Friday, September 8, 2023

Will Hyperscaler Antitrust Really Enable Rise of New Dominant Competitors?

Some observers believe breaking up Google, Meta, Amazon would spur a new wave of upstart competitors. Others believe innovation might not be so robust, as the necessity of hyperscale app provider acquisitions of startups might be reduced. 


On the one hand, antitrust actions could break up the dominant technology leaders, possibly allowing space for smaller companies to develop new products and services. 


On the other hand, antitrust actions could also have negative effects on innovation. Additionally, antitrust actions could discourage investment in new technologies by both today’s giants and venture-backed firms. 


Hyperscalers might be less willing to invest if they expect further antitrust actions. Also, venture capital firms might be less willing to invest in startups if the “exit” formerly available--selling to one of the hyperscalers--is essentially foreclosed. 


The analogy of Google's emergence during the Microsoft antitrust case is considered a relevant precedent by some. In that view, the Microsoft antitrust case helped Google, as the case forced Microsoft to focus on its core businesses, which opened up opportunities for Google to enter the market.


Some might argue other possibilities exist. Antitrust action against IBM does not seem to have produced a big new wave of new competitors. The breakup of the AT&T system also arguably did not produce all that much innovation. 


Some might argue it was the creation of the internet, with its permissionless development model and “death of distance” impact, that enabled the rise of today’s hyperscalers. Antitrust action might have hobbled the targets, it is true. But the opportunities created by the internet to craft new products and business models would have developed in any case. 


Would Microsoft have created the search engine, social media, e-commerce or streaming video services? Would IBM have done so? 


The internet created a new platform for innovation by creating new opportunities for scale, revenue models, enabled by the “permissionless” entry model and dramatically different ways of generating revenue. Would Microsoft or IBM have conceived of advertising as the revenue model for technology services and products?


Would either firm have pioneered e-commerce as the revenue model for technology products?


Would either have invested in consumer services rather than business customers? Would either firm have moved so aggressively to monetize user-generated content? Would retailing have seemed a function ripe for disruption? Would either have created cloud computing? 


We might ask similar questions about whether the Telecommunications Act of 1996, which enabled full competition for local access services, “succeeded or failed.” Some might argue it was the emergence of the internet that disrupted markets and created new avenues for growth, not so much the shift to competitive local access. 


In fact, one might argue that antitrust actions had no impact on other developments that spurred innovation. 


The cost of information technology has been a major barrier to entry for startup software companies. But cloud computing sharply reduced those barriers. A study by McKinsey & Company found that the average startup software company can save an order of magnitude on capex for computing support by using cloud computing. 


The lower costs of cloud computing have made it possible for more startup software companies to enter the market, and had virtually nothing to do with the impact of antitrust action. 


So perhaps we can make an analogy. Policymakers wanted the Telecommunications Act of 1996 because it would bring competition to “telecom services.” That happened just as the internet was emerging as the (arguably) key driver of innovation in computing and communications. 


Likewise, antitrust efforts are predicated on the notion that hyperscale firm breakups will spur innovation. Perhaps that misses the point. Perhaps, as was the case around the turn of the century, innovation will be propelled by forces other than antitrust action. 


Perhaps the next generation of leaders in computing and apps will be produced not by regulatory action but by new technological possibilities, whether that is artificial intelligence or blockchain or cryptocurrency or something else we have yet to identify. 


"Doom Loops" and Legacy Product Declines

It is not always easy to explain why some ideas and terms emerge at specific times in history. But some terms, including the “doom loop,” have emerged before. A doom loop is a self-reinforcing cycle of negative events that can lead to a catastrophic outcome. 


For long-time observers of the cable TV business, perhaps that phrase is current because a major cable operator now believes “the video product is no longer a key driver of financial performance.” 


source: Charter Communications 


That is a profound change for an industry known as “cable TV.” 


The “doom loop” is fundamentally caused by what Charter Communications considers an unsustainable video model.


We are familiar with the notion of the “vicious cycle,”  a situation in which one bad event leads to another bad event, which then leads to even more bad events. Then there is the phrase “death spiral,” referring to  a situation in which a company or organization is caught in a negative feedback loop, itself another phrase expressing a similar idea. 


In the environmental area, the 18th century Malthusian trap argued that population growth will eventually outstrip the carrying capacity of the environment, leading to widespread poverty and starvation. 


In recent decades we have heard about “the tragedy of the commons,” where individuals acting in their own self-interest deplete a shared resource. 


These days, we are apt to hear the term applied to the declining linear video subscription business. But we also have seen similar ideas expressed form time to time about specific companies in the telecom or connectivity business. 

source: NextTV, MoffatNathanson


One example of a doom loop is the Greek debt crisis. In 2010, Greece's government debt was too high and the country was unable to pay its debts. This led to a loss of confidence in the Greek economy, which caused the value of the Greek currency to plummet. This, in turn, made it even more difficult for Greece to pay its debts, and the cycle continued.


Another example of a doom loop is the 2008 financial crisis. The crisis began when the housing market in the United States collapsed. This led to a loss of confidence in the financial system, which caused banks to become more cautious about lending money. This, in turn, made it more difficult for businesses to get loans, which led to a decline in economic activity.


In the telecom industry, a doom loop can occur when a company's financial problems lead to service cuts, which in turn lead to customer losses, which further worsen the company's financial problems. This can create a vicious cycle that is difficult to break.


One example of a doom loop in the telecom industry is the case of Sprint. In the early 2000s, Sprint was one of the leading wireless carriers in the United States. However, the company began to struggle financially. 


In an attempt to save money, Sprint began to cut back on its network investment and service offerings. This led to customer losses, which further worsened the company's financial problems. Sprint eventually filed for bankruptcy in 2012.


Other leading firms also have experienced doom loops. MCI once was a leading provider of long distance services in the U.S. market, second only to AT&T. But MCI began to suffer as its shrinking long distance business was not offset by growth in local access services. MCI eventually was absorbed by Worldcom, which itself collapsed. 


AT&T faced the same problem, more than once. Its declining long distance business could not be countered by new revenues in local services. Eventually, after spinning off mobile, equipment manufacturing and Bell Labs assets, AT&T was acquired by SBC, which then rebranded itself as AT&T. 


Of course, a doom loop is not necessarily fatal for the company or industry in the loop. 


AT&T has a history of getting caught in doom loops. In the early 2000s, for example, the company acquired several large cable companies in an attempt to become a one-stop shop for telecommunications services and solving its local access business problem. 


However, the acquisitions were expensive and led to a significant increase in AT&T's debt. As a result, the company was forced to cut costs and lay off employees, which further damaged its reputation. In 2005, AT&T spun off its cable business.


Then AT&T decided to reimagine itself along the lines of Comcast, and acquired DirecTV and Time Warner assets. That required taking on so much debt that eventually AT&T had to sell off those assets to pay down debt. 


It perhaps goes without saying that such terms as “doom loop” only arise in connection with legacy businesses that are declining. 


By definition, growing businesses are in positive feedback loops; virtuous cycles or experiencing scale or network benefits. So you will not hear anyone applying such terms as “doom loops” to artificial intelligence.


Thursday, September 7, 2023

WAN and Cross Connects Often are Functional Substitutes

One reason the data center and colocation functions have become intertwined with the wide area connections business is that each is a potential substitute for the other. In other words, domains can be connected locally, within a building or between buildings at distance. 


Generally speaking, collocation by cross connect makes sense for larger domains with vast connection requirements, while WAN connectivity makes sense for smaller domains with fewer connectivity nodes. 


With the rise of cloud computing and ecosystems, much spending has shifted from WAN connections to colocation. 


Study

Year

Publication Venue

Enterprise Spending on Colocation $US

Enterprise Spending on WAN Services $US

Uptime Institute

2022

Data Center Industry Report

120 billion

100 billion

IDC

2021

Worldwide Quarterly Data Center Tracker

110 billion

90 billion

Gartner

2020

Market Guide for Colocation and Interconnection Services

100 billion

80 billion

IDC

2023

"Worldwide Quarterly IT Spending Tracker"

1.7 million each

1.5 million each

Gartner

2022

"Market Guide for Data Center Colocation"

1.8 million each


1.6 million each


Cisco

2021

"Cisco Global Cloud Index"

1.9 million each

1.7 million each

How Big Will Data Center AI Be in 2040?

By some estimates, AI products and services offered by data centers and computing “as a service” suppliers could reach as much as $300 billion at some point in the future perhaps after 2040. Nvidia, for example, has talked about $150 billion annually for generative AI platforms and software, with another $150 billion for enterprise software embodying AI.  


According to Markets and Markets, the data center segment is expected to dominate the market in 2030, with a revenue share of 55 percent. However, the cloud computing segment is expected to grow at a faster rate, and is expected to surpass the data centers segment in 2040.


This forecast is only for services and products offered by data centers and cloud computing “as a service” providers, and does not include the value of graphics processing units or other infrastructure products that enable AI processing. 


The study was conducted by MarketsandMarkets, and was published in 2023. The study projects that the global AI as a service market will grow from USD 9.3 billion in 2023 to USD 55.0 billion by 2028, at a CAGR of 42.6 percent.


Product or Service Type

2030 Revenue (USD Billions)

2040 Revenue (USD Billions)

Study

Date of Publication

Publication Venue

Machine Learning

100

200

MarketsandMarkets: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-ai-as-a-service-market-121842268.html

2023

MarketsandMarkets: https://www.marketsandmarkets.com/

Natural Language Processing

50

100

MarketsandMarkets: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-ai-as-a-service-market-121842268.html

2023

MarketsandMarkets: https://www.marketsandmarkets.com/

Computer Vision

30

60

MarketsandMarkets: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-ai-as-a-service-market-121842268.html

2023

MarketsandMarkets: https://www.marketsandmarkets.com/

Robotics

20

40

MarketsandMarkets: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-ai-as-a-service-market-121842268.html

2023

MarketsandMarkets: https://www.marketsandmarkets.com/

Other

10

20

MarketsandMarkets: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-ai-as-a-service-market-121842268.html

2023

MarketsandMarkets: https://www.marketsandmarkets.com/

Total

210

320

MarketsandMarkets: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-ai-as-a-service-market-121842268.html

2023

MarketsandMarkets: https://www.marketsandmarkets.com/

Machine Learning

100

200

IDC

2023

"Worldwide Artificial Intelligence Spending Guide"

Natural Language Processing

50

100

Gartner

2023

"Magic Quadrant for Natural Language Processing Platforms"

Computer Vision

30

60

IDC

2023

"Worldwide Artificial Intelligence Spending Guide"

Robotics

20

40

Gartner

2023

"Magic Quadrant for Robotic Process Automation Platforms"

Other

10

20

IDC

2023

"Worldwide Artificial Intelligence Spending Guide"


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