People are absolutely insane with their takes on AI replacement theory. The complexity of our stacks has grown exponentially since the 70s. Very few people actually comprehend how many layers of indirection, performance, caching, etc. are between their CRUD web app and bare metal these days.

AI is going to increase the rate of complexity 10 fold by spitting out enormous amounts of code. This is where the job market is for developers. Unless you 100% solve the problem of feeding every single third party monitoring tool, logging, compiler output, system stats down to the temperature of RAM, and then make it actually understand how to fix said enormous system (it can't do this even if you did give it the context by the way), then AI will only increase the amount of engineers you need.

> AI is going to increase the rate of complexity 10 fold by spitting out enormous amounts of code.

This is true, and I am (sadly, I'd say) guilty of it. In the past, for example, I'd be much more wary about having too much duplication. I was working on a Go project where I needed to have multiple levels of object mapping (e.g. entity objects to DTOs, etc.), and with LLMs it just spit out the answer in seconds (correct I'd add), even though it was lots and lots of code where in the past I would have written a more generic solution to prevent me from having to write so much boilerplate.

I see where the evolution of coding is going, and as a late middle aged developer it has made me look for the exits. I don't disagree with the business rationale of the direction, and I certainly have found a ton of value in AI (e.g. I think it makes learning a new language a lot easier). But I think it makes programming so much less enjoyable for me personally. I feel like it's transformed the job more to "editor" from "author", and for me, the nitty gritty details of programming were fun.

Note I'm not making any broad statement about the profession generally, I'm just stating with some sadness that I don't enjoy where the day-to-day of programming is heading, and I just feel lucky that I've saved up enough in the earlier part of my career to get out now.

I do not look forward to the amount of incompetence and noise that increasing adoption of these tools will usher in. I've already had to deal with a codebase in which it was clear that the author fundamentally misunderstood what a trie data structure was. I was also having an difficult time trying to talk to them about the implementation and their misconceptions. lo and behold I eventually find out the reason they chose this data structure was because they asked ChatGPT what to do and they never actually understood, conceptually, what they were doing or using. This made the whole engagement with the code and process of fixing things way harder. Not only did I now have to fix the bunk code, I also had to spend significant time disabusing the author of their own misunderstandings...

I've yet to see an LLM response or an LLM generated diff that suggests removing or refactoring code. Every AI solution is additive; new functions, new abstractions added in every step. Increased complexity is all but baked into the system.

Software engineering jobs involve working in a much wider solution space - writing new code is but one intervention among many. I hope the people blindly following LLM advice realize their lack of attention to detail and "throw new code at it" attitude comes across as ignorant and foolish, not hyper-productive.

I agree they cannot handle a complex codebase at all at this moment in time.

But I think I would rather just end my career instead of transitioning into fixing enormous codebases written by LLMs.

The complexity has grown but not the quality. We went from writing ADA code with contracts and all sorts of protections with well thought architectures, to random crap written in ReactJS in web sites that now weigh more than a full install of Windows 95.

I’m really ashamed of what SWE has become and AI will increase that tenfold as you say. We shouldn’t cheer up on that, especially if I will have to debug all that crap.

And if it increases the number of engineers, they won’t be good due to a lack of education (I already experience this at work). But anyway I don’t believe it, managers will not waste more money on us, that would go against modern capitalism.

I expect pretty much the opposite to happen: it makes sense for languages, stacks and interfaces to become more amenable to interfacing with AI. If a machine can act more reliably by simplifying its inputs at a fraction of the cost of the equivalent human labour, the system has always adjusted to accommodate the machine.

The most obvious example of this already happening is in how function calling interfaces are defined for existing models. It's not hard to imagine that principle applied more generally, until human intervention to get a desired result is the exception rather than the rule as it is today.

I spent most of the past 2 years in "AI cope" mode and wouldn't consider myself a maximalist, but it's impossible not to see already from the nascent tooling we have that workflow automation is going to improve at a rapid and steady rate for the foreseeable future.

I wonder if anyone is applying AI to cobol…