0:00:03 – Crypto can help decentralize the power structures
0:00:05 that are emerging in AI.
0:00:08 – Chris always talks about, do you wanna be the indie band
0:00:11 or do you wanna play like the Super Bowl or the mega stadium?
0:00:14 And I think like stable coins really have the ability
0:00:16 to appeal to like a much broader audience.
0:00:18 – Stable coins are beginning to really gain traction.
0:00:21 So there’s something like $16 trillion in volume
0:00:23 on stable coins per year.
0:00:25 – I actually think it’s a great time
0:00:28 for folks to be building token networks.
0:00:32 – Crypto is like a fundamentally radical set of technologies
0:00:34 that is very, very hard for incumbent players
0:00:36 to adopt and run with,
0:00:38 precisely because it is so fundamentally disruptive
0:00:40 to the way that they do things.
0:00:43 – What’s actually working in crypto right now?
0:00:46 For a long time, the space has been defined by bold visions
0:00:48 and has elicited strong skepticism.
0:00:50 So today we’re getting clear on what’s real,
0:00:53 what’s being used at scale and what’s coming next.
0:00:57 Joining me are two of my fellow general partners here at A16Z,
0:01:00 Ali Yahya, who leads investment across crypto infrastructure
0:01:01 and developer tools.
0:01:04 And Ariana Simpson, who focuses on early stage crypto networks
0:01:07 and founders building at the edge.
0:01:09 We get into why stable coins may finally be crypto’s
0:01:13 breakout product, how AI agents are creating new demand
0:01:16 for crypto rails, and what’s changing in policy that could unlock
0:01:18 the next wave of token networks.
0:01:22 We also talk about the enduring vision for decentralized social,
0:01:24 the evolving smart contract landscape,
0:01:27 and why Ethereum is still widely misunderstood.
0:01:29 Let’s get into it.
0:01:35 As a reminder, the content here is for informational purposes only,
0:01:38 should not be taken as legal business, tax, or investment advice,
0:01:41 or be used to evaluate any investment or security,
0:01:45 and is not directed at any investors or potential investors in any A16Z fund.
0:01:50 Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast.
0:01:57 For more details, including a link to our investments, please see A16Z.com forward slash disclosures.
0:02:06 So I’m excited to do a deep dive with you on where we’re at today in this space.
0:02:11 So crypto is a space where people have long been excited about the vision and the potential,
0:02:17 and people have long also been skeptical about where the use case is, what’s happening, what’s actually working.
0:02:20 So here we are in May 2025.
0:02:24 Why don’t you give some context on what’s actually worked so far, what’s working right now?
0:02:30 It’s quite interesting because if you go back all the way to 2009 when the original Bitcoin white paper was published,
0:02:36 one of the first few lines of the paper describes Bitcoin as a peer-to-peer electronic payment system.
0:02:39 which was kind of the original vision behind what a blockchain could do.
0:02:48 And it’s really taken us like 15, 16 years to get to a point where the technology is mature enough to actually make that a reality.
0:02:50 And this is now manifesting with stable coins.
0:02:57 So some of the big issues that Bitcoin had that made it impossible for Bitcoin to become that peer-to-peer electronic payment system
0:03:02 is that one, it was extremely inefficient and very slow, and it still is.
0:03:06 And therefore, it’s become more of a store of value type of system as opposed to stable coins.
0:03:09 And two, Bitcoin is not a stable unit of account.
0:03:11 And so it’s very hard to use it for payments.
0:03:16 And so since then, one of the big things that has happened is that the infrastructure has matured tremendously
0:03:21 to the point at which now we are at a level where a transaction of any amount of money
0:03:25 can be done for less than a penny in cost.
0:03:27 And it can be done in under a second, roughly.
0:03:32 Like those numbers are approximate, which finally makes something like a peer-to-peer transaction
0:03:35 of a few dollars viable on the blockchain.
0:03:41 And that combined with the regulatory clarity that we’re having now as of the new administration
0:03:44 makes stable coins something that are really beginning to happen.
0:03:48 So that’s maybe like the biggest thing that’s going on in the crypto world at the moment
0:03:51 is that stable coins are beginning to really gain traction.
0:03:55 So there’s something like $16 trillion in volume on stable coins per year.
0:04:01 And there are many traditional financial institutions that are beginning to use stable coins
0:04:05 to rip out a lot of the back end of their financial systems.
0:04:06 And these are like fintech companies.
0:04:07 Think Stripe.
0:04:08 Think Revolut.
0:04:08 Think Robinhood.
0:04:13 Some of the companies in the traditional financial system that rely heavily on the trad financial system
0:04:16 are now realizing that stable coins are a much better way to do things.
0:04:19 So that’s kind of like the biggest thing that’s going on.
0:04:23 And we believe that will likely lead to this cascading trend of adoption.
0:04:29 Because once stable coins become a more kind of mainstay of the way that the financial system works,
0:04:35 that opens the door for a lot of the other more advanced and futuristic ideas that crypto has introduced,
0:04:38 like DeFi, to begin to also gain adoption.
0:04:42 And I think that as a result will lead to all of the other things that we believe crypto can offer
0:04:44 to really start to ramp up.
0:04:47 One interesting thing, though, is that stable coins,
0:04:51 at least us who are in the industry full time, have been thinking about for like years and years.
0:04:54 Because I remember talking about them in 2017, 2018.
0:04:58 And I think there was always a narrative about them being useful for remittances
0:05:01 or in countries that have had hyperinflation.
0:05:05 And for those countries, Bitcoin is a better store of value than their native currencies
0:05:07 because sometimes it goes up, unlike those which only go down.
0:05:12 But it’s not ideal because, again, as Ali mentioned, it’s not a stable unit of account.
0:05:17 And so it’s interesting to see that even though this has been talked about for years,
0:05:18 now it’s really having its moment.
0:05:22 And I think, to Ali’s point, a big part of why that’s happening is because the infrastructure
0:05:26 has evolved to a point where you can now efficiently move money
0:05:31 and you’re not having to spend a huge amount of money to move the money, among other things.
0:05:34 So I think that’s why we’re starting to really see it shine now.
0:05:38 I would also add that it’s intersecting in interesting ways.
0:05:42 We’re still super early in this, but there’s obviously a lot of talk about AI and agents.
0:05:46 And if you want to dispatch your agent to go transact on your behalf,
0:05:50 you can’t really give them your bank account or your credit card,
0:05:52 but instead you can give them your crypto wallet.
0:05:56 And so this interplay of agents buying or spending money on behalf of their users
0:06:00 with stable coins is a really interesting theme that we’re starting to explore.
0:06:04 Actually, to that point, it’s kind of ridiculous to think about the way that
0:06:11 the financial system works today, where even a normal local and domestic financial transaction
0:06:14 where you go to a coffee shop and you buy a coffee with a credit card,
0:06:20 that transaction involves like the point of sale, the payment processor, the issuing bank,
0:06:22 the acquiring bank, the credit card network.
0:06:26 And each of these intermediaries takes some cut, some fee on the transaction
0:06:29 to add up to something like multiple percentage points on the transaction.
0:06:32 And that is the case in a domestic transaction.
0:06:34 And if the transaction happens to be international,
0:06:38 then that entire stack of participants and intermediaries gets duplicated
0:06:42 and mirrored on the other side to the point that any kind of financial transaction
0:06:45 across borders is insane in terms of its inefficiency.
0:06:49 It can take up to like three to seven days to move money from one country to another,
0:06:52 and it can cost like up to 10% of the transaction to do it.
0:06:56 So when you have a technology that can now, again, move an arbitrary amount of money
0:07:01 from anywhere in the world to any other place in the world for under a penny and under a second,
0:07:03 that truly is very transformative.
0:07:06 And it’ll be very disruptive to the way that the financial system works.
0:07:13 So to Ariana’s point about AI agents, it’s kind of inconceivable that an AI agent that a human may want
0:07:17 to participate in the financial system would have to go through all of that inefficiency
0:07:22 and deal with all of these arcane human intermediaries, some of which are not even really automated.
0:07:23 That’s inconceivable.
0:07:31 And the only real way to bring online millions or potentially billions or more AI agents into the kind of the financial system
0:07:38 is through a technology that’s fully based on software and as efficient as like the crypto rails that are now available
0:07:39 and now can be used.
0:07:44 Yeah. So say more about some of the use cases that stablecoins are currently enabling.
0:07:47 Is it mostly on an institutional level? Is it on a consumer level?
0:07:48 Or what are the common interactions people are having?
0:07:51 I think it’s both. It depends what markets you’re talking about.
0:07:56 There’s a company in our current accelerator batch called Zarpay, which is operating in Pakistan.
0:08:01 And they’re basically creating a network of small, you know, the little shops if you’ve been to Africa
0:08:06 or wherever they have these little sort of mobile kiosks where you can put money on your phone and that sort of stuff.
0:08:15 And so they’re basically using that network in order to create a way for people to come in and deposit their local currency and get stablecoins.
0:08:20 And then they’re building a whole suite of like financial services around this as the atomic unit.
0:08:26 And I think a lot of countries that have unstable currencies or other financial issues for which holding dollars
0:08:33 or the equivalent of dollars in stablecoins is very appealing immediately understand the value of this and are very attracted to using it.
0:08:38 So I think it goes from that all the way through to banks and financial institutions.
0:08:44 I think in many cases there’s been an interest in crypto and some of the banks and financial institutions have wanted to get involved,
0:08:49 but it’s been very unclear how they could do it, largely as a result of the lack of regulatory clarity,
0:08:52 but also because crypto can be a little scary or whatever.
0:08:56 And so it hasn’t always been obvious for them to see a path.
0:08:57 How do we get involved?
0:08:59 What’s the way that we can bring this to our consumers?
0:09:03 And so stablecoins, I think, are kind of like a baby step in,
0:09:08 in the sense that it’s a lot more clear what the value proposition is.
0:09:11 It’s a non-speculative use case.
0:09:15 And so I think it’s just a good entry point for some of these larger institutions.
0:09:16 Yeah.
0:09:22 Help us understand better the stablecoin landscape around like what big companies or types of companies have emerged or will emerge as a result of it,
0:09:25 or how it impacts the crypto startup ecosystem more broadly.
0:09:30 So right now at the center of all of the action are the stablecoin issuers.
0:09:38 So we’ve got two of the major ones right now are USDC, which is created by this consortium between Coinbase and Circle.
0:09:39 And then there’s Tether.
0:09:42 And so both of these are like the kind of the biggest two issuers of stablecoins today.
0:09:47 Then, of course, both of these stablecoins operate on top of blockchains.
0:09:53 So another important piece of the stack is the infrastructure on top of which some of these stablecoins operate on.
0:10:02 And then you have this kind of collection of companies at the periphery that are generally just companies that help connect the crypto world to the external world.
0:10:04 And that would include wallets.
0:10:08 It would include some of the fintech companies that are using blockchain technology as the back end,
0:10:15 but have a front end that looks more like a Web2 type of front end and doesn’t expose the crypto aspects to the end user as much.
0:10:18 And all of those players will be a part of the story as well.
0:10:24 So one of the things that we talk a ton about is what will this stack look like end to end?
0:10:25 As the space evolves.
0:10:38 And one of the exciting things that we are hoping will happen soon is it will get legislation that sets the rules of the road for stablecoins and for what is required for an issuer to create a stablecoin,
0:10:41 what kind of collateral is needed for the stablecoin to be compliant.
0:10:46 And what that likely will do is if that works, and we strongly believe it will likely happen this year,
0:10:56 it will to some extent commoditize the issuance layer because it’ll be easier for new issuers to emerge and create their own stablecoins that are also USD denominated
0:11:02 to the point that those new stablecoins are somewhat fungible and interchangeable with USDC and with Tether.
0:11:10 Because if all of them are compliant, then you can trust that all of them are likely to be ultimately redeemable for a dollar and they’re equally trustworthy.
0:11:16 Which means that then the issuers may no longer be the ones that capture all the value the way that they do now.
0:11:19 And instead, a lot of the value might be captured by some of the other layers.
0:11:24 Like, for example, the infrastructure is likely to capture a lot of the value because a lot of the activity,
0:11:29 a lot of these sort of transactions, stablecoin transactions that are happening,
0:11:35 happen on blockchains like Solana, Ethereum, Sui, a number of other kind of important layer one blockchains.
0:11:40 And all of those require payment of gas for all of those transactions.
0:11:44 So those blockchains are likely to be important players in the way that this unfolds.
0:11:45 That’s on one another stack.
0:11:50 And then the other end will be kind of the endpoint, the interface that connects this whole crypto world to the end users.
0:11:53 So wallets will be likely important.
0:11:59 One of our portfolio companies, Phantom, likely be well positioned as a gateway or an interface for people to interact.
0:12:04 interact with stablecoins and get kind of exposure to US dollars, regardless of where they may be.
0:12:07 So that’s maybe a bit of a layout for what the ecosystem looks like at the moment.
0:12:14 Yeah. And it seems like for years there’s been this question of, hey, what’s going to make it so that there’s hundreds of millions of users?
0:12:17 I’m not sure what it is at the moment across all of crypto or a billion users.
0:12:19 They asked before that kind of what’s the iPhone moment?
0:12:21 What’s the product that everyone’s going to be using?
0:12:22 That’s also a platform for everything.
0:12:24 Is it stablecoins or is it something else?
0:12:24 Or how do we think about that?
0:12:28 I think the odds are good that stablecoins are that thing.
0:12:31 I also don’t think that necessarily there needs to be one thing.
0:12:33 I think we mentioned AI.
0:12:35 Ali has made some investments in that category.
0:12:37 We’ve done some as a team.
0:12:40 I think there’s going to be different waves that bring in different users.
0:12:44 A while ago, Web3 Games was a big entry point.
0:12:46 Now it’s AI and stablecoins.
0:12:48 So I think the users do come in waves.
0:12:53 I think there’s a lot of it that sort of tracks the cycles that we see every couple of years in crypto.
0:12:58 Chris always talks about, do you want to be the indie band or do you want to play like the Super Bowl or the mega stadium?
0:13:06 And I think like stablecoins really have the ability to appeal to like a much broader audience because, like we said, it’s just a use case that makes sense.
0:13:09 It’s pretty clear what the value proposition is.
0:13:11 And so it appeals to a broader audience.
0:13:11 Yeah.
0:13:12 Yeah.
0:13:15 And in part also because it addresses a very real pain point.
0:13:24 Whether it be for people in third world countries that want exposure to the dollar because their local currency may not be as reliable.
0:13:29 Or people who want to move money between borders, and we talked about how that can be extremely inefficient.
0:13:34 Or even companies that want to move money across borders, they still have to deal with all the inefficiency.
0:13:45 Apparently, companies like SpaceX are already using stablecoins for treasury management to move money from one country to another in a way that’s much more efficient than using the traditional financial rate.
0:13:47 Yeah, I believe they were using Bridge, which Stripe now acquired.
0:13:48 Yeah, I mean, it’s interesting.
0:13:52 Stripe Sessions, their big conference was like all about stablecoins.
0:13:55 And so many of the talk tracks last week were about that.
0:14:00 And so I think it’s really indicative of the fact that this is permeating not just crypto companies, but out more broadly.
0:14:10 And I think, obviously, the other necessary element in addition to the infrastructure improvements and all of that is just the fact that now we have a more friendly regulatory regime, which is interested in seeing these kinds of things flourish.
0:14:10 Yeah.
0:14:12 Just one meta point.
0:14:20 It’s one thing I’ve always appreciated about crypto investing is you guys, as domain experts, don’t just need to understand the technology, which is complex enough in itself.
0:14:29 But you also need to understand the policy regime, law, monetary policy, economics, foreign policy, and how all these things are intersecting with crypto startups.
0:14:29 Yeah.
0:14:36 I mean, I’m certainly not the domain expert on some of the policy stuff, but we’ve really assembled a super strong team who’s been very involved in D.C.
0:14:38 and trying to push the ball forward for the whole industry.
0:14:39 Yeah.
0:14:42 You mentioned Stripe getting deeply involved in crypto.
0:14:46 It’s interesting because people often contrast it with AI and say, hey, AI is mostly sustaining innovation.
0:14:55 And that, of course, there’s massive companies that have been formed, but a lot of the gains have gone to the biggest companies, whereas crypto is mostly a startup, though some bigger companies are getting involved, too.
0:14:58 It’s funny, like maybe Facebook was just a few years too early.
0:15:01 If they launched Libra in a more friendly regime, might that have worked?
0:15:06 How do you think about the startup versus incumbent distinction in this space?
0:15:20 Yeah, crypto is like a fundamentally radical set of technologies that is very, very hard for incumbent players to adopt and run with, precisely because it is so fundamentally disruptive to the way that they do things.
0:15:22 I was at Google a while back.
0:15:26 I was at Google X working on a robotics project, but I was already very interested in crypto.
0:15:33 And Google X is supposed to be like the moonshot factory and it’s supposed to be super innovative and open to new ideas, open to start new companies.
0:15:34 But they don’t want it.
0:15:34 New ideas.
0:15:36 I tried to get Google X to touch crypto.
0:15:37 I tried at Facebook, by the way.
0:15:38 Same thing.
0:15:45 And it’s like Google would not touch crypto with a thousand foot pole unless it was like a very, very kind of vanilla, we will run some note or whatever.
0:15:47 Were they like, this makes no sense or were they like, it’s evil?
0:15:49 I think, yeah, they kind of fundamentally didn’t get it.
0:15:51 They were afraid about the optics.
0:15:56 They were afraid about the regulatory association with it, the reputational consequences.
0:16:08 Also, like the whole Web3 vision, the vision of decentralizing web services, which is, I think, kind of the most futuristic vision for crypto, is fundamentally disruptive to the way that these companies work.
0:16:15 These are centralized companies that make money and have power by virtue of being so centralized.
0:16:37 And if you build something like a social network that is fully decentralized and has no core central company, like no monopolistic tech giant that’s worth $44 billion that controls what recommendation algorithm is used and who gets to follow whom and all of the data and the social graph itself, then that company no longer has a business model.
0:16:45 It’s a very, very different business model to build a social network that’s decentralized in the way that a company like Farcaster currently is.
0:16:55 And so for a company like, say, Facebook or Google in its own way to decentralize itself and to truly embrace crypto with arms wide open, it would have to cannibalize its own business model.
0:16:58 And I think that’s actually becoming true for AI as well.
0:17:07 Like, I think that it was very, very true that AI was a very sustaining innovation before, but it’s gotten so powerful that there are many elements of it that are disruptive.
0:17:11 Google wanted to clearly embrace AI.
0:17:15 It would have to replace search with an LLM instead of having its current model.
0:17:21 And of course, that’s a hard thing for it to do, given the kind of the insanely profitable business model that they currently have.
0:17:23 Yeah, that’s fascinating.
0:17:27 Is that still the vision that we’ll have decentralized social networks, decentralized marketplace?
0:17:28 Or where are we on that vision?
0:17:36 And what are the bottlenecks to that vision being realized of networks at scale that are truly decentralized and competing with some of the centralized ones?
0:17:39 Is it technological or is it that people just don’t really care about this in the same way?
0:17:40 Or like, why hasn’t it happened yet?
0:17:44 I think it’s mostly a consumer preference issue.
0:17:48 I do think like now some of the products have gotten really good.
0:17:51 Like Farcaster, for example, the product experience is very good.
0:17:58 But it’s just challenging to get people to switch because the reason you’re on the social network is for the graph.
0:18:01 And so it’s difficult to export an entire graph.
0:18:06 I think like users are accustomed to being the product.
0:18:08 If you’re not paying for the product, you are the product.
0:18:11 And I think in many cases, consumers are used to that experience.
0:18:14 And, you know, ads are annoying, but they’re not necessarily that bad.
0:18:18 And so people just accept it and don’t think too much about it.
0:18:25 And by the way, this is interesting because if you look at like all of the big social networks, none of them have been started in the last decade.
0:18:27 And that’s not true just of crypto.
0:18:28 It’s true in general.
0:18:37 And so it’s just very difficult, I think, to get over this hurdle of reaching a critical mass whereby people actually say, oh, I’m in the network and I’m going to stay in the network.
0:18:39 So it’s not just a crypto thing.
0:18:40 I think it’s just difficult nowadays.
0:18:42 People only have so much attention.
0:18:47 And with the networks that there are, most of the attention span has already been captured.
0:18:54 So I think we may need to see some of the existing ones falter before there’s like enough room for some of the new ones to really take hold.
0:18:55 We’ll see.
0:19:00 Yeah, we used to believe that these ideas and these companies would be the first to gain adoption.
0:19:07 And that was largely because all of the financial use cases, like the DeFi use cases, even like the stablecoin use cases were illegal.
0:19:08 Yeah.
0:19:10 This was the case in the previous administration.
0:19:17 And so it felt to us like the more kind of innocuous seeming social network use cases, gaming use cases would more likely gain adoption.
0:19:25 And then that will be the gateway for other things to eventually become legitimate and get acceptance from a regulatory standpoint.
0:19:31 But now that the regulatory landscape has shifted so much to the point at which it’s now a much more friendly landscape.
0:19:39 And as a result, we have all these traditional financial institutions getting involved and stablecoins are really having a moment combined with the infrastructure clicking into place.
0:19:44 It’s now much more clear that the more financial use cases are likely to happen first.
0:19:47 Those will act as a legitimizing force for the rest of the space.
0:19:50 And then the consumer use cases, which we still believe in, will take longer.
0:19:53 And I think as Arana is saying, it’s very, very hard to get those things right.
0:20:00 Like the bar that a consumer has on the quality of a consumer facing application is extremely high.
0:20:09 And crypto has not yet figured out all of the UX challenges and kind of the seamlessness and usability challenges of crypto are still, it’s still a nascent technology.
0:20:10 On that front.
0:20:13 So it’ll take longer for all of those things to get resolved.
0:20:22 But in the meantime, we have all these other financial use cases, which I think will solidify the technology, will legitimize the space for a broader group of people, will get more entrepreneurs to come into the space.
0:20:22 Right.
0:20:35 Yeah, I think on the point of the attention span or lack thereof of consumers, it’s interesting when you see a new network created around an area that doesn’t already have somebody in the non-Web3 world occupying it.
0:20:41 So, for example, I think a good example of this is Blackbird, which is kind of a network for restaurant lovers.
0:20:45 And you can think about it as Amex points for restaurants.
0:20:54 And they’re occupying a space that nobody really owns it right now, like the credit card companies do, but it’s still sort of a so-so experience at best.
0:21:15 And when you have a great entrepreneur who is really deep in restaurant tech, like Ben Leventhal, who’s the founder, tackling a problem like that, bringing a consumer Web 2 experience, but using Web 3 as the ownership, therefore allowing the restaurants and the consumers to actually have ownership in this network, which wouldn’t be possible in a Web 2 context.
0:21:19 Then it’s pretty interesting because that’s something that you couldn’t really give the same ownership.
0:21:25 And if you look at some of the platforms like Uber Eats or DoorDash, the restaurants have to work with them because their margins are so slim.
0:21:28 And so they need as much volume as they can, basically.
0:21:35 But it’s not great because the platforms are, in many cases, quite extractive and dig deeper into the margins of the restaurants.
0:21:48 And so if you are using stablecoin payments to bring down transaction costs and you’re also giving restaurants actual ownership in the network, therefore helping their bottom line in that way, too, it’s really interesting.
0:22:00 Yeah, in order for it to match the consumer expectations that we’re talking about, it has to be a new interaction model or new value that’s unlocked that goes straight to the consumer as opposed to something that’s like abstract, like the same product, but just decentralized.
0:22:05 It has to be a forecaster with frames and some of its other experiments has been net new things and only it could do.
0:22:07 And a Web 2 and Blackboard is another example of that.
0:22:08 Yeah, exactly.
0:22:12 Ali, let’s go a bit deeper on AI and the intersection between AI and crypto.
0:22:13 What’s working there?
0:22:14 Where are you most excited?
0:22:24 Peter Thiel actually had this tongue-in-cheek line back in 2018, which I think rings true, which is that AI is communist and crypto is libertarian.
0:22:30 I think the meta point is that these two technologies are very different from one another.
0:22:33 And in many ways, they’re actually counterweights for each other.
0:22:37 So there are many ways in which they are intersecting and we can talk through a few of them.
0:22:55 I think one of the most important ways is that AI is creating a kind of overabundance of media and of human-looking entities, agents that can pretend to be human, or deep fakes of video or audio that seems very human.
0:23:00 And it’s hard to know whether you’re looking at something that’s real or something that’s purely generated.
0:23:07 And crypto happens to be a really good technology to help authenticate media or help authenticate data in general.
0:23:15 One of the ways in which these two worlds will collide is that there are crypto projects that are working on, among many other things, proof of humanity,
0:23:20 which would allow someone, anyone, a user on the internet to prove that they actually are human,
0:23:26 so that anyone on the other end can know that they’re interacting with a human and not with an AI bot or an AI agent.
0:23:32 WorldCoin is one of these companies, is one of our portfolio companies, and they built an orb that uses biometric information
0:23:35 and also uses zero-knowledge proofs to keep all of the biometric data private.
0:23:44 In fact, the data itself never leaves the orb, and only a code or a cryptographic object that is derived from the biometric data ever leaves the orb.
0:23:50 And from that cryptographic object, it is not possible to infer anything about the biometric data itself.
0:23:54 It’s a technology that allows anyone to prove their humanity on the internet.
0:23:58 There was that famous line in the 90s that, on the internet, nobody knows you’re a dog.
0:24:03 And that is very, very true now in 2025, where, like, on the internet, nobody knows you’re a human.
0:24:04 Like, you could be anything.
0:24:05 You could be…
0:24:06 A dog or an ape.
0:24:07 A dog or anything else, yeah.
0:24:17 So that’s one way I think that cryptography blockchains will help deal with the immensity and abundance of signal and noise that AI will generate.
0:24:24 Another big one is that crypto can help decentralize the power structures that are emerging in AI.
0:24:31 At the moment, it seems like there will be a small number of very, very powerful players in the AI world.
0:24:35 Even though it’s unclear as to whether there are things like network effects that drive defensibility,
0:24:41 there are just a handful of really powerful players in the space, at least at the kind of the model layer,
0:24:45 like OpenAI and the other kind of big companies that build foundation models.
0:24:50 Crypto offers an alternative for creating AI systems that’s more decentralized.
0:24:58 So an example of this is a company called Jensen, which is also in our portfolio that builds a kind of marketplace for compute.
0:25:04 So someone on one side of the marketplace can provide their idle GPU capacity to the network.
0:25:11 And then someone on the other side who might want to use the GPU compute for training a model or for doing inference on a model
0:25:14 can, through the network, make use of all of that compute.
0:25:21 And then the network manages all of these heterogeneous computational resources to create something that feels like a unified cloud
0:25:26 on which you can run all of these machine learning AI workloads in a way that’s fully decentralized,
0:25:28 in a way that is not controlled by a single company.
0:25:37 And in a way that could actually be more efficient than a cloud by virtue of using capacity that otherwise would just go idle and unused.
0:25:44 It’s just capacity that’s locked away in all of these like pockets that are far removed and not in one particular data center.
0:25:50 There are many hard technical challenges to get there, but there are a lot of smart people working toward figuring that out.
0:25:52 And we’re very optimistic that’ll also happen.
0:25:58 It’ll also allow for machine learning workloads to run in a way that is also verifiable.
0:26:05 So this way you don’t have to trust a centralized company, say, Facebook or like one of the other social media companies,
0:26:14 that the machine learning model or the AI model that they’re running for, say, like the recommendation algorithm is unbiased or has particular properties.
0:26:21 You can actually, with cryptography, verify that those things are the case and that these things are executed in a way that’s correct.
0:26:23 You can use some of these decentralized systems for that as well.
0:26:33 And then maybe the final one, which I think is the most futuristic and the most challenging, is having crypto help AI figure out the new business models for the internet.
0:26:54 So one of the issues that AI will create with the current business models of the internet is that right now, the way that the internet works is that you have an aggregator, like a search engine, driving traffic to creators of media, like say someone who has written a blog post or someone who has like a page that has content.
0:26:59 And there’s ads as like the business model that mediates that whole interaction.
0:27:06 And that entire business model goes away if you just have an AI that just gives you the answer that you’re looking for.
0:27:16 So instead of doing search on Google, getting exposed to a bunch of ads and then clicking through to a website and having all of those parties be happy because a business model includes all of them.
0:27:25 Now, instead of that, you just interact with an LLM, you get the answer immediately and you never click through to the final page and you never get exposed to an ad.
0:27:27 That kind of completely changes the way that the internet works.
0:27:31 And we’re going to need new business models for the internet if that’s the case.
0:27:46 So one idea is that you could through, there are a lot of these research efforts to try to figure out attribution in the training of a machine learning model for what pieces of data contributed to a particular output.
0:27:59 So you’re asking an LLM a question, you kind of want to know what pieces of data that were used to train that LLM contributed to the answer that the LLM ultimately gives you when you ask it a question.
0:28:06 And if you could know that, then you could come up with a business model that rewards the people who contributed that data originally.
0:28:07 And crypto could be part of that.
0:28:09 So there are open problems on both sides.
0:28:14 Like you have to figure out this attribution challenge in the AI world and there are people working on that problem.
0:28:16 And then there’s a challenge on the crypto side.
0:28:26 Like how do you build a network that can, using that information, compensate all of the parties involved in having the AI actually give you what you ultimately want?
0:28:27 Fascinating.
0:28:30 Are the big labs interested in crypto?
0:28:32 Do they need to be interested in crypto for this to happen?
0:28:33 Or is this largely coming from startups?
0:28:36 It’s funny, Sam Altman, of course, open AI, but also WorldCoin.
0:28:37 So it has some familiarity.
0:28:38 But what can you say about this?
0:28:40 For the most part, I don’t think so.
0:28:43 I think for the most part, the AI labs, they’re just running with AI.
0:28:46 And there’s so much that’s exciting in that world that I think crypto doesn’t really factor in at all.
0:28:55 But there are crypto companies that are very interested in AI and are thinking about the ways in which crypto will ultimately make a difference in that world.
0:29:00 So like this company I mentioned, Jensen, actually the founders are very, very deep in AI.
0:29:08 They have an AI background, but they also happen to have a deep commitment to building things as open source networks that are ultimately decentralized.
0:29:12 And so they are of the few people who really do straddle both worlds.
0:29:18 Zooming out a little bit, what are some of the biggest misconceptions you think people have about the space right now?
0:29:30 Well, I mean, I think for the last few years, it’s been really challenging to launch a token network in the United States in particular, because there was a lack of clear legislation.
0:29:40 And then there were very aggressive folks in several agencies working on basically not allowing entrepreneurs to launch networks.
0:29:46 And that applied to entrepreneurs who were very well-meaning and very much wanted to do things by the books.
0:29:58 And so I think one of the challenges was that people obviously didn’t want to end up in legal trouble and therefore, in many cases, pulled back their plans on that front, which really impeded their progress on the product side as well.
0:30:06 So they couldn’t really build their vision, because I think tokens are part and parcel of what’s valuable and interesting about crypto.
0:30:08 And so if you remove that piece, it doesn’t make any sense.
0:30:13 So the thing that’s a misconception, perhaps, is that the situation is very different now.
0:30:16 We have a much friendlier administration in place.
0:30:21 We have a very different situation in terms of the leadership of these agencies now.
0:30:25 And so I actually think it’s a great time for folks to be building token networks.
0:30:30 And I think that message hasn’t necessarily fully made it out there.
0:30:38 So I’m hopeful that more entrepreneurs realize that the situation is, again, very different from what it was just a few months ago and start to come back in force.
0:30:40 I completely agree with that.
0:30:53 I think another big one is outside of like our immediate circles, like outside of the world of tech, it’s shocking to me that people continue to think of crypto as just like a thing that’s supposed to be money only.
0:30:56 Or they think of blockchain as a kind of ledger for money.
0:31:04 And I think that that misconception comes from Bitcoin, from Bitcoin trying to be money and only money and not really trying to be anything else.
0:31:18 And the fact that this misconception is that like Ethereum is like Bitcoin and Ethereum is actually the silver to Bitcoin’s gold and that all that crypto really is, it’s just another kind of attempt to doing what Bitcoin did.
0:31:24 The fact that Ethereum actually is a fundamentally different thing than Bitcoin is still not widely understood.
0:31:34 The fact that Ethereum is actually a kind of computer where you can build all sorts of different applications, where the software that runs on top of the computer has unique properties.
0:31:40 that no other software has ever had is, I think, not widely understood.
0:31:47 And these are programs that, like the programs that run on a blockchain like Ethereum are programs that have a life of their own.
0:31:54 They are programs that can make commitments that no one has to trust anyone to believe in.
0:32:00 It’s a program that is essentially free from interference from anyone, including the people who originally wrote the program.
0:32:03 And so that’s a very unique property that no other kind of software has.
0:32:17 It’s a kind of technology that inverts the power relationship between the software and the hardware, whereas historically, the hardware has always had power over the software because whomever controls the hardware can turn off the software or change it in some way.
0:32:29 Whereas in crypto with blockchains, the hardware commodities, these are people who run like the miners, for example, or validators in the blockchain context don’t have any power over the software that runs on top.
0:32:31 And that’s what makes a blockchain unique.
0:32:35 And that’s what makes it capable of doing so much more than just money.
0:32:37 You can kind of build far more sophisticated primitives.
0:32:45 So stable coins are the first thing, but the things that come after, things like DeFi, where you can build much more sophisticated financial primitives on chain,
0:32:54 or some of these other more futuristic ideas where you can do AI, you can do deep end, you can do some of these consumer facing applications like decentralized social networks.
0:32:58 All of that relies on the properties of a blockchain computer.
0:32:59 That’s not just a ledger.
0:33:02 It’s a full on computer on which you can build applications.
0:33:05 That I think is not something that most people really get.
0:33:06 Yeah.
0:33:15 Maybe gearing towards closing here, Ali, can you give a bit of an update on kind of the smart contract platform wars as an outsider or someone who’s paid attention at certain times and not at certain times?
0:33:21 What I’ve heard or gleaned is Bitcoin, as you mentioned, has tried to be money, but there’s a little bit of a nascent Bitcoin builder movement.
0:33:24 I’m not sure if that’s led to something particularly meaningful in the space.
0:33:38 And then my understanding is that Ethereum has tried to optimize across multiple dimensions, both trying to be money, but also trying to be the base layer for sort of decentralized internet and committed to decentralization in a way that some people think is at the sacrifice of usability.
0:33:43 And whereas Solana has not had the same commitments to decentralization, has really optimized for usability.
0:33:45 One, is that a fair characterization?
0:33:46 How would you edit the characterization?
0:33:49 And two, how is this all played out or where are we right now on that level?
0:33:50 There’s actually a really good characterization.
0:34:01 The way that I would break things down is that there is a very large and multidimensional trade-off space, and it’s very hard for any one system to cover the entire space.
0:34:09 So it makes sense that you’d end up with different systems specialized for different things, and then as a result, having different use cases and different value propositions.
0:34:14 So Bitcoin, I think, has been extremely successful at becoming like a kind of digital gold.
0:34:26 It’s been extremely volatile, but I think that there is this belief, there’s this mimetic value that Bitcoin is long-term, a pretty good store of value that will be around for a very, very long time.
0:34:27 It’s not going anywhere.
0:34:34 And we’ll have properties that are desirable that are not provided by other things like fiat or gold itself or anything.
0:34:37 It’s funny, it’s only been around for less than 20 years, but in my head, I treat it as gold.
0:34:38 It’s going to be there forever.
0:34:40 Exactly, exactly.
0:34:48 So it’s really succeeded at that, and I think some of the things that has helped it succeed at that is the fact that it is so hard to change and the fact that it is so simple and you can’t do that much with it.
0:34:54 Those things are disadvantages in some contexts, but they’re real advantages when trying to solve for that particular thing.
0:35:00 Then there are like all of the other smart contract platforms that are trying to do much more and are trying to be computers.
0:35:08 And Ethereum lands in some part of the trade-off space here where they do really optimize for decentralization, and they are fully decentralized.
0:35:15 And so it’s hard for Ethereum to change quickly because there are a lot of stakeholders and a lot of people who want to be able to influence its direction.
0:35:25 And so the choices that it has made have made it a pretty good platform for some of the higher stakes like DeFi applications or for the issuance, for example, issuance of new assets on Ethereum.
0:35:32 That might be the default simply because it’s been around the longest and its high amount of decentralization make it very suitable for that.
0:35:36 And then there are blockchains like, say, Solana and Sui, which are extremely high performance.
0:35:42 They are very well suited for transactions and for payments and for things that do require that level of performance.
0:35:47 If you wanted to build something like the Nasdaq exchange on-chain, there’s no way you’re doing that on Ethereum L1.
0:35:56 You probably need a blockchain that has the kind of the level of performance that a Solana or a Sui or some of the other kind of more modern or more recent blockchains have.
0:36:01 So I think I expect that each of these ecosystems will likely find their niche.
0:36:09 It’s obviously very uncertain and there’s all this talk about how maybe Solana will eat Ethereum’s lunch and that’s a possibility.
0:36:11 But it’s still wide open is basically what you’re saying.
0:36:14 Yeah, it’s wide open and there’s like a lot of ways in which it could play out.
0:36:20 Closing out, Ariana, I want to double click on your point about the misconception in terms of how the policy regime has changed.
0:36:25 I mean, I think if you look at the Novi Libra, you know, it had seven different names.
0:36:36 Whichever one you want to use, that was something that could have been incredibly interesting because you have Facebook now Meta with such an enormous distribution network already has all the users.
0:36:41 Integrating payments into that via crypto made all the sense in the world.
0:36:46 But obviously, they were told in no uncertain terms that that was not something that they could proceed with.
0:36:49 And then, unfortunately, the whole project died.
0:36:55 I will say it went on to flourish in other forms because we’re investors in Missin and Sui.
0:36:55 Yeah, they spun out of there.
0:36:58 So there have been actually a number of great teams who came from there.
0:37:03 So I think the diaspora of talent has continued to fight the good fight and build.
0:37:09 But in general, that’s another project that I think, as it was initially conceived, had to die on the vine because of that.
0:37:16 So I think as investors, it’s not necessarily our job to envision, like, what is possible, but rather to recognize it when we see it.
0:37:24 And so I’m personally very excited to see what entrepreneurs come up with in the next couple of years now that we have a new opportunity space.
0:37:25 That’s a perfect place to wrap.
0:37:27 Ali, Ariana, thanks so much for coming to the podcast.
0:37:28 Thanks, Eric.
0:37:29 Appreciate it.
0:37:33 Thanks for listening to the A16Z podcast.
0:37:39 If you enjoyed the episode, let us know by leaving a review at ratethispodcast.com slash A16Z.
0:37:41 We’ve got more great conversations coming your way.
0:37:43 See you next time.

0:00:04 If you read the emotion in the room, it tells you everything you need to know about a team.
0:00:12 I learned quite early that emotion is an indicator in teams. So later on when EI came out
0:00:17 and the focus was on developing emotionally intelligent people, just because you stack a
0:00:24 team with emotionally intelligent people doesn’t mean you get emotionally intelligent behavior.
0:00:31 And the reason for that is that the environment in a team makes a huge difference.
0:00:40 Hello, I’m Guy Kawasaki. This is the Remarkable People podcast. We’re on a mission to make you
0:00:46 remarkable. And we found another person in New Hampshire. Her name is Vanessa Druskat,
0:00:52 and she’s an organizational psychologist and associate professor at the University of New
0:00:59 Hampshire. And believe it or not, she co-developed this whole foundational concept of emotionally
0:01:06 intelligent team. And that’s what we’re going to discuss today about trust and collaboration and
0:01:13 performance. And I dare say her research has influenced hundreds of thousands of people in
0:01:18 teams and thousands of organizations. So welcome to Remarkable People, Vanessa.
0:01:22 Thank you, Guy. I’m really happy to be here with you.
0:01:29 I’ve been on companies that had innovative teams. I’ve had companies that had well-performing teams
0:01:37 or whatever, but nobody ever said, Guy, that is an emotionally intelligent team you’re on. So just as a
0:01:44 basis, can you tell us what is an emotionally intelligent team? I kind of know what an emotionally
0:01:51 intelligent person is. Not that I am one, but I don’t understand the concept of a team like that.
0:01:58 You bet. So to do that, I’m going to have to back up a little bit and tell you that when I was in
0:02:02 graduate school interested in studying teams, I didn’t hear anything about emotion at all. This was
0:02:11 pre-emotional intelligence time, 1990s, early 1990s. And one of the things I learned when I reached out
0:02:19 to learn outside of academia is that if you read the emotion in the room, it tells you everything you
0:02:26 need to know about a team. So I took this two-year course at the National Training Laboratories on how
0:02:34 to facilitate teams, and that’s what they taught me. And so I learned quite early that emotion is an
0:02:41 indicator in team. So later on when EI came out and the focus was on developing emotionally intelligent
0:02:47 people, I knew enough about teams to know that just because you stack a team with emotionally intelligent
0:02:57 people doesn’t mean you get emotionally intelligent behavior. And the reason for that is that the
0:03:04 environment in a team makes a huge difference. And for example, it doesn’t matter how empathetic you are
0:03:10 or how much self-control you have, if you walk into a team and no one else is being empathetic
0:03:17 or people are being disrespectful, you’re not going to be very emotionally intelligent. Does that make sense?
0:03:26 Your empathy is just going to go out the window. And so skills and personality and attitudes tend not to be a
0:03:34 great predictor of behavior in complex teams. A far better predictor is the environment that you’re in
0:03:41 and the norms and routines and the way people behave around you. And so emotionally intelligent teams
0:03:48 build environments that lead to trust and psychological safety and they build relationships
0:03:54 and the positive constructive emotion leads to higher performance. And there’s much more in that,
0:04:03 but we can peel apart. Now, as I understand it, aren’t there nine norms that define an EI team?
0:04:10 Yes. I’m not going to ask you to explain all nine, but I know that they cluster into three different
0:04:16 groups. So can you just explain the clusters so people have an idea about what makes up a team?
0:04:21 Sure. So first, let me again back up for a second and tell you how I came up with those three clusters.
0:04:28 So I went on this quest to figure out what differentiated the truly highest performing teams
0:04:36 from average performing teams. My mentor in my doctoral program was the first person to talk about
0:04:44 competencies and competencies. And so competencies were defined as the behaviors that lead to performance,
0:04:50 the behaviors that differentiate the greatest performance. So I asked him to do that with me with teams.
0:04:54 So I went into many organizations. The first one was a manufacturing organization.
0:04:59 Another one that I can talk a lot about is the drug development teams at Johnson & Johnson.
0:05:06 And we singled out the top 10% performing teams. For example, Johnson & Johnson was heavily invested
0:05:11 in figuring out why is it that some of their drug development teams are so much better
0:05:19 than their average performers than others. And so we identified those top 10% and we interviewed them
0:05:25 and we surveyed them. And in other organizations, I videotaped teams, et cetera, et cetera. Anyway,
0:05:32 what I’ve found in 30 years of doing this kind of work is that there are three categories of behavior
0:05:41 that differentiate the top 10% from average. Okay. And these fall into the three categories. The first
0:05:48 category is about focusing on individuals, how we help one another succeed, but it’s really about
0:05:54 getting to know one another, giving one another feedback, figuring out what distinctive
0:06:02 capabilities you bring to the team and valuing those. So the first cluster is about the individuals and
0:06:07 about relationship development. The second cluster is something that I think you’re going to like
0:06:12 because I’ve heard you talk about the growth mindset quite a bit. The second cluster is really all about
0:06:20 learning and adapting and changing. And so in the second cluster, it’s how you assess yourself.
0:06:28 And again, these are norms. So this is part of the team’s culture. So their routines, habits. And so in
0:06:35 the great teams, they periodically, routinely step back and say, what could we be doing better? What’s going
0:06:40 well? What do we need to change? What’s coming down the pike? Have we heard from everyone? They make
0:06:45 sure that everyone has a sense of control over this conversation and input into the conversation.
0:06:49 So that’s the second cluster. I can talk more about that if you’re interested. But the third cluster is
0:06:57 about reaching outside the team for new ideas. This involves talking to your boss’s boss, to your clients,
0:07:02 your customers, to people in other industries who have information that can help you. And so this cluster
0:07:07 reminds you, it’s used by the high performers, because the high performers recognize they don’t
0:07:12 have all the information they need, and that there’s a lot of information out there that can make them
0:07:16 better. Again, they’re interested in that growth mindset. They’re interested in continuous improvement.
0:07:21 But it all begins with the first cluster, which is about understanding one another.
0:07:28 As I’m listening to this, I’m in Silicon Valley, and let’s just say we’re not the center of the humility
0:07:36 in the world. I think that people, when they hear about the first cluster, their first reaction is,
0:07:42 I don’t want some kind of touchy feely exercise about people explaining their background and where
0:07:47 they’re coming from. I just want to be in this meeting. Let’s figure out how to get sales higher.
0:07:52 Let’s figure out how to get rid of the low performers. Why are we doing all this touchy feely
0:07:57 getting to know each other? So maybe you can shoot down that skepticism.
0:08:07 Yeah, I can shoot that down in two ways. First, we now know that there are a set of social needs
0:08:14 that are activated when people enter groups. And the need that rules them all is one that we’re unaware
0:08:21 that we have, and it’s the need to belong. So let me define belonging for you. It means that first,
0:08:30 we’re genuinely accepted, known, understood, valued, and supported. Okay. That rises above
0:08:38 needs like a need for control, the need to feel valued, the needs for information, the need to be on the
0:08:44 in. It drives things like gossip, because we want to be on the in. We want to know what’s really going on around
0:08:51 here, because we want to maintain our status. Status essentially means you belong, means that you’re
0:08:58 secure. Now, this is a need we don’t know we have, but we certainly know when we don’t have it. And
0:09:06 that’s when we’re ignored or feel like we’re invisible, treated like we’re irrelevant. And that’s the kind of
0:09:14 behavior that will reduce participation and keep a team from being as creative as it could be.
0:09:20 So that’s one piece. One piece is that we’ve got these social needs. They are involuntary, Guy. This
0:09:25 is not a need that we can negotiate where you can tell me that I have it or I don’t. I actually have
0:09:34 an interesting list here, if I can find it, of how the need to belong or our reaction to feeling invisible
0:09:41 affects everyone, regardless of your attachment to your mother, regardless of your personality,
0:09:49 regardless of your social anxiety, regardless of any proclivity that you have, we all have it.
0:09:54 And we know this through neuroscience, and we know this through all kinds of things. So anyway,
0:09:57 that’s the first thing. I just want to plop that there. Hopefully, I’ve convinced you
0:10:02 slightly that that matters. I also want to draw your attention to a book that was written
0:10:07 about the Silicon Valley. It was about Bill Campbell. Are you familiar with Bill Campbell?
0:10:12 We overlapped at Apple, and yeah, I knew him well.
0:10:18 Okay. I read with great interest the book that Eric Schmidt and colleagues wrote about
0:10:24 Bill Campbell’s philosophy. And one of the things that he, at least you can tell me whether this is
0:10:29 true or not. I love that. Eric talked about how at the beginning of meetings, he would do something
0:10:35 called trip reports, where people would check in about where they’d been, what was on their mind,
0:10:42 this kind of thing. Trip report is how we get to know one another. That’s basically what it is.
0:10:50 I get to see how Guy thinks, what he’s noticing. I want you to think of a sports team or team of musicians
0:10:57 I love you. I love your example in the book about, I can’t never pronounce his first name.
0:11:04 I get to know one another’s proclivities. I love your example in the book about, I can’t never pronounce
0:11:09 his first name. It’s the same thing. It’s the same thing that we see in the very best teams.
0:11:14 We see that they do that. They get to know what one another’s proclivities are, if you will.
0:11:22 I love your example in the book about, I can’t never pronounce his first name. It’s Chara is his last name,
0:11:24 the Boston Bruin defenseman.
0:11:26 Oh, yeah, yeah, yeah, yeah.
0:11:32 And so you tell the story about how he stopped rookie hazing and all that, because basically,
0:11:38 he illustrates your concept of he wanted rookies to feel like they belong, right?
0:11:44 Yeah, yeah. He wanted everyone’s energy to come out in that locker room. He wanted everyone to feel
0:11:48 like him and he didn’t use the word belonging. Yeah, there are a lot of organizations where I won’t use
0:11:55 that word because it sounds so touchy-feely, but he wanted everyone in. He didn’t want status or experience
0:12:00 to override the way the rookies felt they needed their energy.
0:12:09 You use the word norm so frequently in your writing. Could you just back up and define what norm means?
0:12:15 Yes, absolutely. It’s a tough one because so many people have asked me to use a different word. I also
0:12:25 try to use the word habits or routines, but norms define normal behavior in this environment. So this is
0:12:32 how we do it here. So one example is when you walk into a meeting, do people greet one another?
0:12:36 Another norm is do you acknowledge people when you pass them in the hallway?
0:12:44 Do you look people in the eye? Do you pick up your phone during meetings and answer things? And here’s
0:12:49 more importantly, when do you pick up your phone? You certainly don’t pick it up when the boss is talking,
0:12:55 but who are you allowed to pick up the phone during their speech while they’re talking? Those are norms.
0:13:01 You’re either going to like this or not like this, but I’ve been called the Jane Goodall of teens. I know
0:13:06 Jane’s a friend of yours, so I don’t mean to insult her in any way, but it’s because I’ve spent so much
0:13:16 time observing team cultures. I go from team to team, teams doing the same task, and I look at the
0:13:24 different ways they interact. What’s normal? Teamwork is about interactions. It’s not about my interaction
0:13:31 with the boss. It’s about our interactions together. And the way team members treat one another and interact
0:13:37 together determines the level of motivation, how much people will speak, the kinds of things they’re
0:13:43 going to say. And if you don’t feel like you belong, then you have to do things that are going to get you
0:13:51 in, which basically means conform. And already we know that when you’re about to disagree with someone,
0:13:56 if you’re going to disagree with the boss or someone with status in the team, your brain sends you an
0:14:03 error message. Yes. So we used to think people just conformed. They would just say,
0:14:08 I think I’m just going to go along with the group. It’s not how it works. The way it works is your brain
0:14:14 wants you, and we can talk about evolution if you’re interested. I’m fascinated by how we’ve evolved to
0:14:22 live in tribes and clans and evolve to collaborate. It’s the collaborative clans that survive longer.
0:14:30 But anyway, we also learn to fit in. Kids learn that in high school, right? They have hormones
0:14:35 that kick in so that they’re interested in fitting in. They build those skills that they use for the
0:14:41 rest of their lives. You don’t want people in your teams behaving in ways to fit in. You want to check
0:14:47 that box, move it to the side, let them know they’re valued, and then encourage them to share their crazy
0:14:55 ideas. Does that make sense? Yeah, that makes terrific sense. Now, can you tell me how are these norms
0:15:01 formed? Is it top down from the leader? Did Bill Campbell say, okay, everybody, we’re starting every
0:15:08 meeting with an update about a trip report. So now when you guys conduct meetings, you also start with
0:15:14 updates. So was it Bill Campbell in leadership, or is it more organic and from the bottoms and the
0:15:20 middles of a team? First of all, there’s norms in every team. There’s no such thing as a team without norms.
0:15:25 The question is whether or not they’re effective, whether or not they suit the environment and the
0:15:32 objectives of the team. Typically, we watch the formal and informal leaders, people with status in the team.
0:15:38 And I got to tell you from what I read about Bill Campbell, I never met him. He was instrumental in
0:15:46 creating these relational, high performance norms. So it’s not just about relationships and getting to
0:15:54 know one another. It’s about giving your best, putting the team first, and performing well. He would ask,
0:16:00 what’s getting in your way? Talk about a way of growth mindset. So anyway, those norms come from
0:16:07 the people who are in charge. Let me tell you a story, a great story about norms in high schools.
0:16:11 This is the kind of thing that captures my attention these days, because I’m constantly trying to figure
0:16:16 out how to explain the power of norms to people. Anyway, wonderful researcher named Elizabeth Pollack
0:16:23 at Princeton, decided to study bullying in middle schools and high schools in New York state. So she
0:16:28 went to 56 schools. And most schools, when they’re trying to stop bullying, what they do is they try to
0:16:34 teach empathy to kids. So in my kids’ school, they brought in these speakers, they made them sign things
0:16:39 that said they were going to be empathetic. Unfortunately, teaching people empathy and putting
0:16:45 them into a system that doesn’t value empathy, where the norms don’t support being empathetic,
0:16:52 doesn’t change behavior. So what Elizabeth Pollack did was she identified who were the influencers,
0:16:59 who were the popular kids. She pulled them out. She did some workshops with them around whether they
0:17:05 wanted bullying, and if they didn’t, to come up with messages that could change the norms. She put them
0:17:13 back in the school. These popular kids shared anti-bullying messages that changed the acceptability of mean
0:17:21 behavior in the school systems. It reduced by 30%. So this is exactly what we see in organizations. And I
0:17:29 can tell you more research done in organizations, including my own, that it’s the norms that predict
0:17:38 how much grit is shown. I know you’re a fan of grit. How much empathy is demonstrated. Whether the growth
0:17:44 mindset is in place is in place. It’s how we do it here. We are social animals. We look to the left,
0:17:52 we look to the right, we figure out what people with status are doing. And we do that too. Right? Unless
0:18:00 we’re encouraged to be ourselves. Okay? And if that’s the norm, that’s what we’ll do.
0:18:08 I’m not sure I got the answer. So are you saying it’s the Bill Campbell’s of the world? Or it’s the
0:18:14 team? Team leaders. It’s the team leaders are the people with status. And Bill Campbell was given that
0:18:18 power. Who’s got the power? Informal leaders, formal leaders are who we look to.
0:18:36 So I’m sitting here, I’m listening and I’m thinking, can you give me some quick diagnostic tips so that I
0:18:42 can assess whether my team is emotionally intelligent or not? It seems to me that should be pretty obvious,
0:18:51 but just in case. Sure, sure. Well, the first thing I would ask is, what’s the emotional context like in
0:18:57 your team? Are people leaning in? Or are they leaning out? In terms of emotion, we tend to lean in or really
0:19:02 tend to lean out. But then what I would do is I would assess your team. What’s working? What’s not
0:19:08 working? But the way we’re working together right now. In the book, I give a sort of a quick survey
0:19:13 that I use with a lot of team leaders. But we have an assessment that we use when we were studying teams
0:19:20 and organizations that ask about the norms. So are you listened to when you speak? Do we respect everyone
0:19:25 equally in this team? Do we stop and reflect on our performance and talk about what we could be doing
0:19:33 better? So we ask about the nine norms in our model, and we look at the level that’s currently being
0:19:39 displayed. We don’t just look at the mean. We look at the range, okay? Because typically what we find is
0:19:45 that if you’ve got status, you think everything’s golden. If you don’t, then you’re at a lower level
0:19:52 of whether or not people are actually heard, and respected, and understood, and supported in this
0:19:58 team. And that’s like a canary in the mine. There’s two questions I get asked all the time from leaders.
0:20:05 One is how do I fix my problem people? And the second one is how do I compose the perfect team?
0:20:11 And so I can answer both of those for you. But first of all, you can’t compose the perfect team.
0:20:16 Everyone who’s ever studied that basically finds that you can’t compose it because it depends on
0:20:24 the norms that emerge. Anyone who’s treated like they don’t matter behaves badly at some level.
0:20:31 And so that links to the second thing. How do you stop that bad behavior? It turns out that we are
0:20:37 funky people. It’s easy to be emotionally intelligent and control our emotions when we have some level of
0:20:44 status or we feel like we matter and we’re valued in a team. When we don’t, we lose our ability to
0:20:50 self-control. It’s fascinating. There have been meta-analyses on this. People like myself who study this stuff
0:20:58 are flabbergasted about how badly people behave when they feel disrespected, when they don’t feel like
0:21:05 they’re in. And these are your outliers. And it’s your outliers that you need to investigate
0:21:12 to figure out how to improve. Innovation comes from the outliers. Improvement comes from hearing from the
0:21:19 outliers. I have helped more leaders turn around their teams by paying attention to outliers. Let me
0:21:25 give you an example. We had one team that we worked with. This was a team, actually it was the British
0:21:30 Football Association, Wembley Stadium folks. I don’t know how much you follow soccer, but it was a
0:21:36 leadership team in their organization. And the leader had a team that she knew wasn’t meeting its potential.
0:21:42 And so she put them through all this kind of training. They had individual coaches,
0:21:47 they had emotional intelligence training. Nothing helped improve the team effectiveness,
0:21:52 the way they were working together. So she brought us in. And the first thing we noticed was there was
0:22:00 this one guy, the bad guy, who every time we used to call him the knee scratcher, because he would scratch
0:22:10 his knee before he would do something that was outrageous. As soon as we got in there with him
0:22:17 and we evaluated the norms, and he had an opportunity to share that he didn’t think things were going
0:22:25 well and that he wasn’t getting listened to and shaping new norms and help create new norms. Six months
0:22:30 later after that workshop, he met me at the door when I was arriving and gave me a big hug and was going
0:22:39 “tee me, I, tee me!” Now that’s a pretty extreme example, but there are people in teams that want to give more
0:22:47 that can’t because they don’t have the opportunity to assess the norms and change the norms. And we know
0:22:56 that human beings are unique in the sense that we are capable of building the environment we want. Animals
0:23:01 are born with instincts. They’re stuck with environments. But Lisa Feldman Barrett, who’s a neuroscientist,
0:23:07 says this is our superpower as human beings, is that we can define the environment we want and we can
0:23:14 create it if we want it. And so what I’m asking for in the emotional intelligence team is you to look at the
0:23:21 model we’ve got, use it as a starting point, and adapt it to your own team. It’s a best practice model. We can
0:23:26 learn from the best. And if they’re building relationships, if they’re figuring out the time,
0:23:31 full collaboration doesn’t happen unless you have everyone in.
0:23:40 Is there a real world limit on the size of a team that can be emotionally talented? Are you telling me
0:23:47 that you could take an IBM with 150,000 people and make it to an intelligent team, or are we only talking
0:23:52 about small pockets within large companies? Yeah, it’s a good question. When a team gets
0:23:59 beyond the size of, say, 12, 13 people, you have to subgroup it a lot, right? And so I’ve worked with
0:24:03 teams that are bigger than that. We’ve changed the norms and it’s improved a little bit. Once you get
0:24:11 to the size of about 20, it becomes unwieldy because people are in subgroups and they subgroup off. And so
0:24:16 you pretty much need to create subgroups in those groups. And so then I talk about,
0:24:21 let’s build norms in those subgroups and then build some vague norms about how we’re going to work
0:24:25 together when we come together. But I can give you another example if you’re interested, but let me
0:24:34 let you ask questions. Vanessa, if I wasn’t interested in your examples, you wouldn’t be on this podcast so far away.
0:24:40 Okay. So let me give you a couple of other examples. I want to give you though, an example
0:24:48 of my mentor, who was Richard Hackman. He’s passed away, but he was a professor at Harvard. Whenever
0:24:52 I mentioned his name, I get a little distracted because he was such a big influence on my life.
0:24:59 But anyway, after 9/11, the FBI and the CIA came to him and said, “We need to work together better. We can’t
0:25:06 get along.” The CIA is a bunch of PhDs in IT and things like that. And the FBI is a bunch of sort
0:25:12 of cops on the beat culture mentality. And they just could not work together well, and they knew that
0:25:19 they could. So Richard, with all of his wisdom and a lot of his research was on norms. He basically ran
0:25:24 leaders through all kinds of leadership development programs, did all kinds of things. But what really
0:25:33 helped the team beat out simulations of terrorists. So what they did was they got a group of MIT PhDs
0:25:38 to play the terrorists, and they ran simulations with these folks, was building norms where they
0:25:43 could actually get along and share their information. And this is the problem with subgroups is that you
0:25:49 have to link them together somehow because they will compete. They won’t share their knowledge with one
0:25:57 another, unless you create norms that encourage you. Wow. Okay, so we answered that question. So now,
0:26:04 well, let’s cross your fingers, hope to die. We read your book, and we achieve this. We achieve a state of
0:26:12 success, as it were. Now, how do you maintain this? Is it a different skill set than achieving it?
0:26:19 Yeah, it’s built into the model. The model tells you what to do. Each norm is quite actionable.
0:26:24 And that middle bundle where it’s all about how we’re going to learn and advance together is about
0:26:29 continuous assessment and continuously tweaking the culture and checking in what’s working well,
0:26:34 what’s not working well. You and I both know that there’s no such thing as the perfect team.
0:26:41 Teams wax and wane, right? And there’s no such thing as a team without problems. And so the best way to
0:26:50 alleviate that is to build in to your routines, into your norms, a continuous assessment process. So we
0:26:56 worked with one team that started off, everyone was competing because they were all wanting to replace
0:27:01 the boss. It was a very high level team. And we helped bring them together and we helped align them around
0:27:06 their goals by helping them learn how each person could contribute. We built these norms essentially
0:27:12 in the team. And they continued those norms through several different iterations of leaders.
0:27:18 That leader left. Another one came in and they said, “Hey, we’re an emotionally intelligent team. We want
0:27:23 to keep up this assessment. We want to keep up this spending time, better understanding one another,
0:27:30 giving one another feedback, helping one another succeed, which is that first bucket.” And so I think we
0:27:36 went through three different leaders that were replaced with this same team until we basically burned out and
0:27:43 moved on and stopped doing that work. But yeah, you can keep doing it. And once you learn it, you pass it on.
0:27:53 So are there any teams that you can highlight for us that’s in the Vanessa Druskat Hall of Fame of
0:28:00 Emotional Intelligence? Like you hold them up as these great examples besides the Boston Bruins?
0:28:08 I want to talk about one team that’s one of my favorite teams that I write about. I can’t tell you the
0:28:14 company that it’s in, but it was a team of engineers. They came to us because their performance was
0:28:18 tanking and they were starting to lose market share. Their competitors were beating them out
0:28:25 and their boss got fired and they were angry and they were blaming one another and they were behaving
0:28:31 really selfishly. And so when we walked in there to help them, their new boss hired us, my colleague and
0:28:37 I, we came in and they immediately started screaming at us. What makes you think you can help?
0:28:41 You know, wait, wait, wait, wait. They screamed at you.
0:28:53 Yes, it was. They were so angry. Okay. They were so angry with one another. So of course we had to break.
0:28:57 What do you do when you, when this is happening, we have to break, take a deep breath, come back,
0:29:05 start over. And we realized that they were too angry to do anything, but get out of their own heads and
0:29:09 start to talk about what their future could hold, what they wanted from their team.
0:29:13 Was this the Tesla cyber truck team by any chance?
0:29:19 Yeah. Could have been. Could have been. What happened?
0:29:28 We spent, I would say at least three hours getting them out of their heads and talking about what
0:29:35 they wanted from a team. We got them talking about what they wanted from one another and they started
0:29:39 getting to know one another. And for example, one guy, this is an international team, by the way,
0:29:44 people from all over the world. One guy said, you know, I don’t talk on the phone. I hate phones.
0:29:49 I do texting, but no phones. And one other guy said, well, oh, well, no wonder you’re not answering
0:29:55 my phone calls because now I know I thought it was something about me you didn’t like. And so this is
0:30:00 the kind of thing that happens, right? Because we interpret people’s behavior. So they got this all out
0:30:06 and they finally selected some norms they wanted to develop. One of the norms was they wanted to
0:30:13 build more respect, more optimism, more proactive strategic thinking amongst the members.
0:30:17 And let me tell you how they decided they were going to demonstrate respect because one of the
0:30:21 things we do is we say, okay, what does respect look like here? What does it mean? They decided that
0:30:27 they were going to put down their phones and they were going to look one another in the eye and they
0:30:35 were going to nod their heads when someone was talking. And it was funny. Yeah. It was hilarious.
0:30:43 A bunch of these guys, these engineers, many of whom had PhDs, they were kings of the world of their worlds.
0:30:48 But guess what happened when they started listening to one another, they started sharing more. They started
0:30:54 helping one another. They had similar challenges, right? And they started sharing and it was a huge
0:31:00 breakthrough. All of a sudden the communication improved. And they took one guy who was the
0:31:06 curmudgeon of the group and they made him the ambassador of optimism. And he was the one that
0:31:14 opened every meeting talking about what he was hopeful for in the team. And hope is a motivator,
0:31:21 as you may or may not know. And we’re wired to need a little bit of optimism periodically so that we can
0:31:28 realize why we’re moving forward, why we’re engaging in this grit, right, together. And anyway, they started
0:31:34 getting more proactive and they really turned themselves around. This team was so great. And we
0:31:41 went on to work with their bosses, their bosses team, and they kept shuffling us up to higher and higher
0:31:47 levels. And it was a beautiful thing, especially when you can shift those norms down. It affects everyone.
0:31:56 Vanessa, is there any such thing as too much emotional intelligence? Can we overshoot the optimal level?
0:32:03 Yes, absolutely. So in the book, for every of the nine norms, I have a table that shows if you’re
0:32:08 doing this too much, are you doing it the right amount? Are you not doing it well enough? You can
0:32:13 spend too much time getting to know one another and it gets in the way of the task at hand. It’s one thing,
0:32:18 if you’re going on trips, Bill Campbell’s in the room and he’s helping facilitate you to get to the point.
0:32:25 But I think the important thing is that people take the leap of faith. Like you, like all your friends
0:32:32 that you talked about, people don’t think this stuff is important. They just don’t. And yet there’s so
0:32:38 many bad teams out there. If we can’t look to the greats and say, what’s going on in the greats
0:32:44 that we can replicate? Who can we look to? We have to learn from them. I set out on a quest. I was in
0:32:51 so many bad teams myself that I said, I need to help. The book is basically a road map that helps people
0:32:56 learn how to do that. And I would also be remiss if I didn’t remind you that there’s a foundation to all
0:33:00 this, which is that you got to have a clear purpose and people need to know what their roles are.
0:33:07 And so there’s a foundation of your typical stuff. But what we don’t talk about often enough is the
0:33:10 environment that brings out the best in people.
0:33:20 Can I ask a very theoretical question, which is if a company or a team is doing well in terms of
0:33:26 revenue, can it not be that they think we’re a well-functioning team, we’re emotionally intelligent,
0:33:34 blah, blah, blah. And that’s because everything is going well, but really they aren’t. And as soon as
0:33:40 things don’t go well, everything falls apart. So which came first, an emotionally intelligent team
0:33:49 begat success or success begat at least a belief in emotional intelligence, which comes first, which
0:33:57 is the chicken and which is the egg? Emotion is the motivator, right? There’s no motivation without emotion.
0:34:04 And so you can have an awful lot of fear or you can have a bad guy in the wings. The cheapest way to
0:34:12 motivate a team is to have a bad team that they’re fighting against, but it can burn people out. So the
0:34:19 question is whether or not you want to build a resilient team that’s capable of adapting constantly
0:34:26 to the next thing coming around the pike. And that’s an emotionally intelligent team. And that’s a team where
0:34:33 everyone’s in, or at least they’re in most of the time. So let me just tell you that I embrace this
0:34:39 concept that Stephen Covey came up with, which is what he called the emotional bank account, which is that
0:34:46 I need to treat you guy like you belong and value you and listen to you and nod my head when you talk
0:34:53 most of the time, or at least enough so that, you know, I care about you, that I really genuinely
0:34:57 want you to succeed. And I’m supportive of you, but there are going to be times when I’m going to have
0:35:04 to say, guy, that’s enough. Move on. We’re getting out of here. And so you need to deposit into this bank
0:35:10 account often enough. And so this is what you need to do in order to build an emotionally intelligent
0:35:15 team, which is put things in people’s bank accounts that let them know you want to hear from them.
0:35:21 I want to give you another quick example of a norm that’s in our model that is quite useful.
0:35:28 You can’t have an emotionally intelligent team that doesn’t have people wanting to participate.
0:35:35 So I can go in and I can observe a team, back to me observing team cultures, and I can see, I can tell
0:35:42 you a lot. Like I told you earlier, what I learned when I was 25 is that you can look at the emotion in
0:35:50 the room and tell a lot. How exhausted are they? How supported do they feel in here? What kind of emotion
0:35:57 are they feeling? But what you’re aiming to build is an environment where people know that their
0:36:04 contribution is something that people want to hear. So here comes back to the norm I was going to tell
0:36:10 you about. We have a norm that’s called support expression. And again, we learn this from our best
0:36:15 teams. All of these norms are from what the great teams did. It falls in that middle bucket of how we
0:36:22 learn in advance. But the way one of the leaders supported expression was he had a hat, a construction
0:36:29 cap that he put on the table where the teams met in his boardroom on that long table. And he said,
0:36:33 anytime you don’t feel like you’re getting heard or that there’s an elephant in the room that’s not
0:36:39 getting talked about, I want you to put that hat on your head and flip the lights on because it was one
0:36:46 of these things that had these light bulbs. That’s support expression. And that’s a reminder when it sits
0:36:53 there that people’s voices need to be heard. Okay. So that’s an emotionally intelligent team. And this is a
0:36:57 resilient team. This is a team where you’re going to have new ideas coming up. You’re going to be
0:37:04 learning from one another constantly. Everybody’s in, everybody’s going to catch you when you fall.
0:37:09 That’s an emotionally intelligent team. Madison tells me when I’m wrong all the time.
0:37:17 Excellent. Excellent. She knows you want to hear it. She knows. I see that in you guy. You have that
0:37:26 openness. You have that growth mindset. Not everyone has. Speaking of Madison. So is a team that’s led by a
0:37:34 a woman more likely to be emotionally intelligent? I have to say that, honestly, I haven’t worked with a
0:37:42 lot of teams that are led by women, unfortunately. What I can tell you is something that I’ve learned,
0:37:52 that a lot of these norms are somewhat feminine, relational. And if you don’t have a leader who embraces that sort of
0:37:58 feminine piece, the relational piece, then you’re not going to build some of this. I have to talk a lot
0:38:05 of team leaders into taking that leap of faith, because I hear it all the time. These are not babies.
0:38:13 These are adults. They don’t want to do this. And yet they come to me because their team’s not meeting their
0:38:19 potential. Teams are people. They’re human. And we have social needs that have to be met.
0:38:25 I suspect that I’m going to get a similar answer to this question, which is,
0:38:32 are teams that are more diverse, more likely to be emotionally intelligent?
0:38:37 teams that are more diverse are more likely to be higher performing. That’s what the research tells
0:38:45 us. The biggest problem with diverse teams is not hearing from everyone. Diverse teams need emotional
0:38:52 intelligence more than other teams. They’re not necessarily that they need it. This is a hot topic
0:38:57 these days, right? I don’t want you to lose all the federal funding for the University of New Hampshire.
0:39:02 Right, right, right. Without a doubt, you want to have a more innovative team. People always tell me,
0:39:06 how big does my team need to be? And I always tell, well, if you’ve got two people with the same
0:39:11 background in your team that think the same way, you’ve got redundancy. You need the smallest team
0:39:15 possible. You need a diverse team so that you can come up with different ideas. We’ve known that for a long
0:39:22 time. The problem is that in diverse teams, not everyone gets hurt all the time. Not everyone has
0:39:29 the influence they need. So the biggest issue has been creating an environment where everybody’s in
0:39:34 and people aren’t holding back. But certainly you’re not saying,
0:39:39 don’t create a diverse team because there’s not enough time for everybody to be heard. You’re saying,
0:39:43 create a diverse team and let everybody be heard. That’s exactly what I’m saying.
0:39:52 I am saying you need a diverse team and you need all those perspectives on the table.
0:39:59 We know that even perspectives that are off, that are outrageous, have an impact on how other people
0:40:05 think. We know this. Team researchers have known this for a long time, that the more ideas you come up
0:40:13 with, even the outrageous ones, impact the way other people think. Again, it’s the outliers that create
0:40:17 innovation. If people are all thinking the same, you’re not going to have that innovation. I want to
0:40:22 add something that’s similar to your question. I think that the more diverse your team, the more you
0:40:27 got to have emotionally intelligent norms. But also, if your team is working remotely,
0:40:33 you got to have clear norms. So the teams that we worked with that had emotionally intelligent norms
0:40:39 during the pandemic were set up. They had that middle bucket of norms, which is that we’re going
0:40:45 to talk, what’s going on here? They learned about one another’s situations quickly. They got up moving
0:40:51 again faster. And they set new norms for how they were going to work together when they weren’t face to
0:40:59 face. Up next, on Remarkable People. I wouldn’t ignore interpersonal skills. Let me take a backstop.
0:41:05 I believe hiring is the most important thing you do. You have to hire. You should prioritize
0:41:17 interpersonal skills, but you should not prioritize them over the skills you need. That’s what I think.
0:41:24 Do you want to be more remarkable? One way to do it is to spend three days with the boldest
0:41:30 builders in business. I’m Jeff Berman, host of Masters of Scale, inviting you to join us at this
0:41:36 year’s Masters of Scale Summit, October 7th to 9th in San Francisco. You’ll hear from visionaries like
0:41:43 Waymo’s Takidra Mawakana, Chobani’s Hamdi Ulukaya, celebrity chef David Chang, Patagonia’s Ryan Gellert,
0:41:52 Promises’ Phaedra Ellis Lampkins, and many, many more. Apply to attend at mastersofscale.com/remarkable.
0:41:58 That’s mastersofscale.com/remarkable. And Guy Kawasaki will be there too.
0:42:05 Become a little more remarkable with each episode of Remarkable People. It’s found on Apple Podcasts
0:42:12 or wherever you listen to your favorite shows. Welcome back to Remarkable People with Guy Kawasaki.
0:42:22 My next question was going to be, what is the impact of the virtual team and are there special
0:42:28 techniques? But I think you’ve already answered that question. Yeah, let me say just a couple things
0:42:32 about that. Yeah, I’ve answered the question, but I also want to say some interesting research on eye
0:42:39 contact shows that eye contact matters more in virtual teams than it does in face-to-face teams.
0:42:43 Now, you know, what does eye contact mean? Does it mean you’ve got to look right at the camera,
0:42:48 which is hard for all of us, right? I think it just means that you’re showing, we see each other so
0:42:55 clearly. So are you paying attention? We can tell very easily whether or not people are attending to us.
0:43:00 When people make eye contact, when they attend to us, it’s a gift. It’s a way of saying,
0:43:07 I accept you, I value you. It’s a small act with a big consequence. There’s so many small acts like
0:43:11 that, that make a difference. I have a friend that used to work in a team where everybody would be
0:43:16 typing the whole time on their computers. And it was like the norm was that if you weren’t typing,
0:43:21 you didn’t have enough work to do, you’re wasting your precious, come on. I mean, I’ve been in meetings
0:43:22 like that before.
0:43:30 As a podcaster, I think maybe I conduct one interview a year in person and every one of
0:43:40 them is virtual like this. And I have the squad cast window behind a teleprompter.
0:43:46 Yeah. Because if I didn’t have a teleprompter and I was looking in your eyes, I would not
0:43:52 be looking at the camera. So this is just technology that I have a teleprompter. And right now I’m
0:43:59 looking right into your eye, but I can also see your face. And so I’m looking in the camera and I’m
0:44:03 looking in your eye because your eye is in front of the camera on the teleprompter.
0:44:05 Sure. Interesting. Yeah. It’s powerful.
0:44:07 It’s worth every penny.
0:44:09 It is. Yeah.
0:44:10 Okay.
0:44:13 So what’s it like for you to be online all the time? How is it exhausting for you?
0:44:19 I have to say that because we’re trying to incorporate more and more videos,
0:44:27 being a virtual interviewer is easier because there’s a camera on you. There’s a camera on me.
0:44:32 If we were in person, we would have to have two cameras. We’d have to have a crew
0:44:38 changing who’s live and all that. It’s much harder. And the other thing, believe it or not,
0:44:46 is I am deaf. And as a deaf person, an in-person interview is much harder because being deaf,
0:44:53 I can have the audio feed come directly into my cochlear implant. If I were just sitting in an office
0:45:02 with you, I would have to depend on the implant microphone picking up your speech. But in this
0:45:07 case, your microphone is coming directly into my head, which is much better for me.
0:45:10 Wow. That’s powerful. Yeah.
0:45:12 So that’s my story.
0:45:13 Yeah. That’s great.
0:45:19 What about return to office? People say return to office. Now we can hang around the water cooler,
0:45:26 we can interact, we can learn more about your trip report, whatever. But there’s a lot of pushback
0:45:29 on return to the office. So where are you on that?
0:45:34 That’s such a tough one. Yeah. One of the things you have to realize is that I’ve been working with
0:45:40 a lot of remote teams. And so for the last 20 years, I think that my colleagues and I were the
0:45:47 first of the Zoom contract, because we worked with so many remote teams. And so I know you can do this
0:45:52 remotely, but I know you have to get together periodically. And when you do get together,
0:45:57 you got to spend a lot of time interacting. You got to spend a lot of time getting to know one another,
0:46:03 breaking bread together. So I should fall on getting people back into the office, but I don’t.
0:46:10 I fall on thinking that you can build relationships. I’ve seen it happen. I’ve seen high-performing
0:46:14 teams. I’ve interviewed high-performing teams. I’ve been doing team-building exercises
0:46:23 online, on phones for 25 years. You can do it. It’s harder. You have to be more intentional.
0:46:28 You have to have your cameras on. And what I can’t stand are teams that meet that people don’t put
0:46:34 their cameras on. That’s my, that’s my thing. Vanessa, can I point out something to you?
0:46:41 You don’t use a teleprompter, right? No. But I am telling you, you make excellent eye contact.
0:46:47 You are really disciplined and you are always looking at the camera. Huh? That’s good to know.
0:46:55 I mean, nobody does it this good. You may be the best person I’ve ever interviewed at looking at the
0:47:03 camera. Oh my gosh, it’s so interesting. Thank you for that feedback. Well, part of it is, I think I’m a good
0:47:09 listener. I’m so curious. Actually, what I would love to do is start asking you questions, both of you.
0:47:18 You want to hear about your team experiences. I’m telling you. Okay. So I’m not imagining it because
0:47:25 Madison would know as much as I would. You win the contest for the best eye contact in the history
0:47:29 of remarkable people podcasting. Wow. That’s great to know. Thank you.
0:47:35 Yeah, that and a quarter will get you a cup of coffee.
0:47:39 I know. I need a sip of water.
0:47:46 All right. A few quick questions and then I’m going to drop a bomb on you. So the quick questions are,
0:47:52 how do your findings impact recruiting? Do you change how you recruit based on wanting to build
0:47:59 the emotionally intelligent team? I still think you need to hire people with interpersonal skills. I
0:48:04 think that those skills are irreplaceable. I think they’re really important in teams. I also think you
0:48:14 need to hire for the skills that you need. So I would lean toward getting the talent, the skills you need
0:48:20 ahead of the interpersonal skills. If you can, if there’s a trade off. That’s the opposite of what
0:48:28 I expected you to say. Yeah. Yeah. Because I think the environment you create can bring the best out of
0:48:35 people. It’s the environment. What I see happening far too often is the wasted talent. Some of those people
0:48:42 with the great interpersonal skills you hire can’t get a word in on your teams. Wait, you threw me for a loop
0:48:49 here. So you’re saying hire the best talent and you can fix their interpersonal skills or you’re saying
0:48:56 hire interpersonal skills and you can fix the talent. I wouldn’t ignore interpersonal skills. Let me take a
0:49:03 backstop. I believe hiring is the most important thing you do. You have to hire. You should prioritize
0:49:11 interpersonal skills, but you should not prioritize them over the skills you need. That’s what I think,
0:49:18 because I think you can create an environment. So let’s just say that you hire somebody who never
0:49:24 shuts up. They dominate conversations. You can get rid of that in your team by managing it. I mean, I have
0:49:31 helped teams do that. That’s a team norm. Next quick question. How do your findings affect
0:49:38 onboarding of new employees? What’s special about onboarding for an emotionally intelligent team?
0:49:45 So much easier to onboard. So let me tell you why. You have a set of norms that define your culture.
0:49:50 This is how we behave in this team. This is what makes us unique. And something about one another,
0:49:55 something about one another’s roles. Some of the things I haven’t talked to you about are some of
0:50:00 these interventions that we use to build these norms. But one of them is sharing information about
0:50:05 one another’s roles, sharing information about one another’s personalities or proclivities,
0:50:13 ways they like to work. And what I recommend is putting that in a charter that you pass on to new
0:50:19 people that are being hired. And I’ve seen teams do it. I’ve helped teams do it that I’ve worked with. So
0:50:24 emotionally intelligent teams. One of the things that I recommend is that if you’re going to build a new
0:50:30 norm, you’d have a couple of team members get in charge of that norm. And so let’s say that one of
0:50:35 the norms is that you’re going to get to know one another better. The classic way to get to know one
0:50:41 another is to take surveys, personality surveys, work style surveys. You pick it. Oh yeah. You’ve
0:50:45 never done that in a team? Oh my God. That’s a riot. That’s what everybody does.
0:50:50 It only works if it’s one of many things you do.
0:50:53 Are you talking like Myers-Briggs and that kind of stuff?
0:50:56 Yeah. That kind of stuff. Yeah. Are you an introvert or an extrovert?
0:50:59 Yeah. I’m a no-vert.
0:51:04 Yeah. Well, you’re probably like I am, which is that you’re an ambivert. You can be either.
0:51:10 Oh, whatever it takes. This is why personality doesn’t predict behavior in teams. It’s because
0:51:16 we adapt to what’s going on around us, but it impacts the way we like to work. And the people
0:51:22 who are in charge of that get to know you norm, put this section into the team charter that’s handed on
0:51:27 to people who are onboarded. I’m not making this up. I didn’t even come up with the idea. This is what
0:51:32 teams I’ve worked with have done. And the new members are up to speed fast. They know what the
0:51:38 norms are. They know how you run your meetings. They know what your goals are because the goals
0:51:45 are on there too, by the way. And it’s beautiful. Okay. The last quick question is how do your
0:51:52 findings and your research affect methods of compensation? People ask me that all the time.
0:51:55 And it’s the questions are really about, was that the bomb you were going to drop on me?
0:51:57 No, no, no. The bomb is coming.
0:52:01 Oh, that’s not the bomb. Okay. Okay. The bomb is coming. It’s circling right now.
0:52:07 Okay, good. I’m looking forward to the bomb. You know, people are not dumb. We’re all compensated
0:52:11 differently. Some of us have more experience or less experience. Some of us have been
0:52:17 negotiated higher salaries when we came in. I’m not talking about having equal compensation
0:52:24 and amongst your team members. I’m not talking about getting rid of individual performance
0:52:31 plans. But what I am talking about is building an environment that supports the people in the team.
0:52:38 It supports the I, it supports the individual, and it supports the we. It can’t only be about the I.
0:52:45 And I got to tell you, in environments where we support one another, people do better. I’ve seen
0:52:52 people get promoted out of these teams because of the feedback and the support they get from their
0:52:56 team members. I’m trying to guess what you’re really thinking about or asking about compensation.
0:53:02 People are going to be compensated differently. They’re going to be promoted differently. And that
0:53:06 one team that was a pretty high level leadership team, the person who was promoted out of it,
0:53:12 people were thrilled for that guy. And the guy, basically, I saw the guy a couple of years later,
0:53:20 and he said to me, “Vanessa, I’m at a loss in this new role because nobody gives me any feedback here.
0:53:26 I don’t know what I’m doing well and what I’m not doing well.” In that emotionally intelligent team,
0:53:34 people would tell me what they wanted to see more of from me. We had that environment. They wanted that
0:53:40 environment. I gave them the building blocks and they wanted it. They created it. That’s what you do in a
0:53:41 great thing.
0:53:51 So let’s say your phone rings and it’s area code 202.
0:53:53 Okay.
0:54:00 And you get to pick how big a bomb you want to bite off. It’s either Hakeem Jeffries,
0:54:08 Mike Johnson or Donald Trump, and they’re saying, “My team is dysfunctional. I want you to come on board
0:54:16 and make my team emotionally intelligent.”
0:54:25 First of all, are you interested in the job? And second of all, can it be done? And third of all,
0:54:32 what would you do? I love that question. So let me say, I work with a lot of teams and there have
0:54:40 been a couple of teams that I have not succeeded with. And those are teams where the leaders won’t let go.
0:54:52 And by the way, I’m the one that’s left. Because I’ve said, “I can’t help you anymore.” They’ll be like,
0:55:00 “Can we redo the contract?” And like, I remember with one leader, I said, “Your team members are afraid
0:55:05 of you. They need you to let go of some control.” And he said, “There’s nobody who’s afraid of me. Why would
0:55:11 they be afraid of me?” And so back to this original question you asked me, which is who’s in control
0:55:18 of the norms? It’s the leader. It’s the people with status. And to build an emotionally intelligent team,
0:55:24 you have to let go of some control. Now, I would be willing to work with Mike Johnson.
0:55:29 I would not be willing to work with Donald Trump.
0:55:31 And how about Hakeem Jeffries?
0:55:32 Sure.
0:55:38 Okay. That’s the bomb. And I like how you answered that question. That was a
0:55:40 very good answer. I appreciate that very much.
0:55:44 Well, thanks. I keep thinking I need to write something on it, but there’s been so much written
0:55:50 about it. Because you want people to share their truth in a team. And that’s not what Donald Trump wants.
0:55:52 It would be hard to convince him to do that.
0:55:56 Careful. I don’t want your university to lose all federal funding.
0:56:06 That’s why I want to tell you something. I told you that you are the best eye contact person
0:56:14 in the history of remarkable people. I will also tell you one more thing. I cannot be as definitive
0:56:24 in what I’m about to say, but I’m pretty sure you may lead the pack here. I read roughly 52 books a year,
0:56:32 a year, because just about every podcast involves reading somebody who’s remarkable has a new book
0:56:39 coming out. Like you have a new book coming out. And I will tell you that your book is one of the best
0:56:52 laid out and the best headings and the best subheadings of the books I have read. I constantly tell Madison,
0:57:02 oh my God, this guy’s book, this gal’s book, it’s pages and pages and pages of paragraphs. There’s no
0:57:08 headings. There’s no subheadings. It’s like reading Tolstoy or something. And this is a business book.
0:57:15 There’s no subheadings, no breaks, no nothing. And I picked up your book and it was like, oh my God,
0:57:21 thank you God for sending me this book. It’s so much easier to read. And I noticed in your
0:57:27 acknowledgments in the back about Harvard Business Review Press and you thank your editor and you thank
0:57:34 your team and you thank your designer. Tell her that Guy Kawasaki says he really likes the design of your book.
0:57:41 You know, I have a lot of books that are similar. I think you and I like books that are similar.
0:57:48 When I write a book, I use Microsoft Word and I have a template for everything. And every section
0:57:57 is a style and I can shift between text and outline so I can see all my heading threes and it’s completely
0:58:02 organized. So I know exactly where the heads and subheads are. And so I’m a little bit OCD that way,
0:58:08 but your book is beautiful. So I congratulate you so much. Yeah, I’m OCD that way to myself.
0:58:16 I get it. I appreciate it. If you ever want a Microsoft Word template that’s completely laid out
0:58:23 for every paragraph, every bullet, every everything that has a style, I’ll be happy to send you my word.
0:58:30 Cool. Cool. I love it. All right. All right, Madison, we’re going to let Vanessa off the hook
0:58:36 so that you can tell me what I did wrong today. Tell me what I did wrong too. I hope I answered your
0:58:41 questions. I hope I didn’t go off too much. You absolutely did it. Good. This was a master class
0:58:47 in looking at the camera. As part of your practice, you could say from now on to improve the emotional
0:58:54 intelligence of your team. I want you to buy every member of your team, a teleprompter.
0:58:59 And for $200, I guarantee you, you will get the value out of that.
0:59:03 I am going to tell them. That’s going to be in my next book and I’m going to cite you on it.
0:59:10 Listen, guy, I just want to say you’re such a positive force in the world. I really appreciate
0:59:16 what you do. Well, I can honestly tell you, I could not do it without Madison. So all right,
0:59:21 Vanessa, we’re going to let you go. Thank you very much. And I want to thank the rest of the
0:59:27 Remarkable People team. And of course, I’m going to thank Madison and also Tessa Neismar,
0:59:33 who’s a researcher who helps him with all the background research. And we have a co-producer
0:59:39 named Jeff C. And finally, we have Shannon Hernandez, who is our sound design engineer. So that’s
0:59:46 the Remarkable People team. This is Remarkable People.

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0:01:43 Welcome to Raging Moderates.
0:01:44 I’m Scott Galloway.
0:01:45 And I’m Jessica Tarlov.
0:01:47 Jess, have you missed me?
0:01:49 Have you missed me?
0:01:49 Yeah.
0:01:51 And I don’t want to say it again,
0:01:52 but, like, I texted you
0:01:53 and you just didn’t respond.
0:01:54 And it was interesting.
0:01:56 It was in response
0:01:57 to your No Mercy, No Malice column
0:01:59 with extra data for you
0:02:00 to be able to use in your shows.
0:02:01 Oh, really?
0:02:02 It’s okay.
0:02:05 I know you have a scarcity clause
0:02:06 in our contract or whatever,
0:02:07 or in your contract with everybody.
0:02:08 Yeah, I don’t.
0:02:11 I would like a new gestalt in our society
0:02:12 that when you don’t respond
0:02:13 to emails or texts,
0:02:14 it means,
0:02:15 I agree,
0:02:16 what a great email,
0:02:18 drop the mic,
0:02:19 I don’t need to respond.
0:02:21 That’s what the thumbs up
0:02:22 was created for.
0:02:24 100%.
0:02:25 It’s not enough for most women,
0:02:26 I will say,
0:02:27 but at least it means
0:02:29 that you are alive,
0:02:30 that you recognize
0:02:31 that this happened
0:02:32 and that you aren’t upset
0:02:33 about something
0:02:34 because that’s where
0:02:35 the estrogen takes you.
0:02:36 Like, I think,
0:02:36 oh my God,
0:02:38 is Scott in Ibiza
0:02:40 upset about something?
0:02:41 But no,
0:02:41 you were probably just
0:02:43 drinking and hanging.
0:02:44 Oh, see,
0:02:46 I think women are more secure.
0:02:47 I think guys are actually
0:02:47 Have you met women?
0:02:48 more insecure
0:02:49 and women,
0:02:51 because women have so much
0:02:53 practice ghosting men.
0:02:54 I think they respect
0:02:55 the slow fade.
0:02:57 I think they respect
0:02:57 the, like,
0:02:58 the polite,
0:03:00 I’m winding down
0:03:01 this dialogue,
0:03:02 whereas men,
0:03:03 especially powerful men,
0:03:04 are used to everyone
0:03:06 responding back
0:03:06 and, you know,
0:03:08 licking them up and down.
0:03:08 And I’m,
0:03:10 I find,
0:03:11 and I’m virtually signaling,
0:03:12 but it’s true,
0:03:14 the more important the person,
0:03:15 the less likely I am
0:03:16 to respond,
0:03:17 because I have spent
0:03:18 my entire career,
0:03:19 Jess,
0:03:20 responding
0:03:22 to powerful people,
0:03:23 whether it’s writing
0:03:23 their speeches,
0:03:24 doing their presentations
0:03:25 for boards,
0:03:26 or telling them
0:03:27 what decisions to make,
0:03:28 or whatever it is.
0:03:30 And now I am done.
0:03:31 I am done
0:03:33 renting my brain
0:03:34 to rich white dudes.
0:03:35 I’m done.
0:03:35 Oh.
0:03:36 Anyways,
0:03:36 probably more than you
0:03:37 were bargaining for.
0:03:38 A little bit.
0:03:39 Back to you.
0:03:40 How are your children?
0:03:41 Oh,
0:03:42 so nice of you to ask.
0:03:43 They are great.
0:03:44 I got some
0:03:45 cute photos
0:03:46 from last week.
0:03:47 I won’t send them to you
0:03:48 because it won’t matter,
0:03:49 probably.
0:03:50 But, uh,
0:03:50 don’t care.
0:03:51 They’re really good.
0:03:53 We’re getting very comfortable
0:03:54 in the pool,
0:03:54 which is important,
0:03:56 the water safety skills.
0:03:56 Oh, God.
0:03:58 It’s so scary
0:04:00 when you’re around water,
0:04:01 which I’m a city kid,
0:04:03 and we don’t have a pool,
0:04:03 obviously, here.
0:04:05 But it’s the scariest thing
0:04:05 in the world
0:04:06 to think that they could
0:04:07 just fall in
0:04:08 when you turn your head
0:04:08 or you’re not,
0:04:08 you know,
0:04:09 they get out of the house
0:04:10 somehow, so.
0:04:11 Oh,
0:04:12 your instincts there
0:04:12 are correct
0:04:13 and common sense.
0:04:15 I have personal experience
0:04:15 with this.
0:04:17 I saw my job
0:04:17 as a father
0:04:18 of young children
0:04:19 to do two things.
0:04:20 Bring home the bacon
0:04:21 and, two,
0:04:22 keep the kids away
0:04:23 from any body of water.
0:04:25 And when we first
0:04:26 moved to Florida,
0:04:27 I was out and back
0:04:28 and my son,
0:04:29 who was,
0:04:30 he was like three or four
0:04:31 and we were out in the back,
0:04:32 he was playing in the pool
0:04:34 and he went to the deep end
0:04:34 and jumped in
0:04:36 and started flailing around
0:04:36 and I was there
0:04:37 so I could jump in
0:04:38 and fish him out.
0:04:39 And I thought,
0:04:40 if I had just gone in
0:04:41 for some water,
0:04:44 if I had just taken a call
0:04:45 and wandered around
0:04:46 the side of the house.
0:04:48 Anyways,
0:04:49 you’re right to be paranoid
0:04:50 about that.
0:04:50 And then,
0:04:52 one summer,
0:04:52 I forget where we were,
0:04:54 we even bought those devices
0:04:55 that you put on their shirts
0:04:57 and when the device
0:04:58 senses water
0:04:59 and alarm goes off
0:05:00 and the alarm went off
0:05:01 on a Sunday
0:05:01 and we’re all like
0:05:03 running around the house
0:05:03 looking for a kid
0:05:04 and a body of water
0:05:06 and someone put the shirt
0:05:07 in the laundry.
0:05:08 I don’t know how
0:05:09 I got here, Jess.
0:05:09 Did I tell you
0:05:10 I’m in Ibiza?
0:05:11 You did,
0:05:12 but now you’re telling everybody
0:05:13 it looks nice
0:05:14 or looks fine.
0:05:15 I’m in Ibiza,
0:05:17 where there are
0:05:18 a ton of young men
0:05:19 who have not earned
0:05:20 their wealth
0:05:20 and are spending
0:05:21 their father’s money
0:05:22 and have some intricate story
0:05:23 about the real job
0:05:24 they supposedly have
0:05:25 and it’s obvious
0:05:26 within about 10 seconds
0:05:26 they’re just,
0:05:28 they’ve got a rich dad
0:05:29 and they bring
0:05:30 a bunch of women,
0:05:30 you know,
0:05:31 looking to be sponsored
0:05:32 by the son.
0:05:34 There’s so many sexist,
0:05:34 classist things
0:05:35 I just made in that statement
0:05:36 but I’m holding by it.
0:05:37 But I do love it here.
0:05:38 They’re also all true
0:05:39 about it there.
0:05:40 I went once,
0:05:41 I was 30
0:05:42 and I was the only person
0:05:44 who wasn’t on Molly
0:05:45 when we went out
0:05:48 and it’s something to behold
0:05:49 watching people
0:05:50 on Molly
0:05:53 dancing for five hours straight.
0:05:54 Like we had just started
0:05:55 getting step counters,
0:05:56 you know,
0:05:57 like people were paying attention
0:05:58 to the number of steps
0:05:58 and then you see that
0:06:00 someone did like 40,000 steps
0:06:01 overnight
0:06:02 and you know
0:06:02 they had a good night.
0:06:04 Yeah,
0:06:04 I’m not going to say
0:06:05 whether or not
0:06:05 I take Molly
0:06:06 but a couple nights ago
0:06:07 at the Black Coffee
0:06:07 DJ said,
0:06:09 I found I really like me.
0:06:11 I really felt good about me
0:06:12 all of a sudden.
0:06:13 All right, Jess,
0:06:15 today we’re talking about
0:06:15 the new phase
0:06:16 of Trump’s trade war.
0:06:17 That was a segue.
0:06:19 The GOP trying to sell
0:06:19 their new bill
0:06:21 and Elon Musk’s
0:06:22 new third party.
0:06:23 Jesus Christ,
0:06:24 you fucking attention monster.
0:06:25 Could you be more addicted
0:06:27 to ketamine
0:06:27 or attention,
0:06:28 you fucking weirdo?
0:06:29 All right,
0:06:29 let’s get into it.
0:06:31 Now that the White House
0:06:32 has pushed its big
0:06:33 legislative package
0:06:34 across the finish line,
0:06:36 it’s turning its attention
0:06:37 back to the global trade war
0:06:38 with a fresh dose
0:06:39 of confusion,
0:06:39 deadlines,
0:06:40 and diplomatic drama
0:06:43 after a 90-day tariff pause
0:06:44 that produced
0:06:45 only a few shaky deals.
0:06:47 That is generous
0:06:48 to describe
0:06:49 what has happened
0:06:49 with the U.K.
0:06:50 By the way,
0:06:51 let’s just talk about
0:06:51 this U.K. deal.
0:06:52 A reduction in tariffs
0:06:54 on the Austin Martin engines
0:06:55 and Rolls-Royce engines.
0:06:55 Wow,
0:06:56 that’s going to change
0:06:56 the economy.
0:06:59 After a 90-day tariff pause
0:06:59 that produced
0:07:00 only a few
0:07:01 of these deals
0:07:03 with the U.K.,
0:07:03 Vietnam,
0:07:04 and China,
0:07:04 although I wouldn’t
0:07:05 even call them deals.
0:07:06 I’m still pretty angry
0:07:07 about this.
0:07:08 There are agreements
0:07:08 or structures
0:07:09 to talk about a deal.
0:07:10 Trump says the U.S.
0:07:10 is ready
0:07:11 to turn up the pressure.
0:07:13 Oh, God.
0:07:14 Hold my beer, bitch.
0:07:14 That is literally
0:07:15 what the world
0:07:16 is saying to this guy
0:07:16 right now.
0:07:18 Starting August 1st,
0:07:20 steep import duties,
0:07:21 some as high as 70%,
0:07:22 are set to kick in.
0:07:24 Yeah, sure they are.
0:07:25 Sure they are.
0:07:26 Mr. Trump
0:07:27 owe his chickens out.
0:07:27 That process
0:07:29 began in earnest Monday
0:07:31 when President Trump
0:07:32 fired off tariff letters
0:07:32 to the leaders
0:07:33 of 14 countries,
0:07:34 including Japan,
0:07:34 South Korea,
0:07:34 Malaysia,
0:07:35 and South Africa.
0:07:37 These letters spell out
0:07:38 new country-specific tariffs,
0:07:40 I guess from some intern
0:07:42 that has a chat GPT account,
0:07:44 ranging from 25%
0:07:45 to 40%,
0:07:46 and warned that rates
0:07:48 could even go higher
0:07:48 if those countries
0:07:49 retaliate.
0:07:50 At the same time,
0:07:51 Trump signed
0:07:52 an executive action
0:07:53 pushing back the deadline
0:07:55 for most reciprocal tariffs
0:07:56 with, oh,
0:07:57 pushed back the deadline.
0:07:59 My red lines
0:08:00 are kind of a beige
0:08:01 invisible line,
0:08:03 said Trump over and over,
0:08:04 with the exception of China
0:08:06 to August 1st.
0:08:07 The move buys more time
0:08:08 for negotiations.
0:08:09 In other words,
0:08:10 I’m folding yet again,
0:08:11 said Donald Trump
0:08:12 to the world,
0:08:13 but not by much.
0:08:14 Now businesses
0:08:15 are bracing for impact,
0:08:16 markets are jittery,
0:08:17 and major questions remain.
0:08:19 Will Canada’s
0:08:20 July 21st deadline hold?
0:08:21 What happens
0:08:22 when the China truce
0:08:24 expires August 12th?
0:08:25 And is this strategy
0:08:28 or just more bullshit,
0:08:28 jazz hands,
0:08:30 false, empty threats?
0:08:30 Jess,
0:08:32 what do you make
0:08:33 of this new phase
0:08:34 of, let’s call it
0:08:35 the tariff limbo?
0:08:37 It’s the same as usual
0:08:38 in that it just feels
0:08:40 deeply unserious.
0:08:41 And this has
0:08:43 an exclamation point
0:08:44 after unserious
0:08:45 or a crescendo
0:08:46 because these letters
0:08:47 letters that he sent
0:08:48 to foreign leaders
0:08:49 were just like
0:08:51 true social posts
0:08:52 on letterhead.
0:08:53 It was like written
0:08:55 by a 14-year-old boy.
0:08:56 He’s capitalizing
0:08:57 random words.
0:08:58 His grammar
0:08:59 makes no sense.
0:09:00 He’s misgendering
0:09:01 certain leaders.
0:09:03 They fixed that, though.
0:09:04 Her Excellency
0:09:05 became a dear
0:09:05 Mr. President
0:09:07 within a few hours.
0:09:09 But there’s always
0:09:10 been an opportunity
0:09:11 for the Trump administration
0:09:13 to take the layup
0:09:14 on this trade war
0:09:14 because when they
0:09:15 buy themselves
0:09:16 more time,
0:09:16 they could just
0:09:17 back out.
0:09:18 And no one would
0:09:19 really say anything
0:09:19 because they’d just
0:09:20 be quietly relieved.
0:09:21 Like, everyone over
0:09:22 at CNBC would be like,
0:09:23 thank God.
0:09:24 Right?
0:09:25 We can just get back
0:09:25 to being normal.
0:09:26 And you could talk
0:09:27 about tariffs on China,
0:09:28 which everyone
0:09:29 broadly agrees with,
0:09:30 and the Biden
0:09:31 administration did as well.
0:09:32 They even jacked up
0:09:33 Trump’s tariffs
0:09:34 on China threefold
0:09:36 and just focus on people
0:09:38 that are actually
0:09:40 at war with us
0:09:40 in some way
0:09:41 or another.
0:09:43 But these blanket
0:09:43 tariffs,
0:09:44 these violations
0:09:45 of, by the way,
0:09:46 free trade agreements,
0:09:47 which creates
0:09:48 larger questions
0:09:49 around what Donald
0:09:50 Trump thinks Congress
0:09:51 actually does
0:09:52 or if he values it
0:09:53 at all,
0:09:53 which, I mean,
0:09:54 he doesn’t,
0:09:54 as we’ve seen
0:09:55 time and time again.
0:09:56 But like on South Korea,
0:09:57 we have a free trade
0:09:57 agreement.
0:09:59 It’s not up to you
0:10:00 what you do with them.
0:10:01 I just,
0:10:02 I don’t want to see
0:10:03 Scott Besson anymore.
0:10:04 Like, this guy
0:10:05 who was supposed
0:10:05 to be the adult
0:10:06 in the room
0:10:07 making the rounds
0:10:08 on the Sunday shows,
0:10:08 then he’s all
0:10:10 over CNBC on Monday
0:10:12 and he’s so smug
0:10:15 and he’s telling us
0:10:16 to not believe
0:10:16 our lion eyes
0:10:17 about what’s going on.
0:10:18 You know,
0:10:19 we had 90 deals
0:10:20 in 90 days.
0:10:21 That’s over.
0:10:22 Peter Navarro says,
0:10:23 oh, I’m very happy
0:10:24 with where we are.
0:10:24 I don’t know how
0:10:26 that’s physically possible
0:10:26 if you said we were
0:10:27 getting 90 deals
0:10:28 in 90 days.
0:10:29 And then we had
0:10:30 Trump in April.
0:10:30 I’m telling you,
0:10:31 these countries
0:10:32 are calling up.
0:10:33 They’re kissing my ass.
0:10:33 They’re dying
0:10:34 to make a deal.
0:10:35 Please, please, sir,
0:10:35 make a deal.
0:10:36 I’ll do anything, sir.
0:10:37 And when he talks like that,
0:10:39 you know that it’s hyperbole.
0:10:40 But now Besson
0:10:41 has admitted as much
0:10:42 that a lot of those countries
0:10:43 didn’t even call us.
0:10:44 And people understand
0:10:45 that you just kind of
0:10:46 sit back and wait
0:10:46 to see what happens.
0:10:48 Because even if you were
0:10:49 to make a plan
0:10:50 that goes along
0:10:51 with what they want
0:10:53 for you to be doing,
0:10:53 right,
0:10:53 that they want you
0:10:54 to build a factory
0:10:55 or whatever,
0:10:56 they’re not giving you
0:10:57 enough time to do it
0:10:59 to any execution whatsoever
0:11:00 because in 10 days
0:11:00 it just changes.
0:11:01 So if I were
0:11:02 these other countries,
0:11:03 I would just sit back
0:11:04 and kind of wait
0:11:05 and see what happens
0:11:06 and hope that he gets
0:11:07 distracted by something
0:11:09 and just keep buying yourself
0:11:10 more and more time.
0:11:11 What do you think?
0:11:14 Yeah, so the entity
0:11:15 which has become
0:11:16 sort of a better
0:11:17 predictor engine
0:11:19 than political pundits
0:11:21 or CNN or Fox
0:11:22 is the markets.
0:11:23 And basically the markets
0:11:25 don’t believe
0:11:26 the tariffs are going
0:11:27 to change that much.
0:11:29 I mean, to be clear,
0:11:30 and I’m a bit
0:11:31 of a catastrophist,
0:11:31 I thought this was
0:11:32 really going to hurt
0:11:32 the markets.
0:11:33 And the markets
0:11:34 have basically said
0:11:35 the tariffs are going
0:11:36 to look remarkably
0:11:37 similar to the way
0:11:37 they did before.
0:11:38 They’re just,
0:11:39 the markets aren’t
0:11:40 worried about this nonsense.
0:11:42 And I did some analysis
0:11:43 because I was very excited
0:11:44 about coming back
0:11:45 to raging moderates.
0:11:47 And I’m fairly certain
0:11:50 that by dollar volume,
0:11:51 there have been
0:11:52 more deals struck
0:11:53 since the president
0:11:55 announced his new
0:11:56 tariff policy,
0:11:57 or what I’ll call threats,
0:11:58 between countries
0:12:00 that are non-U.S.
0:12:02 and that is the threats
0:12:03 of tariffs have actually
0:12:05 inspired a great deal
0:12:06 of deal-making,
0:12:08 just not between the U.S.
0:12:08 and the people
0:12:09 we’ve threatened.
0:12:11 What it’s done
0:12:12 is it’s sent a message
0:12:13 to non-U.S.
0:12:14 countries
0:12:16 that they can’t count
0:12:17 on this incredible
0:12:18 trade relationship
0:12:19 they used to have
0:12:20 with the United States,
0:12:21 which has inspired them
0:12:22 to begin speaking
0:12:23 to each other
0:12:24 and rerouting
0:12:25 their supply chain,
0:12:26 including dialogue
0:12:27 and agreements
0:12:28 around the U.S.
0:12:29 to a few of those.
0:12:31 Vietnam and South Korea
0:12:32 have announced
0:12:33 a $150 billion
0:12:34 more balanced
0:12:35 and sustainable
0:12:36 trade relationship
0:12:37 as they swear
0:12:38 cooperation
0:12:40 following Trump’s tariffs.
0:12:42 EU has struck
0:12:43 more deals
0:12:44 with China,
0:12:45 with Canada,
0:12:46 with India,
0:12:48 the EU
0:12:49 and Mercosur,
0:12:50 a bunch of the
0:12:52 Southeast Asian nations
0:12:53 are talking.
0:12:54 For the first time,
0:12:54 Japan,
0:12:55 South Korea,
0:12:56 and China.
0:12:57 We tend to,
0:12:58 as Americans,
0:12:59 you know,
0:13:00 we’re fairly narcissistic.
0:13:00 We just go,
0:13:01 oh, Asia,
0:13:02 and we think
0:13:02 they’re all the same.
0:13:04 Japan,
0:13:04 South Korea,
0:13:05 and China
0:13:06 are not in love
0:13:06 with each other.
0:13:08 They do not like
0:13:08 each other.
0:13:10 And they are talking
0:13:12 for the first time
0:13:13 about closer ties.
0:13:13 Why?
0:13:15 Because their attitude
0:13:16 is these people,
0:13:18 we can’t count
0:13:19 on this great trading
0:13:20 or pre-existing
0:13:21 trading relationship,
0:13:22 so let’s start
0:13:22 discussing.
0:13:23 So in sum,
0:13:25 Trump did inspire
0:13:27 a great deal
0:13:28 of deal-making,
0:13:30 just not among us
0:13:31 between nations
0:13:32 he’s threatened,
0:13:33 between them
0:13:34 and each other.
0:13:35 Sounds like
0:13:35 what goes on
0:13:36 on foreign policy
0:13:37 as well.
0:13:37 You know,
0:13:39 he did admittedly
0:13:39 have a good
0:13:40 NATO summit
0:13:42 and maybe he’s
0:13:42 going to get
0:13:43 the 5% commitment
0:13:44 in terms of
0:13:45 defense spending
0:13:46 from some nations,
0:13:47 but we know
0:13:48 with the position
0:13:49 that the U.S.
0:13:49 has taken
0:13:50 on Ukraine,
0:13:50 for instance,
0:13:52 that the EU
0:13:53 gets together
0:13:53 with Ukraine
0:13:54 without us
0:13:55 on a pretty
0:13:56 regular basis.
0:13:57 You know,
0:13:57 it’s a go-it-alone
0:13:58 strategy that
0:13:59 we’ve taken
0:14:00 and we’re seeing
0:14:01 the repercussions
0:14:01 of it.
0:14:02 The question
0:14:03 will be
0:14:04 what happens
0:14:04 at home
0:14:06 in terms of
0:14:06 how the American
0:14:07 public feels
0:14:07 about this.
0:14:08 And we know
0:14:08 that Trump’s
0:14:09 disapproval on trade
0:14:10 has skyrocketed
0:14:11 from January.
0:14:13 It was 40%.
0:14:14 Now it’s up
0:14:14 to 54%.
0:14:16 I saw one survey
0:14:16 that actually
0:14:17 had a 65%
0:14:18 disapproval.
0:14:19 The American
0:14:20 public knows
0:14:20 that tariffs
0:14:21 are a tax
0:14:22 on them
0:14:23 because they’re
0:14:24 people that go
0:14:24 out and buy
0:14:25 things.
0:14:25 A lot of them
0:14:26 small business
0:14:27 owners who have
0:14:28 no idea how
0:14:28 to make a plan
0:14:29 for their future
0:14:31 or that they
0:14:31 think that they
0:14:31 can even stay
0:14:32 in business
0:14:33 for the next
0:14:33 six months.
0:14:35 What I saw
0:14:36 that feels
0:14:36 like a bit
0:14:37 of a watershed
0:14:37 moment,
0:14:38 and I didn’t
0:14:38 realize that
0:14:39 this transition
0:14:40 hadn’t happened
0:14:40 yet,
0:14:41 but in the
0:14:42 last month,
0:14:43 Trump voters
0:14:44 have started
0:14:45 saying that this
0:14:45 is Trump’s
0:14:46 economy.
0:14:47 So essentially
0:14:47 this feels
0:14:48 like a reset
0:14:49 moment for the
0:14:50 administration.
0:14:51 So he’s been
0:14:51 in for six
0:14:52 months.
0:14:52 But if you
0:14:53 consider that it’s
0:14:54 only like in the
0:14:54 last few weeks
0:14:56 actually that people
0:14:56 who went out
0:14:57 and voted for him
0:14:59 in November are
0:14:59 saying that he
0:15:00 owns this economy,
0:15:01 it’s a bit of a
0:15:02 blank slate.
0:15:03 And so this new
0:15:04 set of tariffs
0:15:05 and whatever is
0:15:06 to come going
0:15:07 forward in terms
0:15:08 of the economy
0:15:08 is actually going
0:15:09 to be what
0:15:10 Democrats need
0:15:10 to be paying
0:15:11 attention to
0:15:12 and what we’re
0:15:12 going to have
0:15:13 to work for
0:15:13 for the midterm.
0:15:14 So that’s like
0:15:15 18 months versus
0:15:17 24 months of
0:15:18 actual runway
0:15:18 there.
0:15:19 And I was
0:15:20 surprised to
0:15:20 see it.
0:15:21 I know that
0:15:21 everyone, you
0:15:21 know, you
0:15:22 have your
0:15:23 horses and
0:15:23 because you
0:15:24 like this
0:15:24 guy, you
0:15:24 say, oh, it’s
0:15:25 not his
0:15:25 fault.
0:15:26 And all
0:15:26 presidents do
0:15:27 that, right?
0:15:27 They say, you
0:15:27 know, I’m
0:15:28 cleaning up the
0:15:28 mess of the
0:15:29 last guy.
0:15:30 It’s not true
0:15:30 all the time
0:15:30 when they say
0:15:31 it, but they
0:15:32 certainly do.
0:15:33 But I think
0:15:34 that’s a very
0:15:36 different perspective
0:15:36 that we’re going
0:15:37 into this now
0:15:38 where people are
0:15:39 saying Donald
0:15:40 Trump is fully
0:15:41 in control of the
0:15:42 United States of
0:15:42 America now.
0:15:43 And what does
0:15:44 that look like?
0:15:44 It looks like the
0:15:45 one beautiful bill,
0:15:46 which we’re going
0:15:46 to talk about.
0:15:50 trade wars that
0:15:50 are really going
0:15:51 to hurt the
0:15:52 American economy.
0:15:53 I do think it’s
0:15:54 interesting about the
0:15:54 market.
0:15:55 You know, the
0:15:56 ticker is always
0:15:56 running on Fox,
0:15:57 and I, well, I’m
0:15:58 in hair and makeup
0:15:59 for an hour because
0:16:00 it takes a long
0:16:01 time to attach
0:16:02 those fake eyelashes
0:16:03 and bring my hair
0:16:04 closer to God.
0:16:05 You know, I’m
0:16:06 always watching the
0:16:06 direction of things,
0:16:07 and there were
0:16:08 certainly a lot of
0:16:08 very positive
0:16:09 green days.
0:16:11 But yesterday, as
0:16:12 these letters were
0:16:12 trickling in, you
0:16:13 see it go into the
0:16:14 red, and I’m
0:16:15 watching Liz Klayman,
0:16:16 who we both love,
0:16:18 on Fox Business, and
0:16:19 talking to her guests
0:16:20 about what’s going
0:16:20 on in their
0:16:21 companies and how
0:16:22 they’re planning.
0:16:23 And they’re saying
0:16:24 something very similar
0:16:25 to what you said,
0:16:27 which is they’re
0:16:28 making plans for it,
0:16:29 but they’re not
0:16:29 thinking that it’s
0:16:30 the be-all and
0:16:31 end-all.
0:16:32 And I really wish
0:16:34 that more CEOs of
0:16:35 companies, like the
0:16:36 CEO of Ford, sat
0:16:37 down with Lara
0:16:38 Trump and actually
0:16:40 told her why
0:16:40 you need to get
0:16:41 some of these
0:16:42 parts from other
0:16:42 countries and how
0:16:44 unfeasible, is it
0:16:45 unfeasible or
0:16:45 infeasible?
0:16:46 Yes.
0:16:48 How it is not
0:16:49 feasible to totally
0:16:50 produce these cars
0:16:52 on American soil is
0:16:52 what you have to do.
0:16:53 You have to do it
0:16:54 with the kid gloves.
0:16:54 You have to do it
0:16:55 as nicely as
0:16:56 possible, but you
0:16:57 have to show up and
0:16:57 you have to look
0:16:58 these people in the
0:16:59 eye and just say
0:17:00 it’s not possible.
0:17:01 Yeah, if they were
0:17:02 really serious, well,
0:17:04 okay, so the F-150,
0:17:06 I think, goes across
0:17:06 the Canadian or
0:17:07 Mexican border back
0:17:08 and forth or
0:17:08 components of it
0:17:09 like 12 times.
0:17:11 It’s not even clear
0:17:11 how you would even
0:17:12 force these tariffs.
0:17:13 And two, if we were
0:17:15 really interested in
0:17:16 more domestic
0:17:17 manufacturing around
0:17:17 the automobile
0:17:18 industry, we
0:17:19 wouldn’t have cut
0:17:19 those subsidies to
0:17:20 EVs because the
0:17:21 most vertical
0:17:22 automobile manufacturer
0:17:23 is Tesla because
0:17:24 it has dramatically
0:17:25 fewer parts that can
0:17:26 be manufactured and
0:17:27 milled domestically.
0:17:29 And while I’m
0:17:29 loathe to give any
0:17:31 credit to Elon Musk
0:17:33 companies, EVs, if
0:17:33 you were really
0:17:34 interested about having
0:17:35 more domestic
0:17:36 production and
0:17:36 dramatically
0:17:37 simplifying the
0:17:38 supply chain, you
0:17:39 wouldn’t be halting
0:17:40 the EV tax
0:17:41 credits.
0:17:42 What I did find
0:17:43 interesting recently
0:17:45 was that Chairman
0:17:46 Powell at an
0:17:47 economic conference
0:17:48 basically came out
0:17:48 and said, if it
0:17:49 wasn’t for the
0:17:50 tariffs and the
0:17:51 insecurity that the
0:17:52 tariffs are creating
0:17:54 around the possibility
0:17:55 of inflation, if he
0:17:56 actually follows
0:17:57 through on his
0:17:58 threats, which looks
0:17:59 less and less likely
0:18:00 every day as he
0:18:01 continues to
0:18:01 threaten, fold,
0:18:03 threaten, fold,
0:18:04 threaten, see above,
0:18:06 fold, that he
0:18:07 said, we would
0:18:08 have lowered
0:18:08 interest rates
0:18:09 already.
0:18:11 And so, effectively,
0:18:12 the entire economy
0:18:13 is paying a tax of
0:18:14 somewhere between,
0:18:15 call it, 25 and
0:18:16 100 basis points on
0:18:18 your credit cards,
0:18:18 your student loan
0:18:19 payments, your
0:18:20 mortgage payments,
0:18:21 because we would be
0:18:22 in a rate-cutting
0:18:24 cycle right now had
0:18:24 it not been for
0:18:26 someone who is a lot
0:18:26 smarter than anyone
0:18:27 on the administration’s
0:18:28 current economic
0:18:29 team, had he not
0:18:30 said, we have to
0:18:31 wait and see if
0:18:31 these tariffs go
0:18:32 through and the
0:18:32 inflationary
0:18:33 impact they have
0:18:34 before we start
0:18:35 cutting interest
0:18:36 rates, because if
0:18:37 all of a sudden
0:18:39 everything gets
0:18:40 more expensive and
0:18:41 we cut interest
0:18:42 rates and people
0:18:43 get horny about
0:18:44 borrowing money and
0:18:45 buying more shit
0:18:46 and there’s more
0:18:47 money chasing fewer
0:18:48 things and we
0:18:49 start this upward
0:18:50 doom loop of
0:18:51 price where people
0:18:53 start panic buying
0:18:53 because they think
0:18:54 things are going to
0:18:54 get more and more
0:18:56 expensive, you know,
0:18:57 upward inflationary
0:18:58 cycles, unless they
0:18:59 are cauterized early,
0:19:00 can spin out of
0:19:00 control.
0:19:01 And that’s how
0:19:01 nations fail.
0:19:02 Be clear, inflation
0:19:03 is how nations go
0:19:04 out of business.
0:19:06 And so the adult in
0:19:06 the room, Chairman
0:19:07 Powell, has said, he
0:19:08 just came right out
0:19:09 and said it and
0:19:10 said the threat of
0:19:12 the tariffs is why I
0:19:13 have not already cut
0:19:14 rates and why we are
0:19:15 kind of sitting and
0:19:16 waiting.
0:19:18 So be clear, these
0:19:19 tariffs have yet to
0:19:20 take hold in terms
0:19:21 of consumer prices or
0:19:22 inflation because no
0:19:23 one is taking them
0:19:24 seriously because of
0:19:26 the track record of
0:19:27 the president folding,
0:19:28 but it’s already
0:19:31 costing us a great deal
0:19:32 of incremental capital
0:19:33 because interest rates
0:19:35 are probably 25 to 100
0:19:36 bips higher than they
0:19:37 would be had we had a
0:19:38 responsible economic
0:19:39 policy such that the
0:19:40 chairman having beaten
0:19:41 back COVID, having
0:19:43 beaten back inflation
0:19:44 from the supply chain
0:19:46 shocks of COVID and of
0:19:47 Russia’s invasion of
0:19:48 Ukraine, we’d be in a
0:19:49 rate-cutting cycle.
0:19:50 And we’re not because
0:19:52 Chairman Powell correctly
0:19:54 is waiting to see if
0:19:54 these head-up-your-ass
0:19:56 economic policies
0:19:58 companies actually get
0:20:00 traction and register an
0:20:01 impact on the economy.
0:20:02 There’s also a mental
0:20:04 health and paralysis
0:20:06 impact of this as well.
0:20:07 I understand it’s not as
0:20:08 easy to quantify that, but
0:20:09 you have millions of
0:20:10 Americans that essentially
0:20:13 are stuck wondering what
0:20:14 tomorrow, a month from now,
0:20:16 six months from now, are
0:20:16 going to look like.
0:20:18 And that’s everyone who
0:20:19 just needs to buy food for
0:20:21 dinner to someone who has
0:20:22 to run a business.
0:20:25 Yeah, businesses big and
0:20:25 small.
0:20:26 Walmart has said that they
0:20:28 can’t even do, you know,
0:20:30 Q3, Q4 planning because
0:20:32 they don’t know what it’s
0:20:33 going to look like.
0:20:35 And bringing your country,
0:20:37 the engine of the most
0:20:39 powerful country in the
0:20:41 world to a halt because
0:20:44 you want to send strange
0:20:46 letters to heads of state
0:20:47 or you have a bee in your
0:20:49 bonnet about something
0:20:51 that most economists worth
0:20:52 their salt is telling you
0:20:54 is not the way to be
0:20:55 running our country and
0:20:56 certainly not the way to
0:20:57 get the kinds of results
0:20:59 that you are after is an
0:21:01 incredible amount of ego
0:21:02 or hubris.
0:21:02 I don’t even know what
0:21:03 the right term for it is,
0:21:05 but, you know, he was
0:21:06 elected by all of these
0:21:07 people that were laser
0:21:08 focused on lowering
0:21:09 prices.
0:21:10 That was it, right?
0:21:11 They showed up.
0:21:12 Obviously, immigration
0:21:13 voters were about an
0:21:14 eighth of his voters, so
0:21:15 that was the number one
0:21:16 reason.
0:21:17 But in general, people
0:21:18 wanted lower prices.
0:21:20 After a Biden term, he
0:21:21 brought down inflation, but
0:21:21 it was a hugely
0:21:22 inflationary period for
0:21:23 us and for the rest of
0:21:24 the world.
0:21:25 And you look at all of
0:21:26 the actions that he’s
0:21:27 taken and they’re
0:21:29 diametrically opposed to
0:21:30 the goal of lowering
0:21:30 prices.
0:21:32 And Chairman Powell, just
0:21:33 man of steel, right?
0:21:36 This guy just gets up
0:21:37 there and he says exactly
0:21:38 what he wants to say.
0:21:40 He doesn’t sugarcoat any
0:21:40 of it.
0:21:41 There’s the predictable
0:21:42 response that Trump gets
0:21:43 on social media and
0:21:44 interview, whatever it is,
0:21:45 and says, Powell’s got to
0:21:45 go.
0:21:47 You know, he’s bad for
0:21:48 America, et cetera.
0:21:50 But I really admire
0:21:51 someone who is so
0:21:54 fearless in saying
0:21:54 what’s true.
0:21:56 There are a lot of
0:21:57 people who I feel like
0:21:58 are trying to play some
0:22:00 sort of game about how
0:22:01 they treat Trump, right?
0:22:02 They’re either nicer in
0:22:03 for this or they want to
0:22:05 get this kind of reaction.
0:22:06 So they do, you know,
0:22:07 zigzagging around with
0:22:07 it.
0:22:08 And Powell’s just such a
0:22:09 straight shooter about
0:22:10 it.
0:22:11 You know, he says, if you
0:22:12 do this one thing
0:22:13 differently, then I’m
0:22:14 going to be able to give
0:22:15 you the thing that you
0:22:16 want.
0:22:18 And there’s so little
0:22:19 directness, I feel like,
0:22:21 in society right now that
0:22:23 I really love it when I
0:22:24 see it.
0:22:25 And it’s easy for someone
0:22:26 as an analyst to be able
0:22:28 to glom onto that because
0:22:29 you say this is a serious
0:22:30 person who knows what
0:22:32 they’re saying and is not
0:22:34 treating Trump special.
0:22:36 Like, he’s not playing the
0:22:36 game with him.
0:22:37 He’s just saying what’s
0:22:38 true.
0:22:40 Yeah, it’s he’s this guy
0:22:41 will be one of the most
0:22:43 deserving Medal of
0:22:44 Freedom recipients in
0:22:44 history.
0:22:45 And that’ll absolutely
0:22:46 happen as soon as there’s
0:22:47 a Democratic administration
0:22:48 in place.
0:22:49 He really did.
0:22:51 He pulled us back from
0:22:51 COVID.
0:22:53 He basically stuck up the
0:22:54 middle finger and said,
0:22:56 hold my beer to senators on
0:22:57 the far left who were,
0:22:59 you know, crying for people
0:23:00 whose credit card, but that
0:23:01 he needed to lower interest
0:23:02 rates and also on the far
0:23:03 right.
0:23:04 He just didn’t care.
0:23:05 He was very steadfast.
0:23:07 The largest acceleration in
0:23:08 interest rates over a 15
0:23:10 month period in history.
0:23:11 And it was a medicine we
0:23:12 needed to take.
0:23:13 A zero interest rate
0:23:14 environment created some
0:23:16 real externalities and he
0:23:17 immediately course
0:23:17 corrected.
0:23:20 And our inflation under the
0:23:22 Biden administration was
0:23:24 the lowest of the G7 while
0:23:24 our growth was the
0:23:25 strongest.
0:23:27 The affordability thing is
0:23:27 really interesting.
0:23:29 And even if you look at
0:23:31 Momdani’s win in New York
0:23:33 of the Democratic primary,
0:23:34 it was arguably very
0:23:35 similar to Trump.
0:23:37 It was a focus on
0:23:38 affordability and
0:23:39 weaponizing new mediums.
0:23:41 coming at it from a much
0:23:43 different lens, but
0:23:44 basically Trump ran on
0:23:45 affordability and so did
0:23:46 Momdani.
0:23:46 But if you were really
0:23:48 serious about affordability,
0:23:50 you would have a sane
0:23:51 immigration policy that
0:23:52 said, OK, if you’re going
0:23:53 to church and picking
0:23:54 crops and part of our
0:23:55 health care system and
0:23:57 lowering the bills at
0:23:59 grocery stores and in our
0:24:00 health services community,
0:24:01 all right, we’ll figure out
0:24:02 a path to citizenship.
0:24:04 We want to bring in the
0:24:05 most talented immigrants to
0:24:06 start new companies.
0:24:08 We want to ensure there’s
0:24:09 a ton of competition
0:24:10 amongst we’re going to
0:24:11 break up monopolies.
0:24:13 We would never have
0:24:13 tariffs.
0:24:14 We would get together with
0:24:15 some of our partners and
0:24:16 figure out a way to lower
0:24:17 tariffs.
0:24:19 I mean, literally everything.
0:24:21 We would figure out a tax
0:24:22 policy that doesn’t borrow
0:24:24 massive amounts of money
0:24:25 such that interest rates
0:24:27 go down because of the
0:24:28 money or the premium that
0:24:29 we have to offer people on
0:24:30 T-bills doesn’t continue
0:24:31 to increase as our own
0:24:32 balance sheet looks
0:24:33 increasingly risky.
0:24:35 It’s what you said is
0:24:36 exactly right.
0:24:37 I mean, this isn’t the
0:24:38 big, beautiful bill is the
0:24:39 big inflation bill.
0:24:41 I mean, you could assign a
0:24:42 lot of words to this
0:24:44 inflation, depraved, you
0:24:45 know, the anti-Robinhood
0:24:46 bill, whatever you would
0:24:47 want to call it.
0:24:48 But it does appear that he
0:24:52 is dead set on illuminating
0:24:54 or incenting or detonating
0:24:54 inflation again.
0:24:55 All right.
0:24:57 With that, let’s take a
0:24:57 quick break.
0:24:58 Stay with us.
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0:28:22 Welcome back.
0:28:24 Republicans finally muscled
0:28:25 their big, beautiful bill
0:28:26 through Congress, a sweeping
0:28:28 legislative victory for Trump
0:28:30 that slashes $1.7 trillion in
0:28:32 federal spending, extends his
0:28:34 signature tax cuts, and
0:28:35 enacts major changes to safety
0:28:36 net programs, including
0:28:36 Medicaid.
0:28:38 But now comes the harder
0:28:39 part, selling it.
0:28:40 Polls show most Americans
0:28:42 either dislike the bill or
0:28:43 don’t know what’s in it.
0:28:45 And with midterms looming,
0:28:47 GOP lawmakers are sprinting to
0:28:49 define the law before Democrats
0:28:50 do it for them.
0:28:52 Jess, Republicans are touting
0:28:54 the bill’s populist pieces like
0:28:55 eliminating taxes on tips.
0:28:57 But how are they planning to
0:28:59 explain the projected 12
0:29:01 million Americans who are
0:29:02 likely going to lose their
0:29:03 Medicaid coverage?
0:29:04 Well, they’re going to have an
0:29:05 election before you lose your
0:29:06 Medicaid coverage.
0:29:08 So that’s how they’re going to
0:29:08 do it.
0:29:09 You know, they were very
0:29:11 specific about the timing of
0:29:11 everything.
0:29:14 Like, no tax on tips actually
0:29:15 expires in 2028.
0:29:17 Tax cuts for the wealthiest,
0:29:18 that lives forever.
0:29:19 But if you’re going to get no
0:29:20 tax on tips, which, by the way,
0:29:22 only goes up to the first
0:29:23 twenty five thousand dollars
0:29:24 that you make in tips.
0:29:26 So that’s a very low cap.
0:29:29 Anyway, that’ll be around for
0:29:29 the midterms.
0:29:31 But you won’t know if your
0:29:32 Medicaid is going away until
0:29:33 after you cast your vote.
0:29:35 I still expect that the
0:29:37 Democrats will do well in
0:29:38 the midterms because
0:29:39 historically that’s what
0:29:39 happens.
0:29:40 But they were very, very
0:29:43 crafty in the timing about
0:29:44 all of this.
0:29:46 They also told this
0:29:48 monster lie about
0:29:50 what would happen if we
0:29:52 didn’t pass the one big
0:29:52 beautiful bill.
0:29:54 They would say your tax
0:29:55 cuts would expire from the
0:29:56 twenty seventeen
0:29:57 Trump plan.
0:29:59 But that’s not true.
0:30:00 It’s not like you would wake
0:30:01 up the next day and
0:30:02 suddenly you wouldn’t have a
0:30:02 tax cut anymore.
0:30:04 Congress would actually have
0:30:06 six months to deal with
0:30:07 this and they could work out
0:30:07 something in a bipartisan
0:30:08 fashion.
0:30:09 And they’ve done this before.
0:30:12 So that was the pressure
0:30:14 that people felt and they
0:30:15 thought the average
0:30:17 American that you were
0:30:18 going again to wake up on
0:30:20 July 5th or whatever day
0:30:22 we were going to say it is
0:30:23 and that suddenly you were
0:30:24 going to have an enormous
0:30:26 income tax bill or a huge
0:30:28 bill for your small
0:30:28 business.
0:30:31 So that was one pervasive
0:30:31 lie.
0:30:32 And then you have the stuff
0:30:33 about the work requirements
0:30:34 for Medicaid.
0:30:36 Scott Besson, again, very
0:30:37 smug talking about able
0:30:38 bodied Americans that just
0:30:39 don’t want to work.
0:30:40 They always go back to
0:30:41 quote unquote welfare queens
0:30:42 about all of this.
0:30:44 We know that only 8% of
0:30:45 people who receive Medicaid
0:30:46 would even fall into that
0:30:47 category.
0:30:48 You’re not going to pay for
0:30:49 the trillions that you’re
0:30:50 putting into the deficit
0:30:52 with those 8%.
0:30:54 And they just don’t want to
0:30:56 tell you the truth about
0:30:58 what’s going on in these
0:30:58 kinds of bills.
0:30:59 They don’t want to tell you
0:31:00 about who’s getting the
0:31:01 kickbacks.
0:31:04 Steve Ratner created a
0:31:05 beautiful chart of all of
0:31:08 the evaluations and all the
0:31:09 nonpartisan ones, all the
0:31:11 partisan ones, and even
0:31:12 right wing partisan
0:31:14 organizations have talked
0:31:15 about this ballooning the
0:31:17 deficit and that people will
0:31:18 lose their health care,
0:31:19 tax foundation, Cato.
0:31:21 And the only group of
0:31:23 economic advisors or
0:31:25 economic panel that says
0:31:27 that it’s going to be a
0:31:28 boon for the American
0:31:30 economy overall comes
0:31:31 right out of the White
0:31:31 House.
0:31:32 And they obviously have a
0:31:33 vested interest in saying
0:31:33 that.
0:31:36 but it just feels very
0:31:37 much like beating a dead
0:31:37 horse.
0:31:38 The American public hates
0:31:39 this bill.
0:31:40 Net favorables range from
0:31:42 negative 19 to negative 29.
0:31:44 49% say the bill is going to
0:31:45 hurt their family.
0:31:47 23% only say that it’s going
0:31:47 to help.
0:31:48 So you start out with a
0:31:49 baseline that the American
0:31:51 public knows that this is a
0:31:51 bad thing.
0:31:52 But then you get into the
0:31:54 issue of like, well, what are
0:31:55 you going to do about it?
0:31:56 How are you going to talk
0:31:57 about a thing that may not
0:31:59 affect people tomorrow?
0:32:01 like I and I know that
0:32:03 people have made this case,
0:32:04 but I don’t think it’s been
0:32:05 made strongly enough.
0:32:06 And I want to hear it all
0:32:07 the time that a tax and
0:32:09 spend bill more so than
0:32:10 probably anything else that
0:32:12 the government does is a
0:32:13 moral document.
0:32:15 It is a statement of your
0:32:17 values and your priorities.
0:32:19 And the GOP is very
0:32:20 clearly saying our
0:32:22 priorities are the rich.
0:32:23 Our priorities are
0:32:26 deporting millions of
0:32:27 people who are here.
0:32:28 I mean, the ICE funding and
0:32:29 I want to get your take on
0:32:31 that more than the
0:32:33 IDF now, that’s how
0:32:34 much we’re funding these
0:32:37 guys wearing masks that
0:32:38 are driving around in
0:32:39 unmarked vans, picking
0:32:39 people up.
0:32:41 And this not anti-law
0:32:42 enforcement.
0:32:43 I’m thrilled that we have
0:32:45 zero border crossings now.
0:32:46 I think that that’s a very
0:32:47 good thing and something
0:32:48 that the country needed
0:32:50 and desperately wanted,
0:32:51 which is why a lot of
0:32:52 people held their nose and
0:32:53 voted for Donald Trump
0:32:53 because they didn’t think
0:32:54 the Democrats were serious
0:32:55 about immigration.
0:32:58 But when you look at
0:32:59 these priorities and
0:33:00 and I’m sure, you know,
0:33:01 there are bits in there
0:33:02 cutting red tape for small
0:33:03 businesses.
0:33:04 Some seniors will get a
0:33:05 6K deduction.
0:33:06 Those are good things.
0:33:07 You know, I’m not saying
0:33:08 that there’s nothing in the
0:33:11 bill that’s decent, but
0:33:14 overall, it’s a signal that
0:33:15 this is a morally bankrupt
0:33:16 party.
0:33:18 And they all said as much on
0:33:19 the record and then just
0:33:20 went ahead and voted for it
0:33:21 anyway.
0:33:23 Yeah, a lot there.
0:33:24 So let’s go from the small
0:33:26 to the profound.
0:33:27 First off, this populist
0:33:28 bullshit around no taxes on
0:33:29 tips.
0:33:29 What percentage of the
0:33:31 working population would you
0:33:32 guess get tips?
0:33:33 I’m going to make a bad
0:33:34 guess.
0:33:35 It’s 2%.
0:33:36 What?
0:33:36 Yeah.
0:33:38 2% of Americans make money
0:33:39 on tips.
0:33:39 Oh.
0:33:41 And it’s just never made
0:33:42 any sense to me.
0:33:42 I was a waiter.
0:33:44 I was growing up.
0:33:45 I was both a dishwasher and a
0:33:45 waiter.
0:33:47 So when I was a dishwasher, I
0:33:49 wouldn’t get a tax cut.
0:33:50 But when I was a waiter, I got
0:33:50 a tax cut.
0:33:52 And then first off, anyone
0:33:53 who’s getting tips, especially
0:33:56 with a $25,000 limit on it, it
0:33:57 means they’re not paying a lot
0:33:58 of federal income tax to begin
0:33:58 with.
0:33:59 This is populist bullshit that
0:34:01 has no impact on people or the
0:34:02 economy.
0:34:05 And what I find more upsetting,
0:34:07 I’m all down for blaming the
0:34:08 Republicans on this.
0:34:09 I think this is both cruel and
0:34:11 stupid, which adds up to
0:34:12 depraved.
0:34:14 And I think you can lay the
0:34:15 majority of the blame at the
0:34:16 feet of the administration and
0:34:18 the Republicans who are scared
0:34:19 of being primaried and pretend
0:34:21 to give a good goddamn and say,
0:34:22 I would never cut Medicaid and
0:34:24 then grab their ankles when push
0:34:27 comes to shove or decide to sell
0:34:28 out the lower 48 to protect
0:34:30 their folks, Senator Murkowski.
0:34:33 You know, this is absolutely the
0:34:34 majority of the blame lies with
0:34:35 them.
0:34:36 But what’s more frightening, Jess,
0:34:38 is to your point, this isn’t
0:34:39 fiscal policy.
0:34:40 It’s a reflection on our
0:34:40 values.
0:34:42 And I think in America, there’s a
0:34:45 dangerous trend that my dad used
0:34:46 to say America is a terrible place
0:34:47 to be stupid.
0:34:50 And that was sort of an unkind way
0:34:52 of saying it’s a terrible place to
0:34:53 be vulnerable.
0:34:57 And essentially, I think America, not
0:34:58 just Republicans, but America has
0:35:01 decided that we believe in a
0:35:03 Hunger Games-like economy, that the
0:35:04 bottom 90 percent are effectively
0:35:07 nutrition for the top 10 percent
0:35:09 because people are willing to put up
0:35:11 with that depravity because they’re
0:35:13 hoping at some point they’ll be in that
0:35:13 top 10 percent.
0:35:16 And they’re also conflating, you know,
0:35:18 some of these really ugly ice raids and
0:35:21 knees on heads and 14-year-olds crying as
0:35:24 their mother is carted away and hearing
0:35:27 about a kid who is a paraplegic not being
0:35:28 able to afford his medication or his
0:35:29 physical therapy.
0:35:32 They sort of begrudgingly say, well,
0:35:34 thoughts and prayers, but they see that as
0:35:35 leadership.
0:35:36 They see that as, in a weird way,
0:35:38 masculinity and toughness.
0:35:40 I think this goes beyond something much
0:35:43 deeper and more upsetting about America.
0:35:46 And to your point, let’s be hopeful.
0:35:49 It’s that Americans haven’t been or
0:35:51 Democrats haven’t done a good job
0:35:52 connecting this to people because the
0:35:54 majority of people who will probably be
0:35:57 thrown off Medicaid, maybe a lot of us
0:35:59 don’t come in contact with or we don’t
0:36:00 know that our neighbor is on Medicaid.
0:36:04 So I never like to miss an opportunity
0:36:05 to talk about myself.
0:36:09 I’ll go through just how these cuts would
0:36:11 literally pull up the ladder behind me.
0:36:14 I’m sitting here in this, like, out of
0:36:17 control, over-the-top explosion and
0:36:19 wealth in Ibiza.
0:36:21 And, you know, the bad thing about getting
0:36:23 older, and you’ll realize this, I think
0:36:26 you’re further along in sort of self-awareness
0:36:27 than I was at your age.
0:36:29 But up until the point when I was your
0:36:31 age, I credited my grit and my character
0:36:33 for all my success.
0:36:35 It was about me being a baller and me
0:36:37 being talented and me taking risks and
0:36:40 overcoming some hardship.
0:36:42 And then as you get older, you realize a
0:36:44 lot of your success is not your fault.
0:36:47 And what I’ve come to recognize, and I can
0:36:49 attach many of these things to what’s under
0:36:52 attack right now, starting when I was nine
0:36:54 years old, I got assisted lunch.
0:36:57 My mom made $800 a month as a secretary.
0:37:00 And so we qualified for assisted lunch.
0:37:02 And one of the things I remember about that,
0:37:04 and I didn’t find out, I was nine, so I didn’t
0:37:05 know what was going on.
0:37:06 One of the things I found out a few years
0:37:09 later, and it just shows so much dignity and so
0:37:10 much grace on the part of California taxpayers
0:37:14 and our government, was they purposely sent the
0:37:15 coupons to my house.
0:37:18 And every kid had the same coupon, so no one
0:37:21 would know that I was on assisted lunch because
0:37:22 they wanted to avoid the stigma.
0:37:25 And I thought that was the most graceful thing,
0:37:27 one of the most American things.
0:37:30 When I was in high school, when I was 17, and I’ve
0:37:32 talked openly about this, my mom who passed 20
0:37:34 years ago, I don’t think would have a problem
0:37:35 with this.
0:37:39 My mom became pregnant at 47 and was able to
0:37:41 access safe, affordable family planning.
0:37:46 Had we lived in this era in a red state, you
0:37:47 know, we weren’t very sophisticated.
0:37:48 We didn’t have a lot of money.
0:37:51 If my mom had been forced to carry a child and
0:37:54 unwanted pregnancy to term, I was installing
0:37:55 shelving, making decent money at the time.
0:37:57 I would have not gone to UCLA.
0:38:00 I would not have had the opportunity to start
0:38:02 this upward spiral, courtesy of the Regency
0:38:05 University of California, that quite frankly,
0:38:07 and I’m bragging now, has produced tens of
0:38:10 millions of dollars in tax revenue and thousands
0:38:10 of jobs.
0:38:12 I just would have, I would have never had the
0:38:16 opportunity to go to college had it been my mom
0:38:19 and a newborn, and then when I got to UCLA, I got
0:38:20 Pell Grants.
0:38:22 That’s the only way I could afford to be at UCLA.
0:38:26 And a third of Pell Grant recipients, under this
0:38:28 big, beautiful bill, are either going to have their
0:38:30 grants reduced or eliminated.
0:38:33 When I got out of college, I was able to raise a shit
0:38:34 ton of money.
0:38:34 Why?
0:38:38 Because foreign investors loved investing in U.S.
0:38:41 startups because they saw rule of law, because they
0:38:46 saw all types of technology that had been funded by the U.S.
0:38:48 government, which didn’t have to pay a trillion dollars in
0:38:50 interest payments so they could invest in these crazy
0:38:51 things called GPS and the Internet.
0:38:54 All of my companies were built on the backbone of
0:38:58 technologies financed with these extraordinary, irrational
0:38:59 investments from the U.S.
0:39:01 government because they had the capital to make these
0:39:02 forward-leaning investments.
0:39:04 Literally, my company has been built on the back of
0:39:07 immigrants and an America that said, if you are really
0:39:10 fucking talented and want to work hard, come here and we
0:39:11 will put you to work.
0:39:16 All of these things that have built this life and this
0:39:20 prosperity and these millions in tax revenue, every one of
0:39:22 them is under attack.
0:39:26 And it is so disappointing that more people with my blessings of
0:39:32 my generation can’t do the math and reverse engineer this to
0:39:35 one thing, and that is we are torching, we are burning the ships
0:39:37 behind us, we are pulling up the ladders.
0:39:41 It’s so disappointing, beyond the moral argument.
0:39:43 It’s like, you don’t want your kids to have the same
0:39:44 opportunities we had.
0:39:48 And I’ll even go more meta than this and be more dramatic and
0:39:49 more hysterical.
0:39:49 Oh, good.
0:39:57 My mom was a four-year-old Jew sleeping in the tube stations at
0:39:59 night because her house had been bombed during the blitzkrieg.
0:40:03 And America was so alarmed, they decided to convert
0:40:08 factories from producing Buicks to producing tanks.
0:40:12 And they decided that 400,000 households should have a gold
0:40:14 star in the window and lose their sons because it was worth it to
0:40:15 push back on fascism.
0:40:17 They were not pushing back on anti-Semitism.
0:40:20 They were pushing back on fascism.
0:40:21 And what’s fascism?
0:40:25 Demonization of immigrants, a refusal to condemn violence against
0:40:28 your political enemies, and extreme nationalism.
0:40:28 Sound familiar?
0:40:33 And had America not had a gag reflex on emerging fascism in
0:40:36 Europe, my mom’s life would have ended with a train ride.
0:40:37 I wouldn’t even be here.
0:40:44 So all of these things, a gag reflex on fascism, providing
0:40:48 opportunities for young people, safe, affordable family planning
0:40:52 and rights for women, deep pools of capital such that people
0:40:55 could start business, a culture that invites the best and brightest
0:40:59 to help people build businesses and leverage that capital.
0:41:00 All that shit is under attack.
0:41:02 It’s literally under attack.
0:41:05 I find it so deeply rattling and disturbing.
0:41:10 And I’m pissed off that Democrats just scream and get angry and talk
0:41:11 about Medicare.
0:41:11 I get it.
0:41:14 Medicare, that’s that’s one part of the story.
0:41:19 But show me anybody in my generation who has made their wealth, not
0:41:20 inherited, but made their wealth.
0:41:25 In about two fucking minutes, I can show you why this bill is attacking
0:41:31 the reason that you are in Ibiza or in the Hamptons or in Aspen and that
0:41:35 you have decided, no, no one else gets to come here except my kids.
0:41:37 Speech over.
0:41:42 I’m overwhelmed by it and moved.
0:41:44 It’s a great American story.
0:41:51 And it’s not often that people are telling it in such honest terms.
0:41:57 The details are what matter and what create connective tissue amongst Americans.
0:42:00 And right now when we talk about this a lot, that Americans feel completely
0:42:01 disconnected from one another.
0:42:02 You live in your bubbles.
0:42:06 And I wish more people would speak up like that and would be telling those
0:42:07 kinds of stories.
0:42:11 And I know that it is difficult if you also have a business to protect.
0:42:14 And there are a lot of people, even immigrants, that there are heads at these
0:42:18 big companies that feel like they can’t do it, that they have to show up at
0:42:21 inauguration and they have to kiss the ring because they have to make sure that
0:42:23 they continue to make their bottom line.
0:42:28 But it does feel like the very fabric of America is being torn apart.
0:42:31 And I think that’s important to emphasize.
0:42:35 But I also, you know, put on a strategist cap.
0:42:40 And I think about, you know, how much we talked about January 6th or the death of
0:42:46 democracy and, you know, fascism is coming and people didn’t want to vote for that.
0:42:49 They wanted to vote for better grocery prices.
0:42:49 Right.
0:42:51 Or they wanted to vote for a closed border.
0:42:56 And so you have to be really strategic and smart about how you do this.
0:43:00 The reality is that nearly half of Americans haven’t heard anything about the Big
0:43:01 Beautiful Bill.
0:43:03 So those who have heard about it have a very negative view of it.
0:43:10 Only 8% have said that the Medicaid cuts are a detail of a bill that they know about.
0:43:14 That’s going to come for a lot of these people after the midterms, like I said.
0:43:19 So it’s emotional to think about this and to think about the impact on the young people,
0:43:21 like you said, of pulling up the ladder.
0:43:24 You know, what’s going to happen with your student loans, for instance.
0:43:28 I mean, people just are not going to be able to go to graduate school or college for that
0:43:29 matter.
0:43:31 It’s just not going to be happening anymore.
0:43:32 We’re going to become less educated.
0:43:37 We’re also going to be able to import less educated people because why would you want to
0:43:38 come here?
0:43:43 I don’t know what America looks like when this is over, but I do know that millions of Americans
0:43:45 were not happy with the way that it’s going.
0:43:49 And Democrats have got to thread that needle better.
0:43:53 And I don’t want to I don’t want to turn every session into like a shitting on Democrat
0:43:54 session.
0:43:59 Like there’s not a lot that you can do when you don’t have the numbers, but people don’t
0:44:04 feel inspired and they don’t feel like they have a good alternative to this.
0:44:10 A friend of mine who’s a great Democratic strategist was talking about it and he said, essentially,
0:44:16 we’re on trend for 2017 when they tried to do the ACA repeal and we had a very good midterms
0:44:17 there in 2018.
0:44:23 But it’s going to take a lot of work over the next 18 months to impress upon people just
0:44:27 exactly what has happened to the country.
0:44:33 And we know that it’s not that effective to be telling people like, well, this is what your
0:44:34 lived reality is.
0:44:34 Right.
0:44:35 This is what your experience is.
0:44:37 People know what their experiences are.
0:44:42 And if we don’t seem like a decent alternative, then maybe they sit at home.
0:44:43 Maybe they don’t care.
0:44:49 But more so, maybe they just become completely or even further disenchanted with the American
0:44:49 project.
0:44:56 What Democrats do you think are doing a decent job of attaching this bill to real life who
0:45:02 you think that is actually showing some of that fire and ability to connect this to everyday
0:45:04 Americans who other Democrats can model?
0:45:11 I mean, all of the swing Democrats, I think, do a great job of this because sometimes they’re
0:45:14 not as good on social or whatever.
0:45:16 We don’t pay a lot of attention to it.
0:45:21 But like the Jared Goldens of the world, Pat Ryan, you know, when you win races like that,
0:45:26 you know something about how to talk to people and how to make those connections.
0:45:32 If you look at Mallory McMorrow, who we’re going to have on the podcast running for Senate
0:45:40 in Michigan, outraised her primary opponent who has a lot more institutional support because
0:45:43 she’s talking about this like a normal person.
0:45:48 And she’s also saying to the Mom Donnie question, like business as usual, it’s not working for
0:45:48 us.
0:45:53 I mean, people, they want change if they’re not going to get them.
0:45:55 We’re going to talk about the third party thing with Elon.
0:46:01 If these are going to be your options, you have to find a way to turn into an outsider
0:46:03 party while still being on the inside.
0:46:09 And one of my colleagues at Fox, I think it was Kellyanne, said that Donald Trump reformed
0:46:11 the Republican Party from within.
0:46:15 He essentially created a third party from within the Republican infrastructure.
0:46:19 And Democrats need some of that.
0:46:26 They need an internal revolution at this point to inspire people and to make you think that
0:46:29 the status quo is not good enough for any of us.
0:46:33 And I wanted to ask you this because it’s been weighing on me.
0:46:37 Like, I love my job, but I don’t love my job.
0:46:43 And I can’t imagine loving a job enough that I would vote for something that I admitted would
0:46:47 strip health care from hundreds of thousands of people that I represent.
0:46:55 What is the point of staying in office if you can’t help the people that you allegedly signed
0:46:57 up to improve the lives of?
0:47:00 It’s such a profound question.
0:47:06 And I ask myself the same question all the time, that at what point if I mean, literally
0:47:12 if if the president said we have to stop all funding for premature birth wards, would they
0:47:14 do it at this point?
0:47:17 Like, where would they draw the line?
0:47:19 Where would they say we won’t do it?
0:47:23 Because the only people who didn’t vote for this thing were people who basically said I’m
0:47:25 not running again, especially in the Senate.
0:47:28 So I don’t I struggle with this, too.
0:47:33 And I don’t have a good answer other than they’re too fucking old and they literally think if I
0:47:36 leave here, I’m just going to go home and start to die.
0:47:38 And I lose all relevance and all importance.
0:47:44 Do you have any additional thoughts on why these folks just refuse to be the kind of the
0:47:46 leaders we ask them to be?
0:47:48 Power corrupts.
0:47:53 Yeah, these are very important jobs and we treat them like many kings.
0:47:59 And queens to some degree, right, especially with the way the media apparatus works now.
0:48:00 But I can’t.
0:48:06 I can’t come up with what the line would be, except for the very few that actually did find
0:48:11 their line, whether it was the deficit or Medicaid cuts for Tom Tillis.
0:48:13 But, you know, the ball is rolling.
0:48:17 There’s already a rural hospital in Nebraska that’s closed.
0:48:22 And they said that this is because of the bill, that they’re not going to be able to stay in
0:48:24 business moving forward as a result.
0:48:28 And you’re going to see a lot of that and people getting asked tough questions.
0:48:35 And I think the answer is going to be, you know, what caliber of candidate on the Democratic
0:48:40 side is going to show up to run races against these vulnerable Republicans?
0:48:43 Because the map, there are already a lot of them on it.
0:48:45 And they just put even bigger targets on their back.
0:48:51 You know, there are going to be some very interesting races, hard fought races and all of this.
0:49:01 And we got to find the way to be inspirational and different and revolutionary within the system
0:49:07 that we’re working in, because people look at Washington and they just say, I don’t see anything for me.
0:49:14 Yeah. And one of the bright spots about Momdami’s win is, and I want to be clear, I would not have
0:49:18 voted for the guy. A lot of his positions are very troubling to me and a lot of his current, his
0:49:24 public policy ideas make no fucking sense to me, like sponsored bread lines in the form of state
0:49:29 sponsored or state controlled grocery stores. But having said that, along the lines of what you’re
0:49:34 saying, we need a revolution within the Democratic Party to remake the party. And it’s got to start
0:49:38 with young people who understand these new technologies, these new mediums, are unafraid,
0:49:44 new ideas, and are willing to just sort of step up and say, all right, it is time to shed
0:49:50 a new layer of skin. Any ideas? Who do you think, if looking at the positive side of Donald Trump,
0:49:57 who was sort of the, you know, the William Wallace of that revolution, if you had to bet on one or two
0:50:02 or three people who you think could be that, that William Wallace of reshaping the Democratic
0:50:06 Party, do you see any likely candidates or is it still kind of TBD?
0:50:13 I mean, it’s a little TBD, but I think on the more centrist, you know, moderate Dem side of things,
0:50:20 I think Elisa Slotkin is offering people a lot. And she was the one who came out there and said,
0:50:25 you know, this is my plan, right? This is what I think the future of the Democratic Party looks like.
0:50:30 You know, she does the cursing in the right places. She has the resume to back all of it up.
0:50:36 I just think that there’s so much that we can learn from people that have won these difficult races and
0:50:43 that oftentimes we just go back to the folks who have the loudest voices or who give the best
0:50:47 interviews, etc. But they obviously don’t know the same things as the folks that went out there and
0:50:53 connected with people that have supported Republicans their whole life or who split ticket for Donald Trump
0:50:59 and for them. You know, it’s happening all over the country and there are people out there that are
0:51:04 worth your time, even if their politics aren’t exactly aligned with ours. Like we had Greg Kassar
0:51:10 from Texas on the podcast. He’s way to the left of where I am. But, you know, you have a lot of people
0:51:18 in the middle who are saying that they like AOC the best because AOC seems like she’s got the fight in
0:51:20 her and that she’s on the right side of history.
0:51:26 I’m afraid, you know, agreed. Yeah. The problem I have with the far left and I think they’re as guilty
0:51:32 or more guilty of this in the far right and I’ve been subject to it is if you’re a moderate and
0:51:38 occasionally see merit in Republican ideas, you’re treated like an apostate. Yeah. Like, you know,
0:51:42 the right calls me a libtar. They just write me off, but they’ll bring me on Fox and they’re actually
0:51:47 quite polite to me as I think they’re mostly polite to you and appreciate you. On the far left,
0:51:53 I can’t tell you how many mean, angry emails I got from people I know and like and consider me a friend
0:51:58 and I consider them a friend when I started saying Biden’s too fucking old. Yeah. It’s like you either
0:52:05 sign up for the cult and the narrative or you are the enemy. And the far left is as guilty of it as
0:52:12 anybody. And the Democratic Party needs to do a better job of embracing imperfect allies. And when I’m at
0:52:17 conferences and I see people, Democrats playing identity politics and talking about the
0:52:24 patriarchy and if you don’t sign up for every right word for the orthodoxy, you’re the problem.
0:52:29 And it’s like, just as the settlers figured out a way to get Native Americans fighting amongst each
0:52:37 other, kill each other first, and then we’ll come in for cleanup. The Democrats are just the level of
0:52:44 interesting warfare is so unproductive. It’s like, I absolutely think AOC’s policies,
0:52:50 many of them don’t make any fucking sense. I will give money to her. I think she’s wonderful.
0:52:57 I retweet her shit. I think she’s fantastic. But what I find on the left is if you don’t sign up for
0:53:03 the right narrative, basically people attack you and say, OK, maybe we’re allies, but you’re holding
0:53:10 the gun wrong. This is this is the narrative. This demand for ideological consistency is so
0:53:17 ridiculous. No actual human being is totally in lockstep with a party platform. And that’s what
0:53:20 Trump did for the right. It works and it connects.
0:53:26 Yeah. I mean, we’re going to need a bigger boat. But I also think the identity politics of the left
0:53:32 has gotten out of control. It doesn’t do us any good to begin assigning values and identifying or
0:53:38 immediately prescribing validity or a lack thereof based on who’s saying it as opposed to what they’re
0:53:44 saying. And I feel like the Democrats don’t even recognize how biased they are against statements
0:53:51 solely based on who’s saying it. And I feel that the Democrats are actually probably more guilty of
0:53:56 this than Republicans that, OK, if you’re an old white dude, I have to take everything you say with
0:54:01 a grain of salt and I am ready to weigh in and get my guardians of gotcha pen. I am going so far off
0:54:07 script here. Jess, let’s bring it home. Our producer is trying to rein me in. Let’s take a quick break.
0:54:13 Stay with us. We’re going to talk about this guy who’s in technology, who’s actually from South
0:54:16 Africa. His name is Elon Musk. Oh, haven’t heard of him. Yeah.
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0:54:55 Welcome back. Before we go, Elon Musk says he’s launching a third political party.
0:55:03 Oh, God, Jess. Save me. Literally save me. Called the America Party. OK. Yeah.
0:55:10 A South African immigrant who went to school in Canada, who disowns his daughter on Joe Rogan’s
0:55:15 podcast and is being sued concurrently by two women for sole custody of their child because he spends no
0:55:20 time with them. Yeah, that’s the guy to start a third party. He says it’s meant to challenge what he
0:55:26 calls a one-party system that’s driving the country into debt. And he timed the announcement just days
0:55:31 after Trump signed his massive domestic policy bill into law. Bill Musk once backed, then blasted,
0:55:34 and now calls the final straw. He’s had it, Jess. He’s had it. Anyways,
0:55:39 Musk insists the new party will focus on a handful of swing districts in the midterms,
0:55:42 but Trump allies weren’t it could fracture the right at a time when margins are razor thin.
0:55:49 That’s fair. And then there’s the Epstein factor. After months of teasing bombshell revelations,
0:55:53 the Justice Department just announced there isn’t a client list, isn’t a cover-up,
0:55:59 and no more new documents are coming. Well, that’s a 180. That hasn’t stopped Musk from fanning the
0:56:05 flames or from accusing Trump of being part of some kind of cover-up. Jess, what’s going on here?
0:56:09 What’s he trying to accomplish? A third party isn’t exactly, I don’t know, we’ve been to this movie
0:56:13 before. How much trouble do you think he can actually cause for Trump and the GOP?
0:56:22 Not very much. Like, on the fringes, it’s possible to have an effect with a third-party candidate. Like,
0:56:28 actually, in an Ohio district, Marcy Kaptor, who’s the Democrat there, I think this is her 22nd term,
0:56:34 been there a long time, the Democrats funneled money into a libertarian candidate’s campaign,
0:56:39 I think about $400K, to shave off support from the Republican candidate that was challenging her,
0:56:44 and she ended up winning her race. So those are ways that you can use third parties to play around
0:56:49 and make a difference. But in terms of what’s going to happen at the presidential level, it makes no
0:56:57 difference. And if you want to have a government that’s more representative to the public, then you
0:57:02 need to have proportional representation. And we can’t have winner-take-all anymore. And there are a lot
0:57:08 of people that would get on board with that, but would obviously never be able to pass and get through.
0:57:15 So, you know, Musk is throwing his toys out of the proverbial stroller. He’s pissed off. This happened
0:57:20 before, and he essentially came back groveling to Trump. And I imagine that that’s what’s going to happen
0:57:26 because, you know, money matters, of course, and being the richest man on the planet is a very big deal. But
0:57:33 Donald Trump has shown himself to be more powerful than Musk. And I think even the way that he’s treating
0:57:39 him on social media about this, you know, talking down to him, it’s very paternalistic, actually,
0:57:45 how he’s dealing with him. Like, baby Elon is mad. Give him his space. And, you know, they toss around
0:57:49 stuff like, I’m going to look into your immigration status or whatever. But in general, I feel like
0:57:56 he’s going to get over it. And he has to get back to Tesla and, you know, try managing that. And I think,
0:58:01 you know, he’s mad about the debt, but he’s mostly mad about the EV credits. Right. That’s what this is.
0:58:07 Everyone is always just actually concerned with themselves and their personal bottom line. So I
0:58:09 think it’s going to end up being a big nothing burger.
0:58:15 Yeah. I mean, there’s a few things here. There’s the motivation for doing it and the effectiveness.
0:58:21 The motivation is all of a sudden he’s decided the president is a pedophile and that this bill is
0:58:27 fiscally irresponsible. There is no new information from when he loved the president.
0:58:27 Correct.
0:58:33 There are no new revelations about Trump and Epstein. The end of the EV subsidies, the massive increase in
0:58:38 the deficit were all present when he was showing up to the White House in a Hot Topic uniform high on
0:58:45 ketamine. This is about Elon Musk being angry he’s no longer the first friend. So that is not the basis
0:58:52 or the substance or the heft to start a third political party. And then the question is, will
0:58:57 a third political party, does it have any viability? And it doesn’t in America. We have a two party system
0:59:02 because of gerrymandering, because of a winner take all environment. When we have proportional
0:59:07 representation in places like Sweden and Germany, a third, a fourth and a fifth party can have a lot of
0:59:11 influence because they get proportionate representative based on if they get 18 percent of the vote, they
0:59:18 get 18 percent of the representatives. What a third party ends up being is spoilers, right? So Ross
0:59:23 Perot got 18 percent. Ross Perot is the reason Bill Clinton won presidency. George Herbert Walker Bush was
0:59:28 the first incumbent to lose an election when there wasn’t a recession because Perot took 18 percent,
0:59:36 about 11 percent was drawn from Bush’s voter base, seven from Clinton. So a swing of 4 percent, which swung
0:59:42 it from being a landslide for Bush to a decided victory for Clinton. The same thing happened to Gore
0:59:49 because of Nader. Jill Stein played a role. So these third parties are not viable. The last time a third
0:59:57 party won a state was Wallace, I think, in 68. But they can be spoilers. I think this is over before it starts.
1:00:04 I think it’s going to get no traction. What he can have is enormous influence because there’s a decent
1:00:09 argument that he’s the guy that got Trump elected with a quarter of a billion dollars and a huge platform in
1:00:13 seven swing states and a small number of counties in those seven swing states. You can make an argument
1:00:20 that in, you know, two or three of those states, Musk may have swung the election for Trump. If he is able to focus
1:00:28 on four or six senatorial and 12 or 15 house races, he could have a huge impact because those people are
1:00:34 very loyal to whoever puts him in office. One thing that Peter Thiel will never hear from J.D. Vance is the
1:00:41 word no because Peter Thiel put J.D. Vance in office. So he could have enormous influence. But this third
1:00:47 party nonsense is over before it begins. And be clear, folks, Elon Musk isn’t worried about the
1:00:52 deficit. He isn’t worried about America’s future. He’s just, quite frankly, he’s really butthurt and
1:00:55 he’s angry and he’s looking for revenge. Your thoughts?
1:01:02 I agree. And you saw also how quickly Elon Musk faded from favor of the Republican Party. Once
1:01:07 he started opposing the bill, he was persona non grata. I understand that this coincided with him
1:01:13 also leaving the White House, but he’s not walking around with Trump and Dana White anymore. So no one
1:01:20 really cares that much. You’re right about the money, like the example I was giving in Ohio. But he is
1:01:25 on to something that’s really important. You know, we have the highest number of Americans that identify
1:01:29 as a political independent. That doesn’t mean that they don’t have right or left leanings, but it means
1:01:35 that they don’t want to be part of this two party system that pushes you into boxes where you don’t
1:01:41 feel like you belong. And there was a massive study of almost 20,000 people that looked at how
1:01:45 independents feel about the major parties. Sixty four percent have an unfavorable opinion of the
1:01:51 Democratic Party and 71 percent have an unfavorable opinion of the Republican Party. We need to do
1:01:57 better. We need parties that look more like America, that are more responsive to America and their
1:02:03 concerns. It’s a huge branding challenge, you know, something that you’re great at assessing. But when
1:02:11 Musk says we need another option, we need an alternative, almost everybody says that’s objectively true.
1:02:19 We just need to find a way to make that feasible or possible for folks or to at least give them some
1:02:24 inkling that we understand how badly they want things to change.
1:02:29 So just before we wrap up here, Jess, I need you to get under the president’s skin again.
1:02:34 We popped to the fourth biggest news podcast in the world last week solely because…
1:02:36 You didn’t text me about that? I didn’t know that.
1:02:42 Probably because the president is pissed off at you and name checked you. So I need you to continue to
1:02:47 get under his skin because daddy wants to come back to Ibiza. He wants to come back to Ibiza. The
1:02:48 people are so young.
1:02:49 It’ll be my great pleasure.
1:02:56 And so hot here. And it is so expensive. It all reverse engineers to the president getting angry
1:02:58 at you. Can you do that for me?
1:02:59 I will do my best.
1:03:04 I’ll say it again. I can’t say it enough. I am so proud of you. I think that is so impressive.
1:03:08 You literally want to tell your grandkids. You want to be like, yeah,
1:03:13 remember that fascist back at the beginning of the 21st century that we literally vomited out?
1:03:18 Yeah, he he went after me publicly. I think that is going to be I think you’re going to have that
1:03:20 on your tombstone as a point of pride.
1:03:22 A long truth social for a tombstone.
1:03:26 All right, Jess, that’s all for this episode. Thank you for listening to Raging Moderates.
1:03:31 Our producers are David Toledo and Eric Jenakes. Our technical director is Drew Burrows.
1:03:35 Going forward, you’ll find Raging Moderates every Wednesday and Friday. That’s right.
1:03:40 Every Wednesday and Friday. Subscribe to Raging Moderates on its own feed to hear exclusive
1:03:45 interviews with sharp political minds. This week, Jess is speaking with Congressman Seth Moulton.
1:03:48 Make sure to follow us wherever you get your podcasts so you don’t miss an episode.
1:03:52 Jess, have a great rest of the week. It’s so good to see you.
1:03:52 Great to see you.

0:00:06 Hey there, it’s Stephen Dubner.
0:00:09 Today, we’re bringing you one of my favorite episodes from the Archive.
0:00:11 This came out in 2020.
0:00:15 I distinctly remember recording this interview in a COVID-era coat closet.
0:00:19 The episode is called How to Make Your Own Luck.
0:00:24 And it’s a conversation with the writer Maria Konnikova about her decision to become a professional
0:00:25 poker player.
0:00:28 We’ve updated facts and figures as necessary.
0:00:30 As always, thanks for listening.
0:00:50 This is Freakonomics Radio, the podcast that explores the hidden side of everything, with
0:00:51 your host, Stephen Dubner.
0:01:07 I love a book that immediately lets you see what the writer is seeing, lets you hear what
0:01:09 they’re hearing, even smell what they’re smelling.
0:01:13 The room is a sea of people.
0:01:20 Bent heads, pensive faces, many obscured by sunglasses, hats, hoodies, massive headphones.
0:01:25 It’s difficult to discern where the bodies end and the green of the card tables begins.
0:01:29 The smell of stale casino air fills the room.
0:01:34 Old carpet, powder, cold fried food and flat beer.
0:01:40 And the unmistakable metallic tang of several thousand exhausted bodies that have been sharing
0:01:42 the same space since morning.
0:01:46 It’s the first day of the biggest poker tournament of the year.
0:01:50 The main event of the World Series of Poker.
0:01:55 Did you truly not know how many cards are in a deck of cards?
0:01:57 Yes, I thought there were 54.
0:01:59 This is a true story.
0:02:01 It’s not exaggerated for the book.
0:02:06 That is Maria Konnikova.
0:02:08 Her book is called The Biggest Bluff.
0:02:13 How I Learned to Pay Attention, Master Myself and Win.
0:02:17 It chronicles her journey from poker novice to poker professional.
0:02:20 The Biggest Bluff is Konnikova’s third book.
0:02:25 The others are called Mastermind, How to Think Like Sherlock Holmes and The Confidence Game,
0:02:26 which is about con artists.
0:02:32 If you think you are detecting a theme in Konnikova’s writing, yes, there is a theme.
0:02:36 She writes about psychology in her books and for The New Yorker.
0:02:40 She also has a PhD in psychology from Columbia University.
0:02:44 She did not get the PhD in order to teach or to treat patients.
0:02:48 She only wanted to be a writer, and she thought that getting a doctorate in psychology would
0:02:54 give her good insights into how people think and make decisions, whether our lives are shaped
0:02:57 primarily by those decisions or by chance.
0:03:02 Also, insights into how we present our true selves and how we bluff.
0:03:06 These curiosities ultimately brought her to poker.
0:03:13 The deeper I went into poker, the better of a metaphor for life I realized it was, and the
0:03:19 stronger of a tool I realized it was to address so many of the psychological questions that had
0:03:22 been percolating in my head for years.
0:03:26 Life is a game of incomplete information.
0:03:33 You never know everything, and you are able to control a good amount of decisions leading
0:03:37 up to the end because you can control how you present yourself.
0:03:38 You can control whether or not you play.
0:03:40 You can control how you play.
0:03:43 But ultimately, you can’t control the cards.
0:03:48 And I think that that’s a very good reflection of what goes on in life.
0:03:53 There’s so much you can do, but then the ultimate outcome is not up to you, and you have to be
0:03:53 okay with that.
0:04:02 What would you say, Maria, is the luckiest thing that’s ever happened to you?
0:04:05 That is the question for all questions.
0:04:09 I think that it’s a toss-up between two things.
0:04:15 I mean, one, it’s really difficult to call this the luckiest thing, but honestly, being born and being
0:04:20 born to my parents and having the genetic makeup that I have, I think is the luckiest thing that
0:04:20 happened.
0:04:26 But that aside, I think the luckiest thing that ever happened was the fact that when I
0:04:32 was four years old, my parents decided to leave the Soviet Union and come to the United States.
0:04:36 My life would be so different had I grown up in the Soviet Union.
0:04:40 I have no idea what would have happened or what I would have done.
0:04:41 How old are you now?
0:04:42 I’m 36.
0:04:47 So you’re saying that the two luckiest things that ever happened to you,
0:04:49 neither of them were in the past 32 years.
0:04:50 Yes.
0:04:55 I think I’ve had a lot of very lucky things happen along the way, but you ask the absolute
0:05:00 luckiest, and you have to think, you know, what really changed the trajectory of your life
0:05:01 in the most profound way?
0:05:07 And honestly, being a Jew in the Soviet Union, this was before the Berlin Wall fell, was no fun.
0:05:13 I would not have been a writer because, you know, you really couldn’t do anything in the
0:05:13 humanities.
0:05:17 I can’t even begin to imagine what my life would look like.
0:05:25 When her family emigrated, they settled in Acton, Massachusetts, outside of Boston.
0:05:29 From early on, she was an achiever and an overachiever.
0:05:30 She wound up going to Harvard.
0:05:36 Afterward, she was a producer on The Charlie Rose Show, but then back to school for that
0:05:39 PhD under the legendary psychologist Walter Mischel.
0:05:44 He was best known for a series of studies built around the marshmallow test, which examined
0:05:47 the human capacity for self-control.
0:05:52 I asked Konnikova why Mischel and the idea of self-control had appealed to her.
0:05:53 There were two things.
0:06:00 One, Walter Mischel as a person appealed to me because he was someone who liked to think
0:06:02 big and ask big questions about the human mind.
0:06:10 And the other thing is, I did feel like self-control was something that could be incredibly useful
0:06:17 to me as a human and just in general to understand, because it seemed to me that it was such an
0:06:24 important thing in life to learn about emotional management, to learn about how to handle yourself.
0:06:29 So you write that an academic career, had you chosen that, would have been a gamble as
0:06:33 well, which is a really interesting thought because I think a lot of people, when they’re
0:06:38 choosing their careers, there is that sort of fundamental fork in the road of the safe or
0:06:42 at least predictable one and then, you know, the other options.
0:06:45 So what would have been the risk had you chosen the academic career?
0:06:54 I think that you are so dependent on the biases of other people because the academic job market
0:06:58 is an incredibly biased place, as is any job market.
0:07:04 So you’re at the mercy of, you know, what types of things do the people who are in this
0:07:07 particular place want to study?
0:07:09 How do your theories fit into it?
0:07:15 Even as I was going into the graduate program to study with Walter Mischel, I knew that I was
0:07:21 entering an area that was on the outs because the hot areas were neuroscience.
0:07:25 The hot areas were kind of all of the very hard cognition.
0:07:31 And even at Columbia, while I was there, all of the tenure offers went to neuroscientists.
0:07:36 And there were a few amazing social psychologists who didn’t get job offers.
0:07:41 And I was seeing this and thought, uh-oh, if I actually want to do this, that’s a big, big risk.
0:07:45 How meritocratic would you say academic psychology is?
0:07:48 I think it thinks of itself as incredibly meritocratic.
0:07:51 I think that it’s much more biased than that.
0:07:55 I think you need merit up to a certain point.
0:07:57 But then it’s, you know, personal favorites.
0:07:59 Who did you study with?
0:08:01 Who do I owe a favor to?
0:08:05 I mean, the office politics in academia are just insane.
0:08:10 And how would you compare academia to poker in terms of meritocracy?
0:08:13 I mean, I don’t even think there’s a comparison.
0:08:17 I think poker is so much more meritocratic than academia.
0:08:21 I was about to say a million times, but one of the things that poker taught me is to be precise.
0:08:23 So it’s not actually a million times.
0:08:24 That would be an exaggeration.
0:08:25 That would indeed, yeah.
0:08:31 Let me just ask you, how numerate did you consider yourself before playing poker?
0:08:32 Not at all numerate.
0:08:36 I actually still, I know that this isn’t something to be proud of.
0:08:37 It’s just the way my mind works.
0:08:39 I still count on my fingers.
0:08:43 I need that visual and tangible cue to help myself out.
0:08:45 The last math class I took was in high school.
0:08:51 When it comes to probability, however, you don’t have to learn higher order math to understand
0:08:54 probability, but you do need to understand probability to play poker.
0:09:00 So how did you grow from innumerate to a good understanding of probability?
0:09:04 I think it helped that I did once upon a time have a good math background.
0:09:06 I mean, I took calculus.
0:09:08 I was good at math in high school.
0:09:11 I just never really liked it and dropped it soon after.
0:09:15 So it was just a muscle of mine that I hadn’t used at all.
0:09:20 But as Eric Seidel, who became my coach, told me very early on, all you really need to know
0:09:23 how to do is add, subtract, multiply, divide.
0:09:30 And the thing is, you are constantly doing and the human mind learns best by doing.
0:09:34 And in poker, you also have very immediate feedback.
0:09:37 If you make math mistakes, you’re going to lose money.
0:09:43 And I found that as I was put in these high-pressure situations and was forced to think in that
0:09:48 way, my mind eventually unrested itself.
0:09:52 Imagine two players at a table.
0:09:54 The cards are dealt.
0:09:59 Each player must look at her cards and decide whether or not the cards on their own are good
0:10:00 enough to bet.
0:10:05 If she wishes to play, she must at minimum call the big blind.
0:10:10 That is, place as much into the pot as the highest bet that already exists.
0:10:13 She may also choose to fold or raise.
0:10:17 But who knows what factors she’s using to make her decision?
0:10:19 Maybe she has a premium hand.
0:10:25 Maybe she has a mediocre hand but thinks she can outplay her opponent and so chooses to engage
0:10:25 anyway.
0:10:30 Maybe she has observed that the other players view her as conservative because she doesn’t
0:10:35 play many hands and she’s taking advantage of that image by opening up with worse cards
0:10:36 than normal.
0:10:40 Or maybe she’s just bored out of her mind.
0:10:44 Her reasoning, like her cards, is known only to her.
0:10:50 Each decision throws off signals and the good player must learn to read them.
0:10:53 It’s a constant back-and-forth interpretive dance.
0:10:56 How do I react to you?
0:10:58 How do you react to me?
0:11:02 More often than not, it’s not the best hand that wins.
0:11:04 It’s the best player.
0:11:10 Betting on uncertainty is one of the best ways of understanding it.
0:11:15 And it is one of the best ways of conquering the pitfalls of our decision processes in just
0:11:17 about any endeavor.
0:11:20 It doesn’t take a gambler to understand why.
0:11:27 In his critique of pure reason, the German philosopher Immanuel Kant proposes betting as an antidote
0:11:29 to one of the great ills of society.
0:11:35 False confidence bred from an ignorance of the probabilistic nature of the world, from a desire
0:11:39 to see black and white where we should rightly see gray.
0:11:48 from a misplaced faith in certainty, the fact that to our minds, 99%, even 90%, basically means
0:11:53 100%, even though it doesn’t, not really.
0:11:57 Kant offers the example of a doctor asked to make a diagnosis.
0:12:03 The doctor reaches a verdict on the patient’s malady to the best of his knowledge, but that
0:12:05 conclusion isn’t necessarily correct.
0:12:10 It’s just the best he can do given the information he has and his experience in this particular
0:12:11 area.
0:12:14 But will he tell the patient he’s unsure?
0:12:15 Maybe.
0:12:22 But more likely, if his certainty reaches a specific threshold, a different one for different doctors
0:12:25 to be sure, he will just state his diagnosis as fact.
0:12:28 But what if he had to bet on it?
0:12:36 So describe quickly for me the year 2015 for the Konnikova family.
0:12:38 It wasn’t a great year.
0:12:46 The year started off with my mom losing her job and my grandmother dying.
0:12:51 So that was quite a shock because she had been healthy.
0:12:53 She was completely self-sufficient.
0:13:02 She was a volunteer in World War II and had survived so much during the Soviet era.
0:13:05 And she died because she slipped in the night.
0:13:07 She put one foot wrong.
0:13:09 My mom had never lost her job ever.
0:13:15 And so that was quite a shock that she was just let go during a private equity acquisition.
0:13:16 My husband lost his job.
0:13:24 And at the same time, I had this really big health scare and it was really just horrifying.
0:13:27 So that was a really bad year.
0:13:32 I’m sure you know the work of Tom Gilovich and others with headwinds and tailwinds.
0:13:37 Tell me, do most people believe that the good things that happen in their lives are because
0:13:42 of their abilities and the bad things that happen in their lives are because of bad luck?
0:13:46 So this goes back to an idea called the locus of control.
0:13:48 And it was the work of Julian Roeder.
0:13:53 And Roeder found that there are two types of locus of control.
0:13:59 And by that, he meant where you think control over events resides, internal and external.
0:14:04 So if you have an internal locus of control, when good things happen, you take credit for
0:14:06 them and you say, yeah, that was me.
0:14:11 And an external locus of control, you say, oh, no, no, you know, it was events in the
0:14:11 world.
0:14:13 I had nothing to do with it.
0:14:17 Most people, he found, have an internal locus when it comes to good things.
0:14:19 They take credit for their success.
0:14:22 But an external locus, they switch when things go wrong.
0:14:23 They say, oh, it wasn’t my fault.
0:14:27 You know, here are all of the reasons why this went wrong.
0:14:29 But there are exceptions.
0:14:31 There are people who have an external locus always.
0:14:33 That’s not good at all.
0:14:35 There are people who have an internal locus always.
0:14:36 That’s also not good.
0:14:42 And the normal signature is also not great because if you always take credit for the good
0:14:46 things and don’t take blame for the bad things, you’re going to be overconfident.
0:14:48 So you need to learn how to balance the two.
0:14:52 It sounds, though, as if you’ve just drawn an unsolvable puzzle.
0:14:59 You say both extremes are poor, but also the compromise or the moderate version is poor.
0:15:02 So how do you optimize external versus internal?
0:15:07 Yeah, I think that the important thing is to have an internal locus most of the time in
0:15:12 the sense that you understand that you do control a lot of things.
0:15:16 But that also means keeping that internal locus for some of the bad events.
0:15:21 But also, I think the way that you solve that puzzle is through decision-making and analysis.
0:15:24 You actually learn that both modes of thought are possible.
0:15:30 And so before you jump to any conclusions, you break it apart and you say, okay, what was
0:15:31 my decision process?
0:15:32 What did I control here?
0:15:33 What didn’t I?
0:15:34 What was the outcome?
0:15:37 And am I responsible for that outcome or not?
0:15:41 Because sometimes it will be something else and sometimes it will be you.
0:15:47 And pre-poker, how would you describe yourself on the internal versus external scale?
0:15:52 I was probably someone who was more external on a lot of the good things and more internal
0:15:53 on a lot of the bad things.
0:15:57 So the good things that happened were luck and the bad things were my fault.
0:15:57 Yes.
0:16:00 I think that that was a lot of the way that I thought about things.
0:16:04 I actually hadn’t asked myself that question until you just asked me.
0:16:09 But now that I think about it, I think that that was a lot of my mindset pre-poker, which
0:16:09 isn’t ideal.
0:16:15 There is no such thing as objective reality.
0:16:20 Every time we experience something, we interpret it for ourselves.
0:16:23 Do we see ourselves as victims or victors?
0:16:24 A victim.
0:16:27 The cards went against me.
0:16:29 Things are being done to me.
0:16:31 Things are happening around me.
0:16:34 And I am neither to blame nor in control.
0:16:36 A victor.
0:16:37 I made the correct decision.
0:16:40 Sure, the outcome didn’t go my way.
0:16:42 But I thought correctly under pressure.
0:16:45 And that’s the skill I can control.
0:16:47 A victim of the cruel cards?
0:16:51 This may serve as something I think of as a luck dampener effect.
0:16:56 Because you’re wallowing in your misfortune, you fail to see the things you could be doing
0:16:57 to overcome it.
0:17:11 You don’t even attempt certain activities because you think, I’ll lose anyway.
0:17:21 If you think of yourself instead as an almost victor who thought correctly and did everything
0:17:26 possible but was foiled by crap variants, no matter.
0:17:28 You will have other opportunities.
0:17:32 And if you keep thinking correctly, eventually it will even out.
0:17:37 These are the seeds of resilience, of being able to overcome the bad beats that you can’t
0:17:42 avoid and mentally position yourself to be prepared for the next time.
0:17:44 People share things with you.
0:17:48 If you’ve lost your job, your social network thinks of you when new jobs come up.
0:17:53 If you’re recently divorced or separated or bereaved and someone single who may be a good
0:17:55 match pops up, you’re top of mind.
0:17:59 That attitude is what I think of as a luck amplifier.
0:18:05 Sure, you can’t actually change the cards and the variance will be what it will be, but
0:18:09 you will feel a whole lot happier and better adjusted while you take life’s blows.
0:18:14 And your ready mindset will prepare you for the change in variance that will come at some
0:18:17 point, even if that point is far in the future.
0:18:21 Indeed, it’s easy to see how the bad beat seeps into everything.
0:18:24 It’s not just complaining about the runout.
0:18:26 It’s complaining in general.
0:18:31 Once you do that, you slide into dangerous mental waters.
0:18:33 I have a bad table draw.
0:18:38 Why are all the good players at my table while I see so many easier tables around?
0:18:40 I’m card dead.
0:18:44 Why are other people getting all the big pairs and I’m getting unplayable crap?
0:18:47 The great players don’t play that way.
0:18:50 It’s too draining and it makes you too much the victim.
0:18:52 And the victim doesn’t win.
0:18:54 Bad table draw?
0:18:58 It’s a challenging table that will force you to play well.
0:18:59 Card dead?
0:19:01 No one knows that.
0:19:06 If your face reads card dead, everyone will walk all over you as you meekly fold.
0:19:09 Everything is in how you perceive it.
0:19:17 Coming up after the break, Maria Konnikova jumps into the
0:19:18 world of professional poker.
0:19:19 I’m Stephen Dubner.
0:19:21 This is Freakonomics Radio.
0:19:21 We’ll be right back.
0:19:40 As a newcomer to the world of poker, Maria Konnikova knew that she needed a coach.
0:19:44 The one she chose was a legendary player named Eric Seidel.
0:19:50 So Eric ended up being my first choice and he was my only choice because he said yes.
0:19:58 But I did do research and I did look at a number of possibilities before settling on Eric.
0:20:04 So you targeted and subsequently stalked your poker mentor, Eric Seidel.
0:20:06 Well, now that you put it that way.
0:20:08 Well, I think it’s not so.
0:20:08 I did.
0:20:09 No, I did.
0:20:09 I did.
0:20:10 I totally stalked him.
0:20:13 I mean, stalked in a quasi appealing way.
0:20:18 But tell us what he had or represented that made him the mentor you wanted.
0:20:21 He had a few different characteristics.
0:20:23 First, longevity.
0:20:30 There’s actually no comparison between him and any other player in terms of staying at the top
0:20:33 of competitive poker for decades.
0:20:36 And most people, they have kind of this peak and then they go away.
0:20:42 The other component was that he seemed more old school in the sense of being more psychological,
0:20:50 more thinking in his approach rather than a lot of the newer poker players who, while brilliant,
0:20:56 are very mathematically minded and they have just a very calculational approach.
0:20:58 And that’s not my background.
0:20:59 That’s not my strength.
0:21:04 So I wanted to make sure to work with someone who could actually help amplify the skills that
0:21:05 I already had.
0:21:10 Okay, but at this point, knowing how to play poker was not among your skills.
0:21:11 You didn’t know the game at all.
0:21:13 You said you’d never played any card games.
0:21:15 Had you played other games as a kid?
0:21:18 No, we were not a games playing household.
0:21:18 I read.
0:21:23 And because I’m a Russian Jew, my parents decided that maybe I would like chess.
0:21:29 So at some point in elementary school, they enrolled me in this chess club and I lasted
0:21:31 exactly two weeks.
0:21:35 Why was it that poker captured your attention?
0:21:41 I was originally introduced to poker through game theory through the work of John von Neumann.
0:21:47 As I was reading about luck and kind of immersing myself in the world of chance and how to think
0:21:52 about chance, I read John von Neumann’s theory of games, which is the foundational text of game
0:21:52 theory.
0:21:55 And I didn’t know much about von Neumann or his work.
0:21:57 And he loved poker, correct?
0:21:58 Yes.
0:22:00 He was just an avid poker player and he hated games.
0:22:02 By the way, he hated roulette.
0:22:03 He hated chess.
0:22:04 He hated Go.
0:22:08 He thought that they were boring because they were either solvable or unsolvable.
0:22:09 And he loved…
0:22:10 Back up for a second.
0:22:11 So you’re saying he hated roulette.
0:22:12 Mm-hmm.
0:22:19 And on the spectrum of information, those are at opposite ends of the spectrum.
0:22:20 Exactly.
0:22:25 So talk about that and what it was that he was looking for, what it was that he didn’t like
0:22:26 about those.
0:22:31 Von Neumann was drawn to poker because it was a game of incomplete information.
0:22:37 There was a solvable component to it, but there was always an element of the unknown.
0:22:43 And he was working at the time as a national security advisor in the United States government.
0:22:45 He was working on the hydrogen bomb.
0:22:49 I mean, this is someone who was involved at the very highest levels of decision-making.
0:22:55 And when he saw poker, he said, this is a good analog for that because it’s a game of
0:22:56 incomplete information.
0:22:58 Chess is boring because it can be solved.
0:23:02 There is theoretically always a correct move.
0:23:04 And roulette is boring because it can’t be solved.
0:23:05 It’s all chance.
0:23:05 The house wins.
0:23:07 There’s nothing you can do.
0:23:15 Poker is interesting because we can try to find a framework to develop a solution, how to
0:23:16 think through it.
0:23:22 And yet, it’s not solved in the sense that there are these elements of the unknown, this
0:23:27 human element of bluffing, of kind of representing and misrepresenting information.
0:23:30 This is what decision-making in the real world is actually about.
0:23:32 And that was the germ of game theory.
0:23:38 So he came up with game theory as a way to try to solve poker and then ultimately shed
0:23:44 a light onto how to make these very complex strategic decisions at the highest levels of government.
0:23:46 So you write about von Neumann.
0:23:49 He was a god-awful player by every account, poker player.
0:23:55 So this shook me because we think of von Neumann as a few things, you know, the pioneer inventor
0:24:00 of game theory intersecting with the birth of computing, et cetera, et cetera.
0:24:04 But if he was so bad at poker, how good could he have been at game theory?
0:24:07 He was very good at game theory.
0:24:13 He had personal failings that came out at the poker table and he didn’t care.
0:24:15 He liked to have fun when he played poker.
0:24:17 He liked to host poker games at his house.
0:24:19 He liked to drink while he played.
0:24:21 No serious poker player does this.
0:24:23 He had a little too much gamble to him.
0:24:29 So I think that he was one of those people who wasn’t very good at applying the game theory
0:24:31 to the actual game when it came to poker.
0:24:37 Poker isn’t a homogeneous game.
0:24:44 There are multiple varieties of play, with names like stud, Omaha, Raz, Badoogie, and horse.
0:24:47 Each has its own unique set of rules.
0:24:51 But in any style of poker, the basic parameters are essentially the same.
0:24:55 Some cards are dealt face-up, visible to all.
0:24:57 These are the community cards.
0:25:02 And some face-down, so that only the person to whom they are dealt can see them.
0:25:08 You make bets based on how strong your hand is, and how strong you think others’ hands are.
0:25:15 Because the only other cards you know for sure are your own, you’re in a game of incomplete information.
0:25:19 You must make the best decision you can, given the little you know.
0:25:26 But the style I’ve chosen to pursue is one particular variant of the game, which happens to be the most popular.
0:25:29 No Limit Texas Hold’em
0:25:33 How No Limit Hold’em differs from other forms of poker is twofold.
0:25:39 The first is in the precise amount of information that is held in common versus in private.
0:25:44 Each player is dealt two cards face-down, the whole cards.
0:25:47 This is privileged information.
0:25:52 I can try to guess what you have based on how you act, but I can’t know for sure.
0:26:01 The only information I’ll have is your betting patterns once the public information, the cards dealt to the middle of the table face-up, is known.
0:26:09 The amount of incomplete information in Texas Hold’em creates a particularly useful balance between skill and chance.
0:26:13 Two whole cards is just about as practical a ratio as you can have.
0:26:19 Enough unknown to make the game a good simulation of life, but not so much that it becomes a total crapshoot.
0:26:27 The second thing that distinguishes this particular playing style is the concept of no limit, von Neumann’s own preferred style.
0:26:36 The power of the pure bluff is restricted in a game of limit, explains Amarillo Slim, one of the best poker players of his day.
0:26:41 When there’s a limit, it means that the exact amount you bet has a ceiling on it.
0:26:45 In no limit, you can bet everything you have, at any point.
0:26:51 And that’s what makes this game a particularly strong metaphor for our daily decision-making.
0:26:55 Because in life, there is never a limit.
0:27:03 What’s to stop you from risking all your money, your reputation, your heart, even your life, at any point you choose?
0:27:04 Nothing.
0:27:10 There are no rules at the end of the day, save some internal calculus that only you are privy to.
0:27:14 And everyone around you has to know that when they make their decisions.
0:27:19 Knowing you can go all the way, how much should they themselves invest?
0:27:25 It’s the endless game of brinkmanship, popularized by another giant of game theory,
0:27:31 the Nobel-winning economist Thomas Schelling, that plays out everywhere in our lives.
0:27:36 Who will say, I love you first, moving all in in the relationship?
0:27:39 And if you say it, will you be left out, so to speak?
0:27:41 Who will walk away from the business negotiation?
0:27:43 Who will wage war?
0:27:49 The ability to go all in, and the knowledge that going all in is an option for everyone around us,
0:27:55 is the crucial variable that makes so many decisions so very difficult.
0:28:03 You can emerge with the deal of a lifetime, or a life partner, or you can find yourself bankrupt, or emotionally devastated.
0:28:08 Like life, no-limit poker is high risk and high reward.
0:28:13 And it’s no coincidence that that is the style of play I have chosen to learn.
0:28:18 If you’re trying to make the best decisions, you might as well go with the best proxy.
0:28:29 So, in the beginning of the book, where, you know, a smart person who’s a writer who’d never played cards before
0:28:35 decides that they’re going to play competitive poker at a really high level, it feels like a stunt book.
0:28:37 And granted, it’s a good stunt. It’s a really good stunt.
0:28:40 But, you know, it’s a writer who’s got a PhD in psychology,
0:28:49 putting her research about chance and skill to a real test while entering this competitive and alien ecosystem.
0:28:52 So, I didn’t mind the stunt, but it did feel like a stunt.
0:29:05 But over time, your zeal and your desire to learn and study was so contagious that it felt not like a stunt anymore.
0:29:10 And I’m curious whether that experience was a little parallel for you.
0:29:16 In other words, how much of it was a great conceit that would make a good book?
0:29:25 And how much of it was really a way for you to work out your deepest insights about chance and skill and decision making?
0:29:28 That’s an excellent question.
0:29:31 And I think that the way that it felt for you is actually spot on.
0:29:33 This started out as something of a stunt.
0:29:37 I was looking for a way to look, and this seemed like a good idea.
0:29:39 And I was like, bam, this is going to sell.
0:29:40 And it was a stunt.
0:29:45 In a very kind of self-conscious way of knowing that there are lots of books like this,
0:29:48 where people, you know, do something for a year, try something out.
0:29:50 And people like to read that.
0:29:53 And I thought that I could gain some insight along the way.
0:29:57 And boy, did it change as I actually embarked on this project.
0:30:02 And I think that this had to do with Eric and with the fact that Eric loves poker.
0:30:08 He was able to actually instill some of that love in me from the very early days.
0:30:12 And I realized that this was so much more than a stunt,
0:30:18 that this could truly be a way to become a better human, to become a better decision maker,
0:30:19 to learn about myself.
0:30:22 And so it became something very different.
0:30:27 I could never have imagined that that would have been three years of my life,
0:30:29 that the old end point would just come and go.
0:30:31 And it became a different book.
0:30:33 It became a different project.
0:30:34 It became a passion of mine.
0:30:40 And how much of it is also that feedback in poker is immediate and real,
0:30:45 whereas in life, a lot of things we do, we don’t really get great feedback,
0:30:49 especially if it’s from other people that we’re interacting with.
0:30:53 I think the fact that in poker you do get immediate feedback is incredibly valuable,
0:30:57 because that’s also how the mind learns best, so that we know, okay, right or wrong.
0:30:59 You know, how did this feel?
0:31:05 And normally in life, it’s just too noisy of an environment.
0:31:09 There’s too much of a lag between decision and outcome.
0:31:11 There are too many variables going on.
0:31:14 And so it’s just this whole morass that your mind can’t disentangle.
0:31:18 So you can’t figure out, you know, what was me?
0:31:18 What wasn’t?
0:31:19 How do I improve?
0:31:21 It’s incredibly difficult.
0:31:23 It’s what Robin Hogarth calls a wicked environment.
0:31:25 Most of life is wicked.
0:31:34 And at the poker table, you actually are able to make a decision and then you see what happens.
0:31:40 And over time, you start getting feedback as you play through hands over and over and over,
0:31:42 because one time anything can happen.
0:31:45 You can make a horrible decision and you get a good outcome,
0:31:47 or you can make a really good decision and get a bad outcome.
0:31:48 And so the feedback’s wrong.
0:31:51 But if you do that hundreds of times, the feedback becomes aligned.
0:31:56 And so you’re learning, oh, this is the way that I’m thinking correctly.
0:32:00 And these are the mistakes I’m making because I’m losing money and it hurts.
0:32:02 And that also helps you learn.
0:32:06 I raise.
0:32:11 An aggressive hedge fund guy who is running over the table re-raises me.
0:32:14 I make my first mistake by not folding.
0:32:19 I can’t help but think I’m being pushed around and decide to hold my ground.
0:32:24 And that may well be true, but I’m not picking the best spot or way to do it.
0:32:28 Part of me knows that holding my ground with such a marginal hand is a mistake,
0:32:33 but the other part lacks the nerve to raise and is too stubborn to fold.
0:32:37 And so I call, leaving myself precious few chips.
0:32:40 And then I whiff the flop completely.
0:32:43 The board in no way matches my cards.
0:32:48 I have almost no prospects of actually making the best hand.
0:32:50 It’s either bluff or get out.
0:32:52 The hedge fund guy, though, is first to act.
0:32:58 And he puts in a massive bet, enough to force me to go all in if I want to call.
0:33:03 But just as I’m miserably about to fold my cards, a gentleman to my left intervenes.
0:33:04 What?
0:33:06 Are you going to let him get away with that?
0:33:08 I laugh nervously.
0:33:10 Come on, you have to call.
0:33:12 He’s bluffing, can’t you see?
0:33:17 The table all chimes in, confirming my duty to call.
0:33:21 And I, putting aside everything I’ve learned, do so.
0:33:27 The hedge fund guy turns over aces, and my first live poker tournament is at an end.
0:33:30 I wander away, heeding myself.
0:33:33 I knew better than to do that.
0:33:35 That wasn’t my knowledge playing.
0:33:43 That was the worst possible combination of traits, insecurity and gutlessness, leading to half
0:33:44 measures that will never win.
0:33:47 I’ve let them get to me.
0:33:52 I didn’t want to be pushed around, but I wasn’t comfortable doing the pushing around
0:33:52 either.
0:33:55 And the result is this mess of a hand.
0:33:58 I’m hopeless at this game.
0:34:01 And apparently, I’m hopeless at life.
0:34:06 A gutless female who wants to be liked more than she wants to win.
0:34:12 Coming up after the break, Maria Konnikova finds her guts.
0:34:16 And, not coincidentally, she starts to win.
0:34:17 I’m Stephen Dubner.
0:34:18 This is Freakonomics Radio.
0:34:19 We’ll be right back.
0:34:34 Maria Konnikova had long wondered how much of our life outcomes one should attribute to chance
0:34:38 and how much to one’s own volition and skill and decision-making.
0:34:42 In the game of poker, she found the perfect medium to answer this question.
0:34:48 She was a total novice, but she persuaded the poker legend Eric Seidel to coach her.
0:34:52 He appreciated her intellect, and you could see how seriously she took the challenge.
0:34:56 The original plan called for Konnikova to play for a year and write a book about it.
0:34:59 But after that one year, she was just getting started.
0:35:05 She saw that she had made an excellent choice in choosing poker, that unlike most games, it
0:35:07 could hold a mirror up to real life.
0:35:12 I decide to fire out another hefty bet.
0:35:14 Aces, aces, la, la, la.
0:35:20 Except now, instead of calling, he raises, and the raise is a sizable one.
0:35:21 Uh-oh.
0:35:27 Red flags should be waving, horns should be blasting, and I should be folding.
0:35:29 But none of this enters my mind.
0:35:32 I hardly pause a second before calling.
0:35:34 Aces, aces, aces.
0:35:41 So, deception in poker is not only valuable, but it’s necessary.
0:35:45 In real life, deception is usually considered a negative.
0:35:51 Is deception, therefore, an outlier in the poker versus real life parallel, or no?
0:35:52 I don’t think so.
0:35:56 I think that we use deception in real life much more than we realize.
0:35:59 I mean, every single social interaction has deception.
0:36:03 You don’t necessarily like everyone as much as you tell them you like them.
0:36:10 And then, on a much broader level, when you’re in a negotiation, you use deception all the time
0:36:13 to present yourself as a little stronger than you actually are.
0:36:19 You present yourself in the best light possible in order to be hired, or you’re not going to be hired.
0:36:23 The person interviewing you is going to present the job in the best light possible.
0:36:28 All of these are subtle deceptions, and that’s the level of deception that we’re talking about in poker.
0:36:31 It’s not like you’re completely lying.
0:36:39 You are just choosing what and how to present, what and how to present about your hand, about yourself.
0:36:45 So, here’s an amazing statistic that I read in your book, that an analysis done by Ingo Fiedler
0:36:50 found that the actual best hand won, on average, only 12% of the time.
0:36:54 So, that’s interesting because it conveys what makes poker so interesting.
0:36:58 But it also made me wonder what lesson is to be drawn from that.
0:37:04 It seems to perhaps imply that many of us underestimate our strength in real life, I mean.
0:37:05 I do agree.
0:37:06 I do agree.
0:37:10 I think that that is a fascinating analysis that he did of online poker.
0:37:16 First of all, it shows just how much of a skill game poker is because you are convincing people
0:37:19 that you have the best hand when you don’t.
0:37:24 And it does make you realize that in real life, it’s not the people who hold the best cards.
0:37:27 It’s the people who convince everyone else, who are the most confident.
0:37:32 To me, it’s a little bit dispiriting because it’s very hard for me to be that way in real life.
0:37:36 It’s very hard for me to be the person who oversells my hand.
0:37:39 Because one of the things I studied in grad school was overconfidence.
0:37:44 So, it’s one of the things that I’m always aware of and trying not to fall into.
0:37:49 Let me ask you, whether or to what degree do you think that’s a mark of gender?
0:37:55 Because research shows that there’s a big split in confidence and overconfidence between the genders.
0:38:03 And I’m curious whether you feel yours is gender-driven, whether it’s immigrant-driven, whether it’s a quirk of your personality or what?
0:38:05 I think it’s both.
0:38:07 I definitely think it’s gender-driven.
0:38:12 And I actually came to realize that as I immersed myself in the poker world.
0:38:19 Because I never thought of myself as someone who internalized gender stereotypes because, you know, I studied psychology.
0:38:20 I knew what they were.
0:38:22 And I thought that I was above all of that.
0:38:30 And then poker made me realize how much I had and how often I acted because I’d been socialized to act in that way.
0:38:43 But I think you make a very interesting point that there’s also part of it is immigrant me and the fact that, you know, I came to this country and was a total outsider, that I didn’t speak English, that we didn’t have any money.
0:38:46 I wore hand-me-down clothes and not the cool kid clothes.
0:38:52 All of those things probably stayed with me on some level and made me feel a little bit more self-conscious.
0:39:00 And when you say you were socialized to act in that way as a female, what do you mean in that way and how did that translate to playing poker?
0:39:05 When you’re female, you try not to step on people’s toes.
0:39:08 You try to be affable.
0:39:09 You try to be nice.
0:39:11 You try to not be confrontational.
0:39:20 And it’s very adaptive because if you’re confrontational, if you actually push back, it’s not going to go well.
0:39:37 Because all of the research on the psychology of negotiation and negotiating while female shows you that women are judged on very different criteria for men and that women who negotiate more are not only not liked as well, but they’re not going to get what they negotiate for.
0:39:48 And so you become socialized in a sense to really embody a lot of those characteristics because it’s the smarter way to go.
0:40:01 So in poker, I realized that I was being passive, that I was folding more often, that I wasn’t standing up for myself because I always assumed, well, if you’re raising, you must have the better hand.
0:40:05 Oh, well, I don’t want to be too aggressive because then you’re not going to like me.
0:40:11 And it was just this realization that the need to be liked was somehow stronger than the need to actually win.
0:40:20 So as you’re getting acclimated to the game, you recognize that, A, you’ve got these traits that are traditionally female and that you want to adjust them.
0:40:30 But you also understand that your gender can be used to your advantage against male players who may have their own perceptions of how a female should or would play.
0:40:43 It was one of these aha moments for me when I realized that gender could actually be a huge asset at the poker table because people form impressions of everyone right away.
0:40:45 I mean, it’s just something that our brains do intuitively.
0:40:50 The first thing people notice about me is that I’m a woman because that’s what stands out at a poker table.
0:40:56 And so they react not to me as a player, but to me as a female, first of all.
0:41:02 The best players will adjust eventually, but especially at the levels where I started out, these weren’t the best players.
0:41:10 And so to me, the most important thing became how do they see women and how do they see women who play poker?
0:41:13 And they will show you pretty early on.
0:41:19 So I remember one experience where I was playing against this guy and he just kept betting and betting.
0:41:25 And finally, I folded and he placed his cards face up and said, see, I had you beat.
0:41:26 I had the best hand.
0:41:30 And that was one of these moments of realization of, oh, you know, well, thank you so much.
0:41:31 Thank you, sir.
0:41:34 I appreciate the information because in anything, information is power.
0:41:40 He’s showing you because he wants to be seen as someone who wasn’t exploiting you by bluffing, correct?
0:41:41 Exactly.
0:41:44 So he wanted me to know that he was trying to be a gentleman.
0:41:47 And he actually said, I didn’t want to take any more of your chips.
0:41:49 You know, they’re very chivalrous.
0:41:53 They don’t want to be perceived as kind of bullying a woman.
0:41:54 So you adjust to that.
0:41:56 So whenever they bet big, you fold.
0:41:58 You fold very good hands.
0:42:03 Then you have the people who just, they don’t think you should be at the poker table at all.
0:42:05 And they’re going to do a few different things.
0:42:10 When they bet big, you have to call and you have to find it in yourself to keep calling
0:42:14 because they’re going to try to bluff you over and over and over because they don’t think
0:42:15 you should be there.
0:42:17 So they’re going to bully you.
0:42:22 So once you figure out how do you see women, what’s your perception, and how do you want
0:42:28 to kind of play against me because you’re playing against a woman, then all of a sudden I have
0:42:32 this very powerful arsenal of new weapons and new ways to play against you.
0:42:37 There’s a false sense of security in passivity.
0:42:43 You think that you can’t get into too much trouble, but really, every passive decision
0:42:45 leads to a slow but steady loss of chips.
0:42:51 And chances are, if I’m choosing those lines at the table, there are deeper issues at play.
0:42:57 Who knows how many proverbial chips a default passivity has cost me throughout my life.
0:43:02 How many times I’ve walked away from situations because of someone else’s show of strength
0:43:04 when I really shouldn’t have.
0:43:10 How many times I’ve passively stayed in a situation, eventually letting it get the better
0:43:14 of me, instead of actively taking control and turning things around.
0:43:18 Hanging back only seems like an easy solution.
0:43:22 In truth, it can be the seed of far bigger problems.
0:43:29 I know all this, but I had somehow thought that my training in psychology, my knowledge of these
0:43:35 biases, the fact that I’ve achieved some form of professional success in my life, meant
0:43:37 that I had overcome my socialization.
0:43:43 But what poker is showing me, now that I take a moment to really look, is how far that is
0:43:44 from the truth.
0:43:50 It isn’t that I’m incapable of learning an aggressive approach or understanding its merits.
0:43:56 It’s that I have learned and understood and want to make it work, but can’t because of
0:44:02 the emotional baggage that has accumulated without my awareness throughout my entire professional
0:44:03 life.
0:44:05 I’m not a blank slate after all.
0:44:10 It isn’t a pleasant realization, but it is an important one.
0:44:14 Now that I see it, perhaps I can start working through it.
0:44:23 So one would think that even a poker novice having a PhD in psychology, one would think
0:44:27 that that would prove incredibly useful in reading other players, did it?
0:44:29 It did.
0:44:29 It did.
0:44:33 But not necessarily in the way that I thought it would.
0:44:39 I thought that I would actually have some better abilities at tells, at reading players
0:44:41 and figuring out what’s going on.
0:44:47 It ends up that I didn’t, especially at the beginning, that I would fall for actually a
0:44:53 lot of the biases that I’d studied, that I would make assumptions about players in the
0:44:58 same way that they were making assumptions about me, only I didn’t quite realize just how
0:45:00 wrong my assumptions were.
0:45:07 But where it did help eventually is in kind of a metacognitive awareness that helped me identify
0:45:11 those mistakes and fix them because I had the vocabulary.
0:45:15 I realized that I was focusing on the wrong things.
0:45:20 I was focusing on what people looked like, things like that, how they acted.
0:45:25 What I should have been focused on, which is quite funny because it was most of what Walter
0:45:30 Michelle’s work had been for the last 20 years, was on situational dynamics.
0:45:31 What’s going on at this table?
0:45:34 How are these people relating to each other?
0:45:36 How are they reacting?
0:45:39 How do they act under these different situations?
0:45:46 Once I realized that that was the most important thing, then my reading abilities improved and
0:45:48 I was actually able to use my psychology background.
0:45:54 But there have been poker players who dismiss the people reading part of it and the social
0:45:59 dynamic part of it and just are quants, just play the probabilities and succeed.
0:46:00 Yes.
0:46:04 So what’s to say that you’re not overvaluing that part?
0:46:05 Nothing.
0:46:09 And I think that they would probably say that I am overvaluing it.
0:46:11 I think that you play to your strengths.
0:46:13 The quant side is not my strengths.
0:46:20 I’ve mastered it to the point that I don’t make horrible mistakes of addition and subtraction
0:46:22 when I’m calculating pot odds.
0:46:23 But that’s about it.
0:46:30 I think the way that you become a great player is to figure out what works for you.
0:46:36 What Eric taught me is that there’s almost never a right way to do something, a right way
0:46:39 to play in a certain situation.
0:46:41 There’s a right way to think about it.
0:46:47 So one gathers that it took you a little longer to get good than you maybe thought or would
0:46:47 have liked.
0:46:53 But then you really start improving on many dimensions, gameplay and strategy and stamina
0:46:55 and self-control.
0:47:01 And then in 2018, you have a really good year and you win a big tournament.
0:47:01 Congratulations.
0:47:02 Thank you.
0:47:04 Which is super fun to read about.
0:47:07 And then the next year, you have a less good year.
0:47:11 Let me just ask you to summarize on balance, especially the financial part.
0:47:17 We are told that you took in over $300,000 in poker earnings, which sounds like a lot of
0:47:17 money.
0:47:23 But what did it cost you to get that $300,000 over what length of time are we talking?
0:47:25 And then what were the expenses?
0:47:28 Because you’re traveling and staying in places.
0:47:31 And I even want to hear about the opportunity costs.
0:47:33 In other words, did you come out ahead or no?
0:47:37 What people don’t realize when they look at tournament earnings, which is what that over
0:47:44 $300,000 is, is that that’s just earnings that does not count buy-ins, that does not count
0:47:46 travel, that does not count all of those expenses.
0:47:48 So this is over my entire time.
0:47:50 This is over three years.
0:47:53 When I first started playing, I was losing money.
0:47:56 And I was writing it off as kind of an expense for the book.
0:47:57 This was experience.
0:47:59 This was the cost of doing it.
0:48:08 And then in 2018, when I won over $200,000, what I really took home was much less because
0:48:10 I’d been traveling all over the world.
0:48:14 I had played a lot of tournaments in which I hadn’t cashed.
0:48:21 So I made much less than that, maybe somewhere like $50,000, which is still very good.
0:48:22 It’s still an up year.
0:48:26 But as you correctly say, there are also opportunity costs.
0:48:28 You know, I wasn’t taking any writing assignments.
0:48:29 I was on leave from the New Yorker.
0:48:35 I think at the end of the day, it was still worth it because it enabled me to gain this experience.
0:48:37 It enabled me to write the book.
0:48:42 And it also gave me very valuable and marketable experience in other ways.
0:48:50 After I’d had my wonderful year, I was invited to Davos to talk about my poker experience at the World Economic Forum.
0:48:52 I’ve never been invited to Davos.
0:48:55 And that door would not have opened to me otherwise.
0:49:03 So this is one of those books that I really, really didn’t want to end because the journey was so satisfying.
0:49:08 And as you’re learning about poker, we’re learning it with you.
0:49:18 But there was one thing that just kept nagging at me, which is how much does poker really relate to real life?
0:49:32 And the more I read, the more I’d come to think the answer was not really that much, that most interactions we have with people, either individually or in groups or society, that it doesn’t really translate so much.
0:49:34 But you were arguing that it really does.
0:49:38 So I was really happy to see that your last chapter addressed this head on.
0:49:39 It’s called The Ludic Fallacy.
0:49:46 So describe for us where this name comes from, Nassim Taleb, and how you wrestle with it.
0:49:52 Because your book is essentially a willful suspension of this idea that you go at head on.
0:50:01 So Nassim Taleb coined this phrase, The Ludic Fallacy, which basically just tears apart the whole premise of my book.
0:50:09 He says that you can’t use games as a metaphor for life because games are neat and life is messy.
0:50:16 And I actually wholeheartedly agree with the fact that life is messy and games are not.
0:50:22 And I think that that actually makes them much more powerful as teaching tools.
0:50:30 We don’t learn well in life because it’s a noisy environment, because you can’t figure out what’s going on, because there are too many variables.
0:50:32 It’s too uncontrollable.
0:50:44 What poker does is remove some of that noise, yes, in an artificial way, but in a way that actually allows you to access your thought process on a much deeper level.
0:50:50 And then you can go to that noisy arena of life and learn to deal with it better.
0:50:54 Yes, poker doesn’t have as terrible outcomes.
0:50:57 You know, even no limit hold’em where you can wager it all.
0:51:01 All you’re going to lose are your chips or however much money is on the table.
0:51:03 In life, you can lose everything.
0:51:14 But poker, in teaching you how to deal with those losses on a game level, it then translates to a life level where all of a sudden you have the skills.
0:51:16 You have the skills of emotional resilience.
0:51:24 You have the skills of self-analysis to be able to get through those moments where life gets you down much more than poker ever could.
0:51:33 Can you give me an example of a good decision you’ve made in real life recently that you feel was directly influenced by your poker training?
0:51:40 I think that I’ve started approaching relationships in a very different way.
0:52:03 One of the things that poker really brings home is the sunk cost fallacy, which is something that plagues us a lot in our decision process, which is basically we look at things that we’ve already invested and rather than say, I can’t change it, that’s the past, I need to move on and make the best decision I can, knowing what I know now, we actually don’t do that.
0:52:07 And instead we say, well, I’ve already spent so much, I might as well keep going.
0:52:09 And this is true financially.
0:52:13 You know, we put good money after bad when it’s clear that an investment wasn’t working.
0:52:15 It’s true personally.
0:52:19 When we say, well, I’ve already spent so much time on this, I might as well finish it.
0:52:28 Instead, we’d be much better suited to say, hey, you know, that was time I can’t get back, but what I can control is the time I spend now.
0:52:31 So why don’t I actually use it in a more productive and a better way?
0:52:36 Poker has actually forced me to confront that head on in my own life.
0:52:49 And over the last few years, I’ve cut so many toxic relationships from my life that I realized had only been a part of my life because I’d invested so much time and energy into them.
0:52:58 You know, friendships that I’ve had for a very, very long time that I realized weren’t friendships that were draining me more than anything else.
0:52:59 All right.
0:53:00 Let me ask you a last question.
0:53:03 So you’ve gone to a lot of trouble in your life.
0:53:04 I mean, I shouldn’t say trouble.
0:53:06 You’ve enjoyed it and done well with it.
0:53:16 But you’ve gotten a PhD in psychology, then you immersed yourself in a very serious study of poker and traveled around the world to play and get better and ultimately become good and win and so on.
0:53:25 For someone who isn’t going to do any of that, how can we get better at basic decision making and self-control?
0:53:35 A lot of the things that I try to distill in my book are the mini lessons that I learned for myself through doing this.
0:53:44 So I hope that actually, I know this is a terrible answer because it sounds like read my book, but I hope that the book can actually serve as a cheat sheet for people.
0:53:46 You know, I did this so that you don’t have to.
0:53:56 All it comes down to ultimately is something that you can, I think, get without reading my book, which is learn to see yourself from an external perspective.
0:54:03 Learn to be more mindful and more in tune with what you’re thinking, what you’re feeling.
0:54:08 That’s what will enable you to spot the errors that you make in your decision process.
0:54:12 Most of the time, we don’t notice it because we don’t pay attention to ourselves.
0:54:18 We don’t notice that we’re making a decision while angry because we don’t stop to assess, oh, I’m angry.
0:54:19 What’s making me angry?
0:54:22 We just don’t have that sort of internal conversation.
0:54:35 And I think getting into the habit where just before you make any decision, before you do anything, you just stop, take a breath, and take a moment to reflect and to check in with yourself and see, what am I thinking?
0:54:37 Why am I thinking that?
0:54:37 What am I feeling?
0:54:39 Why am I feeling that way?
0:54:41 Okay, now let me act.
0:54:42 Now let me respond.
0:54:54 And I think that that’s one of the most powerful tools that we can use without poker, without anything, that will enable us to be better versions of ourselves and make better decisions at the end of the day.
0:54:58 Nothing is all skill.
0:54:59 Ever.
0:55:03 I shy away from absolutes, but this one calls out for my embrace.
0:55:10 Because life is life, luck will always be a factor in anything we might do or undertake.
0:55:21 Skill can open up new vistas, new choices, allow us to see the chance that others less skilled than us, less observant or less keen, may miss.
0:55:28 But should chance go against us, all our skill can do is mitigate the damage.
0:55:30 And the biggest bluff of all?
0:55:32 That skill can ever be enough.
0:55:39 That’s the hope that allows us to move forward in those moments when luck is most stacked against us.
0:55:43 The useful delusion that lets us push on rather than give up.
0:55:45 We don’t know.
0:55:48 We can’t ever know if we’ll manage or not.
0:55:51 But we must convince ourselves that we can.
0:55:55 That in the end, our skill will be enough to carry the day.
0:55:57 Because it has to be.
0:56:07 That was Maria Konnikova, and her book is called The Biggest Bluff, How I Learned to Pay Attention, Master Myself, and Win.
0:56:10 Like I said, this conversation was recorded in 2020.
0:56:13 Konnikova is now working on a book about cheating.
0:56:20 She also publishes a substack called The Leap, and she co-hosts a podcast with Nate Silver called Risky Business.
0:56:22 And she is still playing poker.
0:56:25 Last year, she won her first World Series of Poker racelet.
0:56:29 We will be back very soon with a new episode of Freakonomics Radio.
0:56:31 Until then, take care of yourself.
0:56:33 And if you can, someone else, too.
0:56:38 Freakonomics Radio is produced by Stitcher and Renbud Radio.
0:56:42 This episode was produced by Mary DeDuke and updated by Dalvin Abouaji.
0:56:49 Audio excerpted courtesy Penguin Random House Audio from The Biggest Bluff by Maria Konnikova, narrated by the author.
0:57:02 The Freakonomics Radio network staff also includes Alina Cullman, Augusta Chapman, Eleanor Osborne, Ellen Frankman, Elsa Hernandez, Gabriel Roth, Greg Rippon, Jasmine Klinger, Jeremy Johnston, Morgan Levy, Sarah Lilly, Tao Jacobs, and Zach Lipinski.
0:57:10 You can find our entire archive on any podcast app or at Freakonomics.com, where we publish transcripts and show notes.
0:57:13 Our theme song is Mr. Fortune by the Hitchhikers.
0:57:16 Our composer is Luis Guerra.
0:57:20 This week’s episode includes additional music by Michael Riola and Stephen Ulrich.
0:57:22 As always, thanks for listening.
0:57:32 I’ve already lost followers.
0:57:45 Because I went down the poker road and I am someone who, you know, espouses gambling and I’m ruining children and I’m sinful and I’m the symbol of everything that’s wrong with America.
0:57:48 Presumably you dispute a few of those charges?
0:57:49 I do. I do. Yes.
0:57:57 The Freakonomics Radio network.
0:57:59 The hidden side of everything.
0:58:03 Stitcher.