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


Sunday, December 21, 2025

Like it or Not, NIMBY Remains a Big Issue for Infrastructure Providers

Local opposition to infrastructure projects (“not in my backyard”) remains a key constraint for any number of initiatives related to housing, energy transmission, power generation, data center siting. Even when citizens agree that “more” of any type of infrastructure is needed, those same citizens oppose local development. 


Local community opposition has become one of the primary non-technical constraints on adding new generation in the U.S., especially for utility‑scale wind, solar, and some gas projects. 


Evidence from developer surveys and studies suggests that opposition now contributes to delays for roughly half of large renewable projects and to cancellation for roughly one‑third, with typical delay times of about a year.​


A Lawrence Berkeley National Laboratory survey of wind and solar developers found that about one‑third of projects in the past five years were cancelled and roughly half experienced delays of six months or more, with local zoning, grid connection problems, and local opposition identified as the main causes. 


Community opposition and restrictive local ordinances often are viewed as leading reasons for delays and cancellations, on par with interconnection issues.​


Project delays tied to community opposition average around 11 months for solar and 14 months for wind, extending already long four‑to‑six‑year development timelines and raising sunk costs by roughly 200,000 dollars per megawatt of capacity when projects are delayed or cancelled, according to the World Resources Institute. 


In 2023, legal and regulatory trackers documented significant opposition to 378 renewable energy projects across 47 U.S. states, and identified nearly 400 local and close to 20 state‑level restrictions that are severe enough to effectively block projects in many jurisdictions, according to developers. 


Cost, regulations, zoning and changing policies all play a role in shaping the pace of infrastructure development, but citizen opposition also matters. 


Sunday, December 14, 2025

NIMBY Slows Infrastructure Deployments

Local opposition to infrastructure projects (“not in my backyard”) remains a key constraint for any number of initiatives related to housing, energy transmission, power generation, data center siting. Even when citizens agree that “more” of any type of infrastructure is needed, those same citizens oppose local development. 


Local community opposition has become one of the primary non-technical constraints on adding new generation in the U.S., especially for utility‑scale wind, solar, and some gas projects. 


Evidence from developer surveys and studies suggests that opposition now contributes to delays for roughly half of large renewable projects and to cancellation for roughly one‑third, with typical delay times of about a year.​


A Lawrence Berkeley National Laboratory survey of wind and solar developers found that about one‑third of projects in the past five years were cancelled and roughly half experienced delays of six months or more, with local zoning, grid connection problems, and local opposition identified as the main causes. 


Community opposition and restrictive local ordinances often are viewed as leading reasons for delays and cancellations, on par with interconnection issues.​


Project delays tied to community opposition average around 11 months for solar and 14 months for wind, extending already long four‑to‑six‑year development timelines and raising sunk costs by roughly 200,000 dollars per megawatt of capacity when projects are delayed or cancelled, according to the World Resources Institute. 


In 2023, legal and regulatory trackers documented significant opposition to 378 renewable energy projects across 47 U.S. states, and identified nearly 400 local and close to 20 state‑level restrictions that are severe enough to effectively block projects in many jurisdictions, according to developers. 


Cost, regulations, zoning and changing policies all play a role in shaping the pace of infrastructure development, but citizen opposition also matters.


Wednesday, November 19, 2025

If the Internet Collapsed "Distance," AI Collapses Time

If the core function of the internet is connectivity and the core value is the collapse of distance, then the core function of artificial intelligence is cognition and the core value is the collapse of time. 


If the internet makes physical location less important, AI makes complexity less important, reducing the time to derive insights. 


But both the internet and AI are going to disintermediate value chains, removing distribution functions and providers. 


Dimension

Internet

AI

Core Function

Connectivity: linking people, machines, data, and services across networks.

Cognition: performing tasks that require perception, reasoning, analysis, prediction, or decision support.

Primary Value Created

Eliminating distance: collapsing geography; enabling instantaneous communication and access.

Saving time: collapsing effort; automating, accelerating, or augmenting cognitive tasks.

Economic Logic

Reduces transaction and coordination costs associated with physical separation.

Reduces cognitive labor costs and enhances productivity by automating thinking tasks.

Primary Constraint

Bandwidth, latency, physical infrastructure (fiber, spectrum).

Quality of data, model capability, alignment with goals, compute.

Main Units of Scarcity

Transport capacity (Mbps/Gbps), access points (ports, routers), spectrum.

Compute, data quality, reasoning ability, task generalization.

User Experience Shift

From location-dependent to location-independent access.

From manual decision-making to automated or assisted decision-making.

Industrial Impact

Creates global digital markets; enables remote work, cloud services, platform economies.

Automates white-collar workflows; reshapes knowledge industries; introduces agentic systems.

Business Models

Subscription access, metered usage, advertising, platform marketplaces.

API usage, per-inference billing, embedded intelligence in existing software, agentic task fees.

Strategic Advantage

Owning the pipes, connectivity footprint, spectrum, and interconnection points.

Owning the models, data, workflows, and user attention for cognitive automation.

Regulatory Focus

Universal access, net neutrality, infrastructure competition.

Bias, transparency, safety, copyright, workforce displacement.

Transformation Pattern

Disintermediation of distance-dependent middlemen (retail → e-commerce; media → streaming).

Disintermediation of cognition-dependent middlemen (analysts, coordinators, support roles via agents).


So one way of understanding AI is to view it as a new form of infrastructure, as is the internet, as was electricity or railroads. In that view, potential over-investment happens because the new infrastructure has to be created, and not because of a mania or bubble over asset values that are illusory. 


That might temper some of the concern over AI asset valuations or investment magnitude, which can appear excessive in the near term, and might well be, in some instances. Such early over-investment tends to happen when a new general-purpose technology emerges, and especially when that GPT involves infrastructure.  


Historically, transformative infrastructure projects such as railroads experienced periods of perceived "over-investment," where excess capacity was common before widespread economic and societal adoption caught up. 


The U.S. railroad boom of the late 19th century and the electricity grid’s rollout involved capital surges, initial overbuilding, and even bankruptcies. However, over time, these investments generated foundational benefits, enabling entirely new industries and reshaping nations.


So although the superficial similarity between an irrational asset bubble and an infrastructure boom can exist, they are quite different. 


While a financial bubble features a disconnect between investment and credible returns, general-purpose infrastructure has long-term value, even if some amount of capital is misallocated. 


But that’s the issue right now: some see the infrastructure for a general-purpose technology being built; others see mostly speculation. It can be hard to tell the difference in the early going. 


Criterion

Productive Infrastructure

Speculative Excess

Cost-Benefit Analysis

Thorough, data-driven

Minimal or absent

Multiplier Effect

High, measurable output/wages

Weak, limited economic return

Demand Alignment

Supported by real user/market needs

Based on future hype, not evidence

Systemic Productivity

Positive spillovers

Neutral or negative impact

Asset Price Relationship

Aligned with long-term value

Driven by short-term speculation

Evaluation Rigor

Institutional, non-partisan reviews

Ad hoc, driven by momentum

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