Does A.I Have a Place in Venture Capital?

The week before Spring Break began, I had the privilege to speak with Angela Jackson of Portland Seed Fund, one of Oregon’s leading venture capital firms. Our conversation wasn’t initially supposed to be about data and the economy, but we ended up having a really interesting discussion about the current startup climate in Portland and the greater Pacific Northwest.

Portland Seed Fund is an early stage VC fund which most often invests in small, emerging companies. The way these venture capital firms thrive is by taking large stakes in companies before they start generating a lot of profit, so that they benefit if the company gets more funding, goes public, or gets acquired later on. As you might imagine, the most crucial part of being a venture capitalist is foresight, or the ability to predict what consumers will want in the near/distant future. Whereas hedge fund managers try to predict future market cycles to find price inefficiencies, venture capitalists try to predict the future market climate to find new business opportunities. For instance, if a venture capitalist thinks that in the next two years, there will be a surge in demand for grocery delivery, then they will likely invest in companies making cutting-edge advances in food delivery services. Or alternatively, if a VC thinks that a new, original idea will have a place in future markets (or even be able to take over established markets), then they will take an early position in the company.

So, there are a lot of similarities between venture capital and managing investment funds, but there are also a lot of crucial differences as well. A big one that comes to mind is the fact that hedge funds don’t really engage with the companies themselves, only the numbers these companies produce. Venture capitalists, in a traditional setting, will observe a pitch from the founder/CEO in person and inquire about their team, to get a sense of how the company functions as a whole (after all, lackluster teams produce lackluster results). After this basic screening, the VCs will delve into the numbers, though often at a more superficial level compared to the quantitative analysis done by financial engineering institutions like hedge funds (keep reading to find out why!).

At the end of the day, though, venture capital is about finding the big companies before they become big, much like how running a hedge fund is about finding the ‘big fish’ opportunities before other investors do. With that in mind, it’s worth thinking about which specific strategies venture capitalists employ to find successful companies early on, and contrasting these strategies to the modern quant finance approach we’re all so familiar with.

So which data points can provide insight into the future of a company?

There are a number of methods and the answers vary based on the organization, but the most common metrics which VCs will analyze are:

  • Scalability: This is a relatively simple concept, but it is much easier to grasp in theory than in practice. A company, algorithm, software, or any other product is scalable if it has the bandwidth and resources to meet increasingly large demands. As companies grow and become more popular, there’s going to be more demand for their products, which often translates to greater overhead and production costs. Tech companies are usually attractive to venture capitalists, especially in the last two decades, because they are incredibly scalable by nature. For instance, a car manufacturing company (I’m not saying Tesla, but we’re all thinking it) can be super popular and lucrative, but for each person who wants to buy one of their cars, a new car needs to be made (it sounds dumb, but bear with me). As more people want to buy your Model 3s cars, you need to build more cars to sell, and building more cars will cost you more money, time, and resources (Economics 101). On the other hand, every time a new every customer wants to open an account on Facebook, it’s the same application process and platform which the other ~2 billion Facebook users used, so no new product needs to be manufactured. Sure, there’s the cost and burden of maintaining servers which can handle growing traffic, but that pales in comparison to the price of opening and operating a vehicle manufacturing and assembly plant.
  • Current Market: Who does this product appeal to? Who will be using this service? This is what people mean when they say “this company is disrupting a 27 billion dollar industry” (i.e: this company is entering a market that can generate $27 billion in revenues). This one can be tricky, because there’s no clear ‘right’ answer. A fourth grader can tell you that a company which is scalable is better than one which is not, provided they know what scalable means. However, the current market to which a product appeals to is always subject to change. Think of a startup which wants to enter a saturated market with intense, established competition like e-commerce or search engines. You might think that this startup is doomed to fail because there’s too much competition and no one needs a new search engine because Google already exists. But what if this startup solves a problem which Google neglects, a problem so significant that users gradually start to move away from Google because it’s no longer the superior product. Don’t forget that MySpace practically owned all online social media before people even knew what a Facebook was! Facebook had no ads on their site while MySpace had a lot of ads which made it cumbersome to use, so people switched to Facebook and the rest is history. People often use market and revenue stream interchangeably, but these are definitely not the same thing!!! Users and customers don’t always translate to profit —Uber claims to have 75 million users worldwide, but they’re losing over a billion dollars per year (based on statistics from 2018).
  • Market Share: What percentage of your respective market do you control? This is a subtle way of comparing a company’s performance with its competitors, and companies with larger market shares tend to make more revenue. This is completely logical, as a company with 40% share of a certain market will have twice the customers than a competitor with 20% share in the market, and should therefore have twice the revenue. But it’s not only revenue, as Harvard Business Revue claims that a company’s market share is almost directly proportional to the return on investment in this company. This is super interesting to me, because HBR claims that high market share is often indicative of quality management and better positioning on the experience curve.
  • Revenue streams: Where are you going to get money from? There’s a myriad of answers to this question, and startups employ all types of different revenue/business models. VCs tend to prefer companies with diverse streams of revenue and companies with scalable revenue streams that can grow at a consistent rate. That’s all there really is to it.
  • Team: This is where we move into the more social and ambiguous metrics for measuring the future success of a company. Countless studies have shown that companies with happier employees perform , and there’s no question that teams with better chemistry are more likely to succeed (the only question is do you really need a study to believe this?). It is very difficult to quantify how well a team works together, and so VCs have to rely on intuition and social intelligence to make decisions about a company’s team. However, with recent advances in AI, we are now able to teach machines more complex undefined tasks such as sentiment analysis and speech recognition. I definitely believe the powers of NLP can be applied for better analyzing startup teams, but more on that later.
  • Enthusiasm: According to Steve Jurvetson, widely recognized as one of the best VCs in the world due to his early stakes in Tesla, Baidu, and SpaceX, a founder’s (or whoever is pitching) disposition during their pitch can make or break the deal. It’s one of those things that sounds too simple to be true, but if those in charge aren’t enthusiastic about their product, then why should customers be?

There are several different articles which explore the importance of these metrics in greater detail, such as this one, this one, and especially this one.

What I find most interesting in all of this is that hedge funds and trading firms have already graduated to quantitative/automatized strategies like trading algorithms, but venture capitalists (the majority of them) have strayed from using these automated methods. The question which interests me is whether or not venture capitalists will start implementing automated algorithms to help guide their decisions, like hedge funds have been doing the past decade plus. Is there a place for A.I in venture capital? If so, what data can be used to learn and gain insights?

My conversation with Angela Jackson spurred this interest, as we talked a lot about what data PSF uses in creating their porfolios, where they get this data, and what specifically they do with this data. AI didn’t come up explicitly, but nowadays that question should be implied everywhere there is data. According to Ms. Jackson, the biggest problem is that venture capitalists have minimal data compared to the expansive databases of bar data, fundamentals, and earnings reports that hedge funds can access.

According to Angela Jackson, there’s been a push in recent years to create aggregates of data between venture capital firms throughout the Pacific Northwest. The main incentive behind this is, among other things, to create a more comprehensive dataset for understanding which startups fail and which succeed. The hope is that as more venture capital firms start working together on such initiatives, the more data there will be to analyze and potentially train neural networks on. However, this doesn’t solve the problem of new startups having little to no data about themselves by the time they pitch to early round investors. This is a challenging problem to address, and I haven’t been able to come up with a reasonable solution. If any of my readers do, I’d love to hear what you think!

So, maybe the answer (right now) isn’t in company data like projected/current market share or scalability. As I mentioned earlier in this article, one great entry point for AI in venture capital is sentiment analysis — specifically, using sentiment analysis to gauge confidence of , or to detect how well teams work together. Sentiment analysis is already used to scan earnings reports, Twitter feeds, news headlines, and all sorts of bodies of text (doesn’t have to be text, as sentiment analysis works for audio clips too) to figure out whether or not there is positive sentiment evoked. The primary technology behind sentiment analysis is natural language processing, through embeddings or other word processing algorithms. These NLP strategies have proven effective in various settings, so there’s no reason why sentiment analysis should be confined to simple stock analysis. In fact, I believe this is one place where the true potential of NLP will come to light.

To summarize, I find it fascinating why venture capitalists have refrained from using automated methods such as AI or NLP to guide their decision making, while hedge funds have been relying so heavily on trading algorithms to find investment opportunities for the past two decades. Despite the potentially limited data sets available for analyzing small-scale startups, I definitely think it is worth exploring the idea of aggregating VC data to eventually train neural networks on. In the even nearer future though, I think we should start exploring the utility of NLP and sentiment analysis to analyze how well startup teams work together, as a metric for anticipating the future success of an emerging company.

Whether or not AI becomes the future of venture capital, I do think it has a lot of potential, and it seems like we are off to a good start.

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