1 00:00:02,720 --> 00:00:07,200 Speaker 1: Bloomberg, Audio Studios, podcasts, radio news. 2 00:00:09,119 --> 00:00:15,080 Speaker 2: Another week, another title weight of AI news, major investments. 3 00:00:14,680 --> 00:00:19,119 Speaker 3: The Disney story, a billion dollar investment from this iconic 4 00:00:19,320 --> 00:00:22,080 Speaker 3: from the House of Mouse in open AI. 5 00:00:22,280 --> 00:00:24,479 Speaker 2: And some significant share drops. 6 00:00:24,720 --> 00:00:26,680 Speaker 1: Oracle sank fourteen percent. 7 00:00:27,080 --> 00:00:29,880 Speaker 3: Investors are worried about whether all the money Oracle is 8 00:00:29,920 --> 00:00:32,640 Speaker 3: spending on AI technology will pay off. 9 00:00:33,000 --> 00:00:36,920 Speaker 2: There's an enormous amount of money flowing in and out 10 00:00:37,040 --> 00:00:38,760 Speaker 2: of the AI industry right now. 11 00:00:39,280 --> 00:00:43,720 Speaker 4: It's just such an all encompassing, all pervasive theme. 12 00:00:44,120 --> 00:00:46,840 Speaker 2: Suzanne Woolley covers personal finance for Bloomberg. 13 00:00:47,400 --> 00:00:50,680 Speaker 4: It's what everyone's talking about, It's what everyone's worried about. 14 00:00:50,920 --> 00:00:52,760 Speaker 3: We've heard a lot of promise, but not a lot 15 00:00:52,800 --> 00:00:55,600 Speaker 3: of actual revenue coming from the companies that are spending 16 00:00:55,600 --> 00:00:58,160 Speaker 3: so much on building out AI. If a few of 17 00:00:58,200 --> 00:01:01,200 Speaker 3: these big AI hyperscalers have a bit of a hiccup, 18 00:01:01,240 --> 00:01:03,160 Speaker 3: we could see a lot of the other market taken 19 00:01:03,200 --> 00:01:04,120 Speaker 3: down with them. 20 00:01:04,520 --> 00:01:08,160 Speaker 2: Every few weeks, Suzanne asks experts to share their advice 21 00:01:08,319 --> 00:01:12,200 Speaker 2: about where people should be investing their money. And right now, 22 00:01:12,319 --> 00:01:14,600 Speaker 2: you'd be hard pressed to find a company or a 23 00:01:14,640 --> 00:01:18,119 Speaker 2: sector to invest in that isn't AI exposed. 24 00:01:18,360 --> 00:01:21,120 Speaker 4: It's a little hard for me to think of a 25 00:01:21,440 --> 00:01:26,560 Speaker 4: pure investment where AI wouldn't at least be embedded in 26 00:01:26,600 --> 00:01:28,039 Speaker 4: the operations of a company. 27 00:01:28,480 --> 00:01:31,880 Speaker 2: So Suzanne and I called up some investment experts to 28 00:01:31,920 --> 00:01:35,759 Speaker 2: help assess when, whether and how to bet on AI 29 00:01:36,280 --> 00:01:40,800 Speaker 2: in the most strategic way possible. Because things are moving quickly, 30 00:01:41,280 --> 00:01:44,080 Speaker 2: and alongside all the excitement about getting in on the 31 00:01:44,120 --> 00:01:47,880 Speaker 2: AI race, there are also a lot of concerns about 32 00:01:47,880 --> 00:01:53,920 Speaker 2: being too exposed. I'm Sarah Holder, and this is the 33 00:01:53,920 --> 00:01:57,200 Speaker 2: big take from Bloomberg News Today. On the show The 34 00:01:57,360 --> 00:02:00,760 Speaker 2: Risks and Rewards of Investing in AI right Now, we 35 00:02:00,800 --> 00:02:03,400 Speaker 2: talk to investment experts about what they think are the 36 00:02:03,440 --> 00:02:13,959 Speaker 2: smartest AI plays and the ones you should avoid, depending 37 00:02:14,040 --> 00:02:18,079 Speaker 2: on who you talk to. AI is poised to revolutionize 38 00:02:18,120 --> 00:02:23,119 Speaker 2: healthcare and technology or destroy jobs and natural resources. It's 39 00:02:23,160 --> 00:02:27,079 Speaker 2: the most profitable investment opportunity we've ever seen, or an 40 00:02:27,080 --> 00:02:31,400 Speaker 2: industry bubble that's about to burst. Suzanne said, the topic 41 00:02:31,480 --> 00:02:33,560 Speaker 2: stirs up a lot of anxiety. 42 00:02:34,000 --> 00:02:38,360 Speaker 4: We know it feels transformative and we're seeing big changes, 43 00:02:38,520 --> 00:02:42,680 Speaker 4: but we can't look out ten years from now with 44 00:02:42,840 --> 00:02:45,799 Speaker 4: any sort of crystal ball and say these are the 45 00:02:45,840 --> 00:02:48,640 Speaker 4: stocks I should have invested in and you know this 46 00:02:48,680 --> 00:02:52,960 Speaker 4: is how it's going to reshape finance or insurance or journalism. 47 00:02:53,240 --> 00:02:55,639 Speaker 4: We're seeing it in our personal lives when we read 48 00:02:55,680 --> 00:02:59,640 Speaker 4: about stories of like layoffs and the entry level jobs. 49 00:02:59,760 --> 00:03:04,920 Speaker 4: So there is this sort of excitement over the promise, 50 00:03:05,760 --> 00:03:11,880 Speaker 4: but worry over the impact on one's life and just 51 00:03:12,360 --> 00:03:13,960 Speaker 4: the uncertainty about what it might lead to. 52 00:03:14,440 --> 00:03:16,840 Speaker 2: I mean, does that remind you of any other sorts 53 00:03:16,880 --> 00:03:20,120 Speaker 2: of investments that you've written about. I guess, like the 54 00:03:20,200 --> 00:03:23,840 Speaker 2: dot com bubble, investing in tech stocks back in the 55 00:03:23,880 --> 00:03:24,560 Speaker 2: early aughts. 56 00:03:25,000 --> 00:03:28,320 Speaker 4: That kind of enthusiasm and the fear about hype and 57 00:03:28,360 --> 00:03:31,360 Speaker 4: all the bubble talk is reminiscent of those times. But 58 00:03:31,400 --> 00:03:33,760 Speaker 4: I feel like with AI it's sort of that on 59 00:03:34,000 --> 00:03:37,560 Speaker 4: steroids in a way. It's seen as a much bigger, 60 00:03:37,800 --> 00:03:41,640 Speaker 4: potentially life altering breakthrough for. 61 00:03:41,640 --> 00:03:44,520 Speaker 2: The AI bulls. That's exactly the reason to put your 62 00:03:44,560 --> 00:03:45,280 Speaker 2: money behind it. 63 00:03:45,520 --> 00:03:49,840 Speaker 4: Getting in now and riding this wave is how you're 64 00:03:49,840 --> 00:03:53,160 Speaker 4: going to make the big money going forward. We've seen 65 00:03:53,320 --> 00:03:59,280 Speaker 4: incredible valuations on companies connected to AI, and that is 66 00:03:59,320 --> 00:04:02,400 Speaker 4: where you get into sort of the bearcase. The cons 67 00:04:02,640 --> 00:04:07,760 Speaker 4: people are worried about these valuations. There's waves, massive waves 68 00:04:07,840 --> 00:04:11,120 Speaker 4: of money flowing into building out the infrastructure and the 69 00:04:11,200 --> 00:04:16,279 Speaker 4: data centers to support AI. Can the industry grow into 70 00:04:16,560 --> 00:04:18,640 Speaker 4: all of the money that's being spent on it. 71 00:04:19,160 --> 00:04:23,359 Speaker 2: That's unclear for a lot of investors, though the idea 72 00:04:23,480 --> 00:04:27,080 Speaker 2: of all this untapped potential makes this moment feel like 73 00:04:27,120 --> 00:04:28,599 Speaker 2: the perfect time to strike. 74 00:04:29,000 --> 00:04:33,719 Speaker 1: I have never seen more fear about innovation than I 75 00:04:33,800 --> 00:04:37,039 Speaker 1: do now, and I'm very comfortable here. I think this 76 00:04:37,200 --> 00:04:39,680 Speaker 1: is a good place. You know, you don't chase the momentum, 77 00:04:39,760 --> 00:04:42,680 Speaker 1: but you buy the dip because you get these opportunities. 78 00:04:43,080 --> 00:04:47,240 Speaker 2: That's Kathy Wood, the CEO of ARC invest She's someone 79 00:04:47,279 --> 00:04:50,800 Speaker 2: who's investments are closely watched by retail investors, so when 80 00:04:50,839 --> 00:04:53,760 Speaker 2: Suzanne was looking for experts to pull, she wanted to 81 00:04:53,800 --> 00:04:54,920 Speaker 2: go to Kathy first. 82 00:04:55,240 --> 00:04:59,560 Speaker 4: She has such an interesting history in investing in innovative 83 00:04:59,560 --> 00:05:01,160 Speaker 4: technology g companies. 84 00:05:01,400 --> 00:05:03,839 Speaker 2: Kathy said that as she tracks the number of users 85 00:05:03,839 --> 00:05:07,360 Speaker 2: of open ai and Gemini, she's reminded of the early 86 00:05:07,480 --> 00:05:09,200 Speaker 2: days of the dot com boom. 87 00:05:09,400 --> 00:05:12,440 Speaker 1: If you think about the Internet and how it evolved, 88 00:05:12,720 --> 00:05:18,000 Speaker 1: we think we are in nineteen ninety five. For the consumer. 89 00:05:18,560 --> 00:05:21,680 Speaker 2: The hope is that as the user base grows so 90 00:05:21,800 --> 00:05:25,240 Speaker 2: will the money that she's invested in companies leveraging AI, 91 00:05:26,200 --> 00:05:29,080 Speaker 2: like one of Kathy's longtime favorites, Tesla. 92 00:05:29,400 --> 00:05:33,520 Speaker 1: It is the robotaxi year. We believe that the autonomous 93 00:05:33,640 --> 00:05:38,880 Speaker 1: taxi ecosystem globally is going to scale right now. I 94 00:05:38,920 --> 00:05:42,000 Speaker 1: think if it's in the billions, I'd be surprised in 95 00:05:42,080 --> 00:05:45,080 Speaker 1: terms of revenue generation. But we think it's going to 96 00:05:45,160 --> 00:05:50,279 Speaker 1: scale to the eight to ten trillion dollar level per 97 00:05:50,400 --> 00:05:53,240 Speaker 1: year within the next five to ten years. 98 00:05:53,760 --> 00:05:57,400 Speaker 2: Investing in a highly valued MAG seven tech company like 99 00:05:57,480 --> 00:06:01,280 Speaker 2: Tesla or Nvideo or Microsoft isn't the only way to 100 00:06:01,279 --> 00:06:05,719 Speaker 2: bet on AI's potential, though. Tasha Wang, a portfolio manager 101 00:06:05,720 --> 00:06:09,720 Speaker 2: for Fidelity International based in Hong Kong, suggests looking at 102 00:06:09,720 --> 00:06:14,080 Speaker 2: the infrastructure that supports the AI ambitions of MAG seven businesses. 103 00:06:14,520 --> 00:06:17,719 Speaker 5: For example, semiconductor, you know you can easily access it 104 00:06:17,839 --> 00:06:21,039 Speaker 5: via ETF and it's something that you know it's a 105 00:06:21,080 --> 00:06:23,320 Speaker 5: structural growth story even before AI. 106 00:06:23,839 --> 00:06:28,240 Speaker 2: In addition to physical technologies like semiconductors, Tasha said, investors 107 00:06:28,240 --> 00:06:31,760 Speaker 2: should be thinking about the underlying commodities that tech relies on. 108 00:06:32,240 --> 00:06:36,120 Speaker 2: With finite supplies, but the prospect of increasing demand, we 109 00:06:36,200 --> 00:06:36,560 Speaker 2: can be. 110 00:06:36,520 --> 00:06:39,520 Speaker 5: Talking about copper, and we can be talking about things 111 00:06:39,560 --> 00:06:43,359 Speaker 5: like uranium. You know that's not traditionally on the radar 112 00:06:43,440 --> 00:06:47,560 Speaker 5: of commality investors, but nuclear is such an important way 113 00:06:47,680 --> 00:06:50,680 Speaker 5: to power the AI power needs. 114 00:06:50,920 --> 00:06:56,160 Speaker 2: Our third expert agreed that anything related to power is interesting. 115 00:06:56,640 --> 00:06:58,200 Speaker 6: That is the bottleneck right now. 116 00:06:58,720 --> 00:07:01,320 Speaker 2: Michael Smith, who runs the Growth equity team at all 117 00:07:01,320 --> 00:07:02,839 Speaker 2: Spring Global Investments. 118 00:07:03,240 --> 00:07:06,560 Speaker 6: When you look at the commitments that have already been 119 00:07:06,600 --> 00:07:09,320 Speaker 6: announced from the major players in the space and add 120 00:07:09,360 --> 00:07:12,560 Speaker 6: it all up between now and twenty thirty, they need 121 00:07:12,600 --> 00:07:17,240 Speaker 6: to obtain enough power to fuel basically the equivalent of 122 00:07:17,280 --> 00:07:20,480 Speaker 6: thirty to thirty five million homes, which to put that 123 00:07:20,520 --> 00:07:23,400 Speaker 6: in perspective, they are over one hundred and thirty million 124 00:07:23,440 --> 00:07:24,800 Speaker 6: households in the US today. 125 00:07:25,240 --> 00:07:28,960 Speaker 2: But Michael also advised being more forward looking in predicting 126 00:07:29,000 --> 00:07:30,840 Speaker 2: where AI is going next. 127 00:07:31,200 --> 00:07:34,240 Speaker 6: To use the surfing analogy. Don't chase the wave that's 128 00:07:34,240 --> 00:07:36,880 Speaker 6: already passed. Get ready for the next one. If you 129 00:07:36,920 --> 00:07:40,560 Speaker 6: miss the infrastructure wave and you feel like it's too 130 00:07:40,600 --> 00:07:43,880 Speaker 6: late to buy Nvidia, don't worry. I think the next 131 00:07:43,880 --> 00:07:47,120 Speaker 6: big wave will probably be the suppliers, the B to 132 00:07:47,200 --> 00:07:51,240 Speaker 6: B companies that develop applications and tools that they sell 133 00:07:51,280 --> 00:07:54,600 Speaker 6: to other businesses and help them use AI. And then 134 00:07:54,640 --> 00:07:57,160 Speaker 6: if if you miss that wave or you're not comfortable 135 00:07:57,160 --> 00:07:59,960 Speaker 6: with that, I think there's a huge wave coming behind them, 136 00:08:00,080 --> 00:08:02,280 Speaker 6: the supplier wave, which will be the consumers of all 137 00:08:02,320 --> 00:08:06,840 Speaker 6: this stuff. And when AI starts to directly improve everyday 138 00:08:06,880 --> 00:08:09,840 Speaker 6: experiences for all of us, that there's going to be 139 00:08:09,880 --> 00:08:10,840 Speaker 6: big opportunities. 140 00:08:12,520 --> 00:08:16,560 Speaker 2: Another way to think about categorizing AI related investment opportunities 141 00:08:16,760 --> 00:08:20,040 Speaker 2: comes from Denny Fish, who's head of Technology research and 142 00:08:20,080 --> 00:08:22,320 Speaker 2: a portfolio manager at Janis Henderson. 143 00:08:22,680 --> 00:08:27,880 Speaker 4: Denny Fish use the buckets of enabler, enhancer, and us 144 00:08:27,920 --> 00:08:29,000 Speaker 4: there we're. 145 00:08:28,800 --> 00:08:32,520 Speaker 3: Going to see waves of adoption and evolution. We are 146 00:08:32,679 --> 00:08:36,640 Speaker 3: clearly in the enablement phase of AI and the infrastructure 147 00:08:36,679 --> 00:08:40,880 Speaker 3: build out, and that's semis and that's power, data center infrastructure, 148 00:08:40,920 --> 00:08:43,319 Speaker 3: all those things that you need to even be able 149 00:08:43,400 --> 00:08:45,560 Speaker 3: to train a model or perform inference. 150 00:08:46,080 --> 00:08:50,200 Speaker 2: In this enablement phase, Companies like Microsoft or Amazon, which 151 00:08:50,240 --> 00:08:54,960 Speaker 2: have major cloud computing businesses are seeing massive growth. So 152 00:08:55,040 --> 00:08:59,760 Speaker 2: are those physical infrastructure providers companies that manufacture chips or 153 00:09:00,080 --> 00:09:04,679 Speaker 2: use liquid cooling systems for data centers. Denny Fish's next 154 00:09:04,720 --> 00:09:06,840 Speaker 2: category is the enhancers. 155 00:09:07,160 --> 00:09:10,960 Speaker 3: There will be companies that will embrace AI in a 156 00:09:11,000 --> 00:09:14,520 Speaker 3: meaningful way to improve their competitive position in areas like 157 00:09:14,600 --> 00:09:15,679 Speaker 3: software and internet. 158 00:09:16,960 --> 00:09:20,640 Speaker 2: Tech companies like into It, which dominates in accounting and 159 00:09:20,720 --> 00:09:23,760 Speaker 2: tax e filing and is trying to use AI to 160 00:09:23,880 --> 00:09:28,600 Speaker 2: improve its product. And finally, there are end users, non 161 00:09:28,679 --> 00:09:31,400 Speaker 2: tech companies that adopt AI early. 162 00:09:32,000 --> 00:09:36,360 Speaker 4: A more sort of motley crew of companies that can 163 00:09:36,520 --> 00:09:42,400 Speaker 4: incorporate AI to have a more competitive edge and operations 164 00:09:42,520 --> 00:09:47,520 Speaker 4: in using agentic AI just really sort of deepening the 165 00:09:47,559 --> 00:09:51,200 Speaker 4: reach of their business and becoming more relevant to their customers. 166 00:09:51,440 --> 00:09:54,000 Speaker 3: You could listen to the transcript of every company in 167 00:09:54,040 --> 00:09:57,120 Speaker 3: the S and P five hundred last quarter, right and 168 00:09:57,320 --> 00:10:01,079 Speaker 3: I don't know, sixty seventy percent of them mention artificial 169 00:10:01,080 --> 00:10:05,240 Speaker 3: intelligence in their transcript. So you can go through and 170 00:10:05,679 --> 00:10:11,000 Speaker 3: pick your poison in financial services, healthcare, agriculture, insurance and 171 00:10:11,120 --> 00:10:14,960 Speaker 3: find unique companies that are actually benefiting from this trend 172 00:10:15,240 --> 00:10:16,920 Speaker 3: that aren't quite as obvious. 173 00:10:17,480 --> 00:10:21,480 Speaker 2: Take John Deere, the agricultural services company that's using AI 174 00:10:21,600 --> 00:10:25,680 Speaker 2: to identify which weeds and plants to spray, or healthcare 175 00:10:25,720 --> 00:10:29,640 Speaker 2: companies like tempess AI, which uses the technology to analyze 176 00:10:29,640 --> 00:10:33,959 Speaker 2: patient data to improve disease diagnosis. And treatment. Here's Kathy 177 00:10:34,000 --> 00:10:34,720 Speaker 2: Wood again. 178 00:10:34,800 --> 00:10:37,840 Speaker 1: That's in our top ten, which we think could become 179 00:10:38,200 --> 00:10:42,320 Speaker 1: one of the most important healthcare information backbones in the 180 00:10:42,440 --> 00:10:43,400 Speaker 1: United States. 181 00:10:44,840 --> 00:10:47,520 Speaker 2: But what happens if all the plans to make AI 182 00:10:47,720 --> 00:10:52,040 Speaker 2: profitable don't pan out exactly the way these companies have promised? 183 00:10:52,520 --> 00:11:04,320 Speaker 2: How to head your bets after the break. The amount 184 00:11:04,360 --> 00:11:07,120 Speaker 2: of money going into the AI space right now is 185 00:11:07,320 --> 00:11:08,680 Speaker 2: frankly staggering. 186 00:11:08,880 --> 00:11:12,480 Speaker 6: The current run rate spendings of the big hyperscaler companies 187 00:11:12,520 --> 00:11:15,520 Speaker 6: alone equals like ten Manhattan projects. 188 00:11:15,960 --> 00:11:17,800 Speaker 2: Michael Smith at all Spring. 189 00:11:17,679 --> 00:11:20,000 Speaker 6: Pretty much AI has to work like we're all in. 190 00:11:20,440 --> 00:11:23,440 Speaker 6: It is a massive percentage of the stock market. 191 00:11:23,640 --> 00:11:26,200 Speaker 5: I think we all sort of in awe of how 192 00:11:26,280 --> 00:11:28,959 Speaker 5: much money is going in right that. The magnitude order 193 00:11:29,000 --> 00:11:32,040 Speaker 5: of magnitude is hundreds of billions of dollars, and they 194 00:11:32,080 --> 00:11:35,000 Speaker 5: are big, they're GDP moving kind of numbers. 195 00:11:35,320 --> 00:11:39,480 Speaker 2: Taosha Wang at Fidelity believes it makes sense that investment 196 00:11:39,600 --> 00:11:42,040 Speaker 2: at this scale would drive GDP. 197 00:11:41,880 --> 00:11:45,720 Speaker 5: But after that, the boost you know, through investment, needs 198 00:11:45,720 --> 00:11:49,640 Speaker 5: to come from productivity. King and productivity is also an 199 00:11:49,679 --> 00:11:54,240 Speaker 5: important driver of GDP. We are seeing anecdotal evidence of 200 00:11:54,280 --> 00:11:58,400 Speaker 5: you know, certain industries really benefiting from the adoption of 201 00:11:58,440 --> 00:12:01,160 Speaker 5: AI in terms of the productivity boost. But you know, 202 00:12:01,360 --> 00:12:05,160 Speaker 5: for us to be broad economy GDP moving that we 203 00:12:05,200 --> 00:12:08,120 Speaker 5: need to see it in many different industries that may 204 00:12:08,160 --> 00:12:11,040 Speaker 5: not necessarily traditionally at the forefront of technology. 205 00:12:11,360 --> 00:12:15,560 Speaker 2: That wide scale adoption and proven profitability are what Tashaw 206 00:12:15,559 --> 00:12:19,400 Speaker 2: believes will determine whether the AI run up continues or 207 00:12:19,440 --> 00:12:21,920 Speaker 2: whether it's more like a bubble that could pop. 208 00:12:22,200 --> 00:12:24,960 Speaker 5: One can never time haul on the bubble is going 209 00:12:24,960 --> 00:12:28,000 Speaker 5: to last and what is going to make the bubble 210 00:12:28,320 --> 00:12:31,680 Speaker 5: burst and make the music stop. I think it's usually 211 00:12:31,679 --> 00:12:34,800 Speaker 5: related to liquidity and cash flow. So to the extent 212 00:12:34,800 --> 00:12:37,680 Speaker 5: that there's still money going around, then I think it 213 00:12:37,720 --> 00:12:38,600 Speaker 5: can continue. 214 00:12:38,840 --> 00:12:41,760 Speaker 2: Those bubble concerns are being driven in part by the 215 00:12:41,840 --> 00:12:45,480 Speaker 2: large number of circular investment deals in this space. In 216 00:12:45,520 --> 00:12:49,400 Speaker 2: other words, companies like open ai, Video and Microsoft all 217 00:12:49,400 --> 00:12:52,400 Speaker 2: investing in each other. The fear is that it's those 218 00:12:52,440 --> 00:12:56,360 Speaker 2: deals that are propping up the industry's growth and valuations. 219 00:12:57,160 --> 00:13:00,720 Speaker 2: But Tasha is among the experts who say mutual investments 220 00:13:00,800 --> 00:13:03,199 Speaker 2: aren't a reason to write the whole industry off. 221 00:13:03,400 --> 00:13:06,440 Speaker 5: I think it's not necessarily a brand new practice that 222 00:13:06,520 --> 00:13:10,360 Speaker 5: you know companies invest in other companies that are in 223 00:13:10,400 --> 00:13:12,920 Speaker 5: their operational sort of sphere. 224 00:13:13,240 --> 00:13:16,440 Speaker 2: But she also says it is reason for investors to 225 00:13:16,520 --> 00:13:17,040 Speaker 2: take care. 226 00:13:17,360 --> 00:13:21,640 Speaker 5: I would generally caution against things from a cash flow 227 00:13:21,679 --> 00:13:25,000 Speaker 5: perspective that do not have, you know, real revenue prove, 228 00:13:25,440 --> 00:13:30,720 Speaker 5: real profitability proof. Alarming amount of circular investments going on. 229 00:13:30,800 --> 00:13:34,040 Speaker 5: That's certainly something that you know, want to be mindful of. 230 00:13:34,480 --> 00:13:37,240 Speaker 2: Michael Smith has been on his team at All Springs 231 00:13:37,240 --> 00:13:40,320 Speaker 2: since the dot com boom and bust, and there's a 232 00:13:40,320 --> 00:13:42,240 Speaker 2: few things he learned from that experience. 233 00:13:42,840 --> 00:13:45,480 Speaker 6: There are a lot of companies that want a piece 234 00:13:45,480 --> 00:13:48,400 Speaker 6: of this pie, and to me, like the big difference 235 00:13:48,760 --> 00:13:53,240 Speaker 6: is who's funding their investments from the profits that their 236 00:13:53,320 --> 00:13:58,560 Speaker 6: legacy businesses generate, and who's dependent on the kindness of strangers, 237 00:13:58,600 --> 00:14:02,840 Speaker 6: whether it would be outside equity investors, lenders, anybody who's 238 00:14:02,880 --> 00:14:06,079 Speaker 6: helping to finance the growth other than the business itself, 239 00:14:06,679 --> 00:14:10,199 Speaker 6: and just be very careful investing in companies that can't 240 00:14:10,200 --> 00:14:13,320 Speaker 6: finance their own growth. I think that was the lesson 241 00:14:13,400 --> 00:14:16,719 Speaker 6: learned in the late nineties and early two thousands. I mean, 242 00:14:16,760 --> 00:14:20,600 Speaker 6: it was basically the inability to access the capital markets 243 00:14:20,600 --> 00:14:24,040 Speaker 6: and continue to finance the growth that changed things that 244 00:14:24,160 --> 00:14:24,760 Speaker 6: time around. 245 00:14:25,160 --> 00:14:28,120 Speaker 2: As you may have noticed, the investment experts we spoke 246 00:14:28,200 --> 00:14:31,800 Speaker 2: to tended to be bullish on AI. They've already bet 247 00:14:31,800 --> 00:14:37,440 Speaker 2: on the industry themselves, after all. When we asked Kathy 248 00:14:37,520 --> 00:14:41,560 Speaker 2: would if there were any AI related investments she'd caution against, 249 00:14:42,040 --> 00:14:45,400 Speaker 2: she said, basically, don't move on to the next thing 250 00:14:45,600 --> 00:14:46,240 Speaker 2: too quickly. 251 00:14:46,560 --> 00:14:48,760 Speaker 1: A lot of people are saying, well, you know, this 252 00:14:48,920 --> 00:14:53,240 Speaker 1: AI movement or opportunity is exploited. Let's move to the 253 00:14:53,280 --> 00:14:58,160 Speaker 1: next thing, which is quantum computing. They've skipped over thematically 254 00:14:58,240 --> 00:15:01,840 Speaker 1: to quantum because they think and I say they meaning 255 00:15:02,320 --> 00:15:06,520 Speaker 1: thematic portfolio teams or what have you. They think AI 256 00:15:06,600 --> 00:15:09,120 Speaker 1: has been exploited. We think it's barely begun. 257 00:15:10,360 --> 00:15:13,160 Speaker 2: To read more about what these investment experts told Bloomberg 258 00:15:13,200 --> 00:15:16,960 Speaker 2: Personal Finance reporter Susan Woolley, head to Bloomberg dot com 259 00:15:17,080 --> 00:15:28,200 Speaker 2: or click the link in our show notes. This is 260 00:15:28,280 --> 00:15:31,560 Speaker 2: the Big Take from Bloomberg News. I'm Sarah Holder. The 261 00:15:31,600 --> 00:15:35,040 Speaker 2: show is hosted by Me, David Gera, and Wanha. The 262 00:15:35,080 --> 00:15:39,200 Speaker 2: show is made by Aaron Edwards, David Fox, Eleanor Harrison, Dengate, 263 00:15:39,560 --> 00:15:45,120 Speaker 2: Patti Hirsch, Rachel Lewis Krisky, Naomi Julia Press, Tracy Samuelson, 264 00:15:45,280 --> 00:15:51,520 Speaker 2: Naomi Shaven, alex Udiura, Julia Weaver, Yangyong and Taka Yasuzawa. 265 00:15:51,600 --> 00:15:54,280 Speaker 2: To get more from the Big Take and unlimited access 266 00:15:54,320 --> 00:15:57,880 Speaker 2: to all of bloomberg dot Com, subscribe today at Bloomberg 267 00:15:57,920 --> 00:16:01,440 Speaker 2: dot Com Slash Podcast Offer. Thanks for listening. We'll be 268 00:16:01,480 --> 00:16:04,680 Speaker 2: back on Monday.