1 00:00:02,520 --> 00:00:07,160 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:07,360 --> 00:00:10,240 Speaker 2: Let's go to one of our favorite tech investors. Kathy Would, founder, 3 00:00:10,360 --> 00:00:12,840 Speaker 2: CEO and CIO of ARC invest joins us around the 4 00:00:12,840 --> 00:00:16,079 Speaker 2: table to talk about this world. Kathy, we want to 5 00:00:16,079 --> 00:00:17,560 Speaker 2: get into your tech investments, we want to get into 6 00:00:17,560 --> 00:00:19,119 Speaker 2: your views, but we got to put this question to 7 00:00:19,160 --> 00:00:22,239 Speaker 2: you around H one B visas and this new application fee. 8 00:00:22,800 --> 00:00:24,400 Speaker 3: What do you make of it? What's the impact? 9 00:00:24,960 --> 00:00:30,960 Speaker 1: Well, again, this is part of President Trump's negotiating process, 10 00:00:31,120 --> 00:00:35,760 Speaker 1: and I think he's negotiating quite intensively right now with India. 11 00:00:36,560 --> 00:00:39,760 Speaker 1: I think India would be have the biggest impact here 12 00:00:39,880 --> 00:00:43,400 Speaker 1: in terms of, you know, workers in the United States. 13 00:00:43,840 --> 00:00:47,040 Speaker 1: So I think this is a little bit like tariffs, 14 00:00:47,240 --> 00:00:50,520 Speaker 1: and it's going to capture all the headlines and it's 15 00:00:50,600 --> 00:00:54,720 Speaker 1: going to really take the oxygen out of the room. 16 00:00:54,760 --> 00:00:59,440 Speaker 1: Because there's a lot of really good fundamental activity happening 17 00:00:59,440 --> 00:01:03,280 Speaker 1: in the United statesating relating to policy. 18 00:01:03,320 --> 00:01:05,000 Speaker 2: Well, a lot of that, a lot of the innovation, 19 00:01:05,040 --> 00:01:07,840 Speaker 2: a lot of that Silicon Valley kind of goodness really 20 00:01:07,880 --> 00:01:09,959 Speaker 2: comes from the talent and the labor pool. 21 00:01:10,000 --> 00:01:11,479 Speaker 3: We've been talking about this for quite some. 22 00:01:11,520 --> 00:01:14,720 Speaker 2: Time in terms of how do you train your engineers. 23 00:01:14,760 --> 00:01:18,200 Speaker 2: You train Americans to do a lot of what Silicon 24 00:01:18,280 --> 00:01:21,240 Speaker 2: Valley depends on foreign workers to do. Does this impact 25 00:01:21,600 --> 00:01:26,800 Speaker 2: innovation in Silicon Valley and doesn't impact megacap tech stocks tech. 26 00:01:26,560 --> 00:01:27,959 Speaker 3: Companies that are dependent on this. 27 00:01:28,240 --> 00:01:33,120 Speaker 1: Well, from what we know of this administration, they ultimately 28 00:01:33,720 --> 00:01:38,279 Speaker 1: want to keep students who have been educated in the US, 29 00:01:38,400 --> 00:01:41,120 Speaker 1: foreign students that have been educated in the US in 30 00:01:41,160 --> 00:01:44,520 Speaker 1: the US. So really, I just think this is part 31 00:01:44,560 --> 00:01:49,160 Speaker 1: of the negotiation with India and that when all is 32 00:01:49,160 --> 00:01:53,080 Speaker 1: said and done, we're probably going to loosen up the 33 00:01:53,280 --> 00:01:55,480 Speaker 1: H one visa visa program. 34 00:01:55,560 --> 00:01:58,080 Speaker 2: But in the short term, is that going to have 35 00:01:58,120 --> 00:01:59,320 Speaker 2: an impact on tech companies. 36 00:01:59,360 --> 00:02:01,960 Speaker 1: I think what it's going to do is force tech 37 00:02:02,000 --> 00:02:04,160 Speaker 1: companies to do what they're already doing and that has 38 00:02:04,200 --> 00:02:08,400 Speaker 1: become more efficient. And the other thing to note is, uh, 39 00:02:08,680 --> 00:02:11,880 Speaker 1: you know, coding is changing dramatically. The number of coding 40 00:02:12,000 --> 00:02:16,799 Speaker 1: jobs and openings has dropped dramatically. Because of AI, all 41 00:02:16,840 --> 00:02:20,000 Speaker 1: of us can become coders. You've heard a vibe good. 42 00:02:21,880 --> 00:02:23,079 Speaker 2: That's a lot of faith gout. 43 00:02:25,160 --> 00:02:30,680 Speaker 1: You know, this is natural language programming generative AI is 44 00:02:31,000 --> 00:02:34,680 Speaker 1: prompting chat GBT to get your programs going. 45 00:02:34,960 --> 00:02:36,000 Speaker 3: We can all do that. 46 00:02:36,000 --> 00:02:38,760 Speaker 1: That's simplifying it, but I do think it gives you 47 00:02:38,800 --> 00:02:42,760 Speaker 1: a sense of the kind of productivity, that possibilities that 48 00:02:43,000 --> 00:02:47,200 Speaker 1: are possible, that that will impact these tech companies longer term. 49 00:02:47,520 --> 00:02:50,200 Speaker 4: Come takes me on to my next question. Is Chinese 50 00:02:50,240 --> 00:02:53,320 Speaker 4: tech getting out perform US tech from a stock market 51 00:02:53,360 --> 00:02:55,239 Speaker 4: point of view over the next year. 52 00:02:56,360 --> 00:02:59,480 Speaker 1: Well, you know, the valuations are quite different. They're they're 53 00:02:59,600 --> 00:03:01,760 Speaker 1: roughly half of what they are in the United States. 54 00:03:01,840 --> 00:03:05,760 Speaker 1: We're very impressed at how quickly China is moving here. 55 00:03:06,200 --> 00:03:09,800 Speaker 1: I think the Deepseek moment gave us an opportunity to 56 00:03:09,919 --> 00:03:14,520 Speaker 1: understand that China is very focused on the open source 57 00:03:14,600 --> 00:03:15,480 Speaker 1: software movement. 58 00:03:15,560 --> 00:03:16,920 Speaker 3: We forced them into that. 59 00:03:17,160 --> 00:03:22,320 Speaker 1: US did because our company stopped selling into China out 60 00:03:22,360 --> 00:03:24,760 Speaker 1: of fear of IP theft. So they've gone the open 61 00:03:24,760 --> 00:03:27,600 Speaker 1: source route and are moving very quickly. 62 00:03:28,120 --> 00:03:29,840 Speaker 3: I think competition is a good thing. 63 00:03:29,919 --> 00:03:31,880 Speaker 1: It's a good thing for the United States, it's a 64 00:03:31,919 --> 00:03:35,640 Speaker 1: good thing for China. I also think what's good about 65 00:03:35,760 --> 00:03:40,080 Speaker 1: China recently is it is focused now on the idea 66 00:03:40,120 --> 00:03:43,440 Speaker 1: that maybe commoditization has gone too far, especially in the 67 00:03:43,480 --> 00:03:48,600 Speaker 1: electric vehicle space, so called involution. They are thinking that 68 00:03:48,760 --> 00:03:52,600 Speaker 1: means whatever that means, so they're thinking that, and it 69 00:03:52,680 --> 00:03:58,160 Speaker 1: is true it is expensive to develop these large language models, 70 00:03:58,200 --> 00:04:00,840 Speaker 1: even though they say it's not a lot of pre 71 00:04:00,920 --> 00:04:03,800 Speaker 1: training that takes place before you get a deep seek 72 00:04:04,480 --> 00:04:09,000 Speaker 1: And if you're not profitable as a company, you're you're 73 00:04:09,000 --> 00:04:11,400 Speaker 1: going to have a lot of trouble competing in that space. 74 00:04:11,440 --> 00:04:14,280 Speaker 1: So I think even that is changing, which is interesting. 75 00:04:14,360 --> 00:04:16,680 Speaker 4: When does AI become profitable? When can I assume a 76 00:04:16,760 --> 00:04:21,200 Speaker 4: margin on AI? And I'm talking about the large language 77 00:04:21,200 --> 00:04:25,120 Speaker 4: model companies here, huge amount of investment going in at 78 00:04:25,120 --> 00:04:29,320 Speaker 4: the moment, Kathy and everybody scratching their head wondering does 79 00:04:29,360 --> 00:04:31,760 Speaker 4: all of it deliver? Does all of it produce a 80 00:04:31,880 --> 00:04:33,920 Speaker 4: rate of return? Are we going to have to see 81 00:04:33,960 --> 00:04:39,200 Speaker 4: some sort of Subbuterian sort of clearing of the clearing 82 00:04:39,240 --> 00:04:41,760 Speaker 4: of the market at some point? What does that look like? 83 00:04:42,120 --> 00:04:43,840 Speaker 4: What is your sense of kind of where we are 84 00:04:43,920 --> 00:04:44,480 Speaker 4: at the moment. 85 00:04:45,360 --> 00:04:49,719 Speaker 1: I think that the number of companies competing, truly competing 86 00:04:49,760 --> 00:04:52,800 Speaker 1: in the large language model space has shrunk. 87 00:04:52,920 --> 00:04:54,840 Speaker 3: These aquihires that. 88 00:04:54,880 --> 00:04:59,240 Speaker 1: Open AI and meta that's all about you know, other 89 00:04:59,360 --> 00:05:01,280 Speaker 1: companies not making it so. 90 00:05:01,240 --> 00:05:03,240 Speaker 4: That the prices is already solved. 91 00:05:03,360 --> 00:05:04,200 Speaker 3: The process has. 92 00:05:04,120 --> 00:05:05,840 Speaker 4: Started, but do you think is going to win? 93 00:05:07,279 --> 00:05:08,200 Speaker 3: Well, I think the. 94 00:05:08,720 --> 00:05:15,800 Speaker 1: Big four right now are Open, Ai, Anthropic, XAI, and 95 00:05:16,160 --> 00:05:20,240 Speaker 1: Gemini those four, and we don't know if this is 96 00:05:20,279 --> 00:05:22,640 Speaker 1: going to be a four horse race or a two 97 00:05:22,680 --> 00:05:26,080 Speaker 1: horse race. Let's see over time how they leapfrog one another, 98 00:05:26,120 --> 00:05:27,640 Speaker 1: and they are doing that regularly. 99 00:05:27,680 --> 00:05:28,440 Speaker 3: It's very interesting. 100 00:05:28,480 --> 00:05:34,200 Speaker 1: But you ask about profitability companies that are not hiring people, 101 00:05:34,279 --> 00:05:39,200 Speaker 1: the number of new job openings is falling. They are 102 00:05:39,560 --> 00:05:43,720 Speaker 1: already enjoying huge productivity increases. That's why margins have been 103 00:05:43,760 --> 00:05:48,400 Speaker 1: sustained even with the tariff hit recently. In fact, tariff's 104 00:05:48,440 --> 00:05:51,359 Speaker 1: being put in place. The counter to that is, okay, 105 00:05:51,520 --> 00:05:53,159 Speaker 1: we have to cut costs. 106 00:05:53,200 --> 00:05:55,120 Speaker 3: And that has been and. 107 00:05:55,080 --> 00:05:58,279 Speaker 1: They're willing to pay twenty dollars a month, some are 108 00:05:58,320 --> 00:06:01,920 Speaker 1: willing to pay two hundred dollars a month, and those 109 00:06:02,080 --> 00:06:06,560 Speaker 1: who are replacing PhDs, they're willing to pay two thousand 110 00:06:06,640 --> 00:06:08,159 Speaker 1: dollars a month or more. 111 00:06:09,080 --> 00:06:10,440 Speaker 3: And so as long. 112 00:06:10,440 --> 00:06:14,560 Speaker 1: As these companies get that signaling that companies are willing 113 00:06:14,600 --> 00:06:17,880 Speaker 1: to pay, they will continue to invest in this race. 114 00:06:18,000 --> 00:06:20,039 Speaker 5: Kathy, you say competition is a good thing. We've talked 115 00:06:20,080 --> 00:06:22,599 Speaker 5: about the landscape. Let's talk about you. So there's a 116 00:06:22,600 --> 00:06:27,080 Speaker 5: lot more tech, innovation, robotics, ETFs out there. 117 00:06:27,120 --> 00:06:31,640 Speaker 1: Now, how do you plan to compete? It's a great question, Lizzie. 118 00:06:32,000 --> 00:06:37,080 Speaker 1: I think our differentiation is our research. We have what 119 00:06:37,279 --> 00:06:41,080 Speaker 1: I believe is the first sharing economy company in the 120 00:06:41,279 --> 00:06:44,120 Speaker 1: asset management space. When it comes to research, we give 121 00:06:44,200 --> 00:06:48,120 Speaker 1: our research away. We have given our Tesla model away 122 00:06:48,160 --> 00:06:51,719 Speaker 1: for example, approach, yes, the open source. From that point 123 00:06:51,720 --> 00:06:55,240 Speaker 1: of view, I think we have a very loud voice 124 00:06:55,240 --> 00:06:58,120 Speaker 1: out there when it comes to innovation, and I think 125 00:06:58,480 --> 00:07:01,320 Speaker 1: some of the calls we've made start in twenty fifteen 126 00:07:01,400 --> 00:07:07,960 Speaker 1: with bitcoin and at the same time roughly Tesla, Pallenteer, Coinbase, 127 00:07:08,200 --> 00:07:11,720 Speaker 1: Nvidia in the earliest days, we were there when most 128 00:07:11,760 --> 00:07:15,080 Speaker 1: people thought it was a PC gaming chip company. So 129 00:07:15,280 --> 00:07:18,040 Speaker 1: I think we've got a loud voice. You're asking about 130 00:07:18,120 --> 00:07:21,280 Speaker 1: conversion because we have these other companies out there which 131 00:07:21,440 --> 00:07:27,560 Speaker 1: with huge distribution. Well thanks to Bloomberg, you know, we 132 00:07:27,600 --> 00:07:30,600 Speaker 1: are able to share with you our research, and it's 133 00:07:30,680 --> 00:07:33,600 Speaker 1: research that others aren't doing in quite the same way. 134 00:07:33,680 --> 00:07:35,440 Speaker 5: But when you look at the numbers arcs of about 135 00:07:35,480 --> 00:07:39,520 Speaker 5: forty percent this year, flows are absolutely flat. What can 136 00:07:39,560 --> 00:07:42,440 Speaker 5: you do about that? If great performance just isn't drawing 137 00:07:42,440 --> 00:07:42,960 Speaker 5: people in. 138 00:07:43,480 --> 00:07:43,680 Speaker 3: Well. 139 00:07:43,760 --> 00:07:50,480 Speaker 1: What's interesting about that is most active managers are outflowing today, 140 00:07:50,560 --> 00:07:53,320 Speaker 1: So the fact that we're flat is very interesting. And 141 00:07:54,080 --> 00:07:58,000 Speaker 1: what's more interesting to us and very gratifying having entered 142 00:07:58,160 --> 00:08:02,760 Speaker 1: UK and Europe, is that we are about to cross 143 00:08:02,960 --> 00:08:07,360 Speaker 1: one billion dollars here in Europe and the UK, which 144 00:08:08,080 --> 00:08:13,080 Speaker 1: in two years time we acquired rise in two years ago, 145 00:08:14,200 --> 00:08:15,320 Speaker 1: is very significant. 146 00:08:15,360 --> 00:08:15,720 Speaker 3: I think. 147 00:08:15,800 --> 00:08:18,840 Speaker 1: I think here in Europe a quarter of our reader base, 148 00:08:18,920 --> 00:08:22,600 Speaker 1: even before we launched was in Europe and the question 149 00:08:22,800 --> 00:08:26,520 Speaker 1: was when are you coming to Europe. So we are 150 00:08:26,640 --> 00:08:30,360 Speaker 1: getting the flows here in the UK and Europe, and 151 00:08:30,400 --> 00:08:33,640 Speaker 1: we're really excited about the momentum behind those flows. 152 00:08:33,960 --> 00:08:36,160 Speaker 2: Kathy, I want to get your take on a deal 153 00:08:36,480 --> 00:08:40,360 Speaker 2: that was preliminarily announced on Friday between the United States 154 00:08:40,400 --> 00:08:42,720 Speaker 2: and China when it comes to TikTok, and I think 155 00:08:42,760 --> 00:08:45,080 Speaker 2: you're a unique person to have this conversation with given 156 00:08:45,160 --> 00:08:50,240 Speaker 2: your early exposure to Chinese tech stocks. TikTok has now 157 00:08:50,240 --> 00:08:53,040 Speaker 2: been We're still waiting for all the details, but we 158 00:08:53,200 --> 00:08:58,040 Speaker 2: understand is being given seven board seats to Americans. There's 159 00:08:58,080 --> 00:09:00,600 Speaker 2: a question of a licensing agreement. There's a question of 160 00:09:00,679 --> 00:09:03,360 Speaker 2: some of the holders, including the Murdos or Larry Ellison, 161 00:09:03,400 --> 00:09:06,440 Speaker 2: among others. What kind of precedent does that set for 162 00:09:06,520 --> 00:09:10,080 Speaker 2: you as someone who is exposed to Chinese tech. Is 163 00:09:10,120 --> 00:09:12,920 Speaker 2: this the new norm or is this an idiosyncratic story. 164 00:09:13,520 --> 00:09:18,640 Speaker 1: I think this is idiosyncratic. I think during our election 165 00:09:19,440 --> 00:09:24,120 Speaker 1: President Trump promised that he would be open minded to 166 00:09:24,960 --> 00:09:29,120 Speaker 1: keeping TikTok us. It's so important to the young people 167 00:09:29,520 --> 00:09:32,800 Speaker 1: and many more people in the United States. So I 168 00:09:32,840 --> 00:09:35,880 Speaker 1: do think it's a one off, special case. I also 169 00:09:35,920 --> 00:09:39,200 Speaker 1: think it's a part of a broader negotiation with China. 170 00:09:40,040 --> 00:09:44,520 Speaker 1: I think if anyone can re enact a Nixon in 171 00:09:44,640 --> 00:09:49,280 Speaker 1: China moment, it is President Trump. He has been toughest 172 00:09:49,440 --> 00:09:54,800 Speaker 1: on China. Everyone trusts that he will strike a good deal. 173 00:09:55,320 --> 00:09:57,680 Speaker 1: In everyone in the US trust he will strike a 174 00:09:57,679 --> 00:10:01,000 Speaker 1: good deal with China, and I think we're probably going 175 00:10:01,040 --> 00:10:05,360 Speaker 1: to get more better than expected news longer term. You'll 176 00:10:05,360 --> 00:10:08,400 Speaker 1: hear a lot of saber rattling in the meantime, but 177 00:10:08,520 --> 00:10:10,360 Speaker 1: this is one indication of that. 178 00:10:10,960 --> 00:10:15,320 Speaker 2: If TikTok a good investment, if the US has more 179 00:10:15,559 --> 00:10:18,319 Speaker 2: hold over it, and counter to that is byedance, a 180 00:10:18,360 --> 00:10:21,800 Speaker 2: good investment. If this is a deal that's being created. 181 00:10:22,240 --> 00:10:25,920 Speaker 1: You know, the one question we always have when there 182 00:10:26,000 --> 00:10:29,920 Speaker 1: is a breakup like that is does a company lose 183 00:10:30,200 --> 00:10:32,800 Speaker 1: some of the network effect in some way? Now they 184 00:10:32,840 --> 00:10:37,360 Speaker 1: were already supposed to be separate China and the US. 185 00:10:38,080 --> 00:10:39,360 Speaker 3: We'll see, We'll see. 186 00:10:39,720 --> 00:10:44,160 Speaker 1: TikTok has been an amazing company. Has taught us a lot. 187 00:10:44,280 --> 00:10:47,280 Speaker 1: This is what I mean about Chinese competition. They taught 188 00:10:47,400 --> 00:10:50,160 Speaker 1: our companies a lot. I will say. I will say 189 00:10:50,160 --> 00:10:53,760 Speaker 1: that a company called Vine in the US was the precursor, 190 00:10:53,840 --> 00:10:56,760 Speaker 1: and Twitter at the time bought it was not able 191 00:10:56,960 --> 00:10:59,559 Speaker 1: to activate it in the way TikTok has. 192 00:11:00,120 --> 00:11:02,520 Speaker 3: TikTok ran away with that. You would you want a 193 00:11:02,520 --> 00:11:03,000 Speaker 3: piece of it? 194 00:11:03,679 --> 00:11:05,560 Speaker 1: You know, we have to do a deeper dive when 195 00:11:05,640 --> 00:11:07,760 Speaker 1: we get into the data room. 196 00:11:08,280 --> 00:11:11,480 Speaker 4: Kryptos down a little bit. Bitcoin's selling off this morning, 197 00:11:11,600 --> 00:11:13,600 Speaker 4: like these kind of levels of you buya Rosilla. 198 00:11:14,760 --> 00:11:20,440 Speaker 1: Well, we've been increasing our exposure generally to the digital 199 00:11:20,480 --> 00:11:25,280 Speaker 1: asset space. I think Bitcoin has been the leader of 200 00:11:25,400 --> 00:11:28,840 Speaker 1: the back. The others are following. Ether has followed now 201 00:11:28,920 --> 00:11:34,040 Speaker 1: Solona and hyper liquid is a new one, the new 202 00:11:34,120 --> 00:11:38,319 Speaker 1: kid on the block. We think this is three revolutions 203 00:11:38,320 --> 00:11:43,200 Speaker 1: in one a monetary revolution, a financial services revolution, and really, 204 00:11:44,240 --> 00:11:48,440 Speaker 1: in a much broader sense, a digital property rights. Immutable 205 00:11:48,480 --> 00:11:52,440 Speaker 1: property rights online never happened before, only possible because of 206 00:11:52,440 --> 00:11:53,640 Speaker 1: blockchain technology. 207 00:11:54,040 --> 00:11:57,959 Speaker 4: Okay, down by a round, cryptozed down big coin is 208 00:11:57,960 --> 00:12:00,680 Speaker 4: that around two? Said this morning Kathy grac very much. Indeed, 209 00:12:00,800 --> 00:12:03,320 Speaker 4: Kathy Wood, founder and CEO of ARC invest