What Is AI Burnout, And How Can It Be Avoided?

One of the most exciting promises of AI is that it will free us from the drudgery of routine and repetitive work, creating more time for us to spend doing things that create value or that we truly enjoy.

Research recently published in Harvard Business Review, suggesting those who have most keenly embraced AI are finding themselves overworked, fatigued, and burning out, might come as a surprise.

The research, by UC Berkley Professor Aruna Ranganathan and Xingqi Maggie Ye, concludes that “AI makes it easier to do more—but harder to stop.”

This has led to those most excited by the potential of new tools and platforms like ChatGPT and Claude Code taking on ever-growing workloads as they try to get to grips with everything that AI makes possible.

So is AI burnout a real phenomenon? Well, increasingly, evidence seems to suggest so, pointing to the fact that rather than solving our problems, AI may in fact be increasing them.

Meanwhile, some believe that the tech sector is starting to reassess the wellness-focused ethos that’s prevailed in workplaces since the pandemic, in favor of a return to a harder-edged, more competitive corporate culture, to match these changing economic and political times.

So, with companies cutting back on hiring for entry-level positions and growing pressure in many professions to work with AI, what does this all mean? And should we forget about the once-sacred principle of work/life balance if we want to succeed in the AI age?

Why Is AI Burnout A Problem?

According to the HBR report, rather than a reduction in workload, keen adopters of generative AI often find that it increases.

Tasks that once felt too technical or required a team of specialist collaborators now seem like they can be achieved solo. Project managers begin vibe coding solutions that they would previously have tasked to engineers. Marketing managers draft landing pages and product descriptions, or create video edits, that might otherwise have been delegated to specialist copywriters and videographers. Finance teams begin building and experimenting with forecasting models and automated dashboards that would once have been the domain of data specialists.

None of this is inherently wrong—any of these initiatives could become drivers of efficiency or innovation. But the cumulative effect can be exhausting, as professionals find that simple prompting isn’t always enough to achieve the desired results, and they need to spend time learning and researching unfamiliar fields and disciplines.

In many cases, once the initial rush of feeling that “anything is possible” fades, workers are left with an expanded workload and half-finished projects that can’t be abandoned without writing off the time spent on them so far.

There is also a growing feeling that AI use can be addictive, due to the instant gratification provided when a prompt works out. If you’ve adopted it into your daily routines, you may already be familiar with the “one more prompt” impulse described by some who report AI fatigue. This refers to the ease with which prompting sessions can slip into lunch hours or the commute home, due to the temptation to fine-tune and improve our AI-augmented creations.

Cultural Shift

While AI burnout is caused by the drive to take on ever-growing workloads, broader shifts in attitudes are also impacting the work wellness landscape.

The Guardian report describes founders and employees regularly working 12 or 16-hour days, seven days a week, due to pressure to outperform competition.

Following waves of layoffs over the last two years, many tech workers say they feel less secure in their jobs—partly due to the fear they could be replaced by AI themselves.

With some estimating that the number of entry-level tech jobs has already fallen by over 30 percent, there are fears that, despite the success of initiatives like four-day working, attitudes could be hardening.

Famously, US president Donald Trump ordered an across-the-board end to working from home for federal employees, a practice credited with improving work/life balance, stating that “Nobody’s working from home … they’re going to play tennis, they’re going to play golf.”

This all adds up to a potential increase in anxiety, as workers feel the need to prove themselves in the age of AI, while also coming under pressure to work longer hours with fewer mental health safeguards in place.

And with expectations around the use of AI spreading far beyond Silicon Valley into finance, healthcare and marketing, this should be a warning for professionals across every sector.

How To Prevent AI Burnout

So does this mean we should all give up using AI to avoid taking on an unhealthy workload?

Unsurprisingly, I don’t believe that’s the answer. Instead, we should approach it with an understanding of the risks involved.

The HBR report recommends building “intentional pauses” into AI workloads. These are moments of reflection where we should assess whether we’re applying AI to a task because it genuinely adds value, or simply because it is possible.

Try to complete ongoing AI projects (or mindfully abandon them) before embarking on new ones. AI projects are very easy to start, but often more difficult to satisfactorily complete, as they have a tendency to increase in scope as more possibilities become apparent.

And try not to think of AI as something we have to do alone. Because projects are often based on ideas that come to us individually, it can be tempting to spin them up without involving others. Setting up collaborative AI working groups lets us assess projects in a peer-to-peer environment. The involvement of others means we’re more likely to spot potential pain points, set and adhere to boundaries, and avoid solo late-night sessions.

Most importantly, remember that a fundamental principle of AI is that it’s supposed to make our lives easier and reduce workloads. So if we find that it’s doing the opposite, we’re probably doing something wrong. Being mindful of how much work we’re taking on, and the impact it’s having, is the best way to head off potential AI burnout before it becomes a real burden on our health and well-being.