Does anybody actually know whether there's a limit to the complexity LLMs are capable of dealing with in a codebase? It's very obvious that they don't write code that is suitable for people to understand it (and it's gonna get worse and worse the more RL is used to train these models), but if there isn't a point at which LLMs also struggle due to the complexity they introduce, then I'm not sure it really matters anymore for a large part of non safety-critical software. I really hope there is, because steering them is, I feel, one of the last competencies through which I can still add value, but is there actually evidence that these models struggle more with poorly maintained code?
> I really hope there is, because steering them is, I feel, one of the last competencies through which I can still add value
Something as simple as output length is a hard linear floor for productivity, even putting aside the obvious context problems that you're intuiting, and it's far from being the biggest cost that arises from steering skill. Learning to make a smaller, faster model do the same work with less tokens is a technical domain that a lot of people don't seem capable of learning. I'm not just talking about "context engineering", but learning how to fine tune, post-train, create better harnesses, design inference setups, etc. If we're both using AI, but I'm beating you to market every single time and with a better product, what is your AI usage actually buying you? Yes, competency and skill is this meaningful right now, and it's highly technical. Not the least of which because you know how to describe the problem in way that gives it a smaller solution and requires less iteration.
Most of the labor who understand the technology enough to do those things lives at the companies selling you these services, but you can absolutely learn to do these things yourself right now. It's actually really fun! A hell of a lot more fun than fucking prompting that's for sure.
Where we're at, I would equate it to the early mainframe era where the programmers came with the computer. I'm placing calls that we follow a similar track and the two will end up decoupling, that "model engineers" are going to move in-house. OpenAI will have a ring to it like IBM does today.
Yeah, exactly my experience. Especially ever since Fable came out, I felt paralysed, many times, about how many decisions I have to hand it. Which is 10x harder when you handed it the reins to build the PR and you practically only knew the compressed initial problem statement. Then it stands up a draft PR and says: "Decisions you owe me: [insert bulleted list with 8 items each a paragraph long]" and you are like: "...oh shit".
It becomes a very heavy and difficult exercise of it walking you through the implementation and the judgment calls it had to make along the way. VERY exhausting.
There's a silver lining though: you do get to gradually clarify a proto project spec and various requirements, but boy does it take time and energy to re-contextualise when the bot tells you that you should make decisions.
