AI and the ghosts of tech booms past

Date:
Mon, 07 Sep 2026 10:39:15 +0000

Description:
What can past tech booms teach us about AI?

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter In its latest financial stability report, the Bank of England warned that an AI crash could plunge
the UK into recession, claiming that a price correction in AI stocks, driven by a change in productivity and profitability among tech-led companies, could cause a 2.2 percent fall in U.K. GDP.

This should serve as a warning to businesses everywhere. The risks
surrounding AI extend far beyond Silicon Valley. Latest Videos From TechRadar Watch full video here: Colin Selfridge Social Links Navigation

Director, Cyber Security at CGI. As leaders prepare for what comes next, however, many are still asking the wrong question.

Is AI real, or is it hype? You may like Why most AI programs stall, and what it will take to scale them The AI investment gap The AI reality check for finance: why experimentation is over and execution has begun

The last 30 years of technology should have taught us that the answer can be both. The last tech crisis of the 20th century Y2K was a real technical risk that came to look, in hindsight, like mass overreaction. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get
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The UK spent billions preparing for the millennium bug, with organizations testing and patching systems that underpinned everything from banking and benefits payments to air travel and the National Grid.

The government established Action 2000 to help businesses prepare, while
banks and major infrastructure providers ran extensive contingency plans
ahead of the deadline.

Then midnight passed, the planes stayed in the sky, cash machines kept dispensing money and the lights stayed on. The whole affair began to look almost laughable. What to read next The AI Scaling gap: why ambition is outpacing readiness Heres what House of the Dragon can teach you about AI strategy AI needs rules and rails: Why governance must move beyond policy

Yet the apparent anticlimax obscured an important point. Much of the disruption people feared was likely avoided because organizations took the risk seriously and prepared for it. Cybersecurity has the same problem.

A threat that is successfully mitigated can look, in hindsight, remarkably like a threat that was exaggerated. Not to burst your bubble The dotcom
bubble taught a different lesson. The internet was not fake, but the valuations often were. Boo.com, for example, raised vast sums to reinvent fashion retail online, only to collapse in 2000 after burning through its funding. Companies with little profit and, in some cases, barely coherent business models were treated as if they had risen above the laws of
economics.

Then the bubble burst, fortunes disappeared and many of the supposed pioneers vanished, but the ideas they were betting on did not. In fact, I bet youre reading this on the internet right now. Style over substance Crypto added a third lesson. Revolutionary language can hide weak use cases. There were serious technical ideas beneath it, but the public boom became dominated by speculation, celebrity endorsements and the strange habit of starting with an asset and then searching for a purpose.

At the height of the NFT frenzy, an NFT of Twitter founder Jack Dorseys first tweet sold for $2.9 million. A year later, its owner attempted to resell it for nearly $48 million, only to see initial bids fall spectacularly short.
The technology had not disappeared. What had disappeared was the markets willingness to treat digital scarcity as lasting value.

AIs rise carries elements of all three stories. A familiar visage Like Y2K,
AI presents risks that may sound exaggerated until they are not. As any cybersecurity expert will tell you, if the worst harms are prevented, the warnings may later look hysterical. This will not prove they were imaginary.

For AI, this means treating cybersecurity and governance as operational disciplines, not innovation-team side projects. Organizations need to understand what data is entering AI systems, what those systems can access, what authority they have, how they could be manipulated and how the business would respond if they failed.

With the dotcom boom, AI looks like a real general-purpose technology wrapped in an overheated investment story. It can already write code, summarize documents, and automate parts of professional work. But that does not mean every AI company is valuable, every AI product is useful, or every data
center will earn its keep.

For businesses learning from this, they must invest in capabilities, data foundations, people and workflows, rather than betting everything on
whichever vendor, model, or product is making the most noise this quarter.

And like crypto, AI has acquired a mythology. AI-powered is becoming what blockchain-enabled briefly was, a phrase that can mean something, nothing, or merely please value us higher.

For the adoption of AI, business leaders must now ask boring, but necessary, questions. What problem does this solve? Who uses it? What data does it see? What systems can it influence? What authority have we given it? What happens if it is wrong, manipulated or unavailable? And who is accountable when that happens?

This combination is what makes the current moment so hard to read. The lazy argument says AI is either a revolution or a bubble. History suggests a more uncomfortable possibility; it could be both. The Spirits of all three shall strive within me. I will not shut out the lessons that they teach. Too often, business leaders are trying to win an argument about whether AI is overhyped when they should be building a strategy that survives either answer. The sensible response to AI is neither a moonshot nor a moratorium. It is disciplined experimentation.

This means treating AI less like a campaign slogan and more like a portfolio of bets. Some of those bets should be defensive, focused on understanding where AI is already being used, what data and systems it can access, and whether the organization can respond when things go wrong.

Others should be exploratory, testing where AI can create genuine value
across areas like coding , customer support and knowledge management. And
some should be deliberately skeptical, particularly when tools sound impressive but cannot demonstrate savings, better outcomes, or clear ownership.

If the aim is simply to have an AI strategy that reads well in a board pack, then the lessons of the past 30 years have been missed. Y2K, dotcom and
crypto each showed us that hype and substance are not opposites. Real risks can be exaggerated. Transformative technologies can attract irrational investment. Powerful ideas can coexist with weak use cases.

AI may prove to be all three at once. The businesses that navigate it best will not be those that predicted the future perfectly. They will be those
that were prepared to adapt when reality arrived. Best cloud storage: tested, reviewed and rated by experts . This article was produced as part of
TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

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