I use AI all day, every day, and I’d like to believe it has made me smarter. If simple exposure caused brain rot, I should be a cautionary tale.
By smarter, I mean my own thinking feels sharper and my taste is easier to articulate. I find the edge of what a new system can do before its failure patterns cost me real time.
A lot of people worry that brain rot is a byproduct of laziness. The kind I fear looks like extraordinary productivity. More pages, more plans, more code, more finished-looking work, all arriving faster than ever. The model forms the first opinion, writes the plan, resolves the ambiguity, interprets the criticism, and explains what the human supposedly learned. The person stays busy and discerning, approving, rejecting, requesting another version, and shipping. Somewhere inside that smooth process, though, they stop making decisions they would know how to make without the machine.
That possibility is hard to see because the work keeps getting better.
AI isn’t harmless. I just fight with it. I make it harder to use on purpose, and I add the resistance at the exact moments where a smooth answer would cost me a decision I need to make. There is a name for this now: friction-maxxing.
Kathryn Jezer-Morton coined it in The Cut in January 2026, writing about people who deliberately pick the slower, more awkward option in ordinary life. She was writing about being a person. I am pointing the same idea at AI.
Most people use AI to delete friction. The whole pitch is less effort, fewer steps, no struggle. When the effort is pointless, I delete it too. On serious work, I run the process in the other direction by adding resistance deliberately, making the AI work harder, and keeping my brain in the work.
The stakes run well past personal productivity. In education, a student can submit a better essay while losing the struggle through which an argument becomes their own. At work, an employee can produce a convincing strategy and have no idea which assumption will fail when reality changes. In product design, an agent can appear magical by hiding its limits and train users to trust completion signals that prove nothing.
Brain rot can coexist with excellent output. That is what makes it dangerous.
Here’s what’s inside:
Where to put the friction back. The five conditions I use to decide whether to slow a task down or let AI run at full speed.
How to size up a new agent. The failure that taught me to judge an agent by how it discloses its limits rather than by what its website claims.
How to keep your taste from flattening. What to do when the model hands you a polished page you dislike, instead of pulling the lever for five more variations.
The kit: how to argue with AI, whether or not you know the domain. One question sorts the task into two modes, each with one habit and one line you write at the end.
You have the argument now. What follows is the practice.
Listen to this episode with a 7-day free trial
Subscribe to Nate’s Substack to listen to this post and get 7 days of free access to the full post archives.
