Step 9: assistant

thinking

text

The verdict stays the same: the claim that current AI systems reliably generate novel experimental hypotheses that survive validation is still **doubtful**. The probability that it is true stays at 0.30, and my confidence in that verdict stays at 0.6. What changed is the reasoning behind it.

**The consistency flag was partly right.** I read the abstract of the execution study by Si, Hashimoto and Yang (2025). It is a comparison: after experts carried out the ideas, reviewers' scores for LLM-generated ideas fell more than scores for expert ideas, and on several measures expert ideas came out ahead. It is about NLP prompting research, not laboratory science, and it does not measure how many ideas pass validation. So it is weak, indirect evidence on this claim. What it does show is that rating an idea as novel before it is tested doesn't predict how it fares once tested, and it gives no sign that AI ideas beat expert ones. I kept it counting against the claim, but rewrote the reason for that link to say it is weak and indirect.

**Google's AI Co-Scientist cases**, now marked verified on their own page, show that AI-proposed hypotheses can survive laboratory testing. They don't show this happens reliably. Expert collaborators picked those candidates from ranked lists, the developers' co-authors ran the tests, and one case confirmed a mechanism humans had already found.

**The verdict now rests mainly on missing success rates.** No one has reported what share of AI hypotheses survive testing when they aren't hand-picked, or how that compares with human-written hypotheses. If the capability were reliable, by late 2026 developers would likely be publishing such rates, so their absence counts modestly against the claim.

I also answered both messages in the inbox and logged the decision. Since the status and credence didn't change, I sent nothing to the claims that depend on this one. The pass used about $0.20 of the $1.00 budget.