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.

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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

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TIMESTAMPS:

00:00:00 Cold open: A demoralizing message to young builders

00:02:04 CyberFund sponsor read

00:02:50 From symbolic AI to machine learning systems

00:05:42 Why AGI is mostly a PR term

00:08:48 A collectivist, economic perspective on AI

00:11:33 Why LLMs need system design, not hype

00:14:50 Predictability beats faux understanding

00:17:55 AlphaFold, bias, and prediction-powered inference

00:21:48 Stop anthropomorphizing intelligence

00:27:44 Drug discovery as an incentive problem

00:32:29 The three-layer data market

00:38:07 Social knowledge, markets, and culture

00:45:39 Creator economics beyond Spotify

00:48:30 How science-fiction AI narratives mislead young builders

00:51:45 AI should improve humans, not replace them

00:56:42 Safety is a property of the whole system

00:58:12 Silicon Valley gurus and the cream off the top

01:00:47 Game theory, mechanism design, and contracts

01:04:39 Conformal prediction, e-values, and anytime inference

01:08:11 A new liberal arts triangle for the AI era

01:11:30 The Bayesian duck and markets as uncertainty reduction

ReScript (transcript, PDF, refs etc) - https://app.rescript.info/public/share/fb68f94af29d3745c6cf6125e01328b5

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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

[00:33:47] 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:

[00:28:20] 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

Beth Barnes and David Rein on the one graph that ate the AI timelines discourse, and why the two people who built it are the most careful about how you read it.**SPONSOR**Prolific - Quality data. From real people. For faster breakthroughs.https://www.prolific.com/?utm_source=mlstInterview: https://youtu.be/cnxZZTl1tkk---Beth Barnes and David Rein from METR on the one graph that ate the AI timelines discourse, and why the people who built it are the most careful about how it gets read.Beth founded METR after leaving OpenAI alignment. David is first author on GPQA and co-author on HCAST and the METR Time Horizons paper. Together they built the measurement Daniel Kokotajlo called the single most important piece of evidence on AI timelines: the log-linear line of "how long a task a frontier model can complete at 50% reliability" vs release date.The conversation opens on reward hacking. Current models can articulate in chat why a behaviour is undesired and then execute it anyway as agents. From there: construct validity, Melanie Mitchell's four-problem taxonomy, and the ARC-AGI 1-to-2 collapse as a worked example of adversarially-selected benchmarks regressing once labs target them. Beth's counter: METR deliberately does not adversarially select. David's: models do not have to do the right thing for the right reasons.Methodology, then specification — David's compiler analogy, Beth on four-month tasks as expensive to evaluate rather than unspecifiable. Then the SWE-bench reality check, the METR finding that half of passing PRs would not be merged, and Beth's horses-versus-bank-tellers analogy for the labour market.The close: monitorability, the coin-spinning boat, two-year recursive self-improvement, and Beth's line that "overhyped now" and "big deal later" are not correlated claims.---TIMESTAMPS:00:00:00 Intro00:02:06 Sponsor break: Prolific human-feedback infrastructure00:02:33 Welcome and the scalable oversight motivation00:06:02 Construct validity, benchmark pathologies and the Chollet worry00:15:45 Time Horizons: human time, HCAST tasks and the 50% logistic00:24:50 Is human difficulty really one variable?00:33:05 Agent harness evolution and the inference-compute dividend00:40:00 Scaffolding bells, token budgets and the credit-assignment problem00:44:15 Look at the damn graph: regularisation bug and reliability nuance00:50:00 Why 50%? Reliability, reward hacking and pizza-party transcripts00:55:20 Extrapolation risk and straight lines on graphs00:59:25 Software engineering as a specification acquisition problem01:07:40 Compilers also made ugly code: vibe-coding quality and Claude on METR Slack01:15:15 Strongest defensible claim, Carlini's compiler swarm and AI 202701:23:45 SWE-bench merge rates, the bank-teller analogy and horses01:31:45 Scheming, alignment faking and the mentalistic vocabulary problem01:40:45 Reward hacking, monitorability and chain-of-thought faithfulness01:45:25 Recursive self-improvement, knowledge vs intelligence and closing

ReScript: https://app.rescript.info/public/share/de3bb40cc02ee39fdf36e2c60366eb4d

(PDF, refs, transcript etc)

Robert Lange, founding researcher at Sakana AI, joins Tim to discuss *Shinka Evolve* — a framework that combines LLMs with evolutionary algorithms to do open-ended program search. The core claim: systems like AlphaEvolve can optimize solutions to fixed problems, but real scientific progress requires co-evolving the problems themselves.

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• Why AlphaEvolve gets stuck — it needs a human to hand it the right problem. Shinka tries to invent new problems automatically, drawing on ideas from POET, PowerPlay, and MAP-Elites quality-diversity search.

• The *architecture* of Shinka: an archive of programs organized as islands, LLMs used as mutation operators, and a UCB bandit that adaptively selects between frontier models (GPT-5, Sonnet 4.5, Gemini) mid-run. The credit-assignment problem across models turns out to be genuinely hard.

• Concrete results — state-of-the-art circle packing with dramatically fewer evaluations, second place in an AtCoder competitive programming challenge, evolved load-balancing loss functions for mixture-of-experts models, and agent scaffolds for AIME math benchmarks.

• Are these systems actually thinking outside the box, or are they parasitic on their starting conditions? When LLMs run autonomously, "nothing interesting happens." Robert pushes back with the stepping-stone argument — evolution doesn't need to extrapolate, just recombine usefully.

• The AI Scientist question: can automated research pipelines produce real science, or just workshop-level slop that passes surface-level review? Robert is honest that the current version is more co-pilot than autonomous researcher.

• Where this lands in 5-20 years — Robert's prediction that scientific research will be fundamentally transformed, and Tim's thought experiment about alien mathematical artifacts that no human could have conceived.

Robert Lange: https://roberttlange.com/

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TIMESTAMPS:

00:00:00 Introduction: Robert Lange, Sakana AI and Shinka Evolve

00:04:15 AlphaEvolve's Blind Spot: Co-Evolving Problems with Solutions

00:09:05 Unknown Unknowns, POET, and Auto-Curricula for AI Science

00:14:20 MAP-Elites and Quality-Diversity: Shinka's Evolutionary Architecture

00:28:00 UCB Bandits, Mutations and the Vibe Research Vision

00:40:00 Scaling Shinka: Meta-Evolution, Democratisation and the Three-Axis Model

00:47:10 Applications, ARC-AGI and the Future of Work

00:57:00 The AI Scientist and the Human Co-Pilot: Who Steers the Search?

01:06:00 AI Scientist v2, Slop Critique and the Future of Scientific Publishing

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REFERENCES:

paper:

[00:03:30] ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution

https://arxiv.org/abs/2509.19349

[00:04:15] AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery

https://arxiv.org/abs/2506.13131

[00:06:30] Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents

https://arxiv.org/abs/2505.22954

[00:09:05] Paired Open-Ended Trailblazer (POET)

https://arxiv.org/abs/1901.01753

[00:10:00] PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem

https://arxiv.org/abs/1112.5309

[00:10:40] Automated Capability Discovery via Foundation Model Self-Exploration

https://arxiv.org/abs/2502.07577

[00:15:30] Illuminating Search Spaces by Mapping Elites (MAP-Elites)

https://arxiv.org/abs/1504.04909

[00:47:10] Automated Design of Agentic Systems (ADAS)

https://arxiv.org/abs/2408.08435

PDF : https://app.rescript.info/api/sessions/b8a9dcf60623657c/pdf/download

Transcript: https://app.rescript.info/public/share/SDOD_3oXOcli3zTqcAtR8eibT5U3gam84oo4KRtI-Vk