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 teams are beginningContinue reading “Your Model Is Not Your Moat”
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if everyone rents the same intelligence, what’s left?
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?”
The AI Boom’s Hidden Payment Schedule
A recent article, “Why the AI Boom Is Hitting a Reset Wall,” makes an argument I think is more useful than the usual debate over whether AI is a bubble. The question is not whether AI demand is real. It is whether the financing structure supporting the buildout can handle the moment when enormous computeContinue reading “The AI Boom’s Hidden Payment Schedule”
AI and the Job Market: What We Know So Far
Table of Contents AI Is Changing Work Faster Than Employment The Entry-Level Squeeze Watch Hiring, Not Just Layoffs Automation and Augmentation Are Different Productivity Does Not Automatically Mean Fewer Jobs The Hiring Market Has an AI Arms Race The Signals Employers May Value More Why the AI Jobs Numbers Are Hard to Read Automating WorkContinue reading “AI and the Job Market: What We Know So Far”
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 many of the most consequentialContinue reading “Nine Practical Rules for Agents Doing Real Work”
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”
What AI Teams Should Know About HBF and Tiered Memory
For years, most of the AI memory conversation has been about bandwidth: can memory feed GPUs fast enough? Frontier inference is adding a second problem. Model weights, KV caches, and expert weights are getting large enough that there may not be enough HBM to keep everything nearby, even though only a fraction of that stateContinue reading “What AI Teams Should Know About HBF and Tiered Memory”
The AI Data Center Backlash Has a Blind Spot
Within my own network, I was early in flagging the growing local opposition to AI data centers. Since then, it has moved fast, from scattered zoning fights to outright moratoriums and statewide political battles, with data centers becoming a poster child for many of the broader anxieties around AI. A lot of that opposition isContinue reading “The AI Data Center Backlash Has a Blind Spot”
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 walked away more certain withoutContinue reading “The biggest AI risks sit outside the model”
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”