My Take:
Nine years ago, I had the opportunity to interview Billy Beane, the General Manager of the Oakland A’s. Beane was a professional baseball player who transitioned into the A’s front office and made a name for himself by using data to identify and recruit undervalued players – transforming the A’s roster in spite of its famously low player payroll. If this sounds familiar, it might be because his story is chronicled in the Oscar-nominated film, Moneyball, where Beane is played by Brad Pitt.
My conversation with Beane focused less on the data-driven decision-making that drove his success and more on the not-so data-driven decision-making behind his early career. He was drafted in the first round of the MLB as a coveted pick. But Beane turned out not to be nearly as good a player as coaches and club owners thought he would be.
In his words, his appeal to recruiters had almost everything to do with “looking the part.” Square shoulders. Successful High School performance. Walked and talked like a successful baseball player. But as he explained in very personal terms, his career as a player didn’t pan out as expected. The coaches, recruiters, and owners were wrong.
Baseball recruiters aren’t the only professionals who over-index on pedigree. The Venture Capital community does it too. In the case of tech, pedigree takes the form of the schools someone went to and the companies where someone worked. Those things are certainly important, but it’s critical not to get caught up on matters of pedigree.
I’ve sat in classes at Harvard and boardrooms with Stanford alums, whom everyone mythologizes, and I can tell you firsthand that, with a few notable exceptions, they’re just not all they’re cracked up to be.
If we flush the traditional markers of a strong hire, how should we think about talent, and how should we discern it?
There are necessary conditions for success: a certain level of training and experience, the context in the relevant space, and they have to have done their 100 push-ups. But from there, we’re making sense of a sea of noisy, frequently conflicting, highly subjective signals. We’ve been at this a while, and we think we’ve gotten pretty good at picking up on the qualities that translate to success in an early-stage startup.
Others look for winners. Look for people with a chip on their shoulder and something to prove instead.
Venture Capital loves winners. When you think about it, that’s a perfectly reasonable risk management strategy. The trouble is that winners have frequently made big money, and it’s hard not to let that go to the head. Everyone who has been in entrepreneurship long enough has seen it many times before – the CEO with an exit who becomes much too certain of his own infallibility. They lose sight of the fact that success in entrepreneurship always entails at least a little luck.
Here’s what gets me and my team excited: a person with an entrepreneurial mindset who has taken a swing or two, but not yet hit a home run. Why? It shows the right risk attitude if they’re raising their hand to have another go, and it shows they’re open-eyed and persistent enough to take another shot. They’ve faced adversity, but they’ve dusted themselves off and gotten back in the game. Others look for named institutions, but grit is more important.
The candidate’s back story is also critical. Were they born on third base, or did they lift themselves by their bootstraps? For example, immigrants’ family stories usually entail a commitment to hard work and the American Dream. They’ve got a point to prove. Look for people who can take a punch and don’t turn their tails and flee when the going gets rough, as it always does in a startup.
Others look for a stacked resume. Look for homegrown heroes instead.
Two decades ago, it was Oracle. These days it’s Google. I’m talking about the golden employer that everybody wants on their resume. It’s good training, depending on the role, but it’s not a reliable indicator of performance in a startup. Why? The number one job when you’re working at one of those huge companies is not to mess up what’s already been built by other people. You’re usually not making big moves that materially change the product, revenue, or customer trajectory.
The difference between working at an already established tech company and working for an early-stage startup is the difference between knowing how to drive a professional Formula 1 race vs. knowing how to build a Formula 1 race car's engine. They’re not the same. In many cases, there are not-yet-famous people hiding in plain sight who can step into larger roles. They’ll do it with more institutional knowledge, more support from their co-workers, a higher capacity to learn fast, and much less ego.
Others look for lone wolves. Look for pack animals instead.
The era of the lone-wolf entrepreneur is over. The problems got too complicated, and the clock speed required to solve them was much too steep for any one brain to do it alone. Hard skills are important, but they don’t mean much if the individual can’t play well with others.
There are a lot of factors that go into what makes a good collaborator, but I think the most important one is this: can they suppress their own ego in the service of a broader cause? Do they need to take the winning shot, or do they see their team’s success as their own? If they can, they’re in a better position to actively discern what other people are good at and then enroll them in their solution.
Company-building is, at its core, a team sport.
Others look for a high IQ. Look for obsessive intensity instead.
Einstein never took an IQ test. Estimates of his IQ are as high as 160 and as low as 135, which are above average for sure, but they hardly correspond to the searing brilliance of his work. What gives? Einstein’s intelligence, it turns out, was in his ability to latch onto a problem and obsess over it.
Calm persistence, verging on obsessiveness, is also exactly what’s required to succeed at an early-stage startup. Being fast and smart is useful, but it matters less if they don’t have the sticktoitiveness and attention-focusing required to solve the hardest, coolest problems. If their first waking thought in the morning and the last conscious one they have before going to sleep at night center on a single company-building puzzle, and they obsess over it for weeks or months, I’ll bet on their odds of success over 30 minutes from a supergenius every time.
Come work with me!
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