Verizon Fixed Wireless Keeps Growing Subscriber Base: What That Suggests About Demand

Most of us "dumb end users" of home broadband have probably realized the value-price proposition for broadband outweighs the "raw bandwidth" claims we are urged by internet service suppliers to consider. The success of relatively bandwidth-constrained fixed wireless is a case in point.

In a market where headline speeds are pushing past 2 Gbps and up to 5 Gbps, it might be easy to dismiss claims that far-lower speeds are adequate for many households and use scenarios. In fact, for most end users, the number of users in a household, and the number of their devices, have more to do with suitability than the actual applications those people engage with on a routine basis.

Verizon, for example, reported fixed wireless net additions of 278,000 in the second quarter of 2025, growing the base to over 5.1 million fixed wireless access subscribers. Keep in mind that those connections tend not to operate faster than about 300 Mbps in most areas where Verizon has not activated its millimeter wave spectrum assets.

Though nobody outside of Verizon actually knows, it is possible that 30 percent to 50 percent of those fixed wireless connections operate at less than 100 Mbps delivered bandwidth. It is possible that 30 percent to 40 percent of accounts can use bandwidth up to about 300 Mbps.

And possibly 10 percent to 20 percent of customers have access to speeds faster than 300 Mbps.

The point is that a growing number of households find those speed ranges to be adequate for their needs and budgets.

The company says it is positioned to achieve the next milestone of eight million to nine million fixed wireless access subscribers by 2028.

Though definitions of “broadband” matter for regulators, advocates and suppliers, in most cases “broadband capability” matters quite little for most users of internet access services. Internet access matters quite a lot, in comparison. 

Use of a Chromebook, for example, absolutely requires internet access. But whether use of a Chromebook requires “broadband,” defined as 100 Mbps downstream, 20 Mbps upstream” is highly questionable, if true at all. 

I’ve used a Chromebook on a symmetrical gigabit-per-second connection and on Wi-Fi connections of varying quality but with downstream speeds not exceeding 100 Mbps and upstream in the mid-single digits. 

Was the user experience on Wi-Fi as good as on my symmetrical gigabit connection? No. But was it a major issue? Also, no. Keep in mind, I do no gaming, do not upload or download large files routinely, have no other users on my connection and might watch 4K but never 8K video. 

Though we often use the terms interchangeably, “internet access” is not the same as “broadband.” 

Internet access is the ability to connect to the internet, regardless of the speed or platform used. 

"Broadband" is a moving target describing internet access at defined minimum speeds. The U.S. Federal Communications Commission defines “broadband” as 100 Mbps download and 20 Mbps upload. 

So, strictly speaking, many access services do not operate at “broadband” speeds, as the definition requires. That does not mean the access is deficient, simply that it might not meet the minimums. Wi-fi access on airplanes or in public locations are typical examples where internet access is available, but not at “broadband” speeds in both directions.

In fact, even cable modem services I have used can fail to meet the definition, even when offering gigabit-per-second downstream speeds, as upstream speeds did not hit 20 Mbps. 

For most of us, the issue is whether such failures matter. Often, they do not matter much, if at all. People can do all the things they want to on many connections that fail to meet the broadband definitions. `

As a practical matter, past a certain point, “broadband” matters relatively little in terms of user experience.

Antitrust Law is Becoming More Subjective: Google Search is a Case in Point

Like everybody else who follows Alphabet, browsers and search, I have trouble figuring out what the judicial system will force Alphabet to do about its “monopoly” in the search business. Among the possible remedies is a forced divestiture of Google Chrome (the browser), among other possibilities. 

That remedy might seem odd in view of the defined problem being a monopoly in “search,” not browser market share. In fact, regulators might have a hard time making the case that “browser monopoly market share” actually matters, in a strict sense, since browser use is “free to use.” 

And, of course, traditional antitrust only applies to products that have an actual price, though the line began to blur when the U.S. government took action against the bundling of the Microsoft operating system with Internet Explorer. 

Even if the browser was essentially a feature, not a “product,” the monopoly danger was seen to exist in the combination of the operating system with the browser. 

So some argue that the remedy in the Google search case has to involve some remedy involving “something else,” as use of search also is “without charge” to the user. 

So the “remedies” now center around “doing something” to a product, or products, that are offered to consumers without charge. In other words, we now are dealing with allegations of “consumer harm” where the “harm” cannot be quantified, as the products are offered free of consumer charge. 

Traditionally, antitrust law has focused on “consumer harm” in the form of  higher prices, reduced output, or diminished quality, though the latter two criteria are subjective and cannot be measured. 

So we wind up with ideas that are basically subjective. The alleged monopoly in search is said to reduce innovation on Google’s part. Or, perhaps, Google search could suffer from quality issues, as it might have little incentive to innovate. 

If consumers "pay" for free services with their data and attention to ads, perhaps that is among the effects of the “monopoly.”

Others might argue that commercial agreements, such as Google paying Apple to be the default search engine on Safari, harms the competitive process.

Others might say the harm falls on advertisers who have to pay higher prices, and therefore incorporate those costs into consumer prices. 

All of those elements might seem logical, but virtually all are subjective or indirect metrics. As always, one has to define “the market” to make a claim of monopolization. 

What are the boundaries of  a market for a "free" product? Is Google Search competing with other search engines, or with social media platforms or apps that offer direct access to information or something else. It is not clear. 

The point is that the Google search monopoly allegation pushes the boundaries of several antitrust concepts. 

Digital platforms often operate in "two-sided" or "multi-sided" markets, connecting different groups (users and advertisers, drivers and riders). The complexity lies in how harm on one side (higher ad prices) impacts the other ("free" users) and how market power is assessed across these interconnected groups. That is quite a bit more subjective than the traditional test of higher consumer prices. 

Also, the arguments against Google often center on its role as a "gatekeeper" to the internet and how it "forecloses" opportunities for competitors through its widespread defaults and exclusive agreements. This avenue does not rely on consumer harm, but rather supposed harm to other competitors. 

Rather than higher consumer prices, which cannot be demonstrated directly, the court focuses on more subjective issues, such as whether dominant firms stifle innovation in the market. 

So we wind up in the novel realm of remedies for problems where consumer harm really cannot be demonstrated in any direct form. So even the remedies involve behavioral or structural changes for products with no actual consumer price. 

The belief is that, although Chrome generates no direct revenue, it is an important distribution channel for other Google services: search, Gmail, YouTube, and Google Drive. If a structural remedy is sought (forcing Google to divest the Chrome browser, for example), it would seem to involve something other than the actual “problem” of search monopoly. 

Divesting Chrome would presumably disrupt this seamless integration, potentially reducing traffic to Google’s other services.

Element	Chrome’s Role	Value to Google Search
Default Search Engine	Chrome sets Google Search as the default engine for most users.	Drives billions of queries and reinforces Google’s search market share.
Data Collection	Chrome tracks user behavior (URLs, clicks, performance, device info).	Enhances ad targeting and search ranking precision.
Search Integration	Omnibox merges the URL bar and search bar.	Increases frequency of search queries, even when users intend direct access.
Cross-product Reinforcement	Chrome integrates well with Google services (Gmail, Docs, YouTube, etc.).	Keeps users in the Google ecosystem, raising switching costs.
User Scale	World's most-used browser (65%+ global share).	Provides a massive funnel of user traffic into Google Search.

Without control over Chrome, Alphabet could lose the ability to set Google Search as the default engine or promote its AI products like Gemini.

Chrome is estimated to contribute significantly to Alphabet’s advertising revenue by driving 35 percent of Google’s search revenue, by driving search activity, enabling data harvesting, ad targeting and therefore ad sales. 

The issue is whether the divestiture of Chrome necessarily destroys that value chain. Commercial agreements could be struck allowing Alphabet access to the data that allows the rest of the stack and ecosystem to function. 

Indeed, perhaps no new owner would be in position to create as robust a revenue model as Alphabet is able to manage. 

Many of us use Google search because we believe it is the best engine. It seems unclear how many of us would switch away from Google no matter what happens to Chrome the browser. 

It would be logical to expect a hit to Alphabet’s equity value if such a development occurs. But some might well argue some of that already is baked into Alphabet’s valuation. There is a reason Alphabet has the lowest price-earnings ratio of the “Magnificent Seven” stocks, for example. 

In the end, we might not know how much impact a Chrome browser divestiture might have on Alphabet. Some might point to pressure on search advertising, which hasn’t been seen to date, but could well happen if large language models disrupt search revenue models. 

The impact on other products in the ecosystem might likewise be hard to pinpoint. Lots of us might already conclude that Gmail, YouTube and other products in the Alphabet ecosystem are preferred and would still be preferred, whether Alphabet owns the Chrome browser or not. 

Some of us would guess that some immediate equity market impact would happen, but that hit would be erased over time. Alphabet obviously has contingency plans and would obviously innovate in other areas. 

Beyond all that, the principles of antitrust for products that “have no price” are being tested. And the tests are increasingly subjective.

ChatGPT Agent Launches

ChatGPT launches its new agent.

Future AI Energy Costs are Hard to Predict

Much has been made of a Goldman Sachs analysis of the impact of artificial intelligence on data center power costs, using the oft-quoted claim that AI queries require 10 times the electrical power of a search query.

The claim that AI queries require approximately 10 times the electrical power of traditional search queries has been widely cited, primarily based on estimates from the International Energy Agency (IEA) and other sources suggesting a single ChatGPT query consumes about 2.9 watt-hours (Wh) compared to 0.3 Wh for a Google search. 

But model efficiency, hardware improvements, workload variability, and the specific nature of queries can significantly affect energy use, suggesting the 10x claim is not universally the case.

Hardware and software efficiency is improving. For example, AI-related computer chips efficiency roughly doubles every 2.5 to 3 years. 

Software optimizations, such as those developed by MIT (the Clover tool), can reduce carbon intensity by 80 percent to 90 percent by adjusting workloads to off-peak times or using lower-quality models for less critical tasks.

Also, AI queries vary widely in computational intensity, with some tasks (text generation) being less energy-intensive than others (video generation).

So do search queries. Complex searches involving real-time data or multimedia can approach the energy use of simpler AI queries. And some estimates suggest that even if all Google searches were replaced with large language model (LLM)-powered searches, the global energy increase would be modest (an additional 10–29 TWh annually, compared to 460 TWh for all data centers in 2022). 

Some studies suggest AI energy demands actually are minimal. 

Table of Studies Challenging the 10x Claim
Study/Source	Publication Date	Key Findings	How It Challenges the 10x Claim
IEA: Energy and AI	April 9, 2025	Data centers account for a small share of global electricity demand growth; efficiency of AI chips has doubled every 2.5–3 years, reducing per-query energy use.	Suggests that efficiency gains and task variability (e.g., text vs. video) mean not all AI queries are 10x more energy-intensive.
MIT Sloan: AI Data Center Energy Costs	January 6, 2025	Software tools like Clover reduce carbon intensity by 80–90%; simple steps can cut 10–20% of data center energy demand.	Demonstrates that optimized AI workloads can significantly lower energy use, narrowing the gap with search queries.
University of Wisconsin: The Hidden Cost of AI	August 20, 2024	Microsoft improved chatbot servers to use 10x less energy; efficient generalization techniques reduce energy needs.	Shows that specific AI implementations can have much lower energy use, challenging the blanket 10x estimate.
Sustainability by Numbers: Impact of AI on Energy Demand	November 17, 2024	If all Google searches used LLMs, energy demand would increase by 10–29 TWh, a modest fraction of total data center use.	Indicates that per-query energy differences are less dramatic when scaled, due to efficiency and workload factors.
Harding & Moreno-Cruz: Watts and Bots	2024	AI’s energy demand increase, including economic spillovers, is very small.	Suggests that AI’s per-query energy impact is not as significant as claimed, due to broader efficiency trends.
Masanet et al.: Recalibrating Data Center Energy Use	2020	Data center energy use is lower than projected due to efficiency improvements.	Implies that AI’s contribution to energy demand is moderated, reducing the per-query energy gap.
Nature: AI Energy Demands	March 4, 2025	Lack of transparency leads to simplistic extrapolations; calls for more precise data on AI energy use.	Questions the reliability of the 10x claim due to insufficient granular data.
Breakthrough Institute: Unmasking AI Energy Demand	July 9, 2024	Energy intensity per computation has decreased 20% annually since 2010; AI’s energy impact may be overstated.	Argues that efficiency gains significantly reduce AI’s per-query energy use, challenging the 10x figure.

The point is that the energy consumption of AI queries depends heavily on the model, hardware, and task complexity, and the same applies to search queries, which can vary in intensity.

On-Device AI Use Cases Likely Greatest When Internet Access is Unavailable or Unreliable

AI apps that run directly on smartphones, such as Google’s new AI Edge Gallery, are said to unlock a range of use cases that include operating without an internet connection present. That has value for remote locations, while on airplanes or other locations where access is limited or non-existent. 

The other set of values often is said to include greater privacy or security, processing speed or reliability. Processing on a smartphone is said to provide greater privacy since no data is sent away to a remote site. In principle, that means less risk of a theft of data in transit. 

And some operations, such as those related to image processing on a local camera, are likely to work with less latency if the app is locally resident on the device. 

That might also apply for any AI operations that only use locally-resident (on the device) data. My own personal use cases almost always require access to external data, so the locally-resident value is quite limited. 

Each of us will decide how much value is provided by a locally-resident ability to generate email or message responses. 

On the other hand, some might find useful the ability to generate images locally, such as creating images based on prompts when offline.

Photo analysis using the camera might be another value, assuming no external data is required. 

But, most of the time, the value might come from processing that can be done without an internet connection. The other area where AI might have value is to optimize the performance of the device itself, to save battery life, for example. 

For many use cases, though, it seems as though the value is greatest when all required data is available locally and devices operate in areas with no internet access.

AI Benefits Will Often Show Up as "Externalities" (What is Enabled)

Every important new technology, and especially all general-purpose technologies, have benefits and “costs” (externalities), though most would likely assume that, on balance, every GPT brings more benefits than costs. 

Some of us assume that will be true for artificial intelligence as it has been the case for earlier GPTs. 

Quantified Benefits and Externalities of Electricity Use
Category	Benefit / Cost	Estimated Value / Impact
Economic Output Enabled	Benefit	>$20 trillion globally (approx. 25%+ of global GDP tied to electricity-enabled sectors)
Job Creation	Benefit	~25 million direct and indirect jobs globally (generation, transmission, electric equipment)
Household Welfare	Benefit	>1 billion people lifted out of extreme poverty since electrification
Time Saved (Lighting & Appliances)	Benefit	~500 billion hours annually saved globally (valued at >$5 trillion/year)
Health Improvements (e.g., refrigeration, medical equipment)	Benefit	Millions of lives improved or saved (e.g., vaccines, surgery enabled by electricity)
CO₂ Emissions from Electricity	Cost	~13.5 billion metric tons/year globally (40% of total CO₂ emissions); $1.35–$6.75 trillion/year (at $100–$500/ton social cost)
Air Pollution from Fossil Power	Cost	~3–4 million premature deaths/year; ~$2–$4 trillion in health damages (WHO, EPA estimates)
Infrastructure Costs	Cost	~$2 trillion/year (generation, transmission, distribution, maintenance)
Unequal Access (Energy Poverty)	Cost (opportunity lost)	~760 million people without access (loss of productivity, education, healthcare)
Blackouts and Reliability Issues	Cost	~$150–$300 billion/year in economic losses globally