To one camp in this discursive space, who of course see themselves to be ever the pragmatists, the essence of the polemic about whether LLMs can "think" is not about whether they think in exactly the same ways we do or capture the essence of human thinking, but whether it matters at all.
I'm not suggesting anything so arrogant as that I cannot be fooled by someone intentionally deploying an LLM with that aim; if they're trained on human input, they can mimic human output, I'm sure. I just mean that the formulations that come out of the mainstream public LLM providers' models, guided however they are by their pretraining and system prompts, are pretty unmistakably robotic, at least in every incarnation I've seen. I suppose I don't know what I don't know, i.e. I can't rule out that I've unknowingly interacted with LLMs without realising it.
In the technical communities in which I move, there are quite a few forums and mailing lists where low-skilled newbies and non-native English speakers frequently try to disgorge LLM slop. Some do it very blatantly, others must believe they're being quite sly and subtle, but even in the latter case, it's absolutely unmistakable to me.
Yes, there's a more streamlined interface to allow them to do things, but that's all it is. You could accomplish the same by copy-and-pasting a bunch of context into the LLM before and asking it what to do. MCP and other agent-enabling data channels now allow it to actually reach out and do that stuff, but this is not in itself a leap forward in capabilities, just in delivery mechanisms.
I'm not saying it's irrelevant or doesn't matter. However, it does seem to me that as we've run out of low-hanging fruit in model advances, the hype machine has pivoted to "agents" and "agentic workflows" as the new VC-whetting sauce to keep the bubble growing.