The Information Bottleneck

In this episode, we talked with Daniel Litt, a mathematician at the University of Toronto. In March 2025, Daniel bet that AI would not be able to autonomously produce an Annals of Mathematics-level paper by 2030. He has since conceded. He told us what changed and why. The models became superhuman at a small set of skills, like knowing the entire literature and grinding through technical arguments, and that turned out to be enough to do real math. They are still weak at the fuzzy part of the job, like figuring out what is actually true about an object or which...

In this episode of the the information bottleneck podcast, we talked with Andy Hall, a Stanford professor and MTS at Anthropic. The conversation explores the political economy of AGI, analyzing how superintelligence could reshape job markets, wealth redistribution, and public trust in government. Hall presents an optimistic view that hyperintelligent agents could enhance democracy by helping citizens make better political decisions, likening AI's potential impact to the invention of the printing press. The discussion also covers the challenges of regulating open-source models, the origins and mitigation of ideological biases in AI, and the geopolitical implications of the AI race...

John Platt is a Google Fellow and leads the Applied Science team at Google Research, the group working on climate and science. He's also the person behind SMO and Platt scaling. In this episode, we talk with him about how Google decides which scientific problems are worth its time, and what happens when AI meets real physical systems. A big part of the conversation is contrails. A small share of flights creates most of the warming from contrails, which together add up to roughly 1% of human-caused warming. John explains how his team detects contrails from weather satellites and predicts...

Yuandong Tian spent many years at Meta FAIR and recently left to co-found Recursive Superintelligence, a company building AI that improves itself. In this episode, he tells us why.We start with his work on computer Go, where he built DarkForest before AlphaGo came out. A few years later came OpenGo, which played Korean professionals on a single GPU and didn't lose a game. He then tried to bring RL to real-world problems and found that the design of the action space mattered more than the algorithm.We also talk about Coconut, his paper on...

Yuandong Tian spent many years at Meta FAIR and recently left to co-found Recursive Superintelligence, a company building AI that improves itself. In this episode, he tells us why.We start with his work on computer Go, where he built DarkForest before AlphaGo came out. A few years later came OpenGo, which played Korean professionals on a single GPU and didn't lose a game. He then tried to bring RL to real-world problems and found that the design of the action space mattered more than the algorithm.We also talk about Coconut, his paper on...

Chris Manning is a legend in NLP. He's a professor of linguistics and computer science at Stanford and ran the Stanford AI Lab. His course CS224N, Natural Language Processing with Deep Learning, is where a whole generation of researchers learned the field. If you've worked on anything in NLP over the last 25 years, you've probably used his work.We start with what linguistics gave machine learning that ML wouldn't have figured out on its own, and what's left for NLP researchers now that LLMs handle most of the classic tasks. Chris thinks the open problems have...

Andrew Dai spent over a decade at Google Brain and DeepMind, where he co-wrote the 2015 paper that introduced language model pre-training followed by fine-tuning, and later co-led pre-training data for Gemini. He's now co-founder and CEO of Elorian AI, which is building models for visual reasoning.We talk about how his pre-training result started as a bug, why next-token prediction scales better than other objectives, and what went wrong for Google in the early LLM race. The second half is about vision: why today's frontier models still can't count objects in a photo, why he thinks reasoning...

Zachary Lipton is an associate professor at Carnegie Mellon University and a co-founder of Abridge, a healthcare AI company (and a jazz saxophonist!). His research spans machine learning, healthcare, and the broader impact of AI.In this episode, we discuss what AI can (and cannot yet) do in healthcare. We talk about why medicine is harder to automate than coding, AI scribes and clinical decision support, drug discovery, whether AI could eventually replace doctors, and the role of open models and intelligent routing.In the second half, we turn to the future of AI research...

What comes after scaling?We talk with Sara Hooker, co-founder and CEO of Adaptation Lab, about why the next generation of AI may look very different from today's static models. Sara argues that models should continuously adapt to new tasks, data, users, and environments—and that doing this efficiently will require rethinking much more than fine-tuning.We discuss continual learning, AutoScientist and automated research, why non-verifiable tasks may become the next major bottleneck, and why interfaces could be as important as the models themselves. We also get into open vs. closed mo...

Alexia Jolicoeur-Martineau is a Principal Researcher at Microsoft and the author of "Less is More: Recursive Reasoning with Tiny Networks," the paper behind the Tiny Recursive Model that hit about 45% on ARC-AGI-1 with a fraction of the parameters of frontier systems. It won the 2025 ARC Prize paper award.She read the hierarchical reasoning paper, thought the potential was real and the explanation was not, and rebuilt it without the mouse brains: a small network that carries a hidden state and a current answer, thinks for a few steps, updates, and repeats, with the gradient truncated at each...

Rohan Anil spent eleven and a half years at Google, where he went from writing memory allocators to large-scale linear solvers, then optimization at Google Brain, where he co-developed distributed Shampoo and led optimization for PaLM and Gemini pre-training, including the work that produced Gemini Flash. He then joined Anthropic's pre-training team, and left before the IPO to co-found Core Automation with Jerry Tworek (ex-VP of Research at OpenAI). We talk with him about how Brain worked at its peak, why he left two of the world's best labs, and what he thinks is missing from today's...

John Langford, one of the heads of Microsoft's AI Labs, the creator of Vowpal Wabbit, and a co-inventor of CAPTCHA, joins us to talk about world models. Transformers need orders of magnitude more data than humans to learn the same thing, and John argues a compact, implicit world model is how you close that gap. He explains why he's skeptical of JEPA-style objectives, why a transformer's KV cache is the Ptolemaic epicycle model of belief states, and what his Next Latent work does differently.We also get into whether research still matters in the age of scale...

DescriptionTabular data is still where most of machine learning actually happens in industry, and the field has changed a lot in the last few years. In this episode we talk with David Holzmüller, a researcher at INRIA and one of the people behind TabArena, TabICL and RealMLP, about what the state of the art looks like right now and how to pick a model for your own data.We cover the shift to TabPFN-style foundation models that learn to learn from whole tables, why TabArena was built and what earlier benchmarks g...

Charles Frye (Modal, ex-Weights & Biases, Berkeley PhD) joins Ravid and Allen to explain why modern AI research is bottlenecked by compute, and why simply buying more GPUs doesn't solve it. We cover the three problems every lab hits (underutilization, saturation, resource sharing), when companies should actually train their own models, why inference is a "bad algorithm" for today's hardware, NVIDIA's monopoly, the OpenAI/Hugging Face hack and what it says about open models, and whether we're in a compute bubble.Key topicsAI infrastructure challenges and when to train your own modelsGPU...

Stella Biderman, Executive Director of EleutherAI, joins us the week an OpenAI model autonomously broke out of its sandbox and hacked Hugging Face. Stella calls it what she thinks it is, an offensive cyber operation, and argues it's part of a pattern: this is not the first containment failure at a frontier lab, and sandboxes have failed basically every time they've been tested for real.So we spend a good chunk of the episode on what actual containment would look like. Stella's argument is that the tools already exist, the labs just don't use them: run dangerous...

In this episode, Frank Hutter joins us to talk about TabPFN and why tabular data is suddenly the hottest problem in deep learning. Frank is a professor at the University of Freiburg and spent 15 years building the AutoML field before founding Prior Labs, which SAP just acquired for over a billion dollars.We get into why deep learning failed on tables for a decade and what in-context learning changed, how TabPFN is trained entirely on synthetic data, and why a model that never saw a real time series ended up beating specialized forecasting models. Frank also explains...

Surya Ganguli is a professor at Stanford and VP at General Catalyst, working at the intersection of physics, neuroscience, and AI. He started in string theory, moved to theoretical neuroscience, and now uses tools from statistical physics to understand both brains and neural networks.We talk about why deep learning theory is finally catching up to practice,  including his group's recent work explaining neural scaling laws, and why smarter data selection could beat them entirely. He also tells the origin story of diffusion models, which were invented in his lab as an attempt to violate the second l...

Brendan O'Donoghue, research director at Google DeepMind, makes the case for text diffusion as a real alternative to autoregressive generation. He walks through how discrete diffusion works, why diffusion samples are far more diverse and what that unlocks for RL, where the Gemma diffusion model actually stands against frontier models, and why the whole training and serving stack being hyper-optimized for autoregression is the main thing holding the approach back. The conversation also covers hardware trends favoring flops over bandwidth, AGI timelines and real-world bottlenecks, and why he thinks RL is still underhyped.Key...

Nathan Lambert spent three years as post-training lead at Ai2, where he built the OLMo models, and he writes Interconnects, one of the most-read technical newsletters in AI. He left Ai2 in June and is now working on a new project. He's also the author of the RLHF book. We talked a lot about open models, their capabilities, and why they are better than he expected. We get into what that means over the next two to five years, why he thinks recursive self-improvement is overblown, what the market for training environments actually looks like now, and why he...

Daphne Koller wrote the book that many of us learned probabilistic graphical models from, founded Coursera, and now runs insitro, which is trying to make drug discovery a machine-learning problem.We start with the bitter lesson. She agrees with most of it and then says where it stops working: biology doesn't have enough data, structure is how people understand anything, and making a drug is a question about an intervention that hasn't happened yet, not a pattern in data you already have.Most of the episode is about why drug discovery is hard. Ninety percent...