Type “should I learn to code” into any search bar and you’ll find two confident, opposite answers. One camp says AI has made programming obsolete — why spend two years learning what a chatbot does in ten seconds? The other insists nothing has changed and grinding algorithm puzzles remains the path. Both are wrong in instructive ways. Here’s the honest middle.
What AI genuinely changed
Let’s not sugarcoat it. AI coding assistants now write solid code for well-defined problems, catch bugs, explain unfamiliar codebases, and scaffold entire small applications from a description. The specific skill of “translating a clear specification into syntax” — the thing junior developers spent their first years doing — has been heavily automated. Entry-level roles built purely on that skill have gotten more competitive, and the market has noticed.
Anyone selling you a coding course with 2019-era promises (“learn syntax, get hired”) is selling nostalgia.
What AI didn’t change
Here’s the part the doom camp misses: writing code was never the hard part of software. The hard parts are deciding what to build, understanding messy real-world requirements, designing systems that survive growth and change, debugging the problem that spans four services and a misconfigured server, judging trade-offs, and — critically now — verifying that generated code is actually correct, secure, and appropriate.
Every one of those requires understanding how software works. AI raised the floor of who can produce code; it simultaneously raised the value of people who can evaluate code. You cannot review what you cannot read. You cannot direct a powerful tool toward a goal you can’t decompose. The developers thriving in 2026 aren’t typing less because they know less — they’re typing less because they’ve moved up a level of abstraction, orchestrating AI the way earlier generations moved from assembly to high-level languages.
So the answer is yes — with a different curriculum
Learning to code in 2026 is still one of the highest-leverage skills available, but how you learn must change:
Learn fundamentals deeply, syntax lightly. Variables, control flow, data structures, how the web works, how data is stored — these concepts transfer across every language and every AI tool. Memorizing syntax details is now genuinely optional; understanding why code works is not.
Use AI from day one — as a tutor, not a vending machine. The single best learning setup in history is currently free: paste code you don’t understand and ask “explain this line by line.” Ask why your approach fails. Ask for three alternative solutions and their trade-offs. Then — and this is the discipline that separates learners from copy-pasters — write your own version without looking. If you can’t reproduce it, you haven’t learned it.
Build real, slightly-too-hard projects. A deployed, ugly, working project teaches more than fifty tutorials. It also produces the portfolio that the modern market actually screens for, since “completed a course” signals little now.
Learn to read and review code. Practice spotting bugs in AI output on purpose. This “verification skill” is arguably the defining junior-developer competency of this decade.
Where to start
Python if you’re drawn to data, AI, and automation; JavaScript if you want to build things people click on. (We compare them in depth in a companion article.) Either is fine — the fundamentals are the product, the language is the packaging.
The honest closing
Is coding still worth learning? Yes — but the reason changed. You’re no longer learning to be a human code-typist; that job is evaporating. You’re learning to be someone who understands software deeply enough to command the most powerful building tools ever created — and to catch them when they’re confidently wrong. That skill isn’t going obsolete. It’s going scarce.