David Duvenaud

This AI:AM highlights cut brings together Cameron Berg, David Duvenaud, Michiel Bakker, Shawn “swyx” Wang, and Bing Xu to examine what we understand about frontier AI systems and what happens as more decisions move into their hands. Berg grounds model-consciousness debates in experiments on architecture, agency, valence, and welfare, while Duvenaud argues that even well-aligned AI could gradually disempower humans through ordinary economic choices. Bakker frames Europe’s AI challenge as a sovereignty problem, and swyx turns to practitioner stakes around agents, evals, maintainable code, and who owns the system of record. Xu closes the loop at the infrastructure layer, arguing that self-improving compute and GPU-kernel automation may deepen rather than weaken the CUDA moat. For full show notes, links, and references, read the episode page: https://www.cognitiverevolution.ai/ai-am-4-cameron-on-model-consciousness-duvenaud-s-gradual-disempowerment-swyx-s-ai-eng-alpha/ Mercury: Command is Mercury’s new conversational interface, giving you natural-language access to your finances and helping you take actions within your existing permissions and approval policies. Visit https://mercury.com to learn more and apply online in minutes. Sponsor: Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr CHAPTERS: (00:00) About the Episode (00:38) Special Sponsor (02:26) Model consciousness indicators (10:47) Valence inside models (Part 1) (17:09) Sponsor: Claude (19:01) Valence inside models (Part 2) (19:01) Misalignment and uncertainty (25:16) Gradual disempowerment threat (35:10) Slow zones and successors (47:41) Europe's AI bind (55:13) Frontier code benchmarks (01:01:59) Routing and memory (01:10:42) Agent infrastructure strain (01:16:25) Self improving infrastructure (01:27:56) Routing compute costs (01:35:39) Sovereign AI financing (01:42:24) Judging AI judges (01:47:26) Building AI DNA (01:52:38) Episode Outro (01:55:09) Outro PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

Cameron Berg

Research Director

AE Studio

David Duvenaud

Assistant Professor

University of Toronto

Michiel Bakker

Shawn Wang

Co-host

Latent SpaceTemporalSmol AIswyx.io

Bing Xu

Today we’re joined by David Duvenaud, Assistant Professor at the University of Toronto, to discuss his research on Neural Ordinary Differential Equations, a type of continuous-depth neural network. In our conversation, we talk through a few of David’s papers on the subject. We discuss the problem that David is trying to solve with this research, the potential that ODEs have to replace “the backbone” of the neural networks that are used to train today, and David’s approach to engineering.

David Duvenaud

Assistant Professor

University of Toronto

In this episode, we hear from David Duvenaud, assistant professor in the Computer Science and Statistics departments at the University of Toronto. David joined me after his talk at the Deep Learning Summit on “Composing Graphical Models With Neural Networks for Structured Representations and Fast Inference.” In our conversation, we discuss the generalized modeling and inference framework that David and his team have created, which combines the strengths of both probabilistic graphical models and deep learning methods. He gives us a walkthrough of his use case which is to automatically segment and categorize mouse behavior from raw video, and we discuss how the framework is applied here and for other use cases. We also discuss some of the differences between the frequentist and bayesian statistical approaches. The notes for this show can be found at twimlai.com/talk/96

David Duvenaud

Assistant Professor

University of Toronto