Unsupervised Learning with Jacob Effron

This episode features the core team behind Sora, OpenAI's groundbreaking video generation platform that became the #1 app in the App Store. Bill Peebles (research lead), Rohan Sahai (product lead), and Thomas Dimson (engineering/product lead with Instagram background) discuss the unexpected viral success of Sora's launch, the product journey that led to the breakthrough "cameo" feature (putting yourself in AI-generated videos), and their philosophy of building a creator-first social network that prioritizes human creativity over passive consumption. They reveal the technical milestones in video generation, their small team size (under 50 people total at launch), navigation of content moderation challenges, early monetization strategy, and their ambitious vision for video models as world simulators that could eventually contribute to scientific breakthroughs by 2028. The conversation captures both the tactical product decisions and strategic philosophy that made Sora a cultural phenomenon.

This episode features a deep dive into the current state of AI model progress with Ari Morcos (CEO of Datalogy AI and former DeepMind/Meta researcher) and Rob Toews (partner at Radical Ventures). The conversation tackles whether model progress is genuinely slowing down or simply shifting into new paradigms, exploring the role of reinforcement learning in scaling capabilities beyond traditional pre-training. They examine the talent wars reshaping AI labs, Google's resurgence with Gemini, the sustainability of massive valuations for companies like OpenAI and Anthropic, and the infrastructure ecosystem supporting this rapid evolution. The discussion weaves together technical insights on data quality, synthetic data generation, and RL environments with strategic perspectives on acquisitions, regulatory challenges, and the future intersection of AI with physical robotics and brain-computer interfaces.

Tri Dao, Chief Scientist at Together AI and Princeton professor who created Flash Attention and Mamba, discusses how inference optimization has driven costs down 100x since ChatGPT's launch through memory optimization, sparsity advances, and hardware-software co-design. He predicts the AI hardware landscape will shift from Nvidia's current 90% dominance to a more diversified ecosystem within 2-3 years, as specialized chips emerge for distinct workload categories: low-latency agentic systems, high-throughput batch processing, and interactive chatbots. Dao shares his surprise at AI models becoming genuinely useful for expert-level work, making him 1.5x more productive at GPU kernel optimization through tools like Claude Code and O1. The conversation explores whether current transformer architectures can reach expert-level AI performance or if approaches like mixture of experts and state space models are necessary to achieve AGI at reasonable costs. Looking ahead, Dao sees another 10x cost reduction coming from continued hardware specialization, improved kernels, and architectural advances like ultra-sparse models, while emphasizing that the biggest challenge remains generating expert-level training data for domains lacking extensive internet coverage.

In this episode, Jacob sits down with Joshua Meier, co-founder of Chai Discovery and former Chief AI Officer at Absci, to explore the breakthrough moment happening in AI drug discovery. They discuss how the field has evolved through three distinct waves, with the current generation of companies finally achieving success rates that seemed impossible just years ago. The conversation covers everything from moving drug discovery out of the lab and into computers, to why AI models think differently than human chemists, to the strategic decisions around open sourcing foundational models while keeping design capabilities proprietary. It's an in-depth look at how AI is fundamentally changing pharmaceutical innovation and what it means for the future of medicine.

In this episode, Jacob sits down with Karol Hausman (Co-Founder) and Danny Driess (Research Scientist) from Physical Intelligence, two of the minds behind some of the most exciting advances in robotics. They unpack the last decade of progress in AI robotics, from early skepticism to the breakthroughs powering today’s generalist robot models. The conversation covers everything from folding laundry with robots to building scalable data pipelines, the limits of simulation, and what it’ll take to bring robot assistants into everyday homes. It's a wide-ranging and thoughtful look at where robotics is headed, as well as how fast we might get there.