The methods used to evaluate pre-revenue tech companies using novel artificial intelligence are not the same as the methods used to evaluate existing tech companies. What kind of issues arise when you apply them to a startup that is developing an artificial intelligence that scales quickly to millions of users? The questions are no longer considered to be academic.
The 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 artificial intelligence startups, and suggests ways to reduce risk.
Scorecard valuation, venture capital and the Berkus Method are the three generally accepted ways of valuing pre-revenue or early stage companies. There are some challenges in applying these methods to an early-stage company.
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.
A startup is compared with others in the market.
The median pre-money valuation for other startup in the same market is determined first. Factors such as the strength of the management team, size of the opportunity, product/technology, competitive environment and marketing/sales channels are taken into account to compare the startup in question.
Each factor is assigned a value similar to a scorecard. If the median pre-money valuation is $1 million and a startup's various factors are less than 1.125, the two numbers are combined to get the pre-money valuation.
The venture capital method tries to determine a startup's pre- and post-money valuations. You need to make assumptions when you compare the startup to the benchmark companies.
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