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Moonshot AI, the company behind Kimi, has four core founders. Their backgrounds are unusually strong: - Founder and CEO Yang Zhilin studied computer science at Tsinghua before earning his PhD from Carnegie Mellon. He was the first author of Transformer-XL and XLNet, and previously worked at FAIR and Google Brain. - Co-founder and CTO Zhang Yutao earned his PhD in computer science from Tsinghua. His earlier work covered knowledge graphs and AMiner, and he previously co-founded Recurrent AI with Yang. - Co-founder Wu Yuxin studied at Tsinghua and CMU before joining FAIR. He worked with Kaiming He on Group Normalization and also created Detectron2. - Co-founder Zhou Xinyu studied computer science at Tsinghua and later joined Megvii, where he worked on turning research algorithms into production systems and co-authored ShuffleNet. They all share one root: Tsinghua University. Tsinghua is widely regarded as one of China’s top universities. In the latest U.S. News Best Global Universities ranking, it reached No. 6 worldwide. Its influence on China’s AI industry extends well beyond Moonshot. https://t.co/DRO96gylNN, the company behind the GLM models, also grew out of Tsinghua. Its co-founder and chief scientist, Tang Jie, was once Yang Zhilin’s teacher. There is also a more personal connection. Yang and Zhou formed a rock band together at Tsinghua. Moonshot AI’s Chinese name, 月之暗面, comes from Pink Floyd’s album The Dark Side of the Moon, one of Yang’s favorites. Kimi may look like a young AI company. Behind it is a much older network of classmates, teachers, research labs, and friendships.

deepseekの論文見てても、普通に技術力では同等と見ている。 CV時代のSOTAトロフィー論文祭り時代と違って実直に底上げされている印象。 むしろ米国のGPU輸出規制は、中国企業に力技のスケールアップを諦めさせ、Multi-Head Latent Attention（MLA）やKimi Delta Attention（KDA）、極限のスパースMoE、そして独自コンパイラといったソフトウェアおよびアーキテクチャの根本的な革新を強制した。結果として、米国の膨大なリソース消費型モデルよりもはるかに効率的なAIエコシステムが誕生することとなったのではないか。 AI開発競争は、「どれだけ多くのGPUを所有しているか」という物量戦のフェーズから、「いかに少ないリソースで高い知能を引き出し、実社会のワークフローに統合するか」という効率性と適応性のフェーズへと完全に移行した。 米国が誇る主要AI会社と中国のスタートアップ群との間の力関係は再定義されつつあり、今後はオープンウェイトモデルを中心とした、より複雑で予測困難な多極的なイノベーションの時代が幕を開けることになるだろう。