Mick Carolan
General Partner, Princap
AI Startups: Valuations and Multiples 2024
By Lior Ronen | Founder, Finro Financial Consulting
Ever seen the Pope rock a big, white puffy coat?
Or caught a self-driving taxi zipping through Manhattan?
These aren't just cool scenes from the future; they're real deals pulled off by AI startups today.
And guess what?
It's not just their tech that's making waves but also their sky-high valuations.
Figuring out what an AI startup is worth isn't just about putting a price tag on it.
It's more about seeing into the future — what investors think this tech goldmine could rake in down the line. We're diving into the nuts and bolts of AI startup valuations, how to stack them up against each other, and what the deal is with revenue multiples.
We'll take a closer look at some big names you've probably heard of — like OpenAI, Anthropic, and Perplexity, the smart search engine.
As a leading startup valuation firm, we're laying out the AI market scene in 2024, shining a light on the multiples making rounds in the AI world.
Just a heads up, though — these multiples are just part of the story. They're not the be-all and end-all for valuing these tech wizards. If you're looking to get the lowdown on a specific AI player's worth, hit us up directly.
tl;dr
AI startups are on the rise, with valuations soaring and investors placing high bets on their future potential. This article breaks down the world of AI startup valuation, exploring methods like revenue multiples and comparing leading companies across various niches. The average revenue multiple of 40.6x highlights the immense expectations for these innovators, and the coming years will be crucial in determining their success and market impact.
AI startups are on the rise, with valuations soaring and investors placing high bets on their future potential. This article breaks down the world of AI startup valuation, exploring methods like revenue multiples and comparing leading companies across various niches. The average revenue multiple of 40.6x highlights the immense expectations for these innovators, and the coming years will be crucial in determining their success and market impact.
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What is AI Startup Valuation?
Valuing an AI startup is all about figuring out how much these cutting-edge companies are worth. But first, let's break down what we mean by an AI startup.
These are the newcomers on the block, harnessing artificial intelligence — technology that enables machines to mimic human learning and decision-making.
From making sense of human speech to recognizing images, AI startups are revolutionizing sectors like healthcare, finance, and retail, aiming to streamline operations and enhance our daily lives.
Investors are keenly watching these startups, betting on their potential to lead the next wave of technological innovation with solutions that automate complex tasks, thereby making our lives easier and more efficient.
At the heart of many of these ventures is AI technology, yet they often operate on a subscription model, similar to Software as a Service (SaaS), offering AI-powered tools and services for a regular fee.
The challenge in valuing AI startups lies in their novelty and the speculative nature of their future success. It's not just about what they're earning now but about predicting how they'll transform industries.
The right valuation method depends on where the startup is in its growth journey and the specifics of its business, as we'll see in the table below.
A deeper dive explaining step-by-step how to value AI startups is available in this guide.
| Valuation Method | Pros | Cons |
|---|---|---|
| Berkus Method | - Simplifies valuation for early-stage startups. - Focuses on qualitative factors. - Quick to apply and understand. |
- Highly subjective. - Limited to a maximum valuation. - May not reflect actual market potential. |
| Scorecard Valuation Method | - Offers a comparative approach against similar startups. - Considers a broader range of factors. - Adaptable to different regions and sectors. |
- Subjective in weighting and scoring. - Relies on the availability of benchmark data. - May not capture unique aspects of AI startups. |
| Revenue Multiple Method | - Straightforward and easy to understand. - Based on actual and projected financial performance. - Widely used and accepted. |
- Relies on finding appropriate comparables. - May not account for long-term growth potential. |
| EBITDA Multiple Method | - Suitable for startups from seed stage with clear a business model. - Reflects profitability and operational efficiency. - Industry multiples provide a market-relevant perspective. |
- EBITDA can be influenced by accounting practices. - Requires accurate industry multiple data. |
| Discounted Cash Flow (DCF) Method | - Detailed and comprehensive. - Incorporates future growth and risk factors. - Can be highly tailored to the startup's specifics. |
- Complex and requires extensive financial forecasting. - Highly sensitive to assumptions used. - Time-consuming and requires financial expertise. |
How to Compare AI Startups Valuation?
When comparing companies of different sizes, the challenge is evident:
How do you draw a fair comparison between a behemoth with a $100 billion valuation and a budding startup worth $10 million?
Or between a company raking in $1 million annually versus another that's pulling in $100 million?
The disparity in size and revenue can make direct comparisons seem impossible.
Enter the revenue multiple, a crucial tool in the valuation toolkit.
The revenue multiple allows us to compare companies by looking at the relationship between their market value and their annual revenues.
This ratio has become an industry standard for a good reason: it levels the playing field, enabling investors and analysts to compare the value of companies, big and small, within the same industry.
This is especially useful in sectors like AI, where a startup's value often hinges more on its growth potential and innovative capabilities than on its current revenue streams.
By using revenue multiples, we can shed light on the future promise of AI startups, making it easier to spot those with the potential to dominate their niches.
For example, in the chart below, we’ve compared eight leading AI companies from different niches. Without the revenue multiple, we couldn’t really compare these usefully and effectively.
The revenue multiple allows us to neutralize the size of the business and annual revenues from the equation.
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What is Revenue Multiple?
Ever wondered how to compare the worth of companies distinctly different in size and earnings?
That's where the term "revenue multiple" comes into play.
Imagine you're at a garage sale deciding between two vintage lamps: one priced for its unique history and another for its appealing design. Similarly, in the business realm, valuing a company involves various factors, but for a uniform comparison, we use the revenue multiple.
What is a Revenue Multiple?
A revenue multiple acts as a universal measure, enabling comparison of companies regardless of their scale or profit margins. It's essentially a ratio reflecting how the market values every dollar of a company's sales. For example, a revenue multiple of 5x suggests the market values the company at five times its current annual revenue. So, a company generating $1 million annually is considered to be worth $5 million by the market.
Calculation of Revenue Multiples
The formula for calculating a revenue multiple is:
Revenue Multiple= Company’s Market Value / Annual Revenue
This straightforward calculation provides a comparative figure to assess how investors value the revenue of different companies.
Significance of Revenue Multiples
Revenue multiples offer insights into market expectations. A high multiple might indicate anticipated growth or significant market advantage, while a lower multiple could signal investor caution due to various challenges.
Pros and Cons
Revenue multiples simplify company valuation, particularly in dynamic industries, by providing a direct comparison metric. However, they don't detail profitability or future earnings potential. Companies with identical multiples might have vastly different financial health or growth prospects. External factors like market trends and economic conditions also affect these multiples, underscoring the importance of comprehensive analysis beyond just revenue multiples.
In essence, while revenue multiples are a valuable part of the valuation toolkit, they're merely the starting point for deeper investigation into a company's overall worth.
AI Valuation Multiples 2024 Analysis: Methodology
We analyzed the latest valuations, revenues, and revenue multiples of what we view as the 30 top private AI startups as of 2024. This offers a snapshot of the sector's meteoric growth.
As background, a revenue multiple compares a company's value to its annual revenue. This ratio helps benchmark high-growth startups regardless of profitability or size. Specifically, we divide the company's valuation by total yearly revenue.
The resulting multiple shows investors' willingness to pay for each revenue dollar - a useful proxy for future prospects. The higher the ratio, the more bullish investors are on substantial expansion ahead.
Our list focuses on trailblazing private companies with AI at the core - from categories like large language models, generative AI, fintech innovations, and advanced health technologies. We prioritized startups with available data on both market valuations and revenues over the past year. Of course, reported figures have their limitations.
There may be other promising startups we missed due to information gaps.
So if you know of any significant player lacking accessible valuation or revenue details, we welcome you to contact us. We intend to continually update this analysis as new data emerges on leading AI startups’ progress and financials.
With open collaboration, we can make this review increasingly comprehensive - an accurate benchmark as AI startups shape the technological landscape. Please reach out with any added intelligence on startup valuations so we can enrich and share these insights.
Lior has consistently delivered analysis of both depth and accuracy. His foresight in identifying pivotal trends and disruptions within the sector has delivered real value. His adeptness and synthesis of large data sets into actionable intelligence based on key comps has also been instrumental in refining our investment strategy. This has been particularly valuable for a pre revenue company.
He is also a very good man, quick to respond despite the time zone and is extremely easy to deal with. I can therefore unreservedly recommend Lior and FINRO to anyone seeking to navigate the complexities of the tech sector with confidence and strategic advantage.
AI Valuation Multiples 2024 Analysis: Results
What stands out from our analysis is the diverse approaches and innovative solutions of these AI startups, showcasing the dynamic and ever-evolving nature of the AI sector.
Interestingly, the average revenue multiple for the listed companies is 40.6x, underscoring investors' significant expectations for their future growth and profitability.
Here are five counterintuitive insights regarding the revenue multiples of leading AI startups:
Generative AI vs. Image Generation: Despite the close relationship between generative AI and image generation in content creation, there is a significant disparity in revenue multiples, with OpenAI at 61.5x and Stability AI at 22.6x.
Fintech Variety: Within the fintech sector, the valuation multiples vary widely, from Gong AI at 40.7x to HighRadius at 12.4x, and AlphaSense at 25.0x, indicating a non-uniform valuation approach across fintech AI startups.
Development Tools Standing Out: Hugging Face, with development tools for AI, has an exceptional revenue multiple of 112.5x, suggesting that the tools enabling AI development may be valued more highly than the applications they help to create.
Search Engines: AI startups in the search engine niche, like Perplexity and Glean, command high multiples of 69.3x and 60.6x, respectively, which is unexpected in a market with dominant players like Google.
Health Tech Discrepancies: The health tech sector shows a broad range of revenue multiples, with Verily Life Sciences at 12.5x, Freenome at 19.3x, and Atomic AI at 50.0x, reflecting the unpredictable nature of valuation in this critical and innovative sector.
As investors continue to place their bets on these companies, the coming years will be key in seeing how their valuations make a real impact and lead to success. We're particularly curious to observe how these average multiples might shift as the market evolves. The valuation differences within the same sectors and between related fields highlight the complexities of the AI market and suggest that the path to profitability might not always be straightforward or predictable.
Revenue Multiples in AI Vs. Non-AI Niches
In the previous sections, we broke down the definition of an AI startup and saw how different leading AI startups are valued. We compared the valuation and multiples of the top AI developers and saw the differences between the niches within the AI market.
The overall multiple in the AI market is 40.6x. But is it high? Low? How does it stand compared to other hot trends in tech like fintech, vertical SaaS, protect, or payment startups?
We already saw in the chart above that the average revenue multiple of AI startups is 5x higher than SaaS companies. This means AI companies have a premium of 32.7x in revenue multiples compared to SaaS.
How are they compared to other niches?
The chart below shows the massive premium on revenue multiples in AI startups compared to other niches.
AI startups have the biggest premium if you compare them to SaaS companies. As you go more specific into the niche, the premium shrinks, as we see in the vertical SaaS, proptech, payments, cybersecurity, insurtech, and fintech below.
We'd like to invite you to share your thoughts on these findings.
What trends do you see? Which AI startup’s approach do you find most promising?
And what do you think about the average revenue multiple of 40.6x - does it align with your expectations?
Do you think the valuation premiums will decline in 2024?
Key Takeaways
AI's Valuation Challenge: Valuing AI startups is complex, involving both current performance and speculation on future impact.
Diverse Revenue Multiples: AI startups show varied revenue multiples, reflecting different investor expectations within the AI sector.
Generative AI's High Value: Generative AI firms like OpenAI have higher revenue multiples, indicating strong market optimism for content creation AI.
Search AI Surprisingly Valued: AI search engines like Perplexity boast high revenue multiples, despite competition from established giants like Google.
Health Tech's Varied Valuation: Health tech AI startups exhibit a wide range in multiples, demonstrating the sector's valuation volatility.
Answers to The Most Asked Questions
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AI startup valuation considers current earnings and forecasts future industry impact; methods vary by growth stage.
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The average revenue multiple for AI startups is 40.6x, indicating high investor expectations for future growth.
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AI companies are valued using revenue multiples, considering speculative future success, and current technological advancements.
