AI and the Rise of Cognitive Overload

Posted March 19, 2026

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Reviewed by Kaja Perina

A few years ago I wrote about digital-induced amnesia—the erosion of memory that occurs as we outsource thinking to our devices. I later reflected on technostress, the strain that occurs as those tools grow more complex.

Lately the phenomenon feels less theoretical.

A recent study published in Harvard Business Review by Julie Bedard and colleagues at Boston Consulting Group (BCG) describes something workers themselves have begun calling “AI brain fry.” The researchers surveyed nearly 1,500 full-time employees across industries in the United States and found that a meaningful share reported symptoms of acute cognitive fatigue linked to heavy AI use, particularly when managing multiple AI systems simultaneously (Bedard et al., 2026). Workers described mental fog, headaches, slower decision-making, and the strange sense that their thinking had become crowded.

They define it as mental fatigue that occurs when interacting with AI exceeds cognitive capacity.

What stands out is not just the symptoms, but the context in which they appear. As an epidemiologist, I’m interested in patterns, and “AI brain fry” appears to reflect a convergence of forces rather than a single cause.

The United States has long been described as one of the most individualistic and competitive societies in the world (Hofstede, 2001). Productivity occupies a central place in our cultural imagination. We measure output, celebrate efficiency, and often equate accomplishment with personal worth.

Artificial intelligence has entered that landscape with astonishing speed, assisting with everything from drafting to analysis.

Researchers studying the “brain fry” phenomenon observed precisely this dynamic. Workers reported that AI did not simply reduce workloads. In many cases it expanded what the researchers called the sphere of accountability, meaning that employees suddenly felt responsible for producing more work, monitoring more outputs, and managing more information in the same amount of time (Bedard et al., 2026).

The technology that promised efficiency often expands both pace and responsibility instead.

Many modern technologies are designed without natural stopping points.

Social media platforms perfected this design through the infinite scroll, a design pattern intentionally created to keep users continuously engaged with new content (Alter, 2017; Harris, 2016). Each new piece of information appears seamlessly below the previous one, eliminating the natural pause that once occurred when a page ended.

Behavioral scientists sometimes describe such systems as bottomless bowls. Like the famous study in which diners consumed significantly more soup when their bowls were secretly refilled from the bottom, people continue engaging with content when no visible endpoint signals completion (Wansink, Painter, & North, 2005).

Increasingly, digital work environments carry the same logic. Emails accumulate, messages continue, and AI tools generate endless variations of work.

When work is structured this way, the brain struggles to answer a simple question that earlier generations of workers encountered more naturally:

We often respond by continuing. Another pass at the document. Another prompt to refine the output. Another dataset to explore. Gradually the workday expands, not because we consciously intend it to, but because the environment never signals that the work has reached a natural endpoint.

The brain is fundamentally responsive to novelty. New information activates dopamine pathways associated with curiosity, anticipation, and reward (Bunzeck & Düzel, 2006). Organisms that explored new stimuli were more likely to discover food sources, detect threats, and adapt to changing environments.