Entries Written By Tobin Arthur
To become wise you have got to have models in your head. And you’ve got to array your experience – both vicarious and direct – on this latticework of models.Charlie Munger
Cloning is valuable in life and in the world of investing. No, I am not referring to genetic manipulation. I am referring to the practice of adopting proven mental models with which to navigate through life.
The fact of the matter is there are few new things under the sun. When it comes to investing, and value investing specifically, Warren Buffett didn’t create the playbook. He adopted models from Benjamin Graham. Certainly he and Charlie Munger have evolved those models to reflect the changing times, but their success has been built on the shoulders of proven models.
Given that what seems new is truly old, it only makes sense to learn more history and pertinent frameworks that have been tested and proven by time.
Charlie Munger summed up mental models in this quote:
Well, the first rule is that you can’t really know anything if you just remember isolated facts and try and bang ’em back. If the facts don’t hang together on a latticework of theory, you don’t have them in a usable form. You’ve got to have models in your head. And you’ve got to array your experience both vicarious and direct on this latticework of models. You may have noticed students who just try to remember and pound back what is remembered. Well, they fail in school and in life. You’ve got to hang experience on a latticework of models in your head.
Farnam Street has outlined some of the most studied mental models including concepts such as Second Order Thinking and Occam’s Razor. Ray Dalio has created an entire body of work on second-order thinking, including frameworks for living well, in an aptly-titled book: Principles. In it, Dalio discusses the importance of being open-minded and how doing so manifests itself in the development of wisdom. I would also recommend the book The Model Thinker by Scott Page. The book walks through the explosion of data and information available to us and articulates the value of both models to make sense of the information, but also the importance of balancing multiple models to ensure you find the right fit.
Want to up your game? Do you value wisdom? Then resolve to regularly spend time studying history, reading books, and listening to podcasts produced by proven experts and leaders… and determine to enhance your future self with proven models.
Like the late Peter Drucker, I find anything written by Jim Collins compelling. He is a keen observer of human behavior through the lens of companies. One of his more poignant insights was that of the Flywheel concept. He first articulated this concept in his bestseller “Good to Great” and has now expanded on it in a short form book (monograph) appropriately entitled “Turning the Flywheel.”
As Collins writes:
“Once you fully grasp how to create flywheel momentum in your particular circumstance, and apply that understanding with creativity and discipline, you get the power of strategic compounding. Each turn builds upon previous work as you make a series of good decisions, supremely well executed, that compound one upon the other. This is how you build greatness.”
The concept is powerful as a framework for understanding why some businesses build momentum while others idle or die. As an investor I particularly love anything that leverages the concept of compounding.
Collins shares the story of how Amazon embraced and honed their flywheel and went on to become a juggernaut. The flywheel behind Vanguard also presents a solid reference.
But flywheels aren’t easy. If they were, every company would get them right. Rather, they require rigorous thought, experimentation and iteration. If there are five key elements of the flywheel and only three of the five are operating efficiently, then the flywheel doesn’t work.
Each of the core elements has to contribute to the momentum,
I suspect there are a lot of flywheels in training out there that need some consideration and polish, but with effort could transform a mediocre business into a force. Part of that consideration and polish is implementing a core system like OKRs that can help a company keep itself guided toward its true north.
This post is being written as a book recommendation for both startups and investors. Every business owner needs to be considering whether or not there is an opportunity to build a flywheel effect into their business. Similarly, every student of business, otherwise known as an investor, should have a clear grasp of this concept in order to determine if an investment candidate has a flywheel embedded into their business model.
Throughout history, gatekeepers have erected barriers to keep people from having direct access to markets. Real estate buyers and sellers needed an agent. Travel agents had access to better deals. Brokerage houses maintained critical data for investing. Heck, even the church conducted mass in Latin so that you had to rely on a priest to tell you what was going on. (Perhaps that’s blasphemous to say during Lent, but I digress…)
The Internet has enabled massive, rapid disruption of the gatekeepers. Consumers now have more direct participation than ever before, and the gatekeepers are floundering.
This disruption is not slowing. We will continue to see major impact where it has already began, and we can expect to see untouched markets become targeted. Healthcare is no exception to this rule. The consumerization of healthcare has a host of implications, and doors are opening to a wealth of opportunity for entrepreneurs. (To be fair, self-diagnosing that red spot on your arm via WebMD and Google is not an especially byproduct of this democratization.)
As an investor, you’re aware of the importance of trends. This democratization of information and markets needs to be part of your calculus. It will create headwinds for traditional players such as brokerage houses and payers, while enabling new blood to thrive. The middle man is gasping for breath as more efficient mechanisms of connecting consumers with providers take hold. Technology-enabled connectors and consumers are going to come out as the winners.
But there is a catch. Disruption of inefficient markets doesn’t happen overnight, and it’s not cheap. It takes pools of capital to gain critical mass, and it takes time to move beyond the early adopter. Amazon is a shining example of these facts. While it’s now obvious how much impact the company has had on traditional players, 1998 told a much different story. Then it was novel, but Barnes & Noble, Borders, and others didn’t take the threat seriously. Even if they had, it’s unlikely that these stalwarts could have evolved fast enough to save themselves.
As you are evaluating startup investments, it’s important to look beyond the potential for disruption. Examine the time horizon that is required to gain market penetration, and the amount of capital needed to get there. The company should have research and thoughts on these topics, so the onus does not rest on you alone. If the numbers or the logic don’t add up, then it may be too early to invest. But, if the management seems to have ideas that are well-thought, and contingencies in place for when things go wrong, it may warrant further consideration.
Early stage investing is not only inefficient, but almost entirely driven by varying levels of guesswork and amateur efforts. In the meantime, virtually every other industry is getting smarter through the adoption of big data or artificial intelligence (A.I.). The primary goal of AngelMD is to revolutionize early stage investing into a legitimate asset class, combining the power of an expert network and artificial intelligence.
To understand the role of A.I. in investing, it’s important to recognize a core weakness of all investors: subconscious bias. Many of you are familiar with Moneyball, the Michael Lewis book chronicling the rise of data in the world of sports (baseball). It has had a fundamental impact on a sport that has remained largely unchanged for a hundred years. For years the statisticians swore that metrics like batting average and home runs should be the focus of scouts and managers. Billy Bean applied real statistical rigor to baseball decision-making and the rest is history. Concepts from Moneyball are now being applied to retail, insurance, travel, real estate, public market investing and more.
Lots of startups talk about being based around “big data”, or being A.I. centric. The reality is that saying you are a big data company doesn’t mean anything until you not only have the data, but you are also using it to make better decisions than you did without it. That’s why the early stages of AngelMD focused on growing the various member types within the network: startups, physicians, investors, and industry. We needed to harness the collective expertise of this community to more effectively identify trends and make better investment decisions.
Now, every time a member of our community logs onto the site, provides a valuation on their startup, reviews a company, follows a startup, takes a poll, attends an event, or invests in a company, we are able to gather that data. As this proprietary body of data grows, we are able to form that data into what we refer to as the Meta Knowledge Engine “Metis”.
The product team at AngelMD is always working to build features that can help you, while also engaging you. This “sticky” factor, where members of the AngelMD network return to the site and complete different actions, enables us to gather more proprietary data than anyone else.
We have also begun to augment this information with third party data sets including external investment transaction information, mergers and acquisitions data, patent data, and much more. All of this feeds an artificial intelligence engine that will get increasingly accurate and predictive with time.
It’s important to understand that adding A.I. to AngelMD does not mean that we are eliminating human judgement. Quite the contrary. The AngelMD network and platform grows from a center of physicians and other allied health experts. It is their input, cultivated and analysed by software, that allows AngelMD to outperform any methodology or system that has come before it.
When Robinhood burst on the scene 5 years ago the ability to trade stock with no fees attached was semi miraculous. That innovation helped drive what today is a startup valued at $5B. Recently, the WSJ dug into the business model and revealed how they are able to thrive while taking no fees.
The fact of the matter is they are taking fees…just not from their trading customers. They are selling those trades on the back end to four high-frequency trading shops. Of course this begs the question as to why those shops would pay for those trades. And this is where it gets interesting. The answer: DATA
I was listening to Tim Ferris interview the CEO of Walmart this week. On the podcast the CEO described Walmart as an increasingly data and technology-driven company. Data is vital to every industry and is the underpinning of AngelMD.
While AngelMD has a long way to go, in the early stages we are aggregating intelligence from our network of physicians and healthcare insiders. We use that intelligence to guide our investment decision making. As we evolve, the data sets will continue to grow and so to will our reliance on this intelligence to give our members an edge in early stage investing.