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Comment by hombre_fatal | Hacker News Reader

While true, I'm not sure it matters.

It kinda seems like wishful thinking on our part as software engineers that one day we'll be able to go "Hah, miss me now?" but that just isn't the writing that I see on the wall.

Over the past year, I've AI-generated two large pieces of software over 2000 commits without having much depth in either domain, and my ability to deliver value as a veteran software engineer has only shrunk over the months or it can be trivially extracted into a reusable markdown file like "design bar: ensure things 'by construction' where possible", a lesson I learned viscerally over decades.

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> without having much depth in either domain

I don't mean to pick on you, but to present this in the service of this kind of fatalism... you know the inevitable follow-up question, right?

Is it: "How do you know the solution is good?"

Because I can still reason and dig into things and ask questions and have the domain explained to me out of curiosity, the same way I could build anything that was originally out of my depth.

But what I've noticed is that the need for any corrective power has gone or is going to nil and I'm mainly doing directional work. And even then, I can constantly have sota models "rank the top options and recommend one", and it's almost always the right way forward.

I used to read every line of every plan that LLMs generated, but over time I've realized I have less and less to correct or, more impressively, the LLM had foresights I never considered. The same thing happens with implementation.

Periodically I can spawn a bunch of agents to evaluate the system adversarially to find improvements, and the findings have been so good that it's evident that soon I can just automate that too.

In other words, the skills I need to excel here are more curiosity and patience than tech expertise -- the things that got me into software engineering in the first place since that was the only way to build things.

You got it. You may well be right, but from my standpoint it's hard to see how you'd know if you weren't. Yeah, the models fill in vagueness with detail sourced from "what's likely and helpful". (In one of my particular domains, that's "use SHAP values for everything everywhere".) Yeah, that detail's increasingly unlikely to be "wrong" in a way that you could explicitly argue. Is it what an expert would come up with? Is it the best it could be? Is there something else that we would have done had we been forced to think it through? Will it ever be _surprising_?

I know, all irrelevant little philosophical roadbumps. The artifact satisfies the buyer. I think I'm going to have to step away from this industry pretty soon, at least for a while.

Yeah, I feel you.

A lot of my nonchalance here is from dealing with an immense sense of loss for the end of my identity-level skillset/profession, then acceptance, then deciding to push the envelope on AI-generated software to see what I can pull off since the start of this year.

Focusing on creating things that I want has been an antidote to the melange/uncertainty for the future.

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