Besides, if we start imagining super models US companies have in secret, why would we expect Chinese companies have them? Why would not Moonshot have their own super intelligence hidden in the cellar?
You can see in this discussions the goalposts being actively moved to desperately create a notion that the lead is still there.
You even see people in this thread referring to imaginary wonder models that they expect US companies to have under development that are supposedly leaps and bounds more impressive, albeit the only reference there is to their existence is the commenters hopes and dreams.
I think people need to realize that there is no longer a cutting edge company that is expected to dominate, and among the multitude of AI companies in the field competition is so tight that no one would even be surprised if tomorrow some other company far away from the US was able to crank out the top performing model, or something like a small local model that outperforms mid/high tier frontier models. The local model alone would be a death sentence to US companies, who are still burning through 10x their revenue.
It’s incredible how little GPU compute they have to work with compared to the US frontier labs.
Two years ago, tool calling was always considered an extra feature on top that some models where good at while other models completely ignored tool calling.
Meanwhile the discourse about how intelligent these models have become and how this is a major step or shift in model intelligence would make you believe that these models suddenly have a more powerful architecture when in practice model optimization has lead to faster but less expressive models that are still more intelligent, meaning that the model architectures didn't really experience meaningful shifts, it's that the training methods had a massive shift away from supervised training which inherently constrains the models.
The AI labs don't have a monopoly on the outcomes of RL training because RL is significantly less dependent on private data. Data helps massively for bootstrapping, but beyond post training, it's up to the model itself and every AI lab has a model so it is much harder to differentiate between your competitor if the RL training environments are the same (I specifically mean coding harnesses and Linux command line tools) because the environment here is the source of the "synthetic" data and everyone has equal access to it.
> Open-weight principles. These evaluations frame our open-weight release decision. First, we aim not to release open weights that would extend the absolute frontier of cyber capability. K3.1 Pro's offensive cyber capability is, by design, below what our pipeline can produce, and remains behind that of frontier proprietary models. Second, we intend to progressively raise the cyber capability available in open weights. We believe making K3.1 Pro available in a non-discriminatory way benefits people who need dual-use capabilities for defensive purposes. When a capable model is open and runs on infrastructure its users control, many more teams gain the means to audit the code they maintain — including legacy systems that rarely receive dedicated security reviews. We believe that letting this capability diffuse gradually — rather than concentrating it in a few proprietary systems — ultimately makes all software more secure.
I don't think Chinese labs are behind at all. I think the tell is that they keep releasing models 2 weeks after new American proprietary models that match or beat them. Suggesting they're sitting on much higher capabilities and releasing just enough to undercut