In recent years, artificial intelligence (AI) has taken centre stage across various industries. From AI-generated art to chatbots in customer service, every sector is seemingly poised for disruption.
It’s not just in your news feed every day – venture capital is pouring in, while CEOs are eager to declare their companies “AI-first.” But for those who remember the lofty promises of other technologies that have since faded from memory, there’s an uncanny sense of déjà vu.
In 2017, it was blockchain that promised to transform every industry. Companies added “blockchain” to their name and watched stock prices skyrocket, regardless of whether the technology was actually used, or how.
Now, a similar trend is emerging with AI. What’s unfolding is not just a wave of innovation, but a textbook example of a tech hype cycle. We’ve been here many times before.
Understanding the hype cycle
The tech hype cycle, first defined by the research firm Gartner, describes how emerging technologies rise on a wave of inflated promises and expectations, crash into disillusionment and, eventually, find a more realistic and useful application.
Recognizing the signs of this cycle is crucial. It helps in distinguishing between genuine technological shifts and passing fads driven by speculative investment and good marketing.
It can also mean the difference between making a good business decision and a very costly mistake. Meta, for example, invested more than US$40 billion into the metaverse idea while seemingly chasing their own manufactured tech hype, only to abandon it later.
When buzz outpaces reality
In 2017, blockchain was everyone’s focus. Presented as a revolutionary technology, blockchain offered a decentralized way to record and verify transactions, unlike traditional systems that rely on central authorities or databases.
US soft drinks company Long Island Iced Tea Corporation became Long Blockchain Corporation and saw its stock rise 400% overnight, despite having no blockchain product. Kodak launched a vague cryptocurrency called KodakCoin, sending its stock price soaring.
These developments were less about innovation and more about speculation, chasing short-term gains driven by hype. Most blockchain projects never delivered real value. Companies rushed in, driven by fear of missing out and the promise of technological transformation.
But the tech wasn’t ready, and the solutions it supposedly offered were often misaligned with real industry problems. Companies tried everything, from tracking pet food ingredients on blockchain, to launching loyalty programs with crypto tokens, often without clear benefits or better alternatives.
In the end, about 90% of enterprise blockchain solutions failed by mid-2019.
The generative AI déjà vu
Fast-forward to 2023, and the same pattern started playing out with AI. Digital media company BuzzFeed saw its stock jump more than 100% after announcing it would use AI to generate quizzes and content. Financial services company Klarna replaced 700 workers with an AI chatbot, claiming it could handle millions of customer queries.
The results were mostly negative. Klarna soon saw a decline in customer satisfaction and had to walk back its strategy, rehiring humans for customer support this year. BuzzFeed’s AI content push failed to save its struggling business, and its news division later shut down. Tech media company CNET published AI-generated articles riddled with errors, damaging its credibility.
These are not isolated incidents. They’re signals that AI, like blockchain, was being over hyped.
Why do companies chase tech hype?
There are three main forces at play: inflated expectations, short-term view and flawed implementation. Tech companies, under pressure from investors and media narratives, overpromise what AI can do.
Leaders pitch vague and utopian concepts of “transformation” without the infrastructure or planning to back them up. And many rush to implement, riding the hype wave.
They are often hindered by a short-term view of what alignment with the new tech hype can do for their company, ignoring the potential downsides. They roll out untested systems, underestimate complexity or even the necessity, and hope that novelty alone will drive the return on investment.
The result is often disappointment – not because the technology lacks potential, but because it’s applied too broadly, too soon, and with too little planning and oversight.
Where to from here?
Like blockchain, AI is a legitimate technological innovation with real, transformative potential.
Often, these technologies simply need time to find the right application. While the initial blockchain hype has faded, the technology has found a practical niche in areas like “asset tokenization” within financial markets.
This allows assets like real estate or company shares to be represented by digital tokens on the blockchain, enabling easier, faster and cheaper trading.
The same pattern can be expected with generative AI. The current AI hype cycle appears to be tapering off, and the consequences of rushed or poorly thought-out implementations will likely become more visible in the coming years.
However, this decline in hype doesn’t signal the end of generative AI’s relevance. Rather, it marks the beginning of a more grounded phase where the technology can find the most suitable applications.
One of the clearest takeaways so far is that AI should be used to enhance human productivity, not replace it. From people pushing back against the use of AI to replace them, to AI making frequent and costly mistakes, human oversight paired with AI-enhanced productivity is increasingly seen as the most likely path forward.
Recognizing the patterns of tech hype is essential for making smarter decisions. Instead of rushing to adopt every new innovation based on inflated promises, a measured, problem-driven approach leads to more meaningful outcomes.
Long-term success comes from thoughtful experimentation, implementation and clear purpose, not from chasing trends or short-term gains. Hype should never dictate strategy; real value lies in solving real problems.
Gediminas Lipnickas is lecturer in marketing, University of South Australia
This article is republished from The Conversation under a Creative Commons license. Read the original article.
