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”
Category Archives: Uncategorized
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”
Multiple Dials, Not One: Reading an Open Model Release
The most useful thing I’ve learned watching this latest round of open model releases is that “open” has stopped predicting anything. It used to be a reliable shorthand for cheap and second-tier. Now the top-ranked open coding model in the world costs roughly thirteen times more per million tokens than the cheapest credible open modelContinue reading “Multiple Dials, Not One: Reading an Open Model Release”
AI Can Win While Data Centers Lose
I keep coming back to the same uncomfortable math on AI data centers: the money going in is piling up faster than any economic proof that it should. Usage of AI systems has reportedly been climbing seven to twenty times a year, while the revenue that usage produces has grown maybe three or four times,Continue reading “AI Can Win While Data Centers Lose”
Three New Models, One Signal About Where AI Spending Goes Next
Three frontier level models landed within weeks of each other this fall, GLM 5.2 from Zhipu, Kimi K3 from Moonshot, and Gemini 3.6 Flash from Google, and I wanted to capture early developer reaction so I can monitor how feelings about these models change over time. The individual verdicts differ, but together they hint atContinue reading “Three New Models, One Signal About Where AI Spending Goes Next”