Nav’s post points to an important recent argument: AI often does not reduce work so much as intensify it. In the Berkeley/HBR research, workers in a 200-person tech company moved faster, took on broader scope, and let work spill into more hours of the day. The issue was not just output. It was expectation creep.

That is the deeper leadership lesson.

When intelligence becomes cheap and instantly available, the constraint shifts. The bottleneck is no longer only doing the work. It is deciding what work should be done, what should stop, what should wait, and what should never have been expanded in the first place. The researchers argue that without intentional norms, AI makes it easier to do more and harder to stop.

So I do not read this as an anti-AI article.

I read it as a warning against unmanaged AI adoption inside systems that already reward speed, responsiveness, and overreach. In that setting, productivity gains do not become margin, recovery, or better judgment. They become more scope, more pressure, and more hidden burnout. That is an organizational design problem before it is a technology problem.

AI increases the number of possible actions.

Leadership must increase the quality of selected actions.

That gap may become one of the central management problems of the next few years.

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Nav Toor surfaced something important this week: AI is not only saving time. In many cases, it is quietly expanding the amount of work people feel able — or expected — to do. That distinction matters.

A recent Harvard Business Review article, published March 5, 2026, argues that certain patterns of AI use are producing what it calls “brain fry” — cognitive fatigue linked to how people interact with AI at work. The article’s key point is not that AI always increases burnout. It is that some use patterns reduce strain, while others increase overload, fatigue, and diminished mental clarity.

This fits what many of us are beginning to notice.

When intelligence becomes cheap, abundant, and instantly available, the real constraint is no longer access. It is discernment.

AI makes drafting easier.
So we draft more.

AI makes research faster.
So we open more loops.

AI makes iteration cheaper.
So “good enough” becomes harder to define.

The result is not always less labor. Sometimes it is more cognitive branching, more review burden, more self-interruption, and more ambient pressure to keep going. TechCrunch’s reporting on the emerging burnout pattern makes a similar point: some of the earliest stress signals are showing up among the people embracing AI most aggressively.

This is not an argument against AI.

It is an argument for boundary conditions.

Without stopping rules, pacing, and a clear definition of RightACTION, speed becomes persuasive. And when speed becomes persuasive, people start to confuse possibility with obligation.

That may be one of the defining leadership errors of the AI transition.

The question is no longer whether AI can help us do more.

The deeper question is whether we know what should not be done, expanded, reviewed, or chased simply because AI made it possible.

That is not a tooling problem.

That is a leadership problem.

#AI
#Leadership
#Burnout
#FutureOfWork
#GenerativeAI
#RightACTION

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Mike R. Jay is a developmentalist utilizing consulting, coaching, advising and helping… emergent from dynamic inquiry as a means to cue, scaffold, support, lift, and protect; offering inspiration to aspiring leaders who are interested in humaning where being, doing, having, becoming, contributing, relating, guiding to produce resilience and wellth help people lead generative lives.

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