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.

I think there’s a kind of laundering that goes on. Like if leadership just told devs to go build something (gave them a prompt) and the devs picked some defaults, leadership wouldn’t like it, they’d want some different interpretation of the prompt, there’d be lots of back and forth.

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.

I disagree. I speak in code to the LLMs. It's just that LLMs are really good at reinventing the wheel that you were supposed to in your codebase. Recent example.

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.

I suspect a lot of "the right abstractions" would be fuzzy and opaque things - more alike to modern AI than to anything from the domain of traditional programming. Because the world is just cursed like that.

There is no right abstraction to make runnable human vagueness.

At one point someone have to take "what you think it should do" into defined unambiguous spec that is called "code"

DNA is still too close to molecular biology than the actual problem of harvesting free energy and replicating.

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.