Telco AI Monetization on the Revenue Front Will be Difficult

Mobile executives these days are talking about ways to monetize artificial intelligence beyond using AI to streamline internal operations. Generally speaking, these fall into three buckets:

• Personalizing existing services to drive higher revenue, acquisition and retention (quality of service tiers of service, for example)

• Creating enterprise or business services (private 5G networks with AI-optimized performance,, for example)

• AI edge computing services for autonomous vehicles, for example

Obviously, those are AI-enhanced extensions of ideas already in currency. But some of us might be quite skeptical that such “AI services” owned by telcos will get much traction. History suggests the difficulty of doing so. How many “at scale” new products beyond voice have telcos managed to create? Text messaging comes to mind. Mobile phone service also was a big success. So is home broadband. 

All those share a common characteristic: they are network services owned directly by the service providers. Generally speaking, other application efforts have not scaled well. 

Mobile service providers have been hoping and proclaiming such new revenue opportunities since at least the time of 3G. But many observers might agree there has been a disconnect between the technical leaps (faster speeds, lower latency, better efficiency) and the ability to turn those into new revenue streams beyond the basic "sell more data" model. 

That is not to say that service providers have had no other ways to add value. Bundling devices, content and other measures have helped increase perceived value beyond the core network features. 

But the core network as a driver of new products and revenue is challenging for a few reasons. 

• Open networks mostly have replaced closed networks (IP versus PSTN) 

• Applications are logically separate from network transport (layers)

• Permissionless app development is the norm (internet is the assumed network transport)

• Vertical control of the value replaced by horizontal functions (telcos had full-stack control of voice, but only horizontal transport functions for IP-based apps)

As I have argued in the past, modern telcos have a hybrid revenue model. They are full-stack “service” providers for voice and text messaging. But they are horizontal transport providers for most IP apps and services, and sometimes are app providers (owned entertainment video services, for example). 

The point is that most new apps and revenue cases can be built by third parties without telco or mobile operator permission, which also takes transport providers out of the direct revenue chain. 

So I’d argue there is a structural reason why telcos and mobile service providers do not directly benefit from most of the innovation that happens with apps. Think about all the customer engagement with internet-delivered apps and services, compared to service provider voice and messaging. 

In their role as voice and text messaging providers, telcos are “service providers” (they own and control the full stack). For the rest of their business, they are transport or access providers (capacity or internet access such as home broadband), a horizontal value and revenue stream. ISPs get paid to provide “internet access,” not the actual end user apps. 

And that has proven a business challenge for now-obvious reasons. Once upon a time, voice services were partly flat-rate and partly usage-based. In other words, telcos earned money by charging a flat fee for access to the network, and then variable usage based on number, length or distance of voice calls. 

In other words, greater usage meant greater revenue. But flat-rate voice and texting usage subverts the business model, as  most of the revenue-generating services become usage-insensitive. That is the real revolution or disruption for voice and texting. 

In their roles as internet access providers, some efforts have been made to sustain usage-based pricing. Customers can buy “buckets of usage” where there is some relationship between revenue and usage. 

Likewise, fixed network providers have used “speed-based” tiers of service, where higher speeds carry  higher prices. Still, those are largely flat-rate approaches to packaging and pricing. And the long-term issue with flat-rate pricing is that it complicates investment, as potential usage of the network is capped but usage is not.  

So as much as ISPs hate the notion that they are “dumb pipes,” that is precisely what home or business broadband access is. So internet access take rates, subscription volumes and prices are going to drive overall business results, not text messaging, voice or IoT revenues. 

To be sure, we can say that 5G is the first mobile generation that was specifically designed to support internet of things applications, devices and use cases. But that only means the capability to act as a platform for open development and ownership of IoT apps, services and value. And even if some mobile service providers have created app businesses such as auto-related services, that remains a small revenue stream for mobile service providers.  

Recall that IoT services are primarily driven by enterprises and businesses, not consumers. Also, the bulk of enterprise IoT revenue arguably comes from wholesale access connections made available to third-party app or service providers, and does not represent telco-owned apps and services (full stack rather than “access services”). 

Optimistic estimates of telco enterprise IoT revenues might range up to 18 percent, in some cases, though most would consider those ranges too high. 

Region/Group	Total Mobile Services Revenue	IoT Connectivity Revenue (Enterprises)	Automotive IoT Apps Share of IoT Revenue	% of Total Revenue from Automotive IoT Apps
Global Average	$1.5 trillion (2025 est.)	10-15% (2025, growing to 20% by 2027)	25-35%	2.5-5.25%
North America (e.g., Verizon)	$468 billion (U.S., 2023, growing 6.6% CAGR)	12-18% (2025 est.)	30-40% (high 5G adoption)	3.6-7.2%
Asia-Pacific (e.g., China Mobile)	$600 billion (2025 est.)	15-20% (strong automotive industry)	35-45% (leader in connected cars)	5.25-9%
Europe (e.g., Deutsche Telekom)	$400 billion (2025 est.)	10-15% (CEE high IoT reliance)	25-35%	2.5-5.25%
Top 10 Mobile Operators	$1 trillion (2025 est.)	12-18% (based on 2.9B IoT connections)	30-40%	3.6-7.2%

Though automotive IoT revenues (again mostly driven by access services) arguably are higher for the largest service providers, their contribution to  total business revenues is arguably close to three percent or so, and so arguably contributing no more than 1.5 percent of total revenues, as consumer services range from 44 percent to 65 percent of total mobile service provider revenues. 

Category	Percentage of Total Revenue	Example products
Services to Consumers	55-65%	Driven by mobile data (33.5% in 2023), voice, and equipment sales; 58% in 2023
Services to Businesses	35-45%	Includes enterprise, public sector, and SMBs; growing at 7.1% CAGR
Business Voice	5-10%	Declining due to VoIP adoption and mobile data preference
Business Internet Access	15-25%	Rising with 5G, IoT (e.g., automotive apps at 2.5-9%), and enterprise demand

The point is that the ability to monetize AI beyond its use for internal automation is likely limited. Changes in the main revenue drivers (consumer and business mobile phone subscriptions and prices) are going to have more impact on revenue and profit outcomes than IoT as a category or automotive IoT in particular.

Cherry Picking Lumen's Consumer Fiber Business

It perhaps always is difficult to value copper access lines when considering an acquisition with the intention of upgrading those lines to fiber access. It might also be somewhat difficult to value fiber lines in neighborhoods and parts of cities, even when there is no intention to buy copper lines and upgrade them. 

Without question, though, the “upgrade” analysis is more difficult. For starters, not all lines really are candidates for upgrading. In some cases, most lines might not be candidates. In such instances, the “upgrade to fiber” business plan will hinge on a minority of lines. 

Assume that perhaps 35 percent to 45 percent  of Lumen Technologies' consumer access lines could be profitably upgraded to fiber. 

But assume the hypothetical $5.5 billion purchase price of the Lumen “consumer fiber business” by a buyer such as AT&T is reasonably accurate, and only includes the already-built fiber assets and customers. 

Without further details, we are left to wonder what assets are included, but It might be reasonable to conclude that it is a “cherry picked” set of assets not including central offices, voice infrastructure and copper lines. 

That might be because the clearest economics are already captured by the existing fiber facilities. Back in 2022 Lumen’s fiber-to-home footprint reached about 27 percent of total access lines. By some estimates it is possible that Lumen or another owner could upgrade between 35 percent to 45 percent of consumer access lines to fiber on a profitable basis. 

But by some estimates Lumen might have built most of the lines it can in markets where it would be the first fiber provider. In many cases the business case for upgrading and becoming the second fiber provider in a neighborhood might not be attractive. 

In markets where a single provider uses fiber, consumer buy rates can hit 40 percent of locations passed. In a market where Lumen is the second provider, it might only get 20 percent take rates. 

The flip side is that more than half of all Lumen’s existing copper facilities likely cannot be upgraded for economic reasons. 

And the copper-based business continues to decline. In early 2024, Lumen had perhaps  4.2 to 4.6 million consumer access lines generating revenue. By early 2025, this number is likely to have further decreased to 3.6 to four million consumer access lines used by paying customers. 

Access Line Type	Total Lines	Total Consumer Accounts
Total Consumer Access Lines	8,200,000	N/A
Fiber Lines	3,600,000	2,100,000
Copper Lines	4,600,000	1,900,000

Basically, a buyer intending to upgrade Lumen consumer lines is basing that decision on perhaps 2.9 million to 3.6 million out of 8.2 million lines, conservatively. By some estimates, Lumen might already have upgraded as many as 3.6 million lines, though that figure also includes small business lines that are routinely counted in the “mass markets” bucket. 

Perhaps there is some revenue to be generated from the copper lines, but it is a declining resource. 

Based on a $5.5 billion purchase price, that implies a per-line investment of between $1897 and $1528 for existing fiber lines, possibly not including any copper lines that are theoretically upgradeable. 

We must assume that there are two different types of potential buyers. In one camp are firms that see the potential to increase equity value by upgrading copper access facilities to fiber. In another camp are firms that primarily want the incremental revenue. The former includes firms that see eventual asset sales. The latter mostly includes operating firms in the business for the long haul. 

If we assume that Lumen would prefer to get out of the consumer mass markets business altogether, a key issue is whether the rest of the consumer business and facilities (central offices, voice infrastructure, non-upgradable lines) would be retained, spun off to another third party if possible, or bundled on a low-cost basis to a potential buyer that really just wants the fiber assets. 

It’s messy. For starters, Lumen (or any new owner acquiring the whole mass markets business) probably would continue to be viewed by regulators as a “carrier of last resort,” meaning it would have to keep offering voice services broadly and might also not be allowed to decommission the copper access network. 

An owner might argue it could use other technologies (mobile network, for example) to supply voice and lower-speed internet access service, even if it decommissioned the whole copper network. But regulators have resisted such pleas in the past. 

The point is that an acquisition of the Lumen mass markets business would be messy. The value is the fiber lines and potential boost in fiber customers. But getting those lines might also entail getting lots of copper lines that actually cannot be upgraded and have declining value. 

And if a potential acquirer only wanted the fiber for internet access and other “data” purposes, the central offices and voice infrastructure would not be very helpful. Beyond that, Lumen’s consumer fiber access lines are scattered about in some neighborhoods in many cities. There are no cities with ubiquitous fiber in place. 

Of course, it always is possible that a potential acquirer really only wants the fiber-to-home facilities that already are in place (neighborhoods), with no intent to buy copper lines and upgrade them. That’s arguably an easier business case to make, as there is not requirement for additional capital for the upgrades from copper. 

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.