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 the system, andContinue reading “Continual Learning Is Arriving in Pieces”
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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?”
Why Data Centers Became the Face of the AI Backlash
AI is no longer being judged only as software. Once the buildout arrives as a massive industrial facility, new transmission lines, continuous power demand, and possible pressure on utility rates, the argument changes. Communities are being asked to absorb costs they can see immediately for benefits that remain distant, uncertain, and spread across people andContinue reading “Why Data Centers Became the Face of the AI Backlash”
What Comes After Language Models
I keep seeing scientific discovery framed as a sufficiently ambitious prediction or data-compression problem. A recent position paper by Tom Zahavy challenges that view by separating reasoning into three capabilities. Induction finds general patterns in examples. Deduction works out what follows from a set of assumptions. Abduction proposes a new explanation when neither the existingContinue reading “What Comes After Language Models”
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 a model is accurate, reliable, fast enough for production, andContinue reading “Passing Your Evals Doesn’t Mean You’re Safe”
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”
If the Labs Wobble, the Clouds Feel It First
I have written separately about the shaky economics behind the data center boom and the growing pressure on the frontier labs. This post connects the two. Microsoft reported $24.1 billion of revenue from its OpenAI relationship, an amount equivalent to almost one-quarter of Azure’s scale, although the figure includes revenue-sharing payments as well as cloudContinue reading “If the Labs Wobble, the Clouds Feel It First”
Inside AMD’s AI Bet
Table of Contents What AMD Actually Sells Now The Software Story, Which Is the Whole Story The Open-Standards Bet Who’s Buying, and How the Money Actually Moves Where This Could Still Come Apart What It Means If You’re Buying AI Compute What AMD Actually Sells Now AMD’s most important change is not that it hasContinue reading “Inside AMD’s AI Bet”
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, and a broad coalition of technology companies is now publiclyContinue reading “Specialized AI Is Getting Easier to Build”
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”