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AI has made generating hypotheses and running simulations cheap, but the actual experimental verification remains slow and expensive — a gap that will determine how much AI truly accelerates scientific discovery. The bottleneck isn't computing power; it's the physical work of building things, measuring them, and getting reproducible results.
- In formal math and software, verification is nearly free once problems are machine-checkable (Lean proofs, AlphaEvolve), so the limiting factor becomes compute. But most science requires physical experiments that can't be sped up by better algorithms.
- Amdahl's law shows the hard limit: if 80% of discovery time is lab work, infinitely fast hypothesis generation only delivers 1.25× speedup. Better models widen the verification gap rather than close it.
- Cheap compute creates "orphaned hypotheses" — promising candidates generated by grad students and small teams that go untested because experimental capacity is concentrated in a few rich institutions and countries. Accessible, affordable lab tools could democratize who can run the full loop of science.
- Surrogates like AlphaFold work only when built on high-quality experimental data and validated with real measurements. A country or company that scales compute without matching lab infrastructure and data capacity will have fast models that are confidently wrong.
+ verification gap
alphafold
alphaevolve
+ scientific discovery
+ experiment
infrastructure
+ research productivity