Multi-Learning: 200 Years of the Wrong Model

For millions of years, our brains evolved in environments that demanded everything at once. We hunted, gathered, raised children, fought, explored territories, developed languages, and sketched on cave walls. The same brain, the same day.

This is called "multi-learning" in scientific journals, but simply put, this is how humans naturally operate.

Then, about 200 years ago, someone realized you could make more money by putting a person on a single task. Cheaper to train. Easier to replace. The industrial revolution redesigned human activity around a principle that had nothing to do with the nature of our cognition.

That was a temporary optimization. A local maximum that lasted two centuries. But these are just a blink compared to the 6 million years of evolution of our species.

Now it's ending.

AI is taking over the repetitive, routine work that justified pinning people to single tasks in the first place. And what's left is exactly what our brains were built for: learning across domains, directing, reasoning, envisioning, orchestrating, making judgment calls that no single-purpose agent can make.

Look at the best practitioners today. They're not narrower — they're broader. They sit at the center of their own organization of agents, directing work across boundaries that used to require separate departments.

In the photo on the right, it is not an AI-generated person; it is Peter Steinberger, the creator of the famous OpenClaw, carefully driving this stack of dozens (hundreds?) of AI agents working for him.

We spent 200 years suppressing our strongest capability to make the factory floor more efficient. The factory floor is automated now.

Time to go back to our strength. To multi-learning at work, which we know so well from non-office hours.