# Biology May Be a Better Fit for AI Than Physics by Richard Socher > Biology may be a better fit for AI than physics. Calculus excels at separated physical phenomena; neural networks excel at combining countless interacting parts. We understand individual neurons, bacteria, and cells, but AI can help reveal what happens when those pieces become a complex living system. > **— Adapted from Richard Socher**, *MOONSHOTS Live, October 2026* ## Sources and Context - **Recording or publication:** [MOONSHOTS Live at 19:15](https://www.youtube.com/watch?v=Blyb1D927pM&t=1155s) — The poster text is an explicit non-verbatim adaptation assembled from the source excerpts below. It preserves the speaker's argument while removing spoken-language filler, restoring the subject, and completing the mechanism or consequence needed for independent use. - **Exact transcript excerpt at [19:15](https://www.youtube.com/watch?v=Blyb1D927pM&t=1155s):** “I think the biggest domain is actually going to be biology. Physics has been interestingly stuck in many ways. Like, they, there's just, um, really powerful ideas like E equals MC squared, so you can get a ton of energy out of potentially a little mass, and then we get nuclear energy out of that. Uh, and so, uh, that then moved into and graduated in some ways into an engineering discipline, uh, which is where you have the real, real-life impact. Um, I think, you know, obviously, like, fusion will be great to finally get figured out, and there's a lot of really cool, like, companies and, and big labs and, you know, tokamaks are ready to balance, uh, like plasma inside a tokamak's like, is a very hard control problem that, where AI is being used. Um, I hope we can eventually make better theories for, uh, you know, quantum gravity and all kinds of other complex issues and, and have, get a better world sort of formula. Um, I-- unfortunately, the dataset collections these days require often like a large hadron colliders and just like billions of dollars, and it's like quite expensive, actually. So in a weird way, biology is a better fit for AI because what calculus did for physics, like understanding really well s- micro, uh, in-individualized separated phenomena, neural nets are great in combining all these little things. Like, we know what a neuron does, we know what one, uh, bacteria does in our microbiome, but then as they all come together and form these really complex interactions, we don't really know anymore how that works, and so that's where AI, I think, will help us more.” - **Reconciled source dossier:** [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] — preserves broadcast order, speaker reconciliation, editorial conventions, and the surrounding argument from which this Reminder was promoted. ## Related Articles and Collections - **Collection:** [[collections/Simple Reminders|Simple Reminders]] - **Collection:** [[collections/Neurotech|Neurotech]] - **Article:** [[articles/Longevity Escape Velocity|Longevity Escape Velocity]] - **Article:** [[articles/Mechanistic Intelligence Is Humanity's Greatest Liberation|Mechanistic Intelligence Is Humanity's Greatest Liberation]] ## Related Topics - [[wiki/Richard Socher|Richard Socher]] - [[wiki/AI-Driven Biology|AI-Driven Biology]] - [[wiki/Biological Simulation|Biological Simulation]] ## Share on Social Media ``` Biology may be a better fit for AI than physics. Calculus excels at separated physical phenomena; neural networks excel at combining countless interacting parts. We understand individual neurons, bacteria, and cells, but AI can help reveal what happens when those pieces become a complex living system. — Adapted from Richard Socher, MOONSHOTS Live, October 2026 https://bryantmcgill.com/simple-reminders-richard-socher-biology-may-be-a-better-fit-for-ai-than-physics ```