219. The Hype Cycle: VCs use it, but should they?

How can you tell which innovations will last and what’s just hype? Are we in a generative AI bubble?

There is a methodology called the Hype Cycle that venture capitalists use to answer these questions.

But is this methodology foolproof? Find out in today’s episode.

Timestamps

00:00:00: Introduction

00:01:34: Innovative tech education.

00:02:34: Distinguishing between innovation and trends

00:03:35: Time as a valuable asset

00:04:39: The Hype Cycle

00:09:42: Five phases of the Hype Cycle

00:13:15: Hype Cycle’s accuracy

00:15:41: Evaluating technology beyond the hype

00:18:47: Conclusion 

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Transcript

00:00:00

How can you tell which innovations will last and what's just hype? And are we in a generative AI bubble? Well, there is a methodology called the Hype Cycle that venture capitalists use to answer these questions, but is this methodology foolproof? Find out in today's episode.

00:00:25

Welcome to the Tech podcast. I'm your host, tech entrepreneur, executive coach at Chicago Booth, MBA Sophia. My aim here is to help you have a great career in the digital age, in a time when even your coffee shop has an app. You simply have to speak tech. On this podcast, I share core technology concepts, help you relate them to business outcomes, and most importantly, share practical advice on what you can do to become a digital leader today. If you want to have a great career in the digital age, this podcast is for you. Hello, smart people. How are you today? I am super proud right now because we at Tech Fronton Techies, we got some data that the return on investment on one of our programs was five x to the client. That's literally five times return on investment. So I taught a no-code program to a group of entrepreneurs and then at the end of this program, they all created MVP, so minimum viable products.

00:01:34

And then we calculated how much money the founder saved on custom development and design by using the tools that they learned in our program. And it turns out that they saved five times more than the cost of the program, and the program was literally six weeks long and I don't think I have ever helped anybody five x their money in less than two months before. So Warren Buffet watch out coming for you next. And if you want to know about our innovation training programs, then clearly you're a very clever person. So just get in touch by the link in the show notes. And today we have a lesson which every innovator and investor must listen to. Today's lesson will help you hold your own with tech bros and venture capitalists, and they are not a humble bunch. Let me tell you. Today's lesson will also help you tell lasting innovation from hyped up nonsense.

00:02:34

And this is especially relevant today for the rise of generative AI and all this talk of are we in an AI bubble? So with this episode, you'll be able to have a discerning view on that. And I know that some of you are professional investors and some of you write me emails, <laugh> as professional investors that you are listening to this podcast episode say, thank you very much. But for those of you who are not professional investors, I want you to think of yourselves as investors because each and every one of you has a very valuable asset that for example, I fight for and that asset is time. And if you think like an investor, then you will invest that precious asset wisely. And that includes deciding what to learn, which company to work at, which podcast episode to listen to. So today you're making a great investment and which idea to pursue.

00:03:35

And all of these require time and your time is a valuable. So think of your time as an asset that you are investing. You can always make more money, but you can't make more time. Anyway, today you are going to learn about the hype cycle, and that's a methodology that Gartner, a research company has come up with and it's really popular. And I'm going to tell you what the hype cycle is, and I'm going to tell you about it in an interesting and fun and non boring way. And also you're going to see why people in Silicon Valley, especially VCs, are really into it. But then it's not all rosy here. I'm going to share some recent research that actually questions the hype cycle. And then at the end I'm going to leave you with some questions to think about when you are evaluating technology trends for yourself, because I want you to be able to use this episode to actually make decisions and not just learn a theoretical concept.

00:04:39

My aim here is for you to demystify tech so you can think for yourself and make decisions based on your discernment and not what some overconfident douche bag is telling you. So firstly, what is Garner? That's the company that created the Hype Cycle framework. Well, garner is a massive company that sells research and market insight to corporates. Their customers use this research for their strategic planning, like where to invest, what trends to watch out for, what sectors and trends to pull out of and so on. And it's important that we start here because you need to know the incentives. The hype cycle is a framework that was created by a commercial business that sells services not by academics in a university. So what is it? The hype cycle aims to explain how a technology goes from an idea to being widely adapted. And it consists of five stages.

00:05:40

And obviously this is why venture capitalists are really into it because they get early stage ideas all the time and essentially they need to make decisions on is this going to be big? Because if they make the right bet, they can become billionaires without doing that much work. But before I start telling you about the stages of tech adaption, I want you to think about your romantic relationships. Think about the long-term relationships versus short-term flinks. And actually you are going to see a similar trajectory. So listen to this. So let's think about a relationship that you've had. So how did it go? First you meet the person and there is an initial attraction. So for example, you maybe meet them on a dating app and you don't know them yet and you then go on a first date and after a first date, you still don't know them even if the first date was like eight hours.

00:06:39

Because even if they make you laugh and it feels like they get you and there's chemistry, it's wonderful. You don't actually really know them. And so because you don't really know them and it's all fabulous, you basically kind of make all this stuff up in your head about how great they are and you're so excited about them. And all you see is the good stuff. You do not yet know about their annoying habits. You don't know their credit score, you have not met their weirdo friends yet their mother has not yet criticized you. And then as time goes on and you two carry on seeing each other, then stuff becomes more real. You get to know the real person and you find out that they're not ideal because none of us are even me. You will be surprised to know. And then you're going to see that there are some things that you really don't like and then maybe you hear them fart, the horror, the horror, and then you enter the trough of disillusionment.

00:07:46

This person that you idolized is no longer a God, but a smelly human with lots of limitations. And often this is when relationships break up and then the two partners go and search for that initial high and chasing that kind of expectation of perfection. But if the two of you make it through this stage, the stage of disillusionment, and you are still together, then you are together and you both have a much more realistic of view of who you both are. You know your partner's good sides, and you know their bad sides. So maybe you love that they're really fun and really spontaneous and you've basically just made peace with the fact that they're always late. And as the relationship goes on, you mold to them and they mold to you. And then a sort of peace descends and you both accept each other and build a life together.

00:08:42

And in a nutshell, this is the path to a happy long-term relationship because there has to be obviously initial attraction, but then after that there is disappointment. And after the disappointment, then if you so desired, you begin building a life together. And by the way, I haven't actually made this up. These stages of relationships have been studied and documented. So if you are interested, you can look them up. But now let's leave a romance and get back to technology. According to the people at Gartner, generative AI and computer vision and technology trends in general follow the same path, the same path as your love life. Isn't that exciting? And now I'm going to give you the five phases of the Gartner hype cycle. And right now I am going to quote directly from Gartner. So what's phase one of the hype cycle? The phase one is called the innovation trigger.

00:09:42

That's when a potential technology breakthrough kicks things off early proof of concept stories and media interest trigger significant publicity. Remember that's what happened with generative AI. In chat GPT, often no usable products exist and commercial viability is unproven. Interesting. So what happens next? We get onto phase two, phase two's called peak of inflated expectations. Early publicity produces a number of success stories, often accompanied by scores of failures. Some companies take action, many do not. We have actually seen this this year with generative ai. So what happens next? Then interface three, the trough of disillusionment. Remember, that's when you first hear your partner fart. So what happens when it comes to tech interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail investments continue only if the surviving providers improve their product to the satisfaction of early adopters. And then what happens after the trough of disillusionment?

00:10:56

We get onto stage four. Remember there are five stages, so we've got two more to go. So stage four is the slope of enlightenment, more instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second and third generation products appear from technology providers. More enterprises fund pilots. Conservative companies though remain cautious. So perhaps chat GPT five is going to become much, much better. There's going to be much less hallucination. There are then more generative AI products that are basically better in hallucinating less so then big companies that tend to be more cautious start funding pilots. Is that going to happen? Well, let's see. This is what OpenAI investors are hoping for. And then what is the final stage? The final stage five, that's the plateau of productivity. Mainstream adoption starts to take off criteria for assessment. Provider viability are more clearly defined and the technology's broad market applicability and relevance are clearly paying off.

00:12:09

So stage five is basically when my mom started using Uber after careful instruction from me. And you know it's actually easiest to see the phases as a graph. So just imagine a line that starts at the bottom, then sharply rises, so sharply goes up, and then it falls just as sharply. And that's from the trigger to inflated expectations to the trough of disillusionment. And then after this sharp hill, the line slowly and gradually moves up. So it's passion, disappointment, and then gradual rise again. And Gartner says that right now generative AI is in the trough of disillusionment. It's kind of exiting the peak of inflated expectations because there was so much hype, there was lots of expectation. And then people started using generative AI and it made things up. People got mediocre results, they couldn't be bothered to learn the prompts. And also we can all tell when we are speaking to generative AI or reading generative AI posts.

00:13:15

So if you're using chat GBT to create LinkedIn posts, we know stop doing it. But according to the hype cycle, investors and generative AI founders should not be discouraged because they should think, well, this is normal after the fall, we are due a slow, steady climb back up. But dears, smart people, does this actually happen? Do technologies bounce back? Do they all bounce back? Well, sometimes, and actually only about 20% of the time, and that's according to the latest research from the economist. And think about this, a 20% hit rate is pretty rubbish. This is why I said that this is super relevant to understand for investors and you are all investors. And so here's what the economist says in an article about the hype cycle from August, 2024. We find that the hype cycle is a rarity tracing breakthrough technologies over time. Only a small share may be a fifth move from innovation to excitement to despondency to widespread adoption.

00:14:31

Lots of tech becomes widely used without such a rollercoaster ride. And they give the example of cloud computing here. Others go from boom to bust but do not come back. We estimate that of all of the forms of tech that fall into the trough of disillusionment six in 10 do not rise again. Six in 10. Our conclusion is that an alarming number of technology trends are flashes in the pan. So my dear smart people, if something looks like hype often, that is all it is. You are not crazy if you didn't get the metaverse and Web3. So what are you to do? Wouldn't it be nice if you just had a framework and then everything fitted into that? Ah, but life, life is way more complicated than that, but that's what makes it interesting. So what are we going to do? Well, first, don't think that you have just wasted your time learning about the hype cycle because whether it's accurate or not, the hype cycle is a thing that people talk about and take very seriously in tech circles.

00:15:41

So you need to know what it is if you want to speak the language of tech and to speak the language of tech investing. And secondly, as I always tell you, be discerning. Think for yourself. Use your own brilliant mind. Why don't hype trap innovations make it out of the trough of disillusionment? Well, because either they don't work that well or there is no clear use case or the use case exists, but it's basically just much smaller than you initially thought or that people initially thought. So for example, metaverse applications are actually really useful. In some cases they're useful for gaming warfare and industrial factories. But I don't want to just hang out in the metaverse for the fun of it no matter what Mark Zuckerberg thinks. So what I want you to see from this is that there is sadly no one size fits all model that we can apply to every tech trend.

00:16:42

It would be super convenient if the Gartner hype cycle was just a neat prediction model for innovation. I mean, that would be wonderful, but as they said, life is more complicated. So think back to your romantic relationships. How often did your disillusionment actually lead to a lasting partnership? How often did your disillusionment lead to love and admiration in the long term for that person? Honestly, not that often. This is why when you do find it, it's really special. So when you're trying to work out whether a technology is going to be with us for the long term, ask yourself these simple questions about a specific tool powered by that technology. So focus on the specifics because if you get really, really general, it's going to be basically impossible to answer these questions. So it's five questions. Question number one, what is the problem that this tool is solving?

00:17:43

Who experiences that problem? Then number three, what are those people doing to solve that problem? Now then number four, does the user have the money to pay for a new solution? 'cause if they don't, what you doing? And finally, number five, is solving this problem a priority for the user. As I've mentioned before, there are all sorts of problems in my life that I know are problems and I can't be bothered to fix them, and my life is just fine. So if something isn't a priority, doesn't really matter if it's a problem. And what we're seeing with the generative AI is that it is a very general technology, well sort of like electricity, and it's wonderful in some cases and it's useless in others right now. So it's use case dependent, but that is also true of literally every other technology in the world. Think about this.

Speaker 0   00:18:47

A spoon is a very useful technology when you are eating ice cream, which a spoon is completely useless. When you need to travel from London to New York, what are you gonna do with a spoon? So the bottom line is, as always, my dear smart person, use your common sense. And I know that tech bro can sound really sure of themselves, but my dears smart person, you know that does not make them right, not all the time anyway, understand the use case and rely on your discernment to tell real lasting innovation from the hype. You've got this, you can do this. You are definitely smart enough. And that's the end of our lesson today. I hope you found it useful, which I assume you did because you are still listening. So have you left this show a rating and a review yet? If not, please do so. It really does help other smart people like you to discover this work. And also it is really encouraging for me to keep on creating this high quality free education from you. So help me keep going by leaving this show a rating and a review. And if you're feeling super generous, then even sharing it on social media. So that note, my dear smart person, I'm wishing you a wonderful day and I shall be back in your delightful smart ears next week. Ciao.