Hello again! These are my latest thoughts on the areas I’m interested in. I hope you’ll enjoy learning more.
In this roundup:
What more can be said about the AI boom that began its ascent less than a year ago? A lot! The potential of AI is immense and its influence on our lives is sure to be significant. And so I’ll continue. . .
In this essay I’ll focus more on Large Language Models (LLMs), but my thoughts apply to all other AI efforts as well.
An easy way to think of it is that LLMs will soon become the “autocomplete for everything”:
What’s common to all of these visions is something we call the “sandwich” workflow. This is a three-step process. First, a human has a creative impulse, and gives the AI a prompt. The AI then generates a menu of options. The human then chooses an option, edits it, and adds any touches they like.
. . . So that’s our prediction for the near-term future of generative AI – not something that replaces humans, but something that gives them superpowers. A proverbial bicycle for the mind. Adjusting to those new superpowers will be a long, difficult trial-and-error process for both workers and companies, but as with the advent of machine tools and robots and word processors, we suspect that the final outcome will be better for most human workers than what currently exists.
There seems to be an unlimited number of areas that language prompting + completion will enable. Some are obvious: a new iteration of Google, help with writing, content generation, help with marketing copy, etc. You see many startups and tools that have already sprung up to tackle these.
Some of the real interesting applications that are incubating now will have action models as a big component. Models will have the ability to take actions like: searching the web; ordering an item; making a reservation; using a calculator; or using any other digital tool that humans are capable of using. Imagine ChatGPT being able to confirm its answers with multiple sources, or having access to all your personal records it can use to assist you.
Prompt engineers are already discovering how much you can do with the existing models, without any new advancements or manipulation of the actual base model. Even if GPT-4 or an open-source LLM from Stability.ai take years to come out, the existing tools are enough for huge changes.
The above is an idea maze I sketched out for products enabled by LLMs. The key question it starts with is “What kind of interface would the use have with the product?”
Continue reading “The New AI Epoch”
Greetings FutureBlind readers!
It’s been a while. Although I have 3 or so posts outlined and in various states of completion, life has gotten in the way. My wife and I’s first child is due in a few months (Are we in the thick of a post-Covid baby boom?) and in an act of complete lunacy this summer we started a major home renovation. This has, to put it mildly, put a damper on my free time.
Nonetheless I really wanted to write a bit and put something out there. So instead of the typical focused post, I’m doing it roundup style. Each section below is an area I follow or find interesting.
Here’s an outline of the roundup so you can jump to whichever section sounds interesting:
The following are passages from the book The First Tycoon by T.J. Stiles. The book is a biography of Cornelius Vanderbilt, who built a steamship and railroad empire in the mid-1800s.
More than that, it’s a history of the early corporation and the beginning of the era of modern business. This is the subject of the quotes below. While reading them, I was constantly reminded of recent (and ongoing) innovations in blockchain tokenization, corporate governance, and new financial mechanisms.
There are a ton of other interesting stories in the book — one epic business battle after another — so I highly recommend it.
On the birth of the corporate structure:
This was the birth of a kind of abstract thinking never before required in everyday life. It sparked a fierce resistance. On a daily basis, most Americans rarely interacted with corporations; they still lived in a society of farms, small businesses, and independent proprietors. Jacksonians viewed corporations in much the same way that the evangelists of the Second Great Awakening saw the Masons or popery: as a corrupt conspiracy, a mysterious encrustation on the beautiful simplicity of the true religion. As artificial beings, Gouge intoned, “corporations have neither bodies to be kicked, nor souls to be damned.”
If ever corporations were necessary, it was now, for railways were far more costly and far more complex than textile mills (almost all of which were owned by individual proprietors or partnerships).
On what stock/equity ownership represented:
They placed great emphasis on the “par value” of stock, usually set at $100 per share. This represented the original investment in a company; it was expected that the total value of all its shares would equal the cost of the physical capital—land, buildings, machinery, livestock. A stock certificate might be a slip of paper, but it was thought to represent something real, much as paper currency represented cold, hard gold that could be retrieved on demand from a bank’s vault.
On the intangible nature of corporations and the tokens that represent them:
Vanderbilt and Drew’s business careers, coming in the first half of the nineteenth century, were acts of imagination. In this age of the corporation’s infancy, they and their conspirators created a world of the mind, a world that would last into the twenty-first century. At a time when even many businessmen could not see beyond the physical, the tangible, they embraced abstractions never known before in daily life. They saw that a group of men sitting around a table could conjure “an artificial being, invisible, intangible,” that could outlive them all. They saw how stocks could be driven up or dropped in value, how they could be played like a flute to command more capital than the incorporators could muster on their own. They saw that everything in the economy could be further abstracted into a substanceless something that might be bought or sold, that a banknote or promissory note or the right to buy a share of stock at a certain price could all be traded at prices that varied from day to day. The subtle eye of the boorish boatman saw this invisible architecture, and grasped its innumerable possibilities. [. . .]
At fifty-four, Vanderbilt could look back on a career of breathtaking leaps of imagination. Steamboats and railroads, fare wars, market-division agreements, and corporations: all were virtually unknown in America when he mastered them. He understood the emerging invisible world far better than those who condescended to him. And this knowledge was about to serve him better than he could have dreamed. He was about to imagine a work of global significance—to create a channel of commerce that would help make the United States a truly continental nation. [. . .]
By 1859, he operated almost entirely through corporations; he proved himself an expert at using the stock market to concentrate capital or avenge himself on his enemies, and emerged as a master of corporate structure. He saw the corporation as just another type of business organization.
Vanderbilt split the stock of his company, doubling its par value (which was ~equivalent to equity value back then). This wasn’t accepted at the time as there was no concept of goodwill or intangibles. So Vanderbilt had someone re-value the assets to make the claim the stock dividend would still account for “real” value.
EVEN BEFORE THE COMMODORE assumed control of the New York Central, his historical legacy as a railroad king began to take shape. He would be no Leland Stanford, no James J. Hill, building transcontinental lines through thousands of miles of unsettled plains and mountains; rather, he would be a creator of the invisible world, a conjurer in the financial ether. What made him powerful—and controversial—was not his riches alone, but his mastery of the corporate golem.
For his first magic trick, he took what was one and made it two. On March 30, 1867, the Hudson River shareholders (himself foremost among them) approved his plan to nearly double the stock by issuing new shares worth $6,963,900 at par value. Called a stock dividend, it was similar to a stock split, an operation that would become common in the twentieth century. In the nineteenth century, it sparked outrage. [. . .]
Stock that did not reflect construction costs was derided as “fictitious capital,” to use the formal term—or, more commonly, “watered stock,” which called up the image of livestock encouraged to gorge on water before weighing and sale at the market. By contrast, new stock was not seen as diluting share value if it reflected actual construction or additional real estate.
On Vanderbilt’s “invisible” world of abstractions of business, money, and markets. It can be hard to imagine what life was like before these abstractions, and how big of a change it truly was to have something as conceptual as a corporation controlling so much of the economy. Tokenization of these concepts created markets where they couldn’t exist before.
For such was the world that swallowed Billy Vanderbilt: a netherworld populated by those artificial persons called corporations that masked the real persons behind them; by paper money, that masked real gold and silver; by whispered rumors, that masked the manipulations of self-serving men. [. . .]
[Vanderbilt] may have left his most lasting mark in the invisible world, by creating an unseen architecture which later generations of Americans would take for granted. The modern economic mind began to emerge in Vanderbilt’s lifetime, amid fierce debate, confusion, and intense resistance. The imagined devices of commerce gradually abstracted the tangible into mere tokens, and then less than tokens. Money transformed from gold coin to gold-backed banknotes to legal-tender slips of paper and ledger entries of bank accounts. Property migrated from physical objects to the shares of partnerships to par-value stock to securities that fluctuated according to the market, that could be increased in number at will [emphasis mine]. Like a ghost, the business enterprise departed the body of the individual proprietor and became a being in itself, a corporation with its own identity, its own character, its own personhood.
[. . .] Vanderbilt lived out the history of this abstraction, the invention of this imagined world. More than that, he took it to a new level by pioneering the giant corporation. By consolidating his New York lines into the New York Central & Hudson River Railroad, he constructed something larger than himself, not to mention virtually every other enterprise that had ever existed. It was a massive organization, one that served to depersonalize, to institutionalize, American business and life. It helped to lead the way to a future dominated by large enterprises possessing wealth and power that changed not only the economic landscape, but the political one as well.
A lot of parallels to our current situation.
Over the next 20 years — What effects will DeFi have on finance and markets? How much of the economy will be run by DAOs and how will this be exploited by the next Vanderbilts? How far can tokenization go and how will having a market in everything change our behavior?
Expansion of life across our solar system and beyond has been a dream of mine since childhood. Of course, this isn’t uncommon among other sci-fi enthusiasts, or anyone who grew up knowing we’ve sent humans to the Moon but haven’t sent them back in nearly 50 years.
Space is fascinating for many reasons. It’s a frontier in every sense: physically, technically, even socially. It’s at the bleeding edge of what humanity is capable of. “Looking to the stars” and “shooting for the moon” are common idioms because space has defined our limits for generations.
Now (finally!) the technical and business tailwinds are coming together to make it possible. The cost and ease of getting to space are about to improve by many orders of magnitude. This will drive the space industry to be one of the biggest sources of growth over the next 10-20 years.[1] It will make existing technologies cheaper and more ubiquitous, like allowing worldwide high-speed internet in even the most remote, rural areas. It will also open up a host of new possibilities previously only imagined in science fiction.
This is the first of a two-part essay on the upcoming future of the space industry. I’ve been closely following SpaceX’s progress in particular since their first launch of the Falcon 9 in 2010, so I’m excited to finally write about it.
TLDR: SpaceX has pushed cost to orbit down by 10x, and will by another 10x in 5 years. Along with further commercialization and government funding, a threshold has been crossed.
The success of commercial launch services puts the space industry in the same place as the early days of railroads in the 1800s or commercial ocean shippers in the 1600s. The key here is early days as things are really just getting started.
The “why now?” can be reduced to one chart — the average cost to get 1 kilogram to orbit:
In the next section I’ll go over the reasons why this makes such a big difference. But first, how did it happen? As should be evident by the chart, this is essentially the story of one company — SpaceX.
The driving ambition for Elon Musk when he founded SpaceX in 2002 was to drastically reduce the cost of escaping Earth’s gravity. Their “MVP” was the Falcon 1, a single-engine rocket that could launch small satellites. Falcon 1 only launched 5 times, with only the last 3 succeeding. Haven proven viability, SpaceX quickly moved onto production of the Falcon 9, a scaled up version with nine Merlin engines eventually capable of delivering over 22,000 kg to Low Earth Orbit (LEO). Here’s the price progression of each SpaceX rocket, starting from the base of what a conventional rocket costs:
Driving the first order-of-magnitude reduction in cost are the following:
And the next 10x reduction with Starship:
Government funding, particularly from NASA, has been a key enabler. Without these contracts it would have been very difficult for SpaceX to fund R&D. And they’ll continue to play a key role for SpaceX and other commercial space providers. In recent years NASA has stepped up their commercial contracts significantly, and with further falling costs this is likely to continue. (See footnote [2] for a list of recent milestones.)
This moment for space companies is the equivalent of 1995 when the NSF dropped all restrictions on Internet commerce, which let private companies take over the backbone. The breaking of the dam that releases a tidal wave of activity.
Expensive launches aren’t just costly in their own right — they lead to cost inflation of everything else. If it costs $100M to get a satellite to orbit, reducing the cost of development from $10M to $5M is only a 5% difference. So why not over-engineer, paying up for components and testing to ensure everything is perfect? Now if a launch costs $10M, there’s more incentive to cut costs. Even if there’s an issue, a second launch is much cheaper. Order-of-magnitude-lower launch costs will lead to similar decreases in payload costs.
From a Morgan Stanley report:
Currently, the cost to launch a satellite has declined to about $60 million, from $200 million, via reusable rockets, with a potential drop to as low as $5 million. And satellite mass production could decrease that cost from $500 million per satellite to $500,000.
More launches will lead to even cheaper costs, which will lead to cheaper payloads, which… see where I’m going here?
SpaceX has initially started the flywheel that got the industry to this inflection point.[3] But it won’t be the only one turning it. Ultimately to truly take advantage of space transportation we’ll be seeing many competing service providers, at all different levels of payload size and capability.
The flywheel is already turning and has led to a higher volume of launches:
At some point in the near future we’ll be seeing a launch per day, with spaceports treated more like shipping ports: hubs of travel and commercial activity.
Before moving on to Part II, I want to quickly review the two main categories of payload currently being launched:
Satellites. Communication and Imaging satellites account for a vast majority of the space industry. Exploratory missions get all the publicity, but they are currently very tiny. This will continue, especially with broadband internet constellations.
The use of communication satellites in particular is already a ubiquitous part of everyday life: from GPS navigation[4] to phone calls, TV signals, internet, and more. Satellite imagery as well: what once was a tool for only the military and intelligence agencies of large governments is now used by anyone with a smartphone.
Satellites come in a range of sizes, from tiny CubeSats the size of a shoebox launched 100s at a time; to huge geostationary satellites that take up the entire payload of a rocket.[5] Most of this hardware — particularly for the larger ones — requires costly, sophisticated engineering and infrastructure. The full stack can include satellite manufacturers, operators, suppliers, and ground equipment. As costs come down, so will satellite size and launch frequency.
I hope I’ve convinced you that getting to space is about to get a whole lot easier.
In Part II, I’ll talk about the progress we will potentially see in space in the upcoming 10 to 20 years: commercial space stations, tourism, manufacturing, mining, exploration and more.
Every product is built on and enabled by one or more technologies.
Understanding where a product fits on its higher-level tech stack is an important part of any long-term strategy or investment thesis.
The following is an exploration of tech stacks: what they are, how to model them, and what roles their components play. I also discuss what insights can be gained, such as potential opportunities to expand the business.
Typically, a tech stack shows what infrastructure a technology is directly built on or requires. A SaaS startup for example could have a front- and back-end software stack with a cloud provider like AWS under it. The tech in focus is on top of the stack, with the supporting layers below it.
A tech stack tree is a higher-level version, branching both above and below the “layer” in focus. It shows both what the technology directly relies on and what relies on it. Stacks are fractal in nature, just like trees. An innovation spawns many others that use it, which further enable others, and so on.
A stack tree shows the relevant “slice” of the full dependency graph, going only a few nodes out. It looks something like this:
Step 1: Determine the core tech. The first step is to decide what the actual technology in focus is. A technology in this case is a physical tool, process or system of other technologies that combine to do a job. It does not include businesses or social innovations. (A “business” is just a group of physical and social technologies united under a strategy — but we’re only concerned with the physical part here.[1])
Examples can range from the simple: hammers, glass, newspapers, or an assembly line process; to the more complex: CPUs, streaming video services, blockchains, smartphones, or nuclear reactors.
Step 2: Layers below. What are the primary technologies and processes needed to create and maintain the core tech? What does it need not only to function but to be sustainable? Clues can be found in:
Step 3: Layers above. What does the tech directly enable? It’s possible there are no layers here. Many well-known innovations don’t directly enable other tech, like Netflix.
Here’s a few examples of stack trees from the tech industry, although they can be drawn out for products any industry:
(The Amazon “Vampire Squid” is the best example I can think of traversing the stack, starting as an online marketplace and expanding outward in all directions: up, down, and sideways (I left out Prime, Music, Video, etc.).
Companies are embedded in value networks because their products generally are embedded, or nested hierarchically, as components within other products and eventually within end systems of use. — Clayton Christensen
A tech stack tree is one way of looking at a company’s value network. This can lead to insights into where value flows, who captures it, and potential opportunities to expand the business.
What layers in the stack capture the most value?
Which technologies accrue most of the value depend on many things: how much value (productivity) created relative to alternatives, availability of potential substitutes, the size of the overall need, or other competitive advantages inherent to the business model.
One of the models Clayton Christensen uses makes sense to apply here: Where is the bottleneck in demand? In other words, where in the stack is the biggest difference between performance demanded and performance supplied? What needs to be better?
Nvidia is a good example here. They keep churning out GPUs with higher capabilities and the market keeps needing more. Supply hasn’t kept up with demand and that’s likely to continue for some time. This bottleneck (along with other factors ) allows the GPU layer to capture a lot of value.
Are there opportunities to expand into adjacent technologies?
Amazon (see stack above) is the prototypical example here. They started as an online marketplace with some fulfillment operations, and over time have expanded in all directions.
In more traditional business thinking, you consider expanding vertically into suppliers and customers or horizontally across industries. Traversing a tech stack is similar, but to me more focused on the true technological and needs-based relationships. Traditional business thinking would have never led to Amazon expanding into internet infrastructure via AWS.
Of course, expanding for the sake of it is never a good strategy. You have to ask:
The core tech here is a mobile or desktop app where you can order food from many local restaurants and get it delivered within ~1 hour. DoorDash, UberEats, Postmates, etc.
Layers below: What are their major cost centers? Restaurants and delivery drivers. What enabled delivery apps? Primarily ubiquitous smartphones and access to GPS-based navigation. Restaurants also need to have some way to communicate, whether by phone or Wifi-based tablets, and be able to package food in proper take-out containers (plus potentially many others to manage operations).
Layers above: What needs delivery apps to run? Cloud kitchens, which operate large strategically located kitchens that can make food for many different branded “restaurants”. Recently a further layer was added with the concept of pop-up branded chains, which uses the cloud kitchen & delivery infrastructure.
What captures the value? In the stack above, smartphones capture far more value than any other tech — but they’re a platform with thousands of other use cases. In this case we just want to focus on value flow within the food delivery market. It may not be clear at first who captures more value: the delivery apps or the restaurants, given companies like DoorDash are losing so much money. But it’s clear that restaurants are not a bottleneck in demand — so it’s likely the apps that capture more value. And it seems their unit economics bear this out.
Opportunities for expansion? The clearest opportunity to expand within the tech stack is into cloud kitchens. This could potentially alienate some restaurant partners, but restaurants are so fragmented it shouldn’t matter. I think this has a lot of potential given: captive customers, synergies with delivery app, and lower costs with economies of scale and not having to operate normal restaurant ops.
How would you classify technologies in the stack? I think it’s most informative to categorize by what pattern (or archetype) they match in the greater ecosystem. These are functions that can exist in any industry or stack tree: Platforms, protocols, etc.
I’ll follow up with another post including examples of different tech functions and stack patterns.
To be continued…
Thanks to Leo Polovets and Jake Singer for helpful feedback on this post. Header photo from veeterzy on Unsplash.
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:
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.
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.
This is another reminder that we live on the thin veneer of civilization — modern society is very fragile if we’re not constantly vigilant about it.
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.
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.
Before continuing, check out Eric Jorgenson’s primer on the flywheel mental model here.
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:
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).
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.
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?
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:
Featured photo from Ruth Hartnup on Flickr.
Thanks to Eric Jorgenson for feedback on the final version.
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.
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.
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.
Evan Spiegel is also heavily influenced by Land and Polaroid. But alas, Snap is not a camera company—they’re a communication company. And I think they’d do better in the future remembering that.
Inspiration, not imitation.
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.”
FutureBlind 