A new analysis from Epoch AI offers a rare, data-grounded look at a question the industry has been debating in the abstract: how much is AI actually accelerating the engineers who build it? The answer, at least for the team shipping OpenAI's Codex, is: measurably, and growing fast.
The Setup: LLM Judges Rating Pull Requests
Epoch's researchers focused on 41 core contributors to OpenAI's public Codex repository on GitHub. The core question was simple but hard to answer: are these engineers merging more code than a human could physically produce in a day, without AI help?
To answer it, they adapted a methodology originally developed by METR. For each merged pull request, an ensemble of three frontier models (Claude Opus 4.8, GPT-5.5, and Gemini 3.1 Pro) was asked to estimate how long a single experienced engineer, working alone and with zero AI tools, would need to reproduce that exact change. The models read the PR title, description, per-file line counts, branch commit messages, and a sampled slice of the diff, then emitted a single time estimate.
The three models agree closely on ordering, with pairwise log-scale correlations of 0.95 to 0.98, but less on scale: on the median PR, the highest of the three estimates is 2.5 times the lowest. To reduce that noise, Epoch takes the per-PR median and groups results into broad buckets: under 6 hours, 6-12 hours, 12-24 hours, 24-48 hours, and 48+ hours.
The Signal: 4x More "Superhuman" Workdays in a Year
In Q2 2026, 8% of contributor-days reflected work estimated at over 24 hours of unassisted effort -- more than a skilled engineer could do in a day, even working around the clock -- up from 2% in Q2 2025. That is a 4x increase in the share of workdays that look physically impossible without AI assistance.
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