A New Law of AI Physics? DeepSeek Proposes Efficiency Over Mass

For years, a fundamental law of AI physics seemed to be that greater intelligence required greater mass—more parameters, more data, more power. Now, DeepSeek is proposing a new law with its V3.2-Exp model, suggesting that efficiency, not sheer mass, is the more elegant path to advanced artificial intelligence.
This new principle is embodied in the DeepSeek Sparse Attention mechanism. It challenges the “brute force” physics of older models by introducing a more refined, targeted application of computational energy. It’s akin to a focused laser beam rather than a massive floodlight, achieving a more precise effect with a fraction of the power.
The first experimental proof of this new law is the 50% reduction in API prices. This is the real-world manifestation of the model’s efficiency, a tangible result that demonstrates the power of its underlying principles. It proves that the “lightness” of the model translates directly into economic value.
This development forces the “physicists” at competing labs like OpenAI and Alibaba to re-examine their own foundational theories. They must now contend with a new model that seemingly bends the old rules of cost and capability, potentially making their own mass-centric approaches look outdated and inefficient.
As an “intermediate step,” V3.2-Exp is just the first published paper on this new field of AI physics. It lays the groundwork for a next-generation architecture that could fully establish this new law, triggering a paradigm shift that will redefine how all future AI models are built.