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.
To determine the post-money valuation, you divide the expected value of the startup in question by the expected return on investment. The expected value is derived by taking the average sales of companies in the same market at the end of a projection period (four to seven years) and then multiplying that number by two. The return on investment should be between 10x to 30x.
To obtain the pre-money valuation, divide the post-money valuation by the amount you are raising.
The Berkus method
Five qualitative and quantitative factors make up this method, including: idea quality, prototype value, quality of the management team, strategic relationships and product roll-out/sales.
Each of these factors is then assigned a monetary value. Like with the other two methods, determining the idea quality and value of the prototype will include a comparison to benchmark companies.
Challenges in applying traditional methods to novel AI startups
AI can scale much faster than other technologies. What works at the beta or minimum viable product stage may not work when an AI product scales to millions of users. Some reasons include:
- Scaling is harder to predict with smaller data sets.
- Cleaning data for testing is relatively easy, but cleaning data at scale is harder.
- Very small internal employee mistakes can lead to catastrophic problems.
As a result, the application of traditional pre-revenue valuation methods is not as straightforward for prototype or novel AI startups. Of course, if the AI in question is very similar to an already existing AI product, comparisons via the methods above can be more accurate. However, such comparisons are trickier in the AI marketplace given the subjective nature of the assumptions you need to make.
Regulatory issues
The law regarding AI is still not concrete in many markets. For example, the EU recently proposed regulations governing the use of AI and related liability issues in self-driving cars. In the U.S., the Algorithmic Accountability Act was introduced in early 2022, which, if passed, would require impact statements from companies that generated more than $50 million during the preceding three-year period.
While this doesn’t affect pre-revenue companies, the implications for a fully scaled company under, say, the venture capital method, are relevant depending on the market. Regulatory uncertainty can give rise to variables around liability exposure when the AI in question scales. These variables will affect assumptions of future value if we use traditional methods.
Suggested approaches
The value of a copycat AI idea can be measured more easily, but competitive advantages usually mean that novel innovations or applications for AI are being marketed for investment. This makes it harder to measure their value using any of the methods we saw above.
There is no silver bullet, but we can minimize the number of uncertain variables in a number of ways. For example, we could examine how relatively comparable AI has scaled with larger data sets; whether the costs of cleaning similar data at scale elsewhere were prohibitive or review the quality of internal measures to lessen the risk of employee mistakes. As for regulatory issues, keeping an eye on the AI proposals being set forth and how they may affect liability is one way to minimize uncertainty.
Company valuation is a separate industry into itself. Investors are better off seeking professional input, along with counsel who is familiar with the issues that commonly arise. This article is but a general roadmap of some of the issues.