What Happens When the AI Bubble Bursts? Will AI Die?

I keep seeing the same question: is AI a bubble, and what happens when it bursts?

There is no reliable way to predict the timing or scale of a market correction. AI is attracting enormous investment, and the returns from that spending are still uncertain in places. At the same time, people already use AI to learn, write, analyse documents, build software, and get through everyday work.

Those facts can coexist. A technology can be useful while some of the businesses built around it are overvalued or financially fragile.

An AI market crash would not mean AI itself has failed

It helps to separate the technology from the business models around it. A company can have a useful product and still struggle to cover the cost of running it. A startup can attract millions of users without finding a sustainable way to serve them.

The dot-com crash is a familiar comparison. Many internet businesses failed, but the internet did not disappear. That history does not prove AI will follow the same path, but it shows why a financial correction and a technology's long-term usefulness are different questions.

A 2026 Bank for International Settlements working paper on the AI investment race examines how competition, large capital commitments, and financing structures can make technology investment booms vulnerable to a disruptive end. The useful question is not simply whether AI is a bubble. It is which investments can produce durable value and which assumptions may not hold.

Character.AI is a warning about business economics, not proof that AI is over

On October 13, 2026, Reuters reported that Character.AI had filed for Chapter 11 bankruptcy and agreed to sell certain assets to Disney for $20 million. Reuters reported that the platform had about 12 million users and was losing between $8 million and $9 million per month, with legal and operating pressures among the issues facing the company.

The point is not that every AI company will fail. Character.AI has its own business model, cost structure, and legal circumstances. Its situation illustrates a more general problem: frequent use does not guarantee that the economics work.

When serving each user is expensive, revenue does not cover operating costs, or legal obligations become difficult to manage, a popular product can still be in trouble. Other companies may face different conditions and outcomes.

What happens to AI wrappers?

Many AI applications package an existing model into a specific interface or workflow. These products are often called wrappers. Some are thin layers around a model API. Others connect models to company data, existing software, specialised processes, permissions, and review steps.

If the underlying model provider adds the same feature to an existing subscription, a simple wrapper may struggle to justify its own monthly fee. If competitors can copy the product quickly and customers have little reason to stay, the business is exposed.

That does not make every wrapper worthless. A product can add real value through its workflow, domain knowledge, integrations, user experience, reliability, and controls. The question is whether customers are paying for a problem being solved, rather than just access to a model that is widely available elsewhere.

My expectation is that some standalone AI apps will close, merge, or become features inside larger products. That is a forecast, not a certainty. Which companies survive will depend on their costs, differentiation, customer retention, and ability to make money.

Will people stop using ChatGPT, Gemini, or Claude?

I do not expect a market correction alone to make most people abandon AI tools they find useful. People have started incorporating chatbots into how they study, write, plan, code, and solve problems. Some have built real habits around them.

That does not mean every user is addicted, loyal to one provider, or guaranteed to keep paying. People can switch models, use free tiers, reduce usage, or stop using AI for tasks where it does not help. Some business use cases will also be cut if the return is not clear.

But changing providers is different from abandoning the technology. If one service gets more expensive or shuts down, a user may move to another product. Large platforms also have distribution advantages because they can add AI to software and services customers already use. Those advantages are meaningful, but they do not guarantee that any company will survive.

The more likely change may be less hype and more scrutiny

If investment slows, companies may become more demanding about what AI features actually deliver. Projects that cannot show measurable value may be cancelled. Teams may be reorganised, subscriptions consolidated, and budgets redirected to products that solve clear problems.

Established software businesses, including companies such as Zoho, could choose to add AI capabilities to existing products instead of asking customers to adopt separate tools. The exact commercial and employment effects will depend on each company's strategy. It would be premature to predict a particular number of layoffs or claim that a specific vendor will definitely benefit.

AI-assisted coding is another example. People can use tools to build a first version of a website or application from natural-language instructions. If model access gets cheaper, that could make prototyping easier. It does not remove the need to check the code, understand the data flows, test the application, and maintain it after launch.

A market correction could push AI away from impressive demos and toward tools that justify their costs. That would change the industry, not necessarily end it.

AI security does not depend on the market staying hot

This is the part I care about most. If AI remains embedded in software, the risks remain too. A business still has to think about what data a model can access, what a connected agent can do, how outputs are checked, and what happens when the system is manipulated or wrong.

For example, an AI assistant that can search internal documents needs access controls. An application that retrieves external content needs to account for prompt injection and untrusted instructions. A coding assistant needs review and testing before generated code is treated as production-ready. A system that can trigger consequential actions may need a human approval step.

These responsibilities do not disappear if a startup closes or a subscription changes. They follow the systems an organization continues to use.

That is why AI security and governance are not simply bets on a booming market. They are operational disciplines. The work includes defining acceptable use, limiting permissions, testing failure modes, monitoring behaviour, keeping evidence, and deciding when a person must make the final call.

Frameworks such as the NIST AI Risk Management Framework can help organizations structure risk management. The OWASP GenAI Security Project provides practical guidance on risks in generative AI applications. Neither framework removes the need to understand the specific system being deployed.

What I think happens next

We do not know whether the AI market will experience a sharp crash, a gradual reset, or years of uneven adjustment. The answer may differ across model providers, infrastructure companies, and application startups.

My view is that a serious correction would likely expose weak business models, force companies to justify spending, and push some AI features into products people already use. Useful capabilities would not automatically vanish. The pace and shape of adoption could change, and some users and businesses would certainly be affected.

The lesson from Character.AI is not that AI is dying. It is that popularity, usefulness, and sustainable economics are separate things.

I am documenting my own journey into AI because I want to understand how these systems work, build useful applications, test where they fail, and learn how to secure and govern them. I cannot predict which providers will dominate the next decade. I can work toward understanding how to build systems that remain useful and accountable as the technology changes.

Frequently asked questions

Sources and further reading

• Reuters: Character.AI files for bankruptcy and agrees to sell some assets to Disney, October 13, 2026.
• Bank for International Settlements: The AI investment race, July 2026.
• NIST AI Risk Management Framework.
• OWASP GenAI Security Project.