Nate Silver on Life's Mixed Strategies

Summary

Conversations with Tyler: Nate Silver on Life’s Mixed Strategies

Podcast: Conversations with Tyler
Host: Tyler Cowen
Guest: Nate Silver
Date: August 13, 2025
Location: Live in New York
Occasion: Release of paperback edition of Nate Silver’s On the Edge: The Art of Risking Everything

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Episode Overview

In this wide-ranging, incisive conversation, Tyler Cowen interviews Nate Silver, statistician, poker player, and author, to explore the art (and science) of risk-taking in life, politics, sports, and prediction. The two dig deep into probabilistic thinking, the limitations and virtues of academic knowledge, prediction markets, and Silver’s personal approaches to learning, forecasting, and living with risk. The discussion is laced with practical examples, especially from sports, politics, and artificial intelligence, and features candid reflection on Silver’s own evolving views.

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Key Discussion Points and Insights

Expected Utility, Mixed Strategies, and Poker

• Poker and Expected Utility: Silver spends "a tenth of his time" playing poker, where expected utility theory and equilibrium are explicit and practical (01:02).
• Equilibrium Misunderstandings: Many grasp expected value but not equilibrium—"what equilibrium that should emerge given everyone has their incentives" (01:18).
• Randomization in Poker: Silver sometimes literally randomizes his play based on external factors like the tournament clock (02:03).
  • “I will literally randomize sometimes. You look at the tournament clock – if it's a high number, you'll bluff, and if it's a low number, then you'll play passively.” – Nate Silver (02:03)
• Tells and Real-World Applications: While useful, reading tells is contextual and not always transferable. Building a “database” of behavioral cues through observation is crucial (03:34–05:06).
  • “If you're right 60% of the time ... that's a huge edge. In gambling, any 55–45 edge is enormous.” – Nate Silver (05:04)
• Translating to Sports and Life: Observing body language in NBA games can provide betting edges—though these are slim and qualitative (05:13).

The Value and Decline of Academic Literature

• Declining Impact: Silver admits reading more substacks than academic journals (05:56). He argues that most academic papers could function as blog posts.
  • “90% of academic papers would work perfectly fine as blog posts, right?” – Nate Silver (08:13)
• Dry Tone and Value of Voice: The “sterile tone” of academic writing loses the nuance, jokes, and speculations newsletters provide (08:25).
• Effect of Politics on Academia: Many academics fall into “reflexively anti-Trump” thinking, losing subtlety and nuance (06:11).

Risk Attitudes and Human Nature

• Segregating Risk: Most people have domain-specific risk attitudes, inconsistent across areas.
  • Example: Ezekiel Emanuel avoids restaurants due to COVID yet rides a motorcycle (11:48).
• Meta-Rationality and Evolution: Apparent irrationalities like loss aversion may be rational “on a higher plane” or evolutionary in origin (12:15).
• Consistent Risk-Takers: Some Silicon Valley types are integrated, but this can limit feedback and lead to overconfidence (13:17).

Maximizing Expected Value and Longevity

• Life Optimization: Silver finds total self-optimization for longevity unappealing; he focuses on “serious but incremental” improvements (08:51).
• Pascal’s Wager: He dismisses Pascal’s Wager: “I don’t really believe in heaven as such... I put P = 0.001” (09:52, 10:00).
  • “I do think that some of the AI stuff ought to have people asking a few more questions about the nature of reality, I suppose.” – Nate Silver (11:00)

Politics, Prediction Markets, and Voting Mechanisms

• Ranked Choice Voting (RCV): Silver is "mildly anti-RCV", citing non-Condorcet optimality, slow counts, and manipulability (16:50–18:22).
• Proportional Representation: In principle, Silver would adopt it due to his critique of the two-party system, though he notes veto points may be good for economic growth (19:24).
• Prediction Markets’ Quality: Liquidity is up, but mispricings remain (e.g., non-eligible candidates appearing in betting) (21:00).
• External 'Savior' Candidates: The chance of an outside centrist emerging is lower now; primaries reflect general election strength (23:29–25:43).
• Democratic Nominee Prediction: AOC leads Silver’s list but with low probability (“no one’s higher than 15%”) (27:37–28:20).

AI, Forecasting, and Human vs Machine

• Superforecasting Timelines: Silver holds to his earlier “10 to 15 years” for AI to reach human superforecaster level, citing the complexity and dynamism of the problems (28:31–29:15).
• Teaching to the Test: Silver is skeptical that AI’s Math Olympiad success translates to real-world dynamic problem-solving (29:15–30:46).
• General vs Super-Intelligence: Large language models are making progress, but a leap to AGI or super-intelligence is not imminent in Silver’s view (31:15–32:22).

Sports: NBA, Betting, and the Nature of Competition

• Sports Betting Edges: Live observation (“courtsiding”) can provide minute but important betting edges (16:16).
• NBA Structure and Injuries: Suggests incremental schedule reductions or individual player rest requirements to combat injuries (39:13–41:43).
• NBA Draft and Socialism vs Capitalism in Sports: Silver favors the “wheel” system; American sports are more “socialist” in structure than European (38:08–39:09).
• LeBron James’s Future: Advises LeBron to join a young team “where I can win a title and mentor some guys,” like the Spurs (42:05–43:33).
• Long-shot vs. Favorite Teams: The theory that only a handful of NBA teams can win is “overstated”; the history is more complex (44:05).
• WNBA Betting: Edges exist in women’s sports due to less information and reporting; “knowledge nobody else has” is valuable (36:37–37:52).

Social Trends, LGBTQ in Sports, and Cultural Norms

• Coming Out in Sports: Surprised by lack of NBA players coming out as gay since Jason Collins (34:36).
• WNBA Comparisons: Large gulf between men’s and women’s leagues in openness; unique “fascinating” demographics at WNBA games (36:13).
• Selection Mechanisms: Possibly harder for gay athletes to stay on the pro track due to early life factors (35:37).

Prediction and Intellectual Community

• Mentorship and Learning: Silver still looks to Bill James, Richard Thaler, and online writers like Zvi Moshkowits for ideas and updates, especially on AI (46:18–48:00).
• Rationalist/EA Community: Sees the rationalist and AI safety communities as open, experimental, sometimes naive, and very “weird” even for tech (48:13).
• Bay Area vs New York: Cultural divergence—Bay Area is more “aloof” and experimental, even in small social signals (51:05–52:15).

Global Politics and Populism

• Populist Right: Momentum for the populist right surged post-pandemic but may be peaking in core countries (54:51–56:28).
• Immigration Politics: Voters want immigration restriction, but policy moves slowly; some leftist leaders (Denmark, Trudeau in Canada) shifting positions (56:28–57:20).
• Thermostatic Politics: Populism could soon recede as anti-status-quo sentiment cycles; US system achieves competitive 50/50 coalitions reliably (55:52–58:35).

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Notable Quotes and Memorable Moments

• On randomness and equilibrium:
  “I will literally randomize sometimes. You look at the tournament clock – if it's a high number, you'll bluff, and if it's a low number, then you'll play passively.” – Nate Silver (02:03)

• On tells and expertise:
  “If you're right 60% of the time ... that's a huge edge. In gambling, any 55–45 edge is enormous.” – Nate Silver (05:04)

• On academic writing:
  “90% of academic papers would work perfectly fine as blog posts, right?” – Nate Silver (08:13)

• On Pascal’s Wager:
  “I don’t really believe in heaven as such... I put P = 0.001.” – Nate Silver (10:00)

• On prediction markets:
  “You still see some obvious mispricings... Zoran actually had a 7% chance of becoming the Democratic nominee when that market launched at Polymarket and was born in Uganda. So there's no workaround to that, I don't think.” – Nate Silver (21:00)

• On AI and superforecasting:
  “I would say relative to a year ago, AI is about at the 40th percentile of progress I would have expected.” – Nate Silver (28:34)

• On writing and mentorship:
  “Good writing about technical subjects can still be good writing, period. If you're like an 8 out of 10 as a writer, and 8 out of 10 as a statistician, that might be more valuable than being a 10 out of 10 and a 2 out of 10 in the other area, potentially.” – Nate Silver (46:27)

• On polyamory and doom:
  “They're not being constrained by the norms of society. That thing is taken very seriously in that group [rationalists].” – Nate Silver (52:45)

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Timestamps for Important Segments

• Expected Utility & Mixed Strategies: 01:02–03:30
• Reading Tells in Poker & Life: 03:30–05:06
• Impact of Academic Literature: 05:50–08:43
• Risk-Taking Attitudes: 11:25–13:17
• Pascal’s Wager & Belief: 09:28–11:00
• Ranked Choice Voting: 16:45–18:22
• Proportional Representation: 19:17–20:38
• Prediction Markets’ Accuracy: 21:00–25:43
• AI Superforecasting Timeline: 28:20–32:22
• NBA Draft, Structure, Injuries: 38:08–41:43
• Mentorship and Learning: 46:14–48:00
• Populism and Immigration Politics: 54:51–58:35

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Conclusion and What’s Next for Nate Silver

Silver is currently working on an NFL modeling project and continues to expand his newsletter, "Silver Bulletin." He hints at future books, possibly on sports or risk, but is keeping specifics under wraps. Poker and forecasting remain central long-term interests, and the next U.S. election cycle still looms large in his plans (62:06–63:08).

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Takeaways

This episode is a masterclass in clear thinking under uncertainty. Silver and Cowen traverse theory and practice, highlighting how mixed strategies, probabilistic reasoning, and human judgment matter in fast-moving fields—be it poker, politics, sports, or AI. The conversation brims with actionable reflections on risk, the complex interplay between information and intuition, and a nuanced skepticism about both academic expertise and technological hype.

For listeners seeking to better understand risk, prediction, and rationality in modern life—this exchange is essential.

Transcript

[A]

Conversations with Tyler is produced by the Mercatus center at George Mason University, bridging the gap between academic ideas and real world problems. Learn [e-mail] for a full transcript of every conversation enhanced with helpful links, visit conversationswithtyler.com hello, everyone, and welcome to Conversations with Tyler. Today I'm chatting with Nate Silver live in New York. We are here to commemorate the paperback edition of Nate's on the the Art of Risking Everything, a book that last year I described sincerely as absolute fun on every page. We're also going to revisit some of our earlier predictions from a year ago and nine years ago and talk about everything I want to talk about. Nate.

[B]

Welcome, Tyler. Always a pleasure.

[A]

At current margins, do you learn anything from studying expected utility theory?

[B]

Well, I mean, I probably spend a tenth of my time playing poker. And like, certainly in that respect, you're quite explicit about calculating things like that. Right.

[A]

But are you learning new ideas, new theories, new concepts, or are you just applying what you learned, say, 13 years ago, whenever?

[B]

No, it still feels fresh to me. I mean, I think so. When I've talked about the book to people, people kind of understand the expected value part. Right. That's easily explicable to economics 101 people. I think the equilibrium part that solve for the equilibrium is something that people don't get as much is like, what equilibrium that should emerge given everyone has their incentives to play this hand optimally, so to speak. Right. Or everyone has different incentives, but they're all being rational in some capacity, like that kind of thing. I think about a lot when it comes to even, like, online discourse, for example, you know, why are people behaving a certain way on Twitter or X.

[A]

For example, let's say random Nash Equilibria. Do you take that seriously? It seems to hold with soccer kicks. Right. But in poker, anything you do, do you ever use the concept?

[B]

Oh, sure. No, I will literally randomize sometimes. Right. Where you look at the tournament clock, and if it's a high number, then you'll bluff. And if it's a low number, then you'll play more passively. For example, you can look at the rotation of your chips or things like that. I mean, look.

[A]

And you think some of the other people do it too.

[B]

Yeah, you can tell there's a certain look. You see, people are like looking up at the clock and it looks like they're kind of, I don't know, daydreaming. And they're. And they're waiting for a number. Right. You can like rotate your cards. One thing people didn't realize until they actually solved the Nash equilibrium for poker is how many mixed strategies there are. Basically every hand is a mix of some kind, right?

[A]

Yeah.

[B]

The flip side to that is that if you have any tell or read on your opponent at all, you move from literal indifference toward a dominant strategy in that context. Right. So I believe actually in, like, reading players tells, picking up on the moment and how you are perceived and how your table image shifts a lot. But no, I mean, poker has gone further than anything else in the literal manifestation of game theory in real life. I mean, you also see it in football and things like that. I'm working on an NFL model now. I wonder if NFL teams are optimally mixing their strategies, for example. But you see it there, right? Like a draw play on third and long. Right. Has a higher expected value because it's not expected and because you're supposed to always pass there. Right. And economics is amazing in the sense that it predicts human behavior, I think, fairly well. When you have repeated trials and people that have good feedback and incentive to be optimal.

[A]

Your ability to read tells, does it help you at all in real life? And if so, how?

[B]

There's probably some people reading, but it's very specific. Right. I was on the subway getting here. Right. I didn't have a seat on the train. I was like, oh, that person's gonna stand up. I can tell. And I was wrong. Right. The tells are not always 100% reliable.

[A]

If someone wants to learn how to read tells other than just, say, playing a lot of poker or doing the thing, how do they do that?

[B]

I think there is not a substitute for how it correlates to real world scenarios because it's so kind of contextual and kind of semantic. So I was at the World Series for most of June, played in the main event, which is a $10,000 tournament, and there was a guy who had like, a very rapid heartbeat that you could kind of see his. It's called, like, the carotid artery or cartridge. I don't know what it's called. Right. But his heart's beating in his neck, and so that's obviously often a tell. However, for some people, they get more nervous when they're bluffing. Some get more nervous when they have a strong hand. Right. So you have to, like, kind of correlate that with. With behavior, and that's contextual. The fact that he was an intermediate player and not a really good player or a complete novice fish, we call him in poker. So you know, you need. You're building up kind of an implicit database in your head. I think just by watching a lot and including like watching when you're not involved in a hand in poker, you're folding. And most films are poker most of the time. But like I'm watching people and like, kind of like making a little prediction. Oh, that feels strong. Something he did. Right. And then, you know, if you're right 60% of the time, 6, 60 instead of 50, random. Right. That's a huge edge. And in gambling, any 55, 45 edge is enormous. Right. And 60, 40 or 65, 35 is, will make you a very winning player.

[A]

And if you're watching an NBA game, is there any meaningful way in which you can read tells? Like, oh, Luka has a bad attitude tonight.

[B]

I'm not. I mean, you can tell from the body language of a team sometimes. I had a fairly good year betting the NBA playoffs. And like, does the team have it tonight? I mean, that should be in principle, you know, actionable, I would think. But, you know, you would think that markets might pick up on that. But yeah, there are things like this team is tired, they don't have the personnel. Right. Like, like the Nuggets in that OKC series. They're a great team, they beat my expectations, but they're not going to be able to keep up with this team for four quarters. But that's not like physical read so much as kind of. Again, it's kind of a little bit of physical observation and a lot of priors in context.

[A]

What was the last interesting thing you learned from the academic? Political science literature.

[B]

Oh, boy. This is going to seem like kind of like an insult to.

[A]

No, no, we're all for insults here.

[B]

I mean, look, I read a lot of substacks and things like that, right? Oh my gosh. I don't know.

[A]

I think that might be the right answer to be clear.

[B]

No, look, I think in some ways what has come out of academic circles have been interesting. I mean, I think effective altruism has been interesting. For example. I mean, certainly critical theory or whatever, the origins of wokeness have had a lot of influence on the discourse more broadly. But yeah, I don't, I don't really read a lot of journal articles anymore. It's also a lot of. It's gone on blue sky, I think, and I can't tolerate blue sky, really. It's too much of a circle jerk, I think. And so I think academics have maybe lost influence in that respect. Even though I have great experiences Talking to academics, and I'll do events at universities a couple of times a year. And they also kind of fall in this trap where it's so reflexively anti Trump, a lot of it. And I'm anti Trump, right. In most senses of that term. But something about it has kind of melted the brains a little bit of like the nuance and the subtlety and the things that, you know, the slow cooking method of academia, where you're supposed to take more time with things, I think is not always a good match. You being one of the major exceptions for kind of rapid fire reaction to the news cycle.

[A]

If there were a paper you wish someone would write, is there such a thing? Something you'd like to know but you don't know now? And in principle an academic could do it.

[B]

Yeah, maybe. Papers is kind of like the wrong part of the curve right now. Where like, yeah, like a substack newsletter that you take a couple of days with or a blog post. Right. Or a book. I mean, I'm still a big believer in books where you kind of go into a conclusion more in depth than, than anyone has before. Right. Or investigative journalism. I was just listening on the way over to the Ross Stouthead podcast with the woman who kind of helped to break the story of Jeffrey Epstein for the Miami Herald. And she's like, this was a major story featuring all types of people that are extremely well known and notorious internationally. But people are kind of lazy, right? And there were not like a lot of reporters really kind of pursuing every detail of that case. And so I think that 90% of academic papers would work perfectly fine as like blog posts, right? Including the tone of like, okay, I ran some regressions. Instead of having like Greek symbols and things like that. And the pretense of all of it.

[A]

Just say what you think, right? Just say what you think and let the world sort it out.

[B]

Say what you think. And also, you know, one of the things I like about writing a newsletter is I can use my tone to say, when am I speculating a little bit more? When am I making a joke, when am I being more serious, presenting some original finding and things like that? And that can be lost with the dry, sterile tone of a typical academic paper.

[A]

So when you think about maximizing expected value, how obsessed should you be with longevity? Should you be the next Brian Johnson?

[B]

Yeah, I mean, I ask myself a lot of these questions, right? I don't know. I think if you kind of hyper optimize for every parameter in your day to day life, then you probably wind up being fairly miserable, I think. Yeah. I mean, I guess it'd be hypocritical if I said I was, like, terribly concerned about longevity. I mean, in your 40s, you are kind of both by choice and by force. You kind of are required to be a little bit more careful about certain things, I suppose. Yeah. I haven't gotten on that bandwagon quite as much. I'm sure I will probably if we do this again in three years, I'll be, I don't know. Right. Some health nut or something. But for now. For now, I'm making serious but incremental.

[A]

Improvements, I guess, as a bet, or. Do you take Pascal's wager seriously?

[B]

I take the notion that we're profoundly uncertain about the nature of the universe seriously. Right.

[A]

Why not believe. Right. It would seem to maximize expected value. Just jump right in. You don't have to say you know the answer, but you would say, well, this is the most likely path I have for getting to. The answer is to start by believing something.

[B]

Well, I don't really believe in heaven as such. Right. Or I don't believe in kind of like Pascal's. I mean, I put, you know, P equals 0.001.

[A]

Well, not P equals. But there's some chance, right?

[B]

There's some chance. But, like, I mean, look, a lot of these pro. A lot of problems in. I guess it's called, like, infinite ethics. You know, you now have this big debate over, like, shrimp welfare.

[A]

Sure.

[B]

For example.

[A]

There's a lot of them.

[B]

And I'm like, we have enough problems that are in the kind of terrestrial, tractable realm. Right. Well, I'm just not going to worry about those things so much. And I think. I think, you know, as thought experiments, they can be helpful and fine. Right. But like, I think if you kind of like, you know, ignore the 1% of edge cases, it's probably good to have some people, Will McCaskill or whatever worrying about those things. We'll have people having, like, some influence on that. But, like, I'm trying to kind of move in the realm of. Of the tractable. I've always been more, you know, poker, you know, what matters is what hand you have. What matters is the read that you have, and the theory matters. But, like, information you have that's local and specific and actionable always predominates that.

[A]

So you dismiss Pascal's wager.

[B]

In short, I don't worry about Pascal's wager. I guess, you know, again, as I said, I've evolved toward being more affirmatively kind of agnostic as opposed to atheist, I suppose, but that's not because of Pascal's wager per se, just because of the strangeness of the world. I do think that some of the AI stuff ought to have people asking a few more questions about the nature of reality, I suppose, yeah.

[A]

There's plenty of material in your book about people who take a lot of risk. Subtitle is the Art of Risking Everything. How much do you think people segregate areas of risk taking? So, for instance, people might say, well, Tyler, you travel to some dangerous locations, but I'm totally afraid to scuba dive. I feel I'm very strongly segregated. I either take a fair amount of risk or close to none at all. Is that your model of most humans?

[B]

That's pretty normal, yeah. I mean, the example I think I use in the book is Ezekiel Emanuel. You can't go to. You shouldn't go to restaurants because we still have some Covid like now in 2022. 2023. Right. But he rides a motorcycle, which is like, known to be one of the most dangerous things per mile that you can do. Yeah, People are usually not super meta rational about risk, Right?

[A]

Should they be, like, should you have one general risk attitude? Is that more meta rational?

[B]

Look, I think things that seem irrational are often kind of rational on a higher plane. Right? Yeah, yeah, like, you know, like loss aversion. You know, you shouldn't. You know, I had a pair of headphones stolen from me the other day, right. And I felt very kind of guilty about that. We hadn't locked the door of our car and it was kind of, you know, kind of my fault.

[A]

But like, this is in New York, in.

[B]

Actually in upstate New York. Okay. Yeah, you gotta be careful of upstate.

[A]

Real home of thieves. Yeah, sure.

[B]

Yeah. But like. And you're like, okay, well, I can afford a new pair of headphones. It's not that big a deal. But if you were chronically sloppy about things like that, then that would cause more hardship and would be wasteful. And so, like, you know, loss aversion. Things like that aren't necessarily. I mean, they come from flawed iterations of rationality that might serve a higher purpose or might have served an evolutionary purpose earlier in kind of human civilization.

[A]

Can you think of cases of people who have truly integrated attitudes toward risk? That is, they have some level of risk taking. They apply it more or less evenly to all the things they do.

[B]

I mean, look.

[A]

Or those people just in jail.

[B]

Yeah, no, I mean, look, I think some of Silicon Valley has that attitude a Little bit. You know, it tends to be correlated with other things that might be less desirable. The calculator. Risk taking part is hard, I think. Right. If you're very successful. I mean, one thing you see when you do play poker is like people play very differently when they're on a winning streak. People play very differently when they're, when they're confident. They usually play better. Quite a bit better, in fact. And probably true for like human beings too. But you know, but I've had moments in my career where I kind of flew way too close to the sun and you stop getting, you begin getting worse feedback from people. That's very, that's very damaging. Right. Where people are sick of sycophantic around you potentially where you shut off criticism. I mean, Elon seems to have this problem a little bit. Although Elon at least has kind of, you know, Elon didn't concede to Trump on tariffs and skilled immigration, things like that. So he had some values at least. But that's a very big problem, I think. And you're surrounding yourself with, with yes men.

[A]

Why haven't you been to Oklahoma yet?

[B]

I'd like to go. I had designs on. I was hoping the Knicks were going to play okc, then I'd have gone for a couple of games there. It's cheaper to fly to okc, stay in a Ramada or something, or maybe, probably in a nice hotel than to go to masses for Garden for playoff games.

[A]

I was thinking of trying to see a playoff game in okc, not the finals. The tickets were so cheap. I was stunned because I'm used to Bay Area New York City prices, which I won't buy at. But I look at them and then I say, oh, that's too much.

[B]

New York City is kind of crazy for. I mean, I think one thing people have figured out is like all the kind of high taste things that like that rich people really like. Like there's been huge inflation. This is like the world's tiniest violin problem in like high end, like sushi Omakaze, for example. Right.

[A]

I don't do it anymore.

[B]

Yeah, no, it's, it's, you know, or like the US Open used to be something really fun going to the US Open in, in Flushing Meadows. Right. You go to like one of the early sessions. You can go like to any of the 17 courts or whatever. It used to be a pretty good deal. You get in for like 50 bucks and now it's like 200 bucks. Right. Which is kind of the fair price. But like you know, you see now Delta using AI tools to price discriminate.

[A]

That's right.

[B]

A little bit more, which I think we probably should.

[A]

That's going to be bad for you.

[B]

Yeah, probably. We should probably have some law. Although in New York we have. In New York we can fly on multiple carriers to almost everywhere. Yeah, no, I think.

[A]

But if they all price discriminate using a common algorithm, it's not literally collusive, but it's in a way like collusion.

[B]

Is that legal or not? You would know more than me.

[A]

There are pending court cases. I'm not sure how they're all being decided, but it will be an increasingly important issue.

[B]

Yeah, I mean, I think there'll be. I think some of this is short sighted in the sense that we'll probably have like a pretty. For profound backlash to it, I would think. Yeah. And I mean it rubs me the wrong way intuitively.

[A]

If you're betting on NBA games or just trying to enjoy them, how much does it help you to have seen the team live? Like if you go to see Oklahoma a few games, do you learn anything?

[B]

I mean, given that the edges are pretty small in general in sports betting, you know, you're probably either losing by 1% ROI or winning by 1% ROI, then any of that is helpful, I think. And also sometimes, you know, the markets are a little bit behind. If you're kind of there in the arena, it's called court signing. You can potentially pick up on things.

[A]

So it is helpful.

[B]

I think so, yeah. I mean again, if it improves you by like 0.5% your ROI, that becomes pretty meaningful in the context of a sports bet.

[A]

Does ranked choice voting really matter? Does it really matter in affecting outcomes?

[B]

It potentially can matter. I mean, particularly you've seen candidates adopt different strategies for it by cross endorsing, for example. Although I think I've kind of turned mildly anti RCV for a couple of reasons.

[A]

Why?

[B]

One is that it's not Condorce optimal. Right. The order in which candidates are eliminated. Hopefully I'm saying that name correctly, right?

[A]

Yeah.

[B]

The order in which candidates are eliminated can affect who wins. Like Brad Lander, for example, is kind of the compromise candidate between Mamdani and Cuomo in the New York mayoral race. It's plausible he would have won any head to head matchup, but he was eliminated third from last or I guess second from last. So he didn't get.

[A]

But would he have had a head to head matchup under a normal voting rule?

[B]

Well, I mean it's plausible. You could, I Mean, I don't know. Or I guess traditional runoff wouldn't be like any better, potentially. No, I mean, fair enough, right? You also almost had Mamdani wound up winning the first choice vote. Although in, in 2021, Eric Adams nearly lost to Katherine Garcia despite being way ahead in the penultimate round. And Garcia nearly was eliminated by Maya Wiley in the round before that. Right. So it's more gamable than you might think. Also, like, it takes a really long time to count the votes and all the tallies. Right. Like just yesterday we're taping this, what is it, July 21st or. Yeah, so three or four weeks after the election, you finally get the official tally of how all the ballots transferred. It wound up not being that close. But like, I think we need it more of a norm in American elections toward counting votes within 24 to 48 hours.

[A]

Brazil does it. Right.

[B]

India does. I was doing a little work on the Indian election, which was, which was fascinating in different ways. And you know, they have a physical polling place up in the Himalayas and things like that. I don't know how many languages they speak and how diverse in every respect that country is. And they count their results very quickly. They also have better exit polling in international countries where in the US the exit pollsters are trying to ask this 100 item, long, proper kind of political science, demographically weighted survey. Whereas in Europe and Latin America, places like that, it's like we just want to know who's going to win to make for more exciting tv. So just ask them one question, who'd you vote for? They do it in a very organized way. And so exit polling is quite good in foreign countries and quite bad elsewhere. But yeah, I think, you know, I don't think it should be a Republican talking point that we want the vote counted quickly and not taking days and weeks.

[A]

And if you could wave a magic wand and bring proportional representation to the United States at the national level, would you do it?

[B]

I think I'm enough of a critic of the two major parties that I'd have to say yes, for the most part. Although, I don't know. I mean, you know, we have more veto points in the American system that's probably been good for capitalism and economic growth and maybe a certain type of liberty, I suppose. Right. I mean, I think a question I'd like to see addressed more, maybe there's some good books is like, I see the US and Europe as diverging more and more economically. Europe is getting longer and longer lifespans. We're not really. Right. We still have fairly good economic growth and they don't really how much of that is kind of culture.

[A]

But our Europeans have long lifespans, right?

[B]

Well, that's what I'm saying.

[A]

If you're a Japanese American woman born in New Jersey, isn't that the world's longest lifespan?

[B]

It's pretty. I mean, New England is almost like Scandinavia or something. Right. Including being like, you know, slightly boring in certain ways. Right. The food's a little, a little plain. But yeah, I'm a big proponent of like kind of New England. It's not where I want to live. But like New England exceptionalism, they did much better under Covid. I mean, Boston had a very big outbreak early. Right. But like Vermont, New Hampshire, Maine had much lower Covid cases than anywhere else by far. In the United States. They're doing something clearly. Right.

[A]

If I look at some of the prediction markets, at times, I've seen Trump at 6 to 7% likely to be the next Republican nominee to run for president. Is that long shot bias that it's hard to shorten those markets? Do you think it's the right number? Is it just expressive bettors who want to see Trump again and they're willing to lose some money to send that message? What do you make of that?

[B]

So the prediction markets and I consult for Polymarket have gotten quite a lot better. They've gotten quite a lot more. More liquid, but you still see some obvious mispricings. Like Zoran actually had a 7% chance of becoming the Democratic nominee when that market launched at Polymarket and was born in Uganda. So there's no workaround to that, I don't think. I think on the papal betting markets, they didn't have Pope Leo priced higher than 1% or things like that. And I think they got overconfident about a system where they had no, no inside knowledge and the outside view was maybe only marginally useful and things like that. Look, I don't know. Yeah, I mean, we talked a little bit before about, like, about how some of the kind of academic historians, political scientists have kind of gotten enraptured by this anti Trump thing. And I do think the scenario where like Trump runs for a third term, I mean, I don't say it's zero. Right. And I'd say if it's 2%, then that's still alarmingly high. But it's probably, probably 2 and not. And not 6 or 7. I mean, you'd have to just totally. My question is, in that world, are we even having an election in the first place. Right. If you're just kind of flagrantly ignoring the Constitution that much. I mean, I think there are, it's.

[A]

Common that weird non democratic governments have elections of some kind. Right. Most of the, even the Soviet Union had quote unquote elections.

[B]

They, they do but like for that transition to occur in the US and also Trump is not particularly popular. His ratings kind of fell pretty fast early because of I think predominantly tariffs and then recovered. Now they've fallen again. I don't know how much of that is more terrorists versus if I think like the Epstein stuff, for example. Yeah, a lot of the scenario. I mean it helps for populists to be popular. We certainly have enough kind of checks and balances in the US People are not being force fed information from, from any one source. And so yeah, I worry about a lot of things with Trump. That's probably not the foremost concern.

[A]

I've seen Stephen Smith, the ESPN sports commentator, you probably even know him, but as high as 9% in the market to be Democratic nominee. What do you think his actual number is?

[B]

Maybe 2%.

[A]

I think it's 4 or 5%. I don't think it's crazy.

[B]

We had a draft of like the 20 most likely Democratic nominees. I think, I think Stephen Smith was like a ninth round. I found draft pick. Right.

[A]

And what does that scenario look like where he wins?

[B]

I think it's where Democrats feel really well. The reason why I would not be as optimistic as you are. Right. Is I think that other people with the platform will emerge. Right. Like I had not frankly heard of Zoran Mandani's name when we taped our last conversation a year ago when he might now be one of the five most recognizable names in the party. The fact that he's from Uganda prevents him from running for president. But you'll see other people emerge. I think there is probably some indication that you're going to have more, more energy on the left. More energy from people who are kind of like explicitly partisan, maybe to the point of being like a little bit paranoid even. Right. You have, you know, there is a little bit of election denialism among certain threads of Democrats. For example, that Trump stole the election from Kamala Harris. It's, it's, you know, it's nascent for now, but I think a partisan fighter. And also you're filtering out. I mean people get confused because they're like, okay, well why is the Democratic electorate? Because Cuomo did very well always in New York City when running statewide. Hillary Clinton did very well in New York City. It kind of diverted Bernie Sanders campaign in 2020, but it's because people in the center have migrated away from the Democratic Party. So you're having these two things that go on at once. I do think there's a chance that Democrats will overestimate how tolerant the rest of the country is for progressive governance. But with that said, I think J.D. vance is also a pretty flawed candidate. I think any other non Vance candidate is going to have a lot of trouble escaping the shadow of Trump. So I regard Democrats like a 55, 45 favorite for 2028 pricing. In some chance he'll nominate what I would think of as a suboptimal from an electoral standpoint candidate. I'm not telling anybody who they should nominate in terms of whose values they have. You know, I think people neglect the importance of the primary system and the optionality and the revealed preferences of voters. It is a pretty good proxy for running a general election campaign, is running a primary campaign that goes through all 50 states and you're raising money, you're giving speeches, things like that. But yeah, I think the idea of like an external savior who's a centrist is becoming a bit less likely than I would have thought a year ago.

[A]

Do you think the major tech companies have proprietary information where they can predict elections better than anyone or anything? They may not even bother to do it. But is it there?

[B]

In principle, I tend to doubt it. I mean one nice thing about polling, for all the flaws that polling has, and I think polling has, I wouldn't call it a crisis, but like on the route to a crisis is you do have a good baseline to compare with, right? You can say we are going to go back to like, you know, 1936, I think was the first time Gallup issued a presidential election poll and look empirically at how accurate those polls are at of days out from the election. Whereas there's nothing to kind of calibrate AI type models, right? I mean, you know, you could probably predict how an individual voter will vote. So if they're gathering, I mean the amount of things I tell.

[A]

But if you spent a lot of money and you have everyone's say Facebook posts and you just fed it into a sufficiently complicated model, wouldn't that be the best predictive model?

[B]

Well, look, if I know your just your name and your zip code, I can probably predict with 90% accuracy who you're going to vote for. Right? From the name you can infer gender, race, age. From the zip code, you can infer socioeconomic status, right. That gets you pretty darn far. The issue with elections is that they operate on this 1% or 2% margin. The polls will have a disaster because one candidate gets 51% instead of 47%. That's still pretty good. If you're estimating how many jelly beans there are in a jar and you're like, oh, 51% of them are purple, and it's really 47, then that's not too bad. Right. But you need a lot of precision for election polling, and you have plenty of ways to get to a kind of blunt, approximate view. But I think those would not be any superior to the other ways like polling.

[A]

Namely, do you have a prediction for most likely Democratic Party nominee? We may be doing another of these in three years. Right.

[B]

AOC was the first pick in the draft. I did. And I think that would be reinforced by the New York Merrill race. I mean, I think she's a very bright person who has demonstrated some tact in certain areas. And look again, I am not a socialist or democratic socialist. Right. But even I am someone who likes seeing new faces in the parties, right. Who thinks. Who had no affection for, like, the Clinton dynasty or the Cuomo dynasty or the. Or the Bidens, who can't seem to stay out of the spotlight. And so I think she's smart enough and fresh enough, good enough on social media. And so, you know, no one's higher than, like, 15%, but if you gave me a free bet, I think I'd pick her.

[A]

Now, speaking of predictions, a year ago, we talked about how long will it take AIs to be as good as human superforecasters? And you made a prediction where you said at least 10 to 15 years.

[B]

Yeah.

[A]

Now a year later, do you want to revisit that and revise?

[B]

I think it's probably about right. I mean, I would say relative to a year ago, AI is about at the 40th percentile of progress. I would have expected. I'd be curious what you would think. Right.

[A]

Well, a year ago, I said two to three years, and right now I'm going to say one to two years, which is the same prediction. So I think you're way too pessimistic in your timetable.

[B]

It depends on kind of how competitive the exercise is. Right. If it's like a.

[A]

Like a Math Olympiad tournament, and, you know, they just did gold medal performance, I said this last year, I said, in a year they're gonna do gold medal. And a year ago, they weren't sure how many Rs were in the word Strawberry. So you don't think on super forecasting, they can.

[B]

I think it's very different when you're dealing with a static problem as compared to a dynamic system where the inputs are changing all the time. So currently the AI labs, or not the AI labs, but like the large language models are very bad, very, very bad at like poker. They're not trained on poker data. I'm sure if you did train them, there are these things called solvers that are trained on poker data that do very well. Right. But like, they cannot quite impute the general patterns just from mediocre text data or amateurish kind of hand analysis. But if you probe them on why they're bad, they're like, yeah, maybe it's tough for us when you have a complicated evolving kind of game theory dynamic and you have to develop exploitive strategies very quickly. I mean, the amount of like, you know, it is amazing. Poker players where if you have a computer solve a poker hand to get to one where there's enough loss minimization, it can take like, you know, a very strong computer can take like minutes. Right. Whereas poker player makes those calculations implicitly in a handful of seconds. For example, I worry with the Math Olympiad stuff, there's like a little bit of teaching to the test where because you kind of set this as the goal that a large language model should have, that therefore there's a lot of prestige when you meet that goal. Potentially.

[A]

It isn't teaching to the test what we should do, even with humans, in a sense. Right. Like the test is what you think is important and that's what you ought to teach.

[B]

Well, but like the poker example, right? Or chess. I mean, I think AI models are very poor at chess from everything that I've heard, for example.

[A]

But other AI models play chess.

[B]

Great, correct? No, look, I mean, this gets to me, the question of kind of like, what's it mean to be generally intelligent? I mean, yeah, we'll probably have like scaffolding of model on top of model and you'll now patch different things. Like right now you can't really very effectively make like a plain reservation using ChatGPT. But I'm sure if you dedicate a resource to that, then you have these agentic models now. Or agent models are just kind of like creeping into the system a little bit.

[A]

But like, but those will work in less than a year, I think. I mean, there's an agentic model now.

[B]

Eight kind of rough AGI versus super intelligence. Right. I am less convinced that we're going to have Some intelligence explosion than I would have been, maybe. I don't think I was ever convinced of it. Right. But like, you know, this emergent superintelligence where you train it on relatively simple data and extrapolates beyond the data set. I mean, I think they do reason sometimes. I think they're kind of quite smart. And I no longer am like bashful about saying, oh, ChatGPT thinks this. I used to avoid that term think. But there's a big gap between approximate general intelligence for kind of desk jobs and then super intelligence on the one hand or then kind of AGI for physical labor on the other hand. I think people are much too quick to make that leap. And I think the Math Olympiad, in part because maybe the answers are somewhere latent in the training data. But even if they're not, if you try to solve. What's this, a Lucas critique, whatever else, Right. It's a Goodhart's law version of that. I think for AI models.

[A]

My intuition is that if you took five superforecasters and just had them write a five page prompt for GPT5, which will be out this summer, that we'd be there already. I don't think it would be super intelligence. You could say it's not AGI. But the human superforecasters, they're not that impressive. Right. Like they're not Einstein's. They just have good methods and they're disciplined.

[B]

It requires so much on general knowledge, I think both general knowledge of how the world works and heuristics. I think it might be a little tougher. I mean, I don't know. I mean, can you train on a thought process? Like I try to do that in real life sometimes. Right. Like my partner is an artist and has a very good eye for art and I don't. I maybe have a B. B plus, Right. But kind of like train myself on him and say, what does he like in art? And so, you know. But that probably caps me at like a B plus understanding, potentially. And in markets, you know, a B plus trader is often on the losing side of a trade.

[A]

How good do you think you could become at appreciating art? Let's say you put in two to three years, not full time, but made it one of your two or three big things.

[B]

After basketball, I'd probably be very studious about it. Look, I have confidence that when I devote myself to things, I can get kind of quite good at them. I think there's no physical limitation there, but still, I mean, look, I have four or five hobbies that kind of occupy all my time, some which have already become kind of professions. Right. I think maybe architecture more in the silver DNA. There are some architecture genes. We're related not by blood to Frank Lloyd Wright. Right. My grandma was an architect, designed the home I live in part time now in Westchester County. My uncle is an architect. So that would kind of be more the direction where I would go. I like things that are spatial. I have a very good spatial memory. If I've been to a restaurant before, even if it was a different restaurant 10 years ago, I remember that space very well. Whereas art, I have just an okay eye for it now.

[A]

Nine years ago, in our first conversation we discussed why more professional athletes had not come out as gay. And you and I both thought then that over the forthcoming time period more players from the NBA would come out as gay.

[B]

Yeah.

[A]

I asked ChatGPT, they said Jason Collins came out 12 years ago and since then it's been zero. Why is that? What did we get wrong?

[B]

I mean there's been a little bit more of a conservative backlash in sports where you see kind of more out spoken Trump supporters.

[A]

But that's very recent. Right. Where you have a nine year period, there's 2020. The extremes of woke are part of this period. Gay marriage is really quite solidified in American life and no one comes out.

[B]

It is surprising I think, in, in a lot of ways. You know, look, I am not so PC. I mean there may be kind of like some selection mechanisms. I don't necessarily know that they're, they're genetic but like maybe it becomes easier for gay people to like come out in high school and things like that. They're attracted to fields apart from sports even though, you know, gay men typically are, are quite in shape and quite athletic relative to straight men of the same demographic cohort otherwise.

[A]

Right.

[B]

So it's not like a physical ability thing, but like, but selecting into sports and as sports becomes more professional, I guess like not the right term at the high school level, but like you're tracked into like, you know, Cooper flag whatever. Right. You know, he didn't kind of spend all his high school in Maine. He went to Monteverde Academy or whatever else it was. And so if anything distracts you from that, whether it's kind of questions about your identity or anything else. Right. You kind of get off track. Maybe it's like kind of harder to make that up later on.

[A]

GPT also claims that 44 players in the WNBA have come out as something other than standard hetero. Yeah, it's a big gap, right?

[B]

It's a big gap for sure. Right. And I went to. I saw Caitlin Clark in the Indiana Fever against the New York Liberty last year. The WNBA is like, fascinating, right? It's a very, very, very different demographic than like any sporting event I've ever been at before. You know, I think it's kind of great. It's getting more cultural salience, I think.

[A]

Does betting help it in relative terms?

[B]

Two years ago, I had a really good time betting the women's NCAA basketball tournament, kind of before the markets had adapted. But I think people figured out like, okay, these are pretty soft lines. And so. Yeah, no, I mean, the issue is, like, in any type of betting, if you're any good, you'll get limited. If you have repeated success or even waitress at. Look ev plus EV at the casino, you'll also get limited quite quickly. But yeah, probably, like betting women's sports probably is. There's probably some smart kid out there, some smart college kid who's going to make a lot of money betting wnba, women's college basketball, women's anything, really.

[A]

And he's building a model or just using intuition or reading a lot on the Internet. How. How might he be doing that?

[B]

You know, my guess is the model combined with knowledge of the league can get you. Get you pretty far. Like, you know, so injury data, for example, is much worse for women's sports than for men's sports. They don't have the same reporting protocols. So if you were, you know, for. Even for the NCAA tournament, we actually were trying to use injury data in our women's NCAA model. And like, you have to like, do one by one individual Google searches for it. Right. And so, yeah, there's still a reward to like, having knowledge that nobody else has. Right. Or that few other people have. And people are basically pretty lazy and don't necessarily do the legwork on that.

[A]

You mentioned Cooper flag before. As you know, he was drafted number one by the Dallas Mavericks, who are quite a good team. And you have what, Houston and Philly drafting two and three and the Wizards draft number six.

[B]

Yeah.

[A]

Do we need to redesign the NBA draft lottery?

[B]

People complained before that there was like too much tanking. And now I guess they're saying that it doesn't reward tanking teams enough. No, I think it's probably. It's probably fairly optimal. I mean, this year was weird in the sense that like, all the top three teams are accomplished in certain ways. Right. Other things going for them. But no, I kind of like the old was it the Mike Zarin proposal for the Wheel where every team gets the first pick once every 30 years and you kind of know ahead of time which one it is. Yeah, I don't like incentivizing failure in any context. And I don't think fans mind like rooting for leagues where one team is great and the other is a perpetual underdog. Right. The most popular sport in the world, of course, is. Is soccer. And there you have teams that are winning teams every year for a century almost. In some cases, it doesn't seem to bother people as much. It's very weird how American sports, it's a cliche, Are kind of socialist and European sports are more capitalist, basically.

[A]

Is there a crisis in NBA regular season design?

[B]

I had been kind of a naysayer of this a little bit until I watched this year's playoffs and kind of how much higher octane they were and how enjoyable it was to watch a game from. From start to finish. Look, I think it's a moving target. If you shorten the season to 66 games or 72, people will still complain about that being too many. And I used to live near Madison Square Garden and for a regular Knicks game, the get in price could be 200 bucks. So I understand why they might not want to change it. But yeah, I think you probably want to go to maybe not 66, but maybe 72 or something like that and see how that goes.

[A]

There were so many decisive injuries this year, I forget the whole list. But it seems like almost half the important players, including the seventh game of the finals, were decided in essence by injury.

[B]

Well, look, in every other sport, right? In football, you're only playing half the downs roughly, right? Right. In hockey, apart from the goalie, you're playing, you know, there are three or four lines that shift, right. In baseball, you're only in the field half the time. The continuous strain on NBA basketball played at such a high. I mean, it's a totally different sport, right? You go back and like watch clips from like the 70s or the 80s and you're like, it's a totally different sport. I think it's a much better sport now. But like, I don't know, I mean, it sucks to see these guys like, you know, so great. Jayson Tatum's a very graceful player. Halliburton's a little herky jerky, but has a certain grace in how he steps. And like, yeah, I think the league has to. I mean, you see that in baseball too, with like Tommy John surgeries and things like that. And you know, there are always downsides to achieving optimization to a degree we didn't necessarily expect. And pushing things to the brink. Now the equilibrium probably is that players always are pushing things to the brink. Right. They're very competitive, they're extremely well compensated. Right. There is appropriately demand for coaches and trainers and everything else for, like, for high effort. I think you also have short term incentives. Teams don't really care that much about, like what happens to their star player if he's on the end of his contract. In theory, they might be happy. Yeah, but I think you have to have. I think you probably have to trim a few games off schedule or have something where maybe you say, okay, the individual player limit is 75 games. Right. Every player is guaranteed at least seven days rest. Maybe you are required to publish them in advance. So if I'm going to see Giannis play for Milwaukee, I have no interest in seeing the Bucks without Giannis in their current form, for example. Maybe something like that could be an intermediate step.

[A]

Let's say you're advising LeBron James and this coming year possibly is his last or certainly his next to last. The Lakers are not good enough to really go anywhere in the next year. Probably you agree with that. But he has a signed contract for what, about $50 million?

[B]

Yeah.

[A]

What do you advise? LeBron, just as a human being, LeBron, what should you maximize? What do you tell him?

[B]

LeBron's in a funny place because I think he's clearly, by the career metrics now the best NBA player of all time.

[A]

I agree.

[B]

I don't think he'll ever win the hearts of people. I mean, you could argue LeBron was not as good as Jordan at the peak. Right. You could argue Kareem's peak was also higher, maybe very early in his career. So, like, there's kind of nothing LeBron could do short of winning like three more titles. That would kind of put him ahead of Jordan in that conversation. I want to go to a young team where I can win a title and mentor some guys. I mean, go to the spurs, for example. That might be a lot of fun, right.

[A]

If they're not going to win a title with LeBron. Not this year.

[B]

Oh, I think the spurs, with LeBron James this year could potentially win a title.

[A]

Really? That would shock me. I would give that, I don't know, 4 or 5%.

[B]

I mean, WEMBY is very, very good. I mean, he was playing at a level where there's this darko NBA metric that kind of tracks performance in real time, which is usually not that relevant for like a 30 year old mid career player, but for like someone like VICT is relevant. Like he was probably the fifth best NBA player by the time he kind of shut down his season.

[A]

But the chance that he's still on the court by the end of the year and as such a young player, you look at the other true greats over time. Kareem won something very early. But usually people need, you know, Michael Jordan needed some years. LeBron.

[B]

So you don't think Wemby and LeBron would be. And you know, and they have little. Let's see.

[A]

I don't bet, but I'd love to bet against them.

[B]

Well, next year maybe then. Right? They have one more year of maturity. They probably fill in. Maybe Dylan Harper's a positive value asset at that point and LeBron can have his like you know, sign a minimum contract in San Antonio anyway.

[A]

We know that's your advice. What do you think of the hypothesis that in a given season there's only a few teams that can win it all and you know in advance who they are. So maybe last year it was Boston, Oklahoma, possibly Denver and you should just bet on those teams. Anything else is a true long shot. What do you think?

[B]

Look, I think every basketball fan believes in some like stylized version of that, but I think it's become like a little bit overstated. Many of the teams now, you know, the Nuggets people, until Jokic was seen as like this ultimate beast of a postseason player, people were very skeptical about their ability to thrive in the postseason. Right. You know, Toronto didn't fit the paradigm a couple of years ago. Even Giannis had struggled in the postseason before they found success later on. So yeah, look, you don't have that many years of relevant NBA history. I think people maybe overindexed a bit to like the Bulls and Warriors dynasty.

[A]

Why are the core young players of the Wizards mediocre as you suggested in a recent substack?

[B]

I mean they haven't had that many elite picks in the most valuable drafts. Right. I mean they're probably doing okay. That was my kind of, I think co authors saying that they were mediocre. Although I agreed with him to be fair.

[A]

Like Halliburton was what, number 12? Steph Curry was number seven. I forget all the numbers, but you know, not everyone's a top pick who's great.

[B]

I haven't looked at how persistent skill in NBA drafting is. To me it seems fairly persistent for San Antonio or OKC to consistently pick up players or even Miami. Right. I mean, that seems to be a pretty consistent, persistent skill set.

[A]

And what's your current update on the Philadelphia 76ers? You were kind of bullish last year and I was down on them. Yeah, I want to claim victory on that one.

[B]

Yeah. I mean, you know Daryl Morey, who. Have you had him on conversation with Tyler?

[A]

No, but would he do it?

[B]

Yeah, of course. Darrell's a big geek. He loves chess, loves a lot of things like that. Right. Okay. He will tell you that, like, his strategies are very high variance. Right. You know, if Embiid is healthy and if Edgecomb is good in his first year and if Paul George is. I mean, they have like, you know, if they won 56 games next year, would it be 56 is a lot. 52, 53. Would that be shocking? I mean, not totally. Right. The east is pretty wide open. You know, they have this kind of like second timeline that now is a little bit more optimistic. No. So we have this piece now where we're ranking all 30 NBA teams by their long term, 10 year chance of winning a championship. And they're right in the middle. For me, I think like 15th out.

[A]

Of 30. Who are the most important mentors in your life, including for basketball.

[B]

I mean, for sports writing in general. You know, Bill James, as somebody who's a mentor, you know, I can say.

[A]

And what did you learn from him personally other than reading, which he's a great writer.

[B]

I mean, Bill is kind of a, kind of a wise ass. Right. But like, but understanding that like good writing about technical subjects can still be good writing, period. That if you're like a, you know, an 8 out of 10 as a writer, and 8 out of 10 is like a statistician, that might be more valuable than being a 10 out of 10 and a 2 out of 10 in the other area, potentially in this era where we're, well, of substantial and things like that. Richard Thaler is somebody I got to know at Chicago a little bit and someone I'd consider a mentor. I followed you for a long time, Tyler as well. But yeah, I'm also someone who's kind of. It's a cliche, but kind of always blazed my own trail a little bit, I think in terms of election forecasting, I mean, there's some people. Robert Ericson's a professor at Columbia who had kind of pretty good election models early on. But in general, all this was done kind of badly. And so I'm like, okay, the product I want doesn't exist. I'm gonna have to Go ahead and make it myself.

[A]

What do you feel you need mentors for now?

[B]

I mean, I don't know if I have mentors for as much. I just want people that are interested.

[A]

In like I need mentors to learn what's new in AI. Right. I can follow it myself, but I need a lot of help.

[B]

Well, maybe mentor is not quite, you know, for like AI stuff. Reading Zvi, is it Moshowitz, Right. Like, you know, he is a mentor for like following AI developments. Right. Because he's kind of very level headed about it and very comprehensive. He'll write like, you know, a novel every week basically on AI, but he.

[A]

Thinks it's going to kill us all. It's funny you would call him level headed. Yeah, you might think he's correct.

[B]

But you know, look, I do think that kind of the AI safety slash rationalist community, I mean, it is kind of a bubble, right. If you go to like the Manifest Conference, for example, have you been in?

[A]

No, but I know what it is.

[B]

Yeah. How do I put this diplomatically? Right. I've been around a lot of weird. These people are weird even relative to the other weird people in that cohort, I think. Right. In ways that allow them to be kind of very experimental and open minded about everything from polyamory to whatever else. But I do think they maybe don't recognize A, political constraints, B, the kind of like adaptability of human beings potentially. C, as I talked about before, kind of. I think it's not at all obvious to me that you leap from rough AGI to comprehensive AGI and then to comprehensive AGI for the physical realm and then to a SI. Right. I think the AI 2027 forecasters, when I was at Manifest, I said, okay, our timelines have moved out by. By a couple of years. That seemed interesting. An interesting update, for example. So I wrote a new preface to on the Edge. And I wrote this back in, I guess February and March, right? At a time when Elon had a lot of influence in the White House, when they were pressing the accelerator on AI stuff more when tariffs were going into effect. And I wrote then that my P doom had increased relative to the book.

[A]

You just don't think it can be a superforecaster, but it can do us all in.

[B]

Well, I worry about humans killing other humans with AIs that make making weapons easier or certain types of terrorism easier. Right. Or chemical compounds could be dangerous. I worry about all of that.

[A]

That wouldn't be doom. Those would be some bad events. Like right now, drones can Be very bad. And they are every day.

[B]

Look, we have not lived with technology. I mean, you've said this before too. We have not lived with nuclear weapons for that many generations. Right. That's still a little bit frightening. You get to get like kind of some of the Nick Bostrom stuff where eventually when we have some technology, we unfortunately invent where it's asymmetric, where any one crazy person can like maybe destroy the world if they get access to the right systems that can't be that well protected and things like that. But you know, look, in some ways I think the AIs are more human like than people expected. And that to me seems like a positive update.

[A]

If you think about the manifold types in terms of the framework in your book, how they think about risk, is there a common feature that they're more risk averse or that they worry more? Is there a common feature that they like the idea that they hold some kind of secret knowledge that other people do not have. Like, how do you classify them? They're just high in openness or what is it?

[B]

They're high in openness to experience. Right. I think they're very high in conscientiousness.

[A]

Are they? I don't know.

[B]

Some of them are.

[A]

Some of them are, yeah. I think of them as high variance in consientes.

[B]

The EAs are and the rationalists are more high variance, I think. I mean, I think there can be like a certain type of gullibility is one problem. Right. I mean, you know, I think obviously EA took a lot of hits for Sam Aikman Freed, but I think if anything they probably should have taken more reputational damage. Right. That was really bad. And there were a lot of signs of it, including like his interviews with you and other people like that. Yeah. Because it contrasts with like poker players who have similar kind of phenotypes. Right. But are much more suspicious and much more street smart. And you know, also the Bay Area is weird. I feel like the west coast is like diverging more from the rest of the country. It's kind of like a long way, long way away. Just the mannerisms are different. Right. You kind of like small thing. Right. You go to like a house party in the Bay Area. Right. There maybe not be very much wine, for example, Right. In New York, if the host isn't drinking, then it'd be considered sacrilege not to have like plenty of booze at a party. Like little things like that, little cultural norms. You go to Seattle, it feels like Canada to me almost. And so These things are diverging more.

[A]

And why is belief in doom correlated with practice of polyamory? And I think it is.

[B]

I mean, if you ask Ayla, I guess she might say, well, if we're all going to die or go to whatever singularity there is, right. We might as well have kind of like fun in the meantime. There's like some of that, some of that kind of hedonism, although in general it's like not a super hedonistic movement.

[A]

It seems too economistic to me. Like even I, the economist, I don't feel people think that economistically that there's more likely some psychological predisposition toward both views.

[B]

No, look, if you, if, I mean, I guess you could argue that society would be better organized, you know, in a more polyamorous relationship. People kind of do it implicitly in a lot of ways anyway. Right. You know, including in like the LGBTQ community has different attitudes toward it. Potentially. There's, and you know, there's not as much childbearing that can have an effect potentially. Yeah, but I think it's kind of like, you know, look, they're not being constrained by the norms of society. Thing is taken kind of very seriously in that group. Right. There's enough kind of disconnectedness and aloofness where they're able to kind of play it out in practice more. And that creeps into a little bit into Silicon Valley too, which can be much more whimsical and fanciful than like the Wall street types I know.

[A]

For example, why can't Canada win the Stanley Cup? I remember it was 1992 the last time. Is that correct?

[B]

I remember forgotten Wayne writing about this in the New York Times in 2012.

[A]

2013, maybe.

[B]

2013, right?

[A]

Yeah. And it's still true.

[B]

Yeah. I go into one of my favorite sports bars. I have the article printed out in the bathroom there. Let people see articles in the bathroom while you're taking a shit, right? You can read some article. I'm reading my own article in the bathroom. Look, tax laws matter a little bit, right? Where like Florida has no state income tax in a league with a salary cap. Actually, a good friend of mine is the assistant general manager for the Florida Panthers, Sonny Mehta. And like, yeah, if you're making de facto 10 or 15% more in a hard capped league, that matters quite a bit.

[A]

If you're playing in Canada, isn't your endorsement income higher maybe, or is that not true?

[B]

It's a smaller market overall. It's kind of less wealthy than us. Right. If you're A Canadian NHL superstar playing in the US like, you know, I don't think Sidney Crosby is any trouble. I don't know if he's on Tim Hortons ads or whatever. Right. Yeah. I don't know.

[A]

Around the world, you know, we used to talk about which countries are immune to populist rights sentiment or whatever you want to call it. Now Japan is flirting with populist rights sentiment. Canada, not at the national level, but it almost happened in some way, the.

[B]

Truck driver thing, a couple years ago in Ottawa. Yeah.

[A]

So what's your update since the last few times we've spoken? Is it just going to come everywhere? Is anywhere immune In Ireland, you see signs of it. They may not win the next election, but it seemed to be totally absent a few years ago. Now MacGregor is running and I don't think he'll win, but he'll have some kind of impact.

[B]

You know, I think parts of Europe that are, and maybe Canada are anti US may become more resistant to populist sentiment. For example. Right. I have a friend who is Irish, actually, and gay and moved here when he was 20. It's like when I was growing up in Ireland, it was quite anti gay. Right. It was a very religious country. Now it's like almost aggressively pro lgbtq. Right. I think.

[A]

But it could be both. As, you know, the head of AfD in Germany, she's lesbian with a Sri Lankan partner. Is that correct?

[B]

Something like that, yeah.

[A]

Yeah. So, but she's populist. Right. And she is pro gay rights and openly and proudly so.

[B]

Look, I. I mostly believe in kind of like thermostatic effects in politics where people run in the opposite direction of the kind of status quo. More and more, I, I think there were reasons why anti incumbent and populist spirit rose from the pandemic onward. But like, we might have been at a high water mark for it potentially.

[A]

And globally, it seems weren't not at a high water mark. It's rising in quite a few other nations.

[B]

I guess I'm thinking about kind of like Canada, Western Europe, other high income countries. Maybe I'm wrong.

[A]

Something that's puzzled me and I've even asked prime ministers this. I've never gotten a good answer. It seems to me really a lot of European voters in some key nations want much lower immigration. Whatever you and I might think, they clearly want it and they don't get it. And the populist parties on the right continue to rise. Why don't they get it? Why don't the centrist politicians in Power, just do something, hold their noses if they don't like doing it, but just do it and stay in office.

[B]

I mean, you have seen some of that in Denmark, for example, you've seen much less immigration. Even Justin Trudeau has said we need to cut down the amount of legal immigration as well as illegal immigration.

[A]

Yeah, I mean, Germany's not doing it, UK's not doing it, Ireland is not doing it, Netherlands is now doing it. But it just seems to be a very slow process given how I think about democratic accountability on most other issues.

[B]

I mean, and there used to be more of a strain of it on the left in the us, Right, where like Bernie Sanders, for example, whenever issues like immigration, where he's a little bit more cautious. Right. If you have a generous welfare state, then admitting somebody new to it is potentially going to cost everybody. Look, I think all this might reverse within a few years where the world realizes we have a shortage of young people in general, a fertility crisis, if you want to call it that. Right. And aging the population and will want to have like, you know, people who are willing to work hard and are skilled in particular. And so maybe they're kind of like skating to where the puck is going, you know? The US also has, I think, particularly efficient political outcomes in some ways. People take for granted the fact that you have these coalitions that are 50, 50, approximately, very consistently, that Trump remade the GOP in a lot of ways, but still every election is roughly 50, 50 with a different coalition than you had before. So maybe our electoral technology is better. Right now, America is quite pro immigrant compared to most of the world. And because of backlash to Trump becoming kind of, at least nominally, more pro immigrant again, it would have been interesting.

[A]

I'm not sure that's true, though. I know all the polls that people cite, but when I look at the electorate's willingness to tolerate, say, ICE activity, my sense is they'll just put up with it.

[B]

Look, I think the consensus is that we have some bad guys, as Trump would call them, and people are not particularly sympathetic to them. I think people are broadly tolerant toward the migrant worker class for the most part. I think people are broadly supportive of skilled immigration. People want more border enforcement. There is a consensus there that kind of the two parties can't seem to actually reach a consensus on. But yeah, look, I agree with the polling issues a little bit. Right. If you kind of ask people, how do you feel about this 10 item list of questions on immigration, they'll kind of side with the left. And who do you like More Trump or Democrats, they'll still say we trust Trump a little bit more. I mean, Democrats do have this problem a little bit where you give them an inch and they'll take a mile. I mean, I think people correctly are worried about those excesses. Right. Where I think during COVID for example, I mean, I was kind of very cautious in the first three months really of COVID The first time I went outdoors to a restaurant, it was like June or something. I was like terrified. I hadn't been around other people apart from a couple of friends for being honest in months. Right. But then you kind of realize that like half of the public health people wanted no real socialization until we had vaccines and maybe not even after that. Right. And so people are worried that like if you kind of let progressive some power, then things do get taken too far. Even though kind of nominally the incremental stuff, they might agree with the liberal side.

[A]

Two last questions. First, do you have a surprising prediction for the United States?

[B]

I mean, how long will the US Last? I don't know. I don't know if I have a prediction there exactly. It's hard to have surprising predictions, Tyler.

[A]

It is. But do you have a surprising prediction for the NBA? I think the Lakers will do poorly this year. I'm not sure it's surprising, but in my records.

[B]

Have the Lakers ranked relatively highly? Actually, yeah.

[A]

Our last session you said Luka was properly rated. I now see his own team didn't want him. LeBron is not crazy about having him around earlier. Jalen Brunson didn't want to play with him. To me that's a lot of negative information. So I think the Lakers will do poorly.

[B]

I'm looking more kind of like a long term brand intangible, which I think does seem to matter a lot in the NBA. They have like new ownership now that can spend all the luxury tax that they want. Look, when I saw Luka last year, he didn't seem to quite like himself. But it's actually a reason why you want to. Do you want to bet on the overachieving athlete? No, they're already overachieving. Whereas an athlete who is a problem as a drinking problem or not drinking, having too many beers or being a little bit lazy, he has much more potential to grow if he gets his act together. So kind of fully healthy. Luka and their salary cap situation is pretty good in the long run. Could be a powerful team.

[A]

Larry Bird is an example on your side. But I tend to bid against such people mentally when it comes to it. Maybe that's a difference between people who.

[B]

Are maximizing currently or no, people who.

[A]

Have some problem, like they drink too much or they're not conscientious enough or bad attitude.

[B]

But you want high variance here, right? You want high variance here. And maybe if Luka takes Ozempic or something, then all of a sudden like you have a Lakers championship again.

[A]

A plug for the new paperback edition on the Edge, the Art of Risking Everything. And the final question is, what will you be doing next?

[B]

I'm working on an NFL model currently. Yeah, I miss having a book project, Tyler, because every day, even days when you're struggling to write or an interview doesn't come through, whatever else, right? Like having these big projects that I work on, I find to be very important.

[A]

No one's stopping you. So what's it going to be? Or you're still looking.

[B]

I don't want to give. I have, I have. So first of all, the newsletter Silver Bulletin did quite a bit better than I expected last year. So that's kind of like the short term project. I have a couple of ideas for books when I write the next book, which might be in a few years. Right. I have ideas for books about sports. I don't want to give away too much that might surprise people a little bit. I kind of still think of poker as a little bit of a project getting. It's satisfying to be really good at something like poker. But for now, the newsletter building toward that is kind of the three and a half year plan here. Until the next election. Then maybe I finally won't escape the election treadmill. Maybe after that. I said that every time. This might be the last one. 28. We'll see.

[A]

Nate Silver, thank you very much.

[B]

Thank you, Tyler.

[A]

Thanks for listening to Conversations with Tyler. You can subscribe to the show on Apple Podcasts, Spotify or your favorite podcast app. If you like this podcast, please consider giving us a rating and leaving a review. This helps other listeners find the show on Twitter. I'm TylerCowen and the show is OwenConvos. Until next time. Please keep listening and learning.