I just noticed something interesting in the AI ecosystem these days. The Chinese tech giants have finally decided to seriously enter the game of global models. Alibaba and Tencent recently launched their proposals, Happy Oyster and HY-World 2.0, aiming to build systems that better understand how the physical world works.

The curious thing is that this is not just a movement from China. World Labs and AMI Labs have also just closed billion-dollar funding rounds. Clearly, money is flowing into this space, but here’s what intrigues me: no one seems to have a clear idea of what exactly a global model is.

The industry is divided. Some talk about 3D reconstruction, others about causal reasoning, and some about completely different things. Without clear technical standards, it’s impossible to compare which solution works best. Evaluations are inconsistent, and each project measures its progress with its own rule.

Moreover, there are unresolved issues that are quite serious. The shortage of high-quality training data remains a bottleneck. Physical accuracy in simulations is still imprecise. And then there’s the topic few mention: who is responsible when these systems fail? Ethical guidelines are practically nonexistent.

I think about applications like autonomous driving or critical industrial operations. If a global model makes a mistake, the consequences can be real. It’s the kind of technology that needs solid accountability frameworks before scaling up. For now, the tech giants are rushing ahead, but the industry needs to slow down a bit to establish firmer foundations.