“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 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.

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. But what happens next is usually shoved under the rug: embarrassing overinvestment, abandoned projects, unmet promises, and ultimately unfulfilled dreams.

Blockchain

“Blockchain is a textbook case of overhype,” says Kyle Campos, chief technology and product officer at CloudBolt. The immutable distributed ledger was supposed to usher in web 3.0 and transform countless industries. Although blockchain still powers cryptocurrency and decentralized finance, an en masse adoption of private blockchains by enterprises never materialized. “I saw the insurance industry pour resources into blockchain. 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. “One supply chain project I saw was shelved after a year and replaced with a simple setup: Kafka, signed records, and S3 immutability,” says Srikara Rao, CTO at R Systems. “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. “There are heaps of faster, cheaper databases out there.” What’s worse, the blockchain industry became a backwater for fraud: in 2024 alone, the FBI reported Americans suffered $9.3 billion in losses due to cryptocurrency-related scams. 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—or so we were told at the height of the pandemic. Despite promises of holographic conference tables and employee avatars, no fully immersive 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. 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 at Tempo Software. “It promised magic but delivered mess.” Back in 2011, McKinsey hailed big data as “the next frontier for innovation.” In practice, 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.” While the promise of big data influenced some enterprises to take their data strategy more seriously, 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 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. The idea was to improve reusability and scalability, but heavyweight standards, orchestration issues, and cultural hurdles caused it to falter. However, there’s a silver lining: 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. REST APIs are now ubiquitous, and the API economy is a multibillion-dollar industry. Lesson learned: Sometimes, it’s what the trend inspires that leaves the everlasting impact.

NFTs

Non-fungible tokens (NFTs) took hype to another level. 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.” By 2023, most NFTs had become virtually worthless. Some defenders still tout niche use cases, such as an airline offering NFT versions of its tickets, but such experiments failed to demonstrate lasting value. 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, citing the recent MIT study showing 95% of generative AI pilots fail. 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.” “The problem isn’t the tech, it’s the approach: broad, abstract use cases instead of targeted pain points,” Mason adds. On the consumer side, only 8% of Americans would pay extra for AI, reports ZDNET. The Wall Street Journal reports that companies are learning to be far more cautious about promoting AI in products. Others agree: “Lessons from blockchain can definitely be applied to today’s AI frenzy,” says Campos. Even so, AI has more staying power than earlier waves. “AI is different because it actually delivers tangibly different results,” says Fong-Jones, noting its success in niches like software development. Lesson learned: Some hyped technologies are praiseworthy, but need maturity and refinement in where exactly to apply them.

Source: InfoWorld News