Showing posts sorted by date for query broadband. Sort by relevance Show all posts
Showing posts sorted by date for query broadband. Sort by relevance Show all posts

Saturday, October 17, 2026

How Big a Competitor to Traditional Mobile Operators can SpaceSXI Become?

Perhaps the notion that SpaceXSI could create a meaningful facilities-based “fourth mobile operator” at reasonable cost is to compare what might be done to what Comcast already has done with its own Wi-Fi network. 


Assume SpaceXSI is able to create a small-cell terrestrial network using existing SpaceX customer locations. In principle, this would allow SpaceXSI to use its low-band spectrum to replicate some of the features of a traditional cellular phone network, without much of the capital investment. 


The analogy to Comcast is that Comcast also uses its fixed network broadband customer base as an “offload” network that reduces its wholesale costs, as data traffic is shifted from the mobile network to the “owned” home broadband network. 


At least in principle, the installed base of Starlink customer terminals might be upgraded so that each also operates as a small cell transmitter for mobile phone service. 


The possible upside is avoiding some building of traditional mobile phone network cell towers. 


Yes, there are many practical issues, ranging from availability of phones able to use the terrestrial frequencies SpaceXSI owns; the extent of coverage that might be possible; what augmentation might have to be done to create a more-robust urban network (since most Starlink customers are in rural areas);  the competitive response by the “big three” facilities-based mobile service providers; and the time and cost required to create all of that infrastructure, versus the revenue upside. 


Still, the key principle remains common: both Comcast and SpaceXSI might replicate the features of a full-fledged mobile phone network at lower cost by leveraging existing assets. 


The functional difference is that Comcast’s Wi-Fi is a capacity layer. It already supplies nationwide coverage using the Verizon network. But offload to its own network reduces the operating cost of buying wholesale capacity from Verizon. 


A Starlink small-cell network would be a coverage layer, replicating the traditional cell tower network.


Comcast’s offloading is about profit margin and operating cost. SpaceXSI’s moves would be about reach: creating the basic network coverage for a terrestrial mobile network. 


The business advantages and issues are different as well. Comcast offloads from rented capacity (per-GB wholesale fees to Verizon) to owned capacity. It only uses unlicensed spectrum for Wi-Fi access, so there is no capital specifically tied up in spectrum licenses.


SpaceXSI has purchased spectrum licenses, so in one respect it has a higher cost base than Comcast does. 


Comcast’s costs to offload traffic are fairly low, whereas SpaceXSI will have a greater cost issue as its satellite capacity is more limited. Also, Comcast’s footprint is contiguous and dense. Starlink’s customer sites are scattered. 


SpaceXSI also will have to concern itself with all regulations governing cell transmitter power levels, for example, something Comcast does not face. 


Comcast’s model is economical because everything it uses is either free (spectrum) or already paid for (backhaul). Starlink’s version is more valuable per node because it provides coverage that doesn’t otherwise exist. But it is much more expensive to run and more heavily regulated.


At a high level, most observers would tend to agree with Comcast executives that the Xfinity mobile service gains advantages by shifting much mobile data traffic to the owned and operated Comcast fixed network.  Even so, nobody outside Comcast really knows what the financial benefits and profit margins might be. 


The larger questions for any SpaceXSI network coverage model based on using existing Starlink receive sites surround the business model: how much market share SpaceXSI might realistically expect to acquire; what its profit margin could be; what its capex and operating costs would be, whether its selection of phones would be commercially competitive. 


But it is basically impossible to clearly quantify the advantages Comcast might possess in using its own fixed network for mobile data access, compared to either a full leased-access model or the alternative of creating a facilities-based mobile radio network.


It might be fair to assume Comcast mobile phone customers use Wi-Fi offload for as much as 90 percent of data traffic, on its owned network and other third-party Wi-Fi sites. 


The central advantage is that Comcast can use a network it already operates to serve a substantial fraction of its mobile traffic. Verizon, AT&T and T-Mobile must generally provide mobile coverage through their cellular networks, including spectrum, radio equipment, towers, transport and core-network infrastructure.


What that implies for the business model is somewhat opaque. If Verizon's wholesale charges vary with traffic, shifting Xfinity customer data traffic onto Wi-Fi instead of cellular can avoid charges on that traffic. However, MVNO contracts can include per-line, minimum-volume and other charges, so the savings will not necessarily be proportional to the traffic shifted.


And, of course, Comcast can add mobile customers without having to construct a corresponding nationwide cellular radio network.


But it might be fair to assume that Comcast saves up to 80 percent on its data capacity wholesale consumption because it is able to offload much cell phone data traffic.


Of course, there are many other costs for operating the mobile phone business, so we cannot assume the overall savings are that high, compared to a standard MVNO approach. 


And even looking only at wholesale charges, it is not clear what the savings magnitude might be. A carrier may charge a flat wholesale fee per subscriber, or sell pooled data, rather than bill every voice minute and gigabyte based on usage or consumption. 


And the value of wholesale services covers quite some range of potential value that might have some bearing on how the wholesale access is priced, if a separation between “voice features” and “data access” features are evaluated. Hypothetically, that can take any number of forms. 


Some features might be heavily attributed to “voice and messaging” services. Other features might be allocated more directly to data network features. In other cases a blended approach is feasible. Nobody outside Comcast or Verizon knows what the specific pattern might be. 

 

Cost component

Voice allocation

Data allocation

Shared or unallocated

Radio access and spectrum capacity

5–15%

65–85%

10–25%

Core network and packet transport

5–15%

65–85%

10–25%

Coverage, towers, radios and site leases

5–15%

35–60%

30–50%

Voice-specific IMS and calling functions

70–100%

0–5%

0–30%

Customer authentication, SIM/eSIM and subscriber provisioning

0–5%

0–5%

90–100%

Wholesale billing, interconnection and roaming

Contract-dependent

Contract-dependent

Contract-dependent

Retail support, marketing and billing

0–5%

0–5%

90–100%


The point is that data access offload might mostly affect the “wholesale data access” features, but not the “voice and messaging” features. 

Wholesale contract structure

How voice and data costs should be modeled

Per-minute voice plus per-GB data

Calculate the two traffic charges separately, then add shared access costs

Flat monthly fee per subscriber

Allocate the fee using estimated underlying cost drivers; do not treat it as a marginal per-GB cost

Pooled data allowance plus voice allowance

Model the cost of each pool, overage charges, minimum commitments and unused capacity

Retail-minus or revenue-based wholesale pricing

Treat the wholesale payment as a commercial cost, not as a direct measure of network resource consumption


Verizon's wholesale wireless service illustrates the nature of these arrangements. A paper on  Cost allocation of WCDMA and wholesale pricing for mVoIP and data services provides a formal framework for distinguishing service-specific costs from shared network costs.


If Comcast pays Verizon a largely fixed per-line wholesale fee, Wi-Fi offload may reduce Verizon's underlying resource consumption without reducing Comcast's bill proportionally. And Comcast’s operating savings might not be as high as some imagine. 


Really “we don’t know” how much value Comcast derives from data access offload. 


Still, assume SpaceXSI wants to avoid MVBO economics, and initially wants to solve an indoor coverage problem for current home broadband customers who might be enticed to buy a SpaceXSI mobile service. 


Every Starlink home terminal comes with a Wi-Fi router, so a Starlink Mobile plan with Wi-Fi calling and data offload would need little new technology aside from compatible phones and radio transmitters working with SpaceXSI’s spectrum assets. 


The real upside would be a licensed-band cellular radio in or beside the dish, so that a customer’s home becomes a femtocell or small cell. 


Starlink does have a global advantage, though, as its network will eventually cover most of the earth’s surface. On the other hand, getting a comparable low-band spectrum globally will be a complicated and cumbersome prospect. It is a non-trivial endeavor! 


Monday, July 20, 2026

Compute Cash Flow: Infrastructure Tends to Lead Other EcosystemRevenue Streams

Some observers rightly note that the “time to revenue” for high-performance computing “as a service” suppliers is crucial. At the moment, for example, some would point to service supplier investments to create the infrastructure.


Some might characterize the cash flow as benefitting chip suppliers at the expense of high-performance computing suppliers, and it is hard to argue with that observation. 


But infrastructure creation first; revenues second is a classic pattern in computing. That pattern has been seen in semiconductors, telecommunications, cloud computing, internet infrastructure, and now AI infrastructure.


Yes, there always is risk, as returns are not guaranteed. 


source: Yahoo Finance 


But it is simply a fact that many computing businesses require supply-leading infrastructure. 


Firms must build capacity before customers can fully exploit it, and that investment often involves high fixed costs, before applications demand and use cases can emerge. 


Principle

Explanation

High fixed costs

Infrastructure requires enormous upfront investment before any customers are served.

Low marginal costs

Once infrastructure exists, serving additional customers becomes relatively inexpensive.

Long construction cycles

Fabs, fiber networks, and data centers often require years to build.

Induced demand

Lower prices and better performance stimulate entirely new applications.

Network effects

Infrastructure becomes more valuable as additional users and complementary services appear.

Option value

Excess capacity allows entrepreneurs to create products that previously were impossible or uneconomic.


So will there be a revenue lag for high-performance computing utilities? Yes. 


Era

Infrastructure investment

Revenue followed later through...

Time lag

Mainframes (1960s)

IBM manufacturing plants, semiconductor production, service organizations

Enterprise computing adoption

Several years

Semiconductor fabs (1970s-present)

Multi-billion-dollar fabrication plants

PC, mobile, cloud, AI chip demand

2–5 years

Personal computers (1980s)

Intel processor fabs, Microsoft software ecosystem, OEM manufacturing

Mass PC adoption

3–5 years

Internet backbone (1990s)

Fiber optic networks, routers, submarine cables

E-commerce, search, streaming

5–10 years

Mobile broadband (2000s)

3G/4G towers, spectrum, fiber backhaul

Smartphone economy, app stores

3–8 years

Hyperscale cloud (2006-present)

Massive global data centers

Cloud software, SaaS, AI services

3–10 years

Content delivery networks

Global edge server deployments

Video streaming and cloud gaming

Several years

AI infrastructure (2023-present)

GPU clusters, AI factories, power generation

AI agents, enterprise AI, robotics, scientific computing

Still developing


But that is a documented pattern:

  • Chip fab investment precedes chip sales revenue

  • Data center investments preceded cloud computing as a service revenues

  • Railroads preceded nationwide commerce

  • Electric grids preceded widespread electrification

  • Fiber preceded streaming


So building GPU clusters will precede AI-native businesses.


But apparent overinvestment will always be a concern. 


Historically, such overinvestment often also occurs, whether that is long-haul fiber; dynamic random access memory, solar panels or data centers. 


The dangers of overinvestment also are real. But the necessity of infrastructure investment before applications, use cases and revenue can develop is a reality we have often seen. 


Monday, July 6, 2026

Value in Technology Value Chains Tends to Migrate to the App Layer

Slow revenue growth and lower average revenue per account are hardly new concerns for suppliers of consumer access services (mobile or fixed). 


But we should not be surprised, either. 


The rule in technology industries is that economic value tends to migrate upward in the technology stack. Network effects are one reason. But opportunities for customer relationships, loyalty and multiple revenue models also make a big difference. 


Asset

Access provider

Application

Customer relationship

Weak

Strong

User data

Limited

Extensive

Workflow integration

None

Deep

Brand loyalty

Moderate

High

Network effects

Small

Often enormous

Pricing flexibility

Low

High


So in the internet value chain, roughly half of ecosystem revenues accrue to app providers, while access providers (internet service providers, mobile service providers) get between 15 percent and 20 percent. 


Value chain layer

Typical participants

Approx. share of ecosystem revenues

Economic characteristics

User applications & digital services

Google, Meta, Microsoft, Netflix, Salesforce

45–55%

Highest margins and strongest network effects

Commerce & digital platforms

Amazon, Uber

20–25%

Transaction-based economics

Cloud & enabling services

Amazon Web Services, Microsoft Azure, Google Cloud, CDNs

10–15%

Infrastructure with higher value-added

Internet access

ISPs, cable, mobile operators

15–20%

Capital intensive, regulated, slower growth

Passive infrastructure

Towers, fiber REITs, colocation

5–10%

Stable but utility-like returns


The economic principle is simple:

  • Infrastructure competes on capacity

  • applications compete on customer outcomes.


Capacity usually becomes abundant, and abundance reduces pricing power.  Solutions for customer problems remain “scarce,” in the sense that customers gravitate to a relatively few apps and tend to stick with them over time. 


And scarcity supports pricing power. 


Economic force

Internet example

AI analogy

Infrastructure becomes commoditized

Broadband, fiber and mobile access become widely available

GPU clusters eventually become standardized compute utilities

User attention concentrates

Search, social media, streaming dominate consumer engagement

AI assistants and vertical AI agents become primary interfaces

Switching costs increase higher in stack

Users stay with Gmail, Office 365, Salesforce—not because of ISP

Users remain with AI workflow platforms because of memory, integrations and data

Network effects strongest near users

Facebook, YouTube, Amazon Marketplace

OpenAI ecosystem, enterprise agent platforms, developer ecosystems

Pricing power follows differentiation

ISP sells Mbps; applications sell outcomes

GPU provider sells tokens; applications sell productivity or decisions

Marginal cost falls faster below than above

Network capacity continually gets cheaper

Compute cost falls faster than value of specialized applications


In the AI ecosystem, similar value chain effects should happen. Value should accrue heavily at the app layer. 


AI layer

Future revenue share

Why

AI applications and agents

40–50%

Own workflows and customer relationships

Vertical enterprise software

20–25%

Industry-specific solutions

Foundation model providers

10–20%

Models become more competitive over time

AI cloud infrastructure

10–15%

Compute utility with economies of scale

Hardware (GPUs, networking)

5–10%

Hardware normalizes after supply shortages

Power and facilities

3–8%

Necessary but infrastructure economics

Living in the "Now"