Nvidia identifies three strategic directions for AI development: from reasoning to protein recognition

At Davos, Nvidia CEO Jensen Huang presented an extensive analysis of the major achievements in artificial intelligence over the past year. His report covered three critical areas that are reshaping the industry and expanding AI capabilities beyond traditional language processing. In particular, Huang emphasized progress in understanding proteins and molecular structures, opening new horizons for biomedical research.

Transformation of AI from Theory to Practical Application

Throughout 2025, the industry witnessed a radical change in the quality of AI models. While previously these systems suffered from frequent hallucinations and inaccuracies, they now demonstrate true logical reasoning, planning, and solving complex problems. This is not just a quantitative improvement — it’s a qualitative leap in technological development.

The practical application of these capabilities in scientific research has become a turning point. AI has begun to serve not just as an assistant but as a genuine research agent capable of independently formulating hypotheses, conducting analysis, and proposing solutions. Thus, a new paradigm was born — Agent AI, fundamentally changing the approach to solving complex scientific problems.

Democratization of AI through Open Ecosystems

The second significant breakthrough is related to the launch of the first large-scale open inference model — DeepSeek. This solution revolutionized access to advanced AI technologies for a broad range of users. In contrast to closed commercial systems, open models allowed companies, research institutions, and educators to adapt AI to their own needs.

Since then, the open model ecosystem has been rapidly developing. This created a network effect, where each new innovative development accelerates the emergence of the next. Today, researchers and developers around the world have real access to cutting-edge technologies that were previously the privilege of large corporations.

Physical AI Recognizes Proteins and Molecular Reality

The third area of progress presents the greatest potential for the future — the development of physical AI. Unlike language models, this technology not only processes text but understands the physical nature of the world.

Physical AI can analyze and recognize biological proteins, understand their structures and functions. This is especially important for medicine and pharmacology, where protein recognition is key to developing new drugs. Additionally, the system understands chemical reactions and interactions between molecules, opening new possibilities for materials science.

At the fundamental physics level, AI has demonstrated the ability to understand concepts such as fluid dynamics, particle behavior in quantum mechanics, and other complex phenomena of nature. This means AI is no longer limited to areas where sufficient textual data exists — it can now work with experimental data and physical process modeling.

These three breakthroughs confirm that AI has entered a new era. From illusions and limitations that models faced just a year ago, the industry has moved to real applications, open access, and deep understanding of physical reality, including protein and molecular structure recognition. Such evolution promises to transform not only the technological industry but also science, medicine, and virtually all fields of human activity.