A junior engineer once asked me, “Will AI take over our jobs?”
I paused for a second not because I didn’t know the answer, but because I wanted to say it in a way that would actually land. “Not if you keep growing” I told them. “But if you stop learning, stop thinking, and just do what you're told then maybe.”
That might sound harsh. But it’s the truth.
This wave of AI won’t crash and take our jobs. But it will force us to evolve. The engineers who thrive won’t just be great at prompting, they’ll be the ones who know when to ignore the prompt and think for themselves.
For a long time, being a good engineer meant you could write clean code, ship working features, and maybe debug a tricky issue once in a while. If you were solid at your craft, kept up with your language of choice, and could learn a new framework now and then you were in good shape.
But that baseline is shifting fast.
AI can now help write code, create tests, and even build simple apps. It’s not flawless, but it’s good enough to change how things work. Skills that used to make you stand out like writing code quickly or knowing all the right syntax are now expected from everyone. And some of those tasks are starting to be done by AI automatically.
And that’s not a threat. It’s a wake-up call.
The engineers who will thrive in the AI era are not just the ones who can prompt well. They’re the ones who:
- Know how to spot edge cases the AI missed
- Make judgment calls when trade-offs aren’t black and white
- Debug messy, AI-generated code and refactor it for clarity
- Collaborate deeply with product, design, and even the AI tools themselves
- Keep asking, “What are we really solving here?”
You’re no longer just the one who types the code. You’re the one who sees the problem, frames it well, and guides both humans and machines toward a thoughtful solution.
So what does staying relevant actually look like in practice? Here are things I keep coming back to:
Master the Fundamentals
Don’t skip the basics. Strong coding skills still matter not because you’ll always write every line yourself, but because you need to review, understand, and improve what the AI gives you.
The feedback loop between you and the machine depends on your ability to see what’s right and what’s wrong.
Great at Debugging
AI code isn’t always clean. It’s often just “reasonable-sounding”. Your debugging skills, your ability to trace problems, form hypotheses, test, and fix will be more valuable than ever.
Practice Problem-First Thinking
Start with the problem, not the solution. Don’t wait for someone to hand you tickets, dig into the real problem. Ask better questions. Learn to break down ambiguity. That’s the kind of thinking AI can’t do well yet.
Work well with different teams
You’ll need to work across roles more often. Product folks might submit Merge Requests. Designers might tweak code. AI might generate specs. Your job is to hold the technical bar while collaborating tightly. Communication, documentation, and shared ownership become core skills.
Experiment and Build With AI
Don’t just read about AI, build with it. Try out tools like GitHub Copilot, ChatGPT, Cursor, or Claude Code. Use AI to write tests, generate documentation, or summarize bug reports. See what works, what doesn’t, and how to guide it better. The more you experiment, the sharper your instincts will become.
AI as your build engine, new type of programming
AI isn’t just a tool for completing your sentences or suggesting the next line of code. We need to see it as a new way of building software. Think bigger:
- Instead of writing every line of logic, you prompt the model to fetch and format data.
- You can build apps that orchestrate LLMs to handle user queries, data processing, and even reasoning.
The real potential of LLMs isn’t just making old workflows faster, but it’s about changing how we build things completely.
Let’s use a simple example: a weather app.
- Before AI: An engineer finds a weather API, reads the docs, writes code to connect to it, pulls the data, and builds a UI.
- With basic AI help: The engineer uses an LLM to search the API, then uses it to write parts of the code faster, like parsing the API response. It saves time, but the process is still mostly the same.
- With a new mindset: The engineer can ask the LLM to gather and organize weather data directly. For example: “Give me temperature, humidity, wind speed, and rain chances for the inputted location in a structured format”. The LLM could return a clean JSON with all that info, skipping the need to write low-level integration code, and engineer can focus more on making a good user experience.
This shows how LLMs can take on bigger roles, not just writing code, but more. That frees up engineers to focus on what makes their app unique. Treat AI as an engine you can build around. Prompting becomes programming. Human creativity and context still matter, but the AI gives us a new layer of abstraction and power. This is the new infrastructure for innovation.
Here’s what to remember as the landscape shifts:
- Coding isn’t going away, it’s just changing form. Strong engineers will still be needed to review, reason, and decide.
- AI won’t replace you, but someone using AI better than you might.
- Stay curious. Stay sharp. Stay human.
This is not the end of your career. It’s the beginning of a new chapter, where the best engineers are thinkers, problem-solvers, and guides for the tools we now work alongside.