And the irony is that AI usage should make onboarding juniors easier.

Before it was "hey $senior_programmer where's the $thing defined in this project?", which either required a dedicated person onboarding or someone's flow was interrupted - an expected cost of bringing up juniors.

Now a properly configured AI Agent can answer that question in 60 seconds, unblocking the Junior to work on something.

And no, it doesn't mean Juniors or anyone else get to make 10k line PRs of code they haven't read nor understand. That's a very different issue that can be solved by slapping people over the head.

Or if code quality stops mattering, in a kind of "ok, the old codebase is irretrievably sphagettified. Lets just have the chatbot extract all the requirements from it, and build a clean room version" kind of way. It's also not impossible we go that route.

How wildly dismissive of the foundation of the X$ billion dollar software industry. You think humans just stumbled into writing code by accident or something?

How does building agentic systems, a "really hard" problem, not just end up a "regular code" problem? Because that is what it is. A distributed systems problem with non-deterministic run lengths. How do you switch agent contexts? Similar to how you solve regular program context switching. How do you search tool capabilities and verify them? How do you effectively manage scheduled tasks?

Oh, look, you've just invented the operating system kernel. Suddenly, those 'dozen or two' experts don't seem so archaic after all!

> How many kernel devs does the world need? A dozen or two?

You're low by several orders of magnitude. "The 2025 development cycle saw 2,134 developers contribute to [Linux] kernel 6.18" [1]

[1] https://commandlinux.com/statistics/linux-kernel-contributor...

Does it even make sense to build everything on top of machines that are 70% reliable? The sheer orchestration and validation overhead at scale risks being more expensive than just keeping most software engineers and having them manage a few AI agents.

Also, 200 years ago we didn't have bike mechanics. Car mechanics. Boat mechanics. Plumbers. Electricians. Not all new professions fade away.

Learning calculus by watching the professor solve integrals on the board for an hour doesn't result in the same level and depth of understanding as working through homeworks every week for a semester. If you ran off to your TA to solve every problem in your homework, you just won't learn calculus.

I've vibe coded plenty. I mostly don't look at the crap coming out. Don't want to. When I do I absorb a tiny bit, but not enough to recreate the thing from scratch. I might have a modicum more surface-level knowledge, but I don't have deep understanding and I don't have skills. To the extent that I've fixed or tweaked AI-generated code, it's not been to restructure, rearchitecture, or refactor. If this is all I did day in and day out, my entire skillset would atrophy.

I think there's a considerable difference in its ability to help with breadth vs. depth of expertise.

For me both are true at the same time.

I vividly remember understanding how calculus works after watching some 3blue1brown videos on youtube, but once I looked at some exercises I quickly realized I was not able to solve them.

Similar thing happens with LLMs and programming. Sure I understand the code but I'm not intimately familiar with it like if I programmed it "old school".

So yes, I do learn more but I can't shake the feeling that there is some dunning kruger effect going on. In essence I think that "banging my head against the wall" while learning is a key part of the learning process. Or maybe it's just me :D

What do you mean by "LLM coding"? That's not a very meaningful term, it covers everything from 100% vibe coded projects, to using the LLM to gradually flesh out a careful initial design and then verifying that the implementation is done correctly at every step with meticulous human review and checking.

> Anyone not learning things via LLM coding right now either doesn't care at all about the underlying code/systems

How many bytes is a pointer in C? How many bytes is a shared pointer in C++? What does sysctl do? What about fsync?

What is a mutex lock? How is it different from a spin lock?

You want to find the n nearest points to a given point on a 2-D Cartesian plane. Could you write the code to solve that on your own?

Can you answer any of these questions without searching for the answer?

I don't use LLMs and I learn things fine. Always have. For several decades. I care deeply about the underlying code and systems. It annoys me when people say they do and they cannot even understand how the computer works. I'm fine with people having domain-specific knowledge of programming: maybe you've only been interested in web development and scripting DOM elements. But don't pretend that your expertise in that area means you understand how to write an operating system.

Or worse: that it prevents you from learning how to write an operating system.

You can do that without an LLM. There's no royal road. You have to understand the theory, read the books, read the code, write the code, make mistakes, fix mistakes, read papers, talk to other people with more experience than you... and just write code. And rewrite it. And do it all again.

I find the opposite is true: those who use LLM coding exclusively never enjoyed programming to begin with, only learned as much as they needed to, and want the end results.

This. I never had patience to figure how to build a from-scratch iOS app because it required too much boilerplate work. Now i do, and i got to enjoy Swift as a language, and learned a lot of iOS (and Mac) APIs.

Because they may have studied history... There are countless examples of eras of lost technology due to a stumble in society. Where those societies were never able to recover the lost "secrets" of the past. Ultimately, yes, humans can rediscover/reinvent how to do things we know are possible. But it is a very real and understandable concern that we could build a society that slowly crumbles without the ability to relearn the way to maintain the systems it relies upon, fast enough to stop it from continued degradation.

Like, yeah, you have the resources right now to boot strap your knowledge of most coding languages. But that is predicated on so many previous skills learn through out your life, adulthood and childhood. Many of which we take for granted. And ultimately AI/LLM's aren't just affecting developers, they are infecting all strata of education. So it is quite possible that we build a society that is entirely dependent on these LLM's to function, because we have offloaded the knowledge from societies collective mind... And getting it back is not as simple as sitting down with a book.

I imagine it being a "does anybody know COBOL?!" but much sooner than sixty years rom now.

>"That's only a brief moment in time. We learned it once, we can learn it again if we have to. "

Yes we can but there is a big problem here. We will "learn it again" after something breaks. And the way the world currently functions there might not be a time to react. It is like growing food on industrial scale. We have slowly learned it over the time. If it breaks now with the knowledge gone and we have to learn it again it will end the civilization as we know it.

We found 500 zero-days in ten year old widely used open-source projects. Was that not a demonstration of the catastrophic failure of human debugging capability?