到2026年1月，这家公司在员工不足10人的情况下，以40亿美元（约280亿人民币）的估值筹集了 3.35亿美元（约23亿人民币）。批评人士指出，真正的自我改进型AI尚未实现，目前只能处理一些 特定的任务。然而，投资者显然相信他们已经发现了具有变革意义的技术。 这家总部位于加州帕洛阿尔托的公司成立仅两个月后就获得了这笔融资。Ricursive 于去年 12 月初 完成了种子轮融资，筹集了 3500 万美元，估值达到 7.5 亿美元。 Lightspeed Venture Partners领投了最新一轮融资，DST Global、NVentures（英伟达的风险投资部 门）和Felicis Ventures也参与了投资。 49 Palms、Radical AI和红杉资本也参与了投资。红杉资本 领投了 Ricursive 的种子轮融资。 公众号记得加星标⭐️，第一时间看推送不会错过。 谷歌两位研究员安娜·戈尔迪和阿扎莉娅·米尔霍塞尼开发了一种能够改进芯片设计的AI。她们设想了 一种革命性的技术——AI能够设计出更优秀的芯片，并在这种循环中不断训练出更智能的AI，从而 实现无限的改进。2025年，她们创立了Ricu ...

Core Insights - Ricursive Intelligence, a startup founded by two former Google researchers, aims to develop AI software that can autonomously design advanced chips, potentially allowing any tech company to create its own chips from scratch [1][2][11] - The vision of Ricursive Intelligence is to create a recursive feedback loop where AI designs chips, which then run more powerful AI, leading to the design of even better chips [2][10] Company Overview - Ricursive Intelligence was founded by Anna Goldie and Azalia Mirhoseini, both of whom have significant backgrounds in AI and chip design [2][11] - Anna Goldie holds a PhD in natural language processing from Stanford and has previously worked at Google DeepMind and Anthropic [2] - Azalia Mirhoseini is currently an assistant professor at Stanford and was also a senior researcher at Google DeepMind [2][11] Technology and Innovation - The core innovation of Ricursive Intelligence lies in applying recursive intelligence principles to complex chip design, aiming to automate and optimize the design process [9][10] - The company plans to reduce the traditional chip design cycle from 2-3 years to just a few weeks by optimizing the most time-consuming parts of the process [10] - Ricursive Intelligence envisions an end-to-end automated chip design process that allows companies without dedicated chip design teams to create custom chips for various applications [10] Market Potential - If successful, Ricursive Intelligence could lead to a significant transformation in the AI and semiconductor industries, enabling a surge in custom silicon chips [2][10][11] - The company has already attracted attention from over 50 venture capital firms and secured $35 million in funding, achieving a valuation of $750 million before launching any products [11]

Core Viewpoint - Ricursive Intelligence aims to revolutionize chip design by using AI to autonomously create advanced chips, which could lead to a self-reinforcing cycle of AI and chip development, significantly impacting the AI and semiconductor industries [1][3]. Company Overview - Ricursive Intelligence was founded by former Google researchers Anna Goldie and Azalia Mirhoseini, both of whom have extensive backgrounds in AI and chip design [5][6]. - The founders previously led the AlphaChip project at Google, which introduced a novel reinforcement learning method for chip layout design, enabling faster and more efficient chip creation [8][10]. Technological Innovation - The core innovation of Ricursive Intelligence lies in applying recursive intelligence principles to complex chip design, aiming to automate the entire design process, which traditionally takes 2-3 years and costs hundreds of millions of dollars [11]. - The company plans to streamline chip design into three phases, allowing any tech company to design custom chips from scratch in a matter of weeks or even days [12]. Market Potential and Investment - Ricursive Intelligence has attracted attention from over 50 venture capital firms and secured $35 million in funding from Sequoia Capital and Striker Venture Partners, achieving a valuation of $750 million before launching any products [12]. - The startup is positioned to disrupt the $800 billion chip industry by optimizing the most time-consuming aspects of chip design and enabling companies without dedicated design teams to create custom chips for various applications [13].

Core Insights - Google DeepMind's AlphaFold has significantly impacted protein structure prediction, driving advancements in scientific research over the past five years [1][4] - AI is reshaping scientific research, particularly in life sciences and biomedicine, due to rich data availability and urgent societal needs [1][3] Group 1: AI in Scientific Research - AI models and tools have achieved breakthroughs in basic research, including protein structure prediction and the discovery of new biological pathways [1][3] - The paradigm of "foundation models + research agents + autonomous laboratories" is emerging in AI-driven scientific research [3][13] Group 2: Advancements in Biology - DeepMind's AlphaFold has solved the protein structure prediction problem, earning the 2024 Nobel Prize in Chemistry and establishing itself as a digital infrastructure for modern biology [4] - The C2S-Scale model, developed by Google and Yale University, has generated new hypotheses about cancer cell behavior, showcasing AI's potential in formulating original scientific hypotheses [8] Group 3: AI in Drug Development - AI-assisted pathology detection has expanded to new disease scenarios, with the DeepGEM model achieving a prediction accuracy of 78% to 99% for lung cancer gene mutations [10] - The AI-optimized drug MTS-004 has completed Phase III clinical trials, marking a significant milestone in AI-driven drug discovery [10] Group 4: AI in Other Scientific Fields - AI applications in materials science are gaining momentum, with startups like Periodic Labs and CuspAI focusing on discovering new materials [11] - DeepMind's WeatherNext 2 model has surpassed traditional physical models in accuracy and efficiency for weather predictions [5] Group 5: Future of AI in Science - The evolution of scientific intelligence technologies is expected to accelerate, with AI foundational models and robotics enhancing research efficiency [19] - The integration of AI into scientific discovery is anticipated to lead to significant breakthroughs, with predictions of achieving near-relativistic level discoveries by 2028 [19]

Core Insights - Ricursive Intelligence aims to revolutionize the $800 billion chip industry by developing software that automates the design of advanced chips, allowing companies to create custom chips from scratch [1][2] - The company has raised $35 million in funding and is valued at $750 million, with plans to launch its first product next year [1][2] - The founders believe that custom silicon chips will proliferate, significantly reducing the time required for chip design from years to weeks or days [2][3] Funding and Valuation - Ricursive Intelligence has secured $35 million in seed funding from investors including Sequoia Capital and Striker Venture Partners [1][3] - The current valuation of the company stands at $750 million [1] Technology and Innovation - The core innovation of Ricursive Intelligence lies in applying "recursive intelligence" to semiconductor design, enabling self-improvement and optimization of chip architecture [4][5] - This approach aims to break down complex design problems into manageable sub-problems, enhancing efficiency and innovation over time [5][10] - The goal is to achieve advanced process nodes like 2nm, significantly improving energy efficiency and performance [5][10] Market Impact - The establishment of Ricursive Intelligence's Frontier AI Lab signifies a major step in merging AI technology with semiconductor design, potentially accelerating the development of artificial superintelligence (ASI) [3][9] - If successful, Ricursive Intelligence could become a key player in the AI hardware space, posing competitive pressure on established companies like NVIDIA, Intel, and AMD [7][8] Future Prospects - Experts predict that Ricursive Intelligence will initially focus on demonstrating the advantages of recursive AI in specific semiconductor design tasks [10] - The long-term potential applications of recursive AI include creating highly specialized AI accelerators for various fields such as drug discovery and climate modeling [10][11] - The company is positioned at the intersection of AI development and hardware manufacturing, which could fundamentally change how AI systems are designed and built [11]