If these guys found some sort of magic formula that actually predicted the value of a startup down the road, they’d keep the information to themselves and make, well, unlimited amounts of money.
This is a bit unfair. It might be that their software isn't a perfect predictor, but just improves the prediction powers of a human VC by some amount. Their publicly available software, then, might be the company's bootstrap. Once they make enough cash to invest and ride out the swings, they can do (approximately) what Arrington is saying.
Oddly enough, the real estate guys who sell seminars and books are the first to admint that they make far more money from the seminars and books than from real estate.
Maybe that says more about their customers than their skills.
Also: even if you could create an excellent predictor with the right inputs, a tiny startup wouldn't be able to collect the inputs -- including inside/detailed info -- all by itself. So starting as a service to the people who can demand such info (investors) makes sense -- and if it works, then they can "raise a fund themselves and beat the tar out of" the old guard. That's how it usually works in finance -- prove yourself first in the service of others, then once you have a track record, run your own show.
Totally agree. A pitch as a service to provide data points makes so much more sense - provide contacts, connections, past track record, competition profile, team profile, current user base, marketing efforts etc.. would be excellent.
First step in building a successful ANYTHING: make sure your product works! They are claiming to possess algorithms that are able to predict the success of a given startup. I'm curious to know how many startups they have successfully invested in that they have developed algorithms against.
I would imagine PG would be in a better position on MANY levels to write something like this, if possible. Indeed if such a thing were possible, I would imagine there would be some serious PhD research in this area.
I find it funny that this got as much initial positive press as it did whereas programs that can predict the stock market reliably (a much simpler problem given the relatively controlled/closed system of any given market compared to the virtual chaotic environment of a startup in the wild) are often passed off as snake oil in many circles. Taking this problem alone (automated trading programs) there are multi-billion dollar investment firms that spend millions on teams of developers and PhD working out formulas and algorithms to crack this problem; I know from personal experience that these systems are only good for a few months and are constantly changing. This fact alone diminishes the predictive value of whatever algorithms they have developed over time...
If two 20 somethings have something that groundbreaking that does NOT require constant tweaking (unlikely) then they must have discovered some new mathematical property governing human decision making at a large scale (even more unlikely).
"..there would be some serious PhD research in this area."
PhD research? I think you mean just 'research'. PhDs aren't the only ones doing research in this world. Neither are they the ones doing the 'hardest' or 'highest' research. Academia is just a different community from that of founder/VCs, or writers, or movie makers, or chess players. It has its own language, its own jargon, and its own objective functions for assigning value to work. But it's not more 'research-y'.*
'PhD research' means 'research work for which the audience is in academia'. The way it's normally used, one can just drop the first word without loss of meaning. Just like in the phrase 'personally believe' that William Zinsser picks on. (http://www.amazon.com/Writing-Well-25th-Anniversary-Nonficti...)
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* - Nor is it more open anymore, with all the emphasis on open software in industry, and the increasing commercialization of academic projects.
If they really wanted to create value through AI prediction of success based on social connections, they should have leveraged LinkedIn Accounts instead of trying to create a new social network.
A lot of people already have their network in there so the barrier to entry would be a lot lower.
Wouldn't their prediction necessarily affect the outcome? Even more so if they intend to market it to investors. If they predict startup X is going to fail, and this prevents people from investing in Startup X, then it's more likely that Startup X will fail. On the other hand if they predict that Startup Y will succeed, and Startup Y gets a lot of press coverage and interested investors because of that, then they helped it happen. It's kind of self-fulfilling.
In order for them to prove that their algorithm is really accurate, they'd have to make the prediction and keep it secret, then see later if it came true or not. Of course, that doesn't really make for a marketable service...
This is a bit unfair. It might be that their software isn't a perfect predictor, but just improves the prediction powers of a human VC by some amount. Their publicly available software, then, might be the company's bootstrap. Once they make enough cash to invest and ride out the swings, they can do (approximately) what Arrington is saying.