Tuesday, November 12, 2024

ISP Marginal Cost Does Not Drive Consumer Prices

As the U.S. Federal Communications Commission opens an inquiry into ISP data caps, some are going to argue that such data caps are unnecessary or a form of consumer price gouging, as the marginal cost of supplying the next unit of consumption is rather low. 


Though perhaps compelling, the marginal cost of supplying the next unit of consumption is not the best way of evaluating the reasonableness of such policies.  


If U.S. ISPs were able to meet customer data demand during the COVID-19 pandemic without apparent quality issues, it suggests several things about their capacity planning and network infrastructure, and much less about the reasonableness of marginal cost pricing.


In fact, the ability to survive the unexpected Covid data demand was the result of deliberate overprovisioning by ISPs; some amount of scalability (the ability to increase supply rapidly); use of architectural tools such as content delivery networks and traffic management and prior investments in capacity as well. 


Looking at U.S. internet service providers and their investment in fixed network access and transport capacity between 2000 and 2020 (when Covid hit), one sees an increasing amount of investment, with magnitudes growing steadily since 2004, and doubling be tween 2000 and 2016.


Year

Investment (Billion $)

2000

21.5

2001

24.8

2002

20.6

2003

19.4

2004

21.7

2005

23.1

2006

24.5

2007

26.2

2008

27.8

2009

25.3

2010

28.6

2011

30.9

2012

33.2

2013

35.5

2014

37.8

2015

40.1

2016

42.4

2017

44.7

2018

47

2019

49.3

2020

51.6


At the retail level, that has translated into typical speed increases from 500 kbps in 2000 up to 1,000 Mbps in 2020, when the Covid pandemic hit. Transport capacity obviously increased as well to support retail end user requirements. Compared to 2000, retail end user capacity grew by four orders of magnitude by 2020. 


Year

Capacity (Mbps)

2000

0.5

2002

1.5

2004

3

2006

6

2008

10

2010

15

2012

25

2014

50

2016

100

2018

250

2020

1000


But that arguably misses the larger point: internet access service costs are not contingent on marginal costs, but include sunk and fixed costs, which are, by definition, independent of marginal costs. 


Retail pricing based strictly on marginal cost can be dangerous for firms, especially in industries with high fixed or sunk costs, such as telecommunications service providers, utilities or manufacturing firms.


The reason is that marginal cost pricing is not designed to recover fixed and sunk costs that are necessary to create and deliver the service. 


Sunk costs refer to irreversible expenditures already made, such as infrastructure investments. Fixed costs are recurring expenses that don't change with output volume (maintenance, administration, and system upgrades).


Marginal cost pricing only covers the cost of producing one additional unit of service (delivering one more megabyte of data or manufacturing one more product), but it does not account for fixed or sunk costs. 


Over time, if a firm prices its products or services at or near marginal cost, it won’t generate enough revenue to cover its infrastructure investments, leading to financial losses and unsustainable operations.


Marginal cost pricing, especially in industries with high infrastructure investment, often results in razor-thin margins. Firms need to generate profits beyond just covering marginal costs to reinvest in growth, innovation, and future infrastructure improvements. 


In other words, ISPs cannot price at marginal cost, as they will go out of business, as such pricing leaves no funds for innovation, maintenance, network upgrades and geographic expansion to underserved or unserved areas, for example. 


Marginal cost pricing can spark price wars and lead customers to devalue the product or service, on the assumption that such a low-cost product must be a commodity rather than a high-value offering. Again, marginal cost pricing only covers the incremental cost of producing the next unit, not the full cost of the platform supplying the product. 


What Would Artificial General Intelligence be Capable of Doing?

Dario Amodei, AnthropicCEO, has some useful observations about the development of what he refers to as  powerful AI (and which he suggests lots of people call  Artificial General Intelligence (AGI). As a one-sentence summary, Amodei suggests powerful AI has the capabilities of  a “country of geniuses in a datacenter”.


It would be “smarter than a Nobel Prize winner across most relevant fields – biology, programming, math, engineering, writing and so forth,” he says.” This means it can prove unsolved mathematical theorems; write extremely good novels; write difficult codebases from scratch.”


It would have “all the “interfaces” available to a human working virtually, including text, audio, video, mouse and keyboard control, and internet access. 


It could engage in any actions, communications, or remote operations enabled by this interface, including taking actions on the internet, taking or giving directions to humans, ordering materials, directing experiments, watching videos, making videos, and so on. It does all of these tasks with, again, a skill exceeding that of the most capable humans in the world.


It would not just passively answer questions. “Instead, it can be given tasks that take hours, days, or weeks to complete, and then goes off and does those tasks autonomously, in the way a smart employee would, asking for clarification as necessary,” Amodei argues.


The resources used to train the model can be repurposed to run millions of instances of it and the model can absorb information and generate actions at roughly 10 times to 100 times human speed. 


However, It may be limited by the response time of the physical world or of software it interacts with. There could well be energy availability constraints; hardware and software cost issues or deficiencies in mimicking human sensory or “fuzzy” reasoning capabilities. 


Also, since such a system would interact with the real physical world, it would remain within the constraints of the physical world. “Cells and animals run at a fixed speed so experiments on them take a certain amount of time which may be irreducible,” he notes. 


Also, “sometimes raw data is lacking and in its absence more intelligence does not help,” he says. In addition, “some things are inherently unpredictable or chaotic and even the most powerful AI cannot predict or untangle them substantially better than a human or a computer today.”


Ethically and legally, “many things cannot be done without breaking laws, harming humans, or messing up society,” he notes. “An aligned AI would not want to do these things.”


The points are that powerful AI (or AGI, as some would call it) will face constraints from the physical world and ethical, moral and legal constraints that could limit its application in many instances as a means of affecting output. 


Analyzing is one thing; the ability to translate that knowledge into outcomes is more tricky. Biological systems offer a case in point. “it’s very hard to isolate the effect of any part of this complex system, and even harder to intervene on the system in a precise or predictable way,” Amodei says. 


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


DIY and Licensed GenAI Patterns Will Continue

As always with software, firms are going to opt for a mix of "do it yourself" owned technology and licensed third party offerings....