Gradient Flow

Like everyone else, I’ve been enjoying the steady improvement in coding agents and the tooling around them, from frameworks and harnesses to evaluation suites. But the more I talk with teams actually deploying agents in enterprises, the more I circle back to plumbing. Agents need data integrations and infrastructure built for them, a theme I explored in a piece on rethinking databases for agents.

Subscribe • Previous Issues Like everyone else, I’ve been enjoying the steady improvement in coding agents and the tooling around them, from frameworks and harnesses to evaluation suites. But the more I talk with teams actually deploying agents in enterprises, the more I circle back to plumbing. Agents need data integrations and infrastructure built for them, a theme I explored in a piece on rethinking databases for agents.

I’ve avoided writing about copyright and AI. Not because it isn’t important, but because it felt like a legal sideshow compared to the engineering and business questions I find more interesting. That’s gotten harder to justify. There is also something mildly funny about naming my podcast The Data Exchange years ago, because I believed data markets would become central to technology, and then mostly ducking this corner of the data conversation.

Subscribe • Previous Issues I’ve avoided writing about copyright and AI. Not because it isn’t important, but because it felt like a legal sideshow compared to the engineering and business questions I find more interesting. That’s gotten harder to justify. There is also something mildly funny about naming my podcast The Data Exchange years ago, because I believed data markets would become central to technology, and then mostly ducking this corner of the data conversation.

The more I dig into the economics, the harder it is to see AI data centers as a good business, and they’re now my leading candidate for what pops the AI bubble in the next 6 to 12 months. The concern isn’t that AI stops improving or that demand vanishes. It’s that spending has raced far ahead of proven revenue while the assets being financed lose value unusually fast.

OpenAI and Anthropic are going public while still capturing much of the money spent on foundation-model usage. But deployment patterns are starting to tell a more complicated story. Companies are building hybrid model portfolios, using proprietary models where convenience, support, and frontier capability matter, while turning to open-weights models where cost, privacy, customization, and deployment control matter more.

Subscribe • Previous Issues OpenAI and Anthropic are going public while still capturing much of the money spent on foundation-model usage. But deployment patterns are starting to tell a more complicated story. Companies are building hybrid model portfolios, using proprietary models where convenience, support, and frontier capability matter, while turning to open-weights models where cost, privacy, customization, and deployment control matter more.

The Cost Reality of Running AI at Scale Budget shock is already happening. Multiple major players have pulled back on AI features or subscriptions due to unexpectedly high token costs. Amazon removed its token leaderboard and Microsoft cancelled Claude Code subscriptions. These are early signals that the deploy-everywhere approach is hitting hard financial limits, not just theoretical ones. Physical infrastructure is the binding constraint.

Back in February, I wrote about what I called the “Data Center Rebellion,” the growing local resistance to the physical infrastructure behind AI. Since then, I have been asking tech people around the Bay Area how closely they are following the backlash. The answer is usually: they know it exists, but not much more than that. There is still a quiet assumption that most of these announced campuses will get built, plugged in, and brought online more or less on schedule. I am much less sure.

Subscribe • Previous Issues Back in February, I wrote about what I called the “Data Center Rebellion,” the growing local resistance to the physical infrastructure behind AI. Since then, I have been asking tech people around the Bay Area how closely they are following the backlash. The answer is usually: they know it exists, but not much more than that. There is still a quiet assumption that most of these announced campuses will get built, plugged in, and brought online more or less on schedule.