October 2022 - Amp it Up by Frank Slootman

This month we cover our third Frank Slootman book, Amp it Up! It covers Slootman’s overall philosophy with a specific focus on achieving significant growth at scale and how companies can push the boundary of their growth potential. Frank only wrote the book because Snowflake’s CMO encouraged him to do so.

Tech Themes

1. Expanding the TAM. One core idea that Slootman has used across both ServiceNow and Snowflake is the idea of expanding the TAM. By expanding the TAM, you lengthen your growth runway because there are more people who are capable of using your software. Slootman employed this strategy perfectly at ServiceNow. When on the IPO roadshow for the company, analysts at Gartner kept telling potential investors that ServiceNow had a small TAM of only $1.5B. An old short report of ServiceNow by Kerrisdale Capital highlights this confusion: “ The overall ITSM market size is only $1.5 billion, less than one-third of NOW's $4.7 billion market capitalization. Leading technology research firm Gartner estimates that the IT Service Management market opportunity is $1.5 billion, and is growing at a modest 7% per year. Furthermore, Gartner's research predicts that only 50% of IT organizations will move to SaaS by 2015, implying that the total market opportunity for NOW's ITSM business is less than $1 billion. Given emerging competition from other SaaS ITSM service providers, we believe that the company will have a difficult time exceeding 30% market share. At $207m of LTM revenue, NOW appears to already control 10% to 15% of the market. So even if NOW's market share rises to 30%, which we don't see happening until 2014 at the earliest, NOW's ITSM business should be generating less than $600m in revenue with limited additional growth opportunities. The result of the limited market size and increasing competition will be flattening growth over the next few years.” Kerrisdale was clearly incorrect. Market size estimates are now closer to $12-15B. Slootman and the team realized that to complete the full remediation of issues, more people in the organization needed to access ServiceNow’s tools and core ticketing system. They deliberately went function by function (network engineers, sys admins, database admins) and added specific functionality to enhance the user experience of these groups. One of these product enhancements was ServiceNow’s configuration management database or CMDB, which keeps a log of every device and its exact specifications to allow for faster triage of issues. Slootman has taken this approach to Snowflake, which started out by focusing on just the data warehousing workload but has since expanded into seven unique workloads: data warehouse, data engineering, data science, collaboration, data sharing, unistore, and cybersecurity. These workloads now bring in more people to the Snowflake platform: database administrators, data engineers, analytics engineers, data analysts, data scientists, and cybersecurity analysts. Each new set of tools added, enhances the overall value of the platform and the stickiness of the solution within the organization. This is a great roadmap for how to keep growth elevated in horizontal markets.

2. Strategy vs. Execution. “Culture eats strategy for breakfast.” Peter Drucker, a famous consultant, and author of the Concept of the Corporation, believed that culture was far more important than strategy. Slootman agrees and even takes it one step further: “Execution has to be your number one goal. Strategy can’t be mastered until you can execute. Great execution is rarer than great strategy.” Slootman actually disagrees with Drucker on the management by objectives framework, “Another source of misalignment is management by objectives, which I have eliminated at every company i’ve joined in the last twenty years. MBOs cause employees to act as if they are running their own show, because they get compensated on their personal metrics, it is next to impossible to pull them off projects. They will be negotiating with you for relief. That is not alignment, that is every man for himself. If you need MBOs to get people to do their jobs, you may have the wrong people, the wrong managers, or both.” In Slootman’s eyes, management by objectives, which sets objectives for an entire organization that are translated into individual goals, ends up being abused by managers. Managers may rely on the objectives solely, and discount the leadership and creative thought necessary to succeed beyond an objective. “A person can do an excellent job according to objective measurement standards, but can fail miserably as a partner, subordinate, superior, or colleague. It is common for people not to be promoted for personal reasons than because of technical inadequacies.” For Slootman, superior execution comes from good judgment, and good judgment comes from bad judgment. Bad judgment is only made clear through experience, which can be the best teacher in his eyes. “New managers have to learn from and through their management chain. Organizations cannot scale and mature around inexperienced management staff.” At Data Domain, Slootman’s team finally started seeing success when they found the right leader for their contract manufacturing organization; at ServiceNow, when they found the right leader for cloud infrastructure; at Snowflake, when they found the right leader for scaling. “The organization needs innovation and discipline, or else the place will simply implode on itself. The common mistake is to rely on our innovators for discipline.” 5 dysfunctions of a team. Why execution is harder than strategy. But need to Prepare your next strategy early so you are ready when you get there.

3. Recruiting Talent. Slootman urges leaders to recruit drivers, not passengers. “Passengers are people who don't mind simply being carried along by the company's momentum, offering little or no input, seemingly not caring much about the direction chosen by management. They are often pleasant, get along with everyone, attend meetings promptly, and generally do not stand out as troublemakers. They are often accepted into the fabric of the organization and stay there for many years. The problem is that while passengers can often diagnose and articulate a problem quite well, they have no investment in solving it. They don't do the heavy lifting. Drivers, on the other hand, get their satisfaction from making things happen, not blending in with the furniture. They feel a strong sense of ownership for their projects and teams and demand high standards from both themselves and others. They exude energy, urgency, ambition, even boldness. Faced with a challenge, they usually say, ‘Why not’ rather than ‘That’s impossible.’ These qualities make drivers massively valuable. Finding, recruiting, rewarding, and retaining them should be among your top priorities.” What I find most interesting about this philosophy is that most jobs train people to be passengers. Most CEOs prefer the calm and non-trouble making attitude of passengers over the outspokenness and aggression that sometimes comes with drivers. So what do you do when you find passengers? Its simple - get them off the bus. Although it can be intense, you need to execute by removing people first, getting the right people in, and then getting the right people in the right spots. We talked about this analogy in the Jim Collins book Good to Great. “At a struggling company, you need to change things fast by switching out people whose skills no longer fit the mission or never really did in the first place. The other advantage of moving fast is that everyone who stays on the bus will know that you are dead serious about high standards. The good ones will be energized by those standards.” The challenge with moving quickly is finding the right balance for what the organization can absorb at any given time. Moving too quickly when the organization is not ready, or moving too quickly when the plan hasn’t been set can lead to drastic consequences.

Business Themes

1. Turnarounds as a Training Ground. Famous football coach Bill Walsh joined the San Francisco 49ers after they were the last placed team in the NFL with a 2-14 record. The next season, Walsh’s first, the 49ers repeated the performance - 2-14 again. Walsh at one point broke down on a flight home from a crushing defeat against Miami. 16 months later, he was Super Bowl champion. Turnarounds provide an unbelievably difficult training ground for young executives. It is sink or swim, it is kill or be killed. As discussed in our last book, Bill McDermott took over the struggling SAP North America division before righting the ship and accelerating SAP to growth. Frank Slootman began his managerial career in similar situations. After stints at Burroughs Corporation in corporate planning and Comshare in product management, Frank joined Compuware as head of non-mainframe Product Management. While there, Compuware acquired the dutch company, uniface, as we touched on in the Tape Sucks book. “I jumped at the opportunity return to Amsterdam to take on the entire operation, which seemed in disarray. Colleagues warned me not to go because the place could not be saved, and they worried I’d go down with the ship. Compuware had bought uniface toward the end of its viable product software. But by now, my career had been about taking on what seemed like long odds, jobs nobody else would touch with a 10 foot pole. It was the only avenue open to me anyway and it didn’t matter how hairy these deals were. As a young person, you easily overestimate your capabilities, this is when I started learning what happens when you step into the wrong elevators. We did manage to stabilize uniface. That became a formative career experience in my mid-30s. I’d never had multiple numerous large, mission-critical customers before and hundreds of employees in my charge. I also started to develop an eye for talent which became a cornerstone of my management focus going forward.” Next, Slootman jumped to Ecosystems, a Compuware subsidiary based in silicon valley. He stabilized the struggling company, but they kept losing talent because mid-western Compuware wasn’t able to retain silicon valley employees. He then joined Borland as SVP of product operations, which had also fallen on hard times. They resurrected the brand and the business. Even by 40 years old, he was taking on problem children, and he kept getting offered CEO jobs at companies that were elevators to nowhere. Slootman interviewed over and over for CEO roles but was passed on because “you’ve never run sales.” He later commented on being passed over: “I led from the front and sold shoulder to shoulder with sales. These rejections left me with an unfavorable opinion of many venture capitalists who couldn’t recognize talent if it smacked them in the face.” Turnarounds, especially those inside big companies offer management challenges that most people don’t get to experience until its too late. For Slootman and McDermott, these were the right opportunities for their personalities and approaches at the right time of their career.

2. Frank doesn’t believe in a Customer Success department. At Snowflake, there is no customer success department. In Slootman’s eyes: “They were happy to follow the trend set up by other companies like ours. But not me. I pulled the plug on these customer success departments in both companies, reassigning the staff back to the departments where their expertise fit best. Here’s why I was so opposed - if you have a customer success department that gives everyone else an incentive to stop worrying about how well our customers are thriving with our products and services. That sets up a disconnect that can create major problems down the road. People can become more focused on hitting the narrow goals of their silo rather than the broader and more important goal of customer satisfaction, which ultimately drives customer retention, word of mouth, profitability, and the long-term survival of the whole company. For instance, at ServiceNow, some of the customer success people grew quite dominant in the interaction with the customer and coordinated all the resources of the company for the customer’s benefit, including technical support, professional services, and even engineering. This had the effect that other departments sat back, became more passive, and felt less ownership of customer success. Customer success is the business of the entire company, not merely one department.” While this approach may work for Snowflake, it is not the norm in the SaaS world. In fact, there are entire companies like Gainsight, Totango, and ChurnZero, that help companies accelerate their Customer Success motion. Openview Venture Partners views customer success as critical for an effective product-led growth sales motion. Sales and Customer Success are important ways of generating product feedback from customers, but organizations need to make sure not to overwhelm product and engineering priorities. Often product teams don’t invest enough time in understanding the sales organization and the sales team views the product team as simply delivering on features to close deals. Leadership is necessary to help set priorities and collaboration across these departments.

3. 5 steps to Amp it Up. Slootman outlines a five-step process for business leaders to accelerate growth and transform their organizations. The first step is to raise your standards and set ambitious goals for your company. This is followed by aligning your people and culture to support your vision, which requires careful attention to hiring, training, and communication. The third step is to sharpen your focus and prioritize the most critical areas of your business for growth. Once you have a clear focus, the fourth step is to pick up the pace and execute with speed and urgency. Finally, the fifth step is to transform your strategy by continually adapting to changes in the market and taking bold actions to stay ahead of the competition. By following these five steps, Slootman believes that business leaders can create a culture of high performance and achieve extraordinary results. Underpinning everything, is a culture of trust. Ultimately high performance cultures can be challenging and Slootman had times where former founders like Fred Luddy disagreed with his decisions. But as Slootman puts it: “In the long run, success trumps popularity. In my early days at several companies, founders openly regretted my hiring and openly complained to the board behind my back. But when companies succeed massively, as all of our companies have, founders will eventually get over it. Yes, its nice if they love you, but you can’t let yourself get rattled if they don’t. Your mission is to win, not to achieve popularity.”

Dig Deeper

• Original Amp It Up Blog Post from 2018

• Snowflake CEO Frank Slootman: taking ownership, increasing velocity & cultivating talent

• The CEO Behind Software's Biggest IPO Ever | Forbes

• Frank Slootman Is a Malcontent—That’s How He Likes It

• The ServiceNow Story by Fred Luddy and Doug Leone

• Knowledge12 Report: The world according to Frank Slootman

January 2022 - Seven Powers by Hamilton Helmer

This month we dove into a classic technology strategy book. The book covers seven major Powers a company can have that offer both a benefit and a barrier to competition. Helmer covers the majority of the book through the lens of different case studies including his favorite company, Netflix.

Tech Themes

1. Power. After years as a consultant at BCG and decades investing in the public market, Helmer distilled all successful business strategies to seven individual Powers. A Power offers a company a re-inforcing benefit while also providing a barrier to potential competition. This is the epitome of an enduring business model in Helmer's mind. Power describes a company's strength relative to a specific competitor, and Powers focus on a single business unit rather than throughout a business. This makes sense: Apple may have a scale economies Power from its iPhone install base relative to Samsung, but it may not have Power in its AppleTV originals segment relative to Netflix. The seven types of Powers are: Scale Economies, Network Economies, Counter-Positioning, Switching Costs, Branding, Cornered Resources, and Process Power.

2. Invention. While Powers are somewhat easy to spot (scale economies of Google's search algorithm), creating them is anything but easy. So what underlies every one of the seven Powers? Invention. Helmer pulls invention through the lens of industry Dynamics - external competitive conditions and the forward march of technology create opportunities to pursue new business models, processes, brands, and products. Companies must leverage their resources to craft Powers through trial and error, rather than an upfront conscious decision to pursue something by design. I view this almost as an extension of Clayton Christensen's Resource-Processes-Values (RPV) framework we discussed in July 2020. Companies can find a route to Power through these resources and the crafting process. For Netflix, the route was streaming, but the actual Power came from a strong push into exclusive and original content. The streaming business opened up Netflix's subscriber base, and the content decision provided the ability to amortize great content across its growing subscriber base.

3. Power Progressions. Powers become available at different points in business progression. This makes sense - what drives a company forward in an unpenetrated market is different from what keeps it going during steady-state - Snowflake's competitive dynamics are different than Nestle's. Helmer defines three stages to a company: Origination, Takeoff, and Stability. These stages mirror the dynamics of S-Curves, which we discussed in our July 2021 book. During the Origination stage, companies can benefit from Cornered Resources and Counter-Positioning. Helmer uses the Pixar management team as an example of Cornered Resources during the Origination phase of 3D animated movies. The company had Steve Jobs (product visionary), John Lasseter (story-teller creative), and Ed Catmull (operations and technology leader). During the early days of the industry, these were the only people that knew how to operate a digital film studio. Another Cornered Resource example might be a company finding a new oil well. Before the company starts drilling, it is the only one that can own that asset. An example of Origination Counter-Positioning might be TSMC when they first launched. At that time, it was standard industry perception that semiconductor companies had to be integrated design manufacturers (IDM) - they had to do everything in-house. TSMC was launched as solely a fabrication facility that companies could use to gain extra manufacturing capacity or try out new designs. This gave them great Counter-Positioning relative to the IDM's and they were dismissed as a non-threat. The Takeoff period offers Network Economies, Scale Economies, and Switching Cost Powers. This phase is the growth phase of businesses. Snowflake currently benefits from Switching Cost dynamics - once you use Snowflake, it's unlikely you'll want to use other data warehouse providers because that process involves data replication and additional costs. Scale economies can be seen in businesses that amortize high costs over their user base, like Amazon. Amazon invests in distribution centers at a significant scale, which improves customer experience, which helps them get more customers - the flywheel repeats, allowing Amazon to continually invest in more distribution centers, further building its scale. Network economies show in social media businesses like Bytedance/TikTok. Users make content that attracts more users; incremental users join the platform because there is so much content to "gain" by joining the platform. Like scale economies, it's almost impossible to go build a competitor because a new company would have to recruit all users from the other platform, which would cost tons of money. The Stability phase offers Branding and Process Power. Branding is hard to generate, but the advantage grows with time. Consider luxury goods providers like LVMH; the older, the more exclusive the brand, the more it's desired, and every day it gets older and becomes more desired. A business can create Process Power by refining and improving operations to such a high degree that it becomes difficult to replicate. Classic examples of Process Power are TSMC's innovative 3-5nm processes today and Toyota's Production System. Toyota has even allowed competitors to tour its factory, but no competitor has replicated its operational efficiency.

Business Themes

1. Sneak Attack. I've always been surprised by businesses that seemingly "come out of nowhere." In Helmer's eyes, this stems from Counter-Positioning. He tells the story of Vanguard, which was started by Jack Bogle in 1976. "You could charitably describe the reception as enthusiastic: only $11M trickled in from investors. Soon after the launch, [Noble Laureate Paul] Samuelson himself lauded the effort in his column for Newsweek, but with little result: the fund had only reached $17M by mid-1977. Vanguard's operating model depended on others for distribution, and brokers, in particular, were put off by a product that predicated on the notion that they provided no value in helping their clients choose which active funds to select." But Vanguard had something that active managers didn't: low fees and consistency. Vanguard's funds performed like the indices and cost much less than active funds. No longer were individuals underperforming the market and paying advisors to pick actively managed funds. Furthermore, Vanguard continually invested all profits back into its funds, so it looked like it wasn't making money while it grew its assets under management. It's so hard to spot these sneak attacks while they are happening. But one that might be happening right now is Cloudflare relative to AWS. Cloudflare launched its low-cost R2 service (a play on Amazon's famous S3 storage technology). Cloudflare is offering a cheaper product at a much lower cost and is leveraging its large installed base with its CDN product to get people in the door. It's unclear whether this will offer Power over AWS because it's confusing what the barrier might be other than some relating to switching costs. However, there will likely be reluctance on AWS's part to cut prices because of its scale and public company growth targets.

2. A New Valuation Formula. Helmer offers a very unique take on the traditional DCF valuation approach. Investors have long suggested the value of any business was equal to the present value of its future discounted cash flows. In contrast to the traditional approach of summing up a firm's cash flows and discounting it, Helmer takes a look at all of the cash flows subject to the industry in which firms compete. In this formula (shown above), M0 represents the current market size, g the discounted market growth factor, s the long-term market share of the company, and m the long-term differential margin (net profit margin over that needed to cover the cost of capital). More simply, a company is worth it's Market Scale (Mo x g) x its Power (s x m). This implies that a company is worth the portion of the industry's profits it collects over time. This formula helps consider Power progression relative to industry dynamics and company stage. In the Origination stage, an industry's profits may be small but growing very quickly. If we think that a competitor in the industry can achieve an actual Power, it will likely gain a large portion of the long-term market. Thus, watching market share dynamics unfold can tell us about the potential for a route to Power and the ability for a company to achieve a superior value to its near-term cash flows.

3. Collateral Damage. If companies are aware of these Powers and how other companies can achieve them, how can companies not take proactive action to avoid being on the losing end of a Power struggle? Helmer lays out what he calls Collateral Damage, or the unwillingness of a competitor to find the right path to navigating the damage caused by a competitor's Power. His point is actually very nuanced - it's not the incumbent's unwillingness to invest in the same type of solution as the competitor (although that happens). The incumbent's business gets trashed as collateral damage by the new entrant. The incumbent can respond to the challenger by investing in the new innovation. But where counter-positioning really takes hold is if the incumbent recognizes the attractiveness of the business model/innovation but is stymied from investing. Why would a business leader choose not to invest in something attractive? In the case of Vanguard competitor Fidelity, any move into passive funds could cause steep cannibalization of their revenue. So in response, a CEO might decide to just keep their existing business and "milk" all of its cash flow. In addition, how could Fidelity invest in a business that completely undermined their actively managed mutual fund business? Often CEOs will have a negative bias toward the competing business model despite the positive NPV of an investment in the new business. Just think how long it took SAP to start selling Cloud subscriptions compared to its on-premise license/maintenance model. Lastly, a CEO might not invest in the promising new business model if they are worried about job security. This is the classic example of the principal-agent problem we discussed in June. Would you invest in a new, unproven business model if you faced a declining stock price and calls for your resignation? In addition, annual CEO compensation is frequently tagged to stock price performance and growth targets. The easiest way to achieve near-term stock price appreciation and growth targets is staying with what has worked in the past (and M&A!). Its the path of least resistance! Counter-positioning and collateral damage are nuanced and difficult to spot, but the complex emotions and issues become obvious over time.

Dig Deeper

• The 7 Powers with Hamilton Helmer & Jeff Lawson (CEO of Twilio)

• Hamilton Helmer Discusses 7Powers with Acquired Podcast

• Vanguard Founder Jack Bogle's '90s Interview Shows His Investing Philosophy

• Bernard Arnault, Chairman and CEO of LVMH | The Brave Ones

• S-curves in Innovation

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

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!

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

May 2021 - Crossing the Chasm by Geoffrey Moore

This month we take a look at a classic high-tech growth marketing book. Originally published in 1991, Crossing the Chasm became a beloved book within the tech industry although its glory seems to have faded over the years. While the book is often overly prescriptive in its suggestions, it provides several useful frameworks to address growth challenges primarily early on in a company’s history.

Tech Themes

1. Technology Adoption Life Cycle. The core framework of the book discusses the evolution of new technology adoption. It was an interesting micro-view of the broader phenomena described in Carlota Perez’s Technological Revolutions. In Moore’s Chasm-crossing world, there are five personas that dominate adoption: innovators, early adopters, early majority, late majority, and laggards. Innovators are technologists, happy to accept more challenging user experiences to push the boundaries of their capabilities and knowledge. Early adopters are intuitive buyers that enjoy trying new technologies but want a slightly better experience. The early majority are “wait and see” folks that want others to battle test the technology before trying it out, but don’t typically wait too long before buying. The late majority want significant reference material and usage before buying a product. Laggards simply don’t want anything to do with new technology. It is interesting to think of this adoption pattern in concert with big technology migrations of the past twenty years including: mainframes to on-premise servers to cloud computing, home phones to cell phones to iphone/android, radio to CDs to downloadable music to Spotify, and cash to check to credit/debit to mobile payments. Each of these massive migration patterns feels very aligned with this adoption model. Everyone knows someone ready to apply the latest tech, and someone who doesn’t want anything to do with it (Warren Buffett!).

2. Crossing the Chasm. If we accept the above as a general way products are adopted by society (obviously its much more of a mish/mash in reality), we can posit that the most important step is from the early adopters to the early majority - the spot where the bell curve (shown below) really opens up. This is what Geoffrey Moore calls Crossing the Chasm. This idea is highly reminiscent of Clay Christensen’s “not good enough” disruption pattern and Gartner’s technology hype cycle. The examples Moore uses (in 1991) are also striking: Neural networking software and desktop video conferencing. Moore lamented: “With each of these exciting, functional technologies it has been possible to establish a working system and to get innovators to adopt it. But it has not as yet been possible to carry that success over to the early adopters.” Both of these technologies have clearly crossed into the mainstream with Google’s TensorFlow machine learning library and video conferencing tools like Zoom that make it super easy to speak with anyone over video instantly. So what was the great unlock for these technologies, that made these commercially viable and successfully adopted products? Well since 1990 there have been major changes in several important underlying technologies - computer storage and data processing capabilities are almost limitless with cloud computing, network bandwidth has grown exponentially and costs have dropped, and software has greatly improved the ability to make great user experiences for customers. This is a version of not-good-enough technologies that have benefited substantially from changes in underlying inputs. The systems you could deploy in 1990 just could not have been comparable to what you can deploy today. The real question is - are there different types of adoption curves for differently technologies and do they really follow a normal distribution as Moore shows here?

3. Making Markets & Product Alternatives. Moore positions the book as if you were a marketing executive at a high-tech company and offers several exercises to help you identify a target market, customer, and use case. Chapter six, “Define the Battle” covers the best way to position a product within a target market. For early markets, competition comes from non-consumption, and the company has to offer a “Whole Product” that enables the user to actually derive benefit from the product. Thus, Moore recommends targeting innovators and early adopters who are technologist visionaries able to see the benefit of the product. This also mirrors Clayton Christensen’s commoditization de-commoditization framework, where new market products must offer all of the core components to a system combined into one solution; over time the axis of commoditization shifts toward the underlying components as companies differentiate by using faster and better sub-components. Positioning in these market scenarios should be focused on the contrast between your product and legacy ways of performing the task (use our software instead of pen and paper as an example). In mainstream markets, companies should position their products within the established buying criteria developed by pragmatist buyers. A market alternative serves as the incumbent, well-known provider and a product alternative is a near upstart competitor that you are clearly beating. What’s odd here is that you are constantly referring to your competitors as alternatives to your product, which seems counter-intuitive but obviously, enterprise buyers have alternatives they are considering and you need to make the case that your solution is the best. Choosing a market alternative lets you procure a budget previously used for a similar solution, and the product alternative can help differentiate your technology relative to other upstarts. Moore’s simple positioning formula has helped hundreds of companies establish their go-to-market message: “For (target customers—beachhead segment only) • Who are dissatisfied with (the current market alternative) • Our product is a (new product category) • That provides (key problem-solving capability). • Unlike (the product alternative), • We have assembled (key whole product features for your specific application).”

Business Themes

1. What happened to these examples? Moore offers a number of examples of Crossing the Chasm, but what actually happened to these companies after this book was written? Clarify Software was bought in October 1999 by Nortel for $2.1B (a 16x revenue multiple) and then divested by Nortel to Amdocs in October 2001 for $200M - an epic disaster of capital allocation. Documentum was acquired by EMC in 2003 for $1.7B in stock and was later sold to OpenText in 2017 for $1.6B. 3Com Palm Pilot was a mess of acquisitions/divestitures. Palm was acquired by U.S Robotics which was acquired by 3COM in 1997 and then subsequently spun out in a 2000 IPO which saw a 94% drop. Palm stopped making PDA devices in 2008 and in 2010, HP acquired Palm for $1.2B in cash. Smartcard maker Gemplus merged with competitor Axalto in an 1.8Bn euro deal in 2005, creating Gemalto, which was later acquired by Thales in 2019 for $8.4Bn. So my three questions are: Did these companies really cross the chasm or were they just readily available success stories of their time? Do you need to be the company that leads the chasm crossing or can someone else do it to your benefit? What is the next step in the chasm journey after its crossed and why did so many of these companies fail after a time?

2. Whole Products. Moore leans into an idea called the Whole Product Concept which was popularized by Theodore Levitt’s 1983 book The Marketing Imagination and Bill Davidow’s (of early VC Mohr Davidow) 1986 book Marketing High Technology. Moore explains the idea: “The concept is very straightforward: There is a gap between the marketing promise made to the customer—the compelling value proposition—and the ability of the shipped product to fulfill that promise. For that gap to be overcome, the product must be augmented by a variety of services and ancillary products to become the whole product.” There are four different perceptions of the product: “1. Generic product: This is what is shipped in the box and what is covered by the purchasing contract. 2.Expected product: This is the product that the consumer thought she was buying when she bought the generic product. It is the minimum configuration of products and services necessary to have any chance of achieving the buying objective. For example, people who are buying personal computers for the first time expect to get a monitor with their purchase-how else could you use the computer?—but in fact, in most cases, it is not part of the generic product. 3.Augmented product: This is the product fleshed out to provide the maximum chance of achieving the buying objective. In the case of a personal computer, this would include a variety of products, such as software, a hard disk drive, and a printer, as well as a variety of services, such as a customer hotline, advanced training, and readily accessible service centers. 4. Potential product: This represents the product’s room for growth as more and more ancillary products come on the market and as customer-specific enhancements to the system are made. These are the product features that have maybe expected or additional to drive adoption.” Moore makes a subtle point that after a while, investments in the generic/out-of-the-box product functionality drive less and less purchase behavior, in tandem with broader market adoption. Customers want to be wooed by the latest technology and as products become similar, customers care less about what’s in the product today, and more about what’s coming. Moore emphasizes Whole Product Planning where you can see how you get to those additional features into the product over time - but Moore was also operating in an era when product decisions and development processes were on two-year+ timelines and not in the DevOps era of today, where product updates are pushed daily in some cases. In the bottoms-up/DevOps era, its become clear that finding your niche users, driving strong adoption from them, and integrating feature ideas from them as soon as possible can yield a big success.

3. Distribution Channels. Moore focuses on each of the potential ways a company can distribute its solutions: Direct Sales, two-tier retail, one-tier retail, internet retail, two-tier value-added reselling, national roll-ups, original equipment manufacturers (OEMs), and system integrators. As Moore puts it, “The number-one corporate objective, when crossing the chasm, is to secure a channel into the mainstream market with which the pragmatist customer will be comfortable.” These distribution types are clearly relics of technology distribution in the early 1990s. Great direct sales have produced some of the best and biggest technology companies of yesterday including IBM, Oracle, CA Technologies, SAP, and HP. What’s so fascinating about this framework is that you just need one channel to reach the pragmatist customer and in the last 10 years, that channel has become the internet for many technology products. Moore even recognizes that direct sales had produced poor customer alignment: “First, wherever vendors have been able to achieve lock-in with customers through proprietary technology, there has been the temptation to exploit the relationship through unfairly expensive maintenance agreements [Oracle did this big time] topped by charging for some new releases as if they were new products. This was one of the main forces behind the open systems rebellion that undermined so many vendors’ account control—which, in turn, decrease predictability of revenues, putting the system further in jeopardy.” So what is the strategy used by popular open-source bottoms up go-to-market motions at companies like Github, Hashicorp, Redis, Confluent and others? Its straightforward - the internet and simple APIs (normally on Github) provide the fastest channel to reach the developer end market while they are coding. When you look at Open Source scaling, it can take years and years to Cross the Chasm because most of these early open source adopters are technology innovators, however, eventually, solutions permeate into massive enterprises and make the jump. With these new go-to-market motions coming on board, driven by the internet, we’ve seen large companies grow from primarily inbound marketing tactics and less direct outbound sales. The companies named above as well as Shopify, Twilio, Monday.com and others have done a great job growing to a massive scale on the backs of their products (product-led growth) instead of a salesforce. What’s important to realize is that distribution is an abstract term and no single motion or strategy is right for every company. The next distribution channel will surprise everyone!

Dig Deeper

• How the sales team behind Monday is changing the way workplaces collaborate

• An Overview of the Technology Adoption Lifecycle

• A Brief History of the Cloud at NDC Conference

• Frank Slootman (Snowflake) and Geoffrey Moore Discuss Disruptive Innovations and the Future of Tech

• Growth, Sales, and a New Era of B2B by Martin Casado (GP at Andreessen Horowitz)

• Strata 2014: Geoffrey Moore, "Crossing the Chasm: What's New, What's Not"

March 2021 - Payments Systems in the U.S. by Carol Coye Benson, Scott Loftesness, and Russ Jones

This month we dive into the fintech space for the first time! Glenbrook Partners is a famous payments consulting company. This classic book describes the history and current state of the many financial systems we use every day. While the book is a bit dated and reads like a textbook, it throws in some great real-world observations and provides a great foundation for any payments novice!

Tech Themes

1. Mapping Open-Loop and Closed-Loop Networks. The major credit and debit card providers (Visa, Mastercard, American Express, China UnionPay, and Discover) all compete for the same spots in customer wallets but have unique and differing backgrounds and mechanics. The first credit card on the scene was the BankAmericard in the late 1950’s. As it took off, Bank of America started licensing the technology all across the US and created National BankAmericard Inc. (NBI) to facilitate its card program. NBI merged with its international counterpart (IBANCO) to form Visa in the mid-1970’s. Another group of California banks had created the Interbank Card Association (ICA) to compete with Visa and in 1979 renamed itself Mastercard. Both organizations remained owned by the banks until their IPO’s in 2006 (Mastercard) and 2008 (Visa). Both of these companies are known as open-loop networks, that is they work with any bank and require banks to sign up customers and merchants. As the bank points out, “This structure allows the two end parties to transact with each other without having direct relationships with each other’s banks.” This convenient feature of open-loop payments systems means that they can scale incredibly quickly. Any time a bank signs up a new customer or merchant, they immediately have access to the network of all other banks on the Mastercard / Visa network. In contrast to open-loop systems, American Express and Discover operate largely closed-loop systems, where they enroll each merchant and customer individually. Because of this onerous task of finding and signing up every single consumer/merchant, Amex and Discover cannot scale to nearly the size of Visa/Mastercard. However, there is no bank intermediation and the networks get total access to all transaction data, making them a go-to solution for things like loyalty programs, where a merchant may want to leverage data to target specific brand benefits at a customer. Open-loop systems like Apple Pay (its tied to your bank account) and closed-loop systems like Starbuck’s purchasing app (funds are pre-loaded and can only be redeemed at Starbucks) can be found everywhere. Even Snowflake, the data warehouse provider and subject of last month’s TBOTM is a closed-loop payments network. Customers buy Snowflake credits up-front, which can only be used to redeem Snowflake compute services. In contrast, AWS and other cloud’s are beginning to offer more open-loop style networks, where AWS credits can be redeemed against non-AWS software. Side note - these credit systems and odd-pricing structures deliberately mislead customers and obfuscate actual costs, allowing the cloud companies to better control gross margins and revenue growth. It’s fascinating to view the world through this open-loop / closed-loop dynamic.

2. New Kids on the Block - What are Stripe, Adyen, and Marqeta? Stripe recently raised at a minuscule valuation of $95B, making it the highest valued private startup (ever?!). Marqeta, its API/card-issuing counterpart, is prepping a 2021 IPO that may value it at $10B. Adyen, a Dutch public company is worth close to $60B (Visa is worth $440B for comparison). Stripe and Marqeta are API-based payment service providers, which allow businesses to easily accept online payments and issue debit and credit cards for a variety of use cases. Adyen is a merchant account provider, which means it actually maintains the merchant account used to run a company’s business - this often comes with enormous scale benefits and reduced costs, which is why large customers like Nike have opted for Adyen. This merchant account clearing process can take quite a while which is why Stripe is focused on SMB’s - a business can sign up as a Stripe customer and almost immediately begin accepting online payments on the internet. Stripe and Marqeta’s API’s allow a seamless integration into payment checkout flows. On top of this basic but highly now simplified use case, Stripe and Marqeta (and Adyen) allow companies to issue debit and credit cards for all sorts of use cases. This is creating an absolute BOOM in fintech, as companies seek to try new and innovative ways of issuing credit/debit cards - such as expense management, banking-as-a-service, and buy-now-pay-later. Why is this now such a big thing when Stripe, Adyen, and Marqeta were all created before 2011? In 2016, Visa launched its first developer API’s which allowed companies like Stripe, Adyen, and Marqeta to become licensed Visa card issuers - now any merchant could issue their own branded Visa card. That is why Andreessen Horowitz’s fintech partner Angela Strange proclaimed: “Every company will be a fintech company.” (this is also clearly some VC marketing)! Mastercard followed suit in 2019, launching its open API called the Mastercard Innovation Engine. The big networks decided to support innovation - Visa is an investor in Stripe and Marqeta, AmEx is an investor in Stripe, and Mastercard is an investor in Marqeta. Surprisingly, no network providers are investors in Adyen. Fintech innovation has always seen that the upstarts re-write the incumbents (Visa and Mastercard are bigger than the banks with much better business models) - will the same happen here?

3. Building a High Availability System. Do Mastercard and Visa have the highest availability needs of any system? Obviously, people are angry when Slack or Google Cloud goes down, but think about how many people are affected when Visa or Mastercard goes down? In 2018, a UK hardware failure prompted a five-hour outage at Visa: “Disgruntled customers at supermarkets, petrol stations and abroad vented their frustrations on social media when there was little information from the financial services firm. Bank transactions were also hit.” High availability is a measure of system uptime: “Availability is often expressed as a percentage indicating how much uptime is expected from a particular system or component in a given period of time, where a value of 100% would indicate that the system never fails. For instance, a system that guarantees 99% of availability in a period of one year can have up to 3.65 days of downtime (1%).” According to Statista, Visa handles ~185B transactions per year (a cool 6,000 per second), while UnionPay comes in second with 131B and Mastercard in third with 108B. For the last twelve months end June 30, 2020, Visa processed $8.7T in payments volume which means that the average transaction was ~$47. At 6,000 transactions per second, Visa loses $282,000 in payment volume every second it’s down. Mastercard and Visa have always been historically very cagey about disclosing data center operations (the only article I could find is from 2013) though they control their own operations much like other technology giants. “One of the keys to the [Visa] network's performance, Quinlan says, is capacity. And Visa has lots of it. Its two data centers--which are mirror images of each other and can operate interchangeably--are configured to process as many as 30,000 simultaneous transactions, or nearly three times as much as they've ever been asked to handle. Inside the pods, 376 servers, 277 switches, 85 routers, and 42 firewalls--all connected by 3,000 miles of cable--hum around the clock, enabling transactions around the globe in near real-time and keeping Visa's business running.” The data infrastructure challenges that payments systems are subjected to are massive and yet they all seem to perform very well. I’d love to learn more about how they do it!

Business Themes

1. What is interchange and why does it exist? BigCommerce has a great simple definition for interchange: “Interchange fees are transaction fees that the merchant's bank account must pay whenever a customer uses a credit/debit card to make a purchase from their store. The fees are paid to the card-issuing bank to cover handling costs, fraud and bad debt costs and the risk involved in approving the payment.” What is crazy about interchange is that it is not the banks, but the networks (Mastercard, Visa, China UnionPay) that set interchange rates. On top of that, the networks set the rates but receive no revenue from interchange itself. As the book points out: “Since the card netork’s issuing customers are the recipients of interchange fees, the level of interchange that a network sets is an important element in the network’s competitive position. A higher level of interchange on one network’s card products naturally makes that network’s card products more attractive to card issuers.” The incentives here are wild - the card issuers (banks) want higher interchange because they receive the interchange from the merchant’s bank in a transaction, the card networks want more card issuing customers and offering higher interchange rates better positions them in competitive battles. The merchant is left worse off by higher interchange rates, as the merchant bank almost always passes this fee on to the merchant itself ($100 received via credit card turns out to only be $97 when it gets to their bank account because of fees). Visa and Mastercard have different interchange rates for every type of transaction and acceptance method - making it a complicated nightmare to actually understand their fees. The networks and their issuers may claim that increased interchange fees allow banks to invest more in fraud protection, risk management, and handling costs, but there is no way to verify this claim. This has caused a crazy war between merchants, the card networks, and the card issuers.

2. Why is Jamie Dimon so pissed about fintechs? In a recent interview, Jamie Dimon, CEO of JP Morgan Chase, recently called fintechs “examples of unfair competition.” Dimon is angry about the famous (or infamous) Durbin Amendment, which was a last-minute addition included in the landmark Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010. The Durbin amendment attempted to cap the interchange amount that could be charged by banks and tier the interchange rates based on the assets of the bank. In theory, capping the rates would mean that merchants paid less in fees, and the merchant would pass these lower fees onto the consumer by giving them lower prices thus spurring demand. The tiering would mean banks with >$10B in assets under management would make less in interchange fees, leveling the playing field for smaller banks and credit unions. “The regulated [bank with >$10B in assets] debit fee is 0.05% + $0.21, while the unregulated is 1.60% + $0.05. Before the Durbin Amendment the fee was 1.190% + $0.10.” While this did lower debit card interchange, a few unintended consequences resulted: 1. Regulators expected that banks would make substantially less revenue, however, they failed to recognize that banks might increase other fees to offset this lost revenue stream: “Banks have cut back on offering rewards for their debit cards. Banks have also started charging more for their checking accounts or they require a larger monthly balance.” In addition, many smaller banks couldn’t recoup the lost revenue amount, leading to many bankruptcies and consolidation. 2. Because a flat rate fee was introduced regardless of transaction size, smaller merchants were charged more in interchange than the prior system (which was pro-rated based on $ amount). “One problem with the Durbin Amendment is that it didn’t take small transactions into account,” said Ellen Cunningham, processing expert at CardFellow.com. “On a small transaction, 22 cents is a bigger bite than on a larger transaction. Convenience stores, coffee shops and others with smaller sales benefited from the original system, with a lower per-transaction fee even if it came with a higher percentage.” These small retailers ended up raising prices in some instances to combat these additional fees - causing the law to have the opposite effect of lowering costs to consumers. Dimon is angry that this law has allowed fintech companies to start charging higher prices for debit card transactions. As shown above, smaller banks earn a substantial amount more in interchange fees. These smaller banks are moving quickly to partner with fintechs, which now power hundreds of millions of dollars in account balances and Dimon believes they are not spending enough attention on anti-money laundering and fraud practices. In addition, fintech’s are making money in suspect ways - Chime makes 21% of its revenue through high out-of-network ATM fees, and cash advance companies like Dave, Branch, and Earnin’ are offering what amount to pay-day loans to customers.

3. Mastercard and Visa: A history of regulation. Visa and Mastercard have been the subject of many regulatory battles over the years. The US Justice Department announced in March that it would be investigating Visa over online debit-card practices. In 1996, Visa and Mastercard were sued by merchants and settled for $3B. In 1998, the Department of Justice won a case against Visa and Mastercard for not allowing issuing banks to work with other card networks like AmEx and Discover. In 2009, Mastercard and Visa were sued by the European Union and forced to reduce debit card swipe fees by 0.2%. In 2012, Mastercard and Visa were sued for price-fixing fees and were forced to pay $6.25B in a settlement. The networks have been sued by the US, Europe, Australia, New Zealand, ATM Operators, Intuit, Starbucks, Amazon, Walmart, and many more. Each time they have been forced to modify fees and practices to ensure competition. However, this has also re-inforced their dominance as the biggest payment networks which is why no competitors have been established since the creation of the networks in the 1970’s. Also, leave it to the banks to establish a revenue source that is so good that it is almost entirely undefeatable by legislation. When, if ever, will Visa and Mastercard not be dominant payments companies?

Dig Deeper

• American Banker: Big banks, Big Tech face-off over swipe fees

• Stripe Sessions 2019 | The future of payments

• China's growth cements UnionPay as world's largest card scheme

• THE DAY THE CREDIT CARD WAS BORN by Joe Nocera (Washington Post)

• Mine Safety Disclosure’s 2019 Visa Investment Case

• FineMeValue’s Payments Overview

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

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)

November 2020 - Tape Sucks: Inside Data Domain, A Silicon Valley Growth Story by Frank Slootman

This month we read a short, under-discussed book by current Snowflake and former ServiceNow and Data Domain CEO, Frank Slootman. The book is just like Frank - direct and unafraid. Frank has had success several times in the startup world and the story of Data Domain provides a great case study of entrepreneurship. Data Domain was a data deduplication company, offering a 20:1 reduction of data backed up to tape casettes by using new disk drive technology.

Tech Themes

Data Domain’s 2008 10-K prior to being acquired

1. First time CEO at a Company with No Revenue. Frank is an immigrant to the US, coming from the Netherlands shortly after graduating from the University of Rotterdam. After being rejected by IBM 10+ times, he joined Burroughs corporation, an early mainframe provider which subsequently merged with its direct competitor Sperry for $4.8B in 1986. Frank then spent some time at Compuware and moved back to the Netherlands to help it integrate the acquisition of Uniface, an early customizable report building software. After spending time there, he went to Borland software in 1997, working his way up the product management ranks but all the while being angered by time spent lobbying internally, rather than building. Frank joined Data Domain in the Spring of 2003 - when it had no customers, no revenue, and was burning cash. The initial team and VC’s were impressive - Kai Li, a computer science professor on sabbatical from Princeton, Ben Zhu, an EIR at USVP, and Brian Biles, a product leader with experience at VA Linux and Sun Microsystems. The company was financed by top-tier VC’s New Enterprise Associates and Greylock Partners, with Aneel Bhusri (Founder and current CEO of Workday) serving as initial CEO and then board chairman. This was a stacked team and Slootman knew it: “I’d bring down the average IQ of the company by joining, which felt right to me.” The Company had been around for 18 months and already burned through a significant amount of money when Frank joined. He knew he needed to raise money relatively soon after joining and put the Company’s chances bluntly: “Would this idea really come together and captivate customers? Nobody knew. We, the people on the ground floor, were perhaps, the most surprised by the extraordinary success we enjoyed.”

2. Playing to his Strengths: Capital Efficiency. One of the big takeaways from the Innovators by Walter Issacson was that individuals or teams at the nexus of disciplines - primarily where the sciences meet the humanities, often achieved breakthrough success. The classic case study for this is Apple - Steve Jobs had an intense love of art, music, and design and Steve Wozniak was an amazing technologist. Frank has cultivated a cross-discipline strength at the intersection of Sales and Technology. This might be driven by Slootman’s background is in economics. The book has several references to economic terms, which clearly have had an impact on Frank’s thinking. Data Domain espoused capital efficiency: “We traveled alone, made few many-legged sales calls, and booked cheap flights and hotels: everybody tried to save a dime for the company.” The results showed - the business went from $800K of revenue in 2004 to $275 million by 2008, generating $75M in cash flow from operations. Frank’s capital efficiency was interesting and broke from traditional thinking - most people think to raise a round and build something. Frank took a different approach: “When you are not yet generating revenue, conservation of resource is the dominant theme.” Over time, “when your sales activity is solidly paying for itself,” the spending should shift from conservative to aggressive (like Snowflake is doing this now). The concept of sales efficiency is somewhat talked about, but given the recent fundraising environment, is often dismissed. Sales efficiency can be thought of as: “How much revenue do I generate for every $1 spent in sales and marketing?” Looking at the P&L below, we see Data Domain was highly efficient in its sales and marketing activity - the company increased revenue $150M in 2008, despite spending $115M in sales and marketing (a ratio of 1.3x). Contrast this with a company like Slack which spent $403M to acquire $230M of new revenue (a ratio of 0.6x). It gets harder to acquire customers at scale, so this efficiency is supposed to come down over time but best in class is hopefully above 1x. Frank clearly understands when to step on the gas with investing, as both ServiceNow and Snowflake have remained fairly efficient (from a sales perspective at least) while growing to a significant scale.

3. Technology for Technology’s Sake. “Many technologies are conceived without a clear, precise notion of the intended use.” Slootman hits on a key point and one that the tech industry has struggled to grasp throughout its history. So many products and companies are established around budding technology with no use case. We’ve discussed Magic Leap’s fundraising money-pit (still might find its way), and Iridium Communications, the massive satellite telephone that required people to carry a suitcase around to use it. Gartner, the leading IT research publication (which is heavily influenced by marketing spend from companies) established the Technology Hype Cycle, complete with the “Peak of inflated expectations,” and the “Trough of Disillusionment” for categorizing technologies that fail to live up to their promise. There have been several waves that have come and gone: AR/VR, Blockchain, and most recently, Serverless. Its not so much that these technologies were wrong or not useful, its rather that they were initially described as a panacea to several or all known technology hindrances and few technologies ever live up to that hype. Its common that new innovations spur tons of development but also lots of failure, and this is Slootman’s caution to entrepreneurs. Data Domain was attacking a problem that existed already (tape storage) and the company provided what Clayton Christensen would call a sustaining innovation (something that Slootman points out). Whenever things go into “winter state”, like the internet after the dot-com bubble, or the recent Crpyto Winter which is unthawing as I write; it is time to pay attention and understand the relevance of the innovation.

Business Themes

1. Importance of Owning Sales. Slootman spends a considerable amount of this small book discussing sales tactics and decision making, particularly with respect to direct sales and OEM relationships. OEM deals are partnerships with other companies whereby one company will re-sell the software, hardware, or service of another company. Crowdstrike is a popular product with many OEM relationships. The Company drives a significant amount of its sales through its partner model, who re-sell on behalf of Crowdstrike. OEM partnerships with big companies present many challenges: “First of all, you get divorced from your customer because the OEM is now between you and them, making customer intimacy challenging. Plus, as the OEM becomes a large part of your business, for all intents and purposes they basically own you without paying for the privilege…Never forget that nobody wants to sell your product more than you do.” The challenges don’t end there. Slootman points out that EMC discarded their previous OEM vendor in the data deduplication space, right after acquiring Data Domain. On top of that, the typical reseller relationship happens at a 10-20% margin, degrading gross margins and hurting ability to invest. It is somewhat similar to the challenges open-source companies like MongoDB and Elastic have run into with their core software being…free. Amazon can just OEM their offering and cut them out as a partner, something they do frequently. Partner models can be sustainable, but the give and take from the big company is a tough balance to strike. Investors like organic adoption, especially recently with the rise of freemium SaaS models percolating in startups. Slootman’s point is that at some point in enterprise focused businesses, the Company must own direct sales (and relationships) with its customers to drive real efficiency. After the low cost to acquire freemium adopters buy the product, the executive team must pivot to traditional top down enterprise sales to drive a successful and enduring relationship with the customer.

2. In the Thick of Things. Slootman has some very concise advice for CEOs: be a fighter, show some humanity, and check your ego at the door. “Running a startup reduces you to your most elementary instincts, and survival is on your mind most of the time…The CEO is the ‘Chief Combatant,’ warrior number one.” Slootman views the role of CEO as a fighter, ready to be the first to jump into the action, at all times. And this can be incredibly productive for business as well. Tony Xu, the founder and CEO of Doordash, takes time out every month to do delivery for his own company, in order to remain close to the customer and the problems of the company. Jeff Bezos famously still responds and views emails from customers at jeff@amazon.com. Being CEO also requires a willingness to put yourself out there and show your true personality. As Slootman puts it: “People can instantly finger a phony. Let them know who you really are, warts and all.” As CEO you are tasked with managing so many people and being involved in all aspects of the business, it is easy to become rigid and unemotional in everyday interactions. Harvard Business School professor and former leader at Uber distills it down to a simple phrase: “Begin With Trust.” All CEO’s have some amount of ego, driving them to want to be at the top of their organization. Slootman encourages CEO’s to be introspective, and try to recognize blind spots, so ego doesn’t drive day-to-day interactions with employees. One way to do that is simple: use the pronoun “we” when discussing the company you are leading. Though Slootman doesn’t explicitly call it out - all of these suggestions (fighting, showing empathy, getting rid of ego) are meant to build trust with employees.

3. R-E-C-I-P-E for a Great Culture. The last fifth of the book is all focused on building culture at companies. It is the only topic Slootman stays on for more than a few chapters, so you know its important! RECIPE was an acronym created by the employees at Data Domain to describe the company’s values: Respect, Excellence, Customer, Integrity, Performance, Execution. Its interesting how simple and focused these values are. Technology has pushed its cultural delusion’s of grandeur to an extreme in recent years. The WeWork S-1 hilariously started with: “We are a community company committed to maximum global impact. Our mission is to elevate the world’s consciousness.” But none of Data Domain’s values were about changing the world to be a better place - they were about doing excellent, honest work for customers. Slootman is lasered focused on culture, and specifically views culture as an asset - calling it: “The only enduring, sustainable form of differentiation. These days, we don’t have a monopoly for very long on talent, technology, capital, or any other asset; the one thing that is unique to us is how we choose to come together as a group of people, day in and day out. How many organizations are there that make more than a halfhearted attempt at this?” Technology companies have taken different routes in establishing culture: Google and Facebook have tried to create culture by showering employees with unbelievable benefits, Netflix has focused on pure execution and transparency, and Microsoft has re-vamped its culture by adopting a Growth Mindset (has it really though?). Google originally promoted “Don’t be evil,” as part of its Code of Conduct but dropped the motto in 2018. Employees want to work for mission-driven organizations, but not all companies are really changing the world with their products, and Frank did not try to sugarcoat Data Domain’s data-duplication technology as a way to “elevate the world’s consciousness.” He created a culture driven by performance and execution - providing a useful product to businesses that needed it. The culture was so revered that post-acquisition, EMC instituted Data Domain’s performance management system. Data Domain employees were looked at strangely by longtime EMC executives, who had spent years in a big and stale company. Culture is a hard thing to replicate and a hard thing to change as we saw with the Innovator’s Dilemma. Might as well use it to help the company succeed!

Dig Deeper

• How Data Domain Evolved in the Cloud World

• Former Data Domain CEO Frank Slootman Gets His Old Band Back Together at ServiceNow

• The Contentious Take-over Battle for Data Domain: Netapp vs. EMC

• 2009 Interview with Frank Slootman After the Acquisition of Data Domain

October 2020 - Working in Public: The Making and Maintenance of Open Source Software by Nadia Eghbal

This month we covered Nadia Eghbal’s instant classic about open-source software. Open-source software has been around since the late seventies but only recently it has gained significant public and business attention.

Tech Themes

The four types of open source communities described in Working in Public

1. Misunderstood Communities. Open source is frequently viewed as an overwhelmingly positive force for good - taking software and making it free for everyone to use. Many think of open source as community-driven, where everyone participates and contributes to making the software better. The theory is that so many eyeballs and contributors to the software improves security, improves reliability, and increases distribution. In reality, open-source communities take the shape of the “90-9-1” rule and act more like social media than you could think. According to Wikipedia, the "90–9–1” rule states that for websites where users can both create and edit content, 1% of people create content, 9% edit or modify that content, and 90% view the content without contributing. To show how this applies to open source communities, Eghbal cites a study by North Carolina State Researchers: “One study found that in more than 85% of open source projects the research examined on Github, less than 5% of developers were responsible for 95% of code and social interactions.” These creators, contributors, and maintainers are developer influencers: “Each of these developers commands a large audience of people who follow them personally; they have the attention of thousands of developers.” Unlike Instagram and Twitch influencers, who often actively try to build their audiences, open-source developer influencers sometimes find the attention off-putting - they simply published something to help others and suddenly found themselves with actual influence. The challenging truth of open source is that core contributors and maintainers give significant amounts of their time and attention to their communities - often spending hours at a time responding to pull requests (requests for changes / new features) on Github. Evan Czaplicki’s insightful talk entitled “The Hard Parts of Open Source,” speaks to this challenging dynamic. Evan created the open-source project, Elm, a functional programming language that compiles Javascript, because he wanted to make functional programming more accessible to developers. As one of its core maintainers, he has repeatedly been hit with requests of “Why don’t you just…” from non-contributing developers angrily asking why a feature wasn’t included in the latest release. As fastlane creator, Felix Krause put it, “The bigger your project becomes, the harder it is to keep the innovation you had in the beginning of your project. Suddenly you have to consider hundreds of different use cases…Once you pass a few thousand active users, you’ll notice that helping your users takes more time than actually working on your project. People submit all kinds of issues, most of them aren’t actually issues, but feature requests or questions.” When you use open-source software, remember who is contributing and maintaining it - and the days and years poured into the project for the sole goal of increasing its utility for the masses.

2. Git it? Git was created by Linus Torvalds in 2005. We talked about Torvalds last month, who also created the most famous open-source operating system, Linux. Git was born in response to a skirmish with Larry McAvoy, the head of proprietary tool BitKeeper, over the potential misuse of his product. Torvalds went on vacation for a week and hammered out the most dominant version control system today - git. Version control systems allow developers to work simultaneously on projects, committing any changes to a centralized branch of code. It also allows for any changes to be rolled back to earlier versions which can be enormously helpful if a bug is found in the main branch. Git ushered in a new wave of version control, but the open-source version was somewhat difficult to use for the untrained developer. Enter Github and GitLab - two companies built around the idea of making the git version control system easier for developers to use. Github came first, in 2007, offering a platform to host and share projects. The Github platform was free, but not open source - developers couldn’t build onto their hosting platform - only use it. GitLab started in 2014 to offer an alternative, fully-open sourced platform that allowed individuals to self-host a Github-like tracking program, providing improved security and control. Because of Github’s first mover advantage, however, it has become the dominant platform upon which developers build: “Github is still by far the dominant market player: while it’s hard to find public numbers on GitLab’s adoption, its website claims more than 100,000 organizations use its product, whereas GitHub claims more than 2.9 million organizations.” Developers find GitHub incredibly easy to use, creating an enormous wave of open source projects and code-sharing. The company added 10 million new users in 2019 alone - bringing the total to over 40 million worldwide. This growth prompted Microsoft to buy GitHub in 2018 for $7.5B. We are in the early stages of this development explosion, and it will be interesting to see how increased code accessibility changes the world over the next ten years.

3. Developing and Maintaining an Ecosystem Forever. Open source communities are unique and complex - with different user and contributor dynamics. Eghbal tries to segment the different types of open source communities into four buckets - federations, clubs, stadiums, and toys - characterized below in the two by two matrix - based on contributor growth and user growth. Federations are the pinnacle of open source software development - many contributors and many users, creating a vibrant ecosystem of innovative development. Clubs represent more niche and focused communities, including vertical-specific tools like astronomy package, Astropy. Stadiums are highly centralized but large communities - this typically means only a few contributors but a significant user base. It is up to these core contributors to lead the ecosystem as opposed to decentralized federations that have so many contributors they can go in all directions. Lastly, there are toys, which have low user growth and low contributor growth but may actually be very useful projects. Interestingly, projects can shift in and out of these community types as they become more or less relevant. For example, developers from Yahoo open-sourced their Hadoop project based on Google’s File System and Map Reduce papers. The initial project slowly became huge, moving from a stadium to a federation, and formed subprojects around it, like Apache Spark. What’s interesting, is that projects mature and change, and code can remain in production for a number of years after the project’s day in the spotlight is gone. According to Eghbal, “Some of the oldest code ever written is still running in production today. Fortran, which was first developed in 1957 at IBM, is still widely used in aerospace, weather forecasting, and other computational industries.” These ecosystems can exist forever, but the costs of these ecosystems (creation, distribution, and maintenance) are often hidden, especially the maintenance aspect. The cost of creation and distribution has dropped significantly in the past ten years - with many of the world’s developers all working in the same ecosystem on GitHub - but it has also increased the total cost of maintenance, and that maintenance cost can be significant. Bootstrap co-creator Jacob Thornton likens maintenance costs to caring for an old dog: “I’ve created endlessly more and more projects that have now turned [from puppies] into dogs. Almost every project I release will get 2,000, 3,000 watchers, which is enough to have this guilt, which is essentially like ‘I need to maintain this, I need to take care of this dog.” Communities change from toys to clubs to stadiums to federations but they may also change back as new tools are developed. Old projects still need to be maintained and that code and maintenance comes down to committed developers.

Business Themes

1. Revenue Model Matching. One of the earliest code-hosting platforms was SourceForge, a company founded in 1999. The Company pioneered the idea of code-hosting - letting developers publish their code for easy download. It became famous for letting open-source developers use the platform free of charge. SourceForge was created by VA Software, an internet bubble darling that saw its stock price decimated when the bubble finally burst. The challenge with scaling SourceForge was a revenue model mismatch - VA Software made money with paid advertising, which allowed it to offer its tools to developers for free, but meant its revenue model was highly variable. When the company went public, it was still a small and unproven business, posting $17M in revenue and $31M in costs. The revenue model mismatch is starting to rear its head again, with traditional software as a service (SaaS) recurring subscription models catching some heat. Many cloud service and API companies are pricing by usage rather than a fixed, high margin subscription fee. This is the classic electric utility model - you only pay for what you use. Snowflake CEO Frank Slootman (who formerly ran SaaS pioneer ServiceNow) commented: “I also did not like SaaS that much as a business model, felt it not equitable for customers.” Snowflake instead charges based on credits which pay for usage. The issue with usage-based billing has traditionally been price transparency, which can be obfuscated with customer credit systems and incalculable pricing, like Amazon Web Services. This revenue model mismatch was just one problem for SourceForge. As git became the dominant version control system, SourceForge was reluctant to support it - opting for its traditional tools instead. Pricing norms change, and new technology comes out every day, it’s imperative that businesses have a strong grasp of the value they provide to their customers and align their revenue model with customers, so a fair trade-off is created.

2. Open Core Model. There has been enormous growth in open source businesses in the past few years, which typically operate on an open core model. The open core model means the Company offers a free, normally feature limited, version of its software and also a proprietary, enterprise version with additional features. Developers might adopt the free version but hit usage limits or feature constraints, causing them to purchase the paid version. The open-source “core” is often just that - freely available for anyone to download and modify; the core's actual source code is normally published on GitHub, and developers can fork the project or do whatever they wish with that open core. The commercial product is normally closed source and not available for modification, providing the business a product. Joseph Jacks, who runs Open Source Software (OSS) Capital, an investment firm focused on open source, displays four types of open core business model (pictured above). The business models differ based on how much of the software is open source. Github, interestingly, employs the “thick” model of being mostly proprietary, with only 10% of its software truly open-sourced. Its funny that the site that hosts and facilitates the most open source development is proprietary. Jacks nails the most important question in the open core model: “How much stays open vs. How much stays closed?” The consequences can be dire to a business - open source too much and all of a sudden other companies can quickly recreate your tool. Many DevOps tools have experienced the perils of open source, with some companies losing control of the project it was supposed to facilitate. On the flip side, keeping more of the software closed source goes against the open-source ethos, which can be viewed as organizations selling out. The continuous delivery pipeline project Jenkins has struggled to satiate its growing user base, leading to the CEO of the Jenkins company, CloudBees, posting the blog post entitled, “Shifting Gears”: “But at the same time, the incremental, autonomous nature of our community made us demonstrably unable to solve certain kinds of problems. And after 10+ years, these unsolved problems are getting more pronounced, and they are taking a toll — segments of users correctly feel that the community doesn’t get them, because we have shown an inability to address some of their greatest difficulties in using Jenkins. And I know some of those problems, such as service instability, matter to all of us.” Striking this balance is incredibly tough, especially in a world of competing projects and finite development time and money in a commercial setting. Furthermore, large companies like AWS are taking open core tools like Elastic and MongoDB and recreating them in proprietary fashions (Elasticsearch Service and DocumentDB) prompting company CEO’s to appropriately lash out. Commercializing open source software is a never-ending battle against proprietary players and yourself.

3. Compensation for Open Source. Eghabl characterizes two types of funders of open-source - institutions (companies, governments, universities) and individuals (usually developers who are direct users). Companies like to fund improved code quality, influence, and access to core projects. The largest groups of contributors to open source projects are mainly corporations like Microsoft, Google, Red Hat, IBM, and Intel. These corporations are big enough and profitable enough to hire individuals and allow them to strike a comfortable balance between time spent on commercial software and time spent on open source. This also functions as a marketing expense for the big corporations; big companies like having influencer developers on payroll to get the company’s name out into the ecosystem. Evan You, who authored Vue.js, a javascript framework described company backed open-source projects: “The thing about company-backed open-source projects is that in a lot of cases… they want to make it sort of an open standard for a certain industry, or sometimes they simply open-source it to serve as some sort of publicity improvement to help with recruiting… If this project no longer serves that purpose, then most companies will probably just cut it, or (in other terms) just give it to the community and let the community drive it.” In contrast to company-funded projects, developer-funded projects are often donation based. With the rise of online tools for encouraging payments like Stripe and Patreon, more and more funding is being directed to individual open source developers. Unfortunately though, it is still hard for many open source developers to pursue open source on individual contributions, especially if they work on multiple projects at the same time. Open source developer Sindre Sorhus explains: “It’s a lot harder to attract company sponsors when you maintain a lot of projects of varying sizes instead of just one large popular project like Babel, even if many of those projects are the backbone of the Node.js ecosystem.” Whether working in a company or as an individual developer, building and maintaining open source software takes significant time and effort and rarely leads to significant monetary compensation.

Dig Deeper

• List of Commercial Open Source Software Businesses by OSS Capital

• How to Build an Open Source Business by Peter Levine (General Partner at Andreessen Horowitz)

• The Mind Behind Linux (a talk by Linus Torvalds)

• What is open source - a blog post by Red Hat

• Why Open Source is Hard by PHP Developer Jose Diaz Gonzalez

• The Complicated Economy of Open Source