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Welcome to Startups Weekly, a fresh human-first take on this week’s startup news and trends.
AngelList’s recently closed early-stage venture fund brings back one of my favorite conversations within the world of early-stage startup fundraising: to data, or not to data. The $25 million fund bases all of its investments off of one key metric that AngelList has been tracking for years: a startup’s ability to hire.
When I spoke to Abraham Othman, head of the investment committee and of data science at AngelList Venture, he told me they win deals because they are less adversarial to portfolio companies than other firms. “Our approach? This is our data set, let’s see if we can put money into them,” he said. No further due diligence? No problem.
Of course, there are some challenges with leaning on such signals to make investments. As history often reminds us, due diligence matters from a human perspective — and vetting a founder beyond their 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 well for success, but could also just be a result of great marketing. In AngelList’s case, they believe that hiring demand’s fluidity adds to its importance.
As you can probably tell, I think the future of data-driven investments will bring a double-edged sword into our Zoom rooms (or lack thereof, perhaps). Traditional investment that prioritizes pedigree and culture, or the “art” of a founder, has left out an entire class of historically overlooked individuals. But that same process, in which you spend five hours in conversation with an aspiring entrepreneur, brings a layer of humanity to decision-makers before they get millions to execute on a vision.
I don’t want to get into the due diligence conversation yet again, and investors leaning on data to dictate their investment decisions is anything but a new strategy. This is the song of late-stage investors, of private equity analysts and your brilliant aunt who loves a good earnings report. Early-stage startups and investors, from ClearCo to SignalFire, have spent years building up advice atop algorithms atop assumed returns.
However, in a bull market for even the most bullish among us, the premise of an unbiased, data-based check feels somewhat more hopeful than before. Money certainly doesn’t solve all woes — the top reason startups fail today is still due to failure to raise new capital. Add in the gender fundraising gap and a more automated decision-making process suddenly doesn’t sound unromantic, it sounds inevitable
For my full take on this topic, check out my TechCrunch+ column: Is algorithmic VC investment compatible with due diligence?
In the rest of this newsletter, we’ll talk about a new graduate-friendly fund, lawyer tech and Plaid’s growing patchwork of startups. As always, you can follow my thoughts on Twitter @nmasc_ or listen to me on Equity, a podcast about the business of startups, where we unpack the numbers and nuance behind the headlines.
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