We are teaching machines to do the work.

Capital always prefers machines.

I do not think, the intention in using AI, will ever be gaining wisdom. It is rather moving computations to a larger computing system.

> I'm no luddite, I’ve integrated AI in my everyday work, while actually teaching my colleagues what I’ve learned

It's appalling that we still hold on to such things that are no longer necessary. Code maintainability is not a problem when you don't have to open a file and inspect how something works anymore. You use english to add to it. You sit on chairs everyday where you don't give a shit how they were created. They fulfill their purpose. hopefully the same can be said for your software.

I'm not quite a doomer. I use AI every single day. I also take a fairly negative view of AI generated code, but I think it's unavoidable while remaining employed these days. A fact I find pretty depressing.

I absolutely agree with the author that humans need to be in the loop reviewing and understand the code they're merging, and generally take a "Hey, build X like Y utilizing Z" approach when using AI to build instead of the "Hey, solve this problem" approach. Our PE overlords actually mandate the latter, but I'm not doing it.

However, a point the author misses is that with AI, major refactors become relatively quick. Hours instead of months/years.

Yes, AI can and probably will land you with major foundational and architectural problems, but your architecture isn't set in stone anymore. Your entire codebase bends like a leaf in the wind.

you don't need to read or write code directly to prevent it from becoming an unmaintainable mess. they way you prompt, the loops and skills you build for your harness and the way you set structure around it can get you most of the benefits of reading & writing code manually

Here's the one simple trick that fixes everything:

- Use the code that your agents write in anger.

There you go. Do I know when my agents fuck up? Yes, I absolutely do -- because I'm a user of the code I have my agents write, and I ask things like "why is it taking 50 ms to start this program ..." and then I go in and find stupidity, and excise it. I do this over and over again.

Is it faster than writing it out by hand? Maybe! It's definitely a different perspective.

Start behaving like a baby "why, why, why" and then do a bit of reading, and you'll be fine.

A lot of these blog posts seem like they're aimed at software written by B2B companies who don't even use their own software ...

AI labs should produce AI that can take support calls. But let it stay away from the fun jobs like coding.

"New technology: cool but the supply chain is vulnerable". A tale as old as time.

This is one of those predictions that sound very wise and do well on Hacker News, but will turn out to be completely wrong. For the simple reason that we’re basing all our assumptions on decades of learned behavior predicated on coding being expensive. Writing code has historically been expensive and, by extension, so is maintaining it.

But when you can literally generate a whole codebase in a few hours, a lot of these assumptions don’t hold up. That doesn’t mean you don’t need sound logic and maintainability. But a lot of the small things you think would go wrong because of a bloated codebase stop being meaningful objections when you can keep generating, testing, and reworking it until those issues are resolved.

A lot of what we consider good engineering judgment is shaped by the constraints we’ve always worked under. I don’t think people appreciate how much of that changes when those constraints go away. The "slop" problem is ultimately a verification and testability problem.

"There will be consequences"

And, so what? Software as an engineering discipline has long lacked standardization and regulation to be on par with other engineering disciplines, and the fact that code and all its surrounding ecosystems are not "visibile" or "malleable" makes this extremely hard.

You can use terraform and yaml to define infrastructure that literally spins up machines _somewhere_ in the internet. With all its issues, bugs and associated consequences mostly being ignored.

I just don't understand the difficulty in KNOWING what needs to be done: NO LLM usage in university/grad/high schools, NO LLM usage in the first 3 years of your professional career.

Once the basics are solidly grasped, then they can use it at will.

The problem has never been about wisdom, knowledge or LLMs writing good, bad, maintainable or terrible code. It has always been about the skill level of people using it AND on the fact that people start off-loading basic things to these models that they wouldn't before.

If you have the knowledge and "suffered" through experience to learn the fundamentals, than not using LLMs becomes more deterimental than beneficial.

You just CAN NOT skip the trial by fire of learning and absorbing knowledge on your own. That's all.