rafaelmn

But if that 20% is not the bottleneck then at most your market is replacing the labor required to get that 20% to the next phase, plus you will probably not be able to capture full value here because of lab/model competition.

Compared to Air 2 :D ?

Reading boost libraries was like reading brainfuck. In fact most of std lib source felt that way too. I never reflected on this since I stopped doing C++ 15 years ago - but yeah I have not worked with a language that's worse to read since. Maybe macro heavy lisp ?

That's why OpenAI hard pivoted from consumer tech into enterprise ?

The only AI that makes money is enterprise. Being open weights/self-hostable is a huge win before the pricing even enters the conversation, but pricing is also adding a ceiling on what US labs get to charge. And stuff like powering other software also works better with fine-tunes, hence legal models, etc.

Agents are pretty shit at making UX decisions unless you're heavily in the training distribution

Depends - if the model asks for permission and you approve - yes. If it's auto mode or something like that then it's probably on authors despite what they claim in ToS/EULA

Realistically once models are good enough at this most of us are out of a job.

Embedding@Work

TBH I'm seeing the opposite, I have a legacy codebase where the people originally writing it were incompetent and inexperienced, but the project took off. It's scaling up but the foundation is shit and has to be gradually rebuilt. Every AI slop commit is still better than the underlying crap.

And this is the pattern I've seen on most (semi)successful projects I've worked on in the past - I'd say correlation between financial success and code quality is 0 (up to a point where the whole thing doesn't fall apart). Once scale (both in load and in code size/features) starts mattering you're stuck building on a foundation of shit. LLMs are very good at identifying and cleaning up said shit layers, as long as you're steering them towards a desireable outcome.

> Your code used to be a masterpiece

Do you even see anything underneath that rose tint ?

A smarter agent can write a better compiler and leave the intent in densely packed/verifiable language structure that can encode propositions low level code cannot.

This is such a bad take given the current LLM agents

High level languages :

  - compress low level patterns into high level abstractions which save context/token
  - express higher level meaning through things like names
  - express and enforce constraints through type system

LLMs are context constrained, do really well with tools that help them iterate towards a correct solution (type systems) and work on human language (although they are compressing it to be more token efficient).

Why would you take something compilers are really good at (translating high level concepts into low level hardware instructions) and bake that into LLMs

If anything LLM programming language will be something like high level IR that's token optimized, but honestly the AI labs are so lazy (or bad) at fundamental engineering (look at their sandboxing solutions LOL), they are going to keep pumping training on shell/python/rust RL environments.

No, not nearly the same living standard.

Entirely different generation

You do realize that St Petersburg dot on the map is Russia ?