Hit the Brakes on the AI Hype Train

I’m old enough to remember when virtualization was the “silver bullet” that would solve all our computing problems (in fact, I was founding editor of the very first physical virtualization-focused magazine, Virtualization Review.) 💡

In fact, virtualization was ground-breaking, and did help change the industry.

I also remember the rise of cloud computing, and how it was also the “silver bullet” that would solve all our computing problems. Much like virtualization, cloud computing did bring about huge changes in the industry, and nothing was the same after as it was before. ☁️

However, what both of these technologies were not were silver bullets that solved all our computing problems. They were massively important, to be sure, but didn’t lead us down the road to the Promised Land of Computing. 🛣️

In that, they have something in common with artificial intelligence, or AI. The AI hype is near-smothering in its promise to free us from all our current IT shackles, ushering in the new Golden Era of friction-free computing that keeps us secure from hackers, skyrockets productivity while reducing headcount, and brews a perfect cup of coffee in the morning. ☕

But if it follows well-established patterns in the industry, AI will settle down and find its rightful place within the next few years. And like virtualization and cloud computing (along with many other trends that have followed suit), it’s likely to fail to fulfill the hype it’s promising. 📉

That’s a big reason AI is currently in the “peak of inflated expectations” in the current Gartner Hype Cycle. Gartner defines the phrase like this: “Early publicity produces a number of success stories — often accompanied by scores of failures. Some companies take action; many do not.” The current state of AI fits this definition. 📈

Here’s a key quote from Gartner about the hype cycle:

“With AI investment remaining strong this year, a sharper emphasis is being placed on using AI for operational scalability and real-time intelligence… This has led to a gradual pivot from generative AI (GenAI) as a central focus, toward the foundational enablers that support sustainable AI delivery, such as AI-ready data and AI agents.”

It feels like “generative AI” is pretty well understood now, and its limitations are generally known and accepted. It’s still being refined all the time—updated versions of ChatGPT are released monthly, and often several times per month—and in that way is still progressing, but its “Wow” factor has faded, and it’s become part of the fabric of business. Most of us now use some form of generative AI on a regular basis. 🤖

The more sophisticated form, involving agents that can problem-solve and act independently, are still largely the great unknown version of AI. This isn’t likely to change soon, as its permutations and use cases are constantly expanding. 🔮

In time, though, it’s likely to move into the category of “plumbing” technology, the type that becomes foundational to all IT infrastructure. In five years or less, the industry will have moved onto the “Next Big Thing,” seeking for the newest silver bullet that will solve all our IT problems. 🚀

When that happens, I may just be writing the next version of this article, discussing how the newest “shiny toy” is all the rage, in the same way that virtualization, cloud computing, and artificial intelligence were, once upon a time. ⏳