In short
Analysis revealed on the Harvard Enterprise Evaluate discovered that AI is accelerating work, not decreasing it.
Productiveness positive factors are morphing into burnout and workload creep.
The true shift isn’t job loss—it’s work intensification and reorganization.
A brand new examine revealed in Harvard Enterprise Evaluate this week confirmed what many staff already suspected: AI instruments do not cut back work, they intensify it.
The examine cited knowledge from UC-Berkeley and Yale, collected throughout eight months of embedded analysis at a 200-person tech firm, the place workers voluntarily adopted AI instruments.
The outcomes confirmed distinct patterns of labor intensification that quietly snowballed into what researchers name “workload creep.”
First got here activity enlargement. Product managers started writing code. Researchers took on engineering work. Roles that after got here with clear boundaries blurred as staff dealt with jobs that beforehand sat exterior their remit. AI made that shift really feel possible.
“You had thought that perhaps, ‘oh, since you could possibly be extra productive with AI, then you definitely save a while, you may work much less,'” one engineer instructed researchers. “However then, actually, you don’t work much less. You simply work the identical quantity or much more.”
This created a ripple impact. Engineers all of the sudden discovered themselves reviewing, correcting, and training colleagues who had been, as one participant completely described it, vibe-coding.
The one who automated a part of their job simply created extra work for another person.
Second got here blurred boundaries. AI’s conversational interface made beginning work really feel easy—no clean web page paralysis, no intimidating studying curve.
So staff began sending “fast final prompts” earlier than leaving their desks, letting AI deal with chores whereas they stepped away. Many even used AI prompts throughout their free time, to the purpose that AI use for work in non-work hours accrued into hours and days with fewer pure pauses.
Third got here a surge in multitasking. Staff had been anticipated to handle a number of workstreams concurrently, as AI seemed that duties could possibly be dealt with within the background.
The promised productiveness positive factors usually translated into fixed attention-switching and longer activity lists.
Put all of it collectively, and also you get what researchers outline as a self-reinforcing cycle during which AI makes issues simpler, so staff do extra of these issues, which finally ends up making them rely extra on AI to make these issues simpler. Rinse, repeat, burnout.
“A number of contributors famous that though they felt extra productive, they didn’t really feel much less busy, and in some instances felt busier than earlier than,” the researchers be aware.
Working within the AI Period
Staff are slowly being laid off, and those that stay are simply being stretched to the purpose of burnout.
A brand new DHR International survey of 1,500 company professionals discovered 83% experiencing burnout, with overwhelming workloads and extreme hours as the highest culprits.
Again in 2024, the Upwork Analysis Institute reported that 77% of workers utilizing AI stated these instruments had decreased their productiveness and elevated their workload.
This yr, the identical institute reported that essentially the most in-demand expertise over the previous few months have been associated to AI.
The Berkeley researchers emphasize that this work enlargement would possibly look productive within the quick time period, however may give solution to cognitive fatigue, weakened decision-making, and ultimately turnover as staff understand their workload has grown whereas they had been busy experimenting with ChatGPT.
Their resolution: corporations want an “AI apply,” or intentional norms round AI use.
Suppose structured pauses earlier than main choices, sequencing work to cut back context-switching, and defending time for precise human connection.
“With out such practices, the pure tendency of AI-assisted work shouldn’t be contraction however intensification, with implications for burnout, determination high quality, and long-term sustainability,” the researchers concluded.
The information additionally confirmed a pointy hole by seniority. Burnout was reported by 62% of associates and 61% of entry-level staff, versus 38% amongst C-suite leaders.
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