There are parallels of the railroad mania of the 1870s and the current AI mania. But that’s only half the story.
The differences between railroads and LLM / generative AI are significant.
AI is not permanent. It is constantly being obsoleted.
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.
While the cost reduction and efficiency benefits of depending on AI are as yet unclear, the costs of sorting “good AI” from “bad AI” are already mounting as real-world expenses.
Cedar Owl recently published a comprehensive overview of the Total Costs of Ownership of AI / Robotics and concluded they may exceed the costs of human employees.
Unlike railroads, whose cost-reduction benefits were immediate and measurable, the sum total of AI benefits is not just unclear but potentially negative.
The structural limits of AI are equally visible but the full consequences of these multi-factor limitations cannot yet be determined. A recent article in Scientific American summarized one key limitation: the illusion that AI is “thinking,” “understanding” and “reasoning”.
AI coding frontloads one form of productivity by backloading the entire system with higher maintenance costs down the line.
Perhaps most importantly, once we subtract the hype, there is no evidence-based answer to the question: will our society / the public benefit from AI?
Put another way: is AI actually solving the core problems undermining our society and economy, or is AI adding new problems--brain rot, dependence on black box systems owned by a handful of tech corporations, AI slop, deepfakes, and a tsunami of malicious AI?
AI Data Centers Are Not the Railroads of Today
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 t…
