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 theContinue reading “if everyone rents the same intelligence, what’s left?”
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I keep hearing the same advice about agents
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 AIContinue reading “I keep hearing the same advice about agents”
The model may not be your biggest risk
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 inContinue reading “The model may not be your biggest risk”
Why do our AI models stop learning the second we deploy them?
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 caseContinue reading “Why do our AI models stop learning the second we deploy them?”
What Workday, OpenAI, and a German court have in common
Subscribe • Previous Issues Passing Your Evals Doesn’t Mean You’re Safe Evals are part of every serious conversation about putting AI into production. Teams define benchmarks, set thresholds, and increasingly run red teams to see how the system holds up against someone actively trying to break it. That combination is reasonably good at telling you whether aContinue reading “What Workday, OpenAI, and a German court have in common”
The Big AI Labs Are Suddenly Competing with Your Own Data
Subscribe • Previous Issues Specialized AI Is Getting Easier to Build Last week I argued that open models will absorb most of the money and compute the world spends on AI. A week later, open weights are even more central to the conversation. Recent releases have made the gap between capable and affordable harder to ignore, andContinue reading “The Big AI Labs Are Suddenly Competing with Your Own Data”
we need to talk about where AI spend is actually going
Subscribe • Previous Issues Open Models Will Absorb Most of the AI Spend Here is my bet: open models (open weights and open source alike) will end up absorbing most of the money and compute the world spends on AI. The proprietary frontier models get the headlines and the IPO valuations, but developers and AI teams seeContinue reading “we need to talk about where AI spend is actually going”
25+ startups all solving the same missing piece
Subscribe • Previous Issues What Startups Taught Me About the Next Layer of AI Infrastructure A little while back I wrote about how teams use reinforcement learning (RL) to make agents reliable. Since then I keep bumping into startups where RL is not a research footnote or a feature buried in the stack. It is central toContinue reading “25+ startups all solving the same missing piece”
What happens when your agent can touch money
Subscribe • Previous Issues Your CLI Was Built for Humans, Not Agents There’s a friendly debate among developers about how to give AI agents reliable ways to use external tools, data, and services so they can do useful work beyond generating text. One side favors CLIs, or Command-Line Interfaces, where agents run text commands against tools likeContinue reading “What happens when your agent can touch money”
I talked to Google’s former AI head about messy data
Subscribe • Previous Issues Agents Need Maps, Not Bigger Context Windows Like everyone else, I’ve been enjoying the steady improvement in coding agents and the tooling around them, from frameworks and harnesses to evaluation suites. But the more I talk with teams actually deploying agents in enterprises, the more I circle back to plumbing. Agents need dataContinue reading “I talked to Google’s former AI head about messy data”