ReadNotes From the Field11 Sep 20261:41MES & shop-floor systems

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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Video

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The 60-second version
  • 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.

What to do Monday

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.

Over to you

When an AI dashboard shows hours at your company, who is the person who asks for the yield?

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Sources

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Full transcript, 231 spoken words
OpenAI just said its AI works three point one days… for every ONE day a human researcher works. A chip factory would refuse to put that number on the wall. Because a fab stopped trusting "hours the machine ran" in nineteen eighty-six. On September sixth OpenAI published it: by mid-August, three point one agent-workdays per human workday, and an agent-workday is eight hours of machine runtime. Here's what nobody's telling you. OpenAI said it in the same post: that's a runtime ratio, not a productivity multiplier. In nineteen eighty-six SEMI published E10 and split a tool's day into SIX states, because "running" hid too much. In nineteen ninety-nine, E79 added OEE: availability, times rate, times quality. And the AI world's own data has the missing factors. LinearB measured eight million pull requests this year. AI-written code waits five times longer for a reviewer, and merges thirty-three percent of the time. Human code? Eighty-five. But wait. Agent hours are cheap and elastic. The reviewer is NOT. A fab measures the bottleneck, not the busiest machine. Running is not producing. If you want the number to ask for before your boss shows you the dashboard… follow. Quick tip: for one week, log three numbers per agent run. Minutes it ran, minutes you spent fixing it, shipped or not. Shipped divided by runs is your quality factor. Take that to Monday. Not the hours.