1 00:00:00,080 --> 00:00:06,760 Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. 2 00:00:11,960 --> 00:00:15,560 Speaker 2: This is the Bloomberg Surveillance Podcast. I'm Tom Keene along 3 00:00:15,600 --> 00:00:18,960 Speaker 2: with Paul Sweeney. Join us each day for insight from 4 00:00:18,960 --> 00:00:23,160 Speaker 2: the best in economics, finance, investment, and international relations. You 5 00:00:23,160 --> 00:00:26,520 Speaker 2: can also watch the show live on YouTube. Visit the 6 00:00:26,520 --> 00:00:31,280 Speaker 2: Bloomberg Podcast channel on YouTube to see the show weekday 7 00:00:31,280 --> 00:00:34,320 Speaker 2: mornings from seven to ten am Eastern from our global 8 00:00:34,360 --> 00:00:39,000 Speaker 2: headquarters in New York City. Subscribe to the podcast on Apple, Spotify, 9 00:00:39,360 --> 00:00:42,920 Speaker 2: or anywhere else you listen and always I'm Bloomberg Radio, 10 00:00:43,080 --> 00:00:47,240 Speaker 2: the Bloomberg Terminal, and the Bloomberg Business App. We turned 11 00:00:47,240 --> 00:00:49,280 Speaker 2: out of Torston Slock. Paul, you read the note, I 12 00:00:49,280 --> 00:00:51,800 Speaker 2: didn't read the note. Why don't you bring in Slock 13 00:00:51,920 --> 00:00:52,960 Speaker 2: of Apollo Management? 14 00:00:53,200 --> 00:00:53,680 Speaker 3: Absolutely? 15 00:00:53,760 --> 00:00:57,440 Speaker 4: Torson Slock joins us here. He is the Apollo partner 16 00:00:57,440 --> 00:01:00,920 Speaker 4: and chief economist Torston. A lot of the parlor game 17 00:01:00,960 --> 00:01:04,839 Speaker 4: here on Wall Street is when will the Federal Reserve 18 00:01:04,920 --> 00:01:08,759 Speaker 4: cut interest rates in twenty twenty four and by how much? 19 00:01:09,040 --> 00:01:11,080 Speaker 3: You came out with a note today saying whoa whoa, whoa, 20 00:01:11,080 --> 00:01:13,240 Speaker 3: whoa whoa. There's a reasonable. 21 00:01:12,880 --> 00:01:15,319 Speaker 4: Argument that they won't cut rates in twenty twenty four, 22 00:01:16,080 --> 00:01:19,360 Speaker 4: tell us what you're thinking, I and I. 23 00:01:19,440 --> 00:01:21,839 Speaker 1: Thanks for having me, Paul and Tom. I think that 24 00:01:22,280 --> 00:01:25,680 Speaker 1: the reality now is that we can no longer ignore 25 00:01:26,120 --> 00:01:29,760 Speaker 1: how much financial conditions have eased since the December thirteen 26 00:01:29,920 --> 00:01:33,120 Speaker 1: FMC meeting. We have seen the stock market reaching new 27 00:01:33,200 --> 00:01:35,520 Speaker 1: all time highs. Of course, every day at the moment, 28 00:01:35,560 --> 00:01:38,559 Speaker 1: as we know, we're seeing credit spreads titan a lot 29 00:01:38,680 --> 00:01:41,959 Speaker 1: in investment grade, in high yield in loans. We're also 30 00:01:42,000 --> 00:01:45,039 Speaker 1: seeing it issuings in January, and now it looks like 31 00:01:45,080 --> 00:01:48,920 Speaker 1: in February also would be the highest January and highest 32 00:01:48,960 --> 00:01:51,280 Speaker 1: beginning of the year that we have had ever in history. 33 00:01:51,480 --> 00:01:54,760 Speaker 1: High yield issuance has been strong, loan issuance has been strong. 34 00:01:54,960 --> 00:01:57,520 Speaker 1: On top of that, IPO activity has been picking up. 35 00:01:57,800 --> 00:01:59,880 Speaker 1: M and A activity has been picking up. It really 36 00:01:59,920 --> 00:02:04,840 Speaker 1: is not surprising, with this significant rebound in capital markets activity, 37 00:02:05,080 --> 00:02:07,640 Speaker 1: that the employment report for January was strong, and it 38 00:02:07,720 --> 00:02:10,520 Speaker 1: is really also not surprising that the inflation data also 39 00:02:10,639 --> 00:02:12,880 Speaker 1: was strong. So the bottom line is I think that 40 00:02:13,080 --> 00:02:16,240 Speaker 1: the market now has to realize that the data is 41 00:02:16,320 --> 00:02:18,960 Speaker 1: just not slowing down, and the fat pivot has given 42 00:02:19,000 --> 00:02:22,920 Speaker 1: an additional tailwind to the economy. And to financial markets 43 00:02:22,919 --> 00:02:25,919 Speaker 1: and financial conditions, and to capital markets, and all that 44 00:02:26,120 --> 00:02:30,760 Speaker 1: is likely to continue to be supporting growth in consumer spending, 45 00:02:30,800 --> 00:02:34,560 Speaker 1: in captic spending, in hiring for most likely the better 46 00:02:34,600 --> 00:02:35,320 Speaker 1: part of this year. 47 00:02:35,960 --> 00:02:38,560 Speaker 4: Is there a scenario for you where the Fed actually 48 00:02:38,800 --> 00:02:40,280 Speaker 4: hikes rates in twenty twenty four. 49 00:02:41,320 --> 00:02:43,520 Speaker 1: So I know Larry Summers and several others have been 50 00:02:43,520 --> 00:02:46,280 Speaker 1: talking about this as a possible scenario. I do think 51 00:02:46,320 --> 00:02:48,760 Speaker 1: that they would be reluctant and very reluctant to do that. 52 00:02:49,639 --> 00:02:51,919 Speaker 1: But I do think at the same time that it's 53 00:02:51,960 --> 00:02:54,880 Speaker 1: clear that the market has, on the back of the 54 00:02:54,880 --> 00:02:58,320 Speaker 1: fit pivot, declared victory and said that inflation is no 55 00:02:58,400 --> 00:03:00,840 Speaker 1: longer a problem. And the problem is that if indeed 56 00:03:01,360 --> 00:03:05,080 Speaker 1: looking like it's becoming a problem again, you're having Obviously 57 00:03:05,320 --> 00:03:07,520 Speaker 1: average hourly earnings is in the reins of four to 58 00:03:07,639 --> 00:03:10,040 Speaker 1: five percent. This has to come further down. This is 59 00:03:10,080 --> 00:03:13,359 Speaker 1: not consistent with a two percent inflation target. If you 60 00:03:13,360 --> 00:03:17,000 Speaker 1: look at the NFIB survey, both for wage expectations and 61 00:03:17,120 --> 00:03:20,560 Speaker 1: selling price expectations, they are all beginning to go up. 62 00:03:20,760 --> 00:03:24,160 Speaker 1: And finally, you also have a number of other indicators 63 00:03:24,520 --> 00:03:27,520 Speaker 1: that are leading in terms of what inflation is doing, 64 00:03:27,840 --> 00:03:30,200 Speaker 1: and all those continue to point in particularly we look 65 00:03:30,200 --> 00:03:32,000 Speaker 1: at some of the underlying trends in the trim mean 66 00:03:32,040 --> 00:03:34,640 Speaker 1: and the media inization to also more upside. So the 67 00:03:34,680 --> 00:03:38,280 Speaker 1: conclusion is we are simply not done fighting inflation, Turstan. 68 00:03:38,840 --> 00:03:41,720 Speaker 2: The hallmark of your work at Deutsche Banking nowt Apollo 69 00:03:42,080 --> 00:03:45,880 Speaker 2: has been a holistic approach to economics. What's the character 70 00:03:46,080 --> 00:03:50,360 Speaker 2: of our nominal GDP? Everybody, not everybody near, everyone's got 71 00:03:50,360 --> 00:03:54,680 Speaker 2: it wrong. What's the character of our animal spirit out 72 00:03:54,720 --> 00:03:55,920 Speaker 2: two in three years? 73 00:03:57,320 --> 00:03:59,360 Speaker 1: I think this is a really important question, Toron, because 74 00:03:59,640 --> 00:04:01,960 Speaker 1: as should said, first of all, let's think about and 75 00:04:02,040 --> 00:04:07,960 Speaker 1: let's evaluate our own meaning, the market's ability to forecast 76 00:04:08,080 --> 00:04:10,600 Speaker 1: things going into twenty twenty three and twenty twenty four 77 00:04:10,600 --> 00:04:14,720 Speaker 1: and twenty twenty three. Everyone everywhere, including most likely also 78 00:04:14,760 --> 00:04:17,640 Speaker 1: the Fed, forecast the consensus. The market was saying we 79 00:04:17,720 --> 00:04:19,760 Speaker 1: will have a recession, and we turn out to not 80 00:04:19,800 --> 00:04:23,279 Speaker 1: have a recession. They continue strength in the economy. Surprised everyone. 81 00:04:23,680 --> 00:04:24,120 Speaker 2: This year. 82 00:04:24,240 --> 00:04:26,320 Speaker 1: Everyone came in and said, oh, the Fed will cut 83 00:04:26,440 --> 00:04:28,680 Speaker 1: rates the six times, maybe even seven times at some 84 00:04:28,760 --> 00:04:31,520 Speaker 1: point because we will get that slowed down. And now 85 00:04:31,520 --> 00:04:33,680 Speaker 1: we're sitting here a few months into the year and 86 00:04:33,720 --> 00:04:36,760 Speaker 1: the economy has simply not slowed down. The employment report 87 00:04:36,800 --> 00:04:39,320 Speaker 1: in January, at more than three hundred thousand jobs created, 88 00:04:39,640 --> 00:04:41,760 Speaker 1: was just really, really strong. So I think a very 89 00:04:41,760 --> 00:04:45,360 Speaker 1: important dimension to your question, Tom is that the US 90 00:04:45,400 --> 00:04:50,120 Speaker 1: economy is just incredibly diversified. It is really difficult for 91 00:04:50,160 --> 00:04:52,640 Speaker 1: the Fed to cool things down. It has turned out 92 00:04:52,680 --> 00:04:55,800 Speaker 1: to be that the consume is much less sensitive to 93 00:04:55,880 --> 00:04:58,360 Speaker 1: rate hikes. Corporates a lessonses to rate hikes. One of 94 00:04:58,600 --> 00:05:01,320 Speaker 1: the consequence the diverse, tight economy continues to do. 95 00:05:01,320 --> 00:05:03,800 Speaker 2: Well, and the scale of it, folks, I think the media, 96 00:05:04,080 --> 00:05:06,600 Speaker 2: and I'm guilty of this as well, does a terrible 97 00:05:06,680 --> 00:05:10,880 Speaker 2: job of showing the size of the American economic experiment 98 00:05:11,040 --> 00:05:14,080 Speaker 2: versus other nations. We're on Apple car Play, We're on 99 00:05:14,160 --> 00:05:18,840 Speaker 2: YouTube type in Bloomberg Podcasts. We've had a huge response 100 00:05:18,880 --> 00:05:21,520 Speaker 2: to YouTube. Paul and I are humbled by at least 101 00:05:21,520 --> 00:05:23,560 Speaker 2: as not humbled by it. Paul and I are humbled 102 00:05:23,560 --> 00:05:25,799 Speaker 2: by it. And there's a live chat there where someone 103 00:05:25,920 --> 00:05:29,000 Speaker 2: just told me their mother throughout their varsity jacket and 104 00:05:29,040 --> 00:05:31,880 Speaker 2: they've been in therapy for thirty eight years. Why don't 105 00:05:31,880 --> 00:05:35,320 Speaker 2: you continue with Torston slock is important research note this. 106 00:05:35,240 --> 00:05:37,760 Speaker 4: Morning, absolutely, Tom, we are speaking with Torston Slack Apollo 107 00:05:37,800 --> 00:05:41,520 Speaker 4: partner and chief economists. Torston, just from an optics perspective, 108 00:05:41,600 --> 00:05:44,880 Speaker 4: not an economics perspective, but from an optics perspective, how 109 00:05:44,880 --> 00:05:48,080 Speaker 4: difficult would it be for bet Cherman Jpel not to 110 00:05:48,200 --> 00:05:52,400 Speaker 4: cut rags this year after I don't know, strongly hinting 111 00:05:52,480 --> 00:05:54,359 Speaker 4: in December that that was going to be the scenario. 112 00:05:55,680 --> 00:05:57,600 Speaker 1: Yeah, I know, But I mean I think that the 113 00:05:57,680 --> 00:06:00,719 Speaker 1: modics have been only interpreting what the Fed actually has 114 00:06:00,760 --> 00:06:04,760 Speaker 1: been saying. He was at the press conference in December 115 00:06:04,839 --> 00:06:08,800 Speaker 1: actually reasonably balanced. He was just saying that, well, for now, 116 00:06:08,800 --> 00:06:11,520 Speaker 1: we're on holes and this was certainly the clear message. 117 00:06:11,920 --> 00:06:14,880 Speaker 1: But more recently several from C members have been coming 118 00:06:14,920 --> 00:06:17,760 Speaker 1: obviously out and saying, well, maybe we do need to 119 00:06:17,800 --> 00:06:20,640 Speaker 1: stay higher for longer. So with that backdrop, I actually 120 00:06:20,720 --> 00:06:23,560 Speaker 1: don't think that it's quite it's quite simple for the 121 00:06:23,600 --> 00:06:26,240 Speaker 1: Fed to just continue to say, well, maybe the risks 122 00:06:26,240 --> 00:06:30,279 Speaker 1: have now tilted more towards some more upside risk, both 123 00:06:30,440 --> 00:06:32,479 Speaker 1: on both sides of the dual mandate, both when it 124 00:06:32,480 --> 00:06:35,960 Speaker 1: comes to employment and when it comes to inflation. So 125 00:06:36,160 --> 00:06:39,119 Speaker 1: on that front, I think that the FED communication challenge 126 00:06:39,240 --> 00:06:43,800 Speaker 1: is actually much much more straightforward relative to the significant 127 00:06:43,880 --> 00:06:46,400 Speaker 1: roller coaster that markets had been through from first again 128 00:06:46,480 --> 00:06:49,440 Speaker 1: pricing six or seven cuts to now pricing three cuts, 129 00:06:49,800 --> 00:06:52,719 Speaker 1: and maybe as the data again we had jobless claims 130 00:06:52,720 --> 00:06:55,320 Speaker 1: that was wrong yesterday, we had inflation chaken up a 131 00:06:55,360 --> 00:06:57,960 Speaker 1: bit high yesterday on the month and month. All that 132 00:06:57,960 --> 00:07:00,240 Speaker 1: would make the market where a lot of the action 133 00:07:00,520 --> 00:07:02,080 Speaker 1: and a lot of the movements needs to be. 134 00:07:02,520 --> 00:07:05,880 Speaker 2: But Turstan, we're here among friends, nobody's listening. That's a Friday. 135 00:07:06,600 --> 00:07:12,160 Speaker 2: Hasn't ISLM theory and aggregate demand supply theory been overwhelmed 136 00:07:12,800 --> 00:07:19,080 Speaker 2: by bulk productivity across America? Hasn't basically the is curve 137 00:07:19,520 --> 00:07:22,880 Speaker 2: taken over from the LM analysis? 138 00:07:22,920 --> 00:07:25,080 Speaker 1: True? I mean, there's certainly a lot to think about it, 139 00:07:25,080 --> 00:07:28,080 Speaker 1: because another way of saying that is this question whether 140 00:07:28,680 --> 00:07:31,480 Speaker 1: was it supply that drove inflation up and now down 141 00:07:31,840 --> 00:07:34,600 Speaker 1: or was it actually also demand? And the San Francisco 142 00:07:34,680 --> 00:07:37,680 Speaker 1: Fed studies that have looked at this have found that 143 00:07:38,160 --> 00:07:41,240 Speaker 1: indeed supply played a role. There two thirds of the 144 00:07:41,280 --> 00:07:44,640 Speaker 1: increase the supply chains was because of supply chains that 145 00:07:44,680 --> 00:07:47,240 Speaker 1: got clocked up and now being straightened out. But one 146 00:07:47,280 --> 00:07:51,000 Speaker 1: third was demand. So that's why the issue now becomes Well, 147 00:07:51,000 --> 00:07:53,360 Speaker 1: if demand did play a role, and maybe now we 148 00:07:53,440 --> 00:07:56,040 Speaker 1: have more demands simply because of the wealth effect, because 149 00:07:56,040 --> 00:07:59,160 Speaker 1: the stock market going up because of the easier financial conditions, 150 00:07:59,280 --> 00:08:02,000 Speaker 1: then maybe we're having a shift in the ISLM curve 151 00:08:02,280 --> 00:08:04,200 Speaker 1: where we now set the hand that is demand that's 152 00:08:04,240 --> 00:08:07,240 Speaker 1: beginning to play a role in driving in nation higher. Yes, 153 00:08:07,320 --> 00:08:10,000 Speaker 1: yesterday's story was that it was supply, but looking ahead, 154 00:08:10,160 --> 00:08:12,280 Speaker 1: it looks like demand is beginning to become more important. 155 00:08:12,440 --> 00:08:17,400 Speaker 2: Turston. One final question, Paul asks, Paul didn't want to 156 00:08:17,400 --> 00:08:20,680 Speaker 2: be rude. Are you buy holders sell on Apple? Which 157 00:08:20,680 --> 00:08:21,320 Speaker 2: way would you go? 158 00:08:21,440 --> 00:08:26,520 Speaker 1: Tourst So I don't have any of you on individual stocks, 159 00:08:27,040 --> 00:08:29,400 Speaker 1: but I would say that my main conclusion from an 160 00:08:29,400 --> 00:08:32,320 Speaker 1: investing perspective is that if there's one thing that we 161 00:08:32,320 --> 00:08:34,760 Speaker 1: should do as investors, it is to listen very carefully 162 00:08:34,760 --> 00:08:36,360 Speaker 1: to what is the FED trying to tell us. 163 00:08:36,440 --> 00:08:41,160 Speaker 2: And you now you're supposed to say, you're supposed to say, 164 00:08:41,160 --> 00:08:44,240 Speaker 2: you have to listen to Bloomberg surveillance Bloomberg. 165 00:08:46,080 --> 00:08:48,920 Speaker 1: If the Fed says they will keep right higher for longer, 166 00:08:49,200 --> 00:08:51,080 Speaker 1: then they will probably keep right higher for longer. So 167 00:08:51,120 --> 00:08:54,359 Speaker 1: that means front end fixed income continues to look attractive. 168 00:08:54,559 --> 00:08:55,800 Speaker 2: Did we get an answer there? 169 00:08:56,960 --> 00:08:57,679 Speaker 3: I'm sure you. 170 00:08:58,640 --> 00:09:06,640 Speaker 2: Turston Slock, thank you so much. Un joining us now, 171 00:09:07,120 --> 00:09:09,920 Speaker 2: and you know, Paul, this is the romance you've lived 172 00:09:09,960 --> 00:09:12,880 Speaker 2: on the cell side. You get on the plane, it's 173 00:09:13,000 --> 00:09:16,319 Speaker 2: romantic and all that. We're looking at him on YouTube here, Dan, 174 00:09:16,400 --> 00:09:18,960 Speaker 2: Iives looks like you could blow him over. He's so tired. 175 00:09:19,280 --> 00:09:22,200 Speaker 2: He's in Bangkok, Thailand right now. We're thrilled. I wanted 176 00:09:22,240 --> 00:09:24,800 Speaker 2: to join us on an Asian tour. Dan, how long 177 00:09:24,840 --> 00:09:27,880 Speaker 2: does it take you to get done with jetlag coming 178 00:09:27,960 --> 00:09:28,800 Speaker 2: back from Asia? 179 00:09:30,440 --> 00:09:34,520 Speaker 5: I mean, look it depends. I mean look it's these 180 00:09:34,600 --> 00:09:38,600 Speaker 5: trips not too well worth it, but yeah, you always 181 00:09:38,600 --> 00:09:41,880 Speaker 5: have some jet lag going in after that twenty one hours. 182 00:09:42,360 --> 00:09:44,079 Speaker 2: For those of you on Apple car Play, you can 183 00:09:44,160 --> 00:09:47,240 Speaker 2: just see that Ives's channeling Deborah Kerr and the King 184 00:09:47,280 --> 00:09:49,560 Speaker 2: and I here, it's the same color scheme as what 185 00:09:49,600 --> 00:09:53,560 Speaker 2: Deborah was doing with Yule Brenner. Dan, serious conversation now, 186 00:09:53,600 --> 00:09:56,719 Speaker 2: and you are more than qualified. I understand AI and 187 00:09:56,880 --> 00:10:00,240 Speaker 2: Vidia AI Microsoft. Paul explains to me they're gonna make 188 00:10:00,280 --> 00:10:04,480 Speaker 2: fancy chips. Apple's not going to do that what's the 189 00:10:04,679 --> 00:10:07,800 Speaker 2: AI plan you glean for Apple? 190 00:10:09,720 --> 00:10:14,319 Speaker 5: Look within Cooper Tino. The next big thing I think 191 00:10:14,480 --> 00:10:18,120 Speaker 5: comes out WWDC. I mean obviously German breaking the story 192 00:10:18,600 --> 00:10:21,000 Speaker 5: how they ripped the band aid off on evs, but 193 00:10:21,200 --> 00:10:24,000 Speaker 5: now it's all focused on AI. We believe the AI 194 00:10:24,160 --> 00:10:28,720 Speaker 5: app store, a separate AI app store, will be the 195 00:10:28,800 --> 00:10:32,880 Speaker 5: first step. And I think for developers that's important because 196 00:10:33,000 --> 00:10:37,040 Speaker 5: for consumers, they're going to be able to now over 197 00:10:37,080 --> 00:10:41,200 Speaker 5: the next one two years be buying separate AI driven 198 00:10:41,240 --> 00:10:46,320 Speaker 5: apps off of an iPhone sixteen that has exclusive AI 199 00:10:46,520 --> 00:10:50,920 Speaker 5: capabilities in llms built in. This is the next phase 200 00:10:50,960 --> 00:10:52,720 Speaker 5: of the growth story within Cooper Tino. 201 00:10:53,480 --> 00:10:55,520 Speaker 4: So Dan, I mean, I think what we've seen and 202 00:10:55,559 --> 00:10:57,840 Speaker 4: you've certainly explained it to us very well over the years, 203 00:10:57,880 --> 00:11:00,840 Speaker 4: that Apple doesn't feel the need to be first, even 204 00:11:00,880 --> 00:11:03,480 Speaker 4: in iPhones, for example, they just need to come in 205 00:11:03,559 --> 00:11:06,559 Speaker 4: with the best and most elegant product. 206 00:11:06,679 --> 00:11:09,280 Speaker 3: Is that kind of the strategy we're seeing with AI. 207 00:11:09,280 --> 00:11:12,200 Speaker 4: Either's certainly not first, they're certainly not leading edge, but 208 00:11:12,920 --> 00:11:15,360 Speaker 4: do you fully expect them to have a play here 209 00:11:15,360 --> 00:11:17,320 Speaker 4: in angle book? 210 00:11:17,360 --> 00:11:19,800 Speaker 5: I mean there's two point two billion reasons. I mean, 211 00:11:19,840 --> 00:11:23,080 Speaker 5: that's the amount of iOS devices for them, they don't 212 00:11:23,200 --> 00:11:26,520 Speaker 5: have to be first because they have a golden install 213 00:11:26,559 --> 00:11:29,720 Speaker 5: base that's unrivaled. Then clearly right now, I mean sentiment's 214 00:11:29,840 --> 00:11:32,520 Speaker 5: negative on Apple out there, and I believe what the 215 00:11:32,600 --> 00:11:36,080 Speaker 5: streets missing is going to be this renaissance of growth, 216 00:11:36,360 --> 00:11:40,199 Speaker 5: not just AI into the Apple ecosystem, but I think 217 00:11:40,200 --> 00:11:43,360 Speaker 5: it's gonna respawn just more and more upgrade opportunities we 218 00:11:43,400 --> 00:11:46,079 Speaker 5: go into the next you're called twelve eight, ten months, 219 00:11:46,080 --> 00:11:48,800 Speaker 5: and that's that's gonna be huge for the some of 220 00:11:48,800 --> 00:11:51,760 Speaker 5: the parts. You know, Keen talked about AI in terms 221 00:11:51,840 --> 00:11:55,560 Speaker 5: of Nvidium, Microsoft, but now it's the second, third, fourth 222 00:11:55,559 --> 00:11:58,520 Speaker 5: derivatives of this AI revolution that hit. 223 00:11:59,760 --> 00:12:02,800 Speaker 3: So, Danny, you're in Asia as we speak. 224 00:12:03,160 --> 00:12:08,480 Speaker 4: What's the feeling there about China and Apple and as 225 00:12:08,480 --> 00:12:12,719 Speaker 4: an end up market for Apple products going forward there? 226 00:12:12,720 --> 00:12:15,880 Speaker 4: Because as you are, well, that's probably the big issue 227 00:12:15,920 --> 00:12:17,280 Speaker 4: for the stock right now. 228 00:12:18,200 --> 00:12:20,760 Speaker 5: Yeah, earlier this week in Hong Kong and you're in 229 00:12:20,880 --> 00:12:26,800 Speaker 5: around China. It's a it's like the the video thriller, 230 00:12:27,760 --> 00:12:30,320 Speaker 5: the negative the horrors show right in terms of the 231 00:12:30,400 --> 00:12:34,960 Speaker 5: view right now of China tech and I think fears 232 00:12:35,040 --> 00:12:37,840 Speaker 5: about just the economy and China, I think when he 233 00:12:37,880 --> 00:12:41,440 Speaker 5: looks at Apple, it all comes down to what does 234 00:12:41,559 --> 00:12:45,560 Speaker 5: demand look like in economy like that. We believe to 235 00:12:45,679 --> 00:12:49,880 Speaker 5: that point it has started to stabilize. I believe the 236 00:12:49,920 --> 00:12:53,000 Speaker 5: streets factoring in a lot more negative than we're seeing here. 237 00:12:53,440 --> 00:12:56,320 Speaker 5: I think the overall sentiment right now is basically anything 238 00:12:56,400 --> 00:13:01,199 Speaker 5: but China and you own us tech revolution. No one 239 00:13:01,280 --> 00:13:05,360 Speaker 5: wants to be outside the bar. At ten pm, Win 240 00:13:05,440 --> 00:13:06,480 Speaker 5: it goes to four am. 241 00:13:06,480 --> 00:13:08,160 Speaker 2: I'm gonna talk with Paul Sweeney on this, and then 242 00:13:08,160 --> 00:13:09,480 Speaker 2: we're going to go to Dan. I I want to 243 00:13:09,480 --> 00:13:11,920 Speaker 2: hear from both you on this. There's got to be 244 00:13:11,960 --> 00:13:15,960 Speaker 2: a point where operating officers of any visible company just 245 00:13:16,000 --> 00:13:19,560 Speaker 2: get angry. Yeah, and the CFO turns to the CEO 246 00:13:19,840 --> 00:13:23,000 Speaker 2: and says, look, we're modeling this out the next eighteen months, 247 00:13:23,000 --> 00:13:26,000 Speaker 2: Paul Sweeney, let's do it now. Is that where Apple 248 00:13:26,120 --> 00:13:26,680 Speaker 2: is now? Paul? 249 00:13:27,200 --> 00:13:29,280 Speaker 3: You know, I think for China and Dan. I'll get 250 00:13:29,320 --> 00:13:29,679 Speaker 3: to Dan on this. 251 00:13:29,880 --> 00:13:31,840 Speaker 4: But for China, it's twenty percent of their sales, it's 252 00:13:31,880 --> 00:13:34,280 Speaker 4: a huge part of their infrastructure. They have to be there. 253 00:13:35,120 --> 00:13:36,960 Speaker 4: Tim Cook is over there all the time trying to 254 00:13:36,960 --> 00:13:39,240 Speaker 4: make it work here. So Dan, I mean, is there 255 00:13:39,240 --> 00:13:44,400 Speaker 4: a scenario where China is not, you know, a critical 256 00:13:44,440 --> 00:13:45,200 Speaker 4: market for Apple. 257 00:13:47,360 --> 00:13:51,319 Speaker 5: Look, I mean it's the hearts and lungs of Apple 258 00:13:52,080 --> 00:13:55,119 Speaker 5: in term, not the growth of course from a supply perspective. 259 00:13:55,720 --> 00:13:59,640 Speaker 5: And that's that's the reality. And I think Apple recognizes 260 00:14:00,760 --> 00:14:03,800 Speaker 5: you have two hundred million iPhones in China. They've gained 261 00:14:03,800 --> 00:14:06,680 Speaker 5: threeing bits of market share the last eighteen months. Just 262 00:14:06,720 --> 00:14:08,800 Speaker 5: because of what they're going through. They're not throwing in 263 00:14:08,800 --> 00:14:09,480 Speaker 5: the white towel. 264 00:14:09,600 --> 00:14:11,680 Speaker 2: But dad, come on, let's see it for they got 265 00:14:12,200 --> 00:14:14,880 Speaker 2: nobody's got a profit machine like Apple. Let's start with 266 00:14:14,920 --> 00:14:18,880 Speaker 2: it basic summary here, there's almost a point where just 267 00:14:19,080 --> 00:14:23,480 Speaker 2: put on Apple they go enough and do something financially 268 00:14:24,120 --> 00:14:26,160 Speaker 2: to write the ship. Is that feasible? 269 00:14:27,840 --> 00:14:30,800 Speaker 5: Look, I think for app anything's possible, and there was 270 00:14:30,800 --> 00:14:33,840 Speaker 5: like all all options are on the table in terms 271 00:14:33,840 --> 00:14:37,160 Speaker 5: of where they can navigate things. And for Apple right 272 00:14:37,200 --> 00:14:39,960 Speaker 5: now they are in just a massive position of strength 273 00:14:40,000 --> 00:14:43,520 Speaker 5: because of the installments, and I think they recognize cook 274 00:14:44,040 --> 00:14:47,040 Speaker 5: they'll they'll navigate headwinds. And China it's again to the 275 00:14:47,080 --> 00:14:51,560 Speaker 5: other side is because of what's on the horizon. It's 276 00:14:51,560 --> 00:14:54,520 Speaker 5: start with the vision bro then it's Ai app store, 277 00:14:54,560 --> 00:14:56,840 Speaker 5: and then it's AI with an iPhone. 278 00:14:57,040 --> 00:14:58,920 Speaker 2: Get one more in here? Can I stay off the 279 00:14:59,040 --> 00:15:03,440 Speaker 2: WACC in a Bulberg terminal? The return on invested capital 280 00:15:03,480 --> 00:15:07,520 Speaker 2: for this dog of Coopertino is fifty five percent. I've 281 00:15:07,520 --> 00:15:10,440 Speaker 2: never said that number of folks in I'm teen years exactly. 282 00:15:10,480 --> 00:15:14,160 Speaker 4: Hey, Dan, real quickly, what's the prognosis here on the 283 00:15:14,200 --> 00:15:17,280 Speaker 4: Apples decision to back out of the car business. 284 00:15:18,760 --> 00:15:21,080 Speaker 5: I think that was just Ryan Wisner law where they 285 00:15:21,200 --> 00:15:23,480 Speaker 5: to rip the band aid off focus on on AI. 286 00:15:23,720 --> 00:15:28,000 Speaker 5: Obviously a decade lost, but for them, you can't keep 287 00:15:28,040 --> 00:15:30,640 Speaker 5: putting good money after bad. They got focused on AI. 288 00:15:31,280 --> 00:15:34,120 Speaker 5: This is no different than Zuckerberg and Meta you know, 289 00:15:34,320 --> 00:15:37,360 Speaker 5: taken away from metaverse going toward AI. It's the smart 290 00:15:37,400 --> 00:15:40,680 Speaker 5: move they needed to do it, and look it was. 291 00:15:40,840 --> 00:15:43,000 Speaker 5: It was a painful learning lesson I think. 292 00:15:42,880 --> 00:15:47,320 Speaker 2: For Cook, right, Dan, seriously, safe travels, your trooper, love 293 00:15:47,360 --> 00:15:49,680 Speaker 2: what you're doing. I don't you know, I don't agree 294 00:15:49,680 --> 00:15:52,840 Speaker 2: with them, but you know, small detail, Dan, the wet bush. 295 00:15:53,000 --> 00:15:55,440 Speaker 2: It's not that we agree or disagree on bihled cell. 296 00:15:55,760 --> 00:15:58,960 Speaker 2: Mister Reive's leading the charge for the enthusiasts YEP of 297 00:15:59,160 --> 00:16:15,120 Speaker 2: artificial intelligence, and this is our technology Conversation of the month. 298 00:16:15,160 --> 00:16:19,680 Speaker 2: It's March first, but we're done, Paul after mister Narrier, 299 00:16:19,720 --> 00:16:22,760 Speaker 2: Antonio Nairy of HPE, their CEO, and I'm going to 300 00:16:22,800 --> 00:16:24,680 Speaker 2: put up here for those of you on YouTube you 301 00:16:24,720 --> 00:16:29,120 Speaker 2: can see one of my most beloved Hewlett and Packard artifacts. 302 00:16:29,480 --> 00:16:33,240 Speaker 2: This is the twelve see that got me into this 303 00:16:33,440 --> 00:16:37,440 Speaker 2: chair with the obligatory CFA stick around it and Sweeney's 304 00:16:37,480 --> 00:16:42,560 Speaker 2: got their killer Antonio Nairy HP twelve c app on 305 00:16:42,640 --> 00:16:45,760 Speaker 2: his Apple iPhone. Mister Nay, honored to have you with 306 00:16:45,840 --> 00:16:48,760 Speaker 2: us today. I want to carry on from Chris Miller's 307 00:16:48,800 --> 00:16:53,080 Speaker 2: wonderful Chip Wars, the battle of say Silicon Graphics coming 308 00:16:53,120 --> 00:16:57,560 Speaker 2: into HPE, and all of his hot air about AI. 309 00:16:58,240 --> 00:17:02,280 Speaker 2: You've got Hpe, Green Lake. How are you going to 310 00:17:02,440 --> 00:17:07,520 Speaker 2: join in Vidia? In Microsoft? You're good competitors in the 311 00:17:07,640 --> 00:17:12,240 Speaker 2: excitement of AI. What's the path over the next two years? 312 00:17:13,880 --> 00:17:15,960 Speaker 6: Well, good one and Tom and Paul, thanks for having 313 00:17:16,000 --> 00:17:20,440 Speaker 6: me today. And obviously we are living an massive inflection 314 00:17:20,560 --> 00:17:22,600 Speaker 6: point where the AI, where I think you know, is 315 00:17:22,640 --> 00:17:25,520 Speaker 6: the most revolution of technology or lifetime, is going to 316 00:17:25,600 --> 00:17:28,080 Speaker 6: change everything the way we work with where we live. 317 00:17:28,960 --> 00:17:32,040 Speaker 6: And when you think about that opportunity I think about 318 00:17:32,080 --> 00:17:35,439 Speaker 6: the opportunity to change everything from a technology standpoint, but 319 00:17:35,480 --> 00:17:38,920 Speaker 6: also from the business standpoint, and so US with HP 320 00:17:39,040 --> 00:17:42,679 Speaker 6: Green Lake, we have a unique opportunity to democratize these 321 00:17:42,800 --> 00:17:47,080 Speaker 6: AI technology to everyone because today AI has been only 322 00:17:47,280 --> 00:17:50,720 Speaker 6: used for the large institution its government and academia. But 323 00:17:50,840 --> 00:17:53,920 Speaker 6: going forward, enterprise have to ads those technology. 324 00:17:53,920 --> 00:17:58,879 Speaker 2: I get like Sid, I get the AI idea within 325 00:17:59,040 --> 00:18:02,480 Speaker 2: computer science. It's like what you've done in your leadership 326 00:18:02,880 --> 00:18:06,320 Speaker 2: over the US at Hewlett Packard. But what I don't understand, 327 00:18:06,359 --> 00:18:08,479 Speaker 2: and I think there's a lot of doubters when you 328 00:18:08,560 --> 00:18:12,840 Speaker 2: say an HPE Greenlake you're going to unleash the potential 329 00:18:13,000 --> 00:18:17,320 Speaker 2: of your people. What does it actually mean two years out. 330 00:18:18,400 --> 00:18:21,560 Speaker 6: Well, it means you now a significant amount of automation 331 00:18:21,880 --> 00:18:24,840 Speaker 6: and business inside that you can gather through the data 332 00:18:24,920 --> 00:18:29,480 Speaker 6: and the application these technologies. We see that across multiple 333 00:18:29,840 --> 00:18:38,040 Speaker 6: vectors or industries, the robotics in manufacturing, through finding cures. 334 00:18:36,840 --> 00:18:38,080 Speaker 2: For major diseases. 335 00:18:38,119 --> 00:18:40,800 Speaker 6: We have some amazing use cases we're working today with AI, 336 00:18:41,400 --> 00:18:45,520 Speaker 6: finding an asserting a queue for Alzheimer's and dementia. You know, 337 00:18:45,640 --> 00:18:47,639 Speaker 6: one of the things we're doing is trying to model 338 00:18:47,920 --> 00:18:52,199 Speaker 6: entirely neural neurological brain to find what the protein is 339 00:18:52,240 --> 00:18:57,120 Speaker 6: that causing those issues, but also better climate research and forecasting. 340 00:18:57,440 --> 00:18:59,440 Speaker 6: But when you bring it to enterprise, it's all about 341 00:18:59,440 --> 00:19:02,800 Speaker 6: productive you know, in the legal space and the finance space, 342 00:19:02,920 --> 00:19:06,720 Speaker 6: in the operation space, and this large language model accelerates 343 00:19:06,800 --> 00:19:09,440 Speaker 6: that set of capabilities, Antonio. 344 00:19:09,480 --> 00:19:10,960 Speaker 4: What I think a lot of investors are trying to 345 00:19:10,960 --> 00:19:13,679 Speaker 4: figure out here in these early innings of AI is 346 00:19:13,680 --> 00:19:17,840 Speaker 4: how much of this AI spending in terms of capital 347 00:19:18,760 --> 00:19:22,000 Speaker 4: expenditures is incremental or how much of it is taken 348 00:19:22,040 --> 00:19:23,680 Speaker 4: away from other tech budgets. 349 00:19:23,680 --> 00:19:26,480 Speaker 3: Maybe it budgets for example. What's your experience so far. 350 00:19:27,760 --> 00:19:30,280 Speaker 6: Yeah, Paula, I will say AI has a life cycle 351 00:19:30,359 --> 00:19:33,560 Speaker 6: from training to fine tune into inferancy. Most of the 352 00:19:33,680 --> 00:19:36,840 Speaker 6: action in the last five quarters or so since we 353 00:19:37,000 --> 00:19:39,960 Speaker 6: have going through this hype has been on the training side. 354 00:19:40,280 --> 00:19:43,360 Speaker 6: These are companies, unique companies that are building large language 355 00:19:43,400 --> 00:19:47,560 Speaker 6: models now exceeding a trillion parameters if you will, and 356 00:19:47,600 --> 00:19:49,840 Speaker 6: they need a lot of compute power right to keep 357 00:19:49,880 --> 00:19:52,800 Speaker 6: training and retraining the model, to reduce the cost of 358 00:19:52,840 --> 00:19:55,760 Speaker 6: those models, but also to make it more accurate so 359 00:19:55,800 --> 00:19:59,720 Speaker 6: you can trust it. Enterprises are in the early stages, 360 00:20:00,040 --> 00:20:02,199 Speaker 6: and I don't think they're going to build the models itself. 361 00:20:02,200 --> 00:20:04,280 Speaker 6: They're going to take a model, They're going to give 362 00:20:04,359 --> 00:20:07,479 Speaker 6: context to the model with their data in a location 363 00:20:07,560 --> 00:20:10,040 Speaker 6: where they can afford. Honestly, I think that's the why 364 00:20:10,200 --> 00:20:12,800 Speaker 6: reason why grileg is important in as a service model, 365 00:20:12,800 --> 00:20:15,920 Speaker 6: you only pay for what you consume. Versus are laying 366 00:20:16,160 --> 00:20:18,800 Speaker 6: tens of millions of dollars of capex upfront, and then 367 00:20:19,160 --> 00:20:22,600 Speaker 6: the value comes from the infancy where you deploy the model, 368 00:20:22,760 --> 00:20:26,280 Speaker 6: where the data is generated so you can actually deliver 369 00:20:26,359 --> 00:20:30,040 Speaker 6: the outcome, whether it's in all processing, video surveillance, or 370 00:20:30,080 --> 00:20:33,280 Speaker 6: whether it is you know, in the manufacturing floor to 371 00:20:33,400 --> 00:20:36,879 Speaker 6: automate processes. This is where we are in the early stages, 372 00:20:37,359 --> 00:20:39,960 Speaker 6: and I think you know that's gonna you know, it's 373 00:20:40,000 --> 00:20:41,960 Speaker 6: going to be one of the biggest growth in twenty 374 00:20:42,000 --> 00:20:42,880 Speaker 6: four and twenty five. 375 00:20:43,480 --> 00:20:45,840 Speaker 4: Antonio, you just reported earnings. You took your four year 376 00:20:45,880 --> 00:20:49,520 Speaker 4: guidance down. That was in part due to the networking 377 00:20:49,560 --> 00:20:52,040 Speaker 4: business leading to that miss, and that was one of 378 00:20:52,080 --> 00:20:55,680 Speaker 4: your better performing businesses last year. What's changed for you 379 00:20:55,720 --> 00:20:56,600 Speaker 4: in that business? 380 00:20:56,880 --> 00:21:01,119 Speaker 6: You know, it's a little bit the the success we 381 00:21:01,160 --> 00:21:03,680 Speaker 6: have had, right so, in the last two years, we 382 00:21:04,400 --> 00:21:08,040 Speaker 6: raised our revenues in the networking business by two billion dollars. 383 00:21:08,040 --> 00:21:11,399 Speaker 6: We took share from Cisco, and we have an amazing portfolio. 384 00:21:11,840 --> 00:21:14,119 Speaker 6: And I just came back from all Congress. You can 385 00:21:14,160 --> 00:21:16,359 Speaker 6: see the use cases of inferencing at the edge of 386 00:21:16,359 --> 00:21:20,280 Speaker 6: the network, for example, the monetization of five G and 387 00:21:20,320 --> 00:21:24,040 Speaker 6: private five G. But what's going on now, Customers are 388 00:21:24,080 --> 00:21:28,119 Speaker 6: digesting the last two years purchases. We're very significant. We 389 00:21:28,160 --> 00:21:31,639 Speaker 6: grew over forty percent year over year, and so this 390 00:21:31,760 --> 00:21:34,280 Speaker 6: comes back. But this is why the merger with Juniper 391 00:21:34,400 --> 00:21:36,920 Speaker 6: in the networking space is going to be a terrific 392 00:21:37,280 --> 00:21:41,200 Speaker 6: addition to our portfolio and the creation for us going forward. 393 00:21:41,240 --> 00:21:43,639 Speaker 2: We have time, sir for one more question. I've just 394 00:21:43,640 --> 00:21:46,600 Speaker 2: got to go back and folks, full disclosures. I'm going 395 00:21:46,640 --> 00:21:48,639 Speaker 2: to redo it as a book of the summer chip Wars, 396 00:21:48,880 --> 00:21:53,600 Speaker 2: Chris Miller's fabulous book. Antonio Seymour Kray was with controlled 397 00:21:53,680 --> 00:21:56,960 Speaker 2: data and all that, and he made a wonder computer 398 00:21:57,200 --> 00:22:01,840 Speaker 2: of my childhood out of Wisconsin and Minnesota. After three mergers, 399 00:22:01,880 --> 00:22:05,920 Speaker 2: Silicon Graphics and all Hewlett Packard Enterprise, which people think 400 00:22:06,000 --> 00:22:09,600 Speaker 2: is stodgy, picks up one of the magic names in 401 00:22:09,680 --> 00:22:13,320 Speaker 2: twenty nineteen in computers. What are you going to do 402 00:22:13,760 --> 00:22:15,280 Speaker 2: with Cray excess scale? 403 00:22:17,000 --> 00:22:21,719 Speaker 6: Well, I mean amazing company, amazing companies that changed the 404 00:22:21,800 --> 00:22:27,239 Speaker 6: landscaping computing. Both now Cray and SGI Silicon Graphic are 405 00:22:27,280 --> 00:22:32,399 Speaker 6: part of our server business delivering these amazing systems. We 406 00:22:32,520 --> 00:22:36,280 Speaker 6: call it supercomputing to give a sense. Those systems today 407 00:22:36,280 --> 00:22:39,639 Speaker 6: are deploying Department of Energy and have many coming online. 408 00:22:40,000 --> 00:22:41,840 Speaker 6: And so our goal is to use the system to 409 00:22:41,840 --> 00:22:44,640 Speaker 6: solve some of the biggest challenges, to design better engines, 410 00:22:44,800 --> 00:22:52,000 Speaker 6: more sustainable solutions in the life science and climate research, sustainability. 411 00:22:52,320 --> 00:22:54,360 Speaker 6: So this is why we made the investment. I think 412 00:22:54,400 --> 00:22:57,879 Speaker 6: I would say, Tom, what people misunderstood when we bought Cray. 413 00:22:57,960 --> 00:23:01,000 Speaker 6: We bought a company that had silicon and software, and 414 00:23:01,000 --> 00:23:04,720 Speaker 6: we're able to turn that intrough a culpability. That's all 415 00:23:04,800 --> 00:23:07,080 Speaker 6: these strategies. That's why we are excited about it. 416 00:23:07,160 --> 00:23:09,240 Speaker 2: We're at a time antony and aary. You got to 417 00:23:09,240 --> 00:23:10,560 Speaker 2: come to New York, you got to come to in 418 00:23:10,600 --> 00:23:13,959 Speaker 2: our studio. I need to show you my HP twelve 419 00:23:14,000 --> 00:23:18,639 Speaker 2: C mister Neary with HPE, the wonderful follow on of 420 00:23:18,720 --> 00:23:20,040 Speaker 2: all the work at cult. 421 00:23:20,119 --> 00:23:23,400 Speaker 7: Packard Cody, I'll come back. 422 00:23:24,600 --> 00:23:27,320 Speaker 2: This is a joy out of Chicago with Bandery and 423 00:23:27,400 --> 00:23:31,840 Speaker 2: Capital Management. Victoria Bills joins us with incredibly detailed notes 424 00:23:31,880 --> 00:23:35,680 Speaker 2: about where we are in, what to do with money, 425 00:23:35,680 --> 00:23:37,840 Speaker 2: tell us what Bandery and does exactly? What is the 426 00:23:38,520 --> 00:23:41,119 Speaker 2: client tele what's your headache on a Monday morning? 427 00:23:41,280 --> 00:23:41,680 Speaker 3: Oh wow? 428 00:23:41,920 --> 00:23:45,000 Speaker 8: Well, personally, my headache this morning is just the jet 429 00:23:45,040 --> 00:23:47,400 Speaker 8: lag getting in. But I'm super excited to be here 430 00:23:48,119 --> 00:23:50,760 Speaker 8: Bannery and Capital. What we do is we're an alternative 431 00:23:50,840 --> 00:23:56,240 Speaker 8: investment platform for financial advisors to access alternam investment solutions 432 00:23:56,240 --> 00:24:01,040 Speaker 8: on behalf of their clients. So we're talking reads, private equity, 433 00:24:01,119 --> 00:24:05,320 Speaker 8: venture capital, anything related to the private markets. Essentially, we 434 00:24:05,400 --> 00:24:09,160 Speaker 8: help to be a facilitator for financial advisors. 435 00:24:09,359 --> 00:24:11,199 Speaker 2: Then, what Paul and I've been talking about all this 436 00:24:11,320 --> 00:24:17,520 Speaker 2: week is the valuation of private equity and credit given 437 00:24:17,560 --> 00:24:21,240 Speaker 2: that there's no transparency. What transparency do you see it? 438 00:24:21,400 --> 00:24:25,400 Speaker 2: Banery in on this new PAM, I write exploding industry. 439 00:24:25,359 --> 00:24:26,600 Speaker 7: Ye oh absolutely. 440 00:24:27,040 --> 00:24:29,440 Speaker 8: A lot of what we're seeing right now is again 441 00:24:29,640 --> 00:24:32,080 Speaker 8: there is a lot of delay or lag in reporting 442 00:24:32,080 --> 00:24:34,879 Speaker 8: when it comes to private equity credit. But what we 443 00:24:34,960 --> 00:24:37,320 Speaker 8: try to do is we work directly with asset managers 444 00:24:37,359 --> 00:24:40,560 Speaker 8: to help them understand how they can better service the 445 00:24:40,560 --> 00:24:44,440 Speaker 8: financial advisory space. Financial advisors work with clients that are, 446 00:24:44,960 --> 00:24:47,840 Speaker 8: for the most part, like people like myself and people 447 00:24:47,840 --> 00:24:49,760 Speaker 8: like you, guys who are just looking to build on 448 00:24:49,800 --> 00:24:51,520 Speaker 8: their wealth and so they want to know that their 449 00:24:51,520 --> 00:24:54,200 Speaker 8: investments are safe. What we try to do is provide 450 00:24:54,240 --> 00:24:57,080 Speaker 8: a lens of due diligence from an investment and operational 451 00:24:57,160 --> 00:25:00,719 Speaker 8: perspective as well to help financial to help the financial 452 00:25:00,800 --> 00:25:04,040 Speaker 8: advisor fully understand the scope and scale of what they're 453 00:25:04,040 --> 00:25:04,680 Speaker 8: getting so. 454 00:25:04,560 --> 00:25:06,159 Speaker 2: She's not worried about what the FED is going to do. 455 00:25:07,960 --> 00:25:08,880 Speaker 7: Partially I am. 456 00:25:08,960 --> 00:25:11,400 Speaker 8: I'm always I'm always worried about the FED. I think 457 00:25:11,440 --> 00:25:14,320 Speaker 8: it's very It's always important to kind of keep what's 458 00:25:14,320 --> 00:25:17,240 Speaker 8: happening in mind. And like for me, growing up, I 459 00:25:17,320 --> 00:25:19,040 Speaker 8: came like I was in a I grew up in 460 00:25:19,080 --> 00:25:22,240 Speaker 8: a zero interest rate environment. So now that things are 461 00:25:22,520 --> 00:25:25,000 Speaker 8: up to five percent, I would say that's kind of normal. 462 00:25:25,359 --> 00:25:27,320 Speaker 7: But I'm definitely assaulting. 463 00:25:27,800 --> 00:25:31,040 Speaker 2: But at least some Mateo and Victoria Bills. I'm getting 464 00:25:31,040 --> 00:25:31,720 Speaker 2: hammered today. 465 00:25:31,920 --> 00:25:34,600 Speaker 9: These guys are just you know, the US. 466 00:25:34,680 --> 00:25:38,960 Speaker 3: He her that it is thankfully so so Victoria. 467 00:25:39,040 --> 00:25:41,080 Speaker 4: I was actually surprised to learn, you know, years ago, 468 00:25:41,640 --> 00:25:47,320 Speaker 4: how much the average retail advisor is allocating to alternatives. 469 00:25:47,320 --> 00:25:49,639 Speaker 4: I thought, but it would have been a really small number, 470 00:25:49,640 --> 00:25:52,479 Speaker 4: if not zero, But I'm hearing it's meaningful. It's more 471 00:25:52,520 --> 00:25:54,360 Speaker 4: than five percent of a port portfolio. For a lot 472 00:25:54,400 --> 00:25:57,600 Speaker 4: of these folks talk to us about kind of what 473 00:25:57,640 --> 00:25:59,359 Speaker 4: you think or what you're hearing from your clients is 474 00:25:59,400 --> 00:26:03,800 Speaker 4: a is a usable allocation in a traditional sixty forty portfolio? 475 00:26:04,160 --> 00:26:05,080 Speaker 3: Two alternatives? 476 00:26:05,320 --> 00:26:08,800 Speaker 8: Great question. I would say, like a traditional allocation, we're 477 00:26:08,800 --> 00:26:11,679 Speaker 8: looking at around ten to fifteen percent in terms of 478 00:26:11,680 --> 00:26:15,200 Speaker 8: allocations to alternatives, But of course that's also contingent upon 479 00:26:15,280 --> 00:26:19,359 Speaker 8: your client's risk profile, timeline, liquidity restraints. There are a 480 00:26:19,400 --> 00:26:22,199 Speaker 8: lot of things that come into play outside of just 481 00:26:22,720 --> 00:26:26,000 Speaker 8: what would be like a standard or like a traditional allocation. 482 00:26:26,400 --> 00:26:29,160 Speaker 8: And always keep in keeping in mind again like those 483 00:26:29,200 --> 00:26:31,680 Speaker 8: liquidity restraints. So one of the things that we do 484 00:26:31,800 --> 00:26:34,920 Speaker 8: is we offer not only just private vehicles, but also 485 00:26:35,000 --> 00:26:37,280 Speaker 8: forty ACT vehicles and forty ACT vehicles. 486 00:26:37,320 --> 00:26:38,840 Speaker 3: So it was a forty ACT vehicle. 487 00:26:39,240 --> 00:26:41,680 Speaker 8: So forty ACT vehicle essentially allows. 488 00:26:41,280 --> 00:26:44,480 Speaker 4: For securities Exchange Act of nineteen forty yes, forty act. 489 00:26:44,520 --> 00:26:48,920 Speaker 8: Okay, So Securities Exchange Act basically allows for fiduciary discretion 490 00:26:49,119 --> 00:26:52,720 Speaker 8: but also higher transparency on the spectrum. 491 00:26:53,560 --> 00:26:55,960 Speaker 4: One of the engining areas on the alternative side that 492 00:26:55,960 --> 00:26:58,159 Speaker 4: it's just blown me away and its growth has been 493 00:26:58,200 --> 00:27:01,040 Speaker 4: private credit. I mean, it's been such a hot area, 494 00:27:01,080 --> 00:27:02,160 Speaker 4: and I always say, if I came back to Wall 495 00:27:02,160 --> 00:27:05,399 Speaker 4: Street again, I might think about going into private credit directly. 496 00:27:05,440 --> 00:27:07,000 Speaker 3: It seems like a great business. 497 00:27:07,000 --> 00:27:09,639 Speaker 4: We all know about private equity, but private credit something 498 00:27:09,680 --> 00:27:10,440 Speaker 4: relatively new. 499 00:27:10,600 --> 00:27:12,200 Speaker 3: How do you guys think about private credit? 500 00:27:12,640 --> 00:27:16,240 Speaker 8: Very positive around private credit. I'm more bullish around actually 501 00:27:16,320 --> 00:27:18,720 Speaker 8: smaller levels of private credit. So when we think about 502 00:27:19,920 --> 00:27:22,919 Speaker 8: the space where essentially now the interest rates have gone up. 503 00:27:23,760 --> 00:27:26,240 Speaker 7: The average I would say, like. 504 00:27:26,720 --> 00:27:29,640 Speaker 8: The average like business owner, someone who has like less 505 00:27:29,680 --> 00:27:32,240 Speaker 8: than like a million in like revenue, they're having a 506 00:27:32,240 --> 00:27:35,840 Speaker 8: difficult time finding loans from banks. So if you, for example, 507 00:27:35,880 --> 00:27:39,760 Speaker 8: are trying to get non deluded capital outside of VC 508 00:27:40,720 --> 00:27:43,959 Speaker 8: small credit loans, private credit private credit loans are actually 509 00:27:43,960 --> 00:27:47,240 Speaker 8: a great opportunity and they're great growing space. So looking 510 00:27:47,280 --> 00:27:50,679 Speaker 8: at private credit funds that have that basically work with 511 00:27:50,720 --> 00:27:53,240 Speaker 8: companies that have less than about five five million or 512 00:27:53,280 --> 00:27:56,760 Speaker 8: working working to provide like five million dollars in. 513 00:27:56,680 --> 00:27:58,800 Speaker 4: Life and there are investment vehicles for your clients to 514 00:27:58,800 --> 00:27:59,879 Speaker 4: get some exposure to that. 515 00:28:00,119 --> 00:28:03,159 Speaker 8: Absolutely, yes, So we have a actually we do have 516 00:28:03,200 --> 00:28:06,120 Speaker 8: a private credit vehicle on our roster right now called 517 00:28:06,160 --> 00:28:09,280 Speaker 8: Meriwether Capital Group. They provide what's called the Hero Fund. 518 00:28:09,760 --> 00:28:12,280 Speaker 8: And the amazing thing about is that they essentially invest 519 00:28:12,320 --> 00:28:15,320 Speaker 8: in businesses within the Pacific Northwest and their mom and 520 00:28:15,320 --> 00:28:20,520 Speaker 8: pops stores particularly, So these are basically small town, small 521 00:28:20,560 --> 00:28:24,280 Speaker 8: time companies, but very good balance sheets, very good like 522 00:28:25,000 --> 00:28:29,760 Speaker 8: debt to ibadah and essentially like they're being managed very well, 523 00:28:29,800 --> 00:28:32,320 Speaker 8: but are not a client when it comes to what 524 00:28:32,359 --> 00:28:35,680 Speaker 8: the traditional banks are looking for, large private credit funds 525 00:28:35,680 --> 00:28:36,280 Speaker 8: would be looking for. 526 00:28:36,440 --> 00:28:40,200 Speaker 2: You came out of the Babson combine. The Babson combine. 527 00:28:40,720 --> 00:28:44,360 Speaker 2: It's not yeah, pound for pound, it's great and part 528 00:28:44,360 --> 00:28:47,600 Speaker 2: of their courage back forty years I lily looked out 529 00:28:47,600 --> 00:28:50,640 Speaker 2: a window years ago at the David Babson Institute building. 530 00:28:50,800 --> 00:28:53,160 Speaker 2: But the answer is up in Wellesley Hills and you 531 00:28:53,240 --> 00:28:55,120 Speaker 2: go up the road past the country Club, and here's 532 00:28:55,160 --> 00:29:01,520 Speaker 2: this oasis of bigger, broader thinking. Sift that through artificial intelligence, 533 00:29:01,920 --> 00:29:06,000 Speaker 2: where do you look on AI? I mean feature in Nvidio. 534 00:29:06,160 --> 00:29:08,880 Speaker 2: But does everyone win an AI or is it a 535 00:29:09,000 --> 00:29:11,040 Speaker 2: narrow group that you've really got to do some work on. 536 00:29:12,360 --> 00:29:13,400 Speaker 8: I love this question. 537 00:29:14,120 --> 00:29:16,800 Speaker 2: Okay, can I go home? I'm leaving right now, go 538 00:29:16,920 --> 00:29:18,560 Speaker 2: back A. 539 00:29:19,080 --> 00:29:22,080 Speaker 8: It's AI is one of those hot topics that everyone 540 00:29:22,120 --> 00:29:25,040 Speaker 8: is very excited about right now without getting into like 541 00:29:25,080 --> 00:29:28,520 Speaker 8: the nitty gritty details, there is a lot of Essentially, 542 00:29:28,560 --> 00:29:30,640 Speaker 8: I would say that there's a lot of noise surrounding it. 543 00:29:30,720 --> 00:29:33,920 Speaker 8: So true AI in the sense we're looking at companies 544 00:29:34,000 --> 00:29:37,520 Speaker 8: or we're looking at essentially the ability to create more 545 00:29:37,560 --> 00:29:40,640 Speaker 8: generative AI. So to me, that's where the real market 546 00:29:40,680 --> 00:29:44,760 Speaker 8: is using AI tools, for example, to create better create 547 00:29:45,120 --> 00:29:49,280 Speaker 8: market efficiencies, whether that's using I use AI for example 548 00:29:49,360 --> 00:29:52,160 Speaker 8: or chat GBT to draft emails for myself, or can you. 549 00:29:52,200 --> 00:29:53,880 Speaker 2: Use AI to draft emails? 550 00:29:54,080 --> 00:29:55,640 Speaker 7: I do, yes, so well. 551 00:29:55,720 --> 00:29:57,520 Speaker 8: Or and I'll even say, like, hey, I want to 552 00:29:57,920 --> 00:30:00,880 Speaker 8: draft an email to say like I I look like 553 00:30:00,920 --> 00:30:03,960 Speaker 8: looking for an opportunity to network, and it'll spout four 554 00:30:04,000 --> 00:30:06,720 Speaker 8: paragraphs and they'll say, okay, make it one paragraph and 555 00:30:06,760 --> 00:30:09,200 Speaker 8: it'll do the exact same thing but in one paragraph. 556 00:30:09,480 --> 00:30:12,320 Speaker 8: And then I can even say for example, I used 557 00:30:12,880 --> 00:30:15,920 Speaker 8: fun example of like using AI to basically ask the 558 00:30:16,000 --> 00:30:19,640 Speaker 8: question of how do I optimize my social media things? 559 00:30:19,680 --> 00:30:21,360 Speaker 8: There are a lot of things that AI. 560 00:30:22,680 --> 00:30:28,480 Speaker 9: She's spouting four paragraphs. Here, here's my AI all capital letters. 561 00:30:28,800 --> 00:30:32,280 Speaker 9: I can't meet at ten fifteen boom. What's AI going 562 00:30:32,360 --> 00:30:33,000 Speaker 9: to do for me. 563 00:30:32,920 --> 00:30:33,640 Speaker 2: On that email? 564 00:30:34,200 --> 00:30:37,200 Speaker 8: It's essentially it will kind of it. I mean, when 565 00:30:37,240 --> 00:30:39,120 Speaker 8: it comes to like, what do you do on that email, 566 00:30:39,200 --> 00:30:41,640 Speaker 8: I'm sure there's not much that AI can do for that. 567 00:30:41,760 --> 00:30:43,960 Speaker 9: Mister Bloomberg can do something for that will show me 568 00:30:44,040 --> 00:30:44,440 Speaker 9: the door. 569 00:30:45,520 --> 00:30:48,560 Speaker 8: But it's essentially there's a lot of opportunities in AI. 570 00:30:48,640 --> 00:30:50,640 Speaker 8: But what we want to focus on more is on 571 00:30:50,680 --> 00:30:53,480 Speaker 8: the generative side of AI. So while it's good for 572 00:30:53,640 --> 00:30:57,520 Speaker 8: operational efficiency making certain that you're sending the right emails, 573 00:30:57,800 --> 00:31:01,920 Speaker 8: trying to figure out what has tags to use where 574 00:31:02,320 --> 00:31:04,560 Speaker 8: a lot of the AI tools are being used right now, 575 00:31:04,640 --> 00:31:07,560 Speaker 8: is essentially to try and get around where other people 576 00:31:07,600 --> 00:31:08,920 Speaker 8: weren't able to work in the space. 577 00:31:09,120 --> 00:31:12,960 Speaker 2: Victoria, thank you so much having me Henryan of Chicago. 578 00:31:13,080 --> 00:31:15,680 Speaker 2: Victoria Bills joins us. This morning, I got to write 579 00:31:15,680 --> 00:31:19,280 Speaker 2: an email in the break good luck with spout it 580 00:31:19,280 --> 00:31:23,680 Speaker 2: out four paragraphs. Longest email I've ever done is three sentences. 581 00:31:34,080 --> 00:31:36,040 Speaker 2: You daily look at the front pages around the world. 582 00:31:36,080 --> 00:31:37,400 Speaker 2: What do you got for the newspapers? 583 00:31:37,640 --> 00:31:40,080 Speaker 7: All right, this one stuck out to me. There finally 584 00:31:40,120 --> 00:31:42,920 Speaker 7: could be an IPO for athletes. This is in the 585 00:31:42,920 --> 00:31:45,000 Speaker 7: New York Post. I had to read through this a 586 00:31:45,000 --> 00:31:48,280 Speaker 7: few times. So it's a new investment platform. It's called Vestible. 587 00:31:48,840 --> 00:31:51,479 Speaker 7: It allows fans to buy and sell shares in the 588 00:31:51,520 --> 00:31:55,640 Speaker 7: future on the field earnings of college professional athlete receive 589 00:31:55,760 --> 00:31:58,720 Speaker 7: federal approval to begin trading on the US stock market. 590 00:31:58,880 --> 00:32:03,200 Speaker 7: Denver Broncos linebacker Baron Browning he's going to headline their 591 00:32:03,320 --> 00:32:07,240 Speaker 7: IPO the week of March eighteenth. So it's really intricate 592 00:32:07,360 --> 00:32:10,080 Speaker 7: as to how it works, but it's an interesting story 593 00:32:10,120 --> 00:32:10,760 Speaker 7: as to how this can. 594 00:32:10,920 --> 00:32:13,440 Speaker 4: So he gets a big chunk of the ip he does. 595 00:32:13,520 --> 00:32:16,200 Speaker 7: He gets about eighty percent of the proceeds. 596 00:32:15,760 --> 00:32:18,120 Speaker 4: From his He gets cash today. Now he's on the 597 00:32:18,160 --> 00:32:19,880 Speaker 4: last year. I guess I'm a rookie contract. So he's 598 00:32:19,880 --> 00:32:23,080 Speaker 4: about to go to free agency. And if he does, 599 00:32:23,200 --> 00:32:25,560 Speaker 4: if I buy a share of this guy, I get 600 00:32:25,560 --> 00:32:29,480 Speaker 4: a piece of his incremental upside in his new contracts 601 00:32:29,720 --> 00:32:33,560 Speaker 4: Now and going forward. So that's my risk return opportunity 602 00:32:33,600 --> 00:32:34,280 Speaker 4: as a shareholder. 603 00:32:34,360 --> 00:32:40,680 Speaker 2: Okay, I'm looking at this. If you equitize his income, 604 00:32:41,280 --> 00:32:44,920 Speaker 2: he gets a capital gains treatment out of an IPO yep, 605 00:32:45,040 --> 00:32:46,440 Speaker 2: versus ordinary income as. 606 00:32:46,360 --> 00:32:48,320 Speaker 3: That makes a good point. Any thinking about that, I 607 00:32:48,360 --> 00:32:50,600 Speaker 3: don't know. I always thinking of taxes. Yeah, possible. 608 00:32:51,360 --> 00:32:54,680 Speaker 4: Just monetizing that revenue stream going forward. 609 00:32:55,200 --> 00:32:57,960 Speaker 7: So what's the depreciation exactly? 610 00:32:58,920 --> 00:33:00,400 Speaker 3: That's the risk yep. 611 00:33:00,520 --> 00:33:04,440 Speaker 7: Okay, good stuff, yeah, okay. We have a big pre 612 00:33:04,560 --> 00:33:05,600 Speaker 7: wedding party today. 613 00:33:05,800 --> 00:33:10,320 Speaker 2: Okay, is there anybody in my isle at Bloomberg not 614 00:33:10,360 --> 00:33:13,920 Speaker 2: getting married? Lee's getting married. Everybody's getting married. 615 00:33:14,200 --> 00:33:14,760 Speaker 3: It's amazing. 616 00:33:14,800 --> 00:33:19,880 Speaker 2: They've all got Bride's marri right, Bride's Magazine, Arizona's over there, 617 00:33:19,880 --> 00:33:22,240 Speaker 2: She's got Bride's Magaze, like four pages thick. 618 00:33:23,280 --> 00:33:27,240 Speaker 4: Well, this this is like big time as riches man 619 00:33:27,280 --> 00:33:30,720 Speaker 4: is getting married and like all the A list slubs 620 00:33:30,720 --> 00:33:31,880 Speaker 4: globally all coming. 621 00:33:31,640 --> 00:33:32,160 Speaker 9: Out to it. 622 00:33:35,160 --> 00:33:37,800 Speaker 2: What do you got, Lisa explained, Do. 623 00:33:37,800 --> 00:33:40,240 Speaker 7: You have Mukesh and Bonnie? It's his youngest son, a 624 00:33:40,400 --> 00:33:43,080 Speaker 7: non m body three day celebration. Okay, this guy doesn't 625 00:33:43,080 --> 00:33:45,600 Speaker 7: get married till July twelfth, but it starts today. You 626 00:33:45,640 --> 00:33:50,360 Speaker 7: have people like Rihanna performing. You have American illusionist David Blaine, 627 00:33:50,480 --> 00:33:53,080 Speaker 7: he's going to be performing there. You have five hundred 628 00:33:53,120 --> 00:33:55,760 Speaker 7: types of dishes. But this is like big wigs showing up. 629 00:33:55,760 --> 00:33:58,400 Speaker 7: We're talking about meta platforms. Mark Zuckerberg, he's going to 630 00:33:58,440 --> 00:34:01,480 Speaker 7: the ding the thing, Micro's co founder Bill Gates, Black 631 00:34:01,520 --> 00:34:05,520 Speaker 7: Rock co founder Larry Think you have Sundar for alphabet CEO. 632 00:34:05,760 --> 00:34:08,560 Speaker 7: I mean this is like big time and it just 633 00:34:08,600 --> 00:34:15,000 Speaker 7: shows like the reliance. Yes, yes, yes, isn't that worth 634 00:34:15,000 --> 00:34:18,400 Speaker 7: there's one hundred and ten billion dollars you have to 635 00:34:18,440 --> 00:34:21,120 Speaker 7: go Now. My question is what kind of gift? What 636 00:34:21,239 --> 00:34:23,040 Speaker 7: kind of check are you handing out at this kind 637 00:34:23,040 --> 00:34:23,440 Speaker 7: of wedding? 638 00:34:24,160 --> 00:34:25,719 Speaker 2: Exactly? Gift? I don't know. 639 00:34:26,400 --> 00:34:27,839 Speaker 3: Handlesticks always make a good gift. 640 00:34:28,719 --> 00:34:31,520 Speaker 2: Hey, mine is a case in there against it that 641 00:34:31,640 --> 00:34:32,399 Speaker 2: really goes well. 642 00:34:32,520 --> 00:34:35,960 Speaker 7: Next, luxury skiing in the Alps, getting even. 643 00:34:35,800 --> 00:34:36,759 Speaker 2: Fa Sweeny story. 644 00:34:37,400 --> 00:34:40,000 Speaker 7: Yes he's gonna love this. Okay, how would you like 645 00:34:40,040 --> 00:34:40,759 Speaker 7: your own ski? 646 00:34:40,880 --> 00:34:41,799 Speaker 3: Butler need it? 647 00:34:41,840 --> 00:34:44,920 Speaker 7: Who shows up with extra gloves boots like right there 648 00:34:44,960 --> 00:34:45,520 Speaker 7: ready for you? 649 00:34:45,680 --> 00:34:48,759 Speaker 2: That's called still to know. 650 00:34:49,480 --> 00:34:52,200 Speaker 7: You have the helicopter for just over two grand. It 651 00:34:52,200 --> 00:34:55,280 Speaker 7: could take you to a mountaintop restaurant. You can indulge 652 00:34:55,320 --> 00:34:58,759 Speaker 7: in two thousand bottle of Tuscan red. You know, here's 653 00:34:58,800 --> 00:35:02,120 Speaker 7: the thing. Winter are getting warmer. Let's know, they're trying 654 00:35:02,120 --> 00:35:04,320 Speaker 7: to attract these big spenders. That's why they're doing the 655 00:35:04,520 --> 00:35:05,600 Speaker 7: Does it matter if. 656 00:35:05,480 --> 00:35:09,160 Speaker 2: The Alps I'm speaking unbriefed is running out of snow? 657 00:35:09,280 --> 00:35:11,560 Speaker 3: Yeah, I mean it's every year. The question is where 658 00:35:11,600 --> 00:35:13,239 Speaker 3: do you go? Do you go east to the to 659 00:35:13,320 --> 00:35:15,319 Speaker 3: Europe or west in the US. And that's what people 660 00:35:15,360 --> 00:35:17,279 Speaker 3: ask I'm going west in a couple of years. 661 00:35:18,239 --> 00:35:20,680 Speaker 2: But I'm showing my age. You stay with me all 662 00:35:20,760 --> 00:35:24,520 Speaker 2: your fossils on YouTube live. That's scene in the beginning 663 00:35:24,520 --> 00:35:28,520 Speaker 2: of Charade where Carrie Grant shows up on the European 664 00:35:29,719 --> 00:35:33,279 Speaker 2: and Audrey heppern is sitting there. That's all I want 665 00:35:33,680 --> 00:35:36,879 Speaker 2: the Can the butler give me the coolness of Carrie 666 00:35:36,960 --> 00:35:38,040 Speaker 2: Grant and Audrey. 667 00:35:38,640 --> 00:35:40,640 Speaker 7: Right there at your beck and call yes. 668 00:35:41,320 --> 00:35:44,480 Speaker 2: It defines for a generation European. 669 00:35:44,040 --> 00:35:48,080 Speaker 3: Skiing, m skiing. It was a whole different gig. Yes, 670 00:35:48,080 --> 00:35:48,399 Speaker 3: it's great. 671 00:35:48,480 --> 00:35:49,240 Speaker 2: Why is it different? 672 00:35:49,920 --> 00:35:51,799 Speaker 3: It's just the like, it's just the gig up in 673 00:35:51,880 --> 00:35:52,480 Speaker 3: the Alps. 674 00:35:52,520 --> 00:35:56,960 Speaker 4: That's just it's so international, so global, and and it's uh, 675 00:35:57,239 --> 00:36:01,319 Speaker 4: it's it's not corporate like you might resorts resorts in 676 00:36:01,360 --> 00:36:03,920 Speaker 4: the US, which are phenomenal, by the way, it's just 677 00:36:03,920 --> 00:36:04,640 Speaker 4: a little bit different. 678 00:36:04,880 --> 00:36:07,040 Speaker 2: The difference there is you got Audrey Yupper, and if 679 00:36:07,080 --> 00:36:10,120 Speaker 2: you say Laurent and Aspen, you've got a guy with 680 00:36:10,200 --> 00:36:13,120 Speaker 2: the Kansas City cheese jacket, right exactly. 681 00:36:13,160 --> 00:36:16,600 Speaker 7: The final one go yes, because it's Friday. Martinis are 682 00:36:16,600 --> 00:36:21,040 Speaker 7: having a moment. Okay, I'm shostingwhere shot. Okay, you're gonna 683 00:36:21,040 --> 00:36:24,640 Speaker 7: pay more for it. Shot thirty dollars to one hundred 684 00:36:24,680 --> 00:36:26,600 Speaker 7: and fifty dollars a glass. That's what they're going for 685 00:36:26,719 --> 00:36:30,040 Speaker 7: places like New York. It's called loud luxury. So post 686 00:36:30,040 --> 00:36:33,200 Speaker 7: a soft luxury, quiet luxury where you don't say this 687 00:36:33,400 --> 00:36:35,480 Speaker 7: is like where you spend a lot and you own 688 00:36:35,560 --> 00:36:38,280 Speaker 7: it and you say, I'm drinking a fifty dollars Martinia 689 00:36:38,320 --> 00:36:40,680 Speaker 7: and you post it wherever you want to post it to. 690 00:36:41,120 --> 00:36:42,840 Speaker 7: There's a place in Manhattan in case you want to go. 691 00:36:42,920 --> 00:36:45,840 Speaker 7: Of course, it is forty five dollars a citrus martini 692 00:36:45,880 --> 00:36:49,040 Speaker 7: at Illis. There's a thirty four dollars martini at Monkey 693 00:36:49,040 --> 00:36:51,520 Speaker 7: Bar in Manhattan. So those are some places you can 694 00:36:51,560 --> 00:36:52,480 Speaker 7: go to. It's Friday. 695 00:36:52,880 --> 00:36:53,440 Speaker 3: I'm shocked. 696 00:36:53,480 --> 00:36:54,400 Speaker 7: I'm no happy. 697 00:36:54,520 --> 00:36:57,879 Speaker 2: I shot at tequila at the Monkey Bar, No over 698 00:36:57,920 --> 00:37:00,920 Speaker 2: in fifty whatever. You sit there at the bar and 699 00:37:01,000 --> 00:37:02,719 Speaker 2: they got a black and white TV up in the 700 00:37:02,800 --> 00:37:06,000 Speaker 2: right of Phil Silvers from like nineteen fifty five playing, 701 00:37:06,840 --> 00:37:09,799 Speaker 2: and you know you'll there'll be like some nurse there 702 00:37:09,840 --> 00:37:12,080 Speaker 2: and you'll go, could I please buy you a shot 703 00:37:12,080 --> 00:37:16,120 Speaker 2: at tequila? And the shot is like an old fashioned 704 00:37:16,160 --> 00:37:19,879 Speaker 2: glass at the Monkey that costs three five dollars too 705 00:37:19,960 --> 00:37:21,960 Speaker 2: as well. So that's what we're that's what we're doing 706 00:37:21,960 --> 00:37:23,560 Speaker 2: with the martinis, right, Yes. 707 00:37:23,760 --> 00:37:28,439 Speaker 7: Very expensive truffle infused vodka serve in a specialty tray 708 00:37:28,520 --> 00:37:30,040 Speaker 7: with olives in there in case you want. 709 00:37:30,120 --> 00:37:31,600 Speaker 2: Now, that's a nice time. They do that at the 710 00:37:31,640 --> 00:37:36,640 Speaker 2: Hotel Chelsea they have the multiple they're beneath that, but 711 00:37:36,719 --> 00:37:40,319 Speaker 2: not much. They're really actually exquisite. What I would say, 712 00:37:40,320 --> 00:37:42,279 Speaker 2: and this is the basic thing I am interested with 713 00:37:42,280 --> 00:37:45,160 Speaker 2: what you two think. I hate when they fill the 714 00:37:45,200 --> 00:37:49,040 Speaker 2: martini to the brim, so sad you spill it. Yes, 715 00:37:50,080 --> 00:37:53,400 Speaker 2: it's so needless. Don't everyone out there when you're pouring 716 00:37:53,440 --> 00:37:56,520 Speaker 2: a beverage this weekend don't fill it to the brim now, 717 00:37:56,600 --> 00:37:59,600 Speaker 2: I mean, that's a messy pro tip. That was brilliant. 718 00:37:59,600 --> 00:38:03,319 Speaker 2: Can you do that series? On Monday, I go over 719 00:38:03,360 --> 00:38:06,960 Speaker 2: from forty dollar Martinez, Lisa Mateo, thank you so much. 720 00:38:07,760 --> 00:38:11,000 Speaker 2: This is the Bloomberg Surveillance Podcast, bringing you the best 721 00:38:11,000 --> 00:38:15,800 Speaker 2: in economics, finance, investment, and international relations. You can also 722 00:38:15,880 --> 00:38:19,920 Speaker 2: watch the show live on YouTube. Visit the Bloomberg Podcast 723 00:38:20,040 --> 00:38:24,080 Speaker 2: channel on YouTube to see the show weekday mornings from 724 00:38:24,120 --> 00:38:27,360 Speaker 2: seven to ten am Eastern from our global headquarters in 725 00:38:27,440 --> 00:38:31,160 Speaker 2: New York City. Subscribe to the podcast on Apple, Spotify, 726 00:38:31,480 --> 00:38:35,040 Speaker 2: or anywhere else you listen, and always on Bloomberg Radio, 727 00:38:35,239 --> 00:38:38,440 Speaker 2: the Bloomberg Terminal, and the Bloomberg Business app.