# The Bitter Lesson Is Coming for Biology by Richard Socher > The bitter lesson is coming for biology: simple end-to-end models trained with enough data and compute can outperform elaborate collections of expert rules. Biology looks impossibly complex today, but massive perturbation studies will make increasingly useful general models possible. > **— Adapted from Richard Socher**, *MOONSHOTS Live, October 2026* ## Sources and Context - **Recording or publication:** [MOONSHOTS Live at 21:37](https://www.youtube.com/watch?v=Blyb1D927pM&t=1297s) — 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 [21:37](https://www.youtube.com/watch?v=Blyb1D927pM&t=1297s):** “I don't know. You're probably joking, but prob- maybe some, some readers, uh, haven't heard of it. So the bitter lesson is basically that human experts had all these really clever ideas and beautiful theories, but really what you needed to do was the sim-- like, to make real progress, is the simplest method that you can come up with, a large neural network that you can train end to end, uh, as a general function approximator. The simplest model you can come up with, and then just a ton of data and compute to like actually train that model, and that usually outperforms at scale all the clever little hacks that human experts had come up with, uh, before. And so the bitter lesson has worked for NLP. Ten, twenty years ago, you would have asked an NLP expert, "Can you have one model to have any conversation with you, prompt of any kind of question?" Prompt engineering I like invented and was nicely cited by the early GPT papers. But like the same thing, the same state, uh, is biology is currently in. There are so many complexities, and the experts know so much. They feel like there's no way you can instill all of that knowledge into one model. But if you have enough data, you can. And now you have companies like Tahoe Therapeutics and so on that are creating these massive perturbation studies. And yes, all models are wrong, but more and more of them will be useful as we collect more and more data about biology. And that is, I think, one of the biggest driving factors, uh, for the impact of AI.” - **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/The Bitter Lesson|The Bitter Lesson]] - [[wiki/Tahoe Therapeutics|Tahoe Therapeutics]] ## Share on Social Media ``` The bitter lesson is coming for biology: simple end-to-end models trained with enough data and compute can outperform elaborate collections of expert rules. Biology looks impossibly complex today, but massive perturbation studies will make increasingly useful general models possible. — Adapted from Richard Socher, MOONSHOTS Live, October 2026 https://bryantmcgill.com/simple-reminders-richard-socher-the-bitter-lesson-is-coming-for-biology ```