AI is supposed to make the work easier. The data so far says it isn’t.
A February Harvard Business Review piece by UC Berkeley researchers, based on an eight-month study at a tech company, found AI intensifies the work rather than reducing it: employees took on more tasks, filled former breaks with prompting, and extended work into more hours of the day. Boston Consulting Group coined a phrase for the cognitive cost, “AI brain fry,” with self-reported productivity dropping for people running four or more AI tools at once.
The hours are only part of it. The shape of the work has changed in a way that hits harder.
The tools compress the mechanical part. What’s left is the part only you can do: judgment, verification, deciding what’s actually safe to ship. That used to be threaded through hours of typing. Now it’s most of the shift.
Sustained judgment has a real cost. A 2022 study put participants through six-plus hours of demanding cognitive control tasks and measured glutamate buildup in the part of the brain that does that kind of work. (Glutamate is the chemistry of focused thought — neurons release it when they fire, and the brain has to clear it to keep working.) The high-demand group ended the day with elevated levels and made more impulsive, low-effort choices afterward. The metaphor people reach for is that the fuel runs out. The mechanism is closer to exhaust accumulating in the exact circuits doing the work, and rest is how those circuits clear.
For AI-augmented engineering, that lands somewhere specific. If you’ve moved up a level — coaching the system, owning the call, defining what good looks like — you’ve moved into the work that runs those circuits hot. Producing 10x the output doesn’t mean working 10x harder, but it does mean a higher ratio of your day is the thing that fatigues fastest.
The tools are still worth it. But it changes the math on what a sustainable shift looks like, and “just sleep less” is a punchline, not a plan.