29 September 2026
Heard in AI
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OpenAI Reports Rising Internal Research-Agent Use

Tracks OpenAI's September research-acceleration report, its internal agent-use metrics and interpretations of research productivity.

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Updated

Overview

OpenAI's research-acceleration report describes rising internal coding-agent activity and improving task outcomes. Moonshots read its 3.1 figure as completed research work exceeding that of human interns. The report instead measures total agent runtime per human workday, not equivalent scientific output, and notes human interventions and measurement limits. OpenAI researcher Noam Brown cited the report on the Dwarkesh Podcast. From memory, he said the top 1 per cent of researchers were spending about 7,000 to 8,000 dollars a day on Codex as of early August. Background notes to the episode say the report puts spending above 7,000 dollars at the 90th percentile. Brown said acceleration is hard to measure because of attribution and uneven task speed-ups. His rough estimate was that research could go about three times faster, within a range from 50 per cent to an unlikely tenfold. The link between higher usage and research progress remains unresolved.

What changed

Dates show when each podcast discussion was published.

  1. New information

    OpenAI researcher Noam Brown cited the internal-acceleration report on the Dwarkesh Podcast. He recalled that as of early August, the top 1 per cent of researchers were spending about 7,000 to 8,000 dollars a day on Codex for internal use, and that this will keep rising. Background notes to the episode say the report attaches spending above 7,000 dollars to the 90th percentile. Brown said acceleration is hard to measure for three reasons: it is unclear how to split credit between humans and AI, some tasks speed up far more than others, and baseline comparisons differ. He said he is confident research is faster than a year ago. His guess was about three times faster, with a range from 50 per cent to an unlikely tenfold.

  2. New information

    The hosts present OpenAI's internal usage figures as evidence that agents have surpassed research interns and may enable recursive improvement. Their output-equivalence interpretation goes beyond the runtime metric in the report.

Podcast discussions

Sources

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Our coverage

Version history

  • 29 Sep 2026 · Version 2

    OpenAI researcher Noam Brown cited the internal-acceleration report on the Dwarkesh Podcast. He recalled that as of early August, the top 1 per cent of researchers were spending about 7,000 to 8,000 dollars a day on Codex for internal use, and that this will keep rising. Background notes to the episode say the report attaches spending above 7,000 dollars to the 90th percentile. Brown said acceleration is hard to measure for three reasons: it is unclear how to split credit between humans and AI, some tasks speed up far more than others, and baseline comparisons differ. He said he is confident research is faster than a year ago. His guess was about three times faster, with a range from 50 per cent to an unlikely tenfold.

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