“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,” says Brian Fox, CTO of Sonatype, a provider of software supply chain security software.

A 2025 HostingAdvice.com survey found that most programming language migrations are driven by hype rather than proven outcomes. And, a MIT report recently 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 Derek Holt, CEO of Digital.ai, 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. But what happens next is usually shoved under the rug: embarrassing overinvestment, abandoned projects, unmet promises, and, ultimately, unfulfilled dreams. 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.

In this post, we’ll do some post-mortems on the biggest recent software trends that failed to deliver on their promises. Beyond indulging nostalgia, we’ll assess the impacts and pick apart the lessons learned from each cycle. Both skeptics and believers should keep these lessons in mind, especially in times like these, when optimism outpaces reality.

Blockchain

“Blockchain is a textbook case of overhype,” says Kyle Campos, chief technology and product officer at CloudBolt, the provider 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.

“I saw the insurance industry pour resources into blockchain,” says Campos. “But after substantial investments, most efforts were abandoned because the cost and complexity far outweighed the benefits.”

Others watched blockchain get replaced by simpler tech that just worked. “One supply chain project I saw was shelved after a year and replaced with a simple setup: [Apache] Kafka, signed records, and [Amazon] S3 immutability,” says Srikara Rao, CTO of cloud and cyber security services at R Systems, a global digital solutions provider. “It lacked the blockchain buzzword, but worked reliably and scaled.”

Blockchain simply didn’t fit many use cases. “Blockchain is fundamentally a very, very slow, expensive database,” says Liz Fong-Jones, field CTO at Honeycomb, an observability platform provider. “There are heaps of faster, cheaper databases out there, and the only reason to use blockchain over those is if you really 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.

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 consultancy Accenture, Meta’s Mark Zuckerberg, Microsoft’s Satya Nadella, and plenty of others 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,” says Honeycomb’s Fong-Jones.

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. Not to mention the conveniently timed rebrand of Meta, the metaverse’s loudest proponent.

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 Shannon Mason, chief strategy officer, Tempo Software, a project and resource management platform. “It promised magic but delivered mess.”

Back in 2011, consultants McKinsey & Company 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,” says Mason. “Instead of simplifying decisions, they created new layers of complexity: multiple tools, governance headaches, and very little actionable output.”

When working at CA Technologies and with other teams, Mason witnessed enterprises investing 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,” she says.

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. For some, it already has.

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 Digital.ai’s Holt.

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, Holt points to heavyweight standards, orchestration and performance issues, lack of reuse, cultural and organizational hurdles, unclear ownership, and governance concerns. 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 did however give way to microservices and API-first architecture, which are still used today,” says Holt.

To his point, REST APIs are ubiquitous. The API economy is now a multibillion-dollar industry projected to expand further with agentic consumption and API-based productization. Nearly 90% of developers use APIs, according to SlashData, with 61% of API usage being for internal services, like microservices, as reported by Postman in 2023.

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 CloudBolt’s Campos 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, The Guardian reported.

Some defenders still tout niche NFT use cases. One web 3.0 proponent, in a sponsored Forbes post in early 2024, pointed to fringe uses outside of art, like an airline that offers NFT versions of its tickets. Necessary? Useful? 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 Mason, who cites the recent MIT study showing 95% of generative AI pilots fail as very telling.

Similarly, a 2025 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,” Mason adds. “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,” as Ted Gioia puts it, has led to increased apathy: only 8% of Americans would pay extra for AI, reports ZDNET. Generative AI features continue to appear in end-user applications, whether they’re helpful or not—and users are pushing back. The Wall Street Journal reports that 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,” says Campos. “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,” says Fong-Jones. Although broader business benefits remain elusive, generative AI has been successfully applied in niches such as software development. It’s undoubtedly here to stay.

Holt also sees many parallels from historical hype cycles to today’s focus on AI and agents, underscoring the need for evolving standards, like Model Context Protocol and Agent2Agent. “Much work is still ahead to continue to improve those standards and to explore more complex use cases,” he says.

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,” reminds Sonatype’s Fox. “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,” says Holt. “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,” says R Systems’ Rao.

For Rao, his 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,” he says.

Chasing the next big thing

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 Holt 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 Mason says, “Novelty is not value.”