Radical AI’s Joseph Krause: The Scientist Building The ‘Waymo’ Lab For New Materials

**Joseph Krause** (0:00)
We really think we're going to be one of the most important companies in the world. When we put a civilization on Mars, the habitat that they're living in will be made with radical materials. That is what we believe.

**Alex Konrad** (0:11)
You might have used AI to write an email or generate a simple image, but a startup called Radical AI is trying to do something much more ambitious. Their goal, to revolutionize the scientific process itself.
CEO and co-founder Joseph Krause says Radical AI is creating self-driving science labs.

**Joseph Krause** (0:29)
Humans work in serial. I make hypothesis, I run experiment, I test results, I learn from results, I redo the whole thing. Our lab is doing all of those things simultaneously.

**Alex Konrad** (0:40)
Today on the podcast, we're going to talk about why Radical AI is developing new metals right here in New York. How Joseph Kold emailed his way into a 55 million funding round to get started, and why Radical is more like a Waymo self-driving car than an AI science lab that might just be more like hands-free driving.
Plus, why you can't buy your way to success with a billion dollars, at least yet. I'm Alex Konrad, founder and editor of Upstarts Media. This is The Upstarts Podcast, our weekly show about startup founders who punch above their weight to take on the status quo. Joseph, thanks for joining the show.

**Joseph Krause** (1:12)
Alex, thanks for having me, man. Excited to be here.

**Alex Konrad** (1:13)
This episode is brought to you by Rippling AI, the only AI built to give you full visibility into your startup, and the ability to take action across every department. So Joseph, what is Radical AI?

**Joseph Krause** (1:25)
Radical AI is a next-generation material science company where we use AI and autonomy to change the way we do the scientific process, where we really move from what is a human-driven process today to an AI and autonomy-driven process of the future that fundamentally removes materials as the biggest bottleneck to our most important industries. That's what the company is, and that's what we're working on.

**Alex Konrad** (1:47)
That's awesome. And you just raised 55 million. You're opening a bigger facility in Brooklyn Navy Yard soon, right?

**Joseph Krause** (1:52)
Yes, that's correct. So we raised 55 million in our seed ground last year and have been off into the races with that. In the new facility, we're taking a whole building, which was previously called Building 20 in the Brooklyn Navy Yard, 45,000 usable square feet. And what's really exciting about this facility is it allows us to, one, grow the team and expand operations, but two, build a multitude of material systems there. So not a single material system like our current facility has today, but actually material systems that span different industries and different material markets. And we'll get into why that's really important, but that's an exciting spot in that facility.

**Alex Konrad** (2:26)
What are the devices, like, at a big picture that are creating these materials?

**Joseph Krause** (2:31)
Yeah, absolutely. So at a big picture, you know, first, let's take one step back. What does a material scientist do? So a material scientist, when they're working on a research problem, will start with hypothesis generation, right? So they'll sit down, they're going to identify this research problem, they're going to read a bunch of scientific publications, they're going to get new ideas, and they're going to make a set of hypotheses on what they want to go make. They're going to go into the research lab, they're going to synthesize these materials, which means to actually create the physical material, they're going to characterize the material. So, hey, what did I make? And what does it look like? Is the structure what I thought it was going to be? Did it come out how I thought it was going to come out, for lack of a better phrase? And then they're going to test the material. So does it have the performance properties I want and was expecting when I made the hypothesis? After they do all this work, they're going to analyze that data, and then they're going to go back in their brain and think about a new hypothesis on how to what we call iterate on those experimental results.
Now come into our facility.
At Radical AI, that entire process that I just explained to you is done autonomously. And all of the hypothesis generation, data analysis, and what we call active learning is done with artificial intelligence. And so you move from this serial-based process that a human scientist must do to this parallel-based process where AI and autonomy can do that scientific experimentation many times over for a material system. So that's what's in the lab. Now for our current facility, we call this System 1