This article is written by Amarnath Simha who is pursuing a Diploma in M&A, Institutional Finance and Investment Laws (PE and VC transactions) from LawSikho.
Table of Contents
Introduction
VC funding means funding of businesses by venture capitalists. Venture capitalists are basically financiers who invest in equity of other businesses like startups, small businesses, etc. which they believe have potential to become huge and their investments would be returned multifold when they sell their stake later. They are a source of private financing for startups which require money for their growth and survival without any security or collateral except that the venture capitalists will be taking a slice of the profits later. Hence there is a demand for VC funding from startups.
But the downslide for a venture capitalist is the identification of a proper startup to invest the money. Because there is no security or collateral for their investment, chances of the venture capitalists losing money is also on the higher side. As a general rule of thumb, it is estimated that out of 10 startups, only one or two succeed, three or four fail and the rest three or four just manage to get back the investment (https://www.investopedia.com/terms/v/venturecapital.asp#:~:text=Venture%20capital%20(VC)%20is%20a,and%20any%20other%20financial%20institutions).
Hence, any mechanism which helps them identify a proper startup to invest would be a welcome opportunity for the venture capitalists. Artificial Intelligence (AI) is one such mechanism which is being used by the venture capitalists for identifying proper startups to invest in. Considering the fact that the VC funding is more and more aligned towards technology oriented startups in various sectors and fields, it is not surprising that the VC funding itself turns towards AI for identifying the startups. Some of the companies like Social Capital and Google Ventures have started using AI in finding the best startups to invest in.
The need for AI
The need for AI arises because of the massive amount of data which is being generated by more than 2 billion smart phones makes it impossible for the human brains to glean all the insights necessary to identify a particular trend at different levels. It is estimated that AI is going to be used more extensively all sectors of economy and fields of life that it would be virtually impossible to gauge its impact from the current level of perception. Considering that the AI is being more and more in startups, it was only a matter of time that the VC funding itself started using AI for identifying the startups to invest in.
Well-established metrics are being used to assess startup potential, and one glance at the market unveils a trove of data points being used by AI algorithms to establish correlations and patterns. These historical points are valuable in assessing how an early-stage startup will perform, while their disjointed and scattered natures provide the perfect environment for AI to thrive in (https://www.entrepreneur.com/article/309198). AI can cut the clutter and allows the VC to decisions inspite of all the business plans submitted to them by the startups. An AI framework arms VCs with the tools and information to use reasoning, knowledge, planning, communication and perception to boil startup viability down to metrics.
AI can internalize data — much like an automated financial adviser — to quickly summate findings and attach a success probability to a company on the basis of previous industry experiences, churn, revenue growth and market size. The speed of the analysis and the wide spectrum of insights which AI can deliver is next to impossible to be expected from that of a human being or a group of them. No doubt ultimately it is a gut instinct but this kind of data analysis would go a long way in making any investment decision.
An example of AI in work would lead to Alice, the first ever AI platform which is said to have been built with the purpose of helping the female founders of business (https://www.entrepreneur.com/article/294148). It is built in collaboration between Dell, Circular Board and Pivotal. This platform more designed from the point of view of female entrepreneurs is in fact a tool for VC funding also because it makes the personal relationship between the venture capitalist and entrepreneur irrelevant by making data more relevant. This platform measures metrics and uses data to provide personalized recommendations using data allowing the entrepreneurs a chance to know where they stand in the industry and what they need to do. This platform promises a few things to the entrepreneurs.
They are static and machine learning based on user inputs, user profiles with data collection capabilities for personal and company demographics (though this raises some issues of privacy), resources, network of global leaders and experts and events calendar, search pages with filtering on various levels, content aggregation and suggestive analytics. The same information would also be available to a venture capitalist and allows the VC to make an informed decision.
Funder driven fundraising process
With the input of the AI, the VC funding is now becoming a funder driver fundraising process. Till now, the startups used to approach the VCs and present business plans and raise money. But now the VCs are taking the first step and are approaching the startups and talk about funding them. It is enabled because of usage of AI wherein the investment trends are tracked by the VCs. It is said these funders can now be called hunters (https://medium.com/entrepreneurship-at-work/hunting-v-s-farming-a-founder-friendly-evolution-of-venture-capital-fd80d00d2bce). In the process, the networking and presentation skills of the startup founders is taking a secondary place and their actual skills and problem solving technology is getting a more prominent place.
A classic case wherein the tracking investment trends could be seen in the case of Fly Ventures, a VC funding operating out of Berlin which as its website https://www.fly.vc/ reveals is involved in investing in automation and applied deep tech by partnering with idiosyncratic founders. It does not just stop at that. Fly Ventures itself makes use of AI to know about the potential startups often before they’ve even begun fundraising. Using its technology, Fly Ventures claims it is able to find and approach startups with a digital footprint, and says its software currently finds more than 1,000 new companies a week (https://techcrunch.com/2017/12/21/fly-ventures/).
The Fly Ventures’ tech group consisting of software engineers being ex-Googlers focuses on automating sourcing i.e. identifying potential investments. It pulls data from hundreds of sources including blogs, job boards, accelerators, and databases like CrunchBase and then its algorithms use a combination of structured and unstructured data derived from these sources to rank and filter the companies its finds. The results are presented to the investment team which takes quick decisions. Before the startups approach them, the Fly Ventures team approaches the startup and talks about funding them.
Another example would be InReach Ventures (inreachventures.com) which is an AI powered venture capital firm investing in early-stage European startups. The website states that they are using a proprietary software called as DIG to discover, evaluate and support investments in the most promising European startups.
The trajectory of a startup
The AI is used not only to find the startup worth investing in but also to find out what makes a startup success i.e., AI is taken as a predictive tools. Hone Capital, a venture capital firm, partnered with AngelList, a platform, to make a comprehensive list of around 30,000 startup deals which happened over the previous ten years and analyse them from around 400 parameters. Ultimately it was found that around 20 parameters are common in all the successful startups and are taken as a predictive tool for the future startup funding. The platform also gives leads of around 20 a week and based on the predictive tool, a decision is taken.
Some of the interesting analysis arrived at was a startup was more likely to succeed if it got 1.5 Million dollars initially than only 0.5 Million dollars and that further it is most likely to succeed if the two founders were from different universities than from the same university. Though the predictive analysis of the AI is not taken to be the most fundamental factor, it is used in combination with human analysis before coming to a conclusion (https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/a-machine-learning-approach-to-venture-capital#).
What should the startups themselves do?
When the venture capitalists are relying on AI for identifying the startups, the startups also have to get aligned with how AI can be used to solve some of the problems they are focusing on. For instance, most of the examples taken above which are funder driven are themselves concentrating on startups which are using AI extensively. This would be become a major factor for AI reliant venture capitalists to invest in.
Conclusion
No doubt while it is ultimately the venture capitalists who take a call on whether to invest or not, the chances of a gut call overriding the AI predictions would reduce as time progresses. This is one such uncertainty for the venture capitalists to grapple with. What would be the future is becoming more and more uncertain to predict because the uses of AI is not yet fully identified or probably even identifiable. Hence, usage of AI by the VC funding will also take various hues and is only going to increase.
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