Category Archives: Business Model

Apple’s New Big Bet: Augmented Reality

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ARKit platform puts it in a race with Facebook, Alphabet and Snap and fuels industry beliefs that it will eventually develop glasses

A demonstration of Apple’s ARKit technology on an iPad Pro on Monday. The system uses camera data to find horizontal planes in a room and estimate the amount of light available.

A demonstration of Apple’s ARKit technology on an iPad Pro on Monday. The system uses camera data to find horizontal planes in a room and estimate the amount of light available. PHOTO: DAVID PAUL MORRIS/BLOOMBERG NEWS

Apple Inc. AAPL 0.60% set its sights on a new target: becoming the world’s largest platform for augmented reality.

The ambitious announcement, which was overshadowed by the introduction Monday of the HomePod speaker, plunges Apple into a race against Alphabet Inc., Facebook Inc.,FB 0.20% Snap Inc. SNAP -3.93% and others to conquer an emerging technology that uses cameras and computers to overlay digital images on a person’s view of the real world.

It also bolsters the belief among many industry observers that Apple will build new augmented-reality features into its coming 10th-anniversary edition iPhone, and eventually develop glasses that relay information about the world so people can view maps or restaurant menus without pulling out a device.

Augmented reality shot to prominence nearly a year ago following the release of “Pokémon Go,” a game in which players scoured the map of the real world, with the help of location-tracking technology, to find digital monsters superimposed through the smartphone screen. The technology is different from virtual reality, which uses computer headsets to create fully immersive digital worlds.

Roughly 40 million people in the U.S. are expected to use augmented reality this year, up 30% from last year, according to research firm eMarketer. It estimates the total will rise to 54 million in 2019.

In a 2.5-hour keynote, Apple announced a slew of new hardware and software products. WSJ’s Joanna Stern recaps what you need to know about the most important announcements.

Craig Federighi, Apple’s head of software, demonstrated the potential of Apple’s new technology platform, ARKit, at the company’s annual Worldwide Developers Conference keynote Monday.

While viewing a table on stage through an iPhone screen, Mr. Federighi added virtual images of a steaming cup of coffee and lamp. The images appeared to rest directly on the table, recognizing the real-world surface rather than floating above it.

Apple Chief Executive Tim Cook has been a big proponent of augmented reality, saying he believes it will have broader success than virtual reality because it is less isolating.

Several companies are already working on augmented reality, including headsets in development from Microsoft Corp. and Magic Leap Inc. Alphabet’s Google Tango platform has been available on some smartphones for a about a year.

Apple’s ARKit, though, has the potential to democratize the technology by bringing it to roughly a billion devices without requiring separate hardware or software, as some competitors do. The company says the system uses camera data to find horizontal planes in a room and estimate the amount of light available.

“It’s a seminal event in the journey toward AR that Apple’s come out and shipped something,” said Matt Miesnieks, co-founder of 6D.ai, a computer-vision startup. He expects developers to use the software because of its relative simplicity and potential to reach across Apple’s large user base.

IKEA International A/S and Lego A/S are already working on augmented-reality apps using ARKit that could allow people to visualize furniture in their home or a virtual image of Lego Batman, Mr. Federighi said.

Representatives of Wingnut AR, an augmented-reality studio from “Lord Of The Rings” director Peter Jackson, showed an ARKit-based experience, seen through an iPad, in which airships battled in a virtual town square that was digitally dropped on a real table on stage, with the audience visible in the background.

Apple’s announcement came two months after Facebook opened its augmented-reality tools to developers. CEO Mark Zuckerberg said he expects the nascent technology to open the world to a new world of apps and services.

Facebook’s smaller rival, Snap, popularized simple augmented-reality tools that overlay bunny ears or dog noses on users’ faces. It also allows users to add special effects to photos and backgrounds.

By creating an augmented-reality tool kit for developers, Apple could spur its more than 680 million iPhone users to share augmented images through its iMessage service rather through Facebook or Snapchat, developers said.

ARKit indicates Apple solved a difficult technical problem—finding a way to use cameras and sensors in an iPhone to track the outside world, said Mr. Miesnieks. He said the same sensors and algorithms would run a pair of glasses, bolstering his belief that Apple plans to launch eyewear in the future.

Apple declined to comment on whether it could develop glasses.

“They’re clearly getting developers ramped up for this,” said Paul Reynolds, founder of Torch 3D, a startup focused on 3D-app development. “I’m sure for the iPhone launch they’ll have nice content around it.”

Warby’s new business model: The Vision Test

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Warby’s at-home vision test:
1. Take a quiz
Answer questions about your vision and eligibility. (Currently, some ocular conditions disqualify you.)
2. Measure
Place a credit card or driver’s license in the corner of the computer screen and point your phone’s camera at it, to determine the computer screen size and display correctly sized images.
3. Swipe away
The smartphone app tells you where to stand (and knows where you are, so you can’t cheat). The computer shows tests, such as C’s in various sizes; swipe the phone in the direction of each.
4. Get your RX
When you’re done, the results are sent to an eye doctor for review. Within 24 hours, you get a new prescription.

YouTube Tops 1 Billion Hours of Video a Day, on Pace to Eclipse TV

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Google unit posts 10-fold increase in viewership since 2012, boosted by algorithms personalizing user lineups

YouTube viewers world-wide are now watching more than 1 billion hours of videos a day, threatening to eclipse U.S. television viewership, a milestone fueled by the Google unit’s aggressive embrace of artificial intelligence to recommend videos.

YouTube surpassed the figure, which is far larger than previously reported, late last year. It represents a 10-fold increase since 2012, YouTube said, when it started building algorithms that tap user data to give each user personalized video lineups designed to keep them watching longer.

Feeding those recommendations is an unmatched collection of content: 400 hours of video are uploaded to YouTube each minute, or 65 years of video a day.

“The corpus of content continues to get richer and richer by the minute, and machine-learning algorithms do a better and better job of surfacing the content that an individual user likes,” YouTube Chief Product Officer Neal Mohan said.

MERRILL SHERMAN

YouTube’s billion-hour mark underscores the wide lead of the 12-year-old platform in online video—threatening traditional television, which lacks similarly sophisticated tools.

Facebook Inc. and Netflix Inc. said in January 2016 that users watch 100 million hours and 116 million hours, respectively, of video daily on their platforms. Nielsen data suggest Americans watch on average roughly 1.25 billion hours of live and recorded TV a day, a figure steadily dropping in recent years.

Despite its size, it is unclear if YouTube is making money. Google parent Alphabet Inc. doesn’t disclose YouTube’s performance, but people familiar with its financials said it took in about $4 billion in revenue in 2014 and roughly broke even.

YouTube makes most of its money on running ads before videos but it also spends big on technology and rights to content, including deals with TV networks for a planned web-TV service. When asked about profits last year, YouTube Chief Executive Susan Wojcicki said, “Growth is the priority.”

YouTube’s success using tailor-made video lineups illustrates how technology companies can reshape media consumption into narrow categories of interests, a trend some observers find worrying.

“If I only watch heavy-metal videos, of course it’s serving me more of those. But then I’m missing out on the diversity of culture that exists,” said Christo Wilson, a Northeastern University computer-science professor who studies the impact of algorithms. “The blessing and curse of cable and broadcast TV is it was a shared experience.…But that goes away if we each have personalized ecosystems.”

YouTube benefits from the enormous reach of Google, which handles about 93% of internet searches, according to market researcher StatCounter. Google embeds YouTube videos in search results and pre-installs the YouTube app on its Android software, which runs 88% of smartphones, according to Strategy Analytics.

That has helped drive new users to its platform. About 2 billion unique users now watch a YouTube video every 90 days, according to a former manager. In 2013, the last time YouTube disclosed its user base, it said it surpassed 1 billion monthly users. YouTube is now likely larger than the world’s biggest TV network, China Central Television, which has more than 1.2 billion viewers.

YouTube long configured video recommendations to boost total views, but that approach rewarded videos with misleading titles or preview images. To increase user engagement and retention, the company in early 2012 changed its algorithms to boost watch time instead. Immediately, clicks dropped nearly 20% partly because users stuck with videos longer. Some executives and video creators objected.

Months later, YouTube executives unveiled a goal of 1 billion hours of watch time daily by the end of 2016. At the time, optimistic forecasts projected it would reach 400 million hours by then.

YouTube retooled its algorithms using a field of artificial intelligence called machine learning to parse massive databases of user history to improve video recommendations.

If I only watch heavy-metal videos, of course it’s serving me more of those. But then I’m missing out on the diversity of culture that exists.

—Northeastern University computer-science professor Christo Wilson

Previously, the algorithms recommended content largely based on what other users clicked after watching a particular video, the former manager said. Now their “understanding of what is in a video [and] what a person or group of people would like to watch has grown dramatically,” he said.

Engineers tested each change on a control group, and only kept the change if those users spent more time on YouTube.

One strategy was to find new areas of user interest. For instance, YouTube could suggest a soccer video to users watching a lot of football, and then flood the lineup with more soccer if the first clip was a hit. “Once you realize there’s an additional preference, exploit that,” the former manager said.

But the algorithm didn’t always deliver. For instance, when Ms. Wojcicki joined as CEO in 2014, YouTube recommended videos to her about eczema because she had recently watched a clip about skin rashes after suspecting one of her children had one, said Cristos Goodrow, YouTube’s video-recommendation chief.

That made the video-recommendation team realize there were certain “single-use videos” to ignore as signals of user interest. But to mark them, they had to think of each example, such as certain health and how-to videos.

Then last year YouTube partnered with Google Brain, which develops advanced machine-learning software called deep neural networks, which have led to dramatic improvements in other fields, such as language translation. The Google Brain system was able to identify single-use video categories on its own.

A survival plan for bricks-and-mortar Starbucks

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Food Business News

by Jeff Gelski

Starbucks Coffee store
The Starbucks stores need to remain a place where people go to seek out human interaction.

SEATTLE — Web sites, e-commerce and what he described as the Amazon effect could lead both large and small companies to close retail stores in the coming years, said Howard Schultz, chairman and chief executive officer for the Starbucks Coffee Co.

Howard Schultz, Starbucks
Howard Schultz, chairman and c.e.o. of Starbucks

“There is no doubt that over the next five years or so we are going to see a dramatic level of retailers not be able to sustain their level of core business as a traditional bricks-and-mortar retailer, and their omni-channel approach is not going to be sustainable to maintain their cost of their infrastructure, and as a result of that, there is going to be a tremendous amount of changes with regard to the retail landscape,” he said in a Nov. 3 earnings call.

Mr. Schultz said Starbucks stores have an advantage in that they maintain a special place in terms of a sense of community, an environment where people go to seek out human interaction. Starbucks could be in a unique position 5 to 10 years from now. Other retail stores closing could mean fewer stores competing for Starbucks’ customers, he said.

“I’m not talking about the coffee category,” Mr. Schultz said. “I am talking overall, but we are in the very, very early stages of a tremendous change in the bricks-and-mortar footprint of retailers domestically and internationally as a result of the sea-change in how people are buying things, and that is going to have I think a negative effect on all of retail, but we believe that it is going to have ultimately a positive effect on the position that we occupy and the environment that we create in our stores.”

Starbucks Roastery in Seattle
New Starbucks roastery stores will be designed to enhance the consumer experience.

New Starbucks roastery stores will be designed to enhance the consumer experience. The Seattle roastery, the only one in operation right now, delivered a comp sales increase of 24% in the fiscal year ended Oct. 2, Mr. Schultz said. A roastery in Shanghai, China, should begin operations next year.

“Opening in late 2017 on Nanjing Road among the busiest shopping destinations in the world, the Starbucks Shanghai roastery will be a stunning two-level, 30,000-square-foot experiential destination showcasing the newest coffee brewing methods and offering customers the finest assortment of exclusive micro-lot coffees from around the world in an immersive all-sensory experience emblematic of our Seattle roastery, respectfully curated through a unique lens that will make it highly impactful and relevant to our Chinese customers,” he said.

Starbucks plans to open roasteries in New York and Tokyo in 2018. A roastery should open in Europe in 2019, but Starbucks has yet to select a city.

Starbucks mobile ordering
Mobile orders now represent 6% of Starbucks transactions.

Starbucks has a digital presence as well. Mobile orders now represent 6% of transactions, said Kevin Johnson, president and chief operating officer.

“We are continuously improving the mobile order and pay experience with newly released functionality that presents our personalized offer directly on the front screen of the mobile app and allows the customer to save favorite stores, favorite customers’ beverages, and we have new features in the pipeline to be released shortly, including real-time personalized product suggestions and the ability to save favorite orders, and there is more coming,” he said.

Starbucks executives discussed results of the 2016 fiscal year in the Nov. 3 earnings call. Net earnings attributable to Starbucks in the year ended Oct. 2 were $2,817.7 million, equal to $1.90 per share on the common stock, which was up 2.2% from $2,757.4 million, or $1.82 per share, in the previous fiscal year. Consolidated net revenues grew 11% to $21,315.9 million from $19,162.7 million in the previous fiscal year. The 2016 fiscal year contained 53 weeks compared to 52 weeks for the previous fiscal year.

From Audi To Zoox, 17 Companies That Are Driving Change In The Car Industry

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Along with GM, these innovators are finding ways to improve the basics of modern transportation.

From Audi To Zoox, 17 Companies That Are Driving Change In The Car Industry
Building a Tesla Model S
 

OLD-GUARD CAR MANUFACTURERS

Audi: In 2015, started test-driving an AI-laden prototype nicknamed “Jack” that lets drivers easily switch to autonomous mode via buttons on the wheel

BMW: Has promised an entirely autonomous car called iNext by 2021; BMW’s ReachNow car-sharing service launched in April in Seattle and expanded to Portland, Oregon, in September

Ford: Announced plans for fully autonomous car with no pedals or steering wheel by 2021; recently invested $75 million in California laser-sensor company Velodyne; bought San Francisco–based private bus service Chariot and plans to expand it

Volvo: Forged partnerships with Microsoft (will incorporate HoloLens augmented-reality technology into its cars) and Uber (which is planning to use Volvos as part of its self-driving test fleet in Pittsburgh); teamed up with safety-system-maker Autoliv to set up a new company focused on autonomous-driving software

TECH GIANTS

Alphabet: Launched self-piloting-car project back in 2009; testing retrofitted Lexus SUVs and its own adorable prototype vehicles in several locations; recently partnered with Fiat Chrysler to build self-driving minivans

Google’s self-driving prototype.[Photo: Brooks Kraft LLC/Getty Images]

Apple: Has invested $1 billion in Chinese ride-share company Didi Chuxing; reportedly rebooting its efforts to develop an Apple car; might also build a system to add autonomous features to preexisting vehicles

Baidu: Chinese search-engine company has teamed up with digital-graphics pioneer Nvidia to create a self-driving-vehicle system that uses 3-D maps in the cloud; is in the testing stage with several different self-driving-car prototypes, including one built with BMW

Tesla: After revolutionizing electric vehicles with the semi-autonomous Model S, will release more-affordable all-electric Model 3, possibly in late 2017; Model S’s involved in a pair of high-profile fatal accidents

IN-DEMAND PROVIDERS

Didi Chuxing: Acquired Uber’s Chinese operations in August, ending a fierce rivalry for Chinese market

Lyft: Partnered with GM to start testing autonomous Chevy Bolt taxis within the next year

Uber: In September, began testing autonomous Ford Fusions in Pittsburgh—the first self-driving fleet available to the public in the U.S.

A fleet of Uber’s autonomous cars.[Photo: Angelo Merendino/Getty Images]

STARTUPS

Comma.ai: Andreessen Horowitz–backed company making an inexpensive kit that turns regular cars into semi-autonomous ones

Mobileye: Israeli software maker that had partnered with Tesla to provide chips and software, but the two companies ended their collaboration in the wake of a fatal accident in May (Tesla cars currently still use Mobileye chips); has teamed up with Delphi Automotive to build a self-driving system by 2019

NextEV: Shanghai-based electric-car innovator headed in the U.S. by former Cisco exec Padmasree Warrior; set to show off a high-performance all-electric sports-car prototype this year

Nutonomy: Born at MIT and backed by Ford, makes self-driving cars, software, and autonomous robots; started testing driverless taxis in Singapore this summer

Quanergy: Silicon Valley–based company developing light- and object-sensing technology for self-driving cars; boasts $1.59 billion valuation thanks to investors Samsung and Delphi

Zoox: Palo Alto startup behind the Boz, a fully autonomous concept vehicle (still in the design phase) with inward-facing seats similar to a train car; company valued at around $1 billion

A version of this article appeared in the November 2016 issue of Fast Company magazine.

How the Best Business Leaders Disrupt Themselves

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And how doing so helps them keep up with technological change.

Why isn’t Intuit INTU -0.94% dead? Its peers from the Pleistocene epoch of PC software (VisiCalc, WordStar) are long gone; only Intuit survives as a significant independent business. The reason is easy to state, hard to emulate: The company has continually disrupted itself, most recently scrapping its desktop-driven business model of the previous 30 years and switching to one based on the cloud. Revenues went down before they went up, but Intuit’s stock recently hit an all-time high.

Such stories are extremely rare. Successful incumbent firms are more likely to follow the trajectory of Kodak, Sears SHLD 2.39% , Bethlehem Steel, and many newspapers, dead or diminished after technology transformed their industries. Little wonder that for the past two years, when we have asked Fortune 500 CEOs to name their single biggest challenge, their No. 1 answer has been “the rapid pace of technological change.”

Yet a few incumbents have defied the odds and succeeded at self-disruption. How they do it is becoming clear.

They see their business as disrupters would see it. This challenge is psychological and requires escaping the aura of headquarters. At the dawn of the web, American Airlines’ AAL -2.31% Sabre subsidiary assembled a team and sent it to another building with orders to disrupt the industry’s travel-agent-based business model. The result was Travelocity. Charles Schwab responded to the rise of “robo-advisers” like Betterment and Wealthfront by forming a full-time team that ignored the company’s corporate playbook. The team developed Schwab Intelligent Portfolios, a robo-product that now manages more assets than any of its disrupter startup rivals.

They find the courage to leap. Netflix NFLX -0.44% CEO Reed Hastings knew that online streaming would disrupt his successful DVDs-by-mail model. He committed to streaming in 2011—and Netflix’s stock plunged 76%. Wall Street called for his head. But Hastings pushed on, and today DVDs are just 7% of the company’s business, while the stock is up 150% from its pre-plunge peak

They never stop. Self-­disruption isn’t something you do just once. Every successful disrupter becomes an incumbent in its transformed industry, and digital business models don’t last long. Amazon AMZN 0.82% disrupted bookstores 20 years ago, then disrupted its own books-by-mail model with Kindle e-readers. Digital evolution is merciless: Intelwas a champion self-disrupter until it missed the mobile revolution; in April it announced 12,000 layoffs.

Leaders can glean these lessons from the first industries to be disrupted by digital tech. But the hardest step for incumbents is the first one, best expressed by Peter Drucker: “If leaders are unable to slough off yesterday, to abandon yesterday, they simply will not be able to create tomorrow.” 

Shoppers Flock to Apps, Shaking Up Retail

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By GREG BENSINGER

Wall Street Journal
April 13, 2016

Hana Pugh, a 29-year-old event planner and new mother, buys most of her household items and baby goods online. But she doesn’t use her computer to shop. Instead, Ms. Pugh taps away at her iPhone.

“It’s quicker to pull out my phone and click ‘buy’ than to log on to my computer,” said Ms. Pugh, of Bowie, Md., who relies on Amazon.com Inc.’s app for essentials such as diapers and wipes.

Shopping on a small screen used to be a pain. But as consumers spend more of their days glued to smartphones, retailers are getting savvier with apps that ease browsing, offer rewards, suggest the right products and simplify the purchase to one click.

The so-called appification of shopping promises to radically transform the retail industry by creating new shopping habits, reshaping sales tactics and carving out winners and losers. Instead of placing one big order from a computer, people are increasingly making smaller purchases in short bursts throughout the day on their phones, a phenomenon retailers call “snacking.”

Mobile sales are booming, especially compared with sales gains from desktop computers. Last year, U.S. sales from mobile devices jumped 56% to $49.2 billion, doubling the previous year’s growth, according to comScore. Desktop sales still dwarf mobile, reaching $256.1 billion last year, but annual growth slowed to 8.1% from 12.5%.

The retailers that are succeeding are training customers to think of their smartphones like an all-day impulse aisle. Apps are able to capture data available on handsets and push consumers to buy when they have a spare moment, whether in line for a morning coffee, or, as in Ms. Pugh’s case, nursing her child.

But merchants say many shoppers on phones still shy away from buying big-ticket items such as sofas, preferring larger photos, expanded reviews and product descriptions, as well as price comparisons available on a desktop computer.

And retailers have to be careful not to seem invasive. Amazon’s shoe retailer, Zappos.com, is testing technology that highlights different products based on a phone’s operating system. Target Corp. pushes coupons to customers who have the app open in its stores. Fashion retailer ModCloth Inc. suggests some products based on a customer’s location.

Amazon’s dominance on the Web extends to mobile, and the online retailer has the top-ranked app, according to Apple. Among the top-ranked apps on both Apple and Android devices are two, young mobile-centric marketplaces, OfferUp Inc. and ContextLogic Inc.’s Wish, which last year were valued at $800 million and $3 billion, respectively, by investors.

“Mobile devices are driving demand,” said Andrew Lipsman, a comScore vice president, who has studied mobile shopping. “They can create an impulsive buying moment at any point in the day because they are with you all the time, right in your pocket.”

BT-AI061_SHOPAP_16U_20160412221805

Selling merchandise on smartphones still poses challenges. The ease of buying single items instead of building a shopping cart can drive up retailers’ shipping costs. And consumers are more likely to window shop for products but not necessarily buy them.

People still make most of their online transactions on desktop computers. Ryan McIntyre, chief marketing officer of men’s fashion retailer JackThreads Inc., said the average cart is about $5 higher on his company’s website than its app. But he says customers spend about 10% more on average on mobile devices in a given six-month period.

Mobile devices also drive Web sales: Nearly 40% of desktop transactions in the fourth quarter took place after a customer visited the retailer’s app or mobile site, according to consulting firm Criteo.

Olivia Bryant, a 19-year-old Starbucks barista in Bakersfield, Calif., said she spends up to two hours a day shopping on her iPhone through apps such as marketplace Etsy Inc. and fashion retailer Poshmark Inc. “It’s much simpler to shop on my phone,” she said. “There aren’t a lot of distractions.”

The average U.S. consumer last year spent 3 hours and 5 minutes a day using apps, compared with 51 minutes surfing the mobile Web, according to eMarketer. Such devotion to apps isn’t lost on retailers. Target has shifted resources and staff away from Web development to its app—which it hopes to make central to all of its digital design.
“With smartphones we’re able to reach you all the time” through texts or lock-screen messages, known as push notifications, said Wish CEO Peter Szulczewski. The San Francisco startup has created an experience that mimics the mall, with a seemingly endless inventory to scroll through and bargain-basement prices.

Some apps, such as those from Zappos and online auction site Tophatter Inc., identify a user’s device to judge a customer’s possible buying power. “Our data shows that the more expensive device you have, the more you might spend,” said Tophatter CEO Ashvin Kumar.

“Millions of Amazon customers shop exclusively on a mobile device all year,” said an Amazon spokeswoman.

Retailers may be most excited to cater results based on a customer’s location, whether it is knowing a user has entered their store or is vacationing in warmer climes.

“If you’re in Australia, we might serve you swimwear when it’s winter in the Northern Hemisphere,” said Matt Kaness, CEO of San Francisco fashion retailer ModCloth, which has about 1.2 million users. EBay Inc. will highlight generators to customers shopping in areas affected by a big storm, said Hal Lawton, the company’s North America senior vice president.
Other retailers such as Wal-Mart Stores Inc. and Nordstrom Inc. have explored ways to tie in mobile shopping with stores. Customers could check in to a store through the app, providing salespeople with their purchase history, even encrypted payment information, for a quicker and more personalized shopping experience.

But the main catch for apps may be the impulsive shoppers. “On bar nights, we see drunk shopping, which is very interesting,” said Alan Tisch, CEO of fashion app Spring Inc. “Maybe there’s an opportunity there.”

 

Other applications for the technology behind bitcoin.

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Blockchains
The great chain of being sure about things
The technology behind bitcoin lets people who do not know or trust each other build a dependable ledger. This has implications far beyond the cryptocurrency

WHEN the Honduran police came to evict her in 2009 Mariana Catalina Izaguirre had lived in her lowly house for three decades. Unlike many of her neighbours in Tegucigalpa, the country’s capital, she even had an official title to the land on which it stood. But the records at the country’s Property Institute showed another person registered as its owner, too—and that person convinced a judge to sign an eviction order. By the time the legal confusion was finally sorted out, Ms Izaguirre’s house had been demolished.

It is the sort of thing that happens every day in places where land registries are badly kept, mismanaged and/or corrupt—which is to say across much of the world. This lack of secure property rights is an endemic source of insecurity and injustice. It also makes it harder to use a house or a piece of land as collateral, stymying investment and job creation.

Such problems seem worlds away from bitcoin, a currency based on clever cryptography which has a devoted following among mostly well-off, often anti-government and sometimes criminal geeks. But the cryptographic technology that underlies bitcoin, called the “blockchain”, has applications well beyond cash and currency. It offers a way for people who do not know or trust each other to create a record of who owns what that will compel the assent of everyone concerned. It is a way of making and preserving truths.

That is why politicians seeking to clean up the Property Institute in Honduras have asked Factom, an American startup, to provide a prototype of a blockchain-based land registry. Interest in the idea has also been expressed in Greece, which has no proper land registry and where only 7% of the territory is adequately mapped.

A place in the past

Other applications for blockchain and similar “distributed ledgers” range from thwarting diamond thieves to streamlining stockmarkets: the NASDAQ exchange will soon start using a blockchain-based system to record trades in privately held companies. The Bank of England, not known for technological flights of fancy, seems electrified: distributed ledgers, it concluded in a research note late last year, are a “significant innovation” that could have “far-reaching implications” in the financial industry.

The politically minded see the blockchain reaching further than that. When co-operatives and left-wingers gathered for this year’s OuiShare Fest in Paris to discuss ways that grass-roots organisations could undermine giant repositories of data like Facebook, the blockchain made it into almost every speech. Libertarians dream of a world where more and more state regulations are replaced with private contracts between individuals—contracts which blockchain-based programming would make self-enforcing.

The blockchain began life in the mind of Satoshi Nakamoto, the brilliant, pseudonymous and so far unidentified creator of bitcoin—a “purely peer-to-peer version of electronic cash”, as he put it in a paper published in 2008. To work as cash, bitcoin had to be able to change hands without being diverted into the wrong account and to be incapable of being spent twice by the same person. To fulfil Mr Nakamoto’s dream of a decentralised system the avoidance of such abuses had to be achieved without recourse to any trusted third party, such as the banks which stand behind conventional payment systems.

It is the blockchain that replaces this trusted third party. A database that contains the payment history of every bitcoin in circulation, the blockchain provides proof of who owns what at any given juncture. This distributed ledger is replicated on thousands of computers—bitcoin’s “nodes”—around the world and is publicly available. But for all its openness it is also trustworthy and secure. This is guaranteed by the mixture of mathematical subtlety and computational brute force built into its “consensus mechanism”—the process by which the nodes agree on how to update the blockchain in the light of bitcoin transfers from one person to another.

Let us say that Alice wants to pay Bob for services rendered. Both have bitcoin “wallets”—software which accesses the blockchain rather as a browser accesses the web, but does not identify the user to the system. The transaction starts with Alice’s wallet proposing that the blockchain be changed so as to show Alice’s wallet a little emptier and Bob’s a little fuller.

The network goes through a number of steps to confirm this change. As the proposal propagates over the network the various nodes check, by inspecting the ledger, whether Alice actually has the bitcoin she now wants to spend. If everything looks kosher, specialised nodes called miners will bundle Alice’s proposal with other similarly reputable transactions to create a new block for the blockchain.

This entails repeatedly feeding the data through a cryptographic “hash” function which boils the block down into a string of digits of a given length (see diagram). Like a lot of cryptography, this hashing is a one-way street. It is easy to go from the data to their hash; impossible to go from the hash back to the data. But though the hash does not contain the data, it is still unique to them. Change what goes into the block in any way—alter a transaction by a single digit—and the hash would be different.

Running in the shadows

That hash is put, along with some other data, into the header of the proposed block. This header then becomes the basis for an exacting mathematical puzzle which involves using the hash function yet again. This puzzle can only be solved by trial and error. Across the network, miners grind through trillions and trillions of possibilities looking for the answer. When a miner finally comes up with a solution other nodes quickly check it (that’s the one-way street again: solving is hard but checking is easy), and each node that confirms the solution updates the blockchain accordingly. The hash of the header becomes the new block’s identifying string, and that block is now part of the ledger. Alice’s payment to Bob, and all the other transactions the block contains, are confirmed.

This puzzle stage introduces three things that add hugely to bitcoin’s security. One is chance. You cannot predict which miner will solve a puzzle, and so you cannot predict who will get to update the blockchain at any given time, except in so far as it has to be one of the hard working miners, not some random interloper. This makes cheating hard.

The second addition is history. Each new header contains a hash of the previous block’s header, which in turn contains a hash of the header before that, and so on and so on all the way back to the beginning. It is this concatenation that makes the blocks into a chain. Starting from all the data in the ledger it is trivial to reproduce the header for the latest block. Make a change anywhere, though—even back in one of the earliest blocks—and that changed block’s header will come out different. This means that so will the next block’s, and all the subsequent ones. The ledger will no longer match the latest block’s identifier, and will be rejected.

Is there a way round this? Imagine that Alice changes her mind about paying Bob and tries to rewrite history so that her bitcoin stays in her wallet. If she were a competent miner she could solve the requisite puzzle and produce a new version of the blockchain. But in the time it took her to do so, the rest of the network would have lengthened the original blockchain. And nodes always work on the longest version of the blockchain there is. This rule stops the occasions when two miners find the solution almost simultaneously from causing anything more than a temporary fork in the chain. It also stops cheating. To force the system to accept her new version Alice would need to lengthen it faster than the rest of the system was lengthening the original. Short of controlling more than half the computers—known in the jargon as a “51% attack”—that should not be possible.

Dreams are sometimes catching

Leaving aside the difficulties of trying to subvert the network, there is a deeper question: why bother to be part of it at all? Because the third thing the puzzle-solving step adds is an incentive. Forging a new block creates new bitcoin. The winning miner earns 25 bitcoin, worth about $7,500 at current prices.

All this cleverness does not, in itself, make bitcoin a particularly attractive currency. Its value is unstable and unpredictable (see chart), and the total amount in circulation is deliberately limited. But the blockchain mechanism works very well. According to blockchain.info, a website that tracks such things, on an average day more than 120,000 transactions are added to the blockchain, representing about $75m exchanged. There are now 380,000 blocks; the ledger weighs in at nearly 45 gigabytes.

Most of the data in the blockchain are about bitcoin. But they do not have to be. Mr Nakamoto has built what geeks call an “open platform”—a distributed system the workings of which are open to examination and elaboration. The paragon of such platforms is the internet itself; other examples include operating systems like Android or Windows. Applications that depend on basic features of the blockchain can thus be developed without asking anybody for permission or paying anyone for the privilege. “The internet finally has a public data base,” says Chris Dixon of Andreessen Horowitz, a venture-capital firm which has financed several bitcoin start-ups, including Coinbase, which provides wallets, and 21, which makes bitcoin-mining hardware for the masses.

For now blockchain-based offerings fall in three buckets. The first takes advantage of the fact that any type of asset can be transferred using the blockchain. One of the startups betting on this idea is Colu. It has developed a mechanism to “dye” very small bitcoin transactions (called “bitcoin dust”) by adding extra data to them so that they can represent bonds, shares or units of precious metals.

Protecting land titles is an example of the second bucket: applications that use the blockchain as a truth machine. Bitcoin transactions can be combined with snippets of additional information which then also become embedded in the ledger. It can thus be a registry of anything worth tracking closely. Everledger uses the blockchain to protect luxury goods; for example it will stick on to the blockchain data about a stone’s distinguishing attributes, providing unchallengeable proof of its identity should it be stolen. Onename stores personal information in a way that is meant to do away with the need for passwords; CoinSpark acts as a notary. Note, though, that for these applications, unlike for pure bitcoin transactions, a certain amount of trust is required; you have to believe the intermediary will store the data accurately.

It is the third bucket that contains the most ambitious applications: “smart contracts” that execute themselves automatically under the right circumstances. Bitcoin can be “programmed” so that it only becomes available under certain conditions. One use of this ability is to defer the payment miners get for solving a puzzle until 99 more blocks have been added—which provides another incentive to keep the blockchain in good shape.

Lighthouse, a project started by Mike Hearn, one of bitcoin’s leading programmers, is a decentralised crowdfunding service that uses these principles. If enough money is pledged to a project it all goes through; if the target is never reached, none does. Mr Hearn says his scheme will both be cheaper than non-bitcoin competitors and also more independent, as governments will be unable to pull the plug on a project they don’t like.

Energy is contagious

The advent of distributed ledgers opens up an “entirely new quadrant of possibilities”, in the words of Albert Wenger of USV, a New York venture firm that has invested in startups such as OpenBazaar, a middleman-free peer-to-peer marketplace. But for all that the blockchain is open and exciting, sceptics argue that its security may yet be fallible and its procedures may not scale. What works for bitcoin and a few niche applications may be unable to support thousands of different services with millions of users.

Though Mr Nakamoto’s subtle design has so far proved impregnable, academic researchers have identified tactics that might allow a sneaky and well financed miner to compromise the block chain without direct control of 51% of it. And getting control of an appreciable fraction of the network’s resources looks less unlikely than it used to. Once the purview of hobbyists, bitcoin mining is now dominated by large “pools”, in which small miners share their efforts and rewards, and the operators of big data centres, many based in areas of China, such as Inner Mongolia, where electricity is cheap.

Another worry is the impact on the environment. With no other way to establish the bona fides of miners, the bitcoin architecture forces them to do a lot of hard computing; this “proof of work”, without which there can be no reward, insures that all concerned have skin in the game. But it adds up to a lot of otherwise pointless computing. According to blockchain.info the network’s miners are now trying 450 thousand trillion solutions per second. And every calculation takes energy.

Because miners keep details of their hardware secret, nobody really knows how much power the network consumes. If everyone were using the most efficient hardware, its annual electricity usage might be about two terawatt-hours—a bit more than the amount used by the 150,000 inhabitants of King’s County in California’s Central Valley. Make really pessimistic assumptions about the miners’ efficiency, though, and you can get the figure up to 40 terawatt-hours, almost two-thirds of what the 10m people in Los Angeles County get through. That surely overstates the problem; still, the more widely people use bitcoin, the worse the waste could get.

Yet for all this profligacy bitcoin remains limited. Because Mr Nakamoto decided to cap the size of a block at one megabyte, or about 1,400 transactions, it can handle only around seven transactions per second, compared to the 1,736 a second Visa handles in America. Blocks could be made bigger; but bigger blocks would take longer to propagate through the network, worsening the risks of forking.

Earlier platforms have surmounted similar problems. When millions went online after the invention of the web browser in the 1990s pundits predicted the internet would grind to a standstill: eppur si muove. Similarly, the bitcoin system is not standing still. Specialised mining computers can be very energy efficient, and less energy-hungry alternatives to the proof-of-work mechanism have been proposed. Developers are also working on an add-on called “Lightning” which would handle large numbers of smaller transactions outside the blockchain. Faster connections will let bigger blocks propagate as quickly as small ones used to.

The problem is not so much a lack of fixes. It is that the network’s “bitcoin improvement process” makes it hard to choose one. Change requires community-wide agreement, and these are not people to whom consensus comes easily. Consider the civil war being waged over the size of blocks. One camp frets that quickly increasing the block size will lead to further concentration in the mining industry and turn bitcoin into more of a conventional payment processor. The other side argues that the system could crash as early as next year if nothing is done, with transactions taking hours.

A break in the battle

Mr Hearn and Gavin Andresen, another bitcoin grandee, are leaders of the big-block camp. They have called on mining firms to install a new version of bitcoin which supports a much bigger block size. Some miners who do, though, appear to be suffering cyber-attacks. And in what seems a concerted effort to show the need for, or the dangers of, such an upgrade, the system is being driven to its limits by vast numbers of tiny transactions.

This has all given new momentum to efforts to build an alternative to the bitcoin blockchain, one that might be optimised for the storing of distributed ledgers rather than for the running of a cryptocurrency. MultiChain, a build-your-own-blockchain platform offered by Coin Sciences, another startup, demonstrates what is possible. As well as offering the wherewithal to build a public blockchain like bitcoin’s, it can also be used to build private chains open only to vetted users. If all the users start off trusted the need for mining and proof-of-work is reduced or eliminated, and a currency attached to the ledger becomes an optional extra.

The first industry to adopt such sons of blockchain may well be the one whose failings originally inspired Mr Nakamoto: finance. In recent months there has been a rush of bankerly enthusiasm for private blockchains as a way of keeping tamper-proof ledgers. One of the reasons, irony of ironies, is that this technology born of anti-government libertarianism could make it easier for the banks to comply with regulatory requirements on knowing their customers and anti-money-laundering rules. But there is a deeper appeal.

Industrial historians point out that new powers often become available long before the processes that best use them are developed. When electric motors were first developed they were deployed like the big hulking steam engines that came before them. It took decades for manufacturers to see that lots of decentralised electric motors could reorganise every aspect of the way they made things. In its report on digital currencies, the Bank of England sees something similar afoot in the financial sector. Thanks to cheap computing financial firms have digitised their inner workings; but they have not yet changed their organisations to match. Payment systems are mostly still centralised: transfers are cleared through the central bank. When financial firms do business with each other, the hard work of synchronising their internal ledgers can take several days, which ties up capital and increases risk.

Distributed ledgers that settle transactions in minutes or seconds could go a long way to solving such problems and fulfilling the greater promise of digitised banking. They could also save banks a lot of money: according to Santander, a bank, by 2022 such ledgers could cut the industry’s bills by up to $20 billion a year. Vendors still need to prove that they could deal with the far-higher-than-bitcoin transaction rates that would be involved; but big banks are already pushing for standards to shape the emerging technology. One of them, UBS, has proposed the creation of a standard “settlement coin”. The first order of business for R3 CEV, a blockchain startup in which UBS has invested alongside Goldman Sachs, JPMorgan and 22 other banks, is to develop a standardised architecture for private ledgers.

The banks’ problems are not unique. All sorts of companies and public bodies suffer from hard-to-maintain and often incompatible databases and the high transaction costs of getting them to talk to each other. This is the problem Ethereum, arguably the most ambitious distributed-ledger project, wants to solve. The brainchild of Vitalik Buterin, a 21-year-old Canadian programming prodigy, Ethereum’s distributed ledger can deal with more data than bitcoin’s can. And it comes with a programming language that allows users to write more sophisticated smart contracts, thus creating invoices that pay themselves when a shipment arrives or share certificates which automatically send their owners dividends if profits reach a certain level. Such cleverness, Mr Buterin hopes, will allow the formation of “decentralised autonomous organisations”—virtual companies that are basically just sets of rules running on Ethereum’s blockchain.

One of the areas where such ideas could have radical effects is in the “internet of things”—a network of billions of previously mute everyday objects such as fridges, doorstops and lawn sprinklers. A recent report from IBM entitled “Device Democracy” argues that it would be impossible to keep track of and manage these billions of devices centrally, and unwise to to try; such attempts would make them vulnerable to hacking attacks and government surveillance. Distributed registers seem a good alternative.

The sort of programmability Ethereum offers does not just allow people’s property to be tracked and registered. It allows it to be used in new sorts of ways. Thus a car-key embedded in the Ethereum blockchain could be sold or rented out in all manner of rule-based ways, enabling new peer-to-peer schemes for renting or sharing cars. Further out, some talk of using the technology to make by-then-self-driving cars self-owning, to boot. Such vehicles could stash away some of the digital money they make from renting out their keys to pay for fuel, repairs and parking spaces, all according to preprogrammed rules.

What would Rousseau have said?

Unsurprisingly, some think such schemes overly ambitious. Ethereum’s first (“genesis”) block was only mined in August and, though there is a little ecosystem of start-ups clustered around it, Mr Buterin admitted in a recent blog post that it is somewhat short of cash. But the details of which particular blockchains end up flourishing matter much less than the broad enthusiasm for distributed ledgers that is leading both start-ups and giant incumbents to examine their potential. Despite society’s inexhaustible ability to laugh at accountants, the workings of ledgers really do matter.

Today’s world is deeply dependent on double-entry book-keeping. Its standardised system of recording debits and credits is central to any attempt to understand a company’s financial position. Whether modern capitalism absolutely required such book-keeping in order to develop, as Werner Sombart, a German sociologist, claimed in the early 20th century, is open to question. Though the system began among the merchants of renaissance Italy, which offers an interesting coincidence of timing, it spread round the world much more slowly than capitalism did, becoming widely used only in the late 19th century. But there is no question that the technique is of fundamental importance not just as a record of what a company does, but as a way of defining what one can be.

Ledgers that no longer need to be maintained by a company—or a government—may in time spur new changes in how companies and governments work, in what is expected of them and in what can be done without them. A realisation that systems without centralised record-keeping can be just as trustworthy as those that have them may bring radical change.

Such ideas can expect some eye-rolling—blockchains are still a novelty applicable only in a few niches, and the doubts as to how far they can spread and scale up may prove well founded. They can also expect resistance. Some of bitcoin’s critics have always seen it as the latest techy attempt to spread a “Californian ideology” which promises salvation through technology-induced decentralisation while ignoring and obfuscating the realities of power—and happily concentrating vast wealth in the hands of an elite. The idea of making trust a matter of coding, rather than of democratic politics, legitimacy and accountability, is not necessarily an appealing or empowering one.

At the same time, a world with record-keeping mathematically immune to manipulation would have many benefits. Evicted Ms Izaguirre would be better off; so would many others in many other settings. If blockchains have a fundamental paradox, it is this: by offering a way of setting the past and present in cryptographic stone, they could make the future a very different place.

 

Commercial drones

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A Chinese firm has taken the lead in a promising market

SOMETHING new is in the air. Look up as you approach the plaza outside the building where Da-Jiang Innovations (DJI) has its headquarters, in the Chinese city of Shenzhen, and you may well see a hovering eye in the sky staring back at you. It belongs to a drone made by DJI, a pioneer in the nascent market for commercial unmanned aircraft.

On March 8th, at press events in New York, London and Munich, the firm launched its new Phantom 3 range of drones. Even the basic model has a built-in camera that takes 12 megapixel stills and video at the “1080p” high-definition standard. The firm, founded in 2006 by a mainlander who studied engineering in Hong Kong, has become a leading light in the industry. It has filed hundreds of patents, and is launching lawsuits against rivals it suspects of infringing its intellectual property.

DJI’s drones are lightweight and relatively easy to use. Newer models come with built-in GPS and a motorised mount that stabilises the camera while letting it rotate in several directions. Considering the technology embedded inside, they are also inexpensive: a new Phantom 3 can be had for about $1,000.

Rather as Boeing did with commercial airliners in the 1930s, DJI is today leading the charge in transforming civilian-drone manufacturing from something for hobbyists into a proper business. The Association for Unmanned Vehicle Systems International, an industry body, predicts that drones will become ubiquitous, with all sorts of uses, from crop monitoring to atmospheric research, from oil exploration to internet provision (see article). WinterGreen, a research firm, forecasts that global sales of civilian unmanned craft will approach $5 billion in 2021.

Venture capitalists and technology companies, from Boeing and GE to Qualcomm, are now pouring money into drone firms. An American outfit, 3D Robotics (founded by Chris Anderson, a former journalist at this newspaper), raised $50m in venture capital in February. Ehang Guangshi Technology, another Chinese drone startup, recently got $10m in venture-capital funding

Now, rumours are swirling in Silicon Valley that DJI is looking for its first injection of outside cash. It is thought to have made around $500m in revenues in 2014 (the company declines to confirm this), and it may be on track to become the first maker of consumer drones to reach a billion dollars in annual sales.

There will be growing pains. As dronemakers’ sales soar, so will its customers’ expectations of good service. On DJI’s website, users grumble that a firm of its size should put more of its resources into this area: “Try to call them…and they treat you like you are an inconvenience,” writes one. Over-regulation is another risk. A Phantom drone crashed onto the White House lawn in Washington, DC, in January; in response, DJI rushed out an upgrade to its drones’ onboard firmware that included many new “no-fly zones”, to head off the risk of outright bans. Although America’s Federal Aviation Administration plans to relax its curbs on drones, they will still have to stay within sight of their human operators and only fly by day.

Such is the civil-drone industry’s potential, DJI is bound to face a rising number of competitors, from China and abroad. It argues that it has a technological edge, including tens of millions of hours of flights, that newcomers will find hard to beat. It scorns the idea that the defence giants who make drones for America’s armed forces will eventually muscle in on its business: yes, they are technologically advanced, says DJI’s Andy Pan, but “they take five to six years to introduce a new model whereas we take five to six months.”

In reviewing the Phantom 2 Vision in January 2014, the New York Times gushed that just five years ago such kit “would have seemed like a science-fiction film prop or a piece of surveillance hardware flown only by the sexiest of superspies.” The fact that this model is now obsolete speaks volumes for how quickly the industry is advancing.

 

Areas businesses need to address to have broader application of IIoT.

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For businesses, three key areas need to be addressed to accelerate the economy-wide, cross-industry application of the IIoT:

Reimagine industry models: If every product is connected and enables a new service, reinventing industry practices and business models becomes paramount. As companies embark on a journey that begins with using the IIoT to improve efficiencies, and progresses to creating outcome-oriented, product- service hybrids, they will need to plan each stage. How can their efforts for improving asset utilization, for example, be used as a platform for new services? Will a company gain most value by offering its own data to an ecosystem of partners, or from incorporating third-party data to enhance its own services? Should a company invest in its own platform or join existing industry platforms? How will its partnerships evolve as a consequence?

Capitalize on the value of data: The power of the IIoT comes not only from generating insightful data from physical objects, but also from sharing it between players within supply chains and cross- industry consortia. According to a survey undertaken by Accenture and GE,10 73 percent of companies are already investing more than 20 percent of their overall technology budget on big data analytics. That shift requires new technical and management skills. Further, it demands a cultural willingness to streamline data flow, not only within enterprises, but also between them. Companies must create new financial and governance models to share the rewards of using common data. Interoperability and security are identified as the greatest hurdles to progress by two- thirds of those companies actively pursuing IIoT initiatives, according to a survey by Accenture, the World Economic Forum and the Industrial Internet Consortium.11 Collaborators should establish their own processes and tests to improve interoperability while establishing common security frameworks. Governments need to work across borders with business and other stakeholders to agree who owns data, what can be shared and how liabilities will be handled across jurisdictions.

Prepare for the future of work: An overwhelming majority of executives (94 percent) believe that the increasing use of smart products and robotics will change the required skill and job mix in the workforce of the future.11 Decision making can be devolved to workers empowered by valuable data, while the design and creative process could become more iterative and experimental. Employees may have to develop working relationships with intelligent machines. And continuous learning could replace traditional training as technologies and business practices evolve quickly. Managers will have to be willing to collapse hierarchies and silos and open up to extended workforces beyond their own walls. Such an approach demands a new culture and tolerance of autonomy. Leaders must also accept the demand for individually tailored working environments and experiences by creative and dispersed workforces, while maintaining core values and a common purpose within their organizations. Companies will have to establish digital platforms to create global talent exchanges that address skills shortages. Digital tools will also accelerate skills development and support a continuous learning culture. Companies will need to reassess their organizational structures and operations. Thanks to technologies such as 3D printing and micro-assembly, in some quarters, the IIoT will reverse today’s trend of centralized manufacturing and localized services, requiring the reconfiguration of operations and talent.

 

From Accenture report: Winning with the Industrial Internet of Things: How to accelerate the journey to productivity and growth. February 2015.