A few weeks ago, the Guesstimate beta came out. It's pretty cool; it’s like Excel with Crystal Ball built right in. You can input a single number or a range of values and build models with it. Guesstimate’s release and the holiday season gave me the perfect chance to explore an idea on the startup industry. I had been meaning to building a model to understand the formation and development of a startup to its eventual failure or exit.
- There’s a huge amount of disagreement in just how many startups are started every year. The Kauffman Foundation says that ~6,000,000 new businesses are created, while not stating how many are high growth startups. Marc Andreessen says there are 4,000 startups that are created. In addition, people still don’t agree on the definition of a startup.
- It’s really hard to build startup. So, so many fail. The vast majority of new businesses fail to attract any angel or VC funds at all.
- Power law distributions are still not internalized by people (and not well represented by this model). The magnitude and difference of returns that one company can generate is just astounding. WhatsApp raised a total of $60 million while exiting at a total valuation of $19 billion, a 316x return on invested capital. 50% of startups will fail to return anything, and the next 40% of startups above that will hopefully return the the total invested capital of investors. It is the WhatsApps of the world, the top 1% that bring home meaningful returns.
- Angel Investors make up a huge not-as-often-recognized pool of capital to startups. $20 billion is invested per year by Angels into startups. Their importance is hard to overstate at the earliest stages where they enter 50 to 70k deals per year. This prominence has grown since the 2000s due to the low cost of doing a startup provided by AWS and other related services. Since the costs of starting a software startup have dropped so low, VCs aren’t able to deploy such little capital in one deal. Their model does not work like that. Angel Investors, do in fact generate a nice return, in line with VC returns.
- Exploring how broader macroeconomic trends influence the startup industry. At the midpoint of 2015, China was on pace to invest $30 billion through venture capital. How will 2016 China influence funding this year, and how will these impact the startup ecosystem 5 - 10 years down the line? (Thanks Daniel)
- How the industry (and cost of doing a startup) affects the rate of formation. While we’ve seen a veritable boom in the formation of software startups, the same can’t be said for life science startups, where the number of initial financings by VCs has remained unchanged. As the cost of doing startups comes down, we should see a pattern of more hardware and biology startups being funded at the early stages. PCH International and Transcriptic are working to do their part to lower costs in their respective industries.
- Making this model more of a simulation to see how the ecosystem evolves over time. I would like to see how exits by the large companies are able to seed the next generation of angel investors and provide landing grounds for acquisitions. Silicon Valley wasn't built overnight. The dynamic process of companies exiting and investors passing on advice to the next generation is an important to creating huge companies and innovative ecosystems.
- Add more data! I’d like to see how individual firms, investors, and entrepreneurs are able to influence the growth of a startup instead of aggregated statistics provided by reports.