AI tools were sold as the antidote to burnout, a way to automate drudgery so humans could focus on higher value work. Instead, a growing number of engineers and office workers describe a quieter crisis: a kind of mental jet lag from trying to keep pace with machines that never tire. The productivity boom is real, but so is the psychological bill that arrives afterward.
The emerging pattern is clear. As companies race to embed chatbots, copilots, and automation into every task, expectations for human output climb just as fast. The result is what one software engineer has bluntly called “AI fatigue,” a mix of exhaustion, anxiety, and creeping cynicism that traditional wellness programs are not built to handle.
The engineer who hit the invisible wall
For many readers, the phrase “AI fatigue” snapped into focus when software engineer Siddhant Khare described hitting a wall that no amount of optimization could fix. He had stacked his workflow with code assistants and chatbots, shaving minutes off every task, yet the more he optimized, the more depleted he felt. In his account, the problem was not a lack of tools but the pressure to keep accelerating, as if every new model reset the baseline for what a “good” engineer should produce.
Khare’s reflection that “AI fatigue is real and nobody talks about it” captured a feeling that had been simmering in private Slack channels and late night DMs for months. He wrote about the moment when even small decisions felt heavy and context switching between prompts, dashboards, and review comments became its own cognitive tax, a pattern he traced in detail on his personal site at length. The essay resonated because it named something many workers sensed but struggled to articulate: the sense that each new AI upgrade quietly raises the bar while eroding the mental margins that make creative work sustainable.
When productivity gains deepen burnout
Khare’s story might have stayed anecdotal if it were not echoed by structured research inside tech companies. An internal study of 200 employees at a United States software firm found that generative tools did, in fact, increase measurable output, but they also intensified stress and fatigue. Workers reported feeling they had to constantly prove they were using AI “enough,” and some described a fear of being outpaced by colleagues who leaned harder on automation, a dynamic detailed in coverage of the 200 employees experiment. The tools did not simply make work faster; they rewired expectations about what “normal” throughput should look like.
That pattern is not confined to one office. Reporting on the broader workforce has found that AI rollouts can have the opposite effect of what executives promise, with some employees saying they are more anxious and less clear about priorities after automation arrives. One engineer, who also builds AI tools himself, described feeling caught between enthusiasm for the technology and dread that he could never fully unplug from it, a tension highlighted in an analysis of how AI is reshaping workloads. The paradox is stark: dashboards show rising productivity, yet the humans behind those metrics report feeling more brittle, not less.
The 996 warning and the global race to automate
Nowhere is the collision between AI acceleration and human limits more visible than in China’s startup scene. As investment floods into generative models and automation platforms, some young companies are reviving the notorious “996” schedule, where employees work from 9 a.m. to 9 p.m., six days a week. In this environment, AI is not a buffer against overwork but a justification for 72 hour weeks, with founders arguing that tools make such intensity sustainable, a trend documented in reporting on AI startups and their schedules.
Khare himself has said he had to rein in his own AI usage after realizing that constant prompting and context switching were eroding his focus rather than sharpening it. That personal adjustment sits in sharp contrast to corporate narratives that treat AI as an almost limitless productivity lever. The risk is that, in the race to keep up with global competitors, companies normalize a culture where human exhaustion is treated as a temporary bug that better tooling will fix, rather than a structural signal that the pace itself is unsustainable.
AI fatigue beyond engineering: from “slop” to blurred boundaries
Although the most visible stories come from coders, the strain is spreading across marketing, customer support, and operations teams that now live inside AI dashboards. Consultants who advise companies on automation warn that leaders often celebrate early efficiency gains while ignoring the downstream costs of what some have started calling “AI slop,” a flood of low quality outputs that humans must still review and correct. One analysis of corporate deployments linked this pattern to “burnout and lack of clarity,” noting that workers felt trapped between rising expectations and the messy reality of cleaning up machine generated drafts, a tension explored in guidance on preventing AI slop.
Wellness experts are now treating AI fatigue as a distinct phenomenon, describing it as the exhaustion that comes from constant interaction with automated systems, from chatbots that handle customer queries to tools that generate emails, reports, or social posts. They argue that the mental load is not just about screen time but about the pressure to supervise, correct, and integrate machine suggestions into human judgment, a pattern outlined in practical advice on workplace fatigue. In this view, AI does not simply automate tasks; it creates a new layer of meta work, where people must constantly decide when to trust the system and when to override it.
The myth of tireless machines and the human cost of keeping up
Part of the problem lies in how AI is marketed. Corporate slide decks often emphasize that AI does not get tired, does not need breaks, and can run all night without complaint. One popular description notes that AI does not experience sugar crashes or exhaustion after solving complex problems, a framing used to sell the technology’s reliability in team building and operations contexts, as seen in a breakdown of AI benefits. The implicit comparison is flattering to the machines and quietly unforgiving to the humans expected to keep pace with them.
Khare has pushed back on this narrative by reminding readers that they are using AI to be more productive at the same time they feel more exhausted than ever, and that these two facts are not unrelated. In a follow up reflection, he framed AI as a kind of cognitive caffeine: a stimulant that can sharpen performance in the short term while masking deeper fatigue, a metaphor he develops when he writes, “You are using AI to be more productive. So why are you more exhausted than ever?” on his personal blog. The more organizations treat AI as a tireless colleague rather than a tool, the more they risk designing workflows that assume human workers can operate at machine tempo indefinitely.
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*This article was researched with the help of AI, with human editors creating the final content.
