Thirteen years ago, when Chris Farmer founded early-stage venture firm SignalFire with data analysis at the core of its investment strategy, many were skeptical about the approach — conventional wisdom dictated that nascent companies didn’t have enough data to make investment algorithms effective.
“This was a very radical idea, and everyone thought I was crazy,” Farmer (pictured above, on the right, alongside CTO and partner Ilya Kirnos) told TechCrunch.
A lot has changed since SignalFire raised its first $53 million fund 2015. Today, more venture firms are adopting data-driven strategies in addition to, or sometimes even instead of, traditional VC methods that often rely on networking.
Indeed, several venture firms today claim they use AI to source deals, and we even have some private markets-focused companies that sell analytical tools to help all kinds of investors to do “qualitative diligence.”
Still, Farmer feels SignalFire’s approach is unique because, unlike other VCs that use data, his firm integrates AI into every aspect of the investing process — from identifying promising early-stage startups to helping portfolio companies with recruiting and product marketing.
And its limited partners seem to agree that its investment methods will continue to hone its edge. On Monday, SignalFire said that it has secured more than $1 billion in fresh capital, bringing its total assets under management to about $3 billion. This is the firm’s largest funding haul ever, exceeding the $900 million it raised two years ago.
At a time when many venture firms have been forced to cut back their fund sizes, Farmer says raising such a large fund means that SignalFire is “well out of the proof-of-concept phase and into the established manager phase.”
The firm’s new LPs include giant pension plans, insurable banks, and an Asian sovereign, Farmer said. Indeed, CalPERS, the largest pension fund in the U.S., has reportedly committed $100 million to SignalFire for the first time.
According to Farmer, a significant reason some of the world’s biggest institutional investors were excited to back in his firm was its focus on seed and pre-seed startups.
Given their size and inherent bureaucracy, giant LPs prefer to write large checks to established firms that are expected to persist. “Most seed funds are small. They have a few great funds, and then they’re done,” he said. “It’s very hard for big institutions to back firms like that.”
Farmer claims that with SignalFire, huge investors can get the best of both worlds: exposure to very young startups at the scale and longevity they need.
Although the firm’s first point of entry into startups is at the pre-seed and seed stages, its model is to use its giant fund to keep investing in companies as they grow. Of course, this strategy isn’t too different from most multi-stage firms, except that most of those investors tend to be more focused on initially backing companies at Series A.
“We use our scale to outgun everyone at the seed,” Farmer said, noting that SignalFire has invested $100 million into some companies. That kind of capital is not readily available to most seed-focused firms.
Farmer said the firm’s model has helped it identify trends before its competitors despite having few significant exits. He points to early investments in startups such as Grammarly, which was last valued at $13 billion; Grow Therapy, which last year raised an $88 million Series C from Sequoia; and EvenUp, an AI software for personal injury lawyers that’s worth over $1 billion.
For this new set of funds, the firm plans to continue investing in sector-specific AI startups, including healthcare and pharma, consumer, infrastructure and developer tools, and cybersecurity.
Despite its AI focus, SignalFire is deliberately avoiding companies building the foundation layer for AI models.
“I think that many venture dollars going into model builders is at a massive risk. They’re getting leapfrogged every couple of weeks by another model. You don’t know whether it’s defensible,” he said.
On the contrary, SignalFire tries to invest in companies whose businesses model or tech can’t be easily replicated. “EvenUp doesn’t have a competitor. I like deep, deep, deep defensibility,” Farmer said.
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