Six months ago I pulled a local language model onto my laptop. Took about 12 minutes with Ollama. No account, no API key, no data leaving the machine. It felt like a small act of sovereignty — exactly the kind of local-first approach I’d been arguing for.

Then I started using it. And I noticed something.

The model’s cultural centre of gravity was somewhere around San Francisco, circa 2022. Ask it about food systems and it defaulted to commodity agriculture and supermarket supply chains. Ask it about community governance and it reached for American municipal frameworks. Ask it about traditional land management and it gave me a careful, earnest summary that read like it had been assembled from university anthropology papers — not from anyone who had actually grown anything, or sat with the country long enough to know it.

I’ve spent 30 years watching tech cycles come and go, from the first dial-up modems in rural Austria to the mesh networks I’m currently stringing across the Australian bush. Each time a “next big thing” arrives, we see the same pattern: a frantic rush to centralise, followed by a slow, painful enclosure of what should have been a common resource.

The current noise around AI in open source feels different. It feels heavier. There’s a justified fear that AI-generated code is hollowing out our commons. Maintainers are being buried under a drift of unvetted, mediocre pull requests, while a handful of platform monopolies strip-mine decades of community work to feed their proprietary black boxes.

I’m tired. Not the kind of tired that a good night’s sleep or a weekend off the grid can fix. It’s a deeper, more pervasive exhaustion—the fatigue of living in a culture of relentless, performative positivity. In the tech world, we’re told to “crush it,” to “move fast,” and to embrace every new “game-changer” with uncritical enthusiasm. If you’re not a believer, you’re a “naysayer” or, worse, a “blocker” of progress.