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, maybe run you, propose you alternative scenarios as to what may happen under,

[00:10:07–00:10:10] and let’s say some assumed probabilities.

[00:10:10–00:10:15] But then, at the end of the day, you need to make, you need to make, you need to judge.

[00:10:15–00:10:20] And they are only as good as the data based on which they are trained.

[00:10:20–00:10:23] And it’s also a matter of judgment, what data do you choose?

[00:10:24–00:10:27] What kind of, let’s say, international relations theory do you follow?

[00:10:27–00:10:31] Or what kind of mix of sources or commentators do you follow?

[00:10:31–00:10:38] So there’s a lot of fine-grained ideas which sets prediction markets apart from financial markets,

[00:10:38–00:10:43] which make them less quantifiable and less quant-like.

[00:10:43–00:10:49] Unless you’re doing short-term contracts on Bitcoin, short-term on prediction markets equities.

[00:10:50–00:10:51] I’m talking about events right now.

[00:10:51–00:10:58] And I think I’m focusing on events because I truly believe that the biggest social and economic value of prediction markets

[00:10:58–00:11:00] is in events forecasting.

[00:11:01–00:11:06] Yeah, I’d actually like to counter that a little bit.

[00:11:07–00:11:12] Because I think that we’re already seeing, especially with the new Frontier models coming out,

[00:11:13–00:11:17] we know Fable came out, was taken away, now it’s back.

[00:11:17–00:11:21] And GPT 5.6 Soul is coming out tomorrow.

[00:11:21–00:11:30] And if we look at here, I can pull up the latest Metaculous Cup.

[00:11:31–00:11:40] And what’s been happening is that with very little human input, just the forecasting models that are ingesting data

[00:11:40–00:11:46] and they’re being trained by whatever company is making them,

[00:11:46–00:11:51] they’re actually becoming more accurate than a lot of the humans.

[00:11:52–00:11:55] Okay, so I can see.

[00:11:55–00:12:00] I mean, they are better at what is measurable, at what you can measure.

[00:12:01–00:12:04] But at what you can’t measure, they are not.

[00:12:04–00:12:05] And you don’t even know.

[00:12:05–00:12:09] So it’s a matter of what you measure.

[00:12:10–00:12:11] And a matter of what kind of question you ask.

[00:12:11–00:12:13] You can measure the calibration.

[00:12:15–00:12:16] Against what?

[00:12:16–00:12:16] Against what?

[00:12:17–00:12:18] Against real world events?

[00:12:19–00:12:20] Yeah.

[00:12:20–00:12:26] My point is that whatever is quantifiable, these models might be good at.

[00:12:26–00:12:26] That’s fine.

[00:12:27–00:12:28] I’m fine with this.

[00:12:28–00:12:30] Because these things might be quantifiable.

[00:12:31–00:12:35] But for the things which are not easily quantifiable, which are qualitative,

[00:12:35–00:12:36] they’re not.

[00:12:37–00:12:37] And they won’t be.

[00:12:37–00:12:41] And I think that events, a lot of events happening in the world,

[00:12:42–00:12:45] they’re just not that much quantifiable, basically.

[00:12:45–00:12:51] You know, it’s like this with Laplace’s demon and Goodall’s theorem.

[00:12:52–00:12:56] Just some things, you just cannot, you just, it’s because the assumption would be

[00:12:56–00:12:59] that you can just pile up the whole universe into the model,

[00:13:00–00:13:02] which is self-contradictory, just impossible.

[00:13:02–00:13:05] Okay, well, I was just pointing out here.

[00:13:05–00:13:08] So if we see forecast by a forecasting research institute here,

[00:13:09–00:13:15] the median forecast of the super forecasters, they ranked the accuracy.

[00:13:16–00:13:21] So this score is the Breyer score index across all of the questions,

[00:13:21–00:13:25] where 100 is perfect, 50 is predicting 50% of the time,

[00:13:25–00:13:28] and zero is maximally wrong.

[00:13:28–00:13:29] So a higher score is better.

[00:13:29–00:13:36] You see, super forecasters got an average score of 69.2 with the best forecasting model,

[00:13:36–00:13:39] only 0.1 point behind that.

[00:13:39–00:13:44] Wouldn’t you expect with the advancement of AI progress that they’ll get better scores

[00:13:44–00:13:47] and begin to beat human forecasters with this?

[00:13:47–00:13:49] They’re basically there already.

[00:13:49–00:13:55] I think that they’ll be good at enhancing your judgment and sharpening your judgment, basically.

[00:13:56–00:14:01] And that’s pretty much about this, basically.

[00:14:01–00:14:03] Because they’re operating on historical data,

[00:14:04–00:14:08] and they just are not aware of stuff that they haven’t saw.

[00:14:09–00:14:12] Well, they can just ingest as much as they want.

[00:14:12–00:14:12] Of what?

[00:14:12–00:14:13] They kind of have free range.

[00:14:14–00:14:16] They can ingest as much data as they choose to.

[00:14:16–00:14:17] But they don’t have eyes.

[00:14:17–00:14:18] They don’t have eyes.

[00:14:18–00:14:19] They don’t have ears.

[00:14:19–00:14:20] They don’t have skin.

[00:14:20–00:14:21] They don’t feel the reality.

[00:14:21–00:14:22] They do have internet.

[00:14:23–00:14:25] They have just digits.

[00:14:26–00:14:27] Whenever there’s a geopolitical event,

[00:14:28–00:14:30] you’re not flying to the Strait of Hormuz.

[00:14:30–00:14:31] You’re reading the news about it.

[00:14:31–00:14:34] You’re reading the same stuff that the AI agent is also reading.

[00:14:35–00:14:37] Yeah, but I can talk with people.

[00:14:37–00:14:39] I can hear you feel the mood.

[00:14:39–00:14:40] Yeah, the people are also reading the same news.

[00:14:40–00:14:43] Why is the agent not getting it?

[00:14:43–00:14:45] But he doesn’t understand the news.

[00:14:45–00:14:46] They don’t understand the news.

[00:14:46–00:14:47] Why not?

[00:14:48–00:14:51] They’re calculating the probability of the good answer.

[00:14:52–00:14:53] It’s something different than understanding.

[00:14:54–00:14:57] They’re not understanding the practical impact

[00:14:57–00:14:59] of whatever they’re doing on the real world.

[00:14:59–00:15:01] They’re just computing probability.

[00:15:01–00:15:05] And we are not computing probability when we think.

[00:15:06–00:15:07] Yeah, okay.

[00:15:07–00:15:11] But they’re also trained on basically all human writing of all time.

[00:15:11–00:15:12] And so they don’t.

[00:15:12–00:15:15] Yeah, so if you stop human judgment, they will be useless.

[00:15:17–00:15:20] Because you won’t have any more qualitative data.

[00:15:20–00:15:22] They will just hallucinate themselves to death.

[00:15:23–00:15:24] Okay, well, that’s true.

[00:15:24–00:15:26] But we’re not at the end of data collection.

[00:15:27–00:15:29] But it will be false data.

[00:15:30–00:15:30] Meaningless data.

[00:15:31–00:15:32] The thing about this data as well.

[00:15:32–00:15:35] If you think hard enough about AI,

[00:15:37–00:15:41] whatever AI outputs is only meaningful because we say so.

[00:15:42–00:15:44] It’s not inherently impactful.

[00:15:45–00:15:47] It’s just kind of a mirror of human thinking.

[00:15:48–00:15:52] It’s like we are projecting our thoughts against AI.

[00:15:53–00:15:55] And then something is outputted.

[00:15:55–00:16:01] And like whether AI makes sense, whatever AI outputs or not make sense or not,

[00:16:01–00:16:03] is only the property of human judgment.

[00:16:04–00:16:09] So we are the evaluators of whether AI works or not, basically.

[00:16:09–00:16:12] Well, we do have benchmarks.

[00:16:12–00:16:21] But your point is that the way that we decide the benchmark is through a human judgment.

[00:16:21–00:16:27] But on the other hand, for forecasting in particular,

[00:16:27–00:16:33] we do have actual ways to measure the agent’s forecasting accuracy over time.

[00:16:34–00:16:34] Right?

[00:16:34–00:16:37] With the prior score and calibration.

[00:16:38–00:16:41] And we can see when the agent is predicting this.

[00:16:41–00:16:45] Well, if the agent predicted something 50%,

[00:16:45–00:16:49] all of those events should happen about 50% of the time.

[00:16:49–00:16:50] That means the agent is calibrated.

[00:16:51–00:16:59] It’s very, very quantifiable whether a human or an agent or any sort of entity is good or bad at forecasting.

[00:16:59–00:17:02] And right now, it looks like the agents are good at forecasting.

[00:17:03–00:17:06] Based on the data humans provided.

[00:17:06–00:17:16] And they were calibrated according to human judgment and human taught them to be good forecasters, basically.

[00:17:17–00:17:18] So they’re just piling up the human judgment.

[00:17:18–00:17:23] You’re saying that they’re good because humans trained the model in the AI.

[00:17:23–00:17:27] If we stop feeding them human interpreted data, they’re useless.

[00:17:28–00:17:28] Okay.

[00:17:28–00:17:29] Well, that’s true.

[00:17:29–00:17:32] But there’s always going to be more money in AI.

[00:17:33–00:17:33] Yeah.

[00:17:34–00:17:34] Yeah.

[00:17:34–00:17:38] But they will need human data, human made data, human interpreted data.

[00:17:38–00:17:42] So they will need still like human written books, interpretation of news.

[00:17:42–00:17:42] Oh, yeah.

[00:17:42–00:17:42] Yeah.

[00:17:43–00:17:43] Yeah.

[00:17:43–00:17:43] For sure.

[00:17:43–00:17:51] At the end of the day, you will always need some kind of human just being there, seeing, interpreting stuff, talking.

[00:17:51–00:17:52] I completely agree.

[00:17:53–00:17:58] I doubt that the human is in your website.

[00:17:58–00:18:04] I doubt that the human chatting with the agent is actually going to help it very much.

[00:18:04–00:18:06] I think it’s more about its training data than.

[00:18:06–00:18:14] No, it’s just my point is basically that they are very formidable and impressive tools, which will be wielded by humans anyway.

[00:18:15–00:18:18] And they’ll be developed by humans and they’ll be enhancing humans.

[00:18:18–00:18:30] I just really, I’m very skeptical about all these narratives trying to anthropomorphize them basically and give them autonomy in a human sense.

[00:18:30–00:18:31] It doesn’t make sense.

[00:18:31–00:18:36] I mean, perhaps there’s a future in which they’ll be trading and forecasting events on polymarket better.

[00:18:37–00:18:37] Okay.

[00:18:37–00:18:38] Good enough.

[00:18:38–00:18:38] Yeah.

[00:18:38–00:18:58] But I think that underneath, and that’s also what we’re thinking about in Elastics, you need to provide, you need some human who will provide proper data, harness, proper harness, context, direction, taste, judgment, however you call it.

[00:18:58–00:19:03] So there will be actually extensions of these humans ultimately.

[00:19:04–00:19:15] Because at the end of the day, you just need this, let’s say, for the lack of the better words, let’s say human discernment, human biological discernment of things.

[00:19:15–00:19:20] That I am here, I’m embodied, and I’m not just, you know, the logical gate.

[00:19:21–00:19:21] Okay.

[00:19:21–00:19:23] Without consciousness.

[00:19:23–00:19:34] It seems like the recapping the disagreement here is whether you just need training data or you also need a human steering the AI at all times.

[00:19:34–00:19:35] But we can...

[00:19:35–00:19:36] Maybe not at all times.

[00:19:37–00:19:42] But, and it’s up to, it will be also the human judgment when do you steer.

[00:19:42–00:19:46] And there’ll be more successful approaches and less successful approaches.

[00:19:46–00:19:54] It just, I just think that humans with AI will be beating just AI.

[00:19:55–00:19:56] Okay.

[00:19:56–00:19:59] Well, we’ll see in the near future.

[00:19:59–00:20:00] Yeah, yeah, yeah.

[00:20:01–00:20:01] Yeah.

[00:20:01–00:20:06] And we’ll need to, let’s say, curate the battlefield carefully.

[00:20:06–00:20:07] But it’s an open question.

[00:20:08–00:20:08] Okay.

[00:20:08–00:20:13] So, let’s talk about the data a little bit that the AI is going to be ingesting.

[00:20:14–00:20:17] How do you vet the quality of the data that’s coming in?

[00:20:18–00:20:33] And how can you prevent, how can you defend against information attacks against the AI if someone is writing, if someone’s like planting headlines or faking posts to try to target AI models to trade on bad information?

[00:20:33–00:20:45] So, what we’re doing in Elastics is that we have curated news sources, curated news sources providers from only reputable sources.

[00:20:45–00:20:50] And we, from, I mean, your question could be a broader question.

[00:20:51–00:20:54] It’s a societal question, but I don’t have a good answer.

[00:20:54–00:20:55] I just can’t tell you what we are doing at Elastics.

[00:20:56–00:20:57] So, we have reputable news providers.

[00:20:57–00:21:09] And we’ll also let you, and that’s what we are working right now, that you’ll be able to connect, choose the Twitter accounts you want to follow and from which you want to have news sources.

[00:21:09–00:21:13] So, it will be also up to you to select the news sources.

[00:21:14–00:21:21] And I think this is the important part that, and it’s also a part of this process where you’re building up the context for your own AI.

[00:21:22–00:21:24] So, you choose, I want to hear Reuters.

[00:21:25–00:21:27] I don’t want to hear this news.

[00:21:27–00:21:28] I want to follow this guy on Twitter.

[00:21:29–00:21:38] And then your AI, you can chat with your AI and perhaps build automations and agents built on your chosen context and data.

[00:21:39–00:21:45] So, I think here it’s very important for a lot of traders to, let’s say, prepare the proper environment.

[00:21:45–00:21:54] And in Elastics, we are creating this environment in which you can, let’s say, imagine that you’re interested in Iran world-related contracts.

[00:21:54–00:21:56] You choose, let’s say, five, six contracts.

[00:21:56–00:21:58] You plug in news sources.

[00:21:58–00:22:02] AI is correlating these news sources and giving, let’s say, impact score.

[00:22:02–00:22:07] And you can chat and then make trades, make automations, make indices out of it.

[00:22:07–00:22:12] Make some agents working based on these news triggers, others for you.

[00:22:12–00:22:14] So, that’s how we approach.

[00:22:14–00:22:21] We don’t want to just give you an LLM wrapper and connect it to Polymarket and good luck, son.

[00:22:21–00:22:35] We just want to provide you a proper environment in which you can create enough confidence and capacity in whatever is happening there that your AI can gradually make bolder moves.

[00:22:36–00:22:38] And you’ll see whether you can trust it or not.

[00:22:38–00:22:39] You can switch models.

[00:22:40–00:22:42] The better model, the more expensive this will be.

[00:22:42–00:22:44] And then you can make judgment.

[00:22:44–00:23:01] Okay, maybe I’ll set up some stop loss or some defensive, let’s say, cut losses automation or maybe indice between this and that venue that will be realizing my portfolio based on if this kind of information hits.

[00:23:01–00:23:05] So, there’s a lot of details there.

[00:23:05–00:23:14] And we are often discussing AI agents in a very, very broad way and understandably because it’s a very hot and interesting topic.

[00:23:15–00:23:18] But then you start to build these things because you’re excited.

[00:23:19–00:23:23] You want to serve this AI wave and you want to fix problems that people have.

[00:23:23–00:23:36] And then you realize that on the ground, it just takes a lot of, lot of, lot of small step and details to make people trust this and make it actually workable.

[00:23:36–00:23:42] So, someone is actually, you know, putting 500 grand on the agent.

[00:23:43–00:23:49] That’s not something, unless you have this quantitative model in big hedge funds.

[00:23:49–00:23:53] That’s not something that’s frequent right now.

[00:23:54–00:23:59] People are still much more into this clicking, checking, et cetera.

[00:23:59–00:24:00] So, it’s a process.

[00:24:01–00:24:03] It’s perhaps the future in which we are going.

[00:24:04–00:24:15] But I really think that the best agents will be, let’s say, the mirror versions of their owners.

[00:24:15–00:24:18] So, you will have Eli, Eli Army of Agents.

[00:24:18–00:24:21] I’ll have my young army of agents.

[00:24:21–00:24:32] Maybe based on our podcast, they can meet in the background and based on what we are talking about, they can be enriched with this context and maybe say,

[00:24:32–00:24:33] okay, Eli, let’s go trade.

[00:24:34–00:24:36] We have this idea and these agents are already trading for us.

[00:24:37–00:24:37] You know what I mean?

[00:24:38–00:24:43] So, I believe in these AIs as an extension of us, basically.

[00:24:43–00:24:50] And this actually is interesting because these are AI agents which really demand human agency.

[00:24:51–00:24:56] So, for agents to work, you need agency because you’re an initiative.

[00:24:57–00:24:58] So, yeah.

[00:24:58–00:24:59] Maybe that’s the future.

[00:25:00–00:25:00] That would be interesting.

[00:25:00–00:25:14] Do you think that – so, when we think about prediction markets, they’re accurate because they harness the wisdom of the crowds to provide forecasts.

[00:25:14–00:25:21] And those forecasts are aggregating lots of independent errors from lots of different traders, right?

[00:25:22–00:25:27] But a lot of the trading agents are going to be prompted in sort of similar ways.

[00:25:27–00:25:30] People have similar ideas.

[00:25:30–00:25:39] But more importantly, they’re all trained on probably the same set of five or six foundation models that you’re using.

[00:25:39–00:25:53] And if agents dominate volume, do you think these humans are going to have different enough ideas to counter the effect that essentially all of the traders have the same brain, essentially?

[00:25:53–00:26:04] So, do you think that could actually make prediction markets less accurate overall as more agents scale to the volume?

[00:26:04–00:26:11] Do you think that there just might not be enough traders because all of the agents are going to have the same ideas?

[00:26:11–00:26:12] There’s not enough counterparties?

[00:26:13–00:26:18] But don’t you think that this is a matter of whether these agents are losing or winning?

[00:26:18–00:26:25] If they’re winning, maybe they will have good streak and then they’ll die or they’ll just die immediately or they’ll be amazing.

[00:26:26–00:26:36] So, I still think that the success of agents will be the property of the humans behind them, how they’re programmed, how they’re trained, what ideas are there basically.

[00:26:36–00:26:44] And because just you will be prompting them, either through code, natural language, however you want.

[00:26:44–00:26:54] So, just imagine this successful hedge fund guy, which is just giving this amazing prompt, basically saying, avoid this, you should do that, ABC.

[00:26:54–00:26:58] Just thinks, I cannot even fathom because I never worked at hedge fund.

[00:26:58–00:27:05] And just, let’s say that he has the best model, just a lot of compute.

[00:27:05–00:27:14] And then he’s just competing against some random dudes who haven’t even read the finance textbook.

[00:27:14–00:27:18] And his prompt is, make me a million dollars, don’t make any mistake.

[00:27:19–00:27:22] So, I guess we know who will win.

[00:27:23–00:27:26] And I think this is, so, AIs are shells.

[00:27:27–00:27:29] They are just like tools, weapons.

[00:27:29–00:27:39] It’s just kind of a bullet that you will shoot and maybe a rifle and just who will have bigger gun and who will have the better gun, who is shooting better, yeah?

[00:27:39–00:27:41] Who have bigger missile.

[00:27:42–00:27:42] It’s a weapon.

[00:27:43–00:27:43] It’s a tool.

[00:27:44–00:27:47] How you calibrate, they won’t be equal at all.

[00:27:47–00:27:50] They’ll have access maybe to this server, to that server.

[00:27:50–00:27:55] Maybe there’ll be some rules discriminating between, let’s say, spamming agents.

[00:27:55–00:27:58] So, it’s an open field.

[00:27:58–00:28:04] But maybe about the efficiency of prediction markets.

[00:28:07–00:28:18] If efficient prediction markets, I guess, are those where you have very high probability of something happening, I guess.

[00:28:19–00:28:19] I don’t know.

[00:28:20–00:28:21] Correct me if I’m wrong.

[00:28:21–00:28:29] Like, efficiency here is a, because at the end of the day, something happened or not.

[00:28:30–00:28:30] Yes?

[00:28:30–00:28:31] Yeah.

[00:28:31–00:28:33] And how do you measure efficiency?

[00:28:34–00:28:37] So, I can better conceptualize what you’re doing here.

[00:28:37–00:28:38] So, you’re right.

[00:28:38–00:28:39] How efficient is the market?

[00:28:39–00:28:41] How accurate was it, essentially?

[00:28:42–00:28:48] Like, how efficient are prediction markets in, how well they are predicting something, basically?

[00:28:48–00:28:48] Yeah.

[00:28:48–00:28:51] So, there’s a couple ways of measuring this.

[00:28:51–00:29:00] The most common way is a Breyer score, as I mentioned a bit earlier, where it’s basically an aggregate of the calibration that shows.

[00:29:01–00:29:05] So, let’s say we take all prediction markets on Polymarket and Kelsey, right?

[00:29:05–00:29:09] Let’s say it’s like 100,000 prediction markets, let’s say.

[00:29:10–00:29:20] And let’s say 1,000 of those at a certain time frame out from the resolution were at exactly 50%.

[00:29:20–00:29:32] So, if the market was efficient, then we’ll see that those markets that fell in that 50% bucket would actually happen 50% of the time.

[00:29:32–00:29:39] And we kind of aggregate all of those across each of the different probability buckets into a single score that scores how efficient the market was.

[00:29:39–00:29:49] So, maybe if agents will be so good at predicting future, they’ll be sitting in some contracts where there is some money to make, basically.

[00:29:51–00:30:02] And the better traders will be either looking for marginal gains or this or that market or will be looking for markets where there is not that much information.

[00:30:02–00:30:09] So, I guess it will be more a property of yet undiscovered information.

[00:30:09–00:30:25] And this is maybe the take that our common friend Adi Rajaprabraharan made, that there might be more and more different and exotic markets about which there’s not that much information ready at hand.

[00:30:25–00:30:34] And maybe there will be people just going there digging this information, feeding it to agents and just having more and more exotic hobbies.

[00:30:34–00:30:39] Or there will be like Citrine researchers seeing whether there’s a traffic in Ormond Strait.

[00:30:39–00:30:40] I don’t know whether you heard about this.

[00:30:40–00:30:46] There was a guy just literally went to see whether the tankers are passing straight or not.

[00:30:47–00:30:52] And he made trade based on this information, I guess, on Polymarket or maybe something like this.

[00:30:52–00:30:55] So, there will be an incentive.

[00:30:56–00:31:09] I know that I cannot directly answer the question about efficiency, but the trend would be then that for the markets about which there’s an abundant data, there will be just priced well.

[00:31:09–00:31:20] And then there will be a strong incentive to just go deeper into more exotic markets to just discover more information about stuff we are just not knowing this much about.

[00:31:20–00:31:24] And just one more thing and interesting dynamics.

[00:31:25–00:31:33] Like if you look at Polymarket right now, the liquidity is where the global attention is because people are talking about it, people are writing about it.

[00:31:33–00:31:37] So, you see Iran, maybe Russia, Ukraine.

[00:31:37–00:31:42] These are fairly, let’s say, efficient liquid markets.

[00:31:42–00:31:50] But then there are other big stuff, big things where, let’s say, the public attention is not yet there.

[00:31:50–00:31:52] Like right now, the issue of Greenland.

[00:31:52–00:31:55] Greenland, it’s still the thing.

[00:31:55–00:31:56] It’s still the thing.

[00:31:56–00:31:57] Greenland is still the thing.

[00:31:58–00:31:59] But there’s not that much liquidity.

[00:32:00–00:32:05] But there will be now liquidity because suddenly Trump’s at Greenland and there will be journalists writing about it.

[00:32:05–00:32:06] And you’ll have reports and stuff.

[00:32:06–00:32:09] So, suddenly you’ll have this, let’s say, liquidity.

[00:32:09–00:32:17] So, the thing I saw with prediction markets is that the liquidity information there is also property of, let’s say, global attention.

[00:32:18–00:32:20] So, where the information is produced and collected.

[00:32:21–00:32:26] So, the people who will be perhaps making a lot of money will be there who will be, let’s say, ahead of the curve.

[00:32:26–00:32:32] That they’ll be not even able to predict what are odds on this particular contract.

[00:32:32–00:32:37] But broadly speaking, where the attention will go given, let’s say, structural forces.

[00:32:38–00:32:40] And there won’t be that much data about this.

[00:32:40–00:32:43] Only, let’s say, broad patterns perhaps.

[00:32:43–00:32:47] Or maybe there will be, like in this previous data, there will be, maybe it will be discoverable.

[00:32:48–00:32:48] I don’t know.

[00:32:49–00:32:51] But that’s what I would say.

[00:32:51–00:33:06] So, do you think that could make it so smaller exchanges that are doing more specific markets, a specific category of markets, could end up having more liquidity and more volume because of agents?

[00:33:06–00:33:12] And do you think that could make it so Calci and Polymarket aren’t really the only large exchanges anymore?

[00:33:12–00:33:15] I think, I think so.

[00:33:15–00:33:31] Also, because Calci and Polymarket seem to be reluctant right now to list contracts which are relevant, very relevant for the general public in order for the US government not to lose edge in its wars or endeavors.

[00:33:32–00:33:39] So, my opinion is that there is less and less interesting contracts from Polymarket related directly to war in Iran.

[00:33:39–00:33:44] So, there are proxy markets like, oh, will be there meeting or not.

[00:33:44–00:33:51] But there are not markets like whether they will invade or much more granular war markets which are very directly informative.

[00:33:51–00:33:54] It’s just we need to make proxies for stuff.

[00:33:54–00:34:05] And proxy markets are, let’s say, telling, there’s some underlying thesis and you need to have bucket of proxy markets.

[00:34:05–00:34:06] So, that’s fine.

[00:34:06–00:34:24] But I would like to have more prediction markets in different jurisdictions that are floating prediction markets which necessarily might not be favorable for this or other government just for the sake of the global public to have an information about this.

[00:34:24–00:34:34] But the thing is, structurally speaking, is that it’s only in the US that you can just, you know, fuel infinite amount of money into unprofitable companies like Calci and Polymarket for them to grow in the future.

[00:34:34–00:34:38] Because you just issue reserve currency so you can just print money and, you know, invest in them.

[00:34:39–00:34:41] So, I hope there will be more prediction markets.

[00:34:41–00:34:51] But I don’t, in short term and midterm, I don’t anticipate anyone as successful as Calci and Polymarket outside of the US.

[00:34:51–00:34:52] Because where else?

[00:34:52–00:34:53] Where else?

[00:34:53–00:34:53] In China?

[00:34:54–00:34:59] They are just, they are jailing professors for telling that the economic data are cooked.

[00:34:59–00:35:00] In Europe?

[00:35:00–00:35:01] Oh, no, it’s a gambling.

[00:35:01–00:35:06] It’s a gambling and we need to regulate everything before we’ll let you do everything, basically.

[00:35:06–00:35:13] So, I think Calci and Polymarket are fine and perhaps there will be some competitors from there.

[00:35:14–00:35:14] Yeah?

[00:35:14–00:35:15] Yeah.

[00:35:15–00:35:20] Well, Laura, I think we can wrap up here.

[00:35:20–00:35:21] But this was a great podcast.

[00:35:22–00:35:27] I’m sure it will be really interesting for our listeners, especially our debate about agents.

[00:35:28–00:35:31] But, yeah, really excited to see where Elastics ends up.

[00:35:32–00:35:34] Hopefully, we could do another one of those events.

[00:35:34–00:35:35] That would be great.

[00:35:35–00:35:36] Let’s do it.

[00:35:37–00:35:38] Let’s do it.

[00:35:38–00:35:40] And always a great pleasure to talk with you.

[00:35:40–00:35:42] And, yeah, it was very, very fun, actually.

[00:35:42–00:35:42] Yeah.

[00:35:43–00:35:49] These talks are inspiring because these are things that are, let’s say, somewhere in my background.

[00:35:49–00:35:54] But you, like, you know, it’s like you prompted me to tell them, like, hey, I...

[00:35:54–00:35:54] Yeah.

[00:35:54–00:35:54] All right.

[00:35:55–00:35:56] All right.

[00:35:56–00:35:57] Thanks, Sean.

[00:35:57–00:35:58] Thanks, Eli.

[00:35:58–00:35:59] Bye.

[00:35:59–00:35:59] Bye.

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 third-party judicial systems. So you’d get sort of privatized law. So one worry here is that this seems strongly... So I’m not sure if this would... If this is how things

[00:10:16] would go. My sense of the historical record is this isn’t how things tend to go. You know, it’s not like the, you know, the mafias in South American countries. So court systems and

[00:10:27] so on. And I’m in general a bit skeptical of sort of like a priori arguments about how the economy will go. I think it’s really hard to predict how complex systems will behave. And

[00:10:47] that as

[00:10:51] a result of this, I think it’s sort of like... Like David Friedman’s a smart guy. Just, you know, people to like work out from the armchair how these sorts of things will go.

[00:11:02] Then I think if... Even if things work out the sort of David Friedman, Brian Kaplan way where people get security firms. In general, because violence is costly, this will be

[00:11:12] heavily biased towards people not interfering with each other. So it’s like you hire your security firm, which protects you basically no matter what. And then I hire my security

[00:11:25] firm, which protects...

[00:11:28] You need kind of a lot of people to be on board with trying... With being willing to go to war with your security firm in order to... Sorry. So like if you hire a security firm that

[00:11:43] will protect you no matter what, then you need a lot of people to be on board for going to war with your security firm in order to defeat it soundly militarily. So then I think you

[00:11:54] get a bias against people regulating other people’s behavior. So my guess is you get a lot less regulation of other people’s behavior in a world, in this world, even if things work

[00:12:06] out as is planned. And the worry there is that... So, I mean, there are cases where we want people’s behavior to be regulated. So we want there to be restrictions on how people

[00:12:19] treat their children, for example. We don’t want people beating their children. It seems like if people hire the security firm that protects the child beaters, it seems like a

[00:12:28] pretty good deal. And it seems like it’s hard to imagine there being enough buy-in. Or at least it’s not obvious there would be enough buy-in to stop this sort of thing. Similarly,

[00:12:39] if the factory farms...

[00:12:45] They, you know, purchase security firms that protect them no matter what they do, and they’re very wealthy and powerful, they know animal welfare laws, which, I mean, this, I

[00:12:55] think, is a really big deal because it affects the welfare of tens of billions of beings. It means that there would be less restrictions on the extent to which private actors were

[00:13:04] able to take actions, like building bioweapons, which impose existential risks. Now, to be clear, I don’t think, like, a guy would be, like, intentionally knowingly building a

[00:13:13] bioweapon in his basement and that you wouldn’t be able to get enough people in to stop them. But it would at least be harder to, like, regulate the materials that are useful for

[00:13:21] building bioweapons. Um, so I think, I think that these are some problems with, um, with anarcho-capitalism. Oh, and then the other thing is I don’t think it would be adequate for

[00:13:34] dealing with externalities, where, I mean, in general, when people impose externalities, there usually isn’t any particular person you can point to who was harmed by the

[00:13:43] externalities that you imposed. So it’s hard to see who would coordinate to take the actions to stop the externalities. Maybe you could get something like this if you had

[00:13:50] enough buy-in, but it at least seems far from obvious. And when you have, you know, externalities that are responsible for, you know, like, pollution is responsible for

[00:13:59] millions of deaths every year, even in the United States, it’s, like, hundreds of thousands. Um, it’s actually a very serious problem if, basically, you don’t have any

[00:14:07] regulation on this sort of thing. I could go on, there’s more to say, but that’s... Do you think that alternative government, governance systems, including, um, anarcho-capitalism,

[00:14:17] uh, or something, uh, more, even, even more, uh, unusual or unconventional, like, like, uh, Robin Hanson’s futarchy, um, do you think those will ever get enough public buy-in, as

[00:14:30] you were talking about, to ever be implemented on a large scale? No. Um, I mean, just in general, there are lots of very clever schemes that economists think of, and then in

[00:14:43] general, no one, no one implements them, even if people would want to. Why is that? Well, I mean, in general, they’re not very popular. The number of people who are pro-futarchy,

[00:14:54] who would even, in principle, be pro-futarchy, is, like, you know, like, 100, because figuring out the upside of futarchy... And I, I think, you know, I think futarchy is a

[00:15:05] very interesting idea, um, but it’s sort of hard to, like, it’s even hard to understand what the idea is. So it’s hard to imagine getting public buy-in, um, enough to implement

[00:15:15] it at scale. Um, and then I also think you can just look to history, or just look, has this been implemented? No. Anarchy, I mean, I guess anarchy occasionally gets implemented,

[00:15:27] but not in the way David Friedman would want it to be implemented. I think, I think he says that, like, medieval Iceland was anarchic-ish, and this went well. I haven’t really

[00:15:37] looked into this claim. Um, uh, I guess anarchy, anarchy is more plausible. Like, anarchy does sometimes get implemented, but not generally anarchic capitalism. Yeah. Do you, so do

[00:15:48] you think that, uh... But definitely not futarchy, I think that... Do you, do you think it’s theoretically a good system of governance if there is enough public buy-in? Uh,

[00:16:00] futarchy, or, yeah. Yeah. Yeah, it’s a good question. It’s at least an interesting enough idea to, um, uh, yeah. So, yes, it’s at least an interesting enough idea that I’d support,

[00:16:16] like, trying it out in some little state somewhere, you know, like, just, you know, have a little futarchy city in New Hampshire. Um, and there are cases where I am sympathetic,

[00:16:27] more sympathetic to, to the, to, like, a futarchic thing, where, um, uh, like, if there is some FDA drug, and I think you could, you could have prediction markets on whether the FDA

[00:16:42] drug would get approved, and then if they predict with super high confidence that it will, then you, you sort of fast track it. Um. Uh, well, that kind of creates a manipulation

[00:16:52] scheme. So it, it is, so it, it’s true that it creates incentive for manipulation. Um, now, the thing that the prediction markets people point out is that if you have a system

[00:17:03] where, so in general prediction markets aren’t manipulated very much, and the reason for this is that if you have a system where people are manipulating it, where they’re not

[00:17:14] pricing it appropriately, then, um. That is an incentive to, to set it back to the fair value price. Right, and here I think the thing you’d want to do is you’d only have it be,

[00:17:23] um, you’d only have this manipulation scheme arise in cases where it reached some very high amount. So it’s like, if there’s like 98% plus. And then, if really the odds of the

[00:17:36] drug working out are only like, uh, 20%, and then it’s at 98% plus. Now there are massive, massive financial incentives for manipulation. Why, why wouldn’t you have the market be,

[00:17:49] will, come up with some metric for the success of the drug, and then be, will this drug, uh, actually cure people at a large scale rather than, will the drug get approved? Um,

[00:18:00] yeah, I, I think that’s another way of doing it. Um. Seems like a bit cleaner. Um, yeah, I guess, I think there are cases where you’d want the drug to be approved, even if it won’t,

[00:18:11] like, cure people at mass scale. Um, where, you know. Or, or, or, or, or not even cure people at mass scale, but at least carry out its intended consequence. Um, yeah, I mean,

[00:18:21] the worry is that if you’re having, uh, some process for determining whether it, after the fact reaches its intended consequence, it just seems like that should be whatever the FDA

[00:18:31] process is using. You know, that should be roughly the process the FDA is using. Yeah, well, that, that’s true, but that’s unfortunately not really the case. Um, yeah. So maybe,

[00:18:43] maybe the, so maybe the optimal thing would be you, you reform the FDA. But talking about food turkey more broadly, um, I mean, it’s, it’s a very interesting idea. Um, uh, I guess,

[00:18:54] should we explain for your listeners, like, what it is? Uh, I, we talk about it in most episodes, so I hope our listeners know, know what it is, but I can give a quick, actually,

[00:19:04] at Manifest, Robin Hanson flamed me for not explaining it to the crowd, um, when we were talking about it. So maybe I should. Moodle, wow. Yeah. Um, so futarchy is a system of

[00:19:14] governance where you’ve, where the citizens of the country or the, whatever entity is being governed will vote on a target metric or like a national welfare index, and calls

[00:19:26] it. So that could be like, um, let’s just simply call it a hundred, the value metric is a hundred percent GDP. We just want to maximize GDP. So, uh, what you do is every time

[00:19:39] there’s a new policy that’s proposed, uh, in a congressional body or some other sort of governance, uh, governing body, then you create two prediction markets. Will this, if, if

[00:19:52] we implement this proposal, will our GDP go up? Will this proposal? Will our GDP go up? And, uh, if the price that says, um, that passing the proposal will increase GDP is higher

[00:20:06] than the price that says passing the proposal will not increase GDP, then the proposal would pass and vice versa. Yeah. Um, so again, it’s been a while since I have thought much

[00:20:17] about food turkey. Um, I thought a lot about it a lot my senior year of high school and then not much since. Um, but so, I mean, I guess, I think the most serious problem with

[00:20:27] food turkey is that it’s very hard to think, to have some metric that nicely captures what we care about. Um, yeah. I mean, I, I would say just for that, um, I mean, it’s, it’s,

[00:20:40] it’s better than the conventional just represented democracy voting system currently in the U.S. because people don’t, in, in, in our system, people don’t even know what they’re

[00:20:50] voting for because of like the, the principal agent issues. Um, in, in, in food turkey, when people vote on the, the value metric, uh, at least they kind of know what they’re

[00:21:00] getting into, which is a lot better than just kind of like taking a blind shot. So I don’t, so I don’t agree with that. Um, where, I mean, first of all, I think in general,

[00:21:13] um, it’s, I find it just hard psychologically to imagine the average voter, like actually doing a lot of research on the very complicated issue of like which system of, um, uh, of

[00:21:32] like which, which welfare index, you know, GDP plus versus GDP plus plus is better. And for any metric along these lines, if you narrowly optimize for some specific metric, even

[00:21:45] if the metric is slightly off, then you will often get really bad things. So, you know, um, suppose, suppose the metric you did was just GDP, which in general, GDP correlates

[00:21:54] extremely well with whatever it is that we care about, you know, like countries with higher GDP or just like better places to live. Um, but obviously if you are just

[00:22:01] maximizing GDP, maybe the way to maximize GDP would be to like enslave some poor nation or something. Like there are lots of ways you might maximize GDP, um, in ways that would be

[00:22:11] highly undesirable. It wouldn’t, it might not support foreign aid. It might not support intervening to stop, you know, genocides. And so like there, there are cases uh, it would

[00:22:21] be seriously suboptimal. Um, now the re, so I, certainly democracy doesn’t have anything like an optimal, uh, um, uh, certainly democracy isn’t anything like optimal with respect,

[00:22:38] um, uh, with, with respect to this. Um, oh, and sorry, one other thing to note is just, my guess is if you were trying to make some index like this, it would end up being super

[00:22:49] complicated. And then the voters wouldn’t be able to like come to informed judgments about it. Um, I mean, voters in general don’t come to very informed judgments about political

[00:23:01] leaders, but I think democracy doesn’t, the case for democracy doesn’t much depend on them coming to informed judgments about political leaders because in general, voters do the

[00:23:11] following two things. If a politician makes their life worse, they tend to vote against them unless they’re pro their, the politician’s ideology and they generally vote for them.

[00:23:20] And so then you have politicians who haven’t, are incentivized not to make voters’ lives worse and not to do other like crazy, evil, diabolical things because this would also

[00:23:28] upset voters. So this system, where it’s just like, um, it incentivizes politicians to do good things and it doesn’t, uh, require that the voters be particularly informed. Um, then

[00:23:39] the other thing is that, um, well, sorry, I’ve forgotten what the other point was. People, people don’t necessarily get a clear vision, uh, and in retrospect can look on whether

[00:23:53] electing that person actually improved them or not. Like, for example, um, I remember a couple of years ago, my grandparents live in this condo complex in Long Island and they

[00:24:04] were about to get a huge tax hike and then Trump was elected and suddenly the, they canceled the tax hike. And then, uh, my grandparents yeah, this is, you know, Trump is

[00:24:16] like so great, he canceled our tax hike where it was actually their representative, uh, in, in the Congress, uh, who was a Democrat, Tom Swasey. And so I think it’s kind of hard

[00:24:27] for people to, to differentiate, to see the direct consequence of, of electing a certain person to see how they’re actually impacting their lives. Um, so I don’t think it’s as

[00:24:37] clear of a, of a comparison in hindsight as you were saying. Uh, I don’t think what I was saying depends on the idea that they can precisely distinguish between which things were

[00:24:48] done by which person. So, in general, so, uh, so let’s imagine that, so if, if politicians do things that make people’s lives worse, then in general, they’re less likely to vote for

[00:25:00] the politicians who were around at the time this happened. And so then politicians are incentivized not to do this. So, um, presumably, uh, if your grandparents, they were

[00:25:10] really upset by this, they’d probably be less likely to vote both for Trump and for the local leaders who would take, to, uh, hike their taxes. Where basically, if they notice

[00:25:20] things are getting worse, and then they in general throw out the political leaders who did this. And, you know, they’re, they’re, this tends to happen, you know. I mean, a big part

[00:25:28] of the reason, like, that, like, Trump started doing a lot worse in the polls after COVID, even though COVID was obviously not his fault, I mean, he did handle COVID really badly.

[00:25:39] So that was his fault. But the, the warp speed thing was good. Yeah. Yeah. I mean, there are certainly some things he did well and some things he did badly with respect to COVID.

[00:25:48] Um, but, uh, yeah. So, um, so then in general, if, if you think like, and then under Biden, inflation, I don’t think was his fault. And yet he paid a big political price for

[00:26:01] it. And so if you notice that voters punish politicians, if the politicians pass laws, and then their lives get worse, then the incentives politicians have is to not pass laws that

[00:26:11] make their lives worse. This is certainly extremely far from the optimal system. Um, but, uh, you know, democracy being the worst form of government, except for all the other ones.

[00:26:23] The other thing is, I don’t actually think like, in general, people’s values are especially consistent. So if you try to codify values, the values we embody with respect

[00:26:35] to foreign policy, my guess is if you try to, if you tried to codify them, you’d either end up getting values that were like fanatical with respect to foreign policy, where they

[00:26:44] implied we should be giving away 10% of our GDP to foreign aid, which, you know, fair enough, we should, but, um, uh, um, or you would get values that basically just don’t

[00:26:53] count foreigners’ interests at all. And then my guess is if deciding between those, people would obviously prefer the not counting foreigners’ interests at all. And so then I think

[00:27:01] having, making decisions sort of within the bounds of common sense, rather than trying to codify values and then do what’s best with respect to them, leads, is, is less likely to

[00:27:12] lead to cases where, like, um, uh, where we, where we sort of jettison something as being a feature of our value because we don’t like what would happen if we acted on it. Do you

[00:27:24] think that, um, so considering that, that voter, obviously this is, implausible, but, uh, assuming a world where the voters always picked value metrics that were optimal for, for

[00:27:37] their society, um, do you think that futarchy would be an effective way to, um, kind of maneuver law to be able to best serve the public interest, um, in that respect? Um, that’s

[00:27:54] a good question. Let me think about that for a moment. Um, possibly, yeah. I mean, in general, you know, I think it’s hard to predict how things will go, um, especially for

[00:28:06] something as complicated as, like, a form of government. Uh, certainly if, if a person was trying to forecast from the armchair how democracy would go, it wouldn’t be very accurate.

[00:28:15] Um, so definitely you want to try things like this on a small scale. Um, and, you know, Robin Hanson, to his credit, agrees with this, you know, so he, he, he wants, wants to,

[00:28:23] like, start with, like, fire the CEO prediction markets. Um, but, uh, what would you say would be the optimal way to test not only futarchy, but any new sort of proposed system of

[00:28:35] government? What’s the best sort of pilot that accurately represents what would happen on a larger scale without, so maybe take some, like, location that is not doing well at all

[00:28:46] with respect to its laws and then you, like, implement the, you, like, have them have that kind of government for, like, some small area and then you have them have that kind of

[00:28:56] government for a short period of time. So I don’t know, maybe, um, uh, I was going to jokingly suggest just implement it in Argentina because however bad it is, it can’t be

[00:29:06] worse than what they have now. Um, or, well, I guess, um, under Malay, things are maybe picking up. Sorry, I guess, yeah, that joke’s a little out of date, but, um, yeah, Malay

[00:29:16] seems to be doing, doing all right, though I guess that’s, that’s disputed. But yeah, so you, you, like, have some city in Maine or something where that’s, like, kind of poor and

[00:29:26] then you, like, have futarki be implemented there and then you, you potentially scale it up if it works well. Yeah. Um, now, the other thing, before you implement futarki, you

[00:29:38] definitely should see if implementing futarki is good so you should have a prediction market on whether the GDP plus metric will, it’ll, implementing futarki would be conducive

[00:29:50] to that. Interesting. I haven’t heard that before. That’s, that’s a good, that’s a good idea. Um, what do you think about, so if, if it’s okay if we move to a slightly different

[00:30:00] topic, um, what do you think about, you’re, you’re obviously concerned about effective altruism and, um, so you, so, and the effective altruism charities are primarily 501(c)(3)s,

[00:30:14] what do you think about impact markets like, uh, like Mana Fund where I, I can give a quick explanation here. So the, the impact markets, um, basically let early funders, uh,

[00:30:27] that are kind of known as like investors in the, the charitable projects, they buy shares in those, in the charitable projects, and then after the project is completed, there’s,

[00:30:39] uh, philanthropic funders, um, and they would review the, the success or the failures of the project and, um, would deploy them a retroactive prize based on how well, uh, they

[00:30:56] grade the project or how well that worked, and then based on what the prize was, the investors would receive, uh, some of that prize proportionally, invested in the project,

[00:31:09] uh, retroactively, kind of like investing in a, in a company. What do you, do you think that’s an effective mechanism? Um, yeah, seems good. Um, I mean, I guess, so I think with

[00:31:19] EA charities, we also, there, there’s a pretty good way of doing it, which is that you have third parties who look into what the charities do, and then they stop recommending

[00:31:27] them. If the charities, they’ll like, if the charities, they do have better evidence and the charities don’t do that much good, then they stop recommending them. So then the

[00:31:36] charities are but I think that the Mana Fund thing also seems like quite a good idea. Has it, uh, how have you seen the effects of Mana Fund? Does it seem to have worked well, um,

[00:31:47] over time? So up until this conversation, I’d seen the name Mana Fund, but I didn’t know how it worked, so unfortunately I don’t, I don’t know anything about the effect it has.

[00:31:57] Other than I guess I’ve seen some impactful charities funded through it, so it seems good. Uh, so, uh, I wanted to ask about your view, so you’re, based on, based on some of your

[00:32:10] earlier writings, uh, and almost all effective altruisms are not theists, um, and so what, can you give a quick summary of your belief and what other effective altruists counter you

[00:32:23] with? Yeah, so I’m a theist in the sense that I think that there’s a God of limitless power, knowledge, and goodness who, you know, made the world and stuff. Um, uh, I guess

[00:32:33] my, my view on the, on God is relatively similar to the views of the typical religious person, but minus the specific religious commitments. So Muslims believe in a God who, you

[00:32:44] know, perfect and made the world, and then they also think that he provided direct revelation to Muhammad in 8th century Arabia. I don’t believe that last bit, obviously.

[00:32:54] Um, but I believe, you know, or, you know, Christians think that there’s a perfect God who, limitless power, knowledge, and goodness, and then this being is triune and became

[00:33:03] incarnate in first century Roman Palestine. I don’t believe that. Um, uh, I mean, so with EAs not agreeing with theism, I mean, in general, EAs don’t have any special expertise

[00:33:18] when it comes to philosophy. I mean, they do relative to the general public, they’re much more informed about philosophy than most people. But, I mean, you know, most of them, most

[00:33:26] of them don’t have a degree in philosophy. Most of them just aren’t aware of most of the serious philosophical arguments for theism. So, um, uh, in general, I don’t, I don’t, I’m

[00:33:37] not that troubled by, like, the fact that the EA general consensus is against theism, given that I think that’s for largely sociological reasons, rather than that sort of,

[00:33:46] like, EA equips people with insights that show them that theism

[00:33:53] then, I mean, in terms of the arguments that EAs make for atheism, they’re just similar to the arguments that people in general make for atheism. So, you know, people appeal to the

[00:34:02] problem of evil, people appeal to, um, uh, you know, in response to fine-tuning, people will appeal to a multiverse, um, so, I guess I can talk more about any of those particular

[00:34:14] arguments if you want to hear about it. Okay, well, um, we, we almost have to wrap up here, but, uh, just one more thing, you, you, you’re one of the more, uh, probabilistic

[00:34:24] forward writers on, on Substack, I would say, uh, for, but for a lot of your claims, they’re not really, um, they’re not really something where you’re, you’re predicting a

[00:34:36] forward-looking outlook, like something that could be predicted with a prediction market. Does Bayesianism still work when you update your beliefs based on arguments rather than

[00:34:47] direct evidence? Yes. Um, well, so in general, when we have these arguments, the arguments provide direct evidence. So these aren’t like a priori, in general, I mean, there are some

[00:34:57] things where we have a priori philosophical arguments for things, and there, maybe there’s some analog of Bayesianism-ish,! you’re not really updating in the same way. Um, I mean,

[00:35:09] there will be considerations that should raise your credence, but, um, and there are, there are times you can employ the Bayesian method, but it doesn’t work exactly the same

[00:35:18] way. So, you know, one case is, so the reason people are non-utilitarians in general is, I mean, there are different reasons, but the most plausible reason is something like they

[00:35:27] find utilitarianism very unintuitive. um, but then I guess just one kind of Bayesian point is, if you think our moral intuitions are fallible, then, are sort of, like, reasonably

[00:35:40] reliable, but imperfect, then even if utilitarianism was correct, you would expect there to be cases where it diverged from our intuitions. And you should have kind of a wide,

[00:35:49] large amount of uncertainty about exactly how much it would diverge. And so then if you look at how much it actually diverges, sure, it diverges in lots of cases from our

[00:35:56] intuitions, but is it obvious that diverge more than the true moral theory? Um, I don’t know, like, I don’t have a strong intuition about, like, how many cases you’d expect the

[00:36:09] true moral theory to diverge from our intuitions. Um, so that’s a case where you can imply, employ Bayesian reasoning to think, to see that this is not decisive. Now,

[00:36:16] obviously, this on its own is not a defense of utilitarianism in that, uh, you’d need some reason to think utilitarianism is right, rather than just a reason to think that, uh, the,

[00:36:27] the fact that it conflicts with our intuitions is non-decisive. Um, but that’s at least some consideration that, that undercuts things to some significant degree. Um, and this is

[00:36:37] a general feature when, if you have a prince, a moral principle, or any other philosophical principle that applies to many, many, many different cases. So if you have,

[00:36:45] like, uh, um, uh, like, for example, the transit, there are apparent counterexamples to the transitivity of a better-than-relation. Transitivity of the better-than-relation a is

[00:36:56] better than b, and b is better than c, then a is better than c. Um, and so you have, you have apparent counterexamples to this in that there are cases where it looks like you have

[00:37:06] one thing better than another, the other better than the third thing, but the third thing’s better than the first thing. But I’m not that troubled by these because even if

[00:37:12] the transitivity of the better-than-relation were accurate, I would expect there to be cases where it was counterintuitive because it applies to every single comparison of all

[00:37:19] things in the history, you know, possible. So it’s not that surprising to find if there’s a principle that applies to many cases, find some cases where it’s unintuitive. In

[00:37:28] contrast, if there’s a case judgment and it conflicts with a principle, well, that’s a much bigger problem because the principles apply to many, many case judgments, while the

[00:37:35] case judgment, you wouldn’t expect it to conflict with many plausible principles. So that’s one case where Bayesianism can be relevant. Then if things are more direct where,

[00:37:47] if we’re talking about cases where they’re not things that you could, will decisively settle, but they are thinking about evidence. So like, there was a while when I was

[00:37:58] looking into what caused the megafauna to go extinct. The megafauna were like large, the large animals that were around 10,000 to 50,000 years ago. Like, you know, giant koalas

[00:38:11] and saber-toothed cats and woolly mammoths. Maybe woolly mammoths weren’t one, I forget. And there, even though it’s unlikely that there will be some direct confirmation of this,

[00:38:25] I think Bayesianism is very helpful for thinking through this. So like, the evidence for, for the megafauna being, having been made to go extinct because of human activity, was

[00:38:37] something like, they kept disappearing right after humans arrived on continents, which is very suspicious if the humans weren’t driving them extinct, that they would like, you

[00:38:46] know, like, we entered like New Zealand, I think it was, and then they went extinct, and we entered in like 1400. And they’re like, that’s weird. The other ones, like, these like

[00:38:55] large classes of birds went extinct right after we arrived. And then, it’s just like, on every continent it happened, it happened right after we arrived. There’s a nice chart from

[00:39:03] our world and data about this. I’m sorry, I might be butchering exactly which continents in which years. But, but yeah, and then there’s, you know, another point is that like,

[00:39:17] and that fact is like good for a massive base factor, maybe like a thousand, where it’s like way likelier that they would keep getting extinct after we arrive if we had driven

[00:39:26] them extinct. And then you, and then you, there’s also the fact that the, the extinction was least severe in Africa where we evolved alongside them. So if we drove them extinct,

[00:39:36] you would expect that cases where we and they co-evolved, extinctions would be least severe. Okay, and so other considerations like this that are good for a hefty base factor.

[00:39:44] And then you look at the considerations on the other side and the considerations on the other side were like, that, that we haven’t found that many fossil sites. And so then

[00:39:55] you’d have people be like, we haven’t found fossil sites of hunting these. There’s no evidence that we, that we hunted them to extinction. But the, I mean, there aren’t that

[00:40:04] many fossil sites in general. It’s like hard to find them. They’re mostly not preserved. So the fact that we haven’t found that many of them and we have found some and we have

[00:40:11] found some for a lot of the animals for any very significant base factor. So I think thinking in Bayes in terms is often helpful for thinking through this. It’s helpful for

[00:40:20] thinking through the existence of God, where you’re like just weighing the base factor from different arguments. So, yeah, so that’s the basic story. Yeah. So we have to wrap up

[00:40:31] soon. Thank you so much for taking the time to do this. Just before we leave, could you do the thing where you say the words backwards in the sentence? Yeah, yeah. Okay. Give me a

[00:40:42] 30 percent. Futarki is the best possible system of governance. Ikratuf Sayetseb Elbisop Mrafo Wait, governance? Yeah. Or government? Governance. Esnan Ravog. Incredible. Right.

[00:41:01] Okay. So, yeah, so thank you. Thank you. Yeah, this has been an interesting interview. Yeah. Good luck. I mean, you seem like a very smart person, so, you know, good luck in

[00:41:13] your future endeavors. Thank you. Bye.

We tried a new podcast layout, let us know what you think in the comments!

One of our most interesting conversations. Trevor has a really unique idea of a futures exchange for superforecaster consensus on a certain disputed truth, which sounds quite vague but you’ll learn more after you take a listen!

Episode #7 of the Supercycle podcast. Next episodes include Robin Hanson, Clément Lesage, and Lucas Kohorst.

I barely figured out the equipment in the Lighthaven podcast room, but nevertheless this was a lot of fun to record! You’ll learn about what goes down at one of the most esoteric conferences in the world. I also want to acknowledge Shreyas Kyada, who did a fantastic job of producing this episode!

Episode #6 of The Supercycle podcast. Episodes to be released soon: Robin Hanson, Trevor Gillan, Martin Shkreli. (!)

We discuss how the CFTC is handling lawsuits against states challenging their authority to regulate derivatives exchanges and how a changing CFTC could impact the industry in the future. How hard is it to put the genie back in the bottle? Markets predict a democratic president will be inaugurated in January 2029. What does that mean for Designated Contract Markets such as KalshiEX?

Episode #5 of The Supercycle podcast. The Supercycle will be live at Manifest to interview Robin Hanson. Hope to see you there!

Mentioned links:

Eli on the Minnesota bill:

Mick’s website: https://mickbransfield.com

Mick’s X: @MickBransfield

Legislation Tracker: https://mickbransfield.com/2026/02/28/prediction-market-2026-legislation-tracker/

Litigation Tracker: https://mickbransfield.com/2025/08/11/summary-of-legal-actions-involving-kalshis-sports-event-contracts/

Database of Prediction Market Projects: https://docs.google.com/spreadsheets/d/1vGjnJPxdnBKwag3Q9Uy_LdVAARaSHrI5CWiYKH7dV9s/edit?gid=1671608647#gid=1671608647

Our best episode yet! We discuss the value and ethics of democratizing information, how PredictIt got their no action letter, where the prediction market industry is going, and (as always) if futarchy could solve nuclear war.

Our strangest podcast. As a suspected AI joins in the middle of the meeting, we quickly decipher that this is, in fact, a real human using a voice filter. Nevertheless, we had some discussions about insurance using protocols such as Augur and how every prediction market is a bounty subject to manipulation.

Episode #3 of The Supercycle podcast. Later this week we’ll be analyzing public comments on the CFTC’s proposed rule-changes, and next week we’ll be airing an episode with Michael Fischer of Endpoint Arena, the first prediction market for clinical trial outcomes.

Eli will be attending a prediction market meetup in Zug, Switzerland, on May 15 courtesy of Elastics. Be sure to stop by if you’re in the area! RSVP here.

Harry has some great insights on market mechanics! One of the most interesting podcasts we’ve done, as well as the first episode co-hosted by both Eli and Flip since the rebrand.

Episode #2 of The Supercycle podcast. We’ve got some very exciting guests coming up, so stay tuned for the next episode drop on Monday! And in case you missed it, read our investigation into left-wing media company More Perfect Union.