Over the decades, software leaders have repeatedly fallen for the trap of chasing "the next big thing," only to face unmet expectations. A 2025 survey found that most programming language migrations are driven by hype rather than proven outcomes. Similarly, a MIT report noted that although 80% of enterprises have attempted generative AI pilots, only 5% succeeded. As Derek Holt, CEO of Digital.ai, observes, "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."
Blockchain
Blockchain was supposed to usher in Web 3.0 and transform industries, but enterprise adoption largely fizzled. Kyle Campos of CloudBolt notes that the insurance industry poured resources into blockchain, only to abandon most efforts due to high cost and complexity. Srikara Rao of R Systems recalls a supply chain project shelved in favor of simpler tech like Apache Kafka and S3 immutability. Liz Fong-Jones of Honeycomb calls blockchain "a very, very slow, expensive database." Beyond poor ROI, the blockchain space saw rampant fraud, with FBI-reported losses of $9.3 billion in 2024 from crypto scams.
Metaverse
The metaverse was hailed as a revolutionary digital realm for work and play, but it never materialized. Fong-Jones notes that both blockchain and VR/metaverse were heavily hyped and failed to achieve success commensurate with investment. High headset costs, lack of a killer app, and low user enthusiasm stalled momentum. The idea of mixed reality taking over work life was wildly overstated.
Big Data
Big data promised magic but delivered mess, says Shannon Mason of Tempo Software. Teams encountered massive storage overheads and data management challenges, turning data lakes into "data swamps." Many enterprises invested heavily only to find programs underused and difficult to operationalize. The lesson: if an initiative can't show business value from day one, it's more burden than breakthrough.
Service-Oriented Architecture (SOA)
SOA aimed to move from monolithic to component-based, reusable services. Holt of Digital.ai points out that heavyweight standards, orchestration issues, and cultural hurdles caused SOA to falter. However, SOA paved the way for microservices and API-first architectures. REST APIs are now ubiquitous, and the API economy is a multibillion-dollar industry.
NFTs
Non-fungible tokens were touted as the future of digital ownership, but collapsed without meaningful use cases. AI images of bored apes briefly sold for millions, but by 2023 most NFTs were virtually worthless. Campos notes that NFTs "took the hype even further." Technology based purely on public perception can disappear as quickly as the hype.
Generative AI
Generative AI is the latest example. A McKinsey survey found 80% of companies using generative AI saw no significant bottom-line impact, with 90% of projects stuck in pilot mode. On the consumer side, only 8% of Americans would pay extra for AI. Yet AI has more staying power than earlier waves; it delivers tangible results in niches like software development. The lesson: some hyped technologies are praiseworthy but need maturity and refinement in application.
These six trends are not the only ones. The industry is quick to downplay past technologies as new approaches emerge. Adding exotic technology without clear, measurable benefit causes more pain than payoff. As one expert suggests, "Novelty is not value." History repeats itself, and hindsight can guide future tech choices.
Source: InfoWorld News