1 00:00:02,920 --> 00:00:07,240 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:09,960 --> 00:00:13,840 Speaker 2: You're listening to the Bloomberg Intelligence Podcast. Catch us live 3 00:00:13,920 --> 00:00:17,280 Speaker 2: weekdays at ten am Eastern on apocarplaying and broud Auto 4 00:00:17,360 --> 00:00:20,280 Speaker 2: with the Bloomberg Business App. Listen on demand wherever you 5 00:00:20,320 --> 00:00:24,000 Speaker 2: get your podcasts, or watch us live on YouTube. 6 00:00:24,920 --> 00:00:28,360 Speaker 3: Let's get more on you Mish my favorite indicator. Joanshu Is, 7 00:00:28,480 --> 00:00:32,120 Speaker 3: University of Michigan, Surveys of Consumer Director breaking down these 8 00:00:32,200 --> 00:00:35,800 Speaker 3: numbers for us, Joanne, it feels like it's a non mover. Really, 9 00:00:35,920 --> 00:00:38,400 Speaker 3: like everything is status quo. Can you walk me through 10 00:00:38,400 --> 00:00:41,040 Speaker 3: what you see in these numbers? Absolutely so. 11 00:00:41,159 --> 00:00:44,960 Speaker 4: Overall it is essentially flat from last from last month. However, 12 00:00:45,040 --> 00:00:48,560 Speaker 4: that does obscure some pretty substantial movements within the population. 13 00:00:49,479 --> 00:00:51,559 Speaker 3: First of all, you know, election news has. 14 00:00:51,400 --> 00:00:53,960 Speaker 4: Been really dominating the headlines, and we see that in 15 00:00:53,960 --> 00:00:58,040 Speaker 4: our data. Sentiment for Democrats improved six percent this month, 16 00:00:58,080 --> 00:01:00,800 Speaker 4: but this ended up being offset by a five decline 17 00:01:01,120 --> 00:01:06,240 Speaker 4: for Republicans. And these movements were entirely in expectations, which 18 00:01:07,080 --> 00:01:10,479 Speaker 4: is sensible given that you know, election developments. Kamala Harris 19 00:01:10,520 --> 00:01:14,560 Speaker 4: becoming the new nominee for the Democrats is not something 20 00:01:14,600 --> 00:01:17,840 Speaker 4: that affects current assessments, But is something that affects people's 21 00:01:17,840 --> 00:01:19,319 Speaker 4: outlook for the future, and. 22 00:01:19,280 --> 00:01:21,240 Speaker 5: How can we expect that to evolve as we get 23 00:01:21,280 --> 00:01:22,080 Speaker 5: closer to the election. 24 00:01:22,160 --> 00:01:24,160 Speaker 6: Since we are still only in mid August. 25 00:01:24,560 --> 00:01:27,080 Speaker 4: It's going to be volatile, or that's what I would expect. 26 00:01:27,720 --> 00:01:31,280 Speaker 4: So far, consumers are really folding in who they think 27 00:01:31,360 --> 00:01:33,240 Speaker 4: is going to win the election. But of course, as 28 00:01:33,240 --> 00:01:37,720 Speaker 4: the campaign season evolves, people's expectations for the election are 29 00:01:37,760 --> 00:01:41,080 Speaker 4: going to change, and as such their their future expectations 30 00:01:41,080 --> 00:01:42,600 Speaker 4: for the macroeconomy will as well. 31 00:01:43,040 --> 00:01:44,720 Speaker 3: What are some of the other things that you noticed, 32 00:01:45,120 --> 00:01:48,400 Speaker 3: just in terms of either future expectation or current conditions, 33 00:01:48,400 --> 00:01:49,600 Speaker 3: I should say which did improve? 34 00:01:51,360 --> 00:01:55,160 Speaker 4: Yeah, current conditions really didn't move much at all, and 35 00:01:55,240 --> 00:01:58,040 Speaker 4: if anything, it edged down just a little bit. Consumers 36 00:01:58,080 --> 00:02:01,640 Speaker 4: continue to be very frustrated by hype prices that continues 37 00:02:01,680 --> 00:02:04,640 Speaker 4: to be top of mind. At the same time, consumers 38 00:02:04,680 --> 00:02:08,840 Speaker 4: do expect inflation to continue stabilizing. They've been already. They 39 00:02:08,880 --> 00:02:11,440 Speaker 4: weren't really reacting to a CPI print or any data 40 00:02:11,440 --> 00:02:14,040 Speaker 4: that came out this month. They were really already folding 41 00:02:14,040 --> 00:02:18,919 Speaker 4: in their experiences throughout the month, their own experiences shopping 42 00:02:19,000 --> 00:02:20,679 Speaker 4: and seeing the prices around them. 43 00:02:20,880 --> 00:02:23,760 Speaker 5: And just looking at the report, so we see thirty 44 00:02:23,760 --> 00:02:26,840 Speaker 5: five percent respondents expect unemployment rate to rise in the 45 00:02:26,880 --> 00:02:30,160 Speaker 5: coming year. Growing number expect interest rates to pull back. 46 00:02:30,360 --> 00:02:33,720 Speaker 5: How does that kind of impact consumer sentiment and kind 47 00:02:33,760 --> 00:02:36,240 Speaker 5: of where we could be in the coming months. 48 00:02:37,080 --> 00:02:39,919 Speaker 4: So consumers aren't really that concerned right now that labor 49 00:02:39,960 --> 00:02:42,760 Speaker 4: markets may be softening. So the thirty five percent expecting 50 00:02:42,840 --> 00:02:45,960 Speaker 4: unemployment rates to go up, that's unchanged from last month, 51 00:02:46,400 --> 00:02:50,520 Speaker 4: So that's pretty stable. Income expectations have edged up, improved 52 00:02:50,560 --> 00:02:52,720 Speaker 4: just a little bit, or have gone sideways. There's no 53 00:02:52,840 --> 00:02:57,240 Speaker 4: weakening in consumer views of labor markets in August. That 54 00:02:57,360 --> 00:03:00,520 Speaker 4: being said, strength and labor markets is precisely It has 55 00:03:00,880 --> 00:03:04,880 Speaker 4: supported robust consumer spending over the last two years in 56 00:03:04,880 --> 00:03:07,440 Speaker 4: spite of the fact that consumers don't feel like they're thriving. 57 00:03:07,680 --> 00:03:10,079 Speaker 4: And so if there is some unraveling in labor markets, 58 00:03:10,120 --> 00:03:12,720 Speaker 4: some softening in labor markets, I would expect consumers to 59 00:03:12,760 --> 00:03:13,480 Speaker 4: pull back. 60 00:03:13,320 --> 00:03:16,960 Speaker 3: All right, Joan, that being yeah, oh no, finish up. Sorry, yeah. 61 00:03:17,000 --> 00:03:20,399 Speaker 4: And part of the reason why that consumers I think 62 00:03:20,520 --> 00:03:22,840 Speaker 4: feel some confidence about labor markets is that they do 63 00:03:22,919 --> 00:03:25,480 Speaker 4: expect interest rates to come down in the year ahead. 64 00:03:25,400 --> 00:03:27,160 Speaker 3: Which leads us right back to the FED. Hey, Joanne, 65 00:03:27,160 --> 00:03:28,960 Speaker 3: we do appreciate it, thank you very much. Joining to 66 00:03:29,280 --> 00:03:32,080 Speaker 3: the University of Michigan Surveys of Consumer Director. Joining us 67 00:03:32,080 --> 00:03:34,680 Speaker 3: on those numbers, A sentiment came in a touch better, 68 00:03:34,920 --> 00:03:38,400 Speaker 3: current conditions a touch flower, and expectations a touch higher 69 00:03:38,760 --> 00:03:39,280 Speaker 3: as well. 70 00:03:40,880 --> 00:03:44,760 Speaker 2: You're listening to the Bloomberg Intelligence Podcast. Catch us live 71 00:03:44,840 --> 00:03:48,360 Speaker 2: weekdays at ten am Eastern on applecar Play and Android 72 00:03:48,400 --> 00:03:51,160 Speaker 2: Auto with the Bloomberg Business App. You can also listen 73 00:03:51,280 --> 00:03:54,360 Speaker 2: live on Amazon Alexa from our flagship New York station. 74 00:03:54,760 --> 00:03:58,240 Speaker 2: Just say Alexa play Bloomberg eleven thirty. 75 00:03:59,120 --> 00:04:01,920 Speaker 3: All right, let's get in here now and talk about 76 00:04:01,920 --> 00:04:04,840 Speaker 3: how you think about these markets after the volatility that 77 00:04:04,840 --> 00:04:06,440 Speaker 3: we've seen the last couple weeks. But if you went 78 00:04:06,480 --> 00:04:08,760 Speaker 3: on vacation two weeks ago and came back now, you 79 00:04:08,800 --> 00:04:10,480 Speaker 3: didn't really miss much at the end of the day, 80 00:04:10,600 --> 00:04:13,560 Speaker 3: which is also kind of crazy. Shana Sissel joins us. 81 00:04:13,560 --> 00:04:17,000 Speaker 3: She's president and CEO of Benri owned Capital Management, and 82 00:04:17,040 --> 00:04:20,360 Speaker 3: she joins us, Now, Shana, isn't that funny two weeks ago. 83 00:04:20,440 --> 00:04:22,359 Speaker 3: It's like it never happened. I mean, that kind of 84 00:04:22,360 --> 00:04:24,960 Speaker 3: rebound in the S and P is quite striking. How 85 00:04:25,000 --> 00:04:27,320 Speaker 3: does that set you up now for the next couple months. 86 00:04:28,279 --> 00:04:30,200 Speaker 7: I always say the best thing we can do for 87 00:04:30,320 --> 00:04:33,120 Speaker 7: clients sometimes is maybe take away their password to their 88 00:04:33,200 --> 00:04:36,279 Speaker 7: brokerage accounts so they're not checking it every day, because 89 00:04:36,279 --> 00:04:38,400 Speaker 7: if they didn't, they would sleep a lot better at 90 00:04:38,480 --> 00:04:43,040 Speaker 7: night for that exact reason. The market has short memory 91 00:04:43,240 --> 00:04:46,000 Speaker 7: these days because we have so much information out there 92 00:04:46,000 --> 00:04:48,800 Speaker 7: in the twenty four hour news cycle. But I think 93 00:04:48,800 --> 00:04:53,760 Speaker 7: the volatility, you know, Professor Siegel was on another network 94 00:04:53,920 --> 00:04:56,880 Speaker 7: earlier this week taking back everything he said when the 95 00:04:56,920 --> 00:05:01,000 Speaker 7: panic set in, and if the professionals that but you know, 96 00:05:01,160 --> 00:05:07,760 Speaker 7: investors look to for guidance, are inclined to panic, I 97 00:05:07,800 --> 00:05:09,839 Speaker 7: think that that says a lot about kind of the 98 00:05:09,960 --> 00:05:14,960 Speaker 7: overall sentiment and psychology of the market right now. So 99 00:05:15,200 --> 00:05:18,679 Speaker 7: it doesn't surprise me that, you know, as information became available, 100 00:05:18,760 --> 00:05:21,920 Speaker 7: people stopped freaking out about the end carry trade. But 101 00:05:21,960 --> 00:05:23,640 Speaker 7: it doesn't change the fact that we are in a 102 00:05:23,640 --> 00:05:28,000 Speaker 7: slowing economy. We've had good data, and so I think 103 00:05:28,080 --> 00:05:30,520 Speaker 7: people feel confident the FED is definitely going to cut 104 00:05:30,520 --> 00:05:34,200 Speaker 7: twenty five basis points in September, and I think that 105 00:05:34,440 --> 00:05:38,120 Speaker 7: if you are a long term investor that there's some 106 00:05:38,200 --> 00:05:41,680 Speaker 7: opportunities when people freak out like that to take advantage of, 107 00:05:42,839 --> 00:05:45,120 Speaker 7: you know, the psychology of the market and buy things 108 00:05:45,120 --> 00:05:45,880 Speaker 7: at a discount. 109 00:05:46,320 --> 00:05:49,599 Speaker 5: Shanna Jackson Hole next week, What are you looking for 110 00:05:49,640 --> 00:05:51,600 Speaker 5: from j Powell? What can you say that can either 111 00:05:51,760 --> 00:05:53,640 Speaker 5: reinforce the view that we're going to get a twenty 112 00:05:53,640 --> 00:05:56,039 Speaker 5: five if not a fifty basis point cut, or could 113 00:05:56,040 --> 00:05:58,320 Speaker 5: he even kind of throw a wrench into things and 114 00:05:58,640 --> 00:06:00,560 Speaker 5: maybe push that for they're out again. 115 00:06:01,680 --> 00:06:03,719 Speaker 7: I can't imagine where he's going to throw a wrench 116 00:06:03,760 --> 00:06:07,840 Speaker 7: in things. I do think that fifty base points is 117 00:06:07,880 --> 00:06:12,640 Speaker 7: a stretch. There's not enough data that suggests that we 118 00:06:12,760 --> 00:06:16,120 Speaker 7: need to do a larger than expected cut, and I 119 00:06:16,160 --> 00:06:19,600 Speaker 7: think the Fed is cautious to not do something so 120 00:06:20,600 --> 00:06:27,279 Speaker 7: abrupt and significant that it would upset the overall market sentiments. 121 00:06:27,360 --> 00:06:31,080 Speaker 7: As I've noted a couple of times, does matter, and 122 00:06:31,400 --> 00:06:35,520 Speaker 7: I expect that he will continue the same narrative that 123 00:06:35,560 --> 00:06:38,839 Speaker 7: he's had for quite some time, which is that the 124 00:06:38,920 --> 00:06:44,680 Speaker 7: economy is stable, it's doing okay. That data shows that 125 00:06:44,720 --> 00:06:48,720 Speaker 7: it's slowing down and softening, but not of concern, and 126 00:06:48,920 --> 00:06:51,720 Speaker 7: it supports a twenty five base point cut, and I 127 00:06:51,760 --> 00:06:55,200 Speaker 7: think that's what he'll continue to say. I've watched him 128 00:06:55,200 --> 00:06:57,640 Speaker 7: long enough now to know that he's not one to 129 00:06:57,800 --> 00:07:01,440 Speaker 7: like very quickly change his tone. Last time he was 130 00:07:01,480 --> 00:07:05,719 Speaker 7: out speaking, he was, you know, not necessarily overly dubvish, 131 00:07:05,720 --> 00:07:11,440 Speaker 7: and I don't expect him to suddenly become overly duvish. 132 00:07:09,800 --> 00:07:14,280 Speaker 3: Which to be fair, most other ex FRED presidents aren't either. 133 00:07:14,440 --> 00:07:16,320 Speaker 3: So there's that as well. So we're kind of like 134 00:07:16,400 --> 00:07:18,520 Speaker 3: wrapping up the earning season. We got some retail that's 135 00:07:18,520 --> 00:07:20,280 Speaker 3: trickling out obviously in video is going to be a 136 00:07:20,280 --> 00:07:23,679 Speaker 3: big event at the end of August. But what stocks 137 00:07:23,680 --> 00:07:25,800 Speaker 3: do you like after we're kind of past the real 138 00:07:25,880 --> 00:07:28,640 Speaker 3: hump of earning season headed into an election cycle or 139 00:07:28,680 --> 00:07:30,200 Speaker 3: policy will be very unclear. 140 00:07:31,680 --> 00:07:34,480 Speaker 7: Yeah, So I have kind of a wide variety of 141 00:07:34,720 --> 00:07:38,160 Speaker 7: different types of stocks that I like. You know, I 142 00:07:38,880 --> 00:07:43,920 Speaker 7: like a name like Atlas Holdings, which is Ensure, a 143 00:07:43,920 --> 00:07:48,800 Speaker 7: commercial insurance firm, followed it for a while since my 144 00:07:48,920 --> 00:07:52,880 Speaker 7: days at Ariel, and it's a really interesting company with 145 00:07:52,920 --> 00:07:56,680 Speaker 7: a really unique niche that is trading at a discount 146 00:07:56,720 --> 00:08:01,400 Speaker 7: and has some relatively attractive upside. Pretty born, it's in financials. 147 00:08:01,960 --> 00:08:05,000 Speaker 7: You know, so people it's not sexy like in video 148 00:08:05,120 --> 00:08:09,240 Speaker 7: or any of the AI plays. V RT is an 149 00:08:09,280 --> 00:08:14,000 Speaker 7: AI play, but it's a second derivative of traditional AI. 150 00:08:14,120 --> 00:08:19,160 Speaker 7: It's data center cooling, which is really important because in 151 00:08:20,040 --> 00:08:24,200 Speaker 7: you know, this growing AI demand and more and more 152 00:08:24,200 --> 00:08:27,520 Speaker 7: people looking to incorporate it, you need greater computing power, 153 00:08:28,320 --> 00:08:31,880 Speaker 7: and thus you need a lot stronger or a lot 154 00:08:31,920 --> 00:08:35,120 Speaker 7: more powerful cooling systems, of which v RT can bring 155 00:08:35,160 --> 00:08:38,120 Speaker 7: to the table. I also like Novartists, which is a 156 00:08:38,120 --> 00:08:40,320 Speaker 7: healthcare company. So I'm a little all over the place, 157 00:08:41,240 --> 00:08:43,760 Speaker 7: happy to go down any one of those roads for you. 158 00:08:44,000 --> 00:08:47,319 Speaker 7: But yeah, I'm trying. I am trying to find ideas 159 00:08:47,520 --> 00:08:52,520 Speaker 7: outside of what has been the hot trend because you 160 00:08:52,600 --> 00:08:55,320 Speaker 7: want to get into other areas when you start to 161 00:08:55,320 --> 00:08:57,680 Speaker 7: see dispersion of returns, which we have seen a little 162 00:08:57,679 --> 00:09:01,160 Speaker 7: bit of, and you want to take advantage of opportunities 163 00:09:01,200 --> 00:09:02,800 Speaker 7: to be a stock picker. 164 00:09:03,280 --> 00:09:05,080 Speaker 5: So yeah, I guess kind of when you look at 165 00:09:05,080 --> 00:09:07,600 Speaker 5: how you're playing AI. You mentioned Verdev. I covered them 166 00:09:07,800 --> 00:09:10,160 Speaker 5: when they went public through a spac so you're looking 167 00:09:10,160 --> 00:09:12,400 Speaker 5: at kind of these derivative plays when you're thinking about 168 00:09:12,520 --> 00:09:15,120 Speaker 5: implementation of artificial intelligence and who can be the winners 169 00:09:15,240 --> 00:09:16,040 Speaker 5: or losers. 170 00:09:16,640 --> 00:09:20,840 Speaker 7: Yeah, that's exactly what I'm doing. I'm someone who's followed 171 00:09:20,880 --> 00:09:23,360 Speaker 7: and loved in video and been talking about in VideA 172 00:09:23,440 --> 00:09:28,120 Speaker 7: now for years, and it has served me well. You know, 173 00:09:28,160 --> 00:09:31,679 Speaker 7: I've made a lot of money for clients and in 174 00:09:31,679 --> 00:09:35,240 Speaker 7: that stock, and I'm still very much bullish on the stock. 175 00:09:35,280 --> 00:09:37,840 Speaker 7: I think the trends are very favorable for the long term, 176 00:09:37,840 --> 00:09:41,400 Speaker 7: and we're still very early in the AI cycle. That said, 177 00:09:42,200 --> 00:09:44,640 Speaker 7: I don't think it can maintain the growth rate that 178 00:09:44,679 --> 00:09:46,560 Speaker 7: it's had going forward. 179 00:09:46,600 --> 00:09:46,760 Speaker 6: Now. 180 00:09:46,800 --> 00:09:50,120 Speaker 7: One hundred percent growth is still really impressive, but when 181 00:09:50,160 --> 00:09:53,560 Speaker 7: you were at three hundred percent growth, it it feels 182 00:09:53,559 --> 00:09:56,439 Speaker 7: a little disappointing. And and so I'm trying to find 183 00:09:56,559 --> 00:10:00,200 Speaker 7: other ideas that will benefit from AI. Vertiv is one 184 00:10:00,280 --> 00:10:03,280 Speaker 7: of them. I've talked a little bit about Cisco in 185 00:10:03,320 --> 00:10:05,120 Speaker 7: the past, and i know they're all in the news 186 00:10:05,160 --> 00:10:09,000 Speaker 7: with the announcement of their layoffs, but that's another player 187 00:10:09,040 --> 00:10:12,160 Speaker 7: that has some second derivative exposure in AI as well, 188 00:10:13,200 --> 00:10:18,400 Speaker 7: and trading very cheaply and obviously making so interesting, unattractive 189 00:10:18,880 --> 00:10:24,920 Speaker 7: or unpopular decisions internally, but typically those decisions are made 190 00:10:25,480 --> 00:10:30,200 Speaker 7: to improve overall financial performance. So that's another name, but 191 00:10:30,520 --> 00:10:33,600 Speaker 7: it's not on my by list. But it isn't another 192 00:10:33,720 --> 00:10:36,240 Speaker 7: like second derivative play And that's kind of what you 193 00:10:36,280 --> 00:10:37,360 Speaker 7: have to look at at this point. 194 00:10:37,679 --> 00:10:40,720 Speaker 3: Heyhana really appreciate it. That's such a great point about Cisco. 195 00:10:40,720 --> 00:10:44,880 Speaker 3: Shana Sizzel joining us. She's president CEO of Benrionna Capital 196 00:10:45,040 --> 00:10:46,839 Speaker 3: Management joining us. 197 00:10:46,880 --> 00:10:52,080 Speaker 2: There you're listening to the Bloomberg Intelligence Podcast. Catch us 198 00:10:52,120 --> 00:10:55,040 Speaker 2: live weekdays at ten am Eastern on Effo card Play 199 00:10:55,040 --> 00:10:57,600 Speaker 2: and then Broud Auto with the Bloomberg Business app. Listen 200 00:10:57,679 --> 00:11:00,800 Speaker 2: on demand wherever you get your podcasts a watch us 201 00:11:00,840 --> 00:11:02,559 Speaker 2: live on YouTube. 202 00:11:03,480 --> 00:11:06,199 Speaker 3: I'm Alex Steel alongside Bailly Lipschutz. Paul Sweeney is setting 203 00:11:06,240 --> 00:11:08,640 Speaker 3: himself at the beach today. This is Bloomberg Intelligence Radio. 204 00:11:08,679 --> 00:11:10,480 Speaker 3: We bring you all the top news in business and 205 00:11:10,520 --> 00:11:13,840 Speaker 3: finance and economics through our lens of our Bloomberg Intelligence analysts. 206 00:11:13,840 --> 00:11:16,080 Speaker 3: They covered two thousand companies and one hundred and thirty 207 00:11:16,120 --> 00:11:19,480 Speaker 3: industries all around the world. One topic we wanted to 208 00:11:19,520 --> 00:11:23,360 Speaker 3: tackle today was empox. So the who declared a fast 209 00:11:23,440 --> 00:11:27,000 Speaker 3: spreading empox outbreak in Africa a global health emergency as 210 00:11:27,000 --> 00:11:29,679 Speaker 3: the agency seeks to contain the spread of the potentially 211 00:11:29,760 --> 00:11:34,200 Speaker 3: deadly virus. WHO Director General sounded that alarm at a 212 00:11:34,200 --> 00:11:35,440 Speaker 3: press conference on Wednesday. 213 00:11:36,040 --> 00:11:39,120 Speaker 8: WHR has been working on the mpox outbreak in Africa 214 00:11:39,160 --> 00:11:42,440 Speaker 8: and raising the alarm that this is something that should 215 00:11:42,520 --> 00:11:47,280 Speaker 8: concern us all. The Emergency Committee met and advised me 216 00:11:47,640 --> 00:11:52,640 Speaker 8: that in its view, the situation constitutes a publical emergency 217 00:11:52,679 --> 00:11:55,920 Speaker 8: of international concern joining us. 218 00:11:55,920 --> 00:11:56,040 Speaker 4: Now. 219 00:11:56,080 --> 00:11:59,440 Speaker 3: Sam Fazzelli, Bloomberg Intelligence director of Research for Global Industries 220 00:11:59,440 --> 00:12:03,160 Speaker 3: and senior Forharmaceuticals analyst, is standing by. I hear you're 221 00:12:03,200 --> 00:12:06,040 Speaker 3: in Bordeaux. I can only assume there's a lot of 222 00:12:06,040 --> 00:12:07,040 Speaker 3: work happening there. 223 00:12:08,080 --> 00:12:11,240 Speaker 9: Oh lots, Alex, lots. I haven't moved from this seat 224 00:12:11,280 --> 00:12:12,640 Speaker 9: for the past seven hours. 225 00:12:12,840 --> 00:12:15,560 Speaker 3: I mean, he could be telling the truth. Hey, Sam, 226 00:12:15,600 --> 00:12:18,400 Speaker 3: when I have you? I assume when I heard the 227 00:12:18,440 --> 00:12:21,200 Speaker 3: Empire's headline, I was like, I gotta get Sam on, 228 00:12:22,200 --> 00:12:25,080 Speaker 3: give us the basics, like what is it and how 229 00:12:25,160 --> 00:12:25,959 Speaker 3: dangerous is it? 230 00:12:27,720 --> 00:12:27,920 Speaker 5: Yeah? 231 00:12:28,000 --> 00:12:32,760 Speaker 9: So, Alex, I think when the WHO Director General declares 232 00:12:32,840 --> 00:12:38,520 Speaker 9: these emergencies, it's that that's already quite a significant and 233 00:12:38,559 --> 00:12:41,040 Speaker 9: important milestone for a disease. And as you know, it 234 00:12:41,120 --> 00:12:45,200 Speaker 9: has already there's been some an individual in Sweden apparently 235 00:12:45,240 --> 00:12:48,199 Speaker 9: there's been diagnosed with it. So and as I said 236 00:12:48,240 --> 00:12:50,960 Speaker 9: all along, infectious diseases are not about a country, they're 237 00:12:50,960 --> 00:12:54,280 Speaker 9: not about a continent. They spread. That's the job of 238 00:12:54,320 --> 00:12:57,680 Speaker 9: an infectious agent. So let's remember that as we talked 239 00:12:57,720 --> 00:13:00,240 Speaker 9: through the rest of this conversation, and what we got 240 00:13:00,320 --> 00:13:03,000 Speaker 9: here is not quite a replay of what we had 241 00:13:03,000 --> 00:13:06,080 Speaker 9: two years ago, which was a slightly different version of 242 00:13:06,120 --> 00:13:10,680 Speaker 9: this monkey pox virus that this is called empox now. 243 00:13:11,360 --> 00:13:14,400 Speaker 9: And the virus comes in two flavors, Played one Clay 244 00:13:14,480 --> 00:13:18,120 Speaker 9: two that kind of looks at it's based on its 245 00:13:18,160 --> 00:13:21,480 Speaker 9: genomic structure, and this particular one is played one, which 246 00:13:21,520 --> 00:13:29,520 Speaker 9: is normally less but often found. I hadn't you know. 247 00:13:29,559 --> 00:13:32,000 Speaker 9: The last one was klay two. And this Klaye one 248 00:13:32,000 --> 00:13:33,920 Speaker 9: has got a major chunk of its genome that's been 249 00:13:34,000 --> 00:13:36,800 Speaker 9: taken out, and those who study these things have been 250 00:13:36,840 --> 00:13:41,280 Speaker 9: writing that it has made it more transmissible through not 251 00:13:41,559 --> 00:13:46,320 Speaker 9: just contact with us, for example, but also through sexual activity, 252 00:13:46,320 --> 00:13:50,760 Speaker 9: which is not what klayed one previously had, but also 253 00:13:51,000 --> 00:13:55,320 Speaker 9: just very close contact. So unlike COVID, if you remember, 254 00:13:55,320 --> 00:13:57,280 Speaker 9: there were stories with COVID where you go into a 255 00:13:57,320 --> 00:14:01,160 Speaker 9: restaurant and you sit diagonally across twenty thirty feet away, 256 00:14:01,160 --> 00:14:03,160 Speaker 9: and you still have a risk of catching it because 257 00:14:03,160 --> 00:14:07,120 Speaker 9: it gets blown around by the air conditioning. I don't 258 00:14:07,120 --> 00:14:09,240 Speaker 9: think we're at that level with empocs yet. I don't 259 00:14:09,240 --> 00:14:11,800 Speaker 9: with this particular clade. Won't be I can't be one 260 00:14:11,880 --> 00:14:13,920 Speaker 9: hundred percent shore, but I don't think it's like that. 261 00:14:14,320 --> 00:14:16,520 Speaker 9: You still do need the close contact, and that's where 262 00:14:16,600 --> 00:14:19,240 Speaker 9: we're That's where my knowledge kind of ends at the minute. 263 00:14:19,440 --> 00:14:23,239 Speaker 9: These viruses do have a nasty habit of bringing surprises 264 00:14:23,280 --> 00:14:23,600 Speaker 9: to us. 265 00:14:23,800 --> 00:14:26,560 Speaker 5: Well, Sam, that's my next question would be how quickly 266 00:14:26,560 --> 00:14:29,240 Speaker 5: can this evolve in what is kind of that look 267 00:14:29,400 --> 00:14:33,600 Speaker 5: like in terms of similarities or differences to as you 268 00:14:33,640 --> 00:14:37,520 Speaker 5: mentioned the twenty twenty two empocs or something that's more 269 00:14:37,920 --> 00:14:39,840 Speaker 5: kind of well not obviously in COVID. 270 00:14:40,880 --> 00:14:43,160 Speaker 9: Yeah. Yeah, So twenty twenty two, we still had ninety 271 00:14:43,160 --> 00:14:45,360 Speaker 9: five thousand people who were infected. It did become a 272 00:14:45,400 --> 00:14:49,600 Speaker 9: major issue in many countries, so let's not forget that. 273 00:14:49,760 --> 00:14:52,960 Speaker 9: And so far from the last count I heard about, 274 00:14:53,000 --> 00:14:55,760 Speaker 9: there's been seventeen and a half thousand infections that have 275 00:14:55,840 --> 00:15:00,280 Speaker 9: been documented in the regions that have reported it. So 276 00:15:01,600 --> 00:15:03,320 Speaker 9: how far can this go? It all depends on that 277 00:15:03,360 --> 00:15:06,680 Speaker 9: mode of transmission. It all depends on Remember the days 278 00:15:06,680 --> 00:15:09,720 Speaker 9: we went from a standard if you want to call it, 279 00:15:09,760 --> 00:15:15,040 Speaker 9: that coronavirus SARSCOBE two to the omicron variant, which had 280 00:15:15,320 --> 00:15:18,040 Speaker 9: so many mutations that made it completely invisible to our 281 00:15:18,040 --> 00:15:21,840 Speaker 9: immune systems, So everybody got another infection round. I'm pretty 282 00:15:21,880 --> 00:15:24,040 Speaker 9: sure you do recall that last lockdown. 283 00:15:24,120 --> 00:15:26,360 Speaker 3: I think it was, Oh, definitely remember all that, zam, 284 00:15:26,600 --> 00:15:27,160 Speaker 3: don't you worry? 285 00:15:27,240 --> 00:15:29,000 Speaker 1: Yeah, But I. 286 00:15:28,960 --> 00:15:31,240 Speaker 9: Mean, it just feels like a really weird period of 287 00:15:31,280 --> 00:15:34,560 Speaker 9: our lives. And so here we have something that if 288 00:15:34,560 --> 00:15:36,880 Speaker 9: the rules are that it plays with us still the 289 00:15:36,920 --> 00:15:39,960 Speaker 9: same as before, it does meet close contact, which means 290 00:15:40,360 --> 00:15:44,400 Speaker 9: you should be able to avoid an infection if you're 291 00:15:44,560 --> 00:15:47,400 Speaker 9: careful enough. The other elements, of course, is that we 292 00:15:47,480 --> 00:15:51,120 Speaker 9: do have vaccines that do work. There is a six 293 00:15:51,200 --> 00:15:56,840 Speaker 9: to seven hundred million stockpile of small pox vaccines around 294 00:15:57,160 --> 00:16:01,360 Speaker 9: both the older generation and new generation, and there are 295 00:16:01,360 --> 00:16:03,360 Speaker 9: two of r n A vaccines in development that could 296 00:16:03,360 --> 00:16:06,120 Speaker 9: be sped up if needed. So we do know that 297 00:16:06,200 --> 00:16:09,880 Speaker 9: vaccination helps, but it does have a higher mortality rate 298 00:16:11,080 --> 00:16:15,400 Speaker 9: standard to even the people who get it that are 299 00:16:16,040 --> 00:16:18,440 Speaker 9: that's not just like elderly people, which was mostly a 300 00:16:18,480 --> 00:16:21,280 Speaker 9: COVID issue than COVID did. 301 00:16:21,720 --> 00:16:25,440 Speaker 3: So it can kill more and it can kill all 302 00:16:25,440 --> 00:16:27,800 Speaker 3: different ranges, not just say the elderly. But we do 303 00:16:27,880 --> 00:16:29,600 Speaker 3: have the vaccine. What about a treatment. 304 00:16:31,320 --> 00:16:36,000 Speaker 9: There was a treatment that actually was being studied by 305 00:16:36,040 --> 00:16:39,560 Speaker 9: a complet called Siga and that just failed in trials 306 00:16:39,600 --> 00:16:42,120 Speaker 9: only two days ago, and it was being tested in 307 00:16:42,240 --> 00:16:48,800 Speaker 9: the Congo and it was being against klayte one. But 308 00:16:49,040 --> 00:16:51,840 Speaker 9: the good, the silver sider, the silver lining of that 309 00:16:52,040 --> 00:16:54,960 Speaker 9: report is that the mortality rate that they came out 310 00:16:55,000 --> 00:16:57,080 Speaker 9: with was lower than what people talk about. People talk 311 00:16:57,120 --> 00:16:59,840 Speaker 9: about three to six percent, but in that trial, mortality 312 00:17:00,200 --> 00:17:03,720 Speaker 9: like not small one and a half one point seven 313 00:17:03,760 --> 00:17:07,960 Speaker 9: percent from what I have read. So that does give 314 00:17:08,000 --> 00:17:11,200 Speaker 9: you a little flavor as to maybe it's not as bad, 315 00:17:11,240 --> 00:17:13,480 Speaker 9: but it's obviously one point six one point seven percent 316 00:17:13,520 --> 00:17:15,040 Speaker 9: is too pretty awful. 317 00:17:16,200 --> 00:17:19,160 Speaker 5: And Sam, how kind of quickly can some of those 318 00:17:19,240 --> 00:17:23,040 Speaker 5: vaccinations be deployed? Is that something that's already playing out 319 00:17:23,119 --> 00:17:26,680 Speaker 5: right now? Is that being centered primarily where the heart 320 00:17:26,680 --> 00:17:28,359 Speaker 5: of the outbreak is is in Congo. 321 00:17:30,000 --> 00:17:34,040 Speaker 9: Yeah, it doesn't sound like it from the latest news 322 00:17:34,040 --> 00:17:35,960 Speaker 9: real force that I've seen that Bloomberg has been doing 323 00:17:35,960 --> 00:17:39,680 Speaker 9: a great job of talking about that. Apparently there've been 324 00:17:39,720 --> 00:17:42,000 Speaker 9: discussions because this has been not this is not something 325 00:17:42,040 --> 00:17:44,679 Speaker 9: that happened overnight. It's been going on at least in 326 00:17:44,720 --> 00:17:47,600 Speaker 9: the Congo unfortunately for a few months, and that there's 327 00:17:47,600 --> 00:17:53,199 Speaker 9: been discussions between groups that provide the vaccines and the Congo, 328 00:17:53,320 --> 00:17:57,000 Speaker 9: and they hadn't got to a the last I read 329 00:17:57,400 --> 00:18:00,520 Speaker 9: to a to an agreement of getting those vacks nations 330 00:18:00,560 --> 00:18:03,200 Speaker 9: out and getting it to people. Let's not forget we've 331 00:18:03,280 --> 00:18:07,280 Speaker 9: learned this. Anywhere you go, having vaccine is not equal 332 00:18:07,280 --> 00:18:10,560 Speaker 9: to people having it in their arms. The more difficult 333 00:18:11,640 --> 00:18:15,480 Speaker 9: a country, a region, reaching people in rural areas, et cetera, 334 00:18:15,720 --> 00:18:19,440 Speaker 9: is cold chain necessities, et cetera. The more difficult that is, 335 00:18:19,680 --> 00:18:22,520 Speaker 9: the fewer people will actually be vaccinated. So that's two 336 00:18:22,520 --> 00:18:25,560 Speaker 9: different things here. But I'm confident that we can have 337 00:18:25,640 --> 00:18:28,680 Speaker 9: enough vaccines to ring fence at least infections and areas 338 00:18:28,720 --> 00:18:32,200 Speaker 9: and travelers. But in order to go into the hundreds 339 00:18:32,240 --> 00:18:34,320 Speaker 9: of millions of doses, I think we need the m 340 00:18:34,400 --> 00:18:35,320 Speaker 9: RNAs to get going. 341 00:18:35,600 --> 00:18:37,680 Speaker 3: It's such a good point, Sam, that's why you're the best. Hey, 342 00:18:37,680 --> 00:18:39,199 Speaker 3: Before we let you go. I just want to hit 343 00:18:39,200 --> 00:18:41,760 Speaker 3: you on this headline here. Pfizer and Beyontech their flu 344 00:18:41,840 --> 00:18:44,040 Speaker 3: covid vaccine failed. 345 00:18:46,160 --> 00:18:49,640 Speaker 9: Well, talking about the mRNA here, it's a complicated thing. 346 00:18:49,720 --> 00:18:53,879 Speaker 9: It was a combination mRNA is against three different types 347 00:18:53,920 --> 00:18:58,600 Speaker 9: of flu and covid, and it failed against one of 348 00:18:58,640 --> 00:19:01,480 Speaker 9: the flu variants, which is the or strains, which is 349 00:19:01,520 --> 00:19:06,600 Speaker 9: the influenza B virus in that setting. So they need 350 00:19:06,640 --> 00:19:09,240 Speaker 9: to go back to the growing board and see why. 351 00:19:09,280 --> 00:19:12,480 Speaker 9: That is what is complicated here and we need to 352 00:19:12,600 --> 00:19:15,639 Speaker 9: understand it better. Is that they had a success in 353 00:19:15,760 --> 00:19:18,359 Speaker 9: phase two, then they took the same thing into phase 354 00:19:18,400 --> 00:19:22,640 Speaker 9: three and it failed against the B influenza B. I've 355 00:19:22,680 --> 00:19:25,680 Speaker 9: been out. I've asked a company whether that's because they 356 00:19:25,720 --> 00:19:29,240 Speaker 9: think that the strain changed during the trial. It's possible 357 00:19:29,280 --> 00:19:31,359 Speaker 9: to remember flu is one of those nasty ones that 358 00:19:31,440 --> 00:19:34,280 Speaker 9: changes all the time, and the change matters a lot more, 359 00:19:34,320 --> 00:19:38,640 Speaker 9: I would say, than covid does. So that is what 360 00:19:39,040 --> 00:19:41,000 Speaker 9: that's what happened here, and it does give them a 361 00:19:41,040 --> 00:19:44,000 Speaker 9: disadvantage versus Moderna which had a successful trial. 362 00:19:44,720 --> 00:19:47,960 Speaker 3: Such good perspective, Sam, you are totally the best. Sam Fazelli, 363 00:19:47,960 --> 00:19:50,800 Speaker 3: Boomberg Intelligence, Director of Research, for Global Industries and Senior 364 00:19:50,840 --> 00:19:54,960 Speaker 3: Pharmaceutical joining us from Bordeaux. I look forward to the picture, Sam, 365 00:19:55,040 --> 00:19:57,960 Speaker 3: I require some of that just to see because do 366 00:19:58,040 --> 00:19:59,679 Speaker 3: you make your own wine there? Like do you have 367 00:20:00,280 --> 00:20:03,240 Speaker 3: Oh he's gone, he already left, already, he's back to 368 00:20:03,240 --> 00:20:06,960 Speaker 3: the vineyard. Ah. Same, thanks Sam, I really appreciate that one. 369 00:20:07,440 --> 00:20:11,320 Speaker 2: You're listening to the Bloomberg Intelligence Podcast. Catch us live 370 00:20:11,400 --> 00:20:14,920 Speaker 2: weekdays at ten am Eastern on applecar Play and Android 371 00:20:14,960 --> 00:20:17,720 Speaker 2: Auto with the Bloomberg Business app. You can also listen 372 00:20:17,840 --> 00:20:20,920 Speaker 2: live on Amazon Alexa from our flagship New York station, 373 00:20:21,320 --> 00:20:24,359 Speaker 2: Just say Alexa play Bloomberg eleven thirty. 374 00:20:25,359 --> 00:20:28,640 Speaker 3: Bloomberg Intelligence Radio. We are broadcasting to live from Interactive 375 00:20:28,640 --> 00:20:31,480 Speaker 3: Broker Studio right here in Midtown Manhattan. Also check us 376 00:20:31,520 --> 00:20:34,159 Speaker 3: out on YouTube. But you know, Bailey doesn't have his 377 00:20:34,280 --> 00:20:36,320 Speaker 3: bro vesque, so the appeal for that is a little 378 00:20:36,359 --> 00:20:40,520 Speaker 3: bit less. A Bay Lipshaltz in for Paul Sweeney. So, Bailey, 379 00:20:40,520 --> 00:20:43,639 Speaker 3: I'm an energy geek nerd. I've been covering energy commodities 380 00:20:43,640 --> 00:20:47,160 Speaker 3: for like seventeen and a half years, so welcome. So naturally, 381 00:20:47,600 --> 00:20:49,440 Speaker 3: every once in a while I have an energy guest 382 00:20:49,480 --> 00:20:52,600 Speaker 3: on and this one's actually quite interesting. So it's a 383 00:20:52,600 --> 00:20:57,280 Speaker 3: company called Sigma Lithium. It's ticker SGML trades about nine 384 00:20:57,320 --> 00:20:59,359 Speaker 3: dollars fifty cents a share. It's been a one billion 385 00:20:59,440 --> 00:21:02,560 Speaker 3: dollar company. It's a Canadian company, but it minds in 386 00:21:02,600 --> 00:21:06,199 Speaker 3: Brazil lithium obviously, hence the name, and the idea is 387 00:21:06,240 --> 00:21:10,040 Speaker 3: to basically make green lithium. Can you make it cost 388 00:21:10,080 --> 00:21:13,440 Speaker 3: competitively and how do you bring your own cost down? Now, 389 00:21:13,480 --> 00:21:16,000 Speaker 3: lithium is used in pretty much everything, but in particular 390 00:21:16,320 --> 00:21:18,560 Speaker 3: the batteries for electric vehicles, So if we want to 391 00:21:18,560 --> 00:21:21,160 Speaker 3: make the transition to green, lithium is something that we're 392 00:21:21,160 --> 00:21:24,080 Speaker 3: going to need. But how you green that is definitely 393 00:21:24,119 --> 00:21:27,080 Speaker 3: the question. So joining us now is Anna Cabral. She's 394 00:21:27,119 --> 00:21:30,919 Speaker 3: Signal Lithium co chair and CEO. The company just reported 395 00:21:30,960 --> 00:21:34,840 Speaker 3: earnings yesterday. What stood out to me really was reduced 396 00:21:34,920 --> 00:21:40,119 Speaker 3: cash costs by twenty two percent an increase in margins there. 397 00:21:40,240 --> 00:21:42,320 Speaker 3: So we wanted to get some more insight into how 398 00:21:42,359 --> 00:21:45,080 Speaker 3: all this works, how it is to go green and 399 00:21:45,119 --> 00:21:47,919 Speaker 3: be sustainable mining lithium, and can you sell it for 400 00:21:48,000 --> 00:21:50,800 Speaker 3: what kind of profit? Anna joins us now, Anna, thank 401 00:21:50,800 --> 00:21:53,040 Speaker 3: you so much for joining us. Can you just talk 402 00:21:53,119 --> 00:21:56,000 Speaker 3: us through for those of you who are on energy geeks, 403 00:21:56,480 --> 00:21:58,520 Speaker 3: what the company does and what it's hoping. 404 00:21:58,280 --> 00:21:58,560 Speaker 2: To do. 405 00:22:00,160 --> 00:22:00,920 Speaker 9: Absolutely well. 406 00:22:01,000 --> 00:22:04,720 Speaker 10: Sigma has been is a twelve year old company. We've 407 00:22:04,760 --> 00:22:08,840 Speaker 10: been operating for over a year now. We're celebrating a 408 00:22:08,920 --> 00:22:12,919 Speaker 10: year of shipments, consistent monthly shipments and we ship an 409 00:22:12,960 --> 00:22:18,280 Speaker 10: equivalent amount. We ship approximately two hundred and fifty thousand 410 00:22:18,280 --> 00:22:21,159 Speaker 10: tons to two hundred and seven thousand tons annually, twenty 411 00:22:21,160 --> 00:22:24,959 Speaker 10: two thousand tons monthly, which translated into a chemical measure, 412 00:22:25,560 --> 00:22:29,119 Speaker 10: is the equivalent of approximately thirty eight thousand tons of 413 00:22:29,200 --> 00:22:32,240 Speaker 10: letum carbonate equivalent. So that's what we do. This is 414 00:22:32,240 --> 00:22:36,560 Speaker 10: who we are. We have shoe operations. They're very distinctive, 415 00:22:36,600 --> 00:22:41,920 Speaker 10: but they're vertically integrated. We have a mine the mines 416 00:22:42,080 --> 00:22:45,919 Speaker 10: letium rock, and then that rock gets fed into an 417 00:22:46,000 --> 00:22:50,000 Speaker 10: industrial pre chemical plant where we process that rock into 418 00:22:50,119 --> 00:22:55,360 Speaker 10: letium materials and at that we are producing zero carbon lithium. 419 00:22:55,720 --> 00:22:57,720 Speaker 10: And so we're the only company in the world that 420 00:22:57,760 --> 00:23:00,520 Speaker 10: produces zero carbon litium in scale. 421 00:23:02,040 --> 00:23:05,760 Speaker 5: And how as Alex pointed out, margins look great at 422 00:23:05,840 --> 00:23:09,280 Speaker 5: least according to the results, and cash costs down twenty 423 00:23:09,280 --> 00:23:09,760 Speaker 5: two percent. 424 00:23:10,119 --> 00:23:10,600 Speaker 10: How does that. 425 00:23:10,560 --> 00:23:13,920 Speaker 5: Play out I'm just thinking, if I'm a car manufacturer, 426 00:23:13,960 --> 00:23:15,840 Speaker 5: I probably want the cheapest lithium I can get my 427 00:23:15,880 --> 00:23:18,440 Speaker 5: hands on because I also want to make money. 428 00:23:19,440 --> 00:23:20,240 Speaker 9: That's the whole point. 429 00:23:20,320 --> 00:23:23,560 Speaker 10: There isn't a green premium. We have been able to 430 00:23:23,600 --> 00:23:27,240 Speaker 10: premiumize our product based on different attributes. The product has 431 00:23:27,240 --> 00:23:32,800 Speaker 10: a chemical and a physical characteristic which brings actual measurable 432 00:23:32,880 --> 00:23:37,320 Speaker 10: cost savings of twenty to thirty percent to the supply chain. 433 00:23:38,040 --> 00:23:40,760 Speaker 10: Either the car maker or the battery maker, whomever owns 434 00:23:40,840 --> 00:23:44,880 Speaker 10: the product within the supply chain will benefit from those 435 00:23:44,920 --> 00:23:49,040 Speaker 10: cost savings. That's the basis for premiumization, and we're very 436 00:23:50,640 --> 00:23:54,360 Speaker 10: let's say reasonable about it. We have a commercially win 437 00:23:54,520 --> 00:23:57,440 Speaker 10: commercial win win approach where we take about ten percent 438 00:23:57,480 --> 00:23:59,520 Speaker 10: of that and then the client takes about twenty to 439 00:23:59,680 --> 00:24:02,160 Speaker 10: twenty center of that. So is a win win situation. 440 00:24:02,200 --> 00:24:05,640 Speaker 10: But there's no green premium. Everybody wants it, nobody wants 441 00:24:05,720 --> 00:24:06,280 Speaker 10: to pay. 442 00:24:06,040 --> 00:24:09,320 Speaker 3: For it exactly. That's one hundred percent what I keep 443 00:24:09,320 --> 00:24:13,560 Speaker 3: hearing about as well. So what enabled you to do 444 00:24:13,640 --> 00:24:17,439 Speaker 3: this sustainably in this area? Like is it? Can you 445 00:24:17,480 --> 00:24:20,719 Speaker 3: replicate this anywhere in any mind? And then uh, and 446 00:24:20,720 --> 00:24:23,720 Speaker 3: then concentration facility or was there something specific to this? 447 00:24:25,040 --> 00:24:27,920 Speaker 10: There are number of factors. The first factor, let's talk 448 00:24:27,920 --> 00:24:30,360 Speaker 10: about scope one. When you when you think about the 449 00:24:30,480 --> 00:24:32,400 Speaker 10: common load of a product, can you need to think 450 00:24:32,440 --> 00:24:35,760 Speaker 10: about what goes inside your operations, which is scope one. 451 00:24:36,240 --> 00:24:40,240 Speaker 10: And then the kind of power energy used to to 452 00:24:40,440 --> 00:24:42,760 Speaker 10: run these operations, which is what we call scope two. 453 00:24:43,040 --> 00:24:45,760 Speaker 10: And then the effect that your product is going to 454 00:24:45,800 --> 00:24:48,680 Speaker 10: have in a supply chain, which is scope three. Right, 455 00:24:48,720 --> 00:24:51,200 Speaker 10: which is we're all in guess for example, has a problem. 456 00:24:51,600 --> 00:24:56,040 Speaker 10: So when we when we made this investment here uh 457 00:24:56,440 --> 00:25:00,240 Speaker 10: which which which was now twelve years ago, the objective 458 00:25:00,400 --> 00:25:04,040 Speaker 10: was to reach eventually a point where we will deliver 459 00:25:04,119 --> 00:25:06,480 Speaker 10: the most sustainable litum in the world. We ended up 460 00:25:06,600 --> 00:25:09,880 Speaker 10: going zero CAB, but we started with scope one, meaning 461 00:25:09,920 --> 00:25:14,199 Speaker 10: we designed when we chose technologies and a processing method 462 00:25:14,720 --> 00:25:17,520 Speaker 10: that were not aggressive to the environment, that we're not 463 00:25:17,640 --> 00:25:23,399 Speaker 10: going to leave a serious, unabatable carbon footprints such as 464 00:25:23,880 --> 00:25:27,640 Speaker 10: tailing dams. We have zero tailing dams because we developed 465 00:25:27,680 --> 00:25:31,639 Speaker 10: a dry stacking module. Water. The use of water in 466 00:25:31,800 --> 00:25:36,119 Speaker 10: our industry is highly problematic. We use sewage water and 467 00:25:36,280 --> 00:25:40,280 Speaker 10: we recite, we recycle that water. So we have what 468 00:25:40,400 --> 00:25:45,120 Speaker 10: we call recirculation, reuse of sewage water, so we don't 469 00:25:45,200 --> 00:25:48,520 Speaker 10: use fresh water, so it's zero fresh water, right, So 470 00:25:48,720 --> 00:25:52,600 Speaker 10: these were elements, and then we chose separation methods that 471 00:25:52,720 --> 00:25:56,320 Speaker 10: do not use toxic chemicals, so we have another zero, 472 00:25:56,400 --> 00:25:59,560 Speaker 10: which is zero toxic chemicals. So this all happens in 473 00:25:59,640 --> 00:26:02,879 Speaker 10: that in the stroke QUOD. So then you moved to 474 00:26:02,920 --> 00:26:06,560 Speaker 10: the mind, what is the responsible of carbon footprint inside 475 00:26:06,560 --> 00:26:08,879 Speaker 10: the gate scope? One on the mind is mainly diesel 476 00:26:09,480 --> 00:26:13,880 Speaker 10: and nitrates in explosives a NFO. So we went out 477 00:26:13,960 --> 00:26:20,679 Speaker 10: to minimize that trying to add bland biodiesel, biofuels, biodiesel 478 00:26:20,720 --> 00:26:26,440 Speaker 10: with diesel, and then using electronic triggers electronic explosives to 479 00:26:26,600 --> 00:26:30,280 Speaker 10: minimize the user nitrate, so we obtained a pretty sizable reduction. 480 00:26:30,880 --> 00:26:34,399 Speaker 10: The result of it, right unabated, like the result of 481 00:26:34,480 --> 00:26:36,480 Speaker 10: all of that, after going through the hard to obey 482 00:26:37,080 --> 00:26:39,080 Speaker 10: after all of it, is that we ended up with 483 00:26:39,640 --> 00:26:43,719 Speaker 10: inside one, meaning point twenty six tons of carbon per 484 00:26:43,800 --> 00:26:47,520 Speaker 10: tonnel material. So we did all that work and we 485 00:26:47,680 --> 00:26:50,119 Speaker 10: got to that low level, which meant that then we 486 00:26:50,160 --> 00:26:52,920 Speaker 10: could go out and offset the rest with credits because 487 00:26:53,000 --> 00:26:55,399 Speaker 10: we are inside one, which meant we had to do 488 00:26:55,520 --> 00:26:59,919 Speaker 10: a huge effort to be inside one. Comparing to our 489 00:27:00,119 --> 00:27:04,800 Speaker 10: piers in these salars in South America is typically five 490 00:27:04,920 --> 00:27:08,240 Speaker 10: tons of carbon per ton, and we were a point 491 00:27:08,320 --> 00:27:11,600 Speaker 10: two six and other minds in other parts of the 492 00:27:11,640 --> 00:27:16,560 Speaker 10: world is fifteen tons of carbon in certain high cost 493 00:27:16,640 --> 00:27:20,240 Speaker 10: minds in Asia it's fourty five tons. So uh, it 494 00:27:20,400 --> 00:27:23,879 Speaker 10: wasn't enormous amount of work on Scope one, so scope 495 00:27:23,920 --> 00:27:25,119 Speaker 10: too though, Yeah, no. 496 00:27:25,160 --> 00:27:28,200 Speaker 3: Go ahead, We only have about the thirty forty seconds left. 497 00:27:28,240 --> 00:27:29,720 Speaker 3: But I do want to know if you can replicate 498 00:27:29,760 --> 00:27:30,360 Speaker 3: this elsewhere. 499 00:27:31,440 --> 00:27:34,120 Speaker 10: Well, that's the point. I mean Scope two. We were 500 00:27:34,160 --> 00:27:36,840 Speaker 10: blessed with the grid in Brazil, which meant that, you know, 501 00:27:37,320 --> 00:27:43,480 Speaker 10: we have access to abundant available renewable energy, so that 502 00:27:43,840 --> 00:27:47,399 Speaker 10: also contributes a lot. Because our Scope two is green, 503 00:27:47,600 --> 00:27:50,440 Speaker 10: it's one hundred percent renewable. So we ended up with 504 00:27:50,640 --> 00:27:53,719 Speaker 10: a situation that's hybrid. Could we replicate Yes, we can 505 00:27:53,800 --> 00:27:56,640 Speaker 10: replicate Scope one, but then we will need to plug 506 00:27:56,680 --> 00:27:59,119 Speaker 10: it into a renewable source. So that's kind of the 507 00:27:59,240 --> 00:28:01,680 Speaker 10: uniqueness of what we do. He is a combination of technology. 508 00:28:02,040 --> 00:28:03,920 Speaker 10: We have a highly benign a grid. 509 00:28:05,240 --> 00:28:07,760 Speaker 3: Well, Ana, we really appreciate it. It's a really interesting 510 00:28:07,840 --> 00:28:10,000 Speaker 3: story because so often we hear about how this just 511 00:28:10,119 --> 00:28:12,800 Speaker 3: isn't possible, and it's nice to get that success story 512 00:28:12,880 --> 00:28:15,600 Speaker 3: as well. Look forward to seeing your growth as well. 513 00:28:15,960 --> 00:28:19,280 Speaker 3: On a cabral Sigma Lithium co chair and CEO of 514 00:28:19,400 --> 00:28:22,639 Speaker 3: joining us on Sustainable Lithium. You good, none, Bailly, you 515 00:28:22,720 --> 00:28:23,720 Speaker 3: got it like you to feel. 516 00:28:23,800 --> 00:28:26,200 Speaker 5: I learned a lot, I will say, though I'm a 517 00:28:26,240 --> 00:28:30,200 Speaker 5: big markets watcher as you know. Uh, Sigma lithium about 518 00:28:30,240 --> 00:28:32,400 Speaker 5: a year ago north of thirty bucks right now trade 519 00:28:32,400 --> 00:28:32,880 Speaker 5: and round nine. 520 00:28:33,000 --> 00:28:35,159 Speaker 3: I mean it's lithium prices they've just been I mean 521 00:28:35,200 --> 00:28:37,760 Speaker 3: that's the thing. It's super cyclical. And look at what's 522 00:28:37,800 --> 00:28:39,680 Speaker 3: happening with the auto demand. At the end of the day. 523 00:28:41,200 --> 00:28:45,080 Speaker 2: You're listening to the Bloomberg Intelligence Podcast. Catch us live 524 00:28:45,200 --> 00:28:48,720 Speaker 2: weekdays at ten am Eastern on applecar Play and Android 525 00:28:48,760 --> 00:28:51,480 Speaker 2: Auto with the Bloomberg Business Act. You can also listen 526 00:28:51,640 --> 00:28:54,720 Speaker 2: live on Amazon Alexa from our flagship New York station 527 00:28:55,080 --> 00:28:57,840 Speaker 2: Just say Alexa play Bloomberg eleven thirty. 528 00:28:59,440 --> 00:29:01,720 Speaker 3: We have a really interesting guest coming up now. So 529 00:29:01,880 --> 00:29:06,040 Speaker 3: Appian is an American cloud computing and enterprise software company. 530 00:29:06,080 --> 00:29:08,920 Speaker 3: It's headquartered in McLean, Virginia. It's been around for quite 531 00:29:09,040 --> 00:29:12,280 Speaker 3: a long time, and the CEO and founder, Matt Calkins 532 00:29:12,920 --> 00:29:15,960 Speaker 3: of Appian joins us. Now, hey, Matt, just dumb question. 533 00:29:16,200 --> 00:29:17,120 Speaker 3: What does your company do. 534 00:29:18,360 --> 00:29:21,200 Speaker 1: Hey, Alex, We do processes. You know, processes make the 535 00:29:21,240 --> 00:29:24,640 Speaker 1: world go around. Every organization is composed of its unique processes, 536 00:29:25,000 --> 00:29:27,760 Speaker 1: and we automate those processes, We orchestrate them, and we 537 00:29:28,120 --> 00:29:30,680 Speaker 1: make them better over time using our expertise in most 538 00:29:30,720 --> 00:29:31,560 Speaker 1: of all our software. 539 00:29:32,440 --> 00:29:34,920 Speaker 5: And how do you guys fit into kind of this 540 00:29:35,120 --> 00:29:37,400 Speaker 5: AI wave? When I look at you know a number 541 00:29:37,440 --> 00:29:39,680 Speaker 5: of stocks that have continued to rip and move higher, 542 00:29:40,240 --> 00:29:42,320 Speaker 5: It doesn't seem like Appian has been caught up in 543 00:29:42,400 --> 00:29:43,000 Speaker 5: that shift. 544 00:29:44,120 --> 00:29:47,000 Speaker 1: Well, we're not the highest profile member of the AI wave, 545 00:29:47,080 --> 00:29:49,520 Speaker 1: but we're really pertinent to it. You see, every process 546 00:29:49,640 --> 00:29:51,560 Speaker 1: is made up of work getting done, and some of 547 00:29:51,600 --> 00:29:53,800 Speaker 1: that work is done by AI. So we've been selling 548 00:29:53,840 --> 00:29:56,160 Speaker 1: AI for nearly a decade and we have a unique 549 00:29:56,240 --> 00:29:59,800 Speaker 1: angle on it. Our angle is that AI should work 550 00:29:59,800 --> 00:30:01,000 Speaker 1: with private data. 551 00:30:01,240 --> 00:30:01,360 Speaker 3: Right. 552 00:30:01,440 --> 00:30:03,240 Speaker 1: You shouldn't as a user, You should not have to 553 00:30:03,360 --> 00:30:06,760 Speaker 1: divulge your data at all to an AI algorithm that 554 00:30:06,880 --> 00:30:09,760 Speaker 1: you don't own. You should preserve the sanctity of your 555 00:30:09,800 --> 00:30:12,680 Speaker 1: information and still be able to get great AI results. 556 00:30:12,760 --> 00:30:15,360 Speaker 1: So we work with kind of a virtual database to 557 00:30:15,480 --> 00:30:17,840 Speaker 1: allow that instead of asking that our customers give us 558 00:30:17,880 --> 00:30:18,280 Speaker 1: their data. 559 00:30:18,600 --> 00:30:22,520 Speaker 3: So that would totally fly in like the large language 560 00:30:22,520 --> 00:30:24,280 Speaker 3: models that we're dealing with now, or they sort of 561 00:30:24,320 --> 00:30:26,640 Speaker 3: sour uce all this public data or so pay Reddit 562 00:30:26,720 --> 00:30:29,080 Speaker 3: to get their data. That's not that would be very different. 563 00:30:30,280 --> 00:30:33,600 Speaker 1: Well it's okay, yeah, I'm not talking about how you 564 00:30:33,680 --> 00:30:36,960 Speaker 1: build the generic AI model. That's what you're talking about. 565 00:30:37,120 --> 00:30:40,760 Speaker 1: You first training a generic model. I'm talking about how 566 00:30:41,040 --> 00:30:45,880 Speaker 1: organizations like corporations are frequently asked to train an AI 567 00:30:46,080 --> 00:30:48,920 Speaker 1: model after it's already incorporated all that public data. But 568 00:30:49,480 --> 00:30:53,040 Speaker 1: train an AI model, which has certain downsides. It means 569 00:30:53,160 --> 00:30:55,520 Speaker 1: that you've made an investment in a model you don't own. 570 00:30:55,560 --> 00:30:57,920 Speaker 1: It means you've divulged your data and that that could 571 00:30:57,960 --> 00:31:00,360 Speaker 1: be risky. It means you can only get one form 572 00:31:00,400 --> 00:31:03,160 Speaker 1: of answer back from that algorithm, because no matter what 573 00:31:03,320 --> 00:31:05,840 Speaker 1: security clearance the question has, you've all got the same 574 00:31:05,880 --> 00:31:08,560 Speaker 1: base of data to ask against. It means it's hard 575 00:31:08,600 --> 00:31:11,360 Speaker 1: to retrain that model. There's a lot of disadvantages to 576 00:31:11,600 --> 00:31:15,280 Speaker 1: training an AI model, and we would like to find 577 00:31:15,360 --> 00:31:18,200 Speaker 1: we do find a way to get great GENAI results 578 00:31:18,320 --> 00:31:19,800 Speaker 1: without that training step. 579 00:31:20,200 --> 00:31:20,600 Speaker 3: Interesting. 580 00:31:21,160 --> 00:31:23,520 Speaker 6: Do you guys have any marquee partnerships? 581 00:31:23,560 --> 00:31:25,720 Speaker 5: It just feels like in terms of kind of this 582 00:31:25,920 --> 00:31:27,840 Speaker 5: AI boom you mentioned that you guys have been operating 583 00:31:27,880 --> 00:31:30,360 Speaker 5: in the space for quite some time has drawn a 584 00:31:30,400 --> 00:31:32,720 Speaker 5: lot of money from Microsoft, Google, you name it. 585 00:31:33,760 --> 00:31:35,760 Speaker 1: For sure. We work with all of them, but we're 586 00:31:35,800 --> 00:31:39,719 Speaker 1: AWS centric, so that's our primary partnership and it has 587 00:31:39,800 --> 00:31:42,120 Speaker 1: been for a couple of decades now. We're also one 588 00:31:42,160 --> 00:31:43,920 Speaker 1: of their early pioneers with the clip. 589 00:31:44,880 --> 00:31:49,120 Speaker 3: What do you think AI regulation should be and where 590 00:31:49,200 --> 00:31:49,600 Speaker 3: is it now? 591 00:31:51,320 --> 00:31:53,680 Speaker 1: It's nowhere near where it should be, that's the answer. 592 00:31:54,120 --> 00:31:55,640 Speaker 3: We have a regulation at all? 593 00:31:56,880 --> 00:31:58,840 Speaker 1: Yeah, we should have some. We should have some to 594 00:31:59,280 --> 00:32:03,480 Speaker 1: establish a a fair and well understood set of ground 595 00:32:03,560 --> 00:32:06,360 Speaker 1: rules so that we can all compete without uncertainty. That 596 00:32:06,400 --> 00:32:09,840 Speaker 1: would be ideal. But today AI regulation is mostly about fear. 597 00:32:09,880 --> 00:32:13,480 Speaker 1: It's mostly about stopping AI from doing something catastrophic, to 598 00:32:13,600 --> 00:32:19,240 Speaker 1: quote the recent California proposal catastrophic instead of worrying about 599 00:32:20,440 --> 00:32:24,800 Speaker 1: bread and butter concerns. Is AI infringing on people's intellectual property? 600 00:32:24,840 --> 00:32:27,720 Speaker 1: As you make the next model? I think we're much 601 00:32:27,800 --> 00:32:30,480 Speaker 1: too driven by fear and too little driven by just 602 00:32:30,600 --> 00:32:34,520 Speaker 1: creating a fair playing field for all participants in this 603 00:32:34,680 --> 00:32:37,720 Speaker 1: emerging industry, which really what we need, less uncertainty, less 604 00:32:38,160 --> 00:32:40,960 Speaker 1: divergent regulation. Depending on where you're doing business, it's this 605 00:32:41,080 --> 00:32:43,560 Speaker 1: in Colorado, it's that in Europe, etc. It would be 606 00:32:43,720 --> 00:32:45,680 Speaker 1: better to just come up with a standard, and that 607 00:32:45,800 --> 00:32:50,200 Speaker 1: standard should address the first harms that AI is committing, 608 00:32:50,560 --> 00:32:53,960 Speaker 1: which are harms of a breach of intellectual property, rather 609 00:32:54,080 --> 00:32:58,479 Speaker 1: than the eventual hypothetical harms that most legislation seems targeted 610 00:32:58,520 --> 00:32:58,959 Speaker 1: to address. 611 00:32:59,320 --> 00:33:02,600 Speaker 6: And how would that actually, in your view, come to fruition. 612 00:33:02,760 --> 00:33:05,880 Speaker 5: It just feels like the kind of climate and DC 613 00:33:06,680 --> 00:33:10,520 Speaker 5: maybe wouldn't be as conducive to actually implementing some of 614 00:33:10,560 --> 00:33:11,920 Speaker 5: these practices at this point in time. 615 00:33:12,480 --> 00:33:15,000 Speaker 1: Look, I live near DC and it's paralyzed right now. 616 00:33:15,040 --> 00:33:17,040 Speaker 1: We're all thinking about the election. But I do have 617 00:33:17,120 --> 00:33:20,480 Speaker 1: a proposal. I think that we should begin with a 618 00:33:20,560 --> 00:33:24,120 Speaker 1: few broad statements like a bill Wright's almost around AI. 619 00:33:24,520 --> 00:33:27,600 Speaker 1: I think AI should disclose its data sources. That would 620 00:33:28,080 --> 00:33:31,120 Speaker 1: raise everybody's visibility into what's going on in AI and 621 00:33:31,200 --> 00:33:33,160 Speaker 1: allow us to better figure out where the value is 622 00:33:33,240 --> 00:33:36,800 Speaker 1: truly coming from. It's coming from the data more than 623 00:33:36,840 --> 00:33:41,280 Speaker 1: we realize. And then for private data, privately identifiable information, 624 00:33:41,440 --> 00:33:43,720 Speaker 1: and copyright information, all of that should be used with 625 00:33:43,840 --> 00:33:47,720 Speaker 1: permission and compensation. So if we started with just that 626 00:33:47,960 --> 00:33:51,320 Speaker 1: full disclosure and permission and compensation for all private information, 627 00:33:51,760 --> 00:33:54,400 Speaker 1: I think we would have a fair baseline for how 628 00:33:54,480 --> 00:33:58,959 Speaker 1: you're allowed to build an AI algorithm. You'd have good transparency, 629 00:33:59,000 --> 00:34:01,840 Speaker 1: we'd be better understand what this technology really means. And 630 00:34:01,960 --> 00:34:05,440 Speaker 1: then also it we'd actually be helping the industry become 631 00:34:05,520 --> 00:34:08,000 Speaker 1: more valuable because we'd shift it out of the current 632 00:34:08,280 --> 00:34:11,640 Speaker 1: race for information into something far more important, the race 633 00:34:11,719 --> 00:34:12,120 Speaker 1: for trust. 634 00:34:13,080 --> 00:34:15,560 Speaker 3: Matt, that is your day job. That is the job 635 00:34:15,640 --> 00:34:17,719 Speaker 3: that you've had for a long time. But you also 636 00:34:17,840 --> 00:34:21,040 Speaker 3: have a side gig, and apparently you're super big into 637 00:34:21,200 --> 00:34:23,520 Speaker 3: board gaming as am I I feel you on that. 638 00:34:23,840 --> 00:34:26,320 Speaker 3: But you also make up your own board games. Is 639 00:34:26,400 --> 00:34:28,080 Speaker 3: this true? Because I am so jealous? 640 00:34:28,880 --> 00:34:29,160 Speaker 4: I do. 641 00:34:29,520 --> 00:34:31,759 Speaker 1: I love it, and I've been designing board games for 642 00:34:31,880 --> 00:34:35,040 Speaker 1: a couple of decades now, and I've got several in print, 643 00:34:35,320 --> 00:34:37,920 Speaker 1: and it's a great pleasure to see other people playing them. 644 00:34:37,960 --> 00:34:39,560 Speaker 1: It's one of my favorite things. 645 00:34:39,840 --> 00:34:41,560 Speaker 3: Do you have like a favorite like? Is there one 646 00:34:41,640 --> 00:34:44,120 Speaker 3: game either that you play that's not yours, and then 647 00:34:44,200 --> 00:34:45,240 Speaker 3: the game that you created. 648 00:34:46,280 --> 00:34:50,040 Speaker 1: Okay, it's hard for me to pick favorites amongst my own, 649 00:34:50,120 --> 00:34:52,439 Speaker 1: but I guess I'd have to point to Sekigahara, which 650 00:34:52,480 --> 00:34:54,840 Speaker 1: is still rated one of the top war games ever written, 651 00:34:54,920 --> 00:34:56,520 Speaker 1: even though it was written more than a decade ago. 652 00:34:57,360 --> 00:34:59,880 Speaker 1: That was a real pleasure to write. And my latest 653 00:35:00,120 --> 00:35:04,160 Speaker 1: is called Chariots here about racing chariots by making simple 654 00:35:04,239 --> 00:35:07,600 Speaker 1: card combinations. My favorites to play that I did not 655 00:35:07,760 --> 00:35:12,520 Speaker 1: write would have to include power Grid, Acquire and Automobile. 656 00:35:12,840 --> 00:35:14,239 Speaker 3: Oh, I don't know, power Grid. 657 00:35:14,280 --> 00:35:14,920 Speaker 6: I need to get that. 658 00:35:15,600 --> 00:35:16,200 Speaker 2: I just put on. 659 00:35:16,640 --> 00:35:19,960 Speaker 6: I'll be the dumbest person in this conversation. Are these 660 00:35:20,040 --> 00:35:21,960 Speaker 6: thinking games? Do I have to be like ready to 661 00:35:22,840 --> 00:35:23,399 Speaker 6: have to think? 662 00:35:25,480 --> 00:35:27,640 Speaker 1: Yeah, you'll do better if you think for sure. In fact, 663 00:35:27,680 --> 00:35:29,239 Speaker 1: that's the fun of it. It gives me something to 664 00:35:29,280 --> 00:35:32,319 Speaker 1: think about, a competition to engage your mind. 665 00:35:32,640 --> 00:35:34,640 Speaker 3: Okay, that's a barely answer that question. 666 00:35:34,680 --> 00:35:36,840 Speaker 5: Every day when we answer the anchor, do I have 667 00:35:37,000 --> 00:35:37,279 Speaker 5: to think? 668 00:35:37,360 --> 00:35:39,000 Speaker 3: Well, normally, I mean that's a TVD. 669 00:35:39,120 --> 00:35:40,880 Speaker 6: To be honest, I'm a Yazi fan. I like to 670 00:35:40,960 --> 00:35:42,719 Speaker 6: you know, you roll the die, you pick out the die. 671 00:35:42,760 --> 00:35:44,920 Speaker 6: It's pretty straightforward strategy. 672 00:35:45,560 --> 00:35:47,719 Speaker 1: There's some decisions, and if there weren't, it wouldn't be fun. 673 00:35:48,360 --> 00:35:53,440 Speaker 3: I mean, I'm totally I'm totally okay, I'm gonna check 674 00:35:53,480 --> 00:35:55,560 Speaker 3: those out. That was really great. We really appreciate it. 675 00:35:55,600 --> 00:35:58,480 Speaker 3: Matt Calkins, thank you so much. A founder and CEO 676 00:35:58,920 --> 00:36:02,400 Speaker 3: of Appy and joining us Nasdaq ticker is a p P. 677 00:36:02,760 --> 00:36:05,200 Speaker 3: And wait, so you guys don't like have parties in 678 00:36:05,320 --> 00:36:06,399 Speaker 3: Jersey with board games? 679 00:36:06,440 --> 00:36:09,160 Speaker 6: Okay? So I love board games, so I didn't. I 680 00:36:09,200 --> 00:36:11,960 Speaker 6: didn't want to downplay it. Okay, but I like Yati 681 00:36:12,200 --> 00:36:14,880 Speaker 6: you play? Were you part of that code games? I 682 00:36:14,920 --> 00:36:17,480 Speaker 6: guess yeah, maybe not. Board games is code names. It's 683 00:36:17,480 --> 00:36:17,960 Speaker 6: a card game. 684 00:36:18,040 --> 00:36:19,360 Speaker 3: It's it's a card Yeah, I don't know. It's a 685 00:36:19,400 --> 00:36:20,359 Speaker 3: good question. Okay, code name. 686 00:36:20,400 --> 00:36:22,560 Speaker 6: So you like the code name names, Yazi, Yati? Have 687 00:36:22,600 --> 00:36:23,360 Speaker 6: you played Chameleon? 688 00:36:23,719 --> 00:36:23,759 Speaker 4: No? 689 00:36:24,040 --> 00:36:26,200 Speaker 6: Chameleons a lot of fun? Okay, it's good. I know 690 00:36:26,280 --> 00:36:29,080 Speaker 6: you're not like, I don't know. If you go to breweries, 691 00:36:29,120 --> 00:36:29,920 Speaker 6: it's good brewery game. 692 00:36:30,080 --> 00:36:32,320 Speaker 3: It's a good brewery game. Okay. I mean, my daughter's 693 00:36:32,320 --> 00:36:34,080 Speaker 3: not quite old enough for that, but it's a fair point. 694 00:36:34,320 --> 00:36:36,479 Speaker 3: I'm a Katon person, so that's see. 695 00:36:36,600 --> 00:36:38,960 Speaker 6: I haven't put in the effort to learn how to 696 00:36:39,000 --> 00:36:39,279 Speaker 6: play it. 697 00:36:39,360 --> 00:36:41,680 Speaker 3: Every once you do play oh man, Yeah, okay, the 698 00:36:41,760 --> 00:36:44,160 Speaker 3: first time is a slog and it's confusing and then 699 00:36:44,200 --> 00:36:45,960 Speaker 3: it might break up your marriage. But once I get 700 00:36:46,000 --> 00:36:50,480 Speaker 3: past that point, it's so good. We're also playing Pandemic Legacy. 701 00:36:50,680 --> 00:36:54,120 Speaker 3: That's interesting. Anyway, we digress. Bailey's looking at me like. 702 00:36:54,560 --> 00:36:56,160 Speaker 5: I don't know what you're talking about. But JT's gonna 703 00:36:56,160 --> 00:36:57,839 Speaker 5: come over. We'll play some Gaitan and build some roos. 704 00:36:57,880 --> 00:36:58,000 Speaker 4: Oh. 705 00:36:58,040 --> 00:36:59,799 Speaker 3: I see what he's doing, and he's like, come over 706 00:36:59,880 --> 00:37:01,600 Speaker 3: and then fix my leak or whatever. 707 00:37:02,280 --> 00:37:06,759 Speaker 2: This is the Bloomberg Intelligence podcast, available on Apples, Spotify, 708 00:37:07,000 --> 00:37:10,120 Speaker 2: and anywhere else you get your podcasts. Listen live each 709 00:37:10,200 --> 00:37:13,359 Speaker 2: weekday ten am to noon Eastern on Bloomberg dot com, 710 00:37:13,680 --> 00:37:17,040 Speaker 2: the iHeartRadio app, tune In, and the Bloomberg Business app. 711 00:37:17,200 --> 00:37:20,160 Speaker 2: You can also watch us live every weekday on YouTube 712 00:37:20,440 --> 00:37:22,240 Speaker 2: and always on the Bloomberg terminal