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.

With that kind of workflow, you might be faster with AI autocomplete :)

Yep! I do use Codex CLI

The lengths some of y'all will go to to avoid using JetBrains products.

I doubt anything other than an LLM auto complete could add something like a tensor reshape

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.

But your job is to write the abstractions. Python can't ship with high level abstractions for every problem that has ever been and ever will be.

Yes.

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.