Over the past year, AngelList has grown from a platform that connects angel investors with startups to an end-to-end suite of tools, working on everything from fund operations to founder cap table management.
Throughout this growth, the company has quietly amassed millions of data points that show appetite, both from investors and regular employees, for burgeoning startups. And, per SEC filings, AngelList isn’t letting those fresh insights go to waste.
AngelList has quietly landed $25 million for the AngelList Early Stage Quant Fund, a new investment vehicle that plans $250,000 checks into over 100 companies. The largest limited partner in the fund is WorldQuant Ventures, an early-stage investment firm founded by Igor Tulchinsky, who is also the founder of WorldQuant, a quantitative asset management firm. Other investors in Quant Fund include Two Sigma Ventures, KAMCO Ventures, Plexo Capital, Tom Tunguz of Redpoint and AngelList founder Naval Ravikant.
The operation, part of AngelList Venture, will be led by a four-person data team from within the company. In an interview with TechCrunch, Abraham Othman, head of the investment committee and of data science at AngelList Venture, explained that Quant Fund’s mission is to use more quantitative factors to decide which startups to invest in.
“If you think about quant investing in venture, there’s a lot of understanding around the metrics that drive SaaS startup success, particularly B2B startup SaaS,” he said. “It is vastly different for other sectors.”
His team tracks the velocity of hiring demand for a startup, looking at how many job applications a single company gets within a specific period of time. The signal strips out factors like investor bias, the founder’s networks and even buzzy valuations.
“I do think, in general, one of the aims of AngelList as it moves forward is to manage more institutional capital,” Othman said. “We are taking advantage of some of the data” the platform already has. Other than AngelList’s access fund, a smaller investment vehicle, this is the company’s first, more traditional-looking venture fund.
About 2 million users use AngelList Talent to apply to startups each quarter. Currently, AngelList is primarily going to be investing in startups in the United States and India, because that’s where the majority of applicants on its platform are coming from.
AngelList talent sees about 35,000 companies a quarter get active interest, but only half of those companies are investable early-stage businesses (the rest being Series B+ companies, consulting companies, venture capital firms, etc). Of those 17,000 companies per quarter, AngelList’s data team reaches out to the 20 companies getting the most hiring demand as potential investments.
Othman thinks they win deals because of the cut-and-dry approach, which he thinks is “less adversarial” than other investors who may be more focused on risk factors, or traditional pitches, before writing a check.
“Our approach? This is our data set, let’s see if we can put money into them,” he said.
Othman says that the data-driven approach has led to greater diversity of the startups, both in mission and founder, compared to traditional generalist funds. He estimates that about 50% of founders within the fund’s portfolio identify as women.
There are some challenges with leaning on one, somewhat broad, signal to make investments. As history often reminds us, due diligence matters — and vetting an investment beyond its ability to attract talent can save firms from headaches or legal woes. Additionally, a startup could get a ton of applicants due to pay, location or even recent coverage in a Well Known Tech Blog — which can bode for success, but could also just be a result of great marketing. To Othman, the fact that hiring demand can be impacted by so many different dynamics makes it a positive signal to look at, not a manipulated one.
A future of data-driven investments comes with a key tension: The bias of the “art” of an investment may be what has left out historically overlooked individuals, but it also adds some layer of humanity to decision-makers before they get millions to execute on a vision.
Algorithm-based investing is getting more attention, with Rocketship VC flexing a data-driven investment strategy, ClearCo writing checks based on startup spend and Hum Capital using artificial intelligence to connect businesses to the available funders on the platform.
AngelList has already made several investments with this strategy, putting money into startups such as Piñata, a reward and credit building platform for renters, and Emile, an on-demand educational service for high schoolers. The fund also plans to put money into roll-up vehicles, which allow founders to raise capital from up to 250 accredited investors with a single line on the cap table, but that will be a minority of investments.
It seems that AngelList has been rethinking its recruitment penetration for quite a while now. In the early innings of the pandemic, April 2020, the startup had layoffs that sources say largely impacted the company’s talent arm, which connects job-seekers with startups looking to hire. Then the layoffs came as a response to hiring freezes from tech startups waiting out the economic downturn.
If there’s a repeat of that freeze, Othman thinks that hiring can still work as a “pretty robust signal across economic conditions.” He explained that his team got a glimpse of which companies were recruiting talent in April and May 2020, and said that “companies at the top of the list have since 6’xd their valuation.”
In the past, AngelList CEO Avlok Kohli said that the AngelList will not return to its fundraising marketplace roots. “Our view is that the market is quite efficient, and we can’t offer an experience that is much better than what is happening today,” he said in September.
Today, Kohli’s sentiment seems to be changing, as his company begins to directly invest in the founders on the platform. In an e-mailed statement, Kohli said that “the new fund is a small yet important step to connect institutional capital with startups in a quantitative fashion…we’re the only platform with the dataset and reach to execute an initiative like this.”
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