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Speaker 1: Welcome to How the Money. I'm Joel and I am Matt,

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Speaker 1: and today we're talking future proofing your career and crypto

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Speaker 1: advice for nubes with Kevin Rush. Yeah. So, with topics

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Speaker 1: like cryptocurrency and artificial intelligence, you can expect that we're

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Speaker 1: going to be talking with someone who's extremely well versed

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Speaker 1: with technology, and that is exactly who we have joining

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Speaker 1: us today. Kevin Ruce is an award winning tech columnist

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Speaker 1: for The New York Times UH and the New York

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Speaker 1: Times bestselling author of three books, Future Proof, Young Money,

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Speaker 1: and The Unlikely Disciple. He's the host of the rabbit

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Speaker 1: Hole podcast UH, and he writes and speaks regularly on

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Speaker 1: all topics related to our modern age, including automation and

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Speaker 1: a I, social media, talks about clibersecurity, digital wellness. But

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Speaker 1: today we are going to attempt to narrow his focus

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Speaker 1: to discussing some of the different ways that we can

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Speaker 1: ensure that your career can continue to grow with the

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Speaker 1: rise of a I. UH. In addition to a we're

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Speaker 1: gonna do a little deep dive into cryptocurrencies and how

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Speaker 1: you should be thinking about them today. Kevin, thank you

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Speaker 1: so much for joining us today on how to money.

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Speaker 1: Thank you so much for having me, Kevin. We're glad

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Speaker 1: to have you. And first question we ask anybody who

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Speaker 1: comes on the show is what's your craft beer equivalent?

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Speaker 1: Matt and I we love craft beer. We spend more

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Speaker 1: money than most people think is wise on good beer,

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Speaker 1: while we're also saving and investing intentionally for the future.

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Speaker 1: So yeah, what's that for you? What do you like

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Speaker 1: to sport on? Well? I I thought of two things, um,

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Speaker 1: one of which is is sort of an early Pandemics

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Speaker 1: blurge in one of which is a more recent one.

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Speaker 1: But my, my, my old, my early Pandemics blurge. I

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Speaker 1: bought myself a fancy gaming PC, and like, I am

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Speaker 1: not like a fancy computer guy, Like I'm sort of

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Speaker 1: like a whatever gets the job done guy. But early

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Speaker 1: in the pandemic, I was like, if I'm gonna be

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Speaker 1: working from home, if I'm gonna be you know, playing

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Speaker 1: video games to pass the time while I'm going to

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Speaker 1: be playing games while I'm also working, I've got to

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Speaker 1: get two separate devices exactly. So I got like the stupidest,

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Speaker 1: biggest gaming computer I could find, and it's like one

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Speaker 1: of these ones that has like, you know, rainbow colored

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Speaker 1: like neon lights coming out. It looks like a like

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Speaker 1: a freaking space machine. And it's so dumb, and I

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Speaker 1: love it and I'm obsessed with it, and I tell

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Speaker 1: all my friends, like by the biggest dumbest gaming PC

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Speaker 1: you can. So that was my early pane. My more

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Speaker 1: my more recent one is beach gear. So I am like,

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Speaker 1: I'm not like a like a total like I don't

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Speaker 1: you know, I live in northern California. Sometimes it's cold here,

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Speaker 1: but when I do go to the beach, I've decided that,

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Speaker 1: like I want to be one of those dads who

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Speaker 1: has like three wagons full of gear like trailing behind him.

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Speaker 1: And so I've just and accumulating this like completely absurd,

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Speaker 1: Like my spouse thinks I'm insane, but I've got like

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Speaker 1: a beach hammock. I've got like, you know, all the

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Speaker 1: flotation devices you could want. I spent like forty dollars

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Speaker 1: on like an infant little like floaty thingy for like

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Speaker 1: my kid the other day. Like it's just it's getting

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Speaker 1: out of control, and it's getting to the point where

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Speaker 1: like multiple multiple trips are necessary to carry all the

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Speaker 1: gear every time we go to the beach. So I

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Speaker 1: think I need to stop, but that has been my

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Speaker 1: splur dry also need to hire a beach tell transported exactly. Yeah,

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Speaker 1: well for for listeners, we we talked. We mentioned before

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Speaker 1: we hit record. You are a new father, and so

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Speaker 1: congrats on that. But I think a lot of times, uh,

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Speaker 1: some of these early months, in those early years of

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Speaker 1: having that first kid, you you want to be prepared,

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Speaker 1: you want to have the gear. But I feel like

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Speaker 1: with four kids, I've reached the point to where I'm like, hey,

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Speaker 1: if you can't carry it, I'm not taking exactly. This

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Speaker 1: might be a one kid privilege showing where I'm like,

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Speaker 1: I can just carry all this stuff myself. That's that's true. Uh. Well, actually,

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Speaker 1: you know, as a tech writer, like what are your

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Speaker 1: favorite pieces of technology that you use regularly that maybe

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Speaker 1: most of us probably don't even know about. Whether that's

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Speaker 1: actual hardware, that's kind of what we're talking about with

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Speaker 1: you going to the beach, or even different software or

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Speaker 1: maybe yeah, just some like kind of odd balls, maybe

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Speaker 1: subscriptions or something like that. But yeah, I'm curious if

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Speaker 1: there are as someone who is more versed and more

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Speaker 1: steeped in this arena, are there some pieces of tech

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Speaker 1: that you use that we don't even know about. Well,

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Speaker 1: I I try to try most things, um once just

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Speaker 1: to for for my job. So like I've got all

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Speaker 1: the sleep rings and wearables and household robots and stuff

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Speaker 1: like that. So I've got like a house full of gadgets. Um.

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Speaker 1: The one that's been bringing me joy recently is I

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Speaker 1: just upgraded my room. But I had, like I had

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Speaker 1: one of the earliest room bas probably ten years old

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Speaker 1: at this point, and it just sucked. It wasn't you know,

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Speaker 1: it is making the floor dirtier every time I vacuum

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Speaker 1: and bad rob I got one of the new ones,

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Speaker 1: and I did not appreciate like how much room but

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Speaker 1: technology has approved in the past like five or ten years. Uh.

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Speaker 1: It now like texts me, first of all, has a name,

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Speaker 1: so it's it's its name is Bruce uh. And so

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Speaker 1: Bruce Ruce and uh. And I get these texts when

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Speaker 1: we're like, you know, out out at the store or

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Speaker 1: something like that. They will say Bruce is stuck and

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Speaker 1: needs your help. And I sort of like have a

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Speaker 1: bond with it now that I didn't have before when

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Speaker 1: it didn't have a name and it was just this

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Speaker 1: like non sentient thing that was vacuuming my floors. So

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Speaker 1: that's my that's my my latest gadget. Okay, the newest

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Speaker 1: gadget and newest friend. I like that. Well, you need

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Speaker 1: to have the room, but to sweep up all that

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Speaker 1: all that beach sand. What model do you have? Because

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Speaker 1: this is something that my wife and I've been talking about.

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Speaker 1: Oh god, it's like whatever the newest one is that

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Speaker 1: was recommended on the wire cutter. All right, I'll look

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Speaker 1: it up. I'll link to it because that is something

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Speaker 1: again with four kids, I'm just like, you know, if

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Speaker 1: we can just have a daily sweep on the lower

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Speaker 1: floor just where we're living, where most of the mess

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Speaker 1: is going down, I think that would Yeah, I would. Yeah,

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Speaker 1: I have two. So it's like my room bow wants

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Speaker 1: to commit suicide. It's like it's just it's overworked and underpaid. Uh,

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Speaker 1: let's talk about We want to talk about bitcoin and

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Speaker 1: cryptocurrency in general in UH later on in this podcast.

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Speaker 1: But but I want to talk about artificial intelligence before

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Speaker 1: we dig too much into that, because you know, so

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Speaker 1: many folks are are bullish on how artificial intelligence is

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Speaker 1: going to impact our society. But you've actually called yourself

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Speaker 1: a sub optimist when it comes to the future of

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Speaker 1: artificial intelligence technology and how it's going to impact our

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Speaker 1: collective future as a whole. So I don't know, I'm curious,

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Speaker 1: why why is it that you're kind of down on AI?

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Speaker 1: And yeah, what are your thoughts on kind of where

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Speaker 1: things are headed? Well, I would say sub optimist is

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Speaker 1: the word that I came up with because I wasn't

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Speaker 1: comfortable with sort of pessimism as a default stance toward AI.

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Speaker 1: I think that a lot of people are just like

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Speaker 1: you know, especially in the media in in over the years,

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Speaker 1: have been sort of just pooh pooing AI in general,

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Speaker 1: and I don't that doesn't really resonate with me, Like

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Speaker 1: I don't think that all AI is bad, but I

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Speaker 1: don't think that all AI is necessarily good either, which

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Speaker 1: I think puts me in some something of an awkward

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Speaker 1: middle position. So sub optimism is just the word that

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Speaker 1: I came up with to to sort of try to

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Speaker 1: capture the feeling of like, I'm I'm kind of optimistic

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Speaker 1: about the technology, but I'm less optimistic about how the

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Speaker 1: technology is going to be implemented and deployed. So I

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Speaker 1: think this is important to separate because we see these

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Speaker 1: advances in AI and conversational AI and you know, protein

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Speaker 1: folding and all the things that are coming out of

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Speaker 1: the big tech companies now that are that are on

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Speaker 1: their own pretty cool. And then we see what happens

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Speaker 1: when that technology is deployed in workplaces, in communities, in families,

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Speaker 1: and it's often much less good. Um. And so you know,

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Speaker 1: I'm thinking a lot these days about workplace AI. UM.

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Speaker 1: You know, there's been this idea for for just decades

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Speaker 1: and decades that AI was going to take away the

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Speaker 1: parts of our jobs that we hate it anyway. You know,

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Speaker 1: it was going to do all of our mundane drudge

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Speaker 1: work for us, and you know, take care of all

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Speaker 1: our expense reports and all the all the boring stuff

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Speaker 1: that we do on a daily basis. And so it

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Speaker 1: would leave us with the most fascinating, interesting human parts.

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Speaker 1: And that's a great idea, and I would love if

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Speaker 1: that happened. But what we've actually seen as AI has

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Speaker 1: been deployed in workplaces is that it's often taking away

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Speaker 1: the parts of people's jobs that they enjoy, the parts

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Speaker 1: that are spontaneous, that are human, that involve, you know,

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Speaker 1: communicating with other people. And so I think about something

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Speaker 1: like an Amazon warehouse where you have essentially AI as

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Speaker 1: the manager. Now, I mean the employees that are tracked

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Speaker 1: with inter nuche of their life. They some of them

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Speaker 1: wear bracelets that you know, vibe rate when they're not

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Speaker 1: meeting their packing targets like it's a it's an atmosphere

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Speaker 1: of AI management. And as a result, it is basically

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Speaker 1: turned to the humans. They're into robots. They are punished

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Speaker 1: when they deviate from the tasks that the machine has

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Speaker 1: set out for them. And so I think that's a

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Speaker 1: really dystopian idea, this idea that instead of freeing us

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Speaker 1: from drudgery, AI in the workplace might actually be creating

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Speaker 1: more drudgery, might be turning us into robots rather than

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Speaker 1: freeing us up to become more human. Yeah, not to

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Speaker 1: mention that it doesn't necessarily perfectly optimize the amount of

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Speaker 1: time that we're spending even outside of work, right, because

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Speaker 1: you see what algorithms and technology has done when it

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Speaker 1: comes to what we're being fed with a community, and

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Speaker 1: we fall more into that sort of wally sort of

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Speaker 1: escape and we're we're all setting there not truly living

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Speaker 1: our lives and floating around being fed what we think

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Speaker 1: we want to be fed. But yeah, I mean you

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Speaker 1: also say that artificial intelligence that is coming for white

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Speaker 1: collar jobs as well. It's not necessarily just robots and conveyance,

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Speaker 1: you know, within the warehouses. It's not just entry level jobs.

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Speaker 1: And so what what types of jobs do you see

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Speaker 1: AI coming for, like right now, UM, as well as

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Speaker 1: maybe a little bit further off, maybe in the near future. Well,

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Speaker 1: right now, AI is is sort of a broad term

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Speaker 1: that encompasses lots of different things. There's like, you know,

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Speaker 1: hardware robotics that are being implemented in factories and warehouses,

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Speaker 1: like like the Amazon warehouse that you mentioned. There's also

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Speaker 1: software automation. This there's a whole category of UH software

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Speaker 1: bought called r PA, which stands for Robotic Process automation

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Speaker 1: and is maybe the most boring thing that is actually important,

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Speaker 1: but it is basically the trend within white collar workplaces

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Speaker 1: where middle UH sort of middle office employees, people who

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Speaker 1: do you know, accounts payable and um, you know, HR

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Speaker 1: tasks and expense approval and things like that, those are

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Speaker 1: all being automated away. And there's actually been some fascinating

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Speaker 1: research UM from UH from Stanford and the Brookings Institution

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Speaker 1: that sort of showed that the jobs most likely to

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Speaker 1: be impacted by the sort of current wave of software

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Speaker 1: automation are actually not the sort of blue collar and

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Speaker 1: retail jobs. They are the kind of middle management jobs

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Speaker 1: at some of these high tech, high skill, highly paid,

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Speaker 1: UM white collar workplaces. And so that's a trend that

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Speaker 1: I'm keeping tabs on. I think the most recent thing

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Speaker 1: that I've been tracking is the use of AI for

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Speaker 1: jobs that we would have considered creative, UM, that we

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Speaker 1: would have maybe even predicted were impossible to automate. So

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Speaker 1: I've been playing around with and looking at other people

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Speaker 1: playing around with this UM new AI model called Dolly two.

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Speaker 1: Have you heard either of you heard of this? So

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Speaker 1: this is something that came out from the AI company

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Speaker 1: open Ai UM just this this year. UM. It's it's

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Speaker 1: brand new, and it basically is that if you've seen

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Speaker 1: on Twitter or on social media people posting these like

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Speaker 1: auto generated images that's say, like, you know, I asked

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Speaker 1: an AI to draw me, you know, a picture of

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Speaker 1: six grizzly bears, UM playing tennis on the moon, UM,

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Speaker 1: and it will it will spit out, you know, several

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Speaker 1: different versions of that, um or you can say, you know,

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Speaker 1: I want to see you know, Vladimir Putin writing on

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Speaker 1: a dolphin, and it will generate that and it's actually

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Speaker 1: quite good. Um And so you know, there are now

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Speaker 1: many aies that are doing this kind of thing at

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Speaker 1: a level that I think is pretty on par with

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Speaker 1: the best human illustrators and much much faster and cheaper

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Speaker 1: obviously than hiring someone to to draw you a picture

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Speaker 1: of Vladimir Putin writing on a dolphin. Um. And So

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Speaker 1: I think that's the kind of job illustrator or um,

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Speaker 1: you know, digital artist that we might have assumed was

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Speaker 1: safe from automation that it turns out is not at all.

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Speaker 1: Oh wow, yeah, that is interesting because you're right, those

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Speaker 1: are kind of maybe some of the last jobs, those

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Speaker 1: most creative ones that I would have said I would

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Speaker 1: be last on the chopping block. But the fact that

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Speaker 1: those are potentially right for the picking is is a

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Speaker 1: little scary. And um. So yeah, if it's got digital

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Speaker 1: in front of whatever the tradial job was, so like, oh, artists,

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Speaker 1: Oh yeah, you're you're fine. I'm a digital artist. Oh actually, actually,

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Speaker 1: if you don't paint on physical canvas, like I'm accountant.

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Speaker 1: Oh yeah, I focus on digital account Oh yeah, maybe

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Speaker 1: maybe not. Yeah. Well, so when it comes to fears

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Speaker 1: about technology replacing humans, those have been around for decades, um,

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Speaker 1: if not centuries. Actually okay, well yeah, I talk to

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Speaker 1: us a little about that. And then maybe, like you know,

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Speaker 1: there's always when these transition transitions happen, there's always a

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Speaker 1: cute pain in certain specific sectors for a specific range

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Speaker 1: of time, but always seems like, uh, that disruption leads

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Speaker 1: to new jobs in other areas. And so, yeah, I

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Speaker 1: don't know, are the fears today of AI replacing these jobs?

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Speaker 1: Like when you just said that, that made me like

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Speaker 1: a little bit scared, Like are those fears right or

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Speaker 1: they misplaced? Are we just like, is the disruption that

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Speaker 1: happens going to eventually end up creating more better things

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Speaker 1: and more better work for people? As as as long as

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Speaker 1: we don't call ourselves digital podcasts, I think we're okay, right,

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Speaker 1: exactly exactly? Well, I think so. I think trying to

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Speaker 1: predict which jobs will and won't disappear is like a

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Speaker 1: fool's game. Um. We have you know, centuries of predictions

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Speaker 1: um that have turned out to be accurate in in

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Speaker 1: some ways, and inaccurate in other ways. So I'm you know,

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Speaker 1: if I knew exactly what which jobs are going to disappear,

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Speaker 1: I would I would, you know, be an oracle. But

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Speaker 1: I do think there are a couple of things we

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Speaker 1: can say. One is the research appears to indicate that

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Speaker 1: today's job losses due to automation and UM are different

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Speaker 1: than job losses of past technological Eras in every technological wave,

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Speaker 1: there's new technology that comes in wipes out some of

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Speaker 1: the jobs but creates other jobs. So in the industry revolution,

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Speaker 1: you know, you had factories that and and you know,

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Speaker 1: automated farming equipment that that took away some of the

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Speaker 1: labor that was needed to run a farm or produce

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Speaker 1: textiles or other things like that. So that eliminated some jobs,

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Speaker 1: but it created many many more jobs in these urban

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Speaker 1: factories UM. And so we could just extrapolate from that

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Speaker 1: and say, well, you know, yeah, there will be illustrators

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Speaker 1: and accountants and you know, maybe podcasters and writers who

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Speaker 1: lose their jobs during this wave, but there will be many,

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Speaker 1: many more jobs created. But there's been some interesting research

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Speaker 1: that sort of pokes holes in that narrative. UM. In particular,

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Speaker 1: there was a paper these two economists Asamaglu and Rostrepo

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Speaker 1: a couple of years ago that introduced the concept of

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Speaker 1: what they call so so automation, which is automation that

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Speaker 1: the kind of automation that is just barely better than

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Speaker 1: a human at a task. So it's it's better than

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Speaker 1: a human by you know, a couple percentage points, which

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Speaker 1: is good enough to eliminate that job and to create

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Speaker 1: the economics to incentivize eliminating that job, but he's not

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Speaker 1: good enough to create new categories of jobs. So an

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Speaker 1: example of this might be, for example, like the you know,

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Speaker 1: the automated uh customer service receptionists. I'm sure we've all

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Speaker 1: had the frustrating experience of like calling in to get

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Speaker 1: you know, customer service with something, you get the robot

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Speaker 1: on the other end, and you know, if you're me,

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Speaker 1: at least you're pushing zero, like you like, I want

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Speaker 1: to get to a human. This robot sucks, it's slow,

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Speaker 1: it can't doesn't know what I'm talking about. But it's

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Speaker 1: good enough that the companies that when these things feel

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Speaker 1: justified in doing that and eliminating some of their customer

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Speaker 1: service staff that way. The problem is it's actually not

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Speaker 1: that good. It's not good enough where you could take

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Speaker 1: out the whole call center. You still have to have

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Speaker 1: people there for those press zero sticklers like me, And

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Speaker 1: so you end up with this technology that is not

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Speaker 1: creating great leaps forward in productivity or output or capability.

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Speaker 1: All it's doing is just shaving down the cost of

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Speaker 1: running a call center a little bit, which is the

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Speaker 1: kind of technology they argue in this paper on soci

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Speaker 1: automation that destroys more jobs than it creates. Is this

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Speaker 1: the kind of thing though, where it's happening in such

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Speaker 1: a way that it's not light years ahead of kind

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Speaker 1: of where things currently stand um and the current human

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Speaker 1: based systems, where it buys us some time, it buys

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Speaker 1: time for people to pursue other avenues, to get retrained

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Speaker 1: in another job. Where if the AI was progressing at

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Speaker 1: such a rate where it was like boom, yes, human obsolete, um,

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Speaker 1: it would maybe make for a more rapid, more frightening transition.

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Speaker 1: But maybe it's it's not going to be quite like that. Yeah,

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Speaker 1: it won't be quite like that. I mean, there's this

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Speaker 1: sort of fiction that you know, the way automation takes

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Speaker 1: your job is that you just show up one day

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Speaker 1: and there's like a robot sitting in your seat and

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Speaker 1: it's like, sorry, Joe, you know, your services are no

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Speaker 1: longer necessary because we've got Joe robot here. Uh, and

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Speaker 1: that that doesn't really happen that way, and it basically

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Speaker 1: never did. I think what's what's hard about sort of

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Speaker 1: the buying more time thesis is that I don't think

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Speaker 1: a lot of people are actually aware that their jobs

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Speaker 1: are in the in the slow process of being automated

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Speaker 1: until they're actually fired. Until they're automated. Um. You know,

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Speaker 1: in in previous waves of technology, like in the middle

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Speaker 1: of the twentieth century, there were a lot of factories,

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Speaker 1: for example, car factories or heavy machinery processing plants that

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Speaker 1: were implementing robots. But like everyone knew when the robot

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Speaker 1: was on the job because there was a big freaking

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Speaker 1: machine that got wheeled into the factory one day and

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Speaker 1: it was right there on the assembly line and you

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Speaker 1: could see as a worker which the job it was doing,

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Speaker 1: and so you could kind of assess your own risk. Um,

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Speaker 1: just you would say, oh, that robot looks like it's

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Speaker 1: pretty good at welding. Um, I'm a welder. I better

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Speaker 1: look for something else to do. But now like it's

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Speaker 1: all software. It's mostly software, and so the way that

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Speaker 1: it's being implemented is much less visible. It's like one

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Speaker 1: day a consultant comes in with an I T team

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Speaker 1: and you know, installs three different apps that each do

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Speaker 1: of a person's job. And so those people aren't even

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Speaker 1: necessarily aware that that's happening. It's just all of a sudden,

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Speaker 1: their department shrinks, there's layoffs or you know, jobs go unfilled,

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Speaker 1: and their jobs are eliminated, even if they don't really

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Speaker 1: grasp that that's what's happening. Okay, let's talk about how

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Speaker 1: we can make ourselves less replaceable. In your book future Proof,

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Speaker 1: you talk about like resisting machine drift, You talk about

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Speaker 1: demoting our devices. Can you explain a little bit to

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Speaker 1: listeners what you mean by that? Yeah, So, I think

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Speaker 1: there's been this idea that's been pretty um, you know,

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Speaker 1: sort of popular and wide spread over the past several

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Speaker 1: decades in sort of American society and education that the

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Speaker 1: way to be competitive in this digital future is to

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Speaker 1: kind of turn yourself into a machine. Is to be

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Speaker 1: super productive, to work all the time, to probably major

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Speaker 1: in like a STEM field, um to get really good

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Speaker 1: at programming, to like, you know, basically get on the

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Speaker 1: side of the machine like that that is how we

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Speaker 1: do yourself to them now exactly like like make yourself

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Speaker 1: as close to a machine as humanly possible, and that

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Speaker 1: that's the way to get ahead. And that was sort

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Speaker 1: of my idea when I started writing this book, and

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Speaker 1: I went out to all these AI researchers and technologists

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Speaker 1: and I asked them, like, what can humans do to

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Speaker 1: protect ourselves? What can we do to make sure that

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Speaker 1: we are not becoming obsolete? And what they said was

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Speaker 1: fascinating because it was basically the exact opposite of what

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Speaker 1: I had expected. Instead of saying like go, you know,

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Speaker 1: study engineering, or go to coding boot camp, or you know,

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Speaker 1: work twenty hours a day, like, what they said was

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Speaker 1: that we need to figure out what our advantages are.

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Speaker 1: We need to tack away from the machines because the

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Speaker 1: things that they do, they will do better than us

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Speaker 1: and cheaper than us and faster than us. And so

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Speaker 1: we need to spend our time figuring out what we

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Speaker 1: can do that they can't do and that they're not

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Speaker 1: likely to be able to do for for many, many years,

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Speaker 1: and to focus our area, our our energy on improving

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Speaker 1: our performance in those areas. So in the book I

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Speaker 1: have I talked about three areas in particular where humans

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Speaker 1: have an advantage over even the best AI today. UM.

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Speaker 1: I call them surprise, using, social and scarce UM. And

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Speaker 1: so those are the kinds of labor, those like surprising

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Speaker 1: jobs that are irregular, that don't have a lot of

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Speaker 1: repetition and pattern to them, that involves sort of meeting

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Speaker 1: people's social needs rather than material needs, and that are

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Speaker 1: involved sort of rare, scarce combinations of skills, abilities, and

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Speaker 1: the things that that you know, machines will not be

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Speaker 1: taught to do for a long time. And so those

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Speaker 1: are the areas where I think we need to focus

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Speaker 1: our energy on rather than trying to you know, one

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Speaker 1: up the machines. You've talked about how your accountant is

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Speaker 1: a former comedian, right, and how he kind of sets

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Speaker 1: himself apart in a way that most accountants aren't able to,

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Speaker 1: and how maybe that's gonna prolong his job security. Exactly

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Speaker 1: what I did when I was researching the book was

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Speaker 1: I looked for industries that were heavily uh, you know,

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Speaker 1: affected by automation UM. And I looked at the survivors

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Speaker 1: in those industries. What what have the people who are

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Speaker 1: still working as travel agents to today doing differently that

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Speaker 1: has allowed them to, uh to persist in that field.

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Speaker 1: What are the people who are doing things like tax accounting,

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Speaker 1: which is you know, highly highly automated at this point,

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Speaker 1: it's it's one of the most automated job fields in

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Speaker 1: in the world. What of the people who have built

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Speaker 1: successful accounting businesses done differently? And I looked at first

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Speaker 1: at my own account Who's this amazing guy named Russ

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Speaker 1: Garoffalo who has a firm um called Brass Taxes and

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Speaker 1: he uh he hires former comedians. He is himself a

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Speaker 1: former comedian, and he pays for all his employees to

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Speaker 1: get improv lessons. And at first I was like, what

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Speaker 1: the heck does that have to do with doing taxies?

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Speaker 1: But for that right exactly, But it turns out that

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00:23:44,040 --> 00:23:47,639
Speaker 1: it actually is the difference between him being able to

429
00:23:47,680 --> 00:23:50,159
Speaker 1: be replaced by Turbo tax or H and R Block

430
00:23:50,280 --> 00:23:53,240
Speaker 1: or one of these other firms and not because what

431
00:23:53,359 --> 00:23:57,200
Speaker 1: he's doing is he's creating, he's turn he's turning a

432
00:23:57,560 --> 00:24:00,639
Speaker 1: function into an experience. He has turned the act of

433
00:24:00,720 --> 00:24:04,080
Speaker 1: preparing your taxes from something that is mind numbing and

434
00:24:04,240 --> 00:24:08,360
Speaker 1: boring and dull into something that is entertaining and enjoyable

435
00:24:08,640 --> 00:24:11,960
Speaker 1: and personable. And he's doing that at scale. And so

436
00:24:12,000 --> 00:24:13,959
Speaker 1: I think there's a lesson in there for all of us,

437
00:24:14,000 --> 00:24:16,639
Speaker 1: which is that even in parts of our jobs that

438
00:24:16,720 --> 00:24:20,360
Speaker 1: are that are prone to being automated, what we can

439
00:24:20,400 --> 00:24:24,040
Speaker 1: do as a kind of bulwark against that is to

440
00:24:25,119 --> 00:24:28,280
Speaker 1: radically up the amount of humanity that we are bringing

441
00:24:28,280 --> 00:24:32,639
Speaker 1: to that job. Exactly. Yeah, injecting that humanity that soul.

442
00:24:32,880 --> 00:24:34,840
Speaker 1: I mean basically doing the exact opposite of what you're

443
00:24:34,840 --> 00:24:38,320
Speaker 1: saying earlier right where you've got factory or warehouse workers

444
00:24:38,359 --> 00:24:43,280
Speaker 1: and if you subscribe to the AI management overlord model,

445
00:24:44,119 --> 00:24:45,720
Speaker 1: like yeah, you are going to be treated like a

446
00:24:45,800 --> 00:24:48,120
Speaker 1: robot and eventually maybe you will be able to be replaced.

447
00:24:48,119 --> 00:24:50,560
Speaker 1: But if you can find ways to inject that humanity

448
00:24:50,600 --> 00:24:53,280
Speaker 1: into all things that you do, I think you're always

449
00:24:53,280 --> 00:24:56,080
Speaker 1: going to have a secure career. I think the same

450
00:24:56,119 --> 00:24:58,679
Speaker 1: thing is true with podcasting, right. It's one of those

451
00:24:58,680 --> 00:25:00,960
Speaker 1: things where can you get some of this information that

452
00:25:00,960 --> 00:25:03,320
Speaker 1: you're getting on a podcast you're listening to elsewhere? Yeah,

453
00:25:03,520 --> 00:25:06,919
Speaker 1: in many cases, yes, but how is information delivered is

454
00:25:07,040 --> 00:25:09,520
Speaker 1: a huge part of how people feel about it, how

455
00:25:09,520 --> 00:25:11,040
Speaker 1: they respond to it, and whether or not they want

456
00:25:11,040 --> 00:25:14,000
Speaker 1: to keep listening. Um and Kevin, we've got more questions

457
00:25:14,000 --> 00:25:15,439
Speaker 1: we're gonna get to with you. We want to kind

458
00:25:15,440 --> 00:25:20,000
Speaker 1: of transition into talking about cryptocurrency and maybe how are

459
00:25:20,000 --> 00:25:22,040
Speaker 1: are how the money listeners should think about that. We'll

460
00:25:22,080 --> 00:25:33,600
Speaker 1: get to some questions on that right after this. All right,

461
00:25:33,640 --> 00:25:36,280
Speaker 1: we are back. We're talking with Kevin Ruce. We just

462
00:25:36,320 --> 00:25:39,240
Speaker 1: talked a good bit about artificial intelligence, how we can

463
00:25:39,280 --> 00:25:41,520
Speaker 1: secure our positions in the future. We're gonna talk now

464
00:25:41,600 --> 00:25:45,160
Speaker 1: about cryptocurrency. And Kevin, you know you you wrote an article,

465
00:25:45,480 --> 00:25:47,440
Speaker 1: uh sort of. It wasn't even an article. It is

466
00:25:47,480 --> 00:25:50,560
Speaker 1: almost like this, uh like a guy idea. Yeah, yeah,

467
00:25:50,640 --> 00:25:52,600
Speaker 1: I mean it was actually called that the the Latecomers

468
00:25:52,640 --> 00:25:55,639
Speaker 1: Guide to Crypto. You're at it in the Times, and

469
00:25:55,760 --> 00:25:58,040
Speaker 1: you know, like it felt like the Super Bowl, the

470
00:25:58,760 --> 00:26:02,040
Speaker 1: tens of millions of dollars in ad spending by all

471
00:26:02,080 --> 00:26:04,760
Speaker 1: the different crypto companies. I felt like that that was

472
00:26:04,800 --> 00:26:08,119
Speaker 1: the top of the crypto madness that we experienced earlier

473
00:26:08,119 --> 00:26:11,200
Speaker 1: this year. Has your opinion about the future of cryptocurrency?

474
00:26:11,320 --> 00:26:13,440
Speaker 1: Has that changed? Since the beginning of the year, and

475
00:26:13,640 --> 00:26:17,439
Speaker 1: since you wrote that piece, it's certainly accelerated some some

476
00:26:17,520 --> 00:26:21,200
Speaker 1: things that I thought might happen. I mean, I am,

477
00:26:21,240 --> 00:26:24,080
Speaker 1: as I say the piece, like, I'm a crypto moderate,

478
00:26:24,200 --> 00:26:29,560
Speaker 1: Like I think there's a lot of scams and speculation,

479
00:26:30,280 --> 00:26:33,879
Speaker 1: and it is not a place where, you know, I

480
00:26:33,920 --> 00:26:37,840
Speaker 1: would recommend people invest like huge portions of their net

481
00:26:37,840 --> 00:26:43,360
Speaker 1: worths um, and I don't I think the most skeptical take,

482
00:26:43,359 --> 00:26:47,000
Speaker 1: which is that it's all uh, it's all crazy, it's

483
00:26:47,080 --> 00:26:53,359
Speaker 1: all gambling and speculation, and there's nothing interesting or novel

484
00:26:53,760 --> 00:26:56,240
Speaker 1: from a technological perspective happening here. I also, I also

485
00:26:56,320 --> 00:26:59,400
Speaker 1: don't buy that perspective totally. So I think I'm I'm

486
00:26:59,400 --> 00:27:01,159
Speaker 1: sort of in a only middle there. It's it is

487
00:27:01,160 --> 00:27:05,159
Speaker 1: a very polarizing issue. Obviously, we're seeing like just a

488
00:27:05,359 --> 00:27:11,359
Speaker 1: huge collapse in the prices of major cryptocurrencies. Um. We've

489
00:27:11,359 --> 00:27:15,680
Speaker 1: got you know, exchanges that are in trouble, We've got

490
00:27:15,720 --> 00:27:18,280
Speaker 1: some you know, liquidity problems in the system. So it's

491
00:27:18,280 --> 00:27:23,360
Speaker 1: clear that there's a crash happening now. Um and so UM,

492
00:27:23,400 --> 00:27:26,159
Speaker 1: you know, I think that's something that has happened before

493
00:27:26,320 --> 00:27:29,840
Speaker 1: in crypto. Unfortunately, I mean, I'm old enough to remember, like,

494
00:27:30,320 --> 00:27:35,560
Speaker 1: you know, the first bitcoin boom and then bust back

495
00:27:35,560 --> 00:27:38,359
Speaker 1: in and I think I wrote an article at the

496
00:27:38,359 --> 00:27:41,560
Speaker 1: time saying like bitcoin is over, it's never coming back,

497
00:27:41,640 --> 00:27:44,760
Speaker 1: it's dead. And you know, fast forward, Uh, you know,

498
00:27:45,160 --> 00:27:47,479
Speaker 1: I think bitcoin was like a hundred and fifty dollars

499
00:27:47,440 --> 00:27:49,640
Speaker 1: apiece at the time, and now it's even at its

500
00:27:49,880 --> 00:27:53,680
Speaker 1: you know, most crashed state. It's like still in the

501
00:27:53,800 --> 00:27:59,680
Speaker 1: you know, twenty thousand range um per bitcoin. So we've

502
00:27:59,720 --> 00:28:03,560
Speaker 1: had you know, booms and busts in crypto. Um it's

503
00:28:03,640 --> 00:28:06,800
Speaker 1: you know, it's not good when it happens, obviously, but

504
00:28:06,920 --> 00:28:10,360
Speaker 1: I think you know, by now, you know, people know

505
00:28:10,560 --> 00:28:13,320
Speaker 1: or should know that this is an incredibly volatile sector

506
00:28:13,840 --> 00:28:17,000
Speaker 1: um and that you know, if you're if you're uh,

507
00:28:17,280 --> 00:28:20,240
Speaker 1: you know, betting money on on crypto, it should be

508
00:28:20,240 --> 00:28:23,360
Speaker 1: money that you're prepared to lose. Yeah, we we completely

509
00:28:23,359 --> 00:28:25,320
Speaker 1: agree with that take. And yeah, I love you call

510
00:28:25,359 --> 00:28:27,119
Speaker 1: yourself a crypto monitory. That's probably why we wanted to

511
00:28:27,119 --> 00:28:29,520
Speaker 1: have you on because it's really hard to find somebody

512
00:28:29,720 --> 00:28:32,080
Speaker 1: who's kind of somewhere in between on crypto. Like the

513
00:28:32,119 --> 00:28:35,719
Speaker 1: takes are so extreme and it's either it's either rat poison,

514
00:28:35,800 --> 00:28:38,240
Speaker 1: it's a scam, or it's going to save the world

515
00:28:38,360 --> 00:28:40,680
Speaker 1: and all of humanity from every single problem that exists.

516
00:28:40,720 --> 00:28:45,080
Speaker 1: And so like, why do you think opinions on cryptocurrency

517
00:28:45,440 --> 00:28:50,640
Speaker 1: are so divisive? Well, I think that a couple of

518
00:28:50,680 --> 00:28:52,760
Speaker 1: things are happening here. One is I think I think

519
00:28:52,880 --> 00:28:57,000
Speaker 1: crypto is essentially a religion. And I don't mean that

520
00:28:57,040 --> 00:28:58,800
Speaker 1: in a pejorative way, like I used to be a

521
00:28:58,800 --> 00:29:03,520
Speaker 1: religion journalist. You know, I have friends who are quite religious.

522
00:29:03,560 --> 00:29:07,840
Speaker 1: Like it's it's not, um, necessarily a bad thing. I

523
00:29:07,880 --> 00:29:10,360
Speaker 1: think there is this element though, that is present in

524
00:29:10,400 --> 00:29:13,840
Speaker 1: many religions though, where it's like we don't speak ill

525
00:29:13,920 --> 00:29:17,800
Speaker 1: of our people, like we don't don't you know, which

526
00:29:17,840 --> 00:29:19,959
Speaker 1: is not true by the way, in the stock market.

527
00:29:20,160 --> 00:29:22,840
Speaker 1: So in the stock market, um, you know, there are

528
00:29:22,840 --> 00:29:26,200
Speaker 1: short sellers, right, and there are short sellers who have

529
00:29:26,480 --> 00:29:32,320
Speaker 1: a financial incentive to make certain prices go down. Um.

530
00:29:32,360 --> 00:29:35,160
Speaker 1: And that, I would argue is actually a healthy part

531
00:29:35,160 --> 00:29:38,120
Speaker 1: of the system that because you have short sellers, you

532
00:29:38,160 --> 00:29:41,840
Speaker 1: also have people doing research and trying to dig up

533
00:29:41,920 --> 00:29:46,000
Speaker 1: frauds and expose them, and they can make a lot

534
00:29:46,040 --> 00:29:48,920
Speaker 1: of money if something that they feel is overvalued actually

535
00:29:48,960 --> 00:29:52,080
Speaker 1: turns out to be overvalued, and they're injecting some honesty

536
00:29:52,080 --> 00:29:56,240
Speaker 1: into the system. Exactly. It's a kind of incentive for honesty,

537
00:29:56,360 --> 00:30:01,160
Speaker 1: for investigation, for honestly reporting. Um. And So I think

538
00:30:01,160 --> 00:30:04,640
Speaker 1: in the stock market you have this kind of self

539
00:30:05,320 --> 00:30:08,560
Speaker 1: self regulating, self written, not self regulating, because it's obviously

540
00:30:08,560 --> 00:30:11,360
Speaker 1: it is regulated, it needs that too, But you have

541
00:30:11,560 --> 00:30:13,840
Speaker 1: this kind of segment of the market that is not

542
00:30:14,000 --> 00:30:16,560
Speaker 1: just saying like everything's going to go up forever and

543
00:30:16,600 --> 00:30:20,600
Speaker 1: that's great, whereas in crypto, like that is the feeling

544
00:30:20,680 --> 00:30:23,320
Speaker 1: that a lot of people have or had until recently,

545
00:30:23,440 --> 00:30:25,600
Speaker 1: is like we're all in this together. We all make

546
00:30:25,640 --> 00:30:28,360
Speaker 1: money together, we all lose money together, and so no

547
00:30:28,400 --> 00:30:30,840
Speaker 1: one is going to blow the whistle on even the

548
00:30:31,040 --> 00:30:35,600
Speaker 1: incredibly obvious frauds in the system. UM. So I think

549
00:30:35,640 --> 00:30:39,400
Speaker 1: to the extent that that crypto is, uh, you know,

550
00:30:39,520 --> 00:30:42,840
Speaker 1: suffers from being a religion. It's that it you know,

551
00:30:42,880 --> 00:30:47,440
Speaker 1: it doesn't have a good sort of like self cleaning function. Yeah,

552
00:30:47,480 --> 00:30:49,120
Speaker 1: it really. I mean, it's got its own language, it

553
00:30:49,120 --> 00:30:53,320
Speaker 1: has its own priests that the different folks who who

554
00:30:53,440 --> 00:30:56,840
Speaker 1: investors or gamblers look to I I David. Basically the pope,

555
00:30:56,880 --> 00:31:00,760
Speaker 1: I think at this point. Right, So, uh, look, so okay,

556
00:31:00,760 --> 00:31:02,560
Speaker 1: while we're talking about crypto, I mean, let's let's kind

557
00:31:02,560 --> 00:31:04,560
Speaker 1: of talk about some of the fundamentals. Can you talk

558
00:31:04,600 --> 00:31:08,480
Speaker 1: to us about the underlying technology and just kind of what, like,

559
00:31:08,520 --> 00:31:12,440
Speaker 1: what makes blockchain so revolutionary? Can you explain that to folks? Well,

560
00:31:12,920 --> 00:31:16,000
Speaker 1: saying that as revolutionary implies that I think it's like good,

561
00:31:16,040 --> 00:31:18,479
Speaker 1: But I just I'll just do this sort of simple

562
00:31:18,600 --> 00:31:24,160
Speaker 1: description here. Yeah, So blockchains are a type of database

563
00:31:24,520 --> 00:31:27,480
Speaker 1: at their most basic core. You can think about it

564
00:31:27,560 --> 00:31:31,760
Speaker 1: a little bit like a Google spreadsheet, but without Google.

565
00:31:32,160 --> 00:31:35,360
Speaker 1: So instead of having sort of one company or one

566
00:31:35,440 --> 00:31:40,480
Speaker 1: person or one entity um controlling what is put on

567
00:31:40,480 --> 00:31:44,200
Speaker 1: the database, instead you have this thing called a blockchain,

568
00:31:44,200 --> 00:31:47,720
Speaker 1: which is basically a way for a network of computers

569
00:31:47,760 --> 00:31:51,440
Speaker 1: all over the world, owned by different people, to coordinate

570
00:31:51,920 --> 00:31:55,000
Speaker 1: and manage a shared database, and to do so in

571
00:31:55,000 --> 00:31:57,800
Speaker 1: a way that is public um, that is permanent, and

572
00:31:57,840 --> 00:32:02,280
Speaker 1: that is very hard to hack or breach or tamper

573
00:32:02,440 --> 00:32:06,040
Speaker 1: with in some way. So the Google spreadsheet without Google

574
00:32:06,520 --> 00:32:08,600
Speaker 1: is sort of the most basic way to think about

575
00:32:08,680 --> 00:32:13,040
Speaker 1: what a blockchain is. And then the next question is, well,

576
00:32:13,080 --> 00:32:15,800
Speaker 1: what's going on to that database. And it can either

577
00:32:15,880 --> 00:32:20,560
Speaker 1: be cryptocurrency transactions that was the original use of block chains,

578
00:32:21,120 --> 00:32:23,240
Speaker 1: or it can be you know, newer things like n

579
00:32:23,320 --> 00:32:25,800
Speaker 1: f T s or smart contracts. And we can talk

580
00:32:25,840 --> 00:32:28,480
Speaker 1: about all that stuff if you want to. Yeah, So,

581
00:32:28,480 --> 00:32:32,160
Speaker 1: so this technology, what are the best use case scenarios? Like,

582
00:32:32,200 --> 00:32:33,960
Speaker 1: what are the ways in which you see at impacting

583
00:32:34,280 --> 00:32:37,440
Speaker 1: our collective futures? Um, you know, most in the most

584
00:32:37,520 --> 00:32:41,920
Speaker 1: impactful ways. Well, I think the thing that is most

585
00:32:42,320 --> 00:32:46,520
Speaker 1: uh sort of compelling about the existence of blockchains is

586
00:32:46,560 --> 00:32:51,080
Speaker 1: that they don't require trust in a centralized authority. UM.

587
00:32:51,160 --> 00:32:53,160
Speaker 1: For a lot of people, that's a big plus, because

588
00:32:53,160 --> 00:32:55,520
Speaker 1: they don't trust big banks, they don't trust the Federal

589
00:32:55,560 --> 00:32:59,920
Speaker 1: Reserve UM. And some of that is is pure ideology

590
00:33:00,080 --> 00:33:02,080
Speaker 1: in politics, and some of it is you know people

591
00:33:02,160 --> 00:33:07,120
Speaker 1: who um, for example, you know people living under authoritarian

592
00:33:07,160 --> 00:33:11,920
Speaker 1: regimes who maybe don't feel like they can or or

593
00:33:11,960 --> 00:33:16,360
Speaker 1: actually aren't allowed to transact within the normal banking system.

594
00:33:16,760 --> 00:33:18,040
Speaker 1: Or even if you just used to be a Wells

595
00:33:18,080 --> 00:33:21,800
Speaker 1: Fargo customer and you're like, damn, that was rough exactly.

596
00:33:21,840 --> 00:33:23,440
Speaker 1: And so I think that's that's where a lot of

597
00:33:23,640 --> 00:33:28,760
Speaker 1: crypto proponents see hope for blockchains. Is that you could

598
00:33:28,800 --> 00:33:31,600
Speaker 1: be living, you know, as a dissident in a in

599
00:33:31,640 --> 00:33:34,800
Speaker 1: a foreign regime somewhere that you know won't allow you

600
00:33:34,800 --> 00:33:38,400
Speaker 1: to have a bank account, and yet this technology allows

601
00:33:38,440 --> 00:33:42,680
Speaker 1: you to transact, to take part in global markets, to

602
00:33:42,720 --> 00:33:45,720
Speaker 1: send money to your family, whatever it is you're doing, um,

603
00:33:45,720 --> 00:33:48,880
Speaker 1: and that you don't need anyone's permission to do that. So,

604
00:33:49,040 --> 00:33:51,080
Speaker 1: I mean you mentioned smart contracts. Is that one of

605
00:33:51,120 --> 00:33:53,840
Speaker 1: the is that one of the applications where you foresee

606
00:33:53,840 --> 00:33:56,360
Speaker 1: it really taking off? Is it? That? Is it the

607
00:33:56,400 --> 00:34:00,920
Speaker 1: modern banking system that we have. Should we expect to

608
00:34:00,960 --> 00:34:03,000
Speaker 1: see that on its way out? Where? Like I guess yeah?

609
00:34:03,000 --> 00:34:04,560
Speaker 1: What are some of the specific ways that we can

610
00:34:04,600 --> 00:34:07,400
Speaker 1: expect to see blockchain more in the future. Well, I

611
00:34:07,600 --> 00:34:10,600
Speaker 1: I don't think banks are going anywhere, like I just

612
00:34:10,680 --> 00:34:12,439
Speaker 1: I think we'll have banks for a good long time.

613
00:34:12,480 --> 00:34:15,520
Speaker 1: And there are lots of reasons for that, um. One

614
00:34:15,520 --> 00:34:17,560
Speaker 1: of which is banks can actually get your money back

615
00:34:17,600 --> 00:34:21,960
Speaker 1: if it's stolen, Like there's some useful function there. I

616
00:34:22,000 --> 00:34:25,279
Speaker 1: think most people are feel safer for having their money

617
00:34:25,320 --> 00:34:27,359
Speaker 1: in a bank then in a crypto wallets somewhere, as

618
00:34:27,360 --> 00:34:31,680
Speaker 1: they should. Um. So I also don't know that I

619
00:34:32,040 --> 00:34:35,400
Speaker 1: think that crypto is necessarily going to be bigger in

620
00:34:35,440 --> 00:34:38,520
Speaker 1: the future than it is today. I'm interested as a

621
00:34:38,600 --> 00:34:41,759
Speaker 1: journalist in why people are using it today, what kind

622
00:34:41,800 --> 00:34:44,719
Speaker 1: of development is taking place in it, and kind of

623
00:34:44,719 --> 00:34:47,359
Speaker 1: the the social and cultural aspects of it. To I mean,

624
00:34:47,400 --> 00:34:53,000
Speaker 1: it is it is both true that um, that crypto

625
00:34:53,160 --> 00:34:58,200
Speaker 1: is an incredibly sort of volatile asset and that it

626
00:34:58,280 --> 00:35:03,960
Speaker 1: has this amazing power or to um unite and uh,

627
00:35:04,040 --> 00:35:09,799
Speaker 1: you know, inspire and uh and radicalized and like it

628
00:35:09,920 --> 00:35:12,200
Speaker 1: is a it is a cultural force as well as

629
00:35:12,200 --> 00:35:16,600
Speaker 1: a technological one. And so I've been fascinated by by

630
00:35:16,640 --> 00:35:19,720
Speaker 1: that piece of it. To the kind of religious aspects

631
00:35:19,760 --> 00:35:24,200
Speaker 1: of crypto, and and honestly, like what happens when the

632
00:35:24,640 --> 00:35:27,799
Speaker 1: prices collapse as we're seeing right now. I mean, it's

633
00:35:27,840 --> 00:35:31,799
Speaker 1: it's fascinating to watch people move through the stages of

634
00:35:31,880 --> 00:35:36,759
Speaker 1: grief over over this and and more seriously, like I

635
00:35:36,800 --> 00:35:41,840
Speaker 1: think it's it's very interesting, um to you know, compare

636
00:35:41,840 --> 00:35:44,680
Speaker 1: and contrast this with something like, you know, the the

637
00:35:44,719 --> 00:35:48,279
Speaker 1: global financial crisis in two thousand eight, UM, which did

638
00:35:48,520 --> 00:35:52,400
Speaker 1: destroy a lot of people's faith in the banking system,

639
00:35:52,440 --> 00:35:55,399
Speaker 1: in the government, in the Federal Reserve, and sort of

640
00:35:55,680 --> 00:35:59,080
Speaker 1: created the conditions for a huge amount of political change.

641
00:35:59,120 --> 00:36:02,480
Speaker 1: And I think there will be some similarities that come

642
00:36:02,480 --> 00:36:05,080
Speaker 1: out of crypto, but of course it'll it'll look totally different.

643
00:36:05,520 --> 00:36:08,200
Speaker 1: Do you think cryptocurrencies are are ever going to be

644
00:36:08,320 --> 00:36:11,719
Speaker 1: used as like a meaningful medium of exchange? Like do

645
00:36:11,760 --> 00:36:14,120
Speaker 1: you see do you see a future where people buy

646
00:36:14,160 --> 00:36:17,240
Speaker 1: milk and they pay for rent with like different crypto coins,

647
00:36:17,280 --> 00:36:20,040
Speaker 1: because you know, right now, that seems really hard to

648
00:36:20,040 --> 00:36:22,960
Speaker 1: fathom um And and that seemed like maybe one of

649
00:36:23,000 --> 00:36:25,759
Speaker 1: the potential use case scenarios was the fact that like, yeah,

650
00:36:25,760 --> 00:36:29,920
Speaker 1: it's just this alternative to your your your U S

651
00:36:29,960 --> 00:36:33,640
Speaker 1: dollars to um some sort of fiat currency. But with

652
00:36:33,680 --> 00:36:37,080
Speaker 1: how volatile it is, and with the lack of you know,

653
00:36:37,280 --> 00:36:40,480
Speaker 1: not many people accepting cryptocurrency as payment anyway, it seems

654
00:36:40,480 --> 00:36:42,040
Speaker 1: like one of those things that I don't know, seems

655
00:36:42,080 --> 00:36:43,719
Speaker 1: like a pie in this guy might never might never

656
00:36:43,760 --> 00:36:48,600
Speaker 1: happen thing. Yeah, I'm not particularly bullish on the case

657
00:36:48,680 --> 00:36:52,279
Speaker 1: that crypto is going to be like people's daily spending money, uh,

658
00:36:52,560 --> 00:36:55,000
Speaker 1: just because like who is going to use bitcoin to

659
00:36:55,040 --> 00:36:57,719
Speaker 1: buy milk? If the price of bitcoin is you know,

660
00:36:57,760 --> 00:37:00,600
Speaker 1: going up or down in a day, Like, that's just

661
00:37:00,640 --> 00:37:02,640
Speaker 1: not gonna it's just not gonna happen, right when there's

662
00:37:02,640 --> 00:37:05,000
Speaker 1: that volatility exactly and the trend and you know it's

663
00:37:05,040 --> 00:37:07,839
Speaker 1: the transaction fees can be quite high on some of

664
00:37:07,840 --> 00:37:10,600
Speaker 1: this stuff. The you know, the settlement time is not

665
00:37:10,800 --> 00:37:14,120
Speaker 1: instantaneous on a lot of stuff. So in in many ways,

666
00:37:14,239 --> 00:37:16,440
Speaker 1: you know, you're better off paying for milk with cash

667
00:37:16,760 --> 00:37:19,560
Speaker 1: or credit card. UM. I think where it gets interesting

668
00:37:19,920 --> 00:37:24,280
Speaker 1: is in an environment where um, you know, inflation is high,

669
00:37:24,360 --> 00:37:27,560
Speaker 1: as we've seen, so the dollar is losing some of

670
00:37:27,600 --> 00:37:32,520
Speaker 1: its purchasing power, um, you know, every every month, every week, um,

671
00:37:33,280 --> 00:37:36,480
Speaker 1: and where you you know, where you you have a

672
00:37:36,560 --> 00:37:40,440
Speaker 1: little bit more um sort of adoption of crypto. I

673
00:37:40,480 --> 00:37:43,680
Speaker 1: think basically, in order for crypto to become people's daily

674
00:37:43,719 --> 00:37:47,520
Speaker 1: spending money, it would have to become totally abstracted from

675
00:37:47,560 --> 00:37:49,960
Speaker 1: the end user because right now, like trying to make

676
00:37:50,000 --> 00:37:54,080
Speaker 1: a crypto payment is quite onerous unless you're a nerd

677
00:37:54,600 --> 00:37:58,759
Speaker 1: like me, Um, it's not easy. And so I think

678
00:37:58,800 --> 00:38:02,080
Speaker 1: if it's all you know, if there were a version

679
00:38:02,440 --> 00:38:05,120
Speaker 1: of you know, Venmo or something where you could just like,

680
00:38:06,000 --> 00:38:09,319
Speaker 1: you know, instead of sending five dollars for milk, you

681
00:38:09,360 --> 00:38:13,000
Speaker 1: could send five dollars worth the bitcoin or eth and

682
00:38:13,200 --> 00:38:15,680
Speaker 1: it didn't require you to do anything different in the

683
00:38:15,719 --> 00:38:18,640
Speaker 1: actual app um. It was just sort of all taking

684
00:38:18,640 --> 00:38:20,600
Speaker 1: place in the background. Like I think that's basically the

685
00:38:20,640 --> 00:38:23,440
Speaker 1: only scenario I see right now for how crypto becomes

686
00:38:23,440 --> 00:38:26,200
Speaker 1: people's daily spending money. And I actually don't know that

687
00:38:26,239 --> 00:38:29,120
Speaker 1: they're interacting with crypto, right Matt and I just started

688
00:38:29,200 --> 00:38:31,319
Speaker 1: using Google Pay and Apple Pay like last year or so,

689
00:38:31,360 --> 00:38:34,279
Speaker 1: we're you know, we're techno late comers in a lot

690
00:38:34,280 --> 00:38:37,040
Speaker 1: of way. So um, yeah, actually using some sort of

691
00:38:37,040 --> 00:38:40,200
Speaker 1: cryptocurrency to buy something that might be a decade off

692
00:38:40,200 --> 00:38:42,680
Speaker 1: for me just based on my previous track record. Well,

693
00:38:42,840 --> 00:38:44,759
Speaker 1: and I totally agree with what he's saying as well,

694
00:38:44,800 --> 00:38:47,759
Speaker 1: because I mean, who's gonna pay like point zero zero, zero,

695
00:38:47,880 --> 00:38:50,239
Speaker 1: one five whatever, you know, Like it's it's just not

696
00:38:50,280 --> 00:38:53,040
Speaker 1: in terms that we can recognize, and we can we

697
00:38:53,040 --> 00:38:55,000
Speaker 1: can't even get folks to look at price per ounce,

698
00:38:55,120 --> 00:38:58,760
Speaker 1: you know, like pricing the unit pricing at the grocery store,

699
00:38:59,160 --> 00:39:02,440
Speaker 1: let alone, like you know, translating in real time a

700
00:39:02,560 --> 00:39:06,120
Speaker 1: currency from something that's that that feels more esoteric to

701
00:39:06,200 --> 00:39:08,600
Speaker 1: something that feels a little more tangible like dollars um.

702
00:39:08,800 --> 00:39:10,799
Speaker 1: But so like, oh that, no, there's there's actually talk

703
00:39:10,880 --> 00:39:14,600
Speaker 1: about digital dollars uh. And I'm sure the defive folks

704
00:39:14,600 --> 00:39:17,680
Speaker 1: out there don't think of that as much of a solution,

705
00:39:17,719 --> 00:39:19,480
Speaker 1: but like, like, what are you're like, what do you

706
00:39:19,480 --> 00:39:22,880
Speaker 1: think the prospects are for that's obviously you're you're moved

707
00:39:23,440 --> 00:39:26,360
Speaker 1: that decentralization aspect of it when you have a digital

708
00:39:26,360 --> 00:39:28,040
Speaker 1: dollar like that. But yeah, I'm curious to hear your

709
00:39:28,040 --> 00:39:30,640
Speaker 1: thoughts on that. Well. I think, you know, we we

710
00:39:30,719 --> 00:39:34,919
Speaker 1: do have digital dollars of a sort now. I mean,

711
00:39:35,520 --> 00:39:39,880
Speaker 1: you know, most transactions are not cash, um, they're just

712
00:39:40,120 --> 00:39:42,680
Speaker 1: entries inner database. But I think what you're talking about

713
00:39:42,760 --> 00:39:46,920
Speaker 1: is something like a you know, a central bank issued cryptocurrency.

714
00:39:47,440 --> 00:39:50,880
Speaker 1: And I write, yeah, I do think that's a little

715
00:39:50,920 --> 00:39:53,840
Speaker 1: bit of of a mirage, Like I just don't. I

716
00:39:53,880 --> 00:39:56,279
Speaker 1: don't know that we're going to have that in the

717
00:39:56,400 --> 00:39:59,120
Speaker 1: US for quite some time. I mean, our our lawmakers

718
00:39:59,160 --> 00:40:02,720
Speaker 1: are still trying to figure out how Facebook makes money.

719
00:40:03,640 --> 00:40:06,000
Speaker 1: I think it's gonna be a while before there's a

720
00:40:06,080 --> 00:40:11,799
Speaker 1: robust enough understanding of digital payments UM in in our

721
00:40:11,840 --> 00:40:14,279
Speaker 1: government to to come up with something like that. And

722
00:40:14,320 --> 00:40:16,960
Speaker 1: to put it into widespread use. I also think, I mean,

723
00:40:17,000 --> 00:40:21,239
Speaker 1: I've talked to a number of um of privacy activists

724
00:40:21,239 --> 00:40:23,840
Speaker 1: who are concerned that something like a central bank digital

725
00:40:23,880 --> 00:40:27,320
Speaker 1: currency could just be a disaster in terms of allowing

726
00:40:27,320 --> 00:40:32,600
Speaker 1: the government to surveil the movement of money, people's assets, um,

727
00:40:32,640 --> 00:40:36,680
Speaker 1: you know, payments they make. Um. It could it could

728
00:40:36,760 --> 00:40:41,160
Speaker 1: be a really um, it could be a really sort

729
00:40:41,160 --> 00:40:45,520
Speaker 1: of dystopian thing where the government suddenly knows everyone you're

730
00:40:45,520 --> 00:40:47,839
Speaker 1: paying and what you're paying them for, and there's no

731
00:40:47,880 --> 00:40:51,400
Speaker 1: way to sort of conduct transactions that aren't monitors surveiled

732
00:40:51,400 --> 00:40:54,200
Speaker 1: in some sort So I don't think that's I think

733
00:40:54,239 --> 00:40:58,280
Speaker 1: that's that's something that may happen in other countries before

734
00:40:58,280 --> 00:41:00,439
Speaker 1: it happens in the US, But I don't not something

735
00:41:00,440 --> 00:41:03,680
Speaker 1: I'm expecting to happen in the next five or ten years. Here, Okay,

736
00:41:03,920 --> 00:41:06,319
Speaker 1: are we have a few more cush it questions for you,

737
00:41:06,400 --> 00:41:09,640
Speaker 1: Kevin about cryptocurrency and you know some of the problems

738
00:41:09,640 --> 00:41:11,760
Speaker 1: that it's creating, but then also some of your predictions

739
00:41:11,800 --> 00:41:13,440
Speaker 1: for the future. We're gonna We're gonna let you go

740
00:41:13,440 --> 00:41:16,160
Speaker 1: a little oracle, uh in this this next segment, we'll

741
00:41:16,160 --> 00:41:27,120
Speaker 1: get to that right after this. Alright, we're back from

742
00:41:27,120 --> 00:41:29,319
Speaker 1: the break. We are talking with Kevin Roose and you know,

743
00:41:29,360 --> 00:41:32,439
Speaker 1: we talked about cryptocurrencies kind of more as a tool.

744
00:41:32,480 --> 00:41:35,600
Speaker 1: We talked about the blockchain. Let's talk now about crypto

745
00:41:35,719 --> 00:41:39,200
Speaker 1: more as as like an investment. And one of the

746
00:41:39,200 --> 00:41:43,760
Speaker 1: criticisms of cryptocurrencies is that it's often been criticized as

747
00:41:44,360 --> 00:41:46,759
Speaker 1: a Ponzi scheme. Uh, and so it's it's creating some

748
00:41:46,840 --> 00:41:50,120
Speaker 1: serious problems. Not only that it's been criticized for its

749
00:41:50,239 --> 00:41:53,640
Speaker 1: energy consumption, it's been criticized for the scams out there

750
00:41:53,680 --> 00:41:57,319
Speaker 1: as well that built individuals out of their money. Kevin like, like,

751
00:41:57,360 --> 00:41:59,560
Speaker 1: how big of a deal do you think are these

752
00:41:59,600 --> 00:42:03,879
Speaker 1: problems to crypto becoming more widely adopted? Well, I think

753
00:42:03,880 --> 00:42:05,879
Speaker 1: there are a couple things here. One, I think these

754
00:42:05,880 --> 00:42:09,120
Speaker 1: are real problems. I mean, the the environmental problems specifically

755
00:42:09,800 --> 00:42:14,280
Speaker 1: is real. Um and there are you know, crypto proponents

756
00:42:14,400 --> 00:42:18,560
Speaker 1: will say, oh, you know, the miners are shifting to

757
00:42:18,600 --> 00:42:23,080
Speaker 1: renewable energy and ethereum is transitioning to proof of steak

758
00:42:23,200 --> 00:42:25,680
Speaker 1: and so there's not going to be this um. You know,

759
00:42:25,760 --> 00:42:28,440
Speaker 1: the the energy consumption of crypto is going to is

760
00:42:28,480 --> 00:42:31,160
Speaker 1: going to fall very soon, but right now it's it's

761
00:42:31,239 --> 00:42:33,880
Speaker 1: a disaster, and it's you know, it's not something that

762
00:42:33,920 --> 00:42:37,040
Speaker 1: anyone in the in the industry is particularly proud of,

763
00:42:37,120 --> 00:42:39,840
Speaker 1: and they attempt to change the subject every time it

764
00:42:39,880 --> 00:42:42,360
Speaker 1: comes up. So UM, So I think that's that's a

765
00:42:42,400 --> 00:42:46,080
Speaker 1: major issue, and the Ponzi scheme of it all. I mean,

766
00:42:46,120 --> 00:42:48,960
Speaker 1: clearly there are Ponzi schemes and frauds, many of them

767
00:42:49,000 --> 00:42:54,239
Speaker 1: within crypto. UM. I'm less persuaded by the case that

768
00:42:54,320 --> 00:42:58,520
Speaker 1: this is all just one giant Ponzi scheme and that um,

769
00:42:58,560 --> 00:43:01,920
Speaker 1: there is nothing of any value being created, and that

770
00:43:02,040 --> 00:43:05,960
Speaker 1: it's purely, you know, a transfer of money from people

771
00:43:05,960 --> 00:43:10,480
Speaker 1: who are less sophisticated later investors to people who are

772
00:43:10,960 --> 00:43:14,200
Speaker 1: were earlier investors. So that's kind of a non answer,

773
00:43:14,239 --> 00:43:17,520
Speaker 1: but I think it's basically, UM, yes, we should, we

774
00:43:17,520 --> 00:43:22,800
Speaker 1: should absolutely UM be concerned about the number of frauds

775
00:43:22,840 --> 00:43:25,480
Speaker 1: and Ponzi schemes. I wrote about UM a couple of

776
00:43:25,520 --> 00:43:28,840
Speaker 1: months ago. This uh, this new scam that is targeting

777
00:43:28,880 --> 00:43:33,360
Speaker 1: people on dating apps using crypto that's been just totally rampant,

778
00:43:33,719 --> 00:43:37,960
Speaker 1: the romance scams, the romance scams, and always had that

779
00:43:38,000 --> 00:43:43,000
Speaker 1: girlfriend up in Canada. I mean, I also had a

780
00:43:43,040 --> 00:43:47,080
Speaker 1: Canadian girlfriend back in the DAD, but she didn't try

781
00:43:47,120 --> 00:43:52,440
Speaker 1: to convince me to invest in dos coin. So so

782
00:43:52,560 --> 00:43:54,840
Speaker 1: I think I think we absolutely do need to, you know,

783
00:43:55,000 --> 00:43:57,520
Speaker 1: keep monitoring this and his reporters. We need to make

784
00:43:57,560 --> 00:44:01,239
Speaker 1: sure that we're you know, that we're boarding on on

785
00:44:01,719 --> 00:44:04,080
Speaker 1: all of it. So yeah, I think I think to

786
00:44:04,120 --> 00:44:07,600
Speaker 1: answer your question, like big problems. But what's interesting to

787
00:44:07,680 --> 00:44:09,839
Speaker 1: me too is that even with these problems, which are

788
00:44:09,880 --> 00:44:12,200
Speaker 1: not new I'm not breaking any news here, people have

789
00:44:12,280 --> 00:44:17,440
Speaker 1: known about them for years, crypto adoption has risen dramatically.

790
00:44:17,520 --> 00:44:21,480
Speaker 1: So you know, something like thirty percent of millennials in

791
00:44:21,560 --> 00:44:24,879
Speaker 1: the US own crypto or have own crypto, and so

792
00:44:25,239 --> 00:44:28,520
Speaker 1: that's a that's millions and millions of people who have

793
00:44:28,600 --> 00:44:31,759
Speaker 1: been convinced to invest in crypto despite the fact that

794
00:44:31,840 --> 00:44:36,000
Speaker 1: it's still pretty jankee um and not the easiest thing

795
00:44:36,120 --> 00:44:39,440
Speaker 1: to to get your arms around. And so I think

796
00:44:39,480 --> 00:44:43,640
Speaker 1: there's an interesting, um sort of deeper question here about

797
00:44:43,800 --> 00:44:48,879
Speaker 1: the set of economic conditions and political conditions that are

798
00:44:49,560 --> 00:44:53,560
Speaker 1: causing people to sort of take on more risks that

799
00:44:53,600 --> 00:44:57,360
Speaker 1: are making them more interested in things like meme stocks

800
00:44:57,480 --> 00:45:02,480
Speaker 1: or crypto or sports gambling. Um we have in this

801
00:45:02,600 --> 00:45:07,920
Speaker 1: country like a kind of overlapping um boom in speculative

802
00:45:07,920 --> 00:45:14,320
Speaker 1: assets and the decline of real wages and and upward

803
00:45:14,360 --> 00:45:17,320
Speaker 1: economic mobility. So there are a lot of people, including

804
00:45:17,360 --> 00:45:19,960
Speaker 1: some that I've interviewed, who feel like investing in stuff

805
00:45:19,960 --> 00:45:22,640
Speaker 1: like crypto and meme stocks and you know, doing game

806
00:45:22,680 --> 00:45:25,960
Speaker 1: stop you know, day trading is the only way that

807
00:45:26,000 --> 00:45:28,520
Speaker 1: they're ever going to be able to say, afford a house.

808
00:45:29,440 --> 00:45:32,560
Speaker 1: And that to me is super super interesting, not that

809
00:45:32,880 --> 00:45:37,000
Speaker 1: not the crypto investment itself, but the the kind of

810
00:45:37,040 --> 00:45:40,719
Speaker 1: economic climate that gives rise to it. Speaking of the

811
00:45:40,719 --> 00:45:43,359
Speaker 1: economic climate, like as far as the overlap too, I mean,

812
00:45:43,360 --> 00:45:45,800
Speaker 1: what are your thoughts regarding the just the massive amounts

813
00:45:45,840 --> 00:45:48,520
Speaker 1: of money that flooded the system, right, because I mean,

814
00:45:48,640 --> 00:45:51,120
Speaker 1: if we're looking, if we're overlaying graphs, I think we

815
00:45:51,120 --> 00:45:54,840
Speaker 1: could see a dramatic increase, say and Robin hood activity

816
00:45:54,880 --> 00:45:57,960
Speaker 1: in the amount of money that was dumped into main

817
00:45:58,000 --> 00:46:02,440
Speaker 1: stocks and bitcoin. Uh, the just yeah, the massive amounts

818
00:46:02,440 --> 00:46:04,920
Speaker 1: of of stimmy money that we all received. Yeah, I

819
00:46:04,920 --> 00:46:09,919
Speaker 1: think there's there's gonna be some interesting research I'm sure

820
00:46:10,440 --> 00:46:14,480
Speaker 1: coming out of economics departments about what happened to speculative

821
00:46:14,480 --> 00:46:19,240
Speaker 1: trading activity as the stimulus checks went out. I suspect that, honestly,

822
00:46:19,280 --> 00:46:21,120
Speaker 1: like more of it is just that people were home

823
00:46:21,280 --> 00:46:24,239
Speaker 1: and they were bored, and they maybe had a little

824
00:46:24,280 --> 00:46:26,719
Speaker 1: extra money jingling around in their pocket, but mostly they

825
00:46:26,719 --> 00:46:30,800
Speaker 1: were just like, I'm bored. Yeah, I want. People literally

826
00:46:30,960 --> 00:46:34,080
Speaker 1: did have eight or more extra money in their accounts

827
00:46:34,080 --> 00:46:35,719
Speaker 1: because of that, and they weren't able to spend some

828
00:46:35,760 --> 00:46:37,359
Speaker 1: of the money that they would normally spend it other

829
00:46:37,400 --> 00:46:39,839
Speaker 1: places coming in. So I'm I think for people who

830
00:46:39,920 --> 00:46:42,120
Speaker 1: did fact keep their jobs, there's a bunch of things

831
00:46:42,160 --> 00:46:45,680
Speaker 1: going on, and you're seeing people just creating just insane

832
00:46:45,680 --> 00:46:49,720
Speaker 1: returns um really quickly overnight, and that gets people interested,

833
00:46:49,840 --> 00:46:52,480
Speaker 1: right like, and so some people were You saw the

834
00:46:52,480 --> 00:46:54,840
Speaker 1: headlines that people making millions of dollars in the spans

835
00:46:54,960 --> 00:46:57,640
Speaker 1: span of like you know, a month or two um,

836
00:46:57,719 --> 00:46:59,840
Speaker 1: and and so I guess on the flip side of that,

837
00:47:00,360 --> 00:47:03,000
Speaker 1: aren't there a lot of people, especially now late commerce

838
00:47:03,040 --> 00:47:05,319
Speaker 1: to crypto, who are just kind of left holding the bag. Yeah,

839
00:47:05,360 --> 00:47:07,680
Speaker 1: we've got these millions of investors, but aren't almost all

840
00:47:07,680 --> 00:47:09,840
Speaker 1: of them upside down at this point in their investment?

841
00:47:10,120 --> 00:47:11,600
Speaker 1: Like I mean, that's the Ponzi side of it. That

842
00:47:11,680 --> 00:47:13,680
Speaker 1: feels a little I don't think it's intentional, but it's

843
00:47:14,080 --> 00:47:15,960
Speaker 1: it's it's happening, it seems like to to most of

844
00:47:15,960 --> 00:47:18,440
Speaker 1: the folks who who are participating. Yeah, I think that's

845
00:47:18,760 --> 00:47:21,560
Speaker 1: I think that's correct. I think there are you know,

846
00:47:22,400 --> 00:47:24,879
Speaker 1: a lot of people who are losing money, especially people

847
00:47:24,880 --> 00:47:28,239
Speaker 1: who came in late. Um. You know, crypto proponents would

848
00:47:28,239 --> 00:47:31,040
Speaker 1: tell you, look, you know everything's down right now, and

849
00:47:31,160 --> 00:47:34,240
Speaker 1: you have you know, Netflix is down what or something.

850
00:47:34,400 --> 00:47:36,080
Speaker 1: You know, a lot of the big tech companies are down,

851
00:47:37,560 --> 00:47:41,279
Speaker 1: off their off their all time highs. So it's not

852
00:47:41,640 --> 00:47:45,640
Speaker 1: just crypto that is that is taking a nose dive

853
00:47:45,760 --> 00:47:49,000
Speaker 1: right now. But I do think, you know, in general,

854
00:47:49,320 --> 00:47:50,640
Speaker 1: I mean, I think that the one thing that is

855
00:47:50,640 --> 00:47:53,040
Speaker 1: a little bit different in crypto is that there is

856
00:47:53,080 --> 00:47:59,959
Speaker 1: this kind of religious, uh mentality around holding. So there's

857
00:48:00,080 --> 00:48:02,239
Speaker 1: this you know, hoddle or whatever you want to call it,

858
00:48:02,320 --> 00:48:05,960
Speaker 1: like in crypto, like it is considered the trail of

859
00:48:06,040 --> 00:48:09,839
Speaker 1: sorts to sell. I mean, there there's some statistics out

860
00:48:09,840 --> 00:48:12,239
Speaker 1: there about like how many of the people who invested

861
00:48:12,480 --> 00:48:16,680
Speaker 1: in bitcoin or something before I don't know, twenty fifteen

862
00:48:16,760 --> 00:48:20,400
Speaker 1: or so have never sold and it's it's a pretty

863
00:48:20,560 --> 00:48:24,759
Speaker 1: high percentage and um. And so I think there are

864
00:48:24,800 --> 00:48:26,719
Speaker 1: a lot of people who got in early and are

865
00:48:26,760 --> 00:48:29,480
Speaker 1: just you know, holding on for your life. And so

866
00:48:29,520 --> 00:48:32,560
Speaker 1: it's not purely that all the early investors sort of

867
00:48:32,640 --> 00:48:36,880
Speaker 1: dumped their bags on the on the schmucks who came later, um,

868
00:48:36,920 --> 00:48:39,279
Speaker 1: but that that certainly has happened. I think in a

869
00:48:39,320 --> 00:48:43,960
Speaker 1: lot of ways, we're returning to pre pandemic pre pandemic times,

870
00:48:43,960 --> 00:48:46,359
Speaker 1: whether it's the amount of cash that Americans have saved

871
00:48:46,400 --> 00:48:48,600
Speaker 1: up in their savings account, whether it comes down to

872
00:48:48,920 --> 00:48:51,160
Speaker 1: the amount of revolving credit we have. I mean, I

873
00:48:51,200 --> 00:48:54,000
Speaker 1: think there's there's a lot that we're seeing quickly return.

874
00:48:54,600 --> 00:48:56,400
Speaker 1: But let's Kevin, we we do want to kind of

875
00:48:56,440 --> 00:48:59,960
Speaker 1: get your predictions for the future. What do you think

876
00:49:00,000 --> 00:49:01,920
Speaker 1: the space is gonna look like in the future, like

877
00:49:01,960 --> 00:49:03,920
Speaker 1: five years from now. Do you think that we're gonna

878
00:49:04,400 --> 00:49:07,720
Speaker 1: still be talking about bitcoin as much as we are today?

879
00:49:08,000 --> 00:49:09,719
Speaker 1: Do you think the term blockchain like is this that

880
00:49:09,760 --> 00:49:12,160
Speaker 1: going to sort of be like a daily necessity for

881
00:49:12,160 --> 00:49:15,160
Speaker 1: for most Americans at that point? Uh? No, I don't

882
00:49:15,160 --> 00:49:18,759
Speaker 1: think that we will all be buying groceries in bitcoin

883
00:49:19,040 --> 00:49:22,600
Speaker 1: and putting our assets on the blockchain. UM. I do

884
00:49:22,719 --> 00:49:26,960
Speaker 1: think that especially in UM, in the financial industry, there

885
00:49:27,000 --> 00:49:30,560
Speaker 1: will be um, you know, continued adoption of this stuff

886
00:49:30,560 --> 00:49:33,000
Speaker 1: in the places where it makes sense. And you know,

887
00:49:33,040 --> 00:49:36,360
Speaker 1: I don't know that will be watching people spend you know,

888
00:49:36,440 --> 00:49:40,480
Speaker 1: hundreds of thousands of dollars on pictures of monkeys. UM.

889
00:49:40,800 --> 00:49:46,279
Speaker 1: But I do, like you know, digital illustrations. I'm not

890
00:49:46,320 --> 00:49:48,080
Speaker 1: the right person ask on this because I have again

891
00:49:48,120 --> 00:49:50,200
Speaker 1: I have been burned before on this. Like I wrote

892
00:49:50,200 --> 00:49:54,319
Speaker 1: in like bitcoin is doomed, It's all going away. We're

893
00:49:54,360 --> 00:49:56,320
Speaker 1: never going to hear about it again. It's going to

894
00:49:56,440 --> 00:49:59,360
Speaker 1: be like, you know, consigned to the dustbin of history.

895
00:49:59,800 --> 00:50:02,520
Speaker 1: And I was like wrong about that, Like it just

896
00:50:02,760 --> 00:50:06,920
Speaker 1: it kept going. And so I'm I'm a little hesitant

897
00:50:06,920 --> 00:50:09,600
Speaker 1: to say like this is all gonna stop mattering. I

898
00:50:09,640 --> 00:50:13,759
Speaker 1: do think there's going to be a lot of projects

899
00:50:13,920 --> 00:50:17,520
Speaker 1: coins and f T collections whatever that just go to

900
00:50:17,640 --> 00:50:20,960
Speaker 1: zero and the people who promoted them just kind of

901
00:50:21,000 --> 00:50:24,240
Speaker 1: slink away and go back to their you know, jobs

902
00:50:24,239 --> 00:50:29,400
Speaker 1: at Goldman Sachs or whatever. But like, I genuinely do

903
00:50:29,560 --> 00:50:34,879
Speaker 1: not feel comfortable saying like this is all going away. Um,

904
00:50:34,920 --> 00:50:38,160
Speaker 1: just because like I have made that mistake before and

905
00:50:38,520 --> 00:50:41,000
Speaker 1: I'm trying to be a little more humble this time around.

906
00:50:42,080 --> 00:50:44,719
Speaker 1: I mean, like you said this multiple times, but it

907
00:50:44,840 --> 00:50:46,759
Speaker 1: is it's not really fun being a moderate. It's it's

908
00:50:46,760 --> 00:50:49,040
Speaker 1: not fun being somebody who's trying to I know, I

909
00:50:49,080 --> 00:50:52,960
Speaker 1: wish the table it's all going to zero and like

910
00:50:53,320 --> 00:50:56,520
Speaker 1: we're going back to you know, we're going back to cash.

911
00:50:56,560 --> 00:51:00,480
Speaker 1: But like I I just genuinely feel like money and

912
00:51:00,600 --> 00:51:02,920
Speaker 1: the way that we think about and use money like

913
00:51:03,040 --> 00:51:06,759
Speaker 1: it has to it has to evolve like that. It

914
00:51:06,840 --> 00:51:11,200
Speaker 1: sounds apprecia, but like, like one moment that was sort

915
00:51:11,200 --> 00:51:16,400
Speaker 1: of epiphany for me was looking into kind of the

916
00:51:16,440 --> 00:51:20,480
Speaker 1: technology that um that powers that are banking system today,

917
00:51:20,560 --> 00:51:22,560
Speaker 1: like the technology that all the big banks used today,

918
00:51:22,880 --> 00:51:24,640
Speaker 1: and it turns out that a lot of it is

919
00:51:24,880 --> 00:51:29,080
Speaker 1: still written in Kobal, which is this programming language that

920
00:51:29,120 --> 00:51:32,520
Speaker 1: was invented in the nineteen sixties, and so a lot

921
00:51:32,560 --> 00:51:35,760
Speaker 1: of the banking infrastructure that we rely on every day

922
00:51:36,040 --> 00:51:41,839
Speaker 1: runs on this like incredibly rickety old technology and that's

923
00:51:41,880 --> 00:51:45,359
Speaker 1: like barely hanging together. It's like bubblegum and you know,

924
00:51:45,480 --> 00:51:51,000
Speaker 1: and scotch tape level technology. And this powers I mean

925
00:51:51,239 --> 00:51:54,120
Speaker 1: billions of dollars in transactions and days running on this

926
00:51:54,400 --> 00:51:59,200
Speaker 1: jankie old uh systems. So I do think that whether

927
00:51:59,280 --> 00:52:02,719
Speaker 1: it's crypto, whether it's fintech, whether it's whatever, like, I

928
00:52:02,760 --> 00:52:06,720
Speaker 1: do think that we are going to see more technology

929
00:52:06,760 --> 00:52:10,480
Speaker 1: improvements in finance rather than fewer. And they think that

930
00:52:10,560 --> 00:52:13,239
Speaker 1: crypto will have some role there. There will be some

931
00:52:13,320 --> 00:52:16,920
Speaker 1: form of decentralization that becomes important, but I think there

932
00:52:16,920 --> 00:52:22,160
Speaker 1: will also just be like much more traditional fintech innovation

933
00:52:22,239 --> 00:52:26,280
Speaker 1: that happens that modernizes the banking system. Awesome, hey, Kevin,

934
00:52:26,320 --> 00:52:28,640
Speaker 1: we really appreciate your insights. Man, Thank you so much

935
00:52:28,680 --> 00:52:30,799
Speaker 1: for joining us on the show today. Where can our

936
00:52:30,800 --> 00:52:33,000
Speaker 1: listeners find out more about you and what you're up to?

937
00:52:33,960 --> 00:52:37,960
Speaker 1: They can at once I'm technically back from parental leave.

938
00:52:38,040 --> 00:52:40,239
Speaker 1: They can read me at the New York Times. I'm

939
00:52:40,280 --> 00:52:43,920
Speaker 1: also on Twitter at Kevin Ruce. Awesome. Well say hey

940
00:52:43,960 --> 00:52:46,760
Speaker 1: to Bruce for us, what do you get off this interview?

941
00:52:46,840 --> 00:52:50,600
Speaker 1: And he's stuck on a cliff now, but I'll help

942
00:52:50,640 --> 00:52:52,439
Speaker 1: him off first, and then I'll say, all right, thanks

943
00:52:52,760 --> 00:52:55,560
Speaker 1: to rescue him. All right, Thanks Kevin. All right, that

944
00:52:55,680 --> 00:52:59,160
Speaker 1: was a very enjoyable, very enlightening conversation we had with Kevin, who,

945
00:52:59,160 --> 00:53:02,000
Speaker 1: by the way, was definitely and not an AI guest. Uh.

946
00:53:02,120 --> 00:53:04,719
Speaker 1: This was a real person who sat down to talk

947
00:53:04,760 --> 00:53:06,960
Speaker 1: to us. This was not a bot. That would be

948
00:53:07,000 --> 00:53:10,279
Speaker 1: awesome if we actually first robot on the show, that

949
00:53:10,400 --> 00:53:13,279
Speaker 1: be incredible. Now he's real life human. Uh yet, So, Joel,

950
00:53:13,280 --> 00:53:14,880
Speaker 1: what is your big takeaway for this one? Okay, so,

951
00:53:14,920 --> 00:53:16,799
Speaker 1: lots of good stuff, lots of interesting stuff. I thought,

952
00:53:16,840 --> 00:53:19,759
Speaker 1: in particular, like future proofing yourself from some of these

953
00:53:19,760 --> 00:53:22,399
Speaker 1: possible changes in society and in the way jobs work,

954
00:53:22,480 --> 00:53:24,680
Speaker 1: jobs functions. It was interesting. But I I love what

955
00:53:24,680 --> 00:53:26,960
Speaker 1: he said about crypto and how he mentioned the religious

956
00:53:27,000 --> 00:53:29,719
Speaker 1: tendencies that it has. And I think it's really hard

957
00:53:29,800 --> 00:53:31,720
Speaker 1: when you're in the middle of some sort of religious

958
00:53:31,719 --> 00:53:34,479
Speaker 1: fervor to actually step outside of it and see that

959
00:53:35,080 --> 00:53:39,000
Speaker 1: there are other emotions and things at work, um in

960
00:53:39,080 --> 00:53:41,520
Speaker 1: maybe that are that are tainting your view of something.

961
00:53:41,880 --> 00:53:44,880
Speaker 1: And so I think it's a good remove yourself from

962
00:53:44,920 --> 00:53:47,319
Speaker 1: the cult, right exactly. I mean it makes me think

963
00:53:47,360 --> 00:53:49,439
Speaker 1: of like truly Emily and we we watched the most

964
00:53:49,440 --> 00:53:54,760
Speaker 1: recent Netflix documentary about fundamentalist Mormon culture in and and

965
00:53:54,920 --> 00:53:58,040
Speaker 1: how Warren Jeff's like he basically brainwashed people into thinking

966
00:53:58,080 --> 00:54:00,120
Speaker 1: a certain way, and when you're in the middle of it,

967
00:54:00,120 --> 00:54:02,160
Speaker 1: it's really really impossible to think that there's some sort

968
00:54:02,160 --> 00:54:05,000
Speaker 1: of other normal way of life. Everyone else on the outside,

969
00:54:05,040 --> 00:54:07,520
Speaker 1: they're the crazy people. And I think there's a similar

970
00:54:07,560 --> 00:54:11,000
Speaker 1: mentality in some parts of cryptoculture. And so I think, UM,

971
00:54:11,040 --> 00:54:13,400
Speaker 1: just for our listeners, we want people to be more skeptical.

972
00:54:13,800 --> 00:54:15,840
Speaker 1: We wan't be able to take a step back and say,

973
00:54:16,040 --> 00:54:17,919
Speaker 1: all right, is there maybe some truth kind of like Kevin,

974
00:54:17,920 --> 00:54:20,560
Speaker 1: be a moderate and think about, uh, maybe maybe some

975
00:54:20,600 --> 00:54:23,080
Speaker 1: ways that it could impact us for good. But when

976
00:54:23,120 --> 00:54:26,120
Speaker 1: it comes to UM, betting your life savings or even

977
00:54:26,120 --> 00:54:31,560
Speaker 1: really putting much of your money your investable assets into cryptocurrencies,

978
00:54:31,719 --> 00:54:33,360
Speaker 1: it's something you need to think long and hard about

979
00:54:33,600 --> 00:54:36,440
Speaker 1: UM and that you need to allocate at most five percent,

980
00:54:36,520 --> 00:54:39,480
Speaker 1: but but probably less for most people. Because I think

981
00:54:39,520 --> 00:54:42,480
Speaker 1: that religious fervor matt it reached fever pitch right earlier

982
00:54:42,480 --> 00:54:44,799
Speaker 1: this year around the Super Bowl, and and since then,

983
00:54:44,840 --> 00:54:46,439
Speaker 1: I think a lot of those people are are maybe

984
00:54:46,520 --> 00:54:49,799
Speaker 1: deconverting from from how they thought about crypto. But yeah,

985
00:54:49,840 --> 00:54:52,000
Speaker 1: what was was your big takeaway? Yeah? No, I thought

986
00:54:52,000 --> 00:54:54,680
Speaker 1: all that was great mind did have to do with

987
00:54:54,840 --> 00:54:57,759
Speaker 1: future proofing, which is the name of Kevin's book, but

988
00:54:57,880 --> 00:55:00,200
Speaker 1: he talks about how we need to step outside, out

989
00:55:00,200 --> 00:55:02,480
Speaker 1: of the system, outside of the paradigm of trying to

990
00:55:03,239 --> 00:55:07,400
Speaker 1: uh perfectly optimize and to become these efficient machines. Essentially.

991
00:55:07,400 --> 00:55:10,400
Speaker 1: I love the example that he gave of the warehouse

992
00:55:10,400 --> 00:55:12,799
Speaker 1: workers and how I hadn't read this, but some of

993
00:55:12,840 --> 00:55:16,000
Speaker 1: the folks who are wearing bracelets that buzz. It makes

994
00:55:16,040 --> 00:55:18,160
Speaker 1: me think of how we do that to ourselves when

995
00:55:18,160 --> 00:55:20,880
Speaker 1: it comes to let's say something positive that we want

996
00:55:20,920 --> 00:55:22,960
Speaker 1: to see in our life, right, whether that's reaching a

997
00:55:22,960 --> 00:55:25,080
Speaker 1: certain number of steps, there might be a little reminder

998
00:55:25,120 --> 00:55:27,360
Speaker 1: and it says, hey, make sure what is it? The

999
00:55:27,600 --> 00:55:29,280
Speaker 1: like on the Apple Watch, it's like get your rings

1000
00:55:29,400 --> 00:55:31,960
Speaker 1: or something like that. Like there's all these different mechanisms.

1001
00:55:32,000 --> 00:55:33,600
Speaker 1: I don't know, I don't know. Abide by those things.

1002
00:55:34,239 --> 00:55:35,880
Speaker 1: We are not a part of that system. But in

1003
00:55:35,920 --> 00:55:39,399
Speaker 1: the same way, if we are trying to out out

1004
00:55:39,440 --> 00:55:42,760
Speaker 1: work essentially the robots, if we're trying to elbow grease

1005
00:55:42,800 --> 00:55:45,960
Speaker 1: our way into the future. Instead, what we need to

1006
00:55:45,960 --> 00:55:49,520
Speaker 1: do is be more human and and focus on the

1007
00:55:49,520 --> 00:55:52,160
Speaker 1: things that make us who we are, because it's a

1008
00:55:52,200 --> 00:55:56,400
Speaker 1: losing battle. Essentially. If we're trying to outwork the robots,

1009
00:55:56,480 --> 00:55:59,719
Speaker 1: out work the machines, outwork the software, there's no way

1010
00:56:00,160 --> 00:56:02,160
Speaker 1: that we're going to be able to do that, especially

1011
00:56:02,200 --> 00:56:06,840
Speaker 1: as I mean, the digital artistry that these artificial that

1012
00:56:06,920 --> 00:56:09,960
Speaker 1: AI is is coming up with sounds incredible, but that

1013
00:56:10,160 --> 00:56:13,600
Speaker 1: is not a game that we want to win at. Instead,

1014
00:56:13,680 --> 00:56:15,839
Speaker 1: we need to focus on the things that allow us

1015
00:56:15,840 --> 00:56:18,760
Speaker 1: to connect with other people, things that make us more social,

1016
00:56:18,800 --> 00:56:21,319
Speaker 1: that make us more human. We should be focusing on

1017
00:56:21,400 --> 00:56:24,680
Speaker 1: those aspects of our jobs, not necessarily becoming a faster,

1018
00:56:24,760 --> 00:56:28,480
Speaker 1: stronger human. Yeah, I agree, Uh yeah, more robot like

1019
00:56:28,600 --> 00:56:30,520
Speaker 1: for sure. We want to avoid that and lean into

1020
00:56:30,600 --> 00:56:33,920
Speaker 1: humanity because that is the way which we're gonna stand apart,

1021
00:56:34,000 --> 00:56:36,960
Speaker 1: stand out what did he say, surprising, social, and scarce

1022
00:56:37,000 --> 00:56:39,560
Speaker 1: like those the elements that you want to develop so

1023
00:56:39,600 --> 00:56:42,359
Speaker 1: that you can succeed in an economy that does rely

1024
00:56:42,480 --> 00:56:45,239
Speaker 1: more and more on artificial intelligence exactly. But let's get

1025
00:56:45,239 --> 00:56:47,600
Speaker 1: back to the beer mat this one fittingly, we drank

1026
00:56:47,600 --> 00:56:51,160
Speaker 1: a beer called Analog Life by Good Word Brewing, and

1027
00:56:51,480 --> 00:56:53,120
Speaker 1: I promise we don't plan it this way, but we

1028
00:56:53,200 --> 00:56:54,719
Speaker 1: see a beer in the phrage, we're like, oh wait, no,

1029
00:56:54,760 --> 00:56:58,160
Speaker 1: actually we're gonna like this one would be. I kind

1030
00:56:58,160 --> 00:56:59,600
Speaker 1: of wanted to mention this one at the beginning, but

1031
00:56:59,640 --> 00:57:02,360
Speaker 1: we forgot. Yeah, I felt like Kevin he would approve

1032
00:57:02,920 --> 00:57:05,279
Speaker 1: of this beer. But yeah, analog life. This is an

1033
00:57:05,280 --> 00:57:10,200
Speaker 1: English style mild from Good Word Brewing here out of Georgia.

1034
00:57:10,239 --> 00:57:12,000
Speaker 1: What your thoughts on this one? But al right, so yeah,

1035
00:57:12,000 --> 00:57:13,440
Speaker 1: I thought it was it was kind of like a

1036
00:57:13,560 --> 00:57:15,440
Speaker 1: light brown ale in a lot of ways, like a

1037
00:57:15,440 --> 00:57:18,760
Speaker 1: really light brown ale. It was like chill, it was relaxed.

1038
00:57:18,800 --> 00:57:20,920
Speaker 1: I thought it was nicely malted, and I thought it

1039
00:57:20,920 --> 00:57:22,720
Speaker 1: was a good change up from the kind of beers

1040
00:57:22,920 --> 00:57:27,360
Speaker 1: that we often drink zero Hop profile zero hops, and

1041
00:57:27,440 --> 00:57:29,920
Speaker 1: so it's kind of it really is more like an

1042
00:57:29,960 --> 00:57:36,840
Speaker 1: older German style beer and or even English, Yeah or English, Yeah, no,

1043
00:57:36,920 --> 00:57:40,080
Speaker 1: this is yeah, it's it's a very traditional old world

1044
00:57:40,120 --> 00:57:43,960
Speaker 1: style beer. And have you ever had Bodington's Yeah, English,

1045
00:57:44,640 --> 00:57:47,640
Speaker 1: I think it's like a blonde English very similar where

1046
00:57:47,720 --> 00:57:50,400
Speaker 1: it feels very drinkable. It's like a it's like the

1047
00:57:50,440 --> 00:57:53,400
Speaker 1: o G session, like like session ales or session beers.

1048
00:57:53,560 --> 00:57:55,280
Speaker 1: They're kind of like the English style of miles of

1049
00:57:55,280 --> 00:57:56,880
Speaker 1: the I p as that we have here over in

1050
00:57:56,880 --> 00:57:59,560
Speaker 1: the States, and so in a similar way, very drinkable,

1051
00:57:59,640 --> 00:58:01,320
Speaker 1: very light. This is like a three point six percent

1052
00:58:01,440 --> 00:58:05,440
Speaker 1: beer that you and I split, but also very enjoyable. Uh.

1053
00:58:05,480 --> 00:58:08,280
Speaker 1: And plus it's just fun to have a beer with

1054
00:58:08,320 --> 00:58:10,840
Speaker 1: this title on an episode like today, we're talking about

1055
00:58:10,840 --> 00:58:13,240
Speaker 1: technolog about future stuff, and you know, an a lot

1056
00:58:13,240 --> 00:58:14,880
Speaker 1: of life. There's a lot of that that ain't bad.

1057
00:58:14,960 --> 00:58:17,120
Speaker 1: It's still pretty good, right, absolutely So, all right, that's

1058
00:58:17,120 --> 00:58:19,640
Speaker 1: gonna do it for this episode. For folks who want, yeah,

1059
00:58:19,760 --> 00:58:22,120
Speaker 1: links to Kevin's books or some of the writing that

1060
00:58:22,160 --> 00:58:24,080
Speaker 1: he does over at the New York Times, Um, you

1061
00:58:24,120 --> 00:58:26,640
Speaker 1: can find those up on our site at how to

1062
00:58:26,680 --> 00:58:28,600
Speaker 1: money dot com. That's right, and we will also make

1063
00:58:28,600 --> 00:58:30,360
Speaker 1: sure to link to that room. But I'm seriously gonna

1064
00:58:30,360 --> 00:58:32,760
Speaker 1: look up the Wirecutter article that he he referenced because

1065
00:58:32,840 --> 00:58:34,480
Speaker 1: Kate and I we have been talking about, all right,

1066
00:58:34,520 --> 00:58:36,439
Speaker 1: what are some ways that we can automate some things

1067
00:58:36,440 --> 00:58:39,000
Speaker 1: in our life because I don't want to have to outsweep,

1068
00:58:39,240 --> 00:58:41,640
Speaker 1: like if it comes down to me or whatever I

1069
00:58:41,720 --> 00:58:45,280
Speaker 1: name my Bruce. Uh, I want to lose to that

1070
00:58:45,360 --> 00:58:47,720
Speaker 1: robot in this game. That's there's a lot of things

1071
00:58:47,760 --> 00:58:50,560
Speaker 1: I don't mind. Robots taking over and cleaning duties is

1072
00:58:50,560 --> 00:58:52,600
Speaker 1: one of them exactly. But yeah, we'll make sure to

1073
00:58:52,680 --> 00:58:55,160
Speaker 1: link to that article. But that is gonna be for

1074
00:58:55,160 --> 00:58:57,720
Speaker 1: this episode, Joel. Until next time, best Friends Out, Best

1075
00:58:57,800 --> 00:59:09,840
Speaker 1: Friends Out,