While Silicon Valley tries to be meritocratic, unbiased, innovative, productive, and unbureaucratic as possible toward early stage founders and companies, it often falls short of these standards.
Venture capital is an old industry.
The idea of asking for money to start ventures has been around longer than we have history to trace it back. Among the earliest record systems of loans include lending grain to farmers and traders. If we compare how it was done in the early days to how it is done now, the fundamental process is exactly the same: people are funded through relationship.
In other words, to get funded, you have to know the people who are funding you. In the past, this was a physical constraint, because the only way to build relationships with potential investors was to interact with them physically. But even in our modern-day financial systems run largely by technology, most funding is still raised through personal relationship, which is why people still flock to Silicon Valley to build better relationships with investors and companies.
But relationships have limits of scale. It’s impossible for any single investor to get to know every single person trying to start a venture to the point of making an informed decision on whether or not to invest. It’s even impossible for any single early stage founder to get to know every single investor that may choose to invest in their company. Even with the network effect, the human capacity has very finite limits on the number of people we can get to know. With this practical limitation described by dunbar’s number, any one person’s true social circles are usually very limited, somewhat homogenous, and undoubtedly biased.
Despite the number of grand ideas that are already being funded by venture capital, just as many Lots of startup founders with billion dollar ideas that may never get a fair chance to change the world. Additionally, a slightly different version of this problem is likely also true: startup founders with mediocre ideas and abilities are overrepresented and over funded.
Current Fundraising Options
Looking at the current landscape of raising funds for an early stage startup, there is a whole array of possible methods of funding. Here are some of the most common options for early stage startups besides bootstrapping with their own money.
Angel Investors and Venture Capital
Angel investors and venture capital are the two areas that generally come to mind immediately when talking about startup investments. Essentially, angels and VCs will give you funding in exchange for a percent of your company that they hope to sell for a profit in the future (Take a look at how term sheets are structured to get a good picture of how it works). Founders usually attempt to convince angels and VCs to invest through a pitch, which is usually done in person. Most VCs tell you that they look for something in terms of the market a startup is trying to address, if it has something unique and proprietary to the company, and whether or not the founders and team are especially strong.
Accelerators / Incubators
Accelerators and Incubators usually bring cohorts of early stage, pre-revenue startups into a program of mentorship and networking over a set time. YCombinator, perhaps the most well-known startup accelerator today, operates two programs every year that connects startups that they select through an application and interview process. The goal of an accelerator is precisely as the name implies: to bring a company from idea to an actual established company in an accelerated amount of time. YCombinator even states on their website that “Our goal is to be the preferred source of seed funding, and to be that we have to do right by everyone.”
Pitch / Startup Competitions
Various types of competitions can also be a source of funding for very early stage companies, usually awarding a cash prize that allows a team to test the viability of an idea. Startup Weekend is an example of a such a competition. Because very few teams at such a competition have any hard data to show, most of these events are based on speculative judgment and a team’s ability to pitch an idea.
Crowdfunding / Syndicate Funds
The last early stage funding method I’ll cover, crowdfunding and syndicates, are a relatively new approach to getting an idea off the ground. Essentially, the crowdfunding model emphasizes quantity over quality, attempting to get a large number of people to invest in an idea at low prices. The power of this method is that it allows you to validate your idea by actually selling a product, gain the money you need to start, and give up absolutely no equity.
Unlike crowdfunding, syndicates such as angellist allow a more sophisticated crowd of people to invest alongside a syndicate lead, bringing in less “investors” than crowdfunding, but each at a higher contribution in exchange for equity instead of the product.
All of these fundraising models have one thing in common: personal interactions. Interactions look different in all of these models, but all of them are ultimately dependent on qualitative judgment and quantitive analysis done by human beings, creating side effects of homogeneity, industry imbalance, and inaccurate conclusions.
The Diversity Problem
Statistically, young white men tend to be the most disproportionately funded group of people when it comes to getting funded at an early stage. Why is this? It can’t be that young white men simply have better ideas than everyone else.
It’s not that they do better either. First Round Capital has shown that in their investments, teams with a female cofounder do 63% better than investments with all-male founding teams.
I believe that it is the result of a combination of base rates and unconscious biases. If the pool of entrepreneurs is dominated by young white men, then it is not too surprising that more young white men get funded than other profiles. When someone comes along looking like Mark Zuckerburg, its easy to overemphasize the potential that such an individual has.
But even normalizing for the percentage of each group of people, having a greater absolute number of a certain persona can unconsciously perpetuate the continuation of the imbalance. It’s the unwritten scripts that bring certain people in and other people out. We believe that a startup founder has to look a certain way, act a certain way, and build a certain thing. We’ve come to select ourselves out, disqualifying ourselves because we don’t fit the stereotypical persona.
Although the diversity argument has become somewhat of a straw man in recent years, it’s worthwhile to explore the chicken and egg problem exemplified in part by relationship driven fundraising and Silicon Valley.
The Industry Problem
Additionally, venture capital is often found overrepresented in a couple of sectors, ignoring the opportunities in sectors that have somehow been deemed “low growth”.
If we’ve learned anything from the history of innovative ideas that are changing the world, many of them start in industries that hardly existed yet, or were in industries that were old and stagnant. Tesla reinvented the automobile industry in a country where automakers are going out of business, Uber refactored the taxi industry by applying technology. etc.
But even these companies became successful because someone understood the vision at an early stage. Who knows how many revolutionary ideas have been passed on because they were considered negligible and unimpressive?
Branching out to new industries and seeing how they can be improved is one of the ways to find revolutionary ideas. But if everyone with the means to accelerate and fund new ideas are all in the same networks of people, how will we ever find the ideas orthogonal to our bubbles?
It’s also extremely naïve to think that the high growth sector will remain forever in American markets. As much as Americans have pioneered many extremely ambitious technologies in the past couple centuries, it’s ignorant to dismiss activity and opportunities that are happening worldwide.
The Human Capacity Problem
This all comes to conclusion in the human capacity problem. Humans have very limited bandwidth. Dunbar’s number claims that we can only have about 150 meaningful relationships at a time, and we only have the energy and ability to interact closely with a small subset of those 150 people.
Thus, when it comes to startup fundraising, the pitch is often used as the main point for making a decision about whether or not to invest. But pitches can be an inaccurate representation of a company, as the skill set required to deliver an excellent pitch is different from the skill required to build a good product.
Thus, relationships can be manipulated easily, unstable, limited, and biased.
The Location Problem
Because all early stage investors seem to interact in the same circles, they also tend to live in the same areas, making funding decisions based largely on the people who have physically interacted with them. This means that unless a startup founder lives in Silicon Valley, his or her chances of building a relationship with angels and VCs is much less feasible.
In light of all the limits of traditional and even the more progressive approaches to fundraising, here is a hypothesis for a system that democratizes fundraising.
Consider what computerized, high frequency trading has done to public markets. Essentially, computers turned trading stocks into less about human intuition and more about speed and volume. And while there are merits and drawbacks to such a system, computerized systems tend to do consistently better than the average human trader.
It’s also good to note that a computerized system for startup fundraising will never completely replace intuitive, strategic, and relationship based venture capital, but is still a valuable model that would offer higher volume, better consistency, and greater equity to founding a startup. If anything, a computerized model will challenge VCs to think more critically about their strategies, while simultaneously increasing the number of good ideas that get funded.
This system would not rely on a pitch nor a prior relationship. Where any idea and team can be evaluated based on a constantly changing set of dynamic data and predictive models. Because if computers trading on public markets can already beat the average investor and be more consistent than most investors, than what’s to stop a similar system from being able to methodically evaluate startups?
However, there are a couple of unique challenges that an algorithm would have to address in startup investing. If writing an algorithm to invest in public stocks is like writing a math interpreter, writing an algorithm for startups is like writing a natural language processor.
For one, startups have a longer maturation period than stocks do, often requiring a long-term investment of a few years before any progress is conclusive. This makes the development period for developing such an algorithm much longer, making it difficult to develop and test an algorithm.
Two, startups have more esoteric moving parts than public companies do, and use a larger variety of metrics in determining viability. Not every startup has a reasonable revenue history, market cap, or otherwise to analyze.
Three, startups trade in lower frequencies than stocks. Whereas a stock can be traded on a per second basis, investing in startups only happens at specific rounds. This lessens the advantage that computers could have, making it more difficult to compete with humans.
Computerizing venture capital is not a new idea. In fact, many firms are already using algorithms to help make decisions. But for the most part, few of these firms have reached a level of ubiquity and accessibility that would allow people from any background to get their business started.
Google Ventures is one of the firms that has experimented with a computerized, methodical approach to startup investing, although much of it happens behind the scenes and appears as a black box to most outsiders.
It’s hard to say who will introduce a computerized model to fundraising, but it has the potential to change the way we approach startups as much as the internet did.