The AI Productivity Paradox: How Artificial Intelligence Is Accelerating Work—and Burnout

In the relentless march toward automation, artificial intelligence has been heralded as the ultimate productivity booster. But for software engineers like Siddhant Khare, the reality is far more complex—and troubling. While AI tools have dramatically increased his coding output, they’ve also accelerated him toward an unexpected destination: burnout.

The Assembly Line of Code Review

“I shipped more code last quarter than any quarter in my career,” Khare wrote in his viral essay “AI Fatigue Is Real.” “I also felt more drained than any quarter in my career.”

Khare’s experience reveals a disturbing paradox at the heart of AI adoption. What was supposed to make work easier has instead transformed software engineers from creators into perpetual reviewers—judges on an endless assembly line of pull requests.

“We used to call it an engineer, now it is like a reviewer,” Khare told Business Insider. “Every time it feels like you are a judge at an assembly line and that assembly line is never-ending, you just keep stamping those [pull requests].”

The Hidden Cost of AI Acceleration

The problem isn’t that AI isn’t delivering on its promise of productivity. It absolutely is. The issue is what happens after that productivity boost—a phenomenon researchers are calling the “AI productivity paradox.”

A groundbreaking study from Harvard Business Review followed 200 employees at a U.S. tech company and discovered something counterintuitive: AI wasn’t reducing workloads; it was intensifying them. The technology accelerated certain tasks, which raised expectations for speed. Higher speed made workers more reliant on AI, which widened the scope of what workers attempted, which further expanded the quantity and density of work.

This creates a vicious cycle. As tasks become faster and easier to complete, the volume of work expands to fill—and exceed—the time saved. The result is what the researchers termed “workload creep,” where employees gradually take on more tasks than is sustainable, often without realizing it until they’re drowning.

The Cognitive Cost of Context Switching

For Khare, the transformation has been profound. Before AI, he might spend an entire day in deep focus on a single problem. Now, he finds himself touching six different problems in a single day.

“Each one ‘only takes an hour with AI,'” he wrote. “But context-switching between six problems is brutally expensive for the human brain. The AI doesn’t get tired between problems. I do.”

This constant switching creates what Khare calls “brutal cognitive overhead.” The human brain isn’t designed for rapid-fire task switching, yet AI tools make it possible—and increasingly expected. The result is a state of perpetual partial attention that leaves workers feeling scattered, overwhelmed, and exhausted.

The Atrophy of Human Skills

Perhaps most concerning is AI’s impact on the fundamental skills that make engineers valuable in the first place. Khare draws a compelling analogy to GPS navigation.

“Before GPS, you built mental maps. You knew your city. You could reason about routes,” he wrote. “After years of GPS, you can’t navigate without it. The skill atrophied because you stopped using it.”

The same phenomenon is happening with coding. As AI handles more of the routine aspects of programming, engineers find their problem-solving muscles weakening. They’re becoming dependent on tools that, while powerful, can’t replace the deep understanding that comes from wrestling with complex problems directly.

The Multitasking Trap

The HBR study found that multitasking became more common as AI adoption increased. Employees who once focused on single tasks found themselves continually switching their attention between different projects, creating the sense that they were “always juggling.”

This isn’t just inefficient—it’s psychologically taxing. Research has consistently shown that multitasking is a myth; what we call multitasking is actually rapid task-switching, and each switch carries a cognitive cost. Over time, this constant switching depletes mental resources and increases stress levels.

The Guardrails We Need

Khare isn’t advocating for abandoning AI tools. He sees their value and believes they can be used in healthy ways. But he’s also realistic about human nature and the addictive qualities of productivity tools.

“You need to keep some sort of guardrails for the humans, so they don’t self-destruct themselves,” he told Business Insider.

This insight points to a crucial responsibility that falls not just on individual workers, but on the companies creating and deploying these tools. AI companies need to build in safeguards that prevent the technology from overwhelming human capacity. This might mean limiting the number of concurrent tasks, building in mandatory breaks, or creating systems that encourage deep work rather than constant context switching.

The Broader Implications

Khare’s experience and the HBR study raise profound questions about the future of work in an AI-powered world. If the technology that’s supposed to make our lives easier is actually making them harder, what does that mean for the trajectory of automation?

The fear is growing that AI isn’t just changing how we work—it’s permanently eliminating the possibility of sustainable work rhythms. As one headline put it, “Fear Grows That AI Is Permanently Eliminating Jobs,” but perhaps the more immediate concern is that AI is permanently eliminating the possibility of balanced, sustainable work itself.

Finding a Path Forward

The solution isn’t to reject AI, but to use it more thoughtfully. This means:

• Setting clear boundaries around AI tool usage
• Maintaining dedicated time for deep, focused work without AI assistance
• Building in regular breaks and recovery periods
• Being intentional about which tasks to automate and which to handle manually
• Creating organizational cultures that value quality and sustainability over pure speed

As Khare continues to experiment with ways to keep his AI habit in check, his journey offers a cautionary tale for the entire industry. The future of work isn’t just about what technology can do—it’s about what humans can sustain.

The AI productivity paradox reminds us that technology should serve human needs, not the other way around. As we continue to integrate these powerful tools into our workflows, we need to be as thoughtful about protecting human capacity as we are about expanding it.

Tags: AI burnout, software engineering, productivity paradox, workplace stress, artificial intelligence, coding fatigue, tech industry trends, AI tools, work-life balance, cognitive overload, multitasking, skill atrophy, future of work, technology ethics, workplace wellness

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