1 00:00:00,080 --> 00:00:13,560 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Bloomberg Tech is alive 2 00:00:13,560 --> 00:00:17,400 Speaker 1: from coast to coast with Caroline Hyde in New York 3 00:00:17,680 --> 00:00:19,680 Speaker 1: and Vla Loow in San Francisco. 4 00:00:23,079 --> 00:00:26,239 Speaker 2: This is Bloomberg Tech coming up. President Trump's families getting 5 00:00:26,280 --> 00:00:29,320 Speaker 2: into the mobile phone business with the Trump branded service 6 00:00:29,520 --> 00:00:30,080 Speaker 2: plus Meta. 7 00:00:30,200 --> 00:00:33,440 Speaker 3: We'll start showing ads in WhatsApp and offer paid subscriptions 8 00:00:33,479 --> 00:00:34,280 Speaker 3: for the first time. 9 00:00:35,040 --> 00:00:38,000 Speaker 2: And robotics firm one Ex Technology says it can evaluate 10 00:00:38,040 --> 00:00:41,560 Speaker 2: a robot's performance without deploying it into the real world. 11 00:00:41,800 --> 00:00:44,760 Speaker 2: But first, President Trump is attending a Group of Seventh 12 00:00:44,760 --> 00:00:48,480 Speaker 2: summit in Canada today. This amid tariff uncertainty, a Middle 13 00:00:48,520 --> 00:00:51,360 Speaker 2: East crisis between Israel and Iran. Let's go over to 14 00:00:51,400 --> 00:00:54,120 Speaker 2: the G seven summit where Bloomberg's and Marie Horden is 15 00:00:54,160 --> 00:00:56,000 Speaker 2: standing by AMH. What do we need to know? 16 00:00:56,680 --> 00:00:58,560 Speaker 4: Well, certainly this is going to be a packed summit 17 00:00:58,600 --> 00:01:00,560 Speaker 4: and what's going on in the Middle East is certainly 18 00:01:00,600 --> 00:01:04,319 Speaker 4: taking center stage now really topping the agenda. We are 19 00:01:04,400 --> 00:01:07,560 Speaker 4: waiting for President Trump to have a first bilateral meeting 20 00:01:07,800 --> 00:01:10,600 Speaker 4: before the summit really kicks off. In earnest with Mark Karney, 21 00:01:10,600 --> 00:01:13,880 Speaker 4: the Prime Minister of Canada, and really the Canadians here 22 00:01:13,880 --> 00:01:16,640 Speaker 4: are trying to avoid what happened in twenty eighteen. You 23 00:01:16,680 --> 00:01:19,479 Speaker 4: may remember that viral photo and the President was sitting 24 00:01:19,480 --> 00:01:22,040 Speaker 4: with his arms crossed looking up at Angela Merkel, the 25 00:01:22,040 --> 00:01:25,720 Speaker 4: then German Chancellor. She was surrounded by a number of leaders, 26 00:01:25,720 --> 00:01:29,600 Speaker 4: the only one that's lasted since a fence, and then, 27 00:01:29,640 --> 00:01:32,520 Speaker 4: of course President Trump pulled the US out of that 28 00:01:32,600 --> 00:01:35,920 Speaker 4: joint communicate. So Mark Kearney's taking a different approach this time, 29 00:01:36,160 --> 00:01:40,880 Speaker 4: looking at different statements that they can have a common 30 00:01:41,080 --> 00:01:46,040 Speaker 4: thread for specifics, not one communicate for wide range of topics, 31 00:01:46,080 --> 00:01:51,440 Speaker 4: maybe something regarding artificial intelligence, critical minerals. Potentially there will 32 00:01:51,480 --> 00:01:54,040 Speaker 4: not be an agreement from all of these countries when 33 00:01:54,080 --> 00:01:56,520 Speaker 4: it comes to things like the war in Ukraine, things 34 00:01:56,600 --> 00:01:58,680 Speaker 4: like climate change, or even when it comes to what 35 00:01:58,760 --> 00:02:01,480 Speaker 4: is going on with Iran and Israel. That is certainly 36 00:02:01,480 --> 00:02:04,160 Speaker 4: going to be top of the agenda today. The President 37 00:02:04,240 --> 00:02:06,520 Speaker 4: was speaking about as he left the White House yesterday 38 00:02:06,560 --> 00:02:08,880 Speaker 4: and his way to Canada, and he was saying that 39 00:02:08,960 --> 00:02:10,920 Speaker 4: these two may have to fight it out, but in 40 00:02:10,960 --> 00:02:12,680 Speaker 4: the end he wants a deal. 41 00:02:13,440 --> 00:02:15,920 Speaker 2: Bloomberg's Amory Horden will stick with what's happening in the 42 00:02:15,919 --> 00:02:18,040 Speaker 2: G seven throughout the day. Thank you. Let's go with 43 00:02:18,080 --> 00:02:20,760 Speaker 2: Donald Trump and over to DC to talk about some 44 00:02:20,800 --> 00:02:24,800 Speaker 2: news in technology. The President's family launching a Trump branded 45 00:02:24,880 --> 00:02:28,600 Speaker 2: mobile phone service that will rely on wireless networks and 46 00:02:28,720 --> 00:02:32,720 Speaker 2: hardware quotes made in America, Bloomberg, Kelsey Griffiths here with more. 47 00:02:33,360 --> 00:02:36,120 Speaker 2: The idea is that they're not building the network from 48 00:02:36,160 --> 00:02:40,440 Speaker 2: the ground upright, They basically licensed capacity from the three 49 00:02:40,480 --> 00:02:42,840 Speaker 2: major US carriers. But what else do we know about 50 00:02:43,000 --> 00:02:46,440 Speaker 2: Trump Mobile? That's right ed? 51 00:02:46,639 --> 00:02:50,440 Speaker 5: So the Trump family's mobile service is they say they're 52 00:02:50,440 --> 00:02:54,640 Speaker 5: going to rely on the three major US telecom networks that. 53 00:02:54,560 --> 00:02:55,440 Speaker 6: Have already been built. 54 00:02:55,480 --> 00:02:58,400 Speaker 5: And this is a pretty common strategy that we see. 55 00:02:58,880 --> 00:03:02,440 Speaker 5: Meant Mobile is one of the pretty well known so 56 00:03:02,520 --> 00:03:05,920 Speaker 5: called mv and os that was eventually acquired by T Mobile. 57 00:03:06,320 --> 00:03:09,720 Speaker 5: We also saw some celebrities last week looking to get 58 00:03:09,720 --> 00:03:12,799 Speaker 5: into the mobile game, launching their own mv and O. 59 00:03:13,080 --> 00:03:17,440 Speaker 5: So the Trump family is following a pretty popular tradition 60 00:03:17,560 --> 00:03:18,919 Speaker 5: here by getting into this. 61 00:03:18,960 --> 00:03:20,800 Speaker 6: Business, Kelsey Popular. 62 00:03:20,880 --> 00:03:23,000 Speaker 3: Last time I checked, there were one hundreds, more than 63 00:03:23,000 --> 00:03:25,600 Speaker 3: one hundred here in the United States of mobile virtual 64 00:03:25,639 --> 00:03:26,639 Speaker 3: network operators. 65 00:03:27,040 --> 00:03:28,400 Speaker 6: But what they're trying to tap into. 66 00:03:28,480 --> 00:03:32,280 Speaker 3: Here is the Trump narrative, the Trump brand. How will 67 00:03:32,280 --> 00:03:35,360 Speaker 3: it be priced versus other competitors in the space. 68 00:03:36,560 --> 00:03:40,360 Speaker 5: That's right, So the brand is it's going to cost 69 00:03:40,400 --> 00:03:43,040 Speaker 5: about just under fifty dollars. I think it's a forty 70 00:03:43,040 --> 00:03:45,720 Speaker 5: seven to forty five per month, and I would say 71 00:03:45,720 --> 00:03:49,400 Speaker 5: that is pretty competitive with the space. These mv and 72 00:03:49,480 --> 00:03:54,320 Speaker 5: os tend to target these niche markets that are looking 73 00:03:54,320 --> 00:03:57,840 Speaker 5: at customers that maybe don't want to or can't pay 74 00:03:58,720 --> 00:04:03,400 Speaker 5: one hundred dollars or or you know, somewhere in that range. 75 00:04:03,480 --> 00:04:05,640 Speaker 5: So I do think that there is a chance they 76 00:04:05,680 --> 00:04:09,360 Speaker 5: will be targeting this niche that you know, maybe other 77 00:04:09,520 --> 00:04:11,040 Speaker 5: operators aren't necessary. 78 00:04:11,080 --> 00:04:14,880 Speaker 2: Are you looking at Kelsey? It's a competitive field, right 79 00:04:14,880 --> 00:04:17,400 Speaker 2: when you're looking at any carrier. When I was reading 80 00:04:17,440 --> 00:04:21,880 Speaker 2: the news from Trump Mobile, they're including everything that you'd expect, 81 00:04:21,920 --> 00:04:26,200 Speaker 2: unlimited text, data, voice protection, overseas call. Just give us 82 00:04:26,200 --> 00:04:27,560 Speaker 2: the basics of the plan. 83 00:04:29,080 --> 00:04:30,800 Speaker 5: Yeah, exactly. So there are going to be some of 84 00:04:30,800 --> 00:04:36,240 Speaker 5: these value added services on top of your mobile services 85 00:04:36,279 --> 00:04:39,040 Speaker 5: that you would expect. There is going to be some 86 00:04:39,080 --> 00:04:43,479 Speaker 5: sort of roadside assistance, there will be international calling thrown in, 87 00:04:44,040 --> 00:04:46,880 Speaker 5: and there's also going to be the opportunity to purchase 88 00:04:47,160 --> 00:04:52,240 Speaker 5: a mobile device that will also be branded with the 89 00:04:52,440 --> 00:04:56,919 Speaker 5: Trump family logo. It will come in a gold variety, 90 00:04:57,120 --> 00:04:59,599 Speaker 5: which you know, I'm looking forward to seeing. 91 00:05:00,360 --> 00:05:04,080 Speaker 3: Kelsey Griffiths puts us perfectly onto our next discussion because 92 00:05:04,120 --> 00:05:07,440 Speaker 3: we want to talk about the hardware perspective. Bomberg's Dana 93 00:05:07,480 --> 00:05:10,080 Speaker 3: woman is, hey, with US four hundred and ninety nine dollars, 94 00:05:10,279 --> 00:05:11,520 Speaker 3: you're going to be able to get it as soon 95 00:05:11,520 --> 00:05:13,800 Speaker 3: as September. You can put one hundred dollars down for 96 00:05:14,000 --> 00:05:15,880 Speaker 3: initial well, name. 97 00:05:15,760 --> 00:05:19,040 Speaker 6: On a list. But can they make that in America? 98 00:05:19,279 --> 00:05:21,800 Speaker 7: Never say never, but certainly the smartphone industry is not 99 00:05:21,839 --> 00:05:24,880 Speaker 7: really making phones here in the US right now, and 100 00:05:24,920 --> 00:05:27,240 Speaker 7: it's unclear from the report so far and what we 101 00:05:27,360 --> 00:05:31,359 Speaker 7: know so far who exactly are with what partners Trump 102 00:05:31,360 --> 00:05:34,120 Speaker 7: and his family would be making these devices. All that 103 00:05:34,160 --> 00:05:35,920 Speaker 7: we know, as Kelsey has said, is that the advice 104 00:05:35,920 --> 00:05:38,520 Speaker 7: would be golden color, which is very on brand for Trump, 105 00:05:38,839 --> 00:05:40,599 Speaker 7: and that it will be caught, that it will be 106 00:05:40,600 --> 00:05:42,680 Speaker 7: priced at four hundred and ninety nine dollars, which even 107 00:05:42,680 --> 00:05:45,839 Speaker 7: before recent inflation, was a pretty budget price for a 108 00:05:45,880 --> 00:05:48,800 Speaker 7: phone and seems especially low these days. So not knowing 109 00:05:48,880 --> 00:05:50,920 Speaker 7: anything about the hardware, we can at least say safely 110 00:05:51,240 --> 00:05:54,960 Speaker 7: these are probably not phones offering the most advanced features. 111 00:05:54,960 --> 00:05:57,000 Speaker 7: And maybe that's not the point, as Kelsey said, for 112 00:05:57,040 --> 00:06:00,000 Speaker 7: this particular niche right. 113 00:06:00,480 --> 00:06:02,679 Speaker 2: The point, Jiner is that this is built as being 114 00:06:02,720 --> 00:06:05,600 Speaker 2: designed and built in America at a time where the 115 00:06:05,640 --> 00:06:09,040 Speaker 2: President is putting pressure on Apple to build the iPhone 116 00:06:09,080 --> 00:06:11,920 Speaker 2: in America or face tariffs of as much as twenty 117 00:06:11,920 --> 00:06:14,320 Speaker 2: five percent. I think it's really notable that it's going 118 00:06:14,360 --> 00:06:15,920 Speaker 2: to run Android OS as well. 119 00:06:17,080 --> 00:06:20,200 Speaker 6: Yes, absolutely, as we said. 120 00:06:20,240 --> 00:06:25,920 Speaker 7: As I said, it's unclear exactly how the Trump organization 121 00:06:26,360 --> 00:06:29,960 Speaker 7: would make this phone come to pass so quickly in 122 00:06:30,000 --> 00:06:33,040 Speaker 7: the US, just knowing from Apple and other manufacturers as 123 00:06:33,080 --> 00:06:37,080 Speaker 7: well all of the human resources, the human labor and 124 00:06:37,440 --> 00:06:42,760 Speaker 7: physical resources that just are lacking here in the US. 125 00:06:43,120 --> 00:06:45,800 Speaker 7: And by human resources, I mean specialized talent that know 126 00:06:45,880 --> 00:06:48,800 Speaker 7: how to use this particular manufacturing equipment. A lot of 127 00:06:48,800 --> 00:06:53,080 Speaker 7: the equipment itself is abroad. So it's unclear exactly how 128 00:06:53,200 --> 00:06:57,240 Speaker 7: the team plans to surmount those challenges that other smartphone 129 00:06:57,279 --> 00:07:00,839 Speaker 7: makers either haven't tackled or just seem to be avoiding. 130 00:07:01,240 --> 00:07:05,840 Speaker 3: It's interesting who ultimately it'll be competing against because we've heard. 131 00:07:05,680 --> 00:07:07,400 Speaker 6: Him take aim President Trump. 132 00:07:07,440 --> 00:07:09,640 Speaker 3: This is of course the phone being made by President 133 00:07:09,720 --> 00:07:12,640 Speaker 3: by Trump organization in his family. But President Trump himself, 134 00:07:12,800 --> 00:07:15,360 Speaker 3: the administration of taken aim at Apple front and center. 135 00:07:15,440 --> 00:07:18,120 Speaker 3: Bring your manufacturing back to the United States, but at 136 00:07:18,120 --> 00:07:19,800 Speaker 3: a four nine to nine price point. Yeah, I might 137 00:07:19,840 --> 00:07:23,360 Speaker 3: compete against an iPhone se but really it's going against 138 00:07:23,360 --> 00:07:26,480 Speaker 3: other Android phones. It's going against the pixel AA for example. 139 00:07:26,840 --> 00:07:30,600 Speaker 7: Yes, maybe perhaps not even the pixel A line, perhaps 140 00:07:30,680 --> 00:07:34,520 Speaker 7: even more budget, sort of bargain basement phones that I'm 141 00:07:34,560 --> 00:07:36,560 Speaker 7: not able to recite offhand, and most of us can't 142 00:07:36,600 --> 00:07:37,520 Speaker 7: recite offhand. 143 00:07:38,440 --> 00:07:39,360 Speaker 6: And I think one. 144 00:07:39,240 --> 00:07:43,240 Speaker 7: Key question is how many does the organization plan to produce. 145 00:07:43,440 --> 00:07:45,480 Speaker 7: I think one of the key lines in our story 146 00:07:45,560 --> 00:07:48,000 Speaker 7: is that smartphone makers aren't producing. 147 00:07:47,640 --> 00:07:49,040 Speaker 2: At scale in the US. 148 00:07:49,520 --> 00:07:52,360 Speaker 7: So it's that, to me, is a really key question. 149 00:07:52,760 --> 00:07:55,880 Speaker 7: Could you produce a small, really sort of limited run 150 00:07:56,000 --> 00:07:58,840 Speaker 7: number of phones here in the US, probably more easily 151 00:07:58,840 --> 00:08:01,360 Speaker 7: than Apple could produce its whole iPhone line. So I 152 00:08:01,360 --> 00:08:04,920 Speaker 7: think the volume that they're expecting is an unknown detail, 153 00:08:04,960 --> 00:08:06,560 Speaker 7: but seems like an important one to me. 154 00:08:07,160 --> 00:08:10,720 Speaker 2: Motorola tried to build the Moto X in Dallas Fort 155 00:08:10,720 --> 00:08:13,920 Speaker 2: Worth in twenty thirteen, and they shut the plant a 156 00:08:14,000 --> 00:08:17,120 Speaker 2: year later. It was an Android based phone because the 157 00:08:17,160 --> 00:08:20,080 Speaker 2: costs were too high. I mean, that's what we're talking 158 00:08:20,080 --> 00:08:22,880 Speaker 2: about here. Karen made a really interesting point in our 159 00:08:22,920 --> 00:08:25,760 Speaker 2: group chat earlier about like what is four nine nine 160 00:08:25,840 --> 00:08:28,960 Speaker 2: relative to the field. It's like iPhone se It's like 161 00:08:29,360 --> 00:08:32,960 Speaker 2: that kind of mid tier of Android smartphone. But making 162 00:08:33,000 --> 00:08:35,160 Speaker 2: it and the number of components go into it. That's 163 00:08:35,200 --> 00:08:36,760 Speaker 2: like the key question here if they can have a 164 00:08:36,800 --> 00:08:37,439 Speaker 2: business in it. 165 00:08:38,600 --> 00:08:42,719 Speaker 8: Yes, absolutely, data on all right, Bluebergs, go for it. 166 00:08:42,800 --> 00:08:44,439 Speaker 2: Cara, I'll wrap it up. 167 00:08:44,679 --> 00:08:47,840 Speaker 3: We've talked so much all things about the future of 168 00:08:48,120 --> 00:08:50,360 Speaker 3: actual hardware and software. But let's take a look at 169 00:08:50,360 --> 00:08:52,880 Speaker 3: what's happening in the world of crypto now ed because 170 00:08:53,120 --> 00:08:54,439 Speaker 3: there's some Trump action there too. 171 00:08:54,640 --> 00:08:56,319 Speaker 6: Let's hina light what's happening with DJT. 172 00:08:56,559 --> 00:08:58,840 Speaker 3: Now we know that we were reporting last week about 173 00:08:58,920 --> 00:09:00,760 Speaker 3: how they're going to be starting to by bitcoin in 174 00:09:00,800 --> 00:09:03,560 Speaker 3: the treasury. Well, now we understand that they're pivoting with 175 00:09:03,679 --> 00:09:07,080 Speaker 3: true social filing to the SEC with an s one 176 00:09:07,520 --> 00:09:11,800 Speaker 3: saying they want a bitcoin in ethereum etf Now they've already. 177 00:09:11,480 --> 00:09:12,720 Speaker 6: Looked at a Bitcoin ETF. 178 00:09:12,760 --> 00:09:15,280 Speaker 3: But this is scaling out to Ether as well. Bitcoin's 179 00:09:15,320 --> 00:09:17,280 Speaker 3: up two point seventy five percent. Ether is up more 180 00:09:17,280 --> 00:09:20,040 Speaker 3: than five percent. Remember perhaps bouncing back after last week's 181 00:09:20,040 --> 00:09:22,720 Speaker 3: sell off amid some of that risk aversion. But check 182 00:09:22,720 --> 00:09:25,600 Speaker 3: out what's happening with Circle, the latest crypto ipo is 183 00:09:25,640 --> 00:09:26,160 Speaker 3: just going from. 184 00:09:26,040 --> 00:09:27,800 Speaker 6: Strength to strength throughout more than twenty percent. 185 00:09:28,080 --> 00:09:32,480 Speaker 3: As we all anticipate that the latest Genius Act that 186 00:09:32,520 --> 00:09:36,520 Speaker 3: will put into legal clarity stable coins is likely to 187 00:09:36,520 --> 00:09:37,319 Speaker 3: occur this week. 188 00:09:37,480 --> 00:09:38,160 Speaker 6: I also want to. 189 00:09:38,160 --> 00:09:40,920 Speaker 3: Say that Tron is being reported in the ft that 190 00:09:40,920 --> 00:09:43,439 Speaker 3: Tron my ipo here in the United States via a 191 00:09:43,520 --> 00:09:47,359 Speaker 3: reverse merger, and that's sending other companies, particularly higher. 192 00:09:47,200 --> 00:09:50,760 Speaker 8: Ed coming up. Robots. 193 00:09:51,320 --> 00:09:54,880 Speaker 2: They're just like us now, possibly more so, as robotics 194 00:09:54,920 --> 00:09:58,480 Speaker 2: firm one X Technologies launches a world model that will 195 00:09:58,480 --> 00:10:01,960 Speaker 2: teach robots to anticipate and understand the physics that's all 196 00:10:01,960 --> 00:10:09,040 Speaker 2: around them. That's coming up next. This is Bloomberg technology 197 00:10:15,080 --> 00:10:18,600 Speaker 2: companies like Tesla betting their future on humanoid robots that 198 00:10:18,640 --> 00:10:22,920 Speaker 2: fill labor shortages, particularly in manufacturing. Today, robotics firm one 199 00:10:23,040 --> 00:10:26,000 Speaker 2: X Technologies is out with a new world model it 200 00:10:26,040 --> 00:10:29,920 Speaker 2: says can evaluate a robot's performance without deploying it into 201 00:10:29,920 --> 00:10:32,160 Speaker 2: the real world. The models are data driven simulator of 202 00:10:32,360 --> 00:10:36,600 Speaker 2: humanoids with the grounded understanding of physics of their surrounding world. 203 00:10:36,920 --> 00:10:39,520 Speaker 2: The CEO and CTO of one X Burn Burnick joins 204 00:10:39,559 --> 00:10:42,280 Speaker 2: us here in San Francisco. You would claim that this 205 00:10:42,440 --> 00:10:44,760 Speaker 2: world model is the first of its kind, and you 206 00:10:44,880 --> 00:10:48,040 Speaker 2: have the data from it that that proves its effectiveness. 207 00:10:48,120 --> 00:10:53,560 Speaker 2: But the main point is validation of capability without collecting 208 00:10:53,600 --> 00:10:54,480 Speaker 2: the real world data. 209 00:10:55,840 --> 00:10:57,959 Speaker 8: Kind of yes, you got to model the right. 210 00:10:58,040 --> 00:11:01,240 Speaker 9: So what had really does us is it gives us 211 00:11:01,240 --> 00:11:04,240 Speaker 9: the ability to basically see the future with respect to 212 00:11:04,280 --> 00:11:06,640 Speaker 9: what would happen if the role what actually takes these 213 00:11:06,640 --> 00:11:10,520 Speaker 9: specific actions. And this allows us to really test at 214 00:11:10,559 --> 00:11:13,959 Speaker 9: scale where these new AI models that we're actually creating 215 00:11:14,320 --> 00:11:16,200 Speaker 9: will be better than the previous ones, right, and that 216 00:11:16,280 --> 00:11:18,280 Speaker 9: really allows us to climb this and really to create 217 00:11:18,320 --> 00:11:20,160 Speaker 9: better and better our models for physical labor. 218 00:11:20,280 --> 00:11:22,720 Speaker 2: I wrote in the Tech and Depth newsletter this morning 219 00:11:22,840 --> 00:11:26,800 Speaker 2: that if you look at particularly manufacturing jobs data, there 220 00:11:26,840 --> 00:11:28,800 Speaker 2: is a need there. You know, it's hard to fill 221 00:11:28,840 --> 00:11:32,840 Speaker 2: those roles. Why does this world model open a path 222 00:11:32,920 --> 00:11:37,920 Speaker 2: to a world where humanoid robotics are genuinely useful, deployable 223 00:11:38,320 --> 00:11:42,040 Speaker 2: in manufacturing or other logistic settings where a human is 224 00:11:42,200 --> 00:11:43,000 Speaker 2: currently needed. 225 00:11:43,720 --> 00:11:45,840 Speaker 9: So I think like taking a step back to us, 226 00:11:45,840 --> 00:11:48,679 Speaker 9: like you know, we're a consumer company. Seen often ask 227 00:11:49,160 --> 00:11:51,400 Speaker 9: right now, but to us, it's really about how do 228 00:11:51,440 --> 00:11:54,800 Speaker 9: you create an actual abundance of artificial labor as quickly 229 00:11:54,800 --> 00:11:58,360 Speaker 9: as possible, which means going into factories, going into enterprise 230 00:11:58,520 --> 00:12:02,840 Speaker 9: services and everything right. But to get there and get 231 00:12:02,840 --> 00:12:05,320 Speaker 9: your robos to be intelligent enough to solve these tasks, 232 00:12:05,840 --> 00:12:08,160 Speaker 9: you just need this incredible diversity of data. 233 00:12:08,760 --> 00:12:11,319 Speaker 8: And that's why we're going to the home first. And 234 00:12:12,960 --> 00:12:14,079 Speaker 8: if you're deploying. 235 00:12:13,679 --> 00:12:17,040 Speaker 9: These robots at scaling to homes, living and learning among us, 236 00:12:17,840 --> 00:12:19,800 Speaker 9: then we also need to be able in a safe 237 00:12:19,840 --> 00:12:24,439 Speaker 9: manner to test how will our models perform? And this 238 00:12:24,480 --> 00:12:26,240 Speaker 9: is really what the world mode allows us to do. 239 00:12:26,520 --> 00:12:28,800 Speaker 9: It allows us to take all of this data that 240 00:12:28,800 --> 00:12:31,960 Speaker 9: we're gathering, create new models, and then see if we 241 00:12:32,000 --> 00:12:34,920 Speaker 9: deployed these to our fleet of robots, how would it 242 00:12:34,960 --> 00:12:37,040 Speaker 9: actually perform, Would all of the behaviors that it do 243 00:12:37,160 --> 00:12:40,600 Speaker 9: actually be safe? Would it be able to do things 244 00:12:40,640 --> 00:12:43,640 Speaker 9: that couldn't do before? And kind of really benchmark the 245 00:12:43,679 --> 00:12:45,600 Speaker 9: models so we can progress. 246 00:12:45,400 --> 00:12:48,000 Speaker 3: How soffiscated is the hardware right now, we're talking about 247 00:12:48,000 --> 00:12:49,680 Speaker 3: how the models and the software is going to really 248 00:12:49,720 --> 00:12:51,760 Speaker 3: bring it to bear. What are they already doing in 249 00:12:51,760 --> 00:12:54,040 Speaker 3: the home those that are out in beta. 250 00:12:55,280 --> 00:12:59,160 Speaker 9: So the hardware is actually really getting there. So I 251 00:12:59,200 --> 00:13:01,319 Speaker 9: have one in my home, for example, and it's doing 252 00:13:01,400 --> 00:13:07,439 Speaker 9: like tidying, cleaning, vacuuming, some laundry, different kind of tasks. 253 00:13:07,679 --> 00:13:09,160 Speaker 8: The whole are on the home. 254 00:13:09,559 --> 00:13:11,800 Speaker 9: And of course also the social aspect of this, right 255 00:13:12,040 --> 00:13:14,680 Speaker 9: being able to have this AI companion together with you, 256 00:13:14,760 --> 00:13:16,880 Speaker 9: which does not allow you doesn't actually require you to 257 00:13:16,880 --> 00:13:18,440 Speaker 9: look at your phone all the time to be able 258 00:13:18,480 --> 00:13:23,280 Speaker 9: to interface with AI. Now, currently this is still pretty 259 00:13:23,280 --> 00:13:26,480 Speaker 9: brittle and the magic is there, and then maybe it 260 00:13:26,559 --> 00:13:29,959 Speaker 9: lasts for a minute and then you need someone to 261 00:13:30,040 --> 00:13:32,280 Speaker 9: nudge it in the right direction. Right, but it's really 262 00:13:32,280 --> 00:13:34,200 Speaker 9: getting to where you see where this will go and 263 00:13:35,320 --> 00:13:37,640 Speaker 9: the hardware can do it. And now it's really about 264 00:13:37,800 --> 00:13:40,280 Speaker 9: how do we gather the data that allows us to 265 00:13:40,280 --> 00:13:43,600 Speaker 9: automate all this behavior. And that's also really what we're 266 00:13:43,600 --> 00:13:46,720 Speaker 9: doing this year, right, So when we're talking about the 267 00:13:46,760 --> 00:13:49,040 Speaker 9: goal being by end of year, we're actually going to 268 00:13:49,080 --> 00:13:51,880 Speaker 9: have this commercially out in the market. In homes, it's 269 00:13:51,920 --> 00:13:54,720 Speaker 9: really important to do expectation management because this is not 270 00:13:55,480 --> 00:13:58,800 Speaker 9: the consumer product at that point that everyone will have 271 00:13:58,840 --> 00:14:00,760 Speaker 9: at home. This is the big beginning of a journey 272 00:14:00,760 --> 00:14:03,400 Speaker 9: where we're inviting people in on our mission to really 273 00:14:03,559 --> 00:14:06,319 Speaker 9: almost like adopt an neo into your family, have it, 274 00:14:06,400 --> 00:14:09,040 Speaker 9: live and learn among us, and have a lot of 275 00:14:09,040 --> 00:14:10,680 Speaker 9: fun around fun along the way. 276 00:14:11,640 --> 00:14:12,440 Speaker 2: Okay, see your. 277 00:14:12,360 --> 00:14:14,480 Speaker 6: Pitch, it's still early adopted here. 278 00:14:14,559 --> 00:14:18,440 Speaker 3: Basically you're pitching an earlier adoptor in the home. Go 279 00:14:18,559 --> 00:14:20,680 Speaker 3: to outside the home for a moment. I really want 280 00:14:20,680 --> 00:14:24,480 Speaker 3: your expertise. When we're talking about having robots help manufacture 281 00:14:24,480 --> 00:14:27,480 Speaker 3: here in the US, President Trump wants to build Apple 282 00:14:27,840 --> 00:14:31,000 Speaker 3: phones here in the US. Is robotics at a level 283 00:14:31,000 --> 00:14:32,520 Speaker 3: in the next few years where we could ever make 284 00:14:32,560 --> 00:14:33,800 Speaker 3: that achievable. 285 00:14:35,320 --> 00:14:36,240 Speaker 8: In the next few years. 286 00:14:36,280 --> 00:14:38,200 Speaker 9: I think like this is going to happen a lot 287 00:14:38,320 --> 00:14:41,640 Speaker 9: sooner than people might think. But it's not happening this year. 288 00:14:42,240 --> 00:14:45,160 Speaker 9: But we are a few years, not a few decades 289 00:14:45,320 --> 00:14:48,760 Speaker 9: away from where actually, most importantly to me, robots can 290 00:14:48,840 --> 00:14:51,840 Speaker 9: build robots, meaning we can have robots build more robots, 291 00:14:51,880 --> 00:14:54,480 Speaker 9: We can have robots build out the energy infrastructure, the 292 00:14:54,560 --> 00:14:57,440 Speaker 9: data centers, the chip fabs and really enable us to 293 00:14:57,480 --> 00:14:57,840 Speaker 9: get this. 294 00:14:57,840 --> 00:15:00,200 Speaker 8: Multiplier on the workforce for artificial labor. 295 00:15:00,240 --> 00:15:02,800 Speaker 9: Which will allow us to automate all kinds of factory 296 00:15:02,880 --> 00:15:05,240 Speaker 9: work across the US. And I think this can have 297 00:15:05,960 --> 00:15:08,480 Speaker 9: a tremendous impact on our GDP right and how we. 298 00:15:08,400 --> 00:15:11,320 Speaker 8: Can grow our way out of the current deficit. 299 00:15:12,480 --> 00:15:14,720 Speaker 9: But I also wanted to just do expectation management and 300 00:15:14,760 --> 00:15:18,440 Speaker 9: say like this is years away, but years it's not 301 00:15:18,480 --> 00:15:20,400 Speaker 9: that long in the ground picture of things, it's not. 302 00:15:20,440 --> 00:15:21,400 Speaker 8: Okay, you first. 303 00:15:21,200 --> 00:15:23,960 Speaker 2: Came on our radar in Video GtC. You had this 304 00:15:24,040 --> 00:15:26,400 Speaker 2: sort of very prominent place, and I know that the 305 00:15:26,440 --> 00:15:30,920 Speaker 2: one X exchanged jackets with Jensen, etc. What is the 306 00:15:31,040 --> 00:15:33,520 Speaker 2: unlock bin from Nvidia? You know they always talk about 307 00:15:33,520 --> 00:15:35,840 Speaker 2: how they work in multiple ways, not just providing the 308 00:15:37,120 --> 00:15:41,080 Speaker 2: basis for training, but the localized silicon for the robot 309 00:15:41,120 --> 00:15:44,280 Speaker 2: itself and then simulated or virtual data. Just to explain 310 00:15:44,360 --> 00:15:45,120 Speaker 2: the relationship. 311 00:15:45,920 --> 00:15:48,680 Speaker 9: Yeah, sure, and Video is an amazing partner, right, and 312 00:15:48,720 --> 00:15:51,480 Speaker 9: we're working very deeply together both with our engineering team. 313 00:15:51,800 --> 00:15:56,720 Speaker 9: We're using their hardware, we're using their simulators, and I 314 00:15:56,720 --> 00:15:59,000 Speaker 9: think they've done an incredible job in just enabling the 315 00:15:59,000 --> 00:16:01,200 Speaker 9: ecosystem and all of the things you've touched upon. 316 00:16:01,240 --> 00:16:02,240 Speaker 8: We are using right. 317 00:16:02,120 --> 00:16:06,720 Speaker 9: So they're very fast simulators that allow us to learn 318 00:16:06,760 --> 00:16:08,680 Speaker 9: a lot in simulation before we have to go into 319 00:16:08,720 --> 00:16:11,360 Speaker 9: the real world. It's really valuable for us. We had 320 00:16:11,360 --> 00:16:16,320 Speaker 9: some great results we published last week from our reinforcement 321 00:16:16,400 --> 00:16:18,960 Speaker 9: learning and our work on how to use the entire 322 00:16:19,000 --> 00:16:20,800 Speaker 9: body like you can see here to robot actually using 323 00:16:20,800 --> 00:16:22,920 Speaker 9: its entire body to do tasks, and this is training 324 00:16:23,000 --> 00:16:27,400 Speaker 9: nvidas simulators. The compute on Border robot actually is nvida's harbor, 325 00:16:27,600 --> 00:16:30,000 Speaker 9: and they've done a great job in creating very good 326 00:16:30,080 --> 00:16:33,840 Speaker 9: embedded compute that can allow us to run the robot 327 00:16:34,280 --> 00:16:37,360 Speaker 9: fully kind of enclosed. Right doesn't actually need to be 328 00:16:37,360 --> 00:16:40,600 Speaker 9: connected to the internet to do its tasks. And of 329 00:16:40,640 --> 00:16:44,040 Speaker 9: course we're training on the infrastructure from NVIDA like everyone else. 330 00:16:44,240 --> 00:16:46,440 Speaker 2: We're talking a lot about your model world model today, 331 00:16:46,480 --> 00:16:49,240 Speaker 2: but we're showing also pictures on the screen of the 332 00:16:49,280 --> 00:16:52,440 Speaker 2: hardware side. What is different, what is your different mission 333 00:16:52,520 --> 00:16:57,080 Speaker 2: statement to a figure, AI, agility, TESLA and optimists all 334 00:16:57,120 --> 00:16:59,080 Speaker 2: the things we've been talking about for many weeks. 335 00:16:59,400 --> 00:17:01,280 Speaker 9: Sure, so of course I don't want to speak on 336 00:17:01,280 --> 00:17:03,240 Speaker 9: other's behalf. But I can see like my view on this, 337 00:17:03,400 --> 00:17:06,679 Speaker 9: so I'd actually say it's pretty different because most of 338 00:17:06,720 --> 00:17:08,719 Speaker 9: the companies in this field they're focusing on how can 339 00:17:08,760 --> 00:17:11,720 Speaker 9: we be useful as quickly as possible in manufacturing or 340 00:17:12,000 --> 00:17:14,840 Speaker 9: some kind of enterprise, while to me, it's actually all 341 00:17:14,880 --> 00:17:17,280 Speaker 9: about how can we make robots that are safe so 342 00:17:17,320 --> 00:17:19,440 Speaker 9: they can live and learn among people, because that's how 343 00:17:19,480 --> 00:17:22,719 Speaker 9: we get to truly intelligent machines. And if you want 344 00:17:22,760 --> 00:17:24,920 Speaker 9: to put them in consumer they need to be extremely affordable. 345 00:17:25,200 --> 00:17:26,960 Speaker 9: So you need to have this combination of something that's 346 00:17:27,119 --> 00:17:30,600 Speaker 9: very safe but still affordable and very capable, and then 347 00:17:30,640 --> 00:17:32,720 Speaker 9: you can have this living and learning among people. 348 00:17:32,840 --> 00:17:35,119 Speaker 8: And basically solve the full problem, right, how do we. 349 00:17:35,119 --> 00:17:37,359 Speaker 9: Get robots that are as intelligent as us so that 350 00:17:37,400 --> 00:17:39,280 Speaker 9: we can easily instruct them in how to do labor 351 00:17:39,280 --> 00:17:45,880 Speaker 9: across society at scale? So I'd say like we're betting 352 00:17:45,960 --> 00:17:48,040 Speaker 9: to go directly to the goal instead of going through 353 00:17:48,080 --> 00:17:48,600 Speaker 9: the factories. 354 00:17:49,240 --> 00:17:52,120 Speaker 3: Ben Berneck, it's been great having you CEO and CTO 355 00:17:52,320 --> 00:17:54,159 Speaker 3: of one X Technology is fascinating. 356 00:17:54,200 --> 00:17:54,480 Speaker 6: Thanks. 357 00:18:00,040 --> 00:18:02,160 Speaker 3: It is time now for talking tech and first up 358 00:18:02,359 --> 00:18:05,120 Speaker 3: shares a Roku surging today. Now the company has announced 359 00:18:05,119 --> 00:18:07,760 Speaker 3: a partnership with Amazon Ads that it says will allow 360 00:18:07,800 --> 00:18:11,200 Speaker 3: advertisers access to more than eighty percent of US households 361 00:18:11,280 --> 00:18:12,439 Speaker 3: with Amazon Connected TVs. 362 00:18:12,480 --> 00:18:12,640 Speaker 6: Now. 363 00:18:12,640 --> 00:18:15,160 Speaker 3: The full integration and roll out to all advertisers that's 364 00:18:15,160 --> 00:18:17,880 Speaker 3: expected by Q four of this year likely drive more 365 00:18:17,880 --> 00:18:19,680 Speaker 3: demand for Roku's US inventory. 366 00:18:19,680 --> 00:18:21,440 Speaker 6: Analysts A plus. 367 00:18:21,160 --> 00:18:24,800 Speaker 3: Imax, while it is set to expand it screens aggressively 368 00:18:24,800 --> 00:18:28,159 Speaker 3: across China, i'm act as Chinese arm alongside partner Wonder Films, 369 00:18:28,400 --> 00:18:31,320 Speaker 3: says they will replace twenty seven screens in IMAX's jumbo screen. 370 00:18:31,560 --> 00:18:34,879 Speaker 3: Imax will currently operate eight hundred screens in China, and 371 00:18:34,920 --> 00:18:38,040 Speaker 3: it drew a record twenty two million customers from January 372 00:18:38,040 --> 00:18:38,360 Speaker 3: to May. 373 00:18:38,840 --> 00:18:40,880 Speaker 6: And Taiwan's International. 374 00:18:40,280 --> 00:18:44,080 Speaker 3: Trade Administration has added Huawei and Smith it's list of 375 00:18:44,160 --> 00:18:47,600 Speaker 3: blacklisted companies. It's a move that undercuts China's efforts in 376 00:18:47,640 --> 00:18:50,840 Speaker 3: developing AI chip technology locally ed. 377 00:18:52,000 --> 00:18:55,000 Speaker 2: Turning to Tesla, the company's full self driving tech stack, 378 00:18:55,040 --> 00:18:58,040 Speaker 2: alongside their ability to scale, is proving to be a 379 00:18:58,040 --> 00:19:01,760 Speaker 2: tailwind against drivers like Weaimo is the robotaxi race ramps up. 380 00:19:01,800 --> 00:19:05,000 Speaker 2: That's the latest from Bloomberg Intelligence Steve Man, who led 381 00:19:05,040 --> 00:19:08,200 Speaker 2: the research, joins us. Now, the kind of key headline 382 00:19:08,240 --> 00:19:11,840 Speaker 2: from the REACT piece is that Tesla's vehicle costs is 383 00:19:11,880 --> 00:19:15,520 Speaker 2: about one seventh of Weaimo's. Why were you so focused 384 00:19:15,520 --> 00:19:17,360 Speaker 2: on that, Steve Well? 385 00:19:17,560 --> 00:19:20,719 Speaker 10: I think is the critical piece in getting it on 386 00:19:20,760 --> 00:19:25,879 Speaker 10: a commercial basis, on a mass scale basis. And also, 387 00:19:26,400 --> 00:19:27,360 Speaker 10: you know the. 388 00:19:27,320 --> 00:19:28,800 Speaker 8: Payback is also important. 389 00:19:28,840 --> 00:19:32,520 Speaker 10: If you can produce the cars at a fraction of 390 00:19:32,560 --> 00:19:35,840 Speaker 10: the price, Look, you can get more of these vehicles 391 00:19:35,880 --> 00:19:39,280 Speaker 10: out on the road. And also you know, whoever is 392 00:19:39,359 --> 00:19:42,280 Speaker 10: running can actually make their money back much sooner. 393 00:19:43,040 --> 00:19:47,520 Speaker 3: The issue you really highlight is the lack of production 394 00:19:47,600 --> 00:19:51,200 Speaker 3: of real vehicles by Weimo versus. Tesla just has hundreds 395 00:19:51,240 --> 00:19:52,840 Speaker 3: of thousands of millions on the road already. 396 00:19:53,040 --> 00:19:54,200 Speaker 6: Is that the key dividing factor? 397 00:19:54,240 --> 00:19:58,879 Speaker 10: Histey, that's one key dividing factor. I think technology is 398 00:19:58,960 --> 00:20:03,480 Speaker 10: also important. Look, I don't want to discount Weymo technology. 399 00:20:03,600 --> 00:20:06,280 Speaker 10: I think you know they've have had a number of 400 00:20:06,280 --> 00:20:07,080 Speaker 10: cars on the road. 401 00:20:07,680 --> 00:20:08,320 Speaker 8: It's working. 402 00:20:09,920 --> 00:20:12,120 Speaker 10: You know, there's always going to be some issues here 403 00:20:12,119 --> 00:20:15,320 Speaker 10: and there. But look, I think if you look at 404 00:20:15,359 --> 00:20:20,679 Speaker 10: Tesla's technology and their approach with using cameras, it's just 405 00:20:21,280 --> 00:20:25,120 Speaker 10: much easier to scale and especially now if you look 406 00:20:25,160 --> 00:20:28,639 Speaker 10: at the recent news, you know, the Chinese are actually 407 00:20:28,640 --> 00:20:31,320 Speaker 10: opening up the market, opening up the market for them 408 00:20:31,560 --> 00:20:35,679 Speaker 10: and allowing them to export some of that data, training 409 00:20:35,760 --> 00:20:39,480 Speaker 10: data out of China. That's a huge that's a huge 410 00:20:39,520 --> 00:20:43,960 Speaker 10: win for Tesla and scaling over the long term. 411 00:20:44,359 --> 00:20:47,560 Speaker 2: Steve I used full self driving supervised the latest version 412 00:20:47,600 --> 00:20:49,800 Speaker 2: this morning to go to thirty miles from my house 413 00:20:49,960 --> 00:20:52,960 Speaker 2: to the studio. I do it every day. You stay 414 00:20:53,080 --> 00:20:55,439 Speaker 2: very clearly it's still a level two system right on 415 00:20:55,480 --> 00:20:58,639 Speaker 2: a technical basis, do you see the jump to a 416 00:20:58,680 --> 00:21:01,520 Speaker 2: software platform that how is a vehicle with no one 417 00:21:01,560 --> 00:21:03,919 Speaker 2: in the driver's seat at all? 418 00:21:04,320 --> 00:21:07,119 Speaker 10: That's that's gonna be a while. I have to admittedly 419 00:21:07,160 --> 00:21:09,560 Speaker 10: say it's going to be a while. I think the 420 00:21:09,640 --> 00:21:13,040 Speaker 10: consumer needs to gain confidence. It's going to be a 421 00:21:13,040 --> 00:21:15,160 Speaker 10: gold standard. It's going to be you know, if if 422 00:21:15,160 --> 00:21:20,080 Speaker 10: a car is ninety nine point ninety nine percent accurate, 423 00:21:20,400 --> 00:21:24,000 Speaker 10: I think then the consumer will be more confidence. Before that, 424 00:21:24,520 --> 00:21:26,639 Speaker 10: I think there's going to be always some level of 425 00:21:26,760 --> 00:21:30,520 Speaker 10: monitoring from from the consumer. We're not at that stage 426 00:21:30,560 --> 00:21:33,159 Speaker 10: where we see you know, this is not like the 427 00:21:33,240 --> 00:21:36,520 Speaker 10: Eye Robot movie that that Will Smith was was on 428 00:21:36,600 --> 00:21:39,520 Speaker 10: a long time ago, where you know cars are actually 429 00:21:39,600 --> 00:21:43,240 Speaker 10: driving itself and making decisions on their own. We're not 430 00:21:43,280 --> 00:21:47,919 Speaker 10: at that at that moment yet. But what's really important 431 00:21:47,960 --> 00:21:51,800 Speaker 10: is the training data, and that's where Tesla also comes 432 00:21:51,840 --> 00:21:54,720 Speaker 10: in as well, where they have just tons of training 433 00:21:54,800 --> 00:21:56,440 Speaker 10: data from around the world. 434 00:21:57,320 --> 00:22:00,280 Speaker 6: Steve Man of BlueBag Intelligence is a great read. Thanks 435 00:22:00,280 --> 00:22:00,720 Speaker 6: for coming on. 436 00:22:00,960 --> 00:22:03,480 Speaker 3: Now coming up Meta, it has to start showing ads 437 00:22:03,480 --> 00:22:06,720 Speaker 3: in WhatsApp, it's private messaging services. We'll find out what 438 00:22:06,840 --> 00:22:09,879 Speaker 3: the change means for users and for the social media giant. 439 00:22:10,520 --> 00:22:23,159 Speaker 3: This is Bloomberg Tech. Welcome back to Bloomberg Tech. I'm 440 00:22:23,200 --> 00:22:24,480 Speaker 3: Caroline Hyde in New York. 441 00:22:24,680 --> 00:22:26,200 Speaker 2: And I met love Loow in San Francisco. 442 00:22:26,359 --> 00:22:28,400 Speaker 3: Quick check on these markets said, because look, we are 443 00:22:28,440 --> 00:22:31,639 Speaker 3: powering back after selling off on Friday and mid geopolitical anxiety, 444 00:22:32,040 --> 00:22:34,840 Speaker 3: we rally as we all eyes atturn our attention to 445 00:22:35,040 --> 00:22:37,520 Speaker 3: Iran Israel and of course what's happening at the g 446 00:22:37,600 --> 00:22:40,399 Speaker 3: seventh summit. But under the hood, chip makers having a 447 00:22:40,440 --> 00:22:43,080 Speaker 3: really nice day, AMD leading the charge up more than 448 00:22:43,119 --> 00:22:46,359 Speaker 3: eight percent after it's unveiled last week of GPUs and 449 00:22:46,400 --> 00:22:47,880 Speaker 3: some analyst notes on the back of that. But move 450 00:22:47,920 --> 00:22:50,520 Speaker 3: on to Meta because it is one of the key 451 00:22:50,640 --> 00:22:53,120 Speaker 3: points contributors, the second biggest points contributor. 452 00:22:53,160 --> 00:22:54,879 Speaker 6: On the day, we're up three percent. 453 00:22:55,320 --> 00:22:59,679 Speaker 3: Why because the announcements when it comes to advertising ED YEP. 454 00:22:59,880 --> 00:23:02,760 Speaker 2: Announce it's introducing ads as well as some new features 455 00:23:02,800 --> 00:23:06,040 Speaker 2: into WhatsApp. It marks a change for the privacy focus 456 00:23:06,080 --> 00:23:08,800 Speaker 2: messaging platform. Blue most Riley Griffin joins us, this is 457 00:23:08,800 --> 00:23:11,119 Speaker 2: all about the updates tab and what people maybe outside 458 00:23:11,119 --> 00:23:13,560 Speaker 2: of America actually don't appreciate. It's like one and a 459 00:23:13,560 --> 00:23:17,359 Speaker 2: half billion visits per day to this updates tab. What's 460 00:23:17,400 --> 00:23:19,720 Speaker 2: the ad strategy here for Meta? Because clearly the shares 461 00:23:19,720 --> 00:23:22,760 Speaker 2: are higher. This is something the markets es is positive. 462 00:23:23,040 --> 00:23:23,240 Speaker 6: Yeah. 463 00:23:23,280 --> 00:23:27,960 Speaker 11: Meta has begun introducing ads to various outlets other than 464 00:23:28,119 --> 00:23:30,760 Speaker 11: Instagram and Facebook, the platforms that we know really well, 465 00:23:30,800 --> 00:23:33,920 Speaker 11: and here with WhatsApp, they're introducing those ads starting today, 466 00:23:34,240 --> 00:23:38,560 Speaker 11: rolling that out over time into the updates tab, which 467 00:23:38,600 --> 00:23:41,320 Speaker 11: is much like stories. You're able to get other kinds 468 00:23:41,320 --> 00:23:43,919 Speaker 11: of information there, and they're not actually introducing it to 469 00:23:44,080 --> 00:23:46,720 Speaker 11: the conversations the messages that you and I might be 470 00:23:46,760 --> 00:23:47,280 Speaker 11: having ED. 471 00:23:48,080 --> 00:23:50,440 Speaker 6: But this is a new revenue line. They're introducing it. 472 00:23:50,480 --> 00:23:53,679 Speaker 11: While at Con this week, advertising is the name of 473 00:23:53,680 --> 00:23:56,840 Speaker 11: the game, and more to come in terms of how 474 00:23:56,880 --> 00:23:58,959 Speaker 11: big of a share of revenue this world pull in. 475 00:23:59,359 --> 00:23:59,639 Speaker 6: Rnie. 476 00:23:59,680 --> 00:24:02,760 Speaker 3: Yeah, got to walk that fine line that they don't 477 00:24:02,800 --> 00:24:06,159 Speaker 3: invade what we feel is very personalized and private conversations, 478 00:24:06,359 --> 00:24:09,800 Speaker 3: the going down the updates route, but also subscriptions. In 479 00:24:09,840 --> 00:24:12,720 Speaker 3: the longer term, the business communications is really going to 480 00:24:12,720 --> 00:24:14,320 Speaker 3: be where it's at. 481 00:24:14,440 --> 00:24:18,600 Speaker 11: No doubt they've been using the platform to bring businesses on. 482 00:24:19,200 --> 00:24:22,840 Speaker 11: You can communicate with airlines, with shops. But a really 483 00:24:22,920 --> 00:24:25,919 Speaker 11: important point that you made, Caroline is that this was 484 00:24:25,960 --> 00:24:29,200 Speaker 11: not the intention of the original WhatsApp owners when met 485 00:24:29,200 --> 00:24:30,760 Speaker 11: about the company in twenty fourteen. 486 00:24:31,160 --> 00:24:32,159 Speaker 6: Even before then. 487 00:24:32,520 --> 00:24:34,920 Speaker 11: The co founders had said that they would never introduce 488 00:24:34,960 --> 00:24:37,399 Speaker 11: ads to the platform. So this is a market change 489 00:24:37,800 --> 00:24:41,360 Speaker 11: and one very specific to Mark Zuckerberg. 490 00:24:41,600 --> 00:24:44,000 Speaker 2: The main thing about Meta right now is that ads 491 00:24:44,040 --> 00:24:46,480 Speaker 2: is still the core. It's still the bread and butter business. 492 00:24:47,080 --> 00:24:50,879 Speaker 2: But success and growth and top line growth all justifies 493 00:24:51,359 --> 00:24:53,719 Speaker 2: investment in AI. I think that's still the formula. 494 00:24:53,760 --> 00:24:54,400 Speaker 6: Absolutely. 495 00:24:54,440 --> 00:24:56,760 Speaker 11: We know that Meta might be spending more than seventy 496 00:24:56,800 --> 00:24:59,200 Speaker 11: billion on AI this week. This comes on the news 497 00:24:59,280 --> 00:25:02,280 Speaker 11: last week that Meta is finalizing a deal for Scale 498 00:25:02,280 --> 00:25:07,119 Speaker 11: AI that is more than fourteen billion investment to be specific. 499 00:25:07,520 --> 00:25:11,040 Speaker 11: But ads are going to be improved by AI, no doubt. 500 00:25:11,160 --> 00:25:13,280 Speaker 11: And that's the conversation in comm this week. 501 00:25:13,560 --> 00:25:14,639 Speaker 6: And it's a perfect set up. 502 00:25:14,720 --> 00:25:16,560 Speaker 3: Riley Griffin, we thank you because let's go out to 503 00:25:16,600 --> 00:25:20,000 Speaker 3: CAM right now and talk about social media advertising more 504 00:25:20,040 --> 00:25:22,760 Speaker 3: with Rachel Tippograph founeracy of Mick Matt. You're out there 505 00:25:23,320 --> 00:25:25,359 Speaker 3: taking a step out of the sam for a moment 506 00:25:25,600 --> 00:25:28,760 Speaker 3: to be with us. Rachel, just talk about the well 507 00:25:28,840 --> 00:25:31,840 Speaker 3: euphoria around how generative AI is going to be helping 508 00:25:31,880 --> 00:25:35,200 Speaker 3: the AI offering and advertising offering. 509 00:25:35,080 --> 00:25:36,800 Speaker 6: But is it going to impact companies and people? 510 00:25:38,359 --> 00:25:42,200 Speaker 12: So everyone's in agreement that AI is going to lower 511 00:25:42,240 --> 00:25:45,199 Speaker 12: the barrier to entry for advertising. Where has all the 512 00:25:45,240 --> 00:25:49,000 Speaker 12: friction been in the creative and ad buying process, generating 513 00:25:49,040 --> 00:25:52,560 Speaker 12: creative figuring out where you're going to invest your dollars, 514 00:25:53,080 --> 00:25:57,639 Speaker 12: launching those ads and auto optimizing experiences and ultimately getting 515 00:25:57,680 --> 00:26:01,399 Speaker 12: reporting back. Where the friction is in the conversation right 516 00:26:01,440 --> 00:26:05,080 Speaker 12: now at con with CMOS is that they absolutely agree 517 00:26:05,280 --> 00:26:08,040 Speaker 12: that there are efficiency disease to be had in the 518 00:26:08,080 --> 00:26:11,000 Speaker 12: process that I just describe, but what they feel cannot 519 00:26:11,040 --> 00:26:15,879 Speaker 12: be outsourced is creative ideas that break through culture. And 520 00:26:15,960 --> 00:26:19,240 Speaker 12: so it's interesting that Zuckerberg has been very bold in 521 00:26:19,280 --> 00:26:22,000 Speaker 12: his statement where he's essentially said, by the end of 522 00:26:22,040 --> 00:26:26,520 Speaker 12: twenty twenty six, all human intervention will be removed from advertising. 523 00:26:27,760 --> 00:26:31,120 Speaker 12: I believe that fifty percent will be removed, but not 524 00:26:31,240 --> 00:26:34,520 Speaker 12: one hundred percent. And the biggest cmos in the world 525 00:26:35,080 --> 00:26:36,360 Speaker 12: share the same sentiment. 526 00:26:36,880 --> 00:26:38,520 Speaker 6: What about the social media companies? 527 00:26:38,560 --> 00:26:40,960 Speaker 3: The biggest in the world is TikTok, is it on 528 00:26:41,080 --> 00:26:43,879 Speaker 3: veilzez It's general to AI offering also as bullish as 529 00:26:43,880 --> 00:26:44,520 Speaker 3: Mark Zukobog. 530 00:26:45,680 --> 00:26:49,040 Speaker 12: Absolutely, there's a place to lower the barrier to entry 531 00:26:49,040 --> 00:26:52,800 Speaker 12: in terms of creative What's interesting with TikTok's announcements this 532 00:26:52,840 --> 00:26:54,439 Speaker 12: week is that they even said that they're going to 533 00:26:54,440 --> 00:26:59,800 Speaker 12: help generate influencer content, which raises the question around authenticity. 534 00:27:00,359 --> 00:27:03,639 Speaker 12: So while AI is going to help lower the barrier 535 00:27:03,680 --> 00:27:07,200 Speaker 12: to entry increase productivity, it's also going to create new 536 00:27:07,280 --> 00:27:08,520 Speaker 12: questions in advertising. 537 00:27:09,000 --> 00:27:09,680 Speaker 2: Is this real? 538 00:27:10,240 --> 00:27:13,560 Speaker 12: How much is an advertiser willing to spend on a 539 00:27:13,560 --> 00:27:16,879 Speaker 12: agentic impression? Will they spend the same amount on a 540 00:27:16,960 --> 00:27:20,760 Speaker 12: human impression. It's going to create a whole new opening 541 00:27:20,840 --> 00:27:23,280 Speaker 12: and advertising for new companies to come to market to 542 00:27:23,320 --> 00:27:26,520 Speaker 12: define what the standardization is in terms of ad buying 543 00:27:26,760 --> 00:27:31,040 Speaker 12: measurement in forms of agentic AI and creative experiences. 544 00:27:31,920 --> 00:27:35,280 Speaker 2: Rachel, what are people talking about on tiktoking? Can there's 545 00:27:35,320 --> 00:27:39,440 Speaker 2: a June nineteenth deadline for sale shut down? We haven't 546 00:27:39,520 --> 00:27:41,680 Speaker 2: yet gotten an extension. What are you hearing? 547 00:27:42,520 --> 00:27:46,000 Speaker 12: Everyone has come to the point in this journey where 548 00:27:46,040 --> 00:27:49,240 Speaker 12: they believe TikTok is here to stay. When we look 549 00:27:49,280 --> 00:27:52,720 Speaker 12: at mickmac data, it's essentially held as our third most 550 00:27:52,720 --> 00:27:56,000 Speaker 12: traffic channel on any given day Meta number one, Google 551 00:27:56,000 --> 00:28:00,560 Speaker 12: Search number two, TikTok number three. When TikTok went dark 552 00:28:00,600 --> 00:28:04,600 Speaker 12: on January nineteenth, it did take about until March first 553 00:28:04,680 --> 00:28:08,360 Speaker 12: for advertisers to get back to Q four levels of traffic. 554 00:28:08,960 --> 00:28:12,240 Speaker 12: By May, we saw it surpass Rach on any given 555 00:28:12,320 --> 00:28:15,440 Speaker 12: day in May, just going to seventy percent of traffic. 556 00:28:15,200 --> 00:28:18,840 Speaker 3: Rachel typograph keeping it global. So good to have you found, Racio, MIKEMP. 557 00:28:24,880 --> 00:28:27,919 Speaker 2: VC firms are continuing to step up their investments in 558 00:28:28,000 --> 00:28:31,560 Speaker 2: AIAI Expentsures is one of those early stage VC firms 559 00:28:31,600 --> 00:28:34,639 Speaker 2: doing just that. Sean Johnson, co founder and general partner 560 00:28:34,680 --> 00:28:38,360 Speaker 2: AIX menures joins us now earlier in the show one X. 561 00:28:38,360 --> 00:28:40,640 Speaker 2: On the robotics side, we've been thinking a lot about 562 00:28:40,640 --> 00:28:45,400 Speaker 2: scale AI data on that side, agentic AI. What they 563 00:28:45,480 --> 00:28:47,200 Speaker 2: all have in common, to my mind is that the 564 00:28:47,280 --> 00:28:50,960 Speaker 2: cost of getting towards this level of intelligence is coming 565 00:28:51,000 --> 00:28:53,480 Speaker 2: down and down and down. How do you invest in 566 00:28:53,520 --> 00:28:55,280 Speaker 2: the early stage to kind of rite that wave? 567 00:28:55,960 --> 00:28:57,440 Speaker 13: Yeah, and thank you so much for having me. 568 00:28:57,920 --> 00:28:58,120 Speaker 6: Yeah. 569 00:28:58,120 --> 00:29:01,000 Speaker 13: Absolutely, we think a lot about this new era where 570 00:29:01,880 --> 00:29:04,120 Speaker 13: the cost of intelligence, just as you called out, is 571 00:29:04,200 --> 00:29:06,960 Speaker 13: going to zero, and so what does that mean for 572 00:29:06,960 --> 00:29:08,880 Speaker 13: the new world we're going to live in. That means 573 00:29:08,880 --> 00:29:11,000 Speaker 13: that you're on the consumer side, you're going to have 574 00:29:11,040 --> 00:29:14,720 Speaker 13: ready access to tutors doctors. On the enterprise side, you're 575 00:29:14,760 --> 00:29:19,800 Speaker 13: going to see more and more knowledge workers be augmented 576 00:29:20,200 --> 00:29:25,560 Speaker 13: and some replaced by AI. And so we invest at 577 00:29:25,560 --> 00:29:28,480 Speaker 13: the early stage, you know, first check. We are looking 578 00:29:28,520 --> 00:29:32,560 Speaker 13: at founders with big visions looking to change the world, 579 00:29:32,920 --> 00:29:35,320 Speaker 13: that want to move very fast to bring that disruption 580 00:29:35,400 --> 00:29:37,560 Speaker 13: to market. And that's how we've positioned the fur. 581 00:29:38,440 --> 00:29:42,120 Speaker 3: What's interesting is there's big visions coming from big companies. Sean, 582 00:29:42,360 --> 00:29:44,480 Speaker 3: we can't help but sort of draw the attention that 583 00:29:44,560 --> 00:29:48,000 Speaker 3: it's phenomenal amounts of money coming from just a few 584 00:29:48,120 --> 00:29:50,920 Speaker 3: key players, whether it be Amazon, whether it be Meta. 585 00:29:51,080 --> 00:29:54,080 Speaker 3: And of course, reports that open Ai might be snapping 586 00:29:54,160 --> 00:29:56,959 Speaker 3: up one of your portfolio companies, Weights and Biases has 587 00:29:57,000 --> 00:29:59,800 Speaker 3: already been eyed as well and bought by call Weave. 588 00:30:00,160 --> 00:30:01,800 Speaker 6: Is M and A going to happen for these smaller 589 00:30:01,800 --> 00:30:02,520 Speaker 6: companies a lot? 590 00:30:04,320 --> 00:30:07,680 Speaker 13: Absolutely? I mean so, I'd say two things there. The 591 00:30:07,720 --> 00:30:10,640 Speaker 13: first is it makes sense for the large companies to 592 00:30:10,640 --> 00:30:13,040 Speaker 13: get a lot of attention. They make a lot of revenue, 593 00:30:13,040 --> 00:30:16,160 Speaker 13: they have large market caps. Venture capital is a business 594 00:30:16,160 --> 00:30:18,720 Speaker 13: that's focused on small companies that start with no revenue 595 00:30:19,640 --> 00:30:23,920 Speaker 13: and then have much smaller prospects in the short term, 596 00:30:23,960 --> 00:30:26,880 Speaker 13: but can grow quickly and under the radar to an 597 00:30:26,920 --> 00:30:30,360 Speaker 13: extent of the large companies. Now, large companies are certainly 598 00:30:30,400 --> 00:30:35,200 Speaker 13: focused on building AI native teams, bringing AI throughout, refusing 599 00:30:35,280 --> 00:30:39,760 Speaker 13: that sort of mindset throughout their organizations, and M and 600 00:30:39,840 --> 00:30:41,880 Speaker 13: A is very important for that. And it's going to 601 00:30:41,920 --> 00:30:44,320 Speaker 13: happen at all stages. It's going to happen at startups 602 00:30:44,360 --> 00:30:46,160 Speaker 13: that are really just getting started. It's also going to 603 00:30:46,160 --> 00:30:48,880 Speaker 13: happen at startups that are fairly far along to. 604 00:30:50,640 --> 00:30:53,240 Speaker 2: Perplexity. We just showed some of the portfolio companies on 605 00:30:53,240 --> 00:30:56,760 Speaker 2: the screen that this thesis building that they're almost peerless. 606 00:30:57,040 --> 00:30:58,520 Speaker 2: They get talked a lot about in the context of 607 00:30:58,560 --> 00:31:01,200 Speaker 2: Google and Search, also get towards about in the context 608 00:31:01,280 --> 00:31:04,200 Speaker 2: of sort of those working on the on the frontier model. 609 00:31:05,120 --> 00:31:08,360 Speaker 2: Just your your thesis on them, please sure. 610 00:31:09,040 --> 00:31:12,440 Speaker 13: So you know, Arvind and the team have been setting 611 00:31:12,480 --> 00:31:16,240 Speaker 13: their sights on building an iconic business. And we all 612 00:31:16,280 --> 00:31:18,840 Speaker 13: know one thing's true in AI as it moves very fast. 613 00:31:19,000 --> 00:31:20,240 Speaker 13: You know, if you never know what's going to be 614 00:31:20,280 --> 00:31:22,160 Speaker 13: launched tomorrow, what's going to be launched the next day. 615 00:31:22,440 --> 00:31:24,880 Speaker 13: And the amazing thing about Perplexity in the team is 616 00:31:25,240 --> 00:31:27,840 Speaker 13: they have a group of some of the world's foremost 617 00:31:27,960 --> 00:31:31,680 Speaker 13: practitioners executing at you know, the speed of light, if 618 00:31:31,720 --> 00:31:34,520 Speaker 13: you will. And so they're all focused right now on, 619 00:31:34,880 --> 00:31:38,600 Speaker 13: you know, redefining what knowledge knowledge engine looks like. But 620 00:31:38,640 --> 00:31:40,680 Speaker 13: then also you're going to see Commet their new browser 621 00:31:40,720 --> 00:31:43,680 Speaker 13: come out very soon, and that's going to redefine again 622 00:31:43,960 --> 00:31:46,680 Speaker 13: what Argentic Solutions can bring you. 623 00:31:46,760 --> 00:31:51,000 Speaker 3: Of course, yourself were very much helming product design at 624 00:31:51,000 --> 00:31:54,360 Speaker 3: startups before you came onto co found aix bench as Sean, 625 00:31:54,600 --> 00:31:56,960 Speaker 3: what'sn't only one question people are coming to your founders 626 00:31:56,960 --> 00:31:57,760 Speaker 3: coming to you at the moment. 627 00:31:57,800 --> 00:31:59,760 Speaker 6: Is it about talent? Is it about taris what is 628 00:31:59,760 --> 00:32:00,280 Speaker 6: it a app? 629 00:32:03,320 --> 00:32:06,320 Speaker 13: Yeah, that's a really good question, Caroline. Founders often talk 630 00:32:06,400 --> 00:32:09,240 Speaker 13: to us about, you know, number one, how do you start, 631 00:32:09,560 --> 00:32:13,200 Speaker 13: you know, connecting with early customers, early prospects. It's all 632 00:32:13,240 --> 00:32:15,600 Speaker 13: about product market fit, and that can be elusive for 633 00:32:15,680 --> 00:32:19,000 Speaker 13: many years into the journey. You know that is an 634 00:32:19,200 --> 00:32:22,920 Speaker 13: entrepreneurs and so what we do is orient ourselves around 635 00:32:22,960 --> 00:32:24,720 Speaker 13: the founder and we think a lot about how do 636 00:32:24,800 --> 00:32:27,200 Speaker 13: we get to product market fit with the founder through 637 00:32:28,200 --> 00:32:31,760 Speaker 13: lots of customer conversations, hiring a team, thinking about business 638 00:32:31,760 --> 00:32:35,080 Speaker 13: strategy execution, but then also connecting with the next wave 639 00:32:35,120 --> 00:32:37,880 Speaker 13: of venture firms will that will continue to back the founders. 640 00:32:38,240 --> 00:32:41,800 Speaker 3: Jean Johnson of AI Expensions, we appreciate you coming on today. 641 00:32:41,800 --> 00:32:42,000 Speaker 7: Thanks. 642 00:32:42,600 --> 00:32:45,200 Speaker 3: Now let's just talk about one time login codes. You 643 00:32:45,280 --> 00:32:47,320 Speaker 3: probably have them a lot that meant to be security 644 00:32:47,400 --> 00:32:51,200 Speaker 3: and verify users identity that may not actually though, be 645 00:32:51,320 --> 00:32:54,719 Speaker 3: all that private. Millions of those codes sent via text 646 00:32:54,920 --> 00:32:57,720 Speaker 3: pastor into madiories, making it possible for entities to actually 647 00:32:57,720 --> 00:33:00,680 Speaker 3: see their content and most, Ryan Gallagher is been really 648 00:33:00,760 --> 00:33:03,880 Speaker 3: needing the charge. This is an investigation, a deep one, Ryan, 649 00:33:04,040 --> 00:33:07,200 Speaker 3: just showing how actually we shouldn't really be using TECHSSMS 650 00:33:07,200 --> 00:33:11,440 Speaker 3: as our form of two factual authentication. That's right. 651 00:33:11,560 --> 00:33:15,720 Speaker 14: Yeah, we looked in depth at this issue because I 652 00:33:15,800 --> 00:33:17,880 Speaker 14: think a lot of people don't realize that when you 653 00:33:17,960 --> 00:33:21,240 Speaker 14: log into whether it's a banking app or whether it's 654 00:33:21,280 --> 00:33:24,560 Speaker 14: Google or Meta, when you receive one of these logging 655 00:33:24,600 --> 00:33:28,880 Speaker 14: codes via SMS, it isn't actually coming directly from the 656 00:33:28,920 --> 00:33:31,440 Speaker 14: tech company or the platform that you're logging into is 657 00:33:31,520 --> 00:33:36,760 Speaker 14: routed through intermediaries all across the world, and there's very 658 00:33:36,800 --> 00:33:40,440 Speaker 14: little oversight of who actually handles those messages. So that's 659 00:33:40,480 --> 00:33:42,640 Speaker 14: what we set about to do is to show look 660 00:33:42,680 --> 00:33:46,480 Speaker 14: who is actually looking at these security codes and is 661 00:33:46,520 --> 00:33:49,719 Speaker 14: there a potential vulnerability in terms of the people who 662 00:33:49,760 --> 00:33:51,800 Speaker 14: are accessing the codes. And that's what we found that 663 00:33:51,800 --> 00:33:54,400 Speaker 14: there's some concerns around some of the entities that are 664 00:33:54,400 --> 00:33:55,560 Speaker 14: handling them. 665 00:33:55,960 --> 00:33:58,360 Speaker 2: Ryan, what's the alternative then to SMS? We just have 666 00:33:58,520 --> 00:33:59,440 Speaker 2: thirty seconds. 667 00:34:00,600 --> 00:34:03,640 Speaker 14: The alternative is to use, for example, an authenticator app 668 00:34:03,680 --> 00:34:06,120 Speaker 14: on your phone which actually gives you the code on 669 00:34:06,160 --> 00:34:08,920 Speaker 14: your phone. You don't have to rely on SMS. It 670 00:34:08,960 --> 00:34:11,880 Speaker 14: doesn't go across any network. It's just on your device 671 00:34:11,960 --> 00:34:14,520 Speaker 14: all the time and it stays there. So authenticator apps 672 00:34:14,719 --> 00:34:16,840 Speaker 14: Google those and you'll find plenty of options. 673 00:34:17,400 --> 00:34:20,600 Speaker 3: Ryan Allagher, it's a really thoughtful piece. We hurt you 674 00:34:20,680 --> 00:34:22,640 Speaker 3: to go and read it from a business perspective, from 675 00:34:22,640 --> 00:34:25,480 Speaker 3: a security perspective. Now that does it for this edition 676 00:34:25,520 --> 00:34:26,520 Speaker 3: of Bloomberg Tech ed. 677 00:34:27,400 --> 00:34:29,719 Speaker 2: Yeah, astonishing way to start the week. Don't forget to 678 00:34:29,760 --> 00:34:32,080 Speaker 2: recap through the podcast. You know where to find the pod. 679 00:34:32,120 --> 00:34:35,080 Speaker 2: It's on the Bloomberg terminal all the Bloomberg platforms, as 680 00:34:35,080 --> 00:34:39,200 Speaker 2: well as online on Apple, Spotify in iHeart all right, 681 00:34:39,640 --> 00:34:41,799 Speaker 2: strong way to start the week. From San Francisco, New 682 00:34:41,880 --> 00:34:43,839 Speaker 2: York City. This IS's Bloomberg Tech