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.

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.

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