1 00:00:02,520 --> 00:00:07,040 Speaker 1: Bloomberg Audio Studios, podcasts, radio News. 2 00:00:07,760 --> 00:00:10,080 Speaker 2: We're going to stay with robin Hood because the company 3 00:00:10,080 --> 00:00:12,559 Speaker 2: this week rolled out updates to was AI assistant and 4 00:00:12,680 --> 00:00:16,520 Speaker 2: prediction market offerings. In fact, prediction markets are now Robinhood's 5 00:00:16,560 --> 00:00:20,080 Speaker 2: fastest growing product line by revenue, with analyst at Citizens 6 00:00:20,079 --> 00:00:23,599 Speaker 2: expecting fivefold growth by twenty thirty. So joining us live 7 00:00:23,680 --> 00:00:25,680 Speaker 2: on the heels of that announcement is the CEO of 8 00:00:25,720 --> 00:00:28,800 Speaker 2: robin Hood, Flat Teneve and my Bloomberg Crypto co host 9 00:00:28,840 --> 00:00:31,720 Speaker 2: Tim Senwik joins us now as well. Loatt, It's great 10 00:00:31,720 --> 00:00:34,040 Speaker 2: to speak with you, and I want to start with 11 00:00:34,360 --> 00:00:39,080 Speaker 2: these sports wagering capabilities that you've added to your suite 12 00:00:39,080 --> 00:00:42,519 Speaker 2: of offerings. Here these preset combination contracts for professional football 13 00:00:42,520 --> 00:00:45,720 Speaker 2: games NFL games. It sounds like this is your version 14 00:00:45,920 --> 00:00:49,159 Speaker 2: of parlays that are offered by traditional sportsbooks. So what 15 00:00:49,240 --> 00:00:52,440 Speaker 2: is the difference between a combination trade and a parlay? 16 00:00:54,160 --> 00:00:56,680 Speaker 3: Yeah, So, as you mentioned, this is part of our 17 00:00:56,760 --> 00:01:02,320 Speaker 3: big unveil of prediction markets, which we've offered for a 18 00:01:02,320 --> 00:01:05,120 Speaker 3: little bit over a year. But remember we started with 19 00:01:05,360 --> 00:01:08,720 Speaker 3: just one prediction market on the platform, which was the 20 00:01:08,720 --> 00:01:12,199 Speaker 3: presidential election in twenty twenty four and Since then, we've 21 00:01:12,240 --> 00:01:16,160 Speaker 3: gone to over one thousand, five hundred contracts on the 22 00:01:16,160 --> 00:01:22,559 Speaker 3: platform across pretty much every domain. We've got politics, we've 23 00:01:22,560 --> 00:01:27,880 Speaker 3: got world affairs, economics, culture, and we recently added weather, 24 00:01:28,240 --> 00:01:31,280 Speaker 3: and of course, as you mentioned, sports. So I think 25 00:01:31,319 --> 00:01:36,840 Speaker 3: the major innovation here and why it's been so disruptive 26 00:01:36,880 --> 00:01:40,240 Speaker 3: to the sports industry is you can take the general 27 00:01:40,280 --> 00:01:45,400 Speaker 3: prediction markets infrastructure, which are markets that trade on regulated 28 00:01:45,720 --> 00:01:49,680 Speaker 3: exchanges where buyers and sellers are matched, and apply them 29 00:01:49,720 --> 00:01:54,240 Speaker 3: to pretty much everything. Now. Of course sports is an 30 00:01:54,280 --> 00:01:58,200 Speaker 3: important subset of that because there's an existing large market there. 31 00:01:59,240 --> 00:02:02,800 Speaker 3: But we see a world where we're actually the percentage 32 00:02:02,840 --> 00:02:07,920 Speaker 3: of nonsports contracts grows much faster than sports, and I 33 00:02:07,960 --> 00:02:12,080 Speaker 3: think several big industries are likely to be disrupted in 34 00:02:12,120 --> 00:02:16,560 Speaker 3: the future, things like insurance. You can imagine with the 35 00:02:16,600 --> 00:02:19,760 Speaker 3: weather contracts we have on a platform. People have already 36 00:02:19,840 --> 00:02:23,760 Speaker 3: pointed out that, you know, trading and hedging with a 37 00:02:24,120 --> 00:02:30,400 Speaker 3: insurance weather contract could be superior to buying traditional insurance 38 00:02:30,400 --> 00:02:33,200 Speaker 3: through a broker. So as the market matures and you 39 00:02:33,240 --> 00:02:38,160 Speaker 3: see increased in institutional participation, I think we could be 40 00:02:38,200 --> 00:02:41,040 Speaker 3: at the very beginning of a prediction market supercycle. With 41 00:02:41,160 --> 00:02:47,280 Speaker 3: growth in volumes and enlisted contracts accelerating from here. 42 00:02:47,760 --> 00:02:50,720 Speaker 2: Well, we've had a few incidents now of alleged insider 43 00:02:50,800 --> 00:02:54,480 Speaker 2: trading in prediction markets. Do you think insider trading poses 44 00:02:54,520 --> 00:02:58,120 Speaker 2: a risk to fairness in prediction markets or could help 45 00:02:58,520 --> 00:03:04,040 Speaker 2: ensure market accuracy by allowing those with privileged information to participate. 46 00:03:06,160 --> 00:03:09,200 Speaker 3: Well, I'm not exactly sure which incident you're referring to. 47 00:03:09,520 --> 00:03:13,760 Speaker 3: There was an incident in the sports betting space, which 48 00:03:13,840 --> 00:03:18,600 Speaker 3: is entirely differently regulated, right, those are regulated on a 49 00:03:18,639 --> 00:03:22,560 Speaker 3: state by state basis, and I think they're obviously working 50 00:03:22,600 --> 00:03:26,239 Speaker 3: hard to address these issues. But what I tell you 51 00:03:26,520 --> 00:03:31,160 Speaker 3: is financial markets have had the infrastructure in terms of 52 00:03:31,240 --> 00:03:36,520 Speaker 3: surveillance and monitoring to deal with these types of concerns, 53 00:03:36,560 --> 00:03:39,920 Speaker 3: and they've been dealing with it for decades. Because obviously, 54 00:03:40,560 --> 00:03:44,320 Speaker 3: insider trading originated as a market integrity measure for the 55 00:03:44,360 --> 00:03:48,040 Speaker 3: equities market. We want to make sure we have protections 56 00:03:48,080 --> 00:03:51,760 Speaker 3: as an industry to make sure corporate insiders and other 57 00:03:51,760 --> 00:03:54,880 Speaker 3: folks weren't using their privileged information about what a company 58 00:03:54,960 --> 00:03:59,240 Speaker 3: was going to release to trade ahead of that disclosure 59 00:03:59,280 --> 00:04:03,720 Speaker 3: being available publicly. So this has pretty much always been 60 00:04:03,920 --> 00:04:07,760 Speaker 3: a financial markets thing, and so I think financial markets 61 00:04:07,800 --> 00:04:11,320 Speaker 3: have the best tools to deal with it. 62 00:04:12,040 --> 00:04:16,120 Speaker 1: Glad, I'm wondering about prediction markets, and specifically Robinhood and 63 00:04:16,240 --> 00:04:20,640 Speaker 1: others too, if they offer enough protections to consumers against 64 00:04:20,720 --> 00:04:25,719 Speaker 1: addictive betting, how do you solve for that? At Robinhood, I. 65 00:04:25,760 --> 00:04:29,200 Speaker 3: Mean, I think our philosophy is, if you look at 66 00:04:29,360 --> 00:04:34,320 Speaker 3: prediction markets, there's a tremendous amount of value in the 67 00:04:34,360 --> 00:04:39,159 Speaker 3: diversity and variety of contracts and what we're actually seeing, 68 00:04:39,240 --> 00:04:43,480 Speaker 3: similar to trading, a lot of people that actually have strategies, 69 00:04:43,800 --> 00:04:46,680 Speaker 3: So it's not you know, it's not as much of 70 00:04:46,839 --> 00:04:50,320 Speaker 3: like I'm buying a team because I'm a fan of it, 71 00:04:50,520 --> 00:04:55,520 Speaker 3: But there's systematic trading and people are analyzing these things 72 00:04:55,560 --> 00:04:59,479 Speaker 3: and they have strategies around it, and it goes beyond sports. 73 00:05:00,600 --> 00:05:06,880 Speaker 3: You guys were mentioning the Warner Brothers merger situation. If 74 00:05:06,880 --> 00:05:11,799 Speaker 3: you notice the question on everyone's mind is what's likely 75 00:05:11,839 --> 00:05:14,400 Speaker 3: going to happen to Warner Brothers. Who's going to acquire 76 00:05:14,480 --> 00:05:17,960 Speaker 3: the company? Is it going to be Netflix or Paramount, 77 00:05:17,960 --> 00:05:23,320 Speaker 3: Skuidance or neither of the above. And right now, we 78 00:05:23,400 --> 00:05:25,480 Speaker 3: look at the stock prices right and we try to 79 00:05:25,600 --> 00:05:29,120 Speaker 3: reverse engineer. What's happening with the situation. Well, there's a 80 00:05:29,160 --> 00:05:32,920 Speaker 3: prediction market that we illustrated on the platform, and you 81 00:05:32,920 --> 00:05:36,560 Speaker 3: can see last night it was showing fifty five percent 82 00:05:36,680 --> 00:05:40,800 Speaker 3: chance Paramount would actually be the ultimate acquier. This morning 83 00:05:40,839 --> 00:05:45,000 Speaker 3: it went to seventy five percent. Netflix. So even in 84 00:05:45,120 --> 00:05:49,440 Speaker 3: terms of what value they provide to the news, it 85 00:05:49,440 --> 00:05:53,080 Speaker 3: can be incredibly powerful and more direct to zero in 86 00:05:53,200 --> 00:05:56,640 Speaker 3: on the results of that specific outcome. And now I'm 87 00:05:56,640 --> 00:06:01,080 Speaker 3: not saying everyone should be trading prediction markets any derivatives market. 88 00:06:01,360 --> 00:06:04,520 Speaker 3: I think people have to make sure it's right for them. 89 00:06:05,240 --> 00:06:08,800 Speaker 3: But I think they're resonating quite strongly with our active traders, 90 00:06:09,279 --> 00:06:12,680 Speaker 3: and also I think they're a great way for other 91 00:06:12,720 --> 00:06:16,400 Speaker 3: folks who may be interested in trading to learn about 92 00:06:16,440 --> 00:06:17,400 Speaker 3: the new asset class. 93 00:06:17,600 --> 00:06:21,400 Speaker 1: It's an incredibly competitive space, flat, I don't need to 94 00:06:21,440 --> 00:06:25,960 Speaker 1: tell you that, and you have companies and people being 95 00:06:25,960 --> 00:06:29,640 Speaker 1: minted billionaires as a result of the competitive space. Right now, 96 00:06:30,120 --> 00:06:32,800 Speaker 1: I'm wondering about the dynamics and how you view the 97 00:06:32,839 --> 00:06:36,440 Speaker 1: competitive dynamics and whether you have an edge after acquiring 98 00:06:37,000 --> 00:06:38,720 Speaker 1: ledger X. Talk about that a little bit. 99 00:06:40,520 --> 00:06:44,360 Speaker 3: Yeah, I mean, right now we're the largest broker in 100 00:06:44,839 --> 00:06:52,640 Speaker 3: the space, and actually the brokerage landscape is not very competitive. 101 00:06:52,839 --> 00:06:55,320 Speaker 3: I mean, we're pretty much the only game in town 102 00:06:55,720 --> 00:07:00,839 Speaker 3: in terms of the exchanges offering prediction market services. I 103 00:07:00,880 --> 00:07:04,120 Speaker 3: think there's a big argument to be made for this 104 00:07:04,160 --> 00:07:07,880 Speaker 3: to be an increasingly institutional business over time. In the 105 00:07:07,880 --> 00:07:13,000 Speaker 3: same way that crypto markets started off being exclusively retail 106 00:07:13,280 --> 00:07:16,880 Speaker 3: and have sort of like built up institutional adoption over time, 107 00:07:18,360 --> 00:07:20,880 Speaker 3: I think you can make a perhaps even stronger argument 108 00:07:20,920 --> 00:07:23,120 Speaker 3: that the same is going to happen with prediction market 109 00:07:23,120 --> 00:07:26,480 Speaker 3: event contracts. If you just look at the trillions that 110 00:07:26,760 --> 00:07:30,800 Speaker 3: are being used are being deployed into interst rate futures 111 00:07:30,840 --> 00:07:36,640 Speaker 3: and swaps to hedge interest rate risk by institutions, prediction 112 00:07:36,760 --> 00:07:41,000 Speaker 3: markets offer a way more direct mechanism for actually hedging 113 00:07:41,040 --> 00:07:45,960 Speaker 3: that risk. So our acquisition, our partnership with SIG to 114 00:07:46,000 --> 00:07:48,680 Speaker 3: acquire ledger x, I think is a way for us 115 00:07:48,720 --> 00:07:51,480 Speaker 3: to tap into what we believe will be a growing 116 00:07:51,520 --> 00:07:55,520 Speaker 3: institutional market for this asset class. And if you couple 117 00:07:55,600 --> 00:07:59,880 Speaker 3: that with our broad retail reach, I think that positions 118 00:08:00,200 --> 00:08:05,240 Speaker 3: well to continue growing with the space, if not faster 119 00:08:05,360 --> 00:08:08,040 Speaker 3: than the aggregate growth in the sector. 120 00:08:08,200 --> 00:08:12,320 Speaker 1: What's the effect that that acquisition has on. Let's say 121 00:08:12,360 --> 00:08:14,800 Speaker 1: call Schaef for example, that is a partner of yours 122 00:08:14,880 --> 00:08:16,160 Speaker 1: right now, but also a competitor. 123 00:08:17,640 --> 00:08:17,840 Speaker 2: Yeah. 124 00:08:17,840 --> 00:08:23,920 Speaker 3: I mean the more prediction market exchanges join the market, 125 00:08:24,760 --> 00:08:28,080 Speaker 3: the more price competition there is, right, and we see 126 00:08:28,080 --> 00:08:33,080 Speaker 3: a world where it's increasingly multipolar. I think there's going 127 00:08:33,160 --> 00:08:37,960 Speaker 3: to be multiple dozens prediction market exchanges, and I think 128 00:08:38,040 --> 00:08:42,400 Speaker 3: that will lead to a great outcome for ultimately the 129 00:08:42,440 --> 00:08:45,600 Speaker 3: traders in the space, because the more exchanges there are, 130 00:08:45,640 --> 00:08:48,199 Speaker 3: the more we put pressure on the transaction fees and 131 00:08:48,520 --> 00:08:51,360 Speaker 3: get them to be close to rock bottom. And what 132 00:08:51,480 --> 00:08:56,520 Speaker 3: happens is more liquidity comes into the space, more institutional interest, 133 00:08:57,160 --> 00:09:00,320 Speaker 3: and prices go down. And as with any ass at, 134 00:09:00,360 --> 00:09:04,679 Speaker 3: you know, crypto equities options Robin who is just focusing 135 00:09:04,679 --> 00:09:08,280 Speaker 3: on providing the best execution for customers. So we connect 136 00:09:08,280 --> 00:09:11,280 Speaker 3: to a wide variety of exchanges and market centers to 137 00:09:11,400 --> 00:09:12,280 Speaker 3: facilitate that. 138 00:09:13,000 --> 00:09:14,920 Speaker 2: A lot for a lot of people, it's not clear 139 00:09:15,120 --> 00:09:18,600 Speaker 2: what the difference is between prediction markets and outright gambling. 140 00:09:18,960 --> 00:09:22,120 Speaker 2: Are you concerned about potential hit to volumes if you're 141 00:09:22,120 --> 00:09:26,120 Speaker 2: forced to exit certain states over conflicts with gambling regulators. 142 00:09:28,200 --> 00:09:30,839 Speaker 3: Yeah, I think a lot of times this discussion of 143 00:09:30,880 --> 00:09:35,600 Speaker 3: you know, is it trading or gambling gets into sort 144 00:09:35,600 --> 00:09:40,600 Speaker 3: of a debate on the meetings of words. But one 145 00:09:40,840 --> 00:09:46,199 Speaker 3: clear benefit of prediction markets is that these contracts are 146 00:09:46,240 --> 00:09:53,319 Speaker 3: traded on regulated exchanges where buyers and sellers meet together. 147 00:09:54,040 --> 00:09:58,000 Speaker 3: And essentially this has a lot of downstream effects, right. 148 00:09:58,080 --> 00:10:01,000 Speaker 3: One of them is you get a wide variety of 149 00:10:01,360 --> 00:10:04,280 Speaker 3: contracts being offered. You know, we've gone from one to 150 00:10:04,480 --> 00:10:06,960 Speaker 3: thousands in the past year. You could see a world 151 00:10:07,000 --> 00:10:10,480 Speaker 3: where it gets to tens of thousands and hundreds of thousands, 152 00:10:10,480 --> 00:10:13,480 Speaker 3: and pretty much any event could be priced in real 153 00:10:13,559 --> 00:10:17,920 Speaker 3: time and tradeable. The second is because these happen on 154 00:10:18,000 --> 00:10:23,160 Speaker 3: an exchange where buyers and sellers meet, our incentives aren't 155 00:10:23,559 --> 00:10:25,840 Speaker 3: to trade against our customers, and that's how a lot 156 00:10:25,880 --> 00:10:31,120 Speaker 3: of these you know, state regulated operators run. Their business 157 00:10:31,120 --> 00:10:36,440 Speaker 3: model is not to exchange orders. It's actually to trade 158 00:10:36,480 --> 00:10:40,240 Speaker 3: against the customer, which basically you know, and they kind 159 00:10:40,280 --> 00:10:43,720 Speaker 3: of jiu jitsu around this when when they get asked, 160 00:10:44,000 --> 00:10:49,880 Speaker 3: but there they make money when their customers lose. Our benefit. 161 00:10:50,559 --> 00:10:54,360 Speaker 3: Our incentive as a business is to bring on customers 162 00:10:54,520 --> 00:10:58,800 Speaker 3: and have those customers account balances grow steadily and monotonically 163 00:10:59,200 --> 00:11:03,680 Speaker 3: over time. So I think we're actually incentive to grow 164 00:11:03,720 --> 00:11:07,600 Speaker 3: with our customers, and that leads us to offering a 165 00:11:07,640 --> 00:11:10,520 Speaker 3: wide variety of products for them to invest and trade in, 166 00:11:11,000 --> 00:11:14,000 Speaker 3: not just prediction markets, which are enticing to bring new 167 00:11:14,000 --> 00:11:18,680 Speaker 3: people in, but also things like Robinhood strategies, retirement, the 168 00:11:18,760 --> 00:11:23,640 Speaker 3: Cortex AI tools that are continuing to proliferate throughout the platform. 169 00:11:23,960 --> 00:11:24,240 Speaker 2: Yeah. 170 00:11:24,280 --> 00:11:26,880 Speaker 3: So our vision is, really, can we build a financial 171 00:11:26,920 --> 00:11:29,400 Speaker 3: super app where all of your financial needs are met 172 00:11:29,400 --> 00:11:32,400 Speaker 3: in one place and all of your all of your 173 00:11:32,480 --> 00:11:38,400 Speaker 3: finances are managed to the best of our ability at Robinhood. 174 00:11:38,440 --> 00:11:40,520 Speaker 2: Well, speaking of all financial needs, there's a lot of 175 00:11:40,600 --> 00:11:43,320 Speaker 2: chatter on Trump accounts for kids, where the government seeds 176 00:11:43,400 --> 00:11:46,280 Speaker 2: investment accounts with one thousand dollars for children as part 177 00:11:46,320 --> 00:11:49,240 Speaker 2: of the One Big, Beautiful Bill. Are you still positioning 178 00:11:49,280 --> 00:11:52,480 Speaker 2: yourself for consideration? Have you heard who else might be 179 00:11:52,480 --> 00:11:53,000 Speaker 2: in the running? 180 00:11:54,800 --> 00:11:58,760 Speaker 3: Yeah? Well, I've spoken to the President and obviously the 181 00:11:58,800 --> 00:12:04,600 Speaker 3: government about this personally, and basically, while we can't give 182 00:12:04,640 --> 00:12:07,480 Speaker 3: you the details of what we're involved in or what 183 00:12:07,520 --> 00:12:11,520 Speaker 3: we're talking about, I think this is incredibly important. I 184 00:12:11,520 --> 00:12:15,520 Speaker 3: think we have a huge risk in this country of 185 00:12:15,600 --> 00:12:19,840 Speaker 3: people not believing in the free markets and in the 186 00:12:19,880 --> 00:12:22,360 Speaker 3: financial system because they don't have skin in the game. 187 00:12:22,800 --> 00:12:26,480 Speaker 3: I think it's especially a problem now when we're in 188 00:12:26,520 --> 00:12:30,480 Speaker 3: this massive wave of AI driven disruption. So you know, 189 00:12:30,800 --> 00:12:35,640 Speaker 3: you're seeing AI products being adopted at an accelerating rate, 190 00:12:35,720 --> 00:12:40,000 Speaker 3: but at the same time AI being generally unpopular because 191 00:12:40,000 --> 00:12:42,880 Speaker 3: people are pretty pretty worried about what impact it's going 192 00:12:42,960 --> 00:12:45,160 Speaker 3: to have on them and their jobs. And I think 193 00:12:45,200 --> 00:12:48,240 Speaker 3: the best way to actually counteract this is from an 194 00:12:48,240 --> 00:12:52,079 Speaker 3: early age, give people a stake in what's being built 195 00:12:52,120 --> 00:12:54,480 Speaker 3: in the great industries of this country. So I think 196 00:12:54,480 --> 00:12:59,520 Speaker 3: it's a great initiative, and we've assured them that we're 197 00:12:59,600 --> 00:13:02,720 Speaker 3: standing ready to help in the maximum way possible. 198 00:13:03,080 --> 00:13:06,000 Speaker 1: To Scarlett's point, Ray Dalio's going to donate to Trump 199 00:13:06,000 --> 00:13:08,720 Speaker 1: accounts and Connecticut Blackrock is going to match Trump account 200 00:13:08,760 --> 00:13:11,160 Speaker 1: contributions for employees. A lot of companies are wondering if 201 00:13:11,160 --> 00:13:13,160 Speaker 1: their companies are going to do it, any plans to 202 00:13:13,160 --> 00:13:14,480 Speaker 1: do that for Robinhood employees. 203 00:13:16,480 --> 00:13:18,760 Speaker 3: I think we're thinking of a lot of things, and 204 00:13:19,679 --> 00:13:23,200 Speaker 3: what I'm mostly interested in is, you know, obviously not 205 00:13:23,280 --> 00:13:29,040 Speaker 3: to downplay Robinhood has thousands of employees, but I think 206 00:13:29,080 --> 00:13:33,000 Speaker 3: our value is we serve tens of millions of customers 207 00:13:33,240 --> 00:13:36,320 Speaker 3: in the US and we've got you know, over a 208 00:13:36,320 --> 00:13:39,200 Speaker 3: third of a trillion in assets on the platform, So 209 00:13:41,520 --> 00:13:44,120 Speaker 3: we're sort of like thinking bigger, like how can we 210 00:13:44,240 --> 00:13:48,280 Speaker 3: help every single company and every single individual in this 211 00:13:48,360 --> 00:13:51,960 Speaker 3: country plug into the system. Of course we're interested in 212 00:13:52,000 --> 00:13:56,240 Speaker 3: thinking for our employees, but the impact of that is 213 00:13:56,320 --> 00:13:59,520 Speaker 3: like relatively low when it comes to the whole country, 214 00:13:59,600 --> 00:14:02,120 Speaker 3: and we're really thinking about how can we help the 215 00:14:02,160 --> 00:14:05,440 Speaker 3: whole country and this program be as successful as possible. 216 00:14:05,679 --> 00:14:07,200 Speaker 1: Heyfa, before we let you go, I want to talk 217 00:14:07,240 --> 00:14:10,360 Speaker 1: a little more about the core tech AI offering and 218 00:14:10,440 --> 00:14:12,840 Speaker 1: what you're trying to build in this financial super app 219 00:14:13,480 --> 00:14:15,840 Speaker 1: at Robinhood. If you think about this from the perspective 220 00:14:15,840 --> 00:14:17,440 Speaker 1: of and you made clear you don't want this to 221 00:14:17,440 --> 00:14:19,440 Speaker 1: be an advisor by any means, but you want people 222 00:14:19,480 --> 00:14:22,160 Speaker 1: to be able to ask questions to the app about 223 00:14:22,280 --> 00:14:26,840 Speaker 1: different stocks or help find stocks. How is it powered, Like, 224 00:14:26,880 --> 00:14:29,400 Speaker 1: what is the AI that powers core techs AI, how 225 00:14:29,480 --> 00:14:33,120 Speaker 1: is it trained? And what's the underlying model that you're using. 226 00:14:34,480 --> 00:14:40,440 Speaker 3: Yeah, so it's an agentic system, which maybe you guys 227 00:14:40,440 --> 00:14:44,400 Speaker 3: have heard this buzzword before, but it's not actually just 228 00:14:44,720 --> 00:14:48,240 Speaker 3: purely the underlying models. We use a variety of models 229 00:14:48,320 --> 00:14:51,920 Speaker 3: under the hood to power different You can think of 230 00:14:51,960 --> 00:14:56,480 Speaker 3: them as experts on our platform. And I think the 231 00:14:56,520 --> 00:15:00,840 Speaker 3: big advantage that we have when you compare it you say, Chat, 232 00:15:00,880 --> 00:15:06,080 Speaker 3: GPT or other lms or agents, is we have not 233 00:15:06,240 --> 00:15:10,000 Speaker 3: just the personal information about customers and their accounts and 234 00:15:10,000 --> 00:15:13,480 Speaker 3: their activity with us, but we also have real time 235 00:15:13,560 --> 00:15:18,120 Speaker 3: market data across multiple assets streaming into the system, and 236 00:15:18,160 --> 00:15:21,640 Speaker 3: that allows us to actually ground the information we provide 237 00:15:22,960 --> 00:15:25,640 Speaker 3: with a high degree of accuracy. So now we can 238 00:15:25,680 --> 00:15:28,880 Speaker 3: tell you not just the real time prices for stocks, 239 00:15:29,400 --> 00:15:33,800 Speaker 3: but also real time predictions and what's likely to happen 240 00:15:34,080 --> 00:15:39,200 Speaker 3: around thousands of real world events. So you can imagine 241 00:15:39,240 --> 00:15:42,760 Speaker 3: all of this is culminating into if you're a customer 242 00:15:42,760 --> 00:15:48,120 Speaker 3: of Robinhood, you no longer have to rely on external 243 00:15:48,160 --> 00:15:52,720 Speaker 3: sources for the inspiration and the idea behind a trade. 244 00:15:52,840 --> 00:15:54,800 Speaker 3: It used to be that Robinhood is pretty much just 245 00:15:54,840 --> 00:15:59,440 Speaker 3: a tool to execute the trade, but now the goal 246 00:15:59,560 --> 00:16:02,680 Speaker 3: is to actually help you with the research, the idea, 247 00:16:02,880 --> 00:16:07,560 Speaker 3: generate the inspiration behind the trade and prediction markets Cortex 248 00:16:07,960 --> 00:16:11,400 Speaker 3: and Robinhood's social which is a new social network we 249 00:16:11,480 --> 00:16:14,200 Speaker 3: unveiled at Hoods Summit a couple of weeks ago. Are 250 00:16:14,240 --> 00:16:18,520 Speaker 3: all converging to drive this evolution and what Robinhood is becoming. 251 00:16:19,080 --> 00:16:22,480 Speaker 2: So Cortex is going to be available to your Gold subscriber, 252 00:16:22,560 --> 00:16:24,440 Speaker 2: so it sounds like it's a premium service. They'll be 253 00:16:24,480 --> 00:16:28,280 Speaker 2: paying an additional fee to get these tools. Do you 254 00:16:28,400 --> 00:16:30,800 Speaker 2: plan to launch any kind of basic version. 255 00:16:30,600 --> 00:16:35,720 Speaker 3: Of this, you know, we're thinking about that. Like Cortex 256 00:16:35,840 --> 00:16:40,040 Speaker 3: and Robinhood Gold in general, it is a subscription service, 257 00:16:40,080 --> 00:16:43,080 Speaker 3: but it's very accessible. So it starts at five dollars 258 00:16:43,160 --> 00:16:47,680 Speaker 3: a month, fifty dollars for the year, and we've seen 259 00:16:47,880 --> 00:16:50,800 Speaker 3: that over a third of customers that sign up to 260 00:16:50,880 --> 00:16:55,320 Speaker 3: Robinhood on a quarterly basis actually adopt the Gold subscription 261 00:16:55,640 --> 00:17:00,640 Speaker 3: relatively quickly. So we see a world where the attachmate 262 00:17:00,720 --> 00:17:05,040 Speaker 3: of our Gold subscription offering goes up even more and 263 00:17:05,480 --> 00:17:10,440 Speaker 3: it really rivals the attachmates of the leading subscription offerings 264 00:17:11,400 --> 00:17:14,680 Speaker 3: industry wide, not just financial services, but also you look 265 00:17:14,720 --> 00:17:20,080 Speaker 3: at music streaming video and I think we can be 266 00:17:20,080 --> 00:17:23,639 Speaker 3: best in class there. So of course, nothing said in stone. 267 00:17:23,680 --> 00:17:26,160 Speaker 3: We're going to continue to iterate. We might end up 268 00:17:26,200 --> 00:17:30,479 Speaker 3: with as you suggest, a basic version, but right now 269 00:17:30,720 --> 00:17:34,920 Speaker 3: we're not seeing any bottlenecks to adoption of Cortex from 270 00:17:34,920 --> 00:17:35,760 Speaker 3: the Gold subscription. 271 00:17:36,160 --> 00:17:39,040 Speaker 2: All right, good stuff, Vlad. Really appreciate your joining us today. 272 00:17:39,160 --> 00:17:41,720 Speaker 2: Lad ten Of is Robin Hood CEO, and I also 273 00:17:41,760 --> 00:17:45,480 Speaker 2: want to thank my co host. I'm Bloomber Crypto Tim Stenovik.