These days, the frontier AI labs are all racing to build self-improving models. Some believe it’s the surest route to superintelligence—as AI improves itself in a mind-melting loop, the thinking goes, it will eventually surpass human comprehension (and perhaps even control).

That’s all well and good, but I have a newsletter to produce. I wondered if recursive self-improvement might also be useful for me. Could I use AI to train and continually improve a model that automates some of this newsletter’s busywork?

After a week or so of experimenting, the answer appears to be a resounding—and surprising—hell yes. What’s more, dabbling with self-improving models shows a different vision for how AI might unfold—one that doesn’t center on a handful of companies that control the whole industry.

I started by trying out a simple self-improving loop

To get my feet wet, I experimented with training a small language model from scratch—by which I mean I dumped all the hard work on Claude’s plate.

I installed AutoResearch, which helps an off-the-shelf AI model build and improve a smaller model. AutoResearch is the brainchild of Andrej Karpathy, a superstar AI researcher who helped found OpenAI, led AI work at Tesla, and recently joined Anthropic.

I fired up Claude and gave it the recommended instruction: “Hi, have a look at program.md and let's kick off a new experiment!” While Claude did the hard stuff, I provided silicon (an Nvidia DGX, a desktop “supercomputer&rd...