Startups valuations in early-stage venture capital

Valuations are a big mystery to many entrepreneurs. And for good reason. In the early stages, valuation is not a factor of revenue or much else.

According to research done by Ilya Strebulaev, a professor of venture capital at Stanford Graduate School of Business, and his collaborators, most VCs, especially early-stage VCs, don’t use techniques such as discounted cash flow or net present value or other financial models to assess.

Instead, in the early stages, valuation is often just a factor of how much money the company is raising in the round. In the first couple of rounds of investment in tech startups, each new round typically gives a total of 15 to 30 percent ownership to investors.

The lead investor takes somewhere around one-half to two-thirds of the round, and smaller investors take the rest, diluting the ownership of the founders and employees by that percentage.

The valuation is calculated based on the dollar amount invested in the round and the 20 to 30 percent dilution; effectively, then, there’s no hard science behind assigning a valuation to a company. (This holds true mostly for earlier-stage rounds and tech companies, though life-science companies typically see larger dilution amounts.)

Valuation is merely the output of this calculation, not the input.

As an example, if a tech company is raising a $ 2.5 million seed round, the post-money valuation is probably somewhere in the range of $ 8.5 to $ 12.5 million, giving the investors a total ownership of 20 to 30 percent.
Or if the company is raising $ 5 million, the valuation is typically somewhere between $ 16 to $ 25 million.

The company may have already earned revenues and gained some traction, or it may just be getting started. The valuation at this point is typically not calculated based on a revenue multiple, but rather on what can help the company get to the next meaningful stage and what can give investors a meaningful stake in the business for the risk they are taking. It’s more a matter of practicality than financial modeling.


This is one of the many passages I read in books and articles on a daily basis. They span many disciplines, including art, artificial intelligence, automation, behavioral economics, cloud computing, cognitive psychology, enterprise management, finance, leadership, marketing, neuroscience, startups, and venture capital.

I occasionally add a personal note to them.

The whole collection is available here.