Your New Rubber Duck is an AI

One of the things we discussed a lot on the LEAD podcast — which I co-host with Geert van der Cruijsen — is how GenAI is affecting the work of developers and architects. And honestly, one question kept coming back: will developers still have a job in a few years?

I use GenAI every day. It is incredibly helpful, especially in coding tasks and architecture discussions. But replacing us? I was not so sure. So Geert and I invited April from GitHub — someone who has been in the cloud and DevOps space for over a decade and works on developer experience and productivity using tools like GitHub Copilot.

Her answer was clear: no, developers are not going away. But your work is definitely changing.

Patterns Repeat Themselves

April reminded us of something important. We have heard this story before. When cloud arrived, people feared for their jobs. Same with virtualisation, and later with containers. Each time, it was not about being replaced. It was about adapting. AI is no different.

The core of our job as developers, architects and engineers has not changed. We make decisions. We design systems. We think critically about trade-offs. That is not something AI can do — at least not yet. GenAI gives suggestions, accelerates repetitive tasks, and acts like a supercharged rubber duck. But we are still the ones in the pilot seat.

A Junior That Knows Everything

The way we described Copilot in the episode really stuck with me. It is like pair programming with a junior developer who somehow has access to everything ever written. But it is still junior. It can make silly mistakes, or suggest code that does not compile. So we still need to test, validate, and bring the right context.

That is where the real challenge is. If you ask the wrong question, you get a half-right answer. If you forget to give business context or overlook the architecture, Copilot cannot fill in those blanks. It does not know your organisational structure, Conway’s Law, or your compliance constraints. It just knows code patterns.

Learning to Work with AI

One important takeaway: not using AI might be a bigger risk than using it wrong.

If your peers use AI to complete a task in 30 minutes that takes you five hours, it is only a matter of time before you fall behind. Just like in sports, technology shifts the baseline. It is not about replacing humans, it is about augmenting them.

So the real question becomes: how fast can you learn to work with it?

Prompting matters. Context matters. Iteration matters. The best developers I know do not just ask AI to “write a unit test.” They keep asking, refining, and learning how to get the best result — just like they would with any tool or colleague.

Rolling It Out Is Not Just Buying Licences

Another thing that stood out in our talk was the gap between management decisions and real developer adoption. Just buying Copilot licences does not magically make your team more productive. It requires cultural change. Education. Time to play and experiment. Communities where developers can share prompts, tricks, and frustrations.

Just like with DevOps, it is not about tooling. It is about process and mindset. And yes, that takes effort.

So, Will You Be Replaced?

Here is my take after this episode. Developers will not be replaced. But developers who refuse to learn, experiment, and adopt new tools might be. It is not about job loss. It is about relevance.

If you want to stay in this field, invest in your own skills. Not just coding, but also in learning how to use GenAI effectively. How to give it better input. How to challenge its output. And maybe most importantly, how to stay curious and open to change.

Because at the end of the day, the thing that keeps you valuable is not just your ability to type out code. It is your ability to think, to design, to collaborate, and to learn.

And AI, at least for now, cannot do any of those without you.