1 00:00:00,280 --> 00:00:10,320 Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. Would you consider yourself 2 00:00:10,480 --> 00:00:13,520 Speaker 1: a swifty? I think it's a scale, like oh, so 3 00:00:13,680 --> 00:00:16,560 Speaker 1: many group chats. This week, I found myself talking with 4 00:00:16,600 --> 00:00:19,480 Speaker 1: my coworkers about Taylor Swift. 5 00:00:19,800 --> 00:00:22,279 Speaker 2: I like music, but I'm not like keeping up with 6 00:00:22,360 --> 00:00:24,040 Speaker 2: every single thing she does with her life. 7 00:00:24,400 --> 00:00:28,400 Speaker 1: That's Bloomberg's Personal Finance reporter Francesca Maglione. I am a 8 00:00:28,480 --> 00:00:32,400 Speaker 1: big fan, and that's Annie Massa, a Bloomberg Wealth reporter. 9 00:00:32,760 --> 00:00:35,880 Speaker 3: I am probably not all the way down the rabbit 10 00:00:35,920 --> 00:00:38,960 Speaker 3: hole on every single development and every single Easter egg 11 00:00:39,000 --> 00:00:43,040 Speaker 3: and every single song. But was I singing August in 12 00:00:43,040 --> 00:00:46,200 Speaker 3: honor of my friend's birthday at a karaoke last week. Yes. 13 00:00:48,200 --> 00:00:52,040 Speaker 1: Taylor Swift and NFL player Travis Kelce announced their engagement 14 00:00:52,120 --> 00:00:55,200 Speaker 1: on Tuesday, and I wanted to talk to Francesca and 15 00:00:55,240 --> 00:00:58,240 Speaker 1: Annie because there's one corner of the universe that's been 16 00:00:58,240 --> 00:01:02,040 Speaker 1: turning their engagement into a money making event, the people 17 00:01:02,080 --> 00:01:06,320 Speaker 1: making predictions and financial bets on where their relationship will 18 00:01:06,319 --> 00:01:06,920 Speaker 1: go next. 19 00:01:07,319 --> 00:01:10,760 Speaker 2: We had like a bet on our team, and people 20 00:01:10,800 --> 00:01:12,160 Speaker 2: had to give their like what they thought was going 21 00:01:12,200 --> 00:01:15,080 Speaker 2: to happen with them in twenty twenty five, and I 22 00:01:15,200 --> 00:01:16,920 Speaker 2: had said that they were going to break up, so 23 00:01:17,240 --> 00:01:19,400 Speaker 2: I think I was expecting that at all. 24 00:01:19,440 --> 00:01:20,760 Speaker 1: Did you lose money on that bet? 25 00:01:20,920 --> 00:01:23,880 Speaker 2: No? No, no money was involved, thankfully. 26 00:01:24,880 --> 00:01:27,640 Speaker 1: The rise of legal online betting in the US over 27 00:01:27,680 --> 00:01:30,320 Speaker 1: the last few years has ushered in all kinds of 28 00:01:30,400 --> 00:01:34,440 Speaker 1: vets like bets on current events and pop culture made 29 00:01:34,440 --> 00:01:37,319 Speaker 1: on prediction markets like Polymarket and call She. 30 00:01:38,000 --> 00:01:40,479 Speaker 2: So far this year, both of these platforms have had 31 00:01:40,520 --> 00:01:43,759 Speaker 2: more than a billion in trading volume, so they've become 32 00:01:43,840 --> 00:01:45,800 Speaker 2: more significant as the years go by. 33 00:01:46,360 --> 00:01:49,840 Speaker 1: Within hours of Tailor and Travis's announcement, gamblers on call 34 00:01:49,920 --> 00:01:53,080 Speaker 1: She had placed about eighty thousand dollars worth of bets 35 00:01:53,120 --> 00:01:56,800 Speaker 1: on the couple's wedding timeline, and by Thursday that number 36 00:01:56,880 --> 00:01:59,960 Speaker 1: had doubled to around one hundred and sixty thousand dollars. 37 00:02:00,040 --> 00:02:00,120 Speaker 3: Yes. 38 00:02:01,000 --> 00:02:04,320 Speaker 1: By Thursday afternoon, the odds of Taylor and Travis getting 39 00:02:04,360 --> 00:02:06,840 Speaker 1: married by the end of the year hovered at around 40 00:02:06,960 --> 00:02:07,760 Speaker 1: seven percent. 41 00:02:08,160 --> 00:02:10,560 Speaker 3: I was not placing any bets personally, but I was 42 00:02:10,639 --> 00:02:13,400 Speaker 3: kind of bearish. So here we are and very happy 43 00:02:13,400 --> 00:02:14,200 Speaker 3: for them, obviously. 44 00:02:14,400 --> 00:02:15,799 Speaker 1: Yeah, bullish now on. 45 00:02:15,800 --> 00:02:17,240 Speaker 3: Travis and Taylor one hundred percent. 46 00:02:22,919 --> 00:02:25,320 Speaker 1: I'm Sarah Holder And this is the big take from 47 00:02:25,360 --> 00:02:29,440 Speaker 1: Bloomberg News Today on the show, How prediction markets work, 48 00:02:29,800 --> 00:02:32,919 Speaker 1: why they're exploding in popularity, and what this all means 49 00:02:32,919 --> 00:02:40,840 Speaker 1: for the people and events they're betting on. Betting isn't new. 50 00:02:41,240 --> 00:02:44,520 Speaker 1: Humans have been doing it for millennia, but online prediction 51 00:02:44,639 --> 00:02:48,400 Speaker 1: markets like polymarket and call She have recently surged in 52 00:02:48,480 --> 00:02:52,239 Speaker 1: popularity in the US. If I've never used polymarket before 53 00:02:52,360 --> 00:02:56,120 Speaker 1: or call she. Is this like sports betting or is 54 00:02:56,160 --> 00:02:58,480 Speaker 1: it different? How does it actually work? 55 00:02:59,200 --> 00:03:02,440 Speaker 3: The easiest explanation would be, in the context of a 56 00:03:02,440 --> 00:03:06,680 Speaker 3: presidential election, who will win Kamala Harris or Donald Trump? 57 00:03:06,919 --> 00:03:09,359 Speaker 1: That's Bloomberg Wealth reporter Annie Massa. 58 00:03:09,520 --> 00:03:13,080 Speaker 3: And you can bet on who will win. But now 59 00:03:13,120 --> 00:03:15,760 Speaker 3: you're able to get odds effectively on all sorts of 60 00:03:15,800 --> 00:03:19,880 Speaker 3: other different things. So will this event happen in the future? 61 00:03:20,040 --> 00:03:23,280 Speaker 3: Yes or no? Will Taylor Swift release an album by 62 00:03:23,320 --> 00:03:25,160 Speaker 3: the end of the year, Yes or no? 63 00:03:25,760 --> 00:03:28,480 Speaker 1: And gods of that went up significantly? 64 00:03:28,680 --> 00:03:31,880 Speaker 3: Yes, exactly, And you can watch in real time as 65 00:03:31,919 --> 00:03:35,440 Speaker 3: these odds change based on who's placing bets for yes, no, 66 00:03:35,840 --> 00:03:38,200 Speaker 3: or a range of other different outcomes for depending on 67 00:03:38,240 --> 00:03:39,280 Speaker 3: the type of market it is. 68 00:03:39,480 --> 00:03:42,680 Speaker 1: Polymarket was founded in twenty twenty and it's gotten major 69 00:03:42,720 --> 00:03:46,840 Speaker 1: funding support from Peter Teel and Ethereum founder Vitolic Buterine. 70 00:03:47,400 --> 00:03:50,560 Speaker 1: This year alone, it's nearly two hundred and thirty thousand 71 00:03:50,560 --> 00:03:54,240 Speaker 1: monthly users have placed nearly sixteen billion dollars of bets 72 00:03:54,320 --> 00:03:59,360 Speaker 1: on real world events. Here's Francesca Magleone, Bloomberg's Personal Finance reporter. 73 00:04:00,200 --> 00:04:03,480 Speaker 2: Started around the presidential election, and it's kind of shifted 74 00:04:03,560 --> 00:04:06,720 Speaker 2: just beyond politics and everything in pop culture when it 75 00:04:06,760 --> 00:04:09,240 Speaker 2: comes to Taylor Swift in general, I think people are 76 00:04:09,240 --> 00:04:11,800 Speaker 2: betting on things as she is she going to have 77 00:04:11,800 --> 00:04:13,480 Speaker 2: a baby this year? Like when are they going to 78 00:04:13,520 --> 00:04:15,600 Speaker 2: get engaged? Is the wedding going to happen this year? 79 00:04:15,680 --> 00:04:18,040 Speaker 2: Like anything you could think of people are betting on. 80 00:04:18,120 --> 00:04:19,160 Speaker 2: There's a market for that for. 81 00:04:19,120 --> 00:04:22,760 Speaker 1: Sure, and what happens when you win and what happens 82 00:04:22,760 --> 00:04:24,680 Speaker 1: when you lose, like how much money is on the 83 00:04:24,680 --> 00:04:25,120 Speaker 1: line here. 84 00:04:25,520 --> 00:04:28,919 Speaker 3: These markets have grown in size over time, but a 85 00:04:28,960 --> 00:04:31,840 Speaker 3: basic example would be you bet a few cents on 86 00:04:31,920 --> 00:04:34,680 Speaker 3: a yes or no and the market settles to a dollar, 87 00:04:34,839 --> 00:04:37,960 Speaker 3: and so effectively you've put down a few cents to 88 00:04:38,040 --> 00:04:40,560 Speaker 3: place the bet based on what the odds are and 89 00:04:40,920 --> 00:04:44,320 Speaker 3: if you were correct, then that market settles to a dollar. 90 00:04:45,480 --> 00:04:48,640 Speaker 1: This can all happen quickly. You type in your card 91 00:04:48,680 --> 00:04:51,120 Speaker 1: info or hook up your crypto wallet and put some 92 00:04:51,160 --> 00:04:53,560 Speaker 1: money on the odds of the Yankees winning tonight, or 93 00:04:53,600 --> 00:04:55,840 Speaker 1: on Travis and Taylor tying the knot by the end 94 00:04:55,839 --> 00:04:58,719 Speaker 1: of the year, depending on what you're wagering on. The 95 00:04:58,760 --> 00:05:02,119 Speaker 1: market closes when in a ends, like after the last 96 00:05:02,120 --> 00:05:04,840 Speaker 1: inning in a baseball game, or it could end at 97 00:05:04,839 --> 00:05:08,479 Speaker 1: a preset cutoff time, and once the market closes, you 98 00:05:08,560 --> 00:05:11,159 Speaker 1: either get your payout or you lose. 99 00:05:11,800 --> 00:05:14,600 Speaker 2: There's been some studies that have said that Americans are 100 00:05:14,640 --> 00:05:17,159 Speaker 2: taking money out of their brokerage accounts to fund some 101 00:05:17,200 --> 00:05:21,520 Speaker 2: of these wages, and in extreme cases there's losses from 102 00:05:21,520 --> 00:05:24,600 Speaker 2: gambling that can contribute to autolone delinquencies and bankruptcies. 103 00:05:25,040 --> 00:05:27,440 Speaker 1: Is this part of a broader trend of online betting 104 00:05:27,440 --> 00:05:30,800 Speaker 1: markets gaining popularity like DraftKings or other websites. 105 00:05:31,200 --> 00:05:34,800 Speaker 2: Yeah, sports betting we've seen grow in popularity, and it's 106 00:05:34,800 --> 00:05:37,839 Speaker 2: also interesting just culturally. Now people will go online to 107 00:05:37,960 --> 00:05:40,240 Speaker 2: check what are the odds of this happening, So it's 108 00:05:40,240 --> 00:05:42,640 Speaker 2: becoming kind of a cultural phenomenon or a place that 109 00:05:42,640 --> 00:05:45,600 Speaker 2: people go to gut check anything that they're questioning or 110 00:05:45,640 --> 00:05:46,279 Speaker 2: thinking about. 111 00:05:46,400 --> 00:05:47,880 Speaker 1: What is a company like this worth. 112 00:05:48,279 --> 00:05:53,880 Speaker 3: So actually, both Calshie and Polymarket have been fundraising, and 113 00:05:54,040 --> 00:05:57,840 Speaker 3: Calsh's valued at two billion dollars now, Polymarket's valued at 114 00:05:57,839 --> 00:06:00,039 Speaker 3: about a billion dollars now. The other thing is the 115 00:06:00,080 --> 00:06:04,400 Speaker 3: intersection of the types of investors in these companies. You 116 00:06:04,480 --> 00:06:09,680 Speaker 3: have an intersection of Silicon Valley and some more traditional 117 00:06:10,080 --> 00:06:14,560 Speaker 3: Wall Street kind of firms. So Susquehanna, which is not 118 00:06:14,640 --> 00:06:17,360 Speaker 3: maybe well known on Main Street, but very well known 119 00:06:17,520 --> 00:06:20,080 Speaker 3: big Wall Street trading firm, is a big backer of Calshi. 120 00:06:20,680 --> 00:06:24,760 Speaker 3: So again like a mix of both the more Silicon 121 00:06:24,839 --> 00:06:27,279 Speaker 3: Valley side of things and the Wall Street side of things. 122 00:06:27,760 --> 00:06:30,320 Speaker 3: And Founder's Fund is a big backer of poly. 123 00:06:30,279 --> 00:06:34,840 Speaker 1: Market and some other notable backers like Donald Trump Junior. 124 00:06:35,880 --> 00:06:39,080 Speaker 3: Donald Trump Junior has been taking on advisory roles at 125 00:06:39,120 --> 00:06:43,880 Speaker 3: a whole bunch of different companies. That's included cryptocurrency realm, 126 00:06:44,360 --> 00:06:48,120 Speaker 3: it's included firearms, all kinds of different companies. But he 127 00:06:48,360 --> 00:06:51,760 Speaker 3: now has a pretty strong foothold in both major prediction 128 00:06:51,839 --> 00:06:54,839 Speaker 3: markets in the US. In January, became an advisor to 129 00:06:54,880 --> 00:07:00,280 Speaker 3: Calshy and just became an advisor to Polymarket, and he 130 00:07:00,520 --> 00:07:03,960 Speaker 3: is a partner at this firm called seventeen eighty nine Capital, 131 00:07:04,400 --> 00:07:06,160 Speaker 3: which invested in Polymarket. 132 00:07:06,640 --> 00:07:08,359 Speaker 1: Why is he so interested in this company? 133 00:07:08,880 --> 00:07:13,600 Speaker 3: He said when he took the role at Calshi that 134 00:07:14,200 --> 00:07:17,960 Speaker 3: prediction markets allowed him and his family, when they were 135 00:07:18,000 --> 00:07:20,680 Speaker 3: watching the returns for the twenty twenty four election at 136 00:07:20,680 --> 00:07:24,120 Speaker 3: mar A Lago, to kind of have a better finger 137 00:07:24,160 --> 00:07:26,000 Speaker 3: on the pulse of what was actually going to happen 138 00:07:26,080 --> 00:07:31,000 Speaker 3: versus polls. Another thing that advocates of prediction markets say 139 00:07:31,080 --> 00:07:34,320 Speaker 3: is that they're usually more accurate than polls, which you 140 00:07:34,360 --> 00:07:37,440 Speaker 3: can debate, but that's what advocates say. And so he 141 00:07:37,480 --> 00:07:40,520 Speaker 3: took the approach that, oh, you know, Calshi helped my 142 00:07:40,560 --> 00:07:45,160 Speaker 3: family understand what was really going on. It's also very 143 00:07:45,160 --> 00:07:49,480 Speaker 3: possible that he sees a lucrative business opportunity in advising 144 00:07:49,560 --> 00:07:52,240 Speaker 3: both of these companies. Who's to say. And so, what's 145 00:07:52,280 --> 00:07:55,720 Speaker 3: really happened is in the past year, and especially under 146 00:07:55,720 --> 00:08:01,120 Speaker 3: the Trump administration, you've seen a more lax regulatory approach 147 00:08:01,320 --> 00:08:04,320 Speaker 3: to policing some of these prediction markets, which have been 148 00:08:04,440 --> 00:08:05,840 Speaker 3: very controversial over. 149 00:08:05,680 --> 00:08:10,520 Speaker 1: Time, controversial, not only because of that murky regulatory landscape, 150 00:08:10,680 --> 00:08:13,720 Speaker 1: but because of the real world implications of betting on 151 00:08:13,840 --> 00:08:14,840 Speaker 1: real world events. 152 00:08:15,160 --> 00:08:17,960 Speaker 3: You can look at it two ways. If you are 153 00:08:18,000 --> 00:08:21,160 Speaker 3: an advocate for prediction markets, you might say, well, this 154 00:08:21,320 --> 00:08:25,640 Speaker 3: is providing some kind of valuable information or wisdom of 155 00:08:25,680 --> 00:08:28,760 Speaker 3: the crowds on anything that will happen next, even if 156 00:08:28,800 --> 00:08:33,480 Speaker 3: a situation is tragic. If you're a detractor, you might say, well, 157 00:08:34,559 --> 00:08:37,640 Speaker 3: you can come up with all kinds of twisted markets 158 00:08:37,720 --> 00:08:40,760 Speaker 3: or sick markets on things, And is that really a 159 00:08:40,800 --> 00:08:43,880 Speaker 3: good idea to start putting that out for people to 160 00:08:43,960 --> 00:08:44,640 Speaker 3: win money on. 161 00:08:45,080 --> 00:08:51,120 Speaker 1: Yeah, it's a really thorny ethical question. Yeah, prediction markets 162 00:08:51,240 --> 00:08:54,480 Speaker 1: rocky road to regulation and how they're influencing the way 163 00:08:54,520 --> 00:08:59,679 Speaker 1: we think about and experience current events. That's after the break. 164 00:09:07,720 --> 00:09:10,400 Speaker 1: A few years after prediction markets hit the scene, the 165 00:09:10,440 --> 00:09:14,400 Speaker 1: industry faced a major roadblock. People were flocking to sites 166 00:09:14,440 --> 00:09:18,040 Speaker 1: like polymarket and call she, but the platforms were operating 167 00:09:18,120 --> 00:09:22,320 Speaker 1: in a regulatory gray area, and Bloomberg reporter Annie Massa 168 00:09:22,400 --> 00:09:26,280 Speaker 1: says they drew the attention of the Commodity Futures Trading Commission, 169 00:09:26,480 --> 00:09:30,400 Speaker 1: or the CFTC. It's a federal agency that regulates the 170 00:09:30,440 --> 00:09:34,800 Speaker 1: derivatives market, which, unlike traditional gambling, is not regulated by 171 00:09:34,800 --> 00:09:35,360 Speaker 1: the States. 172 00:09:36,000 --> 00:09:40,440 Speaker 3: The CFTC has taken a pretty stern approach in the 173 00:09:40,480 --> 00:09:45,080 Speaker 3: past toward these contracts and basically said, if you're betting 174 00:09:45,160 --> 00:09:47,920 Speaker 3: yes or no on the outcomes, if you have money 175 00:09:47,960 --> 00:09:50,240 Speaker 3: on the line, that type of product is something that 176 00:09:50,320 --> 00:09:53,800 Speaker 3: we regulate and we oversee. You need to register with us, 177 00:09:53,920 --> 00:09:58,760 Speaker 3: and we are the watchdogs of this market. And both 178 00:09:58,840 --> 00:10:02,240 Speaker 3: Calshi and Polymarke have had their own skirmishes in the 179 00:10:02,280 --> 00:10:03,319 Speaker 3: regulatory arena. 180 00:10:03,720 --> 00:10:07,200 Speaker 1: The CFTC first targeted call She back in twenty twenty three. 181 00:10:07,720 --> 00:10:11,040 Speaker 1: The regulator claimed that the political betting the platform facilitated 182 00:10:11,120 --> 00:10:14,800 Speaker 1: was illegal and ordered them to stop operating, but Call 183 00:10:14,880 --> 00:10:18,040 Speaker 1: she sued and after a federal court battle, the CFTC 184 00:10:18,240 --> 00:10:20,160 Speaker 1: dropped its appeal in May of this year. 185 00:10:20,559 --> 00:10:24,360 Speaker 3: But the tension for Polymarket had to do with two 186 00:10:24,360 --> 00:10:28,880 Speaker 3: separate inquiries. There was DOJ probe which resulted in this 187 00:10:29,240 --> 00:10:33,800 Speaker 3: big FBI raid on the CEO's penthouse in Soho last year, 188 00:10:34,440 --> 00:10:39,160 Speaker 3: and there was a separate inquiry from the CFTC. The 189 00:10:39,200 --> 00:10:43,960 Speaker 3: CFTC fined them over a million dollars, saying you're allowing 190 00:10:44,000 --> 00:10:46,800 Speaker 3: customers to bet on outcomes and trade contracts that are 191 00:10:46,880 --> 00:10:49,680 Speaker 3: basically something that we should be regulating. You need to 192 00:10:49,720 --> 00:10:50,920 Speaker 3: register with US. 193 00:10:51,200 --> 00:10:55,400 Speaker 1: In twenty twenty two, the US banned Polymarket outright. That 194 00:10:55,480 --> 00:10:58,480 Speaker 1: meant US based users weren't allowed to place bets on 195 00:10:58,520 --> 00:10:59,080 Speaker 1: the site. 196 00:10:59,320 --> 00:11:02,160 Speaker 3: Polymarket was almost an exile in some ways just for 197 00:11:02,520 --> 00:11:03,000 Speaker 3: the US. 198 00:11:03,520 --> 00:11:07,120 Speaker 1: That didn't stop the site from featuring bets about the US. 199 00:11:07,520 --> 00:11:10,760 Speaker 1: In twenty twenty four, Polymarket hosted millions of dollars in 200 00:11:10,800 --> 00:11:13,360 Speaker 1: bets about whether or not Donald Trump would win the 201 00:11:13,360 --> 00:11:18,800 Speaker 1: presidential election. Spoiler alert, he won, just as Polymarket users 202 00:11:18,840 --> 00:11:21,719 Speaker 1: had predicted. I remember that with the presidential election that 203 00:11:21,840 --> 00:11:23,960 Speaker 1: was kind of like an informal poll that actually ended 204 00:11:24,000 --> 00:11:25,080 Speaker 1: up being more accurate. 205 00:11:25,200 --> 00:11:27,240 Speaker 2: Yeah, I have a friend that anytime something going on, 206 00:11:27,360 --> 00:11:29,200 Speaker 2: she just goes on Calshien is like, yeah, no, we 207 00:11:29,200 --> 00:11:31,720 Speaker 2: don't have to worry about this or low or whatever 208 00:11:31,760 --> 00:11:32,400 Speaker 2: the case might be. 209 00:11:32,840 --> 00:11:35,480 Speaker 1: Then in July of this year, the US government and 210 00:11:35,559 --> 00:11:38,800 Speaker 1: Polymarket found a way to move forward. The US shut 211 00:11:38,800 --> 00:11:41,800 Speaker 1: down the two federal probes opened under the Biden administration, 212 00:11:42,280 --> 00:11:46,640 Speaker 1: and Polymarket bought a derivatives exchange and clearing house called QCX, 213 00:11:47,040 --> 00:11:50,240 Speaker 1: which gave them stronger legal standing to operate in the US. 214 00:11:51,600 --> 00:11:57,280 Speaker 1: You both mentioned how influential Polymarket and Calls's predictions have 215 00:11:57,440 --> 00:12:01,800 Speaker 1: grown as a cultural force a political course. Where do 216 00:12:01,840 --> 00:12:03,280 Speaker 1: you see that influence going next. 217 00:12:03,720 --> 00:12:06,680 Speaker 2: I think in the future many people will turn to 218 00:12:06,720 --> 00:12:10,120 Speaker 2: them to kind of gut check events or presidential races. 219 00:12:10,120 --> 00:12:13,880 Speaker 2: They become really helpful to kind of see how people 220 00:12:14,000 --> 00:12:16,400 Speaker 2: online are feeling, Like, will. 221 00:12:16,240 --> 00:12:20,040 Speaker 3: Lisa Cook actually be removed would be an example. There 222 00:12:20,040 --> 00:12:23,720 Speaker 3: are some controversial ones. Earlier in the year, when there 223 00:12:23,720 --> 00:12:27,880 Speaker 3: were wildfires in Los Angeles, Polymarket had a market on 224 00:12:28,280 --> 00:12:32,800 Speaker 3: the trajectory of the wildfires, which people found pretty un towered. 225 00:12:33,000 --> 00:12:36,360 Speaker 3: So it can go all over the place. It's not 226 00:12:36,480 --> 00:12:39,320 Speaker 3: just in the realm of politics or even pop culture. 227 00:12:39,400 --> 00:12:41,320 Speaker 3: You can put odds on a lot of different things. 228 00:12:41,360 --> 00:12:44,360 Speaker 1: Now, yeah, that's disturbing people betting on something that could 229 00:12:44,440 --> 00:12:47,840 Speaker 1: destroy people's livelihoods or their lives. But I guess that 230 00:12:48,480 --> 00:12:51,239 Speaker 1: once you open that door, anything can go on the platform. 231 00:12:51,600 --> 00:12:54,920 Speaker 1: But Francesca says she's also looking at how the demographics 232 00:12:54,920 --> 00:12:58,400 Speaker 1: of people using these platforms could shift over time and 233 00:12:58,400 --> 00:13:01,040 Speaker 1: how that could influence the outcome of certain bets. 234 00:13:01,679 --> 00:13:04,120 Speaker 2: Right now, mostly men are users, Maybe we see more 235 00:13:04,160 --> 00:13:07,520 Speaker 2: women go on there and it become more popular, because 236 00:13:07,520 --> 00:13:10,199 Speaker 2: I do think that it's pretty like the online bro 237 00:13:10,440 --> 00:13:14,319 Speaker 2: crypto bro environment right now, but maybe moving forward it 238 00:13:14,320 --> 00:13:16,600 Speaker 2: will become more mainstream and maybe more accurate in that 239 00:13:16,640 --> 00:13:17,240 Speaker 2: way as well. 240 00:13:17,480 --> 00:13:20,720 Speaker 1: What are the implications of the platform having a certain 241 00:13:20,800 --> 00:13:23,240 Speaker 1: kind of better or a certain kind of user. How 242 00:13:23,320 --> 00:13:27,200 Speaker 1: might that impact the kinds of predictions it will spit out, 243 00:13:27,440 --> 00:13:29,800 Speaker 1: and how might that have an impact on real life. 244 00:13:30,040 --> 00:13:34,439 Speaker 3: There were these markets on throwing things onto the court 245 00:13:34,520 --> 00:13:40,160 Speaker 3: at WNBA games, and the critique there is you're almost 246 00:13:40,280 --> 00:13:45,640 Speaker 3: encouraging certain behaviors by posting these certain markets. 247 00:13:45,760 --> 00:13:47,600 Speaker 2: Yeah, there's some people that are worried that it might 248 00:13:47,679 --> 00:13:51,040 Speaker 2: influence real life events because some people could say it's biased, 249 00:13:51,080 --> 00:13:53,920 Speaker 2: and so that's dangerous in the sense that one opinion 250 00:13:54,040 --> 00:13:57,040 Speaker 2: is getting kind of an advantage over another as more 251 00:13:57,040 --> 00:13:59,640 Speaker 2: people kind of turn to Calci or polymarket, like I said, 252 00:13:59,640 --> 00:14:01,400 Speaker 2: to gut check things, and if you're a gut checking 253 00:14:01,440 --> 00:14:04,000 Speaker 2: against kind of a biased opinion, then that might influence 254 00:14:04,679 --> 00:14:06,520 Speaker 2: real life events or the way that people think about 255 00:14:06,760 --> 00:14:08,480 Speaker 2: races or things like that. 256 00:14:08,640 --> 00:14:12,680 Speaker 1: Yeah, yeah, well, and that's famously one of the impacts 257 00:14:12,720 --> 00:14:14,240 Speaker 1: of political polls. 258 00:14:14,320 --> 00:14:15,959 Speaker 3: Right, if you see your. 259 00:14:15,880 --> 00:14:18,360 Speaker 1: Candidate leading in the polls, maybe you're less likely to 260 00:14:18,440 --> 00:14:21,520 Speaker 1: vote or more likely to switch candidates. Or there are 261 00:14:21,680 --> 00:14:24,080 Speaker 1: impacts of seeing where the wisdom of the crowd is 262 00:14:24,120 --> 00:14:24,800 Speaker 1: pointing and. 263 00:14:24,680 --> 00:14:26,920 Speaker 2: People want to win, and so if you think that 264 00:14:27,000 --> 00:14:29,080 Speaker 2: a outcome is going to go a certain way because 265 00:14:29,080 --> 00:14:31,440 Speaker 2: you saw it on Calshi, then maybe that will make 266 00:14:31,520 --> 00:14:32,920 Speaker 2: you go that way as well. 267 00:14:33,320 --> 00:14:33,600 Speaker 3: Well. 268 00:14:33,640 --> 00:14:37,240 Speaker 1: We've got to hope that Taylor was not influenced to 269 00:14:37,720 --> 00:14:40,520 Speaker 1: get engaged by poly market. I'm sure there's more love. 270 00:14:40,400 --> 00:14:42,000 Speaker 3: There, hope. 271 00:14:57,720 --> 00:15:00,680 Speaker 1: This is the Big Take from Bloomberg News Sarah Holder. 272 00:15:00,920 --> 00:15:03,560 Speaker 1: To get more from The Big Take and unlimited access 273 00:15:03,560 --> 00:15:07,320 Speaker 1: to all of Bloomberg dot com, subscribe today at Bloomberg 274 00:15:07,360 --> 00:15:11,080 Speaker 1: dot com slash podcast offer. If you liked this episode, 275 00:15:11,240 --> 00:15:13,600 Speaker 1: make sure to follow and review The Big Take wherever 276 00:15:13,640 --> 00:15:16,320 Speaker 1: you listen to podcasts. It helps people find the show. 277 00:15:16,680 --> 00:15:18,880 Speaker 1: Thanks for listening. We'll be back tomorrow.