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The Master of 'Influence' - Rob Cialdini - The Knowledge Project

The Master of 'Influence' - Rob Cialdini - The Knowledge Project

A fascinating interview with Cialdini from ‘The Knowledge Project’ podcast. I’ve tried to capture a few of the nuggets below. Full interview: Link Why Charlie Munger gave Cialdini a Berkshire share worth $75,000 McDonalds using ‘reciprocation’ to increase sales by 25% How asterisks created ‘social proof’ for Chinese restaurants The best website star rating is 4.7 not 5 Bose – using the ‘authority’ of doctors to drive a 50% increase in sales The best performing websites rely on ‘scarcity’ Using ‘commitment’ to drop restaurant ‘no shows’ by 67% How to shift someone’s perspective / personal choices A great question to ask a prospective employer or customer The power of saying ‘we have a deal’ (before you do)
Influence on Munger & Buffett: “I got a letter in the mail, maybe 25 years ago from his partner, Charlie Munger. I opened this envelope to find a share, an A share of Berkshire stock, which at the time was worth about $75,000.” “And he said, “Your book has made us so much money that by the principle of reciprocation ... you are owed something in return.” McDonalds – reciprocation driving a 25% increase in sales “In all human societies, we are trained to live by a particular rule, the rule of reciprocation that says we are obligated to give back to others who have first given to us.” “And I love a new study that was done in McDonald’s in Colombia and Brazil, where researchers did a study for a week every family that came into the McDonald’s locations, the children got a balloon from the McDonald’s management. Half of them got the balloon as the family was leaving , as a nice thank you for coming into McDonald’s and purchasing food. The other half each kid got the balloon as they entered … Those families bought 25% more food because they had been given something first” “They (people) don’t recognize the power of this rule … They’re not cognizant of the fact that there’s such a strong rule in every human culture that says you must not take without giving in return.” Chinese restaurants using the social proof of asterisks to increase purchases by 13% to 20% “If we point to a genuine comparability that exists between us and the another individual, that person feels a greater rapport with us.” ‘There was a study done in Beijing, China that I love, it shows you the cross-cultural reach of this. So in Beijing, researchers arranged with the managers of a string of restaurants to put a little asterisk on certain items of the menu that people got when they were to order. Those items that got the asterisk then became purchased 13% to 20% more frequently . So what did the asterisk represent? It was the most popular item on the menu.’ Social proof – the nuances in star ratings “There was a great study that looked at which star rating on a product review was most likely to produce a conversion from a prospect to a customer. It wasn’t a five-point rating, it was a range between 4.2 and 4.7 .” “People were onto the tricksters who were loading their evaluations with all of these positive things. So if it was below 4.2, people will say, “Well, maybe this isn’t such a great product. If it was above 4.7, they got suspicious.”
Bose – using authority + scarcity to drive a 50% increase in sales “People want more of those things they can have less of.” “What we did was to change the word ‘ new ’ to ‘ hear what you’ve been missing ’. That change produced a 45% increase in sales. The idea of loss is what people really want to avoid. ” “Further, by simply by moving the testimonial from the right of the add … to the top left … we were able to increase sales by a further 15%.” The best performing websites rely on the ‘scarcity principal’ “There was a study of 6,700 e-commerce sites and AB tests that were done for various components inside those sites … The top six were all principles of social influence, and the top one was scarcity of supply … Second was social proof … we had 1,000 customers last week … this week we have 1,500. And number three was scarcity of time … that is … limited time offers.” Commitment bias – dropping restaurant ‘no shows’ by 67%
“She would say, “Thank you for calling Gordon’s. Please call if you have to change or cancel your reservation.” He asked her to change two words, “Will you please call if you have to change or cancel your reservation?” And then he asked her to pause and have people fill that moment, and they all said, “Of course, sure, glad to.” And that was their commitment. And ‘no shows’ dropped by 67% immediately and never went up because he had gotten them to make an active, public voluntary commitment to something and now they were going to live up to it to a greater degree.” Commitment bias – getting staff to do what they say they would “For anybody like a manager, anytime you’re running a meeting and you’ve giving members of your team tasks to perform and complete before the next meeting, don’t let anybody out of that room until you ask the question, will you be able to complete this by our next meeting, and pause. If the answer is no, that’s actually good for a manager to know.” How to get someone over their commitment “At the time you made that decision, you did so by evaluating the information you had. However, there is now some new information that has come to light, which I know, as a curious person, you would like to consider. Given this new information, do you still think your original choice is a valid one?” The best way to win a job or customer “I’m very happy to be here today … could you please answer a question before we begin … why did you invite me here today? What was it about my resume that spurred you to make me a candidate?” This would then cause the employer to state back to the candidate the things they found most important … giving the candidate an insight into what attributes they should focus on. The power of saying ‘we have a deal’ when negotiating By stating to the other party … ‘We have a deal … we just need to resolve a few matters’ … the words ‘we have a deal’ creates loss aversion. And we know from Daniel Kahneman - you’re twice as likely to jump at an opportunity because you don’t want to lose it.

Frank Slootman (CEO, Snowflake) - ILTB - the highlander gains strength when he kills another

Frank Slootman (CEO, Snowflake) - ILTB - the highlander gains strength when he kills another

Great interview with Snowflake CEO, Frank Slootman from Invest Like the Best: here Slootman talks about ‘narrowing the focus’ reminds me of the ‘hedge hog’ concept from good to great – again highlighting how simplicity always wins! Lachie. Getting the right people : You’re not inspecting the people – you’re inspecting the work. If something isn’t working properly – make decisions quickly. At Snowflake … (Slootman) made all the changes within 90 days … today (3yrs on) he hasn’t made any other significant changes Finding good people : Slootman believes you don’t learn much from an interview. The key is to find people who worked with them – ask them what they were like – very quickly you will get an understanding of how they operate by talking to their network Focusing : Henry Kissinger – nothing clears the mind like a lack of options . Narrowing the focus : Product must blow your face off – if it doesn’t – why are we bothering? Snowflake uses the word – ‘dazzling’. If you have a list of 5 priorities – you’re already wrong – you should have one. If you spread your resources an inch deep, mile-long … you’re already dead. A lot of organisations can’t get out of their own way – they have too much going on. Building trust : Slootman just did a session with Pat Lencioni (author of The Five Dysfunctions of a Team ) on the importance of trust. You don’t just get trust – it is earnt . Go to incredible lengths built – use the words ‘I’ve got your back’ … The more difficult information you share … the more likely people will trust you. Discipline is key – everyone is on the same price card – never want a customer to hear another customer got a better deal Finding the right solutions : "This is what you need to do." No, they need to say, "This is the problem you need to solve." Otherwise, you're constraining the possible outcomes already. In other words, they have a view of what the solution is. Sales v product : Very few have the intellectual honesty to consider the product is wrong … not the sales. Constantly see founders firing their VP of sales v changing the product Mastering distribution : At Data Domain, they had 15x the exit value of their nearest competitor because they were very effective at sales versus that competitor – sales was inhouse – not farmed out. Playbooks : Don’t believe you can just take a playbook from one company and apply it to another – needs to be situational and first principles. Principals are good guardrails. Growth : It's vitally important – it is what separates the winners from the losers. Must incorporate the question – ‘what can we grow at’ – must be able to think through it No excuses : Need to find ways to continue to grow regardless. When COVID began – started to hear the word every day … told staff that he didn’t want to hear the word anymore … ‘ there is no excuse for not showing up. It’s a discipline. It’s a mindset’ Highlander analogy : When one software company takes another’s top sales person and engineer it is like the Highlander who gets strength from killing another

Morgan Housel - Tim Ferris - ‘Every successful business is a loosely functioning disaster’

Morgan Housel - Tim Ferris - ‘Every successful business is a loosely functioning disaster’

A great interview with Morgan Housel on the Tim Ferris Show – here . Housel’s book the Psychology of Money was mentioned at a number of meetings during our recent trip to the Berkshire Hathaway AGM. Some really good resources are listed below – I got stuck going down a rabbit hole on ‘The Rabbit Hole’ (this guy is hardcore) Lachie. Businesses : Friend Brent Beshore says … ‘every successful business is a loosely functioning disaster’ Leverage : Buffett quote ‘if you’re smart you don’t need it … if you’re dumb you shouldn’t use it’ Planning for risks : The biggest risk is always something that nobody sees coming – all the big events – no one saw them coming. Therefore you have to have a level of savings .. that doesn’t make sense … you need a level of conservatism that seems a bit too much Kindle audible hack : you can listen to the audible through the kindle app – which allows you to ‘clip’ parts of the book you want to note and then log onto your Amazon account and copy those clips into a doc to then review – very fast way to read and take notes on a book People you always read : ‘The mount Rushmore’ Those less known: Nick Maggiulli – Writes Of Dollars and Data – very clear thinker Josh Brown – as funny as he is smart - understands the human side of investing better than anyone Dereck Thompson – Atlantic – one of the smartest Jason Zweig – greatest financial journalist of modern times (don’t think this is an exaggeration) Carl Richards – used to write for the NY Times – now independent – very good at simplifying the complex – writes on behavioral finance Those well-known: James Clear – Atomic Habits Ryan Holliday – The daily stoic Tim Ferris Content : Is a meritocracy – people who have sold millions of books – have typically earnt it Ideas : Every good idea has come when it hasn’t been forced – 90% of writing comes when not sitting at a desk Writer’s block : The majority of the time you have writers' block it is because your ideas suck – good ideas are very easy to write Great resources: Abnormal Returns : Is the best financial news aggregator – visit this site every day The Rabbit Hole : Blast.com – reads more than anyone – makes a detailed summary of every book he has read – does a better job of this than anyone Housel has come across

Brinton Johns & Jon Bathgate (NZS Capital) - acquired - 'expensive valuations force predictions'

Brinton Johns & Jon Bathgate (NZS Capital) - acquired - 'expensive valuations force predictions'

These guys are exceptional thinkers – their paper on ‘ complexity investing ’ – is a great piece. Power law systems: The most used word in the book … Moby Dick … is … ‘the’ … it is used 15T v the next closest which is used 7T … then there is a long tail … that is how a lot of markets work … a few big winners and a lot of small losers Lachie, Complexity investing & Semiconductors with NZS Capital : Link to podcast Complex adaptive systems : Once you accept the fact that all of life is governed by complex adaptive systems, and the markets are also governed by complex adaptive systems, which means emergent behavior, you can't predict the future, you focus on adaptability … those complex adaptive systems tend to be governed by power laws. It's sort of a natural follow on Resilience stocks : Stable, long-term compounders that offer – Mission-critical high switching costs Scale – where a company can take 95% of the profits – big profits begets bigger reinvestment – begets better customer experience Network effects – where one or two players take 80%-95% of the profits Win/win businesses: Don’t like excessive price hikes – business model has to be a win-win for all stakeholders Long-term thinking: If a management team is really thinking long term – I don’t know why they wouldn’t give some of their economics away each year Conviction: Conviction is stupid. Conviction is saying, I think my view of the future is going to be right. Really, what you want is optionality, and you need people to have conviction because otherwise there would be no entrepreneurs … Example on Tesla, I don't think that we have super high conviction that Tesla's going to power-law the EUV market, but is there some probability where they do that and it's worth multiples of what it is today? Sure . That's the way that we think about conviction. Optionality: There are other predictions like we think EUVs are going to dominate the world and Tesla is going to be the power-law winner inside of EVs. That may only happen in 2 out of 10 copies of the multiverse, certainly not 10 out of 10. These predictions are much narrower and the range of outcomes is much broader Investing is a team sport: We think investing is inherently a team sport. It's a terrible solo sport for the most part. The way we view team is our role is calling out bias in each other Great book from Bell Labs – ‘The idea factory’ Primary research : One of the things that are chronically undervalued by investors is Youtube presentations by executives at industry conferences Santa Fe Institute & Moby Dick : Did Santa Fe Institute short course on complex systems - Power laws are everywhere e.g. the most used word in Moby Dick … ‘the’ … is used 15T v the next closest which is used 7T … then there is a long tail Power laws : Constantly looking for these companies that can ‘power law’ – ‘does the embedded optionality have a winner take most payoff’ Conviction is overconfidence : ‘done more work than someone else and therefore can see the future better’ – have ~50 names where they concentrate on ‘resilience’ – stable compounding machines – which is around half of the portfolio and then distribute optionality around 40 names – which is the other half … In VC part of the portfolio – trying to maximise the probability that you get lucky Sizing optionality positions – need to watch evolve – Zoom e.g. was a ‘feature, then COVID came and an ecosystem was built around it and it became a full-blown platform’ … as a result … the range of outcomes would narrow and the predication would get safer .. thus warranting a bigger position … but need to consider what the market is pricing … is valuation forcing us to believe a lot more than what is actually occurring? Valuation: Expensive valuations force predictions … you have to believe a lot more at 10x sales then 10x EBIT Current cycle : We are in an epic shift. We're still early days from the industrial age , the information age, and semiconductors are the new oxygen in this environment … There are 10 out of 10 copies of the multiverse in the future going forward where railroads are important. They are probably also 10 out of 10 copies of the multiverse where semiconductors are important Investing in Candence : He told us what we wanted to hear, but in terms of turning around the culture, really taking a company that was in a very difficult position in the financial crisis, and really re-architecting the product positioning of the company, he's so customer-centric . That was how we first got involved with Cadence.

Sanjay Ayer on the Business Brew

Sanjay Ayer on the Business Brew

Good morning, To help our investors and network hopefully gain a better understanding of what we are reading and listening to … in addition to our blog posts/book reviews, we will be sharing what we’ve listened to … distilled down into our key learnings. The first podcast we are sharing is an interview with Sanjay Ayer on the Business Brew . Sanjay is a portfolio manager and business analyst at WCM Investment Management since 2007. WCM runs ~$120 Billion in assets out of Laguna Beach, California. We hope you find something interesting, Lachie. Focusing on the right things : What percent of the time in this profession goes towards one of the following two things? Minimising career risk Sounding smart Trying to make money : If you trying to make money all of the time , you’re less likely to over time Learning: If you’re not cringing at the work you did five years ago … your probably not getting better Reflection week : Have a reflection week twice a year – turn off all screens and just reflect Leaders : Have set a tone from the top to show mistakes and be vulnerable Specialists : Weaponize information – try to make sure we’ve got a general coverage – everyone is reasonable across most things New ideas : There are few things as dangerous as an analyst with an initiation report and three expert calls Generalists and patterns : Seeing the patterns across industries is what a generalist can do that a specialist will have difficulty doing Adaptable cultures : As an analyst – the question shouldn’t be, how is this company going to be hurt by this or that technology? It should be – does the company have an adaptable culture to change with technology? Thesis : Your thesis needs to evolve as the company evolves What makes a great investor : Requires a set of qualities that are opposing at face value – for instance – you need to be disciplined and you need to be flexible, you need to have conviction but also embrace cognitive dissonance, you need to be able to focus and you need to be able to multitask … and you need to have grit – work ethic and perseverance … you need to have creativity

Investing - The people business & the founder that made a fortune betting in the opposite direction

Investing - The people business & the founder that made a fortune betting in the opposite direction

For the past 18 years, our team has been making the ~20-hour flight to Omaha Nebraska for the Berkshire Hathaway AGM. My colleague, Sam Paradice attended his first meeting in 2004 and has not missed a meeting for 18 years (excluding the past two due to COVID-19). This year was my sixth. Following our trips … we typically get two questions … Why do you keep going back? What were the main takeaways? To address the first question, I typically respond with the following analogy … if you were an aspiring basketball player and you got the opportunity to spend six hours each year with Michael Jordan while he talked all things basketball … would you? Most get the point … a few don’t. To answer the second – investing is a people business. The final meeting of our trip was with one of the greatest investors of all time – Chuck (Charles) Akre from Akre Management. Following a meeting in Indianapolis, we drove 10 hours east to Chuck’s office in Middleburg Virginia. In this little town of 573 people, with just one traffic light, Chuck and his team manage $17 billion and have outperformed the S&P500 by 5% pa since 1989. To put this in perspective … just 5% of active manages outperformed the index over the past 15 years. Chuck has built his track record by investing in compounding machines and holding on for the long haul. He bought his first Berkshire share in the early eighties at around $500 a share (last trade $462,715). In addition to Berkshire, he has had two other ‘100 baggers’ being American Tower and Markel Corporation. Chuck offered many invaluable insights during our two-hour conversation … however the lesson that really cut through was in response to our question about his greatest mistakes … to which Chuck responded … “the misappraisal of the people, not the business”. Chucks comments were timely; prior to our meeting, I had just finished reading the book Richer, Wiser, Happier, by William Green. Green interviews a number of high-quality investors including Nick Sleep and Qais Zakaria from Nomad investors. Sleep and Zakaria built one of the strongest track records in the industry, generating 18%pa net of fees from 2001 to 2013 - investing in businesses that ‘shared the economic benefits’ of their scale through lower prices to their customers. ‘The most powerful MOAT of all is shared economics’ according to Sleep. Using this mental model Sleep and Zakaria bet big on Costco, Carpetright, AirAsia, Amazon, ASOS, and Berkshire Hathaway. The striking commonality amongst this assortment of businesses offering carpets, aeroplane tickets, and consumer staples … was their leadership by a visionary founder … “they have to be almost high on being iconoclastic (criticizing or attacking cherished beliefs or institutions)”, stated Sleep. Back to our meeting with Chuck … in response to my question, “would it make sense to own a portfolio of exclusively founder lead businesses” … Chuck responded … “absolutely”. Further evidence of my conclusion ‘investing is a people business’ … came in the book, Intelligent Fanatics by Sean Iddings and Ian Cassel. As the title suggests, Iddings and Cassel identify nine lesser-known founders that produced remarkable results. The founder who really caught my attention was Roger Milliken of Milliken & Company. Interestingly, Milliken bet in completely the opposite direction to Buffett. As Buffett exited the textile business Milliken set about investing heavily in research and development to become the only remaining textile business in the US. When Roger stepped down as CEO, Milliken & Company had 60 manufacturing facilities scattered across the world, employed more than 12 thousand workers, produced $3.3 billion in revenues, and ranked 38th on Fortune’s 100 Best Companies to work for list. If you didn’t read the Milliken’s & Company’s business description, you’d think it was Google. Researchers were allowed to spend 15% of their time investigating whatever they chose. Further, Milliken devised systems whereby employees could push a suggestion past their direct report in the event they were getting pushed back on an idea – he took decentralisation seriously! Once a researcher had proved him or herself to be an innovator (through new patents and products), their name was published in the Milliken Innovators Hall of Fame, next to their patent, and was then allowed to spend up to half their time on projects they choose. Milliken & Company’s R&D efforts eventually lead to multiple patents in advanced materials and specialty chemicals. Outside of the research department, all employees were allocated 57 hours of continued education each year (far higher than industry averages) and Milliken set up the ‘Quality University’. The ‘Quality University’ opened its doors to outsiders, including competitors to observe Milliken & Company’s operating principles. In explaining why he would let competitors in, Milliken stated “The public relations are good, and they will never do anything with what they learn. They’re not disciplined enough.” Milliken would also invite outside experts in to speak with his associates - allowing them to benchmark the quality of their operations. Further, managers were required to explain their instructions to associates using the ‘5 Ws’ – who, what, where, when, and why. Milliken & Company went from manufacturing textiles as its primary purpose to manufacturing quality people. These quality individuals would lead Milliken to become the forerunner in engineering and designing new advanced fibers. Milliken’s vision of “Quality Leadership Through Research” was always executed in a ‘win-win’ manner – with the business being named Ethisphere Institute’s World’s Most Ethical Company from 2009 to 2017. Jim Collins refers to founders such as Roger Milliken as ‘clock builders’ versus ‘time tellers’. Clock builders can tell you the time forever – good ideas extend beyond the founder. At age 88, Roger Milliken was said to have a hundred-year plan for the business! I find it fascinating what a founder with an overt focus on people and culture can achieve … Milliken bet in the opposite direction to the world's greatest investor in an industry that was in structural decline … yet he produced incredible results. If you find any Roger Milliken’s / clock builders out there … be sure to let us know! Thanks for reading. Lachie

The power of network effects and how they work

The power of network effects and how they work

The Power of Network Effects and how they work This blog has been written based on the book “The Cold Start Problem” by Andrew Chen. It is an exceptional book written from his experience at Andresen Horowitz - a very successful and well know VC fund. We have struggled to find a concise resource that unpacks network effects and this was a great book to develop our understanding. Network Effects A network effect describes what happens when products get more valuable as more people use them. The network effect was first described by AT&T President Theodore Vail who described the telephone as useless without someone on the other end of the connection. As highlighted on the cover photo, AT&T advertised the value of connecting with others, not the telephone hardware. In the case of the telephone, there are two things at play; a) the physical telephone and b) the network of connections that you can call. A successful network requires both a product and its network. For example, Uber has an application (product) and active users to request a ride (network). In today’s world, many network effect products are made of software and the network is the people that are connected to each other. How to tell if a business has a network effect? 1- Does the product have a network of users? Does it connect people with each other whether for commerce, collaboration, communication, or something else? 2- Does the ability to attract new users become stronger as the network grows? Often the answers to the above are not binary and have different degrees of strength. The History of Network effects There was tremendous excitement about network effects in history and the 1999 Dot Com boom was amongst the greatest. From 1995 to 1999 the NASDAQ rose over 400%. In 1996 only 20M users had access to the internet and this connection was 24kbps dial-up speed. Today there are 5B internet users with an average speed of 80Mbps (3,000x greater). Despite how early we were in internet adoption the market became excited, at its peak, AOL was worth $224B and was one of the most valuable companies in the world. The early excitement stemmed from a principle called Metcalfe’s law which is defined as: The systemic value of compatibility communicating devices grows at the number of connections squared. This is arguably why valuations of internet and technology businesses are initially so confronting - the growth is non-linear and therefore difficult to value on a static valuation. As a company doubles its users the network's value is 4x not 2x. Metcalfe’s law however has some drawbacks because it is solely quantitative. It fails to take into consideration the quality of engagement, the differences in network types, and the crowding effect to mention a few. Network effects don’t only exist in PC networks they exist in biology where some animals live longer when in a large group (safer from predators, finding food, etc). This is knowns as the Allee effect. There is a point known as the Allee threshold were below this point growth turns negative, These groups tend to go toward zero. It goes without saying these principles apply to technology products, until you hit density people don’t have a lot of incentive to connect, beyond this tipping point the benefits rapidly accelerate. Networks can become overcrowded though for example when a communication product starts sending too many irrelevant messages or a social product filling up with irrelevant content or too many listings for a marketplace. This overcrowding among other contributors can lead to a network collapse, and this relationship works in a similar way, when a few nodes disconnect the value reduces slightly but when they reduce beyond the tipping point the value disappears rapidly. Self-sustaining small networks and Slack Given the power of network effects, it obviously begs the question of how to get beyond the tipping point to allow the network to create the perpetuating value. Chen outlines in the Cold Start problem that to start a new product with network effects the first step is to build a single, tiny network that’s self-sustaining on its own . This sounds straightforward but it is not. An extreme example of building a small network was Tiny Speck. Tiny Speck was a company that raised $17M from top investors and was led by star founders who had done it all before, they went on to hire 45 developers and build a multi-player game called Glitch. It was a complete flop and within 4years it was shut down. It was observed during prior analysis that 97% of the players that signed up left within 5 minutes and given the benefit of the game was derived from the interaction with other players it seems clear in hindsight that the rapid churn was the issue. It simply never got the scale to produce any benefits for players. The great irony from this story was that the derivative effect of developing Glitch was that Tiny Speck needed a way for the 45 engineers to communicate with each other (The Tiny Speck Network). At the time (2009) communication tools supporting remote work were near non-existent. The team used an old tool called Internet Relay Chat (IRC) which was clunky, but it did the job, to improve it the team built some additional functionality to make their work easier. When Glitch eventually failed Tiny Speck focused on this IRC product which they renamed to “Slack”! it ended up being taken over for $28B by Salesforce. Once companies started communicating with Slack it became self-sustaining as a communication utility, a slightly lower churn rate than Glitch! The key to getting a small self-sustaining network cranked up is to capture the “hard side” of the network – the small percentage of users who do most of the work on the network. Once you have captured the network the point at which they become self-sustaining depends on the product, some examples are: Slack: Requires 3 people to really work but according to founder Stewart Butterfield after 2,000 messages 93% of people are still using Slack today. Zoom: You just need 2 people – one who wants to call someone else. Airbnb: According to Co-Founder Nate Blecharczyk 300 listings with 100 reviewed listings was the magic number to see growth takeoff in a market. Uber: ETA’s down below 3minutes on average and get 15-20 online cars on the road at the same time. The overall key takeaway is to add a small group of the right people at the same time using the product in the right way. A common thread among the big network effect companies is that they all started small and built these small networks into large groups of networks. The 1% - Hard Side of the network As mentioned earlier a key contributor to building the first network is winning the key network contributors. Nearly every network has them, the starkest example is Wikipedia. Wikipedia is one of the top 10 visited websites in the world and generates 18 billion views and 500 million visitors a month. It is a networked product with editors contributing articles and visitors reading them. Since it was founded in 2001, more than 55 million articles have been written and are over 90 times larger than the Encyclopedia Britannica. The amazing story of Wikipedia is that this enormous amount of content has been created by a minuscule population of only around 4,000 active editors. One extremely hyperactive editor is Steven Pruitt; he is a US customs officer during the day and in his spare time voluntarily edits for Wikipedia. He has made nearly 3 million edits and written 35,000 articles. He was named one of the most influential people on the internet in TIME magazine for his contribution. When you look across user-generated products this is the norm, not the exception, some more examples are outlined below: Uber: 100 million riders, 2 million drivers, 20% of these drivers create 60% of the trips. YouTube: Two billion active users but 2 million users that upload videos. Consumers are typically easier to capture and retain than contributors and this is mainly due to the 1/10/100 rule: 1% of the user population might start the group or thread within a group 10% of the user population might participate actively by authoring content or responding 100% of the user population benefits from the activities of all the above groups (Lurkers). The 1% of users becomes extremely valuable to a network and this is because of what’s known as the “Power Law”. Instagram and YouTube both refer to this power law as the top 20% of influencers or content creators capturing most of the engagement, attracting millions of followers and tens of millions of views. It’s important to understand the motivations of these key contributors and focus on aligning product functionality and the business model towards them, some examples are below: Social Media: Status – Social feedback loop. Would the creator be disappointed if no one saw it? Communications Apps: Utility - Deepening relationships with friends. Marketplaces: Economics – making money Multiplayer Games: Fun and status – enjoyment. Marketplaces Marketplaces tend to revolve around sellers, these are the users that are difficult to get. It’s referred to as “supply” and is the workers and small businesses who provide the time, products, and effort to try and generate some income on the platform. The first move for a marketplace is to build a critical mass of supply onto the marketplace. Once you collect the supply, typically you need to bring on-demand but then follow up with more supply quickly. The normal order of events is supply, demand, supply, supply, supply! Sometimes the easiest supply to capture is assets or time that are underutilized: Airbnb: Underutilization of guest bedrooms and second homes Craigslist and eBay: Selling the stuff you don’t want anymore Uber: Using your car to earn some income Killer product – sometimes doesn’t need to be new or complicated Zoom is the best example of a killer product unseating long-standing providers with the best killer feature of all - the “it works” feature. Zoom was founded in 2011 and grew from 10 million daily meeting participants at the end of 2019 to over 300 million just a few months later. The Zoom product that seemed too simple allowed a frictionless experience of inviting and having a meeting that ultimately enabled and accelerated the viral expansion which continued to reinforce the network effects and kept engagement high as its user base grew. It is easy to think that Zoom’s simplicity was its competitive advantage, but this kind of simplicity is very hard to implement in practice. It is a distinctive quality of networked products - they do one thing well. Networked products such as Zoom and Twitter facilitate an experience of interacting with other users. This is why they appear to feature not products. Contrast this to traditional products which attempt to build out functionality improvements which improve the engagement with the product. Eric Yuan, Zoom founder, said when studying the network experience “...I studied the Dropbox pricing strategy and wondered why did they start charging at 2GB instead of 1GB and I realized the more you use a good product the more likely you are to pay – I started Zoom pricing at 40mins so people could get the full experience”. Networked products love to be free. In summary, the ideal product to drive network effects combines both factors: simple to use and understand at the same time. It needs to bring together a network of users that is hard to copy by competitors. User Experiences to build the network One of the most important parts of building a good experience is avoiding what Chen refers to as “Zeroes”. These are events where the experience of the user is a zero. When building a network effect it’s crucial to avoid these user experiences, some examples are below: Uber: Rider opens the app to hail a ride and there are no drivers available Monday: User logs in and missing documentation needed for work that no one has filled out Slack: Log into contact another user and that user hasn’t downloaded yet Social Network: Sign up and no friends or favorite content is available To avoid these zeroes is not as simple as adding a user, it needs constant activity to be built substantially. The real cost of a zero experience has lingering destructive effects because users churn and don’t believe in the reliability of the service so can be lost forever. The Tipping Point There are many ways to get to the tipping point but ultimately it is the point at which the virality, stickiness, and monetization start to scale on their own, “when a product can grow to take over a whole market”. There are many ways to nurture the scalability of growth such as: Invite Only strategy: LinkedIn, Facebook, and Gmail used this approach. The main reason it works so well is that it naturally provides a good experience where your known connections and friends are already connected because they invited you – ensuring you get a good first experience. Come for the Tool, stay for the network: Instagram is probably the best example. It unseated Hipstamatic because they attached a network to a photo filtering app. The tool attracts users, but the network makes them sticky. Some more examples are: Instagram: Create photos + Share Asana: Organize + Collaborate GitHub: System of Record + keep up to date with others Glassdoor: Lookup + Contribute with others Paying up for Launch : It's sometimes smart to get the market quickly by paying up for density. Coca-Cola did this best with coupons - handing out free coupons to everyone to redeem at the store (resulting in lots of stores stocking Coca-Cola). Visa also did this by mailing out credit cards to attract merchants (resulting in the new Visa card being accepted at hundreds of stores). Partnering: Piggybacking off an install base can leapfrog adoption and be a smart way to hit scale quickly. The best example in history of this is Microsoft/IBM . MS-DOS was custom built for IBM and got the immediate scale to attract the most developers, which then went on to attract users and then PC makers. MSFT went on to capture 80% market share for operating systems. Competitive advantage of Network Effect businesses In summary, the competitive advantage of a network effect business model comes from its users, not the product, and this is difficult to replicate. This has been highlighted several times with the best-case study being Airbnb vs Wimdu. Wimdu vs Airbnb: The Samwer brothers in Germany almost exactly copied Airbnb’s website design and business model and scaled extremely fast raising $90M (The largest investment in a European start-up ever) and hiring 400 staff in less than 100days. Wimdu went on to attract 50,000 listings and $130M of revenue within a year. However, within 2 years Wimdu went to zero. All the scale they rapidly attracted was poor quality and the users had a poor experience. One of the early employees at Airbnb quoted: “Not all supply is created equal, Wimdu’s top 10% of inventory was our bottom 10%. Customers were unhappy with their experience, and you need this high NPS to get viral growth”. I would encourage anyone who enjoyed this blog to buy the book as it covers much more detail than I have highlighted here. I hope you enjoyed the read. Regards, Shaun Trewin CA Quality Compounders

The everything store - The pessimists sounded smart ... while the optimists made the money

The everything store - The pessimists sounded smart ... while the optimists made the money

Good morning, In 2007, the consensus analyst forecast for Amazon's 2020 sales revenues ranged from $26 billion to $27 billion. In 2020, Amazon achieved $386 billion. The pessimists armed with their myriad of reasons continually sounded smart undercooking their forecasts, while the optimists made a killing! The pessimists sounded smart in 1996 outlining why Barnes and Nobel would crush Amazon’s bookstore. They sounded smart when eight executives and one board member left between 2002 and 2003. They sounded smart in 2005 when google (who had a large market cap at that point) launched their eCommerce offering – Froogle … and few sounded smarter than hedge fund giant, George Soros when he told the investment community that he was short Amazon in the depths of the GFC. The Amazon story has been retold thousands, if not, millions of times by journalists, analysts, students, and industry participants. Despite this, we still think it is worth taking the time and trying to understand why so many missed it and how one man managed to disrupt two major industries – retail and computing. The everything store, authored by Brad Stone, is possibly the most comprehensive recount of Amazon’s journey from start-up to 2014. Stone does a great job, detailing the major events in the business’s life through 300 interviews with former staff and from fifteen years of reporting on the company for the Newsweek, the New York Times, and Bloomberg Businessweek. Amazon’s skeptics have been well documented through time, none more than Jacqueline Doherty, the Barron’s journalist who published the article Amazon.bomb in May 1999 (attached). However, it was only when reading Stone’s book did, I really grasp the sheer number of times the ‘pessimists would have … sounded smart’ over the past 25 years. For investors, the big gains only come through holding compounding machines over long periods of time, which is far easier said than done. There are few better examples of this than Amazon, trading at $86 ($30.05 billion market cap) and 70x sales in March 1999, before falling 93% over two years and nine months to $5.97 ($2.21 billion market cap) or 2x sales, in September 2001. Today, the stock 547x higher, at $3,271 a share ($1.6 trillion market cap). Like all great founders, it was the crisis that defined Bezos. Prior to the ‘.com crash’, capital allocation was aggressive, with Bezos using debt to bet big on anything that was a ‘.com’. However, it was only the busting of the bubble along with meeting two retailing greats in Sam Walton (Walmart founder) and Jim Sinegal (Costco founder) did Bezos reassess and turn his laser focus to lower prices, developing technology organically, and only betting on an acquisition when he knew the flywheel was turning. Using Stone’s work, I’ve tried to document the many times the ‘pessimists sounded smart’ and how ‘the optimists made money’ in one of the greatest compounders of all time. How the pessimists sounded smart Short sellers & Bezos sell down There are few pessimists who can sound smarter than a short seller armed with a few expert calls and a short report. Amazon was publicly shorted not once but twice! The first by a 28-year-old Lehman Brothers convertible bond analyst – Ravi Suria who drafted a research paper shortly after the Nasdaq peaked in March 2000, ­­­­­outlining why Amazon would run out of cash ‘in just four months’. Ravi continued to pummel the company with negative reports throughout the summer of 2000 causing the stock price to fall from ~$57 to $33 and also causing suppliers to start to question the businesses viability. Adding fuel to the fire was a $12m sell down from Bezos which lead to an SEC investigation after it was found that Amazon had seen a Lehman’s negative research report prior to it being published to the market. It was during this period that Bezos wrote on the wall of his office “I am not my stock price” … telling his staff … ‘don’t feel 30% dumber when your stock is down 30%’ … and … ‘don’t feel 30% smarter when it is up 30%’. Amazon was again shorted in 2008 by one of the biggest names in the business – George Soros. At a hedge fund conference in New York during the depths of GFC Soros name just one stock that he was shorting as the world was falling apart: Amazon. Culture & staff turnover Former staff are rarely a source of unbiased opinion however poor staff reviews coupled with high staff turnover can often tell you something is awry with management. A common quote amongst Amazon employees was ‘if you’re not good, Jeff will chew you up and spit you out. If you’re good, he will jump on your back and ride you into the ground.’ There were numerous bouts of layoffs in key management, Stone noted staff turnover was typically high however the period following the tech bust from 2002 to 2003, was one of note with departures from: Doug Boake, Business Development Executive David Risher, Head of Retail Joel Spiegel, VP of engineering Mark Britto, SPV, Worldwide Services and Sales Harrison Miller, VP, and GM of Platform Services Chris Payne, VP (numerous departments) Warren Jenson, CFO Scott Cook (founder of Intuit) - left the board for eBay Amazon once had a management policy in place that required managers to fire a certain percentage of underperforming staff each year. However, this policy was revoked when managers were found to be hiring those, they knew they would fire! Dealing in the grey market Initially, Amazon faced the ‘chicken and egg’ problem. Customers would only shop where they were offered an adequate range and suppliers would only supply where there was adequate demand. Early on, the likes of Toshiba, Sony, and Samsung viewed internet sellers like Amazon as ‘sketchy’. Their views were only confirmed when Sony executives who were visiting an Amazon fulfillment center, found unauthorized stock all over the floors! Debt used to fund failed acquisitions Between 1998 and 2000, few companies bet bigger on the internet than Amazon, who raised $2.2b via convertible bond offerings to fund acquisitions. To help the process, Amazon hired Randy Tinsley from Intel in 1998, who said to Bezos … “I’m really looking forward to going shopping with you.” Bezos and Tinsely acquired movie database IMBD.com ; the British Web Bookstore BookPages; the German Web Bookstore Telebuch; the online marketplace Exchange.com ; the pioneering social-networking service PlanetAll; and a data collection company called Alexa Internet. They also acquired same-day delivery business kozmo.com and comparison website Junglee, which was shut down quickly after it was diverting traffic from Amazon’s website. In addition, Amazon’s venture arm invested in pets.com , gear.com , wineshopper.com , greenlight.com . homegrocer.com – almost all of which went bust in the .com crash. Aggressive accounting Following the late 90ies acquisition binge and the attention Amazon was getting from short-sellers, Bezos reviewed the cost base and in early 2001 wrote an internal memo to staff declaring that the business would be breakeven by the fourth quarter that year. Bezos announced this publicly to investors later that year, stating that the company would be profitable based on ‘Pro-forma numbers’ which excluded stock-based compensation and some other expenses. Competitors The wall of competition Amazon faced was nothing short of intense. The first major threat came just two years into business (1996) from dominant book retailer, Barnes, and Noble. Founders, the Riggios brothers, told Bezos over lunch that they intended to launch their own website to directly compete with Amazon. At the time, Barnes and Nobel was doing $2 billion in sales v Amazon's $16m. A year later, Barnes and Noble filed a lawsuit just prior to Amazon’s IPO, alleging that Amazon was falsely advertising themselves as the ‘world’s largest bookstore’. In 2005, Google showed up, launching Froogle. A direct competitor to Amazon’s eCommerce business. In 2010, Walmart, Best Buy, Home Depot, and Sears coordinated in backing a new organisation called the Alliance for Main Street Fairness - designed to lobby against online retailers to start collecting sales tax – a significant advantage Bezos didn’t want to lose. In a similar attack, over the course of 2009, the chiefs of the six major US publishing houses – Penguin, Hachette, Macmillan, HarperCollins, Random House, and Simon and Schuster – gathered allegedly to discuss their shared predicament of the Kindle’s $9.99 new releases. How the optimists made money While the pessimists piled on reason after reason, the optimists saw a rare founder CEO who had significant skin in the game and was obsessively frugal and customer-focused. Who instilled a culture of learning and decentralized thinking. Who communicated in an extremely shareholder-focused manner and incentivised his staff to think and act with only one-time horizon in mind: long term. Thanks for reading. Lachlan Morgan, CFA
Adviser, 318999

Watching out of FOG

Watching out of FOG

FOG … ‘Fact Obfuscating Generalisations’ … or as Ray Dalio (founder of Bridgewater – one of the world’s most successful hedge funds) would call it … ‘mouth farting’. FOG is a term I discovered in the book … Investing between the lines by LJ Rittenhouse. Investing between the lines caught my attention whilst skimming the shelves of ‘The Bookworm’ – the local Omaha bookstore at the Berkshire Hathaway annual meeting. Rittenhouse makes the point that a CEO's letter is a far more powerful research tool than many investors appreciate – a conclusion we strongly support. As Mark Twain would say … ‘the difference between the right word and the almost right word is like the difference between lightning and the lightning bug’. We believe the annual letter is the only opportunity that CEOs can address all businesses ’ key stakeholders at once. The annual letter is a document that can be read by shareholders, customers, potential customers, employees, potential employees, suppliers, and in some cases, regulators – hence we find it amazing that most CEOs are willing to outsource a document that could potentially impact both the quality of employees and customers the business could attract. How do we know so many CEOs outsource the writing of their annual letter? Because they are full of what Rittenhouse calls ‘ weasel words, cliches and jargon ’. Words such as ‘ solid, momentum, enhancing, eco-system’ or cliches such as ‘we are a customer-first company’ . These words left on their own or lacking context indicate that a CEO potentially doesn’t have a strong grasp on what is happening at the ‘coalface’ of the business. Rittenhouse provides many examples throughout the book, the most notable being that of notorious corporate failure - Enron. Rittenhouse noted the following passage in CEO Jeff Skilling annual letter from the year 2000: ‘Our talented people, global presence, financial strength, and massive market opportunity have created our sustainable and unique businesses. EnronOnline will accelerate their growth. We plan to leverage all of these competitive advantages to create significant value for our shareholders.’ In one short paragraph, Enron introduced six popular clichés: Talented people Global presence Market knowledge Financial strength Leverage competitive advantages Significant value for our shareholder We look for CEOs who can simply explain, through vivid examples the link between employee and customer satisfaction … with shareholder value. As Rittenhouse explains - without a commitment to transparency from the top, lawyers, professional directors, and communication consultants will do their best to dilute the CEO’s message and keep readers confused. Investors who find CEO letters riddled with generalizations need to ask: does the CEO not understand the business or does he or she not want the owners to understand it?!?! A long-term holding in the Quality Compounders portfolio – Mainfreight – is one of the best examples of a management team that more than understands the importance of using their annual report to communicate to their various stakeholders – the 2021 annual report (attached) is indicative of their typical annual report: Chair, founder, and Mainfreight’s largest shareholder, Bruce Plested mentions ‘customers’ three times and ‘employees’ four times CEO and Mainfreight’s fifth-largest shareholder, Don Braid mentions ‘customers’ 14 times and ‘employees’ nine times Every employee’s name is listed in the annual report … under the heading ‘ Thank you to this extraordinary team …. You are our heart and soul’ Key operating statistics - which include things such as Transport loading errors; Number of training courses completed by their staff; Inventory Record Accuracy … these statistics are provided with year-on-year comparisons … just think how powerful this is when pitching a new account! ‘Five-year road map’ – where management provides details on their key growth initiatives along with targets for these key initiatives Explicit statements about their culture and values ‘ staying our 100-year course ’, ‘culture continues to forge our future’, ‘there is no place for mediocrity’, ‘look after our people and look after our customers’ Well-considered annual letters and reports are few and far between thus we believe it can offer one of the simplest and most effective ways to assess the quality of a manager. A list of the best CEO letters we’ve studied include: Warren Buffett, Berkshire Hathaway Jeff Bezos, Amazon Mark Leonard, Constellation Software Tom Gainer, Markel Corporation Jeff Lawson, Twillo If you know of any exceptional CEO letters – please let us know. Thanks for reading, Lachlan Morgan, CFA
Adviser, 318999

Rags to Riches – The story of the humble uniforms and laundry 400 bagger!

Rags to Riches – The story of the humble uniforms and laundry 400 bagger!

“Pinch Pennies. Work Hard. Make Sacrifices. Reinvest in the business. And go for the long term” Cintas. What an incredible founder-led story about a family-owned business growing up to a 400 bagger since IPO in 1983. Cintas was listed in 1983 at an equivalent share price of $0.94 and has grown to over $400 in 38 years, generating a share price CAGR of ~17.3%. Compounding is the never-ending lesson; these numbers still surprise me. In May 2004 when this book was written, Cintas had completed its 35th consecutive year of growth in sales and profits. At the time of writing the only other public company to have achieved it was Wal-Mart (a few others reached the benchmark but couldn’t maintain it for so long). Cintas achieved a 35yr record of 23% CAGR in sales and 30% CAGR in profits to 2004. What a compounding machine. There are some distinct patterns evident in the way Cintas achieved such enormous scales as decentralized management, incentives, and culture. The book Rags to Riches is written by Richard T Farmer who was the president and second-generation family founder, Richard’s grandfather ‘Doc’ Farmer was the original founder of the business which was invented out of the “grinding poverty of the great depression”. A summary of some elements of overt high-performance culture through the Cintas history include: A 10yr stock option plan with none vesting for the first 5 years and then 20% each year after. Cintas managers always wear business attire, no casual Fridays, our business is making people look sharp - lead by example. Operate exceedingly clean plants, Farmer used to inspect the bathroom as a key indicator of manager quality Even while a private company the profit and loss was shared with all employees every year To improve profitability incentivize the team to satisfy customers, increase competitive advantage and be more productive. Cintas started its life as Acme Wiper and Industrial Laundry. Acme Wipe’s core business was collecting used towels from industrial factories, These towels were then washed and resold back to industrial customers. Acme Wiper then discovered they had a core competence in servicing customers with regular clean garments which eventually lead to outsourced cleaning and provision of uniforms. The business has since grown into one of the largest service businesses in the world. Culture “You’ll hear lots about culture in this book. It is , without doubt, our most important competitive advantage. Competitors can copy our sales material, our products, and even some of our systems but they cannot copy our culture”. Farmer outlines at the end of the book that to achieve the grand ambitions he needed very talented people however, he was always more comfortable with “partners” than “employees” so whenever he came across exceptional people, he saw to it that they were owners and partners in the business, not just employees. Farmer outlines that the best way to communicate the culture of a business is by telling stories about where and how it came about, this is almost exactly the way Bezos describes culture at Amazon –“stories of past successes and failures that become a deep part of company lore”. Some of the stories about how Cintas grew its culture came from near-death experiences. In 1945 when Cintas was a small family business with 12 employees the factory burned down and although there was insurance it wasn’t enough to truly rebuild the business. Doc Farmer exclaimed that “we are not out of business! you can take our equipment, but as long as we have our people we’ll be okay”. Having to rebuild from nothing with only your staff teaches you the true enduring nature of your people. Another story about the workplace environment was developed through many experiences including Richard working in the drying room which was stiflingly hot, lifting heavy drums of wet rags that were 200 pounds apiece, eating lunch in the restrooms because there was no lunchroom, scooping out grease from the sump pit by hand in waist-deep oil and grease. All these examples enforced the culture to provide a safe and enjoyable workplace. Incentives – the rule of 35 “I’ve always believed there is nothing more motivating than ownership” There were a lot of factors involved in creating such a motivated group of people but one element was how we compensated them. The compensation program worked like this. A General manager of a plant was paid a salary of $10,000 a year. That’s it. Everything else depended on how they performed. They were entitled to six percent of the profits after expensing all corporate overhead and taxes. And Cintas still do it that way today. Each business unit (Plant or branch) would have a separate profit and loss statement and the GM is totally responsible for that P&L. They are running their own business. Each GM reports to a vice president who also only earns a modest salary and earns a profit of the group of business units reporting up. During the 80s the rule of 35 was born. It was designed out of the experience that a GM could greatly increase short-term profits by cutting back on investing in advertising and sales. Conversely, a manager could invest in future profits by ramping up sales growth. Some markets also had differing sizes and opportunities. Farmer announced at a managers meeting “wouldn’t we be just as happy if a manager increased sales by 25% and had a profit margin of 10% as we would be with 25% margin and 10% sales growth?”. The rule of 35 was born. The nexus of the Cintas stock option package started in 1967 when Farmer employed Bob Kohlhepp. When Farmer met with Kohlhepp it was suggested they become partners it worked like this, Farmer sold stock to Kohhlepp at a fair price, instead of paying in cash the company lent the money and kept the stock as collateral. This loan was non-interest bearing but would be terminated if certain events occurred such as departure from the company. Farmer always focused on finding ‘partners’ not ‘hired hands’. Vision “I painted my vision for our people at every chance I had – that was their light at the end of the tunnel. That was the great motivator when times got tough” Farmer tells the story of a man walking down the street in the middle of a big city and how he came across a construction site. He came across three men in a ditch, he asked the first man what are you doing? “I’m digging a ditch,” the first man said, he then asked the second man what are you doing? “We’re digging a ditch for the water line for that building going up over there”. He then asked the third man what are you doing? The man looked up and said, “we’re building a cathedral. It will be big and beautiful with five big spires and large stained windows and it will seat 500 people the biggest in this city”. The third man is proud of what he is doing because he shares the vision. This simple story demonstrates why it is important to have a vision and share it with everyone. Tom Dick or Harry? “ One of the disappointments I’ve had over the years is watching promoted individuals experience failure – It’s a fact of life that some people are more talented and capable than others” When conducting performance review Farmer continuously used the Tom Dick or Harry story and it went like this. There was once a gentleman in New York City with three sons. All of his sons had the same education, same household, and worked for the same company but one was much more successful than the others. One day the father called into their place of work to see if he could understand what was happening. After he met the owner and asked “could you explain to me why Tom is doing so much better than the others” the owner said of course why don’t I show you. The owner called Harry into the office and said “Harry, there is a Russian ship that just arrived at the dock. I understand it has some beautiful furs. Would you go down and check it out and let me know what you think I should do?” Harry disappeared and came back shortly after “you’re right boss there is a Russian ship there. They have lots of furs and would like to sell them if possible, would we have any interest?”. The boss said he would consider it. The owner then asked Dick to undertake the same errand. Dick returned and said, “You’re right boss. There is a ship loaded with magnificent furs. They have 5,000 mink furs. 2,000 sable furs, and 3,000 lynx furs. They want $1.3M what should I tell them?”. The owner said he’d think about it. The owner then called in Tom and asked the same. Tom returned and gave his report “Boss you’re right. There is a Russian ship loaded with outstanding furs. They have 5,000 mink furs. I took three people down with me to inspect them. They are A-Grade quality. They have Sable and Lynx furs which we inspected also. They want $1.3M I offered them $1M cash payable in 10days. They agreed and I have the purchase order for you to sign if you would like to proceed. I have also organized our warehouse to take possession, if you sign this I will make it happen”. The owner of the business said to the father – see your other two sons are great but Tom is a star. After telling this story in the performance review Farmer would ask so which son are you? It was a great way to explain the different values. The Cintas Growth Engine “ I was aware early on that our industry was going to consolidate” In the 1940s and 1950s, there were numerous family-owned breweries in a single city, by the 1960s these were gone. The dairy industry was the same. The Cintas strategy was to play a major role in the consolidation of the uniform industry. Cintas made very few acquisitions during the 1970s except for one small company in 1973, this was because of the new fabric technology (Permanent press uniforms). Cintas began making acquisitions from family-owned businesses that didn’t even keep accounting records. By the mid-1970s Farmer and the team figured that there were some 1,000 companies in the uniform industry, most of these were family-owned and operated and the majority had made the upgrade to the new fabric. The team made a list of them and graded them by priority. Alongside the acquisitions, Cintas was rolling out new factories in new regions that leveraged their network. As Cintas continued the aggressive rollout across the country, they encountered some resistance (legal and illegal), when they entered the Cleveland market a large competitor called WorkWear decided to counter-attack Cintas on their home turf in Cincinnati. WorkWear approached every Cintas customer and offered to slash prices, to defend the home market Cintas went and visited every customer and offered them a piece of paper and a pen with the opportunity to “name their price” most customers handed it back and ignored the opportunity for the discount. The IPO At the time of the IPO in August 1983, Cintas had 23 operations and a little over $60M in annual revenues. The company is listed with a market cap of $110M (an adjusted share price of $0.95 today). The stock price was a 50 bagger from the IPO price to December 1999 (26% CAGR), after this it went sideways for almost 14years after which it added another $400 per share or 400bags from the initial investment. Cintas has grown revenues from $60M in 1983 to over $7B today. What an incredible story of growth! Hope you enjoyed the read. Shaun Trewin CA Quality Compounders

The power of Increasing Returns, Lock in and Learning Systems

The power of Increasing Returns, Lock in and Learning Systems

The insights from this blog have been drawn from the book Complexity by M. Mitchell Waldrop. This book outlines how a group of diverse Scientific luminaries including several Nobel Laureates developed the Santa Fe Institute to study complexity – how single elements organize themselves into complex structures. This think tank made several revolutionary discoveries that impacted many sciences from biology to economics. Sounds heavy. There were a few key points my small IQ could understand. Increasing Returns Why have high tech companies scrambled to locate to Silicon Valley? Why did the VHS system take the market from Beta despite it being technically inferior? ‘ increasing returns ’ is a mental model that is well known in Biology and Engineering also referred to as positive feedback – like mild tropical winds growing into a hurricane, seeds and embryos growing into fully developed creatures. The neoclassical economics theory assumes the complete opposite that the economy is dominated by negative feedback – the theory of diminishing returns being that twice the fertilizer doesn’t produce twice the crop yield and that the economy finds an equilibrium and harmony. In some cases, this is very true take for example a business enjoying a large margin with no competitive advantage a competitor is incentivized to enter and share the margin with the incumbent hence finding an equilibrium rather than the incumbent continuing to grow and taking the entire market. Technology however is different. Take for example the QWERTY keyboard, this was invented by Christopher Scholes in 1873 specifically to slow typists down to prevent jamming in typewriters, this technology was then mass produced by the Remington Sewing Machine Company which meant the entire market was trained using this layout, therefore locking this in as the standard forever. Consider the VHS videotape format that by luck gained a slightly larger market share initially which was compounded by the big incentive of video stores wanting to stock only one format and users only wanting one video player which led to VHS dominating the market despite it being inferior to Beta. Interestingly, in any system there are patterns that are the result of a rich mixture of positive feedback and negative feedback. For example, spilling some water on a polished tray would result in a complex pattern of beads that forms as the result of 3 things:
Negative feedback system of gravity forcing the water flat, Positive feedback system of surface tension attracting water molecules together, and LUCK through tiny accidents of history small dust motes and invisible irregularities of the tray Its not a linear process from adoption to lock in and many feedback systems contribute to it, some of which are unpredictable. Technology Lock In An intriguing example of technology lock in is the internal combustion engine. Gasoline powered engines were considered dangerous, less efficient, noisy and gas was hard to obtain in the right grade. Despite all these shortcomings it did eventually win out as the locked in technology arguably due to several lucky events: In 1895 A gasoline powered Duryea won a Horseless-carriage competition that led to Ransom Old’s 1986 patent and mass production of gasoline vehicles. In 1914 there was an outbreak of hoof and mouth disease in North America that led to the withdrawal of horse troughs which were the only places where steam cars could fill up with water. At the point in time where gas vehicles were being mass produced. Despite improvements in technology by the Stanley Brothers condenser and boiler system that didn’t need to be refilled the steam engine never recovered as gasoline power quickly became locked in by gas car owners, gas stations, refineries etc. Increasing returns isn’t an isolated phenomenon at all: the principle applies to everything in high technology. Take for example Microsoft Windows, The company spent close to $50M in Research and Development to get the first copy out the door. The second copy costs ~$10 in materials, it’s the same story in electronics, computers, pharmaceuticals even aerospace (The cost of the first B2 Bomber was $21 billion, cost of the next copy $500 million). High Technology could almost be defined as “Congealed Knowledge” whereby the marginal cost is virtually zero, this makes every copy that is produced cheaper and cheaper. Furthermore, every additional copy that is produced offers the chance for scale efficiencies and improvements. From a customer perspective there is an equally large reward to flock to a standard. For example, an airline would prefer to own a fleet of the same jet, so pilots and engineers don’t have to be retrained and switch. Another customer example is an office choosing one type of software which ensures support and training is more efficient. When you compare all the above to standard bulk commodities industries such as grain, coal, or cement. The know how of this production was acquired many generations ago. Today the direct costs are labor, land and raw materials, areas where diminishing returns can set in easily. (farmers producing more grain resulting in less productive land being utilized). These industries tend be described and understood better by standard neoclassical economics of equilibrium vs increasing returns. Linear vs Non-Linear Systems The name linear refers to the fact that if you plot a relationship on a graph the plot would be a straight line. A lot of study in science has been focused on linear systems in which the whole is precisely equal to the sum of its parts this implies that each part is free to do its own thing regardless of what else is happening. An awful lot of nature works in a linear fashion such. Sound for example can act in a linear way which is why we can hear and recognize two different instruments. Light is a linear system which is why we can recognize and identify a walk/don’t walk sign in daylight. In some ways an economy is linear in that small economic agents can act independently when someone buys a newspaper at a corner store it has no effect on your decision to buy a tube of toothpaste. However, our brain is certainly non-linear which is why the combined sound of multiple musical instruments which becomes music in your brain is worth more than all of them individually. The economy is also non-linear in that millions of buyers can reinforce each other and create booms and busts. As outlined earlier many systems share both properties in differing conditions, and quantities. This is the theory of emergence. The point where one system transitions from linear to non-linear relationships. Take water for example there is nothing very complicated about a water molecule its behavior is governed by well understood equations of atomic physics. But now put a few zillion of these molecules together and you suddenly have a substance that shimmers, sloshes and gurgles they have collectively taken on a new property that none of them possessed alone – liquidity. The non-linear systems thinking is perhaps why stock market behavior is so unexplainable when zillions of rules and expectations are introduced, they create a new property as a collective. Technology networks and change Conventional economic theory of technological development was that technologies came at random and were made possible by inventors that were exogenous to the economy. Another economic model viewed technology development as purely a commodity of Research and Development, if you spent X you delivered Y as an outcome over time. When you look through economic history as opposed to economic theory technology is not a commodity at all, innovations are like an evolving ecosystem and rarely developed in a vacuum. They are made possible by other innovations already in place for example a laser printer is basically a Xerox machine with a little computer circuitry inside, the former enabled by the two latter technologies, also only possible at scale because of the need for high-speed printing. The technological webs are highly dynamic and unstable and can grow in an organic fashion. For example, laser printers give rise to desktop publishing software and desktop publishing opens the need for graphics programs and more powerful hardware and better peripheral devices and on it goes. These technological webs undergo bursts of evolutionary growth and mass extinction events just like biological ecosystems. Say a new technology comes along - the automobile and replaces the old technology - the horse, along with the horse goes the blacksmith, watering troughs, stables and so on. The whole subnetwork collapses below but the automobile brings with it paved roads, gas stations, fast food restaurants, motels and new network is developed. Another example of increasing returns, once a technology is embedded the networks are heavily incentivized to help it grow and prosper. To dislodge an incumbent technology the hurdles are greater than simply the functionality improvement, each technology is different but as an example see below some key elements that are required to be surpassed by sheer new technology functionality: Adaptive Systems Complex systems contain clusters of subs systems that interact and as they continue to interact they reinforce one another. Once a cluster becomes stable it moves on to become a building block for a larger cluster and so on. This hierarchy of building blocks transforms the entire systems ability to learn, evolve and adapt. Take the concepts of red, car and road once these building blocks are understood and refined through experience it can be adapted and recombined into many new concepts such as a “red Saab by the side of the road” this is much more efficient than creating something new and starting over from scratch. It is akin to pattern recognition and identifying similar business models to assign to valuations. Instead of moving slowly through the immense space of possibilities step by step an adaptive system reshuffles its building blocks to take giant steps quickly. A great illustration of this concept is the way police artists used to work in the days before computers when constructing a drawing of a suspect to match a witness description the idea was to divide the face up into 10 different sections (Building blocks) each with 10 strips of paper with different options of noses, ears, eyes, forehead etc. These building block decisions were easy to dissect but together they combine into a complex possibility of over 10 Billion different faces, to identify a face from 10 Billion is almost impossible. Using the building block method can describe a great many complicated things with relatively few building blocks. These building blocks can be informally organized, In the cognitive realm anything we call a “Skill” or “expertise” is an implicit model – or more precisely a huge interlocking set of standard operating procedures (building blocks) that have been inscribed on the nervous system and refined by years of experience. The best example of this is medieval architects who created gothic cathedrals, they didn’t have the tools of modern physics or structural engineering they learned standard operating procedures from rules of thumb passed down from master to apprentice many of these structures are still standing thousands of years later. This system of learning receives environmental feedback, this was Darwin’s great insight. If a model survives in the environment, then it improves through natural selection and this steady improvement is called evolution. Learning Classifier Systems A learning classifier system is a rule-based machine learning method that combines a discovery component with a learning component. Classifier systems seek to identify a set of context dependent rules that collectively store and apply knowledge in a piecewise manner to make predictions. In 1978 an early classifier system was tested to learn how to run a simulated maze and ended up being 10 times faster than the incumbent method. This classifier system also exhibited transfer which is that it could apply learnings from previous mazes to the current one. From this development further applications were built in Poker and Chess to test their effectiveness they worked with great success. The most impressive application of the Classifier system was applied in 1982 to solve the pipeline problem. Running a Gas pipeline is a complex problem that involves hundreds of compressors pumping gas through thousands of miles of large diameter pipe all while customers demands change hourly and pipes and compressors spring leaks continually. Safety constraints demand that pressure remain at certain levels and everything effects everything else in the system. Pipeline operators learn their craft through long apprenticeships and then drive their system with “feel” and “instinct”. The classifier system learned to operate a simulated pipeline beautifully, starting from a set of totally random classifiers it achieved expert-level performance in about 1000 days of experience. The most insightful outcome of the classifier system in the pipeline problem was the way the system organized knowledge about leaks it developed rules and then adapted the rules when the facts changed. The three key principles that underpin the classifier system are: Knowledge can be expressed in terms of mental structures that behave very much like rules; Rules are always in competition so that experience causes useful rules to grow stronger and unhelpful rules to grow weaker Plausible new rules are generated from combining old rules These three rules create a hierarchy of the structure of human knowledge. Artificial Life Imagine a machine that floats around a pond filled with lots of parts, this machine is a constructor and given a description of itself it could construct another machine once it locates the proper parts. That sounds like re-production; however, it isn’t. The newly created copy of the machine won’t have a description of itself which means it won’t be able to make any further copies. Therefore, to achieve true re-production the original machine also requires a description copier, a device that will take the original description, duplicate it, and then attach the duplicate description to the offspring machine. Once that happens the offspring will have everything required to re-produce. To restate this in a more formal way, the genetic material of any self-reproducing system natural or artificial must play two fundamentally different roles: A program/algorithm that can be executed during the construction of the offspring, and Serve as a passive data, a description that can be duplicated and given to the offspring to pass on. This theory was espoused by John Von Neumann in the late 1940’s before Watson and Crick discovered the double helix in 1953. This turned out to be an incredible prediction considering that what we went on discover about DNA and cell division process. Corporate culture is the DNA of a business and the ability for a company to truly replicate its business model to grow requires a) the algorithm of the business model and b) the passive data (Culture) to pass on to the employees that will become the next generation within the business. Without firstly the corporate culture of beliefs, myths, rules and ways of doing things nothing can be passed on at all, secondly that DNA map needs to be implanted in the expanded operations strongly. The Artificial Stock Market One of the most difficult chestnuts to crack in economic theory is stock market behavior. Considering that neoclassical theory considers that all economic agents are perfectly rational then all investors must be perfectly rational. Moreover, since everyone shares the same information, they will always agree about what every stock is worth – simply the net present value of its future cash flows discounted by the interest rate. Therefore, this perfectly rational market will never get caught up in bubbles or busts. Therefore the New York Stock Exchange trading floor would be a quiet place, in reality of course the NYSE is a barely controlled riot, with multiple bubbles and crashes, fear, uncertainty, euphoria and every combination. A Martian that read the Wall Street journal might think that the “market” was a living organism that had moods of being jittery, depressed, upbeat, and confident. If you replace the above assumptions of perfectly rational agents and replace them with Artificial intelligence agents that can learn, adapt, and classify rules they will act more like a human does. The Santa Fe institute modeled this change with one stock that the agents could buy and sell and as they learned rules for trading you could observe what rules they developed and which ones were reinforced and prioritized by different participants. The model began with a stock that had fundamental value of $30. The agents were then introduced to the market with total stupidity, random rules were developed from experience, as expected they learned energetically. On the second run of this model, it was observed that some the agents had developed a primitive form of technical analysis reinforced by positive outcomes from random rules which further reinforced more positive outcomes for new agents further reinforcing these rules even further which ran the price up to $34. In different models the exact opposite happened, and the price fell to $25 as agents tried to continue selling reinforcing further selling. The fascinating reality is that the market is combination of participants that have rules developed and reinforced by individual experiences that drive different expectations of outcomes. Monetary reward is a powerful incentive to overcome especially when reinforced with consecutive positive outcomes, hence as markets rise they can continue to rise for a long time. I hope you enjoyed the read. Thanks, Shaun Trewin

The second most expensive words in investing ...

The second most expensive words in investing ...

If the words ‘I’ve missed it’ … are the most expensive words in investing … then the second most expensive words are ‘I’m waiting for the pullback’. The below extract from REQ Capital’s August newsletter does an incredibly good job of encapsulating the cost of waiting for the correction …

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