Openai's own research org is the clearest case study in agent economics. by mid-august it runs 3.1…
openai's own research org is the clearest case study in agent economics. by mid-august it runs 3.1 agent-workdays for every human workday, with the median researcher burning over $600 a day in api tokens and the 90th percentile over $7,000.
at that burn rate, an agent is infrastructure, not headcount.
Context
OpenAI's post of 6 September 2026 says that as of mid-August its research organization used 3.1 agent-workdays of effort for every workday of human labor, measured on a standard 8-hour day, and that before June 2026 agent runtime was below human labor. It says the median researcher, ranked by agent usage, used more than 600 dollars per day of inference at API prices, and the 90th percentile user more than 7,000 dollars of tokens per day, and that agents still require significant human steering.
The figures match the post, with qualifications the note drops: the dollar amounts are inference valued at API prices and not cash spent, the median is by agent usage ranking, and 3.1 is runtime, not output. It is OpenAI's own research organization, self-reported, with internal compute and its own models. Agent as infrastructure and not headcount is the author's opinion.
Related work
- Earlier note on the same OpenAI post ↗Same figures.
Watch next
- OpenAI's follow-up measurements and independent cost per completed task.
Sources
Provenance
The note above is reproduced unedited from the original post, first published on Threads on 20 September 2026 at 02:48 IST. Sources are the papers and datasets the note draws on.
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