Accelerating Science with AI: Evaluating Information Gain in Experimental Decisions
nathanbenaich · x · 2026-08-13
Investor Nathan Benaich proposed a framework to evaluate AI's actual value in scientific decision-making. The core challenge is that when a lab considers multiple potential experiments and runs only one, the unchosen branches become unobservable counterfactuals.
Merely logging "failures" is of little use because it fails to distinguish between an underpowered assay, degraded reagents, or a genuinely wrong hypothesis. He suggests:
- Experimental Design: Compare models given only papers vs. those also given decision histories, asking them to predict the next experiment.
- Evaluation Metrics: Measure information gained per token, calibration, and how quickly weak branches are abandoned.
- Funding Mechanism: Research funders could reserve a small portion of grants to test the branches not taken, preventing different labs from paying to rediscover the same dead ends.
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