Artificial intelligence — are we in a bubble?
getty
One should be just as cautious about predicting the imminent burst of an AI bubble as skeptical of the exaggerated hype currently surrounding artificial intelligence.
There are concerning signs. The “Magnificent Seven” stocks (Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla) make up more than a third of the S&P 500, with recent growth driven by an AI story. Investors are becoming uneasy with this level of concentration. At the peak of the dot-com bubble in 2000, the top technology stocks from the late 1990s (Cisco, Dell, Intel, Lucent and Microsoft) accounted for 15% of the index. Such concentration heightens risk. The parallels do not stop there.
The AI Bubble And The Dot-com Era
A massive telecommunications infrastructure buildout ushered in the e-commerce era. The world needed the internet pipes to enable high-speed connectivity. This triggered an overly optimistic deployment of fiber optic networks, which led to catastrophic bankruptcies when the demand did not materialize in the short term.
Today, the leading AI companies are investing hundreds of billions of dollars in new data centers. The total capital spending in this area is being discussed in the trillions of dollars, figures that were once only associated with large countries’ GDPs. Will history repeat itself, causing an imminent collapse?
Meanwhile, the connectivity boom and investments from a quarter century ago enabled the always-on world we live in today. They created opportunities for value creation beyond infrastructure, at the application level, and drove the transformation of the information technology industry through the shift to the cloud. Some might argue that data centers are now the new utilities required to provide on-demand information services for an increasingly connected world.
Will The Demand For AI Materialize?
Much of the current attention is focused on the consumer space. OpenAI’s ChatGPT website received over 5 billion visits during July. But that is not the whole story.
The true economic impact will be measured by consumer and enterprise adoption. The National Bureau of Economic Research started publishing its survey of generative AI adoption about a year ago. As of late 2024, about 40% of the U.S. population reported using generative AI, and 23% reported having used it for work at least once in the week before they were polled. When comparing the level of adoption since the initial product launch, generative AI at work is taking off faster than personal computers or the internet, the study concludes. This underscores the potential of AI as what economists call a general-purpose technology, one with deep and pervasive impact on the economy.
But challenges remain. A group of MIT researchers surveyed over 300 publicly disclosed AI initiatives, more than 50 companies and hundreds of senior leaders from January to June 2025 to conclude that 95% were not getting any return for their investment. They were also able to identify three elements that made the remaining 5% successful. Companies with successful AI initiatives are buying instead of building, executing within business units as opposed to central laboratories and choosing tools that integrate with their existing business workflows.
While achieving the returns associated with business transformation is rare, adoption is high, with 90% seriously exploring buying an AI solution. This is a familiar pattern in enterprise technology adoption. It has been captured by what consultants call the hype cycle, tracking innovative technologies from their market entrance to when businesses are likely to benefit from them and the technology has become mainstream.
Gartner’s Hype Cycle
getty
Bank of America, the second-largest bank in the U.S., with a $4 billion budget for new technologies such as AI, is an example of the pattern identified in the MIT study: integrating AI and business workflows. The bank developed a tool to help bankers prepare for client meetings, retrieving information from multiple systems. Previously, a junior banker would have executed this process over multiple hours or days.
How Far Can The Current AI Models Take Us?
As AI usage increases, so does the debate about its ultimate potential and whether the current development model is sustainable.
Much of the progress to date has been made on the back of large language models that benefit from scale. Scale means that with more computing power and more data, one produces better outcomes. Richard Sutton, an AI pioneer, observed in 2019 that general methods leveraging computational power outperform those that rely on human ingenuity and complex heuristics (in what he coined “The Bitter Lesson” for humanity). He has recently criticized the industry’s fixation on scaling and called for a correction toward agents that learn continuously.
Gary Marcus, one of the most vocal critics of the artificial intelligence hype, commented on the mixed reviews received by OpenAI’s latest ChatGPT-5 release. He echoed the sentiment that a development model predicated on scaling is not the path forward, a position he has been sponsoring for decades.
These scientists’ deep skepticism about the current progress represents a technical word of caution. The hype conditions created by investors and large AI laboratories can lead to disappointment. Both, however, are believers in AI’s ultimate potential, while suggesting that alternative approaches are necessary. These may necessitate more, instead of less, investment in research and development.
Is There An AI Bubble?
One should pause when even OpenAI CEO Sam Altman, who helped spark the AI boom, warns that the market may be overheating. He and other investors mention soaring valuations, too much money chasing unproven business models and the risk of building infrastructure faster than demand will justify. Like in the MIT report, they worry that much of the capital outlays are flowing into projects unlikely to deliver results soon. The concern is less about AI’s long-term promise and more about inflated expectations setting the stage for a sharp correction.
Binary thinking that swings between hype and the fear of an AI bubble may limit more nuanced analysis. AI’s long-term potential remains significant, but markets rarely move in straight lines. A correction could slow momentum in the short term while reinforcing the need for discipline. The next phase will depend on advancing research, improving model quality and directing enterprise investments toward measurable economic value.
