The Supercycle

Flip Pidot, Eli Goldfine

The future of prediction market media. supercycle.blog

  1. 2d ago

    Can AI agents replace human forecasters? Debate with Jan Czarnocki

    Featuring Jan Czarnocki. I didn’t think this would end up being a debate, but I think it was quite interesting and worth a listen! My talk at Jan’s event in Switzerland: Transcript: [00:00:10–00:00:34] All right. Welcome back to The Supercycle. Today’s guest is Jan from Elastics AI, which is a startup working on AI agent tooling for prediction market trading. [00:00:34–00:00:48] And I know Jan because his company had an event near Zurich in May, and they flew me out there. We stayed at a hotel in Zurich. [00:00:48–00:00:58] And I gave a little presentation at the event about insider trading in prediction markets, which you can find on our site, supercycle.blog. [00:00:58–00:01:04] And that was a great event. So hopefully we’ll be able to do that again sometime. [00:01:04–00:01:13] Hello, Eli. Pleasure to meet you. Yeah, it was fun. It was fun in Switzerland. We got great photos and your presentation was great. [00:01:13–00:01:18] I’m happy to see you here and hear more about your progress. Where are you? Where are your thoughts? [00:01:18–00:01:29] And I’ll be also happy to tell a bit more about Elastics, where we are, what’s our approach to AI agents, and where we see everything going from here now. [00:01:29–00:01:39] Yeah, great. Yeah, I have some questions about that. I think should be pretty interesting for our listeners here. So if I can start off, that works for you. [00:01:40–00:01:42] Sure. Just shoot at me. [00:01:42–00:01:47] All right. So you trade geopolitical markets, right? [00:01:49–00:01:50] Well... [00:01:50–00:01:52] Oh, yeah. About trading. [00:01:53–00:01:57] I’m doing my best trading, let’s say. But like, sorry, I interrupted you. [00:01:57–00:01:59] All right. Well, yeah. Okay. [00:01:59–00:02:10] What’s an example as a trader of a trade where you might trust your own judgment over your agent’s judgment, and maybe a trade where you just trust the agent over yourself? [00:02:11–00:02:18] Well, what’s kind of the line there between what the agent is good at and what still needs a human in the loop kind of managing it? [00:02:18–00:02:31] Yeah. So just for the record, I’m an aspiring geopolitical trader. I’m doing my best to learn. It’s very, very hard, actually. And it’s very, very hard to reconcile full-time startup job plus serious trading. [00:02:31–00:02:44] But it helps to trade even a bit to empathize with the problems that traders are having, basically. And there’s still a lot of issues and things to be solved on prediction markets and also from the tooling perspective. [00:02:45–00:02:54] And in general, as you hinted already upon, our core thesis is that AI agents are amazing. AI is amazing. [00:02:54–00:03:16] But we really believe that AI is as smart as you are, not more, no less. So the usefulness of AI is the property of the skill, IQ, wisdom, knowledge, everything of people who are using AI, basically. [00:03:16–00:03:45] And my idea is that judgment. Judgment when you’re trading, when you’re not a quantitative trader. So basically, if you’re not relying on models and fast trading and, let’s say, formalizable, let’s call it like this, rules of, okay, I buy this and just operate on very fast intervals in time. [00:03:45–00:03:46] And space. [00:03:46–00:03:57] And space. Then, unless you’re a quant trader, basically. It’s not yet automatable, basically. [00:03:58–00:04:09] But, so, if you’re a quant trader, AI is here and now very helpful because it can analyze data faster and it can, it makes stuff faster, basically. [00:04:09–00:04:21] But there’s a whole world of macro traders and discretionary traders who are like, okay, I think inflation will be like this. Okay, I think there won’t be a peace deal with Iran. [00:04:21–00:04:29] Because they read some article, they read a lot of books, that’s what they feel and that’s where their edge is. [00:04:29–00:04:36] And they, let’s say, time horizon is broader than next few minutes or next few seconds. [00:04:36–00:04:39] And they’re just not operating on these repetitive models. [00:04:39–00:04:45] And here the question is, where AI can be useful for this kind of people? [00:04:45–00:04:51] And our answer is basically that AI can extend your cognition. [00:04:52–00:04:53] It can help you with your judgment. [00:04:53–00:04:55] It can pile up all the data. [00:04:55–00:04:56] It can pile up all the news. [00:04:56–00:04:58] It can help you to browse stuff. [00:04:59–00:05:08] It can help you with simple workflows with, let’s say, defensive positions or defensive quitting from contracts. [00:05:08–00:05:09] It can alert you. [00:05:09–00:05:12] It can be an extension of you. [00:05:12–00:05:16] So we are building a tool in which you land. [00:05:16–00:05:18] You don’t need to move anywhere else. [00:05:18–00:05:25] And you have everything you need as a macro trader, discretionary trader, geopolitical events trader. [00:05:25–00:05:27] You have everything you need to trade. [00:05:27–00:05:29] You have news. [00:05:29–00:05:31] You have AI with the best models. [00:05:31–00:05:34] Let’s say we have plugged in CloudFable. [00:05:34–00:05:38] Right now it’s quite expensive, but that’s what we are plugging in. [00:05:38–00:05:40] We are using Cloud, but you’ll be able to switch models. [00:05:41–00:05:43] Of course, it will be consuming a bit more tokens. [00:05:44–00:05:49] But still, we are giving you a specialized tool for trading. [00:05:49–00:05:52] So imagine Cloud, but for prediction markets. [00:05:52–00:05:59] And you may ask, okay, I might as well just connect some MCP servers or whatever new sources to Cloud, [00:05:59–00:06:00] and I’ll be the same. [00:06:00–00:06:05] No, because you need stuff that is, let’s say, on-chain, aware of what’s happening on-chain. [00:06:05–00:06:13] It needs to be secure in a particular way, and it needs to have tools very specifically made for trading. [00:06:13–00:06:18] And we believe the first step in finance is helping you with the judgment, [00:06:18–00:06:25] and then you can build agentic workflows on top of it when you build enough trust and enough context. [00:06:25–00:06:29] And also a person who’s wielding this AI is now what it’s doing. [00:06:29–00:06:37] So we are very much not as this crazy AI bots on Twitter claiming that, oh, we made so much money just on arbitrage. [00:06:37–00:06:44] I mean, it’s tempting to go there, but we think that lifecycle of these tools is quite short, basically, [00:06:44–00:06:52] because whatever quant strategy you come up with, you will exhaust this alpha structure later, of course. [00:06:52–00:07:04] I think part of what Elastics has been framing their product as part of marketing and stuff is that every prediction market trader [00:07:04–00:07:12] who’s not automating their workflows and using AI for data collection and to kind of automate their process, [00:07:13–00:07:19] all of those traders are at disadvantages because they’re wasting their time on stuff that could easily be automated. [00:07:19–00:07:28] So let’s say Elastics was wildly successful and every prediction market trader was using it. [00:07:28–00:07:31] Doesn’t that disadvantage kind of neutralize? [00:07:31–00:07:37] And then if everyone has these institutional grade AIs that can do all of this fantastic research, [00:07:38–00:07:40] where does alpha come from after that? [00:07:41–00:07:42] From your brains. [00:07:43–00:07:44] Literally. [00:07:44–00:07:47] Well, where are you ingesting the data from? [00:07:47–00:07:58] I mean, the thing is about this event on prediction markets is that the reason why quant models are so good at predicting, [00:07:59–00:08:07] let’s say, stock prices is that stock prices, commodities and everything on financial markets is fairly formalizable [00:08:07–00:08:12] because these are, let’s say, discrete entities with certain mathematical properties. [00:08:12–00:08:14] So let’s say price, yes? [00:08:14–00:08:23] And you can fairly directly take historical data and can learn something about this price given this or that event. [00:08:23–00:08:30] But then in a bigger scheme of things, you also have this whole financial global order, [00:08:30–00:08:36] which assumes that you have respect for contracts, you have an open trade system, and there’s no wild stuff. [00:08:36–00:08:39] So with every quant model, you have so many assumptions. [00:08:40–00:08:44] But to have this quant model, you have to have this formalizable assumptions. [00:08:45–00:08:50] And the thing with prediction markets is that with events, historical data are close. [00:08:51–00:08:56] They’re not, let’s say, patterns formalizable into quantitative model. [00:08:56–00:09:07] So the thing is that it’s about sharpening your judgment about what will happen. [00:09:07–00:09:10] Because with events, it’s about human psychology and stuff. [00:09:11–00:09:17] And with a lot of events on prediction markets, it’s a judgment of one guy, let’s say Donald Trump, [00:09:17–00:09:21] under certain systematic, historical, however you call it, forces. [00:09:21–00:09:25] So there’s less math in this, basically. [00:09:26–00:09:30] So that’s why AI, and AI is running on maths. [00:09:30–00:09:35] It’s probabilistic engine, or however you will call it. [00:09:36–00:09:40] And we humans, we are biological entities, so we feel realities. [00:09:41–00:09:46] And maths and AI, it filters a lot of qualitative stuff out of reality. [00:09:46–00:09:54] So my point, basically, is that, however, in the current AI paradigm, when you have large language models, [00:09:55–00:10:00] there is a limit to which they can be good at forecasting events. [00:10:00–00:10:07] I think they can just, you know, may

    Can AI agents replace human forecasters? Debate with Jan Czarnocki
  2. Aug 8

    Bentham's Bulldog on Libertarianism, Anarcho-Capitalism, and Futarchy

    Featuring Bentham’s Bulldog (a.k.a. Matthew Adelstein). While mostly not about prediction markets, I still think this was a super interesting discussion that many Supercycle readers will enjoy. A special focus on alternative forms of governance. Transcript: [00:00:10] All right, there you go. So, welcome to the podcast. We have Bentham’s Bulldog today, who’s a very prolific writer in the effective altruism and rationalism circles, who’s been [00:00:24] writing for a long time about a wide variety of different topics. I recently read your post on some fun facts about you, and I saw your writing from when you were around my age, [00:00:40] 13 and 14, about libertarianism. Yeah. Which is pretty interesting. So, the first thing I wanted to ask you is, when you were about my age, what was your argument, what was your [00:00:56] general worldview regarding libertarianism, and why has that changed? As you’ve gotten older. So, when I was your age, not a sentence I say very often, or sentence fragment, I [00:01:09] guess. So, yeah, I was quite politically libertarian. I think my views ended up oscillating somewhere between the view that basically the government should only provide [00:01:21] for police, military, and courts, and a small number of other functions, and the view that they should do that, and then also provide a relatively minimal welfare system. [00:01:33] So, now, the things that convinced me of that, I mean, I guess it was a few things. One of them was, I do think I pretty early on got a sense of the amazing efficiency of the free [00:01:44] market, where it turns out that, you know, having a free market system is a very efficient way of allocating goods and services. And so, and then you look at many government [00:01:53] policies that are imposed, and they each have their own distortive effects on the free market. So, you know, the minimum wage was one thing that I wrote about, where, like, it [00:02:03] just, if you impose a minimum wage of $15 an hour, then insofar as people’s labor is less valuable to their employer than $15 an hour, then they simply won’t be hired. And so, then [00:02:14] it seems like you have all these policies that have fairly severe distortive effects. Okay, so that was kind of one class of considerations that I found moving. Another class [00:02:23] of considerations that moved me a lot was just appreciating how the kind of magic of economic growth, where, I mean, if you have 3% economic growth, the GDP will double in [00:02:34] about 23 years. If you have 2% economic growth, it’ll take like 35 years to double. And if you have 1% economic growth, it’ll take about 70 years to double. So, with respect to GDP [00:02:45] growth, even efforts that had fairly small impacts on the GDP really, really compound over time and lead to just massively different, massive differences in quality of life. So, [00:03:04] yeah, I guess in terms of what changed my worldview on this topic, it’s so, I mean, one thing was, there were some of my arguments that I came to appreciate. So, I mean, one [00:03:17] thing is, in lots of cases, my worldview hasn’t changed that much. I’m still more libertarian than the average member of the general public. I still, like, there are lots [00:03:27] of particular solutions that libertarians are fond of to problems that I generally support as well. So, for example, I’m a lot less supportive of regulation on pollution and a lot [00:03:37] more supportive on Pigouvian taxation, where you impose taxes on negative externalities. [00:03:44] So, I don’t know that I’ve changed, I mean, well, I’ve changed a reasonable amount, but I haven’t changed a ton. I guess in some cases, I came to appreciate that my arguments were [00:03:54] bad. So, I think, for example, I think I misunderstood the effects of the minimum wage, where the minimum wage, it’s true that it produces distortive effects, but it also [00:04:03] produces wage increases. And then it’s just an empirical question, which one is more significant? And it’s not obvious to me, I mean, I think the optimal policy would not [00:04:12] include a minimum wage because of the distortive effects, and there are ways of achieving the same ends without the distortive effects. But it’s not obvious whether imposing a [00:04:22] minimum wage at the margins is good or bad, given that it has both distributive effects, which are desirable, and has distortive effects, which are undesirable. So, I realized I [00:04:31] was wrong about some particular things. There were some cases where, you know, you sort of, you try to estimate how much, how much distortion there would be from modest tax [00:04:44] increases. And it seems like, well, it will have some effect on dampening wages. It won’t be enormous. And so then, if you get sort of a lot of distribution without that much [00:04:52] distortion, then it kind of begins to look like redistributing taxes is a fairly good bet. There were some other things where, for example, with respect to health care, I mean, I [00:05:07] think I’m broadly in favor of something Obamacare-ish. Because, I mean, in a civil society, we basically can’t, we don’t want to have it be the case that people are, like, [00:05:17] going into the hospital, and then they’re just not being treated, you know, for a bullet wound, even if they don’t have insurance. And so then, the system that you incentivize if [00:05:25] you don’t have people buy-in, is you incentivize people to, like, not get health insurance, and then they get treated for free if they get really sick, and then they have [00:05:35] an incentive not to get preventative care. And it’s like, this doesn’t seem like a great system. So I became more sympathetic to something Obamacare-ish. [00:05:47] And then there was one other, I guess there was some kind of major respect in which my political sensibilities changed, which didn’t necessarily change my view on any particular [00:05:58] issue, but it changed my view on, like, kind of my ideology a bit more broadly, where, when I was a libertarian, I kind of thought of the world, like, like, I thought of the [00:06:11] consequential errors that are made as much more being about, sort of, mistakes that lead to inefficiency, rather than, kind of, serious moral errors. So, you know, I would think [00:06:24] of, like, you know, the really severe cases of errors that we’re making are, like, we have, you know, burdensome land regulation that makes it hard to build houses, and then [00:06:34] this drives up the price of housing. I mean, I still believe that. I think that’s really bad policy. You know, I’m a big yinni. But it ended up, over time, you know, reading about [00:06:46] factory farming and existential threats and so on, seeming like many of the most serious problems, errors that we make as a society, are distinctly moral and distinct, and come [00:06:55] from neglecting the interests of large classes of entities. And so then, at the, so then if you’re deciding between political parties, then if you think the Democratic Party is [00:07:07] sort of more supportive of these entities, and the libertarians, they might be better economically, but then they’re worse on these most important issues. So, for example, they [00:07:17] would probably, on average, be less supportive of animal welfare laws. They’re, in general, less supportive of foreign aid, which I think is a really, really good thing. [00:07:24] They’re less supportive of cases where U.S. intervention can do a lot of good. Then it began to seem like my worldview was increasingly unward from libertarianism. So, sorry, [00:07:34] that was a kind of long answer, but... No, no, no, don’t worry about it. What do you think about, specifically, like, Brian Kaplan’s views on anarcho-capitalism, and kind of that we [00:07:45] should be basically just privatizing all public government services, just because, as you were saying, that the free market is a lot more efficient in that regard? [00:08:02] But I guess, I mean, so I’m not an anarcho-capitalist for kind of a number of reasons. So one of them is just, I don’t think that the optimal level... So I think anarcho-capitalism [00:08:10] starts to look a lot more plausible as being the kind of government or absence of government that we want if you think that the optimal size of government is extremely [00:08:20] small. So if you’re, like, a minarchist, and then you realize that we can shift, that we can accomplish most of the things that the government does, without needing to... [00:08:37] That we can sort of... That sort of most of the things that the government does, you might be able to do without a government through private security firms, this... It starts to [00:08:50] seem more plausible that it could do that. But insofar as you think the optimal size of the government is actually pretty large. Like, we want a government with an expansive [00:08:59] welfare state. We want a government with... That, you know, ensures that when people are sick and when people are old, they’re adequately taken care of. Then, certainly, you won’t [00:09:11] get that. So I don’t think I’m sort of the target audience for anarcho-capitalism. Then, with respect to the claim that basically... So, you know, the... So I haven’t read as much [00:09:24] of what Brian Kaplan said on the matter. I’ve read David Friedman’s book, The Machinery of Freedom. It’s been a bunch of years. I think I was around your age, in fact, when I read [00:09:33] it. So, what David Friedman says is basically that what you’d get is you’d get people being incentivized to purchase private security firms. And then they would... And then you [00:09:50] wouldn’t get the private security firms fighting because violence is costly. So they would have an incentive to work out optimal deals. And they’d have an incentive. And then you’d [00:10:02] have th

    Bentham's Bulldog on Libertarianism, Anarcho-Capitalism, and Futarchy

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The future of prediction market media. supercycle.blog

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