Steven Sinofsky and Balaji Srinivasan analyze the collision of regulation, capital, and innovation, exploring how an 'anti-tech assault' from regulators is reshaping M&A. They introduce the 'acquifire'—a new deal structure born from pressure—and debate the fundamental conflict between the permissionless 'network' and the regulatory 'state', projecting how this battle will define the future of AI.
Andrew Ng, founder of Google Brain and DeepLearning.AI, discusses the history of neural networks and the foundational ideas that led to modern AI breakthroughs. He covers the controversial early bets on scale and general-purpose algorithms, the technical innovations behind Transformers, and the future democratizing effect of artificial intelligence.
Dr. Umair Shah, former Washington State Secretary of Health, and Dr. Gianrico Farrugia, CEO of Mayo Clinic, discuss the practical applications and strategic imperatives of AI in public health and clinical care. They explore how AI can shift public health from a reactive to a proactive model, the necessity for a new data-centric architecture in healthcare systems, and the transformative potential of generative AI beyond mere optimization of current workflows.
The panel discusses OpenAI's strategic release of open-weight models (`gpt-oss`), the implications of Google DeepMind's immersive 3D world generator (`Genie 3`), the economic realities behind Anthropic's `Claude Code` rate-limiting, and the competing visions of "Personal Superintelligence" from major players like Meta, OpenAI, and Anthropic.
Omar Khattab, creator of DSPy, reinterprets the 'Bitter Lesson' for AI engineering, arguing that the key to building robust and enduring AI systems is to move beyond brittle prompt engineering. He advocates for a declarative, modular approach that separates the fundamental program logic from the rapidly changing landscape of LLMs, optimizers, and inference techniques.
Ido Pesok from Vercel explains why LLM-based applications often fail in production despite successful demos, and presents a systematic framework for building reliable AI systems using application-layer evaluations ("evals").