Folly to forecast startup performance?

Several days ago, Paul Graham, co-founder of noted Silicon Valley accelerator Y-Combinator (YC), wrote an exceptional post, "Black Swan Farming," observing how crazy difficult it is to predict success in the start-up space, and noting that just two companies - Airbnb and Dropbox - account for about 75% of the total value created by all YC-associated companies.

Yesterday, Dave McClure (the white-hot seed-stage Silicon Valley investor, familiar to readers of this column - see this discussion of his small bets style in connection with digital health) responded in a post titled (what else?) "Screw the Black Swans" that his investment model (at 500 Startups) is slightly different. 

While most VCs are looking for the big score, McClure said, he's deliberately seeking singles and doubles, which he basically expects will result in a similar expected value for his portfolio but reduce the chances of getting shut-out.  He anticipates and is hoping for a greater number of successes (albeit more modest ones) than achieved by other VCs.

This will be a familiar dialog not only to investors but also to those in biopharma (who perhaps should be thought of as investors as well), as they continuously need to decide whether to go for a risky potential blockbuster or more of a sure-thing that ostensibly may be associated with a smaller market.

I've been fascinated with this exact question for a while (see here and here), and I've always looked at the problem a bit differently than McClure - which, if I'm right, may actually be good news for him.

While robust data are difficult to come by, so much of what I've seen in the two exception-dominated spaces with which I'm most familiar - startups and in drug development - suggest that Paul Graham (and Nassim Taleb - see this recent WSJ piece arguing most successful investors are lucky not good, nicely summarized here by Business Insider) are right: success, and particularly the magnitude of success, is virtually impossible to predict. 

The folly of forecasting, if taken seriously, is a mind-blowing concept that is at once viscerally uncomfortable and operationally nearly impractical; as a result, most investors and businesses effectively ignore it, and basically (as I've discussed in perhaps my favorite piece) "assume a can opener."

Now add to the mix the well-described tendency of startups to pivot like mad, and you have a situation where I suspect it's not at all clear whether you have a potential single or homerun on your hands, even if McClure (who usually acknowledges the limitations of forecasting) believes he can accurate predict this.   Yet (as McClure certainly recognizes), many incredibly successful companies in the valley started off focused on a very narrow problem, then subsequently realized their solution or approach could be applied or redirected to a larger opportunity.

Sure, I appreciate that the idea of trying to solve huge problem seems more likely to be massively successful than if you're trying to solve a trivial problem, and I also believe that while you can't predict the magnitude of success, you can certainly increase the probability of failure - wrong team, wrong problem, even wrong advice from investors.  And let's be honest - there are a large number of entrepreneurial efforts that are overtly, painfully trivial; it's just that I don't see McClure going after them. 

Having highlighted the problems with predictions, let me go ahead and make a counter-intuitive one: in 10 years, the best performers in McClure's portfolio could very well create more value (to use Graham's standard - and note this value may or may not be realized by the seed-stage investor) than the top performers at YC.

Further Black Swan reading:

WSJ book review of The Black Swan (written before I met Taleb)

Financial Times commentary about serendipity and drug discovery (co-written with Taleb)

 

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