How to keep your skills sharp as an AI-assisted developer
By thinking about how and where you use AI, practicing critical thinking, and using other techniques, you can help avoid AI-induced skills decline.
Dec 8, 2025 • 4 Minute Read
You’re using an LLM to help you create amazing things. Your prompts are on point, you’re spending very little time debugging, and everyone’s pleased with your output. Because it’s doing a good job, you get the LLM to do some more of the work.
One day, you look at the code. The programming language you once recognized is now like alien script, with logic, methods, and features you don’t recognize. And grudgingly, you say to yourself:
“I have no idea what’s going on here.”
Welcome to AI-induced skills decay.
What is AI-induced skills decay?
AI-induced skills decay is when an individual over-uses AI to the point they do not maintain or advance their skills. This typically happens when someone is not applying their skills through hands-on application or engaging in continuous learning and upskilling. For entry-level or junior developers using AI excessively, these foundational technical skills might not get developed to begin with.
What are the consequences of AI-induced skills decay?
When your skills have slipped (or not developed) due to over-reliance on AI, you’re essentially giving the AI a free pass to operate unchecked. This can result in:
Code quality issues
Increased technical debt
Lack of visibility
Code review challenges
Security vulnerabilities (unsafe dependencies, data leaks, etc.)
Compliance and IP infringement
How to keep your skills sharp and mitigate AI-induced skills decay
1. Think about where AI use is going to be beneficial first
Decide where you’re going to use AI based on the value it provides, rather than trying to use AI for every task by default. If you’re working in a dev team, this should ideally be directed from the top down, so ask for guidance on what areas are appropriate for AI use (e.g. “Speeding up writing documentation,” “Refactoring code”) and which need human expertise.
2. Have a clear idea of what you want the AI to produce before prompting
This is just good prompt engineering for development, really. Ask for modular, specific chunks of code, as this helps reduce the chance of you getting AI slop, puts you in the architecting mindset, and means you’re still trying to understand and guide what’s going on.
3. Practice critical thinking and System 2 thinking
Read up on critical thinking and System 2 thinking, which are techniques for going over your work in a slow, logical way. These are great techniques for thinking through what you’re making at a wider level, as well as catching code quality issues and security issues.
4. When you see something unfamiliar, learn what it is
As you’re going through AI output, if you notice a method or reference you’ve never seen before, go learn about it! There might have been a new update to a framework that’s worth learning about, or the tool you’re using could be making things up, which is also worth fixing.
5. Ask for or create dedicated learning time, then use it regularly
Finding time to learn is a pain, but it starts by advocating for it. Block off time in your calendar and spend this learning about new tools, techniques, and frameworks, or keeping up to date with the ones you already know.
Throw together some side projects, watch online tutorials, and learn about new features you could explore. You’ll probably discover tricks that the AI doesn’t “know” yet, which can help you guide it in the right direction on top of keeping your skills sharp.
6. Refactor, don’t assume perfection
Every dev knows that you can solve a problem in many different ways, but many ways are sub-optimal. For example you could create a library management system that didn’t use functions at all, but that would be an awful idea.
AI is very bad at suggesting optimal solutions, and this provides you with an opportunity to keep your skills current by whipping its work into shape. Actively ask yourself if what it produces is the best possible solution, then modify it accordingly, taking into account the wider ecosystem you’re working in. Focus on good work, not just fast work and code that’s “good enough.”
7. Study and apply secure coding best practices
Over 40% of LLM-generated code contains security flaws, so studying and taking Security+ or the upcoming SecurityAI+ can be beneficial, as well as training in secure coding best practices and tools (e.g. SAST, DAST, IAST, SCA, RASP).
Conclusion
If you’re doing most of the above already, like critically evaluating AI output, refactoring what it produces, and applying best practices, congratulations!
Keeping your tech skills current is very much a case of “use it or lose it.” Make the time to learn new things, use AI where it makes sense, and always question its output. It’s far easier to avoid losing your skills bit by bit, and being a continuous learner is typically a positive trait that leaders look for when it comes to hiring, recognition, and rewards.
To learn more about trends like this one, read Pluralsight’s 2026 Tech Forecast, a report based on predictions from 1,500+ tech insiders, business leaders, and Pluralsight Authors.
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