1 00:00:01,600 --> 00:00:06,920 Speaker 1: From Mahart where Innovation, Money and power colle in Silicon Valley, NBN. 2 00:00:07,240 --> 00:00:11,760 Speaker 1: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 3 00:00:24,680 --> 00:00:27,120 Speaker 2: Live from New York and San Francisco. This is Bloomberg 4 00:00:27,160 --> 00:00:30,360 Speaker 2: Technology coming up. In Vidia on track for a record 5 00:00:30,440 --> 00:00:35,080 Speaker 2: close shares or bounced back after successfully calming concerns about 6 00:00:35,280 --> 00:00:38,240 Speaker 2: the product delays and its long term growth plans. 7 00:00:38,960 --> 00:00:41,760 Speaker 3: Adobe's Embrace of AI will talk to the company's CEO 8 00:00:41,840 --> 00:00:44,720 Speaker 3: about its new AI video generated tools as it kicks 9 00:00:44,760 --> 00:00:47,200 Speaker 3: off the annual Adobe Max conference. 10 00:00:47,320 --> 00:00:50,600 Speaker 2: And a successful catch. SpaceX celebrates a major milestone over 11 00:00:50,640 --> 00:00:54,920 Speaker 2: the weekend that Starship Rockets successfully catching its booster midair. 12 00:00:55,400 --> 00:00:57,920 Speaker 2: We discussed but first let's check in on these markets 13 00:00:57,960 --> 00:01:00,360 Speaker 2: because we are ramping high. Then now's that one hundred 14 00:01:00,440 --> 00:01:02,320 Speaker 2: is a seat up five tenths of a percent, and 15 00:01:02,400 --> 00:01:05,080 Speaker 2: we were higher up about a percentage point in earlier trade. 16 00:01:05,160 --> 00:01:07,320 Speaker 2: But we see Mood Music once again wanting to plow 17 00:01:07,400 --> 00:01:10,880 Speaker 2: money into some of the major Magnificent seven names, and 18 00:01:10,959 --> 00:01:13,479 Speaker 2: across the board, we're really seeing a risk on feel 19 00:01:13,720 --> 00:01:15,720 Speaker 2: is it whether China stimulus is going to come into play. 20 00:01:15,760 --> 00:01:17,920 Speaker 2: That's what's being eyed by Bitcoin just a quick look 21 00:01:17,959 --> 00:01:19,760 Speaker 2: at what's happening in the world or crypto at the moment, 22 00:01:19,959 --> 00:01:23,560 Speaker 2: we're up now five percent percent now NASDAC and Bitcoin 23 00:01:23,800 --> 00:01:25,600 Speaker 2: at the highest level since July. 24 00:01:25,840 --> 00:01:26,319 Speaker 4: And what are you. 25 00:01:26,319 --> 00:01:29,080 Speaker 3: Watching, Well, I'm going to go straight to Nvidia and 26 00:01:29,120 --> 00:01:31,080 Speaker 3: if you're just waking up, you're just joining us for 27 00:01:31,120 --> 00:01:34,039 Speaker 3: the first time on Bloomberg Television this morning. It is 28 00:01:34,040 --> 00:01:35,720 Speaker 3: a name we're going to watch all day because, as 29 00:01:35,760 --> 00:01:38,479 Speaker 3: you said, Cara, on a closing basis, if we trade 30 00:01:38,520 --> 00:01:40,640 Speaker 3: at that level one hundred and thirty eight, just below 31 00:01:40,680 --> 00:01:42,520 Speaker 3: one hundred and thirty nine dollars a share, and we 32 00:01:42,600 --> 00:01:46,440 Speaker 3: close there, we will clipse that June record or record 33 00:01:46,520 --> 00:01:48,960 Speaker 3: high that we close that in June. Actually, we're probably 34 00:01:49,000 --> 00:01:51,920 Speaker 3: about a dollar away on an intra day basis from 35 00:01:52,480 --> 00:01:54,520 Speaker 3: a record as well, which I know the team in 36 00:01:54,600 --> 00:01:57,320 Speaker 3: Bloomberg Equities are going to be watching very closely. Is 37 00:01:57,400 --> 00:01:59,520 Speaker 3: kind of like in Video's back, and that's going to 38 00:01:59,520 --> 00:02:01,440 Speaker 3: be our folk as much as it has been for 39 00:02:01,480 --> 00:02:04,000 Speaker 3: the last two years, which I'm very excited about. 40 00:02:03,760 --> 00:02:06,320 Speaker 5: As au It's back. Why is it back? 41 00:02:06,360 --> 00:02:07,960 Speaker 2: Why is it up eleven percent over the course of 42 00:02:07,960 --> 00:02:10,680 Speaker 2: the month. Bruly, Meg's around Blastelica is with us, just 43 00:02:10,760 --> 00:02:15,760 Speaker 2: talk us through the determed risk and feel around in video. 44 00:02:16,160 --> 00:02:17,639 Speaker 2: What have they managed to tell the market? 45 00:02:18,639 --> 00:02:20,360 Speaker 4: Hey, good morning, thanks for having me so. 46 00:02:20,400 --> 00:02:22,680 Speaker 6: I'd say that there was recent weakness in and Video 47 00:02:22,720 --> 00:02:25,680 Speaker 6: shares that was largely concerned about the company's Blackwold chips. 48 00:02:25,680 --> 00:02:27,480 Speaker 6: There were some engineering snags, a little bit of the 49 00:02:27,480 --> 00:02:30,040 Speaker 6: delay there. People didn't quite know how to suss out 50 00:02:30,080 --> 00:02:32,960 Speaker 6: the impact that this would have on future growth. However, 51 00:02:33,040 --> 00:02:35,360 Speaker 6: the CEO came out, he talked about how strong demand 52 00:02:35,400 --> 00:02:38,239 Speaker 6: has been. There's been some positive analyst commentary looking at 53 00:02:38,600 --> 00:02:41,679 Speaker 6: the orders for the Blackwold chip, all this has been 54 00:02:41,800 --> 00:02:43,639 Speaker 6: very strong. I think that's helped to ease a lot 55 00:02:43,639 --> 00:02:46,440 Speaker 6: of concerns that people had about the status of this chip, 56 00:02:46,639 --> 00:02:49,600 Speaker 6: the future product roadmap, and just in general the company's 57 00:02:49,600 --> 00:02:52,680 Speaker 6: flowity to continue executing, keep delivering these chips, which are 58 00:02:52,720 --> 00:02:55,639 Speaker 6: at course at the forefront of the whole AI trade. 59 00:02:56,240 --> 00:02:58,200 Speaker 3: Do you know what's interesting about in video, Ryan, is 60 00:02:58,280 --> 00:03:02,120 Speaker 3: that it's kind of constant communication. Jensen Wong does a 61 00:03:02,120 --> 00:03:05,079 Speaker 3: lot of interviews, but he also appears on stage all 62 00:03:05,160 --> 00:03:08,760 Speaker 3: of the time. He is at investor conferences all of 63 00:03:08,800 --> 00:03:12,000 Speaker 3: the time. You cover such a broad range of technology companies. 64 00:03:12,240 --> 00:03:15,080 Speaker 3: Have you ever known a company in vogue or not 65 00:03:15,280 --> 00:03:16,600 Speaker 3: to communicate that much? 66 00:03:18,280 --> 00:03:19,160 Speaker 4: That's a great question. 67 00:03:19,680 --> 00:03:21,400 Speaker 6: Certainly, he is out there a lot, and he is 68 00:03:21,440 --> 00:03:24,519 Speaker 6: obviously an investor favorite, so every time he speaks, everybody listens. 69 00:03:24,520 --> 00:03:26,359 Speaker 6: And of course everything he has been saying lately has 70 00:03:26,400 --> 00:03:29,040 Speaker 6: been pretty positive. It's hard to find too many, you know, 71 00:03:29,080 --> 00:03:30,680 Speaker 6: presidents for this kind of thing. You'd probably have to 72 00:03:30,680 --> 00:03:32,240 Speaker 6: go back to someone like Steve Jobs. 73 00:03:32,240 --> 00:03:35,760 Speaker 2: I guess maybe we'll have to go back to of course, 74 00:03:36,080 --> 00:03:39,360 Speaker 2: companies that are that much in the spotlight, Apple. 75 00:03:39,120 --> 00:03:39,600 Speaker 4: One of them. 76 00:03:39,800 --> 00:03:42,960 Speaker 2: And with Steve Jobs and now in Video, what is 77 00:03:43,000 --> 00:03:46,600 Speaker 2: there still perhaps remaining in terms of concerns. It looks 78 00:03:46,640 --> 00:03:48,400 Speaker 2: as though people feel they've got a runway for the 79 00:03:48,440 --> 00:03:49,840 Speaker 2: next twelve months in terms of orders. 80 00:03:50,840 --> 00:03:53,000 Speaker 6: Yeah, absolutely, I would say that, you know, they're considered 81 00:03:53,040 --> 00:03:55,720 Speaker 6: pretty far ahead of the curve as far as competition goes. 82 00:03:55,720 --> 00:03:57,760 Speaker 6: There's not too many concerns about that. There's a lot 83 00:03:57,760 --> 00:04:00,240 Speaker 6: of optimism about the new chip, and we've I've seen 84 00:04:00,280 --> 00:04:03,600 Speaker 6: a lot of Video's major customers like Microsoft, Meta Alphabet 85 00:04:03,640 --> 00:04:06,680 Speaker 6: and so board. They're all sticking with their capbex plan 86 00:04:06,840 --> 00:04:09,000 Speaker 6: So there's not really a concern that people are going 87 00:04:09,040 --> 00:04:10,560 Speaker 6: to pull back on their spending and that they're going 88 00:04:10,560 --> 00:04:12,840 Speaker 6: to see a drop up in the maand related to that. 89 00:04:12,960 --> 00:04:15,240 Speaker 6: So I would say that outside of maybe valuation, that's 90 00:04:15,240 --> 00:04:17,880 Speaker 6: probably the biggest concern you'll hear. But people are pretty 91 00:04:17,880 --> 00:04:20,760 Speaker 6: positive aboutting video's fundamentals from here yep. 92 00:04:20,800 --> 00:04:23,240 Speaker 3: As a reminder on an inter day basis, but a 93 00:04:23,320 --> 00:04:25,479 Speaker 3: lot more likely on a closing basis. In VideA a 94 00:04:25,520 --> 00:04:29,000 Speaker 3: fresh record Blimbo's Ryan Vasteliga, thank you very much. Another 95 00:04:29,040 --> 00:04:33,440 Speaker 3: top story, TSMC is expanding its global footprint and planning 96 00:04:33,480 --> 00:04:36,039 Speaker 3: more plants in Europe with a focus on the market 97 00:04:36,320 --> 00:04:40,039 Speaker 3: for AI chips. That's according to Taiwan's National Science and 98 00:04:40,120 --> 00:04:43,960 Speaker 3: Technology Council Minister wucheng Wen, who did not specify a 99 00:04:44,000 --> 00:04:47,640 Speaker 3: timeline for the expansion in Europe. This is TSMC just 100 00:04:47,640 --> 00:04:50,479 Speaker 3: broke round in August on a chip fabrication plant in 101 00:04:50,560 --> 00:04:51,720 Speaker 3: Germany's Dresden. 102 00:04:51,800 --> 00:04:52,440 Speaker 4: Listen to this. 103 00:04:53,640 --> 00:04:57,279 Speaker 7: They had started the construction of the first fab. Interesting. 104 00:04:57,960 --> 00:05:01,039 Speaker 7: They are already planning the next feel app in the 105 00:05:01,080 --> 00:05:07,000 Speaker 7: future for different market sectors as well, but of course 106 00:05:07,120 --> 00:05:09,760 Speaker 7: the most important would be the AIM market. 107 00:05:10,160 --> 00:05:12,800 Speaker 2: That's going to invest to take Michael Green is portfolio 108 00:05:12,839 --> 00:05:16,360 Speaker 2: manager at Simplify Asset Management. Manages a suite of ETFs 109 00:05:16,360 --> 00:05:18,600 Speaker 2: about six billion dollars in assets onder management. You've got 110 00:05:18,839 --> 00:05:22,080 Speaker 2: a rich history in terms of thinking through the macro Michael, 111 00:05:22,680 --> 00:05:26,719 Speaker 2: what then, of the exuberance still around chip names, is 112 00:05:26,760 --> 00:05:28,320 Speaker 2: it right to have that reality? 113 00:05:29,640 --> 00:05:31,760 Speaker 8: Well, I think this is a natural challenge, right. This 114 00:05:31,800 --> 00:05:34,800 Speaker 8: is what's called a positive bubble. It effectively is driving 115 00:05:34,839 --> 00:05:38,200 Speaker 8: innovation and investment into a space that absolutely requires it, 116 00:05:38,240 --> 00:05:41,520 Speaker 8: but there's almost no real opportunity for the company's currently 117 00:05:41,520 --> 00:05:45,039 Speaker 8: involved to earn the returns that are actually built into 118 00:05:45,080 --> 00:05:49,000 Speaker 8: these expectations. We may see that not happen in this 119 00:05:49,200 --> 00:05:52,279 Speaker 8: month or in this quarter, but as we look forward, 120 00:05:52,360 --> 00:05:55,159 Speaker 8: there's going to inevitably be a period of disappointment. This is, 121 00:05:55,160 --> 00:05:57,280 Speaker 8: in a lot of ways very similar to the buildout 122 00:05:57,320 --> 00:05:59,960 Speaker 8: around the dot com cycle, in which the Internet ultimately 123 00:06:00,160 --> 00:06:03,400 Speaker 8: exceeded all of our expectations, radically changing how we shop 124 00:06:03,440 --> 00:06:06,159 Speaker 8: and live our lives. But at the same time we 125 00:06:06,200 --> 00:06:09,680 Speaker 8: saw extraordinary disappointment as many of the companies involved were 126 00:06:09,720 --> 00:06:11,440 Speaker 8: unable to realize their potential. 127 00:06:12,600 --> 00:06:16,040 Speaker 3: Michael, that piece of news about TSMC in Europe shows 128 00:06:16,080 --> 00:06:19,560 Speaker 3: there is data center and chip capacity activity here in 129 00:06:19,600 --> 00:06:23,080 Speaker 3: the States, and there's the same in Europe. Is there 130 00:06:23,200 --> 00:06:26,080 Speaker 3: pros and cons to kind of focusing your attentions as 131 00:06:26,120 --> 00:06:27,880 Speaker 3: an investor on just one. 132 00:06:29,839 --> 00:06:32,160 Speaker 8: I'm not really sure that I would emphasize one versus 133 00:06:32,200 --> 00:06:34,480 Speaker 8: the other, because again, this is all data that can 134 00:06:34,520 --> 00:06:37,040 Speaker 8: be transmitted. A lot of the investment in Europe is 135 00:06:37,080 --> 00:06:39,600 Speaker 8: going to be a byproduct of separate and different privacy 136 00:06:39,680 --> 00:06:42,520 Speaker 8: rules and will probably continue to see investment around that. 137 00:06:43,080 --> 00:06:44,720 Speaker 9: The United States stands head and. 138 00:06:44,680 --> 00:06:46,800 Speaker 8: Shoulders above the rest of the world in terms of 139 00:06:46,880 --> 00:06:49,359 Speaker 8: data center capacity. I forget the exact number, but I 140 00:06:49,360 --> 00:06:52,440 Speaker 8: want to say it's roughly ten x its next largest competitor. 141 00:06:53,240 --> 00:06:55,800 Speaker 8: And at the same time, we're seeing extraordinary gains in 142 00:06:55,839 --> 00:06:59,279 Speaker 8: the efficiency of these data centers, which is actually going 143 00:06:59,320 --> 00:07:02,000 Speaker 8: to of course, that the capacity ends up being much 144 00:07:02,040 --> 00:07:05,240 Speaker 8: greater than we thought. The same underlying phenomenon we saw 145 00:07:05,320 --> 00:07:08,200 Speaker 8: again with fiber optic build out in the late nineteen nineties, 146 00:07:08,760 --> 00:07:11,440 Speaker 8: where we expected to have to use tons of dark fiber, 147 00:07:11,600 --> 00:07:13,600 Speaker 8: we ultimately laid that in the ground, and we've used 148 00:07:13,600 --> 00:07:15,880 Speaker 8: only a small fraction of it as other forms of 149 00:07:15,920 --> 00:07:19,560 Speaker 8: technology improved alongside it. In this case, we're starting to 150 00:07:19,560 --> 00:07:23,280 Speaker 8: see those efficiency gains from rewriting algorithms to optimize them 151 00:07:23,280 --> 00:07:25,640 Speaker 8: for AI or data center applications. 152 00:07:26,160 --> 00:07:28,360 Speaker 9: So ultimately, I think in both places. 153 00:07:28,000 --> 00:07:29,760 Speaker 8: We're going to find that we have at least a 154 00:07:29,840 --> 00:07:33,520 Speaker 8: period of access capacity that emerges, and I think investors 155 00:07:33,520 --> 00:07:35,720 Speaker 8: in both locations should be considered of that. 156 00:07:36,000 --> 00:07:37,400 Speaker 9: Europe probably offers. 157 00:07:37,120 --> 00:07:39,680 Speaker 8: A better construction environment, as I said, because they have 158 00:07:39,760 --> 00:07:42,480 Speaker 8: different rules, they're going to need to make their own investments. 159 00:07:43,080 --> 00:07:46,160 Speaker 2: So everyone going all in on basically the infrastructure build out. 160 00:07:46,160 --> 00:07:48,240 Speaker 2: But where else in the stack, where else in the 161 00:07:48,480 --> 00:07:51,120 Speaker 2: value add of AI should you be boarding out to? 162 00:07:52,120 --> 00:07:53,840 Speaker 8: Well, I think we're starting to get to the point 163 00:07:53,880 --> 00:07:56,440 Speaker 8: that you want to start thinking about the implications of this. Right, So, 164 00:07:56,560 --> 00:08:00,480 Speaker 8: financial institutions could benefit tremendously from an improved and the 165 00:08:00,520 --> 00:08:04,400 Speaker 8: ability to run analysis or process components. We're starting to 166 00:08:04,440 --> 00:08:08,800 Speaker 8: see shopping algorithms. Certainly, we're seeing things like support bots 167 00:08:08,840 --> 00:08:12,760 Speaker 8: in terms of the traditional call center type work. The 168 00:08:12,760 --> 00:08:15,760 Speaker 8: efficiency gains there are immens. Again, I think one of 169 00:08:15,800 --> 00:08:18,320 Speaker 8: the real challenges that we're going to face is that disruption. 170 00:08:18,520 --> 00:08:19,800 Speaker 9: We're really uncertain of it. 171 00:08:20,000 --> 00:08:23,440 Speaker 8: Right, People remember the comparisons of Amazon to Barnes and Noble, 172 00:08:23,480 --> 00:08:25,960 Speaker 8: and now they no longer remember Barnes and Noble except 173 00:08:26,000 --> 00:08:30,440 Speaker 8: as a college textbook destination. My hunches is we're going 174 00:08:30,480 --> 00:08:32,199 Speaker 8: to see a lot of the same things. The world 175 00:08:32,280 --> 00:08:35,800 Speaker 8: is changing very rapidly. It's a period of uncertainty. You 176 00:08:35,920 --> 00:08:39,240 Speaker 8: have to price that into effectively the optionality of these 177 00:08:39,320 --> 00:08:42,400 Speaker 8: names once you start to realize that it may turn 178 00:08:42,440 --> 00:08:45,280 Speaker 8: out that they are the vast majority are unable to 179 00:08:45,360 --> 00:08:47,840 Speaker 8: deliver on the opportunity that was in front of them. 180 00:08:48,480 --> 00:08:51,040 Speaker 8: Against that, I think that we're seeing lots of interesting 181 00:08:51,080 --> 00:08:54,400 Speaker 8: opportunities that are being created in more mundane areas like 182 00:08:54,480 --> 00:08:57,440 Speaker 8: fixed income, where we're beginning to recognize that the level 183 00:08:57,440 --> 00:08:59,559 Speaker 8: of interest rates is unlikely to. 184 00:08:59,480 --> 00:09:06,400 Speaker 3: Be what Wen, I'm sorry to interrupt you, then, Michael, 185 00:09:06,520 --> 00:09:08,120 Speaker 3: I just want to say, when we consider the US 186 00:09:08,200 --> 00:09:11,640 Speaker 3: economy on Bloomberg Technology in particular, it's often through the 187 00:09:11,720 --> 00:09:15,280 Speaker 3: lens of infrastructure, and that's kind of end of the economy. 188 00:09:15,679 --> 00:09:19,280 Speaker 3: But interestingly, specifically when it comes to the labor market, 189 00:09:19,360 --> 00:09:21,520 Speaker 3: you've been thinking about the gig economy, and we're going 190 00:09:21,559 --> 00:09:23,400 Speaker 3: to go very deep on that later in the program. 191 00:09:23,440 --> 00:09:25,800 Speaker 3: So I just wondered if you'd outline your thesis for us. 192 00:09:27,000 --> 00:09:29,040 Speaker 8: Yeah, So one of the things that we're really dealing 193 00:09:29,080 --> 00:09:31,839 Speaker 8: with is again this sort of radical change. Two thousand 194 00:09:31,880 --> 00:09:34,520 Speaker 8: and nine with uber was the advent of the app 195 00:09:34,600 --> 00:09:39,160 Speaker 8: based gig economy that initially targeted professional drivers. In twenty twelve, 196 00:09:39,200 --> 00:09:42,080 Speaker 8: that shifted with the introduction of Lyft and uber x 197 00:09:42,600 --> 00:09:45,600 Speaker 8: to effectively allow anyone to use their car to make 198 00:09:45,679 --> 00:09:48,439 Speaker 8: additional money, either as a side job or as a 199 00:09:48,600 --> 00:09:50,120 Speaker 8: substitute for unemployment. 200 00:09:50,960 --> 00:09:53,040 Speaker 9: The interesting thing that's happened is we haven't. 201 00:09:52,800 --> 00:09:56,520 Speaker 8: Really had a normal cycle or period of unemployment, and 202 00:09:56,600 --> 00:09:58,320 Speaker 8: one of the things that we're finding is that the 203 00:09:58,360 --> 00:10:02,280 Speaker 8: gig economy is effectively better destination for people than filing 204 00:10:02,320 --> 00:10:03,520 Speaker 8: for unemployment claims. 205 00:10:04,400 --> 00:10:06,959 Speaker 9: That would imply that the economy is actually the labor 206 00:10:07,000 --> 00:10:09,520 Speaker 9: markets quite a bit weaker than we're. 207 00:10:09,400 --> 00:10:11,880 Speaker 8: Getting in the headline data, and we're seeing this in 208 00:10:12,000 --> 00:10:14,559 Speaker 8: sentiment surveys of consumers. 209 00:10:13,960 --> 00:10:17,040 Speaker 9: And households, but it's not showing up in the official data. 210 00:10:17,440 --> 00:10:20,079 Speaker 8: One of the examples I give is California, for example, 211 00:10:20,080 --> 00:10:23,160 Speaker 8: where if you file for unemployment, your maximum benefit is 212 00:10:23,200 --> 00:10:25,360 Speaker 8: four hundred and fifty dollars a week for a maximum 213 00:10:25,360 --> 00:10:26,480 Speaker 8: of twenty six weeks. 214 00:10:26,800 --> 00:10:27,959 Speaker 9: That puts you at half of. 215 00:10:27,920 --> 00:10:31,800 Speaker 8: The poverty level for a two person household. In contrast, 216 00:10:31,880 --> 00:10:34,600 Speaker 8: if you already have a car and you're going to 217 00:10:34,600 --> 00:10:38,000 Speaker 8: face that depreciation associated with it, driving for Uber allows 218 00:10:38,040 --> 00:10:41,160 Speaker 8: you to replace a much larger fraction of your income 219 00:10:41,679 --> 00:10:44,440 Speaker 8: by utilizing an asset that you are no longer using. 220 00:10:44,240 --> 00:10:45,559 Speaker 9: In commute to work, et cetera. 221 00:10:46,760 --> 00:10:48,959 Speaker 8: That I think is really a key story that we're 222 00:10:48,960 --> 00:10:50,960 Speaker 8: going to have to figure out the reason why it 223 00:10:51,000 --> 00:10:52,720 Speaker 8: becomes an issue, And I want to emphasize I think 224 00:10:52,720 --> 00:10:55,600 Speaker 8: it's better that people are finding alternatives in the private sector, 225 00:10:56,240 --> 00:10:58,440 Speaker 8: but those private sector alternatives have their. 226 00:10:58,360 --> 00:10:59,320 Speaker 9: Own feedback loop. 227 00:10:59,320 --> 00:11:03,640 Speaker 8: Whereas more people find themselves driving for Uber, simultaneously, the 228 00:11:03,720 --> 00:11:06,640 Speaker 8: demand for these ride sharing services and gig economy and 229 00:11:06,640 --> 00:11:10,840 Speaker 8: applications begins to decline and the competition for the revenues 230 00:11:10,880 --> 00:11:14,360 Speaker 8: associated with them increases. Again, we're starting to see the 231 00:11:14,400 --> 00:11:17,120 Speaker 8: signs of that that people who are driving for Uber 232 00:11:17,160 --> 00:11:20,720 Speaker 8: are suddenly discovering that their incomes are beginning to fall significantly. 233 00:11:21,200 --> 00:11:22,720 Speaker 9: So this is going to be a real challenge for 234 00:11:22,760 --> 00:11:23,160 Speaker 9: our time. 235 00:11:23,200 --> 00:11:25,920 Speaker 8: Do we actually understand the data as that's coming in, 236 00:11:26,440 --> 00:11:29,120 Speaker 8: particularly with central bankers choosing to try to be as 237 00:11:29,200 --> 00:11:32,680 Speaker 8: data dependent as they possibly can. That data could change 238 00:11:32,760 --> 00:11:36,000 Speaker 8: very rapidly if we have a misunderstanding of the system. 239 00:11:36,480 --> 00:11:40,400 Speaker 3: Michael Green, portfolio manager at Simplify Asset Management, thank you 240 00:11:40,920 --> 00:11:42,800 Speaker 3: very much. And in line with what Michael was saying 241 00:11:42,800 --> 00:11:45,360 Speaker 3: coming out, we're going to go in detail into a 242 00:11:45,400 --> 00:11:49,360 Speaker 3: Bloomberg investigation which looks into how Uber and Lyft use 243 00:11:49,400 --> 00:11:53,160 Speaker 3: the loophole to deny drivers in New York City millions 244 00:11:53,200 --> 00:11:57,640 Speaker 3: of dollars in pay also taking a real quick look 245 00:11:57,720 --> 00:12:02,360 Speaker 3: at shares. I think of NAP, which got a downgrade 246 00:12:02,600 --> 00:12:05,280 Speaker 3: in the market earlier this morning and has taken a 247 00:12:05,320 --> 00:12:08,000 Speaker 3: little bit of a hit, maybe actually not as severe 248 00:12:08,000 --> 00:12:11,240 Speaker 3: as it was in the market open, but down about 249 00:12:11,240 --> 00:12:14,439 Speaker 3: a percentage point one point one percent. We'll get a 250 00:12:14,480 --> 00:12:16,880 Speaker 3: little bit more on that later in the program. This 251 00:12:16,960 --> 00:12:31,960 Speaker 3: is Bloomberg Technology. This was the Bloomberg Big take over 252 00:12:31,960 --> 00:12:35,160 Speaker 3: the weekend. Uber and Lyft have found a money saving 253 00:12:35,240 --> 00:12:40,680 Speaker 3: loophole in New York City lockouts. By simply preventing drivers 254 00:12:40,679 --> 00:12:43,760 Speaker 3: from logging into the apps, the companies set themselves up 255 00:12:43,760 --> 00:12:46,880 Speaker 3: to save as much as hundreds of millions of dollars 256 00:12:47,200 --> 00:12:51,000 Speaker 3: in driver payouts. That's according to Bloomberg estimates. Listen to this. 257 00:12:53,000 --> 00:12:55,200 Speaker 10: The summer of twenty twenty four was a stressful time 258 00:12:55,280 --> 00:12:57,480 Speaker 10: to be an Uber or Lyft driver in New York City. 259 00:12:57,679 --> 00:13:00,000 Speaker 11: Kill even Ballmark because there's Lackold's currently. 260 00:13:00,440 --> 00:13:03,880 Speaker 10: In May, Uber began preventing drivers from working seemingly at 261 00:13:03,960 --> 00:13:07,280 Speaker 10: random for hours at a time. Lift soon followed Suit. 262 00:13:07,480 --> 00:13:10,320 Speaker 12: They opened their app and normally you would be able 263 00:13:10,360 --> 00:13:13,160 Speaker 12: to click the go button and go online. But when 264 00:13:13,160 --> 00:13:16,319 Speaker 12: you're locked out, when you press go, nothing happens. 265 00:13:16,360 --> 00:13:20,120 Speaker 13: An individual lockout could happen from anywhere from five minutes 266 00:13:20,800 --> 00:13:22,520 Speaker 13: up to eight hours. 267 00:13:23,000 --> 00:13:25,840 Speaker 12: We understood that this stemmed from a six year old 268 00:13:25,920 --> 00:13:26,400 Speaker 12: pay rout. 269 00:13:26,760 --> 00:13:29,560 Speaker 13: I started driving for Uber to be able to work 270 00:13:29,600 --> 00:13:33,040 Speaker 13: at my own schedule because I went through a lot 271 00:13:33,080 --> 00:13:36,400 Speaker 13: of trauma with a divorce. I was a mess emotionally. 272 00:13:37,040 --> 00:13:40,680 Speaker 13: So basically, a lockout is when you cannot go online 273 00:13:40,800 --> 00:13:43,920 Speaker 13: to work, so essentially your micro fire. 274 00:13:44,120 --> 00:13:45,880 Speaker 9: Every day I would get locked out of Uber. It 275 00:13:45,960 --> 00:13:47,080 Speaker 9: was very frustrating. 276 00:13:47,679 --> 00:13:51,000 Speaker 13: It was also affecting my self respect. What am I 277 00:13:51,240 --> 00:13:53,880 Speaker 13: just the I'm a robot? Like getting locked out like 278 00:13:53,920 --> 00:13:55,800 Speaker 13: I'm a person. I have to feed my family. 279 00:13:55,920 --> 00:13:59,520 Speaker 12: Lockouts happen every hour of the day, every day, and 280 00:13:59,679 --> 00:14:04,520 Speaker 12: that's despite Uber saying their particular schedule of open access 281 00:14:04,559 --> 00:14:07,560 Speaker 12: times where people shouldn't be locked out. We found that 282 00:14:07,679 --> 00:14:10,959 Speaker 12: there were more than four hundred instances of lockouts happening 283 00:14:11,080 --> 00:14:16,120 Speaker 12: insert zones. So there's definitely potential impact there on consumer pricing. 284 00:14:16,240 --> 00:14:19,280 Speaker 12: A lot of people. Through our interviews, we learned that 285 00:14:19,440 --> 00:14:22,120 Speaker 12: they are working way beyond their usual hours to try 286 00:14:22,160 --> 00:14:23,520 Speaker 12: to make what they've made before. 287 00:14:49,360 --> 00:14:52,760 Speaker 3: Check out The Big Take Daily podcasts anywhere you get 288 00:14:52,840 --> 00:14:55,120 Speaker 3: your podcasts. I think that is one you definitely want 289 00:14:55,120 --> 00:14:55,640 Speaker 3: to check out. 290 00:14:55,680 --> 00:14:58,640 Speaker 2: Carrac Yeah, more on the sharing economy because cities around 291 00:14:58,680 --> 00:15:01,640 Speaker 2: the world are debating how to regulate short term rentals, 292 00:15:01,800 --> 00:15:04,240 Speaker 2: for some going as far as banning services like Airbnb 293 00:15:04,360 --> 00:15:07,200 Speaker 2: or erbo, others are trying to find a compromise through 294 00:15:07,240 --> 00:15:08,400 Speaker 2: limits and regulations. 295 00:15:08,640 --> 00:15:10,040 Speaker 4: You just saw her in that package. 296 00:15:10,080 --> 00:15:13,400 Speaker 2: We now have Natalie Lung live on sets talk about 297 00:15:13,400 --> 00:15:15,960 Speaker 2: the different approaches with Airbnb and the like and solving 298 00:15:16,000 --> 00:15:19,680 Speaker 2: some of the concerns and if they work. Natalie, what 299 00:15:19,800 --> 00:15:22,560 Speaker 2: are we currently seeing a city like New York trying 300 00:15:22,560 --> 00:15:23,520 Speaker 2: to compromise on. 301 00:15:23,920 --> 00:15:26,720 Speaker 12: Right The New York City had introduced almost a de 302 00:15:26,800 --> 00:15:31,240 Speaker 12: facto ban on short to rentals last year, basically ruling 303 00:15:31,280 --> 00:15:35,240 Speaker 12: out the short to rentals of less than thirty days 304 00:15:35,720 --> 00:15:38,800 Speaker 12: and more than a year into the law, we've seen 305 00:15:38,880 --> 00:15:42,880 Speaker 12: that vacancies have not gone up, so like hosts have 306 00:15:43,000 --> 00:15:46,640 Speaker 12: not released those short to rentals as expected, turning into 307 00:15:47,000 --> 00:15:50,360 Speaker 12: long term rentals, and the rent decreases we've seen in 308 00:15:50,400 --> 00:15:53,160 Speaker 12: the over the past year were attributed to other factors. 309 00:15:53,440 --> 00:15:55,680 Speaker 12: So our Airbnb is arguing that the law is not 310 00:15:55,840 --> 00:15:58,000 Speaker 12: as effective as the city had hoped. 311 00:15:59,160 --> 00:16:01,880 Speaker 3: As we write in the Natalie, there are two sides 312 00:16:02,000 --> 00:16:05,240 Speaker 3: to this story, and in the middle there is compromise. 313 00:16:05,720 --> 00:16:09,480 Speaker 3: Where have the companies and also like the hosts themselves 314 00:16:09,520 --> 00:16:13,080 Speaker 3: because they want to make money and the regulators found compromise. 315 00:16:14,480 --> 00:16:17,440 Speaker 12: Airbnb has introduced something called a city portal back in 316 00:16:17,480 --> 00:16:21,760 Speaker 12: twenty twenty to basically encourage more transparency, sharing more ways 317 00:16:21,840 --> 00:16:25,160 Speaker 12: that cities can access the data own listing, so cities 318 00:16:25,200 --> 00:16:29,520 Speaker 12: can ensure better licensing registration as well as an easier 319 00:16:29,680 --> 00:16:33,240 Speaker 12: way to collect tax revenue. So by increasing the data 320 00:16:33,440 --> 00:16:37,280 Speaker 12: they share, cities can ensure better enforcement. 321 00:16:38,000 --> 00:16:40,600 Speaker 2: Yeah, they need more transparency ultimately about what people are 322 00:16:40,640 --> 00:16:42,960 Speaker 2: making as well right to be able to declare a 323 00:16:43,000 --> 00:16:46,320 Speaker 2: win win with tax revenue going to the government as 324 00:16:46,320 --> 00:16:47,560 Speaker 2: well as to the people are hosting. 325 00:16:48,320 --> 00:16:52,320 Speaker 12: Yeah, that's the argument basically, and Airbnb is saying a 326 00:16:52,320 --> 00:16:54,960 Speaker 12: de facto band, like a blanket band like New York 327 00:16:55,000 --> 00:16:57,560 Speaker 12: City is almost like a cautionary tail, and cities shouldn't 328 00:16:57,560 --> 00:16:58,200 Speaker 12: go that far. 329 00:17:00,120 --> 00:17:03,040 Speaker 3: The question simply is what happens next. You know, in 330 00:17:03,120 --> 00:17:07,520 Speaker 3: New York City is a really interesting example where there's 331 00:17:07,520 --> 00:17:09,520 Speaker 3: a debate about where the laws get changed. 332 00:17:09,600 --> 00:17:10,240 Speaker 5: Natalie. 333 00:17:11,920 --> 00:17:15,800 Speaker 12: Interestingly, about a year, like a month ago, exactly a 334 00:17:15,880 --> 00:17:18,960 Speaker 12: year of this rule coming into effect, Airbnb came out 335 00:17:19,000 --> 00:17:23,600 Speaker 12: with taking stock of the effectiveness of this rule and 336 00:17:23,640 --> 00:17:26,560 Speaker 12: saying they are kind of appealing to the city to 337 00:17:27,040 --> 00:17:30,960 Speaker 12: perhaps adjust some provisions in the laws so that some 338 00:17:31,160 --> 00:17:35,439 Speaker 12: hosts can come back to their platform. 339 00:17:35,560 --> 00:17:39,040 Speaker 3: Bloomberg's Nasty lung across two very important stories in the 340 00:17:39,080 --> 00:17:39,800 Speaker 3: gig economy. 341 00:17:39,840 --> 00:17:41,600 Speaker 5: We really appreciate it. Thank you very much. 342 00:17:50,280 --> 00:17:52,359 Speaker 10: We can see those chats. 343 00:18:05,240 --> 00:18:06,440 Speaker 9: You should want to hurt cover. 344 00:18:07,520 --> 00:18:11,160 Speaker 3: SpaceX caught the attention of the world over the weekend, 345 00:18:11,320 --> 00:18:14,600 Speaker 3: making a first historic catch mid air for one of 346 00:18:14,600 --> 00:18:17,040 Speaker 3: its boosters. For more and what this leap means for 347 00:18:17,080 --> 00:18:20,600 Speaker 3: space exploration, Bloomberg's Lauren Grush joins US and was on 348 00:18:20,720 --> 00:18:24,320 Speaker 3: shift over the weekend covers SpaceX for us. Actually, there 349 00:18:24,320 --> 00:18:26,560 Speaker 3: were two milestones, and we need to talk about the 350 00:18:26,640 --> 00:18:28,960 Speaker 3: kind of Starship component of it. 351 00:18:29,000 --> 00:18:30,600 Speaker 5: But let's start with the booster. 352 00:18:30,840 --> 00:18:34,440 Speaker 3: Why that was significant and the idea of rapid reusability. 353 00:18:34,600 --> 00:18:36,640 Speaker 5: Lauren, right, Yeah, this. 354 00:18:36,760 --> 00:18:39,600 Speaker 14: Was a big hurdle that SpaceX needed to clear in 355 00:18:39,640 --> 00:18:41,520 Speaker 14: the road of development for Starship. 356 00:18:41,800 --> 00:18:44,560 Speaker 9: As you mentioned, Starship's promise. 357 00:18:44,240 --> 00:18:46,879 Speaker 14: Is that it's going to be fully reusable, so both 358 00:18:47,000 --> 00:18:51,160 Speaker 14: portions of the vehicle need to survive after launch. 359 00:18:51,359 --> 00:18:53,880 Speaker 12: But with the booster, you know, when they. 360 00:18:53,840 --> 00:18:56,720 Speaker 14: First proposed this idea, I think a lot of people 361 00:18:56,760 --> 00:19:01,359 Speaker 14: were skeptical. Obviously SpaceX has perfected landing its rockets with 362 00:19:01,440 --> 00:19:04,800 Speaker 14: its Falcon nine, but when this was proposed, catching the 363 00:19:04,880 --> 00:19:07,720 Speaker 14: rocket in midair rather than landing it, I think a 364 00:19:07,720 --> 00:19:09,879 Speaker 14: lot of people were wondering how this would work. But 365 00:19:10,359 --> 00:19:12,720 Speaker 14: it seemed to work just like a charm, at least 366 00:19:12,760 --> 00:19:15,119 Speaker 14: from what we saw. And so I think they've proved 367 00:19:15,200 --> 00:19:18,520 Speaker 14: a lot of skeptics wrong over the weekend. 368 00:19:18,640 --> 00:19:22,440 Speaker 15: And now NASA going is fall to consider that maybe 369 00:19:22,480 --> 00:19:26,040 Speaker 15: we'll be landing on the Moon, maybe using of cool 370 00:19:26,119 --> 00:19:28,880 Speaker 15: Starship as lotly as twenty twenty five, told us through 371 00:19:28,880 --> 00:19:30,240 Speaker 15: what needs to happen to get that on. 372 00:19:31,200 --> 00:19:33,879 Speaker 14: Yeah, I think the current plan is landing in twenty 373 00:19:33,920 --> 00:19:38,439 Speaker 14: twenty six with people, but you know, just to caution, 374 00:19:38,520 --> 00:19:42,159 Speaker 14: there's still quite a long road ahead for Starship. Obviously, 375 00:19:42,240 --> 00:19:45,399 Speaker 14: this was a huge milestone for the company, but next 376 00:19:45,440 --> 00:19:49,280 Speaker 14: they really need to showcase that they can refuel Starship 377 00:19:49,320 --> 00:19:52,000 Speaker 14: in orbit and so that's essentially like filling up at 378 00:19:52,040 --> 00:19:55,560 Speaker 14: the cast tank, but with a lot more engineering challenges. 379 00:19:56,040 --> 00:19:57,520 Speaker 9: But this will be crucial to. 380 00:19:57,480 --> 00:20:01,080 Speaker 14: That because they have to catch and then relaunched the 381 00:20:01,080 --> 00:20:04,080 Speaker 14: booster over and over again with back to back launches 382 00:20:04,080 --> 00:20:06,280 Speaker 14: in order to perform those refueling missions. 383 00:20:06,960 --> 00:20:11,000 Speaker 3: So the other Starship gets up to orbital altitude, orbital speeds, 384 00:20:11,040 --> 00:20:14,879 Speaker 3: goes around the world, and control lands into the Indian Ocean. 385 00:20:15,080 --> 00:20:17,359 Speaker 5: Taught me through that part please. Yeah. 386 00:20:17,359 --> 00:20:20,440 Speaker 14: So during the last flight, you know, Starship struggled when 387 00:20:20,480 --> 00:20:23,160 Speaker 14: it came back from space. We could see it kind 388 00:20:23,200 --> 00:20:25,760 Speaker 14: of burning up and breaking apart when it was plunging 389 00:20:25,800 --> 00:20:28,439 Speaker 14: through the atmosphere. But SpaceX said that they did a 390 00:20:28,600 --> 00:20:32,520 Speaker 14: total revamp on the heat shield on Starship, and from 391 00:20:32,520 --> 00:20:35,520 Speaker 14: what we saw, it seemed to survive relatively intact. I 392 00:20:35,520 --> 00:20:38,119 Speaker 14: think we saw a little burn through, but definitely not 393 00:20:38,240 --> 00:20:41,520 Speaker 14: nearly as significant as the prior play. And then so 394 00:20:41,680 --> 00:20:44,360 Speaker 14: it did survive, and then when they tried to splash 395 00:20:44,400 --> 00:20:47,159 Speaker 14: down in the ocean, they were able to flip and 396 00:20:47,240 --> 00:20:50,679 Speaker 14: kind of do a pretty precise landing. So yeah, it 397 00:20:50,800 --> 00:20:53,920 Speaker 14: really was a big milestone for both the Booster and Starship, 398 00:20:54,000 --> 00:20:58,800 Speaker 14: showcasing that maybe this rocket can be fully reusable, which is, 399 00:20:58,960 --> 00:21:01,280 Speaker 14: to be fair, something that SpaceX hasn't been able to 400 00:21:01,320 --> 00:21:03,119 Speaker 14: achieve yet with its Falcon nine. 401 00:21:03,280 --> 00:21:04,640 Speaker 9: You know, part of the Falcon. 402 00:21:04,400 --> 00:21:07,879 Speaker 14: Nine still goes unused or is destroyed after launch, and 403 00:21:07,920 --> 00:21:11,000 Speaker 14: so this would be a huge paradigm shift for SpaceX 404 00:21:11,040 --> 00:21:13,520 Speaker 14: and the launch industry in general. 405 00:21:13,880 --> 00:21:24,800 Speaker 2: Line crash, Welcome back to bluemog Technology. I'm Caroline Hyde 406 00:21:24,800 --> 00:21:25,200 Speaker 2: in New York. 407 00:21:25,720 --> 00:21:27,240 Speaker 5: Now I med loll in San Francisco. 408 00:21:27,359 --> 00:21:29,560 Speaker 2: Quick check on these market said, because we've got to 409 00:21:29,600 --> 00:21:31,399 Speaker 2: risk and feel that we're dialing back from our previous 410 00:21:31,440 --> 00:21:33,760 Speaker 2: highs on the NASAQ, we're still up some six tenths percent. 411 00:21:33,800 --> 00:21:36,240 Speaker 2: All eyes on earnings, All eyes on, of course, where 412 00:21:36,280 --> 00:21:40,120 Speaker 2: we'll see stimulus coming from China. Now, interestingly, the NASDAT 413 00:21:40,119 --> 00:21:42,920 Speaker 2: Golden Dragon Index, which tracks some of the key tech 414 00:21:43,040 --> 00:21:45,040 Speaker 2: names over in China. Training here in the US is 415 00:21:45,080 --> 00:21:47,760 Speaker 2: down by a percentage point. Even though Chinese stocks managed 416 00:21:47,760 --> 00:21:50,400 Speaker 2: to ramp up higher in their day of trade. 417 00:21:50,480 --> 00:21:51,320 Speaker 4: We maybe call off. 418 00:21:51,320 --> 00:21:54,000 Speaker 2: We're still waiting for that fiscal stimulus coming from China, 419 00:21:54,119 --> 00:21:57,440 Speaker 2: China being factored into crypto more broadly. But once again, 420 00:21:57,480 --> 00:22:00,040 Speaker 2: Bitcoin up five percent, a good strong play for that, 421 00:22:00,320 --> 00:22:02,240 Speaker 2: and we're seeing and it's training gets higher since July. 422 00:22:02,600 --> 00:22:04,840 Speaker 2: That as we also debate what is happening in terms 423 00:22:04,880 --> 00:22:06,600 Speaker 2: of the US economy where interest rates go and the 424 00:22:06,800 --> 00:22:08,520 Speaker 2: like move on, have a look at what's happening in 425 00:22:08,600 --> 00:22:11,440 Speaker 2: terms of overall big names that drivers higher. I'm looking 426 00:22:11,480 --> 00:22:13,159 Speaker 2: at Adobe up two point three percent. We have a 427 00:22:13,200 --> 00:22:16,480 Speaker 2: crucial conversation coming with a CEO, new generator of AI 428 00:22:16,560 --> 00:22:17,879 Speaker 2: in the world of video. 429 00:22:17,960 --> 00:22:18,600 Speaker 5: We'll get that to. 430 00:22:18,520 --> 00:22:20,359 Speaker 2: You in a moment. A SML though one of the 431 00:22:20,440 --> 00:22:22,600 Speaker 2: key points drivers on the nasat one hundred, of course, 432 00:22:22,680 --> 00:22:26,040 Speaker 2: European chip equipment maker is driving up one two point 433 00:22:26,080 --> 00:22:28,360 Speaker 2: six percent. Look all eyes on earnings they come out 434 00:22:28,560 --> 00:22:31,240 Speaker 2: on October the sixteenth, and I'm looking at Video. Look 435 00:22:31,400 --> 00:22:34,159 Speaker 2: once again, this three trillion dollar company is managing to 436 00:22:34,160 --> 00:22:37,400 Speaker 2: help take the benchmarks higher, all based on optimism around 437 00:22:37,400 --> 00:22:40,679 Speaker 2: the Blackwell focus, but also just AI more broadly. But 438 00:22:40,680 --> 00:22:41,880 Speaker 2: we've got an interesting take on. 439 00:22:41,840 --> 00:22:48,119 Speaker 3: AI now ed okay, buckle up. Open ai is going 440 00:22:48,119 --> 00:22:50,840 Speaker 3: to war with a man who owns a trademark and 441 00:22:50,920 --> 00:22:56,520 Speaker 3: website with the name open Ai with a Space. The 442 00:22:56,520 --> 00:22:59,080 Speaker 3: man at the center of the lawsuit filed a trademark 443 00:22:59,119 --> 00:23:02,800 Speaker 3: on the name open space Ai the same day Sam 444 00:23:02,840 --> 00:23:07,560 Speaker 3: Altman and Greg Brockman announced their company Open Ai. This man, 445 00:23:07,800 --> 00:23:11,520 Speaker 3: Guy Ravine, claims to have invented the video sharing technology 446 00:23:11,600 --> 00:23:15,680 Speaker 3: later made famous by Snapchat and TikTok. Evan Ratliff wrote 447 00:23:15,720 --> 00:23:19,000 Speaker 3: about it in Bloomberg BusinessWeek and joins us, Now, it 448 00:23:19,119 --> 00:23:24,280 Speaker 3: is an incredible tale, some incredible reporting. It's probably one 449 00:23:24,320 --> 00:23:27,920 Speaker 3: where chronology is important. So let's start at the beginning. 450 00:23:28,840 --> 00:23:32,480 Speaker 3: Open space AI and open ai were born at the 451 00:23:32,520 --> 00:23:33,159 Speaker 3: same time. 452 00:23:33,480 --> 00:23:34,240 Speaker 5: Take us from there. 453 00:23:35,880 --> 00:23:39,680 Speaker 16: So essentially, this gentleman, Guy Ravine, who is a kind 454 00:23:39,720 --> 00:23:45,120 Speaker 16: of technologist inventor, who was in Silicon Valley, he had 455 00:23:45,160 --> 00:23:47,679 Speaker 16: come up with an idea and he could document this 456 00:23:47,920 --> 00:23:51,040 Speaker 16: for open ai with a Space, and the idea was 457 00:23:51,600 --> 00:23:55,280 Speaker 16: that it would be a nonprofit that would be engaged 458 00:23:55,320 --> 00:23:58,200 Speaker 16: in deep learning research, and the way that they would 459 00:23:58,280 --> 00:24:01,760 Speaker 16: lure researchers away from Google, which was dominating deep learning 460 00:24:01,760 --> 00:24:04,119 Speaker 16: research at the time, was that they would say that 461 00:24:04,280 --> 00:24:06,680 Speaker 16: everything would be open, the research would be open, it 462 00:24:06,720 --> 00:24:09,280 Speaker 16: would all be done for the benefit of humanity. And 463 00:24:09,359 --> 00:24:11,919 Speaker 16: if you know anything about open ai, the company, the 464 00:24:12,000 --> 00:24:15,240 Speaker 16: organization open ai without a space, that is also. 465 00:24:15,000 --> 00:24:15,959 Speaker 1: Their origin story. 466 00:24:16,760 --> 00:24:21,199 Speaker 16: They just launched some months after Ravine can document that 467 00:24:21,240 --> 00:24:23,639 Speaker 16: he was pitching this story around Silicabaluy, so they launched 468 00:24:23,680 --> 00:24:25,800 Speaker 16: in December of twenty eleven. 469 00:24:26,400 --> 00:24:30,040 Speaker 2: The pitching is important because you originally thought Evan might 470 00:24:30,080 --> 00:24:32,040 Speaker 2: be a bit of a scammer, and then he went 471 00:24:32,160 --> 00:24:35,160 Speaker 2: to some of the really key names in Silicum Valley, 472 00:24:35,320 --> 00:24:39,200 Speaker 2: Jan Lukumb for example, and asked like did he come 473 00:24:39,240 --> 00:24:41,600 Speaker 2: to is he legit? And one key person came back 474 00:24:41,640 --> 00:24:42,480 Speaker 2: and said, I think he is. 475 00:24:43,920 --> 00:24:45,840 Speaker 16: He did, And that's to me is what makes this 476 00:24:45,880 --> 00:24:48,919 Speaker 16: story so interesting and different and kind of the story 477 00:24:48,920 --> 00:24:50,560 Speaker 16: that you don't hear about very often, which is that 478 00:24:50,800 --> 00:24:53,159 Speaker 16: it had first seemed like this was kind of what 479 00:24:53,200 --> 00:24:55,560 Speaker 16: they call a trademark troll lawsuit, like he filed at 480 00:24:55,600 --> 00:24:58,520 Speaker 16: the trademark the day that open ai launched like maybe 481 00:24:58,520 --> 00:25:00,680 Speaker 16: he's just trying to usurp it from them. He owns 482 00:25:00,720 --> 00:25:04,160 Speaker 16: the domain open ai. But then it turned out that 483 00:25:04,480 --> 00:25:06,200 Speaker 16: some of the people that he had pitched to, one 484 00:25:06,240 --> 00:25:09,120 Speaker 16: in particular Tom Gruber, who's the co inventor of Siri, 485 00:25:09,480 --> 00:25:11,840 Speaker 16: who used to be at Apple, who's a respected name 486 00:25:11,880 --> 00:25:15,680 Speaker 16: in AI, he's backing guy r v Up. He says, yes, 487 00:25:15,920 --> 00:25:18,439 Speaker 16: he pitched me this idea. Not only did pitch me 488 00:25:18,480 --> 00:25:21,320 Speaker 16: this idea, I can document that he had it before 489 00:25:22,160 --> 00:25:25,520 Speaker 16: open ai the company launched, And so it's not a 490 00:25:25,560 --> 00:25:28,800 Speaker 16: case of someone just claiming something out there in the blue. 491 00:25:29,000 --> 00:25:31,679 Speaker 16: It's actually there are people to whom he pitched the story, 492 00:25:31,880 --> 00:25:34,280 Speaker 16: and there are documents showing that he pitched the idea 493 00:25:34,760 --> 00:25:37,400 Speaker 16: before it launched. Now whether that legally matters is going 494 00:25:37,480 --> 00:25:39,399 Speaker 16: to be decided in the courts. 495 00:25:40,440 --> 00:25:43,040 Speaker 3: If you're just joining us on Bloomberg Technology, this really 496 00:25:43,080 --> 00:25:47,000 Speaker 3: is a must read in the BusinessWeek magazine. There's litigation 497 00:25:47,080 --> 00:25:49,879 Speaker 3: on going here, right, there's the trademark dispute. There's a suit. 498 00:25:50,040 --> 00:25:52,879 Speaker 3: Just update us on the latest on those front seven. 499 00:25:54,400 --> 00:25:58,000 Speaker 16: So essentially, I mean open ai has has really prevailed 500 00:25:58,040 --> 00:25:59,920 Speaker 16: in the arguments so far. I mean they got a 501 00:26:00,000 --> 00:26:02,720 Speaker 16: preliminary injunction against Guy Ravine, so he can no longer 502 00:26:02,840 --> 00:26:07,359 Speaker 16: use open ai the trademark or the domain open dot ai, 503 00:26:07,400 --> 00:26:10,919 Speaker 16: which he again bought well before open ai launched. 504 00:26:10,680 --> 00:26:12,440 Speaker 1: So he can't use either of those right now. 505 00:26:13,359 --> 00:26:16,080 Speaker 16: He filed a countersuit against them, claiming they stole the 506 00:26:16,119 --> 00:26:18,880 Speaker 16: idea from him. That most of that countersuit was dismissed 507 00:26:18,880 --> 00:26:22,440 Speaker 16: and now it's been refiled, so he's had setbacks in court, 508 00:26:23,160 --> 00:26:24,639 Speaker 16: but they're a long way. 509 00:26:24,440 --> 00:26:26,680 Speaker 1: From you know, what would eventually. 510 00:26:26,200 --> 00:26:28,040 Speaker 16: Be likely a jury trial if they ever got that 511 00:26:28,119 --> 00:26:30,159 Speaker 16: far that that would be minimum months. 512 00:26:30,240 --> 00:26:31,920 Speaker 1: If not, you know, over a year away. 513 00:26:33,080 --> 00:26:36,879 Speaker 2: These things always take an awful lot of time. Evan, 514 00:26:36,920 --> 00:26:39,600 Speaker 2: what do you think the response will be from open 515 00:26:39,640 --> 00:26:42,600 Speaker 2: ai to this investigation of yours? How have you been 516 00:26:42,720 --> 00:26:46,560 Speaker 2: talking to the company without a space, Well. 517 00:26:46,400 --> 00:26:49,840 Speaker 16: The company didn't have much interest in commenting. I mean, 518 00:26:49,880 --> 00:26:52,639 Speaker 16: of course they've they've filed a lot of arguments in 519 00:26:52,680 --> 00:26:55,520 Speaker 16: court and so they're sort of their comments are in 520 00:26:55,640 --> 00:26:58,240 Speaker 16: their legal briefs. Their only sort of general comment was, 521 00:26:58,280 --> 00:27:01,040 Speaker 16: you know, we took legal action action to stop the 522 00:27:01,040 --> 00:27:04,640 Speaker 16: intentional use of open ai from confusing and misleading our users. 523 00:27:05,160 --> 00:27:06,679 Speaker 1: Now, I think one of. 524 00:27:06,680 --> 00:27:08,400 Speaker 16: The big questions around the lawsuit is why don't they 525 00:27:08,400 --> 00:27:11,760 Speaker 16: just settle this lawsuit? Because the money that they're spending 526 00:27:11,960 --> 00:27:16,320 Speaker 16: in lawyers is probably, you could guess, already greater than 527 00:27:16,320 --> 00:27:18,800 Speaker 16: they might have settled the lawsuit for it. And the longer 528 00:27:18,800 --> 00:27:21,040 Speaker 16: it goes on, the more that's true. Now, whether they 529 00:27:21,040 --> 00:27:24,040 Speaker 16: want to make an example of Guyravine or with their 530 00:27:24,080 --> 00:27:26,199 Speaker 16: new funding round, it doesn't really matter to them how 531 00:27:26,280 --> 00:27:27,119 Speaker 16: much money they spend. 532 00:27:27,160 --> 00:27:28,639 Speaker 1: I think those will be the questions. 533 00:27:29,000 --> 00:27:32,199 Speaker 16: But I think the pressure always mounts the closer you 534 00:27:32,280 --> 00:27:33,840 Speaker 16: get to actually having a jury trial. 535 00:27:34,240 --> 00:27:37,520 Speaker 2: Extraordinary story. Go read it, Evan Ratliff. We thank you, 536 00:27:38,200 --> 00:27:38,760 Speaker 2: ed what forgot? 537 00:27:39,760 --> 00:27:43,080 Speaker 3: Many more stories in today's Talking Tech and first start 538 00:27:43,520 --> 00:27:45,840 Speaker 3: what are we looking at? Bank of America has folded 539 00:27:45,920 --> 00:27:50,080 Speaker 3: its fintech investment banking team into its tech practice, moving 540 00:27:50,119 --> 00:27:53,960 Speaker 3: about fifty bankers into its roughly two hundred strong tech group. 541 00:27:53,960 --> 00:27:56,159 Speaker 3: According to the Bank's chairman of Global m and A, 542 00:27:56,520 --> 00:27:59,119 Speaker 3: this is a move reflecting an industry wide shift in 543 00:27:59,160 --> 00:28:03,560 Speaker 3: financial service is toward software. Speaking of fintech, so far 544 00:28:03,600 --> 00:28:05,679 Speaker 3: as Reeds to Bild to use two billion dollars of 545 00:28:05,720 --> 00:28:10,080 Speaker 3: Fortress Investment Group funds for the origination of personal loans. 546 00:28:10,280 --> 00:28:12,600 Speaker 3: The online banks in the agreement will expand its loan 547 00:28:12,640 --> 00:28:16,840 Speaker 3: platform business of brokering deals for pre qualified borrowers and 548 00:28:16,880 --> 00:28:18,480 Speaker 3: originating loans on the. 549 00:28:18,440 --> 00:28:20,000 Speaker 5: Behalf of third parties. 550 00:28:20,280 --> 00:28:23,240 Speaker 3: And South Korea's exports of tech products slowed for a 551 00:28:23,280 --> 00:28:27,040 Speaker 3: third straight month, in indication that global demand may be 552 00:28:27,160 --> 00:28:30,800 Speaker 3: peeking out, according to government data that also showed memory 553 00:28:30,880 --> 00:28:35,080 Speaker 3: chip shipments and prices are starting to lose some momentum. 554 00:28:35,240 --> 00:28:39,360 Speaker 2: Caroline, Now, let's talk fast fashion giant. Shean's potential London 555 00:28:39,400 --> 00:28:43,040 Speaker 2: listing has attracted controversy and mere allegations of forced labor. 556 00:28:43,240 --> 00:28:43,400 Speaker 4: Now. 557 00:28:43,520 --> 00:28:47,280 Speaker 2: UK Prime Minister Kirs Starmer addressed these concerns today. The 558 00:28:47,280 --> 00:28:49,320 Speaker 2: Bloomberg's head of economics, Stephanie. 559 00:28:48,960 --> 00:28:51,560 Speaker 17: Flanders, well, I think we've got to get the balance 560 00:28:51,640 --> 00:28:55,960 Speaker 17: right and clearly we've got to have standards, high stands. 561 00:28:56,000 --> 00:29:00,320 Speaker 17: We do have high standards, not least for example in 562 00:29:00,480 --> 00:29:02,440 Speaker 17: rights at work, and I address that in my speech 563 00:29:02,440 --> 00:29:05,520 Speaker 17: here today to say look better rights and protection for 564 00:29:05,600 --> 00:29:07,200 Speaker 17: people in their workplace are. 565 00:29:07,040 --> 00:29:08,040 Speaker 11: Good for growth. 566 00:29:08,160 --> 00:29:10,920 Speaker 17: So we're clear where we stand on standards, but we're 567 00:29:10,920 --> 00:29:14,800 Speaker 17: future looking, we're pragmatic and subject to those standards. Then yes, 568 00:29:14,800 --> 00:29:17,320 Speaker 17: we do want investment into the United Kingdom because we 569 00:29:17,480 --> 00:29:20,200 Speaker 17: desperately need growth in this come. We have had meaningful 570 00:29:20,200 --> 00:29:22,800 Speaker 17: growth in the economy in the United Kingdom for fourteen 571 00:29:22,880 --> 00:29:25,080 Speaker 17: long years. We're determined to turn that around. 572 00:29:25,400 --> 00:29:27,440 Speaker 18: Just to be clear on that, so the government would 573 00:29:27,480 --> 00:29:31,240 Speaker 18: need to be sure that sheeds not using forced labor 574 00:29:31,440 --> 00:29:34,120 Speaker 18: in Hinjang before it could list in the UK. 575 00:29:34,320 --> 00:29:36,560 Speaker 17: I'm not going to get into individual businesses. What I 576 00:29:36,560 --> 00:29:39,000 Speaker 17: will say it and be very clear about it. Standards 577 00:29:39,000 --> 00:29:41,120 Speaker 17: and high standards do matter to us, So of course 578 00:29:41,160 --> 00:29:43,600 Speaker 17: we'll be looking at any issue that goes to high 579 00:29:43,680 --> 00:29:48,240 Speaker 17: standards with a particular feature on the rights of the workforce. 580 00:29:48,520 --> 00:29:50,920 Speaker 17: And we've been really clear that we see that as 581 00:29:51,640 --> 00:29:54,200 Speaker 17: two sides of the same coin when it comes to growth. 582 00:29:54,680 --> 00:29:57,960 Speaker 17: Good employment rights and protections and a lot of drag 583 00:29:58,080 --> 00:30:01,240 Speaker 17: on growth. They're fundamental for growth, and I think pretty 584 00:30:01,240 --> 00:30:03,720 Speaker 17: well all good businesses understand that, which is why in 585 00:30:03,720 --> 00:30:06,440 Speaker 17: many cases they've already put in place some of the 586 00:30:06,480 --> 00:30:09,400 Speaker 17: rights and protections that we are bringing forward in legislation. 587 00:30:11,520 --> 00:30:15,960 Speaker 3: Okay, coming up, Adobe launches a new AI video generator. 588 00:30:15,960 --> 00:30:19,200 Speaker 3: We're going to speak with Adobe CEO Shantanu Naran. That 589 00:30:19,400 --> 00:30:23,160 Speaker 3: is next, what a conversation. This is Bloomberg Technology. 590 00:30:32,840 --> 00:30:37,560 Speaker 2: Adobe makes its AI video debut, announcing new tools allowing 591 00:30:37,560 --> 00:30:40,480 Speaker 2: for video generation now. The company calls it Firefly, and 592 00:30:40,520 --> 00:30:43,160 Speaker 2: it's set to be implemented in its video editing software 593 00:30:43,160 --> 00:30:45,840 Speaker 2: Premiere Pro, taking on, of course, the likes of Open Ai, 594 00:30:45,960 --> 00:30:49,920 Speaker 2: Meta Google a place to say. Adobe CEO Shantanu Orian 595 00:30:50,080 --> 00:30:54,120 Speaker 2: joins us now on the back of the announcement in Miami. Shantanu, 596 00:30:54,480 --> 00:30:57,040 Speaker 2: what's interesting is you're getting it into the hands of 597 00:30:57,160 --> 00:31:01,320 Speaker 2: users faster than rivals. Is it based on what it's 598 00:31:01,360 --> 00:31:03,360 Speaker 2: trained upon? How are you able to give it to 599 00:31:03,440 --> 00:31:03,960 Speaker 2: us so quick? 600 00:31:05,600 --> 00:31:08,280 Speaker 18: Well, Caroline, first, thanks for having me on the show. 601 00:31:08,440 --> 00:31:08,760 Speaker 4: Max. 602 00:31:08,800 --> 00:31:12,520 Speaker 18: As you know, is our biggest creativity event, and it's 603 00:31:12,560 --> 00:31:15,760 Speaker 18: the time that our engineers really take it upon themselves 604 00:31:16,400 --> 00:31:19,480 Speaker 18: to show their magic to the entire creative community. 605 00:31:20,240 --> 00:31:22,320 Speaker 4: As you point out, we have been. 606 00:31:22,280 --> 00:31:25,440 Speaker 18: Hard at work ensuring that our Firefly set of models 607 00:31:25,760 --> 00:31:29,640 Speaker 18: works on things like images and vector in design. But 608 00:31:29,680 --> 00:31:33,280 Speaker 18: today's a big day because we have been releasing our 609 00:31:33,320 --> 00:31:37,320 Speaker 18: Firefly video models. You know, our strategy has always been 610 00:31:37,400 --> 00:31:39,960 Speaker 18: that it's not just about the models that people develop, 611 00:31:40,320 --> 00:31:42,680 Speaker 18: but it's the integration with the apps. 612 00:31:42,360 --> 00:31:43,719 Speaker 4: Where the real magic is. 613 00:31:43,840 --> 00:31:47,040 Speaker 18: And so we've had thirteen billion generations where people have 614 00:31:47,200 --> 00:31:50,560 Speaker 18: used either Photoshop or illustrator to do it, and today 615 00:31:50,600 --> 00:31:53,959 Speaker 18: that magic comes to video in Premiere Pro, will be 616 00:31:54,000 --> 00:31:58,560 Speaker 18: in after effects as well as on firefly dot com Santani. 617 00:31:59,320 --> 00:32:02,520 Speaker 3: The most interesting part is the accessibility. There are no 618 00:32:02,720 --> 00:32:07,000 Speaker 3: usage limits one, but beyond the basic subscription fees, you're 619 00:32:07,040 --> 00:32:09,520 Speaker 3: not charging for the AI tools. 620 00:32:09,960 --> 00:32:11,440 Speaker 5: Will that change. 621 00:32:12,520 --> 00:32:13,680 Speaker 4: Well, Our focus. 622 00:32:13,320 --> 00:32:17,000 Speaker 18: Really first is on ensuring that we develop these great models. 623 00:32:17,040 --> 00:32:19,880 Speaker 18: We're very differentiated, and these models were designed to be 624 00:32:19,960 --> 00:32:24,120 Speaker 18: commercially safe, so they've only been trained on licensed and 625 00:32:24,200 --> 00:32:25,840 Speaker 18: public domain content. 626 00:32:26,000 --> 00:32:28,600 Speaker 4: And you know, we want people to experiment with it. 627 00:32:28,640 --> 00:32:32,640 Speaker 18: So I think clearly on the imaging, vector and design front, 628 00:32:33,080 --> 00:32:35,200 Speaker 18: we have said as part of the subscription we don't 629 00:32:35,200 --> 00:32:37,800 Speaker 18: want you to think about credits. We just want you 630 00:32:37,840 --> 00:32:40,760 Speaker 18: to experience the magic that exists. A video might be 631 00:32:40,800 --> 00:32:43,200 Speaker 18: a little bit different at what we've announced today is 632 00:32:43,240 --> 00:32:46,200 Speaker 18: that it's available in a beta form. The cost of 633 00:32:46,320 --> 00:32:50,160 Speaker 18: producing video is clearly a little bit more expensive, but 634 00:32:50,320 --> 00:32:52,200 Speaker 18: first steps is to just get it in the hands 635 00:32:52,240 --> 00:32:55,400 Speaker 18: of people, have them try it, give us feedback, make 636 00:32:55,440 --> 00:32:58,480 Speaker 18: sure it's specific, and then as I think the amount 637 00:32:58,520 --> 00:33:03,280 Speaker 18: of video that's being generated is expanding. We will probably 638 00:33:03,320 --> 00:33:05,080 Speaker 18: have different pricing models for that. 639 00:33:06,720 --> 00:33:09,200 Speaker 3: One of the features is to take an existing piece 640 00:33:09,240 --> 00:33:11,760 Speaker 3: of video and use generative AI to. 641 00:33:11,840 --> 00:33:14,240 Speaker 5: Extend it, extend beyond actual. 642 00:33:14,760 --> 00:33:17,600 Speaker 3: As someone that trained in their early career on premiere, 643 00:33:17,880 --> 00:33:19,080 Speaker 3: I'm excited. 644 00:33:18,640 --> 00:33:19,360 Speaker 5: To use that. 645 00:33:19,720 --> 00:33:23,600 Speaker 3: How technically difficult was it for Adobe to achieve that 646 00:33:23,680 --> 00:33:25,280 Speaker 3: versus the work you've done in photo. 647 00:33:27,000 --> 00:33:30,160 Speaker 18: Well, every time you show something in photo and people say, 648 00:33:30,200 --> 00:33:34,440 Speaker 18: oh my god, that's magic. To your point, the ability 649 00:33:34,480 --> 00:33:37,960 Speaker 18: to extend that into video across every frame is so 650 00:33:38,120 --> 00:33:42,160 Speaker 18: much more computationally intensive as well as challenging in order 651 00:33:42,200 --> 00:33:42,400 Speaker 18: to do. 652 00:33:42,520 --> 00:33:45,160 Speaker 4: But the use cases are so dramatic. You've had a 653 00:33:45,240 --> 00:33:47,120 Speaker 4: video for some reason. 654 00:33:46,840 --> 00:33:49,400 Speaker 18: Unfortunately it got clipped off and you just want to 655 00:33:49,440 --> 00:33:52,480 Speaker 18: extend it. You have video that you want to align 656 00:33:52,560 --> 00:33:55,440 Speaker 18: with audio, the song is not long enough. 657 00:33:55,480 --> 00:33:56,120 Speaker 4: How do you do it? 658 00:33:56,360 --> 00:34:00,520 Speaker 18: So I think this generative extend concept, which is you know, 659 00:34:00,600 --> 00:34:04,240 Speaker 18: really something that's very pervasive in video editing, is something 660 00:34:04,280 --> 00:34:05,760 Speaker 18: that the customers will really like. 661 00:34:06,000 --> 00:34:08,320 Speaker 4: The Other thing that we've done is because. 662 00:34:08,040 --> 00:34:10,920 Speaker 18: We can now analyze every frame when you want to 663 00:34:10,960 --> 00:34:14,959 Speaker 18: remove something that inadvertently came into the image. You don't 664 00:34:14,960 --> 00:34:17,839 Speaker 18: have to go through the entire process again, you can 665 00:34:17,960 --> 00:34:20,960 Speaker 18: just through post production change it. So some pretty incredible 666 00:34:21,000 --> 00:34:24,800 Speaker 18: stuff and we're really excited to see what our customers 667 00:34:24,840 --> 00:34:25,239 Speaker 18: do with this. 668 00:34:25,800 --> 00:34:31,560 Speaker 2: You talked about the computational feat the intensity, what about GPUs, 669 00:34:31,680 --> 00:34:34,560 Speaker 2: what about your own access to compute? 670 00:34:35,719 --> 00:34:38,960 Speaker 18: You know, well, we've partnered with you know, Nvideo for 671 00:34:39,320 --> 00:34:42,600 Speaker 18: years in making sure all of this stuff is available. 672 00:34:43,239 --> 00:34:45,480 Speaker 18: You know, it was great to hear from Jensen this 673 00:34:45,560 --> 00:34:49,520 Speaker 18: morning when he heard about our new announcements. But you know, 674 00:34:49,560 --> 00:34:52,759 Speaker 18: we're partnering with all of the chip manufacturers as well 675 00:34:52,800 --> 00:34:56,120 Speaker 18: as all of the hyper scalers because our intent is 676 00:34:56,520 --> 00:35:00,680 Speaker 18: not just to make this magic available through you those chips, 677 00:35:00,719 --> 00:35:04,279 Speaker 18: but also increasingly people are talking about how you can 678 00:35:04,280 --> 00:35:06,799 Speaker 18: make this available on a mobile device or in a 679 00:35:06,840 --> 00:35:10,200 Speaker 18: hybrid environment. So, you know, as long as they keep 680 00:35:10,880 --> 00:35:14,120 Speaker 18: performing in terms of what they are doing innovation on 681 00:35:14,160 --> 00:35:18,800 Speaker 18: the chips, we'll take advantage of every single available GPU 682 00:35:18,840 --> 00:35:20,480 Speaker 18: and CPU for our customers. 683 00:35:20,960 --> 00:35:25,160 Speaker 2: That's expensive and also, look, investors have been so excited 684 00:35:25,200 --> 00:35:28,320 Speaker 2: around just generative AI full stop, but they want to 685 00:35:28,320 --> 00:35:31,279 Speaker 2: see the revenue, they want to see the profitability. Chantanouje, 686 00:35:31,280 --> 00:35:34,279 Speaker 2: you feel that you're able to give more light, more 687 00:35:34,320 --> 00:35:36,960 Speaker 2: clarity to the market as to when this really becomes 688 00:35:37,600 --> 00:35:38,800 Speaker 2: very revenue generating. 689 00:35:40,120 --> 00:35:43,799 Speaker 18: You're absolutely right, Carolin, that all of the focus thus 690 00:35:43,840 --> 00:35:46,480 Speaker 18: far has been on training, and people have been creating 691 00:35:46,520 --> 00:35:49,879 Speaker 18: all the models, and so all of the investment that's 692 00:35:49,960 --> 00:35:53,280 Speaker 18: actually happening is on the training side of the equation. However, 693 00:35:53,680 --> 00:35:57,920 Speaker 18: I think Adobe has delivered more generative AI software than 694 00:35:58,000 --> 00:36:02,360 Speaker 18: virtually any other large company that had a traditional business. 695 00:36:02,400 --> 00:36:04,800 Speaker 18: So if you think about what we've done with Acrobat 696 00:36:04,800 --> 00:36:08,319 Speaker 18: AI Assistant or Photoshop, and clearly the emphasis has to 697 00:36:08,360 --> 00:36:11,760 Speaker 18: go on inference because that's where people are seeing value, 698 00:36:11,800 --> 00:36:16,239 Speaker 18: that's where it's getting embedded in the workflows. We will 699 00:36:16,280 --> 00:36:19,279 Speaker 18: have three or four different ways in which we monetize it, 700 00:36:19,320 --> 00:36:21,439 Speaker 18: and we have a financial analyst Q and A where 701 00:36:21,440 --> 00:36:23,920 Speaker 18: we will talk about this as well. But as you 702 00:36:24,680 --> 00:36:28,520 Speaker 18: insight into that, certainly our subscription tier pricing will have 703 00:36:28,600 --> 00:36:31,480 Speaker 18: the ability to lose some of this AI. We will 704 00:36:31,480 --> 00:36:35,040 Speaker 18: have add on services. Firefly services is being used by 705 00:36:35,080 --> 00:36:38,640 Speaker 18: every large enterprise. They want to understand how they create 706 00:36:38,640 --> 00:36:41,840 Speaker 18: a custom model where they can have, for example, Bloomberg 707 00:36:41,920 --> 00:36:45,200 Speaker 18: assets in a custom model where only Bloomberg is allowed 708 00:36:45,200 --> 00:36:47,680 Speaker 18: to generate with that content. So I think we have 709 00:36:47,800 --> 00:36:51,920 Speaker 18: multiple ways in which we monetize it today. Already, performance 710 00:36:52,120 --> 00:36:55,399 Speaker 18: marketers will have a new product today which is called 711 00:36:55,480 --> 00:36:58,640 Speaker 18: gen Studio, so you can increase the agility by which 712 00:36:58,640 --> 00:37:02,000 Speaker 18: you place ads on every advertising system. So, you know, 713 00:37:02,040 --> 00:37:05,400 Speaker 18: we're both excited about the technology and the value, but 714 00:37:05,480 --> 00:37:08,880 Speaker 18: we're also confident that customers will see the value and 715 00:37:08,960 --> 00:37:10,319 Speaker 18: enable us to monetize it. 716 00:37:11,520 --> 00:37:15,160 Speaker 3: Chantonau intense interest in your M and A strategy. Karen 717 00:37:15,200 --> 00:37:18,040 Speaker 3: and I were with the Runway CEO last week. Will 718 00:37:18,080 --> 00:37:19,760 Speaker 3: you look at companies like Runway. 719 00:37:20,840 --> 00:37:22,160 Speaker 4: We're partnering with Runway. 720 00:37:22,440 --> 00:37:25,160 Speaker 18: You know, I think as it relates to models, we 721 00:37:25,200 --> 00:37:29,720 Speaker 18: believe that Adobe for its core domains such as video 722 00:37:29,800 --> 00:37:33,640 Speaker 18: and imaging and design, and we will certainly have our 723 00:37:33,680 --> 00:37:36,959 Speaker 18: own models, but people will want us to support third 724 00:37:36,960 --> 00:37:40,520 Speaker 18: party models in our applications because that is the interface 725 00:37:40,600 --> 00:37:44,440 Speaker 18: that everybody uses. We've demonstrated that for video at IBC 726 00:37:44,640 --> 00:37:48,240 Speaker 18: with Runway, and certainly we will support these customer models. 727 00:37:48,239 --> 00:37:50,680 Speaker 18: As I said, so if your Coke or Nike or 728 00:37:50,719 --> 00:37:54,040 Speaker 18: Bank of America and you're using it within your context, 729 00:37:54,080 --> 00:37:56,640 Speaker 18: you have access to that, so you know this is 730 00:37:56,680 --> 00:37:57,040 Speaker 18: going to. 731 00:37:57,000 --> 00:37:58,840 Speaker 4: Be such an exciting area. 732 00:37:59,320 --> 00:38:02,360 Speaker 18: Each model, I believe ed will have its own personality. 733 00:38:02,680 --> 00:38:05,560 Speaker 18: So rather than you know, try to create one model, 734 00:38:05,600 --> 00:38:08,359 Speaker 18: that's the super said, We're going to support all these 735 00:38:08,400 --> 00:38:11,480 Speaker 18: models because they will do some things differently and better. 736 00:38:12,760 --> 00:38:15,600 Speaker 3: Adobe CEO shantanoon I ran, it's great to finally have 737 00:38:15,719 --> 00:38:17,520 Speaker 3: you here on Bloomberg Technology. 738 00:38:17,560 --> 00:38:18,520 Speaker 5: Thank you for your time. 739 00:38:26,080 --> 00:38:28,719 Speaker 2: President Biden announcing six hundred and twelve million dollars in 740 00:38:28,800 --> 00:38:32,480 Speaker 2: recovery aid for areas stricken by hurricanes Milton and Helene 741 00:38:32,840 --> 00:38:35,920 Speaker 2: as he surveyed the aftermath of Milton and Florida. Biden 742 00:38:35,960 --> 00:38:38,160 Speaker 2: said last week that the damage from Milton alone could 743 00:38:38,200 --> 00:38:41,680 Speaker 2: be about fifty billion dollars based on early assessments. Let's 744 00:38:41,680 --> 00:38:44,160 Speaker 2: talk about access to aid and how technology might be 745 00:38:44,200 --> 00:38:47,800 Speaker 2: able to help here too. FTW Ventures founder and general 746 00:38:47,800 --> 00:38:50,840 Speaker 2: partner brand Frank has joining us, and look, you are 747 00:38:50,920 --> 00:38:53,799 Speaker 2: all about food security. You're all about food safety and 748 00:38:53,800 --> 00:38:57,200 Speaker 2: innovation around that. One innovation is needed in these moments. 749 00:38:58,680 --> 00:39:01,759 Speaker 19: FTW Ventures were early stage venture fund focused on a 750 00:39:01,760 --> 00:39:05,360 Speaker 19: sustainable food system. We believe technology provides the unlocks like 751 00:39:05,400 --> 00:39:10,160 Speaker 19: it has for many other industries, to create safety, security access, 752 00:39:10,520 --> 00:39:11,840 Speaker 19: optimal nutrition. 753 00:39:12,000 --> 00:39:13,160 Speaker 11: And the outcomes. 754 00:39:13,160 --> 00:39:15,160 Speaker 19: We all want to live a healthy and happy life, 755 00:39:15,320 --> 00:39:17,399 Speaker 19: especially in these times of need when we have war 756 00:39:17,480 --> 00:39:21,400 Speaker 19: and disasters that are taking a toll on our food system. 757 00:39:21,560 --> 00:39:23,640 Speaker 3: Brian, are we talking about hardware or are we talking 758 00:39:23,640 --> 00:39:24,400 Speaker 3: about software? 759 00:39:25,560 --> 00:39:26,640 Speaker 11: We're talking about all of it. 760 00:39:26,920 --> 00:39:30,840 Speaker 19: The reality is technology, from biotechnology to hardware and automation 761 00:39:31,239 --> 00:39:32,280 Speaker 19: to AI and mL. 762 00:39:32,600 --> 00:39:34,719 Speaker 11: All of these technologies that have been pioneered for. 763 00:39:34,760 --> 00:39:37,800 Speaker 19: General purpose and for other industries like the automotive industry 764 00:39:38,000 --> 00:39:40,880 Speaker 19: or energy generation can now be applied directly within the 765 00:39:40,880 --> 00:39:44,080 Speaker 19: food system. And so we're talking about replacing plastics with 766 00:39:44,120 --> 00:39:47,399 Speaker 19: sustainable materials. We're talking about biotechnology to grow our next 767 00:39:47,400 --> 00:39:50,440 Speaker 19: generation food. We're talking about AI and mL to speed 768 00:39:50,480 --> 00:39:53,480 Speaker 19: and optimize the supply chain in which food can reach people. 769 00:39:53,719 --> 00:39:55,960 Speaker 19: So it's any and all technologies that can be applied 770 00:39:56,000 --> 00:39:58,000 Speaker 19: directly within the food and agricultural sectors. 771 00:39:58,560 --> 00:40:01,799 Speaker 2: What sort of ey of check you having to be 772 00:40:01,840 --> 00:40:04,520 Speaker 2: writing at the moment, How facing competitive is it in 773 00:40:04,520 --> 00:40:05,279 Speaker 2: this space or not? 774 00:40:06,640 --> 00:40:09,040 Speaker 19: Yeah, it's the venture industry, and as the whole has 775 00:40:09,080 --> 00:40:12,120 Speaker 19: taken a turn in the recent year, we've seen the 776 00:40:12,200 --> 00:40:16,040 Speaker 19: amount of capital deployed down and the valuation of companies 777 00:40:16,040 --> 00:40:16,680 Speaker 19: coming down. 778 00:40:16,480 --> 00:40:19,160 Speaker 11: Slightly as well. However, we're still writing. 779 00:40:18,880 --> 00:40:21,200 Speaker 19: Significant checks of two hundred and fifty thousand dollars to 780 00:40:21,239 --> 00:40:24,520 Speaker 19: a million dollars into early stage startups that are really 781 00:40:24,600 --> 00:40:26,880 Speaker 19: trying to innovate. They're bringing the first science out of 782 00:40:26,960 --> 00:40:29,239 Speaker 19: loud where they're bringing their first technology out of their 783 00:40:29,239 --> 00:40:32,040 Speaker 19: offices to apply it to the food and agricultural sectors. 784 00:40:32,880 --> 00:40:35,640 Speaker 3: Brian, is this US centric or it's happening around the world. 785 00:40:36,640 --> 00:40:37,720 Speaker 11: It's happening around the world. 786 00:40:38,200 --> 00:40:40,640 Speaker 19: We had FTW focus on the North America market, but 787 00:40:40,719 --> 00:40:43,680 Speaker 19: we believe technology and innovation in North America should be 788 00:40:43,719 --> 00:40:45,720 Speaker 19: brought to all corners of the world. So we're working 789 00:40:45,760 --> 00:40:49,640 Speaker 19: with regions like Singapore and Southeast Asia to really bring 790 00:40:49,640 --> 00:40:53,520 Speaker 19: food security and safety to those members of the community 791 00:40:53,560 --> 00:40:57,239 Speaker 19: as well. We're also I'm also personally a advisor to 792 00:40:57,280 --> 00:41:00,080 Speaker 19: the World Food Programs Accelerator, which helps in places of 793 00:41:00,120 --> 00:41:03,560 Speaker 19: Sub Saharan Africa and Latin America with food access, security 794 00:41:03,600 --> 00:41:04,520 Speaker 19: and safety as well. 795 00:41:04,640 --> 00:41:05,799 Speaker 11: And so it's happening. 796 00:41:05,440 --> 00:41:07,919 Speaker 19: Everywhere and we're just trying to bring the best technologies 797 00:41:08,280 --> 00:41:10,719 Speaker 19: played up level this industry. It needs to see ten 798 00:41:10,760 --> 00:41:13,600 Speaker 19: billion people on the planet by twenty fifty Ran. 799 00:41:13,520 --> 00:41:17,800 Speaker 2: Frank, thank you for joining us. FTW Benure's general partner there. Meanwhile, 800 00:41:18,520 --> 00:41:21,399 Speaker 2: for this edition of Bloomberg Technology, we still have all 801 00:41:21,400 --> 00:41:23,360 Speaker 2: eyes on those record highs for Invidia. 802 00:41:24,000 --> 00:41:26,200 Speaker 3: Yeah, a reminder if you're just joining us, keep an 803 00:41:26,200 --> 00:41:28,800 Speaker 3: eye on Nvidia. There may be a fresh record plenty 804 00:41:28,840 --> 00:41:30,919 Speaker 3: to recap on the podcast. You know where to find 805 00:41:30,920 --> 00:41:34,319 Speaker 3: it online on those platforms and on the Bloomberg platforms. 806 00:41:34,520 --> 00:41:36,719 Speaker 3: Shout out my friends and the team in New York City, 807 00:41:36,800 --> 00:41:38,720 Speaker 3: the big team out here in San Francisco. 808 00:41:39,440 --> 00:41:40,759 Speaker 5: One day down, four to go. 809 00:41:41,000 --> 00:41:47,600 Speaker 3: This is Bloomberg Technology.