Has the AI hype peaked? The data says otherwise

Generative AI has dominated headlines since ChatGPT burst onto the scene in late 2022. Initially, there was a buzz about its seemingly limitless potential to transform industries. More recently, however, the conversation has grown less enthusiastic. Has the excitement faded? Has generative AI failed to live up to expectations?

A study by Juan Pablo Mora-López (Universidad del País Vasco), David Lopez-Lopez, Associate Dean of The Esade MBA, and Olga Rivera-Hernaez (Universidad del País Vasco), published in Electronic Commerce Research, suggests that this story may be rather oversimplified.

Instead of evaluating AI only through one of the technology industry's most influential forecasting tools, Gartner's Hype Cycle, the researchers examined a range of publicly available data. What they uncovered suggests that AI is not entering the so-called "trough of disillusionment" as predicted by the Hype Cycle, but that its popularity is growing.

What is the Gartner Hype Cycle?

Gartner’s Hype Cycle has been an invaluable tool for businesses for decades. It’s a framework that lays out the typical stages that innovations go through. Its five stages include an innovation trigger, followed by a peak of inflated expectations, then a fall into a trough of disillusionment, and eventually reaching productive, mainstream adoption. The model helps businesses make more informed decisions about when and how to adopt emerging technologies.

According to the Gartner assessment, generative AI has already peaked and entered a phase in which interest and investment begin to decline as reality fails to match expectations. Mora-López, Lopez-Lopez, and Rivera-Hernaez investigated whether publicly available evidence actually supports that conclusion.

Looking beyond the hype

The researchers analyzed a series of measurable indicators that reflect how technologies spread and mature. These included venture capital investment, startup creation, website traffic, Google search activity, and global media coverage.

The premise was simple. If businesses, investors, and consumers were actually becoming disillusioned with generative AI, those indicators would also point to that lack of interest. Investment would stall, user engagement would diminish, media attention would wane, and public interest would fade.

But this is not what the study found. The analysis points to growing AI momentum—the opposite of what the Hype Cycle model predicts. While it’s true that media attention has fluctuated, overall, generative AI has maintained strong growth, rather than the classic ‘boom-and-bust’ pattern often seen with emerging technologies. The key to this could be the continuous innovation and regular product improvements.

Investors aren't behaving as though AI is losing momentum

Perhaps the strongest evidence can be seen within the investment community. Despite overall venture funding slowing, venture capital investment in generative AI continues to grow. AI startups continue to attract a growing share of venture capital investment, and the number of AI unicorns—privately held companies valued at more than $1 billion—has continued to increase.

The broader market supports this picture. According to recent reports from PitchBook, AI remained one of the largest recipients of global venture capital investment throughout 2025 and into 2026, despite funding across the technology sector generally leaning towards caution. Investors appear to be distinguishing between general market conditions and the long-term potential of generative AI

Unlike technology booms of the past, where investment dwindled once enthusiasm faded, funding for generative AI has proven highly resilient. It seems that investors believe it presents a long-term opportunity and not just a passing trend.

People are still using AI

Investment tells us a lot, but user behavior is also an important part of the analysis.

Generative AI is now a daily part of life for millions of people. It’s used for everything from writing emails and summarizing documents to writing code, brainstorming ideas, translating languages, and supporting learning. There was a temporary dip in website traffic during 2023, but AI companies then rolled out more capable models and new features, boosting traffic again.

“We suspect that seasonality was a possible cause for passing interest between May and September 2023, due to summer and winter breaks at universities in the US, China, and Europe,” argue the researchers.

Indeed, adoption has continued to expand. OpenAI reports rapid growth in ChatGPT adoption, while Google and Microsoft have steadily expanded the capabilities of Gemini and Copilot, embedding generative AI into widely used workplace products. The technology is being integrated into everyday tasks by businesses and consumers.

AI is different from other new technologies

One reason generative AI may not fit the traditional Hype Cycle is the extraordinary pace at which it is evolving.

AI systems are improving every few months, with new capabilities, reasoning enhancements, and productivity features. These rapid changes keep users engaged, unlike previous technologies that were much slower to improve.

According to the researchers, generative AI appears to be following a different trajectory from many previous emerging technologies, meaning traditional models may no longer tell the whole story.

Generative AI’s continuous improvements seem to be repeatedly resetting expectations.

What business leaders should take away

What can be gleaned from the study is not that every organization should immediately invest in generative AI. Instead, leaders should be cautious about making strategic decisions based solely on headlines—whether optimistic or pessimistic.

By combining open data on investment, public interest, user adoption, and media coverage, organizations can build a more objective picture of how emerging technologies are actually evolving. As the study’s authors explain, "Organizations can gain a more objective understanding of the hype surrounding emerging technologies like AI."

While generative AI continues to evolve at an exceptional pace, the evidence suggests that any "trough of disillusionment" may be shorter or less pronounced than many expected. But perhaps a key idea to be drawn from this research is that we need entirely new ways of understanding how transformative technologies mature.