Learn to code, ignore AI, then use AI to code even better

I guess that's the part I disagree on. The programming language is largely irrelevant now is what I'm seeing. And especially the "time to first result" is orders of magnitude smaller when using AI assistants than having to rtfm/google/so every little problem I encounter in an unfamiliar language.

And I am in no way stating that my code will match an expert in that field, of course.

It's a force constant, rather than a multiplier. If you're low skilled, asking it to do a low skilled task, it works fine. If you're high skilled, and asked it to do the low skilled task, you saved a tiny bit of time (less than that of the low skilled person).

But it cannot do a high skilled task (at least, not right now). It can pretend, which can lead the low skilled person astray (but not the high skilled person).

Therefore, all AI does is raise the floor of what is achievable by the laymen, rather than multiply the productivity of a high skilled programmer.

"How do students learn to code nowadays?" - I think about this pretty much all the time.

In my intro class, the two main goals are to learn about structured programming (using loops, functions, etc.) and build a mental model of how programs execute. Students should be able to look at a piece of code and reason through what it does. I've moved most of the traditional homework problems into class and lab time, so I can observe the students coding without using AI. The out-of-class projects are now bigger and more creative and include specific steps to teach students how to use AI collaboratively.

My upper-level students are now doing more ambitious and challenging projects. What we've seen is that AI moves the difficulty of programming away from remembering details of languages or frameworks, but rewards having a careful, structured development process:

- Thinking hard and chatting about the problem and the changes you need to implement before doing anything

- Keeping components encapsulated and thinking about interfaces

- Controlling the scope of your changes; current AIs work best at the function or class level

- Testing and validation

- Good manual debugging skills; you can't rely on AI to fix everything for you

- General system knowledge: networking, OS, data formats, databases

One of my key theories is that AI might lower the value of "computer science" as a standalone major, but will lead to a lot more coding across fields that currently don't use it. The intersection of "not a traditional engineer" and "can work with AI to solve problems with code" is going to be an emerging skill set that will change a lot of disciplines.

Though you suggest he's non-technical... "writing down exactly what he wants" is just coding!

I learned BASIC as a small kid, using a clone of ZX Spectrum. I was aware that the memory was limited and that I can poke and peek a memory address to set or retrieve the info.

I learned Pascal and then rapidly went to C and C++. I learned about pointers and how the memory is layed out and what are system calls.

I learned about CPUs and I learned some X86 assembly.

Ar the university I learned about digital circuits and how to assemble one using logical gates. Of course I learned much more, data structures, algorithms, operating systems, distributed systems, parallel and concurrent programming, formal languages and automata theory, cryptography, web, lots of stuff.

I learned other lots of stuff by myself.

I've built desktop apps, websites, software for microcontrollers, games, web applications and now I am working on microservices based apps running in cloud.

I was a junior developer, graduated somehow to senior. I worked as a software architect and now I am a team leader.

These days I work almost exclusively with C#, but I am also interested in other languages if I have some spare time to evaluate them.

What I want to say is this: is not enough to learn the highest level of technology of today. Today that is AI, few years ago it was JS frameworks, more years ago it was Java, .NET, Python.

To be good at what you do you always have to learn all layers under the current top layer. Learn from bottom up. You don't have to be good at every technical detail, but you have to understand at least how the things work.