Nvidia(US:NVDA) 3 6 Ke·2025-11-14 00:11Core Insights - Anysphere's AI programming tool Cursor has completed a significant Series D funding round of $2.3 billion, achieving a post-money valuation of $29.3 billion, making it the first AI programming startup to surpass a valuation of 200 billion RMB [1][2] - The funding round included prominent investors such as Nvidia, Google, and top-tier fund Coatue, alongside existing investors like Accel, Thrive, a16z, and DST Global [1][2] - Over the past year, Anysphere has raised more than $3.3 billion, with its valuation increasing by approximately 1026% [1][2] Funding History - The funding history of Anysphere includes: - Series D: $2.3 billion on November 13, 2025, with a post-money valuation of $29.3 billion [2] - Series C: $900 million on May 5, 2025, with a post-money valuation of $9.9 billion [2] - Series B: $105 million on December 19, 2024, with a post-money valuation of $2.6 billion [2] - Series A: $60 million on August 10, 2024 [2] - Seed Round: $11 million on September 1, 2023 [2] Business Performance - Cursor's annual revenue has surpassed $1 billion, with millions of developers and many successful engineering organizations as clients [3] - The company has a team of over 300 engineers, researchers, designers, and operators, with plans for further expansion [3] Product Development - Cursor launched its first self-developed programming model, Composer, in late October, claiming it generates more code than any other large model globally [3][4] - The new funding will enable Cursor to invest further in research and development, aiming to enhance its self-developed models [4] Industry Context - The AI programming sector is witnessing a surge in companies developing their own models, including international players like Windsurf and Cognition, as well as domestic firms such as Alibaba's Qoder and ByteDance's Trae [4] - The unique advantage of these tools lies in their access to vast amounts of real user code and behavior data, which can be used to continuously optimize model performance [4]