OTT Video Growing Globally, But So is Linear Video

OTT video is growing rapidly in the U.S. market, with the country responsible for almost half of global revenue for Internet video services, according to the Global Entertainment and Media Outlook 2017-2021 produced by PricewaterhouseCoopers International (PwC).

The U.S. over-the-top video market is "by far the largest and most established in the world," bringing in 47 percent of global revenue in 2016.

That noted, linear subscription services are saturated in North America and Western Europe.

TV subscription revenue elsewhere remains robust. PwC expects traditional TV revenue to grow at 1.3 percent to $277.4 billion in 2021, with TV subscription steadily increasing its share from 80.2 percent of total  in 2016 to 84 percent in 2021.

source: PwC

Cable TV subscriptions are growing steadily in the Asia-Pacific region, with PwC predicting 600 million households subscribing to cable by 2021, generating $5.9 billion. China and India will be the powerhouses driving this growth and will be responsible for 436 million subscriptions by 2021.

Autonomous Vehicles Might be the Big Opportunity for Mobile Service Providers

 It might be easy to understand what “big data” might mean for mobile operators supporting autonomous vehicles: lots of sensors and processors, communicating status in real time, at the edge of the network, in volumes of perhaps a gigabyte of data per second.

It is harder to understand the eventual implications of ride-sharing services such as Uber that could be part of a larger trend that changes the economics of personal auto ownership. It is not so clear how a change in personal vehicle ownership, in favor of ride sharing, works out.

But it might be reasonable to assume that it is autonomous vehicles, more than ride-sharing, that has positive implications for mobile service providers. That is at least partly because more-efficient ride-sharing actually could reduce auto ownership, and the mobile communications required for those vehicles.

By perhaps 2030, it could cost less for a typical consumer to use a ride-sharing service than to own an auto, argues Morgan Stanley equity analyst Adam Jonas.

Today, ride sharing accounts for perhaps six percent of global miles traveled, but could reach as high 26 percent by 20130, Jonas estimates.

But there are other trends poised to change the economics of the auto business. A shift to autonomous vehicles is among the bigger trends. "Vehicle sharing can only take your vehicle utilization to about 50 percent to 60 percent of its full potential, in our view," says Jonas. "And as long as these vehicles are human-driven, logistical inefficiencies will persist.”

“Autonomous vehicles on the other hand remove the human bottleneck and the economics change substantially,” he argues. And, arguably, represent a huge app case for use of 5G, low-latency mobile communications.

source: Professor Jeffrey Fun

source: Professor Jeffrey Funk

One illustration of benefits is to consider the impact of taxi trips taken in New York City from ride-sharing alone: as much as 70 percent of all such trips can use ride-sharing, eliminating much traffic congestion, reducing fuel costs and reducing pollution, some believe.   

source: Professor Jeffrey Funk

source: Morgan Stanley

Today, a typical consumer likely incurs lower costs than relying solely on shared-ride services for transportation. In other words, the present economics are a bit like renting a hotel room versus owning a house.

But those per-mile operating costs should flip by 2030, when use of autonomous and shared vehicles by many consumers will actually be lower than owning and driving their own vehicles.

source: Morgan Stanley

source: Professor Jeffrey Funk

Thinking the Unthinkable: What if "Cars" Were Free? What does the Business Model Look Like?

source: NBN

source: Wikipedia

It is terribly difficult to imagine your own business operating under conditions of absolute abundance, when the key inputs are relatively scarce, and therefore, costly.

Unthinkably large and important businesses have been built on apparently crazy assumptions, the most important of which turns out to be the assumption that key enabling inputs, though hugely expensive and highly limited in performance, eventually, because of Moore's Law, be rendered very cheap.

So cheap that the phrase "computing wants to be free" or "bandwidth wants to be free" actually make sense, in terms of performance-cost relationships.

It can be argued that Microsoft’s business model, and that of Netflix, were based on astounding assumptions, namely that, as Moore’s Law continued to operate, Microsoft could assume the cost of computing would be very low, to free.

Likewise, Netflix assumed that Moore’s Law, applied to bandwidth, meant that, eventually, bandwidth sufficient to support a video streaming service would be possible, and virtually free.

But if one understands the arguments that “computing will be nearly free,” or that “bandwidth will be nearly free,” then you will understand the argument that “cars will be nearly free.”

Yes, assume your business (even as a auto manufacturer) model has to assume that vehicles themselves will be “nearly free” in terms of cost. That might sound crazy, but Microsoft and Netflix both based their business models on “near zero pricing” of a key input and enabler.

Will automobiles of the future become “mobile real estate, a data pipe, equivalent to telecom spectrum,” asks Morgan Stanley auto and mobility analyst Adam Jonas. To be clear, Jonas does not mean “pipe” as in “access pipe.”

He means a platform for data harvesting, machine learning and content delivery. In other words, autos as data repositories that can be mined, using artificial intelligence, to glean insights useful to third parties that will pay for access to those data stores.

“In the Auto 2.0 business model, we see 100 percent of the revenue and profit eventually coming from the operation of vehicles in a network,” said Jonas.

That might sound crazy, but it is the sort of analysis that underpinned the business models adopted by Microsoft, Netflix and arguably most other “digital” businesses such as Uber, Amazon, Facebook or Airbnb.

It is easy to understand the phrase “connected car,” and try to envision what that means, in terms of increased computing content.

It is quite a bit harder to understand the implications of Moore’s Law for many physical products, or services based on physical products, when there is so much surplus capacity built into the use models. Some estimate that the typical vehicle sits unused 95 percent of the time. That is the inefficiency Uber exploited.

Ability to monetize assets that formerly could not be monetized (spare rooms, spare cottages, spare apartment capacity) was what made Airbnb possible.

Assuming “autos” eventually will cost nearly nothing might make sense if one also assumes “individuals” will not be owning those vehicles. It is a huge leap. It might be wrong. It might not be possible to apply Moore’s Law in such a way to a “physical product” such as an auto.

Traditionally, Moore’s Law has been disruptive mostly to businesses whose “product” is virtual, or can be made virtual (content, transactions). Not to the same extent, but significantly, Moore’s Law has lead to major cost reduction for computing products (all machines that are computational intense).

But the line of reasoning at least suggests ways to think about what one’s business might look like if you make the “wild” assumption (as did Bill Gates and Reed Hastings) that a key input to your business--though prohibitively expensive and underpowered at the moment--eventually will become so affordable that they are not constraints to your planned business.

It might seem crazy to assume that a large physical product such as an automobile could actually become "free." There are lots of caveats, most importantly that the manufacturing of the product will not become "costless." 

But the business model? That is where some big leaps could happen. 

Google Fiber's Webpass Launches $60/Month Gigabit Service in Seattle

Google Fiber’s  Webpass unit has activated fixed wireless internet access service to a building in Seattle, with a retail cost of $60 a month for symmetrical gigabit per second internet access.

The Webpass installation, serving 146 living units, actually uses direct fixed wireless access, not an optical connection, and the building already is wired for internal distribution. Webpass says it plans to similarly connect 100 more over a year’s time.

It is hard, and likely unfair, to compare internet access infrastructure costs to serve a single high-rise building with the cost of serving individual consumer homes. That is simply because getting access to the basement of a high rise building using optical fiber or fixed wireless connection involves one “access” connection, and then riser cabling to reach multiple dwelling units.

That typically is a less-capital-intensive operation than cabling individual homes directly.

Historically, fiber to the curb (in many markets) has been cost effective, compared to use of 4G mobile, at home densities higher than about 210 homes per square mile, according to Delta Partners. That analysis will change with 5G, in all likelihood making 5G a better option in more cases.

Fiber to the home involves higher costs, so LTE arguably works across a wider range of use cases as well, assuming retail pricing and usage patterns are relatively light, in terms of consumed gigabytes per month.

source: Delta Partners

FTTH versus LTE : Friend or Foe from Dr.Joko Suryana

Google Allows End User Choice in Paying for Content

“Choice” is among the most-powerful words in marketing and advertising, along with “new” or “improved,” it is reasonable to argue. And “choice” is precisely the objective of Google’s new Funding Choices capability.

“With Funding Choices, now in beta, publishers can show a customized message to visitors using an ad blocker, inviting them to either enable ads on their site, or pay for a pass that removes all ads on that site through the new Google Contributor, says Sridhar Ramaswamy, Google SVP.

That is one reasonable response to the use of ad blockers, arguably used by many consumers because the number and kinds of advertising they encounter are objectionable. Funding Choices now offers a choice, though.

Consumers can accept the ads, with the content, the way much of the media historically has operated. Or they can opt to pay directly for the content.

Google likely hopes the new capability will reduce regulatory threats, improve end user experience and mollify ad-supported content partners, since ad blocking directly attacks the monetization mechanism for much online content.

Optimal.com has estimated that U.S. online display advertising, shown as the orange bars in this Optimal.com graph (figures in millions of dollars). The loss is estimated at about $12 billion in 2020.

source: Optimal.com

No End to Moore's Law Rates of Improvement?

Concern about the "end of Moore's Law" has been expressed for decades. And there are clear constraints on current platforms. It remains to be seen what can happen with new platforms. But many believe there are a range of options to support continued progress, by tweaking both software and hardware. 

Why AI Investment is Going to (Initially) Disappoint

Despite the promise of big data, industrial enterprises are struggling to maximize its value.  A survey conducted by IDG showed that “extracting business value from that data is the biggest challenge the Industrial IoT presents.”

Why? Abundant data by itself solves nothing, says Jeremiah Stone, GM of Asset Performance Management at GE Digital.

Its unstructured nature, sheer volume, and variety exceed human capacity and traditional tools to organize it efficiently and at a cost which supports return on investment requirements, he argues.

source: Goldman Sachs

At least so far, firms  "rarely" have had clear success with big data or artificial intelligence projects. "Only 15 per cent of surveyed businesses report deploying big data projects to production,” says IDC analyst Merv Adrian.

We should not be surprised. Big waves of information technology investment have in the past taken quite some time to show up in the form of measurable productivity increases.

In fact, there was a clear productivity paradox when enterprises began to spend heavily on information technology in the 1980s.

“From 1978 through 1982 U.S. manufacturing productivity was essentially flat,” said Wickham Skinner, writing in the Harvard Business Review.

In fact, researchers have created a hypothesis about the application of IT for productivity: the Solow computer paradox. Yes, paradox.

Here’s the problem: the rule suggests that as more investment is made in information technology, worker productivity may go down instead of up.

Empirical evidence from the 1970s to the early 1990s fits the hypothesis.  

Before investment in IT became widespread, the expected return on investment in terms of productivity was three percent to four percent, in line with what was seen in mechanization and automation of the farm and factory sectors.

When IT was applied over two decades from 1970 to 1990, the normal return on investment was only one percent.

This productivity paradox is not new. Information technology investments did not measurably help improve white collar job productivity for decades. In fact, it can be argued that researchers have failed to measure any improvement in productivity. So some might argue nearly all the investment has been wasted.

Some now argue there is a lag between the massive introduction of new information technology and measurable productivity results, and that this lag might conceivably take a decade or two decades to emerge.

The problem is that this is far outside the window for meaningful payback metrics conducted by virtually any private sector organization. That might suggest we inevitably will see disillusionment with the results of artificial intelligence investment.

One also can predict that many promising firms with good technology will fail to reach sustainability before they are acquired by bigger firms about to sustain the long wait to a payoff.

So it would be premature to say too much about when we will see the actual impact of widespread artificial intelligence application to business processes. It is possible to predict that, as was the case for earlier waves of IT investment, it does not help to automate existing processes.

Organizations have to recraft and create brand new business processes before the IT investment actually yields results.

One possibly mistaken idea is that productivity advances actually hinge on “human” processes.

Skinner argues that there is a “40 40 20” rule where it comes to measurable benefits. Roughly 40 percent of any manufacturing-based competitive advantage derives from long-term changes in manufacturing structure (decisions about the number, size, location, and capacity of facilities) and basic approaches in materials and workforce management.

Another 40 percent of improvement comes from major changes in equipment and process technology.

The final 20 percent of gain is produced by conventional approaches to productivity improvement (substitute capital for labor).

In other words, and colloquially, firms cannot “cut their way to success.” Quality, reliable delivery, short lead times, customer service, rapid product introduction, flexible capacity, and efficient capital deployment arguably were sources of business advantage in earlier waves of IT investment.

But the search for those values, not cost reduction, were the primary sources of advantage. The next wave will be the production of insights from huge amounts of unstructured data that allow accurate predictions to be made about when to conduct maintenance on machines, how to direct flows of people, vehicles, materials and goods, when medical attention is needed, what goods to stock, market and promote, and when.

Of course, there is another thesis about the productivity paradox. Perhaps we do not know how to quantify quality improvements wrought by application of the technology. The classic example is computers that cost about the same as they used to, but are orders of magnitude more powerful.

It is not so helpful, even if true, that we cannot measure quality improvements in some agreed-upon way that produce far better products sold at lower or same cost. Economies based on services have an even worse problem, since services productivity is both difficult and hard to quantify.

The bad news is that disappointment over the value of AI investments will inevitably result in disillusionment. And that condition might exist for quite some time, until most larger organizations have been able to recraft their processes in a way that builds directly on AI.

source: ITIF