Unpacking the Gartner hype curve

I haven’t posted for a while, have been doing some busy work. But a couple of days ago I was speaking about adoption cycle with the CEO of one of the companies I work with and  Gartner hype cycle  came up. Which made me remember this story.

Many moons ago when I was in business school, me and a few of my co-students did a small course project about adoption of new technologies. We couldn’t refer to the Gartner hype curve for a very simple reason that none of us was aware of it, so we basically came up with our own curve. (If you haven’t picked it up, the previous phrase was a roundabout way of boasting of how smart we were, although it has a side effect of showing how ignorant we were at the same time). 

Unfortunately, I/we can’t take credit for this very useful and powerful concept. I checked, Gartner came up with it first. But the way we did it is worth mentioning, which is why I am writing this post. Essentially, we came up with the same curve by starting from the components that add up to it – and explain it along the way.

The first component is expectations. As a new technology appears, at some point it starts generating expectations. I will ignore the fact that in some cases it takes years for a technology to get to any expectations and the other fact that there are often false starts along the way (after which the name of the technology becomes contaminated and it is consequently often renamed). For example, AI is all the rage at the moment, but the term was coined in 1955 (!) and I myself worked on something that was called “knowledge engineering” in the late 80s – which was essentially an attempt at AI under a different nametag and with things being a lot less ready for real progress. 
Anyway, at some point expectations start to rise and reach an inflated level. Remember Internet boom of the late 1990s? That’s the sort of thing I am talking about. It becomes hype and nothing but hype. 

But any technology (and anything else, really) needs time to develop, people working on it need to learn how to shape it, make it do things, and not least of all, figure out which things are worth making it do. So early technology capabilities grow in this unimpressive fashion.

 
So right bang in the middle of the hype, people find out that the technology can’t do everything they have or have not been told it can, and, more importantly, is by now expected to do. Disappointment follows, expectations level comes crashing down and we swiftly move to disillusionment. To a large extent it’s very similar to how stock markets often operate on expectations (speculations, emotions, greed, fear, crowd chasing and fleeing, etc) rather than rationality and logic - which is one of the biggest reasons for bubbles and crashes. The Internet crash of 2000 fits perfectly as an example again – if you remember, after sprint 2000 the internet was “dead” and largely written off. Who would want to do things online, anyway? Laughable. Who would fund an internet startup, let alone fund it based on a one-page writeup? So silly. And we always looked right through it and never did anything like this, it was that guy in the building next door.

After expectations come down, it’s rather difficult to change public perception. So, it stays down for a long time and the bar for gaining faith again is very high. In fact, many things die at this stage as they can’t survive without customers or funding propping them up – and those aren’t available in sufficient amounts due to disillusionment of providers of both

. 

However, what happens in the background is that technology continues to increase its capabilities. Gradually it can do more and more things that it couldn’t before. Continuing on the internet as an example, people figured out how to sell online, how to deliver things, how business models work, how to promote stuff, how to do consumer reviews for credibility, etc, etc, etc. In the meantime dial up got replaced by broadband, so you didn’t have to wait for the pictures to load. And bang – all of a sudden, it’s “have you tried buying something online? It’s soooo different now! I actually liked it”. This time around, hype isn’t forming (or at least not to the same levels) - who wants to feel stupid (“fool me once, fool me twice”). So, it takes time to come back, but that in itself is helpful in making the comeback real. We end up with this situation
 

So here we have it – technical capabilities and expectations expressed as graphs on a timeline. All we need to do now is to add the two up to describe the situation in one curve. The graph below is a simple mathematical sum of these two graphs. Looks familiar, no?

    Yep, it is the Gartner hype curve that we constructed as a course project. OK, we didn’t get to the later stage of the plateau, but that was not the focus. And we weren’t THAT clever, after all. 

By the way, technology usually surpasses peak expectations at the initial hype moment later in its life. Things we do on the internet today are way beyond what was promised in the late 90s. During the recent electricity blackout, I realised how much I really struggle to live normal life without being connected. I couldn´t communicate with people (chat or calls), couldn’t look basic things up, couldn’t find addresses or get to them even if I knew them (I discovered that the two maps I still have at home are of faraway holiday destinations rather than my surroundings), … the list goes on.

Coming back to the conversation with the CEO that I mentioned in the beginning – often, the main magic trick at startup’s disposal is ability to create time. To be able to survive the trough of disillusionment while the tech develops and you figure things out, that is. 

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