ive heard alot of posts subs about it, they are basically just graduating learning to use AI to bs through the whole degree and wonder why they arnt getting jobs, or they arnt learning anything, plus writing peer-reviewed papers?

the person in your scenario is just fkin stupid. When people say that llms are helpful for coding, they aren't talking about telling an llm to rewrite an entire codebase.

In other words, you're arguing that their usage without discernment is detrimental.

Cory agrees on this very same article, and it also includes the same nuance this chain is trying to give voice to:

The other question Suresh implicitly raises is: "How can you reconcile the failure of AI in the enterprise with the individual claims of skilled technologists who insist that AI is helping them do great work?" The answer is that these AI users are "centaurs" – experienced workers who are assisted by automation on terms that they set for themselves.

Thanks to their skill and experience, these workers possess discernment, the ability to tell good code from bad, and (more importantly) good uses of code-generation tools from bad. They demonstrate the adage that worker-driven automation improves quality, while capital-driven automation improves throughput.

We wouldn't be having this conversation if LLMs had been given the chance to grow into being the same way the web did. Whereas we would be having this same conversation with the letters swapped if corporations were the ones to spawn the WWW instead of the way it came about.

The reason is the same. There is only one war.

@tyler @Magister To add to this: Writing code is easier than reading code.This is why we have comments: understanding the intent helps guide the reader and makes it easier to see where the implementation does not match the intent.This is one of the biggest reasons why we have code review: so that at least one person has read the code before it lands, to make sure that it is possible to understand it by reading it. Reading code is harder than writing code even when that code is written by someone who is trying to make it clear what the code does and why. LLMs make it easier to write code but harder to read because they produce statistically plausible code, which means code that looks right. Spotting bugs is harder. The code is often commented but the comments don’t reflect what it does, they direct you to misunderstand what the code does in subtle ways.But even if they generated code that is as easy to read as a good human programmer, they would not be solving the right problem.And, yes, LLMs can ‘explain’ code. And sometimes those explanations are correct and not subtly misleading. Often enough that they allow skills to atrophy, but not often enough that they’re trustworthy. They are a perfect example of The Paradox of Automation.

I'm not a kid doing some vibe coding, of course I know what I want and basically know how to implement it, the AI is just helping me writing it, auto-complete and all.

I'm sorry but to me your comment is a bit misguided. You raise extremely valid point and are completely right in what you say, and yet all your argument fails to prove that software development hasn't changed.

The difficult part was understanding requirements and problem solving: absolutely true. Yet most of the time of a developer was spent in writing code. Now it's spent refining the analysis so that the LLM stops producing slop. And many programmers are doing it, even with all its downsides, because for them the fun part is understanding the requirements and problem solving, not writing code nor delivering fast. They are delegating those tasks to a machine, even with all the risks and issues.

Your second point (and the anecdote) further proves how programming changed. Before it was unthinkable that some random person, likely with no clue about what they are doing, would refactor an entire codebase in a night.

Both are massive changes. For the best? Arguably not, but I seriously doubt there will be any going back now.