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How AI can make work feel heavier
The mechanism is simple: when a tool makes a task faster or easier, people may use the capacity they gain to do more tasks rather than finish earlier. If the amount of work expands, the saved effort does not necessarily become saved time.
Harvard Business Review published an account of this pattern on February 9, 2026, by UC Berkeley Haas researchers Aruna Ranganathan and Xingqi Maggie Ye. TechCrunch reported that the researchers spent eight months observing a 200-person technology company and conducted more than 40 in-depth interviews. The 200-person figure describes the company, not the number of interviewees.
According to TechCrunch’s account, employees’ task lists grew as AI made additional work seem doable. Work crossed into lunch breaks and late evenings, and stepping away became harder. The report says this occurred without formal instructions to meet higher targets. An engineer interviewed for the study put the experience this way: “You just work the same amount or even more.”
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These are reported experiences in one workplace, not a measurement of how many AI users are burned out. The account does not establish clinical burnout diagnoses or show that AI alone caused any individual’s condition. “Early signs” is best understood here as a warning about work intensification and eroding boundaries.
What this study can—and cannot—tell workers
The Berkeley account is useful for explaining how AI adoption may change the shape of work inside a company. It is not a representative survey or a controlled test that estimates the effect of AI on all jobs. Its findings do not show that every employer raises expectations, or that every worker who uses AI takes on more.
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A separate randomized trial helps illustrate why people’s impressions of productivity and measured task time should not be treated as the same thing. METR’s 2025 study assigned AI use or non-use across 246 software issues for 16 experienced developers working in familiar, large open-source repositories. With early-2025 AI tools, tasks took 19% longer on average when AI was allowed. Yet participants had expected a 24% speedup and, after the tasks, still believed AI had made them 20% faster.
The METR result concerns task duration for that specific developer sample and those coding tasks; it does not measure burnout or prove that AI slows workers in other occupations or with current tools. It and the Berkeley workplace account address different questions: one measured task completion time, while the other described changes in workload and work boundaries. Neither supports a universal verdict about AI’s effect on productivity.
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For a worker, extra output is not automatically extra pay. If AI helps complete more tasks but hours, compensation, and expectations stay unchanged, the financial benefit may accrue to the organization while the worker experiences a denser workload. Conversely, an employer could use efficiency gains to reduce hours, protect breaks, or make room for higher-value work—but the cited workplace account does not establish that any of these outcomes follows automatically.
That makes the relevant question more specific than “Does AI save time?” Ask what happens to the time or capacity a tool appears to free up: does the workday shorten, does the same work become less burdensome, does pay change, or does the volume of expected output increase? The evidence cited here does not determine which arrangement applies at any particular workplace.
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Questions employees and managers can ask
The researchers’ reported findings do not test a particular remedy. The following questions are practical ways for a team to make workload changes visible, rather than interventions proven by this study:
- What is supposed to change? Before adopting an AI tool, clarify whether the aim is to reduce hours, improve quality, handle a defined backlog, or increase output.
- What work will stop or wait? If a task becomes quicker, decide whether the gain will be returned as time or used for additional work—and which existing tasks will be deprioritized if new work is added.
- Are breaks and off-hours staying protected? Track whether work is increasingly happening through lunch or late in the evening, rather than judging the change only by the number of tasks completed.
- Are expectations and compensation aligned? If AI materially changes the volume or pace of expected work, discuss how priorities, staffing, hours, and pay are affected instead of assuming that higher output benefits workers financially.
For employees, keeping a simple record of working hours, interrupted breaks, assigned tasks, and changes in expectations can make a workload discussion more concrete. It cannot, by itself, establish that AI caused burnout, but it can help distinguish a tool that genuinely reduces effort from one that mainly enables more work to fit into the same day.
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