- Earlier this year, Mayfield created its first-ever dedicated seed fund to focus more on AI.
- The firm hired seasoned AI investor Vijay Reddy to lead that fund.
- Reddy and Mayfield managing partner Navin Chaddha reveal the kinds of AI startups on their radars.
Mayfield Fund was one of the first venture-capital firms to quickly realize that artificial intelligence was the dawn of a new tech wave and honed its investing focus on the sector.
In July, the firm announced its first dedicated seed fund focused on AI called AI Start. And Mayfield hired Vijay Reddy, a seasoned AI venture capitalist, who’s been backing companies long before AI was in vogue, to lead the fund’s investing focus.
Prior to launching AI Start, Mayfield had already backed 25 early-stage startups in the space like MindsDB. But now its focus on AI is intensifying with a dedicated seed fund.
Looking ahead to 2024, Reddy and Navin Chaddha, Mayfield’s managing partner since 2009, have big plans for the new fund. Reddy is closely watching for the “maturing” generative AI infrastructure model startups, as many of these buzzy startups will have had enough time to figure out a clearer product-market fit. He and Chaddha are also keeping tabs on open source AI tools, multimodal models and AI agents that are starting to automate many core business functions.
Reddy’s background makes him a perfect fit to lead the $250 million fund, Chaddha told Business Insider.
He’s been backing AI companies since 2014 when he began writing checks to AI startups at Intel Capital, which included the AI audio unicorn, Babblelabs, which Cisco acquired in 2020, and AEye, the AI-powered vision sensor for electric cars that went public via a special purpose acquisition company, or SPAC, in 2021.
Reddy later moved on to Clear Ventures, where he led deals in seed and inception stage AI startups like AI-powered concrete startup AICrete and the accounting startup Spyn. This year, Chaddha tapped him to lead its new AI seed fund.
The capital for the new fund comes from Mayfield’s existing funds, as well as its latest $955 million raised across two new early stage funds, which the firm announced in May. Check sizes will range from $1 million to $4 million, which are smaller than a typical Mayfield investment check, Chaddha said. After investing, the partners will work closely alongside technical founders that are often new to building a startup.
Within the AI world, some of the hottest AI tools and infrastructure startups that are hoping to compete with legacy players have launched within the past two years, and Mayfield plans on being a key partner to these new startups “from day one,” Chaddha explained.
With the AI Start fund, the team plans to double down and lead deals in seed stage and pre-seed generative AI startups, as well as AI infrastructure startups with an enterprise focus. The team also intends on backing startups across all “five layers of the AI stack,” which they define as AI application startups, models and middleware, data infrastructure, AI-powered software-as-a-service companies, and semiconductors and systems.
“There’s a whole blue ocean out there where we can build large companies. Our job is to find these $10 billion, $20 billion market spaces and go invest in those companies and those sectors,” said Reddy.
Reddy has already settled into his new role and has been very hands-on in defining and executing the firm’s AI investing strategy, Chaddha said.
“It comes down to four Cs,” said Reddy. The first is providing capital for running the technical operations of a generative AI startup, including the larger check size of $1 million to $4 million for a pre-seed or seed startup. Other values Reddy wants to bring are a sense of community, support for finding compute power and help with company building for AI founders.
“We have a network of the top experts in AI,” said Reddy, and the team plans to match those experts with founders in the AI Start fund in order to “bring that expertise to startups,” he explained.
In particular, Reddy is interested in generative AI infrastructure startups that are building something that can’t be so easily replicated internally by an incumbent like OpenAI or Google. “When we look at companies which are doing something that is obviously on the roadmap of a cloud company, or the roadmap of OpenAI, we tend to question if they will succeed, when we know these large companies will have to build it themselves.” said Reddy.
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