We measured the "AI review tax" across 5,388 repos. The median repo doesn't pay one.

An analysis of 5,388 public repositories and 444,225 merged pull requests found that the median repository pays no 'AI review tax,' while a quarter pay a real one. The developer behind the scanner, which is sold commercially, says the cost varies more by language than by project size, with TypeScript repos showing the fastest median time-to-merge for AI-attributed PRs.

The claim you hear from one side is that AI-written code is a review burden everywhere. The claim from the other is that it's free velocity. We read 5,388 public repositories — 444,225 merged pull requests — and both sides are wrong in an interesting way: the median repo pays no review tax at all, and a quarter pay a real one . Where the cost lands says more about the repo than about the tools — and which kind of repo yours is turns out to be measurable. Disclosure up front: I built the scanner this data comes from, and I sell a paid version to teams. That's also why the headline is against my interest — a review tax that landed on everyone would be a better sales pitch. It's just not what the data says, and the data is re-checkable by anyone see "Check us" below . For every repo, the last 100 merged PRs, divided into PRs carrying agent authorship and the rest of the same repo. Attribution is commit-level marks only — Co-authored-by trailers naming a known agent, agent commit identities, agent bot accounts. Nothing is inferred from diff shape, code style, or timing; that would be guessing dressed as measurement. Because the comparison stays inside each repository — same reviewers, same conventions, same era — a multiple above 1× means the attributed side took more review; below 1×, less. Full population, backfill complete 2026-08-06. By language, median time-to-merge multiple: TypeScript 0.43× 442 repos , Python 0.54× 361 , Go 0.54× 302 , Rust 0.70× 237 — the slowest-relative of the big five. By project size the spread is flatter than expected: 50k+ stars 0.43×, 10k–20k 0.39×, 5k–10k 0.51×. Size predicts far less than language does . The full cuts, with segments below the sample floor withheld rather than estimated, are on the live page https://forge.ambera.app/review-tax-index . Three limitations are load-bearing, and reading the numbers without them produces wrong conclusions: The sample self-selects twice, too: these are repositories someone chose to scan plus a curated backfill of well-known projects — a population of repos people care about, not a random sample of software. Every repo in the Index is public; every report is a permanent link anyone can re-read; the aggregate recomputes live from the same rows. The full method, signal by signal, including everything it can't see: forge.ambera.app/methodology https://forge.ambera.app/methodology . You can place your own repo against this population at forge.ambera.app/review-tax-index https://forge.ambera.app/review-tax-index — public repositories only, nothing installed, no account. For a private repo there's a local CLI https://forge.ambera.app/scan that reads via your own gh auth and sends nothing. The Index republishes quarterly. Repos already read keep their history, so the next edition carries trend lines: not just where the tax lands, but where it's moving. If your team's numbers look different from the population's, I'd genuinely like to hear about it — that disagreement is more informative than agreement.