Is AI a Bubble?

1. What a bubble actually looks like

A bubble is not just “prices going up”. It’s a specific pattern:

• Asset prices detach from fundamental value.
• Rising prices create FOMO, pulling in more capital.
• That extra capital pushes prices even higher, creating a self‑reinforcing loop.
• At some point, expectations outgrow reality and the whole thing corrects – often brutally.Crunchbase News

We’ve seen this movie with railways, radio, dot‑com stocks, crypto, and plenty more.

2. The AI signs that look bubbly

There are warning signs you can’t ignore:

• Some listed AI‑linked companies now trade at revenue multiples that assume decades of flawless growth.
• Analysts point out that the total stock market value added to “AI winners” is similar to the most optimistic estimates of long‑term AI‑driven profit growth.Business Insider
• Central banks and regulators have started flagging the risk of a sharp correction in AI‑heavy tech indexes.The Guardian
• Venture funding has poured into any startup with “AI” on the slide deck, despite early research suggesting that the majority of generative AI projects do not yet produce positive returns.The Guardian+1

On the capital markets side, parts of AI absolutely look overheated.

3. The fundamentals that are very real

At the same time, underneath the froth, something substantial is happening.

Serious studies estimate:

• Trillions in potential annual productivity gains globally from generative AI and automation.McKinsey & Company+1
• 20–45% productivity boosts in functions like software engineering and customer support in early adopters.trendsresearch.org
• Widespread C‑suite adoption plans: most large enterprises now have an AI strategy, budget and experiments in flight.McKinsey & Company+1

In other words: there’s froth in the pricing, but also real, compounding economic impact.

4. Where the bubble risk is highest

If you’re trying to judge risk, it helps to separate layers of the AI stack:

• Infrastructure layer (chips, cloud, core models) – huge capital requirements, few winners, valuations already stretched. This is where a correction would be most painful.
• Application layer (tools, SaaS products) – thousands of companies competing, many with weak moats. There will be a shake‑out as basic “wrapper apps” struggle to justify their valuations once model capabilities commoditise.Crunchbase News+1
• Enterprise adoption and productivity – slower to move, but more resilient. Once AI is embedded in how a business sells, builds or supports customers, it tends to stick.

The likely outcome: financial bubbles in parts of the stack, not a total collapse of AI as a technology.

5. What this means for your business

For most organisations, the big risk isn’t that “AI is a bubble”. It’s that you either:

• Overpay for shiny tools that don’t deliver value, or
• Sit on the sidelines until your competitors build AI‑powered advantages that are hard to catch up with.

A few practical filters help:

• Focus on clear use cases with measurable outcomes, not vague promises.
• Ask vendors for before/after numbers from similar customers.
• Prefer tools that integrate into your existing systems rather than creating new silos.
• Watch commercial terms – usage‑based pricing can spiral if you don’t monitor it.

AI doesn’t need to “live up to the entire stock market hype” for it to transform your own cost base and customer experience. It just needs to work in the specific areas that matter to you.

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