Government Broadband Policy Too Often Ignores Moore's Law

Government planners often are too optimistic about what their proposed programs can achieve. In the case of broadband, they have tended to be too modest. The U.K. government launched in 2010 an effort to enable superfast internet access across the country. Keep in mind that a year earlier, the government said it wanted a 2 Mbps minimum speed across the country. 

In 2011 the goal goal was bumped up 24 Mbps per household by about 2015. To be sure, there is a difference between a minimum floor and a maximum aspiration. But past experience with speed increases--even in 2010--should have prompted lawmakers and policymakers to aim higher. 

Speeds increase at Moore's Law rates, one can argue, at least for some suppliers, such as the cable companies. 

Comcast has doubled speed every 18 months, for example. In 2010, typical Comcast speeds already were up to 100 Mbps. Few customers bought the fastest-available service, of course. But the minimum speed of about 12 Mbps grew to about 50 Mbps by 2015. Using the Moore’s Law doubling in 18 months would have produced speeds in excess of 100 Mbps by 2015, which is what happened. 

source: Comcast

This example from the Australian National Broadband Network actually is too conservative. Extrapolating from 1985, it suggests typical internet access speeds “should” have grown from about 10 Mbps in 2009 to perhaps 100 Mbps by 2015. 

source: NBN

When at least some suppliers are doubling speeds every 18 months, most targets and goals set by government are going to be eclipsed very quickly, no matter how ambitious the goals seem at the moment.

The point is that although government goals will tend to focus on minimums, as for universal service, aspirational targets need to incorporate what we know about Moore’s Law and its application to internet access bandwidth. 

With or without any specific government policies (other than staying out of the way), typical and minimum speeds would double about every 18 months to 24 months. So, one might argue, the U.K. government goal quickly was surpassed by commercial supply that did, in fact, increase at Moore’s Law rates, as did computing.

Most rational observers would have argued that physical networks could not improve speed so fast, as labor intensive and capital intensive as outside plant remains. Perhaps few thought Moore’s Law  rates of progress were possible for outside plant. On the other hand, few probably believed Moore’s Law would apply to computing hardware, either. 

The most-startling strategic assumption ever made by Bill Gates was his belief that horrendously-expensive computing hardware would eventually be so low cost that he could build his own business on software for ubiquitous devices. .

How startling was the assumption? Consider that, In constant dollar terms, the computing power of an Apple iPad 2, when Microsoft was founded in 1975, would have cost between US$100 million and $10 billion.

source: Hamilton Project

The point is that the assumption by Gates that computing operations would be so cheap was an astounding leap. But my guess is that Gates understood Moore’s Law in a way that the rest of us did not.

Reed Hastings, Netflix founder, apparently made a similar decision. For Bill Gates, the insight that free computing would be a reality meant he should build his business on software used by computers.

Reed Hastings came to the same conclusion as he looked at bandwidth trends in terms both of capacity and prices. At a time when dial-up modems were running at 56 kbps, Hastings extrapolated from Moore's Law to understand where bandwidth would be in the future, not where it was “right now.”

“We took out our spreadsheets and we figured we’d get 14 megabits per second to the home by 2012, which turns out is about what we will get,” says Reed Hastings, Netflix CEO. “If you drag it out to 2021, we will all have a gigabit to the home." So far, internet access speeds have increased at just about those rates.

Enterprises Buy Solutions, Consumers Buy Products

Big buzzwords inevitably are misunderstood and misused. “Solution” is one such word in the software industry, used in place of “product” to emphasize the problem to be solved, rather than the means. 

One might argue that all products are, in reality, solutions to perceived problems. But most of us, as consumers, would likely agree that we tend to buy products, not solutions, where enterprises most often think about problems that are more complex, and therefore require complex "solutions."

We classically argue there is a difference between a product and a “solution.” A product is a good or service that essentially “does something,” whether the product is a screwdriver, notebook PC  or Amazon Elastic Compute Cloud. 

The distinction between product and solution arguably is most relevant for complex problems encountered by large organizations. It seems less an issue for consumer purchases, if only because most consumer outcomes--and the products that produce outcomes--are rather simple. More often than not, “solutions” are unstated and “products” are the functional definition of “the tool to solve a problem.”

A solution, some say, is the application of a product to solve a specific industry need or business problem. The difference can be subtle. Amazon Web Services offers “products” such as compute, storage, databases, security and compliance, migration, analytics, internet of things or security.

But AWS groups its “solutions” by industry vertical, including financial services; digital marketing; enterprise IT; gaming; media and entertainment. 

United Parcel Service offers many products, but groups it solutions into a few industry verticals, including automotive; industrial manufacturing; healthcare; high tech and retail.

AT&T offers a number of products, including mobility, virtual private networks, internet access, voice over IP and web conferencing and network security. But AT&T markets small business solutions including remote information technology; website and marketing; facsimile solutions; data backup and security. 

Some insist that the definition must also include “a set of related software programs and/or services that are sold as a single package.” 

The overarching point is that products represent capabilities, while solutions are products applied to solving specific business problems in particular industries. 

AppDirect provides a commerce platform that enables its customers to directly sell cloud services. Prior to using AppMarket, companies “lacked a platform to offer software-as-a-service (SaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS) products,”  a study by Forrester Research notes. 

So one might say the product is an online marketplace capability, while the solution is the ability to sell a variety of cloud services to small businesses from a single portal. 

AppMarket’s “solution” allows organizations to quickly launch a marketplace to sell their own services, third-party services (Microsoft, Google, Amazon Web Services) or build their own software ecosystem to sell to small businesses. 

The products include automated billing, provisioning, and subscription management, if some might say these are features of the product. That is perhaps the best illustration of the subtlety: it sometimes is hard to tell the difference between a product and a solution. 

Likewise, applications might sometimes be terms used interchangeably with products, though some would say applications are used to create products. 

Perhaps often, the key concept is that a solution creates a business outcome. Outcomes result from the application of tools or products. Or, to put it another way, customers look to buy solutions to their problems, enabled by products. Suppliers hope to sell products that are solutions to customer problems. 

If that is an unsatisfying description, it might be because it is unsatisfying. In principle, every buyer wants a “solution,” but every customer also buys specific products that hopefully will solve the problem. 

I actually buy tools (hammers and nails) which are products, not storage solutions (put up a shelf) or “display solutions” (hang a picture on the wall). Sometimes I buy tools to anticipate that they might be part of the solution to some future problem to be solved.

So, in practice, I often buy products, not solutions, even if the products eventually will be tools used to solve actual problems I encounter. Enterprise buying scenarios are much more complicated, of course. 

But is an e-commerce marketplace capability a product or a solution? Maybe it is both, at different times: a product (capability) before you buy it; a solution (business results) once you get it up and running. 

Or maybe it is simply a matter of complexity: solving many business problems requires use of many tools, capabilities and features to create a single business outcome (sales, churn reduction, incident management, regulatory compliance). 

Most consumer desired outcomes are not complex. Groceries and fast food; going to the store and having food delivered all are “solutions” to the problem of hunger. But it is fairly simple to buy products to solve such problems. 

Seeing friends and family or earning money might require transportation “solutions.” But I mostly consume products: airplane tickets, using public transportation, car purchases or rentals, using Uber or Lyft, or riding with a friend. 

Intel Announces Portfolio for 5G Network Infrastructure

Outcomes, Not Inputs, are the Point of All Advanced Technology Supply and Adoption

If you follow statistics about U.S. communications infrastructure long enough, you know that the United States rarely, if ever, is at the very top of any measure of communications performance, where that is “teledensity” (the number of phone lines compared to population) or internet access speed, adoption or low price. 

The frequent pattern has been lagging adoption followed by a swift uptake once customers figure out the value proposition, networks reach critical mass or some new use case emerges. 

Fundamentally, investment in, and usage of, advanced communications or computing really only makes a difference if it contributes to economic or social outcomes.

In fact, the assertion that the U.S. is behind, on some key measure of communications usage, has been a frequent charge. In the past, it has been argued that the United States was behind, or falling behind, for use of fixed network voice, mobile phones, smartphones, text messaging, broadband coverage, fiber to home, broadband speed or broadband price.

In fact, the “U.S. is falling behind” meme never goes away, where it comes to communications. The latest assertion in some quarters is that the United States is falling behind in 5G. That claim has been made many times in the past, and always has proven wrong.

Consider voice adoption, where the best the United States ever ranked was about 15th globally, for teledensity (people provided with phone service). A couple of thoughts are worth keeping in mind. First, large countries always move slower than small countries or city-states, simply because construction of networks takes time and lots of capital. 

With the caveat that some rural and isolated locations never got fixed network phone service, not many would seriously argue that the supply or use of fixed network voice was an issue of any serious importance for the nation as a whole, though it is an issue for rural residents who cannot buy it.

Some even have argued the United States was falling behind in spectrum auctions.  What such observations often miss is a highly dynamic environment, where apparently lagging metrics quickly are closed.

To be sure, adoption rates have sometimes lagged other regions. 

Some assertions are repeated so often they seem true. Among such statements are beliefs that U.S. internet access is slow and expensive, or that internet service providers have not managed to make gigabit speeds available on a widespread basis. In fact, gigabit coverage is about 80 percent, but take rates might be as low as two percent. 

Other statements, such as the claim that U.S. internet access prices or mobile prices are high, are not made in context, or qualified and adjusted for currency, local prices and incomes or other relevant inputs, including the comparison methodology itself. 

Both U.S. fixed network internet prices and U.S. mobile costs have dropped since 2000, for example. 

source: in2013dollars.com

source: in2013dollars.com

The latest data from Ookla’s Speedtest service shows that U.S. fixed network internet access speeds are on par with South Korea, for example, while mobile access speeds are on par with Western Europe. 

Mobile performance should change significantly, however, as upgrades to 5G using millimeter wave frequencies takes hold. Even before millimeter wave began to be used, mobile speeds were climbing. Between 2018 and 2019, for exampole, U.S. mobile speeds grew about 24 percent. 

source: Ookla Speedtest

Such metrics should always be kept in context, however. What matters with the application of technology is what impact can be wrung from the investments. Speed, coverage, latency and price do matter, but only as enablers of economic and social impact. 

source: Ookla Speedtest

Enterprises Start to Invest in 5G

Currently, 15 percent of enterprises are investing in 5G, with an additional 54 percent planning to invest in the next one to three years, a survey by EY suggests. By the end of 2022, levels of 5G investment by enterprises will be on par with the internet of things, EY believes. 

source: EY

Nokia Network Slicing Available Summer of 2020

Nokia end-to-end network slicing functionality for 4G and 5G New Radio networks will be available in the summer of 2020. Nokia says it is the first supplier to offer this capability. 

The slicing capability can be deployed via a software upgrade to existing LTE and 5G non-standalone (NSA) networks and subsequently 5G standalone (SA) networks. 

At least in principle, network slicing could create a new type of wholesale or managed network capability, potentially allowing end user customers to control core networks as though it were their own managed network.

Think of it as “network as a service.” Granted, the nomenclature is difficult, since connectivity products have always been services. 

There are some important new business issues. What parameters can the slice customer actually control? Aside from the key performance indicators, related to quality of service expectations, what degree of control will a slice customer have over the slice parameters?

Does the slice customer put in a change request to the slice provider? Can the slice user make changes directly? And if so, to which parameters? In other words, how much control will slice customers have over on-the-fly changes to their private networks?

source: ITU News

The Nokia network slicing solution provides sliced mobile broadband connectivity from device to radio, transport, core, all the way to applications in private and public networks and the cloud. It enables new mobile end-to-end services with logical connections, security, quality and traffic management with a seamless service continuity across 4G and 5G networks. 

Private wireless slicing also is supported. Nokia is already trialing live 4G/5G slicing use cases with customers powered by a unique Software Defined Network (SDN) radio slice controller as well as a transport slice controller. 

source: Nokia

The trial includes a Nokia cloud packet core slice orchestrator to support network deployment automation as well as an SD-WAN software solution providing a managed 4G/5G network slice to private and public cloud services. Nokia assurance systems are used to verify per slice key performance indicators.  

But new questions will have to be asked and answered. VPN users do not actually have any control over the network, only the use of a private virtual tunnel through a network. A network slice, in principle, also adds quality of service and functionality guarantees. 

What must be worked out in practice are the degrees of end user programmability of such slices.

Can Fixed Networks Actually be Characterized Like Mobile Networks?

As nearly as I can tell, Huawei was the first entity to refer to fixed network infrastructure in five eras, in terms of use cases, not physical media, analog or digital modes, modulation technology or some other categorization that mimics the evolution of mobile networks. 

The difference is that while one can attribute certain lead apps or use cases to each mobility generation, there is a physical basis for the “G” nomenclature that is not present on the fixed networks. Each mobile generation was a discrete platform and network with distinct technological foundations. 

One can note the physical distinctions between voice switch generations, access media or logical architecture, and come up with some generations. Since the dawn of the internet protocol era, the physical network also has been separated logically from the applications that use networks and essentially abstracted. 

Some might characterize the fixed network eras using various optical platform developments, including the shift from BPON to GPON yp 10G PON to NG-POn2. You can decide whether this is useful or not. 

But European standards group ETSI has formed a new group which aims to specify the ”fifth generation of Fixed Network” (ETSI ISG F5G). 

source: Huawei

At a practical level, the effort seems to address three main issues: full-fiber connections, enhanced fixed broadband and guaranteed reliable experience. But most of the effort seems to focus on how applications drive the need for network performance, arguably something all the other standards groups already essentially are working to ensure. 

“The ETSI ISG F5G aims at studying the fixed-network evolution required to match and further enhance the benefits that 5G has brought to mobile networks and communications.” ETSI says. 

Some of us might argue this is largely a marketing exercise, similar to the phrase “from fiber-to-home to fiber-to-everywhere.”

Encouraging Pacific Islanders

No, nothing directly to do with the internet, communications, mobility or capacity. Just a nice music video reminding Pacific Islanders, wherever they are, that the census is coming. 

Enterprises Say They Need Help with 5G Use Cases

It might not be clear which entities in the 5G value chain will be supplying expertise on 5G apps and use cases, but a study by Accenture suggests there is a substantial market for such advice. About 72 percent of respondents to an Accenture survey indicated “they need help to imagine the future possibilities for connected solutions with 5G.”

As you might guess, “software and services companies” and “cloud businesses” are viewed as the sorts of firms most likely to provide that help. 

source: Accenture

Also, the percentage of businesses expecting to develop 5G applications in-house has dropped over the last year, from 23 percent in the prior-year survey to 14 percent this year, Accenture says. 

As always, though connectivity suppliers are among the most-likely sources of help, there is concern about industry domain knowledge. 

source: Accenture

The survey included responses from more than 2,600 business and technology decision makers across 12 industry sectors in Europe, North America and Asia-Pacific. 

source: Accenture

Telcos are Buying AI Functionality, if Not AI Directly

Artificial intelligence is being adopted by communications networks in many subtle ways, even if AI is not a product but a capability.

Businesses and consumers do not buy “AI” any more than they buy calories in a direct sense. Instead, AI is a capability of some other product a service provider, enterprise or consumer purchases. 

That applies in telecom as much anywhere else. Some note that telecom networks can use AI for network optimization, preventive maintenance, virtual assistants and robotic process automation.  AI also plays a role in self optimizing networks (SONs), software defined networks and Network Function Virtualization as well, which are basic network principles in the 5G era. 

IDC has argued that 64 percent  of network operators are investing in AI systems to improve their infrastructure, for example. 

Some popular AI use cases in telecom include:

• ZeroStack’s ZBrain Cloud Management, which analyzes private cloud telemetry storage and use for improved capacity planning, upgrades and general management
• Aria Networks, an AI-based network optimization solution that counts a growing number of Tier 1 telecom companies as customers
• Sedona Systems’ NetFusion, which optimizes the routing of traffic and speed delivery of 5G-enabled services like AR/VR
• Nokia launched its own machine learning-based AVA platform, a cloud-based network management solution to better manage capacity planning, and to predict service degradations on cell sites up to seven days in advance.

AI functions almost always are used for pattern recognition, to understand typical trends or behaviors. In a consumer context that often is used to monitor customer financial transactions, to  spot anomalies in an account’s spending data that could represent potentially fraudulent behavior. Automated financial advisor services use AI to provide recommendations. 

In a manufacturing or energy industry use case, supply chain optimization, automated detection of defects during production and energy forecasting are use cases based on pattern recognition. 

Prediction, such as forecasting energy consumption, is another common use for AI. Classification or image recognition are other use cases, as when law enforcement agencies use facial recognition. Health and life science users might use AI to help process data from past case notes, biomedical imaging and health monitors to use for  predictive diagnostics.

Consumer speech to text is a frequent AI use case as well. E-commerce engines often use AI in a cognitive search mode to generate personalized recommendations to online shoppers. 

A related use case is natural language interaction, where a software application generates a report on sales revenue predictions without having to run the reports manually, or natural language generation, where a user might hear summaries of everything that has been analyzed from a large document collection. 

Communications networks might use AI to route traffic, optimize server loads or predict future capacity demand. Also, any industry relying on call centers for customer interaction and support use AI to support chatbots. 

Retailers use AI for personalized shopping experiences and customized recommendations.