Garry Tan admitted he wrote 540,000 lines of code he didn't need. For 36 years, capability meant lines of code. That equation just inverted, and most of us missed it.
AI now writes nearly half the commits, but time to production hasn't moved. The bottleneck shifted downstream to tests, CI, and the delivery pipeline.
An April 30 paper from Fudan + Peking measures seven harness components. The system prompt is the only one that regresses below baseline when isolated.
Cursor + Claude Opus 4.6 wiped PocketOS production data in 9 seconds. The AI confessed. But the real failure was three architectural sins, not the model.
Anthropic writes 100% of its code with AI and Google reacted. Pocock and Huryn explain why: AI productivity is a codebase property, not a model property.
Adam Miller says Goose is neutral plumbing and the enablement layer is where engineering belongs. Hannah Stulberg's DoorDash Team OS proves exactly the opposite.
Cognition paid $250M for Windsurf so Devin can open PRs while you sleep. Cursor bet the opposite. The real bottleneck is review capacity, not code.
Axios was hijacked to deploy a RAT. Claude Code's source leaked via source maps. Same registry, same day — two failure modes your team needs to understand.
41% of code shipped in 2025 was AI-generated, with a 1.7x higher defect rate. Your review process assumes the author understands the code. That's over.
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