AI Data Centers Are Not the Railroads of Today

The AI boom shares all the risk profiles of previous speculative manias but lacks society-wide benefits while generating fast-metastasizing negative consequences and costs.

The idea that the current bubble in AI data centers is an echo of the railroad-construction bubble of the 1870s is appealing–but only half-right. The completion of the first transcontinental railroad in late 1869 sparked a speculative mania of raising capital to build railroads, which were seen as “can’t lose” investments in a technology that lowered transport costs from $1 to ten cents.

But not all routes had the potential to become profitable, and the resulting collapse of the railroad bubble devastated the developed-world economies, triggering a deep economic downturn from 1873 to 1879 that was called “The Great Depression” at the time (or “The Long Depression”). Knee Strengthening and... Oz, Ottie Check Amazon for Pricing.

The term for speculative frenzies channeling vast sums into investments with difficult-to-assess risk profiles is mal-investment, and mal-investment on a large scale triggers financial panics and economic depressions in a well-understood feedback loop.

Money invested in digging a mine that doesn’t yield any gold can’t be recovered. That capital is gone. There is an opportunity cost to every investment: that capital could have been invested in something else that was more productive than the speculative bet on something with unclear risks and payback.

As the scale of losses become apparent, credit tightens and the pool of capital available shrinks. Short-term loans that can’t be rolled over into longer duration loans trigger bankruptcies which quickly lead to bank runs (financial panics) and layoffs as businesses close. This decline in wages, revenues and the velocity of money is self-reinforcing, and the recovery process–being both financial and psychological–takes years.

The parallels with the AI speculative investment mania are obvious. Just as any railroad was viewed as guaranteed to be immensely profitable because railroads generated enormous efficiencies that reduced costs, all AI is guaranteed to be immensely profitable because AI generates enormous efficiencies that reduced costs. But in the real world, use cases for specific railroads and AI applications are stretched along a spectrum which isn’t visible in the early stages of a speculative boom.

Individual use cases don’t automatically guarantee an entire class of use cases will be successful. That one railroad–or application of AI–profitably reduced costs does not necessarily extend to all railroads or AI applications. JFK and RFK’s Se... Sterling, Rick Check Amazon for Pricing.

Nobody wants to wait around for the long process of sorting which use cases are actually beneficial and which are mal-investments, as the big money is made by making big bets in the early days. Human greed is a remarkable force, especially when combined with self-serving hype and the euphoria of the herd running.

In the current confluence of greed, hype and euphoria, the possibility that the inevitable aftermath of vast mal-investment is a Great Depression doesn’t exactly resonate. AI isn’t a railroad, it’s the most amazing force in the Universe, etc. This is Wetware 1.0 in action: the psychology of speculative frenzies doesn’t change, and so here we are–again.

Those are the parallels of the railroad mania of the 1870s and the current AI mania. But that’s only half the story. Railroads did dramatically lower costs, turning unprofitable ventures into profitable ventures not by reducing production costs but by reducing transport costs, which prior to railroads might equal production costs.

The differences between railroads and LLM / generative AI are significant. While many railroads went bankrupt when the bubble burst, those that actually served expanding markets were eventually put to use as the tracks were still useful many years after being laid. A new locomotive type might enter service decades later, but the tracks remained useful and valuable for decades–with proper maintenance. The rails were not obsoleted every few years, nor did the the entire rail lines have to be replaced every few years. OK TAPE Self Adherent ... Check Amazon for Pricing.

AI is not permanent. It is constantly being obsoleted. A new class of lower-power consumption chips could obsolete the current class of AI chips, requiring a mass replacement of the entire processing foundation of AI. Innovations in software could reduce the processing demands, turning existing data centers into expenses rather than profit generators. AI software that users download onto their own computers negates the need for “renting” data centers (i.e. buying processing power with tokens) by generating models from the user’s own data. These are just a few potential forces undermining the utility, lifespan and profitability of the current build-out of data centers.

While the cost structure of railroads were relatively straightforward, the costs of AI are complex and difficult to assess as initial costs are not total ownership costs, as maintenance expenses are still unfolding and future costs of resources and energy are trending higher.

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