Fred Sala, Assistant Professor at UW-Madison and Chief Scientist at Snorkel AI, joins us to talk about why personalization might be the next frontier for LLMs, why data still matters more than architecture, and how weak supervision refuses to die.

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 Bruss, VP of Applied AI at Capital One, joins us to talk about building AI systems that can make autonomous financial decisions, and why money might be the hardest problem in machine learning.

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 Mezzetti, creator of TxtAI, joins us to talk about building open source AI frameworks as a solo developer - and why local-first AI still matters in the age of API-everything.

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.