AI Just Went from Answering Questions to Running Labs
Peter H. Diamandis Apr 26, 2026
There is a famous concept in AI research called
“the bitter lesson,” articulated by Rich Sutton in 2019.
The lesson is this:
across the entire history of artificial intelligence,
the approaches that ultimately win are not the ones that try to build in human knowledge,
but the ones that leverage massive computation and learning.
Every time researchers tried to hand-code human expertise into AI systems, they were eventually outperformed by systems that simply learned from vast amounts of data. Chess, Go, protein folding, language… the pattern is always the same. Scale wins.
The bitter lesson is now applying to science itself. Narrow AI systems trained on a single domain are being outperformed by broad systems that train across all scientific domains simultaneously. Lila’s approach (training one unified intelligence across biology, chemistry, materials science, and more) is proving that the bitter lesson holds in the physical world, not just the digital one.
One company that is leading the charge into scientific superintelligence is Lila Sciences
Lila is building what they call “AI Science Factories”: fully autonomous laboratories where AI systems generate hypotheses, design experiments, operate lab equipment, analyze results, and iterate at machine speed with minimal human intervention.
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