# Software: next manifestation of intelligent life

In his book Brief Answers to the Big Questions, Stephen Hawking once again shared fascinating thoughts on physics and cosmology. While it remained the last publication from the undoubtedly clever physicist, the book was certainly not short of brazen predictions on the next manifestation of intelligent life and space travel. If you are a geek like me (or Hawking!) you probably share the pain of knowing that intergalactic travel takes way too long to be viable for a human lifespan as we know it. However, machines face no such hurdles and, at least technically, they can travel as long as they can convert something into power. Thus it could be that Moon or Mars is as far as men will go, and any deeper will be conducted by robots that we've built.

The reason for the long-winded briefing is that before we get to Sir Hawking’s utopia of space travel, the machines will need to not only share our intelligence, but more important learn by themselves. Humans ate the world millions of years ago and software is eating the world today. We became the dominant species largely *because* we learnt to cooperate and learn. Now, we are moving away from monolithic software and with rise of cloud apps and microservices software is taking its first inchoate steps in communicating with each other and getting to its next level of intelligent life. It sounds a lot like AI, but instead in this essay I'll discuss more the importance of interconnectivity in applications and how connectivity will be a crucial piece of all succesfull enterprise software going forward.

![software1.PNG](http://strapi.joelosk.ar/uploads/software1_d56045503e.PNG)

# Why interconnectivity is important now

Two major trends have been molding the world in last 5-10 years: cloud adoption and tech stacks that are growing ever more complex. In a 2018 study by Dynatrace a single mobile transaction now crosses on average 35 systems which is up from 22 systems five years ago. Conveniently we used to have all apps on a single server, a sort of a monolithic approach, but the emergence of software-as-a-service model means that the entire stack now resides on different clouds and servers all of whom need to talk with each other.

Indeed, according to a 2020 study by Cloud Elements cloud app adoption has been the #2 cause for app integrations, only shadowed by the broader digital transformation. First we unbundled the enormous all-in-one legacy stacks residing on physical servers in the company's basement where all sound was subdued by the noise coming from the cornucopia of cooling fans, to vertical cloud apps such as HR SaaS, CRM SaaS, finance SaaS to the point where you start think that this-cant-be-real SaaS also turns out to exist. In any case those apps in turn are now getting torn apart to follow the highly scalable microservices architecture, where each individual component that makes up the software such as login, payments and networking exist as an individual self-sustaining cloud app communicating with all other services. There is hardly a more discernible way to show this need to transfer knowledge than having a look at the below Kafka and Postman trends.

![software5.PNG](http://strapi.joelosk.ar/uploads/software5_58814fb291.PNG)

Think of it this way: a good artisan does everything herself from planning to procuring to crafting, but should he want to scale she would need teams to do all that. And that means setting up means to discuss internally.

![software2.PNG](http://strapi.joelosk.ar/uploads/software2_b444945179.PNG)

# Who is a communicator

According to Cloud Elements’ study, 84% of all businesses consider app integration *critical* to their business strategy.

Think about some of the applications you use the most at work such as Slack, Salesforce or Zendesk. They all have a specific function they fulfil, but they are probably also entangled to many other software you use. Slack has over 2,200 apps and integrations as part of its ecosystem while Salesforce boasts over 4,000. And with tools like Zapier, Tray.io and IFTTT even Margaret from HR can still make smart connections between different tools she uses, as long as that tool provides an API or webhook.

Indeed, should you want to build say, an eCommerce site, you can get really far by utilising custom built blocks. There exists an API component for more or less any part of the eCommerce stack you'd need from payments, to logistics, to inventory management, to supply chain, to analytics... you name it. And of course, you could just use Shopify or Magento that themselves boast massive application ecosystems by itself. Some of these apps provide extensions to the platform while the others allow connecting the platform to the rest of your software stack. In many cases, however, other than the occasional configuration here and there you wouldn't be using the software yourself at all. The software uses the software.

While becoming platform is attractive, it is admittedly not doable for the most emerging software companies. In those cases it is still paramount to have at minimum a one-way API that allows event-based automatisation (e.g. you can read all items in the CRM from Slack) or better a two way (e.g. you can also add an item to the CRM directly from Slack). Luckily, for startups this has never been as easy as today with services like Kong, Tyk.io or Amazon API Gateway that handle all throttling, security, management issues and whatever you may need with ease.

In fact, many software interact little, if at all, with human anymore. Many databases such as Snowflake or search-as-a-service providers such as Algolia or data connectivity platforms such as LiveRamp are basically only used by other software. Even many consumer applications talk more to software than to the consumer itself. A study in 2018 found that roughly two-thirds of all links in Twitter came from bots. Not the most glamorous example, but hey it makes the point.

As a result, like in a Chinese fishmarket there is now so much noise between software-to-software communication that it has become overwhelming to stay on top of what is being discussed without the help of observability tools. Most of the time we don’t need to listen to all of it, but when we do the likes of Datadog and Sumo Logic become invaluable. Invaluable is perhaps not the correct term to describe Datadog’s uhh $30Bn valuation…   

![software3.PNG](http://strapi.joelosk.ar/uploads/software3_672152a117.PNG)

# The damage caused by poor communication

When a fruit is past its expiry date it looks and smells odd. If a car is damaged it has a visible dent. But if there is a bug between software to software communication, however, it is much more difficult to spot, yet can cause a havoc.

In 2017 Patrick Poulin of API Fortress surveyed whether respondents put the same level of effort into testing the API as they do testing websites and apps. Turns out they don’t. The damages due to poor software-to-software communication are very real.

Few years back Etsy was facing a tricky business issue. According to API Fortress, Etsy was missing out on up to $75,000 worth of sales every day due to some items not being findable on the top level navigation nor in external apps. The culprit was ultimately found to be improper categorisation in >3,000 items, where an API field was marked as optional instead of required resulting in some of the items being "lost". In a good or bad they aren’t alone as according to a 2020 study by Cloud Elements, over 80% of respondents would like to see more data standardisation in APIs.

More recently, Tesla with its divine task of bringing us the future filled with electric cars managed to over-engineer itself after Tesla users got locked out of their cars due to severed API communication between the mobile app and the car as reported by Security Boulevard. Indeed, a modern company like Tesla is built on microservices and a seemingly small issue in the microapps' communication could easily bring down the whole service.

![software4.png](http://strapi.joelosk.ar/uploads/software4_305c01498f.png)


# The way forward

There are some similarities with the evolution of work between human and software. With development of technology we’ve seen people specialise into more niche fields. Similarly, as the IT tech advances we see the stacks inside organizations become more complex. Paraphrasing Stephen Hawking, to get to the next level of intelligent life we will need to ensure software is able to use other software. One may ask what is the role of a end user in this new world of software and well, we still sell for the human. They still ultimately make the decisions on who buys what, but often are not the ones who end up using the software. 


Onwards,

Joel-Oskar

Examining the importance of interconnectivity in today's software success stories. 

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Examining the importance of interconnectivity in today's software success stories. You will come to understand how the best software like Slack or Zendesk are truly powerful for their ability to connect anything and everything and how various tools have emerged to enable this connectivity.

No application is an island: Interconnectivity in software

# The most important game you've never played

Roblox is a game that most people without children have never heard of. Together with Minecraft, it is *the* game that the today's kids play before graduating to spend countless hours on Fortnite. 

To be frank, calling Roblox a game is a stretch as the company primarily just provides a platform for players to build games themselves. [According to the CEO David Baszucki](https://www.matthewball.vc/all/digitalthemeparkplatforms) 75% of the new hires do not even work on the game, but rather on the tools that allow players build the games *inside* Roblox.

Whether it is game or a platform is trivial, as Roblox has become so popular that it even [decided to pull its IPO citing the absurd markets and difficulty of pricing its share](https://www.wsj.com/articles/roblox-delays-ipo-until-next-year-11607742940). Yet this week, [Roblox announced raising $520M at almost at a $29.5Bn valuation](https://venturebeat.com/2021/01/06/roblox-raises-520-million-at-29-5-billion-valuation-will-go-public-through-direct-listing/) and while it still plans on going public, it is opting for the direct listing route at an undisclosed date. 

![roblox2.PNG](https://strapi.joelosk.ar/uploads/roblox2_241f8e6d5b.PNG)

As I was listening to [The Prof G Show](https://podcasts.apple.com/gb/podcast/the-prof-g-show-with-scott-galloway/id1498802610) the other day it struck me how effective Roblox is reaching the very young demographic. Children between say 3-13 years old consist of a special and niche age group that is extremely hard to reach effectively. However, Roblox boasts [2 hours 36 minutes of active engagement *per day*](https://www.profgalloway.com/roblox-and-the-dispersal-of-creativity), which is almost three times more than the daily engagement on Youtube. **It implies that most if not all the time children of that age spend online should be on Roblox.**

**I began pondering if we could triangulate a value for Roblox assuming it becomes de facto way of targeting this young digital demographic?** It'd be a fun exercise to conduct given Roblox’s valuation seem to obfuscate even the company itself. Although highly theoretical, it is a simple exercise with all inputs quite readily available. **Essentially the value of Roblox would be the present value of the worldwide advertising spend spent on children.** Let's see.

## The back-of-the-envelope model

[According to Statista](https://www.statista.com/statistics/750865/kids-advertising-spending-worldwide/), the worldwide kids advertising spend was ~$4.6Bn in 2020 growing ~4.6% year over year. More importantly, out of the $4.6Bn roughly $1.7Bn or 37% is digital spend. Knowing nothing of the number, I was frankly surprised that it’s practically nothing compared to the $500Bn+ global advertising industry. Then again, it does make sense given how little purchasing power we all had when were the size of a fire extinguisher. 

To keep things simple, I will use a 10-year model with constant 4.6% year-over-year growth on the spend assuming that the digital channels will reach 50% of total spend. The discount rate can get highly theoretical, so for simplicity I use an arbitrary 10% - a nice round number. Now, all revenue comes with associated expenses, and thus I apply Roblox's latest gross margin of 77.7% on the spend and call it contribution margin. The "model" thus looks as below:

![roblox1.PNG](https://strapi.joelosk.ar/uploads/roblox1_76165ba5c1.PNG)

The $29Bn number I'm getting looks awfully similar to the valuation Roblox just raised in January 2021. It's crucial to understand that my number is an understatement given it excludes all other demographics (to be fair, Roblox claims 15% of its users to be 25+ years old... guys what are you doing?). Not to even mention Roblox's other revenue streams such as in-app purchases and what not. Indeed, this is a highly theoretical thought exercise but coincidentally seem to give a reasonable estimate on its fair value. Just shows how much art valuation can be and sometimes a simple back-of-the-envelop estimate is no worse than a complex one.

Onwards,

Joel-Oskar

Roblox is ubiquoitus among the digital native young generation. Could its valuation thus be triangulated simply by looking at the children's digital ad spend?

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Roblox is the most important game you've never played that reaches the youngest audience of the world. This article conducts a theoretical exercise whether its valuation could be triangulated using children digital ad spend as a proxy.

Triangulating Roblox's value through ad spend



Basically, the TLDR is the following:
- Legacy Enterprise Security Company that’s been using its cash (+$1Bn a year) from its firewall business to turbocharge its transformation into next-gen cloud security offering through acquisitions totalling almost $2.5Bn
- Reliable historical relationships with many CIOs and CTOs built for over 15 years could allow the company to sell these new cloud products much easier than some of the newcomers
- It plays catch-up and is unproven in how it is able to integrate all these separate companies into a complete suite

# CHKPKLR

Jim Goetz at Sequoia [describes](https://www.sequoiacap.com/company-story/palo-alto-networks-story/) meeting Nir Zuk, the founder of Palo Alto Networks, as follows: “*Nir pulled into our parking lot in an old BMW with the license plate CHKPKLR — “Check Point killer” — because Check Point was the incumbent in enterprise security. I knew he had a chip on his shoulder, but I didn’t realize he had the energy around it to actually go out and get custom plates.*”.

![panw1.PNG](http://strapi.joelosk.ar/uploads/panw1_cfe939bb46.PNG)

That person, Nik Zur, had gotten frustrated with his previous employer Juniper Networks and wanted to make next-generation firewalls better for the modern-day. In fact, [their firewalls have always been “next-generation firewalls”](https://web.archive.org/web/20070627092757/https://www.paloaltonetworks.com/) and there was never a first-generation. It’s like that gag sign that goes “Free Beer Tomorrow,” and then the next day, the sign is still there.

Anyhow, Palo Alto Networks IPOd in 2012 after being backed by the likes of Sequoia and Greylock, is today one of the hottest cybersecurity companies out there **with a market cap of $35Bn at the time of writing. Today they have ~$3.5Bn in sales growing ~20% YoY on a ~50% free cash flow margins.** Given its ugly appliance roots, it doesn’t really get the same attention among investors as Crowdstrike and Zscaler, even though Palo Alto has a much more comprehensive and robust value offering and a proven history in enterprise security. While Palo Alto started as a hardware company selling physical firewall appliances for on-premise data centers, it has since evolved to offer a more modern cybersecurity stack and, what I believe to be the most relevant offering of all the cybersecurity companies out there, now that it has aggressively pushed into the cloud security space.

The transition to cloud is what I will focus on in this piece and will start by briefly looking at Palo Alto’s legacy business and then discussing the cloud security part a bit more. I will finish by sharing some back-of-the-envelope thoughts on the upside - something I will probably end up regretting.

# The old Palo Alto

It wouldn’t be a text on Palo Alto without mentioning its firewall appliances which they sell to data centers and enterprises. To be fair, calling them appliances is a bit of a misnomer as a large part of their firewalls are actually virtualized and not in any physical format - at least not anymore. They don’t even hold any inventory as they sell these using a two-tiered system where Palo Alto sells their products, subscription and support offerings to selected distributors who then sell it all to the end customer. Whatever they may manufacture is outsourced to [Flextronics](https://flex.com/). 

Over time, they have also introduced services and subscription-like software integral to the firewalls, with the most well-known being firewall management software Panorama, which gets useful once a company has many Palo Alto appliances to manage. Other subscription add-ons range from URL filtering to tunnel VPNs sold as multi-year contracts priced typically to enterprise contracts.

Because of these nuances, they have relatively admirable software-like gross margins of 70%-80% even on this legacy part. But as a business, it is arguably very mature with stable if not even declining growth (-3% YoY last year). Nothing special - it is similar to its competitors such as Cisco or Juniper Networks and is a result of their clients moving more towards cloud, reducing their need for their own on-premise data centers. While Palo Alto’s dependency on these hardware sales has decreased, they still contribute c. 30% of their sales. Yuck, let’s move on to something more exciting.

![panw2.PNG](http://strapi.joelosk.ar/uploads/panw2_f2ac02a95c.PNG)

# The winds of change

**Cloud security**

So then cloud happened. It started slowly and then became important, well, really fast. But when an app runs on cloud it really just runs somewhere else that is not… here. Sometimes it may even be a bit of a blackbox, as the orchestrator such as Kubernetes launches and stops instances sporadically to ensure a certain level of service. So then you have stuff running a bit of everywhere without really knowing what is happening.

![panw3.PNG](http://strapi.joelosk.ar/uploads/panw3_976d92fc21.PNG)

The simultaneous surge in serverless and stateless applications means security needs to be viewed from a different viewpoint too. Instead of securing an application, the company now needs to secure the individual functions, whether on AWS Lambda, Google Cloud Functions or Azure Functions. 

And then finally, in the cloud era, people access applications from multiple devices and different locations. There is a big concept called zero trust and SASE, which is outside this article but is essentially a VPN on steroids. Anyhow, it had become clear that the enterprise cybersecurity needs had evolved beyond the remedy of traditional firewalls.

**Palo Alto’s transition into cloud really began in 2018 when they acquired [Evident.io and RedLock](https://blog.paloaltonetworks.com/2018/10/time-bring-together-cloud-compliance-security-analytics/) in Mar-18 and Oct-18 respectively.** By mid 2018 the share was already up 44% although pundits seemed to slightly discount the efficacy of this new strategic focus (see e.g. [this pondering](https://uk.finance.yahoo.com/news/palo-alto-networks-finishing-2018-110200151.html) at Motley Fool in Dec-19) - unfairly so, as Palo Alto was very clear that the service wouldn’t even be available until early 2019. By then of course they had already made two more acquisitions, TwistLock and PureSec for a total of $410M, which were announced in May-19 together with their Q3 earnings and the formal launch of Prisma suite and the new era of Palo Alto.

![panw10.PNG](http://strapi.joelosk.ar/uploads/panw10_509c07c868.PNG)

Ever since, **Palo Alto has spent $2.5Bn in pure cash building its cloud security offering**. That’s a massive number I took from their cash flow statement, which excludes any acquisitions made as asset purchases and would thus be considered a capital expenditure. To be honest, as I was about to finish this piece they [acquired one more](https://www.prnewswire.com/news-releases/palo-alto-networks-announces-intent-to-acquire-bridgecrew-301228884.html), [Bridgecrew](https://bridgecrew.io/), for $200M... Too late! I'm gonna act like it haven't happened yet.

![panw8.PNG](http://strapi.joelosk.ar/uploads/panw8_de9bbecb2f.PNG)

To put it into comparison, of the legacy enterprise security players Palo Alto is clearly spending the most to get ahead. **In 2020 they spent roughly 6x more on R&D and acquisitions than Fortinet**. This piece from Deutsche Bank puts it all into perspective:

![panw12.PNG](http://strapi.joelosk.ar/uploads/panw12_77f2c98b07.PNG)

The numbers of their cloud security have been a bit opaque and meshed in with their next-gen firewalls, but they’ve shed more light into it over time. The real paradigm shift happened in their recent Q1-21 report under the section “Increasing Transparency.” Suddenly, Palo Alto completely opened its books on cloud security and how they expect it to contribute to the Palo Alto totals

**In FY20, the cloud security piece contributed $318M on a 63% gross margin. They expect it to grow to $605M in FY21 representing a growth of 90% YoY!** If they can reach those numbers, they are **the fastest-growing cybersecurity company on the street** - a title previously held by Crowdstrike who had the same growth although from a vastly lower base (they grew from $250M to $480M in 2019-2020, representing a growth of 92.7%). Hopefully, over time, they’d also shed some light on the retention figures, which would be key.

![panw4.PNG](http://strapi.joelosk.ar/uploads/panw4_70c9bfcd5b.PNG)

**Security Operations**

The final piece of the new Palo Alto is their Cortex offering which [it launched few months before Prisma](https://www.paloaltonetworks.com/company/press/2019/palo-alto-networks-introduces-cortex-the-industrys-only-open-and-integrated-ai-based-continuous-security-platform). It is easiest to think of as the company’s threat analytics and endpoint solution that enables organizations to go from reactive security to proactive by pooling data from multiple sources to predict malicious behavior.

In a way, this is most similar to Crowdstrike in that it installs an agent to the end-users device that detects any malicious content based on real-time ML data it collects from other Palo Alto networks. Customers do [say](https://www.reddit.com/r/paloaltonetworks/comments/j4fgzw/cortex_xdr_vs_extrahop_ndr/) that it plays a bit of catch-up there. Personally, I position this as an upside. Still, it is not the primary reason I got excited about the company, mainly because it hasn’t received the same love as Prisma and because it doesn’t seem to fully match Crowdstrike yet.

![panw11.PNG](http://strapi.joelosk.ar/uploads/panw11_73375eb9bc.PNG)

# Cash and M&A philosophy

After adding back non-cash items and billings, **the company makes so much cash that it has the flexibility that many of the “hot” cybersecurity companies today don’t.** In fact, for having a reputation as a legacy hardware company **Palo Alto has sustained excellent cash flow margins of ~40-50% for years** that don’t seem to be hindered by the move to the new offering. **Their Q1-21 FCF annualised run-rate is at $2.5Bn**, which hones in on the $4Bn cash flow target they set out for FY22 in 2019.

This abundance of cash is a big reason why Palo Alto is well-positioned. In general, cash can be used for one of the following: investing into a business through capital expenditure, paying back debt, returning to equity investors through share repurchases or dividends, leaving it to business as cash, or making acquisitions. So let’s have a look (note the discrepancy to the total is due to cash on balance sheet or debt financing):

**This abundance of cash is a big reason why Palo Alto is well-positioned**. In general, cash can be used for one of the following: investing into business through capital expenditure, paying back debt, returning to equity investors through share repurchases or dividends, leaving it to business as cash, or making acquisitions. So let’s have a look (note the discrepancy to the total is due to cash on balance sheet or debt financing):

![panw5.PNG](http://strapi.joelosk.ar/uploads/panw5_de60f5d32f.PNG)

Two things strikeout: stock repurchases and acquisitions. The former is nice, but what I’m really excited about are the acquisitions.

While I’m not too keen on stock repurchases, I’d still position them as nice-to-have-if-nothing-else-is-available. Palo Alto has been buying back shares consistently since 2017, and although the amount has varied a bit, it has effectively been $1Bn on a rolling basis. The repurchases have always been made from available working capital.

At least, in theory, those repurchases have been good investments. They have been made at an average share price of $200-220 in 2019, with the stock now trading at closer to $400. Despite this, I’m not sure if I want the company to continue those. Distributions of cash to investors, or repurchases, only make sense if the company believes the investor can use the cash better than the company can. But right now, it’d seem there is a lot of value to be made by reinvesting the cash in the cloud security business. If not for acquisitions, then I’d imagine for sales and marketing. Get those bad boys out to the market!

In any case, the M&A strategy that really began in 2018 is now starting to payout. It takes time to reap the benefits of the acquisitions. Indeed, it seems we are getting to the accretion point with their largest acquisitions, referring to their investor presentation from 2019 when they shed some light on their M&A philosophy. Many of their largest acquisitions, such as Twistlock and PureSec from 2019, are now slowly about to become accretive and show on the bottom line.

![panw6.PNG](http://strapi.joelosk.ar/uploads/panw6_4759b6f5c4.PNG)

Then finally, another advantage Palo Alto has over other up-and-coming cybersecurity vendors is its **existing relationships with decision-makers on the CTO and CIO level.** The company benefits from almost **20 years of enterprise security relationships boasting over 75,000 customers**, which is key in pushing out the new security products. Developing relationships is hard, and if you’re already out there, you have an advantage. Indeed, in Q1-21, the company disclosed an interesting figure that reflects these relationships: **they went from 43% of Fortune 100 coverage to 70% in just one quarter for their cloud security part**. That is big! These are large customers, and while I expect the landing contracts to be small, they represent significant upsell potential.

# In summary

Palo Alto is a legacy firewall appliance business that made its name selling physical firewall appliances to enterprises. They are at $3.5Bn of sales, growing 20% YoY. Their cloud security offering is expected to grow by 90% YoY, boosted by the series of acquisitions the company makes. With its sustained ~50% free cash flow margins, the company generates an abundance of cash that has been offloading to premier acquisitions in the cloud security space.

The thing is, I knew very little about cybersecurity when I began looking at Palo Alto. And in all honesty, I might now know a bit more about the company but arguably still very little about cybersecurity. There are also so many things I didn’t touch at all, such as management which can make or break the case. But I do trust the founder and CTO Nik Zur. How could you not? After all, he paid the bucks 15 years ago for a special license plate in a vow to beat Check Point Software. Today Palo Alto Networks has 2x the market cap, so I’d say he kept his word.

With the most recent acquisition Bridgecrew Palo Alto also seems to be moving more towards the earlier stage of cybersecurity. The so-called “shift-to-the-left” space and closer to the developers. I think this is a really interesting space, although a bit unorthodoxical for Palo Alto. Could we then see even more transformational acquisitions? Could Palo Alto acquire, e.g., [Snyk](http://snyk.io/), [GitLab ](http://gitlab.com/) or [Sonatype](https://www.sonatype.com/)? That’d be something! We could be on the brink of a similar transformation that Satya Nadella did with Microsoft. Could Palo Alto Networks become really cool?

![panw11.PNG](http://strapi.joelosk.ar/uploads/panw11_16431b6d87.PNG)

**Whatever the case, I’m really just excited because they spit out a lot of cash and acquire seemingly cool companies in a hot space**. Yet, the chances of building a great product are always easier if you do it from the ground up rather than trying to stitch together bits and pieces from here and there. And that’s an obvious risk, especially since they are not the only players trying to capture the space. If this doesn’t work out, it’s pretty much just an ugly dividend play selling legacy firewall appliances with $3Bn in sales. In that case, they’d be lucky to get a valuation of more than $10-15Bn, and Nik Zur would need to remove his novelty license plate. But who knows.


A serendipitious coincidence and a look at Palo Alto Networks thinking it was a legacy firewall company but little did I know...

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The other day I looked into the cybersecurity space starting from the usual suspects Crowdstrike and Cloudflare. By sheer coincidence, I stumbled upon Palo Alto Networks thinking it was a legacy firewall firm that would be forgotten in a few years time. That serendipitous misconception ended up becoming a rabbit hole to dig into the profound transformation Palo Alto is going through, which I’ll write about in this post

Palo Alto Network's cloud transformation

# Inflation fears

Recently the stock market has experienced significant turbulence due to the fear of inflation and interest rate hikes. The impact was primarily material on growth stocks over other types of assets. As a growth investor myself, I couldn’t avoid being hit, and although I invest for the longer-term, I wanted to understand the causal relationships better.

Many investors, I included, have invested mainly in low inflation and low interest-rate environment that has been the norm for the past 10 years. Even for the veterans, that is a long enough time to get used to the new normal and forget how the world used to look like.

Sure, the genuinely smart folks have seen the omens for a long time already. [Ray Dalio](https://www.bloomberg.com/opinion/articles/2020-12-10/cpi-why-surprise-inflation-could-push-fed-to-ease-even-more) from Bridgewater Associates was warning about surprise inflation in December. [Michael Burry](https://www.marketwatch.com/story/big-short-investor-michael-burry-warns-stimulus-may-stoke-inflation-11613998762) (Scion Asset Management and the guy who made the lucrative bet on the Big Short) has been predicting inflation after the wave of stimulus. Even the Oracle of Omaha itself, [Warren Buffett](https://news.bitcoin.com/warren-buffett-inflation-bitcoin-all-time-high/), has been shifting its assets amidst inflationary fears. Where did they get the idea from?

The most obnoxious fact is that we are even talking about inflation and interest rate hikes less than 12 months after the biggest financial crash since 2009 and the most severe pandemic the world has seen for decades. If a time traveller visited me a year ago saying the economy will soon overheat at a time when a large part of the leisure and travel industry was being shut down, and bankruptcies and layoffs dominated the press headlines, I would have laughed.

But the difference to the 2009 financial crash was the speed and intensity of the government reaction. Less than two weeks into the European lockdowns, roughly $3 trillion of capital was injected into the economy by various central banks. A big lesson learnt from the global financial crisis was not to rely on banks to act as intermediaries of capital, as it may not be an effective way to get the cash out to the economy. Not only was the quantitative easing of 2009 too small and come with a delay, [but also the banks were so full of bad assets that they used most available cash to strengthen their own balance sheets](https://www.investopedia.com/articles/investing/022615/why-didnt-quantitative-easing-lead-hyperinflation.asp), thus not redistributing and multiplying that money in the economy like they were supposed to. Now, for the first time ever, we have injected money directly into people’s bank accounts. But that money has yet fully reached the economy either because of lockdowns and restrictions around the world.

![The speed and intensity of the response to CV19 has been unprecedented. Over $3Trn capital injected only few weeks into the crisis.](http://strapi.joelosk.ar/uploads/rates1_94e819cfed.PNG)

What has followed is **the abundance of money in the economy together with the pent-up demand and savings that have laid out the foundations for an inflationary environment.** With Covid-19 vaccinations progressing, the risk of the economy overheating once all that pressure is released suddenly became very real. It’s like a spring that has been compressed for 10 months now about to break loose.

And in case you haven’t studied economy, let me give you a refresher on how more of something means the less value it has. Say you had a friendly bet with three of your friends who all earned $1,000 per month. To participate in the bet, you’d all need to chip in $200, and the winner takes it all. Now that’s a decent amount of money at that level of income, and maybe one of your friends doesn’t want to join because it’s quite a chunk of his income. But what if you all earned $10,000 instead? Suddenly the $200 seems more like “fun money” to throw around, and the value of that money is now less. Although an extreme example, this is inflation in a nutshell.

The below chart shows how the money supply (M2) has increased since 1980s.** In the last 12 months alone, over $5Trn of cash has entered the money supply which is more than we’ve seen in the last 8 years combined. **Most importantly, **much of it is simply sitting on bank accounts and balance sheets, waiting for the markets to open** and be spent on goods a bit like a bunch of Siberian Huskies in their cages eagerly waiting for the next morning to pull their sled again.

![The money supply grew as much in the last 12 months as in the last 8 years.](http://strapi.joelosk.ar/uploads/rates2_0a27ea354a.PNG)

Now that we understand what has happened in the economy, let us explore how it impacts our investments.

# How does this impact the growth stocks?

Most introductory courses on finance start with a valuation of a bond. Even though bonds are a bit out of fashion at this point, they are an easy way to teach where the value of an asset comes from. They help understand the importance of cash flows and how the timing of those cash flows can make a big difference in value.

**Growth stocks are not too dissimilar to bonds that pay no coupon.** All the value is at the end of the holding period, often only realised once you sell the share. But contrary to a bond, that pay-off at maturity is not fixed but can and should vary over time.

Value stock is the same, but it also pays an annual coupon (i.e., free cash flow) with the exception that the coupon is also not fixed but (hopefully) grows over time.

The table below exhibits these two different schools of stocks using hypothetical examples: a growth investment that pays nothing until you sell the share and a value stock that pays a 20% FCF yield. The growth stock is equity-financed and riskier, with investors requiring 20% every year. The value is a yield play, and thus only a 10% discount rate is deemed feasible. As a result, they are currently valued essentially the same. 

![Illustrative examples of the characteristics of growth and value shares](http://strapi.joelosk.ar/uploads/rates4_f8d52b4598.PNG)

When inflation looms around the corner, something happens in the required rate of return. Let's take a refresher on the capital asset pricing model, which states that the required rate of return is the sum of the risk-free rate and risk-adjusted market premium. As inflation increases, the risk-free rate increases and the required rate of return increases.

![Refresher into the cost of equity through capital asset pricing model (CAPM)](http://strapi.joelosk.ar/uploads/rates3_1c1c098aa0.PNG)

But why? The risk-free rate is generally quoted as the yield on US treasury bonds (or say Germany bund if we’re in Europe). US treasuries are considered to be essentially riskless as it has strong institutions and a government which can tax its people and businesses. There is a very low likelihood of US going bankrupt. (“[Never bet against America](https://www.youtube.com/watch?v=y5rr1RPdcyM)”)

Then say you bought a bond that paid $100 next year. The current yield on US government bond is 2.0%, meaning you bought the treasury bill today at $98.0. You had budgeted that once the bond matures and you got the capital appreciation and principal back, you could buy 20 Big Macs costing $5.10 each for your upcoming big party.

Suddenly, there is 2% of inflation that will hit the economy, and the Big Macs are expected to cost $5.20 each next year. It’s not much, but it does mean one of your guests will not get a hamburger. To compensate for that difference, the bond must now yield more than it did before. You’d no longer pay $98.0, but $96.0 because that way, your pay-off at maturity ($100 + $100 - $96 = $104) will be equivalent to 20 x $5.2 = $104, i.e., the price of hamburgers after inflation, and all of your 20 friends will get to eat a burger. This then also means that the yield of that bond has now increased to ~4% from 2.0% it was before (because $96 .0 / $100 - 1 = 4.0%)

**If a riskless asset now returns more, it naturally must follow that your risky growth stock has to return more as well.** After all, you could just park your money on those treasury bills and get a better return than before. Plugging this into the CAPM model described earlier, we see that a 2% increase in inflation *could* result in the cost of equity increasing from 11.8% to 14.0%. All this so that you could get that 20 Bic Macs for your friends.

![How the risk-free rates impacts the discount rate and cost of equity](http://strapi.joelosk.ar/uploads/rates6_ed97b320a9.PNG)

Now that we understand why the discount rate changes, we can go back to the value vs. growth examples above. The below table illustrates how the valuations of the two different investments would change as a result. As we’ve seen in the markets, it does seem that growth stocks will take a bigger hit than value stocks. A 200 bps increase in cost of equity results in growth stocks falling 3.4% more than value stocks. For 300bps, the difference increases to 4.9%. This is because **growth investments have longer duration cash flows**.

**“Duration” is financial jargon that just describes how long it takes to get your cash back**. The longer the duration, the more susceptible the investment is to interest rate risk. Growth companies would thus be “long-duration” investments while value investments would be “short-duration” investments. Still, irrespective of the fancy jargon, the important practical point is that, in an inflationary environment, money now is worth more than money further down the line.

![A change in discount rate affects growth stocks more than value stocks because of duration](http://strapi.joelosk.ar/uploads/rates5_d4912aee46.PNG)

But do these results make sense? I pulled up the price movements of the MSCI World Value Index and MSCI World Growth Index from March 2nd to March 13th (time of writing) and compared their relative performance below. I got lucky as it does seem that growth has underperformed value by 3-4%, surprisingly consistent with the model outputs above.

![MSCI Value vs. Growth index relative performance since 3rd March 2021](http://strapi.joelosk.ar/uploads/rates7_704eb4ffee.PNG)

And if we look even more into the data, we see that value stocks have indeed performed better in an inflationary period over growth stocks.

![Value stocks have historically performed better in inflationary environment](http://strapi.joelosk.ar/uploads/rates8_80ab1e3177.PNG)

# What the market forgets

If inflation and interest rates make stocks less attractive, should you then avoid buying stocks altogether? Well, what markets tend to forget is that **stocks are actually a great hedge against inflation**. [Even Warren Buffett recommends equities as the best protection against inflation](https://www.youtube.com/watch?v=BMdUwmhR4Z8).

What the markets also forget is that over the last 150 years, **real (inflation-adjusted) growth rates for the S&P 500’s earnings per share have remained relatively stable in the wake of changes in the inflation rate, while nominal EPS growth rates have tended to rise and fall in line with those changes.**

In other words, two things happen in an inflationary period. First, your discount rate increases as I’ve described in this post. But also, the nominal value of the company’s cash flows increases. In theory, this should be a net-net effect, but frequently, this gets forgotten.

There is even **an academic term for this called “inflation illusion.”** If you are more interested, you should check out [this 2004 paper](https://www.nber.org/system/files/working_papers/w10263/w10263.pdf) from John Campbell and Tuomo Vuolteenaho that describes the phenomena better.

One key finding in their study is “*...However, inflation is highly correlated with mispricing, supporting the Modigliani-Cohn (1979) view that investors form subjective growth forecasts by extrapolating past nominal growth rates without adjusting for changes in inflation.*”

But if equities are such a great hedge, why do they still fall on inflation announcements? [As Mark Hulbert from MarketWatch puts it](https://www.marketwatch.com/story/inflation-is-rising-and-so-are-investors-fears-about-stocks-11613703814): “*Most current investors have lived most of their lives in a low-inflation environment, so they have had no opportunity to learn the lessons that history teaches us about inflation and the stock market*.”

And if Hulbert is right, then a rational investor in the coming months will have an opportunity to pick up stocks at attractive valuations.

Many like me have largely invested in a low inflation environment that has dominated the last 10 years. This post explains why growth stocks have come down recently and what the market tends to forget.

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Many investors, I included, have invested mainly in low inflation and low interest rate environment that has dominated the last 10 years. This post explains why growth stocks have come down recently and the key thing that the market tends to forget.

Inflation and growth stocks

# Introduction

All this talk about tech valuations never stops. For a while many of us actually got concerned about inflation and rotation into value. Now it seems we’re comfortably back on the usual trend for the time being.

It seems high valuations are here to stay. Or are they? This is what I’ve been pondering recently. We can probably all agree that the valuation multiples we see for likes of Snowflake, Crowdstrike and Okta still look vicious. To exaggerate a bit, it almost seems like EV over Sales is the new Price to Earnings.

But I mean, ultimately all value must be based on cash and cash flow. Yet currently we’re not valuing even on EBITDA, let alone on net income basis. Although I am a technology growth investor, occasionally it seems like my metric, Sales, is so far away on the PnL from the actual cash flow that it gets scary. 

In this post I will briefly
- **Show that it's not a new phenomenon** - Some of the tech giants of today had similar multiples in much higher interest rate environment already 15 years ago
- **Describe one approach on how one arrives to these valuations** - it's not too dissimilar to how venture capitalists value companies 
- **Rationalize the uplift in tech multiples since 2011** - we understand subscription models better than before and have bigger respect to tech
- **Show why Covid-19 boosted the multiples to extreme heights** - all value comes to cash flow, so something must've happened in the cash flows to justify 2x higher multiples

# Not a new phenomenon

The famous quote from the scientist Carl Sagan goes “You have to know the past to understand the present.”. So the first question then becomes, is this a new phenomena? And in fact, it’s not. I took a glance at the stock markets between ~2000-2008, i.e. after tech bubble and before global financial crisis, and noticed lots of similarities to this day. 

I quickly found multiple companies that in the past seemed to be similarly “hot” as some high-growth stocks of today: Baidu, Ebay, Google, Tencent, Sirius and even Akamai who had been trading at relatively high EV to Sales multiples even on today’s standards

In fact, all of the before mentioned traded at or above 20x EV / Sales at some point during the last 20 years. The craziest was Google, which at the time had $6-8Bn of revenue yet still traded at 23x EV / LTM Sales! Of course in hindsight we all would’ve happily invested to the firm despite what the multiple said. More peculiar is the fact that they have all eventually converged to somewhere in the 3-8x EV / Sales range.

![High valuations are not a new phenomenon. Many of the giants of today used to trade at >20x EV / Sales.](https://strapi.joelosk.ar/uploads/tv1_d6894a9f9d.PNG)

**Baidu**, the Chinese version of Google, reflects especially well some of the premium stocks we see today on the market. Throughout 2007 Baidu was trading at over 50x EV / LTM Sales or 30x EV / NTM Sales, resembling a lot some of the valuation levels we see with e.g. **Crowdstrike **and **Cloudflare** today.

![Different year, different companies but similar valuation levels. Baidu is a ~$78Bn company today](https://strapi.joelosk.ar/uploads/tv2_68917594da.PNG)

At the time of writing, Baidu is of course a $78Bn company with $16Bn in revenues and $3Bn in annual profit, meaning that **even if you invested at the 2007 levels you would’ve been rewarded with a handsome 15% annual return**. It’s hard to see how large some of these companies can get to when they are only the size of peanuts.

# Arriving to these valuations

A prudent investor will say that if you look at valuations (here cyclically adjusted price-to-earnings ratio or CAPE) over time, you would notice they trend at around ~15-16x. There are times when it’s higher and times when it’s lower, but over long enough of time it tends to reverse to 18x. Ergo, you are overpaying.

![Over time the valuations should reverse to the mean - or should they?](https://strapi.joelosk.ar/uploads/tv3_4a9222b9bd.PNG)

But it doesn’t really tell you much other than given enough time your Snowflakes and Crowdstrikes will eventually converge into ~18x price to earnings. If your friend offers you to invest into his company at 20x price to earnings, is that too expensive? There will always be companies that trade above the average, and always those that trade below.

As an example, according to [Battery Ventures’ Software 2021 study](https://www.battery.com/powered/software-2021/), the average revenue of software companies that IPOd in 2020 was $210M with an average of 40% growth. Meanwhile, the average revenue of the companies in S&P 500 was roughly $23Bn growing a “mere” 10% year-over-year *(Irrelevant sidenote, growing 10% revenue at $23Bn base still means you’re adding $2.3Bn in sales. Roughly 10x the amount of newly IPOd software companies. But I digress...).*

If we then played a hypothetical game where we had a chance to invest into a company that resembled either one of those software companies that IPOd in 2021 or the S&P 500 ones, both valued at the “average 15x multiple” we’d get the following.

![Assuming both companies are equally risky with same cash flow profile, would you ever choose Company #2?](https://strapi.joelosk.ar/uploads/tv4_ec42ee4c02.PNG)

Obvious statement but this kind of thinking does not work. Clearly by year 5 it has become apparent that we got future cash flows much cheaper with the software company - a complete steal. These kinds of arbitrages cannot consistently be found so they disappear. 

So how do we arrive to these valuation differences between high growth and low growth stocks? One way is to resort to the method best explained by [Alex Immerman and David George at a16z](https://a16z.com/2020/08/17/role-of-entry-multiples-in-valuations/) and start from the exit.

If we believe that the beforementioned software company would be worth 15x EV / Sales in 2025 it’d be valued at $12Bn. If you then as an investor required 7% return on this stock, you’d work backwards from $12Bn discounting that enterprise value with 7% all the way to 2021. You would arrive to valuation of $9.2Bn or 44x EV / LTM Sales and 31x EV / NTM Sales. What this means is that you could at most buy the company at $9.2Bn valuation and still get 7% annual returns on your investment, all else equal.

![You can "justify" a high valuation by working from backwards and starting from exit](https://strapi.joelosk.ar/uploads/tv5_6000e91ffd.PNG)

This doesn’t necessarily mean that a company with 44x EV / Sales multiple would be a good deal. If you could get the same company for 15x multiple you’d be jumping with joy. If anything, this gives money managers an excuse to pay up for the deals.

# The increase in valuation 2011-2019

Still, we’d expect that over time even the top quartile multiples would remain roughly average. And again, looking at the data from Battery Ventures’ Software 2021 report, before 2020 they sort of had. We’ve seen ~20x multiples for high +40% YoY growth companies in 2013 too. But admittedly there is a slight upwards trend e.g. EV / Sales multiple for 10-20% growth company is 5.0x in 2013 and 5.2-6.0x between 2017-2019. Even if small, it’d be good to understand if there is a reason for that or if it is all arbitrary?

![Subtle upwards trend in software valuations in last 10 years with peaks in 2013 and 2019. Then they got out of hand in 2020](https://strapi.joelosk.ar/uploads/tv6_ae86c41835.PNG)

One way to think about it is to see if the share of smaller and highly growing IPOs have increased over time. If they had, the average multiple should increase accordingly because higher portion of the indice will consist of companies with low revenue, extremely high growth and thus high multiple.

But it seems that it’s quite the opposite. By borrowing [the data collected by Blossom Street Ventures](https://blossomstreetventures.medium.com/revenue-and-growth-of-tech-ipos-aabd3f6d8847) we actually see **(a) the median revenue of tech companies at the time of IPO has increased and (b) the median growth has decreased** (the reverse relationship to revenue is expected). Also, not shown in the graph but the amount of tech companies that have IPOd since 2010 has not consistently increased year over year and thus we also can't say that there are more fresh highly growing tech companies than before pushing up the multiples.

![Tech IPOs have only got larger over the years](https://strapi.joelosk.ar/uploads/tv7_574b07255f.PNG)

While the was easily debunked, the other more likely point is that **investors now understand software and SaaS models much better than before**. The figure below from Battery Ventures’ Software 2021 presentation illustrates why we may have needed a few years to fully grasp the software model.

![Tech requires understanding a new set of metrics compared to the legacy industries](https://strapi.joelosk.ar/uploads/tv8_e1c06d6085.PNG)

There’s simply different set of numbers and metrics to look at. For years, money managers growing up learning about value investing and book values struggled to find the “book” in asset-light technology companies. Nor was the "E" in P/E anywhere to be found. That together with success stories like [Adobe](https://www.forbes.com/sites/amadoudiallo/2013/06/17/adobe-cc-subscription-release-big-upside-and-risk/?sh=45318ee019c6) that despite early doubts showed how much value a subscription model can actually bring thus proving the value of SaaS for others as well

# And then 2020 happened

Yet the surge in 2020 valuations needs explaining as many companies saw their multiples double compared to 2019 levels. 

At this point we’ve all heard the phrase “two years of digital transformation in two months” to the point of exhaustion. Yet it holds some truth, as **many tech companies today are valued higher than in 2019 for the simple fact that we indeed leapfrogged multiple years ahead in terms of digital transformation**.

What this should mean is our forecasts for some of these companies have jumped forward by few years as well. Below is a quick example I did based on [Cloudflare](https://www.cloudflare.com/), a company that’s equally well known for its ubiquitous reverse proxy product as it is for its high valuation multiple.

Essentially I have two discounted cash flow models reflecting Cloudflare before and after the pandemic. Both assuming it will reach 35% free cash flow margins at ~$6Bn in revenue . 

Cloudflare used to be traded at ~12-13x EV / NTM Revenue before the pandemic, but quickly saw its multiple increase to roughly 30x EV / NTM Revenue during 2020. From the outset it feels like an outrageous uplift in such a short amount of time, but the model below puts it all into perspective. Because the revenue estimates were brought forward by several years, the cash flow break-even now happens sooner and the present value of those cash flows is in turn much higher as they are not as much in the distant future.

![High multiples post CV19 could be explained by growth "moving forward" by several years](https:///strapi.joelosk.ar/uploads/tv10_6e64c6af96.PNG)

We also know that there was a sudden rotation into tech and if an investor wanted exposure to the sector the universe of investable companies is, at the end of the day, rather limited.

As an example, Russell 3000, which is composed of 3000 large U.S. companies, as determined by market capitalization, has only 10% or roughly 300 companies classified as Information Technology. This goes down to 100 if we only take the ones classified as application software or systems software. And further down to roughly 10-15 if we only take the ones who grow 30% YoY. NASDAQ is a bit better. It has >2,500 constituents of which roughly half are considered technology stocks.

While it gets a bit speculative and is tricky to prove, part of the increase in valuations *could* be attributable to simple supply and demand dynamics, where there are more demand for technology stocks than supply (shares). And it’d be naive to think all capital distributes evenly across the investable universe which is why you see best companies getting seemingly more expensive.

# High valuations will remain

I started this post to educate myself on valuation levels. Looking at the history, I noticed this isn't the first time we see seemingly outrageous multiples. Likes of Baidu and eBay both were trading at over 20x EV / Sales not that long ago. The multiples today are arguably higher than in the previous peak during financial crisis, however, back then we also lived in a much higher interest rate environment which would act as a gravity and pull down the valuation levels a bit.

The last decade has been transformational for technology. New types of business models, namely software-as-a-service, has become commonplace and understood by investors much better than before. And then in 2020 we leapfrogged forward in terms of technology adoption, which in turn also boosted the multiples.

In hindsight it seems the valuations in the past / early 2010s were completely wrong. It seems the markets completely misunderstood the potential of software and cloud. The megalomanic returns you could have achieved by investing a 6x EV / Sales to +40% growth companies and holding them to 2020 feels nuts. And I'm sure that in 10 years time we look back and feel equally silly for not owning some of these >40x Sales stocks.

(Cover photo is by [asoggetti](https://unsplash.com/@asoggetti?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText))

To continue learning in public I took a look at the fatty tech multiples of today in an attempt to rationalize them by learning from history and understanding recent events

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Why are tech valuations so high

# Introduction

The rise of software-as-a-service model brought with it a set of completely new metrics for stakeholders to understand. It upended the old large license sales by splitting that large lot into many smaller chunks. As a result, almost over night the mindset shifted from shoving as many new versions down the customer's throat, to keeping that customer happy so they never churn.

Retention became a north star metric over repeat purchases. There are a ton of articles on how to calculate and measure customer and revenue retention, but despite all its simplicity it can be a tricky metric to interpret. 

Especially the relationship between logo and net dollar retention in enterprise SaaS is an interesting one. That relationship can tell a lot about the business, their clientele, pricing model and much more, yet too often we simply take one of the metrics and place judgement without really digging into what happens under the hood.

To help understand that relationship I put together a simple matrix illustrating and summarizing that connection below. To use a number, I’d be defining high logo retention anything >90% and high net dollar retention as ~120%. 

![The four types of companies depending on logo and net dollar retention](https://strapi.joelosk.ar/uploads/retention1_b850164a2a.PNG)


# The SMB and PLG - low logo and high NDR

Starting from the top left quadrant, companies there tend to lose customers, yet grow or keep the dollar value the same. How peculiar? Clearly the customers who churn are not of high value, and the ones retained continue growing their usage. In other words, their customer base looks something like in below

![Product-led companies and companies catering to SMBs keep the high value customer but lose the "triers"](https://strapi.joelosk.ar/uploads/retention2_b086b89b2d.PNG)

The pricing model for these sort of companies is often based on per seat (Slack), per host (Datadog), per data usage (Snowflake) or similar. Often no more than $5-15 per pricing unit per month that allows adopting the product at small scale and ramping up as needed. Comes essentially always with free-tier and trial too.

[DocuSign](https://www.docusign.com/), the company developing rather well known eSignature product, is a good example of this. They have 110-120% net dollar retention, meaning the revenue from their existing customer base grows 10-20% every year, yet logo retention only in the low 80%.

In other words, DocuSign’s customer distribution is likely very skewed similar to above. They lose the "triers" and keep the heavy users. What matters for them is the retention of the customers on the right hand of the graph - the ones who bring the revenue. We could expect that to be in top 90% although a break down like this based on contract sizes is rarely available publicly. 

Sometimes also called product-led growth companies, these customer base characteristics will likely become ever more prevalent in tech. Catering to SMBs has become more viable than before thanks to advancements in back-office (invoicing, accounting) that have made small sales viable and various online marketplaces that have made distribution easier.

Despite their potential, smaller companies are more prone to go bankrupt, and small internal teams may stop using the product for various reasons resulting in the relatively high customer churn for companies operating this model. Without properly splitting out the customer base, the “true” retention is dragged down by this long tail that brings no value to the company in any case. The famous [Tomas Tunguz from Redpoint](https://tomtunguz.com/maximum-viable-churn/) shares some facts:

> “**Startups serving SMBs tend to operate with higher monthly churn, somewhere between 2.5% and 5%+, because SMBs go out of business with greater frequency and tend to be acquired and managed through less retentive channels, e.g. self-service.** In the mid-market, which I’d define by average customer revenue* of between $10k and $250k loosely speaking, the churn rates I’ve seen are between 1% and 2% per month. Enterprise companies, those with customers paying more than $250k per year are typically closer to 1%. As the spend per customer grows, startups can afford to invest significantly more in retaining the customer, hence the improving rates.”

The small percentage who do end up using it (right tail above), however, can grow to become very large customers. They become the clients who bring in the money. [DocuSign’s S-1](https://www.sec.gov/Archives/edgar/data/1261333/000119312518106352/d506878ds1a.htm) describes this well “Approximately 85% of our aggregate ACV and 86% of our revenue for the year ended January 31, 2018 was attributable to our enterprise and commercial customers, with the remaining amount attributable to VSB (very small businesses)”. Only 14% of revenue comes from those small businesses for DocuSign.

So who cares if your smaller customers churn - you really care about that 86% that truly loves your product. Thus when seeing a company with a low logo retention and high net dollar retention have a look at their business model. If it caters to SMBs you might be making premature conclusions.

# The question mark - high logo low NDR

The bottom right quadrant is tricky. There are often few reasons why you might be keeping your customers, but not increasing their wallet or even losing the wallet. Those reasons tend to be related to the clients outgrowing the product or buying it "all-at-once" without a suitable pricing mechanism that’d grow with the business. 

**Enterprise customers outgrow the product**

The first one and more difficult issue to tackle is where the largest companies churn. You may have landed a great enterprises customer, only for them to realise that they can’t get the SLA they need or they save by moving IBM due to synergies with their other products. Tricky part with enterprise products is that it needs to be able to support enterprise needs. 

Say you’re a local webhosting company with few data centers around the country. It might be fine for the local businesses, but a larger company may ultimately need global scale and move its business to AWS. You’re keeping majority of your customers, but your upsell is limited, in which case your customer graph looks something like this.

![If large customers outgrow the product the customer base looks as follows](https://strapi.joelosk.ar/uploads/retention3_96a783f8c5.PNG)

To be fair, there are not too many public companies where you’d see this given they’ve often solved this (ergo reaching IPO), but you do see this with Series B/C product-led companies. 

**Lack of variable pricing mechanism**

What you do see often, however, are cases where the company sells everything at once or lacks variable pricing mechanism that allows upsell. Take Cloudflare, for example, where Motley Fool columnist Brian Stoffel puts it best (it’s a bit long citation but it’s good):

>“Dollar-based net retention takes all the customers in year one and compares how much they spend to the same customers in year two. So you're filtering out all the new customers, which is good because it lets you know, well, are you able to grow once you have a relationship with a customer?
But here's the thing. If you've got all those products, and before someone who was signing up for just one and then adding more as time went on, that would show up in your dollar-base net retention. But if I come to you, Brian, and you're Cloudflare, and I say, "I want the whole board. I want to everything for my whole company." Well, then there's less room to grow. But that's not a bad problem to have. It won't show up in dollar-based net retention, but it will show up in that remaining performance obligation because it just says, "**I don't care how much the pie grows from year one to year two, if you're ordering the biggest pie we have, that's great. Come on onboard.**"”

Cloudflare has talked about this a lot on their earnings calls (which is a bit funny because it’s not like their ~120% NDR is exactly bad either…)

> “... We're also seeing that we're getting more expansion from those large customers. And** sometimes that expansion is actually coming so fast that it's happening within the year. And so it isn't yet reflected in our dollar base net retention numbers**…”

and

>“... others are **small businesses where they might adopt across the entire platform**, but there aren't all that many seats...”

Workday is another example. They may have relatively low net dollar retention, yet it's well known the product is extremely sticky. [Workday has massive >$500K average contract values that are long, often +3 years](https://tomtunguz.com/workday-s-1/), in duration meaning customers buy the whole thing for many years at a time. It is exactly the case Brian Stoffel talks about above. More generally, for companies suffering from this "problem" the customer base may look like something below:

![If all customers buy "everything" with long contracts, the customer base might look quite homogenous](https://strapi.joelosk.ar/uploads/retention4_4906c2462a.PNG)

Thus one way to improve this is to consider a variable pricing mechanism that grows with the customer. The pay-as-you-go model popularised by usage-based cloud products is an example of truly variable pricing that is still contracted. But CFOs prefer predictability in budgets, which is where pricing models such as Snowflake's are great. Their capacity-based pricing allows locking in predictable spend by prebuying certain amount of usage, and if you go over it you simply pay the excess or buy more capacity in a chunk.

# Best-of-breed - high logo high NDR

Not much to say about the companies in the top right quadrant. You are retaining all of your customers while also taking more of their wallet. Many of the best companies do tend to fall in this bucket.

Perhaps one caveat to mention is to be a bit smart of companies with very low landing contract value but highly variable pricing model. If you land a $2K contract with 2 people priced at $1K per seat  which then expands to 4 people you get exorbitant 200% net dollar retention rates, but still have only a $4K contract. That said, the more mature the company gets the more these extremes get "blended" in the cohorts making them less of a concern for mature public firms. 

# The struggle - low logo low NDR

On the complete opposite side, it's equally hard to find much positive to say when customers are leaving and revenue is declining. Only thing to double check is if the company in question is a so called consumer subscription firm which may very well have relatively high logo churn and high dollar churn compared to enterprise SaaS.

# The takeaway

The bottom line is that just looking at a single retention metric may be misleading. Net dollar retention is perhaps still one of the strongest SaaS metrics that tells you about the health and future growth potential of the company, but if you just see a low/high customer retention it's too premature to draw conclusions. 

More importantly, when calculating unit economics it is easy to do shortcuts and calculate lifetime value using the “dumb VC” method and just take 1 divided by aggregate contract churn. When seeing a company with “mismatching” logo and net dollar retention, it is best to take a step back and ask yourself if it's perhaps because they are a product-led company or if their product is so good it's often all bought at once.


Briefer on the peculiar relationship of logo and net dollar retention and what information it can tell you.

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Logo and net dollar retention have become well known enterprise SaaS metrics and there exists plenty of guides on calculating them to defining them. However, there is also an interesting relationship between those two metrics that is often overlooked yet can unveil a lot about the business.

One relationship between logo and net dollar retention

# Introduction

Before I dwelve more into today's topic, I must admit this could possibly be the most dull theme I cover now and perhaps ever in the future. One thousand five hundred words on legal, taxation, and corporate structuring is not for the faint-hearted, so buckle up and hold on to your seat. But if below citation does happen to make sense to you, then I salute you and recommend you to skip the rest of the article:

> Tax Lawyer: “I was trying to structure this acquisition as a reverse triangular merger but there is too much boot in the transaction and a double dummy is too complicated for this crowd,
so I am thinking that so long as the target does not flunk the ‘substantially-all-the-assets test,’ we could flip over and do a forward triangular merger instead, which works because of the
boot relaxation rules and, just to be careful, we can implement the transaction safely by stacking a reverse triangular merger followed immediately by a Type A, so that the transaction
will be aggregated into a forward if it works and disaggregated into a reverse if it doesn’t. That is certainly the right way to implement a forward triangular merger in my opinion. What do you think?” 

> Bewildered Client: “Huh?”

IronSource is an Israel-based software company that specialises in mobile app monetisation. In early 2021 it announced its intention to go public in a SPAC deal with Thoma Bravo. By all means, IronSource has enjoyed quite stellar traction with $332M revenue in 2020 growing 83% YoY on a positive EBITDA and 149% net dollar retention. Naturally, I picked up the prospectus wanting to learn more.

Most of the time these [listing documents](https://sec.report/Document/0001193125-21-166647/d107630d424b3.htm) are a 30 minute read and you’re done, but there was something peculiar on the post-transaction structure in the very first pages of the >300 page thick book full of poetic financial and legal prose. 

![This is how IronSource's organizational structure will look after the IPO. Notice anything peculiar?](https://strapi.joelosk.ar/uploads/is1_59a8ec5ebc.PNG)

See that Showtime Cayman II? It is related to the listing transaction, and tax advisors and lawyers probably know the story immediately, but practitioners like myself will find themselves scratching their heads and quickly falling into a rabbit hole trying to understand what is going on.

Short answer is tax reasons (yes yes, didn't see that one coming did you?) but the long story is still worth the read as the way these cross-border acquisitions are structured can be quite involved. Cue a brief lesson into tax-free merger structuring by the masters of the universe.

# Setting the stage

To understand what is happening, let us start with the way how IronSource describes the transaction. It will sound complex, but we will digest it one step at a time.

> The terms and conditions of the merger of the Merger Sub with and into TBA (the “First Merger”), with TBA surviving the First Merger as a wholly owned subsidiary of ironSource (such company, as the surviving entity of the First Merger, the “Surviving Entity”), and the merger of the Surviving Entity with and into Merger Sub II (the “Second Merger,” and together with the First Merger, the “Mergers”), with Merger Sub II surviving the Second Merger as a wholly-owned subsidiary of ironSource (such company, as the surviving entity of the Second Merger, the “Surviving Company”) (collectively, the “Business Combination”) are contained in the Merger Agreement, which is attached as Annex A to this proxy statement/prospectus. We encourage you to read the Merger Agreement carefully, as it is the legal document that governs the Business Combination

Makes perfect sense, right? Well, to make it easier to digest I’ve drafted the transaction entities for both IronSource and Thoma Bravo Advantage (“TBA”) before anything has happened. IronSource has created two subsidiaries, Merger Subs I and II (called Showtime Cayman I and II, respectively) solely for the purpose of consummating the transaction. 

![How IronSource and Thoma Bravo Advantage (TBA or the SPAC) look just before the transaction](https://strapi.joelosk.ar/uploads/is2_9f393e3be9.PNG)

First thing you'll see is that while Thoma Bravo is US-based acquirer, and IronSource is Israeli-based, the actual transaction happens in Cayman Islands. There are [many benefits](https://www.graubard.com/wp-content/uploads/sites/1400405/2020/08/SPAC-March-a.pdf) of having the acquisition entity in Cayman Islands, where the subsidiaries and the SPACs are domiciled. Not only is Cayman Island [an established and well understood merger regime](https://www.mondaq.com/caymanislands/corporate-and-company-law/970366/the-use-of-cayman-islands-structures-for-spac-ipos) enabling a harmonic deal, but it will also allow IronSource to go public as a so called "foreign private issuer" and be subject to [more lenient SEC regulation](https://www.sec.gov/divisions/corpfin/internatl/foreign-private-issuers-overview.shtml).

Ok, so going back to the transaction (eyes to the graph above), what happens is
- Merger Sub I (“Showtime Cayman I”) acquires Thoma Bravo Advantage ("TBA") in a so-called **“reverse triangular merger”**. IronSource now owns TBA and Merger Sub I will cease to exist
- TBA (or the ex-Showtime Cayman I) that is now owned by IronSource, merges with another IronSource subsidiary Showtime Cayman II in a so-called **“forward triangular merger”**
- Combining these transactions together, and executing them simultaneously, the whole transaction becomes one big forward triangular merger

Forward and reverse triangular mergers are funky tax-free transaction structures that involve acquiring another company using a subsidiary. The names seem frightening at first, but are actually quite important in order to understand what is being orchestrated.

Forward triangular merger is the more logical one where an acquirer acquires a target, which then becomes a wholly owned subsidiary of the acquirer. They are great in transactions where payment is a combination of stock and cash, but do come with a high risk of (double) taxation and can be a challenging as the target gets destroyed.

Reverse triangular merger tends to be more often seen in corporate M&A as it offers many benefits and tends to be clean to execute. The downside is that they are strict around how much cash can be used in the payment (or “consideration”), and depending how the SPAC transaction is structured there might be a large non-stock component in the form of Private Investment to Public Equity ("PIPE") or the cash payment from the SPAC's treasury. 

![Two key tax-free acquisition structures that together enable the IronSource deal](https://strapi.joelosk.ar/uploads/is3_d363b0c33c.PNG)

# The first merger

The transaction begins with a [**reverse triangular merger**](https://www.investopedia.com/terms/r/rtm.asp). As mentioned it is [one of the most popular merger structures](https://witnesseth.typepad.com/blog/reverse-triangular-merger.html) due to its simplicity (subsidiary has essentially only one shareholder, its parent) and has an advantage of isolating acquired company's liabilities into a separate entity. In the first phase, the SPAC, Thoma Bravo Advantage, merges with a subsidiary of IronSource, Showtime Cayman I.

When you put two things together, only one of them can exist (or survive) going forward and it's often more beneficial that the target remains intact. In reverse triangular merger it is the acquired entity i.e. TBA that “survives” while the acquirer Showtime Cayman I gets destroyed. In forward triangular merger it’d be the other way around and while this may sound like a subtle difference it actually matters a lot.

Whoever gets destroyed also finds their legal contracts and obligations destroyed as well, which may need to be renegotiated to the newly formed entity. Needless to say all this legal paperwork can get really messy and may need all sorts of third party consents who have the ability to kill the transaction. With reverse triangular merger where the target survives, the legal entity can operate just as before but under a new shareholder ownership removing all the stress and hustle. Simple.

The technical rule that primarily dictates whether it'll be taxable or not is one that requires the acquisition to contribute into the continuity of the merged company - **the so called Continuity of Business Enterprise Requirement or "COBE"**. As [Investopedia ](https://www.investopedia.com/terms/c/continuity-of-business-enterprise-doctrine.asp) puts it, the doctrine holds that in order to qualify as a tax-deferred reorganization, the acquiring entity must either continue the target company's historic business or should use a substantial portion of the target's business assets when conducting business.

There is some risk that the acquired SPAC would not fill the business continuity requirement because, for example, (a) it doesn’t have an operating history (SPACs by definition have 24 month operating history at best) or (b) the majority of assets in the acquired entity, the SPAC, are considered non-operating assets such as cash.

By the way, notice how annoyingly confusing it is when I am constantly implying it's the SPAC that gets acquired? Intuitively it's easier to think it's the SPAC that needs to acquire a target company, but as described earlier it's actually the reverse in a reverse triangular merger - the subsidiary acquires the SPAC. Anyhow, I digress.

The continuity requirement sounds trivial, but the risk is real. Although on page 275 of the prospectus the counsels of Thoma Bravo, Kirkland & Ellis, do believe the transaction is likely to be considered a tax-free merger they also caveat it as follows:

>Kirkland & Ellis LLP that the Business Combination is more likely than not to qualify as a “reorganization… However, due to the absence of guidance bearing directly on how the above rules apply in the case of an acquisition of a corporation with only investment-type assets, such as TBA, the qualification of the Business Combination as a reorganization is not free from doubt

Here's they juicy bit though: If for some reason the reverse triangular merger blows up because it flunks the above qualifications (i.e. say the regulator agrees to Kirkland & Ellis' doubt and can't accept SPAC contributing to the business continuity requirement), then it's the TBA shareholders who get stuck with the unexpected tax bill.

Not a desirable outcome, but better than in a forward triangular merger where not only do TBA shareholders face the tax bill, but so would IronSource as a company. That would result in a double taxation both on the shareholder and on the corporate level which would obviously be a super bad deal.

So we know reverse triangular merger is simple, allows to retain the original legal entity among all related contracts and has tax benefits in case the transaction flukes but what are we missing? Where do we even need the second merger if it looks like we have already completed the transaction? Well, we haven't touched the payment part yet

You’d think TBA is paying with cash, but actually it’s a stock-for-stock exchange where TBA gets IronSource shares and IronSource gets TBA shares that are promptly converted into IronSource shares. By doing so, IronSource receives the entity that holds all the cash of the SPAC, but TBA is not really "paying" with that cash. Think of it sort of like buying a Porsche by exchanging in an 1983 Datsun that has a glove compartment full of cash. The value of that Datsun is not really in the chassis, in the engine nor in the tires but in the dineros in the cubby.

If it was left at that, we could all go home, but there is also a PIPE from Thoma Bravo, Tiger Global and their rich friends who commit to buy shares directly from existing shareholders. In fact, hidden inside the 300-page guide to tax haven is also an easily missed part where TBA commits to buy more secondaries should there be "excess cash over the company's 'capital needs' after the cash is released from the SPAC’s trust account". There is nothing wrong with these statements or buying secondaries per se (great that they provide liquidity to existing shareholders and employees), but because these secondaries are part of the overall merger payment, things just got 10x more messy for the tax advisors and lawyers.

![Act 1: TBA merges with IronSource's subsidiary Showtime Cayman I in a reverse triangular merger](https://strapi.joelosk.ar/uploads/is4_265e580b75.PNG)

# The second... why do we need this step again?

See, despite the long list of benefits in regards to reverse triangular merger, the major downside is that it limits the amount of non-equity (i.e. cash 💸💸) that can be given as a payment (by the way, the lawyers call it “boot” and [the origin of the word is quite interesting](https://en.wikipedia.org/wiki/Estovers)).

And this is where we introduce **forward triangular mergers** as they do not have this restriction to the same extent which is what this second step is all about. As a reminder, forward triangular transactions are mergers where the *acquirer* survives instead of the target like before. It's the more "logical" acquisition structure. You acquire something and survive the acquisition. Anyways, key benefit is that you can use vastly more non-stock payment as you want

Of course, as mentioned above if it wasn’t for the PIPEs and secondaries we likely wouldn’t need this scene. And to be clear, it's not that PIPEs per se that make it difficult, but in many cases the PIPE is actually injected into the SPAC itself before the transaction, and *then* used to buy the shares of the target company in a stock-for-stock transaction. But not here. No, here rich folks pay pure greenbacks to investors and buy shares as part of the overall consideration meaning there is a massive cash component in the consideration.

And then somewhere in the board room a highly paid, and possibly a bit overworked, lawyer suggests that **TBA should structure the transaction as forward triangular merger but without it being considered as a forward triangular merger.**. People applaud, it's a big brain move but how would you go ahead and do it?

This is where you need the step 2, because **if you follow-up the first reverse triangular merger with a second forward triangular merger into a subsidiary, the entire transaction is now treated as a forward triangular merger** as long as it’s part of a “single-integrated plan” (or how IronSource calls it, “First and Second Plan of Merger”). Best of all, you can use as much cash (or 👢) as you want. Suddenly paying a few million to white-shoe lawyer firm starts to make sense.

And that’s not all, because if for whatever reason the first merger does not qualify as tax-free e.g. it doesn’t satisfy the previously mentioned COBE requirement, then it’s immediately taxed at that point but no more after it. In other words, **while TBA shareholders may be left with the tax bill (ouch, but the prospectus does urge TBA shareholders to consult their tax advisors) there won’t be a double taxation on the corporate level anymore AND you just did a reverse triangular merger using a ton of cash** which otherwise wouldn't be possible. 

Of course, in a likely outcome nobody is left with a tax bill but the point is that all this hustle minimizes the chance of unexpected charges to all parties. And I'm sure it allows the lawyers to tap into the fee flow just a spoonful more.

![Act 2: TBA (ex-Showtime Cayman I) merges with another IronSource subsidiary Showtime Cayman II in a forward triangular merger](https://strapi.joelosk.ar/uploads/is5_e9d0637ffc.PNG)

# Further reading

Now, not all SPACs follow this exact structure and as mentioned, the structure depends a lot on how the consideration is paid.  [Opendoor](https://sec.report/Document/0001104659-21-013241/tm211852-9_424b4.htm) and [Nikola](https://sec.report/Document/0001104659-20-028234/tm2011332d1_8k.htm) were, in fact, both a simple one step reverse triangular merger. If it’s clearly a stock-for-stock deal where the PIPE is injected *before* the merger into the SPAC, then one-step might be fine.

But this two-step dance is not uncommon at all also for cross-border M&A where the acquirers’ and targets’ legal jurisdiction might be different. And it’s not just SPACs, [Grubhub / Just Eat Takeaway](https://www.sec.gov/Archives/edgar/data/1594109/000119312521079863/d114146d8k.htm) utilised essentially the same structure. Sometimes as a second step instead of horizontally acquiring another subsidiary, the merger is to the parent (“upward merger”) and sometimes there are just other structuring reasons. Diamond Eagle Acquisition Corp, for example, also did a two step dance but mainly to incorporate itself to Nevada for more lax gambling laws for acquiring DraftKings. And then there was the [Pershing Square Tontine](https://sec.report/Document/0001193125-21-181943/) one which, uh, was financial engineering at its best.

But the bottom line is that you can do a lot of corporate magic by playing around with the legal entities. How much do you need to care as an investor? Not much - usually it’s in the controlling shareholders’ interest to structure it in a way that’s beneficial for all. 

I'd emphasize that I am not a lawyer nor a tax consultant, so I had to study this topic *a lot* and if you find blatant mistakes do email me. I only scratched the surface here, and if you’re keen to learn more I recommend reading this article: “[The Forward Triangular Merger: A Not-So-Straight-Forward Transaction](https://www.sullivanlaw.com/assets/htmldocuments/Article-WTE-Forward-Triangular-Merger-A-Not-So-Straight-Forward-Transaction-August-2009-GT-46541385-B1594140.pdf)” by Joseph B Darby from Sullivan & Worcester. For practitioners, I’d recommend [Macabus’ page on tax-free acquisitions](https://macabacus.com/taxes/tax-free-acquisitions).

Also known as “reverse triangular merger followed by a forward triangular merger” or “Merger of I-am-doing-a-$10bn-transaction-but-plan-on-paying-no-taxes”

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A brief lesson into tax-free merger structuring by the masters of the universe. Today we will try to learn how to do a “reverse triangular merger followed by a forward triangular merger” or a dance that is also known as “Merger of I-am-doing-a-$10bn-transaction-but-plan-on-paying-no-taxes” by analysing the SPAC transaction between IronSource and Thoma Bravo Advantage.

IronSource and the tax-free tango

When investing into (unprofitable) growth companies the returns math is simple. You buy a share at a certain sales multiple, wait for it to grow, and then sell for another sales multiple resulting in profit as long as the revenues grew.

The problem with this two variable PhD-level equation is that while an investor can often quite reliably craft good enough estimates for the revenue, the multiple part is tricky. In fact, you should probably completely ignore the entry multiple and approach these companies like a venture capitalist. Let me show you what I mean

# The financials are easy

We can build all sorts of market models and bottoms-up estimations with some margin of safety that, despite being completely wrong most of the time, they are ostensibly correct. 

Here is an example of getting really accurate estimates for Cloudflare using just 3 variables. The company made $431M in sales in 2020 (1st variable). It’s [market share was 15.4%](https://w3techs.com/technologies/history_overview/proxy/all) at the end of 2020, up from 12.3% in 2019 (2nd variable), suggesting a $2.8Bn market by revenue ($431 / 15.4%). Finally, the [market is expected to grow 16.9% per year](https://www.mordorintelligence.com/industry-reports/web-application-firewall-market) (3rd variable), and by assuming the market share will continue grow historical pace our super simple revenue model is now complete.

![Forecasting sales "well enough" is easy and with just three variables you get robust estimates](https://strapi.joelosk.ar/uploads/valuations1_0f7b0c4667.PNG)

You see how close it ends up to the analyst consensus? No need to study five years for CFA or have financial services experience to get reliable numbers. Of course this is a simplification and omits things like new markets and products, but the point is, financials are simple.

# But multiple is difficult

Yet multiple has historically driven most of the tech returns but is almost impossible to predict. The P(rice) in P(rice) to (S)ales ratio is dependant on the market, prevalent sentiment, monetary policy, the position of Mars and Jupiter and literally thousands of other things that are simply impossible to estimate unless you are the Medallion Fund. At one point 10x Sales was crazy. Then 20x Sales was seen ludicrous, and then 30x was actually sort of acceptable for a while but 40x was nuts again. It might seem like I am just arbitrarily adding a flat 10 to the numbers but I mean look at this and tell me am I overpaying or underpaying if I buy this company today?

![Multiples keep growing - if you bough this company today, how do you tell whether you're overpaying or underpaying?](https://strapi.joelosk.ar/uploads/valuation3_6be31befad.PNG)

In the classic Benjamin Graham book [Intelligent Investor](https://www.goodreads.com/book/show/106835.The_Intelligent_Investor), investor controls what they can. You can somewhat control sales by choosing companies that operate in high growth markets and have a leadership position, but you can’t control the multiple.

# Ignore the entry multiple

So then, an intelligent investor should ignore the multiple. Yes, ignore. Ignore the multiple. Well, the entry multiple at least.

Going back to what I said earlier, given you can somewhat broadly estimate the revenue, you should then estimate how the revenue will look like in 5 years time and apply a steady-state multiple on it. You can then discount it back to the present date using your own required rate of return to see how much you can pay for it today to achieve the returns you seek.

This limits you to two assumptions: exit multiple, which is easier to assume given we have quite a few mature software companies already, and your own return requirement which you know yourself. See example below, assuming you buy Cloudflare at the end of 2020.

![Ignore the entry multiple and start from the exit and see how much you can pay today to get your returns](https://strapi.joelosk.ar/uploads/valuations2_08b3788d46.PNG)

As I write this, Cloudflare is actually at its historical highs trading $38Bn valuation (~$120 per share) or ~75x LTM Sales and ~50x NTM Sales. If you believe in above analysis then I hope you bought Cloudflare in November 2020 as that was the last time it was trading at $18Bn valuation.

If you didn’t, you’d thus need to believe that they could get a >30x LTM Sales multiple in 2025. You can take that bet knowing that there are only a handful of companies capable of those multiples at >$2Bn revenue. ServiceNow is one software company that is still growing 20-30% YoY but even they peaked at 25x  EV / LTM Sales or 20x EV / NTM Sales. Alternatively, you’d need to accept a longer holding period although who is to say we don’t see 70x sales multiples 5-years from now. 

This methodology is nothing novel. It’s actually often referred to as the “[VC method](https://www.wallstreetprep.com/knowledge/vc-valuation-6-steps-to-valuing-early-stage-firms-excel-template/)”, because it’s commonly used in valuing pre-revenue, early stage companies. Which is funny considering >$400M in sales, yet it seems to be equally applicable in public equities.


Ignore the multiples. I will argue how you should approach current tech stock valuations like a venture capitalist and ignore the entry multiple instead focusing on what you can pay today to get your returns. Again, ignore the multiples.

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Venture capital and public markets are two different extremes. Yet in this article I will show how you should approach tech stock valuations like a venture capitalist and completely ignore the multiple. Yes, ignore the multiple.

The VC method in public stocks

# The Framework

It seems cryptos are here to stay. Although many problems exist, such as onboarding, privacy and compliance, the amount of money being thrown at those issues will likely alleviate them faster than we think. It feels there is too much institutional money and too many bright minds tied up to the ecosystem to go back.

Yet investing into this ecosystem and buying cryptos and calling it investing makes many value investors that follow Ben Graham’s or Peter Lynch’s footsteps quirk. And for a good reason as well given the implied volatility of crypto is effectively 100% and there are no agreed upon methods on how to value them. Yet by “investing” in bitcoin alone, you could’ve doubled the value of your money in the last 12 months.

These quirks in human character, the ever changing regulatory environment, the absence of market makers stabilising the prices and much more has inducted a ton of volatility thus in the process making them rather unsuitable as a method of payment. Slightly ironic given the coinage cryptocurrencies.

But despite the inherent flaw in the name, if you stop thinking of them as currencies and rather as pieces of a network like an internet, they suddenly start to make a lot more sense. In a way, ethereum is nothing more than a network that connects users and applications with each other. People can then build on top of this platform as they please. 

![The map of the internet](https://strapi.joelosk.ar/uploads/eth1_1fee96a0b6.PNG)

So then, what if we thought of crypto as internet 2.0 instead of currency? Financiers and economists have come up with many smart ways to value assets that are based on supply and demand, such as FX and commodities, that have no clear future cash flows. Those methods range from [stock-to-flow](https://en.wikipedia.org/wiki/Stock_and_flow) models that attempt to value something based on how much something is mined (flow) to how much of it is available (stock), to more commonly known [Metcalfe’s law](https://en.wikipedia.org/wiki/Metcalfe%27s_law) that suggests the value of a network is roughly the square of its users. 

But we investors have been taught to know that the value of something comes from the cash flows it will generate in the future. Doesn’t matter if it’s a one big lump sum or a stream of smaller payments. As long as there is more positive cash than negative to look forward to in the future, it has value. Sometimes they are easier to predict, sometimes less so. A bond that pays a coupon every year is very easy to value, whereas exotic options require math that goes right through most of us.

And that said if we think Ethereum as a new internet, I would like to play around with the idea that it could be an asset with somewhat predictable cash flows. 

# What if you could place a value on internet

**What made internet valuable were all the applications built on top of it** that allowed people to flourish their creativity by communicating, creating content, conducting business and much more. 

An application built on top of the internet, say a software company, gets its value from the present value of all the cash it generates from the payments by its customers from here to eternity. To get the value of two software companies, you’d sum together the present values of the two companies. To get the value of 10 software companies you’d sum together the present value of the 10 companies and so on (excluding all synergies and other impacts)… 

So then, it follows that the value of a network infrastructure like the internet, in theory, should be the present value of all the applications that rest on the infrastructure plus maybe another form factor to account for future prospects.

This is basically my shower thought that I wanted to test as a theoretical framework. Except the software companies mentioned above are replaced with applications on the blockchain. 

![The framework: Sum of the value of all the applications on top of Ethereum](https://strapi.joelosk.ar/uploads/eth2_38c6fd1e97.PNG)

That’s the concept and we don’t even need to imagine anything. In practice, it already looks like this today (courtesy of [/u/devboricha at Reddit](https://www.reddit.com/r/ethtrader/comments/l65yst/the_ethereum_ecosystem/)):

![The Ethereum ecosystem is vast](https://strapi.joelosk.ar/uploads/eth3_1_20c20ac129.png)

If the framework holds, the reason Ethereum has increased in value must be because the number of apps on the network have grown in amounts:

![The value of Ethereum has increased as the number of apps and users have increased](https://strapi.joelosk.ar/uploads/eth4_f2eefa5acc.PNG)

Basically it doesn’t seem farfetched: in the last year or two we’ve seen the rise of OpenSea, Compound, Curve, IPFS, Lido, Nifty Gateway, Ethereum Name Service and many others that have brought utility to Ethereum among with users spending money on it.

Well, we’ve simplified a lot. In fact, Ethereum has been generating “cash” for a long time. Every time a user transacts on the network, someone is getting paid through the “gas fees” (or transaction fees). Now, before the [“London” hard fork](https://cointelegraph.com/news/ethereum-s-london-hard-fork-sets-eth-on-a-more-deflationary-path) (which has nothing to do with London though) the miners were the ones getting the fees for spending processing power to validate the transaction.

After the London fork, the fee that the miners would normally get is “burned”. It is still cash in the sense that the supply of ethereum decreases over time and the value of your remaining ethereum increases. Exactly like share buybacks. 

It’s easy to see that Ethereum is generating a good chunk of “revenue” as we speak through transaction fees. In fact ~$1.8 billion of revenue in the last 30 days as I’m writing this.

![Ethereum has been generating revenue for a long time](https://strapi.joelosk.ar/uploads/eth5_d7f6adc1a2.PNG)

Below we see how this transaction fee has fluctuated since 2020. Despite the huge boom in Spring 2021 the average transaction fee has actually remained roughly at ~$5-6 per transaction even though I’m sure we all think it feels more. It feels more, because we use more computationally intensive applications such as OpenSea that require more gas to execute the smart contracts.

![The transaction ("gas") fee has averaged at around $5-6 since 2020](https://strapi.joelosk.ar/uploads/eth6_6cd496c4dc.PNG)

# The model

With all this background I’ve built a simple yet unnecessary long discounted cash flow model of 20 years that tries to put a value on Ethereum by estimating the cash it is expected to generate. The model estimates the number of Ethereum users, the number of apps and ultimately the average number of revenue each app will generate.

The rough assumptions are as follows:
- **Internet users** to grow at ~6% p.a. from 3.9Bn to 5.3Bn by 2023 (based on Cisco) and after that by an assumed 2% every year
- **Crypto penetration** to grow from 5% today (i.e. 220M crypto users as per Crypto.com study in Jun-21) to 10% by 2026 and then 35% by 2041. Very arbitrary growth assumptions, and probably the most conservative of them all
- **Ethereum users of total crypto users** to grow from 10% (i.e. 23M ethereum users as per Crypto.com study in Jun-21) to 20% by 2026 as per Crypto.com study. Afterwards, the % Ethereum users would grow by ~1% step each year reaching 35% of total by the end of forecast period. This is probably the #2 most conservative assumption in play
- **Number of apps on Ethereum** to grow each year by roughly half of the growth in users (assumption, roughly based on the data on State of the Dapps)
- **Average transactions per Ethereum** user per year to remain at ~20 which was the implied number as of August 2021 (data from Etherscan)
- **Average gas fee** to remain at $5.8 which has been the historical average since 2020 (data from Etherscan)

The assumptions imply we’d reach $113Bn of Ethereum revenue by 2040, which is c. 100x of what it generated in the last 30 days. For the discount rate I use a multi-stage approach starting from 25% that decreases by 5% every 5 years reaching 10% by terminal.

**All this results in a present value of $649Bn for Ethereum**, which feels more sensible than I thought when I started this exercise implying c. 20% uplift to today’s ~$500Bn market cap.

![The model summary suggesting Ethereum's fair value should be ~$650Bn today](https://strapi.joelosk.ar/uploads/eth7_1149440f18.PNG)

![Ethereum's "revenue" development according to model](https://strapi.joelosk.ar/uploads/eth8_0643319200.PNG)

The one final bit I would add here is a bit of sensitivity analysis around the adoption of Ethereum as that is the largest individual component of the model.

![The model is highly sensitive to assumptions around Ethereum's adoption](https://strapi.joelosk.ar/uploads/eth9_44ea611a8d.PNG)

One could also argue that it’s better to think Ethereum as a payments network rather than an infrastructure network, in which case you’d rework the model to show the transaction values and implied take rate / interchange revenue instead. 

The resulting value is the same as before given we’re not really changing any underlying value drivers but just showing the lines differently.

![Slight adoption of the same model thinking Ethereum as a payments network instead](https://strapi.joelosk.ar/uploads/eth10_f1e5adce99.PNG)

To emphasize, this is (a) very unlikely to get you to the correct value and (b) how the value is determined but rather just another way to think about it. At the end of the day, the value follows strict supply-demand dynamics and my analysis completely ignores a ton of details. The future is completely open for Ethereum, and it feels almost impossible to try to put a value on it.

Anyways, hope this theoretical exercise was something of value (pun intended). The whole protocol is developing as we speak and I’ve certainly omitted a ton of stuff as mentioned above. 


Shower thought turned into very theoretical analysis of the ethereum’s value using a discounted cash flow model

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Shower thought turned into very theoretical analysis of the Ethereum’s value using a discounted cash flow model. 

Can you put a DCF on Ethereum?

# Story of a lifelong customer

Music has always been a big part of my life. When I’m driving, when I’m commuting, when I’m working or studying, I always have music booming in my ears. As a undeliberate consequence, I’ve developed this magical ability to pinpoint the exact time of my life where I’ve been vibing a certain track sprouting a wave of nostalgia each time.
When Spotify came out in closed alpha around 2007-2008 it wasn’t a surprise that I signed up immediately. For a while it was 100% all-you-can-eat for no charge and when the paid tier was eventually introduced it was pretty much optional, at least for the early users.  

I didn’t mind the ads that were also eventually introduced as Spotify was figuring out its business model (are you OG enough to remember Jonathan from Spotify?), but I do remember that at some point in 2010-2011 they made the free-version so that you can only play your playlists in shuffle-mode effectively forcing you to pay up. Needless to say, being a music connosseur that I was I promptly opened up my wallets and have never looked back since. 

That means I must’ve been a paying user of Spotify for over 10 years now. Easily the only service I’ve continuously been a subscriber of for so long. With quick math that equates to over $1,000 paid to Spotify which admittedly doesn’t feel much now that I wrote it out. Almost a steal considering the value it has added to my life. I’m also sure that I’ve bought individual songs through it (yes, did you know that was a thing on Spotify as well at one point) so add a few dollars on top of that grand.

Now, I’m probably an ideal customer of Spotify. They probably paid nothing to acquire me and I have been loyal to them for a extraordinarily long period of time. My entire cost to them is 75% of the $9 I pay for providing the platform and serving the content for me. I’d be willing to bet that my gross lifetime value of >$1,000 is quite likely in the top 5% of all the customers.

The point is, it’d be amazing if an average customer at Spotify looked like me. But reality is, that many are happy with the free version and [even the paying users churn in a bit over 7 months](http://stockpickinginsights.com/2021/07/07/spotify-churn-rate-39-hurdle/), on average. That means Spotify requires >39% YoY growth just to make up for the lost customers. They can always spend more on marketing to grow and acquire new customers, but if Spotify doesn’t get that marketing investment back in 7 months (i.e. before the customer churns) the business turns into charity. And non-profits tend to be bad investments.

# The simple theory of investing

The theory of investing is simple and goes as follows: Value of a business comes from the positive cash flows that it is expected to generate in the future for the people who own a piece of that business. In order to generate positive cash flows the cash inflows must be higher than the cash outflows. In order to generate net cash inflows each customer must generate well in excess of what it costs to acquire and serve that customer. And in order for that to happen, the unit economics must be positive. Without unit economics the business model is not sound.

Unit economics is the final individual ensemble of the macro-level business management. Once you are clear on your unit economics, you can probe in to its components much more in detail. Perhaps the poor economics are due to customers churning, or inefficient go-to-market. Or perhaps you are missing an upsell component or maybe you are underpricing. But if your starting point is any of the beforementioned, say the engagement or sales efficacy, without first understanding what your unit economics actually is, you are missing the forest from the trees.  Below a simple framework that illustrates this extremely simple yet fundamental equation. 

![The simple theory of investing: Value of business is inherently linked to unit economics](https://strapi.joelosk.ar/uploads/ltv_theory_9e678d6e7e.PNG)  

# The impact of unit economics

How one calculates unit economics depends on the business model. It used to be much easier with traditional goods and services companies where revenue and costs matched 1-1 with both recognized at the point of sale. That meant your gross margin immediately reflected profitability of the business: selling an artisan desk for $10 with the material costs being $4 resulted in $6 of net cash inflow that you can use however you want. 

But the complexity has increased with the rise of tech and resulting popularity of SaaS and marketplace models. Now there are all these repeat purchases, lifetime spends, basket sizes, average contract sizes, supply-side economics, demand-side economics and so on and so on. The fundamentals never changed, however: Businesses must generate cash, and unit economics tell you how much net cash in aggregate you’re getting from a customer.

Let me demonstrate its importance using a simplified example of an enterprise SaaS company. Let’s call it the LTV Co. The firm has 20% R&D costs as % of sales and 5% G&A costs as % of sale. It also acquires ~10-15 customers per year and sells a software product with $30K annual contract value and 75% gross margin. To keep things simple I assume no upsell. The full model thus looks as follows

![Hypothetical operating model of Simplified example of unit economics ](https://strapi.joelosk.ar/uploads/ltvcac_orig_f46f90e97b.PNG)

Now then, let’s say the company either comes up with a product innovation that makes the customer compelled to stay longer on the platform or hires a genius marketer that lowers the customer acquisition costs significantly thus improving its LTV / CAC to 5.0x. With these improved unit economics the key value-drivers, growth and profitability, change tremendously:

![The impact on growth and EBITDA are tangible with improved unit economics](https://strapi.joelosk.ar/uploads/ltv_outputs_1eef470edf.PNG)

By increasing the customer lifetime, the company boosts growth up to a 54% YoY in year 5 as it needs to replace fewer churned customers every year. By improving average sales price (e.g. moving upmarket) or lowering customer acquisition costs (e.g. more organic growth) LTV Co. can acquire profitability faster and reach up to 31% EBITDA margins in year 5.

# Few special cases

Often you hear a 3.0x LTV / CAC cited as some kind of a rule-of-thumb, but I’d argue this all just depends on the company. Let’s explore few special cases:

**1. Special case 1: a company with 10x LTV / CAC but some years with no new customer acquisition**

The first is a very special case of a company that has an extremely high LTV / CAC of 10x, but acquires only a handful, or even no customers at all in a given year. Regardless, because its product comes with high average sales price and is extremely sticky for being mission critical it will grow nicely with the customer on a 20% net dollar retention rate reaching 40-50% EBITDA margins with some fluctuations here and there.

Although definitely more of an exception than a rule, I have seen few private equity backed specialist software companies following this kind of profile where stability of cash flow and stickiness of a client base is more important than just growth. Requires lots of diligence on contracts and individuals customers to get comfortable, however.
 
More often, this is something that emerges when unit economics are split between SMEs and enterprise clients. As it is a profile characteristical to the enterprise segment of a company’s customer base. Ideally, at least. 

![Small number of customers matter less if unit economics are exceptional](https://strapi.joelosk.ar/uploads/ltvcac_30x_5739c54afb.PNG)

**2. Special case 2: a 2x LTV / CAC company**

Whenever we see low LTV / CACs, the first question we must ask is if it’s actually bad or is there anything artificially pulling it down such as free / freemium users who pay nothing. Especially with product-led enterprise SaaS there is a strong pareto-principle where 20% of customers bring in 80% of revenue, but if you look at blended unit economics they look awful because the other 80% of customers have $0 lifetime value.

Consumer software typically has naturally lower LTV / CACs than enterprise software for the reason that there are (a) lots of freemium users that push down the economics, (b) lower gross margins due to having to pay for content (few exceptions such as Match) and (c) for having naturally higher churn (consumers don’t have yearly budgets and commit for few months at a time).

To compensate, they tend to have much lower sales and marketing costs for the simple fact that they don’t need a heavy-weight sales team. As a result, their sales & marketing costs may be only 10-20% of revenue vs. enterprise SaaS at 40-50%. Hopefully at least.

But as shown with the Spotify example above, in order for them to make the economics work consumer software often needs much more customers and effectively need to replace their entire customer base each year.

Below a simple output of a hypothetical consumer subscription company that sells $9.99 / month subscription with 6 months average lifetime and 50% gross margins. It’s easy to see how in order to grow the company must be very aggressive with the new customer acquisition:

![Seemingly low unit economics are not always bad and are typical to consumer software](https://strapi.joelosk.ar/uploads/ltvcac_2x_c9c6a23d0a.PNG)

So how can you spot positive unit economics in a public company? After all, only a few of us have access to full customer cubes or cohorts to perform detailed analysis of customer acquisition costs, lifetime or churn.

Perhaps the simplest way is to look at the path to profitability. If unit economics are positive, the math should make it so that the burn becomes smaller every year. Clear path to profitability. While not bulletproof (e.g. a company can simply cut down S&M and balance that with reasonable growth) it's a relatively good indicator of a sound business.

At least most enterprise SaaS companies tend to have blended ARPU, gross margin, net retention and fully-loaded customer acquisition costs from their 10Ks and 10Qs. And if they don’t report the total customer base at least you can approximate through their large  >$100,000 per year customer base. And after that it just becomes a question of how to estimate lifetime. For that, one can try to infer something from net retention or try to google what is e.g. the average number of years a financial system gets replaced in an organization.


Unit economics is the #1 metric for technology companies. In this post I will show a few simple concrete examples of how unit economics can make or break the financial profile.

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Unit economics is the #1 metric for modern technology companies. We all know that, but many don’t know just how much. In this post I will show a few simple concrete examples of how unit economics can make or break the financial profile.

Unit economics and the short theory of tech investing

# Story of two farmers

Imagine yourself in a quintessential farmhouse. It’s a warm morning, the sun is rising and you’re on the porch drinking the first coffee of the day. You know this feeling is only evanescent and that sense of relaxation won’t last long because you are a farmer and it is the busiest harvesting season for your corn.

While finishing the coffee you glance over the commodities pages on local newspaper and notice how the price of eggs just keeps surging. People are increasingly eating more eggs with all the protein and wellness fad that people keep talking about. "Hmm, didn't Aunt Sally just inquire whether I had any eggs available...?", you remember. You didn’t because you are a corn farmer.

“It might be a good idea to expand the business a bit”, you start to ponder. There is conveniently an unused plot of land next to your cornfield, so you figure it doesn’t take much to build a small chicken coop and buy a few hens from the local farmers’ market. 

Surely enough, it doesn’t take long until you wake up one day to find a dozen eggs in your coop! Happy farmer that you are, you go back to the local farmers’ market to sell your loot. You figure that it may not be a bad investment to use the cash to buy a few more hens to double down on the lucrative egg business. 

Meanwhile, however, your neighbor farmer Joe has also got into the bandwagon to capitalize on the rise of egg prices. His farm is similar in size to yours and you know that his cornfield should produce roughly the same amount of corn every year so all else equal he should not be any more wealthy than you are.

Yet every time you see him at the farmers’ market you can't help but envy how many eggs he is selling! His business is booming but you can’t understand where he gets all the money to buy hens for so many eggs. After all, he started exactly the same as you.

So one day your curiosity takes over and you go to Joe’s: “Hey Joe, I’ve been watchin’ and you seem to have way more hens than I do even though we used to sell the same amount of corn back in the days. I’m doing alright and keep buying more hens with all the proceeds I get from selling my goods, but I can tell it’s still not even close to the amount of hens that you must have! Tell me Joe, what is your secret?”. Joe laughs and says “Silly neighbor. Don’t you also have a rooster? Every hen I buy not only lays eggs, but also spits out new hen! It’s like free money. You can’t lose! And most importantly it makes my production go from linear to exponential growth”. And with that, you go and buy yourself a rooster as well.

# The one metric to rule them all

What the farm example showed was that there are really two simple ways to grow. The first is what the protagonist did i.e. expanded your means of production to sell more. This works fine but fixes you on a linear growth path. After all, one hen produces one egg, two hens produce two eggs, three produce three and so on. 

The other way to grow is what Joe understood: That if each hen created more hens that all laid eggs, then the growth would not be linear but exponential: One hen produced another hen who then both laid eggs, these two hens then produced two new hens who then produced four eggs and so on.

To move away from farming and generalize a bit, every company’s growth is equal to new customers + upsell to existing customers. While you only focused on the former, Joe did both.

Say you want to grow 40% every year. If your existing customer base does not grow the math is simple: your sales team needs to generate that 40% growth from scratch.

But if you were able to upsell to the existing customer base so that their revenue grows 20% every year, then you have half of your projected growth in the bank already. The end result is the same, but your sales team needs to work only half as much for the growth. The below illustrates this in graphical form

![Sales team needs to work only half as hard if you can grow with your customer](https://strapi.joelosk.ar/uploads/ret1_a177255548.PNG)

Thus your growth should always be the sum of (1) new sales plus (2) net upsell. When you see a company with 120% net dollar retention they should have grown their revenue by at least 20% that year and it tends to be quite safe to expect them to grow at least 20% next year as well.

The other point I’d like to highlight is even more important: the cost of serving an existing customer is lower than the cost of new sales. The below graph from [Bessemer Venture Partners](https://www.bvp.com/atlas/how-one-b2b-saas-company-revamped-pricing-for-an-ultra-successful-land-and-expand-play) shows how new logo acquisition can be over 4x more expensive than simply selling to the customers you already have.

![Growth through upsell tends to be cheaper than new logo acquisition](https://strapi.joelosk.ar/uploads/ret2_243e6b186e.png)

What that means is not only do you grow faster with better net dollar retention, but you also generate more cash flow, all else equal. And more free cash flow means higher value of the business.


# Careful on definitions

The thing astute investors need to be aware of is the definition of net retention. To get a “true” net retention you should always compare apples with apples, i.e. revenue from the customers at the beginning of the period to the revenue of the exact same customers at the end of period, including all churned ones. This “true” net retention should always be lower than your total growth so that the formula Total Growth = New Sales + Net Upsell holds. 

For whatever reason software companies with transactional revenue, like Marqeta or IronSource, tend to like to report their net revenue as revenue from ending customers to the same revenue of those customers in the beginning of the year. While the difference may seem like a play on words, it is crucial as it disregards all churned customers during that period giving them much higher net revenue retention than it actually is.

As an example, if they report their net revenue retention at 150% what they are effectively saying is that “hey guys the customers that are still on our platform grew 150%..” but what they might omit is “... but we lost 99% of our customers and actually only retain 10% of the revenue we had last year.”. Good thing is, this crookery is usually easy to spot because if their net revenue retention is 180% but they only grew their revenue by 40% you know they are playing games.

The last definition pitfall I’ve seen comes from when upsell is calculated at the time of renewal comparing the dollar amount at the end of contract time to the new contract that is negotiated. What this might mean is your net revenue retention is effectively 100%, because it is blind to all the upsell that happens between the renewal periods.

As an example, the customer may land at $100, then buy another module for $10 and another for $20 meaning that at the time of renewal the company has a $130 contract at stake. If the customer then renews that contract for $130 it would result in 100% net revenue retention using this obscure net revenue retention definition even though the upsell clearly was 30%.

Only a handful of companies use this definition with Workday coming to my mind first. Not sure why, given how many investors are honed in on net revenue retention. Yet this is how they define the net revenue retention from the [2018 investor day](https://www.workday.com/content/dam/web/en-us/documents/investor/workday-financial-analyst-day-2018.pdf) (note keyword is “up for renewal” in the denominator).

> Dollar-base net retention rate – (Amount of Annual Recurring Revenues renewed in a given period including upsell)/(Amount of total Annual Recurring Revenues that was up for the renewal in a given period)

Hard to spot, but if it’s a growing enterprise software with large ACV, extremely high gross revenue retention you could probably assume their net revenue retention is not what it looks like.

That’s all folks. I think net retention is the metric I always check first when I see a software company for the information it entails, and I hope you do as well now.


There is one key metric for software companies that boosts growth and cash flow more than others. In this article I will explore more how and talk through few pitfalls regarding its definition.

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The one metric that actually matters


# 10 Snow Crash

![Snow Crash by Neal Stephenson.PNG](https://strapi.joelosk.ar/uploads/book_snowcrash_e4a19ccc38.PNG)

Snow Crash is the book that got its reputation for coining the term metaverse. The term, that was impossible to avoid in 2021. Neal likely sold more Snow Crash this year than in any other year before that, and to be fair the metaverse Neal described in 1992 *is* indeed scaringly accurate to how the “metaverse” today is supposed to look like.

That said, other than the novelty of coming up with the metaverse, Snow Crash as a book was quite average. In short, it takes place in a really dystopian West Coast US starring a sword-wielding protagonist called Hiro Protagonist (yeah…) and his 15-year old sidekick girl getting themselves involved in shady activities of a modern cult.

All the reviews you read online are true both in good and bad. Although Neal got a lot of technology predictions right (e.g. noise cancelling headphones, cryptocurrencies, even portable computers…), the whole premise is mediocre and writing is full of unnecessary innuendos.

**Verdict:** 1/5 - for being the first fictional book I read in many years it deserves a place on the list. Without the novelty of metaverse, probably a 0/5.

# 9 Common Stocks and Uncommon Profits

![Common Stocks and Uncommon Profits by Philip A. Fisher](https://strapi.joelosk.ar/uploads/book_commonstocks_932a4b16b3.PNG)

I *love* investing, but I’ve come to learn that reading about investing is peak dull. Having been an investor for >10 years, studied B.Sc and M.Sc in Finance and having worked professionally in investing for many years, many of the “learnings” these investment books tout tend to be already common knowledge by now.

This same goes for Common Stocks and Uncommon Profits. Published in 1996 and always considered a classic on investing from Philip Fisher, an investor who Morninstar has praised as “one of the best investors of all time”, tends to fall short on teaching much new to an experienced investor. Even more so when his examples are companies that were big in 1950 but less so today. Heard of people pitching Motorola or Texas Instruments recently?

Don’t get me wrong, what he says is all true and important e.g. in regards to the quality of management teams, talking to employees and ex-employees, talking to suppliers and vendors and so on and so on, but it suffers from the same lack of relevancy and relatability that Benjamin Graham’s Intelligent Investor. 

**Verdict:** 2/5 - Philip coined a much cooler name “scuttlebutt” to what we in the industry 30-years later call “commercial due diligence”. Read it if you are passionate about investing and want to educate yourself with the classics akin to Homer’s Odyssey or To Kill a Mockingbird.

# 8 Rise of the Data Cloud

![Rise of the Data Cloud by Frank Slootman](https://strapi.joelosk.ar/uploads/book_datacloud_827fc06683.PNG)

I love Snowlake. I think it’s one of the most relevant enterprise SaaS companies out there, and this is the book on Snowflake from none other than Dutch enterprise SaaS guru Frank Slootman itself.

Those who don’t know Frank, he is one of the most experienced executives in the enterprise software space. Before Snowflake he used to helm ServiceNow, where he grew the company’s revenue from $100M to $1.4Bn during his tenure. Before that he also exited Data Domain, an electromagnetic tape data storage company, to EMC (now Dell) for $2.4Bn.

The book is both an intro to the data warehouse space and a description around Frank’s management style. Overall, however, it felt Frank tried to do something similar to Satya Nadella’s Hit Refresh and write a book to justify the need for data warehouses and the vision for Snowlake. But whereas Satya Nadella’s book was more about his own life, Frank’s book definitely came across as a long white paper on data warehouses, even dedicating entire chapters to some of the partners in the ecosystem.

**Verdict:** 3/5 - I love Snowflake and found Frank’s leadership style relatable, but it’s really a long white paper.

> “It’s not the C players that kill organizations. It’s easy to spot them. It’s the B players. They destroy you with mediocrity.”

# 7 No Filter

![No Filter by Sarah Frier](https://strapi.joelosk.ar/uploads/book_nofilter_8c5da9a20c.PNG)

No Filter was meant to be the founding story of Instagram. Which it indeed was, but even more so I found it being a great inside story on how Facebook operates and primer on corporate development and post-merger integration.

As a reminder, Facebook (now Meta) acquired Instagram in 2012 when the company had mere 13 employees generating $1Bn in revenue or roughly $77M per employee. Instagram was always frugal and very product-focused, thinking that under Facebook they’d get access to much better resources than they’d get continuing alone. In contrast, however, they were forced to remain frugal under Facebook mainly due to the internal politics and conflicts between having the best product or making the most money through Zuckerberg’s advertising machinery.

As we later know, the founder-vision had to cave in to the new owners as Facebook got the better end of the stick and in 2019 Instagram delivered around $20Bn in revenues. At the time it was roughly a quarter of Facebook’s overall sales and in that sense Facebook’s acquisition likely goes down as one of the best corporate M&As in the history of business.

**Verdict:** 4/5 - the research Sarah Frier did for this book was outstanding.

# 6 Flash Crash

![Flash Crash by Liam Vaughan](https://strapi.joelosk.ar/uploads/book_flashcrash_9073255d68.PNG)

Despite being on investing (read my rant above), Flash Crash was a bit different perhaps because it was written more as a story rather than as a how-to guide. Flash Crash depicts the story leading to the 6th May 2010 sudden market crash that wiped trillions of market value in seconds as trading algorithms around the world went havoc all due to the actions of one London-based trader Navinder Sarao (“The Hound of Hounslow”) trading a bit aggressively from his parents’ bedroom.

I don’t do day trading myself nor want to, but it was fascinating to get a glimpse on how the traders actually sense the pulse of the market and can recognize other participants simply based on how certain tickers behave on their computer screen. More so with Navinder not being the typical Wall Street egojunkie, but rather quite a relatable eccentric hermit who just wanted to beat others at trading made it all the better. Best of all, he was trading all *manually* without relying on algorithms or machines like, say, Jim Simons (whose book The Man Who Solved the Market was also good, but not good enough to be on the list!).

**Verdict:** 3/5 - well written and easy read on an important financial event for a keen investor wanting to educate herself more

# 5 Unit 731 Testimony

![Unit 731: Testimony by Hal Gold](https://strapi.joelosk.ar/uploads/book_unit731_00cac095db.PNG)

And then for something completely different. While many people know about Japan’s kamikaze pilots and the attack on Pearl Harbor, few people know of the absolute horrors that Japan did during the Second World War in the Chinese Manchuria region.

Unit 731 unveils the story behind Japan’s highly secretive biochemical military group which makes the entire German holocaust look like a joke. Brazen statement, but once you read the book and learn about the chemical experimentations they did to fully live human subjects by e.g. injecting syphilis and gonorrhea through vaccines, chopping people’s arms, or freezing people to death, you’ll understand what I mean. 

Japan had realised that diseases killed more soldiers than bullets and Unit 731 was set up to come up with solutions to decrease deaths due to illness. Thus the silver lining of all Japan’s dreadful work was that much of it has been captured and passed on to general public knowledge afterwards. For example, in contrast to the best medical practices at the time, the best cure against frostbite is not to rub the limb but rather soak it in a water less than 122 degrees. Yeap, it was clinically tested and no, not using rats. 

**Verdict:** 4/5 - Nightmare fuel but the world needed to hear these stories unveiled. Don’t buy it for a mother’s day gift.

# 4 Bitcoin Billionaires

![Bitcoin Billionaires by Ben Mezrich](https://strapi.joelosk.ar/uploads/book_bitcoinbillionaires_38899bce8a.PNG)

Although this is definitely one of those biographies that was written two decades too early, I had to include it purely due to how well it was written. Bitcoin Billionaires is the story of the Winkless Brothers who effectively made a fortune by being early adopters of bitcoin after a serendipitous trip to Ibiza. At one point they held 1% of all bitcoin supply in circulation and at a $1Trn bitcoin market cap that makes their stake worth at least ~$10Bn.

The story itself is a bit meh and abruptly ends to the point where the Winklewoss Brothers came up with Gemini, which those who follow bitcoin know is still extremely early in the whole crypto lifespan. But the sole reason I loved the book was the way Ben Mezrich, the award winning American author, had written the book. It’s commendable how well he managed to describe all the key concepts of blockchain and bitcoin by fluidly weaving them into the dialogue of the characters.

**Verdict:** I marked it as a 5/5 for myself purely due to the writing, but perhaps 4/5 would be more justified given how it was definitely written a few years too early. Even if you don’t like the topic, I’m sure you’ll be able to finish the book.

# 3 Trillion Dollar Coach

![Trillion Dollar Coach by Eric Schmidt, Jonathan Rosenberg and Alan Eagle](https://strapi.joelosk.ar/uploads/book_1000_b7749173c5.PNG)

Those who read tech biographies *must’ve* found one common denominator in all of them. A recurring character of sorts, that is not too dissimilar to some games where the game changes but the universe remains the same. In Silicon Valley success stories, that character was Bill Campbell and after seeing him as a side character countless times I finally figured it might make sense to read his own story. 

Bill Campbell is the Trillion Dollar Coach. Trillion, because that (was) the combined market value of the executives he helped to succeed: Larry Page, Sergey Brin, Eric Schmidt, Sundar Pichai, Steve Jobs, Jeff Bezos, Jack Dorsey, Dick Costolo and Sheryl Sandberg... Coach, because he was an executive coach with intimate relationships with his coachees explaining why he appeared in so many other biographies.

The whole book is a **must read** for managers. It is so influential that one of the authors is Eric Schmidt himself, the ex- CEO and Chairman of Google of almost two decades. There are too many individual things to list out here, but if I were to summarise his thesis it’d be inclusive and empowering leadership. In fact, the way he describes leadership is just like parenting: high expectations, but with sufficient encouragement needed to reach them. Tough love.

**Verdict: **5/5, easily. Will revisit my notes many times in years to come. RIP, Bill Campbell.

> “... your title makes you a manager, your people makes you a leader”

# 2 No Rules Rules by Reed Hastings

![No Rules Rules: Netflix and the Culture of Intervention by Reed Hastings and Erin Meyer](https://strapi.joelosk.ar/uploads/book_norules_1bdafba356.PNG)

One of those books where you think you’re ordering a kebab but actually get pizza in a wrap called calzone. I bought No Rules Rules thinking it was a book on Netflix, but quickly found out it was another *fantastic* book on management.

No Rules Rules talks about the importance of having a high talent density, which is a concept Reed Hastings found after a dreadful pivot where they had to lay off all except their top performers. What Reed quickly realised was that contrary to what he thought, the organization was actually performing now *better* with fewer people, compared to the old status quo where they had more hands on deck. Indeed, the entire thesis of the book is summarized early on:

> “At most companies policies and control processes are put in place to deal with employees who exhibit sloppy, unprofessional or irresponsible behavior. But if you avoid or move out these people you don’t need the rules. If you build an organization made up of high performers, you can eliminate most controls. The denser the talent, the greater the freedom you can offer.”

The *how* you do it sounds simple: hire brilliant people and pay them top of the range. Excellent people do not want comfortable offices or kombucha on tap, but a wage that matches their market value and an environment where they can keep learning from other smart people. This then becomes a feedback loop where smart people will attract more smart people and so on.

As I’m here reading my notes I also find the core thesis of the book actually quite similar to Bill Campbell’s (Trillion Dollar Coach) in regards to culture of excellence. His management style was also centered around top performers, empowerment and responsibility of the individual. 

And just like Bill thinks in the Trillion Dollar Coach, even a few adequate performers can “*...reduce efficiency, drive staff who seek excellence to quit, show the team you accept mediocrity, thus multiplying the problem*”.

**Verdict:** 5/5 - great people make great things to happen. The difficult part is finding and retaining those great people. That is where this book comes in. Will revisit my notes in the future.

# 1 Will by Will Smith

![Will by Will Smith and Mark Manson](https://strapi.joelosk.ar/uploads/book_will_3631dcf72b.PNG)

Never did I see this book becoming one of my top 3 books ever. I was not a fan of Will Smith, I don’t watch his movies (or any movies really) and certainly didn’t know he started as a rapper. I had zero affinity to him whatsoever (yea I did know he was in Men in Black), but I had to read it after a strong endorsement from a good friend of mine. In hindsight, perhaps exactly not having any preconceptions on Will and being oblivious about the “ending” was the sole reason this book was such a great ride.

As it turns out, the book barely talks about Will’s rapper career let alone his movies. There was maybe one chapter on Mohammed Ali but all the other movies barely got screen time. Will is really an excellent story on life, relationships and the hard work needed to succeed. His background is relatable to anyone, with similar ups and downs yet magical enough that the story keeps you wanting to know what happens next.

Biographies are one of my favorite reads, especially when they’re written so well that the personality of the biographee jumps at you. Will talks a lot about the importance of storytelling in his life, and you can tell that he took it seriously with this book. It sounded just like Will was telling his own story from his own perspective, and the pages went by faster than I realised. Usually biographies are made “too early” (like with Bitcoin Billionaires above) but here it feels the book captures Will’s whole life and career perfectly. What else could be there to conquer for someone who has done it all?

The only con was that there was a bit too much “egoism” around “being the biggest movie star in the world who had it all, BUT xyz could be better” which, while probably deserved, admittedly got a bit old after the 10th time it was mentioned. 

**Verdict:** 5/5 - Happy stories, sad stories, funny stories. I had no idea Will Smith even was so huge, but when finishing a book makes you sentimental you know it’s a five out of five.

Finally, the list of books that didn’t make it into the list:
- **Investing for Growth: How to Make Money only Buying the Best Companies in the World**  by Terry Smith - meh, structured as series of investor letters. Also an investment book…
- **Crypto Asset Investing in the Age of Autonomy** by Jake Ryan - way too high level
- **The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution** by Gregory Zuckerman - actually pretty good, just not good enough
- **Considering Stocks: Hits and Misses of a Sensible Investor** by Heikki Keskivali - nothing you wouldn’t know already
- **The Anatomy of the Swipe: Making Money Move** by Ahmed Siddiqui - actually one of the more useful books, but a bit high level
- **The Hundred-Year Marathon: China's Secret Strategy to Replace America as the Global Superpower** by Michael Pillsbury - the core message is good but my god it was boring




Every year I try to read 12 books. Looking at Goodreads, it seems I actually did 16 in 2021 so here is an excuse to revisit my Kindle notes and list the best ones.

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Every year I try to read 16 books. Looking at Goodreads, it seems I actually did 15 in 2021 so here is an excuse to revisit my Kindle notes and list the best ones.

2021 book wrap-up

# The digital asset paradigm

It’s taken me a long time to become familiar with blockchain. Like many I’ve been around bitcoin since 2011 at least it wasn’t until the beginning of 2021 when I went from effectively no cryptoexposure to having roughly a third of my investable wealth in cryptos.

Most recently, I’ve become a huge advocate of non-fungible tokens or NFTs. At a glance they seem mere pictures, but as I’ll come to show you under the hood they are fully fledged computers with the image/audio/asset just being a physical representation of the code. 

The most important reason, however, is that NFTs have allowed digital ownership. This is a concept I struggle to put into words, simply due to its sheer significance to the world. Major contributor to the development of the human race is the fact that we can own things. Those who’ve read [Sapiens by Yuval Noah Harari](https://www.goodreads.com/book/show/23692271-sapiens) know that if we go back in time long enough, humans had little possessions before we settled into cities as they primarily moved from one place to another in search of food. 

However, when we invented farming two things happened: (a) we were able to settle down as there was now a consistent way to get food and (b) we were able to start accumulating possessions that allowed us to mix and match materials to make better equipment to create more food. Owning anything was a wild west until institutions were also invented that ensured the things you owned were indeed actually yours. In theory, a wealthy lord could had come to your land and claim it for himself given there were little ways for you as a peasant to prove that you owned it. Now of course we can simply check from a registry that that piece of land belongs to you and have police and various justice systems in place to enforce those ownerships.

The point is, we wouldn’t be here if there was no way to have ownership and that same paradigm shift is now happening in digital form. Although we’ve had IP contracts and digital rights managements (DRMs) for a long time it’s been hard to enforce unless you’ve got the resources to first implement and then enforce those contracts. But with blockchain, it’s simple, fast, and extremely cost effective to see who owns what.

While in its nascence, I can’t emphasize how significant this invention is.The amount of money that will start to flow into digital goods by individuals and companies alike will be tremendous. Admittedly it’s currently limited to dog pictures and digital art, but the fact that we can own digital assets is fundamentally a key concept. This is not a new iPhone, not even a new Tesla, but a whole new paradigm shift - a new concept that didn’t exist before. We’ve invented ownership for anything in digital form. Just like we moved from hunters to farmers and began the most profound development trend in human history that still continues today.

# Investing is not about what but how

John Doerr talks about bringing a [“prepared mind”](https://podcastnotes.org/20vc-with-harry-stebbings/john-doerr-on-buying-12-of-google-for-12m-his-biggest-investing-lesson-from-30-years-in-venture-the-climate-crisis-why-governments-are-the-biggest-problem-and-where-the-biggest-opportunities-are/) to investing meaning that it’s not only enough to know the what but also the how. For example, probably most of us know what a blockchain or Ethereum is, but few of us actually know more than it’s a distributed ledger and there are some sort of smart contracts involved. How do you actually interact with it? Where is the blockchain stored? Where are my bitcoins stored?

Getting this prepared mind was my primary reason why I wanted to know Solidity, the programming language for Ethereum, but there were two other reasons as well. Firstly, I believe that a big portion of the world will run on Solidity. Not in a way that you’d interact with it, but rather in the background similarly to how javascript or C++ powers the world today. Knowing how the car works prepares you for the visit at the car dealer.

The second reason I wanted to learn Solidity was more sentimental. When I started programming ~15 years ago code optimisation and memory management were key as computers were rather slow. But as we know CPUs/GPUs got more and more powerful and compilers more forgiving, reducing the need to make efficient code. However, with Ethereum code execution is costly again due to the gas fees. The more complex the program is, the more expensive it is to run and the more you pay fees in gas. Suddenly it’s like programming for Amiga again where you’re stretching the limits of what is possible.

# Introducing J-Onchains

So in order to become more acquainted with the world of tomorrow I decided to create simple smart contracts that interact with the blockchain: an ERC-721 digital asset a.k.a. Non-fungible token or colloquially an NFT.

Before I even started I knew I wanted my NFT to be 100% on-chain. What most don’t know is that NFTs usually only store the ownership data and whatever other procedures it may have scripted on the actual blockchain. The physical representation of the asset itself, whether it’s picture or an audio file, often resides off-chain on IPFS (InterPlanetary File System - effectively distributed storage).

With 100% on-chain digital assets, all the data, including images and rendering scripts, are on the blockchain meaning that as long as Ethereum exists, so will everything regarding the asset. Does it actually matter? Probably not as much as people think, but I’m a big fan of Chain Runners and Anonymice projects and want to act like the cool guys.

![The three Michelin stars of on-chain NFTs by @dom](https://strapi.joelosk.ar/uploads/jon1_8c7ae88e23.PNG)

# The art

Before I even started with Solidity I cranked on with the “art”. No need for an artist because if it’s on-chain, the whole picture must exist in code-format and rendered to the output. I chose to do it with SVG mainly because it’s simple and because I knew it from before (the moving background of this website is an SVG render, for example). This naturally limits the quality of the “art” (yes definitely that, not my skills!), but that’s fine given this is more of a proof-of-concept anyways. Plus it keeps it infinitely scalable as it’s all vectorised!

The very first ugly draft looked like below on the left. It was already animated, because like music, I believe art needs to “flow”. We are in the 2021, after all. Then I wanted to simplify it a bit and came up with the version in the middle. I also added the same rotating square that is in the logo of the minting webpage. The final NFT on the right changed only little with some stereoscopic “3D” effect in the font. With that, I had the on-chain art ready.

![J-Onchain saw a few revisions of the final form. The final one is animated as well.](https://strapi.joelosk.ar/uploads/jon2_79537acccc.PNG)

# The code

Now you don’t really need to know any coding at all to do an NFT. In fact, you can mint one directly using [OpenSea](https://opensea.io/), the largest NFT marketplace in the world, if you’d like. But the beauty of NFTs is not in the picture, but in the code, because you can program the NFT to do pretty much whatever you want. One popular trend right now is to code in staking mechanisms for the NFT, which brings in DeFi elements for the owner of the picture. And NFTs rarely consist of just one program (or smart contract) as the “best” NFTs consist of multiple smart contracts that they interact with. See for example the Grade S+ contract of [Terraforms by Mathcastles](https://etherscan.io/address/0x4e1f41613c9084fdb9e34e11fae9412427480e56). So when you think you’re buying “just an image”, what you’re really buying is a fully fledged software. Heck, that “image” could do your groceries and get your kids from school if it wasn’t for the gas fees that limit it.

In my contract I followed [this guide](https://ethereum.org/en/developers/tutorials/how-to-write-and-deploy-an-nft/) from Ethereum.org pretty much one-to-one. This simple proof-of-concept smart contract will implement the following methods:

![The methods behind NFT. It's really simple.](https://strapi.joelosk.ar/uploads/jon3_8fd6b04643.PNG)

There are only two, slightly ugly, non-standard methods I had to create: the first which builds the SVG based on the unique token ID the user has and the second is the token URI. The SVG renderer is really just the entire SVG script dumped into one single string line that is returned to the caller. In this function I also create the uniqueness through pseudorandom rand() function that takes in the token ID as seed to keep the metadata also on-chain. Pseudorandom, because it will be random enough to make every NFT slightly different, but still fully deterministic to guarantee each NFT is always rendered the same. Works? Yes. Elegant? No, but hey I am an investor and not a software engineer.

![The inelegance of dumping the whole SVG script on-chain](https://strapi.joelosk.ar/uploads/jon4_eb1b5d41d4.PNG)

The tokenURI() method was certainly more tricky, but needed if you want your NFT on OpenSea. Effectively this is the one single end-point that tells other applications interacting with the NFT everything they need to know about it: it’s name, description, website, where the image is and so on.

The way it is supposed to work is that it points to an URL where the metadata is actually held at in JSON format. Usually IPFS, but in any case effectively always off-chain. And since I want the whole thing to be 100% on-chain I had to find a way to “cheat” the URL request by feeding OpenSea headers that make it think like it’s reading a URL and receiving the JSON simultaneously.

Luckily in programming you rarely have to reinvent the wheel and I was able to leverage the work made by [SVG721](https://github.com/mikker/svgnft), a project by Mikker, who had already made a simple library for on-chain SVG metadata. With little tuning I was able to customize it for myself and also ended up using his base64 encoding methods.

![The unorthodox tokenURI() method that tries to deceive being a web URL](https://strapi.joelosk.ar/uploads/jon5_12182bfb82.PNG)

The other methods were relatively straightforward to implement (after uh a few trial and error) and not worth documenting here. You can have a look at the original contract on Etherscan, however, if you are keen.

Three things I learnt on Solidity:
1. Creating a 24kb contract is harder than I thought. Without turning on Hardhat’s code optimisation I would’ve busted it already with this super simple on-chain contract. To be more frugal I would have had to come up with more programmatic ways to build the SVG on-chain instead of just dumping 99.5% of it in the code as plain text. Again, works.
2. Debugging a deployed contract is really, really annoying. There are no console outputs you can read, so you have to deploy the contract to testnet and read it through a custom script. I did find out about [Hardhat’s console.sol debugger](https://hardhat.org/tutorial/debugging-with-hardhat-network.html), but figured it’s not worth the time to get into just yet.
3. Instead of memory management, Solidity is neurotic about storage management, because actually storing data on Ethereum blockchain is bloody expensive. Everything must be defined to be either run in memory (preferred) or stored on the chain (nah uh). There are a ton of visibility options for functions (private, public, internal, external, view, pure) that all define how the program should handle the storage of resources.
 
Shout out to Alchemy and Hardhat for creating really easy tools to develop on Solidity. While the code itself looks simple, it took a lot of time given the difficulty of debugging.

# The mint page

Finally, I had to put together a simple web page. I wanted to keep it as simple as possible, so I pulled in the simplest one-page bootstrap template and started to plug in stuff. 

The first thing was of course, how to interact with Ethereum blockchain? Luckily, there is a fantastic library out there called Ethers.js which made everything much easier than I was prepared for. Effectively [Ethers.js](https://docs.ethers.io/v5/) abstracts away all the complexities and simplifies connecting MetaMask to a webpage into a single API call. Similarly, calling all methods on the smart contract was effectively done using one line of code. Can’t get easier than this!

The web page itself took a few iterations. The first revision looked a lot like below - a basic NFT output with metamask and minting buttons for debugging. Those were clicked maany times. 

![The first ugly version of the web page to help with debugging](https://strapi.joelosk.ar/uploads/jon6_af017db60a.PNG)

The second iteration had a bit more lipstick and and already resembles the final output a lot. Thankfully, bootstrap makes these layouts really quick to do. At this point, I added a bit more stereoscopic 3D style to the headers and title, and added an animated background that flows in pastel colours. Lots of inspiration from Equilibrium’s web page, which has a fascinating blog post on how they came up with the design for with website with concepts that I may or may not have borrowed a bit.

![The second and final version started to shape up](https://strapi.joelosk.ar/uploads/jon7_6addfefd58.PNG)

# Final thoughts

Obviously the reason I spent some time with the art and graphics was because the intention was to deploy this whole thing to the mainnet. Then once I finalised it all I read how it [costs ~$436 to deploy a smart contract](https://news.ycombinator.com/item?id=28625485). So I figured I’ll just keep this on Rinkeby-testnet for the time being given it’s really just a project to learn more. But if, for whatever reason, you regardless want to mint one yourself, go to [the mint page](https://joelosk.ar/web3/) and you can get yourself a J-Onchain for free. I even saw the effort to ensure it’s visible on OpenSea. Well [OpenSea’s Testnet](https://testnet.opensea.com) only, until I decide to deploy it on mainnet.

Anyhow, this was a fun exercise to do in order to learn more about Solidity and digital assets.




Digital asset ownership might be one of the greatest paradigms shifts happening at the moment. But how is John Doerr related to my desire to learn Solidity and create my own NFT?

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Digital asset paradigm and J-Onchain

# The poor unit economics of Zara and Adidas

[Unit economics are the lifeblood of technology companies](https://joelosk.ar/article/unit-economics-and-the-short-theory-of-tech-investing). If you don’t get more cash from each customer than it costs to serve that customer (or an equivalent “unit”) the business hardly makes economic sense. 

Before the rise of subscription business the profitability analysis was rather simple: If you were a furniture artisan you made money on a chair when it sold for more than it cost to manufacture. Your gross margin told you exactly how much was left for overheads after paying the direct costs. Selling a chair for $100 that cost $60 to make meant you ended up with $40 left for salaries, rent and whatnot. Life was simple.

Rise of subscription businesses made this equation more difficult as now that $100 is actually divided among payments split over multiple months. Since then, many “rules of thumbs” have emerged such as the need to have >3x LTV / CAC in order to have a successful business.

Rules of thumbs are of course great for simplifying the world, but taking them for granted risks becoming too blind to see the forest from the trees. 

Take an example: Some of the best companies in the world, like H&M, Starbucks, NYT or McDonalds enjoy a meager 20-50% gross margins. Yet such margins are absolutely frowned upon for a software company where the standard is 70%+. “Terrible unit economics”, they say. **Why exactly is 3x LTV / CAC great but the moment you go down to 2x LTV / CAC things get difficult?**

![Gross margins of 20-50% are hardly acceptable within software](https://strapi.joelosk.ar/uploads/lowltv2_e8a21461f1.PNG)

But here’s the thing: what if I told you that 2x LTV / CAC could be equivalent to ~40-50% gross margin i.e. the same margins that Adidas, Mcdonalds and H&M enjoy? 

Gross margin on a product should include the direct costs related to serving that product and there is hardly a more direct cost than the cost of acquiring the customer. More so, there is nothing preventing you from being creative and taking that CAC from the LTV / CAC denominator and pushing it to the nominator **to get a “fully-loaded gross margin” thus defining Gross Profit as Revenue less COGS less CAC**.

Let me exemplify below what I mean with 2x LTV / CAC = 40% gross margins. A company that is selling a $33 / month subscription to a customer that churns in 3 months will make the same amount of cash as a company that sells $100 perpetual license. This is simple arithmetics and a 101 of subscription business model - so far nothing new.

![Dividing a lump sum into many smaller payments creates no extra value by itself](https://strapi.joelosk.ar/uploads/lowltv3_b530541b82.PNG)

If the gross margins are the same, customer acquisition costs the same, it makes no difference whatsoever whether you sell the product as a perpetual license or as a subscription model. In fact, if nobody told you this was a software company this could actually be H&M in disguise. **At short enough lifetimes, when time value of money is negligible, the difference between subscription models and one-off / license sales is nonexistent and it might even be conceptually easier to think of the software company as a one doing one-off sales.**

# Needs volume to work

There is one common denominator between H&M, Starbucks, Zara and others and it’s that they are what the industry calls fast moving consumer goods (“FMCG”) companies that largely rely on volume. In order to be viable businesses with their gross margins Starbucks is estimated to sell almost [3Bn cups of coffee](https://www.leozqin.me/leo-does-the-math-how-many-cups-does-starbucks-use-in-a-day/) and [Zara to produce over 450 million products per year](https://beeketing.com/blog/zara-growth-story/).

Indeed, in order for this 2x LTV / CAC or 40-50% gross margin software company to also be viable it needs volume - a lot of volume. Volume either through (a) acquiring a lot of customers or reactivating old customers periodically or (b) shoving out new products consistently. Most of the time it is the former, while the latter is more difficult in practice but used by e.g. many hypercasual games.

Of course, acquiring new customers is also hard and for an enterprise software with 6 month lifecycles, $100K acquisition costs and 2.5m potential customer universe in the entire world the economics become difficult. 

**But for consumer companies with potentially >6bn of customers and low customer acquisition costs this is more doable** - let’s take a look at one example next.

# Tinder does it right

To give an example of this let’s look at Tinder. In a way it should be a great example of a subscription app with a low lifetime built into the design (people find a match!). Yet it should thrive, because it addresses an extremely large global market that creates new customer volume and (perhaps unfortunately) sees a high number of reactivations for churned customers as some of the matches weren’t meant to be. Bottom line: it sees a lot of customer volume.

Using a combination of information from [Tinder’s Q4-21 report](https://s22.q4cdn.com/279430125/files/doc_financials/2021/q4/Earnings-Letter-Q4-2021-vF.pdf), and data from this excellent [Lazard Consumer Subscription Software report](https://www.lazard.com/media/451703/consumer-subscription-economics-report_2021.pdf) I’ve built a simple model that shows both the subscription unit economics, and the pro-forma perpetual license / fully-loaded gross margin economics.

![Tinder effectively has 45% gross margins once its subscription with 6 month lifetime is converted into a lump sum sale](https://strapi.joelosk.ar/uploads/lowltv4_c6a23acd1d.PNG)

We see that using the rough inputs above we get ~2.7x LTV / CAC which is below the magical 3x threshold that makes most practitioners shiver. Yet by “converting” that subscription revenue to perpetual license revenue it’s actually a rather decent 45% fully-loaded gross profit margin vastly above Starbucks’s 29% and not too far off from McDonald’s 50%. Furthermore, all this results in 23% net income which is quite close to Match Group’s actual 29% net margins, giving me further comfort that the numbers above are probably not too much off.

So there we have Match with seemingly “poor” unit economics but regardless a thriving company: look at the below growth and profitability year-over-year (sidenote: all analysis so far also excludes Match’s other product portfolio such as Hinge, OKCupid, OurTime…).

![Despite <3x LTV / CAC Match Group (Tinder) is growing >20% YoY with almost 40% operating margins](https://strapi.joelosk.ar/uploads/lowltv5_aadf2ae6a2.PNG)

# Sales cycle makes it difficult in B2B

As mentioned earlier, in order for low unit economics to “work” you need high volume of sales which is more typical to B2C than B2B.

**While you could make it work with B2B as well by getting an army of sales people to hit the phones, it gets vastly more expensive due to longer sales cycles.** When you get a lead it can take months until that lead goes through the whole sales funnel to convert into a hopefully thick contract.

All that time enticing the customer the B2B company is not generating any revenue but incurring a lot of sales-related expenses. **Thus a B2B company will always end up with lower margins at the same LTV / CAC simply because the timing of expenses and revenues are further away unlike with B2C where CAC and all revenue is generated effectively simultaneously.** Indeed, because each contract takes time the unit economics in B2B are ever more important as exemplified by this citation from [David Vandergrifte](https://www.linkedin.com/pulse/brass-tacks-caveats-ltvcac-ratio-b2b-vs-b2c-startups-david-vandegrift/):
> ... In our professional opinions, LTV and CAC are all the more important to get right for enterprise startups precisely because the number of contracts is so small…

See below as a visual example of acquiring one customer. The B2C one roughly follows the Tinder example, while the B2B one assumes a 6 month sales cycle and $60K CAC that results in $120K 12 month contract (i.e. 2x LTV / CAC) after which the customer churns. 

![Sales cycle is a key reason why B2B companies burn more despite having the same LTV CAC](https://strapi.joelosk.ar/uploads/lowltv6_a76bdd3900.PNG)

The difference in margins is palpable. The B2C one is able to roll with ~40% margins while the B2B is rocking at 10% for the calendar year.

The usual argument against the different margins includes arguments about B2B needing an expensive enterprise sales force. While that is true, it should be netted off by vastly larger contracts. Yet in practice it doesn’t and the reason tends to come down to the sales cycle.

To be clear, this does not mean B2B software is a bad investment. Because of how the math works it will eventually reach the same margins, but just needs more time to reach that point due to the built-in sales cycle. Plus the quality of the revenue is often higher given B2B is easier to upsell and more recurring in nature as B2B is tied to budgets and dependent on the opinions of many people in the organisation. More so, on a free cash flow basis B2B will likely generate higher margins given large amounts prepaid contracts.

Finally, see below some illustrations from B2C and B2B margin differences from beforementioned [Lazard’s consumer subscription software benchmarking](https://www.lazard.com/media/451703/consumer-subscription-economics-report_2021.pdf) with [Jamin Ball’s fantastic B2B software benchmarks](https://cloudedjudgement.substack.com/) layered on top. Only a handful of B2C companies have negative profit margins whereas effectively all B2B companies do. Although this is only a subset of B2B companies, you'd see the results hold when looking [at the complete B2B software list](https://cloudedjudgement.substack.com/p/clouded-judgement-4122?s=r).

![The margin difference is palpable between B2C and B2B](https://strapi.joelosk.ar/uploads/lowltv7_min_2638adffef.PNG)

That's all this time folks

In this article I will challenge the 3x LTV / CAC rule-of-thumb by creating a "fully-loaded gross profit metric" and having a look at Tinder as an example. I will also explain why these economics are more viable in B2C than in B2B. 

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Occasionally I like to tear apart "rules of thumbs" and in this article I will challenge the 3x LTV  / CAC standard by creating a "fully-loaded gross profit" and having a look at Tinder as an example. I will also explain why these economics are more viable in B2C than in B2B. 

When seemingly poor unit economics work

[Pershing Square invested in Netflix in January 2022](https://www.reuters.com/business/media-telecom/ackmans-pershing-square-takes-new-position-netflix-2022-01-26/) after familiarizing themselves with the company as their SPAC Pershing Square Tontine Holdings was contemplating to merge with Universal Music Group earlier in Fall 2021. The famous hedge fund finally pulled the trigger on Netflix as the stock price tumbled to its long-term lows of ~$360 / share on January 21st on subscriber slowdown news.

Bill Ackman, the Founder of Pershing Square, shed light on the thesis behind Netflix in his [2021 Pershing Square Annual Letter to Shareholders](https://assets.pershingsquareholdings.com/2022/03/30135611/Pershing-Square-Holdings-Ltd.-2021-Annual-Report-1.pdf) released in March 2022 and I found the argument such an excellent and succinct example of thesis building that I had to dissect it down. 

The thesis goes as follows and is structured from top-to-bottom starting from high level market-related topics to the competition to the nuts and bolts of the company:

Streaming video services are still early with low penetration
1. Netflix is able to increase the market penetration as the market grows due to its unparalleled value offering
2. Netflix is able to remain as market leader over PayTV and Big Tech
3. Netflix has durable competitive moat and is safe from new competitors, also abroad
4. Netflix has will be highly cash flow generative business with strong operating leverage
5. Netflix will generate returns through double digit growth and share buybacks

As I will show, not only is it clear the Pershing Square team has done a tremendous amount of work behind the scenes to validate this thesis, but also the way the theses are presented in the annual letter is well constructed. Indeed, I suggest the reader to look at how Bill lays out his thesis. Almost every paragraph has a thesis with supportive validation data.

Let’s dig in.

# Market penetration is low
**Streaming video services are still early with low penetration**

Growth has through history been the key component of returns. Not just for the so-called “growth” investors, but even more so for value investors such as Bill Ackman. As the Oracle of Omaha, Warren Buffett, puts it, there are no growth and value stocks but rather “[growth and value are part of the same equation](https://www.youtube.com/watch?v=rwC_5tB-5lI)”.

As such it is no surprise that Bill’s first argument is rather simply on the potential of the entire video streaming market. He has looked at the total addressable market for fixed broadband access and PayTV amounting to 800-900m subscribers, while noting that Netflix still only has ~220m subscribers - and this is excluding China. Thus, the penetration is still low with a lot of room to grow as those PayTV customers cut their cords and move to OTT platforms like Netflix.

![netflix2.PNG](https://strapi.joelosk.ar/uploads/netflix1_48c9eb6d53.PNG)

# Will grow faster than market
**Netflix is able to increase the market penetration as the market grows due to its unparalleled value offering**

Bill’s second thesis is slightly hidden in the middle of the paragraph, but relates to Netflix’s superior offering at an unparalleled price point (lots of content at low cost) that will ensure Netflix is **able to increase the penetration (see Thesis #1) as also the underlying addressable market grows.** In practice they argue that** as the market grows, Netflix will grow even faster.**

To support the thesis Bill’s team has compared Netflix to various U.S. PayTV packages and benchmarked the cost per hour of engagement between all of them noticing Netflix beats the competition. *(Cost per engagement meaning if you watched 5 hours of TV per week and paid $100 per month your cost per hour of engagement would be $20)*

Bill’s believes that because of Netflix’s ability to offer an abundance of high quality content at low costs, they can grow with this growing market while gradually also increasing its prices. This pricing power is key, because once penetration becomes high enough and the market matures, the key growth lever will be the ability to increase the monthly subscription fee when the demand is price inelastic. Just like [Amazon](https://www.thisismoney.co.uk/money/bills/article-10498689/Amazon-Prime-prices-rise-customers-Britain.html) and [Spotify](https://www.theverge.com/2021/4/26/22403813/spotify-price-increase-subscriptions-2021) have done recently.

To support the argument for the growing market the team has looked at the improved broadband connectivity and proliferation in the number of [connected devices](https://iot-analytics.com/wp/wp-content/uploads/2018/08/Number-of-global-device-connections-2015-2025-Number-of-IoT-Devices.png) that can consumer OTT content. It’s interesting to note that the [fixed broadband penetration in the world is still surprisingly low at 15% as of 2019](https://en.wikipedia.org/wiki/List_of_countries_by_number_of_broadband_Internet_subscriptions). Mobile broadband is much higher at 85%, but for a streaming service like Netflix which requires high transfer speeds 4G may only just barely be enough.

![netflix3.PNG](https://strapi.joelosk.ar/uploads/netflix2_9225d1d232.PNG)

# Leader among current competition
**Netflix is able to remain as market leader over PayTV and Big Tech**

Bill’s third thesis is on **competition arguing that Netflix is ahead of legacy PayTV and Big Tech** for its ability to release content faster than the likes of Prime Video, Disney+ and HBO Max.

Again, the Pershing Square team has done a lot of benchmarking noting** Netflix releases 150-200 original content episodes per month which was more than the combined aggregate from Prime Video, Hulu, Disney+ and HBO Max**. In an industry where content is king, this is the key metric to differentiate the market leader, and an interesting point they have diligenced.

Bill continues saying that the reason Netflix can push out content at high velocity is due to their subscriber lead (seems true based on [this comparison](https://www.newsweek.com/netflix-amazon-hulu-disney-most-subscribers-streaming-service-1590463)). More subscribers means more cash which means more content for existing subscribers which attracts more new subscribers which lowers the content cost per subscriber and so on… great example of “winner-takes-the-most” dynamics which is so prevalent in tech.

But the natural argument that would go against the above is that even though Amazon Prime or Disney+ don’t have the same subscriber numbers, they have other business that can effectively finance their video streaming operations! Or you could straight up say that PayTV already has more subscribers than Netflix and can thus “outcontent” them.

Bill defends Netflix saying that due to cord cutting these PayTV companies will struggle to finance their content acquisition costs thus inevitably falling behind the content competition and losing customers to likes of Netflix.

The point on Amazon and Disney+ is more difficult to validate, however. Although they are highly cash flow generative and growing, Bill believes they are unlikely to channel this cash to their OTT businesses for profitability reasons. This is a classic “must believe” that is hard to prove, but that the investment thesis largely relies on. Every investment thesis has a “must believe” and in Netflix this is the only explicit one that I found in the letter.

![netflix3.PNG](https://strapi.joelosk.ar/uploads/netflix3_4ccda8c5da.PNG)

# Safe from new entrants
**Netflix has durable competitive moat and is safe from new competitors**

This is one of my favorite bits from the letter and shows the Pershing Square team has done their commercial due diligence. If the previous thesis was on *current *competition, this one is more on the *future *competition. How do you ensure Netflix won’t be outcompeted should Amazon Prime double down on their OTT services?

What Pershing Square argues is that the cash freed from cancelling ~$80 / month cost of PayTV could be used to support at least 2-3 OTT services priced at $10-15 / month. This means that even if a customer has multiple video streaming services, it’s unlikely that Netflix would be immediately dethroned.

More importantly, Bill thinks that out of those 2-3 video streaming services Netflix will actually always be the customers’ “core OTT platform”. i.e. the one they will be subscribed to no matter what. The Pershing Square team supports this thesis with a retention benchmark that shows Netflix’s churn remaining stable despite the “launch of several new competitors”.

And what about local competition abroad? The Pershing Square team is likewise doubtful of that. Bill claims Netflix is in great position to keep winning as they have already proven their ability to source international success stories such as Squid Game, Casa de Papel and Lupin.

I have followed Bill a lot over the past few years and come to learn how important getting comfort on “durable competitive moat” is. Indeed, it does seem the team has spent a lot of time in getting this right.

![netflix4.PNG](https://strapi.joelosk.ar/uploads/netflix4_e93013967d.PNG)

# Cash flow generative
**Netflix has will be highly cash flow generative business due to operating leverage**

Theses built on financial and operational KPIs only come last in Bill's letter. Perhaps an oddity, given at the end of the day Pershing Square’s mantra is investing into predictable free cash flow generative businesses. But in Netflix’s case he makes no exceptions.

Bill’s first point is about growing margins from from 4% to 21% in 2016-2021. Despite spending billions in content costs that grow 23% per year, Netflix has managed to improve unit economics by keeping the cost per subscriber constant, and increasing subscription prices by 7% per annum. This is a textbook example of operating leverage in action and will enable Netflix to generate cash going forward.

Because every dollar spent in content benefits every subscriber equally, the more subscribers Netflix has the better the content acquisition cost is split among the subscribers. I.e., if Netflix had 10 customers and spent $10m to acquire a film, each customer would effectively pay $1m for the film. If instead Netflix had 100 customers the content cost per customer is only $100,000, yet they all benefit from it equally.

This means more content not only improves retention (more things to watch) but also helps attract new customers (largest content library), which means future content will again cost less for Netflix on per subscriber basis, and the cycle continues. Content is king and operating leverage the queen.

![netflix5.PNG](https://strapi.joelosk.ar/uploads/netflix5_b4f0fa9440.PNG)

# Clear return drivers
**Netflix will generate returns through double digit growth and share buybacks**

Finally, Bill finishes the letter with a glimpse at how the all above will translate into returns. Or put another way, how Pershing Square will make their returns on Netflix: namely they argue the free cash flow per share will increase through growth + margin improvements (nominator) and share buybacks (denominator).

Bill reveals how their financial model has “double-digit” revenue growth built in and +20% earnings per share growth per year. That would mean all else equal an IRR between 20-40% from improved cash flow and margins. Pershing Square is certainly not an investor that invests on a vision alone, and this proves there is once again a lot of quantitative analysis done behind the scenes. After a quick look at analyst estimates on [Koyfin](https://www.koyfin.com/), Bill’s prediction on the future also look rather similar to what Wall Street is forecasting, suggesting they may not even see anything the Wall Street is not seeing.

To finish it all off, Bill sees share buybacks going forward. It is what Pershing Square often looks at companies for their tax efficient way of distributing cash to investors - to get the cash flow in discounted cash flow analysis.

![netflix6.PNG](https://strapi.joelosk.ar/uploads/netflix6_528268c50c.PNG)

Overall I liked how Bill clearly yet succinctly spelled out his whole Netflix thesis and the lengths he went to show his team done the work behind it so the thesis is not just words. Generally it’s rare to get a glance at how a top-tier investor like Pershing Square forms and validates its investment theses - especially in tech - and while it’s obviously high level I thought it’s great for learning for all aspiring investors.

Below the full excerpt from the letter.

![Full excerpt on Netflix from Pershing Square's 2021 Annual Letter](https://strapi.joelosk.ar/uploads/joelosk_ar_vertical_min_aae8cbb2e1.jpg)




Pershing Square, the activist investor Bill Ackman's hedge fund, tends to avoid technology until in January 2022 they bought $1Bn stake in Netflix. This article looks at their thesis outlaid in Pershing Square's 2021 Shareholder Letter.

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Dissecting Pershing Square's Netflix Thesis

# Back in the 70s

I’ve written about inflation [before](https://joelosk.ar/article/inflation-and-growth-stocks) in March 2021 but back then it was more as a precaution, whereas now the inflation is here.

I don’t need to go too deeply into the numbers but the last 12 months have shown a lot of resemblance to the inflationary environment of 1970s and 1980s. Back then there were many unfortunate events happening at the same time with Nixon running a highly liberal budget deficit and low interest rates as he was aiming for full employment, the break-up of Bretton Woods arrangement that led to the demand of US dollar, and of course the OPEC oil shock. As inflation rose, employees demanded higher wages that kept the inflation going higher until the new Fed chairman had the cajones to slam in 20% interest rates and put a full stop to the party.

![Brief look at inflation over the years](https://strapi.joelosk.ar/uploads/inf1_4037885949.png)

In many ways, today is the same. On the demand side we have lots of pent-up spending from the stimulus checks and accumulated savings over two years that are now released to the market as the world opens up. People don’t want to come back to the office and the Great Resignation is forcing firms to pay higher salaries both to retain and to attract employees. People have money, people want to spend it and prices go higher. This is great - the world is normal and the economy is plowing forward.

But to add fuel to the fire the Ukraine/Russia conflict is now also bringing in supply-side inflationary pressures quite akin to the 70s. Instead of currency devaluation and OPEC issues we now have oil prices along with other commodities shooting off as a big chunk of supply has been cut off due to the large suppliers of oil and gas being at war. Not only that, but also as mentioned the dearth of employees is pushing up the wages and thus the costs of providing goods and services that are being passed on to consumers. Inflation.

Anyhow, we all read the news but the reason I bring this topic up again is due to the harsh market reaction on growth, and mainly technology, equities. The reason for this is simple and comes through the higher interest rate as the cash flows are now discounted with higher cost of equity given the risk-free rate is higher than before: Why would I pay the same of Netflix as before if Uncle Sam pays me higher rates risk free? This especially hurts growth stocks where a lot of value is in the terminal value far in the future (you can read more on my previous post here).

# Equities as inflation hedge

However, if what Warren Buffet says is true, that [good businesses are the best protection against inflation](https://www.youtube.com/watch?v=BMdUwmhR4Z8), then technically there shouldn’t be such a market reaction right? The argument roughly follows the fact that good businesses should be able to transfer the cost of inflation to the customer in the way of higher prices. This should then change the nominal cash flows of the company which should result in higher cash flows in terminal value. Thus, even though your discount rate is higher, so is your cash flow leaving you with net neutral effect.

Indeed, I’ll bring up the term “inflation illusion” again which is a term coined (or at least used) by John Campbell and Tuomo Vuolteenaho in their 2004 paper “[Inflation illusion and stock prices](https://www.nber.org/system/files/working_papers/w10263/w10263.pdf)”. Effectively one of their arguments is:

> “...However, inflation is highly correlated with mispricing, supporting the Modigliani-Cohn (1979) view that** investors form subjective growth forecasts by extrapolating past nominal growth rates without adjusting for changes in inflation**.”

# Example

The bolded part above is key, given it suggests that many investors simply adjust the discount rate without appropriate adjustment in the PnL. Perhaps because the former is easier to predict than latter.

But if the statement holds then for good businesses the valuations should remain *roughly* the same pre- and post inflation, once they pass over the cost of inflation to customer and even after taking a hit on the their cost basis. This is obviously a little bit theoretical but let me demonstrate it with the following example of an anonymized public tech company 10-year DCF that I quite fancy and that I own in my own public equities portfolio. 

The example is highly simplified. The model is a 10-year cash flow model with simple irrelevant assumptions first using a stable 10% WACC (completely arbitrary), and then doing the same example but with 13% WACC to reflect the change in risk-free rates. However, the latter example also has the inflation modeled in both for revenue and for cost items. The former I'm estimating slightly higher than the latter to account for the fact that salaries adjust more slowly, often in annual cycles, versus selling prices that can be changed nearly on the fly. Also, there could be suppliers that simply won't change any their prices, or may change them only years later, whereas our example company is high quality business that will promptly raise its own sales prices.

![If a company can transfer the cost of inflation to customers, the impact of higher inflation should be rather modest](https://strapi.joelosk.ar/uploads/inf2_e15ae393d7.PNG)

As we see the resulting enterprise value is only ~10% lower in the post-inflation case than the pre-inflation one. Because the company is able to pass a big portion of the inflation to the consumers  through price hikes it will enjoy slightly higher nominal growth for the first few years after which the growth will follow the old estimates. And because it is able to increase its prices faster than the cost basis (or at higher magnitude) it's free cash flow is also higher on nominal basis.

These adjustments cascade all the way to the terminal value, where the changes early on in the model compound over time to higher terminal cash flow. Once prices have been raised they rarely, if ever, go down. I’m sure we would all love to buy a [Chevrolet for $525](https://www.countryliving.com/life/g33398396/what-things-cost-100-years-ago/#:~:text=A%20fresh%20set%20of%20wheels,for%20that%20price%20in%202020%3F&text=%22Fill%20'er%20up!%22,to%20%243.87%20per%20gallon%20now.) like it was in 1920 but instead in order to drive it out of the shop we'd have to pay 100x that today.

**The bottom line here being, all these inflations translate into permanently higher free cash flow and thus higher terminal value which, albeit discounted at higher WACC, results in rather similar enterprise values in both cases. Hence the 50-60% drops we've seen in last ~6 months or so (writing this in early May 2022) feel quite punitive**.

But do companies increase prices in practice? I did a really quick unscientific research where I googled “[company] raises prices 2022” and if I found anything on the company hiking up its prices on the front page within 5 seconds I added its logo below. Turns out, there are quite a few of them ranging from Autodesk to Adobe to IBM to Atlassian.

![Companies that have announced increasing their prices in 2022](https://strapi.joelosk.ar/uploads/inf3_1266547844.PNG)

Most of them are consumer companies that are more public about their prices, but I suspect there are more on the enterprise side where prices are not announced as openly and are negotiated more over-the-counter.

# Requires inelastic demand

Now, this is obviously theoretical and, unlike naively assumed in the example, whether companies can do this is another matter. Many contracts have prices tied to multiyear agreements that will need to wait for renewals to adjust, and many companies have products that are simply not price inelastic enough to not lose customers as a result of higher prices (like uh Netflix, sorry Bill). Thus for most the impact is probably still net-negative like in my example, **but the more mission critical, sticky, and price inelastic the product is, the less its cash flows and thus valuation will be impacted by the inflation and interest rates**. This is because it can promptly change its selling prices according to inflation without losing customers in the process.

Still especially for technology I believe this works more than for other industries. There are no “menu costs” where distribution channels would need to be updated or channel partners that need to know about the changes. Instead, a company can quite centrally change the price and it will rather automatically be promulgated to its customer base with little friction near instantaneously.

Someone could also argue that this whole article is a way to justify paying up. That is not the case. The sample above, for example, is my estimate of the current fair value of that particular company, regardless of the market. During October/November 2022 when the market was at all time high I certainly wouldn’t have been a buyer (seller maybe), whereas if the price now goes below my estimate of fair value I wouldn’t hesitate doubling down. 

But, all this must be done carefully because otherwise, well…

![Few examples from the past where price hikes didn't quite work out](https://strapi.joelosk.ar/uploads/inf4_ccf5016faf.PNG)


Buffett has once said that good businesses are the best inflation hedges. In this article I will assess this argument, and whether we should be alarmed about the rising interest rates as growth investors.

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Equities as inflation hedge

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I’ve worked as a venture and growth investor @GP Bullhound and advised technology
giants IPO to NYSE at @Citigroup in London.

Joel-Oskar Raisanen