Sarah Sachs, Head of AI Engineering at Notion, discusses the economic traps of AI model contracts and advocates for a "win on product" strategy. She details how Notion maintains optionality and leverage by treating suppliers as competitors, implementing a model-agnostic "AI Switzerland" approach with an auto model, leveraging open-weight models, and prioritizing data flywheels and orchestration over token economics to build sustainable AI products.
Stanislas Polu, co-founder of Dust, shares his journey from Stripe to OpenAI and his motivations for building Dust. He discusses Dust's model-agnostic approach, the challenges of fundraising in an environment dominated by Frontier Labs, the strategic decision to build in France, and critical insights into pricing models and defensibility for AI product companies amidst commoditized intelligence.
Matt, the organizer of the AI.engineer conference, shares insights into its origin, the challenges of early adoption, and its current value as a neutral ground for AI labs. He delves into AI hardware trends, discussing specialized chips like Etched, and gives a nuanced take on Anthropic's Fable, addressing performance concerns and compute limitations. The conversation then explores OpenAI's rumored equity offer to the US government, discussing implications for regulation and societal involvement. Matt shares his perspective on AI existential risk and alignment, emphasizing the need for pragmatic engineering solutions. Finally, he outlines the limitations of current LLMs, the critical need for data efficiency, and offers strategic advice for "Agent Labs" navigating the "model capability overhang" versus multi-model agnosticism.