Comment by mrinfinitiesx
Comment by mrinfinitiesx 4 days ago
I can know literally nothing about a programming language, ask a LLM to make me functions and a small program to do something, then read documentation and start building off of the base immediately, accelerating my learning allowing me to find new passions for new languages and new perspectives for systems. Whatever's going on in the AI world, assisting with learning curves and learning disabilities is something it's proving strong in. It's given me a way forward with trying new tech. If it can do that for me, it can do that for others.
Diminishing returns for investors maybe, but not for humans like me.
If you "know literally nothing about a programming language", there are two key consequences: 1) You cannot determine if the code is idiomatic to that language, and 2) You may miss subtle deficiencies that could cause problems at scale. I’ve used LLMs for initial language conversion between languages I’m familiar with. It saved me a lot of time, but I still had to invest effort to get things right. I will never claim that LLMs aren’t useful, nor will I deny that they’re going to disrupt many industries...this much is obvious. However, it’s equally clear that much of the drama surrounding LLMs stems from the gap between the grand promises (AGI, ASI) and the likely limits of what these models can actually deliver. The challenge for OpenAI is this: If the path ahead isn’t as long as they initially thought, they’ll need to develop application-focused business lines to cover the costs of training and inference. That's a people business, rather than a data+GPU business. I once worked for an employer that used multi-linear regression to predict they’d be making $5 trillion in revenue by 2020. Their "scaling law" didn’t disappoint for more than a decade; but then it stopped working. That’s the thing with best-fit models and their projections: they work until they don’t, because the physical world is not a math equation.