Saturday, October 21, 2023

Debt, Not Strategy, Often is the Telco Problem

The conventional wisdom is that AT&T and Verizon should never have ventured into the subscription video business or content ownership in general. Perhaps more to the point, neither firm should have taken on so much debt to acquire content assets. 


Similarly, many mobile operators got into financial difficulties in the 3G era by overpaying for spectrum assets.


In other words, the problem is debt load, not the strategy. Lots of telcos and service providers globally are in the subscription video business, and all are profitable. Beyond that, executives believe their bundled video offers help with customer acquisition, reduce churn and boost cash flow and overall profits.


The problem AT&T has had a few times in the past is making acquisitions that required assumption of excessive debt. It was the debt, not necessarily the strategy, that proved to be a mistake. When long-distance providers MCI and AT&T was casting about for a strategy to enter the local access business at scale after deregulation, AT&T decided to acquire and then upgrade cable TV assets.


Given the success cable TV companies have had in the home broadband market, the strategy was rational. The issue was simply that AT&T took on $64 billion in debt to acquire Tele-Communications Inc. in 1998, and then another $54 billion in 1999 to acquire MediaOne.


You might wonder why AT&T did not simply embark on a massive fiber to home building plan. The reason is simple: it did not have time to do so.

If any of us were asked whether AT&T could afford to build a national FTTH network--within 10 years--we would rightly doubt it was possible. Even if it had the money, it did not have the time.

No single firm could afford to spend $300 billion over 10 years to connect even 100 million homes, which is the scale of the problem AT&T faced. 
And even if it had the money, AT&T did not have the time. 


At a high level, growth by acquisition has generally worked for telcos when using their own currency (stock) to purchase similarly-valued telco assets. Generally speaking, many telcos have gotten into trouble when using debt to fuel such acquisitions.


Acquisition Target

Acquisition Amount (USD billions)

P/E Multiple Paid

Financing

Acquisition Type

Sprint (by T-Mobile US)

26.5

10.4

Debt

Telco

T-Mobile US (by Deutsche Telekom)

31

11.2

Debt

Telco

DirecTV (by AT&T)

67.1

9.8

$67.1 billion. $48.5 billion debt.


Telco

Time Warner (by AT&T)

85.4

12.3

$85.4 billion. $45 billion debt.


Content

HBO Max (by Warner Bros. Discovery)

43

10.1

Debt

Content

Crunchyroll (by Sony Group)

1.2

15.2

Equity

Content

TCI acquired by AT&T

64


$48 billion stock, $16 billion debt

Telco

MediaOne acquired by AT&T

54


Equity

Telco

WhatsApp (by Facebook)

19

10.2

Equity

Application

Instagram (by Facebook)

1

12.0

Equity

Application

LinkedIn (by Microsoft)

26.2

14.5

Equity

Platform


But globally, many tier-one telcos do provide subscription video services and own content assets. 


We might debate the revenue or profit margins such efforts produce. But it is hard to escape the conclusion that it is debt burdens, more than anything else, that have proved troublesome for AT&T and Verizon in content-related businesses. 


Estimates of revenues and profit margins sometimes have to be inferred, but, generally speaking, video services generate five-percent to 15-percent profit margins for telcos. 


Firm

Video Service

Revenue (USD billions)

Profit Margin (%)

Comcast

Linear TV

29.0

20.0

Comcast

Peacock

8.0

10.0

AT&T

AT&T TV

4.0

5.0

Verizon

Fios TV

3.0

10.0

Orange

OCS

2.0

15.0

Vodafone

Sky TV

1.5

10.0

Telefónica

Movistar+

1.0

5.0

China Mobile

Migu Video

0.8

15.0

China Unicom

Wo+

0.7

10.0

China Telecom

Tianyi TV

0.6

5.0

SK Telecom

Wavve

0.5

15.0

KT Corporation

Olleh TV

0.4

10.0

LG Uplus

U+ TV

0.3

5.0

Deutsche Telekom

MagentaTV

0.2

15.0

Telefónica Deutschland

O2 TV

0.1

10.0

Altice USA

Optimum TV

0.1

5.0


Likewise, many telcos own content assets, in addition to selling video subscriptions. 


Firm

Subscription Video Service

Content Assets Owned

Comcast

Peacock

NBCUniversal

Orange

OCS

Orange Studio

Vodafone

Sky TV

Sky Studios

Telefónica

Movistar+

Telefónica Studios

China Mobile

Migu Video

China Mobile Migu

China Unicom

Wo+

China Unicom Wo+

China Telecom

Tianyi TV

Tianyi TV

SK Telecom

Wavve

SK Planet

KT Corporation

Olleh TV

KT Corporation

LG Uplus

U+ TV

LG Uplus


Friday, October 20, 2023

T-Mobile Probably Can Compete for 25% of the Home Broadband Market

A new Ookla report on mobile speed performance cites T-Mobile’s 5G network at 221 Mbps downstream. Compare that to Ookla’s average for fixed network internet service provider speeds at 213 Mbps. 


To the extent that T-Mobile’s fixed wireless service can attain such speeds, it arguably would be faster than the typical home broadband service purchased by, and experienced by consumers. 


That does not mean home broadband providers are not, in many instances, able to sell faster services in the gigabit ranges, for example. It simply is not a service “most” consumers of home broadband are buying, right now. 


Looking at U.S. customer behavior, it appears that about 25 percent of the market is willing to buy service that tops out at about 200 Mbps. If T-Mobile can boost fixed wireless speeds to about 400 Mbps, it could appeal to about 60 percent of U.S. customers. 


source: OpenVault 


AI is Going to Change Data Centers

It seems clear that the amount of AI processing and workloads are going to grow quite substantially, assuming AI proves to be as useful as most of us now believe. It might be at the high end of estimates, but some believe AI operations will consume as much as 80 percent of data center power loads by about 2040. 


Other estimates also are significant. Digital Bridge CEO Marc Ganzi believes AI will mean a new or additional market about the size of the whole public cloud computing market, eventually. 


If the public cloud now represents about 13 gigawatts of capacity, AI might eventually require 38 gigawatts, says Ganzi. 


The whole global data center installed base might represent something on the order of 700 gigawatts, according to IDC. Other estimates by the Uptime Institute suggest capacity is on the order of 180 GW. 


According to a report by Synergy Research Group, the global public cloud computing industry now represents 66.8 gigawatts (GW) of capacity. 


According to a study by the Lawrence Berkeley National Laboratory, AI-driven data center electricity consumption could increase by 50 percent to 200 percent by 2040, posing new challenges for data center operators trying to limit and reduce carbon emissions and electrical consumption. 


Study

Year Published

AI-driven electricity consumption (GWh)

Increase over 2023 (%)

Lawrence Berkeley National Laboratory

2020

130

40%

Gartner

2021

200

50%

IDC

2022

300

75%

DigiCapital

2023

400

100%





Study

Year

Projected AI-Driven Data Center Electricity Consumption (2040)

Growth from 2023 (%)

Lawrence Berkeley National Laboratory

2018

10% of total data center electricity consumption

50%

Gartner

2020

15% of total data center electricity consumption

75%

IDC

2021

20% of total data center electricity consumption

100%


Of course, data center operators will continue to seek ways to reduce impact, as well. 


Study

Year Published

Energy Efficiency Savings (%)

Methods Used

Lawrence Berkeley National Laboratory

2020

20-30%

Using more energy-efficient hardware, optimizing the use of data center resources, and using renewable energy sources

McKinsey & Company

2021

30-40%

Using more energy-efficient hardware, optimizing the use of data center resources, using renewable energy sources, and improving cooling efficiency

IDC

2022

40-50%

Using more energy-efficient hardware, optimizing the use of data center resources, using renewable energy sources, improving cooling efficiency, and deploying AI-powered energy management solutions


But there seems little doubt that AI model training and inference generation will become a much-bigger part of data center compute activities and therefore energy load. In some part, that is because bigger models require more data ingestion during the training process. 



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....