What History Suggests about Generative AI Markets

Lots of people now are required to make estimates of the size of the generative artificial inatelligence and other AI market s, if only to analyze the value of companies that should be affected, for better or worse.

One might not believe history is very useful for market forecasting exercises, but I’ve always found history a form of data-driven analysis. Past patterns often exist and can be used to establish a range of possible outcomes in various industries. 

For example, past general-purpose technologies often have initially favored suppliers of infrastructure. Think Nvidia, graphics processors or memory, for example.

Internet accerss providers and data transport companies were early beneficiaries of the internet. Railroad and electrical generation and transmissions firms were early winners for the railroad and electricity GPTs.

Beyond, that, once activity spreads to industries that can take advantage of the GPT infrastructure, some industries historically grow fast; some slowly. Some industries are highly-concentrated; others less so.

So one early assumption is that any industry (young or old; physical or virtual products) has to be categorized as akin to others: fast-growing or slow-growing; susceptible to fragmentation or not. 

Then one can examine historical adoption rates for various types of business and consumer products, to get an idea of possible faster or slower adoption (growth) rates. That tends to establish a reasonable upper and lower bound for potential growth patterns. 

In the early days of telecom deregulation in the United States (in the wake of the 1986 Bell system breakup; followed by the Telecommunications Act of 1996, competition and fragmentation momentarily reigned, but rather quickly resulted in high concentration again. 

Many software-driven industries start out highly fragmented but consolidate into moderately- to highly-concentrated structures, based on market shares. And, sometimes, high concentration, where markets are led by three or so leaders (share), also coexists with a fair amount of fragmentation among small firms serving niches. 

Industry	Concentration Level	Notable Characteristics
Search Engines	Very High	Google dominates with over 90% market share
Commercial Aircraft	High	Duopoly between Airbus (>50%) and Boeing
Automobiles	Moderate to High	Concentrated but with weak pricing power due to competition
Telecom	High	Oligopoly is the rule
Oil Refining	Moderate to High	Capital intensive, high barriers to entry/exit
Software	Moderate to High	Some software segments have a few dominant players, while others are more fragmented. Generative AI is moderately concentrated, but almost certainly will become highly concentrated over time.
Chip Industry	Moderate	Oligopoly within segments; somewhat fragmented across the full industry
Content	Moderate to High	High concentration for video streaming; studios; TV broadcasting, newspapers. Less concentrated in support services, radio broadcasting, online media, “magazine” content
Biotechnology	Moderate	Top 4 firms hold 84% market share (oligopoly)
Retail	Low to Moderate	Many players, but some large chains dominate certain segments
Restaurants	Low	Fragmented market with many local and chain options
Professional Services	Low	Legal, accounting, engineering and other services typically are highly fragmented
Agriculture	Low	Numerous small farms and producers
Retail	Low	Fragmented generally, but more concentrated in segments such as grocery

Think about mobile service, where a few U.S. firms hold as much as 97 percent share, while dozens of firms make up the remaining three percent to six percent of accounts or revenue, for example. Three firms control about 95 percent of the branded account volume. Mobile Virtual Network providers hold perhaps five percent share, but that must be qualified since the larger MVNOs are owned by the top-three mobile operators. 

Mobile Service Provider	Estimated Market Share (%)
Verizon	~35
AT&T	~35
T-Mobile	~25
MVNOs (Mobile Virtual Network Operators)	~5

For example, it is estimated that U.S. MVNOs book about $13.7 billion in annual revenue. Assume an average account revenue of $300 per year ($25 a month). But assume about half those accounts are offered by MVNOs owned by the big three providers. 

That implies the independent, non-affiliated MVNOs book about $6.8 billion annually, representing about 22.8 million accounts. Against a total market of 372.7 million accounts, that suggests a share of about six percent for independent providers. 

So, yes, the U.S. mobile services market is highly concentrated, but also features a fragmented independent MVNO pattern as well. 

As a practical matter, for analysts of market dynamics in the mobile service provider space, that means paying attention to the three firms holding perhaps 94 percent to 95 percent share, on the clear assumption that the overall market is driven by the leaders. On the other hand, even when the market is not driven by the MVNOs, many still exist. 

Roughly the same dynamics happen in the U.S. home broadband market, again driven by a handful of firms, but with a growing number of small independent providers. Just two service providers claim 55 percent market share, according to Leichtman Research Group. 

source: Leichtman Research Group

The point is that even when total market dynamics are dictated by the few leading firms, there also can exist a fragmented set of small providers coexisting with the leaders. Analytically, one can understand market dynamics by understanding outcomes of a relatively few firms with scale, even when a fragmented base of contenders also operate. 

In other words, studying the dynamics of the leaders (Amazon, Walmart and a few others) tells us most of what the market is doing, even when a huge fragmented market of retailers also operates.

For analytical purposes, past retail behavior (history) are good starting points for future projections about markets, even when restricting the analysis to just a few firms. So, yes, history can be a useful tool for predicting future developments.