A few years ago the advice was simple. If you want to learn to code, find a good tutorial and follow it. Watch the video, type out the code, build the project, repeat. That path worked and millions of developers walked it.
Then AI coding tools arrived. Now you can describe what you want in plain English and get working code back in seconds. So the question is obvious: do coding tutorials still matter, or have they been made irrelevant?
The honest answer is more complicated than either side usually admits.
What Tutorials Were Actually For
Tutorials were never really about the code. They were about building a mental model. When you follow a tutorial that walks you through building a CRUD app from scratch, you are not just learning the syntax. You are learning how the pieces connect, what order things happen in, why certain decisions get made, and what goes wrong when you do it wrong.
That mental model is what lets you debug something you have never seen before. It is what lets you read an error message and know roughly where to look. It is what lets you tell the difference between a working solution and a fragile one.
AI tools do not give you that model. They give you output. If you do not have the underlying understanding, you cannot evaluate whether the output is correct, whether it will hold up under real conditions, or why it broke when something changed.
What Has Actually Changed
The things that have changed are real and significant. You no longer need to memorise syntax. You do not need to spend twenty minutes looking up how to write a specific SQL query or remember the exact arguments for a particular function. AI handles that retrieval work almost instantly and usually correctly.
The starting point for building something has also moved dramatically. A founder with no coding background can now get a working prototype running in an afternoon using tools like Lovable, Bolt, or Claude Code. That was not possible three years ago regardless of how many tutorials you had watched.
For experienced developers, AI has compressed the time it takes to move into an unfamiliar framework or language. You can be productive in something new much faster when you have an assistant that can explain conventions, generate boilerplate, and answer questions inline.
Where Tutorials Still Win
Fundamentals still require deliberate learning. If you want to understand how memory works, how databases handle transactions, how HTTP actually functions, or why your asynchronous code behaves unexpectedly, a tutorial written by someone who deeply understands those things is still one of the best ways to build that knowledge.
AI is genuinely bad at teaching you to think. It is very good at giving you answers. Those are different things. When you ask an AI to explain why something works, you get an explanation. When you work through a well-designed tutorial that makes you predict what will happen before you see it, you build intuition. That intuition is what separates developers who can ship reliable software from those who are always one unexpected error away from being stuck.
Debugging is the other place where foundational knowledge still pays off. AI assistants can suggest fixes, and they are often right. But when they are wrong, you need to know enough to recognise it. Following a dependency chain through an unfamiliar codebase, reading a stack trace, understanding what the runtime is actually doing, these things require knowledge that tutorials build and AI prompting does not.
The Trap That Is Catching a Lot of People
There is a pattern showing up constantly right now. Someone uses AI tools to build something, it works, they build more, then something breaks in a way the AI cannot fix with a simple prompt, and they have no foundation to fall back on. They are stuck not because the problem is hard, but because they skipped the part where you learn what is actually happening.
This is not an argument against AI tools. It is an argument for using them on top of real knowledge rather than instead of it. The founders and developers who are getting the most out of tools like Cursor and Claude Code are typically people who already knew how to code. The AI is accelerating them. For people with no foundation, the AI is papering over gaps that will eventually cause real problems.
What to Do If You Are Learning Right Now
Use both. Learn the fundamentals through structured content, whether that is a course, a tutorial series, or a well-written book. Build things from scratch at least some of the time, even when it would be faster to use AI. Understand what the AI is generating before you ship it.
Then use AI tools to go faster once you have enough mental model to evaluate what they produce. The combination of foundational knowledge and AI assistance is genuinely powerful. Either one without the other has real limitations.
For Founders Who Are Not Trying to Become Developers
If you are a non-technical founder and you are not trying to become a developer, you do not need to work through coding tutorials. Your job is to understand enough to have good conversations with your technical team, evaluate what you are being told, and make good product decisions.
For that, a light pass through some foundational material so that terms like API, database, deployment, and version control make sense to you is worth more than a deep tutorial series. You do not need to write the code. You need to understand what the code is doing well enough to ask good questions.
The Bottom Line
Coding tutorials are not dead. They are less necessary for experienced developers who already have a foundation and are using AI to go faster. They are more important than ever for anyone who is just starting out and is tempted to skip the fundamentals because AI makes it feel optional.
The foundation is what makes the AI useful. Without it, you are just hoping the output is right. With it, you can actually tell.
If you are building a product and trying to work out how much technical knowledge your team actually needs, we are happy to talk it through at Cystall.