What is the Value of a Copper Access Line That Cannot be Upgraded to Optical Fiber?

Some of us used to wonder what many telcos were going to do as they phased out their copper access facilities, since many are still covered by older laws mandating they provide service to any customer who asks for it. The problem is that those telcos must still have the means to provide service, even if they cannot use copper or optical fiber facilities. 

Wireless, in some form, always has been the only realistic alternative. Whether that is satellite service, mobile phone service or fixed wireless, untethered platforms might always be the only way to provide universal service in areas where a fixed network using cables is impractical. 

The latest advances allow standard mobile phones to communicate with low earth orbit satellites without any extra gear or software. 

Which still leaves us with the problem of how to value stranded copper assets, which are declining in value, especially when they cannot be upgraded to optical fiber, and as consumers continue to migrate away from use of fixed networks for voice services. 

Many potential fixed network “home broadband” assets owned by smaller telcos must use a blended valuation approach, as such firms generally own a mix of copper access lines that cannot be profitably upgraded to fiber-to-home; copper lines that can be upgraded as well as existing fiber lines that are operational.

Each asset has distinct valuation characteristics, with built fiber lines being the most valuable, non-upgradable copper lines the lowest valuation. 

The big U.S. telcos arguably always have grown more from acquisitions than from organic growth. Verizon, AT&T, Charter, Comcast and T-Mobile, for example, have done so. In the FTTH business, which always is capital intensive, there also is a “time to market” advantage. 

Building lines takes time. And even when built, take rates might remain in the 40-percent range. In other words, up to 60 percent of the assets are essentially stranded, with no revenue produced. So buying lines that do produce revenue (have subscribers on them) has lots of value. 

If building FTTH lines costs between $800 and $1200 per location, and we assume 40-percent take rates, the cost per subscriber can range from $2000 to $3000. 

If we add in marketing and acquisition costs, the full cost of provisioning a line with a customer on it can range from $2350 to $3700, assuming sales and marketing cost between $200 and $400 per sub, plus installation costs between $150 and $300 per subscriber. 

Scenario	Cost per Passing	Take Rate	Cost per Subscriber
Low Cost	$800	40%	$2,000
High Cost	$1,200	40%	$3,000
Average	$1,000	40%	$2,500

If that is the case, then it often makes sense to pay a premium to acquire customers and facilities rather than build them, if “buying rather than building” accelerates cash flow and also allows some additional economies of scale. 

When copper access lines are upgradable, there is additional upside. And even dwindling copper access revenues provide some amount of revenue and cash flow, even if they are declining assets. For providers who own mobile network assets, there additionally is the potential to serve former copper customers with fixed wireless access as well, as a longer-term alternative to using copper access. 

Eventually, that also will change the valuation of access network assets.

Using AI to Automate Spreadsheet Tasks

 People who spend lots of time creating spreadsheets will appreciate the automation artificial intelligence can bring to such tasks, and might be especially helpful for less-proficient users. 

No "Build It and They Will Come" for AI

"Build it and they will come" was among the fundamental assumptions of dot-com era startups. The belief was that getting big fast, creating a large audience of users, was the foundation for monetization. So, many venture-backed firms essentially spent more money to acquire a user than the lifetime value of that user was reasonable to anticipate, if in fact entrepreneurs even had an idea what that number might be. 

The goal was to build market share first and figure out sustainable economics later. So instead of revenue indicators, startups used non-financial metrics such as page views, unique visitors, and user growth, rather than revenue metrics. 

But artificial intelligence  represents a fundamentally different paradigm. Rather than hoping to monetize attention, AI tools aim to address concrete business problems with measurable returns from day one: productivity gains, cost savings, or revenue improvements. 

To be sure, there is some element of “user engagement” that observers note, as market share matters in big emerging industries. But, for the most part, investment seems more pragmatic for AI, compared to the dot-com era.

That doesn’t eliminate the danger of overinvestment. But the focus on financial returns does ground AI investments in financial reality. Instead of “build it and they will come,” AI developers are much more grounded in  "build it because they're already asking for it and willing to pay."

Also, the funding of AI firms is not led by venture capital, but by profitable, revenue-generating, profitable  hyperscalers. 

Also, where many dot-com companies were essentially trying to become digital magazines or shopping malls, competing for finite consumer attention. AI companies are building tools that enhance work. So the focus is on productivity rather than entertainment spending. 

Key Dimension	Dot-Com Era Startups	AI Firms (Current Era)
Core Offering	Web-based services (e-commerce, portals, online marketplaces, content)	AI products/services, AI-as-a-service, automation, predictive analytics, platform APIs
Value Driver	Website traffic, user acquisition, and “eyeballs”; hope for future monetization	Data-driven solutions that improve efficiency, automate tasks, and deliver predictive/personalized value
Monetization	Advertising, IPO capital, early focus on growth over profits	Subscriptions (SaaS, AIaaS), API usage, licensing, and value-based enterprise sales
Profit Timeline	Delayed, speculative—often prioritizing fast scaling “at any cost” over profitability	Earlier monetization; many AI companies achieve profitability within 2-3 years of launch
Main Users	Primarily B2C (consumers)—focus on internet adoption and convenience	Mix of B2B and B2C—solutions for enterprises (automation, analytics) and consumers (personalization)
Technology Focus	Utilized the internet for delivery; basic web technologies and e-commerce platforms	Core focus on AI/ML, deep learning, neural networks, data pipelines, and process automation
Data Dependence	Limited data collection/analysis; basic web analytics	Highly data-centric; model development, product improvement, and scaling depend on high-quality data
Scaling	Slow initial scaling, limited by server costs, bandwidth, and infrastructure	Rapid scaling via cloud computing; platform and ecosystem approaches facilitate global reach
Investment Focus	Aggressive, speculative VC inflows; little diligence; focus on market “potential” rather than fundamentals	Cautious, more rigorous VC due diligence (product, financials, market fit, defensibility)
Entry Barriers	Comparatively low—basic website, small tech team	High—requires AI/ML expertise, large datasets, significant computing resources, regulatory compliance
Sustainability	Many ventures failed after burning through capital; often lacked clear viable business models	More robust, industry-adaptable models, often with recurring revenue and adaptable offerings
Market Focus	Novelty-driven; unproven digital business models targeting new online audiences	Problem-solving, industry-agnostic, targeting specific verticals to deliver tangible value]

Some amount of overinvestment will likely happen. That tends to be the case for many new general-purpose technologies. Not all the efforts will succeed, but the assets will be rationalized, as was the case for railroads, for example. 

Phase	Description of Overinvestment	Typical Examples (Historical)	Rationalization & Consolidation Phase	Results of Consolidation
Technological Breakthrough	Emergence of new general-purpose technology (GPT); triggers widespread excitement and investment	Railroads (19th c.), Electricity, Telephony, Internet (1990s), AI (2020s)	Initial indications of value mismatch and inefficiencies	Early signs: Maintenance costs rise, market saturation
Investment Bubble/Boom	Massive capital flows seeking profit, often outpacing initial real returns or productive deployment	“Canal mania,” “Railway mania,” Dot-com bubble, early AI startups	Asset values diverge from fundamentals	Overcapacity, fragmented assets, irrational valuations
Overinvestment	Realization that not all investments are viable; bust phase, financial losses, bankruptcies, job cuts	Great Depression (1929), Dot-com bust (2000), other sector crashes	Rationalization push by firms, investors, and regulators	Asset sell-offs, bankruptcies, sector exits
Rationalization,  Reallocation	Surviving firms acquire distressed/undervalued assets; asset consolidation reduces duplication	Telecom mergers post-dot-com, Cloud provider consolidation, AI sector	Consolidation into larger, more viable entities	More efficient use of capital, streamlined offerings
Deployment,  Productivity Growth	Consolidated sector harnesses matured tech; rational investment leads to true productivity gains	Sustainable “deployment period” post-crash (see Perez, 2025)	Stable sector, less speculation, measured investment	Stable profits, innovation resumes sustainable growth

The point is that AI might not be analogous to the “dot com bubble.” It might be more akin to the investment pattern for lots of general-purpose technologies, where some amount of overinvestment eventually happens, but the assets are rationalized over time. 

So far, no single phrase captures the investment mindset for AI. If "build it and they will come" expressed the unbridled optimism of the dot-com era, it is not yet clear what will emerge for AI.

Manufacturing Might be Growing Where We Do Not Expect

Manufacturing employment in the United States has surpassed its pre-Covid pandemic levels, the first time since the 1970s that the sector has regained all the jobs it lost in a recession. But the places where growth is happening have changed.

The manufacturing recovery has not reached the “Rust Belt” states of Pennsylvania, Ohio, Indiana, Illinois, Michigan, and Wisconsin. But states in the Sun Belt and Mountain West, such as Florida, Texas, and Utah, are well above pre-pandemic manufacturing employment.

The post-pandemic period also shifted manufacturing growth away from rural areas and towards small urban counties, which have become the sector’s primary drivers of job creation.

Before COVID 19, large urban and suburban counties enjoyed the fastest manufacturing jobs growth.  Since 2019, small urban counties have become dominant in manufacturing job creation. 

These areas, which previously accounted for less than 20 percent of new manufacturing jobs in the four years before the pandemic, have accounted for 61 percent of all manufacturing jobs added from 2019 to 2023, according to Bureau of Labor Statistics data. 

source: Economic Innovation Group

And what seems clear is that although most manufacturing industries have recovered from their pandemic job losses, computer and electronics manufacturing and chemical manufacturing are growing faster than before the pandemic.

The new jobs increasingly feature higher-skill roles, have grown most in small urban counties and seem to feature more contingent labor (contractors rather than employees). 

Change	Pre-Covid	Post-Covid
Automation/Digitalization	Gradual, uneven	Rapid, industry-wide
Workforce skill requirements	More low-skill jobs	Shift to high-skill roles
Supply chain strategy	Cost-driven, global	Resiliency, domestic focus
Growth Patterns	Rural & urban	Mostly small urban counties
Job Structure	More permanent	More temp/contract work
Government/Private Investment	Limited	Massive new investment

For economic development advocates, perhaps a takeaway is the growing importance of electronics and computer manufacturing, which seems to be growing faster and perhaps in locations one might not expect, especially smaller urban areas.

Jobs Most, and Least Affected by Generative AI, According to Microsoft Research Analysis

A study by Microsoft Research finds generative artificial intelligence might well have the greatest impact on jobs for interpreters, historians, writers, and customer service representatives. While you might expect impact on CSR operations, the other cognitive roles are going to look large as well.

Roles in sales and education also showed high overlap with generative AI capabilities, the study authors argue.

As always, we might find we are wrong about the way generative AI affects many of the roles, but the consensus right now seems to be that many cognitive roles could be affected, as generative AI is especially capable of performing tasks involving language, analysis, and communication areas that dominate many office, media, and teaching professions.

source: Microsoft Research

Of course, what list would be complete without some notion of jobs that are least likely to be displaced by language models. Most of those jobs involve physical operations rather than cognitive tasks, including:

Dredge Operators
Bridge and Lock Tenders
Water Treatment Plant Operators
Foundry Mold and Coremakers
Rail-Track Maintenance Equipment Operators
Pile Driver Operators
Floor Sanders and Finishers
Orderlies
Motor boat Operators
Logging Equipment Operators

source: Microsoft Research

Of course, all general-purpose technologies, which many of us believe AI is going to prove to be, lead to widespread economic and role changes, which necessarily reshape occupational demand.