1 00:00:00,040 --> 00:00:06,760 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. 2 00:00:11,960 --> 00:00:15,560 Speaker 2: This is the Bloomberg Surveillance Podcast. I'm Tom Keene along 3 00:00:15,600 --> 00:00:18,960 Speaker 2: with Paul Sweeney. Join us each day for insight from 4 00:00:18,960 --> 00:00:23,160 Speaker 2: the best in economics, finance, investment, and international relations. You 5 00:00:23,160 --> 00:00:26,520 Speaker 2: can also watch the show live on YouTube. Visit the 6 00:00:26,520 --> 00:00:31,280 Speaker 2: Bloomberg Podcast channel on YouTube to see the show weekday 7 00:00:31,280 --> 00:00:34,320 Speaker 2: mornings from seven to ten am Eastern from our global 8 00:00:34,360 --> 00:00:39,000 Speaker 2: headquarters in New York City. Subscribe to the podcast on Apple, Spotify, 9 00:00:39,360 --> 00:00:42,920 Speaker 2: or anywhere else you listen, and always I'm Bloomberg Radio, 10 00:00:43,080 --> 00:00:47,199 Speaker 2: the Bloomberg Terminal, and the Bloomberg Business app. Joining us 11 00:00:47,280 --> 00:00:50,240 Speaker 2: right now, she's with Airbus Adventures. She's not with the Airbus, 12 00:00:50,520 --> 00:00:54,200 Speaker 2: although she likes the Beluga, but she's with Airbus Ventures. 13 00:00:54,520 --> 00:00:57,840 Speaker 2: No Cole Connor joins us here on investment in all 14 00:00:57,880 --> 00:01:01,800 Speaker 2: of this, is there too much money chasing too few 15 00:01:01,920 --> 00:01:04,000 Speaker 2: ideas or the other way around? 16 00:01:04,400 --> 00:01:07,520 Speaker 3: It thinks the other way around. There's so many opportunities 17 00:01:07,600 --> 00:01:09,759 Speaker 3: right now, and I think this is a really special 18 00:01:09,880 --> 00:01:13,160 Speaker 3: time for venture capital and for these startups who are 19 00:01:13,200 --> 00:01:17,040 Speaker 3: working on taking these important emerging technologies out of the 20 00:01:17,120 --> 00:01:20,080 Speaker 3: lab and into the real world. At our fund, we're 21 00:01:20,120 --> 00:01:24,920 Speaker 3: investing in everything from autonomy to electrification, the low carbon economy, 22 00:01:26,200 --> 00:01:31,240 Speaker 3: news space, advanced materials, quantum computing, networking and sensing. And 23 00:01:31,319 --> 00:01:33,960 Speaker 3: as we've seen a climate week, there's no end to 24 00:01:34,000 --> 00:01:35,600 Speaker 3: the number of startups who are trying to solve these 25 00:01:35,600 --> 00:01:36,440 Speaker 3: important challenges. 26 00:01:36,560 --> 00:01:40,400 Speaker 2: You have a wonderful transatlantic view at Airbus Ventures. What 27 00:01:40,480 --> 00:01:42,959 Speaker 2: I see when I'm in London or Paris is a 28 00:01:43,080 --> 00:01:48,040 Speaker 2: huge success or belief in the future of climate versus 29 00:01:48,080 --> 00:01:52,960 Speaker 2: a hugely fractious United States debate on climate. Describe that 30 00:01:53,200 --> 00:01:55,560 Speaker 2: debate and how it affects investment. 31 00:01:56,440 --> 00:01:59,040 Speaker 3: What we see is that we're you know, we're investing 32 00:01:59,120 --> 00:02:01,880 Speaker 3: in CEEDD, siri B companies who are working on bricks 33 00:02:01,880 --> 00:02:06,080 Speaker 3: of technology that are hoping to solve this challenge. We 34 00:02:06,240 --> 00:02:09,560 Speaker 3: are uniquely positioned as one of the few global VC funds. 35 00:02:09,639 --> 00:02:13,639 Speaker 3: We're investing all over the world, and what we see 36 00:02:13,680 --> 00:02:16,520 Speaker 3: as an optimism on seeing how these technologies can help 37 00:02:16,560 --> 00:02:19,600 Speaker 3: shape the future of tomorrow. Some of these technologies that 38 00:02:19,639 --> 00:02:24,120 Speaker 3: will be changing products, you know, in decades time, So 39 00:02:24,680 --> 00:02:28,720 Speaker 3: we have a very optimimistic view on the current environment. 40 00:02:28,800 --> 00:02:31,919 Speaker 4: Help me with the term deep technology, So you invest 41 00:02:31,960 --> 00:02:35,200 Speaker 4: in deep technology entrepreneurship, what does it mean and how 42 00:02:35,200 --> 00:02:37,040 Speaker 4: does that set you apart from other VC shops. 43 00:02:37,320 --> 00:02:41,639 Speaker 3: We're investing in the hardest engineering problems to solve, and 44 00:02:42,040 --> 00:02:46,239 Speaker 3: the deep tech investment practice is extremely important in unlocking 45 00:02:46,280 --> 00:02:49,720 Speaker 3: these opportunities down the chain. So when you think about 46 00:02:50,000 --> 00:02:52,720 Speaker 3: I think about one of our portfolio companies who's sitting 47 00:02:52,760 --> 00:02:55,359 Speaker 3: in the Brooklyn Navy Yard called que Net. They're working 48 00:02:55,440 --> 00:02:59,080 Speaker 3: on quantum networking hardware. This is a product suite that 49 00:02:59,080 --> 00:03:04,519 Speaker 3: will sit on existing telecom infrastructure to allow for quantum communications. 50 00:03:05,040 --> 00:03:08,560 Speaker 3: Why that matters is that's ultra secure communication. So the 51 00:03:08,680 --> 00:03:12,120 Speaker 3: national security sector is very interested in seeing even how 52 00:03:12,120 --> 00:03:14,120 Speaker 3: if you can use their product suite to see if 53 00:03:14,160 --> 00:03:17,760 Speaker 3: someone is tapping the line, and ultra secure communication matters 54 00:03:17,800 --> 00:03:22,360 Speaker 3: for so many industries, finance, defense, national security, of course. 55 00:03:22,560 --> 00:03:25,160 Speaker 3: So that's where we're focused on. How can we work 56 00:03:25,200 --> 00:03:28,079 Speaker 3: on using these bricks of technology to unlock other problems. 57 00:03:28,200 --> 00:03:30,160 Speaker 4: Nicole, We'll get Tom over to the Brooklyn Navy Yard here, 58 00:03:30,160 --> 00:03:31,880 Speaker 4: we'll get you to Brooklyn at some point. 59 00:03:32,480 --> 00:03:36,200 Speaker 3: Bagels crave that that's incredible community over there. 60 00:03:36,200 --> 00:03:38,960 Speaker 4: Tell me about your background. I mean you are somebody's 61 00:03:39,000 --> 00:03:41,120 Speaker 4: a lawyer. You're based in DC. I remember talking with 62 00:03:41,120 --> 00:03:43,960 Speaker 4: Steve case a while back about sort of broadening the 63 00:03:44,000 --> 00:03:48,320 Speaker 4: reach of VC beyond Boston, beyond the Bay Area. What 64 00:03:48,360 --> 00:03:50,760 Speaker 4: added value perspective does that give you being based in 65 00:03:51,080 --> 00:03:52,560 Speaker 4: Washington versus those other places. 66 00:03:52,800 --> 00:03:56,080 Speaker 3: Well, certainly in the areas that we're investing. These are 67 00:03:56,320 --> 00:03:59,080 Speaker 3: all on the Critical and Emerging Technologies list for the US. 68 00:03:59,120 --> 00:04:01,960 Speaker 3: So many of our folio companies, they are in and 69 00:04:02,000 --> 00:04:05,920 Speaker 3: out of DC, meeting with their customers, the agencies, the 70 00:04:06,080 --> 00:04:09,520 Speaker 3: aerospace primes in Northern Virginia, their headmans on the hill. 71 00:04:09,840 --> 00:04:12,600 Speaker 3: I think there is a strong appetite to better understand 72 00:04:12,680 --> 00:04:17,320 Speaker 3: how these technologies can be plugged into other areas and 73 00:04:17,400 --> 00:04:21,479 Speaker 3: challenges that we have. I would say, with my background, 74 00:04:21,480 --> 00:04:23,520 Speaker 3: what we'd like to say within our fund is that 75 00:04:23,560 --> 00:04:26,120 Speaker 3: we have a Swiss Army knife of a team. Founders 76 00:04:26,200 --> 00:04:29,000 Speaker 3: have no end to the challenges that they need to navigate, 77 00:04:29,480 --> 00:04:32,440 Speaker 3: and so yes, I bring my financial and operational background. 78 00:04:32,680 --> 00:04:36,600 Speaker 3: We have aerospace engineers and mechanical engineers, physicists, all of whom 79 00:04:36,640 --> 00:04:40,040 Speaker 3: are working with our founders to get their technologies out 80 00:04:40,040 --> 00:04:40,560 Speaker 3: into the market. 81 00:04:40,760 --> 00:04:45,280 Speaker 2: You are living the skepticism of America over climate change 82 00:04:45,520 --> 00:04:48,640 Speaker 2: in this election of David's expert on the election. I'm not, 83 00:04:48,839 --> 00:04:52,880 Speaker 2: but you go state to state, it's almost an unmentionable 84 00:04:53,360 --> 00:04:58,760 Speaker 2: rather too candidates, how does America become more like Europe 85 00:04:58,839 --> 00:05:01,960 Speaker 2: in climate change? Or do you suggest we stay distant 86 00:05:02,320 --> 00:05:04,120 Speaker 2: from the enthusiasm. 87 00:05:04,520 --> 00:05:09,240 Speaker 3: I think it's important to focus on the technologies themselves 88 00:05:09,680 --> 00:05:13,400 Speaker 3: because in the debate it's getting lost, Like for example, 89 00:05:13,480 --> 00:05:17,000 Speaker 3: we could look at hydrogen aircraft. These are exciting opportunities. 90 00:05:17,000 --> 00:05:19,320 Speaker 3: This is what the market wants, is they want to 91 00:05:19,360 --> 00:05:22,320 Speaker 3: have novel ways of flying that has less climate impact. 92 00:05:22,680 --> 00:05:25,279 Speaker 3: And so we like to stay out of the noise 93 00:05:25,400 --> 00:05:29,359 Speaker 3: and in terms of any debate on this, because we 94 00:05:29,400 --> 00:05:31,440 Speaker 3: think the technologies will show themselves. 95 00:05:31,720 --> 00:05:33,679 Speaker 4: What do folks need to know just about the environment? 96 00:05:33,720 --> 00:05:35,480 Speaker 4: For venture capital? At this point, we've been through a 97 00:05:35,520 --> 00:05:37,280 Speaker 4: couple of years when the environment wasn't as great as 98 00:05:37,279 --> 00:05:40,400 Speaker 4: as it had been. Where are we in the arc 99 00:05:40,480 --> 00:05:43,400 Speaker 4: of sort of its energy or success or how does 100 00:05:43,440 --> 00:05:45,320 Speaker 4: it feel at this moment being in VC. 101 00:05:46,680 --> 00:05:50,000 Speaker 3: Feeling optimistic? Actually, I think that there's a lot it's 102 00:05:50,040 --> 00:05:53,640 Speaker 3: important right now, and we as a fund want to 103 00:05:54,000 --> 00:05:58,920 Speaker 3: really bring the expertise into bringing these technologies out, and 104 00:05:58,960 --> 00:06:00,760 Speaker 3: so I think it's important that right now we're seeing 105 00:06:00,920 --> 00:06:05,520 Speaker 3: VC funds who have special expertise really focused on the founders, 106 00:06:06,839 --> 00:06:10,800 Speaker 3: and I think the interest rate helps helps. It certainly helps, 107 00:06:10,800 --> 00:06:13,320 Speaker 3: and so we're feeling good and I think hopefully getting 108 00:06:13,360 --> 00:06:14,960 Speaker 3: through the election process itself. 109 00:06:15,000 --> 00:06:16,800 Speaker 5: Well, I've got my question of the day. 110 00:06:16,920 --> 00:06:20,680 Speaker 2: Is a shock worldwide that Microsoft is going to team 111 00:06:20,760 --> 00:06:25,159 Speaker 2: up for electrical generations three Mile Island with Constellation Energy. 112 00:06:25,320 --> 00:06:27,640 Speaker 2: How did you react when you saw that, I mean 113 00:06:27,839 --> 00:06:32,360 Speaker 2: talk about a jump condition for energy or energy generation, 114 00:06:32,520 --> 00:06:33,760 Speaker 2: I should say yeah. 115 00:06:33,960 --> 00:06:36,360 Speaker 3: I think it's it's just reflective of the fact that 116 00:06:36,440 --> 00:06:40,640 Speaker 3: companies are eager for anyone to find solutions for the 117 00:06:40,760 --> 00:06:43,800 Speaker 3: energy crisis that they that they have, I mean a lot. 118 00:06:43,920 --> 00:06:45,760 Speaker 3: I mean, if you think about AI, there's a huge 119 00:06:45,800 --> 00:06:48,359 Speaker 3: amount of energy consumption in order to run some of 120 00:06:48,400 --> 00:06:51,800 Speaker 3: these models, and so it's it's unsurprising that they're looking 121 00:06:51,839 --> 00:06:55,880 Speaker 3: for any particular technology that can help solve their problem. 122 00:06:56,040 --> 00:06:57,039 Speaker 5: Nicole, thank you for j. 123 00:06:59,279 --> 00:07:02,080 Speaker 2: So there must be not today getting us started here 124 00:07:02,120 --> 00:07:05,360 Speaker 2: at the Earth Shot A Prize awards. 125 00:07:04,920 --> 00:07:16,000 Speaker 6: It this is a joy. 126 00:07:16,200 --> 00:07:18,600 Speaker 2: He is someone at Morgan Stanley who's done the tour 127 00:07:18,640 --> 00:07:21,760 Speaker 2: of duty and very rarely do you see such an 128 00:07:21,800 --> 00:07:24,560 Speaker 2: extended commitment to one project. 129 00:07:24,960 --> 00:07:25,760 Speaker 5: That project. 130 00:07:25,800 --> 00:07:28,400 Speaker 2: Well, maybe it's not as iconic as the Dow Jones 131 00:07:29,000 --> 00:07:34,360 Speaker 2: Industrial Average, but within Global Wall Street, MSCI is understood 132 00:07:34,400 --> 00:07:38,040 Speaker 2: by all. Henry Fernandez joins us this morning with your 133 00:07:38,160 --> 00:07:41,960 Speaker 2: baby ms CI. What was it like the first years 134 00:07:42,000 --> 00:07:45,160 Speaker 2: of MSCI where you had to go into every meetia. 135 00:07:44,840 --> 00:07:47,000 Speaker 6: To explain what are we doing there? 136 00:07:47,040 --> 00:07:51,480 Speaker 2: Now you own Emerging market in Global Index Analysis. 137 00:07:52,440 --> 00:07:55,480 Speaker 7: So I created a company thirty years ago on top 138 00:07:55,560 --> 00:07:59,240 Speaker 7: of the what was then the morning Stanley Capital International 139 00:07:59,280 --> 00:08:02,640 Speaker 7: in this is and that was the only problem we had, right, 140 00:08:02,720 --> 00:08:05,560 Speaker 7: you know, market cup in this is and it was 141 00:08:05,560 --> 00:08:07,960 Speaker 7: a call center, so we didn't have a lot of resources, 142 00:08:08,400 --> 00:08:11,840 Speaker 7: and the world wanted a lot from us. So we 143 00:08:11,920 --> 00:08:15,800 Speaker 7: had to educate a lot of people and expand and 144 00:08:16,080 --> 00:08:18,520 Speaker 7: you know, eventually brought a lot of companies and spin 145 00:08:18,600 --> 00:08:21,080 Speaker 7: out of Morgan Stanley and it's a forty billion dollar 146 00:08:21,120 --> 00:08:22,239 Speaker 7: market cap company today. 147 00:08:22,520 --> 00:08:26,480 Speaker 2: You were focused on the innovation here of the accountability 148 00:08:26,680 --> 00:08:32,120 Speaker 2: of data over to the climate challenges we have. How 149 00:08:32,120 --> 00:08:35,199 Speaker 2: are you going to bring your data acumen over to 150 00:08:35,400 --> 00:08:38,760 Speaker 2: a better measurement of our climate disaster? 151 00:08:39,640 --> 00:08:43,760 Speaker 7: So clearly climate risk is an existential risk for all 152 00:08:43,800 --> 00:08:46,800 Speaker 7: of us. But in the Capital industry. You know, you 153 00:08:46,840 --> 00:08:50,600 Speaker 7: can decimate portfolios. There'll be winners and losers, and all 154 00:08:50,640 --> 00:08:53,480 Speaker 7: of them arender your portfolio today. So the question is 155 00:08:53,480 --> 00:08:56,320 Speaker 7: how are you going to discern who those are and 156 00:08:56,360 --> 00:08:59,960 Speaker 7: how you're going to start transitioning to a low carbon PORTFOLI. 157 00:09:00,920 --> 00:09:03,720 Speaker 7: So it's a lot of it a question of measurement. 158 00:09:04,240 --> 00:09:07,400 Speaker 7: So what's the nature of your problem? Whereas the carbon emissions, 159 00:09:07,400 --> 00:09:09,800 Speaker 7: where are the big climate risk? What do you do 160 00:09:09,840 --> 00:09:11,959 Speaker 7: about it? Where do you start? Do you start in 161 00:09:12,000 --> 00:09:15,200 Speaker 7: private assets and public acids? And very importantly, at what 162 00:09:15,280 --> 00:09:17,120 Speaker 7: pace do you go? If you go too fast, you 163 00:09:17,120 --> 00:09:18,680 Speaker 7: get underperform because. 164 00:09:18,480 --> 00:09:22,280 Speaker 8: You're gone too fast. Is Europe going too fast? 165 00:09:22,800 --> 00:09:23,400 Speaker 5: No? 166 00:09:23,400 --> 00:09:25,439 Speaker 7: No, I mean nothing in the world is going too 167 00:09:25,480 --> 00:09:29,000 Speaker 7: fast for you know, the Parents Agreement put a day 168 00:09:29,040 --> 00:09:31,560 Speaker 7: of twenty fifty because we needed to get two hundred 169 00:09:31,559 --> 00:09:34,839 Speaker 7: countries to agree. But it was so far out that 170 00:09:35,040 --> 00:09:37,400 Speaker 7: you know, people think that this is a long term problem. 171 00:09:37,760 --> 00:09:39,520 Speaker 7: Of course it is a long term problem, but it's 172 00:09:39,520 --> 00:09:41,320 Speaker 7: also a short to medium term problem. 173 00:09:41,679 --> 00:09:42,000 Speaker 5: You know, we. 174 00:09:42,080 --> 00:09:44,840 Speaker 7: Already see an enormous physical risk around the world. 175 00:09:45,040 --> 00:09:47,040 Speaker 2: I mean, you've got a risk in Brooklyn that your 176 00:09:47,040 --> 00:09:48,960 Speaker 2: house can float away, right, Well, I. 177 00:09:48,960 --> 00:09:52,439 Speaker 4: Mean thankfully out of the bottom floor. Yeah, get around 178 00:09:52,440 --> 00:09:55,960 Speaker 4: the great way up park slope. I remember talking to 179 00:09:56,000 --> 00:09:58,800 Speaker 4: Anfinuca on her last day at Bank of America about ESG, 180 00:09:59,000 --> 00:10:01,200 Speaker 4: something about which she is so passionate, has been for 181 00:10:01,240 --> 00:10:03,800 Speaker 4: so long, and we talked about the issue of standardization 182 00:10:04,000 --> 00:10:06,320 Speaker 4: that company is trying to keep up with this kind 183 00:10:06,360 --> 00:10:09,360 Speaker 4: of data, have so many ways which they're filing or 184 00:10:09,400 --> 00:10:12,960 Speaker 4: making filings known. Help me understand that where we are 185 00:10:13,000 --> 00:10:15,760 Speaker 4: in that process of winning and simplifying data when it 186 00:10:15,800 --> 00:10:18,040 Speaker 4: comes to ESG and climate issues and in particular, is 187 00:10:18,040 --> 00:10:20,080 Speaker 4: it's still too much of a wild west that it 188 00:10:20,080 --> 00:10:20,480 Speaker 4: should be? 189 00:10:20,559 --> 00:10:21,840 Speaker 5: No, really there is. 190 00:10:22,120 --> 00:10:26,679 Speaker 7: First of all, ESG is investment risk and opportunities. It's 191 00:10:26,840 --> 00:10:30,840 Speaker 7: term politicized, you know, into political philosophies, but you know 192 00:10:30,920 --> 00:10:34,680 Speaker 7: it's about understanding the coupit of markets and the risk 193 00:10:34,720 --> 00:10:37,679 Speaker 7: associated with it with additional information, right in terms of 194 00:10:38,040 --> 00:10:40,320 Speaker 7: non financial information as you know. 195 00:10:40,400 --> 00:10:40,679 Speaker 4: Well. 196 00:10:41,160 --> 00:10:44,520 Speaker 7: Secondly, unfortunately, we call them ratings, and we have a 197 00:10:44,559 --> 00:10:47,600 Speaker 7: metric system that is similar to credit ratings. So a 198 00:10:47,600 --> 00:10:51,000 Speaker 7: lot of people think that there has to be convergence 199 00:10:51,000 --> 00:10:53,640 Speaker 7: of the ratings, just like credit ratings. Of convergence where 200 00:10:53,679 --> 00:10:56,760 Speaker 7: credit ratings are your dimensional. It's about cash flows and 201 00:10:56,800 --> 00:10:59,360 Speaker 7: the stability of the cash flows. So how far can 202 00:10:59,400 --> 00:11:00,840 Speaker 7: you be in different people? 203 00:11:01,080 --> 00:11:01,240 Speaker 5: Right? 204 00:11:01,280 --> 00:11:04,920 Speaker 7: In terms of the rating, ESG measures a huge number 205 00:11:04,920 --> 00:11:09,160 Speaker 7: of various things, so it's almost like Seales side opinion. 206 00:11:10,200 --> 00:11:12,800 Speaker 7: So therefore it's very hard to think that you're going 207 00:11:12,840 --> 00:11:15,800 Speaker 7: to end up with, you know, standardization of the ratings 208 00:11:16,120 --> 00:11:18,800 Speaker 7: because you know, my opinion not MSCI may be different 209 00:11:18,840 --> 00:11:22,040 Speaker 7: than our competitor's opinion. They may think that governance is 210 00:11:22,040 --> 00:11:24,319 Speaker 7: more important. We may think that climate is more important. 211 00:11:24,559 --> 00:11:27,120 Speaker 7: The key is not on the ratings. The key is 212 00:11:27,160 --> 00:11:29,360 Speaker 7: to make sure the quality of the data. 213 00:11:30,280 --> 00:11:33,679 Speaker 2: Henry Fernandez with us with the MSCI here at the 214 00:11:33,720 --> 00:11:35,880 Speaker 2: Earthshot Prize a Plaza Hotel. 215 00:11:36,040 --> 00:11:38,720 Speaker 4: David, let's talk about sort of one area in particularly 216 00:11:38,760 --> 00:11:41,280 Speaker 4: through voluntary carbon credit market. I know that MSCI has 217 00:11:41,320 --> 00:11:44,360 Speaker 4: kind of looked at the integrity of that data, rated 218 00:11:44,400 --> 00:11:47,439 Speaker 4: that data. How representative is that of the work that 219 00:11:47,679 --> 00:11:49,640 Speaker 4: you're doing when it comes to climate in particular. What 220 00:11:49,720 --> 00:11:52,360 Speaker 4: did you find of looking at those data in particular? 221 00:11:52,480 --> 00:11:56,040 Speaker 7: So, first of all, we believe strongly that voluntary carbon 222 00:11:56,120 --> 00:11:59,520 Speaker 7: markets have to be part of the process of the 223 00:11:59,640 --> 00:12:03,040 Speaker 7: risk and the portfolios of the world, and the world itself, 224 00:12:03,440 --> 00:12:06,040 Speaker 7: you know, every airplace else we go markets serve a 225 00:12:06,080 --> 00:12:09,440 Speaker 7: function of transferring value and transferring risk and in climate 226 00:12:09,480 --> 00:12:12,000 Speaker 7: we're going to say they don't play any function. You know, 227 00:12:12,080 --> 00:12:13,079 Speaker 7: it's not it's. 228 00:12:13,040 --> 00:12:14,560 Speaker 5: Crazy to think that they want. 229 00:12:15,040 --> 00:12:17,680 Speaker 7: The issue is how do we build integrity and you 230 00:12:17,720 --> 00:12:20,960 Speaker 7: know and understanding that what you buy is what you 231 00:12:20,960 --> 00:12:23,160 Speaker 7: thought you were buying and all of that. So we 232 00:12:23,280 --> 00:12:25,959 Speaker 7: have come up with ratings. We have four thousand projects 233 00:12:26,040 --> 00:12:28,640 Speaker 7: right now that we're rating. We just announced that last week, 234 00:12:28,920 --> 00:12:31,880 Speaker 7: and we're going to bring more integrity, more data and quality. 235 00:12:31,920 --> 00:12:35,120 Speaker 2: How are you rating something in Chengdu, China? Explain to 236 00:12:35,200 --> 00:12:40,200 Speaker 2: me the dating data veracity we can bring to the 237 00:12:40,360 --> 00:12:46,120 Speaker 2: distrust around what China is doing in carbon management and 238 00:12:46,280 --> 00:12:49,960 Speaker 2: coal management. Are you optimistic that we can get a 239 00:12:50,000 --> 00:12:50,599 Speaker 2: handle on that? 240 00:12:51,160 --> 00:12:53,520 Speaker 7: Well, I was just in chang Dou, China a couple 241 00:12:53,640 --> 00:12:55,320 Speaker 7: of months ago to begin with. 242 00:12:55,480 --> 00:12:56,600 Speaker 5: But important did you. 243 00:12:56,600 --> 00:12:58,800 Speaker 8: Go see the pandas absolutely good? 244 00:13:00,320 --> 00:13:01,600 Speaker 5: A number one thing we've learned from. 245 00:13:03,360 --> 00:13:08,199 Speaker 7: And but it's not different that how we how we 246 00:13:08,400 --> 00:13:12,600 Speaker 7: classify countries, how we classify the ESGO companies, you know, 247 00:13:12,840 --> 00:13:15,000 Speaker 7: how we so how we look at carbony, so we 248 00:13:15,040 --> 00:13:19,560 Speaker 7: collect an enormous amount of data from independent sources, and 249 00:13:19,600 --> 00:13:23,880 Speaker 7: then we sort of package it into a classification system 250 00:13:23,920 --> 00:13:26,520 Speaker 7: of the various sources, and then you know, we create the. 251 00:13:26,559 --> 00:13:29,160 Speaker 8: Radio for our American audience coast to coast. 252 00:13:29,240 --> 00:13:32,320 Speaker 2: Thank you this morning for listening and tuning in on 253 00:13:32,360 --> 00:13:36,680 Speaker 2: YouTube as well. There's a belief here that China is cheating. 254 00:13:37,520 --> 00:13:42,360 Speaker 2: Does MSc I have the data amount or veracity to 255 00:13:42,480 --> 00:13:44,560 Speaker 2: say that China is or is not. 256 00:13:44,640 --> 00:13:46,959 Speaker 8: Cheating or any other countries for that matter. 257 00:13:47,880 --> 00:13:50,480 Speaker 7: Well, I mean they could be cheating in many different aspects, 258 00:13:50,520 --> 00:13:55,720 Speaker 7: you know, obviously, but in our case, we have incredible 259 00:13:55,800 --> 00:13:59,720 Speaker 7: norse sources of independent data, you know, the the global press, 260 00:14:00,120 --> 00:14:05,360 Speaker 7: the local press. Sometimes it is obviously control to find 261 00:14:05,400 --> 00:14:08,559 Speaker 7: independent sources and verify those sources, like in the newspaper 262 00:14:08,559 --> 00:14:12,439 Speaker 7: would to understand what is the nature of the climate 263 00:14:12,480 --> 00:14:14,760 Speaker 7: emissions of a company, what is the nature of ESG, 264 00:14:15,000 --> 00:14:16,400 Speaker 7: what is the nature of you know, in. 265 00:14:16,320 --> 00:14:17,920 Speaker 5: The west of China and all of that. 266 00:14:18,040 --> 00:14:21,920 Speaker 7: And it doesn't make us popular in China necessarily, but 267 00:14:22,120 --> 00:14:25,520 Speaker 7: we have an independent view and an unbiased view, and 268 00:14:25,560 --> 00:14:28,160 Speaker 7: that's how we rate everything, you know, including in China, right. 269 00:14:28,160 --> 00:14:32,360 Speaker 7: But it's not different than Indonesia sometimes no different than Nigeria, 270 00:14:32,560 --> 00:14:34,160 Speaker 7: no different than Mexico and the like. 271 00:14:35,080 --> 00:14:37,720 Speaker 4: The last question here, just given your perspective having traveled 272 00:14:37,720 --> 00:14:39,920 Speaker 4: to the bamboo for us and trained to do and elsewhere, 273 00:14:40,360 --> 00:14:42,840 Speaker 4: what don't we understand about emerging markets today? So we're 274 00:14:42,920 --> 00:14:46,040 Speaker 4: coming out of the pandemic as we see the world 275 00:14:46,080 --> 00:14:49,400 Speaker 4: reopening again, what are people misunderstanding or not understanding about 276 00:14:49,400 --> 00:14:49,880 Speaker 4: the health of. 277 00:14:49,920 --> 00:14:52,880 Speaker 7: First of all, emergent market is a catch all, yes, right, 278 00:14:52,960 --> 00:14:55,840 Speaker 7: so you have to then break it down into you know, 279 00:14:56,200 --> 00:15:00,080 Speaker 7: commodity exporting markets, consumer markets, and markets that don't have 280 00:15:00,120 --> 00:15:01,560 Speaker 7: a lot of competitive advantages. 281 00:15:01,640 --> 00:15:01,800 Speaker 5: Right. 282 00:15:02,640 --> 00:15:07,120 Speaker 7: Secondly is emerging markets are where the demographics you know, 283 00:15:07,160 --> 00:15:09,480 Speaker 7: growth is in the world. They have a lot of 284 00:15:09,480 --> 00:15:11,960 Speaker 7: problems obviously, you know the emerging markets is where the 285 00:15:12,000 --> 00:15:15,320 Speaker 7: climate problem will be the biggest because they don't have 286 00:15:15,320 --> 00:15:18,880 Speaker 7: a lot of resources, but they also present significant opportunities 287 00:15:18,920 --> 00:15:21,560 Speaker 7: you know, in the world. So there is a little 288 00:15:21,560 --> 00:15:25,000 Speaker 7: bit of too much of a glossover, you know, merging. 289 00:15:24,760 --> 00:15:27,160 Speaker 4: Market attaps needs to you need to go more. 290 00:15:26,920 --> 00:15:30,280 Speaker 7: In depth into each category and understand Brazil is different 291 00:15:30,280 --> 00:15:31,560 Speaker 7: than Turkey, different than China. 292 00:15:32,360 --> 00:15:35,320 Speaker 2: Thank you so much. I'd like to continue this conversation. 293 00:15:35,880 --> 00:15:37,080 Speaker 4: Thank you for how I make Yeah. 294 00:15:36,920 --> 00:15:39,600 Speaker 2: We'd love to see you over at Bloomberg. Henny Fernandez 295 00:15:39,600 --> 00:15:47,200 Speaker 2: with us here with the ms CI on the politics 296 00:15:47,200 --> 00:15:49,560 Speaker 2: of the moment. There's no one better than David Gerr 297 00:15:49,600 --> 00:15:51,240 Speaker 2: to be looking state by state. 298 00:15:51,760 --> 00:15:52,280 Speaker 5: So when you. 299 00:15:52,240 --> 00:15:55,560 Speaker 2: Slice in die fifty states for the selection of November fifth, 300 00:15:55,840 --> 00:15:59,720 Speaker 2: there's no question Colorado can be front and center with. 301 00:15:59,720 --> 00:16:02,720 Speaker 8: A really rich and interesting politics. 302 00:16:02,800 --> 00:16:05,880 Speaker 9: I like the ballot that the Governor of Colorado put 303 00:16:05,920 --> 00:16:09,400 Speaker 9: as a ballot initiative, which is to bring back Tullagis 304 00:16:09,400 --> 00:16:12,960 Speaker 9: in Boulder, Colorado. The shutting down of Tulagi's as a 305 00:16:13,000 --> 00:16:15,120 Speaker 9: club was a national scandal. 306 00:16:15,160 --> 00:16:16,800 Speaker 2: I hope you can bring Tulagi's back. 307 00:16:17,080 --> 00:16:19,440 Speaker 4: We'll get into that. We'll let Tom Wax nostalgic about 308 00:16:19,440 --> 00:16:21,480 Speaker 4: his time at the Sink, his time on the hill. 309 00:16:21,480 --> 00:16:24,360 Speaker 4: Maybe we'll talk about Tom's tavern on Pearl Street as well. 310 00:16:25,000 --> 00:16:26,960 Speaker 4: But we're here at the Earth Shot Summit at the 311 00:16:26,960 --> 00:16:30,360 Speaker 4: Plaza Hotel on this Tuesday morning with us now the 312 00:16:30,400 --> 00:16:32,840 Speaker 4: forty third governor of the State of Colorado, Jared Poulis, 313 00:16:33,160 --> 00:16:35,160 Speaker 4: who is of course a supporter of and a surrogate 314 00:16:35,160 --> 00:16:38,280 Speaker 4: for the Harris Walls campaign. And Governor Paul is great 315 00:16:38,280 --> 00:16:39,480 Speaker 4: to speak with you. And I got to start with 316 00:16:39,520 --> 00:16:41,040 Speaker 4: what has been in the news. We talked about it 317 00:16:41,080 --> 00:16:44,359 Speaker 4: just a moment ago. That is the degree to which Aurora, Colorado, 318 00:16:44,400 --> 00:16:46,600 Speaker 4: has been in the news. And I saw an interview 319 00:16:46,600 --> 00:16:50,400 Speaker 4: on KUSA on nine news in Colorado, your former congressional colleague, 320 00:16:50,440 --> 00:16:53,680 Speaker 4: Mike Kaufman, now the mayor of that city, evincing a 321 00:16:53,760 --> 00:16:56,040 Speaker 4: kind of unease as he was asked about whether he 322 00:16:56,040 --> 00:16:58,600 Speaker 4: would stand by former President Trump as he came to 323 00:16:58,680 --> 00:17:01,680 Speaker 4: Aurora for a row to address what he's been talking 324 00:17:01,720 --> 00:17:03,840 Speaker 4: about over and over again. That is what's really an 325 00:17:03,840 --> 00:17:07,320 Speaker 4: apparition here. The dangers, the dangers as he describes them, 326 00:17:07,400 --> 00:17:10,399 Speaker 4: Venezuelan gangs in that city, give us a sense of 327 00:17:10,400 --> 00:17:13,040 Speaker 4: what's really happening on the ground there in Aurora. 328 00:17:13,080 --> 00:17:15,440 Speaker 1: Well, I mean, the way the former president talks about 329 00:17:15,440 --> 00:17:18,080 Speaker 1: it is frankly one of the reasons he's so unpopular here. 330 00:17:18,160 --> 00:17:20,919 Speaker 1: I mean, Aurora is a thriving Auroras, a thriving city. 331 00:17:21,520 --> 00:17:25,800 Speaker 1: I was there last week. Most residents are are, you know, 332 00:17:25,880 --> 00:17:28,240 Speaker 1: enjoy living in one of our most culturally diversities in 333 00:17:28,280 --> 00:17:30,879 Speaker 1: the state. Violent crime has been down two years in 334 00:17:30,920 --> 00:17:33,639 Speaker 1: a Row car thefts are way down two years in 335 00:17:33,680 --> 00:17:36,080 Speaker 1: a row. They have a new police chief. We've been 336 00:17:36,119 --> 00:17:37,960 Speaker 1: working with them on law enforcement for a number of 337 00:17:37,960 --> 00:17:40,760 Speaker 1: different years. So I mean, that's that's the reality in Aurora. 338 00:17:41,240 --> 00:17:43,160 Speaker 1: And then you have somebody talking about it like it's 339 00:17:43,160 --> 00:17:46,200 Speaker 1: some alternate reality place in Colorado and just sort of 340 00:17:46,240 --> 00:17:47,760 Speaker 1: roll our eyes and say, that's not us. 341 00:17:48,119 --> 00:17:50,359 Speaker 4: Governor. We look at the latest polling, which has gotten 342 00:17:50,400 --> 00:17:52,720 Speaker 4: pretty granular, especially on the state by state level, looking 343 00:17:52,760 --> 00:17:55,600 Speaker 4: at the issues that matter most to voters, and chief 344 00:17:55,600 --> 00:17:57,280 Speaker 4: among them in the latest set of polls we've gotten 345 00:17:57,320 --> 00:18:00,280 Speaker 4: has been the economy has been inflation. What are you 346 00:18:00,280 --> 00:18:03,639 Speaker 4: hearing from Coloraden's about that? And what more does the 347 00:18:03,680 --> 00:18:06,320 Speaker 4: ticket you support need to do to make the case 348 00:18:06,400 --> 00:18:08,640 Speaker 4: that they're going to make life better for those who 349 00:18:08,680 --> 00:18:12,320 Speaker 4: are still struggling, still dealing with the effects of high 350 00:18:12,359 --> 00:18:13,680 Speaker 4: inflation and higher prices. 351 00:18:14,160 --> 00:18:16,480 Speaker 1: Certainly high costs of inflation are one of the big 352 00:18:16,560 --> 00:18:19,280 Speaker 1: challenges we face, and I think that the type of 353 00:18:19,320 --> 00:18:23,080 Speaker 1: thing that we saw from the Wharton School pen came 354 00:18:23,119 --> 00:18:26,400 Speaker 1: out and said that Donald Trump's big spending plan would 355 00:18:26,400 --> 00:18:29,600 Speaker 1: increase the deficit more than two times more than Kamala 356 00:18:29,680 --> 00:18:32,720 Speaker 1: Harris's plan. People understand that this kind of big spending 357 00:18:32,760 --> 00:18:35,879 Speaker 1: mentality is inflationary. That's more of what the former president 358 00:18:35,920 --> 00:18:38,160 Speaker 1: brought last time he was president. That's what he's likely 359 00:18:38,240 --> 00:18:41,360 Speaker 1: to bring again. I think Kamala Harris has a responsible 360 00:18:41,400 --> 00:18:46,280 Speaker 1: plan and encourages entrepreneurship, innovation, really leans into the opportunity 361 00:18:46,359 --> 00:18:48,760 Speaker 1: economy from the bottom up and doesn't rely on big 362 00:18:48,800 --> 00:18:50,240 Speaker 1: government spending like Donald Trump. 363 00:18:50,840 --> 00:18:54,080 Speaker 2: Governor, you are living there for decades, way back before 364 00:18:54,119 --> 00:18:55,560 Speaker 2: this fractious politics. 365 00:18:55,960 --> 00:18:58,200 Speaker 8: The Latino vote and the turnout. 366 00:18:58,560 --> 00:19:01,720 Speaker 2: What is your prediction for the size of the Latino 367 00:19:01,840 --> 00:19:05,960 Speaker 2: vote in Colorado, and particularly the youth vote that many 368 00:19:06,080 --> 00:19:09,040 Speaker 2: say finds an affection with the former president. 369 00:19:09,320 --> 00:19:12,800 Speaker 1: There's a lot more excitement now with Kamala Harris's the nominee. 370 00:19:12,840 --> 00:19:15,199 Speaker 1: I think this was a legitimate problem when Joe Biden 371 00:19:15,280 --> 00:19:18,760 Speaker 1: was talking about running again, especially among the younger vote. 372 00:19:19,000 --> 00:19:21,080 Speaker 1: I think we now have a candidate that really speaks 373 00:19:21,119 --> 00:19:23,880 Speaker 1: to a new generation, to their aspirations, to their goals. 374 00:19:24,359 --> 00:19:26,000 Speaker 1: And I'm getting a lot of assignment, whether I'm at 375 00:19:26,040 --> 00:19:30,480 Speaker 1: our campuses at CU and DU and CSU, or whether 376 00:19:30,520 --> 00:19:34,159 Speaker 1: it's at our community colleges or in stores and on restaurants. 377 00:19:34,359 --> 00:19:36,800 Speaker 1: I think people really get that she is an agenda 378 00:19:36,840 --> 00:19:39,359 Speaker 1: that works for them. Our Latino population in Colorado is 379 00:19:39,359 --> 00:19:41,760 Speaker 1: incredibly diverse, right. We have people that have been families 380 00:19:41,760 --> 00:19:43,640 Speaker 1: that have been here hundreds of years. We have families 381 00:19:43,640 --> 00:19:46,000 Speaker 1: that arrive here in a generation or two ago. Like 382 00:19:46,040 --> 00:19:50,560 Speaker 1: any group, there's a broad political diversity among them. Overwhelmingly, 383 00:19:50,600 --> 00:19:54,040 Speaker 1: of course, Latino voters, just like all Colorado's, are supporting 384 00:19:54,080 --> 00:19:56,400 Speaker 1: Kamala Harris and what she offers to bring down inflation, 385 00:19:56,760 --> 00:19:59,040 Speaker 1: secure our border, and help grow opportunity. 386 00:19:59,400 --> 00:20:02,480 Speaker 4: Gunnary, You've got to know the vice president's running made 387 00:20:02,480 --> 00:20:05,119 Speaker 4: through his service and the Democratic Governor's Association. Imagine you've 388 00:20:05,119 --> 00:20:06,880 Speaker 4: gotten know him well over the course of that. Tom 389 00:20:06,920 --> 00:20:09,680 Speaker 4: asked me yesterday on air, so what he can bring 390 00:20:09,720 --> 00:20:13,080 Speaker 4: to states like Arizona and Colorado As he makes this 391 00:20:13,119 --> 00:20:15,600 Speaker 4: way along that campaign trail, it's bound to get busier. 392 00:20:15,960 --> 00:20:17,479 Speaker 4: What if voters want to hear from him? What does 393 00:20:17,520 --> 00:20:19,600 Speaker 4: he offer that the vice president doesn't that the incomon 394 00:20:19,640 --> 00:20:22,720 Speaker 4: president didn't offer before him? What's he able to do 395 00:20:22,840 --> 00:20:26,000 Speaker 4: to kind of galvanize or energize the electorate there in Colorado. 396 00:20:26,200 --> 00:20:27,960 Speaker 1: So you know, Tim Walls was somebody I served with 397 00:20:28,000 --> 00:20:30,040 Speaker 1: in the United States Congress as well. This is somebody 398 00:20:30,080 --> 00:20:33,360 Speaker 1: who won a red district in the United States Congress. 399 00:20:33,359 --> 00:20:38,320 Speaker 1: How by really understanding and being authentic with people, I mean, 400 00:20:38,320 --> 00:20:41,480 Speaker 1: this is a guy who can get along with almost anybody. 401 00:20:41,560 --> 00:20:44,240 Speaker 1: He's comfortable in his own skin. He's able to hear 402 00:20:44,280 --> 00:20:46,840 Speaker 1: people where they're at and help them imagine a better 403 00:20:46,880 --> 00:20:49,840 Speaker 1: world for themselves and others. So that's really who Tim is. 404 00:20:49,920 --> 00:20:52,639 Speaker 1: And through his service to our country in the military 405 00:20:52,680 --> 00:20:55,359 Speaker 1: as a teacher, he's really put his work where his 406 00:20:55,440 --> 00:20:57,960 Speaker 1: mouth is, and people are really excited across the country 407 00:20:58,000 --> 00:20:59,520 Speaker 1: to get to know him as our next vice president. 408 00:21:00,000 --> 00:21:00,439 Speaker 5: Thank Governor. 409 00:21:01,000 --> 00:21:02,880 Speaker 8: How do you feel about the tradition? 410 00:21:03,040 --> 00:21:04,959 Speaker 2: And I'm going to go back to Ronald Reagan, who 411 00:21:05,040 --> 00:21:06,800 Speaker 2: maybe was on the right and. 412 00:21:06,800 --> 00:21:10,600 Speaker 8: Ran to the center in the days to November fifth. 413 00:21:10,280 --> 00:21:13,119 Speaker 2: Are we going to see the vice president run to 414 00:21:13,200 --> 00:21:17,920 Speaker 2: the middle and become verbally less progressive and more centrist? 415 00:21:18,280 --> 00:21:20,159 Speaker 8: Is maybe we see from Jared Pois? 416 00:21:20,720 --> 00:21:23,040 Speaker 1: Well, Look, I mean she is who she is. She 417 00:21:23,200 --> 00:21:25,960 Speaker 1: obviously evolves on issues and learns just like any of 418 00:21:26,040 --> 00:21:28,679 Speaker 1: us do. It's a different job to be a DA 419 00:21:28,760 --> 00:21:30,919 Speaker 1: It's a different job to be a senator from California. 420 00:21:30,920 --> 00:21:33,000 Speaker 1: It's a different job to be vice president, and it's 421 00:21:33,000 --> 00:21:35,320 Speaker 1: a different job to want to be the next president 422 00:21:35,359 --> 00:21:37,840 Speaker 1: of all America. And that's really what she's leading into. 423 00:21:38,080 --> 00:21:40,399 Speaker 1: She wants to be a uniter. She's the person we 424 00:21:40,440 --> 00:21:43,600 Speaker 1: need as America approaches our two hundred and fiftieth anniversary 425 00:21:43,880 --> 00:21:46,560 Speaker 1: in twenty twenty six. We don't need a nation divided. 426 00:21:46,600 --> 00:21:49,080 Speaker 1: We need a nation united, and that means somebody who 427 00:21:49,080 --> 00:21:52,840 Speaker 1: speaks to everybody. And that's why conservative Republicans, liberal Democrats, 428 00:21:52,920 --> 00:21:56,760 Speaker 1: moderates are all flocking to Takamala Harris, including former Vice 429 00:21:56,760 --> 00:21:57,679 Speaker 1: President Dick Cheney. 430 00:21:57,920 --> 00:22:00,119 Speaker 4: I'm the vice president. You say she is who she is? 431 00:22:00,240 --> 00:22:01,439 Speaker 4: I think there are a lot of people still in 432 00:22:01,440 --> 00:22:03,399 Speaker 4: this country who don't know who she is, and we 433 00:22:03,440 --> 00:22:05,240 Speaker 4: see that in the polling data as well. Is she 434 00:22:05,280 --> 00:22:08,400 Speaker 4: doing herself with the service by not doing more interviews, 435 00:22:08,480 --> 00:22:11,600 Speaker 4: doing more one on ones with reporters? How do you 436 00:22:11,640 --> 00:22:13,359 Speaker 4: respond to that. It's great to speak with you this morning, 437 00:22:13,400 --> 00:22:15,800 Speaker 4: to have the opportunity to do so, of course, but 438 00:22:16,280 --> 00:22:18,000 Speaker 4: is she missing an opportunity by not doing more of 439 00:22:18,000 --> 00:22:18,520 Speaker 4: that her self? 440 00:22:18,560 --> 00:22:21,800 Speaker 1: Do you think she's a thoughtful, diligent leader who studies 441 00:22:21,840 --> 00:22:25,160 Speaker 1: the issues, and she's a prosecutor, put criminals behind bars, 442 00:22:25,160 --> 00:22:27,160 Speaker 1: stared them in the face. Nothing's harder than that. 443 00:22:27,480 --> 00:22:27,600 Speaker 7: Oh. 444 00:22:27,640 --> 00:22:29,440 Speaker 1: Look, she is doing more interviews and I'm sure she'll 445 00:22:29,440 --> 00:22:32,560 Speaker 1: do even more going forward. She's working very very hard 446 00:22:32,600 --> 00:22:34,879 Speaker 1: on the campaign trail, doing far more campaign events and 447 00:22:34,920 --> 00:22:37,120 Speaker 1: outreaching her opponent. I'm going to I think that'll help 448 00:22:37,160 --> 00:22:38,320 Speaker 1: carry the day in November. 449 00:22:38,720 --> 00:22:41,840 Speaker 2: Governor, thank you so much for joining Bloomberg this morning. 450 00:22:42,000 --> 00:22:46,440 Speaker 2: The governor of Colorado, Jared Poles, greatly appreciated. 451 00:22:45,880 --> 00:22:58,280 Speaker 8: Her advantage New Haven. If you're in New Haven and. 452 00:22:58,240 --> 00:23:01,760 Speaker 2: You're double E electrical engineer, you spend a lot of 453 00:23:01,840 --> 00:23:03,320 Speaker 2: time at Sally's Pizza. 454 00:23:03,480 --> 00:23:06,479 Speaker 8: I mean, it's just no other way to put it. 455 00:23:06,560 --> 00:23:10,240 Speaker 2: You're grinding, you're cramming differential equations and the rest of it. 456 00:23:10,440 --> 00:23:12,960 Speaker 2: I assume you took the Wheatstone Bridge to Sally's. 457 00:23:12,960 --> 00:23:15,320 Speaker 6: What was it like Double E at. 458 00:23:15,200 --> 00:23:18,240 Speaker 2: Yale in a school acclaimed for liberal arts. 459 00:23:18,440 --> 00:23:19,440 Speaker 6: How lonely were you? 460 00:23:19,640 --> 00:23:22,000 Speaker 10: We spent a lot of time in a basement away 461 00:23:22,000 --> 00:23:23,040 Speaker 10: from sunlight. 462 00:23:23,760 --> 00:23:26,240 Speaker 2: Am Trepani with us? I heard this morning on what 463 00:23:26,320 --> 00:23:29,920 Speaker 2: he's doing in this innovation. Explain to me your innovation 464 00:23:30,119 --> 00:23:31,240 Speaker 2: project right now today. 465 00:23:31,720 --> 00:23:33,879 Speaker 10: Yeah, it's great to be with you. So I'm the 466 00:23:33,880 --> 00:23:37,600 Speaker 10: CEO of atlas Ai, Wh're a Palo Alto, California based 467 00:23:37,640 --> 00:23:42,240 Speaker 10: startup that is building models based on geospatial data. So 468 00:23:42,280 --> 00:23:46,040 Speaker 10: geospatial is data that has a location or a geographic 469 00:23:46,080 --> 00:23:50,920 Speaker 10: element associated with it. So our models aim to understand 470 00:23:50,960 --> 00:23:53,880 Speaker 10: and anticipate what the future of the planet is likely 471 00:23:53,920 --> 00:23:55,960 Speaker 10: to look like, where people are likely to live in 472 00:23:56,000 --> 00:23:59,679 Speaker 10: the future, how economy is likely to evolve and adapt, 473 00:23:59,720 --> 00:24:04,000 Speaker 10: and really to macro factors like conflict or climate change. 474 00:24:04,480 --> 00:24:06,879 Speaker 10: And we aim to help international organizations. 475 00:24:07,000 --> 00:24:09,120 Speaker 2: I would go to climate and flooding today, but this 476 00:24:09,240 --> 00:24:12,199 Speaker 2: morning I have to go to conflict. As you consider 477 00:24:12,320 --> 00:24:15,720 Speaker 2: three wars Ukraine, Gaza and North to Lebanon as well, 478 00:24:15,960 --> 00:24:19,840 Speaker 2: what is your value add to people assessing the conflict 479 00:24:19,880 --> 00:24:20,520 Speaker 2: in the levant? 480 00:24:20,720 --> 00:24:24,760 Speaker 10: So there's immediate elements of monitoring the impact of the 481 00:24:24,800 --> 00:24:29,800 Speaker 10: conflict on the environment itself, but also on communities. How 482 00:24:29,800 --> 00:24:33,640 Speaker 10: are people being displaced forced to move from the places 483 00:24:33,680 --> 00:24:36,280 Speaker 10: that they live because of the nature of the conflict. 484 00:24:36,920 --> 00:24:39,640 Speaker 10: But more so than that, some of the interesting factors 485 00:24:39,680 --> 00:24:43,119 Speaker 10: are how is the conflict impacting the agricultural industry in 486 00:24:43,240 --> 00:24:46,080 Speaker 10: Ukraine and what global implications does that have on supply 487 00:24:46,200 --> 00:24:49,960 Speaker 10: chains that flow from this critical area of agriculture. 488 00:24:50,280 --> 00:24:53,119 Speaker 4: Talk about your role in the broader AI sector if 489 00:24:53,119 --> 00:24:54,960 Speaker 4: we can christen that or call it that now and 490 00:24:55,040 --> 00:24:57,320 Speaker 4: we talk a lot about a handful of companies in particularly, 491 00:24:57,320 --> 00:24:59,720 Speaker 4: talk a lot about chip manufacturing as well. As you 492 00:25:00,000 --> 00:25:02,679 Speaker 4: look at the sort of ncency evolution, early evolution of it, 493 00:25:02,720 --> 00:25:04,160 Speaker 4: where are we in that process now? 494 00:25:05,160 --> 00:25:08,240 Speaker 10: So not surprising. A lot of the early focus has 495 00:25:08,320 --> 00:25:11,840 Speaker 10: been on modalities of information that we work with almost 496 00:25:11,880 --> 00:25:18,040 Speaker 10: every day, text, video, imagery, tom's phone, Tom's phone, where 497 00:25:18,080 --> 00:25:22,040 Speaker 10: to find pizza and Newhagen. Yes, some of the interesting 498 00:25:22,119 --> 00:25:24,679 Speaker 10: next set of questions that we're dealing with relate to 499 00:25:24,720 --> 00:25:28,199 Speaker 10: different types of data. How do we help organizations understand 500 00:25:28,560 --> 00:25:30,719 Speaker 10: the information that they have access to that may not 501 00:25:30,760 --> 00:25:34,240 Speaker 10: be as neatly structured in a document or in an 502 00:25:34,280 --> 00:25:37,920 Speaker 10: image file like Satellite imagery is a good example. There 503 00:25:37,960 --> 00:25:41,440 Speaker 10: is an immense amount of information about economic activity, about 504 00:25:41,520 --> 00:25:45,119 Speaker 10: the climate, about other dimensions of change that's happening on 505 00:25:45,119 --> 00:25:48,320 Speaker 10: the planet, and we have satellite images that are capturing 506 00:25:48,359 --> 00:25:51,080 Speaker 10: every square foot of the planet every single day. Now, 507 00:25:51,840 --> 00:25:55,000 Speaker 10: using AI gives us a real time lens on what 508 00:25:55,240 --> 00:25:57,840 Speaker 10: change is unfolding across the planet in a way that 509 00:25:58,200 --> 00:26:02,159 Speaker 10: some of the other other aspects of AI are not 510 00:26:02,520 --> 00:26:03,639 Speaker 10: able to capture today. 511 00:26:03,760 --> 00:26:05,240 Speaker 4: I think that so many of us glam on to 512 00:26:05,359 --> 00:26:08,880 Speaker 4: kind of the Aeschucks factor of the modality that you're 513 00:26:08,920 --> 00:26:11,280 Speaker 4: talking about, how it can affect our lives individually. Do 514 00:26:11,359 --> 00:26:14,159 Speaker 4: you think that people, as if yet comprehend kind of 515 00:26:14,200 --> 00:26:17,200 Speaker 4: the wider implications of what AI will be able to offer, deed, 516 00:26:17,200 --> 00:26:20,000 Speaker 4: what it may offer right now they're. 517 00:26:19,840 --> 00:26:22,080 Speaker 10: Starting to, in part because it's on our phone and 518 00:26:22,119 --> 00:26:26,280 Speaker 10: we have access to magical technology that allows us to 519 00:26:26,320 --> 00:26:29,920 Speaker 10: do things that we couldn't do yesterday. The reality is 520 00:26:29,920 --> 00:26:32,639 Speaker 10: is that a lot of this is just scratching the 521 00:26:32,680 --> 00:26:34,879 Speaker 10: surface of what we're seeing come out of the labs 522 00:26:34,920 --> 00:26:37,919 Speaker 10: in Stanford and Yale and other schools in terms of 523 00:26:38,240 --> 00:26:42,399 Speaker 10: not just how individual models, but how models that can reason, 524 00:26:42,520 --> 00:26:45,760 Speaker 10: that can engage with the world are able to now 525 00:26:46,440 --> 00:26:49,680 Speaker 10: start to enhance the productivity of companies and organizations. 526 00:26:49,760 --> 00:26:54,400 Speaker 2: You are hugely qualified to answer a question. It's understanding, 527 00:26:57,040 --> 00:27:02,760 Speaker 2: But Microsoft and Constellation and three Island fine the thermodynamics 528 00:27:02,760 --> 00:27:06,159 Speaker 2: of all this AI chit chat in the need for electricity. 529 00:27:06,600 --> 00:27:09,359 Speaker 2: To me, there's almost a free lunch to it. What 530 00:27:09,440 --> 00:27:12,040 Speaker 2: will be the cost of having to generate all that 531 00:27:12,119 --> 00:27:14,879 Speaker 2: electricity to power your world? 532 00:27:15,160 --> 00:27:17,760 Speaker 10: Well, I think there's probably two questions in bed there. 533 00:27:17,800 --> 00:27:20,320 Speaker 10: What's the cost and what's the sustainability of being able 534 00:27:20,359 --> 00:27:22,600 Speaker 10: to do so? And the good news is that from 535 00:27:22,640 --> 00:27:27,159 Speaker 10: a sustainability angle, we are now seeing the acceleration of 536 00:27:27,200 --> 00:27:31,240 Speaker 10: new technologies like solar and wind, but also hydrogen and 537 00:27:31,280 --> 00:27:36,280 Speaker 10: other newer energy sources that are going to be prevalent 538 00:27:36,400 --> 00:27:38,600 Speaker 10: in in the next couple of decades. So the energy 539 00:27:38,640 --> 00:27:42,840 Speaker 10: demand will be significant, but I'm also very optimistic that 540 00:27:43,240 --> 00:27:47,080 Speaker 10: this energy will be powered by renewable, sustainable sources of energy. 541 00:27:47,200 --> 00:27:50,080 Speaker 2: What do the seven big stocks? What do they get 542 00:27:50,200 --> 00:27:52,640 Speaker 2: from you? When you talk to Nvidio, when you talk 543 00:27:53,000 --> 00:27:56,440 Speaker 2: to Microsoft? How do they listen to what atlas Ai 544 00:27:56,600 --> 00:27:57,040 Speaker 2: is doing. 545 00:27:57,359 --> 00:27:59,680 Speaker 10: So I'll give you a specific example, and that where 546 00:27:59,760 --> 00:28:02,600 Speaker 10: we partner with Google Cloud extensively. Our technology is built 547 00:28:02,640 --> 00:28:05,800 Speaker 10: on Google Cloud and we're a member of a sustainability 548 00:28:05,800 --> 00:28:09,720 Speaker 10: ecosystem within Google Cloud that's focused on questions of how 549 00:28:09,720 --> 00:28:14,800 Speaker 10: do we help companies advance their sustainability priorities. Many of 550 00:28:15,320 --> 00:28:18,240 Speaker 10: Google has Gemini they have one of the leading large 551 00:28:18,280 --> 00:28:22,480 Speaker 10: language models, but many of their customers are asking a 552 00:28:22,520 --> 00:28:27,280 Speaker 10: whole array of questions related to how does air Bus 553 00:28:27,359 --> 00:28:32,600 Speaker 10: think about anticipating the demand of air travel in Indonesia 554 00:28:32,640 --> 00:28:35,800 Speaker 10: in the future, And so there are questions that language 555 00:28:35,800 --> 00:28:38,360 Speaker 10: models are not by themselves able to answer that at 556 00:28:38,400 --> 00:28:39,640 Speaker 10: let's a I can at. 557 00:28:39,520 --> 00:28:42,440 Speaker 2: Let's say, I question, how about that midtown traffic this 558 00:28:42,600 --> 00:28:46,960 Speaker 2: istout getting over to the lunch. 559 00:28:47,440 --> 00:28:48,720 Speaker 10: I wish I could have told you how to get 560 00:28:48,720 --> 00:28:49,880 Speaker 10: to the east side last night. 561 00:28:51,960 --> 00:28:54,040 Speaker 4: A Let me just stick with infrastructure a little bit here, 562 00:28:54,040 --> 00:28:55,640 Speaker 4: and we hear from Jensen Kwang about, you know, how 563 00:28:55,640 --> 00:28:57,680 Speaker 4: he deploys the chips that his company is making, and 564 00:28:57,720 --> 00:29:00,880 Speaker 4: there's such demand for them, which comes to shove. Is 565 00:29:00,920 --> 00:29:02,960 Speaker 4: that a difficulty for folks like you who are starting 566 00:29:03,040 --> 00:29:06,719 Speaker 4: these businesses in this space, just getting access to the infrastructure, 567 00:29:06,720 --> 00:29:08,960 Speaker 4: getting the chips, to put it bluntly, getting the access 568 00:29:09,000 --> 00:29:11,280 Speaker 4: to these data centers. How much of that is yet 569 00:29:11,360 --> 00:29:13,480 Speaker 4: a hurdle to the kind of development that you'd like 570 00:29:13,520 --> 00:29:14,120 Speaker 4: to see. 571 00:29:14,640 --> 00:29:16,840 Speaker 10: It's a question that we constantly have to face because 572 00:29:16,880 --> 00:29:19,040 Speaker 10: not only are we thinking about the current generation A model, 573 00:29:19,040 --> 00:29:21,920 Speaker 10: but we're also now thinking about next year's generation. The 574 00:29:21,960 --> 00:29:25,880 Speaker 10: pace of training new models is accelerating at such a 575 00:29:25,880 --> 00:29:27,520 Speaker 10: pace that you need to be thinking one or two 576 00:29:27,520 --> 00:29:31,040 Speaker 10: generations down the line. And with each generation the compute 577 00:29:31,120 --> 00:29:36,920 Speaker 10: requirements are increasingly increasing exponentially. I'll say that the last 578 00:29:36,920 --> 00:29:40,040 Speaker 10: few years are likely tougher than the next few years 579 00:29:40,080 --> 00:29:43,680 Speaker 10: will be. The supply chains are starting to work themselves out. 580 00:29:44,440 --> 00:29:47,160 Speaker 10: Nvidia and other companies have been able to now adapt. 581 00:29:47,400 --> 00:29:50,320 Speaker 10: New generations are coming out that are more efficient, so 582 00:29:51,040 --> 00:29:53,560 Speaker 10: that's not one of the elements that we worry most 583 00:29:53,560 --> 00:29:55,440 Speaker 10: about when we think about the next few years growth 584 00:29:55,480 --> 00:29:56,240 Speaker 10: of the business. 585 00:29:56,360 --> 00:29:58,560 Speaker 2: So wonderful come to our world headquarters sometime. I'd love 586 00:29:58,600 --> 00:30:01,440 Speaker 2: to talk to you from our studios. Am Terror Parne 587 00:30:01,440 --> 00:30:09,360 Speaker 2: with us with alis Ai, finally for Global Wall Street. 588 00:30:09,920 --> 00:30:13,320 Speaker 2: An important conversation you think of the Securities and Exchange 589 00:30:13,320 --> 00:30:18,360 Speaker 2: Commission in America f s A in London. How about 590 00:30:18,360 --> 00:30:25,160 Speaker 2: the International Organization of Securities Commissions is OSCO ISCO IOSCO. 591 00:30:25,320 --> 00:30:28,000 Speaker 2: Thank you joining us from my Oscar and Brussels. With 592 00:30:28,160 --> 00:30:32,960 Speaker 2: all the headaches of regulation and cryptocurrencies. Future Jean Paul 593 00:30:33,000 --> 00:30:35,720 Speaker 2: Survey joins us this morning. Wonderful to have you with us. 594 00:30:36,400 --> 00:30:41,360 Speaker 2: Different presidential candidates are saying crypto is part of a 595 00:30:41,440 --> 00:30:47,240 Speaker 2: legitimate American political and economic future. Where does that take 596 00:30:47,360 --> 00:30:52,200 Speaker 2: our understanding of crypto if we see politicians talking it up. 597 00:30:52,400 --> 00:30:56,120 Speaker 11: Yeah, by spot of the political legitimacy debate, it's about innovation. 598 00:30:56,600 --> 00:31:00,360 Speaker 11: We have Securities Markets Regulator. I'm not against imidation, but 599 00:31:00,480 --> 00:31:03,360 Speaker 11: all job is to ensure that there is stress, that 600 00:31:03,440 --> 00:31:06,000 Speaker 11: there are no accidents. And due to the fact that 601 00:31:06,040 --> 00:31:08,680 Speaker 11: we know that most of the time, in fact, we 602 00:31:08,840 --> 00:31:12,040 Speaker 11: have a business developed by cross border I would say 603 00:31:12,120 --> 00:31:15,640 Speaker 11: crypto asset provider, we need to be an iOS come 604 00:31:15,720 --> 00:31:19,480 Speaker 11: means in fact, the Global Membership Organization we have one 605 00:31:19,600 --> 00:31:22,600 Speaker 11: hundred and thirty jo addiction. It means that my coroeague 606 00:31:22,640 --> 00:31:25,280 Speaker 11: and members are involving in the supervision of ninety five 607 00:31:25,320 --> 00:31:28,360 Speaker 11: percent of the financial like the US SEC and usc FTC. 608 00:31:28,840 --> 00:31:30,640 Speaker 11: So we need to be there and that's what we did. 609 00:31:30,720 --> 00:31:34,440 Speaker 11: We endorsed last year a toolkit in order to help 610 00:31:34,520 --> 00:31:37,760 Speaker 11: all job addiction to have an appropriate supervision. It's not 611 00:31:37,880 --> 00:31:40,880 Speaker 11: about the enforcement assets does the job of the national 612 00:31:41,000 --> 00:31:44,160 Speaker 11: agency like in the US. So we try to avoid 613 00:31:44,320 --> 00:31:47,000 Speaker 11: that there are collapses, that they are accident, that there 614 00:31:47,000 --> 00:31:48,600 Speaker 11: are problem about the quality. 615 00:31:48,920 --> 00:31:50,840 Speaker 5: I would say of the of the business. 616 00:31:50,520 --> 00:31:52,640 Speaker 8: There's about there's a new legitimacy lawrence. 617 00:31:52,720 --> 00:31:59,240 Speaker 2: Think of black rockets tracking up everybody else, Airbelt Tuness, Blombergers, encyclopedic. Honest, 618 00:31:59,400 --> 00:32:01,400 Speaker 2: that's all great, but how is. 619 00:32:01,400 --> 00:32:03,320 Speaker 8: The bitcoin underlying? 620 00:32:04,440 --> 00:32:07,800 Speaker 2: The Bank of International Settlements is studied so hard? Is 621 00:32:07,800 --> 00:32:11,480 Speaker 2: there an underlying value like there is to gold or 622 00:32:11,840 --> 00:32:14,000 Speaker 2: you know, what's the latest stock I had at crater? 623 00:32:14,160 --> 00:32:15,040 Speaker 6: There's something there. 624 00:32:15,280 --> 00:32:18,000 Speaker 11: That's a very difficult question. And you are for there, 625 00:32:18,080 --> 00:32:21,360 Speaker 11: of course, because it's a never ending story. All I 626 00:32:21,360 --> 00:32:24,920 Speaker 11: would say objective is to be helpful for discovery. 627 00:32:24,400 --> 00:32:26,200 Speaker 5: Price to avoid. 628 00:32:26,400 --> 00:32:29,120 Speaker 11: Let us be clear many of the accident which took 629 00:32:29,120 --> 00:32:31,760 Speaker 11: place during the crypto meet it was about the absence 630 00:32:32,160 --> 00:32:36,720 Speaker 11: of segregation of upset, something that is quite manageable, I 631 00:32:36,720 --> 00:32:39,320 Speaker 11: would say, and something that it had to be managed 632 00:32:39,360 --> 00:32:43,360 Speaker 11: for thirty years by trading, venue, stocking, change asset manager. 633 00:32:43,680 --> 00:32:46,240 Speaker 11: What we are proposing is to copy and paste this 634 00:32:46,360 --> 00:32:49,640 Speaker 11: well known principle and then to provide that for the 635 00:32:49,680 --> 00:32:50,720 Speaker 11: sake of the supersion. 636 00:32:50,720 --> 00:32:51,960 Speaker 5: When working very. 637 00:32:51,800 --> 00:32:54,600 Speaker 11: Hard with the imas, and I was very happy to 638 00:32:54,640 --> 00:32:57,880 Speaker 11: see that now the I'm start by using old toolkit. 639 00:32:58,240 --> 00:33:01,160 Speaker 11: When they performed their well known mission article for f 640 00:33:01,240 --> 00:33:03,200 Speaker 11: s UP and so it means that we are useful 641 00:33:03,360 --> 00:33:05,880 Speaker 11: and that we are working together with other global standards. 642 00:33:06,040 --> 00:33:07,560 Speaker 4: You'll forgive the phrase, but I imagine a lot of 643 00:33:07,560 --> 00:33:10,120 Speaker 4: your job is hurting cats. You're having to deal with 644 00:33:10,200 --> 00:33:12,480 Speaker 4: all of these regulatory agencies all over the world's which 645 00:33:12,520 --> 00:33:15,200 Speaker 4: have different priorities. How much unanimity is that you can 646 00:33:15,200 --> 00:33:17,000 Speaker 4: take crypto as an example and how we've seen this. 647 00:33:17,400 --> 00:33:19,520 Speaker 4: Maybe arbitrage is a good way of putting it. But 648 00:33:19,960 --> 00:33:22,640 Speaker 4: companies moving from place to place, jurisdiction to jurisdiction because 649 00:33:22,640 --> 00:33:25,240 Speaker 4: they offer different things. Are we seeing a movement toward 650 00:33:25,240 --> 00:33:26,840 Speaker 4: a more unified regulatory lands? 651 00:33:26,920 --> 00:33:28,440 Speaker 5: Yeah, well, thanks for your question. 652 00:33:29,480 --> 00:33:32,960 Speaker 11: That's maybe mycro league thought that a Belgium guy had 653 00:33:33,000 --> 00:33:35,280 Speaker 11: to be elected because the DNA of Belgium is about 654 00:33:35,320 --> 00:33:38,080 Speaker 11: Belgium compromise because we are not sure that our countries 655 00:33:38,120 --> 00:33:41,240 Speaker 11: still exist today after so we need compromise every day. 656 00:33:41,400 --> 00:33:42,120 Speaker 5: We never vote. 657 00:33:42,320 --> 00:33:45,080 Speaker 11: It's like United Nations, except that I think that we 658 00:33:45,120 --> 00:33:45,800 Speaker 11: are able to. 659 00:33:45,800 --> 00:33:47,120 Speaker 5: Take get things do OK. 660 00:33:48,520 --> 00:33:52,680 Speaker 11: So it's consensus because it's a global membership organization, and 661 00:33:52,720 --> 00:33:55,560 Speaker 11: I think that we are able to find consensus because 662 00:33:55,880 --> 00:33:59,280 Speaker 11: whenever you have global challenges, risk and ability. We are 663 00:33:59,280 --> 00:34:01,920 Speaker 11: here because some members are involved in the supersion of 664 00:34:02,000 --> 00:34:03,800 Speaker 11: ninety five percent or much of. 665 00:34:03,760 --> 00:34:06,360 Speaker 5: It of members are in fact grow and emergent market. 666 00:34:06,760 --> 00:34:07,240 Speaker 5: So when we. 667 00:34:07,160 --> 00:34:10,920 Speaker 11: Speak about crypto assets, that's a danger also for crypto 668 00:34:10,960 --> 00:34:14,800 Speaker 11: and an opportunity also for many countries in Africa. 669 00:34:14,920 --> 00:34:17,000 Speaker 5: The same for climate fans. It's cross border. 670 00:34:17,640 --> 00:34:21,560 Speaker 2: I understand that can be banking transactions, a cross water transactions, 671 00:34:21,960 --> 00:34:24,160 Speaker 2: but I still don't see out there a lot of 672 00:34:24,360 --> 00:34:27,920 Speaker 2: use of crypto in day to day transactions. Do you 673 00:34:28,080 --> 00:34:31,319 Speaker 2: envision out there while reptore will actually be a daily 674 00:34:31,280 --> 00:34:32,239 Speaker 2: to day transaction. 675 00:34:33,000 --> 00:34:35,160 Speaker 11: I think that you are right to be product. There 676 00:34:35,160 --> 00:34:38,880 Speaker 11: are many expectations. I think the first step if you 677 00:34:38,960 --> 00:34:41,920 Speaker 11: want to have confidence in crypto is to have a 678 00:34:42,000 --> 00:34:44,399 Speaker 11: solid frame at global frame, and that's what we are 679 00:34:44,400 --> 00:34:46,560 Speaker 11: doing for the time being, and we are going the 680 00:34:46,640 --> 00:34:49,480 Speaker 11: right direction because I think that all tool kit was 681 00:34:49,520 --> 00:34:52,160 Speaker 11: able to take into account what happens last year, the 682 00:34:52,239 --> 00:34:55,439 Speaker 11: crypto winter, some accident, and that's the objective of IO 683 00:34:55,800 --> 00:34:56,600 Speaker 11: to be helpful for the. 684 00:34:56,600 --> 00:34:59,040 Speaker 5: Members, like for climate finances. That's the reason why the 685 00:34:59,080 --> 00:35:00,000 Speaker 5: pleasure to be here. 686 00:35:00,920 --> 00:35:02,799 Speaker 4: Let's pull back a bit as you look at the 687 00:35:02,840 --> 00:35:05,319 Speaker 4: broader landscape, are there pockets of risk or things that 688 00:35:05,760 --> 00:35:07,879 Speaker 4: give you pause for concern at this point in time 689 00:35:07,920 --> 00:35:10,680 Speaker 4: when it comes to sort of the regulatory risk regulatory landscape, 690 00:35:10,719 --> 00:35:11,400 Speaker 4: yeh as. 691 00:35:11,320 --> 00:35:15,080 Speaker 11: Ioscop chair, I think that there are too global issue 692 00:35:15,480 --> 00:35:21,080 Speaker 11: climate finance nbify the former shadow banking climate finance. If 693 00:35:21,080 --> 00:35:23,440 Speaker 11: you are not able to speak more or less the 694 00:35:23,480 --> 00:35:26,839 Speaker 11: same language to use the same I would say, sustainty 695 00:35:26,920 --> 00:35:30,520 Speaker 11: reporting standard together data as much as possible, and that's 696 00:35:30,520 --> 00:35:33,600 Speaker 11: the reason why the initiative taken by Mike Bloomberg and 697 00:35:33,680 --> 00:35:37,280 Speaker 11: Emmanuel McCoy is going the right diction to extract data. 698 00:35:37,440 --> 00:35:41,239 Speaker 11: We will have many questions about greenwashing because green washing 699 00:35:41,360 --> 00:35:44,360 Speaker 11: means most of the time the lack of capacity to 700 00:35:44,440 --> 00:35:46,360 Speaker 11: use the same language. And that's the reason why we 701 00:35:46,480 --> 00:35:49,120 Speaker 11: endorsed last yearsb stun. It means at the end of 702 00:35:49,200 --> 00:35:52,879 Speaker 11: the day, one hundred and thirty thousand company will use 703 00:35:52,880 --> 00:35:53,520 Speaker 11: the same stand. 704 00:35:53,560 --> 00:35:54,720 Speaker 5: It's a giant step. 705 00:35:54,840 --> 00:35:58,200 Speaker 11: Let us be clear, who could believe that some years ago, 706 00:35:58,480 --> 00:36:00,960 Speaker 11: and it's a recent and it's thanks to the devolvement 707 00:36:01,000 --> 00:36:03,719 Speaker 11: of I school. The same for shadow banking, I would say, 708 00:36:03,719 --> 00:36:07,120 Speaker 11: we know the evolution after the subprime crisis, and we 709 00:36:07,160 --> 00:36:10,960 Speaker 11: are not working I would say, very intensely with the BIS, 710 00:36:11,000 --> 00:36:14,160 Speaker 11: with the FSB, we are reporting to the G twenty 711 00:36:14,760 --> 00:36:16,640 Speaker 11: leaders in order to develop new tools. 712 00:36:16,680 --> 00:36:17,520 Speaker 5: For instance, we were. 713 00:36:17,440 --> 00:36:20,760 Speaker 11: Able to get with this SB to publish a useful 714 00:36:20,800 --> 00:36:23,560 Speaker 11: toolkit for the liquidity of manager tool. We are not 715 00:36:23,680 --> 00:36:26,040 Speaker 11: working on leverage in order to keep to account that 716 00:36:26,120 --> 00:36:28,480 Speaker 11: ons if our chare goes, the games and so on, 717 00:36:28,840 --> 00:36:31,040 Speaker 11: so there is always something to learn, I would say, 718 00:36:31,080 --> 00:36:33,960 Speaker 11: and then to make progress about new toolkits. 719 00:36:34,040 --> 00:36:34,799 Speaker 8: Thank you so much. 720 00:36:35,600 --> 00:36:37,080 Speaker 5: We thank you with I ask. 721 00:36:37,480 --> 00:36:40,640 Speaker 2: This is the Bloomberg Surveillance Podcast, bringing you the best 722 00:36:40,640 --> 00:36:45,440 Speaker 2: in economics, finance, investment, and international relations. You can also 723 00:36:45,520 --> 00:36:49,560 Speaker 2: watch the show live on YouTube. Visit the Bloomberg Podcast 724 00:36:49,680 --> 00:36:53,680 Speaker 2: channel on YouTube to see the show weekday mornings from 725 00:36:53,760 --> 00:36:57,000 Speaker 2: seven to ten am Eastern from our global headquarters in 726 00:36:57,080 --> 00:37:00,799 Speaker 2: New York City. Subscribe to the podcast on Apple, Spotify, 727 00:37:01,160 --> 00:37:04,680 Speaker 2: or anywhere else you listen. And always I'm Bloomberg Radio, 728 00:37:04,880 --> 00:37:08,080 Speaker 2: the Bloomberg Terminal, and the Bloomberg Business App.