How to find co-founders / collaborators: advice for myself 2 years ago

[Today I read a mailing list question from an entrepreneur asking how to find developers who would work for no pay. That struck a chord since I wasted a lot of time trying to do that myself when I was starting out on LinerNotes. So I wrote this blurb that I wish I could send back in time to myself. Also NB that I’m still working solo, so take this with a grain of salt!]

The first thing you need to do is to reverse the way you’re approaching the problem.

Unless you’re extremely lucky, you’re not going to have a lot of success with “how can I get people to do what I want?” No one else cares what you want. They care what they want. So instead you need to think in terms of “how can I give other people what they want (and satisfy my own needs in the process)?”

No one is going to work for you for free (would you personally spend months helping a stranger out of the kindness of your heart?) They may work for no cash, but only if you can compensate them in some other way that’s as valuable as cash. So think about what the type of people you need (i.e. programmers) want and think about which of those things you can offer them. The best thing you can offer is traction (i.e. a hugely reduced risk of failure) because everyone wants a seat on a rocket ship. You might offer equity, but if you haven’t raised money yet then your equity is basically worthless.[1]  Or it might be some connection or hard expertise you can share. If you don’t have anything at all to offer, then you need to go focus on yourself for a while and figure out how to either develop yourself to be a more useful partner or how to acquire the resources you need to attract people.

Applying this framework will give you a “candidate archetype” of people with whom you can make a mutually agreeable transaction. Then figure out where that type of person hangs out (e.g. in the UT-Austin computer labs) and go talk directly to them. Computer people are friendly and most of them will talk to strangers as long as you’re humble in your initial contact.

If you’re talking to a lot of people and not finding anyone, the problem is probably with you. Either you’re talking to the wrong people (because you didn’t target well) or you don’t have enough to offer at this point as a partner.

[1] Rule of thumb – think about the amount of cash you’d sell your company for today, and assume the whole equity pie is worth less than 1/10 of that.

Future the Economy Part 4 – Computers and Productivity

Dear reader, this is a unusual day. For the first time (possibly) ever, I’m actually writing my next future the economy post while NOT airborne.

When I left you, we were talking about productivity. I claimed that productivity is the root of all wealth and nearly all improvements in the human condition. And I told you about the two ways that productivity improves:

  1. Doing the same thing faster
  2. Doing different things

It’s pretty obvious how computers let you do #1. Once upon a time, rocket scientists had to compute thousands of ballistic equations using slide rules. “Computer” used to be the job title of a person who simply did computations all day.

Now scientists can do the same thing, but faster. And Moore’s law tells they’ll be able to do that about 50% faster ever year. That’s pretty good, unless of course I want to do something that currently takes a million years (e.g. perform a 1 second/byte calculation on 10 terabytes of data).

For that, I’m going to have to get creative. My only option is to choose an algorithm that does substantially fewer computations or does each computation substantially faster.

Computer scientists have a fancy way of talking about how long a certain type of algorithm will take. When you see expressions like “O(n^2)” (i.e. “big O notation”) or “polynomial time”, people are talking about how much slower an algorithm will run as the amount of input data increases. For some problems, like sorting a list of n integers, a good algorithm might take (n)*(log n) nanoseconds while a bad one takes n^2 nanoseconds. For a billion item list, that’s a difference of about 30 years.

By using the better algorithm, I’ve increased my productivity by about 4,000,000,000%.

These examples are most dramatic in computer science but they apply just as much to normal life. Say I need to attend a meeting in Houston. That’s a ~20 hour travel ordeal OR a 1 hour Skype conversation. If they’re both equivalently good[1], I can 20x my productivity by choosing wisely.

Next time, we’ll get a little meta and talk about how to choose better algorithms.

[1] In real life, alternative solutions are rarely perfect substitutes. But they’re often close enough along the attributes that matter.