Will AI Mess Up Revenue Multiples?
10/27/24 Benchmarks for Operators
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Will AI Mess Up Revenue Multiples?
AI is shaking up a lot of industries, but one question I keep coming back to: what does it mean for how we value companies? Specifically, will AI revenue kill the beloved ARR multiples that SaaS companies rely upon?
As we move to a pricing model based on outcomes rather than access, AI revenue feels more transactional, and less predictable.
Am I wrong?
I asked Kyle Poyar from
this very question, and his response gave me a lot to think about.Recurring vs. Reoccurring Revenue – What's the Difference?
Kyle pointed out something that’s easy to gloss over—there’s a big difference between recurring and reoccurring revenue. Let’s break it down:
Recurring revenue is predictable, happening at regular intervals for the same amount. Think about my monthly Spotify subscription of $11.99, billed like clockwork.
Reoccurring revenue happens more than once, but it’s irregular, both in timing and amount. Think Uber rides or payments revenue for a company like Toast—restaurants continue to use the service, but the amounts vary.
A lot of AI products, especially those linked to a successful outcome, like support tickets solved or meetings booked, fall into this "reoccurring" category. You might keep using the product, but it’s not as predictable or consistent as a standard SaaS subscription.
What About Valuations?
Here’s where Kyle's insight really hit home:
"If you think about valuations, they're very tied to ARR multiples in software, not like other industries where it's maybe tied to EBITDA multiples.
We're very attached to ARR multiples because the revenue is high quality. It's usually high margin, at least 70%, often 80%. So where ARR is kind of a proxy for gross profitability, it’s highly sticky, with net dollar retention over 100%. You can feel confident that ARR will stay consistent or increase over time.
But with AI products, margins are often worse than classic SaaS. Some AI products even have negative or highly variable margins. Plus, AI revenue isn’t as predictable, with high churn from early adopters who might not have a mission-critical use case yet.
ARR won’t be the best predictor of valuations for AI companies. We’ll need alternative metrics to help us assess the quality of these businesses."
-Kyle Poyar on the RTN Podcast
Is ARR Still King?
In SaaS, we’ve long relied on ARR multiples because, as Dave Kellogg joked, "If we don’t mess up, this is what we get next year."
But with AI, things get a little messier. AI products don’t have the same margins, and churn is higher because many early users are still just "kicking the tires." So, does this really count as ARR?
ARR assumes steady, predictable revenue streams, and AI doesn’t always fit that mold.
Time for a New Metric?
If ARR multiples don’t work as well for AI companies, what’s next? Kyle suggests we start thinking about annual revenue run rate (ARRR) instead. This would capture all types of revenue—whether it’s from software, AI, or payments—and then assess the quality of that revenue based on mix. We should be asking: what’s the margin? How predictable is it? How sticky is the customer base?
Perhaps we stop looking forward, and start looking backwards at the trailing twelve months of revenue? At least that way we’ll know what actually occurred. But that, of course, would mess up discounted cash flow models, which are predicated on forecasting the future.
Maybe, as someone commented on one of my LinkedIn posts: "We just go back to GAAP revenue and cut out all the fluff?" He is annoying. But he’s got a point.
Another issue: a lot of public companies don’t even give you a proper ARR number. Some just multiply quarterly revenue by four, which isn’t really what ARR is meant to reflect.
There’s hope that AI margins will improve as products scale, but until then, we should be cautious about blindly applying ARR multiples to AI businesses without deconstructing revenues. Kyle summed it up well: AI isn’t taking CFO jobs anytime soon—in fact, it’s giving them even more to think about.
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TL;DR: Multiples are UP week-over-week.
Top 10 Medians:
EV / NTM Revenue = 14.1x (-0.2x w/w)
CAC Payback = 17 months
Rule of 40 = 52%
Revenue per Employee = $531K
Figures for each index are measured at the Median
Median and Top 10 Median are measured across the entire data set, where n = 110
Population Sizes:
Security: 17
Database and Infra: 14
Backoffice: 16
Marcom: 16
Marketplace: 15
Fintech: 16
Vertical SaaS: 16
If you’d like the company level metrics used in these reports, upgrade to paid and you can download the excel sheet at the bottom of this post
Revenue Multiples
Revenue multiples are a shortcut to compare valuations across the technology landscape, where companies may not yet be profitable. The most standard timeframe for revenue multiple comparison is on a “Next Twelve Months” (NTM Revenue) basis.
NTM is a generous cut, as it gives a company “credit” for a full “rolling” future year. It also puts all companies on equal footing, regardless of their fiscal year end and quarterly seasonality.
However, not all technology sectors or monetization strategies receive the same “credit” on their forward revenue, which operators should be aware of when they create comp sets for their own companies. That is why I break them out as separate “indexes”.
Reasons may include:
Recurring mix of revenue
Stickiness of revenue
Average contract size
Cost of revenue delivery
Criticality of solution
Total Addressable Market potential
From a macro perspective, multiples trend higher in low interest environments, and vice versa.
Multiples shown are calculated by taking the Enterprise Value / NTM revenue.
Enterprise Value is calculated as: Market Capitalization + Total Debt - Cash
Market Cap fluctuates with share price day to day, while Total Debt and Cash are taken from the most recent quarterly financial statements available. That’s why we share this report each week - to keep up with changes in the stock market, and to update for quarterly earnings reports when they drop.
Historically, a 10x NTM Revenue multiple has been viewed as a “premium” valuation reserved for the best of the best companies.
Efficiency Benchmarks
Companies that can do more with less tend to earn higher valuations.
Three of the most common and consistently publicly available metrics to measure efficiency include:
CAC Payback Period: How many months does it take to recoup the cost of acquiring a customer?
CAC Payback Period is measured as Sales and Marketing costs divided by Revenue Additions, and adjusted by Gross Margin.
Here’s how I do it:
Sales and Marketing costs are measured on a TTM basis, but lagged by one quarter (so you skip a quarter, then sum the trailing four quarters of costs). This timeframe smooths for seasonality and recognizes the lead time required to generate pipeline.
Revenue is measured as the year-on-year change in the most recent quarter’s sales (so for Q2 of 2024 you’d subtract out Q2 of 2023’s revenue to get the increase), and then multiplied by four to arrive at an annualized revenue increase (e.g., ARR Additions).
Gross margin is taken as a % from the most recent quarter (e.g., 82%) to represent the current cost to serve a customer
Revenue per Employee: On a per head basis, how much in sales does the company generate each year? The rule of thumb is public companies should be doing north of $450k per employee at scale. This is simple division. And I believe it cuts through all the noise - there’s nowhere to hide.
Revenue per Employee is calculated as: (TTM Revenue / Total Current Employees)
Rule of 40: How does a company balance topline growth with bottom line efficiency? It’s the sum of the company’s revenue growth rate and EBITDA Margin. Netting the two should get you above 40 to pass the test.
Rule of 40 is calculated as: TTM Revenue Growth % + TTM Adjusted EBITDA Margin %
A few other notes on efficiency metrics:
Net Dollar Retention is another great measure of efficiency, but many companies have stopped quoting it as an exact number, choosing instead to disclose if it’s above or below a threshold once a year. It’s also uncommon for some types of companies, like marketplaces, to report it at all.
Most public companies don’t report net new ARR, and not all revenue is “recurring”, so I’m doing my best to approximate using changes in reported GAAP revenue. I admit this is a “stricter” view, as it is measuring change in net revenue.
Operating Expenditures
Decreasing your OPEX relative to revenue demonstrates Operating Leverage, and leaves more dollars to drop to the bottom line, as companies strive to achieve +25% profitability at scale.
The most common buckets companies put their operating costs into are:
Cost of Goods Sold: Customer Support employees, infrastructure to host your business online, API tolls, and banking fees if you are a FinTech.
Sales & Marketing: Sales and Marketing employees, advertising spend, demand gen spend, events, conferences, tools
Research & Development: Product and Engineering employees, development expenses, tools
General & Administrative: Finance, HR, and IT employees… and everything else. Or as I like to call myself “Strategic Backoffice Overhead”
All of these are taken on a Gaap basis and therefore INCLUDE stock based comp, a non cash expense.
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