How to Have Meetings That Don't Suck

A PDF of the paper can be downloaded here.

How to Have Meetings That Don’t Suck | A Different Approach to Meetings

Assertion #1: Most meetings are about people sitting around a table or tiled on a zoom call trying to sound smart. They have less to do with the task at hand and more to do with career advancement. In short, most meetings...well, they suck.

Assertion #2: In meeting eutopia, meetings are about energetic debate and removing bias from the process SO THAT the group can solve for better decisions.

Assertion #3: We rarely experience meeting eutopia because of misaligned incentives. Namely, most organizations are structured in a hierarchy that makes pandering to the person(s) holding the power almost impossible to resist. At the same time, who doesn’t love sounding smart? Ego combined with incentive marks a difficult combination to overcome.

So, how do we get to a place where meetings are both fun AND result in better decision making? Here are some practical steps to improve meetings that we use at NZS Capital. While we are focused on investing and portfolio construction, these tenets should apply to any team involved in high-stakes decision making.

Seven paths to better meetings:

1.      Eliminate hierarchy.

We’ve all been in meetings where we have put in days of work. As we begin to present, the senior person in the room quickly offers their opinion based on some previous bias. Heads around the table begin to nod in agreement. Our hard-fought idea quickly becomes diminished and, eventually, squashed. Is there anything more annoying than the opinion of someone who hasn’t taken the time to understand what they are talking about? For the person who has done the work, it feels demoralizing.

In the meeting room, take out the hierarchy. Acknowledge the conversational power law and take steps to flatten it.

At NZS, there is no portfolio manager/analyst dichotomy. Instead, all five investors are responsible for generating and researching ideas AND constructing the portfolio. We all have the same title: investor. And, we are all owners of the company. Our interests are aligned.

You might be thinking, “Okay, but how do you make decisions when there is no final authority?” We’ll get there...

2.      Expect preparedness. Meetings should cost something – for everyone.

At NZS, we believe the power for better decision making resides in unfiltered group collaboration. To accomplish this, we don’t allow presentations. We have found that most presentations are aimed at convincing the group of something. Instead, we try to foster discussion – working together to analyze a question/topic to come as close as possible to the truth. We follow a simple structure of sending a one-pager at least 24 hours in advance. The one-pager follows a standardized format that includes several hours of homework tasks for the rest of the group, which might include listening to podcasts, listening to an analyst day, or specified reading. We also frequently do our own research beyond what’s specified in the circulated note so that we can come to the meeting ready with a unique perspective. We all learn something. Instead of the presenter teaching the group, the group members teach each other, resulting in higher quality research.

To sum up: eliminate presentations; the person doing the research should send out their work (confined to one page) 24 hours in advance and assign homework; participants should do their own independent work beyond the one-pager.

3.      Avoid talking about the content of the meeting with the team beforehand.

We’ve probably all experienced politics before a meeting. It works like this: The presenter talks with teammates individually around the office, going out to coffee, whatever. They convince others of the merits of the idea beforehand and informally recruit teammates to support the idea. If the presenter is effective, they will sway the room to their point of view before the meeting even starts, rendering the actual meeting a formality with lots of head nods and little debate.

There is another less insidious form of such bias that comes from just discussing an idea that you’re excited about with a colleague during your research phase. Without intending to influence your co-worker, they too begin to see things from your point of view. It’s sort of the Heisenberg effect on pre-meeting banter. At the end of the day, decisions are a form of storytelling. We paint a picture of the future and decide which path to pursue. If we’ve already convinced people our story is true, we greatly reduce the odds of coming to a more robust decision.

At NZS, we seek to eliminate both politics and the unintended consequences of talking about a new idea by restricting talk about a topic before the meeting AND limiting the amount of time we spend together. It might sound counter intuitive to spend less time with your team. We have found, however, that time away from the team fosters diversity of thinking and more productive debate.

4.      Foster an environment of trust.

In 2015, Google set out to understand what separated average performing teams from stand out performing teams. Their conclusion? It’s not how smart someone is, where they went to school, or even how good they are at their job. What separates the ordinary from the extraordinary team is psychological safety. Here’s an excerpt from a 2016 NYT article about Google’s experiment:

In other words, if you are given a choice between the serious-minded Team A or the free-flowing Team B, you should probably opt for Team B. Team A may be filled with smart people, all optimized for peak individual efficiency. But the group’s norms discourage equal speaking; there are few exchanges of the kind of personal information that lets teammates pick up on what people are feeling or leaving unsaid. There’s a good chance the members of Team A will continue to act like individuals once they come together, and there’s little to suggest that, as a group, they will become more collectively intelligent.

 In contrast, on Team B, people may speak over one another, go on tangents and socialize instead of remaining focused on the agenda. The team may seem inefficient to a casual observer. But all the team members speak as much as they need to. They are sensitive to one another’s moods and share personal stories and emotions. While Team B might not contain as many individual stars, the sum will be greater than its parts.

 Within psychology, researchers sometimes colloquially refer to traits like ‘‘conversational turn-taking’’ and ‘‘average social sensitivity’’ as aspects of what’s known as psychological safety — a group culture that the Harvard Business School professor Amy Edmondson defines as a ‘‘shared belief held by members of a team that the team is safe for interpersonal risk-taking.’’ Psychological safety is ‘‘a sense of confidence that the team will not embarrass, reject or punish someone for speaking up,’’ Edmondson wrote in a study published in 1999. ‘‘It describes a team climate characterized by interpersonal trust and mutual respect in which people are comfortable being themselves.’’

When Rozovsky and her Google colleagues encountered the concept of psychological safety in academic papers, it was as if everything suddenly fell into place.

Trust is the pixie dust for better meetings. In our paper “Slowing Down Time in Organizations” we go into this aspect in more depth:

Like Pirsig’s Quality, trust can be difficult to define, but we all instinctively know what it feels like when there is trust in a team and when it’s absent. And, like Quality, trust just doesn't magically happen. Rather, trust is the product of deliberate intention, agreed-upon rules among team members, and lots of practice. Trust is also asymmetric – it can take seemingly forever to build and be lost in an instant. Trust is the “x” factor missing in average teams, yet it can catapult team productivity and success to seemingly impossible levels.

In our prior discussion of hierarchy, I said we’d come back to how decision making works when there’s disagreement among the team. Here’s how we do it:

At NZS, we don’t require consensus to put a new stock in the portfolio. We trust each other so much that even if only one person believes we should invest in a stock and everyone else disagrees, then the stock goes into the portfolio. Granted, the position size will be relatively small, but, if the thesis proves out, it can become a much bigger position over time. Surprisingly, after having implemented this practice for some time, we’ve found that the person going against the team is right most of the time. It’s also notable how infrequently this situation actually occurs (which is perhaps explained by the group being united in their goal of ferreting out the truth). And, once a decision has been made, we ALL support it, no matter our opinion. That said, we don’t stop looking for evidence that supports or disconfirms the thesis. Instead, we work harder to understand the company and help the team come to the best longer-term decision.

5.      Debate the idea, not the person. Cultivate a culture of candor.

It’s all too easy for energetic debate to be taken personally. Likewise, it’s easy for debate to devolve from dissecting the idea to attacking a person. It’s important that group members develop the emotional and communication skills necessary to facilitate good meetings. We’ve learned that ideas are not tied to our identity. We’ve also found that conviction is overrated – often, it’s just another word for overconfidence. Openness to changing one’s mind when presented with superior facts remains key, which means we need to divorce our ego from the decision. We’ve come to understand that individual value to the group does not come from being right, but in helping the group ask better questions to collaboratively uncover key truths.

In his book, Creativity Inc., Ed Catmull has this to say about the unique culture of candor that permeates the meetings of Pixar’s “Braintrust”[i], a group that seeks to help struggling directors develop their ideas into amazing movies.

Candor isn’t cruel. It does not destroy. On the contrary, any successful feedback system is built on empathy, on the idea that we are all in this together, that we understand your pain because we’ve experienced it ourselves. The need to stroke one’s own ego, to get the credit we feel we deserve—we strive to check those impulses at the door. The Braintrust is fueled by the idea that every note we give is in the service of a common goal: supporting and helping each other as we try to make better movies.

Most of us have never been in meetings where debate goes beyond ego, politics, and merely trying to convince the group of an idea or agenda. It can be a vulnerable experience to change one’s mind. Too often, team members pull punches in a meeting to avoid offense. But, when the debate concerns the idea and not the person, it becomes impossible to offend (so long as individual respect governs the debate). What we have found at NZS is a sense of freedom from the need to always be right. Said another way, when you don’t feel like anyone on the team is out to get you, you can put a lot more energy into helping the group make a better decision.

Catmull shares the story of Jennifer Lee as the Braintrust worked to rewrite the storyline of Frozen:

Jenn is incredible at listening to other people. She’ll write every idea down, but if yours is an idea that seems headed for a detour or is going to take us off the rails, she’ll say, “Let’s put a pin in that.” So she doesn’t shut the room down; it’s very much the improv “yes, and” energy in the room.

Jenn recalls the offsite on Frozen: “There was no judgment. There was no doubt. It was this incredible collaboration of what inspires us. That’s the key: You have to come in generous and open, bringing all your skills with you. And you have to leave your ego at the door.”

6.      Focus on calling out bias in others. Allow your own bias to be called out by others.

As we build trust, mutual respect, and a culture of candor, we can become more open to allowing others to help us see our own biases. Most of us excel at selective amnesia. We organize our memories in such a way that we are the hero of our own story. Bias is almost impossible to identify in ourselves, but it’s very easy for us to identify in others. The problem is that we seldom put ourselves in a position to voluntarily invite others to point out our own biases. It’s just too uncomfortable, for us and them! But, as we become more concerned with team collaboration than ego protection, it becomes clear that nothing is more important to the team than awareness of our individual biases.

Here are Catmull’s meeting guidelines for working towards a state of egolessness.

·        Do not become attached to your ideas. (You are not your ideas.)

·        Do not judge the value of your own contribution by whether your ideas are adopted.

·        Put all your attention on the problem. Keep your focus on whether the idea thread is advancing or stagnating.

·        Withhold quick judgement.

·        As you wait to find a break in the banter in which to speak up and make your points, try not to stop listening to what is happening.

Finally, pre-mortems offer another effective way to eliminate bias in a decision by putting oneself in the future and looking back on why the decision proved to be successful or disastrous. We wrote about this process in more depth in our paper “Time Travel to Make Better Decisions”. For those wanting a deeper dive, we’d recommend the fabulous Capital Allocators podcast on conducting pre-mortem analysis with cognitive psychologist Gary Klein.   

 7.      Embrace the initial discomfort that comes with increased debate.

Perhaps the most difficult aspect of eliminating hierarchical decision making and increasing the level of debate is that it forces the team to live with dissonance. It isn’t that dissonance is absent inside a traditional hierarchical structure, it’s just that, without an authoritarian figure, the illusion of certainty is often ripped away. It’s far more difficult to live in the reality of uncertainty and have our own overconfidence (a synonym for conviction) challenged by the group while remaining open to different points of view that are likely better than our own. The facts never fully align. We never map out our exact future path through time. There is no pot of gold at the end of the rainbow. We want certainty, we get complexity. Because trying to predict the future is largely a waste of time, adaptability becomes the goal. We find that the group often adapts far better than an individual. And adaptability is messy.

An individual decision maker will often offer the illusion of certainty due to quick decision making with conviction. However, our experience is that these decisions tend to be more bravado than substance. What really leads to better decisions is listening to and understanding others’ informed perspectives. These additional perspectives tend to increase the level of spirited debate, which, in turn, leads to new perspectives that go beyond individuals to something greater. From this foundation, the group can become more than the sum of its parts; one plus one can equal three. Instead of placing confidence in one’s opinion, we change the focus such that the confidence lies in the group – that the group will come to fantastic decisions, and those decisions will not be reliant upon anyone’s ability to predict the future better than others.

At NZS, we’ve taken the additional step of recording our meetings. We use an AI tool that transcribes the meetings so that we can become more aware of what is happening inside of our discussions. Eventually, we hope to have enough data to create a simple GPT. For example, if we made a particularly good or bad decision, we could ask the AI about the primary factors, based on what we said in the meeting, that led to that good or bad decision. As the Jesuit priest Anthony de Mello once said in his book Awareness:

What you are aware of you are in control of; what you are not aware of is in control of you. You are always a slave to what you’re not aware of. When you’re aware of it, you’re free from it. It’s there, but you’re not affected by it. You’re not controlled by it; you’re not enslaved by it. That’s the difference.

A parting thought:

Not everyone will be able to make the transition to the types of meetings we’re talking about. Unfortunately, these people are completely toxic to creating a new environment. No matter how great their individual contributions might be, they must be cut out to allow the collective intelligence of the group to thrive. There will be people that like the traditional meeting format because they get the biggest payoff from the status quo. These same folks also tend to garner the most resentment from others and tend to keep even better people from staying at the firm. Firing a toxic team member, even one with superior performance, is perhaps the single most value-maximizing thing a team can do. In our experience, editing the team to focus on a shared culture of trust and debate far exceeds the loss of any one contributor, as it allows morale to rise and collaboration to deepen. Remember, firing a team member who cannot get past their own biases and ego is great for everyone. It’s a no brainer for the team AND it’s great for the individual getting fired – there’s a whole world of toxic teams out there where they can shine.

Conclusion

Meetings don’t have to suck. As we begin to align incentives, assign homework, expect preparedness, eliminate hierarchy, and cultivate an environment of trust, meeting productivity skyrockets. There’s an aspect of holding meetings in this manner that becomes deeply uncomfortable. We must truly listen to others’ opinions and encourage them to call out our own persistent biases in the process. No matter how much we trust others, having our shortcomings highlighted never really feels great in the moment. However, that’s the price of growth.

Another drawback worth mentioning is that this manner of decision making can be painfully slow compared to command-and-control decisions. Meetings can take longer. Getting to the root of a question through debate can result in numerous dead ends before there is a breakthrough. Frustrations can run high. This can take some people by surprise, especially those who were previously in control. It’s easy to give up on the process too soon. However, at NZS, we have consistently found that, as we push through initial discomfort and resist the urge to come to snap decisions, our thought processes are richer, the insights more nuanced and defined, and our conclusions are consistently higher quality. Oh yeah, and our meetings don’t suck.

Sources:

Duhigg, Charles. “What Google Learned From Its Quest to Build the Perfect Team.” The New York Times, Feb. 25, 2016. (https://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html)

Slowing Down Time in Organizations. NZS Capital, 2021. (https://www.nzscapital.com/news/slowingtime)

Catmull, Ed. Creativity Inc. Random House, 2014; Revised Edition 2023.

Time Travel to Make Better Decisions. NZS Capital, 2021. (https://www.nzscapital.com/news/time-travel)

Seides, Ted, host. “Conducting Pre-Mortem Analysis with Gary Klein, Paul Johnson, and Paul Sonkin.” Capital Allocators, episode 109, September 22, 2019. (https://www.capitalallocators.com/podcast/conducting-pre-mortem-analysis/)

De Mello, Anthony. Awareness. Image, 2023.

[i] In recent years Pixar has seen somewhat less commercial success combined with higher than historical expenses. In an updated version of Creativity Inc. (The Expanded Edition) published in 2023, Catmull discusses what appears to be an evolution in the Braintrust culture at Pixar away from the primary creative directors and towards the collective opinion of the employees. While it’s hard to comment as outsiders, we wonder if this shift in culture may have contributed to lower commercial success.

Time Travel to Make Better Decisions

A PDF of the paper can be downloaded here.

Time is the school in which we learn,
Time is the fire in which we burn.
-Delmore Schwartz

Marty McFly: “What about all that talk about screwing up future events? The space-time continuum?”
Dr. Emmett Brown: “Well, I figured, what the hell.”

Every time we contemplate a decision, whether big or small, we are attempting to see into the future. In other words, the act of scrutinizing options and possible outcomes is a form of mental time travel. Can we see how this decision will play out? What are the odds we make the right decision? And, the single most important (and emotional) question: will I regret this decision?!? If I could somehow communicate with my future (and ostensibly more knowledgeable) self, what would I want to know now to make the right decision today? The idea of mental time travel is especially relevant to investment decisions, a topic that I’ll return to later in this essay.

Despite wanting to make better decisions and predictions, we are constantly stymied by the fact that the future is largely unknowable and becomes more opaque the farther into it we attempt to peer. We know from complex adaptive systems that there are too many factors, agents, and relationships to know precise details of the future state of the Universe with any meaningful degree of accuracy – chaos ensures we’re always betting against the house. As such, we all have rather spotty decision-making track records. And yet, we tend to think that we’re pretty good decision makers, largely thanks to our brain’s serial overconfidence (perhaps our survival as a species is predicated on having a heightened sense of control, however false, over the unknown). In reality, luck factors into our successes far more prominently than our brain wants to admit.

One of the biggest inhibitors of good decision making is our brain’s inability to see things as nonlinear. We tend to think in an analog, incremental way, but the world itself is dominated by exponentials, power laws, and compounding – all of which we struggle to conceptualize. From an evolutionary perspective, linear thinking is likely a lot more energy efficient and less mentally overwhelming, which would be important for quick decision making to ensure day-by-day survival, as was required of our human ancestors for hundreds of thousands of years. Under such challenging circumstances, linear thinking apparently yielded a decent enough solution in a sufficient number of cases to let us wade through life (while the slower, more cerebrally-intensive thinkers were perhaps subject to a higher number of predation events). For those of us fortunate enough to exist in the modern world, however, we have the luxury (or perhaps the imperative) to become more cognizant of our path through time and the myriad possibilities/probabilities encompassed by our endlessly branching future.

The path we took through time to get to the present moment and which we will follow into the future is only one of an infinite number of possibilities. This visual from Tim Urban is a great illustration of the alternative paths we didn’t follow to reach the present moment and the vast (and constantly evolving) array of future paths. Existentially, our narrow slice of the multiverse is the only path we can travel simply because it is the path we travel.

One of my favorite movie genres concerns time travel and the speed of time passing. These movies are a trove of insight into decision making, regret, and the folly of trying to change the past or predict the future. I think our cultural fascination with time travel boils down to our own regret over how little we confront the actual present, and, more specifically, how we often fail to be fully aware and present when we make decisions. What I’ve learned is that having a fascination with the present moment might be the only way to make better decisions about the future. As Russell Ackoff puts it: “I have no interest in forecasting the future, only in creating it by acting appropriately in the present.”

Time Travel Genres and What They Teach Us
I group movies involving time travel and/or relativity into five key categories. In the following discussion, I tried to limit my references to some obvious examples where knowing the genre wouldn’t be too much of a plot spoiler. This necessitated omission of some great examples, which I have instead listed at the end of this essay.

1. The devastating consequences of general relativity and speeding up/slowing down the passage of time: One of our biggest fears is that we’ll blink and life will be over. Indeed, our time on Earth isn’t even measurable in the timescale of the Universe. When movies highlight the idea that years can pass in mere moments, there is a special kind of discomfort that sweeps over us. Perhaps the most well-known example of this idea is Interstellar. And, Flight of the Navigator is a sentimental favorite from my youth. There are two ways to slow down time relative to an external reference: you can be near a large source of gravity, or you can travel at near the speed of light. In our paper Redefining Margin of Safety, we discussed this concept as it relates to company strategy and investing decisions:
Ultimately, what highly nimble companies are able to do is act in a way that slows down time relative to their competitors. The world is moving and changing at an accelerating pace, but with a Quality company operating in a long-duration, slow-growth industry dynamic, it’s possible to operate in a bubble in which time appears to move more slowly than in the frantic world around you. Imagine two paths connecting two points in time: one short path, where time is normal, and one long path, where time is stretched and slowed. Because time moves slower on the longer, time-dilated path, you have more time to react and adapt relative to your competition on the direct route, so when you both arrive [in the future], you have out-thought and out-innovated your competition.

This particular sub-genre of time travel also highlights an important element of decision making: the often futile attempt to beat entropy. Every time we make a decision, we are putting in energy and effort in an attempt to fight the chaos of the unpredictable future. Entropy is the slow cooling of the Universe from high information to low information. As things become more disordered and contain less information, entropy rises. This phenomenon is also what gives a vector to how we experience the passage of time (e.g., instead of experiencing the future before the past). As we wrote in Redefining Margin of Safety, “Much of what society has done is to try to create temporary order despite the long-arching trend toward disorder in the Universe. We build buildings, cities, communities and companies – we take organized energy and reshape it into all sorts of literal and figurative structures. But, it’s only a temporary, local increase in order, and, in the long run, the information value is lost and entropy rises. The trend toward more disorder means that predicting the future is very hard, if not impossible.” On the plus side, we’ve got a lot of time before the Universe fully cools (so much so that our existence will register as a miniscule blip on the timeline), and we have lots of free energy to play around with in the meantime. However, if we can heighten our awareness of the awesome and persistent power of entropy, it can help us be more aware and appreciative of the present. The more details we take in – the more we learn in each given moment – the slower we perceive the passage of time. This present awareness is the greatest advantage we can achieve in decision making.

2. Time traveling teams, solo do-overs, and unintended consequences: This popular premise typically involves two or more agents (either working together or unsuspecting of the other) or a single person attempting to change something that happened in the past or set up a better future. This storyline exemplifies our desire to repair past regrets and avoid future ones – sometimes it works, but only with an escalating set of moral dilemmas. The Back to the Future trilogy is a well-known example of team time travel. For solo time travel, The Butterfly Effect is a good example. Often, the protagonist seeks to redress regrets, save someone, or, the most common of all, win over their romantic interest. Relationships gone wrong might be the primary reason that people write time travel scripts! Trying to resolve unintended consequences tends to be the most popular theme for independent time travel films (often with a common writer/director), which offer wonderful glimpses into someone’s heart and past regrets.

There are a wide array of plot points/mechanisms for shifting in time that manifest in this and other time travel sub-genres. Frequently, some yet-to-be-discovered genius invents a time machine. Or, sometimes, time travel is all in someone’s mind. And then there are the odd ones, such as a camera that can snap a photo of tomorrow or a portal hidden in a bathroom (surprisingly, I am aware of at least two examples of the latter!). And, there is always the chance the Universe is just messing with someone.

The dilemma of unintended consequences reminds us of just how densely interconnected our world is. Even small changes can have vastly compounding effects that ripple forward through time, thwarting our attempts to predict the future and emphasizing the importance of being aware and intentional regarding our actions in the present.

3. Time loops: Groundhog Day with Bill Murray is the epitome of this gem of time travel movies. In life, we often make the same decisions over and over leading to the same bad outcomes. To redeem ourselves and get unstuck from the loop requires a lightbulb moment of insight. It’s a sad reality of life that, while we can easily spot the flaws in other people's reasoning, we are terrible at seeing our own biases and blind spots, leading to years, or even a lifetime, of lost time while we try to sort out what’s wrong. If only we could short-circuit the loop! Sometimes the answer lies in asking better questions, but, typically, it’s about seeing what’s right in front of us – something obvious we fail to notice, like the fish that doesn’t know what water is. The key to becoming unstuck is usually a fresh perspective on a situation from a completely different angle (e.g., what we try to achieve in our team discussions). This idea is similar to Galilean relativity – you cannot fully grasp a system within which you are embedded; therefore, you need to put yourself outside of the system.

4. The mental fog of time travel: This group of movies tends to have more of a sci-fi element. It’s tricky to give examples here without giving too much away, but 12 Monkeys comes to mind as a good one. When actions in the past can impact the future, shifting through time can be very disorienting. I view this as analogous to the mental gymnastics we often must go through in decision making or when we analyze our decisions, which can be uncomfortable and confusing. Could we have done things differently? If so, how? If the alternative required greater courage/risk, would we have? Was there information we missed? Sometimes it feels like we have memories of different branches through time even though we only traveled one of them. While we can always benefit from identifying previous mistakes, an obsession with the past can detract from our awareness of the present and our focus on what is within our power to change, leading to missed opportunities to craft a better future. Pattern recognition can be dangerous in a complex, unpredictable world, where we are likely to frequently encounter emergent behavior rather than just history repeating. As such, it’s important to fight the urge to rely on the past, even though doing so can create a feeling of instability.

5. Seeing your future self and messages from the future: This sub-genre is a good example of our desire to fast forward to the future to see how today’s decisions might play out. There is of course the Terminator series, and a special case of this idea is represented by Tom Hanks’ character in Big, where a chance encounter with an arcade fortune telling machine transforms him into an adult overnight. This plot type reminds me of the ‘work backward’ concept popular at Amazon: when they have an idea for a new product/service, they write the press release for its intended launch and then work backward to today when they are starting to develop it, asking the question: How did we get there from here? So, picture the world as you want it to be 10, 50, 100 years from now. Then ask: What step should I take in the present to put me on the path to making that future a reality? As we discussed in Complexity Investing:
“It’s important to distinguish between long-term intent or desires and shorter-term plans or actions. Intentions are the things that DON’T change...Plans tend to be linear and shorter term, but complex systems are nonlinear, placing a premium on the ability to adapt our short-term plans to a changing landscape. Intent should serve as a northstar throughout the winding paths life takes us down...To begin to move toward a new intention requires a plan and a step toward that plan. To take the step, we need a lot of confidence – after all, the path of least resistance is often to do exactly what we did yesterday. However, life acts like a huge noise field where it’s incredibly difficult to discern signal – there is an excess of possibilities out there. This is why mindfulness is so central to our framework. Identifying and avoiding cognitive bias helps us see and accept mistakes as we make them. An understanding of complexity crafts our ability to be humble. The ‘noisy’ nature of life (and complex systems) often results in our initial step of confidence being slightly off course. Because we get off course, it’s important to balance confidence with the humility to admit mistakes and course correct...once a better direction becomes clearer.”

Better Decisions through Time Manipulation and Mental Time Travel
Poet David Whyte asks the following questions: “What would it be like to start a conversation with myself that my future self would thank me for? What would it be like to become the saintly ancestor of my future happiness?” Oftentimes it’s not the answers we are looking for, but the right questions to ask. How can we converse with ourselves today in a way that we ask better questions and arrive at better decisions? There are a few exercises that I find helpful, including a few specific conversations we have at NZS Capital when we are analyzing companies.

Slow down time: Try and spot the figurative gravity wells and light speed hacks that allow your clock’s gears to turn more slowly than others’, which will create a huge advantage in decision making. This entails figuring out how you should be spending your time so that you are asking the right questions, gleaning the most useful information, and giving yourself time to analyze, digest, and connect dots. If reading the news or scrolling social media is not causing you to ask better questions, or if it’s pulling you out of your awareness of the present, then stop – it’s needlessly spinning your gears and speeding up time. Remember to focus on intentions. Focus on your awareness of the present and being extremely intentional about what you want to accomplish, and you will find you can achieve more in less time.

Focus on what won’t change: We often reference this concept from Jeff Bezos who famously said his primary focus at Amazon was on what won’t change: people will always want more selection, lower prices, and faster delivery. Rather than focus on the competition, Amazon tried to continue to improve on these three dynamics of their ecommerce business. While we spend a lot of our time desiring to know what will change when we make decisions, often inverting the problem and seeing what is unlikely to change is more useful. So, fast forward through time or imagine an array of different multiverses. What remains invariant despite changing time/space and the unpredictable future paths?

Perform pre-mortems: This exercise helps you determine what could go wrong before it happens. A pre-mortem is a way to try and picture yourself in the future and work backward to decisions made today. It’s similar in concept to Jeff Bezos’ regret minimization framework: “I wanted to project myself forward to age 80 and say, ‘Okay, now I'm looking back on my life. I want to have minimized the number of regrets I have.’” We do pre-mortems for every stock we consider investing in by transporting ourselves into the future and trying to guess at the answers in these scenarios: 1) We didn’t buy enough. Why? What questions/data would have clarified our understanding of the potential? And, 2) We should not have bought it. Why did we? What did we miss about the range of outcomes, the degree of predictions forced by the valuation, etc.? Similarly, if we are contemplating selling a stock, we try to answer these questions as our future selves: 1) We regretted selling it and ended up buying it back at a higher price. Why? And, 2) We never regretted selling. What negatives were there that we were right about? Often, the question isn’t about buying or selling outright, but getting to the truth of what position size an investment should be. We use specific metrics from our Complexity Investing paper to answer these pre-mortem questions in the categories of Quality, Growth, and Context (chapter 3) and Resilient or Optionality position sizes (chapter 6).

This exercise may sound simplistic and obvious, but the key is to make time travel feel as real as possible to fully experience the thoughts and emotions of your future self. Making mistakes in investing (and life in general) is personal and painful – it’s a gut punch of regret. So, we try to literally vault ourselves into the future and see what it feels like to be selling a stock at a major loss – it’s a horrible feeling, how could we have avoided it? The answer can only be in the present. What information are we missing today, or, more likely, what questions are we failing to ask? What is it about the range of outcomes that we need to better grasp? Imagine you have an actual time machine to travel five years into the future. Imagine which path you took through time to get there and which ones you avoided.

The Importance of Awareness
One of the most freeing concepts that can improve decision making is: it’s worthless to dwell on regret because there’s no going back. Whatever happened, happened, and it’s now permanently out of your control. So, take a few moments to learn what you can about the factors that influenced your decision and then put it out of your mind. There are thousands of factors that can play into decisions over which you have no control. Neuroscientist Robert Sapolsky shares some devastatingly great insights on this concept in his book Behave. Here is a passage listing just a few of the unconscious influences on decision making: “blood glucose levels; the socioeconomic status of your family of birth; a concussive head injury; sleep quality and quantity; prenatal environment; stress and gluticocorticoid levels; whether you’re in pain; if you have Parkinson’s disease and which medication you’ve been prescribed; perinatal hypoxia; your Dopamine D4 receptor gene variant; if you have had a stroke in your frontal cortex; if you suffered childhood abuse; how much cognitive load you’ve borne in the last few minutes; your MAO-A gene variant; if you’re infected with a particular parasite; if you have the gene for Huntington’s disease; lead levels in your tap water when you were a kid; if you live in an individualist or collectivist culture; if you’re a heterosexual male and there’s an attractive woman around; if you’ve been smelling the sweat of someone who is frightened. On and on. Of all the stances of mitigated free will, the one that assigns aptitude to biology and effort to free will, or impulse to biology and resisting to free will, is the most pernicious and destructive.” (p. 597-598). So, focus on what you can control – your awareness of the present – to create a better backdrop for decision making, and then try not to regret decisions as soon as they are relegated to the immutable past.

The idea of cultivating awareness and how it goes beyond plain old thinking is an important concept that’s vastly too complex to address herein. We touched on mindfulness in chapter 5 of Complexity Investing, and, as a starting point, I might suggest Sam Harris’ Waking Up app. The following is from Loch Kelly’s book (which I highly recommend) Shift into Freedom:
“One of the most important developments in human evolution is the ability to think. However, an even more important development is the ability to grow beyond thinking. To do this, we need to discover the intelligence that’s inherent in awareness itself. It is important to note that growing beyond thinking is not a regressive, dumb, or irrational state. Consider the innocence of a young child at an adult party, who asks the group of adults what they would do in this situation: ‘Imagine you are surrounded by hungry tigers with a cliff behind you. What would you do?’ Each adult comes up with a different creative solution, but the boy just shakes his head. So they turn to him and ask, ‘What would you do?’ The boy smiles and says, ‘I’d simply stop imagining.’”

Conclusion
Time travel movies are full of paradoxes. For example, the classic causal loop: a future event is caused by a change in the past, which causes the same future event. Or, there's the grandfather paradox: altering the past means you might not be the same person, who in the future goes back to alter the past (literally interpreted, if you travel back in time to kill your grandfather, you would never be born to travel back in time to kill your grandfather, therefore you would be born, etc.). Marty McFly learned this paradox firsthand when he accidentally stopped his parents from dating in high school. Making decisions is also a paradox: we desire to see into a future that we can never truly know. It would be nice if reading (or, in my case, writing) this essay were sufficient to erase the longing to travel back in time to fix decisions and instantly transport forward to see the future. I'm afraid these paradoxes cannot be resolved. Instead, I hope that I’ve made the case for cultivating awareness in the present, which should ease the dual burdens of decision remorse and wanting to know the unknowable/predict the unpredictable. While we can harness deliberate and intentional mental time travel to our advantage, as with my example of the pre-mortem, our obsession with the impossible is just a mental trap that drains energy and shifts our attention away from the present. And, the actual present is our only window of opportunity to make decisions that positively shape the future. By cultivating awareness, trying to slow down time, and finding the right questions to ask (e.g., working backward, what won’t change, pre-mortem analysis) we can attempt to create a landscape for decision making that allows us to see good fortune when it comes knocking and take the next incremental step toward a better future. Since we can never know the future, perhaps it’s best to take Doc Brown’s advice: “Roads? Where We’re Going, We Don’t Need Roads.”

Select time travel movies by category:
[Warning: even knowing a movie involves time travel can be a spoiler, so feel free to skip this section! The following are just a few selected highlights of time travel flicks I've seen, but if you have a favorite not listed here, send it my way.]

1. The devastating consequences of general relativity and speeding up/slowing down the passage of time: Time Trap, Interstellar, Flight of the Navigator

2. Time traveling teams, solo do-overs, and unintended consequences: Teams: Project Almanac, Primer, Time Freak, Frequently Asked Questions About Time Travel, Time Lapse; Solo do-overs: The Butterfly Effect, About Time, 41. Misc: Midnight in Paris

3. Time loops: Groundhog Day, The Map of Tiny Perfect Things, Source Code, Palm Springs, The Endless, The Infinite Man

4. The mental fog of time travel: 12 Monkeys, Donnie Darko, Timecrimes, Your Name (2016)

5. Seeing your future self and messages from the future: Terminator series, Looper, Big

6. The popcorn classics of my youth: The Bill & Ted and Back to the Future trilogies

Complexity Investing

Download Complexity Investing PDF

We believe that the economy and the stock market are best understood as biological systems: specifically, complex adaptive systems. Complex systems have unpredictable outcomes; therefore, as investors, we focus on companies that are adaptable, long-term focused, innovative, possess long-duration growth, and maximize non-zero-sum outcomes. Our whitepaper published in 2014, Complexity Investing, describes this process in detail and can be downloaded here.

A condensed version of the paper is also available here.

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Redefining Margin of Safety

Redefining "Margin of Safety"

How the Nature of Growth and Adaptability Informs Investing

A PDF of this paper can be downloaded HERE.

At NZS Capital, we don’t believe it’s possible to accurately predict the future with any meaningful degree of precision. As far as we can tell, no one is very good at it. A few people, most notably economists, are embarrassingly bad at it. The prediction game has such low odds of winning (as it favors blind luck over innate foresight), that we prefer to play a different game.  

Ben Graham posited the idea of “margin of safety” in The Intelligent Investor back in 1949. It was so important to him that he devoted the entire final chapter of the book to the concept: Chapter 20 - "Margin of Safety" as the central concept of investment. Graham’s ideas have since been popularized by Buffett and become the cornerstone of value investing. Here’s Graham’s definition:

“The function of margin of safety is, in essence, that of rendering unnecessary an accurate estimate of the future.  If the margin is a large one, then it is enough to assume that future earnings will not fall far below those of the past in order for an investor to feel sufficiently protected against the vicissitudes of time.” 

We agree, but approach the question differently. Using value investing as an example: given a stock’s current market valuation, we focus on the type of predictions we have to make for forecasting the company’s future prospects. If the valuation is expensive, then we have to make more narrow (i.e., highly precise) predictions. If the valuation is cheap, then we have a bit more leeway and can make broader (i.e., less precise) predictions about the future. Since we don’t believe it’s possible to predict the future with a high degree of both precision and accuracy, we’re always looking for situations where we can make predictions with the broadest range possible, thus giving us higher odds of being correct – what Graham would call a ‘large margin of safety’. 

We don’t expect successful business managers to be fortune tellers either. So what do you do if you can’t predict the future, and don’t want your company caught off-guard as you navigate an uncertain, ever-changing economic landscape? We’ve found that nimble, adaptive companies tend to be successful over the long term and offer investors a wider margin of safety. Indeed, we would argue that relying exclusively on valuation for safety, especially given the accelerating pace of disruption in the Information Age, is downright dangerous. Age-old concepts such as mean reversion and intrinsic value now have become misleading, and using valuation alone to determine margin of safety is akin to the bumper sticker we used to see around Silicon Valley after the dotcom crash: “Please God, Just One More Bubble”. ‘Value traps’ seem to be a symptom of the digital paradigm shift, occurring when Industrial Age companies fail to adapt to the Information Age – hoping the world will cycle back to a bygone era is not a productive business model and an even worse investment strategy. 

The lifecycle of a company: most new ventures start off as gambles. As the products/services gain in the marketplace, they become Optionality businesses with positive asymmetry. Over time, adaptable companies can build Resilience. However, eventually most companies lose their market position and become classic Value Traps, and may even end up becoming gambles at the end of their lifecycle.

The lifecycle of a company: most new ventures start off as gambles. As the products/services gain in the marketplace, they become Optionality businesses with positive asymmetry. Over time, adaptable companies can build Resilience. However, eventually most companies lose their market position and become classic Value Traps, and may even end up becoming gambles at the end of their lifecycle.

As with predicting the future, picking companies with eroding profits that will have a eureka moment and successfully pull out of their death spiral is a low-probability game we’d rather avoid entirely. Companies that are caught on the wrong side of time tend to focus capital allocation on share buybacks, debt repurchase, or dividends rather than R&D and innovation, hastening their decline.

We’ve previously detailed our investment philosophy, centered around deep analysis of Quality, Growth, and Context (with valuation factoring into Context), in Complexity Investing. Here, we advance the idea that ‘nature of growth’ is a critical factor in determining a company’s ability to adapt, and explain why long-duration, slow-growth companies offer a desirable margin of safety. 

Quality Management and Slow Growth Confer High Adaptability 

In Complexity Investing, we wrote at some length about the importance of management team Quality for fostering a culture of adaptability. Quality is easy to spot but hard to define. Pirsig, in Zen and the Art of Motorcycle Maintenance, offers the best definition of quality we know of: 

“Any philosophical explanation of Quality is going to be both false and true precisely because it is a philosophic explanation. The process of philosophic explanation is an analytic process, a process of breaking something down into subjects and predicates. What I mean (and everybody else means) by the word ‘quality’ cannot be broken down into subjects and predicates. This is not because Quality is so mysterious, but because Quality is so simple, immediate and direct.”

The key factors we identify with Quality management are: long-term thinking, decentralization, and ability to foster a culture of adaptability/innovation. What we’ve come to realize, however, is that Quality management is only part of the story – necessary but not sufficient – because a company's ‘nature of growth’ also factors heavily into adaptability. 

Investors often celebrate brilliant managers and criticize those that fail to see future pitfalls. But perhaps investors have put too much emphasis on management teams. It seems likely that success isn’t determined exclusively by the management team per se, but also by the time constraints under which the management team has to adapt, which is governed by the speed of their business.  

When we look at classic, gentle sloping ‘S-curve’ businesses, the managers tend to look like geniuses when it comes to adaptability; however, maybe management teams running long-duration, slow-growth businesses are adaptable because they have adequate time to see and react to change. In other words, perhaps brilliant managers don’t necessarily have exceptional foresight and instincts, they just have the luxury of a longer reaction time (or an apparent longer reaction time...more on that idea later).

In this example of a gentle sloping S-Curve, a company has time to adapt and innovate – stacking on new S-Curves to become Resilient with Out-of-the-money Optionality (ROOTMO) – rather than slowly dying as a Value Trap (where most Investors equate “cheap” with “margin of safety”).

In this example of a gentle sloping S-Curve, a company has time to adapt and innovate – stacking on new S-Curves to become Resilient with Out-of-the-money Optionality (ROOTMO) – rather than slowly dying as a Value Trap (where most Investors equate “cheap” with “margin of safety”).

Imagine a driver following a car in front of them at a two-second distance versus a driver tailgating a car. If the car in front stops suddenly, the tailgater is highly likely to crash whereas the two-second follower may avoid the accident. It’s not that the safe follower has any better adaptability genes than the tailgater, they simply have more time to make course corrections before it’s too late. Perhaps that’s the genius of management teams that are seen as brilliant adapters: slower growth, dictated (intentionally or unintentionally) by their business model, gives them WAY more time to react. Of course, nowadays, many cars have integrated collision-avoidance technology, which provides hazard data to the car far more quickly than can the human driver. In the same manner, Quality data can serve as an advanced warning system for economically-relevant changes ahead, thereby expanding a company’s reaction-time window. 

Faster-growth businesses with shorter product cycles generally don’t offer managers any advanced warning of change and allow only a narrow window for adaptation. These managers often end up as the ‘One Hit Wonders’ of the corporate world: they lead businesses to explosive growth only to be blindsided by sudden change. Groupon, Zynga, PortalPlayer, Synaptics, GoPro, FitBit – history is littered with these victims of disruption. 

This challenge of adaptation faced by fast-paced businesses brings a Buffettism to mind: “When a management with a reputation for brilliance tackles a business with a reputation for bad economics, it’s the reputation of the business that remains intact.” We might adapt this saying to something like: When a Quality manager with a long-term track record of adaptability takes the helm of a fast-changing, steep-S-curve business, they may not emerge with their reputation intact. No traditionally-defined margin of safety would have been enough to protect investors from the downfall of these seemingly promising companies; however, by considering the window for adaptability, it becomes clear that businesses exhibiting rapid growth are much more likely to be shooting stars rather than sure bets. 

Characteristics of Slow-Growth, Long Duration Businesses: Governors, Necessity, and NZS

In addition to conferring improved conditions for adaptability, slow growth has the benefit of elongating the duration of growth, with time acting as a magnifier through the magic of compounding. Long-duration, slow-growth companies tend to have a governor, or negative feedback loop, that tamps down the magnitude of their short-term growth. Given a sufficient TAM (total available market), this rate limitation means that a company can expect positive returns for a very long time. 

For example, a software company might offer a new process (e.g., virtual simulation instead of physical prototyping) that radically changes the nature of R&D at a product company. Upgrading to the virtual simulation would allow for a quicker design process with more iterations. However, in order to implement this process, the entire R&D department would have to be restructured around the new software platform, a high hurdle that most companies would be slow to tackle. Over the long term (barring disruption), every company in that industry would have to eventually modernize to stay relevant. As a result, the software company is unlikely to experience explosive growth in any one year but could post reasonable growth over decades. 

A common thread with most long-duration, slow-growth companies is that they have become so ‘mission critical’ to their customers that tight integration develops between producer and consumer. R&D becomes a collaborative effort with prototypes, refinements, and tailor-made products. As a result, the producer has so much (essentially real-time) aggregate data on customer requirements, complaints, and challenges, that they are able to anticipate what the next-generation products should be, layering on new capabilities often before individual customers even fully understand their own needs. This data-driven foresight virtually eliminates guesswork on the part of the producer, dramatically reducing the chances of being blindsided by shifting customer demands. In this type of environment, adaptation becomes second nature.

Now, contrast that scenario with steep S-curve growth. Mercurial customer demands and new disruptions come fast and hard, often because companies don’t have access to meaningful, predictive data. To adapt, management teams may have to upend their entire business model. They might only have one shot to get it right – and within a relatively short time frame as well. If the company survives one round, the nature of their business leaves open the possibility they’ll have to do the same thing over and over again. Successful adaptation in this type of environment goes way beyond manager skill and skews heavily toward luck.

Another hallmark of a long-duration, slow-growth company revolves around something we call NZS or non-zero sum. We look for companies that are delivering more value to their constituents (customers, employees, society at large, the environment, etc.) than they do for themselves – the essence of NZS. We explored the topic of NZS and its beneficial effects on businesses in a previous white paper: NZS - Non-Zero Outcomes in the Information Age. Briefly, in the world today, the increasing transparency and velocity of information make it challenging for companies to extract high margins from their customers/constituents. While traditional investors may seek businesses with ‘high barriers’ and ‘wide moats’, these can rapidly become vulnerabilities exploitable by a higher-NZS competitor (who, by definition, will be more attractive to customers). In contrast, companies focusing on maximizing NZS become invaluable to their customers and less vulnerable to disruption. These types of companies overwhelmingly exhibit long-term thinking, as shorter-term sacrifices are often required (which effectively act as negative-feedback loops, slowing and elongating growth). 

Time Dilation: Slowing Down The Game Clock

Ultimately, what highly nimble companies are able to do is act in a way that slows down time relative to their competitors. The world is moving and changing at an accelerating pace, but with a Quality company operating in a long-duration, slow-growth industry dynamic, it’s possible to operate in a bubble in which time appears to move more slowly than in the frantic world around you. Imagine two paths connecting two points in time, 2020 and 2021: one short path, where time is normal, and one long path, where time is stretched and slowed. Because time moves slower on the longer, time-dilated path, you have more time to react and adapt relative to your competition on the direct route, so when you both arrive at 2021, you have out-thought and out-innovated your competition. 

In physics, Einstein discovered two ways to think about time dilation. The first way is described by Special Relativity: as objects move at higher speeds, their “clocks” will appear to run slower to outside observers. Second, in General Relativity, your “clock” will run slower as you approach large masses (black holes being an extreme example). In fact, since your feet are closer to Earth than your head, they are actually younger than your brain, which is less affected by our planet’s gravity. Luckily, the effects are negligible at these scales!

If you are a competitor at a poorly run company looking across space and time at a high-Quality business, you will be running around putting out fires and focusing on the wrong things while the Quality business will be calm and functional, buying themselves time to focus on their customers, products, etc. When your clock runs slowly, you have far more time to react to change and disruption. Quality is a way to slow down time; it’s like a black hole that allows you to focus on the long term. 

Access to data concerning customer needs and future disruptions is another way to effect time dilation, expanding the reaction time window and facilitating early adaptation. 

Likewise, innovation also slows down time. Think of data/innovation in terms of Special Relativity – if you can anticipate customer needs and innovate more efficiently, that’s like a moving clock; it buys you time relative to the fast moving clocks of your competition, essentially like time traveling to the future!

Tesla is a good example of how to think about time dilation. The company has out-innovated every automaker, which has bought them at least a five-year headstart – the watch is ticking away quickly for legacy car manufacturers wasting their time on combustion engines and failing to develop the electric drivetrain, batteries, software, data, and sensors they need to build a modern car. But, at Tesla, they’ve enacted a paradox: by quickly innovating around the needs of their customers, they’ve slowed down time and pulled years ahead of the competition. If Tesla continues to gain market share, they will be like a gravity well, or a back hole, allowing them to operate far into the future ahead of their competition.

Amazon is another great example – for years, the company innovated to stay ahead of the competition in retail and cloud computing, buying themselves time along their way. And now they have created such large, defensible gravity wells of network effects around logistics and technology that they exist years ahead of their competition.

We’d be remiss if we didn’t extend our physics metaphor by talking about time’s arrow itself: entropy. Entropy is a measure of disorder: around the start of the Universe (at least the part that’s visible to us) ~14B years ago, matter and energy were very organized – i.e., there existed an extremely low entropy state. When you have information (a.k.a. order) entropy is very low. As information (or matter and energy) become disordered, entropy grows over time. 

Life, as it turns out, is uniquely suited to taking ordered, high-information matter/energy and turning it into disordered, low-information states; indeed, this seems to be the vector of the Universe and life’s role in it. For example, take sunlight, plants, and animals: sunlight is highly-ordered electromagnetic rays that help plants grow through photosynthesis; then animals eat those plants (and sometimes animals eat the animals that eat those plants); and then animals (e.g., humans), turn that energy into all sorts of interesting things, ultimately scattering that neat, organized solar energy into myriad disorder around the planet and surrounding space.

Much of what society has done is to try to create temporary order despite the long-arching trend toward disorder in the Universe. We build buildings, cities, communities and companies – we take organized energy and reshape it into all sorts of literal and figurative structures. But, it’s only a temporary, local increase in order, and, in the long run, the information value is lost and entropy rises. The trend toward more disorder means that predicting the future is very hard, if not impossible; therefore, companies that can slow down time don’t need to operate with rigid views of the future. Thus, they are more adaptable and durable.

Conclusion: Beyond valuation. Slow-Growth, Long-Duration Companies with Adaptable Management Maximize Margin of Safety

Graham offered a helpful lens by introducing ’margin of safety’ as the central concept of investment. At its core, the concept is about investors’ inability to predict the future. We agree that humans are terrible at accurately and narrowly predicting the future, but question the over-emphasis on valuation. We posit that value-based margin of safety is all too often used to justify ownership of dying businesses. While cheap, these value traps do not control their own destiny, devoting resources to life support and/or blind attempts at reinvention; and, all too often, it’s too late. Instead, the ability of a company to adapt – which, in turn, is dependent upon management Quality and nature of growth – is critical to any formulation of margin of safety. Hallmarks of gentle sloping ‘S-curve’ businesses that we look for are negative feedback loops, tight-knit customer relations, and positive NZS. Slow, long-duration growth allows for timely innovation, decelerating the game clock so managers can make smart decisions and maintain their lead through adaptation. 

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Disclaimers:

The content of this newsletter is my personal opinion as of the date published and are subject to change without notice and may not reflect the opinion of NZS Capital, LLC (“NZS”).  This newsletter is simply an informal gathering of topics I’ve recently read and thought about. It generally covers topics related to the digitization of the global economy, technology and innovation, macro and geopolitics, as well as scientific progress, especially in the fields of cosmology and the brain. I will frequently state things in the newsletter that contradict my own views in order to be provocative. I often I try to make jokes, and they aren’t very funny – sorry. 

I may include links to third-party websites as a convenience, and the inclusion of such links does not imply any endorsement, approval, investigation, verification or monitoring by NZS Capital, LLC (“NZS”). If you choose to visit the linked sites, you do so at your own risk, and you will be subject to such sites' terms of use and privacy policies, over which NZS Capital has no control. In no event will NZS be responsible for any information or content within the linked sites or your use of the linked sites.

Nothing in this newsletter should be construed as investment advice. The information contained herein is only as current as of the date indicated and may be superseded by subsequent market events or for other reasons. There is no guarantee that the information supplied is accurate, complete, or timely. Past performance is not a guarantee of future results. 

Investing involves risk, including the possible loss of principal and fluctuation of value. Nothing contained in this newsletter is an offer to sell or solicit any investment services or securities. Initial Public Offerings (IPOs) are highly speculative investments and may be subject to lower liquidity and greater volatility. Special risks associated with IPOs include limited operating history, unseasoned trading, high turnover and non-repeatable performance.

Slowing Down Time with Organizational Structure

Slowing Down Time in Organizations

How Organizational Structure and Functional Teams Influence the Ability to Adapt

How is it that organizations spend so much time on mission, vision, and culture, yet most employees experience chaos in the day-to-day environment? How can network theory help us gain a fresh perspective on our everyday personal and organizational realities? This paper will attempt to address two organizational behemoths: structure and team culture. Said differently, we’ll try to illustrate how to slow down time in organizations and then how to capitalize on it. A PDF of the paper can be downloaded here.

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The Improbability of Rising Rates

The following two posts are taken from SITALWeek newsletters on the topic of interest rates and inflation. The first post discusses the difficult path for sustainably higher rates and the second looks at the long term disinflationary trends. If inflation manifests, but rates cannot rise for existential reasons, then targeted policy would be required to keep prices in check. For example, to combat rising housing costs there could be rent increase caps or regulation against institutional home ownership. For rising food costs there could be targeted subsidies. To tamp down runaway asset price bubbles, higher taxation would limit some speculation and aid in redistribution. Ultimately these are all anti-market forces with unknown consequences long term. From today’s vantage point, it is more likely the global economy continues to experience a low rate environment driven by deflationary technology trends and an abundance of debt. Declining birth rates also offer a long term disinflationary force. Ultimately, the economy will move to a distributive mechanism from a growth mechanism.

The Myth of Interest Rate Mean Reversion
(From SITALWeek #257 on August 9th, 2020)

There is a certain profile of investor I call an “interest rates will Mean Revert” investor, or MR for short. There are a lot of really smart MRs with great track records, like Warren Buffett. In May of this year, at the annual Berkshire gathering, Buffett had the following to say:
“So if the world turns into a world where you can issue more and more money and have negative interest rates over time, I’d have to see it to believe it, but I’ve seen a little bit of it. I’ve been surprised. So I’ve been wrong so far...if you’re going to have negative interest rates and pour out money and incur more and more debt relative to productive capacity, you’d think the world would have discovered it in the first couple thousand years rather than just coming on it now. But we will see. It’s probably the most interesting question I’ve ever seen in that economics is can you keep doing what we’re doing now and we’ve been able to do it or the world’s been able to do it for now a dozen years or so but we may be facing a period where we’re testing that hypothesis that you can continue it with a lot more force than we’ve tested before.”

MRs like Buffett believe that there is a certain anti-gravity that should cause rates to be structurally higher (largely to offset inflation stemming from higher debt levels), which they are counting on so that bank stocks rise and value stocks reverse their long period of underperforming growth stocks. There is a funny paradox worth noting about the MR view of rates: companies at lower valuations tend to be more mature and therefore tend to have more debt (though certainly that’s not true in all cases), which means that, if rates rise by several hundred basis points, catastrophe could ensue for those companies unable to sustain their high levels of debt. And, it’s not just value companies at risk, but the whole system, since one company’s debt is another’s asset. Likewise, higher rates could easily create a geopolitical crisis owing to the massive government borrowing around the globe. If borrowers can’t service and pay back that debt, then the debtholders don’t have assets. And that’s bad. I discussed this conundrum in more detail in our mid-year update last month:
“Did low rates increase debt, or did debt demand low rates? As an economy grows and debt increases, the borrowers – those people who need to make the interest payments and eventually return the principle – tend to be disproportionately less-wealthy, while the people who lend money out and make a return on it tend to be wealthier. As time goes on, the wealth of the wealthier is more and more tied to the interest payments from the less wealthy – one person’s indebtedness is another person’s asset. And, as inequality marches higher, the less wealthy have an ever-rising debt burden that can only be maintained by perpetually lowering interest rates. It’s in the best interest of the lenders to lend at lower and lower rates to preserve their assets. This explanation is somewhat at odds with the general narrative – that lower rates are the driving force behind rising debt. Certainly lower rates allow rising debt; however, the common view misses the crucial point that increasing debt necessitates lower rates...” (Note: this concept is explored in more mathematical detail in this essay from Ole Peters).

Now, let’s consider inflation. Rates can go up for different reasons, but one view is that rates should be increased to vacuum money out of the economy to offset inflation (or preemptively counter expected future inflation). The main inflationary fear right now is that excess liquidity from COVID-driven fiscal and monetary stimulus will be the source of that inflation (I’m rather puzzled by this fear because much of the stimulus has been replacing lost GDP, not adding to it, but let’s shelve that point for now). And, there can be short-term shocks which cause inflation – e.g., war can reduce oil supply and drive up prices, or drought can increase crop prices. There are also localized bubbles of long-term structural inflation, e.g., US healthcare costs.

But, let’s try to take a first principles look at systemic, structural inflation – in particular, long-term price increases on the scale of hundreds of years. Why would prices go up, on average, for everything over a prolonged period of time? I couldn’t really find an answer that made sense to me when I researched this question (and, let's generously say that my degree in economics was less helpful here than my degree in astrophysics😁; so, to be clear: I am guessing here). So, here is my interesting, simple hypothesis: a long-term cause of structural inflation would be upward pressure from population growth, which would be offset by downward pressure from technological progress. A growing population outpacing the production of goods and services (produced/provided for their own consumption) and leveraging their growing wealth would cause inflation, while technological progress (i.e., making more for less) would offset inflation. Seems reasonable, right?

Humans (Homo sapiens) took at least 200,000 years to get to a population of ~400 million by the early 1400s (following a dip in the 14th century due to the Black Death). But then we went from 400 million to nearly eight billion in the last ~600 years. One of the reasons for population growth was the burgeoning expansion of the economic pie. A dash of Renaissance, a touch of Enlightenment, and a heavy pour of science and the Industrial Revolution all created a lot of hope for the future – and a lot more people. There was more to go around, and people believed that there would be even more to go around in the future. When you believe the future will be bigger than the present, you are also more inclined to borrow and invest in that future.

Inflation over this 600-year period has been a little over 1% on average*; however, sustainable inflation has been the highest over the last 60 years, at around 2%, which corresponds to a period when aggregate borrowing in the US (both public and private sector) went from around 1.5x GDP to around 3.5x GDP. So, there seems to be at least a modest correlation between a significant increase in borrowing and an increase in inflation (borrowing driving up inflation makes intuitive sense, since lenders are conceptually printing money; that said, certainly a lot else has changed in the last 60 years as well). More recently, inflation has been declining (from its most recent high ~1980) while borrowing continues to grow (US public and private debt).

So, what have been the overriding deflationary pressures that account for this recent decline? The pace of technological development has been massively accelerated and global population growth has slowed. Over the last 60 years, the annual global population growth rate has dropped from around 2% to a little over 1%. And, in the US, the birth replacement rate has been steadily falling to the point where, without immigration, the US population would be shrinking marginally.

While we’ve had a clear, declining interest rate trend since the 1980 peak, real rates have actually been declining since the 1400s*, tracking the population increase, GDP, and, in turn, rising debt. So, in some ways, the trend of rising debt and falling rates has been happening for a long time. Perhaps it is the natural way of civilization, per Ole Peters’ theory referenced above, i.e., falling rates are necessary to sustain the value of an ever-increasing pool of debt (one person’s debt is another person’s asset). Rates may dance around the mean short term, but there’s no historical evidence that suggests we should expect an increasing interest rate trend in the future (indeed, quite the opposite!).

Let’s turn briefly to potential systemic, sustained deflationary pressures we might face, which are a little easier to guess at. Advances in technology, as well as improvements in productivity, are constantly giving us more for less. While we might have a period of oil price shocks, green energy will solve that long term. As the economy becomes increasingly digital, from less than 10% now, to 100% over the coming decades, and as AI increases, we will see unprecedented deflationary pressures. Look at what’s happened just this year – thanks to broadband, our houses have become offices, gyms, schools, etc. – talk about a lot of technologically-enabled bang for your buck!

Putting all the variables together, we see inflationary pressure from increased borrowing and deflationary pressure from slowing population growth and increased technological progress. Of course, there are a thousand other variables – like how quickly AI can destroy or create jobs, whether or not de-globalization will follow decades of globalization, etc. It’s incredibly complex. Indeed, the world’s economy is a complex adaptive system, and no one can accurately and narrowly predict its behavior given its sensitivity to small perturbations and propensity for spawning fat-tail events. With near certainty, we can say that short-term inflationary shocks will happen, but when, why, and how big are anyone’s best guess. Moreover (MRs pay attention here), it seems clear that we can no longer treat the “symptom” of inflation with the “cure” of raising rates because it would destroy the asset value of our highly leveraged global economy. Therefore, government tactics will likely turn to treating the localized inflation directly via offset, e.g., using fiscal stimulus to offset an oil or food price shock. Sure, fiscal stimulus could temporarily add inflationary pressure; but, unless we somehow deleverage the economy without destroying it, it’s hard to make a case for structurally high rates. However, I am rather obsessed with making a case for higher rates since I cannot find one that passes both logical and mathematical scrutiny. For years, I’ve searched for answers that would support the counterargument, and have utterly failed thus far. So, if you have a theory for why rates will be sustainably higher that considers or falsifies what I’ve written here, please let me know!

*several numbers in this section were taken from this post for convenience, but are generally available across the web as well.

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Can We Harness Technology’s Deflationary Pressure?

(From SITALWeek #258 on August 16th, 2020)

Can We Harness Technology’s Deflationary Pressure?
I studied prior disinflationary/deflationary periods in the Industrial Age this past week (thanks to a suggestion from a reader!). Historically, significant advancements in technology seem to be coupled with: 1) investment cycles (funded by debt), 2) a digestion of overinvestment, and 3) disinflation or outright deflation (majority of cases). One theory put forth by Irving Fisher in the 1930’s paper The Debt-Deflation Theory of Great Depressions would suggest that any borrowing-driven inflation would likely be overwhelmed by disinflationary/deflationary forces wrought by the unserviceable debt burden following the (largely inevitable) bust. A more common market view is that low rates drive increased borrowing, which in turn drives inflation, putting less emphasis on the risk of deflation (on a longer time scale, however, increased borrowing causes low rates rather than the other way around; see our mid-year update, as well as the end of SITALWeek #257, for more details).

Fisher was perhaps on the right track; but, I wonder if it’s the technological advancement itself that causes the subsequent long term disinflation/deflation pressure, while the debt-fueled bust/recession is a smaller, secondary factor? For example, putting your delivery on a canal or train (anteceding technological advancements) was much cheaper than the horse/person-powered alternative. Canals and rails were heavy, physical, capital-intensive advances. But, what about technological investments in today’s Information Age? Forging leading-edge technology is capital intensive for a handful of large cloud infrastructure providers, but the resulting productivity increases and technological advancements far exceed the capital invested. Think of the productivity output of a single Nvidia A100 system: a $100,000 investment could produce a breakthrough that creates billions of dollars of value...every day! So, although (at present) we are in a period of significant debt expansion in the economy, we are in a much more significant, overarching phase of ever-accelerating technological advancement. If I were to attempt a first principles analysis on this topic, I would start with the following question: does accelerating deflationary pressure – from nonlinear advances in technology – enable the expansion of the money supply without the corresponding risk of inflation?

This question is perhaps even more critical now that we are on the cusp of unprecedentedly rapid change/disruption as we move from the Information Age to the AI Age. Around 40 years ago, the pace of technological advancement went from analog speed to digital speed, and with AI it’s about to go to ludicrous speed. Technology was always jumping ahead with nonlinear improvements, but the pace of change accelerated even more with the introduction of the PC and the software revolution. Many activities and ways of doing business in the year 2000 would have been unrecognizable in the year 1980. Indeed, I have a difficult time remembering what it was like a decade ago without a smartphone and ubiquitous high-speed connections; so, in many ways, 2010 is unrecognizable to me today (and vice versa). I expect 2025 will look unrecognizable to us from today’s viewpoint. And, to follow this acceleration, 2028 may look unrecognizable from 2025. 2030 from 2028, 2031 from 2030, etc.

The late-1970s/early-1980s pivotal shift from analog to digital likely played into other society-changing forces that began around the same time, including increased globalization (enabled in many ways by digital technology and communication) and the beginning of real earnings stagnation for a large part of the population. The accelerated pace of change and the shift from an assets- to an information-based economy helped accrue wealth for the wealthy, and steadily declining rates over the last four decades enabled wealth concentration as well (again, for more detail on this see our mid-year update as well as the end of last week’s SITALWeek #257). For the last four decades, the pace of change has become much more nonlinear and exponential.

For millions of years, we experienced progress as iterative analog changes. Our evolutionary heritage hasn’t really prepared us for exponential, ongoing advances. So, in some ways, the onset of accelerated, nonlinear change is breaking down our ability to cope. Most of us are struggling to adapt and react as the ground shifts faster and faster underneath our feet. As we wrote in Pace Layers: Tech Platforms, Regulation, and Finite Time Singularities: “Historically, we would expect fashion or technology to have slow and small impacts on the thousand-year-old institutions of Culture, but recently the increased velocity and transparency of information flow is causing rapid behavioral shifts in humans.” I suspect much of the turmoil and rising problems of society we have now, including inequality, nationalism, racism, fake news, etc., stem from humans’ inability to process rapid, nonlinear change (our fallback coping mechanism for uncertainty/misfortune is: if you can’t identify the real motive force or enemy, invent a story and create one). Fear of the increasingly unknown has been subconsciously motivating human behavior.

That said, we humans are remarkably intelligent, resilient creatures. And, a younger generation – whose adaptable neural networks were established during the increasingly nonlinear world of the last 40 years – may have enough of an edge over us older folks in coping with rapid change. With any luck, they will engineer solutions to the difficult socio-economic problems we now face. In any case, let us hope we can figure out a way to harness the power of our technological wild ride before its spawned problems become totally overwhelming. Returning to the point above, it seems quite plausible that our current era of unprecedented technological growth offers its own solution – providing sufficient disinflationary/deflationary pressure that we might be able to buy our way out of our current, untenable societal problems, but time will tell.

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Interest Rates’ Historical Downward Trend

(From SITALWeek #305 on July 18th, 2021)

A new working paper from the Bank of England (PDF link) has one of the more detailed looks at interest rates’ downward march to zero over the last 700 years. I’ve covered the myth of interest rate mean reversion in detail in #257 as well as the deflationary impact of technology in #258, which I believe to be vastly underestimated in analysis of long-term rate trends. Whenever I see a chart on long-term rates, I am reminded of an essay by Ole Peters that I’ve previously discussed: “Did low rates increase debt, or did debt demand low rates? As an economy grows and debt increases, the borrowers – those people who need to make the interest payments and eventually return the principle – tend to be disproportionately less-wealthy, while the people who lend money out and make a return on it tend to be wealthier. As time goes on, the wealth of the wealthier is more and more tied to the interest payments from the less wealthy – one person’s indebtedness is another person’s asset. And, as inequality marches higher, the less wealthy have an ever-rising debt burden that can only be maintained by perpetually lowering interest rates. It’s in the best interest of the lenders to lend at lower and lower rates to preserve their assets. This explanation is somewhat at odds with the general narrative – that lower rates are the driving force behind rising debt.”

The BoE author reached a similar conclusion: “There is no reason, therefore, to expect rates to ‘plateau’, to suggest that ‘the global neutral rate may settle at around 1% over the medium to long run’, or to proclaim that ‘forecasts that the real rate will remain stuck at or below zero appear unwarranted’ as some have suggested...the long-term historical data suggests that, whatever the ultimate driver, or combination of drivers, the forces responsible have been indifferent to monetary or political regimes; they have kept exercising their pull on interest rate levels irrespective of the existence of central banks, (de jure) usury laws, or permanently higher public expenditures.” As previously mentioned, I like Brian Arthur’s views that we are entering the distributive era of economics. Rather than rates cracking through zero and continuing to negative infinity, it seems much more likely the wealth of the world will be redistributed in a way that creates some inflation to offset dropping rates. In a potential Goldilocks scenario, deflationary pressure from technology would make the porridge of inflation, rates, and redistribution taste just right over the long term.

Disclaimers:

The content of this newsletter is my personal opinion as of the date published and is subject to change without notice and may not reflect the opinion of NZS Capital, LLC.  This newsletter is simply an informal gathering of topics I’ve recently read and thought about. It generally covers topics related to the digitization of the global economy, technology and innovation, macro and geopolitics, as well as scientific progress, especially in the fields of cosmology and the brain. I will frequently state things in the newsletter that contradict my own views in order to be provocative. Often I try to make jokes, and they aren’t very funny – sorry. 

I may include links to third-party websites as a convenience, and the inclusion of such links does not imply any endorsement, approval, investigation, verification or monitoring by NZS Capital, LLC. If you choose to visit the linked sites, you do so at your own risk, and you will be subject to such sites' terms of use and privacy policies, over which NZS Capital, LLC has no control. In no event will NZS Capital, LLC be responsible for any information or content within the linked sites or your use of the linked sites.

Nothing in this newsletter should be construed as investment advice. The information contained herein is only as current as of the date indicated and may be superseded by subsequent market events or for other reasons. There is no guarantee that the information supplied is accurate, complete, or timely. Past performance is not a guarantee of future results. 

Investing involves risk, including the possible loss of principal and fluctuation of value. Nothing contained in this newsletter is an offer to sell or solicit any investment services or securities. Initial Public Offerings (IPOs) are highly speculative investments and may be subject to lower liquidity and greater volatility. Special risks associated with IPOs include limited operating history, unseasoned trading, high turnover and non-repeatable performance.

Semiconductors

This page is home to our whitepaper on semiconductors, along with our accompanying podcast episode. In November 2020, Brinton and Jon joined Shane Parrish on The Knowledge Project podcast for a wide ranging discussion of semiconductors.

How a Handful of Chip Companies Came to Control the Fate of the World

June 28th, 2020

Click HERE for a PDF of this WhitepaperAt the risk of sounding like hyperbolic doom mongers, let’s grab your attention from the outset: Because of a series of complex and unexpected global developments over the past several decades, the fate of the world now lies in the hands of fewer than a dozen semiconductor companies.

A good exercise in assessing a potential investment is to ask “could the world get by without this company?”. The answer is usually, “in time, probably just fine.” But should we wake up tomorrow to find that any among Taiwan Semiconductor Manufacturing Co. (TSMC), ASML Holding, Lam Research, Cadence, Synopsys and KLA-Tencor have suddenly ceased to exist, the answer at best is “well, yes, but global progress will suffer a setback of several decades at least.” The loss of a handful of critical chipmakers that depend on these companies – such as NVIDIA, Samsung, Intel, AMD, Texas Instruments, Xilinx, Broadcom and Microchip Technology – would dramatically impede the digital transition of the economy still further.

Why? Because these companies are at the core of a half a trillion-dollar industry that enables us to do pretty much everything, from flying across oceans to booking a ride and ordering a pizza when we get there; from diagnosing and treating a disease to boosting agricultural yields; from supporting the military to powering our cities.

ENGINE ROOM
No critical system can function without semiconductors and chip companies provide the wherewithal necessary to design, build and test ever-faster and more powerful sensors, microcontrollers, CPUs, GPUs, FPGAs, network switches, modems, memory, and all the other components that power our devices and, by extension, the entire digital economy.

Marc Andreessen famously proclaimed in 2011 that “software is eating the world,” and evidence of this truth can be seen more and more every day. But without the semiconductors that allow the machines to run the software, there would be no Information Age.

Chips are the engine driving the digital transformation of the world.

But how did we get to a place where more than half of all contract semiconductor manufacturing is handled by one company on an island whose sovereignty is in dispute; where a single organization holds what looks like an unassailable lead in photolithography; where virtually all memory is made by just three companies; and where it’s well-nigh impossible to even design a chip without software that only a couple of companies provide?

INCREASING COMPLEXITY

For one thing, as chips used in mobile phones, artificial intelligence and cloud computing applications become more complex, so too do the design, manufacturing and testing processes needed to produce them, making it impossible for all but a few of the most adaptable companies to keep pace. And as development becomes more complex, so does the cost to design, test and build an advanced chip, which has spiraled to as much as $500 million, creating an additional barrier for wannabe disruptors.

MAGICAL PROCESS
Photolithography is a good example. In short, when the light source used in the process had to change from a wavelength of 193nm to 13.5nm to accommodate smaller, more intricate patterns on leading-edge chips of ever-decreasing geometry, only one company even tried to do it.

Extreme ultraviolet lithography (EUV) is an almost magical process. In a vacuum, 50,000 microscopic droplets of molten tin are fired every second in a stream as one laser strikes each one so precisely that they flatten into discs before another bombards them with so much power that they become balls of plasma shining with EUV light. The machines cost almost $200 million, can be the size of a house and are contained within ultraclean environments to keep out even a single speck of dust. The scanners and lasers that power EUV lithography are so complex that a decade ago many scientists believed them to be an impossibility, and Nikon, ASML’s key competitor, viewed the technology as so complicated that it didn’t even attempt to develop an EUV tool.

Because of its unique mastery of EUV, ASML has built a de facto monopoly in manufacturing the machines that make the most advanced chips. The Dutch company expects to ship about 35 scanners this year, taking the total used by foundries around the world to around 100. TSMC and Samsung are already in high-volume manufacturing with EUV, while Intel will be using the process from 2021.

Without EUV, Moore’s Law, which states that the density of transistors on a chip will double about every two years, would likely have reached its limitations. But because of the process, TSMC is building 7nm and 5nm fabs, and is investing another $20 billion on a 3nm node foundry, while Samsung, South Korea’s biggest company, said in May 2020 it started building a 5nm facility near Seoul based on EUV as part of a $116 billion plan outlined in April 2019 to compete with TSMC in contract chipmaking. 

CLOSE COOPERATION
But even before you reach the manufacture and testing stages, intricate chips have to be designed, and as competition drives device makers to pack more performance, functionality and power efficiency into ever-smaller chips, the creation of semiconductors has become impossible without electronic design automation (EDA). A key provider is Cadence Design Systems, whose software is used to map advanced semiconductors. Cadence and its competitor Synopsys work in extremely tight partnership with chip designers and manufacturers (often TSMC) to ensure that those hundreds of millions of dollars invested into the next chip design will yield a functioning device. Initiatives such as TSMC’s EDA Alliance and Digital Reference Flow – which have no equivalent at Samsung or Intel – increase the likelihood that designs will be compatible with TSMC’s manufacturing processes, creating network effects by making it more likely that future projects will also be successful.

CONSOLIDATION EXPLOSION
While increasing complexity played a significant role in reducing the number of players, consolidation helped the process along. In 2009, there were 10 DRAM memory makers. Now, Samsung, SK Hynix and Micron Technology account for 95% of revenue in a roughly $100 billion sector.

Consolidation elsewhere was triggered in large part by Hock Tan, the longtime CEO of Avago, now Broadcom, who saw an opportunity following the Great Financial Crisis to buy underpriced and under-appreciated assets using cheap money. In addition to noticing that semiconductor companies were undervalued, Hock’s key insight was that they were often poorly managed – chasing growth markets that promised low returns. Working more like a private equity executive than a technology CEO, Hock shuttered or sold off underperforming divisions of acquired companies, while investing in established highly profitable franchises. After the resounding success of Avago’s 2014 acquisition of storage chipmaker LSI, rival CEOs saw the need to increase scale and returns to survive.

(As an aside, another more morbid reason for the quickening pace of consolidation was that many founders died, making it easier to build alliances that were previously unthinkable.)

So after lying dormant for a decade or so, semiconductor M&A exploded in 2015, surpassing $100 billion in transaction value, more than six times any of the preceding five years, and remained elevated for the rest of the decade. Broadcom was the poster child of the consolidation wave. During this period, Tan spent $50 billion on acquisitions, taking the company from a niche supplier of wireless technology to a powerhouse in wireless, networking and data center solutions at industry-leading profitability, a move that helped the stock to surge 1,800%, making it the best performer in the semis sector and one of the top-10 stocks in the S&P 500 in the decade.

DECLINING CYCLICALITY
Money poured into the space as Tan led others to realize that the industry’s traditional cyclicality was ebbing. For decades, sales revolved around events such as upgrades to the Windows operating system and mobile phone releases. But as the number of use cases and potential profit pools widened due to mega themes such as artificial intelligence, cloud computing, mobility and the Internet of Things, along with expanding industrial and healthcare applications, those cyclical peaks and troughs became less pronounced, making chips even more appealing.

Inevitably, the M&A activity eventually drew the attention of the US government, whose Committee on Foreign Investment (CFIUS) became concerned that key American intellectual property was leaving the country through some consolidations. The most ambitious transaction in the industry’s history, the $120 billion planned  acquisition of wireless giant Qualcomm by Broadcom, was blocked by CFIUS — even after Broadcom relocated to California from Singapore — over concerns that the target’s key wireless technology, including 5G, were critical to national security.

THE MOMENT EVERYTHING CHANGED
This decision made it clear that the US government appreciated the lack of resilience in the supply chain while also signaling that it would act to protect America’s strategic interests. That made it obvious that one key part of this critical supply chain would move directly into the crosshairs – fabrication.

And for that, we can blame Texas Instruments (TI).

In the early 1980s, Morris Chang was head of TI’s semiconductor business. Despite being an exceptionally talented leader and technologist, he was passed over for CEO, many believe because he is not American. So Chang returned to his native Taiwan at the government’s request to work on a way to build up the island’s tech chops. With no history, infrastructure, obvious skills or other resources in any aspect of semiconductor design and manufacture, Chang hit on the idea of creating a fabrication only model.

Until then, semiconductor companies both designed and manufactured their chips, and constructing increasingly large factories was the biggest impediment to innovation as it was the most costly part of starting a company. Chang removed that barrier by undertaking to never compete with customers in chip design. TSMC  grew over the proceeding decades into by far the biggest contract chipmaker, controlling 53% of the almost $70 billion third-party fabrication market by January 2020, according to TrendForce, compared with 18% for Samsung and 8% for GlobalFoundries, the next two biggest producers.

In 2019, TSMC manufactured almost 11,000 products for around 500 customers, including AMD, Apple, Alphabet, Amazon, Microsoft, Facebook, NVIDIA and Qualcomm, while in a deliciously ironic twist, Texas Instruments, whose market cap is less than half TSMC’s, now relies on the company to manufacture some of their advanced products.

TAIWAN’S STRATEGIC IMPORTANCE
What spooks the US government is that American companies account for about 60% of TSMC’s revenue, according to analysts’ estimates, with China bringing in a further 20%.

US-based companies are responsible for about 45% of global semiconductor sales, according to the Semiconductor Industry Association, yet almost 70% of chips are made in Taiwan or pass through the island during the manufacturing process.

Given that China as recently as May 20, 2020, said that reunification “cannot be stopped by anyone or by any force,” the US is keen to loosen the stranglehold of TSMC on the fabrication of leading-edge chips in Taiwan.

As a result, TSMC this year entered an accord to build a $12 billion, 5nm fab in Arizona, which it plans to open in 2024, while at time of writing Congress is also debating a multibillion-dollar package of incentives designed to further boost third-party fabrication options in the US. While these moves aren’t enough to significantly reduce the US’s exposure to Taiwan, they signal intent.

UNASSAILABLE LEAD?
While the US has sought to deny China’s Huawei access to the semiconductors needed to build 5G mobile networks, as a nation China remains several years behind its American and European counterparts, a gap that would likely be decades without access to western tools. Moreover, China has little to no hope of closing the gap, since it’s impossible to reverse engineer the complex processes, making traditional industrial espionage techniques pointless.

The $100 billion “Made in China 2025” effort, which seeks to essentially replicate the global semiconductor industry to reduce the country’s dependence on US and European knowhow, is therefore likely to result in success in lower-end parts of the market and not achieve the goal of technology independence. While total Chinese output is rising, the chips are still designed with US-produced software and manufactured with American and European gear. China is in no position to go it alone and won’t be for the foreseeable future.

FINANCIAL RESILIENCE
We go into the geopolitics of semi fabrication in more detail in this MarketWatch op-ed, but the fragility inherent in the supply chain stands in stark contrast to the financial resilience created by the concentration of capabilities in a few privileged hands.

It may come as a surprise to many that the best-performing sector of the S&P 500 since the Great Financial Crisis is semiconductors. From a nadir in November 2008, the 30-member Philadelphia Semiconductor index doubled in value in a year and surged 377% in the decade through 2019, returning more than twice the 182% of the main S&P 500.

Another example  is the stock of Lam Research, which increased in value 14 times since languishing around $20 in February 2009. Lam’s tech is a key enabler of both NAND and DRAM memory, which are seeing rising demand as the world generates and analyzes more data. Lam is the only provider of high aspect ratio etch tools, which are critical in the NAND market as the device architecture has started to scale vertically, leaving the latest chips looking more like skyscrapers than their one-story house antecedents. The magic in Lam’s process allows its tool to etch more than a trillion perfectly uniform holes — each one-thousandth of the diameter of a human hair — on a wafer. Lam’s leverage to the NAND market and a broader renaissance in memory helped the company to grow annual revenue by 16% on average from 2013 through 2019, double the rate of the semiconductor equipment sector. The company has also quietly built up about a third of its business to be a highly profitable recurring revenue stream of services, refurbs and consumables that improves its ability to serve customers while adding a stabilizing force to the cyclicality from the rest of the business.

IMPROVING PROFITABILITY
Despite that perception of heightened cyclicality, the profitability of many semiconductor companies now compares with top tier software providers. By transforming from a pure-play chip company into one that produces software that is monetized through silicon, graphics processing leader NVIDIA – which now employs more software than hardware engineers – is this year expected to earn a 38% operating margin based on consensus estimates, slightly higher than Microsoft.

Such performance is not limited to the leading edge. While some NVIDIA products sell for hundreds or thousands of dollars each, companies such as Microchip Technology and TI are earning software-like margins by pushing billions of sub-$5 embedded processing and analog chips a year to tens of thousands of customers. Both companies are earning higher margins at the current cyclical trough than at previous cyclical peaks seen earlier last decade. Moreover, the outlook for such products is brighter than ever as their market potential explodes in coming years to encompass trillions of connected devices as the mega themes lead to more chips being deployed across countless applications in healthcare, industrial, agriculture, automotive and other parts of the burgeoning digital economy.

WHAT NOW?
So having examined how we got here, the question now is what happens next?

While 2020 has shown us that forecasting can often be a futile pursuit, three broad predictions seems safe to make. China is in no position to go it alone, we’ll continue to rely on the key ecosystem enablers named at the start of this article, and the semiconductor renaissance will keep gaining steam.

In the final audio portion of this look at the space, we’ll talk about what’s happening in individual parts of the chip market, while also touching on why we’re excited about the absence of a Next Big Thing.

Disclaimers:

The content of this newsletter is my personal opinion as of the date published and is subject to change without notice and may not reflect the opinion of NZS Capital, LLC.  This newsletter is simply an informal gathering of topics I’ve recently read and thought about. It generally covers topics related to the digitization of the global economy, technology and innovation, macro and geopolitics, as well as scientific progress, especially in the fields of cosmology and the brain. I will frequently state things in the newsletter that contradict my own views in order to be provocative. Often I try to make jokes, and they aren’t very funny – sorry. 

I may include links to third-party websites as a convenience, and the inclusion of such links does not imply any endorsement, approval, investigation, verification or monitoring by NZS Capital, LLC. If you choose to visit the linked sites, you do so at your own risk, and you will be subject to such sites' terms of use and privacy policies, over which NZS Capital, LLC has no control. In no event will NZS Capital, LLC be responsible for any information or content within the linked sites or your use of the linked sites.

Nothing in this newsletter should be construed as investment advice. The information contained herein is only as current as of the date indicated and may be superseded by subsequent market events or for other reasons. There is no guarantee that the information supplied is accurate, complete, or timely. Past performance is not a guarantee of future results. 

Investing involves risk, including the possible loss of principal and fluctuation of value. Nothing contained in this newsletter is an offer to sell or solicit any investment services or securities. Initial Public Offerings (IPOs) are highly speculative investments and may be subject to lower liquidity and greater volatility. Special risks associated with IPOs include limited operating history, unseasoned trading, high turnover and non-repeatable performance.

NZS - Non-zero Outcomes in the Information Age

Broadening the Definition of Fiduciary Duty for the Mindful Investor and Company

Brinton Johns and Brad Slingerlend

March 8, 2019

Click HERE to download a PDF of this White Paper

Transactions between companies and customers in a capitalist system can often be explained by the economic concept of game theory. Assuming people and companies are acting rationally (not always a safe assumption, but it works for the discussion here), game theory provides a strategy for logical decision making to maximize outcomes for participants. A zero-sum outcome is one where every time one person wins, another person loses equally. A negative non-zero-sum (NZS) outcome implies all parties net out to being worse off. We are focused on positive NZS outcomes wherein a decision made by one party improves, on the whole, all parties involved. Historically, game theory takes into account only the obvious players impacted by the decision. However, we argue here that this legacy way of playing the game of capitalism is increasingly dangerous. Instead, companies and consumers need to take into account the impact of their decisions on the silent players of the game – the environment and society as a whole. In this broader context, the idea of fiduciary duty, a loosely defined term governing how a company or investor operates with a duty to their investors, must be broadened to include a larger set of players and impact. Once you make that leap in logic, it highlights the rising vulnerability of many companies and investors. We have seen, with rising frequency, examples of management teams and investors rooted in “Industrial Age logic” that will no longer produce value for their constituents, which constitutes a breach of fiduciary duty in the 21st century.

In the paper Complexity Investing, first made public in 2014, we argued that investors and corporate strategies that were overly focused on legacy concepts of “barriers to entry” and “competitive moats” are missing the more important characteristic of NZS outcomes, or win-win results. Here is an excerpt from the paper:

A company that operates a platform focused on creating value for all participants, including itself, is creating large amounts of NZS. Specifically, when companies create significantly more value for their ecosystem than for their own treasury, the win-win positive spiral is optimal. The relative level of NZS between customers and companies is generally more important than the absolute level – it will vary by industry.

As transparency and the velocity of information sharing increase in the world, it will become increasingly challenging for companies to extract positive sums from their customers. While traditional investors seek businesses with “high barriers” and “wide moats,” we believe this practice is misinformed. A barrier or moat today becomes a vulnerability tomorrow. Rather than create large barriers (which often turn out to be temporary and/or artificial), companies should focus on maximizing NZS.

Long-term thinking (beyond 5 years) is crucial for creating NZS because shorter-term sacrifices are often required. Significant ongoing, long-term investments are also required to continue innovation and non-zero value creation.

Companies that are disrupting large, established markets often do so with a value proposition that offers more opportunity for NZS. Often these companies are attacking an industry with large existing switching barriers, which allows the challenger to grow slowly (small position in a very large, addressable market with the negative feedback loop of high switching costs) and invest for the long term with a disruptive model that creates more NZS for the ecosystem constituents.

For further context on the idea of NZS, Robert Wright in his book “Nonzero: The Logic of Human Destiny” notes:

In short, both organic and human history involve the playing of ever-more-numerous, ever-larger, and ever-more-elaborate non-zero-sum games. It is the accumulation of these games—game upon game upon game that constitutes the growth in biological and social complexity...I like to refer to this accumulation as an accumulation of "non-zero-sumness." Non-zero-sumness is a kind of potential—a potential for overall gain, or for overall loss, depending on how the game is played.

If you will allow us to get a little bit more technical on game theory, the concept of Pareto efficient outcomes is an important factor in increasing NZS outcomes. For anyone who slept through the economics class on game theory, a Pareto efficient game outcome implies that no decision would make any one party better off without making another party worse off. In other words, we think of it as the point at which you stop creating NZS or positive-sum, win-win outcomes. Further, in our context, a Pareto optimal outcome is one in which any further changes in a transaction between a company and its constituents no longer produces more NZS.

So, what does that mean for investors, boards, and management teams? In terms of traditional ways of running a business, we argued in Complexity Investing that it does not always make sense to maximize pricing or earnings short term. Instead, a clear focus on long-term goals, innovation, and creating win-win outcomes are more important. In that context we argued for looking more broadly at a company and its interactions with customers, suppliers, employees, and shareholders – the more obvious upstream and downstream players in the game theory outcome.

In the first couple of centuries of capitalism as we know it, the Pareto optimal outcome for a company selling a widget to a customer generally assumed that you charge as much as a customer is willing to pay for the product. This generally involved exploiting some sort of information advantage – you had knowledge your customers didn’t have that allowed you to make an outsized return on a supply chain, brand, or pool of data. These are largely the pillars of Michael Porter’s “5 Forces” of competitive advantage, which we believe are increasingly tenuous in the modern era of capitalism. In the rising transparency of the Information Age and velocity with which information now travels, these legacy exploitations to maximize shareholder returns not only fall short, but can become an Achilles’ heel for companies and their employees, shareholders, suppliers, etc.

Our view of Pareto optimal outcomes in the 21st century Information Age, which we covered in Complexity Investing 5 years ago, defined win-win outcomes as creating more value for your constituents (customers, employees, suppliers) than for the company itself. This somewhat paradoxical strategy is often seen inside the big Internet platforms – in some ways the creators, or at least commercializers, of the Information Age. The compounding externalities of products like Google Search, Amazon Web Services, Microsoft’s Azure cloud computing platform, Netflix are obvious – customers save while also receiving a better solution to the legacy alternatives.

Our view has evolved since then, and we feel we did not go far enough in Complexity Investing when discussing NZS and optimal outcomes. Although we alluded to a broader context of game theory participants with an example on fast food (“Another example is fast food – it is quite cheap and appears to offer a NZS scenario, but when you take into account the long term healthcare costs and burden to society, it is not Pareto optimal.”), time has enabled us to see that company strategy must take into account the broader consequences of the environment and social impact of their products. The players of the game are no longer just a board of directors and a set of equity shareholders. The players of the game in the 21st century include our planet, the environment, society at large, and every human, animal, and plant on earth!

You can get wrapped up in a web of unintended consequences given the interconnectedness and complexity of the global economy – so rather than becoming too paralyzed to make any decision lest you harm a flea on the back of a rodent in some distant land – perhaps we need to simplify the situation. A simple question can help in the decision process to maximize NZS for the long term: “Are we creating more value for our constituents than for ourselves?” Here constituents includes employees, customers, shareholders, suppliers, the environment, and the broader social context. If a decision significantly makes one of these players worse off in order to grow corporate free cash flow in the short term, it’s probably not the best decision.

Microsoft CEO Satya Nadella highlights this strategy perfectly in comments to industry analysts in January of 2019:

"...so that’s why I’m not one of those guys who celebrates some market cap measure because I think all of that is – I mean that’s just not stable, at least not with our business model – because our business model fundamentally is about creating more surplus outside us. We will only be long term successful if people are making more money around us.

"...simply put for a multinational company we have to accept that unless you are really adding an economic service in every country you are operating in, truly, that is measured by employment, measured by taxes you pay, measured by essentially all of the prosperity around your activity that gets created locally, if that is not true, I just don’t see how the world just kind of says “let’s just go back to this thing”. I mean basically, look, globalization was fantastic except that it hollowed out the middle in many parts, not just in the United States, but all over the world and given that, I think everybody is going to be looking to say, “Ok how do I get back?” and the equation between innovation and democracy or whatever form of government they have and economic prosperity that’s broadspread because that’s the only way to stay in power and so given that we as a business community better be sure we are in harmony with those goals.

"...It means structuring simply a business model that allows you to create for every dollar that you make multiple orders of dollars beyond that in our channel broadly speaking. What are the local startups? What are the localized feeds? Who are the local SI’s? What’s the total employment digital skills inside of the companies?"

To contrast this idealized view of a NZS-driven corporate strategy, we were struck by news of activist hedge fund investors who claimed a board of a public company filing for bankruptcy had a singular duty to equity holders only. The specific company and investor involved here is less important than the illustration of a growing naivete on the part of investors who fail to see a broader duty for corporate boards emerging that encompasses both social and environmental issues. If a company neglects needed investment, perhaps even endangering lives or harming the environment, in order to maintain high dividends and share repurchases, such a company will likely find themselves without customers and perhaps even bankrupt long term.

We have largely focused on the consequences of a broader definition of fiduciary duty encompassing more players of the game for higher NZS outcomes, but there is a clear analogy to investors as well. As investors, we not only need to support and push management teams toward higher NZS decisions with broader constituents, we ourselves need to assure we are making decisions that create more value for our investors – active managers should not earn fees when they serially underperform benchmarks after fees. Whether we are managing a hedge fund, mutual fund, or act as an investment advisor or consultant in the industry, our customers need to win more than we win. That means aligning portfolio construction with the goal of long-term positive outcomes. It also means investing in companies that understand the rapidly evolving and highly complex world we operate in – companies that are accounting for all possible players in the game for optimal win-win outcomes. This also means lower initial management fees with a higher degree of alignment on performance fees tied to long-term value creation. Further, the employees and corporate structure of an investment firm need to take into account the same broader set of constituents as we recommend corporate boards and managements contemplate.

These last points are increasingly important as younger investors demand more accountability from the companies they work with and work for. The following is excerpted from Blackrock’s annual letter to CEOs of companies they invest in:

Companies that fulfill their purpose and responsibilities to stakeholders reap rewards over the long-term. Companies that ignore them stumble and fail. This dynamic is becoming increasingly apparent as the public holds companies to more exacting standards. And it will continue to accelerate as millennials – who today represent 35 percent of the workforce – express new expectations of the companies they work for, buy from, and invest in...

In the years to come, the sentiments of these generations will drive not only their decisions as employees but also as investors, with the world undergoing the largest transfer of wealth in history: $24 trillion from baby boomers to millennials. As wealth shifts and investing preferences change, environmental, social, and governance issues will be increasingly material to corporate valuations.

The Paradox of Platforms and NZS

Before concluding, there is an important point to make regarding platform businesses that are growing to dominate capitalism in the 21st century. We expanded considerably on this topic in Complexity Investing, so will briefly set the table for a discussion here on NZS and platforms. When we talk about platforms, we are discussing those businesses that drive significant network effects or positive flywheel outcomes where the bigger they get, the bigger they get, and so on. For example, as Netflix obtains more data on viewing habits, it informs their spending on content, which drives more viewers, which drivers more data, etc. Likewise, as more people use Google’s products like search and maps, these products become smarter and drive more usage, which allows them to be smarter, etc.

The heart of this trend is data. Data feed artificial intelligence, which feeds decision making, which tends to make successful companies even more successful. This is the power law math that we discussed at length which governs complex adaptive systems such as the global economy. The paradox is that we have already seen many companies who generate significant amounts of NZS fall victim to questionable decision making as they grow more and more powerful. Examples abound of abuse of privacy, environmental damage, or unnecessary increases in social inequality.

This problem of high NZS outcomes co-existing alongside the risk of abuse of power by large network effect platforms in the digital age calls for a high degree of mindful and conscious decision making by boards, management teams, and investors. When a decision is made to collect and leverage certain types of data, we have to ask whether the long-term optimal outcome will be derived from the decisions we make today. If we foresee potential for a zero-sum or negative-sum outcome, we have to rethink the decision. As Satya Nadella stated in the prior quote, worth repeating here:

“...simply put for a multinational company we have to accept that unless you are really adding an economic service in every country you are operating in, truly, that is measured by employment, measured by taxes you pay, measured by essentially all of the prosperity around your activity that gets created locally, if that is not true, I just don’t see how the world just kind of says “let’s just go back to this thing.”

Conclusion

Our goal here is to convince economic actors – specifically boards, management teams, and investors – to think much more broadly about business and investment decisions. While we used to generally try to optimize for two players in a game – company and customer or mutual fund and investor, for example – that is no longer sufficient. The definition of fiduciary duty is changing in the 21st century and now includes disclosure of a wide range of bad behaviors. Transparency is rising and the velocity of information requires a focus on long-term non-zero-sum-maximizing decisions. Those decisions often, paradoxically, do not maximize traditional measures of shareholder returns in the short term, but will create bigger and stronger companies longer term that have an ability to more positively impact society and the environment. This critical type of thinking requires a high degree of mindful and conscious decision making with longer time horizons. Reorienting a corporate culture toward a long-term decision making framework is crucial to success, and that reorientation must start at the top of an organization and align incentives all the way down to impact even the smallest of decisions. Rather than become paralyzed by a series of compounding unintended consequences of every decision no matter how big or small, we argue to take into account your employees, customers, suppliers, and the broader environment and social consequences and simply ask the question: “Are we creating more value for our constituents than for ourselves?”

Tech Regulation White Papers

How I Learned to Stop Worrying and Love the Monopoly
Successfully building a business with potential to extract monopoly-like profits is a prize that, so far, has moved civilization forward faster than any other pot of gold at the end of any rainbow. It’s the carrot of capitalism that has pulled more people out of poverty and improved more lives than any other system in the dozen millennia since we settled down from our hunter/gatherer lifestyle.

Monopolies are awesome. Well, at least for a while, but then they get a little too big, a little too powerful, and sometimes a little too greedy. Maybe it’s intentionally bad behavior, maybe it’s just the system they operate under, or maybe it’s just a law of nature. Then society and the government catches up and restricts monopolies either by placing guardrails on them or breaking them up. In doing so, we often see the phenomenon of regulatory capture: new regulation cements current monopolies by raising the barriers to new entrants. Monopolies are kept in their lanes and they can’t enter new vertical or horizontal businesses. Sometimes, this regulation turns into a quagmire of unintended consequences with devastating impacts, where big business and big government create significant damage and inequality – it's a tale as old as capitalism. 

Certain gears of civilization, like technology, turn faster than others, like government. Eventually the government gear spins fast enough to catch up...and then technology races ahead again. We’ve seen this phenomenon over and over in multiple industries over the last 200 years – it's at the heart of our paper Pace Layers: Tech Platforms, Regulation, and Finite Time Singularities.

As monopolies amass more power, they eventually slow down under their own weight. They reach the top of their classic S-curves, providing welcome governors or brakes, on the pace of progress. In this sense, monopolies are an effective mechanism to slow down time (we talked in more detail about the importance of slowing down time in our paper Redefining Margin of Safety). If they propelled us forward at an unstoppable pace, innovation and progress would run too fast for the slower layers of society to catch up. On the flipside, if we had perfect textbook competition, we would probably either end up with 1) severely attenuated innovation (as there would be little chance of gaining majority market share and amassing a pot of gold, and thus there would be reduced motivation to try), or 2) creative destruction would move society forward at an unstable pace in a state of ongoing punctuated equilibrium.

Monopolies can be good for humans because they allow innovation to push us forward as fast as possible without completely breaking the machine. They allow more people to live better lives in total over time. And, when the government catches up and cements their position while keeping them from expanding their territory, they can often enable brand new businesses and innovation to thrive. AT&T birthed the semiconductor. Standard Oil enabled cars. Microsoft built the monopoly operating system of the PC, and then, through government action, enabled Google, Amazon, Facebook, etc. who in turn enabled Uber, Lyft, AirBnB, and countless new and innovative businesses. If Microsoft had controlled the Internet, we’d be much worse off, but if Microsoft didn’t exist at all, or was created decades later, we wouldn’t have seen the massive waves of innovation that have positively impacted lives around the world.

There are, however, many instances of destructive government involvement enabling destructive monopolies – such as the case with the entire US healthcare industry. The banking and agriculture sectors are also examples of monopoly-government relations gone bad. But, even then, the outcomes are not entirely black and white. Monsanto has a near monopoly in seeds and pesticides, but their mission was to feed the world. The complexities here require us to be very thoughtful about the interplay of innovation, monopolies, lobbying, and regulation. In particular, the political lobbying machine in the US has murdered innovation in industries like healthcare and finance, and there’s a good chance the ramping political engine of Silicon Valley could do the same damage in the tech sector as well. 

In the Information Age, monopolies will form faster and accumulate more data and network effects than in the Industrial Age, which is a problem because they can do more damage at a faster pace. There are victims along the way, and it’s a philosophical question as to whether the harm justifies the lifting of countless others out of poverty (oversight and a case-by-case analysis is warranted here). Clearly, the progress of capitalism has harmed the environment and stretched inequality to a breaking point; however, we can’t go back to living in caves. These are issues humans need to solve as the first round of Information-Age giants are regulated to enable the next round of AI, augmented reality, IoT, and other innovations we can’t even yet imagine. 

We cannot approach regulating Information-Age monopolies the same way we tackled Industrial-Age giants, which brings us to our paper Tech Regulation: Jamming a Power Law Back into a Bell Curve Won’t Work. Rather than over regulate, break up, or punish Information-Age monopolies, we should instead free the data. Consumers and companies should have 100% control of who collects what data when, and which companies can use that data. We should have complete control of our data and we should be able to allow new, innovative companies access to that data to build new products to improve more lives – to build the next generation of monopolies that, 20 years from now, will require regulatory action. I look forward to reading about that situation on my augmented-reality glasses while I am flying in my autonomous, electric copter eating sustainable, lab-grown meat while free of all the medical conditions for which I am genetically at risk. 

I’ll bet it’s monopoly-like businesses that create the innovation that solves the environmental crisis, the healthcare crisis, inequality, and other challenges facing humans in the 21st century. At NZS Capital, we believe it’s those companies creating maximum non-zero-sum, or win-win outcomes, that will be the preeminent businesses of the future: rendering themselves irreplaceable to their customers, insulating themselves from competition, and creating more value for their customers, employees, and the world than they create for themselves. The principle of NZS is a logical way for Information Age platforms, and even monopolies, to secure their own success and propel the world forward.

So, we would do well to stop overly worrying about monopolies because they will enable the most progress for society. That's not to say we shouldn't keep a watchful eye, and step in with a heavy hand when necessary, but we should not attack large companies or billionaire entrepreneurs as an evil to be eradicated from the planet. Eventually, monopolies will either collapse under their own weight, or they will be limited by regulation. However, negative feedback loops of government regulation, regulatory capture, and lobbying can cripple innovation and progress. We should be especially sensitive to this potential risk today with tech platforms and push for regulation via data democratization and personal control. 

The Following Two White Papers explore NZS Capital’s thoughts on regulation and the technology sector.

Tech regulation: trying to jam a power law back into a bell curve won’t work (PDF)

Pace Layers: Tech Regulation (PDF)