Is Payback on Open Access ISP Networks Faster?

One argument for open access fiber-to-home networks is that such networks enable competition while also requiring less capital investment in multiple networks. And there is at least some evidence that open access networks reach payback a bit faster than single ISP-owned facilities.

For example, a sampling of ISP FTTH networks that are not operated on an open access basis suggest payback in competitive markets between seven and 15 years, with payback happening faster with higher take rates. 

Network	Country/Region	Market Competition	Capital Investment	Payback Period	Penetration Rate
Google Fiber (Kansas City)	USA (Kansas City)	Competing with Cable & DSL	$94 million	7-9 years	30-40%
Orange FTTH (France)	France	Competing with Free and SFR	$4 billion (nationwide)	10-12 years	25-35%
BT Openreach FTTH	United Kingdom	Competing with Virgin Media	£12 billion	12-14 years	20-30%
Verizon Fios (NYC)	USA (New York City)	Competing with Spectrum & Altice	$23 billion (total Fios)	9-11 years	35-45%
TDC FTTH (Denmark)	Denmark	Competing with Fiberby and Waoo	€500 million	10-12 years	20-30%
Bell Canada FTTH	Canada	Competing with Rogers	$1.5 billion	8-10 years	25-35%
Chorus FTTH (New Zealand)	New Zealand	Competing with Vodafone NZ	NZ$3 billion	10-11 years	30-40%
T-Mobile Fiber (Germany)	Germany	Competing with Vodafone & O2	€2 billion	12-15 years	15-25%

But some open-access networks get payback in seven to 18 years. The point is that it is not always clear open-access networks reach payback faster than ISP proprietary networks. 

Network	Country	Network Type	Capital Investment	Payback Period
Ammon Fiber Optic Network	USA (Idaho)	Municipal Fiber Network	$3.5 million	16-18 years
Stockholm’s Stokab Network	Sweden	City-Owned Fiber Network	$300 million	8-10 years
Utopia Fiber (Utah)	USA (Utah)	Open Access Fiber	$200 million (Phase 1)	12-14 years
NGA Initiative (UK)	United Kingdom	Public-Private Partnership	£1.7 billion	10-15 years
Nuenen (NL) Fiber Network	Netherlands	Rural Fiber Network	€8 million	7-8 years
Lunet	France	Rural Municipal Network	€2.5 million	10-12 years

Why Firm Productivity Might Drop in the Near Term as AI Gets Deployed

Among other issues, such as potential payback from deploying generative artificial intelligence, is the timing of the payback, and history suggests payback will take far longer than many expect. If AI does develop as a general-purpose technology, as were earlier GPTs including steam power and electricity, and even granting that many technological innovations--which are largely virtual--can propagate much faster than did earlier innovations.  

The initial impact of steam power and electricity on productivity was not as immediate or dramatic as expected. 

Consider steam power. Early adoption was slow. The first practical steam engine was invented by Thomas Newcomen in 1712. James Watt significantly improved the steam engine in 1765 and kicked off the process of commercialization. Still, by 1830, only 165,000 horsepower of steam was in use in Britain, for example.

Even in 1870, about two-thirds of steam power was concentrated in just three industries: coal mining, cotton textiles, and metal manufactures. So, while invented in the early 18th century, it took about 50 to 75 years for steam power to begin having truly widespread and transformative effects on industry and the economy.

The major productivity gains from electricity in the United States came in the 1920s, about 40 years after Thomas Edison first distributed electrical power in New York in 1882.

And there is ample prior evidence of actual productivity dips in  the early days of new technology diffusion. The J Curve, for example, illustrates the pattern that there is an early period of disruption and actual productivity decline when a major new technology is introduced. Only later are the tangible benefits seen. 

source: Flexible Production 

The J-curve effect in GPT adoption typically follows a few stages, from initial investment to realized productivity. AI clearly is in the early investment phase, which ought to imply significant costs without immediate financial returns.

Which ought to clue us in to the fact that investors are likely to be quite disappointed when most entities cannot show significant financial returns. 

And though the J curve might not apply when innovations do not require value chain disruption and displacement, Verizon’s experience with fiber-to-home upgrades still show that even innovations that do not require business model change can take a while to reach maturity. 

As significant as fiber-to-the-home was deemed to be by Verizon, one would be very hard pressed to show significant financial returns to Verizon for five years from mass deployment.

FTTH was not a GPT that required changes in consumer behavior or disruptions of Verizon’s supply and value chains. 

The thing about GPTs (and if AI is a GPT the J curve should apply) is that disruption is required. Still, Verizon arguably reached scale in about four to five years of construction, with very-significant revenue contributions for new video entertainment services enabled by the FiOS network. In the second quarter of 2011, for example, Verizon had about 4.5 million broadband accounts, as well as3.8 million video accounts. 

In the second quarter of  2011, FiOS generated 57 percent of consumer wireline revenues, up from 48 percent a year earlier, Verizon said that year. 

 

By the third quarter of 2011, FiOS accounted for nearly 60 percent of consumer wireline revenues. In the last quarter of 2014, FiOS contributed 75 percent of consumer wireline revenues. Keep in mind that statistic also includes the diminution of Verizon’s landline voice business, plus the maturation and decline of its linear video entertainment business as well. 

In other words, FiOS revenue became the driver of Verizon consumer fixed network revenue in part because the voice and video entertainment businesses declined. 

Year	Cumulative Capital Investment ($B)	Annual FiOS Revenue ($B)	FiOS Subscribers (Millions)
2006	3.6	0.5	0.7
2010	23.0	7.5	4.1
2014	30.0	12.7	6.6
2018	34.0	11.9	6.1
2022	36.5	12.8	6.3

The main take away is that productivity might actually dip in the near term as firms deploy AI technologies.

General-Purpose Technology	Initial Productivity Dip	Adaptation Period	Productivity Surge
Steam Engine	Slow adoption in early 19th century	1820s-1840s	1850s-1890s
Electricity	Limited productivity gains in 1890s-1910s	1920s-1930s	1940s-1950s
Computers	Productivity paradox in 1970s-1980s	1980s-1990s	Late 1990s-2000s
Internet	Initial investment costs in 1990s	Late 1990s-early 2000s	Mid 2000s-present

AI Monetization? Look at 5G

Buyers of infrastructure and services to use artificial intelligence might be forgiven their angst about payback or monetization of those investments. Sellers have few such qualms. 

Roughly the same argument happens around monetization of 5G services: executives complain that they have spent a lot on 5G and have perhaps not seen the financial returns they were expecting, in terms of new or higher revenues. 

That is not a new problem, and our experience with fiber-to-home and 5G provides instructive insight.

For some of us, the debate is an old one. In the mid-1990s, for example, it would not have been hard to find an argument about the payback from fiber-to-home networks, either. In the specific context of new competition between telcos and cable operators for voice, internet access and entertainment revenues, the argument was that FTTH would allow telcos to compete with cable in internet access and video, while cable operators took market share in voice. 

Then, as now, the issue was the new investments would enable assaults on various markets. Assuming a rough split of new internet access share, telcos expected to take share from cable in video services, while cable two-way networks took some telco voice share. 

Financial analysts and operating executives might have hoped for higher returns, but essentially the rationale came down to an existential argument: “do you want to remain in business or not?” Without FTTH upgrades, few, if any, telcos could expect to survive against competitors able to supply hundreds of megabits to gigabits per second home broadband speeds. 

That argument applies to 5G investments and clearly will apply to AI investments as well. Though many expect new revenues, use cases, products and services to be possible, the bottom line is that the new investments essentially allow firms to “remain in business.”

“You get to keep your business” might not be highly appealing, in one sense. One would rather be able to claim that investments will produce high financial returns. 

But that is not really the choice. The choice is “keep your business or go out of business.” The new investments in 4G and AI are essentially strategic and existential; not fully driven by traditional “return on investment” criteria. 

All that noted, some segments of each value chain will have an easier time showing results. As always with a new technology, the initial investments are required to enable use of the technology, and that often means infrastructure suppliers are first to benefit. 

If one agrees that the artificial intelligence market can be viewed as consisting of three layers of infrastructure; models and applications, as do analysts at UBS, value creation and supplier revenue also are in layers. As generally is the case for software layers, AI involves layers that also drive or dictate business, revenue and monetization models. 

The most-direct monetization will happen at the infrastructure layer, involving direct purchase of hardware, software and capabilities as a service. Nvidia and other creators of graphics processing units and acceleration hardware, as well as servers, are in this category. 

Monetization possibilities are mostly direct, in the form of licenses and subscriptions, at the model layer, with some possible indirect monetization for open source models. Subscriptions to use OpenAI; Copilot or Gemini are in this category. 

At the applications layer, monetization will mostly be indirect, in the form of improved existing products and services. UBS estimates “enabling” layer products and services including semiconductor production; chip design, cloud and data centers, and companies involved in power supply will generate at least $185 billion in 2027, with total segment revenues closer to $331 billion. 

Companies developing large language models and those that own data assets that can be turned into intelligence

Application layer: The companies which embed the tools from the intelligence layer into specific use cases. This layer likely offers the largest monetization potential over time, yet this opportunity is difficult to quantify at this early stage. Presently, the report expects a directly addressable market of USD 395 billion in revenue opportunities for the application layer by 2027.

In the 5G markets, one might note a similar trend. The clearest initial winners were the suppliers of 5G network infrastructure, such as Ericsson and Nokia; construction firms and so forth. 

Typically, it takes longer for application success to be discovered. 

In that regard, the salient example of direct new 5G revenue is fixed wireless for home broadband. Since about 2022, virtually all net account additions in the U.S. home broadband market have been supplied by fixed wireless platforms. 

source: CTIA 

Other gains attributable to 5G are mostly indirect or hard to quantify, since in most markets supporting 5G services, all the providers offer 5G. In some markets the quantities of various spectrum resources might provide an advantage to one or more providers, such as in the U.S. market, where T-Mobile’s greater trove of mid-band spectrum arguably has allowed it to take market share from the other leading providers. 

Still, over time, most of the value of 5G or AI, for most applications, use cases and users, is likely to be realized in more-subtle and indirect ways.