1 00:00:00,080 --> 00:00:06,080 Speaker 1: M. This is Mesters in Business with Very Results on 2 00:00:06,240 --> 00:00:11,760 Speaker 1: Bloomberg Radio. This week on the podcast, I have an 3 00:00:11,760 --> 00:00:16,360 Speaker 1: extra special guest, Luana Lopez. Laura is a co founder 4 00:00:16,400 --> 00:00:21,079 Speaker 1: of CALCI. They are a derivatives trading marketplace where you 5 00:00:21,120 --> 00:00:27,080 Speaker 1: can go and trade event contracts on such disparate occurrences 6 00:00:27,120 --> 00:00:33,840 Speaker 1: such as COVID nineteen, economic outcomes, interest rates, Federal Reserve, politics, 7 00:00:33,960 --> 00:00:39,280 Speaker 1: climate and weather, culture, the Oscars, the Grammys, science and technology, 8 00:00:39,320 --> 00:00:43,720 Speaker 1: all sorts of really fascinating places. They are the only 9 00:00:44,200 --> 00:00:47,600 Speaker 1: such marketplace that has been approved for this sort of 10 00:00:47,640 --> 00:00:53,040 Speaker 1: events trading by the Commodities Futures UH Trading Commission, the CFTC, 11 00:00:53,640 --> 00:00:57,760 Speaker 1: which makes them both fascinating and unique. There's nothing else 12 00:00:57,800 --> 00:01:02,560 Speaker 1: like them. UH. This pro vides away for individuals and 13 00:01:02,720 --> 00:01:08,240 Speaker 1: institutions to hedge all sorts of really interesting events and 14 00:01:08,440 --> 00:01:13,039 Speaker 1: as opposed to having think about, well, if this happens, 15 00:01:13,080 --> 00:01:18,560 Speaker 1: what's the ramification in gold or oil or inflation or 16 00:01:18,680 --> 00:01:22,040 Speaker 1: interest rates, you can actually bet on that exact event 17 00:01:22,560 --> 00:01:27,199 Speaker 1: and hedge your business or your portfolio. It's really quite fascinating. 18 00:01:27,319 --> 00:01:30,880 Speaker 1: I thought this was a really interesting conversation and I 19 00:01:30,920 --> 00:01:33,679 Speaker 1: think you will also so with no further too, my 20 00:01:33,880 --> 00:01:40,800 Speaker 1: conversation with cal She co founder Luana Lopez Laura. This 21 00:01:41,280 --> 00:01:45,720 Speaker 1: is Mesters in Business with Very Results on Bloomberg Radio. 22 00:01:48,000 --> 00:01:51,120 Speaker 1: My special guest this week is Luana Lopez Laura. She 23 00:01:51,440 --> 00:01:55,440 Speaker 1: is the co founder of Calci, one of the only 24 00:01:55,600 --> 00:02:00,240 Speaker 1: derivative trading marketplaces that allows the trading of a event 25 00:02:00,320 --> 00:02:04,640 Speaker 1: contracts in order to hedge against major business and political events. 26 00:02:05,080 --> 00:02:08,680 Speaker 1: CALCI is the only marketplace to receive approval from the 27 00:02:08,720 --> 00:02:15,080 Speaker 1: Commodity Futures Trading Commission, who regulates the trillion dollar derivatives industry. 28 00:02:15,440 --> 00:02:19,560 Speaker 1: Luana Lopez Laura, Welcome to Bloomberg. Thank you so much. 29 00:02:19,600 --> 00:02:22,520 Speaker 1: I'm very happy to be here. So so let's start 30 00:02:23,040 --> 00:02:28,079 Speaker 1: just with that unusual UM intro. You're the only CFTC 31 00:02:28,160 --> 00:02:33,120 Speaker 1: approved way to trade on the outcome of events. Explain 32 00:02:33,160 --> 00:02:35,799 Speaker 1: that a little bit right exactly, UM. Cal She is 33 00:02:35,800 --> 00:02:38,160 Speaker 1: a financial exchange that allows people to trade on the 34 00:02:38,200 --> 00:02:42,000 Speaker 1: outcome of a lot of different events. UM. So things 35 00:02:42,040 --> 00:02:45,400 Speaker 1: from will inflation keep going as high as it is 36 00:02:45,480 --> 00:02:48,959 Speaker 1: right now? Will the Feds raise rates to like? Will 37 00:02:49,000 --> 00:02:51,720 Speaker 1: twenty twenty two be the hottest year on record? Um? 38 00:02:51,800 --> 00:02:53,520 Speaker 1: And already sets us A part is that we're the 39 00:02:53,560 --> 00:02:57,160 Speaker 1: only the first and only ones regulated by the CFTC 40 00:02:57,360 --> 00:03:00,320 Speaker 1: to do this um in the United States. So let's 41 00:03:00,360 --> 00:03:03,840 Speaker 1: talk about that, because I love this story about you guys, 42 00:03:04,200 --> 00:03:08,120 Speaker 1: you and your co founder. You start calling attorneys and 43 00:03:08,160 --> 00:03:10,480 Speaker 1: one day you end up calling like sixty or seventy 44 00:03:10,560 --> 00:03:13,720 Speaker 1: lawyers in a single day, and pretty much every one 45 00:03:13,760 --> 00:03:16,160 Speaker 1: of them said, people have been trying to do this 46 00:03:16,240 --> 00:03:20,360 Speaker 1: since the nineteen eighties. It's never been approved. Just forget 47 00:03:20,400 --> 00:03:23,960 Speaker 1: about it. It's not happening to tell us about that, right. So, 48 00:03:24,120 --> 00:03:26,400 Speaker 1: we really wanted to build Cauchi the right way, so 49 00:03:26,680 --> 00:03:29,440 Speaker 1: to be that exchange that is sustainable and can be 50 00:03:29,440 --> 00:03:32,320 Speaker 1: a pillar of the financial world. UM. We wanted to 51 00:03:32,360 --> 00:03:34,880 Speaker 1: make this really big, get the right partners on board, 52 00:03:34,920 --> 00:03:36,920 Speaker 1: and really try to build something that's gonna out last 53 00:03:37,200 --> 00:03:39,160 Speaker 1: seeing me, you know, like c's around there for like 54 00:03:39,200 --> 00:03:41,520 Speaker 1: a hundred fifty years. And the way to do that 55 00:03:41,600 --> 00:03:44,280 Speaker 1: for us was to build a proper financial exchange, build 56 00:03:44,320 --> 00:03:47,080 Speaker 1: this right. Uh. And we knew that getting regulated was 57 00:03:47,120 --> 00:03:49,080 Speaker 1: the first step in like figuring out how to do 58 00:03:49,080 --> 00:03:51,920 Speaker 1: it right. But obviously me and my co founder of 59 00:03:51,960 --> 00:03:55,360 Speaker 1: both computer scientists, we knew nothing about regulations. So we 60 00:03:55,440 --> 00:03:58,680 Speaker 1: sat down and put on a spreadsheet that names and 61 00:03:58,680 --> 00:04:02,000 Speaker 1: and emails of five different lawyers that we thought maybe 62 00:04:02,040 --> 00:04:04,600 Speaker 1: could be related to this, and we called one by one. 63 00:04:04,680 --> 00:04:07,640 Speaker 1: I think we split who was gonna call um who, 64 00:04:07,680 --> 00:04:10,080 Speaker 1: and all of them were just like, that's not gonna happen. 65 00:04:10,240 --> 00:04:12,920 Speaker 1: The CFC won't allow this. It has already they already 66 00:04:12,920 --> 00:04:16,600 Speaker 1: said no to this in the past. Um. But because 67 00:04:16,640 --> 00:04:18,520 Speaker 1: of a friend of a friend of a friend, we 68 00:04:18,640 --> 00:04:21,040 Speaker 1: ended up getting to Jeff Benman, who works with us 69 00:04:21,080 --> 00:04:25,520 Speaker 1: to today. Uh, he's an ex official the CFTC, and 70 00:04:25,640 --> 00:04:28,719 Speaker 1: he really understood the commission and helped us navigate started 71 00:04:28,960 --> 00:04:32,520 Speaker 1: helping us start navigating the entire situation. Um. And Yeah, 72 00:04:32,520 --> 00:04:36,159 Speaker 1: it was two years of of that entire engagement and 73 00:04:36,200 --> 00:04:39,599 Speaker 1: iteration of the CFTC with all their core principles and 74 00:04:39,640 --> 00:04:42,440 Speaker 1: concerns that they had that to address them and really 75 00:04:42,680 --> 00:04:47,120 Speaker 1: end up getting regulated in November. So it sounds like 76 00:04:47,600 --> 00:04:50,760 Speaker 1: it wasn't so much that the CFTC was against the 77 00:04:50,839 --> 00:04:56,560 Speaker 1: idea of event contracts in order to hedge on these circumstances. 78 00:04:57,080 --> 00:05:01,400 Speaker 1: They just didn't like what was um presented them previously 79 00:05:01,640 --> 00:05:06,240 Speaker 1: over the previous forty years, or did something change that 80 00:05:06,279 --> 00:05:08,680 Speaker 1: they suddenly said Oh, we used to think this was 81 00:05:08,720 --> 00:05:10,599 Speaker 1: a bad idea, and now we think it's a good idea. 82 00:05:10,920 --> 00:05:12,960 Speaker 1: I think it was it's more of the first I 83 00:05:13,000 --> 00:05:16,360 Speaker 1: think it was about presenting to them why we thought 84 00:05:16,400 --> 00:05:19,960 Speaker 1: event contracts were so important and how they could really 85 00:05:19,960 --> 00:05:23,600 Speaker 1: be used for hedging um and every day hedging like 86 00:05:23,600 --> 00:05:27,279 Speaker 1: like retail and Americans every day can hedge things like inflation, 87 00:05:27,400 --> 00:05:30,200 Speaker 1: like rates, the risks that we see and read about 88 00:05:30,279 --> 00:05:33,240 Speaker 1: like in the news or or on TV every day. Uh. 89 00:05:33,320 --> 00:05:35,479 Speaker 1: And it was really like presenting to them and getting 90 00:05:35,520 --> 00:05:38,280 Speaker 1: them to comfort with how these markets work, how they 91 00:05:38,279 --> 00:05:42,200 Speaker 1: weren't easy to manipulate, how the rules could could operate. 92 00:05:42,240 --> 00:05:44,960 Speaker 1: So really getting them to comfort with how the exchange, 93 00:05:45,000 --> 00:05:47,640 Speaker 1: the markets, and all of our contracts could could operate. 94 00:05:47,680 --> 00:05:51,240 Speaker 1: And that's what took that long. It wasn't um in 95 00:05:51,279 --> 00:05:53,880 Speaker 1: my opinion. It was more like explaining what we wanted 96 00:05:53,920 --> 00:05:56,559 Speaker 1: to do. They were fantastic from the beginning to really 97 00:05:56,560 --> 00:05:59,760 Speaker 1: listening and working with us. UM. It wasn't that they 98 00:05:59,800 --> 00:06:01,640 Speaker 1: were just like, no, wait, we're never going to do this. 99 00:06:02,200 --> 00:06:04,640 Speaker 1: I think it's interesting that it took people from outside 100 00:06:04,640 --> 00:06:08,000 Speaker 1: of the world to finance to bring an idea into 101 00:06:08,080 --> 00:06:13,640 Speaker 1: finance from a technology perspective and say, whatever the logistical 102 00:06:13,720 --> 00:06:19,080 Speaker 1: hurdles we have to meet in order to receive regulatory approval. 103 00:06:19,760 --> 00:06:25,040 Speaker 1: That wasn't like an ideological problem to you, was a well, 104 00:06:25,080 --> 00:06:27,040 Speaker 1: this is a logistical problem that we have to solve, 105 00:06:27,080 --> 00:06:29,640 Speaker 1: and once we solve it, we can get this going. 106 00:06:29,880 --> 00:06:31,600 Speaker 1: So how long did the back and forth with the 107 00:06:31,680 --> 00:06:36,200 Speaker 1: CFTC take to get approval? Yeah, it was two years 108 00:06:36,279 --> 00:06:38,560 Speaker 1: or two years and a half. Um. And yeah, we 109 00:06:38,640 --> 00:06:41,040 Speaker 1: used to say, it's like we were climbing a very 110 00:06:41,120 --> 00:06:44,320 Speaker 1: high mountain, and then as we would started climbing more, 111 00:06:44,360 --> 00:06:46,640 Speaker 1: we would see it's actually so, I says high, and 112 00:06:46,680 --> 00:06:49,560 Speaker 1: we would keep and it would keep multiplying. Because the 113 00:06:49,560 --> 00:06:51,440 Speaker 1: thing is we would go to them and they would 114 00:06:51,440 --> 00:06:54,039 Speaker 1: have concerns and issues, so we would go back and 115 00:06:54,080 --> 00:06:55,800 Speaker 1: solve the issues. A lot of it, as you mentioned, 116 00:06:56,320 --> 00:06:59,919 Speaker 1: was related to technology really doing analysis and similar markets, 117 00:07:00,080 --> 00:07:02,599 Speaker 1: what we could do and view the surveillance systems and 118 00:07:02,600 --> 00:07:04,640 Speaker 1: all of those things, and going back to them and 119 00:07:04,640 --> 00:07:06,599 Speaker 1: then they're like, okay, that's fine, but we have all 120 00:07:06,600 --> 00:07:08,440 Speaker 1: these other issues now, and then we would go back 121 00:07:08,480 --> 00:07:12,440 Speaker 1: and and figure them out and do that one by one. Um. 122 00:07:12,560 --> 00:07:14,400 Speaker 1: It was like walking in the desert a little bit. 123 00:07:14,440 --> 00:07:16,800 Speaker 1: We didn't know where where the end was, but it 124 00:07:16,920 --> 00:07:18,880 Speaker 1: ended up working out. So so let's talk a little 125 00:07:18,880 --> 00:07:22,560 Speaker 1: bit about your platform. This is unlike futures, and it's 126 00:07:22,640 --> 00:07:27,280 Speaker 1: unlike derivatives in that when you are purchasing a contract, 127 00:07:27,960 --> 00:07:30,360 Speaker 1: you're putting up the full dollar amount. It's not like 128 00:07:30,480 --> 00:07:33,680 Speaker 1: where you're putting up ten cents on the dollar or 129 00:07:33,880 --> 00:07:36,239 Speaker 1: one cent on the dollar. If you're making a thousand 130 00:07:36,280 --> 00:07:40,000 Speaker 1: dollar bet, you were posting a thousand dollars. How much 131 00:07:40,320 --> 00:07:45,160 Speaker 1: did that factor influence the CFTC that this wasn't just 132 00:07:45,200 --> 00:07:49,800 Speaker 1: going to be reckless speculation and people um falling around. 133 00:07:49,800 --> 00:07:53,160 Speaker 1: This was really hedging right. So we are fully cash collateralized. 134 00:07:53,200 --> 00:07:56,720 Speaker 1: So every as you said, every dollar that you can lose, 135 00:07:56,840 --> 00:07:58,760 Speaker 1: every dollar that you your trade, you have to have 136 00:07:58,800 --> 00:08:01,200 Speaker 1: it with us before UM. And I think this really 137 00:08:01,240 --> 00:08:03,280 Speaker 1: helps with the safety off the platform, and it really 138 00:08:03,280 --> 00:08:06,160 Speaker 1: started from us. UM. We really want to start in 139 00:08:06,200 --> 00:08:07,920 Speaker 1: a way that it's very safe for everyone and we 140 00:08:07,920 --> 00:08:12,880 Speaker 1: can really understand the system before going like too far ahead. UM, 141 00:08:12,920 --> 00:08:15,080 Speaker 1: and we really see this as very important. So all 142 00:08:15,120 --> 00:08:18,040 Speaker 1: the funds are fully cash collatalized, but obviously from from 143 00:08:18,080 --> 00:08:20,680 Speaker 1: the CFTC perspective. It adds to their comfort to the 144 00:08:20,720 --> 00:08:23,720 Speaker 1: fact that there can be like leverage or marginal more 145 00:08:24,040 --> 00:08:27,040 Speaker 1: risk added to the system that all the money's collatalized 146 00:08:27,040 --> 00:08:30,240 Speaker 1: and the retail is protected because of that. So equities 147 00:08:30,320 --> 00:08:33,600 Speaker 1: you can put up half the dollar amount. Two to 148 00:08:33,679 --> 00:08:37,920 Speaker 1: one futures are something like ten to one options. If 149 00:08:37,960 --> 00:08:40,440 Speaker 1: you go out of out of the money and far 150 00:08:40,640 --> 00:08:43,760 Speaker 1: enough into the future, it's it's a hundred to one. 151 00:08:44,360 --> 00:08:46,760 Speaker 1: Is there ever a plan to move away from the 152 00:08:46,880 --> 00:08:50,720 Speaker 1: one to one dollar for dollar Maybe not option hundreds 153 00:08:50,720 --> 00:08:53,880 Speaker 1: on one, but certainly margin and equity market seems to 154 00:08:53,920 --> 00:08:56,720 Speaker 1: be pretty reasonable at two to one UM. At the moment, 155 00:08:56,760 --> 00:08:59,960 Speaker 1: we really focused on retail and fully cash collateralization fullick 156 00:09:00,120 --> 00:09:03,360 Speaker 1: being fully cash colatalized UM. But at in the future, 157 00:09:03,360 --> 00:09:05,280 Speaker 1: as think, our goal is to be like the New 158 00:09:05,320 --> 00:09:08,480 Speaker 1: York Stock Exchange for events, so having being really the 159 00:09:08,800 --> 00:09:12,440 Speaker 1: central place of the ecosystem and having like different brokers 160 00:09:12,440 --> 00:09:16,240 Speaker 1: and institutions, hedge funds, market makers plugged into us the exchange. 161 00:09:16,480 --> 00:09:19,000 Speaker 1: At that point it would make sense to start considering 162 00:09:19,440 --> 00:09:21,960 Speaker 1: UM something like that, But right now we're completely focused 163 00:09:21,960 --> 00:09:24,800 Speaker 1: on retail and UM having a fully cash glatalized stuff. 164 00:09:25,120 --> 00:09:28,440 Speaker 1: Once it becomes a big institutional exchange, then then you 165 00:09:28,480 --> 00:09:31,679 Speaker 1: can explore that. So since it's retail, let's talk a 166 00:09:31,679 --> 00:09:35,480 Speaker 1: little bit about retail. Gamification is a real big issue. 167 00:09:35,520 --> 00:09:37,520 Speaker 1: We've seen robin Hood do this, We've seen a number 168 00:09:37,600 --> 00:09:41,400 Speaker 1: of other UH sports gambling platforms doing this. What are 169 00:09:41,440 --> 00:09:45,840 Speaker 1: your thoughts about gamification when it comes to events trading. Yeah, 170 00:09:45,880 --> 00:09:48,480 Speaker 1: I think the gamification question is a very interesting one 171 00:09:48,520 --> 00:09:50,720 Speaker 1: because I think it's less about the asset class and 172 00:09:50,760 --> 00:09:54,280 Speaker 1: more about the actual platform and the mechanics. So, for example, 173 00:09:54,360 --> 00:09:57,520 Speaker 1: you can trade equities on robin Hood or Charles shob Um. 174 00:09:57,559 --> 00:10:00,640 Speaker 1: The conversation about gamification is a lot more on Robin 175 00:10:00,640 --> 00:10:03,520 Speaker 1: hood Um then Altarl shrob even though the underlying like 176 00:10:04,000 --> 00:10:06,760 Speaker 1: it's the same, you're trading equities. So we really believe 177 00:10:06,840 --> 00:10:10,839 Speaker 1: event contracts are have a very big economic purpose and 178 00:10:11,120 --> 00:10:13,320 Speaker 1: can be used for hedging and all of those things 179 00:10:13,360 --> 00:10:16,839 Speaker 1: that we talked about, and the gamification would come only 180 00:10:16,840 --> 00:10:20,080 Speaker 1: in the platform. But we're very, very um focused on 181 00:10:20,200 --> 00:10:23,000 Speaker 1: beauting a platform that's safe, easy to understand and to use, 182 00:10:23,080 --> 00:10:26,520 Speaker 1: but not not gamified. So let's go over some of 183 00:10:26,559 --> 00:10:30,120 Speaker 1: the type of events that you guys trade. You could 184 00:10:30,280 --> 00:10:35,319 Speaker 1: you can make bets on COVID nineteen and vaccination, on economics, inflation, 185 00:10:35,520 --> 00:10:40,959 Speaker 1: mortgage rates, politics, climate and weather, world culture, science and technology. 186 00:10:41,400 --> 00:10:44,439 Speaker 1: Let's let's uh, let's take some examples from this. I 187 00:10:44,559 --> 00:10:48,040 Speaker 1: love the idea, will the thirty year fixed rate mortgage 188 00:10:48,080 --> 00:10:52,079 Speaker 1: be above three point nine percent on April fift In 189 00:10:52,200 --> 00:10:55,720 Speaker 1: other words, if I'm buying a house and closing on 190 00:10:55,760 --> 00:10:59,640 Speaker 1: it and concerned that rates might rise, I could take 191 00:10:59,640 --> 00:11:03,760 Speaker 1: a trade against that and hedge that position. And I 192 00:11:03,800 --> 00:11:07,160 Speaker 1: don't have to be a billion dollar hedge fund. I 193 00:11:07,200 --> 00:11:10,120 Speaker 1: could just be someone buying a house exactly. I think 194 00:11:10,160 --> 00:11:13,760 Speaker 1: all of our contracts UM have economics purpose and they 195 00:11:13,760 --> 00:11:16,520 Speaker 1: can really be used for hedging. UM. For example, all 196 00:11:16,520 --> 00:11:19,320 Speaker 1: of our COVID markets during the omicron wave, you could 197 00:11:19,360 --> 00:11:22,040 Speaker 1: really see, like even before the news started reporting it 198 00:11:22,520 --> 00:11:24,719 Speaker 1: UM the amount that it was ticking up, and then 199 00:11:24,720 --> 00:11:26,880 Speaker 1: we would talk to the users and there, oh wow, 200 00:11:26,960 --> 00:11:28,959 Speaker 1: like I might not be able to go back to school. 201 00:11:29,280 --> 00:11:32,720 Speaker 1: I want to hedge like that that situation and all 202 00:11:32,760 --> 00:11:35,000 Speaker 1: of that UM. SO a lot of the contracts I'm 203 00:11:35,080 --> 00:11:38,120 Speaker 1: very interested in. For example, is the half point rate 204 00:11:38,200 --> 00:11:40,680 Speaker 1: hike for for March. I think it's it's a market 205 00:11:40,679 --> 00:11:43,280 Speaker 1: that went up a lot to during I think one 206 00:11:43,320 --> 00:11:45,400 Speaker 1: of the there was some news that that he was 207 00:11:45,400 --> 00:11:47,520 Speaker 1: going to go up by that and then he went 208 00:11:47,920 --> 00:11:51,880 Speaker 1: down again, and and um. Other ones are GDP and inflation. 209 00:11:51,960 --> 00:11:54,480 Speaker 1: Really just getting into the economic situation we have in 210 00:11:54,520 --> 00:11:57,560 Speaker 1: nowadays number of Americas. So these are all yes or 211 00:11:57,679 --> 00:12:03,360 Speaker 1: no contracts, pretty clearly determined. It's black and white. Will 212 00:12:03,400 --> 00:12:06,559 Speaker 1: two hundred and fifty four million Americans be vascinated by 213 00:12:06,600 --> 00:12:11,160 Speaker 1: May one? But I saw a contract will America achieve 214 00:12:11,280 --> 00:12:16,280 Speaker 1: her immunity by September one? Who is the determiner of 215 00:12:16,600 --> 00:12:20,360 Speaker 1: whether or not her immunely? How do you define those terms? Yeah, 216 00:12:20,400 --> 00:12:22,679 Speaker 1: that's a great question. All of our markets are like 217 00:12:22,920 --> 00:12:27,080 Speaker 1: legal binding documents, so they're like forty pages determining what 218 00:12:27,160 --> 00:12:30,120 Speaker 1: the real rules are, uh to really make sure that 219 00:12:30,160 --> 00:12:33,559 Speaker 1: there's no room for indeterminacy or anything of the sort. 220 00:12:33,960 --> 00:12:36,680 Speaker 1: Uh So this market specifically, I'm not exactly sure. I 221 00:12:36,679 --> 00:12:39,040 Speaker 1: think it's definitely the CDC or some number around there. 222 00:12:39,360 --> 00:12:41,559 Speaker 1: But if you like all of our rules, if you 223 00:12:41,600 --> 00:12:44,439 Speaker 1: go to our rule book, it has very specifically defining 224 00:12:44,920 --> 00:12:48,480 Speaker 1: um where which number we're using, how we're using, which target, 225 00:12:48,520 --> 00:12:50,000 Speaker 1: if it has to be above or below a certain 226 00:12:50,080 --> 00:12:52,920 Speaker 1: number and it ends up being very determined. But for 227 00:12:52,960 --> 00:12:56,480 Speaker 1: COVID markets, we're using CDC numbers for settlement sources. So 228 00:12:56,520 --> 00:13:00,240 Speaker 1: I mentioned world culture. Um, that's kind of interest thing. 229 00:13:00,800 --> 00:13:03,079 Speaker 1: Is there a lot of activity in who's gonna win 230 00:13:03,160 --> 00:13:05,679 Speaker 1: Best Picture or who's going to be the best actress 231 00:13:05,679 --> 00:13:09,040 Speaker 1: at the oscars? How is that a seasonal thing when 232 00:13:09,080 --> 00:13:12,360 Speaker 1: each year or how does that trade? Yeah, the launching 233 00:13:12,360 --> 00:13:15,319 Speaker 1: the oscar markets were was very important for us because 234 00:13:15,360 --> 00:13:19,000 Speaker 1: they were the very very first regulated derivatives I guess 235 00:13:19,040 --> 00:13:22,840 Speaker 1: in the entertainment industry and in academy awards. Um. We 236 00:13:22,920 --> 00:13:25,920 Speaker 1: have traded more than a hundred and fifty thousand contracts 237 00:13:26,240 --> 00:13:27,920 Speaker 1: in the oscars so far, and it's only been a 238 00:13:27,960 --> 00:13:30,760 Speaker 1: couple of weeks, and we really expect the trading there 239 00:13:30,800 --> 00:13:33,480 Speaker 1: to to be a lot higher closer to to the 240 00:13:33,520 --> 00:13:36,679 Speaker 1: ceremony or during the ceremony. UM. But it's interesting a 241 00:13:36,720 --> 00:13:38,560 Speaker 1: lot of people say that the oscars are are dead 242 00:13:38,640 --> 00:13:42,760 Speaker 1: or irrelevant, but the movie industry is so big to 243 00:13:42,880 --> 00:13:45,480 Speaker 1: nowadays that there's so much so many people that are 244 00:13:45,520 --> 00:13:48,120 Speaker 1: so impacted by the results of these awards and things 245 00:13:48,160 --> 00:13:50,960 Speaker 1: of the sort. Um. And yeah, well this is analogy point. 246 00:13:51,000 --> 00:13:54,599 Speaker 1: I think The interesting thing about the entertainment industry is 247 00:13:54,640 --> 00:13:56,760 Speaker 1: that you have awards, for example, like the Oscars or 248 00:13:56,800 --> 00:13:59,120 Speaker 1: the Grammys, that we also have markets on. But you 249 00:13:59,200 --> 00:14:04,560 Speaker 1: have weekly things, for example, album sales numbers, UM billboards, 250 00:14:04,760 --> 00:14:07,200 Speaker 1: charts and things like that that that we offer markets 251 00:14:07,200 --> 00:14:08,800 Speaker 1: on every week and have a lot of room for 252 00:14:08,880 --> 00:14:11,200 Speaker 1: like modeling and aupha and things of the sort. So 253 00:14:11,200 --> 00:14:14,280 Speaker 1: so I know, studios spend a lot of money on 254 00:14:14,880 --> 00:14:18,880 Speaker 1: marketing and promoting leading up to the Oscars, because if 255 00:14:19,160 --> 00:14:23,840 Speaker 1: let's say a small independent film wins Best Oscar, it 256 00:14:23,960 --> 00:14:27,120 Speaker 1: seems a you it gets a huge uptick in subsequent 257 00:14:27,640 --> 00:14:33,000 Speaker 1: box office and other sales or streaming rights. I'm wondering 258 00:14:33,040 --> 00:14:35,479 Speaker 1: if part of their marketing plan is going to include 259 00:14:36,120 --> 00:14:40,320 Speaker 1: hedging on Best Oscar. They can not only spend you know, 260 00:14:40,360 --> 00:14:43,400 Speaker 1: a million dollars on promotion, they could buy a contract 261 00:14:43,440 --> 00:14:47,840 Speaker 1: that offsets not winning Best Oscar. Yeah, that's our goal. 262 00:14:48,000 --> 00:14:50,200 Speaker 1: Let's forget all of them to come and really hedge 263 00:14:50,240 --> 00:14:52,880 Speaker 1: all this risk that they have. So so where's the 264 00:14:53,000 --> 00:14:57,560 Speaker 1: volume today? Where are you seeing the most amount of activity? 265 00:14:57,600 --> 00:15:01,560 Speaker 1: Is it? Is it inflation and fed activity? Is a GDP? 266 00:15:02,160 --> 00:15:06,040 Speaker 1: What where where's all the money flowing? On your platform. Right. 267 00:15:06,160 --> 00:15:09,680 Speaker 1: It's actually interesting because when we launched, we really expected 268 00:15:09,680 --> 00:15:13,560 Speaker 1: it to be categories specific or concentrated in specific UM 269 00:15:13,960 --> 00:15:18,800 Speaker 1: categories or economics, UM, entertainment, transportation, technology, But it really 270 00:15:18,880 --> 00:15:21,760 Speaker 1: is about what what the news are, So what's top 271 00:15:21,800 --> 00:15:24,240 Speaker 1: of the New York Times, what's in the newspaper the 272 00:15:24,240 --> 00:15:27,600 Speaker 1: whole day? Uh, what's in the news? And right now, 273 00:15:27,680 --> 00:15:31,880 Speaker 1: as you mentioned the Fed March meetings, is is very 274 00:15:32,080 --> 00:15:34,440 Speaker 1: is a very uh is a market with a lot 275 00:15:34,440 --> 00:15:38,800 Speaker 1: of right, it's very hot, yeah, for sure. But for us, 276 00:15:38,920 --> 00:15:41,920 Speaker 1: we've we've seen this like news based activity, a lot 277 00:15:42,000 --> 00:15:44,320 Speaker 1: like the onm ground wave um as like Dojo and 278 00:15:44,360 --> 00:15:46,480 Speaker 1: the infrastructure bill was passing, there was a lot of 279 00:15:46,520 --> 00:15:49,640 Speaker 1: activity over there, or when j. Powell was going to 280 00:15:49,720 --> 00:15:52,240 Speaker 1: get renominated, there was a lot of activity in that market. 281 00:15:52,560 --> 00:15:54,120 Speaker 1: So it's really about what's in the news and what 282 00:15:54,200 --> 00:15:56,880 Speaker 1: people see UM there was associated with and and where 283 00:15:56,920 --> 00:15:59,960 Speaker 1: they can they think there's most room to make money. 284 00:16:00,000 --> 00:16:03,280 Speaker 1: And right now the Federates UM people are really disagreeing 285 00:16:03,280 --> 00:16:05,280 Speaker 1: on that and there's a lot of volume of volatively 286 00:16:05,280 --> 00:16:08,000 Speaker 1: on that market. So so you guys didn't exist when 287 00:16:08,080 --> 00:16:11,800 Speaker 1: Brexit had come up. That was before your time. But 288 00:16:12,040 --> 00:16:15,720 Speaker 1: you have been around with Russia and Ukraine and I 289 00:16:15,840 --> 00:16:18,960 Speaker 1: noticed there's not a lot of activity there. Why not 290 00:16:19,160 --> 00:16:22,720 Speaker 1: do a futures contract on will Russia. It's obviously too 291 00:16:22,800 --> 00:16:26,160 Speaker 1: late today, but in January or December you could have 292 00:16:26,200 --> 00:16:30,720 Speaker 1: done a will Russia invade Ukraine by February one, March 293 00:16:30,800 --> 00:16:34,320 Speaker 1: first April one. Right, we avoid any contract that's related 294 00:16:34,360 --> 00:16:38,760 Speaker 1: to war, terrorism, assassination, or violence of any kind. Um. 295 00:16:38,960 --> 00:16:42,680 Speaker 1: We don't want to have those those markets on our platform. Um. 296 00:16:42,720 --> 00:16:45,960 Speaker 1: But we do have markets there are adjacent to that. 297 00:16:46,080 --> 00:16:48,480 Speaker 1: So for example, markets on the price of ruble or 298 00:16:48,600 --> 00:16:51,320 Speaker 1: or the price of oil natural gas in the US 299 00:16:51,360 --> 00:16:53,720 Speaker 1: and Europe, So we have markets that adjacent. We just 300 00:16:53,720 --> 00:16:57,680 Speaker 1: don't want to have markets directly related to war, terrorism, assassination, 301 00:16:57,760 --> 00:17:01,040 Speaker 1: or makes sense, you don't want to incentivize anybody misbehave. 302 00:17:02,160 --> 00:17:05,280 Speaker 1: In the past, I've heard futures described as a marriage 303 00:17:05,359 --> 00:17:09,760 Speaker 1: between hedgers and speculators. So if you're an airline, you 304 00:17:09,800 --> 00:17:13,160 Speaker 1: want to hedge the price of oil, but someone's got 305 00:17:13,160 --> 00:17:15,040 Speaker 1: to be on the other side of that trade, so 306 00:17:15,480 --> 00:17:19,000 Speaker 1: incomes the speculators. Are you seeing that same sort of 307 00:17:19,040 --> 00:17:23,639 Speaker 1: relationship amongst CALCI clients. Yeah, I think she is one 308 00:17:23,640 --> 00:17:26,520 Speaker 1: of the most pure forms of exactly this hedging and 309 00:17:26,560 --> 00:17:30,240 Speaker 1: speculation UM match. I think one a very simple example 310 00:17:30,280 --> 00:17:32,680 Speaker 1: to understand this. If you think of rain in New 311 00:17:32,720 --> 00:17:35,360 Speaker 1: York City right like you can have like an ice 312 00:17:35,400 --> 00:17:38,800 Speaker 1: cream truck buying. UH. An ice cream truck will be 313 00:17:38,840 --> 00:17:41,720 Speaker 1: ready really hit if if it rains for like a 314 00:17:41,720 --> 00:17:44,480 Speaker 1: lot of days, because people will buy less ice cream, 315 00:17:44,720 --> 00:17:46,879 Speaker 1: so they can buy a guess contract to really hedge 316 00:17:47,119 --> 00:17:50,240 Speaker 1: that offset that they have UM. On the other side, 317 00:17:50,240 --> 00:17:52,760 Speaker 1: there can be someone that is going to speculate and 318 00:17:52,800 --> 00:17:56,720 Speaker 1: seeing there's a forecast of for rain UM in the 319 00:17:56,800 --> 00:18:00,200 Speaker 1: next couple of days, and they're willing to take UM 320 00:18:00,640 --> 00:18:03,480 Speaker 1: the no side because they think that there's only twenty 321 00:18:03,480 --> 00:18:05,600 Speaker 1: percentages it's going to rain, and it seems like they 322 00:18:05,600 --> 00:18:07,520 Speaker 1: can make money. So then you can really have a 323 00:18:07,560 --> 00:18:09,560 Speaker 1: match of like people that actually need to have a 324 00:18:09,600 --> 00:18:12,920 Speaker 1: contract for hedging almost like insurance, and people who who 325 00:18:12,960 --> 00:18:16,040 Speaker 1: because of forecasting and probability and what they think the 326 00:18:16,080 --> 00:18:18,200 Speaker 1: fair value is, is going to take the other side. 327 00:18:18,480 --> 00:18:20,840 Speaker 1: And then at the settlement, for example, if it does rain, 328 00:18:21,160 --> 00:18:23,000 Speaker 1: it ends up being that everyone is happy because the 329 00:18:23,040 --> 00:18:27,400 Speaker 1: speculator makes money because they work correct. UH. The head 330 00:18:27,800 --> 00:18:31,520 Speaker 1: is protected against and the speculator on the trade right 331 00:18:31,600 --> 00:18:35,199 Speaker 1: exactly and exactly, you got it totally right. So let 332 00:18:35,480 --> 00:18:39,000 Speaker 1: that raises a really interesting question, who are your clients? 333 00:18:39,040 --> 00:18:43,159 Speaker 1: Are they hedge funds and institutions, are they retail investors 334 00:18:43,280 --> 00:18:45,399 Speaker 1: or is it a whole spectrum of people. Um, we 335 00:18:45,520 --> 00:18:48,879 Speaker 1: really focused now on on retail, and our our biggest 336 00:18:48,920 --> 00:18:51,560 Speaker 1: amount of users right now is the traditional option trader, 337 00:18:51,720 --> 00:18:55,960 Speaker 1: like informed retail option traders. UM. But the way that 338 00:18:56,000 --> 00:18:59,000 Speaker 1: we see this this growing is we want to keep 339 00:18:59,040 --> 00:19:02,080 Speaker 1: growing within the so trading an options trading community. And 340 00:19:02,119 --> 00:19:05,280 Speaker 1: then our next steps getting brokerages on board so that 341 00:19:05,320 --> 00:19:08,159 Speaker 1: you can now go and trade on event contracts for 342 00:19:08,280 --> 00:19:11,280 Speaker 1: your interactive brokers or each trade account um. And then 343 00:19:11,320 --> 00:19:14,560 Speaker 1: after debuting enough liquidity to start bringing more prop shops 344 00:19:14,560 --> 00:19:18,600 Speaker 1: in and smaller firms, and then hedge funds and and 345 00:19:18,640 --> 00:19:22,840 Speaker 1: then institutions and mimic can have maybe um burger king hedging, UM, 346 00:19:23,000 --> 00:19:25,439 Speaker 1: I don't know, price of plastic draws or something like that. 347 00:19:25,520 --> 00:19:29,840 Speaker 1: So so the platform eventually becomes an exchange exactly exactly. 348 00:19:29,840 --> 00:19:31,439 Speaker 1: I think we see it as a beaut up of 349 00:19:31,520 --> 00:19:35,639 Speaker 1: liquidity from from retail. That's like smaller amounts, but but 350 00:19:35,640 --> 00:19:39,720 Speaker 1: but higher velocity to to higher bigger and bigger institutions 351 00:19:39,920 --> 00:19:43,199 Speaker 1: all the way to become like a full flash financial 352 00:19:43,240 --> 00:19:45,159 Speaker 1: exchange like the New York Stock Exchange or seeing me. 353 00:19:45,480 --> 00:19:48,240 Speaker 1: So let's talk a little bit about how you guys, 354 00:19:48,320 --> 00:19:53,000 Speaker 1: you and your co founder created Calci. You kind of 355 00:19:53,040 --> 00:19:57,040 Speaker 1: were the opposite of Facebook. You know, Mark Zuckerberg famously 356 00:19:57,119 --> 00:20:01,760 Speaker 1: said move fast and break things. Companies like you and 357 00:20:02,040 --> 00:20:05,359 Speaker 1: coin base and block fi spent a lot of time 358 00:20:05,600 --> 00:20:09,720 Speaker 1: getting approval from the regulators. Tell us a bit about 359 00:20:09,840 --> 00:20:13,520 Speaker 1: why you took that approach as opposed to moving fast 360 00:20:13,520 --> 00:20:16,040 Speaker 1: and breaking things. Yeah. I think a lot of times 361 00:20:16,240 --> 00:20:19,280 Speaker 1: UM people are making the short term trade off for speed. 362 00:20:19,640 --> 00:20:22,440 Speaker 1: UM and in finance, I think it's different. You can, 363 00:20:22,440 --> 00:20:24,760 Speaker 1: obviously you go to market faster if you choose the 364 00:20:24,800 --> 00:20:27,840 Speaker 1: unregulated route, but with financed there's been like a lot 365 00:20:27,880 --> 00:20:32,439 Speaker 1: of historical examples of unregulated platforms getting meaningful volume and 366 00:20:32,440 --> 00:20:35,119 Speaker 1: then being shut down by regulators because they weren't properly 367 00:20:35,160 --> 00:20:38,080 Speaker 1: regulated and doing things right from the start. UM. We 368 00:20:38,119 --> 00:20:41,080 Speaker 1: really think that the opportunity really shrinks if if you 369 00:20:41,080 --> 00:20:43,680 Speaker 1: don't take regulation into account, because then you can't get 370 00:20:43,920 --> 00:20:47,040 Speaker 1: real money in the platform, you can't get real good partners, 371 00:20:47,080 --> 00:20:50,760 Speaker 1: as we just talked about brokers, market makers, hedge funds 372 00:20:50,760 --> 00:20:53,440 Speaker 1: on board. Sometimes you can't even offer products to you 373 00:20:53,560 --> 00:20:57,399 Speaker 1: ask customers. You really boxes into something small very quickly. 374 00:20:57,440 --> 00:20:59,600 Speaker 1: And and that's for us to be the New York 375 00:20:59,680 --> 00:21:02,240 Speaker 1: chockics change for events, because that's our goal. The only 376 00:21:02,240 --> 00:21:04,440 Speaker 1: way to do that was to do uh A right 377 00:21:04,520 --> 00:21:07,800 Speaker 1: from the start, going through the regulated path and and 378 00:21:08,000 --> 00:21:09,600 Speaker 1: eating on the cost of the two years and a 379 00:21:09,640 --> 00:21:13,200 Speaker 1: half waiting, but but making sure that we're set for success. 380 00:21:13,440 --> 00:21:16,800 Speaker 1: So you're your co founder, Turek Monsour. He was an 381 00:21:16,880 --> 00:21:21,960 Speaker 1: equity derivatives intern at Goldman Sachs in the same year 382 00:21:22,000 --> 00:21:25,800 Speaker 1: you were a quantitative trader at Citadel Securities. So you 383 00:21:25,840 --> 00:21:29,439 Speaker 1: guys both had a pretty bright career path had you 384 00:21:29,520 --> 00:21:32,960 Speaker 1: not decided to go out and launch this whole new platform. 385 00:21:33,119 --> 00:21:37,679 Speaker 1: Tell us what motivated you to say, Goldman Citadel, that 386 00:21:37,720 --> 00:21:41,119 Speaker 1: looks too easy, let's let's launch a new startup. Well 387 00:21:41,280 --> 00:21:44,520 Speaker 1: that that's funny because actually most of our M I 388 00:21:44,520 --> 00:21:46,800 Speaker 1: T time we were both very focused on just getting 389 00:21:46,800 --> 00:21:50,320 Speaker 1: finance jobs and never even thought about um starting a company. 390 00:21:50,359 --> 00:21:53,359 Speaker 1: But yeah, we were both very interested in math, financial 391 00:21:53,400 --> 00:21:58,000 Speaker 1: history finance from from the very start of our school years, 392 00:21:58,040 --> 00:22:00,600 Speaker 1: and and we worked at various finding show firms as 393 00:22:00,600 --> 00:22:03,159 Speaker 1: you Man Shtaric worked at Goldman UM, I worked at 394 00:22:03,200 --> 00:22:06,399 Speaker 1: Bridgewater UM, Five Rings Capital, which is a small prop shop, 395 00:22:06,440 --> 00:22:10,640 Speaker 1: and then Citadel Securities UM. At the internships, we really 396 00:22:10,680 --> 00:22:13,240 Speaker 1: saw the behavior that we say is the cowship behavior 397 00:22:13,600 --> 00:22:17,119 Speaker 1: over and over again. It's like firms making trading decisions 398 00:22:17,400 --> 00:22:20,040 Speaker 1: based on events. As we think the European Central Bank 399 00:22:20,080 --> 00:22:22,760 Speaker 1: is gonna raise rates, Let's take this massive position already 400 00:22:22,960 --> 00:22:25,800 Speaker 1: find the structure to make that work. But the idea 401 00:22:25,840 --> 00:22:28,960 Speaker 1: really crystallized in our heads when we were working both 402 00:22:29,000 --> 00:22:32,719 Speaker 1: together at Five Rings and there we were playing this 403 00:22:32,760 --> 00:22:35,000 Speaker 1: game almost the whole day. It's called the makeup market 404 00:22:35,040 --> 00:22:38,040 Speaker 1: game that people that the everyone would be putting like 405 00:22:38,119 --> 00:22:41,760 Speaker 1: bids and offers in the probability of something UM, and 406 00:22:41,800 --> 00:22:45,240 Speaker 1: then the other person can only tighten the spread or 407 00:22:45,600 --> 00:22:48,439 Speaker 1: or trade against you UM. And there was a single 408 00:22:48,480 --> 00:22:51,000 Speaker 1: there was a day that we were just trading, playing 409 00:22:51,000 --> 00:22:53,920 Speaker 1: this game the entire day. And then I, I don't 410 00:22:53,960 --> 00:22:56,960 Speaker 1: remember exactly what market it was, but I took a 411 00:22:57,040 --> 00:23:00,439 Speaker 1: massive position on Trump doing something I don't remember exactly 412 00:23:00,520 --> 00:23:03,119 Speaker 1: what it was UM, and everyone thought I was crazy 413 00:23:03,240 --> 00:23:04,760 Speaker 1: and and debated me a lot on that, but I 414 00:23:04,840 --> 00:23:07,199 Speaker 1: ended up being right UM. And then when I was 415 00:23:07,280 --> 00:23:09,720 Speaker 1: we were walking back to to where the insurance were staying, 416 00:23:10,280 --> 00:23:12,719 Speaker 1: it was stuck in my head like why isn't there 417 00:23:12,760 --> 00:23:14,720 Speaker 1: a place for people to do this? Like we love 418 00:23:14,920 --> 00:23:16,520 Speaker 1: doing this, We do this the whole day, like we 419 00:23:16,600 --> 00:23:19,880 Speaker 1: see in every place we work at, like very big 420 00:23:19,920 --> 00:23:22,320 Speaker 1: positions people were trading based on events, Like why is 421 00:23:22,359 --> 00:23:25,240 Speaker 1: there no place to do this? And then I sat 422 00:23:25,280 --> 00:23:27,520 Speaker 1: down and started talking to talk about it, like why 423 00:23:27,600 --> 00:23:29,960 Speaker 1: isn't there, why don't why don't we do it? Um? 424 00:23:30,000 --> 00:23:32,199 Speaker 1: And we stayed the whole night up talking about it, 425 00:23:32,280 --> 00:23:34,879 Speaker 1: and it was just something we were so passionate about 426 00:23:35,200 --> 00:23:38,240 Speaker 1: UM from the finance side, the product side, everything we 427 00:23:38,280 --> 00:23:41,520 Speaker 1: always loved and if it was gonna be someone to 428 00:23:41,520 --> 00:23:42,840 Speaker 1: figure it out, I was gonna be us. It just 429 00:23:42,880 --> 00:23:45,359 Speaker 1: didn't leave us the idea for another six months up 430 00:23:45,440 --> 00:23:48,600 Speaker 1: until we were like, okay, like this is a calling, 431 00:23:48,600 --> 00:23:51,440 Speaker 1: we have to do it. So when you say your 432 00:23:51,520 --> 00:23:56,320 Speaker 1: desks are and you guys are trading back in trading events, 433 00:23:57,240 --> 00:24:01,840 Speaker 1: you couldn't incredibly bet any sort of volume on events 434 00:24:01,960 --> 00:24:04,560 Speaker 1: like cal she does today, you had to go to 435 00:24:04,640 --> 00:24:08,399 Speaker 1: secondary or tertiary markets. So you're betting on gold, if 436 00:24:08,440 --> 00:24:11,560 Speaker 1: you're thinking about inflation, you're betting on oil. If you're 437 00:24:11,560 --> 00:24:15,399 Speaker 1: concerned about war, it's it's always once removed, which raises 438 00:24:15,400 --> 00:24:18,480 Speaker 1: the issue. Even if you're right, it may not express 439 00:24:18,480 --> 00:24:20,920 Speaker 1: itself in a market the same way that the bet 440 00:24:20,960 --> 00:24:23,439 Speaker 1: was supposed to go right exactly. I think that in 441 00:24:23,480 --> 00:24:26,080 Speaker 1: the beginning of COVID you had this exact thing happening 442 00:24:26,080 --> 00:24:28,119 Speaker 1: with the economy and how we would think about the 443 00:24:28,240 --> 00:24:31,600 Speaker 1: smp UM and the beauty about it in contracts is 444 00:24:31,640 --> 00:24:33,800 Speaker 1: that is direct exposure and what you think there's not 445 00:24:33,880 --> 00:24:36,200 Speaker 1: like a lot of variables for you to keep track 446 00:24:36,280 --> 00:24:38,879 Speaker 1: of or or think about are things that can go wrong. 447 00:24:39,080 --> 00:24:41,040 Speaker 1: That's why we also think it's very it's the most 448 00:24:41,080 --> 00:24:44,280 Speaker 1: like natural way of investing, especially if you think for 449 00:24:44,359 --> 00:24:47,200 Speaker 1: retail they can't like keep track or have food desks 450 00:24:47,200 --> 00:24:50,359 Speaker 1: of people trying to understand what's going on. It's a 451 00:24:50,400 --> 00:24:52,399 Speaker 1: lot easier to do when you have one opinion and 452 00:24:52,400 --> 00:24:55,399 Speaker 1: you have a very clear way to get the exposure 453 00:24:55,440 --> 00:24:58,160 Speaker 1: on what you believe and being right or wrong. So 454 00:24:58,160 --> 00:25:02,520 Speaker 1: so you've spoken about the gambling industry and how incentives 455 00:25:02,520 --> 00:25:07,640 Speaker 1: are somewhat cloudy. Um, how does your platform correct for that? Right? 456 00:25:07,640 --> 00:25:10,800 Speaker 1: The key part about gambling is that the house takes 457 00:25:10,800 --> 00:25:13,480 Speaker 1: the position in the bets, So the house has an 458 00:25:13,520 --> 00:25:16,120 Speaker 1: interest on the outcome of of the bet or or 459 00:25:16,200 --> 00:25:17,560 Speaker 1: the market if you want to call it that, but 460 00:25:17,600 --> 00:25:20,600 Speaker 1: it's more just the beat. We are just a financial exchange, 461 00:25:20,640 --> 00:25:22,719 Speaker 1: so you can think of Guacha as a matching engine. 462 00:25:22,840 --> 00:25:26,000 Speaker 1: We match people that believe something will happen with people 463 00:25:26,000 --> 00:25:28,560 Speaker 1: that believe something will not happen. Um. If they have 464 00:25:28,640 --> 00:25:32,000 Speaker 1: equivalent prices, we match them. So we have no uh 465 00:25:32,160 --> 00:25:34,760 Speaker 1: interest in in in whether the market will go away 466 00:25:34,760 --> 00:25:36,840 Speaker 1: in a certain way. We do have an affiliate trader 467 00:25:36,920 --> 00:25:39,600 Speaker 1: that's there to provide liquidity so that people can trade, 468 00:25:39,680 --> 00:25:42,399 Speaker 1: especially as we start the exchange, but the exchange doesn't 469 00:25:42,400 --> 00:25:46,000 Speaker 1: take any positions ever, we're simply matching other participant orders, 470 00:25:46,000 --> 00:25:48,400 Speaker 1: so there's no conflict of interest between us and our members. 471 00:25:48,440 --> 00:25:50,280 Speaker 1: So so when you look at a race track and 472 00:25:50,320 --> 00:25:54,040 Speaker 1: the odds are set on horses, those odds don't quite 473 00:25:54,080 --> 00:25:57,600 Speaker 1: add up, and the shortfall is the house take, so 474 00:25:57,680 --> 00:26:01,520 Speaker 1: it's never quite fifty fifty. What is it costs to 475 00:26:01,720 --> 00:26:05,400 Speaker 1: trade on this platform. What what's the so in other words, 476 00:26:05,400 --> 00:26:07,359 Speaker 1: if I'm betting a hundred dollars that something is going 477 00:26:07,400 --> 00:26:10,439 Speaker 1: to happen and I win, do I get two hundred 478 00:26:10,440 --> 00:26:13,120 Speaker 1: dollars back? Or how how does that work? Right? So, 479 00:26:13,119 --> 00:26:15,400 Speaker 1: so the way that it works is that the yes 480 00:26:15,480 --> 00:26:19,120 Speaker 1: and the noe prices are from one to and whoever's 481 00:26:19,240 --> 00:26:23,240 Speaker 1: right gets one dollar. UM. So let's say I'm buying 482 00:26:23,240 --> 00:26:25,520 Speaker 1: a yes for forty cents. It means there's someone buying 483 00:26:25,520 --> 00:26:28,359 Speaker 1: a no for sixty cents UM and if I am correct, 484 00:26:28,520 --> 00:26:31,400 Speaker 1: I make one dollar, which means I'm profiting sixty cents, 485 00:26:31,440 --> 00:26:34,159 Speaker 1: which is from my counter party. Right, what's the cost 486 00:26:34,200 --> 00:26:37,359 Speaker 1: of that trade? Meaning? How does calcie make money? And 487 00:26:37,400 --> 00:26:41,400 Speaker 1: I assume, since it's fully collateralized, there's a float that's 488 00:26:41,400 --> 00:26:45,160 Speaker 1: going to be a good source of revenue UM over time. UM. 489 00:26:45,359 --> 00:26:47,880 Speaker 1: We don't make money on float. All of our are 490 00:26:48,000 --> 00:26:50,560 Speaker 1: all of the user member funds are in a fully 491 00:26:51,119 --> 00:26:55,400 Speaker 1: regulated CFTC clearing house which is f t x UM derivatives, 492 00:26:55,400 --> 00:26:58,800 Speaker 1: the US derivatives there are clearing house UM and we 493 00:26:58,920 --> 00:27:00,719 Speaker 1: make money on the transaction and fee. So we have 494 00:27:00,760 --> 00:27:03,439 Speaker 1: a small transaction fee that varies on the price of 495 00:27:03,440 --> 00:27:06,400 Speaker 1: the contract. What is it average bullpark? What does that cost? 496 00:27:06,480 --> 00:27:09,840 Speaker 1: I think alright, So we will have a conversation after 497 00:27:09,840 --> 00:27:13,480 Speaker 1: we're done, and I will show you that. UM I 498 00:27:13,480 --> 00:27:16,400 Speaker 1: think it was Schwab when they moved to free trading, 499 00:27:17,080 --> 00:27:20,280 Speaker 1: their float became fifty seven percent of the revenue. So 500 00:27:20,320 --> 00:27:22,720 Speaker 1: we'll have a conversation. We'll see if we can help 501 00:27:23,119 --> 00:27:26,560 Speaker 1: raise your your revenue target and we'll go from there. 502 00:27:26,600 --> 00:27:30,280 Speaker 1: Because especially it's one thing if you're looking at events 503 00:27:30,280 --> 00:27:33,119 Speaker 1: that are days and weeks out, but if you're making 504 00:27:33,160 --> 00:27:38,800 Speaker 1: bets on will be the hottest year in history. Hey, 505 00:27:38,800 --> 00:27:42,720 Speaker 1: you're sitting with that money for twelve, eleven, ten months. 506 00:27:42,440 --> 00:27:45,000 Speaker 1: There's a lot of top line to be gained from 507 00:27:45,040 --> 00:27:47,600 Speaker 1: from a little float. We'll we'll work that out with 508 00:27:47,600 --> 00:27:50,600 Speaker 1: the SEFTC. That'll be that'll be easy. You guys raised 509 00:27:50,720 --> 00:27:54,760 Speaker 1: thirty six million dollars in a series A. Sequoia Capital 510 00:27:55,000 --> 00:27:59,120 Speaker 1: was the lead, probably the most storied venture capital firm 511 00:27:59,160 --> 00:28:02,560 Speaker 1: in Silicon Valle. Charles Schwab not the entity I was 512 00:28:02,600 --> 00:28:06,040 Speaker 1: talking earlier about Charles Swab in the float, but Mr 513 00:28:06,160 --> 00:28:10,880 Speaker 1: Charles Swab was an investor. Henry Cravis is an investor 514 00:28:11,160 --> 00:28:15,200 Speaker 1: Silicon Valley angel Um is one of the early investors. 515 00:28:15,240 --> 00:28:18,760 Speaker 1: And were you at y Combinator when you were first launching. Yeah, so, 516 00:28:18,760 --> 00:28:23,520 Speaker 1: so that's quite an esteemed list of of people who said, hey, 517 00:28:23,520 --> 00:28:26,280 Speaker 1: there's some value here. Tell us a little bit about 518 00:28:26,320 --> 00:28:30,439 Speaker 1: the experience UM at ye Combinator and then doing an 519 00:28:30,480 --> 00:28:33,400 Speaker 1: a round with some really bold faced names. Yeah, our 520 00:28:33,480 --> 00:28:36,720 Speaker 1: experience at White Combinator was actually very different from most 521 00:28:36,720 --> 00:28:40,240 Speaker 1: of the other startups. Like we were measuring regulatory traction 522 00:28:40,600 --> 00:28:43,800 Speaker 1: um and other start ups are measuring user growth or 523 00:28:44,080 --> 00:28:47,720 Speaker 1: revenue or or things things of the sort. Um. Yeah, 524 00:28:47,800 --> 00:28:50,080 Speaker 1: and about the Series A, getting a d c M 525 00:28:50,240 --> 00:28:53,040 Speaker 1: was was a key part of of that Series A. 526 00:28:53,120 --> 00:28:55,680 Speaker 1: I think cow she is really one of those asymmetric 527 00:28:55,720 --> 00:28:58,880 Speaker 1: type of investments. Um, We're going to face obviously a 528 00:28:58,920 --> 00:29:01,400 Speaker 1: lot of challenges, and but we if we execute against 529 00:29:01,400 --> 00:29:04,800 Speaker 1: the challenges, we're got to have massive outlier potential. And 530 00:29:04,840 --> 00:29:09,000 Speaker 1: we were really trying to find partners and investors. They 531 00:29:09,120 --> 00:29:12,400 Speaker 1: really understood the long term vision of the company and 532 00:29:12,440 --> 00:29:15,680 Speaker 1: shared that obsession that we have with event contracts and 533 00:29:15,680 --> 00:29:19,800 Speaker 1: and buting this entire trading ecosystem. UM. So Alfred from 534 00:29:19,800 --> 00:29:23,000 Speaker 1: Sequoia is one of those people he did a PhD 535 00:29:23,400 --> 00:29:26,200 Speaker 1: in these types of markets. He really really understands it 536 00:29:26,240 --> 00:29:30,320 Speaker 1: and sees the potential and obviously it's it's it's Sequoia, Sequoia, 537 00:29:30,360 --> 00:29:32,920 Speaker 1: as you said, So that was that was definitely something 538 00:29:32,960 --> 00:29:36,920 Speaker 1: we thought about. But but Alfred specifically has historically invested 539 00:29:36,960 --> 00:29:39,360 Speaker 1: in a lot of like paradigm shifting companies like Airbnb 540 00:29:39,440 --> 00:29:41,360 Speaker 1: and door Dash, so he really thought it was a 541 00:29:41,360 --> 00:29:43,760 Speaker 1: good it was a good fit. Um. And then after 542 00:29:43,760 --> 00:29:46,320 Speaker 1: Sequoia was our lead investor, we were really trying to 543 00:29:46,360 --> 00:29:49,080 Speaker 1: feel the round with Wall Street investors that could really 544 00:29:49,080 --> 00:29:52,640 Speaker 1: help us navigate this industry. UM. So yeah, talk my 545 00:29:52,680 --> 00:29:57,240 Speaker 1: co founder. He's obsessed with barbarians at the gate. So 546 00:29:57,240 --> 00:30:01,600 Speaker 1: so when um right, So when when one of our 547 00:30:02,120 --> 00:30:05,640 Speaker 1: seating vessels, Joli Barto vi Um said he could intro 548 00:30:06,000 --> 00:30:08,760 Speaker 1: and we could talk to Henry, I think um Taric 549 00:30:08,920 --> 00:30:12,800 Speaker 1: was just like absolutely fascinated and they had a fantastic conversation. 550 00:30:12,880 --> 00:30:15,360 Speaker 1: He was very interested from from the very beginning, and 551 00:30:15,440 --> 00:30:17,920 Speaker 1: with with Charles Schab it was something similar. It was 552 00:30:17,960 --> 00:30:21,640 Speaker 1: also Ali Bartovi introguing us too. To him also very 553 00:30:21,640 --> 00:30:24,160 Speaker 1: interested from the start, and he actually told us that 554 00:30:24,800 --> 00:30:26,720 Speaker 1: our early days that call she looked very similar to 555 00:30:26,880 --> 00:30:29,720 Speaker 1: his early days starting Charles Schwab, So that was very 556 00:30:29,720 --> 00:30:32,200 Speaker 1: exciting and and yeah, they help us so much to today, 557 00:30:32,240 --> 00:30:35,080 Speaker 1: so it's fantastic. The funny thing about Schwab is people 558 00:30:35,080 --> 00:30:37,640 Speaker 1: don't realize the guy you see with the gray hero 559 00:30:37,760 --> 00:30:41,480 Speaker 1: and commercials, that's Charles Swab. That's not an actor. He 560 00:30:41,640 --> 00:30:44,480 Speaker 1: really exists and has been running the company. Now I 561 00:30:44,480 --> 00:30:48,240 Speaker 1: think he's UM chairman, but that was really him for 562 00:30:48,240 --> 00:30:50,080 Speaker 1: for a long time. So so let's talk a little 563 00:30:50,080 --> 00:30:54,640 Speaker 1: bit about vent hedging. And I like this quote. These 564 00:30:54,680 --> 00:30:57,960 Speaker 1: markets are a little like an aggregator of public opinion 565 00:30:57,960 --> 00:31:01,080 Speaker 1: in real time. So so what are the implications of 566 00:31:01,080 --> 00:31:04,160 Speaker 1: this And is that the sort of stuff your UM 567 00:31:04,440 --> 00:31:10,040 Speaker 1: lead investor at Sequoia was studying when he went to school, right, Um, yeah, 568 00:31:10,040 --> 00:31:12,320 Speaker 1: this is a very important part of our vision UM 569 00:31:12,360 --> 00:31:14,920 Speaker 1: over time, we really want CALCI to become the sorts 570 00:31:14,920 --> 00:31:17,760 Speaker 1: of truth for forecasting these events that we have markets 571 00:31:17,800 --> 00:31:20,760 Speaker 1: on because because the prices that call should go from 572 00:31:20,760 --> 00:31:24,280 Speaker 1: one to n cents, they directly translate to the probability 573 00:31:24,360 --> 00:31:27,680 Speaker 1: of the event happening. So let's say, uh, the market 574 00:31:27,840 --> 00:31:30,400 Speaker 1: might be saying there's a twenty percentions there's a recession 575 00:31:30,400 --> 00:31:34,160 Speaker 1: this year. UM. It means the twenty cents means that 576 00:31:34,160 --> 00:31:36,720 Speaker 1: there's a twenty percentions that the market believes there's a 577 00:31:36,760 --> 00:31:39,080 Speaker 1: twenty percentions that there will be a recession this year. 578 00:31:39,640 --> 00:31:41,960 Speaker 1: And the amazing thing is that there's a lot of 579 00:31:42,000 --> 00:31:46,080 Speaker 1: theoretical and empirical evidence that they are the most effective 580 00:31:46,120 --> 00:31:48,960 Speaker 1: and most accurate ways of forecasting the future. They're way 581 00:31:49,000 --> 00:31:51,320 Speaker 1: better than polls, way better than like pundits on on 582 00:31:51,360 --> 00:31:54,640 Speaker 1: the news trying to uh say what's going on? Um. 583 00:31:54,760 --> 00:31:56,640 Speaker 1: And it's mainly for two reasons. I think. The first 584 00:31:56,680 --> 00:31:59,840 Speaker 1: one is because when people put money where their mouth is, 585 00:32:00,280 --> 00:32:02,920 Speaker 1: they are more more likely to say what they really 586 00:32:02,920 --> 00:32:05,959 Speaker 1: think and actually do research and everything. And the second 587 00:32:05,960 --> 00:32:08,520 Speaker 1: one is that markets really aggregate the wisdom of the crowds. 588 00:32:08,560 --> 00:32:11,240 Speaker 1: You're getting a lot of different people's opinions when they're 589 00:32:11,240 --> 00:32:14,120 Speaker 1: putting money behind their opinion and really aggregating that, and 590 00:32:14,160 --> 00:32:16,360 Speaker 1: which makes there's a very powerful tool. And I mean 591 00:32:16,400 --> 00:32:20,600 Speaker 1: any market lover understands what I'm saying, um and yeah, 592 00:32:20,720 --> 00:32:23,160 Speaker 1: making and and part of our our vision and what 593 00:32:23,240 --> 00:32:25,800 Speaker 1: we really want to do long long term is make 594 00:32:25,880 --> 00:32:28,880 Speaker 1: these forecasts score to people's lives. UM. It's really part 595 00:32:28,880 --> 00:32:32,320 Speaker 1: of our mission with with event contracts becoming more widespread, 596 00:32:32,520 --> 00:32:34,560 Speaker 1: we really hope that people we use data in their 597 00:32:34,560 --> 00:32:38,240 Speaker 1: lives to prepare better for the future, addressed uncertainty, inform 598 00:32:38,360 --> 00:32:41,240 Speaker 1: themselves better, and like try to address a little bit 599 00:32:41,280 --> 00:32:44,480 Speaker 1: of the very biased world and not very data driven 600 00:32:44,520 --> 00:32:47,320 Speaker 1: world that we live in nowadays. UM. So we're trying 601 00:32:47,360 --> 00:32:49,200 Speaker 1: to get started with that. We're already trying to get 602 00:32:49,240 --> 00:32:52,360 Speaker 1: We have market tickers like any other UM equity or 603 00:32:52,360 --> 00:32:54,160 Speaker 1: things like that. We have takers for all of our markets. 604 00:32:54,160 --> 00:32:56,760 Speaker 1: So we're trying to have tickers and prices be used 605 00:32:56,760 --> 00:32:58,560 Speaker 1: by news and things of the store to really try 606 00:32:58,560 --> 00:33:01,400 Speaker 1: to get this very important data that we believe is 607 00:33:01,480 --> 00:33:05,960 Speaker 1: very important data out there. But for Alfred specifically, I 608 00:33:06,040 --> 00:33:08,840 Speaker 1: think he was doing more than like mathematical and like research. 609 00:33:09,320 --> 00:33:12,160 Speaker 1: He was doing a status PhD. So someone related to this, 610 00:33:12,200 --> 00:33:14,080 Speaker 1: but not really on the on the on this side. 611 00:33:14,120 --> 00:33:16,200 Speaker 1: But yeah, so so let's let's talk a little bit 612 00:33:16,240 --> 00:33:20,440 Speaker 1: about prediction markets that are out there. Historically they've only 613 00:33:20,480 --> 00:33:25,880 Speaker 1: done a so so job, partly because they're not very broad, 614 00:33:25,960 --> 00:33:28,920 Speaker 1: they're not that very deep, and the dollar amounts that 615 00:33:28,920 --> 00:33:32,040 Speaker 1: are traded have been modest. I saw an overlay of 616 00:33:32,080 --> 00:33:36,840 Speaker 1: about half a dozen different prediction markets before the Russian 617 00:33:36,880 --> 00:33:40,120 Speaker 1: invasion of Ukraine, and you would think they would all 618 00:33:40,160 --> 00:33:42,520 Speaker 1: be kind of similar, but they weren't. They were all 619 00:33:42,560 --> 00:33:44,959 Speaker 1: over the map. Do you have to get to a 620 00:33:45,000 --> 00:33:50,200 Speaker 1: certain scale that will fix that problem of prediction markets 621 00:33:50,240 --> 00:33:52,880 Speaker 1: being kind of thin and easily I don't want to 622 00:33:52,880 --> 00:33:57,640 Speaker 1: say manipulated, but one big trade really has an impact 623 00:33:57,720 --> 00:34:01,280 Speaker 1: on on how those UM markets trade, right exactly. I 624 00:34:01,280 --> 00:34:04,080 Speaker 1: think we need a base level of liquidity and volume 625 00:34:04,160 --> 00:34:07,600 Speaker 1: for for the forecast to really work and be really useful. 626 00:34:07,640 --> 00:34:10,480 Speaker 1: In a lot of these, like UM other prediction markets 627 00:34:10,480 --> 00:34:13,440 Speaker 1: out there, as as we talked about their unregulated they 628 00:34:13,440 --> 00:34:16,560 Speaker 1: have UM, they're very new. They just pop up, especially 629 00:34:16,600 --> 00:34:19,480 Speaker 1: the crypto ones, every other day UM, and it's hard 630 00:34:19,480 --> 00:34:22,839 Speaker 1: to build liquidity and proper volume like that. But UM, 631 00:34:23,040 --> 00:34:25,040 Speaker 1: we really think the prediction markets are the way to 632 00:34:25,040 --> 00:34:28,400 Speaker 1: go to have these these very good forecasts of events. 633 00:34:28,520 --> 00:34:30,560 Speaker 1: But it needs liquidity and needs volume, and that's what 634 00:34:30,640 --> 00:34:33,359 Speaker 1: we're working on. Huh. Really really kind of interesting, which 635 00:34:33,360 --> 00:34:36,200 Speaker 1: which raises the question, UM, how are you going to 636 00:34:36,280 --> 00:34:37,880 Speaker 1: scale this up? How are you going to get to 637 00:34:38,560 --> 00:34:41,640 Speaker 1: a hundred million and then a billion and then who 638 00:34:41,640 --> 00:34:43,680 Speaker 1: knows what from there. Right, we have a lot of 639 00:34:43,680 --> 00:34:46,160 Speaker 1: ways to to scale the exchange. It's kind of what 640 00:34:46,400 --> 00:34:49,960 Speaker 1: we talked about with with building up liquidity. Right now 641 00:34:49,960 --> 00:34:52,920 Speaker 1: we're really focused on retail, so getting UM we have 642 00:34:53,080 --> 00:34:55,319 Speaker 1: a lot of options traders are like what we call 643 00:34:55,400 --> 00:34:58,480 Speaker 1: informed retail traders in the platform, trying to go in 644 00:34:58,560 --> 00:35:01,359 Speaker 1: more deeper into different communities and trying to get them 645 00:35:01,360 --> 00:35:04,080 Speaker 1: into test the platform brings off the sword, and then 646 00:35:04,080 --> 00:35:06,600 Speaker 1: the next step for us is getting brokers in to 647 00:35:06,760 --> 00:35:11,360 Speaker 1: offer our markets in their platform, so UM E trade, 648 00:35:11,760 --> 00:35:15,600 Speaker 1: interactive brokers, all of those um and then bringing up 649 00:35:15,600 --> 00:35:18,360 Speaker 1: the volume we can bring up like actual liquidity providers, 650 00:35:18,360 --> 00:35:22,319 Speaker 1: prop shops, hedge funds, and then up until UM I 651 00:35:22,360 --> 00:35:26,600 Speaker 1: guess insurance companies even um off floating some risk or 652 00:35:26,920 --> 00:35:30,400 Speaker 1: or like actually big institutions natural hedgers bringing them in. 653 00:35:30,680 --> 00:35:32,279 Speaker 1: So the way that we're seeing is really starting to 654 00:35:32,280 --> 00:35:35,080 Speaker 1: build with retail with getting more and more of the 655 00:35:35,080 --> 00:35:38,239 Speaker 1: current users that we have which are options traders, and 656 00:35:38,400 --> 00:35:40,600 Speaker 1: having more retail as we go to the to the 657 00:35:41,040 --> 00:35:44,200 Speaker 1: I guess brokers. So so how big can this get? 658 00:35:44,360 --> 00:35:47,040 Speaker 1: I mean, is this ever a billion dollars a month. 659 00:35:47,080 --> 00:35:50,840 Speaker 1: How how large can this sort of event hedging scale 660 00:35:50,920 --> 00:35:53,839 Speaker 1: up to? Right, so, event contracts are a lot more 661 00:35:53,880 --> 00:35:57,640 Speaker 1: like tangible, relatable, and more direct as we talked about UM, 662 00:35:57,680 --> 00:36:01,640 Speaker 1: then all these other assets that that preceded it UM. 663 00:36:01,680 --> 00:36:03,480 Speaker 1: So we really think when we actually plug it in 664 00:36:03,520 --> 00:36:07,000 Speaker 1: the financial ecosystem, it can properly scale. Obviously, it takes 665 00:36:07,000 --> 00:36:08,239 Speaker 1: a lot of time to get there because we need 666 00:36:08,280 --> 00:36:10,920 Speaker 1: to be the entire ecosystem around events a completely new thing. 667 00:36:11,280 --> 00:36:15,000 Speaker 1: But once it's properly plugged in the financial system, UM 668 00:36:15,280 --> 00:36:17,359 Speaker 1: I can give you some numbers to give some idea, right. 669 00:36:17,480 --> 00:36:19,200 Speaker 1: I think you mentioned that in the beginning with the 670 00:36:19,200 --> 00:36:23,600 Speaker 1: CFTC regulating a trillion UM dollar industry like grain futures 671 00:36:23,640 --> 00:36:28,320 Speaker 1: are seven trillion dollars industry commodities to any trillion. Interest 672 00:36:28,400 --> 00:36:31,520 Speaker 1: rate swaps are around I think trillion UM. So I'm 673 00:36:31,520 --> 00:36:33,479 Speaker 1: not exactly how big the market is, but I think 674 00:36:33,520 --> 00:36:36,920 Speaker 1: as we expand, event contracts definitely has a potential to 675 00:36:36,920 --> 00:36:39,880 Speaker 1: be one of these interest rate swaps are five billion 676 00:36:40,000 --> 00:36:44,920 Speaker 1: or trillion trillion Really that's the notational, not the but 677 00:36:45,000 --> 00:36:49,040 Speaker 1: at that point that's a giant amount of money. Right, So, really, 678 00:36:49,560 --> 00:36:52,960 Speaker 1: startups have a tendency to have this defining moment in 679 00:36:53,000 --> 00:36:56,799 Speaker 1: their lifespans where they sort of either pivot or just 680 00:36:56,880 --> 00:36:59,160 Speaker 1: a moment of clarity and you could see the whole 681 00:36:59,560 --> 00:37:02,640 Speaker 1: roadman up laid out. Um, did you guys have that 682 00:37:02,680 --> 00:37:05,799 Speaker 1: sort of defining moment at Calcy? I would say the biggest, 683 00:37:06,040 --> 00:37:09,880 Speaker 1: the earliest defining moment we had was actually before we 684 00:37:09,920 --> 00:37:12,440 Speaker 1: really started the company. We went to a white Combinator 685 00:37:12,480 --> 00:37:16,480 Speaker 1: hackathon UM because before we were like fascinated by it, 686 00:37:16,480 --> 00:37:18,239 Speaker 1: but we didn't think it was got like gonna work. 687 00:37:18,280 --> 00:37:21,720 Speaker 1: It's like it seems so complicated and like are we crazy? 688 00:37:21,760 --> 00:37:23,239 Speaker 1: I think that was a big question our head, like 689 00:37:23,280 --> 00:37:26,160 Speaker 1: are we going crazy? We're here? Um. Then we went 690 00:37:26,200 --> 00:37:29,799 Speaker 1: to a combinated for a hackathon and there were like 691 00:37:30,040 --> 00:37:33,880 Speaker 1: these teams with like a bunch of servers, crazy computers 692 00:37:33,960 --> 00:37:36,200 Speaker 1: like and it was just me and Taragraph are like 693 00:37:36,520 --> 00:37:39,799 Speaker 1: MacBooks like trying to to go like a demo. Before 694 00:37:39,800 --> 00:37:42,360 Speaker 1: we were talking about UM And then we first presented 695 00:37:42,400 --> 00:37:44,840 Speaker 1: to Michael them, the CEO of y C, and he 696 00:37:44,880 --> 00:37:46,640 Speaker 1: really didn't like what we were saying from the beginning. 697 00:37:46,680 --> 00:37:48,839 Speaker 1: He cut us like the first five seconds He's like 698 00:37:49,080 --> 00:37:52,880 Speaker 1: that like this is illegal, Like what are you doing UM? 699 00:37:53,040 --> 00:37:54,759 Speaker 1: And then we would get very upset. We went in 700 00:37:54,880 --> 00:37:57,920 Speaker 1: like we I think Tara even started drinking UM beer. 701 00:37:58,000 --> 00:37:59,440 Speaker 1: He's like, there's no way we're gonna be in the 702 00:37:59,440 --> 00:38:02,319 Speaker 1: top ten, which had to present again, and we ended 703 00:38:02,360 --> 00:38:04,680 Speaker 1: up being in the top ten. We presented again, and 704 00:38:04,719 --> 00:38:06,319 Speaker 1: then we ended up being in the top three, which 705 00:38:06,320 --> 00:38:09,040 Speaker 1: were the winners of the hackathon. And I remember that 706 00:38:09,160 --> 00:38:12,040 Speaker 1: night when we're going back to to our friends place 707 00:38:12,040 --> 00:38:15,080 Speaker 1: where we were staying in San Francisco UM for the hackathon, 708 00:38:15,480 --> 00:38:17,920 Speaker 1: we were like, wow, like maybe we aren't crazy like 709 00:38:18,000 --> 00:38:21,439 Speaker 1: we should like maybe like people believe in what we're doing. 710 00:38:21,480 --> 00:38:24,120 Speaker 1: And it was a very like happy moment for us. 711 00:38:24,120 --> 00:38:25,879 Speaker 1: And I think right after that we actually got into 712 00:38:25,920 --> 00:38:28,120 Speaker 1: the White Combinator batch and it was one of the 713 00:38:28,160 --> 00:38:31,720 Speaker 1: happiest moments we've we've had UM we've had of the company. 714 00:38:31,760 --> 00:38:34,440 Speaker 1: So that was really like motivating and encouraging because I 715 00:38:34,480 --> 00:38:36,800 Speaker 1: was like, told you, we never thought about being founders. 716 00:38:36,800 --> 00:38:39,239 Speaker 1: We thought about being like he was going to be. 717 00:38:39,400 --> 00:38:42,160 Speaker 1: We were both going to be traders UM full time. 718 00:38:42,400 --> 00:38:44,360 Speaker 1: So it was like a big shift for us. So 719 00:38:44,400 --> 00:38:46,880 Speaker 1: that was a very exciting moment. Huh, really really interesting. 720 00:38:46,960 --> 00:38:49,640 Speaker 1: Let me throw a couple of curveballs at you. Uh. 721 00:38:49,760 --> 00:38:53,040 Speaker 1: You and your co founder Tarik both were named to 722 00:38:53,080 --> 00:38:57,120 Speaker 1: the Forbes thirty Under thirty list in in the Finance category. 723 00:38:57,719 --> 00:39:01,080 Speaker 1: Tell us a little bit about that. What was that parents, Like, Yeah, 724 00:39:01,080 --> 00:39:03,360 Speaker 1: I know, it was very excited. We were very honored 725 00:39:03,400 --> 00:39:07,239 Speaker 1: to be to be to be nominated, especially being like 726 00:39:07,280 --> 00:39:09,719 Speaker 1: the head up of the of the finance category. Um, 727 00:39:10,000 --> 00:39:12,040 Speaker 1: we were really excited after all the work we've done. 728 00:39:12,320 --> 00:39:14,840 Speaker 1: And actually a funny story is that because of the 729 00:39:14,880 --> 00:39:17,800 Speaker 1: four of study under Dirty, I went Vio in Brazil 730 00:39:17,840 --> 00:39:22,120 Speaker 1: for a little bit because the Brazilian Forbes wrote wrote 731 00:39:22,120 --> 00:39:25,560 Speaker 1: a piece about how Brazilian was in the American fourb 732 00:39:25,600 --> 00:39:28,560 Speaker 1: study under Dirty and that because it's very rare to 733 00:39:28,560 --> 00:39:31,160 Speaker 1: have resilience in the list here. So that was that 734 00:39:31,239 --> 00:39:33,600 Speaker 1: was that was a funny story. But yeah, because of 735 00:39:33,640 --> 00:39:36,600 Speaker 1: the four study under Dirty, we also um ended up 736 00:39:36,680 --> 00:39:40,319 Speaker 1: ringing the opening bell at the Nasdak, which was very exciting. 737 00:39:40,800 --> 00:39:44,760 Speaker 1: And one more, one more curveball. You were a ballet 738 00:39:44,920 --> 00:39:47,960 Speaker 1: dancer with the ball show. You studied ballet tell us 739 00:39:48,000 --> 00:39:52,000 Speaker 1: about that, right, so very different from what I do 740 00:39:52,080 --> 00:39:54,920 Speaker 1: now for sure, But um, I'm from Brazil originally and 741 00:39:55,040 --> 00:39:57,919 Speaker 1: I just came to the US for college, and most 742 00:39:57,960 --> 00:40:00,680 Speaker 1: of my life before college, I was split between ballet 743 00:40:00,719 --> 00:40:03,719 Speaker 1: and school. What what I really loved about ballet was 744 00:40:03,760 --> 00:40:07,000 Speaker 1: the intensity of it all. Um, it was extremely hard 745 00:40:07,000 --> 00:40:09,680 Speaker 1: to get to the top. It's extremely competitive, uh. And 746 00:40:09,680 --> 00:40:11,480 Speaker 1: there's nowhere to hide. You need to be completely on, 747 00:40:11,520 --> 00:40:14,360 Speaker 1: you need to give it your all. And yeah, and 748 00:40:14,400 --> 00:40:16,880 Speaker 1: I studied at the Bolshoi Ballet School and it was 749 00:40:17,239 --> 00:40:21,080 Speaker 1: extremely intense and and um we had to be extremely disciplined, 750 00:40:21,120 --> 00:40:23,160 Speaker 1: like measuring our food down to like a fourth of 751 00:40:23,160 --> 00:40:27,319 Speaker 1: a strawberry before this rehearsal to be able to get there. 752 00:40:27,600 --> 00:40:29,319 Speaker 1: But that was that was one part. And the other 753 00:40:29,360 --> 00:40:33,080 Speaker 1: part my my parents are both um um engineers and 754 00:40:33,120 --> 00:40:35,880 Speaker 1: have stand backgrounds. Uh, so I was surrounded by that 755 00:40:35,920 --> 00:40:38,400 Speaker 1: outside of ballet, doing like math, olympiads and all of that. 756 00:40:38,480 --> 00:40:40,759 Speaker 1: I also had to get a hundred on everything on 757 00:40:40,800 --> 00:40:43,640 Speaker 1: the math and science side. So I used to do 758 00:40:44,520 --> 00:40:47,319 Speaker 1: like normal school, I guess from like seven am because 759 00:40:47,400 --> 00:40:49,920 Speaker 1: Brazil schools hours are different, so seven am to like 760 00:40:49,960 --> 00:40:52,480 Speaker 1: one pm, and then ballet from one like one thirty 761 00:40:52,480 --> 00:40:55,040 Speaker 1: pm to like nine ten pm, and then I would 762 00:40:55,080 --> 00:40:58,160 Speaker 1: actually go study. So that was a very intense part 763 00:40:58,160 --> 00:40:59,600 Speaker 1: of my life. But I think it really set me 764 00:40:59,680 --> 00:41:01,920 Speaker 1: up for for being able to go to M I 765 00:41:01,960 --> 00:41:05,480 Speaker 1: T and and and and injure everything there. And it's 766 00:41:05,480 --> 00:41:07,240 Speaker 1: something that talks very similar to me. He was actually 767 00:41:07,280 --> 00:41:10,400 Speaker 1: professional skier before going to college and and we have 768 00:41:10,520 --> 00:41:13,799 Speaker 1: very similar backgrounds. And I think that level of intensity 769 00:41:13,880 --> 00:41:17,120 Speaker 1: and discipline is really what help us get through the 770 00:41:17,120 --> 00:41:20,560 Speaker 1: regulatory process and be where we are today. So tough times, 771 00:41:20,560 --> 00:41:23,480 Speaker 1: but it's good now. I do the same thing. I 772 00:41:23,560 --> 00:41:28,200 Speaker 1: measure my food input down to the quarter strawberry. You see, 773 00:41:28,560 --> 00:41:32,239 Speaker 1: It's how I maintained my girl. So, so we only 774 00:41:32,280 --> 00:41:34,680 Speaker 1: have a certain amount of time left. Let me jump 775 00:41:34,719 --> 00:41:38,240 Speaker 1: to my favorite questions that I ask all of our guests, 776 00:41:38,800 --> 00:41:41,879 Speaker 1: starting with what kept you entertained during lockdown? What were 777 00:41:41,880 --> 00:41:46,040 Speaker 1: you streaming, watching, or listening to? Right? Um, I listened 778 00:41:46,040 --> 00:41:49,400 Speaker 1: to All In and I'm very into American politics nowadays. 779 00:41:49,440 --> 00:41:53,400 Speaker 1: So I'm finishing up the ten American President's podcast. Um, 780 00:41:53,400 --> 00:41:55,759 Speaker 1: But on TV, I think I'm more mainstream. So I 781 00:41:55,800 --> 00:41:59,759 Speaker 1: just love Succession, House of Cards, West Wing, and so on. Uh. 782 00:42:00,040 --> 00:42:04,200 Speaker 1: Let's talk about your mentors who helped shape your career. Um. Right, 783 00:42:04,239 --> 00:42:06,440 Speaker 1: So I think at M I T. I had two 784 00:42:06,760 --> 00:42:09,960 Speaker 1: professors are very UM impactful in my career and to me. 785 00:42:10,000 --> 00:42:12,360 Speaker 1: I think the first one was Patrick Winston. He was 786 00:42:12,400 --> 00:42:14,640 Speaker 1: my adviser and professor of UM a lot of our 787 00:42:14,719 --> 00:42:17,840 Speaker 1: Fisher Intelligence classes. He really helped me navigate m I 788 00:42:17,920 --> 00:42:21,680 Speaker 1: T and set me up to and set my mindset 789 00:42:21,680 --> 00:42:23,800 Speaker 1: to where I wanted to be to like really start CauSci. 790 00:42:24,280 --> 00:42:27,319 Speaker 1: And the other great mentor was Peter Kempthorne. He's also 791 00:42:27,320 --> 00:42:30,240 Speaker 1: a professor of stats and really I started being interested 792 00:42:30,239 --> 00:42:33,120 Speaker 1: in finance in his classes, and funnily enough, he's actually 793 00:42:33,160 --> 00:42:35,640 Speaker 1: one rectors off of Cauch nowadays. Because we kept very 794 00:42:35,640 --> 00:42:38,640 Speaker 1: close contact UM and we talked a lot to him 795 00:42:38,680 --> 00:42:41,160 Speaker 1: about like the dynamics of markets and all that stuff 796 00:42:41,160 --> 00:42:44,040 Speaker 1: we talked about UM. And since we started the company, 797 00:42:44,080 --> 00:42:47,759 Speaker 1: I think our biggest mentors have been Michael's the CEO 798 00:42:47,760 --> 00:42:50,080 Speaker 1: of y C up until today. He's helps us so much. 799 00:42:50,360 --> 00:42:53,520 Speaker 1: And Ali Partovi, who's who runs NEOUM, is one of 800 00:42:53,520 --> 00:42:56,960 Speaker 1: our seat investors, and they have been really instrumental and 801 00:42:57,040 --> 00:43:00,239 Speaker 1: like making us better founders UM, not just like making 802 00:43:00,239 --> 00:43:02,400 Speaker 1: the company succeed, but better founders and how to like 803 00:43:02,760 --> 00:43:06,319 Speaker 1: do have employees growing company like growing pains, negotiation, all 804 00:43:06,320 --> 00:43:08,359 Speaker 1: of those things that you like. M I ten Nerds 805 00:43:08,560 --> 00:43:11,400 Speaker 1: didn't really know what to do. So let's talk about books. 806 00:43:11,440 --> 00:43:13,080 Speaker 1: What are some of your favorites and what are you 807 00:43:13,120 --> 00:43:16,480 Speaker 1: reading right now? Right? Some of my favorite, um, my 808 00:43:16,560 --> 00:43:19,239 Speaker 1: favorite book is is this book called Americana, but it's 809 00:43:19,280 --> 00:43:22,640 Speaker 1: not a novel. It's actually the four year history of 810 00:43:22,680 --> 00:43:25,520 Speaker 1: American capitalism. But whenever I say American and everyone thinks 811 00:43:25,520 --> 00:43:27,759 Speaker 1: it's the novel. Um. And the other one is this 812 00:43:27,800 --> 00:43:33,160 Speaker 1: book called predict Predictably Rational um, which is right, yeah, 813 00:43:33,200 --> 00:43:35,799 Speaker 1: and it's it's very uh some. It has a lot 814 00:43:35,840 --> 00:43:37,080 Speaker 1: to do with what cow she does, and I think 815 00:43:37,120 --> 00:43:39,840 Speaker 1: it's one of the early books I read on prediction, 816 00:43:39,880 --> 00:43:42,000 Speaker 1: markets and decision making, and I thought it was a 817 00:43:42,040 --> 00:43:45,080 Speaker 1: fantastic book. Um. And at the moment I am finally 818 00:43:45,160 --> 00:43:47,880 Speaker 1: Tart would be very happy to hear this. I'm finally 819 00:43:47,880 --> 00:43:50,480 Speaker 1: reading Barbarians at the Gate after he told me for 820 00:43:50,600 --> 00:43:54,480 Speaker 1: years that I should, but I barely started. So yeah, 821 00:43:54,560 --> 00:43:58,960 Speaker 1: and Americana is boo swing enough Boston and might pronounce 822 00:43:59,000 --> 00:44:01,080 Speaker 1: it right, he was a asked here a couple of 823 00:44:01,160 --> 00:44:05,560 Speaker 1: years ago. I love that book. That book because people 824 00:44:05,719 --> 00:44:10,080 Speaker 1: think that oh, all these companies were, you know, freestanding. 825 00:44:10,680 --> 00:44:14,279 Speaker 1: It was a public private partnership for a long time. 826 00:44:14,880 --> 00:44:17,560 Speaker 1: That that is a fascinating book. And I'm surprised someone 827 00:44:18,120 --> 00:44:20,440 Speaker 1: as young as you has found it. It's sort of 828 00:44:20,520 --> 00:44:22,480 Speaker 1: off the beaten path. Yeah, no, it's a it's a 829 00:44:22,560 --> 00:44:25,680 Speaker 1: fascinating book. It made me, especially in not being American, 830 00:44:25,719 --> 00:44:28,080 Speaker 1: I think it made me understand the country and how 831 00:44:28,160 --> 00:44:31,200 Speaker 1: it works so well. I think UM way better. So 832 00:44:31,440 --> 00:44:33,440 Speaker 1: so this is the first time I'm going to ask 833 00:44:33,719 --> 00:44:38,560 Speaker 1: this question of somebody who is so recently out of college. 834 00:44:38,840 --> 00:44:42,680 Speaker 1: But you're twenty five now, is that right? So what 835 00:44:42,880 --> 00:44:46,200 Speaker 1: sort of advice would you give to a college student 836 00:44:46,360 --> 00:44:49,440 Speaker 1: or a recent college grad who is interested in a 837 00:44:49,560 --> 00:44:57,000 Speaker 1: career in either startups and technology or finance and derivative training? Right? 838 00:44:57,120 --> 00:44:59,880 Speaker 1: I think the finance industry is very there's a very 839 00:45:00,040 --> 00:45:03,919 Speaker 1: traditional path that people can take. UM And what really 840 00:45:04,320 --> 00:45:08,080 Speaker 1: helped me and and Tart understand and and really come 841 00:45:08,160 --> 00:45:11,080 Speaker 1: up with the Cauchy idea and and and understand it 842 00:45:11,160 --> 00:45:13,000 Speaker 1: and work on it. But then we got a lot 843 00:45:13,080 --> 00:45:15,400 Speaker 1: of exposure to a lot of different types of firms 844 00:45:15,440 --> 00:45:17,560 Speaker 1: and a lot of different types of roles as well, 845 00:45:17,640 --> 00:45:19,680 Speaker 1: Like we did I did more of the engineering side 846 00:45:19,719 --> 00:45:20,960 Speaker 1: than in a bit of the trading than a bit 847 00:45:21,000 --> 00:45:24,040 Speaker 1: of research and tart that like all types of different tradings, 848 00:45:24,080 --> 00:45:26,800 Speaker 1: because he also worked at Citadel and Five Rings and Goldman. 849 00:45:27,320 --> 00:45:29,880 Speaker 1: And I think that giving yourself a lot of breath, 850 00:45:30,120 --> 00:45:32,960 Speaker 1: especially when you're in college, is very important to just 851 00:45:33,120 --> 00:45:35,760 Speaker 1: understand the industry as a whole, understand in their gaps 852 00:45:36,239 --> 00:45:39,439 Speaker 1: and and seeing like finding patterns like how we found 853 00:45:39,480 --> 00:45:42,560 Speaker 1: the coucil behavior. So I really think it's about putting 854 00:45:42,560 --> 00:45:45,799 Speaker 1: yourself out there, trying to learn different things, do different things, 855 00:45:45,880 --> 00:45:48,320 Speaker 1: and trying to get a global vision of of what 856 00:45:48,520 --> 00:45:50,960 Speaker 1: the industry is and why you want to do um 857 00:45:51,200 --> 00:45:53,240 Speaker 1: and and not be too tied to like the traditional 858 00:45:53,320 --> 00:45:56,160 Speaker 1: path of like entering us like this level and then 859 00:45:56,200 --> 00:45:58,279 Speaker 1: going up in a big firm and and things like that. 860 00:45:58,520 --> 00:46:00,880 Speaker 1: And our final question, what do you know about the 861 00:46:00,960 --> 00:46:05,279 Speaker 1: world of trading and hedging and investing today that you 862 00:46:05,360 --> 00:46:08,400 Speaker 1: didn't know? What do I say four years ago when 863 00:46:08,440 --> 00:46:10,640 Speaker 1: you guys were first starting out even doing it since 864 00:46:11,560 --> 00:46:14,680 Speaker 1: so let's call it six years ago, right, Yeah, So 865 00:46:14,880 --> 00:46:17,840 Speaker 1: what we're really doing is is enabling trading and investing. 866 00:46:17,960 --> 00:46:20,640 Speaker 1: But if I were an investor. What I think I 867 00:46:20,680 --> 00:46:23,080 Speaker 1: would have liked to know a couple of years ago 868 00:46:23,280 --> 00:46:25,719 Speaker 1: is that bold bets are I would take a lot 869 00:46:25,760 --> 00:46:28,879 Speaker 1: of both bets. I think generally, uh, that's the best 870 00:46:28,960 --> 00:46:31,480 Speaker 1: that seem ridiculous efforts and there's a lot of debate 871 00:46:31,560 --> 00:46:34,080 Speaker 1: and there's no way that's gonna work. Are usually the 872 00:46:34,160 --> 00:46:37,480 Speaker 1: ones that achieved, like the large outlie results. Definitely, I'm 873 00:46:37,520 --> 00:46:40,360 Speaker 1: biased because caw she is hopefully one is going to 874 00:46:40,400 --> 00:46:42,360 Speaker 1: be one of those bets for a lot of our investors. 875 00:46:42,480 --> 00:46:45,399 Speaker 1: But I really think it's about seeing what the world 876 00:46:45,440 --> 00:46:48,040 Speaker 1: can be in the future and taking both beats to 877 00:46:48,160 --> 00:46:49,719 Speaker 1: get there. I think a couple of years ago they 878 00:46:49,880 --> 00:46:51,920 Speaker 1: very if I were an investor a couple of years ago, 879 00:46:52,000 --> 00:46:54,600 Speaker 1: they're very scared to do that. But now I would 880 00:46:54,600 --> 00:46:57,120 Speaker 1: think that's the way to go to really do meaningful investing. Huh. 881 00:46:57,280 --> 00:47:01,600 Speaker 1: Quite fascinating. We have been speaking to Luana Lopez Laura. 882 00:47:01,719 --> 00:47:05,400 Speaker 1: She is the co founder of Derivatives Training marketplace CALC. 883 00:47:06,040 --> 00:47:08,600 Speaker 1: If you enjoyed this conversation, be sure and check out 884 00:47:09,000 --> 00:47:12,440 Speaker 1: any of the previous four hundred interviews we've done over 885 00:47:12,520 --> 00:47:16,520 Speaker 1: the past eight years. You can find those at iTunes, Spotify, 886 00:47:17,040 --> 00:47:20,440 Speaker 1: wherever you get your podcast fix. We love your comments, 887 00:47:20,480 --> 00:47:24,440 Speaker 1: feedback and suggestions right to us at m IB podcast 888 00:47:24,520 --> 00:47:27,560 Speaker 1: at Bloomberg dot net. You can sign up for my 889 00:47:27,719 --> 00:47:30,520 Speaker 1: daily reads at Ridolts dot com. Follow me on Twitter 890 00:47:31,000 --> 00:47:33,560 Speaker 1: at rid Halts. I would be remiss if I did 891 00:47:33,600 --> 00:47:36,400 Speaker 1: not thank the crack staff that helps put these conversations 892 00:47:36,840 --> 00:47:42,000 Speaker 1: together each week. Sean Russo is my research assistant. Mohammed 893 00:47:42,360 --> 00:47:45,920 Speaker 1: Mui is my audio engineer. Paris Wald is my producer. 894 00:47:46,400 --> 00:47:50,640 Speaker 1: Atika val Bron is my project manager. I'm Barry Rihlts. 895 00:47:50,960 --> 00:47:54,240 Speaker 1: You've been listening to Masters in Business on Bloomberg Radio.