AI adoption promised efficiency but delivered employee burnout and increased workload instead

The first signs of burnout are coming from the people who embrace AI the most

The most seductive narrative in American work culture right now isn't that AI will take your job. It's that AI will save you from it. That's the version the industry has spent the last three years selling to millions of nervous people who are eager to buy it. Yes, some white-collar jobs will disappear. But for most other roles, the argument goes, AI is a force multiplier. You become a more capable, more indispensable lawyer, consultant, writer, coder, financial analyst -- and so on. The tools work for you, you work less hard, everybody wins. But a new study published in Harvard Business Review follows that premise to its actual conclusion, and what it finds there isn't a productivity revolution. It finds companies are at risk of becoming burnout machines. As part of what they describe as "in-progress research," the researchers spent eight months inside a 200-person tech company watching what happened when workers genuinely embraced AI. What they found across more than 40 "in-depth" interviews was that nobody was pressured at this company. Nobody was told to hit new targets. People just started doing more because the tools made more feel doable. But because they could do these things, work began bleeding into lunch breaks and late evenings. The employees' to-do lists expanded to fill every hour that AI freed up, and then kept going. As one engineer told them, "You had thought that maybe, oh, because you could be more productive with AI, then you save some time, you can work less. But then really, you don't work less. You just work the same amount or even more." Over on the tech industry forum Hacker News, one commenter had the same reaction, writing, "I feel this. Since my team has jumped into an AI everything working style, expectations have tripled, stress has tripled and actual productivity has only gone up by maybe 10%. It feels like leadership is putting immense pressure on everyone to prove their investment in AI is worth it and we all feel the pressure to try to show them it is while actually having to work longer hours to do so." It's fascinating and also alarming. The argument about AI and work has always stalled on the same question -- are the gains real? But too few have stopped to ask what happens when they are. The HBR study isn't entirely novel. A separate trial last summer found experienced developers using AI tools took 19% longer on tasks while believing they were 20% faster. Around the same time, a National Bureau of Economic Research study tracking AI adoption across thousands of workplaces found that productivity gains amounted to just 3% in time savings, with no significant impact on earnings or hours worked in any occupation. Both studies have gotten picked apart. This one may be harder to dismiss because it doesn't challenge the premise that AI can augment what employees can do on their own. It confirms it, then shows where all that augmentation actually leads, which is "fatigue, burnout, and a growing sense that work is harder to step away from, especially as organizational expectations for speed and responsiveness rise," according to the researchers. The industry bet that helping people do more would be the answer to everything. It may turn out to be the beginning of a different problem entirely.

Researchers Studied Work Habits in a Heavily AI-Pilled Workplace. They Sound Hellish

One could be forgiven for thinking that automation tools would make arduous tasks redundant, and make work more relaxing overall. But this elides an important law of the universe: the ratchet of productivity only turns one way. That is, it's a modern day truism that if automationâ€"AI or otherwiseâ€"makes any sort of positive change in your work life, you’ll feel a sort of squeezing sensation, and additional work will materialize to erase any momentary feelings of relief. According to a case study highlighted in some “in-progress research†from Aruna Ranganathan, who teaches management at UC-Berkeley and Xingqi Maggie Ye, a Ph.D. student who is part of Ranganathan’s Berkeley program, AI “intensifies†work, and certainly doesn’t make people’s days easier. It sounds, in other words, like hell on earth. If that is, paradoxically, what you want in your workday, then you probably work in a place like Silicon Valley, or even at OpenAI, where CEO Sam Altman has described AI’s ability to intensify his own work in ways that make him sound strangely awed and humbled (even as he expresses little to no regret about his ambition to annihilate knowledge worker jobs). “I don't think I can come up with ideas fast enough anymore,†he said in an interview in October of last year, adding “I think it will mean that stuff just happens faster and that you canâ€| that you can try a lot more stuff, and figure out the better ideas quickly.â€Â  Altman's experience may resonate with the workers mentioned in the article about Ranganathan and Ye’s research for Harvard Business Review. They describe an eight-month study into generative AI’s effects on working life at a company with about 200 employees. Employees “worked at a faster pace,†the authors write, covered a “broader scope of tasks,†and found themselves working “more hours of the day, often without being asked to do so.†This was a workplace that, Ranganathan and Ye explain, didn’t mandate AI use. It just made enterprise AI tools available. This doesn’t sound like a 200-person workplace where widgets were being glued together. Instead, many of the roles described in the article involve engineering, writing code, and communicating in Slack, so it’s safe to say these were knowledge workers and software engineers, quite possibly making use of tools like Claude Code. Due to AI, many of Ranganathan and Ye’s subjects, it seems, started expanding the scope of their jobs, usurping one another’s roles, and taking on roles coaching others on coding, or correcting their vibe-coded work. Hiring new employees may have been postponed or circumvented altogether, because employees “absorbed work that might previously have justified additional help or headcount.†Workers also, it seems, furtively fed tasks into their AI tools while they were ostensibly in meetings, and submitted prompts while on breaks, while waiting for things to load, or while they were supposed to be having lunch. How you interpret this case study is going to vary. If your workplace is a startup in “founder mode†and everyone in your office is working punishing hours in exchange for equity in a company that everyone hopes will be a unicorn, I’m guessing you’ll probably love the sound of thisâ€"particularly if you’re a CEO/founder and you’re planning to become a billionaire. That’s far from a universal experience, however. According to a 2024 Pew survey, about half of U.S. workers reported that they were either somewhat satisfied or “not too/not at all satisfied,†and the other half said they were “extremely/very satisfied.†That “extremely/very satisfied†group shrinks from 50% to 42% when the respondent has a lower income. That survey also found that far and away the most satisfying aspects of a job according to respondents are other humans, with 64 percent reporting being “extremely/very satisfied†with their relationships with their co-workers. Skills development, meanwhile, ranked low, with 37 percent reporting being “extremely/very satisfied†with that aspect of a given job. So I don't get the impression that fewer people, having to learn to do more things, and work that seeps into breaks will help most people's job satisfaction, but maybe I lack a certain kind of vision. In other words, if instead of building an app, you’re someone who works as, say, a hospital receptionist or a school administrator, you’re probably not all that stoked about a hypothetical where hiring is postponed, you have to do other people’s jobs, you’ll work on your breaks, and instead of getting new, helpful software, you’re getting enterprise AI tools so you can make your own software. But let's not assume that all tech workers love this kind of productivity theater, or that the sense of greater productivity in Ranganathan and Ye's case study is necessarily anything other than an illusion. An anonymous worker at the cybersecurity firm Crowdstrike wrote into the newsletter Blood in the Machine last year, and said workers at that company “have been encouraged to handle the additional per capita workload by simply working harder and sometimes working longer for no additional compensation,†and that “While our Machine Learning systems continue to perform with excellence, I have yet to be convinced that our usage of genAI has been productive in the context of the proofreading, troubleshooting, and general babysitting it requires.†According to this person, “The net result is not a lightening of the load as has been so often promised,†and “Morale is at an all-time low.â€

AI Promised to Save Time -- Instead It's Created a New Kind of Burnout - Decrypt

The real shift isn't job loss -- it's work intensification and reorganization. A new study published in Harvard Business Review this week confirmed what many workers already suspected: AI tools don't reduce work, they intensify it. The study cited data from UC-Berkeley and Yale, collected during eight months of embedded research at a 200-person tech company, where employees voluntarily adopted AI tools. The results showed distinct patterns of work intensification that quietly snowballed into what researchers call "workload creep." First came task expansion. Product managers began writing code. Researchers took on engineering work. Roles that once came with clear boundaries blurred as workers handled jobs that previously sat outside their remit. AI made that shift feel feasible. "You had thought that maybe, 'oh, because you could be more productive with AI, then you save some time, you can work less,'" one engineer told researchers. "But then, really, you don't work less. You just work the same amount or even more." This created a ripple effect. Engineers suddenly found themselves reviewing, correcting, and coaching colleagues who were, as one participant perfectly described it, vibe-coding. The person who automated part of their job just created more work for someone else. Second came blurred boundaries. AI's conversational interface made starting work feel effortless -- no blank page paralysis, no intimidating learning curve. So workers started sending "quick last prompts" before leaving their desks, letting AI handle chores while they stepped away. Many even used AI prompts during their free time, to the point that AI use for work in non-work hours accumulated into hours and days with fewer natural pauses. Third came a surge in multitasking. Employees were expected to manage multiple workstreams simultaneously, as AI gave the impression that tasks could be handled in the background. The promised productivity gains often translated into constant attention-switching and longer task lists. Put it all together, and you get what researchers define as a self-reinforcing cycle in which AI makes things easier, so workers do more of those things, which ends up making them rely more on AI to make those things easier. Rinse, repeat, burnout. "Several participants noted that although they felt more productive, they did not feel less busy, and in some cases felt busier than before," the researchers note. Workers are slowly being laid off, and those who remain are just being stretched to the point of burnout. A new DHR Global survey of 1,500 corporate professionals found 83% experiencing burnout, with overwhelming workloads and excessive hours as the top culprits. Back in 2024, the Upwork Research Institute reported that 77% of employees using AI said these tools had decreased their productivity and increased their workload. This year, the same institute reported that the most in-demand skills over the last few months have been related to AI. The Berkeley researchers emphasize that this work expansion might look productive in the short term, but could give way to cognitive fatigue, weakened decision-making, and eventually turnover as workers realize their workload has grown while they were busy experimenting with ChatGPT. Their solution: companies need an "AI practice," or intentional norms around AI use. Think structured pauses before major decisions, sequencing work to reduce context-switching, and protecting time for actual human connection. "Without such practices, the natural tendency of AI-assisted work is not contraction but intensification, with implications for burnout, decision quality, and long-term sustainability," the researchers concluded. The data also showed a sharp gap by seniority. Burnout was reported by 62% of associates and 61% of entry-level workers, versus 38% among C-suite leaders.

AI adoption increased workload, didn't reduce it, says Harvard study

Without effective guardrails, AI turns work efficiency into employee burnout Automation will free us from drudgery, from repetitive work. Gone will be the tyranny of the overflowing inbox and the never-ending to-do list. Humans will be free to do other creative tasks, with more free time. Doesn't this sound like every major tech leap's promise through history? Generative AI arrived harping on that same promise, when ChatGPT announced itself to the world back in 2022. However, early conclusions on an eight month study being conducted by Harvard research is now revealing something far less comforting. In a US-based tech company with 200 employees, the Harvard study found that instead of reducing work, AI is quietly intensifying it. The Harvard study claims that once employees gained access to generative AI tools, they didn't work less. They worked faster, took on more tasks, and extended their work across more hours of the day - often voluntarily. In other words, AI didn't shrink the workday. It expanded it. On paper, the logic seems simple. If AI can draft documents, write code, analyse data and summarise reports, surely the human workload must fall? It's the assumption that's driving GenAI adoption at workplaces across the board, not just in the Harvard research study. Also read: Will AI take over jobs? Goldman Sachs predicts automation of 25 pct of work hours In practice, the opposite happens. AI lowers the barrier to starting almost any task. The difficulty of writing something on a blank page disappears. For someone who's never coded before, the unfamiliar coding language becomes approachable. Researching anything becomes easier. And just like that, tasks that once required specialists or postponed indefinitely feel doable. So people do them. Work that once required collaboration, delegation or new hiring quietly gets absorbed into existing roles. For example, product managers dabble in writing code, designers experiment with data analysis, as AI starts to erase functional boundaries of employees. They start to feel everything is possible with the help of AI - at least in the beginning of the adoption curve, suggests the Harvard study. What emerges isn't efficiency of work, but expansion of work hours. Because prompting an AI feels conversational - almost casual - work begins to slip into the cracks of daily life. A quick prompt during lunch. A draft refined while waiting for a meeting to start. A "last prompt" before stepping away from the desk so the AI can work in the background. None of these moments feel like real work. Yet together, they create a workday with no pause button and almost no true downtime. Also read: Workspace Studio explained: AI agents will automate more work, believes Google AI also allows employees to feel they can juggle several things at once in parallel. Employees write while AI generates alternatives. Long-ignored tasks are revived because "the AI can handle it." Expectations rise accordingly as well - if work can be done faster, more work will be done. And soon, what was once impressive starts to become the new normal. Over time, this always-on workday directly threatens the efficiency it claims to unlock during the early adoption curve of GenAI at workplaces. Without deliberate breaks, cognitive fatigue builds in employees, and their quality of decision-making starts to fall. What initially feels like a GenAI-fuelled productivity surge slowly and eventually morphs into quiet burnout. None of this means AI is harmful by itself, of course. It just means AI is powerful - and power, without structure (in this case) can exhaust the system supporting it. At least, that's what the Harvard study is trying to get at. Without clear norms around when to use AI at work, when to pause, and when to stop, work will naturally intensify. Productivity gains will be real, but so will fatigue, errors and attrition. Perhaps the real promise of AI was never about doing less work. Maybe it's about redefining what meaningful work looks like, and how much of it should we be doing at all. Food for thought?