1 00:00:01,400 --> 00:00:06,680 Speaker 1: From Marhart where Innovation, Money and Power Collie in Silicon Valley, NBN. 2 00:00:07,040 --> 00:00:10,480 Speaker 1: This is Bloomberg Technology with Caroline Hyde. 3 00:00:10,160 --> 00:00:11,559 Speaker 2: And Ed Ludlow. 4 00:00:25,760 --> 00:00:29,240 Speaker 3: Live from London for the Bloomberg Technology Summit. I'm Caroline Hyde. 5 00:00:29,160 --> 00:00:32,680 Speaker 2: And I'm Ed Ludlow in San Francisco. This is Bloomberg Technology. 6 00:00:33,760 --> 00:00:35,640 Speaker 3: Coming up. We'll have full earnings coverage. 7 00:00:35,680 --> 00:00:38,360 Speaker 4: Ahead is Spotify reports the results, and Google and Microsoft 8 00:00:38,560 --> 00:00:39,000 Speaker 4: we're going to. 9 00:00:39,040 --> 00:00:40,960 Speaker 3: Deliver after the bell, We've got you covered. 10 00:00:41,760 --> 00:00:44,839 Speaker 2: Plus, we'll hear from Reddick co founder Alexis o'hanian to 11 00:00:44,920 --> 00:00:48,280 Speaker 2: talk his investments in AI and crypto are they living 12 00:00:48,320 --> 00:00:49,080 Speaker 2: up to the hype? 13 00:00:50,280 --> 00:00:53,160 Speaker 4: And with the Bloomberg Tech Summit underway in London, we'll 14 00:00:53,159 --> 00:00:57,120 Speaker 4: hear from executives from Anthropic Delivery and so much more so. 15 00:00:57,160 --> 00:01:00,240 Speaker 2: After the bell we get the giants Microsoft in alphabet. 16 00:01:00,400 --> 00:01:02,280 Speaker 2: There's going to be interested in the cloud division of 17 00:01:02,280 --> 00:01:05,280 Speaker 2: those two names, right, There always is, But there will 18 00:01:05,280 --> 00:01:07,959 Speaker 2: also be intense interests in artificial intelligence. I guess by 19 00:01:07,959 --> 00:01:10,679 Speaker 2: this stage we ask how is that showing up in sales? 20 00:01:10,720 --> 00:01:12,600 Speaker 2: All of the R and D and investment in large 21 00:01:12,640 --> 00:01:15,800 Speaker 2: language or foundation models, are they actually making any money 22 00:01:15,800 --> 00:01:17,760 Speaker 2: from it? Top of mine right now is what's top 23 00:01:17,800 --> 00:01:20,080 Speaker 2: of the list. Spotify on track for its biggest jump 24 00:01:20,280 --> 00:01:23,160 Speaker 2: since January thirty first of this year, trading near its 25 00:01:23,200 --> 00:01:26,240 Speaker 2: highest level since July. Top line beat, bottom line beat, 26 00:01:26,400 --> 00:01:29,679 Speaker 2: premium subscriber beat, monthly active users beat. Let's get straight 27 00:01:29,680 --> 00:01:32,760 Speaker 2: to the details with Bloomberg's Ashley Carmen and Ashley I 28 00:01:32,760 --> 00:01:35,520 Speaker 2: guess the question is what is in the earnings playlist 29 00:01:35,520 --> 00:01:37,080 Speaker 2: for Spotify? What were the top numbers? 30 00:01:38,360 --> 00:01:41,160 Speaker 5: Yeah, so the big headline item is that Spotify turn 31 00:01:41,200 --> 00:01:44,240 Speaker 5: to profit in the third quarter at thirty two million 32 00:01:44,280 --> 00:01:47,200 Speaker 5: euro and that definitely was a surprise for analysts who 33 00:01:47,200 --> 00:01:50,160 Speaker 5: were expecting a pretty significant loss. 34 00:01:50,960 --> 00:01:52,840 Speaker 4: And they'd say it's an inflection point. 35 00:01:52,880 --> 00:01:54,680 Speaker 3: Can you tell us what really been the drivers of 36 00:01:54,720 --> 00:01:55,240 Speaker 3: growth here? 37 00:01:56,400 --> 00:01:57,160 Speaker 5: Yeah, for sure. 38 00:01:57,280 --> 00:01:59,960 Speaker 6: So over the past year, we've seen significant cost reduct 39 00:02:00,280 --> 00:02:03,040 Speaker 6: whether it be looking to lease real estate that they 40 00:02:03,080 --> 00:02:08,680 Speaker 6: previously rented, significant calling back on their podcast efforts, all 41 00:02:08,720 --> 00:02:12,240 Speaker 6: of that. Plus in July they initiated their first price 42 00:02:12,280 --> 00:02:15,400 Speaker 6: hikes on their standard plan in quite a while, which 43 00:02:15,440 --> 00:02:18,160 Speaker 6: we're starting to see some of the revenue growth from 44 00:02:18,200 --> 00:02:18,880 Speaker 6: that decision. 45 00:02:20,040 --> 00:02:22,360 Speaker 2: Actually here on Bluemog Technology, we've talked a lot about 46 00:02:22,360 --> 00:02:25,880 Speaker 2: what they've been doing in the artificial intelligence contexts and 47 00:02:25,960 --> 00:02:28,440 Speaker 2: in the podcast context And it's interesting, right because they 48 00:02:28,520 --> 00:02:31,400 Speaker 2: kind of did not green light some projects. They made 49 00:02:31,400 --> 00:02:34,160 Speaker 2: cost cuts, headcount reduction, but if you look at the 50 00:02:34,200 --> 00:02:36,359 Speaker 2: earnings that's showing up in a positive way. 51 00:02:38,480 --> 00:02:38,919 Speaker 3: Totally. 52 00:02:39,160 --> 00:02:43,040 Speaker 6: Yeah, they are really mentioning this year as their quote 53 00:02:43,080 --> 00:02:45,560 Speaker 6: unquote efficient year. They're really trying to find all these 54 00:02:45,560 --> 00:02:48,720 Speaker 6: different efficiencies, whether it be through AI, but really truly 55 00:02:48,800 --> 00:02:51,320 Speaker 6: through this cost cutting and trying to make sure they're 56 00:02:51,360 --> 00:02:53,920 Speaker 6: delivering on the businesses that they're pursuing. 57 00:02:56,160 --> 00:02:58,240 Speaker 4: Well, we see total active views as well as some 58 00:02:58,280 --> 00:03:01,120 Speaker 4: twenty six percent more than half billion. It's clear that 59 00:03:01,160 --> 00:03:05,000 Speaker 4: we're seeing some well key year of efficiency starting to 60 00:03:05,040 --> 00:03:07,320 Speaker 4: benefit in terms of growth too. Actually, Carmen, it's great 61 00:03:07,360 --> 00:03:08,640 Speaker 4: to have some time with you, thank you very much, 62 00:03:08,680 --> 00:03:10,520 Speaker 4: and deep breaking down the Spotify numbers and in need 63 00:03:10,680 --> 00:03:12,959 Speaker 4: the rally that we saw in the shares and and look, 64 00:03:13,000 --> 00:03:14,760 Speaker 4: that just sets us up for why there is so 65 00:03:14,880 --> 00:03:17,520 Speaker 4: much optimism around some of these big tech names that 66 00:03:17,520 --> 00:03:19,799 Speaker 4: are set to report after the bell, Microsoft. 67 00:03:19,320 --> 00:03:21,959 Speaker 3: Alphabet of course, parent at Google going to be coming 68 00:03:22,000 --> 00:03:22,560 Speaker 3: a little bit later. 69 00:03:22,639 --> 00:03:25,080 Speaker 4: Let's bring in Ana agran Or a Bloomberg intelligence for 70 00:03:25,160 --> 00:03:28,760 Speaker 4: what we can expect from Microsoft first and are we 71 00:03:28,880 --> 00:03:31,080 Speaker 4: expecting key revenue growth here as well? 72 00:03:32,280 --> 00:03:34,280 Speaker 7: Yeah, you know, the number really to catch for is 73 00:03:34,320 --> 00:03:37,600 Speaker 7: their cloud revenue. I mean it's a slight deceleration from 74 00:03:37,720 --> 00:03:39,920 Speaker 7: last quarter, but guidance for next quarter I think is 75 00:03:40,160 --> 00:03:43,400 Speaker 7: going to be really critical because in the software world, 76 00:03:43,560 --> 00:03:46,640 Speaker 7: Microsoft is the you know, the biggest player in AI 77 00:03:46,840 --> 00:03:49,600 Speaker 7: or anything generated AI right now. So I think that's 78 00:03:49,600 --> 00:03:51,880 Speaker 7: really where our eyes are going to be, our years 79 00:03:51,880 --> 00:03:53,760 Speaker 7: are going to be. You know, when they talk about 80 00:03:53,760 --> 00:03:55,080 Speaker 7: guidance for next quarter. 81 00:03:55,720 --> 00:03:57,520 Speaker 2: You know the battleground that I love to track and 82 00:03:57,640 --> 00:03:59,640 Speaker 2: a rag is the hyper scale cloud, right and I 83 00:03:59,640 --> 00:04:03,720 Speaker 2: think sure it's topline growth forecasts for around twenty seven percent. 84 00:04:04,080 --> 00:04:08,320 Speaker 2: I know we're so excited about artificial intelligence, we genuinely are, 85 00:04:09,080 --> 00:04:11,400 Speaker 2: but ultimately, how much do you focus on the bread 86 00:04:11,440 --> 00:04:12,280 Speaker 2: and butter business? 87 00:04:13,400 --> 00:04:13,640 Speaker 8: See? 88 00:04:13,720 --> 00:04:15,560 Speaker 7: Right now, the bread and burder business is not going 89 00:04:15,600 --> 00:04:18,280 Speaker 7: to accelerate the way the cloud should over the next 90 00:04:18,279 --> 00:04:20,799 Speaker 7: twelve to eighteen months. So that's why we really trying 91 00:04:20,800 --> 00:04:23,800 Speaker 7: trying to figure out if enterprise customers are still in 92 00:04:23,839 --> 00:04:26,360 Speaker 7: the cost cutting mode or have they started to invest 93 00:04:26,440 --> 00:04:29,160 Speaker 7: Because that data point I think is the single most 94 00:04:29,160 --> 00:04:33,080 Speaker 7: important thing for technology companies because unless that happens, you know, 95 00:04:33,120 --> 00:04:34,880 Speaker 7: you're not going to see a recovery in a lot 96 00:04:34,880 --> 00:04:37,080 Speaker 7: of these valuations that have gotten beaten up over the 97 00:04:37,160 --> 00:04:39,680 Speaker 7: last you know, couple of I would say, what. 98 00:04:39,720 --> 00:04:43,280 Speaker 4: Was what about spending at the moment? Anak how much 99 00:04:43,279 --> 00:04:44,920 Speaker 4: are they willing to be investing in their own business? 100 00:04:44,960 --> 00:04:46,479 Speaker 4: Because boy, they've been investing in open Ai. 101 00:04:47,360 --> 00:04:49,680 Speaker 7: Yeah, see the investment part I am. I personally, I 102 00:04:49,680 --> 00:04:51,960 Speaker 7: am not concerned about whether you know, I think it's 103 00:04:52,000 --> 00:04:55,200 Speaker 7: going to remain strong for all cloud providers only because 104 00:04:55,240 --> 00:04:57,960 Speaker 7: the backlog or the long term models are still there. 105 00:04:58,000 --> 00:05:01,159 Speaker 7: People need to invest and you know, move away from 106 00:05:01,200 --> 00:05:04,560 Speaker 7: on premise infrastructure. So I'm not concerned about CAPEX as 107 00:05:04,600 --> 00:05:07,479 Speaker 7: much as some you know, maybe investors are. But you know, 108 00:05:07,520 --> 00:05:09,880 Speaker 7: from my site, I think it's going to continue for 109 00:05:09,880 --> 00:05:11,160 Speaker 7: for many years to come. 110 00:05:12,320 --> 00:05:14,800 Speaker 2: All Right, I was being a bit sassy about cloud 111 00:05:15,080 --> 00:05:18,479 Speaker 2: and artifisial intelligence. Get it sasy software as a anyway, 112 00:05:18,520 --> 00:05:21,120 Speaker 2: Anna rag Rana of Bloomberg Intelligence, thank you very much. 113 00:05:21,160 --> 00:05:23,919 Speaker 2: Let's get at Google's results after Caroline love that I 114 00:05:23,960 --> 00:05:27,920 Speaker 2: know I knew you with all right, hard pivot Google's 115 00:05:27,920 --> 00:05:30,039 Speaker 2: after the bell. Who else is here but Mandy seeper 116 00:05:30,080 --> 00:05:34,440 Speaker 2: Bloomberg Intelligence, Mandy ads it's going to be a story 117 00:05:34,600 --> 00:05:37,400 Speaker 2: of how the ad business is done. We talked just 118 00:05:37,480 --> 00:05:40,280 Speaker 2: there about the sort of bread and butter business for Microsoft. 119 00:05:40,360 --> 00:05:43,200 Speaker 2: Is that what Bloomberg Intelligence looks for in Google's context 120 00:05:43,200 --> 00:05:43,600 Speaker 2: as well? 121 00:05:43,960 --> 00:05:47,560 Speaker 9: Yeah, and to me, you know, the bogie for YouTube 122 00:05:47,680 --> 00:05:51,000 Speaker 9: is quite low. When you think about Search, you know, 123 00:05:51,080 --> 00:05:54,719 Speaker 9: growing at nine percent consensus expectations and YouTube at ten percent. 124 00:05:54,800 --> 00:05:58,160 Speaker 9: You have to wonder why is YouTube not growing faster 125 00:05:58,279 --> 00:06:01,200 Speaker 9: when Meta is expected to grow twenty one percent? And 126 00:06:01,279 --> 00:06:05,039 Speaker 9: the real comparison here is YouTube shorts versus Meta reels. 127 00:06:05,080 --> 00:06:07,960 Speaker 9: Meta reels we know is a ten billion dollars run 128 00:06:08,040 --> 00:06:11,160 Speaker 9: read business. They have three exit over the last twelve months. 129 00:06:11,440 --> 00:06:13,800 Speaker 9: I think investors want to know what's going on with 130 00:06:13,920 --> 00:06:16,320 Speaker 9: YouTube shorts and if it's going to drive that top 131 00:06:16,360 --> 00:06:18,960 Speaker 9: line growth. And on the cloud side, I mean, look, 132 00:06:19,480 --> 00:06:22,440 Speaker 9: I mentioned about large anguid models. The good thing with 133 00:06:22,520 --> 00:06:25,799 Speaker 9: Google Cloud is they don't have a legacy business where 134 00:06:25,880 --> 00:06:28,920 Speaker 9: you know, Google Cloud is going to cannibalize its legacy business. 135 00:06:28,920 --> 00:06:32,280 Speaker 9: It's all incremental revenue from AI and large anglid models. 136 00:06:32,320 --> 00:06:34,880 Speaker 9: And I think if they give any details around the 137 00:06:34,960 --> 00:06:38,600 Speaker 9: licensing of their large anguid models, that would be quite interesting. 138 00:06:40,040 --> 00:06:40,360 Speaker 3: Mandid. 139 00:06:40,440 --> 00:06:42,520 Speaker 4: There was a lot of handwringing though previous couple of 140 00:06:42,600 --> 00:06:46,160 Speaker 4: quarters about well the competitive threat that generator of AI 141 00:06:46,320 --> 00:06:49,720 Speaker 4: certainly maybe coming from being Finally have they managed to 142 00:06:49,760 --> 00:06:51,840 Speaker 4: shake off any anxiety that search is going to be 143 00:06:51,920 --> 00:06:52,520 Speaker 4: upended here? 144 00:06:53,120 --> 00:06:56,640 Speaker 9: I mean, Caroline, this search is one business that has 145 00:06:56,680 --> 00:07:01,760 Speaker 9: still the strongest mode. And granted, you know GPT got traffic, 146 00:07:01,839 --> 00:07:03,760 Speaker 9: you know, the one hundred million users, but when you 147 00:07:03,800 --> 00:07:06,400 Speaker 9: look at the ad revenue, I don't think it's gonna 148 00:07:06,760 --> 00:07:10,200 Speaker 9: make a dent, at least for now and over time. Look, 149 00:07:10,560 --> 00:07:13,600 Speaker 9: Google has the advantage that they have over four billion 150 00:07:13,640 --> 00:07:16,960 Speaker 9: monthly active users with search, and even if you know, 151 00:07:17,680 --> 00:07:20,640 Speaker 9: the volume goes down because they are more large anguage 152 00:07:20,680 --> 00:07:23,880 Speaker 9: models doing searches, they still have the distribution. And so 153 00:07:24,120 --> 00:07:27,600 Speaker 9: to me, Search will continue to grow, you know, high 154 00:07:27,680 --> 00:07:30,520 Speaker 9: single digit and it's all about the other drivers, the 155 00:07:30,640 --> 00:07:34,400 Speaker 9: YouTube and the cloud that's going to accelerate the growth 156 00:07:34,440 --> 00:07:35,320 Speaker 9: through double digits. 157 00:07:36,320 --> 00:07:38,400 Speaker 2: Madip, there's part of this week which is the kind 158 00:07:38,440 --> 00:07:41,080 Speaker 2: of timing and structure of it. So Alphabet kind of 159 00:07:41,120 --> 00:07:44,080 Speaker 2: reports first, and you have meta Snap down the road 160 00:07:44,160 --> 00:07:47,160 Speaker 2: and Alphabet or Google kind of set the tone for 161 00:07:47,240 --> 00:07:50,480 Speaker 2: social media companies that make money from ads. Do you 162 00:07:50,560 --> 00:07:52,720 Speaker 2: expect it to play out that way that way this 163 00:07:52,800 --> 00:07:53,440 Speaker 2: week as well? 164 00:07:54,080 --> 00:07:54,160 Speaker 10: No. 165 00:07:54,640 --> 00:07:56,800 Speaker 9: I think it's going to be tough for the smaller 166 00:07:56,840 --> 00:08:00,600 Speaker 9: players simply because companies are still prudent about their sales 167 00:08:00,640 --> 00:08:02,800 Speaker 9: and marketing spend and they want to focus on the 168 00:08:02,880 --> 00:08:06,160 Speaker 9: highest ROI, and we know the highest ROI is search 169 00:08:06,360 --> 00:08:09,760 Speaker 9: and meta, Instagram and you know the social media, large 170 00:08:09,760 --> 00:08:13,400 Speaker 9: social media properties. I think Snap and Pinterest will have 171 00:08:13,560 --> 00:08:16,920 Speaker 9: easier comms, So that's good, But in terms of acceleration, 172 00:08:17,120 --> 00:08:19,280 Speaker 9: it's too early to say that we are headed for, 173 00:08:19,440 --> 00:08:21,880 Speaker 9: you know, a big rebound in AD spending next year. 174 00:08:23,800 --> 00:08:26,120 Speaker 4: Man name saying a Bloomberg Intelligence, We thank you so 175 00:08:26,200 --> 00:08:27,960 Speaker 4: much as we look ahead to those big earnings and 176 00:08:28,000 --> 00:08:30,360 Speaker 4: a key discussion about AI. We've got more of that 177 00:08:30,400 --> 00:08:33,440 Speaker 4: to come from One Alexis Ohanian, founder of VC firm 178 00:08:33,559 --> 00:08:34,559 Speaker 4: seven seventy six. 179 00:08:34,640 --> 00:08:36,040 Speaker 3: Co founder of course of Reddit. 180 00:08:36,520 --> 00:08:38,560 Speaker 4: He's just sat down with our own at Ludlow. Listen 181 00:08:38,559 --> 00:09:08,479 Speaker 4: to it in a minute. Listen bloom Bag Technology. 182 00:08:57,080 --> 00:08:58,199 Speaker 3: Time now for talking tech. 183 00:08:58,400 --> 00:09:01,680 Speaker 4: First up, in Vidia in processes from arm holdings to 184 00:09:01,760 --> 00:09:05,040 Speaker 4: develop chips for personal computers. Sources say that in Vidia 185 00:09:05,120 --> 00:09:06,359 Speaker 4: plans to make CPUs. 186 00:09:06,400 --> 00:09:09,079 Speaker 3: You know, we always associated with GPUs, but they would. 187 00:09:08,960 --> 00:09:10,920 Speaker 4: Run on Microsoft Windows and go on sales in as 188 00:09:10,960 --> 00:09:13,920 Speaker 4: twenty twenty five. Now AMD is also working with ARM 189 00:09:14,160 --> 00:09:17,880 Speaker 4: based processes. Of course, that's a chip design company to 190 00:09:18,000 --> 00:09:20,640 Speaker 4: move is really all putting pressure on the rival Intel, 191 00:09:20,679 --> 00:09:23,120 Speaker 4: which makes similar technology for PCs. 192 00:09:23,160 --> 00:09:24,760 Speaker 3: Also talking to AI chips. 193 00:09:24,800 --> 00:09:27,440 Speaker 4: The startup Rebellions is hoping to raise abou one hundred 194 00:09:27,480 --> 00:09:29,839 Speaker 4: million dollars in global investors. Based in South Korea, the 195 00:09:29,880 --> 00:09:32,640 Speaker 4: startup is in talks over Series B financing. They may 196 00:09:32,720 --> 00:09:35,600 Speaker 4: value the company at more than half a billion. Rebellions 197 00:09:35,760 --> 00:09:38,200 Speaker 4: is one of several players trying to capitalize off of 198 00:09:38,240 --> 00:09:42,199 Speaker 4: the rapid appeal of artificial intelligence software. Plus a group 199 00:09:42,240 --> 00:09:46,079 Speaker 4: of bipartisan centers will host yet another crop of tech 200 00:09:46,160 --> 00:09:49,000 Speaker 4: leaders and executives discuss well how to regulate this AI. 201 00:09:49,400 --> 00:09:51,760 Speaker 4: It will be the second in a series of forums 202 00:09:51,800 --> 00:09:55,120 Speaker 4: led by Senate Majority Leader Chuck Schumer, and indeed the 203 00:09:55,200 --> 00:09:58,199 Speaker 4: VC billionaire Mark and Risen is expected to attend and 204 00:09:58,280 --> 00:10:00,160 Speaker 4: then the previous forum took place on sept to the 205 00:10:00,200 --> 00:10:03,960 Speaker 4: thirteenth and included pearances from well Elon Musk for example, ed. 206 00:10:04,760 --> 00:10:07,760 Speaker 2: Yeah, let's stick with the artificial intelligence conversation. Earlier today, 207 00:10:07,760 --> 00:10:10,240 Speaker 2: I caught up with Alexis o'hanian, the founder of seven 208 00:10:10,320 --> 00:10:13,160 Speaker 2: seven six of course the co founder of Readit as well. 209 00:10:13,400 --> 00:10:16,760 Speaker 2: We discussed his approach to investing in AI companies and 210 00:10:16,760 --> 00:10:19,480 Speaker 2: how he's thought about the hype around AI this year. 211 00:10:19,559 --> 00:10:21,920 Speaker 10: Have a listen, there's a lot of hype right now. 212 00:10:22,200 --> 00:10:24,120 Speaker 10: I've been investing in the space for over a decade, 213 00:10:24,160 --> 00:10:26,520 Speaker 10: back when it was just narrow AI companies, you know, 214 00:10:26,559 --> 00:10:30,120 Speaker 10: seating companies like Athellis and Cruise. So this is really 215 00:10:30,160 --> 00:10:34,280 Speaker 10: now a big breakthrough. Generalized AI is a giant buzzword, 216 00:10:34,760 --> 00:10:38,120 Speaker 10: but there is some real special truth there, and we're 217 00:10:38,120 --> 00:10:40,840 Speaker 10: looking for companies that are using this technology to enhance 218 00:10:40,840 --> 00:10:43,080 Speaker 10: the user experience and outside of ways. It really is 219 00:10:43,480 --> 00:10:46,240 Speaker 10: as simple as that, and we're seeing across the portfolio 220 00:10:46,320 --> 00:10:51,040 Speaker 10: from AI produced dubbing like Deep Tune to sports media 221 00:10:51,080 --> 00:10:53,680 Speaker 10: Rita's been companies like score Plates. It's not about just 222 00:10:53,760 --> 00:10:56,520 Speaker 10: the buzzword, it's about how are you improving users' lives 223 00:10:56,640 --> 00:10:58,160 Speaker 10: using this technology effortlessly. 224 00:10:58,840 --> 00:11:00,720 Speaker 2: When I was looking through the port you know, the 225 00:11:00,760 --> 00:11:03,000 Speaker 2: examples of score play and deep tune. You kind of 226 00:11:03,000 --> 00:11:06,400 Speaker 2: split it maybe into a tool, an AI tool which 227 00:11:06,440 --> 00:11:10,520 Speaker 2: we call generative AI, and then an existing technology platform 228 00:11:11,080 --> 00:11:15,360 Speaker 2: which is kind of improved or added to using AI. 229 00:11:16,080 --> 00:11:20,400 Speaker 2: You know, explain to us why score play and deep 230 00:11:20,480 --> 00:11:23,960 Speaker 2: tune fit those two kind of categories and why you'd invested. 231 00:11:23,559 --> 00:11:24,439 Speaker 3: In them for sure. 232 00:11:24,679 --> 00:11:27,000 Speaker 10: Well, you know, as the owner of two professional sports teams, 233 00:11:27,080 --> 00:11:30,480 Speaker 10: I know how important media management is and this process 234 00:11:30,480 --> 00:11:33,560 Speaker 10: of actually capturing the clips, the photos or everything happening 235 00:11:33,640 --> 00:11:36,440 Speaker 10: on the pitch or in the stands, and then you know, 236 00:11:36,480 --> 00:11:39,080 Speaker 10: getting those out to the athletes, to social media, to 237 00:11:39,120 --> 00:11:42,560 Speaker 10: your media partners. That is a ton of work, and 238 00:11:43,000 --> 00:11:46,520 Speaker 10: software should automatically be able to seamlessly make all that 239 00:11:46,559 --> 00:11:49,079 Speaker 10: happen way more effectively. But now you layer in AI 240 00:11:49,200 --> 00:11:51,760 Speaker 10: and you have something that now does it ten times faster. 241 00:11:51,760 --> 00:11:55,839 Speaker 10: Whether it's identifying you know, this is Sidney LaRue, this 242 00:11:55,880 --> 00:11:58,920 Speaker 10: is her kicking a goal, this is the door dash 243 00:11:59,000 --> 00:12:01,880 Speaker 10: logo visible, and so all of that stuff can now 244 00:12:01,960 --> 00:12:04,960 Speaker 10: be automated away and so smaller teams can get far 245 00:12:05,000 --> 00:12:09,720 Speaker 10: more done. And it's not reinventing a whole new technology. 246 00:12:09,760 --> 00:12:13,280 Speaker 10: It's leveling up existing software that already has deep relationships 247 00:12:13,320 --> 00:12:15,240 Speaker 10: with customers. And so there are going to be these 248 00:12:15,280 --> 00:12:17,840 Speaker 10: types of companies that have strong moats and lock in 249 00:12:18,040 --> 00:12:20,400 Speaker 10: that are going to win. And you know, there's going 250 00:12:20,440 --> 00:12:21,319 Speaker 10: to be big winners in. 251 00:12:21,240 --> 00:12:22,000 Speaker 3: The space as well. 252 00:12:22,080 --> 00:12:24,719 Speaker 10: Chat GBT is probably the most famous one, which I'm 253 00:12:24,760 --> 00:12:27,400 Speaker 10: not an investor in, though I really should have bugged 254 00:12:27,440 --> 00:12:30,160 Speaker 10: Sam about that a lot earlier. You know, I use 255 00:12:30,200 --> 00:12:32,600 Speaker 10: it to tell bedtime stories with my daughter, and so 256 00:12:32,640 --> 00:12:37,240 Speaker 10: you're seeing this very generalized, you know, approach from NLM 257 00:12:37,320 --> 00:12:38,679 Speaker 10: like Open Eye that's going to solve a lot of 258 00:12:38,679 --> 00:12:40,400 Speaker 10: problems for a lot of people, and then much more 259 00:12:40,400 --> 00:12:43,600 Speaker 10: specific approaches that are solving at least right now, strong 260 00:12:43,679 --> 00:12:46,360 Speaker 10: business needs and you know, offering it like any other 261 00:12:46,480 --> 00:12:47,960 Speaker 10: subscription as a service business. 262 00:12:48,760 --> 00:12:51,640 Speaker 2: Just real quick if you do bug Sam Altman. I 263 00:12:51,720 --> 00:12:55,880 Speaker 2: reported last week that there's a tender off underway right 264 00:12:55,920 --> 00:12:58,480 Speaker 2: and there's pretty big, big blocks of shares on the 265 00:12:58,520 --> 00:13:03,320 Speaker 2: secondaries market. Yes that you know value open AI eighty 266 00:13:03,400 --> 00:13:07,000 Speaker 2: six ninety billion, more than one hundred billion. Some of 267 00:13:07,000 --> 00:13:09,680 Speaker 2: the prospectus that I've seen, is that a way for 268 00:13:09,720 --> 00:13:11,120 Speaker 2: you to get in or do you just you just 269 00:13:11,160 --> 00:13:14,319 Speaker 2: stay away? From open Ai given its late growth stage. 270 00:13:14,400 --> 00:13:16,520 Speaker 10: I am such an early investor. I want to be 271 00:13:16,600 --> 00:13:18,920 Speaker 10: there at the point of inception all the way to 272 00:13:18,960 --> 00:13:21,040 Speaker 10: maybe the Series A. That's that's when we like leading 273 00:13:21,080 --> 00:13:23,600 Speaker 10: and writing that first check. At this stage, I still 274 00:13:23,640 --> 00:13:25,760 Speaker 10: think there is value, but it's not you know, it's 275 00:13:25,760 --> 00:13:29,920 Speaker 10: above my pay grade. I enjoy I enjoy being super 276 00:13:29,960 --> 00:13:34,320 Speaker 10: early and right, but you know, it's still it's going 277 00:13:34,400 --> 00:13:36,920 Speaker 10: to continue to surprise us. I think what these technologies 278 00:13:36,920 --> 00:13:38,520 Speaker 10: are able to do, and yes, there is a ton 279 00:13:38,559 --> 00:13:41,040 Speaker 10: of hype, but I do think the sky is the limit. 280 00:13:41,080 --> 00:13:43,440 Speaker 10: And I've known Sam since he did y Combinator together 281 00:13:43,559 --> 00:13:46,560 Speaker 10: back in two thousand and five, and one thing he 282 00:13:46,640 --> 00:13:50,319 Speaker 10: has never lacked is ambition. And so if there's anyone 283 00:13:50,360 --> 00:13:52,719 Speaker 10: who can turn this into you know what all the 284 00:13:53,040 --> 00:13:56,880 Speaker 10: sort of hype is about, it would be him. 285 00:13:57,000 --> 00:14:00,319 Speaker 2: So a big emphasis there on artificial intelligence. Alexis Hani 286 00:14:00,400 --> 00:14:03,200 Speaker 2: and seven seven six found a co founder of Reddit. 287 00:14:11,120 --> 00:14:13,560 Speaker 10: I have been in crypto for over a decade. I 288 00:14:13,600 --> 00:14:16,120 Speaker 10: have invested through every single winter, and none of them 289 00:14:16,160 --> 00:14:19,720 Speaker 10: phase me. They're all healthy because they sort of clear 290 00:14:19,800 --> 00:14:23,640 Speaker 10: out the tourists and the grifters in every sector, in 291 00:14:23,640 --> 00:14:28,040 Speaker 10: every industry, and I think here we're seeing a response 292 00:14:28,360 --> 00:14:32,000 Speaker 10: to my guests, a sort of broader macro and global uncertainty. 293 00:14:32,600 --> 00:14:35,960 Speaker 10: And what's wild is I know, for some viewers it 294 00:14:36,000 --> 00:14:38,920 Speaker 10: may seem a little surprising that people would find safety 295 00:14:38,960 --> 00:14:42,320 Speaker 10: in a volatile cryptocurrency like bitcoin, but the fact that 296 00:14:42,360 --> 00:14:46,840 Speaker 10: it is truly decentralized and the fact that it is 297 00:14:46,880 --> 00:14:50,240 Speaker 10: backed by conviction, you know, to me, bitcoin has never 298 00:14:50,280 --> 00:14:53,120 Speaker 10: felt really all that different from gold. 299 00:14:55,280 --> 00:14:57,360 Speaker 3: Ah the age old digital gold. 300 00:14:57,360 --> 00:15:00,240 Speaker 4: Alexasirhanian there are seven seven six, just talking a little 301 00:15:00,280 --> 00:15:02,720 Speaker 4: bit more about his thesis around Web three, around crypto, 302 00:15:02,760 --> 00:15:05,280 Speaker 4: around bitcoin, and of course it has just hit thirty 303 00:15:05,280 --> 00:15:08,440 Speaker 4: five thousand dollars for the first time this year. Of course, 304 00:15:08,440 --> 00:15:11,320 Speaker 4: it's all kind of based on fresh demands, some of 305 00:15:11,360 --> 00:15:14,880 Speaker 4: them maybe for resilience in conflict, others well because we 306 00:15:14,880 --> 00:15:16,920 Speaker 4: think an ETF is going to be signed off sometimes 307 00:15:16,920 --> 00:15:19,040 Speaker 4: soon for spot bitcoin. Let's talk about it all with 308 00:15:19,080 --> 00:15:21,440 Speaker 4: Amy James, co executive director of Web three Working Group. 309 00:15:21,480 --> 00:15:22,960 Speaker 4: You join us, and of course a lot of our 310 00:15:23,000 --> 00:15:25,400 Speaker 4: advocacy work is about making sure everyone has got the 311 00:15:25,440 --> 00:15:27,640 Speaker 4: access the information to be able to be playing a 312 00:15:27,680 --> 00:15:33,720 Speaker 4: part in Web three. The ATF exposure, the ability to invest. 313 00:15:34,360 --> 00:15:37,160 Speaker 4: Is that really what's behind this thirty five thousand dollar pop. 314 00:15:38,720 --> 00:15:41,480 Speaker 1: It could be absolutely thank you so much for having 315 00:15:41,480 --> 00:15:42,960 Speaker 1: me on the show, just wanting to say that before 316 00:15:42,960 --> 00:15:46,800 Speaker 1: you get started. And it absolutely could be the speculation 317 00:15:46,920 --> 00:15:52,360 Speaker 1: that that's happening. And it also could be the economic 318 00:15:52,440 --> 00:15:55,680 Speaker 1: uncertainty as the previous guest was talking about, that is 319 00:15:55,760 --> 00:15:59,200 Speaker 1: driving people into bitcoin because it is potentially a more 320 00:15:59,280 --> 00:16:04,160 Speaker 1: stable asset over time. I personally think that the bitcoin 321 00:16:04,240 --> 00:16:08,080 Speaker 1: price action is the least interesting aspect of bitcoin, and 322 00:16:08,120 --> 00:16:11,440 Speaker 1: it's the technology underneath it that is far more important, 323 00:16:11,840 --> 00:16:14,840 Speaker 1: more exciting, and will have a much more profound impact 324 00:16:14,840 --> 00:16:15,920 Speaker 1: on our daily lives. 325 00:16:16,360 --> 00:16:18,400 Speaker 2: Yeah, Amy, this is why I'm so excited to have 326 00:16:18,440 --> 00:16:20,520 Speaker 2: you on the program. You know, you can see it 327 00:16:20,560 --> 00:16:23,960 Speaker 2: as bitcoin as a currency and the underlying blockchain technology 328 00:16:24,000 --> 00:16:27,720 Speaker 2: what makes it secure? If bitcoin has this kind of 329 00:16:27,840 --> 00:16:31,840 Speaker 2: upward trajectory thirty five thousand dollars now, Sunny Singh was 330 00:16:31,840 --> 00:16:35,160 Speaker 2: on the show last week talking about above one hundred 331 00:16:35,240 --> 00:16:38,440 Speaker 2: thousand at some point next year. Does that help the 332 00:16:38,520 --> 00:16:43,680 Speaker 2: technology become accepted and utilized in societies around the world. 333 00:16:44,880 --> 00:16:49,360 Speaker 1: That's absolutely right. Every time we have a ball run, 334 00:16:49,600 --> 00:16:52,680 Speaker 1: more and more people find out about the technology. It's 335 00:16:52,680 --> 00:16:55,200 Speaker 1: the price action that is, you know, the kind of 336 00:16:55,280 --> 00:16:57,080 Speaker 1: headline that gets people drawn in. 337 00:16:57,520 --> 00:17:00,000 Speaker 3: But once they once they come. 338 00:16:59,880 --> 00:17:02,920 Speaker 1: Into the space, they learn about the technology that really 339 00:17:03,000 --> 00:17:08,400 Speaker 1: has the potential to reshape the foundation of the Internet 340 00:17:08,760 --> 00:17:11,840 Speaker 1: and return it to its original vision of being a 341 00:17:11,880 --> 00:17:15,800 Speaker 1: free and fair marketplace for ideas. And so you know, 342 00:17:15,880 --> 00:17:19,000 Speaker 1: as they say, bear markets are for builders, and we 343 00:17:19,080 --> 00:17:22,480 Speaker 1: have been in a bear market since the Terra Luna 344 00:17:22,560 --> 00:17:27,200 Speaker 1: collapse in May of last year, and during that time 345 00:17:27,320 --> 00:17:31,600 Speaker 1: a tremendous amount of building has happened. And now I 346 00:17:31,640 --> 00:17:35,320 Speaker 1: would say that the deep PIN sector of the cryptospace, 347 00:17:35,320 --> 00:17:40,280 Speaker 1: which stands for decentralized physical infrastructure networks, is poised to 348 00:17:40,320 --> 00:17:43,400 Speaker 1: be sort of the breakout hit of this next bull run, 349 00:17:43,800 --> 00:17:47,720 Speaker 1: similar to NFTs and stable coins in previous runs. 350 00:17:48,960 --> 00:17:50,800 Speaker 3: Oh boy, another acronym. 351 00:17:51,320 --> 00:17:54,560 Speaker 4: So talk to us about deep in and what decentralized 352 00:17:54,560 --> 00:17:58,600 Speaker 4: physical infrastructure It actually feels a lot more real, tangible. 353 00:17:59,040 --> 00:18:00,920 Speaker 4: What exactly is it that you're building me? 354 00:18:02,359 --> 00:18:06,040 Speaker 1: That's right, So decentralized physical infrastructure networks, I like to 355 00:18:06,080 --> 00:18:09,040 Speaker 1: call them the plumbing of the web. So just as 356 00:18:09,080 --> 00:18:12,440 Speaker 1: we rely on water flowing from our tap, we rely 357 00:18:12,560 --> 00:18:15,800 Speaker 1: on these networks for the things that we do every day, 358 00:18:15,840 --> 00:18:18,240 Speaker 1: the things that allow us to be on this video 359 00:18:18,359 --> 00:18:21,879 Speaker 1: call together for you to broadcast your show to the world, 360 00:18:21,960 --> 00:18:26,720 Speaker 1: things like video trans coding with networks like live peer, 361 00:18:26,920 --> 00:18:30,960 Speaker 1: things like file storage networks. Those would be protocols like 362 00:18:31,440 --> 00:18:35,760 Speaker 1: r weave and IPFS. File coin is what most people 363 00:18:35,760 --> 00:18:39,239 Speaker 1: would know that as. And then also for things like 364 00:18:40,240 --> 00:18:43,679 Speaker 1: GPU rentals, which I'm sure you know. GPUs have been 365 00:18:43,720 --> 00:18:48,080 Speaker 1: a really constrained product because of supply issues, and so 366 00:18:48,480 --> 00:18:50,840 Speaker 1: builders have had a hard time getting their hands on 367 00:18:50,880 --> 00:18:53,760 Speaker 1: them to train their AI models, and marketplaces like a 368 00:18:53,800 --> 00:18:57,960 Speaker 1: cash network are making those available on a rental sort 369 00:18:58,000 --> 00:18:59,920 Speaker 1: of basis so that they can use them when they 370 00:19:00,080 --> 00:19:02,879 Speaker 1: need them without having to outlay a tremendous amount of 371 00:19:02,880 --> 00:19:06,480 Speaker 1: cash to purchase them and also really having to help 372 00:19:06,520 --> 00:19:10,760 Speaker 1: overcome those supply issues. So these networks are going to 373 00:19:11,440 --> 00:19:16,280 Speaker 1: return the Web to its decentralized shape. What we've seen 374 00:19:16,440 --> 00:19:18,840 Speaker 1: over the eras of Web one, Web two, and Web 375 00:19:18,880 --> 00:19:23,240 Speaker 1: three is a change from a decentralized structure in the 376 00:19:23,320 --> 00:19:25,480 Speaker 1: very beginning of the web and Web one, when everybody 377 00:19:25,520 --> 00:19:27,840 Speaker 1: ran it on their own computers, and then it became 378 00:19:27,960 --> 00:19:30,680 Speaker 1: centralized as We've entered this big heech. 379 00:19:30,520 --> 00:19:34,919 Speaker 2: Era and web three really really quick, just before we 380 00:19:35,000 --> 00:19:37,119 Speaker 2: run out of time here very quickly, just on what 381 00:19:37,160 --> 00:19:40,199 Speaker 2: you said, is the United States a good place for 382 00:19:40,280 --> 00:19:44,680 Speaker 2: this dream to become realized? Is a regulatory jurisdiction very quick. 383 00:19:46,119 --> 00:19:48,359 Speaker 1: So our hope is that it will be right now, 384 00:19:48,400 --> 00:19:50,920 Speaker 1: it is very precarious, and I think that the industry 385 00:19:50,960 --> 00:19:54,359 Speaker 1: feels very nervous about what will happen. We have some 386 00:19:54,480 --> 00:19:58,520 Speaker 1: great champions in the legislatorture who are helping to move 387 00:19:58,520 --> 00:20:00,560 Speaker 1: that forward, and we wish them the best and hope 388 00:20:00,560 --> 00:20:03,240 Speaker 1: that they are successful, because if not, we do face 389 00:20:03,280 --> 00:20:06,440 Speaker 1: the really serious danger of losing the tech industry here 390 00:20:06,440 --> 00:20:07,000 Speaker 1: in the US. 391 00:20:07,880 --> 00:20:10,120 Speaker 2: All right, Amy James at the WED three working group 392 00:20:10,119 --> 00:20:12,160 Speaker 2: here on Bloomberg Technology, Thank you so much. 393 00:20:20,880 --> 00:20:23,880 Speaker 4: Welcome back to Bloomberg Technology. I'm Caroline Hyde in London. 394 00:20:24,200 --> 00:20:26,080 Speaker 2: And I'm Ed Lovela in San Francisco. I think you'd 395 00:20:26,119 --> 00:20:29,680 Speaker 2: agree that this sort of candor third quarter earning season 396 00:20:30,280 --> 00:20:32,480 Speaker 2: Artificial intelligence is what we're going to look for in 397 00:20:32,560 --> 00:20:34,520 Speaker 2: terms of how's all of the news, all of the 398 00:20:34,640 --> 00:20:36,880 Speaker 2: R and D actually showing up in the top line 399 00:20:36,880 --> 00:20:39,720 Speaker 2: of these businesses invest in AI what do you have 400 00:20:39,760 --> 00:20:41,000 Speaker 2: to show for it AI everything? 401 00:20:42,320 --> 00:20:44,080 Speaker 4: And how many hundreds of times is it going to 402 00:20:44,119 --> 00:20:45,680 Speaker 4: be cited in various earning releases. 403 00:20:45,680 --> 00:20:47,680 Speaker 3: I know you'll be counting that one uphead, but in. 404 00:20:47,600 --> 00:20:49,680 Speaker 4: Fact it has been front and center and our own 405 00:20:49,680 --> 00:20:52,560 Speaker 4: Bloomberg Technology Summit today right here in London, and in 406 00:20:52,600 --> 00:20:55,040 Speaker 4: fact we were hearing from Joe Kaplan and Bropic co 407 00:20:55,160 --> 00:20:58,600 Speaker 4: founder and chief science officer, who waited on howard Thropic 408 00:20:58,840 --> 00:21:02,760 Speaker 4: has made commitments to hold itself accountable for safer and 409 00:21:02,840 --> 00:21:03,680 Speaker 4: more ethical AI. 410 00:21:03,840 --> 00:21:05,480 Speaker 3: He spoke to Bradstone, haven't listen. 411 00:21:05,920 --> 00:21:08,440 Speaker 11: One thing that we'd really like to see, And that's 412 00:21:08,440 --> 00:21:10,240 Speaker 11: sort of part of part of the reason why we're 413 00:21:10,280 --> 00:21:15,640 Speaker 11: excited to have started Anthropic is we think that there 414 00:21:15,640 --> 00:21:17,520 Speaker 11: should be kind of a race to the top on 415 00:21:18,280 --> 00:21:22,280 Speaker 11: safer AI, more ethical AI in preparation for the fact 416 00:21:22,320 --> 00:21:24,840 Speaker 11: that we believe there will be more powerful systems on 417 00:21:24,880 --> 00:21:27,360 Speaker 11: the horizon. So I think the goal is for there 418 00:21:27,359 --> 00:21:31,320 Speaker 11: to be competition in the direction of safer and more 419 00:21:31,359 --> 00:21:35,680 Speaker 11: reliable systems. So as to sort of make that concrete, 420 00:21:36,240 --> 00:21:40,600 Speaker 11: we recently made a set of commitments, a responsible Scaling policy. 421 00:21:40,640 --> 00:21:46,320 Speaker 11: Responsible scaling commitments about basically standards that will hold ourselves 422 00:21:46,400 --> 00:21:49,840 Speaker 11: to as we build more powerful systems. So the current 423 00:21:49,880 --> 00:21:54,720 Speaker 11: systems we have now like Claude two, we have information security, 424 00:21:54,800 --> 00:21:57,960 Speaker 11: we have constitutional AI. We think that that's sufficient, but 425 00:21:58,400 --> 00:22:02,959 Speaker 11: we're imagining systems that are effectively able to operate autonomously, 426 00:22:03,400 --> 00:22:05,080 Speaker 11: a system that might be able to sort of install 427 00:22:05,119 --> 00:22:08,320 Speaker 11: itself and run itself on new computers all on its own, 428 00:22:08,560 --> 00:22:12,040 Speaker 11: or a system potentially that might be used by bad 429 00:22:12,119 --> 00:22:17,760 Speaker 11: actors to say, build or operate cyber weapons, other kinds 430 00:22:17,800 --> 00:22:19,960 Speaker 11: of other kinds of other kinds of weapons. We all 431 00:22:20,000 --> 00:22:22,639 Speaker 11: know that current AI systems are really helpful to software 432 00:22:22,640 --> 00:22:24,920 Speaker 11: engineers for coding, but if you take that a step further, 433 00:22:25,560 --> 00:22:29,959 Speaker 11: the more powerful equivalent systems could be used used for hacking. 434 00:22:30,240 --> 00:22:34,680 Speaker 11: So we're very concerned about those possibilities and even more 435 00:22:34,720 --> 00:22:38,199 Speaker 11: speculative possibilities over the next few years. And so we 436 00:22:38,280 --> 00:22:41,200 Speaker 11: made a set of commitments about the sort of information 437 00:22:41,280 --> 00:22:45,000 Speaker 11: security and the level of safety and robustness to what's 438 00:22:45,040 --> 00:22:47,640 Speaker 11: called red teaming, where people try to break these models 439 00:22:47,880 --> 00:22:51,400 Speaker 11: to get them to violate their principles and do something bad. 440 00:22:53,840 --> 00:22:57,439 Speaker 4: Jared Kaplan, then Eanthropic co founder chief, starts efforts talking 441 00:22:57,480 --> 00:22:58,400 Speaker 4: with Bradstone. 442 00:22:58,760 --> 00:22:59,919 Speaker 3: Let's still keep to. 443 00:23:00,000 --> 00:23:03,560 Speaker 4: Talking about artificial intelligence and Synthashia is with us, the 444 00:23:03,600 --> 00:23:08,000 Speaker 4: world's leading AI video creation platform for enterprises. It's making 445 00:23:08,080 --> 00:23:11,359 Speaker 4: video production simple and intuitive without the need for cameras 446 00:23:11,400 --> 00:23:12,000 Speaker 4: or studios. 447 00:23:12,400 --> 00:23:14,560 Speaker 3: Very pleased to say that Victor Rippabelli is with us. 448 00:23:14,640 --> 00:23:17,919 Speaker 4: He's a Synthesia CEO, part of the inaugural list of 449 00:23:17,880 --> 00:23:21,760 Speaker 4: Bloombag UK tech startups to watch as well. So Victor, great, congratulations, 450 00:23:21,800 --> 00:23:24,199 Speaker 4: wonderful to see you singled out as really wanted to 451 00:23:24,200 --> 00:23:26,800 Speaker 4: be watching and boy is everyone watching AI and boys 452 00:23:26,840 --> 00:23:28,320 Speaker 4: the UK government watching AI? 453 00:23:28,640 --> 00:23:29,840 Speaker 3: How much do you think. 454 00:23:29,640 --> 00:23:34,120 Speaker 4: This conversation around, well, the direction of travel the guardrails 455 00:23:34,119 --> 00:23:36,680 Speaker 4: has changed since you were first building this company. 456 00:23:37,560 --> 00:23:40,119 Speaker 8: I think what we've seen we've been building for almost 457 00:23:40,119 --> 00:23:43,840 Speaker 8: seven years and has always been a topic for regulators, 458 00:23:43,880 --> 00:23:45,560 Speaker 8: always been a topic for the take country. Of course, 459 00:23:45,640 --> 00:23:47,879 Speaker 8: this is in many ways not new technology. It's just 460 00:23:47,920 --> 00:23:50,640 Speaker 8: had its moment where I think the world really woke 461 00:23:50,720 --> 00:23:52,480 Speaker 8: up to the fact how powerful these technologies are going 462 00:23:52,520 --> 00:23:53,600 Speaker 8: to be and how much they're going to be a 463 00:23:53,600 --> 00:23:57,960 Speaker 8: part of everything in our lives. The chet ChiPT moment 464 00:23:58,000 --> 00:24:00,000 Speaker 8: as we usually call it, which happened late last year. 465 00:24:00,240 --> 00:24:03,159 Speaker 8: I think really just gave us a version of this 466 00:24:03,200 --> 00:24:06,480 Speaker 8: technology that was incredibly accessible. Everyone with the Internet connection 467 00:24:06,560 --> 00:24:08,320 Speaker 8: and an email address could sign up and try to 468 00:24:08,320 --> 00:24:10,639 Speaker 8: play around with these tools to see all the amazing 469 00:24:10,640 --> 00:24:12,679 Speaker 8: things they can do, but also discover some of the 470 00:24:12,680 --> 00:24:14,639 Speaker 8: pitfalls of these technologies and some of the things we 471 00:24:14,680 --> 00:24:17,720 Speaker 8: have to watch out for. And the government right now 472 00:24:17,800 --> 00:24:20,600 Speaker 8: is obviously very focused on how we put the right 473 00:24:20,600 --> 00:24:23,200 Speaker 8: guard rails, but at the same time capture all the 474 00:24:23,240 --> 00:24:24,080 Speaker 8: amazing opportunity. 475 00:24:24,600 --> 00:24:26,800 Speaker 3: So Thedia, of course, has captured the attention of a 476 00:24:26,800 --> 00:24:28,320 Speaker 3: lot of clients. I mean, what is it. 477 00:24:28,359 --> 00:24:30,800 Speaker 4: You're in forty seven percent of Fortune one hundred companies 478 00:24:30,840 --> 00:24:35,320 Speaker 4: already using it, I mean hundreds thousands if not deploying 479 00:24:35,320 --> 00:24:38,800 Speaker 4: your videos. Why what are they doing to use your 480 00:24:38,920 --> 00:24:40,320 Speaker 4: tech in particular. 481 00:24:39,880 --> 00:24:41,320 Speaker 3: How are they working with it? 482 00:24:41,920 --> 00:24:44,000 Speaker 8: So the core thing is that we live in twenty 483 00:24:44,040 --> 00:24:46,840 Speaker 8: twenty three and people want to watch and listen to content. 484 00:24:47,040 --> 00:24:49,040 Speaker 8: They don't want to read that much anymore, and for 485 00:24:49,080 --> 00:24:50,879 Speaker 8: most people in their private lives, this is actually how 486 00:24:50,880 --> 00:24:52,800 Speaker 8: they consume content. Like most people spend a lot of 487 00:24:52,800 --> 00:24:56,359 Speaker 8: time on YouTube, TikTok and listen to podcasts reading is 488 00:24:56,400 --> 00:24:58,399 Speaker 8: on the decline, no matter if you like that or not. 489 00:24:59,080 --> 00:25:02,560 Speaker 8: But in the sector, it's really difficult to produce audio 490 00:25:02,560 --> 00:25:04,680 Speaker 8: and video content, right, like filming things with a camera, 491 00:25:04,760 --> 00:25:07,360 Speaker 8: recording things to the microphone. If you compare the production 492 00:25:07,480 --> 00:25:10,560 Speaker 8: process to writing a work document and making a PowerPoint slide, 493 00:25:10,560 --> 00:25:12,200 Speaker 8: for example, it's very very different. 494 00:25:12,280 --> 00:25:12,440 Speaker 9: Right. 495 00:25:12,840 --> 00:25:13,600 Speaker 3: What we offer is. 496 00:25:13,560 --> 00:25:17,000 Speaker 8: An alternative to traditional video production where you just like 497 00:25:17,000 --> 00:25:19,560 Speaker 8: you would make a PowerPoint presentation, you simply select your avatar, 498 00:25:19,680 --> 00:25:21,800 Speaker 8: you type out the script. You make these very kind 499 00:25:21,840 --> 00:25:24,160 Speaker 8: of simple videos and for the enterprise, but this means 500 00:25:24,200 --> 00:25:28,080 Speaker 8: that now they can train their employees or their customers 501 00:25:28,760 --> 00:25:30,840 Speaker 8: way better than they could before. So the information retention 502 00:25:30,920 --> 00:25:32,760 Speaker 8: of watching a video is around eight to ten times 503 00:25:32,800 --> 00:25:35,399 Speaker 8: as high as we read something in text. That really 504 00:25:35,520 --> 00:25:42,000 Speaker 8: is the utility that our customers get out of using SYNTHESIAIK. 505 00:25:40,359 --> 00:25:43,240 Speaker 2: So thank you for explaining how the technology works. One 506 00:25:43,280 --> 00:25:46,040 Speaker 2: of the stories we've covered here on the show is 507 00:25:46,080 --> 00:25:49,439 Speaker 2: in the context of the Israel mass war and video 508 00:25:49,560 --> 00:25:54,600 Speaker 2: content in circulation on social media platforms that purports to 509 00:25:54,640 --> 00:25:57,960 Speaker 2: be one thing, but in reality is not that thing. 510 00:25:58,000 --> 00:26:00,760 Speaker 2: It is fake or false. In some cases, video game 511 00:26:01,080 --> 00:26:04,240 Speaker 2: video which is claimed to be video of what's happening 512 00:26:04,240 --> 00:26:08,440 Speaker 2: on the ground. What is Synthesia doing to make sure 513 00:26:08,480 --> 00:26:12,520 Speaker 2: that video content generated through the platform is not shared 514 00:26:12,560 --> 00:26:15,000 Speaker 2: in such a way that is not used by bad 515 00:26:15,040 --> 00:26:18,520 Speaker 2: actors to share false information. 516 00:26:21,160 --> 00:26:22,719 Speaker 8: When we found the company, we did it on an 517 00:26:22,720 --> 00:26:25,800 Speaker 8: ethical framework, which is around consent, control, and collaboration of 518 00:26:25,880 --> 00:26:29,080 Speaker 8: the free seas as we call them. Content is around 519 00:26:29,119 --> 00:26:32,000 Speaker 8: any avatar advice that we reproduced. The likeness of it 520 00:26:32,040 --> 00:26:34,520 Speaker 8: is done with full consent from whoever's voice, a likeness 521 00:26:34,560 --> 00:26:37,320 Speaker 8: that is second one around control, it's around content moderations. 522 00:26:37,320 --> 00:26:39,359 Speaker 8: So we have very strict policies in place, but what 523 00:26:39,440 --> 00:26:41,119 Speaker 8: kind of content you're allowed to create and what kind 524 00:26:41,160 --> 00:26:43,439 Speaker 8: of content you're not allowed to create, and we monitor 525 00:26:43,480 --> 00:26:45,920 Speaker 8: that both with automatic systems and also humans in the loop. 526 00:26:46,920 --> 00:26:50,400 Speaker 8: For example, if you're creating news like content on current events, 527 00:26:50,480 --> 00:26:53,000 Speaker 8: that's only allowed if you're on an enterprise plan, which 528 00:26:53,040 --> 00:26:55,639 Speaker 8: means that we know who you are, and we know 529 00:26:55,720 --> 00:26:59,359 Speaker 8: that you have a good reputation and most likely a 530 00:26:59,400 --> 00:27:01,720 Speaker 8: well known media company. So we have a lot of 531 00:27:01,720 --> 00:27:05,000 Speaker 8: safeguards in place to make sure that the technology isn't misused. 532 00:27:05,000 --> 00:27:07,000 Speaker 8: In the case of what you're talking about, here. That's 533 00:27:07,040 --> 00:27:10,919 Speaker 8: sort of a different technologies than hours hours around the 534 00:27:11,000 --> 00:27:14,600 Speaker 8: AI avatars talking to the camera presenting something, whereas what 535 00:27:14,640 --> 00:27:16,879 Speaker 8: we're seeing here right is so something is then an 536 00:27:16,920 --> 00:27:19,800 Speaker 8: involvement of some of the disinformation and misinformation tactics that's 537 00:27:19,800 --> 00:27:21,840 Speaker 8: been deployed the last ten twenty. 538 00:27:21,600 --> 00:27:23,200 Speaker 3: Years, where you take a video of. 539 00:27:23,200 --> 00:27:25,400 Speaker 8: An explosion, for example, that actually happened five years ago, 540 00:27:25,400 --> 00:27:28,000 Speaker 8: where you say it happened yesterday. Now we're getting to 541 00:27:28,080 --> 00:27:30,879 Speaker 8: a point where computer graphics is getting good enough to 542 00:27:31,000 --> 00:27:33,560 Speaker 8: actually fool people into thinking that something that might have 543 00:27:33,600 --> 00:27:36,159 Speaker 8: been done in a gaming engine is actually happening in 544 00:27:36,160 --> 00:27:39,080 Speaker 8: real life. I think the ultimate technological solution to this 545 00:27:39,200 --> 00:27:42,840 Speaker 8: is around fingerprinting content that we generate both with AI 546 00:27:42,960 --> 00:27:44,560 Speaker 8: but also things we capture with a camera, so we 547 00:27:44,560 --> 00:27:47,560 Speaker 8: can begin to build a provenance chain of where content 548 00:27:47,640 --> 00:27:50,240 Speaker 8: came from, who created it, and I'm watching the original 549 00:27:50,280 --> 00:27:52,959 Speaker 8: or edited version of it. It's a really our technical problem, 550 00:27:53,560 --> 00:27:56,080 Speaker 8: but we're working on it with Adobe. Google is working 551 00:27:56,119 --> 00:27:58,480 Speaker 8: on as well, and I have a lot of optimism 552 00:27:58,600 --> 00:28:01,160 Speaker 8: that this technology will be are part of the media 553 00:28:01,200 --> 00:28:02,800 Speaker 8: landscape within the next couple of years. 554 00:28:03,359 --> 00:28:05,679 Speaker 3: Absolutely fascinating. We could talk to you for much longer. 555 00:28:05,840 --> 00:28:09,800 Speaker 4: Sadly we can't, but the Synthesia CEO there, Victor Ripperbelly, 556 00:28:10,040 --> 00:28:14,240 Speaker 4: UK based startup course ed. We've been focusing a lot 557 00:28:14,320 --> 00:28:16,280 Speaker 4: on those that are currently being built right here in 558 00:28:16,320 --> 00:28:17,920 Speaker 4: the capital of the EAED. 559 00:28:18,080 --> 00:28:19,600 Speaker 2: Yeah, it's so important to have you there on the 560 00:28:19,640 --> 00:28:21,520 Speaker 2: ground in London. Coming up we will have more from 561 00:28:21,560 --> 00:28:24,159 Speaker 2: the Bloomberg Technology Summit and here from more of the 562 00:28:24,240 --> 00:28:27,359 Speaker 2: startups on our Bloomberg Startups to Watch list. That's next. 563 00:28:27,400 --> 00:28:28,639 Speaker 2: This is Bloomberg Technology. 564 00:28:38,720 --> 00:28:42,560 Speaker 12: Earth ecosystems are delicately balanced, but they're under threat from 565 00:28:42,600 --> 00:28:46,320 Speaker 12: our actions, and so monitoring biodiversity will allow business leaders 566 00:28:46,320 --> 00:28:50,240 Speaker 12: to opt for more sustainable practices with so many data points, 567 00:28:50,280 --> 00:28:53,400 Speaker 12: though traditionally that's been easier said than done. 568 00:28:53,720 --> 00:28:57,000 Speaker 13: So Nature Metrics is a global nature technology company. We 569 00:28:57,200 --> 00:29:01,600 Speaker 13: make biodiversity measurable by analyzing the tiny traces of DNA 570 00:29:01,720 --> 00:29:05,120 Speaker 13: that all organisms leave behind in the environment. The process 571 00:29:05,200 --> 00:29:08,040 Speaker 13: works by literally just taking water in a syringe and 572 00:29:08,080 --> 00:29:10,960 Speaker 13: pushing it through a filter like this, and it captures 573 00:29:11,000 --> 00:29:12,640 Speaker 13: all of the DNA from the water. So the water 574 00:29:12,680 --> 00:29:14,760 Speaker 13: comes out the other side and all of the DNA 575 00:29:14,840 --> 00:29:17,120 Speaker 13: from the water gets stuck inside and then this is 576 00:29:17,160 --> 00:29:20,360 Speaker 13: sent to our lapse for analysis. So it's something that 577 00:29:21,000 --> 00:29:22,880 Speaker 13: literally anybody anywhere. 578 00:29:22,440 --> 00:29:24,600 Speaker 3: In the world can do. Yes, I got some. 579 00:29:24,520 --> 00:29:26,920 Speaker 13: Strange looks when I went to investors and said. 580 00:29:26,880 --> 00:29:30,160 Speaker 4: I'm going to revolutionize the scale of biodiversity monitoring and 581 00:29:30,200 --> 00:29:32,760 Speaker 4: they went, what, Well, who's going to pay for that? 582 00:29:32,800 --> 00:29:36,160 Speaker 13: I mean, buidiversity is not important enough for anyone with money. 583 00:29:36,360 --> 00:29:38,560 Speaker 13: But actually that was wrong because there was a there 584 00:29:38,680 --> 00:29:42,280 Speaker 13: was a market already, particularly in companies that were doing 585 00:29:42,360 --> 00:29:45,760 Speaker 13: environmental impact assessments. Don't let perfect be the enemy of 586 00:29:45,760 --> 00:29:46,120 Speaker 13: the good. 587 00:29:46,360 --> 00:29:47,840 Speaker 3: Especially if you're a scientist. 588 00:29:48,280 --> 00:29:50,360 Speaker 13: You can often feel like it's got to be ready 589 00:29:50,360 --> 00:29:52,960 Speaker 13: to publish or ready to write a PhD paper on 590 00:29:53,080 --> 00:29:55,520 Speaker 13: or you've tested it fifteen times before you go out 591 00:29:55,560 --> 00:29:57,360 Speaker 13: and start using it, and you have to take such 592 00:29:57,360 --> 00:29:58,240 Speaker 13: a different approach. 593 00:29:58,320 --> 00:30:07,120 Speaker 4: In business, let's stick with startups here in Europe. Well, 594 00:30:07,120 --> 00:30:09,440 Speaker 4: actually there's one that's been a key exit for the 595 00:30:09,520 --> 00:30:12,160 Speaker 4: tech scene here in the UK, and we were discussing 596 00:30:12,200 --> 00:30:14,240 Speaker 4: it at the Tech Summit just on today, online food 597 00:30:14,280 --> 00:30:17,200 Speaker 4: delivery company Delivery. In fact, the CEO was just telling 598 00:30:17,280 --> 00:30:20,080 Speaker 4: me that it is fielding ten thousand job applications a 599 00:30:20,120 --> 00:30:22,360 Speaker 4: week in the United Kingdom and as one hundred percent 600 00:30:22,360 --> 00:30:25,360 Speaker 4: committed to remain in the country. I sat down with Willshoes, 601 00:30:25,400 --> 00:30:27,960 Speaker 4: the delivery CEO, earlier at the Bloomerg Technology Summit. 602 00:30:28,000 --> 00:30:28,480 Speaker 3: Take listen. 603 00:30:29,120 --> 00:30:32,959 Speaker 14: We're still well above where we were pre COVID, but 604 00:30:33,000 --> 00:30:36,000 Speaker 14: I think what happened is as COVID on wound, we 605 00:30:36,120 --> 00:30:40,240 Speaker 14: then also were hit with a extremely high inflation on 606 00:30:40,320 --> 00:30:43,120 Speaker 14: the food side. So in some of our markets you've 607 00:30:43,120 --> 00:30:47,040 Speaker 14: had food inflation three x that of wage inflation. So 608 00:30:47,320 --> 00:30:49,840 Speaker 14: the UK was in that position probably about nine months ago. 609 00:30:50,240 --> 00:30:52,800 Speaker 14: Now some of that subsided, and so if you look 610 00:30:52,840 --> 00:30:55,400 Speaker 14: at that, for many people, what was I think a 611 00:30:55,520 --> 00:31:00,360 Speaker 14: regular purchase sort of became discretionary. But for people who 612 00:31:01,080 --> 00:31:05,200 Speaker 14: maybe are in London or in more affluent areas, it's persisted, right, 613 00:31:05,440 --> 00:31:08,560 Speaker 14: And so what really we've seen is as inflation has 614 00:31:08,600 --> 00:31:11,360 Speaker 14: slowed down for food, we're starting to see more and 615 00:31:11,440 --> 00:31:14,000 Speaker 14: more recovery, which is good, but it's tough when it's 616 00:31:14,000 --> 00:31:15,280 Speaker 14: three x out of wage inflation. 617 00:31:15,520 --> 00:31:15,720 Speaker 10: Right. 618 00:31:16,400 --> 00:31:18,800 Speaker 4: You've also those seen improvements, as you say you weren't 619 00:31:18,800 --> 00:31:20,600 Speaker 4: standing still, You've made changes to the app. 620 00:31:20,800 --> 00:31:22,680 Speaker 3: Yeah, like, what how have. 621 00:31:22,640 --> 00:31:26,120 Speaker 14: You watched I'd say, you know, the big ones grocery right, 622 00:31:26,520 --> 00:31:29,760 Speaker 14: it's you know, it's over eleven percent of our business. 623 00:31:29,760 --> 00:31:29,960 Speaker 6: Now. 624 00:31:30,000 --> 00:31:32,240 Speaker 14: We've built that over kind of three to four years 625 00:31:32,240 --> 00:31:34,840 Speaker 14: from a standing start, so that's been really great. 626 00:31:34,920 --> 00:31:35,560 Speaker 10: We've built some. 627 00:31:35,600 --> 00:31:38,840 Speaker 14: Dark stores called hop which is a compliment to our 628 00:31:38,880 --> 00:31:42,480 Speaker 14: grocery business. We've done a lot of different things, but 629 00:31:42,520 --> 00:31:44,520 Speaker 14: I think the thing I'm probably most proud of in 630 00:31:44,520 --> 00:31:48,880 Speaker 14: the last twelve months is just a series of really 631 00:31:48,920 --> 00:31:53,400 Speaker 14: sort of i'd call them incremental improvements to our service reliability. 632 00:31:54,680 --> 00:31:58,800 Speaker 14: So for example, a better new user address flow, or 633 00:31:59,320 --> 00:32:02,760 Speaker 14: for example, a better handover when the writer shows up, 634 00:32:03,000 --> 00:32:05,760 Speaker 14: to really minimize code for example, Yeah, like a code 635 00:32:05,840 --> 00:32:09,240 Speaker 14: right to minimize these sort of like errors that can happen, 636 00:32:09,960 --> 00:32:13,480 Speaker 14: And the cumulative impact of those are very, very large, 637 00:32:13,720 --> 00:32:16,320 Speaker 14: And the collective creativity it takes to actually do all 638 00:32:16,320 --> 00:32:18,800 Speaker 14: of those things, to me, is as important as the 639 00:32:18,840 --> 00:32:22,600 Speaker 14: shiny new thing like you know, grocery or a rolling 640 00:32:22,600 --> 00:32:23,960 Speaker 14: out pharmacy. 641 00:32:24,040 --> 00:32:27,040 Speaker 4: Or the shiny new thing on everyone's lips right now, 642 00:32:27,080 --> 00:32:30,040 Speaker 4: which is AI. I mean, how have you inherently been 643 00:32:30,040 --> 00:32:31,120 Speaker 4: an AI company for ages? 644 00:32:31,160 --> 00:32:33,480 Speaker 3: You just haven't been. It wasn't sexy, so you didn't 645 00:32:33,480 --> 00:32:34,480 Speaker 3: so much talk about it. 646 00:32:34,880 --> 00:32:36,960 Speaker 14: I don't know if I can go that far, but yeah, 647 00:32:36,960 --> 00:32:39,200 Speaker 14: I mean it's definitely a question we get asked a 648 00:32:39,240 --> 00:32:42,520 Speaker 14: lot about and we've been definitely utilizing jen Ai. 649 00:32:42,920 --> 00:32:43,800 Speaker 3: So in what way? 650 00:32:44,560 --> 00:32:47,320 Speaker 14: I think a few different ways. We have in our 651 00:32:47,360 --> 00:32:50,600 Speaker 14: employee version of the app a recommendation engine, so you 652 00:32:50,640 --> 00:32:54,800 Speaker 14: can type I want, you know, a healthy Mexican sort 653 00:32:54,840 --> 00:32:58,640 Speaker 14: of you know meal, or within this caloric range, you 654 00:32:58,680 --> 00:33:00,880 Speaker 14: can type something. It doesn't always work, if I'm honest, 655 00:33:00,880 --> 00:33:03,680 Speaker 14: but it gives cting it it's it is iterating and 656 00:33:03,720 --> 00:33:06,680 Speaker 14: it comes back with what is our increasingly better and 657 00:33:06,720 --> 00:33:10,000 Speaker 14: better recommendations. That's one thing we're doing on the consumer side. 658 00:33:10,280 --> 00:33:14,200 Speaker 14: I think on the customer care side, we've done a 659 00:33:14,200 --> 00:33:17,400 Speaker 14: lot of really cool things where Jenna I will summarize 660 00:33:17,440 --> 00:33:20,440 Speaker 14: the last ten interactions with the consumer and then tell 661 00:33:20,480 --> 00:33:22,920 Speaker 14: the agent, hey, do we think this consumer is happy? 662 00:33:23,000 --> 00:33:25,120 Speaker 14: And what's the summary of how the last few interactions 663 00:33:25,160 --> 00:33:27,920 Speaker 14: went without having to look all the stuff up yourself. 664 00:33:27,960 --> 00:33:30,280 Speaker 14: I think something like that's been really really powerful. 665 00:33:32,480 --> 00:33:35,840 Speaker 3: Will shoot delivery CEO there. Meanwhile, look, you want to 666 00:33:35,920 --> 00:33:37,280 Speaker 3: order a takeout, well you've got. 667 00:33:37,200 --> 00:33:39,040 Speaker 4: To pay for it, So let's talk about that space 668 00:33:39,080 --> 00:33:41,840 Speaker 4: here in the UK right now, the fintech landscape Britain 669 00:33:41,960 --> 00:33:44,360 Speaker 4: is well imursioning one. Monso is one of the largest 670 00:33:44,400 --> 00:33:47,120 Speaker 4: digital bangs in the UK, if not the found in 671 00:33:47,160 --> 00:33:49,560 Speaker 4: eight years ago. Monso wants to be one and one 672 00:33:49,600 --> 00:33:52,280 Speaker 4: stop shop basically the key app customers turned to in 673 00:33:52,360 --> 00:33:55,280 Speaker 4: order to manage their entire financial lives. Is on bluemg's 674 00:33:55,320 --> 00:33:57,240 Speaker 4: UK Startups to Watch, And we're now very pleased to 675 00:33:57,280 --> 00:33:59,960 Speaker 4: welcome from the company the CEO Sujata Party. 676 00:34:00,080 --> 00:34:02,520 Speaker 3: Yeah, thanks so much for joining us, Thanks for having me. 677 00:34:03,160 --> 00:34:06,560 Speaker 4: So we are all about financial inclusion, all about focusing 678 00:34:06,560 --> 00:34:09,319 Speaker 4: on people basically in the UK, realizing you don't need 679 00:34:09,360 --> 00:34:11,560 Speaker 4: to be wealthy to invest, but also how easy it is. 680 00:34:11,640 --> 00:34:13,600 Speaker 3: How much of a cultural shift is that. 681 00:34:14,880 --> 00:34:18,480 Speaker 15: I think it's money causes a lot of anxiety. That 682 00:34:18,640 --> 00:34:20,640 Speaker 15: is a commonality. You don't have to be wealthy or 683 00:34:20,680 --> 00:34:23,160 Speaker 15: poor to feel anxious about your money, and certainly all 684 00:34:23,200 --> 00:34:25,600 Speaker 15: the research we've shown shows that it is one of 685 00:34:25,640 --> 00:34:27,960 Speaker 15: the biggest barriers people have to advancement. Right you can 686 00:34:28,000 --> 00:34:30,640 Speaker 15: change the world through money, education and healthcare, and so 687 00:34:30,760 --> 00:34:33,400 Speaker 15: money is definitely the common thread. Certainly, when we launched 688 00:34:33,400 --> 00:34:35,640 Speaker 15: our most recent investments product, what we found was that 689 00:34:35,719 --> 00:34:38,279 Speaker 15: seventy percent of people in the UK did not know 690 00:34:38,320 --> 00:34:40,680 Speaker 15: where to turn to to find something easy and accessible 691 00:34:40,680 --> 00:34:44,239 Speaker 15: to start investing, and sixty percent of people said that 692 00:34:44,280 --> 00:34:46,319 Speaker 15: they didn't have enough money to get start investing, which 693 00:34:46,320 --> 00:34:48,880 Speaker 15: is counterintuitive, right, which is why actually our investment product 694 00:34:48,920 --> 00:34:51,319 Speaker 15: that we launched in partnership with Blackrock allows you to 695 00:34:51,320 --> 00:34:53,080 Speaker 15: invest with as little as a pound, Because how are 696 00:34:53,080 --> 00:34:55,400 Speaker 15: you going to learn the muscles and learn the habits 697 00:34:55,400 --> 00:34:57,200 Speaker 15: to be able to help you grow your money if 698 00:34:57,239 --> 00:34:59,800 Speaker 15: you don't actually get access to it and have some 699 00:35:00,080 --> 00:35:01,799 Speaker 15: one there by your side to help you learn. 700 00:35:02,239 --> 00:35:05,440 Speaker 3: What has been the recipe? Do you think of Monzo's 701 00:35:05,520 --> 00:35:08,680 Speaker 3: scale in the UK? It was a different way of marketing. 702 00:35:08,719 --> 00:35:12,120 Speaker 4: It's a different way of organically growing. What has it 703 00:35:12,239 --> 00:35:14,680 Speaker 4: Monso been able to offer other than you know, you 704 00:35:14,800 --> 00:35:17,560 Speaker 4: came from Amex, the CEO came from These are another 705 00:35:17,960 --> 00:35:20,000 Speaker 4: age old financial institutions. 706 00:35:20,000 --> 00:35:21,280 Speaker 3: What was this fintech doing differently? 707 00:35:21,520 --> 00:35:23,040 Speaker 15: Yeah, well it's not just one thing. I think if 708 00:35:23,040 --> 00:35:25,279 Speaker 15: it was, somebody would have replicated it by now. But 709 00:35:25,360 --> 00:35:28,080 Speaker 15: we have quite a very different business model than mostly right, 710 00:35:28,120 --> 00:35:31,520 Speaker 15: we have eight point four million customers, three hundred thousand 711 00:35:31,520 --> 00:35:34,920 Speaker 15: small businesses and growing and that growth momentum is accelerating. 712 00:35:34,960 --> 00:35:37,319 Speaker 15: We're growing by almost two hundred thousand customers a month 713 00:35:37,360 --> 00:35:39,759 Speaker 15: and mostly by word of mouth, so there's an incredibly 714 00:35:39,800 --> 00:35:43,240 Speaker 15: strong customer advocacy there. We're known for our hot coreld card. 715 00:35:43,400 --> 00:35:46,160 Speaker 15: I think some of our uniqueness starts with just the 716 00:35:46,200 --> 00:35:48,920 Speaker 15: fact that we build really delightful, intuitive products, and we 717 00:35:49,000 --> 00:35:51,480 Speaker 15: build them for and with our customers. So you know, 718 00:35:51,520 --> 00:35:54,040 Speaker 15: in the very early days, we invited customers literally into 719 00:35:54,040 --> 00:35:56,400 Speaker 15: our offices and they were able to tell us what 720 00:35:56,440 --> 00:35:58,680 Speaker 15: they wanted and we built with them. Even now, at 721 00:35:58,680 --> 00:36:01,239 Speaker 15: eight and a half million customers and counting, we're still 722 00:36:01,320 --> 00:36:03,319 Speaker 15: talking to them. We have more connection points across our 723 00:36:03,360 --> 00:36:06,040 Speaker 15: business and almost any other company or a single product 724 00:36:06,120 --> 00:36:07,880 Speaker 15: might have five hundred points of feedback in it in 725 00:36:07,920 --> 00:36:10,200 Speaker 15: just a single month, So we're hearing from them and 726 00:36:10,239 --> 00:36:11,560 Speaker 15: we're acting on it really nimbly. 727 00:36:11,719 --> 00:36:13,560 Speaker 3: But there's the products that we build. 728 00:36:13,600 --> 00:36:17,560 Speaker 15: We also own our own tech stack, and that's industry leading, 729 00:36:17,560 --> 00:36:19,279 Speaker 15: and we continue to invest in it. That means that 730 00:36:19,320 --> 00:36:21,359 Speaker 15: we can be both resilient, which is important when you're 731 00:36:21,360 --> 00:36:24,080 Speaker 15: dealing with people's money, but also be able to be 732 00:36:24,160 --> 00:36:26,360 Speaker 15: nimble in terms of speed to market and create inventive 733 00:36:26,400 --> 00:36:29,160 Speaker 15: things that nobody else has done before. And then finally, 734 00:36:29,200 --> 00:36:31,160 Speaker 15: we're not sharing our margins with a lot of other suppliers, 735 00:36:31,200 --> 00:36:33,160 Speaker 15: so we can invest in customer value. 736 00:36:34,840 --> 00:36:40,640 Speaker 2: So Jata, you heard the delivery CEO there talk about inflation. 737 00:36:41,000 --> 00:36:43,719 Speaker 2: We're very conscious about the jobs market in the UK, 738 00:36:44,400 --> 00:36:47,520 Speaker 2: interest rates, all these fun things. But you talk about 739 00:36:47,520 --> 00:36:51,719 Speaker 2: being nimble, and I wondered if Monzo kind of thrives 740 00:36:51,719 --> 00:36:54,719 Speaker 2: in this environment when the consumer has to make kind 741 00:36:54,719 --> 00:36:57,759 Speaker 2: of really proactive decisions about their money, or if you're 742 00:36:57,800 --> 00:37:00,440 Speaker 2: subject to the same challenges, is sort of traditional buyers. 743 00:37:01,960 --> 00:37:02,839 Speaker 3: Oh that's a great question. 744 00:37:02,880 --> 00:37:06,160 Speaker 15: Actually, I think we're built for kind of all stages 745 00:37:06,200 --> 00:37:08,920 Speaker 15: of finance, but we really come into our own right now. 746 00:37:09,160 --> 00:37:11,480 Speaker 15: So if you think about it, we're able to give you, 747 00:37:11,560 --> 00:37:14,719 Speaker 15: whether it's a simple savings challenge or helping you open 748 00:37:14,760 --> 00:37:17,120 Speaker 15: a pot to be able to set goals. We launched 749 00:37:17,120 --> 00:37:22,280 Speaker 15: an instant access product, savings product with no barriers to entry, 750 00:37:22,560 --> 00:37:24,600 Speaker 15: no lock up periods. We had over eight hundred and 751 00:37:24,600 --> 00:37:27,399 Speaker 15: fifty thousand customers pour into that in the last six 752 00:37:27,480 --> 00:37:29,840 Speaker 15: months and start to be able to make their money 753 00:37:29,840 --> 00:37:30,719 Speaker 15: work harder for them. 754 00:37:30,960 --> 00:37:32,759 Speaker 3: We launched a home ownership solution to. 755 00:37:32,760 --> 00:37:35,279 Speaker 15: Allow people to get visibility of their mortgage within our 756 00:37:35,360 --> 00:37:38,000 Speaker 15: Monzo product, and that allows them to think about we 757 00:37:38,040 --> 00:37:40,359 Speaker 15: can help them give nudges on what's the right loan 758 00:37:40,440 --> 00:37:42,720 Speaker 15: to value ratio, how do you might maybe get access 759 00:37:42,760 --> 00:37:44,440 Speaker 15: to a better rate, and what might you do in 760 00:37:44,520 --> 00:37:47,400 Speaker 15: terms of paying it down over time our investments product. 761 00:37:47,400 --> 00:37:49,040 Speaker 15: You might say, actually, this is the wrong time to 762 00:37:49,080 --> 00:37:51,000 Speaker 15: launch an investments product, but we've found is that like 763 00:37:51,040 --> 00:37:53,479 Speaker 15: a quarter of people out there actually saying this cost 764 00:37:53,480 --> 00:37:56,279 Speaker 15: of living crisis is making them think about investing more 765 00:37:56,280 --> 00:37:58,200 Speaker 15: than ever, and so we want to help them proactively 766 00:37:58,200 --> 00:37:59,000 Speaker 15: build that muscle. 767 00:38:00,239 --> 00:38:01,960 Speaker 3: Great tows some time with you. Thank you for joining us, 768 00:38:02,000 --> 00:38:06,160 Speaker 3: Thanks for having meat. Of course, the Monzo's coo. 769 00:38:14,040 --> 00:38:16,480 Speaker 2: Meta was sued by California in a group of more 770 00:38:16,520 --> 00:38:20,560 Speaker 2: than thirty states overclaims that it's social media platforms Instagram 771 00:38:20,600 --> 00:38:24,520 Speaker 2: and Facebook exploit youths for profit and feed them harmful 772 00:38:24,560 --> 00:38:27,000 Speaker 2: content and carry This is a company that faces a 773 00:38:27,000 --> 00:38:30,000 Speaker 2: lot of litigation at the moment, and. 774 00:38:29,880 --> 00:38:32,839 Speaker 4: Indeed so to Do, the parent company of YouTube, so 775 00:38:32,880 --> 00:38:34,680 Speaker 4: To does TikTok so to do a lot of these 776 00:38:34,680 --> 00:38:37,600 Speaker 4: social media companies, and it's notable that Meta was showing 777 00:38:37,600 --> 00:38:38,239 Speaker 4: the response there. 778 00:38:38,239 --> 00:38:39,480 Speaker 3: They've reacted by saying that they. 779 00:38:39,480 --> 00:38:44,360 Speaker 4: Share the agg's commitment to providing teens with safe, positive 780 00:38:44,400 --> 00:38:46,959 Speaker 4: experiences online. But they've already introduced more than thirty tools, 781 00:38:46,960 --> 00:38:49,839 Speaker 4: they say, to support tools and their families. So it's 782 00:38:50,040 --> 00:38:53,280 Speaker 4: notable that these attorneys general are being sort of pushed 783 00:38:53,280 --> 00:38:55,319 Speaker 4: that back by Meta. Will be interesting if they comment 784 00:38:55,360 --> 00:38:57,680 Speaker 4: about it amid their earnings, which come out what them 785 00:38:57,719 --> 00:38:58,360 Speaker 4: the twenty. 786 00:38:58,080 --> 00:39:02,239 Speaker 2: Fifth Yeah, and we stick with that story in twenty 787 00:39:02,239 --> 00:39:02,879 Speaker 2: four hours time. 788 00:39:04,480 --> 00:39:06,680 Speaker 3: Meanwhile, that does it for this edition of Bluebow Technology. 789 00:39:07,640 --> 00:39:10,600 Speaker 2: Don't forget check out the podcast apples Spotify, iHeart