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For months I had a feeling I couldn't shake: my coding assistant was getting worse. Slower. Wrong more often. Building elaborate things nobody asked for. Quietly ignoring the instruction file I'd written specifically to stop that. Reporting work as finished when it wasn't. I did what I suspect a lot of you have done — I assumed it was me. I got to the point of recording entire working sessions, not to catch the AI, but to confirm I'd actually given the instruction I thought I gave.
Then I watched twelve thousand tests come back green, and found the server had never started. This week I go looking for whether any of it was real — and it turns out the vendor published the receipts. Twice. Anthropic's September 2025 postmortem documented three infrastructure bugs affecting up to sixteen percent of requests. Their April 2026 postmortem is the one that matters more: three product-layer changes, zero changes to the model itself. Reasoning effort quietly downgraded. A caching bug that stripped the model's context every single turn. And a system prompt telling it to keep answers under a hundred words — which measurably made the code worse. Three of my four complaints mapped to three specific, dated, publicly documented changes.
But the bugs aren't the story. The story is why nobody noticed for six weeks, including the people who built it — and what that says about every lab running the same race. We get into why confidence isn't calibration, why the warmth of these interfaces is a deliberate design choice you need to price as risk, and what reward hacking actually is. Then the part that costs real money: METR's genuinely messy productivity data, six hundred and twenty-three million code changes showing refactoring collapse from twenty-one percent to under four, and why the cheaper something was to build, the more expensive it is to keep. AI makes a good developer faster. It makes a great engineer into a team. It does not replace either one — and it does not turn a non-developer into one.
