1 00:00:02,640 --> 00:00:05,320 Speaker 1: Welcome to the Bloomberg Penl Podcast. I'm Paul swing you. 2 00:00:05,360 --> 00:00:07,680 Speaker 1: Along with my co host Lisa Brahma Wicks. Each day 3 00:00:07,720 --> 00:00:10,240 Speaker 1: we bring you the most noteworthy and useful interviews for 4 00:00:10,280 --> 00:00:12,520 Speaker 1: you and your money. Whether at the grocery store or 5 00:00:12,560 --> 00:00:15,480 Speaker 1: the trading floor. Find a Bloomberg Penl podcast on Apple 6 00:00:15,520 --> 00:00:17,960 Speaker 1: podcast or wherever you listen to podcasts, as well as 7 00:00:17,960 --> 00:00:23,159 Speaker 1: at Bloomberg dot com. Well, given a sharp rally we 8 00:00:23,200 --> 00:00:25,000 Speaker 1: have seen in a fixed income market so far in 9 00:00:26,000 --> 00:00:29,320 Speaker 1: many investors are asking themselves what's next. So to try 10 00:00:29,360 --> 00:00:31,840 Speaker 1: to get the answer to this question and others, let's 11 00:00:31,840 --> 00:00:35,160 Speaker 1: bring on Tad Revel Tad as a chief investment officer 12 00:00:35,200 --> 00:00:37,479 Speaker 1: for fixed income at tc W t c W haws 13 00:00:37,479 --> 00:00:40,960 Speaker 1: about a hud billion dollars under management, and it is 14 00:00:41,000 --> 00:00:44,760 Speaker 1: based in Los Angeles. Tad, thanks so much for joining us. 15 00:00:44,760 --> 00:00:46,280 Speaker 1: I wonder if, just to start off, if you could 16 00:00:46,280 --> 00:00:48,599 Speaker 1: give us a sense where do you think we are 17 00:00:48,680 --> 00:00:51,880 Speaker 1: in the credit cycle right now? We think we're pretty 18 00:00:51,960 --> 00:00:55,360 Speaker 1: late in the credit cycle, and when we think about 19 00:00:55,520 --> 00:00:59,360 Speaker 1: the economic cycle and the investing cycle, we actually try 20 00:00:59,400 --> 00:01:01,720 Speaker 1: not to think about it in the narrower sense of 21 00:01:01,760 --> 00:01:04,640 Speaker 1: the credit cycle, but rather in a more expansive way 22 00:01:04,959 --> 00:01:07,800 Speaker 1: in the sense that I think that apperusal of the 23 00:01:07,880 --> 00:01:11,600 Speaker 1: last twenty or thirty years of economic downturn suggests that 24 00:01:12,000 --> 00:01:15,760 Speaker 1: we actually live in a world of interlocked credit asset 25 00:01:15,800 --> 00:01:19,080 Speaker 1: price business cycles. Like a three legged stool, you knock 26 00:01:19,120 --> 00:01:22,240 Speaker 1: out one one leg, in this case maybe the credit, 27 00:01:22,760 --> 00:01:26,039 Speaker 1: and you're in inevitably going to have a decline in 28 00:01:26,120 --> 00:01:29,360 Speaker 1: risk based asset prices as well as an economic downturn. 29 00:01:29,880 --> 00:01:32,280 Speaker 1: And there's many reasons that we do think that it's 30 00:01:32,440 --> 00:01:34,520 Speaker 1: in the late stage of the credit cycle, or the 31 00:01:34,560 --> 00:01:36,640 Speaker 1: asset price cycle as we put it, and we can 32 00:01:36,680 --> 00:01:40,399 Speaker 1: discuss that if that's of interest. So given that we 33 00:01:40,440 --> 00:01:42,800 Speaker 1: are in the later stages of the credit cycle, as 34 00:01:42,840 --> 00:01:47,520 Speaker 1: you say, where are you in TCW seeing value right now? Well, 35 00:01:48,320 --> 00:01:50,720 Speaker 1: the way we think about it in terms of a 36 00:01:50,800 --> 00:01:54,840 Speaker 1: late cycle playbook for fixed income is that rather than 37 00:01:54,880 --> 00:01:58,080 Speaker 1: think about the fixed income market as a risk on, 38 00:01:58,240 --> 00:02:01,600 Speaker 1: risk off type of flavoring, that you have to think 39 00:02:01,600 --> 00:02:03,360 Speaker 1: about it in a little bit with a little bit 40 00:02:03,400 --> 00:02:05,880 Speaker 1: more nuance and a little bit more complexity. So what 41 00:02:06,000 --> 00:02:08,480 Speaker 1: that means is that as you look to your fixed 42 00:02:08,520 --> 00:02:11,600 Speaker 1: income allocations, in our view, you should be thinking about 43 00:02:11,639 --> 00:02:14,600 Speaker 1: it is really having three pieces to it, three broad pieces. 44 00:02:14,919 --> 00:02:17,680 Speaker 1: One piece is sort of your traditional risk off type 45 00:02:17,680 --> 00:02:21,280 Speaker 1: of securities like treasuries, and they're there for liquidity and 46 00:02:21,400 --> 00:02:26,760 Speaker 1: for maintenance of some hedging risk against the potentiality of 47 00:02:26,800 --> 00:02:29,600 Speaker 1: the clients and risk based asset prices. You're supposed to 48 00:02:29,639 --> 00:02:32,760 Speaker 1: have bendable assets. And by bendable assets, what we're typically 49 00:02:32,760 --> 00:02:36,600 Speaker 1: referring to are things like investment grade credit, triple A 50 00:02:36,680 --> 00:02:39,720 Speaker 1: rated commercial mortgages, things that are subject to marking to 51 00:02:39,840 --> 00:02:44,720 Speaker 1: market volatility, but assets that will not go into bankruptcy 52 00:02:44,840 --> 00:02:48,800 Speaker 1: or provide haircuts or discounts to principle through some type 53 00:02:48,800 --> 00:02:51,880 Speaker 1: of a workout situation. And so you balance your portfolio 54 00:02:51,960 --> 00:02:54,760 Speaker 1: between vendable and risk off type assets, and you do 55 00:02:54,800 --> 00:02:58,680 Speaker 1: your darn disk to try to avoid allocations to breakable assets. 56 00:02:58,720 --> 00:03:01,440 Speaker 1: And by breakable asset, that's what we're talking about, our 57 00:03:01,480 --> 00:03:04,680 Speaker 1: assets that are going to experience principal haircuts that are 58 00:03:04,720 --> 00:03:08,040 Speaker 1: so to speak, get swept into the late cycle environment 59 00:03:08,320 --> 00:03:12,040 Speaker 1: that inevitably has a degree of debt for equity swapping 60 00:03:12,360 --> 00:03:17,120 Speaker 1: associated with it. Bankruptcy is distress, debt, exchanges, rescue financing, 61 00:03:17,440 --> 00:03:20,600 Speaker 1: that sort of stuff understood. I'm just I'm curious what 62 00:03:20,639 --> 00:03:22,880 Speaker 1: you think about the quality of the U S high 63 00:03:22,919 --> 00:03:24,919 Speaker 1: yield market right now. There's lots of people talking about 64 00:03:24,919 --> 00:03:28,600 Speaker 1: how investment grade quality is deteriorating with so many triple 65 00:03:28,680 --> 00:03:30,960 Speaker 1: B s um and we're also at the same time 66 00:03:31,000 --> 00:03:33,320 Speaker 1: seeing more rising stars this year, that is, high yield 67 00:03:33,360 --> 00:03:36,480 Speaker 1: names are getting upgraded to investment grade. Does that mean 68 00:03:36,480 --> 00:03:39,000 Speaker 1: the high old qualities getting worse if the best of 69 00:03:39,000 --> 00:03:42,600 Speaker 1: the names are leaving well. Um, when when we ask 70 00:03:42,720 --> 00:03:45,400 Speaker 1: questions about high yield, we're probably supposed to also think 71 00:03:45,440 --> 00:03:49,520 Speaker 1: about that a little bit more expensively. Because the sibling, 72 00:03:50,040 --> 00:03:53,560 Speaker 1: the the nearly identical twin of the high yield market 73 00:03:53,720 --> 00:03:57,840 Speaker 1: is the bank loan, the syndicated bank loan market, and 74 00:03:57,920 --> 00:04:01,560 Speaker 1: the syndicated bank loan market has always been another vehicle 75 00:04:01,760 --> 00:04:04,800 Speaker 1: by which companies, or leverage companies or below investment grade 76 00:04:04,800 --> 00:04:08,640 Speaker 1: companies have been able to to to borrow, and that 77 00:04:08,800 --> 00:04:11,960 Speaker 1: has been basically the part of the credit markets, the 78 00:04:12,080 --> 00:04:15,200 Speaker 1: leverage credit markets that has grown so in such an 79 00:04:15,200 --> 00:04:18,200 Speaker 1: outsized way over the course of this cycle. In the 80 00:04:18,320 --> 00:04:24,080 Speaker 1: leverage bank loan market, we see absolutely substantial evidence of 81 00:04:24,400 --> 00:04:27,800 Speaker 1: major deterioration in terms of the quality of underwriting that 82 00:04:27,839 --> 00:04:29,919 Speaker 1: has gone on over the course of the cycle. And 83 00:04:29,960 --> 00:04:32,360 Speaker 1: the simplest way to put it, and I'm sure it's 84 00:04:32,360 --> 00:04:35,000 Speaker 1: been spoken too many times, is that this was a 85 00:04:35,080 --> 00:04:38,840 Speaker 1: marketplace that number one was a lot smaller ten years ago, 86 00:04:38,960 --> 00:04:42,640 Speaker 1: but number two and more importantly, was dominated with covenant 87 00:04:42,680 --> 00:04:46,120 Speaker 1: heavy issuance. Covenant light issuance, which now is something like 88 00:04:46,160 --> 00:04:49,880 Speaker 1: eight of this market is something that basically came along 89 00:04:50,240 --> 00:04:53,080 Speaker 1: over the course of this cycle, and covenant light lending 90 00:04:53,120 --> 00:04:57,520 Speaker 1: almost by definition, means that the borrower has substantial ability 91 00:04:57,560 --> 00:05:01,919 Speaker 1: to work um mostly eclusively for the shareholders, to the 92 00:05:01,960 --> 00:05:05,160 Speaker 1: detriment of bondholders. So I think that when you look 93 00:05:05,200 --> 00:05:09,560 Speaker 1: at leverage lending in a sort of holistic way, the 94 00:05:09,600 --> 00:05:11,760 Speaker 1: only conclusion that you can reach is actually is that 95 00:05:11,800 --> 00:05:16,479 Speaker 1: the quality of underwriting in general is actually rather poor. Well, 96 00:05:16,560 --> 00:05:18,920 Speaker 1: are there any sectors in particular than this leverage loan 97 00:05:18,960 --> 00:05:23,280 Speaker 1: market that are concerning to you. Well, the the leverage 98 00:05:23,320 --> 00:05:26,839 Speaker 1: loan market has has found its way into most every 99 00:05:26,960 --> 00:05:29,560 Speaker 1: nook and cranny. But I think that, you know, maybe 100 00:05:29,720 --> 00:05:32,840 Speaker 1: maybe one way to make the discussion just a little 101 00:05:32,880 --> 00:05:36,120 Speaker 1: bit more pointed or a little bit more concrete, if 102 00:05:36,160 --> 00:05:38,159 Speaker 1: I could, Maybe I'll put it this way, because I 103 00:05:38,200 --> 00:05:41,480 Speaker 1: think people talk about covenants and it sounds like maybe 104 00:05:41,480 --> 00:05:44,120 Speaker 1: it should be important, but maybe people don't totally get 105 00:05:44,160 --> 00:05:48,480 Speaker 1: why it matters, and maybe an example helps that. I think, 106 00:05:48,520 --> 00:05:52,359 Speaker 1: first of all, to personalize it is consider the fact 107 00:05:52,400 --> 00:05:54,600 Speaker 1: that when you buy a home and you take out 108 00:05:54,600 --> 00:05:57,320 Speaker 1: a mortgage on it, you may not think about the 109 00:05:57,320 --> 00:05:59,480 Speaker 1: fact that you're agreeing to a covenant, but you are 110 00:05:59,480 --> 00:06:03,159 Speaker 1: agreeing to very important covenant. That that being essentially that 111 00:06:03,320 --> 00:06:05,839 Speaker 1: when the day comes that you sell that house, the 112 00:06:05,880 --> 00:06:08,679 Speaker 1: first use of proceeds is to pay off the lender. 113 00:06:08,920 --> 00:06:10,760 Speaker 1: You don't get to sell your house, put the money 114 00:06:10,760 --> 00:06:13,520 Speaker 1: in the bank, take a trip around the world, come back, 115 00:06:13,640 --> 00:06:16,400 Speaker 1: have a discussion with your lender. Maybe I'll pay you back. Now. 116 00:06:16,760 --> 00:06:20,039 Speaker 1: That type of covenant protection is necessary so that there 117 00:06:20,080 --> 00:06:23,880 Speaker 1: isn't a separation of collateral from the lender the house, 118 00:06:23,920 --> 00:06:26,919 Speaker 1: so to speak, from the mortgage loan. In the in 119 00:06:27,040 --> 00:06:29,520 Speaker 1: the bank loan market, it doesn't work that way. We've 120 00:06:29,560 --> 00:06:34,599 Speaker 1: seen numerous examples of situations in which companies have transferred assets, 121 00:06:34,800 --> 00:06:38,320 Speaker 1: sold divisions, sold important assets of the business, and with 122 00:06:38,400 --> 00:06:41,920 Speaker 1: the proceeds of that of those funds, they make dividends 123 00:06:41,920 --> 00:06:44,719 Speaker 1: to shareholders. And that sounds like a good thing, except 124 00:06:44,760 --> 00:06:46,800 Speaker 1: that from the point of view of the lender, I'm 125 00:06:46,839 --> 00:06:50,159 Speaker 1: watching you sell valuable pieces of your business, and the 126 00:06:50,200 --> 00:06:53,599 Speaker 1: money is basically leaving the collateral package, which is to say, 127 00:06:53,800 --> 00:06:57,560 Speaker 1: your company or specific entities in your company. It's going 128 00:06:57,600 --> 00:07:00,320 Speaker 1: to shareholders and leaving me, the bond holder, with the 129 00:07:00,320 --> 00:07:04,479 Speaker 1: potentiality of a uh, not exactly an empty shell, but 130 00:07:04,800 --> 00:07:08,360 Speaker 1: a shell that is emptying in the process and reducing 131 00:07:08,400 --> 00:07:12,360 Speaker 1: my protections. The important, the critical point here, of course, 132 00:07:12,720 --> 00:07:15,520 Speaker 1: is that credit needs to be understood not just statically 133 00:07:15,560 --> 00:07:18,480 Speaker 1: how do you look visa via your borrower today, but 134 00:07:18,640 --> 00:07:21,000 Speaker 1: what it could look like in the event that that 135 00:07:21,080 --> 00:07:24,960 Speaker 1: business got stressed. As as bankers love to say, there 136 00:07:25,080 --> 00:07:27,520 Speaker 1: is no genius so to speak, in making the loan. 137 00:07:27,640 --> 00:07:29,800 Speaker 1: All of the hard work is involved in collecting on 138 00:07:29,880 --> 00:07:33,280 Speaker 1: the loan. Yep, interesting, very interesting moving. We've had this 139 00:07:33,480 --> 00:07:35,600 Speaker 1: big move up in the market and investors are trying 140 00:07:35,600 --> 00:07:38,200 Speaker 1: to figure out do I go to quality and uh. 141 00:07:38,400 --> 00:07:41,360 Speaker 1: Lots of questions being asked by fixed income investors. Thanks 142 00:07:41,400 --> 00:07:44,360 Speaker 1: for some of these answers. Tad Ravel Tad is a 143 00:07:44,400 --> 00:07:47,120 Speaker 1: chief investment officer for fixed income at tc W. Joining 144 00:07:47,160 --> 00:08:04,320 Speaker 1: us on the phone from Los Angeles, The right sharing 145 00:08:04,320 --> 00:08:06,880 Speaker 1: company Lift is about to begin its I p O 146 00:08:07,000 --> 00:08:09,680 Speaker 1: road show and investors are pouring through its perspectus to 147 00:08:09,760 --> 00:08:13,480 Speaker 1: better understand the company's business and financial prospects. So it 148 00:08:13,520 --> 00:08:16,280 Speaker 1: help us evaluate the Lift story. Is our good friends 149 00:08:16,280 --> 00:08:19,840 Speaker 1: Shire overday Share is a technology calumnist for Bloomberg Opinion. 150 00:08:20,320 --> 00:08:23,920 Speaker 1: She joins us in our Bloomberg Interactive Broker Studio. So Shia, 151 00:08:23,960 --> 00:08:25,640 Speaker 1: thanks so much for being with us. You know, when 152 00:08:25,640 --> 00:08:28,040 Speaker 1: you take a look at the perspectus for Lift, one 153 00:08:28,040 --> 00:08:30,320 Speaker 1: thing that jumps out is it is not profitable at 154 00:08:30,320 --> 00:08:33,719 Speaker 1: lost nine million dollars last year. So what are investors 155 00:08:33,960 --> 00:08:36,720 Speaker 1: looking at to try to get a sense of the 156 00:08:36,800 --> 00:08:39,480 Speaker 1: prospects for this company? And maybe evaluation and so on. Yeah, 157 00:08:39,640 --> 00:08:42,680 Speaker 1: So look, there's two things that investor two big things 158 00:08:42,679 --> 00:08:45,400 Speaker 1: that investors will be looking at. One is the rate 159 00:08:45,440 --> 00:08:48,400 Speaker 1: of growth, which is very fast. We should give them 160 00:08:48,440 --> 00:08:51,760 Speaker 1: credit for revenue is sort of doubling or so a 161 00:08:51,920 --> 00:08:54,360 Speaker 1: year over year, which is an impressive growth rate for 162 00:08:54,360 --> 00:08:56,800 Speaker 1: a company with two billion dollars in revenue last year. 163 00:08:57,080 --> 00:09:00,839 Speaker 1: The other thing is Lift points people to a number 164 00:09:00,880 --> 00:09:05,840 Speaker 1: of metrics that are intended to show the improving financial 165 00:09:05,880 --> 00:09:10,000 Speaker 1: economics of the main business it's in, which is you know, 166 00:09:10,080 --> 00:09:13,880 Speaker 1: single use on demand rides. And the piece that I 167 00:09:13,920 --> 00:09:17,000 Speaker 1: wrote about was on something sort of nerdy. So everybody, 168 00:09:17,000 --> 00:09:20,319 Speaker 1: hold onto your ears. Um called the take rate. That's 169 00:09:20,360 --> 00:09:23,240 Speaker 1: the kind of common vernacular in a business like Lift, 170 00:09:23,600 --> 00:09:27,320 Speaker 1: which is the effective share of money that lift gets 171 00:09:27,679 --> 00:09:30,760 Speaker 1: from every dollar of that People are excuse me that 172 00:09:30,760 --> 00:09:33,000 Speaker 1: people are spending on rides, right, So when I take 173 00:09:33,040 --> 00:09:35,280 Speaker 1: a ride in the lift, I think the the you know, 174 00:09:35,679 --> 00:09:38,840 Speaker 1: uber or lift or whatever gets twenty or thirty cents 175 00:09:38,880 --> 00:09:40,440 Speaker 1: of every dollar I spend. Is that what you're talking about? 176 00:09:40,480 --> 00:09:42,000 Speaker 1: The take That's exactly what I'm talking about. And and and 177 00:09:42,320 --> 00:09:45,440 Speaker 1: it's the complicated it's complicated formula because it's not like 178 00:09:45,800 --> 00:09:49,200 Speaker 1: I pay twenty dollars for a lift ride and the 179 00:09:49,320 --> 00:09:52,679 Speaker 1: driver gets some portion and and lift gets some portion. 180 00:09:52,720 --> 00:09:57,240 Speaker 1: It's a complicated formula. But effectively, yes, uh, Lift is 181 00:09:57,679 --> 00:10:01,120 Speaker 1: keeping something like five or thirty cents of every dollar 182 00:10:01,320 --> 00:10:03,319 Speaker 1: that that I spend on that. And how how is 183 00:10:03,360 --> 00:10:06,560 Speaker 1: that trending? It's it's up, It's up significantly. You know 184 00:10:06,679 --> 00:10:10,240 Speaker 1: that that percentage is going more and more towards lift 185 00:10:10,440 --> 00:10:14,240 Speaker 1: lifts favor, which again, Lift is trying to say, look, 186 00:10:15,000 --> 00:10:18,760 Speaker 1: yes we are. Losses are extremely large, but you can 187 00:10:18,800 --> 00:10:21,480 Speaker 1: see that we're getting more efficient with our spending over time. 188 00:10:21,679 --> 00:10:24,880 Speaker 1: The problem that I wrote about is that number that 189 00:10:24,880 --> 00:10:28,720 Speaker 1: that take rate, that efficiency rate for LIFT is getting 190 00:10:29,120 --> 00:10:32,720 Speaker 1: muddier and muddier with every passing moment. And that's because 191 00:10:32,720 --> 00:10:36,319 Speaker 1: of the changing nature of lifts business. So Lift bought 192 00:10:36,440 --> 00:10:40,720 Speaker 1: a company called Motivate just recently. Motivated is the company 193 00:10:40,760 --> 00:10:44,600 Speaker 1: behind city bike and these other kind of city bike 194 00:10:44,679 --> 00:10:49,199 Speaker 1: rental programs. Lift also offers electric scooters in the doctor 195 00:10:49,559 --> 00:10:53,800 Speaker 1: or so US cities. And the distinction here is when 196 00:10:53,800 --> 00:10:57,040 Speaker 1: I rent a scooter or a bicycle from Lift, there's 197 00:10:57,040 --> 00:10:59,880 Speaker 1: no driver, there's no driver. With the share of it, 198 00:11:00,240 --> 00:11:03,240 Speaker 1: they keep a dcent of the revenue, and so that 199 00:11:03,320 --> 00:11:07,079 Speaker 1: affects the company's overall take rate. It lifts it higher 200 00:11:07,120 --> 00:11:11,080 Speaker 1: and higher, and it says us Yeah. So investors need 201 00:11:11,120 --> 00:11:12,960 Speaker 1: to be prepared to kind of dig through that a 202 00:11:12,960 --> 00:11:14,760 Speaker 1: little bit, right exactly, that that is going to be 203 00:11:14,800 --> 00:11:17,080 Speaker 1: a complicated number for them to sift through. So one 204 00:11:17,120 --> 00:11:18,680 Speaker 1: of the interesting things about the Lift I p O 205 00:11:18,800 --> 00:11:21,720 Speaker 1: is there first, they're coming up before Uber. Is there 206 00:11:21,760 --> 00:11:25,280 Speaker 1: a sense that that is an advantage for them? I mean, 207 00:11:25,320 --> 00:11:26,719 Speaker 1: how how are they trying to spin it? Do you think? 208 00:11:26,800 --> 00:11:29,280 Speaker 1: I know there's a lot of game theory about this 209 00:11:29,440 --> 00:11:32,840 Speaker 1: race between Uber and Lift to the public markets. I'm 210 00:11:32,880 --> 00:11:35,679 Speaker 1: not sure there's really much of an advantage. I think, yes, 211 00:11:35,880 --> 00:11:39,640 Speaker 1: Lift as the smaller player, as the more focused on 212 00:11:39,800 --> 00:11:43,280 Speaker 1: just on demand ride player, they do have an advantage 213 00:11:43,320 --> 00:11:47,439 Speaker 1: from going first, from getting more attention than they would 214 00:11:47,440 --> 00:11:51,120 Speaker 1: have if they were after Uber um, and from kind 215 00:11:51,160 --> 00:11:54,280 Speaker 1: of being able to focus investors on these are the 216 00:11:54,320 --> 00:11:56,800 Speaker 1: markets we think you should pay attention to, These are 217 00:11:56,800 --> 00:12:00,079 Speaker 1: the metrics we think are important to our business, and 218 00:12:00,240 --> 00:12:02,840 Speaker 1: they get to plant a flag in the ground on 219 00:12:02,960 --> 00:12:05,839 Speaker 1: those points. I don't think Uber has any disadvantage from 220 00:12:05,880 --> 00:12:09,120 Speaker 1: being second. They're so large, they're gonna suck up so 221 00:12:09,200 --> 00:12:12,160 Speaker 1: much oxygen when and if they do go public this year. 222 00:12:12,800 --> 00:12:15,600 Speaker 1: UM So, I think this was the natural order of 223 00:12:15,640 --> 00:12:18,439 Speaker 1: IPO timing. It's interesting as you look at this model, 224 00:12:18,480 --> 00:12:20,599 Speaker 1: the Lift model, and presumably when we get the the 225 00:12:21,000 --> 00:12:24,559 Speaker 1: numbers from Uber, it will be similar. The path to profitability, 226 00:12:24,600 --> 00:12:28,120 Speaker 1: it's not nearly as clean as it was for some 227 00:12:28,200 --> 00:12:31,040 Speaker 1: of the advertising driven tech companies that also came out 228 00:12:31,040 --> 00:12:33,679 Speaker 1: of big valuations, because you do have to subsidize the 229 00:12:33,880 --> 00:12:36,559 Speaker 1: these drivers. Are you getting a sense from investors or 230 00:12:36,600 --> 00:12:39,560 Speaker 1: the marketplace that people are comfortable that there is some 231 00:12:39,600 --> 00:12:42,840 Speaker 1: path to profitability for these companies. I hope so, because 232 00:12:43,080 --> 00:12:46,080 Speaker 1: otherwise I don't see how these businesses can survive. But yeah, 233 00:12:46,120 --> 00:12:49,520 Speaker 1: you're right that snap Snapchat, for example, is a company 234 00:12:49,520 --> 00:12:53,640 Speaker 1: that had enormous losses when that company would public, really 235 00:12:53,720 --> 00:12:58,040 Speaker 1: staggering losses and negative free cash flow. But as you said, 236 00:12:58,240 --> 00:13:02,000 Speaker 1: there are examples from other advertising focus businesses like Google 237 00:13:02,080 --> 00:13:06,400 Speaker 1: and Facebook that have become very profitable and and that 238 00:13:06,440 --> 00:13:09,200 Speaker 1: business model is well understood by investors. What Lift and 239 00:13:09,320 --> 00:13:13,640 Speaker 1: Uber are doing is is is a totally different thing. 240 00:13:14,240 --> 00:13:16,520 Speaker 1: And it's really going to be hard to model this 241 00:13:16,559 --> 00:13:20,440 Speaker 1: out because you don't know the ultimate economics of this business. 242 00:13:21,000 --> 00:13:25,240 Speaker 1: You don't know the total market size of this business. 243 00:13:25,280 --> 00:13:27,880 Speaker 1: I think it's all just a big string of question marks. Yeah, 244 00:13:27,880 --> 00:13:30,320 Speaker 1: I think it's I'm guessing just some you're talking to 245 00:13:30,400 --> 00:13:32,760 Speaker 1: some investors that the company is kind of positioning themselves. 246 00:13:32,800 --> 00:13:35,960 Speaker 1: Is that that total addressable market out there? We think 247 00:13:36,000 --> 00:13:38,199 Speaker 1: it's just huge. We don't know, and quite fact that 248 00:13:38,240 --> 00:13:40,640 Speaker 1: we're probably under reporting it because more and more people 249 00:13:40,640 --> 00:13:44,360 Speaker 1: are you know, really turning to ride sharing across you know, 250 00:13:44,640 --> 00:13:47,120 Speaker 1: different vehicles and so on and so forth, and Bloomberg 251 00:13:47,120 --> 00:13:49,440 Speaker 1: business Week even wrote a story a couple of weeks ago, 252 00:13:49,440 --> 00:13:51,920 Speaker 1: but maybe peak auto and one of the reasons that 253 00:13:51,960 --> 00:13:54,320 Speaker 1: we may be at peak auto production globally is because 254 00:13:54,360 --> 00:13:56,880 Speaker 1: of the rise of ride sharing. So I suspect that 255 00:13:56,880 --> 00:13:58,839 Speaker 1: that's what these companies will try to play up. Is 256 00:13:58,880 --> 00:14:01,280 Speaker 1: the long term. That's the bull is case on on 257 00:14:01,360 --> 00:14:04,760 Speaker 1: both Uber and Lift, right, is that the transportation market, 258 00:14:04,840 --> 00:14:07,200 Speaker 1: the the amount of money that people spend on buying 259 00:14:07,240 --> 00:14:10,400 Speaker 1: cars and operating cars is large, and if Lift and 260 00:14:10,520 --> 00:14:13,679 Speaker 1: Uber can can steal some of that spending, that's going 261 00:14:13,720 --> 00:14:15,920 Speaker 1: to be an enormous business. But I just don't know 262 00:14:16,520 --> 00:14:21,160 Speaker 1: whether rides on demand will ever be that big outside 263 00:14:21,160 --> 00:14:24,560 Speaker 1: of a handful of cities in the world where it's 264 00:14:24,600 --> 00:14:27,440 Speaker 1: sort of appealing and viable. Is there a sense just 265 00:14:27,640 --> 00:14:29,480 Speaker 1: switching from Lift, is there a sense for Uber? What 266 00:14:29,600 --> 00:14:32,240 Speaker 1: the the timing is on that IP? I know they 267 00:14:32,280 --> 00:14:34,680 Speaker 1: talked about it being, uh, you know, obviously later in 268 00:14:34,680 --> 00:14:36,640 Speaker 1: the year, but at any sense of whether it's a 269 00:14:36,720 --> 00:14:39,280 Speaker 1: kind of first half or second half. I certainly the 270 00:14:39,880 --> 00:14:42,520 Speaker 1: last time I asked around it sounded more like second 271 00:14:42,600 --> 00:14:47,720 Speaker 1: quarter um than second half of the year, but who knows. 272 00:14:47,760 --> 00:14:49,400 Speaker 1: I think a lot of a lot of the timing 273 00:14:49,440 --> 00:14:52,920 Speaker 1: got affected by the government shutdown, which impacted the ability 274 00:14:52,960 --> 00:14:56,120 Speaker 1: of Uber and left to get feedback on their their 275 00:14:56,200 --> 00:14:59,360 Speaker 1: draft I p O filings. So well, we'll see what happens. 276 00:14:59,640 --> 00:15:01,720 Speaker 1: And the I mean on Lift, I mean they keep 277 00:15:01,760 --> 00:15:03,440 Speaker 1: saying it's imminent. Do you have any sense of whether 278 00:15:03,520 --> 00:15:06,200 Speaker 1: it's how imminent it might be. It seems like they're 279 00:15:06,200 --> 00:15:09,240 Speaker 1: going to meet with investors later this month, and then 280 00:15:09,400 --> 00:15:13,040 Speaker 1: I assume the listing would happen either late March or 281 00:15:13,080 --> 00:15:15,840 Speaker 1: early April. Interesting, and I think, yeah, this will be interesting. 282 00:15:15,920 --> 00:15:17,680 Speaker 1: It would be interesting to see about the valuation, what 283 00:15:17,760 --> 00:15:19,800 Speaker 1: kind of valuation they get in the marketplace. And the 284 00:15:19,880 --> 00:15:22,320 Speaker 1: question is will Uber come out at a premium to Lift? 285 00:15:22,360 --> 00:15:25,200 Speaker 1: Presumably I guess they would because it's bigger. It's a 286 00:15:25,200 --> 00:15:29,040 Speaker 1: little bit hard to know because on the one hand, 287 00:15:29,120 --> 00:15:32,680 Speaker 1: Lift is growing much faster. On the other hand, Uber's 288 00:15:32,720 --> 00:15:35,720 Speaker 1: business is much more diverse, for better for worse. Right, 289 00:15:35,760 --> 00:15:39,800 Speaker 1: they do restaurant food delivery. They operate in many many countries, 290 00:15:39,840 --> 00:15:42,600 Speaker 1: whereas Lift is pretty much just the U. S and Canada. 291 00:15:43,240 --> 00:15:46,000 Speaker 1: Lift owned Steaks and all of these global right Herring 292 00:15:46,280 --> 00:15:49,240 Speaker 1: healing companies, So it may deserve a premium or maybe not. 293 00:15:49,480 --> 00:15:52,160 Speaker 1: Got it? Interesting? Very interesting? Okay, but keep on top 294 00:15:52,160 --> 00:15:54,840 Speaker 1: of this one share over date technology commist for Bloomberg 295 00:15:54,840 --> 00:16:13,600 Speaker 1: Opinion joining us here in the Bloomberg Interactive Broker Studio. Well, 296 00:16:13,600 --> 00:16:16,880 Speaker 1: despite today's sell off of over one percent, yes and 297 00:16:16,960 --> 00:16:20,400 Speaker 1: p this year to date is up about ten and 298 00:16:20,400 --> 00:16:22,080 Speaker 1: after the meltout we saw in the fourth quarter. The 299 00:16:22,160 --> 00:16:26,120 Speaker 1: question is is this a continuation of the bowl market? 300 00:16:26,200 --> 00:16:28,720 Speaker 1: Is this more of a bear market kind of rally? 301 00:16:28,800 --> 00:16:30,600 Speaker 1: If you will, to help us kind of dig into 302 00:16:30,640 --> 00:16:34,160 Speaker 1: that as Bill Smead. Bill is the chief executive officer 303 00:16:34,200 --> 00:16:37,440 Speaker 1: and chief investment officer of SMED Capital Management, a little 304 00:16:37,480 --> 00:16:40,960 Speaker 1: over two point one billion dollars under management. Bill joins 305 00:16:41,040 --> 00:16:44,720 Speaker 1: us here on our Bloomberg Interactive Broker Studio. So, so, Bill, 306 00:16:44,760 --> 00:16:46,720 Speaker 1: what do you make of what we've seen in the 307 00:16:46,760 --> 00:16:49,640 Speaker 1: marketplace over the last three or four months? Is this 308 00:16:49,800 --> 00:16:51,960 Speaker 1: um Is this performance we've seen in the in the 309 00:16:52,000 --> 00:16:56,200 Speaker 1: beginning of this year real? Oh, it's always real. It's 310 00:16:56,240 --> 00:16:59,240 Speaker 1: real money, right, It's it's real money. Uh. In the 311 00:16:59,320 --> 00:17:02,120 Speaker 1: in the long run, the market's a weighing machine. And 312 00:17:02,160 --> 00:17:06,200 Speaker 1: in the short run, it's kind of a snapshot, and uh, 313 00:17:06,240 --> 00:17:09,080 Speaker 1: we fall into the camp of people that believes that 314 00:17:09,200 --> 00:17:16,160 Speaker 1: the excitement around e commerce was a parabolic bubble. And 315 00:17:16,440 --> 00:17:18,720 Speaker 1: we've got a chart that shows over the last forty 316 00:17:18,800 --> 00:17:21,600 Speaker 1: five years there it's the third biggest bubble behind the 317 00:17:21,680 --> 00:17:25,760 Speaker 1: dot com and the ridiculous housing thing that we didn't 318 00:17:25,760 --> 00:17:31,320 Speaker 1: know four oh six. So if you believe that we 319 00:17:31,320 --> 00:17:35,800 Speaker 1: we would not trust the market, uh if in it. 320 00:17:36,119 --> 00:17:39,040 Speaker 1: In fact, it's going to continue to be led by 321 00:17:39,440 --> 00:17:45,159 Speaker 1: a the most aggressive growth sector in a tenure stretch 322 00:17:45,200 --> 00:17:49,720 Speaker 1: where growth is completely pasted value. Right, right, let's talk 323 00:17:49,760 --> 00:17:52,120 Speaker 1: about I was gonna ask you about some some sectors 324 00:17:52,160 --> 00:17:54,159 Speaker 1: and some names dream but let's let's lead off the 325 00:17:54,200 --> 00:17:56,080 Speaker 1: one that is not a good story for you. Today. 326 00:17:56,160 --> 00:17:59,520 Speaker 1: Kroger put out some weaker than expected numbers stocked down 327 00:18:00,040 --> 00:18:03,440 Speaker 1: today around that's just a holding of years. What woul 328 00:18:03,440 --> 00:18:06,040 Speaker 1: happened with the company this quarter? Well, you have to 329 00:18:06,080 --> 00:18:09,399 Speaker 1: go back to what got us involved, which which is 330 00:18:09,520 --> 00:18:12,439 Speaker 1: coming up on two years ago Amazon announced they were 331 00:18:12,440 --> 00:18:15,880 Speaker 1: going into the grocery step space. We're in Seattle, our 332 00:18:15,960 --> 00:18:20,920 Speaker 1: headquarters is about ten twelve blocks from Amazon and at 333 00:18:20,920 --> 00:18:24,879 Speaker 1: the time they bought Whole Foods, they said they bought 334 00:18:24,920 --> 00:18:31,080 Speaker 1: it um for reasons of, you know, accelerating their move 335 00:18:31,080 --> 00:18:33,399 Speaker 1: into the grocery store business. But those of us that 336 00:18:33,440 --> 00:18:37,200 Speaker 1: were watching their beta tests on Amazon French Fresh locally, 337 00:18:37,480 --> 00:18:40,320 Speaker 1: we're seeing that no one was using Amazon Fresh. And 338 00:18:40,840 --> 00:18:45,760 Speaker 1: you've got a young, very highly target it's a target market. 339 00:18:45,800 --> 00:18:48,639 Speaker 1: And if Amazon Fresh isn't working in Seattle, it's not working. 340 00:18:48,960 --> 00:18:52,520 Speaker 1: So then they had to try something else. Uh So 341 00:18:52,600 --> 00:18:54,879 Speaker 1: what happened was Kroger stock fell all the way to 342 00:18:54,920 --> 00:18:57,440 Speaker 1: twenty one, and that's what got us involved. Okay, it 343 00:18:57,600 --> 00:19:01,240 Speaker 1: sent rebounded to about thirty three, and we just stayed 344 00:19:01,240 --> 00:19:03,879 Speaker 1: with the original position. And now it's back down to 345 00:19:04,000 --> 00:19:11,800 Speaker 1: about to share and we believe, we sincerely believe that, uh, 346 00:19:12,040 --> 00:19:15,359 Speaker 1: the grocery business is going to be to Amazon what 347 00:19:15,600 --> 00:19:19,679 Speaker 1: invading Russia was for Napoleon. Right, let's let's go to 348 00:19:19,720 --> 00:19:22,240 Speaker 1: that first of all. For you've been in this business 349 00:19:22,359 --> 00:19:25,520 Speaker 1: forever thirty some odd years and money management business. When 350 00:19:25,520 --> 00:19:27,760 Speaker 1: you have a position like this is pretty significant position 351 00:19:27,760 --> 00:19:30,320 Speaker 1: for you, it takes a hit today. What do you 352 00:19:30,320 --> 00:19:33,200 Speaker 1: do on that position? Well, when you're a value guy 353 00:19:33,560 --> 00:19:37,840 Speaker 1: and value has significantly underperformed growth over the course of 354 00:19:37,840 --> 00:19:42,199 Speaker 1: the last two or three years. Uh, it's kind of 355 00:19:42,240 --> 00:19:46,080 Speaker 1: a body below. You hate to have x amount of 356 00:19:46,080 --> 00:19:48,120 Speaker 1: of the value of your portfolio get hit on one 357 00:19:48,160 --> 00:19:52,720 Speaker 1: stock like that. But it's been a fairly regular exercise 358 00:19:53,119 --> 00:19:55,760 Speaker 1: for the last couple of years, so we're just getting 359 00:19:55,880 --> 00:19:59,439 Speaker 1: used to it. We're getting because, Uh, the fact of 360 00:19:59,480 --> 00:20:03,720 Speaker 1: the matter is that our portfolio trades at about twelve 361 00:20:03,800 --> 00:20:07,280 Speaker 1: times earnings in a market that trades about sixteen and 362 00:20:07,320 --> 00:20:11,480 Speaker 1: so we're paying way less for our future luck than 363 00:20:11,680 --> 00:20:15,399 Speaker 1: than the index ers are. And and really, how you 364 00:20:15,480 --> 00:20:19,720 Speaker 1: do relatively the index is a combination of what you buy, 365 00:20:19,760 --> 00:20:23,960 Speaker 1: but also what you paid to participate in what you buy, 366 00:20:24,080 --> 00:20:26,720 Speaker 1: and and that's where the value is. If if if 367 00:20:27,119 --> 00:20:31,000 Speaker 1: we analyze the company and use very very high interest 368 00:20:31,080 --> 00:20:33,720 Speaker 1: rates to do an intrinsic value calculation, and there's a 369 00:20:33,760 --> 00:20:36,199 Speaker 1: big spread there, as there are with most of the 370 00:20:36,200 --> 00:20:39,439 Speaker 1: things we own right now. Uh. We we know that 371 00:20:39,600 --> 00:20:42,159 Speaker 1: time is our ally and so we're operating in a 372 00:20:42,240 --> 00:20:45,000 Speaker 1: five to ten year continuum, and most of the market 373 00:20:45,040 --> 00:20:47,640 Speaker 1: participants are working in a six month continuum. If you're 374 00:20:47,640 --> 00:20:49,800 Speaker 1: in a six month continuum and you get kicked around 375 00:20:49,800 --> 00:20:52,960 Speaker 1: on a stock like Kroger, you're sunk because you're not 376 00:20:53,000 --> 00:20:55,520 Speaker 1: gonna so do you take to do you just say 377 00:20:55,680 --> 00:20:57,760 Speaker 1: we still like this position, that the thesis is still 378 00:20:57,760 --> 00:20:59,560 Speaker 1: there and we buy more on the weakness. Yeah, the 379 00:21:00,000 --> 00:21:03,000 Speaker 1: grocery business has a history of not being very good 380 00:21:03,960 --> 00:21:07,360 Speaker 1: unless there's some inflation. It's actually good for the grocery 381 00:21:07,359 --> 00:21:10,560 Speaker 1: store companies to have the produce and the meat and 382 00:21:10,560 --> 00:21:13,679 Speaker 1: and those ingredients going up in price, and they just 383 00:21:13,720 --> 00:21:16,359 Speaker 1: aren't going up right now, and so therefore it was 384 00:21:16,400 --> 00:21:18,680 Speaker 1: going to be a more difficult time. But I think 385 00:21:18,680 --> 00:21:21,320 Speaker 1: if you dig into the numbers with with Kroger, what 386 00:21:21,359 --> 00:21:25,399 Speaker 1: they're doing to insulate themselves and be the grocery store 387 00:21:25,880 --> 00:21:30,360 Speaker 1: of millennials ten years from now is investing in online 388 00:21:30,480 --> 00:21:32,840 Speaker 1: and pick up at the store and and and and 389 00:21:33,320 --> 00:21:36,600 Speaker 1: in their mobile capability. That that stuff costs money, and 390 00:21:36,600 --> 00:21:39,280 Speaker 1: they're doing it, but they're doing it so that they 391 00:21:39,320 --> 00:21:41,440 Speaker 1: are as successful in the future as they've been in 392 00:21:41,480 --> 00:21:43,959 Speaker 1: the past. Ten seconds. Why do you think that this 393 00:21:44,200 --> 00:21:47,720 Speaker 1: grocery business will be very difficult from Amazon? As you mentioned, Oh, well, 394 00:21:48,160 --> 00:21:51,320 Speaker 1: the the answer is having the physical stores is a 395 00:21:51,440 --> 00:21:55,920 Speaker 1: huge positive attribute. In fact, having physical stores when when 396 00:21:56,000 --> 00:21:59,639 Speaker 1: when you're doing anything online targets set in December of 397 00:21:59,680 --> 00:22:02,439 Speaker 1: their wine orders were picked up the store that saves 398 00:22:02,480 --> 00:22:06,560 Speaker 1: them five dollars or seven dollars per delivery. Interesting and 399 00:22:06,600 --> 00:22:09,400 Speaker 1: that's real money. Good Bill Smith, thanks so much for 400 00:22:09,400 --> 00:22:10,800 Speaker 1: for being here with us. I don't want a tough day. 401 00:22:10,800 --> 00:22:12,240 Speaker 1: I for you're on with one of your big names. 402 00:22:12,280 --> 00:22:15,120 Speaker 1: Share Bill Smee, chief executive officer in chief Investment officer 403 00:22:15,160 --> 00:22:19,120 Speaker 1: of SMED Capital Management, talking value investing, which has been 404 00:22:19,200 --> 00:22:22,879 Speaker 1: underperforming and tough relative to growth in this bull market. 405 00:22:23,160 --> 00:22:25,359 Speaker 1: Talking a little b about the supermarket business, which continues 406 00:22:25,400 --> 00:22:28,720 Speaker 1: to be a challenging business as it deals with changing 407 00:22:28,720 --> 00:22:45,280 Speaker 1: consumer taste as well as the Amazon effect. Well, yesterday 408 00:22:45,320 --> 00:22:49,720 Speaker 1: Facebook had some very interesting news. CEO Mark Zuckerberg published 409 00:22:49,760 --> 00:22:54,600 Speaker 1: a plan to quote pivot to privacy. This is obviously 410 00:22:54,600 --> 00:22:57,879 Speaker 1: in response to consumer backlash against big tech as it 411 00:22:57,920 --> 00:23:00,520 Speaker 1: relates to privacy. So it helps par through what is 412 00:23:00,560 --> 00:23:03,040 Speaker 1: going on in big tech and privacy. We welcome Amy 413 00:23:03,080 --> 00:23:06,399 Speaker 1: Webb Amy as Professor of Strategic Foresight at the n 414 00:23:06,520 --> 00:23:08,639 Speaker 1: y U Stern School of Business. She is also a 415 00:23:08,680 --> 00:23:11,600 Speaker 1: founder of the Future Today Institute. She joins us in 416 00:23:11,600 --> 00:23:14,800 Speaker 1: our Bloomberg eleven three OH studios. Professor Webb is also 417 00:23:14,880 --> 00:23:17,760 Speaker 1: the author of a new book entitled The Big Nine, 418 00:23:18,080 --> 00:23:23,280 Speaker 1: How the Tech Titans and their Thinking Machines Could Warp Humanity. Professor, 419 00:23:23,320 --> 00:23:25,360 Speaker 1: thank you so much for joining us. What are the 420 00:23:25,400 --> 00:23:30,399 Speaker 1: main takeaways of this book in terms of warping humanity? Sure? So, 421 00:23:30,440 --> 00:23:32,679 Speaker 1: I think the pieces of this that everybody needs to 422 00:23:32,760 --> 00:23:36,400 Speaker 1: understand is that, for a very long time are um 423 00:23:36,600 --> 00:23:40,760 Speaker 1: market economy. Free market economy has enabled big tech companies 424 00:23:40,760 --> 00:23:43,879 Speaker 1: to flourish and to deliver great returns for their investors. 425 00:23:44,240 --> 00:23:48,040 Speaker 1: The challenges that during that process UM Oftentimes there have 426 00:23:48,119 --> 00:23:52,800 Speaker 1: been decisions made in relationship privacy, UH and automation and 427 00:23:52,840 --> 00:23:55,359 Speaker 1: a bunch of other things UM that have started to 428 00:23:56,160 --> 00:23:59,560 Speaker 1: UH make the general public a little nervous, and that 429 00:23:59,720 --> 00:24:02,879 Speaker 1: is ending ripples throughout the hill. And so what we 430 00:24:02,960 --> 00:24:05,960 Speaker 1: could be facing, I think in the future is regulation 431 00:24:06,040 --> 00:24:08,240 Speaker 1: coming from from new places, which is neither good for 432 00:24:08,520 --> 00:24:11,199 Speaker 1: investors nor good for for all of us who are 433 00:24:11,200 --> 00:24:13,120 Speaker 1: going to be living with the future of AI. Well, 434 00:24:13,200 --> 00:24:16,040 Speaker 1: let's let's talk about AI, and kind of specifically about 435 00:24:16,040 --> 00:24:19,200 Speaker 1: a AI. What are the I guess the applications and 436 00:24:19,240 --> 00:24:23,240 Speaker 1: implications of AI that you think are most concerning sure, well, 437 00:24:23,440 --> 00:24:25,560 Speaker 1: you know, there's a lot of misplaced optimism and fear 438 00:24:25,640 --> 00:24:27,879 Speaker 1: when it comes to artificial intelligence, and it very much 439 00:24:27,920 --> 00:24:30,240 Speaker 1: feels like something out in the distance. In fact, it's 440 00:24:30,240 --> 00:24:32,800 Speaker 1: been with us for many years. Most businesses in some 441 00:24:32,920 --> 00:24:36,440 Speaker 1: ways use AI, whether it's their risk and compliance systems 442 00:24:36,840 --> 00:24:39,720 Speaker 1: or the auto complete and there are inboxes. All of 443 00:24:39,800 --> 00:24:42,080 Speaker 1: us are using AI all of the time. Now, the 444 00:24:42,160 --> 00:24:45,280 Speaker 1: challenges come in to play when we have a consolidation 445 00:24:45,320 --> 00:24:48,000 Speaker 1: of power among just a few companies with a fairly 446 00:24:48,040 --> 00:24:50,800 Speaker 1: homogeneous group of people whose job it is to make 447 00:24:50,840 --> 00:24:53,280 Speaker 1: decisions for all of us. And you know, for a 448 00:24:53,280 --> 00:24:56,280 Speaker 1: lot of businesses that are currently choosing which AI and 449 00:24:56,320 --> 00:24:59,960 Speaker 1: the cloud systems to use, um, you know, which Auto 450 00:25:00,040 --> 00:25:03,480 Speaker 1: made it services to use, which frameworks, um. You know, 451 00:25:03,520 --> 00:25:08,200 Speaker 1: they're they're having to choose now between Microsoft, Amazon, and Google, 452 00:25:08,520 --> 00:25:10,160 Speaker 1: which means that they're going to have to start making 453 00:25:10,240 --> 00:25:13,119 Speaker 1: much much smarter decisions. Right. So when we think so 454 00:25:13,160 --> 00:25:16,760 Speaker 1: you identifying your book, a number of players to be concerned. 455 00:25:16,760 --> 00:25:20,119 Speaker 1: What you mentioned Google, Amazon, So what species? Who are 456 00:25:20,640 --> 00:25:23,040 Speaker 1: they just us companies? Are they're also companies outside the 457 00:25:23,160 --> 00:25:25,880 Speaker 1: US show So there are nine companies that are essentially 458 00:25:25,920 --> 00:25:28,160 Speaker 1: building the future of AI doesn't mean that there aren't 459 00:25:28,200 --> 00:25:32,920 Speaker 1: others in the mix. However, these nine have the majority 460 00:25:32,920 --> 00:25:36,040 Speaker 1: of patents, they own the biggest part of market share, 461 00:25:36,359 --> 00:25:39,040 Speaker 1: They attract the best talent, They have the most significant 462 00:25:39,040 --> 00:25:42,879 Speaker 1: amount of funding. So so these nine companies are in China, 463 00:25:43,320 --> 00:25:45,720 Speaker 1: there are three the bat Bay, Do, Ali, Baba and 464 00:25:45,800 --> 00:25:48,760 Speaker 1: ten Cent. In the United States, I like to call 465 00:25:48,800 --> 00:25:55,840 Speaker 1: them the g mafia. So those six are Google, Amazon, Microsoft, IBM, Facebook, 466 00:25:55,880 --> 00:25:59,600 Speaker 1: and Apple. Doesn't mean that Uber and Salesforce and others 467 00:25:59,640 --> 00:26:01,920 Speaker 1: aren't in the mixed doing great things. But these nine 468 00:26:02,080 --> 00:26:05,680 Speaker 1: companies for the most part, have consolidated power um and 469 00:26:05,680 --> 00:26:08,960 Speaker 1: and are are pulling shots. And so what you mentioned 470 00:26:09,000 --> 00:26:12,720 Speaker 1: regulatory risk, and I think for investors the big concern 471 00:26:12,840 --> 00:26:15,720 Speaker 1: out there has always been regulatory risk, not coming out 472 00:26:15,720 --> 00:26:17,719 Speaker 1: of Europe the East. You know, the European Union has 473 00:26:17,760 --> 00:26:19,600 Speaker 1: always been tough on US tech, going all the way 474 00:26:19,640 --> 00:26:22,280 Speaker 1: back to Microsoft and the operating system and and now 475 00:26:22,320 --> 00:26:24,520 Speaker 1: they're obviously, you know, tough on some of these social 476 00:26:24,560 --> 00:26:27,520 Speaker 1: media companies. But boy, when you bring Mark Zuckerberg and 477 00:26:27,800 --> 00:26:29,960 Speaker 1: some of these other CEOs in front of Congress, that's 478 00:26:30,000 --> 00:26:32,080 Speaker 1: a different game. Do you think the US Congress has 479 00:26:32,119 --> 00:26:35,480 Speaker 1: any appetite for regulating big tech. Well, I think this 480 00:26:35,520 --> 00:26:38,360 Speaker 1: new Congress certainly does. Uh. And but but that's that's 481 00:26:38,359 --> 00:26:40,879 Speaker 1: a problem for many reasons. First of all, uh, you 482 00:26:40,880 --> 00:26:43,160 Speaker 1: know there listen, there's a lot of smart engineers working 483 00:26:43,200 --> 00:26:45,720 Speaker 1: at these big companies, and I actually don't believe that 484 00:26:45,760 --> 00:26:48,480 Speaker 1: any of these companies are intentionally doing anything evil. I 485 00:26:48,520 --> 00:26:50,200 Speaker 1: think just you know, when you get to be big 486 00:26:50,240 --> 00:26:52,840 Speaker 1: and you've got different business units and staff, sometimes they 487 00:26:52,880 --> 00:26:54,840 Speaker 1: don't all talk to each other, and you've got to 488 00:26:54,880 --> 00:26:58,800 Speaker 1: start making quick decisions because investors, you know, are are 489 00:26:58,880 --> 00:27:02,840 Speaker 1: hungry for high return earns and and good margins. So um, 490 00:27:02,880 --> 00:27:05,600 Speaker 1: the challenges that you know, we're gonna have one too 491 00:27:05,640 --> 00:27:08,320 Speaker 1: many calls for privacy, one too many sets of constituents 492 00:27:08,359 --> 00:27:11,639 Speaker 1: who get really upset. Um. And given who's currently in 493 00:27:11,720 --> 00:27:14,959 Speaker 1: Congress right now, I can guarantee you that they're you know, 494 00:27:15,040 --> 00:27:18,199 Speaker 1: they're they're looking at regulatory action, which is which is 495 00:27:18,520 --> 00:27:20,320 Speaker 1: is going to be bad for all of these companies, 496 00:27:20,359 --> 00:27:23,960 Speaker 1: but also for us because any regulations that get constructed 497 00:27:24,000 --> 00:27:26,560 Speaker 1: now are definitely not going to keep pace with how 498 00:27:26,560 --> 00:27:29,000 Speaker 1: technology evolves. Yeah, yeah, exactly. I think that's one of 499 00:27:29,040 --> 00:27:31,080 Speaker 1: the issues we've seen some other tech tech industries, they're 500 00:27:31,080 --> 00:27:34,520 Speaker 1: always oftentimes behind behind the terms. Amy Web, thank you 501 00:27:34,560 --> 00:27:37,639 Speaker 1: so much for joining us. Amys Professor of Strategic Foresight 502 00:27:37,680 --> 00:27:39,600 Speaker 1: at n y U Stern School of Business, also a 503 00:27:39,680 --> 00:27:43,080 Speaker 1: founder of the Future Today Institute. Her book The Big Nine, 504 00:27:43,080 --> 00:27:45,800 Speaker 1: How the Tech Titans and their Thinking Machines Could Warp Humanity. 505 00:27:45,880 --> 00:27:49,160 Speaker 1: Thank you very much, Professor. That's very interesting about technology. Again, 506 00:27:49,440 --> 00:27:53,480 Speaker 1: the big risk for tech investors is US regulatory oversight, 507 00:27:53,520 --> 00:27:56,600 Speaker 1: and that's something they want to avoid at all all costs. 508 00:27:57,000 --> 00:27:59,240 Speaker 1: Thanks for listening to the Bloomberg P and L podcast. 509 00:27:59,359 --> 00:28:01,480 Speaker 1: You can subscribe. I've been listening to interviews at Apple 510 00:28:01,520 --> 00:28:04,880 Speaker 1: Podcasts or whatever podcast platform you prefer. I'm Paul Sweeney. 511 00:28:04,920 --> 00:28:07,639 Speaker 1: I'm on Twitter at pt Sweeney. I'm Lisa abram Woits 512 00:28:07,680 --> 00:28:10,680 Speaker 1: I'm on Twitter at Lisa abram Woits one. Before the podcast, 513 00:28:10,720 --> 00:28:13,320 Speaker 1: you can always catch us worldwide. I'm Bloomberg Radio