The early years of your career shape the kind of developer you’ll become. They’re when you build the problem-solving skills and knowledge that set apart excellent engineers from average ones. But what happens if those formative years are spent outsourcing that thinking to AI?
Generative AI (GenAI) coding assistants have rapidly become popular tools in software development, with as many as 81% of developers reporting to use them. 1Developers & AI Coding Assistant Trends by CoSignal
Whilst I personally think the jury is still out on how beneficial they are, I’m particularly worried about junior developers using them. The risk is they use them as a crutch – solving problems for them rather than encouraging them to think critically and solve problems themselves (and let’s not forget: GenAI is often wrong, and junior devs are the least likely to spot its mistakes).
GenAI blunts critical thinking
LLMs are impressive at a surface level. They’re great for quickly getting up to speed on a new topic or generating boilerplate code. But beyond that, they still struggle with complexity.
Because they generate responses based on statistical probability – drawing from vast amounts of existing code – GenAI tools tend to provide the most common solutions. While this can be useful for routine tasks, it also means their outputs are inherently generic – average at best.
This homogenising effect doesn’t just limit creativity; it can also inhibit deeper learning. When solutions are handed to you rather than worked through, the cognitive effort that drives problem-solving and mastery is lost. Instead of encouraging critical thinking, AI coding assistants short-circuit it.
Several studies suggest that frequent GenAI tool usage negatively impacts critical thinking skills.
- A recent study by Gerlich (2025) found a strong negative correlation between GenAI tool usage and critical thinking skills, largely due to cognitive offloading – where individuals delegate thinking to external tools instead of engaging deeply in the problem-solving process. 2AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking by Micheal Gerlich, 2025
- Another study by Çela, Fonkam, and Potluri (2024) found similar results and also each unit increase in reliance on GenAI tools, there was a corresponding decrease in problem-solving ability. 3Risks of AI-Assisted Learning on Student Critical Thinking: A Case Study of Albania by Çela, Fonkam, and Potluri (2024)
I’ve seen this happen. I’ve watched developers “panel beat” code – throwing it into an GenAI assistant over and over until it works – without actually understanding why 😢
GenAI creating more “Expert Beginners”
At an entry-level, it’s tempting to lean on GenAI to generate code without fully understanding the reasoning behind it. But this risks creating a generation of developers who can assemble code but quickly plateau.
The concept of the “expert beginner” comes from Erik Dietrich’s well known article. It describes someone who appears competent – perhaps even confident – but lacks the deeper understanding necessary to progress into true expertise.
If you rely too much on GenAI code tools, you’re at real risk of getting stuck as an expert beginner.
And here’s the danger: in an industry where average engineers are becoming less valuable, expert beginners are at the highest risk of being left behind.
The value of an average engineer is likely to go down
Software engineering has always been a high-value skill, but not all engineers bring the same level of value.
Kent Beck, one of the pioneers of agile development, recently reflected on his experience using GenAI tools:

This is a wake-up call. The industry is shifting. If your only value as a developer is quickly writing pretty generic code, the harsh reality is: if you lean too heavily on AI, you’re risking making yourself redundant.
The engineers who will thrive are the ones who bring deep understanding, strong problem-solving skills, the ability to understand trade-offs and make pragmatic decisions.
My Plea…
Early in your career, your most valuable asset isn’t how quickly you can produce code – it’s how well you can think through problems, how well you can work with other people, how well you can learn from failure.
It’s a crucial time to build strong problem-solving and foundational skills. If GenAI assistants replace the process of struggling through challenges and learning from them (and from more experienced developers), and investing time to go deep into learning topics well, it risks stunting your growth, and your career.
If you’re a junior developer, my plea to you is this: don’t let GenAI tools think for you. Use them sparingly, if at all. Use them in the same way most senior developers I speak to use them – for very simple tasks, autocomplete, yak shaving. But when it comes to solving real problems, do the work yourself.
Because the developers who truly excel aren’t the ones who can generate code the fastest.
They’re the ones who problem solve the best.