Most AI-for-science companies are selling shovels. Qichao Hu wants the gold.

In this episode, we talk with Qichao, the founder and CEO of Molecular Universe, the AI-for-science platform that grew out of SES AI, a high-energy-density battery developer he's run for fourteen years. His core distinction is that companies from the AI world build tools, such as foundation models that predict properties, while companies from the science world care about the final product, such as the new battery or material that actually ships. Molecular Universe sits firmly on the science side, and the difference shows up everywhere from what they publish to what they refuse to.

We get into the actual workflow of materials discovery and where AI compresses it. A single trial in a traditional lab can take a year with maybe a 40% success rate; the goal is to run a thousand candidates in parallel and turn that year into a week. Qichao walks through improving low-temperature fast-charging for EV batteries:  from hypothesis generation through molecule-, material-, and device-level property prediction, down to autonomous labs that synthesize and test the top candidates without a human touching a pipette.

The hardest problem, it turns out, isn't predicting molecular properties or measuring device performance, but it's the black box connecting the two. In batteries, that's the solid-electrolyte interface, which the field has been hand-waving about since the seventies. And the thing standing in the way of cracking it isn't a clever training trick but data: companies sitting on twenty years of records are finding it too messy, incomplete, and poorly labeled to train on, and are having to start collecting from scratch with new protocols and robots.

Timeline

• 00:13 — Intro and welcome;
• 01:19 — Shovel vs. gold
• 05:18 — Why the world's smartest scientist doesn't automatically give you a better battery
• 07:25 — The discovery workflow
• 09:37 — Exploration vs. exploitation
• 11:54 — Safety and filtering: screening novel molecules against banned and toxic-substance lists
• 17:55 — How hypotheses get generated, and where frontier LLMs help
• 20:29 — From hypothesis to ~400 formulations: property prediction, ranking, and handing off to autonomous labs
• 26:37 — "A foundation model for everything" — and the black box between molecular properties and device performance
• 30:01 — World models and physics
• 33:09 — The great unknown in batteries
• 37:08 — Simulation vs. reality: calibrating massive simulated datasets with a sliver of experimental data
• 41:47 — Lab robotics: how fast the hardware has caught up, and what a floor of autonomous labs looks like
• 43:50 — The real bottlenecks
• 50:21 — Pre-training from scratch vs. post-training LLMs, and why training tricks haven't reduced the need for good data
• 52:42 — Evaluation
• 55:42 — Publish the B+ model, keep the A model
• 58:05 — Five years out
• 1:00:37 — Closing thoughts and wrap

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We talk why the real bottleneck is power, not chips, and why that favors Nvidia and Google. Why MoE keeps winning even when dense models look better on paper and why he'd rather run fungible capacity at 95% than specialized chips at 60%. We also talk about quantization limits, where RL efficiency has to go next, and his case that AI is still under-hyped. We also get into cross-region training, sparse autoencoders and why interpretability hasn't taken off in open source, whether open models can close the gap, and a frank read on Anthropic's go-to-market.

Timeline

• 00:00 — Intro: the part of AI nobody talks about
• 01:20 — What "infrastructure for AI" actually means: the layers, from GPUs up to routing
• 02:59 — Why not just buy your own GPUs and do it yourself?
• 05:17 — The scale Fireworks runs at
• 06:35 — Hardware inflation, GPU costs, and the real risk hiding in commit duration
• 10:14 — Nvidia vs AMD vs TPUs, and why power is the bottleneck
• 11:57 — Mixing GPU types and generations; fungibility vs. specialization
• 14:22 — Once you have the GPUs, what's the next layer to build?
• 17:04 — Dense vs. MoE, and why the hardware picks the winner
• 21:07 — Quantization: is FP4 the floor? TurboQuant and INT vs. FP
• 24:28 — How tied are the algorithms to the hardware?
• 25:12 — DeepSeek, DeepGEMM, and next-token prediction as reconstruction loss
• 28:50 — Why RL is still wildly inefficient compared to pre-training
• 30:08 — Speculative decoding, AI-generated kernels, and auto-research
• 34:00 — The AGI question: why text gets automated but vision may stay expensive
• 37:07 — Hype check: why Benny thinks AI is still under-hyped
• 41:28 — Training vs. inference at the infrastructure level
• 44:12 — Scaling across data centers: cross-region training with Cursor
• 45:40 — Sparse autoencoders, interpretability, and why open source is human-constrained
• 49:04 — Will open models catch up — on quality and on compute?
• 51:41 — Are we plateauing? Opus 4.7 vs. 4.6 and the coming data wars
• 54:41 — Physical limits, HBM, and whether chips keep getting faster
• 58:17 — The belief about inference everyone gets wrong
• 59:31 — Anthropic, mythos, and a frank take on go-to-market
• 1:04:41 — Wrap-up

  ---

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We start with what's wrong with peer review and grant funding: why it takes years to publish, why reviewers are often your own competitors, and why the whole thing is locked to an economic model that rewards publishing more papers, not better ones. Oded explains why he doesn't call QED "peer review" at all, and what it would take to actually validate science instead of just stamping it.

Then we get into the biology. C. elegans has exactly 959 cells, every one of them named, and a fully mapped brain. Oded's lab studies how a worm's experiences get passed to its offspring through RNA rather than DNA — meaning what happens to a worm in its lifetime can change its descendants. We also talk about using ancient DNA to reassemble the Dead Sea Scrolls, what AI can and can't do for biology, and why he wants to build an "Ironman suit" for researchers rather than replace them.

00:00 Intro

01:35 Why scientific publishing is broken

04:02 Years to publish, and what it costs science

07:20 Bad reviewers, conflicts of interest, and the money

10:47 Why preprints don't fix it

15:37 How AI conferences handle review

22:07 Conferences vs. journals — does slow review help?

25:22 Building QED: review, not peer review

30:02 Tracking a paper from idea to submission

33:11 What writing a grant actually involves

35:00 The ERC reviewer crisis

37:06 Tailoring feedback to your field

41:48 Switching to biology

44:30 Every cell has a name: inside C. elegans

46:28 Inheritance without DNA

48:16 What the worm "thinks" changes its offspring

51:58 Reassembling the Dead Sea Scrolls with ancient DNA

56:07 Psychedelics and worms

58:36 Can AI run the research itself?

1:04:49 Automation vs. validation

1:07:12 The origin of life

1:08:49 Why people reject AI-written work

1:16:18 Will humans still have a role?

1:17:39 Wrap-up

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Alex makes the case that a job is a bundle of tasks, not a single thing AI either does or doesn't do, and that the number of people who should actually care about is how much consumer demand responds to falling prices. Get that wrong and you predict mass layoffs. Get it right and you sometimes predict more hiring. We get into why the automation panic is two centuries old, why he thinks blue-collar work is in more danger than white-collar, and why the people already winning are the ones adopting AI fastest.

We also cover the AGI versus ASI distinction and why it changes everything for the economy, what happens when there's no moat and open models stay six to eight months behind, the three-tier pricing future he sees coming after the 2026 compute crunch, and what any of this means if you're deciding whether to send your kids to college.

• The episode was recorded before Alex joined Google

Timestamps

00:00 Meeting Alex Imas

00:44 Will AI take our jobs?

03:35 Is this an AI question or an economics question?

06:18 The economy is already behind the AI we have

07:43 Why AI adoption is K-shaped

12:51 Was Andrew Yang right?

13:45 The automation panic is 200 years old

16:46 Dario's six-month claim, and why we don't see it yet

17:22 A job is not a task

22:38 The three numbers that actually predict the labor market

22:42 The chess engine analogy and the centaur phase

25:45 Recursive self-improvement and the hamburger problem

30:06 Should AI labs be the ones answering alignment questions?

31:17 The "invisible hand wave" and why nobody wants fully autonomous AI

33:27 AGI vs ASI, and why the difference is everything

35:28 Commodities vs relational goods

41:14 Star Trek, replicators, and predicting with sci-fi

45:20 Inequality and the Upper West Side VCs

46:21 Your money manager was automated in the 1960s

50:47 Are OpenAI and Anthropic overvalued? The moat problem

54:29 What has to be true for the losses to make sense

55:43 Cognitive atrophy and monopoly fears

57:00 The 2026 compute crunch and the three-tier pricing future

1:01:52 The Apple vs Android analogy

1:03:54 A rich-country perspective

1:04:16 Protecting the skills that actually matter

1:07:02 Will not using AI become a status symbol?

1:08:53 Does capitalism even survive?

1:13:44 Redistribution becomes the political battleground

1:18:16 Blue collar vs white collar: who's really at risk

1:21:18 Advice for parents in an AI world

1:22:43 Saving for retirement when the Valley says don't

1:25:06 Will non-elite colleges survive?

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

  ---

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We also talk about ClawBench, his new benchmark that deploys agents to over 140 real production websites to do things people actually want done, such, such as ordering food, booking tickets, and applying for jobs. The best model in the world completes about a third of these tasks. We unpack why: bot detection, models that refuse to click "pay," agents that give up the moment an environment doesn't match their training, and harnesses that can swing results by 20% without changing the model at all.

Along the way, we cover the overlooked science of evaluating pre-training, data flywheels, and synthetic environments for agent training, and whether RL teaches models to reason or just surfaces what's already there. We close with Wenhu's predictions: exploration and adaptability will improve rapidly, but security will become the field's hardest problem as agents gain real permissions in the real world.

Timestamps

00:00 – Intro
00:55 – What good evaluation means, and how it's changed since the early GPT days
03:35 – Benchmarks as the field's loss function
05:50 – Contamination: the problem nobody fully solves
08:08 – MMLU-Pro scores: real progress or training on the test set?
11:05 – Can you measure creativity?
12:34 – Why human judges and arenas are unreliable — and what to use instead
19:22 – What a good benchmark actually looks like
22:34 – Chain of thought: signal or scratchpad?
26:01 – Auto-research and hill-climbing agents
28:52 – Harnesses: 20% swings without touching the model
32:28 – Safety, model release, and an "FDA for models"
36:53 – The overlooked science of pre-training evaluation
43:49 – Designing pre-training benchmarks when one run costs a billion dollars
49:45 – ClawBench: agents on 140+ live websites, and why the best model gets 33%
54:42 – How MMLU-Pro and MMMU-Pro were born from public complaints
59:16 – Pixel agents vs. APIs: will MCP kill computer use?
1:02:11 – Training agents: data flywheels and synthetic environments
1:05:43 – SFT vs. RL, and does RL teach reasoning or reveal it?
1:09:21 – What gets solved next year — and what doesn't
1:14:32 – Undervalued ideas, and what's next for ClawBench

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

  ---

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We start with JEPA. Jürgen argues that the method LeCun named in 2022 is the same family he published in 1992 as Predictability Maximization. From there he traces the adversarial lineage back further still, to his 1990 world-model paper and 1991 Predictability Minimization  -  the curiosity-driven minimax games he sees as the real origins of GANs.

We also talk about why these ideas took thirty years to land, why today's trillion-dollar data-center buildout is driven by AGI fear, and why he thinks Apple may come out ahead.

The back half turns to what he sees as the real frontier: physical AI. Today's systems are superhuman behind the screen but helpless at a leaky pipe, and until a robot can use human tools, there's no AGI. He discusses self-replicating, self-improving machines as "a new kind of life," reframes continual learning and test-time training as ideas from his 1991 fast-weight work, and detours through Solomonoff's universal prior, Hutter's AIXI, and the Gödel machine.

We close on the subject Jürgen is famous for: scientific credit. He makes his case for rigorous attribution, casts himself as a "speaker for the dead" championing forgotten pioneers like Ivakhnenko, and reflects candidly on whether the fights are personal.

Timeline

00:30 — What JEPA is, and the 1992 Predictability Maximization story

04:54 — Implementing PMAX: autoencoders, Siamese networks, Infomax

09:10 — Predictability Minimization, factorial codes, and the roots of GANs

16:00 — Why it took 30 years: the economics of compute

20:52 — Data, the web, and 1990 as the origin point

23:09 — Hardware inflation, the trillion-dollar buildout, and the coming crash

34:05 — Physical AI: the plumber problem and self-replicating machines

41:14 — Which 90s ideas are being scaled right now

45:26 — Continual learning and test-time training as "old hats"

55:19 — Measuring intelligence: Solomonoff, AIXI, and the Gödel machine

1:05:26 — Self-replication and von Neumann

1:09:51 — Will he see AGI in his lifetime?

1:10:42 — Credit, integrity, and being a "speaker for the dead"

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

• ---

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We go back to his lab in the early 90s and ask how one small group came up with so many of the ideas that are now being scaled to a thousand billion dollars, back when compute was ten million times more expensive. A lot of the episode comes down to one distinction he keeps making: prediction vs. decision-making. His take is that LLMs are very good prediction machines that imitate the web, but that’s only half the problem. To actually act in the world, you need a controller that uses a world model to plan. He talks about his 1990 work on world models and artificial curiosity, where the controller gets rewarded for running experiments that improve its own model (an adversarial setup years before GANs), why planning millisecond by millisecond doesn’t scale, and why you need sub-goals instead.

We also talk about compression as the core of understanding, from falling apples to Kepler to Einstein, and why we still don’t have a robot that can do what a plumber does, even though the AI behind the screen keeps getting better. Then the conversation moves to credit assignment: how “to Schmidhuber” became a verb, what he thinks is broken about the award system, and a long exchange on PMAX vs. JEPA. He ends on the real origins of deep learning and a prediction about self-replicating machines in space.

Timeline

00:00  Intro
00:55  1991 in Munich, and why that lab mattered
02:38  "I'm not very smart"  and why compute getting 10× cheaper every 5 years changed everything
04:25  Chess as an AI proxy
08:27  Artificial curiosity in the 90s vs. today's RL exploration
09:10  Why RL is harder than supervised learning
20:48  Coding agents vs. robots, and how a baby learns its own hands
26:20  Compression as understanding
33:40  What's actually missing on the road to AGI
37:30  Why millisecond-by-millisecond planning is stupid
47:44  Convergence to LLMs, GPUs, and how far we still are from the Bremermann limit
51:49  Unsupervised learning, factorial codes, and predictability minimization
58:12  Credit assignment: the fights with LeCun and the Nobel critique
1:02:13  On his last name becoming a verb
1:05:17  The award system's missing peer review
1:07:03  Closed labs and the decline of open research
1:13:23  Audience questions
1:34:02  Closing: who really invented deep learning?

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

  ---

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Max starts with what impresses him and what worries him about the LLM era, then makes the case that the next leaps will come from physical AI and from science itself. We dig into how machine learning actually works in the lab, world models and whether priors like geometry and symmetry should be built in or simply learned, and whether transformers will still rule a decade from now. At the end, we talk about CuspAI's climate mission, AI risk and regulation, Max’s new book, and where neuroscience might inspire the next wave of ML.

Timeline

• 00:00 — Intro
• 00:47 — Are we happy with the LLM era?
• 03:14 — Embodiment and physical AI
• 08:05 — Does "AGI" even matter as a term?
• 11:34 — Verifiers, RL, and why math/coding are tractable
• 13:17 — What actually shifted to make materials discovery work
• 14:42 — From molecules to biology and wet labs
• 16:26 — Working with real labs: timescales, friction, and the "Mira" agent
• 20:29 — Balancing simulators vs. experiments: the exploration–exploitation trade-off
• 23:44 — Active learning for experimental design
• 24:23 — Why active learning hasn't been central to LLMs
• 25:24 — A general loop for ML-for-science across domains
• 27:10 — Foundation models for chemistry: a "mother ship" plus a zoo of fine-tuned models
• 30:04 — Quantum mechanics, interpretation, and AI as a creative theorist
• 31:54 — World models and Yann LeCun's view; priors vs. learning
• 34:57 — Should world knowledge be explicit? (responding to Stefano Ermon)
• 36:41 — Vision: equivariance vs. transformers, and the role of optimization
• 40:32 — Best model for molecular properties in 10 years? Will transformers survive?
• 43:16 — CuspAI's climate focus and what motivated it
• 47:10 — One platform for every material class — what transfers and what doesn't
• 48:42 — Where does the risk of human extinction really come from?
• 51:06 — The "pause AI" debate and the arms-race reality
• 52:40 — Regulating powerful models: government vs. self-regulation
• 55:16 — Who should design AI regulation?
• 56:29 — The new book
• 1:00:31 — Compression, the information bottleneck, and renormalization
• 1:03:30 — The role of foundational principles in modern AI
• 1:04:06 — Waves in computing, the brain, and the next wave of innovation
• 1:07:11 — Neuroscience and ML: are we in a better position now?
• 1:09:17 — Conferences, the ICLR keynote, and finding the right people

  ---

  Music:

  • "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
  • "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
  • Changes: trimmed

  ---

  About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

In this episode, we sit down with Julia Kempe, a Professor at NYU's Center for Data Science and researcher at Meta FAIR's Foundations of Reasoning team,  for a wide-ranging conversation on the future of AI research.

We dig into why verifiable domains like mathematics may be on track to "fall" the way Go did. With formal verification through Lean and the Mathlib infrastructure, LLM agents can now generate and check proofs at scale, and Julia makes the case that a new industry of automated mathematical discovery is closer than most mathematicians believe. We explore why Erdős problems are already falling, what's still missing for harder fields like analysis and physics, and how synthetic data, curation, and verification fit together.

From there we get into the energy and scaling limits of frontier models, the case for academic research that big labs can't pursue, how to advise PhD students when Claude can already do their first-year work, the rise of AI safety and security as research priorities, and Julia's optimistic argument that AI tools are bringing back the Renaissance generalist  -  the researcher who can finally work fluently across math, biology, and beyond.

Timeline

• 00:00 — Introductions
• 01:00 — Defining reasoning and verifiable domains
• 04:00 — Lean, Mathlib, and the formalization of mathematics
• 10:00 — Constructive proofs, Erdős problems, and the new wave of "AI mathematicians"
• 14:00 — Will math be "solved"? Art, photography, and the changing nature of creative work
• 18:00 — Why physics is harder than math
• 22:00 — Moravec's paradox, evolution, and why robotics lags behind language
• 27:00 — The Renaissance is back: generalist researchers in the age of AI
• 29:00 — Advising students: math, programming, and what core education still matters
• 32:00 — Teaching and assessment when GPT can do the homework
• 35:00 — Anti-AI backlash, energy costs, and the security threat
• 40:00 — Scaling vs. efficiency
• 42:00 — Model collapse, synthetic data, and what's left to squeeze from the internet
• 44:00 — What's exciting next: AI for science, safety, robotics, memory, and planning
• 47:00 — Annotation costs as a proxy
• 50:00 — Superhuman models and what security even means against them
• 52:00 — AlphaGo as precedent for verifiable superhuman performance
• 54:00 — Hallucination, the Mirage paper, and whether these are solvable problems
• 56:00 — Why coding isn't fully solved yet
• 58:00 — Agent security, prompt injection, and the Wild West of deployed agents
• 1:01:00 — Regulation: what's needed and what's possible
• 1:04:00 — Advice for PhD students and what research academia should pursue
• 1:09:00 — Startup opportunities: robotics, security, and AI for finance
• 1:12:00 — Closing thoughts: use the tools, and build grassroots AI for good

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We discussed why children can learn language from around 100 million words while LLMs need trillions, and the surprising finding that as models get better at predicting the next word, they become worse models of how humans actually process language. Tal walked us through how his lab uses eye-tracking and reading-time data to compare model behavior to human behavior, and what that reveals about prediction, working memory, and the limits of current architectures.

We also got into nature versus nurture and how inductive biases can be instilled by pre-training on synthetic languages, world models and whether transformers actually use the geometric structure they encode, the BabyLM challenge and data-efficient language learning, and what mechanistic interpretability can offer cognitive science beyond just fixing model bugs. The conversation closed on academia versus industry, the role of PhDs in the current AI moment, and how AI coding tools are changing the way Tal teaches and evaluates students at NYU.

Timeline

• 00:13 — Intro and what cognitive science means
• 02:16 — Using computational simulations to understand how humans learn language
• 05:26 — How children learn language vs. how LLMs are pre-trained
• 07:53 — Why mainstream LLMs are not good models of humans
• 10:07 — Comparing humans and models with eye-tracking and reading behavior
• 13:52 — Sensory modalities, smell, and how much you can learn from language alone
• 16:03 — Animal cognition and decoding animal communication
• 17:00 — Nature vs. nurture, inductive biases, and what transformers can and can't learn
• 21:21 — Instilling inductive biases through synthetic languages
• 27:34 — The bouba/kiki effect and cross-linguistic sound symbolism
• 28:33 — Latent causal structure in language and whether models discover it
• 31:13 — Does knowing linguistics help build better models?
• 35:07 — World models: what they mean, and why transformers encode geometry but don't use it
• 39:13 — Tokenization, and why Tal doesn't like it
• 41:35 — Scaling laws and the inverse-U curve of model quality vs. human fit
• 44:34 — Where the human–model mismatch comes from: architecture, memory, and data
• 47:08 — Diffusion language models and sentence planning
• 48:21 — Data quality, synthetic data, and curriculum effects
• 50:54 — Comparing models at different training stages to human development; BabyLM
• 54:40 — What level of the model should we actually probe? Representations vs. behavior
• 1:01:04 — Mechanistic interpretability, Deep Dream, and human dreaming
• 1:02:11 — Cognitive neuroscience, intracranial recordings, and working memory
• 1:10:31 — Should you still do a PhD in 2026?
• 1:12:31 — Will software engineers lose their jobs to AI?
• 1:17:43 — Teaching in the age of coding agents: what changes in the classroom
• 1:20:54 — What's next: human-like LLMs as user simulators, and recruiting

  ---

  Music:

  • "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
  • "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
  • Changes: trimmed

  ---

  About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We get into why intelligence has to be defined in open environments, not closed ones, and what that means for how we measure progress. We push on the creativity question: today's models sample bottom-up from a softmax or a Gaussian, with no internal loop of consideration, and as Mengye puts it, we haven't understood creativity yet and we're already prepared to hand it over.

We also talk about what's missing for the next paradigm: continual learning, memory, embodied grounding, and smaller models that actually accumulate experience instead of re-deriving everything from scratch each call. Along the way, we get into JEPA and latent variables, biology as inspiration vs. blueprint, why frontier labs don't lean on explicit latents, the limits of synthetic data and world models, agent-to-agent communication, model uncertainty and forecasting, and whether ML education still matters when AI writes the experiments.

A grounded, contrarian conversation about where AI research should be looking next, beyond benchmarks, beyond scale.

Timeline

00:00 — Intro and welcome

01:24 — What is intelligence? Defining it relative to objectives and open environments

04:19 — Is intelligence really the path to human flourishing, or is it productivity?

04:57 — Safety, scalable oversight, and whether stronger models help or hurt

06:09 — What does "alignment" actually mean?

07:18 — Centralized vs. decentralized models: objectivity vs. personal meaning

08:50 — Hinton vs. LeCun: where Mengye stands on AI risk

10:29 — Bottom-up vs. top-down architectures and feedback loops

21:28 — Biology and AI: inspiration, not blueprint

24:14 — Biological plausibility, spiking nets, and where the analogy breaks

25:39 — JEPA, Mamba, and architectures beyond the transformer

27:31 — Language as a special modality: abstraction built for communication

29:04 — Are we too locked into the current paradigm? Risk of creativity collapse

30:09 — Synthetic data, simulation, and the brain's own generative models

31:43 — World models and physical AI: how babies actually learn 33:03 — The case for smaller, continually learning models

37:02 — The role of academic research in a frontier-lab world

39:47 — Why LLMs aren't funny: the creativity gap

40:35 — What research areas matter most: embodiment, continual learning, creativity

42:05 — Creativity is bounded by experience — and why bottom-up sampling isn't enough

45:35 — Agent-to-agent communication and the limits of sub-agents

46:39 — Model confidence, epistemic uncertainty, and forecasting

49:44 — Tokenization, static vs. dynamic worlds, and always-learning systems

52:20 — Latent variables, JEPA, and why frontier models skip them

53:40 — The future of ML education when AI writes the experiments

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We start with what a generative model actually is, and what it means to "generate" an image or a sound. Jesse explains the core idea behind diffusion in plain terms. You start with pure noise, and a neural network gradually cleans it up, step by step, until a realistic image emerges.

From there, we talk about why diffusion has come to dominate so much of generative AI. Because the model builds an image gradually, you can guide it along the way, nudging the output toward what you actually want, refining details, or combining it with other controls. We also discuss the common critique that diffusion is slow and how the field has largely addressed it through new techniques.

We zoom out to the bigger picture, too. Jesse shares his view on world models and whether diffusion is the right foundation for them. We talk about what makes a generative model genuinely good versus just good at gaming benchmarks, and why evaluating creativity and realism is so much harder than scoring a multiple-choice test.

Timeline

00:12 — Intro and welcoming Jesse

00:47 — Why Jesse wrote the book, and who it's for

03:29 — The three families of diffusion models, and why they're really one idea

05:14 — What makes a good generative model

07:39 — How do you even measure if a generated image is good

08:59 — Why diffusion beats autoregressive models for images

10:33 — Is diffusion still slow? How fast generation got fast

11:12 — A simple intuition for what a "score" is

14:12 — How the different flavors of diffusion connect under the hood

14:42 — Diffusion for text and proteins

17:12 — Consistency models and the push for one-step generation

22:12 — Diffusion for world models: simulating reality in real time

26:12 — Do world models need to understand language

35:12 — Is diffusion the right tool, or just a convenient one

38:12 — What benchmarks actually tell us, and what they miss

46:12 — Closing thoughts and where to find the book

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

We got into his bet with François Chollet that AI will hit superhuman mathematician level by 2026, and what that actually unlocks beyond math itself: verified software instead of vibe-coded apps that break when you refactor, AI systems you can actually trust because their reasoning is checkable, and a path to handling protein folding, chemistry, and parts of biology with real guarantees instead of hand-waving. Christian also walked us through how Math Inc's Gauss system pulled off a proof in two weeks that human experts had estimated would take another year.

We also covered xAI's first 12-person year, why Christian no longer buys the original batch normalization story, why he's sure transformers won't be the dominant architecture in five years, what mathematicians do in a world of cheap proofs, and his take on whether humanity will handle AI well. He distrusts humanity more than he distrusts AI.

Timeline

00:12 — Intros: Christian's background (Inception, Batch Norm, xAI, Math Inc)

01:29 — Building a frontier lab from scratch: the first 12 people at xAI

04:15 — Hiring for proven track records when 200K GPUs are at stake

06:07 — Elon's "dependency graph" and balancing long-term vision with investor demos

07:28 — Gauss formalizes the strong prime number theorem in 2 weeks

12:25 — What "formalization" actually means (and why it's not what most people think)

14:39 — Why Lean gives 100% certainty and why that matters for RL

15:26 — ProofBridge and joint embeddings across mathematical subfields 18:07 — Does math formalization transfer to coding and other fields?

21:44 — Can every domain be mathematized?

23:14 — Verified software, chip design, and why vibe-coded apps are dangerous

26:35 — Scaling Mathlib by 100–1000x

28:27 — Artisan formalizers vs. invisible machine-language formalists

33:26 — Can verification generalize?

45:19 — Revisiting Batch Norm: covariate shift, loss landscape, and what really happens

48:22 — Is normalization even necessary?

50:10 — What's actually fundamental in modern AI architectures

51:41 — Why Christian thinks transformers won't last 5 years

52:38 — The 2026 superhuman AI mathematician bet

55:15 — What's missing: better verification + a much larger formalized math repository

56:13 — Lean vs. Coq vs. HOL Light -  does the proof assistant actually matter?

59:26 — The role of mathematicians in 5–10 years

1:02:00 — A human element to mathematics: Newton, Leibniz, and competitive proving

1:03:25 — The telescope analogy: AI as the instrument that lets us see the math universe

1:05:19 — Job apocalypse or Jevons paradox?

1:08:41 — Advice for students

1:09:50 — Can we formally verify AI alignment?

1:11:52 — Closing thanks

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Rao has been working on planning and decision-making since long before deep learning, which makes him one of the most grounded voices on what today's reasoning systems actually do. We start with definitions of what reasoning is, why planning is the hard subset of it, and what changed when systems like o1 and DeepSeek R1 moved the verifier from inference into post-training. From there we get into where these models generalize, where they don't, and why benchmarks can be misleading about both.

A big chunk of the conversation is on chain-of-thought: what intermediate tokens are actually doing, why they help the model more than they help the reader, and what outcome-based RL does to whatever semantic content was there to begin with. We also cover world models and why Rao thinks the video-only framing is the wrong bet, the difference between agentic safety and existential risk, and what the planning community figured out decades ago that the LLM community keeps rediscovering.

Timeline

• (00:12) Intros
• (01:32) Defining "reasoning" and the System 1 / System 2 framing
• (04:12) Blocksworld vs Sokoban, and non-ergodicity
• (06:42) Pre-o1: PlanBench and "LLMs are zero-shot X" papers
• (07:42) LLM-Modulo and moving the verifier into post-training
• (10:12) Is RL post-training reasoning, or case-based retrieval?
• (13:12) τ-Bench and benchmarks that avoid action interactions
• (14:12) OOD generalization and what we don't know about post-training data
• (19:02) Does it matter how they work if they answer the questions we care about?
• (21:27) Architecture lotteries and why no one tries different designs
• (23:42) Intermediate tokens and the "reduce thinking effort" cottage industry
• (26:12) The 30×30 maze experiment
• (27:42) Sokoban, NetHack, and Mystery Blocksworld
• (34:58) Stop Anthropomorphizing Intermediate Tokens — the swapped-trace experiment
• (46:12) Latent reasoning, Coconut, and why R0 beat R1
• (50:12) How outcome-based RL erodes CoT semantics
• (52:12) Dot-dot-dot and Anthropic's CoT monitoring paper
• (53:42) Safety: Hinton, Bengio, LeCun
• (57:12) Existential risk vs real safety work
• (59:42) World models, transition models, and video-only approaches
• (1:03:12) Why linguistic abstractions matter — pick and roll
• (1:05:42) What the planning community knew in 2005
• (1:08:12) Multi-agent LLMs
• (1:09:57) Closing thoughts: the bridge analogy

  ---

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Zhuang is behind some of the most important papers in recent years (with more than 100k citations): ConvNeXt (showing ConvNets can match Transformers if you get the details right), Transformers Without Normalization (replacing LayerNorm with dynamic tanh), ImageBind, Eyes Wide Shut on CLIP's blind spots, the dataset bias work showing that even our biggest "diverse" datasets are still distinguishable from each other, and more.

We got into whether architecture research is even worth doing anymore, what "good data" actually means, why vision is the natural bridge across modalities but language drove the adoption wave, whether we need per-lab RL environments or better continual learning, whether LLMs have world models (and for which tasks you'd need one), why LLM outputs carry fingerprints that survive paraphrasing, and where coding agents like Claude Code fit into research workflows today and where they still fall short.

Timeline

00:13 — Intro

01:15 — ConvNeXt and whether architecture still matters

06:35 — What actually drove the jump from GPT-1 to  GPT-3

08:24 — Setting the bar for architecture papers today

11:14 — Dataset bias: why "diverse" datasets still aren't

22:52 — What good data actually looks like

26:49 — ImageBind and vision as the bridge across modalities

29:09 — Why language drove the adoption wave, not vision

32:24 — Eyes Wide Shut: CLIP's blind spots

34:57 — RL environments, continual learning, and memory as the real bottleneck

43:06 — Are inductive biases just historical accidents?

44:30 — Do LLMs have world models?

48:15 — Which tasks actually need a vision world model

50:14 — Idiosyncrasy in LLMs: pre-training vs post-training fingerprints

53:39 — The future of pre-training, mid-training, and post-training

57:57 — Claude Code, Codex, and coding agents in research

59:11 — Do we still need students in the age of autonomous research?

1:04:19 — Transformers Without Normalization and the four pillars that survived

1:06:53 — MetaMorph: Does generation help understanding, or the other way around?

1:09:17 — Wrap

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

A big part of the conversation was about why coding has become such a powerful setting for these tools. We discussed what makes code different from other domains, why agents seem to work especially well there, and how much of today’s progress comes not just from better models, but from better ways of using them. Sasha also gave an inside look at how Cursor thinks about training coding models, long-running agents, context limits, bug finding, and the balance between autonomy and human oversight.

We also talked about the broader shift happening in software engineering. Are developers moving to a higher level of abstraction? Is this just a phase where we “babysit” models, or the beginning of a deeper change in how software gets built? Sasha had a very thoughtful perspective here, including what he’s seeing from students, researchers, and engineers who are growing up native to these tools.

More broadly, this episode is about what it means to do serious technical work in a moment when the tools are changing incredibly fast. Sasha brought both optimism and skepticism to the discussion, and that made this a really grounded conversation about where coding agents are today, what they are already surprisingly good at, and where all of this might be going next.

Timeline
00:00 Intro and Sasha joins us
01:11 What “coding agents” actually mean
02:34 Why coding became the breakout use case
08:56 Long-running agents and autonomous workflows
15:08 How these tools are changing the work of engineers
17:15 Are people just babysitting models right now?
22:11 How Cursor builds its coding models
26:29 Rewards, training, and what makes agents work
34:53 Memory, continual learning, and agent communication
38:00 How context compaction works in practice
41:29 Why coding agents recently got much better
50:31 Refactoring, maintenance, and self-improving codebases
52:16 Bug finding, oversight, and verification
54:43 Will this pace of progress continue?
56:42 Can this spread beyond coding?
58:27 The future of Cursor and coding agents
1:03:08 Model architectures beyond standard transformers
1:05:37 World models, diffusion, and what may come next

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Peyman Milanfar is a Distinguished Scientist at Google, leading its Computational Imaging team. He's a member of the National Academy of Engineering, an IEEE Fellow, and one of the key people behind the Pixel camera pipeline. Before Google, he was a professor at UC Santa Cruz for 15 years and helped build the imaging pipeline for Google Glass at Google X. Over 35,000 citations.

Peyman makes a provocative case that denoising, long dismissed as a boring cleanup task, is actually one of the most fundamental operations in modern ML, on par with SGD and backprop. Knowing how to remove noise from a signal basically means you have a map of the manifold that signals live on, and that insight connects everything from classical inverse problems to diffusion models.

We go from early patch-based denoisers to his 2010 "Is Denoising Dead?" paper, and then to the question that redirected his research: if denoising is nearly solved, what else can denoisers do? That led to Regularization by Denoising (RED), which, if you unroll it, looks a lot like a diffusion process, years before diffusion models existed. We also cover how his team shipped a one-step diffusion model on the Pixel phone for 100x ProRes Zoom, the perception-distortion-authenticity tradeoff in generative imaging, and a new paper on why diffusion models don't actually need noise conditioning. The conversation wraps with a debate on why language has dominated the AI spotlight while vision lags, and Peyman's argument that visual intelligence, grounded in physics and robotics, is coming next.

Timeline

0:00 Intro and Peyman's background

1:22 Why denoising matters more than you think Sensor diversity and Tesla's vision-only bet

15:04 BM3D and why it was secretly an MMSE estimator

17:02 "Is Denoising Dead?" then what else can denoisers do?

18:07 Plug-and-play methods and Regularization by Denoising (RED)

26:18 Denoising, manifolds, and the compression connection

28:12 Energy-based models vs. diffusion: "The Geometry of Noise"

31:40 Natural gradient descent and why flow models work

34:48 Gradient-free optimization and high-dimensional noise

45:13 Image quality and the perception-distortion tradeoff

48:39 Information theory, rate-distortion, and generative models

52:57 Denoising vs. editing

54:25 The changing role of theory

57:07 Hobbyist tools vs. shipping consumer products

59:40 Coding agents, vibe coding, and domain expertise

1:05:00 Vision and more complex-dimensional signals

1:09:31 Do models need to interact with the physical world?

1:11:28 Continual learning and novelty-driven updates

1:13:00 On-device learning and privacy

1:15:01 Why has language dominated AI? Is vision next?

1:17:14 How kids learn: vision first, language later

1:19:36 Academia vs. industry

1:22:28 10,000 citations vs. shipping to millions, why choose?

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Timeline

00:12 — Introduction and Yaroslav's background at OpenAI and Google Brain

01:16 — Why deep learning isn't such a good idea

02:03 — The three definitions of AGI: religious, financial, and vibes-based

07:52 — The SAI framework: do we need the term AGI at all?

10:58 — What matters more than AGI: efficiency and refactoring the AI stack

13:28 — Jevons paradox and the coming energy wall

14:49 — The recipe: replaying 70 years of AI with hindsight

17:23 — Memory, energy, and gradient checkpointing

18:34 — Why you can't just optimize the current stack (the recurrent laryngeal nerve analogy)

21:05 — What a redesigned AI might look like: hierarchical message passing

22:31 — Can a small team replicate decades of research?

24:23 — Why non-experts outperform domain specialists

27:42 — The GPT-2 benchmark: what success looks like

29:01 — Ian Goodfellow, Theano, and the origins of TensorFlow

30:12 — The Muon optimizer origin story and beating Google on ImageNet

36:16 — AI coding agents for software engineering and research

40:12 — 10-year outlook and the voice-first workflow

42:23 — Why start with text over multimodality

45:13 — Are AI labs like SSI on the right track?

48:52 — Getting rid of backprop — and maybe math itself

53:57 — The state of ML academia and NeurIPS culture

56:41 — The Sutra group challenge: inventing better learning algorithms

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Timeline:

(00:00) Intro and welcome

(01:25) Why healthcare is uniquely hard

(04:46) Who owns your medical records? — The case for patient-controlled data and tapping your phone at the doctor's office

(06:43) Centralized vs. decentralized healthcare — comparing Israel, Korea, and the US

(13:19) Why most existing health data isn't as useful as we think — selection bias and the lack of randomization

(16:53) The "toss a coin" proposal — continuous clinical trials through automated randomization, and the surprising connection to LLM sampling.

(23:07) Drug discovery's broken pipeline — why stage-wise optimization is failing, and we need end-to-end thinking

(28:30) Why the current system is already failing society — wearables, preventive care, and the case for urgency

(31:13) Allen's personal healthcare journey and the GLP-1 conversation

(33:13) GLP-1 deep dive — 40 years from discovery to weight loss drugs, brain receptors, and embracing uncertainty

(36:28) Why antibiotic R&D is "economic suicide" and how AI can help

(42:52) Language models in the clinic and the lab — from clinical notes to back-propagating clinical outcomes, all the way to molecular design

(48:04) Do you need domain expertise, or can you throw compute at it?

(54:30) The $10 billion question — distributed GPU clouds and a patient-in-the-loop drug discovery system

(58:28) Vertical scaling vs. horizontal scaling for healthcare AI

(1:01:06) AI regulation — who's missing from the conversation and why regulation should follow deployment

(1:06:52) Professors as founders and the "neo-lab" phenomenon — how Ilya cracked the code

(1:11:18) Can neo-labs actually ship products? Why researchers should do research

(1:13:09) Academia got spoiled — the deep learning anomaly is ending, and that's okay

(1:16:07) Closing message — why it's a great time to be a PhD student and researcher

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

  ---

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

In this episode, we talk with Stefano Ermon,  Stanford professor, co-founder & CEO of Inception AI, and co-inventor of DDIM, FlashAttention, DPO, and score-based/diffusion models, about why diffusion-based language models may overtake the autoregressive paradigm that dominates today's LLMs.

We start with the fundamental topics, such as what diffusion models actually are, and why iterative refinement (starting from noise, progressively denoising) offers structural advantages over autoregressive generation.

From there,  we dive into the technical core of diffusion LLMs. Stefano explains how discrete diffusion works on text, why masking is just one of many possible noise processes, and how the mathematics of score matching carries over from the continuous image setting with surprising elegance.

A major theme is the inference advantage. Because diffusion models produce multiple tokens in parallel, they can be dramatically faster than autoregressive models at inference time. Stefano argues this fundamentally changes the cost-quality Pareto frontier, and becomes especially powerful in RL-based post-training.

We also discuss Inception AI's Mercury II model, which Stefano describes as best-in-class for latency-constrained tasks like voice agents and code completion.

In the final part, we get into broader questions  - why transformers work so well, research advice for PhD students, whether recursive self-improvement is imminent, the real state of AI coding tools, and Stefano's journey from academia to startup founder.

TIMESTAMPS

0:12 – Introduction
1:08 – Origins of diffusion models: from GANs to score-based models in 2019
3:13 – Diffusion vs. autoregressive: the typewriter vs. editor analogy
4:43 – Speed, creativity, and quality trade-offs between the two approaches
7:44 – Temperature and sampling in diffusion LLMs — why it's more subtle than you think
9:56 – Can diffusion LLMs scale? Inception AI and Gemini Diffusion as proof points
11:50 – State space models and hybrid transformer architectures
13:03 – Scaling laws for diffusion: pre-training, post-training, and test-time compute
14:33 – Ecosystem and tooling: what transfers and what doesn't
16:58 – From images to text: how discrete diffusion actually works
19:59 – Theory vs. practice in deep learning
21:50 – Loss functions and scoring rules for generative models
23:12 – Mercury II and where diffusion LLMs already win
26:20 – Creativity, slop, and output diversity in parallel generation
28:43 – Hardware for diffusion models: why current GPUs favor autoregressive workloads
30:56 – Optimization algorithms and managing technical risk at a startup
32:46 – Why do transformers work so well?
33:30 – Research advice for PhD students: focus on inference
34:57 – Recursive self-improvement and AGI timelines
35:56 – Will AI replace software engineers? Real-world experience at Inception
37:54 – Professor vs. startup founder: different execution, similar mission
39:56 – The founding story of Inception AI — from ICML Best Paper to company
42:30 – The researcher-to-founder pipeline and big funding rounds
45:02 – PhD vs. industry in 2026: the widening financial gap
47:30 – The industry in 5-10 years: Stefano's outlook

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Naomi argues that many structures researchers find inside trained models are vestigial, they mattered early in training but are meaningless by the end. Grokking is one case of a broader phenomenon: models go through multiple consecutive phase transitions during training, driven by symmetry breaking and head specialization, but the smooth loss curve hides all of it. We talk about why training is nothing like human learning, and why our intuitions about what's hard for models are consistently wrong  -  code in pretraining helps language reasoning, tokenization drives behaviors people attribute to deeper cognition, and language already encodes everything humans care about. We also get into why SAEs are basically topic models, the Platonic representation hypothesis, using AI to decode animal communication, and why non-determinism across training runs is a real problem that RL and MoE might be making worse.

Timeline:

(00:12) Introduction and guest welcome

(01:01) Why training dynamics matter - the evolutionary biology analogy

(03:05) Jennifer Aniston neurons and the danger of biological parallels

(04:48) What is grokking and why it's one instance of a broader phenomenon

(08:25) Phase transitions, symmetry breaking, and head specialization

(11:53) Double descent, overfitting, and the death of classical train-test splits

(15:10) Training is nothing like learning

(16:08) Scaling axes - data, model size, compute, and why they're not interchangeable

(19:29) Data quality, code as reasoning fuel, and GPT-2's real contribution

(20:43) Multilingual models and the interlingua hypothesis

(25:58) The Platonic representation hypothesis and why image classification was always multimodal

(29:12) Sparse autoencoders, interpretability, and Marr's levels

(37:32) Can we ever truly understand what models know?

(43:59) The language modality chauvinist argument

(51:55) Vision, redundancy, and self-supervised learning

(57:18) World models - measurable capabilities over philosophical definitions

(1:00:14) Is coding really a solved task?

(1:04:18) Non-determinism, scaling laws, and why one training run isn't enough

(1:10:12) Naomi's new lab at BU and recruiting

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• Changes: trimmed

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Stefano sees LLMs as stochastic dynamical systems that need to be controlled, not just prompted. He introduces "strands coding," a new framework AWS is building that sits between vibe coding and spec coding, you write a skeleton with AI functions constrained by pre- and post-conditions, verifying intent before a single line of code is generated. The surprising part: even as AI coding adoption goes up, developer trust in the output is going down.

We go deep into the philosophy of models and the world. Stefano argues that the dichotomy between "language models" and "world models" doesn't really exist, where a reasoning engine trained on rich enough data is a world model. He walks us through why naive realism is indefensible, how reverse diffusion was originally intended to show that models can't be identical to reality, and why that matters now.

We also discuss three types of information, Shannon, algorithmic, and conceptual, and why algorithmic information is the one that actually matters to agents. Synthetic data doesn't add Shannon information, but it adds algorithmic information, which is why it works. Intelligence isn't about scaling to Solomonov's universal induction; it's about learning to solve new problems fast.

Takeaways:

• Vibe coding is local feedback control with high cognitive load; spec coding is open-loop global control with silent failures, neither scales well alone.
• Trust in AI-generated code is declining even as adoption rises.
• The distinction between next-token prediction and world model is mostly nomenclature - reasoning engines operating on multimodal data are world models.
• Algorithmic information, not Shannon information, is what matters in the agentic setting.
• Intelligence isn't minimizing inference uncertainty - it's minimizing time to solve unforeseen tasks.
• The intent gap between user and model cannot be fully automated or delegated.

  ---

Timeline

(00:13) Introduction and guest welcome

(01:12) How the agentic era changed machine learning

(06:11) Vibe coding one year later

(07:23) Vibe vs. spec vs. strands coding

(14:30) Why English is not a programming language

(16:36) Constrained generation and agent choreography

(20:44) Diffusion models vs. autoregressive models (25:59) The platonic representation hypothesis and naive realism

(31:14) Synthetic data and the information bottleneck

(36:22) Three types of information: Shannon, algorithmic, conceptual

(38:47) Scaling laws and Solomonov induction

(42:14) World models and the Goethian vs. Marrian approach

(49:00) Encoding vs. generation and JEPA-style training

(55:50) Are language models already world models?

(59:13) Closing thoughts on trust, education, and responsibility.

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0. Changes: trimmed

About

The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Sophont is trying to be something like an OpenAI or Anthropic but for healthcare  - training models across pathology, neuroimaging, and clinical text, to eventually fuse them into one multimodal system. The surprising part: their pathology model trained on 12,000 public slides performs on par with models trained on millions of private ones. Data quality beats data quantity.

We talk about what actually excites Tanishq, which is not replacing doctors, but finding things doctors can't see. AI predicting gene mutations from a tissue slide, or cardiovascular risk from an eye scan.

We also talk about the regulation and how the picture is less scary than people assume. Text-based clinical decision support can ship without FDA approval. Pharma partnerships offer near-term impact. The five-to-ten-year timeline people fear is really about drug discovery, not all of medical AI.

Takeaways:

• The real promise of medical AI is finding hidden signals in existing data, not just automating doctors
• Small, curated public datasets can rival massive private ones
• Multimodal fusion is the goal, but you need strong individual encoders first
• AI research itself might get automated sooner than biology or chemistry
• FDA regulation has more flexibility than most people think

Timeline

(00:12) Introduction and guest welcome

(02:32) Anthropic's ad about ChatGPT ads

(07:26) XAI merging into SpaceX

(13:32) Vibe coding one year later

(17:00) Claude Code and agentic workflows

(21:52) Can AI automate AI research?

(26:57) What is medical AI

(31:06) Sofont as a frontier medical AI lab

(33:52) Public vs. private data - 12K slides vs. millions

(36:43) Domain expertise vs. scaling

(41:54) Cancer, diabetes, and personal stakes

(47:52) Classification vs. prediction in medicine

(50:36) When doctors disagree

(54:43) Quackery and AI

(57:15) Uncertainty in medical AI

(1:03:11) Will AI replace doctors?

(1:07:24) Self-supervised learning on sleep data

(1:10:10) Aligning modalities

(1:13:17) FDA regulation

(1:22:28) Closing

Music:

• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.
• "Palms Down" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.

Changes: trimmed

About

The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Anastasios sits at a fascinating intersection -   a theoretical statistician running the platform that every major lab watches when they release a model. We talk about the messiness of AI-generated code slop (yes, he hides Claude's commits too), then dig into the statistical machinery that powers Arena's leaderboards and why getting evaluation right is harder than most people think.

We explore why style control is both necessary and philosophically tricky, where you can regress away markdown headers and response length, but separating style from substance is a genuinely unsolved causal inference problem. We also get into why users are surprisingly good judges of model quality, how Arena serves as a pre-release testing ground for labs shipping stealth models under codenames, and whether the fragmentation of the AI market (Anthropic going enterprise, OpenAI going consumer, everyone going multimodal) is actually a feature, not a bug. Plus, we discuss the role of rigorous statistics in the age of "just run it again," why structured decoding can hurt model performance, and what Arena's 2026 roadmap looks like.

Timeline:

(00:12) Introduction and Anastasios's Background

(00:55) What Arena Does and Why Static Benchmarks Aren't Enough

(02:26) Coverage of Use Cases - Is There Enough?

(04:22) Style Control and the Bradley-Terry Methodology

(08:35) Can You Actually Separate Style from Substance?

(10:24) Measuring Slop - And the Anti-Slop Paper Plug

(11:52) Can Users Judge Factual Correctness?

(13:31) Tool Use and Agentic Evaluation on Arena

(14:14) Intermediate Feedback Signals Beyond Final Preference

(15:30) Tool Calling Accuracy and Code Arena

(17:42) AI-Generated Code Slop and Hiding Claude's Commits

(19:49) Do We Need Separate Code Streams for Humans and LLMs?

(20:01) RL Flywheels and Arena's Preference Data

(21:16) Focus as a Startup - Being the Evaluation Company

(22:16) Structured vs. Unconstrained Generation

(25:00) The Role of Rigorous Statistics in the Age of AI

(29:23) LLM Sampling Parameters and Evaluation Complexity

(30:56) Model Versioning and the Frequentist Approach to Fairness

(32:12) Quantization and Its Effects on Model Quality

(33:10) Pre-Release Testing and Stealth Models (34:23) Transparency - What to Share with the Public vs. Labs

(36:27) When Winning Models Don't Get Released

(36:59) Why Users Keep Coming Back to Arena

(38:19) Market Fragmentation and Arena's Future Value

(39:37) Custom Evaluation Frameworks for Specific Users

(40:03) Arena's 2026 Roadmap - Science, Methodology, and New Paradigms

(42:15) The Economics of Free Inference

(43:13) Hiring and Closing Thoughts

Music:

• "Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.
• "Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.
• Changes: trimmed

  ---

About:

The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Fred sits at a rare intersection,  building the theory of data-centric AI in academia while shipping it to enterprise clients at Snorkel. We talk about the chaos of OpenClaw (the personal AI assistant that's getting people hacked the old-fashioned way, via open ports), then focus on one of the most important questions: how do you make a model truly yours?

We dig into why prompting your preferences doesn't scale, why even LoRA might be too expensive for per-user personalization, and why activation steering methods like REFT could be the sweet spot. We also explore self-distillation for continual learning, the unsolved problem of building realistic personas for evaluation, and Fred's take on the data vs. architecture debate (spoiler: data is still undervalued). Plus, we discuss why the internet's "Ouroboros effect" might not doom pre-training as much as people fear, and what happens when models become smarter than the humans who generate their training data.

Takeaways:

• Personalization requires ultra-efficient methods - even one LoRA per user is probably too expensive. Activation steering is the promising middle ground.
• The "pink elephant problem" makes prompt-based personalization fundamentally limited - telling a model what not to do often makes it do it more.
• Self-distillation can enable on-policy continual learning without expensive RL reward functions, dramatically reducing catastrophic forgetting.
• Data is still undervalued relative to architecture and compute, especially high-quality post-training data, which is actually improving, not getting worse.
• Weak supervision principles are alive and well inside modern LLM data pipelines, even if people don't call it that anymore.

  ---

Timeline:

(00:13) Introduction and Fred's Background

(00:39) OpenClaw — The Personal AI Assistant Taking Over Macs

(03:43) Agent Security Risks and the Privacy Problem

(05:13) Cloud Code, Permissions, and Living Dangerously

(07:47) AI Social Media and Agents Talking to Each Other

(08:56) AI Persuasion and Competitive Debate

(09:51) Self-Distillation for Continual Learning

(12:43) What Does Continual Learning Actually Mean?

(14:12) Updating Weights on the Fly — A Grand Challenge

(15:09) The Personalization Problem — Motivation and Use Cases

(17:41) The Pink Elephant Problem with Prompt-Based Personalization

(19:58) Taxonomy of Personalization — Preferences vs. Tone vs. Style

(21:31) Activation Steering, REFT, and Parameter-Efficient Fine-Tuning

(27:00) Evaluating Personalization — Benchmarks and Personas

(31:14) Unlearning and Un-Personalization

(31:51) Cultural Alignment as Group-Level Personalization

(41:00) Can LLM Personas Replace Surveys and Polling?

(44:32) Is Continued Pre-Training Still Relevant?

(46:28) Data vs. Architecture — What Matters More?

(52:25) Multi-Epoch Training — Is It Over?

(54:53) What Makes Good Data? Matching Real-World Usage

(59:23) Decomposing Uncertainty for Better Data Selection

(1:01:52) Mapping Human Difficulty to Model Difficulty

(1:04:49) Scaling Small Ideas — From Academic Proof to Frontier Models

(1:12:01) What Happens When Models Surpass Human Training Data?

(1:15:24) Closing Thoughts

Music:

• "Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.
• "Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.
• Changes: trimmed

Bayan leads Capital One's AI Foundations team, where they're working toward a destination most people don't associate with banking: getting AI systems to perceive financial ecosystems, form beliefs about the future, and take actions based on those beliefs. It's a framework that sounds simple until you realize you're asking a model to predict whether someone will pay back a loan over 30 years while the world changes around them.

We get into why LLMs are a bad fit for ingesting 5,000 credit card transactions, why synthetic data works surprisingly well for time series, and the tension between end-to-end learning and regulatory requirements that demand you know exactly what your model learned. We also discuss reasoning in language vs. in latent space - if you wouldn't trust a self-driving car that translated images to words before deciding to turn, should you trust a financial system that does all its reasoning in token space?

Takeaways:

• Money is a behavioral science problem - AI in finance requires understanding people, not just numbers.
• Foundation models pre-trained on web text don't outperform purpose-built models for financial tasks. You're better off building a standalone encoder for financial data.
• Synthetic data works surprisingly well for time series - possibly because real-world time series lives on a simpler manifold than we assume.
• Explainability in ML is fundamentally unsatisfying because people want causality from non-causal models.
• Financial AI needs world models that can imagine alternative futures, not just fit historical data.

  ---

Timeline:

(00:24) Introduction and Bayan's Background

(00:42) Claude Code, Vibe Coding - Hype or AGI?

(05:59) The Future of Software Engineering and Abstraction

(11:20) Abstraction Layers and Karpathy's Take

(13:54) Hamming, Kuhn, and Scientific Revolutions in AI

(19:24) Stack Overflow's Decline and Proof of Humanity

(23:07) Why We Still Trust Humans Over LLMs

(30:45) Deep Dive: AI in Banking and Consumer Finance

(34:17) Are Markets Efficient? Behavioral Economics vs. Classical Views

(37:14) The Components of a Financial Decision: Perception, Belief, Action

(42:15) Protected Variables, Proxy Features, and Fairness in Lending

(45:05) Explainability: Roller Skating on Marbles

(47:55) Sparse Autoencoders, Interpretability, and Turtles All the Way Down

(51:57) Foundation Models for Finance — Web Text vs. Purpose-Built

(53:09) Time Series, Synthetic Data, and TabPFN

(59:44) Feeding Tabular Data to VLMs - Graphs Beat Raw Numbers

(1:03:35) Reasoning in Language vs. Latent Space

(1:08:24) Is Language the Optimal Representation? Chinese Compression and Information Density

(1:13:37) Personalization and Predicting Human Behavior

(1:21:36) World Models, Uncertainty, and Professional Worrying

(1:24:07) Prediction Markets and Insider Betting

(1:26:33) Can LLMs Predict Stocks?

(1:29:11) Multi-Agent Systems for Financial Decisions

Music:

• "Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.
• "Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0. Changes: trimmed

  ---

About: The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

David's path from running a 50-person IT company through acquisition to building one of the most well-regarded AI orchestration libraries tells you how sometimes constraints breed better design. TextAI started during COVID when he was doing coronavirus literature research and realized semantic search could transform how we find information.

We get into the evolution of the AI framework landscape - from the early days of vector embeddings to RAG to LLM orchestration. David was initially stubborn about not supporting OpenAI's API, wanting to keep everything local. He admits that probably cost him some early traction compared to LangChain, but it also shaped TextAI's philosophy: you shouldn't need permission to build with AI.

We also talk about small models and some genuinely practical insights: a 20-million parameter model running on CPU might be all you need. On the future of coding with AI, David's come around on "vibe coding" and notes that well-documented frameworks with lots of examples are perfectly positioned for this new world.

Takeaways:

• Local-first AI gives you control, reproducibility, and often better performance for your domain
• Small models (even 20M parameters) can solve real problems on CPU
• Good documentation and examples make your framework AI-coding friendly
• Open source should mean actually contributing - not just publishing code
• Solo developers can compete by staying focused and being willing to evolve

  ---

Timeline:

(00:14) Introduction and David's Background

(07:44) TextAI History and Evolution

(12:04) Framework Landscape: LangChain, LlamaIndex, Haystack

(15:16) Can AI Re-implement Frameworks?

(24:14) API Specs: OpenAI vs Anthropic

(26:46) Running an Open Source Consulting Business

(32:51) Origin Story: COVID, Kaggle, and Medical Literature

(43:08) Open Source Philosophy and Giving Back

(47:16) Ethics of Local AI and Developer Freedom

(01:06:44) Human in the Loop and AI-Generated Code

(01:09:31) The Future of Work and Automation

Music:

• "Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.
• "Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0. Changes: trimmed

  ---

About:

The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Cody's path from writing CUDA kernels to spending his days staring at weird internet text tells you something important: data quality can account for half or more of a model's final performance. That's on par with major architectural breakthroughs.

We get into the differences between pre-training, mid-training, and post-training data. Mid-training in particular has become a key technique for squeezing value out of rare, high-quality datasets. Cody's team stumbled onto it while solving a practical problem: how do you figure out if a 5-billion-token dataset is actually useful when you can't afford hundreds of experimental runs?

We also talk about data filtering and some genuinely surprising findings: the documents that make the best training data are often short and dense with information. Those nicely written blog posts with personal anecdotes? Turns out models don't learn as well from them.

On synthetic data, Cody thinks pre-training is still in its early days, where most techniques are variations on a few core ideas, but there's huge potential. He's excited about connecting RL failures back to mid-training: when models fail at tasks, use that signal to generate targeted training data.

Takeaways:

• Data work is high-leverage but underappreciated
• Mid-training helps extract signal from small, valuable datasets
• Good filters favor dense, factual text over polished prose.
• Synthetic data for pre-training works surprisingly well, but remains primitive.
• Optimal data mixtures depend on model scale, where smaller models need more aggressive distribution shifts.

  ---

  Timeline

  (00:12) Introduction to Data Correlation in LLMs

  (05:14) The Importance of Data Quality

  (10:15) Pre-training vs Post-training Data

  (15:22) Strategies for Effective Data Utilization

  (20:15) Benchmarking and Model Evaluation

  (28:28) Maximizing Perplexity and Coherence

  (30:27) Measuring Quality in Data

  (32:56) The Role of Filters in Data Selection

  (34:19) Understanding High-Quality Data

  (39:15) Mid-Training and Its Importance

  (46:51) Future of Data Sources

  (48:13) Synthetic Data's Role in Pre-Training

  (53:10) Creating Effective Synthetic Data

  (57:39) The Debate on Pure Synthetic Data

  (01:00:25) Navigating AI Training and Legal Challenges

  (01:02:34) The Controversy of AI in the Art Community

  (01:05:29) Exploring Synthetic Data and Its Efficiency

  (01:11:21) The Future of Domain-Specific vs. General Models

  (01:22:06) Bias in Pre-trained Models and Data Selection

  (01:28:27) The Potential of Synthetic Data Over Human Data

  ---

  Music:

• "Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.
• "Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

Changes: trimmed

About

The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

In this episode, we dive into AI privacy, frontier model capabilities, and why academia still matters.

We kick off by discussing GPT-5.2 and whether models rely more on parametric knowledge or context. Niloofar shares how reasoning models actually defer to context, even accepting obviously false information to "roll with it."

On privacy, Niloofar challenges conventional wisdom: memorization isn't the problem anymore. The real threats are aggregation attacks (finding someone's pet name in HTML metadata), inference attacks (models are expert geoguessers), and input-output leakage in agentic workflows.

We also explore linguistic colonialism in AI, or how models fail for non-English languages, sometimes inventing cultural traditions.

The episode wraps with a call for researchers to tackle problems industry ignores: AI for science, education tools that preserve the struggle of learning, and privacy-preserving collaboration between small local models and large commercial ones.

Timeline

[0:00] Intro

[1:03] GPT-5.2 first impressions and skepticism about the data cutoff claims

[4:17] Parametric vs. context memory—when do models trust training vs. the prompt?

[9:28] The messy problem of memory, weights, and online learning

[16:12] Tool use changes model behavior in unexpected ways

[17:15] OpenAI's "Advances in Sciences" paper and human-AI collaboration

[24:17] Why deep research is getting less useful

[28:17] Pre-training vs. post-training—which matters more?

[30:35] Non-English languages and AI failures

[33:23] Hilarious Farsi bugs: "I'll get back to you in a few days" and invented traditions

[37:56] Linguistic colonialism—ChatGPT changed how we write

[41:20] Why memorization isn't the real privacy threat

[47:14] The three actual privacy problems: inference, aggregation, input-output leakage

[54:33] Deep research stalking experiment—finding a cat's name in HTML

[1:01:13] Privacy solutions for agentic systems

[1:03:23] What Niloofar's excited about: AI for scientists, small models, niche problems

[1:08:31] AI for education without killing the learning process

[1:09:15] Closing: underrated life advice on health and sustainable habits

Music:

"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

"Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

Changes: trimmed

About

The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

"The path to superintelligence - just train up the LLMs, train on more synthetic data, hire thousands of people to school your system in post-training, invent new tweaks on RL-I think is complete bullshit. It's just never going to work."

After 12 years at Meta, Turing Award winner Yann LeCun is betting his legacy on a radically different vision of AI. In this conversation, he explains why Silicon Valley's obsession with scaling language models is a dead end, why the hardest problem in AI is reaching dog-level intelligence (not human-level), and why his new company AMI is building world models that predict in abstract representation space rather than generating pixels.

Timestamps

(00:00:14) – Intro and welcome

(00:01:12) – AMI: Why start a company now?

(00:04:46) – Will AMI do research in the open?

(00:06:44) – World models vs LLMs

(00:09:44) – History of self-supervised learning

(00:16:55) – Siamese networks and contrastive learning

(00:25:14) – JEPA and learning in representation space

(00:30:14) – Abstraction hierarchies in physics and AI

(00:34:01) – World models as abstract simulators

(00:38:14) – Object permanence and learning basic physics

(00:40:35) – Game AI: Why NetHack is still impossible

(00:44:22) – Moravec's Paradox and chess

(00:55:14) – AI safety by construction, not fine-tuning

(01:02:52) – Constrained generation techniques

(01:04:20) – Meta's reorganization and FAIR's future

(01:07:31) – SSI, Physical Intelligence, and Wayve

(01:10:14) – Silicon Valley's "LLM-pilled" monoculture

(01:15:56) – China vs US: The open source paradox

(01:18:14) – Why start a company at 65?

(01:25:14) – The AGI hype cycle has happened 6 times before

(01:33:18) – Family and personal background

(01:36:13) – Career advice: Learn things with a long shelf life

(01:40:14) – Neuroscience and machine learning connections

(01:48:17) – Continual learning: Is catastrophic forgetting solved?

Music:

"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

"Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

Changes: trimmed

About

The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

On the symbolic AI side, Atlas shares his recent work proving that neural networks can learn symbolic equations through gradient descent, a surprising result given that gradient descent is continuous while symbolic structures are discrete. We talked about why neural nets learn clean, compositional mathematical structures at all, what the mathematical tools involved are, and the broader implications for understanding reasoning in AI systems.

The conversation then turns to neuro-symbolic approaches in practice: agents that discover rules through continued learning, propose them symbolically, verify them against domain-specific checkers, and refine their understanding.

On the finance side, Atlas pulls back the curtain on what AI research looks like at a high-frequency trading firm. The core problem sounds simple (predict future prices from past data). Still, the challenge is extreme: markets are dominated by noise, predictions hover near zero correlation, and success means eking out tiny margins across astronomical numbers of trades. He explains why synthetic data techniques that work elsewhere don't translate easily, and why XTX is building time series foundation models rather than adapting language models.

We also discuss the convergence of hiring between frontier AI labs and quantitative finance, and why this is an exceptional moment for ML researchers to consider the finance industry.

Links:

• Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning - arxiv.org/abs/2506.21797
• Atlas website - https://www.vita-group.space/

Guest: Atlas Wang (UT Austin / XTX)

Hosts: Ravid Shwartz-Ziv & Allen Roush

Music: “Kid Kodi” — Blue Dot Sessions. Source: Free Music Archive. Licensed CC BY-NC 4.0.

Key topics covered:

Robotics challenges: We discussed why robotics remains harder than many expected, compared to LLMs. The real world is unpredictable and unforgiving, and mistakes have physical consequences. Sim-to-real transfer remains a major bottleneck because simulators are tedious to configure accurately for each robot and environment. Unlike text, robotics lacks foundation models, partly due to limited clean, annotated datasets and the difficulty of collecting diverse real-world data.

Reproducibility crisis: We discussed how self-reported benchmarks can lead to p-hacking and irreproducible results. Centralized evaluation systems (such as Kaggle or ImageNet challenges), where researchers submit algorithms for testing on hidden test sets, seem to drive faster progress.

Smart buildings: Judah's work at Google focuses on using ML to optimize HVAC systems, potentially reducing energy costs and carbon emissions significantly. The challenge is that every building is different. It makes the simulation configuration extremely labor-intensive. Generative AI could help by automating the process of converting floor plans or images into accurate building simulations.

Links:

• Judah website - https://judahgoldfeder.com/

Music:

"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

"Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

Changes: trimmed

Chapters

00:00 Introduction to Reinforcement Learning and Will's Journey

03:10 Theoretical Foundations of Multi-Agent Systems

06:09 Transitioning from Theory to Practical Applications

09:01 The Role of Game Theory in AI

11:55 Exploring the Complexity of Games and AI

14:56 Optimization Techniques in Reinforcement Learning

17:58 The Evolution of RL in LLMs

21:04 Challenges and Opportunities in RL for LLMs

23:56 Key Components for Successful RL Implementation

27:00 Future Directions in Reinforcement Learning

36:29 Exploring Agentic Reinforcement Learning Paradigms

38:45 The Role of Intermediate Results in RL

41:16 Multi-Agent Systems: Challenges and Opportunities

45:08 Distributed Environments and Decentralized RL

49:31 Prompt Optimization Techniques in RL

52:25 Statistical Rigor in Evaluations

55:49 Future Directions in Reinforcement Learning

59:50 Task-Specific Models vs. General Models

01:02:04 Insights on Random Verifiers and Learning Dynamics

01:04:39 Real-World Applications of RL and Evaluation Challenges

01:05:58 Prime RL Framework: Goals and Trade-offs

01:10:38 Open Source vs. Closed Source Models

01:13:08 Continuous Learning and Knowledge Improvement

Music:

"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

"Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

Changes: trimmed

Papers and links:

• Chain-of-Thought Hijacking - https://arxiv.org/pdf/2510.26418
• Kosmos: An AI Scientist for Autonomous Discovery - https://t.co/9pCr6AUXAe
• JustRL: Scaling a 1.5B LLM with a Simple RL Recipe - https://relieved-cafe-fe1.notion.site/JustRL-Scaling-a-1-5B-LLM-with-a-Simple-RL-Recipe-24f6198b0b6b80e48e74f519bfdaf0a8

Chapters

00:00 Navigating the Peer Review Process

04:17 Kimi K2 Thinking: A New Era in AI

12:27 The Future of Tool Calls in AI

17:12 Exploring Google's New TPUs

22:04 The Importance of Real-World Data in Robotics

28:10 World Models: The Next Frontier in AI

31:36 Nvidia's Dominance in AI Partnerships

32:08 Exploring Recent AI Research Papers

37:46 Chain of Thought Hijacking: A New Threat

43:05 Simplifying Reinforcement Learning Training

54:03 Cosmos: AI for Autonomous Scientific Discovery

Music:

"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

"Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

Changes: trimmed

We break down what the information bottleneck really means, a principled approach to retaining only the most informative parts of data while compressing away the irrelevant. We discuss why compression is still important in our era of big data, how it prevents overfitting, and why it's essential for building models that generalize well.

We also dive into scaling laws: why they matter, what we can learn from them, and what they tell us about the future of AI research.

Papers and links:

• Alex's website - https://www.alexalemi.com/
• Scaling exponents across parameterizations and optimizers - https://arxiv.org/abs/2407.05872
• Deep Variational Information Bottleneck - https://arxiv.org/abs/1612.00410
• Layer by Layer: Uncovering Hidden Representations in Language Models - https://arxiv.org/abs/2502.02013
• Information in Infinite Ensembles of Infinitely-Wide Neural Networks - https://proceedings.mlr.press/v118/shwartz-ziv20a.html

Music:

“Kid Kodi” — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

“Palms Down” — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

Changes: trimmed

We also talked about Stanford's "Cartridges" paper, which presents an innovative approach to long-context language models. The paper tackles the expensive computational costs of billion-token context windows by compressing KV caches through a clever "self-study" method using synthetic question-answer pairs and context distillation. We discussed the implications for personalization, composability, and making long-context models more practical.

Additionally, we explored the "Continuous Autoregressive Language Models" paper and touched on insights from the Smol Training Playbook.

Papers discussed:

• The fragile intelligence of GPT-5 in medicine: https://www.nature.com/articles/s41591-025-04008-8
• Cartridges: Lightweight and general-purpose long context representations via self-study: https://arxiv.org/abs/2506.06266
• Continuous Autoregressive Language Models: https://arxiv.org/abs/2510.27688
• The Smol Training Playbook: https://huggingface.co/spaces/HuggingFaceTB/smol-training-playbook

Music:

“Kid Kodi” — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

“Palms Down” — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

Changes: trimmed

This is an experimental format for us, just news and papers without a guest interview. Let us know what you think!

Chapters

00:00 Introduction and Guest Introduction

01:03 Peer Review in Preprint Servers

06:57 New Developments in Coding Models

09:34 Open Source Models in Europe

11:00 Dynamic Layers in LLMs

26:05 Training Playbook Insights

30:05 Recurrent Depth Models and Reasoning Tasks

43:59 Exploring Recursive Reasoning Models

46:46 The Role of World Models in AI

48:41 Innovations in AI Training and Simulation

50:39 The Promise of Recurrent Depth Models

52:34 Navigating the Future of AI Algorithms

54:44 The Bitter Lesson of AI Development

59:11 Advising the Next Generation of Researchers

01:06:42 Safety and Interpretability in AI Models

01:10:46 Scaling Laws and Their Implications

01:16:19 The Role of PhDs in AI Research

Links and paper:

• Jonas' website - https://jonasgeiping.github.io/
• Scaling up test-time compute with latent reasoning: A recurrent depth approach - https://arxiv.org/abs/2502.05171
• The Smol Training Playbook: The Secrets to Building World-Class LLMs - https://huggingface.co/spaces/HuggingFaceTB/smol-training-playbook
• VaultGemma: A Differentially Private Gemma Model - https://arxiv.org/abs/2510.15001

Music:

“Kid Kodi” — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

“Palms Down” — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

Changes: trimmed

Links:

Jack's Website - https://jxmo.io/

TextAttack - https://arxiv.org/abs/2005.05909

How much do language models memorize? https://arxiv.org/abs/2505.24832

DeepSeek OCR - https://www.arxiv.org/abs/2510.18234

Chapters:

00:00 Introduction and AI News Highlights

04:53 The Importance of Fine-Tuning Models

10:01 Challenges in Open Source AI Models

14:34 The Future of Model Scaling and Sparsity

19:39 Exploring Model Routing and User Experience

24:34 Jack's Research: Text Attack and Adversarial Examples

29:33 The Platonic Representation Hypothesis

34:23 Implications of Inversion and Security in AI

39:20 The Role of Compression in Language Models

44:10 Future Directions in AI Research and Personalization

Links:

• TravelAgent: Generative agents in the built environment - https://journals.sagepub.com/doi/10.1177/23998083251360458
• Ariel Neumann's websites -
  • https://www.arielnoyman.com/
  • https://www.media.mit.edu/people/noyman/overview/

Papers and links:

• Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies, Timor et al, ICML 2025, https://arxiv.org/abs/2502.05202
• Distributed Speculative Inference (DSI): Speculation Parallelism for Provably Faster Lossless Language Model Inference, Timor et al, ICLR, 2025, https://arxiv.org/abs/2405.14105
• Fast Inference from Transformers via Speculative Decoding, Leviathan et al, 2022, https://arxiv.org/abs/2502.05202

• FindPDFs - https://huggingface.co/datasets/HuggingFaceFW/finepdfs

Chapters

00:00Introduction to AI Coding and Recent Developments

03:10OpenAI's Paper on Hallucinations in LLMs

06:03Critique of OpenAI's Research Approach

08:50Copyright Issues in AI Training Data

12:00The Value of Data in AI Training

14:50Watermarking AI Generated Content

17:54The Future of AI Investment and Market Dynamics

20:49AI Coding and Its Impact on Software Development

31:36The Evolution of AI in Software Development

33:54Vibe Coding: The Future or a Fad?

38:24Navigating AI Tools: Personal Experiences and Challenges

41:53The Limitations of AI in Complex Coding Tasks

46:52Security Vulnerabilities in AI-Generated Code

50:28The Role of Human Intuition in AI-Assisted Coding

53:28The Impact of AI on Developer Productivity

56:53The Future of AI in Creative Fields

In this episode of the Information Bottleneck Podcast, Ravid Shwartz-Ziv and Alan Rausch discuss the latest developments in AI, focusing on the controversial release of GPT-5 and its implications for users. They explore the future of large language models and the importance of sampling techniques in AI.

Chapters

00:00 Introduction to the Information Bottleneck Podcast

01:42 The GPT-5 Debacle: Expectations vs. Reality

05:48 Shifting Paradigms in AI Research

09:46 The Future of Large Language Models

12:56 OpenAI's New Model: A Mixed Bag

17:55 Corporate Dynamics in AI: Mergers and Acquisitions

21:39 The GPU Monopoly: Challenges and Opportunities

25:31 Deep Dive into Samplers in AI

35:38 Innovations in Sampling Techniques

42:31 Dynamic Sampling Methods and Their Implications

51:50 Learning Samplers: A New Frontier

59:51 Recent Papers and Their Impact on AI Research