I’m just gonna be straight with you… there’s a TON of bad business advice flying around right now…
“Companies are only hiring AI engineers”…
“You need VC money to scale”…
“This economy is bad for starting a business”…
If you’ve fallen for any of these oft-repeated assumptions, you need to read Mercury’s data report, The New Economics of Starting Up. Mercury surveyed 1,500 leaders of early-stage companies across topics ranging from funding, AI adoption, hiring, and more, to set the record straight.
What they discovered is equal parts surprising and encouraging:
79% of companies surveyed, who have adopted AI, said they’re hiring more because of it.
Self-funding is the number one avenue for accessing capital — even for tech companies, with half likely to bootstrap.
87% of founders are more optimistic about their financial future than they were last year, despite prevailing uncertainties.
To uncover everything they learned in the report, click the link below.
*Mercury is a financial technology company, not a bank. Banking services provided through Choice Financial Group, Column N.A., and Evolve Bank & Trust; Members FDIC.
Why the Hell Is It So Hard to Price an IPO?
In 2025, day one IPO performance feels like a total crapshoot.
Figma went parabolic (but now sits at the failed Adobe acquisition price), Coreweave was flat out the gates (but now has doubled in market cap), Navan and Stubhub popped (but in the wrong direction). There seems to be no rhyme or reason to the day one movements.
Naturally, everyone wants to blame the banks (myself included).
The process is broken, we scream!
Let’s do direct listings!
Retail is driving prices up (but somehow not getting any shares at all?)!
So I asked someone who actually knows what they’re talking about.
He’s sat on every side of the IPO table - as a Goldman Sachs banker launching the IPOs, as a hedge fund manager buying (and selling) the IPOs, and now as an advisor guiding companies through the process. He currently works as Senior Managing Director of Equity Capital Markets at Riveron, guiding companies make the leap. We initially connected when he slid into my LinkedIn comments as I publicly vented about the Figma IPO pop, and how it felt like bad practice.
Naturally, I invited him on the pod.
Here’s what I learned from our conversation on Run the Numbers.
TL;DR: The crazy day 1 price movements are not caused just by “one” thing. It’s a confluence of incentive driven factors, simultaneously supported and restrained by benchmarks.
Because even if you know the stock is going to rippppp on day one, you still have to price it with a long-term, peer-relative lens.
As Jeff told me:
“Banks underwrite not for day one. They price for the valuation relative to the peer group and what we call a ‘seasoned valuation’… meaning after they report their first quarter and the stock starts to settle. That’s what they’re pricing for.”
Let’s try to deconstruct what’s going on…
1. There’s a Supply Problem, Which Creates a Pricing Problem
There haven’t been many IPOs in the past few years. That scarcity creates pent-up demand for any company with a half-decent growth story.
So when something solid finally comes to market, whether it's vertical SaaS helping plumbers optimize dispatch routes or a DeFi platform letting you 50x lever your Fart Coin, investors pile in.
This demand flood makes it hard to parse: is this company truly hot, or is it just the only kid you can ask to the dance?
2. A Bull Market Warps Investor Brains
Bull markets create false confidence. Risk feels muted. Multiples stretch like Gumby. Investors chase deals more aggressively, even when the fundamentals are... shaky.
Jeff again:
“A bull market de-risks a lot of buying the IPO.”
So demand feels strong, but it might just be momentum tourists (I think I just made that term up) hoping to flip their allocation in 40 minutes.
3. Underwriters Walk a Tightrope
Even if there’s enough demand to send the stock to the moon, bankers can’t just price it at 2x or 3x its peers and call it a day.
This is where Jeff got me during our Figma kertuffle. If peer companies are trading at 6–8x forward revenue, how do you justify pricing an IPO at 50x?
“You would no longer be oversubscribed,” he said.
“If Adobe is trading at 8x and you tell investors you’re pricing this at 50x, you're not going to get the book filled—at least not with the people you want in it.”
And in the background of all of this, we have algorithms causing increasing volatility.
“Traders can sense through price movements and software and social media feeds and a whole bunch of other things, what price people are willing to pay in any moment.”
Not to put our tinfoil hats on or anything (carefully taking mine out of desk drawer)…
“They’ve developed these algorithms to buy or sell before all of that is realized by a broader base.”
With these predictive tools, an anticipated 2% or 3% swing in price gets ran up to something much, much larger and faster moving.
“In a hot IPO where they know there’s lots of demand, they will just play the greater fool theory.”
On the surface? It’s a party.
Under the hood? It’s a feeding frenzy.
L/S HF’s eating up shares like Kobayashi
Everyone’s Playing a Different Game
At the center of it all: misaligned incentives.
Companies want the highest valuation so they can put as much capital as possible on the balance sheet (that’s what this is after all… a fundraising event). But they also don’t want a valuation that will be impossible to maintain in the medium term. Not too hot, not too cold, but please more hot than cold.
Banks want a smooth debut with a pop that’s bigger than 10% but smaller than 40%, and a nice logo to market to the next wave of companies. If you pop more than 30% to 40% it looks like you sandbagged at the company’s expense. But if you don’t pop at all, you put them into a reputational and motivational hole.
Investors want instant upside with little to no risk. They want their cake, and to sell it too.
Employees want liquidity (but usually can’t sell for six months and watch the stock revert back to below its issuance price (or worse), resulting in psychological warfare)
What really stuck with me from the convo with Jeff is how little control anyone actually has once the IPO goes live. Day-1 takes on a life of its own.
It’s like trying to get a boat out of a narrow slip in a crosswind (shoutout Sesuit Harbor in Dennis, Cape Cod, where my father is the lucky renter of an absolute death trap of a dock slip) - you can steer a bit, but the wind decides most of it. And in this market, it’s not a steady gust…it’s swirling.
As a result, price discovery reflects the behavior of traders far more than the business they’re buying into. Even more reason to make sure you have a big enough boat before heading out…
Me and Jeff jam out on IPO pricing dynamics, SPACS (they’re back!), and IPO readiness. He’s a wealth of knowledge and one of the more entertaining capital markets gurus I’ve spoken to.
TL;DR: Multiples are DOWN week over week.
Top 10 Medians:
EV / NTM Revenue = 17.1x (DOWN 0.4x w/w)
CAC Payback = 25 months
Rule of 40 = 50%
Revenue per Employee = $438k
Figures for each index are measured at the Median
Median and Top 10 Median are measured across the entire data set, where n = 147
Recent changes
Added: Navan, Bullish, Figure, Gemini, Stubhub, Klarna
Removed: Olo, Couchbase
Population Sizes:
Security & Identity = 17
Data Infrastructure & Dev Tools = 13
Cloud Platforms & Infra = 15
Horizontal SaaS & Back office = 20
GTM (MarTech & SalesTech) = 19
Marketplaces & Consumer Platforms = 18
FinTech & Payments = 28
Vertical SaaS = 17
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
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.
OPEX
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 in the cloud, 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.
Please check out our data partner, Koyfin. It’s dope.
Wishing you a Revenue per Head that makes sense for your business model,
CJ
