This article won't offer false reassurance, nor alarm you for the sake of it. The situation has genuinely changed, and pretending otherwise would be a disservice. But the conclusion to draw is more interesting than the simple yes or no the question invites.
This week, a Chinese model took the top spot in a blind-tested frontend code generation benchmark, ahead of Anthropic's best model. Full agents write, test, and fix code without human intervention for hours on end. Faced with that, the question legitimately arises, and it comes up often among young people unsure about their career path: why spend years learning something a machine already does better and instantly?
The "no" camp has real arguments
Let's start by taking the reasons for doubt seriously, because they aren't absurd.
AIs today write functional, clean code, often better commented than that of a rushed developer. Tools like Claude Code, which we covered in a dedicated article, turn a plain-language description into a working application. A beginner can now produce in an afternoon what previously took weeks of learning.
On the job market, the effect is visible. The tech sector recorded more than 100,000 job cuts in 2026, many directly attributed to automation, as we saw with the layoffs at Meta. Simple execution tasks, the ones traditionally handed to junior profiles, are precisely what AI absorbs best. Denying that reality would be dishonest.
The "yes" camp has a deeper argument
Yet here's what the previous argument misses. It conflates writing code with building software. These are two different things, and the latter has never been in such demand.
A developer doesn't spend their days typing lines. They spend most of their time understanding a poorly framed problem, deciding on an architecture, weighing trade-offs, figuring out why something breaks, and assessing whether a solution will hold up in six months. AI excels at the text-production part. It remains far behind on judgment.
You can't supervise what you don't understand. When an agent proposes a command for you to run, only your understanding lets you spot that it's dangerous. That's exactly the mechanism exploited by agentjacking, the attack that slips fake instructions into data the AI believes is legitimate, with an 85% success rate. It doesn't target a flaw in the machine; it targets the trust of a human who no longer checks. Those who can read the code are protected. Those who only copy-paste are not.
There's a second argument, less obvious. The more capable AI becomes, the more the quality of what you get depends on the quality of what you ask. To properly direct an AI on a software project, you need to be able to name what you want, tell a good answer from a plausible but shaky one, and catch the subtle error that will only surface in production three months down the line. That discernment only comes from having gotten your hands dirty yourself.
What really changes: the content of learning
The real answer is therefore neither yes nor no, but a shift. What you need to learn has changed.
Memorising the exact syntax of a function, knowing a library's methods by heart, writing a loop without mistakes: this part of the job is indeed losing value, because AI covers it perfectly. Spending six months memorising what a model produces in two seconds is now a poor investment.
On the other hand, understanding why an architecture holds up or collapses, breaking down a vague problem into clear steps, being able to read code written by someone else and spot what's wrong, understanding what actually happens when a program runs: all these skills are gaining value, because they're precisely what lets you steer an AI rather than be steered by it.
The paradox of the entry barrier
There's one last subtlety worth naming. AI has spectacularly lowered the barrier to starting. Anyone can now produce something that works from day one, without enduring the months of frustration that once discouraged most beginners. That's a tremendous democratisation.
But in the same move, it has raised the bar for what sets a professional apart. If anyone can produce a working prototype, value shifts to those who can keep a system running in production, weigh trade-offs, and take responsibility for what's shipped. The middle of the spectrum compresses; the two ends pull apart.
So should you learn to code in 2026? Yes, but not to become a machine for typing lines—that job is disappearing, and that's a good thing. Learn to understand what you build, to keep control over what you delegate, and because the one who understands the system remains, in all circumstances, the one who decides. AI is an extraordinarily powerful tool in the hands of someone who knows what they're doing, and a trap for those who don't. The difference between the two is exactly what you learn by learning to code.