Gradient Flow’s analysis of data, technology, and business, with a focus on machine learning and AI — one of the Top 10 Sites for Data Scientists.
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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.…
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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…
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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…
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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…
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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…
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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…
