The ongoing AI buildout has similarities with the railroad expansion of the 20th century. Both are capital intensive undertakings with the potential to reshape the entire economy. Just as railroads transformed how we navigate physical space, AI is poised to transform how we navigate the information space.1 It’s obvious that railroads were useful and AI is no different.
During the railway boom, railroads proliferated amid intense competition. Overcapacity was common, some companies went bankrupt, and the industry took years to consolidate. Eventually, railroads became commoditized.
The same dynamics may play out with AI. Semiconductors and datacenters are the tracks and rolling stock. AI applications are the railway companies operating the lines. The coming years will reveal which segments of the AI ecosystem are truly profitable.
At the peak of the railroad era, rail companies accounted for roughly 60 percent of market capitalization. Today, AI makes up about 30 percent of the stock market. Such valuations are only justifiable if AI adoption becomes widespread. For semiconductors and datacenters, this means continuing infrastructure buildout. For AI applications, this means acquiring enough users to finance that growth.
The investment in AI is enormous—around $220 billion per year. But it does not need to replace all labor to be justified. Global labor is about $60 trillion per year, and information work accounts for roughly 10–20 percent of that. By this math, AI only needs to replace 1.8 to 3.7 percent of information work per year to pay off the investment.
At the individual level, that is about one or two days of work saved per information worker per year. With AI agents, improving information work—searching, aggregating, writing, and generating information—is already within reach. This means the current investment is economically justified even if AI only captures a small portion of information work.
More
- The metaphor is not as stretched as it seems. Large language models literally encode information in multi-dimensional vector spaces, computing distances between vectors to find similarities. ↩︎
://ewernli 