Doge Chubuk, co-founder of Periodic Labs, explains why his company chose superconductivity as a moonshot target, comparing it to OpenAI building useful intermediate tools on the path to AGI.
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“To discover a exciting superconductor, we probably have to develop so many capabilities on the way there that's By themselves very useful. For example, automated synthesis, automated characterization, being able to model or predict high temperature superconductivity because we don't have a theory for it yet.”
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More from Doge Chubuk
Insight
Chubuk: AI cannot reason to breakthrough superconductors from training data alone
“I think it's still true that it would be difficult to just reason your way into a much better superconductor. I actually would guess that there's a law out there that we haven't discovered yet that says that you can't just look at your training set that's diff…”
Doge ChubukNov 6, 2025▶ 12:56Inside a $300 million bet on AI for physical R&D
Insight
Chubuk: LLMs already bridge solid-state chemistry and physics better than human specialists
“Like, there was probably a time when a physicist could contribute and be one of the best in the world on many fields of physics, but it's definitely not true today, and this is one of the reasons I think we are very excited about LLMs, because when you talk to…”
Doge ChubukNov 6, 2025▶ 26:37Inside a $300 million bet on AI for physical R&D
“So what O-one showed is if you spend test time compute, you can get better results. So that was very exciting to me because there was one way of investing resources that was beyond the training set.”
Doge ChubukNov 6, 2025▶ 6:30Inside a $300 million bet on AI for physical R&D
AssertionNot checkable as stated
Chubuk: AI is currently not better than humans at hypothesis generation
“It does seem like today there are things that ML, AI is better than humans, but one of those things is not hypothesis generation.”
Doge ChubukNov 6, 2025▶ 22:54Inside a $300 million bet on AI for physical R&D
Disclosure
Chubuk: GPU compute and training costs drove Periodic Labs' $300M seed
“We are going to train LLMs, we are going to use GPUs to run simulations, so that does end up being a large part of the cost. Yeah, it's funny, like, before, you know, if you asked me this question 10 years ago, I would have thought that the biggest part of the…”
Doge ChubukNov 6, 2025▶ 24:05Inside a $300 million bet on AI for physical R&D
Insight
Chubuk: Science requires out-of-domain generalization unlike standard ML
“Machine learning works best on the training set distribution. But in science and technology, we almost only care about auto-domain generalization, right?”
Doge ChubukNov 6, 2025▶ 6:14Inside a $300 million bet on AI for physical R&D
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