301. How to catch the next tech investment wave

Whether you're starting a tech venture or investing in one, you need to understand how to spot the next tech investment trend.

Why?

Because if your venture or your portfolio is riding the wave of a genuinely transformative technology, you have a structural advantage. If it isn't, you're pushing uphill.

And here is the good news: you do not need a computer science degree to do this!

In this episode, Sophia Matveeva speaks with Igor Pejic — an expert on tech-driven shifts and investing  — about the framework that lets any intelligent person spot transformative technology waves before they go mainstream.

Igor is an award-winning author whose previous books earned him the Independent Press Award and a finalist place at the Bracken Bower Prize — awarded by McKinsey and the Financial Times.

His work has appeared in the New York Times, Forbes and Bloomberg. He has advised Fortune 100 companies and held senior positions in banking and payments. 

His new book is out in May: Tech Money: A Guide to the New Game of Technology Investing is out this spring.

Listen to learn:

• Why the metaverse failed while AI succeeded — and the signals that told you this years in advance
• How McKinsey predicted natural language AI wouldn't arrive until the 2040s — and what that tells us about relying on expert opinion
• The four non-technical indicators you can track right now to spot the next transformative technology
• Whether the AI bubble is coming — and how it compares to the dot-com era
• Why being too deep in the technical details can actually make you a worse technology investor

Timestamps:

• 00:00 - Introduction: Why non-experts can catch tech waves
• 03:53 - Why the metaverse failed and Gen AI succeeded
• 06:07 - The adoption problem: $100 billion for 900 daily users
• 09:30 - Catching tech trends early: The technology adoption lifecycle
• 12:10 - How much tech knowledge do you really need?
• 16:31 - Finding the sweet spot: Risk vs reward in tech investing
• 19:20 - The AI bubble: Comparing to the dot-com era
• 23:12 - Why this time is different: Big tech vs dot-com startups
• 25:41 - Using investment frameworks for founding decisions
• 27:04 - Closing and resources

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Transcript:

[00:00:00] Igor Pejic: The age of tech investing has actually made it easier for non-experts to benefit from those tech waves than it has been previously in the 20th century. You don't have to be a tech genius. Actually, it's better if you have range, if you understand how mass psychology is working, if you understand how tech trajectories are working. In fact, I would say that metaverse is the best idea that being too deep into the details can make you subject to tunnel vision.

[00:00:30] Sophia Matveeva: Hello and welcome to the Tech for Non-Techies podcast. I'm your host, Sophia Matveeva. If you're a non-technical founder building a tech product or adding AI to your business, you're in the right place. Each week, you'll get practical strategies, step-by-step playbooks, and real-world case studies to help you launch and scale a tech business without learning to code. And this is not another startup show full of jargon, venture capital theater, or tech-bro bravado. Here, we focus on building useful products that make money without hype and without code.

[00:01:04] I've written for the Harvard Business Review and lectured at Oxford, London Business School and Chicago Booth. So you are in safe hands. I've also helped hundreds of founders from concept to scalable product. And now it's your turn. So let's dive in.

[00:01:30] Hello smart people. How are you today? In this episode, we are going to cover how to spot the next tech investment trend. But I don't want you to switch off if you're not formally an investor and you know what I say about being an investor. If you're investing your time and your energy into something, you are an investor. So you should think like one. Anyway, I digress. The same framework for evaluating tech investments applies to deciding whether to build a venture or to fund one. So if your startup is riding the wave of a genuinely transformative technology, you have a structural advantage. And if it isn't, you're pushing uphill. So this is why this episode is useful, obviously, for investors and also for founders and for corporate innovators.

[00:02:16] And in this episode, you're going to learn how to build a framework for catching the next tech trend. And you don't need a computer science degree to do this. Your liberal arts one is going to be completely fine. And today you are going to hear from a very impressive man whose name is Igor Pejic. Igor is a global leading expert on tech driven shifts. And he's an award winning author, a keynote speaker and a banker. His work has appeared in the New York Times, Forbes and Bloomberg. His previous books have earned him the Independent Press Award and he was also a finalist at the Brackenbaugh Prize. That's a prize awarded by McKinsey and the FT and it's basically a big deal. And his new book, Tech Money, uncovers the new rules of investing in the technology age and it is out in May 2026.

[00:03:08] In this conversation, we get into why the metaverse failed, where AI is succeeding and how you could have spotted that difference early. We also talk about the AI bubble. And we also cover what a comparison with the dot com era actually tells us about where we are with AI today. And we discuss how the same signals that help investors spot winning technologies also help founders decide whether their venture is riding the right wave. And before we get to the interview, I have a question for you. Are you a subscriber to this show? Do you follow Tech for Non-Techies on YouTube, on Apple and on Spotify? Well, you should because it will definitely make you smarter. Just look at the caliber of conversations we have on here. So make sure to give this show a follow so you don't miss another high quality episode. And now let us learn from Igor.

[00:03:53] Why the metaverse failed and Gen AI succeeded

Igor, from an investor standpoint, why does the metaverse look like a disaster and Gen AI look promising?

Igor Pejic: Well, today it seems like a no-brainer to see why the metaverse has failed whereas Gen AI has transformed or is currently transforming the economy. But from the time actually when the metaverse was coming out, there were some people that were very skeptical for good reasons, but there was also a lot of excitement and there was this tremendous hype that was created, especially by Mark Zuckerberg's announcement to go into the metaverse. He did a multi-billion dollar bet on this new technology and other brands, all of them followed, right? And this is something a little bit comparable to AI in terms of the hype because we've had tremendous media interest in the topic. Everybody's speaking about it. Companies were investing a lot in it. So every major company was in the metaverse, was selling NFTs and then raising capital. And there was a tremendous market also in terms of end users or retail investors that were buying virtual land and stuff like that.

[00:05:02] So you can say in terms of hype, it was comparable to Gen AI. And it was also of course, a novel technology with reportedly huge potential, but there was one critical difference. And I think this is the gold standard always in any technology when you're trying to judge it is, is this really a technology that is productive at the end of the day? And productive can mean a lot of things. It can mean, can you do stuff you're doing more efficiently, can you do new stuff? Can you bring down the costs of something that you're already doing? Can you increase the speed? And actually AI, if you look at AI, it's excelling at all of those things. Whereas the metaverse, nobody actually knew what we needed it for, right? There was a huge discussion, you know, why do we need the metaverse for? And there were many ideas and many visions, but nobody could actually tell you why you should spend time in the metaverse.

[00:05:54] Now, judging the productivity, of course, is always very, I would say subjective matter. Everybody is assessing this from his or her own experiences and beliefs. But there are objective metrics and perhaps the most important metric that you can have is adoption. And if you look at AI, it took ChatGPT even as one application, it took it just one or two years to bring the whole technology into the very big mainstream market. Whereas the metaverse just had no users. Let's put it like this.

[00:06:07] The adoption problem: $100 billion for 900 daily users

Sophia Matveeva: Well, as we previously discussed on previous episodes, it was something that gamers were already doing and Roblox was doing that really successfully and gamers had a really cool and like pretty good looking metaverse already and Mark Zuckerberg's was pretty crappy in comparison. So it didn't really make sense.

Igor Pejic: It was looking crappy, you know, even his own avatar had no legs when he was playing poker in the metaverse and people were asking, you know, why do we need this? Right? Is this some kind of, if this were some kind of killer video game, you know, maybe there would have been an argument to be there, but there was no adoption. And there were reports that actually Mark Zuckerberg was trying to force his own employees to spend time in the metaverse. And if you have to force your employees to go there, those people who are working on this project, then this is actually a very bright red flag.

[00:06:58] And if you look at the numbers, it's just even more sobering. You know, Mark Zuckerberg reported we burned through $100 billion US dollars with this metaverse idea. And at the climax of the metaverse, Horizon Worlds, which was Facebook's metaverse, they had like 900 daily active users, you know, for $100 billion investment. The other metaverses were not doing much better. If you look at Decentraland or Sandbox, which are decentralized variants of the metaverse, like those initial metaverse proponents were envisioning it, they were doing much more poorly or just as poorly as Horizon Worlds. They were not doing poorly in terms of investment, right? As I said, they were a billion dollars in market cap. People were buying virtual land. Some were paying up to a couple of million dollars for virtual parcels of land that, sure, they cannot be copied within one metaverse, but there were just many of those metaverses. You could basically just move to another one if one got too expensive. So it didn't make any sense.

[00:08:08] So adoption is really the key and you can track adoption by looking at productivity as we discussed earlier, but you can also look at other things like where is the money going to? Because with ChatGPT and the metaverse, we have both a parallel that there was so much money going into the space, but the targets were different, right? With AI, it was more going in the beginning towards application, the application layer, later of course into data centers and infrastructure. But if you look at the metaverse, most of the money was not going to build new applications that people wanted, but it was actually going into buying virtual land, which was highly speculative. So people were investing not so it gets better or because there was utility value, but simply because they were hoping that they can resell it to somebody else at a much higher price, which tells you that this is a setup that will not work in the long run.

[00:08:42] Sophia Matveeva: I mean, that's one of the criticisms that people have of cryptocurrency, but we will not go down that rabbit hole because then that will be a completely different episode. But you really focus on figuring out how to get the next wave of tech investing right. And so when we're talking about this, it seems that we're already talking about a fairly late stage decision, right? Like we can assess ChatGPT or Gen AI and the metaverse because these are already established technologies. People can already see the results and so then you can make the decision, you know, making the decision now is a fairly late-stage decision. So would the same kind of metric, would the same kind of methodology really apply to much earlier technologies or technologies that are still in the making, let's say like quantum computing?

[00:09:30] Catching tech trends early: The technology adoption lifecycle

Igor Pejic: Yeah. So it depends how early we're talking about. But it was, I believe you would have been able with this methodology to catch the AI boom, for example, much, much earlier. Right. And we can actually put concrete figures on this. So there's something called the technology adoption life cycle, which basically tells you there's different kinds of groups of people or segments of the market you have to address. So first you start with the visionaries, then you get the pioneers, then the early majority, late majority and the laggards. And there is a critical point in time with every technology where there is a chasm in which many technologies fall and don't recover. And if they pass this, the likelihood is extremely high that they will be successful in the end. And this occurs usually between the shift from early adopters and the early majority, right? So this is somewhere between 15 to 20% of the market.

[00:10:32] For example, this would have given you a very strong hint to get into this technology already a year after ChatGPT. So, of course, you wouldn't have been able to foresee it, but you would have still caught a tremendous steep growth curve. Now, if you're asking me about even earlier technologies, what we don't call emerging technologies, but frontier tech, there is a possibility to gain like one, two, three years of advantage to the overall market. Of course, it's always a very risky game, but it's possible. And there are a couple of indicators that you can track that I also discussed in my book. One of them would be, for example, to look at the number of patents, right? How many patents are filed in a certain technology? By which companies and which regions?

[00:11:18] Another possibility would be to look at the interest, you know, media interest, but also interest in terms of the people that are hearing about this technology, which you can track with things like Google Trends. Then there's even the category of scientific papers, because if you look at big technologies, and blockchain is one of those, if you had tracked blockchain scientific papers, you would have beaten the market two to three years in catching these very steep growth curves. And finally, one of my favorites is the talent trends, right? So you can, for example, look at where are the people going, right? If you look at AI and what we've seen over the past 10, 15, maybe even 20 years, there was a steady shift of AI PhDs from academia to the private sector, right? So there were more and more, I think in the end, it was 70, 80% of all AI PhDs ending up in private corporations, even before OpenAI came up with ChatGPT.

[00:12:10] How much tech knowledge do you really need?

Sophia Matveeva: And with the last one, a non-technical person can definitely understand that because we all understand what a PhD is. But the other ones, like how much do you actually really need to understand about technology to essentially apply these frameworks to your investment decision-making?

Igor Pejic: I would say the earlier you're in there, the more you have to understand it. So for example, today understanding quantum computing and the trajectory is extremely difficult because you have to spend a lot of time reading scientific papers and very detailed and very technical analysis. As soon as there is an early breakthrough moment, like for example, ChatGPT, even an investor who has no background in technology, if they apply strategically and systematically a framework, they really have the possibility to get into those technologies. And I would say that the age of tech investing has actually made it easier for non-experts to benefit from those tech waves from corporations than it has been previously in the 20th century where you had to go deep down into technical and mathematical analysis of stocks and so on.

[00:13:18] Now it's more the bigger picture thinking, right? So it's very tough. It's very difficult to figure it out, but you don't have to be a tech genius or somebody who is very, very deep in one technology. Actually, it's better if you have range, if you understand how mass psychology is working, if you understand how tech trajectories are working, you know your way about supply chains. But as I said, on a very strategic level, you don't have to be a coder yourself to understand what is the best direction. In fact, I would say that metaverse is the best idea that being too deep into the details can make you subject to tunnel vision, can make you, it can force you to make errors that you wouldn't have made if you were a much broader range investor. Right?

[00:14:08] So Mark Zuckerberg, the best idea, he's obviously a visionary because he wouldn't have been right with Facebook and WhatsApp and Instagram. But he wanted the metaverse to work out. He was so into this idea and he said, you know, wouldn't it be perfect if this was the next platform instead of the smartphone? And then if you're just thinking along these lines, you start or you stop looking at the world outside of you. And it's very difficult to get a neutral assessment of a technology.

[00:14:37] Sophia Matveeva: And it's interesting what you said about user psychology and that kind of adoption cycle because it actually made me think of a company that I'm an advisor to and it's a fashion tech company. And essentially what they do is they scan the internet to work out the next fashion trend, but not the next one like tomorrow, but the one that's going to come in three years time. And then they can essentially sell that information to brands and retailers and say that, okay, in three years time, this is the shade of blue that everybody's going to want, which means that, okay, manufacturers need to start making that shade of blue. And if you get that right, then okay, if you've got the exact perfect blue handbag at that particular time, then you sell out.

[00:15:22] And the way they do it is they essentially, they figure out how to spot a super early adopter because, you know, there are the super early adopters who are the kind of very, very weird looking fashion forward people, you know, who dress in a way that normal people would never dress. And then there comes a different wave of people who take what the very fashionable people are wearing and tone it down and maybe instead of wearing a full-on tartan outfit, they'll have a tartan scarf, for example. Essentially, this company's secret sauce is being able to figure out when does a trend go from just the super weirdo, highly fashionable people, when does it go to mass market. And also they predict when do they think that the mass market is going to tire of it and move on to the next thing.

[00:16:08] And I've worked with them quite closely and I've seen essentially how they figure this out. And this is, I mean, there's like, there's a data science PhD there. There are some very serious mathematicians. And so when you're saying that, okay, you don't need to be a coder to figure this out, but you just need to look at this adoption. When I'm literally working with this company and there are some really serious mathematicians there figuring this out. So how do we as non-technical people do it?

[00:16:31] Finding the sweet spot: Risk vs reward in tech investing

Igor Pejic: That's an excellent point. What you're describing is that they are trying to pinpoint the tipping point, right? It's very, very difficult to forecast this. It's almost impossible, at least for a lay person or for a non-expert, it would have been impossible to forecast ChatGPT. Actually, even for experts, and there's a great study, I quote in my book, done by McKinsey, who have interviewed experts pre and post ChatGPT about their expectations when AI will actually hit the market. Most of them said, you know, things like natural language understanding and translation, all of those things, they would come sometimes in the 2040s, 2050s. And then along comes ChatGPT and they revised this by like 20 years and each one of them, right?

[00:17:22] So it's very, very difficult to really pinpoint that point in time. That's why I said earlier, getting into those super early technologies, even much, much more difficult. And of course you don't get the same benefit when you're a little bit later to the party or the same upside. But at the same time, you're limiting your downside. And I think that's the critical thing for retail investors. Because you shouldn't try to get the jackpot, right? It's almost impossible to make two bets and get Nvidia and Palantir right. So that's not happening. What you have to do is basically get this point in time where there is still a huge upside, but where the downside is limited or strongly limited. So I think that's really the goal of every tech investor.

[00:18:08] And also, you know, being too early might be very tricky because if you're too early, it means maybe you wait five years or 10 years in order to really get the returns that you were seeking. Just ask any AI investor who's been in this space since the early 2000s, right? So they waited 15, 20 years until really the steep growth curve comes. That's why I think it's not about getting the tipping point, but getting the sweet spot with the risk and the reward ratio in the best position for just regular investors like us.

[00:18:34] Sophia Matveeva: This actually reminds me of an episode we had recently with a growth investor who was talking about the differences between what she does versus an early stage venture capitalist. Because an early stage venture capitalist, yes, they have massive upside, but also very high likelihood that none of it is going to work out. At her end, it was okay, well, the company is actually already pretty established. And now it's just a question of scale, which yes, means that there is upside, but it's not at the same level, but the downside is also capped. You know, Igor, I don't think we can really talk about tech investing and catching the next trend and discuss AI without mentioning the bubble. So what are your thoughts on the AI bubble? Is it coming? Is it here? Has it already passed and we just didn't notice?

[00:19:20] The AI bubble: Comparing to the dot-com era

Igor Pejic: Yeah, that's definitely the question keeping up at night all of the decision makers, investors, and pretty much everybody else. And actually we will only see if it really is or was a bubble once if it pops. But I think we can do very well to compare it to previous bubbles to see, you know, are there similarities? And even if it is a bubble, the question is then more important, I believe, of how bad will it be? And of course, what comes to mind is the dot com bubble because we are seeing so many similarities.

[00:19:54] And I like to look at those things from the prism of those tech cycles that we're discussing. And if you do so, you will see that those two eras, pre dot com bubble burst and today's AI, they resemble each other in all ways but one, but that difference is critical. So just to give you a brief rundown through the parallels. So on the one hand, we have two technologies that promise extreme productivity that are not just transversal technologies, but they're what we call general purpose technologies, meaning that they basically transform radically every sector of the economy and they are platform businesses. So they foster also very profitable or promise very profitable business models.

[00:20:42] We also see a lot of parallels in terms of the hype. We see a lot of parallels also in the overvaluation of companies. If you look at P to E ratios, the P to E ratios of tech companies as compared to price to earnings, right? So that's the willingness of investors to overpay for a company as compared to what it's worth on paper. So the relationship of P to E ratios to general GDP is roughly the same as it was at pre dot com bust levels. So there's certainly the same amount of hype. What we're now seeing also is parallels coming up in the financing of the AI investments, because in the dot com era, there was a lot of circular financing, which means, for example, the telecommunication providers would buy each other's fiber, buy each other's networks and so on. So they would artificially inflate their revenues.

[00:21:42] And we're seeing now, for example, chip makers that are buying equities or buying stakes in LLM producers. But those LLM producers, so large language models, those are the guys that are doing the big algorithms, for this equity deal, they have to buy chips. So it's kind of again, a circle of financing that obfuscates the real need and the real market. So there's a lot of parallels. I, however, believe that they are fundamentally different in one regard. And that's if you look at a concept called techno-economic paradigm. So every 50 to 60 years, there's a paradigmatic change in the economy. Think of the age of electricity before that age of steam or the age of oil and gas. And since the 1970s, we're in the age of information, right? With the first Intel chip coming out and internet and so on.

[00:22:38] And within each of those techno-economic paradigms, you have a bundle of transformational technologies. But the interesting thing is that each of those paradigms is split in two big phases. One is the installation period and the second is deployment. And in the installation period, it's all very dynamic. It's unclear how companies are making money or there's a lot of failures in the corporate world. But at the end of this installation period, there is a big crash and that's happening in every of those paradigms. There's a big market collapse. A lot of players go out of business, a lot of value is shed and it takes quite a long time to return actually for those companies that have been driving the change. But when they return, they outperform.

[00:23:12] Why this time is different: Big tech vs dot-com startups

Now, we had that crash in the dot com bubble and it was actually a productive crash because it left behind all of this infrastructure. And of course you were able to build this infrastructure because of the hype. So what we have today is a fundamentally different tech and IT ecosystem than what we had in the 90s. So we don't have any more some kind of startups that live only of promises and so on, but we have big tech giants. We have actually the biggest companies we have seen in history, companies that are bigger in terms of market cap than most countries in the world. So this is significant. And they are financing most of the investments from their free cash flows, which means that the risk is not as big.

[00:24:08] So even if there is a downturn in valuations, it will certainly not ripple across the economy as the dot com bust has done because after all, we know after 2000, it took about 15 years for many of those companies that survived to get back to the levels they were. But again, at the same time, you had companies like Amazon that grew a thousand times and more in value. So it's really a big crash and now we might see smaller crashes, but I think even if we do, it will be nothing as compared to the dot-com bust. And also there's quite some encouraging developments if you look at how much those companies are investing in AI and that they're actually already gaining revenues. If you look at Google, about 20 plus percent of their cloud revenues are actually from AI solutions, the same with Microsoft is also earning quite a significant portion. So those ratios of investment to revenues from AI, they are improving, right? They're still high, but they're improving, which is a very healthy sign. It's a sign of a very healthy trajectory. So there might be crashes coming, but it will certainly not be as bad as we have seen from bubbles in the past.

[00:24:52] Sophia Matveeva: That's a really interesting comparison. Thank you very much, Igor. So if people want to learn about these frameworks and how to spot the next tech investing trend or how to evaluate what's in front of them, where can they find out more?

[00:25:06] Igor Pejic: So yeah, definitely. I did spend quite a lot of time thinking about those things and researching. And I wrote it down in my new book that's coming out in May. It's called Tech Money, How the Age of Technology is Changing the Rules of Investing. You can find it basically at every big retailer. And while you're at it, I'm also running a pre-order campaign, giving out a free online course on tech investing for everybody who does pre-order the book and gets in touch. So now it's the best time to get your copy and make sure that you're using those opportunities that the world of tech is offering.

[00:25:41] Using investment frameworks for founding decisions

Sophia Matveeva: Awesome, thank you. Actually, I just thought of one last question I want to ask you before I let you go. If somebody is evaluating an opportunity to invest in, but they're not just going to hand their money to it, they actually are thinking, well, I'm going to not only invest my money, but I'm actually going to do it. Like I'm thinking, should I go and create this product and start this venture? Would the same frameworks apply?

Igor Pejic: Great question and yes, yes, because I'm always asked, you know, what is a defining characteristic of a successful company? It doesn't matter if it's a startup or whether it is one of those tech giants. It's always that the company is riding on the wave of a successful technology, right? iPhone was so successful, the most successful product in history because it was riding the wave of mobile. Of course, ChatGPT or Gen AI and I could go on and on and on. And of course, if you're a startup, you will probably not create a large language model. But even if you're, say in cryptography and post-quantum cryptography is now coming out, that is the major indicator of whether you are on the right track. And there's many of those models in the book and many of those tech cycles. I think it's definitely applicable, not just to public investors, but also to managers and founders.

[00:26:57] Closing and resources

Sophia Matveeva: Awesome. Well, we will link that in the show notes. On that note, thank you very much for joining us, Igor, and have a wonderful day.

Igor Pejic: Thank you for having me.

Sophia Matveeva: Wasn't that interesting? What stood out for you? I'm genuinely curious. I'd love to know. So send me a connection request on LinkedIn, that's Sophia Matveeva on LinkedIn, and let me know. And on that note, I wish you a wonderful day and I shall be back in your delightful smart ears next week. Ciao.