Nathan Chen, born in 2009 in Shenzhen, has become a striking example of how self‑directed learning can open doors in cutting‑edge AI. His rapid rise from a high‑school student to a contributor at top labs makes his story worth following.

Born in 2009 in Shenzhen, Nathan Chen started teaching himself artificial intelligence in 2024. He used research papers and Google’s Gemini chatbot to understand complex concepts.

In May 2025 he published a blog post describing an AI technique. The post caught the attention of MIT researcher Yang Songlin and resulted in a seven‑week internship at Tilde Research in Palo Alto, where he worked alongside experienced AI researchers.

He joined Moonshot AI at 16, helped develop the Kimi K3 model and was named a co‑first author of the March research paper “Attention Residuals,” which improved AI model efficiency. The paper’s technique was later incorporated into the Kimi K3 system.

Chen repeatedly stressed that his achievements were a team effort and he resisted being portrayed as a lone genius. His departure from Moonshot sparked speculation about his next move, highlighting intense competition between Chinese and U.S. AI companies for talent.