LLM utility is exponential with increased LLM capability. Even if improvements to LLMs slow down their impact will continue to increase. And there is no evidence LLMs are slowing down. In fact improvements still seem to be accelerating.
But arithmetically speaking, is any of that true? Is model progress truly "accelerating"? How can you compare the delta from GPT-2 to GPT-3 with the delta from GPT-5 to GPT-6 with no sense of irony? And, at the risk of being quite blunt, do you know the meaning of the term "exponential"?
On the MMLU benchmark, GPT-2 had an accuracy of 32.4%. GPT-3 improved this to 43.9%.
GPT-4 scored 86.4%. On GPQA Diamond, it scored 31%, vs GPT-5 at 86%. GPT-6 scores 96%.
If we take ARG-AGI-2, it would be 9.9% for GPT-5 vs 95% with GPT-6.
The benchmarks do not corroborate the picture you were drawing about improvements slowing down between GPT major versions.
I remember their capabilities like so:
GPT-3 could produce convincing looking texts, sometimes.
GPT-4 was somewhat smarter and would give more accurate answers. At that time image understanding was released, wasn't it?
Then with GPT-5 we have a reasoning model, another jump in capabilities.
Now, compare GPT-6 Astra with its ability to implement software, work on long horizon tasks, and visual understanding.
This is true of every frontier model. The real agentic coding breakthrough in Claude land was around Opus 4.5 or so, though it's hard to say if that might have just been coeval with the release of the Claude Code harness.
On the Chinese front (my preferred landscape because of the open weight aspect, not because I'm Chinese), Kimi has been perfectly viable since K2.5, DeepSeek since however many major releases ago, and GLM and MiMo and the rest of them too, although they all achieved breakout moments of publicity at different times. There's no denying that every release brings improvements, but if we're going to be honest, they're fairly marginal.
You're right that many of the less capable / open weights models are very good themselves, but that's part of showing how much more capable the frontier models are becoming. Cheaper (smaller) models are able to do the same things, in many cases better, as the way larger models were able to do a year ago. The efficiency is getting way better alongside the 'intelligence'. That'll also cause the models to become much faster over time which is the real improvement I'm waiting for.
I think a part of why many people aren't seeing big deltas if they're using it for code and they're actually coders is we actually sit down and reason out the project with the LLM. Someone who knows nothing about coding ~1 year ago wouldn't come even close to the kind of app they'd get now with the same instructions. If you just give it a task like "help me decompile this game" without knowing a whole lot about decompiling suddenly in the last 3 or so months you start to get some half-decent results.
The sea-change I see lately is about lifting up the people who couldn't do these things before more than the people who could. And I actually see problems coming from that already as people I've met think they can make enterprise grade software if they just tell their LLM to make it safe.