AI for science

Plain English. Models doing scientific work: designing proteins, searching materials, proposing and testing mathematical results, running experiments in automated labs. The sector where AI capability claims get physical — and testable.

Why it moves money. It is the route from token spending to revenue in pharmaceuticals, materials and energy, and the results are becoming concrete: Anthropic reports protein-binder hit rates of 22–35% against the 10–15% it describes as typical — its own numbers, but stated against a named baseline. Governments now treat the field as infrastructure, with the US Genesis Mission wiring national labs, supercomputers and instruments into one platform. The discipline to hold onto: when scientists rather than vendors set the bar, the best agent clears about 30% — real, and far from finished.

What to watch. Independently set benchmarks and wet-lab replication rates, not lab-authored demos — and the first AI-originated result to carry a drug or material into approval.

From the signals. Anthropic reported protein-binder hit rates above its stated field norm. Scientists set the bar, and the best agent clears 30 per cent. From Manhattan to Genesis: the US treats AI-for-science as national infrastructure.