Lessons from the Dot-Com Boom and Bust: Is AI Headed for the Same Fate?

The late 1990s witnessed an unprecedented technological gold rush. The rise of the internet sparked a wave of investment, innovation, and speculation that fuelled the infamous dot-com boom. Start-ups with little more than a domain name and a vague promise of digital transformation attracted billions in capital. The Nasdaq soared, businesses rebranded overnight to add ".com" to their names, and Wall Street embraced the hype with open arms.

Then, in 2000, the bubble burst. The crash was swift and merciless, wiping out trillions in market value, shuttering countless start-ups, and delivering a sobering reality check to investors who had bought into the frenzy.

Now, more than two decades later, artificial intelligence is experiencing a similar surge in attention and investment. Tech companies, venture capitalists, and governments are pouring billions into AI, driving valuations of AI-focused start-ups to dizzying heights. The question looms: will AI follow the same trajectory as the dot-com era, culminating in a spectacular collapse? Or is this a fundamentally different technological revolution with a more sustainable future?

Boom, Bubble, or Sustainable Growth?

Every era of technological transformation has its excesses. The railway boom of the 19th century led to speculative manias and financial ruin before it reshaped global transport. The early days of electrification were fraught with overpromises, false starts, and monopolistic battles. Even the rise of personal computing saw bursts of hype and subsequent contractions.

AI is no different. It is revolutionary. It is also, at times, vastly overstated.

The Familiar Symptoms of a Bubble

Exponential Investment and Sky-High Valuations

During the dot-com bubble, companies that had no revenue, no sustainable business models, and, in some cases, no actual products were seeing their stock prices multiply. Investors threw money at anything with "internet" in the business description, fuelling an unsustainable valuation bubble.

The AI industry today shows similar signs of overexuberance. Companies like OpenAI, Anthropic, and DeepMind are securing staggering amounts of investment, with valuations soaring despite unclear paths to profitability. Many start-ups claim AI capabilities as a buzzword rather than a fundamental part of their offering—mirroring how companies in the 90s tacked ".com" onto their names to boost stock prices.

The Race for Market Dominance

In the 90s, there was an intense race to capture market share before competitors could establish themselves. Companies operated under the assumption that acquiring users and brand recognition was more important than building profitable, sustainable businesses. Amazon survived the crash, but many others—such as Pets.com and Webvan—burned through billions before collapsing.

AI is seeing a similar land grab. Tech giants and start-ups alike are aggressively launching AI-powered products, integrating large language models, and attempting to secure first-mover advantages. Many companies are spending heavily without clear revenue streams, betting that market dominance will translate into profits later.

The Talent Bubble and Rising Costs

The dot-com boom created an insatiable demand for developers, engineers, and digital strategists. Salaries skyrocketed, and companies spent lavishly on perks to attract talent. But when the crash came, many of those high-paying jobs evaporated.

Today, AI talent is among the most sought-after in the world, with salaries for top engineers reaching seven figures. Companies are aggressively poaching talent from one another, leading to unsustainable cost structures. If AI investments fail to deliver tangible revenue, we could see a similar industry-wide correction.

Hype vs Reality: The Mismatch of Expectations

During the dot-com boom, the internet was indeed revolutionary—but not in the immediate way that many investors had anticipated. E-commerce, social media, and digital advertising would eventually transform the world, but it took decades, not months.

AI is facing a similar challenge. While its capabilities are impressive, many companies are exaggerating what AI can currently achieve. Hallucinations in generative AI models, legal and ethical concerns, and massive computing costs mean that AI is far from the all-knowing, fully autonomous force some claim it to be.

Could AI Face a Similar Crash?

Given these similarities, it’s reasonable to ask whether AI will follow the dot-com trajectory and experience a severe crash. While no one can predict the future with certainty, there are three possible scenarios:

The AI Bubble Bursts (Dot-Com Redux)

In this scenario, AI investments reach an unsustainable peak before the industry undergoes a sharp correction. Many overhyped start-ups would collapse, wiping out billions in venture capital. Public enthusiasm would wane, and only the strongest, most fundamentally sound AI companies would survive—much like how Amazon and Google emerged from the ashes of the dot-com crash.

A Gradual Correction, Not a Catastrophe

A softer landing could involve a cooling-off period where speculative investments slow down, but AI development continues steadily. Companies would focus more on real-world applications, sustainable revenue models, and incremental progress rather than massive, high-risk bets.

AI Avoids the Bubble Altogether

Unlike the internet in the 90s, AI is already integrated into many core business functions. Industries from healthcare to finance are embedding AI-driven automation, analytics, and decision-making into their workflows. If AI continues to prove its value in practical applications, the industry could sustain long-term, stable growth without a bubble-like collapse.

Historical Perspective: Learning from Jevons' Paradox

In 1865, economist William Stanley Jevons observed that technological improvements in efficiency do not necessarily lead to reduced resource consumption—instead, they often drive increased usage. This principle, known as Jevons' Paradox, is highly relevant to AI. Rather than simply replacing traditional processes and reducing costs, AI's growing efficiency is likely to fuel greater demand, more intensive data consumption, and heightened expectations.

If this demand outpaces sustainable growth, AI could find itself caught in an unsustainable cycle of expansion, much like the dot-com era.

How Businesses and Investors Can Navigate This Moment

Separate the Hype from the Substance

Ask the tough questions. What does this AI solution actually do? How does it improve efficiency? Is it cost-effective? If a business cannot articulate its AI strategy in real, measurable terms, it risks investing in little more than a PR stunt.

Plan for Inevitable Disruption

AI is not going away, but the industry will change. Regulatory landscapes are shifting. Consumer expectations are evolving. A prudent strategy involves preparing for both short-term gains and long-term restructuring.

Avoid Over-Reliance on AI as a Silver Bullet

Just as the internet did not eliminate traditional business fundamentals, AI will not render human judgment, creativity, and strategic thinking obsolete. Companies that approach AI as a tool rather than a total solution will fare better in the long run.

Expect a Correction, but Not an Apocalypse

A downturn is coming. Some AI firms will collapse, just as many dot-com start-ups did. But the technology itself is not a fad. When the dust settles, AI will remain a fundamental part of modern business—leaner, more focused, and better understood.

Final Thoughts: The Shape of Things to Come

The AI boom mirrors the dot-com era in many ways—sky-high valuations, a rush to claim market dominance, and speculative investments. However, AI also differs in that its applications are already proving their worth across industries. While a market correction may be inevitable, it is unlikely to spell the end of AI as a transformative technology.

Just as the internet didn’t die in 2000 but rather matured into the backbone of the modern economy, AI is poised to follow a similar path. The challenge for investors, businesses, and policymakers is to temper enthusiasm with realism, ensuring that AI’s promise doesn’t get lost in a wave of overhype and disillusionment, remember its taken over 20 years to get to this point with the internet, and the biggest change in human behaviour with COVID.

The AI boom is real. So is the risk of overextension. If history teaches us anything, it is that technological revolutions rarely unfold in a straight line. There will be breakthroughs, setbacks, and—inevitably—some high-profile failures.

But there will also be enduring success stories. Just as Amazon and Google emerged from the dot-com wreckage to define the digital age, the strongest AI innovators will weather any storm and shape the future.

For those navigating this moment, the challenge is clear: move with ambition, but not recklessness. The future of AI will belong to those who balance innovation with discipline, who understand both the lessons of the past and the demands of the present.

Because, as history reminds us, revolutions do not fail. But many of their early champions do.