From sourcing new investments to due diligence and far beyond, more and more investment firms are leveraging Outside Insight from AI and computer-based aids to help them make better, faster, more informed decisions.
New categories of technology are popping up around every major industry to support data-driven decision-making. Startups from AlphaSense and Kensho, to StartupYard’s Decissio, are specifically helping investors leverage AI to sift through the massive amount of data available, sort out which information is most relevant and therefore make better investment decisions.
But it’s not just startups. Silicon Valley VC bigwigs and hungry new entrants alike are increasingly relying on smart algorithms to help their humans make the best investment decisions.
Evaluating Potential Seed Deals
In a recent interview with McKinsey Quarterly, Veronica Wu, managing partner at Hone Capital, described how her firm has developed a data-driven approach to analyzing potential seed deals, through a partnership with Silicon Valley platform AngelList.
A foreign firm based in Beijing, Hone was looking to access the top deals and build a network of trust – an important step for any new entrants to the SV market. They partnered with AngelList, stating, “We saw the huge potential of the data that AngelList had. There’s not a lot of visibility into early seed deals, and it’s difficult to get information about them. I saw it as a gold mine of data that we could dig into.”
They created a machine-learning model based on a database of over 30,000 deals from the last decade, drawing from sources like Crunchbase, Mattermark and PitchBook. For each deal they looked at whether a team made it to a series-A round, and explored 400 characteristics, from investors’ historical conversion rates, to total money raised, the founding team’s background, and the syndicate lead’s area of expertise, finally landing on a list of 20 that they consider most predictive of future success.
Some insights Hone uncovered (via McKinsey):
Startups that failed to advance to series A had an average seed investment of $0.5 million Average investment for start-ups that did advance to series A was $1.5 million A deal with two founders from different universities is twice as likely to succeed as one with founders from the same university; diverse perspectives are a strength About 16% of all seed-stage companies backed by VCs went on to raise series-A funding within 15 months.
Due to the progress her firm has been able to make as newcomers to Silicon Valley in such a short period of time, Wu believes there will be a shift in the way the space operates in the future.
In the venture-capital world, success has historically been driven by a relatively small group of individuals who have access to the best deals. However, we’re betting on a paradigm shift in venture capital where new platforms provide greater access to deal flow, and investment decision making is driven by integrating human insight with machine-learning-based models.
AI lends the assist in major investment decisions
In 2014, Hong Kong life science venture firm Deep Knowledge Ventures appointed an AI system called VITAL to its board of investors, giving it a vote in every investment decision to be made. As the company is focused on age-related disease drugs and regenerative medicine projects, VITAL makes its decisions by scanning prospective companies’ financing, clinical trials, intellectual property and previous funding rounds.
Deep Knowledge Ventures made waves three years ago as one of the first to employ a machine on staff, but this is quickly becoming the new norm.
Akkadian Ventures’ data-driven diligence software tracks 14,000 companies that fit their investment criteria – more than $20 million in revenue and growing between 75% and 100% per year – to understand the development of the hottest Silicon Valley startups and determine which companies to pre-approve for investment. Today it has more than $100M under management.
Swedish firm EQT Ventures is famous for its invention, Motherbrain, which Andreas Thorstenssons claimed would ‘disrupt the traditional venture capital industry’ using software. EQT Ventures has € 566 million ($ 630 million) in commitments, leveraging data in the form of “Motherbrain which proactively sources investment opportunities, and Together, a matchmaking tool for angels and early-stage startups.”
According to the team, “EQT Ventures is designed to be the VC we would have wanted when on our own startup journeys.” Sourcing data from a multitude of sources, EQT Ventures and Motherbrain are leading the charge in computer-aided investment decisions with their “hybrid startup and VC firm” approach.
AI’s wider implications
Arguably one of Silicon Valley’s most successful investors, Steve Jurvetson of DFJ argues that the benefits of machine learning software will extend far beyond investing, into every industry imaginable. “I think the application of iterative algorithms to build complex systems is the most powerful advance in engineering since the Scientific Method. Machine learning allows us to build software solutions that exceed human understanding, and shows us how AI can innervate every industry.”
While it’s not likely to replace human intelligence anytime soon, we should take notes on the increased VC focus on AI and data science-driven startups, diligence methods and investment strategies.