Cheeky Pint / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
25 published records
01 / belief
“Sure, but that could just be like a handy thing, whereas again, I think with social media, the candidates are different, the debates are different.”
02 / belief
“I think that the way that we think about sports, how we decided to first list sports, is obviously something that a lot of people are interested in.”
03 / belief
“If you're a CEO of a public company and you announce something and it just keeps going down, you're like, "Maybe I'm wrong." And I think the same thing you see with politicians where you can see in the live debate, if they say some answers and they see their prices going lower, they're like, "Well, maybe the answers aren't great.”
04 / belief
“I think that like even the classic retail brokerages should also be adopting a lot of these customer protections that we're talking about that they don't.”
05 / belief
“I think CEOs of public companies would say it is not always possible to choose to ignore it.”
06 / uncertainty
“For example, if you are a government official, like if you're in Congress, you cannot trade on bills passing, even though I don't know if they have an agreement.”
07 / belief
“I don't think we've seen that necessarily yet, but I feel like if I were to say, there was a, for example, the Texas primary, and I think that the polls were saying one person was really going to win, another person that was way higher in prediction markets won.”
08 / belief
“You still need the commentators, but they're going to be able to show a number and be like, this is what the forecast is and this is my opinion on it.”
09 / belief
“You can have margin, you can have more like institutional grade liquidity and all of that. And I think that that's a great example of like when we start going more in outside of binary markets and more into the traditional ones, then what we're doing is expanding kind of that from grain to compute.”
10 / belief
“I think that the existence of that made the CFTC also be like, "We need a legal regulated alternative to this.”
11 / belief
“The last step, if you go to the Department of Justice. And I think the biggest difference is when people look at the SEC cases, most of the time they take a long time because the exchanges did their research and their investigation and they put some fine, they block someone, and it kind of goes through the process.”
12 / belief
“Where, I think people don't think so much about this dynamic, but for a sports bookie, the best possible punter is someone who is kind of unsophisticated, bets on, like, their home soccer team's game.”
13 / belief
“I think what Matt's getting at is like, who is a market maker on Kalshi? There's all these Jane Street conspiracy theory memes.”
14 / belief
“If the elections have economic impact, they need to be allowed to trade on a futures exchange or derivatives exchange. And it was basically, I think what the lawsuit did is it told the CFTC that they couldn't just do whatever they wanted.”
15 / uncertainty
“Yeah, I don't know if there's a full... I know that, for example, there's a lot for international elections, just on translating all documents posted in English to do all of that.”
16 / evaluation
“There is also, when you're an exchange like this, you have to spin up market making. And in the end, the New York Stock Exchange doesn't have to think too much about market making because just the economic incentive is there.”
17 / evaluation
“We think that would be a bad outcome for all the reasons, because there's a lot of upside to having liquid markets and a variety of different things.”
18 / recommendation
“We don't know yet, but I think you could roughly let the models run for, make predictions on same set of markets for a month or two and see which ones perform that percentage of predictions that were correct, P&L over time, etc.”
19 / evaluation
“You cannot find like a clearly defined demographic who they are. And I think that the thing where we got to now, and that took a very long time and you had to incentivize, is we have the community, a strong community of superforecasters that are on Kalshi that can help price these things extremely effectively and fast.”
20 / evaluation
“High odds don't always correlate to like a good outcome. So you saw Mamdani when his odds were 94% on Kalshi, the thing that he was mentioning pretty consistently, and I think there was a little bit of worry there, it's like you've got to show up.”
21 / evaluation
“I think that maybe the simple frame for this is you are increasing market efficiency for all these questions.”
22 / evaluation
“It's like, are we feeling too certain about this person? And I think that depolarizes things because the dimension is not, we're not one-dimensional anymore, which is like Republican, Democrat.”
23 / preference
“I think people, when they think about, have a difference of opinion on something, it's sort of becoming synonymous to like, "Oh, let me pull up Kalshi and see the odds," or "Let me sort of place a position on Kalshi.”
24 / prediction
“Like the incentive structure for most things that we read these days is clickbait, whether it's a lot of traditional news or social media or other. So there was more of a pressing problem, I think, that helped create the wave and this sort of adoption that you're seeing in prediction markets that I don't think happened or would have happened 15 years ago.”
25 / evaluation
“Because at least I believe, but you don't have to trust me, maybe you should trust markets, that this sort of analysis around DoorDash was actually quite poor.”