1 00:00:02,600 --> 00:00:07,320 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:09,560 --> 00:00:12,320 Speaker 2: From the heart where Innovation, money and power. 3 00:00:12,440 --> 00:00:14,800 Speaker 3: Collie in Silicon Valley, Nbon. 4 00:00:15,160 --> 00:00:18,440 Speaker 4: This is Bloomberg Technology with Caroline Hyde and. 5 00:00:18,600 --> 00:00:19,680 Speaker 5: Ed Ludlow. 6 00:00:32,800 --> 00:00:35,000 Speaker 6: And Caroline Heide at Blomberg's world headquarters in New York. 7 00:00:35,080 --> 00:00:38,239 Speaker 7: Ed Ludlow, he's off today. This is Bloomberg Technology coming up. 8 00:00:38,479 --> 00:00:39,640 Speaker 7: Full earnings coverage. 9 00:00:39,640 --> 00:00:41,440 Speaker 6: Ahead. Is Adobe fools by the most of this two 10 00:00:41,479 --> 00:00:44,519 Speaker 6: thousand and two. That's on phears of AI competition. We'll 11 00:00:44,560 --> 00:00:45,400 Speaker 6: break down the results. 12 00:00:45,720 --> 00:00:48,520 Speaker 7: Plus bitcoins slide from its record high continues. 13 00:00:48,640 --> 00:00:50,879 Speaker 6: That's a bubble talk escalate. 14 00:00:51,280 --> 00:00:54,040 Speaker 7: We'll discuss whether the bull run can continue, and we'll 15 00:00:54,040 --> 00:00:56,600 Speaker 7: wrap the week that was for TikTok after the House 16 00:00:56,680 --> 00:00:59,800 Speaker 7: voted to ban the social app or have it divested. 17 00:01:00,240 --> 00:01:01,920 Speaker 6: What can we expect from the Senate. 18 00:01:02,360 --> 00:01:05,520 Speaker 7: Adobe the biggest mover on the Nasdaq one hundred today, 19 00:01:05,520 --> 00:01:07,720 Speaker 7: we're off by almost fourteen percent. As we said, I mean, 20 00:01:07,760 --> 00:01:10,959 Speaker 7: having its worst way day in several decades. Let's talk 21 00:01:11,000 --> 00:01:13,280 Speaker 7: about why it's straight to the earnings with Bertie Ford 22 00:01:13,400 --> 00:01:16,200 Speaker 7: and could you actually dig into the individual numbers. Perhaps 23 00:01:16,240 --> 00:01:18,720 Speaker 7: it wasn't that big a miss, but the amount. 24 00:01:18,440 --> 00:01:21,279 Speaker 6: That the stock ran up last year. People are worried. 25 00:01:21,920 --> 00:01:25,320 Speaker 8: You ask investors which software companies will make money from AI. 26 00:01:25,440 --> 00:01:28,679 Speaker 8: In twenty twenty four, it was two names, Microsoft and Adobe. 27 00:01:29,160 --> 00:01:32,280 Speaker 8: After last night, investors are saying, maybe it's just Microsoft, 28 00:01:32,480 --> 00:01:36,200 Speaker 8: right because they expected to see this big surge in 29 00:01:36,440 --> 00:01:39,960 Speaker 8: new business because of the AI products. Remember, Adobe has 30 00:01:39,959 --> 00:01:42,640 Speaker 8: spent a lot of time and money putting its model 31 00:01:42,800 --> 00:01:46,399 Speaker 8: into Photoshop illustrators so you can press that button generate 32 00:01:46,959 --> 00:01:48,080 Speaker 8: and create a new image. 33 00:01:48,160 --> 00:01:50,960 Speaker 2: Right But it appears that right now they are. 34 00:01:50,840 --> 00:01:54,200 Speaker 8: Still more focused on new user acquisition and that kind 35 00:01:54,240 --> 00:01:57,880 Speaker 8: of monetization of this new innovation is not coming. 36 00:01:57,640 --> 00:01:58,960 Speaker 2: At least for a couple of quarters. 37 00:02:00,200 --> 00:02:03,760 Speaker 7: In the biggest full for Adobe since twenty twenty two, 38 00:02:04,080 --> 00:02:05,240 Speaker 7: September the fifteenth. 39 00:02:05,880 --> 00:02:08,120 Speaker 6: I'm interested as to how. 40 00:02:07,960 --> 00:02:10,240 Speaker 7: That is really focusing a light or some of the 41 00:02:10,320 --> 00:02:11,840 Speaker 7: startups that aren't publicly traded. 42 00:02:11,960 --> 00:02:13,320 Speaker 6: We can't see these sorts of moves in. 43 00:02:13,600 --> 00:02:15,400 Speaker 7: I mean, Sora must have been the big one that 44 00:02:15,440 --> 00:02:16,840 Speaker 7: everyone looked at and thought. 45 00:02:16,800 --> 00:02:19,680 Speaker 6: Oh boy, he's got to race to catch up here. 46 00:02:19,880 --> 00:02:22,239 Speaker 8: It was a funny moment about a year ago Dolly 47 00:02:22,440 --> 00:02:25,040 Speaker 8: came out and mid Journey came out. That image generation 48 00:02:25,720 --> 00:02:28,600 Speaker 8: people got real scared for Adobe. They responded with new 49 00:02:28,600 --> 00:02:32,600 Speaker 8: innovations and photoshop. Investors said, Okay, we'll be fine. Sora 50 00:02:32,760 --> 00:02:35,360 Speaker 8: came out and it's like Groundhog Day, right, everybody is 51 00:02:35,400 --> 00:02:35,960 Speaker 8: afraid again. 52 00:02:36,000 --> 00:02:38,600 Speaker 2: There's this same energy in the air that hey. 53 00:02:39,240 --> 00:02:43,639 Speaker 8: This new piece of technology could make Adobe entirely obsolete, right. 54 00:02:43,680 --> 00:02:46,079 Speaker 8: I mean, there was an interesting conversation on the earnings 55 00:02:46,160 --> 00:02:49,519 Speaker 8: last night where an analyst had to Adobe, Hey, folks 56 00:02:49,520 --> 00:02:52,240 Speaker 8: are getting worried that in the long run this software 57 00:02:52,320 --> 00:02:54,960 Speaker 8: could be unused. Can you explain to us why that's 58 00:02:54,960 --> 00:02:55,600 Speaker 8: not the case. 59 00:02:56,120 --> 00:02:56,720 Speaker 2: That's not a. 60 00:02:56,720 --> 00:02:59,720 Speaker 8: Level of candor and anxiety you tend to hear on these. 61 00:02:59,600 --> 00:03:03,880 Speaker 7: Calls CA I mean, this is what investors ultimately want, 62 00:03:04,000 --> 00:03:08,040 Speaker 7: is transparency, and actually we're kind of hearing that from 63 00:03:08,320 --> 00:03:09,040 Speaker 7: the CEO. 64 00:03:08,919 --> 00:03:10,040 Speaker 6: And a direction of trouble. 65 00:03:10,120 --> 00:03:13,079 Speaker 7: What is there anyone that's bullish that's thinking, actually, look, 66 00:03:13,080 --> 00:03:15,400 Speaker 7: they've just been conservative here. Here we will start to 67 00:03:15,400 --> 00:03:17,359 Speaker 7: see generator of AI coming to the bottom line, to 68 00:03:17,480 --> 00:03:18,760 Speaker 7: profitability to sales. 69 00:03:19,120 --> 00:03:22,080 Speaker 8: Yeah, there's this argument that right now, the beauty of 70 00:03:22,120 --> 00:03:24,920 Speaker 8: being a big company is that you can price for 71 00:03:25,480 --> 00:03:26,640 Speaker 8: customer acquisition, and. 72 00:03:26,680 --> 00:03:27,720 Speaker 2: That's what Adobe's doing. 73 00:03:27,760 --> 00:03:29,600 Speaker 8: Now, that's done so well over the years that it's 74 00:03:29,639 --> 00:03:32,200 Speaker 8: going to kind of get all the creatives in the 75 00:03:32,200 --> 00:03:35,400 Speaker 8: ecosystem using its products and then monetize it. 76 00:03:35,880 --> 00:03:37,000 Speaker 2: Right, that's the argument. 77 00:03:37,800 --> 00:03:40,760 Speaker 8: Some other folks might see the numbers and say, we 78 00:03:40,800 --> 00:03:42,680 Speaker 8: want a revenue uplift. Now, I don't know if we're 79 00:03:42,680 --> 00:03:45,560 Speaker 8: willing to wait that long. When open AI is generating 80 00:03:45,640 --> 00:03:46,960 Speaker 8: videos with prompts. 81 00:03:46,560 --> 00:03:50,200 Speaker 7: Already to that point, I mean videos with prompts, it 82 00:03:50,240 --> 00:03:53,760 Speaker 7: does feel as though that actually isn't just around the corner. 83 00:03:53,800 --> 00:03:54,960 Speaker 6: I mean, what was it that. 84 00:03:54,880 --> 00:03:56,960 Speaker 7: I think Nryan was saying that this notion that the 85 00:03:56,960 --> 00:03:59,960 Speaker 7: next Oppenheim or will be done video prompt is not 86 00:04:00,160 --> 00:04:01,240 Speaker 7: going to happen for decades. 87 00:04:01,400 --> 00:04:03,960 Speaker 8: Yeah, And it's funny because he said this notion that 88 00:04:04,000 --> 00:04:05,920 Speaker 8: the next op at Haybro we don't have prompts, it's 89 00:04:05,960 --> 00:04:06,720 Speaker 8: not going to happen. 90 00:04:07,080 --> 00:04:09,160 Speaker 2: Then he kind of pauses us at least for a 91 00:04:09,200 --> 00:04:11,400 Speaker 2: couple decades, right, because there's. 92 00:04:11,200 --> 00:04:14,600 Speaker 8: This understanding that AI will fundamentally change the way that 93 00:04:14,640 --> 00:04:17,240 Speaker 8: applications for image and video happen. 94 00:04:17,360 --> 00:04:18,880 Speaker 2: Right for now, you're. 95 00:04:18,680 --> 00:04:20,480 Speaker 8: Just going to be able to tweak little things at 96 00:04:20,520 --> 00:04:23,640 Speaker 8: the margins. A decade or two down the line are 97 00:04:23,680 --> 00:04:27,200 Speaker 8: you going to have, you know, four hundred million creatives 98 00:04:27,240 --> 00:04:28,279 Speaker 8: paying for photoshop. 99 00:04:28,680 --> 00:04:30,599 Speaker 2: You might not need that many people. 100 00:04:31,440 --> 00:04:33,839 Speaker 7: But if the training data is when they say it is, 101 00:04:33,880 --> 00:04:35,960 Speaker 7: and you've got to put a protection there might be 102 00:04:36,000 --> 00:04:37,800 Speaker 7: helpful to them in some way, shape or form. 103 00:04:38,160 --> 00:04:38,440 Speaker 3: Yeah. 104 00:04:38,560 --> 00:04:40,680 Speaker 8: No, absolutely right, And that's the big question around the 105 00:04:40,720 --> 00:04:44,520 Speaker 8: training data, right is it that Adobe's big pitch as 106 00:04:44,560 --> 00:04:47,279 Speaker 8: it relates to AI is that we have this safety, 107 00:04:47,400 --> 00:04:50,960 Speaker 8: we have the copyright credentials. Whereas when it comes to 108 00:04:50,960 --> 00:04:53,240 Speaker 8: open AI, there's this idea that, hey, where are you 109 00:04:53,279 --> 00:04:55,680 Speaker 8: scraping it from? I think we all saw this very 110 00:04:55,680 --> 00:04:57,920 Speaker 8: famous interview with our CTO a couple of days ago. 111 00:04:58,040 --> 00:04:59,480 Speaker 8: He said, are you scraping from YouTube? 112 00:05:01,080 --> 00:05:02,000 Speaker 6: You answer it? 113 00:05:02,560 --> 00:05:03,000 Speaker 2: Answer it. 114 00:05:03,040 --> 00:05:05,000 Speaker 7: I'm sure she has more answers to that question now, 115 00:05:05,040 --> 00:05:07,600 Speaker 7: but some plenty of means being made with that particular 116 00:05:07,640 --> 00:05:10,000 Speaker 7: interview by the Wall Street Journal. Thirty two buys still 117 00:05:10,000 --> 00:05:12,360 Speaker 7: though on Adobe two sounds brilliant. 118 00:05:12,040 --> 00:05:12,440 Speaker 6: To have you on. 119 00:05:12,520 --> 00:05:15,000 Speaker 7: Thank you, Jody Forward running us through the latest and 120 00:05:15,040 --> 00:05:17,240 Speaker 7: the big dive in the Adobe share price as we say, 121 00:05:17,640 --> 00:05:19,600 Speaker 7: having its worst day since twenty twenty two. 122 00:05:19,720 --> 00:05:21,560 Speaker 6: Coming up. Bitcoin it's retreating from. 123 00:05:21,440 --> 00:05:24,760 Speaker 7: Its latest record high too, and that's going to take 124 00:05:24,800 --> 00:05:27,960 Speaker 7: on why we've got citizens JMPS Devin Ryan joining us. 125 00:05:28,160 --> 00:05:44,120 Speaker 9: This is Blueberg Technology. 126 00:05:46,400 --> 00:05:48,440 Speaker 7: Let's just talk about crypto because there's been a week 127 00:05:48,480 --> 00:05:51,159 Speaker 7: and Bitcoin there is actually pulling back from its recent 128 00:05:51,240 --> 00:05:54,120 Speaker 7: record highs that it's recently set, and there's an intensifying 129 00:05:54,160 --> 00:05:56,360 Speaker 7: debate about whether the ball run that we've seen across 130 00:05:56,360 --> 00:05:59,520 Speaker 7: the cryptosphere is kind of evidence of just speculative froth 131 00:05:59,520 --> 00:06:01,080 Speaker 7: that we're seeing across global markets. 132 00:06:01,240 --> 00:06:02,719 Speaker 6: Let's break that down with Shannali beassec. 133 00:06:02,760 --> 00:06:06,760 Speaker 7: And there's been some wild headlines, wild expectations today, the 134 00:06:06,760 --> 00:06:10,239 Speaker 7: pullback yesterday, we're hearing of a monster cycle in crypto 135 00:06:10,320 --> 00:06:11,360 Speaker 7: that was only just starting. 136 00:06:11,480 --> 00:06:13,920 Speaker 10: Yeah, it's interesting because you have seen just super inflows 137 00:06:13,920 --> 00:06:16,000 Speaker 10: into those ETFs, You have the having in front of you, 138 00:06:16,080 --> 00:06:17,560 Speaker 10: and you have a lot of people saying you have 139 00:06:17,640 --> 00:06:21,240 Speaker 10: this never before seen dynamic in bitcoin where you're going 140 00:06:21,320 --> 00:06:24,120 Speaker 10: to have more demand while supply is decreasing, but at 141 00:06:24,160 --> 00:06:27,120 Speaker 10: the same time there's leverage in these markets, Caroline, so. 142 00:06:27,200 --> 00:06:28,760 Speaker 5: Not surprising to see a pullback. 143 00:06:28,800 --> 00:06:30,760 Speaker 10: A lot of people have been calling from it, everyone 144 00:06:30,839 --> 00:06:34,400 Speaker 10: from Glenn Goodman to Mike Novograts have expected there to 145 00:06:34,480 --> 00:06:37,000 Speaker 10: be a pullback given the leverage that's been seen. At 146 00:06:37,000 --> 00:06:39,760 Speaker 10: some point, when you're buying things on margin or buying 147 00:06:39,800 --> 00:06:42,400 Speaker 10: things with money you don't have, there is always that 148 00:06:42,440 --> 00:06:45,880 Speaker 10: potential for a little bit of a reversal. However, you 149 00:06:45,960 --> 00:06:48,480 Speaker 10: do only see really us come full circle. 150 00:06:48,560 --> 00:06:49,080 Speaker 6: For the week. 151 00:06:49,480 --> 00:06:52,919 Speaker 10: We have still been well above sixty seven thousand, but 152 00:06:52,960 --> 00:06:55,520 Speaker 10: we have seen bitcoin hit those new highs even just 153 00:06:55,560 --> 00:06:57,320 Speaker 10: this week above seventy two thousand. 154 00:06:57,680 --> 00:06:59,760 Speaker 7: Talk about the leverage and where it's coming in from 155 00:06:59,800 --> 00:07:02,800 Speaker 7: the beause many would associate that with Asian investors, but 156 00:07:02,920 --> 00:07:05,360 Speaker 7: we'll keep talking about the institutional bar that's just come 157 00:07:05,360 --> 00:07:07,799 Speaker 7: into the market with these US bitcoins, bail etm. 158 00:07:08,000 --> 00:07:10,160 Speaker 10: Just think about how much leverage there is in the 159 00:07:10,160 --> 00:07:12,040 Speaker 10: system now. It's not the same type of leverage you 160 00:07:12,040 --> 00:07:14,120 Speaker 10: saw in twenty twenty one with the same types of players. 161 00:07:14,160 --> 00:07:17,760 Speaker 10: Of course, we saw those leverage intermediaries blow up, but 162 00:07:17,880 --> 00:07:20,280 Speaker 10: now the type of leverage you're seeing are from things 163 00:07:20,400 --> 00:07:24,440 Speaker 10: like finance perpetual futures, which are largely outside of the 164 00:07:24,520 --> 00:07:27,040 Speaker 10: United States. You have them in bitcoin futures as well, 165 00:07:27,080 --> 00:07:29,680 Speaker 10: which you buy futures on margin, and then you have 166 00:07:29,800 --> 00:07:33,200 Speaker 10: other forms of leverage. You think micro strategy issuing bonds 167 00:07:33,600 --> 00:07:37,720 Speaker 10: to buy bitcoin, and other big crypto players also tapping 168 00:07:37,760 --> 00:07:41,520 Speaker 10: debt markets in order to really capture the part of 169 00:07:41,560 --> 00:07:44,200 Speaker 10: this boom. So you can make an argument here that 170 00:07:44,320 --> 00:07:47,840 Speaker 10: the leverage you're seeing is happening through safer entities, more 171 00:07:47,880 --> 00:07:50,560 Speaker 10: regulated markets. Even in the United States. You think about 172 00:07:50,600 --> 00:07:54,000 Speaker 10: some of the leverage you also see in leveraged ETFs 173 00:07:54,520 --> 00:07:57,680 Speaker 10: for people trying to really amplify their exposure to bitcoin 174 00:07:57,760 --> 00:08:01,200 Speaker 10: at these prices. Now, the big question is is everything 175 00:08:01,200 --> 00:08:04,560 Speaker 10: we've expected out of the positivity in bitcoin priced in 176 00:08:04,720 --> 00:08:07,080 Speaker 10: right now or is there another thing that will add 177 00:08:07,120 --> 00:08:08,600 Speaker 10: some fuel to this cycle after that. 178 00:08:08,720 --> 00:08:12,280 Speaker 7: Having we can keep an eye on correlations, correlations within crypto, 179 00:08:12,280 --> 00:08:14,360 Speaker 7: Bitcoin down, Solana still up, but we also think about 180 00:08:14,400 --> 00:08:17,400 Speaker 7: the correlations between tech stocks, more badley, the markets, and 181 00:08:17,440 --> 00:08:19,560 Speaker 7: Cryptotionalibasik always does it all for. 182 00:08:19,640 --> 00:08:20,840 Speaker 6: Us, and we thank her so much. 183 00:08:20,960 --> 00:08:23,600 Speaker 7: Let's dig in really about some of the outsized returns 184 00:08:23,600 --> 00:08:25,680 Speaker 7: we've seen of late, whether we can continue what this 185 00:08:25,720 --> 00:08:27,800 Speaker 7: is in terms of a healthy or not pullback with 186 00:08:27,960 --> 00:08:31,560 Speaker 7: citizens Damp Director of Financial Technology Research, Devin Ryan, it's 187 00:08:31,560 --> 00:08:34,080 Speaker 7: great to speak to you again, Devon, and some of 188 00:08:34,120 --> 00:08:36,960 Speaker 7: your notes have been pretty extraordinary in terms of the 189 00:08:37,000 --> 00:08:39,960 Speaker 7: sheer scale of money that is entering this space and 190 00:08:40,000 --> 00:08:41,840 Speaker 7: where you see some of the price points going. Can 191 00:08:41,840 --> 00:08:44,080 Speaker 7: you just talk us through your main thesis of what 192 00:08:44,120 --> 00:08:47,120 Speaker 7: the ets could do for bitcoin and the ecosystem more generally. 193 00:08:48,200 --> 00:08:50,640 Speaker 3: Yeah, happy too, Caroline, Thanks so much for having me on. 194 00:08:50,720 --> 00:08:53,400 Speaker 11: So we put on a note earlier this week, and 195 00:08:53,640 --> 00:08:57,160 Speaker 11: we are talking about two hundred and twenty billion dollars 196 00:08:57,320 --> 00:08:59,720 Speaker 11: of net inflows into these ETFs. 197 00:08:59,320 --> 00:09:00,480 Speaker 3: Over the next three years. 198 00:09:00,520 --> 00:09:03,319 Speaker 11: So for context, there's been about ten billion of net 199 00:09:03,360 --> 00:09:06,240 Speaker 11: inflows over the first two months of launch, but we 200 00:09:06,280 --> 00:09:08,600 Speaker 11: see that accelerating quite a bit, and the reason being 201 00:09:08,679 --> 00:09:12,120 Speaker 11: is that we've essentially opened up a lot of additional 202 00:09:12,160 --> 00:09:16,079 Speaker 11: capital that historically, up until January this year, was shut 203 00:09:16,080 --> 00:09:19,559 Speaker 11: out of investing in crypto and bitcoin specifically. The biggest 204 00:09:19,559 --> 00:09:22,080 Speaker 11: pool that is retail. 205 00:09:21,960 --> 00:09:23,640 Speaker 3: Investors advisor led money. 206 00:09:23,640 --> 00:09:27,000 Speaker 11: There's about twenty five trillion dollars of assets where advisors 207 00:09:27,000 --> 00:09:30,000 Speaker 11: really want to custody the money within their own custodian, 208 00:09:30,080 --> 00:09:32,240 Speaker 11: not move it out to somewhere like coinbase. And so 209 00:09:32,320 --> 00:09:36,000 Speaker 11: ultimately now these advisors with the ETF approval. 210 00:09:35,800 --> 00:09:37,079 Speaker 3: Can start to look at that. But I think we're 211 00:09:37,080 --> 00:09:37,640 Speaker 3: in the first inning. 212 00:09:37,640 --> 00:09:39,920 Speaker 11: I don't think advisors have really been allocated yet, so 213 00:09:40,320 --> 00:09:43,079 Speaker 11: we see flows actually accelerating from here. 214 00:09:43,080 --> 00:09:44,319 Speaker 3: So there will be volatility. 215 00:09:44,400 --> 00:09:47,880 Speaker 11: But you know, in my career covering financials and fintech 216 00:09:47,920 --> 00:09:50,640 Speaker 11: for twenty years, you know, follow the flows and when 217 00:09:50,640 --> 00:09:53,079 Speaker 11: there's money coming into a new market and it's substantial, 218 00:09:53,080 --> 00:09:56,040 Speaker 11: which we think it will be with these ETFs, it's 219 00:09:56,080 --> 00:09:57,040 Speaker 11: really transformational. 220 00:09:57,520 --> 00:10:01,560 Speaker 7: It is amazing that we've been talking some of these cycles. 221 00:10:01,120 --> 00:10:03,760 Speaker 6: For years, but only now do we hear the like. 222 00:10:03,800 --> 00:10:06,840 Speaker 7: So you Devin talking about two hundred and twenty billion 223 00:10:06,880 --> 00:10:09,920 Speaker 7: dollars coming in, and we hear Bernstein yesterday out with 224 00:10:10,000 --> 00:10:10,880 Speaker 7: a note saying. 225 00:10:10,679 --> 00:10:14,800 Speaker 6: We are entering this monster cycle for crypto. What changed 226 00:10:14,800 --> 00:10:16,240 Speaker 6: your tune? Was it the ETF? 227 00:10:17,240 --> 00:10:20,760 Speaker 11: Yeah, We've been consistently constructive on the space, and you know, 228 00:10:20,760 --> 00:10:23,160 Speaker 11: I cover Coinbase, which is in our view, kind of 229 00:10:23,160 --> 00:10:26,319 Speaker 11: the biggest on ramp in the public markets into the industry. 230 00:10:26,559 --> 00:10:28,120 Speaker 11: I think you guys touched on it. There's a lot 231 00:10:28,160 --> 00:10:30,800 Speaker 11: of speculation going on. You have Bitcoin and then you 232 00:10:30,840 --> 00:10:33,520 Speaker 11: have you know, twenty thousand other cryptocurrencies, and I think 233 00:10:33,559 --> 00:10:36,160 Speaker 11: that many of those cryptocurrencies are going to be worthless 234 00:10:36,160 --> 00:10:36,440 Speaker 11: when you. 235 00:10:36,440 --> 00:10:38,200 Speaker 3: Look out over the next couple of decades. 236 00:10:38,280 --> 00:10:40,640 Speaker 11: That being said, we are in the early innings, and 237 00:10:40,640 --> 00:10:43,559 Speaker 11: so people are putting their chips on various blockchains that 238 00:10:43,600 --> 00:10:46,440 Speaker 11: they think will have development on top of them. And 239 00:10:46,600 --> 00:10:48,400 Speaker 11: just like you would in a technology stock or even 240 00:10:48,440 --> 00:10:51,199 Speaker 11: a biotech stock that is, you know, in its formation days, 241 00:10:51,240 --> 00:10:53,280 Speaker 11: you know, it's going to take ten years to get 242 00:10:53,320 --> 00:10:56,000 Speaker 11: to commercial application, and a lot of these blockchains are 243 00:10:56,040 --> 00:10:57,880 Speaker 11: in their first inning and people are speculating on which 244 00:10:57,880 --> 00:11:00,800 Speaker 11: ones will be utilized. And so I'm very bullish on 245 00:11:01,080 --> 00:11:04,760 Speaker 11: the applications the block technology that's kind of the broader 246 00:11:04,800 --> 00:11:07,920 Speaker 11: crypto ecosystem that coinbase is benefiting from. And then the 247 00:11:07,920 --> 00:11:10,600 Speaker 11: ETF for bitcoin is in my opinion, kind of its 248 00:11:10,600 --> 00:11:13,440 Speaker 11: own animal. But we do see again, you know, we 249 00:11:13,480 --> 00:11:16,680 Speaker 11: think people are going to increasingly move some money, you know, 250 00:11:16,760 --> 00:11:19,240 Speaker 11: small amounts, but small amounts now that you've opened the 251 00:11:19,280 --> 00:11:21,480 Speaker 11: door to, in our opinion, over one hundred trillion dollars 252 00:11:21,480 --> 00:11:24,080 Speaker 11: of capital in the US that potentially could look at 253 00:11:24,120 --> 00:11:27,200 Speaker 11: allocating something here, and we're shut out, you know, much 254 00:11:27,200 --> 00:11:28,720 Speaker 11: of that until January eleventh. 255 00:11:29,400 --> 00:11:32,280 Speaker 7: You mentioned coinbase, and just a disclosure that my husband 256 00:11:32,320 --> 00:11:34,760 Speaker 7: is a director over at coinbase, but I am interested 257 00:11:34,800 --> 00:11:37,760 Speaker 7: in outside of the space of just Bitcoin. 258 00:11:37,800 --> 00:11:39,280 Speaker 6: Bitcoin is sort of hoovered. 259 00:11:39,080 --> 00:11:40,880 Speaker 7: Up all the oxygen in terms of being able to 260 00:11:40,880 --> 00:11:42,280 Speaker 7: shine all out on the price, being able to talk 261 00:11:42,320 --> 00:11:45,040 Speaker 7: about the ets but there is talk of an e ETF. 262 00:11:45,080 --> 00:11:48,160 Speaker 7: There is still Solana doing very well, for example, other 263 00:11:48,240 --> 00:11:48,840 Speaker 7: old coins. 264 00:11:49,600 --> 00:11:51,920 Speaker 6: How does that spillover effect continue. 265 00:11:52,480 --> 00:11:53,920 Speaker 3: Yeah, it's a great question, Caroline. 266 00:11:53,920 --> 00:11:55,880 Speaker 11: So I think there was a lot of discussion in 267 00:11:55,920 --> 00:11:58,520 Speaker 11: the market leading to the Bitcoin ETF that would be 268 00:11:58,520 --> 00:12:01,360 Speaker 11: cannibalistic and that everybody would just trade the ETF and 269 00:12:01,400 --> 00:12:03,559 Speaker 11: then that would hurt Coinbase. We were taking the other 270 00:12:03,600 --> 00:12:05,160 Speaker 11: side of that argument. But I think the evidence is 271 00:12:05,200 --> 00:12:08,840 Speaker 11: now clear that in reality what's happened is there's been 272 00:12:08,880 --> 00:12:10,960 Speaker 11: a significant amount of more money that's moved into the 273 00:12:11,000 --> 00:12:14,880 Speaker 11: broader space, both Bitcoin, where Coinbase is participating a lot 274 00:12:14,880 --> 00:12:17,959 Speaker 11: of the trading of the underlying assets in the ETF 275 00:12:18,000 --> 00:12:20,480 Speaker 11: and then elsewhere. But their volumes are up over one 276 00:12:20,559 --> 00:12:24,480 Speaker 11: hundred percent trading volumes over the first quarter last year, 277 00:12:24,720 --> 00:12:26,480 Speaker 11: and a lot of that's happening in all of these 278 00:12:26,520 --> 00:12:29,480 Speaker 11: other assets, because again, people are more interested in learning 279 00:12:29,480 --> 00:12:32,960 Speaker 11: about the space and taking views on other blockchain technologies 280 00:12:32,960 --> 00:12:35,800 Speaker 11: and which ones you could be successful, and so that's 281 00:12:35,880 --> 00:12:37,960 Speaker 11: really what's happening. I think we're still kind of in 282 00:12:37,960 --> 00:12:40,400 Speaker 11: the early days of that as well. That's coinbase. But 283 00:12:40,400 --> 00:12:43,360 Speaker 11: then Coinbase is also, you know, in our opinion, going 284 00:12:43,400 --> 00:12:45,400 Speaker 11: to have their hand in virtually every aspect of how 285 00:12:45,440 --> 00:12:48,640 Speaker 11: this industry grows, whether it's tokenization of real world assets, 286 00:12:48,880 --> 00:12:53,400 Speaker 11: whether it's you know, payments and remittance and development of 287 00:12:53,520 --> 00:12:54,600 Speaker 11: Web three applications. 288 00:12:54,600 --> 00:12:56,280 Speaker 3: You know, they have their own blockchain called Base. 289 00:12:56,720 --> 00:12:58,360 Speaker 11: You know, they're very active and staking and there's going 290 00:12:58,400 --> 00:13:00,400 Speaker 11: to be a lot of innovation in this industry that 291 00:13:00,440 --> 00:13:01,760 Speaker 11: I don't think has even happened yet. 292 00:13:01,800 --> 00:13:02,839 Speaker 3: So coinbas to. 293 00:13:02,800 --> 00:13:05,080 Speaker 11: Us is really a play on that growth, even more 294 00:13:05,120 --> 00:13:08,040 Speaker 11: so than just trading volumes and you know, the exchange activity. 295 00:13:08,120 --> 00:13:11,199 Speaker 7: It's interesting that Bernstein called out robin Hood is the 296 00:13:11,280 --> 00:13:11,880 Speaker 7: key play. 297 00:13:12,040 --> 00:13:13,000 Speaker 6: You've got a twenty. 298 00:13:12,720 --> 00:13:15,160 Speaker 7: Five dollars price target, I believe in that particular company. 299 00:13:15,640 --> 00:13:18,560 Speaker 7: Where else could be benefiting in terms of brokerages. Is 300 00:13:18,600 --> 00:13:21,959 Speaker 7: it all about the institutional play and the retail play 301 00:13:22,000 --> 00:13:23,760 Speaker 7: Still yeah. 302 00:13:23,559 --> 00:13:26,800 Speaker 11: I think you know, Galaxy is a name they're Canadian 303 00:13:26,840 --> 00:13:29,800 Speaker 11: listed right now, but that's Michael Lenovogratz. As you guys mentioned, 304 00:13:30,360 --> 00:13:32,920 Speaker 11: They're clearly a public market play here. We know a 305 00:13:32,960 --> 00:13:34,640 Speaker 11: lot of the private players in this space, and there's 306 00:13:34,679 --> 00:13:37,360 Speaker 11: a lot of really interesting companies in the private markets. 307 00:13:37,640 --> 00:13:40,760 Speaker 11: Robin Hood, you know, clearly has an investor base that 308 00:13:40,960 --> 00:13:44,480 Speaker 11: is I think in the demographic that is active in crypto. 309 00:13:44,559 --> 00:13:46,760 Speaker 11: It's not as big of a driver for their business model. 310 00:13:47,040 --> 00:13:50,520 Speaker 11: Coinbase is really a pure play on the crypto ecosystem, 311 00:13:50,559 --> 00:13:54,199 Speaker 11: both trading but then also development and how was blockchain 312 00:13:54,520 --> 00:13:56,960 Speaker 11: used over the next five to ten years, and they're 313 00:13:56,960 --> 00:13:59,280 Speaker 11: going to participate in all that. I think Robinhood has 314 00:13:59,440 --> 00:14:00,920 Speaker 11: you know, their tenacles into some of that. 315 00:14:01,120 --> 00:14:03,960 Speaker 3: But you know, robin Hoood's a broader brokerage, which is good. 316 00:14:04,200 --> 00:14:06,600 Speaker 11: A lot of the other brokerages really, you know, they're 317 00:14:06,600 --> 00:14:08,800 Speaker 11: barely there yet. And that's also, in our opinion, part 318 00:14:08,840 --> 00:14:12,000 Speaker 11: of the opportunity the self directed market. I would put 319 00:14:12,040 --> 00:14:16,040 Speaker 11: in that bucket of retail investors and we estimate that 320 00:14:16,080 --> 00:14:18,679 Speaker 11: two hundred and twenty billion dollars of net inflows. You 321 00:14:18,720 --> 00:14:20,800 Speaker 11: one hundred and sixty one hundred and seventy billion of 322 00:14:20,840 --> 00:14:23,400 Speaker 11: that is still going to come from retail, whether self 323 00:14:23,440 --> 00:14:26,600 Speaker 11: directed or advisor led. And I've covered the wealth management 324 00:14:26,600 --> 00:14:30,400 Speaker 11: market for twenty years. These are folks that again they're 325 00:14:30,680 --> 00:14:33,640 Speaker 11: relatively conservative, but they want to invest where the customers 326 00:14:33,680 --> 00:14:34,920 Speaker 11: want to invest, and I think the people are going 327 00:14:34,960 --> 00:14:39,480 Speaker 11: to want some small access to this new asset class. 328 00:14:39,520 --> 00:14:43,040 Speaker 7: Sticking with it, citizens, JMP Director of Financial Technology Research 329 00:14:43,080 --> 00:14:45,320 Speaker 7: Devin Ryan. Great to talk through your note through some 330 00:14:45,360 --> 00:14:46,200 Speaker 7: of the players within it. 331 00:14:46,400 --> 00:14:57,000 Speaker 6: Thank you time now for talking tech. First up, TikTok. 332 00:14:56,680 --> 00:15:00,000 Speaker 7: Owner bike Dance is human resources chief taking the helm. 333 00:15:00,120 --> 00:15:03,440 Speaker 7: The companies sharply reduced gaming division and we'll refocus the 334 00:15:03,480 --> 00:15:06,240 Speaker 7: business on more personal content in look, an effort to 335 00:15:06,280 --> 00:15:09,640 Speaker 7: avoid the head on clash with Tencent is a pivot 336 00:15:09,760 --> 00:15:11,880 Speaker 7: for a unit that once hoped to take on the 337 00:15:11,960 --> 00:15:16,320 Speaker 7: leading Chinese game distribution leaders like Tencent and of course Nettes. Meanwhile, 338 00:15:16,760 --> 00:15:20,360 Speaker 7: the Chinese government is quietly encouraging eed makers from BYD 339 00:15:20,520 --> 00:15:24,000 Speaker 7: to Giely to sharply increase their purchases from local auto 340 00:15:24,080 --> 00:15:26,400 Speaker 7: chip makers. Is in an effort to reduce reliance on 341 00:15:26,480 --> 00:15:27,280 Speaker 7: Western imports. 342 00:15:27,400 --> 00:15:30,040 Speaker 6: Of course boosts China's own domestics semi conductor industry. 343 00:15:30,520 --> 00:15:33,320 Speaker 7: Plus, the US plans to award more than six billion 344 00:15:33,360 --> 00:15:36,240 Speaker 7: dollars to Samsung to help the chip maker expand beyond 345 00:15:36,360 --> 00:15:39,600 Speaker 7: its project in Texas, the federal funding for South Korea's 346 00:15:39,640 --> 00:15:43,480 Speaker 7: leading chip maker we're becoming, alongside significant additional US investment 347 00:15:43,560 --> 00:15:44,480 Speaker 7: by the firm. 348 00:15:44,640 --> 00:15:46,400 Speaker 6: All of that, according to sources. 349 00:15:47,120 --> 00:15:49,760 Speaker 7: Now, let's turn our attention to cybersecurity, always top of 350 00:15:49,800 --> 00:15:53,880 Speaker 7: mind for executives, the board of businesses, and you and 351 00:15:53,920 --> 00:15:57,160 Speaker 7: me as individuals. Microsoft Corporate Vice President Security, Compliance, Identity 352 00:15:57,160 --> 00:15:59,360 Speaker 7: and Management, Basu Jaka is here to tell us all 353 00:15:59,360 --> 00:16:02,840 Speaker 7: about microsf co Pilot for Security. It's a generative AI 354 00:16:02,880 --> 00:16:05,960 Speaker 7: offering that will be being made widely available at prefers. 355 00:16:06,360 --> 00:16:07,920 Speaker 6: What's the aim here for you. 356 00:16:08,120 --> 00:16:09,920 Speaker 7: To support security it professionals? 357 00:16:09,960 --> 00:16:11,440 Speaker 6: How much easier is their life going to get? 358 00:16:12,440 --> 00:16:12,640 Speaker 3: Well? 359 00:16:12,840 --> 00:16:15,160 Speaker 5: Caroline, it's so great to be here with you. 360 00:16:15,240 --> 00:16:18,760 Speaker 4: Thank you for having me to understand why we did 361 00:16:18,800 --> 00:16:21,040 Speaker 4: go by it for security. Step back and look at 362 00:16:21,040 --> 00:16:25,400 Speaker 4: what we're facing against and today the threat landscape is unprecedented. 363 00:16:25,440 --> 00:16:28,800 Speaker 4: The speed, the scale, the sophistication of attacks is increasing. 364 00:16:29,240 --> 00:16:31,560 Speaker 4: Just as an example, we're seeing a tanex increase in 365 00:16:31,640 --> 00:16:34,560 Speaker 4: identity related attacks from three billion to thirty billion over 366 00:16:34,560 --> 00:16:36,160 Speaker 4: the same time frame year over year. 367 00:16:36,600 --> 00:16:39,080 Speaker 5: And this is why we designed GO Pilot for Security. 368 00:16:39,480 --> 00:16:43,160 Speaker 4: It's the industry's first generative AI product and the industry's 369 00:16:43,240 --> 00:16:46,680 Speaker 4: leading generative AI product that's based on open AICHATGBT four 370 00:16:46,680 --> 00:16:50,240 Speaker 4: models plus a Microsoft Security model. And I do believe 371 00:16:50,480 --> 00:16:53,920 Speaker 4: that the next eighteen months of AI innovation are going 372 00:16:53,960 --> 00:16:57,360 Speaker 4: to determine the next eighteen years of cybersecurity. So that's 373 00:16:57,360 --> 00:16:59,640 Speaker 4: why we did this, because we believe that Go Buy 374 00:16:59,640 --> 00:17:03,160 Speaker 4: It for Beauty is going to help all defenders defend 375 00:17:03,160 --> 00:17:04,520 Speaker 4: at machine speed and scale. 376 00:17:04,760 --> 00:17:06,480 Speaker 7: I mean, this has been in beta, this has been 377 00:17:06,600 --> 00:17:07,960 Speaker 7: quietly rolled out and tested. 378 00:17:08,560 --> 00:17:11,520 Speaker 6: What does uptake Binlin, what is interest inbound men like? 379 00:17:12,680 --> 00:17:16,240 Speaker 4: Yeah, we actually announced co part for security last March, 380 00:17:16,760 --> 00:17:19,679 Speaker 4: and we went into private preview with tons of customers 381 00:17:19,680 --> 00:17:21,840 Speaker 4: because we wanted to do co creation. We wanted our 382 00:17:21,880 --> 00:17:25,199 Speaker 4: customers in the journey with us, and last fall we 383 00:17:25,280 --> 00:17:28,199 Speaker 4: then expanded that to hundreds of customers as well as 384 00:17:28,240 --> 00:17:31,639 Speaker 4: one hundred plus partners. What we've seen through that is 385 00:17:31,760 --> 00:17:34,680 Speaker 4: it's making defenders faster no matter who you are. So 386 00:17:34,680 --> 00:17:37,719 Speaker 4: if you're early in career, twenty six percent faster, if 387 00:17:37,760 --> 00:17:41,840 Speaker 4: your seasoned professional twenty two percent faster. We've seen ninety 388 00:17:41,880 --> 00:17:44,200 Speaker 4: seven percent of people want to use the tool again, 389 00:17:44,280 --> 00:17:46,560 Speaker 4: so there's a lot of sentiment, of positive sentiment, a 390 00:17:46,560 --> 00:17:50,160 Speaker 4: sentiment of joy using it, and a lot of organizations 391 00:17:50,160 --> 00:17:52,800 Speaker 4: are ready to embrace this or really positive feedback. 392 00:17:53,520 --> 00:17:55,600 Speaker 7: I'm going to ask a sensitive question here because you are, 393 00:17:55,640 --> 00:17:58,680 Speaker 7: of course in charge of the security business. More broadly 394 00:17:58,720 --> 00:18:01,560 Speaker 7: over at Microsoft, we've had some recent disclosures. I think 395 00:18:01,600 --> 00:18:03,560 Speaker 7: of last summer, the Chinese hackers. I think of just 396 00:18:03,600 --> 00:18:07,679 Speaker 7: a couple of months ago, the Russian affiliated hack that 397 00:18:07,720 --> 00:18:09,040 Speaker 7: we understand. 398 00:18:08,520 --> 00:18:11,280 Speaker 6: That you know, Senator Ron Wyden was saying, this is 399 00:18:11,320 --> 00:18:15,159 Speaker 6: as a compromise, is inexcusable? How much questioning if you 400 00:18:15,240 --> 00:18:15,800 Speaker 6: got on that? 401 00:18:16,040 --> 00:18:20,480 Speaker 7: And is it impacting demand for security products coming from Microsoft. 402 00:18:21,440 --> 00:18:24,440 Speaker 4: We recently publish an update on what are the Microsoft 403 00:18:24,560 --> 00:18:28,159 Speaker 4: actions that we are taking to address the cyber attack 404 00:18:28,240 --> 00:18:29,720 Speaker 4: that you just reference scale line. 405 00:18:29,760 --> 00:18:33,080 Speaker 5: It was published and Friday. We continue to do to 406 00:18:33,119 --> 00:18:34,000 Speaker 5: do all we can. 407 00:18:34,080 --> 00:18:36,959 Speaker 4: To protect Microsoft, to protect our customers, and we are 408 00:18:37,040 --> 00:18:40,959 Speaker 4: using our cybersecurity tools to really integrate the latest knowledge 409 00:18:40,960 --> 00:18:44,159 Speaker 4: from the threat landscape and then continue to protect comprehensively. 410 00:18:45,000 --> 00:18:48,119 Speaker 7: Has it hit sales though, because many would say the 411 00:18:48,119 --> 00:18:50,800 Speaker 7: compromise from the Russian hackers it was like cybersecurity one 412 00:18:50,800 --> 00:18:51,080 Speaker 7: I won. 413 00:18:53,320 --> 00:18:55,480 Speaker 5: We are continuing to our investigation. 414 00:18:55,640 --> 00:18:58,080 Speaker 4: I can't talk more than what we said on Friday, 415 00:18:58,560 --> 00:19:01,480 Speaker 4: and we continue to see our gusts using more. 416 00:19:01,359 --> 00:19:02,679 Speaker 5: Of our cybersecurity tools. 417 00:19:02,800 --> 00:19:05,439 Speaker 4: Today, we have more than a million customers who use 418 00:19:05,520 --> 00:19:08,600 Speaker 4: Microsoft security products, and as we talked about for our 419 00:19:08,600 --> 00:19:10,800 Speaker 4: co Pilate for Security, we are seeing great interest. 420 00:19:11,640 --> 00:19:14,840 Speaker 7: How is that interest coming globally at the moment? Where 421 00:19:14,840 --> 00:19:18,080 Speaker 7: are you seeing the demand come from? What kind of clients? 422 00:19:18,080 --> 00:19:19,920 Speaker 7: Are they predominantly US Western Mas? 423 00:19:20,000 --> 00:19:20,960 Speaker 6: Do you seeing it elsewhere? 424 00:19:21,920 --> 00:19:25,119 Speaker 4: We're seeing actually global demand for this, and we're making 425 00:19:25,119 --> 00:19:29,399 Speaker 4: it available globally as well. Our general availability starting April 426 00:19:29,440 --> 00:19:32,960 Speaker 4: first is global. We started in English, but we're rolling 427 00:19:32,960 --> 00:19:38,520 Speaker 4: out eight new languages Japanese and Portuguese, Spanish, et cetera, 428 00:19:39,000 --> 00:19:41,800 Speaker 4: and we expect to continue to roll this out globally 429 00:19:41,960 --> 00:19:45,760 Speaker 4: across the year. The interest in customers is also worldwide. 430 00:19:45,800 --> 00:19:48,200 Speaker 4: There is no specific region. And then again, the way 431 00:19:48,200 --> 00:19:51,440 Speaker 4: we've designed Copilate for Security is for all, so we 432 00:19:51,640 --> 00:19:54,960 Speaker 4: wanted all kinds of defenders to use it, all organizations, 433 00:19:54,960 --> 00:19:58,480 Speaker 4: public sector, private sectors, small and medium businesses, large enterprise, 434 00:19:58,920 --> 00:20:00,399 Speaker 4: So we are seeing that as well. 435 00:20:01,560 --> 00:20:04,840 Speaker 7: What's interesting is because I think being is present in China, 436 00:20:04,960 --> 00:20:08,320 Speaker 7: for example, how much do you have inbound interest for 437 00:20:08,359 --> 00:20:11,760 Speaker 7: your products from a security basis coming from China. 438 00:20:11,920 --> 00:20:14,840 Speaker 4: We have interest across the world for our security products, 439 00:20:14,840 --> 00:20:16,119 Speaker 4: and we have it available. 440 00:20:16,160 --> 00:20:17,960 Speaker 5: We have different products today. 441 00:20:17,960 --> 00:20:20,719 Speaker 4: We integrate fifty plus categories and bring you to life 442 00:20:20,720 --> 00:20:24,400 Speaker 4: in six product families which form our Microsoft Security Cloud. 443 00:20:24,760 --> 00:20:27,679 Speaker 4: And depending on where customers are in the journey, they 444 00:20:27,680 --> 00:20:30,439 Speaker 4: are using different parts of our products. Some use the 445 00:20:30,480 --> 00:20:33,280 Speaker 4: full stacks, some use just one or two products, and 446 00:20:33,359 --> 00:20:36,399 Speaker 4: we have seven hundred thousand plus customers who are using 447 00:20:36,520 --> 00:20:37,800 Speaker 4: four or more of our products. 448 00:20:37,800 --> 00:20:41,920 Speaker 6: Now, have you had any a pressure to be pulling back? 449 00:20:41,960 --> 00:20:42,600 Speaker 6: You're offering that. 450 00:20:44,720 --> 00:20:47,560 Speaker 4: We haven't seen any pressure to pull back our product. 451 00:20:48,560 --> 00:20:50,480 Speaker 7: We want to thank you for your transparency today to 452 00:20:50,520 --> 00:20:53,320 Speaker 7: talk through the product to the uptake already and the. 453 00:20:53,280 --> 00:20:54,600 Speaker 6: Demand that you're continuing to see. 454 00:20:54,640 --> 00:20:56,280 Speaker 7: We thank you so much of the time today, Microsoft 455 00:20:56,320 --> 00:21:00,560 Speaker 7: Corporate Vice President's Security, Compliance, Identity and Management Aspassion, we 456 00:21:00,640 --> 00:21:10,359 Speaker 7: thank her. Welcome back to Bloue Meg Technology. I'm Caroin 457 00:21:10,400 --> 00:21:12,280 Speaker 7: Hid in New York. Let's just talk more, borady about 458 00:21:12,280 --> 00:21:13,960 Speaker 7: what we've had across this week. 459 00:21:14,000 --> 00:21:15,440 Speaker 6: When it comes to TikTok and. 460 00:21:15,440 --> 00:21:18,000 Speaker 7: China, We've heard from the chairman of the Senate Intelligence 461 00:21:18,000 --> 00:21:19,640 Speaker 7: Committee potentially about. 462 00:21:19,359 --> 00:21:21,320 Speaker 6: That ban or divestment. Take listen. 463 00:21:22,680 --> 00:21:26,679 Speaker 12: They're collecting enormous amount of personal data about Americans. The 464 00:21:26,720 --> 00:21:29,000 Speaker 12: genius of TikTok it kind of knows what you like 465 00:21:29,160 --> 00:21:32,480 Speaker 12: even before you may know. And that kind of personal 466 00:21:32,560 --> 00:21:35,440 Speaker 12: data of the one hundred and seventy million Americans who 467 00:21:35,440 --> 00:21:37,920 Speaker 12: are on ninety minutes a day. If you don't think 468 00:21:37,960 --> 00:21:40,800 Speaker 12: that as a security risker, potentially you could be blackmailed 469 00:21:40,800 --> 00:21:45,000 Speaker 12: at some future time by Asians of the Chinese government, 470 00:21:45,240 --> 00:21:48,760 Speaker 12: then I think you don't understand the unfortunate real world 471 00:21:48,760 --> 00:21:49,399 Speaker 12: that we live in. 472 00:21:50,400 --> 00:21:53,840 Speaker 7: So there's a senator who potentially wants to see an 473 00:21:53,920 --> 00:21:57,600 Speaker 7: expedited bill pass through his part of the US government. 474 00:21:57,640 --> 00:21:59,960 Speaker 7: But let's get to Alex Barinka for the latest on tikto, 475 00:22:00,240 --> 00:22:02,720 Speaker 7: because it was such a rapid week in terms of 476 00:22:02,720 --> 00:22:06,359 Speaker 7: it getting through the House with real momentum, the idea 477 00:22:06,400 --> 00:22:08,840 Speaker 7: of a ban or be bought by someone else. 478 00:22:09,119 --> 00:22:10,680 Speaker 6: But the Senate seems to be slowing down. 479 00:22:11,680 --> 00:22:13,399 Speaker 1: I think for those of us who've watched this process 480 00:22:13,400 --> 00:22:16,239 Speaker 1: are taking a deep breath on this Friday, because it 481 00:22:16,240 --> 00:22:18,080 Speaker 1: does seem like it's slowing down. In the Senate, and 482 00:22:18,119 --> 00:22:21,280 Speaker 1: that's for a few reasons. We've heard from senators on 483 00:22:21,359 --> 00:22:25,160 Speaker 1: both sides of the aisle who are basically in support 484 00:22:25,359 --> 00:22:28,159 Speaker 1: of some kind of regulation either of TikTok or of 485 00:22:28,200 --> 00:22:31,440 Speaker 1: data privacy on social media more broadly, but are saying 486 00:22:31,480 --> 00:22:33,480 Speaker 1: that maybe not so fast, we need to take a 487 00:22:33,520 --> 00:22:34,200 Speaker 1: look at this bill. 488 00:22:34,359 --> 00:22:35,320 Speaker 6: One example on the. 489 00:22:35,240 --> 00:22:39,560 Speaker 1: Democrat side is Senator Richard Blumenthal, who said that he's 490 00:22:39,600 --> 00:22:43,280 Speaker 1: in support of separating TikTok from byte Dance, it's parent company, 491 00:22:43,280 --> 00:22:46,879 Speaker 1: it's Chinese parent company, but warned that the deadline in 492 00:22:46,920 --> 00:22:49,320 Speaker 1: the bill about one hundred and sixty five days was 493 00:22:49,359 --> 00:22:52,240 Speaker 1: too short. So that's one kind of knock against this 494 00:22:52,320 --> 00:22:56,840 Speaker 1: moving forward. Another very interesting name, Caroline was Republican Senator 495 00:22:56,840 --> 00:23:00,600 Speaker 1: Ted Cruz, who has consistently been a voice again TikTok 496 00:23:00,640 --> 00:23:04,040 Speaker 1: and sits on the Commerce Committee, which has jurisdiction. 497 00:23:03,640 --> 00:23:04,320 Speaker 6: Over this bill. 498 00:23:04,560 --> 00:23:07,600 Speaker 1: He also said that this bill should be referred to 499 00:23:07,640 --> 00:23:10,560 Speaker 1: the Senate panel for more work, that this bill would 500 00:23:10,600 --> 00:23:14,240 Speaker 1: go through the full amendment process and that would slow 501 00:23:14,240 --> 00:23:17,240 Speaker 1: it down. I will point out we are now also 502 00:23:17,560 --> 00:23:20,520 Speaker 1: just months out from the November US election. 503 00:23:21,040 --> 00:23:22,160 Speaker 6: Does anyone want to be. 504 00:23:22,200 --> 00:23:24,520 Speaker 1: Kind of going back and forth on this bill right now, 505 00:23:24,640 --> 00:23:26,800 Speaker 1: or do they want to be out on the road campaigning. 506 00:23:27,000 --> 00:23:29,879 Speaker 1: I think that's a very valid question to ask as 507 00:23:29,960 --> 00:23:33,280 Speaker 1: the Senate Chamber considers the timing on whether or not 508 00:23:33,440 --> 00:23:35,040 Speaker 1: they move this one forward. 509 00:23:34,760 --> 00:23:37,639 Speaker 7: And ultimately whether the US population thinks this is a 510 00:23:37,680 --> 00:23:40,720 Speaker 7: priority or indeed to the opposite side. 511 00:23:40,720 --> 00:23:41,239 Speaker 6: Hates it. 512 00:23:41,359 --> 00:23:45,040 Speaker 7: And just tell us about how we've seen immobilized gen z. 513 00:23:45,240 --> 00:23:47,840 Speaker 7: Have they been doing what TikTok costs and calling their 514 00:23:47,960 --> 00:23:52,040 Speaker 7: representatives because suddenly maybe Trump, who originally floated the idea 515 00:23:52,080 --> 00:23:54,399 Speaker 7: of a TikTok ban, seems to be going against it, 516 00:23:54,400 --> 00:23:58,119 Speaker 7: in large part because he was of a backlash. 517 00:23:56,920 --> 00:23:57,320 Speaker 6: They have been. 518 00:23:57,400 --> 00:24:00,480 Speaker 1: Lawmakers phones have been flooded at different times this week 519 00:24:00,600 --> 00:24:03,520 Speaker 1: with calls from their constituents who are saying, don't vote 520 00:24:03,520 --> 00:24:04,240 Speaker 1: this bill through. 521 00:24:04,440 --> 00:24:05,199 Speaker 3: There are one hundred and. 522 00:24:05,160 --> 00:24:09,040 Speaker 1: Seventy million Americans who use this app on a monthly basis. 523 00:24:09,200 --> 00:24:11,639 Speaker 1: The average age of those users is over thirty, So 524 00:24:11,760 --> 00:24:15,520 Speaker 1: absolutely the voting contingent. And for the Democrats in particular, 525 00:24:15,560 --> 00:24:18,360 Speaker 1: they've really relied on young people on the last presidential 526 00:24:18,400 --> 00:24:22,040 Speaker 1: election and the midterms to help them win on the ground. 527 00:24:22,359 --> 00:24:25,080 Speaker 1: So though President Biden has said he would sign this 528 00:24:25,160 --> 00:24:28,000 Speaker 1: bill if it passed through both chambers and hit his desk. 529 00:24:28,280 --> 00:24:31,320 Speaker 1: That is certainly something that strategists we are talking to 530 00:24:31,359 --> 00:24:34,760 Speaker 1: say that politicians need to pay attention to youth voter 531 00:24:34,880 --> 00:24:37,840 Speaker 1: turnout poles say might be a lot lower in the past. 532 00:24:37,920 --> 00:24:40,440 Speaker 1: That could be a real problem for Democrats in particular. 533 00:24:40,720 --> 00:24:44,120 Speaker 1: So is upsetting that cohort of people a smart idea? Well, 534 00:24:44,160 --> 00:24:46,760 Speaker 1: I do think that some people are potentially thinking twice 535 00:24:46,960 --> 00:24:50,000 Speaker 1: even though they think that this might be really important legislation, 536 00:24:50,640 --> 00:24:54,160 Speaker 1: either on data privacy or specifically on TikTok and. 537 00:24:54,520 --> 00:24:58,159 Speaker 6: Just remind us Alex. This whole debate throws up so 538 00:24:58,240 --> 00:24:59,000 Speaker 6: many questions. 539 00:24:59,119 --> 00:25:01,800 Speaker 7: One key one is who would buy it if they 540 00:25:01,800 --> 00:25:03,160 Speaker 7: were divested and. 541 00:25:03,080 --> 00:25:05,240 Speaker 6: What sort of price point? Who the names in the ring? 542 00:25:06,800 --> 00:25:10,920 Speaker 1: The names are, it's an interesting shortlist. Bloomberg Intelligence puts 543 00:25:10,960 --> 00:25:13,480 Speaker 1: the US business at about thirty five to forty billion 544 00:25:13,520 --> 00:25:14,840 Speaker 1: dollars in valuation. 545 00:25:15,760 --> 00:25:16,680 Speaker 6: Not too many. 546 00:25:16,480 --> 00:25:18,359 Speaker 1: People could write a check that big, and some of 547 00:25:18,359 --> 00:25:20,720 Speaker 1: the names like Meta, Amazon or Alphabet who would be 548 00:25:20,760 --> 00:25:23,040 Speaker 1: the obvious ones, might have a hard time getting that 549 00:25:23,080 --> 00:25:26,680 Speaker 1: through antitrust review. We've already had people like former Treasury 550 00:25:26,720 --> 00:25:29,680 Speaker 1: Secretary Steve Manuchin come out and say, hey, I want 551 00:25:29,680 --> 00:25:31,479 Speaker 1: to get a group of buyers together to buy this 552 00:25:31,560 --> 00:25:34,359 Speaker 1: asset if this bill goes through. But it is an 553 00:25:34,400 --> 00:25:37,720 Speaker 1: incredibly valuable one. So while that list of names might 554 00:25:37,760 --> 00:25:40,600 Speaker 1: be short right now, if this bill does get signed 555 00:25:40,600 --> 00:25:44,040 Speaker 1: into law, I can probably guarantee we'll have folks across 556 00:25:44,040 --> 00:25:46,560 Speaker 1: the board who are saying, hey, this is an incredibly 557 00:25:46,600 --> 00:25:49,240 Speaker 1: powerful asset. What can we do to kind of put 558 00:25:49,280 --> 00:25:52,400 Speaker 1: together either a consortium, sell a piece of it public, 559 00:25:52,760 --> 00:25:55,720 Speaker 1: or keep it private so that we can keep this 560 00:25:55,920 --> 00:25:59,600 Speaker 1: really kind of money making, lucrative potential asset in the 561 00:25:59,640 --> 00:26:02,760 Speaker 1: hands of Americans, but keep it making money for those owners. 562 00:26:02,960 --> 00:26:05,240 Speaker 7: Your old M and A be coming straight back at you, 563 00:26:05,480 --> 00:26:08,080 Speaker 7: Alex Spirenka, perfect person be talking about all of this. 564 00:26:08,200 --> 00:26:11,880 Speaker 6: We thank you so much. Meanwhile, let's turn to the legal. 565 00:26:11,600 --> 00:26:15,720 Speaker 7: Implications of any sort of potential ban or indeed divestment 566 00:26:15,800 --> 00:26:18,359 Speaker 7: of TikTok. David Greens with US Senior staff attorney and 567 00:26:18,400 --> 00:26:21,320 Speaker 7: Civil Liberties Director over the Electronic Frontier Foundation. Now that's 568 00:26:21,320 --> 00:26:25,400 Speaker 7: a nonprofit organization that defensible liberties in the digital works. 569 00:26:25,440 --> 00:26:28,920 Speaker 7: And David, what's been interesting is some of the reticence 570 00:26:28,960 --> 00:26:33,119 Speaker 7: from certain senators has been we can't just deploy a 571 00:26:33,240 --> 00:26:34,680 Speaker 7: leaf out of China's book here. 572 00:26:34,720 --> 00:26:37,280 Speaker 6: We can't do what they do, which has banned. 573 00:26:37,560 --> 00:26:40,439 Speaker 7: Ultimately social media forms of expression that we don't like. 574 00:26:41,000 --> 00:26:42,399 Speaker 6: Is that something you're thinking about. 575 00:26:43,359 --> 00:26:45,240 Speaker 13: Oh, yes, I'm thinking about it quite a bit, and 576 00:26:45,400 --> 00:26:48,240 Speaker 13: I'm actually glad to hear that senators are thinking about 577 00:26:48,280 --> 00:26:53,280 Speaker 13: it as well. The US, you know, historically, really really 578 00:26:53,320 --> 00:26:57,119 Speaker 13: without exception, over the over the past or at least 579 00:26:57,160 --> 00:26:59,720 Speaker 13: sixty years, has been a champion for the free flow 580 00:26:59,760 --> 00:27:03,760 Speaker 13: of in around the world, and we've chastised other governments 581 00:27:04,280 --> 00:27:08,200 Speaker 13: when they have shut down apps and calling those actions undemocratic. 582 00:27:08,280 --> 00:27:12,159 Speaker 13: I think most recently, or maybe most famously, when the 583 00:27:12,440 --> 00:27:17,960 Speaker 13: government of Nigeria shut down Twitter because it accused Twitter 584 00:27:18,119 --> 00:27:23,320 Speaker 13: of disseminating misinformation about the Nigerian government, and they shut 585 00:27:23,359 --> 00:27:25,560 Speaker 13: down Twitter for six months, and the US State Department 586 00:27:25,600 --> 00:27:28,320 Speaker 13: issued a very strong statement telling them that even if 587 00:27:28,359 --> 00:27:30,920 Speaker 13: they didn't like the content and thought it might be harmful, 588 00:27:30,920 --> 00:27:33,600 Speaker 13: that it was undemocratic to cut off a mode of 589 00:27:33,600 --> 00:27:36,479 Speaker 13: communication that was used by its people. And we are 590 00:27:36,520 --> 00:27:38,640 Speaker 13: now doing the same thing, And it is quite ironic 591 00:27:38,680 --> 00:27:41,560 Speaker 13: that's happening in the context of China, which is, you know, 592 00:27:41,600 --> 00:27:44,119 Speaker 13: a nation that doesn't have a good human rights and 593 00:27:44,160 --> 00:27:47,560 Speaker 13: civil liberties history, and yet here we are really, you know, 594 00:27:47,720 --> 00:27:51,960 Speaker 13: taking a page out of an undemocratic playbook to try 595 00:27:52,000 --> 00:27:53,000 Speaker 13: to address concerns. 596 00:27:53,880 --> 00:27:58,840 Speaker 7: Those concerns, let's dwell on them, because maybe people would say, Okay, 597 00:27:58,880 --> 00:28:01,959 Speaker 7: we need to adopt a leaf adrada out of their 598 00:28:02,000 --> 00:28:06,800 Speaker 7: book because we cannot risk manipulation on the platform itself. 599 00:28:06,800 --> 00:28:09,560 Speaker 7: We cannot risk us data getting into the hands of 600 00:28:09,560 --> 00:28:12,800 Speaker 7: the Chinese. But couldn't they just access the data any 601 00:28:12,800 --> 00:28:13,840 Speaker 7: how without TikTok? 602 00:28:14,680 --> 00:28:18,439 Speaker 13: Yes, absolutely, And I think there's really good reasons to 603 00:28:18,480 --> 00:28:23,119 Speaker 13: be concerned about the amount of data that TikTok and 604 00:28:23,160 --> 00:28:28,320 Speaker 13: really almost every other social media service collect about US users. 605 00:28:28,320 --> 00:28:31,840 Speaker 13: They collect a tremendous amount of data, they retain it, 606 00:28:31,880 --> 00:28:33,879 Speaker 13: and then they share it widely. And there's a whole 607 00:28:33,880 --> 00:28:37,560 Speaker 13: industry of data brokers that buy information from social media 608 00:28:37,560 --> 00:28:39,920 Speaker 13: companies and other online services and then sell it all 609 00:28:39,960 --> 00:28:43,440 Speaker 13: across the world, including to governments like China and other 610 00:28:43,600 --> 00:28:47,480 Speaker 13: foreign adversaries, even those who don't operate their own social 611 00:28:47,560 --> 00:28:50,360 Speaker 13: media companies. So I really think it's safe to assume 612 00:28:50,600 --> 00:28:56,280 Speaker 13: that China has regardless of ownership by ByteDance. China has 613 00:28:56,320 --> 00:28:59,200 Speaker 13: a lot of information about US users. The way to 614 00:28:59,240 --> 00:29:02,680 Speaker 13: address that, of course, is to pass comprehensive data privacy law. 615 00:29:03,000 --> 00:29:05,800 Speaker 13: And this, the bill that the House past, is not 616 00:29:05,920 --> 00:29:08,360 Speaker 13: a privacy law. It says very very little, It has 617 00:29:08,440 --> 00:29:12,040 Speaker 13: very few provisions in it that actually guarantee privacy for 618 00:29:12,320 --> 00:29:15,959 Speaker 13: US users, and really just shutting down TikTok will do 619 00:29:16,560 --> 00:29:21,080 Speaker 13: will be just a very tiny step and largely ineffectual 620 00:29:21,120 --> 00:29:23,240 Speaker 13: one in protecting US data. 621 00:29:24,120 --> 00:29:28,440 Speaker 7: So, David, why if we hear from Elizabeth Warren? She 622 00:29:28,480 --> 00:29:32,000 Speaker 7: seems to want to broader privacy law, But what would 623 00:29:32,040 --> 00:29:36,120 Speaker 7: stop that actually being executed? Is it lobbying from social 624 00:29:36,120 --> 00:29:38,480 Speaker 7: media companies here in the US? Is it ultimately the 625 00:29:39,480 --> 00:29:41,520 Speaker 7: negatives to some of that privacy overreach? 626 00:29:42,560 --> 00:29:44,400 Speaker 5: Well, it's I don't know. 627 00:29:44,440 --> 00:29:47,880 Speaker 13: I don't know what the reluctance of Congress has been, 628 00:29:49,040 --> 00:29:52,520 Speaker 13: but I and it's not. It's not completely easy to 629 00:29:52,560 --> 00:29:55,480 Speaker 13: write a good comprehensive data privacy law. There have been 630 00:29:55,520 --> 00:29:58,800 Speaker 13: some proposed, and we eff haven't actually been completely pleased 631 00:29:58,800 --> 00:30:00,720 Speaker 13: with a lot of the with a lot of the 632 00:30:00,760 --> 00:30:02,960 Speaker 13: proposal because we don't think they go we don't they 633 00:30:03,080 --> 00:30:03,920 Speaker 13: go far enough. 634 00:30:04,240 --> 00:30:06,440 Speaker 5: I don't know what the political obstacle is. 635 00:30:06,600 --> 00:30:10,280 Speaker 13: It seems, and it really makes me question because it 636 00:30:10,360 --> 00:30:13,200 Speaker 13: has been so difficult to pass a data privacy law 637 00:30:13,520 --> 00:30:16,280 Speaker 13: and it was so easy to get this TikTok bill through. 638 00:30:16,720 --> 00:30:20,920 Speaker 13: It really makes me question whether the motivations behind this 639 00:30:21,120 --> 00:30:25,320 Speaker 13: most recent TikTok bill are data privacy because this sailed 640 00:30:25,320 --> 00:30:28,880 Speaker 13: through the House in three days, and we've and privacy 641 00:30:28,960 --> 00:30:30,760 Speaker 13: legislations had a much tougher slock. 642 00:30:31,720 --> 00:30:35,120 Speaker 7: It was fast bipartisanship, which we don't say much TikTok 643 00:30:35,160 --> 00:30:37,800 Speaker 7: issuing a statement of course saying that it's jammed through 644 00:30:37,800 --> 00:30:38,480 Speaker 7: for one reason. 645 00:30:38,800 --> 00:30:39,239 Speaker 6: It's a ban. 646 00:30:39,600 --> 00:30:42,000 Speaker 7: David Green, we thank you so much, Senior staff attorney 647 00:30:42,000 --> 00:30:44,560 Speaker 7: and Civil Liberties director over at the Electronic Frontier Foundation. 648 00:30:44,720 --> 00:30:46,440 Speaker 6: Some great resources there. 649 00:30:46,680 --> 00:30:48,520 Speaker 7: I Meanwhile, coming up that we're going to be speaking 650 00:30:48,520 --> 00:30:52,000 Speaker 7: with Alis Spentink, CEO and co founder of Entrepreneur First 651 00:30:52,200 --> 00:30:55,040 Speaker 7: is about opening up a new office in San Francisco, 652 00:30:55,120 --> 00:30:58,600 Speaker 7: bringing entrepreneurs over from London, from Bangalore, from. 653 00:30:58,520 --> 00:31:00,400 Speaker 6: New York to the Bay Area. 654 00:31:00,720 --> 00:31:03,600 Speaker 7: Meanwhile, let's just focus in on what's happening in Africa 655 00:31:03,640 --> 00:31:06,000 Speaker 7: at the moment, because well, there's been a real lack 656 00:31:06,480 --> 00:31:09,360 Speaker 7: of access to the internet ultimately at the moment, and 657 00:31:09,480 --> 00:31:13,320 Speaker 7: undersea cable damage has caused internet outages across Africa, we 658 00:31:13,400 --> 00:31:17,520 Speaker 7: understand and we're seeing an impact not so much on 659 00:31:17,520 --> 00:31:19,840 Speaker 7: an MTN group today it's up two point three percent. 660 00:31:19,960 --> 00:31:23,200 Speaker 7: This in Votercom, Africa's biggest wireless carry is basically trying 661 00:31:23,240 --> 00:31:26,320 Speaker 7: to tackle these issues of connectivity and they say multiple 662 00:31:26,360 --> 00:31:29,520 Speaker 7: undersea cables failures between South Africa and Europe are currently 663 00:31:29,520 --> 00:31:33,280 Speaker 7: impacting the network providers. Microsoft also impacted as well. 664 00:31:33,320 --> 00:31:35,800 Speaker 6: They're saying they're reported disruptions to its cloud services. 665 00:31:36,000 --> 00:31:53,960 Speaker 7: And Microsoft three sixty five is the Blombo technology Entrepreneur 666 00:31:54,080 --> 00:31:58,320 Speaker 7: First and invests in early career technical founder talent that 667 00:31:58,480 --> 00:32:00,880 Speaker 7: is now opening up in San Francisco, an office there 668 00:32:00,880 --> 00:32:04,000 Speaker 7: to connect the founders in the world's best following ecosystem. Well, 669 00:32:04,040 --> 00:32:07,560 Speaker 7: so they say, let's bring on Alice Bentink, co founder 670 00:32:07,640 --> 00:32:10,080 Speaker 7: CEO of Entrepreneur First, which I know well from having 671 00:32:10,440 --> 00:32:13,200 Speaker 7: covered the London scene for a couple of decades and 672 00:32:13,400 --> 00:32:16,120 Speaker 7: seen the rise of EF You've then, of course built 673 00:32:16,160 --> 00:32:17,520 Speaker 7: offices in New York. 674 00:32:17,680 --> 00:32:20,719 Speaker 6: They're over in Bangalore even as well as in Paris. 675 00:32:20,800 --> 00:32:24,400 Speaker 7: I'm interested as to why now San Francisco, What does 676 00:32:24,400 --> 00:32:26,120 Speaker 7: that ecosystem have that the others don't. 677 00:32:27,720 --> 00:32:27,920 Speaker 2: Well. 678 00:32:27,960 --> 00:32:30,000 Speaker 14: For a number of years now, we've been working directly 679 00:32:30,040 --> 00:32:33,120 Speaker 14: with the San Francisco ecosystem. So back in twenty seventeen, 680 00:32:33,240 --> 00:32:36,480 Speaker 14: we raised the Series A from led by Graylock alongside 681 00:32:36,480 --> 00:32:39,000 Speaker 14: reied Hoffmann and Founder's Fund. As we've been plugged into 682 00:32:39,000 --> 00:32:41,440 Speaker 14: this ecosystem for a while, and what we've seen is 683 00:32:41,600 --> 00:32:44,560 Speaker 14: there's a huge amount of demand from Bay Area investors 684 00:32:44,720 --> 00:32:48,200 Speaker 14: to invest in exceptional talent that comes from Europe and Asia. 685 00:32:48,560 --> 00:32:53,000 Speaker 14: We've had vcs like Andresen and Kosler and Sequoia invest 686 00:32:53,000 --> 00:32:53,920 Speaker 14: in our companies at. 687 00:32:53,800 --> 00:32:54,920 Speaker 6: A very early stage. 688 00:32:55,080 --> 00:32:57,160 Speaker 14: And what we're doing now is we're building a bridge 689 00:32:57,320 --> 00:32:59,800 Speaker 14: between the exceptional talent and the exceptional founders that were 690 00:32:59,800 --> 00:33:02,480 Speaker 14: seeing in Europe and Asia and bringing them to the 691 00:33:02,520 --> 00:33:05,200 Speaker 14: Bay Area to access not just the investors, but the 692 00:33:05,240 --> 00:33:08,680 Speaker 14: wider ecosystem and the magic, if you like, of the 693 00:33:08,760 --> 00:33:09,640 Speaker 14: Bay Area. 694 00:33:09,800 --> 00:33:13,400 Speaker 7: If you've already had this talent and success. For example, 695 00:33:13,480 --> 00:33:15,440 Speaker 7: I mean what we're seeing portfolio now worth more than 696 00:33:15,480 --> 00:33:17,800 Speaker 7: ten billion dollars. As you say, some of these companies 697 00:33:17,800 --> 00:33:20,640 Speaker 7: that are well known, particularly in Europe and more broadly 698 00:33:20,680 --> 00:33:23,720 Speaker 7: worldwide of received funding from read Hoffmann, who I know 699 00:33:23,840 --> 00:33:24,080 Speaker 7: is going. 700 00:33:24,040 --> 00:33:25,240 Speaker 6: To be opening your office today. 701 00:33:26,120 --> 00:33:28,360 Speaker 7: Why there need to be even more plugged in if 702 00:33:28,360 --> 00:33:29,520 Speaker 7: it's already worked thus far. 703 00:33:31,120 --> 00:33:33,080 Speaker 14: I mean, it's a great point, but I think if 704 00:33:33,120 --> 00:33:35,920 Speaker 14: you look, the bridge is actually building in both directions. 705 00:33:36,240 --> 00:33:38,760 Speaker 14: We see many of the firms that I just mentioned 706 00:33:38,840 --> 00:33:40,760 Speaker 14: opening offices in London. 707 00:33:40,480 --> 00:33:41,560 Speaker 6: To access Europe. 708 00:33:41,680 --> 00:33:43,400 Speaker 14: But we really see this as a strengthening of the 709 00:33:43,440 --> 00:33:45,240 Speaker 14: ties between the two ecosystems. 710 00:33:45,920 --> 00:33:46,720 Speaker 6: I think it's also. 711 00:33:46,600 --> 00:33:49,880 Speaker 14: Word saying that by bringing our companies here and helping 712 00:33:49,880 --> 00:33:53,120 Speaker 14: them access the level of ambition, the depth of network, 713 00:33:53,320 --> 00:33:55,440 Speaker 14: and it's not just about the investors here, it's also 714 00:33:55,480 --> 00:33:58,400 Speaker 14: about who you can hire, who you're surrounded by, and 715 00:33:58,400 --> 00:34:00,760 Speaker 14: we're excited to give our companies deep access to that. 716 00:34:01,640 --> 00:34:05,240 Speaker 7: It's expensive to hire that talent on the West Coast. 717 00:34:05,400 --> 00:34:07,640 Speaker 7: What have been some of the nerves coming into those 718 00:34:07,760 --> 00:34:10,120 Speaker 7: foulers coming over because you've got a whole raft of 719 00:34:10,160 --> 00:34:12,920 Speaker 7: companies coming, but where do they see some of the worries? 720 00:34:14,120 --> 00:34:16,359 Speaker 14: So we've got thirty three companies that have joined us 721 00:34:16,400 --> 00:34:16,920 Speaker 14: out here. 722 00:34:17,160 --> 00:34:18,560 Speaker 6: I think some of the worries. 723 00:34:18,239 --> 00:34:22,360 Speaker 14: Are around the cost of running a company here, but 724 00:34:22,440 --> 00:34:25,120 Speaker 14: I would say that is offset by the benefits of 725 00:34:25,160 --> 00:34:30,200 Speaker 14: the ecosystem. I think the real opportunity here is to 726 00:34:30,280 --> 00:34:33,120 Speaker 14: expand their ambitions, give them access to the US market, 727 00:34:33,640 --> 00:34:37,920 Speaker 14: and to give them access to a opportunity that they can. 728 00:34:37,840 --> 00:34:38,640 Speaker 6: Access in Europe. 729 00:34:38,640 --> 00:34:41,160 Speaker 14: But our companies are trying to build globally from day one, 730 00:34:42,080 --> 00:34:45,080 Speaker 14: and the US market is so large and recently consistent 731 00:34:45,800 --> 00:34:47,759 Speaker 14: that we believe this gives them a better opportunity to 732 00:34:47,760 --> 00:34:50,680 Speaker 14: build a globally important company from the very beginning. 733 00:34:50,800 --> 00:34:52,920 Speaker 7: What kind of companies have come, I mean we're just 734 00:34:52,960 --> 00:34:55,480 Speaker 7: thinking of why Combinator, for example, and guess what a 735 00:34:55,480 --> 00:34:58,120 Speaker 7: whole load of their recent batch have been AI and 736 00:34:58,160 --> 00:35:01,240 Speaker 7: generative AI focused. Is Is that the kind of slant 737 00:35:01,280 --> 00:35:04,920 Speaker 7: that you're seeing with the companies you're enticing over I mean, 738 00:35:05,080 --> 00:35:08,320 Speaker 7: AI is the defining technology of our generation, and so yes, 739 00:35:08,360 --> 00:35:10,160 Speaker 7: a lot of our companies. 740 00:35:09,760 --> 00:35:12,279 Speaker 6: Are the majority are using AI in some way. 741 00:35:12,920 --> 00:35:15,720 Speaker 14: We've actually been investing in AI companies since twenty fourteen, 742 00:35:15,880 --> 00:35:18,360 Speaker 14: and we built Europe's first computer vision Unicorn, and in 743 00:35:18,400 --> 00:35:20,600 Speaker 14: some ways that's a great example of why we're doing this. 744 00:35:20,760 --> 00:35:24,239 Speaker 14: So it was a founder from Romania, a founder from 745 00:35:24,280 --> 00:35:27,160 Speaker 14: France that built together in our office in London and 746 00:35:27,200 --> 00:35:29,640 Speaker 14: then actually got their seed investment from a US based 747 00:35:29,680 --> 00:35:32,960 Speaker 14: firm called Zetta that's based in San Francisco, actually next 748 00:35:32,960 --> 00:35:35,160 Speaker 14: to our office in South Park, and I think that 749 00:35:35,200 --> 00:35:38,720 Speaker 14: demonstrates the importance of the bridge between the two ecosystems. 750 00:35:39,080 --> 00:35:42,279 Speaker 7: You're clearly bringing geographical diversity as you talk about those 751 00:35:42,280 --> 00:35:44,560 Speaker 7: two technical founders you brought together, and it is quite 752 00:35:44,560 --> 00:35:48,760 Speaker 7: such an interesting concept you built basically find those founders early, 753 00:35:49,120 --> 00:35:52,919 Speaker 7: back them, pre idea pre team, pre product market fit, 754 00:35:53,200 --> 00:35:56,239 Speaker 7: and actually build it within What is the. 755 00:35:56,160 --> 00:35:57,919 Speaker 6: Cohort looking like as well? 756 00:35:58,000 --> 00:36:00,120 Speaker 7: I mean it's a boring question for one woman to 757 00:36:00,120 --> 00:36:03,680 Speaker 7: ask another, but there is a question of diversity. 758 00:36:04,480 --> 00:36:07,240 Speaker 14: If you work in technology, diversity is always a challenge. 759 00:36:07,640 --> 00:36:11,440 Speaker 14: So about twenty percent of our founders are women, and 760 00:36:11,480 --> 00:36:14,440 Speaker 14: we know the technology industry does struggle with gender diversity 761 00:36:14,440 --> 00:36:16,800 Speaker 14: in particular, and it's something that we've been very intentional 762 00:36:16,840 --> 00:36:19,600 Speaker 14: about ever since the beginning of founding Entrepreneur First. We 763 00:36:19,640 --> 00:36:22,840 Speaker 14: actually built an organization alongside EF called Code First Girls, 764 00:36:23,040 --> 00:36:26,920 Speaker 14: which teaches women to code for free across Europe, and 765 00:36:26,960 --> 00:36:29,880 Speaker 14: that has been an important driver to help shift the 766 00:36:29,960 --> 00:36:33,919 Speaker 14: narrative around diversity within technology, but also to actually change 767 00:36:33,960 --> 00:36:36,200 Speaker 14: the numbers of women that we see founding companies. So 768 00:36:36,239 --> 00:36:38,640 Speaker 14: it is something we see that we take very seriously. 769 00:36:38,800 --> 00:36:40,960 Speaker 14: We have some amazing female founders in this cohort that 770 00:36:41,000 --> 00:36:43,080 Speaker 14: we're very excited by spending. 771 00:36:43,400 --> 00:36:44,960 Speaker 6: Good luck with the launch. We thank you so much. 772 00:36:45,000 --> 00:36:46,680 Speaker 6: Co founder and CEO of Entrepreneur. 773 00:36:46,840 --> 00:36:56,440 Speaker 7: First, let's stig into general to AI a little bit more. 774 00:36:56,480 --> 00:36:58,880 Speaker 7: Now we're going to be talking with Cerebras Systems. It's 775 00:36:58,880 --> 00:37:02,799 Speaker 7: announcing it's third generation wafer scale chip. Basically the way 776 00:37:02,840 --> 00:37:05,120 Speaker 7: in which we're seeing the infrastructure being supplied to the 777 00:37:05,160 --> 00:37:07,960 Speaker 7: training of data for generative AI, but also a new 778 00:37:07,960 --> 00:37:11,480 Speaker 7: AI supercomputer digging into all of it. Andrew Feldman, co 779 00:37:11,560 --> 00:37:14,640 Speaker 7: founder CEO of Cerebras, and just tell us with this 780 00:37:14,760 --> 00:37:19,480 Speaker 7: new third generation, what would people see feel trained differently? 781 00:37:21,160 --> 00:37:23,839 Speaker 15: Thank you so much for having me again, very much 782 00:37:23,880 --> 00:37:28,880 Speaker 15: appreciate it. In a new generation, one hopes to be 783 00:37:28,960 --> 00:37:30,920 Speaker 15: able to do the same work in half the time, 784 00:37:31,560 --> 00:37:33,920 Speaker 15: right and to pay the same price. 785 00:37:34,120 --> 00:37:35,640 Speaker 2: And that's what we were able to achieve. 786 00:37:36,320 --> 00:37:36,680 Speaker 6: And so. 787 00:37:38,160 --> 00:37:42,640 Speaker 15: This is a four trillion transistor chip. This is the 788 00:37:42,719 --> 00:37:45,440 Speaker 15: largest chip ever made in the history of the compute industry, 789 00:37:45,960 --> 00:37:50,600 Speaker 15: and it's optimized for the training of AI work, a 790 00:37:50,640 --> 00:37:54,279 Speaker 15: big generative AI and it slashes the amount of time 791 00:37:54,320 --> 00:37:57,200 Speaker 15: it takes to complete one of these projects in half. 792 00:37:57,719 --> 00:38:00,560 Speaker 2: And so this is a huge st forward. 793 00:38:00,600 --> 00:38:06,080 Speaker 15: It cuts the cost per unit compute in half and 794 00:38:06,719 --> 00:38:09,759 Speaker 15: is the foundation of a new supercomputer we're building. And 795 00:38:09,840 --> 00:38:12,920 Speaker 15: so it's a big, big lead both for us and 796 00:38:12,960 --> 00:38:13,640 Speaker 15: for the industry. 797 00:38:14,440 --> 00:38:16,360 Speaker 7: We can see from the art behind you almost the 798 00:38:16,440 --> 00:38:19,040 Speaker 7: architecture is within and throughout you. 799 00:38:19,360 --> 00:38:22,239 Speaker 6: I'm interested in the ultimately what does this mean? Like 800 00:38:22,280 --> 00:38:23,920 Speaker 6: what when you talk. 801 00:38:23,800 --> 00:38:26,640 Speaker 7: To us For the audience who don't exactly know how 802 00:38:27,000 --> 00:38:30,920 Speaker 7: the biggest chip in the world is made, what technology 803 00:38:30,960 --> 00:38:31,600 Speaker 7: goes into that? 804 00:38:33,280 --> 00:38:35,640 Speaker 15: Well, the art behind us actually is the chip and 805 00:38:35,920 --> 00:38:38,720 Speaker 15: that was really cool. One of our engineers got fired 806 00:38:38,800 --> 00:38:42,759 Speaker 15: up on a on a project. A chip of the 807 00:38:42,840 --> 00:38:52,440 Speaker 15: size takes years of development. It involves a thinking of 808 00:38:52,719 --> 00:38:57,400 Speaker 15: how you're going to do the math underneath artificial intelligence. 809 00:38:57,520 --> 00:39:01,919 Speaker 2: And what it means for your every day user is. 810 00:39:04,239 --> 00:39:08,600 Speaker 15: That their their mapping software UH that uses AI is 811 00:39:08,640 --> 00:39:12,600 Speaker 15: more efficient, that their recommendation engines that select or recommend 812 00:39:12,640 --> 00:39:16,759 Speaker 15: things for them in UH, in their shopping or on 813 00:39:16,920 --> 00:39:22,040 Speaker 15: Netflix is even better. It means that they're the generative 814 00:39:22,080 --> 00:39:25,720 Speaker 15: AI models. They play with, whether it's uh chat, GPT 815 00:39:26,280 --> 00:39:31,200 Speaker 15: or others. These are less expensive, These are more powerful. 816 00:39:31,320 --> 00:39:36,480 Speaker 15: These have bigger impact at work, not just at play. 817 00:39:37,080 --> 00:39:41,360 Speaker 15: And so I think as you build faster compute, the 818 00:39:41,360 --> 00:39:46,399 Speaker 15: the AI infrastructure enables the what everybody cares about, which 819 00:39:46,440 --> 00:39:51,040 Speaker 15: is the AI to permeate through UH the economy, through 820 00:39:51,080 --> 00:39:52,720 Speaker 15: the way we live, work and play. 821 00:39:53,440 --> 00:39:56,200 Speaker 7: Well. It's a big announcement, a notable one ahead of 822 00:39:56,560 --> 00:39:59,719 Speaker 7: in videos, big event next week, and we thank you 823 00:39:59,719 --> 00:40:01,960 Speaker 7: so much Andrew Feldman, who's a key player in the 824 00:40:02,000 --> 00:40:04,799 Speaker 7: infrastructure of AI co found or CEO Cerebras. 825 00:40:05,080 --> 00:40:05,920 Speaker 6: Meanwhile, that does. 826 00:40:05,800 --> 00:40:07,719 Speaker 7: It for this edition of Bloomberg Technology. You do not 827 00:40:07,719 --> 00:40:09,879 Speaker 7: want to forget to check out our podcast. You can 828 00:40:09,880 --> 00:40:12,000 Speaker 7: find it on the terminal as well as online on Apple, 829 00:40:12,080 --> 00:40:14,799 Speaker 7: Spotify and iHeart Boy, who've had a big one in 830 00:40:14,880 --> 00:40:17,120 Speaker 7: terms of TikTok, go back, consume it all. 831 00:40:17,160 --> 00:40:19,520 Speaker 9: This is Blomberg Technology.