We’re on the brink of a major shift. For decades, the stereotype of the ideal developer has been the same: someone who lives, breathes, and dreams in code. They memorise algorithms, wrestle endlessly with syntax, and churn through lines faster than anyone else in the room. And for a long time, that’s been enough.

But, to quote Bob Dylan: that old road is rapidly fading.

As AI-driven coding assistants like GitHub Copilot - and soon, far more advanced tools - take over the bulk of actual code-writing, the value of human developers shifts dramatically. Suddenly, being able to memorise an entire codebase or recite obscure JavaScript methods off the top of your head isn’t particularly impressive. After all, AI can do that better, faster, and more reliably, without ever needing a coffee break.

So what’s left for humans?

Simple: Communication.

Think about your average tech project. What’s the biggest challenge? Is it writing the actual code? Rarely. Usually, it’s figuring out what the hell you’re supposed to be building in the first place. It’s navigating conflicting requirements from stakeholders, deciphering half-formed ideas that were cobbled together in a Slack message, and resolving ambiguities before they become tech debt. It’s translating vague client-speak into something actionable. And it’s explaining highly technical decisions in a way that makes sense to non-technical people, without sounding like you’re talking down to them.

The devs who’ll thrive in the next decade won’t necessarily be the ones who’ve spent their evenings memorising LeetCode solutions, it will be the ones who can step into a room, listen to what’s actually needed, and clearly articulate what comes next. It’ll be the people who can simplify the complex, who can confidently stand between the tech stack and the client, and who can ensure that what’s being built genuinely solves the right problems or even understanding that the best solution is to not build this new feature at all!

Experience still matters

I remember clearly, my first day on a new project, walking into a kick-off meeting. There was a developer who had been involved since the discovery phase, two weeks ahead of me, trying to explain a crucial concept to a stakeholder. From the stakeholder’s puzzled responses and the developer’s quietly frustrated tone, it was clear this wasn’t their first attempt at this conversation and the relationship was starting to sour.

Thanks to my years of experience and my strong communication skills, I was quickly able to translate the developer’s technical jargon into plain English. The stakeholder immediately responded, “Ah! Finally, I get it!” and, from that moment, despite being brand new to the project, I became the technical leader in that meeting. Stakeholders began directing their questions to me - even though I often had to defer to others on context-based knowledge - simply because I was the clearest communicator in the room. Suddenly, I was perceived as the authority and “subject matter expert” purely based on my ability to communicate clearly and empathetically.

Conversely, the “eat, sleep, code, repeat” devs - the ones who’ve dominated the recruitment process for years - might find the ground shifting beneath their feet sooner than they think. Their deep, technical memorisation skills, impressive though they may have been, won’t be enough when AI can effortlessly replace them. Not because they’ve become worse at what they do, but because the value of that particular skill set is depreciating fast.

It's not all doom and gloom

Now, to be clear: software development isn’t going anywhere. In fact, I suspect we’re about to experience a major boom. AI isn’t killing software engineering; it’s turbocharging it. Throughout history, when the means of production are simplified - and that simplification leads to increased output - the market booms. Think industrial revolution. Think mass printing. Think digital publishing. Companies finding themselves more productive won’t decide to cut costs - they’ll decide to increase output, because more output = more profit.

But the people leading the charge won’t be the stereotypical coders who write insane code as easily as breathing but can’t communicate a technical concept without sounding like they are in an episode of Star Trek. It’ll be those who understand complexity in a product rather than merely in a codebase- - those skilled at breaking tasks down, clearly communicating requirements, and managing outputs effectively. They’ll be part dev, part translator, part project manager, part educator.

Right now, AI tools still rely heavily on people who understand the fundamentals. For example, they don’t instinctively add performance optimisations like memoization. They don’t automatically factor in essential considerations such as accessibility, localisation, or security unless they’re told to. Humans still need to point the AI in the right direction and ensure these critical elements aren’t overlooked. They also struggle in less-popular or more esoteric languages and frameworks.

But sooner or later - and probably sooner than we think - we’ll have ‘chain of command’ and ‘quality-control’ AI’s that pipe output through rigorous checkers. These advanced systems will catch performance bottlenecks, accessibility oversights, and security gaps automatically. They’ll enforce design tokens, spot logic bugs, lint your code, and even make suggestions in real time - all without breaking a sweat. At that point, AI will become perfectly capable of consistently outputting production-level code.

The nay-sayers who say things like “AI will never be able to handle complex C++ code” will have a rude-awakening. The chances are, they are wrong about that, eventually AI will likely advance to the point where it won’t struggle with any programming language or framework. However even if that turns out to be true, that just means that those languages will probably start to die off. After all - why hire an expensive C++ developer to take 5 months to code a feature when it would be quicker and cheaper to hire an AI-assisted Python developer to replace the entire codebase in 2 months.

Mark my words here, if you are not on board the AI train, you have no future in this industry. Pure coding will be about as useful a skill today as typesetting was when desktop publishing software became commonplace.

When that happens, it won’t be the pure coders who lead software engineering - it’ll be the communicators, the strategists, the orchestrators. Those devs who previously relied on coding muscle alone might find themselves floundering, while those who can clearly understand, simplify, and communicate will thrive.

Your job will still exist but it will become niche and rare. Those lucky enough to land one may find that they get much higher pay for it but most people will find that their livelihood as gone because there is no longer a mass-market need for your skillset.

So, what’s a dev to do?

If you’re a dev worried about your future in this changing landscape, start sharpening your communication skills now. Practise translating complex ideas into simpler ones. Get comfortable explaining your decisions in plain English. Embrace your role as a bridge-builder and problem solver rather than just a “coder.”

Become a ‘systems’ thinker, don’t consider ‘how’ to build that feature you’ve been given, look at ‘why’ it is being built, what problem does it solve? How does it fit into the bigger picture? Learn to see and understand the entire picture instead of focusing on carving out your own jigsaw piece.

The future isn’t code; it’s connection. And those who master the art of making those connections and communicating them clearly and effectively are going to be the ones thriving in this brave new world.