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June 2021 - Letters to the Nomad Partnership 2001-2013 (Nick Sleep's and Qais Zakaria's Investor Letters)

This month we review a unique source of information - mysterious fund manager Nick Sleep’s investment letters. Sleep had an extremely successful run and identified several very interesting companies and characteristics of those companies which made for great investments. He was early to uncover Amazon, Costco, and others - riding their stocks into the stratosphere over the last 20 years. These letters cover the internet bubble, the 08/09 crisis, and all types of interesting businesses across the world.

The full letters can be found here

The full letters can be found here

Tech Themes

  1. Scale Benefits Shared. Nick Sleep’s favored business model is what he calls Scale Benefits Shared. The idea is straight forward and appears across industries. Geico, Amazon, and Costco all have this business model. Its simple - companies start with low prices and spend only on the most important things. Over time as the company scales (more insured drivers, more online orders, more stores) they pass on the benefits of scale to the customer with even further lower prices. The consumer then buys more with the low-cost provider. This has a devastating effect on competition - it forces companies to exit the industry because the one sharing the scale benefits has to become hyper-efficient to continue to make the business model work. “In the case of Costco scale efficiency gains are passed back to the consumer in order to drive further revenue growth. That way customers at one of the first Costco stores (outside Seattle) benefit from the firm’s expansion (into say Ohio) as they also gain from the decline in supplier prices. This keeps the old stores growing too. The point is that having shared the cost savings, the customer reciprocates, with the result that revenues per foot of retailing space at Costco exceed that at the next highest rival (WalMart’s Sam’s Club) by about fifty percent.” Jeff Bezos was also very focused on this, his 2006 annual letter highlighted as much: “Our judgment is that relentlessly returning efficiency improvements and scale economies to customers in the form of lower prices creates a virtuous cycle that leads over the long-term to a much larger dollar amount of free cash flow, and thereby to a much more valuable Amazon.com. We have made similar judgments around Free Super Saver Shipping and Amazon Prime, both of which are expensive in the short term and – we believe – important and valuable in the long term.” So what companies today are returning scale efficiencies with customers? One recent example is Snowflake - which is a super expensive solution but is at least posturing correctly in favor of this model - the recent earnings call highlighted that they had figured out a better way to store data, resulting in a storage price decrease for customers. Fivetran’s recent cloud data warehouse comparison showed Snowflake was both cheaper and faster than competitors Redshift and Bigquery - a good spot to be in! Another example of this might be Cloudflare - they are lower cost than any other CDN in the market and have millions of free customers. Improvements made to the core security+CDN engine, threat graph, and POP locations result in better performance for all of their free users, which leads to more free users, more threats, vulnerabilities, and location/network demands - a very virtuous cycle!

  2. The Miracle of Compound Growth & Its Obviousness. While appreciated in some circles, compounding is revered by Warren Buffett and Nick Sleep - it’s a miracle worth celebrating every day. Sleep takes this idea one step further, after discussing how the average hold period of stocks has fallen significantly over the past few decades: “The fund management industry has it that owning shares for a long time is futile as the future is unknowable and what is known is discounted. We respectfully disagree. Indeed, the evidence may suggest that investors rarely appropriately value truly great companies.” This is quite a natural phenomenon as well - when Google IPO’d in 2004 for a whopping $23bn, were investors really valuing the company appropriately? Were Visa ($18Bn valuation, largest US IPO in history) and Mastercard ($5.3Bn valuation) being valued appropriately? Even big companies like Apple in 2016 valued at $600Bn were arguably not valued appropriately. Hindsight is obvious, but the durability of compounding in great businesses is truly a myth to behold. That’s why Sleep and Zakaria wound down the partnership in 2014, opting to return LP money and only own Berkshire, Costco, and Amazon for the next decade (so far that’s been a great decision!). While frequently cited as a key investing principle, compounding in technology, experiences, art, and life are rarely discussed, maybe because they are too obvious. Examples of compounding (re-investing interest/dividends and waiting) abound: Moore’s Law, Picasso’s art training, Satya Nadella’s experience running Bing and Azure before becoming CEO, and Beatles playing clubs for years before breaking on the scene. Compounding is a universal law that applies to so much!

  3. Information Overload. Sleep makes a very important but subtle point toward the end of his letters about the importance of reflective thinking:

    BBC Interviewer: “David Attenborough, you visited the North and South Poles, you witnessed all of life in-between from the canopies of the tropical rainforest to giant earthworms in Australia, it must be true, must it not, and it is a quite staggering thought, that you have seen more of the world than anybody else who has ever lived?”

    David Attenborough: “Well…I suppose so…but then on the other hand it is fairly salutary to remember that perhaps the greatest naturalist that ever lived and had more effect on our thinking than anybody, Charles Darwin, only spent four years travelling and the rest of the time thinking.”

    Sleep: “Oh! David Attenborough’s modesty is delightful but notice also, if you will, the model of behaviour he observed in Charles Darwin: study intensely, go away, and really think.”

    There is no doubt that the information age has ushered in a new normal for daily data flow and news. New information is constant and people have the ability to be up to date on everything, all the time. While there are benefits to an always-on world, the pace of information flow can be overwhelming and cause companies and individuals to lose sight of important strategic decisions. Bill Gates famously took a “think week” each year where he would lock himself in a cabin with no internet connection and scan over hundreds of investment proposals from Microsoft employees. A Harvard study showed that reflection can even improve job performance. Sometimes the constant data flow can be a distraction from what might be a very obvious decision given a set of circumstances. Remember to take some time to think!

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Business Themes

  1. Psychological Mistakes. Sleep touches on several different psychological problems and challenges within investing and business, including the role of Social Proof in decision making. Social proof occurs when individuals look to others to determine how to behave in a given situation. A classic example of Social Proof comes from an experiment done by Psychologist, Stanley Milgram, in which he had groups of people stare up at the sky on a crowded street corner in New York City. When five people were standing and looking up (as opposed to a single person), many more people also stopped to look up, driven by the group behavior. This principle shows up all the time in business and is a major proponent in financial bubbles. People see others making successful investments at high valuations and that drives them to do the same. It can also drive product and strategic decisions - companies launching dot-com names in the 90’s to drive their stock price up, companies launching corporate venture arms in rising markets, companies today deciding they need a down-market “product-led growth” engine. As famed investor Stan Druckenmiller notes, its hard to sit idly by while others (who may be less informed) crush certain types of investments: “I bought $6 billion worth of tech stocks, and in six weeks I had lost $3 billion in that one play. You asked me what I learned. I didn’t learn anything. I already knew that I wasn’t supposed to do that. I was just an emotional basketcase and I couldn’t help myself. So maybe I learned not to do it again, but I already knew that.”

  2. Incentives, Psychology, and Ownership Mindset. Incentives are incredibly powerful in business and its surprisingly difficult to get people to do the right thing. Sleep spends a lot of time on incentives and the so-called Principal-Agent Conflict. Often times the Principal (Owner, Boss, Purchaser, etc.) may employ an Agent (Employee, Contractor, Service) to accomplish something. However the goals and priorities of the principal may not align with that agent. As an example, when your car breaks down and you need to go to a local mechanic to fix it, you (the principal) want to find someone to fix the car as well and as cheaply as possible. However, the agent (the mechanic) may be incentivized to create the biggest bill possible to drive business for their garage. Here we see the potential for misaligned incentives. After 5 years of really strong investment results, Sleep and Zakaria noticed a misaligned incentive of their own: “Which brings me to the subject of the existing performance fee. Eagle-eyed investors will not have failed but notice the near 200 basis point difference between gross and net performance this year, reflecting the performance fee earned. We are in this position because performance for all investors is in excess of 6% per annum compounded. But given historic performance, that may be the case for a very long time. Indeed, we are so far ahead of the hurdle that if the Partnership now earned pass-book rates of return, say 5% per annum, we would continue to “earn” 20% performance fees (1% of assets) for thirty years, that is, until the hurdle caught up with actual results. During those thirty years, which would see me through to retirement, we would have added no value over the money market rates you can earn yourself, but we would still have been paid a “performance fee”. We are only in this position because we have done so well, and one could argue that contractually we have earned the right by dint of performance, but just look at the conflicts!” They could have invested in treasury bonds and collected a performance fee for years to come but they knew that was unfair to limited partners. So the duo created a resetting fee structure, that allowed LPs to claw back performance fees if Nomad did not exceed the 6% hurdle rate for a given year. This kept the pair focused on driving continued strong results through the life of the partnership.

  3. Discovery & Pace. Nick Sleep and Qais Zakaria looked for interesting companies in interesting situations. Their pace is simply astounding: “When Zak and I trawled through the detritus of the stock market these last eighteen months (around a thousand annual reports read and three hundred companies interviewed)…” Sleep and Zakaria put up numbers: 55 annual reports per month (~2 per day), 17 companies interviewed per month (meeting every other day)! That is so much reading. Its partially unsurprising that after a while they started to be able to find things in the annual reports that piqued their interest. Not only did they find retrospectively obvious gems like Amazon and Costco, they also looked all around the world for mispricings and interesting opportunities. One of their successful international investments took place in Zimbabwe, where they noticed significant mispricing involving the Harare Stock Exchange, which opened in 1896 but only started allowing foreign investment in 1993. While Nomad certainly made its name on the Scaled efficiencies shared investment model, Zimbabwe offered Sleep and Zakaria to prioritize their second model: “We have little more than a handful of distinct investment models, which overlap to some extent, and Zimcem is a good example of a second model namely, ‘deep discount to replacement cost with latent pricing power.’” Zimcem was the country’s second-largest cement producer, which traded at a massive discount to replacement cost due to terrible business conditions (inflation growing faster than the price of cement). Not only did Sleep find a weird, mispriced asset, he also employed a unique way of acquiring shares to further increase his margin of safety. “The official exchange rate at the time of writing is Z$9,100 to the U$1. The unofficial, street rate is around Z$17,000 to the U$1. In other words, the Central Bank values its own currency at over twice the price set by the public with the effect that money entering the country via the Central Bank buys approximately half as much as at the street rate. Fortunately, there is an alternative to the Central Bank for foreign investors, which is to purchase Old Mutual shares in Johannesburg, re-register the same shares in Harare and then sell the shares in Harare. This we have done.“ By doing this, Nomad was able to purchase shares at a discounted exchange rate (they would also face the exchange rate on sale, so not entirely increasing the margin of safety). The weird and off the beaten path investments and companies can offer rich rewards to those who are patient. This was the approach Warren Buffett employed early on in his career, until he started focusing on “wonderful businesses” at Charlie Munger’s recommendation.

Dig Deeper

  • Overview of Several Scale Economies Shared Businesses

  • Investor Masterclass Learnings from Nick Sleep

  • Warren Buffett & Berkshire’s Compounding

  • Jim Sinegal (Costco Founder / CEO) - Provost Lecture Series Spring 2017

  • Robert Cialdini - Mastering the Seven Principles of Influence and Persuasion

tags: Costco, Warren Buffett, Berkshire Hathaway, Geico, Jim Sinegal, Cloudflare, Snowflake, Visa, Mastercard, Google, Fivetran, Walmart, Apple, Azure, Bing, Satya Nadella, Beatles, Picasso, Moore's Law, David Attenborough, Nick Sleep, Qais Zakaria, Charles Darwin, Bill Gates, Microsoft, Stanley Druckenmiller, Charlie Munger, Zimbabwe, Harare
categories: Non-Fiction
 

February 2021 - Rise of the Data Cloud by Frank Slootman and Steve Hamm

This month we read a new book by the CEO of Snowflake and author of our November 2020 book, Tape Sucks. The book covers Snowflake’s founding, products, strategy, industry specific solutions and partnerships. Although the content is somewhat interesting, it reads more like a marketing book than an actually useful guide to cloud data warehousing. Nonetheless, its a solid quick read on the state of the data infrastructure ecosystem.

Tech Themes

  1. The Data Warehouse. A data warehouse is a type of database that is optimized for analytics. These optimizations mainly revolve around complex query performance, the ability to handle multiple data types, the ability to integrate data from different applications, and the ability to run fast queries across large data sets. In contrast to a normal database (like Postgres), a data warehouse is purpose-built for efficient retrieval of large data sets and not high performance read/write transactions like a typical relational database. The industry began in the late 1970s and early 80’s, driven by work done by the “Father of Data Warehousing” Bill Inmon and early competitor Ralph Kimball, who was a former Xerox PARC designer. In 1986, Kimball launched Redbrick Systems and Inmon launched Prism Solutions in 1991, with its leading product the Prism Warehouse Manager. Prism went public in 1995 and was acquired by Ardent Software in 1998 for $42M while Red Brick was acquired by Informix for ~$35M in 1998. In the background, a company called Teradata, which was formed in the late 1970s by researchers at Cal and employees from Citibank, was going through their own journey to the data warehouse. Teradata would IPO in 1987, get acquired by NCR in 1991; NCR itself would get acquired by AT&T in 1991; NCR would then spin out of AT&T in 1997, and Teradata would spin out of NCR through IPO in 2007. What a whirlwind of corporate acquisitions! Around that time, other new data warehouses were popping up on the scene including Netezza (launched in 1999) and Vertica (2005). Netezza, Vertica, and Teradata were great solutions but they were physical hardware that ran a highly efficient data warehouse on-premise. The issue was, as data began to grow on the hardware, it became really difficult to add more hardware boxes and to know how to manage queries optimally across the disparate hardware. Snowflake wanted to leverage the unlimited storage and computing power of the cloud to allow for infinitely scalable data warehouses. This was an absolute game-changer as early customer Accordant Media described, “In the first five minutes, I was sold. Cloud-based. Storage separate from compute. Virtual warehouses that can go up and down. I said, ‘That’s what we want!’”

  2. Storage + Compute. Snowflake was launched in 2012 by Benoit Dageville (Oracle), Thierry Cruanes (Oracle) and Marcin Żukowski (Vectorwise). Mike Speiser and Sutter Hill Ventures provided the initial capital to fund the formation of the company. After numerous whiteboarding sessions, the technical founders decided to try something crazy, separating data storage from compute (processing power). This allowed Snowflake’s product to scale the storage (i.e. add more boxes) and put tons of computing power behind very complex queries. What may have been limited by Vertica hardware, was now possible with Snowflake. At this point, the cloud had only been around for about 5 years and unlike today, there were only a few services offered by the main providers. The team took a huge risk to 1) bet on the long-term success of the public cloud providers and 2) try something that had never successfully been accomplished before. When they got it to work, it felt like magic. “One of the early customers was using a $20 million system to do behavioral analysis of online advertising results. Typically, one big analytics job would take about thirty days to complete. When they tried the same job on an early version of Snowflake;’s data warehouse, it took just six minutes. After Mike learned about this, he said to himself: ‘Holy shit, we need to hire a lot of sales people. This product will sell itself.’” This idea was so crazy that not even Amazon (where Snowflake runs) thought of unbundling storage and compute when they built their cloud-native data warehouse, Redshift, in 2013. Funny enough, Amazon also sought to attract people away from Oracle, hence the name Red-Shift. It would take Amazon almost seven years to re-design their data warehouse to separate storage and compute in Redshift RA3 which launched in 2019. On top of these functional benefits, there is a massive gap in the cost of storage and the cost of compute and separating the two made Snowflake a significantly more cost-competitive solution than traditional hardware systems.

  3. The Battle for Data Pipelines. A typical data pipeline (shown below) consists of pulling data from many sources, perform ETL/ELT (extract, load, transform and vice versa), centralizing it in a data warehouse or data lake, and connecting that data to visualization tools like Tableau or Looker. All parts of this data stack are facing intense competition. On the ETL/ELT side, you have companies like Fivetran and Matillion and on the data warehouse/data lake side you have Snowflake and Databricks. Fivetran focuses on the extract and load portion of ETL, providing a data integration tool that allows you to connect to all of your operational systems (salesforce, zendesk, workday, etc.) and pull them all together in Snowflake for comprehensive analysis. Matillion is similar, except it connects to your systems and imports raw data into Snowflake, and then transforms it (checking for NULL’s, ensuring matching records, removing blanks) in your Snowflake data warehouse. Matillion thus focuses on the load and transform steps in ETL while Fivetran focuses on the extract and load portions and leverages dbt (data build tool) to do transformations. The data warehouse vs. data lake debate is a complex and highly technical discussion but it mainly comes down to Databricks vs. Snowflake. Databricks is primarily a Machine Learning platform that allows you to run Apache Spark (an open-source ML framework) at scale. Databricks’s main product, Delta Lake allows you to store all data types - structured and unstructured for real-time and complex analytical processes. As Datagrom points out here, the platforms come down to three differences: data structure, data ownership, and use case versatility. Snowflake requires structured or semi-structured data prior to running a query while Databricks does not. Similarly, while Snowflake decouples data storage from compute, it does not decouple data ownership meaning Snowflake maintains all of your data, whereas you can run Databricks on top of any data source you have whether it be on-premise or in the cloud. Lastly, Databricks acts more as a processing layer (able to function in code like python as well as SQL) while Snowflake acts as a query and storage layer (mainly driven by SQL). Snowflake performs best with business intelligence querying while Databricks performs best with data science and machine learning. Both platforms can be used by the same organizations and I expect both to be massive companies (Databricks recently raised at a $28B valuation!). All of these tools are blending together and competing against each other - Databricks just launched a new LakeHouse (Data lake + data warehouse - I know the name is hilarious) and Snowflake is leaning heavily into its data lake. We will see who wins!

An interesting data platform battle is brewing that will play out over the next 5-10 years: The Data Warehouse vs the Data Lakehouse, and the race to create the data cloud

Who's the biggest threat to @snowflake? I think it's @databricks, not AWS Redshifthttps://t.co/R2b77XPXB7

— Jamin Ball (@jaminball) January 26, 2021

Business Themes

Lakehouse_v1.png
architecture-overview.png
  1. Marketing Customers. This book at its core, is a marketing document. Sure, it gives a nice story of how the company was built, the insights of its founding team, and some obstacles they overcame. But the majority of the book is just a “Imagine what you could do with data” exploration across a variety of industries and use cases. Its not good or bad, but its an interesting way of marketing - that’s for sure. Its annoying they spent so little on the technology and actual company building. Our May 2019 book, The Everything Store, about Jeff Bezos and Amazon was perfect because it covered all of the decision making and challenging moments to build a long-term company. This book just talks about customer and partner use cases over and over. Slootman’s section is only about 20 pages and five of them cover case studies from Square, Walmart, Capital One, Fair, and Blackboard. I suspect it may be due to the controversial ousting of their long-time CEO Bob Muglia for Frank Slootman, co-author of this book. As this Forbes article noted: “Just one problem: No one told Muglia until the day the company announced the coup. Speaking publicly about his departure for the first time, Muglia tells Forbes that it took him months to get over the shock.” One day we will hear the actual unfiltered story of Snowflake and it will make for an interesting comparison to this book.

  2. Timing & Building. We often forget how important timing is in startups. Being the right investor or company at the right time can do a lot to drive unbelievable returns. Consider Don Valentine at Sequoia in the early 1970’s. We know that venture capital fund performance persists, in part due to incredible branding at firms like Sequoia that has built up over years and years (obviously reinforced by top-notch talents like Mike Moritz and Doug Leone). Don is a great investor and took significant risks on unproven individuals like Steve Jobs (Apple), Nolan Bushnell (Atari), and Trip Hawkins (EA). But he also had unfettered access to the birth of an entirely new ecosystem and knowledge of how that ecosystem would change business, built up from his years at Fairchild Semiconductor. Don is a unique person and capitalized on that incredible knowledgebase, veritably creating the VC industry. Sequoia is a top firm because he was in the right place at the right time with the right knowledge. Now let’s cover some companies that weren’t: Cloudera, Hortonworks, and MapR. In 2005, Yahoo engineers Doug Cutting and Mike Cafarella, inspired by the Google File System paper, created Hadoop, a distributed file system for storing and accessing data like never before. Hadoop spawned many companies like Cloudera, Hortonworks, and MapR that were built to commercialize the open-source Hadoop project. All of the companies came out of the gate fast with big funding - Cloudera raised $1B at a $4B valuation prior to its 2017 IPO, Hortonworks raised $260M at a $1B valuation prior to its 2014 IPO, and MapR $300M before it was acquired by HPE in 2019. The companies all had one thing in problem however, they were on-premise and built prior to the cloud gaining traction. That meant it required significant internal expertise and resources to run Cloudera, Hortonworks, and MapR software. In 2018, Cloudera and Hortonworks merged (at a $5B valuation) because the competitive pressure from the cloud was eroding both of their businesses. MapR was quietly acquired for less than it raised. Today Cloudera trades at a $5B valuation meaning no shareholder return since the merger and the business is only recently slightly profitable at its current low growth rate. This cautionary case study shows how important timing is and how difficult it is to build a lasting company in the data infrastructure world. As the new analytics stack is built with Fivetran, Matillion, dbt, Snowflake, and Databricks, it will be interesting to see which companies exist 10 years from now. Its probable that some new technology will come along and hurt every company in the stack, but for now the coast is clear - the scariest time for any of these companies.

  3. Burn Baby Burn. Snowflake burns A LOT of money. In the Nine months ended October 31, 2020, Snowflake burned $343M, including $169M in their third quarter alone. Why would Snowflake burn so much money? Because they are growing efficiently! What does efficient growth mean? As we discussed in the last Frank Slootman book - sales and marketing efficiency is a key hallmark to understand the quality of growth a company is experiencing. According to their filings, Snowflake added ~$230M of revenue and spent $325M in sales and marketing. This is actually not terribly efficient - it supposes a dollar invested in sales and marketing yielded $0.70 of incremental revenue. While you would like this number to be closer to 1x (i.e. $1 in S&M yield $1 in revenue - hence a repeatable go-to-market motion), it is not terrible. ServiceNow (Slootman’s old company), actually operates less efficiently - for every dollar it invests in sales and marketing, it generates only $0.55 of subscription revenue. Crowdstrike, on the other hand, operates a partner-driven go-to-market, which enables it to generate more while spending less - created $0.90 for every dollar invested in sales and marketing over the last nine months. However, there is a key thing that distinguishes the data warehouse compared to these other companies and Ben Thompson at Stratechery nails it here: “Think about this in the context of Snowflake’s business: the entire concept of a data warehouse is that it contains nearly all of a company’s data, which (1) it has to be sold to the highest levels of the company, because you will only get the full benefit if everyone in the company is contributing their data and (2) once the data is in the data warehouse it will be exceptionally difficult and expensive to move it somewhere else. Both of these suggest that Snowflake should spend more on sales and marketing, not less. Selling to the executive suite is inherently more expensive than a bottoms-up approach. Data warehouses have inherently large lifetime values given the fact that the data, once imported, isn’t going anywhere.” I hope Snowflake burns more money in the future, and builds a sustainable long-term business.

Dig Deeper

  • Early Youtube Videos Describing Snowflake’s Architecture and Re-inventing the Data Warehouse

  • NCR’s spinoff of Teradata in 2007

  • Fraser Harris of Fivetran and Tristan Handy of dbt speak at the Modern Data Stack Conference

  • Don Valentine, Sequoia Capital: "Target Big Markets" - A discussion at Stanford

  • The Mike Speiser Incubation Playbook (an essay by Kevin Kwok)

tags: Snowflake, Data Warehouse, Oracle, Vertica, Netezza, IBM, Databricks, Apache Spark, Open Source, Fivetran, Matillion, dbt, Data Lake, Sequoia, ServiceNow, Crowdstrike, Cloudera, Hortonworks, MapR, BigQuery, Frank Slootman, Teradata, Xerox, Informix, NCR, AT&T, Benoit Dageville, Mike Speiser, Sutter Hill Ventures, Redshift, Amazon, ETL, Hadoop, SQL
categories: Non-Fiction
 

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