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The article primarily discusses how AI is transforming scientific research by making knowledge work faster while physical experiments remain the bottleneck, a dynamic central to the AI x Science sector. It highlights the emerging role of "foundries" that industrialize experimental measurement to generate data at unprecedented speeds, directly addressing the core mechanics of AI-driven discovery.
Foundries vs Navigators: Lowering the Cost of Science
Guest Post: In science, thinking has gotten cheap but doing has not. This asymmetry is reshaping how research companies operate, largely inconspicuously.
What does the future of science look like in the world of AI? Anthropic has some lofty goals for science and is even opening a wet lab. Meanwhile a quiet transformation1 is happening all across AI x Science. In this guest post, Adrian Sanborn talks about the less flashy but more immediate ways he sees AI transforming front-line scientific research in his own company, Endura Therapeutics. Adrian did a CS PhD at Stanford and spent much of it running experiments at the bench, which makes him one of the rare people who can tell you what an LLM is doing to a codebase and to a wet lab. Enjoy! Language models have transformed how software gets built. Writing code, wrangling data, and architecting systems now move at a speed unthinkable three years ago. When the product is software and the result is verifiable, cheaper coding turns directly into more software and more builders.2 But in science, everything is ultimately gated by physical experiments that take days or weeks to verify anything. Knowledge work around the experiment has become dramatically faster while AI has done little for the throughput of the experiment itself. Thinking got cheap and doing did not. Foundries…
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