Rising apes and falling angels

In psychology, we talk often about the survivorship bias: the tendency of people to focus on winners, rather than losers, as a way of trying to insure future wins.  The prototypical example is airplanes in war (one used in a great recent post on the topic): if you want to figure out how to keep planes from getting shot down, don’t look at where the holes are, look at where the holes aren’t (because you can presume that the planes with holes in those places didn’t make it back alive).

But there is actually a larger trend here than just losses and wins.  In my traditional style, I’ll call it a true two-by-two matrix: expectation given resources and outcome.  That is, some winners seem to have been always meant to win, like a startup that of experienced people with a solid idea and good funding.  When they succeed, we’re not shocked.  Ditto the reverse, like people born into poverty who stay in poverty.

The problem with those two groups is that they rarely tell us anything interesting: we look at them, apply our existing knowledge of winners and losers, and our expectations are confirmed.  It is the other opposing corners of the matrix that always teach me the most, both with startup teams and individuals.

First, there are “those who lose, despite having everything they need to win.”  Occasionally, I’ll see startups who just seem to have it all figured it out: market fit, resources, team.  And then I watch them struggle and fail to gain traction and die out.  We rarely dissect these losses, as a startup community, often because it is hard to do so: ours is a way in which the bodies quickly disappear as the limbs crawl off in separate directions to do other things.  And yet I think these failures are the ones that are most interesting, because rather than confirming what we already know about what makes teams succeed or fail, it hints at new variables that we should take into account, things we may have missed.  I think of this group as the falling angels, divine right up to the moment that they crash headfirst into the ground.

And there is, of course, the reverse: “those who win, despite having everything they need to lose.”  Here are our rising apes, teams that seem destined to fail so utterly and completely, and then they succeed and leave everyone wondering why they didn’t invest.  They too hint at variables as yet unmeasured, despite our fervent desire to right them off as simply luck.  Luck is actually how most people explain both groups, because a serious study of them would force us to reevaluate our worldview, which is pretty much the antithesis of what our brain is designed to do.  We love the path of least resistance, which is at the heart of the survivorship bias: the winners win because they were always going to win, the losers lose because they were always going to lose, and our desire to confirm that means studying only living winners and dead losers.

Fortunately, we have science, which demands that we draw out the two-by-two’s and investigate all four quadrants.  So look around, all you who say you believe in The Method – there are lessons in the fallen angels and all those rising apes.

Blame the hackers, not the Hackathon

There has been a recent spate of folks hating on Hackathons lately that seems, to me, a bit like throwing the baby out with the bathwater.  It is part of a larger movement (that I’m mainly in agreement with) to point out the ways in which tech folks these days are desperately trying to “change the world for the better” while also having billion dollar exits, but exists in its own special hate-world.

Before I try to talk about why I think some of the critiques are misguided, a few things to make clear.  For one, I actually met my current employer through a Hackathon: I worked with Bing on prototypes to help kids deal with asthma during the TEDActive conference.  And I’m currently involved in sponsoring, proposing, or running all sorts of different hacks on social problems.  So there are all sorts of biases running around here to be aware of.

As a kid, my father had a knack for explaining things in way that I could grasp.  Take guns: why rifles but not handguns?  Because rifles (of the non-automatic variety) have a purpose, they are a tool.  Whether it is helping to put down an injured animal or hunting for food (yes, lots of people still hunt for food in this country), there is a reason to have a .22 locked away and to know how to safely and correctly use one, in the same way a tablesaw is worth spending some time with.  But handguns have no purpose other than hunting human beings: if they are a tool, they are a tool for only those whose job it is to kill people, which is almost certainly not anyone reading this blog.

So let’s apply that same reasoning to Hackathons.  If they are a tool, what is their purpose?  I’ve spent a decent amount of time thinking about that question, because I’ve spent a decent amount of time trying to run better Hackathons, and knowing what it is that you’re trying to do is half the battle.  To me, a Hackathon is useful for creating multiple solutions to a single problem, as a way of creatively prototyping potential answers that others can use.  Done well, it should know in advance the specific behaviors it wants to encourage, it should bring all the tools (or at least as many as you can reasonably anticipate) to the table,

Another way to think about this is the variety of ways in which a Hackathon can go wrong.  Like when it becomes an app competition; you shouldn’t be hacking on something to which you already think you know the answer and have built it.  Or when you go in with the expectation that you’ll get fully-formed products; it takes more than 48 hours to truly think something all the way through and then thoughtfully execute a solution.  Or when you go in thinking you’ll arrive at the best answer; the very best solution for something will rarely emerge from a bunch of amateurs taking a weekend stab at it.

Part of this anti-Hackathon mentality actually seems to be about the Valley’s sense of entitlement and using social good to whitewash profit motive.  That’s a fine conversation to be having, but it is a little like trying to use emotional arguments to discern between rifles and handguns.  You’ll notice when my father talked about guns, he didn’t say anything about “to make you feel safe in the world”.  A gun shouldn’t exist to make you feel a certain way and in the same way, Hackathons shouldn’t exist so that you can feel like you’re contributing.  But if used properly, a rifle can make you feel safe, because you actually are safer: able to provide for your family, able to protect animals for painful deaths, etc.  And a good Hackathon can make you feel like you are contributing because you actually are.

Put more broadly, we should never try to argue against a thing by citing only its misapplications.  If we did that, most STEM subjects would be out the window: the amount of times people use stats to bad ends is astonishing.  Hence the current “Big Data is not Truth” bandwagon, which hopes to have people use Big Data responsibly,  but will almost certainly result in some people abandoning it entirely.