1 00:00:00,840 --> 00:00:04,000 Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney, alongside 2 00:00:04,040 --> 00:00:05,240 Speaker 1: my co host Matt Miller. 3 00:00:05,640 --> 00:00:09,600 Speaker 2: Every business day, we bring you interviews from CEOs, market pros, 4 00:00:09,720 --> 00:00:13,600 Speaker 2: and Bloomberg experts, along with essential market moven news. 5 00:00:14,160 --> 00:00:17,279 Speaker 1: Find the Bloomberg Markets Podcast on Apple Podcasts or wherever 6 00:00:17,400 --> 00:00:20,480 Speaker 1: you listen to podcasts, and at Bloomberg dot com slash podcast. 7 00:00:20,680 --> 00:00:22,840 Speaker 1: And let's get figure out what's happening now in Washington, DC, 8 00:00:22,960 --> 00:00:25,240 Speaker 1: because they have no idea what's going on down there. 9 00:00:25,239 --> 00:00:27,160 Speaker 1: They're not even back like in session. I mean they're 10 00:00:27,160 --> 00:00:29,560 Speaker 1: coming back tomorrow. I mean, the government's about the shutdown 11 00:00:29,600 --> 00:00:31,360 Speaker 1: and they're still on some holiday. 12 00:00:31,400 --> 00:00:36,640 Speaker 2: I think it's very popular in Washington, d C. To 13 00:00:36,920 --> 00:00:39,519 Speaker 2: not do your job, right. I think that's what the 14 00:00:39,560 --> 00:00:40,760 Speaker 2: constituency wants. 15 00:00:41,200 --> 00:00:43,720 Speaker 1: You think, So it seems like it all right. Nathan Dean, 16 00:00:43,760 --> 00:00:45,920 Speaker 1: I'm gonna blame him for all this. Nathan Dean is 17 00:00:45,920 --> 00:00:49,360 Speaker 1: our senior US policy analyst for Bloomberg Intelligence. His job 18 00:00:49,400 --> 00:00:51,600 Speaker 1: is to make sure this government works. He's not getting 19 00:00:51,640 --> 00:00:54,400 Speaker 1: it done here. Nathan, talk to us about where we 20 00:00:54,560 --> 00:00:57,800 Speaker 1: are with this possible shutdown. What's the feeling in Washington, 21 00:00:57,880 --> 00:00:58,680 Speaker 1: DC these days? 22 00:00:59,480 --> 00:00:59,680 Speaker 3: Yeah? 23 00:01:00,080 --> 00:01:01,600 Speaker 4: You know, over the weekend, you know, Speaker of the 24 00:01:01,600 --> 00:01:05,280 Speaker 4: House Kevin McCarthy talked about trying to pass for appropriation bills. 25 00:01:05,440 --> 00:01:08,920 Speaker 4: You know, there's been talk about some Republicans bypassing the 26 00:01:08,959 --> 00:01:11,880 Speaker 4: Speaker and working with Democrats to do something called a 27 00:01:11,959 --> 00:01:14,479 Speaker 4: discharge petition. But if you're sitting in New York City, 28 00:01:14,520 --> 00:01:16,600 Speaker 4: all you need to know is is that nothing really 29 00:01:16,640 --> 00:01:20,160 Speaker 4: has changed. I mean, we're heading towards the shutdown Saturday night. 30 00:01:20,600 --> 00:01:22,679 Speaker 4: You know, there's a lot of political fireworks that have 31 00:01:22,720 --> 00:01:25,360 Speaker 4: to happen this week, but we just aren't seeing any 32 00:01:25,440 --> 00:01:29,040 Speaker 4: movement at this point that the Speaker wants to actually 33 00:01:29,080 --> 00:01:31,720 Speaker 4: do a deal with the Democrats and try and avoid 34 00:01:31,760 --> 00:01:35,759 Speaker 4: this type of situation. So I think we're probably around 35 00:01:35,800 --> 00:01:37,360 Speaker 4: seventy eighty percent chance and we're gonna. 36 00:01:37,160 --> 00:01:38,160 Speaker 5: Have a shutdown next week. 37 00:01:38,200 --> 00:01:40,960 Speaker 1: So Nathan is the choice for the speaker. I'm assuming, 38 00:01:41,000 --> 00:01:43,959 Speaker 1: you know, Speaker McCarthy's is kind of driving the bus. 39 00:01:44,000 --> 00:01:46,920 Speaker 1: Here is this choice, I do a deal with these 40 00:01:47,040 --> 00:01:51,320 Speaker 1: radical Republicans, these small group Republicans, or I reach across 41 00:01:51,360 --> 00:01:54,760 Speaker 1: the aisle to Democrats and deal with the political backlash. 42 00:01:54,880 --> 00:01:55,760 Speaker 1: There is that his choice. 43 00:01:55,760 --> 00:01:57,200 Speaker 2: At this point, there's no deal to be done with 44 00:01:57,200 --> 00:02:00,520 Speaker 2: the radical Republicans. They want to shut down the government, right, 45 00:02:00,680 --> 00:02:02,160 Speaker 2: Isn't that yes what they want? 46 00:02:02,240 --> 00:02:04,920 Speaker 4: It's so much more of like, do I actually ask 47 00:02:05,000 --> 00:02:09,320 Speaker 4: to the radical Republicans and then shut down the government 48 00:02:09,360 --> 00:02:11,880 Speaker 4: and then get to a point where the Democrats just say, look, 49 00:02:11,880 --> 00:02:13,680 Speaker 4: we're not going to deal. I mean, the odds of 50 00:02:13,760 --> 00:02:16,800 Speaker 4: what the Republicans, some of those conservative Republicans are wanting, 51 00:02:17,200 --> 00:02:19,160 Speaker 4: are less than a two percent chance of ever going 52 00:02:19,200 --> 00:02:19,840 Speaker 4: to become law. 53 00:02:19,880 --> 00:02:21,240 Speaker 5: And that's being grateful. 54 00:02:21,600 --> 00:02:24,960 Speaker 4: So Speaker McCarthy's choice here is essentially saying, look, do 55 00:02:25,080 --> 00:02:27,920 Speaker 4: I go through the motions of a government shutdown and 56 00:02:28,080 --> 00:02:30,600 Speaker 4: then do I deal with the Democrats? Or do I 57 00:02:30,600 --> 00:02:33,640 Speaker 4: deal with the Democrats now and get it over with? Ultimately, 58 00:02:33,720 --> 00:02:36,040 Speaker 4: this is ending with a deal with the Democrats. And 59 00:02:36,080 --> 00:02:38,560 Speaker 4: so I think from the Speaker's perspective, really what he 60 00:02:38,639 --> 00:02:41,119 Speaker 4: has to do here is he has to do this deal, 61 00:02:41,160 --> 00:02:42,360 Speaker 4: but he has to do it in a way that 62 00:02:42,440 --> 00:02:44,720 Speaker 4: saves him face. Look, he's got to be showing that 63 00:02:44,800 --> 00:02:47,079 Speaker 4: he's fighting for his ideals and so forth like that, 64 00:02:47,320 --> 00:02:49,200 Speaker 4: And that's why I think he's going to accept the 65 00:02:49,240 --> 00:02:52,680 Speaker 4: government shutdown if he makes a deal before then I 66 00:02:52,680 --> 00:02:54,800 Speaker 4: think there are those people that are seeking to oust 67 00:02:54,880 --> 00:02:57,280 Speaker 4: him get a little bit more ammunition, saying, look, he 68 00:02:57,320 --> 00:02:59,560 Speaker 4: didn't even fight, so let's just kick him out. So 69 00:03:00,040 --> 00:03:02,240 Speaker 4: I think speaker, I think the speaker's just playing a 70 00:03:02,240 --> 00:03:05,040 Speaker 4: little bit of what if scenario NELS is here, But 71 00:03:05,440 --> 00:03:07,160 Speaker 4: I think he's just going to say, look, we got 72 00:03:07,160 --> 00:03:10,079 Speaker 4: to do this after sometime next week, after the shutdown. 73 00:03:10,600 --> 00:03:13,200 Speaker 2: By the way, is it do we call them conservatives 74 00:03:13,240 --> 00:03:14,679 Speaker 2: because they don't really act like that? 75 00:03:14,760 --> 00:03:17,360 Speaker 6: Do we that the Freedom Club people? Nathan? 76 00:03:17,600 --> 00:03:21,920 Speaker 2: I mean, they're not exactly acting like Ronald Reagan or 77 00:03:21,960 --> 00:03:22,840 Speaker 2: George H. W. 78 00:03:22,960 --> 00:03:28,440 Speaker 6: Bush. It's more like, I don't know what word you use. 79 00:03:28,520 --> 00:03:32,919 Speaker 2: How do you describe that other than you know, radical populists. 80 00:03:33,360 --> 00:03:36,640 Speaker 4: Well, you know, I could say somebody is a conservative Washington, 81 00:03:36,680 --> 00:03:39,000 Speaker 4: and I'll get one hundred different answers what that means. 82 00:03:39,560 --> 00:03:41,640 Speaker 4: But you know, ultimately what's going to happen here is 83 00:03:41,680 --> 00:03:44,880 Speaker 4: it's the House Freedom Caucus. There's about fifteen or so members, 84 00:03:45,320 --> 00:03:47,960 Speaker 4: maybe as much as twenty. But I would also point 85 00:03:47,960 --> 00:03:50,800 Speaker 4: out that there's also another caucus called the House Problem 86 00:03:50,880 --> 00:03:53,560 Speaker 4: Solvers Caucus. And one of the things that they're trying 87 00:03:53,560 --> 00:03:56,080 Speaker 4: to do, and we've heard whispers on this is that 88 00:03:56,120 --> 00:03:57,960 Speaker 4: there's about six of them. It would take six of 89 00:03:57,960 --> 00:04:01,280 Speaker 4: them to say, look, I'm going to bypass Speaker. I'm 90 00:04:01,280 --> 00:04:04,120 Speaker 4: going to work with the Democrats on something called the 91 00:04:04,160 --> 00:04:09,280 Speaker 4: discharge petition, where I can actually overrule the Speaker. We'll 92 00:04:09,320 --> 00:04:12,280 Speaker 4: have the Democrats have control of the floor. They'll make 93 00:04:12,320 --> 00:04:14,680 Speaker 4: the deal. Now, I don't think Speaker McCarthy would be 94 00:04:14,680 --> 00:04:16,400 Speaker 4: all that upset with it. Look, I mean, look, you 95 00:04:16,440 --> 00:04:18,400 Speaker 4: could say, look, this is horrible that they're taking me 96 00:04:18,600 --> 00:04:20,680 Speaker 4: my power, you know, so forth like that, but also 97 00:04:20,760 --> 00:04:22,960 Speaker 4: as a get out of jail card free for you know, 98 00:04:23,040 --> 00:04:26,039 Speaker 4: free get out of jail free card for him. So 99 00:04:26,480 --> 00:04:28,520 Speaker 4: you know, I think what the Speaker is doing at 100 00:04:28,520 --> 00:04:30,440 Speaker 4: this moment is just saying, look, I've got these people 101 00:04:30,440 --> 00:04:33,320 Speaker 4: over here that want this. This is never going to happen. 102 00:04:33,680 --> 00:04:36,560 Speaker 4: I got over here this idea of working with the Democrats, 103 00:04:36,560 --> 00:04:39,839 Speaker 4: which I know eventually will happen, But I just can't 104 00:04:39,839 --> 00:04:40,799 Speaker 4: go out there and say. 105 00:04:40,600 --> 00:04:42,160 Speaker 6: I'm ready to do it just yet. 106 00:04:42,320 --> 00:04:45,120 Speaker 4: So unfortunately, you know, it's the people that are going 107 00:04:45,200 --> 00:04:46,920 Speaker 4: to be stuck at you know, the American people are 108 00:04:46,920 --> 00:04:48,280 Speaker 4: going to suffer a little bit because of this. 109 00:04:48,560 --> 00:04:50,640 Speaker 1: I mean, this seems from like, I don't know anything 110 00:04:50,640 --> 00:04:53,280 Speaker 1: about politics and political calculus, but this seems like a 111 00:04:53,360 --> 00:04:57,839 Speaker 1: disaster for the Republican Party. How does the leadership allow A? 112 00:04:58,080 --> 00:05:00,599 Speaker 1: Is it a disaster? And B? How would the leadership 113 00:05:00,600 --> 00:05:02,200 Speaker 1: at the Republican Party allow this to happen? 114 00:05:02,760 --> 00:05:05,440 Speaker 4: Well, you know, it's not like they can come out 115 00:05:05,440 --> 00:05:07,280 Speaker 4: and just wave their wand and say do as I say. 116 00:05:07,400 --> 00:05:09,840 Speaker 4: I mean that, you know, the speaker McCarthy's. 117 00:05:10,520 --> 00:05:12,960 Speaker 1: But we have whips and majority leaders and things like that. 118 00:05:13,080 --> 00:05:16,720 Speaker 2: You know, if the party has fallen apart, you know, 119 00:05:16,839 --> 00:05:21,359 Speaker 2: if they've lost their minds, then all right, But I'll. 120 00:05:21,200 --> 00:05:23,520 Speaker 6: Say, but then you can't control them, right, I mean. 121 00:05:24,120 --> 00:05:25,960 Speaker 4: But if they also make a deal within the next 122 00:05:26,000 --> 00:05:28,080 Speaker 4: two weeks or so, we won't be talking about this 123 00:05:28,080 --> 00:05:31,560 Speaker 4: come January. We'll be talking about you know, the presidential 124 00:05:31,600 --> 00:05:32,560 Speaker 4: election cycle. 125 00:05:32,279 --> 00:05:32,880 Speaker 7: And so forth. 126 00:05:32,960 --> 00:05:35,839 Speaker 4: So there are people out there that say, look, shutdowns 127 00:05:35,880 --> 00:05:37,720 Speaker 4: will be negative for the party that cause it, in 128 00:05:37,720 --> 00:05:41,000 Speaker 4: this case would be the Republicans. But the independent voter's 129 00:05:41,080 --> 00:05:44,120 Speaker 4: mindset is sort of short term, and per your inflation 130 00:05:44,240 --> 00:05:47,200 Speaker 4: talk prior to this segment, you know, inflation may be 131 00:05:47,240 --> 00:05:49,240 Speaker 4: around in January, and that may be the big ticket 132 00:05:49,360 --> 00:05:52,159 Speaker 4: so there are some saying, look, shutdowns are bad, but 133 00:05:52,360 --> 00:05:54,240 Speaker 4: you know how much of a political price we're gonna 134 00:05:54,240 --> 00:05:56,120 Speaker 4: have to pay? Maybe not so much. 135 00:05:56,920 --> 00:05:57,719 Speaker 5: It's a good point. 136 00:05:57,920 --> 00:05:59,520 Speaker 2: It's a good point by but Nathan, let me get 137 00:05:59,560 --> 00:06:03,880 Speaker 2: your takes of inflation on the UAW and President Biden, 138 00:06:04,080 --> 00:06:07,800 Speaker 2: Oh yeah, going there, which I think is I mean, 139 00:06:07,880 --> 00:06:11,400 Speaker 2: such an exciting story because I love the car makers 140 00:06:11,640 --> 00:06:14,480 Speaker 2: and now you've got these one hundred and fifty thousand 141 00:06:14,480 --> 00:06:16,640 Speaker 2: workers that are potentially going to make a lot more money. 142 00:06:16,960 --> 00:06:18,080 Speaker 6: It's inflationary. 143 00:06:18,800 --> 00:06:21,560 Speaker 2: President Biden says he's pro union, but he's also super 144 00:06:21,600 --> 00:06:25,240 Speaker 2: pro green transition and EVS, which the union feels is 145 00:06:25,600 --> 00:06:27,080 Speaker 2: in some ways is like their enemy. 146 00:06:27,400 --> 00:06:28,960 Speaker 6: How do you look at that whole situation? 147 00:06:29,120 --> 00:06:32,280 Speaker 2: Can he does he have the vitality to go and 148 00:06:32,320 --> 00:06:33,520 Speaker 2: stand on a picket line. 149 00:06:34,240 --> 00:06:35,200 Speaker 6: I'm just for me. 150 00:06:35,279 --> 00:06:38,080 Speaker 4: This is all about Michigan. I mean, he needs Michigan 151 00:06:38,120 --> 00:06:41,240 Speaker 4: to win reelection, and going to that picket line is 152 00:06:41,400 --> 00:06:43,760 Speaker 4: just way to say that. Look, Michigan is one of 153 00:06:43,760 --> 00:06:48,560 Speaker 4: those five or six states that I need. Going there 154 00:06:48,640 --> 00:06:50,760 Speaker 4: is just to make sure that President Trump doesn't steal 155 00:06:50,800 --> 00:06:53,599 Speaker 4: the press cycle by him going there. So you know 156 00:06:53,640 --> 00:06:55,840 Speaker 4: the actions of President Biden actually walking the picket line 157 00:06:55,839 --> 00:06:57,960 Speaker 4: and so forth like that. This was a political call. 158 00:06:58,440 --> 00:07:01,400 Speaker 4: I'm not sure how much impact. You know, I haven't 159 00:07:01,400 --> 00:07:02,960 Speaker 4: looked at the impact of how it would impact the 160 00:07:03,000 --> 00:07:04,240 Speaker 4: UAW negotiations. 161 00:07:04,560 --> 00:07:06,000 Speaker 7: But this was an easy call for him. 162 00:07:05,920 --> 00:07:08,240 Speaker 4: To make because if he doesn't go there, President Trump 163 00:07:08,279 --> 00:07:10,600 Speaker 4: was going to go there. Other Republican candidates would go 164 00:07:10,600 --> 00:07:12,880 Speaker 4: there in one of your key states would be you know, 165 00:07:12,920 --> 00:07:15,040 Speaker 4: you wouldn't actually be in one of those key states 166 00:07:15,040 --> 00:07:15,560 Speaker 4: that you need. 167 00:07:16,400 --> 00:07:18,840 Speaker 2: It'll be tough, though, if some of the workers on 168 00:07:18,880 --> 00:07:22,280 Speaker 2: the picket line say, hey, Joe, what's with the billions 169 00:07:22,280 --> 00:07:25,120 Speaker 2: of dollars in subsidies for electric vehicle transitions. 170 00:07:26,400 --> 00:07:28,960 Speaker 4: Absolutely, I mean, you know, he's going to have a 171 00:07:28,960 --> 00:07:31,640 Speaker 4: tough time talking to this. But you know, President Biden 172 00:07:31,680 --> 00:07:35,280 Speaker 4: also claims to be the most pro union president ever Scranton, Pennsylvania, 173 00:07:35,320 --> 00:07:38,360 Speaker 4: and this is something so I wouldn't put it past 174 00:07:38,400 --> 00:07:39,920 Speaker 4: him to be able to rely on some of the 175 00:07:40,000 --> 00:07:43,320 Speaker 4: institutional knowledge he's had over those seven years, seventy years 176 00:07:43,320 --> 00:07:45,480 Speaker 4: to try and go forth and you know, at least 177 00:07:45,520 --> 00:07:47,840 Speaker 4: get some talking points out of there. So I don't 178 00:07:47,840 --> 00:07:50,000 Speaker 4: think it's going to be that much impactful in the 179 00:07:50,040 --> 00:07:54,600 Speaker 4: way of the negotiations. But again it's a symbolic gesture 180 00:07:54,640 --> 00:07:57,760 Speaker 4: that he needs to ensure that he wins the rust 181 00:07:57,840 --> 00:08:02,120 Speaker 4: belts come twenty twenty four, because that in Arizona and 182 00:08:02,120 --> 00:08:04,480 Speaker 4: Nevada are going to be pretty much all the states 183 00:08:04,520 --> 00:08:06,520 Speaker 4: that matter going into this election. 184 00:08:07,600 --> 00:08:10,560 Speaker 1: So is it expectation in DC? We're getting off topic 185 00:08:10,560 --> 00:08:12,360 Speaker 1: a little bit. It's going to be Biden for the 186 00:08:12,360 --> 00:08:15,680 Speaker 1: Democratic Party. Is or any credible discussion otherwise? 187 00:08:16,320 --> 00:08:16,400 Speaker 8: No. 188 00:08:16,560 --> 00:08:19,400 Speaker 4: I mean, look, we're three months away from a primary process, 189 00:08:19,400 --> 00:08:21,440 Speaker 4: and if President Biden were to stand up and say, look, 190 00:08:21,760 --> 00:08:23,480 Speaker 4: I'm not going to run for president, you know who 191 00:08:23,480 --> 00:08:26,400 Speaker 4: would take his place? Obviously have Vice President Kamala Harris. 192 00:08:26,440 --> 00:08:29,120 Speaker 4: But you know Vice President Harris was the first one 193 00:08:29,160 --> 00:08:31,280 Speaker 4: to bow out in the twenty you know, in the 194 00:08:31,320 --> 00:08:35,280 Speaker 4: prior election. That person doesn't automatically get the party's mantle. 195 00:08:35,559 --> 00:08:38,120 Speaker 4: So if President Biden were to say today I'm not running, 196 00:08:38,440 --> 00:08:41,079 Speaker 4: you would kick off a chaotic process in which the 197 00:08:41,120 --> 00:08:44,520 Speaker 4: Democrats would be completely on you know, disjointed, and there'd 198 00:08:44,559 --> 00:08:47,320 Speaker 4: be a really fast paced fight to try and see 199 00:08:47,320 --> 00:08:50,800 Speaker 4: who can win. Governor Newsom, probably Governor Pritzker from Illinois, 200 00:08:51,160 --> 00:08:53,679 Speaker 4: for example. It would just be very chaotic. So I 201 00:08:53,720 --> 00:08:56,040 Speaker 4: really don't see President Biden saying that he's done. 202 00:08:56,480 --> 00:08:58,200 Speaker 5: I think he's in this for the long bow. 203 00:08:58,400 --> 00:09:00,079 Speaker 1: All right, Nathan, thanks so much for joining us. We 204 00:09:00,120 --> 00:09:01,960 Speaker 1: can go a million ways with you. You've always got 205 00:09:02,280 --> 00:09:05,559 Speaker 1: everything on topic. Nathan Deane, Senior Polo Saneles for the 206 00:09:05,640 --> 00:09:09,240 Speaker 1: US government for Bloomberg Intelligence. He's based down in Washington, 207 00:09:09,280 --> 00:09:10,800 Speaker 1: d C. And he is our go to guy. So 208 00:09:10,840 --> 00:09:13,720 Speaker 1: there's a lot of balls in the air down in DC. 209 00:09:14,280 --> 00:09:17,920 Speaker 1: Most notably, is this government gonna shut down? And it 210 00:09:17,960 --> 00:09:21,480 Speaker 1: looks like appears at this stage at least that the 211 00:09:21,520 --> 00:09:22,720 Speaker 1: answer is probably yes. 212 00:09:23,800 --> 00:09:27,160 Speaker 9: You're listening to the Team Ken's are Live program Bloomberg 213 00:09:27,280 --> 00:09:30,680 Speaker 9: Markets weekdays at ten am Eastern on Bloomberg dot Com, 214 00:09:30,720 --> 00:09:33,880 Speaker 9: the iHeartRadio app, and the Bloomberg Business app, or listen 215 00:09:33,920 --> 00:09:36,199 Speaker 9: on demand wherever you get your podcasts. 216 00:09:38,520 --> 00:09:40,400 Speaker 1: You know, Matt, you might remember two or three months ago, 217 00:09:40,440 --> 00:09:43,200 Speaker 1: I took that whole recession talk off the table for me. 218 00:09:43,600 --> 00:09:47,200 Speaker 1: But you know what, this federal reserve higher for longer, 219 00:09:47,360 --> 00:09:49,120 Speaker 1: I don't know. I think they might run the risk 220 00:09:49,160 --> 00:09:51,920 Speaker 1: of pushing us into something that we don't really need. 221 00:09:52,000 --> 00:09:55,000 Speaker 1: So I'm going to talk to and ask a professional 222 00:09:55,040 --> 00:09:59,400 Speaker 1: about this. Lydia Bussor senior economists at e Y Parthenon. So, 223 00:09:59,480 --> 00:10:01,840 Speaker 1: Lydia was I a little bit early in taking the 224 00:10:01,840 --> 00:10:04,439 Speaker 1: recession talk off the table. Is this Federal Reserve with 225 00:10:04,559 --> 00:10:08,079 Speaker 1: its news or not new but continued effort to keep 226 00:10:08,120 --> 00:10:11,080 Speaker 1: rates higher for longer? Are they bringing that recession talk 227 00:10:11,120 --> 00:10:12,679 Speaker 1: back to the forefront? 228 00:10:14,080 --> 00:10:17,400 Speaker 8: Hi, thanks for having me. I mean the higher for 229 00:10:17,480 --> 00:10:21,239 Speaker 8: longer interest rate paradigm that the Federal Reserve strongly signaled 230 00:10:21,760 --> 00:10:24,760 Speaker 8: after the meeting, and with this new dot plot showing 231 00:10:24,800 --> 00:10:29,200 Speaker 8: interest rates remaining above five percent throughout next year, means 232 00:10:29,200 --> 00:10:31,280 Speaker 8: that you know, we're going to see we're going to 233 00:10:31,320 --> 00:10:34,920 Speaker 8: be in that higher interest rate environment for longer, higher 234 00:10:34,920 --> 00:10:37,600 Speaker 8: cost of capital, and that's going to be waiting on 235 00:10:37,640 --> 00:10:42,000 Speaker 8: spending decisions for consumers and businesses. I think this is 236 00:10:42,040 --> 00:10:44,800 Speaker 8: going to be an additional headwind for the economy. This 237 00:10:44,960 --> 00:10:48,560 Speaker 8: has already been impacting the economy, but we do believe 238 00:10:48,559 --> 00:10:51,240 Speaker 8: that there remains a pathway for a soft lending. I 239 00:10:51,280 --> 00:10:54,800 Speaker 8: think it's important to keep in mind that these higher 240 00:10:54,840 --> 00:10:58,199 Speaker 8: for longer interest rates are also reflection of a more 241 00:10:58,280 --> 00:11:02,800 Speaker 8: resilient and stronger economy and also expectations for that trend 242 00:11:02,920 --> 00:11:04,080 Speaker 8: to continue moving forward. 243 00:11:05,480 --> 00:11:08,640 Speaker 2: Well, first of all, Lydia, we just got a red 244 00:11:08,920 --> 00:11:14,440 Speaker 2: hot sticky headline crossing the terminal and on China and 245 00:11:14,480 --> 00:11:19,360 Speaker 2: the property issue there. So Evergrand Unit missed payment on 246 00:11:19,679 --> 00:11:23,840 Speaker 2: four billion you on Onshore bond. We've all been watching 247 00:11:24,400 --> 00:11:28,559 Speaker 2: the Chinese property sector again lately. Right, it looks worse 248 00:11:28,640 --> 00:11:34,120 Speaker 2: than feared previously, and the short lived boost it got 249 00:11:34,120 --> 00:11:38,480 Speaker 2: from the Chinese sort of stimulus activities is gone. 250 00:11:38,520 --> 00:11:39,520 Speaker 1: Now. 251 00:11:40,600 --> 00:11:42,000 Speaker 6: Is it possible that this. 252 00:11:42,000 --> 00:11:44,680 Speaker 2: Spreads around the world or is it contained to the 253 00:11:44,760 --> 00:11:45,640 Speaker 2: Chinese economy? 254 00:11:45,640 --> 00:11:45,960 Speaker 3: You think? 255 00:11:47,559 --> 00:11:50,040 Speaker 8: I mean, we believe that the China, the China, the 256 00:11:50,120 --> 00:11:53,320 Speaker 8: China's economic explow down has emerged as a top risk 257 00:11:53,400 --> 00:11:56,360 Speaker 8: for the global economy. When we look at the US 258 00:11:56,400 --> 00:12:00,840 Speaker 8: in particular, when you purely looked at the trailing and 259 00:12:00,960 --> 00:12:05,120 Speaker 8: financial linkages, they're fairly limited in terms of the US 260 00:12:05,160 --> 00:12:09,640 Speaker 8: exposure and looking at US exports to China. But when 261 00:12:09,679 --> 00:12:12,960 Speaker 8: you think about the impact on financial conditions and how 262 00:12:13,040 --> 00:12:16,720 Speaker 8: that could potentially lead to China growth care the way 263 00:12:16,760 --> 00:12:20,440 Speaker 8: we saw back in twenty fifteen twenty sixteen, that could 264 00:12:20,520 --> 00:12:23,080 Speaker 8: have an impact on confidence, and that could have an 265 00:12:23,120 --> 00:12:26,800 Speaker 8: impact on a more significant impact on the US economy. Now, 266 00:12:26,840 --> 00:12:29,720 Speaker 8: looking at the broader global economy, this is going to 267 00:12:29,760 --> 00:12:33,559 Speaker 8: have some ramification for economies that are more exposed, and 268 00:12:33,640 --> 00:12:37,000 Speaker 8: this is potentially leading also to a sharper economy slow 269 00:12:37,040 --> 00:12:37,880 Speaker 8: down globally, So. 270 00:12:37,920 --> 00:12:39,040 Speaker 6: Not the US, right Lydia. 271 00:12:39,200 --> 00:12:41,520 Speaker 2: When you say economies that are more exposed, I guess 272 00:12:41,520 --> 00:12:43,640 Speaker 2: you're talking more about Europe than America. 273 00:12:43,920 --> 00:12:47,520 Speaker 8: Europe is more exposed, but as I mentioned, the US, 274 00:12:48,040 --> 00:12:52,079 Speaker 8: the exposure is really through more through financial conditions and 275 00:12:52,320 --> 00:12:55,960 Speaker 8: a potential deeper global slowdown as some of the other 276 00:12:56,080 --> 00:12:58,400 Speaker 8: trade partners for the US get impacted as well. 277 00:12:59,200 --> 00:13:02,200 Speaker 1: Leading you back here in the US. One of the 278 00:13:02,240 --> 00:13:05,400 Speaker 1: real points of resilience in this iconomy has been the 279 00:13:05,480 --> 00:13:07,040 Speaker 1: labor market. And a lot of folks are kind of 280 00:13:07,040 --> 00:13:10,600 Speaker 1: confused about why this labor market is so strong, given 281 00:13:11,120 --> 00:13:13,640 Speaker 1: you know, the inflation editors are given the higher rates. 282 00:13:14,440 --> 00:13:16,360 Speaker 1: How do you view and how do you explain the 283 00:13:16,440 --> 00:13:17,600 Speaker 1: labor market to every one? 284 00:13:19,200 --> 00:13:22,640 Speaker 8: Yeah, I mean, we've seen some persistent tightness in the 285 00:13:22,679 --> 00:13:26,320 Speaker 8: label market and those lingering labor shortages in some sectors. 286 00:13:26,760 --> 00:13:29,320 Speaker 8: So a lot of that tightness that we have seen 287 00:13:29,400 --> 00:13:31,640 Speaker 8: is really tied to some of these post pandemic label 288 00:13:31,679 --> 00:13:34,640 Speaker 8: market dynamics. We've seen a lot of these locations during 289 00:13:34,640 --> 00:13:38,640 Speaker 8: the pandemic, and so what we're seeing today's companies essentially 290 00:13:38,920 --> 00:13:42,559 Speaker 8: holding on to the workforce, their talent that they really 291 00:13:42,600 --> 00:13:46,720 Speaker 8: had a hard time attracting and retaining over the past 292 00:13:46,720 --> 00:13:50,199 Speaker 8: two years. So we are seeing some labor hoarding. And 293 00:13:50,600 --> 00:13:52,640 Speaker 8: that is, you know, one of the reasons why even 294 00:13:52,679 --> 00:13:55,839 Speaker 8: though you're saying that rebalancing, even though you're saying labor 295 00:13:55,880 --> 00:13:59,400 Speaker 8: dement coming down, we haven't seen that a significant rising 296 00:13:59,480 --> 00:14:02,880 Speaker 8: layoff that you would expect in this environment of higher 297 00:14:02,960 --> 00:14:04,520 Speaker 8: interest rates. 298 00:14:04,800 --> 00:14:07,120 Speaker 2: In terms of the you know, the path to a 299 00:14:07,200 --> 00:14:09,640 Speaker 2: soft landing, you know, with all the risks that you 300 00:14:09,720 --> 00:14:15,200 Speaker 2: mentioned Lydia, and we've been you know, we've been listing 301 00:14:15,240 --> 00:14:19,280 Speaker 2: them here on Bloomberg Radio as well, from increased credit 302 00:14:19,320 --> 00:14:23,920 Speaker 2: card debt to delinquencies on those cards and on auto loans, 303 00:14:24,080 --> 00:14:28,760 Speaker 2: the return of student loan payments, higher oil and higher 304 00:14:28,800 --> 00:14:30,840 Speaker 2: gas prices at the pump. 305 00:14:31,960 --> 00:14:34,000 Speaker 6: What am I missing, Paul, No, those are all good. 306 00:14:34,400 --> 00:14:38,240 Speaker 2: I mean, it seems like the strike, the UAW strike 307 00:14:38,320 --> 00:14:41,160 Speaker 2: lasting a long time and expanding is a risk. I 308 00:14:41,160 --> 00:14:44,760 Speaker 2: don't know if the government shutdown is a financial risk, 309 00:14:44,880 --> 00:14:47,920 Speaker 2: or if it's just an amazing annoyance. But all of 310 00:14:47,960 --> 00:14:52,280 Speaker 2: these things are bad, right, So where do you see 311 00:14:52,280 --> 00:14:53,680 Speaker 2: a soft landing coming out of that? 312 00:14:54,920 --> 00:14:57,760 Speaker 8: Yeah, I mean we all these headwinds, the headwinds you 313 00:14:57,840 --> 00:15:01,840 Speaker 8: mentioned are really combining and will combine to slow down 314 00:15:01,880 --> 00:15:04,880 Speaker 8: the economy. So we do expect to see a downshift 315 00:15:04,880 --> 00:15:08,000 Speaker 8: in economic activity as we head into twenty twenty four. 316 00:15:08,480 --> 00:15:11,440 Speaker 8: We think that growth will fall below potential, and then 317 00:15:11,880 --> 00:15:15,800 Speaker 8: we're expecting a number of quarters where growth is going 318 00:15:15,840 --> 00:15:19,320 Speaker 8: to be fairly modest as a result of some of 319 00:15:19,360 --> 00:15:23,760 Speaker 8: the old headwinds higher interest rates, tighter credit conditions, and 320 00:15:23,800 --> 00:15:26,960 Speaker 8: still high inflation and some of these new headwinds that 321 00:15:27,000 --> 00:15:29,040 Speaker 8: you mentioned that are going to be waiting on growth 322 00:15:29,280 --> 00:15:32,120 Speaker 8: in the fourth quarter. What's important to keep in mind 323 00:15:32,240 --> 00:15:36,760 Speaker 8: with the strike, with the potential government shutdown is some 324 00:15:36,800 --> 00:15:39,320 Speaker 8: of the impact is going to be reversed in the 325 00:15:39,320 --> 00:15:42,720 Speaker 8: first quarter of next year. So this is also something 326 00:15:42,760 --> 00:15:44,600 Speaker 8: that I think is important to keep in mind when 327 00:15:44,600 --> 00:15:48,400 Speaker 8: you think about the outlook. But certainly a nuanced outlook 328 00:15:48,680 --> 00:15:52,400 Speaker 8: with resilient economy, but some headwinds that are going to 329 00:15:52,440 --> 00:15:54,400 Speaker 8: be waiting on private sector activity. 330 00:15:55,520 --> 00:15:59,040 Speaker 1: So Lydia, we've seen inflation come down from the peak 331 00:15:59,120 --> 00:16:03,400 Speaker 1: of eighteen month to go roughly, But now maybe the 332 00:16:03,440 --> 00:16:06,000 Speaker 1: hard work starts. As a lot of economers are suggesting, 333 00:16:06,000 --> 00:16:08,840 Speaker 1: how do you see inflation moving over the next six 334 00:16:08,920 --> 00:16:10,000 Speaker 1: or twelve months. 335 00:16:11,040 --> 00:16:13,720 Speaker 8: Yeah, we are in the camp that maybe the last 336 00:16:13,720 --> 00:16:17,320 Speaker 8: inflation mile won't be as strainerous as some people think. 337 00:16:17,840 --> 00:16:19,840 Speaker 8: There is a lot of ground to cover in terms 338 00:16:19,880 --> 00:16:22,880 Speaker 8: of bringing inflation back to target, but we still think 339 00:16:22,920 --> 00:16:27,080 Speaker 8: that there is there is this inflationary environment that remains 340 00:16:27,120 --> 00:16:27,560 Speaker 8: in place. 341 00:16:27,880 --> 00:16:29,040 Speaker 7: When you look further. 342 00:16:28,800 --> 00:16:32,240 Speaker 8: Ahead at the next few months and going into twenty 343 00:16:32,320 --> 00:16:35,760 Speaker 8: twenty four, when you look at core inflation, we are 344 00:16:35,800 --> 00:16:39,040 Speaker 8: expecting to see modes inflation on the core side, with 345 00:16:39,680 --> 00:16:43,800 Speaker 8: housing inflation having turned the corner, with the label market 346 00:16:43,920 --> 00:16:47,760 Speaker 8: loosening as well, and also with the fact that you 347 00:16:47,760 --> 00:16:49,880 Speaker 8: know we're going to see a slow down in demand 348 00:16:50,320 --> 00:16:53,080 Speaker 8: and in economic activity and that will put further downwald 349 00:16:53,080 --> 00:16:56,320 Speaker 8: pressure on inflation. So we think that we're going to 350 00:16:56,320 --> 00:17:00,120 Speaker 8: see continue this inflation and looking at poor and headline inflation, 351 00:17:00,680 --> 00:17:02,680 Speaker 8: it will still take some time, but by the end 352 00:17:02,720 --> 00:17:05,399 Speaker 8: of next year we may have gotten closer to the 353 00:17:05,800 --> 00:17:07,760 Speaker 8: two person target on both headline and poor. 354 00:17:08,600 --> 00:17:12,760 Speaker 1: Lady health concerned. Are you about the European economies over there? 355 00:17:12,800 --> 00:17:15,360 Speaker 1: I mean again, we talked about it earlier, but boy, 356 00:17:15,400 --> 00:17:18,960 Speaker 1: their exposure to China and just compounds some some problems 357 00:17:18,960 --> 00:17:21,800 Speaker 1: over there. What's about your forecast for just Europe in 358 00:17:21,800 --> 00:17:22,680 Speaker 1: the UK going forward. 359 00:17:24,040 --> 00:17:27,200 Speaker 8: Yeah, we are seeing more weakness for the deer and 360 00:17:27,359 --> 00:17:30,840 Speaker 8: the European economy, and we are seeing more headwinds as 361 00:17:30,840 --> 00:17:34,560 Speaker 8: well from the China slowdown. When we look at economic activity, 362 00:17:35,000 --> 00:17:38,000 Speaker 8: we are seeing some economic indicators pointing to a further 363 00:17:38,080 --> 00:17:41,919 Speaker 8: down shift. And now the outlook is, you know, not 364 00:17:42,080 --> 00:17:45,199 Speaker 8: the same for each individual country within the Eurozone. We 365 00:17:45,240 --> 00:17:49,760 Speaker 8: are seeing localized weakness in Germany with this this recession 366 00:17:49,960 --> 00:17:53,840 Speaker 8: recessionary conditions in Germany. The UK is also showing most 367 00:17:53,840 --> 00:17:56,760 Speaker 8: signs of weakness. But overall, this is a region where 368 00:17:56,800 --> 00:17:59,879 Speaker 8: we are expecting to see more weakness this year and 369 00:18:00,280 --> 00:18:02,639 Speaker 8: this is going to be a soft spot for the 370 00:18:02,680 --> 00:18:05,359 Speaker 8: global economy along with the Chinese economy. 371 00:18:05,800 --> 00:18:07,719 Speaker 1: Lydia, thank you so much for joining us. Always appreciate 372 00:18:07,760 --> 00:18:11,680 Speaker 1: getting your comments. Lydia bussor senior economists at e Y 373 00:18:11,720 --> 00:18:12,960 Speaker 1: Partha that you're. 374 00:18:12,760 --> 00:18:16,160 Speaker 9: Listening to the tape Cat's Are Live program Bloomberg Markets 375 00:18:16,240 --> 00:18:19,600 Speaker 9: weekdays at ten am Eastern on Bloomberg Radio, the tune 376 00:18:19,680 --> 00:18:21,480 Speaker 9: in app, Bloomberg dot Com. 377 00:18:21,200 --> 00:18:22,640 Speaker 3: And the Bloomberg Business App. 378 00:18:22,680 --> 00:18:25,480 Speaker 9: You can also listen live on Amazon Alexa from our 379 00:18:25,480 --> 00:18:30,600 Speaker 9: flagship New York station, Just say Alexa Play Bloomberg eleven thirty. 380 00:18:31,240 --> 00:18:33,560 Speaker 1: Jess Larsen joins and sees the CEO and founder of 381 00:18:33,600 --> 00:18:36,520 Speaker 1: Briarcliff Credit Partners. He joins us live here in our 382 00:18:36,520 --> 00:18:40,560 Speaker 1: Bloomberg Interactive Broker studio. Just you guys, just focus on 383 00:18:40,560 --> 00:18:43,520 Speaker 1: this exclusively. Matt and I. We know private credit as 384 00:18:43,600 --> 00:18:47,520 Speaker 1: like direct lending. Well you're telling us it's more than that, right. 385 00:18:48,000 --> 00:18:51,240 Speaker 10: That is, that's a good question, right, and private credit 386 00:18:51,280 --> 00:18:52,800 Speaker 10: means a lot of things to a lot of people. 387 00:18:53,240 --> 00:18:56,240 Speaker 10: And there's a little bit of a misunderstanding that directlenning 388 00:18:56,400 --> 00:18:58,600 Speaker 10: is all rage of private credit. Dorek Lanny is a 389 00:18:58,600 --> 00:19:01,760 Speaker 10: great sub strategy with in private credit, but private credit 390 00:19:01,880 --> 00:19:04,720 Speaker 10: is really a lot more so. What we have identified 391 00:19:05,119 --> 00:19:07,680 Speaker 10: is fall pillars of private credit. You got corporate credit, 392 00:19:07,720 --> 00:19:11,520 Speaker 10: structure credit, real asset, and sparcusally finance underneath that twenty 393 00:19:11,640 --> 00:19:13,000 Speaker 10: six different strategy way. 394 00:19:13,280 --> 00:19:14,520 Speaker 5: It doesn't matter where in. 395 00:19:14,480 --> 00:19:17,720 Speaker 10: The economic economic cycle we are there's always strategies for 396 00:19:17,760 --> 00:19:20,520 Speaker 10: you to make money, and that's exciting about private credit. 397 00:19:20,359 --> 00:19:23,080 Speaker 1: Right, and private credit seems to me to come out 398 00:19:23,080 --> 00:19:24,960 Speaker 1: of nowhere over the last four or five. 399 00:19:24,840 --> 00:19:26,760 Speaker 2: Well yeah, I mean I've been going to the super 400 00:19:26,920 --> 00:19:29,840 Speaker 2: Turn conference for the past I don't know, seven or 401 00:19:29,880 --> 00:19:33,840 Speaker 2: eight years in Berlin, which is which was a private 402 00:19:33,840 --> 00:19:37,280 Speaker 2: equity conference, right, But more and more of the people 403 00:19:37,320 --> 00:19:39,719 Speaker 2: there are just talking about private credit. And a lot 404 00:19:39,720 --> 00:19:42,240 Speaker 2: of the big shops that you think of as PE 405 00:19:42,320 --> 00:19:46,919 Speaker 2: shops are actually the majority of their investments are on 406 00:19:46,960 --> 00:19:48,679 Speaker 2: the credit side now absolutely. 407 00:19:48,680 --> 00:19:50,760 Speaker 10: I mean the big firms are pivoting from PE to 408 00:19:50,880 --> 00:19:51,520 Speaker 10: private credit. 409 00:19:51,600 --> 00:19:51,760 Speaker 6: Right. 410 00:19:52,160 --> 00:19:54,159 Speaker 10: What we're seeing right now is we on the golden 411 00:19:54,200 --> 00:19:57,040 Speaker 10: age of private credit. What we will see in ten years, 412 00:19:57,040 --> 00:19:59,600 Speaker 10: if I can be so bold, is in ten years 413 00:19:59,600 --> 00:20:01,920 Speaker 10: private credit it's gonna be bigger than private equity. Wow, 414 00:20:02,080 --> 00:20:04,280 Speaker 10: this is our age, right. And I have to say, Matt, 415 00:20:04,520 --> 00:20:07,439 Speaker 10: Berlin is great, But next year come to our summit. 416 00:20:07,480 --> 00:20:10,320 Speaker 10: We had it last week. Over one hundred lpiece came 417 00:20:10,359 --> 00:20:11,919 Speaker 10: to hear just about private credit. 418 00:20:12,400 --> 00:20:13,879 Speaker 5: This is really a sou Where is it? 419 00:20:13,920 --> 00:20:15,199 Speaker 6: Because the city is important to me. 420 00:20:15,480 --> 00:20:17,160 Speaker 5: We did it here in the metropolitan club. We made 421 00:20:17,160 --> 00:20:17,760 Speaker 5: it easy for you. 422 00:20:17,960 --> 00:20:20,040 Speaker 2: Ah, I thought you're gonna say I could go to Copenhagen, 423 00:20:20,040 --> 00:20:21,920 Speaker 2: which I would like to do, that's where I'm from. 424 00:20:21,920 --> 00:20:23,600 Speaker 5: But you're more than welcome. We can do that as well. 425 00:20:23,800 --> 00:20:25,880 Speaker 1: It's easier for us to take a subway to that's 426 00:20:25,960 --> 00:20:28,280 Speaker 1: that's true. We can walk to the Metropologic Club. We 427 00:20:28,320 --> 00:20:30,760 Speaker 1: can do that as well. We can do just talk 428 00:20:30,800 --> 00:20:33,280 Speaker 1: to us about how this industry came about. Is it 429 00:20:33,320 --> 00:20:37,040 Speaker 1: just simply the regular the regulators put the traditional big 430 00:20:37,080 --> 00:20:38,360 Speaker 1: banks out of this business? 431 00:20:39,720 --> 00:20:40,080 Speaker 3: That is? 432 00:20:40,359 --> 00:20:42,639 Speaker 10: It is a really good question, right, because if we 433 00:20:42,760 --> 00:20:45,920 Speaker 10: think about private credit, this is not a new thing. 434 00:20:46,520 --> 00:20:49,439 Speaker 10: People have been lending to each other since the start 435 00:20:49,480 --> 00:20:52,960 Speaker 10: of humankind, right, So we go back to Plato and 436 00:20:53,080 --> 00:20:56,639 Speaker 10: those times the Medici family, it was all about lending. 437 00:20:56,800 --> 00:20:59,160 Speaker 5: Now we call the usury. At the time, it was all. 438 00:20:59,080 --> 00:21:02,359 Speaker 10: About lending, right, So lending a private credit has been 439 00:21:02,359 --> 00:21:07,119 Speaker 10: around since the early times. Now, you're right, during the GFC, 440 00:21:08,080 --> 00:21:11,359 Speaker 10: we've got the regulatory environment really supporting getting the credit 441 00:21:11,520 --> 00:21:13,600 Speaker 10: out of the banks because he's not the right place 442 00:21:14,040 --> 00:21:16,880 Speaker 10: for lending to mid market companies and get it into 443 00:21:16,880 --> 00:21:19,639 Speaker 10: the private credit space. So that was really the catalyst 444 00:21:19,800 --> 00:21:21,200 Speaker 10: of what I would call the golden nature. 445 00:21:21,840 --> 00:21:24,600 Speaker 2: I mean, the Germans would have a problem with that, 446 00:21:24,720 --> 00:21:26,600 Speaker 2: right because that's their bread and butter. 447 00:21:26,520 --> 00:21:27,960 Speaker 6: Of German banking. 448 00:21:28,040 --> 00:21:32,119 Speaker 2: And I think, uh, you know, there's no sort of 449 00:21:32,160 --> 00:21:35,639 Speaker 2: middelstand credit here the way there there is on the 450 00:21:35,640 --> 00:21:36,760 Speaker 2: other side of the Atlantic. 451 00:21:37,160 --> 00:21:39,639 Speaker 6: But we would call that shadow banking. Is that a 452 00:21:39,680 --> 00:21:41,200 Speaker 6: bad Is that a dirty term? 453 00:21:41,640 --> 00:21:44,200 Speaker 5: I think private credit sounds a lot better than shadow banking. 454 00:21:44,440 --> 00:21:47,600 Speaker 2: Come on, so it is, though, because you're pushing it 455 00:21:48,000 --> 00:21:52,360 Speaker 2: out of the regulated banking sector into more of a 456 00:21:53,080 --> 00:21:54,879 Speaker 2: I don't don't want to say wild West, because we 457 00:21:54,920 --> 00:21:58,200 Speaker 2: talked to private credit men and women here all the time, 458 00:21:58,240 --> 00:22:03,040 Speaker 2: who are obviously very responsible, learned individuals used to dealing 459 00:22:03,080 --> 00:22:04,320 Speaker 2: with sophisticated investors. 460 00:22:04,320 --> 00:22:06,520 Speaker 6: But you do have less regulation. 461 00:22:06,800 --> 00:22:10,120 Speaker 10: You do have less regulations. I would be contrarian and say, 462 00:22:10,240 --> 00:22:13,280 Speaker 10: we're pushing it out to where it belongs. Mid market 463 00:22:13,320 --> 00:22:16,119 Speaker 10: lending does not necessarily belong in a bank. We saw 464 00:22:16,240 --> 00:22:18,879 Speaker 10: the actual the risk you're taking on if you're a 465 00:22:18,880 --> 00:22:20,720 Speaker 10: mid market company and you want to borrow some money, 466 00:22:21,320 --> 00:22:23,760 Speaker 10: doing it with a bank adds a whole other layer 467 00:22:23,760 --> 00:22:25,800 Speaker 10: of risk, right. We saw that, we see a compative bank. 468 00:22:25,840 --> 00:22:28,280 Speaker 10: We saw that with credit and Signature Bank and so forth. 469 00:22:28,560 --> 00:22:31,080 Speaker 10: Now that private credit funds are set up and have 470 00:22:31,160 --> 00:22:34,480 Speaker 10: a much better asset liability match, so this is a 471 00:22:34,560 --> 00:22:37,640 Speaker 10: much better place for our mid market. The economy needs it, right. 472 00:22:37,760 --> 00:22:41,239 Speaker 10: The economy cannot survive without mid market lending, so we 473 00:22:41,320 --> 00:22:43,359 Speaker 10: need it. But we just need to having a better place, 474 00:22:43,400 --> 00:22:44,560 Speaker 10: and that's where we're going, all. 475 00:22:44,520 --> 00:22:47,320 Speaker 1: Right, mid market lending? Do you envision the day when 476 00:22:47,560 --> 00:22:50,359 Speaker 1: you characterize your industry is something more than mid market lending? 477 00:22:50,359 --> 00:22:53,119 Speaker 1: Not that that's not a great business, great returns, but 478 00:22:53,520 --> 00:22:55,840 Speaker 1: bigger deals, bigger tickets. Is that where we're going. 479 00:22:56,240 --> 00:22:58,520 Speaker 10: That's also where we're going. I think you're one hundred 480 00:22:58,520 --> 00:23:01,320 Speaker 10: percent of right, poll Mark. The terms of private credit 481 00:23:01,440 --> 00:23:03,240 Speaker 10: is going to go from mid market to upper market 482 00:23:03,280 --> 00:23:05,480 Speaker 10: to where we need to be right simply because it 483 00:23:05,560 --> 00:23:08,119 Speaker 10: makes sense for the borers. If not, we wouldn't have it. 484 00:23:08,320 --> 00:23:11,240 Speaker 2: What about the lenders, I mean right now or previously 485 00:23:11,280 --> 00:23:14,280 Speaker 2: it was big institutional money, as I said, sophisticated investors, 486 00:23:14,320 --> 00:23:17,040 Speaker 2: so people who you know, could afford to take losses. 487 00:23:17,200 --> 00:23:20,320 Speaker 2: We've had people on the program that are looking to 488 00:23:20,359 --> 00:23:23,639 Speaker 2: start platforms that allow retail money in is everybody going 489 00:23:23,720 --> 00:23:24,320 Speaker 2: to get involved. 490 00:23:25,440 --> 00:23:26,119 Speaker 5: It's coming, right. 491 00:23:26,160 --> 00:23:28,600 Speaker 10: We all need to be careful when there's retail money, right, 492 00:23:28,960 --> 00:23:31,680 Speaker 10: there is a high level of scrutiny, the high level 493 00:23:31,720 --> 00:23:34,159 Speaker 10: of regulatory environment when it comes to retail money. I 494 00:23:34,200 --> 00:23:36,879 Speaker 10: think we started the right place with the big institutional 495 00:23:36,920 --> 00:23:39,280 Speaker 10: money or cabs are coming from the pension funds and 496 00:23:39,280 --> 00:23:42,359 Speaker 10: dominance foundations, the big family jobs. We started with that money. 497 00:23:42,680 --> 00:23:45,240 Speaker 10: We're slowly moving into the retail but it does require 498 00:23:45,240 --> 00:23:46,920 Speaker 10: a little bit more of a regulatory overview. 499 00:23:47,440 --> 00:23:50,639 Speaker 1: So what's a typical deal that Briarcliffe invests in. 500 00:23:51,200 --> 00:23:52,320 Speaker 5: So we actually don't invest. 501 00:23:52,640 --> 00:23:54,920 Speaker 10: What we really do is we take the smart funds 502 00:23:55,240 --> 00:23:57,600 Speaker 10: and actually bring them out to the institutional investors their 503 00:23:57,640 --> 00:24:00,680 Speaker 10: placement firm basically gotcha, okay, And the only one in 504 00:24:00,720 --> 00:24:01,840 Speaker 10: private credit, believe. 505 00:24:01,560 --> 00:24:01,800 Speaker 3: It or not. 506 00:24:02,440 --> 00:24:05,240 Speaker 10: So you're the only one with the only place maiden 507 00:24:05,320 --> 00:24:07,080 Speaker 10: in the world that is completely He wants. 508 00:24:06,840 --> 00:24:09,520 Speaker 6: To hear the numbers. All Paul cares about in life 509 00:24:09,880 --> 00:24:10,679 Speaker 6: is making money. 510 00:24:11,320 --> 00:24:11,800 Speaker 3: I love it. 511 00:24:11,880 --> 00:24:14,920 Speaker 10: So there's three thousand place maidens globally, there's only one 512 00:24:14,920 --> 00:24:16,240 Speaker 10: that is dedicated to private credit. 513 00:24:16,280 --> 00:24:17,879 Speaker 5: It happens to be in your studio right now. 514 00:24:18,680 --> 00:24:21,360 Speaker 2: So are the what are the deals look like. I mean, 515 00:24:21,440 --> 00:24:24,359 Speaker 2: especially as in this rising rate environment. On the one hand, 516 00:24:24,400 --> 00:24:27,480 Speaker 2: I think it's going to get juicy. On the other hand, 517 00:24:28,119 --> 00:24:31,480 Speaker 2: I may be worried about companies that have to refinance 518 00:24:31,560 --> 00:24:34,000 Speaker 2: or companies that you know have been lent to at 519 00:24:34,040 --> 00:24:35,080 Speaker 2: much lower levels. 520 00:24:35,400 --> 00:24:38,359 Speaker 10: Yeah, yeah, I think it is a worry or it's 521 00:24:38,400 --> 00:24:40,760 Speaker 10: an opportunity, whether which way you want to look at it. 522 00:24:40,880 --> 00:24:43,680 Speaker 10: Because as we mentioned earlier, private credit is more than 523 00:24:43,800 --> 00:24:46,359 Speaker 10: just direct lending, right, So there are special sits that 524 00:24:46,480 --> 00:24:50,159 Speaker 10: distress managers out there. There are all sorts of strategies 525 00:24:50,160 --> 00:24:53,439 Speaker 10: that can capitalize if the default rate starts to spike. 526 00:24:54,040 --> 00:24:54,959 Speaker 5: Let's see if it happens. 527 00:24:54,960 --> 00:24:57,160 Speaker 10: But if we are going to get more losses, there 528 00:24:57,160 --> 00:24:59,359 Speaker 10: are credit funds out there that can actually capitalize, go 529 00:24:59,440 --> 00:25:01,600 Speaker 10: in and save these companies. And that's what we need 530 00:25:01,720 --> 00:25:03,200 Speaker 10: where the banks would shy away. 531 00:25:03,160 --> 00:25:08,119 Speaker 2: Right right, because private lenders work more closely with management. 532 00:25:08,520 --> 00:25:11,600 Speaker 5: It's other portfolio companies. And thank you for the pitch. 533 00:25:12,359 --> 00:25:15,520 Speaker 6: Well, I think we need to be skeptical as well. 534 00:25:15,600 --> 00:25:18,679 Speaker 2: That's our job, right And I wonder what you do 535 00:25:18,800 --> 00:25:22,040 Speaker 2: when regulation does come knocking, because there's going to be 536 00:25:22,040 --> 00:25:27,000 Speaker 2: a day, you know when some senator gets a craw 537 00:25:27,080 --> 00:25:29,800 Speaker 2: in her bonnet or whatever and decides you're the problem. 538 00:25:30,040 --> 00:25:32,919 Speaker 10: Yeah, Well the question is whether it's a problem. But 539 00:25:33,320 --> 00:25:36,800 Speaker 10: I don't think regulation is necessarily a bad thing, right 540 00:25:37,080 --> 00:25:39,359 Speaker 10: What we're doing right now is moving it out of 541 00:25:39,400 --> 00:25:43,159 Speaker 10: the banking system to the private cretic system where. 542 00:25:42,920 --> 00:25:45,439 Speaker 5: It is better placed. That's the first step. 543 00:25:45,760 --> 00:25:47,960 Speaker 10: The next step is probably be getting a little bit 544 00:25:48,000 --> 00:25:51,120 Speaker 10: more oversight, and we're already see in the regulatory environment 545 00:25:51,400 --> 00:25:54,399 Speaker 10: starting to looking at valuation reporting and so forth. So 546 00:25:54,520 --> 00:25:57,360 Speaker 10: it's coming and it's a good thing because it gives 547 00:25:57,520 --> 00:25:59,639 Speaker 10: everybody a little bit more comfort. 548 00:26:00,280 --> 00:26:02,280 Speaker 1: All right, Jess, thanks so much for joining us. Jess Larson, 549 00:26:02,400 --> 00:26:05,960 Speaker 1: CEO and founder Briar Cliff Credit Partners. 550 00:26:06,640 --> 00:26:10,080 Speaker 9: You're listening to the team Ken's Are Live program Bloomberg 551 00:26:10,119 --> 00:26:13,480 Speaker 9: Markets weekdays at ten am Eastern on Bloomberg dot com, 552 00:26:13,560 --> 00:26:16,720 Speaker 9: the iHeartRadio app, and the Bloomberg Business App, or listen 553 00:26:16,760 --> 00:26:18,880 Speaker 9: on demand wherever you get your podcasts. 554 00:26:21,160 --> 00:26:24,520 Speaker 1: A little bit of news out of Amazon today, they 555 00:26:24,560 --> 00:26:26,960 Speaker 1: are going to invest up to four billion dollars in 556 00:26:27,000 --> 00:26:30,040 Speaker 1: an AI firm by the name of Anthropic. It's a 557 00:26:30,040 --> 00:26:31,879 Speaker 1: big number to me, four billion, but I guess if 558 00:26:31,920 --> 00:26:34,240 Speaker 1: you're Amazon and you got a market cap at one 559 00:26:34,240 --> 00:26:37,679 Speaker 1: point three to five trillion, maybe not that big a deal, but. 560 00:26:37,720 --> 00:26:39,760 Speaker 2: Let's get it at it is though, because they don't 561 00:26:39,800 --> 00:26:42,520 Speaker 2: typically go out and buy businesses exactly. 562 00:26:42,640 --> 00:26:45,199 Speaker 1: And I think this is big because it's AI. So 563 00:26:45,240 --> 00:26:47,560 Speaker 1: I pay attention on a Rock Ronick he also pays attention. 564 00:26:47,560 --> 00:26:50,520 Speaker 1: He's a tech analyst at Bloomberg Intelligence. He joined us 565 00:26:50,560 --> 00:26:55,240 Speaker 1: via Zoom from his happy place Bloomberg Chicago office. Loves 566 00:26:55,280 --> 00:26:58,560 Speaker 1: that Honor Rock talk to us about this deal here, Anthropic. 567 00:26:58,920 --> 00:27:01,160 Speaker 1: What's Amazon doing here? What does Anthropic do? And why 568 00:27:01,200 --> 00:27:03,119 Speaker 1: is Amazon investing up the four billion? 569 00:27:04,040 --> 00:27:05,719 Speaker 11: Yeah, I think it's a little bit of catching up 570 00:27:05,760 --> 00:27:08,000 Speaker 11: to do because for the last twelve months, all we 571 00:27:08,040 --> 00:27:11,600 Speaker 11: have heard is Microsoft and open ai investments in how 572 00:27:11,920 --> 00:27:15,280 Speaker 11: OpenAI has the large language models and the foundation models 573 00:27:15,320 --> 00:27:19,080 Speaker 11: that people can use, but they only run on Microsoft's cloud. 574 00:27:19,440 --> 00:27:22,760 Speaker 11: So Amazon does not have that relationship with open Ai. 575 00:27:22,880 --> 00:27:26,359 Speaker 11: So they're looking at other AI companies that have similar products, 576 00:27:26,960 --> 00:27:30,080 Speaker 11: investing money in them, trying to offer that service. And 577 00:27:30,119 --> 00:27:33,360 Speaker 11: that's really what's happening here for AWS. Remember one thing, 578 00:27:33,560 --> 00:27:37,120 Speaker 11: AWS is a much bigger player in cloud infrastructure then 579 00:27:37,200 --> 00:27:39,800 Speaker 11: Microsoft and I think it's for the first time they've 580 00:27:39,800 --> 00:27:41,200 Speaker 11: been caught on the wrong floort. 581 00:27:42,400 --> 00:27:48,320 Speaker 2: In terms of Claude, which is that name of their Claude, 582 00:27:48,840 --> 00:27:53,159 Speaker 2: that's the name of the bot, the chatbot that Anthropy 583 00:27:53,400 --> 00:27:56,359 Speaker 2: or whatever it's called runs right, what's it called anthropy 584 00:27:56,440 --> 00:27:57,960 Speaker 2: anthropic anthropotropic. 585 00:27:58,960 --> 00:28:03,240 Speaker 6: So in terms of Clauds usage, it's going. 586 00:28:03,200 --> 00:28:07,760 Speaker 2: To be all on now AWS servers, and it's going 587 00:28:07,840 --> 00:28:10,879 Speaker 2: to use Amazon chips, which I didn't even know that 588 00:28:11,000 --> 00:28:13,919 Speaker 2: they were, you know, building this much of their own silicon. 589 00:28:14,400 --> 00:28:17,840 Speaker 6: They have one chip for training it's called like trainium, 590 00:28:17,960 --> 00:28:20,679 Speaker 6: and one chip for read in on this. 591 00:28:20,880 --> 00:28:22,840 Speaker 2: Well, what's the other one is for like inference, It's 592 00:28:22,840 --> 00:28:24,480 Speaker 2: called inferyon or something like that. 593 00:28:25,840 --> 00:28:28,440 Speaker 6: How big of a business is that for them? 594 00:28:28,600 --> 00:28:31,120 Speaker 11: So these are all emerging businesses. I mean, you know, financially, 595 00:28:31,200 --> 00:28:33,720 Speaker 11: there's a probably negligible compared to the size of the 596 00:28:33,720 --> 00:28:36,199 Speaker 11: business right now. But this is one thing that I 597 00:28:36,240 --> 00:28:39,000 Speaker 11: think that really stood out to us was Amazon's not 598 00:28:39,280 --> 00:28:41,640 Speaker 11: talking about using in video chips for this, but their 599 00:28:41,680 --> 00:28:43,760 Speaker 11: own internal one. Now, it's going to be a while 600 00:28:43,800 --> 00:28:46,560 Speaker 11: before we really figured out if it works, but you know, 601 00:28:46,560 --> 00:28:49,400 Speaker 11: if AWS is making a big splash around it They're 602 00:28:49,440 --> 00:28:52,280 Speaker 11: basically saying that we can run some of these foundation 603 00:28:52,400 --> 00:28:55,440 Speaker 11: models or the large language models without the need of 604 00:28:55,560 --> 00:28:58,520 Speaker 11: Nvidio GPUs, which to me is a big deal. A 605 00:28:58,640 --> 00:29:01,960 Speaker 11: most large cloud companies eventually will design their own chips 606 00:29:02,000 --> 00:29:05,000 Speaker 11: and get them made only because they want to, you know, 607 00:29:05,200 --> 00:29:08,360 Speaker 11: have much higher performance than what they get from let's say, 608 00:29:08,360 --> 00:29:11,680 Speaker 11: an Intel and an AMD or or anybody else out there. 609 00:29:12,200 --> 00:29:15,240 Speaker 1: So Anrak, I'm probably like most investors out there, Like 610 00:29:15,280 --> 00:29:16,920 Speaker 1: over the last twelve months, I've been trying to run 611 00:29:16,960 --> 00:29:18,800 Speaker 1: around like crazy trying to figure out how I get 612 00:29:18,840 --> 00:29:21,960 Speaker 1: exposure to this thing called AI, which I don't even 613 00:29:21,960 --> 00:29:23,280 Speaker 1: really know what it is, but I know I got 614 00:29:23,320 --> 00:29:27,800 Speaker 1: to own it. So you know, in Nvidia, the chip stocks, 615 00:29:27,840 --> 00:29:30,640 Speaker 1: maybe a Microsoft something like that we kind of fell into. 616 00:29:31,120 --> 00:29:35,000 Speaker 1: But I did not think about Amazon. And is that 617 00:29:35,080 --> 00:29:38,080 Speaker 1: a problem for Amazon that it's not doesn't really it's 618 00:29:38,080 --> 00:29:40,000 Speaker 1: not proceived to have a real AI angle. 619 00:29:40,840 --> 00:29:43,440 Speaker 11: Yeah, I think this is this investment is basically they're 620 00:29:43,480 --> 00:29:45,400 Speaker 11: telling that you know, they are also serious and they're 621 00:29:45,400 --> 00:29:48,480 Speaker 11: gonna partner with other people. Oracle, for example, is partnering 622 00:29:48,480 --> 00:29:51,920 Speaker 11: with another company called cohere in a similar way. You know, 623 00:29:52,000 --> 00:29:54,600 Speaker 11: for us, when you get rid of or when you 624 00:29:54,640 --> 00:29:58,400 Speaker 11: when you are over the hype of the the chip makers, 625 00:29:58,680 --> 00:30:01,120 Speaker 11: when you move onto the software side, then the clear 626 00:30:01,240 --> 00:30:05,840 Speaker 11: winners actually are the cloud infrastructure companies, which is Awos, Microsoft, Google, 627 00:30:05,920 --> 00:30:08,560 Speaker 11: and to some extent, Oracle, because that's where you will 628 00:30:08,560 --> 00:30:11,360 Speaker 11: host some of these models and do your work on it. 629 00:30:11,560 --> 00:30:14,160 Speaker 11: And then on the downstream you have companies like you know, 630 00:30:14,680 --> 00:30:17,560 Speaker 11: Ccenture and Captaremini and so forth that will implement some 631 00:30:17,600 --> 00:30:20,480 Speaker 11: of that stuff. So on the software side, cloud infrastructure 632 00:30:20,600 --> 00:30:22,320 Speaker 11: is one of the biggest beneficiaries in our view. 633 00:30:22,680 --> 00:30:25,040 Speaker 2: Where do they get by the way their data sets 634 00:30:25,320 --> 00:30:29,000 Speaker 2: because a lot of these companies, you know, Microsoft has 635 00:30:29,040 --> 00:30:31,560 Speaker 2: a ton of our data. Obviously Apple has a ton 636 00:30:31,600 --> 00:30:34,440 Speaker 2: of our data. But Amazon might have more of our 637 00:30:34,480 --> 00:30:35,520 Speaker 2: data than anybody else. 638 00:30:35,640 --> 00:30:38,440 Speaker 11: Right, Yeah, but Matt, they're not using your or my 639 00:30:38,600 --> 00:30:41,520 Speaker 11: consumer data to run any of these things. What Amazon 640 00:30:41,520 --> 00:30:44,000 Speaker 11: Web Services is selling is going to you know, let's 641 00:30:44,000 --> 00:30:46,520 Speaker 11: say JP Morgan or Bank of America and saying, if 642 00:30:46,520 --> 00:30:49,080 Speaker 11: you want to train your models, we are giving you 643 00:30:49,120 --> 00:30:50,760 Speaker 11: the raw material for it. We are giving you the 644 00:30:50,800 --> 00:30:54,160 Speaker 11: computing power, We're giving you the algorithms. You bring your 645 00:30:54,200 --> 00:30:57,720 Speaker 11: own data, keep it in a contained environment, and test 646 00:30:57,760 --> 00:31:00,160 Speaker 11: it out. Whatever the results you get, you get the 647 00:31:00,160 --> 00:31:03,520 Speaker 11: benefit of it. They're basically just you know, selling compute 648 00:31:03,560 --> 00:31:06,520 Speaker 11: power and storage. They're not really you know, selling their 649 00:31:06,560 --> 00:31:09,360 Speaker 11: own or you're in my customer data to these companies. 650 00:31:09,960 --> 00:31:13,239 Speaker 1: Is this a good deal for Amazon? And do they 651 00:31:13,280 --> 00:31:14,120 Speaker 1: need to do more here? 652 00:31:14,760 --> 00:31:16,280 Speaker 11: Oh, they need to do a lot more. They need 653 00:31:16,320 --> 00:31:18,760 Speaker 11: to do a lot more and actually showcase a lot 654 00:31:18,760 --> 00:31:21,200 Speaker 11: of the enterprise use cases, Paul, What we have seen 655 00:31:21,280 --> 00:31:23,880 Speaker 11: so far is a lot of use cases when it 656 00:31:23,920 --> 00:31:26,640 Speaker 11: comes to consumer consumers. So when it you know, when 657 00:31:26,720 --> 00:31:29,560 Speaker 11: we see what's happening with chat GPT because it has 658 00:31:29,880 --> 00:31:32,560 Speaker 11: all the Internet data behind it, We're seeing some of 659 00:31:32,600 --> 00:31:35,400 Speaker 11: the stuff that Microsoft is doing because it has some 660 00:31:35,480 --> 00:31:38,440 Speaker 11: of the user data around it, you know, Excel and Word. 661 00:31:38,800 --> 00:31:40,800 Speaker 11: But the real use case in the long run is 662 00:31:40,960 --> 00:31:43,760 Speaker 11: enterprises B to B. In the case of B to B, 663 00:31:43,880 --> 00:31:46,400 Speaker 11: the data is diseggregated and it's in a lot of 664 00:31:46,440 --> 00:31:49,960 Speaker 11: different systems, and it's people like Amazon, Microsoft and Google 665 00:31:50,000 --> 00:31:52,280 Speaker 11: that are going to sell the infrastructure and say, you 666 00:31:52,360 --> 00:31:55,480 Speaker 11: create your own bots, you create your own large language 667 00:31:55,520 --> 00:31:58,520 Speaker 11: model trained on your own data, but we're going to 668 00:31:58,520 --> 00:32:00,880 Speaker 11: sell you all the services that allow you to do that. 669 00:32:02,360 --> 00:32:05,600 Speaker 1: So what I mean, it's it's when I think about Amazon, 670 00:32:05,640 --> 00:32:09,120 Speaker 1: I mean the cloud is there's such a leader in cloud. 671 00:32:10,000 --> 00:32:12,120 Speaker 1: Can I not say that that's a way to play 672 00:32:12,160 --> 00:32:13,080 Speaker 1: this whole AI thing. 673 00:32:13,120 --> 00:32:15,719 Speaker 11: This is the way, yeah, for software, that is the 674 00:32:15,760 --> 00:32:18,240 Speaker 11: only way to play it and argue because Amazon has 675 00:32:18,280 --> 00:32:21,480 Speaker 11: over a forty percent market share in cloud infrastructure. The 676 00:32:21,560 --> 00:32:25,280 Speaker 11: problem was they didn't have a relationship with OPENINGI. Today 677 00:32:25,280 --> 00:32:27,800 Speaker 11: they are, you know, So it's it's that's so the 678 00:32:27,840 --> 00:32:32,200 Speaker 11: companies that are selling the algorithms or these big foundation 679 00:32:32,360 --> 00:32:35,440 Speaker 11: models that are called or large language models. There are 680 00:32:35,520 --> 00:32:38,320 Speaker 11: multiple companies out there, and you you should expect to 681 00:32:38,360 --> 00:32:41,480 Speaker 11: see multiple deals like this with everybody down the road. 682 00:32:42,600 --> 00:32:44,000 Speaker 6: Who has the best product? 683 00:32:44,840 --> 00:32:45,040 Speaker 3: Have you? 684 00:32:45,160 --> 00:32:47,600 Speaker 6: Have you been playing with all of the with claud 685 00:32:47,720 --> 00:32:49,400 Speaker 6: and chat GBT and. 686 00:32:49,480 --> 00:32:51,840 Speaker 11: It's it all depends on who has the underlying data. 687 00:32:51,880 --> 00:32:53,880 Speaker 11: If you're going to ask me, who can summarize a 688 00:32:53,920 --> 00:32:56,840 Speaker 11: picture for me or a write poem, that doesn't really 689 00:32:56,880 --> 00:32:59,080 Speaker 11: move the needle for what I do for B to B. 690 00:32:59,320 --> 00:33:01,840 Speaker 11: It really needs to be able to get into enterprise 691 00:33:01,960 --> 00:33:04,560 Speaker 11: data and figure out whether I have the who's my 692 00:33:04,640 --> 00:33:08,440 Speaker 11: best customer over the next twelve months for that I 693 00:33:08,480 --> 00:33:11,800 Speaker 11: really need to tap into enterprise data warehouses and that's 694 00:33:11,840 --> 00:33:13,280 Speaker 11: not an easy thing to do right now. 695 00:33:14,160 --> 00:33:16,520 Speaker 1: All right, So what's what do you think is next 696 00:33:16,560 --> 00:33:19,360 Speaker 1: here for Amazon in terms of their cloud and maybe 697 00:33:19,360 --> 00:33:23,200 Speaker 1: in terms of AI specifically, because they have no shortage 698 00:33:23,200 --> 00:33:26,000 Speaker 1: of capital to invest here, so. 699 00:33:25,960 --> 00:33:27,800 Speaker 11: They're going to make investments like this. They're going to 700 00:33:27,840 --> 00:33:30,320 Speaker 11: invest in ships, they're going to invest in large language model. 701 00:33:30,480 --> 00:33:32,200 Speaker 11: You know, margin is not going to be a problem. 702 00:33:32,600 --> 00:33:35,320 Speaker 11: We are coming to a point that the tough comparisons 703 00:33:35,320 --> 00:33:39,040 Speaker 11: for cloud decline is almost over. We are expecting a 704 00:33:39,080 --> 00:33:41,600 Speaker 11: bounce back either the end of this quarter or perhaps 705 00:33:42,240 --> 00:33:45,120 Speaker 11: next quarter, and then we should see a rebound next year, 706 00:33:45,320 --> 00:33:48,400 Speaker 11: just because we have seen a fair amount of shrinkage 707 00:33:48,480 --> 00:33:51,160 Speaker 11: or the usage of cloud over the last twelve to 708 00:33:51,200 --> 00:33:53,760 Speaker 11: fourteen months, and we are coming to a point where 709 00:33:53,960 --> 00:33:56,680 Speaker 11: we should start to see a rebound. Now that's irrespective 710 00:33:56,720 --> 00:33:59,520 Speaker 11: of what happens to the AI. But once we see 711 00:33:59,520 --> 00:34:02,600 Speaker 11: more AI investments, hopefully next year, you know, that only 712 00:34:02,640 --> 00:34:04,800 Speaker 11: adds to the fuel of the cloud rebound that we 713 00:34:04,840 --> 00:34:05,520 Speaker 11: really believe in. 714 00:34:06,040 --> 00:34:07,959 Speaker 1: All right, Donnroroq, thanks so much for joining us. 715 00:34:08,400 --> 00:34:10,960 Speaker 9: You're listening to the tape, can to our live program 716 00:34:11,000 --> 00:34:14,960 Speaker 9: Bloomberg Markets weekdays at ten am Eastern on Bloomberg Radio, 717 00:34:15,120 --> 00:34:17,399 Speaker 9: the tune in app, Bloomberg dot Com, and the. 718 00:34:17,280 --> 00:34:18,479 Speaker 3: Bloomberg Business App. 719 00:34:18,520 --> 00:34:21,319 Speaker 9: You can also listen live on Amazon Alexa from our 720 00:34:21,360 --> 00:34:26,400 Speaker 9: flagship New York station. Just say Alexa play Bloomberg eleven thirty. 721 00:34:28,000 --> 00:34:31,080 Speaker 12: We want to welcome our Bloomberg television and radio audiences 722 00:34:31,400 --> 00:34:36,320 Speaker 12: worldwide joining us now. Amazon Web Services CEO Adam Selipsky, 723 00:34:36,360 --> 00:34:38,839 Speaker 12: and Adam Welcome to Bloomberg Technology. I want to start 724 00:34:38,840 --> 00:34:42,120 Speaker 12: with some of the mechanics of this deal. Does Anthropic 725 00:34:42,520 --> 00:34:46,440 Speaker 12: still pay Amazon to use AWS Cloud or is it 726 00:34:46,520 --> 00:34:50,640 Speaker 12: structured such that the investment that you make in Anthropic 727 00:34:50,719 --> 00:34:54,320 Speaker 12: is in the form of cash and credits for AWS Cloud? 728 00:34:55,800 --> 00:34:57,719 Speaker 7: No, good morning, Thanks for having me. 729 00:34:57,760 --> 00:35:03,319 Speaker 13: We're very excited for this expanded relationship with Anthropic, and 730 00:35:04,080 --> 00:35:08,160 Speaker 13: the investment is a financial investment, as you say. And 731 00:35:08,680 --> 00:35:14,319 Speaker 13: in addition, Anthropic will be training future versions of its 732 00:35:14,320 --> 00:35:19,000 Speaker 13: models and running its models on AWS using our. 733 00:35:19,000 --> 00:35:22,040 Speaker 7: Training chips and Inferentia chips. 734 00:35:22,640 --> 00:35:25,520 Speaker 13: Those models will be guaranteed to be available for years 735 00:35:25,560 --> 00:35:30,040 Speaker 13: to come in our Amazon Bedrock Managed service for llms, 736 00:35:30,120 --> 00:35:34,440 Speaker 13: which provides the very wide choice of models, and AWS 737 00:35:34,480 --> 00:35:38,400 Speaker 13: customers will actually receive early access to key features in 738 00:35:38,480 --> 00:35:42,000 Speaker 13: Anthropics models in the future, such as fine tuning and 739 00:35:42,080 --> 00:35:47,640 Speaker 13: customization of models. In addition, Anthropics on very talented technical teams, 740 00:35:48,080 --> 00:35:51,880 Speaker 13: and we anticipate working closely with them to actually improve 741 00:35:52,360 --> 00:35:56,960 Speaker 13: future versions of our training and inferential chips. So there 742 00:35:57,000 --> 00:35:59,560 Speaker 13: are a lot of different benefits for our joint end 743 00:35:59,600 --> 00:36:02,480 Speaker 13: customers from this relationship and we're very excited to be 744 00:36:02,560 --> 00:36:03,440 Speaker 13: leaders in this together. 745 00:36:04,640 --> 00:36:07,320 Speaker 12: Adam, there's a lot of emphasis on moving a maker 746 00:36:07,360 --> 00:36:10,560 Speaker 12: of foundation models at that scale onto your proprietary silicon. 747 00:36:11,040 --> 00:36:16,160 Speaker 12: How quickly will Anthropics start running AI workloads on Trainium 748 00:36:16,200 --> 00:36:16,960 Speaker 12: and Inferentia. 749 00:36:18,080 --> 00:36:21,839 Speaker 13: We've been working with Anthropic, they've been a customers of 750 00:36:21,840 --> 00:36:24,920 Speaker 13: ours since I think they're founding over a couple of 751 00:36:24,960 --> 00:36:28,000 Speaker 13: years ago, and so they use a variety of different 752 00:36:28,480 --> 00:36:35,080 Speaker 13: technologies for a variety of different workloads on AWS that 753 00:36:35,120 --> 00:36:39,279 Speaker 13: they'll be using GPUs on AWS and will also be 754 00:36:39,400 --> 00:36:43,239 Speaker 13: using large quantities of Trainium and Inferentia. So I think 755 00:36:43,280 --> 00:36:46,400 Speaker 13: everything's going to move very quickly and it'll all be 756 00:36:46,920 --> 00:36:49,359 Speaker 13: a mix of technologies depending on their needs at the time. 757 00:36:50,680 --> 00:36:54,200 Speaker 12: Adam, what's the mood like within Amazon and AWS this morning. 758 00:36:54,239 --> 00:36:57,640 Speaker 12: There are lots of talented engineers that have been working 759 00:36:57,719 --> 00:37:02,160 Speaker 12: on large language models generative tools internally, and now you're 760 00:37:02,200 --> 00:37:05,319 Speaker 12: turning to a third party who's highly regarded as a 761 00:37:05,400 --> 00:37:07,640 Speaker 12: leader in building foundation models. 762 00:37:09,600 --> 00:37:10,560 Speaker 7: The mood here is great. 763 00:37:11,320 --> 00:37:13,760 Speaker 13: We are a company of inventors who we love to build, 764 00:37:14,200 --> 00:37:16,239 Speaker 13: and there's never been a better time to be a 765 00:37:16,239 --> 00:37:18,839 Speaker 13: builder at AWS than right now. 766 00:37:19,440 --> 00:37:21,480 Speaker 7: And as I mentioned. 767 00:37:21,120 --> 00:37:24,560 Speaker 13: Before, a big part of our strategy in AI and 768 00:37:24,640 --> 00:37:28,200 Speaker 13: generative AI specifically, it is all about customer choice and 769 00:37:28,360 --> 00:37:32,400 Speaker 13: there's not going to be any one solution that works 770 00:37:32,400 --> 00:37:35,600 Speaker 13: for all customers for all use cases. And Thropic has 771 00:37:35,600 --> 00:37:39,280 Speaker 13: done an amazing job. They're clearly a leader in this space, 772 00:37:39,719 --> 00:37:42,560 Speaker 13: and it's really important for customers that we continue to 773 00:37:42,600 --> 00:37:47,239 Speaker 13: generate new capabilities together at the same time. Really, one 774 00:37:47,239 --> 00:37:50,600 Speaker 13: of the hallmarks of our Amazon Bedrock managed service for 775 00:37:51,040 --> 00:37:54,600 Speaker 13: generative AI is choice, and so Amazon is going to 776 00:37:54,600 --> 00:37:57,160 Speaker 13: continue to build its own Titan models, which are going 777 00:37:57,200 --> 00:38:02,560 Speaker 13: to be available later this year. Obviously, Anthropics models are 778 00:38:02,600 --> 00:38:05,680 Speaker 13: prominent in Bedrock, and we will have models from other 779 00:38:05,920 --> 00:38:09,040 Speaker 13: leading providers as well as we have today. So it's 780 00:38:09,040 --> 00:38:12,360 Speaker 13: still an amazing time to build here at Amazon. We 781 00:38:12,400 --> 00:38:14,000 Speaker 13: think our models are going to be great as well, 782 00:38:14,040 --> 00:38:18,439 Speaker 13: and it's about customers choosing the right tool for the job, talking. 783 00:38:18,080 --> 00:38:20,600 Speaker 14: About choice, and I just want to re welcome our 784 00:38:20,640 --> 00:38:24,920 Speaker 14: TV and radio audiances with Adam Sleipski. What's so notable 785 00:38:25,120 --> 00:38:28,040 Speaker 14: is that, well, Anthropic took a chunk of change one 786 00:38:28,120 --> 00:38:32,640 Speaker 14: hundred million dollars worth from Google already, and I'm interested 787 00:38:32,680 --> 00:38:35,240 Speaker 14: as to how you feel that is perhaps a concern 788 00:38:35,320 --> 00:38:38,520 Speaker 14: for you or not the relationship that Anthropic already has 789 00:38:39,160 --> 00:38:40,920 Speaker 14: with a previous cloud provider. 790 00:38:42,160 --> 00:38:45,640 Speaker 13: Now, we feel great about the relationship with Anthropic. It's 791 00:38:45,680 --> 00:38:48,600 Speaker 13: been a good relationship and I think today's announcement just 792 00:38:48,680 --> 00:38:55,120 Speaker 13: makes it a deeper and longer term. Anthropic will use 793 00:38:55,160 --> 00:38:58,960 Speaker 13: AWS as its primary cloud provider for mission critical workloads, 794 00:38:59,040 --> 00:39:05,400 Speaker 13: including building foundational foundation models and doing AI safety research, 795 00:39:05,719 --> 00:39:09,160 Speaker 13: and will run the majority of its workloads on AWS. 796 00:39:09,200 --> 00:39:12,040 Speaker 13: So we feel great about being able to provide the 797 00:39:12,080 --> 00:39:15,520 Speaker 13: capacity and the expertise and of course the security, the 798 00:39:15,640 --> 00:39:19,320 Speaker 13: enterprise grade security that is so important to AWS customers. 799 00:39:19,480 --> 00:39:21,880 Speaker 13: And we also feel great about working with Anthropic to 800 00:39:21,960 --> 00:39:26,240 Speaker 13: make sure that our trainingum and inferential technology our chips 801 00:39:26,360 --> 00:39:30,080 Speaker 13: are as cutting edge as possible going forward for years 802 00:39:30,080 --> 00:39:30,439 Speaker 13: to come. 803 00:39:31,120 --> 00:39:33,279 Speaker 14: I'm interested in drilling down sort of on why ed 804 00:39:33,440 --> 00:39:36,239 Speaker 14: was going about the feeling internally right now, because I 805 00:39:36,239 --> 00:39:38,480 Speaker 14: look at some of the analyst reaction to this Adam 806 00:39:38,520 --> 00:39:41,160 Speaker 14: and Webbush, for example, they say this signals a new 807 00:39:41,280 --> 00:39:45,200 Speaker 14: found urgency in Amazon's strategy to further integrate generative AI 808 00:39:45,320 --> 00:39:49,120 Speaker 14: among your AWS suite of services. That urgency was there 809 00:39:49,480 --> 00:39:53,200 Speaker 14: a lack of understanding or indeed a reality that Amazon 810 00:39:53,520 --> 00:39:55,680 Speaker 14: was behind the curve here a little bit when it 811 00:39:55,719 --> 00:39:59,160 Speaker 14: came to the integration of generative AI. Because we've been 812 00:39:59,200 --> 00:40:01,359 Speaker 14: looking at open Ai Microsoft for a while now. 813 00:40:02,480 --> 00:40:05,600 Speaker 13: We've been saying for many, many months, Carolyn, that we 814 00:40:05,640 --> 00:40:08,399 Speaker 13: are fully urgent. We have a strategy that we really love. 815 00:40:08,719 --> 00:40:11,280 Speaker 13: It is different than some other cloud provider strategies. 816 00:40:11,280 --> 00:40:11,920 Speaker 7: It's true. 817 00:40:12,640 --> 00:40:18,479 Speaker 13: We have a strategy of providing absolutely uncompromising security, which 818 00:40:18,520 --> 00:40:20,759 Speaker 13: I don't think is true for all cloud providers. We 819 00:40:20,840 --> 00:40:24,120 Speaker 13: have a strategy of providing customers the choices to use 820 00:40:24,160 --> 00:40:28,200 Speaker 13: whatever is best for their job at hand. So Andthropic 821 00:40:28,320 --> 00:40:30,719 Speaker 13: is going to be an amazing set of models for 822 00:40:30,800 --> 00:40:35,279 Speaker 13: many many use cases. And Amazon is fully invested in 823 00:40:35,320 --> 00:40:38,160 Speaker 13: building its own Titan models, which I think will be 824 00:40:38,160 --> 00:40:41,799 Speaker 13: really useful for other customers and other circumstances, and of 825 00:40:41,800 --> 00:40:47,000 Speaker 13: course our other model provider partners through Bedrock. So I 826 00:40:47,040 --> 00:40:49,759 Speaker 13: really think it's an ill founded premise that there's been 827 00:40:49,760 --> 00:40:52,680 Speaker 13: some change in urgency. We're fully urgent here on generative 828 00:40:52,719 --> 00:40:55,640 Speaker 13: AI for one reason or one reason alone. It's because 829 00:40:55,680 --> 00:40:59,600 Speaker 13: our customers need us to have great generative AI capabilities. 830 00:40:59,640 --> 00:41:02,520 Speaker 13: So many of them have their data platforms on AWS, 831 00:41:02,560 --> 00:41:05,960 Speaker 13: and if you got your data here, you really want 832 00:41:06,000 --> 00:41:09,080 Speaker 13: to have your generative AI and all the powerful capabilities 833 00:41:09,120 --> 00:41:11,920 Speaker 13: that you need from those capabilities are in the same place. 834 00:41:12,000 --> 00:41:15,040 Speaker 13: And so we have been, are and will continue to 835 00:41:15,080 --> 00:41:17,480 Speaker 13: be very motivated to deliver for customers. 836 00:41:18,000 --> 00:41:20,000 Speaker 12: Adam, what does this mean for the kind of ramp 837 00:41:20,080 --> 00:41:23,359 Speaker 12: up or path forward for Trainium and Inferentia. You put 838 00:41:23,360 --> 00:41:27,040 Speaker 12: a lot of emphasis that anthropic brings you a maker 839 00:41:27,400 --> 00:41:30,879 Speaker 12: or creator foundation models at scale. Well, you now need 840 00:41:30,920 --> 00:41:35,280 Speaker 12: to ramp up I guess your third party manufacturing relationships 841 00:41:35,280 --> 00:41:38,000 Speaker 12: to say, okay, let's get more Trainium on more inferential 842 00:41:38,080 --> 00:41:40,200 Speaker 12: online to support the workloads. 843 00:41:41,320 --> 00:41:44,600 Speaker 13: Well, it's absolutely true that there is a huge demand 844 00:41:45,200 --> 00:41:49,520 Speaker 13: for all of the different ships with which people do 845 00:41:49,960 --> 00:41:54,040 Speaker 13: a generative AI workloads, and so We absolutely have already 846 00:41:54,080 --> 00:41:58,560 Speaker 13: been ramping up our training and inferential supply chain and 847 00:41:58,880 --> 00:42:00,920 Speaker 13: ramping up the apply that we can. 848 00:42:00,800 --> 00:42:02,480 Speaker 7: Create as quickly as possible. 849 00:42:02,840 --> 00:42:07,799 Speaker 13: And yes, Anthropic will have access to very significant quantities 850 00:42:07,800 --> 00:42:10,919 Speaker 13: of compute which we'll have trainum and inferentia in them. 851 00:42:11,000 --> 00:42:14,960 Speaker 13: So yes, that's one of many reasons why we continue 852 00:42:15,000 --> 00:42:18,640 Speaker 13: to ramp up and to provide a very robust AWS 853 00:42:18,680 --> 00:42:21,680 Speaker 13: controlled supply chain for AI chips. 854 00:42:21,960 --> 00:42:25,400 Speaker 14: And is that where the revenue boost comes, Adam, because 855 00:42:25,440 --> 00:42:27,600 Speaker 14: we're looking at the share price reaction is higher on 856 00:42:27,640 --> 00:42:31,400 Speaker 14: the day. When does this all start to really drive adoption, 857 00:42:31,920 --> 00:42:33,760 Speaker 14: money and the bottom line for Amazon? 858 00:42:35,040 --> 00:42:37,400 Speaker 7: Well, I think that AI in general. 859 00:42:37,480 --> 00:42:41,000 Speaker 13: Look, AWS has had machine learning services since at least 860 00:42:41,040 --> 00:42:45,560 Speaker 13: twenty seventeen when we released our sage Maker machine learning service, 861 00:42:45,600 --> 00:42:48,480 Speaker 13: which has over one hundred thousand AWS customers on it. 862 00:42:48,520 --> 00:42:51,040 Speaker 13: So we've been doing machine learning for a long time 863 00:42:51,160 --> 00:42:55,160 Speaker 13: inside of AWS and obviously more recently have had a 864 00:42:55,200 --> 00:42:59,520 Speaker 13: significant number of generative AI customers, and we will certainly 865 00:43:00,160 --> 00:43:03,000 Speaker 13: continue to ramp up anticipate, you know, quite steeply. We 866 00:43:03,080 --> 00:43:07,480 Speaker 13: have many sources of growth inside of AWS where a 867 00:43:07,560 --> 00:43:12,360 Speaker 13: scaled and relatively sizable business at this point, and customers 868 00:43:12,360 --> 00:43:17,080 Speaker 13: are running their data platforms on AWS. They are building 869 00:43:17,080 --> 00:43:19,800 Speaker 13: out more and more applications for things like supply chain 870 00:43:20,400 --> 00:43:23,080 Speaker 13: and contact center management on AWS. 871 00:43:23,320 --> 00:43:24,399 Speaker 7: I've still a whole lot of. 872 00:43:24,360 --> 00:43:28,000 Speaker 13: Storage and compute and database workloads ramping on our AWS, 873 00:43:28,040 --> 00:43:31,600 Speaker 13: so we have many sources of growth I anticipate, but 874 00:43:31,680 --> 00:43:34,759 Speaker 13: there's absolutely no doubt that generative AI looks like it's 875 00:43:34,800 --> 00:43:38,040 Speaker 13: going to be an explosive additional source of growth in 876 00:43:38,080 --> 00:43:38,800 Speaker 13: the years ahead. 877 00:43:40,239 --> 00:43:42,560 Speaker 12: Adam, we put a lot of emphasis on the up 878 00:43:42,600 --> 00:43:46,120 Speaker 12: to four billion dollars, and you know, I understand and 879 00:43:46,160 --> 00:43:49,959 Speaker 12: thank you for explaining how the relationship will work in practice. 880 00:43:50,200 --> 00:43:52,239 Speaker 12: If I put to you this is an example of 881 00:43:52,280 --> 00:43:56,080 Speaker 12: Amazon or AWS basically paying a leader in the field 882 00:43:56,080 --> 00:43:59,960 Speaker 12: of AI, handing over cash to allow to make them 883 00:44:00,160 --> 00:44:03,360 Speaker 12: use trainum and inferentia, how would you respond to that 884 00:44:03,400 --> 00:44:05,600 Speaker 12: and explain to me how you bring new customers on 885 00:44:05,680 --> 00:44:09,040 Speaker 12: board who are really interested in the AI accelerators that 886 00:44:09,120 --> 00:44:13,359 Speaker 12: you have built without having to invest in them as 887 00:44:13,400 --> 00:44:14,280 Speaker 12: a sort of backup. 888 00:44:15,400 --> 00:44:15,680 Speaker 7: Sure. 889 00:44:15,760 --> 00:44:20,279 Speaker 13: Well, the I think the really big news today is 890 00:44:20,440 --> 00:44:27,320 Speaker 13: the new expanded relationship between Anthropic and Amazon, in which 891 00:44:28,040 --> 00:44:31,800 Speaker 13: they will have access to really large quantities of training 892 00:44:31,880 --> 00:44:35,360 Speaker 13: and in FRENTI at Chips, customers will have access to 893 00:44:35,360 --> 00:44:39,440 Speaker 13: those models, including early access to critical features through Amazon 894 00:44:39,480 --> 00:44:43,400 Speaker 13: Bedrock and Amazon will get to AWS, We'll get to 895 00:44:43,400 --> 00:44:47,320 Speaker 13: work with Anthropic to ensure that, you know, we optimize 896 00:44:47,360 --> 00:44:49,560 Speaker 13: our training and Inferentia technology going forward. 897 00:44:49,760 --> 00:44:52,920 Speaker 7: That's the benefit for customers. And yes, as part of this. 898 00:44:53,239 --> 00:44:56,560 Speaker 13: We're pleased to be making an adial an initial investment 899 00:44:56,600 --> 00:45:00,960 Speaker 13: of one point twenty five billion dollars into Anthropic's financial investment, 900 00:45:01,480 --> 00:45:04,320 Speaker 13: and that could go up as high as four billions, 901 00:45:04,760 --> 00:45:07,840 Speaker 13: as you said, over time. But it's really driven around 902 00:45:07,840 --> 00:45:09,719 Speaker 13: customer value and what this is going to mean to 903 00:45:09,800 --> 00:45:14,040 Speaker 13: customers who are very, very determined as they should be, 904 00:45:14,560 --> 00:45:18,280 Speaker 13: to figure out generative AI strategies. We already are working 905 00:45:18,360 --> 00:45:22,400 Speaker 13: in depth with customers, as is Anthropic on forming those 906 00:45:22,440 --> 00:45:25,279 Speaker 13: strategies and actually moving to execution. We have a lot 907 00:45:25,280 --> 00:45:29,719 Speaker 13: of great customers from Lonely Planet to Nexus, Lexus and 908 00:45:29,800 --> 00:45:32,760 Speaker 13: a number of others who are actually moving to production 909 00:45:33,160 --> 00:45:37,320 Speaker 13: with generative AI on AWS and Anthropic, And in addition, 910 00:45:37,360 --> 00:45:40,919 Speaker 13: as you alluded to, we'll be working with all of 911 00:45:40,960 --> 00:45:44,200 Speaker 13: the partners that our customers want to do business with. 912 00:45:44,280 --> 00:45:46,920 Speaker 13: If it's an important partner to our customers. It's going 913 00:45:46,960 --> 00:45:48,680 Speaker 13: to be an important partner to us as well. 914 00:45:50,200 --> 00:45:54,320 Speaker 12: Amazon Web Services CEO Adam Selipski, thank you. 915 00:45:54,320 --> 00:45:55,520 Speaker 5: You're listening to the tape. 916 00:45:55,760 --> 00:45:59,080 Speaker 9: Catch our live program Bloomberg Markets weekdays at ten am 917 00:45:59,120 --> 00:46:03,120 Speaker 9: Eastern on Bloomberg Radio, the tune in app, Bloomberg dot Com, and. 918 00:46:03,080 --> 00:46:04,399 Speaker 3: The Bloomberg Business App. 919 00:46:04,440 --> 00:46:07,239 Speaker 9: You can also listen live on Amazon Alexa from our 920 00:46:07,280 --> 00:46:12,360 Speaker 9: flagship New York station, Just say Alexa, play Bloomberg eleven thirty. 921 00:46:13,080 --> 00:46:17,400 Speaker 1: Last month, existing US home sales the United States fell 922 00:46:17,440 --> 00:46:21,319 Speaker 1: by zero point seven percent from the prior month. That's 923 00:46:21,360 --> 00:46:24,719 Speaker 1: a seven month low. So higher interest rates and a 924 00:46:24,800 --> 00:46:28,120 Speaker 1: lack of supply contributing to those declinents. Let's see kind 925 00:46:28,120 --> 00:46:29,719 Speaker 1: of where we go from here. We can check in 926 00:46:29,760 --> 00:46:33,080 Speaker 1: with Lisa's start Event, chief economists at Bright Mls. She 927 00:46:33,160 --> 00:46:36,640 Speaker 1: joins us via zoom. Lisa again, it's nobody's putting. There's 928 00:46:36,680 --> 00:46:39,760 Speaker 1: no there's really no supply out there. So the net result, 929 00:46:39,800 --> 00:46:42,279 Speaker 1: there's not a lot of transactions. What do you see 930 00:46:42,320 --> 00:46:44,080 Speaker 1: going forward? 931 00:46:46,040 --> 00:46:49,680 Speaker 15: Of course, have made it more difficult for buyers, but 932 00:46:49,760 --> 00:46:52,560 Speaker 15: those higher rates have also caused a lot of existing 933 00:46:52,600 --> 00:46:55,359 Speaker 15: homeowners to stay put when they might have thought about 934 00:46:55,440 --> 00:46:58,960 Speaker 15: listing their home for sale. I think mortgage rates are 935 00:46:58,960 --> 00:47:02,680 Speaker 15: going to stay elevated throughout the fall and into the winter, 936 00:47:02,800 --> 00:47:05,359 Speaker 15: so I think inventory is going to still be very 937 00:47:05,480 --> 00:47:07,959 Speaker 15: very low for the last part of the of the year. 938 00:47:08,520 --> 00:47:10,400 Speaker 15: I think we're I do think though, we're going to 939 00:47:10,400 --> 00:47:13,719 Speaker 15: start to see buyer demand pull back even further. You 940 00:47:13,760 --> 00:47:17,120 Speaker 15: mentioned August numbers were the lowest we've seen in several months, 941 00:47:17,200 --> 00:47:18,880 Speaker 15: and I think we're going to see an even further 942 00:47:18,960 --> 00:47:22,399 Speaker 15: contraction of buyer activity in the market simply because those 943 00:47:22,480 --> 00:47:24,040 Speaker 15: rates are so high. 944 00:47:24,200 --> 00:47:28,040 Speaker 1: Is there a rate, Lisa, that you think would entice 945 00:47:28,080 --> 00:47:29,879 Speaker 1: people to come back into this market. I mean we're 946 00:47:29,880 --> 00:47:32,799 Speaker 1: now looking at the UH, you know, the thirty year 947 00:47:33,120 --> 00:47:38,160 Speaker 1: fixed like seven point sixty four percent. Here. Is there 948 00:47:38,160 --> 00:47:40,800 Speaker 1: a level, whether it's six percent, five percent, four percent, 949 00:47:40,840 --> 00:47:42,840 Speaker 1: where people start coming back into the market. 950 00:47:43,480 --> 00:47:44,200 Speaker 6: Yeah, I don't think. 951 00:47:44,040 --> 00:47:46,239 Speaker 15: We're going to see four percent UH in an eight 952 00:47:46,400 --> 00:47:48,919 Speaker 15: in any short order, for sure. You know, I think 953 00:47:48,920 --> 00:47:50,960 Speaker 15: it's I think it's not only about the level, but 954 00:47:51,000 --> 00:47:54,799 Speaker 15: also about the timing. Last year, we saw rates go 955 00:47:54,920 --> 00:47:57,920 Speaker 15: over seven percent, like right around the beginning of November, 956 00:47:58,160 --> 00:48:00,720 Speaker 15: and we saw the market just sort of shut down. 957 00:48:00,840 --> 00:48:04,200 Speaker 15: People were reacting to the higher rates, but they were 958 00:48:04,200 --> 00:48:06,160 Speaker 15: also reacting to the fact that, you know, the holidays 959 00:48:06,160 --> 00:48:07,920 Speaker 15: were coming and people were sort of just going to 960 00:48:07,920 --> 00:48:10,440 Speaker 15: take a break before they returned to the market in 961 00:48:10,719 --> 00:48:12,520 Speaker 15: the first of the year. So I think, you know, 962 00:48:12,560 --> 00:48:15,400 Speaker 15: if we see mortgage rates come down to you know, 963 00:48:15,480 --> 00:48:17,319 Speaker 15: six six and a half percent, which I don't think 964 00:48:17,320 --> 00:48:20,520 Speaker 15: we're going to see until next year, Frankly, I do 965 00:48:20,560 --> 00:48:24,200 Speaker 15: think that'll be the level that will change the landscape 966 00:48:24,200 --> 00:48:27,120 Speaker 15: for a lot of buyers and sellers, importantly bringing more 967 00:48:27,120 --> 00:48:28,560 Speaker 15: of those listings onto the market. 968 00:48:28,960 --> 00:48:31,160 Speaker 1: So, I mean, I guess the key issue here for 969 00:48:31,200 --> 00:48:33,520 Speaker 1: most people is just affordability. I mean, you know, the 970 00:48:34,160 --> 00:48:37,399 Speaker 1: given the mortgage rates and given where housing prices are, 971 00:48:37,520 --> 00:48:40,799 Speaker 1: just the affordability is just the key issue here for 972 00:48:40,800 --> 00:48:41,920 Speaker 1: a lot of potential buyers. 973 00:48:42,320 --> 00:48:43,759 Speaker 15: I think that's right. You know, we've been talking about 974 00:48:43,800 --> 00:48:46,600 Speaker 15: inventory as the main constraint on the market, but affordability 975 00:48:46,640 --> 00:48:48,279 Speaker 15: is going to be more and more of what we're 976 00:48:48,320 --> 00:48:51,640 Speaker 15: talking about. You know, rates over the last fifty years. 977 00:48:51,640 --> 00:48:55,320 Speaker 15: Mortgage rates have averaged about seven point seventy five percent 978 00:48:55,400 --> 00:48:58,000 Speaker 15: over the last four decades, So the rates we're seeing 979 00:48:58,040 --> 00:49:01,960 Speaker 15: now aren't particularly high by historic standards. 980 00:49:02,120 --> 00:49:02,960 Speaker 6: But what's very. 981 00:49:02,800 --> 00:49:05,520 Speaker 15: Different is that home prices have been rising much much 982 00:49:05,560 --> 00:49:09,640 Speaker 15: faster than incomes over the last decade or two. And 983 00:49:09,680 --> 00:49:13,360 Speaker 15: we're seeing now that affordability, when you compare household incomes 984 00:49:13,400 --> 00:49:17,080 Speaker 15: to the price of buying a home, affordability is worse 985 00:49:17,120 --> 00:49:21,359 Speaker 15: than it's been in history and is really the main 986 00:49:22,280 --> 00:49:25,160 Speaker 15: driver of people having to leave the market, particularly first 987 00:49:25,160 --> 00:49:28,000 Speaker 15: time home buyers. And that's a real problem as the 988 00:49:28,040 --> 00:49:31,040 Speaker 15: ability to accumulate well through home ownership is you know, 989 00:49:31,160 --> 00:49:33,600 Speaker 15: being is there's an obstacle to that for many first 990 00:49:33,600 --> 00:49:34,479 Speaker 15: time buyers right now. 991 00:49:34,920 --> 00:49:38,560 Speaker 1: So where are first time home buyers going? I mean, 992 00:49:39,400 --> 00:49:41,680 Speaker 1: do they just continue to rent? What do they do? 993 00:49:42,320 --> 00:49:43,919 Speaker 15: Yeah, So there's a couple of things we've been seeing. 994 00:49:43,960 --> 00:49:44,080 Speaker 3: You know. 995 00:49:44,120 --> 00:49:45,960 Speaker 15: We're seeing that first time buyers are getting a little 996 00:49:45,960 --> 00:49:49,000 Speaker 15: bit more creative. We're seeing more first time buyers who 997 00:49:49,120 --> 00:49:51,640 Speaker 15: might be you know, looking to buy a home with 998 00:49:51,680 --> 00:49:56,080 Speaker 15: their parents, for example, or looking to buy with buy 999 00:49:56,120 --> 00:49:57,680 Speaker 15: a home where they could rent out part of the 1000 00:49:57,680 --> 00:49:59,719 Speaker 15: home to have that income be able to make the 1001 00:49:59,760 --> 00:50:02,640 Speaker 15: month payment a little more doable. We're also seeing that 1002 00:50:02,680 --> 00:50:05,440 Speaker 15: maybe first time buyers are more willing to take on 1003 00:50:05,480 --> 00:50:07,160 Speaker 15: a fixer upper when that might not have been the 1004 00:50:07,160 --> 00:50:09,720 Speaker 15: case a couple of years ago. But frankly, we're seeing 1005 00:50:09,719 --> 00:50:12,800 Speaker 15: that folks in that first time home buying age actually 1006 00:50:12,840 --> 00:50:15,799 Speaker 15: maybe deciding that renting makes the most sense. As more 1007 00:50:16,080 --> 00:50:19,200 Speaker 15: apartment construction has come online, rents are actually falling in 1008 00:50:19,239 --> 00:50:22,120 Speaker 15: some markets across the country, and renting might be a 1009 00:50:22,120 --> 00:50:24,840 Speaker 15: better financial decision than buying at this point for those folks. 1010 00:50:25,080 --> 00:50:27,560 Speaker 1: You know, when you listen to the home builders, they 1011 00:50:27,640 --> 00:50:31,680 Speaker 1: talk about building these larger homes than McMansions, if you will, 1012 00:50:31,920 --> 00:50:34,720 Speaker 1: because that's simply where the profit margin is, but arguably 1013 00:50:34,719 --> 00:50:37,279 Speaker 1: where the demand is, or where the need is is 1014 00:50:37,320 --> 00:50:39,560 Speaker 1: for some of those first time buyers as smaller homes. 1015 00:50:39,600 --> 00:50:42,480 Speaker 1: But do are we seeing the homebuilders kind of move 1016 00:50:42,520 --> 00:50:44,719 Speaker 1: that way? Are they sticking with where the margin is? 1017 00:50:45,400 --> 00:50:47,160 Speaker 15: Yeah, you know, that's a great question. And you know, 1018 00:50:47,200 --> 00:50:50,200 Speaker 15: we have seeing that the size of new build homes 1019 00:50:50,200 --> 00:50:52,279 Speaker 15: has come down, and I do think that is in 1020 00:50:52,320 --> 00:50:56,000 Speaker 15: response to the demand from first time home buyers. The 1021 00:50:56,040 --> 00:50:59,440 Speaker 15: biggest constraint that home builders have really is the local 1022 00:50:59,520 --> 00:51:03,000 Speaker 15: zoning RD relations on the ground that limit the amount 1023 00:51:03,040 --> 00:51:05,120 Speaker 15: of homes that they can buy. So you talk about 1024 00:51:05,200 --> 00:51:09,120 Speaker 15: profit margins, if builders were able to build more homes 1025 00:51:09,160 --> 00:51:11,520 Speaker 15: on a given lot, they could build them smaller and 1026 00:51:11,600 --> 00:51:14,759 Speaker 15: still make the numbers pencil out. And that really is, 1027 00:51:14,920 --> 00:51:17,959 Speaker 15: you know, where the constraint is. Builders see that first 1028 00:51:18,000 --> 00:51:20,719 Speaker 15: time home buyer demand and if they could make it 1029 00:51:20,800 --> 00:51:24,439 Speaker 15: work in order to make a profit, they certainly would 1030 00:51:24,440 --> 00:51:27,520 Speaker 15: be building there. But there's still demand all along the 1031 00:51:27,560 --> 00:51:30,400 Speaker 15: price ranges, and so they're still seeing folks coming in 1032 00:51:30,440 --> 00:51:31,920 Speaker 15: for those larger homes. 1033 00:51:32,480 --> 00:51:34,919 Speaker 1: Is the tightness in the real estate market, the lack 1034 00:51:34,960 --> 00:51:37,120 Speaker 1: of liquiditying in the real estate market. Is that a 1035 00:51:37,120 --> 00:51:40,520 Speaker 1: are we seeing that across the country or the regional variances. 1036 00:51:40,080 --> 00:51:43,239 Speaker 15: Of note, Yeah, you know, I think we could characterize 1037 00:51:43,280 --> 00:51:46,239 Speaker 15: it as a tight inventory situation across the country for sure, 1038 00:51:46,560 --> 00:51:48,960 Speaker 15: But there are places where the market is loosening up 1039 00:51:48,960 --> 00:51:52,759 Speaker 15: a little bit. For example, we're seeing in markets that 1040 00:51:52,800 --> 00:51:55,600 Speaker 15: have been traditionally second home and vacation home markets. We're 1041 00:51:55,600 --> 00:51:59,120 Speaker 15: seeing those are places where inventory is increasing, perhaps as 1042 00:51:59,160 --> 00:52:01,040 Speaker 15: people who had per just a second home or a 1043 00:52:01,080 --> 00:52:03,640 Speaker 15: vacation home, or maybe a home to use as a 1044 00:52:03,680 --> 00:52:06,799 Speaker 15: short term rental are listing those homes for sale. So 1045 00:52:07,080 --> 00:52:10,240 Speaker 15: we are starting to see supply growing in some markets 1046 00:52:10,239 --> 00:52:13,120 Speaker 15: and I expect we'll see an uptick in inventory in 1047 00:52:13,200 --> 00:52:15,719 Speaker 15: other markets as we head into the end of the 1048 00:52:15,800 --> 00:52:17,080 Speaker 15: year and into the first part. 1049 00:52:16,880 --> 00:52:17,399 Speaker 3: Of next year. 1050 00:52:17,800 --> 00:52:19,480 Speaker 1: If I want to get a mortgage, can I get 1051 00:52:19,480 --> 00:52:20,880 Speaker 1: a mortgage? 1052 00:52:21,160 --> 00:52:21,399 Speaker 14: Yeah? 1053 00:52:21,600 --> 00:52:23,120 Speaker 15: You know, you still can get a mortgage. It still 1054 00:52:23,120 --> 00:52:25,360 Speaker 15: pays to shop around, for sure. We talked about what 1055 00:52:25,440 --> 00:52:28,960 Speaker 15: the average rate was on a thirty year fixed, but 1056 00:52:29,320 --> 00:52:31,920 Speaker 15: it obviously varies depending on the borrower. And I think 1057 00:52:31,960 --> 00:52:34,520 Speaker 15: the most important thing for a perspective home buyer right 1058 00:52:34,520 --> 00:52:37,000 Speaker 15: now is to make sure you're out there shopping around 1059 00:52:37,360 --> 00:52:40,000 Speaker 15: to find a lender who can provide you with the 1060 00:52:40,440 --> 00:52:42,719 Speaker 15: rate and the terms that make sense for your situation. 1061 00:52:43,360 --> 00:52:47,080 Speaker 15: More folks are looking at adjustable rate mortgages. We've seen 1062 00:52:47,120 --> 00:52:50,240 Speaker 15: in some markets where there are a lot of VA loans, 1063 00:52:50,280 --> 00:52:53,320 Speaker 15: for example, we're actually hearing more talk about assumable mortgages, 1064 00:52:53,360 --> 00:52:56,080 Speaker 15: which of course is limited to the VA product and 1065 00:52:56,080 --> 00:52:58,480 Speaker 15: to some other products. But I think you do need 1066 00:52:58,520 --> 00:53:00,640 Speaker 15: to cast your net a little bit why to make 1067 00:53:00,640 --> 00:53:02,840 Speaker 15: sure you're getting all of the options that are available 1068 00:53:02,880 --> 00:53:03,040 Speaker 15: to you. 1069 00:53:03,560 --> 00:53:05,279 Speaker 1: All right, Lisa, thank you so much for joining us. 1070 00:53:05,400 --> 00:53:08,560 Speaker 1: I really appreciate gatting your thoughts there. Lisa Sturtevant, Chief 1071 00:53:08,600 --> 00:53:12,239 Speaker 1: Economists at Bright MLS. Again, it is a tight real 1072 00:53:12,360 --> 00:53:15,239 Speaker 1: estate market out there, folks. US existing home sales fell 1073 00:53:15,280 --> 00:53:18,400 Speaker 1: to seventh month low on rates and supply. That was 1074 00:53:18,440 --> 00:53:20,840 Speaker 1: in the month of August, down zero point seven percent 1075 00:53:20,840 --> 00:53:24,120 Speaker 1: from the prior month. So not a lot getting transacted 1076 00:53:24,120 --> 00:53:25,640 Speaker 1: out of there, not a lot for sale, and then 1077 00:53:25,680 --> 00:53:27,680 Speaker 1: if you do find something, we've got to pay a 1078 00:53:27,760 --> 00:53:30,040 Speaker 1: pretty steep mortgagery. We'll keep on top of that. 1079 00:53:30,840 --> 00:53:33,960 Speaker 2: Thanks for listening to the Bloomberg Markets podcasts. You can 1080 00:53:34,000 --> 00:53:37,800 Speaker 2: subscribe and listen to interviews at Apple Podcasts or whatever 1081 00:53:37,840 --> 00:53:39,360 Speaker 2: podcast platform you prefer. 1082 00:53:39,719 --> 00:53:40,520 Speaker 6: I'm Matt Miller. 1083 00:53:40,800 --> 00:53:44,200 Speaker 2: I'm on Twitter at Matt Miller nineteen seventy three and 1084 00:53:44,320 --> 00:53:44,800 Speaker 2: on Fall. 1085 00:53:44,719 --> 00:53:47,560 Speaker 1: Sweeney I'm on Twitter at pt Sweeney Before the podcast. 1086 00:53:47,600 --> 00:53:51,080 Speaker 1: You can always catch us worldwide at Bloomberg Radio