Valued at $750 million immediately upon establishment! Former Google researcher’s startup shortens chip design from 2-3 years to just a few days!

Two former Google researchers are attempting to use artificial intelligence software to reshape the $800 billion chip industry. They founded Ricursive Intelligence, which is dedicated to automating chip design, enabling every tech company to design custom chips from scratch within weeks or even days.

According to media reports on Wednesday, Ricursive Intelligence recently completed a $35 million financing round led by Sequoia Capital and Striker Venture Partners. The company was valued at $750 million at founding and is expected to release its first product next year. Ricursive was founded by Anna Goldie and Azalia Mirhoseini, who previously developed the AlphaChip software used for designing chips such as TPUs at Google.

This startup aims to compress the current chip design process, which takes two to three years and involves high costs, down to just weeks or even days. Automating chip design will lower the threshold for custom chips, driving more tech companies to develop chips targeted for specific applications, thereby gaining advantages in cost, energy efficiency, and performance.

Ricursive has attracted attention from more than 50 venture capital institutions. This reflects the unprecedented investment enthusiasm for companies founded by former top AI lab researchers; other similar cases include Periodic Labs, co-founded by former OpenAI researcher Liam Fedus, and Reflection, founded by a former Google DeepMind researcher.

Market Opportunity for Automated Chip Design

Custom chips are becoming a competitive advantage for tech giants. Amazon and Google have developed specialized chips for AI and data centers, Apple has saved billions of dollars with its self-developed chips, and the M series chips have revived its MacBook laptops. These specialized chips are designed for dedicated functions and are superior to general-purpose chips in terms of cost, energy efficiency, and size.

However, the threshold for developing custom chips is extremely high. The current process covers architecture design, testing, and mass production preparation, usually taking two to three years and being heavily reliant on manual operations. Tiny errors discovered late in the design process can result in costly, often irreversible delays.

Mirhoseini is currently a professor of computer science at Stanford University. She and Goldie believe automation can break through this bottleneck. “We believe a large number of custom chips will emerge,” Goldie said.

Continuation of Google's Technical Genes

Ricursive’s core team has deep experience in AI chip design. In a top-floor office of an ordinary house in Palo Alto, California, five researchers and engineers squeeze around giant computer screens, analyzing research papers and sorting through code. Two team members previously worked with Goldie and Mirhoseini at Google, participating in the development of the AlphaChip software.

AlphaChip was used by Google to design layouts for multiple chips, including TPUs for AI applications. This technical foundation has paved the way for Ricursive.

Sequoia Capital partner Stephanie Zhan will join Ricursive’s board of directors. She stated: “They have truly invented an entirely new category of AI-powered chip design.” The goal is “to unleash a whole new world where people can create custom chips for their own applications.”

Wave of Entrepreneurship Among Top AI Talent

Ricursive’s rapid financing reflects investors’ strong interest in startups founded by former top AI lab researchers. Goldie revealed the company received investment intentions from more than 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, founded by veteran AI researchers, are attracting unprecedented attention and funding.

Ricursive’s high starting valuation and fundraising scale show that investors are optimistic about the application prospects of AI technology in verticals such as chip design, and the potential of these technologies to disrupt traditional industries.

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