The success of LLM's (by usage) tells us that programming languages are still too close to the machine than the actual problem domain as defined by humans.
If we truly had the right abstractions, no one would care to use LLM's for programming.
If we truly had the right abstractions, no one would care to use LLM's for programming.
Somehow when it’s the LLM that makes the choices, everyone is impressed with what AI did. It’s really just whatever defaults have been trained in, but somehow we’re ok with this.
Part of it is better marketing and communication. Basically the defaults of OpenAI and Anthropic are better than what a random dev will pick. But it’s not really that natural language is a better interface, it’s more that having “AI” for now somehow intermediates responsibility so everyone is ok with what it picked, when they probably wouldn’t accept the same if the internal team came up with it. It’s not too different from hiring consultants.
struct TensorView<T>{ body: Arc<[T]>, shape: [usize], stride: [usize], offset: usize, }
Okay now fill in all the helper methods. And GPT 5.6 Sol did a good job.
At one point someone have to take "what you think it should do" into defined unambiguous spec that is called "code"
Our programming languages are far too low level, and have been for a long time.
I've long held this view, LLMs are fairly clear evidence that this is true, because it looks like the much, much more compact prompt(s) have enough information content to create a much larger program in our current languages.
So it should be possible to create a non-natural language with the same information density.
I think we see this pattern over and over and it might just be that the problem domain is a weird projection into more dimensions of complexity than it makes sense to directly model.