As an underrepresented venture fund CEO, I believe in meritocracy—and I invest in underrepresented entrepreneurs for a reason

It mattered little that Wang clarified that “the belief that meritocracy somehow conflicts with diversity” is “mistaken.” The fact that he directly replaced the acronym DEI with MEI ironically demonstrates the one thing these two sides agree on—that the two concepts are fundamentally in opposition with one another. Two ends of a spectrum, one that closely maps to American politics. 

Of course, equity and meritocracy are not two ends of a spectrum. The opposite of meritocracy is not equity—it’s nepotism. And the opposite of equity is not meritocracy, it’s inequity. The ideal we should all be striving for is a meritocratic system that is free of bias, one where there is equality of opportunity for all. 

I have long believed that the true binary isn’t between right and left, it’s between the center and the extremes. Unfortunately, it’s the extremes that dominate the conversation, and adding a second dimension to the conversation is too complicated to fit into today’s highly polarized discourse, particularly when it plays out online. Subtlety doesn’t shine on TikTok. Thoughtfulness doesn’t go viral on X. Nuance doesn’t make for catchy headlines. 

I’ll give you a litmus test for your beliefs: I am a female, a Latina, and an immigrant. I am a graduate of Harvard Business School, where I attended on a full scholarship. I am a venture capitalist running my own fund, and the founder and leader of the largest community of VC-backed female founders in the country. 

Think fast: Did I achieve any of this because of, or in spite of, those pieces of my identity that make me stand out from most VCs?

However you answer likely has little to do with me, and a lot to do with how you see the world. I’ve been told that I’d never raise a VC fund because I’m a woman. I’ve also been told that raising my fund was easier because I’m a woman. Meanwhile, my male counterparts, who outnumber women in the space about nine times over, raise funds an order of magnitude larger in less than half the time I did. I’ve also been told it was easier for me to get into HBS because I’m from Costa Rica. I have spent many years counting how many Costa Ricans I know applied to HBS that same year, just to prove to myself that fewer than 7% of us—the overall HBS admission rate—got in. I hate the idea of being admitted as part of some quota, and not because—among other things—I aced the GMAT. 

Most importantly, either answer—because of, or in spite of—has the same consequence: It erases me from my own accomplishments. It detracts from my actual merit, not to mention the years of sacrifice, mountains of rejection, bouncing back from failure, refusing to take no for an answer, and yes, even the luck. None of these things matter if, when you see me, all you see are the boxes I check. It’s like you don’t see me at all. 

This erasure is in large part why many who are underrepresented in the most competitive professions, but who are otherwise stereotypical type A overachievers, tend to walk around with a massive chip on their shoulders. Because we’re damned if we do, and damned if we don’t. Contrary to what you may think, most of us are staunchly pro-meritocracy, even as we experience first-hand that most systems claiming to be meritocratic are highly inequitable in practice. They are rife with bias that affects people like us. 

Consider a Harvard study that found that when a man and a woman deliver the exact same pitch to investors, it results in higher investment for the man. Is this because men are better? Unlikely. It’s because investors bring bias—good and bad—into their investment decisions. Now compare this to findings—from BCG and from First Round Capital, for example—that show that female founders deliver better results than all-male teams. Is it because women are better? Also unlikely. I think it’s because we’re equally talented but have a much harder time raising capital. The result is a self-selection of relentless entrepreneurs that rises to the top, forced to create much stronger companies from the get-go. Iron is forged in fire.

All this being said, let’s all face the ugly truth: Data doesn’t matter! People on both sides will ignore whatever data I cite, unless it reinforces what they already believe to be true. If data mattered, all teams, boards, and levels of leadership would be more diverse. Opportunity would be equally distributed. Schools wouldn’t drop standardized testing. Performative DEI initiatives that don’t actually work would stop. Or they wouldn’t be needed in the first place. 

Early in my career, after studying under him at HBS, I took a job in Clayton Christensen’s strategy consulting firm, helping clients identify and leverage his theories of disruptive innovation. For the uninitiated, and the 95% of you who routinely get this wrong, disruptive innovation is a powerful economic phenomenon whereby upstarts achieve outsize success by targeting market segments that are overlooked by, or unattractive to, established incumbents. In disruptive theory, these “unattractive” market segments are ignored because they’re thought to be less profitable and less desirable, all the way up until the disruptor shows they’re not. 

This is why in my own investing activities I’ve started thinking of underrepresented founders—women, minorities, immigrants—as having outsized disruptive potential. Disruptive theory teaches us that there is huge power in being unseen. Because when you’re unseen, then they’ll never see you coming. 

A chip on the shoulder is a powerful motivating tool—however you feel about fairness. A recent study of 845 unicorns—startups that reach a $1 billion valuation—and 2,018 unicorn founders found a “chip on your shoulder” to be one of three dominant factors in the “DNA” of a unicorn founder. It also found that 70% of unicorn founders were immigrants, women, or people of color. Compare this to the proportion of VC-backed companies that are founded by immigrants (20%), women (4%), and BIPOC (around 2%). Given the disparate sources of data, I’ll refrain from turning these statistics into a mathematical likelihood. But you get the idea. 

And if you don’t? That’s just more opportunity for me.

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