Valuing pre-revenue technology Startups are an established process today, but are the methods employed using novel artificial intelligence equally applicable to early-revenue companies? What issues arise when applying AI to growing startups that can quickly scale to millions of users? These questions are not academic.
This article provides a primer on the traditional methods used to value pre-revenue startups, examines some of the limitations that arise when these methods are used for new AI startups, and suggests ways to mitigate risk.
Let’s start by looking at three generally accepted ways to value pre-revenue or early-stage companies: scorecard pricing, venture capital, and the Berkus method. Later we look at some of the challenges of implementing these methods for an early-stage company with novel AI applications.
Scorecard evaluation method
AI can scale more quickly than other technologies, so an AI product that works at the beta or minimally viable production stage may not work for millions of users.
This evaluation method seeks to compare a startup with others in the market.
First, the average pre-fund valuation for other startups in the same market is determined. Next, this valuation is used to compare the startup in question, taking into account factors such as the strength of the management team, the size of the opportunity, product/technology, competitive environment, and marketing/sales channels.
Although very subjective, each of these items is valued – just like a scorecard. If the average pre-money price for startups in the market is $1 million and a startup’s variance factor is 1.125, the two numbers are multiplied to get the pre-money valuation.
Venture capital method
The venture capital method seeks to determine a startup’s pre-funding valuation by extrapolating its post-funding value. As with scorecard pricing, you need to make assumptions by comparing your startup to benchmark companies in the same market.
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