If OpenAI and Anthropic drive 48 percent of Google Cloud revenue in 2027, is that a problem?
Some might say “yes,” to the degree that customer concentration is generally viewed as a problem.
Others might say “maybe,” if customer concentration or solvency danger is not ameliorated.
But the history of computing might suggest “no” is a possible answer, as there are parallels in mainframes, supercomputers, military computing, and even early semiconductors.
The closest historical analogy is probably the mainframe/supercomputer market of the 1960s to 1980s.
The current AI infrastructure boom therefore looks less like the PC industry and more like an earlier era when a computer system could be a multimillion-dollar capital project.
The interesting question is not simply "Are there few buyers?" There are. It is why there are few buyers and what dimensions of concentration matter.
In the 1950s and 1960s, computers were so expensive that the customer base was intrinsically concentrated.
IBM's 1401, introduced in 1959, in the mid-1960s, represented more than half of the world's computers. The IBM System/360 arguably deepened the pattern, as a relatively small number of customers represented a very large percentage of industry revenue.
Early on, government was an extraordinarily important buyer of such computers, for example.
There also are parallels in the semiconductor industry, where military and aerospace customers were key.
NASA's Apollo program, for example, purchased roughly 60 percent of U.S. integrated-circuit output in the early 1960s.
And the Minuteman missile program subsequently became an even larger individual consumer of ICs.
Of course, skeptics will argue that was different as the U.S. federal government was in no danger of defaulting, where OpenAI or Anthropic are not immune from that outcome.
In the context of demand for high-performance computing services, though, optimists might argue we must separate the small number of direct buyers from the much-larger end-user demand.
The number of organizations using HPC can be much larger than the number actually buying the infrastructure, for example, even if a few buyers stand out.
So today's AI compute services market resembles other industries where direct buyers are few, such as:
aircraft;
electric utilities;
telecommunications infrastructure;
semiconductor fabs;
power generation.
The closest non-computing analogy may actually be aircraft, where large commercial aircraft are sold to a relatively small number of airlines.
In that sense, the small number of passenger airline providers represents an aggregation of relatively large and dispersed demand.
That arguably resembles Nvidia selling to a few hyperscalers whose customers are highly concentrated at the moment, but also representing lots of dispersed enterprise demand.
There might also be similarities to telecommunications or power generation, where a concentrated buyer base is not necessarily evidence of a small market.
In essence, there is a two-level demand structure, with a few key buyers (frontier language model suppliers) supporting a relatively small number of large HPC suppliers, which in turn support end-user demand that is highly distributed.
And, of course, optimists say the market will broaden over time.
The point is that customer concentration in computing is not unusual, especially at early stages of deployment.
Aside from the resemblance to mainframe, minicomputer, integrated circuit precedents, HPC might also suggest parallels to railroads, electric utilities and telephone service industries, where a small number of infrastructure buyers also has been key.
Are there risks? Yes. But are the risks also manageable over time and structurally consistent with other capital-intensive industries? Perhaps also yes.
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