Polaroid, Apple’s spiritual successor

I just finished 2 books on the history of Polaroid 🌈*. 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

Poloroid-Apple.jpg

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.

Snapshot

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.

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Polaroid Variable Day Glasses; Snap Glasses.

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.”

 


* “Instant: The Story of Polaroid” by Christopher Bonanos (2012).
Land’s Polaroid: A company and the man who invented it” by Peter Wensberg (1987)

The Scale of Large Projects

$100 million +

  • Midsize commercial airplane — $120m ^
  • Big budget video game — $150m ^
  • F-22 Raptor jet — $157m ^
  • iPhone R&D (2007) — $185m ^
  • Titanic (1912) — $190m ^
  • Big budget movie — $250m ^
  • SpaceX Falcon 9 v1 R&D — $350m ^
  • Empire State Building (1931) — $400m ^
  • Modern cruise ship — $750m ^
  • Hoover Dam (1936) — $863m ^

$1 billion +

  • Modern sports stadium — $1.3b ^
  • Modern skyscraper — $1.5b ^
  • Space Shuttle launch — $1.5b ^
  • Erie Canal (1825) — $4b ^
  • Human Genome Project (2003) — $5b ^
  • Panama Canal (1912) — $9b ^
  • Hubble Space Telescope (1990) — $9b ^

$10 billion +

  • Global Positioning System (1989) — $10b ^
  • Large Hadron Collider (2009) — $13b ^
  • Great Pyramid of Giza (~2500 BCE) — $20b ^
  • Three Gorges Dam (2009) — $25b ^
  • Transcontinental railroad (1863) — $30b ^
  • Manhattan Project (1945) — $30b ^
  • F-22 Raptor development (1997) — $42b ^
  • Great Wall of China (220 BCE) — $50b ^
  • SR-71 Blackbird development (1964) — $90b ^

$100 billion +

  • International Space Station — $150b ^
  • Apollo program (1969) — $200b ^
  • U.S. Interstate Highway System (~1980) — $500b ^

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.

Related: The Tallest Skyscrapers in the World, Pyramids vs. Skyscrapers

Tokenized Securities and the Future of Ownership

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:

token_types.png
A breakdown of token types from The Token Handbook. Tokens will have many uses but I think the biggest will be on the Securities and Asset side — not currencies as many believe.

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). Continue reading “Tokenized Securities and the Future of Ownership”

Books: 2017 Reading List

Competing Against Luck — finally a full writeup on “Jobs Theory”, and required reading for anyone involved in product strategy & UX design (i.e. all startups).

The Change Function — good, simple model to think about how valuable a new innovation is (all about UX, or if (perceived crisis > cost of adoption)).

Marketing High Technology — best book on distribution you can find, for technology or otherwise.

Shoe Dog — Great story; wish he would have spent more time in the later years of Nike’s growth.

Doing the Impossible — too dense overall, but I loved hearing the story of the moon mission from the inside, especially from such a talented project manager that made it happen.

Scale — not as good as hoped, but a good “skim” with lots of interesting ideas around a theme.

21 Irrefutable Laws of Leadership — great leadership advice + stories to go along with, Dale Carnegie style (but could have been much shorter).

Hard Drive — 3rd reading of the best bio of Bill Gates & Microsoft’s early years.

The Elements of Computing Systems — I never had formal CS education so this was a great practical explainer, from translating binary to assembly, to how an OS works.

A Mind at Play — always been a huge fan of Claude Shannon’s work, mind, and humility.

Turing’s Cathedral — a little long in places, but great overall history of computing & early people who shaped it.

Softwar — Reading now. Interesting insights about early Oracle, also gives me new appreciation for Ellison. [Update: I would not recommend this book. First part is good but last half rambles on, fawning over Ellison with random stories. “The Difference Between God and Larry Ellison” is much better.]

Product Study: Falcon 9

Last week I was outside of Vandenberg Air Force Base to watch the launch of SpaceX’s Falcon 9 rocket. (It was perfect weather and an amazing experience for my first launch!) To commemorate it, this is another one of a handful of product case studies I wrote to help understand successful product launches.

Falcon 9 was finished in early 2010, and had been in development since 2005. Its first flight occurred on June 4, 2010, a demonstration flight to orbit where it circled Earth over 300 times before reentry.

  • 1st flight to ISS: May 22, 2012
  • 1st cargo resupply (CRS-1): October 7, 2012
  • 1st successful commercial flight: September 29, 2013

Development costs for v1.0 were estimated at $300M. NASA estimated that under traditional cost-plus contracts costs would have been over $3.6B. Total combined costs for F9 and Dragon up to 2014 were ~$850M, $400M of that provided by NASA. 

By September 2013, the SpaceX production line was manufacturing 1 F9 every month.

(1) Value created — Simply describe the innovation. How did it create value? 

The Falcon 9 is a two-stage rocket that delivers payloads to Earth orbit or beyond. It’s a transportation vehicle to space. F9 drastically reduced launch costs, allowing NASA and small satellite companies to send payloads at a fraction of the cost.

(2) Value captured — Competitive advantages, barriers to entry. Why didn’t incumbents have a reason to fight them?

  • Ahead on the learning curve — highly advanced, experiential, expert knowledge
  • Capital and time barriers — lots of money and time needed to get to scale
  • F9 was a disruptive innovation, built from the ground up at low cost. Incumbent launch companies had no reason to start from scratch and lower their profits when they had strong (mainly cost-plus) contracts with existing customers. Industry was viewed as very inelastic and that little demand existed at low end.

Continue reading “Product Study: Falcon 9”

Product Study: iPhone

One of a handful of product case studies I wrote last year to help understand successful product launches.

Apple’s iPhone was announced December 9, 2007 and released June 29, 2007. It was $499 for the 4GB version, $599 for 8GB. After 8 years it had captured 50% of U.S. smartphone market and >66% of sales, with 100 million users.

(1) Value created — Simply describe the innovation. How did it create value?

The iPhone is a pocket computer. It has typical phone capabilities including phone calls and text messaging, along with cellular internet connectivity. Differences between other smartphones at the time were:

  • Large multi-touch screen with no tactile keyboard, no need for stylus — this allowed full use of screen when not using keyboard
  • Ability to browse normal, non WAP, websites (can zoom easily using multi-touch)
  • Ability to run desktop-class applications
  • Multiple sensor inputs — proximity, light, accelerometer

(2) Value captured — Competitive advantages, barriers to entry. Why didn’t incumbents have a reason to fight them?

  • Distribution:
    • Extension from existing Apple network — iTunes, Mac OS, iPod.
    • Brand attachment to Apple.
    • Economies of scale exist with integration and complexity of engineering.
  • Switching costs once owning an iPhone.
  • Strong habit attached to usage many times / day — strong attachment to UX.
  • Phone makers saw it as toy for rich people at first. Computer makers didn’t see it as a computer (low-end disruption).

Continue reading “Product Study: iPhone”

Mashgin: The Future of Computer Vision

twitter-picAbout a year ago I invested in and joined a startup called Mashgin. In this post I want to talk a little about what we’re working on.

Mashgin is building a self-checkout kiosk that uses cameras to recognize multiple items at once without needing barcodes.

The current version of the kiosk is designed for cafeterias, where customers can slide their tray up and everything on it is recognized instantly. Excluding payment, the process takes around 2 seconds. No more waiting for a single line held up by a price check!

But retail checkout is just a package around Mashgin’s core fundamental technology. We believe there is an opportunity to apply recent technical advancements to many fields. Advancements such as:

  • Smartphone dividends — cheap sensors and ubiquitous, miniaturized electronic components
  • Cheap parallel processing power including low-cost GPUs
  • An explosion in collaborative, open-source software tools
  • Machine learning methods, in particular convolutional neural networks (a byproduct of the 2 preceding trends)
  • Cheap cloud infrastructure

Chris Dixon talks more about some of these trends in his post What’s Next in Computing?

So how is Mashgin applying this technology?

Adaptive Visual Automation

IMG_0330
Face swap: billionaire edition

Computer Vision transforms images into usable data (descriptions) using software. If cameras are the “eyes” of a machine, computer vision would be the brain’s visual cortex–processing and making sense of what it sees.

When computers know what they’re looking at, it opens up a world of potential. You can see it in existing use cases from facial recognition in Facebook photos (…or face swap apps) to Google Image Search and OCR. Newer, much more sophisticated applications include driverless cars, autonomous drones, and augmented reality.

Gradient Descent
A visual example of using gradient descent (the reverse of hill climbing in a fitness landscape) as part of the learning process of a neural network

These recent applications tend to be more complex, and as a result use machine learning in addition to traditional image processing methods. Machine learning, and in particular deep learning through neural networks, has changed the game in many areas of computer science, and we are just beginning to see its potential. ML can simplify a large amount of data into a single algorithm. As the name implies, it can learn and adapt to new information over time with little or no “teaching” from engineers.

Both CV and ML can be applied to many fields, but one of the biggest immediate needs is in Automation. There are a surprising amount of simple (to humans) visual tasks ripe for automation. This includes industrial use cases in manufacturing and distribution, and consumer use cases in household robotics and relief of everyday bottlenecks.

I call the above combination adaptive visual automation: using machine learning to automate vision-based tasks. Although relatively new, this combination covers a large and quickly growing class of real-world problems. Autonomous cars (and especially trucks) are a good up-and-coming example that will have huge ramifications.

Mashgin’s future

Mashgin uses adaptive visual automation to improve the speed, accuracy and cost of applications in recognition, measurement, and counting in a closed environment. That was a bit of a mouthful, so here’s the short version: Mashgin wants to make visual automation intelligent.

There’s a broader category of AI vision companies whose purpose is giving computers the ability to understand what they see. Mashgin is a subset of this group, focusing on automating well defined real-world problems.

There are further subsets such as eliminating bottlenecks in everyday circumstances — speeding up checkout lines being one example. In many of the activities you do on a daily basis, intelligent automation has the ability to save a huge amount of time and money.

Retail checkout is a big market (even for just cafeterias) but it only scratches the surface of the value Mashgin will eventually be capable of. We have already established a foundation for applying recent advancements to these problems and it will only get better from here.