> Just because there's time between big releases doesn't mean progress stopped.

No, but progress not stopping doesn't mean it's not plateauing. I believe 'plateauing' is understood as the process of approaching a plateau, not being stuck on a plateau already. So, the question is about the rate of progress, not its existence.

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usef- 8 hours ago | parent [-]

I guess we draw a different line then. This year has been full of a lot of great releases so far. Most normal people didn't even use Agents before January. It does not at all feel slower than previous years.

HN commenters have been saying that LLMs plateaued ever since the first ChatGPT release.

6 months ago:

> LLMs are amazing, but they have reached a plateau.

https://news.ycombinator.com/item?id=46109534

1 year ago:

> generative AI has languished in the same place, even in my kindest estimations, for several months, though it's really been years.

https://news.ycombinator.com/item?id=43085885

2 years ago:

> 2024 has seen nothing substantially good and the only notesworthy thing is this article finally hitting into the public consciousness that we are past of the AI peak and beyond the plateau and freefalling has already begun.

https://news.ycombinator.com/item?id=42125888

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Many more that I haven't time to look up.

I think the present just always feels slow.

	▲	jijijijij 4 hours ago \| parent [-]
		It doesn't matter where we draw the line, or what someone else said at some point. I merely pointed out your argument wasn't a sound rebuttal. Criticizing subjective experiences to assess the situation is, but that cuts both ways. > HN commenters have been saying that LLMs plateaued ever since the first ChatGPT release. Your earliest example was 2 years after release, when LLMs were already widely used and there is literally a source to support the claim. Now, you need to show research efforts, time and resource investments, ... are producing proportional results, disproving diminishing returns. If there are diminishing returns, LLMs are plateauing. Also, manual capability extensions, or use/edge case adaptations, which may improve subjective usability are not exactly advances in AI as technology. LLMs still hallucinate. LLms still fundamentally struggle with certain classes of problems (e.g. counting), but you increasingly need to come up with different problem dress-ups because of targeted interventions to manufacture hype and a limited supply of test cases. Can you make the case AI got actually more intelligent, fundamentally? That is, not an increase in case specific usability, but a decrease in fundamental limitations. And is this proportional to improvement efforts?