I think it just needs to be efficient or small enough for companies to deploy their own models on their hardware or cloud, for more inference providers to come out of the woodwork and compete on price, and/or for optimized models to run locally for users.

Regarding the latter, smaller models are really good for what they are (free) now, they'll run on a laptop's iGPU with LPDDR5/DDR5, and NPUs are getting there.

Even models that can fit in unified 64GB+ memory between CPU & iGPU aren't bad. Offloading to a real GPU is faster, but with the iGPU route you can buy cheaper SODIMM memory in larger quantities, still use it as unified memory, eventually use it with NPUs, all without using too much power or buying cards with expensive GDDR.

Qwen-3.5 locally is "good enough" for more than I expected, if that trend continues, I can see small deployable models eventually being viable & worthy competition, or at least being good enough that companies can run their own instead of exfiltrating their trade secrets to the worst people on the planet in real-time.

Depends on your circle. On HN I would argue that there are still a fair number of people that would be surprised to see what heavy organizational usage of AI actually looks like. On a non programming online group, of which I am a member of several, people still think that AI agents are the same as they were in mid 2025 and they can't answer "how many R's are in the following word:". Same thing even when chatting with my business owner friends. The majority of the public has no clue of the scale of recent advancement.