Riding the Latest AI Hype Cycle: Myths, Realities, and Future

Any time a promising new technology enters the channel, it gets hyped. This is what marketers do for a living, after all.

Artificial intelligence (AI) is the latest tech darling, but it has the dubious honor of being the focus of both hype and anti-hype. On one hand, it will help the enterprise streamline operations, lower costs, and make the most of the vast amounts of data needed to succeed in the digital economy. On the other, it will destroy jobs, take over the world, and kill us all.

If past technology hype cycles are any guide, however, it will do neither.

The Winter of Artificial Intelligence

The one consistency in the tech industry is that it tends to over-promise and under-deliver. As Medium.com’s Clive Thompson points out, the risk of today’s AI hype is that it will lead to an AI winter. This is when technology becomes so tarnished that demand fades even if it proves useful in some lesser capacity than what was originally advertised.

In fact, we’ve been through this before with AI – twice. Both the 1970s and 1980s saw AI hype peak and then trough in quick succession. Today’s hype curve actually began in the mid-1990s and then saw a jump in the 2000s as rapid improvements in data generation and analytics led to renewed hope that, this time, AI will deliver.

Now that regulatory concerns and plain, old fear, doubt, and uncertainty (FUD) are on the rise, we might be on the cusp of another winter.

Approaching the Summit

This is where generative AI – the technology fueling ChatGPT and other content-creation apps – sits in Gartner’s Hype Cycle: at the height of the so-called Peak of Inflated Expectations.

Organizations are quickly deploying the technology in the hopes that it will do all the amazing things that its backers say it will do. When it does not – and this is a tall order for any technology – it enters the Trough of Disillusionment along with myriad other technologies – edge AI and autonomous vehicles among them.

This does not mean generative AI is doomed to failure. Many technologies rebound on the Slope of Enlightenment and then settle in on the Plateau of Productivity for the long term. In other words, this process is not about living up to the inflated expectations of the initial hype but finding the real-world applications that justify ongoing investment.

AI is unlike most other technologies, however, in that its public perception has been influenced by a robust mythology that dates back to the automata of ancient civilizations. More recently, thanks to sci-fi books and movies, the idea of an infallible, all-knowing digital brain is the first thing that jumps to mind whenever AI is mentioned. Even though current AI models are nowhere near that sophisticated.

Myths Vs. Reality

Techopedia’s contributor Dr. Claudio Buttice recently listed a number of AI myths that still cloud the understanding of the technology and what it can do, even among much of today’s knowledge workforce. For one thing, AI is merely software that can alter its perception of data and make adjustments to changing conditions. A robot or an android is something else entirely and involves a wide set of technologies to create even a reasonable facsimile of human functionality.

And while AI does have the capacity to learn, it is capable of only limited self-instruction. Even then, it must be fed properly curated data, lots of it, in order to achieve basic levels of understanding. And no, AI does not always outperform humans. In fact, it only excels at the mundane, repetitive tasks that most people don’t want to do anyway.

In most cases, technology hype is not a natural phenomenon. It is the result of what marketers call “demand push”; that is, fire up the chatter over a new development – in this case, AI – in order to fuel the fear of losing out to the competition. After all, if everyone is talking about it, it must be real.

Awash in AI

From there, the next step is to relabel existing platforms with the new technology while, at best, adding it in only a minor, tangential way. In the past, this was virtualization-washing, cloud-washing, green-washing, and the like. The current phase of AI-washing is already in full swing, says Techopedia’s Linda Rosencrance, and is driven largely by the need to raise capital or stall for time while system architectures get retooled.

The best way to counter this is to educate yourself as to what AI is and what it is not and to focus on deploying solutions to actual problems, not just buzzwords.

The Bottom Line

Even though technology hype is usually manufactured, it still tends to pursue a natural course. At some point, the world will come to understand what it can and cannot do, then both the overblown fears of annihilation and expectations of grandeur will fade, and a comfortable reality will set in – just in time for the next hyped technology to come along.