🔬 The Self-Driving Lab — Joseph Krause, Radical AI
June 17, 2026
AI Summary
5 min read"Materials are long" is the kind of phrase that gets thrown around casually in the industry, but Joseph Krause, CEO of Radical AI, gives it real weight: a new alloy for a jet turbine can take 15 to 30 years to go from discovery to application. The reason, he argues, is not that scientists are slow, but that the process is brutally fragmented. Discovery happens in academia, early testing in small companies, and manufacturing optimization inside large incumbents—and the data almost never connects across those silos. Radical AI's bet is that the only way to compress that timeline is to build a self-driving lab that closes the loop between synthesis, characterization, and property testing, generating the experimental ground truth that AI models need to actually predict materials that work in the real world.
Why materials are harder than biology
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What you'll learn
- 1 (00:52) **Podcast intro and guest background** - Joseph Krause, CEO of Radical AI, joins to discuss materials AI
- 2 (01:38) **Core thesis: experimental data and self-driving labs** - Ground truth comes from making, testing, and characterizing materials
- 3 (03:05) **Why discovery alone fails in materials** - Characterization, synthesis, and processing dominate real performance
- 4 (04:33) **Industry fragmentation as root cause of long timelines** - Academia, small companies, and large manufacturers operate in disconnected silos
- 5 (05:57) **Scaling problems across orders of magnitude** - Discovery (N=1) to manufacturing (N=millions) changes every constraint
- 6 (07:21) **Current lab capabilities** - Synthesis, characterization (SEM, EDS, XRD, XRF, TGA), and early property testing are running today
- 7 (09:05) **Progress metrics and remaining gaps** - 1200 alloys made recently, 300 novel; manufacturing and full qualification still external
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Show Notes
On the Science pod, we’ve been covering a lot of the ground on how AI is revolutionizing STEM, but one of our favorite off the record topics since our launch is which field is harder to accelerate: math, bio, or physics? Today we’re back in Materials Science land with Radical — Unlike biological molecules that can be represented (and predicted!) by token strings, the success of materials involve many more macro complex variables like supply chains, microstructures, and manufacturing processes. If you recall the LK99 drama of 2023, while the basic ingredients were known, part of the confusion came from the lack of disclosure around manufacturing, and therefore defeated reproducibility. There is probably no "one-shot" model capable of designing a material that works perfectly at scale.
How Radical is accelerating materials discovery >10x the pace of DARPA/GE MACH
Joseph Krause is a materials scientist through and through. And after spending his career watching industries stall out waiting for better materials, he founded Radical AI to do something about it.
We recently sat down with Joseph to talk about Radical AI, materials discovery, self-driving labs, and the future of AI science. Joseph did not sugar coat anything: accelerating the materials discovery pipeline is a hard problem. But it’s one that he strongly believes we need to invest in, for the future of consumer products, aerospace, computing, and defense, and get them into every day use:
“We count it as a discovery when you pick up your phone and there’s a new material sitting inside of it.”
How does Joseph plan on accelerating the rate of discovery? To understand this, it’s important to understand why this is such a hard problem in the first place. The first thing to keep in mind is that the material that is manufactured is far more than a chemical formula going into it. The process of mixing, annealing, growing, or generating the final material can result in wildly different outcomes. The entire materials discovery process, both from early discovery to large scale manufacturing, needs to be understood and characterized.
The Self-Driving Lab
This philosophy has grown into a key insight at Radical AI: The construction of the self-driving lab. This lab is one that is not just automated, b
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