A clear look at the key risks software engineers face when working with AI tools.
Look, AI tools are brilliant. They speed up development, help you write boilerplate faster, and can even suggest solutions you hadn’t thought of. But there’s a trap here that too many engineers are falling into, and it’s worth talking about.
From over-trusting outputs to losing technical depth, this video explains what you must stay vigilant about to keep providing real value as a software engineer.
Here’s the thing: when you rely too heavily on AI-generated code without understanding what it’s doing, you’re essentially becoming a copy-paste engineer. You lose the ability to debug properly when things break. You stop questioning whether the solution is actually the right one for your use case. You miss edge cases because you didn’t write the code yourself and didn’t think through the problem deeply enough.
And over time? Your technical depth erodes. You become dependent on tools that might give you plausible-looking code that’s actually inefficient, insecure, or just plain wrong.
Don’t get me wrong, AI tools are fantastic when used correctly. They’re brilliant for speeding up repetitive tasks, exploring different approaches, or handling the boring stuff. But you need to stay in control. You need to understand the code you’re shipping. You need to verify outputs, question suggestions, and keep your problem-solving skills active.
In this video, I’ll break down the specific risks you should watch out for and how to use AI tools as a force multiplier rather than a crutch. Because at the end of the day, the engineers who thrive aren’t the ones who blindly trust AI outputs. They’re the ones who use AI strategically whilst maintaining their technical judgement and depth.
I’ve been building several projects on my GitHub over the years that might interest you. Feel free to check them out for inspiration or jump in with contributions!
There are so much more to come on my LinkedIn as well! Don’t forget to follow and stay tuned! 🔥
