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The AI bubble debate, set against 10 market bubbles since the 1630s

Weigh the AI bubble debate against history with 10 market bubbles, from Dutch tulip mania to dot-com and crypto, and how each compares with today's AI boom

OpenAI launched ChatGPT on November 30, 2022, and interest in artificial intelligence took off. Investors then bid up shares of companies seen as likely to profit from AI. Nvidia $NVDA, the main supplier of chips to AI developers, became a focal point for that enthusiasm. The AI boom has also drawn heavy spending on data centers and the hardware inside them.

A technology can change the world and still be overpriced. That tension sits at the center of the AI bubble debate, and it is not new. Railways reshaped Britain in the 19th century, yet many of the companies formed to build them failed. The internet became part of daily life even as many early dot-com companies vanished.

Whether today's prices reflect future earnings or excess optimism cannot be settled in advance. Bubbles are usually identified with confidence only after they burst. What history can offer is comparison. Past manias show which patterns repeat, which ones differ and where a real innovation and an inflated market have existed side by side.

This list covers 10 market bubbles in chronological order. It starts with Dutch tulip contracts in the 1630s and moves through John Law's Mississippi scheme in France and the South Sea Company in Britain. It then covers British railway mania, the 1929 U.S. stock bubble, the Nifty Fifty, Japan's 1980s asset bubble and the dot-com era. It ends with the U.S. housing bubble and the crypto boom and bust, which included the bankruptcy of the exchange FTX in 2022.

Each slide explains how one bubble formed and draws one specific comparison with the current AI rally. Four questions run through the piece. What story justified the prices? How was the buying financed? How concentrated was the money in a few companies? And how long did it take prices to recover once the mania ended?

Some comparisons point to similarities with AI stocks, and others point to sharp differences. None of them amounts to a forecast. Together they give readers a way to judge the AI boom against market history from the 1630s to 2022.

Tulip mania in the 1630s

Tulip mania is the earliest bubble on this list. Its lesson for the AI boom is that the size of a mania can stay in dispute long after prices fall. Prices for tulip bulb contracts in the Netherlands collapsed in early February 1637, and historians still argue over how much that crash mattered.

The collapse hit contracts for bulbs, not flowers already in bloom. Buyers agreed to prices for bulbs they would take possession of later. When confidence broke that February, those agreed prices no longer held, and the market for the contracts fell apart.

The Semper Augustus is the variety most often named as the costliest of the period. Its name appears in many retellings of the episode. The figures attached to it differ between accounts. Because the sources disagree, no single price for a single bulb can be stated with confidence.

That uncertainty sits at the center of the modern debate. The historian Anne Goldgar has argued that the economic fallout from the collapse was limited. Popular retellings often present tulip mania as a frenzy that gripped a whole country. The two pictures are hard to reconcile, and historians have not settled the question.

The gap between legend and record is what links tulip mania to the AI debate. A bubble story is simple to tell and simple to repeat. A dramatic version can travel further than the documented one, and it can harden into accepted fact.

Debate over an AI bubble runs into the same problem. A vivid historical comparison can make a case sound convincing without proving anything about current prices. Anyone weighing the AI boom against tulip mania is comparing it with an episode whose own size remains contested.

The record from 1637 raises a set of questions that can be put to any boom. Who was actually buying? What were they paying for? How far did losses spread when prices fell? Those questions apply to AI stocks as much as to bulb contracts. The tulip case shows how hard they can be to answer, even centuries later.

John Law's Mississippi Bubble

The Mississippi Bubble was a French share mania built on promised profits from colonial Louisiana, and it collapsed during 1720. Its lesson for the AI boom is that a persuasive story about a new frontier can carry a share price well beyond what a company earns today.

The scheme was the work of John Law, a Scottish financier who ran two linked institutions in France. One was the Banque Générale. The other was the Mississippi Company, formally known as the Compagnie d'Occident.

Shares in the Mississippi Company drew their appeal from two sources. The first was France's colonial trade in Louisiana, a territory on the other side of the Atlantic. The second was the company's role in handling the French state's debt. That role tied the company's fortunes to the government's own finances.

Louisiana stood for new and untapped wealth. Buying the company's shares was a way to own a piece of that trade. Buyers expected large returns from it in the years ahead. What the company earned at the time was small next to the expectations priced into its shares.

The scheme's collapse in 1720 left shareholders who had bought on the Louisiana story holding shares worth far less than they had paid.

The parallel with AI lies in the structure of the bet rather than in the technology itself. Investors value many AI companies on what the technology may earn in the years........

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