How to stop AI-induced skills decay in dev roles

AI coding tools can make costly mistakes. While they can speed up development, they have been known to create solutions that are not fit for purpose, wipe out production databases, and generate security flaws. That’s why they’re best as an assistive rather than fully autonomous solution, with skilled software engineers guiding and reviewing what is produced. This solution, known as AI-assisted development, mitigates a wide range of business risks.

But what happens if your engineers lose the ability or desire to do that job? 

The perishable nature of tech skills

Technical skills don’t last long—only a mere two and a half years, according to industry research. And there’s plenty of research that shows these skills—or the lack thereof—has an outsized impact on the success of your projects: 

• 48% of IT professionals have had to abandon projects due to a lack of the right tech skills, according to Pluralsight’s latest Tech Skills Report.

• Talent tech shortages will cost organizations $5.5 trillion by 2026, due to product delays, the inability to compete, and loss of business. 

Normally, software engineers maintain and advance these skills through hands-on application, which they get in their day-to-day roles. But when they start outsourcing everything to Large Language Models (LLMs) like Claude, GPT, or GitHub Copilot—particularly in the case of vibe coding—they risk losing the skills they have.

To quote an old adage, tech skills are a case of “use it or lose it.”

The consequences of AI-induced skills decay

When your AI-assisted development workflows no longer have a skilled human component, it’s the same as giving the AI a free pass. 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

Catching AI mistakes requires motivation, not just tech skills

To stop mistakes, your engineers need to be vigilant. This takes critical thinking and System 2 thinking—going over your work in a slow, logical way. However, they may not do this when:

• They have not had sufficient training in using these techniques.

• They and/or leadership do not understand the risks of unvetted AI use in development.

• They are tired from debugging high volumes of low-quality AI-generated code.

• They are experiencing less job satisfaction due to the increased use of AI.

• There is a focus on speed and delivery rather than quality results and best practice.

• There are no reward or recognition schemes to encourage them to do more than the minimum.

• They subscribe to the philosophy of vibe coding, which outsources critical thinking to the AI tool.

• They no longer understand what the AI tools are producing and are too overwhelmed to try.

This is especially important when it comes to avoiding security headaches, since over 40% of LLM-generated code contains security flaws. Your engineers need to be trained and motivated to stop these issues falling through the cracks, creating future business issues.

How to mitigate AI-induced skills decay in dev teams

1. Train and test your teams in foundational AI concepts and secure coding 

Don’t assume developers know all about AI risk just because they can use AI tools: these two concepts are separate. Make sure to use objective skill assessment methods, not self reporting, as 79% of professionals admit to overstating their AI knowledge, and confidence in their AI skills can be misplaced. 

Have your staff study and take certifications like Security+ or the upcoming SecurityAI+, and train in secure coding best practices and tools (E.g. SAST, DAST, IAST, SCA, RASP). This will help them better understand and catch any mistakes made by AI coding tools. You should also arrange for regular penetration tests to catch and fix vulnerabilities. 

2. Promote a balanced approach to using AI for development

Yes, your development teams should be using AI, but not for everything. Clearly define what areas are appropriate for AI use (E.g. “Speeding up writing documentation,” “Refactoring code”) and which need human expertise, then communicate this clearly to set expectations.

While it may be tempting to just ban AI coding to eliminate risk, this can be counterproductive. It’s also not very practical—the best way to stop plane crashes is to keep every aircraft on the ground, but it’s a terrible way to run an airline.

3. Reward and recognize good work (and not just fast work)

Building an employee recognition programme is overall a good idea—when employees are recognized, they are nearly three times more likely to be highly engaged. To learn some ways to do this, read Pluralsight’s Tech Upskilling Playbook. 

4. Make sure your teams are properly resourced to avoid corner cutting

If your teams are under considerable pressure to deliver fast results, the temptation to get AI to create something that “just works” and not properly check it over significantly increases. Make sure you have enough headcount to avoid creating more costly bottlenecks further down the line.

Where resources stay flat and increasing headcount is not possible, you need to scale through technology and process instead.

5. Create a culture of continuous upskilling (not just targeted upskilling)

There are a lot of strategies to doing this: incentivising learning, company hackathons, certification challenges, leading by example. Developers should be given free access to guided learning paths, hands-on labs, and mentorship programs that allow them to grow and pursue what they are passionate about while still aligning this to real business goals. Be aware that offering targeted upskilling is not the same as continuous upskilling.

6. Create protected learning time as part of your business model

The number one barrier to upskilling is finding the time to actually learn, according to Pluralsight’s latest Tech Skills Report (in fact, it’s topped the list for the last four years). In second place was low employee engagement, followed by lack of leadership support. 

To make sure your workforce stays skilled, you need to carve out learning time for them and keep it sacred, even when there are conflicting priorities.

7. Practice empathy and watch for signs of burnout

Senior engineers must now understand software development, AI systems, security, and compliance simultaneously. All of this learning can create pressure that then translates into burnout. On top of incentivization and upskilling, leaders need to practice kindness to avoid stressing out their teams while empowering them to succeed.

Conclusion: Stopping AI-induced skills decay starts from the top

AI-induced skills decay isn’t something visible—it shows up by what falls through the cracks, and at that stage, you’ve already got problems. It’s far less costly to avoid the problem before it affects the software development lifecycle and the organization, rather than trying to fix it after the fact.

To learn more about trends like this one that may affect your organization in the year ahead, read Pluralsight’s 2026 Tech Forecast, a report based on predictions from 1,500+ tech insiders, business leaders, and Pluralsight Authors.