Opinion: AI as a Fractal: Signal, Noise, and Recursive Nature of Progress

AI as a Fractal: Signal, Noise, and the Recursive Nature of Progress

When I think about artificial intelligence, I often picture a fractal. Zoom in, and you see the same structure repeating. Step back, and the pattern persists. It’s endless, recursive, and both beautiful and overwhelming.

AI is supposed to help us “learn more” — to analyze faster, to uncover insights more easily, to extend our reach into complexity. At first glance, that sounds like pure progress. But here’s the paradox: every time AI helps us generate more knowledge, it also creates more noise. Each new tool, summary, or dataset adds to the pile we already have to sift through. In chasing clarity, we’ve multiplied the clutter.

In this sense, knowledge creation starts to feel fractal. We zoom in — hoping for sharper detail — only to rediscover the same fundamental struggle: how do we separate signal from noise? How do we discern what really matters amid the recursion of information?

This creates something of a rat race. Faster analysis gives us more output, which demands faster filtering, which produces even more output. The cycle repeats. Like a fractal, the structure doesn’t end; it just scales.

And yet, there’s reason for optimism. The “rat race” doesn’t mean we’re running in circles without progress — it means the floor has moved up. AI may not make life simpler in the way we first imagined, but it has raised the baseline of what’s possible. In the past, accessing certain kinds of analysis or creativity required years of specialized training. Now, those same capabilities are just a prompt away. The challenge isn’t that AI has replaced the need to learn, but that it has shifted what we need to learn.

Take careers as an example. AI tools don’t eliminate the need for skill — they change the definition of skill. Everyone has access to the same baseline capabilities, so being “qualified” now means going one level deeper: interpreting results, asking better questions, and weaving AI’s output into meaningful action. The bar is higher, but so are the opportunities.

So maybe AI’s fractal-like recursion isn’t a trap but a feature of progress itself. Each layer of complexity opens new ground. Each wave of noise forces us to become better at finding signal. If we accept this, the paradox becomes less discouraging: AI won’t free us from the work of learning, but it will continually expand what’s worth learning.

In that way, the recursion isn’t endless futility. It’s an invitation. An invitation to refine, to prioritize, and to rise to the next level of the pattern — not to escape the fractal, but to grow with it.