“We are such stuff as dreams are made on,” says Prospero in The Tempest. While he was reflecting on humanity’s fleeting existence, he could just as well have been describing the tech industry’s fascination with shiny new objects that come and go.

Over the decades, countless software leaders have fallen into the age-old trap of chasing “the next big thing,” only to find themselves trying to fit a round peg into a square hole. “There are plenty of examples of this, where we jumped the gun and paid the price,” notes a prominent CTO in the software supply chain security space.

A 2025 survey found that most programming language migrations are driven by hype rather than proven outcomes. And a recent MIT report noted that although 80% of enterprises have attempted generative AI pilots, only 5% of those pilots succeeded. The vast majority of those projects stalled or failed to deliver benefits in production.

“As outlined in Amara’s Law, humans tend to overestimate the impact of technology in the short term and underestimate the effect in the long run,” says the CEO of an AI-powered software delivery platform.

When the market is saturated with lofty excitement and surging VC interest, it’s human nature to fall for the headlines and lose a bit of sanity to FOMO in the process. In the aftermath of a hollow hype wave, we’re often left with a handful of legitimate use cases, wavering support, and sometimes outright scams.

Blockchain

“Blockchain is a textbook case of overhype,” says the CTO of a hybrid cloud management platform. The immutable distributed ledger was supposed to usher in web 3.0 and transform countless industries. Although blockchain still powers cryptocurrency and decentralized finance via Bitcoin or Ethereum smart contracts, an en masse adoption of private blockchains by enterprises never materialized.

One supply chain project was shelved after a year and replaced with a simple setup: Apache Kafka, signed records, and Amazon S3 immutability. It lacked the blockchain buzzword but worked reliably and scaled. Blockchain simply didn’t fit many use cases. It is fundamentally a very, very slow, expensive database. There are heaps of faster, cheaper databases out there, and the only reason to use blockchain over those is if you require zero trust in a central party.

What’s worse, beyond not delivering on ROI, the blockchain industry became a backwater for web 3.0 fraud. In 2024 alone, the FBI reported Americans suffered $9.3 billion in losses due to cryptocurrency-related scams. In the end, “blockchain for everything” collapsed under high friction, low reward, and few real-world use cases. Lesson learned: Watch out for technologies that offer solutions to problems that don’t exist.

Metaverse

Have you heard of the metaverse? It’s “the next digital revolution” that businesses are racing toward and executives are calling “breakthrough” and “transformational.” It will redefine everything you do, from social interactions to factory floors to business meetings. At least, that’s what many had us believing at the height of the pandemic’s surreal, chronically online atmosphere.

Given those futuristic claims, you would think we’d all be meeting around holographic conference tables, talking to employee avatars by now. But no fully immersive, transhumanistic workplace reality has arrived. Both blockchain and VR and the metaverse were heavily hyped and have failed to achieve success commensurate with the amount of money and hype poured into them.

While AR/VR thrived in niche communities, gaming, and certain training scenarios, the idea of mixed reality taking over work life was wildly overstated. The absence of a “killer” app, zero desire for VR meetings, and high headset costs stalled the metaverse’s momentum from the get-go. Lesson learned: Don’t buy into paradigm shifts with low user enthusiasm and unproven ROI.

Big Data

“Big data was one of the most hyped trends of the last decade,” says the chief strategy officer of a project and resource management platform. “It promised magic but delivered mess.” Back in 2011, consultants hailed big data as “the next frontier for innovation.” The idea was that by storing all the data a company could get its hands on, you could unearth valuable insights and inform predictive analytics to direct decision-making.

In practice, the reality was far messier. Teams encountered massive storage and data management overheads, and were left unsure how to turn swelling data lakes into something useful. Too often, the reality was sprawling expensive data lakes that became data swamps. Instead of simplifying decisions, they created new layers of complexity: multiple tools, governance headaches, and very little actionable output.

Many enterprises invested heavily in big data programs only to find them underused and difficult to operationalize. They stood up enormous infrastructures, often taking months to implement, and then struggled to define how the data would actually inform business outcomes. While the promise of big data may never have fully materialized, it arguably influenced some enterprises to take their data strategy more seriously. And hopes are that AI could one day make mining trends in large data stores more feasible. Lesson learned: If a large-scale tech initiative can’t show how it drives business value from day one, it’s probably more burden than breakthrough.

SOA

“After years of hype, SOA never really materialized,” says the CEO of a digital delivery platform. Service-oriented architecture (SOA) was an idea trumpeted in the early 2000s as a move from monolithic architecture to component-based, loosely-coupled, reusable services, often coordinated through an integration or management layer. The idea was to improve reusability, interoperability, and scalability across internal systems, ultimately enhancing business agility and time to market. But like many things that sound too good to be true, it was.

As for why SOA faltered, heavyweight standards, orchestration and performance issues, lack of reuse, cultural and organizational hurdles, unclear ownership, and governance concerns all played a role. Too often, the people and process elements of the transformation came too late. However, there’s a silver lining here—SOA paved the way for modern cloud-based designs. The SOA trend gave way to microservices and API-first architecture, which are still used today. REST APIs are ubiquitous, and the API economy is now a multibillion-dollar industry. Nearly 90% of developers use APIs, with most usage being for internal services like microservices. Lesson learned: Sometimes, it’s what the trend inspires that leaves the everlasting impact.

NFTs

Non-fungible tokens (NFTs)… where to begin? The fact that enough people were convinced a digital image of a bored ape was worth millions should make anyone do a double-take. As one CTO puts it, “NFTs took the hype even further, touted as the future of digital ownership but ultimately collapsing without meaningful use cases.”

To their credit, NFTs were a novel idea for digital artists and collectors: a blockchain-based asset to verify ownership and authenticity of a digital work. NFTs were extolled as the next big investment class and even a transformative technology for business. But by 2023, most NFTs had become virtually worthless. Some defenders still tout niche NFT use cases, like an airline that offers NFT versions of its tickets, but necessary and useful are debatable. Beyond such experiments, NFTs failed to demonstrate lasting value in IT. Public perception plummeted as the bubble burst, copycats proliferated, and major crypto exchanges collapsed. Lesson learned: Technology based 100% on public perception can disappear as quickly as the hype that created it.

Generative AI

“Generative AI is the latest example,” says a chief strategy officer, citing the recent MIT study showing 95% of generative AI pilots fail as very telling. Similarly, a McKinsey survey found that 80% of companies using generative AI found no significant bottom-line impact, with 90% of projects still stuck in “pilot mode.” While the numbers don’t sound promising, the AI hype cycle is more nuanced than others. “The problem isn’t the tech, it’s the approach: broad, abstract use cases instead of targeted pain points.” The future belongs to smaller, focused AI applications that reduce complexity and solve real problems.

On the consumer side, the “force-feeding of AI on an unwilling public” has led to increased apathy: only 8% of Americans would pay extra for AI. Generative AI features continue to appear in end-user applications, whether they’re helpful or not—and users are pushing back. Companies are learning to be far more cautious about promoting AI in products. Others agree that AI could use a dose of realism. “Lessons from blockchain can definitely be applied to today’s AI frenzy. Focus on solving real problems, not chasing buzzwords.”

Even so, AI has more staying power than earlier waves. AI is different because it actually delivers tangibly different results, at a convenience and price point that is much less of an issue. Although broader business benefits remain elusive, generative AI has been successfully applied in niches such as software development. It’s undoubtedly here to stay. Much work is still ahead to continue to improve standards and explore more complex use cases. Lesson learned: Some hyped technologies are praiseworthy, but need maturity and refinement in where exactly to apply them.

The Bigger Picture

Of course, these six trends aren’t the only hype waves we’ve lived through. Tech is full of other high promises and low failures. “These hype cycles have been around for years. They’re a constant reminder to stay practical and pragmatic about new technologies without abandoning reasoning.” It’s hard to know when you’re getting swept up in the bandwagon of tech trends, let alone where the road is heading. Sometimes, the confusion can fog up what works in the current moment.

“The industry is often quick to downplay technology trends of the past as new approaches emerge. While AI and agents are getting nearly all of the hype today, I have little doubt the many innovations over the past few decades will continue to drive impact at scale.” Regardless, history repeats itself, and hindsight can help guide future tech choices.

For instance, many of the trends above required a high degree of friction and complexity compared to other “mainstream” technologies of the time, making their end payoffs unclear. “Adding exotic technology without a clear, measurable benefit will only cause more pain than payoff.” One organization’s dalliance with blockchain proved that people need incentives and accountability to embrace new technology. It also inspired the company to instigate kill switches for new experiments. “Now, if we don’t see real usage by a set date, we pivot or stop.”

He goes on to note that even some mainstream tech that appears to be “the status quo” is overhyped. “Survivorship bias ensures that only the few success stories are covered.”

This isn’t to say that all the ideas lampooned above are worthless. Many sparked innovation and will continue to evolve in their own ways. Moreso, the gulf between promise and reality, and the tendency for hype to overheat the market, is very apparent in retrospect. So, what’s driving tech’s insatiable lust for the next big thing? Human psychology. VC dollars. FOMO. Plain curiosity. Excitement and hype, after all, is what drives invention.

As one executive acknowledges: “Without these motivations, many breakthroughs may have never received the resources, attention, and early adoption required to break through.” He continues, “From railroads and electricity to the internet and AI, the hype around ‘game-changing technology’ drives us forward.” So, some hype around ‘the next big thing’ ain’t all that bad. It’s knowing how to tell when wishful thinking replaces sanity that makes all the difference. Or, as another expert says, “Novelty is not value.”

Source: InfoWorld News