The AI Bubble: What Happens If the Boom Goes Bust?
As the artificial intelligence (AI) sector continues to dominate headlines and investment portfolios, concerns are mounting that the industry may be on the brink of a major correction. With leading firms like OpenAI reporting staggering losses despite record revenues, and financial analysts warning of unsustainable growth, the question on everyone’s mind is: What happens if the AI bubble bursts?
Unsustainable Growth and Mounting Losses
For the first half of 2025, OpenAI brought in $4.3 billion in revenue, yet still posted a net loss of $13.5 billion. The reason? The immense computing power required to run generative AI products like ChatGPT is so costly that each interaction often results in a financial loss. According to a recent Deutsche Bank report, the current AI boom is “unsustainable,” and Bain & Company estimates that the industry will need $2 trillion in annual revenue by 2030 to maintain its current pace—a figure that many experts believe is nearly impossible to achieve.
The Bank of England has also issued warnings, cautioning that the market could experience a “sharp correction” due to the overvaluation of AI companies. This sentiment is echoed by industry leaders, including Jamie Dimon, CEO of JPMorgan Chase, who recently stated that “a lot of assets look like they’re entering bubble territory.”
What a Burst Could Mean for the Economy
If the AI bubble does burst, the consequences could be severe. Companies could lose billions, and the U.S. economy, in particular, would likely take a significant hit. Data centers, which have expanded rapidly to meet the demands of AI, may suddenly need to downsize, leading to job losses and economic disruption.
Experts like Cory Doctorow and Alex Hanna warn that the aftermath could resemble the fallout from the crypto crash, with years of court cases, exposed fraud, and corporate infighting. Billions could be lost, companies could go out of business, and governments may struggle to stabilize the economy after years of wishful thinking and overinvestment.
The Productive Residue
Despite the grim outlook, not all experts believe the aftermath will be entirely negative. Doctorow suggests that the burst could leave behind a “productive residue”—a buyer’s market for GPUs, skilled statisticians, and open-source AI models that have yet to be fully optimized. Large language models may have reached their peak utility, but they will continue to be useful, and ongoing research will make them more efficient to train and run.
Image, video, and audio generation technologies will continue to impact creative industries, but people will eventually find ways to coexist with these tools. Over time, the hype will fade, and AI technologies will become more mundane, integrated into everyday life rather than dominating headlines.
Regulatory and Governance Risks
AI’s rapid growth has also exposed significant governance and regulatory gaps. Much like the cryptocurrency exchanges of the early 2020s, the AI industry is characterized by disparate governance practices and minimal oversight. This lack of regulation increases the risk of fraud, misuse, and catastrophic failures.
Industry leaders like Anthropic CEO Dario Amodei and Google CEO Sundar Pichai have raised concerns about the “probability of doom” from AI misuse, with Amodei estimating a 25% chance that AI could go “really, really badly.” Recent incidents, such as tampering with AI models leading to unintended consequences, highlight the potential for major disruptions to financial markets or national security systems.
Looking Ahead
As the AI bubble shows signs of strain, firms and governments are beginning to shift focus from speculative AI tools to more resilient and adaptive strategies. Cybersecurity, in particular, is moving toward investments in diversified infrastructure and digital resilience, recognizing that long-term value lies in fundamentals rather than hype.
The burst of the AI bubble could be catastrophic in the short term, but it may also pave the way for a more sustainable and responsible approach to AI development. The technologies left behind could continue to drive innovation, even as the industry learns from its mistakes and adapts to a new reality.
For now, the world watches and waits, hoping that the AI industry can navigate its current challenges and emerge stronger on the other side.