Against the backdrop of a frosty market for startup fundraising, AI companies are raising large rounds on fairly relaxed terms. Driven by an unprecedented hype surrounding AI technologies, these companies are commanding valuations that are reminiscent of the highs of 2021.
While the excitement is understandable, both founders and investors must navigate this terrain with a clear understanding that, at its core, a company’s valuation is fundamentally anchored in its future cash-generating potential. Whatever hype-driven premium they can command today should be justifiable based on a reasonable (though optimistic) view of the future.
So, as the founder of an AI startup, how should you think about your valuation as you approach your first funding round? The terms you get will set the tone for future raises, and recent history is a reminder that raising at too-high a valuation can be seriously problematic down the road.
The Bedrock of Valuation
The allure of AI startups is undeniable, but the basic principles of valuation remain unchanged. A company’s worth is intimately tied to its ability to generate revenue and profits in the future. This reality prompts a crucial reflection for both founders looking to raise capital and investors searching for the next breakthrough.
How do you reconcile the seemingly incredible potential of AI with the pragmatic outlook of valuation? Essentially, it’s through understanding the economic energy your company can unlock, which can be viewed through four lenses:
Value Proposition: business model perspective
In a basic sense, the value that your company provides plays a pivotal role in customer acquisition and growth. Whether it’s through enabling revenue growth or reducing costs, the financial proposition of your AI solution is how you can justify a premium (and how large a premium) over competing offerings.
Depending on your pricing (and a venture backed startup is likely to go for aggressive growth over higher margins) you can then project growth based on the speed at which you can onboard new customers. This all connects together into a ‘growth engine’, helping you to present a coherent story.
Market Potential: top-down perspective
One of the crucial factors to consider is the market in which an AI company operates. An AI-driven enterprise servicing the insurance industry, for example, has its long-term growth potential intrinsically linked to the overall size and expansion rate of the insurance market itself. Similarly, a startup that applies AI to the hospitality industry will face similar existential risks to the rest of that industry, such as the COVID pandemic.
This relationship between startup and industry highlights the importance of rooting your company’s potential in the rational constraints of existing markets, which are better understood than emerging technologies looked at in isolation.
Budget Allocation: bottom-up perspective
Another aspect of understanding growth potential is identifying the budget that your product or service taps into. For example, as an AI startup focused on data analysis in retail, you should research the typical budget allocated by retailers for that purpose. This insight allows you to gauge the potential value each customer brings to the table, thereby informing pricing strategies and revenue forecasts.
Even when your product promises great return on the associated fee, in terms of revenue growth or cost savings, you will often find that customers have to justify it in the context of existing budgets. This can evolve over time, as you build traction and proof in the market, but it is likely to be a hurdle with early customers.
Benchmarking: market perspective
Finally, another way to get perspective on your financial projections is to look at how other companies are doing. This provides a very broad picture which shouldn’t necessarily shape your projections unless you appear wildly out of alignment, and is more useful for understanding investor perspective.
For example, you can look at our data on average startup revenue growth to get an idea of what the standard might look like. Additionally, and perhaps especially appropriate to startups at the cutting-edge of AI, you can compare your growth to some of the fastest growing companies in history. If you are forecasting exceptional future growth which puts you on a par with Google or Amazon, do you have a similarly compelling ‘dotcom boom’ style story to tell investors?
You can also use Equidam to build benchmarking on valuation, revenue and EBITDA directly into your valuation report.
Projecting Into an Uncertain Future
It must be said that financial projections are rarely reliable, and investors are often hesitant to put too much stock in their accuracy. However, the goal here is to illustrate that your future vision is based on tangible and well researched factors. It is as robust and reliable as possible, and you aren’t just using hype as a screen for questionable economics or market opportunity. We have a library of resources which can help you get to grips with solid financial projections along with a template file.
The alternative approach often employed by investors is to look at comparable deals as a benchmark, an easy tool to find terms that seem appropriate for your startup. This may be particularly risky for AI, as the currently buoyant market with intense optimism about the potential AI will not last. Either AI will not live up to expectations, and valuations will fall, or it will, and the saturation of the market with new AI companies will ultimately bring valuations down anyway.
Beginning your fundraising journey by exploiting current market conditions for a high valuation – as mentioned above – is likely to cause problems down the line, and you will find yourself competing for the attention of the best investors or looking at an uphill struggle against growth expectations that may not be entirely reasonable.
Rationality in the Face of Hype
The past three years have served as a cautionary tale about the dangers of succumbing to market trends without a solid foundation in value. The initial wave of excitement around generative AI startups, for instance, is already facing challenges as the reality of sustaining hype-inflated valuations becomes apparent. This has led to consolidation among the affected startups, with many struggling to raise subsequent rounds, accepting reduced valuations, or closing their doors entirely.
In this context, the allure of ‘hot’ deals for investors and the temptation for founders to pursue inflated valuations should be tempered with a rational perspective on value. By focusing on the fundamentals of what drives company growth and valuation, founders can leverage the AI hype to attract attention from desirable investors. This approach enables the closing of deals at fair valuations and on favorable terms, ensuring a sustainable and prosperous future for the company – the goal that valuation should aim towards.
In summary, while AI brings with it a whirlwind of excitement and potential, the principles of valuation remain firmly rooted in the future cash flows a company is expected to generate. By adhering to a disciplined and rational approach to fundraising, AI startups can navigate the hype to achieve long-term success and stability.