1 00:00:01,639 --> 00:00:05,960 Speaker 1: From Marhard where Innovation, Money and power Collie in Silicon 2 00:00:06,040 --> 00:00:06,920 Speaker 1: Vallet NBN. 3 00:00:07,280 --> 00:00:11,320 Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 4 00:00:25,040 --> 00:00:27,880 Speaker 3: I'm Caroline Heinder Blomberg's world headquarters in New York, and 5 00:00:27,920 --> 00:00:29,480 Speaker 3: I met Lovelow in San Francisco. 6 00:00:29,680 --> 00:00:31,600 Speaker 4: This is Bloomberg Technology. 7 00:00:31,080 --> 00:00:34,000 Speaker 3: Coming up US inflation it tops forecast in a blow 8 00:00:34,040 --> 00:00:36,600 Speaker 3: to rate cut hopes. We'll discuss the implication of high 9 00:00:36,640 --> 00:00:38,360 Speaker 3: for longer rates on the tech sector. 10 00:00:38,960 --> 00:00:41,720 Speaker 2: Plus we'll dive into the names reporting tonight after the 11 00:00:41,760 --> 00:00:43,640 Speaker 2: bell and get a read on the health of the 12 00:00:43,760 --> 00:00:44,720 Speaker 2: gig economy. 13 00:00:45,080 --> 00:00:46,440 Speaker 5: And we'll get an outlook for crypto. 14 00:00:46,600 --> 00:00:49,879 Speaker 3: That's as bitcoin drops, having hit fifty thousand dollars for 15 00:00:49,880 --> 00:00:52,479 Speaker 3: the first time in two years. All that and so 16 00:00:52,680 --> 00:00:55,000 Speaker 3: much more coming up. First we check on in the 17 00:00:55,040 --> 00:00:59,000 Speaker 3: macro picture today is dictated by that hotter than anticipated 18 00:00:59,040 --> 00:01:02,320 Speaker 3: CPI print, which which means well, the market now anticipates 19 00:01:02,400 --> 00:01:05,119 Speaker 3: rate cuts not happening until at least July, you let 20 00:01:05,160 --> 00:01:08,080 Speaker 3: alone March that we'd previously been anticipating. Look, it is 21 00:01:08,560 --> 00:01:10,960 Speaker 3: not therefore to those rate cut views and the S 22 00:01:11,000 --> 00:01:12,960 Speaker 3: and P five hundred falls off that all important five 23 00:01:13,000 --> 00:01:15,160 Speaker 3: thousand level that we do only just hit on Friday, 24 00:01:15,400 --> 00:01:17,920 Speaker 3: the NASDAC under pressure then as that one hundred in particular, 25 00:01:17,959 --> 00:01:19,360 Speaker 3: one of the worst performing off by one. 26 00:01:19,240 --> 00:01:22,399 Speaker 5: Point two percent. So big tech really being squeezed at the. 27 00:01:22,360 --> 00:01:25,319 Speaker 3: Moment, and you see those boring costs absolutely leaping to 28 00:01:25,319 --> 00:01:27,720 Speaker 3: the higher side, the two year yield eclipsing the high 29 00:01:27,720 --> 00:01:29,800 Speaker 3: since we've seen back in December when the FED did 30 00:01:29,840 --> 00:01:32,440 Speaker 3: pivot and really make the market think we were going 31 00:01:32,520 --> 00:01:34,800 Speaker 3: to see some rate cuts coming in twenty twenty four, 32 00:01:34,800 --> 00:01:37,760 Speaker 3: we're currently up by some thirteen basis points in ed. 33 00:01:37,760 --> 00:01:39,480 Speaker 3: Before we get into the micro we want to go 34 00:01:39,600 --> 00:01:43,319 Speaker 3: broader with clear Bridge Investment senior research analysts Hillary Frish, 35 00:01:43,360 --> 00:01:45,840 Speaker 3: who can really give us a perspective of what's been 36 00:01:45,880 --> 00:01:49,480 Speaker 3: happening on a resasset basis, and ultimately we are seeing 37 00:01:49,520 --> 00:01:53,080 Speaker 3: Hillary a perspective that well, maybe rate cuts aren't happening 38 00:01:53,120 --> 00:01:54,680 Speaker 3: and what that means in terms of all the bets 39 00:01:54,680 --> 00:01:58,400 Speaker 3: and the valuations ultimately they've been seen in big tech names. 40 00:01:58,520 --> 00:02:03,440 Speaker 3: Do you think it's the right time to be such. 41 00:02:01,640 --> 00:02:04,320 Speaker 6: A good question, Caroline, Thank you, it's terrific being here. 42 00:02:05,160 --> 00:02:07,960 Speaker 5: It's we've had. 43 00:02:07,880 --> 00:02:11,239 Speaker 6: Such a great run in technology and a relative relatively 44 00:02:11,280 --> 00:02:13,640 Speaker 6: short period of time, and going into today's print, I 45 00:02:13,720 --> 00:02:17,919 Speaker 6: was thinking that we're already entering a seasonally weaker part 46 00:02:17,960 --> 00:02:22,400 Speaker 6: of the year. Now the tenures CPI is looking hotter, 47 00:02:22,919 --> 00:02:26,720 Speaker 6: and also the US dollars up, so we can and 48 00:02:26,919 --> 00:02:30,799 Speaker 6: absolutely likely will have some sell off at some period 49 00:02:30,840 --> 00:02:33,200 Speaker 6: of time. I think at a minimum, we should have 50 00:02:33,240 --> 00:02:37,520 Speaker 6: some consolidation, some digestion. That said, I'm about as bullish 51 00:02:37,560 --> 00:02:39,600 Speaker 6: as I can be on the intermediate to long term. 52 00:02:39,639 --> 00:02:42,600 Speaker 6: I'm not sure who was expecting rate cuts to really 53 00:02:42,600 --> 00:02:45,520 Speaker 6: take place in March at this point given the Fed speak, 54 00:02:45,639 --> 00:02:49,239 Speaker 6: and then finally the pain trade has really felt up 55 00:02:49,480 --> 00:02:52,000 Speaker 6: not down. Today could change that. I think if we 56 00:02:52,080 --> 00:02:54,720 Speaker 6: get to a four and a half percent level on 57 00:02:54,760 --> 00:02:58,120 Speaker 6: the tenure, that's an important point of demarcation psychologically and 58 00:02:58,160 --> 00:02:59,239 Speaker 6: also for valuations. 59 00:02:59,480 --> 00:03:02,440 Speaker 3: What was in thing is basically how all in investors 60 00:03:02,440 --> 00:03:04,640 Speaker 3: seem to have been going in tech stocks, particularly at 61 00:03:04,639 --> 00:03:06,280 Speaker 3: the end of last week. Bank of America, as they 62 00:03:06,280 --> 00:03:09,360 Speaker 3: always do, really dictating the flows that they've been seeing, 63 00:03:09,680 --> 00:03:12,040 Speaker 3: and the fact that basically everyone has gone along this 64 00:03:12,080 --> 00:03:12,800 Speaker 3: particular sector. 65 00:03:12,840 --> 00:03:14,520 Speaker 5: Are we just seeing too much of a crowded trade 66 00:03:14,520 --> 00:03:15,280 Speaker 5: at the moment. 67 00:03:15,800 --> 00:03:18,600 Speaker 6: We're seeing some crowding. We're seeing some signs of exuberance, 68 00:03:18,600 --> 00:03:20,440 Speaker 6: but I think what we also saw was the fact 69 00:03:20,440 --> 00:03:23,959 Speaker 6: that investors were really off sides for this move heading 70 00:03:24,000 --> 00:03:27,840 Speaker 6: into it. There wasn't enough participation. We were seeing some chasing. Absolutely, 71 00:03:28,160 --> 00:03:30,799 Speaker 6: I wouldn't call it abject crowding, but I would say 72 00:03:30,840 --> 00:03:34,640 Speaker 6: that last year was a year of reset and also 73 00:03:34,720 --> 00:03:37,960 Speaker 6: profitability improvements. We saw six hundred basis points of profitability 74 00:03:37,960 --> 00:03:40,760 Speaker 6: improvement across software in particular, five hundred to six hundred. 75 00:03:40,960 --> 00:03:43,920 Speaker 6: That was pretty impressive in a dicceleration year, and this 76 00:03:44,080 --> 00:03:48,120 Speaker 6: year should actually be a year of revenue reacceleration, although 77 00:03:48,160 --> 00:03:50,640 Speaker 6: SQL if the economy doesn't absolutely fall apart, which doesn't 78 00:03:50,680 --> 00:03:54,720 Speaker 6: look like it. So I think investors are willing to 79 00:03:54,800 --> 00:03:57,280 Speaker 6: anticipate some of that. We're seeing some signs they're willing 80 00:03:57,360 --> 00:03:59,240 Speaker 6: to look out a little bit. We'll see how they 81 00:03:59,240 --> 00:03:59,880 Speaker 6: feel in the spring. 82 00:04:01,680 --> 00:04:02,040 Speaker 4: Hary. 83 00:04:02,360 --> 00:04:04,960 Speaker 2: The root of that Boa survey that Caro is talking 84 00:04:04,960 --> 00:04:09,000 Speaker 2: about is just optimism about global growth, right earnings in 85 00:04:09,040 --> 00:04:11,480 Speaker 2: the context of a strong global economy. This is what 86 00:04:11,520 --> 00:04:14,160 Speaker 2: Aaron Brown and PIMCO told the show earlier today. 87 00:04:14,200 --> 00:04:17,880 Speaker 7: This is this you're still seeing this market where you 88 00:04:17,960 --> 00:04:20,800 Speaker 7: have one sector like the tech sector, which is up 89 00:04:20,800 --> 00:04:24,160 Speaker 7: forty percent, and you have another sector like healthcare, which 90 00:04:24,200 --> 00:04:27,479 Speaker 7: delivered earnings down twenty percent. It's a huge bifurcation in 91 00:04:27,520 --> 00:04:30,039 Speaker 7: the market right now, and I think that that really 92 00:04:30,080 --> 00:04:33,560 Speaker 7: presents a lot of opportunity for equity investors. 93 00:04:34,640 --> 00:04:38,080 Speaker 2: A huge bifurcation. Who even cares about inflation? Just go 94 00:04:38,160 --> 00:04:40,360 Speaker 2: with the tech sector? Is that your attitude? 95 00:04:41,120 --> 00:04:43,479 Speaker 6: Oh, I think you have to care about inflation to 96 00:04:43,920 --> 00:04:46,040 Speaker 6: a degree. And again above four and a half percent, 97 00:04:46,200 --> 00:04:49,680 Speaker 6: you start to see some incremental concern over technology. 98 00:04:49,720 --> 00:04:50,000 Speaker 8: For sure. 99 00:04:50,040 --> 00:04:53,800 Speaker 6: It's a terrific point by Aaron. I think if we 100 00:04:54,320 --> 00:04:57,120 Speaker 6: see a real broadening out of the economy, and yes 101 00:04:57,120 --> 00:05:01,240 Speaker 6: we've seen some sectors absolutely annihilated, those representrificpportunities. We think 102 00:05:01,279 --> 00:05:05,360 Speaker 6: that as a firm at Clearbridge, I personally also tend 103 00:05:05,400 --> 00:05:08,040 Speaker 6: to believe that as we move further through time toward 104 00:05:08,080 --> 00:05:10,560 Speaker 6: the second half of this year, where I think we 105 00:05:10,640 --> 00:05:14,599 Speaker 6: will see a point of inflection in technology, in part 106 00:05:14,680 --> 00:05:18,240 Speaker 6: driven by AI, in part driven by initiatives around AI, 107 00:05:18,800 --> 00:05:22,640 Speaker 6: you will also see tech do well ultimately, but tech 108 00:05:22,680 --> 00:05:24,400 Speaker 6: will have to tread water for a period of time 109 00:05:24,440 --> 00:05:27,119 Speaker 6: to the extent that either the tenure continues to rise, 110 00:05:27,400 --> 00:05:30,600 Speaker 6: or the economy the market broadens out to other sectors. 111 00:05:30,640 --> 00:05:33,600 Speaker 6: So I think we're I don't know that we have 112 00:05:33,680 --> 00:05:36,280 Speaker 6: to ultimately have an either or, although we might have 113 00:05:36,320 --> 00:05:37,720 Speaker 6: one on an interim basis. 114 00:05:38,480 --> 00:05:40,120 Speaker 4: You go back to economics one oh one. 115 00:05:40,279 --> 00:05:44,720 Speaker 2: The idea simply is that the FED uses rates and 116 00:05:44,839 --> 00:05:50,280 Speaker 2: higher rates to dampen demand, right impact consumption. In a minute, 117 00:05:50,400 --> 00:05:51,720 Speaker 2: Karen and I are going to go deep on some 118 00:05:51,760 --> 00:05:54,000 Speaker 2: of the gig economy companies. If you look at Uber 119 00:05:54,160 --> 00:05:59,080 Speaker 2: last week, there is no evidence that consumer demand really 120 00:05:59,440 --> 00:06:03,279 Speaker 2: or corporate command is slowing down. So it puts into 121 00:06:03,320 --> 00:06:05,960 Speaker 2: questionable what was the point of having higher rates? Right 122 00:06:06,000 --> 00:06:08,720 Speaker 2: if that is the only mechanism available to them, What 123 00:06:08,720 --> 00:06:10,000 Speaker 2: do you make of that thesis? 124 00:06:10,520 --> 00:06:13,880 Speaker 6: Well, it's not the only mechanism available to them, and 125 00:06:13,920 --> 00:06:15,919 Speaker 6: we're going to see liquidity coming out of the market 126 00:06:15,960 --> 00:06:19,080 Speaker 6: over time. It could be a lag. It certainly was 127 00:06:19,080 --> 00:06:23,320 Speaker 6: a hotter print. We're not at five percent. Five percent 128 00:06:23,320 --> 00:06:26,760 Speaker 6: would be another story, but we'll see how things trend 129 00:06:26,760 --> 00:06:27,520 Speaker 6: in the coming months. 130 00:06:28,000 --> 00:06:30,880 Speaker 3: What I'm interested in is some of your individual companies 131 00:06:30,880 --> 00:06:33,720 Speaker 3: that you call out, where you see the opportunities perhaps 132 00:06:33,720 --> 00:06:36,800 Speaker 3: in this pullback to be putting more into like Microsoft, 133 00:06:37,120 --> 00:06:41,280 Speaker 3: like Salesforce, Oracle. What is this a moment to think 134 00:06:41,320 --> 00:06:44,720 Speaker 3: about corporate spending rather than perhaps consumer spending, which we 135 00:06:44,760 --> 00:06:46,719 Speaker 3: are worried about, particularly if we start to see more job. 136 00:06:46,600 --> 00:06:47,560 Speaker 5: Losses in the back of AI. 137 00:06:47,920 --> 00:06:51,599 Speaker 6: Absolutely well, what I do like about technology in general 138 00:06:51,680 --> 00:06:54,400 Speaker 6: is that I think we went through many recessions last year. 139 00:06:54,640 --> 00:06:57,560 Speaker 6: Last year companies were concerned about recession. They were laying 140 00:06:57,560 --> 00:06:59,600 Speaker 6: off people, they were optimizing costs, they went on a 141 00:06:59,640 --> 00:07:03,039 Speaker 6: buyer's strike. This year, there's an imperative to spend. Even 142 00:07:03,080 --> 00:07:05,680 Speaker 6: if you're not spending directly in AI, you need to 143 00:07:06,160 --> 00:07:08,640 Speaker 6: lay the foundation to be able to leverage AI. So 144 00:07:08,720 --> 00:07:13,320 Speaker 6: that's a lot of data related investments, harmonization, getting data 145 00:07:13,320 --> 00:07:16,120 Speaker 6: in order, it's a lot of infrastructure investment. It's migration 146 00:07:16,240 --> 00:07:20,240 Speaker 6: of the cloud. You're starting to see the signs of that. Interestingly, 147 00:07:20,440 --> 00:07:23,760 Speaker 6: Data Dog a consumption name and consumption names tend to 148 00:07:23,880 --> 00:07:26,960 Speaker 6: lead reported this morning they report in a much better number. 149 00:07:26,960 --> 00:07:31,000 Speaker 6: They reported slight acceleration. They guide it extraordinarily conservatively in 150 00:07:31,000 --> 00:07:35,200 Speaker 6: my view, and rightly so. But nonetheless, the stock is 151 00:07:35,240 --> 00:07:37,520 Speaker 6: actually down less than the NASDAK I think less than 152 00:07:37,560 --> 00:07:40,400 Speaker 6: the SMP and I think it's because investors can see 153 00:07:40,400 --> 00:07:43,200 Speaker 6: the signs of this improvement, the signs of this need 154 00:07:43,240 --> 00:07:47,040 Speaker 6: to spend. So if the economy again, if we see 155 00:07:47,320 --> 00:07:50,920 Speaker 6: outsized layoffs, that will be more of an issue. So far, 156 00:07:51,040 --> 00:07:52,040 Speaker 6: we're seeing the opposite. 157 00:07:52,280 --> 00:07:54,559 Speaker 5: At dieta dog now down by one point two percent. 158 00:07:54,600 --> 00:07:57,160 Speaker 3: It was off more than six percent at one point, 159 00:07:57,160 --> 00:07:59,640 Speaker 3: but that needs at reaction, just pushing it back off 160 00:07:59,640 --> 00:08:00,600 Speaker 3: to the and traded. 161 00:08:01,760 --> 00:08:01,960 Speaker 4: Yeah. 162 00:08:01,960 --> 00:08:04,160 Speaker 2: I think one final question I've got for Hillary is, Okay, 163 00:08:04,200 --> 00:08:06,600 Speaker 2: we know the AI story, we know about inflation, and 164 00:08:06,640 --> 00:08:06,920 Speaker 2: we know. 165 00:08:06,880 --> 00:08:07,520 Speaker 4: About the FED. 166 00:08:07,760 --> 00:08:09,760 Speaker 2: How much of a wild card is the election this 167 00:08:09,880 --> 00:08:11,480 Speaker 2: year for the technology sector? 168 00:08:13,160 --> 00:08:13,320 Speaker 5: Hm? 169 00:08:13,960 --> 00:08:16,680 Speaker 6: Well, I have to imagine it's a bit of a 170 00:08:16,680 --> 00:08:22,200 Speaker 6: wildcarde it was last time Trump was on the ballot, 171 00:08:22,680 --> 00:08:25,320 Speaker 6: but it frankly will get closer. But it feels like 172 00:08:25,360 --> 00:08:27,320 Speaker 6: an autrinity from now when I think about how these 173 00:08:27,360 --> 00:08:30,040 Speaker 6: tech stocks moved here to day. So I'll look forward 174 00:08:30,040 --> 00:08:31,400 Speaker 6: to coming on and discussing that with you. 175 00:08:31,480 --> 00:08:33,720 Speaker 4: Vn Ed all right, we'll hit you up on that. 176 00:08:33,840 --> 00:08:37,680 Speaker 2: Hillary Fish of Clearbridge Investments, great analysis of the macro picture. 177 00:08:37,720 --> 00:08:41,000 Speaker 4: Thank you. Let's go from what is the macro to 178 00:08:41,080 --> 00:08:41,480 Speaker 4: the micro? 179 00:08:41,559 --> 00:08:43,000 Speaker 2: There's a lot of stories that are happening in the 180 00:08:43,080 --> 00:08:44,800 Speaker 2: ernies context. One of the things I wrote about at 181 00:08:44,800 --> 00:08:46,520 Speaker 2: the beginning of the week in the Tech Daily news 182 00:08:46,600 --> 00:08:47,800 Speaker 2: letter was the gig economy. 183 00:08:48,120 --> 00:08:49,520 Speaker 4: Uber had a blowout quarter. 184 00:08:49,960 --> 00:08:52,720 Speaker 2: Now its competitors are up next, Lift down three percent 185 00:08:52,880 --> 00:08:55,360 Speaker 2: earnings today after the bell Airbnb. 186 00:08:54,920 --> 00:08:57,320 Speaker 4: As well later in the Weekdoor Dash. What do we 187 00:08:57,360 --> 00:08:58,520 Speaker 4: actually know about the. 188 00:08:58,480 --> 00:09:01,320 Speaker 2: Demand side for the gig economy but also the supply side. 189 00:09:01,360 --> 00:09:04,400 Speaker 2: Let's go to Bloomberg's Natalie Lung who covers this feat 190 00:09:04,600 --> 00:09:07,960 Speaker 2: for Bloomberg Technology. Uber really set the bar high, right 191 00:09:08,080 --> 00:09:10,960 Speaker 2: because Lift lives in their shadows. So let's start there. 192 00:09:11,160 --> 00:09:13,880 Speaker 2: What are you expecting this week from Lyft for Lift? 193 00:09:14,040 --> 00:09:18,080 Speaker 9: Yes, as you mentioned, they're kind of in a tough 194 00:09:18,160 --> 00:09:21,240 Speaker 9: spot in comparison to Uber, who has the scale of 195 00:09:21,360 --> 00:09:25,960 Speaker 9: international markets and as well as scale in other verticals 196 00:09:26,000 --> 00:09:32,240 Speaker 9: like delivery and advertising. So for Lift, their performance, based 197 00:09:32,280 --> 00:09:36,280 Speaker 9: on analyst estimates pretty conservative. Some analysts say they could 198 00:09:36,360 --> 00:09:39,680 Speaker 9: deliver a beat, But then the concern is also like 199 00:09:39,760 --> 00:09:43,959 Speaker 9: how much can they balance in terms of offering incentives 200 00:09:43,960 --> 00:09:47,160 Speaker 9: to get more drivers get more drivers over from Uber, 201 00:09:47,440 --> 00:09:52,120 Speaker 9: and how much subsidies and that sense and so, and 202 00:09:52,360 --> 00:09:56,680 Speaker 9: especially because they announced like a seventy percent pay guarantee 203 00:09:56,760 --> 00:10:01,480 Speaker 9: last week to sort of more drivers onto the platform 204 00:10:01,520 --> 00:10:06,760 Speaker 9: and other features like a better response on deactivation account deactivations, 205 00:10:06,960 --> 00:10:10,640 Speaker 9: and so the conversation will be around how much can 206 00:10:10,679 --> 00:10:13,960 Speaker 9: you do that and not like eat into the margins. 207 00:10:14,360 --> 00:10:17,040 Speaker 3: Let's talk about margins when it comes to food delivery 208 00:10:17,600 --> 00:10:21,320 Speaker 3: in Siicar, we're anticipating this is one of the most 209 00:10:21,360 --> 00:10:24,040 Speaker 3: shortened stocks out there at the moment. I think forty 210 00:10:24,080 --> 00:10:26,719 Speaker 3: percent of the free flowt is currently being better against 211 00:10:27,040 --> 00:10:28,000 Speaker 3: one of the worries here. 212 00:10:28,520 --> 00:10:28,679 Speaker 7: Right. 213 00:10:28,840 --> 00:10:33,640 Speaker 9: It's definitely again around the competition. So Uber door Dash 214 00:10:33,760 --> 00:10:38,160 Speaker 9: they have they have not only just grocer delivery, they 215 00:10:38,240 --> 00:10:41,960 Speaker 9: also the majority of the business is restaurant meals takeout, 216 00:10:42,120 --> 00:10:44,600 Speaker 9: and so they do have that scale there which Instacart 217 00:10:44,640 --> 00:10:47,959 Speaker 9: may be more limited right now. And also international ubers 218 00:10:48,240 --> 00:10:50,960 Speaker 9: in a lot of international markets. DoorDash is in some 219 00:10:51,000 --> 00:10:54,240 Speaker 9: parts of Europe and instacart is mostly in the US Canada. 220 00:10:54,280 --> 00:10:57,079 Speaker 9: So the growth rate there in terms of orders of 221 00:10:57,200 --> 00:11:00,400 Speaker 9: grosse transaction value will be of interest. Whether that can 222 00:11:01,520 --> 00:11:06,240 Speaker 9: reaccelerate will be definitely If Chris, it's. 223 00:11:06,160 --> 00:11:10,280 Speaker 2: The economy week lift kicking ourself after the bell this afternoon, Bloombgs, 224 00:11:10,360 --> 00:11:11,600 Speaker 2: Natalie Lung, thank you so much. 225 00:11:12,040 --> 00:11:12,520 Speaker 5: Firefly. 226 00:11:12,640 --> 00:11:16,079 Speaker 3: It's Adobe's new flagship AI product that has been seen 227 00:11:16,200 --> 00:11:18,040 Speaker 3: as perhaps a bit of a late comer to the 228 00:11:18,160 --> 00:11:20,480 Speaker 3: likes of Mid Journey in Dahli, but now the company 229 00:11:20,559 --> 00:11:24,479 Speaker 3: is banking one aspect that as competitors lack a commercially 230 00:11:24,600 --> 00:11:28,040 Speaker 3: safe data set which provides IP guards, including legal support 231 00:11:28,040 --> 00:11:29,360 Speaker 3: if anyone tries to sue them. 232 00:11:29,520 --> 00:11:31,679 Speaker 5: Let's bring in Bloombogs Brodie Ford for more on this. 233 00:11:31,720 --> 00:11:34,560 Speaker 3: And look, it's not exactly a sexy growth story to 234 00:11:34,600 --> 00:11:35,320 Speaker 3: a certain extent. 235 00:11:35,480 --> 00:11:37,120 Speaker 5: It's more about, look, we've got you. 236 00:11:37,360 --> 00:11:39,679 Speaker 3: If you're a big client and you want legal protection, 237 00:11:40,160 --> 00:11:40,959 Speaker 3: you got it with us. 238 00:11:42,240 --> 00:11:46,119 Speaker 10: Yeah, everyone knows the story of your big tech incumbent 239 00:11:46,400 --> 00:11:49,439 Speaker 10: and then this cool startup comes and smokes you. Right, 240 00:11:49,480 --> 00:11:52,080 Speaker 10: and everyone thought out what would happen with open Ai 241 00:11:52,800 --> 00:11:56,280 Speaker 10: in mid Journey with the image creations. But Anooahi has 242 00:11:56,280 --> 00:11:59,200 Speaker 10: been coming out saying you're going to get sued right 243 00:11:59,280 --> 00:12:02,719 Speaker 10: unless you us use these other guys. What's going to 244 00:12:02,800 --> 00:12:06,000 Speaker 10: happen is they sucked in images from across the Internet 245 00:12:06,280 --> 00:12:10,720 Speaker 10: with no copyright. It's a legal hazard. That's the argument. 246 00:12:10,760 --> 00:12:13,839 Speaker 10: They've made, and so they've trained their system on their 247 00:12:13,840 --> 00:12:16,800 Speaker 10: own Adobe stock, meaning that they have their own rights 248 00:12:16,800 --> 00:12:20,520 Speaker 10: to use it. Obviously, some of the creators might not 249 00:12:20,760 --> 00:12:24,480 Speaker 10: love that. There's probably still some legal dispute, but so 250 00:12:24,720 --> 00:12:28,640 Speaker 10: far this pitch appears to be working with many big corporations. 251 00:12:30,040 --> 00:12:32,520 Speaker 2: You give the example in the BusinessWeek piece of them 252 00:12:32,559 --> 00:12:36,960 Speaker 2: commissioning photographers like eighty Bucks to go out and photograph animals, 253 00:12:36,960 --> 00:12:39,480 Speaker 2: and the animals specifically are not allowed to wear clothes, 254 00:12:39,520 --> 00:12:42,240 Speaker 2: which is what fun is that. The thing that always 255 00:12:42,240 --> 00:12:44,760 Speaker 2: surprises me about Adobe, right Brodie, is that it is 256 00:12:44,800 --> 00:12:48,840 Speaker 2: in the top forty biggest publicly traded companies in the world. 257 00:12:49,240 --> 00:12:52,079 Speaker 2: And what it has in common with Microsoft is it's 258 00:12:52,120 --> 00:12:55,280 Speaker 2: got a very good track record of selling subscriptions on 259 00:12:55,400 --> 00:12:58,280 Speaker 2: software where you can get a similar tool for free 260 00:12:58,320 --> 00:13:01,760 Speaker 2: pretty much anywhere will still pay for it. So let's 261 00:13:01,760 --> 00:13:03,439 Speaker 2: go back to the intro. We said they want to 262 00:13:03,480 --> 00:13:07,000 Speaker 2: be profitable. How do they plan to make firefly profitable? 263 00:13:08,320 --> 00:13:12,679 Speaker 10: As you said, Adobe's creative cloud is very profitable. I 264 00:13:12,720 --> 00:13:14,240 Speaker 10: mean I've had people say to me it's one of 265 00:13:14,320 --> 00:13:19,359 Speaker 10: the most profitable things ever, not even just software, just products. 266 00:13:19,400 --> 00:13:22,600 Speaker 10: It is a complete cash cout and it is not 267 00:13:22,880 --> 00:13:25,960 Speaker 10: because it's the easiest to use or the cheapest, but 268 00:13:26,040 --> 00:13:28,600 Speaker 10: because it works well together. Right, if you have a 269 00:13:28,640 --> 00:13:34,040 Speaker 10: creative agency of a marketing agency, they need photo, video, audio, 270 00:13:34,080 --> 00:13:36,960 Speaker 10: it needs to work well together. And now they need AI. 271 00:13:37,520 --> 00:13:40,680 Speaker 10: And so Adobe's bet is that when they put AI 272 00:13:40,800 --> 00:13:43,880 Speaker 10: into each one of these products, into the content supply 273 00:13:44,040 --> 00:13:46,920 Speaker 10: chain as they call it, it'll work well together and 274 00:13:46,960 --> 00:13:49,440 Speaker 10: people will be willing to pay extra for it, and 275 00:13:49,480 --> 00:13:52,439 Speaker 10: more importantly, that they won't go to open AI or 276 00:13:52,480 --> 00:13:55,840 Speaker 10: mid journey and then cancel their Photoshop subscription. I mean 277 00:13:55,920 --> 00:13:58,360 Speaker 10: right now they're barely even charging for this. It's more 278 00:13:58,360 --> 00:14:02,559 Speaker 10: of a plan that stay the ecosystem. 279 00:14:02,520 --> 00:14:04,199 Speaker 4: And within Adobe. 280 00:14:04,240 --> 00:14:08,360 Speaker 2: Inside Adobe, though, there are employees and staff that are 281 00:14:08,400 --> 00:14:13,040 Speaker 2: worried about that ecosystem and the creatives that'll operate within it. 282 00:14:14,440 --> 00:14:14,600 Speaker 9: Well. 283 00:14:14,679 --> 00:14:17,800 Speaker 10: Yeah, there's a funny story we hear. We have it 284 00:14:17,840 --> 00:14:20,360 Speaker 10: in the story about you know, there's a presentation in 285 00:14:20,400 --> 00:14:23,920 Speaker 10: twenty nineteen. Someone is saying, listen, you guys, you are 286 00:14:24,000 --> 00:14:26,960 Speaker 10: going to get disrupted if you don't take this stuff seriously, 287 00:14:27,440 --> 00:14:31,440 Speaker 10: and the executives in the room being like, we're good, 288 00:14:32,640 --> 00:14:35,480 Speaker 10: They're not going to leave us, you can know. So yeah. 289 00:14:35,520 --> 00:14:39,160 Speaker 10: I mean it's a classic story a big company doesn't 290 00:14:39,160 --> 00:14:42,160 Speaker 10: invest enough in an emerging technology and gets disrupted. In 291 00:14:42,200 --> 00:14:45,080 Speaker 10: one year ago, everyone expected this to be the case 292 00:14:45,120 --> 00:14:48,080 Speaker 10: for Adobe. Today the picture is a little blurrier. I mean, 293 00:14:48,120 --> 00:14:51,800 Speaker 10: their argument has been landing, and I talked to a 294 00:14:51,840 --> 00:14:53,560 Speaker 10: lot of Wall Street types to say that the two 295 00:14:53,640 --> 00:14:56,080 Speaker 10: companies who are going to see a material uplift this 296 00:14:56,160 --> 00:14:59,200 Speaker 10: year from AI Microsoft first and then Adobe. 297 00:15:00,000 --> 00:15:02,080 Speaker 3: You see that coming from our own boombug intelligence team 298 00:15:02,120 --> 00:15:04,080 Speaker 3: members and thinking Anna Ragrana putting out a note this 299 00:15:04,120 --> 00:15:05,840 Speaker 3: morning saying it over you can see a one billion 300 00:15:05,880 --> 00:15:09,000 Speaker 3: dollar mid term generative AI lift at the moment in 301 00:15:09,040 --> 00:15:12,880 Speaker 3: terms of creative cloud, is there still any ramifications from 302 00:15:13,000 --> 00:15:15,200 Speaker 3: the big m and a deal that fell apart the Figma, 303 00:15:15,360 --> 00:15:19,920 Speaker 3: the anticipation that they've been distracted by having to go 304 00:15:20,000 --> 00:15:22,680 Speaker 3: through such legal wranglings rather than focusing in on what 305 00:15:22,800 --> 00:15:25,680 Speaker 3: is for many, you know, the new electricity when it 306 00:15:25,720 --> 00:15:29,960 Speaker 3: comes to data visa, the new way in which we're 307 00:15:30,040 --> 00:15:31,200 Speaker 3: thinking about technology. 308 00:15:32,040 --> 00:15:35,240 Speaker 10: The way most folks understand it is that Figma was 309 00:15:35,280 --> 00:15:39,000 Speaker 10: supposed to transform Adobe, and then AI was supposed to 310 00:15:39,040 --> 00:15:42,560 Speaker 10: transform Adobe. So the fact that Figma fell apart that 311 00:15:42,720 --> 00:15:45,320 Speaker 10: means AI really has to work out. I mean a 312 00:15:45,360 --> 00:15:48,400 Speaker 10: lot of their eggs are in this basket, and we 313 00:15:48,520 --> 00:15:51,600 Speaker 10: haven't even touched on the idea that this technology might 314 00:15:51,720 --> 00:15:54,120 Speaker 10: kind of disrupt their own user base, right, I mean, 315 00:15:54,160 --> 00:15:57,560 Speaker 10: millions of people pay for these tools. Are they going 316 00:15:57,600 --> 00:15:59,520 Speaker 10: to be out of work? Are they just going to 317 00:15:59,520 --> 00:16:01,680 Speaker 10: want a general images and not need to pay Adobe? 318 00:16:01,760 --> 00:16:04,800 Speaker 10: I mean, even those who are bullish on Adobe's AI 319 00:16:04,880 --> 00:16:08,560 Speaker 10: efforts see that there is some potential for this to 320 00:16:08,680 --> 00:16:11,960 Speaker 10: knock out a bit of their user base. So I mean, yeah, 321 00:16:12,160 --> 00:16:14,920 Speaker 10: now that Figma's gone, this needs to work, and it 322 00:16:14,920 --> 00:16:18,840 Speaker 10: appears to be at this moment, but there is uncertainty remaining. 323 00:16:20,080 --> 00:16:23,400 Speaker 2: In Berg's Brady Ford with the Adobe AI deep dive. 324 00:16:23,480 --> 00:16:24,960 Speaker 2: Thanks coming on the show Mate, Now coming up here 325 00:16:24,960 --> 00:16:28,360 Speaker 2: on Bloomberg Technology. Elon Musk's X looks to compete with 326 00:16:28,360 --> 00:16:32,000 Speaker 2: the likes of YouTube by adding new advertisers targeting features 327 00:16:32,000 --> 00:16:32,600 Speaker 2: to the platform. 328 00:16:32,600 --> 00:16:33,760 Speaker 4: Will bring you all those details. 329 00:16:33,800 --> 00:16:38,400 Speaker 2: Next is the company also looking to glurin creators, Karl, 330 00:16:38,440 --> 00:16:39,080 Speaker 2: what are you looking at? 331 00:16:39,520 --> 00:16:40,640 Speaker 5: I love the story that we're watching. 332 00:16:40,680 --> 00:16:43,200 Speaker 3: And this is a European company of course, ASML the 333 00:16:43,280 --> 00:16:44,320 Speaker 3: most valuable. 334 00:16:44,080 --> 00:16:46,680 Speaker 5: Tech stock in Europe. A little bit of pressure. 335 00:16:46,400 --> 00:16:49,120 Speaker 3: Today at one point, some significant pressure, a so called 336 00:16:49,160 --> 00:16:51,840 Speaker 3: fat finger. Basically an eriness trade, whether that's by a 337 00:16:51,880 --> 00:16:54,960 Speaker 3: human or an algorithm, is what many are blaming for 338 00:16:55,000 --> 00:16:57,280 Speaker 3: a seven percent plunge that we saw in the stock 339 00:16:57,520 --> 00:17:00,480 Speaker 3: at the open. Now the URINEX the overall that it's 340 00:17:00,560 --> 00:17:02,080 Speaker 3: on and said, look, that wasn't to blame. 341 00:17:02,080 --> 00:17:03,480 Speaker 5: Had no alerts that were triggered. 342 00:17:03,600 --> 00:17:06,560 Speaker 3: But now people are basically rather questioning the valuations that 343 00:17:06,560 --> 00:17:08,800 Speaker 3: we've seen, the run up that you've seen on ASML 344 00:17:08,920 --> 00:17:11,040 Speaker 3: and in SAP for example, or by two point eighther 345 00:17:11,040 --> 00:17:13,080 Speaker 3: center for the last couple of trading days. There's a 346 00:17:13,119 --> 00:17:24,520 Speaker 3: rumot technology time now for talking tech. First up Open 347 00:17:24,560 --> 00:17:27,119 Speaker 3: AI CEO Sam Altman, but he said that the UAE 348 00:17:27,280 --> 00:17:30,400 Speaker 3: could serve as the world's regulatory sandbox to test AI 349 00:17:30,440 --> 00:17:33,480 Speaker 3: technologies and then lead to spearhead global rules limiting their use. 350 00:17:33,800 --> 00:17:35,520 Speaker 5: The comments calm, of course, as Altman. 351 00:17:35,359 --> 00:17:37,680 Speaker 3: Is calling investors in the Middle East for a Semiconductor 352 00:17:37,680 --> 00:17:42,600 Speaker 3: initiative to advance AI. Meanwhile, X has won a dismissal 353 00:17:42,640 --> 00:17:45,400 Speaker 3: of claims that it conspired to supply the Saudi government 354 00:17:45,640 --> 00:17:49,040 Speaker 3: with confidential user information to identify a Sali man who 355 00:17:49,080 --> 00:17:51,960 Speaker 3: was arrested, tortured and sentenced to twenty years in prison. 356 00:17:52,160 --> 00:17:54,600 Speaker 3: That's for posting remarks critical of the government. Now, the 357 00:17:54,640 --> 00:17:57,080 Speaker 3: person in question had alleged that the social media company 358 00:17:57,119 --> 00:17:59,639 Speaker 3: conspired with the Kingdom of Saudi Arabia to violate the 359 00:17:59,760 --> 00:18:02,880 Speaker 3: rack t Influenced and Corrupt Organizations Act and the Alien 360 00:18:03,040 --> 00:18:04,520 Speaker 3: Taught Statute. 361 00:18:05,080 --> 00:18:07,000 Speaker 5: Then, in a fura monex. 362 00:18:06,760 --> 00:18:10,600 Speaker 3: Spaces, of course, Elon Musk told US Republican senators that 363 00:18:10,640 --> 00:18:14,360 Speaker 3: there is no way in hell that Russian President Vladimir Putin. 364 00:18:14,119 --> 00:18:15,679 Speaker 5: Could lose the war on Ukraine. 365 00:18:15,800 --> 00:18:19,040 Speaker 3: It was also in that space's opponents of a Senate 366 00:18:19,040 --> 00:18:21,919 Speaker 3: bill that will provide further assistance for Ukraine to continue 367 00:18:21,960 --> 00:18:24,720 Speaker 3: battling the full scale Russian invasion, and of course began 368 00:18:24,960 --> 00:18:25,640 Speaker 3: two years ago. 369 00:18:25,800 --> 00:18:28,600 Speaker 4: Ed well, let's stick with X. 370 00:18:28,640 --> 00:18:32,800 Speaker 2: Elon Musk's platform is adding you advertiser targeting features to 371 00:18:32,840 --> 00:18:37,199 Speaker 2: better entice video creators and compete against YouTube, joining us 372 00:18:37,200 --> 00:18:40,240 Speaker 2: with the details Bloomberg's Asia councin Asia. We reported this 373 00:18:40,359 --> 00:18:43,480 Speaker 2: idea back in October. Right, so, Musk and Lindy Yakarino 374 00:18:43,880 --> 00:18:47,400 Speaker 2: did their first ever join all hands in the company, 375 00:18:47,400 --> 00:18:50,479 Speaker 2: and they said, our competitor in the future is going 376 00:18:50,520 --> 00:18:53,200 Speaker 2: to be YouTube. And what's happened since October is all 377 00:18:53,240 --> 00:18:56,359 Speaker 2: of these video focused developments, and that's what you've been 378 00:18:56,359 --> 00:18:59,560 Speaker 2: writing about today exactly. 379 00:19:00,200 --> 00:19:02,720 Speaker 11: Has been pushing into video hard, as you mentioned, must 380 00:19:02,720 --> 00:19:05,280 Speaker 11: have been talking about this forever and now we're starting 381 00:19:05,320 --> 00:19:07,399 Speaker 11: to see some realization of it. So, just in the 382 00:19:07,480 --> 00:19:10,560 Speaker 11: past number of weeks, they've signed content partnerships with people 383 00:19:10,600 --> 00:19:13,600 Speaker 11: like Don Lemon, former CNN anchor, and Tulsa Gabbert, who 384 00:19:13,640 --> 00:19:17,199 Speaker 11: was the US representative and a presidential candidate. They signed 385 00:19:17,280 --> 00:19:19,879 Speaker 11: content deals with them to produce shows and put them 386 00:19:19,920 --> 00:19:22,439 Speaker 11: exclusively on X First. They did the same thing with 387 00:19:22,480 --> 00:19:26,439 Speaker 11: the World Wrestling Entertainment so WWE, and they're continuing to 388 00:19:26,440 --> 00:19:28,600 Speaker 11: push to go after creators. And now what we see 389 00:19:28,800 --> 00:19:31,880 Speaker 11: is now they're giving advertisers more control to actually put 390 00:19:31,920 --> 00:19:34,480 Speaker 11: their ads on the profile of a creator that they 391 00:19:34,760 --> 00:19:38,000 Speaker 11: like in order to generate more revenue from them and 392 00:19:38,119 --> 00:19:41,280 Speaker 11: just allow those advertisers to take advantage of the profile 393 00:19:41,320 --> 00:19:42,920 Speaker 11: and the followers that these creators are going to bring 394 00:19:42,920 --> 00:19:43,679 Speaker 11: to the platform. 395 00:19:43,920 --> 00:19:46,760 Speaker 3: And also I expose to a certain extent, would it 396 00:19:46,800 --> 00:19:50,199 Speaker 3: provide some certainty to an advertiser that I'm just putting 397 00:19:50,200 --> 00:19:54,439 Speaker 3: my brand, my name, my message via one particular content 398 00:19:54,480 --> 00:19:56,960 Speaker 3: creator rather than who knows where it ends up being. 399 00:19:56,840 --> 00:19:57,240 Speaker 5: Next to it. 400 00:19:58,280 --> 00:20:01,160 Speaker 11: Absolutely, I mean that's been a huge topic of conversation 401 00:20:01,240 --> 00:20:03,720 Speaker 11: with Exit particular. As you both know, over the past 402 00:20:03,880 --> 00:20:06,240 Speaker 11: year or so, they've dealt with a lot of content 403 00:20:06,280 --> 00:20:09,320 Speaker 11: safety issues. So we saw a bunch of advertisers leave 404 00:20:09,359 --> 00:20:11,159 Speaker 11: when they found out that their ad was next to 405 00:20:11,200 --> 00:20:14,960 Speaker 11: antisemitic content, for example, not too long ago. And so 406 00:20:15,560 --> 00:20:17,960 Speaker 11: absolutely this is another control to say, hey, I want 407 00:20:17,960 --> 00:20:20,800 Speaker 11: my advertisement to only be on a video by mister Beast, 408 00:20:20,880 --> 00:20:23,560 Speaker 11: for example, So you know you're reaching those followers. You 409 00:20:23,640 --> 00:20:25,479 Speaker 11: know that your content is going to be right in 410 00:20:25,480 --> 00:20:28,040 Speaker 11: front of their videos, so it lessens the chance that 411 00:20:28,080 --> 00:20:30,080 Speaker 11: it's going to be next to some harmful content. So 412 00:20:30,119 --> 00:20:32,600 Speaker 11: it is a little bit of an assurance for an advertiser, 413 00:20:32,600 --> 00:20:34,720 Speaker 11: So you can imagine that might be something that's attractive 414 00:20:34,760 --> 00:20:35,040 Speaker 11: to them. 415 00:20:35,359 --> 00:20:38,280 Speaker 3: As she counts all over the developments, X, we thank 416 00:20:38,320 --> 00:20:38,840 Speaker 3: you so much. 417 00:20:39,160 --> 00:20:41,639 Speaker 2: I'm also taking a look at bitcoin, and I'm sorry 418 00:20:41,640 --> 00:20:44,560 Speaker 2: to say that we've also changed direction on bitcoin. We're 419 00:20:44,600 --> 00:20:47,119 Speaker 2: lover for the first time in around eight sessions. But 420 00:20:47,280 --> 00:20:50,080 Speaker 2: over those eight sessions we posted a significant gain of 421 00:20:50,080 --> 00:20:53,639 Speaker 2: fifteen percent. We're below fifty thousand US dollars per token 422 00:20:53,680 --> 00:20:56,640 Speaker 2: as it stands, but when we did hit that milestone, 423 00:20:56,720 --> 00:20:58,560 Speaker 2: I know that all of us were very excited because 424 00:20:58,560 --> 00:21:01,600 Speaker 2: it's kind of psychological flat level of fifty thousand US 425 00:21:01,680 --> 00:21:02,399 Speaker 2: dollars be tooken. 426 00:21:03,280 --> 00:21:04,240 Speaker 5: It is a focus. 427 00:21:04,320 --> 00:21:07,719 Speaker 3: The vying of the market capitalizations is one we focus on, 428 00:21:07,800 --> 00:21:09,840 Speaker 3: and then we think about the vying when it comes 429 00:21:09,920 --> 00:21:12,240 Speaker 3: to which crypto ecosystem is really on the win. Let's 430 00:21:12,240 --> 00:21:14,240 Speaker 3: talk about all of this as to what's bringing us 431 00:21:14,320 --> 00:21:16,359 Speaker 3: up to that fifty thousand dollars level and then dragging 432 00:21:16,440 --> 00:21:19,679 Speaker 3: us back down. August co founder and Coca Aya Kontorovitch 433 00:21:19,760 --> 00:21:20,640 Speaker 3: is joining us for more. 434 00:21:20,680 --> 00:21:22,680 Speaker 5: And yes, perhaps. 435 00:21:22,320 --> 00:21:24,400 Speaker 3: The CPI print takes the wind own of its sales 436 00:21:24,440 --> 00:21:26,320 Speaker 3: a little bit like every risk I said on the day. 437 00:21:26,680 --> 00:21:30,120 Speaker 3: But what has been blowing us higher when it comes 438 00:21:30,119 --> 00:21:33,040 Speaker 3: to bitcoin? Is it ETF flows or is it something 439 00:21:33,040 --> 00:21:34,520 Speaker 3: else more fundamental that's happening. 440 00:21:36,640 --> 00:21:39,199 Speaker 12: Sure, you know, I'm sure that folks would like to 441 00:21:39,240 --> 00:21:40,840 Speaker 12: say that a part of it has to do with 442 00:21:40,920 --> 00:21:44,080 Speaker 12: Jack Dorsey wearing this SOCI shirt during the Super Bowl, 443 00:21:44,640 --> 00:21:47,159 Speaker 12: But it really is, to your point, us inflow is 444 00:21:47,200 --> 00:21:50,000 Speaker 12: coming in around the ETF We saw April and June 445 00:21:50,160 --> 00:21:53,280 Speaker 12: BTC call buyers with strekes at around seventy K to 446 00:21:53,280 --> 00:21:56,760 Speaker 12: seventy five K, which are all time high prices. We're 447 00:21:56,800 --> 00:21:59,040 Speaker 12: also seeing a lot of that narrative around the April 448 00:21:59,200 --> 00:22:04,600 Speaker 12: nineteenth coming up, and overall spot and option positions that 449 00:22:04,640 --> 00:22:08,399 Speaker 12: are really gearing up for pricing to break all time highs. 450 00:22:08,400 --> 00:22:11,879 Speaker 12: In Q two we saw BDC per futures open interest 451 00:22:11,920 --> 00:22:14,560 Speaker 12: also cross eleven billion, which is the highest ever since 452 00:22:14,600 --> 00:22:17,800 Speaker 12: December twenty twenty one. So a lot of US inflow 453 00:22:17,840 --> 00:22:19,480 Speaker 12: momentum going on right now. 454 00:22:20,640 --> 00:22:23,440 Speaker 3: I hate to always just talk about and focus obsessively 455 00:22:23,480 --> 00:22:26,520 Speaker 3: about price, but ultimately the market being dictated that we're 456 00:22:26,560 --> 00:22:28,280 Speaker 3: going to eclipse this fifty thousand is I think we're 457 00:22:28,280 --> 00:22:30,760 Speaker 3: going to eclipse to sixty nine thousand that we've seen previously. 458 00:22:32,560 --> 00:22:35,639 Speaker 12: Absolutely, it looks like, I mean again, from anywhere between 459 00:22:35,680 --> 00:22:37,200 Speaker 12: seventy k to seventy five. 460 00:22:37,080 --> 00:22:39,880 Speaker 5: K that is very, very bullish, and we really. 461 00:22:39,720 --> 00:22:43,240 Speaker 12: Haven't seen those prices historically, So yes, it does look 462 00:22:43,320 --> 00:22:46,520 Speaker 12: like we're going to eclipse those and it really does 463 00:22:46,560 --> 00:22:49,000 Speaker 12: look like that's coming from the US side. You're seeing 464 00:22:49,000 --> 00:22:52,840 Speaker 12: that reflected in coinbas BDC pricing, which was trading at 465 00:22:52,840 --> 00:22:56,679 Speaker 12: a premium to finance BDC pricing, which again reflects the 466 00:22:56,720 --> 00:22:59,440 Speaker 12: fact that you're seeing a lot of US inflow coming 467 00:22:59,480 --> 00:23:03,119 Speaker 12: in that's pushing that price higher at a premium, and 468 00:23:03,359 --> 00:23:07,359 Speaker 12: as well reflected by the ETF influence, which continues to 469 00:23:07,920 --> 00:23:11,200 Speaker 12: make that narrative strong US specifically focused. 470 00:23:12,119 --> 00:23:12,199 Speaker 9: Hi. 471 00:23:12,359 --> 00:23:14,600 Speaker 2: We were reflecting on the show yesterday that if you 472 00:23:14,600 --> 00:23:17,280 Speaker 2: think about it in really simple terms, from kind of 473 00:23:17,400 --> 00:23:20,200 Speaker 2: end of twenty two to present day, there's this kind 474 00:23:20,200 --> 00:23:23,560 Speaker 2: of minimal jump on bitcoin from forty four thousand to 475 00:23:23,600 --> 00:23:26,840 Speaker 2: forty eight thousand dollars per token, and in between all 476 00:23:26,920 --> 00:23:30,560 Speaker 2: kinds of chaos and volatility lows of fifteen thousand, five 477 00:23:30,680 --> 00:23:32,760 Speaker 2: hundred a brief peak. 478 00:23:33,440 --> 00:23:34,200 Speaker 4: But what also. 479 00:23:34,080 --> 00:23:37,159 Speaker 2: Happened in that time is that thousands of old coins 480 00:23:37,160 --> 00:23:40,200 Speaker 2: and other digital tokens just cease to exist, right they 481 00:23:40,280 --> 00:23:43,040 Speaker 2: or they become a liquid How much of this story 482 00:23:43,080 --> 00:23:46,520 Speaker 2: is about bitcoin kind of being the only stable option 483 00:23:46,680 --> 00:23:49,080 Speaker 2: if you want some exposure to a cryptocurrency. 484 00:23:51,600 --> 00:23:54,880 Speaker 12: I think it's pretty crazy that we're talking about bitcoin 485 00:23:54,960 --> 00:23:58,080 Speaker 12: now as being the stable currency, but you're even seeing 486 00:23:58,119 --> 00:24:01,040 Speaker 12: that reflected in some of the language that's is pushed out. 487 00:24:01,560 --> 00:24:04,240 Speaker 12: I would say that's very much true. However, ethereum is 488 00:24:04,280 --> 00:24:07,360 Speaker 12: still playing into that narrative. A lot of what's going 489 00:24:07,400 --> 00:24:10,639 Speaker 12: on right now is with regards to the restaking narrative 490 00:24:10,640 --> 00:24:13,639 Speaker 12: and Ethereum driven by an eigenlayer, and so you're seeing 491 00:24:13,720 --> 00:24:16,240 Speaker 12: a lot of narratives around both. 492 00:24:15,960 --> 00:24:17,560 Speaker 5: That and in Eth ETF. 493 00:24:18,160 --> 00:24:21,760 Speaker 12: Yesterday you saw that Franklin Templeton also submitted an application 494 00:24:21,840 --> 00:24:25,920 Speaker 12: for ETF, which caused a small bounce in the Eth price, 495 00:24:25,960 --> 00:24:28,639 Speaker 12: and so we're likely going to see that happening with 496 00:24:28,840 --> 00:24:32,280 Speaker 12: Eth as well. And you are seeing other applicants for 497 00:24:32,480 --> 00:24:36,520 Speaker 12: an ETF, whether it's Solana or other all coins where 498 00:24:36,600 --> 00:24:40,600 Speaker 12: that may also lead to some of that lower volatility 499 00:24:40,600 --> 00:24:41,840 Speaker 12: around those tokens as well. 500 00:24:42,320 --> 00:24:45,080 Speaker 3: You've been building for a long time, whether it's August, 501 00:24:45,160 --> 00:24:48,159 Speaker 3: whether previously you're helping thing on the founding team of Falconnects, 502 00:24:48,359 --> 00:24:51,080 Speaker 3: but also you're in the investing space and understanding what 503 00:24:51,119 --> 00:24:54,320 Speaker 3: was going on in Pantera Capital. What is the building 504 00:24:54,480 --> 00:24:56,679 Speaker 3: like right now when we think about sort of the 505 00:24:56,680 --> 00:24:59,199 Speaker 3: oxygen being sucked out the rim by the OG's a 506 00:24:59,200 --> 00:25:01,520 Speaker 3: bitcoin and eighth but still salon again and look in 507 00:25:01,600 --> 00:25:03,520 Speaker 3: some of the olt coins, what's been built from an 508 00:25:03,520 --> 00:25:07,160 Speaker 3: infrastructure layer, from adapt level, from an actual go out 509 00:25:07,160 --> 00:25:10,160 Speaker 3: there and use this in some way, shape or form. 510 00:25:11,240 --> 00:25:14,000 Speaker 12: It has never been a hotter time to build in 511 00:25:14,080 --> 00:25:17,520 Speaker 12: this space than now. You are seeing folks doubling down, 512 00:25:17,640 --> 00:25:20,000 Speaker 12: people who have been in the space for years, who 513 00:25:20,000 --> 00:25:23,000 Speaker 12: have really clarified what are the to your point, the 514 00:25:23,040 --> 00:25:25,919 Speaker 12: problems in on the infrastructure layer that need to be 515 00:25:26,000 --> 00:25:28,480 Speaker 12: solved for people to actually be able to use. 516 00:25:28,400 --> 00:25:29,040 Speaker 11: This at scale. 517 00:25:29,359 --> 00:25:31,520 Speaker 12: And so the biggest thing right now is account abstraction 518 00:25:31,960 --> 00:25:34,320 Speaker 12: being able for these applications to be used on a 519 00:25:34,320 --> 00:25:37,080 Speaker 12: front end without having to deal with all of the 520 00:25:37,280 --> 00:25:39,600 Speaker 12: interactions with the blockchain on the back end. 521 00:25:39,840 --> 00:25:41,120 Speaker 5: You know, we've been talking about that. 522 00:25:41,119 --> 00:25:43,600 Speaker 12: Narrative for years. How do you get to a point 523 00:25:43,640 --> 00:25:46,280 Speaker 12: where you can just send a transaction using a front 524 00:25:46,359 --> 00:25:49,160 Speaker 12: end UI And we are getting closer to that point. Again, 525 00:25:49,200 --> 00:25:52,840 Speaker 12: this infrastructure takes time to build, and every single day 526 00:25:52,840 --> 00:25:55,520 Speaker 12: we're getting closer to that. Like I said, it really 527 00:25:55,560 --> 00:25:58,000 Speaker 12: just hasn't been a hotter time to be a builder 528 00:25:58,160 --> 00:25:59,200 Speaker 12: in crypto than today. 529 00:25:59,240 --> 00:26:00,000 Speaker 5: So it's very exciting. 530 00:26:01,080 --> 00:26:04,440 Speaker 4: A real quick eye harving due in April. Do you care. 531 00:26:06,560 --> 00:26:07,200 Speaker 5: Absolutely? 532 00:26:07,400 --> 00:26:09,639 Speaker 12: I would say there's always a narrative around it, you know, 533 00:26:09,840 --> 00:26:11,800 Speaker 12: it's whether it's going to be by the rum or 534 00:26:11,840 --> 00:26:14,160 Speaker 12: sell the news. That's typically what we've seen to date. 535 00:26:14,920 --> 00:26:17,320 Speaker 12: But again it does take a while for the flows 536 00:26:17,760 --> 00:26:20,520 Speaker 12: to come into the ets, you know, especially on the 537 00:26:20,640 --> 00:26:23,240 Speaker 12: RIA space. So it'll be interesting to see what happens 538 00:26:23,240 --> 00:26:26,280 Speaker 12: on the retail side, what happens on the RIA side, 539 00:26:26,359 --> 00:26:28,240 Speaker 12: and if that narrative is going to be reflected with 540 00:26:28,400 --> 00:26:31,800 Speaker 12: users there. And of course Asia. We haven't seen as 541 00:26:31,840 --> 00:26:34,960 Speaker 12: much Asia inflow with all of this being historically led 542 00:26:35,040 --> 00:26:38,040 Speaker 12: right now with the US sides, so very very interesting. 543 00:26:38,200 --> 00:26:40,040 Speaker 5: And then the eth BTC ratio. 544 00:26:40,800 --> 00:26:44,160 Speaker 2: August co founder and co CEO Iakontarovitch Greats catch up 545 00:26:44,160 --> 00:26:46,040 Speaker 2: here on the program. Now, Carrie, you went out the 546 00:26:46,040 --> 00:26:48,560 Speaker 2: building in the snow this morning for a pretty important conversation. 547 00:26:48,920 --> 00:26:51,879 Speaker 3: I thought the element said for a breakfast meeting, because 548 00:26:52,040 --> 00:26:53,639 Speaker 3: I was lucky enough to be sitting down with the 549 00:26:53,880 --> 00:26:57,080 Speaker 3: Sunivation Ventures chairman and the CEO, that's Kaifoon Lee. It 550 00:26:57,160 --> 00:26:59,000 Speaker 3: was over at the Economic Club of New York and 551 00:26:59,440 --> 00:27:03,040 Speaker 3: fifty four we were chatting about what else artificial intelligence. 552 00:27:03,160 --> 00:27:06,080 Speaker 3: Of course, he's written many a book, forward thought on 553 00:27:06,160 --> 00:27:08,879 Speaker 3: many an impact, And we start the conversation by asking 554 00:27:09,240 --> 00:27:11,400 Speaker 3: if big tech companies, the ones that are still here, 555 00:27:11,960 --> 00:27:14,359 Speaker 3: can actually truly pivot to be AI first. 556 00:27:14,400 --> 00:27:14,960 Speaker 5: Take a listen. 557 00:27:15,880 --> 00:27:19,719 Speaker 13: Theoretically, they're the best equipped to do AI first because 558 00:27:19,840 --> 00:27:22,919 Speaker 13: they have the most AI experts in those two companies 559 00:27:23,160 --> 00:27:26,720 Speaker 13: if you coun't open AI in Microsoft's camp. But what 560 00:27:26,840 --> 00:27:30,480 Speaker 13: they face is a fundamental business issue of cannibalizing your 561 00:27:30,480 --> 00:27:31,320 Speaker 13: own business. 562 00:27:31,640 --> 00:27:32,360 Speaker 14: So for a. 563 00:27:32,359 --> 00:27:35,640 Speaker 13: Large company, imagine if you will your Microsoft or Google, 564 00:27:36,040 --> 00:27:37,919 Speaker 13: the first thing you're going to think to want to 565 00:27:37,960 --> 00:27:40,439 Speaker 13: do once you invent this technology is how do you 566 00:27:40,480 --> 00:27:41,000 Speaker 13: make money? 567 00:27:41,320 --> 00:27:43,920 Speaker 14: And of course Google will make the most money from 568 00:27:43,920 --> 00:27:44,720 Speaker 14: putting it in search. 569 00:27:45,040 --> 00:27:47,879 Speaker 13: Microsoft will make the most money by putting it in office, 570 00:27:48,280 --> 00:27:49,680 Speaker 13: so that will be. 571 00:27:49,560 --> 00:27:53,640 Speaker 14: The low hanging fruit. But low Microsoft. 572 00:27:53,000 --> 00:27:55,919 Speaker 13: Become a disruptor that says I'm going to build a 573 00:27:56,000 --> 00:27:59,600 Speaker 13: new engine that's AI first, that causes more people to 574 00:27:59,640 --> 00:28:00,520 Speaker 13: no longer need. 575 00:28:00,560 --> 00:28:02,640 Speaker 14: Or want to use office or pay for it. 576 00:28:03,720 --> 00:28:07,679 Speaker 13: Few companies, if any, has ever done that. Right, when 577 00:28:07,760 --> 00:28:11,840 Speaker 13: Kodak invented the digital camera, they shelfed. 578 00:28:11,359 --> 00:28:14,520 Speaker 14: It because as they think it would kill the film business. 579 00:28:15,080 --> 00:28:18,520 Speaker 13: It did, but it wasn't because of them, but other companies, right, 580 00:28:18,920 --> 00:28:24,520 Speaker 13: So the same thing happens with you, Intel not embracing mobile, 581 00:28:24,680 --> 00:28:26,840 Speaker 13: Qualcom not embracing AI, and. 582 00:28:26,840 --> 00:28:30,000 Speaker 14: The list goes on. So it's fundamentally really hard. 583 00:28:29,800 --> 00:28:33,119 Speaker 13: For a large company not to take this technology and 584 00:28:33,480 --> 00:28:36,320 Speaker 13: put it in the most best selling product to make 585 00:28:36,359 --> 00:28:37,760 Speaker 13: that easy low hang through. 586 00:28:38,200 --> 00:28:41,520 Speaker 14: And it's incredibly hard for them to build a disruptive 587 00:28:41,680 --> 00:28:42,720 Speaker 14: product that. 588 00:28:42,640 --> 00:28:47,000 Speaker 13: Will challenge, cannibalize, and potentially kill the revenue that is 589 00:28:47,080 --> 00:28:48,600 Speaker 13: currently a cash car for them. 590 00:28:49,520 --> 00:28:49,680 Speaker 9: Now. 591 00:28:49,680 --> 00:28:52,320 Speaker 3: Of course, Kai fee Lee I worked for both Microsoft 592 00:28:52,480 --> 00:28:55,320 Speaker 3: and for Google over there in China. He's been, of 593 00:28:55,400 --> 00:28:57,680 Speaker 3: course an investor, he's an author. 594 00:28:57,880 --> 00:28:59,160 Speaker 5: He's actually getting back into the. 595 00:28:59,080 --> 00:29:02,680 Speaker 3: World of building businesses and he's got his own was 596 00:29:02,960 --> 00:29:05,240 Speaker 3: one zero one dot Ai and this is all about 597 00:29:05,280 --> 00:29:07,719 Speaker 3: sort of the Chinese models that he can build in 598 00:29:07,840 --> 00:29:11,280 Speaker 3: focus is open source. And he actually said one of 599 00:29:11,360 --> 00:29:13,280 Speaker 3: his great lines when we were sitting down was he 600 00:29:13,320 --> 00:29:16,120 Speaker 3: thinks open AI should rebrand. We've heard that one before, 601 00:29:16,120 --> 00:29:18,080 Speaker 3: but he thinks it's one of those closed shops there. 602 00:29:18,000 --> 00:29:20,040 Speaker 4: Is closed AI. 603 00:29:20,280 --> 00:29:22,520 Speaker 2: I mean, he's seen as a bridge right understanding what's 604 00:29:22,520 --> 00:29:25,920 Speaker 2: happening in China's domestic efforts on AI. He knows what 605 00:29:25,960 --> 00:29:28,280 Speaker 2: the Western World is doing. But as he said, he's 606 00:29:28,280 --> 00:29:31,040 Speaker 2: going back to being a startup founder while being plugged 607 00:29:31,080 --> 00:29:34,880 Speaker 2: into the megacaps that have basically competing business models, is 608 00:29:34,880 --> 00:29:37,680 Speaker 2: what I heard. He's trying to outline here their AI 609 00:29:37,880 --> 00:29:40,400 Speaker 2: work and then their call legacy business, and. 610 00:29:40,360 --> 00:29:42,440 Speaker 3: He thinks they will be disrupted and eventually the winners 611 00:29:42,480 --> 00:29:44,480 Speaker 3: will be AI first. I have to say, I haven't 612 00:29:44,480 --> 00:29:47,840 Speaker 3: seen him quite as well worried in a long time. 613 00:29:47,880 --> 00:29:51,240 Speaker 3: He's usually an optimist, but he is concerned about the scale, 614 00:29:51,360 --> 00:29:54,200 Speaker 3: the pace at which we're innovating. But he did talk 615 00:29:54,240 --> 00:29:56,440 Speaker 3: about China, and look he phil still thinks that China 616 00:29:56,480 --> 00:30:00,320 Speaker 3: can get to an never beat us an innovation, certainly 617 00:30:00,360 --> 00:30:02,200 Speaker 3: on implementation, he thinks it can get there. 618 00:30:03,040 --> 00:30:04,160 Speaker 4: We're coming up here on the program. 619 00:30:04,240 --> 00:30:07,280 Speaker 2: We will continue to talk about AI and opportunities in 620 00:30:07,320 --> 00:30:10,480 Speaker 2: the investment space, innovation more broadly. That conversation with alic 621 00:30:10,560 --> 00:30:14,360 Speaker 2: Ross board member Amplo, Okay, I. 622 00:30:14,280 --> 00:30:16,600 Speaker 3: Just want to focus a little bit on what's happening 623 00:30:16,760 --> 00:30:20,760 Speaker 3: with IBM today because New York parents amid this snow 624 00:30:20,960 --> 00:30:23,520 Speaker 3: ed have had real tech issues basically getting their kids 625 00:30:23,560 --> 00:30:26,360 Speaker 3: to do remote schooling, and there are statements coming from 626 00:30:26,400 --> 00:30:28,600 Speaker 3: the New York City Department of Education on exiting. 627 00:30:28,640 --> 00:30:30,240 Speaker 5: We're currently experiencing issues. 628 00:30:29,960 --> 00:30:34,920 Speaker 3: With services that require IBM authentication login. We're actively working 629 00:30:34,960 --> 00:30:37,800 Speaker 3: with the IBM to resolve it and we provide an update. 630 00:30:37,800 --> 00:30:38,800 Speaker 5: And in fact they did. 631 00:30:38,720 --> 00:30:42,120 Speaker 3: Roll out some added capacity and improvements to the system. 632 00:30:42,200 --> 00:30:44,840 Speaker 3: But like every stock today, it's down by one point 633 00:30:44,840 --> 00:30:47,160 Speaker 3: one percent. Sorry to all those parents out there. This 634 00:30:47,240 --> 00:30:50,560 Speaker 3: is BLUEBG technology. 635 00:30:57,000 --> 00:30:58,560 Speaker 4: Okay, time for the VC roundup. 636 00:30:58,600 --> 00:31:02,800 Speaker 2: First up VC firm found previously called Wingman Ventures, is 637 00:31:02,880 --> 00:31:05,000 Speaker 2: raising one hundred and twenty million dollars for a new 638 00:31:05,080 --> 00:31:09,080 Speaker 2: fund to target Swiss AI and tech startups. The firm 639 00:31:09,120 --> 00:31:11,960 Speaker 2: says it's already raised eighty five million and aims to 640 00:31:11,960 --> 00:31:15,520 Speaker 2: make up the rest. By July, an AI startup Marco 641 00:31:15,680 --> 00:31:18,080 Speaker 2: raised twelve point five million dollars in a Series A 642 00:31:18,280 --> 00:31:22,120 Speaker 2: round led by Lightspeed Venture Partners, gaining funds to develop 643 00:31:22,160 --> 00:31:25,560 Speaker 2: its technology that helps companies search the vast amounts of 644 00:31:25,680 --> 00:31:30,320 Speaker 2: unstructured data in their systems. Blackbird Ventures and January Capital 645 00:31:30,760 --> 00:31:34,120 Speaker 2: also took part in the round. Plus Bioage Labs, a 646 00:31:34,160 --> 00:31:39,720 Speaker 2: clinical stage biotech company developing therapies for obesity and metabolic disease, 647 00:31:40,040 --> 00:31:43,200 Speaker 2: raised one hundred and seventy million dollars in an oversubscribed 648 00:31:43,480 --> 00:31:48,120 Speaker 2: series definancing round led by Sophenova Investments. We are going 649 00:31:48,200 --> 00:31:50,200 Speaker 2: to have the CEO and co founder of Bioage with 650 00:31:50,280 --> 00:31:52,320 Speaker 2: us later in the program, Cara. 651 00:31:52,640 --> 00:31:55,160 Speaker 3: Well, first, let's go back to sort of what Bioage 652 00:31:55,200 --> 00:32:00,680 Speaker 3: is using AI to spearhead moves forward in healthcare VC Spotlight. 653 00:32:00,760 --> 00:32:03,200 Speaker 3: We want to talk about the broader opportunities but also 654 00:32:03,200 --> 00:32:05,680 Speaker 3: the strains that AI presents the economy much much more 655 00:32:05,680 --> 00:32:08,160 Speaker 3: with alic Rossi's tech policy analyst and board member for 656 00:32:08,280 --> 00:32:12,280 Speaker 3: vcfern Amplos, also the author of the Raging twenty twenties, Companies, Countries, 657 00:32:12,320 --> 00:32:14,600 Speaker 3: People and the Fight for Our Future, and of course 658 00:32:14,640 --> 00:32:17,240 Speaker 3: he served as Senior Advisor for Innovation to the Secuary 659 00:32:17,240 --> 00:32:21,080 Speaker 3: of State during the Obama administration and Alec, we sit 660 00:32:21,160 --> 00:32:24,480 Speaker 3: here at a time of ultimate disruption. It feels like 661 00:32:24,640 --> 00:32:28,320 Speaker 3: we're thinking of companies that are all in on operational 662 00:32:28,320 --> 00:32:31,560 Speaker 3: efficiency in their earnings, but also letting go of people 663 00:32:31,680 --> 00:32:34,440 Speaker 3: at a rapid rate. We're thinking of investment in technology, 664 00:32:34,520 --> 00:32:37,480 Speaker 3: but job's ultimately going to be disrupted by this. Where 665 00:32:37,520 --> 00:32:41,440 Speaker 3: do you sit in the optimist versus personimistic side of 666 00:32:41,520 --> 00:32:42,320 Speaker 3: AI application? 667 00:32:43,880 --> 00:32:48,520 Speaker 1: I'm net positive over the medium to long term. Over 668 00:32:48,560 --> 00:32:50,040 Speaker 1: the short term, though, it's going to be a little 669 00:32:50,080 --> 00:32:53,480 Speaker 1: bit tricky, because what we see is AI enabling automation 670 00:32:53,600 --> 00:32:58,880 Speaker 1: of labor that isn't just manual and routine for cognitive. 671 00:32:58,640 --> 00:32:59,680 Speaker 4: And non routine. 672 00:33:00,160 --> 00:33:03,040 Speaker 1: Is it's wiping out a lot of jobs, the likes 673 00:33:03,040 --> 00:33:05,720 Speaker 1: of which, like my father, who is a lawyer, the 674 00:33:05,800 --> 00:33:09,120 Speaker 1: kind of work my father did as a lawyer ought 675 00:33:09,200 --> 00:33:12,200 Speaker 1: to be ninety nine percent done by software at this point. 676 00:33:12,440 --> 00:33:15,480 Speaker 1: And that's not necessarily a bad thing, but it is 677 00:33:15,600 --> 00:33:18,840 Speaker 1: a bad thing for people later in their careers who 678 00:33:19,200 --> 00:33:21,360 Speaker 1: aren't going to be able to pivot necessarily from. 679 00:33:21,240 --> 00:33:23,120 Speaker 4: A statistical standpoint. 680 00:33:23,160 --> 00:33:26,720 Speaker 1: From a statistical standpoint, it's not going to be as 681 00:33:26,840 --> 00:33:30,440 Speaker 1: bad as one might imagine the impact of AI on labor, 682 00:33:30,640 --> 00:33:32,480 Speaker 1: but over the short term we're going to see some 683 00:33:32,800 --> 00:33:34,640 Speaker 1: automation of a lot of white collar jobs. 684 00:33:35,480 --> 00:33:38,080 Speaker 2: The interesting dynamic right now, Alec, if you look at 685 00:33:38,160 --> 00:33:42,320 Speaker 2: the entirety of the newsflow, is layoffs and cost cuts 686 00:33:42,320 --> 00:33:45,680 Speaker 2: at bigger companies are in part being carried out to 687 00:33:45,800 --> 00:33:51,360 Speaker 2: free up funds to invest in AI. Ironically, is the 688 00:33:51,480 --> 00:33:54,400 Speaker 2: earliest stage ecosystem of all these startups that say we're 689 00:33:54,480 --> 00:33:58,600 Speaker 2: using AI to automate or make more efficient specific function 690 00:33:59,320 --> 00:34:02,960 Speaker 2: feel any of that, you know, the reallocation of capital 691 00:34:03,040 --> 00:34:05,960 Speaker 2: from say headcount to a new AI. 692 00:34:05,800 --> 00:34:07,680 Speaker 4: Tool, Well, yes to no. 693 00:34:07,760 --> 00:34:10,239 Speaker 1: And first of all, let's be let's have some you know, 694 00:34:10,480 --> 00:34:13,840 Speaker 1: intellectual honesty around a lot of the drama around layoffs. 695 00:34:14,719 --> 00:34:18,399 Speaker 1: Unemployment is really really low in the United States right now. 696 00:34:18,600 --> 00:34:20,640 Speaker 1: Some of the layoffs that are getting lots of attention 697 00:34:20,680 --> 00:34:23,120 Speaker 1: are getting lots of attention because they are inside platform 698 00:34:23,160 --> 00:34:26,200 Speaker 1: companies that have been hiring wildly for years. But if 699 00:34:26,239 --> 00:34:29,719 Speaker 1: you take take a sort of cold blooded look at 700 00:34:29,719 --> 00:34:33,400 Speaker 1: employment in the United States, unemployment is shockingly low. I 701 00:34:33,440 --> 00:34:35,920 Speaker 1: live in Maryland, where the unemployment rate is like two 702 00:34:36,000 --> 00:34:36,920 Speaker 1: point four percent. 703 00:34:37,120 --> 00:34:37,760 Speaker 4: That's nothing. 704 00:34:38,080 --> 00:34:40,200 Speaker 1: So I don't think we need to be sounding alarm 705 00:34:40,239 --> 00:34:43,040 Speaker 1: bells yet about layoffs, in part because there's people being 706 00:34:43,120 --> 00:34:45,839 Speaker 1: laid off inside these platform companies can go get work 707 00:34:45,880 --> 00:34:49,319 Speaker 1: inside startups the likes of which I invest in. And 708 00:34:49,400 --> 00:34:53,640 Speaker 1: what we're seeing inside these startups is they are using 709 00:34:53,760 --> 00:34:58,520 Speaker 1: AI in a way that is enabling them to not 710 00:34:58,680 --> 00:35:02,160 Speaker 1: have to build their headcount too fast. So this is 711 00:35:02,320 --> 00:35:05,839 Speaker 1: enabling the growth of really lean startups. So I think 712 00:35:05,880 --> 00:35:09,800 Speaker 1: it's actually helping the startup ecosystem a little bit more 713 00:35:09,880 --> 00:35:13,080 Speaker 1: so than some of the flabber year old platform companies 714 00:35:13,120 --> 00:35:14,520 Speaker 1: that are doing some of these layoffs. 715 00:35:14,960 --> 00:35:17,319 Speaker 3: Alik, we were just speaking with Kaifu Lee a little 716 00:35:17,360 --> 00:35:19,719 Speaker 3: bit earlier, who does feel that there's going to be 717 00:35:19,760 --> 00:35:22,799 Speaker 3: a limitation on these big tech juggernauts that have been 718 00:35:22,840 --> 00:35:25,560 Speaker 3: so ahead of the curve in AI development thus far, 719 00:35:26,000 --> 00:35:28,160 Speaker 3: But it's going to lead to cannibalization of their own 720 00:35:28,160 --> 00:35:31,000 Speaker 3: business models and ultimately they're not going to be AI first. 721 00:35:31,600 --> 00:35:34,360 Speaker 3: Are all of the companies that you're backing now ultimately 722 00:35:34,440 --> 00:35:38,800 Speaker 3: AI first companies or are they having to re establish themselves. 723 00:35:38,840 --> 00:35:41,120 Speaker 3: We think about their headcount, We think about the way 724 00:35:41,120 --> 00:35:43,960 Speaker 3: in which their business model works, for example, like a grammarly. 725 00:35:44,440 --> 00:35:47,200 Speaker 1: Yeah, so not all of them are necessarily AI first. 726 00:35:47,440 --> 00:35:49,560 Speaker 1: For a lot of them, AI will be a new 727 00:35:49,600 --> 00:35:52,680 Speaker 1: tool in their toolkit. The most exciting examples of an 728 00:35:52,680 --> 00:35:55,279 Speaker 1: AI first company the one that leads to mind. I'm 729 00:35:55,320 --> 00:35:58,280 Speaker 1: speaking to you today from Italy, and I spent today 730 00:35:58,400 --> 00:36:02,200 Speaker 1: visiting with a company we're backing in Switzerland called key 731 00:36:02,239 --> 00:36:07,480 Speaker 1: in Health Ayaan and they are using AI to provide 732 00:36:07,640 --> 00:36:12,040 Speaker 1: mental health services into big companies and they're doing so 733 00:36:12,239 --> 00:36:16,640 Speaker 1: AI first, something like this wouldn't have been possible. Providing 734 00:36:16,800 --> 00:36:21,680 Speaker 1: AI enabled enabling mental health services to tens of thousands 735 00:36:21,719 --> 00:36:24,480 Speaker 1: of employees that has a human component on the back 736 00:36:24,600 --> 00:36:27,000 Speaker 1: end but has an AI component on the front end. 737 00:36:27,280 --> 00:36:29,560 Speaker 4: That kind of thing wouldn't have been possible years ago. 738 00:36:29,719 --> 00:36:33,080 Speaker 1: And so this, ultimately, I think is showing what AI 739 00:36:33,400 --> 00:36:36,600 Speaker 1: and can do to help the world a little bit. 740 00:36:37,880 --> 00:36:42,080 Speaker 2: Alec Ross Techic analyst and board member at AMPLO, thank you. 741 00:36:41,960 --> 00:36:42,880 Speaker 4: So much for your time. 742 00:36:43,480 --> 00:36:46,080 Speaker 2: Let's get back to Bioage Labs, which we mentioned earlier, 743 00:36:46,440 --> 00:36:48,120 Speaker 2: just to announce the close of a one hundred and 744 00:36:48,160 --> 00:36:51,799 Speaker 2: seventy million dollars Series D round to develop new therapies 745 00:36:52,000 --> 00:36:55,880 Speaker 2: for metabolic diseases using the power of AI. Co founder 746 00:36:55,920 --> 00:36:59,640 Speaker 2: and CEO Christin Forney joins us. Now, so I guess 747 00:36:59,480 --> 00:37:02,560 Speaker 2: it's a d What is it that you need to 748 00:37:02,640 --> 00:37:06,720 Speaker 2: invest in in the context of AI. Help us understand 749 00:37:06,760 --> 00:37:10,960 Speaker 2: how AI will ultimately accelerate I guess the work that 750 00:37:11,000 --> 00:37:11,520 Speaker 2: you're doing. 751 00:37:13,440 --> 00:37:15,560 Speaker 8: Sure, kirst and thanks for having me. It's really great 752 00:37:15,600 --> 00:37:18,879 Speaker 8: to be here. So how we use AI at Bioage 753 00:37:19,000 --> 00:37:24,440 Speaker 8: is really to understand human aging. So human biology is 754 00:37:24,480 --> 00:37:27,759 Speaker 8: incredibly complicated. You have, for example, tens of thousands of 755 00:37:27,800 --> 00:37:30,080 Speaker 8: proteins in your body, each with a different role to 756 00:37:30,120 --> 00:37:32,959 Speaker 8: play in health and disease, and it's fundamentally a data 757 00:37:33,040 --> 00:37:36,640 Speaker 8: question to figure out which proteins matter for certain processes 758 00:37:36,719 --> 00:37:37,400 Speaker 8: versus others. 759 00:37:37,840 --> 00:37:38,720 Speaker 4: And at bio read. 760 00:37:38,560 --> 00:37:42,839 Speaker 8: If you really study what makes individuals, helps individuals live 761 00:37:42,920 --> 00:37:45,919 Speaker 8: long and live healthy for that for a long period 762 00:37:45,960 --> 00:37:49,000 Speaker 8: of time, right, because some people are very healthy well 763 00:37:49,000 --> 00:37:52,120 Speaker 8: into their nineties, they're still very physically capable, still very 764 00:37:52,120 --> 00:37:54,480 Speaker 8: mentally capable. So AI for us is really at the 765 00:37:54,520 --> 00:37:57,640 Speaker 8: beginning of the funnel helping do target ID and now 766 00:37:57,680 --> 00:38:00,759 Speaker 8: with this financing, this incredible Series D finance, but that's 767 00:38:00,760 --> 00:38:03,040 Speaker 8: really going to power as our clinical development. So phase 768 00:38:03,080 --> 00:38:05,080 Speaker 8: two clinical trials of our lead drug. 769 00:38:04,880 --> 00:38:08,160 Speaker 3: For obesity and that you're doing in combination with Eli Lilly, 770 00:38:08,440 --> 00:38:10,600 Speaker 3: and I'm interested as to how much you have to 771 00:38:10,680 --> 00:38:13,640 Speaker 3: go hand in hand with the older god those that 772 00:38:13,680 --> 00:38:15,480 Speaker 3: are already bringing these to market. 773 00:38:16,320 --> 00:38:19,160 Speaker 8: That's a really great question. So our drug, which improves 774 00:38:19,239 --> 00:38:23,160 Speaker 8: muscle and metabolic function, really shines in combination with these 775 00:38:23,160 --> 00:38:26,520 Speaker 8: increten drugs, these drugs that Lily and Novo are are 776 00:38:26,520 --> 00:38:29,799 Speaker 8: currently building. So for the phase true trial, we're going 777 00:38:29,840 --> 00:38:32,799 Speaker 8: to be combining our drug together with one of these 778 00:38:32,800 --> 00:38:35,239 Speaker 8: with Lily's drug to show if we can increase the 779 00:38:35,400 --> 00:38:37,960 Speaker 8: quantity of weight loss so that get more weight loss 780 00:38:38,000 --> 00:38:39,799 Speaker 8: as well as the quality of weight loss, to have 781 00:38:39,840 --> 00:38:42,680 Speaker 8: it be really healthier weight loss with a better balance 782 00:38:42,840 --> 00:38:44,800 Speaker 8: of muscle the fat at the end of the treatment. 783 00:38:45,440 --> 00:38:48,279 Speaker 8: Importantly too, our drug is an oral drug. It's a pill, 784 00:38:48,600 --> 00:38:50,719 Speaker 8: and the future here is really to instead of having 785 00:38:50,800 --> 00:38:53,560 Speaker 8: to reject yourself all the time, have a weight loss 786 00:38:53,600 --> 00:38:56,480 Speaker 8: pill that achieves the weight loss that you need and 787 00:38:56,520 --> 00:38:57,319 Speaker 8: in a healthy way. 788 00:38:58,360 --> 00:39:01,520 Speaker 4: Kristin what's your biggest cost? Real quick? We had thirty seconds. 789 00:39:01,840 --> 00:39:03,520 Speaker 2: In the funds that you've gained, what are you going 790 00:39:03,560 --> 00:39:07,360 Speaker 2: to spend on headcount or software or compute? 791 00:39:07,719 --> 00:39:10,319 Speaker 8: For sure? So for a biotech, the largest cost is 792 00:39:10,360 --> 00:39:13,440 Speaker 8: the clinical trial itself. So you're going to require hundreds 793 00:39:13,440 --> 00:39:15,400 Speaker 8: of people that are taking your drug for months at 794 00:39:15,400 --> 00:39:18,160 Speaker 8: a time and look at a really comprehensive battery of tests, 795 00:39:18,440 --> 00:39:21,920 Speaker 8: so that more than the headcount or the software is 796 00:39:21,920 --> 00:39:23,120 Speaker 8: the big cost user. 797 00:39:23,960 --> 00:39:25,759 Speaker 5: Well, so you put that money to work. Tell us 798 00:39:25,840 --> 00:39:27,560 Speaker 5: how trials go come back on the show. 799 00:39:27,560 --> 00:39:31,120 Speaker 3: Bioah Labs co founder CEO Kristin Fortney on that one 800 00:39:31,160 --> 00:39:32,719 Speaker 3: hundred and seventy million dollar raise. 801 00:39:33,320 --> 00:39:35,320 Speaker 5: Meanwhile, well that does it for this edition. 802 00:39:35,160 --> 00:39:38,759 Speaker 3: Of Bloomberg Technology Comraine sun or snow or bring it 803 00:39:38,800 --> 00:39:39,759 Speaker 3: to air here in New York. 804 00:39:39,800 --> 00:39:40,880 Speaker 5: But so much to digest that. 805 00:39:41,880 --> 00:39:44,400 Speaker 2: Yeah, and it's a big week for the gig economy. 806 00:39:44,440 --> 00:39:46,080 Speaker 2: I really think this is going to be something that 807 00:39:46,120 --> 00:39:48,000 Speaker 2: we'll be talking about and learning a lot about the 808 00:39:48,000 --> 00:39:50,400 Speaker 2: world we live in through those earnings. Check out the 809 00:39:50,440 --> 00:39:55,520 Speaker 2: pod recap, the conversations Apple, Spotify, iHeart from a snowy, 810 00:39:55,760 --> 00:40:00,800 Speaker 2: blizzardy New York City and a standard luke warm sunny 811 00:40:00,840 --> 00:40:01,640 Speaker 2: San Francisco. 812 00:40:02,120 --> 00:40:02,960 Speaker 4: This is Bloomberg