Artificial intelligence was supposed to make software development easier. Instead, AI burnout in software engineers is becoming an uncomfortable reality.

While AI tools are helping developers ship more code than ever, many say the mental toll is rising just as fast. What began as a productivity revolution is quietly turning into a burnout accelerator.

And programmers are starting to speak up.

“I Shipped More Code — But Felt More Drained”

Siddhant Khare, a software engineer, recently described the paradox in an interview and a blog post titled AI Fatigue Is Real.

AI tools made him significantly more productive. In fact, he shipped more code in one quarter than at any other point in his career.

But there was a cost.

“We used to call it an engineer, now it is like a reviewer,” Khare said, describing how his role shifted from building systems to approving an endless stream of AI-generated pull requests. The work felt less like engineering and more like standing at a never-ending assembly line.

This shift is central to the growing problem of AI burnout in software engineers. AI may reduce the effort required to generate code, but it increases the burden of reviewing, coordinating and making decisions — all of which still depend on humans.

The Productivity Paradox of AI

Khare argues that AI lowers production costs but raises coordination costs. That dynamic creates what he calls a productivity “paradox.”

A recent Harvard Business Review study supports that view. Researchers tracked 200 employees at a U.S. tech company and found that AI adoption intensified work rather than easing it.

Here’s how the cycle played out:

• AI accelerated certain tasks
• Faster output raised expectations
• Higher expectations increased reliance on AI
• Greater reliance expanded the scope of work
• Expanded scope increased workload density

In short, AI didn’t reduce pressure. It amplified it.

The study described a phenomenon known as “workload creep,” where employees gradually took on more tasks without realizing the long-term strain. Multitasking increased, deep focus declined and many workers felt they were constantly juggling responsibilities.

That pattern aligns directly with the rising trend of AI burnout in software engineers.

The Hidden Cost of Context Switching

Before AI tools became embedded in daily workflows, Khare says he could spend an entire day in deep focus solving a single complex problem.

Now, AI allows him to tackle multiple issues in short bursts. On paper, that looks efficient.

But in practice, constantly switching between six different problems — even if each only takes an hour — comes with a heavy cognitive cost.

“The AI doesn’t get tired between problems. I do,” he wrote.

Context-switching fatigue is emerging as a major contributor to AI burnout in software engineers. While AI handles output tirelessly, human brains struggle to maintain sustained clarity across fragmented tasks.

Are Coding Skills Regressing?

Khare also compares AI coding tools to GPS navigation.

Before GPS, people developed strong mental maps. Over time, relying on automated directions caused those skills to weaken. He fears the same thing may be happening with programming.

If AI writes most of the code, engineers may gradually lose the deep reasoning skills that once defined their craft.

This psychological erosion adds another layer to AI burnout in software engineers — not just exhaustion, but anxiety over long-term skill degradation.

Not Anti-AI — But Calling for Guardrails

Importantly, Khare isn’t rejecting AI. He acknowledges its value and continues to experiment with ways to use it more sustainably.

But he believes responsibility doesn’t rest solely on developers.

AI companies, he argues, need to build guardrails that protect users from self-destructive productivity spirals. When tools remove friction, they also remove natural limits — and without boundaries, expectations can become unsustainable.

The broader takeaway? AI may be revolutionizing software development, but without thoughtful implementation, AI burnout in software engineers could become one of the unintended consequences of this technological leap.

More output doesn’t automatically mean better work — especially if the humans behind the code are running on empty.