1 00:00:02,520 --> 00:00:10,600 Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. 2 00:00:13,440 --> 00:00:15,800 Speaker 2: Welcome back to Bloomberg Tech. Let's have a quick check 3 00:00:15,800 --> 00:00:18,720 Speaker 2: in on these markets, because we are continuing to see 4 00:00:18,800 --> 00:00:20,919 Speaker 2: pressure on the tech stocks only by about two tens 5 00:00:20,960 --> 00:00:22,720 Speaker 2: per percent, and as that one hundred coming off of 6 00:00:22,840 --> 00:00:25,880 Speaker 2: what was a five tens percent increase the previous day. 7 00:00:26,160 --> 00:00:28,200 Speaker 2: A lot of macro, a lot of digestion this week 8 00:00:28,240 --> 00:00:30,920 Speaker 2: as we anticipate, of course, the inflation readings, and a 9 00:00:30,960 --> 00:00:33,920 Speaker 2: lot of FED speak. We're just questioning though, perhaps some 10 00:00:33,960 --> 00:00:36,519 Speaker 2: of the valuations in the AI space. Let's move on 11 00:00:36,600 --> 00:00:39,040 Speaker 2: to see what's happening on this day with Micro and 12 00:00:39,080 --> 00:00:41,360 Speaker 2: get earnings After the bell, We're up more than two percent. 13 00:00:41,400 --> 00:00:44,360 Speaker 2: This is a stock that has increased forty percent in 14 00:00:44,400 --> 00:00:47,000 Speaker 2: the month of September alone, But can it live up 15 00:00:47,040 --> 00:00:49,640 Speaker 2: to the hopes for revenue increases to also be up 16 00:00:49,680 --> 00:00:52,839 Speaker 2: some forty percent in their fiscal quarter on which they're reporting. 17 00:00:52,920 --> 00:00:56,560 Speaker 2: Of course, key is the demand for high bandwidth memory 18 00:00:56,840 --> 00:00:59,400 Speaker 2: and the use within AI accelerators. Talking about AI accelerators, 19 00:00:59,440 --> 00:01:00,880 Speaker 2: check out in video. We're off by one point eight 20 00:01:00,880 --> 00:01:03,520 Speaker 2: percent after yesterday's rally. Remember that one hundred billion dollar 21 00:01:03,560 --> 00:01:07,319 Speaker 2: investment commitment into open AI. If open Ai in return 22 00:01:07,400 --> 00:01:11,480 Speaker 2: commits to buying GPUs the future from Invidea, the merry 23 00:01:11,480 --> 00:01:13,920 Speaker 2: go round of money continues. Let's break that all down 24 00:01:13,959 --> 00:01:17,319 Speaker 2: with Bloomberg Semiconductors report Ian King just going back to 25 00:01:17,360 --> 00:01:21,280 Speaker 2: that extraordinary one hundred billion dollar commitment getting equity from 26 00:01:21,280 --> 00:01:25,120 Speaker 2: open Ai, but only as they bring on AI accelerators, 27 00:01:25,280 --> 00:01:30,160 Speaker 2: GPUs from Invidea, and ten gigawatts eventually in the next 28 00:01:30,160 --> 00:01:32,920 Speaker 2: few years. Ian, are we questioning the way in which 29 00:01:33,160 --> 00:01:34,800 Speaker 2: money is floating Right now. 30 00:01:36,840 --> 00:01:40,080 Speaker 3: That's a large number, and it seems excessive, even for 31 00:01:40,360 --> 00:01:42,840 Speaker 3: in Video. But then you sort of take a step 32 00:01:42,880 --> 00:01:44,680 Speaker 3: back and you realize that this is going to happen 33 00:01:44,720 --> 00:01:47,760 Speaker 3: in an incremental basis, and it's going to be put 34 00:01:47,800 --> 00:01:51,440 Speaker 3: to work only when the actual hardware is put in 35 00:01:51,480 --> 00:01:54,920 Speaker 3: place and the actual capacity is put in place. So yes, 36 00:01:54,960 --> 00:01:57,960 Speaker 3: it's a massive headline. Yes, even for Invidea, that seems huge, 37 00:01:57,960 --> 00:01:59,720 Speaker 3: But if you take it over a number of years, 38 00:01:59,720 --> 00:02:02,560 Speaker 3: then maybe it isn't that excessive. Maybe it's just what 39 00:02:02,720 --> 00:02:04,080 Speaker 3: is needed for this market. 40 00:02:04,200 --> 00:02:06,760 Speaker 2: And in Video continues to be a deeply strategic investor, 41 00:02:06,840 --> 00:02:09,040 Speaker 2: often putting money to work in the like sort of 42 00:02:09,040 --> 00:02:13,360 Speaker 2: core Weaver n scale or long term demand for their GPUs. 43 00:02:13,480 --> 00:02:15,680 Speaker 2: In talking of demand, we're going to get a bit 44 00:02:15,720 --> 00:02:17,840 Speaker 2: of a fundamental check on it, right with Micron's earnings 45 00:02:17,840 --> 00:02:18,320 Speaker 2: after the bell. 46 00:02:19,400 --> 00:02:23,760 Speaker 3: Yeah, the Micron story is everybody is focused. As you 47 00:02:23,880 --> 00:02:26,720 Speaker 3: mentioned on HBM high bind with memory one of Ed 48 00:02:26,760 --> 00:02:31,679 Speaker 3: lard Low's favorite topics. Everybody wants to see this company 49 00:02:31,919 --> 00:02:37,240 Speaker 3: converting what they know is strong demand into very rapid growth. 50 00:02:37,280 --> 00:02:41,080 Speaker 3: Everybody wants to see Micron really having a say in 51 00:02:41,120 --> 00:02:43,960 Speaker 3: the AI world, and that's what people be focused on. 52 00:02:44,000 --> 00:02:47,440 Speaker 3: Because the market overall for other types of memory, maybe 53 00:02:47,600 --> 00:02:50,480 Speaker 3: isn't that strong. Maybe isn't recovering as quickly as as 54 00:02:50,520 --> 00:02:51,160 Speaker 3: some had hoped. 55 00:02:51,720 --> 00:02:54,080 Speaker 2: Eleven point one five billion dollars is a consensus up 56 00:02:54,120 --> 00:02:56,640 Speaker 2: forty four percent in terms of revenue for fiscal fourth quarter. 57 00:02:56,919 --> 00:03:00,680 Speaker 2: Bloomberg's inking as always a very busy man. Thank him. 58 00:03:00,800 --> 00:03:04,560 Speaker 2: Look as we await earnings. Interestingly, anxiety around AI funding 59 00:03:04,600 --> 00:03:07,680 Speaker 2: just continues to be intensifying. The industry. We understand will 60 00:03:07,720 --> 00:03:11,400 Speaker 2: need two trillion dollars in combined annual revenue to fund 61 00:03:11,480 --> 00:03:14,280 Speaker 2: the computing power that's needed by twenty thirty, but their 62 00:03:14,280 --> 00:03:17,920 Speaker 2: revenue is likely to fall short by a tune of 63 00:03:17,960 --> 00:03:20,600 Speaker 2: eight hundred billion dollars. It's all according to Bain's annual 64 00:03:20,639 --> 00:03:24,200 Speaker 2: Global Technology Report. It was released today Tuesday, as bringing 65 00:03:24,200 --> 00:03:26,600 Speaker 2: in j Jacobs for more US head of Equity ETS 66 00:03:26,600 --> 00:03:29,600 Speaker 2: over at black Rock who you really get an analysis 67 00:03:29,680 --> 00:03:33,440 Speaker 2: of the insatiable demand of investors into the AI data 68 00:03:33,480 --> 00:03:36,839 Speaker 2: center story. But are they starting to question ultimately whether 69 00:03:36,880 --> 00:03:39,440 Speaker 2: we can meet that insatiable demand where the funding is 70 00:03:39,480 --> 00:03:40,560 Speaker 2: there in the longer term. 71 00:03:40,800 --> 00:03:43,440 Speaker 4: No, I think it's quite the opposite. Investor enthusiasm is 72 00:03:43,440 --> 00:03:46,320 Speaker 4: only growing. If you look at one of our ETFs, BAI, 73 00:03:46,360 --> 00:03:49,600 Speaker 4: it's an actively managed AIETF. It's brought in over five 74 00:03:49,680 --> 00:03:52,480 Speaker 4: billion dollars this year and is now the largest AIETF 75 00:03:52,520 --> 00:03:54,920 Speaker 4: in the United States. And a lot of the exposure 76 00:03:54,960 --> 00:03:58,920 Speaker 4: that is providing to investors is around that digital infrastructure layer, 77 00:03:58,920 --> 00:04:02,240 Speaker 4: the hardware producers in the semiconductor space, the digital infrastructure owners, 78 00:04:02,760 --> 00:04:05,200 Speaker 4: some of the data owners that are becoming really valuable 79 00:04:05,240 --> 00:04:08,200 Speaker 4: in this explosion of AI models. And so there's a 80 00:04:08,240 --> 00:04:10,960 Speaker 4: ton of investor enthusiasm, and I think there's a lot 81 00:04:11,000 --> 00:04:14,520 Speaker 4: of expectations that revenues will match the expenditures, if not 82 00:04:14,600 --> 00:04:16,080 Speaker 4: exceed them, in the next several years. 83 00:04:16,520 --> 00:04:21,200 Speaker 2: But how much should we get comfortable with companies like 84 00:04:21,360 --> 00:04:25,640 Speaker 2: in Nvidia basically plowing their own revenues of today into 85 00:04:25,680 --> 00:04:29,640 Speaker 2: hopes of revenue of tomorrow, basically funding their end clients. 86 00:04:30,240 --> 00:04:32,640 Speaker 4: I think it's beyond comfort. This is a necessity for 87 00:04:32,680 --> 00:04:35,360 Speaker 4: some of the leading technology companies. They have a tremendous 88 00:04:35,440 --> 00:04:37,520 Speaker 4: mode because they have low cost of capital, they have 89 00:04:37,560 --> 00:04:41,360 Speaker 4: tremendous access to dollars, and they're able to really kind 90 00:04:41,360 --> 00:04:43,240 Speaker 4: of leverage their own strengths, which is if you look 91 00:04:43,240 --> 00:04:47,640 Speaker 4: at a semiconductor company owning GPUs, owning this manufacturing process 92 00:04:47,680 --> 00:04:50,800 Speaker 4: is really valuable in the semiconductor space. That's the short 93 00:04:50,960 --> 00:04:53,719 Speaker 4: that's the shortfall that's needed by large language model developers. 94 00:04:53,760 --> 00:04:56,760 Speaker 4: So this investment across the industry makes a ton of sense. 95 00:04:56,880 --> 00:04:59,039 Speaker 2: So going back to your I shares AI Innovation in 96 00:04:59,080 --> 00:05:03,120 Speaker 2: Tech actively managed ETF, how much are clients begging you 97 00:05:03,160 --> 00:05:05,720 Speaker 2: for more diversification when it comes to the AI trade. 98 00:05:05,760 --> 00:05:08,880 Speaker 2: Because we have put so much stock into a few names. 99 00:05:09,080 --> 00:05:11,640 Speaker 4: There's a lot of ask to go beyond the MAG seven. 100 00:05:11,680 --> 00:05:14,000 Speaker 4: I think people understand the MAG seven story. They understand 101 00:05:14,040 --> 00:05:16,760 Speaker 4: why these companies are so successful in AI space, and 102 00:05:16,760 --> 00:05:18,440 Speaker 4: why they're leading it, but they do want to know 103 00:05:18,480 --> 00:05:20,720 Speaker 4: what's beyond that. What are the other semiconductor companies, why 104 00:05:20,720 --> 00:05:22,200 Speaker 4: are some of the data owners, what are some of 105 00:05:22,200 --> 00:05:25,560 Speaker 4: the companies that aren't necessarily a consensus view as an 106 00:05:25,600 --> 00:05:28,719 Speaker 4: AI company but are involved in the space. And so 107 00:05:28,800 --> 00:05:30,919 Speaker 4: that's where and actively managed fund I think can provide 108 00:05:30,920 --> 00:05:33,320 Speaker 4: a lot of value to investors is by yes, it's 109 00:05:33,320 --> 00:05:35,840 Speaker 4: going to have Max seven exposure, but beyond two thirds 110 00:05:35,839 --> 00:05:37,359 Speaker 4: of it is going to look beyond it and provide 111 00:05:37,440 --> 00:05:38,920 Speaker 4: kind of the next names that are going to rise 112 00:05:38,920 --> 00:05:39,800 Speaker 4: in this ecosystem. 113 00:05:39,920 --> 00:05:42,400 Speaker 2: And all those names rising in the United States. We've 114 00:05:42,440 --> 00:05:47,080 Speaker 2: also just seen President Trump, alongside Jensen Wang and many others, 115 00:05:47,080 --> 00:05:49,040 Speaker 2: go to the United Kingdom to talk up an investment. 116 00:05:49,160 --> 00:05:51,400 Speaker 2: There are we seeing more names being added to the ets. 117 00:05:51,400 --> 00:05:52,960 Speaker 2: They're coming from Europe, coming from Asia. 118 00:05:53,360 --> 00:05:55,840 Speaker 4: It's a global theme, but predominantly it is being led 119 00:05:55,880 --> 00:05:57,600 Speaker 4: out of the United States today, and that's where we've 120 00:05:57,600 --> 00:05:59,920 Speaker 4: seen some of these kind of new up and come 121 00:06:00,120 --> 00:06:02,320 Speaker 4: AI risers largely coming from We sell have a really 122 00:06:02,440 --> 00:06:05,640 Speaker 4: robust startup environment and venture capital environment around AI. So 123 00:06:06,080 --> 00:06:08,720 Speaker 4: the US is very much at the center of this ecosystem, 124 00:06:08,720 --> 00:06:10,120 Speaker 4: but it is a global theme. 125 00:06:09,960 --> 00:06:13,200 Speaker 2: So the members of the ETF a larger US. Are 126 00:06:13,200 --> 00:06:16,520 Speaker 2: the investors in that INTF getting ever any more global 127 00:06:16,600 --> 00:06:18,800 Speaker 2: or is it predominantly US investors that want to stick 128 00:06:18,800 --> 00:06:19,240 Speaker 2: with this theme. 129 00:06:19,800 --> 00:06:20,320 Speaker 5: It's both. 130 00:06:20,400 --> 00:06:23,039 Speaker 4: We certainly have seen a ton of global appetite, but 131 00:06:23,200 --> 00:06:24,960 Speaker 4: some of the biggest investors today are coming from the 132 00:06:25,040 --> 00:06:27,960 Speaker 4: United States. I think AI is not surprising anyone. Everyone 133 00:06:28,000 --> 00:06:30,240 Speaker 4: knows globally this is the biggest area right now. They 134 00:06:30,320 --> 00:06:32,400 Speaker 4: want to have access to the best investment tools to 135 00:06:32,400 --> 00:06:35,560 Speaker 4: get exposure to it. Our ETF is really risen as 136 00:06:35,600 --> 00:06:38,280 Speaker 4: the number one tool in the US for AI exposure. 137 00:06:38,560 --> 00:06:41,440 Speaker 2: What about other parts, like the energy side of the equation? 138 00:06:42,040 --> 00:06:44,839 Speaker 2: Are the areas that eventually will do well. We're always 139 00:06:44,839 --> 00:06:47,440 Speaker 2: talking about healthcare predominantly being a user of generals of 140 00:06:47,480 --> 00:06:49,440 Speaker 2: AI and therefore more productive. 141 00:06:49,760 --> 00:06:52,800 Speaker 4: I would say the energy space is coming up more 142 00:06:52,839 --> 00:06:55,880 Speaker 4: and more with clients as they look to the AI 143 00:06:55,920 --> 00:06:58,560 Speaker 4: trade beyond technology kind of what's the next shoot to drop, 144 00:06:58,560 --> 00:07:00,520 Speaker 4: what's the area of opportunity. We've seen a ton of 145 00:07:00,520 --> 00:07:05,200 Speaker 4: interest around different energy sources to be able to power 146 00:07:05,240 --> 00:07:08,080 Speaker 4: the AI trade, whether that's nuclear, whether it's fossil fuels, 147 00:07:08,080 --> 00:07:10,960 Speaker 4: whether it's renewables. Really looking across the entire energy ecosystem, 148 00:07:11,760 --> 00:07:14,360 Speaker 4: It's a way of looking at utilities from a more 149 00:07:14,440 --> 00:07:17,440 Speaker 4: forward looking lens. If you take the utility sector, but 150 00:07:17,480 --> 00:07:19,480 Speaker 4: say who's really going to grow here, who really has 151 00:07:19,520 --> 00:07:21,920 Speaker 4: this great ten urre trajectory because of AI. It's a 152 00:07:21,960 --> 00:07:23,480 Speaker 4: more thematic cut of that space. 153 00:07:24,000 --> 00:07:27,000 Speaker 2: We've just been coming out of the United Nations General Assembly. 154 00:07:27,120 --> 00:07:29,560 Speaker 2: It does feel as though fossil fuels very much have 155 00:07:29,640 --> 00:07:31,440 Speaker 2: to be put back on the map by President Trump 156 00:07:31,480 --> 00:07:33,760 Speaker 2: at least. Is that something you're seeing echoed by investors 157 00:07:33,760 --> 00:07:36,240 Speaker 2: they want to be recommitting once again after years of 158 00:07:36,240 --> 00:07:40,080 Speaker 2: perhaps rebalancing, moving towards renewables, looking at cleanoforms of energy. 159 00:07:40,240 --> 00:07:42,520 Speaker 4: It's across the entire energy value chain. I think you 160 00:07:42,520 --> 00:07:44,080 Speaker 4: have to look at There are going to be energy 161 00:07:44,080 --> 00:07:45,880 Speaker 4: fuels that are going to be really necessary, there's going 162 00:07:45,920 --> 00:07:47,960 Speaker 4: to be energy producers that are really necessary, and there's 163 00:07:48,040 --> 00:07:50,920 Speaker 4: energy distribution that's going to be really necessary in this 164 00:07:51,080 --> 00:07:53,200 Speaker 4: AI theme, and so I think people are looking very 165 00:07:53,200 --> 00:07:55,040 Speaker 4: broadly at where it's going to come from. 166 00:07:55,120 --> 00:07:58,360 Speaker 2: Every now and then the market goes through moments of anxiety, 167 00:07:58,440 --> 00:08:01,520 Speaker 2: whether it's about fact that maybe we've got this revenue 168 00:08:01,560 --> 00:08:04,240 Speaker 2: mismatch when it comes to compute funding, whether it's about 169 00:08:04,280 --> 00:08:07,080 Speaker 2: actual productivity gains with the MIT report that came out 170 00:08:07,560 --> 00:08:11,600 Speaker 2: last month, are you seeing any anxiety pull back in 171 00:08:11,680 --> 00:08:13,880 Speaker 2: actual fun flows wanting to go into AI? 172 00:08:13,960 --> 00:08:15,000 Speaker 5: More broadly, like. 173 00:08:14,920 --> 00:08:18,120 Speaker 4: I said, our aifun flows have been accelerating recently, so 174 00:08:18,160 --> 00:08:20,160 Speaker 4: we're not seeing a lot of anxiety. Look, this is 175 00:08:20,200 --> 00:08:21,880 Speaker 4: a long term theme. This is a bet for the 176 00:08:21,920 --> 00:08:24,679 Speaker 4: next five to ten and beyond years. I'm sure within 177 00:08:24,720 --> 00:08:27,080 Speaker 4: that timeframe there will be bouts of anxiety. There will 178 00:08:27,120 --> 00:08:30,000 Speaker 4: be questions around what the valuation should be. But if 179 00:08:30,000 --> 00:08:32,800 Speaker 4: you take a long term view of artificial intelligence, we 180 00:08:32,880 --> 00:08:34,120 Speaker 4: believe this is going to be one of the most 181 00:08:34,160 --> 00:08:36,640 Speaker 4: powerful drivers of the markets for the next decade and beyond. 182 00:08:37,120 --> 00:08:40,240 Speaker 2: Okay, and more broadly, when we're thinking about fundamentals, how 183 00:08:40,320 --> 00:08:43,679 Speaker 2: much to earnings matter for your portfolio managers, but more 184 00:08:43,720 --> 00:08:45,079 Speaker 2: broadly for the investor sentiment too. 185 00:08:45,480 --> 00:08:48,400 Speaker 4: So they matter, but when you're looking over the long term, 186 00:08:48,400 --> 00:08:50,280 Speaker 4: it's more about what does the earning say about the 187 00:08:50,320 --> 00:08:53,080 Speaker 4: direction of travel rather than what's happening today. There's not 188 00:08:53,160 --> 00:08:57,160 Speaker 4: a ton of concern about what is your revenue today. 189 00:08:57,240 --> 00:08:59,880 Speaker 4: If you're a large language model developer. What we prefer 190 00:09:00,480 --> 00:09:04,160 Speaker 4: is have you gotten more paying subscribers? Have you developed 191 00:09:04,160 --> 00:09:06,360 Speaker 4: the best model? Are you starting to see more penetration 192 00:09:06,440 --> 00:09:08,160 Speaker 4: in the corporate world where you're going to have more 193 00:09:09,040 --> 00:09:12,160 Speaker 4: ability to spend on these technologies? The direction of travel 194 00:09:12,440 --> 00:09:13,920 Speaker 4: is what we're really looking at. The earnings. 195 00:09:14,000 --> 00:09:16,079 Speaker 2: J Jacobs is always great to have you here. Thankscoming 196 00:09:16,120 --> 00:09:19,240 Speaker 2: in the studio. Black rocks. J Jacobs there. Meanwhile, I 197 00:09:19,320 --> 00:09:21,760 Speaker 2: want to talk other areas of AI now, the application 198 00:09:21,880 --> 00:09:25,559 Speaker 2: of it. Snowflake, alongside industry leaders and key partners, just 199 00:09:25,640 --> 00:09:28,840 Speaker 2: unvailed a new initiative aiming to set a new industry 200 00:09:28,840 --> 00:09:32,640 Speaker 2: standard for AI interoperability. But they call it the Open 201 00:09:32,840 --> 00:09:35,920 Speaker 2: Semantic Interchange and it will be open sourced in an 202 00:09:35,920 --> 00:09:39,079 Speaker 2: effort to remove what they call data islands and accelerate 203 00:09:39,120 --> 00:09:41,160 Speaker 2: AI for all. Treat our Ramaswami and piece to say 204 00:09:41,240 --> 00:09:44,360 Speaker 2: is with us. Snowflake CEO set the context of the 205 00:09:44,400 --> 00:09:48,160 Speaker 2: problem here, shout out how difficult is it to get 206 00:09:48,200 --> 00:09:50,640 Speaker 2: access to all data out there at the moment to 207 00:09:50,720 --> 00:09:52,960 Speaker 2: make the generative AI become this reality. 208 00:09:54,480 --> 00:09:56,640 Speaker 5: Well, great to be here at Caroline. 209 00:09:56,840 --> 00:09:59,719 Speaker 1: AI has a lot of promise for making data very 210 00:09:59,760 --> 00:10:05,360 Speaker 1: broad available to everyone in the enterprise, including business users using. 211 00:10:05,200 --> 00:10:07,839 Speaker 5: Natural language, which they which all of us like doing. 212 00:10:08,320 --> 00:10:12,280 Speaker 1: But the problem often is that the meaning behind the 213 00:10:12,400 --> 00:10:16,439 Speaker 1: data is in multiple places. It can be in databases, 214 00:10:16,480 --> 00:10:19,360 Speaker 1: it can be in business intelligent tools. This just makes 215 00:10:19,400 --> 00:10:22,600 Speaker 1: developing AI solutions on top of this data very hard. 216 00:10:23,040 --> 00:10:26,760 Speaker 1: And what we announced today with a consortium of industry 217 00:10:26,800 --> 00:10:31,160 Speaker 1: folks is an open standard for exchanging this semantic information. 218 00:10:31,320 --> 00:10:34,040 Speaker 1: And what that means is this is going to enhance 219 00:10:34,080 --> 00:10:40,800 Speaker 1: interoperability between different folks between Salesforce, DBT Labs, Blackrock that 220 00:10:41,280 --> 00:10:44,760 Speaker 1: we would like to see happen. It can help accelerate 221 00:10:44,880 --> 00:10:47,560 Speaker 1: value that is going to be gotten from AI. It 222 00:10:47,720 --> 00:10:51,160 Speaker 1: also just simplifies things for everybody that's involved, and that's 223 00:10:51,160 --> 00:10:54,880 Speaker 1: why we're super excited about working with these giant industry 224 00:10:54,920 --> 00:10:57,800 Speaker 1: players to announce this new interchange format. 225 00:10:58,080 --> 00:11:01,920 Speaker 2: Yeah, universal translator is a nice way about talking about it. 226 00:11:01,920 --> 00:11:05,440 Speaker 2: With Snowflake, the sell Source, DBT Labs, black Rot, Thought Spot, 227 00:11:06,120 --> 00:11:08,760 Speaker 2: Wig Giki on this program, can you tell us about 228 00:11:08,840 --> 00:11:11,559 Speaker 2: how difficult the technology exchange is going to be? Here? 229 00:11:11,559 --> 00:11:13,440 Speaker 2: What actually you need to do beneath the surface. 230 00:11:14,360 --> 00:11:15,959 Speaker 5: Yeah, it's a standard. 231 00:11:16,040 --> 00:11:19,800 Speaker 1: What this means is that people that want to be 232 00:11:19,920 --> 00:11:22,920 Speaker 1: part of the standard or want to confirm to the standard, 233 00:11:23,200 --> 00:11:26,520 Speaker 1: just for going to make it easy to send and 234 00:11:26,880 --> 00:11:29,760 Speaker 1: receive this semantic information. This is you know, this is 235 00:11:29,840 --> 00:11:33,160 Speaker 1: business stuff, stuff like OS revenue defined, how is profit defined, 236 00:11:33,360 --> 00:11:34,600 Speaker 1: how is EBITA defined? 237 00:11:34,720 --> 00:11:35,320 Speaker 5: And so on. 238 00:11:35,640 --> 00:11:38,920 Speaker 1: So we intentionally wanted it to be a lightlift for 239 00:11:39,120 --> 00:11:43,280 Speaker 1: each of these players. But what happens collectively is that 240 00:11:43,440 --> 00:11:46,120 Speaker 1: all of a sudden, the semantic information is free to 241 00:11:46,160 --> 00:11:50,520 Speaker 1: move about. It just makes value creation, especially with AI, 242 00:11:51,040 --> 00:11:53,960 Speaker 1: just a whole lot faster. And it's a big step 243 00:11:54,080 --> 00:11:56,240 Speaker 1: forward for the industry, and we expect more and more 244 00:11:56,280 --> 00:11:59,040 Speaker 1: folks in the data industry to be a part of 245 00:11:59,080 --> 00:11:59,960 Speaker 1: this standard. 246 00:12:00,120 --> 00:12:02,640 Speaker 2: Interesting, so that's for the data industry, going to the 247 00:12:03,000 --> 00:12:06,120 Speaker 2: large language model industry in and of itself. Anthropics seem 248 00:12:06,160 --> 00:12:09,079 Speaker 2: to have seen this issue with data and inter proability 249 00:12:09,120 --> 00:12:12,240 Speaker 2: as well. They've been seeing silos. They introduce something called 250 00:12:12,280 --> 00:12:16,760 Speaker 2: model context protocol that's again open standard up. How is 251 00:12:16,800 --> 00:12:17,280 Speaker 2: that different? 252 00:12:18,760 --> 00:12:20,120 Speaker 5: This is one layer below that. 253 00:12:20,280 --> 00:12:23,760 Speaker 1: What a model context protocol does is it creates a 254 00:12:23,880 --> 00:12:28,080 Speaker 1: standard way, for example, to talk to an AI agent. 255 00:12:28,440 --> 00:12:30,079 Speaker 5: But on the other hand, even to. 256 00:12:30,080 --> 00:12:32,520 Speaker 1: Create the AI agent, you need to make sense of 257 00:12:32,559 --> 00:12:35,120 Speaker 1: the underlying data. It can be the case that you 258 00:12:35,200 --> 00:12:38,640 Speaker 1: have data sitting in one place while the definition for 259 00:12:38,720 --> 00:12:41,000 Speaker 1: what the data means is in a different place. 260 00:12:41,679 --> 00:12:42,520 Speaker 5: This makes it. 261 00:12:42,480 --> 00:12:45,600 Speaker 1: Easier to create agents, which can then take advantage of 262 00:12:45,640 --> 00:12:49,880 Speaker 1: protocols like Model Control Protocol MCP that you mentioned to 263 00:12:49,960 --> 00:12:53,720 Speaker 1: make agent interoperability work a whole lot better. So you 264 00:12:53,800 --> 00:12:57,120 Speaker 1: need all of these layers for AI value to get created. 265 00:12:56,840 --> 00:12:59,120 Speaker 2: And we need the agents to get better for the 266 00:12:59,160 --> 00:13:03,240 Speaker 2: productivity to really result. Should I ask you, after months 267 00:13:03,240 --> 00:13:07,040 Speaker 2: of worry perhaps about MIT reports saying that ultimately the 268 00:13:07,040 --> 00:13:10,040 Speaker 2: productivity gains aren't being seen yet, what are you seeing 269 00:13:10,040 --> 00:13:13,000 Speaker 2: with your clients using your own offerings and products. 270 00:13:13,800 --> 00:13:17,280 Speaker 1: Yeah, we are seeing a lot of productivity gain in 271 00:13:17,400 --> 00:13:22,000 Speaker 1: specific areas, and I can speak even personally to examples 272 00:13:22,080 --> 00:13:25,040 Speaker 1: like my sales data is just a whole lot easier 273 00:13:25,040 --> 00:13:28,680 Speaker 1: for me to get to with the snowflake intelligence. Blackrock, 274 00:13:28,760 --> 00:13:32,440 Speaker 1: one of the customers in the OSI standard, also is 275 00:13:32,559 --> 00:13:35,199 Speaker 1: using our AI products to create what's called the customer 276 00:13:35,240 --> 00:13:38,400 Speaker 1: three sixty. So when you call Blackrock, the person on 277 00:13:38,440 --> 00:13:41,200 Speaker 1: the other side has access to all of the relevant 278 00:13:41,320 --> 00:13:45,160 Speaker 1: information about you. It's things like that that are creating value, 279 00:13:45,160 --> 00:13:48,240 Speaker 1: and what's unique about Snowflake is we let our customers 280 00:13:48,280 --> 00:13:53,360 Speaker 1: create AI products without a ton of investment on their part. 281 00:13:53,440 --> 00:13:56,520 Speaker 1: We make it much easier to iterate and create these 282 00:13:56,600 --> 00:13:59,640 Speaker 1: products and use them and see for themselves what's creating 283 00:13:59,720 --> 00:14:03,560 Speaker 1: value before turning on this figot and having these products 284 00:14:03,600 --> 00:14:07,559 Speaker 1: exposed more and more and in areas like coding agents, 285 00:14:07,600 --> 00:14:11,080 Speaker 1: the productivity gains that we are seeing is truly remarkable, 286 00:14:11,400 --> 00:14:13,920 Speaker 1: and I think that's an area where you will continue 287 00:14:13,920 --> 00:14:18,160 Speaker 1: to see a ton of adoption across the board in enterprises. 288 00:14:18,920 --> 00:14:21,000 Speaker 5: You know, finally or strangely. 289 00:14:20,520 --> 00:14:22,840 Speaker 1: It turns out that coding is one of the most 290 00:14:22,840 --> 00:14:27,440 Speaker 1: interesting and important applications of language models we've seen. 291 00:14:27,440 --> 00:14:29,760 Speaker 2: The adoption of your products is shown up in your earnings. 292 00:14:29,760 --> 00:14:33,040 Speaker 2: People have seen really the growth that you're talking about. 293 00:14:33,120 --> 00:14:35,520 Speaker 2: For that, you need sales and marketing people to go 294 00:14:35,560 --> 00:14:37,720 Speaker 2: out and sell your products. But that's where your Talent 295 00:14:37,880 --> 00:14:39,520 Speaker 2: edition has been. But I go to the news of 296 00:14:39,520 --> 00:14:41,600 Speaker 2: the day for example around at h one b visas 297 00:14:41,880 --> 00:14:45,040 Speaker 2: how much is that becoming an issue for you with talent? 298 00:14:45,160 --> 00:14:47,080 Speaker 2: Is that something that you're worried about access to the 299 00:14:47,160 --> 00:14:48,680 Speaker 2: right people here in the United States? 300 00:14:49,560 --> 00:14:53,320 Speaker 1: Yeah, it is. It is an issue roughly less than 301 00:14:53,360 --> 00:14:56,280 Speaker 1: ten percent of our workforce is on H one B 302 00:14:56,440 --> 00:15:00,360 Speaker 1: Visas you know, I'm an immigrant, and I think there 303 00:15:00,400 --> 00:15:02,560 Speaker 1: are a ton of folks like me that come in, 304 00:15:02,680 --> 00:15:05,440 Speaker 1: create massive amounts of value and then become and then 305 00:15:05,480 --> 00:15:08,560 Speaker 1: become proud citizens. If we don't see it as an 306 00:15:08,560 --> 00:15:13,240 Speaker 1: immediate problem, but we do think that tech workers from 307 00:15:13,280 --> 00:15:15,640 Speaker 1: around the world have a lot to add to the 308 00:15:15,680 --> 00:15:20,240 Speaker 1: tech industry. We're watching and work, you know, working as 309 00:15:20,280 --> 00:15:22,880 Speaker 1: closely with the administration as we can to make sure 310 00:15:22,960 --> 00:15:25,480 Speaker 1: that this is more of a win win for everybody involved. 311 00:15:25,680 --> 00:15:29,720 Speaker 2: Trida Ramaswami, so thank you for that answer and talking 312 00:15:29,760 --> 00:15:33,400 Speaker 2: us through your announcement today on AI interrooperability. We appreciated 313 00:15:33,480 --> 00:15:36,880 Speaker 2: the CEO of Snowflake coming up, Michelle Geider of Produce 314 00:15:36,960 --> 00:15:39,800 Speaker 2: Crowd Institute. A tech diplomacy we're going to break down 315 00:15:40,000 --> 00:15:51,560 Speaker 2: instead of US China relations, this is blombag Tech. Welcome 316 00:15:51,560 --> 00:15:53,520 Speaker 2: back to Bloomberg Tech. I quick check on these markets, 317 00:15:53,520 --> 00:15:55,640 Speaker 2: which pretty sanguine at the moment, is we have a 318 00:15:55,640 --> 00:15:58,000 Speaker 2: big week ahead in terms of inflation data and FED speaking. 319 00:15:58,040 --> 00:16:00,360 Speaker 2: That's that one hundred just down about two tech of 320 00:16:00,480 --> 00:16:03,920 Speaker 2: percent having been on a record high streak across most 321 00:16:03,920 --> 00:16:07,120 Speaker 2: of the benchmarks. We pause for breath on equities more 322 00:16:07,160 --> 00:16:09,560 Speaker 2: broadly in video in the red, which cause tugs down 323 00:16:09,880 --> 00:16:12,040 Speaker 2: in large part the rest of the index. But we 324 00:16:12,080 --> 00:16:14,480 Speaker 2: want to set a global context here because the movement's 325 00:16:14,520 --> 00:16:17,160 Speaker 2: being made geopolitically and what's in the eye of the 326 00:16:17,200 --> 00:16:20,320 Speaker 2: investor right now. We've just had the United Nations General 327 00:16:20,360 --> 00:16:25,320 Speaker 2: Assembly speech by the United States President, really big claims 328 00:16:25,400 --> 00:16:27,920 Speaker 2: and blasting the United Nations more broadly, but all of 329 00:16:27,920 --> 00:16:30,960 Speaker 2: this in a context of geopolitical relationships perhaps the US 330 00:16:31,040 --> 00:16:33,640 Speaker 2: and China, for example, we have been understanding they're nearing 331 00:16:33,680 --> 00:16:36,160 Speaker 2: an agreement to hive off the US operations and social 332 00:16:36,200 --> 00:16:40,320 Speaker 2: media platform TikTok to a consortium that inclosed software giant Oracle. 333 00:16:40,720 --> 00:16:42,720 Speaker 2: We want to just give you the global context right now. 334 00:16:42,760 --> 00:16:45,800 Speaker 2: With Bloomberg Senior Tech editor Mike Sheppard standing by, you've 335 00:16:45,840 --> 00:16:49,000 Speaker 2: just been sat tuned in to President Trump where he 336 00:16:49,040 --> 00:16:53,160 Speaker 2: talked about whole wealth of issues that he sees right now. 337 00:16:53,400 --> 00:16:56,240 Speaker 2: Any of them catch your attention, and. 338 00:16:56,200 --> 00:16:59,680 Speaker 6: Certainly for our context, Cara on this program, what really 339 00:16:59,760 --> 00:17:01,680 Speaker 6: caught my year was what he was talking about in 340 00:17:01,800 --> 00:17:04,879 Speaker 6: terms of investment and what he's been trying to attract 341 00:17:04,960 --> 00:17:08,400 Speaker 6: to the US when you think about all the announcements 342 00:17:08,400 --> 00:17:11,640 Speaker 6: that we've seen related to artificial intelligence and more recently, 343 00:17:11,680 --> 00:17:13,760 Speaker 6: as you just alluded to, this push to try to 344 00:17:13,800 --> 00:17:16,680 Speaker 6: reach a deal with China on TikTok, the President was 345 00:17:16,720 --> 00:17:20,760 Speaker 6: talking about seventeen trillion dollars in announcement investment pledges so 346 00:17:20,840 --> 00:17:24,520 Speaker 6: far this year. That number bears some skepticism, but there 347 00:17:24,600 --> 00:17:26,119 Speaker 6: is some kernel of truth in it. 348 00:17:26,200 --> 00:17:28,359 Speaker 7: Of course, when you towed up the. 349 00:17:28,240 --> 00:17:31,200 Speaker 6: Pledges that we have seen from Nvidia, from open Ai, 350 00:17:31,359 --> 00:17:34,520 Speaker 6: from soft Bank and others to build plants here in 351 00:17:34,560 --> 00:17:37,240 Speaker 6: the US and TSMC, we can't leave them out either, 352 00:17:37,400 --> 00:17:40,520 Speaker 6: and all that fits into the picture that the President 353 00:17:40,560 --> 00:17:43,200 Speaker 6: is trying to portray of the US on the move. 354 00:17:43,320 --> 00:17:45,920 Speaker 6: During this address to the UN General Assembly, he talked 355 00:17:45,920 --> 00:17:49,199 Speaker 6: about this golden age of America, and he defended his 356 00:17:49,359 --> 00:17:52,560 Speaker 6: tariff program as well, and some of the trade agreements 357 00:17:52,560 --> 00:17:56,400 Speaker 6: that he's reached with the UK, the EU and other nations. 358 00:17:56,400 --> 00:17:58,840 Speaker 6: And he even talked about trying to meet with Brazil's 359 00:17:58,920 --> 00:18:01,280 Speaker 6: leader next work to try to sort out some of 360 00:18:01,320 --> 00:18:05,760 Speaker 6: the trade and other differences with that trading partner as well. 361 00:18:05,800 --> 00:18:08,560 Speaker 6: So there's a lot happening here on the international trade 362 00:18:08,600 --> 00:18:09,840 Speaker 6: and deal making front. 363 00:18:09,720 --> 00:18:11,800 Speaker 2: And it had a market reaction sending on the Brazilian 364 00:18:11,840 --> 00:18:15,439 Speaker 2: reality gaining in the session, Mike, What's interesting is he 365 00:18:15,480 --> 00:18:18,119 Speaker 2: did go on to chastise China when it comes to 366 00:18:18,680 --> 00:18:22,600 Speaker 2: the Russian relationship, that they hold anything that could put 367 00:18:22,840 --> 00:18:26,000 Speaker 2: at risk any sort of ultimate deal around TikTok. 368 00:18:27,280 --> 00:18:30,280 Speaker 6: Well, it's a great question, because for the President, the 369 00:18:30,320 --> 00:18:33,600 Speaker 6: TikTok deal is of paramount importance. He's talked about it 370 00:18:34,200 --> 00:18:36,120 Speaker 6: for more than a year. It was something that came 371 00:18:36,240 --> 00:18:39,280 Speaker 6: up on the campaign trail. It's something that he credits 372 00:18:39,480 --> 00:18:42,960 Speaker 6: with helping him in his comeback victory to the White House, 373 00:18:43,000 --> 00:18:47,000 Speaker 6: and yet it has been a very difficult victory for 374 00:18:47,080 --> 00:18:50,520 Speaker 6: him to bring home, in part because China is reluctant 375 00:18:50,560 --> 00:18:55,119 Speaker 6: to sell this precious asset from Beijing based Byte Dance Limited. 376 00:18:55,680 --> 00:18:59,520 Speaker 6: China is reluctant also to see Byte Dance let go 377 00:18:59,640 --> 00:19:02,639 Speaker 6: of an any way, shape or form the algorithm that 378 00:19:02,880 --> 00:19:06,280 Speaker 6: powers TikTok, and that has really been the secret sauce 379 00:19:06,320 --> 00:19:09,399 Speaker 6: to the app and its appeal to young users. Now, 380 00:19:09,560 --> 00:19:12,159 Speaker 6: the question in this deal that is taking shape is 381 00:19:12,400 --> 00:19:14,639 Speaker 6: perhaps there could be a way for a copy to 382 00:19:14,680 --> 00:19:17,480 Speaker 6: be handed to the new US venture that would be 383 00:19:17,600 --> 00:19:21,000 Speaker 6: led by American investors and have six of seven board 384 00:19:21,040 --> 00:19:25,520 Speaker 6: seats occupy by Americans. Now, would China go along with that? 385 00:19:26,040 --> 00:19:28,399 Speaker 6: Oracle would have a role with the new venture in 386 00:19:28,560 --> 00:19:32,280 Speaker 6: retraining this new model, But will China go along? We 387 00:19:32,359 --> 00:19:36,320 Speaker 6: have not heard enough from China yet about whether they 388 00:19:36,359 --> 00:19:39,000 Speaker 6: will approve this deal in full. On Friday, we heard 389 00:19:39,000 --> 00:19:42,200 Speaker 6: Donald Trump talk about how Shijinping has counterpart hours earlier 390 00:19:42,240 --> 00:19:45,680 Speaker 6: in a conversation they had had on Friday, really blessing 391 00:19:45,720 --> 00:19:48,800 Speaker 6: the deal. But China's Foreign Ministry was far more circumspect 392 00:19:49,160 --> 00:19:51,840 Speaker 6: in its statement. So we'll have to see where this 393 00:19:51,960 --> 00:19:55,119 Speaker 6: goes and whether some of that leverage on Russian oil 394 00:19:55,160 --> 00:19:57,840 Speaker 6: purchases may factor into the conversation. 395 00:19:57,960 --> 00:20:02,120 Speaker 2: Here, ybugs, Mike sheffind Up, thank you very much. Indeed, 396 00:20:02,600 --> 00:20:05,040 Speaker 2: now let's discuss this more broadly with Michelle Geider. She 397 00:20:05,160 --> 00:20:07,240 Speaker 2: is the CEO of the Crack Institute for Tech Diplomacy 398 00:20:07,240 --> 00:20:09,880 Speaker 2: at Purdue used to serve as Assistant Secretary of State 399 00:20:09,920 --> 00:20:12,600 Speaker 2: for Global Public Affairs under the first Trump administration. Wonderful 400 00:20:12,640 --> 00:20:15,919 Speaker 2: to have you here in New York. Michelle. Taking a 401 00:20:15,960 --> 00:20:20,720 Speaker 2: step back, the fact that TikTok is becoming so central 402 00:20:20,800 --> 00:20:23,840 Speaker 2: to the future discussions between the two leaders of the 403 00:20:23,920 --> 00:20:26,680 Speaker 2: United States and China. Does that surprise him at all? 404 00:20:27,160 --> 00:20:30,480 Speaker 8: You know, it's a really interesting reflection of the importance 405 00:20:30,520 --> 00:20:33,600 Speaker 8: of technology today when it comes to national security, when 406 00:20:33,600 --> 00:20:36,080 Speaker 8: it comes to economic prosperity. But when you talk about 407 00:20:36,080 --> 00:20:39,040 Speaker 8: taking a step back, whether it's TikTok, whether it's export 408 00:20:39,080 --> 00:20:43,560 Speaker 8: controls on semiconductor chips, whether it's tariffs, bans on TikTok 409 00:20:43,600 --> 00:20:46,960 Speaker 8: bands on Huawei, all of these things are really important 410 00:20:47,240 --> 00:20:51,240 Speaker 8: defensive tactical tools the US government can use to ensure 411 00:20:51,280 --> 00:20:55,440 Speaker 8: that we're trying to keep our competitive edge. But when 412 00:20:55,480 --> 00:20:58,440 Speaker 8: you think about what's really happening with the US China relationship, 413 00:20:58,480 --> 00:21:01,240 Speaker 8: the fundamentals are still the same right now as they 414 00:21:01,240 --> 00:21:04,760 Speaker 8: have been for years. And we're in this sort of grinding, 415 00:21:04,920 --> 00:21:08,080 Speaker 8: incremental struggle on TikTok, on terrafts, on this and on that. 416 00:21:08,560 --> 00:21:12,119 Speaker 8: And the only way that America actually breaks away is 417 00:21:12,160 --> 00:21:14,119 Speaker 8: if we build an event like crazy. We have to 418 00:21:14,160 --> 00:21:16,480 Speaker 8: go on offense. In addition to the defensive measures that 419 00:21:16,480 --> 00:21:18,800 Speaker 8: are happening. We have to build chips, we have to 420 00:21:18,840 --> 00:21:21,600 Speaker 8: build ships, we have to build data centers, we have 421 00:21:21,640 --> 00:21:23,959 Speaker 8: to build rare earth magnets. We have to design new 422 00:21:24,000 --> 00:21:26,439 Speaker 8: frontier models. We have to do all of these things 423 00:21:26,480 --> 00:21:29,480 Speaker 8: and by the way, export them around the globe to 424 00:21:29,560 --> 00:21:31,439 Speaker 8: all of our allies and partners. And so going on 425 00:21:31,520 --> 00:21:34,320 Speaker 8: offense is what's going to change the fundamental nature of 426 00:21:34,320 --> 00:21:37,320 Speaker 8: this relationship, and that ultimately comes down to the private sector. 427 00:21:37,320 --> 00:21:40,600 Speaker 8: The government can do all of these really important defensive tactics, 428 00:21:40,640 --> 00:21:42,639 Speaker 8: but when it comes to breaking away and unleashing the 429 00:21:42,680 --> 00:21:44,800 Speaker 8: full force of United States, that's a private sector. 430 00:21:44,920 --> 00:21:46,720 Speaker 2: It feels like the private sector. Call the memo and 431 00:21:46,840 --> 00:21:49,440 Speaker 2: Vidia one hundred billion dollars going to open AI. We've 432 00:21:49,440 --> 00:21:52,240 Speaker 2: got just five billion going to Intel. It seems tiny 433 00:21:52,280 --> 00:21:55,640 Speaker 2: in comparison. Was an important move and signal for Intel. 434 00:21:56,080 --> 00:21:58,680 Speaker 2: But should there be more dumb by the government because 435 00:21:58,720 --> 00:22:01,280 Speaker 2: there's actually been a pulling away from the chipsacked from 436 00:22:01,359 --> 00:22:04,600 Speaker 2: support from a financial perspective instead that taking equity stakes 437 00:22:04,600 --> 00:22:05,640 Speaker 2: in Intel for example. 438 00:22:05,760 --> 00:22:08,680 Speaker 8: Well, I think the Trump administration has really motivated the 439 00:22:08,720 --> 00:22:11,040 Speaker 8: private sector in this direction. If you think about the 440 00:22:11,080 --> 00:22:14,080 Speaker 8: AI Action Plan, which is about unleashing our AI, exporting 441 00:22:14,320 --> 00:22:17,280 Speaker 8: American AI around the globe. If you think about unleashing 442 00:22:17,320 --> 00:22:20,640 Speaker 8: American energy and the national Energy Dominance Council. Right, All 443 00:22:20,680 --> 00:22:22,879 Speaker 8: of these things are to enable the private sector to 444 00:22:22,920 --> 00:22:24,760 Speaker 8: go run and to do their thing, and so I 445 00:22:24,760 --> 00:22:26,880 Speaker 8: think we're headed in the right direction. But to your point, 446 00:22:26,920 --> 00:22:30,679 Speaker 8: that's why these deals like the US UK Prosperity Partnership 447 00:22:30,760 --> 00:22:33,639 Speaker 8: on technology are really interesting. All of the deals on 448 00:22:33,720 --> 00:22:36,400 Speaker 8: semiconductor and AI and energy coming out of the Middle 449 00:22:36,440 --> 00:22:38,800 Speaker 8: East trip that President Trump took a few months ago, 450 00:22:38,840 --> 00:22:42,000 Speaker 8: that's really interesting. You mentioned Nvidia and all of these investments. 451 00:22:42,200 --> 00:22:44,760 Speaker 8: There's also this five hundred and fifty billion dollar fund 452 00:22:45,200 --> 00:22:47,520 Speaker 8: that the Japanese government has built part of our trade 453 00:22:47,560 --> 00:22:50,000 Speaker 8: agreement with them, that now the administration is taking a 454 00:22:50,040 --> 00:22:52,360 Speaker 8: look at to say can we use that to spur manufacturing. 455 00:22:52,440 --> 00:22:54,560 Speaker 8: Those are really interesting because that's what's going to give 456 00:22:54,640 --> 00:22:56,439 Speaker 8: us not only allow us to go on offense, but 457 00:22:56,480 --> 00:22:59,280 Speaker 8: really be a full force multiplier and working with our allies. 458 00:23:00,240 --> 00:23:04,159 Speaker 2: Seems to be a more difficult square to circle is 459 00:23:04,160 --> 00:23:07,200 Speaker 2: whether or not Nvidio or other powerhouses in the United 460 00:23:07,240 --> 00:23:10,040 Speaker 2: States should be able to export their technology into China 461 00:23:10,080 --> 00:23:12,240 Speaker 2: to be able to ensure that they win the tech 462 00:23:12,280 --> 00:23:14,359 Speaker 2: stack race to a cain extent. David Sachs has been 463 00:23:14,359 --> 00:23:17,879 Speaker 2: outspoken about this. How are you seeing that very sensitive 464 00:23:17,880 --> 00:23:21,320 Speaker 2: discussion percolate through the government and the administration. 465 00:23:21,520 --> 00:23:24,880 Speaker 8: Well, that's a really important point because there's an underlying 466 00:23:24,920 --> 00:23:28,720 Speaker 8: assumption on both sides of the aisle about or two 467 00:23:28,760 --> 00:23:31,720 Speaker 8: camps really about whether or not China actually wants American 468 00:23:31,720 --> 00:23:33,439 Speaker 8: tech in the long run. Yes, and you see some 469 00:23:33,480 --> 00:23:36,240 Speaker 8: project if you just look at chips, for example, they 470 00:23:36,240 --> 00:23:38,679 Speaker 8: are on a mission to be self sufficient when it 471 00:23:38,680 --> 00:23:42,520 Speaker 8: comes to semiconductor technology. There's some estimates now there'll be 472 00:23:42,640 --> 00:23:45,960 Speaker 8: eighty percent self sufficient in domestic chips within two to 473 00:23:46,000 --> 00:23:48,480 Speaker 8: three years, and that's up from one third last year. 474 00:23:48,680 --> 00:23:52,160 Speaker 8: And so they want Chinese chips, they want the Chinese 475 00:23:52,200 --> 00:23:54,840 Speaker 8: tech stack. And so really it's a question of how 476 00:23:54,840 --> 00:23:56,879 Speaker 8: are we going to go on offense and get global 477 00:23:56,920 --> 00:23:59,240 Speaker 8: market share with all of our friendly allies who share 478 00:23:59,240 --> 00:24:02,200 Speaker 8: the same values and make China less of an issue 479 00:24:02,240 --> 00:24:04,560 Speaker 8: when it comes to market share. 480 00:24:04,680 --> 00:24:07,240 Speaker 2: So you were on the camp, yes, restrained from sending 481 00:24:07,280 --> 00:24:09,359 Speaker 2: into China, but just win everywhere else. En sure that 482 00:24:09,400 --> 00:24:13,480 Speaker 2: we're supplying to Africa are one hundred percent? That makes sense. 483 00:24:13,520 --> 00:24:16,760 Speaker 2: What's interesting more broadly about the China US context is 484 00:24:16,760 --> 00:24:19,320 Speaker 2: we do keep feeling as though there is this ultimate 485 00:24:19,440 --> 00:24:23,600 Speaker 2: race is there. What can you remind the audience what's 486 00:24:23,640 --> 00:24:27,560 Speaker 2: at stake if America quote unquote doesn't win it. Yeah. 487 00:24:27,600 --> 00:24:29,480 Speaker 8: Well, you know, it's interesting. We're talking right now on 488 00:24:29,480 --> 00:24:31,639 Speaker 8: the sidelines of the UN General Assembly and it's been 489 00:24:31,680 --> 00:24:34,320 Speaker 8: eighty years. And if you look at the past eighty 490 00:24:34,400 --> 00:24:36,320 Speaker 8: years and the backbone of the free world, it's been 491 00:24:36,400 --> 00:24:39,120 Speaker 8: institutions that the United States has built and led. Right, 492 00:24:39,160 --> 00:24:42,199 Speaker 8: whether it's United States itself or the UN or the 493 00:24:42,240 --> 00:24:45,959 Speaker 8: G seven or the OECD, all of these important institutions 494 00:24:45,960 --> 00:24:49,320 Speaker 8: have helped in the twentieth century, they're not as relevant 495 00:24:49,320 --> 00:24:51,800 Speaker 8: heading into the twenty first century. The new backbone of 496 00:24:51,800 --> 00:24:54,159 Speaker 8: the free world is going to be technology, and it's 497 00:24:54,200 --> 00:24:55,760 Speaker 8: going to be all the businesses we build off the 498 00:24:55,760 --> 00:24:58,800 Speaker 8: back of the technology, all the values that the technology propagates. 499 00:24:59,040 --> 00:25:02,119 Speaker 8: And so we we have to re earn our leadership 500 00:25:02,160 --> 00:25:05,080 Speaker 8: in the twenty first century by inventing a new framework 501 00:25:05,320 --> 00:25:07,600 Speaker 8: for freedom and prosperity. And the backbone of that is 502 00:25:07,640 --> 00:25:10,120 Speaker 8: going to be technology. And so the really good news 503 00:25:10,200 --> 00:25:11,719 Speaker 8: is America is good at this stuff. This is our 504 00:25:11,760 --> 00:25:15,000 Speaker 8: superpower innovation and enterprise. And so if we lean into that, 505 00:25:15,160 --> 00:25:16,240 Speaker 8: we can lead the twenty First. 506 00:25:16,160 --> 00:25:19,240 Speaker 2: Are we good at governance of it? Yes? I think we. 507 00:25:19,320 --> 00:25:21,880 Speaker 8: I mean, look at what we did in the twentieth century. 508 00:25:22,359 --> 00:25:24,360 Speaker 8: But it's not going to be the status quo. We're 509 00:25:24,359 --> 00:25:26,680 Speaker 8: going to have to think and operate in really different ways. 510 00:25:26,680 --> 00:25:28,840 Speaker 8: That's what tech diplomacy is all about, right, It's about 511 00:25:28,840 --> 00:25:32,080 Speaker 8: bringing tech companies in the private sector together with government 512 00:25:32,160 --> 00:25:35,040 Speaker 8: officials to have a conversation around how do you unleash 513 00:25:35,240 --> 00:25:38,600 Speaker 8: innovation and also make sure that it accelerates our values 514 00:25:38,640 --> 00:25:41,360 Speaker 8: of freedom and prosperity and human rights and data privacy 515 00:25:41,359 --> 00:25:43,840 Speaker 8: and things like that. That conversation needs to happen where 516 00:25:43,840 --> 00:25:44,879 Speaker 8: it traditionally doesn't. 517 00:25:45,320 --> 00:25:47,040 Speaker 2: One you're probably having here right here in New York, 518 00:25:47,080 --> 00:25:49,320 Speaker 2: Michelle Geide making time for us. We so appreciate it. 519 00:25:49,359 --> 00:25:53,800 Speaker 2: Perduce cruct Institute for Tech Diplomacy. Now we're going to 520 00:25:53,840 --> 00:25:56,439 Speaker 2: have an update now. In the world of entertainment, Jimmy 521 00:25:56,480 --> 00:25:59,480 Speaker 2: Kimmel Live, it's set to return to the airwaves tonight, 522 00:26:00,080 --> 00:26:02,680 Speaker 2: a suspension imposed by Disney following remarks made by the 523 00:26:02,680 --> 00:26:05,920 Speaker 2: host about the assassination of Charlie Kirk. Meanwhile, Next Star, 524 00:26:06,119 --> 00:26:09,920 Speaker 2: alongside Sinclair Broadcast Group, announced it will continue to pre 525 00:26:09,960 --> 00:26:13,000 Speaker 2: empt the airing of the late night program across its stations. 526 00:26:13,119 --> 00:26:15,960 Speaker 2: According to sources, Kimmel is set to address the controversy 527 00:26:16,200 --> 00:26:25,880 Speaker 2: during his show. Defense tech startup or Tarryon has raised 528 00:26:25,920 --> 00:26:27,760 Speaker 2: one hundred and thirty million dollars in new funding led 529 00:26:27,760 --> 00:26:30,880 Speaker 2: by Bessemer Benure Partners. The drone software maker says it's 530 00:26:30,920 --> 00:26:34,880 Speaker 2: platform tas individual drones into coordinated combat forces and it's 531 00:26:34,880 --> 00:26:39,480 Speaker 2: already deploying AI strike kits to Ukraine. Speak with Autarion 532 00:26:39,680 --> 00:26:42,600 Speaker 2: CEO Lorenzmeyer, Lorenzi is great to have you on. You're 533 00:26:42,640 --> 00:26:44,880 Speaker 2: not building the hardware, You're all in on the software. 534 00:26:45,000 --> 00:26:47,840 Speaker 2: Why is that the right strategy? 535 00:26:48,800 --> 00:26:49,639 Speaker 7: Thank you for having me. 536 00:26:49,960 --> 00:26:55,239 Speaker 9: Yes, it is a very unique strategy, surprisingly given their 537 00:26:55,440 --> 00:26:59,400 Speaker 9: software is such a force in every other industry. 538 00:27:00,359 --> 00:27:03,479 Speaker 7: But what we're building is the common operating system. 539 00:27:03,520 --> 00:27:06,560 Speaker 9: You can think of it as the Windows or Android 540 00:27:06,600 --> 00:27:09,639 Speaker 9: of drones, along with the app store for it. And 541 00:27:09,760 --> 00:27:13,200 Speaker 9: right now, the most urgent need for our technology is 542 00:27:13,560 --> 00:27:16,920 Speaker 9: on the battlefield in Ukraine. We're delivering tens of thousands 543 00:27:16,960 --> 00:27:20,119 Speaker 9: of strike kits, which is software pre installed on a 544 00:27:20,160 --> 00:27:26,520 Speaker 9: small computer to improve the combat effectiveness of Ukrainian forces, 545 00:27:27,480 --> 00:27:33,640 Speaker 9: and we are already supplying US forces native forces. So 546 00:27:34,000 --> 00:27:37,760 Speaker 9: the learnings that we're making on the battlefield on Ukraine 547 00:27:37,960 --> 00:27:41,400 Speaker 9: are coming back quickly to all of our allies and partners. 548 00:27:41,600 --> 00:27:44,080 Speaker 2: And talk about your partners, because many would say, okay, 549 00:27:44,080 --> 00:27:46,400 Speaker 2: I can see here in the US perhaps the hardware 550 00:27:46,440 --> 00:27:51,000 Speaker 2: competition of ANDRAIL along with its full vertical integration, but 551 00:27:51,080 --> 00:27:53,480 Speaker 2: actually working with those sorts of companies too. 552 00:27:55,400 --> 00:27:58,800 Speaker 9: So we are happy to work with everybody, so we're 553 00:27:58,840 --> 00:28:02,959 Speaker 9: not discriminative on that. But our focus is the warfighter. 554 00:28:03,880 --> 00:28:08,439 Speaker 9: Our mission is to give warfighters the unified swarm of 555 00:28:08,640 --> 00:28:14,240 Speaker 9: different drones, different sizes, different purposes, and to allow them 556 00:28:14,280 --> 00:28:16,840 Speaker 9: to choose the right tool for the mission. And that's 557 00:28:16,920 --> 00:28:19,640 Speaker 9: only possible if you run them all with one operating 558 00:28:19,680 --> 00:28:23,040 Speaker 9: system which enables them to speak a single language. And 559 00:28:23,080 --> 00:28:26,199 Speaker 9: a single language is necessary for them to move and 560 00:28:26,240 --> 00:28:30,840 Speaker 9: swarm together and fight together. And in the future, what 561 00:28:30,880 --> 00:28:36,640 Speaker 9: we're expecting more and more are robot wars where drones 562 00:28:36,720 --> 00:28:40,920 Speaker 9: are fighting not just today tanks, but actually other drones. 563 00:28:41,000 --> 00:28:44,400 Speaker 9: And so it is important, it is imperative that we 564 00:28:44,600 --> 00:28:49,480 Speaker 9: achieve supremacy in that autonomy field to defend our freedoms. 565 00:28:49,800 --> 00:28:52,400 Speaker 2: And so when you say we are selling to the 566 00:28:52,440 --> 00:28:54,640 Speaker 2: Western world and allies. At the moment, I understand you 567 00:28:54,800 --> 00:28:57,560 Speaker 2: just want a contract with Taiwan. You've also been winning 568 00:28:57,600 --> 00:29:00,120 Speaker 2: contracts in Europe and the United States. Where is the 569 00:29:00,200 --> 00:29:02,400 Speaker 2: largest amount of demand coming from Lorentz for you? 570 00:29:04,240 --> 00:29:08,560 Speaker 9: I would say medium term definitely from the Department of War, 571 00:29:08,920 --> 00:29:12,360 Speaker 9: and we're super excited about the fact that we've been 572 00:29:13,200 --> 00:29:14,480 Speaker 9: supplying back the n. 573 00:29:14,520 --> 00:29:16,320 Speaker 7: DUD for the past eight years. 574 00:29:17,200 --> 00:29:21,400 Speaker 9: But short term definitely on the battlefield in Ukraine because 575 00:29:22,040 --> 00:29:26,120 Speaker 9: with the additional aion board, these drones are a lot 576 00:29:26,160 --> 00:29:29,480 Speaker 9: easier to operate and more precise, and so we're helping 577 00:29:29,560 --> 00:29:33,200 Speaker 9: Ukrainian units to be a lot more effective in combat 578 00:29:33,240 --> 00:29:34,479 Speaker 9: with the same amount of drones. 579 00:29:34,720 --> 00:29:37,239 Speaker 2: This money one hundred and thirty million dollars. What does 580 00:29:37,280 --> 00:29:40,240 Speaker 2: that help you do, then, Lorenz? Is it about being 581 00:29:40,240 --> 00:29:44,280 Speaker 2: able to negotiate navigate big government entities. Is it more 582 00:29:44,360 --> 00:29:48,480 Speaker 2: about scaling and bringing in demand, making acquisitions. 583 00:29:50,280 --> 00:29:53,400 Speaker 7: So we're already cashful positive. 584 00:29:53,520 --> 00:29:57,080 Speaker 9: So this is all about non organic growth in terms 585 00:29:57,120 --> 00:30:02,360 Speaker 9: of growing our international footprint the Nordics. We have entered 586 00:30:02,440 --> 00:30:06,600 Speaker 9: the British market, we are in Asia. You've mentioned the 587 00:30:06,680 --> 00:30:10,880 Speaker 9: partnership with Taiwan, and we're expanding our product line. So 588 00:30:11,240 --> 00:30:14,360 Speaker 9: a year ago we did the operating system for individual 589 00:30:14,480 --> 00:30:18,880 Speaker 9: drones with terminal guidance. Recently we announced as the first 590 00:30:19,040 --> 00:30:23,560 Speaker 9: software company in our space swarming at a very large 591 00:30:23,640 --> 00:30:28,400 Speaker 9: number of drones. And so we are expanding our product 592 00:30:28,400 --> 00:30:33,160 Speaker 9: portfolio continuously. And because we are software centric, that is 593 00:30:33,480 --> 00:30:37,880 Speaker 9: as simple as a software update, as an app install. 594 00:30:38,120 --> 00:30:41,040 Speaker 2: And then the money. You've course got better men leading 595 00:30:41,040 --> 00:30:43,680 Speaker 2: these serious b But where has the interest been coming 596 00:30:43,880 --> 00:30:47,080 Speaker 2: from a venture capital perspective? You're based out in Europe, 597 00:30:47,120 --> 00:30:49,520 Speaker 2: you were born in Switzerland, But has it been US 598 00:30:49,520 --> 00:30:51,160 Speaker 2: investors knocking on your door? Where do you think the 599 00:30:51,200 --> 00:30:52,320 Speaker 2: pools of capital are for you? 600 00:30:54,520 --> 00:30:57,680 Speaker 9: Well, we actually planned to fundraise later in the years, 601 00:30:57,680 --> 00:31:00,120 Speaker 9: so best we're preempted the around, which shows how how 602 00:31:00,520 --> 00:31:06,360 Speaker 9: excited they are about what we're building. The technology has 603 00:31:06,440 --> 00:31:09,040 Speaker 9: a lot of i would say European heritage. We have 604 00:31:09,120 --> 00:31:15,080 Speaker 9: fantastic developers. But where we're pushing forward is the American market, 605 00:31:15,240 --> 00:31:16,880 Speaker 9: which is the largest market. 606 00:31:17,880 --> 00:31:21,720 Speaker 2: Arensmeyer, founder and CEO of Autarion. Keep coming back as 607 00:31:21,760 --> 00:31:24,120 Speaker 2: you build out here in the United States too. Look 608 00:31:24,280 --> 00:31:27,080 Speaker 2: now that's pivot and take a look at fireflies first 609 00:31:27,160 --> 00:31:29,640 Speaker 2: earning results as a public company. Of course, this is 610 00:31:29,680 --> 00:31:31,680 Speaker 2: a space company in many ways. Some would see this 611 00:31:31,760 --> 00:31:35,080 Speaker 2: defense falling short of expectations. The rocket makers revenue miss 612 00:31:35,160 --> 00:31:38,520 Speaker 2: Tall Street expectations, currently off by twelve percent in the 613 00:31:38,560 --> 00:31:42,440 Speaker 2: market after we digest those numbers. Interestingly similar thing happened 614 00:31:42,440 --> 00:31:44,680 Speaker 2: to Figma, of course, after its first earnings as a 615 00:31:44,680 --> 00:31:47,920 Speaker 2: public company. Now coming up, we're going to be taking 616 00:31:47,920 --> 00:31:51,080 Speaker 2: a look at kind Body, once one of the fastest 617 00:31:51,080 --> 00:31:54,200 Speaker 2: growing fertility chains in the United States, and it's spiraling 618 00:31:54,280 --> 00:31:57,240 Speaker 2: into crisis. It's the latest Bloomberg Big Tape podcast. That's next. 619 00:31:57,320 --> 00:32:08,600 Speaker 2: This is Bloomberg Tech US fertility chain kind Body. It 620 00:32:08,680 --> 00:32:12,000 Speaker 2: was one's booming. The venture backed company cultivated a modern 621 00:32:12,120 --> 00:32:15,280 Speaker 2: millennial friendly esthetic that did help but attract one of 622 00:32:15,360 --> 00:32:19,000 Speaker 2: three hundred million dollars in investments, including backing some celebrity 623 00:32:19,000 --> 00:32:23,000 Speaker 2: investors like Gwyneth Paltrow Chelsea Clinton. But kind Body has 624 00:32:23,040 --> 00:32:27,280 Speaker 2: been riddled with clinical problems with embryos being mislabeled, lost, 625 00:32:27,520 --> 00:32:31,720 Speaker 2: accidentally destroyed, and has spiraled into crisis as more people's 626 00:32:31,720 --> 00:32:34,680 Speaker 2: seek fertility treatment than ever. The startup story illustrates really 627 00:32:34,680 --> 00:32:38,040 Speaker 2: the dangers are moving fast in a largely unregulated industry. 628 00:32:38,400 --> 00:32:41,960 Speaker 2: Bloomberg's Jackie Devolos took a deep dive into this for 629 00:32:42,000 --> 00:32:46,200 Speaker 2: Bloomberg's latest podcast, IVF Disrupted. It's such an important deep 630 00:32:46,240 --> 00:32:50,400 Speaker 2: investigation you've done, one that just highlights turmoil and real 631 00:32:50,440 --> 00:32:53,080 Speaker 2: pain points for individuals. Can you just tell us a 632 00:32:53,080 --> 00:32:55,760 Speaker 2: little bit about how you first understood that this was 633 00:32:55,800 --> 00:32:56,760 Speaker 2: a spiral occurring? 634 00:32:57,480 --> 00:32:59,880 Speaker 10: Absolutely well, of course, I'm a venture capital and stre 635 00:33:00,160 --> 00:33:03,280 Speaker 10: ups reporter, and like with any story I look into, 636 00:33:03,520 --> 00:33:07,360 Speaker 10: I was trying to identify whether these one off occurrences 637 00:33:07,480 --> 00:33:10,320 Speaker 10: I was hearing about from employees and patients was a 638 00:33:10,360 --> 00:33:14,080 Speaker 10: pattern of mistakes or if it really was just that 639 00:33:14,240 --> 00:33:17,960 Speaker 10: a one off. And two years of investigating, I was 640 00:33:18,000 --> 00:33:22,000 Speaker 10: able to uncover multiple incidents across several kind body clinics 641 00:33:22,280 --> 00:33:25,200 Speaker 10: that really came down to the high turnover that led 642 00:33:25,240 --> 00:33:29,920 Speaker 10: to understaffing, as well as inconsistently applied protocols in the 643 00:33:29,920 --> 00:33:33,000 Speaker 10: embryology lab. This is the place where embryos are made 644 00:33:33,040 --> 00:33:36,920 Speaker 10: and have to be carefully handled, and because of how 645 00:33:37,000 --> 00:33:40,400 Speaker 10: quickly the company was growing, a lot of those protocols 646 00:33:40,440 --> 00:33:43,360 Speaker 10: were going by the wayside in some cases. Now the 647 00:33:43,400 --> 00:33:46,440 Speaker 10: company does dispute at least some of these incidents, but 648 00:33:46,520 --> 00:33:49,720 Speaker 10: it did acknowledge that accidents did occur, but it also 649 00:33:49,800 --> 00:33:52,080 Speaker 10: said that these issues are not unique to kind Body, 650 00:33:52,480 --> 00:33:54,920 Speaker 10: but illustrative of the broader industry as well. 651 00:33:55,200 --> 00:33:57,120 Speaker 2: Okay, so that's a worry for people who are out 652 00:33:57,120 --> 00:33:59,720 Speaker 2: there seeking fertility help, that the fact that this is 653 00:33:59,720 --> 00:34:03,440 Speaker 2: an industry wide issue. Are there instances where femtech in 654 00:34:03,480 --> 00:34:06,560 Speaker 2: particular has done good things, managed to keep the balance 655 00:34:06,640 --> 00:34:09,400 Speaker 2: of growth versus security and safety, and where has it 656 00:34:09,480 --> 00:34:10,480 Speaker 2: dropped by the wayside. 657 00:34:10,520 --> 00:34:12,160 Speaker 10: Well, this is one of the areas in which I 658 00:34:12,160 --> 00:34:14,520 Speaker 10: found Kind Body was unique. It was one of the 659 00:34:14,560 --> 00:34:18,040 Speaker 10: only venture backed and private equity backed startups that was 660 00:34:18,160 --> 00:34:21,280 Speaker 10: building brick in mortar clinics. This is a really difficult 661 00:34:21,320 --> 00:34:23,719 Speaker 10: thing to do because, like I said, the embryology lab 662 00:34:23,840 --> 00:34:27,839 Speaker 10: has to be very carefully put together. It's very expensive 663 00:34:27,880 --> 00:34:29,800 Speaker 10: to put together. And they were going out and putting 664 00:34:29,800 --> 00:34:33,080 Speaker 10: these clinics in areas where there's a lot of foot traffic, 665 00:34:33,200 --> 00:34:35,560 Speaker 10: and that can potentially open it up to other issues 666 00:34:35,920 --> 00:34:38,600 Speaker 10: like flooding or other things that come along with being 667 00:34:38,600 --> 00:34:41,480 Speaker 10: next to a Berrie's boot camp, for example. You do 668 00:34:41,560 --> 00:34:45,520 Speaker 10: have other femtech providers in the fertility space, like a maven, 669 00:34:45,640 --> 00:34:48,480 Speaker 10: but they don't open up brick and mortar. Clinics and 670 00:34:49,000 --> 00:34:51,040 Speaker 10: experts have told me that that's for a reason. So 671 00:34:51,120 --> 00:34:54,480 Speaker 10: of course investors got very excited about the prospect of 672 00:34:54,680 --> 00:34:57,480 Speaker 10: a new company disrupting the way fertility had been done. 673 00:34:57,560 --> 00:34:59,719 Speaker 10: But of course, as I found, there were real human 674 00:34:59,760 --> 00:35:01,200 Speaker 10: costs and consequences to that. 675 00:35:01,960 --> 00:35:06,799 Speaker 2: This is an enormous industry and what's the takeaway here 676 00:35:06,920 --> 00:35:09,680 Speaker 2: ultimately is that it shouldn't be venture backed or what 677 00:35:09,920 --> 00:35:12,279 Speaker 2: things that can be learned from this that means a 678 00:35:12,320 --> 00:35:14,440 Speaker 2: company is able to scale, but at the pace that 679 00:35:14,480 --> 00:35:16,760 Speaker 2: it needs you to have security for those that it's serving. 680 00:35:17,239 --> 00:35:20,400 Speaker 10: I think this reporting can really be helpful for it 681 00:35:20,640 --> 00:35:24,600 Speaker 10: not just patients, but also investors looking at these companies, 682 00:35:24,880 --> 00:35:27,799 Speaker 10: for families of people going through some of these procedures. 683 00:35:27,840 --> 00:35:30,560 Speaker 10: You get to hear in this five part podcast from 684 00:35:30,600 --> 00:35:35,920 Speaker 10: patients themselves who went through these treatments, which are incredibly 685 00:35:35,960 --> 00:35:39,480 Speaker 10: taxing on the human body. There's a lot of what 686 00:35:39,560 --> 00:35:43,480 Speaker 10: they would describe kind of dismissing of symptoms and doubts, 687 00:35:43,920 --> 00:35:46,920 Speaker 10: and you get to hear more about where some of 688 00:35:46,960 --> 00:35:50,600 Speaker 10: those pressures inside the company were coming from. A worry 689 00:35:50,640 --> 00:35:53,520 Speaker 10: about the bottom line, and so while there is no 690 00:35:53,640 --> 00:35:56,399 Speaker 10: prospect for regulation for the industry, at least people who 691 00:35:56,480 --> 00:36:00,239 Speaker 10: listen can come out better equipped with more information about 692 00:36:00,239 --> 00:36:02,720 Speaker 10: what they should know when they're walking through in IVF 693 00:36:02,760 --> 00:36:03,839 Speaker 10: clinic stories. 694 00:36:04,120 --> 00:36:06,799 Speaker 2: Listen to the podcast, read your print stories on it. 695 00:36:06,800 --> 00:36:09,680 Speaker 2: It has been a two year thorough investigation and phenomenal 696 00:36:09,680 --> 00:36:12,920 Speaker 2: work from Jackie Devodos. We so thank her. Meanwhile, you 697 00:36:12,960 --> 00:36:15,839 Speaker 2: can see, as I say, the IVF Disrupted series from 698 00:36:15,840 --> 00:36:18,720 Speaker 2: the Big Takes podcast. Get it on the Terminal, online 699 00:36:18,760 --> 00:36:21,160 Speaker 2: on Apple, Spotify, and iHeart you can get ours there too, 700 00:36:21,760 --> 00:36:24,399 Speaker 2: But that does it for this edition. A Boomberg Tech