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.

Atlastory: Mapping the history of the world

Certain ideas are “inevitable” over time. Paul Graham calls them “[squares] in the periodic table” — if they don’t exist now, they’ll be created shortly. It’s only a matter of when, not if.

I believe that Atlastory is one of those ideas. The following is a long post about a project I’ve been passionate about for some time now and am currently in the process of winding down.

The Idea

Atlastory is an open source project to create an interactive map that chronicles the history of life on earth. It’s a “Google Maps” for history. The ultimate goal is the ability to see what the world looked like 50, 200, 1000+ years ago. It was inspired by OpenStreetMap & Wikipedia: combining historic maps with cultural & statistical data.

Atlastory map in action

I started Atlastory at first because I’m a fan of both history and good data visualizations. I was surprised something like this didn’t already exist and thought that it would be an amazing educational tool.

Maps are one of the best ways to clearly show an enormous amount of information. Since everything in the past took place at a certain time and location, maps are an obvious choice to visualize that knowledge. Understanding history requires seeing changes and interactions over time, and a four-dimensional map allows this.

To envision information—and what bright and splendid visions can result—is to work at the intersection of image, word, number, art.” — Edward Tufte

Good design will be a key aspect of the final product. Good information design can communicate a huge amount of knowledge in a small window of time or space. Great information design has a high amount of density and complexity while remaining completely understandable.

The Vision (version ∞)

Atlastory’s purpose is to improve understanding of the past by organizing and visualizing historic knowledge.

My vision for Atlastory was that one day it would become a tool like Wikipedia that’s used regularly around the world. A journalist could use it to go back 20 years to see the geography and timeline of a major world event. A student could use it to go back 20,000 years to see the expansion of human culture across the globe. A climatologist could use it to visualize the historic overlap of population growth with changes in global climate patterns.

Wikipedia organizes information by creating a searchable network of interconnected articles that combine text and other multimedia. Atlastory can be the first medium that allows completely visual navigation, displaying information at a much higher density and level of interactivity.

1937-WORLD

Imagine students in a classroom learning about World War II. You’d be able to see the country borders of Europe as they existed in 1942. Drag the timeline, and see the borders change as the years go on. Turn on an overlay of population density or GDP per capita and see the flow of activity throughout the war. Zoom in and see the troop movements of a pivotal battle.

The visual interactivity would make it much more enticing for people, young and old. Almost game-like in terms of exploration and discovery.

Eventually, the timeline could go back far enough that you’re able to see continental drift and other pre-historic geographic or environmental changes.

Map content

Maps can be broken down into a few different types:

  • Physical — shows the physical landscape including mountains, rivers, lakes.
  • Political — sovereign, national and state boundaries, with cities of all sizes. The typical world map you see will be political with some physical features.
  • Road — shows roads of various sizes along with destinations and points of interest. Google Maps & other navigation apps fall into this category.
  • Statistical — shows statistics about human populations such as economic stats, population density, etc.
  • Scientific — thematic maps that can show climate, ecological regions, etc. (see the climate map below)
  • Events — shows how a specific event played out geographically, like WWII or Alexander the Great’s conquests.

Climate patterns

Any map type that has enough data to span long periods could eventually go into the Atlastory system. Event, thematic, statistical, and scientific maps could all seamlessly layer on top of the main “base map”.

Base map

The Atlastory base map should be an elegant combination between 3 map types: physical (basic landscape features), political (sovereign and administrative boundaries), and cultural (see below). Major roads and infrastructure would be added only after a worldwide “structure” of the base map was created.

Importantly, map creation should be top down, from global to local. The purpose of an Atlastory map is not navigation, it is understanding of history. Creating a global structure will also provide context and make it easier to interest other users/contributors.

Cultural cartography

Most world maps made today (of the present time or of the last few hundred years or so) are of the political variety. But what happens when you go back a few thousand years? What about areas of the world where, even now, aren’t necessarily defined by geopolitical boundaries?

The solution is mapping cultural regions. Culture, in this case, being human societies with common language, belief systems, and norms. “A cultural boundary (also cultural border) in ethnology is a geographical boundary between two identifiable ethnic or ethno-linguistic cultures.”

A cultural map would have different levels, just like political maps: from dominant cultural macroregions to local divisions between subcultures or classes within a society (blue collar vs. white collar, etc.).

Combining cultural cartography with typical map types allows for a much better understanding of both modern and ancient history. Culture plays a major role in world events & limiting the map to only defined borders paints an inaccurate view of history.

Cultural regions

(Notice any overlap between cultural regions and the climate regions in the map above it?)

The Tech

The technical infrastructure behind Atlastory has a few basic components:

  1. A database of nodes (latitude/longitude points) organized into shapes, layers, types, and time periods.
  2. An API that manages, imports and exports data from the database.
  3. crowdsourced map editor interface (like iD for OpenStreetMap, but designed specifically for top-down time-based editing).
  4. A map rendering service that turns raw map data from the database into vector tiles that can be styled for viewing.
  5. The map itself: a web interface to view and navigate the maps.

Most of the components would be built from existing open-source tools created by organizations like OpenStreetMap, MapBox, and CartoDB. There has been a lot of technical innovation in this field over the past few years which is one of the main reasons something like Atlastory is now possible to build. (Although given what I known about the requirements still very challenging.)

Read more about the technical requirements…

The current status and future of Atlastory

I’ve been working on this as a side project for more than 3 years now. Originally I imagined being able to quickly find a way to profit from the service. But as development dragged on and other commitments began taking up more of my time, I realized I’d never be able to finish it alone.

Earlier this year I joined Mashgin, a startup in the Bay Area, as a full-time “Generalist.” My spare time completely dried up and I decided everything needed to be completely open sourced and distributed to anyone interested in the project.

Due to personal time constraints, I can’t continue with it so I’m looking for others who are interested. This could mean taking over / adapting the codebase or using other means to pursue the idea. See below for more details on what’s currently done. Although many of the back-end components are functional, the infrastructure is in a rather unusable state right now.

Please contact me or leave a comment below if this strikes your curiosity or you know anyone else who would be interested. I’m happy to answer any questions.

Resources

Education & Elon Musk’s School Startup

One of my “later in life” goals has always been to start my own school. A “School Startup” rather than a Startup School, if you will. The school would be radically different than traditional education. Charter schools, Montessori education, and AltSchool are steps in the right direction but don’t go far enough. (See my post a year ago on mental model education as an example.)

Once again, Elon Musk has stolen my idea. Running two revolutionary billion-dollar companies just wasn’t enough. But of all the people who have and will try something like this, I think Musk has much higher odds of pulling it off. (Then again, the same could be said about a lot of undertakings…)

I hadn’t seen this news before, but Eric Jorgensen linked to a video from a May interview with Beijing News where Musk discusses creating Ad Astra, a private school in L.A for his 5 kids and 15 or so others (primarily kids of SpaceX employees).

“I created a little school,” Musk began. “It’s small, it’s only got 14 kids now and it will have 20 kids in September. It’s called ‘Ad Astra’ which means ‘to the stars.’ ” He continues:

What’s a bit different from most other schools is that there aren’t any grades — there’s no grade 1, grade 2, grade 3 type of thing — and not making all the children go in the same grade at the same time like an assembly line. Because some people love English, or languages, some people love math, some people like music, and have different abilities at different times. It makes more sense to cater the education to match their aptitudes and abilities. So that’s one principle.

Another is that it’s important to teach problem solving — or teach to the problem, not to the tools. . . . Let’s say you’re trying to teach people about how engines work. A more traditional approach would be to say we’re going to teach all about screwdrivers and wrenches, and you’re going to have a course on screwdrivers, a course on wrenches . . . A much better way would be like “Here’s the engine, now let’s take it apart. How are we going to take it apart? Oh, you need a screwdriver. That’s what the screwdriver is for. You need a wrench — that’s what the wrench is for. And then a very important thing happens which is that the relevance of the tools becomes apparent.

I think the problem solving aspect is key. Having kids work alone or in teams to solve problems, with a teacher to guide and review after. As they get older, using a “case study” approach to learning can supplement this as a way to learn multidisciplinary mental models.

Imagine learning about the space race in the 1960s. For 1 or 2 weeks students can learn (through a mixture of lectures, media, internet research, experimentation, etc.) about a whole range of disciplines: history of the space race & cold war tensions, politics, math & science of getting to space, reading chapters of “The Right Stuff”, engineering by building model rockets, and so on.

I’m sure there are schools/teachers who already do this but I wish it was the norm rather than the exception.

Berkshire’s Best Investments + Poster Now Available

[This is a cross post from the Explorist Productions blog. Explorist is a media company I founded that publishes content related to business, innovation, and discovery.]

The Berkshire Hathaway limited hardcover letters book and “50 Years of Berkshire” wall print are now available for purchase online. Both of these items were available at the meeting a month ago and I’ve received lots of praise about them from other shareholders, so I’m glad to finally make them available to everyone.

In the process of doing research for the visualization, I collected a lot of data on Berkshire’s financial history — much more data than could fit in the charts on the print.

So in addition to the wall print, I hope to release a few more posts further exploring the story of how Warren Buffett transformed Berkshire over the years. Once I reformat and clean-up it up, I’ll eventually release the raw data so that others can do their own analysis.

Berkshire Hathaway’s Best and Most Notable Investments

The following chart shows the cumulative contribution to book value* of selected investments over 50 years. This is a good yardstick for comparing how successful investments were over time. It doesn’t include insurance companies other than GEICO, as it’s too difficult to separate individual performance given available data.

BRK-individual-investments

Notes:

  • See’s Candy: Income for some years after 23 are estimated.
  • Buffalo News: No data available after year 23.
  • BNSF: Post-acqusition performance only (pre-2009 stock return not included).
  • Dividend income for stock holdings calculated in most cases on average shares held during year.

Some interesting tidbits:

  • One-third of Coca-Cola’s total gain to Berkshire is in dividends paid over the 27 year holding period. One-quarter of the Washington Post gains are from dividends, the remainder from realized gains in the 2014 sale/transfer.
  • With underwriting gains, GEICO has added 7,119% to book value since purchase in 1976. This means that had the rest of Berkshire’s investments returned 0% over those 38 years, annual book value growth would still have been 12%.

* A simple example to show the calculation: ABC Corp. is purchased in year 1, adding $100 (either in net income for subs, or change in unrealized gains + dividends for investments) that year to an initial equity base of $1,000. So contribution after year 1 would be 10%. In year 2, ABC Corp. adds another $100 to a starting equity base of $1,300. Contribution for that individual year would be 100/1300 = 7.7%, but cumulative contribution would be 20%, as ABC Corp. has contributed $200 to an initial equity base of $1,000.

This measurement puts investments on an equal footing, allowing comparison across different timeframes. It implicitly accounts for both individual return and capital allocated to the investment. What is not accounted for is excess capital reinvestment — in other words, contribution is based on GAAP net income, not true free cash flow.

1976 Buffett Letter About Geico

July 22nd, 1976

Mr. George D. Young,
National Indemnity Company,
3024 Harney Street,
Omaha, Nebraska. 68131.

Dear George:

Thanks very much for your memo of July 19th regarding GEICO which I believe summarizes well the problems attendant to the specific property treaty we are discussing, as well as the general problems associated with reinsurance of any type at GEICO. I still am willing to explore further the GEICO property treaty—if they subsequently decide that it fits their needs—and today committed to Jack Byrne that we would take a 1% quota share of their entire book. This increase from .8 of 1% was pursuant to his request in order to help him attain the 25% mark by the shareholders meeting tomorrow.

I consider the overall quota share to be an acceptable—but not exciting—piece of business. Under normal conditions we would take nothing like 1%, obviously, since that makes it by far the largest reinsurance treaty on our books, and involves substantial risks along with a limited prospect of profit. I also do not like the feature that provides for a credit to GEICO for interest earnings on funds held by us. In effect, we are making this contract number one in size for the reinsurance department, whereas the contractual terms make it less attractive than most of our other contracts.

However, I have three reasons for taking this unusually large portion of the quota share arrangement, and these same reasons also apply to my interest in the property treaty.

  1. I hope it is not a governing factor in any way, but I do have some sentimental reasons for wishing GEICO to survive. GEICO has enumerated all of the hard headed reasons, such as the State Financial Guaranty funds, etc. I just have pulled out of the bottom drawer of my desk a statement of my net worth at the end of 1951 when I was 21 years old. I showed net assets of $19,737, of which $13,125 was in GEICO stock. That was the year when I first started selling securities, and I told everyone who would listen to me that they should put every cent they could scrape together into GEICO. A number of friends and relatives did so, and enjoyed a significant change in their financial fortunes because of this. It provided the first big boost to my own small savings, as well as an even more important boost to my reputation in the Omaha investment community.

    During those early years, when I followed the company, the people involved couldn’t have been nicer. Leo Goodwin was running things then and was helpful. Even moreso was L. A. Davidson. He was personally encouraging and forthcoming with information regarding the business, which enabled me to develop a depth of conviction which I have felt few times since about any security.

  2. At that time I felt that GEICO possessed an extraordinary business advantage in a very large industry that was going to continue to grow. Since that time they never have lost that advantage—the ability to give the policyholder back in losses a greater percentage of the premium dollar than any other auto insurance company in the country, while still providing a profit to the company. I always have been attracted to the low cost operator in any business and, when you can find a combination of (i) an extremely large business, (ii) a more or less homogenous product, and (iii) a very large gap in operating costs between the low cost operator and all of the other companies in the industry, you have a really attractive investment situation. That situation prevailed twenty-five years ago when I first became interested in the company, and it still prevails.

    The company managed to nullify this advantage—and even more than nullify it—by inadequate recognition of loss costs through poor techniques of loss reserving. This led to improper pricing of product with the result that a product which *could* have been sold at a profit *was* sold at a loss.But the important point to note is that the company had not lost its position as a low cost operator; they merely had mismanaged their loss information which caused the product to be priced inadequately. I believe the advantages of a 13% acquisition cost ratio are as important as ever. I also believe that practically no other companies are going to achieve costs near that figure in the future. Therefore, GEICO, properly managed, should prosper if they can pull themselves back from the financial precipice.

    I like very much what Jack Byrne says about reducing policies in force. It seems to me that such an approach a rather than an obsession with growth is very likely to reconstruct the situation whereby they can give the policyholder an unusually high percentage of the dollar back in losses and still make good profits for themselves.

  3. The crucial factor, then, becomes whether they can get past their present financial difficulties. Much of the press –witness Time last week—assumes that they can’t. Until recently, I was unclear myself as to their possibilities in this regard. If they had been at all wishy-washy in obtaining rate increases or biting the bullet generally, I don’t think they would have made it. However, the size of the rate increases they have instituted, along with the underwriting results they have published for April and May, have convinced me that their combined ratio will come down to tolerable limits within a fairly short time.

    Even this would not have been enough if Mr. Wallach were inclined to put them into receivership because of the unwillingness of the industry to accept his 40% plan. When he did not move to do so after the June 23rd deadline, it convinced me that he was not going to act precipitously to terminate a business that fundamental economic logic still dictated had a bright future ahead of it. When he did not bow his back over the non-subscription to his 40% plan, I believe the company’s future became assured. I decided then to buy stock, which is the most tangible evidence I can give you as to my assessment of the Company’s chances for survival.

Therefore, George, I will take the responsibility for making the decision that GEICO survives as a business entity. You should make any underwriting judgments that you wish, with this as the premise—if I am wrong about their survival, it will be my fault and not yours. I do not want to go overboard because of sentiment, but I certainly want us to make every effort to come up with proposals that make business sense to us and are useful to them. I do not want mare of the overall quota share because I consider the terms too disadvantageous to the reinsurer, all things considered. But, if a property treaty can be put together with a prospect of gain that more than balances the risk of loss, let’s proceed.

Sincerely,

Warren E. Buffett

WEB/glk