Kalshi Surges to $10B Monthly Volume After Winning CFTC Election Suit

赢得CFTC大选诉讼后，Kalshi月交易量飙升至100亿美元 预测市场将颠覆传统民调与金融交易所，因为经济激励促使参与者趋向客观事实，而非意识形态极化。 本次对话探讨了Kalshi提前寻求监管合规的战略抉择，而非遵循硅谷“先斩后奏”的传统做法。联合创始人详细介绍了为上线大选合约而经历的艰苦法律斗争、“超级预测者”驱动的点对点流动性独特机制，以及其基于交易手续费的商业模式如何天然倾向于金融准确性，而非传统博彩公司的掠夺性策略。展望未来，他们讨论了向微观资产指标的无限市场拓展、部署高级保证金系统，以及应对内幕交易和政治反身性带来的独特监管边界。 Kalshi联合创始人Tarek Mansour和Luana Lara性格截然相反却互为平衡，将坚定的乐观主义与交易员式的偏执相结合，成功应对了高风险挑战。 (0:00) 与典型硅谷初创公司“事后请求原谅”的做法不同，Kalshi从第一天起就坚持严格的“监管与市场契合”策略，认为金融服务必须做到绝对合规，以防范FTX式的灾难性溃败。 (1:37) 在CFTC对其备受期待的大选合约实施“口袋否决”并推迟批准至中期选举之后，Kalshi挺过了2022年底的公司低谷与裁员困境。 (7:01) 作为一家仅有25人的公司，Kalshi采取了一项打破常规且极具风险的举措，通过证明大选具有不可否认的经济影响，成功起诉其主要监管机构CFTC，从而获准上线大选合约。 (7:01) 算法信息流的普及、社交媒体的极化以及公众对体制的普遍不信任，催生了对预测市场的需求，而预测市场正是利用经济激励来提供客观、实时的真相。 (13:41)

Kalshi cofounders Tarek Mansour and Luana Lopes Lara argue that prediction markets operate as a necessary truth-seeking financial utility in an era of media polarization and inaccurate polling. By choosing onshore compliance over offshore avoidance and successfully suing the CFTC, the exchange established a regulated model that derives market forecasts from decentralized, peer-to-peer liquidity. Regulatory Strategy and the CFTC Lawsuit (1:37) Rejecting Silicon Valley's standard playbook of launching first and seeking forgiveness later, Kalshi spent four years working alongside the Commodity Futures Trading Commission (CFTC) before launching in 2022. Mansour contends that operating without regulatory approval in financial markets carries catastrophic systemic risks, pointing to the collapse of FTX. Every contract on the exchange is submitted to the CFTC under a 24-hour review window. When the regulator issued a pocket veto blocking political election contracts ahead of the 2022 midterms, the company was forced to lay off a significant portion of its staff, yet opted to file a lawsuit in late 2023 with the backing of board members Alfred Lin and Michael Seibel. Kalshi prevailed in federal court at the end of 2024 because statutory law permits the CFTC to prohibit contracts only if they cause demonstrable public harm or involve specific unlawful activities such as terrorism, war, or assassination. Mansour and Lara established that elections exert profound economic consequences, legally placing election contracts under the purview of regulated derivatives exchanges. The founders maintain that the platform's 2024 election forecasts provided an objective baseline of voter sentiment that proved far more accurate than traditional political polling. Information Utility and Marketplace Growth Dynamics (13:41) The founders attribute the rise of prediction markets to a cultural deficit of trust rather than crypto or stablecoin technology. With algorithmic feeds and political polarization impairing traditional news and polling, financial stakes incentivize participants to supply accurate information. Lara highlights that roughly 80% of Kalshi's users never trade on the platform, relying on it solely to consume real-time probabilistic data. Platform trading volume reached $10.4 billion in February, representing an 11-fold expansion across six months. While initial volume was heavily catalyzed by distribution partnerships with brokers such as Robinhood and Webull, direct engagement via Kalshi Direct now constitutes the primary growth channel. Mansour notes that liquidity improvements increase user retention, unlocking network effects that turn prediction contracts into a mainstream reference point for real-world event probabilities. Peer-to-Peer Liquidity Versus Traditional Bookmaking (24:15) Unlike traditional equity or futures exchanges dominated by institutional market makers, over 95% of Kalshi's maker orders are supplied peer-to-peer by more than 2,000 retail traders and boutique funds. Institutional makers account for under 5% of maker volume because Wall Street algorithmic firms struggle to price fast-moving pop culture and political developments. The ecosystem relies on specialized independent forecasters—including an individual in Kansas who became Kalshi's top inflation trader and a tax accountant who made a large, profitable short against early Department of Government Efficiency (DOGE) target figures based on statutory research. This structure diverges fundamentally from sports betting platforms. Sportsbooks operate a house-managed model that charges roughly 10% rakes, bans profitable 'sharp' bettors, and offers deposit bonuses to retain losing customers. Kalshi operates as a pure exchange funded by transaction fees of roughly 1%, actively encouraging sharps to trade to ensure fair pricing. Kalshi manages spreads and market depth by discounting fees for continuous liquidity providers while assessing higher fees on opportunistic snipers. Surveillance, Ethical Boundaries, and Structural Expansion (47:35) Kalshi maintains an internal 24-hour surveillance division serving as the first tier of enforcement before referring cases to the CFTC and the Department of Justice. The team recently levied penalties of over five times trading profits against two insiders and banned them from the platform. While the exchange enforces a total ban on trading by members of Congress and individuals with non-disclosure obligations, it defends 'mention markets' regarding corporate calls or political speeches provided the speakers and their staffs are barred from taking positions. The exchange maintains strict ethical boundaries prohibiting contracts on war, assassinations, terrorism, or natural disasters. Looking forward, Kalshi plans to scale from 10,000 to over 50,000 markets by introducing compute commodities, collectibles, traditional derivative structures like futures and swaps, and advanced margining to replace 100% upfront collateral. Citing Kevin Hassett's 'infinite markets' hypothesis, the founders argue that dissecting complex macroeconomic outcomes into atomic, individually traded variables offers better capital allocation, while giving policymakers real-time, reflexive feedback on public initiatives. Kalshi's model demonstrates how regulated event contracts can align financial incentives with objective public information, though its long-term viability requires navigating ongoing regulatory oversight, enforcing strict insider trading boundaries, and scaling capital-efficient margining systems for institutional adoption.

Kalshi 联合创始人 Tarek Mansour 和 Luana Lara 认为，预测市场不仅是受监管的金融衍生品交易所，更是对抗信息极化与失真的真实性基础设施；通过坚持合规并在联邦诉讼中战胜监管机构 CFTC，Kalshi 证明了预测市场的经济价值与公共效用。 监管契合度与起诉 CFTC 的法律突破 (7:01) 与硅谷常见的“先斩后奏”（如早期 Uber 或 PayPal）策略不同，Kalshi 从 2019 年创立起就将合规视为首要产品目标，选择在美国本土建立受监管的交易所，而非避走离岸市场。创始人认为，金融与医疗等领域涉及公众资金与重大利益，不能承受无序试错的后果。在长达数年的时间里，Kalshi 将监管机构视为核心用户来设计流程，其平台上的每份合约都须向美国商品期货交易委员会（CFTC）申报并接受 24 小时审查机制。 双方的合作在选举预测合约上陷入僵局。面对 CFTC 连续两年的拖延与否决，Kalshi 经历了团队大幅裁员的低谷期，但在红杉资本 Alfred Lin 与 YC 的 Michael Seibel 等董事的支持下，公司选择打破常规起诉自己的直接监管机构。2024 年底，Kalshi 赢得了这场诉讼。法庭认定选举对宏观经济具有不可否认的重大影响，且现行法律并未禁止此类合约，这确立了合法预测合约属于受监管金融衍生品范畴的法律地位。 点对点流动性与超级预测者生态 (24:15) 与依赖华尔街专业做市商的传统金融市场不同，Kalshi 平台的撮合订单中，传统机构做市商占比不足 5%，超过 95% 的流动性由 2000 多名独立个人或小型团队通过点对点方式提供。创始人解释，传统机构缺乏对政治动向或流行文化事件进行实时定价的模型，而分散在各地的“超级预测者”能够凭借对细分领域的深入研究（如税法条款或音乐榜单机制）迅速捕捉信息差并获得回报。 针对不同深度的市场，Kalshi 采取了差异化的流动性维护策略。对于需求天然较低、定价难度大的“长尾利基市场”（例如预测特定乐队是否重组），平台直接设立流动性激励计划来构筑护城河；对于体育、政治和加密货币等高频高需求市场，则不直接支付补贴，而是通过返还交易手续费的方式，要求做市商满足严苛的在线时长、狭窄买卖价差与挂单深度标准，以确保即使在比赛突发变动时盘口也不会出现失真剧震。 交易平台机制与传统博彩业的本质分野 (33:42) 传统博彩公司依赖“庄家模式”，通过输赢对赌赚取高达约 10% 的抽成，并会利用算法识别并封禁高胜率的专业玩家（sharps），同时向亏损者提供返还奖励以诱导沉迷。Kalshi 则采用类似纽约证券交易所或芝加哥商业交易所的清算与撮合架构，纯粹通过收取约 1% 或更低水平的交易手续费盈利。平台需要专业交易者的参与来发现真实价格，因而绝不限制盈利者，并借助阶梯费率奖赏提供稳定流动性的行为、惩罚价值榨取型的恶意抢单。 创始人强调，预测市场具有强烈的社会信息属性，目前约 80% 的 Kalshi 用户仅将其作为实时客观的数据源查看事件概率，并不参与实际交易。在重大选举中，受利益驱动的交易者倾向于采用高质量模型以求精准，使得预测市场在信息聚合的即时性与准确度上展现出优于滞后且存在偏差的传统民调的效果。 内幕交易监管与资产拆解的无限市场 (47:35) Kalshi 划定了严格的伦理边界，明确拒绝上线涉及战争、暗杀、恐怖主义或自然灾害等悲剧事件的合约。针对内幕交易，平台设立了 24 小时运行的内部监控部门，并直接对接 CFTC；近期已对两名违规使用非公开信息的交易者处以其收益 5 倍以上的罚款并予以封禁。创始人倡导对国会议员等政府决策人员实施明确的交易禁令，防止利用职务便利在法案或政策合约中牟利。 关于预测市场的长期演进，创始人引用经济学家 Kevin Hassett 的理论指出，随着社会复杂性提高，单一资产的价格解析度在衰退，未来需要建立“无限市场”将宏观结果拆解为底层的原子变量（如芯片出货量或汽车交付量）分别定价。Kalshi 计划从二元预测扩展到期货、掉期等传统金融结构，引入更高效的保证金系统，并将标的拓展至算力、大宗商品和高端收藏品，力图构建下一代多功能衍生品交易所。 Kalshi 在 2025 年 2 月实现了单月 104 亿美元的交易量，半年内增长 11 倍。然而，作为受联邦严格监管的金融实体，公司员工被法律禁止在自有平台上进行交易测试（dogfooding），同时平台在向传统金融机构拓展大宗交易、引入人工智能交易模型，以及应对政治事件引发的社会心理反身性方面，仍面临持续的制度调试与技术验证。

[1:37] Tarek Mansour: you can't ask for forgiveness [7:01] Luana Lara: Are you fucking kidding me? [7:01] Alfred Lin or Michael Seibel: even if you win, you will probably lose [10:52] Luana Lara: the law applies to companies, but it also applies to the government. [33:42] Tarek Mansour or Luana Lara: The sharps are the market [33:42] Tarek Mansour or Luana Lara: We want all the winners. We need the sharps because how do you get market accuracy without the sharps? [45:00] Tarek Mansour or Luana Lara: For example, if you are a government official, like if you're in Congress, you cannot trade on bills passing. [45:00] Tarek Mansour or Luana Lara: Because we're also regulated, we're able to charge them a lot of fines. So we charge them over five times what they made [51:25] Tarek Mansour or Luana Lara: Fed meeting minutes are the original mention markets [51:25] Tarek Mansour or Luana Lara: If you are Gavin Newsom, there's a market on what you're going to say, you cannot trade it, and your staff cannot trade it. [53:28] Tarek Mansour or Luana Lara: If you start losing... the first thing that they're going to do is give you a bonus... so that they can hook you to keep you coming back because they want to incentivize the losers. [61:09] Tarek Mansour or Luana Lara: If you don't think the second-by-second stock price is good, you can just choose to ignore it. [61:09] Tarek Mansour or Luana Lara: If we pass this bill, will unemployment go up or down? And you can price these things for better decision-making. [65:07] Tarek Mansour or Luana Lara: What do I actually think is going to happen?

[0:00] I brought some Brazilian beers. [0:01] I'm going to try a Brazilian beer then. [0:03] Do you want a Brazilian beer or Guinness? [0:05] Well, I think I need to have Guinness. You need to have Guinness, that is true. [0:08] Ok. We lost Tarek already. [0:09] It's been like one minute. [0:12] Tarek Mansour and Luana Lara are cofounders of Kalshi, [0:15] one of the new prediction market firms that rose to prominence [0:18] in the November '24 elections. [0:19] They spent four years pre-launch fighting for regulatory approval to build the first [0:23] onshore prediction market in the US and now trade more than $10 billion [0:26] each month in prediction contracts. [0:29] Cheers. Cheers. [0:32] What is this that we're drinking, Luana? [0:33] It's a Brazilian beer. Ok. [0:35] It's our most famous Brazilian beer, I would say. [0:38] Very light. I don't know if you like it. [0:39] I like it. So what is the split between you guys? [0:42] Maybe in terms of responsibilities, but more interestingly, in terms of outlook? [0:46] Well, we actually come from the exact same background. [0:47] We studied math and CS at MIT, same internships, everything. [0:51] But I'm a very, very optimistic person. [0:54] I love taking risks. I think everything's going to work out. [0:57] He's very paranoid, more on the negative side. [0:59] So it's always like a very good balance. [1:02] And I think that, outside of what we do day-to-day, [1:04] that's what really the difference between us that works out. [1:07] I mean, there's a little bit of background. [1:08] So I was going to be a trader. [1:10] That was really what I was going to do. [1:12] And you know, when you're a trader and you probably... [1:15] I don't know if you've ever met sort of or spent enough time with the persona, [1:18] but it's like a very— John is a secret trader. [1:22] At heart, yes. [1:23] But if you're a trader, you're like an expected-value calculator. [1:26] I think about these sort of tail, really bad outcomes all the time. [1:31] And one oftentimes doesn't. [1:32] And I think this is the thing that actually leads to great outcomes. [1:35] Ok. So I want to ask about that starting out. [1:37] This is really interesting. [1:38] You guys started Kalshi and for several years were not able to operate [1:44] until you got CFTC approval. [1:46] And that's interesting where just, one, most companies don't start out that way. [1:51] And secondly, I feel like the Silicon Valley [1:54] standard that people sometimes trot out as a criticism is sort [1:59] of the PayPal, Uber early days model, where you start doing the thing and maybe [2:04] retroactively a structure is put on top of it, but you do a bit of ask forgiveness [2:08] rather than permission in the very early days. [2:11] And so can you just tell a little bit of the story of how you started [2:15] and that approval process? [2:17] And then I want to get into whether that generalizes to other companies. [2:20] Yeah, I think that the approach we took from the start was that financial [2:25] services or healthcare, I think you can't ask for forgiveness. [2:28] I think there's a big difference between losing people's money. [2:31] See what goes wrong with like an FTX example that can go very wrong. [2:35] With healthcare, there's a lot of other massive examples of it going wrong. [2:38] And we wanted to do things the right way because also when we look at the market, [2:41] we thought the biggest question to be answered was not, [2:44] "Is this going to grow?" It was "Can we do this legally in the US?" And we're like, [2:48] let's just actually address the biggest problem first and go from there. [2:53] And I think the strategy for a long time, people looked at it as the wrong strategy. [2:57] Up until we won the election lawsuit, everyone was saying the folks that went [3:01] offshore, they're doing a lot better, they're growing a lot more. [3:04] But I think once we won the election lawsuit and proved [3:08] that the legal interpretation we had was right and we could do the company as we [3:11] wanted in the US, I think it just really, really took off. [3:14] What are the timelines here? [3:15] So when did you start and when did you win the election lawsuit? [3:17] Right. So we started the company in 2019. [3:19] We started in YC 2019, and then it took us three years to be [3:22] able to get regulated and launch. [3:24] I think it was 2022 at that point. [3:28] And then we won the election lawsuit at the end of 2024. [3:32] And that's when we really started ramping up. [3:35] There were a lot of like elongated... [3:37] I mean, there's some overlap between the timelines, [3:40] but maybe going back to the question and maybe we can talk a little bit more [3:43] about the sort of history afterwards, but I think it was like a twofold, [3:49] or two-step process. One, it was a pragmatic thing, which is [3:56] we felt like to get proper mainstream [4:00] adoption and institutional adoption, the elephant in the room was, could we do [4:03] it in a regulated, credible, and safe way? [4:07] Because it's a complex marketplace, you're moving people's money, and we're [4:11] like, we have to solve that problem first. [4:13] That is the hard problem to solve. [4:15] And, you know, that will be the road to success. [4:17] The second thing was a bit more principled. [4:21] We were... [4:22] What excited us, like when we created this doc, one page on Google Docs, [4:26] and we wrote a set of things like why should we do this company and why [4:29] are we so excited about this? [4:30] We wanted to build the next generation New York Stock Exchange. [4:34] We wanted to build a financial market that is in the US, [4:38] that is credible, that is regulated. [4:40] We were not very excited about this idea of building something offshore. [4:46] And so that was really important because it's like, what kind of company do you [4:49] want to build and why are you doing this in the first place? [4:51] There's many paths to success. [4:53] We just weren't very excited about the other idea. [4:55] We wanted it to be here. [4:57] You're the first CFTC-approved prediction markets at any scale. [5:01] Yes. [5:02] And still to this day, all the contracts are individually approved. [5:06] And so— [5:07] Yeah, every single contract we file with the [5:10] CFTC and they have 24 hours to stop it. [5:13] Ok, so they get kind of a real-time feed of the contract. [5:16] Exactly. [5:17] Yeah. And it was a kind of... [5:21] It was a very long journey to get to where we are in terms of the contract [5:24] process and how it works. [5:25] Because you've got to imagine the first time we walked into the building—actually, [5:28] the picture is right here. [5:29] This is the first time we ever walked into the CFTC. [5:32] You know, you walk in and you're talking about this idea and it's... [5:37] You've got to imagine the regulator's head starts spinning. [5:40] You're talking about things that don't have a financial underlying. [5:45] And then there's this idea of potentially hundreds of tens of contracts, [5:48] hundreds of contracts a week. And I mean, now we're like... [5:51] But there's all these things where the model wasn't really set up for this. [5:55] So a lot of the process was actually like this iterative process where you're trying [6:00] to figure out how to actually regulate this as you get feedback [6:03] from the regulators and what can we do to satisfy the concerns. [6:06] So it was a bit like building a product, but you're not building it [6:10] for a customer, you're actually— [6:12] Yeah, it's a regulatory-market fit in a way. [6:14] And so now you've gotten them comfortable with you shipping them unless they say no. [6:20] Yes, right. Have they said no to anything recently? [6:23] Well, the biggest they said no to was the elections. [6:26] That's why we had to end up suing them. [6:28] They said no for two years. [6:30] But at this point, I think we've worked with them for so [6:33] long that we know exactly... [6:34] They trust us as well as a self-regulated entity to know kind of what [6:38] we can do and cannot do. [6:39] So we don't do anything around war, assassination, those [6:42] things that we don't do. [6:43] So within the parameters that we've worked with them, it's a lot faster. [6:47] So the election lawsuit was they were willing to approve contracts generally. [6:51] They were not willing to approve contracts around who would win the election, [6:55] which is a pretty popular contract. [6:59] It's the holy grail. The US presidential election. [7:01] And so you sued the CFTC? Yeah. [7:03] Our own regulator. I mean, and— [7:05] Which is generally not considered a best practice. [7:07] It was. I mean, ok, so we— [7:10] It worked out. [7:11] We started talking about the election market at the end of '21 and we started [7:14] engaging with policymakers, talking with Congress a bunch and the regulator. [7:19] And they're like, "Yeah, I think it's a good idea, it's a good [7:21] idea." But then they weren't moving. [7:23] We started noticing something is off. [7:26] By the end of '22, they delayed the approval till after [7:30] the election, what we call a pocket veto. [7:33] That was brutal. [7:34] So that was one of the hardest times in the company where we had to [7:38] lay off a bunch of people. [7:40] But the harder part of this is that your team and some of your investors, [7:44] or majority of investors, kind of stopped— Believing the idea. [7:48] Yeah. [7:49] But believing in the strategy, the idea, and it's a bit like, [7:53] "This is getting a little bit unhealthy. [7:55] You guys should do something else. [7:57] Clearly it's not going to work out." But we could not... [8:02] We couldn't get ourselves to do something else. [8:05] We just couldn't. [8:06] So we're like, "Ok, we're going to try again." And end of '22, [8:10] so imagine the team is at an all-time low on morale. [8:13] They're waiting for a new strategy. [8:14] A bunch of people left, a bunch of people got laid off because we had to downsize. [8:18] And our message in that next stand-up was actually, "Guys, here's the '23 strategy, [8:24] is we're going to try again." [8:25] We're going to do the same thing, but this time it'll work. [8:28] And exactly, but this time it's going to work, even though every inch [8:31] of evidence was pointed in the other direction. [8:33] And I will say, a lot of this is her. [8:36] I wanted this to happen so bad, but my rational brain was like [8:40] "Got to listen to these people." And Luana is much more dogmatic. [8:44] So we try again, end of '23 they block it again. [8:47] And I was really at the point where I'm like— [8:50] "Ok, this prediction market thing is just not going to work." [8:53] It's just... [8:53] And then Luana was like, "Well, the only thing we can do right now out [8:59] of the entire range of possibilities is we got to sue the government." And [9:07] at the beginning it was like, "This is crazy." And we took it [9:09] to the board and we had Alfred and Michael at the time and they're all like— [9:14] Alfred Lin and Michael... [9:15] Seibel from YC. [9:17] And I remember that board meeting, it took a few board meetings, it took a few times. [9:23] At the beginning, I was like, "Well, we have to tell you guys it's a bad idea. [9:26] These are all the ways it's a bad idea." Because you're a regulator and you're [9:29] like now a 25-people company. [9:31] The government can do anything; they can shut you down, take out the license. [9:36] I mean, it is true. [9:37] So rarely does it work. [9:39] And even if you win, you will probably lose. [9:41] You will end up getting killed in the process. [9:44] And it took a few times. [9:45] And I remember very vividly, there was a meeting we had internally [9:49] before talking to the board. [9:50] And this was the night before. [9:52] We had lined up the lawyers and everything. [9:54] And I got cold feet. [9:56] I was like, "Let's just focus on getting a clearinghouse. [10:01] We could focus on financial products. [10:02] We can focus on all these other things. [10:04] We don't need to sort of tank it all on this and really bet the farm on this." [10:08] And I remember Luana in that call—I forgot the exact [10:12] wording, but it was something along the lines of like, [10:13] "Are you fucking kidding me?" [10:15] That sounds like me. [10:18] And I realized, all right, I'm not going to win this fight. [10:22] `But the other part of me is like, "We got to do this." So we went [10:25] to the board, and the response was basically, [10:28] "It's an anti-pattern, it's a bad idea, but a lot of great companies [10:31] are built by an anti-pattern. [10:33] There's something off. [10:35] That is weird. That happens and maybe this is yours." [10:39] Good way of putting it, that every company is different in some [10:43] new way, and so yeah, this could be yours. [10:46] What was the basis for the decision where you won the election lawsuit? [10:52] Was there any interesting policy angle? [10:54] Right, so the whole point is that the government cannot stop [10:59] any type of contract unless it makes a finding that's against public interest [11:03] and it has to fall within certain categories of war, [11:05] terrorism, assassination. [11:07] And the CFTC was taking the stance that they were trying to fit [11:10] elections into any of these things. [11:12] They're like, "Oh, elections might be illegal under state [11:14] law." Because betting on elections, there's this one state [11:18] that in bucket shop law... [11:19] They try to find something to stop it. [11:21] And we knew we were very, very clear on the law, [11:23] like elections have economic impact. [11:25] If the elections have economic impact, they need to be allowed to trade on a [11:28] futures exchange or derivatives exchange. [11:31] And it was basically, I think what the lawsuit did is it told [11:35] the CFTC that they couldn't just do whatever they wanted. [11:37] That kind of like— [11:39] The categories it prohibited, it needs to actually fall under [11:42] one of the prohibited categories. Exactly. [11:43] Which elections did not. Right. [11:45] Exactly. [11:45] And I think that's important because the law... [11:47] The thing that we always say, the law applies to companies, [11:49] but it also applies to the government. [11:51] But you should make your point about maybe suing your— [11:55] Well, I think certainly in crypto and in prediction [11:59] markets, it's sort of this unique thing of suing the government. [12:02] But I was sort of surprised to realize that Coinbase has [12:06] sued their primary regulator. [12:09] In govtech, SpaceX, Anduril, and Palantir [12:13] all had to sue for various reasons. [12:16] So it seems like it's actually more common than Silicon Valley conventional wisdom. [12:20] So what advice would you have having dealt with the government to people out [12:25] there trying to build businesses? [12:29] What sort of situation would be, you think, ripe to actually [12:32] make that kind of challenge? [12:35] I think it's a sort of no other option situation. [12:39] So it's still painful. But did you actually have no other option? [12:42] Couldn't Kalshi have done fine without elections? [12:44] I mean, elections are obviously very helpful because they're such a big shiny [12:46] thing, but I presume elections are not a majority of your contracts today. [12:50] I think it was just too important and maybe that's the dogmatic or whatever, [12:54] but it is the holy grail of market. [12:57] That's the one that you could see the use case of the data the best [13:00] and you can see the use of the... [13:03] And you can see the 2024 election, the polls were completely wrong [13:06] and the markets were so much better at bringing that sort of information. [13:09] And I think that it's the shining example of why these markets are a force for good [13:14] and we need to have them in the US and regulated, which other [13:17] markets just won't have. [13:18] So to John's point on PayPal and Uber and asking for forgiveness, [13:22] there were other prediction markets operating and showing real usage offshore. [13:29] And so I'm wondering, how much did that help demonstrate [13:33] that election markets weren't against the public interest? [13:36] Did that factor at all into the court case, the fact that people [13:40] are already doing it? [13:41] I don't know, but specifically on the court, [13:45] it was much more grounded in sort of like— The law. [13:49] Yeah, the law. [13:49] Reading our law is the Commodities Exchange Act. [13:53] So that's one of the financial statutes. [13:55] The other one is the Securities Exchange Act. [13:57] And reading it and really interpreting it and is the regulator overstepping? [14:01] Now, I think for us it was a good way to learn. [14:07] Because we could not learn about our product because we took this [14:12] regulatory-first approach, where we will ask for permission [14:16] first before doing something. [14:18] And so in some ways it was helpful that you could have some data, [14:23] some evidence that could guide our decisions over time. [14:27] And I think it also helped educate some people about the existence [14:30] of prediction markets. They are here. [14:34] Here's how you could use them, etc. [14:36] But I don't think unregulated offshore players really [14:39] help from a policy perspective. [14:42] Could Kalshi have been started 10 or 15 years prior, or was there some [14:46] moment of openness in the CFTC? [14:48] Was there some tech enablement that was required? [14:52] Were stablecoins required? [14:54] I do think that there is a part of it that crypto at the time was augur [14:58] and there was like some very early prediction markets. [15:01] I think that the existence of that made the CFTC also be like, [15:05] "We need a legal regulated alternative to this." Because before you [15:09] could just say no to things. [15:11] I do think that played a role, but maybe like 5%, maybe 10%. [15:15] I don't think it's more than that. [15:16] The broader thing is there's always intellectual interest [15:20] in prediction market. [15:21] And I think that started in the '50s. [15:22] It is a better source of signal than most other mechanisms of getting signal. [15:27] But there wasn't a real pain. [15:29] I think 10, 15 years ago in the way that there is pain in the last few years. [15:33] And that pain, I think, is the country is more polarized. [15:37] I would say the world is more polarized. [15:39] Social media has really bifurcated social feeds. [15:43] Clickbait is rampant. [15:44] Like the incentive structure for most things that we read these days is [15:48] clickbait, whether it's a lot of traditional news or social media or other. [15:52] So there was more of a pressing problem, I think, that helped create the wave [15:58] and this sort of adoption that you're seeing in prediction markets that I don't [16:01] think happened or would have happened 15 years ago. [16:04] Because the problem wasn't that— [16:07] Yeah, and that's because most of our users, like 80% of our users are actually [16:11] just looking, consuming information. [16:13] They're just coming in and seeing who's going to win the Texas primary yesterday [16:17] and seeing, "Ok, the polls are saying they're tied, but [16:20] they're not tied" and all those things. [16:22] That consumption of information is way more important and relevant now. [16:26] So you're saying like algorithmic feeds, Kalshi markets do very well [16:31] on algorithmic feeds and just maybe people wouldn't have been as [16:34] interested 10 or 15 years ago. [16:36] Yeah, I just think that sort of there's a meaningful and accelerating rise [16:42] of distrust in traditional sources of information. [16:47] And so, you need a new one. [16:50] And this does work, right? [16:52] Like the incentive structure for a prediction market is truth. [16:56] It is more volume. [16:57] It is more liquidity, which translates to better and more accurate forecasts. [17:03] And it took a few iterations for people to start trusting it. [17:06] But as you start building a track record, people start trusting it and they're [17:10] never going to use a better product. [17:12] Well, to the point of substance and volume, can you guys [17:14] give us the outline of... [17:16] It seems like it's growing very quickly. [17:18] So volume in February was $10.4 billion. [17:21] Dollars of contracts traded. [17:26] And that's up 11x over 6 months, [17:31] I think, or a little bit— [17:33] It's growing so quickly, you don't even bother to go back a year [17:36] because that's just ancient history. Yeah. [17:38] I mean, a year ago, it really is. [17:41] It's like we just had, for example, one sports market. [17:43] We only had the Super Bowl. [17:44] In February... Yeah. [17:45] I mean, it's growing very fast. [17:47] Fastest-growing company outside of AI. [17:49] Yes, I think so. [17:51] And we compete with, I think, even some of the top AI companies. [17:54] I don't know what Cursor and Anthropic's latest numbers are— [18:00] I think 11x is very quick, even in AI. It's quick. [18:04] And I think because it's an... [18:05] So we are a marketplace. [18:07] It's a true marketplace that has all the attributes of, it has network effects. [18:13] So what happens in those situations is that users retain better because there's [18:17] more diversity and more liquidity. [18:22] Their participation and volume grows over time, which obviously grows their usage, [18:27] but it also grows other people's usage because there's more [18:29] liquidity in the system. [18:31] And then they share it more with other people because the product [18:33] is getting better. [18:34] And so the sort of trifecta of factors is leading to this sort of growth. [18:38] When some of your early growth depended mostly on other brokers, [18:43] and I think you've evolved that mix today. [18:46] What's the broker mix and how do you think about that? [18:49] Sorry, what's a broker in this context? Like Robinhood. [18:51] Ok, like a distribution. To explain what that is. [18:54] Which is interesting. I mean, the— [18:56] Well, I can explain the broker part, but I don't know what we want [18:58] to show in the numbers part. But basically— [19:01] That was a tough one. Ok. [19:03] We could delete that part. [19:05] All right. [19:06] So because we're an exchange and clearinghouse, we basically function [19:09] like the New York Stock Exchange. Another beer for her. [19:11] Yeah, exactly. [19:13] The Chicago Mercantile Exchange. [19:15] So brokers can connect to us. [19:17] You can go to Robinhood to trade stocks. [19:19] You can go to Robinhood to trade on Kalshi, same with Coinbase or whatever. [19:23] And it's always been part of how we think about... [19:26] We always wanted to be an exchange and a clearinghouse first, [19:28] and actually connection to a Goldman Sachs or Robinhood is very important for how we [19:32] thought about this ecosystem as a whole. [19:35] In the beginning of last year, we launched the first broker partner [19:38] that we had was actually Robinhood and then Webull. [19:42] And at the start, actually, when we were starting to ramp up, [19:44] the brokers were a very, very big part of kind of how we started [19:47] growing, which was actually very great because the brokers bring so much demand. [19:51] Then we get all the market makers to come in because they want [19:53] to trade against retail flow. [19:55] And then we could kind of buy ourselves time to ramp up the direct product a lot, [19:59] to where it is now. [20:00] But basically how you think about it is, well, we really are at the core is [20:03] an exchange and a clearinghouse, and then you can access us through our [20:06] app, website, API, but also any broker. [20:09] We're investing more into institutional now, international brokers, [20:12] so you can be in Brazil and you can trade on Kalshi, all those things. [20:15] They're coming soon. But on the numbers, you can take it. [20:18] I mean, maybe we won't share numbers, but what we call direct, [20:24] Kalshi Direct, which is our Kalshi. [20:25] com, Kalshi app, the consumer business. [20:28] That has grown. [20:29] That has sort of dramatically outpaced the rest, our other [20:34] sort of intermediary or broker business. [20:37] And I think it's just that the brand has gone mainstream. [20:40] I think people, when they think about, have a difference of opinion on something, [20:45] it's sort of becoming synonymous to like, "Oh, let me pull up Kalshi and see [20:48] the odds," or "Let me sort of place a position on Kalshi." And [20:53] that's contributing... There's just a lot of organic growth now. [20:55] And I think that's going to continue over the next few months. [20:58] You're describing how you grow the individual retail side of the market, [21:02] whether people are coming through brokers like a Robinhood or people coming [21:06] directly to the Kalshi website. [21:07] There is also, when you're an exchange like this, [21:10] you have to spin up market making. [21:13] And in the end, [21:17] the New York Stock Exchange doesn't have to think too much about market making [21:20] because just the economic incentive is there. [21:23] And so when something is at large scale, that's not as big of an issue. [21:26] But I'm curious what that was like in the beginning. [21:29] Were you guys doing the market making? [21:30] Did you work with market-making partners? [21:32] Now, how do you incentivize market makers to participate? [21:35] I'm just curious what the market making scale-up has looked like. [21:39] So there's actually two groups of contracts on markets [21:43] on Kalshi, and they behave very differently, and the market-making [21:46] incentives are actually pretty different. [21:47] So you have the long tail of markets, right? [21:49] The ones like, will One Direction have a reunion or all those things. [21:53] And they are actually very hard to price. [21:56] And because there's not necessarily a lot of demand, we actually have [21:58] to incentivize market makers to come in. [22:01] And there's like liquidity incentives, all those things for them to come in. [22:04] And it's actually how we think about how to build our moat long term is how do we [22:08] get very sustainable, solid liquidity in this long tail of markets so we can get... [22:13] We have like, I think, 10,000. [22:15] How do we get to 50,000, 100,000 markets? We've still... [22:17] But on the other side, you have the more classic like [22:20] crypto, sports, all of those guys. [22:22] And on that side is actually a lot easier to market because you have very clear [22:26] proven demand, is a lot easier to price. [22:28] So the market-making incentives on this side is actually we don't pay them for it. [22:32] We just rebate fees. [22:33] But they have very, very, very hard conditions to meet. [22:36] They need to have uptime of a certain amount, spreads, and top-of-book [22:40] size and all of those things. [22:42] Because we see it more as like incentivizing stability of the book than [22:45] it is incentivizing them being there. [22:47] What does incentivizing stability of the book mean? [22:49] For example, if you think of a live game or like if you're [22:52] trading an hourly crypto— [22:54] You actually don't want the price flying around a whole bunch if [22:58] there's no new information. Right, exactly. [23:00] Or even if there is, if someone is about to sort of touch down, [23:03] you don't want the book to just have like no liquidity whatsoever. [23:05] You want it maybe to go a little bit wider, but you want people [23:08] to be able to trade. [23:08] And actually, when we go into the intermediated model, [23:11] the brokers come with expectations that they have from traditional markets. [23:14] So they are expecting, "We want this spread and this size at any point in time. [23:19] It doesn't matter." So we need to go to the market makers and we're like, [23:22] how do we incentivize this? [23:23] Even though if you think about, you should just let the markets [23:26] do whatever they want to do. [23:27] And if they're going to go way wider because they need to, it is. [23:30] But we have to kind of play with incentives in a way to... [23:34] For all of our users, including the brokers. [23:36] So during those moments when the spreads would normally blow out wide, [23:40] are market makers losing money then and they're cross-subsidizing to the other [23:45] parts where like it's more stable? [23:47] Well, now there's so much demand that I don't think they're... [23:50] You can make money on spread even if the spread is a little lower. [23:52] But that's the point of the program. [23:54] You have to think about all the benefits you get in this program. [23:57] And then even if you're losing a little bit in this time, having [24:01] the benefits is worth it. [24:03] So you want tight spreads all the time for the major markets. [24:07] Yes. That's what market stability is. [24:08] And that actually takes work to engineer. It's hard to get there. [24:11] You have to... But there's more to it, which is... [24:15] So I think the magic and the uniqueness of, I would say, [24:19] or the special thing about prediction markets is that a lot of the liquidity [24:23] is not what you consider a market maker. Right, right. [24:27] It's people. [24:30] And so this goes back to the whole point. [24:31] So maybe let's just go back to first principles. [24:34] There was the regulatory thing that we figured out, but then there's a liquidity [24:37] problem, which is historically it's a bit like we said, [24:41] the New York Stock Exchange or the CME. [24:43] They're like, "We're going to create a green future. [24:45] We're going to take two years to figure out what it looks like. [24:48] And we're going to call all of our buddies, like the 50 market makers that we [24:53] all know, we all hang out at Christmas parties together, etc. [24:58] We're going to get them ready and they're going to start helping us [25:01] get this product launched. [25:02] And then we're going to market it for the next three years." And [25:04] then it's just the same thing. The liquidity is there. [25:06] But prediction markets is really different because now you have to create liquidity [25:10] in these products on like a weekly, daily, maybe even hourly basis. [25:15] How do you do that? It's much more dynamic. [25:17] There's new things all the time. [25:19] I think it's counterintuitive to people that you have to incentivize market makers [25:23] to create liquidity because in the stock exchange, you don't have to incentivize [25:26] high-frequency trading firms to create sub-second liquidity. [25:31] They are very excited to take on that project themselves and build [25:35] the high-speed interconnect between New York and Chicago to accomplish [25:38] and everything like that. [25:39] And so is this just the stage that prediction markets are at, or is [25:44] there something fundamentally different? [25:45] I think this is where I was talking about, which is this idea that you need... [25:49] So, so maybe finishing that sort of line [25:54] of thought and then like I'll get to the answer there. [25:56] You now have a model where you need liquidity built on the fly much [26:00] faster, much more dynamically. [26:02] And the market makers, the traditional Wall Street market [26:04] makers are not geared up for that. [26:05] It's not like they can spin up a new desk to price politics or price [26:09] culture in an hour. [26:12] And so, but this is the part that gets really [26:14] interesting, and this goes back to the foundational [26:17] principles around prediction markets, is like a lot of these markets, [26:21] the people that will price them the best may not actually be the experts or [26:26] the authority figures that you usually would think about. [26:29] It's actually random people that live— [26:34] Internet anons. [26:36] Exactly. The super forecasters, those are... [26:39] It's like extremely dispersed. [26:40] You cannot find like a clearly defined demographic who they are. [26:45] And I think that the thing where we got to now, and that took a very long time [26:49] and you had to incentivize, is we have the community, [26:51] a strong community of superforecasters that are on Kalshi that can help price [26:55] these things extremely effectively and fast. [26:58] Where you don't have... [26:59] But it took a while to kind of get them incentivized and come in and spend [27:03] the time and resources to take it from a hobby, which is—it was [27:06] a hobby to for a part-time job. [27:07] Now it's a full-time job because the pie is so big. [27:10] And a metric that we can share is actually the... [27:12] When you think about traditional market makers, the biggest percentage [27:15] on a platform of a traditional market maker is less than 5% of the maker [27:20] orders in that market, of the liquidity. [27:23] Sorry, say that stat again? [27:24] Yeah, so less than 5% of the order—people come and make orders. [27:29] Less than 5% of the ones that match actually come from the big institutional [27:32] market makers you'd think about. I see. [27:34] Over 95% are just like—Peer-to-peer. [27:36] Peer-to-peer or funds that have like two people that just got set up. [27:40] Which is unusual for— [27:41] How many of those small full-time little shops are there? [27:45] There's over 2,000 people that market-making. [27:48] People/small shops. [27:49] Yeah, on like a specific— [27:50] I think what Matt's getting at is like, who is a market maker on Kalshi? [27:53] There's all these Jane Street conspiracy theory memes. [27:58] Is it like—That's my Twitter feed. [27:59] Or is it like some guy in his garage, you know, drinking Red Bull at 3 AM, [28:04] market-making by himself? [28:05] The guys in the garage are the most crucial. [28:06] And you're saying those are 95% of the flow? [28:10] They're extremely crucial to the ecosystem because they price fast. [28:13] They're monitoring the situation all the time. [28:17] They're the original situation monitors. [28:20] Yeah, I see. [28:20] Kalshi is built on people who are monitoring the situation. [28:24] And so one example I'll give, and I've given this in the past. [28:30] The best inflation forecaster on Kalshi over the last few years is not— none [28:34] of the institutions or the big-name hedge funds. [28:38] It's this guy who lives in Kansas, never traded financial markets before, [28:42] just likes to read the news and just knows how to predict inflation. [28:47] He can feel it. [28:49] And you have so many of these people. [28:51] Now you have a few thousand that are formally committed. [28:55] But there's tens of thousands of these people that know a bunch about [29:02] a bunch of different topics. [29:03] And they're sort of actively pricing these things. [29:05] And they do it as a full-time job. [29:07] And they get rewarded for that. [29:08] You need to talk about my favorite user. Oh, yeah. [29:12] Or— [29:12] Well, I have a new favorite user, by the way. [29:14] Ok, each of you can tell us your favorite user. [29:16] I was thinking about it this morning. What's your new favorite user? [29:18] The Washington Journal article tax guy. [29:20] Oh, yeah, that's true. [29:22] He's a good candidate. [29:23] But no, my favorite user is this Ariana Grande superfan. [29:26] And he found Kalshi during the election season, and he's like, [29:29] "I don't like the elections, whatever." Then he found our Billboard [29:32] ranking markets of like charts. [29:34] He's going to work on important markets. Important markets. [29:37] To me, very important. And he's made over $150,000. [29:40] He's getting every single thing. [29:42] He paid back student loans. [29:44] He put himself through a master's degree, bought a car. [29:46] And all those things. And he just like loves these markets. [29:49] And he's never really traded, never done anything like that before. [29:52] But it's the first time that he actually has a way to monetize this very compulsive [29:57] hobby that he had on music charts. [29:59] And he's able to do it. [30:00] And he's also very, very nice to us on Twitter. [30:02] So I like him. [30:04] So I had many over the years, but maybe I shift a lot. [30:09] But like the—He's not loyal. [30:10] See, I'm loyal to my guy. [30:12] Well, you know, I love all of our users, But [30:16] there was an article last week in the Washington Journal about [30:21] a tax accountant who was very active on Kalshi, Alan. [30:25] And when DOGE came around and like there was [30:30] a lot of talk about how much they could cut, [30:34] he actually read a bunch of tax codes and a bunch of [30:41] statutes, just dug extremely deep, and then realized there is no [30:46] way they could hit the targets. [30:48] And even if... [30:49] He really deterministically realized. [30:51] And then he basically talked to his wife and he's like, "I have extremely [30:56] high conviction in this trade. [30:59] I know I..." It's a bit like Michael Burry with The Big Short. [31:04] So this guy put a big short but on DOGE this time. [31:08] And it was big, he really went all in. [31:11] And he won. [31:13] And it's just like one of those amazing sort of showcases of what this can do. [31:17] Now you have a market that if you have that sort of knowledge, [31:20] which maybe oftentimes is esoteric, like I'm assuming none of us have read all [31:23] these tax codes, you can actually go out in the world, [31:27] do research, get smarter about the world, and then get rewarded for that. [31:31] And that's awesome. [31:32] You know, an early field of AI was poker bots. [31:35] Are you seeing any good AI market makers? [31:38] When you say no one's read all these tax codes, I mean, no one except Claude. [31:45] That's fair. We should ask. [31:46] We are seeing more, increasingly more people using agents to trade. [31:49] So that's definitely— [31:50] Especially on the API side. [31:51] On the API side, it's very big. And, you know, like— [31:54] But do you have users who are successfully running market-making businesses [31:57] that are mostly agentic? [31:58] Users don't exactly tell us their strategy. [32:01] But you talk to them, you know, you just— Generally, yes. [32:04] But the way I think about it is [32:10] like, do we think RenTech back [32:11] in the days was using agentic models? [32:14] I'm talking about Renaissance Technologies to trade. [32:16] Like, yeah, the early versions of them. [32:18] And so I think they're just evolving and they're getting better. [32:21] And like most of our traders in their stack have some sort of summary [32:26] and synthesis module that's AI-driven. [32:30] I guess what I'm curious about is, fully autonomous, no human in the loop, [32:35] consuming information and providing a market based on that? [32:38] That feels like it's coming quite soon, if it's not like your Claw making a market. [32:43] Yeah, I don't know if there's a full... [32:45] I know that, for example, there's a lot for international elections, [32:48] just on translating all documents posted in English to do all of that. [32:53] But I don't know if it's all fully— We're doing... [32:56] So we don't know if the models are there yet. [32:59] So we launched Kalshi Research recently. [33:03] And one of the threads that we want to work on is we're [33:07] talking to some of the research labs to create a new benchmark [33:10] around which models actually predict the future better, [33:13] which could be a unique benchmark around are these models [33:17] developing some understanding of the world that goes beyond memorizing old patterns? [33:23] And I'm honestly excited to see how it goes. [33:25] And what's the eval for that? [33:27] We don't know yet, but I think you could roughly let the models run for, [33:32] make predictions on same set of markets for a month or two and see which ones [33:36] perform that percentage of predictions that were correct, P&L over time, etc. [33:40] Right. [33:42] Ok. Another market-making question. [33:46] So sports bookies have this need to crack down on what [33:52] in their industry is called sharps, you know, people who are too good. [33:56] Where, I think people don't think so much about [33:59] this dynamic, but for a sports bookie, the best possible punter is someone who is [34:05] kind of unsophisticated, bets on, like, their home soccer team's game. [34:10] Irrespective of the odds. [34:12] Exactly, and just wants the home team to win or whatever. [34:15] And the worst kind is someone who's super sophisticated, [34:20] finding the narrow markets, because for a bookie, maybe they're [34:23] making odds on 10,000 different markets. [34:26] They only need to be wrong once or twice for people get to choose [34:30] which they play on. [34:31] And so they can't be right on all the odds that they're offering. [34:34] And these sophisticated people go find those. [34:38] And so what happens is they basically use behavioral signals [34:42] to identify if you are just signed up and you're betting on your home [34:46] sports team's game, that's good. [34:48] And if you appear to be really sophisticated with all the signals [34:52] that they would use, they shut them down. But it's interesting, right? [34:55] You think, I'm just booking on the bets that, on the odds that you're offering. [35:00] But if you're too good, they'll shut you down. [35:02] It's maybe like card counting in Vegas. [35:04] Do you have this dynamic with sharps? [35:05] I would have thought no, that you just are fine with it. [35:08] But do the market makers worry about too sophisticated counter-parties [35:13] on the other side? The sharps are the market. [35:15] I mean, to be clear, we don't limit any winners. [35:18] We don't have any of the... [35:19] We want all the winners. [35:23] We need the sharps because how do you get market accuracy without the sharps? [35:26] This is the difference of the— [35:29] Well, yes and no, right? [35:30] Because what you want is different because the sharps can snipe. [35:35] They can just turn up once when the odds are wrong, grab a big win, [35:40] and then disappear. [35:41] Whereas what you're describing is you want during the game or during the election, [35:45] You want narrow spreads all the time. [35:47] And so I feel like providing good market-making is different [35:50] than being right. [35:51] But a lot of the sharps can actually do better if they provide market making and [35:55] become part of the liquidity. [35:56] So this is the main difference, which is very important, the [36:02] maybe disclaimer—I don't gamble, I trade, which I've always found the difference. [36:07] And I think gambling is this idea where the business model is [36:11] you are the house and your revenue is your customer's losses. [36:15] So a lot of dynamic that you describe has to be true because your incentive is like, [36:19] well, if somebody's making money, I got to stop them because [36:22] they're making me lose. [36:23] That's just going straight from my bottom line. [36:25] And the opposite is true. [36:26] If somebody is losing money, I got to figure out how to bring them back. [36:29] That's a very different model from traditional financial markets where [36:32] the structure is you have to incentivize fairness and transparency. [36:37] That's the structure. [36:38] You want to create fair rules of the game for people to participate. [36:41] Maybe Matt is better than Luana, and maybe Luana is better than Matt, [36:44] and they can battle it out. They can figure it out. [36:46] But you think the incentive system is very different, where you do not, [36:49] like a casino or something, you do not monetize on some [36:52] zero-sum other person losing. [36:54] You monetize just on transaction fees. [36:56] The best outcome for us is that people are like, "This is fair. [36:58] They have good prices. They have stable liquidity. [37:00] I'm going to go there." But of course, for us to get there, we also need to [37:02] incentivize different players differently. [37:04] So that's why, for example, a lot of the liquidity programs come into effect. [37:07] They're like, for providing liquidity, you're taking a lot more risks because [37:10] you're putting yourself out to be sniped. [37:12] Then we're going to lower your fees. [37:13] But if you're taking and you're going to snipe, you're going to have higher [37:16] fees so you can pay for that activity. [37:18] So in a lot of ways, you use fees to incentivize pro-social behavior. [37:21] Exactly. [37:22] And I think that that's actually how we see a lot of how financial [37:24] markets actually do the same. Financial markets are the same. [37:26] The same thing. [37:27] But it's more balancing out the marketplace so that people are [37:29] providing value to the marketplace. [37:31] Have a little bit more tilt. [37:33] And then people that are taking away value have a little bit less. [37:36] What behavior is pro-social and what behavior is antisocial? [37:39] Well, insider trading is antisocial. Right. [37:42] That's a big one. Yeah. [37:43] And illegal. [37:46] But it's... [37:49] And look, sniping is part of it, right? [37:52] You need people that all of a sudden have gained some information edge and they do [37:56] it in traditional financial markets all the time. [37:58] But to have liquidity and make sure that people are there and they're [38:02] investing the resources, they have to get, as you said, some incentives. [38:06] But the interesting point, and I think this is part of why prediction [38:11] markets are being adopted so much, is people like this idea that if [38:17] your edge is proportional to your research, how informed you are, how [38:21] much time and energy you put into this. [38:24] And I think that only exists in prediction markets or traditional financial markets, [38:28] except that for a lot of people, traditional financial markets, [38:30] they're just less interesting. [38:32] Like in here, you're researching about DOGE and what's going to happen or what [38:35] the election and how people think about elections and how they vote. [38:39] That, at least to me, feels a little bit more interesting than, [38:42] let me think about IBM's quarterly earnings every quarter. [38:48] Kalshi has built a new kind of marketplace where real-world outcomes are traded, [38:52] like whether the US will confirm whether aliens exist before 2027. [38:56] You have thousands of participants opening, transferring, settling [38:59] their positions all in real time. [39:01] And underneath it, as you can imagine, there's a complex [39:03] multi-party flow of funds. [39:05] That choreography on Kalshi is powered by Stripe Connect—onboarding participants, [39:09] processing payments, routing funds, managing payouts. [39:12] When money movement becomes programmable, new products or even new market [39:16] structures become possible. [39:18] So, if you're building something new with complex money flows, [39:21] Stripe Connect was built for you. [39:26] Sorry, guys. No, you're good. [39:33] Let's talk different market verticals. [39:36] And I think today everyone gets elections, they get sports, [39:40] they get economic indicators. [39:42] But I think you can look at prediction markets as this kind of search function [39:45] across the set of interesting markets humanity wants to trade. [39:50] And it's kind of a weird artifact that like the CME used to greenlight [39:54] like wheat and oil and corn. [39:58] But now you get to greenlight 1,000 markets a day. [40:02] So what do you think we're going to find as we do that? [40:05] One thing that we're very excited for, we're actually starting to go [40:08] in the direction of, for example, things like [40:11] watches and bags and all of those things are more going to the collectible side. [40:15] They're actually able to do derivatives on those things. [40:17] One thing that you should talk about is the— [40:19] Yeah, I feel like compute could be a huge one. [40:22] And I think the compute that... [40:24] What we're thinking a lot about is that there's a lot of these types [40:27] of things that they function better as a more traditional future. [40:30] So things that don't have a binary, "Will it be at this price? [40:34] Yes/no." But it's more like an actual future. [40:36] You can have margin, you can have more like institutional [40:39] grade liquidity and all of that. [40:41] And I think that that's a great example of like when we start going more [40:44] in outside of binary markets and more into the traditional ones, [40:49] then what we're doing is expanding kind of that from grain to compute. [40:53] Because it strikes me that obviously the futures markets that have worked best [40:58] are these large commodity categories. [41:00] But we're sort of in an era where humanity is spending more money than it's ever [41:05] spent before on a specific new kind of commodity. [41:10] And so, and the other traditional markets don't seem to be attacking compute. [41:15] So the way that we think about—we want to be the biggest derivatives [41:18] exchange in the world. [41:18] And for that, when we think about product roadmap, there's four things that matter. [41:22] The first one is breadth of topics of markets. [41:24] So we think about compute, we think about sports, [41:26] think about elections, think about securities, we think about all of that. [41:29] The second bucket is really market structure. [41:31] So right now we only have the binary yes/no. [41:34] We want to have things like futures, like swaps, options, all of that. [41:37] The third one is really margining systems. [41:39] Right now it's very bad. [41:41] You have to put all the money up front— You have to tie up all the capital. [41:44] Which makes a lot of, for example, will a hurricane happen this year, very, [41:48] very bad for you to be actually like market making or selling those contracts. [41:50] Doesn't make almost any sense, like capital-wise. [41:53] And then the last one is liquidity. [41:54] And when we think about it, it's like if we win these, [41:57] we have the broadest set of markets, we have the broadest set of market [42:00] structures, we have great and very... [42:04] I don't want to say cheap margin, but in a way around that and then good [42:07] liquidity, we're going to win on everything that we do. [42:09] So everything that we do in the company is like, it needs to be in one [42:12] of these four buckets. [42:13] And I think a lot of the topic side is like, how do we actually match the right [42:17] topic with the right market structure, the right margining, and how do we make [42:20] sure that it's all coming together? [42:22] But you're completely right. [42:23] I think that being able to build all the margining systems, [42:27] all those things from scratch, we're going to be able to do margin models [42:31] a lot faster and list a lot of these kind of new markets a lot faster. [42:36] Because of your direct mobile app interface and the fact that you target [42:41] a lot of retail, do you worry that sort of the markets [42:46] you're going to gravitate towards are the ones that are most [42:49] interesting to just retail? [42:50] And how much do you think about— As opposed to like the pro markets? [42:53] Yes, the kind of institutional markets. [42:54] Like I think of compute as much more of an institution-to-institution market. [42:58] How do you think about building liquidity and interest upmarket? [43:01] Yeah, we almost divide the company again, in a way. [43:06] We divide the markets and like sports, crypto and everything else. [43:08] But in how do we make what we have great but very new things? [43:12] Because I think what got Kalshi here was not the regulatory side, was not... [43:17] It was really that we're just really pushing what is the next thing. [43:20] It was elections and then after election, sports. [43:22] And for us it's like, we need to be pushing what the next [43:25] thing is and doing that very well. [43:26] And I think that if we stop doing that, we're not going to win. [43:29] The company is kind of structured that we [43:30] have the market operations, [43:32] we have the engineering side, all of that that's set up for maintenance [43:35] and improvement of what we have. [43:36] And then the new teams like institutional, the margin team, international [43:40] that are kind of pushing forward. [43:42] And we just try to kind of find a balance on those teams and then a platform layer [43:47] that is like the core exchange and compliance and all of that. [43:50] But it is tricky because we're still like 120 people to do it. [43:55] Are you seeing pull already on the institutional side and certain topics? [43:59] Yeah, for sure. [44:01] And I think that we are... [44:03] We actually just launched a week ago this thing called block trades. [44:06] I don't know if you know, it's a very institutional way to do it [44:09] that I can call you and negotiate a trade. [44:11] And then we go and put it on the exchange versus trying to do everything that way. [44:14] So we're trying to build a lot of features to start getting more institutional side. [44:18] Are they trading the same things that are on the Kalshi retail or are you [44:21] offering new types of products? Yes and no. [44:24] So whenever they're interested in something like the... [44:26] There was a lot of interest on the tariff situation. [44:29] Is there going to be a tariff or not? [44:31] A lot now with the petroleum, like the reserve and kind [44:34] of how that's going to go. [44:36] So whenever we hear, "We want to trade this market." We just [44:39] list it directly and then it's accessible to everyone. [44:41] But I do think there's going to be a very big gap on what the institutions are [44:45] going to end up trading versus not. But we just list it to everyone. [44:47] It's very cheap for us at this point to list new markets. [44:50] In the early days of Uber, it wasn't bad for the taxi business [44:54] because it was just excess capacity and, you know, it was serving unmet needs. [44:58] But then after a while, it was bad for the taxi business. [45:02] Are there existing businesses that will feel the effects of Kalshi and other [45:07] prediction markets because it's a bigger market, there's more liquidity. [45:11] Like, I can think about the just existing futures exchanges. [45:14] Maybe Kalshi is a better place to hedge your soybean prices or what have you. [45:20] There's sports bookies, obviously. [45:22] There's political polling firms where maybe you can get kind of the same [45:26] information way cheaper. [45:28] So who do you think will start feeling the effects of prediction markets? [45:32] Because they have been in some way superseded. [45:34] Yeah, there's that funny meme of like the guy knocking on the door [45:37] and it's like, "Who's the next one?" The grim reaper. [45:39] So you don't want to do that. [45:40] But I think that a lot of what you mentioned, that just traditional [45:46] betting that we talked about, all the issues that industry has [45:49] that we're very different from. [45:51] There is a traditional futures, now we're going way more into their space. [45:54] So I think there's going to be a difference there. [45:57] There is the political polling that I think since the last election, [45:59] there's just a lot of campaigns are using our data and all of that. [46:02] There's parametric insurance. [46:03] Once we have margin, we can start going to more hurricane, [46:06] natural disaster insurance, all of that side. [46:09] Is there a tragedy of the commons with the polling? [46:11] Like, part of the reason the prediction markets [46:14] are accurate is because they interpret the polls, right? [46:17] So like, if people stop using polls, in some sense, polls are the sensor [46:22] that you get of what people's opinions are, and then the prediction markets are [46:28] like the mathematical interpretation of the polls? [46:31] Yeah, my take is that polls are just going to get a lot better because what people [46:35] are going to be is like, "Ok, I can make money if my polls are right, [46:38] so I'm just going to commission this poll and I'm going to do this." And now you can [46:41] actually compete a lot of polling models into one market. [46:44] It's like FiveThirtyEight did, kind of a meta-poll interpretation. [46:47] Exactly. [46:47] And you can have one number that's aggregating all of that. [46:50] And even in the last election, there was someone that actually did this. [46:54] They commission a specific poll to do nearest neighbors type of thing. [46:57] It's like it was... [46:59] I don't know how it was, but a different type of poll. [47:01] And then they were able to make a lot more money in the markets. [47:03] And that's the whole point of like having money and skin in the game [47:07] aligns the incentives with truth. [47:09] And then the polls are not just paid for tell me what I want [47:12] to hear but the real numbers. [47:14] So I think it's complementary. Same with the news. [47:16] A lot of people are like prediction markets will destroy the news. [47:18] I think it's way more complementary. [47:20] It's like when you're talking about an election, [47:22] you're going to give your opinion. [47:23] The market is not going to give you an opinion. [47:25] You still need the commentators, but they're going to be able to show [47:28] a number and be like, this is what the forecast is and this is my opinion on it. [47:32] I don't think the opinions are going to disappear, but— [47:35] You guys referenced insider trading earlier, [47:38] and there's just the policy question as to what the right policy should be around [47:44] insider trading when it comes to prediction markets. [47:47] I think it's pretty nuanced, like it's nuanced in the stocks case, right? [47:49] Where famously there's lots of, you see SEC enforcement actions all [47:55] the time against the things that aren't allowed. [47:57] But there are cases like a hedge fund can have proprietary satellite data [48:02] of the Walmart parking lot and use that to trade earnings. [48:06] And that is information that only that hedge fund has. [48:09] But that is permissible. [48:11] And so similarly, I think there's a complex set [48:13] of line-drawing exercises here, where presumably we don't think [48:18] government officials should be trading in advance of military actions. [48:22] What about leading up to the Super Bowl, you know, predicting the Bad Bunny [48:26] halftime show length? [48:27] I mean, you know, people have that information, right? [48:29] And so where do you think the lines should get drawn on insider trading? [48:33] Yeah. [48:34] And as you said it perfectly, it's a very complicated question, and it's [48:38] a complicated question for stocks. [48:41] Way more into a bigger scale than it is in prediction markets. [48:43] The line that we take now is that we follow what the federal law is. [48:47] So it's basically if you have— [48:49] And these are CFTC rules. [48:50] Well, CFTC and SEC, they both have it. [48:52] So basically, if you have signed an agreement that says [48:55] you cannot share some part of data. [48:57] So if I work at the Bureau of Labor Statistics and I have [49:00] in my confidentiality that I'm not able to say what the inflation number is [49:03] before, then you cannot be sharing that data. [49:07] But if you know that they're going to be rehearsing [49:09] the Thursday before the Super Bowl and you're outside and you're like, [49:12] "I hear Lady Gaga singing," that's fine. [49:15] And that's the same thing that a lot of hedge funds do with Starbucks [49:19] and people noting, ok, there's more people, fewer people in the store. [49:23] And that's the point. [49:24] Markets are very good at incentivizing information. [49:26] We want information to come to the markets, but we [49:28] don't want it to be unfair. [49:29] And if you have access to it in an unfair way, you should not be trading on it. [49:33] Ok, so you cannot trade on information where you have some duty to hold [49:37] that information confidential. [49:38] We actually take it even a step further. [49:40] For example, if you are a government official, [49:43] like if you're in Congress, you cannot trade on bills passing, [49:46] even though I don't know if they have an agreement. [49:48] Famously, congresspeople can trade on stocks. [49:50] Right, right. [49:50] So we're actually like taking a step further there. [49:53] And we're working a lot with the regulators because obviously it's [49:56] a very new problem for them and for us. [49:58] But we have an entire... [50:00] Part of being regulated, we have an entire surveillance division [50:03] that is looking at every single flag. [50:05] They pretty much don't sleep and they just try to figure out everything. [50:08] And we put out two cases two weeks ago of two insiders that then... [50:13] Because we're also regulated, we're able to charge them a lot of fines. [50:16] So we charge them over five times what they made and all those things. [50:19] They're banned and all that. [50:20] What's very interesting to me about these stories is that you guys were doing [50:24] that where with the public equities markets, [50:29] the SEC is extremely enthusiastic about enforcing their insider trading doctrine. [50:34] Just what has the CFTC been like on this topic? [50:37] It's a great question. [50:38] So obviously the CFTC, you can think about it at three steps, right? [50:42] The first step is our own surveillance and enforcement. [50:45] Then the next step is it goes to the CFTC and their own surveillance [50:48] and enforcement. [50:48] The last step, if you go to the Department of Justice. [50:52] And I think the biggest difference is when people look at the SEC cases, [50:57] most of the time they take a long time because the exchanges did their [51:01] research and their investigation and they put some fine, they block someone, [51:04] and it kind of goes through the process. [51:06] So it's still like that. [51:07] Every single trade on Kalshi goes to the CFTC. [51:10] They have every single thing, every single case goes to the CFTC for them to review. [51:14] So they might take action. We don't know. [51:16] But now the— Ok, so you refer cases to them? [51:18] We refer cases to them. [51:20] But we kind of do our first step of our first level of protection there. [51:25] I'm curious, there are clearly markets where nobody knows the answer yet [51:29] of some event in the future, so like it's sort of impossible to insider trade. [51:33] And then there are markets where a single person can change the outcome, [51:38] like the mention markets in a speech or maybe a sports player doing [51:42] a specific number of shots. [51:45] So how do you think about that spectrum? [51:48] Like, are mention markets a bad idea because they're just [51:51] so inherently gameable? [51:51] Yeah, or are they like limited in scale fundamentally because— [51:56] Mention markets like the Brian Armstrong Coinbase earnings thing. [52:01] Yes, I think that, well, inherently I think major markets are [52:05] actually great if you think about the Fed. [52:07] The amount of hedge funds that are just sitting down being like, "Is he going [52:10] to tilt his head this way or this way? [52:11] And if he does that, it means he's not very sure." [52:13] Fed meeting minutes are the original mention markets. [52:15] Exactly. [52:16] And it was because we just know that specific words being [52:21] used mean very specific things, and you can move the market so much. [52:25] The same thing with Trump. [52:26] If Trump says we're going to war, that's going to move the markets a lot. [52:30] Or if he says a lot of things like tariff, everyone knows moves the market a lot. [52:36] Even in... [52:37] Yeah, just a lot of everywhere, things that people say move markets [52:41] and move a lot of different things. [52:43] So I think the mainstream market is very important. [52:45] Obviously, the person that is working on the speech or [52:49] that is saying the speech cannot trade. [52:51] And that's kind of how we enforce it. [52:52] If you are Gavin Newsom, there's a market on what you're going [52:56] to say, you cannot trade it, and your staff cannot trade it. [52:58] That's part of the political kind of cuts that we do there that they cannot trade. [53:03] But I think the point is like, if there is a way [53:07] to restrict some players in the market so that the market's fair [53:10] and the market's positive and there's an economic utility for it, [53:13] the market should exist. [53:13] We shouldn't say "Ok, there are five people that could [53:16] manipulate the market and the market shouldn't exist." Then you say [53:19] the stock market shouldn't exist, right? [53:20] So I think it's more about how do we build a system that is strong and resilient [53:24] enough and with the right prohibitions that you can have the market. [53:28] The other big debate you guys are in the middle of is just that about [53:33] sports contracts generally. [53:34] And I was trying to reason about my own thoughts here on this debate, where [53:42] on the one hand, the criticism is that with more [53:48] sports gambling comes proven bad effects. [53:52] You can measure some of the bad effects that it has on people. [53:57] And, you know, we have this especially in the US, where there was a lot [54:00] of legalization of sports betting over the past 10 years, [54:02] and there's some data on that. [54:04] On the other hand, I have no real issue with alcohol, [54:08] despite the fact that it has kind of a similar distribution where many [54:10] people enjoy it and then there's a very bad set of outcomes for a small [54:14] fraction of the population. [54:15] And so I feel like the societal discussion of, you know, the morals of alcohol [54:19] and the morals of betting are different, despite the fact that, again, [54:24] it's a similar shape to the distribution. [54:26] And, you know, also just thinking from my experience, [54:30] online sports betting has been legal in—well, legal is a complex term—has [54:37] been available in Europe for a very long time, basically [54:41] since the start of the internet. [54:42] And there was all these cross-border hacks in Malta. [54:44] And now it's a bit more regularized. [54:46] But it's been available to people who want it for a long time. [54:48] And life goes on. [54:50] And it hasn't led to any kind of major societal collapse over there. [54:55] But clearly, this is one of the big debates that raged around Kalshi. [54:59] And so I'm curious how you guys think about increasing access to sports [55:03] contracts and the effects there. [55:06] Well, there's a lot of parts to what you're saying. [55:09] I think that the way that we think about sports, how we decided to first list [55:14] sports, is obviously something that a lot of people are interested in. [55:16] That's unquestionable. [55:18] But also there's something that a lot of people do a lot of research [55:20] and know a lot about. [55:21] And there aren't traditional ways for you to make money on that. [55:24] That are actually good, or we talk about the winners, [55:27] they get cut and all those things, and it doesn't really work. [55:30] And regardless of whether people like that some people bet or dislike [55:34] that some people bet, people bet. [55:36] And it's just about what is the best way for them to have access to something [55:39] that they can get exposure to sports. [55:41] And I think that the whole point of markets versus a bookie is [55:44] that markets are just objectively better. [55:47] I think that it's almost... [55:49] I'd never heard someone make a case that a state-by-state regulated [55:52] casino is actually a good thing. [55:55] I'm actually hearing nowadays that a lot of the paid propaganda by the gambling [55:59] guys trying to say that. [56:03] And just to put numbers on that, the order of magnitude rake for sports [56:06] betting companies is around 10% and the order of magnitude for prediction [56:10] markets is, you know, 1% or a few points. Right. [56:13] But the predatory part doesn't even come from that. [56:15] Like for sports betting, if you start losing—because they want the losers, [56:19] they don't want the winners—if you start losing, the first thing that they're [56:22] going to do is give you a bonus. [56:23] They're going to be like, "Oh, here it is." Oh, look at that. [56:26] Welcome back. [56:28] We were talking about sports, and what they do is you start losing [56:33] and then they're going to give you $1,000 for you to come back or a deposit [56:36] boost and all those things. [56:37] So that they can hook you to keep you coming back because they want [56:40] to incentivize the losers. We don't do that. [56:42] The people losing the most money are the most profitable for sports bookies, [56:48] which creates a bad incentive. [56:51] Yeah, and we don't have that at all. [56:53] And I think that the whole point's like, there's a moral question. [56:55] Like, some people are going to go into Robinhood or Coinbase or whatever [56:59] and speculate on stocks and speculate in crypto and whatever they want to do. [57:03] And some people want to speculate on sports, and they should have the best, [57:07] the access to the best possible thing for that. [57:08] And right now it's just the sportsbooks are just not it. [57:12] And we really firmly believe that what we do in our markets are significantly [57:16] safer for all of that. [57:17] And if you just take a stance of prohibiting, it's like same [57:20] what you said with alcohol. It didn't change. [57:22] People just went to a speakeasy and drank. [57:25] And people are just going to go offshore when there's way less protections. [57:28] There's none of the self-exclusion, deposit limits, [57:30] all those things that we do. [57:32] Don't have any information about them. [57:33] And they're going to actually... [57:34] It's actually very bad. Bad for them. [57:36] So I think it's just this prohibition concept just never really works. [57:40] It's also the whole policy discussion around this stuff is also very interesting [57:45] when it kind of reminds me of, in Canada all the liquor stores are run [57:49] by the government, or at least in British Columbia. [57:53] And you have the government saying this must be very carefully controlled, [57:56] but also we will sell it to you and have the revenue source. [57:58] And obviously that's much more of a factor in lotteries and things like that. [58:02] It's all about money at the end of the day. [58:03] The states want their money, the casinos want their money. [58:06] It's just, yeah, it is what it is. [58:08] Speaking of sports, one thing we were talking about while you [58:10] were gone was just what interesting new verticals are there going to be? [58:14] And so I'm just curious, which ones are you most excited about? [58:18] Well, I think that anything around dissecting like a stock [58:25] into its more atomic components. [58:29] So this would be like betting directly on Nvidia GPU shipments— [58:34] Versus Tesla deliveries or whatever. [58:36] Their earnings... [58:37] Because then you can expand that to things like [58:42] dissecting the macro economy and what are the main sort of like factors are [58:47] influencing the economy broadly, like AI. [58:49] And I have a series of questions to price what's going on with AI. [58:53] Things like health scares like COVID and so on. [59:00] But the sort of where things get really interesting is this idea where... [59:03] So there was a paper written by Kevin Hassett around this idea that like as [59:08] society gets increasingly more complex, our asset prices, [59:15] our understanding of asset prices will naturally decay. [59:19] Entropy will go up because the things that influence or the vector [59:26] that, the number of factors— [59:28] Much higher dimensional vector. [59:29] Becomes very high dimensional. [59:31] And if those dimensions you don't have a good understanding of x1 to [59:36] xN, you cannot get a good estimate of Y. [59:41] And so the paper basically says you need infinite markets. [59:44] And prediction markets started this notion of infinite markets, [59:47] which is like you have to have a market for each one of these Xs so that you can [59:51] then take that back into pricing traditional asset, getting [59:56] good traditional asset prices. [59:57] And an example of that this last week, so you know that there was [60:02] the Citrini put out a research report. [60:05] And it got a surprising amount of— [60:07] People got obsessed with it. [60:08] Yeah, I would say it got a surprising amount of love and interest. [60:11] This is the AI 2028 we're all doing. [60:13] Yeah, 38% on a bull run. [60:16] I think there's a little bit of like our society wants to believe [60:18] that AI is going to end us all. [60:20] I think right now there's a bit of that. [60:22] But this is where... [60:23] And that impacted markets. [60:25] Like the stocks got, there was a sell-off. [60:28] And so we launched a prediction market on that. [60:30] And well, before that, Citadel came out with a rebuttal and we [60:33] launched a prediction market on that. And, you know, the odds are 10%, right? [60:37] Of the economic scenario that they predicted being true as of 2028. [60:41] It's three out of five things. [60:42] So they have like five conditions. If three hit, we— [60:44] Yeah, if three out of these five conditions hit, you could reasonably say [60:47] that, ok, this outcome has somewhat materialized. [60:50] And it's just 10%. [60:51] Five out of five is much lower. [60:54] So that is important. [60:55] If you can put that back into pricing models, maybe the markets wouldn't have [60:58] reacted, maybe people wouldn't have sold off DoorDash. [61:03] Because at least I believe, but you don't have to trust me, [61:05] maybe you should trust markets, that this sort of analysis around [61:08] DoorDash was actually quite poor. [61:09] So one thing you're sort of envisioning is this world where everything [61:13] has a price all the time. [61:15] And I'm curious, is that a good world to live in? [61:19] And I would note that Stripe, I think, benefits from being private and not having [61:23] the real-time price and smoothness for employees and comp and all that. [61:29] I think there's some noise in the sub-second pricing. [61:30] Right. [61:31] Because clearly sentiments swings publicly. [61:33] Like there's on average in the long run, it's a truth-telling machine, [61:37] but in the short run can be a panic. [61:40] So just curious for you guys to think about, yeah, is that the [61:43] world we want to live in? [61:45] I mean, we are obviously biased because we love markets. [61:48] We think markets are good, so we're definitely biased here. [61:52] But I think our view on this is that it's always better to have [61:55] more data than less data. [61:57] If you don't think the data is good, if you don't think [62:00] the second-by-second stock price is good, you can just choose to ignore it. [62:04] It might... [62:05] The world might not just ignore it, but you could use it. [62:07] I think CEOs of public companies would say it is not always possible [62:09] to choose to ignore it. [62:11] That's fair. [62:11] But in a way, it's like it's better to have the data and then use it [62:16] as an input to something than not. [62:18] But when we say we want to have prices on a lot of things, [62:22] it doesn't mean everything. [62:22] There are a lot of things that we wouldn't do, like wildfires we don't do. [62:26] War, terrorism, assassination. Those things are bad. [62:28] There's a moral side of these markets and we're not going to ever go there. [62:32] But in general, it's in a world of social media is like, you don't [62:36] know what's true anymore. My feed is like, is it real? [62:38] Is it not real? Did this happen? [62:40] It's just better to have a source, an unbiased source of information that you [62:43] can kind of use it for other things. [62:45] And I think that that value is there. [62:49] I think that maybe the simple frame for this is you [62:58] are increasing market efficiency for all these questions. [63:03] That's what's happening. [63:05] It's including potentially some events or things that relate [63:09] to maybe private companies. [63:10] And I was just thinking about the question, it's an interesting [63:12] question, why do companies go public? [63:18] Why is it important to get a real-time market price? [63:22] Because there are downsides. [63:24] Sometimes markets are erratic. [63:25] Sometimes they overshoot in either direction. [63:29] But the market on the long enough time horizon is a good sort of allocator. [63:33] It's a good weighing mechanism. [63:34] It's a good allocator of capital. [63:36] And I don't really see that... [63:38] I just think that pricing a lot of these questions will just increase efficiency. [63:44] Make our function, our allocating function better over time. [63:47] And there will be some net losers. [63:49] Like some people that maybe capital shouldn't be allocated to. [63:53] It's also a good feedback loop. [63:55] If you're a CEO of a public company and you announce something and it just [63:58] keeps going down, you're like, "Maybe I'm wrong." And I think the same thing you see [64:02] with politicians where you can see in the live debate, [64:04] if they say some answers and they see their prices going lower, they're like, [64:08] "Well, maybe the answers aren't great." And I think a lot of these things, [64:12] when we see even the, for example, the use case of prediction markets [64:14] in government, a lot of it is conditional markets. [64:16] They can say, if we pass this bill, will unemployment go up or down? [64:19] And you can price these things for better decision-making and just [64:23] like a tighter feedback loop tied with good incentives. [64:27] Do you guys feel like we have started to see... [64:29] Like, clearly we have seen the effects of social [64:34] media on politics, where politics is a different game now. [64:37] And different politicians are popular and the political discourse is changed [64:42] by the existence of first Twitter and now to some extent short-form video. [64:47] Do you guys feel like we have seen the effects on politics [64:52] of prediction markets yet? Definitely. [64:54] I mean, the—What are they? [64:56] Well, the candidates are using the prediction market prices. [65:00] Sure, but that could just be like a handy thing, whereas again, [65:01] I think with social media, the candidates are different, the debates are different. [65:05] Reflexive. [65:07] I do think that what the markets are good at is that they are more [65:12] unbiased by party dynamics. [65:14] So if there is an underdog that the public really likes, there's a lot of... [65:19] Maybe there's like a party that's like, "We don't like this guy, [65:21] we like this guy from the establishment." But the markets are very good at actually [65:25] showing the real odds for that person. [65:26] Ok, so you think the party machines have lost a little bit of power? [65:29] I think you can shine more light into what people really want, [65:33] which might not be necessarily what the parties want to put forward. [65:37] I don't think we've seen that necessarily yet, but I feel like if I were to say, [65:40] there was a, for example, the Texas primary, [65:43] and I think that the polls were saying one person was really going to win, [65:46] another person that was way higher in prediction markets won. [65:49] I think a lot of it was more of like, they give a more, [65:53] a fairer view of the state of the race than a lot of the party polls. [65:58] I think that we do see this a lot with also... [66:01] There's sort of the piece where people use it and that's sort of reacting [66:04] in real time to certain things. [66:05] But I think there's some degree of depolarization, and that's [66:08] what Luana's alluding to. [66:10] And in some ways, it's an antidote to social media. [66:12] Social media has really polarized. [66:15] When you have two candidates running for a Senate race, we're sort of set. [66:22] Your feed is set. [66:22] Either your feed is saying the Republican candidate is awesome, or the feed is [66:26] saying the Democrat candidate is awesome. [66:29] Prediction markets don't really have that. [66:31] Because the people that are engaging in this are not in the sort of like [66:36] who's great and who's, you know— [66:38] I think what you're saying is social media feeds ultimately try to resolve upfront. [66:43] They're like, "I need to figure you out. Are you a Democrat? [66:45] Are you a Republican? [66:46] Like what post should I show to you?" And pretty quickly, and you know, [66:49] people have complained about this, of this phenomenon where you end up down [66:52] a particular rabbit hole because they have pigeonholed you as kind of this type. [66:56] And you're saying just prediction markets do not have that phenomenon. [66:58] There's a little bit of a reverse phenomenon. [67:00] It's like, are we feeling too certain about this person? [67:02] And I think that depolarizes things because the dimension is [67:06] not, we're not one-dimensional anymore, which is like Republican, Democrat. [67:09] And that's what you're seeing and what you're hearing about. [67:11] Now it's like, "Well, this guy's kind of cool." And that's Talarico, [67:16] and maybe he might do good in Texas even though he's a Democrat. [67:20] The same thing happened with the New York mayor situation. [67:23] Everyone was like Cuomo was going to win 100%, 100% Cuomo was going to win, [67:26] there's not even a chance. [67:27] And we were just seeing Mamdani's odds going up. [67:29] And I think it's just the progressive message was really working [67:32] with New Yorkers and the markets were really seeing that uptick. [67:35] And that decrease in polarization really comes from people taking a step back [67:39] and being like, "What do I actually think is going to happen?" [67:43] So there's a bit of like an Iowa-New Hampshire Iowa-New Hampshire [67:45] effect here, where in presidential elections in the US, there's like some [67:49] big name leading into the election. [67:51] You know, maybe it was Hillary Clinton in '08. [67:53] And then Iowa and New Hampshire are measurements of the sentiment of [68:00] those two states, but they also create narrative. [68:03] And I think what you guys are saying is prediction markets create this [68:06] Iowa-New Hampshire effect, where they can create narrative in a way [68:10] that changes the ultimate outcome. [68:11] I don't know if it changes, though. [68:13] I wouldn't say it changes the ultimate outcome. [68:14] I think it sheds light into what the ultimate outcome is going to be. [68:18] But it potentially changes it. [68:20] There clearly is some reflexivity, right? [68:22] I mean, there's always, but it's like with the polls too. [68:24] Like, yes, I mean, there's polls— [68:26] Aren't you guys hiding your lamp under a bushel here? [68:28] You're like, "Well, we're not changing anything here." [68:31] I just think that the one thing I would say, the reason why we're like— [68:35] Prediction markets are a big deal. [68:37] It's ok to say they'll change things a little bit. [68:38] High odds don't always correlate to like a good outcome. [68:43] So you saw Mamdani when his odds were 94% on Kalshi, the thing that he was [68:47] mentioning pretty consistently, and I think there was a little bit [68:49] of worry there, it's like you've got to show up. [68:52] Because if you're very high odds, that could also lead people to be like, [68:55] "Ok, this isn't it." So it's not as clear. [68:58] And I don't think this changes things more than polls changes. [69:01] Does that make sense? So the response is more like in a vacuum. [69:04] If you had nothing else If you didn't have social media, if you didn't have news, [69:07] if you didn't have any of that, yes. [69:09] But because we have all these other things and you add prediction market to it, [69:12] like the impact model... [69:13] But I want to make one point that I think is actually very interesting, [69:16] and we're seeing this often. We'll be the judge of that. [69:18] Ok, you can judge that. [69:19] Let me know if it's interesting. [69:21] But a lot of times when you see people start participating in prediction markets, [69:26] they get more engaged in the underlying. [69:29] They legitimately just get more informed. [69:31] It pulls people in. [69:32] It pulls people in, but to do research. Now you have... [69:34] Because you have some skin in the game, or you're about to put some skin [69:36] in the game, you read, everything changes. [69:39] You're not just like saying something crazy on Twitter anymore. [69:42] You're putting money. [69:43] And now it's like, "Let me read, let me actually figure out, oh, [69:45] who's this person, what's happening?" [69:46] It's like, are they pro this, are they against this, [69:48] what's their view on everything? And you go... [69:49] It's amazing because this happened a bit in the New York race. [69:53] Or it happened a bit with Brexit. [69:56] People voted for Brexit. [69:59] And after they voted for it, they're like, "Oh God, wait, I don't think [70:01] we wanted this. [70:02] Wait, we didn't even understand what we were voting for." And I think this [70:06] heightened engagement, it engages people further [70:10] to learn and understand. [70:11] You know, in sports, basically, if you ask a lot of the leagues, [70:14] they would tell you the same thing, but people got more engaged [70:16] with the statistic, which player is good, what's happening. [70:18] And I think that will happen, is already happening, it's sentiment [70:21] in politics, which is a good thing. [70:22] I think that the I'm going to say something and then [70:26] I'm going to qualify what I'm saying. [70:27] I think that politics are going to get better because you're going to have a way [70:32] faster feedback loop on the messaging and the policies. [70:35] Right now, when a candidate says 10 things and they win or they lose, [70:39] you're trying to make one assessment of so many things that the candidate did. [70:43] And did that go well or not? And which point was it? [70:46] And even if you do a poll, it's like always delayed. [70:48] It's a very specific example. [70:50] But now you can have real-time of they said this, what was the response? [70:54] And you can get that and that faster feedback loop, which I [70:57] think makes startups great. [70:58] You're able to iterate very fast. [71:00] And I think if candidates are able—everyone wants to win at the end [71:02] of the day, and if they're able to optimize their [71:04] message to what really people want and what policies people want, [71:07] I think they're going to end up being better because they're just [71:09] going to know what people want better. [71:11] It's like you get a score on all of them, all the different things you've done, [71:14] not one score that encompasses the good and bad. [71:16] Which is basically like when you ship a new feature, you're able [71:19] to have like 10 metrics. [71:19] And you're like, "This went up, this went down." And you're able to, [71:23] markets can kind of contribute to that. [71:25] But also I think to a lot of other things as well. [71:29] Music charts. [71:30] When someone listens to the song and they're like, "Definitely not going [71:32] to hit number 1," you're like, "Ok, that was that song. [71:34] So we should do something else." [71:36] Do you guys try to use prediction markets in any way internally to make decisions? [71:41] Every single decision we make is always probability. [71:44] Even the election lawsuit, right, when we were doing it, it's like— [71:46] But that's you guys evaluating the probability. [71:48] Do you ever think about creating markets for your employees to participate in? [71:52] So we have one nit. [71:55] Which is, as a regulated exchange, we can't trade. [71:59] But we do it like internal markets. [72:01] There's like a separation of church and state thing. [72:03] Exactly, exactly. [72:04] And so we've been asking, we've been working with regulators, [72:07] could we do something small? [72:08] We do small dollars where, so that, you know, because obviously that's [72:13] one that we really want to do. [72:14] And you can't even dogfood the product, I guess. [72:15] And then dogfooding the product. Right. [72:17] Which has been hard. [72:18] It's hard. [72:20] Sorry, employees cannot trade in their personal capacity? [72:23] They can't. Not at all. [72:24] Oh, interesting. Yeah. [72:25] But yeah, that makes it very hard when you, as you say, you just can't dogfood [72:27] your—People at Facebook use Facebook and that's how you make the product good. [72:32] So that's why it's so important for us to you should just be asking [72:35] the users all the time. Yeah. [72:37] Oh, that's so interesting. Yeah. [72:38] It sucks. [72:39] But the power users, the superforecasters, are a lot of what influences sort of where [72:43] it goes because they're very engaged. [72:44] You guys must presumably spend a lot of time with those power user, [72:48] superforecaster types. Yeah. [72:49] Just have them on speed dial. Yeah. [72:51] They were there day one. [72:52] So, we want to make sure they're happy. [72:55] Last question. [72:56] Where do you guys want to see prediction markets policy go? [73:01] Like when you're talking to someone in government or if you had a magic wand, [73:04] what are you arguing for? [73:07] So our stance as a company, and I think this may differ a little bit [73:10] from I would say like your average tech [73:14] company or big tech company. [73:18] So we are pro-innovation and innovation needs to happen in America. [73:22] You know, we have to lead and we have to do it right, and we have to win. [73:26] We have to be, you know, all the things that Americans want to do [73:29] or we need to win as a country, we should have here. [73:32] But we're also pro-regulation. [73:34] And so at a high-level principle, there's usually this tension that [73:39] generally it's like the policymakers want to regulate— [73:42] And I think you're maybe more like a traditional financial [73:44] firm in that way, right? [73:45] Where maybe Silicon Valley, a lot of firms grew up in an unregulated [73:49] way, but financial firms have always had a regulator. [73:52] And that's just a fact of life. [73:55] Yes. So it's part of the culture. [73:56] And I mean, again, we spent four years getting regulated up front. [73:59] So, so it's part of... [74:00] But we believe in regulation. [74:01] I think it's important because, regulation is a bit like insurance. [74:05] It's like it's protecting you from things going wrong at bad times. [74:09] And so when I think about where this lands, I think anything that is oriented [74:14] around preserving these in America and making sure we win, [74:17] but then elevating the fairness and transparency of the market. [74:21] Anything that's oriented like how do we make it more fair? [74:23] Ban insider trading, add more restrictions on, for example, government officials, [74:28] members of Congress trading on like, you know, information they shouldn't trade. [74:31] You know, I'm obviously a big fan of like banning insider trading [74:33] for members of Congress. That's what we talked about. [74:35] Yeah. [74:36] But presumably you mean just banning trading period for members? [74:39] I think it's not a bad idea. [74:42] That's how we kind of— [74:43] But I think... [74:44] And then things around like creating also social fairness and [74:51] transparency because of all the questions that we asked, if people are basically [74:53] trading on politics, let's have all the trade data be as public [74:56] as possible so anyone can audit it, anyone can see it, which is a good thing. [74:59] You don't, now you don't know, imagine a poll where you can check every [75:04] single person that basically got, the general public can check who was [75:07] polled and what the sample was like. [75:09] And then anything around customer protection. [75:12] And because that is important long term in the sense that [75:16] when you build a consumer product and it goes mainstream, there is a massive [75:21] burden on the other companies to educate. [75:24] And you've seen it over and over. [75:28] And in our case, you want to make sure that people know what [75:31] they're getting into. [75:32] They're not overextending themselves in terms of how much they're sort of trading. [75:38] They're not getting into an area of discomfort. [75:40] And how do you, we can do as much as we can do [75:43] on the marketing and on the product side, but like we need policymakers [75:46] and regulators' help to basically make it an industry standard, [75:49] but also help us elevate ours. [75:50] And by the way, we're pro that. [75:51] I think that like even the classic retail brokerages should also be adopting a lot [75:55] of these customer protections that we're talking about that they don't. [75:58] I think that every retail trading platform should be taking a lot of these steps. [76:04] Yeah, I mean, that's our general view, and we hope that this is sort [76:07] of the direction that things take. [76:09] Because there's kind of... [76:10] You can have a variety of views. [76:13] And some people believe that any type of speculation should be banned, [76:17] whether it's in the stock market or crypto or prediction market. [76:20] We don't believe that. [76:21] We think that would be a bad outcome for all the reasons, because there's a lot [76:24] of upside to having liquid markets and a variety of different things. [76:28] But also because if you ban it, you're actually [76:30] heightening the risks that you're trying to prevent because now that activity is [76:34] going offshore where you cannot monitor it or police it or do anything to protect it. [76:40] Awesome. Cool. [76:41] Tarek, Luana, thank you guys. Thank you.