If you ever wanted to see a collection of Unicorns, you might want to take a look at the portfolio of GV (launched in 2009 as Google Ventures), the venture capital arm of Alphabet, Inc. [1]. GV\u2019s current and past investments impressively include Uber, Nest, Lime and Jet; all private companies with over one billion-dollar valuations (Unicorns) [2].<\/p>\n
Given my background in investing in private companies, I found myself fascinated with how GV\u2019s investment process has yielded such successful results. Fortunately for my curiosity, a few months ago, Axios shined a light on GV\u2019s investment approval process aid called \u201cThe Machine\u201d, a machine learning algorithm that opines on potential investments [3]. This begs the question: Is GV\u2019s incredible portfolio a result of human investment judgement or The Machine?<\/p>\n
According to Axios, The Machine is fed numerous inputs on an investment opportunity (i.e. industry, fundraise size, investors, etc.), then the algorithm scores it on a 10-point scale and gives each a green (scores above an 8), yellow or red rank [3]. The Machine recommends only proceeding with green ranked investment opportunities. Given venture investments are in young companies without much history to analyze, investors tend to rely on human judgment (\u201cgut\u201d). Decisions left up to gut are prone to human biases and error that can derail a firm like GV from making successful investments [4]. In order to try and gain a competitive advantage against other venture capital firms, GV is attempting to create a data-driven investment process that outperforms simply using gut. Unlike the issue with company specific data of young firms, industry, founder, round and investor data is almost limitless! Through The Machine, GV is using machine learning to help analyze the infinite amount of data surrounding an investment opportunity to find the factors that make certain investments successful. If cultivated correctly, The Machine would create a massive advantage for GV in its investment approval process.<\/p>\n
In 2013, GV\u2019s former CEO, Bill Maris, stated “We have access to the world’s largest data sets you can imagine… It would be foolish to just go out and make gut investments” [5]. In order to maximize The Machine\u2019s potential, GV is presumably focused on harnessing the information access they uniquely have as a subsidiary of Alphabet today (it is important to note GV declined to comment on Axios\u2019 story). There are other venture capital firms trying to also use machine learning to improve their investment process (most notably Hone Capital and EQT Ventures [3,6]), but they all lack the incredible data sets privy to Alphabet. With vast amounts of data, GV needs to now focus on deciphering between signals for good investments and noise to build The Machine to be a flexible model that is able to look at a myriad of different investment opportunities [4]. As GV continues to fine tune its machine learning process, it certainly seems that the longer-term goal is to have The Machine be the \u201cde facto investment committee\u201d [3], removing the gut aspect of investing. Even with superior results, a machine-based process being the final arbiter of investments will take time to get full buy-in.<\/p>\n
The benefits of a data-driven approach to the venture investing is undeniable, but relying entirely on algorithms, like The Machine, can also bare risks. In the more immediate term, GV needs to focus on the casual relationship that data inputs create and how they define successful investments. For example, including previous\/current investors as inputs can create a bias to invest in firms with backing from prestigious capital providers (especially if success is measured via a company\u2019s record of raising money). The reality is that these types of companies might just be able to raise capital due to their enhanced reputation, not because they are actually a good investment opportunity. Moreover, some of venture capital\u2019s most lucrative opportunities are the ones that might seem most risky to a machine-learned process looking for common fact patterns [3]. As to making final investment decisions, there should always be an integration of machine-learning and gut. Through studies conducted by the aforementioned Hone Capital on venture investments from 2015, combined human and machine teams sustainably outperformed teams with only humans or machines (which each interestingly performed at the same level on a standalone basis) [6]. Machine learning provides relatively consistent result, while gut provides potential for home-run investment. In my opinion, the right mix of both is essential for GV to succeed in the future.<\/p>\n
As GV moves forward with The Machine, there are a few critical questions. When a human investment professional and The Machine disagree on an investment opportunity in the future, what should GV do? Furthermore, given the shift in venture investing toward more machine-learning processes, what are the biggest differences between today\u2019s human venture capitalist and the future\u2019s?<\/p>\n
(795 Words)<\/p>\n
References
\n1) GV. \u201cOur Story\u201d. https:\/\/www.gv.com\/, accessed November 2018
\n2) GV. \u201cPortfolio\u201d. https:\/\/www.gv.com\/portfolio\/, accessed November 2018
\n3) Primack, Dan. \u201cScoop: Inside Google’s Venture Capital \u2018Machine\u2019\u201d. https:\/\/www.axios.com\/, July 18, 2018, accessed November 2018.
\n4) Yeomans, Mike. \u201cWhat Every Manager Should Know About Machine Learning\u201d. 性视界 Business Review Digital Articles, July 7, 2015. pp.5-6.
\n5) Miller, Claire Cain. \u201c Google Ventures Stress Science of Deal, Not Art of the Deal\u201d New York Times, June 23, 2013. https:\/\/www.nytimes.com\/2013\/06\/24\/technology\/venture-capital-blends-more-data-crunching-into-choice-of-targets.html?utm_source=newsletter&utm_medium=email&utm_campaign=newsletter_axiosprorata&stream=top.
\n6) Wu, Veronica. Interviewed by Chandra Gnanasambandam. McKinsey Quarterly. https:\/\/www.mckinsey.com\/. June 2017<\/p>\n","protected":false},"excerpt":{"rendered":"
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