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Briefings and ideas about AI scientific discovery from AI podcast discussions and the research they cite, with links to the original sources.
Goodfire's Eric Bigelow said its Silico agent did the research for his Forking Fast paper within a week. He says researchers still need to supply context, close guidance and the scientific taste that agents don't yet have.
Google DeepMind's Pushmeet Kohli said scientists need models with calibrated confidence and known limits more than human-readable inner workings. Biohub's Sal Candido said protein models hold knowledge not yet unlocked.
Biohub's Sal Candido and Google DeepMind's Pushmeet Kohli argued on a panel that scaling laws in biology must be found, not assumed, and that the problem should decide whether to build better models or gather new data.
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.
Nathan Labenz argues an AI-assembled music track shows models generalizing beyond tasks with checkable answers. Mercor's Edward Hu is more conditional: progress depends on how clearly and cheaply 'better' can be measured.
After the 2026 medicine Nobel for optogenetics, Moonshots panelists argued the prizes lag AI-era science. Alexander Wissner-Gross said he once nudged Ed Boyden toward the field after reading Vernor Vinge.
On Moonshots, Emad Mostaque argued governments should buy AI compute for universities and open science. Alexander Wissner-Gross said private labs and markets should decide. Dave Blundin described being outbid for a 72-GPU system.