Wednesday, March 26, 2025

Asking the Wrong Question about 5G

The claim  that "5G has failed” is in some ways an odd one. On one hand, critics tend to cite the unfulfilled promises of exciting new use cases. On the other hand, critics tend not to focus on the lower latency, faster speeds or energy efficiency that each successive network also is founded upon. 


But that might be the main point: each successive mobile generation has been successful and necessary precisely for the reasons that consumer home broadband experiences have been based on ever-increasing bandwidth, capacity and access speeds. 


So alter the question just a bit to understand the real impact. Do you ever really hear observers arguing that mobility services (mobile phone service) actually have failed? One does not hear such claims because mobile service clearly has been a raging global success. 


Some 71 percent of humans presently use a mobile phone, according to the GSMA.  


source: World Economic Forum 


So “mobility” has clearly succeeded, even if some feel particular mobile platforms have not. To be sure, proponents have touted the creation of platforms for futuristic use cases (the network will support them), not the extent of usage. Some examples can always be cited, though often not mass market adoption. 


To be sure,  every mobile generation since 3G has made such claims. And we might advance some very-practical reasons for the claims. Each mobile generation requires the allocation of additional spectrum from governments, which have to be convinced to do so.


Pointing out the new potential applications; the contribution to economic growth; educational advantages and so forth are part of the effort to secure the new spectrum. 


Also, infrastructure suppliers have a vested interest in enticing operators to create whole new networks precisely because it might be possible to create new revenue streams, or provide


Still, each successive mobile platform has promised, and delivered, latency improvements of about 10 times over the preceding generation, as well as potential bandwidth (internet access speeds) of 10 times more, and typically also energy consumption efficiencies as well. 


The practical improvements always vary from laboratory tests, though. The actual behavior of all radio waves in real-world environments is an issue. So are the realities of impediments to signal propagation (walls, trees, other obstacles) and signal interference.


Cell geometry also matters. Higher bandwidth is possible when smaller cells are used. 


Higher bandwidth is possible when channel sizes are increased (as when channels are bonded together to create a single wider channel from two or more narrower channels). 


And real-world “customer-experienced speeds” also are dependent on which actual frequencies are used widely by each mobile generation. Lower frequencies propagate better, but higher frequencies support higher speeds, all other things being equal. 


Still, the point is that observers never question the “success” of the mobile phone and mobile networks, only the “failure” of futuristic apps to emerge. 


That is not the point. The primary and essential value of each successive mobile platform comes from network performance (lower latency, higher bandwidth) and not the possible new apps, which cannot be created by mobile operators in any case, anymore than internet service providers having created Facebook. Google, Amazon, YouTube or Uber. 


Mobile operators can only create the physical infrastructure third parties can use to create new use cases. And that has been accomplished. But then innovation leading to new apps rests in the hands of entrepreneurs and investors.  


That’s the whole implication of “permissionless innovation” the internet is based upon: innovators do not have to own networks to build apps that use the networks. The entities that own the access or transport networks do not necessarily or primarily create and own the apps. 


Oddly, the reverse tends to be the case: highly-successful consumer app providers find they can vertically integrate into core network transport as a means of lowering their costs. That is why most of the world’s long distance networks (subsea, especially) are built and owned by a relative handful of big app providers such as Alphabet (Google) and Meta. 


It is fair to note that few of the futuristic apps touted for 3G, 4G or 5G networks have become mass market realities. On the other hand, lots of highly-useful apps not envisioned for any of those networks have emerged.


Net

Predicted "Futuristic" Use Cases

Unexpected "Everyday" App Developments

3G

Video conferencing, mobile TV, advanced multimedia

Mobile social media (early stages), basic GPS navigation, early app stores

4G

Immersive VR/AR, high-definition mobile gaming, remote surgery

Ride-sharing apps (Uber, Lyft), widespread video streaming (YouTube, Netflix), robust social media (Instagram, TikTok), advanced turn-by-turn navigation (Google Maps)

5G

Holographic communication, tactile internet, massive IoT deployments

Enhanced real-time location based services, very high definition mobile video streaming, cloud gaming, very reliable real time social media interactions. Increased use of live streaming services, and the further enhancement of cloud based applications.


All of which suggests we are very bad at predicting the future; innovations often emerge unexpectedly and only when users see the value. 


Consider only the industrial, commercial, medical and other applications generally centered around the use of sensors and mobile networks as the connectivity mechanism. Most have not taken off in a significant way, even if there are some instances of viable and routine deployment. 


Generation

Touted Possible New Applications

3G

- Telematics for automotive industry5


- Smart home devices (thermostats, security cameras)1


- Traffic light systems1


- Vending machines with remote monitoring1


- GPS trackers for livestock1


- Wearable devices and e-readers1


- Medical alert devices1


- Remote weather stations1

4G

- Enhanced mobile broadband for video streaming and gaming6


- Smart home applications2


- Internet of Things (IoT) connectivity2


- Remote monitoring systems2


- Vehicle communications (real-time road information, navigation)2


- VoIP calls and video conferencing6


- Mobile payments6

5G

- Telesurgery and remote medical procedures4


- Fully autonomous vehicles4


- Advanced connected homes4


- Portable Virtual Reality (VR) experiences4


- Smart city infrastructure4


- Ultra-reliable low latency communication (URLLC)3


- Massive Machine Type Communication (mMTC)3


- Industrial automation and robotics8


- Remote patient monitoring in healthcare7


- Large-scale IoT deployments in agriculture, utilities, and logistics


For the most part, the futuristic appl;ications have not developed as expected, and when they do take hold, it often is in the subsequent generation.


Many expected 3G to produce mass market usage of videoconferencing. That did happen, but only in the 4G era, with social media and other multimedia messaging apps, for example. That is a fairly common pattern: we overestimate routine adoption by at least a decade. 


Use Case Prediction

Actual Adoption (at least early stage)

Delayed Applications Likely Emerging in Later Generations

3G Expectations

(Medical devices, telematics, mobile TV)1

4G Realizations (IoT connectivity, smart meters, vehicle telematics)2

4G Concepts for 5G Era

- Advanced industrial automation3

- Mobile medical monitoring systems3

- Smart grid controls3

- HD public safety cameras3

4G Expectations

(Massive IoT, Industry 4.0)2

5G Realizations (Network slicing, enhanced mobile broadband)4

5G Concepts for 6G Era

- Holographic communications5

- Autonomous vehicle networks57

- Network-as-sensor technology5

- Microsecond-latency telesurgery7

5G Expectations

(URLLC, mMTC)34

6G Projections

- 1,000x faster latency than 5G7

- AI-optimized networks5

- Energy-efficient massive IoT6

6G Horizon

- Real-time digital twins5

- Military-grade AR simulations5

- Advanced environmental sensing5

- 8K holographic streaming


The point is that mobile services and smartphone services have proven wildly successful. In fact, nobody doubts that. What often gets criticized are the many futuristic apps that could be developed with each next-generation mobile network.


That misses the point. As fixed network home broadband has to continually extend internet access speeds and bandwidth, so too do mobile networks. The bottom line is that each successive mobile generation succeeds to the extent it does so.


Tuesday, March 25, 2025

Internet and AI: It's "Different This Time"

Investors, as all humans, tend to see the future through the lens of the past. And the thinking that "it is different this time" tends to be dangerous. So many have warned of an investment  “dot com bubble” in artificial intelligence.


So some worry about the size of AI infra investments, compared to the near-term and immediate revenue generation from those investments. 

source: Seeking Alpha 


But investment in AI stands on much-firmer ground than did internet startup investing a quarter century ago. 

To be sure, the past emergence of general-purpose technologies (assuming AI will one day be deemed to be a GPT), have led to over-investment. But it also is true that the past GPTs did emerge as transformative and profitable, even if there was a period of investment excess. 


And it might also be correct to say concern over the present investment boom is not anchored in the magnitude of the investment so much as the magnitude of the near-term revenues. 


Would-be leaders of the coming AI markets have a different perspective, of course. They believe the future markets will be huge and will be led by just a handful of firms. So the risk of falling behind is commensurately great. 


There is a risk of over-investment, to be sure. But that might be deemed the lesser of evils. The risk of some temporary over-investment has to be weighed against the risk of losing out on permanent, long-term market leadership. 


Some over-investment is temporary and quantitative. Missing out on the chance to lead in AI markets is lasting and qualitative. 


General-Purpose Technology

Time Period

Investment Boom/Bubble

“Boom”

“Bust”

Railroads

1840s

Railroad Mania

Rapid expansion of rail networks, speculative investments

Many companies went bankrupt, but rail infrastructure remained

Automobiles

Early 20th century

Automotive boom

Proliferation of car manufacturers, increased road construction

Industry consolidation

Internet

Late 1990s

Dot-com Bubble

Excessive speculation in internet-related companies, skyrocketing valuations

NASDAQ crashed 78%, many startups failed

Artificial Intelligence

2020s-present

AI Boom

Massive investments in AI companies, high valuations for AI-related stocks

?


But there might also be many differences between the “internet” investment bubble of the last turn of the century and the current AI investment trend. For starters, AI infrastructure is so hugely expensive that most of the leading investors are deep-pocketed, profitable firms with established businesses and huge cash flows. 


The internet investment bubble was much more speculative, with a greater role played by venture capital and even retail investors, where AI investment is led by established technology giants and institutional investors. 


Internet firms often raised money on the assumption they would “find a business model.” Today’s AI leaders already have logical avenues to  monetize their investments, for the most part. And, for the most part, all those models hinge on vast improvements to the performance of existing use cases, not the creation of new use cases. 


Aspect

Internet Bubble (Late 1990s)

AI Investment Wave (2020s)

Investor Composition

Primarily speculative retail investors and venture capital

Predominantly established, profitable tech giants and institutional investors

Company Financials

Many dot-com startups with no proven business models

AI companies backed by companies with substantial existing revenue streams

Revenue Potential

Highly speculative, based on potential internet reach

More concrete, with clear applications in existing industries

Technology Maturity

Nascent internet infrastructure and capabilities

More advanced technological foundation with demonstrable AI capabilities

Valuation Basis

Primarily "eyeballs" and website traffic

Tangible metrics like AI model performance, integration potential, and efficiency gains

Market Penetration

Theoretical internet transformation

Proven AI applications across multiple sectors (healthcare, finance, technology)

Investment Sources

Retail investors, IPOs, venture capital

Large tech companies (Microsoft, Google, NVIDIA), institutional investors, strategic corporate investments

Economic Context

Emerging digital economy

Established digital infrastructure with clear productivity enhancement potential

Risk Profile

Extremely high speculative risk

More measured risk with clearer value proposition

Competitive Landscape

Numerous undifferentiated internet startups

Fewer, more technologically advanced AI companies with distinct competitive advantages


And where internet metrics often were indirect or non-financial (usage, attention), AI metrics already are largely operationally quantifiable (time saved, code generated, output per hour increased), even if direct revenue increases are harder to measure. 


And even if some parts of the AI infrastructure must be created (graphics processing unit as a service; model training and inference as a service), most of the rest of the infrastructure (broadband internet access; high-capacity cloud computing and data transport facilities; high existing use of applications and devices) is basically in place. 


The internet investment occurred when broadband access had yet to be created; when smartphones were not common; search, social media, e-commerce and content streaming were still developing; and the widespread availability of cloud computing as a service had yet to develop. 


Perhaps the point is that the internet and AI investment context is quite different. There will be over-investment, but by many large, profitable firms that can take the short-term hit. The fate of many would-be startups remains unknown. 


But there are many significant differences between the internet and AI investment contexts. While firms might still falter for any number of reasons, monetization paths are quite a bit clearer; the finances of big investors are sturdier; the use cases clear, in principle. 


We do not have to guess at the value of AI embodied in the form of robo-taxis or autonomous vehicles; factory and other robots. We already know AI can enhance all personalization efforts for all types of software and consumer processes. We are aware of the many ways AI can speed up output by automating repetitive processes. 


The value of the internet was far less clear in early days.


Monday, March 24, 2025

Will AI Exceed Internet in Terms of Producitivity Gains?

If the value of the internet had to be summed up in just one word, that would probably be “connectivity:” people to people; people to apps; people to devices; people to information; devices to devices. 


And though we cannot be fully sure yet, if we had to sum up artificial intelligence in just one word, that might be “augmentation” today, but most observers probably would agree we are on a road to some form of  “intelligence” eventually. 


That might raise the question of whether the internet or AI will have more impact on life, business and economies, though few seem to doubt that both are huge innovations.


Today, AI mostly augments human capabilities, which is not so different from other general-purpose technologies of the past that amplified human muscle power, sight, sound, mobility, memory or speech.


But it will be hard to determine whether communicatiion is more important than decision making; information access more valuable than knowledge creation.  


General-Purpose Technology (GPT)

Amplified Capability or Sense

Printing Press (15th century)

Knowledge Sharing, Memory

Steam Engine (18th century)

Physical Strength, Mobility

Electricity (19th century)

Vision (Lighting), Strength (Machines)

Telegraph & Telephone (19th century)

Communication (Hearing, Speech)

Automobile (19th-20th century)

Mobility, Speed

Radio & Television (20th century)

Hearing, Vision

Computers (20th century)

Calculation, Memory, Logic

Internet (20th century)

Communication, Knowledge Access

AI & Machine Learning (21st century)

Pattern Recognition, Decision-Making


But many observers might already suggest that AI’s potential impact could be greater than the value added by the internet. While the internet broke geographic and physical limitations, connecting people and information faster,  AI has the potential to automate and augment human capabilities across a wider range of tasks and industries.   


AI has the potential to automate cognitive tasks, automate routine processes of all sorts and amplify pattern recognition in almost any sphere of life or industry.


The internet's productivity gains arguably were largely driven by increased connectivity and information access. AI's productivity gains are expected to come from advanced automation and “intelligent” systems. 

   

Feature

Internet Impact

AI Impact (Expected)

Primary Productivity Drivers

Increased information access.  Enhanced communication.  Automation of information-based tasks E-commerce and digital markets

Advanced automation of cognitive and physical tasks. Optimization of complex processes. Creation of AI-driven products and services. Data driven decision making.

Quantifiable Productivity Gains

Significant increase in total factor productivity (TFP) during the "internet boom" of the late 1990s and early 2000s. Studies indicate a notable contribution to GDP growth. 

Estimates vary widely: some predict a substantial boost to GDP within the next decade (e g , Goldman Sachs projecting a potential 15% GDP boost).  Studies suggest potential increases in annual TFP growth by 0 25 to 0 6 percentage points.  Micro level studies show very high increases in productivity in specific sectors

Key Productivity Sectors

Information technology

Finance 

Retail Communication

Manufacturing Healthcare 

Finance 

Transportation Customer service Software development


Has Generative AI Begun to Slow Search Engine Usage?

With the caveat that data for all of 2024 and early 2025 has to be estimated, it might be the case that new ChatGPT activity (to look at just one of the generative artificial intelligence chatbots) has begun to affect search engine volume growth, which is what most observers likely expected. 

Forecast assumptions:


  • Google: Assumes a one- to two- percent  annual growth rate, consistent with reported traffic stability and slight increases (1.4 percent from May 2023–2024).

  • Bing: Reflects a slight uptick post-ChatGPT integration in 2023, stabilizing at four-percent market share.

  • Yahoo & DuckDuckGo: Minimal growth, holding steady at two percent and one-percent market share, respectively.

  • ChatGPT: Starts low (reflecting its early web traffic of 55 million visits by late 2023) and grows rapidly, aligning with claims of 5.25 times growth in AI search use by early 2025.

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