Valuing pre-revenue tech startups is an established process today, but do the methods employed apply equally to pre-revenue companies using novel artificial intelligence? What kind of issues arise when you apply them to startups that are developing AI that can scale rapidly to millions of users? These questions are no longer academic.
This article provides a primer of the traditional methods used to value pre-revenue startups, examines some of the limitations that arise when these methods are used for novel AI startups and suggests ways to reduce risk.
Let’s start by looking at the three generally accepted ways of valuing pre-revenue or early-stage companies: Scorecard valuation, venture capital and the Berkus Method. We’ll later delve into some of the challenges in applying these methods to an early-stage company with novel AI applications.
Scorecard valuation method
AI can scale much faster than other technologies, so what works at the beta or minimum viable product stage may not work when an AI product scales to millions of users.
This valuation method seeks to compare a startup with others in the market.
First, the median pre-money valuation for other startups in the same market is determined. Then, this benchmark of value is used to compare the startup in question taking into account factors such as the strength of the management team, size of the opportunity, the product/technology, competitive environment and marketing/sales channels.
While highly subjective, each of these factors is assigned a value — akin to a scorecard. If the median pre-money valuation for startups in the market is $1 million and a startup’s various factors amount to 1.125, the two numbers are multiplied to obtain the pre-money valuation.
Venture capital method
The venture capital method seeks to determine a startup’s pre-money valuation by extrapolating its post-money valuation. Like with scorecard valuation, you need to make assumptions by comparing the startup to benchmark companies in the same market.