For most use cases, you don't actually need frontier performance either. Customization, cost, and data sovereignty are far bigger practical concerns. If you can run your own model on prem and tune it exactly what you need, then you're both saving money and getting better quality output.
It's also wroth noting that tooling can go a long way to improve the quality of output from the models as well, and this is very much an under explored area right now. For example, ATLAS agentic harness does a clever trick where it gets the model to generate multiple candidates then uses a second lightweight model as a heuristic to score them keeping the promising ones. And this drastically improves coding capability.
https://github.com/itigges22/ATLAS
There's also a paper along similar lines discussing how using a harness to force a project structure also allows it to work on much larger projects successfully.
https://arxiv.org/abs/2509.16198
So, I don't think that raw power of the model is even the most important part at this point. We can squeeze a lot more juice out of smaller models we can run locally by using them more effectively.
We're basically in the mainframe era of this tech, but the pendulum always swings to tech getting more optimized and moving to edge devices over time. And I think we're already starting to see this happen with local models becoming good enough to do real work.
And there are also occasional statements like the one by Airbnb here disclosing what they use https://www.bloomberg.com/news/articles/2025-10-21/airbnb-ce...
"Chinese models are what pretty much every AI company in the US is using now" - just untrue. you think people inside Cursor use composer for most of their work? haha
the talent at the labs far surpasses the global research community its just not comparable
I'm not saying I prefer it this way, I want open source to do well but it's just not happening at the current pace
The idea that the talent in the US surpasses the global research community is laughable. China already tops the world in artificial intelligence publications. https://www.science.org/content/article/china-tops-world-art...
China also has a population of 1.4 billion people, and an excellent education system. Pretty much all top universities are Chinese. https://www.nature.com/nature-index/institution-outputs/gene...
And let's not forget that top AI researchers from US are now fleeing to China. https://www.scmp.com/news/china/science/article/3353398/lead...
Not denying that China is a close #2 btw.
And specifically to AI, practically all major innovation that's been published and is used in the wild comes from Chinese companies. Before DeepSeek, everybody just assumed you needed a gigantic date centre to train models. Qwen is showing that you can get near frontier quality on your desktop. Nothing of the sort is coming out from the US.
And frankly when you look at the recent report from Stanford, it's embarrassing af for the US. Look at the chart on how much money is going into AI in US relative to China, and then at the chart showing how there's practically no difference in quality of the models. The only thing the US is ahead in is burning through capital like there's no tomorrow.
https://hai.stanford.edu/news/inside-the-ai-index-12-takeawa...
And that's in the US, the rest of the world is all using Chinese models as well. Which means these models get far more collaboration from the global research community being developed in the open. They will set the standards in terms of how APIs work. And they will be what everyone uses going forward.
The closed approach simply can't compete with that. The same way Linux destroyed Windows on servers, open AI models will destroy proprietary solutions as well.