Apr 25, 2025
The rise of AI assistants in coding has sparked a paradox: we may be increasing productivity, but at risk of losing our edge to skill atrophy if we’re not careful. Skill atrophy refers to the decline or loss of skills over time due to lack of use or practice.
Would you be completely stuck if AI wasn’t available?
Every developer knows the appeal of offloading tedious tasks to machines. Why memorize docs or sift through tutorials when AI can serve up answers on demand? This cognitive offloading - relying on external tools to handle mental tasks - has plenty of precedents. Think of how GPS navigation eroded our knack for wayfinding: one engineer admits his road navigation skills “have atrophied” after years of blindly following Google Maps. Similarly, AI-powered autocomplete and code generators can tempt us to “turn off our brain” for routine coding tasks. (Shout out to Dmitry Mazin, that engineer who forgot how to navigate, whose blog post also touched on ways to use LLM without losing your skills)
Offloading rote work isn’t inherently bad. In fact, many of us are experiencing a renaissance that lets us attempt projects we’d likely not tackle otherwise. As veteran developer Simon Willison quipped, “the thing I’m most excited about in our weird new AI-enhanced reality is the way it allows me to be more ambitious with my projects”. With AI handling boilerplate and rapid prototyping, ideas that once took days now seem viable in an afternoon. The boost in speed and productivity is real - depending on what you’re trying to build. The danger lies in where to draw the line between healthy automation and harmful atrophy of core skills.
Recent research is sounding the alarm that our critical thinking and problem-solving muscles may be quietly deteriorating. A 2025 study by Microsoft and Carnegie Mellon researchers found that the more people leaned on AI tools, the less critical thinking they engaged in, making it harder to summon those skills when needed.
Essentially, high confidence in an AI’s abilities led people to take a mental backseat - “letting their hands off the wheel” - especially on easy tasks It’s human nature to relax when a task feels simple, but over time this “long-term reliance” can lead to “diminished independent problem-solving”. The study even noted that workers with AI assistance produced a less diverse set of solutions for the same problem, since AI tends to deliver homogenized answers based on its training data. In the researchers’ words, this uniformity could be seen as a “deterioration of critical thinking” itself.
There are a few barriers to critical thinking:
What does this look like in day-to-day coding? It starts subtle. One engineer confessed that after 12 years of programming, AI’s instant help made him “worse at [his] own craft”. He describes a creeping decay: First, he stopped reading documentation – why bother when an LLM can explain it instantly?
Then debugging skills waned – stack traces and error messages felt daunting, so he just copy-pasted them into AI for a fix. “I’ve become a human clipboard” he laments, blindly shuttling errors to the AI and solutions back to code. Each error used to teach him something new; now the solution appears magically and he learns nothing. The dopamine rush of an instant answer replaced the satisfaction of hard-won understanding.
Over time, this cycle deepens. He notes that deep comprehension was the next to go – instead of spending hours truly understanding a problem, he now implements whatever the AI suggests. If it doesn’t work, he tweaks the prompt and asks again, entering a “cycle of increasing dependency”. Even the emotional circuitry of development changed: what used to be the joy of solving a tough bug is now frustration if the AI doesn’t cough up a solution in 5 minutes.
In short, by outsourcing the thinking to an LLM, he was trading away long-term mastery for short-term convenience. “We’re not becoming 10× developers with AI – we’re becoming 10× dependent on AI” he observes. “Every time we let AI solve a problem we could’ve solved ourselves, we’re trading long-term understanding for short-term productivity”.
It’s not just hypothetical - there are telltale signs that reliance on AI might be eroding your craftsmanship in software development:
It’s worth noting that some skill loss over time is natural and sometimes acceptable.
We’ve all let go of obsolete skills (when’s the last time you manually managed memory in assembly, or did long division without a calculator?). Some argue that worrying about “skill atrophy” is just resisting progress - after all, we gladly let old-timers’ skills like handwritten letter writing or map-reading fade to make room for new ones.
The key is distinguishing which skills are safe to offload and which are essential to keep sharp. Losing the knack for manual memory management is one thing; losing the ability to debug a live system in an emergency because you’ve only ever followed AI’s lead is another.
Speed vs. Knowledge trade-off: AI offers quick answers (high speed, low learning), whereas older methods (Stack Overflow, documentation) were slower but built deeper understanding
In the rush for instant solutions, we risk skimming the surface and missing the context that builds true expertise.
What happens if this trend continues unchecked? For one, you might hit a “critical thinking crisis” in your career. If an AI has been doing your thinking for you, you could find yourself unequipped to handle novel problems or urgent issues when the tool falls short.
As one commentator bluntly put it: “The more you use AI, the less you use your brain… So when you run across a problem AI can’t solve, will you have the skills to do so yourself?”. It’s a sobering question. We’ve already seen minor crises: developers panicking during an outage of an AI coding assistant because their workflow ground to a halt.
Over-reliance can also become a self-fulfilling prophecy. The Microsoft study authors warned that if you’re worried about AI taking your job and yet you “use it uncritically” you might effectively deskill yourself into irrelevance. In a team setting, this can have ripple effects. Today’s junior devs who skip the “hard way” may plateau early, lacking the depth to grow into senior engineers tomorrow.
If a whole generation of programmers “never know the satisfaction of solving problems truly on their own” and “never experience the deep understanding” from wrestling with a bug for hours, we could end up with a workforce of button-pushers who can only function with an AI’s guidance. They’ll be great at asking AI the right questions, but won’t truly grasp the answers. And when the AI is wrong (which it often is in subtle ways), these developers might not catch it – a recipe for bugs and security vulnerabilities slipping into code.
There’s also the team dynamic and cultural impact to consider. Mentorship and learning by osmosis might suffer if everyone is heads-down with their AI pair programmer. Senior engineers may find it harder to pass on knowledge if juniors are accustomed to asking AI instead of their colleagues.
And if those juniors haven’t built a strong foundation, seniors will spend more time fixing AI-generated mistakes that a well-trained human would have caught. In the long run, teams could become less than the sum of their parts – a collection of individuals each quietly reliant on their AI crutch, with fewer robust shared practices of critical review. The bus factor (how many people need to get hit by a bus before a project collapses) might effectively include “if the AI service goes down, does our development grind to a halt?”
None of this is to say we should revert to coding by candlelight. Rather, it’s a call to use these powerful tools wisely, lest we “outsource not just the work itself, but [our] critical engagement with it”). The goal is to reap AI’s benefits without hollowing out your skill set in the process.
How can we enjoy the productivity gains of AI coding assistants and still keep our minds sharp? The key is mindful engagement. Treat the AI as a collaborator – a junior pair programmer or an always-available rubber duck – rather than an infallible oracle or a dumping ground for problems. Here are some concrete strategies to consider:
By integrating habits like these, you ensure that using AI remains a net positive: you get the acceleration and convenience without slowly losing your ability to code unaided. In fact, many of these practices can turn AI into a tool for sharpening your skills. For instance, using AI to explain unfamiliar code can deepen your knowledge, and trying to stump the AI with tricky cases can enhance your testing mindset. The difference is in staying actively involved rather than passively reliant.
The software industry is hurtling forward with AI at the helm of code generation, and there’s no putting that genie back in the bottle. Embracing these tools is not only inevitable; it’s often beneficial. But as we integrate AI into our workflow, we each have to “walk a fine line” on what we’re willing to cede to the machine.
If you love coding, it’s not just about outputting features faster - it’s also about preserving the craft and joy of problem-solving that got you into this field in the first place.
Use AI it to amplify your abilities, not replace them. Let it free you from drudge work so you can focus on creative and complex aspects - but don’t let those foundational skills atrophy from disuse. Stay curious about how and why things work. Keep honing your debugging instincts and system thinking even if an AI gives you a shortcut. In short, make AI your collaborator, not your crutch.
The developers who thrive will be those who pair their human intuition and experience with AI’s superpowers – who can navigate a codebase both with and without the autopilot. By consciously practicing and challenging yourself, you ensure that when the fancy tools fall short or when a truly novel problem arises, you’ll still be behind the wheel, sharp and ready to solve. Don’t worry about AI replacing you; worry about not cultivating the skills that make you irreplaceable. As the saying goes (with a modern twist): “What the AI gives, the engineer’s mind must still understand.” Keep that mind engaged, and you’ll ride the AI wave without wiping out.
Bonus: The next time you’re tempted to have AI code an entire feature while you watch, consider this your nudge to roll up your sleeves and write a bit of it yourself. You might be surprised at how much you remember – and how good it feels to flex those mental muscles again. Don’t let the future of AI-assisted development leave you intellectually idle. Use AI to boost your productivity, but never cease to actively practice your craft.
The best developers of tomorrow will be those who didn’t let today’s AI make them forget how to think.
I’m excited to share I’m writing a new AI-assisted engineering book with O’Reilly. If you’ve enjoyed my writing here