OpenAI Just Published the Number Chip Fabs Stopped Trusting in 1986
OpenAI says its AI now works 3.1 days for every day a human researcher works. Why would a chip factory refuse to put that number on the wall?

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- In this Fabspeak episode: • The figure OpenAI published on 6 September 2026: 3.1 agent-workdays per human workday, an agent-workday being 8 hours of machine runtime; the median researcher using over $600 of inference a day, the top 10% over $7,000.
- • SEMI E10 (1986): why a tool's day is split into six states, and why "running" hid too much.
- • SEMI E79 (1999): OEE = availability × rate × quality, and why a busy fab lands at 50–70%.
- • The missing two factors the software world already measured: LinearB's 8.1 million pull requests (AI code waits 5.3× longer for review, merges 32.7% vs 84.5%) and Faros AI's 22,000-developer telemetry (review time up 441%).
Why this matters
• OpenAI's own caveat: a runtime ratio, not a productivity multiplier. • The practitioner's insight: agent-hours are cheap and elastic; the reviewer is the bottleneck, and a fab measures the constraint, not the busiest machine. Minutes it ran, minutes you spent fixing it, shipped yes or no. Shipped ÷ runs is your quality factor.
for one week, log three numbers per agent run: minutes it ran, minutes you spent fixing it, shipped yes or no. Shipped divided by runs is your quality factor. Take that to Monday's meeting, not the hours. Save this.
When an AI dashboard shows hours at your company, who is the person who asks for the yield?
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