Technology often has a fairly predictable adoption cycle, from innovators and early adopters to mainstream use, to the point where even those behind the curve catch up and start using the technology.
But there’s another cycle at play—the hype cycle—and this affects everything from budgeting to forecasting to startup investments. Conceived in 1995 by research firm Gartner, each annual Hype Cycle report seeks to see whether a technology is on track for productive use, or is still in the smoke-and-mirrors phase of its life.
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Gartner defined five important phases in the cycle.
Five phases of the hype cycle
The Innovation Trigger phase is all about building excitement. This is where a new technology like generative AI starts to show some serious promise, and where engineers, marketers, and investors can see the potential—even though most of that potential is still unfulfilled and, in many cases, not even possible with current technology.
Then comes the Peak of Inflated Expectations. By this point, the press coverage was breathless and overwhelming, entrepreneurs pitched new startups, marketers added allusions to the technology to everything they pitched, and… enough, already!
AI is a good example of that. I mean, wow. Are you not reaching a saturation point with all the over-the-top AI hype being thrown around? I just got a 3D printer that was soaked in an AI wash. Although the tech was in this printer exactly same as it has always been, the product came with “AI assisted” plastered all over the product packaging, website and promotional material.
Next—and I think this is the real innovation in Gartner’s cycle—comes the Trough of Disillusionment. Just like teenagers go through a phase where nothing is ever good enough, so do tech products. After what seems like an endless promotion with little real uptake and commitment, the technology that was previously subject to such high and exuberant hype now seems to have wings made of wax. Expectations come to the ground.
Although Gartner does not describe this, I have often seen this phase accompanied by mockery. Anyone who – post-peak – recommends or discusses the so-called “failed” technology is considered an incorrigible or a fanboy who has not accepted reality.
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VR has been through this phase several times, and – I expect – will go through it again. Take Apple’s Vision Pro headset. It’s very expensive, great to use, inconvenient, 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 has covered the developments of the technology throughout the year, I would suggest that spatial computing – at least as far as the Vision Pro is concerned – 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 ride the Hype Cycle curve again, 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 to find its footing, its specific value propositions are proven, and it reaches a certain level of productive use, although the associated hype dogging its every step.
Gartner’s Hype Cycle for Emerging Technologies, 2024
Each year, Gartner releases 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 won’t. It also helps companies predict where to put their money, where to allocate staff to evaluate potential, and where it might be practical to innovate.
But you have to take the hype cycle with a grain of salt. Back in 2021, we wrote that Gartner predicted, “The impact of artificial intelligence on code generation, design augmentation and innovation is all 5 to 10 years away.” That was wrong. Generative AI began to make a substantial impact in just two years, at the beginning of 2023.
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But that was then, and this is now. In 2024, Gartner has identified four major themes that are just beginning to climb the great Innovation Trigger hill. These are: autonomous AI, developer productivity, total experience, and human-centric security. We will break down each of these themes next.
Autonomous AI
The obvious first point of contact here is self-driving car technology. Furthermore, think of big action models (where AIs take action, not just spit 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 over tasks that humans used to do. This goes beyond generative AI essay writing for college students who just want to have fun. Instead, we look at machines that can perform physical tasks (cars and robots, for example), and machines that interact with the rest of the world (such as printers that automatically order printer ink or cars that drive their own automatically schedule maintenance visits to the local dealer).
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Of course, there are quite a few hurdles before autonomous AI can achieve real productivity, not the least of which is that most of us are nervous about letting robots out into 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, general computing requirements (such as battery power duration), and more.
Keep in mind that different projects can 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.
AI-augmented software development
While the hype around AI writing code is huge, even the leading players are failing 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 honest, it’s exciting. When I actually got ChatGPT to write a WordPress plugin for my wife’s e-commerce business, I was amazed. After that I used ChatGPT to help me write a ton of code. Overall, I estimate that it has saved me weeks, if not a month or two, on my projects over the last year.
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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 simply generate the app you have in mind as long as you type “Write me an app that will make me a million dollars” into the prompt bar can type.
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.
Empower with total experience
Every few years there’s another customer-centric buzzword that promises endless profits. It used to be 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 hurts my head too.
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It may make more sense if you look at the emerging technologies that Gartner attributes to this trend: 6G, spatial computing, and digital twins of customers.
No one has yet fully defined 6G, but the best description was the one a telecommunications executive told me during a discussion about future technology: super-fast 5G with lots of AI help. Specifically, think of this as collapsed latency, so it’s possible to react in real time to what’s happening. This will also help self-driving cars.
Spatial computing is something that we get to know in the Vision Pro and the Meta Quest 3, but it will become much more constructive once it works in regular glasses, instead of headsets that weigh the same as a brick.
The concept of customer digital twins is creepy. Basically, it describes a way that companies can model consumer interests and behavior so accurately that they can simulate customer interaction and affinity based on their established data history. Anything to better manipulate people into buying! And yes, the same technology can be used to influence elections. Yikes.
Deliver human-centric security and privacy
The last big trend has to do with the need for overall improved security. The concept behind “human-centricity” is that individuals should be part of the overall security footprint. That includes a focus on the user experience, finding behavioral patterns, encouraging security behaviors and building trust through transparency.
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But Gartner sees a bunch of technology trends that support this effort. They include AI TRISM (AI Trust, Risk, and Security Management), which approaches security from a reliable, 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 and responding to threats.
AI comes into play here too, across all solution areas. One major push is in the idea of ​​federated machine learning, where the learning captured in one part of the enterprise network is federated (made available) to the entire network.
Are Gartner’s predictions on track?
Every year it seems we get 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 film.
Gartner’s predictions are just that: predictions. As the table above shows, the research firm has identified more emerging trends than the ones I have discussed. However, these four trends are the ones you should look out for this year, for next year.
What do you think? Is Gartner on the right track? Are there other trends we should be watching? Let us know in the comments below.
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