The Myths of Disruptive Innovation and Accelerating Speed

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The Economist:

More creative destruction would seem to imply that firms are being created and destroyed at a greater rate. But the odds of a company dropping out of the S&P 500 index of big firms in any given year are about one in 20—as they have been, on average, for 50 years. About half of these exits are through takeovers. For the economy as a whole the rates at which new firms are born are near their lowest since records began, with about 8% of firms less than a year old, compared with 13% three decades ago. Youngish firms, aged five years or less, are less important measured by their number and share of employment.

Some studies suggest that the period over which firms could sustain a competitive advantage shortened in the 1970s and 1980s, perhaps owing to deregulation. But for today’s incumbents life looks sweet and stable. In 2000 about half the S&P 500 had been making a pre-tax return on capital of at least 12% every year for five years. The share is the same today. The rate at which listed firms depreciate their plant and software has held fairly steady, too.

Many bosses complain that capital markets amplify a wired-up society’s hyperactive impulses. But on some measures they are becoming less short-term. The average maturity of a newly issued corporate bond has risen to 17 years from ten years in the 1990s, reflecting the attractions of borrowing for longer maturities when interest rates are low. The average holding period for a share of an S&P 500 firm is still a pitifully low 200 days, but that has doubled since 2008 and is comparable with levels a decade ago. The big fall was in the 1990s.

Intra-day churn by high-frequency trading programs accounts for about half of stockmarket turnover, according to Ana Avramovic of Credit Suisse. But that masks the rise of more stable investors. Large “passive” fund managers such as BlackRock and Vanguard have got much bigger in the past decade and their holding periods are indefinite. The average holding period of actively managed mutual funds, meanwhile, has risen to about two years. In 2000 it was closer to one.

Some executives are doubtless spivs, willing to cut investment to hit earnings targets. And economists have shown that investors discount the value of far-off profits more than they should. But it is not clear that long-term investment has shrunk. For both S&P 500 firms and the economy as a whole, corporate investment (including plant and equipment, software and R&D spending) has been steady relative to sales, assets and GDP.

Investment has fallen relative to profits, but that is because margins are at a record high thanks to lower wage costs. Companies are generously giving their owners dividends and share buy-backs while being stingy with their staff. But by historical standards they are not being miserly about investment. Were firms to invest what they spent on buy-backs, investment would have to rise to 15% of sales, far above the 25-year average of 9%. Low interest rates may mean the cost of capital is cheap, but most firms worry that they reflect the risk of slow economic growth.

Bosses grumble they are under constant pressure to perform, but they are being pushed down the gangplank more slowly. The median tenure of serving CEOs was five years in 2014, up from three in 2007. The average retiring chief executive of an S&P 500 firm in 2014 had been in office for ten years—the highest figure since 2002.

The result is a puzzle. Business people feel time is accelerating—but the figures suggest they are largely talking guff. One possibility is that their perception of speed is a leading indicator, and that a giant wave of disruption is just about to strike. But many of the reputational giants of Silicon Valley are financial tiddlers. Uber has $2 billion of sales—if it were listed it would be the world’s 3,882nd-biggest public firm. Airbnb’s sales account for 1-2% of the hotel industry’s total. These firms are platforms for purchasing services, but beneath them, the assets and people—cars, rooms, drivers—change far less dramatically, if at all. People who use dating apps still go to restaurants. Overall, McKinsey & Co, a consulting firm, estimates that technology disruption could lower global corporate profits in 2025 by 6%: significant but not overwhelming.

A better explanation of the puzzle comes from looking more closely at the effect of information flows on businesses. There is no doubt that there are far more data coursing round firms than there were just a few years ago. And when you are used to information accumulating in a steady trickle, a sudden flood can feel like a neck-snapping acceleration. Even though the processes about which you know more are not inherently moving faster, seeing them in far greater detail makes it feel as if time is speeding up.

This unsettling sensation is common to most chief executives—a straw poll suggests that they receive 200-400 e-mails a day. Their underlings are deluged with information, too. AT&T now tracks faults on its telecoms networks by monitoring social media for grumpy customers letting off steam online. Big consumer brands are subject to a rolling online plebiscite from their customers. This abundance of information gives firms a cloak of hyperactivity.

Lift up the hem, however, and the illusion of acceleration gives way to a dangerously stolid reality. As well as lower rates of new company creation, industries have become more oligopolistic. Of 13 industrial sectors in America, ten were more concentrated in 2007 than they had been in 1997. Since then there has been a huge round of mergers in health care, consumer goods, airlines, cable-TV, telecoms and technology hardware. Most of these deals have created bigger firms with higher market shares and more pricing power.

The technology platforms through which people get information and shop—those of Google, Apple and the like—have high market shares too. These firms are extraordinarily profitable and have accumulated a lot of cash—41% of the total held by big American firms outside the financial sector sits with tech companies. Perhaps they are clinging to these safety belts because they fear that they will be swept away by new rivals. Perhaps they will use their huge resources to buy other firms and further increase their pricing power.

For managers the illusion of acceleration is dangerous if it prompts them to churn their portfolio of businesses ever faster. GE has bought and sold businesses worth over 100% of its capital base in the past decade or so. American pharmaceutical firms have attempted $1.1 trillion of deals since the start of 2014, equivalent to 51% of their current stockmarket value. …

Forget frantic acceleration. Mastering the clock of business is about choosing when to be fast and when to be slow.

Financial Times:

You can always go faster than you think you can.

Those are not my words. They are the words of Meg Whitman, Hewlett-Packard Enterprise chief executive, who said them about 10 days ago at Davos, where they were duly jotted down and published in a collection of quotes from world leaders during their week in the snow.

I admire the Whitman aphorism for its simple syntax and nice short words. The only trouble with it is that it’s nonsense. Often in business you can’t go nearly as fast as you fondly think you can. When you try, you fall on your face — and Ms Whitman, of all people, should know that. If her predecessor at HP hadn’t been quite so hasty in buying Autonomy, it would have saved itself a big mess.

The 35 other quotes are almost all as dismal; variously moronic (“The fourth industrial revolution should be a revolution of values”), silly (“Let’s put our optimism goggles on”) or empty (“We are not the prisoners of a predetermined future”).

When I first read the collection I thought it was a spoof. Then I thought the quotes were real but selected maliciously to make the speakers look foolish. I now discover they were specifically picked by the World Economic Forum not as the stupidest things famous people said at Davos 2016, but as the smartest.

Most disturbingly — given that Davos is kept afloat on corporate money — every quote devoted to business is not just empty, but wrong. Marc Benioff, CEO of Salesforce, picks up where Ms Whitman left off by declaring: “Speed is the new currency of business.” On the contrary, there is no evidence that business is speeding up. …

What are we to conclude from these worthless soundbites? Do they prove that world leaders are fools? Not at all. I think they establish the truth of a new law that was proposed to me recently by a smart European Commission functionary. He has noticed that the amount of guff talked is always in direct proportion to the size of the audience.

Business leaders like talking to big audiences as it’s good for their prestige. Equally, they know it is bad form to say anything new or interesting to lots of people as it would mean giving away something for nothing. All the other important speakers are equally reluctant to cast pearls before swine, and so forgive each other their dull platitudes.

The idiots are not the waffling speakers. They aren’t even the people in the audience who have paid through the nose to hear nothing — as their own prestige is enhanced simply by being there. The only idiots are people like me who look at the quotes and are outraged to find them an empty collection of bien pensant twaddle.

 

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The History of Artificial Intelligence (Video)

[youtube https://www.youtube.com/watch?v=NGZx5GAUPys]

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Internet Publishing Industry Grew 13% to $110 Billion in 2014

Services industries 2014

Revenue for Internet publishing and broadcasting and Web search portal (NAICS 519130) employer firms increased 13.1 percent to $109.6 billion between 2013 and 2014. These information sector firms are primarily engaged in publishing and/or broadcasting content on the Internet exclusively or in operating websites that use a search engine to generate and maintain extensive databases of Internet addresses and content in an easily searchable format. Revenue among wireless telecommunications carriers (NAICS 517210) – another industry in the information sector – grew 8.0 percent between 2013 and 2014 to $251.8 billion.

All in all, information sector (NAICS 51) revenue increased 5.3 percent to $1.4 trillion for employer and nonemployer firms. This sector is primarily comprised of publishing industries, including software and Internet publishing, motion picture and sound recording, broadcasting and telecommunications.

These revenue data come from the Service Annual Survey, which provides the most comprehensive national statistics available each year on service industry activity in the United States. Dating from 1982 as an annual survey and last expanded in 2009, the survey collects data on revenues, expenses, and other measures from firms in most service industries. Together, these industries account for approximately 55 percent of the U.S. gross domestic product and had aggregate revenue of $13.4 trillion in 2014 for employer and nonemployer firms. …

Newspaper publishers (NAICS 511110) saw employer revenue decline 1.4 percent to $28.1 billion in 2014.

The television broadcasting industry (NAICS 515120), which includes local stations and television networks, experienced a 12.5 percent rise in employer revenue to $50.4 billion in 2014.

Source: Census Bureau

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Present and Future IoT: Numbers from IDC (Infographic)

IDC-IoT

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Worldwide Shipments of Wearables to Surpass 200 Million in 2019

IDC

The worldwide wearable device market will reach a total of 111.1 million units shipped in 2016, up a strong 44.4% from the 80 million units expected to ship shipped in 2015. By 2019, the final year of the forecast, total shipments will reach 214.6 million units, resulting in a five-year compound annual growth rate (CAGR) of 28%.

One of the most popular types of wearables will be smartwatches, reaching a total of 34.3 million units shipped in 2016, up from the 21.3 million units expected to ship in 2015. By 2019, the final year of the forecast, total shipments will reach 88.3 million units, resulting in a five-year CAGR of 42.8%.

Apple’s watchOS will lead the smartwatch market throughout our forecast, with a loyal fanbase of Apple product owners and a rapidly growing application selection, including both native apps and Watch-designed apps. Very quickly, watchOS has become the measuring stick against which other smartwatches and platforms are compared. While there is much room for improvement and additional features, there is enough momentum to keep it ahead of the rest of the market.

Android/Android Wear will be a distant second behind watchOS even as its vendor list grows to include technology companies (ASUS, Huawei, LG, Motorola, and Sony) and traditional watchmakers (Fossil and Tag Heuer). The user experience on Android Wear devices has been largely the same from one device to the next, leaving little room for OEMs to develop further and users left to select solely on price and smartwatch design.

Smartwatch pioneer Pebble will cede market share to AndroidWear and watchOS but will not disappear altogether. Its simple user interface and devices make for an easy-to-understand use case, and its price point relative to other platforms makes Pebble one of the most affordable smartwatches on the market.

Samsung’s Tizen stands to be the dark horse of the smartwatch market and poses a threat to Android Wear, including compatibility with most flagship Android smartphones and an application selection rivaling Android Wear. Moreover, with Samsung, Tizen has benefited from technology developments including a QWERTY keyboard on a smartwatch screen, cellular connectivity, and new user interfaces. It’s a combination that helps Tizen stand out, but not enough to keep up with AndroidWear and watchOS.

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Price is #1 Barrier to the Purchase of IoT Devices

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IoT_Devices Owned

Accenture:

Consumers report that price is the top barrier to the purchase of IoT devices, with 62 percent believing these devices are too expensive. This perception is almost consistent across age groups and countries—with mature markets only slightly less concerned about the price than emerging markets. Russia, Romania and the Philippines report the highest share of consumers stating price is a barrier.

Source: eMarketer

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Data visualization: Plotting life expectancy against income for 200 countries over 200 years

 

 

[youtube https://www.youtube.com/watch?v=jbkSRLYSojo?rel=0]

Hans Rosling’s famous lectures combine enormous quantities of public data with a sport’s commentator’s style to reveal the story of the world’s past, present and future development. Now he explores stats in a way he has never done before – using augmented reality animation. In this spectacular section of ‘The Joy of Stats’ he tells the story of the world in 200 countries over 200 years using 120,000 numbers – in just four minutes. Plotting life expectancy against income for every country since 1810, Hans shows how the world we live in is radically different from the world most of us imagine.

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A Reasonable Discussion of Deep Learning’s Great Expectations

[youtube https://www.youtube.com/watch?v=aygSMgK3BEM?rel=0]
Luke Hewitt:

I have no doubt that the next few years will see neural networks turn their attention to yet more tasks, integrate themselves more deeply into industry, and continue to impress researchers with new superpowers. This is all well justified, and I have no intention to belittle the current and future impact of deep learning; however, the optimism about the just what these models can achieve in terms of intelligence has been worryingly reminiscent of the 1960s.

Extrapolating from the last few years’ progress, it is enticing to believe that Deep Artificial General Intelligence is just around the corner and just a few more architectural tricks, bigger data sets and faster computing power are required to take us there. I feel that there are a couple of solid reasons to be much more skeptical.

To begin with, it is a bad idea to intuit how broadly intelligent a machine must be, or have the capacity to be, based solely on a single task. The checkers-playing machines of the 1950s amazed researchers and many considered these a huge leap towards human-level reasoning, yet we now appreciate that achieving human or superhuman performance in this game is far easier than achieving human-level general intelligence. In fact, even the best humans can easily be defeated by a search algorithm with simple heuristics. The development of such an algorithm probably does not advance the long term goals of machine intelligence, despite the exciting intelligent-seeming behaviour it gives rise to, and the same could be said of much other work in artificial intelligence such as the expert systems of the 1980s. Human or superhuman performance in one task is not necessarily a stepping-stone towards near-human performance across most tasks. ……

The many facets of human thought include planning towards novel goals, inferring others’ goals from their actions, learning structured theories to describe the rules of the world, inventing experiments to test those theories, and learning to recognise new object kinds from just one example. Very often they involve principled inference under uncertainty from few observations. For all the accomplishments of neural networks, it must be said that they have only ever proven their worth at tasks fundamentally different from those above. If they have succeeded in anything superficially similar, it has been because they saw many hundreds of times more examples than any human ever needed to.

Deep learning has brought us one branch higher up the tree towards machine intelligence and a wealth of different fruit is now hanging within our grasp. While the ability to learn good features in high dimensional spaces from weak priors with lots of data is both new and exciting, we should not fall into the trap of thinking that most of the problems an intelligent agent faces can be solved in this way. Gradient descent in neural networks may well play a big part in helping to build the components of thinking machines, but it is not, itself, the stuff of thought.

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The Economy of Cloud Computing (Infographic)

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Source: Soliant Consulting

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IoT Data Traffic Growth: As many machines as people roaming by 2020

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Machina Research estimates that there are now 350 million cellular based connections worldwide, and this will grow to 1.3 billion over the next five years. However, the proportion of M2M connections accounted for by roaming is growing even faster. As a global provider of roaming services including billing and clearing to network operators, Starhome Mach is able to determine that the number of roaming registrations that can be attributed to M2M devices increased by 100% last year to reach 7% of all roamers. The rate of growth is such that it is entirely possible that there will be as many machines as people roaming by 2020.

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