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The Poison of Inertia
By now I’m sure everyone has seen Jack Dorsey’s tweet. If not, you can find it here. For those who haven’t seen it (or don’t care to read it), he announced a ~40% headcount reduction at Block (formerly Square). This is a massive move… You rarely see headcount reductions this large. Throughout the post he used the word “intelligence” – which really can be replaced with “AI.”
“we’re already seeing that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company. and that’s accelerating rapidly”
and later:
“we’re going to build this company with intelligence at the core of everything we do.”
I’ve broadly seen two different reactions to this post:
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AI is coming and it will take all our jobs!!
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Block massively inflated their headcount in the COVID period, never reverted, was running hyper inefficiently, and is now unwinding the inefficiencies.
Most will want to ignore #1 and blame the large RIF on #2, but the reality is it’s a bit of both (and kind of a related #3). The related #3:
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Large organizations are riddled with inertia. And during a tectonic platform shift (none bigger than what we’re going through with AI), that inertia can become fatal, beginning with the innovator’s dilemma and ending in irrelevance. The only way to avoid this inertia is to forcefully reduce it.
In the past, I’ve compared the rise of AI to the rise of the internet, using GDS platforms like Sabre as an example of how incumbents were reshaped by that shift. Article here. To summarize – the internet “squeezed” the value the GDS systems captured and OTAs (Priceline, Booking, etc) took the majority of the incremental profits earned. With the help of some ChatGPT research, GDS systems used to make ~$20 / booking. Post internet (and rise of OTAs) that dropped >10x.
The natural question – why didn’t Sabre and other GDS systems end up owning the OTA layer? Surely they saw the internet coming, and understood the risk of someone sitting on top of the GDS layer and capturing their value? Why didn’t they innovate?
The short answer is of course they tried! The more interesting part is how they tried. Instead of setting up their own OTA inside of Sabre, they created an entirely new entity – Travelocity! New brand, that was born technically inside of Sabre, but lived as it’s own thing. In 2000 Travelocity was carved out entirely and went public as it’s own standalone entity. So why did Sabre structure it this way? Why not just make the “Sabre OTA?” Because of inertia! (I say that definitively, but of course I don’t actually know, this is just my speculation)…
Simply “adopting” a new platform technology (in this case the internet) wasn’t the hardest part for an incumbent to do. It was reversing the inertia that existed everywhere in the business. The only way to fight that inertia was to start something new untethered by the existing org. Business models changed (and therefore sales commission structures needed to change). Marketing shifted from trade relationships and agency channels to consumer acquisition and performance marketing. Distribution moved from proprietary terminals and long term contracts to open web traffic and price comparison. Product development had to become software driven and release cycles accelerated from years to weeks. Even the culture had to evolve, from protecting existing margins to being willing to cannibalize them.
There are tons of other examples I could give – but to pull out a common thread it’s this: the playbooks in the old world had to be rewritten for the new world. And it’s realllllly hard for people to rewrite playbooks. Most can’t. Sometimes the only way to do it is to start fresh.
I think the same thing is playing out now in software. Playbooks are being rewritten. Take something like developer relations. How can you create broad developer love. Create word of mouth virality that leads to strong product lead growth (PLG). Well, if agents become the customer, you now need agent to agent signaling not “word of mouth.” You’re now marketing to a new audience. Hiring structures change. For an enterprise sales driven company, maybe you used to have 2 SDRs for every 1 AE. Well, in a world of agentic AI, maybe you have 100 agentic SDRs for every 1 human SDR, who supports 10 AEs (and then continue this loop once AEs become more automated!)
No longer can you just hire the sales exec who “took company X from $10m to $100m in revenue because we’re now at $10m and we want him to help us scale like he did company X.” That exec knows the old playbooks, not the new ones. This doesn’t mean he can’t adapt, but you really have to evaluate him critically.
So back to Block and their ~40% RIF. Sometimes the only way to transition into the new world is to remove the “old way group think.” Force new ways of thinking by bringing in fresh blood. The natural question – is 40% enough, or should it have been larger?
Every large incumbent SaaS company should consider a similar cut. It sounds harsh, but a) you probably have bloat and inefficiency EVERYWHERE, and b) It’s going to be very hard for you to move quickly with all the inertia that exists.
And I’ll end with this – large technological shifts often come with huge changes to cost structures. A crude analogy I love is looking at a world before mass production existed. If you wanted to buy a dining room table, someone had to hand craft it for you, Carving the wood by hand, 3 layers of paint by hand, etc. Because of the effort required, the merchant had to charge a price that compensated them for the time / effort to produce. Then mass production came along – that same table could be made for a fraction of the cost, and therefore sold at a fraction of the cost. The old artisanal craft vendor simply couldn’t compete on price, because the more modern vendor was structurally able to produce the unit at a lower cost, and therefore sell it at a lower cost. If the artisanal craft vendor matched the new market price, they’d go out of business.
Something similar will happen in software – modern AI vendors will be able to produce software at a materially lower cost. Therefore allowing them to sell it at a materially lower cost. If the incumbents want to compete, they will have to materially lower their cost basis. There’s really only one way to do this…They should all get ahead of this before it’s too late.
Quarterly Reports Summary
Top 10 EV / NTM Revenue Multiples
Top 10 Weekly Share Price Movement
Update on Multiples
SaaS businesses are generally valued on a multiple of their revenue – in most cases the projected revenue for the next 12 months. Revenue multiples are a shorthand valuation framework. Given most software companies are not profitable, or not generating meaningful FCF, it’s the only metric to compare the entire industry against. Even a DCF is riddled with long term assumptions. The promise of SaaS is that growth in the early years leads to profits in the mature years. Multiples shown below are calculated by taking the Enterprise Value (market cap + debt – cash) / NTM revenue.
Overall Stats:
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Overall Median: 3.3x
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Top 5 Median: 16.6x
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10Y: 4.0%
Bucketed by Growth. In the buckets below I consider high growth >22% projected NTM growth, mid growth 15%-22% and low growth <15%. I had to adjusted the cut off for “high growth.” If 22% feels a bit arbitrary, it’s because it is…I just picked a cutoff where there were ~10 companies that fit into the high growth bucket so the sample size was more statistically significant
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High Growth Median: 10.3x
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Mid Growth Median: 6.3x
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Low Growth Median: 2.6x
EV / NTM Rev / NTM Growth
The below chart shows the EV / NTM revenue multiple divided by NTM consensus growth expectations. So a company trading at 20x NTM revenue that is projected to grow 100% would be trading at 0.2x. The goal of this graph is to show how relatively cheap / expensive each stock is relative to its growth expectations.
EV / NTM FCF
The line chart shows the median of all companies with a FCF multiple >0x and <100x. I created this subset to show companies where FCF is a relevant valuation metric.
Companies with negative NTM FCF are not listed on the chart
Scatter Plot of EV / NTM Rev Multiple vs NTM Rev Growth
How correlated is growth to valuation multiple?
Operating Metrics
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Median NTM growth rate: 12%
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Median LTM growth rate: 15%
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Median Gross Margin: 76%
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Median Operating Margin (1%)
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Median FCF Margin: 20%
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Median Net Retention: 109%
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Median CAC Payback: 34 months
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Median S&M % Revenue: 35%
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Median R&D % Revenue: 24%
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Median G&A % Revenue: 15%
Comps Output
Rule of 40 shows rev growth + FCF margin (both LTM and NTM for growth + margins). FCF calculated as Cash Flow from Operations – Capital Expenditures
GM Adjusted Payback is calculated as: (Previous Q S&M) / (Net New ARR in Q x Gross Margin) x 12. It shows the number of months it takes for a SaaS business to pay back its fully burdened CAC on a gross profit basis. Most public companies don’t report net new ARR, so I’m taking an implied ARR metric (quarterly subscription revenue x 4). Net new ARR is simply the ARR of the current quarter, minus the ARR of the previous quarter. Companies that do not disclose subscription rev have been left out of the analysis and are listed as NA.
Sources used in this post include Bloomberg, Pitchbook and company filings
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