Chasing The Next Big Thing: Why AI Is Starting To Look A Lot Like Blockchain 2.0
In Brief
AI is generating massive excitement and investment but faces challenges similar to past tech hype cycles, with many companies struggling to realize meaningful, sustained value without clear strategy, governance, and focused leadership.
Artificial intelligence (AI) has taken over the headlines. From AI-generated art to sophisticated chatbots, the excitement has reached nearly every industry. Venture capital is flooding in, and business leaders are rushing to declare their organizations “AI-first.”
Yet for many who lived through the blockchain boom of the late 2010s, there’s a sense of déjà vu. This isn’t the first time a technology promised the world only to deliver far less in practice.
Back in 2017, blockchain carried similar hype. Companies merely tacked “blockchain” onto their names and watched their stock prices skyrocket, often without real products or application strategies.
Now, AI is following a strikingly similar trajectory. What we’re witnessing isn’t just innovation; it’s another ride on the classic tech hype cycle.
Understanding the Hype Cycle
Coined by Gartner, the tech hype cycle maps out how emerging technologies often surge on inflated expectations, then crash into disillusionment before finally delivering sustainable value. Recognizing this cycle helps businesses separate meaningful innovation from overblown marketing, and avoid costly missteps.
One recent example is Meta’s $40 billion plunge into the metaverse—a project many now view as an internally generated hype bubble that ultimately failed to deliver.
As Konstantine Buhler, a partner at Sequoia Capital, observed at The Wall Street Journal’s CIO Network Summit, “We are definitely in a hype cycle, especially for generative AI.” He added that in terms of realizing real business value, “we are not even in the beginning.”
When Buzz Outpaces Results
The blockchain hype was marked by dramatic but often hollow developments. One soda company rebranded itself as Long Blockchain Corporation and saw its stock jump 400% overnight, despite lacking any blockchain offering. Kodak introduced a cryptocurrency initiative, KodakCoin, which briefly lifted its share price before fading into irrelevance.
Today’s AI movement carries similar warning signs. Companies like Klarna, which leaned heavily into AI-based customer service, later walked back the decision after customer satisfaction declined. BuzzFeed’s attempt to turn to AI-generated content failed to rescue its struggling business, and CNET’s AI-written articles were found to contain multiple factual errors, eroding trust.
These cases reveal a pattern: AI, like blockchain before it, is being sold with promises that are difficult to fulfill quickly. The results, more often than not, are underwhelming.
Companies Struggling to Implement AI
Despite immense buzz, most organizations are still unsure how to extract consistent, measurable value from AI. Boardrooms are filled with questions: Will this revolutionize our operations or simply distract us? Is it an opportunity—or a costly misdirection?
Research from McKinsey and Deloitte offers sobering insights. Deloitte’s recent survey suggests that companies are gradually shifting from experimentation to serious enterprise-level adoption. But there’s friction: compliance concerns are rising, jumping from 28% to 38% in a matter of months. Meanwhile, 69% of respondents believe it will take over a year to fully implement robust AI governance frameworks.
McKinsey’s January 2025 report, based on interviews with global business leaders, finds that while nearly all companies invest in AI, only 1% view their efforts as mature. Employees are generally ready to embrace the technology—but leadership often lags in translating potential into action.
The numbers reflect this disconnect:
- Only 19% of C-level executives report revenue growth over 5% from AI initiatives.
- 39% report modest growth between 1% and 5%.
- 36% report no change at all.
The big question remains: Is AI just another overhyped tech promise? Or is there a path to genuine return on artificial intelligence investment (RoAI)?
According to Jim Rowan, Applied AI Leader at Deloitte, the answer lies in long-term strategy. He noted that while “anticipation is high,” meaningful returns require governance, collaboration, and a willingness to iterate. Future-focused leaders understand that AI’s rewards will unfold gradually—not overnight.
Why Businesses Keep Chasing the Hype
The impulse to jump on emerging tech is driven by three key forces: inflated expectations, short-term thinking, and flawed execution.
Under pressure to stay competitive and impress investors, executives often pitch grand visions without laying the groundwork. In the rush to appear innovative, companies deploy unproven systems and expect novelty alone to deliver returns.
The reality is that this approach frequently leads to disappointment—not because the technology lacks promise, but because the foundation to support it isn’t there. When applied too broadly and without strategic intent, even the most powerful tools falter.
Five Obstacles Blocking AI’s Potential
Michael de Kare-Silver, Managing Partner at Signium UK, observes that despite all the effort, no organization has yet found the “silver bullet” for consistent RoAI.
The barriers are numerous and familiar:
- Scattered Pilots and Experiments: Many companies allow employees to experiment with tools like Microsoft CoPilot or ChatGPT for small tasks. But these trials are uncoordinated—different teams testing different tools without central oversight or shared learning.
- Lack of Unified Strategy: Without an organization-wide framework, AI adoption becomes fragmented. There’s no clarity on who should use AI, for what purposes, or when it’s appropriate.
- Fear of the Unknown: Uncertainty surrounding AI’s long-term value keeps many companies in wait-and-see mode. As de Kare-Silver puts it, “No leader wants to risk throwing money in the water.”
- Poor Data Hygiene: High-quality data is the backbone of effective AI. Unfortunately, many companies lack clean, standardized databases, leading to flawed outputs and unpredictable behavior.
- No Dedicated AI Leadership: AI is often dumped into the portfolios of already-overloaded CTOs or data heads. But meaningful AI deployment demands focused, full-time leadership.
De Kare-Silver commended companies like BT, Schneider Electric, and ING for appointing senior executives specifically responsible for AI strategy.
So, Is AI Just Another Tech Mirage?
The answer isn’t binary. AI isn’t just a bubble, but it’s not a magic wand either. Its impact depends entirely on how it’s deployed. For one company, AI chatbots might clear support backlogs. For another, fraud detection tools might prevent costly breaches.
What’s clear is that AI will transform the future of work—what gets done, who does it, and how. But that transformation will only be sustainable if businesses proceed with clarity, discipline, and foresight.
Rather than chasing trends blindly, organizations should focus on actual use cases and adopt AI with a clear understanding of risks. Implement enterprise-grade governance. Design strategies that align with both current operations and future objectives.
The “why” should always come before the “how.”
By treating AI not as a savior but as a tool—one of many in the digital toolbox—companies can make smarter decisions and avoid the fate of those that fell victim to earlier hype cycles.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
