From AI to blockchain, technologies rise and fall on waves of inflated expectations. We are told to treat hype with scepticism: inflated claims, frothy valuations, and moonshots that rarely come to fruition. For much of the twentieth century, hype was considered a nuisance or even a danger, something that distorted innovation.
But what if hype is no longer background noise? What if it has become one of the key infrastructures through which the digital economy is organised?
In our forthcoming book After Hype: The Business of Taming the Digital Economy (Pollock & Williams, 2026), we argue that hype has moved from the margins to the mainstream. No longer ephemeral or chaotic, it has become structured, calculable, and governable. To understand today’s innovation systems, we must study hype itself. We refer to this new research agenda as Hype Studies.
From wild to tamed hype
The dotcom boom of the late 1990s displayed hype in its most unrestrained form—what we call ‘hype in the wild’. Charismatic entrepreneurs, excitable journalists, and speculative investors inflated expectations to breaking point, with nobody held accountable for undelivered claims.
Since the crash, however, a quieter transformation has taken place. Powerful intermediaries such as analyst firms and ranking systems transformed hype into a more structured system. Gartner’s ‘Hype Cycle’ chart and ‘Magic Quadrant’ ranking do not simply report on technological trends; they actively shape them. These frameworks influence when firms invest, how governments prioritise funding, and which start-ups are granted credibility.
Hype, once spontaneous and unruly, has been partially domesticated. Today, it is managed through evaluative infrastructures that stabilise promises, distribute resources, and sequence innovation.
Hype as a resource
This domestication means hype is not only a distortion to be avoided. It is also a ‘resource’—something that organisations can learn to cultivate and mobilise.
Consider Juvo, a fintech we analyse in the book. Its initial pitch—providing the underbanked with a digital financial identity—failed to capture the attention of market gatekeepers. Juvo eventually prospered when it swapped its generic hype for a narrative tailored to analyst categories—reframing itself as ‘financial identity as a service’. This repositioning won analyst endorsement, secured a partnership with Mastercard, and enabled the start-up to carve out an entirely new market.
Who wins the hype game?
Yet hype as a resource is not equally available to all. Success increasingly depends on knowing how to ‘play the hype game’: how to craft narratives that fit analyst categories, cultivate ties with intermediaries, and frame promises in ways that resonate with evaluative infrastructures. Firms that master these skills gain visibility, credibility, and resources. Those that do not—even if technologically innovative—risk being ignored.
This creates winners and losers. Established hubs in North America, where ecosystems for managing hype are well developed, often dominate analyst frameworks. European and emerging-market firms face steeper barriers to recognition. The taming of hype, therefore, redistributes advantage, privileging those who can navigate its rules.
Hype, then, is not simply a marketplace of ideas. It is a structured game that rewards those who know its rules and penalises those who do not. This reshapes not just markets, but the geography of innovation itself.
Towards Hype Studies
These developments demand systematic investigation. We must open the ‘black box’ of hype to see how futures are ranked, staged, and circulated.
Hype Studies would ask:
- How do bold promises about new technologies get turned into concrete roadmaps and adoption curves?
- Who decides which innovations are credible, and what tools or rankings give them that authority?
- Which companies and regions get visibility and investment from hype—and who gets overlooked?
- How do hype cycle charts influence the direction of industries like AI, quantum computing, or blockchain?
- And crucially: how can we ensure hype is managed in ways that inspire ambition without misleading or excluding?
Why this matters now
Generative AI offers a vivid case in point. In 2024, extraordinary claims sparked both feverish excitement and deep scepticism. Yet within months, analyst firms had moved to domesticate these expectations, segmenting the field into subdomains—generative design, autonomous coding, and AI safety tooling—each mapped onto its own adoption curve.
These frameworks did not merely describe emerging possibilities; they configured them, shaping how markets, regulators, and investors perceived the technology. Firms that engaged early with these structures were better positioned to anticipate regulatory shifts, identify partnerships, and mobilise resources than those who dismissed the charts as ‘just buzz’.
Conclusion
Hype is no longer fleeting noise. It has become institutionalised, calculable, and consequential. By advancing Hype Studies, we aim to establish a new agenda: one that treats hype not as distortion but as infrastructure.
The challenge ahead is clear. To understand innovation in the twenty-first century, we must study the expectations that drive it. And to shape more inclusive and responsible futures, we must ask not just how hype works, but who it works for.
Neil Pollock is our Professor of Innovation and Social Informatics.