These days, it seems everyone wants to be a venture capitalist, right? I mean, I get it: the lure of riches, the chance at fame, the cloak of prestige — all immensely compelling drivers.
But far less discussed (and perhaps intentionally) are the challenges that are par for the course. Indeed, much is on the venture capitalist’s plate:
- finding and closing quality deals amid fierce competition and ambiguity;
- evaluating how much support portfolio companies need (or merit) and discerning the best ways to assist; and
- achieving attractive exits to please (and court) limited partners.
All the while, venture capitalists face an uneasy truth: their feedback cycles are lengthy, making it difficult to accurately grade their job performance.
Because of this, venture capitalists are often kept in the dark, hoping that subsequent returns vindicate their investment decisions.
So, clearly, venture capitalists face an uphill battle. Still, they have a job to do — and do it well they must. To maximize their odds of success, many turn to pattern matching, a topic that I wish to discuss with you.
The Duality of Pattern Matching
Pattern matching is an observation and selection method. It deals with matters of correlation, if not causality. The aim is to discern which factors (traits) yield specific outcomes. In other words: which variables best predict what I want — or don’t want?
This can mean identifying all-star founders to avoiding uninspiring deals to building high-caliber teams. Unsurprisingly, these outcomes tend to have monetary implications.
In Silicon Valley, pattern matching is huge. Venture capitalists swear by it. Some call it their “secret sauce”, their proprietary blueprint, their compass of sorts (remember, they need help navigating through a sea of competition and noise). If possible, entrepreneurs get in on the action too, using it to their advantage. And that’s not all.
Pattern matching reinforces expectations, shaping the “outfit” of success. It steers capital, consenting what is conferred legitimacy. It influences what we see and consume, cementing who or what is legible in the eyes of power. In other words, pattern matching is a big deal.
A. Very. Big. Deal.
Understandably, pattern matching is a sensitive subject. Some suggest it’s too exclusionary. Others counter, arguing it’s necessary to remove (or limit) the randomness of picking “hits”. Notwithstanding your leanings on the subject, we can agree that fitting the pattern is not always easy. Certainly, then, we can agree that doing so offers rare perks, not the least of which is access to exclusive networks.
But for every person who fits the pattern, many more do not — or cannot, for that matter. Consequently, much-sought-after talent is overlooked, a costly mistake in and of itself. And as history suggests, mistakes have consequences, which in turn have costs. And with any cost, somebody is the bearer — upon whom a burden rests. Yet the “overlooked” need not suffer alone. Society joins them.
When talent is untapped, society feels it (though the ensuing damage is not always apparent, much less understood). Repeatedly doing so, however, corrodes the faucet of innovation, the lifeblood of any society. This stymies the flow of fresh ideas, products, and discoveries — resources upon which societies rely, especially during a productivity slump.
This, in my determination, reflects the troubling duality of pattern matching. What was fashioned as a remedy has worked in some respects and fallen short in others, forming the basis of what I believe is the Venture Capitalist’s Dilemma:
Investing in precedent-defying founders and teams, which data suggest are good for performance and returns, runs counter to venture investing in practice, thereby leaving scores of talent on the sidelines.
By virtue of the demands they face, venture capitalists are pressured to perform and convinced that few options exist:
- Stick to the script (read: continue pattern matching) or
- Explore uncharted territory (read: discontinue pattern matching)
While I’m sympathetic to their predicament, I’m less so to how this conversation is framed. Left unquestioned, we risk accepting a menu of false choices — self-made intellectual dead-ends that betray our allegiance to the status quo.
There is, I believe, a better way.
Humanity + Science = Pattern Matching
Humans are innately social. Bonding is what we do. Yet we remain isolated, especially along gender and racial lines. The implications of this are far-reaching. Indeed, our networks influence our behavior. They also shape what we see — and, sometimes more importantly, don’t see. And as the clock ticks, forging new bonds becomes harder. To put it plainly: our networks are diversely thin and increasingly resistant to newcomers.
Truly inspiring, I know.
We also gravitate to conformity (a double-edged sword), to which our ancestors owe much of their survival. In many ways, we do as well.
While Hollywood glamorizes the “maverick” persona, “real life” does not. Bucking the crowd is hard. In a way, societal pressures maintain “order” — slapping penalties on those deviating from custom. Silicon Valley’s struggle with “groupthink” may be an example of this.
Last but not least, we’re also creatures of habit. We adopt routines: swapping uncertainty for consistency, variability for regularity. And when it comes to habits, we might have a favorite: seeking control. From shaping our surroundings to resisting them, we yearn for it.
To get it, we set out to observe. We search for clues — the vestigial footprints of truth. We analyze. We pursue prescience. Why? Because with it, we can anticipate the future and position ourselves accordingly. For without it, the whimsical mood of fate roams free — our capacity to self-determine put in doubt.
With respect to prescience, its root word is science: the domain of observation, experimentation, and prediction. Silicon Valley is forged from this, and technology depends on this. I submit that pattern matching is an outgrowth of this. To be sure, the very nature of venture capital borrows from this tradition. I’ll explain why.
Scientists are in the business of truth discovery. They begin by forming a hypothesis — an unproven explanation of a phenomenon — and testing it against a series of experiments. Feedback is collected and adjustments are made however necessary to inch closer to truth.
In venture capital, the process is similar. First, a thesis is adopted. What follows is capital allocated along “thesis-driven” lines. How investments fare is analogous to signals from scientific experiments. Feedback on a thesis’ efficacy is collected, patterns are noted, the best ones are applied, and the process is repeated.
These similarities are not accidental. Rather, pattern matching is likely an extension of the scientific process, with the pursuit of prescience as the impetus behind both.
Yet prescience (insofar that history is a reliable signpost) merits studying representative data sets. Not doing so leads to flawed models — predictive only in title. Artificial intelligence is a prime example, given its challenges with biased training data. But so too is venture capital, as it risks enshrining lessons borne from unrepresentative data sets (read: networks), limiting what it means (or looks like) to be a successful tech entrepreneur.
Because of this, hidden talent remains so.
“if A then B” ≠ “if not A then not B”
Historically, the go-to “pattern” is: young, white, male, computer science, and Ivy League (but let’s not forget those at Stanford, MIT, and Berkeley, etc). And to be fair, that playbook can work. Many brilliant technologists and entrepreneurs share some of those traits, as evidenced by the following chart:
Nevertheless, we must avoid substituting partial “truths” for holistic ones, for we court falsehoods should we presume that no other narrative can “tech”, launch a unicorn, or replicate the success to which Silicon Valley is accustomed.
Similarly, we must reject notions that treat brilliance as a function of gender, ethnicity, or Alma mater. Genius is ubiquitous. It inhabits not a single address — but many. Sadly, hiring practices and capital flows suggest a different opinion. Certain narratives are preferred. Without one, you must swim against the counter-currents of suspicion.
If you don’t believe me, look to the few women in tech, as they continue to fight for equality and respect. Or look to the underrepresented founder. You know… the one who must square her ambitions with the reality that investors may not take her seriously, much less provide funding.
It’s 2017, almost 2018, folks. We can and must do better. Here are a few thoughts on how.
Found below are some thoughts on tweaking pattern matching for the better. The hope is to reduce the likelihood of missing amazing founders of all stripes.
Identifying those with practical expertise is the goal, not finding those with stellar resumes…
Many believe that educational attainment and work experience correlate with competence, especially in the context of entrepreneurship. I tend to agree.
Undoubtedly, certain institutions attract powerful minds and provide useful instruction, experiences, and resources.
Nevertheless, it takes more than a stellar resume to launch a startup, much less to monetize it.
Just ask the founders of the seventy-five percent of venture-backed startups that fail. I’m sure that they (or at least their backers) would agree.
So the question is not whether an entrepreneur attended an Ivy League institution or spent time on Wall Street — though those experiences can certainly help.
Rather, the question is whether an entrepreneur reasonably conveys a level of expertise that’s consistent with the challenges they are likely to face.
Do they fundamentally understand their product, their competition, their markets? Do they have a plan (a good one) that can lead to profitability? Those, among others, are the critical questions to raise.
Expertise is expertise. Knowledge is knowledge. If you know your stuff, it matters not where or how you learned it.
Isn’t that the basis of Silicon Valley’s goal to democratize education?
Thus, let’s see if that can be applied to venture investing.
Entrepreneurship is a battle… find someone who’s battle-ready…
Entrepreneurship is grueling. It’s lonely. Few can do it. Fewer can do it well. Without question, it’s a battle (a lengthy one for that matter).
It follows that it’s not enough to invest in smart people. No doubt picking smart founders is important and predictive of startup success. Yet it’s hardly sufficient.
One must also inquire whether an individual is amply resilient and resourceful — capable of getting knocked down and coming back for more.
Few can frame it as eloquently as Mr. Tyson does:
Everyone has a plan ‘till they get punched in the mouth…
Adversity is an-all-too familiar theme to the “overlooked” entrepreneur. Their upbringing certainly plays a role.
But I suspect they’re even more familiar with resiliency — an attribute founders can’t do without.
Perhaps, then, identifying entrepreneurs with the figurative “iron-clad jaw” is the way to go, for it’s the critical ingredient of successful entrepreneurship.
Not recognizing a problem/opportunity is not evidence that it’s not there…
Venture capitalists often embrace what they understand. That’s not the case for what they’re less familiar with.
What they “get” is often based on their experiences, which are linked to their networks — which few would mistake as the paragons of diversity.
This explains, in part, why underrepresented founders struggle to raise capital at levels mirroring their majority counterparts. A shortage of cultural competency is likely to blame. Luckily, this “shortage” need not be permanent.
So instead of dismissing these opportunities outright, maybe it’s best to treat them as a chance to learn — to discover new paths to higher returns.
Chances are that with patience, thoughtful questions, and the right facts, one might discover how lucrative these markets truly are. I believe Mayvenn is the perfect example of this.
This piece is not about bashing Silicon Valley. Rather, it’s about offering suggestions to make it better. That’s all.
My assumption is that pattern matching, like most tools, can be improved. I would hope that you agree.
Allowing for new “stories” and backgrounds to “fit” the “pattern” not only benefits entrepreneurs, it aids the entire tech ecosystem: from investors to limited partners to consumers.
If more stakeholders can succeed, perhaps that’s something we should collectively rally around.
As always, thanks for reading! If you liked the story, feel free to share. You can also let me know what you think on Twitter: @Gmasejr
Gerald is a graduate of Cornell University, where he was a Fund Manager in Big Red Ventures and Fellow at Impact America Fund. He is passionate about the intersection of technology, culture, and impact.