Be Patient About Evaluating 5G

In some ways, it is premature to evaluate the value of 5G, as the networks are still early in deployment (or non-existent) in most countries. Early in 2023, for example, only about 35 percent of humans globally had the option of buying 5G service from at least one mobile service provider. 

And where it is already widely deployed, the variety of types of spectrum used mean coverage and capacity are uneven. So even where theoretically available, 5G signal strength might be low enough that devices revert to 4G. 

Beyond that, some customers might not own devices capable of using 5G, or might not enable 5G on their devices. 

Country	Population	5G Availability (Percentage)	5G Usage (Percentage)
South Korea	51.8 million	72.9%	45.2%
United States	332 million	62.5%	30.8%
United Kingdom	67 million	53.2%	33.4%
Canada	38 million	50.4%	29.2%
Germany	83 million	48.8%	27.9%
Australia	25.7 million	47.3%	26.7%
China	1.44 billion	46.2%	26.1%
Japan	125.5 million	45.1%	25.2%
France	67.3 million	44.0%	24.2%
India	1.4 billion	42.9%	23.2%

The point is that, whatever one thinks about 5G value and development of new use cases, apps and business models, we still are too early to draw definitive conclusions, as most humans still cannot buy 5G or use it routinely. 

And even once deployment is substantially complete, the expected upside from 5G might still lag. That was the case for 3G and 4G as well. 

3G was first introduced in 2001, and it was expected to enable new applications such as video calling, mobile TV and mobile gaming. Some of that happened, but often not what was expected. In many ways, 3G apps such as messaging, email and turn-by-turn directions seem to predominate.  

4G was supposed to enable new applications such as real-time video streaming, virtual reality, and self-driving cars. In most cases, 4G enabled apps many thought would happen during the 3G era, or at least enabled an adequate user experience for anything related to video. 

By some estimates, apps allowing people to communicate seem to predominate on 3G networks. 

3G Apps	Estimated number of users	Hours used per week
WhatsApp	2 billion	25 hours
Facebook Messenger	1.3 billion	20 hours
YouTube	1.5 billion	15 hours
Skype	660 million	10 hours
Instagram	1 billion	5 hours
Snapchat	500 million	3 hours
Spotify	365 million	2 hours
Netflix	203 million	2 hours
Google Maps	1.5 billion	1 hour
Gmail	1.5 billion	1 hour

4G apps tend to revolve around working or transactions, while content consumption and connectivity trends from 3G remain in place. But 4G seems much more useful for entertainment on the go.

4G Apps	Estimated number of users	Hours used per week
Uber	110 million	10 hours
Airbnb	800 million	5 hours
WeWork	500,000	3 hours
DoorDash	50 million	2 hours
Lyft	100 million	2 hours
Slack	12 million	1 hour
Zoom	300 million	1 hour
Google Drive	1.5 billion	1 hour
Microsoft Office 365	300 million	1 hour
Salesforce	150,000	1 hour

Overall, it can be said that many expected use cases for 3G did not actually emerge as mass market realities. 

3G use case	Experts' expectations	Actual outcome
Video streaming	Experts believed that 3G would enable streaming of high-quality video, such as live sports events and movies.	While 3G did enable some video streaming, it was not as widespread as experts had predicted. This was due to the limited bandwidth of 3G networks and the high cost of data plans.
Mobile gaming	Experts believed that 3G would enable a new generation of mobile games that were more immersive and interactive.	Mobile gaming did indeed become more popular with the advent of 3G, but the games were not as groundbreaking as experts had predicted. This was due to the limitations of 3G networks, such as latency and packet loss.
Mobile office	Experts believed that 3G would make it possible for people to work from anywhere, using their mobile devices to access email, files, and applications.	Mobile office did become a reality with 3G, but it was not as widespread as experts had predicted. This was due to the lack of 3G coverage in many areas and the high cost of data plans.
Mobile commerce	Experts believed that 3G would enable a new wave of mobile commerce, with people using their phones to make purchases online and in stores.	Mobile commerce did indeed grow with the advent of 3G, but it did not reach the levels that experts had predicted. This was due to security concerns and the lack of a standardized mobile payment system.
Location-based services	Experts believed that 3G would enable a new generation of location-based services, such as turn-by-turn navigation and targeted advertising.	Location-based services did become popular with the advent of 3G, but they were not as revolutionary as experts had predicted. This was due to the limited accuracy of 3G location data and the high cost of data plans.

The same might be said of 4G networks as well. 

4G use case	Experts' expectations	Actual outcome
Ultra-high-definition (UHD) video streaming	Experts believed that 4G would enable streaming of UHD video, such as 4K and 8K content.	UHD video streaming is now possible over 4G networks, but it is not as widespread as experts had predicted, in part because mobile operators often do not support it.
Virtual reality (VR) and augmented reality (AR)	Experts believed that 4G would enable the widespread adoption of VR and AR applications.	VR and AR applications are becoming more popular, but they are still not as widespread as experts had predicted. T
Real-time gaming	Experts believed that 4G would enable real-time gaming, such as multiplayer games that require fast and reliable connections.	Real-time gaming is now possible over 4G networks, and it is becoming increasingly popular. However, it is still not as widespread as experts had predicted.
Cloud computing	Experts believed that 4G would enable cloud computing applications, such as the ability to run high-performance applications on remote servers.	Cloud computing applications are now possible over 4G networks, and they are becoming increasingly popular. However, they are still not as widespread as experts had predicted.
Connected cars	Experts believed that 4G would enable connected cars, which would be able to communicate with each other and with the infrastructure around them.	Connected cars are still not as widespread as experts had predicted.

The bottom line is that it still is too early to tell how 5G will develop, and whether the expected new apps and use cases will emerge as expected. History suggests patience will be needed.

NaaS Actually is an Old Goal

“Network as a service” is one of those baffling terms the connectivity business periodically comes up that in many ways defies explanation. 

After all, the traditional connectivity service is, in fact, “a service.” That noted, enterprises often have ways to “create their own services” using their own infrastructure, as when they use private branch exchanges (phone systems), create their own edge-based software-defined wide area networks or their own virtual private networks. 

In such cases, “connectivity” might be called a “product” created by the user, not a service supplied by a connectivity provider. Enterprises can use phone systems to “create their own voice services” or their own edge-based SD-WAN or VPNs, for example. 

Definitions of “NaaS” often focus on “cloud based” or “on demand” or “pay as you go” or “pay only for what you use” concepts, and those are generally apt concepts. 

Feature	NaaS	Traditional Telecom
Ownership	In most cases, the customer does not own the network infrastructure or services.	In most cases, the customer does not own the network infrastructure and services.
Cost	Customers pay for the services they use on a pay-as-you-go basis.	Customers pay a fixed monthly fee for the network infrastructure and services.
Flexibility	Customers can scale their network capacity up or down as needed.	Customers buy a fixed amount of capacity
Management	Customers do not need to manage the network infrastructure or services.	Customers do not need to manage the network infrastructure or services.
Security	Connectivity providers are responsible for public network security.	Connectivity providers are responsible for public network security.
Architecture	Might or might not be “cloud based”	Might or might not be “cloud based”
Ease of use	Supposed to be easier for end user to change levels of service	Difficult and manual changes of service levels
Scalability	Supposed to be easy to scale	Not terribly easy to scale up or down

So some of us might argue that NaaS) simply restates the older goal of an automated, flexible, on-demand architecture for telecom services that allows customers to add features and services easily and fast; scale usage up and down. 

The concept also means  the network operator can scale features and services on and off; bandwidth up or down “on demand,” easily and fast in response to customer demand or desires. 

So NaaS is simply a new way of stating the older goal of a flexible, automated, scalable network. 

Who Wins Next from Generative AI?

In the early stages of a technology change cycle, the firms that benefit the most are those that provide the enabling platform. These are the firms that develop the basic technologies that other firms will use to build new products and services. 

In the generative AI business, that means Nvidia selling graphics process units. That fits an older pattern of new technology opportunities, where infra has to be built first, before use cases, apps and industries can develop. 

For example, in the early days of the internet, the firms that made the most money were the ones that built the infrastructure, such as the internet service providers and the web hosting companies. These firms provided the platform upon which other firms could build their businesses.

As the technology matures, the firms that benefit the most are those that develop applications of the technology. These are the firms that use the technology to create new products and services that meet the needs of consumers. 

For example, in the early days of the internet, once the “plumbing” was in place, the firms that made the most money were the ones that developed e-commerce websites and online advertising platforms.

Only after some time was it possible for substantial new industries and revenue streams to develop. A good road system was required before a mass market auto industry could develop, for example. 

Enabling Infrastructure	Subsequent App and Use Case Development
Roads	Suburbs, car culture, trucking industry
Airports	Passenger airlines, cargo flights, air travel
Power grids	Computers, appliances, electronic devices
Internet	Email, social media, online shopping, cloud computing
Cloud computing	Large-scale applications, such as Netflix and Amazon
Broadband internet	Netflix
Smartphones	Social media
High-speed computing	Generative AI, machine learning, artificial intelligence
Large amounts of data	Natural language processing, computer vision, medical diagnosis
Open standards	Interoperability between systems, collaboration between developers

In perhaps the same way, the early money to be made in generative AI will be reaped by Nvidia and GPU suppliers. Cloud computing will follow fairly closely, as more of the demand for cloud computing services shifts to support of training models and generating inferences. 

Over a period of time, generative AI will reshape consumer and business software, be incorporated into devices and then reshape business processes on a wider scale. 

The bottom line is that suppliers of GPUs have been the first to see significant revenue impact from generative AI. It is easy to predict that cloud computing “as a service” suppliers will see impact next, as well as data centers hosting such operations. 

It will take much longer for existing firms and processes to incorporate generative AI in ways that produce significant financial outcomes, as most of those applications will be indirect: gen AI will be used by existing processes. 

In most cases, applied gen AI will augment or improve existing processes, but without clearly-measurable financial impact. In the medium term, we should start to see some measurable changes as functions are revamped, replacing some legacy methods with gen AI replacements. 

Only eventually will whole new industries arise, supplying novel products.