Joseph Krause

speaker

1,040 appearances 1 recordings 1 series first heard Jun 2026 last heard 17 Jun

Joseph Krause’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jun 2026 with 1.

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Latent Space: The AI Engineer Podcast · 🔬 The Self-Driving Lab — Joseph Krause, Radical AI · 17 Jun 2026 podcast

the infrastructure to start to speed that gap up, to start to show you can shorten that gap.

That's important.

The third one is, I think, maybe where we have to be different, public-private partnership.

And this is what we talk a lot about.

This is the perfect opportunity for a private enterprise to work with public research to create the greatest scientific tool, as the DOE likes to say, in the world.

Why?

Because we have all the HPC that we need, high-performance compute,

We have all the researchers that we need.

We have some of the best scientists in the world at the national labs and the infrastructure from a material tooling or a science tooling perspective.

And then lastly, you actually have the data.

You actually have more experimental data in all of those national labs than anywhere in the world.

or we think anywhere in the world from all the science that we've run.

So if you can couple all three of those things together and you can bring in private enterprise, you can help you make sense of that, help you close that system, help you tie the loop together.

Now I think the entire national infrastructure runs in this way.

the research infrastructure whether that's corporate r d or again like sbir sttr r d runs this way and private enterprise runs this way now you've changed the fabric of all of r d in the us now you're at a place where you can compete with china not by owning everything and doing unforced labor and the unethical practices that they might employ but rather changing the mentality and the approach to how we do r d that's how i think we can compete that's the only way we can compete

I think if we want to move forward, if we do not do that, then they will continue to win because they will outpace us on cost and they will outpace us on people.

And so if you try to play that game, it feels like, you know, we're going to lose that game, it feels like.

But if you play the game of changing the system and building a better workforce and a better system to do R&D than they have, then we can beat them with withdrawal output.

I don't think this is removable, so that's why it's not a good answer.

But the hardest part about AI for science is that our feedback loops are long.

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