Peter Smith
⬤ A recent debate about where the AI market is headed has put Oracle's strategy under the microscope. Oracle Chairman Larry Ellison has been making the case that large language models are turning into commodities since they're mostly trained on the same public internet data. The real differentiator? Proprietary enterprise datasets. Companies that control massive amounts of private data and have the infrastructure to securely hook it up to AI models should come out on top.
⬤ Oracle's been positioning itself as the critical "data layer" sitting between AI models and enterprise information. They're betting big on retrieval-augmented generation infrastructure, vector databases, and secure access protocols. The thinking goes like this: as models get more similar in capability and inference costs keep dropping, enterprises will start treating models like swappable parts. When that happens, managing, governing, and integrating proprietary data becomes the real value driver in AI deployments.
⬤ But there's pushback on this thesis. Critics point to Stanford's 2025 AI Index showing that performance gaps between top models on platforms like Chatbot Arena have basically disappeared—including the gap between U.S. and Chinese models. Meanwhile, how enterprises actually use AI tells a different story. Multi-homing is the norm now, with companies routinely running multiple models at once. New models get adopted fast but don't necessarily knock out the incumbents. Anthropic's enterprise market share grew while OpenAI's dropped, showing there's real ongoing competition rather than everyone consolidating around a few winners.
⬤ This debate cuts to the heart of how value gets distributed in the AI ecosystem. Instead of a winner-take-all scenario driven purely by data ownership, falling model costs are lowering switching barriers and pushing companies toward best-of-breed selection for different tasks. Differentiation is shifting to workflow integration, orchestration, and domain-specific tuning. For Oracle, the debate highlights both the opportunity and the risk: in a multi-model world, lots of infrastructure providers might end up splitting a much bigger but also much more fluid AI market.
Peter Smith
