Motley Fool Money - “No One Will Ever Need a PC in Their Homes.”

Episode Date: June 10, 2023

A new technology won’t go far unless it solves a migraine-level problem.  Motley Fool Live’s “This Week in Tech” co-hosts Tim Beyers and Tim White discuss: - How investors can think about adoption lifecycles and tech investments - Where generative AI lands on the hype cycle - One key sign that a new product has “crossed the chasm” for widespread adoption - ChatGPT’s “nice to haves” Companies discussed: IT, AAPL, MDB, HUBS Host: Tim Beyers Guest: Tim White Producer: Ricky Mulvey Engineer: Rick Engdahl Learn more about your ad choices. Visit megaphone.fm/adchoices

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What? Oh boy. Fantastic. You guys go hard, man. Daredevil Born Again, official podcast Tuesdays, and stream season two of Marvel Television's Daredevil Born Again on Disney Plus. If you think about the Apple one, maybe that's sort of where the innovators were people who were willing to take a risk on a, at the time, you know, in inflation adjusted five or six thousand dollars on a computer that basically did nothing, right? You had to make it do anything you wanted to make it do. And it really led to a lot of people at the time, you know, in that trough of disillusionment that happened with personal computers of like, no one will ever need a PC in their homes, right? Right.

Big statements from big fancy people, right? I'm Mary Long, and that's Tim White, who co-hosts this week in tech on Motley Fool Live alongside Tim Byers. Tim and Tim caught up on Motley Fool Money to discuss hype cycles, adoption curves, and why investors should pay attention to their key differences. Let's talk about promising technologies,

and you've seen a boatload of them. So have I over the course of years, how what do you think when we think about how long it takes genuinely like a really promising technology i mean a i's in the news now right a genuinely promising technology something like an artificial intelligence toolkit or artificial intelligence generally generative AI like chat gptt how long do you really think that takes to become part of our daily lives We're hearing about it constantly, but it's not really part of our daily lives yet. Right.

So, again, we were talking about two different cycles, the hype cycle, which is people getting excited about things and the beginning of technology. And then that adoption cycle where you have early innovators getting on board with things and then eventually adopting things in the general public way at the end of that cycle. So all of the stuff we've seen from AI has been around for a long time. what made it sort of cross the chasm to more people knowing about it and more people using it in their daily lives was making a chatbot free. And I think the free part is so important there, right? Absolutely. We should talk about the difference between what we call the hype cycle,

or more specifically, what Gartner, which is a research firm, which you may or may not have heard about, their public company and ticker symbol IT. And Gardner defines something called the hype cycle. And then there is the more commonly referred to technology adoption lifecycle. And each of them are a bell curve. The hype cycle is a really compressed bell curve. And the technology adoption cycle is a bit more like a regular bell curve with a pretty big gap in it.

And that gap was defined in 1993. I hope I have this year right, Tim. But if I have my history right, 1993, Jeffrey Moore, who's a consultant, and still to this day, operates the chasm group. Consultant who wrote a book called Crossing the Chasm. And in Crossing the Chasm, more defined what a technology adoption life cycle look like. And he said there comes a point when all the enthusiasm, all the stuff in the hype cycle has to go across this big chasm where the people who are really excited sort of convince the rest of us to say like, okay, this is real. We'll actually spend some money on this. So when we talk about these two different cycles, the differentiator, I think we both sort of have identified, and we've talked about this so many times, is if you're talking about tech adoption, you're talking about solving what you and I have called a migraine level problem.

And if you're talking about hype, what you're really talking about is some spending around excels. Boy, this thing is needo, and I want to do some things with it. And so this is why I'm going to come back to what you just said about free. Like, free is so important in the hype cycle part of the phase. Right. So if we think about hype is the idea that, wow, this technology could change everything. We'll never have to drive cars again. We'll never have to think for ourselves again.

We'll never have to listen to the radio again because we can watch television, right? Like all these, like, it'll change everything, right? That's what the hype cycle is. And it ramps up to the peak of inflated expectations, as Gardner calls it, where everyone's like, oh, this is going to do everything. And then there's a crest at which suddenly it doesn't deliver. And things start to fall apart. And actually turning that, this will change everything, just falls apart.

And the technology is cool. But it doesn't solve anybody's particular migraine level problem. And that's where I think you really transition over to a. option where there's a particular problem that some piece of technology solves, and that's when it starts to become more mainstream. If you had to, I'm going to guess here, but if you had to say where generative AI is in the hype cycle, is it at the peak of inflated expectations where now the hallucinations are like, oh, wait a minute, maybe this thing doesn't. doesn't give us exactly what we thought it was going to give us.

Yeah, I think it's somewhere along the top there. I think it's kind of funny that in 2021, Gartner listed chatbots and the trough of disillusionment, right? At the worst case, right, where everyone's like, oh, chatbots, they were going to save everything. They were going to let us fire all of our customer service agents. And now they're terrible and no one wants to use them anymore. So they were in the trough of disillusionment in 2021. and then they magically vanished from the hype cycle chart for AI in 2022. I think.

Right. It was a Jedi mind trick. This is not the technology you're looking for. Right. So I think we are absolutely at the peak of inflated expectations around generative AI. We are absolutely at the peak of inflated expectations around generative AI right now. But I think we're starting to slide down the backside and toward that trough of disillusionment, where people are wondering, okay, so this is cool, but there's so many little gotchas,

will we actually be able to make this a real part of our business? Yeah, for sure. And so let's talk a little bit about that switchover when the hype moves into the adoption and what more defined as the chasm here. And we said this. products always solve a migraine level problem. There's always something. And so if we can look back through history, and when we talk about the chasm, the chasm is sort of defined by the types of customers that are using a product. So on the left side, so you think of a bell curve,

on the left side, the real enthusiasts are the innovators and the early adopters. And then you jump the chasm to what's called the early majority, then the late majority, then the conservatives, then the skeptics. In other words, that group on the right side of the chasm has to have a reason, like a real business case in order to spend money, Tim. And I'm wondering if we think about this, there are different types of technologies that we've seen cross the chasm. So yesterday when we were prepping for this, we talked a little bit about home computers, which really were. I mean, I know we've been at this for 30 years. So when we were kids, home computers were, I mean, boy, it was a privilege to have. I think you said, and you said, I felt the same way.

We got our Apple 2E from my uncle, who was a very early adopter of computing. And I mean, it was unusual in the early 1980s. Yeah, I think, you know, if you think about the Apple one, maybe that's sort of where the innovators were people who were willing to take a risk on a, at the time, you know, in inflation adjusted five or six thousand dollars on a computer that basically did nothing. right? You had to make it do anything you wanted to make it do. And it really led to a lot of people at the time, you know, in that trough of disillusionment that happened with personal computers of like, no one will ever need a PC in their homes, right? Right. Big statements from big fancy people, right? And the, but the early adopters, people like your uncle, my father who bought an Apple 2 Plus were like, we need to have our children have a chance to use this because these computers will be the future. And right. And that was a huge. privilege to be able to have a computer like that in my home. And of course, I immediately grabbed onto it and then really never let go.

But those early adopters are what give companies enough money and enough feedback, right? This is the beautiful thing about a first version product is you get feedback from your customers. And then you can make your product better and better and better. And that's where the Apple 2E, like you talked about, suddenly hit the education market and really exploded and took off and really made Apple. up until the Macintosh came out. Yeah, and there were. I mean, it also found its way. So Apple in some ways found its way at least into the chasm

and started bridging across through things that made that computer or the computers that Apple was making a lot more useful for solving a business problem. And so I'll use the example of one of the great early apps that made, Apple's machines incredibly useful for the business community. I mean, I know you know this one, but there's a lot of people probably never heard of VisiCalc. Yeah. Dan Brickland created VisiCalc while he was watching a presentation

at Harvard Business School. He was watching this presentation and realized that the financial model that was drawn on the blackboard is something that he could create on his computer and started working on it as the sound. on the side and it really became the first spreadsheet as we know it. Yep. And of course led to Lotus 1,23 and Excel

and all the things that we use today. But VisiCalc really gave people a true reason, right? Solved a migraine level problem of people needing to keep track of budgets and other kinds of things that we now use spreadsheets for. And so people suddenly said, oh, I do need to have a computer because I can use Zizzacalc. Right, I can use it to, and so people, this is probably,

I would say the very beginning of some businesses deciding, as things were crossing the chasm here, I can use this to actually, I can use a computer to manage my business. I actually don't need to use a paper ledger anymore. I can automate some of this. And we've never gone back from that. So you end up with these little use cases that end up being worth spending quite a lot of money on. So the through line, let's talk about the through line here because there is, there's enthusiasm and then there's practical desire to spend. You do, you, because you just pointed this out that you need the enthusiasm, you need the cheerleading to get people thinking about the practical. But when does, when do you think that that flips? Because there are, and I'm going to, bring up another one that we talked about yesterday. There are moments where a technology has all

sorts of promise. And you do have a lot of cheerleaders and it ends up going all wrong. And I think you'd know where I'm going with this one. It's on our list. There's the case tools, which I know we've talked about before. Yeah. So in the 90s, there was this huge rush towards computer-aided software engineering, right? So if you think about CAD, you may have heard CAD as computer-rated design, Case was computer-rated software engineering. And it sort of was this idea that you can take a piece of software and make a drawing, like a diagram of what you want your software to look like, press a button, and it will generate all the code for you to do that. And of course, that never really turned out to be true in the same way that the current generation of generative AI can't really

write all of your code for you. It certainly can help, just like the case tools could help. But in the end, I think a lot of people realized that the case tools were really just adding time and not actually eliminating work. Right. And I think this is, you end up, I'm going to come back to the free tools in a minute here, because some of the economics of what's changed is making the technology adoption lifecycle, arguably a little more compressed. But at that time, the cheerleaders were so vocal about this that there was a lot of investment, things like unified modeling and tools like Rational Rows. And we think, this is going to change everything. We're going to have business people, marketers and salespeople are going to be able to define what business process

they need and I literally like draw. They're going to learn unified modeling language and they're going to draw the workflow that they need and then the code is just going to magically pop out. And it just became an exercise in disappointment here. Coming back to free, which is where we are now, a lot of tools due to a whole confluence of things, the open source movement and so forth, we can try a lot of things for free right now. And generative AI, chat GPT. We're trying AI for free, and we're just getting enthralled with it. Do you think because of this prevalence of free tiers that what used to cost us something, like it cost you something to be a cheerleader in the 1970s, 1980s, and now it doesn't cost you anything anymore, does that

dramatically alter the economics of the technology adoption lifecycle? I think it does because your expectations can be very low. If you're spending $3,000 on something, unless you're a super early adopter, I'm looking at you, you know, Apple Vision Pro, right? Unless you're a super early adopter, spending that kind of money, you have very high expectations that this is going to be a product that's going to solve some problems for you, whether that problem be boredom, right, entertainment, whatever. But if you get it for free, your expectations are at literally the bottom.

And so it really helps to get innovators in the door. If they can get people to use things for free, give them feedback, get increasingly better and better products out the door to the point where eventually they can charge for things because they actually have a product that does meet expectations. And I think Linux is another great example of a tool that were initially free and was very limited operating system when it first came out. But because of a lot of work that happened in the 70s and 80s creating free software for Unix operating systems, it immediately had a bunch of tools that solved people's problems. And it was the peak of the time when Linux came out when companies like Sun and HP were charging really large amounts of money for licenses for Unix.

Oh, you don't have to be that kind. You could say obscene. Right. And I just remember like 18T Linux was costing upwards of $1,000 per machine that you installed it on at the time. And it was just crazy because people would have racks and racks of these machines that they would all have to have licenses for. And Linux absolutely changed the game by saying, no, you can literally spend up a computer with an operating system on it, put it on the internet for no money. But what's interesting about this, Tim, is if the cost is eliminated up for, front, the worry I have is that you'll see more bad products because there, there really is no

gating factor. Cost is not a gating factor anymore. You just release it out into the wild and it can be a terrible product. Sure. I do think that there's some fear of flooding the market. And I think we're certainly seeing that with AI tools now, just like a couple years ago, we saw that with cryptocurrencies, right? Like, hey, there's a new cryptocurrency every week. And that's because Because the cost of entering the market was zero, right? It cost you nothing to make a new one, and so everyone made one. So I think that's still going to be true. But the good news is, you know, as we've often discussed, user experience trumps everything, right?

If you've got a really easy-to-use tool that's very simple and very reliable, that will win over a tool that is otherwise similarly priced, eG-free. And so I think you end up competing a lot on user experience. So let's talk about when do we know? Like, as investors, so a lot of bad products can come to market quickly because the cost to introduce products now has gone way, way down. Free is the new model here. How do we know when a product or a company has sort of found its way across the chasm? There's a couple of indicators.

I think we can talk about here. I'll kick it to you first and tee you up with this one. I think when you have seen either in a vertical industry or a set of customers, something you can define, you can point and say, those people have made it very clear that they need this product. So in the case of like the original Mac, the desktop publishing as a practice and the graphic design community said you can have this computer if you take it from my cold dead hands.

Yeah, I think what you just said is the classic business version of crossing the chasm, which is as soon as your salespeople start telling the IT department to shove it, when the IT department says, no, you can't have that. That's when you've crossed the chasm, right? And a great example, of course, is when the iPhone came out, suddenly every sales exec had one of those, everybody had to have one and they really wanted to use them for everything for for mail and for all the stuff and of course the IT department freaked out and said they're not secure you can't use that you can't have it and you know of course President

Obama notoriously wouldn't give up his Blackberry right so I mean there's certain those are the things that you know you've crossed the chasm when people are demanding that they use them in their business environment even if there's strong resistance yeah so there is right the the loyalty indicates to you that, look, this solves my problem, what we said before, this is a migraine level problem for me, and there's absolutely no way you're taking this away from me. So some kind of sign that a group has said, absolutely, there's no way you're taking this away from me. That's the definition of evidence of a migraine level problem being solved.

when we when we think about this i'll take an example of a company that i think has crossed the chasm not not recently it's it's been a while but i do think there there's ample evidence to say just using a software product i think mongo db crossed the chasm a really long time ago because there is a number of instances where it's so easy to develop a piece of software and attach that database to it, that developers are never letting that go. Yeah, I think that's true. And of course, any of the cloud hosting companies are in that place where, you know, people want to host on Google Cloud, want to host on Azure, want to host on AWS.

And there's a lot of people just assuming that that's going to happen now. And I think when you assume that's going to happen, that's a big difference from, like, I interviewed the CTO of a company called TU years ago. And he said, when I first said, we're not having any servers of our own, we're doing everything on AWS, everyone thought I was crazy. And now they're like, wow, yeah, of course, that's how you do things now. Right, right. Yes. When you reach that, of course, that's what you're doing moment. I think that's evidence that you have crossed the chasm.

So as investors, if we're wrapping this up a little bit here, I think what we've said, as we were talking about this, it's probably better as a public market investor to be on the other side of the chasm. In fact, I would say it is universally better to be on the other side of the chasm. The left side of the chasm where you're still working with cheerle, leaders, that's a good place for venture capitalists. Right. I mean, the true huge money gets made from there, but the true huge money also gets lost from there, right? They're making a lot of very expensive bets on that before adoption side of the chasm, and most of them don't pan out. Right. And so there's a common term that gets bandied about a lot, particularly amongst executives who specialize in things like product-led growth. And they venture capitalists use this too. They call it kind of a tipping point. And the phrasing you'll hear sometimes is called product market fit. Product market fit. And product market fit means, I mean, just really dumbing it down, Tim,

this is the way I think about it. It's, you know, we've got a product and we found a migraine level problem and those two have met. And now there's an explosion of demand. Right. We actually have a product that people want to buy eventually and all we need to do is figure out how to get more people to buy it not to get anyone to buy it right we need to be able a satisfied demand at scale get those people satisfied grow the pool of those people and then get other people around them talking about it right so i think in terms of investing one of the main takeaways that i am always suggesting to people is to look for things that are right after that adoption is really and companies that are really ready to really hit really hard on some big question.

So one that hit that way for me personally in my investment career was HubSpot. That was a product that is a and has a lot of different features now. But at the time, it was mostly a CRM, so customer relationship management and email marketing platform. And at the time, Salesforce was dominating that industry. And no one thought that another product could really crack into the market. But HubSpot found the product market fit of small businesses, very small businesses, solopreneers, designers, folks like that who just need something more than a spreadsheet and a

simple WordPress website, but they're not wanting to pay the premium to use Salesforce. And they have utterly dominated that market in the last few years. Yeah, and they showed no signs of slowing down. And they've been able to, once where these companies get really interesting and once you want to hold for a long time and HubSpot is this kind of company, once you solve one migraine level problem for a particular type of audience, that same audience gives you permission to solve another migraine level problem for them. and HubSpot, boy, did they lean into that like very few other companies I've ever seen. So it was inbound marketing. And then you had those customers saying, hey, could maybe you help with my sales pipeline? Yes, we can.

And they built a hub around that. And then they built a hub around web design. And they built a hub around support. So customers, where this ends up, again, sort of drawing the through line between hype and when you actually get adoption. When you're at the hype stage and you found cheerleaders, they are very excited about what your potential is. When you're on the other side of the chasm, you are now pain relief for a well-defined customer base, and that customer will come back to you and say, what else can you do?

Right. And as long as you can continue to deliver on that, which not every company can, right? Including Salesforce, right? They have done it for a long time and now maybe you're perhaps struggling a bit. If you can continue to deliver on that, then you can continue to increase your revenue per customer and solve customer pain and they will stick with you for a long time. So let's end this by making, because we always like to make reckless predictions here. So this one's a bonus.

We didn't talk about this up front here. I think we both agree that. Generative AI is still stuck in the hype cycle. It's still on the hype side of the equation that we haven't yet seen general product market fit for generative AI yet. So, Tim, if I had to give you a time frame, how long do you think it takes generative AI to get genuine product market fit where it is solving a migraine level problem? I think it's already solving some problems for some people today.

Right? So there are people who get benefit from using chat GPT as is right now. But for free, right? Yes. That's the thing. Where I think we really want to think about is when is the product worth enough money to someone that if it went away, right, the David Gardner snap test, if this goes away, will I be like, all right, well, it went away for free, I'm willing to pony you?

up $5 a month, $20 a month, $50 a month to keep using it. That's where I think the real heart of your question lies. I think that could happen in less than a year if the people who make these tools continue to push. And there is definitely an arms race between tools now, which definitely leads to an accelerated, you know, tech excitement. Let me try this again. There's a lot of people competing on this right now, which definitely leads to tech going very, very fast in terms of how well it gets better. But will that be really useful to a lot of people in their daily life soon? I don't know. Apple was very careful not to say AI in their big announcements this week. And I think that's telling that they don't think it's there yet. Well, so I'm going to take that side of the prediction equation here and play, you know, as I sometimes do.

play get off my lawn guy here for a second and say I think it's at least three years. And the reason I say that is because I think you need to identify what kind of data and what kind of data problems are so specific and so hairy that they need AI to solve them. And I don't think we've defined that yet. I think we found the, to your point, I think chat GPT has has found a whole bunch of nice to haves. And that's interesting. And that's where the cheerleaders live. But I think the need to have must pay for. Don't do this. And we feel severe pain. Those sort of data problems. I don't think they've been well defined yet, Tim. So I'm giving it three years. But then again, I get curmudgeonly on this kind of stuff. As always, people in the program

may have interests in the stocks they talk about. And the Motley Fool may have formal recommendations for or against, so don't buy stocks based solely on what you hear. I'm Mary Long. Thanks for listening. We'll see you tomorrow.