While $9.3 billion was invested in AI companies in 2018, many VC investors aren’t leveraging that tech in their businesses. AI models are especially beneficial tool for VC investors because it can comb through massive data sets and sort information to fuel better investment decisions and higher ROI.
In its purest form, AI is a method of curve-fitting. Imagine a random plot of points on a piece of paper and then ask yourself, “Which straight line most closely fits those points?” Instead, we can envision a more complex version of that same diagram. Given a random plot of red and green dots, can we draw a curve that will separate the red dots from the green ones? Can we calculate a mathematical equation for such a curve? If so, then we can predict the color of a new (previously uncolored) dot using that equation.
That’s a lot of mathematical jargon, so let’s apply this theory to a real-life situation: VC investing. In this case, the red dots represent failed companies; the green ones are successful organizations. The goal is to come up with the equation of a curve that separates the two so that you can accurately categorize each new investment (or uncolored dot) as a potential success or failure.
Unlike the dots on a piece of paper, however, we aren’t confined to two dimensions. Instead, we must evaluate startup characteristics using hundreds (sometimes thousands) of variables. The problem becomes far more complex, but it’s certainly solvable with the help of modern AI technology.
AI Modeling Takes More Than a Good Face
The challenge is identifying the dimensions necessary to model the problem. Do they even exist?
In venture capital, it’s common to look at founders’ academic pedigrees, their past experiences and successes, demonstrations of their entrepreneurial traits, and their levels of passion. You should then look at market-specific conditions like competition, pricing, product-market fit, traction, and so on.
Some of these dimensions are easier to model than others. For example, it’s mathematically easier to model entrepreneurs’ alma maters and the academic rigors of those schools than it is to track how passionate they are about their products. Similarly, how do you model product-market fit when buying decisions can be based on such nuanced factors as design, aesthetics, speed, and features?
Understandably, many VC investors might be reluctant to embrace technology to help evaluate these dimensions to make a decision. It can be difficult to alter your long-trusted processes to fit modern conveniences. However, investors who rely on antiquated processes will not see as much success in their investments as those who embrace AI and other innovative technologies.
Technology Is Here to Help Rather Than Hinder
A lot of tenured VCs will tell you that venture investing is more of an art than a science. This may very well be true given that more than 627,000 new businesses open each year, and 70% of them usually fail within five years. Sadly, no scientific/mathematical solution can prevent failure.
Still, it’s critical for VCs to shortlist potential candidates from the ever-growing pool quickly — and the most efficient way to do this is with technology like AI. Once you’ve done that, you can take a more “artful” approach to decision-making.
Another cliché associated with high-stakes decision-making is to “listen to your gut.” This gut instinct was once thought to be what separated best-in-class decision makers from their peers, but recent discoveries in neuroscience suggest these gut feelings are just our brains giving us a push in one direction based on previous experiences. AI models, which are trained on historical data, could behave similarly to “gut instincts” — and that artificial instinct would get better as it consumes more data.
Finally, most everyone falls into the trap of feeling threatened by technology. After all, it’s common practice for businesses to replace blue-collar workers with automation or retail tellers with self-serve machines. The obvious logical next step would be to replace white-collar workers, right?
This fear is warranted in certain instances, as some jobs are threatened by intelligent machines (think robo-advisors for investments.) It’s not hard to imagine a company like Crowdfundr building AI-based VCs that investors can use to make smart investment decisions while bypassing the venture capital community. The VC community could go from backing disruption through AI to being hoisted by its own petard.
That said, I don’t foresee a near future where technology takes over the jobs of white-collar workers. Instead, I see innovative technologies — like AI — becoming critical tools in decision makers’ arsenals. Without these tools, I imagine it will be hard for VC companies to thrive.
The Future of VC With AI Models at the Helm
We’re far from the days of robo-partners running VC firms, but we’ve reached the point that easy-to-model dimensions can give us a glimpse of future possibilities. AI models can analyze parameters like funding history, investor activity, and sector-wise M&A data from sources such as Crunchbase, AngelList, and PitchBook. We can then use the analyzed data to make better investment predictions.
Similarly, LinkedIn data related to founder and employee background, headcount growth, key hires, and more can be used to generate models around technological superiority and expected growth. Additionally, web traffic data aggregators such as SimilarWeb and SEMrush can be used to model traction in the market, traffic estimates, monthly active users, growth rates, and so on. Finally, we can extract customer sentiment from user comments posted via social media, online discussions, and app store reviews.
This wealth of data can be used as dimensions to model startups. You can then use historical data to train AI models, and eventually, use those models to predict the potential outcome of new investment opportunities.
There are many different ways to use technology like AI to help you make better investment decisions. As long as you embrace technology rather than shy away from it, you’ll gain a competitive edge and see greater ROI.
This article originally appeared in ValueWalk on March 27, 2020