1 00:00:00,120 --> 00:00:04,360 Speaker 1: Bloomberg is now on your dashboard with Apple CarPlay and 2 00:00:04,360 --> 00:00:08,160 Speaker 1: Android Auto. It gives you access to every Bloomberg podcast, 3 00:00:08,280 --> 00:00:11,560 Speaker 1: live audio feeds from Bloomberg Radio, print stories from Bloomberg 4 00:00:11,640 --> 00:00:14,920 Speaker 1: News in audio form, and the latest headlines of the 5 00:00:14,920 --> 00:00:18,600 Speaker 1: click of a button with Bloomberg News. Now it's free 6 00:00:18,680 --> 00:00:21,439 Speaker 1: with the latest version of the Bloomberg Business App. That's 7 00:00:21,680 --> 00:00:24,400 Speaker 1: the Bloomberg Business App. Get it on your phone in 8 00:00:24,440 --> 00:00:27,760 Speaker 1: the Apple App Store or on Google Play. Just download 9 00:00:27,800 --> 00:00:30,560 Speaker 1: the app, connect your phone to your car and get started. 10 00:00:30,960 --> 00:00:34,400 Speaker 1: And it's all presented by our sponsor, Interactive Brokers. 11 00:00:35,400 --> 00:00:38,600 Speaker 2: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney alongside 12 00:00:38,640 --> 00:00:39,800 Speaker 2: my co host Matt Miller. 13 00:00:40,200 --> 00:00:44,279 Speaker 1: Every business day we bring you interviews from CEOs, market pros, 14 00:00:44,320 --> 00:00:48,160 Speaker 1: and Bloomberg experts, along with essential market moven News. 15 00:00:48,720 --> 00:00:51,839 Speaker 2: Find the Bloomberg Markets podcast called Apple Podcasts or wherever 16 00:00:51,960 --> 00:00:54,560 Speaker 2: you listen to podcasts, and at Bloomberg dot com slash 17 00:00:54,600 --> 00:00:58,120 Speaker 2: podcast here. And you want to talk rates and fed 18 00:00:58,240 --> 00:01:01,360 Speaker 2: and all those bankers stuff. You turn to Ira, Jersey. 19 00:01:01,680 --> 00:01:04,679 Speaker 2: You know, you head down the turnpike, the Parkway Route one. 20 00:01:04,720 --> 00:01:07,480 Speaker 2: You get to Princeton, New Jersey, and that's where Ira 21 00:01:07,640 --> 00:01:10,800 Speaker 2: Jersey is with the Bloomberg Intelligence Strategy team. 22 00:01:10,880 --> 00:01:13,120 Speaker 1: O way on your way there, you can stop for 23 00:01:13,280 --> 00:01:16,360 Speaker 1: gas and fill up for what three dollars and thirteen cents? 24 00:01:16,640 --> 00:01:19,360 Speaker 2: Yes on roote seventy one in Wall Township this weekend, 25 00:01:19,480 --> 00:01:20,679 Speaker 2: which all. 26 00:01:20,640 --> 00:01:24,400 Speaker 1: Time, you know, just a drop for the energy prices. 27 00:01:24,440 --> 00:01:26,840 Speaker 1: And that's part of the reason, Ira, that we saw 28 00:01:26,880 --> 00:01:32,440 Speaker 1: this inflation report come in light. It's good news obviously 29 00:01:32,760 --> 00:01:35,880 Speaker 1: for the Fed if you buy it, it's good news 30 00:01:36,120 --> 00:01:37,440 Speaker 1: for the country as well. 31 00:01:37,800 --> 00:01:41,400 Speaker 3: Does this mean no more hikes are necessary? 32 00:01:41,520 --> 00:01:41,720 Speaker 4: Yeah? 33 00:01:41,760 --> 00:01:44,400 Speaker 5: I think you know, this number takes hikes off the table, 34 00:01:44,440 --> 00:01:46,679 Speaker 5: I think at this point. And yeah, you know, like 35 00:01:46,760 --> 00:01:49,400 Speaker 5: you mentioned the WORP function and just looking at what 36 00:01:49,760 --> 00:01:53,040 Speaker 5: short term interest rate products are doing, we're now pricing 37 00:01:53,080 --> 00:01:57,040 Speaker 5: in for even more cuts in twenty twenty four. I'm 38 00:01:57,040 --> 00:01:59,880 Speaker 5: still skeptical that the Federal Reserve will actually cut, but 39 00:02:00,040 --> 00:02:02,920 Speaker 5: certainly the market is thinking that there's a risk of that. 40 00:02:03,120 --> 00:02:05,440 Speaker 5: And you know, this is what's been happening though, is 41 00:02:05,480 --> 00:02:09,240 Speaker 5: that the market's been extrapolating these trends from single numbers, 42 00:02:09,400 --> 00:02:12,160 Speaker 5: and you know, we have to think that, Okay, maybe 43 00:02:12,160 --> 00:02:15,280 Speaker 5: we got a zero inflation this month, but next month. 44 00:02:15,280 --> 00:02:16,280 Speaker 4: If we get a point. 45 00:02:16,000 --> 00:02:18,640 Speaker 5: One or a point two, it's still slower inflation than 46 00:02:18,639 --> 00:02:21,120 Speaker 5: we had earlier this year, but it's still, you know, 47 00:02:21,200 --> 00:02:24,200 Speaker 5: not going to be necessarily near the Fed's comfort zone, 48 00:02:24,200 --> 00:02:25,000 Speaker 5: at least not yet. 49 00:02:25,720 --> 00:02:28,280 Speaker 2: So what other parts of the market do you look 50 00:02:28,280 --> 00:02:31,280 Speaker 2: at to get a sense of kind of where you 51 00:02:31,280 --> 00:02:34,200 Speaker 2: know future rates may go here? 52 00:02:35,919 --> 00:02:38,560 Speaker 5: Yeah, so the big market now used to be the 53 00:02:38,639 --> 00:02:41,760 Speaker 5: euro dollar market and not meeting the actual dollar, but 54 00:02:41,880 --> 00:02:46,640 Speaker 5: meaning dollars in Europe and interest rates on short term 55 00:02:47,080 --> 00:02:48,760 Speaker 5: on short term products. So if you look at the 56 00:02:48,880 --> 00:02:52,960 Speaker 5: SOFUR futures now you've seen a big shift over. 57 00:02:52,800 --> 00:02:53,680 Speaker 4: The last two weeks. 58 00:02:53,720 --> 00:02:57,079 Speaker 5: Where SOFA was thinking at the end of twenty twenty 59 00:02:57,080 --> 00:03:01,160 Speaker 5: five was going to be that we were going to 60 00:03:01,240 --> 00:03:04,440 Speaker 5: have short term interest rates somewhere around three and a 61 00:03:04,480 --> 00:03:07,760 Speaker 5: half percent. Now it's even lower than that. Right, So 62 00:03:08,360 --> 00:03:13,040 Speaker 5: we're seeing some shifts in the market that like twenty 63 00:03:13,040 --> 00:03:15,520 Speaker 5: five basis points over the last two weeks in the 64 00:03:15,560 --> 00:03:17,520 Speaker 5: market's expectation for where rates are going to be in 65 00:03:17,520 --> 00:03:18,079 Speaker 5: two years. 66 00:03:18,120 --> 00:03:20,079 Speaker 4: So SOFA futures are a big one. 67 00:03:20,400 --> 00:03:23,560 Speaker 5: Obviously Fed funds futures as well, but Fed funds futures 68 00:03:23,600 --> 00:03:26,120 Speaker 5: most of the volume is really in the first six 69 00:03:26,240 --> 00:03:30,200 Speaker 5: months of those contracts. So you looking at longer term 70 00:03:30,240 --> 00:03:33,160 Speaker 5: and what the market's thinking for policy rates, SOFA is 71 00:03:33,160 --> 00:03:34,680 Speaker 5: the much better product for that. 72 00:03:34,800 --> 00:03:37,280 Speaker 3: By way, Paul, you have made a killing on your 73 00:03:37,280 --> 00:03:37,840 Speaker 3: two years. 74 00:03:38,400 --> 00:03:38,720 Speaker 4: Sure. 75 00:03:38,880 --> 00:03:41,640 Speaker 1: You know, as the rate obviously comes down the price 76 00:03:41,680 --> 00:03:44,400 Speaker 1: goes up. You don't need to worry about reinvestment risk. 77 00:03:44,440 --> 00:03:46,640 Speaker 1: You can sell those things out of profit. Exactly right. 78 00:03:46,680 --> 00:03:48,960 Speaker 1: I mean just today, I mean we had five spots 79 00:03:49,040 --> 00:03:51,320 Speaker 1: zero four. Now we're four point eighty five percent of 80 00:03:51,440 --> 00:03:52,520 Speaker 1: just a big move into short term. 81 00:03:52,640 --> 00:03:54,880 Speaker 3: Yeah, these big moves keep happening. 82 00:03:55,120 --> 00:03:58,920 Speaker 1: Is there any problem with this much volatility in the 83 00:03:58,960 --> 00:04:04,680 Speaker 1: treasury market? I'm looking at twenty basis point drops on threes, fives, 84 00:04:04,720 --> 00:04:07,680 Speaker 1: and sevens and almost that much eighteen and change on 85 00:04:08,000 --> 00:04:08,880 Speaker 1: twos and tens. 86 00:04:09,200 --> 00:04:10,400 Speaker 3: Is that a problem? 87 00:04:11,160 --> 00:04:12,240 Speaker 4: I don't think it's a problem. 88 00:04:12,240 --> 00:04:14,520 Speaker 5: I think it's a sign of the new environment that 89 00:04:14,560 --> 00:04:18,200 Speaker 5: we're in with the amount of with the amount of 90 00:04:18,360 --> 00:04:22,200 Speaker 5: bonds that are outstanding versus the capacity of dealers and 91 00:04:22,279 --> 00:04:26,200 Speaker 5: other intermediaries to actually see all the flow and take 92 00:04:26,240 --> 00:04:29,240 Speaker 5: on risks to redistribute that risk at some point is 93 00:04:29,320 --> 00:04:31,400 Speaker 5: much lower today. And you know, part of this is 94 00:04:31,480 --> 00:04:34,760 Speaker 5: electronification of the markets and the like, so you're. 95 00:04:34,680 --> 00:04:35,920 Speaker 4: Likely to continue to see these. 96 00:04:36,200 --> 00:04:40,120 Speaker 5: So actually, we were just looking this morning at comparing where, 97 00:04:40,600 --> 00:04:43,120 Speaker 5: you know, the types of moves we're seeing now versus 98 00:04:43,400 --> 00:04:46,200 Speaker 5: say nineteen ninety seven, when you had interest rates that 99 00:04:46,240 --> 00:04:47,680 Speaker 5: were a little bit higher than they are now, but 100 00:04:47,800 --> 00:04:50,320 Speaker 5: six seven percent on the ten year instead of you know, 101 00:04:50,320 --> 00:04:53,760 Speaker 5: four and a half five, But you actually had regularly 102 00:04:54,200 --> 00:04:58,000 Speaker 5: fifteen twenty basis point moves. So you know, given the 103 00:04:58,120 --> 00:05:00,400 Speaker 5: level of rates, this is not that unusual. Well, I 104 00:05:00,400 --> 00:05:02,040 Speaker 5: think the thing is that we were used to the 105 00:05:02,120 --> 00:05:05,320 Speaker 5: last decade when interest rates were near zero and when 106 00:05:05,360 --> 00:05:07,960 Speaker 5: you had tenure yields at two percent, you know, a 107 00:05:08,000 --> 00:05:11,200 Speaker 5: five basis point move seemed really big. Now that you're 108 00:05:11,240 --> 00:05:14,080 Speaker 5: at you know, five percent, then you know, maybe a 109 00:05:14,120 --> 00:05:16,520 Speaker 5: ten basis point move or a fifteen basis point move 110 00:05:16,640 --> 00:05:18,960 Speaker 5: is kind of you know, on the larger size, but 111 00:05:19,040 --> 00:05:20,719 Speaker 5: also not particularly unusual. 112 00:05:20,800 --> 00:05:22,960 Speaker 4: So so I think you'll see more of these. 113 00:05:23,240 --> 00:05:27,080 Speaker 5: Partially doe to rate, partially due to shifts in the 114 00:05:27,080 --> 00:05:31,320 Speaker 5: market structure that you know, just going to provide for more, 115 00:05:32,080 --> 00:05:34,280 Speaker 5: you know, more jumps in the market when people are 116 00:05:34,400 --> 00:05:36,760 Speaker 5: one way and you know, want to get want to 117 00:05:36,800 --> 00:05:40,000 Speaker 5: get out of risk or into risk in because of 118 00:05:40,080 --> 00:05:42,360 Speaker 5: data or because of some news out of the Fed, 119 00:05:42,400 --> 00:05:42,960 Speaker 5: for example. 120 00:05:43,400 --> 00:05:46,200 Speaker 2: So I when I first got the Salomon Brothers, the 121 00:05:46,440 --> 00:05:49,679 Speaker 2: Salomon had like an army of government bond traders, no kidding, 122 00:05:49,720 --> 00:05:51,360 Speaker 2: I mean just dozens of them, and they were all 123 00:05:51,360 --> 00:05:55,440 Speaker 2: trading for their book, trading for clients, and they made 124 00:05:55,440 --> 00:05:57,480 Speaker 2: a lot of money. I'm guessing they also lost money 125 00:05:57,760 --> 00:05:59,839 Speaker 2: from time to time. Does that even exist on Wall Street? 126 00:05:59,880 --> 00:06:03,200 Speaker 2: And more? Do government trading desks exists? And do they? 127 00:06:03,320 --> 00:06:04,640 Speaker 2: Are they profit centers anymore? 128 00:06:05,920 --> 00:06:10,000 Speaker 5: So there are government Yeah, there are clearly government bond traders. 129 00:06:10,040 --> 00:06:13,320 Speaker 5: I think that the what what you've seen, and this started, 130 00:06:13,440 --> 00:06:16,080 Speaker 5: you know, in the last twenty odd years, you know, 131 00:06:16,120 --> 00:06:19,320 Speaker 5: and progressed while I was on the street myself, is 132 00:06:19,360 --> 00:06:22,600 Speaker 5: that you went from having you know, five six people 133 00:06:22,800 --> 00:06:27,000 Speaker 5: trading treasuries and just treasuries to having somewhat less than 134 00:06:27,040 --> 00:06:29,720 Speaker 5: that because you have more automated pricing. 135 00:06:29,800 --> 00:06:31,680 Speaker 4: So you get auto pricers. 136 00:06:31,200 --> 00:06:35,240 Speaker 5: And electronic trading platforms that basically you know, look at 137 00:06:35,240 --> 00:06:38,279 Speaker 5: supply and demand and and you know price smaller trades 138 00:06:39,240 --> 00:06:43,480 Speaker 5: in in you know, just electronically without necessarily the dealer 139 00:06:43,560 --> 00:06:46,520 Speaker 5: having to take much risk. But for those bigger trades, 140 00:06:46,560 --> 00:06:50,039 Speaker 5: and for trades that you know of decent size, you 141 00:06:50,120 --> 00:06:52,360 Speaker 5: still have a person who has to accept that trade 142 00:06:52,640 --> 00:06:54,919 Speaker 5: because whether or not it fits their risk limits of 143 00:06:54,960 --> 00:06:55,719 Speaker 5: their trading book. 144 00:06:56,200 --> 00:06:57,359 Speaker 4: And banks make money on this. 145 00:06:57,480 --> 00:06:59,560 Speaker 5: You know, you talk to you know, Alison Williams, who 146 00:06:59,560 --> 00:07:02,799 Speaker 5: covers the lot of the dealers here at Bloomberg Intelligence, 147 00:07:02,839 --> 00:07:06,080 Speaker 5: and you look and you know, sometimes fixed income trading. 148 00:07:07,360 --> 00:07:09,080 Speaker 4: Is a big profit center and in other times. 149 00:07:09,080 --> 00:07:11,600 Speaker 5: Obviously twenty twenty two, for example, was not a particularly 150 00:07:11,640 --> 00:07:15,440 Speaker 5: good year for trading, primarily because you get stuck with risk, 151 00:07:15,760 --> 00:07:18,560 Speaker 5: and because you get stuck with that risk, you wind 152 00:07:18,640 --> 00:07:20,960 Speaker 5: up losing money when when bond prices go down and 153 00:07:21,000 --> 00:07:24,920 Speaker 5: yields go up. So I think that that you know, 154 00:07:25,160 --> 00:07:29,640 Speaker 5: the market's changed, and certainly the size of the market 155 00:07:29,680 --> 00:07:32,080 Speaker 5: and the size of the dealer community has shrunk a 156 00:07:32,120 --> 00:07:35,000 Speaker 5: little bit, right in terms of personnel and liquidity and 157 00:07:35,320 --> 00:07:38,360 Speaker 5: also risk taking. Yeah, liquidity is definitely lower. So you 158 00:07:38,360 --> 00:07:41,440 Speaker 5: look at the Bloomberg Liquidity Index, which basically shows you 159 00:07:41,520 --> 00:07:43,880 Speaker 5: how much off the run bond. So these are aren't 160 00:07:43,960 --> 00:07:46,640 Speaker 5: like the current tenure, but the tenure issued. 161 00:07:46,360 --> 00:07:47,280 Speaker 4: Say six months ago. 162 00:07:47,880 --> 00:07:51,600 Speaker 5: Liquidity in those securities in particular has gone way down, 163 00:07:52,400 --> 00:07:54,680 Speaker 5: and that's really where I think you see some of 164 00:07:54,720 --> 00:07:58,200 Speaker 5: the pain points and one reason why dealers, you know, 165 00:07:58,240 --> 00:08:00,360 Speaker 5: aren't willing to take as much risk and some of 166 00:08:00,360 --> 00:08:03,160 Speaker 5: those off the run security. So getting out of out 167 00:08:03,200 --> 00:08:05,360 Speaker 5: of risk sometimes is going to be much more difficult 168 00:08:05,400 --> 00:08:08,160 Speaker 5: today than it had been in past years. 169 00:08:08,480 --> 00:08:10,280 Speaker 2: Yeah, I don't know. I'm sticking with my thought that 170 00:08:10,520 --> 00:08:12,560 Speaker 2: we had way more funnel Wall Street than the kids 171 00:08:12,600 --> 00:08:15,280 Speaker 2: do today. I'm sure you're sticking with that point. Well, 172 00:08:15,280 --> 00:08:17,720 Speaker 2: it depends which kids, right, Yeah, I'm just you know, 173 00:08:17,720 --> 00:08:20,560 Speaker 2: I walk on a desk today, I'm like, there's nobody there. 174 00:08:20,600 --> 00:08:22,880 Speaker 2: I mean, you know, there's a relatively no. I don't know, 175 00:08:23,200 --> 00:08:25,280 Speaker 2: but anyway, they're making tons of money over there, so 176 00:08:25,320 --> 00:08:28,240 Speaker 2: good for them. Iver Jersey, chief US interust Rate Strategists 177 00:08:28,240 --> 00:08:29,440 Speaker 2: for Bloomberg Intelligence. 178 00:08:30,600 --> 00:08:33,960 Speaker 6: You're listening to the team Can's are live program Bloomberg 179 00:08:34,080 --> 00:08:37,440 Speaker 6: Markets weekdays at ten am Eastern on Bloomberg dot com, 180 00:08:37,520 --> 00:08:40,640 Speaker 6: the iHeartRadio app and the Bloomberg Business app, or listen 181 00:08:40,720 --> 00:08:42,840 Speaker 6: on demand wherever you get your podcasts. 182 00:08:45,280 --> 00:08:47,880 Speaker 2: Mikey Shaw he is one of our senior analysts over 183 00:08:47,880 --> 00:08:51,040 Speaker 2: in London. Covering a farmer space. He's been doing it 184 00:08:51,080 --> 00:08:53,360 Speaker 2: a long time along with that guy, Sam Fazzelli. Mike's 185 00:08:53,400 --> 00:08:55,920 Speaker 2: got much better hair than Sam Fazelli. Will go there 186 00:08:56,000 --> 00:09:01,280 Speaker 2: right away. I'm jealous exactly. It's over flowing. So Mike, 187 00:09:01,360 --> 00:09:03,200 Speaker 2: you guys have done a ton of work on these 188 00:09:03,280 --> 00:09:06,320 Speaker 2: g LP one drugs. Can you frame out We've had 189 00:09:06,360 --> 00:09:08,280 Speaker 2: a little bit of time now to kind of think 190 00:09:08,320 --> 00:09:10,720 Speaker 2: about this and look at the different products out there. 191 00:09:11,080 --> 00:09:15,240 Speaker 2: But where are we in terms of developing that market, 192 00:09:15,320 --> 00:09:17,160 Speaker 2: that part of the market. 193 00:09:16,840 --> 00:09:19,360 Speaker 7: I mean, in terms of penetration rates with GLP one 194 00:09:19,400 --> 00:09:22,120 Speaker 7: drugs for obesity, I think we're still in the very 195 00:09:22,120 --> 00:09:26,920 Speaker 7: early stages. Excitement around the classes, you know, is constantly growing, 196 00:09:27,920 --> 00:09:31,200 Speaker 7: even more so after this weekend where we saw the 197 00:09:30,559 --> 00:09:34,480 Speaker 7: car the detailed cardiovascular outcomes data in the best patients 198 00:09:34,880 --> 00:09:38,720 Speaker 7: forward go V. I think that was met very positively 199 00:09:39,440 --> 00:09:43,680 Speaker 7: by the prescriber community in general. We saw kind of 200 00:09:43,720 --> 00:09:47,040 Speaker 7: early benefits as early as day one in terms of 201 00:09:47,679 --> 00:09:52,240 Speaker 7: you know, these drugs can reduce major adverse cardiovasco events 202 00:09:53,080 --> 00:09:57,120 Speaker 7: in you in these heavy abest patients. I mean, but 203 00:09:57,160 --> 00:09:59,400 Speaker 7: there's also still a lot to be kind of learnt 204 00:09:59,840 --> 00:10:03,720 Speaker 7: in in terms of you know, the mechanism itself. When 205 00:10:03,720 --> 00:10:06,840 Speaker 7: we look at the I mean, the two curves of 206 00:10:06,840 --> 00:10:10,720 Speaker 7: interests are are the you know, the curve aster outcomes 207 00:10:10,760 --> 00:10:15,200 Speaker 7: curve and then the body weight curve so well. 208 00:10:14,840 --> 00:10:18,880 Speaker 1: And diabetes, right because if you're pre diabetic and you 209 00:10:18,920 --> 00:10:22,720 Speaker 1: can't stop eating hagendas, this thing could be a lifesaver. 210 00:10:23,320 --> 00:10:24,280 Speaker 8: Yeah. So, I mean. 211 00:10:25,800 --> 00:10:27,760 Speaker 7: So, I mean when we look at obesity, it's obviously 212 00:10:27,800 --> 00:10:32,440 Speaker 7: it's a risk factor for you know, diabetes, and in 213 00:10:32,480 --> 00:10:37,400 Speaker 7: that data set, it showed that, you know, it definitely 214 00:10:37,440 --> 00:10:43,880 Speaker 7: cut the move of pre diabetics to you know, developing diabetes. 215 00:10:45,320 --> 00:10:48,320 Speaker 7: And I mean the key question though with with the 216 00:10:48,440 --> 00:10:52,280 Speaker 7: data is, yes, body weight is a contributing factor, but 217 00:10:52,400 --> 00:10:55,400 Speaker 7: given the disparities we saw in terms of maximum weight 218 00:10:55,440 --> 00:10:58,920 Speaker 7: loss being achieved and that early separation of the core 219 00:10:59,320 --> 00:11:02,680 Speaker 7: of the of the Calleavascar outcomes curve, it's not the 220 00:11:02,760 --> 00:11:06,000 Speaker 7: soul you know driver, so that there's. 221 00:11:05,840 --> 00:11:07,880 Speaker 3: A secret ingredient what's doing it? 222 00:11:08,280 --> 00:11:10,679 Speaker 7: Yeah, I mean that's what Nave said. It's got an 223 00:11:10,800 --> 00:11:13,640 Speaker 7: X factor which we you know, we need to find 224 00:11:13,679 --> 00:11:14,280 Speaker 7: out more about. 225 00:11:14,440 --> 00:11:15,240 Speaker 3: But that's what you did. 226 00:11:15,240 --> 00:11:18,200 Speaker 2: Like this weekend, you're at a conference, right, Yeah, up 227 00:11:18,200 --> 00:11:19,200 Speaker 2: in Boston. Where was it. 228 00:11:19,200 --> 00:11:20,040 Speaker 3: It was in Philadelphia. 229 00:11:20,040 --> 00:11:20,559 Speaker 2: Philadelphia. 230 00:11:20,800 --> 00:11:24,280 Speaker 1: Oh so you saw the apparently multiple rounds of applause 231 00:11:24,520 --> 00:11:25,800 Speaker 1: when the data were released. 232 00:11:25,920 --> 00:11:27,760 Speaker 3: Yeah, so the room was full. 233 00:11:28,000 --> 00:11:29,680 Speaker 7: It's like the sounding evasion. 234 00:11:29,800 --> 00:11:33,480 Speaker 2: Okay, Okay. When I go to a conference, this is 235 00:11:33,480 --> 00:11:37,480 Speaker 2: my schedule. Get up, have a little business breakfast, maybe 236 00:11:37,480 --> 00:11:39,600 Speaker 2: a little business lunch, maybe meeting in afternoon, and then 237 00:11:39,640 --> 00:11:41,840 Speaker 2: boom dinner. Then we go to the craps table in Vegas. 238 00:11:42,040 --> 00:11:45,040 Speaker 2: That's how you do an investor conference. These guys healthcare. 239 00:11:45,200 --> 00:11:47,880 Speaker 2: They go on the weekend and they go to these 240 00:11:47,880 --> 00:11:50,440 Speaker 2: conference rooms for like ten hours and have to deal 241 00:11:50,480 --> 00:11:53,600 Speaker 2: with all this medical scientific data. And you guys actually 242 00:11:53,640 --> 00:11:56,640 Speaker 2: have to write research notes based upon this stuff on 243 00:11:56,720 --> 00:11:59,560 Speaker 2: a weekend. By the way, that's what you guys do, right, 244 00:11:59,640 --> 00:12:01,960 Speaker 2: Yeah about See, that's no way to give it. 245 00:12:02,000 --> 00:12:03,960 Speaker 1: Your badge is worth two drinks at the meat and 246 00:12:04,000 --> 00:12:05,640 Speaker 1: greet after exactly. 247 00:12:05,920 --> 00:12:09,240 Speaker 2: All right, So but in reality, how have you guys 248 00:12:09,240 --> 00:12:10,080 Speaker 2: sized out this market? 249 00:12:10,160 --> 00:12:13,480 Speaker 1: Because listen, listen, I have some important questions, okay, because 250 00:12:13,520 --> 00:12:15,680 Speaker 1: I want to get this. I've got a doctor's appointment 251 00:12:15,679 --> 00:12:18,200 Speaker 1: on December seventh, and hopefully he's gonna hook me up. 252 00:12:18,240 --> 00:12:19,600 Speaker 3: I need to lose twenty pounds. 253 00:12:19,880 --> 00:12:22,480 Speaker 1: I want to avoid getting diabetes and I don't want 254 00:12:22,520 --> 00:12:25,959 Speaker 1: to die of a heart attack. Then, you know, and 255 00:12:26,320 --> 00:12:28,760 Speaker 1: I have very weak I have no willpower, right, so 256 00:12:28,920 --> 00:12:30,760 Speaker 1: I'm hoping that this drug can do this for me 257 00:12:30,840 --> 00:12:31,319 Speaker 1: and maybe do. 258 00:12:31,280 --> 00:12:32,240 Speaker 2: My taxes as well. 259 00:12:33,400 --> 00:12:36,240 Speaker 3: I don't want to lose muscle. I only I only 260 00:12:36,280 --> 00:12:37,240 Speaker 3: want to lose fat. 261 00:12:37,440 --> 00:12:40,480 Speaker 1: Is there a competitor to ozempic slash we goo v 262 00:12:41,120 --> 00:12:42,679 Speaker 1: that is going to help with me with that? 263 00:12:43,160 --> 00:12:46,400 Speaker 7: I mean in terms of the data with regardless to 264 00:12:46,480 --> 00:12:51,040 Speaker 7: kind of lean the loss of lean muscle. It's limited 265 00:12:51,040 --> 00:12:53,440 Speaker 7: at the moment, but you know, Lily has done a 266 00:12:53,480 --> 00:12:57,680 Speaker 7: deal for a drug which potentially when you used in 267 00:12:57,720 --> 00:13:01,439 Speaker 7: compination with the drug which is well just go to 268 00:13:01,440 --> 00:13:07,200 Speaker 7: approve to z bound, that could potentially address that issue. 269 00:13:07,280 --> 00:13:08,720 Speaker 3: Okay, so that's one question I have. 270 00:13:09,200 --> 00:13:13,120 Speaker 1: The other one is do I go zepic because I 271 00:13:13,160 --> 00:13:15,720 Speaker 1: really want to avoid diabetes and a heart attack. 272 00:13:15,800 --> 00:13:17,480 Speaker 3: I mean, I need to lose twenty pounds. I don't 273 00:13:17,520 --> 00:13:20,400 Speaker 3: have to write not morbidly obese? Or do I go 274 00:13:20,720 --> 00:13:21,080 Speaker 3: we go. 275 00:13:21,200 --> 00:13:26,120 Speaker 1: Vy because it's a stronger dose and actually has better 276 00:13:26,200 --> 00:13:27,960 Speaker 1: results for hard issues. 277 00:13:28,920 --> 00:13:31,199 Speaker 7: I mean, in order to get we'll gav as in 278 00:13:31,840 --> 00:13:35,280 Speaker 7: because of the supply issues, it needs to be on label, right, 279 00:13:35,320 --> 00:13:39,120 Speaker 7: So you need to be either overweight with a weight 280 00:13:39,160 --> 00:13:43,200 Speaker 7: related to comobidity so that might be diabetes. Oh you 281 00:13:43,280 --> 00:13:46,319 Speaker 7: need to be you know, clinically abese, so BMI have 282 00:13:46,440 --> 00:13:47,320 Speaker 7: thirty or above. 283 00:13:48,040 --> 00:13:49,920 Speaker 1: I don't check that box, but I feel like I 284 00:13:49,960 --> 00:13:52,040 Speaker 1: know people. I can go to Canada, you know, I 285 00:13:52,040 --> 00:13:56,040 Speaker 1: can get the drug elsewhere. So you're saying like, is 286 00:13:56,080 --> 00:13:58,160 Speaker 1: we GOV the right choice but it's gonna be hard 287 00:13:58,240 --> 00:13:58,480 Speaker 1: to get? 288 00:13:58,559 --> 00:13:59,960 Speaker 3: Or should I just settle for ozempic. 289 00:14:00,520 --> 00:14:02,400 Speaker 7: I mean, at the moment, there's a lot of off 290 00:14:02,480 --> 00:14:05,720 Speaker 7: label use of a zempiic simply because of the supply 291 00:14:06,200 --> 00:14:12,080 Speaker 7: supply situation with WEGOV. And in essence, you know, the 292 00:14:12,120 --> 00:14:14,920 Speaker 7: active ingredient of these of these drugs are both sema 293 00:14:14,960 --> 00:14:18,600 Speaker 7: glue tide, and in both cases you would start off 294 00:14:18,920 --> 00:14:21,600 Speaker 7: at a lower dose and then start tie trading up. 295 00:14:21,640 --> 00:14:25,680 Speaker 7: So the maximum dose for for a zempick is two milligrams, 296 00:14:26,280 --> 00:14:28,640 Speaker 7: maximum days FORGOVY is two point four. 297 00:14:28,680 --> 00:14:31,120 Speaker 2: All right, Amy from South Carolina rates in how long 298 00:14:31,160 --> 00:14:34,520 Speaker 2: does someone have to stay on the weight loss drugs? Forever? 299 00:14:34,880 --> 00:14:37,000 Speaker 2: A few weeks? And when you start taking it there's 300 00:14:37,000 --> 00:14:38,680 Speaker 2: a weight cut back on what's the show there. 301 00:14:38,760 --> 00:14:42,480 Speaker 7: Yeah, so there's clinical data from both Lily and Novos 302 00:14:42,480 --> 00:14:44,800 Speaker 7: showing that you know, as soon as you stop these drugs, 303 00:14:45,560 --> 00:14:48,640 Speaker 7: you eventually, you know, put on you put the weight 304 00:14:48,680 --> 00:14:53,320 Speaker 7: back on. Prescribers we've spoken to as well, they're all 305 00:14:53,400 --> 00:14:56,960 Speaker 7: kind of saying, you know, chronic dosing of these drugs 306 00:14:57,240 --> 00:15:00,320 Speaker 7: are needed. But I mean there are you there are 307 00:15:00,440 --> 00:15:03,400 Speaker 7: various kind of things that you could potentially do. You 308 00:15:03,440 --> 00:15:06,640 Speaker 7: could potentially, you know, reach your targeted weight loss and 309 00:15:06,720 --> 00:15:08,560 Speaker 7: then perhaps you lower the dose. 310 00:15:08,360 --> 00:15:08,880 Speaker 8: Of the drug. 311 00:15:09,240 --> 00:15:10,320 Speaker 2: Who pays for this stuff? 312 00:15:10,880 --> 00:15:13,080 Speaker 7: So that's one of the main questions at the moment. 313 00:15:13,000 --> 00:15:13,960 Speaker 2: So you got to go to the money. 314 00:15:14,000 --> 00:15:15,840 Speaker 3: But Mikey, we have the same insurance provider. 315 00:15:17,760 --> 00:15:20,720 Speaker 1: If I go in there and I test like positive 316 00:15:20,760 --> 00:15:23,400 Speaker 1: for pre diabetes, or if I have a BMI of 317 00:15:23,440 --> 00:15:25,920 Speaker 1: like twenty seven twenty eight, are they gonna is my 318 00:15:25,960 --> 00:15:26,600 Speaker 1: insurance going. 319 00:15:26,560 --> 00:15:27,040 Speaker 8: To pay for this? 320 00:15:27,720 --> 00:15:30,040 Speaker 7: I think as we get more and more data, we're 321 00:15:30,080 --> 00:15:32,360 Speaker 7: going to see kind of access and reinbursement for these 322 00:15:32,400 --> 00:15:36,560 Speaker 7: drugs improve. I think the outcomes data for w GOOVI, 323 00:15:36,800 --> 00:15:38,600 Speaker 7: you know, it's a big step in the right direction. 324 00:15:38,760 --> 00:15:39,000 Speaker 4: There. 325 00:15:40,120 --> 00:15:43,400 Speaker 7: The whole I mean what insurers need to do is 326 00:15:43,440 --> 00:15:47,880 Speaker 7: you know, balance that the short term costs, which are 327 00:15:47,920 --> 00:15:50,440 Speaker 7: going to be high of you know, jailping one therapy 328 00:15:50,560 --> 00:15:53,240 Speaker 7: versus the long term benefits in terms of you know, 329 00:15:53,520 --> 00:15:58,640 Speaker 7: saving cost savings through reducing kind of hospitalizations. 330 00:15:58,720 --> 00:16:04,120 Speaker 1: And there's an actuary at Novo Nordisk and the boss 331 00:16:04,160 --> 00:16:07,760 Speaker 1: comes into his office and says, how much does heart 332 00:16:07,800 --> 00:16:12,920 Speaker 1: disease and diabetes and obesity cost the world? 333 00:16:13,040 --> 00:16:14,119 Speaker 8: And he says brillions. 334 00:16:14,200 --> 00:16:16,560 Speaker 3: Yeah, and then he says, Okay, let's price our drug 335 00:16:16,640 --> 00:16:19,440 Speaker 3: for that price. Right, That's exactly what they want to do. 336 00:16:19,480 --> 00:16:20,200 Speaker 3: They want to get it. 337 00:16:20,240 --> 00:16:22,880 Speaker 1: So some studies say it saves some studies say it doesn't, 338 00:16:22,880 --> 00:16:24,200 Speaker 1: but it's basically at the same level. 339 00:16:24,960 --> 00:16:27,880 Speaker 7: Yeah, I mean, like the if you look at the 340 00:16:28,040 --> 00:16:32,800 Speaker 7: direct and indirect costs of of of obesi and diabetes, 341 00:16:33,080 --> 00:16:36,600 Speaker 7: it runs into the trillions, right. And you know, all 342 00:16:36,640 --> 00:16:39,640 Speaker 7: the prescribers we spoke to at the conference, you know, 343 00:16:39,760 --> 00:16:43,000 Speaker 7: they believe in these drugs. They believe that eventually they'll 344 00:16:43,040 --> 00:16:45,640 Speaker 7: be cost effective in terms of as we get more 345 00:16:45,680 --> 00:16:49,040 Speaker 7: competition coming into the market, that price point will come down. 346 00:16:49,200 --> 00:16:52,000 Speaker 7: As we see orals which are cheaper to produce also 347 00:16:52,240 --> 00:16:54,080 Speaker 7: to prove that going to come down to. 348 00:16:54,040 --> 00:16:56,080 Speaker 2: What we need, all right. Mikey Shaw joins us. He's 349 00:16:56,080 --> 00:16:58,120 Speaker 2: a senior industry anist Bloomberg Contology. Dude, you got to 350 00:16:58,120 --> 00:16:59,160 Speaker 2: come here to New York more often. 351 00:16:59,200 --> 00:16:59,480 Speaker 4: Stuff. 352 00:16:59,520 --> 00:17:02,040 Speaker 2: I mean, we see Sam a lot who cares. We'd 353 00:17:02,120 --> 00:17:05,200 Speaker 2: rather talk to the smart money behind the healthcare research 354 00:17:05,200 --> 00:17:07,760 Speaker 2: of Bloomberg Intelligence. Mikey Shaw joining us here in our 355 00:17:07,760 --> 00:17:09,399 Speaker 2: Bloomberg Interactive Brooker's Studio. 356 00:17:09,720 --> 00:17:12,840 Speaker 6: You're listening to the tape cans Are live program Bloomberg 357 00:17:12,880 --> 00:17:16,480 Speaker 6: Markets weekdays at ten am Eastern on Bloomberg Radio, the 358 00:17:16,520 --> 00:17:19,760 Speaker 6: tune in app, Bloomberg dot Com, and the Bloomberg Business App. 359 00:17:19,800 --> 00:17:22,640 Speaker 6: You can also listen live on Amazon Alexa from our 360 00:17:22,640 --> 00:17:27,040 Speaker 6: flagship New York station Just say Alexa playing Bloomberg eleven thirty. 361 00:17:28,840 --> 00:17:30,600 Speaker 2: Jake Saber knows is did I consent Rais? 362 00:17:30,600 --> 00:17:30,840 Speaker 6: He did? 363 00:17:31,040 --> 00:17:35,000 Speaker 2: Jake's Safer founder, general partner of Emergence Capital. He's out 364 00:17:35,000 --> 00:17:37,359 Speaker 2: there in Silicon Valley. He does all that stuff. So 365 00:17:38,480 --> 00:17:40,880 Speaker 2: when we talk about AI, Jake, I'd like to ask people, 366 00:17:41,080 --> 00:17:43,520 Speaker 2: just say, from your perspective, what is AI? 367 00:17:44,240 --> 00:17:46,080 Speaker 8: It's machines helping humans do their jobs. 368 00:17:46,280 --> 00:17:48,919 Speaker 2: Machines helping humans do their jobs, right, I'm down with that. 369 00:17:49,359 --> 00:17:53,320 Speaker 2: So how do you guys view that space and maybe 370 00:17:53,359 --> 00:17:56,600 Speaker 2: how you view investment opportunities for your venture capital? 371 00:17:56,680 --> 00:17:58,600 Speaker 8: Yeah, totally. Well, I'll start with some context on like 372 00:17:58,640 --> 00:18:01,000 Speaker 8: where we think this fits in our software. More broadly, 373 00:18:01,200 --> 00:18:03,320 Speaker 8: we're focused just on enterprise software B to B software, 374 00:18:03,480 --> 00:18:05,640 Speaker 8: something that I near and dear the hearts of Bloomberg folks. 375 00:18:05,680 --> 00:18:08,720 Speaker 8: Probably presumably our firm have started twenty years ago with 376 00:18:08,720 --> 00:18:10,760 Speaker 8: the thesis that software would move from on premise to 377 00:18:10,800 --> 00:18:14,000 Speaker 8: the cloud. That thesis largely worked, right, So the very 378 00:18:14,000 --> 00:18:16,760 Speaker 8: first investment for US in four was in salesforce and 379 00:18:16,840 --> 00:18:19,400 Speaker 8: investing in all sorts of enterprise software companies like Zoom 380 00:18:19,400 --> 00:18:22,639 Speaker 8: ever since. And we think that this AI wave is 381 00:18:22,760 --> 00:18:25,159 Speaker 8: very similar to the wave of on prem to the cloud. Okay, 382 00:18:25,240 --> 00:18:27,760 Speaker 8: So just like that was transformational for tech, we think 383 00:18:27,760 --> 00:18:30,479 Speaker 8: this will be transformational. We're very bullish and we're very 384 00:18:30,480 --> 00:18:32,040 Speaker 8: focused on how we can help B to B software 385 00:18:32,040 --> 00:18:33,080 Speaker 8: companies make that transition. 386 00:18:33,320 --> 00:18:35,240 Speaker 2: But should we have a should we have a fast 387 00:18:35,280 --> 00:18:38,920 Speaker 2: food company talking about AI on an earn quarterly earning 388 00:18:38,920 --> 00:18:39,560 Speaker 2: conference call? 389 00:18:39,960 --> 00:18:42,760 Speaker 8: I think it's real, right, Like, there's so much hysteria 390 00:18:42,880 --> 00:18:44,440 Speaker 8: and so you have to cut through all the crap 391 00:18:44,480 --> 00:18:46,000 Speaker 8: to figure out what's real. But like in the case 392 00:18:46,000 --> 00:18:48,400 Speaker 8: of fast food, the idea of using this to help 393 00:18:48,440 --> 00:18:50,440 Speaker 8: augment the people doing the drive throughs and make drives 394 00:18:50,520 --> 00:18:53,080 Speaker 8: is more efficient. Like, that's very real. Voice recognition technology 395 00:18:53,160 --> 00:18:56,439 Speaker 8: is real. So I think what may be in danger 396 00:18:56,520 --> 00:18:58,520 Speaker 8: is I think a lot of tech companies public companies 397 00:18:58,560 --> 00:19:00,719 Speaker 8: over the past six months have gotten so excited they 398 00:19:00,720 --> 00:19:02,560 Speaker 8: may have over committed, over promised on what they can 399 00:19:02,560 --> 00:19:04,480 Speaker 8: do with A in the near term. So we may 400 00:19:04,480 --> 00:19:07,159 Speaker 8: be you know, near term negative, But I think over 401 00:19:07,200 --> 00:19:08,359 Speaker 8: the long run, this thing is gonna work. 402 00:19:08,480 --> 00:19:11,280 Speaker 1: Dude, think about you don't want a high school like 403 00:19:11,320 --> 00:19:13,560 Speaker 1: a stoner that you hired for your drive through? 404 00:19:13,600 --> 00:19:15,280 Speaker 3: It'd be like, do you want fries with that? 405 00:19:16,320 --> 00:19:18,399 Speaker 1: He's not going to be enthusiastic about it. He's not 406 00:19:18,400 --> 00:19:20,640 Speaker 1: going to remember to ask every time, like AI can 407 00:19:20,680 --> 00:19:24,320 Speaker 1: be better at up selling in the drive through a pretty. 408 00:19:26,200 --> 00:19:27,280 Speaker 3: Man, these fries are. 409 00:19:27,160 --> 00:19:31,359 Speaker 2: So goodly all right, So what's the next? Well, I 410 00:19:31,480 --> 00:19:32,720 Speaker 2: want to say what's the next because I don't eve 411 00:19:32,720 --> 00:19:34,040 Speaker 2: know where the kind of the starting point is. 412 00:19:34,040 --> 00:19:35,639 Speaker 1: But well in video is the only one that we 413 00:19:35,760 --> 00:19:38,680 Speaker 1: really know, right, I mean, Microsoft, okay with open AI, 414 00:19:39,880 --> 00:19:42,480 Speaker 1: But where do you invest I mean you're not You're 415 00:19:42,520 --> 00:19:44,960 Speaker 1: not looking solely at public markets, so you have a 416 00:19:45,040 --> 00:19:48,280 Speaker 1: much better view of you know, what's what the roots 417 00:19:48,320 --> 00:19:49,560 Speaker 1: are down underground? 418 00:19:49,640 --> 00:19:50,440 Speaker 3: What are you looking at? 419 00:19:50,600 --> 00:19:52,120 Speaker 8: So the way I think about it is like there's 420 00:19:52,119 --> 00:19:54,800 Speaker 8: there's different layers to the AI stuff. There's nvideo, which 421 00:19:54,840 --> 00:19:56,480 Speaker 8: is at the base layer, right, it's the actual you know, 422 00:19:56,520 --> 00:19:59,479 Speaker 8: chips that are helping power this. Then there's the foundational models. 423 00:19:59,480 --> 00:20:02,440 Speaker 8: This is opening and Microsoft developing their own addition to 424 00:20:02,440 --> 00:20:04,320 Speaker 8: open AI, and there's a bunch of others. And then 425 00:20:04,359 --> 00:20:06,720 Speaker 8: there's the application layer, and that's the stuff we're most 426 00:20:06,760 --> 00:20:08,399 Speaker 8: excited about, which is what do you do with this 427 00:20:08,400 --> 00:20:10,520 Speaker 8: stuff once it's built? Right, you don't just build it 428 00:20:10,560 --> 00:20:12,080 Speaker 8: for the sake of building it. But how can you 429 00:20:12,080 --> 00:20:14,360 Speaker 8: make the stoner dude more effective at taking your order? 430 00:20:14,560 --> 00:20:17,160 Speaker 8: How could you help doctors treat patients more effectively? How 431 00:20:17,160 --> 00:20:19,800 Speaker 8: can you help lawyers go through contracts more effectively? So 432 00:20:19,840 --> 00:20:22,800 Speaker 8: we're investing in companies that do all that stuff aided 433 00:20:22,800 --> 00:20:23,280 Speaker 8: by AI. 434 00:20:24,200 --> 00:20:26,679 Speaker 2: So all right, now, given it feels like we're in 435 00:20:26,720 --> 00:20:30,560 Speaker 2: the top of the first inning for AI. Yeah, where 436 00:20:30,600 --> 00:20:33,359 Speaker 2: are you guys looking and what types of deals are 437 00:20:33,359 --> 00:20:34,000 Speaker 2: you guys looking at? 438 00:20:34,040 --> 00:20:34,600 Speaker 4: Yeah, for sure. 439 00:20:34,800 --> 00:20:38,480 Speaker 8: So we're seeing a lot of activity in vertical specific 440 00:20:38,840 --> 00:20:42,919 Speaker 8: AI solutions, and this actually mirrors software. So in the 441 00:20:42,960 --> 00:20:45,520 Speaker 8: first wave of cloud technology, horizontal SaaS was the thing, 442 00:20:45,600 --> 00:20:48,400 Speaker 8: so salesforce, work day, et cetera. In the second wave, 443 00:20:48,400 --> 00:20:51,359 Speaker 8: it was vertical SaaS or what we'd emergence call industry cloud. 444 00:20:51,600 --> 00:20:53,600 Speaker 8: These are companies like Viva Systems, which is a thirty 445 00:20:53,600 --> 00:20:57,000 Speaker 8: billion dollar public company selling into the pharmaceutical industry. The 446 00:20:57,040 --> 00:20:59,280 Speaker 8: same things starting to happen in AI, where you're taking 447 00:20:59,320 --> 00:21:01,960 Speaker 8: this technology instead of just trying to broadly apply it anywhere, 448 00:21:01,960 --> 00:21:04,439 Speaker 8: you're actually choosing a specific job to be done and 449 00:21:04,480 --> 00:21:07,960 Speaker 8: you're having it solve that problem. So we have invested 450 00:21:07,960 --> 00:21:10,560 Speaker 8: in companies, as I said, that use this technology to 451 00:21:10,600 --> 00:21:13,359 Speaker 8: do help lawyers draft contracts better. So the idea is 452 00:21:13,600 --> 00:21:15,919 Speaker 8: you can, as you're writing contracts, say hey, help me 453 00:21:16,000 --> 00:21:18,080 Speaker 8: make this clause mutual, and it will use AI to 454 00:21:18,440 --> 00:21:21,560 Speaker 8: help draft that. We've invested in companies that help doctors 455 00:21:21,800 --> 00:21:24,520 Speaker 8: communicate with insurance companies and write the letters to get 456 00:21:24,520 --> 00:21:27,239 Speaker 8: approvals for different treatments. A company called Doctimity that does 457 00:21:27,320 --> 00:21:29,959 Speaker 8: that so that kind of concept we think is going 458 00:21:30,040 --> 00:21:32,119 Speaker 8: to be really powerful. The question is what are the 459 00:21:32,200 --> 00:21:34,800 Speaker 8: jobs to be done for which this technology is most 460 00:21:34,800 --> 00:21:36,000 Speaker 8: applicable in your term. 461 00:21:36,200 --> 00:21:38,639 Speaker 3: And what are the data sets that they use to 462 00:21:38,640 --> 00:21:39,080 Speaker 3: train on. 463 00:21:39,280 --> 00:21:43,080 Speaker 1: Is that like a sticky issue because recently Elon Musk 464 00:21:43,080 --> 00:21:45,760 Speaker 1: came out with his competitor to open aiy Grock. 465 00:21:46,040 --> 00:21:50,920 Speaker 3: And apparently it's supposed to use real time data Twitter data. 466 00:21:51,359 --> 00:21:54,640 Speaker 1: I didn't even know they could do that without breaking 467 00:21:54,720 --> 00:21:58,320 Speaker 1: certain you know, privacy clauses. Maybe they don't mind, but 468 00:21:58,680 --> 00:22:01,720 Speaker 1: you know, I think of Google, they have so much 469 00:22:02,760 --> 00:22:04,640 Speaker 1: data at their fingertips. 470 00:22:04,680 --> 00:22:05,639 Speaker 3: Can they use all that? 471 00:22:06,160 --> 00:22:08,560 Speaker 8: So I think companies, the larger companies the ones that 472 00:22:08,600 --> 00:22:10,159 Speaker 8: have to be the most conservative about this, which is 473 00:22:10,240 --> 00:22:12,280 Speaker 8: kind of interesting actually, like in many ways, those of 474 00:22:12,280 --> 00:22:14,239 Speaker 8: the folks that have the most to lose, and so 475 00:22:14,280 --> 00:22:16,160 Speaker 8: startups I think can be more innovative on this front. 476 00:22:16,160 --> 00:22:17,800 Speaker 8: They can you know, find different sets of data and 477 00:22:17,840 --> 00:22:20,600 Speaker 8: actually kind of outmaneuver some of the incumbents. There's a 478 00:22:20,640 --> 00:22:23,560 Speaker 8: real innovative's dilemma problem for the incumbents here, right If 479 00:22:23,600 --> 00:22:25,439 Speaker 8: your Google, if your salesforce, if you're one of these 480 00:22:25,480 --> 00:22:28,199 Speaker 8: established public companies, you have a lot to lose in this. 481 00:22:28,560 --> 00:22:31,160 Speaker 8: From a legal perspective, you also if you drastically try 482 00:22:31,200 --> 00:22:34,040 Speaker 8: to change your UX your user interface, let's say doing 483 00:22:34,040 --> 00:22:36,360 Speaker 8: a chat based interface is better than doing the traditional interface, 484 00:22:36,680 --> 00:22:38,439 Speaker 8: you could piss off a lot of your existing users. 485 00:22:38,720 --> 00:22:40,800 Speaker 8: So there's risk there. There's also risk on business models. 486 00:22:40,840 --> 00:22:42,560 Speaker 8: I think a lot of these technologies are starting to 487 00:22:42,560 --> 00:22:44,280 Speaker 8: think about how do I charge not on a per 488 00:22:44,280 --> 00:22:48,000 Speaker 8: seat basis or usage basis, but actually on an outcomes basis. 489 00:22:48,320 --> 00:22:51,159 Speaker 8: Because the technology actually can help you process contracts more 490 00:22:51,240 --> 00:22:53,760 Speaker 8: quickly or you know, cure patients more quickly. Would have 491 00:22:53,800 --> 00:22:56,080 Speaker 8: you what if you could start actually charging on that basis, 492 00:22:56,280 --> 00:22:58,760 Speaker 8: which would be disruptive to the incumbents. So from as 493 00:22:58,760 --> 00:23:00,960 Speaker 8: a startup investor perspective, which is what I'm focused on, 494 00:23:01,240 --> 00:23:02,679 Speaker 8: I'm spending a lot of time thinking about what are 495 00:23:02,680 --> 00:23:04,879 Speaker 8: the advantages of startup hads over the incumbent in this 496 00:23:04,920 --> 00:23:05,320 Speaker 8: new era? 497 00:23:06,640 --> 00:23:09,639 Speaker 2: How much incremental CAPEX? If I just look at the 498 00:23:09,680 --> 00:23:13,439 Speaker 2: macro tech CAPEX summers I get from IDCU whatever, and 499 00:23:13,480 --> 00:23:15,200 Speaker 2: I see some of these big growth rates, how much 500 00:23:15,200 --> 00:23:18,080 Speaker 2: of that do you think is incremental AI or maybe 501 00:23:18,119 --> 00:23:20,359 Speaker 2: taking from some other bucket and putting it into AI. 502 00:23:20,480 --> 00:23:22,520 Speaker 2: I don't know how much. Does think about it as 503 00:23:22,520 --> 00:23:23,360 Speaker 2: how much is incremental. 504 00:23:23,400 --> 00:23:25,119 Speaker 8: It's a really good question. I think some of it 505 00:23:25,160 --> 00:23:30,720 Speaker 8: will actually come from labor. Right, So, if you had 506 00:23:30,760 --> 00:23:33,800 Speaker 8: hired a consulting firm before to help you implement some 507 00:23:33,840 --> 00:23:36,280 Speaker 8: new technology, and you had a big budget for that 508 00:23:36,320 --> 00:23:38,800 Speaker 8: consulting firm, it's very possible that you can use AI 509 00:23:38,920 --> 00:23:41,560 Speaker 8: to help you implement this technology more effectively, and so 510 00:23:41,600 --> 00:23:44,240 Speaker 8: you don't need to spend as much on consulting firms. Interesting, 511 00:23:44,400 --> 00:23:47,040 Speaker 8: which actually brings me to another point I've been reflecting on, 512 00:23:47,080 --> 00:23:49,480 Speaker 8: which is we thought that it was like the stoner 513 00:23:49,560 --> 00:23:51,639 Speaker 8: dude that was going to be most at risk in 514 00:23:51,680 --> 00:23:53,000 Speaker 8: this age of AI, that it was a lot of 515 00:23:53,040 --> 00:23:55,480 Speaker 8: the kind of more blue collar workers, you know, the janitors, 516 00:23:55,560 --> 00:23:59,719 Speaker 8: the nannies, et cetera. What's happened with this past year 517 00:23:59,760 --> 00:24:02,359 Speaker 8: of A is it seems like white collar workers are 518 00:24:02,359 --> 00:24:05,639 Speaker 8: actually the ones that may have more infringement upon their 519 00:24:05,680 --> 00:24:07,800 Speaker 8: daily work, and as a result, they're the ones who 520 00:24:07,840 --> 00:24:09,679 Speaker 8: have to probably get up to speed faster on how 521 00:24:09,720 --> 00:24:11,720 Speaker 8: to make use of this technology to augment what they do, 522 00:24:12,080 --> 00:24:13,879 Speaker 8: otherwise they may fall by the wayside. 523 00:24:13,920 --> 00:24:18,160 Speaker 3: I always think about this as well the jobs at risk. 524 00:24:18,600 --> 00:24:20,159 Speaker 3: But what will be created? 525 00:24:20,560 --> 00:24:23,280 Speaker 1: You know, because every time there's a technological wave or 526 00:24:23,280 --> 00:24:27,760 Speaker 1: an industrial wave, it does knock out a whole group 527 00:24:27,800 --> 00:24:30,200 Speaker 1: of jobs, but it also creates so many more. 528 00:24:30,359 --> 00:24:32,199 Speaker 3: So what's going to be created here? It's not just 529 00:24:32,240 --> 00:24:33,320 Speaker 3: more coders, right please? 530 00:24:33,400 --> 00:24:34,560 Speaker 8: No, No, it's not more code. 531 00:24:34,640 --> 00:24:34,680 Speaker 6: No. 532 00:24:34,760 --> 00:24:37,800 Speaker 8: In some ways we may have different types of coders 533 00:24:37,800 --> 00:24:39,520 Speaker 8: in the sense that you may not need to know 534 00:24:39,600 --> 00:24:41,919 Speaker 8: as much code because the AI can help you do it. 535 00:24:42,000 --> 00:24:43,840 Speaker 8: So you'll have probably more of democratization, So therell be 536 00:24:43,840 --> 00:24:45,600 Speaker 8: more people to code, but you may have less people 537 00:24:45,600 --> 00:24:48,040 Speaker 8: to do it at their full time job. I do 538 00:24:48,119 --> 00:24:50,439 Speaker 8: think like in the near term, figuring out how to 539 00:24:50,440 --> 00:24:53,400 Speaker 8: implement this technology and existing text acts is something that 540 00:24:53,560 --> 00:24:56,760 Speaker 8: everyone's figuring out real time. Remember like this chatchipte thing, 541 00:24:57,119 --> 00:24:59,399 Speaker 8: it's a year old, Like we've aged a ton and 542 00:24:59,480 --> 00:25:00,879 Speaker 8: a year, So what's it going to look like in 543 00:25:00,880 --> 00:25:02,720 Speaker 8: a year two years from now? So I think I 544 00:25:02,720 --> 00:25:04,400 Speaker 8: think the world will look different, in jobs will look 545 00:25:04,440 --> 00:25:04,919 Speaker 8: very different. 546 00:25:05,119 --> 00:25:08,119 Speaker 2: Jake, just on the VC business today, talk to us 547 00:25:08,119 --> 00:25:10,600 Speaker 2: about what it's like at in sand Hill Road out there, 548 00:25:10,640 --> 00:25:13,320 Speaker 2: and if I have a cool technology, can will you 549 00:25:13,359 --> 00:25:13,719 Speaker 2: fund it? 550 00:25:13,800 --> 00:25:15,679 Speaker 8: Well, I'll start by saying sand Hill Road isn't what 551 00:25:15,720 --> 00:25:16,239 Speaker 8: it used to be. 552 00:25:16,480 --> 00:25:17,200 Speaker 3: Really, Yeah. 553 00:25:17,240 --> 00:25:18,000 Speaker 8: What I mean by that. 554 00:25:18,000 --> 00:25:19,640 Speaker 2: Is like, and where did sand Hill Road come from? 555 00:25:19,760 --> 00:25:23,399 Speaker 8: Sand Hill Road went to San Francisco from from sand Hill, So. 556 00:25:23,240 --> 00:25:25,200 Speaker 3: Sand Hill from New Jersey actually. 557 00:25:25,000 --> 00:25:26,120 Speaker 8: Oh sure, yeah? Yeah? 558 00:25:26,160 --> 00:25:29,080 Speaker 2: And Menlo Park, California, where'd that come from? 559 00:25:29,960 --> 00:25:32,080 Speaker 8: Much love to the East Coast, Like there's so much. 560 00:25:32,520 --> 00:25:36,320 Speaker 2: For advanced study, Albert Einstein my backyard, go ahead. 561 00:25:36,680 --> 00:25:38,760 Speaker 8: There's there's a lot of history that came from the 562 00:25:38,760 --> 00:25:40,159 Speaker 8: East Coast that made West Coast what it is. And 563 00:25:40,320 --> 00:25:42,399 Speaker 8: actually the military is a lot of what made the 564 00:25:42,400 --> 00:25:45,520 Speaker 8: West Coast and Silicon Ugway was anyway, Silicon Valley and 565 00:25:46,440 --> 00:25:49,840 Speaker 8: sand Hill in particular have moved up to San Francisco largely. 566 00:25:49,840 --> 00:25:52,399 Speaker 8: I think there's more action there. We're at peer five actually. 567 00:25:52,160 --> 00:25:56,080 Speaker 3: Right, yeah, but I get that Humphrey slocom action. 568 00:25:56,440 --> 00:25:58,280 Speaker 8: Yeah, Like there's a lot of ways to you were 569 00:25:58,280 --> 00:26:00,359 Speaker 8: talking about with Goovi before. So if you want to 570 00:26:00,920 --> 00:26:02,520 Speaker 8: get on that train, and I'm like to the right 571 00:26:02,520 --> 00:26:04,040 Speaker 8: way to do it, I need to get some secret 572 00:26:04,040 --> 00:26:07,239 Speaker 8: breakfast first exam. I'll do the would go exactly, but 573 00:26:07,240 --> 00:26:08,680 Speaker 8: to answer a question on like what's the state of VC. 574 00:26:09,680 --> 00:26:12,439 Speaker 8: There's kind of a weird bipolar state of VC. 575 00:26:12,480 --> 00:26:14,200 Speaker 2: Right now, and we got about twenty seconds cool. 576 00:26:14,240 --> 00:26:15,560 Speaker 8: What I mean by that is like, there's lots of 577 00:26:15,640 --> 00:26:18,200 Speaker 8: hysteraian segment on AI, some of which is justified. And 578 00:26:18,240 --> 00:26:20,119 Speaker 8: there's also some real challenges that these companies who are 579 00:26:20,119 --> 00:26:22,040 Speaker 8: dealing with high interest rates and having slower sales cycles 580 00:26:22,040 --> 00:26:22,440 Speaker 8: are facing. 581 00:26:22,760 --> 00:26:22,960 Speaker 6: RP. 582 00:26:23,359 --> 00:26:24,880 Speaker 2: We'll get you back next time you're in the big 583 00:26:24,880 --> 00:26:27,560 Speaker 2: town here, Jake Safer, he is a general partner of 584 00:26:27,640 --> 00:26:28,680 Speaker 2: Emergency Can. 585 00:26:28,600 --> 00:26:29,720 Speaker 3: We can do it over Zoom too. 586 00:26:29,800 --> 00:26:31,240 Speaker 8: We can do it with Zoom. We love Zoom. 587 00:26:31,400 --> 00:26:34,080 Speaker 2: No, I don't, I don't, I don't. We go We 588 00:26:34,119 --> 00:26:36,680 Speaker 2: go real time here and that's how we do it. Jake, 589 00:26:36,720 --> 00:26:39,960 Speaker 2: thanks so much for joining us here. Markets tearing up 590 00:26:39,960 --> 00:26:42,240 Speaker 2: here today, own a back some good inflation data. 591 00:26:42,400 --> 00:26:45,480 Speaker 6: You're listening to the tape cans are live program Bloomberg 592 00:26:45,600 --> 00:26:49,199 Speaker 6: Markets weekdays at ten am Eastern on Bloomberg Radio, the 593 00:26:49,240 --> 00:26:52,480 Speaker 6: tune in app, Bloomberg dot Com, and the Bloomberg Business App. 594 00:26:52,520 --> 00:26:55,320 Speaker 6: You can also listen live on Amazon Alexa from our 595 00:26:55,359 --> 00:27:01,360 Speaker 6: flagship New York station, Just say Alexa play Bloomberg eleven thirty. 596 00:27:01,480 --> 00:27:04,200 Speaker 2: I did not know what CGM met until just recently. 597 00:27:04,240 --> 00:27:08,160 Speaker 2: Continuous glucose monitoring. Yeah, that's a big deal. 598 00:27:08,240 --> 00:27:10,879 Speaker 1: Well, and I've been interested in it. You know, I 599 00:27:10,920 --> 00:27:13,160 Speaker 1: don't have diabetes yet. 600 00:27:13,200 --> 00:27:14,200 Speaker 3: You're in good shape. 601 00:27:14,240 --> 00:27:15,160 Speaker 2: What are you talking about? 602 00:27:15,359 --> 00:27:18,200 Speaker 1: But I'm sure it's in my future because I eat 603 00:27:18,240 --> 00:27:21,359 Speaker 1: so many sugary foods. But well, I mean, I'm fortunately 604 00:27:21,359 --> 00:27:24,320 Speaker 1: I trying to stop. I've been interested in these continuous 605 00:27:24,359 --> 00:27:27,080 Speaker 1: glucose monitors for a while just because I'd like to 606 00:27:27,119 --> 00:27:30,920 Speaker 1: see how my body reacts different foods. For example, you 607 00:27:30,960 --> 00:27:33,240 Speaker 1: could eat you and I could eat the exact same 608 00:27:33,359 --> 00:27:37,880 Speaker 1: meal and we could have very different responses in terms of, 609 00:27:38,359 --> 00:27:42,280 Speaker 1: you know, how that makes our blood sugars react. And 610 00:27:42,400 --> 00:27:47,040 Speaker 1: I think it's really interesting information to have, especially interesting 611 00:27:47,160 --> 00:27:51,240 Speaker 1: for athletes or for people who are really monitoring their 612 00:27:51,240 --> 00:27:52,119 Speaker 1: health closely. 613 00:27:52,560 --> 00:27:56,520 Speaker 3: But for now, diabetics are getting to use these products, 614 00:27:56,760 --> 00:27:58,080 Speaker 3: good stuff. Insurance pay for it. 615 00:27:58,200 --> 00:28:01,479 Speaker 2: Jeremy Silvan joins the CSC dex coom that is a 616 00:28:01,760 --> 00:28:05,560 Speaker 2: NASTAK listed company. D x CM is a ticker uh 617 00:28:05,720 --> 00:28:07,880 Speaker 2: to put in there. They are based in San Diego. 618 00:28:07,960 --> 00:28:10,160 Speaker 2: I believe, nice choice. Jeez, lame. 619 00:28:10,359 --> 00:28:12,160 Speaker 3: Why aren't we all based on San Diego. 620 00:28:12,280 --> 00:28:14,520 Speaker 1: I don't understand when I don't know, you know, when 621 00:28:14,520 --> 00:28:16,480 Speaker 1: the founding fathers set everything. 622 00:28:16,200 --> 00:28:19,119 Speaker 2: Up exactly why in New York and watch it. 623 00:28:19,280 --> 00:28:20,160 Speaker 3: I mean it's nice, but. 624 00:28:20,160 --> 00:28:21,800 Speaker 2: All right, Jeremy, thanks so much for joining us here 625 00:28:21,800 --> 00:28:25,320 Speaker 2: in our Bloomberg Nactior broker studio d X dex comp 626 00:28:25,960 --> 00:28:27,920 Speaker 2: for ourn listeners. Tell us what you guys do kind 627 00:28:27,920 --> 00:28:29,960 Speaker 2: of how you play the game here? 628 00:28:30,119 --> 00:28:33,359 Speaker 9: Yeah, sure, and thanks for having us here. Dexcom we 629 00:28:33,440 --> 00:28:36,359 Speaker 9: make continuous glucose monitors, as you mentioned, our G six 630 00:28:36,400 --> 00:28:39,760 Speaker 9: and our G seven series as well as Dexcom one. 631 00:28:39,840 --> 00:28:42,280 Speaker 9: And what the really the product does is that there's 632 00:28:42,280 --> 00:28:45,560 Speaker 9: a small interest needle that goes right beneath your skin 633 00:28:45,600 --> 00:28:49,080 Speaker 9: into your interstitial fluid. It monitors your glucose throughout the 634 00:28:49,080 --> 00:28:51,640 Speaker 9: course of the day. And so for anybody that is 635 00:28:52,200 --> 00:28:55,480 Speaker 9: monitoring their glucose levels because they take insulin, because they 636 00:28:55,480 --> 00:28:59,440 Speaker 9: take other drugs like glp ones, as they monitor their food, 637 00:28:59,480 --> 00:29:02,720 Speaker 9: their sleep patterns, and ultimately their exercise patterns, we're able 638 00:29:02,720 --> 00:29:05,200 Speaker 9: to monitor in real time and give feedback as to 639 00:29:05,280 --> 00:29:08,960 Speaker 9: how your glucose levels are moving in your body really 640 00:29:09,240 --> 00:29:11,680 Speaker 9: minute by minute. Every five minutes, we send a ping 641 00:29:11,800 --> 00:29:14,160 Speaker 9: to your phone or to the receiver. We do that. 642 00:29:14,240 --> 00:29:17,000 Speaker 9: It's obviously very beneficial to those that need to manage 643 00:29:17,040 --> 00:29:20,240 Speaker 9: their glucose levels, and by the way, that really spans 644 00:29:20,280 --> 00:29:21,360 Speaker 9: the entire population. 645 00:29:21,400 --> 00:29:22,800 Speaker 3: It's really the fifth vital sign. 646 00:29:23,400 --> 00:29:27,400 Speaker 1: So, but it's interesting that you say you can use 647 00:29:27,440 --> 00:29:30,400 Speaker 1: it to work with GLP ones, to work with semi 648 00:29:30,440 --> 00:29:34,000 Speaker 1: glue tites likeozepic and we govy are you seeing more 649 00:29:34,520 --> 00:29:36,000 Speaker 1: more customers do that? 650 00:29:36,080 --> 00:29:36,840 Speaker 3: We are, in fact. 651 00:29:36,840 --> 00:29:38,680 Speaker 9: You know what we see is if you look at 652 00:29:38,680 --> 00:29:42,600 Speaker 9: the prescribing patterns, you're really realizing these are companion therapies. 653 00:29:42,600 --> 00:29:46,680 Speaker 9: You're seeing our prescriptions actually increase when you have somebody 654 00:29:46,680 --> 00:29:48,920 Speaker 9: who's using a GLP one, and what you find is 655 00:29:48,960 --> 00:29:52,200 Speaker 9: the outcomes are better. There's an increase in a one 656 00:29:52,280 --> 00:29:55,000 Speaker 9: C reduction when you have somebody using both of these products. 657 00:29:55,040 --> 00:29:57,080 Speaker 9: And so, you know, we think about it as if 658 00:29:57,160 --> 00:29:59,480 Speaker 9: you want to think about it as therapy and diagnosis 659 00:29:59,480 --> 00:30:02,720 Speaker 9: coming toge other. The therapy is the drug, the diagnosic 660 00:30:02,840 --> 00:30:05,280 Speaker 9: is the real insight into what's happening in your body. 661 00:30:05,520 --> 00:30:08,520 Speaker 9: Those two things are an incredibly powerful combination. You're seeing 662 00:30:08,560 --> 00:30:11,040 Speaker 9: positions prescribing more and more in that pattern. 663 00:30:11,240 --> 00:30:13,320 Speaker 2: What are that so for your CGM business, that is 664 00:30:13,600 --> 00:30:16,240 Speaker 2: that is your business. Yes, okay, what are the growth 665 00:30:16,280 --> 00:30:17,640 Speaker 2: drivers on the top line for you? 666 00:30:17,800 --> 00:30:18,040 Speaker 6: Yeah? 667 00:30:18,040 --> 00:30:21,520 Speaker 9: Absolutely, I mean the total addressable market is huge, and 668 00:30:22,160 --> 00:30:23,880 Speaker 9: you know, it's one of those things when we sought 669 00:30:23,920 --> 00:30:24,400 Speaker 9: out to. 670 00:30:24,280 --> 00:30:25,920 Speaker 8: Meet an unmet need, we. 671 00:30:25,840 --> 00:30:28,800 Speaker 9: Realized and you mentioned sugary foods, a lot of us 672 00:30:28,800 --> 00:30:30,880 Speaker 9: are sitting down more often than we have in the past. 673 00:30:31,240 --> 00:30:33,440 Speaker 9: There is a challenge with glucose levels in the body. 674 00:30:33,480 --> 00:30:36,240 Speaker 9: It started with folks using insulin, but it's really progressed 675 00:30:36,240 --> 00:30:39,040 Speaker 9: beyond that. And when you think about the entire population 676 00:30:39,280 --> 00:30:43,160 Speaker 9: diabetes thirty million in the US, pre diabetes ninety million 677 00:30:43,200 --> 00:30:48,080 Speaker 9: in the US, Global diabetes over five hundred million YEA, 678 00:30:48,320 --> 00:30:50,760 Speaker 9: and only about one percent of the people that have 679 00:30:50,800 --> 00:30:53,280 Speaker 9: diabetes in the world are on this therapy, so there's 680 00:30:53,400 --> 00:30:55,920 Speaker 9: ninety nine percent to go, and so there's a real 681 00:30:55,960 --> 00:30:59,400 Speaker 9: opportunity for us to continue to drive adoption really change people. 682 00:31:00,520 --> 00:31:02,880 Speaker 2: Is your customer, the physician. 683 00:31:03,040 --> 00:31:06,240 Speaker 9: Our customer is the physician, the payer, and the actual 684 00:31:06,280 --> 00:31:09,600 Speaker 9: customer themselves. I mean, really, this is incredibly personal to 685 00:31:09,960 --> 00:31:12,000 Speaker 9: the actual patient. They wear it on their body, yep. 686 00:31:12,320 --> 00:31:15,240 Speaker 9: The physician clearly wants to know it, how it helps 687 00:31:15,240 --> 00:31:19,000 Speaker 9: them titrate medication. And then obviously the payer wants outcomes, 688 00:31:19,080 --> 00:31:21,280 Speaker 9: and so you have to have a product that delivers outcomes. 689 00:31:21,320 --> 00:31:22,520 Speaker 9: Ours delivers outcomes. 690 00:31:22,800 --> 00:31:27,680 Speaker 1: So I've seen you see the YouTube videos or Instagram 691 00:31:27,760 --> 00:31:30,440 Speaker 1: videos where somebody just kind of slaps it on the 692 00:31:30,440 --> 00:31:33,440 Speaker 1: bottom of her arm or his arm. It doesn't look 693 00:31:33,520 --> 00:31:35,920 Speaker 1: like it hurts. Explain to you, say needle, But I 694 00:31:35,920 --> 00:31:38,280 Speaker 1: feel like it's more of a wire almost, isn't it. 695 00:31:38,320 --> 00:31:40,719 Speaker 9: That's what it is. I mean, it's I'm wearing one now. 696 00:31:40,760 --> 00:31:42,360 Speaker 9: Basically you put on your body, a click, a button, 697 00:31:42,400 --> 00:31:45,120 Speaker 9: it comes on. I don't feel it. Most folks don't. 698 00:31:45,840 --> 00:31:48,920 Speaker 9: When I say it's a little needle under it. It's 699 00:31:48,920 --> 00:31:50,960 Speaker 9: about the size of a hair and about about a 700 00:31:51,040 --> 00:31:51,600 Speaker 9: quarter of an inch. 701 00:31:51,680 --> 00:31:52,959 Speaker 3: And how long do you leave it in your arm? 702 00:31:53,120 --> 00:31:55,400 Speaker 9: For ten days? So stays in there for ten days, 703 00:31:55,400 --> 00:31:57,880 Speaker 9: giving you feedback over the course of those ten days. 704 00:31:58,200 --> 00:32:00,520 Speaker 9: Real easy to put on, real easy to take off. 705 00:32:00,560 --> 00:32:03,360 Speaker 9: Pairs with your foam, pairs with other devices as well. 706 00:32:03,600 --> 00:32:05,160 Speaker 3: And do you leave it on all the time? Do 707 00:32:05,200 --> 00:32:06,960 Speaker 3: you constantly have one on always? 708 00:32:07,000 --> 00:32:08,680 Speaker 9: You have it on twenty four to seven, you swim 709 00:32:08,720 --> 00:32:11,400 Speaker 9: with it? I noticed you mentioned San diego, you surf 710 00:32:11,440 --> 00:32:12,840 Speaker 9: with it. If you guys ever want to try it 711 00:32:12,840 --> 00:32:14,320 Speaker 9: and come out surfing with us, you're more. 712 00:32:14,240 --> 00:32:14,840 Speaker 8: Than welcome to. 713 00:32:15,600 --> 00:32:18,600 Speaker 9: But absolutely part of wearing it twenty four to seven 714 00:32:18,640 --> 00:32:20,320 Speaker 9: So it gives you that twenty four to seven feet back. 715 00:32:20,400 --> 00:32:22,840 Speaker 3: What do you notice? I mean when you have I 716 00:32:22,840 --> 00:32:25,800 Speaker 3: don't know, a glass of wine. Do you see a 717 00:32:26,000 --> 00:32:31,240 Speaker 3: change when you have you know, peanut butter? I mean, 718 00:32:31,640 --> 00:32:33,560 Speaker 3: what are the things that you notice? You change? 719 00:32:33,640 --> 00:32:35,800 Speaker 9: You change your habits, you really do. I'll tell you 720 00:32:35,840 --> 00:32:39,160 Speaker 9: what's good whiskey. That's a good one, that's not good beer. 721 00:32:39,720 --> 00:32:41,440 Speaker 9: So there you go, and you're gonna have to battle 722 00:32:41,440 --> 00:32:43,240 Speaker 9: between those if you want to keep it. But there 723 00:32:43,240 --> 00:32:45,320 Speaker 9: are foods that you eat that you realize to your point, 724 00:32:45,320 --> 00:32:48,120 Speaker 9: your body interacts with differently. For me, it's rice. Rice 725 00:32:48,160 --> 00:32:50,320 Speaker 9: sends me through the roof. And so what I've done 726 00:32:50,400 --> 00:32:53,040 Speaker 9: is I've stayed. I've changed my behavior around my eating habits. 727 00:32:53,080 --> 00:32:55,160 Speaker 9: I stay away from rice. I don't want that to 728 00:32:55,200 --> 00:32:58,200 Speaker 9: impact my glucose levels, which obviously impacts your health. 729 00:32:58,200 --> 00:32:59,560 Speaker 2: Why are you wearing the patch? 730 00:33:00,080 --> 00:33:02,560 Speaker 9: So I wear it for the similar reason I like 731 00:33:02,640 --> 00:33:05,160 Speaker 9: to one. I'm a bit of a sensor nerd since 732 00:33:05,160 --> 00:33:05,959 Speaker 9: I work at the company. 733 00:33:05,960 --> 00:33:07,960 Speaker 2: You know, is this a prescription thing? 734 00:33:08,280 --> 00:33:08,920 Speaker 9: It is a prescription. 735 00:33:09,120 --> 00:33:09,960 Speaker 3: I have a prescription. 736 00:33:10,480 --> 00:33:11,800 Speaker 2: And why did you get the prescription? 737 00:33:12,280 --> 00:33:14,800 Speaker 9: Because I wanted to monitor the post levels. It was 738 00:33:14,880 --> 00:33:16,440 Speaker 9: really important me for long term health. 739 00:33:16,480 --> 00:33:16,960 Speaker 3: Concert But the. 740 00:33:16,960 --> 00:33:20,040 Speaker 2: Target market is what it's still those? Is it you 741 00:33:20,240 --> 00:33:22,440 Speaker 2: or is it just somebody who has an underlying condition 742 00:33:22,520 --> 00:33:24,720 Speaker 2: that requires this type of monitoring. 743 00:33:24,800 --> 00:33:28,160 Speaker 9: Today it's really targeted at the diabetes popularation and those impacted, 744 00:33:28,240 --> 00:33:31,480 Speaker 9: so that population, but longer term it will, believe go 745 00:33:31,520 --> 00:33:34,360 Speaker 9: well beyond that to folks that are monitoring their health 746 00:33:34,760 --> 00:33:36,360 Speaker 9: before your physical. 747 00:33:36,000 --> 00:33:38,000 Speaker 2: So the Apple Watch isn't enough. We got to go 748 00:33:38,200 --> 00:33:38,800 Speaker 2: another step. 749 00:33:38,880 --> 00:33:40,040 Speaker 3: You have to step. 750 00:33:40,120 --> 00:33:42,280 Speaker 9: You have to be accurate. And that's the big thing. 751 00:33:42,320 --> 00:33:45,320 Speaker 9: The difference between a healthy person and someone with pre 752 00:33:45,360 --> 00:33:48,360 Speaker 9: diabetes is very very small, so accuracy is paramount. 753 00:33:48,600 --> 00:33:51,479 Speaker 3: Yeah, ninety million people have pre diabetes. 754 00:33:51,000 --> 00:33:52,960 Speaker 2: And I go once a year from my checkup. 755 00:33:52,960 --> 00:33:53,680 Speaker 4: That's not good enough. 756 00:33:53,800 --> 00:33:58,080 Speaker 1: You, I'm sure, are a healthy eater and an active 757 00:33:58,240 --> 00:33:59,040 Speaker 1: fitness guy. 758 00:34:00,080 --> 00:34:01,240 Speaker 3: You don't need to worry about it. 759 00:34:01,280 --> 00:34:04,040 Speaker 1: But I'm cramming like peanut butter and jellies down my 760 00:34:04,120 --> 00:34:05,360 Speaker 1: throat as fast as I can. 761 00:34:05,880 --> 00:34:07,640 Speaker 3: You know, and I like to watch TV. 762 00:34:07,840 --> 00:34:09,919 Speaker 8: How much does this cost from Matt when he doesn't 763 00:34:09,920 --> 00:34:10,640 Speaker 8: have a prescription. 764 00:34:10,760 --> 00:34:12,359 Speaker 9: When you don't have a prescription, you can get online. 765 00:34:12,360 --> 00:34:14,120 Speaker 9: It's one hundred and seventy nine for a month. That's 766 00:34:14,120 --> 00:34:15,879 Speaker 9: three different sensors, ten days each. 767 00:34:16,680 --> 00:34:19,719 Speaker 1: And then I just think it's interesting. You know that 768 00:34:19,920 --> 00:34:22,840 Speaker 1: rice drives your blood sugar higher. I would not have 769 00:34:23,000 --> 00:34:24,839 Speaker 1: any idea if I'm sitting in front of a bowl 770 00:34:24,880 --> 00:34:28,000 Speaker 1: of rice that that's gonna do it. So, you know, 771 00:34:28,120 --> 00:34:31,080 Speaker 1: we take these tests. The doctor will say, hey, avoid 772 00:34:31,120 --> 00:34:35,160 Speaker 1: sugary foods, don't eat anything after dinner, and then come 773 00:34:35,200 --> 00:34:39,479 Speaker 1: in the next morning. I will avoid ice cream and 774 00:34:39,560 --> 00:34:42,719 Speaker 1: you know obviously cake and candy, but I probably would 775 00:34:42,719 --> 00:34:45,640 Speaker 1: eat a bowl of rice no problem, and even sneak 776 00:34:45,680 --> 00:34:46,600 Speaker 1: one in after dinner. 777 00:34:46,680 --> 00:34:47,920 Speaker 2: Wow, look at you going crazy. 778 00:34:48,040 --> 00:34:50,799 Speaker 3: Well, but then I would get a bad result on 779 00:34:50,840 --> 00:34:51,280 Speaker 3: the test. 780 00:34:51,680 --> 00:34:53,640 Speaker 9: You might even find out that the ice cream is 781 00:34:53,680 --> 00:34:56,359 Speaker 9: actually good for you and the rice isn't. 782 00:34:56,360 --> 00:34:58,279 Speaker 3: Don't take my work all right? 783 00:34:58,360 --> 00:35:01,560 Speaker 2: Twenty one by ratings three whole and zero cells. 784 00:35:02,120 --> 00:35:05,840 Speaker 1: The stock has outperformed Medtronic Insulate Tandem Diabetes and Abbot 785 00:35:05,920 --> 00:35:07,799 Speaker 1: labs over the last five years. I'll look at those 786 00:35:07,800 --> 00:35:10,200 Speaker 1: because they make you know, competing products or in the 787 00:35:10,239 --> 00:35:11,320 Speaker 1: same space in the diabetes. 788 00:35:11,360 --> 00:35:13,320 Speaker 2: What's the investment message you give to the street. 789 00:35:13,680 --> 00:35:16,680 Speaker 9: You know, we have a product, it's the most accurate, simple, 790 00:35:16,680 --> 00:35:19,160 Speaker 9: it's easy to use, it's covered. So we have a 791 00:35:19,239 --> 00:35:21,239 Speaker 9: population in front of us, a TAM in front of 792 00:35:21,320 --> 00:35:24,520 Speaker 9: us that we think is absolutely unparalleled. On top of that, 793 00:35:24,560 --> 00:35:26,960 Speaker 9: we've been working very, very hard on profitability. I know 794 00:35:27,000 --> 00:35:28,920 Speaker 9: that's top of mind for investors. And if you look 795 00:35:28,920 --> 00:35:33,320 Speaker 9: at our performance, a twenty six hundred basis point increase 796 00:35:33,440 --> 00:35:35,759 Speaker 9: over the last five years in profitability. So when you 797 00:35:35,800 --> 00:35:39,240 Speaker 9: combine incredible top line growth we grew twenty six percent 798 00:35:39,360 --> 00:35:42,480 Speaker 9: organically last quarter and the prior quarter, along with that 799 00:35:42,520 --> 00:35:46,160 Speaker 9: profitability in this unmatched TAM, I think you really have 800 00:35:46,800 --> 00:35:50,160 Speaker 9: an incredible investment opportunity. And we've been communicating that to 801 00:35:50,200 --> 00:35:52,640 Speaker 9: the investing public for some time, and you know you've 802 00:35:52,640 --> 00:35:54,600 Speaker 9: seen that through our growth over the years. Just really 803 00:35:54,640 --> 00:35:56,520 Speaker 9: passionate about the company and the opportunity ahead. 804 00:35:56,560 --> 00:35:58,239 Speaker 2: Yeah, I'm looking at the projectors out there on the 805 00:35:58,280 --> 00:36:00,960 Speaker 2: street kind of high teens, low twenty top line growth, 806 00:36:01,160 --> 00:36:04,439 Speaker 2: IBATHAM margin mid twenties going to high twenties and free 807 00:36:04,440 --> 00:36:06,839 Speaker 2: cash a positive. If you can't sell that, I think 808 00:36:06,840 --> 00:36:08,400 Speaker 2: you need another job. So that's why we got all 809 00:36:08,440 --> 00:36:10,759 Speaker 2: those buys out there. So Jeremy Silvin, thank you so 810 00:36:10,880 --> 00:36:14,320 Speaker 2: much for joining us. Jeremy is the CFO of dex Com, 811 00:36:14,680 --> 00:36:17,080 Speaker 2: the symbol to put into your Bloomberg Professional Terminals d 812 00:36:17,600 --> 00:36:21,759 Speaker 2: XCM there, and he got his undergraduate from Arizona State. 813 00:36:21,760 --> 00:36:25,799 Speaker 2: Who's basketball team just coach by the greatest point guard 814 00:36:25,800 --> 00:36:27,719 Speaker 2: in college history. That's Dandy Early good bud Man. 815 00:36:29,360 --> 00:36:32,480 Speaker 1: Thanks for listening to the Bloomberg Markets podcasts. You can 816 00:36:32,480 --> 00:36:36,280 Speaker 1: subscribe and listen to interviews at Apple Podcasts or whatever 817 00:36:36,360 --> 00:36:40,080 Speaker 1: podcast platform you prefer. I'm Matt Miller. I'm on Twitter 818 00:36:40,280 --> 00:36:42,200 Speaker 1: at Matt Miller nineteen seventy three. 819 00:36:42,640 --> 00:36:45,040 Speaker 2: And I'm Paul Sweeney. I'm on Twitter at pt Sweeney. 820 00:36:45,160 --> 00:36:47,799 Speaker 2: Before the podcast. You can always catch us worldwide at 821 00:36:47,840 --> 00:36:49,560 Speaker 2: Bloomberg Radio