374v1, 5 Oct 2025; ~29pp full read). The wiki’s first direct AI-capability-measurement primary source — a third labour lens beside Anthropic’s adoption index and the AI Index academic benchmarks. 1,320 tasks / 44 occupations / top-9 US-GDP sectors (~$3T wages), built from real expert work (avg 14 yrs; tasks avg 7 hrs, up to weeks). Metric = non-saturating win rate vs human experts (swap the baseline as models improve). Oct-2025 result: Claude Opus 4.1 best, 47.6% wins-or-ties; improving roughly linearly; Claude strong on aesthetics/file-formats, GPT-5 on accuracy/instruction-following. Frontier+oversight cheaper & faster than unaided experts; reasoning effort + context + scaffolding all lift scores. Closes a deferred-ingest named in the Wolfe guide.
supports→ AI Index 2026. One W&W tag (digital-sensing/digital-scouting). - 2025-09-28-husain-ai-evaluations-clearly-explained-50-min — AI Evaluations Clearly Explained in 50 Minutes (Real Example) — Hamel Husain (Peter Yang YouTube podcast, 28 September 2025; ~52:30). Hamel Husain (independent AI-evaluation educator; co-instructor of the Maven AI evals course; “trained over 2,000 PMs and engineers from companies like OpenAI, Anthropic, and Google”). The wiki’s practitioner-trainer anchor on AI evaluation as a teachable, spreadsheet-first discipline, complementing Braintrust with a vendor-neutral curriculum stance. Three contributions: (1) The spreadsheet-first workflow on a real production agent (Nurture Boss property-management assistant, ~100 traces) — open codes → axial coding via LLM → Google-Sheets
=AI(…)formula categorisation → pivot tables → binary LLM-as-judge → continuous in-prod sampling. “Annotation and counting is the most valuable process, and the one part that everyone skips.” (2) Binary pass/fail beats 1–5 scoring — every time. “When you see an average score of 3.2 versus 3.7, no one really knows what the hell that means. Honestly, like nobody really knows whether it’s getting better or not.” Likert agreement is dominated by category-boundary noise; binary collapses it. Under a dozen judges per app is usually right. (3) The agreement-metric trap — “as a PM, if you ever see the word ‘agreement’, you need to pause and dig”; naive accuracy is misleading at low failure base-rates; always report TPR (recall over failures) and TNR (recall over passes) separately. 3 concept pages updated (agent-development-lifecycle 4→6 / ai-benchmarks 4→6 (paired with HF Agentic Evals) / agent-harness implicitly via Contracts/Compounding layer references). Dangling first-mentions: Hamel Husain, Peter Yang, Nurture Boss, Maven; Braintrust now second-source-eligible for entity promotion. - 2025-07-31-wang-agentspec-runtime-enforcement-llm-agents — AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents (Wang, Poskitt, Sun — Singapore Management University, arXiv:2503.18666v3, 31 July 2025; accepted to ICSE ‘26). The wiki’s first peer-reviewed academic paper in the harness-engineering cluster — all three other 2026 academic primaries are arXiv preprints. Introduces a domain-specific language (
rule / trigger / check / enforce / end) for runtime enforcement of LLM-agent behaviour. Built on LangChain 0.13.13; framework-agnostic (porting to Microsoft AutoGen + Baidu Apollo demonstrated). Enforcement actions:user_inspection/llm_self_examine/invoke_action(Params)/stop. Empirical anchors across three domains: prevents >90% unsafe code executions (RedCode-Exec, 750 scenarios across 25 vulnerability types); eliminates all hazardous actions in embodied agent tasks (SafeAgentBench, 10 categories); enforces 100% AV law compliance in 5/8 scenarios (FixDrive). LLM-generated rules (OpenAI o1, few-shot): 87.26% / 95.56% / 5-of-8 precision. Millisecond-scale runtime overhead — effectively free at LLM-call latency. The formal operationalisation of Chatterjee’s Constraints layer as an executable DSL — first peer-reviewed academic treatment of harness safety. Three named failure modes for LLM-generated rules (overfitting / over-broad rules / insufficient specification). 1 concept page updated: responsible-ai 10→11 with new “Runtime enforcement as a declarative discipline” section. Confidence 0.92. - 2025-07-02-joshi-venkatraman-fowler-expert-generalists — Expert Generalists (Unmesh Joshi, Gitanjali Venkatraman & Martin Fowler, martinfowler.com / Thoughtworks; 02 Jul 2025; ~25 pp, full article). Names the Expert Generalist as a first-class, hire-able, trainable skill: spanning specialties via tool-independent fundamentals and patterns. Six characteristics (curiosity / collaborativeness / customer-focus / favoring fundamental knowledge / generalist+specialist blend / sympathy for related domains); rejects “T-shaped.” LLM thesis: an LLM is an on-tap specialist, so generalists who interrogate output get more valuable — “exactly the behavior needed to overcome the unreliability inherent in LLM-given advice.” Still need ≥1 deep specialist per core tech (optimise Cost of Delay). Miniatures workshop (build pocket Kafka / Kubernetes / Delta Lake) teaches fundamentals via patterns. New concept expert-generalist; new entities Martin Fowler, Thoughtworks.
supportsArgenti, Forsgren & Macvean, Ng. Updated durable-skills (32→33), ai-deskilling (12→13). 4 W&W tags. Dangling: Unmesh Joshi, Gitanjali Venkatraman, Martin Thompson, Kathy Sierra, Kent Beck, Jackie Stewart. - 2025-06-27-guthrie-braintrust-evals-101-ai-engineer-worlds-fair — Evals 101 — Doug Guthrie, Braintrust (AI Engineer World’s Fair, 27 June 2025; 48:31). Doug Guthrie (Braintrust solutions engineer; ex-dbt Labs). The wiki’s earliest source on AI evaluation as a formalised discipline — predates the harness-cluster sources by 9-11 months. Four ingredients of an eval: task / dataset / scores (LLM-as-judge + code-based) / experiment. Three operational modes that compound: offline (pre-production iteration) / online (production tracing with sampling-rate scoring at span-level) / flywheel (low-scored production logs → human review → offline dataset). Anker Goyal’s “evals as offense” reframing: “tests are defense; evals are offense — drive intent forward, not catch defects post-hoc”. Operator-advice: just-start-don’t-optimise-the-dataset-first; use higher-quality models for scoring than for the prompt; break scoring into focused areas; evals-as-CI-check pre-merge gate. 3 concept pages updated (agent-development-lifecycle 2→3 / agent-harness 15→16 / agentic-engineering 6→7).
- 2025-06-09-krakowski-human-centered-ai-field-experiment — Human-Centered Artificial Intelligence: A Field Experiment (Management Science 72(1):57-72, INFORMS Special Issue on the Human-Algorithm Connection; published online 9 June 2025, ingested 25 May 2026; ~17 pages, full ingest; peer-reviewed; DOI 10.1287/mnsc.2022.03849; CC BY-NC-ND open access). Authors Sebastian Krakowski (Stockholm School of Economics, House of Innovation; corresponding author + co-author of Raisch & Krakowski 2021 on AI augmentation — cited self-referentially as the theoretical prior), Darek Haftor (Uppsala University, Department of Informatics and Media), Johannes Luger (University of Zurich, Department of Business Administration), Natallia Pashkevich (Södertörn University, School of Social Sciences), Sebastian Raisch (University of Geneva, Geneva School of Economics and Management). Submitted Dec 8 2022; revised three times across 2024–early 2025; accepted March 13 2025. The wiki’s first RCT-grade DiD field experiment showing that untailored augmentation produces negative performance vs the legacy IT control. N=72 sales experts across 12 business units in the four Nordic subsidiaries (Denmark, Finland, Norway, Sweden) of a multinational pharmaceutical firm; three conditions (D1 = legacy-IT control, D2 = untailored AI, D3 = tailored AI per sales expert’s cognitive style per Kirton 1976 KAI Index, sample distribution: 47 adaptors / 25 innovators); 9 quarters of pre-intervention data confirm common-trends. Six substantive contributions: (1) Headline DiD finding — D2 (untailored AI) negatively impacts performance vs D1 (legacy IT) — market share gradually declines post-intervention; D3 (tailored AI) yields positive effects vs both D2 and D1. (2) The four interaction-design parameters — work procedure (imposed-predefined vs flexible), decision-making authority (constrained vs high), training (mandatory vs on-demand), incentives (extrinsic vs intrinsic); tailored to adaptors (well-defined + constrained + mandatory + extrinsic) or innovators (flexible + high + on-demand + intrinsic). (3) Work-procedure as most-impactful parameter — most of the D2 negative effect originates from adaptors despite only one parameter being misaligned for them; “work procedure emerged as the most critical parameter, shaping daily operational interactions.” (4) The Kirton 1976 KAI Index as empirical anchor for cognitive-style moderation of human-AI interaction outcomes; sample mean KAI = 91.92 (theoretical mean 96; pharma industry favours adaptors). (5) The prison quote — Danish innovator in D2: “We got this super tool, and at the same time, I felt like in prison. There was no freedom to work the way I wanted to work. (…) I wanted to work more spontaneously and in an improvised way to deal with each situation. This level of controlling killed my internal drive!” + Norwegian innovator in D2: “In a way, after the change, I felt like a less good sales representative, because of that prison-like context.” + Swedish adaptor in D3: “I think I did more than double as much relevant work after the change.” (6) Login-data mediation analysis — utilization in D2 gradually decreased post-intervention while D3 gradually increased, with mediation evidence that utilization is the primary driver of the performance result; the divergence is rational adaptation, not algorithm aversion. W&W tags:
digital-seizing/strategic-agility,digital-transforming/redesigning-internal-structures,digital-transforming/improving-digital-maturity,strategic-renewal/organizational-culture,contextual/internal-enablers,contextual/internal-barriers. 6 typedsupportsrelationships — Generative AI at Work (two-field-experiment pair on the augmentation pole — one shows the success case, one shows the failure case), Jagged Frontier (same INFORMS-journal family + same DiD/RCT method + same outside-the-frontier-loses pattern, with bounded region being task-shape there and interaction-design here), GenAI Playbook (deployment-quadrant frame + interaction-design frame as complementary layers), HBR 2026 (two 2026 RCT-grade empirical sources on organisational-design dimension of AI deployment outcomes), Warner & Wäger (empirical operationalisation of W&W redesigning-internal-structures + improving-digital-maturity microfoundations), Google DORA (productive-struggle + cognitive-style-tailoring convergence). Dangling first-mentions: Sebastian Krakowski, Darek Haftor, Johannes Luger, Natallia Pashkevich, Sebastian Raisch. Open follow-up: ingest Raisch & Krakowski 2021 — Artificial Intelligence and Management: The Automation-Augmentation Paradox (cited self-referentially as the theoretical prior). Confidence 0.85. - 2025-05-17-turc-llms-low-precision-quantization-fundamentals — How LLMs survive in low precision | Quantization Fundamentals (Julia Turc YouTube channel, 17 May 2025; 20:34). Julia Turc — solo educator on ML fundamentals; Patreon-supported. The wiki’s first source on model efficiency / inference optimisation as a topic in its own right, and the wiki’s second-earliest source by publication date (after Leskovec 2023). Five contributions: (1) The dual-end-point quantization motivation — at the top, DeepSeek R1 (671B params, ~720 GB at FP16) shrunk by −82% to ~131 GB to fit on 2× Nvidia H100; at the bottom, Google’s Coral Edge TPU “doesn’t even support floating-point operations at all” — INT8 quantization becomes a prerequisite, not optimisation. (2) The training-vs-inference precision asymmetry — training trajectory FP32 → FP16 / Bfloat16 → FP8 (DeepSeek R1, Llama 4); “natural end to this trend… unlikely gradient descent will allow us to go much lower”. Inference goes to 8-bit, 4-bit, even 1-bit. (3) PTQ vs QAT — Meta and DeepSeek publish FP-only; Unsloth quantizes post-publication; Qwen publishes pre-quantized variants (GGUF / GPTQ / AWQ); 1-bit LLMs require QAT. (4) The fixed-point-arithmetic integer-only-multiplication trick — decompose scale s = (negative-power-of-2) × s₀ with s₀ > 0.5; precompute once before deployment; the multiplication becomes integer multiply + bit shifts, the load-bearing operational bridge between the math and the hardware. (5) Memory-bandwidth-not-FLOPs as the real bottleneck on modern GPUs — “float and int multiplications can be similarly fast. … The real gain from quantization is the reduced bit width.” Inverts the naive quantization-saves-compute mental model. Bfloat16 vs IEEE FP16 discussed in operator detail (1+8+7 vs 1+5+10 → dynamic-range vs mantissa-precision tradeoff). Substrate-layer companion to Karpathy 2026’s Software 3.0 framing. 1 entity touched (Google 4→5, confidence 0.82→0.87; new sub-sections for Google Brain / Bfloat16, Coral Edge TPU, TensorFlow). Surfaces 18 dangling entities (Julia Turc; DeepSeek + DeepSeek R1; Meta + Llama 4 + PyTorch; Nvidia + H100 + TensorRT; Qwen; Unsloth; Coral Edge TPU; Bfloat16; ONNX Runtime; GGUF/GPTQ/AWQ formats; IEEE 754; Severance / MDR cultural-reference flag) and 9 concept candidates (quantization, PTQ, QAT, fixed-point arithmetic, clipping range, zero point, Bfloat16, memory-bandwidth bottleneck, training-vs-inference precision asymmetry). Empirical anchors are 12 months old at ingest time — temporal-drift flag in the source page.
- 2025-05-06-jassy-amazon-agility-ai-strategy-changing-role-of-managers — Amazon CEO Andy Jassy on Agility, AI Strategy, and the Changing Role of Managers (HBR IdeaCast, 6 May 2025; 29:39). Adi Ignatius interviews Andy Jassy (Amazon CEO since 2021, AWS founder). First wiki ingest from before the December 2025 phase change — a pre-phase-change major-vendor-CEO vantage on enterprise AI. Manual caption track (wiki’s second
kind: manualvideo source after Sternfels). Three substantive contributions: (1) The three-layer AI stack framing — infrastructure (Trainium / SageMaker) → orchestration (Bedrock with guardrails / RAG / agentic capabilities) → applications (Q / Rufus / 1,000+ internal apps); the wiki’s earliest first-party-CEO mention of a productised harness primitive (~8 months before Anthropic’s Managed Agents launch). (2) A target-firm CEO operator-of-micro-productivity-trap-escape worked example: +15% IC-to-manager ratio target (already beaten in Q1 2025), 1,000+ no-bureaucracy emails received with 375 processes changed in response, 5-day RTO mandate. Concrete operational counter-evidence to “speed comes only from technology” — at Amazon scale. (3) A May-2025 vibe-coding anchor: Jassy at 22:00 predicts “the number of people who are going to be able to be software developers is going to go up exponentially. Because you’re going to have these coding apps that allow you to use natural language to describe what you want to go build.” Earliest vendor-CEO articulation of the floor-raising thesis — ~12 months before Karpathy coined the term, ~7 months before Nika’s December-2025 PM-side worked example. Promoted two entities on this ingest: Amazon (parent firm; second source after Werner-Le-Brun) and Adi Ignatius (HBR editor-in-chief; second source after Sternfels). 3 concept pages updated (vibe-coding 5→6 / agent-harness 13→14 / micro-productivity-trap 10→11) + HBR entity 9→10, AWS entity 2→3. - 2024-12-19-anthropic-building-effective-agents — Building effective agents (Anthropic Engineering blog, 19 December 2024). Erik Schluntz and Barry Zhang. The wiki’s earliest primary-source on the harness-engineering construct by ~14 months — predates the naming of “harness engineering” (Feb 2026, Lopopolo + LangChain) while articulating the construct in essentially mature form. The architectural foundation: workflows-vs-agents distinction (“systems where LLMs are orchestrated through predefined code paths” vs “systems where LLMs dynamically direct their own processes”). Five composable patterns (prompt chaining / routing / parallelization (sectioning + voting) / orchestrator-workers / evaluator-optimizer) plus the autonomous-agent extreme. Three core principles: (1) Maintain simplicity in your agent’s design; (2) Prioritize transparency by explicitly showing the agent’s planning steps; (3) Carefully craft your agent-computer interface (ACI) through thorough tool documentation and testing. The augmented LLM as the foundational building block. First wiki source naming Model Context Protocol (MCP) as part of the agentic systems architecture. Strong vendor-side prediction from Dec 2024: “start by using LLM APIs directly; frameworks often create extra layers of abstraction that can obscure the underlying prompts and responses” — anticipating Osmani’s HaaS substrate-shift framing by 17 months. Cited by all four academic primaries in the harness cluster (Pan, Lee, Lou, Karten) and by every major practitioner essay (Chatterjee, Kokane, Osmani, Bockeler). The construct’s intellectual foundations are December 2024; the naming is February 2026 — the 14 intervening months were spent rediscovering, formalising, and operationalising what was already in this post. Confidence 0.78.
- 2024-12-01-csaszar-ketkar-kim-ai-strategic-decision-making — Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors (Felipe A. Csaszar·Harsh Ketkar·Hyunjin Kim — Strategy Science 9(4):322–345, INFORMS, Dec 2024; peer-reviewed; Special Issue on the Theory-Based View). The wiki’s first academic-strategy anchor on AI + strategic decision-making and first to engage the theory-based-view directly. Two empirical studies (a start-up accelerator: LLM-generated business plans vs entrepreneurs seeking VC; a start-up competition: LLM evaluations vs VC/angel investors) find LLMs generate and evaluate forward-looking entrepreneurial strategies at a level comparable to humans. Framework: SDM rests on search / representation / aggregation; AI changes the generation and evaluation of strategies. Competitive-advantage trichotomy: as AI advances, advantage could stay Ricardian (unique resources), become Schumpeterian (innovation-driven), or erode entirely — “the ultimate impact on firm performance will depend on competitive dynamics,” the strategy-theoretic paradox-of-access statement. TBV tension: LLMs are next-word predictors (may limit forward-looking causal-theory creation; risk reproducing conventional strategies) yet can expand theory-based strategising (virtual strategy simulations). W&W:
digital-sensing/digital-scenario-planning. 3 typedsupports: Dell’Acqua (extends AI-augments-knowledge-work into SDM), Boussioux (LLMs generate/evaluate ideas at human levels), Carroll & Sørensen (shared TBV lens). Concept changes: theory-based-view 1→2, strategy 4→5. Dangling: Felipe A. Csaszar, Harsh Ketkar, Hyunjin Kim. Confidence 0.78. - 2024-06-13-jordan-hbr-7-key-tensions-every-leader-must-balance — 7 Key Tensions Every Leader Must Balance (Harvard Business Review YouTube, 13 June 2024; ~10:03; ~278 ASR segments after rolling-caption dedup). Jennifer Jordan (Professor of Leadership and Organizational Behaviour, IMD Business School Lausanne; social psychologist). The wiki’s first explicit paradox-leadership source. Framework: competent contemporary leadership requires holding seven explicit style-tensions and moving across each pair contextually rather than committing to one pole. The seven dichotomies (from the companion HBR Feb 2020 article Every Leader Needs to Navigate These 7 Tensions): power holder ↔ power sharer / tactician ↔ visionary / teller ↔ listener / perfectionist ↔ accelerator / analyst ↔ prospector / miner ↔ prospector / constant ↔ adapter. “A good leader is never standing fully on one side of that tension. They’re learners. They oscillate based on context.” Operational heuristic for switching modes: “what am I sensing from the people around me?” — if the team needs voice, listener; if they need direction, teller. Decision criterion: “buying it” — if the team isn’t, you’re in the wrong mode. Skill/fear failure-mode distinction as the diagnostic for stuck leaders. Worked cases: Angela Ahrendts (CEO Burberry / SVP Retail Apple) as exemplar; Mathias Döpfner (likely identification; Axel Springer CEO) as the 15-20-years-ago Silicon Valley pilgrimage case. Pairs with Sinek 2018 (mindset reframe) and Ross & Schneider 2026 (adaptability replacing resilience) on the rejection of single-style command-and-control leadership. Acquisition fallback: yt-dlp captions used after youtube-transcript-skill panel-render path timed out at
--timeout 60000. Dangling first-mentions: Jennifer Jordan, IMD Business School, Angela Ahrendts, Mathias Döpfner (provisional), Axel Springer SE. Confidence 0.70. - 2024-04-18-caldwell-lennys-podcast-lessons-1000-yc-startups-tarpit-ideas — Lessons from 1,000+ YC startups: Resilience, tar pit ideas, pivoting, more (Lenny’s Podcast YouTube channel, published 18 April 2024, ingested 22 May 2026; ~80:52; 2,244 deduped ASR segments via yt-dlp fallback after Playwright path failed at both 30s and 60s timeouts). Host Lenny Rachitsky interviews Dalton Caldwell (managing director / group partner at Y Combinator, 10+ years across 21 batches). The wiki’s first deep evergreen-YC-partner anchor on the pre-AI-substrate-shift practitioner-wisdom layer of the YC corpus — predates the wiki’s spring 2026 YC partner-content burst (Tan / GStack / Hu / Stanford CS153 / etc.) by two years and provides the foundational vocabulary (tar pit ideas / just don’t die / find an incumbent with low NPS / customer-validation-first) the later episodes inherit and assume. Six substantive contributions: (1) Tar pit ideas as a named primitive — “By definition it is only a tar pit if it seems like it’s not… an idea that a lot of people come up with and that it seems like an unsolved problem and you get lots of positive feedback for… part of being a true tar pit is that you can get good initial validation.” Canonical examples: friend-coordination apps, music discovery, Foursquare-clone location apps. Concept-page candidate flagged for promotion next ingest. (2) The pivot-as-methodical-not-artful doctrine + Zip / Procurement worked example (~14:00–22:00) — Caldwell coached Rujul Zaparde toward the prompt “intentionally find large publicly-traded or PE-owned companies that are hated by customers, combined with software is horrible” → Zip discovered procurement after six pivots → billion-dollar business. The named template that Campfire, Vori, Luminai later instantiate operationally. (3) The struggle-is-universal anchor — Caldwell’s ~50% base-rate estimate that founders hit “actually truly got down to very very hard situations” at some point; the Winter 17 batch / Brex anecdote as empirical-base. (4) Don’t optimise for TAM at seed stage — “trying to be super pedantic about market size when it’s like a pre-seed company is not something I put a lot of thought on” + Brex India “the TAM of credit cards in India in 2015 was tiny” worked example. (5) Growth-hacking advice is actively harmful pre-product-market-fit — Caldwell’s contrarian-corner claim on Lenny’s own channel: “growth and growth hacking and doing all this analytics AB testing stuff is a total waste of time for very early startups”. (6) Customer-validation-first as final tactical advice — “my tactical advice is start doing customer validation first versus building a PowerPoint deck versus trying to raise money.” Plus the 2024 request-for-startups inventory (~9 categories named explicitly): ERPs (Caldwell-personally-authored — Glasgow / Campfire emerged from this prompt), open source companies, space companies, way-to-end-cancer, spatial computing, defence technology, manufacturing-back-to-America, better enterprise glue, small fine-tuned models. W&W tags:
digital-sensing/identifying-needs,digital-sensing/digital-scouting,digital-seizing/strategic-agility,digital-seizing/rapid-prototyping,digital-seizing/balancing-digital-portfolios,strategic-renewal/business-model. 5 typedsupportsrelationships: Campfire (the Zip-procurement-pivot template instantiated at AI-native-ERP scale), Luminai (Caldwell’s customer-validation-first principle operationalised at the post-AI-wave enterprise-altitude), Letter AI (twin YC-pivot-during-batch anchors at opposite ends of the YC corpus’s time-window), Yhangry (struggle-is-universal at single-founder-vantage), Lenny’s (Lenny’s Podcast channel-cluster on what disposition / discipline survives the substrate change). Entity changes: Dalton Caldwell promoted from Dangling to entity page (second substantive source — first was Glasgow / Campfire); Y Combinator 10→11; Lenny’s Podcast 3→4. Dangling first-mentions: Rujul Zaparde, Zip, Brex, Michael Seibel, Scott Belsky, Danny Alberon, Pete Kazi, Mixed Media Labs, App.net. Confidence 0.78. - 2023-12-07-leskovec-stanford-cs224w-knowledge-graph-embeddings — Stanford CS224W: Knowledge Graph Embeddings (Stanford Online YouTube, 7 December 2023; 70:04). Jure Leskovec (Stanford CS professor; SNAP Group). The wiki’s oldest video source by publish date — academic-foundation lecture on KG theory. Manual
en-UScaption track. The KG-completion task — given a head entity + relation type, predict the missing tail. Four shallow embedding methods × five relation patterns coverage matrix — TransE / TransR / DistMult / ComplEx vs symmetric / antisymmetric / inverse / composite / 1-to-N. FreeBase scale anchor: ~80M entities, 38k relation types, 3B edges; 93% of people in FreeBase don’t have a place-of-birth. Worked example: question-answering systems (Siri / Alexa / Bing) all run on graph-traversal of underlying KGs — “what are the latest films by the director of Titanic?” → parse → look up director edge → look up films-directed-by edges → return ranked. Pre-GenAI-era foundational reference for knowledge-graphs — the LLM-era practitioner sources (Manditereza / Bratanic / SurrealDB) operate on top of this academic theory. 1 concept page touched — promotes knowledge-graphs as a new concept page (this source + SurrealDB + Manditereza + Bratanic = 4-source threshold). - 2022-11-08-yao-cao-react-google-research-blog — ReAct (Google Research Blog, 8 Nov 2022; Shunyu Yao & Yuan Cao). The accessible popularisation of the paper — same claims, same headline tables, general-audience register. Typed
supports→ the paper. The wiki’s secondary/non-peer-reviewed register of ReAct. - 2022-10-06-yao-et-al-react-synergizing-reasoning-acting — ReAct: Synergizing Reasoning and Acting in Language Models (Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao; Princeton + Google Research Brain team; ICLR 2023, arXiv:2210.03629v3). The wiki’s foundational primary source for the agent loop — introduces ReAct: augment action space
Awith language spaceL(thoughts that update context without env feedback);reason to act+act to reason. Frozen PaLM-540B, 1–6 shot. HotpotQA/FEVER beat act-only & cut CoT hallucination (best=ReAct+CoT-SC 35.1/64.6); ALFWorld +34%, WebShop +10% over RL/IL on 10³–10⁵ instances; human-trace-editing correction. Promotes Shunyu Yao + Yuan Cao to entities (2nd-source rule via the blog). No W&W tags (methodology, outside the lens). - 2022-06-29-martin-hbr-a-plan-is-not-a-strategy — A Plan Is Not a Strategy (Harvard Business Review YouTube, 29 June 2022; ~9:31; 6.5M views; 364 segments). Roger Martin (former dean, Rotman School of Management, U Toronto). One of two foundational anchors for the new strategy concept page, paired with Oberholzer-Gee 2022. Load-bearing definition: “A strategy is an integrative set of choices that positions you on a playing field of your choice in a way that you win. Strategy has a theory: here’s why we should be on this playing field, not this other one, and here’s how, on that playing field, we’re going to be better than anybody else.” Planning ≠ strategy — planning lives on the cost side (you control it); strategy specifies a competitive outcome (customers decide). Southwest Airlines case: incumbents “playing to play” (more planes, more gates, no theory of winning); Southwest had a Greyhound-substitute theory + 5 cost-aligned choices (point-to-point, 737-only, no meals, online booking) → most passenger-seat-miles in America. Three practices for escaping the planning trap: (1) accept the angst — strategy can’t be proven in advance, “that is being a great leader because you’re giving your organization the chance to do something great”; (2) lay out the logic — “what would have to be true about ourselves, the industry, competition, customers for this strategy to work?” then watch the world unfold and tweak; (3) keep it to one page — where to play / how to win / capabilities / management systems / aspiration. Closing line: “If you plan, that’s a way to guarantee losing. If you do strategy, it gives you the best possible chance of winning.”
roles: [ceo, cso, coo, strategy-consultant, transformation-lead, rd-director, innovation-lab-lead]. Dangling first-mentions: Roger Martin, Rotman School of Management, Southwest Airlines. Confidence 0.78. - 2022-02-23-oberholzer-gee-hbr-what-is-strategy-value-stick — What Is Strategy? It’s a Lot Simpler Than You Think (Harvard Business Review YouTube, 23 February 2022; ~9:32; 1.5M views; human-curated captions, 207 clean segments). Felix Oberholzer-Gee (Andreas Andresen Professor of Business Administration, Harvard Business School; author of Better, Simpler Strategy, HBS Press 2021). Second foundational anchor for strategy, complementing Martin’s theory-of-winning with the value-stick value-creation framework. Definition: “Strategy’s simple. It’s a plan to create value.” The value stick: WTP (top) ↔ WTS (bottom); value = WTP − WTS; splits 3 ways (customer delight = WTP − price / firm margin = price − cost / employee value = compensation − WTS). Three ways to raise WTP: quality / complements (razor-razorblade, printer-cartridge, espresso-machine-capsule) / network effects (Instagram). Two ways to lower WTS — and the critical distinction: pay-more-money redistributes value (no creation); make-the-job-better-job creates value. Implication: invest in job quality, not just compensation. Best Buy turnaround case: ~$1B quarterly loss → 20%+ ROIC via stores-as-warehouses (raises WTP via faster shipping) + store-in-a-store with Microsoft/Samsung/Lenovo/Sony (lowers WTS for vendors + lowers WTS for employees via specialisation). Methodological punchline: “We started with ideas about how to create value before we thought about how to capture a fraction of the value that we created.”
roles: [ceo, cfo, cso, coo, strategy-consultant, transformation-lead, product-manager, chro]. Dangling first-mentions: Felix Oberholzer-Gee, Harvard Business School, Best Buy, Hubert Joly (unnamed in video). Confidence 0.80. - 2018-05-31-sinek-nyt-the-infinite-game — The Infinite Game (New York Times Events YouTube, 31 May 2018; ~25:48; 1.1M views; ~633 segments after rolling-caption dedup). Simon Sinek — keynote previewing his 2019 book of the same name. The wiki’s first leadership / corporate-purpose source. The framework Sinek adapts from James P. Carse (Finite and Infinite Games, 1986): finite games = known players / fixed rules / agreed objective (football, chess); infinite games = known + unknown players / changeable rules / objective is to perpetuate the game. Most business leaders treat business as a finite game; it’s an infinite game. The mismatch produces quagmire — “the finite player finding themselves in quagmire running through will and resources trying to win.” Worked examples: (1) Vietnam War / Tet Offensive — 85,000 troops over 125 US/allied targets; US tactically won; “the Americans were trying to beat the North Vietnamese where the North Vietnamese were fighting for their lives” — finite/infinite mismatch; (2) Apple/Microsoft Zune — “Microsoft gave me the new Zune and it is so much better than your iPod touch” / Apple exec: “I have no doubt” — “the infinite player understands […] you’re actually only competing against yourself.” Five elements of playing an infinite game: Just Cause / Courageous Leadership / Trusting Teams / Worthy Rival / Existential Flexibility. Declaration of Independence as a Just Cause document (not a complaint against Britain). Pairs with Jordan 2024 on the rejection of single-style leadership and with Sterman 2026 on multi-decade-horizon multi-stakeholder reasoning. Productive tension with Martin 2022’s finite-game framing of Southwest’s win. Acquisition fallback: yt-dlp captions. Dangling first-mentions: Simon Sinek, NYT Events, James P. Carse (Finite and Infinite Games, 1986). Confidence 0.65 (high reach + single-anecdote cases + 8-year-old talk).
Entities
- Aaron Chatterji — Mark Burgess Professor of Business and Public Policy at Duke; Chief Economist at OpenAI; co-author of the Bain/OpenAI HBR transformation article.
- Addy Osmani — Software engineer at Google (Chrome team) and web-developer advocate; author of the wiki’s first article-altitude treatment of harness engineering, [[2026-05-15-osmani-agent-harness-engineering|Agent Harness Engineering]] (O’Reilly Radar, May 2026) — Ralph Loop, AGENTS.md, hooks, compaction.
- Adi Ignatius — Editor-in-Chief of Harvard Business Review; host of the HBR IdeaCast podcast; interviewer on the wiki’s Jassy (Amazon) and Sternfels (McKinsey) IdeaCast episodes.
- Adrienn Lawson — Researcher/analyst at The Linux Foundation (Linux Foundation Research); co-author of both 2026 State-of-Tech-Talent reports with Marco Gerosa.
- AI Index — Independent annual-report initiative at Stanford HAI; 8 editions through 2025.
- Alex Singla — Senior Partner at McKinsey; global co-leader of QuantumBlack; contributing co-author of Rewired 2nd ed.
- Alexander Sukharevsky — Senior Partner at McKinsey; global co-leader of QuantumBlack (with Singla); contributing co-author of Rewired 2nd ed.
- Amazon — US retail + cloud conglomerate; CEO Andy Jassy since 2021; parent of Amazon Web Services; wiki’s target-firm case for AI adoption (three-layer AI stack, flatten-management, 5-day RTO) and an Octopus-Organization exemplar.
- Amazon Web Services — Cloud hyperscaler; Anthropic partnership ($8B total); host of the Octopus-Org executives-in-residence program.
- Amy Webb — Quantitative futurist; CEO of Future Today Strategy Group; professor of strategic foresight at NYU Stern; author of The Signals Are Talking, The Big Nine, The Genesis Machine.
- Andrej Karpathy — AI researcher and educator; co-founder of OpenAI (2015–2017, 2023–2024); led Tesla Autopilot computer-vision (2017–2022); founder of Eureka Labs (2024–) for AI-native education. The upstream-spec author for this entire repo (
llm-wiki.md, CLAUDE.md). Coined Software 2.0 (2017), vibe coding (2024), and named Software 3.0 in the Sequoia AI Ascent interview (29 April 2026). Cross-page-presence promotion (precedent: Jack Clark). - Andrew Ng — Founder of DeepLearningAI; Stanford adjunct professor; ex-Google Brain founding lead; ex-Baidu chief scientist; managing GP at AI Fund. The wiki’s AI-educator-and-conference-keynote voice on the PM-bottleneck thesis, building-blocks framing, and 100%-AI-coding claims.
- Andy Wu — HBS Associate Professor (Strategy Unit); Wharton Mack Institute senior fellow; co-author of the 2×2 GenAI Playbook.
- Anthropic — AI safety and research company; publisher of Claude and the Anthropic Economic Index; ~$8B total AWS investment. Public Benefit Corporation + Long-Term Benefit Trust two-tier governance (LTBT mechanism narrated in operator detail by Ries 2026; Ries personally advised the Amodeis on the structure at founding).
- Anthropic Economic Index — Recurring measurement initiative by Anthropic; five reports through March 2026; introduces “economic primitives” framework in fourth report; introduces skill-biased-technological-change / learning-curves framing in fifth.
- Antigravity — Google’s single agent harness (built by the ex-Windsurf team); an IDE + web agent surface + CLI + SDK + Gemini-API managed agent powering search, the Gemini app, Cloud, and AI Studio; a concrete instance of agent-harness at hyperscaler scale.
- Arjun Dutt — Partner at Bain & Company (AI/ML focus); lead author of the AI Experimentation to AI Transformation HBR article.
- Bain & Company — Global management consulting firm; co-publishing partner with OpenAI Economic Research on the 2026 transformation framework; reports 10–25% client EBITDA gains.
- Bharat N. Anand — Dean & Professor at NYU Stern; co-author of the 2×2 GenAI Playbook.
- Bidyut Dumra — Group Head of Innovation and Future of Work at DBS Bank; joined from outside banking (Cathay Pacific); architect of DBS’s Managing Through Journeys, Innovation Pyramid, and the information-gap → action-gap thesis. (Per MIT SMR 2026.)
- Boris Cherny — Engineering leader at Anthropic; lead of the Claude Code team. Public-facing exemplar of the AI-native engineering practice the wiki tracks: 10–15 concurrent Claude instances + CLAUDE.md as in-workflow learning capture (per Kiron-Schrage 2026). Cross-page-presence promotion (precedent: Andrej Karpathy, Jack Clark).
- Boston Consulting Group — Global management consulting firm; partnered with Harvard/Wharton/MIT/Warwick on the 758-consultant Jagged Frontier RCT.
- Cisco — Tech vendor; sponsor of MITTRI/Cisco report; source of the 5-foundations framework and the chatbot → agent → multi-agent progression.
- Claire Vo — Product leader; founder/creator of ChatPRD; host of the How-I-AI podcast. The wiki’s product-leader-altitude voice on AI practice — PRD-as-input and loops-as-designing-jobs. (Promoted 2026-06-22 on her 2nd source: agent loops + Marily Nika episode.)
- Claude Code — Anthropic’s coding agent (CLI-first; desktop “Claude Cowork”); the wiki’s reference harness benchmark, mentioned in ~45 sources. Harness primitives: AGENTS.md instruction files, hooks, subagents, skills, routines (scheduled loops) + the
/goalprimitive. (Created 2026-06-22 as cross-reference repair — resolved 12 pre-existing broken[[Claude Code]]wikilinks.) - Cline — Open-source AI coding agent / VS Code extension (formerly Claude Dev); a customer-of-model-vendor coding harness that reportedly out-performed Claude Code on Opus 4.5 evals via harness optimisation (Stanford terminal-bench, Harbor/Modal infrastructure).
- Continuum Laboratory — San Francisco AI firm (ContinuumLab.AI); partnered with Harvard / U. Washington researchers on the Crowdless Future circular-economy crowdsourcing study.
- Dalton Caldwell — Managing Director and Group Partner at Y Combinator (10+ years, 21 batches); ex-founder of Mixed Media Labs/Picplz/App.net; the wiki’s canonical evergreen YC-partner voice on tar-pit ideas, methodical pivots, and customer-validation-first founder advice.
- Daron Acemoglu — Institute Professor of Economics at MIT; 2024 Nobel laureate (with Simon Johnson and James A. Robinson); co-author of Why Nations Fail and Power and Progress; the wiki’s anchor for the productivity-is-five-things bound on AI’s macroeconomic impact.
- David Kiron — Editorial director, research, of MIT Sloan Management Review; program lead for Big Ideas research initiatives. Co-author of both 2026-05-07-ransbotham-augmented-learners (Augmented Learners) and 2026-05-07-kiron-schrage-compound-benefits (return on iteration).
- DBS Bank — Singapore-headquartered bank (~39,000 employees), Southeast Asia’s largest; the wiki’s headline incumbent digital-transformation case. GANDALF aspiration (“be the D in GANDALF”); Managing Through Journeys; innovation-as-KPI (20%-of-scorecard). Also a deferred [[2026-05-03-rewired-second-edition-sample|McKinsey Rewired]] case study. (Per MIT SMR 2026.)
- Deep Nishar — Technologist and investor; ex-Google senior product (2003–2008), ex-LinkedIn CPO (2009–2014); investments include Anthropic, Figma, Glean, Slack; co-author with Nohria of the End of One-Size-Fits-All Enterprise Software HBR piece.
- DeepLearningAI — AI-education company founded by Andrew Ng in 2017; runs the Coursera Deep Learning Specialization, the Batch newsletter, and the AI Dev conference series; the wiki’s educator-and-conference channel-entity for the 2026 AI-engineering community.
- Diana Hu — General Partner at Y Combinator; ex-CTO/co-founder of Escher Reality (acquired by Niantic); the wiki’s canonical partner-altitude voice on AI-native company architecture — closed-loop vs open-loop companies, the IC/DRI/AI-founder-type org structure, and the $1–2M revenue-per-employee benchmark.
- Eric Lamarre — Senior Partner at McKinsey; lead author of Rewired 2nd ed (Wiley, 2026).
- Erik Brynjolfsson — Stanford economist; director of Stanford Digital Economy Lab; AI Index steering committee; lead author of the customer-support productivity study (2023) and the Canaries-in-the-Coal-Mine employment study (2025).
- Ethan Mollick — 2 sources. Wharton associate professor; co-director of Wharton AI Initiative; co-author of the Jagged Frontier paper; author of Co-Intelligence; the wiki’s non-doomer/non-zealot voice. Per the June 2026 A Bit of Optimism interview + the Dell’Acqua study.
- Eva Lyubich — Researcher contributing to the Anthropic Economic Index program; co-author of the 5th AEI report (Learning curves) and the Agentic coding and persistent returns to expertise study of ~400,000 Claude Code sessions.
- Fabrizio Dell’Acqua — HBS Digital Data Design Institute researcher; lead author of the Jagged Frontier paper.
- Future Today Strategy Group — Strategy/foresight consultancy led by Amy Webb; publisher of the Convergence Outlook 2026; formerly Future Today Institute; produced 19 annual Tech Trends Reports.
- Garry Tan — President & CEO of Y Combinator since 2023; ex-Palantir employee #10; Posterous co-founder; the wiki’s canonical first-party AI-founder-type archetype — builder of the open-source GStack Claude Code toolkit and the GBrain autonomous knowledge base.
- Gawesha Weeratunga — Member of OpenAI Economic Research team; co-author of the Bain/OpenAI HBR transformation article.
- Gene Rapoport — Partner and head of AI for the Private Equity practice at Bain & Company; co-author of the AI Experimentation to AI Transformation HBR article.
- GitHub — Microsoft-owned developer platform (version control + the Copilot AI-coding-agent family). Promoted via cross-page-presence (~31 source mentions). Home of the Copilot coding agent, Agent HQ (open agent-orchestration surface — runs OpenAI Codex + Claude/Gemini agents under one license), GitHub Advanced Security (secret/code scanning + Copilot autofix), and the State of the Octoverse report. “The center of the universe for Microsoft’s developer agentic tooling.”
part-ofMicrosoft. - Glenn R. Carroll — Stanford GSB professor; co-author of Strategy Theory Using Analogy with Sørensen.
- Goldman Sachs — NEW (13 June 2026), 2 sources. Global investment bank; in the wiki as a first-party enterprise-AI practitioner (via CIO Marco Argenti’s [[2026-06-12-argenti-hbr-thrive-alongside-ai-mindset-not-skillset|HBR mindset-not-skillset essay]] — client-onboarding evals, “AI transformation follows data transformation”) and as the legal-and-life-sciences-most-exposed analyst voice in WP. Marco Argenti dangling (single-source author).
- Google — Big-tech platform company; operating subsidiary of Alphabet. Parent of Google Research (Gemini family / durable-skills measurement) and Google Cloud / Google Developers (the Agents CLI in Agent Platform announcement, ADK harness framework, Agent Runtime / Cloud Run / GKE substrate, Gemini Enterprise distribution). Also the substrate-layer player via Google Brain (Turc 2025 names Bfloat16 as Google Brain’s ML-bespoke 16-bit float format that won the training-precision war over IEEE FP16), Coral Edge TPU (edge-ML processor with no floating-point support, mandating INT8 quantization), and TensorFlow (the framework with built-in quantization support).
- Google DeepMind — Google’s consolidated AI research division (2023 merger of DeepMind and Google Brain); produces the Gemini model family, the Antigravity agent harness, and the Omni multimodal model; led by Demis Hassabis.
- Google Research — Research arm of Google; producer of the Gemini model family and recurring data partner of the AI Index. Anchor of the durable-skills measurement work (Globerson et al. 2026 Vantage / Executive LLM platform).
- Grady Booch — Chief Scientist for Software Engineering at IBM (Fellow); co-author of the Gang of Four Design Patterns book; creator of UML. Cited by Paul Everitt as the conceptual-lineage anchor for the agentic engineering reframe and the call for a “Gang-of-Four-for-AI” of agentic design patterns.
- Harrison Chase — Co-founder and CEO of LangChain (since 2022); the wiki’s canonical vendor-CEO voice on agent-engineering infrastructure — coined the frameworks/runtimes/harnesses/no-code Build taxonomy and the model/harness/context continual-learning model.
- Harrison Satcher — Member of OpenAI Economic Research team; co-author of the Bain/OpenAI HBR transformation article.
- Harvard Business Review — Management magazine; publisher of multiple wiki sources (Anand-Wu, Werner-Le-Brun, Bansal-Birkinshaw, Reitz-Higgins, Webb, Dutt-Chatterji).
- How-I-AI — Practitioner podcast / YouTube channel hosted by Claire Vo (the How I AI companion show to Lenny); worked-examples and live builds at the product-leader altitude. Sources: agent loops, Marily Nika on the PM toolkit. (Promoted 2026-06-22.)
- InfoQ — software-engineering news site + QCon/Dev Summit organiser; the publishing venue/channel (
author:) for two of the wiki’s agent-harness practitioner talks (QCon London + “From Demo to Production”). Promoted 2026-06-26 on second source mention. - Ivey Business School — Business school at Western University, London, Ontario; houses Innovation North; affiliated with Bansal & Birkinshaw.
- Jack Clark — Co-founder of Anthropic and Head of Public Benefit (leads the new Anthropic Institute); previously Anthropic Head of Policy and OpenAI Policy Director; technical journalist before that. Founding member of the AI Index (2017–2024); inaugural NAIAC member (2021–2024); OECD AI co-chair. Author of the Import AI newsletter.
- Jacqueline N. Lane — Assistant Professor at HBS / LISH; corresponding author of the Crowdless Future paper.
- James Manyika — Senior Vice President at Google–Alphabet for Research, Labs, Technology & Society (since 2022/2023); previously Chairman & Director of McKinsey Global Institute (2009–2022); UN AI High-Level Advisory Body co-chair; AI Index Steering Committee member across editions.
- Jesper B. Sorensen — Stanford GSB professor; co-author of Strategy Theory Using Analogy with Carroll.
- John Higgins — Researcher at GameShift / The Right Conversation; co-author of Spacious Thinking with Reitz; co-author of Speak Out, Listen Up (2024).
- Juan Carlos Niebles — Research Director at Salesforce Research and Adjunct Professor of Computer Science at Stanford; co-Director of the Stanford Vision and Learning Lab; AI Index Steering Committee member.
- Julian Birkinshaw — Dean of Ivey Business School; co-author of Why You Need Systems Thinking Now with Bansal.
- Julie Bedard — NEW (15 June 2026), 2 sources. BCG (Boston Consulting Group Henderson Institute) researcher; co-author of the agents-as-employees RCT + the labor-disruption-segments report.
- Karim Lakhani — HBS professor; chair of the Digital Data Design Institute; co-author of the Jagged Frontier paper.
- Karl S.R. Warner — Edinburgh Napier University researcher; lead author of Building Dynamic Capabilities for Digital Transformation with Wäger.
- Kate Smaje — Senior Partner at McKinsey; co-author of Rewired 2nd ed.
- LandingAI — Computer-vision / document-AI company founded by Andrew Ng (~2018). The wiki’s first document-intelligence vendor entity. Flagship Agentic Document Extraction (ADE): vision-first (reads the page as a structured image), zero-shot, visually grounded extraction on proprietary DPT (Document Pre-trained Transformer) models, deployed via Cloud / VPC / on-prem-air-gapped for regulated industries. Promoted on the [[2026-05-26-landingai-touchpoint-to-outcome-front-office-processes|Touchpoint to Outcome webinar]] (first LandingAI-channel source). Partner ecosystem: TCG/OCTO (Dangling).
- LangChain — US-based AI company (founded 2022 by Harrison Chase); ships at every layer of the ADLC Build phase: LangChain (framework) / LangGraph (runtime) / Deep Agents (harness) / LangSmith Fleet (no-code) — plus the LangSmith observability/evaluation/deployment platform. The vendor whose product taxonomy is the wiki’s vocabulary for the ADLC, both because Chase coined the four-layer Build split and because LangChain ships at all four layers. Promoted to entity from cumulative cross-page mentions (agent-harness, ai-agents, generative-ai, plus Chase 2026 as first first-party LangChain source).
- Langfuse — open-source LLM-engineering platform for observability and tracing of agent/LLM applications; an implementation of the agent-harness observability layer (traces, per-step context, tool-call capture). Promoted 2026-06-26 (by request; single source — InfoQ). Paired with LiteLLM.
- Lapo Santarlasci — Economics PhD researcher at IMT School for Advanced Studies Lucca (Italy); doctoral work in finance, applied econometrics, machine learning; published on global geography of AI patents; AI Index 2025 + 2026 co-author.
- Lenny’s Podcast — Long-form interview podcast hosted by Lenny Rachitsky (ex-Airbnb PM; founder of Lenny’s Newsletter and the How I AI companion show). Promoted on second
author:mention (Ries 2026 / Spiegel 2026). Standing disclosure: “Lenny may be an investor in the companies discussed.” - Leonard Boussioux — University of Washington Foster School / HBS LISH; lead author of the Crowdless Future paper.
- Lisa Krayer — Principal at Boston Consulting Group (People & Organization, Technology and AI teams; BCG Henderson Institute Ambassador); co-author of the Jagged Frontier field experiment and the Don’t Treat AI Agents Like Employees HBR study.
- LiteLLM — open-source Python library exposing one OpenAI-compatible interface over many LLM providers — the model-routing seam that keeps a harness’s model dependency swappable. Promoted 2026-06-26 on second source (Wolfe τ-bench loop + InfoQ observability). Paired with Langfuse.
- Logan Kilpatrick — Product lead for Google AI Studio and the Gemini API at Google DeepMind; ex-OpenAI developer relations; source of the wiki’s model eats the harness thesis and jagged/narrow superintelligence framing (Sequoia Training Data interview, June 2026).
- Loredana Fattorini — Research Manager at Stanford HAI; spearheads the AI Index in collaboration with the AI Index Lead; co-author of the Global AI Vibrancy Tool; PhD Applied Economics, IMT Lucca.
- Lowe’s — U.S. home-improvement retailer; OpenAI partner (Mylow / Mylow Companion across 1,700+ stores; March 2025 launch).
- Marco Gerosa — Professor at Northern Arizona University; software-engineering researcher; lead author of The Linux Foundation’s 2026 State-of-Tech-Talent reports (Global + Europe).
- Martin Fowler — NEW ENTITY (25 June 2026), 3 sources. Chief Scientist at Thoughtworks; author of Refactoring and Patterns of Enterprise Application Architecture; co-author of the Agile Manifesto; runs martinfowler.com. Anchor author of the expert-generalist concept (Expert Generalists, 2025). Long referenced incidentally across concept pages; promoted on his first first-party-authored source.
part-ofThoughtworks. - Matthew Kropp — NEW (15 June 2026), 2 sources. Managing Director & Senior Partner at Boston Consulting Group (Henderson Institute); lead BCG voice on AI’s organizational/labor impact — the agents-as-employees RCT + the labor-disruption-segments report.
- Maxim Massenkoff — NEW (15 June 2026), 2 sources. Economist (Naval Postgraduate School) with the Anthropic Economic Index; lead author of the observed exposure labor-impacts report (Massenkoff & McCrory 2026) + the 5th AEI report.
- Maximilian Wager — Nunatak Group researcher; co-author of Building Dynamic Capabilities for Digital Transformation with Warner.
- McKinsey & Company — Global management consulting firm; runs QuantumBlack as its AI arm; recurring data partner of the AI Index; publisher of Rewired. McKinsey Global AI Survey is the underlying instrument for several wiki adoption headlines.
- McKinsey Global Institute — Research arm of McKinsey & Company (founded 1990); editorially independent from client work; publisher of Agents, Robots, and Us and The Race Takes Off — the wiki’s highest-reliability-tier source on AI-and-economy structural questions.
- Megan Hsu — NEW (15 June 2026), 2 sources. BCG (Boston Consulting Group Henderson Institute) researcher; co-author of the agents-as-employees RCT + the labor-disruption-segments report.
- Megan Reitz — Associate fellow at Saïd Business School Oxford; adjunct prof at Hult; co-author of Spacious Thinking with Higgins; author of Dialogue in Organizations, Mind Time, Speak Up, Speak Out, Listen Up.
- METR — AI evaluations organization; introduced task-horizon benchmark (50%-success duration); referenced by the fourth Anthropic Economic Index report.
- Miaomiao Zhang — ContinuumLab.AI / HBS researcher; co-author of the Crowdless Future paper.
- Michael Schrage — NEW (15 June 2026), 2 sources. Research fellow at the MIT Sloan Initiative on the Digital Economy; co-author of Kiron & Schrage (consumption→compounding) and speaker at the 2026 MIT Sloan CIO Symposium (in-the-loop vs on-the-loop). Throughline: the human-judgment / expert-as-evaluator role in agentic AI.
- Microsoft — Cloud-and-developer-tools giant; owns GitHub (since 2018), partners with OpenAI. Promoted via cross-page-presence (~21 source mentions). Ships the agentic-dev surfaces in the Agentic DevOps keynote: Azure, Azure DevOps, Visual Studio Code (Ask/Edit/Agent/Plan modes), the responsible-AI pipeline (validates Copilot I/O even for third-party models), and the Azure SRE Agent (autonomous cloud monitoring + remediation). Channel-author of Microsoft Visual Studio on YouTube.
- Mike Loukides — VP of Content Strategy at O’Reilly Media and editorial lead of O’Reilly Radar; recurring author of the monthly Radar Trends to Watch digest (five Jan-May 2026 installments ingested). Quoted by Julie Baron on the signs-of-the-future-in-the-present framing; added Claude as named co-author on the May 2026 digest. First recurring-digest curator entity — editorial weighting itself is a signal worth citing.
- MIT CISR — MIT Center for Information Systems Research; produces the Four Stages of AI Maturity framework.
- MIT Sloan Executive Education — Executive-education arm of MIT Sloan School of Management; webinar channel promoting non-degree courses; the wiki’s channel-entity for MIT’s system-dynamics lineage (Sterman’s Systems Thinking for Leaders, Carrier’s Industrial AI That Works).
- MIT Sloan Management Review — MIT Sloan School of Management’s research-and-management magazine; runs the Big Ideas research initiatives. Distinct from MIT Technology Review Insights. Publisher of multiple wiki sources including the 8-year MIT SMR × BCG annual AI survey.
- MIT Technology Review Insights — Custom publishing arm of MIT Technology Review; publisher of MITTRI/Cisco report.
- Nestor Maslej — Editor-in-Chief of the AI Index at Stanford HAI; lead author of the 2025 report.
- Nitin Nohria — Harvard professor; 10th Dean of HBS (2010–2020); co-author with Nishar of the End of One-Size-Fits-All Enterprise Software HBR piece introducing the Build/Compose/Collaborate/Buy-Outcomes firm-boundary framework.
- O’Reilly Media — US technology publisher founded 1978 by Tim O’Reilly; publisher of books, the O’Reilly learning platform, and O’Reilly Radar — the editorial trend-curation channel under Mike Loukides. The wiki’s most-engaged trade-press source as of May 2026 (8 ingested sources: 5 monthly Radar Trends digests + Baron’s annual Signals for 2026 + Shyamsundar-Jain’s Organizational Strategies from the Collective Wisdom of Nature + Addy Osmani’s Agent Harness Engineering). Editorial signature: explicit vocabulary curation (the Signals piece names the 2026 paradigm vocabulary that aligns with Karpathy at Sequoia from the practitioner side); deliberate cross-platform reposting (Osmani’s piece reposted with permission from his blog — O’Reilly Radar as editorial consolidator); AI-assisted-curation transparency (May 2026 digest carries Claude as named co-author).
- Omni — Google DeepMind’s single any-input-to-any-output multimodal model, launched at Google I/O 2026; replaces ~8 separate systems (text, audio, music, image, video); first iteration “Omni Flash,” strongest at video editing.
- OpenAI — AI research and deployment company; provider to >1M businesses; co-publishing partner on the 2026 transformation framework via its Economic Research team; Lowe’s AI partner; central to the wiki’s GenAI substrate references. Governance trajectory narrated by Ries 2026: founded as nonprofit foundation (2015) → “two or three OpenAI crises ago” the Amodeis departed to start Anthropic → mid-2025 conversion to Public Benefit Corporation structure (PBC alone, no outside trustee body — structurally weaker than Anthropic’s PBC + LTBT).
- Pete Koomen — General Partner at Y Combinator; co-founder of Optimizely; architect of YC’s internal AI agent infrastructure (shared tool/skill registries, self-improving dream-cycle); author of the Horseless Carriages essay and the multiplayer-harness gap framing.
- Peter McCrory — NEW (15 June 2026), 2 sources. Economist at Anthropic on the Anthropic Economic Index; co-author of the observed exposure labor-impacts report + the 5th AEI report. Paired with Maxim Massenkoff.
- Peter Weill — Senior research scientist at MIT Sloan; chairman of MIT CISR. Cited in Rewired 2nd ed (Lamarre et al. 2026, p. 26).
- Pydantic — Open-source Python data-validation library and its commercial company (Pydantic Inc); builder of Pydantic AI (agent framework), Monty (sandboxed Rust-based Python-subset tool execution), and LogFire (system + AI observability).
- Raymond Perrault — Distinguished Computer Scientist Emeritus at SRI International (Director of SRI AI Center 1987–2017); CALO/Siri-lineage NLP researcher; AI Index Steering Committee chair (2025) and co-chair (2026, with Yolanda Gil).
- Rob Levin — Senior Partner at McKinsey; co-author of Rewired 2nd ed.
- Roberta Fusaro — NEW (4 July 2026), 2 sources. Editorial Director at McKinsey & Company; host of The McKinsey Podcast. Interviewer on the serial-builder-advantage episode; previously credited (body prose) on 2026-06-18-ramaswamy-mckinsey-every-company-software-company.
- Ron Carucci — Cofounder and managing partner at Navalent; author of 10 leadership/change-management books including To Be Honest (2021); HBR contributor on the resistance-as-data framework (2026).
- Russell Wald — Executive Director of Stanford HAI (since 2024); previously HAI’s first Director of Policy and then Managing Director for Policy and Society; AI Index Steering Committee member; argues academia needs structural reform (incl. public compute) to stay competitive with frontier labs.
- Ryan Heller — Contributor to the Anthropic Economic Index program; co-author of the 5th AEI report (Learning curves) and the Agentic coding and persistent returns to expertise study of Claude Code usage data.
- Ryan Lopopolo — NEW (20 June 2026), 2 sources. Member of Technical Staff at OpenAI (Codex); coiner of the term “harness engineering.” Promoted on the second substantive source: the OpenAI Codex blog case study + the AI Native DevCon talk. The wiki’s practitioner-origin voice on harness engineering.
- Sam Altman — CEO of OpenAI since 2019; ex-President of Y Combinator (2014–2019); co-founder of Loopt. Source of the wiki’s AI washing counter-framing on layoff narratives and a named voice in the AGI-bull / FOOM-scenario camp.
- Sam Ransbotham — Professor of analytics at Boston College Carroll School of Management; guest editor of MIT SMR’s AI and Business Strategy initiative; lead author of the 8th annual MIT SMR × BCG global AI report (2024).
- Schmidt Sciences — Nonprofit research and philanthropy organization; new analytics partner for AI Index 2026, collaborated on the standalone Medicine chapter.
- Sequoia Capital — US venture-capital firm (founded 1972); appears in the wiki as publisher of frontier-AI interviews via its Training Data podcast and AI Ascent event series, including the Logan Kilpatrick and Andrej Karpathy interviews.
- Sha Sajadieh — Editor-in-Chief of the AI Index 2026 (9th edition); replaces Nestor Maslej.
- Shunyu Yao — NEW (12 June 2026), 2 sources. First author of ReAct (2022); PhD in the Princeton NLP Group (advised by Karthik Narasimhan), work done during a Google Research Brain-team internship. The genealogical-root author of the wiki’s agent/harness cluster.
- Simon Sinek — NEW (20 June 2026), 2 sources. Leadership author (Start With Why, Leaders Eat Last, The Infinite Game) and host of A Bit of Optimism. Promoted on the second substantive source: subject of the 2018 NYT infinite-game keynote + host of the June 2026 Ethan Mollick podcast.
- Simon Willison — Co-founder of Django; creator of Datasette; prolific AI-engineering blogger dubbed “the developer AI developer whisperer” by Paul Everitt. Sources the wiki’s dark factory pattern, challenger disaster worry, and red-green-TDD-for-agents framings.
- Stanford Digital Economy Lab — Research initiative at Stanford directed by Brynjolfsson; publisher of the Canaries paper; distinct from Stanford HAI.
- Stanford GSB — NEW ENTITY (25 June 2026), 3 sources. Stanford Graduate School of Business — both a research institution and a publishing channel (YouTube + the If/Then podcast). Promoted on its second
author:-value appearance, resolving pre-existing broken[[Stanford GSB]]links. Sources: Guilbeault (AI limits), Jones (AI & growth), Carroll & Sørensen (analogy). Distinct from Stanford HAI. - Stanford HAI — Stanford Institute for Human-Centered Artificial Intelligence; publisher of the AI Index; founded 2019.
- Stanford Online — Stanford University’s online-education delivery channel (YouTube + course platform); wiki source for both academic-foundation lectures (Leskovec’s CS224W on knowledge graphs) and frontier-systems guest lectures (CS153 with Nikhyl Singhal on product management in the AI era).
- Stephanie Woerner — Principal research scientist at MIT Sloan; director of MIT CISR. Cited in Rewired 2nd ed (Lamarre et al. 2026, p. 286).
- Terah Lyons — Founding Executive Director of the Partnership on AI; previously White House OSTP Policy Advisor under Obama (co-directed the Future of AI Initiative producing the 2016 federal AI strategy reports); AI Index Steering Committee member.
- The Linux Foundation — Nonprofit, vendor-neutral open-source foundation (est. 2000); via Linux Foundation Research (the two 2026 State-of-Tech-Talent reports), Linux Foundation Education (certifications), and its conference channel (the Headroom talk). 3 wiki sources.
- Thoughtworks — NEW ENTITY (25 June 2026), 3 sources. Global software consultancy known for agile delivery, continuous delivery, and the Technology Radar; professional home of Martin Fowler (Chief Scientist) and publisher of martinfowler.com. Institutional voice behind the expert-generalist talent thesis (Expert Generalists) and AI-coding-agent practice (Böckeler’s QCon harness talk).
employsMartin Fowler. - Tima Bansal — Canada Research Chair in Business Sustainability at Ivey; founder of Innovation North; co-author of Why You Need Systems Thinking Now with Birkinshaw.
- Vanessa Parli — Director of Research Programs / Managing Director of Programs and External Engagement at Stanford HAI; manages industry partnerships, executive education, policy engagement, and the AI Index; AI Index Steering Committee member.
- Vladimir Jacimovic — ContinuumLab.AI / HBS researcher; co-author of the Crowdless Future paper.
- Y Combinator — Silicon Valley startup accelerator founded 2005; President & CEO Garry Tan since 2023. The wiki’s deepest AI-native-startup-formation vantage across ten-plus sources and four channels (Startup School, Founder Firesides, YC Root Access, partner personal channels).
- Yoav Shoham — Professor Emeritus of CS at Stanford; co-founder & co-CEO of AI21 Labs; author of the canonical Multiagent Systems textbook with Leyton-Brown; Allen Newell Award (2012), IJCAI Research Excellence Award (2019); AI Index Steering Committee member.
- Yolanda Gil — USC / Information Sciences Institute researcher; chair of the AI Index 2026 (was chair-elect in 2025).
- Yuan Cao — NEW (12 June 2026), 2 sources. Research Scientist at Google Research Brain team; senior Google-side co-author of ReAct (2022), paired with first author Shunyu Yao on both the paper and the blog.
Concepts
- agent-development-lifecycle — ADLC. Process construct paralleling SDLC — the ordered (but iterating) stages of building / evaluating / deploying / distributing / operating an AI agent. Two formalizations as of 10 May 2026 (confidence 0.80): Google’s 9-stage wheel (Cheung et al. 2026: Capability & Tool Exploration → Design Cognitive Architecture → Implement Tools → Context Engineering → Evaluate → UAT → Deploy Infrastructure → CI/CD & Production → Iterate) and Chase’s 4-phase loop + governance ring ( LangChain 2026: Build → Test → Deploy → Monitor → Iterate, wrapped by Govern). Both compatible; the two are different granularities of the same construct. Worked example at vendor scale: OpenAI Codex 2026.
- agent-harness — The runtime engineering layer that wraps a foundation model to make a production agent. Context / Constraints / Contracts / Compounding (Chatterjee 2026); 7 building blocks + 4-layer stack (Kokane 2026); refined as frameworks / runtimes / harnesses / no-code by Chase 2026. “The model is what you rent. The harness is what you own.” 20 sources converging — vendor productisations (Anthropic Managed Agents, Google Agents CLI in Agent Platform), practitioner essays (Kokane, Chatterjee, Kiron-Schrage), the empirical anchor (Prompt Engineering YouTube — same-model 6× variance + transferable harness + subtraction principle), the paradigm-vocabulary anchor (Karpathy at Sequoia AI Ascent); plus the wiki’s first vendor-side production case study at scale ( OpenAI Codex — 5 months, 0 manually-written lines, ~1M LOC), the hooks-as-portable-primitive anchor ( TDS — same five lifecycle events across Claude Code / Codex / Cursor; memory portable across harnesses), and the lifecycle-vocabulary refinement ( LangChain) sharpening the framework/harness boundary.
- agentic-engineering — The engineering discipline of writing software with AI agents that preserves the quality bar of professional software while going much faster. Coined as paired-with-vibe-coding in Karpathy at Sequoia AI Ascent (29 April 2026). Vibe coding raises the floor; agentic engineering raises the ceiling. “The 10× engineer used to be the upper bound; agentic engineering pushes far past 10×.” Humans own aesthetics/judgment/taste/oversight/spec; agents own fill-in-the-blanks. Tool mastery is now the load-bearing craft. Hiring is broken — needs project-scale build-and-defend formats (write a Twitter clone for agents, secure it, fleet attempts to break it). 5 sources (10 May 2026): Karpathy named it; Fung showed the team-norms rewrite at Anthropic; Lopopolo (OpenAI Codex) adds five months of operational data — 0 manually-written lines, ~1M LOC, 7 engineers, 3.5 PRs/day with throughput increasing as the team grew; Böckeler (Thoughtworks at QCon London) propagates the harness engineering name to the broader consultancy audience and adds the cross-client observer vantage; Thompson (NYT The Daily) is the journalist-observer field-report on 75 working developers — “feel like Steve Jobs picking from nine designs”. Operational invariants now explicit: repository-as-system-of-record, AGENTS.md as table-of-contents, layered architecture with mechanical enforcement, encoded golden principles + scheduled GC, application legibility for the agent.
- ai-agents — Software systems pursuing complex goals autonomously; chatbot → agent → multi-agent progression; deployed today in low-cost-of-error tasks. Practitioner framing as intern entities (Karpathy 2026) — remarkable but error-prone in surprising ways; humans must own aesthetics/judgment/taste/oversight.
- ai-benchmarks — Umbrella for standardized AI evaluations; covers MMLU, MMMU, GPQA, SWE-bench, HELM Safety, RE-Bench, METR task horizons, etc.
- ai-deskilling — Task-composition shift mechanism: jobs persist while higher-education-content tasks are AI-handled; most-affected named occupations include technical writers, travel agents, teachers.
- ai-employment-effects — Empirical record of AI’s effects on jobs, hiring, and wages. Headline: ~13% relative decline for early-career workers in AI-exposed occupations; +1.0–1.2 pp/yr aggregate productivity contribution (reliability-adjusted); equalizing effect among elite knowledge workers per Dell’Acqua et al. 2026.
- ai-knowledge-hiding — NEW CONCEPT PAGE (16 June 2026), 1 source. The deliberate withholding by employees of the AI workflows/prompts they discover — the “suppression of solutions” (vs the classic silence literature’s suppression of problems). Newly consequential because AI gains are individual, portable, and concealable. Driver is organizational trust via psychological safety, not governance/tooling (Anicich & Brouwers 2026: 30.3% withhold; 47% vs 14% by trust quartile; policy/tools alone predict nothing). Three disclosure costs (reputational / workload / replaceability); Edmondson’s exploratory-testing-vs-deviance misclassification.
instance-ofenterprise-ai-adoption;supportsmicro-productivity-trap. - ai-washing — NEW CONCEPT PAGE (26 June 2026), 2 sources. Companies citing AI as the reason for layoffs they’d have done anyway — narrative cover by analogy to greenwashing. Mechanism: a stock-market valuation premium (saying “we pivoted to AI” beats “we overhired” for shareholders), amplified by downturn peer pressure (BBC). Attribution near-unmeasurable (“inconclusive forever”); weak market penalty (firms often lack consumer discipline); the amplified narrative distorts career decisions while counter-data (Indeed software-dev postings up ~4×) is ignored. Converges with Sam Altman’s executive-altitude AI washing counter-framing (via Everitt).
part-ofai-employment-effects (the attribution-confound layer). - analogical-reasoning — Use of source-target analogies in strategy formulation; rhetorical and generative roles; Carroll-Sørensen tools for disciplined use; theory-based view connection.
- attack-surface-management — NEW CONCEPT PAGE (18 May 2026), 2 sources. The continuous discipline of discovering, cataloguing, and monitoring every externally observable asset of an organisation, so exposure is known before an attacker uses it. “You can’t protect what you can’t see.” ASM = defensive OSINT organised as continuous practice — the platforms attackers use for recon (Shodan / Censys / FOFA / ZoomEye / FullHunt / SecurityTrails / SpiderFoot) are the platforms defenders use for ASM; the asymmetry is who runs them first and how often. The 5-step OSINT workflow (discover infrastructure → enumerate domains & certs → analyse web tech → check identity exposure → correlate vulnerabilities) is the ASM workflow. Shadow IT named as the load-bearing failure mode — Khan’s 3-year-old public-GitHub-repo-with-internal-credentials is the canonical worked example. Five operational best-practices (continuous scanning / credential-leak monitoring / public-repo auditing / shadow-IT hunting / SIEM-SOC automation). Two sources: Khan 2026 (narrative one-off audit), TechLatest 2026 (platform catalogue + continuous-workflow framing).
- automation-vs-augmentation — A load-bearing distinction: does AI substitute for labor or complement it? Strategic, task-design, and labor-market consequences.
- document-intelligence — Extracting accurate, structured, grounded data from unstructured documents (PDFs, scans, forms, handwriting, tables). 2026 shift from rule-based OCR toward vision/multimodal transformers. Load-bearing claim: the OCR accuracy gap (generic 80–90% vs the “high 99.x%” an agentic pipeline needs) makes document accuracy a gate on enterprise-ai-adoption. Grounding (visual / page-level citations) is the trust primitive for regulated-industry use (responsible-ai). Two corpus approaches: vision-first extraction (LandingAI ADE) + verifiable multimodal RAG (Gemini File Search). Confidence 0.72 (vendor-tier cap; metrics uncorroborated).
- durable-skills — Human skills resistant to AI substitution: collaboration, creativity, critical thinking. Globerson et al. 2026 (Google Research) introduce the Vantage / Executive LLM platform — first scalable, ecologically-valid, psychometrically-controllable assessment methodology. The inverse measurement frame to ai-deskilling. Now corroborated from the hiring-criteria angle by Sternfels (McKinsey) who names four durable leadership skills models lack — aspiration / judgment / discontinuous-leap thinking / human-to-human skill — and describes McKinsey’s 20-year-self-analytics-driven hiring overhaul.
- dynamic-capabilities — Teece’s sense/seize/transform framework; nine digital microfoundations per Warner & Wäger; the substrate of which AI adoption is one current instantiation.
- enterprise-ai-adoption — Pace, depth, and pattern of org AI use; multi-lens framing (breadth / stage / readiness / capabilities / foresight / transformation / six-capability / org-design / task / firm-boundary); 88% adoption (AI Index 2026) but agent deployment in single digits per function; load-bearing concept anchoring the wiki’s organizational-frameworks synthesis.
- expert-generalist — NEW CONCEPT PAGE (25 June 2026), 3 sources. Joshi, Venkatraman & Fowler (martinfowler.com) name the Expert Generalist — a practitioner whose first-class skill is spanning specialties via tool-independent fundamentals and patterns. Six characteristics: curiosity, collaborativeness, customer-focus, favoring fundamental knowledge, a generalist+specialist blend, sympathy for related domains. Rejects “T-shaped” (generalists grow several legs). LLM thesis: an LLM is an on-tap specialist, so generalists who interrogate rather than accept output get more valuable. Cited and applied by AWS Enterprise Strategy in both editions of its “advanced team structures” keynote (Allen, Brovich).
supportsdurable-skills;contradictsai-deskilling;authored-byMartin Fowler. - foundation-models — Large pretrained models that serve as substrate; industry produced 90% of notable 2024 models.
- founder-led-sales — NEW CONCEPT PAGE (25 June 2026), 4 sources. The doctrine that an early-stage tech company’s founder is the irreplaceable sales engine — early customers buy the founder’s credibility (which doesn’t transfer to a hired seller), so the founder runs sales until the motion repeats. Four vantages: YC (first-10 tactics), HBR (250-founder study + SPRINT framework), Luminai (enterprise worked example), Campfire (“stay in founder-sales mode”). Sharpens under AI-market saturation: mistaking attention for traction.
supportsenterprise-ai-adoption (the seller-side mirror of the adoption gap). - generative-ai — Content-generating AI; >20% of all AI private investment ($33.9B in 2024); MS-DOS → GUI access democratization; substrate for agents; field-experimental evidence (Dell’Acqua et al. 2026) shows jagged-frontier capability profile.
- industrial-ai-agents — 2 sources. The application class of ai-agents deployed against industrial / OT environments (manufacturing, process industries, energy, logistics). Distinguished from the SaaS / coding-agent cluster by data-fabric primacy (semantic grounding of fragmented MES / CMMS / QMS / ERP / SCADA data is the dominant problem), action-precondition governance (preconditions / validation rules encoded in the data layer itself via ontology action types, not just runtime guardrails), and continuous real-time data dependency. Three-layer arch (Data Streaming + Data Intelligence + Agentic AI) per HiveMQ 2026; four ontology pillars (object types / properties / link types / action types); five mechanisms (unified operational awareness / semantic layer / compounding returns / closed-loop learning / governed autonomy). SurrealDB 2026 adds the second-vendor cross-domain anchor on KG-as-substrate at the general-purpose-developer vantage.
- industry-4-0 — German-government 2011 framing of cyber-physical-systems-driven manufacturing; the digital side of the Lean 4.0 synergy.
- infinite-game — NEW CONCEPT PAGE (18 May 2026), 1 source. Carse/Sinek’s frame for which game you are in — finite (fixed players, fixed rules, agreed endpoint) vs infinite (known-and-unknown players, changeable rules, no terminal state). Anchored on Sinek 2018; five-element checklist (Just Cause / Courageous Leadership / Trusting Teams / Worthy Rival / Existential Flexibility). Operates one layer above the strategy lenses — Martin asks how to win the round, Sinek asks which game? Cross-walk to nine wiki strategy lenses filed in strategy-finite-vs-infinite-game synthesis. Pending: Sinek 2019 book + Carse 1986 deferred-ingest (would lift confidence 0.65 → ~0.75).
- jagged-frontier — Boundary of current AI capability; uneven and invisible; introduced by Dell’Acqua et al. 2026; per-task fit determines whether AI augments or harms performance. Cause-of-jaggedness mechanism added by Karpathy 2026: LLMs automate what you can verify; verifiability + labs care explain why models fly in some circuits and struggle in others (canonical 2026 example: Opus 4.7 refactors 100k-line codebases yet tells you to walk to a 50m-away car wash). Includes animals vs ghosts mindset framing.
- knowledge-graphs — NEW CONCEPT PAGE (12 May 2026), 4 sources. Heterogeneous graphs with typed nodes (entities) and typed edges (relationships) as deterministic, queryable, updatable substrates for AI-agent grounding. Four vantages: academic-foundation (Leskovec 2023 — KG-completion task + shallow-embedding methods TransE/TransR/DistMult/ComplEx + relation-pattern taxonomy + FreeBase scale); industrial-OT (Manditereza 2026 — three-tier semantic data layer); agentic-memory (Bratanic 2026 — linked-list session graph + dream-phase distillation); practical-engineering ( Martin 2026 — full KG-ETL pipeline + GraphRAG + SurrealQL hybrid-query). Decision criteria for KG vs vector-only RAG: manual-driven Q&A → vector OK; decisions / explainability / dynamic-knowledge → KG required. Chunking-strategy taxonomy (recursive / structural / semantic / agentic / no-chunking). LLM-driven entity-extraction inverts the KG-construction-cost curve by ~1-2 orders of magnitude vs pre-GenAI hand-curation.
- lean-4-0 — The integration of Lean Manufacturing principles with Industry 4.0 technologies; 23 × 23 tool-mapping.
- llm-wiki — 8 sources (concept page since 12 May 2026; confidence 0.93). Karpathy’s LLM Wiki pattern (GitHub gist, 4 April 2026; 17M views, 5K stars, 4.3K forks in days) — the foundational architecture this repo implements. Three-layer architecture (raw sources / wiki / schema=CLAUDE.md) with explicit ownership semantics (LLM owns the wiki layer). Three operations (ingest / query / lint) matching this repo’s own CLAUDE.md §“The four operations” exactly (minus v0.3 synthesize). Compounding-not-evaporation principle: synthesis happens once at ingest time, not on every query. A single ingest typically touches 10-15 pages. Eight wiki sources span the full spectrum: Karpathy’s upstream Sequoia interview + the explainer/comparison cluster (Raju / Liu) + implementations/extensions ( ex-brain / Mysore WikiZZ / Nodus Labs InfraNodus) + the interoperability-standardization vantage Google Cloud — the first major-vendor open spec (OKF v0.1) formalizing the pattern; partially resolves the enterprise-implementations open question. Three limitations honestly named (Raju): scale ceiling at ~100 pages without retrieval substrate; hallucination baking (“organized, persistent mistakes are harder to spot”); ingest cost. Convergence prediction: 2023 RAG era → 2025 Wiki + Skills emerge → 2026+ hybrid. Claude Code already partial-implements the convergence (CLAUDE.md = mini-wiki / auto-memory = compounding / skills = action).
- micro-productivity-trap — Failure pattern in enterprise AI deployment: task-level gains failing to translate to firm-level results. Two lock-ins (offering, process); escape via four-step transformation framework; 10–25% Bain client EBITDA gains. Two consulting-firm vantages now converge on the same diagnosis (Dutt-Chatterji 2026 from Bain; Sternfels 2026 from McKinsey: “half-or-more of the secret sauce is organizational change”) — structural evidence the trap is real, not a single-firm framing. Storoni 2026 adds the neuroscience-mechanism vantage — gear-3 reactive work as the individual-level cognitive failure that aggregates to the firm-level trap pattern; six stack layers, eleven vantages now corroborate.
- osint — NEW CONCEPT PAGE (18 May 2026), 2 sources. Open-source intelligence — disciplined gathering and analysis of publicly available information about a target (people, organisations, infrastructure, code) using only legal channels. Same techniques used by attackers and defenders; difference is intent and authorization. Hacker search engines (Shodan, Censys etc.) index open ports, IoT, databases, cloud buckets, certificates, vulnerabilities, leaked credentials — fundamentally different from Google’s webpage index. 5-category platform taxonomy + 5-step recon workflow per TechLatest 2026; 7-vector narrative walkthrough of a real one-off audit per Khan 2026 (developer comments, EXIF, LinkedIn+password-reset enumeration, 3-year-old public GitHub credential leak, job-posting tech-stack disclosure, badges-in-photos, Google dorks). Unifying mechanism: organisations don’t audit what they themselves publish. Bridge into existing wiki agent thread via TechLatest’s AI-Augmented Offensive & Defensive Security category (LLMs + AI agents correlating OSINT sources, generating attack graphs, automating recon) — currently vendor-narrative depth.
- react-reasoning-acting — NEW CONCEPT PAGE (12 June 2026), 3 sources. ReAct (Reason + Act) — the Shunyu Yao et al. (2022, ICLR 2023) paradigm interleaving free-form reasoning traces with tool actions in one LLM trajectory; the genealogical root of the wiki’s agent/harness cluster. The “LLM that uses tools in a loop” definition the wiki adopts from 2026 practitioners is ReAct’s reason → act → observe cycle, named four years earlier. Augments action space
Awith language spaceL(thoughts that update context without touching the environment); two regimes (dense thoughts for QA, sparse for decision-making); frozen PaLM-540B, 1–6 shot. Headline empirics: HotpotQA+FEVER beat act-only and reduce CoT hallucination (best = ReAct+CoT-SC); ALFWorld +34% / WebShop +10% over RL/IL trained on 10³–10⁵ instances with just 1–2 shots; legible thoughts enable human-trace-editing correction. Three sources: paper + Google Research blog (same work, two registers) + ADK tutorial (independent 2026 operationalization). “ReAct named the loop; the harness owns the reliability.” - responsible-ai — Risk-management discipline for AI; incidents +56.4% YoY; AI security as a discipline; Stewardship as one of the Four S pillars; labor-market disruption as an under-attended concern.
- software-3.0 — Karpathy’s third paradigm of software (1.0 = explicit rules / code; 2.0 = learned weights / datasets; 3.0 = prompts / context windows). The LLM is a programmable computer; the context window is the program; the LLM is the interpreter. Load-bearing claim: “new things are now possible, not just speed-ups of what existed” — entire glue-code app classes are now spurious (Menu Gen as worked example). Architecturally upstream of agent-harness (a Software 3.0 program needs a runtime; that runtime is the harness).
- strategic-centering — NEW CONCEPT PAGE (16 June 2026), 1 source. McGrath’s organizing-principle lens for strategy in a dematerializing economy (~90% of value intangible; Porter / RBV / Blue Ocean exhausted). Choose one center (“What are we really about?”) that bounds the opportunity set, resolves capital-allocation dilemmas, and enables “permissionless action.” Five centers: mission / customer / technology / national ecosystem / friction erasure.
part-ofstrategy;supportsdynamic-capabilities. - strategic-foresight — Disciplined identification of where to play / how to win in the future; FTSG’s 10-step methodology; STREEEP+W uncertainty taxonomy; convergence as a 2026 reframing.
- strategy — NEW CONCEPT PAGE (18 May 2026), 3 sources. The discipline of making integrative choices that position an organisation to win on a chosen playing field by creating value. Two complementary foundational lenses: Martin (HBR 2022) — strategy = integrative set of choices with a theory of winning; planning ≠ strategy because planning is on the cost-side (you control it), strategy is on the customer-side (customers decide). Oberholzer-Gee (HBR 2022) — strategy = a plan to create value, measured by the value stick (WTP − WTS). The two are complementary: Martin tells you where and how to win; Oberholzer-Gee tells you what you’re optimising. Worked cases: Southwest Airlines (Greyhound-substitute theory + 5 cost-aligned choices → most passenger-seat-miles in America) and Best Buy turnaround ($1B loss → 20%+ ROIC via stores-as-warehouses + store-in-a-store). Critical value-stick distinction: pay-more redistributes value; make-the-job-better creates value. Third source Carrier 2026 supplies the 2026 industrial-AI restatement — “winners determined not by who has access to the technology, but whose organization adopts it faster” + the Heineken Mexico worked case (6h→15min changeover, ~1M extra cases/month). Three escape practices for the planning trap: accept the angst, lay out the logic (“what would have to be true”), keep it to one page.
- systems-thinking — Innovation mode that traces flows, relationships, feedback loops; complement to breakthrough and design thinking; four-principle approach for wicked problems (North Star / reframe / flows / nudge).
- theory-based-view — NEW CONCEPT PAGE (18 May 2026), 1 source. Felin & Zenger’s (2009, 2017) framing of strategy formulation as construction, communication, and progressive validation of a firm-specific falsifiable theory of value. Four canonical questions (theory of value / novel-simple-elegant / falsifiable-generalizable-generative / who-must-you-convince). The falsifiability discipline applied to [[concepts/strategy|Martin’s theory of winning]] and [[infinite-game|Sinek’s Just Cause]] — same construct at greater logical hygiene. Currently anchored second-hand via Carroll & Sørensen 2024; Felin & Zenger primary sources deferred-ingest (Phase B4).
- vibe-coding — The intuitive, exploratory mode of writing code with an AI agent. Coined by Andrej Karpathy in 2024; widely adopted as practitioner vocabulary through 2025–26. Raises the floor (everyone can vibe code anything) — the democratisation half of software-3.0; the professional-quality-bar half is agentic-engineering. The December 2025 phase change (Karpathy 2026): vibe coding tipped from “useful but needs corrections” → “I just trust it now” in a single month for someone at Karpathy’s exposure level. Now anchored at both engineer (Karpathy) and PM (Nika 2025) ends — the latter contemporary primary-source evidence of the trustworthy-tool-stack practitioner workflow inside the December 2025 window. The stakeholder-influence-prototype use case (PRD → v0 → product review) is the canonical non-engineer professional application of vibe coding.
- warner-wager-process-model — NEW CONCEPT PAGE (14 May 2026), 1 source. The process-model elaboration of transform specialised for digital transformation. Doubles as the wiki vocabulary spec for the v0.4-era
dynamic_capabilities:source-page tag (GH #4) — fifteen closed slugs across nine W&W microfoundations, three strategic-renewal outcomes, and three contextual factors. Carries therole_defaults:matrix mapping each cell to 2–4 of fifteen org-chart roles (nine C-suite + six functional), enabling role-scoped corpus navigation without per-source role tags. Single source of truth;scripts/lint-page.mjsreferences it for vocabulary validation; CLAUDE.md §Dynamic-capabilities tagging documents the contract.
Syntheses
- is-rag-dead — NEW synthesis filed 2026-05-12, 5 sources. Closes the thread of the same name. RAG is not dead. The term is being retired in favour of context engineering; the technique persists as a substrate primitive. Three independent sources (Raju / Liu / Huber) plus two substrate-implementation worked examples (OceanBase ex-brain / SurrealDB GraphRAG) converge on complement, not replace. Names the three context-failure axes (too much / too little / confusing), maps Liu’s three named failure points (chunking / re-derivation / passivity) and SurrealDB’s three vector-only failure modes (context clash / context confusion / dense neighbourhood) onto the unified taxonomy. Bitter-lesson direction: context engineering will be folded back into the models themselves (Huber + Karpathy-compatible). Open: the Cherny-vs-Huber agentic-search-vs-RAG empirical disagreement.
- knowledge-architectures-for-llm-agents — NEW synthesis filed 2026-05-12, 7 sources. Closes the thread of the same name. The three-architecture decision framework: RAG (retrieve, scale 200K+ docs, low setup, search at scale) / LLM Wiki (compile, ~1K sources, medium setup, deep expertise) / Fat Skills (act, 17K+ pages, high setup, autonomous ops). No single architecture wins everywhere; choice depends on agent’s job. Hybrid production reality: RAG at retrieval substrate + LLM Wiki at synthesis layer + Fat Skills at action layer. Claude Code as the partial-convergence example: CLAUDE.md = mini-wiki + auto-memory = compounding + skills = action. Liu’s convergence prediction: 2023 RAG era → 2025 Wiki + Skills emerge → 2026+ hybrid. Per-architecture failure-mode summaries; operational-instantiation table; concrete Claude-Code mapping.
- organizational-frameworks-for-ai-adoption — Closes the thread of the same name. Six frameworks (MIT CISR Four Stages + Four S; Anand-Wu 2×2 + leakage points; Cisco 5 Foundations; Werner-Le-Brun Octopus/Tin Man; McKinsey Rewired 6 capabilities; Bain/OpenAI 4-step transformation) mapped to seven decision layers (org-design / readiness / capability progression / transformation playbook / trap escape / task deployment / diagnostic), with a decision tree and an empirical-validation gap analysis.
- strategy-finite-vs-infinite-game — NEW synthesis filed 2026-05-18, 7 sources. Closes the thread of the same name. Resolves the Sinek/Martin tension flagged across strategy, Sinek 2018, and the
/wqanswer thread. Headline finding: Sinek operates one layer above the strategy lenses — he asks which game you are in, Martin asks how to win the round you are in. The apparent contradiction is a layered difference, not a supersession. Three-altitude strategy stack: game-frame (Sinek/infinite-game) → theory-of-winning (Martin / theory-based-view as falsifiability discipline) → value-creation-instrument (Oberholzer-Gee value stick). Nine-lens cross-walk: eight of nine lenses align with the infinite-game frame once the layered structure is named; only the Martin-on-Southwest residual remains. Three lessons: layered-not-competing / time-horizon+optimisation+falsifiability stack / foresight-and-infinite-game are vocabulary cousins. Open: Sinek 2019 book + Carse 1986 + empirical anchoring all deferred.
Threads
- ai-maturity-measurement-comparison — open. How do AI Index, MIT CISR, Cisco, Werner-Le-Brun, and now Brynjolfsson all measure “AI’s organizational impact” differently? The 1% / 7% / 12% / 13% spread on a single order of magnitude, plus employment-effects as a third measurement dimension.
- is-rag-dead — opened retroactively 2026-04-09; closed 2026-05-12; synthesized into the synthesis page. Tracks the “is RAG dead?” question from Karpathy’s 4 April 2026 LLM Wiki gist onward. Closed when 5 sources converged on complement-not-replace.
- knowledge-architectures-for-llm-agents — opened retroactively 2026-04-27; closed 2026-05-12; synthesized into the synthesis page. Tracks the broader three-architecture decision framework (RAG / LLM Wiki / Fat Skills). Closed when 7 sources converged on the hybrid-convergence prediction.
- organizational-frameworks-for-ai-adoption — closed 2026-05-05; synthesized into the synthesis page. Original thread retained for history.
- strategy-finite-vs-infinite-game — opened and closed 2026-05-18 in the same session (Phase A4 of strategy-gap remediation); synthesized into the synthesis page. The Sinek/Martin tension cross-walk material was already assembled in the prior
/wqanswer — the thread is the schema-compliant antecedent, not a substantive investigation.