5 min read

What is AI fatigue

tldr; AI automates the output of mental and creative effort, though sometimes it does so with no respect in its own peculiar way - you still have to control, direct, evaluate, and make decisions. AI compresses the interval between asking for a result and making the next decision, shifting attention to the later stages of solving a task. Human attention (for now) doesn't automate and doesn't scale. When all 5 of your AI agents keep shoving more and more output at you that you have to react to - the speed gain turns into fatigue. People are starting to call this AI fatigue. Reducing the fatigue is mostly a matter of balance. Agent speed and autonomy live in the harness - they won't cure the fatigue.

Using AI, I started catching myself on this: sometimes it hurts. You do something, keep doing it, and you're tired. Yes, that's part of adult life, but with AI it happens really fast and vividly.

It looks like this:

  • At first you're all fired up, cranking away, seeing you can move mountains: 5 (or 155) agents with a hundred subagents are each busy with their thing: the robots toil, the human is happy, fully automated luxury communism, all of that.
  • Your robots aren't tired, but you got tired instead of the them.

I decided to figure out what's going wrong.

Let's try science

As usual, I read the research AI finds and collects the papyruses for me, and then I interrogate them. What did we find? That's right - a new scientific term is emerging: ai fatigue. There's also a media term, but I don't want to go there. There's no canonical definition; I like mine. AI fatigue is cognitive and emotional exhaustion during or after using AI, tied to the need to process AI's output, where processing means directing, validating, controlling, interpreting, correcting.

Without immersing ourselves in scientific rituals and the rest of the advancement of scientific knowledge, what have we got? Not that much. There's a new phenomenon, and what's clear about it is:

  • it exists; there's a chance it's not just my anecdotal feeling [1]
  • there are guesses as to why it shows up
  • it's not yet clear whether it's a standalone thing or not - for example, ai fatigue scores correlate with digital fatigue and technostress
  • tools are appearing for describing and measuring it: I vibe-coded an ai fatigue assessment questionnaire for you based on a paper [2]

As for ai fatigue specifically, science so far offers few practical takeaways:

  • Something is taking shape; subjectively, people talk about ai fatigue. They say they're cutting back on AI use or planning to.
  • Sometimes usefulness and fatigue grow at the same time [3], and distress coexists with eustress [4].

Everything else looks to me like early-stage research: some are trying to figure out whether exposure matters, some are looking at readiness, fears, responsibility [5], whether all users are susceptible, whether all activities are, and so on [6]

My personal conclusion from the scientific standpoint: weeeell, my feelings aren't entirely detached from reality, but what to do about it - science doesn't know yet, and fine, I'll try some speculating.

My unscientific fantasies and value judgments

When I'm solving some research or creative task, in the broad sense, I have enough time to be alone with it: I formulate the question inside myself, read, search, talk to people, change the question, adjust the goals, and so on. I don't have a ready answer; I have to find that answer, read my way to it, create it on my own. If I delegate a task instead, I have time until the people come back to me with questions or a result. I don't have to make a decision right away. I can switch to something else and come back later.

With AI it's different: I get an answer, even to a complex question, within tens of minutes. Yes, sometimes Pro/Ultra/Fable can think for 4-5 hours inside /goal, but that's more the exception. With development it's a bit longer, but the next chunk of output that demands a reaction is at most hours of waiting away.

With AI there's a thrill: so many unformulated questions, so many ideas for pet (and not-so-pet) projects you want to build right now.

There's FOMO too:

  • I'm paying for the subscriptions, after all: the agents should be working all that time
  • that guy and that other guy on X have a bazillion agents, and I've only got 5, so I'm a loser, going to die under a bridge now
  • look, here's a technique that makes agents work better, I have to try it right away

The bottom line:

  1. AI solves some things very fast; I start making decisions more often than I'd like. I'm not used to this speed, and I need to learn to live and work with it. That doesn't mean I should keep accelerating: time to simply be is, for me, one of the ingredients of a good decision.
  2. Working with a large number of agents - and with an accelerated stream of output in general - pushes me toward even more multitasking.

What to do about ai fatigue?

Ideally, all this reflection should turn into some actions or decisions that make my life better.

Here's my reasoning. To get rid of the fatigue itself, you have to deliberately take the path of slowing down and stepping away from multitasking (thanks, CO). Flow, all that - Csikszentmihalyi approves. I have no other recommendations for myself yet.

You can explain to an agent how to make some of the decisions, and sooner or later it will come to you with an escalation - meaning everything gets even denser. So I'll have 50 agents instead of 5 and the same old problems with AI fatigue and control. Jevons paradox, in a way. The way I see it, AI fatigue lives in the overall balance, not in workflows or the harness. What lives there is speed, quality, and density of output.

Sources

  1. ↩ Parreño et al. (2026), How do College Students Express AI Fatigue? A Content Analysis of Students' Perceptions and Experiences.
  2. ↩ Lau et al. (2026), AI fatigue in human–AI interaction: Scale development and validation.
  3. ↩ Fan et al. (2026), When Help Hurts: Verification Load and Fatigue with AI Coding Assistants.
  4. ↩ Högemann et al. (2025), Technostress and generative AI in the workplace: a qualitative study.
  5. ↩ Savolainen et al. (2026), Social comparison contributes to work exhaustion in the context of workplace AI use.
  6. ↩ Soriano et al. (2026), AI-induced fatigue among students in higher education: a latent profile analysis.