Problems & concepts
The core concepts behind the AI coding verification gap: verification debt, the review bottleneck, and why AI-generated code fails in the ways it does - defined, sourced, and measurable.
Concept
Verification Debt
The gap between how fast AI tools generate code and how reliably teams can verify it before merge – definition, data, and a practical framework to measure your own.
Updated: July 2, 2026Read article →
Concept
The Verification Gap
96% distrust AI code, only 48% always check it: all key numbers behind the verification gap from Sonar's 2026 survey - sourced, tabulated, and explained.
Updated: July 2, 2026Read article →
Concept
The AI Code Review Bottleneck
Generation got cheap, reading did not: merged PRs nearly double while review time rises 91%. The mechanics of the new constraint, the measured numbers, and what actually relieves it.
Updated: July 2, 2026Read article →
Concept
Comprehension Debt
The gap between the code a team ships and the mental model its people hold of it - rooted in Naur's theory building, accelerated by AI, and compounding with verification debt.
Updated: July 2, 2026Read article →
Concept
Why AI-Generated Code Fails
Five characteristic failure classes - hallucinated APIs, silent edge-case errors, scope creep, self-confirming tests, plausible-but-wrong logic - why review misses each, and which check catches it.
Updated: July 2, 2026Read article →
Concept
Vibe Coding's Bill
Prompting and shipping without review trades verification for speed - the deferred bill in churn, ~55% security pass rates, fix cycles and cleanup costs, plus the honest counter-position.
Updated: July 2, 2026Read article →
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