Does it still make sense to learn how to code?
This is a question I’ve been thinking about a lot recently.
When I started learning programming ...
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The real shift is from writing the code to owning the code. And therefore being able to read, understand and judge the code yourself is more important than ever. Not always, not all the code, but when a problem occurs, when AI starts turning itself in circles unable to address the core issue properly, the ones capable of stepping in will be those to save the day.
I mean, just yesterday I used agentic AI to two impressive tasks - identify potential memory leaks in one codebase and fully describe the state of second. AI was done within minutes and it was an incredible time saver. But should I just blindly trust the output and don't care? I don't think so. But to verify anything, you need to know how. Or would you bet everything on second (third, fourth,...) agent? Who watches the watchmen?
as someone still learning i think about this more than i'd like to admit. i keep landing on yes, mostly because reading code i didn't write is still where i learn the most.
I think when people say “you should learn to code,” what they often mean is: learn to think algorithmically.
Learn to take a messy problem, reduce it to its essential parts, understand the constraints, and recognise which problem-solving techniques might apply. Coding is one way to express and test that thinking, but the abstract thinking is the durable skill.
What you are describing is rubbing elbows with the role of a technical product manager, though admittedly, the dev team should know the same things but for the purpose of remaining in scope and assisting with potential blindspots from your where you're sitting as the data engineer, designer, etc. The product manager role and tech roles are collapsed in 2026, and it's something we haven't said aloud, but you can certainly see the shift when speaking with hiring managers. They haven't been able to name it plainly, but essentially they want product managers with more technical depth, and it's easier to make the dev team go through exercises and even courses on product design thinking than it would be to mandate a non-technical product manager to learn how to code and understand what's happening on a deeper level with digital products. Though the best product managers have an intimate understanding of this presently.
You absolutely need to learn how to code. This is coming from someone who came into tech as a non-technical product manager. I'm actually aiming to become a data engineer at this point, because I like it...a lot. But also because it's easier to be intuitive and think like a good product manager if I understand the architecture, pipelines/workflows on a granular level. I've built my own ai-assisted products to demonstrate my capabilities and propose new solutions, but earlier this week, I coded my own project based on a need that my family has. I learned so much from that alone - in ways that building a Replit-generated product cannot teach you in a weekend (absolutely no offense). Understanding code helps with managing tech debt, which becomes extremely costly when budgeting tokenization.
And large enterprises are pulling back from AI because it is actually more expensive to use it at maximum capacity because you cannot entirely trust it, and there is constant debate over governance, ownership when the agents get it wrong, and essential humans in the loop. AI has proven essential when used strategically for operational efficiency and lower-stakes for automated processes that require minimal human oversight. I think the shift back to developers who can identify issues the code cannot is essential. Bloated code and tech debt can be managed well with humans who understand the code. It is extremely important, as it has always been, to understand code.
Even with the AI boom, I always knew that coding was still very important in the software landscape; you worded it way better than I could have. Code can generate thousands of lines of code, but you're going to need someone who understands the logic behind the code, someone who knows how to think as a programmer.
I think that, while anyone can generate their own code through an AI model without understanding it all that much, coding has become even more valuable. There's millions of lines of code AI has generated in all kinds of projects, but someone has to understand and review it; everyone can generate code, but not everyone can read the code.
Very interesting read, thanks for sharing. What resonated most is the point that the cost of producing code collapsed while the cost of verifying it didn't move at all — that imbalance explains a lot of what I'm seeing.
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▎ I'd add one thing though: I don't think the answer is learning to code without AI. That's not the environment anyone will actually work in. The part that matters is still doing the loop yourself — forming an expectation, testing it, being wrong, and understanding why. Learning to develop with AI is fine, as long as you're the one deciding what needs to be verified.
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▎ Because that's the trap: verification only works if you already have a sense of what correct looks like. Without it, "I verified it" quietly becomes "it ran without errors."
I agree. AI can help you get into an unfamiliar codebase much faster and quickly debug for you, but it can’t take responsibility for the final decision. You still need enough programming knowledge to tell whether its reasoning makes sense and whether a fix will create new problems somewhere else. So I don’t think learning to code has become less important. What’s changing is that understanding and judging code may matter more than writing every line yourself.
The point about reading legacy code being a highly paid skill in the 2000s really lands — we're basically back there, just with AI-generated codebases now. Reviewing and judging code you didn't write is the daily job again, and that only works if the fundamentals are solid.
The question is whether you would pass technical interviews if you don't know how to code. Furthermore, I think we still need to keep that knowledge to be able to verify the generated code.
I particularly appreciated the emphasis on understanding the problem deeply enough to ask the right questions, which resonates with my own experience working on complex projects where defining clear requirements and evaluating trade-offs was crucial. The mention of algorithms, requirements, and system thinking as fundamental skills that are still relevant today, even with the rise of AI, is also well-taken. It's interesting to consider how these skills can be applied to designing AI workflows and reviewing AI-generated code, and I'd love to hear more about how you've seen these skills play out in practice, especially in terms of orchestrating multi-agent systems.
I agree that we should know the basics of coding to use AI agents. I believe that the trend is moving toward a basic understanding of business hardware systems in general. A programmer will likely be more like an implementation specialist. In the past, a programmer used code libraries developed over many years; now, they’ll be using prompt libraries ;-) I also believe that every AI project must be properly structured in terms of supervising the work of AI agents.
The core problem is that unless you write code you'll never be skilled enough to review it, but the routes to becoming a skilled programmer are fast disappearing.
There's a deeper issue here. In conversation between two people, it's rare for each to speak a different language. Not impossible, but rare. And yet when we converse with AI to create software, we speak to the machine using human language but it replies with computer code that most of us can't understand. Why do we expect this to work?
Our societies are built on communication that, for centuries before the invention of computers, used human language, where necessary constrained to be unambiguous. What is needed is forms of coding language that closely resemble human language but which avoid the ambiguity normally associated with the latter. Think of SQL, for example. And the great thing is, AI itself is more than capable of devising such forms of language. It just needs the right questions to be asked.
The assumption the software industry clings to, that code has to be the way it is, urgently needs challenging. Otherwise, as AI continues to get more powerful we will eventually find ourselves without any part to play in the process. Obsolescence tends to be closely followed by extinction, and I'm not joking. If we are to continue as the dominant species on this planet we need to maintain our place in the loop. Right now that's by making sure AI serves our needs rather than the other way round.
allspeak.ai
True. Take this opportunity to learn how a codebase works. Go to a open source repo, assess the system and take advantage of AI if you have some problems understanding why the codebase is structured that way. I promise you, you will learn more than just coding itself. Reading is a skill too and it is more important today than ever. Coding, languages, and frameworks are just tools. Your thinking is the most valuable skill you could ever invest in.
Really resonates with me. AI makes writing code almost free, but understanding it is still on us, catching the wrong assumption or the edge case that only breaks in production takes real problem understanding, not just a good prompt.
Actually, reviewing AI-generated code has become the harder (and more important) half of the job for us these days. Curious how you're seeing teams build that reviewing muscle in newer devs who didn't go through the "algorithms before syntax" path you mentioned?
The question isn't whether to learn coding — it's what layer of coding matters now. AI handles the syntax layer (writing boilerplate, generating standard patterns) well enough that spending months on memorizing APIs is diminishing returns. But the architecture layer — understanding why a system is structured a certain way, where the failure modes are, what the tradeoffs mean — that still requires a human who has actually built and broken things. The people who will struggle are those who only learned to write code. The ones who learned to think about systems will find AI makes them faster, not redundant.
It absolutely makes sense to understand programming languages... assuming that is the same as "learn how to code", but it might not be, to be true. Only you would know the answer.
Instead of slinging code, maybe consider what else can be done creatively from the same frame of mind as "coding".
E.g. Have you explored the realm of Spec-Driven Project Governance for AI Agents?
Check this out: tvox.online/books/1 - audiobook covers some of what you're discussing actually.
IT IS DEAD 2021 CSE GRAD 8 GPA I worked for 1.5 years as SWE and unemployed from like 3 years it's over guys dead Don't waste your time
This really resonates with my daily workflow. AI makes generating boilerplate almost free, but checking whether a solution is robust, secure, and maintainable is where the real engineering happens.
Whenever an AI agent starts spinning in circles on a complex bug, it's always those foundational lessons—algorithms, inputs/outputs, and edge-case reasoning—that save the day. Tools change, but mental models are permanent!
I still tell people yes, and this is roughly why. The models produce code fine, but someone has to know when the output is quietly wrong, and that judgment only comes from having written enough of it yourself. Learning to code now is less about syntax and more about being the person who can tell good from plausible.
I support learning to code
However, in my personal opinion, just knowing the basics should be enough
In the age of AI, we do not know how or in which direction we will go
It seems that just learning the basics should be enough, and that will help you understand the overall flow
I don’t know much about coding either..But acquire knowledge from the tools
Because of that, I also created a decent library for the AI era
"Does it still make sense to learn how to code?" - I would say more than ever, even if AI writes most of your code - if you don't understand the fundamentals and a lot of the 'specifics' (details, and that even included 'syntax'), then how are you gonna review the code that AI has generated?
That's why I say it still pays off (a lot) to get down and dirty with code, and with the basic principles of HTTP, databases/SQL, etc ...
Otherwise we're just reduced to 'vibe coders', or to "mouse and keyboard monkeys" who know how to click the "Approve" button - and that's fine for very simple and non-critical systems, but it's gonna wreak havoc when things like performance and security matter ...
Thanks to all the valuable comments and perspectives I received on this article, I kept thinking about the topic and decided to explore it from a different angle.
This article was mostly about learning to code in the age of AI. In the new one, I look at the same transformation from the perspective of someone who is already a software developer: how our role, the value we create, and even the economics of software development are changing.
Same topic, different angle. 🙂
The Next Evolution of Software Developers: From Implementation to Intent, Orchestration, and Ownership: dev.to/robertobutti/the-next-evolu...
Waw great piece! I still struggle daily on choosing to code or not, but this gave me a whole new perspective, the tools changed but the fundamentals are the same!
Coding is still worth it for problem-solving, not just jobs—fundamentals trump hype. Which should beginners focus first today?
perhaps we are less writers of code today, and more designers of code now? at any rate we are the builders and architects still - at least for now 🤖
A big Yes. "No knowledge is waste" 😀
And your end note - " no matter how much AI evolves", is what makes a human a "being" as far as I know 😉