The Hidden Cost of AI Productivity: When Efficiency Turns Into “Brain Fry”

 A new HBR study reveals that the race to build and manage AI agents may be pushing knowledge workers toward a new form of cognitive overload.

If you spend any time on LinkedIn these days, you’ve probably seen the same type of post over and over.

Someone proudly announces they built an AI agent that now writes their emails, analyzes data, drafts presentations, and maybe even ships code. The post usually ends with something like:

“AI has made me 10x more productive”

And to be fair, there’s truth in that narrative. AI is already eliminating large amounts of repetitive work across industries. Many professionals are genuinely seeing meaningful productivity gains.

But a recent article I came across in Harvard Business Review made me pause and rethink the conversation around AI productivity.

The article introduces a concept called “AI brain fry”, a type of mental fatigue that occurs when workers spend too much time managing, supervising and interacting with AI tools.

It turns out the story of AI at work might be more complicated than the productivity headlines suggest.

When AI Doesn’t Simplify Work, It Intensifies It

The promise of AI has always been simple. Automate repetitive tasks so humans can focus on more meaningful work. But the HBR researchers point out something interesting happening in many workplaces today. Instead of reducing work, AI often changes the nature of it.

Workers aren’t dong their jobs anymore. They’re also managing the tools that help them do their jobs.

In modern AI workflows, it’s common to see people juggling multiple tools at once:

• A coding assistant generating drafts
• A research agent summarizing document
• A data model analyzing datasets

Each tool produces outputs that still require human oversight. Prompts must be written. Results must be validated. Errors must be corrected.

What was supposed to be automation can quickly turn into a constant loop of prompting, checking, refining, and switching between tools. And that mental juggling has consequences.

The Emergence of “AI Brain Fry”

In the study discussed in the HBR article, researchers surveyed 1488 full-time US workers across industries to understand how AI is affecting cognitive workload. What they discovered was a pattern of mental fatigue tied specifically to intensive AI use and oversight.

Participants described the experience in strikingly similar ways:

• A “buzzing” feeling in the head
• Mental fog
• Slower decision making
• Difficulty focusing

The researchers coined a term for this phenomenon: AI brain fry, defined as mental fatigue caused by excessive interaction with AI tools beyond a person’s cognitive capacity.

One engineer quoted in the research described it perfectly. After juggling several AI tools simultaneously, he said it felt like having “a dozen browser tabs open in my head, all fighting for attention”.

The engineer’s description stuck with me because it mirrors something I’ve started to notice in my own work.

Leading tech marketing at a lean company means I’m constantly switching between strategy and execution. On any given day, I might be thinking about competitive positioning, refining messaging, working on SEO or AEO strategy, researching a completely new topic on AI, writing content about it, and sometimes even stepping into pseudo web developer mode to quickly code something for our website.

AI has undeniably made me more efficient across all of these areas. It helps eliminate the tedious subtasks that used to consume most of my time.

But the tradeoff is that my scope of work and the area of accountability have expanded exponentially. I’m now required to own several outcomes in the same amount of time. Because AI makes it possible to move quickly across so many domains, my mind often feels like it’s processing multiple streams of thought at once. There’s always another idea to test, another prompt to run, another problem that suddenly feels solvable.

So while the tasks themselves may get easier, I’ve noticed something interesting: by the end of the day, even after getting a lot done, I sometimes feel more mentally exhausted than before.

And while this experience looks different from traditional burnout, it resembles something closer to cognitive workload, exactly what researchers behind the HBR article set out to study.

The Surprising Role of AI Oversight

One of the most interesting insights from the research is that the most mentally taxing part of AI work isn’t using tools, it’s supervising them.

Workers whose AI workflows required constant monitoring reported:

• 14% higher mental effort
• 12% higher mental fatigue
• 19% more information overload

That makes intuitive sense. When multiple systems are generating outputs simultaneously, the human operator becomes responsible for interpreting and validating everything.

In many cases, employees aren’t actually doing less work. They’re simply managing a larger surface area of information and decisions.

More AI Tools Doesn’t Always Mean More Productivity

Another finding from the study stood out to me.

The researchers examined how productivity changed depending on how many AI tools people used simultaneously.

They found productivity increased when workers went from one AI tool to two, and again when they added a third tool. But after that, something surprising happened. Productivity started to decline. The explanation is familiar to anyone who has ever tried to multitask across too many applications: the human brain has limits.

At a certain point, managing additional tools becomes more work than the value they generate.

The Business Risks of AI Brain Fry

What makes this phenomenon particularly important is that it isn’t just an employee wellness issue.

The study found that workers experiencing AI brain fry reported:

• 33% more decision fatigue
• 11% more minor errors
• 39% more major errors in their work.

Even more concerning, employees experiencing this kind of mental strain were significantly more likely to consider leaving their jobs.

Ironically, these employees are often the ones most actively experimenting with AI — the very people organizations rely on to drive innovation.

AI Can Still Reduce Burnout, If Used Correctly

Despite these findings, the research isn’t anti-AI. In fact, it highlights an important distinction. AI does reduce burnout when it replaces routine tasks.

Workers who used AI to offload repetitive work reported 15% lower burnout levels than those who didn’t. This reinforces something many people intuitively understand. AI is most valuable when it removes toil: the repetitive tasks that drain energy without adding much value.

Problems arise when AI introduces new layers of oversight, monitoring, and decision-making. In highly automated environments, this challenge becomes even more prominent as each of these AI agents produces signals that may require attention.

Without thoughtful alerting strategies, engineers can easily become overwhelmed by the sheer volume of information generated by the very automation meant to help them. Tools that filter noise and escalate only the events that truly require action, like OnPage, play a critical role in protecting human attention.

The Real Lesson: Human Attention Is the Bottleneck

The biggest takeaway from the article isn’t that AI is harmful. It’s that human attention remains the most limited resource in modern work.

As organizations race to adopt AI, the temptation is to keep layering tools on top of each other.

More copilots. More agents. More automation layers.

But if every system still requires human supervision, then the bottleneck isn’t compute power. It’s cognition.

The companies that succeed with AI adoption may not be the ones that deploy the most tools, but the ones that design workflows that respect the limits of human attention.

Because if the goal of AI is to make work better, we should probably ensure it doesn’t leave people mentally fried in the process.