[samdjstephens]

If LLMs stopped improving today I’m sure you would be correct- as it is I think it’s very hard to predict what the future holds and where the advancements take us.

I don’t see a particularly good reason why LLMs wouldn’t be able to do most programming tasks, with the limitation being our ability to specify the problem sufficiently well.

[maccard]

I feel like we’ve been hearing this for 4 years now. The improvements to programming (IME) haven’t come from improved models, they’ve come from agents, tooling, and environment integrations.

[bigiain]

> I feel like we’ve been hearing this for 4 years now.

I feel we were hearing very similar claims 40 years ago, about how the next version of "Fourth Generation Languages" were going to enable business people and managers to write their own software without needing pesky programmers to do it for them. They'll "just" need to learn how to specify the problem sufficiently well.

(Where "just" is used in it's "I don't understand the problem well enough to know how complicated or difficult what I'm about to say next is" sense. "Just stop buying cigarettes, smoker!", "Just eat less and exercise more, fat person!", "Just get a better paying job, poor person!", "Just cheer up, depressed person!")

[elAhmo]

Both is true, models have also been significantly improved in the last year alone, let's not even talk about 4 years ago. Agents, tooling and other sugar on top is just that - enabling more efficient and creative usage, but let's not undermine how much better models today are compared to what was available in the past.

[dwohnitmok]

> The improvements to programming (IME) haven’t come from improved models, they’ve come from agents, tooling, and environment integrations.

I disagree. This almost entirely model capability increases. I've stated this elsewhere: https://news.ycombinator.com/item?id=46362342

Improved tooling/agent scaffolds, whatever, are symptoms of improved model capabilities, not the cause of better capabilities. You put a 2023-era model such as GPT-4 or even e.g. a 2024-era model such as Sonnet 3.5 in today's tooling and they would crash and burn.

The scaffolding and tooling for these models have been tried ever since GPT-3 came out in 2020 in different forms and prototypes. The only reason they're taking off in 2025 is that models are finally capable enough to use them.

[PaulRobinson]

LLM capability improvement is hitting a plateau with recent advancements mostly relying on accessing context locally (RAG), or remotely (MCP), with a lot of extra tokens (read: drinking water and energy), being spent prompting models for "reasoning". Foundation-wise, observed improvements are incremental, not exponential.

> able to do most programming tasks, with the limitation being our ability to specify the problem sufficiently well

We've spent 80 years trying to figure that out. I'm not sure why anyone would think we're going to crack this one anytime in the next few years.

[eru]

> Foundation-wise, observed improvements are incremental, not exponential.

Incremental gains are fine. I suspect capability of models scales roughly as the logarithm of their training effort.

> (read: drinking water and energy)

Water is not much of a concern in most of the world. And you can cool without using water, if you need to. (And it doesn't have to be drinking water anyway.)

Yes, energy is a limiting factor. But the big sink is in training. And we are still getting more energy efficient. At least to reach any given capability level; of course in total we will be spending more and more energy to reach ever higher levels.

[majormajor]

> the limitation being our ability to specify the problem sufficiently well

Such has always been the largest issue with software development projects, IMO.