This podcast episode explores the application of the Lean Startup process to the AI industry and the challenges related to big rounds and investments in AI models. The conversation emphasizes the importance of understanding customer preferences through experimentation, adapting to the unique characteristics of AI, and achieving product-market fit. The speakers also discuss the motivation behind creating widely applicable and accessible AI applications, the importance of coupling researchers with the application, and the need to break abstraction boundaries in R&D labs. The section further delves into the significance of cost-effectiveness and resource-constrained solutions in AI development, the hindrances to technology deployment in the tech industry, and the importance of laws in protecting individuals and ensuring access to education. The conversation also explores the differences between using product-based AI models versus accessing the raw model directly and the challenges faced by foundation model companies. The section concludes with a focus on maintaining coherence and alignment within AI organizations, exploring the limitations of autoregressive models in language understanding, and emphasizing the importance of understanding customer needs and building impactful products in the healthcare space.