The author reflects on the convergence of the data and AI communities in 2024, where traditional boundaries between them are becoming increasingly blurred. They discuss three major problems: RAG (Representational Artificial Generalism), specialized vector databases, and complex RAG startups. The author believes that general-purpose databases like SingleStore can solve the RAG problem by providing efficient ANN search capabilities. They also argue that specialized vector databases will become irrelevant as general-purpose databases improve their performance. Complex RAG startups, however, are likely to survive due to their focus on data retrieval and innovation in this area. The author highlights the need for a unified speedy layer that combines database and AI capabilities, and expresses skepticism about trusting AI with control over physical systems in the near future. They also disappointingly note that AI-powered analytics have not yet delivered on their promise of replacing human analysts. Finally, they predict that agentic apps, which enable AI to build workflows and solve problems, will become increasingly important in the enterprise world.