Aileen Lee (@aileenlee) | TechCrunch
It’s been over a year since we wrote our original post sharing our analysis of the last decade’s most successful U.S.-based, venture-backed tech companies.
As we wrote in our original post – many entrepreneurs, and the venture investors who back them, seek to build big, impactful companies valued at a billion dollars or more.
We called these companies “unicorns” because what they had achieved seemed very difficult, rare, and relatively unstudied.
That billion-dollar threshold is important, because historically, top venture funds have driven returns from their ownership in just a few companies that grow to be super-successful.
And as most traditional funds have grown in size, they require larger “exits” to deliver acceptable returns (Cowboy Ventures is different – we’re small by design, although we’d be happy to invest in Unicorns).
For example, to return just the initial capital of a $400 million venture fund, that might mean needing to own 20 percent of two different $1 billion companies at exit, or 20 percent of a $2 billion company when the company is acquired or goes public.
The post and term generated more attention than expected. It’s been a nice surprise – it’s a special word for a special thing, and we love it’s not traditional business lingo.
From some, there’s even concern now that pursuit of ‘unicornhood’ is both annoying and may have somehow changed the nature of tech valuations. To that, we’d emphasize two points:
First, our project uses valuation as a filter (an admittedly imperfect one) to identify and learn from the fastest-scaling tech companies of our time.
Our goal is learning, not list making. Nor is it to encourage companies to optimize point-in-time paper valuations, which have a lot of downside if they are not sustainable.
Second, as noted, today’s traditional venture firms are sized to need unicorn exits to deliver returns.
Some investors may grumble about entrepreneurs wanting ‘unicorn valuations.’ But let’s be honest, most investors want them, too, and are supporting the massive capitalization of these companies. (More on that later.)
So with the big caveats that our data is based on publicly available sources, as well as on a snapshot in time (which has definite limitations), here is a summary of new learning and reinforced lessons from an updated set of companies1: