Sumeet Singh, an investor with experience at Andreessen Horowitz and now leading Worldbuild, identifies a troubling trend among AI founders: they're replicating outdated SaaS product strategies instead of embracing the potential of generative AI. He highlights Richard Sutton's "bitter lesson," which asserts that scaling and leveraging data outpace specialization in AI development. This shift is crucial in a market that demands multi-billion dollar exits, as founders cling to old frameworks and risk becoming irrelevant. Singh outlines two promising paths for AI entrepreneurs. The first is the "model economy," which focuses on creating infrastructure and data that enhance AI capabilities rather than just applications. Companies like Oracle and CoreWeave are already capitalizing on this trend, providing essential resources for AI labs. He identifies four key opportunities in this space: treating compute like a commodity, developing local AI that operates on devices due to latency or privacy concerns, and preparing for the volatility of GPU supply. The second path involves discovering entirely new workflows made possible by AI. This approach emphasizes innovation that only AI can facilitate, pushing beyond traditional applications. Founders who adapt to these evolving dynamics will likely define the future of AI and avoid the pitfalls of specialization.