---
title: "AI Burnout at Work: Why Your Days Run Longer | Openskill"
description: "AI burnout at work often starts with doing more by choice. How to set limits that keep your day short."
url: "https://openskill.ai/blog/ai-makes-workdays-longer/"
---
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# Why AI is making your workday longer, and how to set limits

Oct 4, 20266 min read[Openskill team](https://openskill.ai/about/)

[AI at work](https://openskill.ai/tag/ai-at-work/) · [burnout](https://openskill.ai/tag/burnout/) · [productivity](https://openskill.ai/tag/productivity/) · [work-life balance](https://openskill.ai/tag/work-life-balance/)

### In this article

1. [What the Berkeley researchers saw](https://openskill.ai/blog/ai-makes-workdays-longer/#what-the-berkeley-researchers-saw)
2. [Your job quietly gets bigger](https://openskill.ai/blog/ai-makes-workdays-longer/#your-job-quietly-gets-bigger)
3. [The breaks stop being breaks](https://openskill.ai/blog/ai-makes-workdays-longer/#the-breaks-stop-being-breaks)
4. [Juggling feels like momentum](https://openskill.ai/blog/ai-makes-workdays-longer/#juggling-feels-like-momentum)
5. [Build your own AI practice](https://openskill.ai/blog/ai-makes-workdays-longer/#build-your-own-ai-practice)
6. [Why limits help your career](https://openskill.ai/blog/ai-makes-workdays-longer/#why-limits-help-your-career)

The pitch for AI at work was that it would give you time back. Draft the routine email in seconds, summarize the long thread, fix the boring bug, and spend the saved hours on something that matters. Plenty of people now use these tools every day and notice something odd. They’re getting more done, and their days are getting longer.

Two researchers at UC Berkeley’s Haas School of Business watched that happen up close. Their findings, published in a February 2026 [Harvard Business Review article](https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies-it) by Aruna Ranganathan and Xingqi Maggie Ye, are one of the clearest accounts so far of how AI burnout at work builds up. It doesn’t arrive as a crisis. It creeps in through choices that each feel reasonable at the time.

## What the Berkeley researchers saw

The study followed a US technology company of about 200 people for eight months, from April to December 2025. The team observed in person two days a week, followed internal communication channels, and conducted more than 40 in-depth interviews across engineering, product, design, research, and operations. The authors call it in-progress research, so the patterns are early, but they come from close observation rather than a survey.

One detail matters a lot. The company never required anyone to use AI. It paid for subscriptions and left the rest to employees. Nobody’s manager handed them extra tasks. People took on more because the tools made more feel possible, and often enjoyable.

## Your job quietly gets bigger

The first pattern the authors describe is task expansion. With AI filling gaps in know-how, product managers and designers started writing code. Researchers picked up engineering tasks. People tried jobs they would once have handed off or put off indefinitely. Each experiment felt small. Together they widened job scope enough that, in the authors’ reading, employees absorbed work that might once have justified another hire.

That expansion had a cost for others too. Engineers ended up reviewing and fixing AI-assisted code from colleagues, often in Slack threads and quick desk-side chats that never showed up as formal tasks. If you’ve read our piece on [workslop](https://openskill.ai/blog/workslop-makes-careful-workers-valuable/), this is the same mechanism seen from inside one company. The cleanup lands on whoever knows enough to catch the problem.

## The breaks stop being breaks

The second pattern is blurred boundaries. Because starting a task with AI takes almost no effort, people slipped prompts into lunch, meetings, and the minute spent waiting for a file to load. Some sent one last prompt before leaving their desk so the tool could keep going without them.

None of this felt like extra hours. Typing a line to a chatbot feels closer to texting than to sitting down to a task. Over months, though, the day lost its natural pauses, and several employees realized only in hindsight that their downtime no longer felt restful.

## Juggling feels like momentum

The third pattern is multitasking. People coded by hand while an agent drafted another version, ran several agents at once, or revived old tasks because AI could handle them in the background. It felt like having a partner. In practice it meant constant switching, frequent checking of outputs, and a growing pile of open threads.

Expectations for speed rose along with it, not through any memo but through what became normal to see. One engineer summed up the result: “You just work the same amount or even more.”

The authors warn that this cycle, with faster tasks raising expectations and wider scope raising the load, can lead to cognitive fatigue, burnout, and weaker decisions, and eventually to lower quality and turnover. Because the extra effort is voluntary and often fun, it’s easy for everyone, including you, to miss how much you’re carrying.

## Build your own AI practice

The researchers’ answer is what they call an “AI practice,” a set of deliberate norms for how AI gets used, when to stop, and how far a job should expand just because it can. They pitch it mainly to organizations and argue that asking employees to self-regulate isn’t enough. That’s fair. Most early-career people can’t redesign their company’s norms, though, and they can still set their own. A version you can start this week:

Set a decision pause. Before you send anything that commits you or your team, stop and note one counterargument and how the choice connects to what the team is trying to achieve. This comes straight from the authors’ suggestions, and it takes two minutes.

Sequence instead of reacting. Batch AI jobs and check the results at set points, say mid-morning and mid-afternoon, rather than every time one finishes. Keep at least one block a day where no agent is running and nothing needs checking.

Stop at a fixed hour. Pick a time after which you stop sending prompts. If a background job is useful overnight, start it earlier or let it wait until morning.

Decide what not to take on. Before you pick up a task because AI makes it feasible, ask whether it’s yours and who would normally own it. Saying “I could draft that, but design should own it” protects both your time and your colleagues’ review load.

Talk to a person. The authors call this human grounding. A short check-in with a colleague, or a problem talked through before it gets prompted, breaks the solo loop and usually improves the thinking.

## Why limits help your career

Early on, it’s tempting to prove yourself by saying yes to everything, and AI makes yes cheaper than ever. The trouble is that burnout and sloppy decisions show up later, often right when people start relying on your judgment. The people who last are the ones who can deliver and also explain how they chose what to do and what to skip. A manager can hand you more tasks at any point. Knowing which ones deserve your best hours is harder to teach, and it is what gets you trusted with bigger decisions.

That second ability comes up in hiring more than people expect. [Openskill](https://openskill.ai/) runs the most realistic AI voice interviews, with follow-up questions based on your answers, so you can practice explaining how you decide which tasks to hand to AI, and where you draw the line, before a hiring manager asks. A roleplay session is also a low-stakes way to try saying no to extra work, which is harder in the moment than it sounds.

If a longer workday is part of why you are looking, [openskill](https://openskill.ai/) is a complete career platform: search current openings in [Jobs](https://app.openskill.ai/jobs), practice for the exact interview, get scored feedback, and track your applications.

AI can make the day faster. Whether it also makes the day longer is, for now, mostly down to the limits you set.

## Questions, *answered.*

### Does AI reduce your workload?

Not in the Berkeley study reported in Harvard Business Review in February 2026. At a US tech company of about 200 people, AI sped tasks up, but employees used the time to take on more, slipped tasks into breaks and evenings, and juggled more threads at once, often without being asked.

### What causes AI burnout at work?

The researchers saw three patterns: people took on tasks that used to belong to others, prompts crept into breaks and off hours, and running several AI threads at once meant constant switching. They warn this can lead to cognitive fatigue, burnout, and weaker decisions.

### How can I set limits on my own AI use?

Build a personal AI practice. Pause before big decisions, batch AI jobs and check them at set times, stop prompting after a fixed hour, decide which tasks are not yours to take on, and talk problems through with a colleague instead of working alone with the tool.

### How do I answer interview questions about managing my workload with AI?

Describe one concrete limit you set, such as a task you chose not to take on or a fixed time you stop prompting, and what it protected. Openskill runs the most realistic AI voice interviews, and its follow-up questions based on your answers show whether the example holds up past the first question.

Compare tools

## Choosing where to practice?

See how Openskill compares with other interview practice tools, and how its job search and AI match score fit alongside realistic voice practice.

- [Best AI mock interview apps (2026)](https://openskill.ai/best-ai-mock-interview-apps/)
- [Best AI product manager interview tools](https://openskill.ai/compare/best-ai-product-manager-interview-tools/)
- [Best mock interview apps for job-specific practice](https://openskill.ai/compare/best-mock-interview-apps-for-job-specific-practice/)
- [Best realistic voice AI mock interview apps](https://openskill.ai/compare/best-realistic-voice-ai-mock-interview-apps/)
- [All interview tool comparisons](https://openskill.ai/compare/)

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