Source podcast
Conversations with AI engineers about the models, tools and systems they build.
Google DeepMind's Pushmeet Kohli said AlphaFold reproduces structures deposited in the Protein Data Bank, not proteins' shifting shapes. He sees raw cryo-EM images as one possible route to that motion, though his own attempt needs more work.
Liam Fedus said Periodic Labs is taking AI tools built for its own materials research to semiconductor partners through onsite engineers. He said the company could later charge for reaching goals, not only for assisting researchers.
On Latent Space, Periodic Labs' Liam Fedus and Ekin Doğuş Çubuk discussed why experiments remain essential in materials discovery. The conversation covered how the standard simulation method, density functional theory, usually models perfect crystals and can't easily predict properties like superconducting temperature.
Periodic Labs' Liam Fedus says the startup puts AI on lab instruments to capture richer data and automates bottlenecks one at a time. He calls full autonomy a non-goal and says humanoid robots would be a slower path to its aims.
Periodic Labs' Liam Fedus says its records of failed experiments, and of the whole process behind each result, give its AI data that published research lacks. He argues even stronger future models will still need physical experiments.
Liam Fedus of Periodic Labs says rewarding AI for finding a superconductor is too slow and noisy. The startup instead rewards it for identifying what X-ray data show a lab actually made, though ambiguity remains.
MIT PhD student Alex Zhang said AI generates nearly all recent GPU Mode leaderboard solutions, yet one expert's AI-steered kernel was essentially the only top-10 entry stable end to end. He argues experts remain strong verifiers.
MIT researcher Alex Zhang argues that TypeSafe's Jev, which returns fast typed decisions instead of prose, matters as a test of new language-model designs, not as a mere classifier. He says the open question is how it was trained.
Alex Zhang, an MIT PhD student, described Prime Agent, built with Prime Intellect: the agent acts only through Python and keeps its history on disk. He also cited outside RLM work, including Harvey's legal agents and Laude Institute's Headlong.
MIT's Alex Zhang says Claude Code, Codex and Pi share nearly all their design. He argues code-based recursive harnesses could help models generalize, citing his own early experiments on one open model as support.
MIT PhD student Alex Zhang guesses that about 95% of an agent swarm like OpenAI's Navier–Stokes run burns tokens on dead ends. He still calls OpenAI's approach right and says getting a swarm to converge is the hard part.
OpenAI's Ari Weinstein says computer-use agents can now operate software made for people, citing a two-hour meal order an agent did in 15 minutes. He adds that builders must earn trust through reliability, consent before payments and site limits.
