In recent years, artificial intelligence (AI) has dominated conversations across industries.
From AI-generated art to customer service chatbots, it seems every sector is on the brink of AI-driven disruption. It’s everywhere – from your social feeds to boardroom strategy sessions. Venture capital is flowing, and CEOs are eager to brand their companies as “AI-first.”
But for anyone who recalls the overblown promises of past tech trends, it all feels very familiar.
Back in 2017, it was blockchain that was poised to change everything. Companies rebranded with “blockchain” in their names and saw stock prices soar – often without ever actually using the technology in any meaningful way.
Now, AI appears to be following a similar arc. What we’re seeing isn’t just innovation – it’s a textbook example of the tech hype cycle, and it’s far from the first time.
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The Tech Hype Cycle Explained
The tech hype cycle – originally described by research firm Gartner – tracks how emerging technologies tend to rise on inflated expectations, plummet into disillusionment, and eventually settle into practical, useful roles.
Understanding this cycle helps separate lasting innovation from fleeting fads fueled by speculation and marketing spin.
It can also prevent costly missteps. Take Meta, which poured over US$40 billion into the metaverse in pursuit of a self-created hype – only to retreat from the idea later.
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When Buzz Outruns Reality
In 2017, blockchain was heralded as a revolutionary, decentralized solution to everything. It promised to replace centralized systems for recording and verifying transactions.
But what actually happened?
The US-based Long Island Iced Tea Corporation renamed itself Long Blockchain Corporation and saw its stock price quadruple overnight – despite lacking any blockchain product. Kodak jumped on the bandwagon too, launching a vague cryptocurrency called KodakCoin that briefly spiked its share price.
Most of this was speculative frenzy, not meaningful innovation. Companies rushed in to avoid missing out, chasing hype instead of solving actual problems.
Solutions often didn’t fit the use case. From tracking pet food ingredients to launching crypto loyalty programs, many blockchain applications added little or no real value.
By mid-2019, roughly 90% of enterprise blockchain projects had failed.
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Déjà Vu: The Generative AI Boom
By 2023, AI had taken the spotlight. Digital media outlet BuzzFeed’s stock more than doubled after announcing plans to use AI to generate quizzes and content. Klarna, a financial services firm, laid off 700 workers in favor of an AI chatbot it said could handle millions of queries.
But the results were largely disappointing. Klarna later had to scale back, rehiring human agents after a drop in customer satisfaction. BuzzFeed’s AI experiment failed to reverse its fortunes, and its news division ultimately shut down.
CNET, a tech media site, published AI-written stories riddled with factual errors – damaging its credibility.
These weren’t isolated mistakes; they reflected a broader pattern of overhyping AI.
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Why the Hype?
Three main forces drive tech hype:
Inflated expectations
A short-term mindset, and
Poor execution.
Tech companies, under pressure from investors and media buzz, often overpromise. Executives pitch grand visions of transformation without adequate planning or infrastructure.
Many jump in too early, hoping novelty alone will deliver returns. But deploying untested systems, overlooking complexity, and underestimating the human element usually leads to failure.
This isn’t because the technology lacks potential – but because it’s misapplied, rushed, or used without clear purpose.
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So What Happens Next?
AI, like blockchain, is a legitimate and powerful technology. But it needs time to mature and settle into its most useful roles.
Blockchain’s hype may have died down, but it has since found a niche in areas like asset tokenization – allowing assets like shares or real estate to be traded more efficiently as digital tokens.
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Generative AI will likely follow a similar trajectory. As the current wave of hype recedes, the consequences of rushed, poorly designed implementations will become clearer.
But that doesn’t mean generative AI is finished. On the contrary, it’s entering a more realistic phase, where it can be refined and aligned with actual needs.
One key takeaway is that AI should complement human productivity – not replace it. Public backlash, AI errors, and a growing awareness of its limitations all point to a future where human-AI collaboration becomes the norm.
Recognizing these tech cycles is critical. Rather than jumping on every new bandwagon, companies should take a measured, problem-driven approach.
Success depends on:
Thoughtful planning
Strategic implementation, and
Solving real-world problems
not hype.
In short, value comes from purpose – not buzz.
This article is republished under a Creative Commons license (CC BY-ND).
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