Erik Brynjolfsson

Confidence 0.85 · 3 sources · last confirmed 2026-04-28

Erik Brynjolfsson is a leading academic on the economics of digital technology and AI. Stanford University and NBER affiliated. Director of the Stanford Digital Economy Lab. Member of the AI Index Steering Committee at Stanford HAI (so cross-affiliated between Stanford’s two major AI-research initiatives).

Role in the wiki

Brynjolfsson recurs across multiple sources and is the author of two of the most-cited empirical findings in the wiki:

1. The “Equalizing Effect” customer-support study (2025 QJE / 2023 NBER WP)

Brynjolfsson, Li & Raymond (2025) “Generative AI at Work”, The Quarterly Journal of Economics 140 (2025): 889–942. Working paper predecessor: NBER 31161 (2023). Field study with 5,172 customer-support agents, 3M+ chats, fall 2020 – early 2022, at a Fortune 500 firm using a GPT-3-based AI assistant. Key findings:

• +15% productivity in resolutions per hour (preferred specification with year-month + agent + agent-tenure FE: +15.2%; +23.9% with location-only FE).
• Equalizing effect with quality nuance: low-skill workers +30% RPH and quality up; top performers small speed gains AND small quality DECLINE.
• AI-exposed workers maintain higher efficiency during AI outages — durable learning, not just real-time scaffolding.
• Treated 2-month-tenured agents perform like untreated 6-month-tenured agents — AI accelerates the experience curve ~3×.
• Convergence in communication patterns — low-skill agents begin communicating more like high-skill agents.
• Customers more polite, less likely to escalate; reduced worker attrition driven by retention of newer workers.

Note: the wiki previously cited the working-paper version (+14.2%) via AI Index 2025 §4.4. The QJE version of the paper is now the canonical primary source — slight upward revision and added top-performer-quality-decline nuance.

2. The “Canaries in the Coal Mine” employment study (2025)

Brynjolfsson, Chandar & Chen (2025), Stanford Digital Economy Lab working paper, Aug 26, 2025. Six facts using ADP payroll data showing:

• Early-career workers (22–25) in AI-exposed occupations: ~13% relative decline since late 2022.
• Older workers and less-exposed occupations stable or growing.
• Declines concentrated in automation uses, not augmentation uses.
• Adjustments visible in employment more than wages (wage stickiness).

This is the wiki’s headline empirical evidence for AI labor displacement.

Cross-paper synthesis

The two papers together describe the task-level vs. occupation-level paradox of AI’s labor impact:

• At the task level (within a job): AI raises productivity of low-skill / early-career workers more — equalizing.
• At the occupation level (across firms): AI is reducing entry-level employment in occupations where it can substitute for labor — disequalizing.

Both can be true simultaneously. The mechanism: when AI raises individual productivity, total employment in that role depends on how elastic demand is for the role’s output. See ai-employment-effects and automation-vs-augmentation.

Notable affiliations

• Stanford University — faculty
• Stanford Digital Economy Lab — director
• Stanford HAI — affiliated; member of AI Index Steering Committee
• NBER (National Bureau of Economic Research) — research associate

Open questions

• Brynjolfsson’s books (The Second Machine Age with Andrew McAfee; Race Against the Machine; Machine, Platform, Crowd) — to be filled in as more sources reference them.
• The Productivity J-Curve (Brynjolfsson, Rock, Syverson) framework — relevant to the time-lag between AI investment and measurable productivity. Mentioned only obliquely in the wiki so far.
• His earlier work on IT productivity paradox — which his AI-productivity work builds on intellectually.