At the 2026 World Economic Forum Annual Meeting in Davos, Zhang Yuting, President of Moonshot's Kimi, shocked the global tech community with a bold statement: Kimi successfully launched globally leading open-source large models such as Kimi K2 and Kimi K2 Thinking, using only about 1% of the computing resources of top U.S. AI laboratories, and surpassed mainstream closed-source models in some key performance indicators.

This statement not only highlights the extreme pursuit of efficiency and innovation by Chinese AI teams, but also reveals a technical path different from "computing power accumulation" - AI development driven by engineering thinking. Zhang Yuting emphasized that the Kimi team invested a lot of effort into "making algorithms truly run in production systems": from training stability, inference latency to multi-task generalization ability, each research was based on large-scale deployment, ensuring that the model is not only SOTA in papers, but also a reliable tool in users' hands.

This "research for practical application" philosophy has enabled Kimi to achieve remarkable breakthroughs with limited resources. For example, Kimi K2 Thinking excels in complex task chain processing and long context consistency through an innovative reasoning architecture; its open-source strategy has also accelerated community feedback and iteration, forming a virtuous cycle.

Zhang Yuting also revealed that Kimi's latest generation model will be released soon, further enhancing multi-modal understanding, Agent collaboration, and cost efficiency advantages. Although specific details were not disclosed, considering its previous leading performance in areas such as long text and code generation, the new model is expected to spark a new round of competition in the enterprise-level AI application market.

Against the backdrop of the global AI competition increasingly becoming an "computing power arms race", Moonshot's choice provides a clear alternative: true intelligence is not about how much power is consumed, but about how to solve the most problems with the least resources. While U.S. giants are racing towards AGI with thousands of GPU clusters, Chinese teams are proving with engineering wisdom that efficient, stable, and deployable AI can also stand at the top of the world.