Data centers may play a role in the AI era comparable to the one railroads played in industrial expansion: they make a powerful technology usable at scale. That is the argument Hans Tung, managing partner at Notable Capital, makes in a conversation reported by Allie Garfinkle in Fortune on October 13, 2026. The analogy is about infrastructure and reach, not proof that AI will reproduce the Industrial Revolution or deliver a particular geopolitical result.
Why compare AI infrastructure with ships and railroads?
The comparison starts with a distinction between inventing a technology and making it useful to large numbers of people. An invention can be powerful and still have limited practical effect if the physical systems needed to build, move, or operate it are missing. Tung’s argument, as Fortune reports it, is that infrastructure determines whether technology can be deployed and distributed at scale.
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He puts the point plainly: “The models that win are the ones that are available.” In the current AI context, availability depends not only on model design, but also on the computing capacity and supporting infrastructure that let organizations access and use models.
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What do steel steamships and railroads contribute to the analogy?
Steel ships: technology needs industrial capacity
Fortune’s historical example is Britain’s adoption of steel steamships. The feature reports that British shipyards produced 80% of the world’s ships in 1890. That figure is reported by Fortune; the underlying historical source is not identified in the available material, so it should be read as an attributed figure rather than an independently verified statistic.
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The point of the example is the combination of a new technology with the capacity to manufacture and deploy it. The ships matter in the analogy not just as an invention, but as products of an industrial system capable of putting them into service.
Railroads: infrastructure expands reach
Tung’s railroad comparison focuses on the movement of resources and the reach created by a network. He invokes the North’s railroad system during the U.S. Civil War as an example of infrastructure that helped move resources across distance. The parallel he draws is that compute capacity can help make AI models available to users and organizations.
These systems are not interchangeable: railroads moved people and physical goods, while data centers provide computing resources. The analogy is useful for asking whether infrastructure can deliver a technology broadly; it does not establish that AI infrastructure will produce the same economic or military effects as railroads did.
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What do the reported data-center figures say?
Fortune reported approximately 24 gigawatts of working data-center compute capacity in China and at least 47 gigawatts in the United States, described as conservative estimates. These are estimates reported in an October 13, 2026 article, not independently confirmed measurements in the material available here. Capacity changes quickly, and the figures depend on how “working” compute capacity is defined, so they should not be treated as a current, directly comparable census.
Even with those qualifications, the numbers illustrate the scale of infrastructure in the argument: access to AI depends on an underlying physical capacity that must be built and made operational. A headline capacity estimate alone, however, cannot show how effectively that capacity is used, who can access it, or what economic or strategic outcomes it produces.
How strong is the Industrial Revolution comparison?
The analogy is strongest when used as a framework for examining four questions:
- Scale: How much infrastructure is being built to support deployment?
- Delivery: Can it move or provide the resources needed to put the technology to work?
- Execution: Can the infrastructure be completed and operated effectively?
- Reach: Does it make the technology available beyond a narrow set of users?
Those questions help distinguish technical achievement from practical adoption. They also keep the comparison grounded: a large buildout may be necessary for broad access, but scale by itself does not demonstrate good execution, widespread availability, or a specific geopolitical advantage.
What does the GDP estimate mean?
Fortune also reported that a Columbia Business School professor estimated AI infrastructure could account for an average of 3.6% of U.S. GDP each year through 2032. The feature material available here does not identify the professor or the underlying estimate, and the figure has not been independently verified. It is therefore best understood as a reported projection, not an established measure of AI’s contribution to GDP.
The estimate points to the possible economic scale of investment in AI infrastructure, but it does not mean that AI itself will add 3.6% to GDP annually. The wording concerns infrastructure’s estimated share of GDP, and the basis for that estimate is not specified in the reported material.
What the analogy can—and cannot—show
Steel ships, railroads, and data centers all illustrate how enabling systems can shape the reach of a technology. Tung’s argument is that AI models’ practical importance will depend partly on whether the infrastructure to serve them is available at scale. That is a serious and useful lens for thinking about AI deployment.
It is not, by itself, evidence that any country will prevail in an AI contest, that more capacity automatically means better access, or that today’s buildout will match the Industrial Revolution in its effects. Those outcomes depend on more than infrastructure, and the historical comparison should be treated as an analogy that raises questions—not as a prediction.
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