I believe that's why 90% of the focus in these firms is on coding. There is a natural difficulty ramp-up that doesn't end anytime soon: you could imagine LLMs creating a line of code, a function, a file, a library, a codebase. The problem gets harder and harder and is still economically relevant very high into the difficulty ladder. Unlike basic natural language queries which saturate difficulty early.

This is also why I don't see the models getting commoditized anytime soon - the dimensionality of LLM output that is economically relevant keeps growing linearly for coding (therefore the possibility space of LLM outputs grows exponentially) which keeps the frontier nontrivial and thus not commoditized.

In contrast, there is not much demand for 100 page articles written by LLMs in response to basic conversational questions, therefore the models are basically commoditized at answering conversational questions because they have already saturated the difficulty/usefulness curve.

Whenever we get the locally runnable 4k models things are going to get really awkward for the big 3 labs. Well at least Google will still have their ad revenue I guess.

Depends a lot on the task demands. "Got 95% of the way to designing a successful drug" and "Got 100% of the way" is a huge difference in terms of value, and that small bump in intelligence would justify a few orders of magnitude more in cost.

It probably depends what you're using the models for. If you use them for web search, summarizing web pages, I can imagine there's a plateau and we're probably already hitting it.

For coding though, there is kind of no limit to the complexity of software. The more invariants and potential interactions the model can be aware of, the better presumably. It can handle larger codebases. Probably past the point where humans could work on said codebases unassisted (which brings other potential problems).

I'm seeing a lot of sentiment, and agree with a lot of it, that opus 4.6 un-nerfed is there already and for many if not most software use cases there's more value to be had in tooling, speed, and cost than raw model intelligence.

yeah thats is my biggest issue - im okay with paying 20-30% more but what is the ROI? i dont see an equivalent improvement in performance. Anthropic hasnt published any data around what these improvements are - just some vague “better instruction following"

At normal viewing distance(let's say cinema FOV) most people won't see a difference between 4k and 8k never mind 16k.

And it's not that they "don't notice" it's that they physically can't distinguish finer angular separation.

It's more like, if it gets it right 99% of the time, that sounds incredible.

Until it's making 100k decisions a day and many are dependent on previous results.

I agree, but also the model intelligence is quite spikey. There are areas of intelligence that I don't care at all about, except as proxies for general improvement (this includes knowledge based benchmarks like Humanity's Last Exam, as well as proving math theorems etc). There are other areas of intelligence where I would gladly pay more, even 10X more, if it meant meaningful improvements: tool use, instruction following, judgement/"common sense", learning from experience, taste, etc. Some of these are seeing some progress, others seem inherent to the current LLM+chain of thought reasoning paradigm.

Longer version of the comment https://www.linkedin.com/pulse/imperceptible-upgrade-petko-d...

This will probably happen but I wouldn't plan on it happening soon

yeah there needs to be a corresponding increment improvement in model archetecture.

>IMHO there is a point where incremental model quality will hit diminishing returns.

It's not necessary a single discrete point I think. In my experience, it's tied to the quality/power of your harness and tooling. More powerful tooling has made revealed differences between models that were previously not easy to notice. This matches your display analogy, because I'm essentially saying that the point at which display resolution improvements are imperceptible matters on how far you sit.

Does anyone here use 8k display for work? Does it make sense over 4k?

I was always wondering where that breaking point for cost/peformance is for displays. I use 4K 27” and it’s noticeably much better for text than 1440p@27 but no idea if the next/ and final stop is 6k or 8k?

> IMHO there is a point where incremental model quality will hit diminishing returns.

You mean a couple of years ago?