Joseph Krause, co-founder and CEO of Radical AI, joins the show to break down how scientific discovery is being reinvented. From the limitations of the traditional trial-and-error model to the rise of AI-driven self-driving labs, Joseph explains how science is moving from slow, serial processes to a parallel model that unlocks breakthroughs at scale. He also dives into the economics of materials, why big companies can’t pivot fast enough, and how the role of scientists is being transformed.

Key Takeaways

The old model of science is serial: slow, linear, and limited by human capacity to read, experiment, and analyze.

Negative results—failed experiments—are the true fuel for breakthroughs, but they’re rarely captured or shared.

Self-driving labs powered by AI create a “materials flywheel,” running 30,000+ experiments a year and learning continuously.

Big corporations are trapped by the innovator’s dilemma and talent challenges, leaving space for startups to lead.

Scientists in the future will focus less on repetitive lab work and more on shaping hypotheses and applying intuition at scale.

Timestamped Highlights

02:00 How science traditionally works and why it’s so slow

05:50 Why mistakes and negative results matter more than we admit

09:40 The fragmentation of research and why labs don’t share data

17:15 Inside a self-driving lab and how AI accelerates discovery

23:40 Why big material companies can’t innovate like startups

35:40 The new role of scientists in an AI-powered discovery world

Memorable Line

“You don’t get a PhD to learn to pipette—you get it to think about how and why the world will change.”

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