Will We Break Traditional Computing Era Leadership Paradigm?

What are the odds that the next Google, Meta or Amazon--big new leaders of new markets--will be one of the leaders of the present market,  breaking from historical patterns? 

Historically, we can argue that the leaders of each era of computing were different from the leaders of the prior era. The leaders in the mainframe era (1945-1980) included IBM, Honeywell and Burroughs. 

In the succeeding personal computer era, the leaders were Apple, Microsoft and Dell. 

The era that follows the “PC” period is more contested. Some might say it is the internet era. Others might say the mobile or cloud computing eras also followed since 2006 or 2007. It also is possible that mobile and cloud computing are merely evolutions of a single internet (or other name we do not universally agree upon) era. 

In the internet era, we might argue the leaders were Google, Amazon and Meta. Some might argue the internet era largely overlaps, since about 2007, with the mobile era, whose leaders might be said to include  Apple, Google (Android) and Samsung.

The cloud computing era might include Amazon Web Services, Microsoft Azure, Google Cloud. 

And that might suggest a possible outcome that reflects our current inability to define the present era (internet, mobile, cloud computing). The leaders in segments or eras tend to overlap with each other. That might suggest there are phases to a single era. 

Some believe the next era will center on artificial intelligence, perhaps led by generative AI frontier models. And one characteristic of the model business is its capital intensity. 

source: Pure AI 

And keep in mind that LLMs are updated, as are operating systems. Creating one version of a model necessarily includes t he necessity of updating that model every year to three years or so. 

Model Family	Company	Update Cycle	Notes
GPT (3, 4)	OpenAI	1-2 years	GPT-3 was released in 2020, GPT-4 in 2023
PaLM, Gemini	Google	1-2 years	PaLM released in 2022, Gemini in 2023
BERT	Google	2-3 years	Initial release in 2018, with subsequent variants
LLaMA	Meta	1-2 years	LLaMA 1 released in 2023, LLaMA 2 in 2023
Claude	Anthropic	6-12 months	Frequent iterative updates reported

And note: leaders include many of the same names we see in the internet, mobile or cloud computing “eras.” OpenAI is the most-prominent “new” name, but the others are familiar: Google, Meta, AWS and Microsoft,  for example. 

source: IoT Analytics 

And that suggests a possibility: the leaders of the generative AI era of computing might well be one or more of the firms said to lead in the internet, mobile or cloud computing eras as well.

That might break a pattern we have seen since the mainframe era. On the other hand, there is some divergence of opinion about which “era” computing now is in. But whether we focus on internet, cloud or mobility-based nomenclature, many of the leaders are the same. 

So though we might not know for some time to come, it is possible that the leaders of the internet era could be the leaders (mostly) of the next era, the exception being OpenAI. 

It might also be worth noting that since the PC arrived, eras have been defined by applications and platforms rather than hardware. But many observers might agree that a single computing era can last 30 years to 40  years. 

No Matter What, 6G Revenues Will Still be Led by Phone Usage

Some might argue that 5G was the first mobile platform intentionally designed to support internet of things services in addition to mobile phone services. That noted, IoT mobile service revenues arguably represent less than four percent of total mobile service provider revenues using any mobile platform (2G, 3G, 4G and 5G combined).

Revenue Source	Percent of Total Revenue
Voice Services	20-30%
Data Services	30-50%
Messaging Services (SMS)	2-5%
Roaming Charges	3-7%
Value-Added Services	5-10%
Device Sales	5-15%
Content and Digital Services	5-10%
Enterprise and IoT Solutions	5-10%
Wholesale Services	5-10%
Other Revenues	1-5%

On the other hand, some estimates suggest IoT will be a significant portion of the enterprise customer revenue stream, eventually. 

source: IoT Analytics 

IoT percentage of connections is higher, but revenue per connection is an order of magnitude lower than traditional phone connections, generally speaking. 

Study	Date	Publisher	Estimate
Global Cellular IoT Connectivity Tracker & Forecast	June 2024	IoT Analytics	Cellular IoT (2G, 3G, 4G, 5G, LTE-M, and NB-IoT) makes up nearly 21% of global IoT connections
Global IoT Connections Forecast	2024	IoT Analytics	Global cellular IoT connections grew 24% year-over-year in 2023
Ericsson Mobility Report	June 2023	Ericsson	5.5 billion cellular IoT connections by the end of 2027, majority on 4G/5G.
GSMA Intelligence IoT Report	2023	GSMA Intelligence	3.2 billion IoT connections on mobile networks by 2025, with rapid 5G growth.
Cisco Annual Internet Report	March 2023	Cisco Systems	10% of global IoT connections will be 5G by 2025.
Statista IoT Connectivity Forecast	2023	Statista	2.7 billion IoT devices connected via cellular (4G/5G) by 2025.
IoT Analytics Cellular IoT Report	2023	IoT Analytics	4.3 billion active cellular IoT connections by 2026.

At least one reason connections might not be as high as some might have forecast is that there are other ways to connect IoT devices, including unlicensed wireless such as Wi-Fi or Bluetooth and other methods. 

The point is that new or "lead" applications for a next-generation mobile network often do not dirrectly drive high amounts of new revenue, though the indirect effect can be important (each important internet-based app makes internet access more valuable).

IoT, so far, seems to fit that notion. Even if 5G was purpose-built to support IoT, such revenues remain less than five percent of total mobile service provider revenues, which continue to be led by mobile phone accounts and services.

Study	Date	Publisher	Estimate
Global IoT Connectivity Tracker	2024	IoT Analytics	Wi-Fi makes up 31% of all IoT connections4
Global IoT Connectivity Tracker	2024	IoT Analytics	Bluetooth accounts for 25% of connected IoT devices worldwide4
IoT Device Connections Report	2023	Pondiot	Bluetooth offers a maximum data transfer rate of approximately 3 Mbps for IoT devices1
IoT Connectivity Analysis	2023	Very Technology	Bluetooth range for IoT devices can be anywhere from 1 meter to 1 kilometer depending on device class and context2
IoT Project Connectivity Study	2023	Euristiq	Bluetooth Low Energy (BLE) can transfer data at a rate of approximately 100-250 KBps for IoT applications3
Global IoT Connections Forecast	2024	IoT Analytics	There were 0.7 billion wired IoT aggregation nodes in 2023, representing 4% of total IoT connections

That experience is worth keeping in mind as we start to hear about 6G platforms and their ability to support other types of enterprise or consumer applications, such as virtual reality, autonomous vehicles and so forth.

One always hears about such “futuristic” new use cases whenever a next-generation mobile platform is proposed. Rarely do the proposed innovations reach revenue scale, compared to supporting mobile devices such as smartphones. 

Will Generative AI Follow Development Path of the Internet?

In many ways, the development of the internet provides a model for understanding how artificial intelligence will develop and create value.

For example, the internet’s evolution from a platform primarily for finding and learning to a platform for doing and creating is a path AI could follow. Also, the internet flipped technology adoption on its head.

Where pre-internet technology adoption typically was "enterprise first, followed by small business; followed by consumer adoption," in the internet era we often saw the reverse: consumers first, then adoption of those tools by businesses.

The "information at first; transactions later" pattern should also hold.

Initially, the internet served mostly as a repository of information, where users would search for data, read articles, or view static content. Over time, it shifted into a space where people could interact, transact, create, and collaborate in real-time.

That is in many ways an apt description of the state of generative AI as well. Early search (pre-Google) really was not very useful, in large part because the range of sources was highly limited, with output pretty much limited to text and static images.

Social media had not been invented; payment mechanisms were clumsy and limited; support for video and audio input and output was limited as well. The ability to personalize was rudimentary. 

In contrast, even early generative AI already provides lots of value, but has far to go. The natural language interface is rapidly expanding to include multimedia input and output; highly-personalized content creation with important degrees of contextual awareness. 

But we still are at a relatively early stage of development.

The arrival of AI “agents” and functions provides an example. Right now, generative AI is mostly “ask a question, get an answer.” In the future it will expand to include “do things on my behalf, without an active prompt on my part.”

Feature	Advancements	Examples and Models	Impact on Agent Capabilities
Contextual Awareness	Improved ability to retain, understand, and apply context across interactions; models can remember conversation flow or details over time.	Google Gemini 2, OpenAI ChatGPT, Anthropic Claude 3	Enables smoother, more human-like conversations that adapt based on user history and context.
Multimodal Input Handling	Capability to process and respond to multiple input types, including text, images, video, and even voice.	OpenAI GPT-4 with vision, Google Gemini 2, Meta’s LLaMA 2	Supports richer interactions where users can input questions or prompts across media types.
Enhanced Code Execution	Models can run code to calculate and retrieve specific outputs, especially helpful for technical users.	OpenAI’s Codex, Google Gemini 2’s Python execution capabilities	Useful for problem-solving in technical domains; increases AI’s utility in engineering and data science.
Task-Specific Agents	Models can act as task-specific agents (e.g., research assistants, customer service agents) with focused functions.	Anthropic Claude, Microsoft Copilot, Jasper AI	Tailored functionality helps automate specialized workflows and repetitive tasks across business applications.
Real-Time Interaction and Responsiveness	Models have improved speed and conversational fluidity, supporting real-time, hands-free interactions.	Google Gemini Live, OpenAI ChatGPT with voice capabilities	Facilitates conversational agents that can handle continuous, free-flowing dialogue more naturally.
Voice and Speech Recognition	Integration of natural language processing with voice recognition for spoken commands and responses.	OpenAI Whisper, Google Assistant with Gemini 2	Enhances accessibility and allows for hands-free operation, useful for on-the-go or mobile settings.
Complex Problem Solving	Ability to handle complex problem-solving using mathematical reasoning, programming, and logical deduction.	Google DeepMind's AlphaCode, OpenAI’s GPT-4 with expanded logic	Enables advanced technical applications, from coding to scientific research support.
Interactive and Connected App Ecosystem	Deep integration within app ecosystems allows AI to manage tasks across multiple apps seamlessly.	Google Gemini 2 with Google apps, Microsoft Copilot	Helps users complete cross-app workflows, such as scheduling meetings and drafting emails, autonomously.