At our private luncheon, experts from OpenAI, Anthropic, XAI, NVIDIA, and the academic community gathered to discuss the evolving infrastructure and applications of foundation models. A strong consensus emerged: the convergence of search and recommendation is accelerating. With LLMs serving as the primary interface for content discovery, product databases and user logs are becoming commoditized infrastructure. In this new paradigm, competitive advantage shifts to the experience layer—namely, how well systems orchestrate conversational flows, multimodal handoffs, and agent-driven task scripts. Despite rapid model progress, building reliable, end-to-end agents for commerce remains an open frontier, offering opportunities for startups to innovate on top of existing APIs and fulfillment systems.

NVIDIA shared its perspective on operational excellence as a long-term differentiator. Beyond GPU performance, their hybrid enterprise support model—combining remote assistance with on- premise engineering—has become a key moat in high-stakes AI deployment. Attendees also highlighted a broader industry trend: the growing importance of organizational velocity. While data and compute remain critical, fast iteration cycles, tight feedback loops, and hands-on engineering leadership are increasingly defining the winners in applied AI. As the foundation model stack becomes more complex, execution speed and system-level intuition may matter as much as architectural novelty.