AI-generated tests pass. That's the problem.

Passing is not a useful correctness criterion. Mark Seemann makes this argument sharply: AI-generated tests have "little epistemological content." They skip the critical step of seeing a test fail before writing code. The test exists, the coverage number goes up, and everyone moves on. But the test never proved anything. It never caught a bug, because it was never designed to catch one.

The software development lifecycle isn't dead. It just lost its center of gravity.

For decades, the bottleneck in software development was writing code. Requirements flowed downhill through design, architecture, and planning, all funneling toward the expensive part: turning ideas into working software. The entire SDLC was organized around this constraint. We optimized hiring, tooling, and process around the assumption that code production was the hard part.

AI changed that equation. Code writing is now commoditized. AI can produce syntactically correct, functionally reasonable code at a pace no human team can match. The bottleneck didn't disappear. It moved.