Introduction

Generative AI and Copilot are spreading fast as “magic tools that reduce human labor.” Reports, code completion, research, slide decks—what once took hours now finishes in minutes.

So if AI relieves us from tedious work, what do humans get tired of? Does our job truly become easier?

Some teams are already seeing signs of the opposite. Ask ChatGPT a question and it gives an answer, but the burden of deciding which answer to adopt only grows. Copilot proposes code, yet the time spent verifying whether it is correct does not shrink.

Instead of being exhausted by manual work, humans may soon be drained by the constant demand to judge and to take responsibility. That is the true face of what I call “AI fatigue.”

Part 1: The Anatomy of AI Fatigue

AI fatigue is not about sore eyes from overusing AI tools. Its essence lies in a shift in cognitive load.

What Is Decision Fatigue?

AI always presents multiple options. Ask a marketer’s ChatGPT to write ad copy, and back come ten candidates. Prompt Copilot to write a function, and several implementations appear.

Convenient, yes—but we must choose one every time. Most options sit in a gray zone: “half right but slightly off,” “each has trade-offs.”

Having to make indecisive judgments again and again becomes the first driver of AI fatigue.

What Is Verification Fatigue?

AI outputs are especially insidious because they look correct. The Japanese is fluent, the code is neatly formatted—yet behind the scenes lie hallucinations and hidden security risks.

On the ground, “checking everything just in case” is unavoidable, and verification fatigue will likely intensify.

From Work Fatigue to Responsibility Fatigue

Fatigue used to stem from “too much typing” or “slides take forever.” In a future where AI shoulders the work, what remains is “choosing” and “taking responsibility.”

AI fatigue is the exhaustion caused by repeatedly making responsible decisions.

Part 2: A Historical View—How Human Fatigue Has Evolved

This change is not abrupt; it follows the trajectory of human labor.

  • The era of physical fatigue: Factory and manual labor consumed muscle and stamina.
  • The era of repetitive-task fatigue: As white-collar work and computers spread, repetitive tasks dominated what tired us out.
  • The era of cognitive fatigue: IT and automation pushed humans into decision and verification roles, making mental load central.
  • The era of responsibility fatigue (AI fatigue): As AI takes over the work, humans become tired of judging and owning outcomes.

Part 3: Designing Ways of Working That Prevent AI Fatigue

Preventing AI fatigue requires more than personal effort. We must redesign how we work.

Meta-AI—Using AI to Evaluate AI

Let one AI check what another produces. Code-review agents and fact-checking agents are already being tested.

If a three-layer workflow—AI proposes, AI verifies, humans only do the final check—becomes standard, the burden of judgment and verification will ease.

Designing the Chain of Command—Decide Who Decides

Today, many workplaces assume “humans decide on every AI proposal.” That concentrates fatigue.

We should clarify what can be delegated to AI and what humans must own. Low-risk areas can be handed off; high-risk domains must always be reviewed by people.

Spreading the Load Across the Team—Do Not Pile Decisions on One Person

AI fatigue worsens when responsibility concentrates on a single leader or owner. Rotating reviewers and sharing responsibilities are not mere process tweaks—they are mechanisms for protecting human health.

Part 4: The Challenge of Leadership Fatigue

The final stage of AI fatigue may be “leadership fatigue.”

AI can propose and advise, but it cannot decide where to steer the ship. Humans still determine direction for organizations and projects, and humans bear the responsibility.

Leaders face a flood of choices and must keep deciding. They even need reasons to reject AI suggestions, and accountability piles up on them.

Part 5: A Philosophical Question—What Will Humans Get Tired Of Next?

If AI eventually replaces even judgment, what will humans get tired of?

  • Can uniquely human underpinnings—kyoji (dignified self-respect), conviction, a sense of responsibility—be replicated by AI? See “What Generative AI Is Critically Missing.”
  • Even if AI learns to “simulate responsibility,” can it truly match the human experience without pain or shame?

AI fatigue will likely become a way to discuss how humans remain responsible agents.

Conclusion

AI reduces the amount of work. In exchange, it is poised to increase the burden of judgment and responsibility.

The essence of “AI fatigue” is the shift from work fatigue to responsibility fatigue.

The future demands:

  • Automating verification through meta-AI
  • Clarifying roles by organizing who judges and who owns responsibility
  • Distributing judgment workloads across teams
  • Redefining leadership

In the end, what tires people out is owning responsibility. How we share and design that weight will determine what work looks like in the age of AI.

FAQ

Q: What is AI fatigue?
A: It is the phenomenon in which AI reduces manual work, but humans become exhausted by the remaining “judgment” and “responsibility.”

Q: How can we prevent AI fatigue?
A: Use meta-AI to automate verification, clarify who judges and who owns responsibility, and design team structures that spread the load.

Q: Is AI fatigue an entirely new problem?
A: No. It is the next phase in the historical shift of human fatigue: from physical to manual to cognitive and now to responsibility.