The Supercycle

Transcript:

[00:10–00:19] all right all right welcome back to the super cycle today our guest is theo jaffe who

[00:19–00:24] previously had his own podcast called the theo jaffe podcast and then went to work on the new

[00:24–00:33] media team for for a16z and now is the co-host of mts or monitoring the situation which is one of

[00:33–00:40] of the best tech live streams. It’s probably one of the only tech live streams, but I watch it all

[00:40–00:47] the time. And it’s a part of the main places where I get my AI news, not only from their stream,

[00:47–00:51] but from their newsletter as well, which I definitely recommend subscribing to. And there,

[00:51–00:58] you can find them at mts.now. And so really excited to be able to get to chat with Theo today

[00:58–01:06] about new media and his opinions on AI takeoff and forecasting, et cetera.

[01:06–01:07] So welcome.

[01:07–01:09] Yeah, Eli, thanks for having me on.

[01:09–01:10] You know, I’m 22 years old.

[01:10–01:13] I’m the youngest person in the room in SF most of the time.

[01:13–01:15] Eli is one of the few people that makes me feel old.

[01:16–01:17] Yeah.

[01:17–01:18] Oh, yeah.

[01:18–01:23] And I forgot to mention we’re here at the Overhang Conference in Washington, D.C.,

[01:23–01:28] which is run by, it’s a project of Sparrow Infrastructure,

[01:29–01:32] which is also kind of light cone in which it’s like the manifest crowd,

[01:33–01:35] but sponsored by MNX.

[01:36–01:41] And so really excited to be here and start talking about this.

[01:41–01:44] And so for those who don’t know, can you explain A16Z,

[01:45–01:48] because they’re the sponsor of MTS, what’s their thesis about new media

[01:48–01:51] and why is it important to have new media?

[01:51–02:05] Well, A16Z is an investor in MTS. We were sort of incubated, spun out of the A16Z new media team. A lot of the original MTS team members, including me, were previously on the A16Z new media team.

[02:05–02:23] I think the theory behind new media at A16Z is like for the longest time, trad media, especially trad tech media, stuff like, well, to say nothing of the New York Times and the Wall Street Journal and whatnot, which are generally pretty bad on tech coverage, especially the New York Times.

[02:23–02:33] But even, you know, tech media like The Verge and TechCrunch and Wired have become like extremely negative to, hostile towards technology.

[02:34–02:41] Most of what you read in the Verge Wired Tech Crunch and so on is written by people who kind of hate technology.

[02:41–02:43] And they don’t really hide that.

[02:43–02:52] You know, to them, journalism is about, quote, speaking truth to power, which they interpret as just like being really critical and negative of everything.

[02:52–03:06] And so, you know, E16Z is an investment firm, Andreessen Horowitz, that invests in all kinds of amazing disruptive technologies from, you know, AI to space to biotech to software to like everything you can imagine, really.

[03:07–03:15] And so the idea behind new media is just to create media that is unapologetically positive towards technology.

[03:15–03:26] And not that this is dishonest or anything, like it is just true that technology is like one of the greatest levers that humanity has ever had for improving its own material living conditions.

[03:27–03:35] And so like to have media that is unapologetically pro-tech and also, you know, novel and interesting in other ways is very important.

[03:35–03:59] So while I agree with you that in general, in almost all cases, new innovation and new technologies are a net positive, but I’m wondering if you might feel that like traditional media has swung to bias negative on technology gradually.

[03:59–04:04] gradually and now they hold this bias, it’s something that they’re kind of like, they have

[04:04–04:10] to conform to. So like, even if there’s a single pro-tech writer at New York Times, they have to,

[04:10–04:14] you know, kind of agree with the PAC and say, you know, data centers are bad for the environment,

[04:14–04:18] which is obviously not true. Do you think the same thing could happen kind of in the other

[04:18–04:24] direction where you become so committed to the pro-tech premise that if it looked like, for

[04:24–04:29] example, even if you don’t think it’s the case now, if it looks like there’s a lot more AI

[04:29–04:34] safety incidents in the near future and you might like not want it to cover them honestly do you

[04:34–04:38] think it’s possible that it could become too biased in the other direction yes and like you

[04:38–04:44] do see this with some of the pro tech people on twitter who basically deny that ai could ever

[04:44–04:49] pose a risk of any kind especially an existential risk which is a very different position than ai

[04:49–04:55] as a technology will be immensely net positive i believe ai as technology in expectation will be

[04:55–04:59] immensely that positive but that doesn’t mean that there are absolutely no risks associated

[04:59–05:05] with ai it doesn’t mean that existential risk is complete sci-fi nonsense like it’s definitely a

[05:05–05:12] possibility at the minimum and and like there are people on x who kind of just deny this altogether

[05:12–05:18] they’re they’re just like they’re too accelerationist it it becomes ideological

[05:18–05:22] and it you know messes with their epistemics it messes with their model of the world

[05:22–05:28] like the the correct take here is like technology is good almost all the time

[05:28–05:34] all like almost all the time meaning there are very very very few edge cases where technology

[05:34–05:38] might not be good can you give an example of one of this yeah existential risk from ai like i think

[05:38–05:47] that if ai labs with the current level of if you want to call it safety that’s one thing but

[05:47–05:53] techniques to ensure that AI systems are controllable, steerable, reliable, and interpretable.

[05:53–05:58] If they were to vastly rapidly increase capabilities through recursive self-improvement

[05:58–06:05] without a corresponding increase in our capabilities to control, steer, and interpret

[06:05–06:10] these systems, then you could end up in a very, very bad situation where you have like

[06:10–06:14] immensely powerful technology that we’re not able to adequately steer.

[06:14–06:20] do you do you believe that that that kind of takeoff scenario is the path that we’re on

[06:20–06:26] currently it’s hard to say i think like a lot of ai forecasting sort of assumes that

[06:26–06:31] we’ll just like speed through a takeoff by default i don’t think that’s really the case i think that

[06:31–06:37] there are pretty strong incentives for companies to if they’re about to undergo like ai like full

[06:37–06:43] pretty hard takeoff for personal self-improvement and they have not yet

[06:43–06:51] received enough assurance that the resulting technology will go well for them like I think

[06:51–06:55] the incentive is just to not do that right like OpenAI doesn’t want to blow up the world

[06:55–07:01] even in the most like selfish narrow sense you can’t IPO if you’re dead

[07:01–07:31] Yeah, that’s true.

[07:31–07:37] be like 70% that we like achieve like a $30 trillion market cap or something based on the

[07:37–07:43] expected value for him that might be that might kind of contradict that no I think not I mean I

[07:43–07:48] think in like first of all if that happened like the researchers a lot of the researchers would

[07:48–07:55] just rebel like they would not be okay with kicking off full RSI if they thought there was a 10%

[07:55–08:03] chance of human extinction secondly i think that sam all that wouldn’t do that like i i

[08:03–08:04] You’re saying St. Albany wouldn’t do that?

[08:05–08:06] I think St. Albany wouldn’t do that.

[08:06–08:09] I think that like what opening I did after the hugging face incident,

[08:09–08:14] where they decided to pause their frontier RL training and implement a bunch of

[08:14–08:19] new safeguards and monitoring measures and whatnot was the correct and

[08:19–08:21] responsible thing to do.

[08:21–08:23] The hugging face incident itself, by the way,

[08:23–08:25] it was not a big deal at all.

[08:25–08:26] Like it was a big deal in,

[08:26–08:29] in its implications for the alignment of future systems,

[08:29–08:32] but in terms of what it actually was like opening,

[08:33–08:33] I hacked hugging face.

[08:33–08:36] It was a felony, but you know, no one was hurt.

[08:36–08:38] There was not a lot of monetary damage.

[08:39–08:48] If Sam Allman were like a full maximum accelerationist, he would have said like, oh, well, this, you know, safety incident clearly was just like a little bit of a monitoring failure.

[08:48–08:50] We’re not going to pause our frontier RL training.

[08:50–08:52] We’re not going to invest more resources in alignment.

[08:53–08:53] We’re going to go full speed ahead.

[08:54–08:54] And he didn’t do that.

[08:54–08:57] So I like I reject the premise sort of.

[08:57–09:10] Okay, and do you think, how effective currently do you think we can actually have safeguards in place? What’s your personal opinions on how well these are actually helping?

[09:10–09:17] I think they’re helping pretty substantially. I think that our level of...

[09:18–09:22] of alignment techniques are not sufficient to fully trust

[09:22–09:25] that if we went through a hard takeoff,

[09:25–09:27] things on the other end would be all right.

[09:27–09:32] But I think that like in the vast majority of cases,

[09:33–09:34] models do behave in aligned ways

[09:34–09:37] and in the vast majority of deployments, right?

[09:37–09:39] Like you don’t have to worry about your codex

[09:39–09:42] or your cloud code agent, you know,

[09:42–09:44] hacking into other companies.

[09:44–09:46] The worst it will do is sort of mislead you

[09:46–09:48] about the nature of its work.

[09:48–09:51] It will tell you it has done something that it hasn’t.

[09:51–09:56] But the misalignment of models in normal deployments

[09:57–09:58] is not a huge problem.

[10:01–10:04] Remember, companies are incentivized to spend a lot

[10:04–10:07] making sure that their models are well aligned

[10:07–10:09] in normal deployments.

[10:09–10:11] Most of the serious misalignment incidents

[10:11–10:13] in the hugging face incident, for example,

[10:13–10:15] that we’ve seen were in evals.

[10:16–10:35] And many of the other incidents that we’ve seen between Anthropic, OpenAI, Meta, and so on, aside from the Hugging Face incident, had the common factor of dealing with a cybersecurity firm called Irregular, which makes testing environments for the labs, which did a really shitty job making these testing environments.

[10:35–10:47] And so if you are training your models, you know, to oversimplify, if you’re training your models in faulty testing environments, it will learn to exploit those faults, which is what reward hacking is.

[10:48–10:54] And so you don’t want your models to be trained in sloppy, vibe-coded environments.

[10:55–10:56] And, okay, so if I could,

[10:57–11:01] I want to start talking a little bit about how you think,

[11:01–11:05] just to get back to our prediction market angle here with the super cycle,

[11:05–11:10] how do you think current AI forecasting has been?

[11:10–11:18] maybe AI 2027 and how do you think that forecasting is going to play a role in advising labs and the

[11:18–11:26] government about how to proceed with AI development what do you mean by curt like

[11:26–11:31] like that the forecast like AI forecasters like Dan Cook, Tylo and other people who are making

[11:31–11:37] predictions about how takeoff is good like different takeoff scenarios right yeah so I have a huge

[11:37–11:44] amount of respect for Daniel Povetajlo, Eli Liflund, and the rest of the AI Futures project

[11:44–11:52] team, Thomas Larson, Romeo Dean, and so on, everyone else involved. I think that they

[11:52–12:00] produce some of the most rigorous forecasts of anyone in AI. Like, there are definitely

[12:00–12:06] things to criticize about the way they arrive at their conclusions. But, you know, the critiques

[12:06–12:12] are definitely not that they lack rigor or effort or quantitative chops. Like they’re really,

[12:12–12:18] really good at this stuff. So the AI Futures project has produced three main things. AI 2027,

[12:18–12:24] which is their original scenario forecast of what things might look like if humanity sort of

[12:24–12:32] maximally raced to superintelligence with pretty aggressive estimates for both timelines and takeoff.

[12:32–12:48] Then they produced the AI Futures model, which is their quantitative model of AI takeoff with a whole bunch of different parameters you can check out, like, for example, how much uplift do coding models give to researchers today, for example.

[12:48–12:54] And you can change the parameters in the model and it will produce different estimates for timelines and takeoff.

[12:55–13:00] And then AI 2040, their most recent piece is kind of a mix of a scenario forecast.

[13:00–13:07] I think they explicitly said it’s not really a forecast, but it’s just their target scenario for Planet.

[13:08–13:13] Yeah, it’s a forecast under specific assumptions that they don’t expect to actually happen.

[13:13–13:14] Okay, yeah, that’s fair.

[13:14–13:20] And it’s a combination of a scenario forecast and policy recommendation paper.

[13:22–13:28] And AI 2040 is, I think, one of the best AI policy papers that has been written.

[13:28–13:33] there’s like there’s definitely things to critique about it it’s a little bit too authoritarian for

[13:33–13:38] my taste but like it involves ai actually continuing to get better and diffuse throughout

[13:38–13:44] the economy and produce sort of a slow rolling singularity rather than a lot of ai policy that’s

[13:44–13:50] basically just shut down ai which would be terrible so so i know you personally have a low p doom but

[13:50–13:57] now you’re also giving a lot of credit to the ai futures project which has a very doomer outlook

[13:57–14:02] can you explain that discrepancy between your PDOOM and the PDOOM of the AI Futures Project,

[14:02–14:06] whereas if you think there’s a lot of credibility in their forecasts?

[14:08–14:14] The PDOOM of the different AI Futures Project members varies. I think Daniel Kropotajlo is

[14:14–14:20] like 70%. I think Eli Liflund is like 30%, something like that. I think some of the others

[14:20–14:28] or lower but like i i don’t how do i say this like your estimations of timelines and takeoff

[14:28–14:34] don’t have like a ton to do with pdoom like pdoom is largely about like part of it is like

[14:34–14:39] is the ai capable enough to kill everyone i mean but ultimately their forecasts end with human

[14:39–14:49] extinction like sort of 2027 and 2027 in in in the the takeoff scenario in the race it sounds

[14:49–14:56] like we we’re we are going to be in a race so no i don’t think so why not because like the

[14:56–15:02] incentives aren’t really there like nobody wants to risk it like all of them well i mean i think

[15:02–15:07] it would be kind of unfair to say the incentives aren’t there it’s trillion tens of trillions of

[15:07–15:11] dollars of market cap well there’s tens of trillions of dollars of market cap that you can

[15:11–15:17] get with a slower takeoff that will lower debune yeah and of course there are all kinds of exogenous

[15:17–15:23] political pressures as well, which we’re starting to see now. Can you explain a scenario in which

[15:23–15:31] there’s a bilateral slowdown between the U.S. and China, where we can verify that China would

[15:31–15:35] actually be slowing down and how that would actually lead to a lower P2? Because I kind of

[15:35–15:41] just don’t see that. Well, they describe such a scenario in AI 2040, which is the U.S. and China,

[15:41–15:47] both sort of spooked by AI capabilities and the potential for very rapid RSI decide to make a

[15:47–15:55] bilateral agreement that’s like both leaders understand RSI would be really bad. If one of us

[15:55–16:00] decides to go full RSI, we risk like completely losing control rather than gaining unilateral

[16:00–16:05] dominance over the other. I think this is kind of where things are headed. Like I think

[16:05–16:12] politicians on both sides of the Pacific are increasingly worried about what would happen if

[16:12–16:21] we did very rapid RSI. And of course, like, we’re going to go leaders for a second, like so many

[16:21–16:26] American politicians, like American society in general is pretty primed to reject new technology.

[16:26–16:31] Like, look at how difficult it’s been to get like Waymo’s on the road in blue cities like New York.

[16:31–16:37] yeah yeah it’s like and a waymo is like one of the most ev positive technologies you can imagine

[16:37–16:43] i was saying you can you can go to prison for for murdering someone but mom donnie can’t go to

[16:43–16:48] prison for letting all those car taxi deaths happen when you get hit by a taxi exactly like

[16:48–16:54] waymo is literally like a policy button that you can press to not have tens of thousands of people

[16:54–17:00] die every year and yet like the u.s finds it so difficult to do that so if it’s if there’s there’s

[17:00–17:05] much political pressure against these like very clearly ev positive technologies of course there’s

[17:05–17:10] going to be massive political backlash against the technology whose ev is much more unclear which is

[17:10–17:18] you know artificial super intelligence right but i would say there’s like for for waymo it’s kind

[17:18–17:24] of like a smaller a smaller thing that’s like okay there’s like these one or two kind of

[17:24–17:30] There’s like a Google, it was Waymo, and then Tesla, who are trying to put the robo-taxis on the road.

[17:30–17:40] But then AI is kind of like a totally different reference class with much more pressures on all sides and much more financial incentives going around to accelerate.

[17:41–17:45] But I mean, you do have a point, but that would just be my pushback.

[17:45–17:51] The financial incentives don’t push towards maximum acceleration if you believe in existential risk, basically.

[17:51–17:59] right but the ev that doesn’t mean the ev wouldn’t be positive necessarily for the ai lab

[17:59–18:06] leaders well like open ai for example which has been criticized many times for not caring about

[18:06–18:11] safety like has slowed down in costly ways like pausing their for trl training you don’t think

[18:11–18:17] it’s marketing no why not why would it be marketing be because they want to show because

[18:17–18:21] because there’s gonna be so much backlash, as you were saying, they want to look like they’re slowed down.

[18:21–18:23] there are ways that they could-

[18:23–18:30] could signal slowdowns in non-costly ways that don’t actually involve slowdowns like they could

[18:30–18:35] you know put out some paper claiming like they’re going to increase safety with like very vague

[18:35–18:39] details then just not do it but like pausing frontier training runs is like very costly

[18:39–18:45] like a lot is riding on okis ability to produce better models that help create more economic

[18:45–18:50] value that help them pay back all their compute commitments for example okay yeah but like like

[18:50–18:57] You’ve got to remember, the people who are launching these labs really believe in, they’re really AI-pilled.

[18:57–18:58] They’re really AI-pilled.

[18:58–18:59] They believe in the singularity.

[18:59–19:00] They believe in existential risk.

[19:00–19:11] If you look at Sam Ullman’s blog post from 2015, before he started OpenAI, he was very clear that he thinks that the largest existential threat to humanity over the coming decades is artificial intelligence.

[19:11–19:14] Obviously, it’s also the largest opportunity for humanity.

[19:14–19:21] but these are not you know total mercenary businessmen who don’t understand the potential

[19:21–19:28] of the risk of AI and Dario even more so what do you think I I watched your your podcast

[19:28–19:33] with Liron Shapira and one of the things you asked him which I actually kind of argued when I

[19:33–19:39] was on his podcast in debates is that there’s there’s little rigor in in like just throwing

[19:39–19:46] out a p doom totally and do you think that it kind of lowers credibility for the doomers that

[19:46–19:57] many of them do that yes basically like i the the differences between what causes you know

[19:57–20:03] evan hubbinger from anthropic to arrive at 10 or greater than 10 p doom versus daniel

[20:03–20:05] to arrive at 70% PDOOM.

[20:06–20:09] I don’t understand how you would actually

[20:09–20:12] lay out those cruxes in kind of

[20:12–20:15] a useful, rigorous, and quantitative way.

[20:15–20:18] I definitely think it detracts from the credibility of the Doomers

[20:18–20:21] to some extent, and all the more so if they

[20:21–20:24] then make up a PDOOM number and then use it

[20:24–20:27] as a parameter in some

[20:27–20:28] EV calculation equation.

[20:30–20:32] That’s how you get these Pascal’s mugging

[20:32–20:38] kind of things where it’s like oh you know even if pdoom is like five percent it’s like the worst

[20:38–20:45] thing in the universe and so we just slow down around like basically if you’re going to like

[20:45–20:50] come up with policy recommendations based on like quantitative measured quantitative estimates of

[20:50–20:55] things you should ensure that those quantitative estimates are pretty rigorous and like it’s much

[20:55–21:01] easier to do rigorous forecasting of ai timelines of ai takeoff because like it’s it’s a lot of

[21:01–21:07] extrapolation from stuff we’ve seen in the past, like the rate of growth and effective compute,

[21:07–21:13] for example, the rate of algorithmic improvements, the rate of progress in Moore’s law, you know,

[21:13–21:18] transistor density per chip or performance per watt. Like these, these are all things that are

[21:18–21:23] much easier to forecast. And like, if we do have artificial super intelligence, like,

[21:23–21:29] would it want to kill us? And if so, like, how would it do it? And if so, like, how long would

[21:29–21:35] to take and so on and so on these are all very like hard to answer questions yeah i think i i

[21:35–21:40] kind of agreed with with lear on at the end of the podcast that while i wasn’t going to form a

[21:40–21:46] specific pdoom estimate i think what he calls the sane zone a 10 to 90 pdoom sounds

[21:46–21:53] pretty reasonable to me yeah i don’t know 90 sounds pretty unreasonable unless we’re talking

[21:53–22:00] about very long time presence but like 90% PDOOM by the end of the decade seems like implausible

[22:00–22:06] yeah it’s it’s possible I just think there’s like way too much uncertainty to to pin down any sort of

[22:06–22:12] range it makes sense yeah and like you know into the medium term future like I think PDOOM

[22:12–22:19] under 10% totally makes sense maybe I don’t know I might push back a bit there but what do

[22:19–22:27] you think about, especially over the past week or two weeks, like the big push into the public,

[22:27–22:33] into the mainstream that the doomers have had, what do you think about how that’s going to impact

[22:33–22:39] the discourse among the general public? It’s going to make it suck.

[22:39–22:48] like i hmm you know i i am concerned about ai risks i’m not even sure that raising public

[22:48–22:52] salience about these things broadly is the best way to address them like i think it’s just going

[22:52–22:57] to end up with massive backlash basically butlerian jihad against the technology and it’s

[22:57–23:02] going to suck it’s like we already have like bernie sanders saying we’re going to do a

[23:02–23:06] moratorium on data centers and we’re going to pause all ai training and we’re going to permanently

[23:06–23:11] banned superintelligence, where superintelligence is defined as, I’m going to mess up the exact

[23:11–23:13] wording, but something like...

[23:13–23:15] Yeah, so what was I saying?

[23:15–23:17] AI discourse is gonna suck.

[23:17–23:19] Oh yeah, you were saying what Bernie- AI discourse is gonna suck.

[23:19–23:21] Yeah. Super intelligence,

[23:21–23:23] how Bernie defines it. How Bernie defines-

[23:23–23:25] Yeah, it’s just like,

[23:25–23:28] I tweeted the other day, this is the least stupid AI discourse we’ll ever be.

[23:28–23:40] I think that, you know, those of us who have been in this world for some time around a lot of SF people, lab people, forecasting people, rationalists, very rigorous sort of world models and probability estimations.

[23:40–23:56] Like all of this is just going to get swept away and AI is going to become just another political topic with, you know, dumb people saying stupid things, mostly for the sake of their own aura farming rather than, you know, genuine desire to arrive at a sort of shared truth.

[23:57–24:01] It’s like, it’s going to be so bad. It already has become much worse than it was.

[24:02–24:07] Like, I think that the epistemics around this stuff are just going to like kind of get poisoned.

[24:07–24:16] and you know like this is part of the vision of mts actually is like we’re trying to help fix

[24:16–24:20] the discourse we’re trying to help steer the discourse in a positive direction that helps

[24:20–24:28] people understand the truth about things that are going on yeah yeah that makes total sense

[24:28–24:46] I saw a Politico poll a couple days ago that showed 17% of Americans think that it’s almost certain, like their PD is essentially, they say, almost certain that AI is going to kill everyone, which seems kind of extreme to me.

[24:46–24:47] Well, it’s extreme.

[24:47–24:53] Even the most doom-pilled of doomers wouldn’t say it’s almost certain.

[24:53–24:57] Well, the most doom-pilled of doomers, like Ginkowski would say 99%.

[24:57–24:59] I think you’d count it like 90%.

[24:59–25:01] Of the quantitatively rigorous...

[25:01–25:05] 75th percentile of rationalists wouldn’t say almost certain.

[25:05–25:08] Yeah, I mean, of the quantitatively rigorous kind of doomers,

[25:08–25:12] like Daniel Kovatajla, his PDO is 70%, which is not almost certain.

[25:13–25:15] But regardless, it’s also silly because...

[25:15–25:17] You say Yudkowsky is not quantitatively rigorous?

[25:18–25:19] Not really.

[25:20–25:21] But what are your thoughts on it?

[25:21–25:26] I think Yudkowsky’s beliefs on this are sort of more religious.

[25:27–25:33] he, you know, he has this very like kind of elegant theory of the way intelligence works.

[25:33–25:40] you know, like me, Yuri has had a lot of like very, like thoughtful writing on this

[25:40–25:45] that I think is just like wrong. I think it’s kind of pretty paradigmatic, so to speak. Like,

[25:45–25:51] I don’t think that, building AI is akin to like summoning alien intelligence, unless you like

[25:51–25:57] do it really really wrong i don’t think that like i think that yudkowsky was just wrong that

[25:57–26:02] there’s no fire alarm before agi we just had a fire alarm before agi it was a hugging face incident

[26:02–26:07] like there will be lots of other fire alarms i think that he was wrong that we have like one

[26:07–26:13] critical try to get alignment right and if we don’t then it kills everyone like a lot of these

[26:13–26:20] assumptions only make sense in like an extremely foony world that we do not appear to be in

[26:20–26:31] In reality, the world that we live in now is we have a lot of different actors with a lot of sort of AGI-ish things that are sort of mostly aligned.

[26:32–26:38] Obviously, the situation can still go quite wrong from here, but it looks just much better than what Yukowsky imagined 20 years ago.

[26:40–26:42] Yeah, I think that makes sense.

[26:42–26:49] And I think I want to go to a 415 session, so I think we can stop here.

[26:49–26:52] but Theo, thanks so much for, for doing the, the interview.

[26:53–26:58] Absolutely. yeah. So, go watch, go watch MTS. It’s good.

[26:58–27:02] X.com slash MTS live. Yeah. Thanks for having me on. It’s good to be a guest.

[27:03–27:06] I’m so used to being a host. Yeah. Yeah. All right. all right.

Get full access to The Supercycle at supercycle.blog/subscribe

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, 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.

Get full access to The Supercycle at supercycle.blog/subscribe

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 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.

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Featuring Clément Lesage.

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

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Cool live mention market quoting at the beginning, a bit off the rails at the end.

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It was very cool to be able to interview Robin Hanson at Manifest. If you’re into alternative futarchy concepts and market mechanisms, this interview is for you.

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Featuring Trevor Gillan.

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.

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Featuring Quroe (a repeat guest), Tumbles, and Chris Gerlacher.

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. (!)

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Featuring Mick Bransfield.

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

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I was honored to speak at this Elastics event at CV Labs Zug. My talk is about 10 minutes, but you can watch Adhi Rajaprabhakaran and Noah Zingler-Sternig speak at the same event in the full recording.

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