> This has been discussed a lot, but the generated code is nowhere close to good enough for large projects where you really need intelligence.

> Except that it's not linear scaling. The larger NLP models consume absurdly large resources, it's not straightforward to "get enough representational power"

When allowing maximizers to run wild, just like reinforcement learning, they will find hidden solutions, and when the model can provide an action in the form of a dense representation, it can also use code generation models with much more precision that we do because it can skip the encoding part.

> Also, most models fail to adapt to new tasks outside of their narrow training scope, that's a massive problem. Even if you make models large, you will find that getting data covering all edge cases is exponentially expensive.

We are still 6-7 years in. Deepmind's last paper on general agents has them generalizing to new tasks relatively easily. It's still not there, but we miles ahead than we were 5 years ago.

Or the whole movie?

And comedy, could the AI do standup comedy? Where the jokes are generated by it, rather than the human?

The current fastest production car in the world (and of all time) is an electric car, but most cars are still not electric. That doesn't mean gas cars are "better cars" than electric ones.

The most interesting thing Tesla is doing to make "acceptable" FSD possible is to open an insurance company.

So commercial acceptance is proxy for capability, but it is not immune to regulatory moat building (or alternatively, go the other way - like, force 80+ year olds to drive enhanced cars).

There's some ways around it, but that problem isn't a technology one.