Unlocking Data and AI
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Subscribe • Previous Issues Your Model Is Not Your Moat In a recent article, I wrote about companies turning their own data, workflows, and production feedback into specialized intelligence they increasingly control. The deeper idea was compounding. What matters is not simply whether a model performs well today, but whether using it creates assets that make the system better tomorrow. More AI…
Subscribe • Previous Issues Nine Practical Rules for Agents Doing Real Work In recent conversations with crews building agents, I keep hearing the same lessons. Teams with very different products are arriving independently at almost the same architectural choices. That convergence feels important. In recent posts, I argued that passing your evals does not mean an AI system is safe, and that…
Subscribe • Previous Issues The biggest AI risks sit outside the model The most revealing AI failures right now are not stories about models becoming too capable. They are stories about everything around the model. One system received more access than its test environment could contain. Another was trained on material whose acquisition created $1.5 billion in exposure. In a third, people…
Subscribe • Previous Issues Continual Learning Is Arriving in Pieces A while back I wrote about how startups are using reinforcement learning to make agents more reliable. A deeper problem behind that whole trend keeps resurfacing: a model can improve during training, but the moment it’s deployed, learning largely stops. A policy changes, a new edge case shows up, a user corrects…
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