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How sheep-like behavior breeds innovation in Silicon Valley

Once you’ve been working in Silicon Valley for a bit, you’re often offered advice such as:

  • Are you launching at X conference? ¬†… where X is whatever hot conference is coming up, like SXSW or Launch
  • Do you have an X app? … where X is whatever new platform just emerged, be it Open Social, iPhone, or whatever
  • Have you pitched X venture capitalist? … where X is a prolific headline-grabbing investor with a recent hot deal
  • You should do feature X that company Y does! … where X is some sexy (but possibly superficial feature) that a hot startup has done
  • Do you know what your X metric is? … where X is some metric a recent blog post was written about
  • Have you met X? … where X is some highly connected expert in the field
  • Maybe you should pivot into X space! … where X is a space with a hot company that just raised a ton of funding
  • Did you think about applying framework X to this?¬†… where X is a new framework, be it gamification or viral loops or Lean

Sound familiar? I confess that I’ve both received and given much advice along the lines of the above.¬†I call it “advice autopilot.”

The perils of “advice autopilot”
Advice autopilot is when you’re too lazy to think originally about a problem, instead regurgitate whatever smart thing you read on Quora¬†or¬†Hacker News. If you’re a bit more connected, instead you might parrot back what’s being spoken at during Silicon Valley events and boardrooms, yet the activity is still the same – everyone gets the same advice, regardless of situation.¬†The problem is, the best advice rarely comes in this kind of format – instead, the advice will start out with “it depends…” and takes into account an infinite array of contextual and situational things that aren’t obvious. However, we are all lazy and so instead we go on autopilot, and do, read, say, and build, all the same things.

That’s not to say that sometimes generic advice isn’t good advice – sometimes it is, especially for noob teams who are working off an incomplete set of knowledge. Often you may not have the answers, but the questions can lead to interesting conversations. You may not be able to say “you should do an iPhone app” but it’s definitely useful to ask, “how does mobile fit into this?” This can help a lot.

The other manifestation of this advice autopilot is the dreaded use of “pattern matching” to recommend solutions and actions.

Pattern-matching in a world of low probability, exceptional outcomes
One of Silicon Valley’s biggest contradictions is the love of two diametrically opposed things:

  • The use of pattern-recognition to predict the future…
  • … and the obsession with a small number of exceptional successes.

Exceptional outcomes for startups are limited – let’s say it’s really only 5-10 companies per year. In this group, you’d include companies like Facebook and Google that have “made it” and hit $100B valuations. On the emerging side, this would include startups who might ultimately have a shot at this, like Dropbox, Square, Airbnb, Twitter, etc. This is an extraordinarily small set of companies, and it isn’t much data.

The problem is, we’re hairless apes that like to recognize patterns, even in random noise. So as a result, we make little rules for ourselves – Entrepreneurs who are Harvard dropouts are good, but dropping out of Stanford grad school is even better. It’s good if they start a company in their 20s unless they’re Jeff Bezos. Being an alum of Google is good, but being an alum of Paypal is even better. Hardcore engineers as founders is good, but the list of exceptions is long: Airbnb, Pinterest, Zynga, Fab, and many others. And whatever you do, don’t fund husband-wife teams, unless they start VMWare or Cisco, in which case forget that piece of advice.

As anyone who’s taken a little statistics knows, when you have a small dataset and lots of variables, you can’t predict shit. And yet we try!

The intense focus on a small set of companies also introduces a well-known logical fallacy called Survivorship Bias. Here’s the Wikipedia page, it’s interesting reading. Basically, the idea is that we draw our pattern-recognition from well-publicized successful companies while ignoring the negative data from companies that might have done many of the same things, but end up with unpublicized failures. We’re all so intimately familiar with stories like “two PhDs from Stanford start Google” that we ignore all the cases where two PhDs from Stanford try to start a company and fail. Or similarly, YCombinator has built a great rep on companies like Airbnb and Dropbox, and yet you’d think that if you invest in 600+ startups that you’d get a few hits. Because of factors like this, it might seem as though A predicts B when in fact, it does nothing of the sort – we’re just not taking the entire dataset into account.

Conformity leads to average outcomes when we seek exceptional outcomes
The problem with giving and taking so much of the same advice is that ultimately it breeds conformity, which is another way if saying it reduces the variance in the outcome. And if you conform enough, you end up creating the average outcome:

The average outcome for entrepreneurs is, your startup fails.

Lets not forget that. And so one part of¬†Naval Ravikant’s talk on fundable startups¬†that resonated with me is the idea of playing to your extremes. He says in the talk:

‚ÄúInvestors are trying to find the¬†exceptional outcomes, so they are looking for something exceptional about the company.¬†Instead of trying to do everything well (traction, team, product, social proof, pitch, etc),¬†do one thing exceptional.¬†As a startup you have to be exceptional in at least one regard.‚ÄĚ –Naval Ravikant @naval

Be extremely good at something, and invest in it¬†disproportionately¬†relative to your competition – this gives you the opportunity to actually create an extreme outcome. Otherwise, the average outcome doesn’t seem so good.

The flipside of innovation
The funny thing with all of this, of course, is that this is what innovation looks like. The remarkable ability for practical knowledge to disseminate amongst the Bay Area tech community is what makes it so strong. Before something becomes autopilot advice for a wide variety of people, often a small number of hard-working teams who know what they’re doing leverage it to great success. Follow those people, and you might find yourself successful – just like them.

So the billion dollar question is – how do you separate out trendy/junk advice from what really matters?

… well, it depends!

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