Those who know me are aware that what has obsessed me for the past few years is how to use analytics and artificial intelligence (AI) to improve the venture industry. While I tended to focus on scouting and evaluation, I learned that AI can also be used to spot general trends, identify market gaps, improve portfolio management, match co-investors and deals better, gather intelligence on competitors’ landscape, identify potential acquirers and improve pricing models.
I am clearly not the first one who is arguing for a data-driven approach to investing, and I thought it would make sense to write about other like-minded investors I have heard of. Knowing exactly what they do is quite cumbersome without having inside information, so I am simply reporting public knowledge.
645 Ventures: a series A investor, they follow a data-intensive approach that mostly helps them in deal-sourcing and evaluation, and they have a fairly specific metrics-driven process to invest in a bunch of different sectors. They also seem to automate many of the manual tasks of traditional VCs.
Ardian: a world-leading private investment house, they are enhancing their AI capabilities through partnerships with startups that can collect and analyse unstructured market data.
Connetic Ventures: their data analytics platform collects, analyses and ranks startups, and supports the due diligence process.
Correlation Ventures: probably the first real data-driven investor, they reach a decision on whether to invest in two weeks, plus another two for extra due diligence. They co-invest only in the US and do not take board seats.
EQT Ventures: with more than $560m of assets under management, and equipped with an AI system called the Motherbrain, they have done more than 40 investments in less than three years. Apparently, most of its backbone is based on convolutional neural networks.
E.ventures: in addition to having a public dashboard to spot internet startups, they have been playing with analytics since 2012 to inform their investment process.
Fyrfly: there is not much information available publicly on their process, but this is another fund claiming a data-driven approach as part of their foundational principle.
Fly Ventures: they recently closed a first $46m data-driven fund to do small investments – up to $1.1m – and have invested in companies like Bloomsbury AI, recently acquired by Facebook.
Follow[the]seed: a post-seed global algorithmic VC, they have developed two data-driven methodologies – one business to business and one business to customer – to simplify the investment process.
Georgian Partners: one of the most prominent Canadian VCs, they are not simply looking at AI to improve their investment process, but they also put machine learning at the service of their portfolio companies – a differentially private machine learning software for their ecosystem.
GV: formerly Google Ventures, they are using AI and machine learning to inform their investment process, although almost no one knows exactly what they are doing and how.
Hatcher-plus: they use a data-driven approach to offer their partners quality analysis and opportunity scoring. They identify early-stage opportunities and have created what they call a “resilient investment model”.
Hone Capital: the Palo Alto-based US arm of CSC Group partnered AngelList to create their proprietary model.
InReach Ventures: led by Roberto Bonanzinga and Ben Smith, InReach has quickly built a name as the software powered house able to scout early-stage European startups even before other VCs have realised they need funding.
Nauta Capital: a business-to-business software-focused European VC, they recently brought in a few very good software engineers and data scientists with an ambitious roadmap in mind. They are working on a prediction engine that assesses the probability of investment success, a dealflow engine that gathers and analyses investment opportunities, and a reserves planner that calculates the optimal distribution of reserves for follow-on.
NorthEdge Capital: a private equity investor that developed a platform that identifies new investment and buy-and-build opportunities by analysing regional-specific companies.
Origin Ventures: they claim to have built their own scoring software to assess the quality of startups.
Redstone: a renowned VC that introduced the VC-as-a-service model, it hired a talented scientist, Stefano Gurciullo, who is, using Redstone’s words, “building technologies that help them invest based on evidence and on a quantitative understanding of innovation”.
Right Side Capital Management: with more than 800 pre-seed investments so far, they make small investments – $100,000-$500,000 at valuations of less than $3m.
Scale Venture Partners: their Scale Studio is a platform that allows startups to compare their progress with similar startups across a handful of key business metrics.
SignalFire: the firm run by Chris Farmer uses analytics to pick the right companies and helps them grow by providing market intelligence and talent-matching services.
Social Capital: led by Chamath Palihapitiya, the firm is better known to have started the capital-as-a-service concept, and more recently they created an analytics due-diligence tool, hosted on their webpage, to help them invest in early-stage companies and identify trends in customer cohorts.
Switch Ventures: they are using a mathematical and predictive approach to sourcing.
Ulu Ventures: they use “decision analysis” to inform investment decisions – creating market maps, assessing risks, quantifying uncertainty, performing sensitivity analysis and computing the risk-return profile of a potential investment.
Venture/science: a quant-driven VC led by Matt Oguz, it uses AI and decision theory to compute the risk associated with different attributes such as team completeness and vision.
WR Hambrecht Ventures: Thomas Thurston is the key man behind this and Growth Science, its sister tech company, advocating the use of data science to guide growth investments.
We now have more than 25 funds using AI in different ways. Even though this may look like a drop in the ocean of the venture industry, it seems to me it is something that may change the way we think about investing.
Is this the whole story? Not quite. Even though I started this article focusing on venture funds that use AI in different ways, I eventually discovered that VCs are not the only players in this niche industry. There are several startups and tools worth mentioning for the sake of completeness because they are trying to democratise VC investors’ skills:
Aingel.ai: they have recently filed a patent for a machine learning system that scores startups and founders and also matches the companies to the most suitable investor.
Capital Pilot: another service that facilitates the match between companies and investors.
Crunchdex: a new company focusing on identifying the fastest-growing startups.
Kognetics: they have a proprietary framework to identify interesting deals and offer extra insights on trends, markets and competition.
Preseries: another fully automated solution to discover and evaluate startups, which also has a voice interface through Alexa.
Radicle: their proprietary software can be used to detect novel interesting sectors, and I believe they have something to say on new ways to evaluate startups.
Rocket DAO: a decentralised crowdfunding and startup evaluation platform, still in beta, that helps to match companies and investors.
Valsys: they provide professionals with the tools they need to make data-driven decisions in valuation and estimation processes. They focus, however, more on a later stage.
This is likely to be only a partial list, but it conveys and bolsters the point mentioned above – having an AI-driven investment engine is becoming a trend, and we should expect more of those solutions in the future.
It is also interesting to note that are more funds are pouring money into development of these engines than companies selling those systems as a service. In other words, VCs seem to prefer building over buying when it comes to intelligent software for their own internal use. Intuitively, this is paramount in creating a moat and a competitive advantage with respect to other investors, but it could segment the market and polarise it.
While bigger funds may have the resources to invest in building their own platforms, this may not be true for smaller funds, and this could result in wrong signalling to limited partners and potential deals – if you buy software rather than building it, you may be seen as a second-class investor.
I listed funds and mainly software companies that are offering different types of AI services, but these are not the only two options. There are intermediate alternatives such as the one provided by Clearbanc and 20-Min Term Sheet, which use algorithms to review the startup’s marketing and revenue data and decide whether to grant a loan in about 20 minutes. Similar capital-as-a-service offers are provided by companies such as BlueVine, Lighter Capital, Corl, always with an automated process that speeds up investment decisions.
I am pretty optimistic about data-driven VCs being the future, and I am spending a lot of time thinking and working on how to push it further. I do not believe the future of the industry is likely to be fully automated and VC is and always will be a people business. On the other side though, it sounds astonishing that algorithmic thinking has not so far permeated the way investors work on a daily basis. I spent time researching and talking to many of those people, but it is also very likely that I might have misunderstood something or missed someone out there working on similar approaches. If so, feel free to reach out.
This is an edited version of an article first published by Forbes