U.S. Cable Operators Will Lose Home Broadband Share, But How Much, and to Whom?

Comcast says it will lose about 100,000 home broadband accounts in the fourth quarter of 2024, a troublesome statistic given that service’s past-decade role in fueling company revenue growth. 

By most estimates, the U.S. cable operators will lose market share to other contestants to 2030. The issue is “to whom” the losses will occur. By volume, the shift to telcos is likely to be the biggest. Satellite access might gain, but the magnitude remains unclear. Share held by third-party independents might not change. 

ISP Segment	2025 Market  Share	2030 Market Share	Key Drivers
Cable TV Providers	58%	45%	Increasing competition from 5G fixed wireless Legacy infrastructure becoming less competitive Price pressure from new entrants
Telcos (Combined)	30%	38%	5G fixed wireless growth in suburban areas Fiber deployment acceleration Mobile/fixed service bundling
Satellite	7%	12%	LEO constellation maturity (Starlink, Project Kuiper) Improved latency and speeds Rural market penetration
Independent ISPs	5%	5%	Municipal networks growth Local fiber deployments Consolidation pressure from larger players

The issue is growing competition for new fixed wireless services on one end of the demand spectrum, plus fiber-to-home services on the other end. Put simply, fixed wireless seems to be taking market share from cable services among customers content to buy services offering 100 Mbps to 200 Mbps of downstream bandwidth, while FTTH is taking share among customers who want 1 Gbps or faster, and sometimes more upstream bandwidth. 

In my own case, I can get around 1 Gbps from both my hybrid fiber coax provider and a FTTH provider. That isn’t the issue. The HFC upstream runs at about 17 Mbps. The FTTH connection is reliably operating at 940 Mbps. 

And the point is not that I “need” 940 Mbps upstream. I don’t. The point is that upstream performance is 55 times greater for the FTTH provider than the HFC provider, at zero cost premium. 

For that matter, I don’t “need” 1 Gbps in the downstream direction, either. The point is that I wouldn’t consider buying any service operating at speeds less than 1 Gbps. It is not a matter of “need” but of preference or “want.”

Somewhat ironically, U.S. cable TV operators face almost the same issues as do telcos when pondering upgrades of their legacy networks. Traditionally, telcos have had to fund a complete replacement of their copper access networks with fiber-to-home platforms to support broadband services. 

And telcos have generally tried to be rational about the capital expenditures, generally deploying FTTH in greenfield areas (new home construction, for example). But that might only represent about one percent to two percent of housing locations per year. At that rate, it will take quite some time to complete a full transition to FTTH. 

Cable operators face the same dilemma. 

Telcos also have justified FTTH upgrades in neighborhoods where demand is greater and willingness to pay is higher. Cable operators might make the same decisions. 

And much hinges on changes in customer demand for symmetrical bandwidth and faster speeds, as there is a point where HFC cannot compete with FTTH (perhaps at about 10 Gbps). That might give cable operators about a decade of running room before a network replacement is required. 

That might assume that “typical” U.S. home broadband speeds reach 1 Gbps by perhaps 2026, with upgrades beyond that to 3 Gbps to 10 Gbps over a decade. 

But that also assumes the key issue is downstream bandwidth, not “symmetrical” or “more nearly symmetrical” bandwidth. Though most observers arguably do not believe upstream bandwidth symmetry is a huge issue for the near future, its importance seems likely to grow. The issue is whether demand for symmetry grows slowly or faster. 

Market demand for products sometimes is not based on “need” but “want,” and some users might already make buying decisions as though symmetrical bandwidth is preferable, even if no application currently requires it, and even if multi-user demands do not require it. 

source: ITIF 

So bandwidth demand beyond the capabilities of the HFC network will force a platform upgrade that telcos already have been facing with the upgrade to FTTH from copper access, even if HFC has a more-evolutionary path remaining, before a full platform shift is necessary. 

Cable operators have been able to gradually and incrementally upgrade their once-copper networks to hybrid networks featuring fiber backbones and retaining copper distribution. But a disruption is coming. No matter how far cable operators extend fiber closer to end user locations, increasingly more-difficult adaptations are necessary. 

Traditionally, the simple remedy was to replace coaxial cable in the backbone with fiber, which was fairly simple, as the rest of the network remained untouched. But moving in the direction of more-symmetrical bandwidth is tougher, requiring revamping all active elements of the copper network. 

High-split hybrid fiber coax networks allocate up to 204 MHz for upstream traffic, compared to only 42 MHz (USA) or 65 MHz (Europe) in sub-split networks. That represents as much as five times more upstream capacity compared to 42-MHz sub-split upstreams.

But even a high-split network will not be able to support symmetrical bandwidth, as FTTH systems now do. So long as customers do not demand symmetrical bandwidth, perhaps that is not an existential issue. 

But if the market shifts to a preference for symmetrical bandwidth, cable operators will, at some point, have to invest quite a bit more than they presently do in network capital investment, as they will essentially have to replace HFC with FTTH access networks. 

There also is a new wrinkle, namely that some demand for lower-bandwidth connections apparently has grown for fixed wireless alternatives. 

We can see that demand shift in statistics on home broadband net gains and losses. 

Company	Q1 2024 Net Broadband Subscribers	Q2 2024 Net Broadband Subscribers	Total Net Additions (Losses) Q1, Q2
Charter	(81,000) losses	(72,000) losses	(153,000) losses
Comcast	(38,000) losses	(34,000) losses	(72,000) losses
AT&T	Slight gains	Slight gains	Approximately 50,000 gains
Verizon	Minor losses	Minor losses	Approximately (50,000) losses
T-Mobile	226,000 gains	246,000 gains	Approximately 472,000 gains

Company	Net Change (Q3 2024)
Charter	-113,000
Comcast	-87,000
AT&T	+50,000
Verizon	+28,000 (Fios) plus 363,000 fixed wireless
T-Mobile	+415,000 fixed wireless

AI "Performance Plateau" is to be Expected

There is much talk now about generative artificial intelligence model improvement rates slowing. But such slowdowns are common for most--if not all--technologies. In fact, "hitting the performance plateau," is common. 

For generative AI, the “scaling” problem is at hand. The generative AI scaling problem refers to diminishing returns from increasing model size (number of parameters), the amount of training data, or computational resources.

In the context of generative AI, power laws describe how model performance scales with increases in resources such as model size, dataset size, or compute power. And power laws suggest performance gains will diminish as models grow larger or are trained on more data.

Power laws also mean that although model performance improves with larger training datasets, but the marginal utility of more data diminishes.

Likewise, the use of greater computational resources yields diminishing returns on performance gains.

But that is typical for virtually all technologies: performance gains diminish as additional inputs are increased. Eventually, however, workarounds are developed in other ways. Chipmakers facing a slowing of Moore’s Law rates of improvement got around those limits by creating multi-layer chips, using parallel processing or specialized architectures for example

Technology	Performance Plateau	Key Challenges	Breakthroughs or Workarounds
Steam Engines	Efficiency plateaued due to thermodynamic limits (Carnot cycle).	Material limitations and lack of advanced thermodynamics.	Development of internal combustion engines and electric motors.
Railroads	Speed and efficiency stagnated with steam locomotives.	Limited by steam engine performance and infrastructure capacity.	Introduction of diesel and electric trains.
Aviation	Propeller-driven planes hit speed and altitude limits (~400 mph).	Aerodynamic inefficiency and piston engine limitations.	Jet engines enabled supersonic and high-altitude flight.
Telecommunications	Copper wire networks reached data transmission capacity limits.	Signal attenuation and bandwidth limitations of copper cables.	Transition to fiber-optic technology and satellite communication.
Automotive Engines	Internal combustion engine efficiency (~30% thermal efficiency).	Heat losses and material constraints in engine design.	Adoption of hybrid and electric vehicle technologies.
Semiconductors (Moore's Law)	Scaling transistors beyond ~5 nm became increasingly difficult.	Quantum tunneling, heat dissipation, and fabrication costs.	Development of chiplets, 3D stacking, and quantum computing.
Renewable Energy (Solar)	Silicon solar cells plateaued at ~20–25% efficiency.	Shockley-Queisser limit and cost of advanced materials.	Emerging technologies like perovskite solar cells and tandem cells.
Battery Technology	Lithium-ion batteries plateaued at energy density (~300 Wh/kg).	Materials science constraints and safety issues.	Development of solid-state batteries and alternative chemistries.
Television Display Technology	LCD and OLED reached practical resolution and brightness limits.	Manufacturing cost and diminishing returns in visual quality.	Introduction of micro-LED and quantum dot technologies.

The limits of scaling laws for generative AI will eventually be overcome. But a plateau is not unexpected. 

The Fulcrum of Human History

As some see it, not even artificial intelligence is the "fulcrum of human history," the central event around which all other historical occurrences can be understood. Blessings to all, irrespective of belief. 

"God bless us everyone"

AI's "iPhone Moment" Will Come. We Just Don't Know When

Some observers might be underwhelmed with the current state of smartphone AI use cases, as they might see somewhat-limited value for other artificial intelligence use cases. There has not yet been an equivalent of an “iPhone moment” when value crystallized in a new way. 

But that is a common theme for any new computing technology. 

In fact, we might argue that prior “iPhone” moments have happened for prior waves of computing technology.

The introduction of the IBM PC in 1981 was a pivotal moment for personal computing within the business world. 

The launch of the Apple Macintosh in 1984 popularized the graphical user interface, revolutionizing how people interacted with computers and making computing more intuitive and accessible to a broader audience.

The Mosaic web browser release in 1993 played a crucial role in popularizing the World Wide Web, making the internet more user-friendly and visually appealing.

The launch of the App Store in 2008 created a new ecosystem for mobile software. 

The debut of Siri on the iPhone 4S in 2011 changed how people interacted with their smartphones.

There arguably is a predictable pattern of new technology incremental improvement, infrastructure development, and the creation of compelling use cases, even if the first implementations are unspectacular. 

Network effects might often explain why value increases over time, but attractive experiences people desire also have to be created. And that typically takes some time and much trial and error, plus creation of ecosystems of capability. 

Ride hailing doesn’t work without smartphones. E-commerce doesn’t work without secure and easy payments. Visual media doesn’t work without broadband. Food delivery doesn’t work without smartphones, location ability, navigation, ordering, payment and fulfillment systems. 

Internet value, for example, grew over time. In the 1970s and 1980s the internet was primarily a text-based tool for researchers and government agencies, used for sharing files and messages.

The World Wide Web brought user-friendly multimedia browsers while internet access moved from slow dial-up to broadband.

Likewise, early web apps were static and limited, offering basic interactivity like online forms. Today’s apps are highly-dynamic, personalized and capable of transaction support of many types. 

Cloud computing, social media, search and e-commerce likewise progressed in similar fashion. 

And there are network effects. Online maps lead to turn-by-turn directions (navigation) to contextual information to ride hailing using smartphones. 

The point is that usefulness develops over time as the ecosystem grows; the platforms improve and innovators create new and desired experiences. 

The “iPhone moment” for smartphone AI might likewise take some time. But it will come.