AI isn’t going anywhere—but the hype surrounding it might be.
Just a few years after blockchain mania fizzled, generative AI is now showing signs of a similar cycle.
Is it déjà vu, or are we witnessing a necessary shift from inflated promises to practical reality? The real test isn’t who shouts loudest, but who builds lasting value.
What’s the News?
In the last two years, artificial intelligence has dominated headlines and boardroom agendas. From AI-powered art and productivity tools to customer service chatbots, the technology is being pitched as the solution to just about everything.
Venture capitalists have poured billions into AI startups. CEOs regularly tout their companies as “AI-first.” But to seasoned observers, this fever feels familiar.
Back in 2017, it was blockchain that promised to revolutionize every industry. Companies tacked “blockchain” onto their names and watched share prices soar, regardless of whether they had a viable product.
Now, AI seems to be following the same trajectory: high expectations, rushed adoption, and inevitable disappointment when real-world outcomes fall short.
The Conversation, CC BY-ND
The pattern follows what Gartner famously labeled the “hype cycle.” New technologies rise on a wave of publicity and promise. But as exaggerated expectations collide with implementation reality, disillusionment often sets in before real utility emerges.
Take Meta’s $40 billion investment in the metaverse. Or Kodak’s brief pivot to crypto. Each chased a trend that sounded transformative, but ultimately couldn’t sustain momentum without substance.
AI has had its KodakCoin moments, too. BuzzFeed’s stock more than doubled after it announced AI-generated content. Klarna laid off hundreds of employees, replacing them with a chatbot it claimed could handle millions of support queries.
Yet both saw setbacks. Klarna had to reverse course as customer satisfaction tanked. BuzzFeed’s AI push failed to revive its core business. CNET also took a reputational hit after publishing error-filled AI articles.
These cases aren’t isolated—they’re red flags that the AI hype curve may be peaking.
Why It Matters
Understanding hype cycles helps leaders separate future-defining innovations from short-lived fads. AI is not just another buzzword—it has real potential. But poor execution can sabotage its credibility.
Like blockchain, many early AI deployments lacked alignment with actual business needs. Leaders rushed in, fueled by media narratives and investor pressure, without adequate strategy or infrastructure.
That doesn’t mean AI is doomed. It means we’re entering a more discerning phase—one where value must be proven, not presumed. Just as blockchain found useful applications in asset tokenization, generative AI could thrive in roles that amplify human productivity, rather than attempt to replace it.
The failure isn’t in the technology. It’s in treating it like a silver bullet rather than a smart tool.
💡 Expert Insight
“[AI] should be used to enhance, not replace, human work,” noted Gary Marcus, cognitive scientist and AI critic. “Without proper oversight and context, these systems are bound to disappoint.”
He has long argued that many AI systems are being deployed too broadly, too soon, without a full understanding of their limitations. His view echoes a growing sentiment: usefulness, not novelty, should drive adoption.
GazeOn’s Take
We’re likely at the start of AI’s “slope of enlightenment”—the stage where real applications get tested, refined, and integrated more thoughtfully.
Expect a shift from flashy announcements to quieter, problem-solving implementations. The companies that succeed will be the ones who treat AI as a tool, not a talking point.
💬 Reader Question
Is the AI hype dying—or just maturing? Tell us how your company is navigating this shift.
Main picture: credit Izf/Shutterstock
About Author:
Eli Grid is a technology journalist covering the intersection of artificial intelligence, policy, and innovation. With a background in computational linguistics and over a decade of experience reporting on AI research and global tech strategy, Eli is known for his investigative features and clear, data-informed analysis. His reporting bridges the gap between technical breakthroughs and their real-world implications bringing readers timely, insightful stories from the front lines of the AI revolution. Eli’s work has been featured in leading tech outlets and cited by academic and policy institutions worldwide.
