Oct 14, 2026

Can Open Models Keep Up With OpenAI and Anthropic? | Nathan Lambert

Nathan Lambert, AI researcher and founder of the nonprofit Trillium, joins Lisha Li to examine how durable the gap is between frontier closed labs like OpenAI and Anthropic and open-weight models. The discussion covers compute, data, and talent as the drivers of model progress, how much distillation really matters for Chinese open labs, the evolution of post-training toward RL environments, and why open research on RL environments and recursive self-improvement is needed to demystify what frontier systems can and cannot do.

Oct 07, 2026

Why Reflection AI Is Making the Open Intelligence Bet - and What It Means for Deploying Models

Joe Spisak of Reflection discusses why open models and open intelligence matter, covering RL-driven customization, environments as the new dataset, the OpenEnv project, PyTorch and Llama lessons, inference scaling, and the case for ecosystem control and open safety.

Sep 22, 2026

Debating RSI, the US-China Gap, and Jaggedness of Frontier AI

Jean-Stanislas Denain of Epoch AI and Nathan Lambert debate the nuanced trajectory of AI. They discuss the current state of RSI, the US-China AI capabilities gap (estimated 6-8 months), and the role of distillation and compute in closing it. The conversation also covers the challenges and differing paces of AI in robotics, insights from Chinese AI job postings, and the complex trade-offs between open and closed models regarding safety and misuse. They touch on Epoch AI's research philosophy and speculate on the intricate, evolving post-training recipes at frontier AI labs.

Aug 06, 2026

How Open Source Became AI's Backbone | Inferact with a16z

Simon Mo, CEO of Inferact and lead maintainer of vLLM, discusses how open-source AI, exemplified by vLLM, transformed into critical infrastructure. The conversation highlights the technical complexities of serving LLMs, the evolving economics and licensing of open-weight models, the need for control over guardrails, and the rapidly disappearing capability gap between open and proprietary AI.

Jul 30, 2026

Jeff Dean: The 1% Rule for Building in AI

Jeff Dean discusses the evolution of AI, drawing parallels between Google's past breakthroughs (like fitting search in RAM and the origin of TPUs) and current challenges. He emphasizes that AI is becoming an energy problem, driving the need for specialized inference hardware. Dean highlights 'context engineering' and multi-agent systems as crucial for long-running, complex AI tasks, and offers advice for startups on finding niches where they can outperform larger entities by focusing on specific domains, data, and models. He stresses the importance of clear specifications for agents and 'taste' in problem selection, encouraging founders to question fundamental assumptions and automate the scientific method to build 'AI that builds AI.'

Jul 22, 2026

Open Models: Kimi K3, Qwen 3.8, Xi's WAIC Speech, Distillation, The Open-Closed Gap, and What's Next

Nathan Lambert and Florian Brand discuss the accelerating open model landscape, focusing on the surge in Chinese models like Kimi K3 and GLM 5.2. They explore the reasons behind China's progress, the evolving US ecosystem, the cybersecurity implications of open-source bans, and debunk common misconceptions about distillation, particularly challenging Ben Thompson's recent claims. The episode concludes with predictions and a frontier model tier list.