Eugene Cheah, CEO of Featherless AI, presents a four-step framework for cutting AI inference costs at scale: measure token spend by team and workload, right-size models through evals, classifiers, and fine-tuning, buy GPU capacity with an understanding of the speed-versus-cost tradeoff, and check whether organizational processes can absorb the extra output.
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.
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.
This podcast explores the friction between frontier AI labs and cybersecurity experts, who disagree on AI safety priorities (alignment vs. traditional controls). It discusses the dual risks of open-weight AI models enabling sophisticated hacks like the TikTok camera exploit, and the challenges of CISA's call for transparent breach disclosures amidst corporate self-interest and the complexities of global AI regulation.
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.
Ollama Co-founder Jeffrey Morgan discusses the enterprise shift to open models, driven by cost, customization, and coding agents, leading to a 150X token growth. He highlights the dominance of Chinese models in cloud consumption, the hardware renaissance enabling local LLMs, and the rise of ultra-efficient "flash" models. Morgan also shares Ollama's journey, from its YC pivot to its monetization strategy, and reflects on lessons for building AI companies.