What should we learn now?

This is, unfortunately, contrary to previous advice of specializing.

It’s a great time to be a jack of all trades and master of none.

I had to learn coding when a reference book was all I had to help when I got stuck. Then the Google and StackOverflow came and people complained about no-knowledge coders copy-pasting their way through tasks. AI is just an extension of that.

Yes, you can push out 5,000 line PRs blindly, but you can also use AI to help you speed past blockers, to investigate why things are structured in a certain way, explain what a block of code is doing, propose 3 alternative approaches and explain each, etc.

We say "coding is only part of what a SWE does" but you can't do the rest without understanding what the code is doing and meant to do, and why.

I can understand every problem, ask and explanation for every paragraph evenreorganizae a clearly suboptimal PDF notes from my teacher's class or Latex.

It is an aceleretor of my education.

They pursue OOP but end up with too much abstraction. They pursue ML but end up making a complete mess.

So if you ask me: Are you worried about Code generated by AI?

I want to ask you back: Can you trust the Code written by your colleagues?

The nature of the problem is the same. It's better to rely on powerful E2E and AI detection to handle this problem.

As for learning and writing code yourself, I think that's correct, but this has become something with a very high barrier.

It's a bit like sugar. There's more and more industrial sugar, people like eating it more and more, and actively stopping sugar intake becomes very difficult, especially for those who eat more sugar.

Most people now use LLMs for development, and they've always been focused on developing features. Their roles have changed from engineers to PMs or Makers.

And sitting down to write code yourself is like spending a lot of time training your muscles. It's painful, growth is slow, and even if you have muscles all over your body, it doesn't necessarily mean a higher salary.

Many people only care about "features" and lack understanding of code, architecture, and core logic, including why to abstract a Pattern, why to use async, technology selection, and so on.

Experienced engineers might write Prompts like: Use SQLite for the backend Database, and make sure to use transactions. Create a class that automatically Commits when leaving its lifecycle, and automatically rollbacke if something fails. The created connect should be a long-lived connection, use a Pool to manage seesion, and work with me on Database Table design and normalization, and so on.

But ordinary inexperienced new engineers or regular people: Help me save this thing.

Without these basic design abilities, and with descriptions becoming more and more high-level, you basically know nothing about your own projects and code. Later, during interviews, you can only tell me what you've done, but you can't tell me how you did it.

Although there's always been a joke among engineers that as long as the system works, it's fine. One bug is a bug, but multiple Bugs can work.

This is indeed what LLM development is like now.

I can't say for sure what the future will be like, or whether this kind of design ability will still have any meaning.

Most people who aren't software engineers, even when releasing to Prod, only care that it works. They care more about being product-driven rather than technology.

If even design is already like this, then going back to writing code by hand, experiencing how it takes several hours to develop one feature, and at the beginning only being able to write a complete mess of code, with an extremely low sense of achievement, is something where the time investment simply isn't worth it.

I don't know how many new and future engineers or students will still be willing to do this.

So, to be honest, what exactly should we learn?

Even now, I still haven't reached a conclusion. AI is developing too fast. Many skills are becoming useless and making people's thinking rigid. At the same time, there's a strong sense of rejection and resistance, and you have to convince yourself to go look at it, experience it, and master it.

Just like illustrators, translators, and software developers, these fields require extremely high investment in the early stages, including limited time and money. The sunk costs are so high, and personal interests are also involved. How should we face this kind of impact? I'm still very confused.

Even now, I'm starting to question: What exactly do humans need to learn?

Recently, I had a realization. I've been writing novels for a long time, probably almost 10 years now. Looking back at when I first started writing, turning the ideas in my head into words was really a disaster. I had so many ideas in my head, but writing them down was extremely difficult.

Now, I can turn whatever I think of into words and improve them while writing. Because of this, I've always had a hard time getting used to voice input.

And recently, I've been learning Japanese, relying on LLMs to help me learn. Until yesterday, when I went to take an exam, I didn't expect that I would have to write an essay.

I had ideas in my head, but I couldn't turn them into effective written words.

With the help of LLMs, the text can indeed become cleaner, but basically, I've also lost the ability to "construct a system of thought and language."

I think back to when I read the text structures given by LLMs. At the moment I read them, I thought: That's exactly right.

But when facing a blank piece of paper, I couldn't construct them myself. I could only write content in a way similar to "speaking."

So now I've made up my mind that at least for written expression, I need to write it myself, building my own language system and structured output.

I really didn't expect the cognitive damage LLMs could cause to learning to be this strong.

It's like a teacher explaining mathematics on a blackboard. Every step looks so simple and reasonable.

But when you have to do it yourself, it's a completely different story.

Based on my own experience, many people using LLMs to learn programming are like listening to a piano piece, watching someone else play the piano, and then thinking they know how to play it themselves.

In the future, many people may not even have the ability to produce structured output by themselves, including basic written expression.

But if you say we should go back to learning in the old traditional way, it also feels like we can't keep up with the times.

I don't have an answer, but personally, I'll adjust my ability to "output" after absorbing information, as well as my ability to think structurally and ask further questions.

Spell checking via AI seems... pointless? Unless this is a means of stating "AI did a once-over of this before I hit submit" in which case this is somewhat disingenuous.