Should kids learn to code in 2026?

It's a question I hear a lot, and the uncertainty around it is at an all-time high. As educators we want the best for our students, and we're obligated to prepare them for the real world, even if they're a decade away from entering the workforce. So if the value of coding is already being called into question today, what does that say about where we'll be in 2036?

Spoiler: in 2026, learning to code is more important than ever. But a few things are changing, and it's worth understanding exactly what's going on and where things are likely headed.

What AI is good at, and where it falls down

The first question worth tackling: what's the value of learning to code if AI is writing all the code now? It's a fair question. The short answer is that AI is very good at some things and quite bad at others.

If you have a technical background, you can probably skip this section. But briefly: AI models are trained on a huge amount of data. In the context of coding, that means tons of examples of real-world code, technical discussions, documentation, and so on. The patterns that show up frequently in that training data (writing a tic-tac-toe game, say) the model handles well. Things it hasn't seen as much, it tends to struggle with. And while it can reason about new problems by analogy to old ones, even the best models routinely get stumped or make bad decisions inside a real codebase.

That leads to the second limit: context. Context is roughly the amount of information the model can hold in its head at one time. When you ask it for help on a problem, it needs your code loaded into context to reason about it. On any non-trivial project, your codebase is bigger than what fits, so the model starts dropping information to make room. That's where the subtle mistakes creep in.

There's a third limit that's easy to miss: even when an AI is helping you reason about your code right now, it isn't actually learning anything. Once a model is trained, its weights are frozen. It can be told new things and use them within the current conversation, but the next conversation starts over. Anything that happened after the model was trained (a new library, a recently documented bug, a convention your team just adopted) only enters the picture if you explicitly bring it in. Humans accumulate intuition over years of building things. A model has whatever intuition it was given at training time, and that's where it stays.

There's a bigger point too, which isn't really about AI capability at all: writing software isn't a one-shot translation from idea to code. Real projects are an iterative loop. You ship something, users push back, requirements shift, edge cases surface, the team realizes a different feature matters more. A good engineer is constantly weighing competing requirements, anticipating how a design will evolve, and pulling information out of users who often don't know what they actually want. AI can absolutely help inside that loop, but the loop itself, with all its judgment and conversation and revisiting of decisions, is the part a person has to drive.

What beginners should focus on

The skills that matter most are shifting. Producing syntactically correct code is the part AI handles best, which means it's the least useful thing for a beginner to spend years grinding on. The more valuable skills sit one level up: reading code and understanding what it actually does, spotting where things could go wrong, and reasoning about how a change will ripple through the rest of a system.

That last part deserves extra emphasis. Real software is almost never one isolated program. It's a database connected to a web server connected to a frontend connected to a payments provider, each with its own quirks, performance characteristics, and failure modes. Understanding how those pieces fit together under a particular set of constraints (this team's budget, this product's latency target, this customer's legal requirements) is genuinely hard, and AI today is nowhere close to doing it on its own.

Communication matters more than ever too. A coder who can explain a tradeoff clearly to a non-technical teammate, write a tight bug report a colleague can actually reproduce, or summarize a thorny design decision in a few sentences is the coder who gets trusted with the interesting work. AI can help you produce text, but only if you already know what you're trying to say. The skill underneath is being able to think clearly about technical detail and translate it for whoever's listening.

A beginner who builds those habits early is going to be in great shape, regardless of how good the tools get.

Why now is the time to start

So why is 2026 the best year yet to learn to code? Because getting good at it now is what lets you unlock the productivity boost from the tools that are coming. AI is going to reinforce, not erode, the value of software skills across industries.

There's a bigger reason too: the next decade is going to be one of the busiest stretches of software engineering in history. AI models are powerful general-purpose tools, but a powerful tool isn't value. Turning AI into something a business or a customer actually uses takes an enormous amount of new software wrapped around it. Every chatbot that serves a company's real customers has to be wired into that company's real data, authentication, billing, monitoring, error handling, and whatever existing systems it needs to talk to. Every "AI agent" that does real work needs guardrails, audit logs, retries, fallback paths, and a UI a human can actually use. Every new model release breaks somebody's prompt and forces a re-test of the pipeline downstream.

None of that builds itself. The bottleneck for getting AI to deliver real value isn't the model anymore; it's the software around the model, and there isn't enough engineering talent in the world to keep up.

AI won't replace human coders, but human coders using AI will replace the ones who don't. Coding is how AI gets integrated into the real world, so coders are the people best positioned to take full advantage of this new technology.

What about 2036?

Will AI eventually get good enough to do all the coding? Maybe. Who knows. Definitely not with the current technology. It's easy to get caught up in the hype and assume that if AI is already this good in 2026, then by 2036 it must be able to do anything.

But that's a bit like watching the moon landing in 1969 and concluding that by 2026 we'd all be vacationing on Mars.