Jensen Huang's comments at the Davos Conference are quite intriguing. He points out some highly suggestive insights regarding the three major breakthroughs in AI models over the past year.

First, the emergence of Agentic AI. Until now, AI models had remained in a theoretical stage, but with the approach of agent-based models, practical implementations in research are becoming a reality. As Jensen Huang notes, this is not just a technological evolution but a significant turning point in the commercialization of AI.

Second, the expansion of open-source models. The advent of open-source inference models like DeepSeek has revitalized the entire ecosystem. There is a shift from predominantly closed models to an open competitive environment. Jensen Huang also seems to emphasize this trend.

Third, physical AI. The evolution of AI that understands complex phenomena in the real world, such as proteins, chemistry, and physics, beyond just language. This can be applied to biological problem-solving and holds great potential in fields like medicine and materials science.

Listening to Jensen Huang's analysis, it becomes clear that AI's evolution is not merely about increased computational power but a shift toward multi-dimensional understanding. These three axes are likely to be key indicators for future trends.