Making progress — in society, a team, or life — isn’t straightforward most of the time. Knowing where you want to go is generally the first step, but the destination can be very broad. And even if there’s a specific goal, the path to get there may be very indirect.
As strategy transitions into execution, it’s important to understand how these attributes affect progress. If an effort is managed or guided the wrong way, it may be doomed to failure no matter how difficult it is.
Project management as a discipline works great, but not with everything. Managing large, more uncertain endeavors in particular has been problematic recently. The wide-scale ongoing effort to fight COVID-19 and return the world to normalcy brings this challenge front and center.
Why are certain efforts harder than others and how do we navigate them? How were we able to accomplish such large scale collaborative efforts such as the Apollo program or Manhattan Project, but can’t do the same thing for curing cancer?
If we want to build, we need to understand the answers to these questions. The following is a framework for classifying efforts by the certainty of both their objectives and the paths to achieve them. Knowing which “mode” an effort is in is critical to understanding and managing its progress.
How do we classify efforts into modes? The best paradigm I’ve come across is the how/what quadrants.
In his 1994 book “All Change!”, Eddie Obeng described 4 different types of projects along with the difficulties and peculiarities of each: quests, movies, painting by numbers, and fog. It turns out putting a project on both the know how and the know what scales tells you a lot about how it should be managed.
Venkatesh Rao explored the concept much further in his essay on the “Grand Unified Theory of Striving”, pulling in other ideas like convergent thinking, critical paths, and lean methodology. Venkat’s visualization of the critical paths and point frontiers of each quadrant is a particularly insightful way to think about the concept.
Defining the dimensions and modes
Here’s how I’d describe the axes of the 2×2:
“Why”-axis — Know what vs. don’t know what. Do you know what the goal is? How specific is the desired outcome? Not knowing the goal (or having a very broad idea) is in the realm of divergent thinking: there are many potential solutions and progress can be non-linear. It’s the exploration phase of the explore vs. exploit tradeoff, searching for goals or areas of value.
Knowing what and why is in the realm of convergent thinking: there is a single “correct” solution or destination. It squarely aligns with Peter Thiel’s deterministic approach of viewing the future: “There is just one thing—the best thing—that you should do.”
Horizontal-axis — Know how vs. don’t know how. Is it known how to accomplish the goal? Are the bottlenecks or resource-sensitive parts generally understood? When you know exactly how to accomplish something, there is a clear critical path1 (the red lines in the diagram below). Other paths of effort may still be required, but they are oblique with more slack, running parallel to the critical path.
Knowing how allows you to operate lean because you can—in theory—use the least amount of resources necessary to get the job done. In the fat mode of operation, you don’t really know how to reach your goal. You can’t be efficient because you don’t know how to be, and there will be a lot of slack in the system. The path is determined opportunistically as you go, with critical paths only in smaller subsections.
“An old story tells of a visitor who encounters three stonemasons working on a medieval cathedral and asks each what he is doing. ‘I am cutting this stone to shape,’ says the first, describing his basic actions. ‘I am building a great cathedral,’ says the second, describing his intermediate goal. ‘And I am working for the glory of God,’ says the third, describing his high-level objective. The construction of architectural masterpieces required that high objectives be pursued through lesser, but nonetheless fulfilling, goals and actions.”
John Kay, Obliquity
All efforts, from daily personal projects to global collaborative endeavors, fit in a webbed hierarchy of abstraction.
Understanding the full hierarchy of an effort is critical to accomplishing it, along with its higher-level objectives in the long-term. Not understanding it can result in bad planning, mismanagement, and failed expectations.
Ladder of Abstraction
“…the most powerful way to gain insight into a system is by moving between levels of abstraction.” — Bret Victor
The ladder represents a top-level concept or domain, with each rung a subset of the one above it. The rungs move from abstract at the top, to concrete at the bottom. The lower down, the more detailed and specific. The higher up, the broader and more abstract the concept.
The model is very simple, and can be applied to almost any discipline with a hierarchy of nested groups. This includes applying it to efforts.
First of all — what do I mean by effort?
An effort is the active search for the best outcome of an objective. It encompasses both the objective and the pursuit of that objective — both of which are not fixed and can evolve over time. The objective always has some boundaries, but otherwise can be very broad (“solving climate change”) or very narrow (“double next-month’s sales volume”).
All efforts are multi-scale and nested.1 This means we can put them on a ladder of abstraction, each rung with an objective or method that’s a prerequisite of the one above it. Lower-level goals are nested in higher-level purposes. Good project managers do this intuitively when breaking an objective down into tasks and sub-goals, mapping their dependencies.
Because efforts can have many dependencies and relationships aren’t just one-to-one, they exist in more of a webbed hierarchy of abstraction than a ladder. A simple one-dimensional ladder of abstraction is just a slice of the larger hierarchy.
Here’s an example of a hierarchy of abstraction for the efforts relating to Covid-19:
What’s the best way to determine the hierarchy of abstraction for an effort?
A simple way to move up and down the ladder is the Why/How Chain. To move up, ask “Why?”; to move down, ask “How?”. Many know this technique from the Toyota Production System’s method of asking 5 Why’s to find the root cause of an issue.
You can start by finishing the phrase: In what ways might we ___? This method can work on almost everything, from large-scale efforts to small-scale jobs-to-be-done:
⬆️ Why? To make my home look good.
⬆️ Why? To hang a picture.
❇️ In what ways might we drill a hole?
⬇️ How? Use a drill.
In the Covid-19 example, you could start at whatever level is most relevant to you.
❇️ In what ways might we provide better medical care for COVID patients?
⬆️ Why? To stop people from dying and reopen the economy.
⬇️ How? Protect medical workers from getting sick.
⬇️ How? Source and distribute PPE.
⬇️ How? Contact regional manufacturers.
There will always be multiple “how”s, which is the essence of breaking a goal down into sub-goals. There can be multiple “why”s as well, especially the further you go down the ladder. But high up in the hierarchy the whys and hows become more and more vague. This means you have to approach them in a completely different way.
Abstraction = Obliquity
Knowing where an effort fits on the hierarchy is the first step. Now we need to understand how the different levels of scale need to be treated.
This is where John Kay’s concept of obliquity, from his book of the same title, comes in.
To solve a problem obliquely is to solve it through experiment and adaptation. In general, the bigger the scope and complexity of an objective, the more indirect the path is to achieve it.
The ladder of abstraction is a proxy for obliquity. The higher on the ladder, the more adaptive the problem should be solved. John Kay: “High-level objectives — live a fulfilling life, create a successful business, produce a distinguished work of art, glorify God — are almost always too imprecise for us to have any clear idea how to achieve them.” In the process of making progress on these objectives, we don’t only learn how improve, but “about the nature of the objectives themselves.” You’re wayfinding, rather than following a prescribed path.
The lower on the ladder, the more direct. “Directness is only appropriate when the environment is stable, objectives are one-dimensional and transparent and it is possible to determine when and whether goals have been achieved.”
The following table compares the different aspects of both ends of abstraction.
Clear and simple
Loosely defined and multidimensional
Most outcomes are intended
Outcomes arise through complex processes with no simple cause and effect
Interactions with others
Limited and predictable
Dependent on many variables, including interpretation of them
Range of available options is fixed and known
Only a subset of options are available from successive limited comparison
Can be described probabilistically
Uncertain: Range of what might happen is not known
Insists on consistency: always treating the same problem the same way
Consistency is minor and possibly dangerous — rare that same problem is encountered twice
Conscious maximization of objectives
Adapt continuously to changing circumstances
Consistency is vital when you’re low on the ladder, not so much higher up. “The oblique decision maker, the fox,” John Kay remarks, “is not hung up on consistency and frequently holds contradictory ideas simultaneously.”
But the real power of solving an oblique problem lays in adaptation: “If the environment is uncertain, imperfectly understood and constantly changing, the product of a process of adaptation and evolution may be better adapted to that environment than the product of conscious design. It generally will be.” There is no map, so instead you have to wayfind and look for clues in front of you, making your way with the tools you have at hand.
Keep in mind again that this is a scale — it’s rare that an effort would completely check all the above boxes for either Direct or Oblique. The point is that efforts always fall somewhere on the scale and that this determines the best methods to pursue them.
Consequences of Misplaced Directness
I’ll try to keep this section short, as whole books have been written on the consequences of misplaced directness. See Nassim Taleb’s Incerto for example.
Attempting to approach a large, complex effort too directly almost always leads to failure — or at the very least a failure to meet initial expectations. Directness is only appropriate when the objective is one-dimensional and the path to achieve it is known.
The intention was to create a new Brazilian capital from scratch that was truly unique and modern, paying special attention to cars and traffic flow. (This was the same time period the U.S. began building out the intercontinental highway system.)
As time went on, unforeseen circumstances in the messiness of the real world intervened. Overpopulation drove traffic congestion, slums, and general inequality. The focus on form over function from the top-down design caused alienation and poor quality of life. This is exactly why any such an effort can’t be planned with precision. Not only are the details of the true goal not understood, but the methods to achieve it involve unpredictable complexity. They’re in the world of “extremistan” as Taleb would say.
“The structures in this artificial capital are impressive,” read an FT article on one of the architects, “yet few want to walk its barren streets. Politicians leave as soon as possible to return to grittier, but livelier, Brazilian cities.”
The Brasilia master plan was partly based on architect Le Corbusier’s misplaced utopian vision of creating the ideal city. Corbusier’s work also included the Indian city of Chandigarh with similar consequences. This was, in the words of John Kay, “the hope that rational design by an omniscient planner could supersede practical knowledge derived from a process of adaptation and discovery.”
Many overfunded startups suffer from the same fate. When you have access to seemingly unlimited resources, it’s easy to be fooled into thinking you can build your exact vision into reality. But these visions generally exist in a complex world of human culture, desires, and economic feedback loops.
Quibi — the $1.8 billion funded short-form media startup — is case in point. Ultimate success or failure remains to be seen but results have yet to come close to expectations. They had the resources to build a particular product and business model into reality — a reality where most customers don’t seem to want what they’re selling. A destination was chosen on a map that couldn’t be seen.
Magic Leap has an amazing vision of seamless AR glasses to enable a digital layer on top of the real world. The actual objective in this case might actually be the right destination. But the complexity of the problem means the approach still can’t be direct. Currently their product has seen minimal success as they struggle to find a sustainable business model.
WeWork seemed to start at the right level of abstraction, then an ambitious “visionary” founder is given unlimited funds, and a direct approach is applied to a still-oblique problem.
Summary: The Right Strategy for the Right Level
All efforts, and the efforts within them, can be placed on a ladder of abstraction. The higher up you go, the less concrete the objectives and less straightforward the methods to achieve them.
Where the effort falls on the scale is critical to the strategies for making progress on them. Direct, methodical approaches are only appropriate at lower, smaller-scale levels. This is where it’s good to plan details, use processes, and keep things consistent.
You can still have a grand, abstract vision. You just need to wayfind to get there: working from the bottom up, adapting and evolving the path while shaping and refining the details of the goal. Keep things flexible, adaptive, and opportunistic at the top.
Organizations: Given the definition of an effort above, what about coordinated groups of efforts or goals? ↩︎
In the book The Origin of Wealth, Eric Beinhocker describes organizations as “goal directed, boundary-maintaining, and socially constructed systems of human activity. . . . There is a boundary distinguishing the inside world from the outside world, and the goals of the organization drive activities that lower entropy inside the organizational system.” This is the best description I’ve come across given its abstract nature. But I’d like to propose simpler, yet still compatible definition.
An organization is a group of people pursuing one or more ongoing efforts, generally with the same high-level objective. This means an organization can be anything from seven-person hunting parties, to fleets of exploratory vessels, to philanthropies, to a multinational conglomerate.
In April, Marc Andreessen put out the call to build. It was in response to our failure to control and mitigate the effects of Covid-19 — institutions on every level were unprepared for the pandemic, and have continued to show their inability to quickly find and scale solutions.
But more than anything it was in response to our failure to build in general. We chose not to build, he claims. “You see it throughout Western life, and specifically throughout American life.” The problem isn’t a lack of resources or technical ability — it’s with supply and demand of desire. Demand is limited by our ambition and will to build. Supply is limited by the people and organizations holding it back.
Andreessen is generally an optimist, which is why I see his essay as positive in overall tone. But it was also somewhat of a mea culpa. Andreessen has for years been on the other side of Peter Thiel’s view of modern technical stagnation.
Thiel’s view may be too pessimistic, but there’s a kernel of truth to it. If you’re familiar with the history of tech and innovation, something feels different. The late-1800s to mid-1900s had explosions of innovation in fields from medicine to consumer products, transportation, energy, communication, computing, food, and more.1
This is the introduction to a series of ongoing essays centered around the question:
What frameworks can help us build more, better?
And further attempting to investigate the answers to the following:
What are the best ways to approach solving big, complex problems?
Why are certain efforts so much harder to achieve than others?
How are these efforts best managed at every level?
How do we build things faster? (Without sacrificing quality or safety.)
What is holding us back from building more?
How do we overcome these barriers?
Many of these lessons apply not just to “building” in the physical sense, but for solving problems, scientific discoveries, improving systems, and making progress overall. Building in a way is symbolic. It represents making big, necessary changes to move humanity and our planet forward. This can be building something physical or digital, pushing the boundaries of fundamental research, or trying new uncertain ways to solve problems.
It doesn’t even have to be anything new or innovative per se. Andreessen gives many examples of expanding existing tech: housing, infrastructure, education, manufacturing. Even preservation and restoration — in many ways opposites of building — can still apply. In the early 1900s, President Teddy Roosevelt established over 230 million acres of public lands and parks. This added an incalculable amount of value to future generations. I would love to see E.O. Wilson’s Half-Earth Project executed at scale. This is in the spirit of building: making progress and pushing humanity toward a better future.
Here’s a preview of some of the specific topics I want to explore in the series: Ladders of Abstraction (why/how chains), Oblique vs. direct approaches, Modes of effort (why/how quadrants), traversing fitness landscapes, the explore vs. exploit tradeoff, the role of trust in building things fast, forcing functions, and the specific methods we used to accomplish large-scale collaborative efforts such as the Apollo program, the Manhattan Project, etc.
Wayfinding Through the Web of Efforts[8 minutes] — Putting goals on a ladder or hierarchy of abstraction. Defining efforts and their multi-scale nature. Determining the hierarchy of efforts using a why/how chain. The difference between making progress directly and obliquely, and the consequences of misplaced directness.
Managing Modes of Effort[10 minutes] — A framework for understanding how managing progress differs across scales of effort. Classifying efforts into four modes on the how/what quadrants. Defining the modes and how they fit on the hierarchy of abstraction. A Covid-19 case study. How to manage an effort based on its mode.
What was different about this era? The following is a good rundown from Vaclav Smil’s book “Creating the Twentieth Century” on the remarkable attributes of the pre-WWI technical era:
The impact of the late 19th and early 20th century advances was almost instantaneous, as their commercial adoption and widespread diffusion were very rapid. A great deal of useful scientific input that could be used to open some remarkable innovation gates was accumulating during the first half of the 19th century. But it was only after the mid-1860s when so many input parameters began to come together that a flow of new instructions surged through Western society.
The extraordinary concentration of a large number of scientific and technical advances.
The rate with which all kinds of innovations were promptly improved after their introduction—made more efficient, more convenient to use, less expensive, and hence available on truly mass scales.
The imagination and boldness of new proposals. So many of its inventors were eager to bring to life practical applications of devices and processes that seemed utterly impractical, even impossible, to so many of their contemporaries.
The epoch-making nature of these technical advances. Most of them are still with us not just as inconsequential survivors or marginal accoutrements from a bygone age but as the very foundation of modern civilizations. ↩︎
Whole Earth Discipline: An Ecopragmatist Manifesto
Ecological balance is too important for sentiment. It requires science. The health of natural infrastructure is too compromised for passivity. It requires engineering. What we call natural and what we call human are inseparable. We live one life.
We are forced to learn planet craft — in both senses of the word: craft as a skill and craft as cunning. The forces in play in the Earth system are astronomically massive and unimaginably complex. Our participation has to be subtle and tentative, and then cumulative in a stabilizing direction. If we make the right moves at the right time, all may yet be well.
“Find (a) simple solutions (b) to overlooked problems (c) that actually need to be solved, and (d) deliver them as informally as possible, (e) starting with a very crude version 1.0, then (f) iterating rapidly.” — Paul Graham
For sensitive ecosystem engineering at planet scale, what we need most is better knowledge of how the Earth system works. We are model-rich and data-poor. We need to monitor in detail and map in detail what’s really going on, and the measuring has to be sustained and consistent. Donella Meadows laid down the commandment: “Thou shalt not distort, delay, or sequester information.” You can drive a system crazy by muddying its information streams. You can make a system work better with surprising ease if you can give it more timely, accurate, and complete information. We must build a digital Gaia.
“A project is sustainable if it is cheap enough to be the first of a series continuing indefinitely into the future. A project is unsustainable if it is so expensive that it cannot be repeated without major political battles. A sustainable project marks the beginning of a new era. An unsustainable project marks the end of an old era.” — Freeman Dyson
One important negative feedback may be operative. The world’s land areas are absorbing more carbon dioxide than they’re releasing lately. “Believe it or not, plant life is growing faster than it’s dying. This means land is a net sink for carbon dioxide, rather than a net source.” This might be due to simple CO2 fertilization–additional CO2 stimulates plant growth.
In Jim Lovelock’s worst-case climate scenario, Earth stabilizes at 9°F warmer; a fraction of the present human population survives. But the exact outcome in such a complex system is unpredictable. Threshold effects are sneaky. At some point, though, a threshold is reached. Then in an unstoppable cascade the rain forests melt like Arctic ice, leaving savannah, scrub, and desert in their place.
Humanity currently runs on about 16 terawatts of power. We have to cut our fossil fuel use to around 3 terawatts a year, and we have to do it in about 25 years.
On the old astronomical schedule, a new ice age should have begun a couple thousand years ago. “A glaciation is now overdue, and we are the reason.”
Our terraforming thus far has been unintentional. Now that we have the curse and blessing of knowing what’s going on, unintentional is no longer an option. We finesse climate, or climate finesses us.
The following are my thoughts taken from a memo to family office investors I sent out today regarding the pandemic.
These are unprecedented times in modern history. Not since World War II has there been such a large disruption in daily lives across the world at such a quick pace.
The pandemic we’ve entered is a classic Black Swan — an unpredicted event that has extreme consequences. Of course, Black Swan events are relative. A surprise to you or I may have been wholly anticipated by others. And in this case, it very much was.
To epidemiologists and people who had seriously thought it through, a global pandemic quickly sweeping humanity was an inevitability. It was a matter of when, not if. In 2018 Bill Gates gave a short TED Talk about the dangers of a global flu-like pandemic and the measures we could take to help prevent or reduce it. As we’re now aware, the advice was unheeded.
The human lives lost from the virus will be a tragedy of epic proportions. The current and upcoming economic malaise may be nearly as bad — particularly affecting those without the means to ride it out. Recent wide-ranging government stimulus and intervention can soften the blow, but ultimately the only solution is getting rid of the virus.
We will get through this, as humanity has always done in the past. When the entire world has a common enemy, people get creative. Everyone should expect the world to look different after. Especially in areas like healthcare, biotech, and government.
These differences will all be for the better. Humanity is always searching for higher peaks of “fitness”, and on the rough landscape of possibilities sometimes you have to go down to eventually go up. Life getting worse before it gets better has always been a common theme. From the shift to agricultural societies, to world wars, to global pandemics.
We just need to work together to get through it first.
Back in December I read the book “Why We Get Sick” (1992) by Randolph Nesse and George Williams. While some of the information was outdated due to its age, overall I loved the book as it took a more wholistic, evolutionary approach to explaining sickness.
Given the global pandemic of 2019-nCoV (novel coronavirus) underway and the timely nature of my read, here are my brief notes I took from the book.
Why We Get Sick
Two kinds of explanations for disease:
Proximate explanations — Answer “what” and “how” questions about structure and mechanism. Address how the body works and why some people get a disease and other’s don’t. A proximate explanation describes a trait — its anatomy, physiology, and biochemistry, as well as its development from the genetic instructions provided by DNA.
Evolutionary explanations — Answer “why” questions about origins and functions. Show why humans, in general, are susceptible to some diseases and not to others. (Or why some parts of the body are so prone to failure.) An evolutionary explanation is about why the DNA encodes for one kind of structure and not some other.
Defenses. Mechanisms our body and immune systems designed specifically to combat an issue. A protective response to a problem. Coughing is a defense. The distinction between defenses and defects is important — defects are not preprogrammed responses, they are results of a problem. Skin turning blue from lack of oxygen is a defect.
Causes of disease:
Infection. External agents such as bacteria and viruses.
Novel environments. Environments our evolved bodies aren’t used to handling. A mismatch between our design and our environment.
Genes. Some of our genes are perpetuated despite the fact the cause disease. In the environments we evolved in, they didn’t harm us enough not to be selected out. DNA can also be mutated and create new bad genes.
Design compromises. There are costs associated with every major structural change preserved by natural selection.
Evolutionary legacies. Evolution is incremental and can’t make major changes quickly. Many of the design choices are not optimal and carry on anyway.
Signs and symptoms of infectious diseases
Symptoms of colds and other sicknesses and diseases can be unpleasant. But most of them are useful. It is an adaptation shaped by natural selection specifically to fight infection.
Fever is an adaptation to raise body temperature enough to assist with fighting infection. Body temperature is carefully regulated even during fever; the thermostat is just set a bit higher. Children who take Acetaminophen take about a day longer to recover from chicken pox. There are costs of a fever, of course. Otherwise the body would just always stay at 103F at all times. It depletes nutrient reserves 20% faster and causes temporary male sterility. Still higher fevers can cause delirium and lasting tissue damage. And because regulatory precision is limited, fever will sometimes rise too much and at other times not enough.
Competitive advantage can be represented visually as 1 or more feedback loops. These create the advantage “flywheel” that maintain and grow a moat over time. Think of a big, heavy wheel that takes some effort to get started but then coasts off its own momentum.
Here are 6 simple examples of common advantages represented as flywheels (or “causal loops” in systems terminology). These loops are generalized — they’ll be expressed uniquely in every company that has them.
A few examples of how each advantage flywheel can vary:
In the Economies of Scale flywheel above, the primary driver of more volume is low prices. This fits for most consumer businesses, but lower prices aren’t always the outcome of lower unit costs. If prices are maintained or increase, scale will yield higher margins → more resources to spend on growth → more sales volume.
The Brand Habit flywheel exhibits the typical loop for habit-reinforcing association of a brand with a specific quality or job-to-be-done. Think “thirst quenching happiness” for Coca-Cola and “low prices” for Wal-Mart. Another example of brand advantage is more of a social proof effect: Product has success → the cool kids want it → improved perception of product → …
As Eric discussed in his flywheel post, each wheel needs a push to get started. Written in green on a few of the archetypes above are initial advantages to get the wheels moving. Whether it’s a better user experience, a technical breakthrough, or a bootstrapped network based off of an existing network (college campuses for FB) or a useful utility (Instagram).
Real world examples
The above archetypes can be combined to create more comprehensive flywheels modeling the driving “engines” of each company’s moat:
The most successful moats have multiple flywheels that feed off of each other’s momentum. Google’s technical advantages enable stronger brand allegiance and vice versa. Coca-Cola’s marketing-driven brand feeds off of it’s distributor/bottler based network effects. Facebook’s brands have at least 3 reinforcing network effects: direct (social network), 2-sided aggregator (advertising and developers), and brand-driven social proof.
Friction and limiting factors
In systems thinking, reinforcing feedback loops are almost always slowed by a balancing loop attached to it. Growth doesn’t continue unchecked, and flywheels always run into friction.
Some of these limiting factors are overcome, others are so strong they stop or reverse the entire growth engine.
What are some typical examples?
Switching costs & network effects — product quality slips as the incentives to improve aren’t strong when customers can’t leave → value of a competitive offering overcomes switching cost.
Learning curve of proprietary tech — hitting top of the S-curve, output efficiency declines, and competitors catch up.
Direct network effects — any source of decreasing value to users, which could cause users to exit and turn the virtuous cycle into a vicious one.
Using the analogy of a feedback loop helps to think of an advantage as a moving, changing system. A system that needs catalysts to get started, and will gain momentum at first but still be slowed by friction over time.
When thinking about how a business will grow over time, ask:
What advantage archetypes does it fit?
Where are the sources of positive feedback?
How do you get the flywheels moving? What strategies can help get inertia? (For example, “doing things that don’t scale.”)
I just finished 2 books on the history of Polaroid 🌈1. A remarkable tech company with enormous success in consumer and industrial applications for decades. It’s also remarkable just how much Apple was influenced by Polaroid.
A brief history
As a child Edwin Land found a copy of the 1911 edition of Physical Optics, a textbook by the physicist Robert W. Wood. He obsessed over its contents, lingering on one chapter in particular: the polarization of light.
In 1928, Ed Land was 19 when he invented the first thin-sheet polarizer. He cofounded Land-Wheelwright Labs with a friend in 1932 after dropping out of Harvard. Their first products were polarized versions of headlights, sunglasses, etc.
They grew slowly with mostly small industrial contracts for 6 years, then reincorporated as Polaroid Corporation. During the war sales grew an order of magnitude, 80% of which went to the military for products like polarized goggles.
In 1943 Land came up with the idea for a film camera that can process right away instead of in a lab. R&D started immediately, but it wasn’t until 1948 their first camera, the Model 95, was released. It went on to sell 900k units in 5 years.
The 95 was a classic disruptive innovation: worse quality than traditional film cams, dismissed as not “real” photography, but appealing to a new market of customers. And profitable: camera for $90, film packages with 60% gross margins.
With all the new cash flow, they could plow it back into R&D. To Land, they had “. . . created an environment where a man was expected to sit and think for two years.”
Polaroid’s growth lasted decades longer, peaking in the ’80s right when, ironically, they won an historic years-long lawsuit against Kodak for patent infringement.
Apple, the spiritual successor
Back to the Apple comparison. The similarities are clear: from values, to marketing, to org structure, to product launches and demos.
Just like Jobs, Land was at the top of every invisible organizational chart. An anonymous former colleague: “Don’t kid yourself, Polaroid is a one-man company.”
When faced with scientific illiteracy or lack of imagination, Land resorted to a restrained bit of showbiz. As it turned out, he was strikingly good at explaining his work to people, and powerfully persuasive.
Ed Land was one of Jobs’ childhood heroes. Jobs met with him later and connected when when Land said his products have always existed, they were just invisible: waiting to be discovered. Apple exemplified Land’s motto “Don’t do anything that someone else can do.“
Polaroid’s downfall started long before the digital apocalypse with their sidelining of Land in the ’80s. His final mistake was giving little thought to his own succession and the future of the company in the new generation. When they all but kicked Land out, Jobs met with and scolded management, saying Polaroid would turn into “a vanilla corporation”.
And it did. Jobs would take this lesson to heart many years later with his own succession plan.
I’ll finish with a Land quote from 1970: “We are still a long way from the… camera that would be, oh, like the telephone: something that you use all day long … a camera that you would use as often as your pencil or your eyeglasses.”
Many of these numbers are rough estimates. Figures adjusted for inflation after 1900 that weren’t already. Any figure before 1900 was adjusted via per capita GDP to more accurately reflect the scale of the undertaking.
If it were possible, the best metric to compare the scale of projects would be something like “Man-years + Value of Raw Materials (possibly in ounces of gold)“. This is especially true for projects like the Great Pyramid, the Suez Canal, the Great Wall of China, or the Manhattan Project which used mostly unpaid or low-paid labor.
In the coming years, Tokenized Securities are poised to take over existing financial markets and create many where they didn’t exist before. This is only now possible due to the invention of decentralized blockchains along with the recent influx of interest and capital.
So what are they? Here are a few good resources to start with:
There’s plenty of related buzzwords like blockchain, crypto, ICOs, colored coins, etc., but forget all of those for now. Tokenized Securities are digitized, programmable ownership. Legal ownership requires enforceable scarcity. Normally anything digital isn’t scarce, but they can be thanks to decentralized ledgers (blockchains).