Burnout in the AI Era: When Humans Are Expected to Work Like Machines

AI was supposed to set us free. From the mundane. From repetition. From the kind of work that drained energy but didn’t add meaning. And for the most part, it has—AI tools today generate code in seconds, write copy, optimize campaigns, summarize meetings, and even predict what we might need before we ask for it.

But here’s the paradox: The more we automate, the more we accelerate. And the faster our tools get, the more we’re expected to keep up.

The Rise of a New Burnout

This isn’t the classic burnout we’ve been discussing for years—the kind that comes from overwork, lack of rest, or poor work-life boundaries. This is something deeper. It’s the kind of burnout that comes from cognitive overload, constant decision-making, and a creeping loss of creative agency.

Many of us now spend our days prompting AI systems, reviewing their outputs, editing drafts, choosing between versions. It sounds efficient in theory. But in practice, it often feels like our brains are always in sprint mode—switching tabs, context, and tasks without pause. Ironically, AI hasn’t always reduced our workload; in some cases, it’s made us the managers of machines. And being the human-in-the-loop isn’t always glamorous.

When Tools Become Expectations

There’s a subtle shift happening in how organizations operate. AI tools don’t get tired. They don’t procrastinate. They don’t pause. And whether we admit it or not, that bar—of being always available, always sharp, always “on”—has quietly crept into human expectations too.

We feel it in the way Slack pings late into the night. In the way emails arrive with AI-generated summaries that make us feel we’re already behind. In the pressure to turn things around faster, because “the AI already did the first draft.” It’s not that the tools are flawed—it’s that we’ve failed to recalibrate our culture around them.

What the Research Says

Burnout in this era isn’t anecdotal. It’s supported by frameworks that many researchers have studied over the years.

The Technostress Theory (Tarafdar et al., 2007) talks about the anxiety and fatigue caused by advanced technology systems—particularly when they blur the lines between work and personal life, or when users feel pressured to constantly adapt to new tools.

Then there’s Cognitive Load Theory (Sweller, 1988), which is especially relevant in AI-heavy workflows. While AI tools are designed to simplify, they often provide too many choices—multiple prompts, recommendations, and action items—which increases mental load instead of reducing it.

And the Job Demands–Resources (JD-R) Model (Bakker & Demerouti, 2007) makes it even clearer: burnout is not just about having too much to do. It’s about having insufficient resources—be it time, autonomy, support, or mental space—to balance those demands. If AI raises the ceiling for productivity, we need to raise the floor for support too.

The Slow Fade of Human Identity at Work

Beyond the mental strain, there’s a quieter loss many don’t talk about: the fading sense of identity. When AI starts doing the writing, coding, summarizing, and strategizing—it’s easy to wonder: What’s left for me?

Many professionals today feel like they’re curating more than creating. Their role becomes more about reviewing and less about expressing. Over time, this shift can impact confidence, joy, and even our sense of contribution.

This is where the original idea of Human-Machine Symbiosis (Licklider, 1960) becomes important. It envisioned a world where technology and humans collaborate to make each other stronger. Today, however, we risk treating AI as a substitute—not a partner. And when that happens, we lose not just productivity—we lose people’s engagement and purpose.

Leadership Needs to Catch Up

If burnout in the AI era is systemic, not personal, then the solution must be structural. That means rethinking leadership—radically.

Leaders must stop looking at AI as a pure efficiency tool and start asking: What does this do to the human experience of work?

Practices like compassionate leadership (Boyatzis & McKee, 2005) are no longer nice-to-haves. They’re essential. This means understanding that digital tools don’t just change workflows—they change the emotional texture of work.

We also need to foster psychological safety (Amy Edmondson, 1999) in teams, especially when expectations are rising. Employees need to feel safe to say “I need time” or “This is too much,” even when AI is producing things in minutes.

Redefining What Productivity Looks Like

In this era, productivity needs a reboot. It’s not about volume anymore—it’s about value. It’s not about speed—it’s about sustainability.

We should stop asking:

• “How much more can you do with AI?” And start asking:

• “How much more meaningful can your work become with AI?”

Because burnout isn’t just a sign of weakness. It’s a sign that the systems we’ve built need healing. And as we race forward with automation, we must also slow down—to design workplaces where humans can thrive, not just survive.

References

• Bakker, A. B., & Demerouti, E. (2007). The Job Demands‐Resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10.1108/02683940710733115

• Boyatzis, R. E., & McKee, A. (2005). Resonant Leadership: Renewing Yourself and Connecting with Others Through Mindfulness, Hope, and Compassion. Harvard Business Press.

• Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999

• Licklider, J. C. R. (1960). Man-computer symbiosis. IRE Transactions on Human Factors in Electronics, HFE-1(1), 4–11. https://doi.org/10.1109/THFE2.1960.4503259

• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

• Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301–328. https://doi.org/10.2753/MIS0742-1222240109