Thu. Jan 20th, 2022

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 managing founder cap tables.

During this growth, the company has quietly amassed millions of data points demonstrating interest, both from investors and regular employees, in nascent startups. And, according to SEC filings, AngelList isn’t letting those new insights go to waste.

AngelList has quietly raised $25 million for the AngelList Early Stage Quant Fund, a new investment vehicle that plans $250,000 in checks with more than 100 companies. The largest limited partner in the fund is WorldQuant Ventures, an early stage investment firm associated with a quantitative asset manager. Other investors in Quant Fund include Two Sigma Ventures, KAMCO Ventures, Plexo Capital, Redpoint’s Tom Tunguz 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 BestFitnessBands, Abraham Othman, head of investment committee and 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 when you think about investing quantitatively in enterprises, there is a lot of understanding about the metrics that drive the success of SaaS startups, especially B2B startup SaaS,” he said. “It is very different for other sectors.”

His team monitors the speed of hiring demand for a startup and sees how many applications a single company gets in a given time frame. The signal removes factors such as investor bias, founder’s networks, and even buzzy valuations.

“I think, in general, one of AngelList’s goals is to manage more institutional capital,” Othman said. “We’re taking advantage of some of the data” the platform already has. Aside from AngelList’s entry fund, a smaller investment vehicle, this is the company’s first, more traditional-looking, venture capital fund.

Every quarter, approximately 2 million users use AngelList Talent to sign up with startups. Currently, AngelList is mainly going to invest in startups in the United States and India, as that’s where most of the applicants on its platform come from.

AngelList talent sees about 35,000 companies per quarter gaining active interest, but only half of those companies are early stage investable companies (the rest are Series B+ companies, consultancies, venture capital firms, etc.). Of those 17,000 companies per quarter, AngelList’s data team hits the 20 companies that get the most demand for staff as potential investments.

Othman thinks they win deals because of the sharp approach, which he says is “less hostile” than other investors who may be more focused on risk factors or traditional pitches before writing a check.

“Our approach? This is our dataset, let’s see if we can put money into it,” he said.

Othman says the data-driven approach has led to a greater diversity of the startups, both in mission and founder, compared to traditional generalist funds. He estimates that about 50% of the founders in the fund’s portfolio identify as women.

There are some challenges in relying 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 companies headaches or legal trouble. In addition, a startup can get a lot of applicants due to salary, location or even recent coverage in a Well Known Tech Blog – which can be a harbinger of success, but also can simply be the result of great marketing. For Othman, the fact that workforce demand can be affected by so many different dynamics makes it a positive signal to watch, not a manipulated signal.

A future of data-driven investment comes with an important tension: the bias of the ‘art’ of an investment may be what historically overlooked individuals have left out, but it also adds a touch of humanity to decision-makers before they get millions to make a decision. execute vision.

Algorithm-based investing is gaining traction, with Rocketship VC adopting a data-driven investment strategy, ClearCo issuing checks based on startup spend, and Hum Capital using artificial intelligence to connect companies to the funders available on the platform.

AngelList has already made several investments with this strategy and put money into startups such as Piñata, a rewards and credit-building platform for tenants, and Emile, an on-demand educational service for high school students. The fund also plans to put money into roll-up vehicles, which will 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 recruiting penetration for quite some time now. At the start of the pandemic, April 2020, the startup had made layoffs that, according to sources, had a major impact on the company’s talent arm, which connects job seekers with startups looking for staff. Then came the layoffs in response to the hiring freeze for tech startups awaiting the economic downturn.

If that freeze repeats itself, Othman thinks hiring could still act as a “pretty robust signal about economic conditions.” He explained that his team had glimpsed which companies were recruiting talent in April and May 2020, saying that “companies at the top of the list have been getting their valuations since 6’xd.”

In the past, AngelList CEO Avlok Kohli has said that the AngelList will not go back to the roots of the fundraising market. “We believe that the market is quite efficient and we cannot offer an experience much better than what is happening today,” he said in September.

Today, Kohli’s sentiment seems to be changing as his company starts investing directly in the founders on the platform. In an emailed statement, Kohli said that “the new fund is a small but important step to quantitatively connect institutional capital with startups … we are the only platform with the dataset and reach to launch an initiative.” like this.”

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