Every "AI is transforming finance" pitch skips the part where your actuals don't tie to your CRM, your hiring plan disagrees with your headcount tracker, and three people have three different numbers for marketing spend.
(I’ve been there… unfortunately)
The AI isn't the hard part. The plumbing underneath it is.
Abacum published a whitepaper that actually engages with this - why deterministic models still matter in an AI world, how planning architecture is changing, and what to realistically expect versus what's getting oversold.
One of the more honest breakdowns I've read on where FP&A is actually going.
How Much Did the Bankers Actually Make on the SpaceX IPO?
Someone tell the manager at Land Rover of Darien, CT to cancel their weekend plans. The investment bankers are coming.
The Chads will be participating in the Lexus December to Remember Event this year
While everyone and their mother is talking about SpaceX achieving a +$2 trillion valuation on day one, I found myself knuckles deep in the 424B4 prospectus to check the final numbers. This is the vFinal documentation to amend any changes or placeholders in the original S1 (here’s my S1 breakdown).
You’ll see on the cover that the underwriting commissions were $500M. This was a very round number and heavily negotiated (it is a 0.67% gross spread, and as anticipated, it is well below 1%).
For context, this is VERY low as an absolute percentage for an IPO. Most IPOs raise between $500M and $1B (not $75B). And the fees hover between 4% and 5% of gross proceeds. If you raise less, it’s typically a larger fee. If you raise more, the fee gets driven down below that range (like CoreWeave and Cerebras were able to do).
SpaceX drove the fee into the ground. And yet, it was still the largest IPO fees paid in absolute dollars.
To go a level deeper, you can check the number of shares allocated to each bank. In recent years we’ve shifted from a “single lead left” to a “joint lead” model for larger IPOs. With an easy calc you can see that Goldman Sachs and Morgan Stanley had 20% of the allocation and therefore got paid $100,000,000 each.
Now, that $100M is pre-greenshoe, and normally this is where the number creeps up. Underwriters get an over-allotment option, the right to sell another 15% of stock for thirty days after pricing, and on a book this oversubscribed they exercise it basically every time. That would be another 83,333,333 shares, and on a normal deal they'd collect at the same $0.90, padding the pool by another $75M, and nudging Goldman and Morgan Stanley toward $115M apiece.
SpaceX zeroed it out. Buried in the underwriting section is a line that says the banks collect no discount and no commission on a single greenshoe share. So the syndicate will hand out an extra $11 billion of stock for free, the $500M is capped, and Goldman and Morgan Stanley stay at $100M.
When you run the full raise, the effective spread drops from 0.67% to 0.58%, because they're distributing $86 billion of stock and only getting paid on $75 billion of it (on the largest IPO ever printed, no less).
Five things to leave you with:
0.58% is less than what most people pay a financial advisor to trail the S&P for a year.
It's the biggest IPO fee pool ever in raw dollars. But it’s also roughly what Goldman books in a single quarter ($535M in equity underwriting revenue last quarter).
$500M actually undersells it. The money keeps coming via future trading commissions that come back from every fund the banks let into the deal. Scratch my back, I’ll scratch yours.
This syndicate just set the benchmark for what OpenAI and Anthropic might pay when they go public later this year. Let’s see if they can get the % any lower.
The manager at Land Rover of Darien better get more than a 0.58% for opening the show room on a Sunday.
“Yes, it’s me, Dennis. What? Do you know who you’re talking to? I own 2 whole shares of SpaceX… Hello?”
Weekly Valuation and Efficiency Metrics
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.
Companies Included
1. Security & Identity (17 companies)
Endpoint, network, IAM, security operations. The CISO budget.
CrowdStrike, Palo Alto Networks, Fortinet, Cloudflare, Zscaler, Okta, SentinelOne, SailPoint, CyberArk, Check Point, Qualys, Tenable, Rapid7, Varonis, Rubrik, Mitek, OneSpan
2. Data & AI Infrastructure (12 companies)
Modern data stack, AI/ML platforms, vector and analytics infra, GPU compute. Software-native by design. The legacy hardware names (HPE, NetApp, Lumen, Rackspace, Cisco) that used to live in the old “Cloud Platforms” bucket are gone.
Snowflake, Arista Networks, Equinix, CoreWeave, MongoDB, DigitalOcean, Elastic, Akamai, Fastly, Teradata, C3.ai, Cerebras
3. Dev Tools & Observability (10 companies)
Anything bought out of the engineering budget. The observability names used to live separately. Combined them with dev tools because they’re sold to the same buyer through the same procurement motion.
Datadog, Atlassian, Figma, Dynatrace, Nutanix, GitLab, UiPath, JFrog, AvePoint, PagerDuty
4. Horizontal SaaS & Back Office (18 companies)
Software sold across industries to ops, HR, finance, and collaboration teams. Not vertical-specific.
Oracle, ServiceNow, Workday, ADP, Paychex, Paycom, Paylocity, Zoom, DocuSign, Navan, monday.com, Asana, Workiva, BlackLine, RingCentral, 8x8, Box, Dropbox
5. GTM (MarTech & SalesTech) (18 companies)
Anything bought out of the revenue org. Marketing automation, sales engagement, CRM, ad tech, customer experience.
Salesforce, Adobe, HubSpot, The Trade Desk, Twilio, Klaviyo, Braze, ZoomInfo, Freshworks, Amplitude, Semrush, Five9, Zeta Global, Wix, Sprout Social, ON24, Yext, Criteo
6. Vertical SaaS (16 companies)
Software built for a specific industry without take-rate or transaction economics. This bucket used to include Toast, Olo, and Shopify. They don’t belong here. They make money on transaction volume, not seat licenses or SaaS usage. They’re in #7 now.
Palantir, Autodesk, Veeva, Aspen Technology, Samsara, ServiceTitan, Guidewire, Tyler Technologies, Doximity, Procore, AppFolio, CCC Intelligent Solutions, Blackbaud, nCino, CareCloud, CS Disco
7. Take-Rate Platforms (19 companies)
Marketplaces and commerce platforms that earn money on transaction volume. This is a new bucket. It’s the single biggest reason your old Vertical SaaS median was hard to use, especially if you’re a hospitality or commerce CFO trying to find your comp set.
Uber, Airbnb, Shopify, MercadoLibre, DoorDash, eBay, Zillow, CarGurus, Instacart, Etsy, Toast, Lyft, Opendoor, StubHub, Olo, Upwork, Coursera, Ethos, Fiverr
8. Payments & Money Movement (11 companies)
The rails. Payment processors, payment infrastructure, B2B payments, treasury. Volume game, utility margins. Used to be jumbled in with consumer fintech in a 28-company FinTech bucket. Now they’re on their own.
Intuit, Fiserv, Adyen, PayPal, Block, Shift4, BILL, Clearwater Analytics, Flywire, Marqeta, Lightspeed
9. Consumer Fintech, Lending & Crypto (16 companies)
The front-end. Consumer-facing financial apps, BNPL, lending platforms, crypto exchanges. CAC-driven, marketing-heavy, totally different unit economics from #8.
Coinbase, Robinhood, SoFi, Chime, Affirm, Upstart, Circle, Bullish, Figure, Klarna, Sezzle, Gemini, Blend, Remitly, MoneyLion, LendingClub
Please check out our data partner, Koyfin. It’s dope.
Wishing you trade at a high revenue and EBITDA multiple,
CJ
