Established with a valuation of $750 million! A former Google researcher has shortened chip design from 2-3 years to just a few days!

Two former Google researchers are attempting to reshape the $800 billion chip industry with artificial intelligence software. Their company, Ricursive Intelligence, is dedicated to automating chip design, enabling every tech company to design custom chips from scratch in just weeks or even days. According to media reports on Wednesday, Ricursive Intelligence recently completed a $35 million funding round, led by Sequoia Capital and Striker Venture Partners, with a valuation of $750 million at its inception. The company is expected to launch its first product next year. It was founded by Anna Goldie and Azalia Mirhoseini, who previously developed the AlphaChip software for designing chips like TPUs at Google. The startup aims to compress the current chip design process, which takes two to three years and is costly, down to just weeks or even days. Automating chip design will lower the barriers to custom chips, encouraging more tech companies to develop application-specific chips, gaining advantages in cost, energy efficiency, and performance. Ricursive has attracted attention from over 50 venture capital firms. This reflects the unprecedented investment enthusiasm for companies founded by former top AI lab researchers, with similar cases including Periodic Labs co-founded by former OpenAI researcher Liam Fedus and Reflection founded by former Google DeepMind researcher. Market Opportunities in Chip Design Automation Custom chips are becoming a competitive advantage for tech giants. Amazon and Google have developed dedicated chips for AI and data centers, while Apple has saved billions through its self-developed chips, with its M-series chips revitalizing the MacBook line. These custom chips are designed for specific functions and outperform general-purpose chips in cost, energy efficiency, and size. However, the development of custom chips has a very high barrier to entry. The current process includes architecture design, testing, and preparation for mass production, typically taking two to three years to complete and heavily relying on manual operations. Minor errors discovered late in the design process can lead to costly delays that are difficult to recover from. Mirhoseini, now a professor of computer science at Stanford University, and Goldie believe that automation technology can break this bottleneck. "We believe that custom chips will emerge in large numbers," Goldie stated. Continuation of Google's Technical Gene Ricursive's core team has extensive experience in AI chip design. In a top-floor office of a regular house in Palo Alto, California, five researchers and engineers are crowded around a large computer screen, analyzing research papers and sorting through code. Two of them previously worked with Goldie and Mirhoseini at Google, contributing to the development of the AlphaChip software. AlphaChip has designed layouts for multiple chips for Google, including TPU chips for AI applications. This technological accumulation has laid the foundation for Ricursive. Sequoia Capital partner Stephanie Zhan will join the board of Ricursive. She stated, "They have truly pioneered a new category in AI chip design." The goal is to "unlock a whole new world that allows people to create custom chips for their applications." The Wave of Top AI Talent Entrepreneurship Ricursive's rapid fundraising reflects strong investor interest in startup projects from former top AI lab researchers. Goldie revealed that the company has received investment intentions from over 50 venture capital firms. This trend is spreading across the industry. Former OpenAI researcher Liam Fedus co-founded Periodic Labs, while a former Google DeepMind researcher founded Reflection. These companies, established by seasoned researchers in the AI field, are receiving unprecedented attention and funding support. Ricursive's high initial valuation and fundraising scale indicate that investors are optimistic about the application prospects of AI technology in vertical fields such as chip design, as well as the disruptive potential of these technologies in traditional industries