Subscribe • Previous Issues Stale Data Used to Be Annoying Imagine a procurement agent checking inventory and deciding that stock has fallen below the reorder threshold. It places another order. The problem is that a large delivery was recorded a few minutes earlier, and the copy of the data the agent queried has not caught up. AContinue reading “why your data stack is about to get less forgiving”
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so Google just paid $10 million for an airline’s Teams messages
Subscribe • Previous Issues When Work History Becomes an AI Asset Google recently agreed to pay $10 million for the internal business data of Spirit Airlines. The airline is bankrupt, but its emails, Teams messages, software, spreadsheets, and operating records apparently still have value. Google plans to use the material for product development and AI. Spirit’s flightContinue reading “so Google just paid $10 million for an airline’s Teams messages”
Your AI model is a rental (but the loop is an asset)
Subscribe • Previous Issues Self-Improvement Without the Science Fiction A few weeks ago I wrote about AI systems that keep learning after deployment instead of treating every interaction as a fresh start. That prompted a few readers to ask about recursive self-improvement (RSI). The connection is real, but I think it helps to separate three ideas. ContinualContinue reading “Your AI model is a rental (but the loop is an asset)”
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?”
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”