1 00:00:02,920 --> 00:00:10,840 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the 2 00:00:10,880 --> 00:00:15,040 Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am 3 00:00:15,080 --> 00:00:18,079 Speaker 1: Eastern on Apple Car playing Android Auto with the Bloomberg 4 00:00:18,120 --> 00:00:21,440 Speaker 1: Business app. Listen on demand wherever you get your podcasts, 5 00:00:21,640 --> 00:00:24,040 Speaker 1: or watch us live on YouTube. 6 00:00:25,079 --> 00:00:28,800 Speaker 2: Let's talk technology here. There's a lot happening again. The 7 00:00:28,800 --> 00:00:31,560 Speaker 2: Bloomberg News story today, which is just amazing to me. Nvidia, 8 00:00:32,200 --> 00:00:35,200 Speaker 2: Microsoft and Apple are bigger than the China stock market. 9 00:00:35,240 --> 00:00:35,519 Speaker 3: That's it. 10 00:00:35,960 --> 00:00:37,559 Speaker 2: I don't know who did that math, but that's like 11 00:00:37,600 --> 00:00:39,519 Speaker 2: a good math. That makes a good story to me. 12 00:00:39,880 --> 00:00:41,840 Speaker 4: That's a great story. I mean, does it mean they're 13 00:00:41,840 --> 00:00:43,879 Speaker 4: overvalued or China's undervalued? 14 00:00:43,880 --> 00:00:45,239 Speaker 2: All right, let me check in with somebody who might 15 00:00:45,280 --> 00:00:47,279 Speaker 2: have an opinion here on Rock ran a Bloomberg Intelligence 16 00:00:47,320 --> 00:00:50,920 Speaker 2: senior tech analyst on a Rock. I mean, ever since 17 00:00:51,080 --> 00:00:53,560 Speaker 2: I've known you, this is we're going fourteen years. You've 18 00:00:53,560 --> 00:00:55,800 Speaker 2: got like the easiest job on Wall Street. All you 19 00:00:55,840 --> 00:00:58,480 Speaker 2: got to do is say tech spending is going up. 20 00:00:59,000 --> 00:01:02,280 Speaker 2: Buy these things that's good for technology. Step back today, 21 00:01:02,280 --> 00:01:05,760 Speaker 2: you walk into a portfolio manager's office here, and the 22 00:01:05,800 --> 00:01:08,000 Speaker 2: portfolio manager he's been in he or she has been 23 00:01:08,040 --> 00:01:11,039 Speaker 2: in industrials their entire career. Now they're told they need 24 00:01:11,040 --> 00:01:13,480 Speaker 2: to get some tech exposure. Where do you send them? 25 00:01:14,240 --> 00:01:15,959 Speaker 5: Yeah, so this is you know, you just made a 26 00:01:15,959 --> 00:01:18,160 Speaker 5: comment that you know, eighteen months ago or so when 27 00:01:18,160 --> 00:01:20,360 Speaker 5: you heard of AI, you said, go by in video 28 00:01:20,400 --> 00:01:22,840 Speaker 5: and that's you know, that has worked out very well. 29 00:01:22,959 --> 00:01:25,360 Speaker 5: But now the question is for the next leg of 30 00:01:25,440 --> 00:01:29,279 Speaker 5: AI beneficiaries, do you go downstream or you still stick 31 00:01:29,319 --> 00:01:32,360 Speaker 5: to the hardware players? And that's a very tough question, frankly, 32 00:01:32,600 --> 00:01:35,080 Speaker 5: but there are enough players in the software area, in 33 00:01:35,120 --> 00:01:37,399 Speaker 5: the in the services area that have not seen the 34 00:01:37,400 --> 00:01:39,640 Speaker 5: benefit of AI, and I think those are the companies 35 00:01:39,680 --> 00:01:42,480 Speaker 5: they're going to see sales improve over the next you know, 36 00:01:42,560 --> 00:01:45,400 Speaker 5: let's say a couple of years, because the downstream effect 37 00:01:45,520 --> 00:01:48,080 Speaker 5: of all the increased spending has to reach them. 38 00:01:48,120 --> 00:01:49,240 Speaker 6: It's just a matter of time. 39 00:01:49,720 --> 00:01:52,360 Speaker 5: The difficult part is to put a real pulse on 40 00:01:52,440 --> 00:01:53,400 Speaker 5: when that's going to happen. 41 00:01:53,560 --> 00:01:56,400 Speaker 4: Yeah, let me throw a fly into the ointment. Can 42 00:01:56,440 --> 00:01:59,320 Speaker 4: the Justice Department screw all this up at some point? 43 00:02:00,560 --> 00:02:01,840 Speaker 6: Yeah, they will try very hard. 44 00:02:01,880 --> 00:02:04,280 Speaker 5: And you know, I think these companies are also getting 45 00:02:04,320 --> 00:02:07,320 Speaker 5: smarter and smarter. But frankly speaking, I think what we 46 00:02:07,400 --> 00:02:11,400 Speaker 5: saw after the financial crisis was banks did become much bigger, 47 00:02:12,040 --> 00:02:15,360 Speaker 5: and I think unfortunately for the Justice Department and other regulators, 48 00:02:15,560 --> 00:02:18,560 Speaker 5: I think the large tech companies will become much bigger 49 00:02:18,600 --> 00:02:21,280 Speaker 5: because of AI, just because they have the capacity to 50 00:02:21,360 --> 00:02:23,920 Speaker 5: run these workloads. It's not easy to run them in 51 00:02:23,960 --> 00:02:27,280 Speaker 5: your you know, backyard or so you really need powerful 52 00:02:27,360 --> 00:02:30,520 Speaker 5: computers to do these processing and they have an unfair 53 00:02:30,560 --> 00:02:31,280 Speaker 5: advantage here. 54 00:02:31,680 --> 00:02:34,239 Speaker 3: All right, Anrarag. We can't get you on the horn 55 00:02:34,280 --> 00:02:35,280 Speaker 3: without talking about Apple. 56 00:02:35,320 --> 00:02:38,359 Speaker 2: And I know next week June tenth, I believe, will 57 00:02:38,400 --> 00:02:40,920 Speaker 2: be the Developer Conference for Apple, and that's a you know, 58 00:02:41,080 --> 00:02:42,799 Speaker 2: an event every year where people like John Tucker and 59 00:02:42,840 --> 00:02:45,960 Speaker 2: I who don't know much about technology, we do pay 60 00:02:46,000 --> 00:02:49,240 Speaker 2: attention because that tends to be when Apple introduces some 61 00:02:49,320 --> 00:02:52,880 Speaker 2: cool stuff, maybe that it moves the stock of Apple, 62 00:02:52,919 --> 00:02:56,120 Speaker 2: maybe it moves the socks in other tech companies. Give 63 00:02:56,200 --> 00:02:59,160 Speaker 2: us a preview of June tenth at in Cupertino, California. 64 00:03:00,080 --> 00:03:02,400 Speaker 5: Yeah, so I would say that you know, these companies 65 00:03:02,440 --> 00:03:05,840 Speaker 5: hold these events every year, but I would say for Apple, 66 00:03:05,880 --> 00:03:08,160 Speaker 5: I don't think it has ever been so important than 67 00:03:08,240 --> 00:03:10,560 Speaker 5: this year as to what big updates they're going to 68 00:03:10,560 --> 00:03:13,919 Speaker 5: do their software because right now, Apples was caught off 69 00:03:13,960 --> 00:03:17,040 Speaker 5: guard like most of the companies did when chat GPD 70 00:03:17,200 --> 00:03:19,600 Speaker 5: came out and people figured out they are really behind 71 00:03:19,600 --> 00:03:22,720 Speaker 5: the curve when it comes to AI adoption or their 72 00:03:23,520 --> 00:03:25,239 Speaker 5: you know, investments in this area. 73 00:03:25,280 --> 00:03:27,520 Speaker 6: So Apple's been behind, there's no lying about it. 74 00:03:28,080 --> 00:03:31,000 Speaker 5: But we are expected to see you know, you could 75 00:03:31,000 --> 00:03:35,560 Speaker 5: say alliance between Apple and Open Ai. We don't know 76 00:03:35,600 --> 00:03:38,520 Speaker 5: if they're going to do a similar deal with with Google. 77 00:03:38,640 --> 00:03:40,800 Speaker 5: That's pending, So there's going to be a lot of 78 00:03:40,840 --> 00:03:44,280 Speaker 5: interesting stuff that shows up on Monday when Apple's going 79 00:03:44,320 --> 00:03:47,880 Speaker 5: to showcase how this technology is going to get into 80 00:03:47,920 --> 00:03:50,280 Speaker 5: your phone and make your life a little bit easier 81 00:03:50,280 --> 00:03:53,200 Speaker 5: than what it is. And if that, if that thing 82 00:03:53,320 --> 00:03:57,400 Speaker 5: goes through, if it's executed, well, then the the shipment 83 00:03:57,480 --> 00:03:59,720 Speaker 5: data or the unit unit sales data that we have 84 00:03:59,800 --> 00:04:02,240 Speaker 5: seen for iPhones over the last three years, which has 85 00:04:02,280 --> 00:04:04,640 Speaker 5: typically been flat, I mean, the Apple's not seen any 86 00:04:05,000 --> 00:04:05,880 Speaker 5: improvement there. 87 00:04:06,000 --> 00:04:07,600 Speaker 6: I mean you could see a lift over there. 88 00:04:08,400 --> 00:04:10,760 Speaker 4: Let me ask you about Microsoft real quickly. What's the 89 00:04:10,800 --> 00:04:13,560 Speaker 4: deal that they struck with this AI startup. 90 00:04:13,560 --> 00:04:19,480 Speaker 7: I think it's Inflection, Yeah, so basically they paid them 91 00:04:19,520 --> 00:04:24,599 Speaker 7: a fee as you could say compensation to hire a 92 00:04:24,600 --> 00:04:27,640 Speaker 7: lot of their employees and make them part of Microsoft's 93 00:04:27,640 --> 00:04:28,800 Speaker 7: internal AI division. 94 00:04:29,040 --> 00:04:31,480 Speaker 5: And that's the you know, that's what it's been looked 95 00:04:31,480 --> 00:04:36,280 Speaker 5: at because, as you know, larger companies cannot easily buy 96 00:04:36,279 --> 00:04:39,920 Speaker 5: any companies at this point, or AI companies, so they 97 00:04:39,920 --> 00:04:41,120 Speaker 5: are trying to see if they could. 98 00:04:41,160 --> 00:04:42,320 Speaker 6: They are you know, you could. 99 00:04:42,120 --> 00:04:45,560 Speaker 5: Say, skirting the rules around acquisitions by doing this end 100 00:04:45,560 --> 00:04:47,480 Speaker 5: mass hiring folks. 101 00:04:47,520 --> 00:04:49,520 Speaker 2: One of the I think the real key advantages of 102 00:04:49,560 --> 00:04:53,799 Speaker 2: Bloomberg Intelligence is that they have access to the best 103 00:04:54,120 --> 00:04:58,279 Speaker 2: data for every industry that they cover in a technology space. 104 00:04:58,320 --> 00:04:59,120 Speaker 3: One of those data. 105 00:04:58,920 --> 00:05:03,000 Speaker 2: Providers Thatloomberg Intelligence has access to is id C. That 106 00:05:03,080 --> 00:05:07,120 Speaker 2: is the gold plated provider of really independent data on 107 00:05:07,440 --> 00:05:11,440 Speaker 2: tech spending, amongst other things a RAG. I know, you've 108 00:05:11,440 --> 00:05:13,320 Speaker 2: worked a lot with the IDC folks. You use your 109 00:05:13,720 --> 00:05:17,400 Speaker 2: the id data a lot in your research. What are 110 00:05:17,400 --> 00:05:19,799 Speaker 2: you getting, What are you sensing in terms of total 111 00:05:19,839 --> 00:05:24,240 Speaker 2: tech spend, in terms of what's incremental for AI and 112 00:05:24,279 --> 00:05:27,839 Speaker 2: maybe what's just kind of taking from one tech you know, 113 00:05:27,960 --> 00:05:28,839 Speaker 2: capex budget and. 114 00:05:28,800 --> 00:05:30,280 Speaker 3: Putting it into AI. What do we know? 115 00:05:31,240 --> 00:05:33,400 Speaker 5: Yeah, so at least this for this year, for twenty 116 00:05:33,440 --> 00:05:36,279 Speaker 5: twenty four. It's more of a reallocation at this point, 117 00:05:36,480 --> 00:05:39,000 Speaker 5: and you know, you have to really think you're long 118 00:05:39,040 --> 00:05:40,960 Speaker 5: and hard and say whether that's going to be the 119 00:05:41,000 --> 00:05:42,840 Speaker 5: same case in twenty five and twenty six. 120 00:05:43,240 --> 00:05:44,880 Speaker 6: We think things are going to improve. 121 00:05:45,440 --> 00:05:49,160 Speaker 5: Geopolitical conditions are not the best right now, so CIOs 122 00:05:49,440 --> 00:05:53,000 Speaker 5: or CFOs are not that excited about ramping up their 123 00:05:53,040 --> 00:05:55,920 Speaker 5: tech budgets. But we think tech as a percentagea of 124 00:05:55,960 --> 00:05:59,320 Speaker 5: global GDP is still a small portion. It's you know, 125 00:05:59,360 --> 00:06:01,880 Speaker 5: we talk about a lot, but it's not as big 126 00:06:01,960 --> 00:06:04,560 Speaker 5: as let's say, healthcare spending in the US, for example, 127 00:06:04,600 --> 00:06:05,960 Speaker 5: when you compare both the numbers. 128 00:06:06,160 --> 00:06:07,400 Speaker 6: So we really feel. 129 00:06:07,120 --> 00:06:10,320 Speaker 5: That, I mean, there are enough you could say, legs 130 00:06:10,320 --> 00:06:14,000 Speaker 5: to this particular story out now. We think it's going 131 00:06:14,040 --> 00:06:16,560 Speaker 5: to be in the form of more software spending over 132 00:06:16,600 --> 00:06:20,560 Speaker 5: the next several years than services spending. Hardware is doing well, 133 00:06:20,600 --> 00:06:22,680 Speaker 5: So I think that may continue or that may not. 134 00:06:22,800 --> 00:06:25,440 Speaker 5: That it's a big question, but we think we think 135 00:06:25,480 --> 00:06:27,480 Speaker 5: it's going to be incremental spent coming in the next 136 00:06:27,480 --> 00:06:28,080 Speaker 5: few years. 137 00:06:28,200 --> 00:06:32,720 Speaker 4: The expectations are really high for technology. What if they 138 00:06:32,760 --> 00:06:36,360 Speaker 4: don't meet the expectations, What's what's in the unexpected bin 139 00:06:36,440 --> 00:06:38,600 Speaker 4: at this point and the potential downside. 140 00:06:39,320 --> 00:06:42,920 Speaker 5: Yeah, you see, you talked about two or three names, 141 00:06:42,920 --> 00:06:45,839 Speaker 5: you know, Nvidia, Apple, and Microsoft. But if you look 142 00:06:45,880 --> 00:06:48,320 Speaker 5: at other companies, let's say, if you were to pull 143 00:06:48,320 --> 00:06:51,960 Speaker 5: a chart of salesforce or workday or accenture or you 144 00:06:52,000 --> 00:06:54,919 Speaker 5: know tcs, they've all been going down or they have 145 00:06:55,000 --> 00:06:58,000 Speaker 5: not seen the same benefit coming through. And that's where 146 00:06:58,080 --> 00:07:01,400 Speaker 5: the allocation is being taken away from and going into 147 00:07:01,440 --> 00:07:04,080 Speaker 5: the hardware areas. So those are the companies that you 148 00:07:04,120 --> 00:07:07,479 Speaker 5: know could see slight improvement next year. The sentiment is 149 00:07:07,520 --> 00:07:10,560 Speaker 5: really bad in the software arena right now because of 150 00:07:10,600 --> 00:07:12,680 Speaker 5: the lack of that spending. I think you know that 151 00:07:12,760 --> 00:07:16,160 Speaker 5: may change next year if economic conditions improve, or I 152 00:07:16,160 --> 00:07:18,720 Speaker 5: would say geopolitical conditions improve. 153 00:07:19,080 --> 00:07:20,760 Speaker 2: All right, Don Rod, thanks so much for joining us 154 00:07:20,760 --> 00:07:23,360 Speaker 2: on Rod Rana. He's a senior technology channels for Bloomberg 155 00:07:23,440 --> 00:07:27,960 Speaker 2: Intelligence based in that burgeoning tech hub of Chicago, Illinois. 156 00:07:28,240 --> 00:07:31,440 Speaker 2: We appreciate him checking in there. So even HPE, you 157 00:07:31,440 --> 00:07:35,280 Speaker 2: think about HP Hewlett Packard Enterprises, they actually came out 158 00:07:35,280 --> 00:07:39,120 Speaker 2: with numbers yesterday were really strong and they cited AI servers. 159 00:07:39,160 --> 00:07:42,760 Speaker 2: So there's a company like Dell. You think Dell PC's right. 160 00:07:42,800 --> 00:07:46,320 Speaker 2: You think HP PCs printer stuff like that. But they're 161 00:07:46,320 --> 00:07:48,760 Speaker 2: doing these servers that are being used by these AI 162 00:07:48,920 --> 00:07:51,720 Speaker 2: folks and boomits moving their numbers. 163 00:07:51,920 --> 00:07:53,720 Speaker 4: Are you saying, well you have to do is attach 164 00:07:53,840 --> 00:07:56,080 Speaker 4: AI something else one of your products? 165 00:07:56,080 --> 00:07:57,520 Speaker 2: Well, one of the one of the things I'm thinking 166 00:07:57,560 --> 00:07:59,560 Speaker 2: about when you think about AI, what we have not 167 00:07:59,760 --> 00:08:03,480 Speaker 2: had like we had with Google and you know, kind 168 00:08:03,520 --> 00:08:05,600 Speaker 2: of the Internet and all that kind of stuff. Where's 169 00:08:05,600 --> 00:08:10,239 Speaker 2: that big IPO company that is the I AI ipo? 170 00:08:10,840 --> 00:08:12,720 Speaker 3: Like Google was, Oh how do I play the Internet? Oh? 171 00:08:12,760 --> 00:08:15,080 Speaker 3: You go buy this Google thing or you buy Yahoo. 172 00:08:15,160 --> 00:08:17,560 Speaker 2: Back in the day, you know, there isn't that IPO 173 00:08:17,640 --> 00:08:20,280 Speaker 2: that's coming out and everybody says, oh, this is the 174 00:08:20,520 --> 00:08:21,280 Speaker 2: AI play. 175 00:08:21,680 --> 00:08:25,280 Speaker 4: Well, I think startups and where the angel investors are 176 00:08:25,280 --> 00:08:27,960 Speaker 4: putting money right now, who's out there you've never heard of? 177 00:08:28,160 --> 00:08:28,320 Speaker 8: Right? 178 00:08:28,440 --> 00:08:29,960 Speaker 2: That's kind of where you know, I'm looking to the 179 00:08:30,040 --> 00:08:31,520 Speaker 2: dan Ives of the world to say, hey, where's that 180 00:08:31,600 --> 00:08:33,760 Speaker 2: next company that's going to come public that's going to 181 00:08:33,800 --> 00:08:34,760 Speaker 2: be the AI play. 182 00:08:36,320 --> 00:08:40,199 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 183 00:08:40,280 --> 00:08:43,800 Speaker 1: weekdays at ten am Eastern on applecard Play and Android 184 00:08:43,840 --> 00:08:46,600 Speaker 1: Otto with the Bloomberg Business App. You can also listen 185 00:08:46,720 --> 00:08:49,800 Speaker 1: live on Amazon Alexa from our flagship New York station. 186 00:08:50,200 --> 00:08:53,679 Speaker 1: Just say Alexa play Bloomberg eleven thirty. 187 00:08:54,640 --> 00:08:57,440 Speaker 3: John Tucker sitting in for Alex Steele. Paul Swinge. 188 00:08:57,480 --> 00:09:00,120 Speaker 2: You're live here in a Bloomberg Interactive Brooker's studio so 189 00:09:00,559 --> 00:09:02,400 Speaker 2: in New York City. Were streaming live on YouTube as well. 190 00:09:02,440 --> 00:09:04,400 Speaker 2: So check us out there on Bloomberg Podcast. Is how 191 00:09:04,400 --> 00:09:06,840 Speaker 2: you search for us there? As least was just reporting 192 00:09:06,880 --> 00:09:10,839 Speaker 2: Lululemon has what Bloomberg first ward is saying, Lululemon jumps 193 00:09:10,840 --> 00:09:13,960 Speaker 2: on quote better than feared ONEQBE reports. I think the 194 00:09:13,960 --> 00:09:17,520 Speaker 2: expectations a little bit low there, but the stock's ratling 195 00:09:17,559 --> 00:09:20,320 Speaker 2: about two and a half three percent here today. But 196 00:09:20,960 --> 00:09:24,160 Speaker 2: the shares of Lululemon are down thirty eight percent year 197 00:09:24,440 --> 00:09:26,840 Speaker 2: to date. So let's figure out what's going on there 198 00:09:26,840 --> 00:09:30,520 Speaker 2: in Lululemon. Again, I'm a big fan, but it's interesting 199 00:09:30,520 --> 00:09:32,920 Speaker 2: the stock has really been under pressure. Punhum Goyle, she's 200 00:09:32,920 --> 00:09:35,920 Speaker 2: a senior US retail analyst for Bloomberg Intelligence. She joins 201 00:09:35,960 --> 00:09:39,360 Speaker 2: us via zoom from Princeton, New Jersey. Punham, what did 202 00:09:39,400 --> 00:09:43,000 Speaker 2: you see in Lululemon's earnings and what does it tell 203 00:09:43,000 --> 00:09:44,360 Speaker 2: you about maybe the retail consumer. 204 00:09:45,400 --> 00:09:47,600 Speaker 8: Sure, So I think the earnings are better than expected 205 00:09:47,640 --> 00:09:50,160 Speaker 8: because well, we do see slow in growth at Lululemon, 206 00:09:50,160 --> 00:09:54,040 Speaker 8: where it's decelerating from the mid teens to low double 207 00:09:54,040 --> 00:09:57,760 Speaker 8: digits near ten percent. There is concern that is that 208 00:09:57,880 --> 00:10:01,080 Speaker 8: because of a slower macro and the fact that Lulu 209 00:10:01,160 --> 00:10:04,559 Speaker 8: Lemon is losing out to competition Alo Yoga, et cetera. 210 00:10:04,960 --> 00:10:07,600 Speaker 8: We discovered that part of that is true, but part 211 00:10:07,600 --> 00:10:09,440 Speaker 8: of that is not true, and I think that's why 212 00:10:09,480 --> 00:10:11,360 Speaker 8: investors have a little more confidence. 213 00:10:12,640 --> 00:10:15,199 Speaker 4: How big is the US market for them? And are 214 00:10:15,200 --> 00:10:17,040 Speaker 4: they selling overseas and how much? 215 00:10:17,960 --> 00:10:20,760 Speaker 8: Yeah, so the US market the America is eighty percent 216 00:10:20,800 --> 00:10:24,440 Speaker 8: of their business. International is twenty percent, with China half 217 00:10:24,480 --> 00:10:24,720 Speaker 8: of that. 218 00:10:26,200 --> 00:10:28,800 Speaker 4: And is demand picking up or slowing in China or 219 00:10:28,840 --> 00:10:30,880 Speaker 4: is it new to the Lulu Lemon market. 220 00:10:31,760 --> 00:10:35,240 Speaker 8: So international was a bright spot this quarter. International sales 221 00:10:35,320 --> 00:10:39,640 Speaker 8: grew about thirty five percent, and within that, China was 222 00:10:39,720 --> 00:10:42,480 Speaker 8: up Mainland China was up forty five percent, So a 223 00:10:42,520 --> 00:10:45,520 Speaker 8: lot of growth there. It's a bright spot for lul Lemon. 224 00:10:45,600 --> 00:10:48,680 Speaker 8: International is part of their three pronged strategy to double 225 00:10:48,720 --> 00:10:52,920 Speaker 8: sales into twenty twenty seven. It's been doing very very 226 00:10:52,920 --> 00:10:54,680 Speaker 8: well and we expect that to continue. 227 00:10:55,240 --> 00:10:55,600 Speaker 3: All right. 228 00:10:55,800 --> 00:10:58,560 Speaker 2: So and folks, if you want the best research on 229 00:10:59,040 --> 00:11:01,840 Speaker 2: the street on Lululan, Lululemon just gonna be I go 230 00:11:02,080 --> 00:11:04,120 Speaker 2: and you can find Punam's research there. 231 00:11:04,160 --> 00:11:05,600 Speaker 3: And I'm looking at your research here. Punham. 232 00:11:06,200 --> 00:11:08,280 Speaker 2: You're talking about you know, when you talk about that 233 00:11:08,360 --> 00:11:11,120 Speaker 2: doubling sales to twelve and a half million, you know 234 00:11:11,120 --> 00:11:14,320 Speaker 2: by twenty twenty six the company, and you're saying strong 235 00:11:14,400 --> 00:11:17,679 Speaker 2: international growth. Okay, I get that men's growth. Talk to 236 00:11:17,720 --> 00:11:19,800 Speaker 2: us about Lululemon and men's I mean, it's just not 237 00:11:20,400 --> 00:11:21,600 Speaker 2: John and I don't know where to go with this. 238 00:11:21,679 --> 00:11:23,480 Speaker 4: Do real men go to Lululemon? 239 00:11:24,400 --> 00:11:28,240 Speaker 8: Is what it's. Real men are going to Lululemon. You know, 240 00:11:28,280 --> 00:11:30,800 Speaker 8: it's one of their bright spots right now. And they 241 00:11:30,960 --> 00:11:33,800 Speaker 8: actually think that men's could become overtime. I think you'll say, 242 00:11:33,880 --> 00:11:36,640 Speaker 8: take some time fifty percent of the business. So there's 243 00:11:36,800 --> 00:11:39,240 Speaker 8: a lot of opportunity in men's. And you know when 244 00:11:39,280 --> 00:11:41,679 Speaker 8: you walk those stores and I looked at the men's inventory, 245 00:11:41,760 --> 00:11:44,920 Speaker 8: it actually looks great. It's not just about outfitting the 246 00:11:44,960 --> 00:11:47,520 Speaker 8: men to go to the gym. It's about outfitting the 247 00:11:47,559 --> 00:11:51,880 Speaker 8: men to go into work with casual, comfortable where that they. 248 00:11:51,760 --> 00:11:56,000 Speaker 4: Can where everybody who listens to this program of views 249 00:11:56,000 --> 00:12:00,360 Speaker 4: this on YouTube knows Paul is the company fashion police. Yes, 250 00:12:00,440 --> 00:12:03,600 Speaker 4: if you saw somebody in like Lululemon come into the office, 251 00:12:04,080 --> 00:12:04,640 Speaker 4: might have a. 252 00:12:04,600 --> 00:12:05,880 Speaker 3: Film, it might be a problem. 253 00:12:05,880 --> 00:12:07,880 Speaker 2: But I'm going to refer to Punum because she knows 254 00:12:07,960 --> 00:12:11,360 Speaker 2: fashion here and she knows what works. So if I'm 255 00:12:11,440 --> 00:12:15,160 Speaker 2: Lulu Lemon, talk to us just about what they're saying 256 00:12:15,200 --> 00:12:18,160 Speaker 2: about the consumer out there, Punham. 257 00:12:18,520 --> 00:12:20,600 Speaker 8: So the consumer, We've been talking about this for a 258 00:12:20,640 --> 00:12:24,360 Speaker 8: while now. The consumer is selective. They're choosing where to shop, 259 00:12:24,520 --> 00:12:26,840 Speaker 8: when to shop, how to shop, and if they see 260 00:12:26,880 --> 00:12:29,360 Speaker 8: the right product, they are still purchasing it. The one 261 00:12:29,360 --> 00:12:32,080 Speaker 8: thing that struck out during the call was that traffic 262 00:12:32,280 --> 00:12:35,079 Speaker 8: was up in the quarter, which means people are still 263 00:12:35,120 --> 00:12:38,040 Speaker 8: going to Lulu Lemon. They still have purchased intent. But 264 00:12:38,320 --> 00:12:41,160 Speaker 8: there were some product missteps which they're working to correct 265 00:12:41,160 --> 00:12:43,560 Speaker 8: into the second half of the year, where they didn't 266 00:12:43,600 --> 00:12:45,880 Speaker 8: have the right colors, or they didn't have enough colors. 267 00:12:45,880 --> 00:12:48,600 Speaker 8: I should say, they didn't have enough depth and sizes. 268 00:12:48,640 --> 00:12:50,640 Speaker 8: They were running out of the small sizes. So these 269 00:12:50,679 --> 00:12:53,200 Speaker 8: are problems that I would think are execution related and 270 00:12:53,400 --> 00:12:55,760 Speaker 8: are being fixed and can be fixed into the back half. 271 00:12:56,080 --> 00:12:58,520 Speaker 8: So while there is still part of a macro problem, 272 00:12:58,800 --> 00:13:02,000 Speaker 8: the story isn't that Lemon people are not going to 273 00:13:02,040 --> 00:13:04,439 Speaker 8: shop there. People still are interested in the apparel. They 274 00:13:04,480 --> 00:13:07,440 Speaker 8: just have to fix the product a little and really 275 00:13:07,440 --> 00:13:09,280 Speaker 8: improve our execution into the back half. 276 00:13:09,600 --> 00:13:12,640 Speaker 4: They're known for form fitting, but just to get back 277 00:13:12,640 --> 00:13:15,720 Speaker 4: to the men's fashion, they have anything for old fat guys. 278 00:13:17,320 --> 00:13:21,160 Speaker 8: Well, their ABC men's franchise is really popular and it's 279 00:13:21,240 --> 00:13:24,920 Speaker 8: not form fitting. It's about the casual flare and they 280 00:13:24,960 --> 00:13:28,080 Speaker 8: have done very well there. And you don't need to 281 00:13:28,080 --> 00:13:30,960 Speaker 8: wear tight leggings for men's that's not the appeal there. 282 00:13:31,000 --> 00:13:33,559 Speaker 8: That's for the women. But I'd say even for the woman, 283 00:13:33,600 --> 00:13:35,760 Speaker 8: you know what worked really well in the corner where 284 00:13:35,760 --> 00:13:38,920 Speaker 8: they're loose fitting pants and their biker shorts. So yes, 285 00:13:39,000 --> 00:13:41,600 Speaker 8: leggings is a big business for them, but they are 286 00:13:41,640 --> 00:13:42,240 Speaker 8: broadening that. 287 00:13:43,320 --> 00:13:44,800 Speaker 3: Hey, let's switch gears just a little bit. 288 00:13:44,800 --> 00:13:47,040 Speaker 2: I saw it yesterday reporting in a Wall Street journal 289 00:13:47,040 --> 00:13:51,319 Speaker 2: Punum Dollar Tree is among the potential sales spin off 290 00:13:51,320 --> 00:13:52,240 Speaker 2: of Family Dollar. 291 00:13:52,440 --> 00:13:54,960 Speaker 3: What is going on with the dollar stores here. 292 00:13:55,080 --> 00:13:57,880 Speaker 2: I would have thought that if a part of the 293 00:13:57,880 --> 00:14:01,439 Speaker 2: consumer market is weak and the low end economically, that. 294 00:14:01,440 --> 00:14:04,600 Speaker 3: Perhaps that would be good for the dollar stores. But 295 00:14:04,960 --> 00:14:05,920 Speaker 3: what's going on there? 296 00:14:06,679 --> 00:14:10,160 Speaker 8: So they're really two very different types of dollar stores. 297 00:14:10,240 --> 00:14:13,880 Speaker 8: Gen Bartashus covers the space, but from my coverage over 298 00:14:14,080 --> 00:14:17,440 Speaker 8: years ago, I can tell you that Dollar Tree targets everyone, right. 299 00:14:17,520 --> 00:14:19,360 Speaker 8: I would shop at Dollar Tree, Paul. You would shop 300 00:14:19,400 --> 00:14:21,760 Speaker 8: a Dollar Tree. You buy that one twenty five item 301 00:14:21,840 --> 00:14:25,840 Speaker 8: there and you go treasure hunt. But a family Dollar customer, 302 00:14:25,960 --> 00:14:28,960 Speaker 8: that's their principal discount store. That is where they buy 303 00:14:29,000 --> 00:14:31,040 Speaker 8: their groceries, that is where they buy their day to 304 00:14:31,120 --> 00:14:34,440 Speaker 8: day needs. So as that customer's wallet is being stretched, 305 00:14:34,760 --> 00:14:36,920 Speaker 8: that business does come into more pressure. 306 00:14:37,920 --> 00:14:40,880 Speaker 4: I'm going to tell you my Dollar Tree story. I 307 00:14:40,920 --> 00:14:43,160 Speaker 4: bought a loaf of bread I brought to the counter 308 00:14:43,600 --> 00:14:48,640 Speaker 4: and the casher said, sir, you don't want that. Apparently 309 00:14:48,640 --> 00:14:51,040 Speaker 4: they had some sort of rat problem. I didn't know 310 00:14:51,080 --> 00:14:51,520 Speaker 4: about it. 311 00:14:53,360 --> 00:14:55,680 Speaker 3: All right, Punam, thank you so much for joining us there. 312 00:14:55,800 --> 00:14:58,520 Speaker 2: Putnam Goyle, Senior US e Commerce and retail Anams Bloomberg 313 00:14:58,560 --> 00:15:02,200 Speaker 2: Intelligence give us a breakdown on Lululemon again, some better 314 00:15:02,240 --> 00:15:04,800 Speaker 2: than feared or results, and n actually lifted their guidance 315 00:15:04,840 --> 00:15:07,360 Speaker 2: a little bit here. So some good news there for 316 00:15:07,440 --> 00:15:09,440 Speaker 2: Lululemon stocks up about two and a half percent, but 317 00:15:09,480 --> 00:15:13,200 Speaker 2: it's down pretty big this year, concerns about growth. 318 00:15:13,200 --> 00:15:13,440 Speaker 4: There. 319 00:15:15,800 --> 00:15:19,680 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 320 00:15:19,760 --> 00:15:22,840 Speaker 1: weekdays at ten am Eastern on Apple Car playing Android 321 00:15:22,880 --> 00:15:25,960 Speaker 1: otto with the Bloomberg Business app. Listen on demand wherever 322 00:15:26,040 --> 00:15:29,880 Speaker 1: you get your podcasts, or watch us live on YouTube. 323 00:15:31,120 --> 00:15:33,840 Speaker 2: If you look at the equal weighted standard and Poor's 324 00:15:33,880 --> 00:15:36,800 Speaker 2: index of about five percent, the question is kind of 325 00:15:36,840 --> 00:15:38,440 Speaker 2: where do we go from here with We've got a 326 00:15:38,480 --> 00:15:40,280 Speaker 2: FED that appears at. 327 00:15:40,240 --> 00:15:42,480 Speaker 3: Some point it's going to be cutting rates. You got 328 00:15:42,520 --> 00:15:43,000 Speaker 3: that on one hand. 329 00:15:43,040 --> 00:15:45,800 Speaker 2: The other hand, you've got decent earnings growth That kind 330 00:15:45,800 --> 00:15:48,240 Speaker 2: of seems pretty constructive here in the marketplace. Let's check 331 00:15:48,280 --> 00:15:50,200 Speaker 2: in with somebody who does this stuff for a living. 332 00:15:50,280 --> 00:15:52,960 Speaker 2: Nicole Webbs. She's the senior vice president. She's a financial 333 00:15:52,960 --> 00:15:56,760 Speaker 2: advisor at Wealth Enhancement Group, joining us via that Zoom thing. 334 00:15:57,080 --> 00:15:58,840 Speaker 2: So Nicole, is you sit down and talk to your 335 00:15:58,840 --> 00:16:02,320 Speaker 2: clients here today. They've had a pretty good start to 336 00:16:02,360 --> 00:16:04,760 Speaker 2: the year by and large. What are you telling them 337 00:16:04,760 --> 00:16:06,160 Speaker 2: about the back half of this year. 338 00:16:07,160 --> 00:16:09,160 Speaker 9: Yeah, you know, I think the back half is a 339 00:16:09,160 --> 00:16:13,880 Speaker 9: little bit to be determined, But in the interim, we 340 00:16:13,960 --> 00:16:16,240 Speaker 9: expect it to be pretty choppy. And I say that, 341 00:16:16,720 --> 00:16:19,160 Speaker 9: you know, for a myriad of reasons. You know, I 342 00:16:19,240 --> 00:16:22,320 Speaker 9: think the first being, there was so much expectation that 343 00:16:22,600 --> 00:16:25,680 Speaker 9: entering this year and your lead in was so perfect, 344 00:16:26,080 --> 00:16:28,280 Speaker 9: was that you know, we're going to see this broadening 345 00:16:28,400 --> 00:16:31,880 Speaker 9: out in the market, a breath that would come through 346 00:16:31,880 --> 00:16:34,800 Speaker 9: the first two quarters of twenty twenty four, and that's 347 00:16:34,840 --> 00:16:37,440 Speaker 9: not really been the case. And actually over the last 348 00:16:37,480 --> 00:16:40,560 Speaker 9: couple of weeks which we've seen is less breath and 349 00:16:40,840 --> 00:16:44,720 Speaker 9: more leadership in mega technology again, and so you know, 350 00:16:45,120 --> 00:16:50,120 Speaker 9: probably to get that next stickiness the next run from here, 351 00:16:50,640 --> 00:16:53,840 Speaker 9: one would expect that we need more confidence throughout the 352 00:16:53,880 --> 00:16:57,840 Speaker 9: cap spectrum, not just concentrated in technology. And then you 353 00:16:57,880 --> 00:16:59,560 Speaker 9: couple that with the fact that we've had a lot 354 00:16:59,560 --> 00:17:01,960 Speaker 9: of interest rate volatility. I mean, really even in the 355 00:17:02,040 --> 00:17:06,680 Speaker 9: last five, six, seven trading days, the tenure has moved 356 00:17:06,680 --> 00:17:09,600 Speaker 9: a lot, and so you know, there's there's some underpinnings 357 00:17:09,600 --> 00:17:13,080 Speaker 9: to this market right now that we expect to keep 358 00:17:13,119 --> 00:17:15,320 Speaker 9: it choppy for the coming weeks ahead. 359 00:17:15,840 --> 00:17:19,640 Speaker 4: The data that we've been getting, does that show slowing 360 00:17:19,680 --> 00:17:22,840 Speaker 4: that indicates, uh, interest rate cut that's going to be 361 00:17:22,880 --> 00:17:26,320 Speaker 4: good for equities, or does it show slowing of the 362 00:17:26,400 --> 00:17:28,960 Speaker 4: economy that's going to be bad for corporate profits. 363 00:17:30,480 --> 00:17:35,360 Speaker 9: You know, our stance on cuts is that it kind 364 00:17:35,359 --> 00:17:38,399 Speaker 9: of circles back to the ECB today, which I I 365 00:17:38,480 --> 00:17:42,200 Speaker 9: loved that their words were a bit hawkish in tone, 366 00:17:42,800 --> 00:17:46,440 Speaker 9: but their actions were that they want to see rate stabilize. 367 00:17:47,800 --> 00:17:50,280 Speaker 4: Yeah, they ec be cut, but at the same time 368 00:17:50,359 --> 00:17:54,800 Speaker 4: Lagarn also said there were inflation concerns or if she 369 00:17:54,880 --> 00:17:57,119 Speaker 4: doesn't know how close they are to the neutral radar 370 00:17:57,280 --> 00:17:59,600 Speaker 4: words to that effect exactly. 371 00:17:59,680 --> 00:18:01,720 Speaker 9: And so you know, I think as we keep seeing 372 00:18:01,760 --> 00:18:05,440 Speaker 9: economic data that is in support of you know, many 373 00:18:05,520 --> 00:18:08,840 Speaker 9: consumers that are slowing, but then many who are benefiting 374 00:18:08,880 --> 00:18:12,040 Speaker 9: from the run in the markets also clipping five percent 375 00:18:12,119 --> 00:18:15,520 Speaker 9: coupons on risk free assets, and you kind of need 376 00:18:15,560 --> 00:18:18,560 Speaker 9: to start hearing a tone from our FED that we 377 00:18:18,680 --> 00:18:22,200 Speaker 9: need to start the process of normalization of rates because 378 00:18:22,200 --> 00:18:27,120 Speaker 9: there are certain elements of the inflationary trends around us, 379 00:18:27,119 --> 00:18:31,200 Speaker 9: some of these pockets of stickiness and service, the undersupply 380 00:18:31,320 --> 00:18:35,800 Speaker 9: in housing that simply probably can't be fixed by leaving 381 00:18:35,960 --> 00:18:38,399 Speaker 9: rates as restrictive as they are. And so, you know, 382 00:18:38,440 --> 00:18:40,320 Speaker 9: I think it was a really balanced message from the 383 00:18:40,320 --> 00:18:43,960 Speaker 9: ECB and one that we would love to see, you know, 384 00:18:44,040 --> 00:18:47,000 Speaker 9: the Fed here take more of that stance on. You 385 00:18:47,040 --> 00:18:52,000 Speaker 9: can remain hawkish in your tone, but you can start 386 00:18:52,040 --> 00:18:56,119 Speaker 9: to move towards normalization and do it at a slow 387 00:18:56,119 --> 00:18:59,760 Speaker 9: and steady pace, versus continuing this narrative of going into 388 00:18:59,760 --> 00:19:03,040 Speaker 9: a cutting cycle, which you know, sounds very different. 389 00:19:03,080 --> 00:19:06,000 Speaker 10: It sounds like fixing a new problem. 390 00:19:06,680 --> 00:19:09,440 Speaker 3: So Nicole, talk to us about valuation here. 391 00:19:09,440 --> 00:19:11,160 Speaker 2: I'm looking at kind of s and P earnings growth 392 00:19:11,280 --> 00:19:14,639 Speaker 2: kind of ten eleven maybe twelve percent over the you know, 393 00:19:14,760 --> 00:19:17,760 Speaker 2: next year or so. Is that enough to support this 394 00:19:17,880 --> 00:19:20,199 Speaker 2: valuation in the market or do you feel like you 395 00:19:20,280 --> 00:19:22,359 Speaker 2: need to maybe get a little defensive in valuation. 396 00:19:25,040 --> 00:19:25,280 Speaker 10: There? 397 00:19:25,320 --> 00:19:30,400 Speaker 9: You know, there's there's this convergence of themes, one of 398 00:19:30,440 --> 00:19:35,080 Speaker 9: them being we went in to the year with earning expectation, 399 00:19:35,680 --> 00:19:42,320 Speaker 9: you know, consensus among analysts. Generally, you see analysts start 400 00:19:42,320 --> 00:19:45,120 Speaker 9: to cut those earnings forecasts, which in the first month 401 00:19:45,200 --> 00:19:47,080 Speaker 9: or two we did, and then we've seen a steady 402 00:19:47,200 --> 00:19:50,400 Speaker 9: increase in those earnings expectations and where we think this 403 00:19:50,440 --> 00:19:53,440 Speaker 9: market is going to take us. You know, again, though, 404 00:19:53,440 --> 00:19:56,560 Speaker 9: I go back to the earlier comment, which is we 405 00:19:56,680 --> 00:20:00,239 Speaker 9: need more than just the support of mega technology or 406 00:20:00,240 --> 00:20:04,359 Speaker 9: that next leg or the next wave of this bull market, 407 00:20:04,520 --> 00:20:06,280 Speaker 9: or to have more confidence in it. 408 00:20:06,880 --> 00:20:08,159 Speaker 10: But at the same time. 409 00:20:08,160 --> 00:20:12,320 Speaker 9: It's pretty clear that you need a lot of free 410 00:20:12,359 --> 00:20:14,800 Speaker 9: cash flow, that there's going to be a huge capex 411 00:20:14,840 --> 00:20:20,439 Speaker 9: expenditure over the next many years to build the new infrastructure, 412 00:20:20,520 --> 00:20:26,000 Speaker 9: to build the AI factories, and that the megatech companies 413 00:20:26,160 --> 00:20:29,119 Speaker 9: are the ones who have the cash flow and the 414 00:20:29,320 --> 00:20:33,320 Speaker 9: access to do that. And so you know, aside from 415 00:20:33,480 --> 00:20:37,520 Speaker 9: more government intervention or more fiscal money put at building 416 00:20:37,600 --> 00:20:41,720 Speaker 9: out this infrastructure, we do believe that you're probably going 417 00:20:41,800 --> 00:20:45,760 Speaker 9: to continue to see multiples that are substantiated by both 418 00:20:45,800 --> 00:20:47,119 Speaker 9: the investment. 419 00:20:46,760 --> 00:20:50,000 Speaker 10: Towards future growth the breadth of that growth. 420 00:20:50,119 --> 00:20:54,080 Speaker 9: And then you know, also, though, when do you start 421 00:20:54,119 --> 00:20:59,720 Speaker 9: to see some of those productivity reshoring metrics for further 422 00:20:59,800 --> 00:21:02,840 Speaker 9: se access again down that cap spectrum within the s 423 00:21:02,880 --> 00:21:05,400 Speaker 9: and P five hundred, And then as we think even 424 00:21:05,440 --> 00:21:08,040 Speaker 9: further about the broadening out of the market, it really 425 00:21:08,080 --> 00:21:12,639 Speaker 9: does seem like until the FED starts to enter a 426 00:21:12,800 --> 00:21:16,240 Speaker 9: normalization narrative around interest rates that we're not going to 427 00:21:16,320 --> 00:21:19,760 Speaker 9: get that further support from mid and small cap, which 428 00:21:19,840 --> 00:21:23,040 Speaker 9: again has just been very volatile amongst you know, the 429 00:21:23,400 --> 00:21:28,320 Speaker 9: consumer data, the inflation data, and then again interest rate volatility. 430 00:21:28,640 --> 00:21:33,280 Speaker 4: Do you move beyond the big megacap stocks and maybe 431 00:21:33,280 --> 00:21:37,280 Speaker 4: into the derivatives of artificial intelligence? Do you have to 432 00:21:37,320 --> 00:21:40,440 Speaker 4: be more strategic in that area now? 433 00:21:40,960 --> 00:21:45,040 Speaker 9: Overall, you know, we think it's really important to think 434 00:21:45,119 --> 00:21:51,040 Speaker 9: about artificial intelligence, the build out of the infrastructure as 435 00:21:51,280 --> 00:21:52,560 Speaker 9: being future. 436 00:21:52,240 --> 00:21:54,639 Speaker 10: Table stakes among many companies. 437 00:21:54,680 --> 00:21:58,480 Speaker 9: So if we think, you know, of the Internet, at 438 00:21:58,480 --> 00:22:02,400 Speaker 9: one point it was incredibly interesting and now it's as 439 00:22:02,400 --> 00:22:04,520 Speaker 9: if you would never run a business without it. And 440 00:22:04,560 --> 00:22:07,360 Speaker 9: I know that is a little bit cliche and simplified, 441 00:22:07,359 --> 00:22:10,760 Speaker 9: but I do think you have to look beyond just 442 00:22:11,760 --> 00:22:17,160 Speaker 9: the productivity within each company and think more about end benefactors. 443 00:22:17,200 --> 00:22:21,200 Speaker 9: So right now there's so much onus put on the suppliers. 444 00:22:21,520 --> 00:22:23,960 Speaker 9: We've seen the pressure on software because there's a lot 445 00:22:24,000 --> 00:22:26,960 Speaker 9: of curiosity about you know, is that more of a 446 00:22:27,040 --> 00:22:31,399 Speaker 9: middleman that gets gets stripped out of this equation. But 447 00:22:31,560 --> 00:22:34,280 Speaker 9: you know, we think of and it's a very easy 448 00:22:34,280 --> 00:22:38,640 Speaker 9: example would be you know, John Deere, you know things 449 00:22:38,720 --> 00:22:45,760 Speaker 9: that become end benefactors in terms of access to you know, 450 00:22:45,920 --> 00:22:51,359 Speaker 9: robotic arms and the ability for artificial intelligence instead of 451 00:22:51,359 --> 00:22:52,960 Speaker 9: fertilizing entire fields. 452 00:22:53,080 --> 00:22:55,199 Speaker 4: It's the tractor that doesn't need a farmer. 453 00:22:56,400 --> 00:22:58,479 Speaker 10: It is the tractor that doesn't need a farmer. 454 00:22:58,560 --> 00:22:58,919 Speaker 9: Thank you. 455 00:22:58,920 --> 00:23:00,440 Speaker 10: You got there faster than I could. 456 00:23:00,480 --> 00:23:03,399 Speaker 9: And so you have to think about what are businesses 457 00:23:03,400 --> 00:23:08,000 Speaker 9: that are truly revolutionized and what businesses just become table 458 00:23:08,200 --> 00:23:12,440 Speaker 9: stakes owners of you know AI as part of its 459 00:23:12,640 --> 00:23:14,000 Speaker 9: operating technology. 460 00:23:14,760 --> 00:23:17,040 Speaker 2: Nicole, I'm guessing you probably get some phone calls from 461 00:23:17,040 --> 00:23:18,800 Speaker 2: your clients saying, hey, I can just buy a two 462 00:23:18,880 --> 00:23:21,240 Speaker 2: year treasury and get close to five percent here. That's 463 00:23:21,240 --> 00:23:23,159 Speaker 2: not a bad place to be. What do you tell 464 00:23:23,200 --> 00:23:24,960 Speaker 2: them about fixed income these days? 465 00:23:25,320 --> 00:23:30,680 Speaker 9: Yeah, you know, it is the I never thought that 466 00:23:31,200 --> 00:23:34,000 Speaker 9: I would spend so much of my time for consecutive 467 00:23:34,080 --> 00:23:35,359 Speaker 9: years talking about treasuries. 468 00:23:35,400 --> 00:23:37,760 Speaker 10: It is incredibly interesting. 469 00:23:37,800 --> 00:23:41,520 Speaker 9: And you know, I would say post the Great Financial Crisis, 470 00:23:41,760 --> 00:23:44,440 Speaker 9: we started to really change the way we ran financial 471 00:23:44,480 --> 00:23:48,000 Speaker 9: models because we sat in a zero percent interest rate 472 00:23:48,080 --> 00:23:51,200 Speaker 9: environment for so long, and effectively you had to think 473 00:23:51,240 --> 00:23:54,840 Speaker 9: about how you then modeled total return as a component 474 00:23:54,920 --> 00:23:59,040 Speaker 9: of that. And so now if you were running projections 475 00:23:59,040 --> 00:24:03,399 Speaker 9: at five six percent, maybe seven, but someone can clip 476 00:24:03,640 --> 00:24:06,840 Speaker 9: five point four on the short end, it becomes really 477 00:24:06,920 --> 00:24:10,359 Speaker 9: hard to get them to take equity premium risk over 478 00:24:10,800 --> 00:24:14,280 Speaker 9: that risk free rate. And so it's really a combination 479 00:24:14,680 --> 00:24:21,600 Speaker 9: of thinking about public markets, private markets, and then how 480 00:24:21,720 --> 00:24:25,480 Speaker 9: much duration are you going to build into your portfolio today? 481 00:24:27,000 --> 00:24:28,359 Speaker 9: You know how far out are you going to go? 482 00:24:28,400 --> 00:24:31,000 Speaker 9: Because for many the tax equivalent yield on a tenure 483 00:24:31,440 --> 00:24:36,240 Speaker 9: is still incredibly lucrative, and so that is becoming a 484 00:24:36,320 --> 00:24:38,560 Speaker 9: bigger part of the overall conversation. 485 00:24:39,119 --> 00:24:40,879 Speaker 2: All right, Nicole, thank you so much for joining us. 486 00:24:40,880 --> 00:24:43,359 Speaker 2: As always, Nicole Web She's a senior vice president financial 487 00:24:43,359 --> 00:24:46,960 Speaker 2: advisor at Wealth Enhancement Group. Joining us from New York 488 00:24:47,520 --> 00:24:48,720 Speaker 2: via zoom. 489 00:24:48,520 --> 00:24:53,520 Speaker 1: Here, you're listening to the Bloomberg Intelligence Podcast. Catch us 490 00:24:53,560 --> 00:24:56,960 Speaker 1: live weekdays at ten am Eastern on applecar Play and 491 00:24:56,960 --> 00:24:59,959 Speaker 1: Android Auto with the Bloomberg Business App. You can also 492 00:25:00,200 --> 00:25:03,480 Speaker 1: and live on Amazon Alexa from our flagship New York station, 493 00:25:03,840 --> 00:25:06,800 Speaker 1: Just Say Alexa, playing Bloomberg eleven thirty. 494 00:25:08,520 --> 00:25:09,320 Speaker 3: For the month of June. 495 00:25:09,440 --> 00:25:12,840 Speaker 2: Bloomberg Radio is committed to bringing new segments and guests 496 00:25:12,840 --> 00:25:16,080 Speaker 2: focused on the topic of equality. Today, we're speaking with 497 00:25:16,160 --> 00:25:19,600 Speaker 2: Norman chen CEO of the Asian American Foundation. He joins 498 00:25:19,680 --> 00:25:22,440 Speaker 2: us via zoom from San Francisco. Norman, thanks so much 499 00:25:22,440 --> 00:25:23,919 Speaker 2: for joining us here. I love to just start at 500 00:25:23,960 --> 00:25:26,960 Speaker 2: the really basics here. Tell us what you do at 501 00:25:26,960 --> 00:25:30,800 Speaker 2: the Asian American Foundation, what's the focus and how are 502 00:25:30,840 --> 00:25:32,640 Speaker 2: you guys impacting this environment. 503 00:25:33,640 --> 00:25:36,520 Speaker 11: Well, first, thanks for having me. The Asian American Foundation 504 00:25:36,680 --> 00:25:39,399 Speaker 11: is a three year old organization that was formed at 505 00:25:39,440 --> 00:25:41,920 Speaker 11: the peak of anti Asian hate in twenty twenty one, 506 00:25:42,400 --> 00:25:45,480 Speaker 11: where we work together across the country, across all sectors 507 00:25:45,600 --> 00:25:48,280 Speaker 11: on safety, belonging, and prosperity for our community. 508 00:25:48,960 --> 00:25:52,480 Speaker 4: Does the China rhetoric, which in Washington has been focused 509 00:25:52,480 --> 00:25:58,080 Speaker 4: on technology and trade, does something as subtle as that 510 00:25:58,119 --> 00:26:01,560 Speaker 4: does that conversation need to to change or at least 511 00:26:01,680 --> 00:26:06,040 Speaker 4: acknowledge that it can result in what you see in 512 00:26:06,119 --> 00:26:13,399 Speaker 4: terms of hate speech and hate action against the AAPI community. 513 00:26:14,880 --> 00:26:17,600 Speaker 11: Yeah, language is really important, as we saw during COVID 514 00:26:17,880 --> 00:26:22,760 Speaker 11: rhetoric blaming China for COVID kung flu Wuhan virus severely 515 00:26:22,760 --> 00:26:25,720 Speaker 11: affected our community, leading to thousands and thousands of attacks 516 00:26:25,720 --> 00:26:29,280 Speaker 11: that continue today. While they are legitimate competitive issues with 517 00:26:29,400 --> 00:26:33,520 Speaker 11: China that need to be addressed. Inappropriate or inflammatory language 518 00:26:33,600 --> 00:26:36,639 Speaker 11: does lead to perceptions and misperceptions of our community that 519 00:26:36,800 --> 00:26:40,040 Speaker 11: continues to lead to feelings that Asian Americans are threats 520 00:26:40,160 --> 00:26:43,879 Speaker 11: in this country, that we're more loyal to Asia than 521 00:26:43,920 --> 00:26:46,560 Speaker 11: to the US, and other misunderstandings. 522 00:26:47,200 --> 00:26:51,040 Speaker 2: So, practically speaking, Norman, think you practically speaking Norman, how 523 00:26:51,080 --> 00:26:54,040 Speaker 2: do you guys go about trying to affect change? 524 00:26:54,080 --> 00:26:54,760 Speaker 6: How do you do that? 525 00:26:56,040 --> 00:26:59,640 Speaker 11: Yeah, we have four pillars to achieve safety belonging to prosperity. 526 00:27:00,119 --> 00:27:02,920 Speaker 11: Number one is to address the immediate hate and tax 527 00:27:02,960 --> 00:27:05,439 Speaker 11: against our community. And we work with a network of 528 00:27:05,480 --> 00:27:08,879 Speaker 11: fifty six national partners or partners across the country and 529 00:27:08,960 --> 00:27:12,480 Speaker 11: fourteen cities that provide care to victims and survivors that 530 00:27:12,520 --> 00:27:16,240 Speaker 11: have suffered these terrible attacks. So those partners are on 531 00:27:16,280 --> 00:27:20,480 Speaker 11: the ground. They provide medical, financial, legal support to those 532 00:27:20,480 --> 00:27:23,359 Speaker 11: who have been traumatized by these incidents. But longer term, 533 00:27:23,400 --> 00:27:26,480 Speaker 11: we work in education and narrative change. So we promote 534 00:27:26,520 --> 00:27:30,120 Speaker 11: the teaching of Asian American history in schools around the country, 535 00:27:30,440 --> 00:27:33,000 Speaker 11: and now seven states have mandated the teaching of Asian 536 00:27:33,000 --> 00:27:36,320 Speaker 11: American history because Asian American history is a critical part 537 00:27:36,480 --> 00:27:39,359 Speaker 11: of American history. And then on our Narrative Change piece, 538 00:27:39,400 --> 00:27:43,040 Speaker 11: we're working to tell more authentic stories about Asian Americans 539 00:27:43,040 --> 00:27:46,359 Speaker 11: and less the stereotypical kinds of stories. One survey that 540 00:27:46,400 --> 00:27:49,040 Speaker 11: we did as people around the country, can you name 541 00:27:49,119 --> 00:27:52,240 Speaker 11: a prominent or famous Asian American And for most people 542 00:27:52,240 --> 00:27:55,400 Speaker 11: in the country, they cannot, were largely invisible to them. 543 00:27:55,640 --> 00:27:57,560 Speaker 11: The number one name that has come up over the 544 00:27:57,600 --> 00:28:01,440 Speaker 11: past four years, over twenty thousand Peace book is Jackie Chan, 545 00:28:02,520 --> 00:28:04,800 Speaker 11: who is actually not even Asian American, He's from Hong Kong. 546 00:28:05,320 --> 00:28:07,080 Speaker 11: And then it's Bruce Lee. So you can see the 547 00:28:07,080 --> 00:28:12,040 Speaker 11: impact that movies and TV shows have on the general psyche, 548 00:28:12,440 --> 00:28:14,760 Speaker 11: and so that's really important to address that as well. 549 00:28:15,960 --> 00:28:18,720 Speaker 4: The Alan Mole shooting that was what a little more 550 00:28:18,760 --> 00:28:22,400 Speaker 4: than a year ago at this point, where Young targeted 551 00:28:22,760 --> 00:28:27,280 Speaker 4: Texas location because presumably, at least we're told by authorities 552 00:28:27,359 --> 00:28:31,879 Speaker 4: because of its aa PI population in Texas. Can you 553 00:28:31,880 --> 00:28:33,240 Speaker 4: tell us what the response has been. 554 00:28:34,640 --> 00:28:36,880 Speaker 11: Yeah, we have wonderful partners in Texas that are really 555 00:28:36,960 --> 00:28:40,080 Speaker 11: advocating for the teaching of Asian American history. They're also 556 00:28:40,120 --> 00:28:43,440 Speaker 11: fighting against land laws that prohibit Asian Americans from buying 557 00:28:43,480 --> 00:28:48,560 Speaker 11: property in Texas, so wonderful partners. There's a real activity 558 00:28:48,720 --> 00:28:51,640 Speaker 11: in Texas, and we also work with corporate partners that 559 00:28:51,640 --> 00:28:54,680 Speaker 11: are helping to promote Asian American programs in that state. 560 00:28:55,160 --> 00:28:57,400 Speaker 4: Yeah, you mentioned the property, but I believe there was 561 00:28:57,440 --> 00:29:01,000 Speaker 4: also a bill in Georgia that I think had the 562 00:29:01,480 --> 00:29:06,680 Speaker 4: backing of Governor Brian Kemp that would ban for Asian 563 00:29:06,720 --> 00:29:10,600 Speaker 4: foreign nationals Chinese foreign nationals from owning property in the state. 564 00:29:10,640 --> 00:29:11,960 Speaker 4: Can you give us an update on that. 565 00:29:13,040 --> 00:29:16,760 Speaker 11: Yeah, there's legislation and multiple states around the country. Unfortunately, 566 00:29:16,880 --> 00:29:19,320 Speaker 11: there has been fear mongering, as you pointed to earlier, 567 00:29:19,680 --> 00:29:24,080 Speaker 11: that people who are Chinese nationals shouldn't own property, and 568 00:29:24,120 --> 00:29:27,720 Speaker 11: they're trying to focus it around certain military or sensitive institutions. 569 00:29:28,320 --> 00:29:30,840 Speaker 11: While there are legitimate national security issues that need to 570 00:29:30,840 --> 00:29:35,440 Speaker 11: be addressed, this perception that Asian buyers Chinese buyers are 571 00:29:35,560 --> 00:29:39,480 Speaker 11: potentially a threat really affects the market, so that realtors 572 00:29:39,480 --> 00:29:42,280 Speaker 11: in these markets now are afraid or hesitant to bring 573 00:29:42,320 --> 00:29:46,040 Speaker 11: on Asian buyers, and so that really has a ripple 574 00:29:46,080 --> 00:29:49,680 Speaker 11: effect to the broader community. The sense that somehow Asians 575 00:29:49,720 --> 00:29:52,320 Speaker 11: are not loyal to America, that will provide and provide 576 00:29:52,400 --> 00:29:54,240 Speaker 11: risks to America, and we're really trying to fight that. 577 00:29:54,400 --> 00:29:57,320 Speaker 4: Yeah, you sound like we can go back to World 578 00:29:57,360 --> 00:30:03,200 Speaker 4: War two and Hawaii interment of Asian Americans exactly. 579 00:30:03,280 --> 00:30:06,120 Speaker 11: Yes, one hundred and twenty thousand Japanese Americans, seventy percent 580 00:30:06,160 --> 00:30:09,520 Speaker 11: who are citizens, were incarcerated for three years because people 581 00:30:09,520 --> 00:30:11,040 Speaker 11: thought that they were going to be disloyal, and it 582 00:30:11,040 --> 00:30:13,920 Speaker 11: turns out there was no proof at all during that 583 00:30:14,000 --> 00:30:16,959 Speaker 11: time that any of those people or any national security 584 00:30:17,040 --> 00:30:17,680 Speaker 11: risk of the country. 585 00:30:17,720 --> 00:30:21,200 Speaker 4: It's an unfortunate a long history of the United States. 586 00:30:21,720 --> 00:30:24,440 Speaker 2: Norman talk to us about is there a role for 587 00:30:24,920 --> 00:30:29,160 Speaker 2: you know, corporations, businesses and their leaders to kind of 588 00:30:29,200 --> 00:30:29,920 Speaker 2: get involved here. 589 00:30:29,960 --> 00:30:30,600 Speaker 3: What are we seeing? 590 00:30:31,000 --> 00:30:35,080 Speaker 11: Yeah, well, thanks for the question. So one very interesting 591 00:30:35,160 --> 00:30:37,920 Speaker 11: fact about TAP is that we were formed by prominent 592 00:30:38,000 --> 00:30:41,320 Speaker 11: Asian American business people such as Joe Tai, owner of 593 00:30:41,320 --> 00:30:46,480 Speaker 11: the Brooklyn Nets, Joe Bay co CEO of KKR, Jerry Yang, 594 00:30:46,560 --> 00:30:49,160 Speaker 11: co founder of Yahoo. So we have very much a 595 00:30:49,240 --> 00:30:53,040 Speaker 11: DNA with business background. And so one of the real 596 00:30:53,080 --> 00:30:56,200 Speaker 11: strengths of TAFF is that we are working across all sectors, 597 00:30:56,480 --> 00:30:59,440 Speaker 11: so not just nonprofit or philanthropy, but also across the 598 00:30:59,480 --> 00:31:04,600 Speaker 11: corporate sector, across government sector, and also with media and entertainment, 599 00:31:04,800 --> 00:31:07,600 Speaker 11: and in our gatherings around the country. We have such 600 00:31:07,640 --> 00:31:09,880 Speaker 11: a mix of people with who all want to support 601 00:31:09,880 --> 00:31:12,680 Speaker 11: our community but come at it from different backgrounds. So 602 00:31:12,720 --> 00:31:15,200 Speaker 11: our fourth pillar, which I didn't mention, is called representation 603 00:31:15,280 --> 00:31:17,320 Speaker 11: and resources, and a lot of that is focused on 604 00:31:17,360 --> 00:31:18,400 Speaker 11: our corporate partners. 605 00:31:18,600 --> 00:31:19,360 Speaker 3: So what does that mean. 606 00:31:19,760 --> 00:31:23,600 Speaker 11: We know from our Status Index research that feelings that 607 00:31:23,720 --> 00:31:25,680 Speaker 11: Asian Americans are the least likely to feel like we 608 00:31:25,720 --> 00:31:28,440 Speaker 11: truly belong in this country, and in fact, only eighteen 609 00:31:28,480 --> 00:31:32,080 Speaker 11: percent believe we truly belong and are accepted. Major reasons 610 00:31:32,080 --> 00:31:35,360 Speaker 11: for that are discrimination, racism. Clearly someone spits at you 611 00:31:35,440 --> 00:31:37,960 Speaker 11: or insult you, you don't feel very good, but also because 612 00:31:37,960 --> 00:31:40,360 Speaker 11: we don't see enough of our people in senior positions. 613 00:31:40,920 --> 00:31:44,200 Speaker 11: Asian Americans make up fifteen to thirty percent of workforces, 614 00:31:44,200 --> 00:31:47,280 Speaker 11: but only three percent of the c suite. Despite the 615 00:31:47,360 --> 00:31:50,480 Speaker 11: model minority myth which makes people believe that, oh, Asian 616 00:31:50,480 --> 00:31:55,120 Speaker 11: Americans are highly successful in business, it's not proportional across 617 00:31:55,160 --> 00:31:58,760 Speaker 11: the organization. So we're working with our corporate partners to 618 00:31:58,760 --> 00:32:02,520 Speaker 11: not only add and increase the presence of Asian Americans 619 00:32:02,560 --> 00:32:06,120 Speaker 11: on boards right at the c suite level, even among ergs, 620 00:32:06,200 --> 00:32:09,040 Speaker 11: but also working with our corporates to funnel more resources 621 00:32:09,280 --> 00:32:11,880 Speaker 11: to our community to make these changes. 622 00:32:12,080 --> 00:32:14,240 Speaker 2: Norman, thanks so much for joining us. Really appreciate getting 623 00:32:14,240 --> 00:32:16,640 Speaker 2: some of your time. Norman Chen, he's the CEO of 624 00:32:16,680 --> 00:32:18,240 Speaker 2: the Asian American Foundation. 625 00:32:18,920 --> 00:32:21,760 Speaker 3: Joining us via zoom from San Francisco. 626 00:32:24,320 --> 00:32:28,200 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 627 00:32:28,280 --> 00:32:31,800 Speaker 1: weekdays at ten am Eastern on applecard Play and Android 628 00:32:31,840 --> 00:32:34,600 Speaker 1: Auto with the Bloomberg Business app. You can also listen 629 00:32:34,720 --> 00:32:37,840 Speaker 1: live on Amazon Alexa from our flagship New York station, 630 00:32:38,200 --> 00:32:40,880 Speaker 1: Just say Alexa Play Bloomberg eleven. 631 00:32:40,680 --> 00:32:44,520 Speaker 2: Thirty John Tucker, He's sitting in for Alex Steel today 632 00:32:44,520 --> 00:32:45,200 Speaker 2: on Paul Sweeney. 633 00:32:45,200 --> 00:32:47,360 Speaker 3: We're live here on Bloomberg Interactive Brokers Studio. 634 00:32:47,400 --> 00:32:49,560 Speaker 2: We're also streaming on YouTube, so you can head over 635 00:32:49,600 --> 00:32:51,600 Speaker 2: to YouTube dot com and search Bloomberg Podcast. 636 00:32:52,240 --> 00:32:52,880 Speaker 3: John. I don't know. 637 00:32:52,960 --> 00:32:54,600 Speaker 2: I'm at the point of my life here. All the 638 00:32:54,680 --> 00:32:56,800 Speaker 2: kids are out of the house. I'm eating out more 639 00:32:57,000 --> 00:32:58,760 Speaker 2: in restaurants than they ever have, and maybe. 640 00:32:58,600 --> 00:33:00,800 Speaker 3: It's also a post COVID type of thing. 641 00:33:01,640 --> 00:33:04,040 Speaker 2: I don't know, restaurants I think are packed. I mean, 642 00:33:04,080 --> 00:33:06,040 Speaker 2: I think the restaurant business is doing pretty good. I 643 00:33:06,040 --> 00:33:07,560 Speaker 2: know there's different levels of well. 644 00:33:07,520 --> 00:33:11,280 Speaker 4: The restaurants you're going to are different than the restaurants 645 00:33:11,320 --> 00:33:13,600 Speaker 4: like somebody like me and Charlie Pellett go to. 646 00:33:13,800 --> 00:33:16,400 Speaker 2: You think I'll go into the squad tavern tonight, Pete's 647 00:33:16,400 --> 00:33:16,920 Speaker 2: and Beer Knight. 648 00:33:16,960 --> 00:33:17,880 Speaker 3: That's what I'm doing tonight. 649 00:33:17,880 --> 00:33:22,239 Speaker 2: But anyway, Barry Ridholts, he's got a piece out on 650 00:33:22,280 --> 00:33:23,920 Speaker 2: this whole restaurant thing. What does it mean as an 651 00:33:23,960 --> 00:33:26,040 Speaker 2: economic indicator? So someone in the touch base with Barry 652 00:33:26,080 --> 00:33:27,840 Speaker 2: and see what he's doing there. Barry Ridholts hosts of 653 00:33:27,880 --> 00:33:32,440 Speaker 2: Masters in Business on Bloomberg Radio. Fantastic podcast folks, if 654 00:33:32,440 --> 00:33:34,920 Speaker 2: you want a business podcast markets all that kind of stuff. 655 00:33:34,920 --> 00:33:38,080 Speaker 2: He gets the greatest guest. It's called a Master's in Business. 656 00:33:38,080 --> 00:33:39,640 Speaker 2: He's also got a day job. He's a founder of 657 00:33:39,680 --> 00:33:43,560 Speaker 2: Ridholts Wealth Management. He joins his via zoom. Barry, I 658 00:33:43,600 --> 00:33:45,400 Speaker 2: know you've been known to go to a restaurant or 659 00:33:45,480 --> 00:33:49,080 Speaker 2: to maybe not the kind that John Tucker goes to. 660 00:33:49,240 --> 00:33:52,680 Speaker 2: But how do you look at the restaurant business as 661 00:33:52,720 --> 00:33:54,600 Speaker 2: just an indicator of the economy? 662 00:33:55,320 --> 00:33:58,600 Speaker 12: These issues whenever you hear me complaining about some terrible 663 00:33:58,760 --> 00:34:02,480 Speaker 12: bit of economic and nowais or discussion. It's because some 664 00:34:02,600 --> 00:34:06,000 Speaker 12: clients said, Hey, I know you've been constructive on stocks 665 00:34:06,000 --> 00:34:08,760 Speaker 12: for the past two years, but look at this video 666 00:34:08,880 --> 00:34:12,000 Speaker 12: a friend just sent me. The restaurant industry is collapsing. 667 00:34:12,600 --> 00:34:15,359 Speaker 12: We have to get out of stocks. And you know, 668 00:34:15,560 --> 00:34:17,840 Speaker 12: I go look at the video, and not only is 669 00:34:17,880 --> 00:34:23,240 Speaker 12: it mostly incorrect, it violates every rule of statistical analysis. 670 00:34:23,840 --> 00:34:27,920 Speaker 12: It's filled with band information and denominator blindness, and so 671 00:34:28,680 --> 00:34:31,959 Speaker 12: it sends me down the rabbit hole of hey, what's 672 00:34:32,000 --> 00:34:34,640 Speaker 12: really happening in the world to restaurants. And as it 673 00:34:34,760 --> 00:34:40,080 Speaker 12: turns out, the restaurant industry is on target. In twenty 674 00:34:40,120 --> 00:34:44,280 Speaker 12: twenty four, it will be the US restaurant industry's biggest 675 00:34:44,400 --> 00:34:48,960 Speaker 12: year ever in sales one point one trillion dollars by 676 00:34:48,960 --> 00:34:52,200 Speaker 12: the end of December, and that's based on the first 677 00:34:52,239 --> 00:34:55,960 Speaker 12: half a year of actual sales and then estimates from 678 00:34:56,000 --> 00:34:59,520 Speaker 12: the Restaurant Association National Restaurant Association for the rest of 679 00:34:59,560 --> 00:35:03,000 Speaker 12: the year. Now, again, I'm a little skeptical when people 680 00:35:03,080 --> 00:35:07,520 Speaker 12: are forecasting a five point four percent increase in revenue, 681 00:35:07,920 --> 00:35:12,399 Speaker 12: maybe inflation adjusted. Some of that's right, but when they 682 00:35:12,480 --> 00:35:15,879 Speaker 12: survey restaurant operators, about a third of them said they're 683 00:35:15,920 --> 00:35:19,880 Speaker 12: expecting to see higher real sales for the full year, 684 00:35:20,400 --> 00:35:22,680 Speaker 12: and two thirds of them have said the restaurants in 685 00:35:22,719 --> 00:35:27,279 Speaker 12: their area have rebounded from the pandemic fully. So, you know, 686 00:35:27,400 --> 00:35:29,920 Speaker 12: it's taken a while for this sector the. 687 00:35:29,920 --> 00:35:32,040 Speaker 4: Economy to recover. 688 00:35:32,680 --> 00:35:36,000 Speaker 12: But man, you try and make reservations at a decent restaurant, 689 00:35:36,360 --> 00:35:38,960 Speaker 12: you'd better be looking out two or three weeks or 690 00:35:39,280 --> 00:35:40,120 Speaker 12: you're not going to get in. 691 00:35:40,280 --> 00:35:42,839 Speaker 4: It also depends on where you're located. Barry, I think 692 00:35:42,880 --> 00:35:45,759 Speaker 4: you did an interview with Bobby Flay some years ago 693 00:35:46,040 --> 00:35:49,560 Speaker 4: who was saying it's almost impossible for a restaurant owner 694 00:35:50,160 --> 00:35:52,520 Speaker 4: an individual to turn a profit in a place like 695 00:35:52,600 --> 00:35:54,800 Speaker 4: Manhattan because the rents are so high. 696 00:35:55,760 --> 00:35:59,360 Speaker 12: Right, Well, first of all, it's a very difficult business 697 00:35:59,400 --> 00:36:04,120 Speaker 12: no matter where you are. It's razor thin profits. The 698 00:36:04,160 --> 00:36:09,000 Speaker 12: Restaurant Industry Association says that two thirds of restaurants will 699 00:36:09,000 --> 00:36:13,160 Speaker 12: fail in the first five years. It is tough. You know, 700 00:36:13,840 --> 00:36:18,839 Speaker 12: a great restaurant is a really good business, but restaurants 701 00:36:18,840 --> 00:36:24,080 Speaker 12: and as an industry, are incredibly competitive, incredibly difficult. Now, 702 00:36:24,320 --> 00:36:28,000 Speaker 12: the interesting thing about the discussion with Bobby Flay, where 703 00:36:28,040 --> 00:36:30,040 Speaker 12: I'm a giant fan of my wife and I have 704 00:36:30,120 --> 00:36:30,759 Speaker 12: his cookbooks. 705 00:36:30,760 --> 00:36:31,160 Speaker 4: We've missed. 706 00:36:32,800 --> 00:36:35,480 Speaker 12: Back when Miracle Grill was his first restaurant and I 707 00:36:35,520 --> 00:36:39,000 Speaker 12: was living on twenty seventh Street, we would go over there, 708 00:36:39,320 --> 00:36:41,399 Speaker 12: and that was before Mesa and some of his other 709 00:36:41,440 --> 00:36:48,160 Speaker 12: bigger restaurants. But given what's been taking place with work 710 00:36:48,200 --> 00:36:53,200 Speaker 12: from home and how empty office spaces are, the proposal 711 00:36:53,239 --> 00:36:55,960 Speaker 12: I've made is, hey, if you're an office building owner 712 00:36:56,360 --> 00:37:00,600 Speaker 12: and you want to get more office space rented, well, 713 00:37:00,600 --> 00:37:03,000 Speaker 12: then you better cut a deal with your ground floor 714 00:37:03,040 --> 00:37:07,040 Speaker 12: tenants to make sure it's filled. Because nothing reduces the 715 00:37:07,160 --> 00:37:10,839 Speaker 12: value of an office floor than coming in and seeing 716 00:37:10,880 --> 00:37:14,279 Speaker 12: a boarded up restaurant or a a null retailer on 717 00:37:14,400 --> 00:37:18,400 Speaker 12: the ground floor. I think retail as a service, restaurants 718 00:37:18,440 --> 00:37:21,880 Speaker 12: as a service is something that the major urban areas 719 00:37:21,920 --> 00:37:25,799 Speaker 12: need to consider because their bread is buttered ll upon 720 00:37:25,880 --> 00:37:30,920 Speaker 12: intended with the twenty year office leases that they're gonna 721 00:37:31,320 --> 00:37:36,120 Speaker 12: you know, sell tens of thousands of dollars worth over 722 00:37:36,160 --> 00:37:38,320 Speaker 12: the next twenty years, tens of thousands of square foot. 723 00:37:38,760 --> 00:37:41,279 Speaker 12: A restaurant that's down on the corner, a deli, a 724 00:37:41,320 --> 00:37:44,799 Speaker 12: half decent restaurant, even a high end restaurant. They need 725 00:37:44,840 --> 00:37:47,080 Speaker 12: to be a whole lot more flexible than they've been. 726 00:37:47,360 --> 00:37:49,880 Speaker 12: And they really have to think about this in a 727 00:37:49,960 --> 00:37:50,759 Speaker 12: very different way. 728 00:37:51,080 --> 00:37:51,239 Speaker 3: You know. 729 00:37:51,280 --> 00:37:53,440 Speaker 2: I saw a article on the Bloomberg yeshay, I've been 730 00:37:53,719 --> 00:37:55,960 Speaker 2: waiting waiting to read and it finally came out. 731 00:37:56,040 --> 00:37:58,760 Speaker 3: I say about San Francisco and how. 732 00:37:58,600 --> 00:38:02,040 Speaker 2: There are signs at San Francisco is bottoming and turning 733 00:38:02,440 --> 00:38:07,200 Speaker 2: for the first year. Last year they actually added people 734 00:38:07,239 --> 00:38:10,960 Speaker 2: in San Francisco, eight hundred and forty additional. 735 00:38:10,480 --> 00:38:13,880 Speaker 3: People on the on you know, it was positive. They 736 00:38:13,960 --> 00:38:15,840 Speaker 3: got to start somewhere. I mean, Barry, what do you 737 00:38:15,840 --> 00:38:16,600 Speaker 3: think about a market? 738 00:38:16,600 --> 00:38:18,440 Speaker 2: I know, you spend a lot of time in San Francisco, 739 00:38:18,480 --> 00:38:21,880 Speaker 2: a lot of you know, there's a lot of ingenuity there, 740 00:38:21,880 --> 00:38:23,759 Speaker 2: a lot of money there. How do you think about 741 00:38:23,760 --> 00:38:27,000 Speaker 2: a city like San Francisco, which, in my opinion, before 742 00:38:27,040 --> 00:38:30,680 Speaker 2: the pandemic was a jewel of this country in terms 743 00:38:30,760 --> 00:38:31,919 Speaker 2: of you gotta go there. 744 00:38:32,600 --> 00:38:34,360 Speaker 12: How do you think one of the most beautiful cities 745 00:38:34,360 --> 00:38:38,080 Speaker 12: in the country, No doubt, you know there there are 746 00:38:38,200 --> 00:38:41,840 Speaker 12: everybody wants that one answer, like a binary yes or no. 747 00:38:41,920 --> 00:38:46,360 Speaker 12: And here's a solution. We have a lot of problems 748 00:38:46,400 --> 00:38:48,919 Speaker 12: that are multi factor. There are a lot of things 749 00:38:49,000 --> 00:38:51,560 Speaker 12: driving it. When you look at when you look at 750 00:38:51,600 --> 00:38:55,319 Speaker 12: a city like New York or San Francisco, nimby is 751 00:38:55,360 --> 00:38:59,880 Speaker 12: a giant problem. The all the rules that prevent a 752 00:39:00,000 --> 00:39:05,560 Speaker 12: additional building and construction that that's an ongoing issue. 753 00:39:05,760 --> 00:39:06,600 Speaker 3: Like there isn't a. 754 00:39:06,520 --> 00:39:09,600 Speaker 12: Whole lot of things that can't be solved when prices 755 00:39:09,640 --> 00:39:12,040 Speaker 12: are too high. You know, the old joke is the 756 00:39:12,120 --> 00:39:14,799 Speaker 12: cure for high prices is high prices. You either have 757 00:39:14,880 --> 00:39:18,760 Speaker 12: to lower your prices or bring on more supply. We're 758 00:39:18,800 --> 00:39:23,480 Speaker 12: starting to see that in suburbia. It hasn't quite happened 759 00:39:23,560 --> 00:39:27,440 Speaker 12: in New York City or San Francisco, at least not rapidly. 760 00:39:28,040 --> 00:39:32,160 Speaker 12: And I have vivid recollections of watching the entire Lower 761 00:39:32,200 --> 00:39:39,160 Speaker 12: Manhattan region following September eleventh completely pivot from uptown downtown 762 00:39:39,600 --> 00:39:43,080 Speaker 12: to much more of a residential area. And I think 763 00:39:43,200 --> 00:39:45,680 Speaker 12: that has to happen in more cities. Yes, I know 764 00:39:45,719 --> 00:39:49,680 Speaker 12: a lot of these buildings don't lend themselves to fast 765 00:39:49,719 --> 00:39:52,920 Speaker 12: and easy conversions. Let a few half a billion or 766 00:39:52,960 --> 00:39:56,640 Speaker 12: billion dollar buildings go into receivership. You'll see how fast 767 00:39:56,640 --> 00:40:00,719 Speaker 12: and easy. They get converted. They'll get converted to residential. 768 00:40:00,800 --> 00:40:04,000 Speaker 12: And I think we have to recognize that what the 769 00:40:04,000 --> 00:40:07,719 Speaker 12: pandemic taught us, which is we are no longer in 770 00:40:07,840 --> 00:40:11,960 Speaker 12: a you know, nine to five Monday to Friday, nineteen 771 00:40:12,080 --> 00:40:17,560 Speaker 12: fifties mad men era of office work. That ship has sailed. 772 00:40:18,000 --> 00:40:21,160 Speaker 12: There are some firms that insist on five days a week. 773 00:40:21,640 --> 00:40:24,680 Speaker 12: For us, we give employees a whole lot more flexibility. 774 00:40:24,719 --> 00:40:28,239 Speaker 12: We think that's a great recruiting and retention advantage over 775 00:40:28,280 --> 00:40:33,279 Speaker 12: some larger firms. But it's pretty obvious the hybrid methodology 776 00:40:33,520 --> 00:40:36,759 Speaker 12: is here to stay, and that means we have too 777 00:40:36,840 --> 00:40:41,240 Speaker 12: much office space and too little residential in these urban centers. 778 00:40:41,400 --> 00:40:44,000 Speaker 2: Yep, all right, great stuff has always Barry, appreciate your perspective. 779 00:40:44,000 --> 00:40:46,320 Speaker 2: Barry Redholts, host Semester's in Business on Bloomberg Radio and 780 00:40:46,320 --> 00:40:48,360 Speaker 2: also founder of Ritholtz Wealth Management. 781 00:40:48,480 --> 00:40:53,000 Speaker 1: This is the Bloomberg Intelligence Podcast, available on apples, Spotify, 782 00:40:53,200 --> 00:40:56,120 Speaker 1: and anywhere else you will get your podcasts. Listen live 783 00:40:56,200 --> 00:40:59,800 Speaker 1: each weekday ten am to noon Eastern on Bloomberg dot Com, 784 00:41:00,120 --> 00:41:03,520 Speaker 1: iHeartRadio app, tune In, and the Bloomberg Business app. You 785 00:41:03,560 --> 00:41:06,719 Speaker 1: can also watch us live every weekday on YouTube and 786 00:41:06,880 --> 00:41:08,520 Speaker 1: always on the Bloomberg Terminal