1 00:00:05,600 --> 00:00:07,880 Speaker 1: Welcome to the Fear and Greed Business Interview. I'm sure 2 00:00:08,480 --> 00:00:11,800 Speaker 1: the AI boom has generated huge profits for companies like 3 00:00:11,960 --> 00:00:15,680 Speaker 1: Nvidia in created tools like chat GPT that are significantly 4 00:00:15,720 --> 00:00:19,119 Speaker 1: impacting the way we work. But has it been overhyped? 5 00:00:19,360 --> 00:00:21,560 Speaker 1: Is it a bubble? And the real benefits for both 6 00:00:21,720 --> 00:00:25,240 Speaker 1: users and investors will come in the next wave AI 7 00:00:25,360 --> 00:00:28,440 Speaker 1: boom two point zero. Roger Montgomery has written a piece 8 00:00:28,480 --> 00:00:32,040 Speaker 1: about this very question, suggesting that history is littered with 9 00:00:32,159 --> 00:00:36,279 Speaker 1: transformational technologies that have benefited society much more than their 10 00:00:36,440 --> 00:00:40,320 Speaker 1: benefited shareholders. Roger Rizsy, founder and chief investment officer at 11 00:00:40,360 --> 00:00:43,960 Speaker 1: Montgomery Investment Management, a great supporter of this podcast, offering 12 00:00:44,000 --> 00:00:47,519 Speaker 1: investors access to a range of managed funds, including equities 13 00:00:47,560 --> 00:00:50,000 Speaker 1: in private credit. We've spoken to Roger plenty of times 14 00:00:50,040 --> 00:00:52,720 Speaker 1: on Fear and Greed. He's got more than three decades 15 00:00:52,720 --> 00:00:57,600 Speaker 1: of experience in funds management, equities analysis, investment strategy, trading, stockbroking. 16 00:00:57,920 --> 00:01:01,120 Speaker 1: As always, this podcast contains general only and you should 17 00:01:01,120 --> 00:01:05,120 Speaker 1: seek professional advice before making investment decisions. Roger Montgomery, Welcome 18 00:01:05,120 --> 00:01:05,920 Speaker 1: back to Fear and Greed. 19 00:01:06,319 --> 00:01:08,240 Speaker 2: Glat to be with this Shawn and to be with everyone. 20 00:01:08,600 --> 00:01:10,720 Speaker 1: I imagine this idea that AI could be one of 21 00:01:10,720 --> 00:01:14,760 Speaker 1: those technologies that benefits society more than shareholders. What did 22 00:01:14,800 --> 00:01:15,560 Speaker 1: you mean by that? 23 00:01:16,160 --> 00:01:21,120 Speaker 2: Yeah, okay, So if we think about transformational technology of 24 00:01:21,160 --> 00:01:26,680 Speaker 2: the past, there's lots of examples where we as consumers 25 00:01:26,880 --> 00:01:31,679 Speaker 2: and in fact society in general benefited enormously, but shareholders 26 00:01:31,720 --> 00:01:35,360 Speaker 2: didn't do so well. So a classic example in eighteen 27 00:01:35,400 --> 00:01:41,640 Speaker 2: eighty six Carl Ben's tro the first Ben's patent multiwagen, 28 00:01:42,200 --> 00:01:45,720 Speaker 2: which was a horseless carriage. And you know, if you'd 29 00:01:45,800 --> 00:01:49,800 Speaker 2: been there at the time, Firstly, not a lot of 30 00:01:49,800 --> 00:01:53,000 Speaker 2: people thought it was going to be transformational at the time. Yeah, 31 00:01:53,080 --> 00:01:55,840 Speaker 2: there was, you know, we had famously Henry Ford subsequently 32 00:01:55,840 --> 00:02:00,000 Speaker 2: Henry Ford's lawyer who said, mister Ford, we don't need motive, 33 00:02:00,360 --> 00:02:03,440 Speaker 2: we need faster horses. So you may not have picked 34 00:02:03,440 --> 00:02:06,160 Speaker 2: that it was going to be transformational technology. But even 35 00:02:06,200 --> 00:02:09,440 Speaker 2: if you did, even if you thought that technology was 36 00:02:09,480 --> 00:02:13,320 Speaker 2: going to succeed, it would have been impossible to predict 37 00:02:13,800 --> 00:02:17,640 Speaker 2: which manufacturers were going to win. In the US alone, 38 00:02:18,280 --> 00:02:24,640 Speaker 2: there've been sixteen, one hundred and sixty five car manufacturers. Wow, 39 00:02:25,440 --> 00:02:30,440 Speaker 2: and that's between eighteen ninety four and today, And today 40 00:02:30,480 --> 00:02:35,040 Speaker 2: there's only four really that exist of any note, and 41 00:02:35,080 --> 00:02:40,680 Speaker 2: that is four General motors, Tesla and Chrysler, and two 42 00:02:40,800 --> 00:02:46,200 Speaker 2: of those were rescued by a government bailout during the 43 00:02:46,200 --> 00:02:49,520 Speaker 2: global financial crisis, so they wouldn't exist but for the 44 00:02:49,560 --> 00:02:52,840 Speaker 2: fact that they were bailed out. And so, you know, 45 00:02:53,200 --> 00:02:56,400 Speaker 2: the technology has been great, we've all benefited from it, 46 00:02:56,880 --> 00:03:00,919 Speaker 2: but as shareholders of those businesses, you haven't. You haven't 47 00:03:00,919 --> 00:03:03,600 Speaker 2: made great returns. Another example that I can give you 48 00:03:03,680 --> 00:03:07,399 Speaker 2: is television. You know, since the late nineteen forties, there's 49 00:03:07,440 --> 00:03:11,639 Speaker 2: been four hundred and fifty eight manufacturers of TVs globally, 50 00:03:12,320 --> 00:03:15,960 Speaker 2: and less than ninety exist today. And in the United 51 00:03:15,960 --> 00:03:18,519 Speaker 2: States there was seventy eight and there are now none. 52 00:03:18,880 --> 00:03:19,079 Speaker 1: Wow. 53 00:03:19,360 --> 00:03:22,560 Speaker 2: And so again there's a technology that transformed the way 54 00:03:22,600 --> 00:03:26,360 Speaker 2: we communicate with each other, are entertained and so forth. 55 00:03:26,720 --> 00:03:29,440 Speaker 2: But shareholders probably didn't do so well. And the last 56 00:03:29,480 --> 00:03:32,520 Speaker 2: one I'll give you is commercial air travel. Now. Warren 57 00:03:32,560 --> 00:03:35,760 Speaker 2: Buffett famously said if he'd been at Kitty Hawk when 58 00:03:35,760 --> 00:03:39,400 Speaker 2: Norvel and Right first took that faithful flight, he once 59 00:03:39,440 --> 00:03:41,880 Speaker 2: said that he hoped he would have had the presence 60 00:03:41,880 --> 00:03:44,600 Speaker 2: of mind for the benefit of all future capitalists, to 61 00:03:44,680 --> 00:03:49,880 Speaker 2: have shot him down. Because they've lost collectively so much money. 62 00:03:49,880 --> 00:03:53,640 Speaker 2: And I did some numbers using some official data between 63 00:03:53,680 --> 00:03:57,040 Speaker 2: nineteen forty five and twenty twenty three, seventy nine years, 64 00:03:57,600 --> 00:04:03,240 Speaker 2: the total aggregate proffit of commercial air travel in that 65 00:04:03,320 --> 00:04:06,440 Speaker 2: seventy nine years has been twenty billion dollars. Now, to 66 00:04:06,440 --> 00:04:10,440 Speaker 2: put that in perspective, Saudi Ramco makes that in a month, 67 00:04:11,560 --> 00:04:15,800 Speaker 2: Microsoft makes that in three months. It's taken eighty years 68 00:04:15,840 --> 00:04:17,920 Speaker 2: for airlines to make that, and it could be wiped 69 00:04:17,960 --> 00:04:22,680 Speaker 2: out in another pandemic next year. So you know, collectively, 70 00:04:23,279 --> 00:04:28,520 Speaker 2: these industries that are all we all agree they're transformational technologies, 71 00:04:29,040 --> 00:04:33,039 Speaker 2: they haven't been very successful for their shareholders. And that's 72 00:04:33,120 --> 00:04:35,560 Speaker 2: the difficulty with new technology. You don't know who's going 73 00:04:35,640 --> 00:04:40,240 Speaker 2: to win, and collectively, often the consumer benefits, not investors. 74 00:04:41,320 --> 00:04:44,760 Speaker 1: Okay, so let's bring this to AI as the transformational 75 00:04:44,800 --> 00:04:48,760 Speaker 1: technology we're talking about now. I know you've written about 76 00:04:48,839 --> 00:04:52,479 Speaker 1: thinking about it as upstream and downstream companies. Is that 77 00:04:52,560 --> 00:04:53,760 Speaker 1: a good way of approaching it? 78 00:04:54,279 --> 00:04:57,719 Speaker 2: I think so. So. So the upstream companies are the 79 00:04:57,800 --> 00:05:01,240 Speaker 2: pick and shovel suppliers to you know, to the gold rush, 80 00:05:01,279 --> 00:05:05,760 Speaker 2: and we can safely safely describe the AI boom at 81 00:05:05,760 --> 00:05:07,240 Speaker 2: the moment is a bit of a gold rush. I'll 82 00:05:07,240 --> 00:05:09,280 Speaker 2: tell you why in a minute when we look at valuations. 83 00:05:09,760 --> 00:05:16,960 Speaker 2: But upstream, you've got companies like Nvidia, which everyone knows about, Intel, Qualcolm, Oracle, 84 00:05:17,480 --> 00:05:20,799 Speaker 2: you know other semiconductor companies. You know, they're the biggest 85 00:05:20,839 --> 00:05:23,919 Speaker 2: beneficiaries at the moment of this revolution. You know, the 86 00:05:23,960 --> 00:05:28,560 Speaker 2: companies that are providing cloud infrastructure, not just data centers, 87 00:05:28,880 --> 00:05:32,800 Speaker 2: but the technology that links all those data centers together 88 00:05:32,880 --> 00:05:35,360 Speaker 2: in all the cables and wires and so forth. You know, 89 00:05:35,400 --> 00:05:38,520 Speaker 2: they're benefiting and we'll see that when we talk about 90 00:05:38,560 --> 00:05:42,080 Speaker 2: valuations in a minute. And then downstream you've got the appliers. 91 00:05:42,760 --> 00:05:45,599 Speaker 2: These are the companies that are taking the upstream technology 92 00:05:46,200 --> 00:05:50,400 Speaker 2: and trying to craft a business model that sells it. 93 00:05:50,520 --> 00:05:52,080 Speaker 2: So it could be B to B or B two C, 94 00:05:52,320 --> 00:05:55,440 Speaker 2: so business to business or business to consumer. And they're 95 00:05:55,440 --> 00:05:58,960 Speaker 2: trying to create models that they can charge more for, 96 00:05:59,600 --> 00:06:03,440 Speaker 2: that will use and pay for. And what's interesting is 97 00:06:03,480 --> 00:06:08,280 Speaker 2: that the growth of profits for the downstreamers, the appliers, 98 00:06:08,680 --> 00:06:16,720 Speaker 2: is starting to slow down. And downstream you've got you know, Apple, Alphabet, Meta, Microsoft, IBM, Adobe, 99 00:06:17,080 --> 00:06:20,720 Speaker 2: you know, they're enhancing their products with AI, Adobe, I 100 00:06:20,720 --> 00:06:24,680 Speaker 2: think is a particularly fascinating one. But they're applying the 101 00:06:24,720 --> 00:06:30,240 Speaker 2: technology to their services, they're enhancing their offers, and they're 102 00:06:30,279 --> 00:06:34,760 Speaker 2: trying to charge more. What's interesting is the uptake is 103 00:06:34,800 --> 00:06:38,480 Speaker 2: slowing down, and so is the revenue growth. But this 104 00:06:38,560 --> 00:06:44,719 Speaker 2: will feedback to the upstreamers because the downstreamers aren't making 105 00:06:44,760 --> 00:06:48,599 Speaker 2: more money or the growth isn't what they had planned 106 00:06:49,080 --> 00:06:51,880 Speaker 2: or what they had hoped for, then they'll be buying 107 00:06:51,960 --> 00:06:55,280 Speaker 2: less of the upstream services. Now. I don't think, as 108 00:06:55,279 --> 00:06:57,160 Speaker 2: you said in your introduction, I don't think this is 109 00:06:57,200 --> 00:06:59,960 Speaker 2: a short term phenomena. I think AI is here to start, 110 00:07:00,480 --> 00:07:03,080 Speaker 2: and I think it's going to be transformative. But I 111 00:07:03,080 --> 00:07:06,400 Speaker 2: don't necessarily think that the valuations that we're seeing currently 112 00:07:06,440 --> 00:07:08,159 Speaker 2: and we can talk about those in a sec I 113 00:07:08,160 --> 00:07:11,800 Speaker 2: don't think those valuations are sustainable based on current growth rates. 114 00:07:12,360 --> 00:07:16,760 Speaker 1: Stay with me, Roger, we'll be back in a minute. 115 00:07:21,400 --> 00:07:25,960 Speaker 1: I'm speaking to Roger Montgomery from Montgomery Investment Management. Okay, 116 00:07:26,000 --> 00:07:28,320 Speaker 1: so those valuations, let's talk about days are you talking 117 00:07:28,360 --> 00:07:30,680 Speaker 1: about for both upstreamers and downstreamers. 118 00:07:31,080 --> 00:07:34,200 Speaker 2: Well, it's mostly the downstreamers at the moment, where the 119 00:07:34,280 --> 00:07:39,680 Speaker 2: vacuations don't seem to be meeting the growth and the profits. So, 120 00:07:39,760 --> 00:07:42,760 Speaker 2: for example, there's been a couple of couple of what 121 00:07:42,800 --> 00:07:47,240 Speaker 2: I would describe as massively hyped valuation uplifts as a 122 00:07:47,280 --> 00:07:51,040 Speaker 2: result of recent capital raisings. One of them was chat GPT, 123 00:07:51,160 --> 00:07:54,400 Speaker 2: which is the dominant capital raising. They close probably one 124 00:07:54,400 --> 00:07:59,440 Speaker 2: of the largest funding rounds in history of certainly Silicon 125 00:07:59,520 --> 00:08:02,840 Speaker 2: Valley history, and it was a six point six billion 126 00:08:02,880 --> 00:08:07,640 Speaker 2: dollar raising at one hundred and fifty seven billion dollar valuation. 127 00:08:08,520 --> 00:08:10,880 Speaker 2: So that was the first one. The second one is 128 00:08:10,880 --> 00:08:15,640 Speaker 2: another competitor in the LM or the large language model space, 129 00:08:16,000 --> 00:08:19,280 Speaker 2: and that's a business called Perplexity. Now let's put some 130 00:08:19,560 --> 00:08:23,920 Speaker 2: perspective on this. Perplexity in January was valued at five 131 00:08:24,000 --> 00:08:29,080 Speaker 2: hundred and twenty million dollars. Okay, by June the valuation 132 00:08:29,480 --> 00:08:33,840 Speaker 2: had risen to three billion dollars and they're now looking 133 00:08:34,040 --> 00:08:37,400 Speaker 2: at doing a fundraising. So they're in talks for fundraising 134 00:08:37,440 --> 00:08:41,000 Speaker 2: at the moment at eight billion dollars. So they're gone 135 00:08:41,000 --> 00:08:43,360 Speaker 2: from five hundred and twenty million less than a billion 136 00:08:43,800 --> 00:08:48,680 Speaker 2: to eight billion dollars in nine months. And in March 137 00:08:48,920 --> 00:08:54,839 Speaker 2: their annualized revenue was ten million dollars. Wow, so that 138 00:08:55,040 --> 00:08:57,880 Speaker 2: annualized revenue. So that's what they are now that's grown, 139 00:08:58,600 --> 00:09:01,839 Speaker 2: and rumors at the moment that their annualized revenue is 140 00:09:01,920 --> 00:09:05,760 Speaker 2: about fifty forty or fifty million dollars. But still you're 141 00:09:05,800 --> 00:09:09,079 Speaker 2: talking about an eight million dollar valuation on a business 142 00:09:09,240 --> 00:09:14,360 Speaker 2: that generates revenue per year of fifty million dollars. You know, 143 00:09:14,400 --> 00:09:18,600 Speaker 2: the go further downstream, what are these lms? You know, 144 00:09:18,600 --> 00:09:23,080 Speaker 2: there are companies now that are taking those lms and applying, 145 00:09:23,200 --> 00:09:28,640 Speaker 2: So they're applying the applier's technology. This is a second 146 00:09:28,640 --> 00:09:32,480 Speaker 2: derivative or third derivative. The most interesting or the most 147 00:09:32,480 --> 00:09:38,520 Speaker 2: popular use case at the moment is personal chatbots companion 148 00:09:38,720 --> 00:09:42,960 Speaker 2: chat bots. That's what people are using this software for 149 00:09:43,120 --> 00:09:47,400 Speaker 2: the most in terms of a revenue generation model. That 150 00:09:47,520 --> 00:09:50,800 Speaker 2: is the most the moment or growing the fastest. So 151 00:09:50,840 --> 00:09:55,280 Speaker 2: these are people who are maybe lonely or unable to 152 00:09:55,320 --> 00:10:00,640 Speaker 2: go outside and unable to talk to people for legitimate reasons, 153 00:10:00,720 --> 00:10:05,240 Speaker 2: want to use companion bots, and that seems to be 154 00:10:05,880 --> 00:10:09,080 Speaker 2: the most popular use case. They're not big, we're not 155 00:10:09,160 --> 00:10:14,360 Speaker 2: talking about massive transfertional use of this technology right now. 156 00:10:15,120 --> 00:10:17,880 Speaker 1: So as an investor, let's bring it back to investors 157 00:10:17,920 --> 00:10:20,920 Speaker 1: how does an investor who wants to get in on 158 00:10:20,960 --> 00:10:24,640 Speaker 1: the AI boom, whatever that is, how do they think 159 00:10:24,640 --> 00:10:25,920 Speaker 1: about it? How do they do it? 160 00:10:26,520 --> 00:10:28,760 Speaker 2: Well, there's probably a few ways to think about it. 161 00:10:29,320 --> 00:10:31,360 Speaker 2: The way I or the framework guy would use to 162 00:10:31,400 --> 00:10:34,440 Speaker 2: be thinking about it is in terms of phases the 163 00:10:34,480 --> 00:10:39,679 Speaker 2: way this technology rolls out. So phase one is in video, right, 164 00:10:40,000 --> 00:10:44,400 Speaker 2: and then phase two is which might not be today, 165 00:10:44,640 --> 00:10:47,120 Speaker 2: and it might not be next year, it could be 166 00:10:47,160 --> 00:10:50,800 Speaker 2: in two or three years time. Phase two are the 167 00:10:50,920 --> 00:10:57,920 Speaker 2: companies that take the technology and apply it directly to 168 00:10:58,080 --> 00:11:02,199 Speaker 2: generate revenue from existing services. We've described those the adobes 169 00:11:02,600 --> 00:11:05,199 Speaker 2: of the world. Right, So if you're using Adobe Photoshop, 170 00:11:05,760 --> 00:11:09,600 Speaker 2: it becomes much easier to use if it's enhanced by AI. 171 00:11:10,480 --> 00:11:14,160 Speaker 2: Or Netflix might have an AI version of a subscription 172 00:11:14,880 --> 00:11:19,320 Speaker 2: that actually helps me from searching for a movie that 173 00:11:19,440 --> 00:11:22,680 Speaker 2: I saw five years ago? What was that movie again, Sean? 174 00:11:22,720 --> 00:11:25,040 Speaker 2: You know it had that guy in it, you know 175 00:11:25,120 --> 00:11:28,360 Speaker 2: that guy with the I can't even remember his name. 176 00:11:28,720 --> 00:11:32,160 Speaker 2: You know. Maybe AI helps me find that movie, and 177 00:11:32,200 --> 00:11:34,440 Speaker 2: that makes it just simply to use. And if I 178 00:11:34,480 --> 00:11:36,360 Speaker 2: want that service, I've got to pay an extra ten 179 00:11:36,400 --> 00:11:38,360 Speaker 2: bucks a month to get it, and you know what, 180 00:11:38,760 --> 00:11:40,760 Speaker 2: that's going to be much simpler, save me a lot 181 00:11:40,800 --> 00:11:42,360 Speaker 2: of time and a lot of arguments at home about 182 00:11:42,400 --> 00:11:46,040 Speaker 2: what we're going to watch tonight. So that's phase two, 183 00:11:46,679 --> 00:11:49,839 Speaker 2: and that's not really happening yet in a meaningful way. 184 00:11:50,440 --> 00:11:53,920 Speaker 2: You know, we're not seeing rapid increases in revenue growth 185 00:11:54,280 --> 00:11:58,600 Speaker 2: from the adoption of Adobe's AI enhanced version. And then 186 00:11:58,640 --> 00:12:02,240 Speaker 2: there are versions like you know, there's Meta and Meta's 187 00:12:02,440 --> 00:12:05,720 Speaker 2: version of you know, Facebook, which is already AI enhanced. 188 00:12:06,000 --> 00:12:08,600 Speaker 2: No one's paying any more for it, you know, there's 189 00:12:08,600 --> 00:12:11,960 Speaker 2: no revenue uplift from it. So that's Phase two and 190 00:12:12,040 --> 00:12:17,680 Speaker 2: that will take time to evolve. And then Phase three 191 00:12:17,720 --> 00:12:22,079 Speaker 2: is the final phase, and that takes years to roll out. 192 00:12:22,440 --> 00:12:26,240 Speaker 2: And that's where sewn, you and I doing this today 193 00:12:26,840 --> 00:12:31,000 Speaker 2: is enhanced by AI, and that's where everything's enhanced by AI. 194 00:12:31,480 --> 00:12:34,120 Speaker 2: If indeed AI is going to be that good that 195 00:12:34,160 --> 00:12:38,160 Speaker 2: it enhances everything, you know, there's there's a real debate 196 00:12:38,240 --> 00:12:41,360 Speaker 2: going on in the AI community at the moment, you know, 197 00:12:41,400 --> 00:12:47,040 Speaker 2: whether AI l m's large language models chat GPT becomes 198 00:12:47,920 --> 00:12:55,280 Speaker 2: this general intelligence artificial general intelligence, which is AGI, and 199 00:12:55,320 --> 00:12:57,560 Speaker 2: there are those who are arguing that in order for 200 00:12:57,720 --> 00:13:02,920 Speaker 2: large language models to into AGI, which is what everyone's 201 00:13:02,960 --> 00:13:05,600 Speaker 2: worried about, a lot of people are worried about, you know, 202 00:13:05,640 --> 00:13:10,280 Speaker 2: the whole, the whole idea that Ai destroys humanity in 203 00:13:10,360 --> 00:13:12,240 Speaker 2: order for that to happen. There's there are those who 204 00:13:12,320 --> 00:13:16,880 Speaker 2: argue there's not enough energy on earth to power those 205 00:13:17,000 --> 00:13:21,000 Speaker 2: large language models enough to be able to get that going. So, 206 00:13:21,480 --> 00:13:24,080 Speaker 2: you know, we're a long way, by some accounts from 207 00:13:24,120 --> 00:13:28,720 Speaker 2: a world where large language models forget about AGI, where 208 00:13:28,840 --> 00:13:33,360 Speaker 2: large language models are helping us in everything. That's not 209 00:13:33,480 --> 00:13:37,440 Speaker 2: Phase one, that's not even Phase two. That's Phase three, 210 00:13:37,480 --> 00:13:41,040 Speaker 2: and that's some years away. And so for investors, you know, 211 00:13:41,080 --> 00:13:45,160 Speaker 2: the prices you're paying today are factoring in that happening 212 00:13:45,280 --> 00:13:48,600 Speaker 2: right now. And all you need is a bump in 213 00:13:48,640 --> 00:13:55,560 Speaker 2: the road like China, you know, circumnavigating Taiwan permanently annexing 214 00:13:55,640 --> 00:13:59,200 Speaker 2: Taiwan and saying to the US, come and get me. 215 00:14:00,000 --> 00:14:02,559 Speaker 2: But that could be enough for all of the hype 216 00:14:02,559 --> 00:14:07,880 Speaker 2: Innai to correct violently, and then you know, then you're 217 00:14:07,920 --> 00:14:09,679 Speaker 2: in a better position to say, you know what it's 218 00:14:09,720 --> 00:14:12,960 Speaker 2: worth taking the risk. Now, Yes, I think Phase three 219 00:14:13,040 --> 00:14:15,120 Speaker 2: is going to happen. I don't know when, but now 220 00:14:15,120 --> 00:14:18,880 Speaker 2: I'm paying a much cheaper price and it's worth doing now. 221 00:14:19,880 --> 00:14:21,600 Speaker 1: The moral of the story, we're out of time now, 222 00:14:21,600 --> 00:14:26,000 Speaker 1: But the moral of the story, Roger is by beware 223 00:14:25,600 --> 00:14:28,040 Speaker 1: if you're going to jump into the AI story. 224 00:14:28,560 --> 00:14:30,680 Speaker 2: Yeah. Look, I think there are going to be winners 225 00:14:30,680 --> 00:14:32,640 Speaker 2: and they're going to be losers. Number one, really hard 226 00:14:32,680 --> 00:14:35,560 Speaker 2: to pick who they are. And number two, you paid 227 00:14:35,600 --> 00:14:37,560 Speaker 2: too high a price. Even if you get it right, 228 00:14:37,960 --> 00:14:39,440 Speaker 2: you could end up with a poor return. 229 00:14:40,080 --> 00:14:41,720 Speaker 1: Roger, thank you for talking to Fear and Greed. 230 00:14:42,080 --> 00:14:43,720 Speaker 2: Always a pleasure show. Thanks for your tom. 231 00:14:43,920 --> 00:14:46,800 Speaker 1: That was Roger Montgomery, founder and chief investment officer at 232 00:14:46,880 --> 00:14:50,360 Speaker 1: Montgomery Investment Management, a great supporter of this podcast. Visit 233 00:14:50,560 --> 00:14:53,160 Speaker 1: mont invest M O N T, I n V, E 234 00:14:53,360 --> 00:14:56,400 Speaker 1: s T mont invest dot com for more information, or 235 00:14:56,480 --> 00:15:00,000 Speaker 1: sign up for Roger's insights at Roger Montgomery dot com. 236 00:15:00,600 --> 00:15:02,840 Speaker 1: This is the Fear and Greed Business Interview. Remember this 237 00:15:02,920 --> 00:15:05,640 Speaker 1: is general information only, and you should see professional advice 238 00:15:05,680 --> 00:15:08,640 Speaker 1: before making investment decisions. Join us every morning for the 239 00:15:08,640 --> 00:15:10,840 Speaker 1: full episode of Fear and Greed Daily business years for 240 00:15:10,880 --> 00:15:13,760 Speaker 1: people who make their own decisions. I'm Shane elma Enjoy 241 00:15:13,800 --> 00:15:18,720 Speaker 1: your day.