Discovered Materials is playing AI whack-a-mole to hunt cooler chips

0:01This is TechCrunch.

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0:50So you can focus on other important things, like savoring that last bit of pumpkin pie. From early startups to Fortune 500 companies, thousands are turning to Framer to build a website they'll love. Learn how you can get more out of your site from a Framer specialist or get started building for free today at framer.com slash tcstartups for 30 % off a Framer Pro annual plan. That's framer.com slash tcstartups for 30 % off. Rules and restrictions may apply. Chips running AI workloads are too hot. That's one reason why data centers consume so much electricity and require cooling systems, and inevitably, entrepreneurs are turning to AI to solve the problem it created.

1:37Discovered Materials is the latest, with plans to use swarms of AI agents to find new materials that can be used to build more efficient, integrated circuits. Founders Advaith Sridhar and Akash Ramdas teamed up to launch the company, drawing on Ram Dass' experience earning a doctorate in materials science from Stanford and Sridhar's work on agents at Persona AI and Luma Labs. The two have created a software pipeline that uses anthropic models in a custom harness to generate material leads and then turns to foundational physics models they've trained to run simulations that verify if the candidate materials are actually of interest.

2:20Tridar told TechCrunch Ram Dass was doing maybe 20 guesses a day during his PhD. We're able to do thousands of guesses a day now by having these agents run 24-7 on the cloud, exploring research directions that he gives them. Discovered Materials released examples of hundreds of new materials today, as well as their Material Discovery Bench, which is designed to track how frontier models take on this challenge. Companies like Matnex, Sandbox AQ, and Cusp AI have all launched similar efforts, but Discovered Materials is betting that a laser focus on the thermal problems of semiconductor materials is the path to success.

3:01The startup says it has already discovered several materials that match the properties of existing materials used by major chip makers, but they can't share more details about them at this point. One challenge is the engineering trade space. If they find a material that might reduce heat generation or improve dissipation, it might be too difficult to actually manufacture a chip out of it, or its electrical properties are compromised. Hamant Moapatra, the Lightspeed partner who led the round, told TechCrunch, it's a bit of playing whack-a-mole with atomic structures. A material is only useful in the real world if all of them converge at once, which is what makes this a really interesting search problem.

3:45Mohapatra expects that the business of predicting novel substances will be commoditized as models continue to improve. The difference with discovered materials is Ram Dass' deep experience in the field and the ability to run a lab that can rapidly experiment and validate the candidates, something he says the two founders have already done with several new materials. When they find valuable candidates, Shudar says the company will attempt to patent the use of the materials in GPUs or the process by which chips can be made out of the substance, licensing them out to chip makers. He hopes that they will have new materials worth patenting in the next year.

4:24However, for all the excitement, we still have not seen any drugs or materials discovered by AI actually make a commercial impact. The closest is perhaps in Silico Medicine's Renterosib, the first drug discovered with generative AI to make it into a phase two clinical trial. On the materials side, promising candidates have been found, like Matnex's rare earth free permanent magnets or new semiconductor materials worked out by Panasonic and Citrine Informatics, but these have not been commercially deployed at scale yet. These techniques may be coming into their own now as AI continues to improve, but it's one reason why Moapatra says that he does not believe finding more candidates is the holdup for AI materials science.

5:13Instead, filtering them correctly and synthesizing them is the bottleneck. While Sridhar believes that Discovered Materials' unique data and expertise will help the startup compete with deep-pocketed frontier labs, He acknowledged that the reality is that a lot of this will involve actually going into wet labs and making things as well. And this is the process that cannot be sped up.

5:41Situational awareness may have had to sell off the majority of its public portfolio last month, but the AI-focused hedge fund is still making some big bets. This week, the fund invested$400 million into SourceFoundry, a startup founded by Stanford researchers aiming to make chip manufacturing faster and cheaper, according to the Wall Street Journal. That brings its total investment in SourceFoundry to$500 million. Situational Awareness was founded by Leopold Aschenbrenner, a former OpenAI researcher in his mid-20s who had no trading experience when he launched the fund in 2024. Early returns were reportedly strong, but the fund faced steep losses in recent months amidst the decline in AI infrastructure stocks.

6:30At the end of July, situational awareness sold off the majority of its public portfolio to Ken Griffin's Citadel, although the fund held on to its Anthropic shares. Its assets under management reportedly fell from$20 billion to$10 billion. But on the bright side, Aschen Brenner did not let those setbacks get in the way of his wedding. That's all for now. For the latest in tech news, go to TechCrunch.com.

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