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.
How to use this collection
Use problems & concepts when you need a bounded answer rather than a generic promise about AI coding. The 6 articles separate observed evidence, working assumptions, product boundaries, and decisions that still belong to a human reviewer. Each guide is dated and keeps its source limitations visible.
Start with “Verification Debt” for the broadest entry point, then use “Vibe Coding's Bill” when you need the collection's more specific edge. The cards below state what each article covers, so you can choose by task instead of reading a manufactured sequence. Related links inside each guide connect methods, risks, evidence, and next actions without treating one check as universal proof.
Check each article's publication date, cited source, and stated scope before applying it to a live repository. Examples explain a method; they do not replace your project rules, threat model, tests, or accountable reviewer. If two guides appear to conflict, compare their assumptions and evidence rather than selecting the more confident wording.
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: August 15, 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: August 15, 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 17, 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: August 15, 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: August 15, 2026Read article →
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