On AI Fatigue

I turned from a composer to a DJ and it's exhausting

I’m in the middle of a thirty-day sabbatical away from work. A full calendar month without programming - something I never thought I’d need.

I feel exhausted.

I had planned to take this break later in the year, when the days were longer and the weather a bit better. But I was too tired to wait. I couldn’t get through a full workday without falling asleep.

I thought that twelve years of programming had caught up to me, but it was more than that.

I think I’m experiencing what some people are calling AI fatigue.

How I used to work

In the past, when I had a challenging feature or a hard architectural problem, the process was clear. I’d sit down, make a cup of coffee, read the documentation, and tunnel-vision into the problem.

Deep focus.

One thing at a time.

If the problem was big enough, I’d sleep on it and let the background process in my mind do its work. That’s how I’ve always operated.

I loved those afternoons where I had a big, gnarly problem ahead of me and no idea how I’d solve it - researching, reading, trying one approach until it failed, then trying another. I’d lose all track of time. The whole afternoon would pass just like that. I’d forcefully pull myself away from the computer to have dinner.

The threshold for problems that can suck me in like this is so high now.

How I (try) to work

AI finds solutions more quickly than I can keep up.

That space where I used to lose myself, that deep exploratory space, has shrunk almost to nothing.

Here’s the problem. Here are the constraints. Here’s what I think.

And just like that, I have a solution to review.

Is it good enough?

Hard to say because I haven’t gotten to it on my own.

I have the input and the output, but I haven’t spent enough time in the in-between.

It’s very difficult for me to keep a deep understanding of codebases (without losing my mind) with the speed at which they get created now. AI can design a solution more quickly than I can understand it and think about the trade-offs and future needs.

I try to be the reviewer, the manager, the DJ.

But surface-level understanding is not enough to build good software. If I don’t want to be the bottleneck, I need to review quickly, but reading is not reviewing. To review properly, I need intense focus, and that, combined with the speed of AI output, is exhausting.

Why don’t I slow down?

Honestly, the industry peer pressure got to me.

Everyone is talking about how quickly they’re building things, how many agents they’re using at the same time, and how much time they’re saving.

I feel like if I’m working at a regular speed, I’m not doing enough.

And this is probably true to an extent.

Coding agents are a time-saver, no doubt about that.

But when I tried juggling three agents at once - one updating dependencies, one working on an easier task, and discussing a more complex feature with another - my mind felt like mincemeat.

I’m not sure if I can train myself to do this all the time.

Maybe if it’s the only mentally-intensive task I do the whole day, but I have other things I want to focus on after I close the laptop. I want to read challenging books, write these articles, explore art and science.

Adapting

How much longer do I have in the industry if performing at the top level requires both intensity and higher speed?

I don’t know.

But the fact that I’m asking this question at all says something.

I don’t think I’m wired for this, but I think I can adapt. Maybe if I have a large mental library of solutions and architectures, I can recognize patterns and good solutions more easily? If I can do the mentally-taxing part upfront?

Maybe not knowing what I want from the start is the problem?

But every domain is so special and different. Every business has its own peculiar problems that you can’t find in a public repository to learn in advance.

These are questions to think about after my sabbatical.