Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters. SPONSOR: --- Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open. Apply now: https://cyber.fund --- Jordan trained as a statistician and cognitive scientist, and his career has been spent building machine learning systems that work in the real world: supply chains, commerce, healthcare, and large economic systems. When the field rebranded itself as AI and then AGI, he did not follow. Instead he argues that the framing is wrong. AI is better understood as a collective economic system than as a race to build a disembodied superintelligence. We talk about why AGI is mostly a PR term, what machine learning achieved before the LLM hype cycle, and why the assistant-on-your-shoulder vision may be less compelling than it sounds. Jordan explains why explanations need to be actionable, not merely mechanistic; why AlphaFold's missing error bars matter; how prediction-powered inference changes the picture; and why drug discovery is an incentive-design problem rather than a pure pattern-matching problem. ERRATA: Science magazine ranked him the most influential computer scientist, not Nature --- --- REFERENCES: person: [00:02:50] Michael I. Jordan (homepage) https://people.eecs.berkeley.edu/~jordan/ paper: [00:06:01] A Collectivist, Economic Perspective on AI https://arxiv.org/abs/2507.06268 [00:18:09] AlphaFold https://www.nature.com/articles/s41586-021-03819-2 [00:20:36] Prediction-Powered Inference https://arxiv.org/abs/2301.09633 [00:24:38] On the Measure of Intelligence https://arxiv.org/abs/1911.01547 [00:33:47] On Three-Layer Data Markets https://arxiv.org/abs/2402.09697 [01:04:39] Conformal Prediction with Conditional Guarantees https://arxiv.org/abs/2107.07511 [01:04:51] A Tutorial on Conformal Prediction https://www.jmlr.org/papers/v9/shafer08a.html [01:06:00] E-Values Expand the Scope of Conformal Prediction https://arxiv.org/abs/2503.13050 [01:08:23] Computational Thinking https://www.cs.cmu.edu/~CompThink/papers/Wing06.pdf other: [00:11:33] The Bitter Lesson http://www.incompleteideas.net/IncIdeas/BitterLesson.html [00:20:50] How to use AI for discovery without leading science astray https://news.berkeley.edu/2023/11/09/how-to-use-ai-for-discovery-without-leading-science-astray/ [00:28:20] How Should the FDA Test? https://rdi.berkeley.edu/events/sbc-assets/pdfs/Summit%20session%20speaker%20slides%20submission%20form-s1-5%20%28File%20responses%29/Slides%20in%20PDF%20%28Please%20name%20the%20submitted%20file%20as%20_firstname_-_lastname_-slides.pdf%29.%20%28File%20responses%29/27-Michael%20Jordan-Session%20V.pdf#page=15 [00:28:40] Michael I. Jordan Session V Slides https://rdi.berkeley.edu/events/sbc-assets/pdfs/Summit%20session%20speaker%20slides%20submission%20form-s1-5%20%28File%20responses%29/Slides%20in%20PDF%20%28Please%20name%20the%20submitted%20file%20as%20_firstname_-_lastname_-slides.pdf%29.%20%28File%20responses%29/27-Michael%20Jordan-Session%20V.pdf#page=1 [01:08:13] Three Foundational Disciplines https://rdi.berkeley.edu/events/sbc-assets/pdfs/Summit%20session%20speaker%20slides%20submission%20form-s1-5%20%28File%20responses%29/Slides%20in%20PDF%20%28Please%20name%20the%20submitted%20file%20as%20_firstname_-_lastname_-slides.pdf%29.%20%28File%20responses%29/27-Michael%20Jordan-Session%20V.pdf#page=35 organization: [00:45:58] UnitedMasters https://www.unitedmasters.com/ book: [00:48:30] Human Compatible: Artificial Intelligence and the Problem of Control https://aima.cs.berkeley.edu/~russell/hc.html [01:00:56] Theory of Games and Economic Behavior https://press.princeton.edu/books/paperback/9780691130613/theory-of-games-and-economic-behavior