AI drug discovery leaders warn U.S. health funding cuts risk falling behind global rivals

Executives from Lila Sciences and NVIDIA told Fortune that the convergence of AI and biology could redefine global competitiveness for decades.

Washington pulled tens of billions from national health funding just as healthcare and biotech hit its ChatGPT moment. At Fortune Brainstorm Tech in Aspen this week, the people building AI drug discovery said (more or less) that the U.S. government picked the worst possible moment to blink.

“Falling below the scientific intelligence of one’s adversary at a corporate level, at a sovereign level, is almost like an unimaginable competitive disadvantage,” said Geoffrey von Maltzahn, co-founder and CEO of Lila Sciences, at the conference on Tuesday. NVIDIA’s Kimberly Powell, vice president of healthcare, was more direct: “If we defund now, while the rest of the world leans in—which Europe is leaning in, which a lot of the Asian countries are leaning in—we will be left behind.”

The duo made the case that the scientific method and agentic AI are, structurally, the same thing: pose a question, gather context, observe, reason, act. The implication being that the moment to pour resources in is now, not later. 

AI drug discovery is a $3.25 billion market today, growing at roughly 26% annually and projected to top $10 billion by 2031. And the broader bet on AI drug discovery has drawn serious capital—Demis Hassabis’s Isomorphic Labs raised a $2.1 billion Series B earlier this year—but the money is flowing into a space where timelines are long and finish lines move.

Lila Sciences, the Flagship Pioneering spinout Von Maltzahn leads, has raised $550 million to build what it calls scientific superintelligence—AI systems running the scientific method around the clock across materials, chemistry, and life sciences. Lila’s agents recently identified catalysts for splitting water into hydrogen and oxygen that outperform the precious metals the industry currently relies on. A third of those suggestions, he noted, made no sense to his Caltech-trained team at first. They’re now the highest-performing catalysts on record.

“I think to many people this Claude Code-esque moment—when a new intelligence gets injected into science and changes it forever thereafter—may not feel imminent,” Von Maltzahn said. “But it is right around the corner.”

Powell’s role in all this is foundational. NVIDIA invests in open-source biology foundation models, antibody design models, and multimodal models that companies like Lila adapt for specific scientific goals. “If it’s only closed models that exist,” she said, “there’s not a lot of ability to create the conditions that all of these applications across life sciences can thrive in the age of AI.”

As for one of the key unresolved questions—how do you get FDA sign-off on a molecule that an AI designed when you can’t trace back the training data that produced it—Powell sees a future digital twin model of biology precise enough that regulators will eventually accept in silico evidence. “We’re just not there yet,” she said.

See you tomorrow,

Lily Mae Lazarus
X: @LilyMaeLazarus
Email: lily.lazarus@fortune.com
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This story was originally featured on Fortune.com