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ZDNET’s key takeaways
- Programmers find AI tools addictive, but exhausting.
- Coding speed can be lost due to delays in fixing AI-written code.
- AI programming tools can lead to programmer workaholism and burnout.
Yes, yes, we get it. Thanks to GitHub Copilot, Claude Code, Cursor, whatever the hot new AI tool is for developers, you can get more work done than ever before as a programmer. But even as developers use them ever more to generate boilerplate, explain unfamiliar code, draft tests, refactor modules, and troubleshoot errors, many are also finding that the same tools can be pains in the rump.
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AI-induced workaholism
That’s because, as Quentin Rousseau, CTO and co-founder of the AI-powered incident report company Rootly, described in a LinkedIn post, “It’s 2:47 a.m… I’m not debugging an outage. There’s no deadline. I’m just watching Claude Code refactor a module… and I can’t stop.” Why? Because, he continued, “Agentic coding is addictive. When the agent gets things right, you get a dopamine hit. When it fails, you get an adrenaline rush.” Rousseau confessed that he couldn’t sleep and had to seek medical help.
This isn’t too surprising. Programmers have long been prone to workaholism. However, AI has brought a new pace to the work where, as Rousseau put it, “Watching an agent’s work is passive enough to feel like rest, active enough to keep you hooked.” The result is a new kind of burnout.
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This isn’t just one programmer’s experience. AI coding can turn software development into an always-on feedback loop. Rather than completing a task and stepping away, developers can continually ask an agent for another implementation, rewrite, optimization, or refactor, or combine it with the nagging worry that stopping means leaving work undone.
When AI becomes a ball-and-chain instead of a helper
Coddy Tech, a programming training company, found in a survey of 305 developers that “Four in five developers (80%) say their AI use has felt more like dependence than an advantage.” Sure, they found these tools useful, but they also worry that habitual reliance on AI is weakening their own problem-solving process, expanding their workload, and fostering an unhealthy relationship with work.
So it is that “more than two-fifths of developers (43%) keep coding with AI after hours even when they meant to stop, and 32% have put off sleep to keep going.” Also, 39% said AI tools have made it harder to switch off from work.
Also: 75% of developers I surveyed prefer Claude Code – here’s why they choose it over Codex
It’s not just the addictive nature of AI tools. Heavy use of AI, reported 74% of developers, made it more likely they’d earn a raise or promotion. However, 51% also said they were more likely to burn out.
Trust but verify AI annoyances
It also doesn’t help any that, as the 2025 Stack Overflow Developer Survey found, 45% of respondents were frustrated by AI answers that were “almost right, but not quite. The result? Output that appears convincing while creating difficult debugging work.
The Stack Overflow study also found that while AI tool adoption has continued to climb, with 80% of developers now using them in their workflows, trust in AI accuracy has fallen from 40% in previous years to just 29% this year. As a result, programmers’ positive favorability toward AI has decreased from 72% to 60% year over year.
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The friction between ease of use and a heavier workload matters. Besides trying to work out what the AI almost got right, developers must still understand the requirements, recognize when generated code conflicts with a system’s architecture, test edge cases, address security risks, and own the production consequences.
This creates “verification debt.” The output arrives quickly, but you’re still stuck establishing whether it is correct, secure, maintainable, and appropriate to the specific codebase.
On top of that, the dependence described in the Coddy survey may be amplified by how employers interpret AI-driven output. If an organization treats AI as a way to multiply developer capacity, workers can face pressure to ship more features, close more tickets, and perform more reviews in the same number of hours.
That, in turn, can erase the time saved on individual coding tasks and shift the burden elsewhere: Larger pull requests, more generated changes to inspect, more dependencies to validate, and more operational risk to manage.
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AI-assisted programming is, therefore, becoming as much a life-work balance issue as a tooling issue. Teams that use agents to remove routine toil may see genuine benefits. Teams that use them to accelerate every part of the software-production pipeline risk creating a faster, more relentless version of the same job.
For developers like Rousseau, the concern is no longer merely whether AI can write code. It can. We get that. The question going forward is whether developers can still decide when the workday and the agent loop end.
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