Technology often has a fairly predictable adoption cycle, going from innovators and early adopters to mainstream use, to the point where even those who are way behind the curve catch up and start using the technology.

But there's another cycle at play -- the hype cycle -- and this impacts everything from budgeting to forecasting to startup investments. Coined back in 1995 by research firm Gartner, every annual Hype Cycle report attempts to show whether a technology is on track for productive use, or is still in the smoke-and-mirrors phase of its life.

Gartner defined five key phases in the cycle.

The Innovation Trigger phase is all about building excitement. This is where a new technology like generative AI begins to show some serious promise, and where engineers, marketers, and investors can see the potential -- even though most of that potential is as yet unfulfilled and, in many cases, not even possible with current technology.

Then comes the Peak of Inflated Expectations. By this point, press coverage has been breathless and overwhelming, entrepreneurs have been pitching new startups, marketers have been adding allusions to the technology to everything they're pitching, and… enough, already!

AI is a good example of this. I mean, wow. Aren't you reaching a saturation point with all the over-the-top AI hype getting thrown around? I just got a 3D printer that was drenched in an AI washing effort. Although the tech in this printer was exactly the same as it's always been, the product came with "AI assisted" plastered all over the product casing, the website, and the promotional materials.

Next -- and I think this is the real innovation in Gartner's cycle -- comes the Trough of Disillusionment. Just as teenagers go through a phase where nothing's ever good enough, so too do tech products. After what seems like an unending promotion with little real uptake and deployment, the technology previously subjected to such lofty and exuberant fuss now appears to have wings made of wax. Expectations come crashing to the ground.

Although Gartner doesn't describe this, I've often seen how this phase is accompanied by ridicule. Anyone who -- post-peak -- recommends or discusses the so-called "failed" technology is considered a out of touch or a fanboi who hasn't accepted reality.

VR has been in this phase repeatedly, and -- I expect -- will go through it again. Take Apple's Vision Pro headset. It's wildly expensive, amazing to use, uncomfortable, and -- at least for now -- pretty much a novelty except for some specific vertical uses.

In fact, in Gartner's 2024 Hype Cycle for Emerging Technologies, the analyst firm places spatial computing at the early edge of the Innovation Trigger phase. But I'm not so sure. As someone who's been covering the technology's developments all year, I'd suggest that spatial computing -- at least as it pertains to the Vision Pro -- has landed in the Trough of Disillusionment. In a few years, when Apple introduces a cheaper and lighter headset, I'm sure the Vision product line will once again run the Hype Cycle curve, possibly with better results.

Finally, some technologies crawl out of the Trough of Disillusionment and begin their climb up the Slope of Enlightenment and the Plateau of Productivity. These two phases refer to the time when a technology begins finding its footing, its specific value propositions are proven, and it enters some level of productive use, albeit without the associated hype dogging its every step.

Each year, Gartner issues a total of 25 different hype cycles. ZDNET has been covering their cycle for emerging technology since, well -- I found an article from 2009. What makes this particular hype cycle about emerging technologies so compelling? It helps us predict what will be hot and what will not. It also helps businesses predict where to put their cash, where to assign staff to evaluate potential, and where it might be practical to innovate.

But you need to take the hype cycle with a grain of salt. Back in 2021, we wrote that Gartner predicted, "Artificial intelligence's impact on generating code, augmenting design and innovation is all 5- to 10-years away." That was wrong. Generative AI began making a substantial impact in just two years, at the very beginning of 2023.

But that was then, and this is now. In 2024, Gartner has identified four major themes that are just starting to climb the big Innovation Trigger hill. These are: autonomous AI, developer productivity, total experience, and human-centric security. We'll break each of these themes down next.

Autonomous AI

The obvious first point of contact here is self-driving car technology. Beyond that, think of large action models (where AIs take action, not just spew information), machine customers (where machines buy stuff), humanoid working robots (every science fiction movie you've ever seen), autonomous agents, and reinforcement learning.

The big idea here is that AI systems will take on tasks that humans performed previously. This goes beyond generative AI writing essays for college students who just want to have fun. Instead, we're looking at machines that can perform physical tasks (cars and robots, for example), and machines that interact with the rest of the world (like printers that automatically order printer ink or cars that automatically schedule their own maintenance visits to the local dealer).

Obviously, there are quite a few obstacles before autonomous AI can achieve real productivity, not the least of which is that most of us are nervous about letting robots loose in the world. I mean, who hasn't seen Terminator?

But there are other issues, including regulatory concerns, areas where data is scarce and yet AIs need to make decisions, lack of trust, overall computational requirements (as well as battery power duration), and more.

Keep in mind that different projects may be at different points along the hype cycle. For example, Apple canceled its multi-billion dollar self-driving car project, while Alphabet's robo-taxi service actually doubled the number of riders over the last few months.

Developer Productivity

While the hype over AI writing code is huge, even the leading players fail miserably --  as we've seen through ZDNET's hands-on testing. The hype is incredible, and perfectly in line with the idea that AI-augmented software development is on the Innovation Trigger rocket flight.

And, to be fair, it is exciting. When I actually got ChatGPT to write a WordPress plugin for my wife's e-commerce business, I was astounded. Subsequently, I have used ChatGPT to help me write a ton of code. Overall, I estimate that it saved me weeks, if not a month or two, on my projects over the last year.

But here's the thing: The AI didn't write my code. The AI helped me write my code. Most of the hype around AI coding implies that the AIs will just generate the app you have in mind, as long as you can type "Write me an app that will make me a million dollars" into the prompt bar.

Those who rely too much on AI coding will take a deep dive into that Trough of Disillusionment. But those who use AI to help write carefully defined and tested snippets of code will find some very big benefits.

Total Experience

Every few years, there's another customer-centric buzzword that promises endless profits. Once upon a time, it was multichannel -- the idea that you meet the customer wherever they want you to be, whether that's on their phone, in their desktop browser, on social media, or even in a physical location.

Gartner's premise for "total experience" is that vendors will create super-salient shared experiences that "intertwine customer experience, employee experience, multi-experience, and user experience practices."

I know. It makes my head hurt, too.

It might make more sense if you look at the emerging technologies Gartner attributes to this trend: 6G, spatial computing, and digital twins of customers.

Nobody has fully defined 6G yet, but the best description was the one a telecommunications executive told me during a discussion of future technology: super-fast 5G with a lot of AI help. Specifically, think of this as collapsed latency, so it's possible to respond in real-time to whatever is happening. This will also aid self-driving cars.

Spatial computing is something we're getting to know in the Vision Pro and the Meta Quest 3, but it will become far more constructive once it works in regular glasses, rather than headsets that weigh the same as a brick.

The digital twins of customers concept is creepy as heck. Basically, it describes a way companies can model consumer interests and behaviors so accurately that they can simulate customer interaction and affinity based on their established data history. All to better manipulate folks into buying! And yes, this same technology can be used to influence elections. Yikes.

Human-Centric Security

The last major trend has to do with the need for across-the-board improved security. The concept behind "human-centric" is that individuals have to be part of the overall security footprint. That includes a focus on the user experience, finding behavioral insights, encouraging security behavior, and building trust through transparency.

But Gartner sees a bunch of technological trends supporting this effort. They include AI TRISM (AI trust, risk, and security management), which approaches security from a trustworthy, secure, transparent, and ethical approach. Mesh architecture security environments are intended to make security scalable and modular. The idea of a digital immune system combines technologies and practices to build resilience by proactively identifying threats and responding to them.

AI comes into play here as well, across all the solution areas. One big push is into the idea of federated machine learning, where the learnings captured in one part of the enterprise network are federated (made available) to the entire network.

Every year, it looks like we're getting closer and closer to the world of Blade Runner. I found the idea of customer twins and spatial advertising particularly evocative of replicants and the customized marketing shown in the classic movie.

Gartner's predictions are just that: predictions. As the chart above shows, the research firm has identified more emerging trends beyond those I've discussed. These four trends, however, are the ones you should look out for this year, going into next year.

What do you think? Is Gartner on the right track? Are there other trends we should be looking at? Let us know in the comments below.

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