Tuesday, September 3, 2024

What AI Market Structure Will Emerge?

In what sorts of markets does rapid market share growth really matter for the long term? In what markets is it possible to shift share positions once markets are mature? When should firms focus on specialties and niches? When is an aggressive growth strategy called for, and when is it ill advised?  


When and why should leaders shift to profitability rather than growth, in growth markets? When will business leaders reach the limits of their growth strategies and have to consider becoming asset sellers?


The "Rule of Three" is a concept in business strategy originally introduced by the Boston Consulting Group that might inform business leader decisions of these types. 


The rule suggests that in mature, competitive markets where there are barriers to entry; economies of scale or are capital intensive, three companies tend to dominate, with the largest player holding 40-50 percent of the market, the second-largest holding 20-30 percent, and the third-largest holding 10-20 percent market share.


The concept is useful for strategists and market researchers as it suggests reasonable strategy possibilities, such as whether it makes sense to undertake disruptive actions to gain share, and if so, what rational possibilities for gains could occur. 


The other corollary is that profitability also tends, in such markets, to correspond to market share. 


So the rule suggests the importance, in capital intensive industries, of taking advantage of barriers to entry; economies of scale and industry segment leadership, especially when young industries are emerging. 


Hence the importance of rapid growth and share gains in software, some types of hardware, connectivity services, commercial aircraft manufacturing, search or cloud computing,  for example. 


Such Rule of Three markets generally are not susceptible to disruptive attacks, once the pattern is set. If one assumes market share is roughly correlated with profitability, then the market leader will have twice the profitability of provider number two, which in turn will have twice the profitability of provider number three. 


A 40-20-10 pattern could hold, with the balance held by numerous other specialty firms. That advantage in profitability contributes to all other efforts to maintain market leadership on the part of the leader, and also limits the possible range of actions by providers two and three to compete. 


Pricing attacks might generally fail for the simple reason that the market leader can simply match any price reductions attempted by the smaller providers. A firm with 10-percent margins could easily see zero margin, which is not sustainable. And a firm with a 20-percent margin that is sliced in half could not easily sustain such outcomes for too long, much less a permanent halving of profit margin. 


That also tends to be true of attempting value attacks (bundling, for example), which often can be matched by the market leader. 


The Rule of Three does not apply to fragmented industries, though. Consider the U.S. fast food market, where brand matters; product segments are diverse; barriers to entry are quite low and economies of scale might not be decisive. 


Brand

Share

McDonald's

43.80%

Starbucks

9.60%

Chick-fil-A

8.60%

Taco Bell

6.60%

Wendy's

5.70%

Burger King

5.40%

Dunkin'

3.70%

Subway

3.40%

Domino's

3.20%

Chipotle

2.80%

Sonic Drive-In

2.40%

Pizza Hut

2.30%

KFC

2.10%

Panda Express

1.50%

Arby's

1.40%


And though lots of markets--consumer and business--are concentrated, many are not. Concentrated industries are more likely to resemble the Rule of Three pattern, while fragmented industries tend not to show the pattern. The exceptions are that some segments of fragmented industries might well show a quasi-Rule of Three pattern.  


Athletic footwear might provide one example of a Rule of Three pattern, even within a larger industry category (fashion and apparel) that might be fragmented. 


Market Type

Industry

Market Characteristics

Example Companies

Concentrated

Consumer




Soft Drinks

Dominated by a few large companies.

Coca-Cola, PepsiCo


Smartphones

High market share held by a few players.

Apple, Samsung


Athletic Footwear

Leading brands control most of the market.

Nike, Adidas


Credit Cards

Few major issuers dominate the market.

Visa, Mastercard


Fast Food

A small number of chains dominate.

McDonald's, Starbucks, Chick-fil-A

Concentrated

Business




Commercial Aircraft

Duopoly structure in many regions.

Boeing, Airbus


Operating Systems (PC)

Few companies dominate the market.

Microsoft, Apple


Cloud Computing

Concentrated among a few major players.

Amazon AWS, Microsoft Azure, Google Cloud


Professional Services (Big 4)

Dominated by a few global firms.

Deloitte, PwC, EY, KPMG


Search Engines

One company holds a massive market share.

Google

Fragmented

Consumer




Restaurants

Highly localized, many small competitors.

Local and regional chains


Fashion and Apparel

Wide range of brands, trends, and niches.

Zara, H&M, many independent brands


Home Improvement

Multiple large and small players.

Home Depot, Lowe's, Ace Hardware


Organic Food

Numerous small and regional producers.

Whole Foods, Trader Joe's, local farms


Craft Beer

Many small, independent breweries.

Local craft breweries

Fragmented

Business




Marketing Agencies

Numerous small firms, specialized services.

Local and regional agencies


Construction

Many small to medium-sized firms.

Local contractors, regional builders


Logistics

Highly fragmented with many local operators.

Local trucking and shipping companies


Real Estate

Numerous small firms, localized markets.

Local agencies and independent agents


Consulting

Many small and specialized firms.

Local consultants, boutique firms


The U.S. home broadband market provides another example. Looking at all providers, the Rule of Three does not seem to hold. But within the category, looking only at legacy telcos, the pattern does seem to hold. AT&T has 15- to 18-percent share; Verizon seven to 10; Lumen two to four. 


The caveat is that AT&T, Verizon and Lumen do not actually compete head to head in most markets. In fact, market share corresponds to homes within the respective provider service territories. In contrast, where AT&T, Verizon and T-Mobile compete head to head across virtually all markets, shares are roughly equal. 


So even if mobile service is highly capital intensive, mature, with high barriers to entry, there seem to be offsetting factors, even when brand preference might be relatively stable. At some level, the regulatory context might prevent any of the providers from amassing too much more share. And most observers would likely agree that offers are highly competitive. 


ISP

Share

Comcast (Xfinity)

27-30%

Charter Communications (Spectrum)

23-26%

AT&T

15-18%

Verizon (Fios)

7-10%

Cox Communications

6-8%

Altice USA (Optimum, Suddenlink)

4-5%

CenturyLink (Lumen Technologies)

2-4%

Frontier Communications

1-2%

Mediacom

1-2%

Windstream

1-2%


Likewise, the Rule of Three seems to apply in the U.S. cloud computing “as a service” industry. Some will point to Microsoft’s share as deviating from the expected pattern. But real world markets often do not perfectly match what theory tells us to expect. 


Also, Microsoft’s revenue in the “intelligent cloud” segment historically has included productivity software, for example. But Microsoft has gradually been realigning revenue reporting to better reflect performance of the “cloud computing as a service” activities that compete head to head with Amazon Web Services and Google Cloud. 


The point is that Microsoft cloud computing revenue has for some time not been an easy “like to like” comparison with AWS or Google Cloud “computing as a service” revenues, as Microsoft once included other revenues, such as game platforms, within intelligent cloud.


Company

Market Share

Amazon Web Services

32.00%

Microsoft Azure

22.00%

Google Cloud Platform

10.00%

IBM Cloud

6.00%

Oracle Cloud

4.00%

Salesforce

3.00%

Alibaba Cloud

2.00%

Other Providers

21.00%


On the other hand, Microsoft, in removing productivity software subscription revenue from intelligent cloud, has added advertising revenues to the intelligent cloud category. 


The upshot is that there should be a temporary resetting of Azure market share, in a downward direction. 


The Rule of Three might be relevant early in a concentrated industry’s emergence as well as once the market share pattern is established, as it suggests disruption will be highly unlikely. 


The rule will be less useful--or break down--under some circumstances, such as when a major technology disruption threatens the legacy business model; when governments decide to regulate or deregulate an established industry; when some innovation enables non-traditional suppliers to enter a market or when consumer preferences change significantly. 


Economic downturns, new business models, supply or distribution chain disruptions or cultural or societal shifts could, in principle, be disruptive to established industries. Auto manufacturing might provide an example, as consumer shifts in preference for higher-mileage vehicles; sport utility vehicles rather than sedans; trucks rather than passenger vehicles; or hybrid and electric vehicle demand occur. 


As the artificial intelligence market grows, business leader strategies might well turn on expectations about whether the Rule of Three actually applies, as if so, where in the business.


Monday, September 2, 2024

When "Less" Personalization is a Good Thing

Recommendation and personalization algorithms almost always use a user’s past behavior as a guide to predicting content. That is very useful--and creates ad efficiency--for sellers of specific products and services purchased by specific consumers. 


But it is not a completely beneficial practice, in some instances.


When algorithms leverage user data, such as search history, clicks, purchases, and interactions or dwell time with content, to tailor results and recommendations, they also create echo chambers and filter bubbles for content related to ideas, news and information useful for citizens (not as consumers). 


That might not be an issue for advertisers selling niche, specialty or inherently-targeted products, or the users who have those interests. Suppliers selling products for surfers--and surfers--might not care at all about echo chambers or filter bubbles. 


The issues are more acute for news and information related to citizens rather than consumers. In such cases, past behavior can mean that  users are exposed to a limited range of information that aligns with their existing beliefs and preferences. And we can argue that this is generally unhelpful for civic life. 


So filter bubbles and echo chambers arguably are not much of an issue for advertisers. The same cannot be said for news and information providers whose products supposedly are designed to inform the public; deal with truth; and do so in fair and balanced ways. 


Study Name

Date

Publishing Venue

Key Conclusions

"The Filter Bubble: What the Internet Is Doing to Your Brain"

2011

Farrar, Straus and Giroux

Argues that online algorithms can create personalized filter bubbles, limiting users' exposure to diverse information.

"The Effect of Algorithmic Personalization on Political Polarization"

2018

Proceedings of the ACM on Web Science

Finds that algorithmic personalization can exacerbate political polarization by exposing users to content that reinforces their existing beliefs.

"Algorithmic Fairness in Recommender Systems"

2019

IEEE Transactions on Knowledge and Data Engineering

Examines the potential for bias in recommender systems and proposes techniques to mitigate bias.

"The Impact of Algorithmic News Personalization on Political Polarization"

2020

Proceedings of the ACM on Human-Computer Interaction

Investigates how algorithmic news personalization can affect political polarization and engagement.

Through the Newsfeed Glass: Rethinking Filter Bubbles and Echo Chambers

2022

NCBI

Most empirical research found little evidence of algorithmically generated informational seclusion. People online engage with information opposing their beliefs.

What are Filter Bubbles and Digital Echo Chambers?

2022

Heinrich Böll Foundation

The role of algorithmic curation in creating bias is limited. User vulnerability to lack of diverse content depends more on motivation and broader information environment.

Understanding Echo Chambers and Filter Bubbles: The Impact of Social Media on Diversification and Partisan Shifts in News Consumption

2020

MIS Quarterly

Increased Facebook use was associated with increased information source diversity and a shift toward more partisan sites in news consumption.

A scientific study from Wharton on personalized recommendations


Wikipedia

Found that personalized filters can create commonality, not fragmentation, in online music taste.

Through the Newsfeed Glass: Rethinking Filter Bubbles and Echo Chambers

2022

NCBI

Most empirical research found little evidence of algorithmically generated informational seclusion. People online engage with information opposing their beliefs.

"The Filter Bubble: What the Internet is Hiding from You"

2011

Book by Eli Pariser

Personalization algorithms can isolate individuals from diverse perspectives, reinforcing their pre-existing beliefs and creating a "filter bubble."

"How Algorithms Create and Prevent Filter Bubbles: A Theory of Refracted Selective Exposure"

2015

Journal of Communication

Algorithms can both reinforce and mitigate filter bubbles. The extent to which they do depends on the design of the algorithm and users' existing preferences.

"Breaking the Echo Chamber: Mitigating Selective Exposure to Extreme Content"

2017

Proceedings of the ACM

Echo chambers can be mitigated by introducing diverse content in algorithmic recommendations, though this depends on user engagement with such content.

"Exposure to Ideologically Diverse News and Opinion on Facebook"

2015

Science

Personalization on Facebook does expose users to some ideologically diverse content, but the overall effect is that users tend to see more content that aligns with their pre-existing views.

"Algorithmic Accountability: A Primer"

2016

Data & Society Research Inst.

Algorithms often lack transparency, which makes it difficult to address issues like filter bubbles. Greater accountability and transparency are needed to ensure diverse content exposure.

"Echo Chambers on Facebook"

2016

PLoS ONE

Users on Facebook are likely to be exposed to content that aligns with their own views, leading to the formation of echo chambers. The network structure and algorithmic sorting contribute.

"Polarization and the Use of Technology in Political Campaigns"

2018

Political Communication

Political campaigns' use of personalization algorithms can exacerbate polarization by targeting individuals with content that reinforces their existing political beliefs.

"Online Echo Chambers and the Effects of Selective Exposure to Ideological News"

2017

Public Opinion Quarterly

Selective exposure to ideological news through personalized algorithms can deepen echo chambers, leading to more polarized opinions among users.

"The Role of Personalization in Political Polarization"

2019

Digital Journalism

Personalization in news feeds can contribute to political polarization by filtering out dissenting viewpoints and reinforcing users' existing beliefs.

"Algorithmic Personalization and the Filter Bubble: A Literature Review"

2020

Internet Policy Review

A review of existing studies that suggests while filter bubbles exist, their impact is variable and depends on individual behavior, platform design, and other factors.


What is not so clear is how algorithms can be redesigned to counteract such issues. In principle, algorithms might be deliberately designed not to respond so directly to user behavior, perhaps by increasing “serendipity” into recommended content (recommending content that is unrelated to a user's typical preferences). 


That might work better for social media or other news content than e-commerce; worse in the legal or medical domain; arguably better for food, travel, hospitality recommendations. Serendipitous content might help or might not, for advertisers. 


When the objective is the largest-possible audience, it might not matter what the specific content happens to be. If the objective is to reach a defined buying public, content will matter more. 


And perhaps some elements of the traditional journalistic profession’s emphasis on fairness and balance could help as well, such as the necessity of “showing both sides” or multiple viewpoints and using multiple sources. 


It might also be possible to enhance transparency and provide some measures of user control. For example, it might be possible to give users more control over their recommendations, such as the ability to opt out of personalized content or request alternative viewpoints.


In  some cases it might be possible to use a broader contextual approach, such as embracing the broader context of user queries and recommendations and avoiding overly-narrow personalization. 


Of course, these sorts of techniques may run counter to the targeting features that have driven advertisers to highly-personalized content and venues. What made personalized content and venues so compelling for advertisers was the belief that they provided a more-efficient way to reach likely buyers of any product. 


To the extent that less reliance on past behavior influences content presentation, it might also reduce the “personalization” that advertisers prefer. 


But that is less an issue--if an issue at all--for advertisers selling products and services. The problems are centered on news and information deemed important for people as citizens, not consumers.


Saturday, August 31, 2024

Who Needs to Invest Heavy in AI Right Now, Who Doesn't?

Perhaps the best advice for most enterprises, at the moment, is to be cautious about investment in generative artificial intelligence and to avoid all investment in artificial general intelligence.


But despite analyst and investor worries, some firms must invest heavily, right now. If a firm hopes to be a leader in the generative AI model business, it has to invest heavily, right now. If a firm hopes to be a leader in the “generative AI as a service” business, it likewise has to invest heavily, right now. 


For a few firms that hope to lead in the future AGI business, it has to invest heavily, right now. For all three types of efforts, “return on investment” in immediate financial results is not expected. Instead, the investments are strategic, aimed at creating leading positions in new businesses and markets. 


Entity

Capex Magnitude

Timing

Large Language Model Developers

High

Early

AI-as-a-Service Providers

High

Early

Future AGI Firms

Very High

Early

End-User Firms

Moderate to High

Later


Such strategic investments always are criticized, and yes, there is danger of overinvestment. Few recall it now, but Verizon faced huge skepticism about its at-scale shift to “fiber to home” for fixed network access. 


As positive as Verizon leaders were about future new revenue streams and operating cost reductions, a few observers might have been privately willing to say that the real upside was simply “you get to keep your business.” In other words, Verizon and others viewed FTTH as the necessary precondition for remaining in business as leading connectivity service providers. 


Financial analysts worried about FTTH for reasons similar to today’s concern about AI infrastructure investments: the potential revenue upside remains uncertain and the hit to earnings and profit margins is real. 


From about 2005 to 2011, when Verizon put into place most of its FiOS FTTH network, it seems to have spent about $23 billion. But some might point out that Verizon's construction budgets showed no significant increase during the FiOS rollout period (2005-2011) compared to the previous years (2000-2004).


In fact, construction spending as a percentage of wireline revenues decreased from 22.2 percent in 2000-2004 to 19.7 percent in 2005-2011. So a significant portion of the build was financed from the existing capital budget, by shifting spending on the copper network to the new FTTH network. 


That noted, capex did increase. By 2006, if the average capital expenditure to pass a home with fiber was $850, and Verizon is correct in estimating that its FiOS program cost about $23 billion, that also implies passing about 27 million homes. The cost to connect a customer might have ranged from $930 in 2006 to $650 by 2010. 


Revenue upside appears to have been relatively modest initially, as gains provided by subscription TV and internet access revenues were balanced by losses of voice customers. 


The bigger change was the rise of mobility as the source of a majority of Verizon’s revenues. In 2005 mobile services contributed more than 40 percent of total Verizon revenues. Today, mobility is the majority driver of Verizon revenue, and arguably the driver of total revenue growth and profits. 


The point is that AI investments by some firms are strategic and existential, believed related to ultimate survival and growth, and less driven by expectations of immediate revenue growth, as was arguably true of FTTH investments by Verizon. 


Some say Larry Page, Google cofounder, is now saying "I am willing to go bankrupt rather than lose this race." That’s an example of the view of AI as strategic, not tactical, for firms who believe they must become leaders in AI models and platforms. 



Sundar Pichai, Alphabet/Google CEO has argued AI will be more important than fire or electricity or even the internet.


"I've always thought of A.I. as the most profound technology humanity is working on: more profound than fire or electricity or anything that we've done in the past,” Pichai has said. And most leaders of technology firms seem to agree.  


Andy Jassy, Amazon CEO, likewise believes AI will "be in virtually every application that you touch and every business process that happens."


On the other hand, most end user firms will want to be more deliberate in their deployment of AI.


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