Are Telcos "Trapped" by Language or Operating Metrics?

Sebastian Barros, Circles managing director, might be quite right that average revenue per user and gross additions (new subscribers) no longer make sense. The “KPIs (key performance indicators) they still cling to are built for a world of selling minutes and megabytes, not for today’s digital ecosystems,” he argues.

One might agree with the sentiment, but the logic misses something important. It might be correct to argue that “consumers no longer just buy ‘minutes and megabytes,’” as Barros somewhat rightly notes. “They buy ecosystems: streaming, wallets, commerce, and cloud.” 

Perhaps more correctly, consumers use ecosystems, wallets, streaming, commerce, cloud, minutes and megabytes. What we actually buy from mobile and fixed network access providers is the use of their network resources so we can talk, text and use the internet. 

But “telcos” and “internet service providers” are not in businesses where metrics such as units shipped, book-to-bill ratio or monthly active users or advertising revenue per user make sense as operating indices that inform us about how the business is faring. 

Where ISPs sit in the value chain means they make their money selling internet access and some communication services. So ARPU, churn rates and net additions actually are the relevant operating metrics. 

Those metrics will not make sense for others in the value chain. But ARPU, gross adds, net adds and churn are very much the right numbers to track operating performance. 

Here’s the point: ARPU and gross adds do not “trap” ISPs in old ways of thinking. Those metrics are the right ones for the business they are in and where they are situated in the value chain. ARPU and gross adds might make no sense for other participants in the value chain. 

But just because “ARPU and gross adds make no sense for the business I am in” does not mean “they make no sense for the business ISPs are in.” 

Segment	Example Players	Key Operating Metrics	Typical Characteristics
Semiconductors / Chip Suppliers	Nvidia, Intel, AMD, Qualcomm, Broadcom	- Gross Margin (% of sales) - Fab Utilization Rate - R&D Intensity (% of revenue) - Average Selling Price (ASP) - Units Shipped	Capital-intensive; cyclical demand; high fixed costs; margins vary by product (leading-edge chips much higher).
Networking / Hardware Equipment	Cisco, Ericsson, Huawei, Arista	- Gross Margin - Book-to-Bill Ratio - Installed Base Growth - CapEx as % of Revenue - Service Revenues %	Dependent on telco/enterprise capex; recurring service revenues important; margin pressure from competition.
Access Providers (Telcos, ISPs)	AT&T, Verizon, Comcast, Deutsche Telekom	- ARPU (Average Revenue per User) - Churn Rate - Network CapEx (% of revenue) - EBITDA Margin - Subscriber Growth	Capital-intensive, slow growth, regulated; sticky customer base but high infrastructure costs; moderate margins.
Cloud / Hyperscalers	AWS, Microsoft Azure, Google Cloud	- Revenue Growth Rate - Gross Margin - Utilization of Datacenters - Customer Retention - Operating Income Margin	High scalability; strong growth; capex heavy but high-margin once scaled; sticky enterprise contracts.
Search Engines	Google, Bing, Baidu	- MAUs (Monthly Active Users) - ARPU (via advertising) - Ad Load (ads per page/search) - Click-through Rate - Operating Margin	Network effects; ad-driven monetization; extremely high margins; market concentration.
Social Media Platforms	Meta, TikTok, Snapchat, X (Twitter)	- DAUs / MAUs - ARPU (ad revenue per user) - Engagement Time per User - Ad Fill Rate - Operating Margin	Strong network effects; monetization via ads; margins high, but sensitive to user growth and ad demand.

It is quite true that “pipes and bundles are the only game in town,” as Barros notes. Unfortunately, most of those other businesses in the value chain are not the ones telcos and ISPs are in, for the most part. 

I find it is common in business for people to make a certain sort of mistake. They might argue something “cannot be done” because “my company, with its resources and business model, cannot do so.” That never means some other company, with different resources, cannot succeed in doing so.

Nor, in this case, do the prevailing operating metrics “trap” participants. Telcos and ISPs are access providers. It is their role in the value chain. If they were to substantially change roles, and move elsewhere in the value chain, of course the operating KPIs would change. 

But ISPs are not limited by their choice of metrics: they are “limited” by their chosen roles in the value chain.

EchoStar to Sell Mid-Band Spectrum Assets to AT&T, in Shift of Business Model

AT&T is purchasing spectrum licenses from Echostar (30 MHz of nationwide 3.45 GHz mid-band spectrum and approximately 20 MHz of nationwide 600 MHz low-band spectrum). The all-cash transaction for $23 billion represents licenses covering virtually every market across the United States, AT&T notes. 

The spectrum sale to AT&T marks a significant strategic shift for EchoStar's mobile communications business, refocusing the company from a facilities-based network operator to a mobile virtual network operator (MVNO) that also operates its own core 5G network.

After selling its 3.45 GHz and 600 MHz spectrum licenses to AT&T, EchoStar will still retain rights to spectrum assets for mobile service in the AWS-4 (2 GHz) band and S-band MSS (Mobile Satellite Service), including 40 MHz in the 2000–2200 MHz range. 

EchoStar's strategy following the sale to AT&T includes both terrestrial 5G and emerging satellite connectivity use cases, including potential direct-to-device applications. 

EchoStar’s Boost Mobile operations will primarily be powered by wholesale access to AT&T’s network, though it also uses T-Mobile radio access assets as well. 

Though EchoStar began life as a satellite services provider, it began in the 2010s to move into terrestrial mobility. Early on, the company envisioned a role as a major facilities-based retailer of mobile services. As the U.S. mobile market solidified as a market led by AT&T, Verizon and T-Mobile, the opportunity to compete effectively as a fourth provider dimmed. 

The latest thinking is to focus on more niche roles such as satellite direct to device use cases. 

Era	Focus	Strategic Action	Outcome/Impact
1980s–2000s	Satellite TV and broadband	Launch of DISH, Hughes acquisition	Dominant satellite TV provider
2010s	Diversification, spectrum acquisition	AWS-4, 5G, Open RAN initiatives	Entry into mobile and 5G market
2019–2023	Mobile network buildout, M&A activity	DISH/EchoStar merger, network expansion	Boost Mobile, 5G deployment on owned facilities
2024–2025	Hybrid MNO, satellite-terrestrial innovation	AT&T spectrum sale, Open RAN/D2D focus	Partnership  model; spectrum held for direct-to-device services

Well-Intentioned Regulations Can Backfire

Land use regulations including zoning laws, density restrictions, minimum lot sizes, height limits, rules on parking spaces and growth controls increase the cost of housing and limit its construction. 

That matters if the U.S. housing supply is 4.7 million homes short of demand. It’s just basic supply and demand economics. 

And focusing on supply matters. Such regulations often limit the amount, type, and location of new housing development, effectively constraining supply even as population growth, urbanization, and economic demand for housing rise. 

 This supply shortage pushes up prices and rents, making housing less affordable, particularly for low- and middle-income households. 

 For instance, regulations can impose lengthy permitting processes, environmental reviews, or inclusionary requirements that raise development costs, which are then passed on to buyers or renters. 

 But the bigger problem is simply that such rules are among the reasons more housing is not created. And there are several reasons, one might argue. Labor availability and costs; lumber availability and cost; tax rules and other government policies play a role. 

 

But land use planning rules matter. To be sure, the rationale often is compelling: preserving community character, protecting the environment, or preventing urban sprawl. 

 But those very same rules create disincentives to build affordable housing, as they all restrict housing density or volume. 

Study Title	Authors	Year	Methodology and Key Findings
The Effect of Land Use Regulation on Housing and Land Prices	Keith R. Ihlanfeldt	2007	Used an endogenous index of regulatory restrictiveness across over 100 Florida cities; found greater restrictiveness increases house prices, decreases land prices, and leads to larger new homes.
The Effects of Land Use Regulation on the Price of Housing: What Do We Know? What Can We Learn?	John M. Quigley and Larry A. Rosenthal	2005	Reviewed empirical literature using surveys, econometric models (e.g., OLS, hedonic pricing), and regulatory indices; regulations like zoning and growth boundaries are associated with higher prices, but causality is not firmly established due to endogeneity and data limitations.
https://www.urban.org/research/publication/land-use-reforms-and-housing-costs	Christina Plerhoples Stacy et al.	2023	Analyzed a panel dataset of 180 reforms in 1,136 U.S. cities (2000–2019) using machine learning, manual coding, and fixed-effects models; loosening restrictions increases supply by 0.8% over 3–9 years (mainly high-end units), while tightening raises median rents and reduces affordable units.
How Land-Use Regulation Undermines Affordable Housing	Sanford Ikeda and Emily Washington	2015	Reviewed literature and urban policy data; regulations reduce supply relative to free-market levels, increase costs (e.g., 10%+ "regulatory tax" in major cities), and disproportionately affect low-income households, potentially lowering GDP by limiting growth in productive areas.
Regulation and Housing Supply	Joseph Gyourko and Raven Molloy	2014	Literature review with surveys, panel data, and regression analyses (e.g., OLS, instrumental variables); strong positive link between regulation and prices (17–22% increases), reduced construction (4–22%), and lower supply elasticity, leading to volatility.
Zoning, Land-Use Planning, and Housing Affordability	Randal O'Toole	2017	Regression analysis of court decisions as proxies for regulation intensity (2000–2010 data); rising land-use and zoning regulations correlate with higher home prices in 44 and 36 states, respectively, with federal aid flowing more to restrictive states.
The Impact of Zoning on Housing Affordability	Edward L. Glaeser and Joseph Gyourko	2002	Compared house prices to construction costs across U.S. markets; zoning drives prices above costs in high-regulation areas (e.g., NYC, California), suggesting supply restrictions exacerbate affordability issues more than demand alone.
Do Restrictive Land Use Regulations Make Housing More Expensive Everywhere?	John Landis and Vincent J. Reina	2021	Examined 336 metro areas with multiple stringency measures and growth variables; restrictive regulations pervasively raise home values and rents, especially in growing/prosperous economies, but effects on supply vary by market.

When is a "Commodity" Not a Commodity?

Real markets often do not conform to our efforts to categorize or change them. Sometimes products are hard to classify, in that regard. Consider home broadband and mobile phone services, which might be considered commodities by some; but the opposite by others. 

Any “commodity market” requires products that are standardized and therefore interchangeable across providers, with competition primarily driven by price, with  little differentiation in quality or features. Agricultural products, metals and energy products provide good examples. 

But it is a judgment call whether mobile services have such properties, as competitive as the market might be. 

Mobile phone service has some commodity traits (price sensitivity, partial standardization), but significant differentiation through network quality, branding, and bundled services, along with limited fungibility (one provider’s product is quite similar to all others), makes it a non-commodity.

Broadband is the most commodity-like (compared to either mobile phone service or electricity) due to high standardization, fungibility, and price-driven competition. However, bundling and infrastructure variations introduce slight quasi-commodity traits. 

Product	Standardization	Price Competition	Fungibility	Differentiation	Category
Electricity	High	High (deregulated); Low (regulated)	High (deregulated)	Low	Quasi-commodity
Mobile Phone Service	Moderate	Moderate	Low	High	Non-commodity
Home Broadband	High	High (competitive markets)	High (competitive markets)	Moderate	Commodity

Home Broadband might be the most commodity-like due to high standardization, fungibility, and price-driven competition in markets with multiple providers. Its differentiation through bundles (streaming services, mobility, linear video) is secondary to price and speed.

Electricity might be a quasi-commodity, highly standardized and fungible in deregulated markets but constrained by regulated monopolies and infrastructure dependence in many regions.

Mobile phone service might be considered a non-commodity, with significant differentiation through network quality, branding, and bundled perks, despite some price sensitivity.

Whether an industry’s products are commodities or not also shapes business and revenue models. Even products we might assume are definitely commodities, such as electricity, might actually be “quasi” commodities. 

In most regions, electricity, gas, and water are delivered by regulated monopolies, meaning consumers have no choice in providers. This lack of competition undermines the commodity trait of market-driven pricing. 

Unlike true commodities (oil traded globally), these utilities are tied to local infrastructure (grids, pipelines, water systems), which effectively means an electron for sale in Atlanta, Ga. is not interchangeable with an electron to be sold in Los Angeles, Calif. 

That might not seem so important, but it means that retail electricity for consumers is not an actual commodity (implying alternate suppliers), but more a “quasi-commodity” as there are, in many markets, no competitors. 

The point is, our efforts to categorize and understand ”commodity” products, and company and industry efforts to prevent products becoming commodities, is harder than it might seem.