Lessons From the DotCom Bubble

bubble.jpg

Here is an argument you don't hear very often – there were too few failures during the dot-com bubble. Yes, you read that correctly. Too. Few. As in, not enough. As in, we needed more. Here's the argument…

The 50 percent failure rate of the dot-com era still seems high, until we put it into perspective. Compare the dot-coms to other business realms: From 1996 to 1998, for example, the survival rate for independent restaurants open for three years ran 39 percent. That is, a form of business with a very measurable market, using cooking technology that has existed for decades or more, failed 61 percent of the time. By comparison, the failure rate of Internet-based businesses tapping unknowable market opportunities with an unproven technology platform seems far more tame.

Perhaps this data simply suggests that the dot-com era was an overall success. Despite the trillions of dollars of market capitalization lost when the Internet bubble burst, maybe one should celebrate that the losses were not greater. But we disagree with this perspective. In fact, we bemoan the low failure rate.

To be clear, we do not wish that more startups had failed. Rather, to us, the low failure rate indicates that too few entrepreneurs were funded and too few new ventures launched. Had twice as many been launched, the short-term failure rate for individual businesses might have been higher, but a larger number of successful business models would probably have emerged, and these would have led to more enduring businesses in the long run.

The article concludes with an important point. A business is very much the test of a theory. I think people will buy my product. Let me try to sell it. I was wrong. Let me change my assumptions. And so on, until you figure it out.

Scaling before you have proven the theory doesn't make a lot of sense. That's why the first mover advantage is a fallacy. You have to be the first to really get it right, no to just sorta-kinda get it close. And the first mover is usually the one who is testing a partially incorrect theory.