Latest / In the Loop AI / Why It's Not Too Late To Invest in AI | Dan Ives

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Why It's Not Too Late To Invest in AI | Dan Ives · In the Loop AI

• Speaker0: [0:00] That's what we spend all of our time doing, right? We're in the field, Speaker0: [0:03] like, 3 million air miles the last 25 years, right? Like, not for my health. I mean, the point is, like, that's been our secret sauce, trying to find who the winners are. Who are going to be the winners in, like, robotics, autonomy, physical AI. The amount that's going to be spent in those areas could potentially dwarf what we've already seen today. Speaker1: [0:26] We have Dan Ives on the podcast today. He leads tech research at Wedbush. They launched an Ives AI Revolution ETF. That's an AI ETF. Speaker1: [0:35] And he's been pretty early and pretty loud about the AI CapEx super cycle. So, Dan, welcome to the show. Speaker0: [0:43] Yeah, great to be here. Speaker1: [0:44] Okay, so Josh and I are investors. I have experience mainly in crypto. Speaker1: [0:49] Josh has more experience in AI. And I want to start with this question. Is it too late for me to get rich on AI, Dan? Speaker0: [0:56] I mean, look, I believe we're in the second inning of the AI revolution. Because, see, it's my view, and this goes back to 2022 when it started with, you know, Godfather of AI, Jensen, NVIDIA, and my view of when it was all starting. It's a 10-year build-out. So I strongly disagree. I go in a bit like, oh, the stocks have run so much. I missed it. Dude, this is just the beginning. Because the second, third, fourth derivatives across enterprise, consumer, semis, energy, robotics hasn't even started in terms of full scale. Autonomous hasn't started as well. So to me, that's going to be the holy grail. Speaker1: [1:42] Okay, that makes sense. So you're saying we're in the second inning of a nine-inning game, of course, but coming from crypto, we know that these innings, of course, can span multiple cycles and you can have boom-bust cycles across them. And so this is the NVIDIA chart that I'm showing right now. And even by crypto standards, okay, we don't often see charts that look like this, which is just like a parabolic line up from 2023 up to now. This is an incredible chart. And you're saying, Dan, You think that buying this chart is actually a good thing to do at this point in time? It's hard for me to believe. Speaker0: [2:18] Yeah, and that's been our... But remember, the last... I might have my career, what, 25 years in tech? I mean, really, for the last 15, investors have just fought it. No, it can't go up more. No, it don't. Because it's my view, we're in a fourth industrial revolution. And that's really been our view, not just NVIDIA, Palantir, of Microsoft, of everything we've seen across the tech sphere, is that you have $3 to $4 trillion that's going to be spent in the next two or three years. I'm not saying you're not going to have pullbacks. But it's my view. We're going to be talking about $5 trillion, $6 trillion markups. And a lot of it just comes down, when you look at NVIDIA specifically, like there's one chip fueling the AI revolution. And it's led by godfather of AI, Jensen, NVIDIA. There's no one else that's within miles of NVIDIA. Speaker1: [3:16] Okay. Do you think that this fourth industrial revolution will play out across cycles, though, is kind of my question. So 25 years in tech investing, so you've seen a number of like, you know, bull and bust cycles, including, of course, you know, the full dot-com cycle, right? You had 1995 all the way to 1999. And then you had a, like a massive boom, of course, the end of the 90s, and then a bust, which a lot of people said it's over, Internet's never coming back, all of this thing. There was some recovery time. And ultimately, I think the Internet bulls turned out to be right on that, but it took some time. And so even a fourth industrial revolution, even, you know, second inning of the game here. But do you think that this still could play out in cycles? And the cycles may be just because of human psychology, right? Like we're in crypto. We've seen so many of these cycles. Things get ahead of their skis. Yeah. Do you think this plays out in cycles? Speaker0: [4:07] Yeah. See, normally I'd say yes, like in terms of cycles, right? Because my whole career, that's really what I've seen. Here's the reason I don't think this plays out in traditional cycles. It's because when you go back, when I cover tech stocks in the late 90s and the boom and the bust, those were basically where frothy companies, business models that were really untested, the use cases weren't really there. And these companies were essentially back by VCs and a lot of companies that were losing tons of money. now because big tech is sending you back in 325 billion of cap backs, Sovereigns haven't even gotten in it. Enterprise in the U.S., only 4% are actually starting to focus on AI. There's none in Europe, none in Asia, ex-China. I believe the next few years, this trend actually just multiplies. Like, it's our view. Like, look, we spend so much of our time in Asia. I believe there's more growth over the next two years. When it comes to semis and when it comes to just overall demand. Globally than the last 10 years combined. Speaker1: [5:20] So you believe this chart just keeps kind of going up? Maybe there's some pullbacks, but it's nothing like kind of like a 90... I think Amazon had a 95% drawdown, for instance. One of the dot-com darlings, of course, that's quote-unquote went bust. And then I think it took them, what, to 2008, 2010, something like that, to like recover just from the dot-com highs. You don't think anything like that will happen in AI. Speaker0: [5:44] Big tech companies, they have a trillion dollars of cash in the balance sheet, and they generate $200 billion a year in free cash flow. So see, the difference, that's why a lot of times, when I talk to investors, retail, institution, whatever it is, when it's about dot-com bubble, I was a tech analyst in dot-com bubble. So I was there front and center. it's so dramatically different because of the use cases and because of computing power and because of what we're seeing on the enterprise but on the consumer side a lot of people are like oh chat GPT I'm not using it that much it's not about that it's about, one in every 10, 12 households is going to have a robot. It's about autonomous is going to be 25% of mobility. See, we're talking about the beginning of a transformational period of time Speaker0: [6:43] as a, not just an enterprise, but as a consumer. Speaker1: [6:47] And we talked about cycles. I want to talk about the super cycle and particularly the CapEx super cycle. And I'd love for you to kind of walk us through, kind of map the dollars for us, Dan. So what exactly turns into shareholder returns over the next few years. I mean, you flagged $2 trillion of AI spend over the next three years. Who captures that margin first? Who captures it last? And where is it all heading to? Speaker0: [7:07] Look, it speaks to our Ives, AI30, and obviously ETAFs based on that. It's that, okay, it starts with semis. Of course, it starts with NVIDIA, but then TSMC, Broadcom, you know, where I view AMD, and there's a number of other partners in there. Okay, so it starts with semis. And of course, semi-equipment. Then it goes to software, hyperscalers, Microsoft, Amazon, Google. Look at everything, what Oracle's doing, renaissance to growth. You've even seen with IBM and others. But then it goes back to who wins in the use cases. So I'm just going through like almost a pattern. That's where Messi of AI palantir, right? They hate the stock at 12, despise it at 50, yelling from mountaintop's at 100. And the bears, when they're in hibernation mode, they can't see AI in spreadsheets when the stock's 200. Then you think about Datadog, MongoDB, Elastic. Okay, so now how do you protect it? Cybersecurity, CrowdStrike, Z-Scale. Think about Palo Alto by CyberArk. You start to see more and more consolidation. The consumer side, Meta. Speaker1: [8:14] Alphabet. Speaker0: [8:15] And then it's my view, ultimately, where does this go with physical AI, robotics, autonomous, Tesla, nuclear names like aqua so that's my view my view is like you can think about the theme, in a very like tunneled vision you have to now think about in terms of the second third fourth derivatives playing out so. Speaker1: [8:38] When you look at these is there anything that possibly derails spending because it looks like everything's going well right everyone has a lot of money they're pouring into these data centers is there anything along like a power issue or supply chain or policy Anything that you're looking for that would raise a red flag when you're evaluating these things? Speaker0: [8:54] No, look, like down the road, call it 6 to 8x more power, given the amount of data centers out there. That's a restricting factor. Speaker0: [9:03] If you think about where we are, 3%, 4% in terms of through, that doesn't really till we're about 20% through where that's a restriction. That's probably not until 2028. So I'm just trying to give you some maybe goalposts or timelines. Look, the biggest risk to this whole thing was US-China. Trump tariffs, cut tech off at the knees, chips selling into Nvidia. But obviously, look, we've spent a lot of time in DC meeting with lawmakers. People on the air like cool ads prevailed right like they recognize like okay we cannot cut tech off and these that gives china advance for the first time in 30 years u.s is ahead of china, when it comes to tech so that's why you're starting a csnj barter system right nvidia amd you want to go in there pay us 15 fine it's a tax it's a toll on the gw bridge so to me it's like okay there's geopolitical. There's supply issues. There's power issues. But the biggest thing is if the use cases work, this story plays out. And if I think about where we were, being early in AI in 2022 relative to where we are in 2025, we are probably a year to a year and a half ahead where I thought we'd be. Speaker1: [10:24] While we're talking about this CapEx, so $2 trillion CapEx, where's the CapEx coming from? Is it primarily just strength of these big tech company balance sheets so far, Speaker1: [10:33] or is some of it debt at this point? Because that starts to, I guess, trigger my meter for how far we are into a CapEx super cycle. It's just like when we start needing more and more debt, let's say, and going up the risk curve from a debt perspective. But so far, is it all retained earnings, basically? Speaker0: [10:51] All retained earnings. Speaker1: [10:52] Really? Really? So it's these just large tech companies who've made billions selling ads in cloud and everything else, and they have that on their balance sheet, and now they're just spending it. Speaker0: [11:03] Spending it. I mean, see, that's the biggest difference. You know, if you're ever with a bunch of friends, like, oh, there's like 99. Yeah, look at Cisco. They're like, oh, look at Cisco's chart. Compare it to NVIDIA, and it looks like it's the same. Look what happens. Yeah, but there's one thing you're missing, Like Silicon Valley essentially didn't exist then. And when you think about cat backs and everything that we're seeing, it's new rules of the road, right? It's new rules of the game because big tech is essentially running it. But here's what's crazy. Sovereigns haven't even gotten involved. Middle East, they're just starting. Enterprises, just start. The consumer piece, that's just starting. Robotics, still in the lab. Autonomous. Just starting to get whatever, Waymo in five cities, RoboTax, obviously now, Austin and some other cities. So I'm just trying to show you where we are in this. Speaker1: [12:03] Help me navigate the AI stack here a little bit further, Dan. So say I'm kind of new to this. I'm newer to AI investing than Josh is, right? So you mentioned kind of semis at the bottom, right? And this is, to me, maybe this is the compute layer. as I look at like what you're holding in your ETF, things like NVIDIA, AMD, TSMC, even Broadcom, which is somewhat interesting to me. That's one layer. And then there's maybe the data center layer. Like we have to build out Speaker0: [12:31] A whole bunch of data centers. I just want to stop you right there. Yeah. Think about like foundation of a house. Speaker1: [12:35] Okay. Speaker0: [12:36] The foundation of everything are chips. Speaker1: [12:40] Okay. Speaker0: [12:41] So just think about foundation house because now we built it. Those are the chips. As the concrete's getting filled in and you're starting to build the actual structure of a house, that's the hyperscalers. Microsoft, AWS, Google, Oracle. Then when you're thinking about use cases, software, that's the actual building of a house, the rooms, the furniture. Protecting it, that's a ring. That's an alarm. That's what cybersecurity is doing. See, I'm trying to like explain to you, but here's the thing about it like this. You're essentially building Vegas. Speaker0: [13:21] Or Dubai, and it's desert, and you're just starting like 1945, 50 style to where we were when you think about build out Vegas. That's what AI is. Speaker1: [13:37] Interesting Vegas using the casino analogy here, which is the fast money analogy. But let's continue with this kind of house analogy. And for maybe two innings in, we're like 15% of the way there, 20% of the way there, something like that. So 80% remaining. Speaker0: [13:51] I think it's really more 5%, 10%. Speaker1: [13:52] 5% to 10%. Okay. All right. So we've got semis and compute, NVIDIA chips. That's kind of the foundation of the house then, right? And I guess the framing of the house, we'll have to get some construction people on here because I don't know my house part, but the framing, okay, of the house, that's like you're talking about the hyperscalers, all right? So we've got the Google. Speaker0: [14:11] The storage. Speaker1: [14:12] Okay. And storage, all of that. And then we have kind of the app layer, which I guess is like things like Palantir. I'm referring to all of these things as publicly traded companies because that's what's in your ETF, okay? And then where do data centers fit? Is that like the attic space or something or is that what they, you know? Speaker0: [14:32] Yeah, I mean, but then when you think about the hyperscalers, okay, and you think about, remember they have different, they have like different functional areas. Their storage, their computing, they're the application layer. Speaker1: [14:46] Right. Speaker0: [14:47] They build out the cloud. So when you think about like part of how Microsoft and Nadella will be on the Mount Rushmore of CEOs for the next 200 years. Because Nadella recognized, okay, we're Windows. We're enterprise. Going back to like when he, 2014, you know, when Nadella came in 2015, he recognized all about cloud. So when he built out the cloud, Amazon and obviously Bezos was ahead in terms of AWS. But here's the key. They built out cloud, built out cloud, built out cloud. When AI came... Everything has to be in the cloud. So that was a further catalyst for these companies where it's not just AI-driven workloads. It accelerates the cloud. It accelerates companies going from on-prem to cloud. So that's why these companies have almost had an accelerated growth, like a Microsoft, like Google Cloud. Speaker1: [15:44] Like, look at Oracle. Speaker0: [15:45] If you look at that stock, that's a good example. Speaker1: [15:48] Okay, I get it. So most of the time, the hyperscalers then, And they have a ton in terms of data center and, you know, compute resources, I suppose. And there's not generally like independent companies here. So in each of these kind of layers of the house or the Speaker0: [16:01] Parts of the house. And I would just say one thing. And remember, a lot of the data centers, those are data centers all around the United States, all around the world. Speaker1: [16:08] Right. Speaker0: [16:09] Where they're basically physical data centers, REITs, Equinox. And there's different like players that play in the data center space. And those, look, there's more data centers under construction today than active data centers. Speaker1: [16:25] That's incredible. That does remind me of the 1990s, actually, when we were building out all that. Speaker0: [16:29] But the deal with the 1990s, this is a 1995, 1996 moment. Speaker1: [16:35] Right. Speaker0: [16:36] Not a 1999 moment. Now, then you could be like, oh, in three years, it's going to be a 1999 moment. Look, my view is it's totally different because the system's not leveraged. The business models are there, and it's being supported not by debt, but by essentially trillions that can be generated, not just big tech, but across the board. Speaker1: [17:00] In each of these layers, do you see a kind of a breakout network effect winner? You know, sort of like NVIDIA has sort of won the semis. I mean, they haven't won. I guess, you know, there's TSMC. Speaker0: [17:10] I think Palantir has clearly been that one on the software. Speaker1: [17:14] Okay. Speaker0: [17:15] I think Microsoft's been the one on the hyperscaler. I think on the consumer side, it's been Meta because of the way they're going to monetize their user base, the three and a half billion. And, you know, that meta is going to be the one on the consumer side. But we're just starting. It's like, okay, who's going to be a winner on autonomous? It's my view, like, autonomous and robotics, Tesla is going to be the clear winner. It's a whole part of our thesis, right? Like, with Tesla, like, it's very easy. When you look at Tesla, like, oh, it's an EV, it's a car. I've never viewed it like that. And I view the holy grail from Musk and Tesla, which is why Musk is now a wartime CEO. go to focus on autonomous robotics. Speaker1: [17:59] Is it the case that there are these power law winners in each of these categories from some kind of return to economy to scale? Or actually, we had Kathy Wood on the podcast not too long ago, and she talked about the notion of rights law, which is basically the more of the thing you make, the cheaper they get to make, and you kind of build a network effect this way. And if you're saying, look, Tesla for robotics, maybe it's because they make the most robots effectively. That's why they win. Speaker0: [18:23] NVIDIA, maybe they're making the most cost. Speaker1: [18:26] Yeah. So is it the case that for each of these categories, we should expect some really dominant players and returns to scale? Speaker0: [18:34] But it's not a zero-sum game. Now, at one point in the next five, seven years, you'll have more and more competition. But that's why it's my view. You're going to have winners in China, Baba, Baidu, and others. This is not one of those, it's a zero-sum. It's one or the other because the market opportunity is so massive so early to where we are and that's why it's my view that like we're going to be talking about nasdaq 25 000 30 000 coming years but again like for something myself has done 25 years like you know how many investors i've known, that have missed every transformational growth stock because they're focused on. Speaker1: [19:20] Valuation over They're too expensive? They're always too expensive? No, at the time, Speaker0: [19:23] It's like, well, the Greek crisis in 2000, no, but the geopolitical, no, but the Fed, There's always an excuse, right? So the point is, I think we're most well-known for just seeing more disruptive themes, picking the winners, and we don't focus, we don't get so bogged down with valuation over the next year. Because I think with transformational growth trends, you can't do that. Speaker1: [19:51] So if I'm looking at AI and I was looking through the stocks in your ETF, like the 30 ETFs, one thing that struck me too is it's primarily American, right? Speaker0: [20:00] Yeah, just a few Chinese players, yeah. Okay. Speaker1: [20:03] So is that because you think America is going to kind of win or because it's just too hard to access China's capital markets? This fourth revolution, where do you think it happens? Is there a... Like the internet primarily happened in America. Do you think AI primarily happens in one geography? Speaker0: [20:19] First time in 30 years. Like I can tell you, someone's like always like, you know, in Asia four times a year. And all the time I'd have those trips, then, you know, I land in Newark airport and I'm like, oh, we're so behind. Like, okay. Guess what? The last few years, we're ahead. See, it's the first time, in my view, since mid-90s, that U.S. Is ahead of China when it comes to tech. Because AI, NVIDIA. China's going to have clear winners. Baidu, Baba. You'll have other one, Tencent. I mean, you're going to have winners there, but it's because it's the one chip fuel in the AI revolution, is NVIDIA. And you're not selling. When you think about Blackwell and next-gen chips, you'll have scaled-down, restricted chips you sell to China, but you're not going to sell the best chips to China's companies. You sell it to U.S. companies. And that's why it also goes back to, like, when Deep Seek happened. You know, right away, like, over that weekend, we're like, dude, there's a better chance of me playing an NBA than them spending $6 million. Like, add two commas. The chance that they didn't use NVIDIA chips, again, same chance, like, I'm playing Ryder Cup, September and Beth Page. Speaker0: [21:40] So it comes down, like, there's a lot of, like, those moments, like, oh, my God, this, but look what ended up happening, right? Like they essentially had NVIDIA chips and you had two commas to what they really were spending. But that's why Trump administration understands the biggest asset they have when it comes to U.S. China. Skyfire, the Bay Area, Gen2, NVIDIA. Speaker1: [22:02] Okay, so we're investing in the United States. We're investing in these categories. We have the semis. We have data centers, hyperscalers. Curious, I mean, for the people who are listening, is there a section of this house that we've just built that you see the most potential in? That is the most exciting, that Speaker1: [22:16] has the most upside that maybe most people aren't paying attention to? Is it in the foundational data center layer? Is it the hyperscalers like Palantir, who's had tremendous returns in the past couple of quarters? Is there a place that kind of is asymmetric in expected returns versus the others? Speaker0: [22:31] I think the area, when you said where people are missing, it's not really chips. And it's not even cyber secure. To me, it's software. What what ultimately this is going to do to Salesforce, Adobe, Microsoft, Alphabet, in my view of Amazon and some of the parts, you know, specifically on AWS, the MongoDBs, the Elastics. Like, you know, this is going to be like, look, for every dollar spent on NVIDIA chip, we estimate there's a $10 multiplier across the restack. But it all happens in the software layer. that's the golden goose. Speaker1: [23:14] In the software layer, how do you identify like who's executing against this? Because it strikes me that some companies will, some companies will probably miss it. Have you identified that yet? And what do you look for if you haven't yet? Speaker0: [23:25] Yeah, I mean, being like exposing, identifying Palantir like so early, right? That was a good example. Where it was like we identified it and it just took time to play out. Speaker1: [23:38] Yeah. Speaker0: [23:39] That's what we spend all of our time doing. We're in the field, like, 3 million air miles for the last 25 years, right? Like, not for my health. I mean, the point is, like, that's been our secret sauce, trying to find who the winners are. But no different, like, who are going to be the winners in, like, robotics. Atomic, physical AI. I mean, I believe the amount that's going to be spent in those areas could potentially dwarf what we've already seen today. Speaker1: [24:09] I think it's interesting because it's also going to be difficult to sort the signal from the noise because, I mean, I'm sure this has already happened. I don't listen to many quarterly earnings calls, but I'm sure every CEO in the country is talking about their AI strategy and what they're doing and using more and more buzzwords. And so the software layer, you have to really separate the substance from the noise. Speaker0: [24:32] Yeah, and that's actually, that's probably the most difficult thing because there's so much noise. Just because you say AI 40 times in a conference call doesn't mean you're AI. You're right, it's a challenge because there's so many companies I'll meet with. And Managing's like, we are reconverting into AI. And, like, I'll sit down with them. And after an hour, I'm like, you speak in Mandarin? Like, I don't understand. Like, I don't understand what you're doing. Like, no, but the AI is doing. And I'm like, oh, it's really your core technology that you basically have reconverted with some change in code to AI. But AI is really 5% of the code. 95% of it's away, you see. It's an AI play. So I'm just trying to explain. That's why we've spent so much of our time trying to identify the next steps. Speaker1: [25:35] Back to these areas. So America versus China, you think America has the lead. Of course, we haven't quite seen robotics and maybe what China will do there. Speaker1: [25:43] That could be another area. But like when it comes to, I guess, Chinese AI exposure, is it even possible to get the sort of exposure that you'd ideally want in US capital markets? Or is that just like not available to U.S. investors. No, you can't because like, Speaker0: [25:58] Look, it's like Huawei has like a massive, you know, scale product offering. I mean, you're going to be able to like put, see, I almost view it as like, not only does it decoupled, but I actually do think it's important to play China because, because I don't view it as like, it's usher that it like, it's my view. Like you also have to play that because that's exposing, not just China, but you're exposing to the rest of Asia and a lot of the other areas. Speaker0: [26:25] But it's an arms race. And the good thing in arms race, a lot of money spent, a lot of winners. Now, to your point, like, okay, are we going to go through a point where there's a cat-backs digestion? We could go through points into the next year that you have cat-backs digestion. But, again, it goes back to it. I don't view that as, like, a multi-quarter digestion. It's like, you're just going to go through some of these pockets where investors will be hypersensitive. Are they lowering growth? Is it over? But, but it's my view. Like we're just, these companies have to continue to propel to the metal. You go back, like, and like going in, the question is like, oh my God, is Nadella going to recommit to 80 billion that they said that they're going to do? And everyone's like, and then Nadella's like, I'm good for my 80 billion. Okay, it's seven months later, they're doing 120 billion run rate. So just go back, I mean... Because they understand you take foot off pedal, like look at Intel. That didn't exactly work out great. Speaker1: [27:35] Another way too that I'm trying to understand this market is like where the primary value accrues. Is it all going to be weighted towards the large cap side of things? Or is there some room for small cap plays? Because what I've been astounded in this most recent kind of like run, the AI stock market run, is how lopsided it's been in favor of like large tech company incumbents. I mean, the returns on the S&P, Speaker0: [27:59] The NASDAQ for that matter. Speaker1: [28:01] Are heavily weighted towards like, I don't know, the top five companies. And that's like, that feels different from a market structure perspective than other things I've seen in the past. But do you think there's a small cap play? Maybe they make a comeback? I understand. Speaker0: [28:16] Because big tech, they're first ones to benefit. Then the ripple effect is going to be small mid-catch. But there's so many names that we've identified, like, I mean, there's so many small cap names, like a sound town, like stocks, like $2, like, uh, you know, now it's 15, like that's the example of the small cap name on like the AI speech side's benefit, but just got a small benefit, like names, like Pegasus. I'm just giving them names, like InnoData. Like there's a lot of small cap players. There's a lot of small cap players that are going to be mid caps. A lot of mid caps are going to be large caps. Speaker1: [28:54] So if you're talking to someone like me, let's say, and I'm just kind of like, I started the question of like, hey, is it still possible to get rich on AI, right? So what are the plays now that you think are undervalued, right? Because it does give me some pause to sort of 500% of an allocation. I don't necessarily want to pour it all into that Nvidia stock that's already kind of, you know, gone to the moon. I will put a portion there. Okay, I'll put a portion there. Because that's the not that's more than the foundation of the house. That's kind of like everything. That's the basement that's like the core piece. So I get that. But I'm also looking for the undervalued play. Do you think I should look more at sort of mid-cap, small-cap type equities for that? And like, is there an easy button there or do I just have to do the analyst research? Speaker0: [29:38] I mean, look, that's why we created the IVEs AI30. Like if those are the names at all, but there could be names. Like right now it's rotating like where I think software and cybersecurity and the autonomous MSPs are the ones where maybe there's more pedal to the metal relative to, you know, valuation upside. But it's what I view it less about small cap, mid cap, and more about sectional software, cybersecurity, the buckets, the way that we break it out. Speaker1: [30:11] Tell me more about the Ives AI 30 then. So because I was looking at the stocks in there, there's most many I recognize, some I didn't. But how do you guys kind of like adjust what's in that basket? Speaker0: [30:22] Yeah, and every quarter we adjust. You know, we just launched a few months ago in terms of based on the Ives Ad 30 Research, And it's really, look, it's giving investors the ability, okay, how do I play AI? How do I play AI? It's not just these names. Like, okay, you got to focus on second, third, fourth derivative. It's us putting it into the buckets the way that we view it, whether it's enterprise software, cybersecurity, consumer, autonomous, semis. And that is something where every quarter, if we feel like there's names that are going to be more relevant based on our research and others that are less than on a quarterly basis, like some come in, some come out. And that's the way the ETF is based off of our research. Speaker1: [31:06] So that's a way to easy button play it, I suppose, for a retail investor. Can I also just buy the S&P? Can I just buy NASDAQ as an indice as well? Is that a way to play it? Of course, you can always buy. Speaker0: [31:17] It's my own view. The whole reason I created IBSA out there is that this is giving true AI exposure. The problem is in so many of these others, it's like, they're not there's pieces of it but this is more contrary to what i've used the ai theme true ai a to z and you know and look and i think it's the biggest tech theme we've seen the last 40 50 years it's fourth industrial revolution but it's not one or two year theme is. Speaker1: [31:46] There a point like at what point would you look at kind of the market and say okay okay at this point i think ai is overvalued for the point in time? Like, of course, long-term, it could be the fourth industrial revolution, but are there some metrics or numbers in the back of your mind where you're like, hey, if this happens, maybe things are getting a little frothy at this point in time. Speaker0: [32:10] Look, a lot of it's based on when we do, if I see deal activity, it's just continuing to massively increase. Conversion, ROI, use cases went from 10 to 80 to what could be one exponential. Then to me, we'll continue to look out next two, three, four years. And if we could justify the valuation, and I believe the street is always underestimating. Like that's always been my view as tech analyst for decades. Like street underestimates, maybe in the near term, but it underestimates transformational growth themes. Speaker1: [32:50] I agree. Speaker0: [32:51] Because it gets caught up in, she had political, fed, this valuation, you know, like typical themes. And I think that has created the opportunities, right? Speaker1: [33:01] The other monkey wrench, it seems, could be like the use cases. For whatever reason, kind of the chatbot intelligence, LLMs, that doesn't matriculate, that doesn't kind of get into the app layer and create economic value. I think I just saw, you know, OpenAI hit what, 10 billion in annualized revenue, which is like a pretty good sign. I mean, that basically means they've got 10 billion people paying them $10 billion just for access to their model. That's a good sign. But there could come a point where we just can't bake the intelligence in our systems and automate, reduce costs, generate new revenue sources. I mean, is a fundamental of driver here how smart these models actually are and how far they distribute in our economy? And how are you watching for that Speaker0: [33:49] Yeah and the enterprise piece that's been that box i think has essentially been checked right like you're seeing that with hyperscalers the oracle and then i mean the enterprise is booming and that's remember the ai revolution is really enterprise is driving it the consumer piece hasn't started so when we go to the consumer piece 26 27 28, That's going to be the key in terms of the consumer side. But that's why use cases are key. It's key to understand where this is all heading. And it goes back to like when we talked about in 2022, like we viewed it as like, this is a theme we're going to see till 2030 and beyond. But we're only early on because it's just the enterprise. Speaker1: [34:38] So I've went through the allocation of these 30 and I kind of looked at a lot of the stocks that you had And some of them were really exciting. I want to talk about the software because you mentioned a bit earlier, a lot of the value accrues to that software layer, particularly around Palantir, who I guess would be the poster child of this. Speaker0: [34:53] I was looking at the chart. Speaker1: [34:54] It's unbelievable. It's up 500%, over 500% just in the last year. The last earnings report was incredible. I'd say half of the people invested in it probably still don't even know what it does. But what Bush has said, I mean, Palantir is going to hit a $1 trillion market cap. Speaker0: [35:10] Wait, what is it now. Speaker1: [35:11] Josh? Speaker0: [35:12] I'm not sure. Speaker1: [35:12] Dan, do you have a, do you know? Speaker0: [35:14] It's like 350. Speaker1: [35:15] 350, yeah. Speaker0: [35:16] It's a long way to go. Basically, for it. Speaker1: [35:18] To hit a trillion dollars, it needs to continue this type of growth. Speaker0: [35:21] Look, it's my view. What are you looking for? First of all, I'm the hugest believer and fan of Karp. Like, I believe Karp. I put him up there with Musk, Jensen, you know, Nadella. Just, you know, hook to somebody. In terms of, like, seeing the vision. Well, Palantir has a mousetrap that no one else has. I mean, remember, it started off as government. Basically, you know, they really foundationally are essentially the big data platform for most Western governments, military, three-letter agencies. And it was taking that technology from a data perspective on the enterprise. And basically, like, doing what they did in government to the enterprise. CARP from the beginning and Palantirians and Palantir, they understood AI wasn't going to be about the LMs. It's going to be about data. See, they made very strategic decisions early on. So they were so ahead of the trend that when companies basically started to buy NVIDIA chips and wanted to build out AI strategies, there was essentially one company to call. It's a pound tier. Speaker0: [36:38] Now, I get the whole like, so expensive. But again, it goes back to like, I could have said the same thing when Facebook bought Instagram and everyone's like, how is you? That acquisition makes no sense. That's been a hundred bagger. It's my view. You have to be able to look out the next five, 10 years where these companies are going. Speaker1: [37:01] I'm looking at the numbers here. So the market cap, 400, just over 400 billion, trading at 600 times price-to-earnings ratio, which is outrageous. Speaker0: [37:08] But again, that's, but see, that's my view. It's like, you know, so many times, like I've had companies like, well, it's true. Then all of a sudden, like it's a year later and you're like, huh, free cash flow is 2 billion. It's now 8 billion. What's free cash flow going to be next year? 12 billion. Then all of a sudden, the company's not trading a rev, trading free cash flow. Like that switch flips. Speaker1: [37:32] So what milestones are you looking for along the way that signal we're on our way? So we're at about 400 billion, we're going to a trillion. Is there anything in particular you're looking for along the way to kind of check marks? Speaker0: [37:43] The biggest thing that I look for, Palantir, it's customers we talk to. What do deployments look like? What are the use cases? As deal sizes continue to increase? What the commercial U.S. business could ultimately look like over the coming years. The government, I mean, like the U.S. government, like when they go toward AI, the red phone is essentially Palantir, NVIDIA, Microsoft, Oracle. Speaker1: [38:13] Okay, there's another company on this list here that feels a little out of place, although it shouldn't. It's Apple. Apple is, I mean, very clearly missing on the AI front. I saw it recently. They've spent about a trillion dollars in buying back shares, And it doesn't seem like they're putting nearly enough money in R&D and AI development. And yet it's still on your list. So I see you shaking your head. Speaker0: [38:33] Why is Apple there? Because they're on the list because, in my view, the consumer AI revolution, 1.5 billion iPhones, 2.4 billion iOS devices, comes through Cupertino. They're a toll collector on the highway. But probably the most disappointed I've been at any point in covering Apple over the 15, 20 years, probably now, because they're missing the AI revolution. Innovation's not gonna happen internally. You have wartime CEOs across tech, Zuckerberg, Musk, Nadella, Jensen with the black leather jacket. It's an F1 race in Monza and Cook's watching it on the park bench drinking a cappuccino. So it's my view, whether it's perplexity, whether it's doubling down the Google-Gemini partnership, bringing new leadership in, that's what they need to do. But look, I think they're going to do something significant. Speaker1: [39:32] One other area to kind of look at is private markets. I think there have been a lot of people, retail investors, myself included, who have been worried that some of the biggest gains that we've seen, at least from tech, you know, maybe since Sarbanes-Oxley, I don't know, people blame various things, have been on Sarbanes-Oxley. Speaker1: [39:49] Yeah. It was a blast in the past. It's the private side though, right? The returns going to the private side and then companies waiting to go IPO and go public later and later. That's a big worry I have with respect to AI. It's just like, well, I mean, if the value doesn't accrue to these public companies, it has so far. What if it goes into private? And most retail investors don't have access to that. Is that something you're monitoring? Is that something you- Yeah, Speaker0: [40:13] It's been an issue, right? Backlog, you know, of IPUs, a lot of the most successful companies, OpenAI, Anthropic, Perplexity, you know, they're private. Companies don't need to tap public market. But look, ultimately, you know, with Circle and you've seen other, I mean, public is the path. Like, I think you're going to see more companies forced into the public market just because I think brand, currency, valuation. And then there's a lot of companies where, like, they had down rounds after down rounds after down rounds. Like, okay, rip the band off, get a public valuation. Here is the private value. Here is the public. Do it, prove it, and then you'll get it. So I think there's some of that that definitely needs to go on. Speaker1: [41:02] Dan, I want to give you kind of a crossover section. So the Limitless podcast where we do a lot of our frontier tech and AI investing, and we've got another podcast called Bankless where we cover crypto all the time. We recently had Tom Lee on the podcast. So you probably know he's hugely bullish in many of the same things you're bullish on. He's also started this Ethereum treasury company called BMNR. Basically, part of his thesis there is that stable coins are going to be a very big deal. We have a new money system. It's a smart contract driven. It's programmable money, all of these things. And you dot, dot, dot into the future a little bit, and you have a world where you have LLMs and AI agents. The question is, what money system are they going to use? You think an LLM, an AI agent can walk into a bank and get an account? No, they're going to use crypto systems. I guess my question is, do you see some signs of convergence there? I noticed there's no crypto stocks in your portfolio, but there could be. And what do you think about that? Speaker0: [42:00] Look, first of all, like, seller, Tom, you know, Scarmucci, I mean, some of the people I've known for, you know, basically my whole career, like, I agree 100% with their views, right? In other words, like, crypto and disruptive tech and where everything's heading, it actually aligns with the broader thesis in terms of AI revolution. And AI revolution, when you think about where it's going to go, it's not getting there without Because ultimately, there's a convergence that's going to happen here. So I'm very in line with that view. And I think more investors are starting to piece it all together in terms of crypto. There's Bitcoin, Ethereum, and some others with what's happening in AI. Speaker1: [42:52] Very cool. Well, we're definitely very excited about that convergence. Dan, as we end, are you ready for a quick lightning round? Speaker0: [42:57] Yeah, let's do it. Speaker1: [42:58] All right. You can only hold one AI stock, which is it and why? Speaker0: [43:02] NVIDIA, godfather of AI. Only one chip in the world, few in AI. Speaker1: [43:07] What's the best AI model right now? Speaker0: [43:10] Oh, I believe perplexity. Speaker1: [43:12] Perplexity, interesting. Speaker0: [43:13] Perplexity is probably the one that's probably the... I think the underdog, ChatGBT clearly being the leader. But I want to put perplexity in there because ChatGBT is the leader, but perplexity I think is the one under the radar. Speaker1: [43:31] Best AI leader CEO specifically for war mode? Speaker0: [43:36] Carp, Palantir. Speaker1: [43:38] Carp, okay. Over Elon Musk? Speaker0: [43:40] Look, Musk clearly obviously is a wartime CEO, but I'm just saying if you look at what Carp's done with AI, with Palantir, You got to give him a little nod over Musk for now. Speaker1: [43:52] Most undervalued, underrated AI asset? Speaker0: [43:55] I believe it's hyperscalers. I think that's the one from Microsoft to Google to Amazon. I think those are the ones that are very undervalued in terms of everything they're doing. I could have said Oracle, put them in the same category. Speaker1: [44:08] Okay. So maybe I want to end this episode with where we started, which I asked you the question, is it still possible to get rich on AI? You said yes. Now my question, I guess, is so how rich are we talking? All right. How much do you think this will continue to run? I don't know what the size is now for all of AI and the stocks that you put in that category, but tell me about the size now and where you think this could get in the next three to five years. Speaker0: [44:34] Look, it's the biggest tech trend in the last 40, 50 years. And it's our view in this AI party, it was 9 p.m. It's now 10 p.m. That party goes to 4 a.m. Everyone's waiting behind the velvet at ropes, who's getting onto a dance floor, Jensen, Karp, that obviously others are waiting. I would much rather be a bull in that party than a bear outside the party looking through the windows. When you meet up at 6 a.m. at the diner, bulls had a lot better night than the bears. Speaker1: [45:07] It's no fun being a bear. Dan Ives, thank you so much for joining us today. It's been a pleasure. Thanks for joining.