Redefining What Efficiency Means in the Age of AI

Storoni — Redefining What Efficiency Means in the Age of AI

What is the real definition of efficiency in a world powered by AI? What if it was quality, not quantity? Neuroscientist Mithu Storoni researches how our brains are most creative and truly productive at knowledge work. She offers techniques to be most effective at work that matters, including how brains tackle different kinds of work, how to schedule the day to align with optimal states of mind, and the implications for leaders and organizations. — HBR IdeaCast episode description

A ~32-minute episode of HBR’s IdeaCast podcast (audio-first; not on YouTube). Host Curt Nickisch (HBR senior editor) interviews Mithu Storoni — neuroscientist, physician, and author of Hyperefficient: Optimize Your Brain to Transform the Way You Work — about a definitional reframe of efficiency for the AI era. The 2026-05-13 release is a re-cut of Storoni’s earlier 2024-09 IdeaCast appearance (Training Your Brain to Work More Effectively) repositioned around the AI-era quality-over-quantity argument.

This is the wiki’s second HBR IdeaCast source (after Sternfels 2026) and the first source from a neuroscience vantage on AI-era work design. It supplies a physiological-mechanism layer to claims the wiki has so far only seen from leadership-practice (Reitz & Higgins), consulting-engagement (Sternfels, Bain/OpenAI), and labour-economics (Brynjolfsson) vantages.

TL;DR

• Definitional reframe of efficiency: quantity-per-unit-time (assembly-line era) → quality-per-unit-time (AI era). “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 — AI does the quantity, humans do the quality.

• The three-gear framework (Storoni’s central neuroscience metaphor), governed by norepinephrine on an inverted-U curve:

  Gear	Norepinephrine	State	When	What it does well
  Gear 1	Low	Slow, hazy, mind-wandering, attention loose	Wake-up window, pre-sleep, breaks, walks	Creativity, idea hatching, problem incubation
  Gear 2	Goldilocks zone	Sustained focus, alertness, judgment, nuance-detection	~10am–lunch and late afternoon (typical)	High-quality cognitive work, second/third-order thinking, learning under uncertainty
  Gear 3	High	Reactive, distractible, speed-up + accuracy-down, prefrontal cortex disengages	Slack/email pressure, deadline panic, emergencies	Fast simple maneuvers; misses nuance and 2nd/3rd-order consequences

• Gear 3 is the neurological mechanism for the micro-productivity-trap: workers in constant-reaction mode (replying to Slack and email as messages arrive) feel productive but produce low-value work. “It’s a trade-off between speed and accuracy. Your speed becomes faster, but your accuracy goes down. … You will make mistakes, errors, and you will miss nuances.”

• Body-brain co-regulation: walking is the canonical hack — keeps attention moving (no rumination, no fixation), aligns physiology, prevents drift into lethargy. The named worked example: a managing director with the rule “10 minutes stuck at the desk → leave for a walk.” The analogy: “there is a reason why we can’t daydream while we sprint. … You can daydream while you run slowly for long distances, but you can’t daydream while you sprint.” Desk-bound mental sprinting is the equivalent physiological mismatch.

• Time-of-day cognitive peaks (the chronobiology argument):

  • Creativity peak: just after waking, before coffee/work — the slow-thinking mind-wandering window. Lost by anyone starting work at 9:30–10am.
  • Second creativity peak: late evening, after most people have stopped work. Lost by anyone working a conventional 9-to-5.
  • Focus peak: ~10am to lunch, with a post-lunch dip and a late-afternoon revival into early evening.
  • Implication: the uniform 9-to-5 schedule destroys both creativity windows by design. “Instead of imposing the same or using 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.”

• Learning under uncertainty is the critical AI-era durable skill (durable-skills): the sudden burst of norepinephrine when you encounter an unpredictable situation also primes neural plasticity for learning. The trick is staying at the top edge of gear 2 (slightly apprehensive, jittery, but self-controlled) without tipping into gear-3 panic. “That is the state that, if we can train ourselves to embrace it, will last us and take us far through this era of rapid change.”

• Counter-intuitive boredom fix: bored workers (especially in AI-oversight / supervisory work) get more engaged when work is made harder — multitasking adds channels, feedback adds engagement. Named case: an air-traffic-control simulation study where a mouse-click-on-new-data-appearing requirement measurably reduced boredom and improved engagement. This runs counter to conventional anti-multitasking advice.

• Three org-level prescriptions for managers:

  1. Flexible schedules tailored to work type. Creativity teams come in early (protect the wake-up creativity window), focus teams come in later (protect the 10am-to-lunch focus window). Routine meetings cluster post-lunch (the trough between creativity and focus). Brainstorming meetings cluster in creativity windows.
  2. Protect peak windows from meetings — meetings during the creativity or focus peak destroy the peak.
  3. 20%-time / learning-progress-mechanism — Google and 3M cited as named exemplars. Give workers protected time to pursue something they are curious about, relevant to the organisation but without guaranteed outcome. Generates intrinsic motivation (“learning progress mechanism” is the neuroscience term Storoni uses). Especially important in the AI era because the old promises (job guarantees, career progression, financial-incentive certainty) have become fluid.

What was actually ingested

Full episode transcript (user-supplied, ~4,500 words, 27 speaker turns). HBR does not routinely publish full transcripts on its episode pages, so this is the user’s faithful transcription rather than an officially-published one — accuracy treated as high but not certified. Audio duration not surfaced on the HBR episode page; the 30–35 min estimate is a word-count derivation.

The 2026-05-13 cut is a re-release of Storoni’s 2024-09 IdeaCast appearance (Training Your Brain to Work More Effectively) reframed around the AI-era quality-over-quantity argument. The earlier episode is not ingested; if/when ingested it would be a paired source to this one.

Key claims (expanded)

1. The definitional reframe of efficiency

Storoni opens with a sharp historical claim: our current operational definition of efficiency descends from the assembly-line era, where quantity per unit time was the measurable output of a worker. In the AI era, quantity is being taken over by AI and technology, so the productivity contribution of humans is no longer proportional to the quantity of their output. Humans contribute through the quality of their output — the ideas, the judgment, the second- and third-order thinking that AI does not (yet) reliably produce.

The argument is prescriptive, not descriptive — Storoni is not claiming organisations already measure efficiency this way; she is claiming they should. The descriptive baseline she names is the opposite: most knowledge workers are still measured (and reward themselves) on assembly-line metrics like emails replied to, messages responded to, presentations completed. This descriptive baseline is what produces the gear-3 reactive-work pathology she diagnoses later.

The “we are all becoming AI managers” framing (“we now have at our disposal this entire armory of tools, of methods … and so we now all need to really think like senior executives, like managers, no matter where we are in this hierarchy”) is the corollary: if AI handles the tool-use layer, humans must operate at the management/judgment layer — and the management/judgment layer requires gear-2 cognitive states.

2. The gear framework as load-bearing metaphor

Storoni’s gear framework is a metaphor on top of a real and long-established neuroscience claim: norepinephrine (a neurotransmitter/neuromodulator) influences cognitive performance on an inverted-U curve — too little and you’re underaroused, too much and you’re overaroused, and there is a Goldilocks zone in the middle (gear 2) where alertness and attention are optimal. “We’ve known for the best part of a century now” — i.e. this is the Yerkes-Dodson law territory (Storoni does not name it but the curve she describes is exactly that).

The framework’s contribution is not the inverted-U claim itself (which is textbook) but its operationalisation for modern knowledge work:

• Gear 3 is easy to drift into in modern knowledge work because Slack, email, and deadline-driven workflows continuously inject low-grade urgency that shifts norepinephrine rightward on the curve. The result is a state that feels productive (fast thoughts, fast typing, fast responses) but is systematically biased toward speed-over-accuracy. The prefrontal cortex (judgment, planning, nuance) is the part of the brain that cannot fully engage in this state.
• Gear 2 requires active protection — it does not naturally arise in a Slack-saturated workday. Without explicit design (protected windows, walking breaks, schedule-by-work-type), the modal state of a modern knowledge worker drifts toward gear 3.
• Gear 1 is undervalued — the slow mind-wandering state in the morning, while walking, in the shower, mowing the lawn — is where creative ideas hatch. The argument is that creative ideas don’t come during focused work; they come during gear-1 attention-loose states triggered by gear-2 problem-engagement earlier. Squeezing out gear-1 (by working the moment you wake up, by skipping walks, by filling the slow windows with meetings) doesn’t accelerate creative output — it suppresses it.

3. The mind-is-not-like-muscle principle

A quotable opening on the gears section: “The mind is not like muscle. It rests while it works, and it works while it rests.” This is Storoni’s compact statement of the principle behind the framework — sustained focus is not the same as sustained gear-2; the brain naturally oscillates between focused and wandering states during deep cognitive work, and the wandering states are part of the work (not a failure of discipline). Forcing continuous gear-2 (the modern open-plan-office, always-on-Slack ideal) is neurobiologically unsustainable and produces the gear-3 collapse pattern.

This is the load-bearing claim behind the “protect mind-wandering windows” prescription that follows.

4. The body-brain physiology argument

Storoni makes a strong claim about physical body state determining cognitive state — not the weaker “exercise is good for the brain” framing, but a specific mechanistic claim about how the kind of movement you do shapes the kind of cognitive state available to you.

• Walking is the canonical hack because it produces a very specific physiological mismatch that maps to the gear-2/gear-1 boundary: you are alert enough not to fall asleep (no gear-1 collapse), your attention is moving with your surroundings (no fixation, no rumination), and you have to pay enough attention to where you’re walking that you can’t lock onto a single problem too long. Net effect: attention drifts into your head and explores problems from multiple avenues simultaneously.
• Sprinting is the opposite — physiological arousal so high that mind-wandering is impossible. “You can daydream while you run slowly for long distances, but you can’t daydream while you sprint.”
• Desk-bound focused work is the mental equivalent of sprinting — the body is in a low-arousal state (sitting still) while the mind is in a high-arousal state (cognitive sprint). Storoni claims this body-brain physiological mismatch is a systematic source of cognitive degradation.

The implication: physical posture and movement are not separate from cognitive performance — they are inputs to it. This is a much stronger claim than “take breaks for your health”; it is “your body-brain coupling determines which gear is available to you.”

5. The chronobiology argument

Storoni names two creativity peaks and one focus peak in the typical daily cycle. The exact times are typical-not-universal — chronotype (morning person vs night person) shifts the windows earlier or later — but the pattern is general.

• Creativity peak 1: just after waking, before caffeine and full alertness, in the transition zone between sleep and full focus. The window is short (one to two hours) and is destroyed by any activity that pushes alertness to full gear-2 too quickly — coffee, a run, immediately checking email and entering reactive mode.
• Focus peak 1: ~10am to lunchtime. The prime hours for deep work, problem-solving, learning.
• Focus peak 2: late afternoon into early evening. Lower-amplitude than the morning focus peak.
• Creativity peak 2: late evening, after most people have stopped work. Same gear-1-flavoured mind-wandering quality as the morning peak.

The organisational implication is sharp: the uniform 9-to-5 schedule is structurally hostile to creativity work — it begins after the morning creativity window has closed and ends before the evening one opens. Workers in creativity-dependent roles who work a conventional schedule are categorically denied access to their two peak windows.

The proposed fix is time-of-day-aware team scheduling: let creativity teams come in early (protect the morning window), let focus teams come in later (protect the focus peak from morning meetings), cluster routine meetings post-lunch (the trough between creativity and focus), schedule brainstorming meetings in creativity windows.

6. Learning under uncertainty as the AI-era durable skill

This section is where the source connects most directly to the durable-skills concept page. Storoni’s claim: when you encounter a situation more uncertain than you want it to be, your brain steps on the accelerator — releasing norepinephrine to drive faster information processing. The subjective experience is tension and apprehension. The neurological function is priming neural plasticity for learning.

This means: the discomfort of facing an AI-era re-skilling demand is itself the neurological condition that enables the re-skilling to succeed. Workers who flee the discomfort (by avoiding learning, or by tipping into gear-3 panic that destroys learning capacity) lose the opportunity to use that priming. Workers who can stay at the top edge of gear-2 — apprehensive but self-controlled — get the maximum learning benefit.

Storoni names this self-regulation skill as the critical AI-era durable skill: “learning how to control yourself, regulate your brain from tipping into gear three … that is the state that, if we can train ourselves to embrace it, will last us and take us far through this era of rapid change.”

This is a meta-skill on top of the conventional durable-skills list (collaboration, creativity, critical thinking — see durable-skills). It is the regulation skill that determines whether the conventional durable skills can be exercised under AI-era uncertainty pressure.

7. The counter-intuitive boredom fix

A surprising claim: workers who are bored (especially in AI-oversight / supervisory work where the AI does most of the work and the human watches) get more engaged — not less — when the work is made harder. Multitasking adds channels of information; adding feedback to passive monitoring adds engagement.

The named worked example: an air-traffic-control simulation study (Storoni dates it “some decades ago” but does not cite specifically) in which simulated controllers were required to click a mouse every time new data appeared. The trivial action requirement measurably reduced boredom and improved engagement compared to passive monitoring.

The forward-looking implication: as AI automates more execution and humans shift toward oversight, the boredom problem will become a load-bearing UX/HR challenge. Conventional advice (reduce multitasking, simplify) is exactly wrong for this work pattern; the fix is to re-introduce action requirements and feedback loops.

This connects to a ai-deskilling-adjacent concern that the wiki has not deeply explored: pure-oversight work both atrophies skills (deskilling) and produces boredom (engagement collapse). Storoni’s fix addresses the engagement side; the skill-atrophy side remains an open question.

8. The 20%-time / learning-progress-mechanism prescription

Storoni’s third organisational prescription names Google and 3M as exemplars of giving workers protected time to pursue something relevant to the organisation but without guaranteed outcome. The neuroscience term she uses is learning progress mechanism — the brain registers progress in a self-chosen pursuit as intrinsic reward, generating motivation and pleasure regardless of external incentive structures.

The argument’s AI-era specificity: the old motivational scaffolding has weakened — job guarantees, career-progression certainty, even financial-incentive predictability have all become fluid in the AI era. If organisations rely on those external scaffolds to keep workers engaged, engagement collapses as the scaffolds collapse. The replacement is internal motivation built through the learning-progress mechanism. Workers who can derive pleasure from the process of learning something they chose stay engaged even when the external rewards become uncertain.

This is not an argument for unstructured slack time. The 20% must be relevant to the organisation and pursued with enough structure to enable progress-detection. It is a structured-autonomy prescription, not a free-time prescription.

Why this matters for the wiki

Three under-served gaps Storoni fills:

1. Physiological mechanism for the micro-productivity-trap. The wiki has the trap from seven other vantages (consulting, investor + academic, executive coaching, academic survey + practitioner column, runtime engineering, target-firm CEO, NYT journalism) — but it has not had a neuroscience mechanism. Storoni’s gear-3 framing is that mechanism at the individual level: the trap’s organisational pattern (high throughput, low value) has a brain-level analogue (reactive gear-3 work, low prefrontal-cortex engagement, missed nuance). The two scales reinforce each other — organisational pathologies persist because they emerge from individual neurology that the work design actively reinforces.

2. Normative voice on the augmentation pole of automation-vs-augmentation. The wiki’s automation-vs-augmentation concept is dominated by descriptive sources — Brynjolfsson on labour-market data, Anthropic Economic Index on conversation patterns, MITTRI/Cisco on deployment patterns. Storoni is prescriptive: she does not just describe how augmentation should work; she names what humans should be doing when AI takes the quantity layer (gear-2 quality work, gear-1 creative incubation), and what work design enables them to do it (protected windows, flexible schedules, intrinsic-motivation structures). This is a missing voice in the existing cluster.

3. Meta-skill layer above durable-skills. The durable-skills concept has converged on collaboration / creativity / critical thinking as the canonical cluster. Storoni adds a regulation meta-skill — the ability to stay at the top edge of gear 2 under AI-era uncertainty pressure — that determines whether the conventional durable skills can be exercised at all. This is not a fourth durable skill; it is the operating condition under which the other three become usable.

Cross-source positioning

• Reitz & Higgins (spacious thinking, HBR 2025) — direct phenomenon overlap. Reitz/Higgins’s spacious mode maps to Storoni’s gear 1 plus the top of gear 2; their doing mode maps to Storoni’s gear 2 driven hard, drifting into gear 3. The vocabularies differ (attention modes vs cognitive gears) but the organisational diagnosis is identical: modern work over-rewards the reactive/task-focused state and systematically suppresses the receptive/wandering state that produces strategic thinking, insight, and creative work. Storoni’s contribution beyond Reitz/Higgins: the neuroscience mechanism (norepinephrine inverted-U) and the physiological prescription (walking, time-of-day, body-brain coupling). Reitz/Higgins’s contribution beyond Storoni: the leadership-behaviour layer (how managers unknowingly suppress spacious mode, the advantage blindness phenomenon, the three enabling behaviours). Two-source convergence from neuroscience and management-research vantages — strong cross-source agreement on the underlying claim.
• McKinsey) — convergent on the organisational change > technology implementation argument, from a different stack layer. Sternfels’s “half, if not more, of the secret sauce is organizational change as opposed to technology implementation” is a consulting-engagement evidence statement; Storoni names the neuroscience mechanism for why the organisational change is load-bearing. The two together form an evidence + mechanism pair. Also a venue cluster — both HBR IdeaCast, with Adi Ignatius hosting Sternfels and Curt Nickisch hosting Storoni; the wiki’s first observable IdeaCast-as-vantage cluster on AI-era work redesign.
• OpenAI HBR.org) — supplies the firm-level diagnosis of the micro-productivity-trap (task gains, no firm-level value, offering lock-in and process lock-in). Storoni supplies the individual-level cognitive mechanism for why workers in trap-organisations produce the throughput pattern they do. Same trap, different stack layer.
• Brynjolfsson et al. 2025 (Canaries in the Coal Mine) — descriptive labour-market data showing augmentation use does not displace, automation use does. Storoni is the normative organisational-design counterpart — what work design enables augmentation-pole human contribution to actually be high-value (and therefore retained). Together they form descriptive-floor + prescriptive-ceiling on the augmentation argument.
• Lenny’s Podcast) — two-cell W&W overlap (redesigning-internal-structures + strategic-renewal/organizational-culture) plus direct phenomenon overlap. Schoening’s agency-as-durable-skill thesis (curiosity, ownership, taste as the AI-era moat) is the personality/cultural counterpart to Storoni’s self-regulation-under-uncertainty meta-skill. Where Schoening names what makes individual contributors valuable in the augmentation era, Storoni names the cognitive states they need to be in to deliver that value. Two-vantage convergence (product-leader vs neuroscientist) on the same underlying claim: the AI-era human contribution is agentic, self-regulating, top-of-gear-2 knowledge work, and the org’s job is to design work-environments that make it possible.
• Developer Intelligence team) — the engineering-team correlate of Storoni’s individual-cognition critique. The DORA programme has empirically observed that “increasing AI adoption can lead to individual productivity benefits while at the same time decreasing team-level benefits” — the same divergence pattern Storoni names at the individual-vs-firm level (gear-3 reactive workers feel productive, the firm’s value output collapses) operating at the engineering-team scale. Forsgren & Macvean’s AI is an amplifier and a mirror framing is the convergent claim from the other vantage: AI doesn’t create dysfunction, it exposes and accelerates whatever dysfunction is present in the existing work design — gear-3 work design amplified by AI produces more, faster, lower-quality output. The two sources together cover three nested scales of the same phenomenon (individual neurology → engineering team → firm).

Dynamic-capabilities tagging (W&W)

Per the Warner & Wäger process model vocabulary, Storoni’s organisational prescriptions touch:

• digital-transforming/redesigning-internal-structures — flexible-schedule prescription, time-of-day-aware team design, and the “design team-based structures” activity bullet that W&W list under this microfoundation. Storoni’s prescription is what this microfoundation looks like when applied to neuroscience-of-knowledge-work principles.
• strategic-renewal/organizational-culture — the 20%-time / learning-progress-mechanism prescription is exactly W&W’s “refreshing legacy cultures toward digital-mindset, entrepreneurial, fast-and-flexible cultures; embedding new shared values across the workforce.” Google and 3M are Storoni’s named exemplars.
• contextual/internal-enablers — Storoni’s three managerial prescriptions (flexible schedules, peak-window protection, intrinsic-motivation structures) are operational instantiations of W&W’s “cross-functional teams; fast decision making; executive support” enablers, specialised for the neuroscience-of-attention layer.

Inherited roles per the W&W role-defaults matrix: ceo, cdo, chro, transformation-lead. This source is squarely CHRO/CEO territory — work-design for cognitive performance is an HR-led, top-sponsored decision.

Methodology and source-quality notes

• Format: HBR IdeaCast podcast, audio-first, distributed via HBR.org + Apple Podcasts + Spotify. Transcript is user-supplied (HBR does not routinely publish transcripts); treated as faithful but not officially certified.
• Speaker: Mithu Storoni — neuroscientist (Cambridge-trained), physician, author. The book Hyperefficient: Optimize Your Brain to Transform the Way You Work is the underlying corpus; the podcast is a popularisation interview, not a primary research output.
• Evidence weight: Storoni references neuroscience research at framework level (norepinephrine inverted-U, prefrontal cortex disengagement, learning-progress mechanism) but cites no specific studies in the interview format. The framework is the integration claim; the underlying neuroscience pieces are real and well-established but the integration into a gear metaphor is Storoni’s synthesis. Treat the framework as a useful and broadly-grounded model, not as a single-study empirical finding.
• Source-quality flag: this is a prescriptive popularisation by an author promoting a book. The strongest empirical anchors (norepinephrine inverted-U, prefrontal cortex during reactive states, mind-wandering and incubation effects in creativity research) are well-grounded; the chronobiology specifics (exact time-of-day windows) are typical-pattern claims, not universal — chronotype variation is real. The 20%-time / Google / 3M exemplars are reported in their conventional popular form; the actual organisational implementation is more nuanced than the popular framing suggests.
• Per the wiki’s Lifecycle confidence rules: single popularisation source by an author promoting their book. Should not lift dependent concept-page confidence above the prior baseline on its own — its contribution is mechanism articulation and prescription, not new empirical evidence. Where it appears in concept-page citation panels, it appears as a mechanism or prescriptive voice, not as an empirical anchor.

Quotes worth saving

“Our idea of efficiency really stems from the era of assembly line work, where the more products you assembled on an assembly line, the better your output was. … But right now, we are going through a period of tremendous change in AI and in technology, and 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.”

“We have spent a very large part of the Knowledge Work Era being the tools that we are now using. Because of this, we have now effectively all become AI managers.”

“The mind is not like muscle. It rests while it works, and it works while it rests.”

“There is a reason why we can’t daydream while we sprint. … You can daydream while you run slowly for long distances, but you can’t daydream while you sprint. And in many ways, when we are sitting at our desk and performing what is the equivalent to a mental sprinting, we are forcing our bodies to stay in a very different physiological state to our minds while we’re working.”

“It’s a trade-off between speed and accuracy. Your speed becomes faster, but your accuracy goes down. … You will make mistakes, errors, and you will miss nuances, and you will miss subtle aspects of anything you’re going through.” — on gear-3 reactive work

“Learning how to control yourself, regulate your brain from tipping into gear three, that sort of anxious, panicky state, and just staying at the top edge of gear two, where you’re still slightly apprehensive, slightly jittery, but you have enough self-control and focus to be able to sit down and learn, that is the state that, if we can train ourselves to embrace it, will last us and take us far through this era of rapid change.”

“Instead of imposing the same or using 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.”

“We think that you’re working only while you’re working on what is relevant, but actually, the brain is working on whatever is relevant even when it is doing things that are not relevant.”

Linked entities and concepts

• Harvard Business Review — publisher
• Mithu Storoni — guest; neuroscientist, physician, author of Hyperefficient: Optimize Your Brain to Transform the Way You Work. Dangling (single-source mention, deferred): promote to entity page on second source.
• Curt Nickisch — host; HBR senior editor. Dangling (single-source mention, deferred): promote to entity page on second source.
• Google — named as 20%-time exemplar
• 3M — named as 20%-time exemplar. Dangling (single-source mention, deferred): promote to entity page on second source.
• automation-vs-augmentation — Storoni is a normative voice on the augmentation pole
• micro-productivity-trap — gear 3 is the neurological mechanism
• durable-skills — self-regulation-under-uncertainty as meta-skill
• warner-wager-process-model — for the W&W tagging vocabulary

Related in this wiki

• 2026-04-28-reitz-higgins-spacious-thinking — closest phenomenon-overlap source; same diagnosis from leadership-practice vantage
• 2026-02-09-sternfels-mckinsey-survive-ai-and-reinvent-consulting — closest venue + vantage neighbour (also HBR IdeaCast; organisational-change-dominates-technology framing)
• 2026-05-02-dutt-chatterji-ai-experimentation-to-transformation — Bain/OpenAI’s firm-level micro-productivity-trap diagnosis; Storoni supplies the individual-cognition mechanism
• 2026-04-28-brynjolfsson-canaries-coal-mine — empirical labour-market data on the augmentation/automation cut Storoni argues normatively about