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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Masters in

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Speaker 1: Business with Barry Ritholts on Bloomberg Radio.

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Speaker 2: I'm Barry Ridholts. You're listening to Masters in Business on

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Speaker 2: Bloomberg Radio. My extra special guest this week is Hillary Allen.

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Speaker 2: She is a professor at the American University Washington College

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Speaker 2: of Law in DC, where she specializes in financial regulation,

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Speaker 2: banking law, securities regulation, and technology law. She published a book,

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Speaker 2: Fintech Dystopia, a Summer Beach Read about how Silicon Valley

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Speaker 2: is ruining things, covering the intersection of finance, technology, law, regulation,

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Speaker 2: and politics. It's a perfect subject for us to talk about.

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Speaker 2: Hillary Allen, Welcome to Bloomberg.

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Speaker 3: Thank you so much for having me.

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Speaker 2: So fascinating conversation, fascinating topic that you write about. Before

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Speaker 2: we jump into that, let's spend a few minutes going

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Speaker 2: over your background. You get a bachelor's in Laws from

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Speaker 2: the University of Sydney in Australia, a Master of Laws

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Speaker 2: in Securities and Financial Regulation law from Georgetown here in

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Speaker 2: the States, and you graduated first in your class there.

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Speaker 2: What was the original career plan? Was it simply I'm

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Speaker 2: going to go be a lawyer. What were you thinking.

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Speaker 4: The original career plan was I'm just going to be

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Speaker 4: a lawyer. And then I loved law school and I

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Speaker 4: practiced for seven years and discovered there wasn't so much

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Speaker 4: law always in the practice of law, and I'm a

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Speaker 4: nerd and I missed it. And so the drive was

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Speaker 4: to go back to Georgetown and get my master's, do

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Speaker 4: some academic writing, and then launch a career as a

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Speaker 4: professor where I could.

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Speaker 3: Really sort of think slowly about the law.

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Speaker 2: And you practiced, you were in London, you were in Sydney,

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Speaker 2: Shaman and Sterling here in New York. Tell us a

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Speaker 2: little bit about the sort of legal work you were

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Speaker 2: doing when you were practicing attorney.

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Speaker 4: So basically there's sort of two broad categories of the

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Speaker 4: work I did. I did transactional work banking transactional, typically

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Speaker 4: acting for banks in leverage buyouts.

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Speaker 3: But the work I.

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Speaker 4: Think I enjoyed more was the regulatory compliance advisory. So

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Speaker 4: there was more law in that, especially when you had

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Speaker 4: new financial laws being handed down in Australia and changes

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Speaker 4: in the US with Dot Frank and sort of trying

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Speaker 4: to figure out how to comply with those new rules.

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Speaker 2: So how do you go from practicing bank transactions and

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Speaker 2: some regulatory law to ultimately working with the Financial Crisis

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Speaker 2: in Greek Commission. Tell us a little bit about your

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Speaker 2: experiences there.

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Speaker 4: So that was a serious of a series of fortunate events.

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Speaker 4: While I was doing my master's at Georgetown, I had

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Speaker 4: a professor who was tapped to be on the staff

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Speaker 4: of the financialis Inquired Commission, and he pulled me into

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Speaker 4: work with them two days a week and we were

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Speaker 4: investigating the causes of the two thousand and eight financial

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Speaker 4: crisis to put together the report that came out, which

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Speaker 4: really was.

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Speaker 2: Sort of a nice thick book that they published.

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Speaker 4: It's a really thick book with a really thick index even,

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Speaker 4: and the idea was to tell the story. And that's

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Speaker 4: really sort of stuck with me throughout my career, the

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Speaker 4: importance of being able to explain complex things and how

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Speaker 4: they knit together to cause things.

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Speaker 2: So working with the FCIC, how did that affect how

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Speaker 2: you looked at regulation in general, but more specifically the

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Speaker 2: government's responds to technology, new financial products, the regulatory world

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Speaker 2: in general.

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Speaker 4: So the gift that I got from working with the

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Speaker 4: Financial Crisis in quir Commission is sort of understanding that

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Speaker 4: there are a lot of things that come together and

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Speaker 4: you need to really look very broadly to understand systemic changes.

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Speaker 4: Another gift that it gave me was I think a

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Speaker 4: healthy skepticism of innovation rhetoric, right, because if you think

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Speaker 4: back to two thousand and eight and what caused it,

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Speaker 4: you know there were all these stories about well, these

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Speaker 4: new financial products, these complex new derivatives, we don't need

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Speaker 4: to regulate them, their innovation, sophisticated parties involved, We don't

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Speaker 4: want to tamp down on innovative potential. And so that

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Speaker 4: that skepticism has been a helpful skill set as I've

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Speaker 4: been navigating the sort of post two thousand and eight

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Speaker 4: financial world where you have the innovation rhetoric from Silicon

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Speaker 4: Valley infiltrating into financial services.

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Speaker 2: You raise a really interesting issue that I have to

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Speaker 2: ask about. So, how much of what we see as

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Speaker 2: regulation is either an adherence to an ideology that sometimes

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Speaker 2: says regulation is good and our guardrails and capitalism and

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Speaker 2: other ideology says regulation is expens and anti innovative and

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Speaker 2: reduces job creation. It seems like, regardless of the facts

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Speaker 2: on the ground, each side has their belief system. How

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Speaker 2: do you contextualize that.

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Speaker 4: Well, I mean, I think I don't think there were

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Speaker 4: too many people in the depths of the two thousand

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Speaker 4: and eight crisis who were saying there's too much regulation.

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Speaker 4: I think it's a function of where you are in

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Speaker 4: a particular time. I think people's memories fade really quickly,

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Speaker 4: and as soon as regulation has solved the problems it

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Speaker 4: was intended to solve, or the crisis that spurred the

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Speaker 4: regulation has dissipated, people quickly forget why that regulation is

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Speaker 4: there in place, and then it becomes much easier to

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Speaker 4: see it as something that is just a hindrance, something

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Speaker 4: that is just expensive, that doesn't have a role to play.

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Speaker 4: But I think what we're actually seeing right at this

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Speaker 4: moment is the erosion of the securities laws that really

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Speaker 4: have stood investors in goodstead since the nineteen thirties. Not

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Speaker 4: to say they're perfect, but the general sort of investor

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Speaker 4: protection regime that the Securities and Exchange Commission is always

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Speaker 4: implemented has really encouraged trust in the US stock market,

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Speaker 4: and it sort of made it the endvy of the world,

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Speaker 4: and people wanted to list here. That's really getting peeled

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Speaker 4: back right now, and so I think, you know, it'll be.

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Speaker 3: Pretty soon a moment.

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Speaker 4: Where we realize why we had all that regulation, and

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Speaker 4: we'll miss it.

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Speaker 2: So heading into the financial crisis, I recall looking at

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Speaker 2: some of what I called radical deregulation prior. And this

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Speaker 2: isn't by no means the sole cause of the financial crisis.

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Speaker 2: Lots of factors led to this. But you had the

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Speaker 2: Commodity's Futures Modernization Act, which allowed what was essentially an

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Speaker 2: insurance product to be issued without any insurance reserves and

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Speaker 2: a risky and then you had the repeal of glass

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Speaker 2: steel that kept depository banks separate from speculative Wall Street banks.

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Speaker 2: It probably didn't cause the crisis, but certainly allowed it

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Speaker 2: to get much bigger at the very least. And yet

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Speaker 2: there didn't seem to be any desire after the crisis. Hey,

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Speaker 2: maybe we should put these things back into place. Maybe

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Speaker 2: we should repeal what was added and restore what was repealed.

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Speaker 2: Nobody wanted. They want to go a totally different direction.

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Speaker 4: Well, I think again, this is a story of political economy,

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Speaker 4: and there are still a lot of people who are

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Speaker 4: mad at the Obama administration for prioritizing health care over

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Speaker 4: financial reform. Because basically they had one shot at doing

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Speaker 4: something big and if they had, and I'm not weighing

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Speaker 4: in to say that this was the right or the

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Speaker 4: wrong move, but if they had gone right out of

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Speaker 4: the gates with financial reform, I think we would have

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Speaker 4: seen more of the bigger structural things that you're talking about. So,

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Speaker 4: you know, in that immediate aftermath of the two thousand

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Speaker 4: and eight crisis, you had Sandy will who had been

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Speaker 4: the head of City Group and had sort of engineered

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Speaker 4: the end.

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Speaker 3: Of the glass Stegel legislation.

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Speaker 4: And from this maybe apocryphal, but apparently he had a

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Speaker 4: deal toy that said shatterer of glass Stegel that he

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Speaker 4: kept on his desk. And again this may be apocryphal,

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Speaker 4: but I heard that he basically sort of had a

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Speaker 4: conversion after two thousand and eight said oh, yeah, probably

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Speaker 4: shouldn't have done that.

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Speaker 2: Well, well, a lot of people did. Alan Greenspin famously said,

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Speaker 2: I incorrectly assumed people's concern over their own reputation would

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Speaker 2: have prevented some of the excesses we've seen. I'm paraphrasing,

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Speaker 2: but that was pretty close to what he said.

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Speaker 4: Yeah, he said, the world sort of didn't work the

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Speaker 4: way I thought it did, and I think, you know,

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Speaker 4: had they gone straight out of the gates with financial reform,

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Speaker 4: you might have seen some of that structural reform. But

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Speaker 4: by the time they got around to it, you know,

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Speaker 4: DoD Frank wesn't passed two twenty ten. You know, then

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Speaker 4: the political economy calculus had shifted. The industry was in

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Speaker 4: more of a position to sort of argue for weaker

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Speaker 4: rules and fewer structural changes.

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Speaker 2: It's amazing how rapidly memories fade and people just quickly, Oh, no,

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Speaker 2: that was then, Now it's new. You've worked inside the

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Speaker 2: global financial system as well as studying it from the outside.

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Speaker 2: How did being part of the FCICE affect how you

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Speaker 2: perceive technology, new financial products, regulation, and deregulation. How did

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Speaker 2: that affect your perspective?

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Speaker 4: You know, I didn't think a ton about technology at

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Speaker 4: that time. That's sort of been a later addition to

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Speaker 4: the work that I do. But the broader themes of

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Speaker 4: financial innovation, regulation, deregulation, you know, I see the value

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Speaker 4: in financial stability regulation in particular. So financial stability regulation

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Speaker 4: are the rules that are supposed to prevent financial crises,

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Speaker 4: and they work often sort of hand in hand with

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Speaker 4: investor protection regulations, but they also aim to do something differently.

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Speaker 4: And part of the challenge when you're trying to prevent

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Speaker 4: a financial crisis is this silo mentality where people just

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Speaker 4: think about their own little piece of the world, and Okay,

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Speaker 4: we can deregulate our little piece and we don't won't

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Speaker 4: think about the flow on consequences and what incentives it'll create,

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Speaker 4: et cetera. And so you know, my real takeaway was

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Speaker 4: always to have the most holistic perspective possible to break

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Speaker 4: down that silo mentality, and later in my career that

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Speaker 4: meant learning about the new technologies that are sort of

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Speaker 4: infiltrating the financial system.

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Speaker 2: So I want to talk about technology and I want

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Speaker 2: to talk about fintech dystopia. But there is a quote

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Speaker 2: from within that that applies directly to what you're describing

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Speaker 2: with stability, which was, it's the economic precarity. Stupid paraphrasing

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Speaker 2: James Carville'll tell us a little bit about the economic precarity.

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Speaker 4: Yeah, So, I think a mistake that we have made

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Speaker 4: collectively in recent years is to say, well, look, the

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Speaker 4: economy's doing well, everything's fine, and that really doesn't mesh

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Speaker 4: with many people's experience of the economy. So it used

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Speaker 4: to be, well, probably not always the case, but closer

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Speaker 4: to the case in the Clinton years, where there was

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Speaker 4: less economic inequality than there is now, that you could

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Speaker 4: sort of say, a rising tide lifts ale boats. But

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Speaker 4: now what we're seeing is over half of Americans live

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Speaker 4: from paycheck to paycheck, even in a good economy, right,

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Speaker 4: And so in that kind of circumstance, the financial systems,

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Speaker 4: not the economy, aren't working for everybody. And so I

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Speaker 4: think when we think about what we're trying to achieve

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Speaker 4: with our financial system, it should be that we are

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Speaker 4: trying to find a solution to this economic precarity. And

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Speaker 4: also that begs the question of whether the financial system

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Speaker 4: and investing is in actually the way to get there.

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Speaker 4: And maybe we need broader public policies to address that

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Speaker 4: economic precarity so that no one or at least not

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Speaker 4: half of the population are just scraping by.

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Speaker 2: So we just passed a new set of laws that

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Speaker 2: include thousand dollars accounts for newborns is not going to

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Speaker 2: solve financial inequality, or these kids, by the time they're thirty,

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Speaker 2: they'll be worth millions.

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Speaker 4: I think you might need to offset against the people

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Speaker 4: losing their health insurance subsidies. I don't think that one

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Speaker 4: thousand dollars is going to go very far right.

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Speaker 2: And what's fascinating is watching just a parade of billionaires

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Speaker 2: come out and no, no, we need to supplement that

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Speaker 2: thousand dollars. So first it was Michael Dell, and then

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Speaker 2: it was Ray Dalio. I don't know who else is

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Speaker 2: going to step forward, but it appears, hey, we're not

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Speaker 2: really paying a whole lot in axes, we might as

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Speaker 2: well throw some money at some babies. That seems to

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Speaker 2: be the philosophy.

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Speaker 4: Yeah, I mean, I don't love philanthropy in that sense.

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Speaker 4: Supplementing democratically sort of elected policies. You know, it gives

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Speaker 4: a lot of sort of discretion and power to people

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Speaker 4: as to how they want to distribute their large ss.

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Speaker 3: And to some degree that's fine.

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Speaker 4: But again, when we have a society where half of

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Speaker 4: the population is barely scraping by, I don't think their

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Speaker 4: liveability should be predicated on the whims of billionaire large s.

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Speaker 2: Fair enough, You talked about technological innovation in your book.

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Speaker 2: You argue that that is financial technology innovation is driven

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Speaker 2: largely by legal design rather than technical brilliance. Explain that

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Speaker 2: a little bit. What is it about fintech that seems

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Speaker 2: to be working the perspective from an attorney rather than

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Speaker 2: an engineer.

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Speaker 4: Yeah, so this was something that, as I said, I

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Speaker 4: came to a little later in my career. I think

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Speaker 4: earlier in my career when I first started looking at fintech,

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Speaker 4: I generally accepted the party line, this technology is revolutionary,

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Speaker 4: this technology is making things more efficient, this technology is

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Speaker 4: fixing things. And then I realized that the people who

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Speaker 4: were saying that had something to sell, and I probably

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Speaker 4: should learn a little more about the technology, because if

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Speaker 4: you want to work on financial regulatory policy now, you

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Speaker 4: need to understand the extent to which the technology actually

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Speaker 4: lives up to what it's claimed it can do.

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Speaker 3: And so sort of my first sort of.

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Speaker 4: Foray into this was I've looked really in detail at blockchain,

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Speaker 4: which is truly, frankly a terrible technology. It's a clunky

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Speaker 4: database and it's not something you would ever choose for

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Speaker 4: any kind of financial market infrastructure, but for the fact

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Speaker 4: that it's been very easy to convince regulators not to

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Speaker 4: regulate it, and so the value add comes from crypto

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Speaker 4: has never been blockchain technology as a technology. It's been

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Speaker 4: whipping up stories about that technology that have justified avoiding regulation.

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Speaker 3: And we see it in other instances as well.

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Speaker 4: You know, there are fintech lending that is replicating some

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Speaker 4: of the predatory payday lending that we've seen before.

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Speaker 2: So buy now, pay later sort of financing or.

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Speaker 4: Well, payday loans have been around a lot longer than that.

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Speaker 4: This is sort of a sort of it's like a

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Speaker 4: four hundred dollar loan that you get to bridge you

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Speaker 4: over till your next payday. And you know, there's been

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Speaker 4: a lot of predation in that market in some states

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Speaker 4: had banned those products essentially.

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Speaker 2: Do you think twenty nine percent interest is not fair?

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Speaker 2: You have a problem with that. We're just trying to

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Speaker 2: make a profit here.

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Speaker 3: Some of these interest rates are three hundred percent?

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Speaker 2: Get out. Yeah, that's insane. And what is New York

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Speaker 2: turned out? It like nineteen something.

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Speaker 3: I don't know about New York. Yeah, but but.

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Speaker 2: But normally, anything you know, mid double digits is thought

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Speaker 2: of as usurious. Three hundred percent is just next level.

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Speaker 4: Yeah, I mean it's not set as an interest rate

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Speaker 4: per se their fees, but once you actually convert that

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Speaker 4: into a paranum that can be in the hundreds of percentages.

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Speaker 4: And so that has always been a problem, and we've

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Speaker 4: had states act and then we've had new fintech lenders saying, well, actually,

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Speaker 4: we're different from payday lenders because we use AI to

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Speaker 4: screen our borrowers and.

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Speaker 3: So you should treat us differently.

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Speaker 4: And yet they're charging interest rates that are equivalent to

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Speaker 4: what payday lenders do. And then you mentioned by now

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Speaker 4: pay later. Again they say, well, we're not even extending loans.

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Speaker 4: This isn't a loan at all, so we shouldn't have

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Speaker 4: to comply with the laws around lending, around disclosure, around

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Speaker 4: that kind of thing.

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Speaker 2: How is that not alone? You're buying a product that

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Speaker 2: you don't have money for someone is paying for that.

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Speaker 2: Isn't that alone?

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Speaker 3: I would say?

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Speaker 2: So okay, but but what what's the counter to This

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Speaker 2: isn't a loan, This is a free layaway essentially?

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Speaker 3: Yeah, no, you know, we don't charge interest.

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Speaker 4: There are late fees if you don't pay, but that's

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Speaker 4: not the same as interest.

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Speaker 2: You know, that's there like we bought a couch no

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Speaker 2: interest for six months, so as long as you pay

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Speaker 2: it off within six months. That sort of thing seems

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Speaker 2: to be interest free.

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Speaker 4: But then when you look at the business model and

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Speaker 4: you see that a significant chunk of the people are

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Speaker 4: incurring these late fees.

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Speaker 2: Well, that's their fault, isn't it. That's human nature. You

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Speaker 2: can't blame us if we take advantage of people procrastinating

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Speaker 2: and not paying off their fees in time.

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Speaker 4: Well, it's not that they're procrastinating, it's that they're choosing

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Speaker 4: between paying rent or paying this off.

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Speaker 2: So this is yeah, medicine exactly.

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Speaker 4: So this is coming back to it's the economic procarity

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Speaker 4: stupid right. If people are in these dire straits, we

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Speaker 4: should not be surprised that fintech firms are trying to

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Speaker 4: capitalize on that and profit from it, which is why

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Speaker 4: I think, you know, what we need are some kind

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Speaker 4: of public safety nets to sort of make and to

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Speaker 4: hire minimum wage and hire social security benefits.

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Speaker 2: Coming up, we continue our conversation with Professor Hillary Allen

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Speaker 2: discussing her new book, Fintech Dystopia, a summer beat read

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Speaker 2: about Silicon Valley and how it's ruining things. I'm Barry Ridults.

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Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm

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Speaker 2: Barry Ridults. You're listening to Masters in Business on Bloomberg Radio.

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Speaker 2: My extra special guest this week is Hillary Allen. She

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Speaker 2: teaches at the American University Washington College of Law in Washington,

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Speaker 2: d C. Where she specializes in regulation of financial and

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Speaker 2: technology laws. So let's talk about the Digital Only book

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Speaker 2: Ironic Right, Fintech Dystopia, where you describe modern financial technology

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Speaker 2: simply as Silicon Valley ruining things. Explain that seems like

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Speaker 2: an extreme example, and give us some examples of how

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Speaker 2: Silicon Valley is ruining things.

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Speaker 4: So, just to be clear, not all modern technology is

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Speaker 4: ruining things. There's a particular business model approach that I

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Speaker 4: think is ruining things, and that is derivative in many

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Speaker 4: ways of the venture capital model in Silicon adventure capital

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Speaker 4: just venture, Okay, yeah, venture capital model in Silicon Valley.

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Speaker 4: So it's sort of got thischene around it that's iconoclastic,

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Speaker 4: and they make bets on these moonshots that'll save all

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Speaker 4: of humanity and YadA, YadA YadA. But in fact it's

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Speaker 4: pretty well established as a play at this point. You know,

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Speaker 4: there's a lot of subsidies that go to venture capital

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Speaker 4: by virtue of their having access to pension funds by

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Speaker 4: virtue of sort of capital gains taxation, and so they've

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Speaker 4: got sort of and especially in low interest rate environments,

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Speaker 4: they attract a lot of money, so they have pretty

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Speaker 4: cheap money available to them. And then they go shopping.

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Speaker 4: And what they go shopping for is not the iconoclastic

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Speaker 4: sort of outlier that we think of, but what we've

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Speaker 4: seen and what the evidence shows is that they tend

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Speaker 4: to go shopping for the same things that their friends

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Speaker 4: are going shopping for, and they go shopping for the

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Speaker 4: businesses that their friends have developed. And so there's this

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Speaker 4: sort of very sort of insular mentality in what they're

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Speaker 4: looking for. And they're also looking for something that they

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Speaker 4: can cash out of very quickly, because you know, the

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Speaker 4: average venture capital fund has a ten year, sometimes twelve,

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Speaker 4: but usually ten year duration.

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Speaker 3: That's really not that much.

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Speaker 4: Time to find something to invest in, have it grow,

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Speaker 4: and then cash out, and so they're not looking for

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Speaker 4: things that are going to take decades to develop. They're

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Speaker 4: looking for things that they can grow quickly and get

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Speaker 4: out of in about five or six years.

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Speaker 2: So give us a few examples. What do you think

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Speaker 2: is the sort of you know, not adding a whole

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Speaker 2: lot of value venture backed businesses.

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Speaker 4: So not intentionally, but it just turned out that way.

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Speaker 4: As I wrote this book, almost every fintech business I

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Speaker 4: looked at had been funded by Andresen Horowitz.

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Speaker 3: They had been sort of the lead. So you know,

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Speaker 3: they they're.

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Speaker 2: The hot VC these days. I've full disclosure, I've interviewed Andresen,

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Speaker 2: I've interviewed Kapor, I've interviewed Horowitz. So I've sat with

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Speaker 2: them and talked about a lot of their businesses. But

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Speaker 2: the past few years they've been very front and center,

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Speaker 2: very active.

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Speaker 4: Yeah, no, and they sort of they have their as

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Speaker 4: a markete name. As you said, they're the hot vcs.

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Speaker 4: Once they say they like something, they can basically attract

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Speaker 4: other venture capital to those businesses. And so they're essentially

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Speaker 4: taste makers.

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Speaker 2: Which which is fascinating you say that, because before that

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Speaker 2: it was Sequoia, Before that it was Kleina Perkins, Like

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Speaker 2: you work your way, there's a hot firm for a decade.

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Speaker 2: The nineties had it, the two thousands out at the

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Speaker 2: twenty ten's had it. They tend not to maintain that

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Speaker 2: position forever. Although to Andres and Horwitz's credit, they've been

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Speaker 2: the it girl for a good good run so far.

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Speaker 4: Yeah, I mean I wouldn't say that that's a good thing,

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Speaker 4: but yeah, so you know, they they basically built the

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Speaker 4: crypto industry. So you know, the narrative around crypto is

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Speaker 4: that is this organic sort of community of cyberpunks and libertarians,

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Speaker 4: but they really built that industry. They were early investors

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Speaker 4: in coinbase, that was their first crypto investment, and then

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Speaker 4: they have plowed a lot of money into the industry

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Speaker 4: and it's sort of their seal of approval has been

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Speaker 4: what's attracted people to it. And you know, part of

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Speaker 4: what Andreeson Horowitz does is it doesn't just invest, it's

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Speaker 4: does aggressive marketing campaigns for the things that they've invested in,

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Speaker 4: aggressive lobbying. So they've really been at the forefront for

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Speaker 4: trying to get the laws changed to accommodate their business models. So, yeah,

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Speaker 4: there's there's crypto, but they've also been at the sort

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Speaker 4: of the forefront of.

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Speaker 3: I always there's one of the do not pace. I

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Speaker 3: think it's a firm that's theirs. I always get get

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Speaker 3: mixed up.

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Speaker 4: They they were very early investors in Robin Hood, the

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Speaker 4: fintech trading stock app.

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Speaker 2: Which originally started out as a stock app and then

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Speaker 2: it became eventually a crypto app, and now it's a

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Speaker 2: been on anything app.

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Speaker 4: Yeah, and again that is a company that, by the

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Speaker 4: time at iPod, had wracked up all kinds of fines

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Speaker 4: from the SEC and FINRA because it was violating laws

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Speaker 4: left right and center.

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Speaker 3: You know, it's.

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Speaker 4: It was one of the first to offer commission for brokerage.

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Speaker 4: But as the chestnut goes, if you're not paying for

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Speaker 4: the product, you are the product. And it makes most

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Speaker 4: of its money from payment for order flow and was

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Speaker 4: not clear with its customers in the early years about

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Speaker 4: that how that was going on, and how that they

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Speaker 4: get paid a lot more for your options trades than

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Speaker 4: your regular stock trades because more profitable, Yeah, more profitable

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Speaker 4: for the citadel securities of this world to take those

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Speaker 4: Yeah huh.

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Speaker 2: Really kind of interesting. And yet at the same time,

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Speaker 2: you have a chapter in your book Silicon Valley Welfare

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Speaker 2: Queen explain. I thought that these are you know, Ain

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Speaker 2: Randian libertarians that don't want to suckle off the teat

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Speaker 2: of big government, and these are people that are builders

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Speaker 2: and self made people. You're arguing not so much.

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Speaker 4: Well, they don't want us suckling on the teat of

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Speaker 4: the state because they might have to fund that with taxes.

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Speaker 3: But they're okay suckling themselves, right, So.

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Speaker 2: Give us a few examples what companies started out as

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Speaker 2: welfare queens.

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Speaker 4: Well, I mean, again, the whole story of tech, the

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Speaker 4: Internet and smartphone boom is very much based on technologies

439
00:25:24,000 --> 00:25:24,680
Speaker 4: developed by.

440
00:25:24,600 --> 00:25:27,400
Speaker 3: The government, DARPA and the whole Internet exactly.

441
00:25:27,600 --> 00:25:30,359
Speaker 4: And you know, and I think if you look at

442
00:25:30,400 --> 00:25:33,080
Speaker 4: the iPhone, a lot of the individual technologies that went

443
00:25:33,119 --> 00:25:33,880
Speaker 4: into that again.

444
00:25:33,800 --> 00:25:36,840
Speaker 2: Came everything with microwaves comes out of NASA, right.

445
00:25:37,240 --> 00:25:41,399
Speaker 4: So you know, first of all, this entirely self made

446
00:25:41,480 --> 00:25:45,760
Speaker 4: story falls apart right there, because, as I mentioned earlier,

447
00:25:46,119 --> 00:25:48,719
Speaker 4: if you've only got six years to turn around to technology,

448
00:25:48,760 --> 00:25:52,800
Speaker 4: you're not really investing in prototypes, in thinking really hard

449
00:25:52,840 --> 00:25:55,600
Speaker 4: about physical hardware and how that works. You're really looking

450
00:25:55,640 --> 00:25:58,439
Speaker 4: for a software thing that you can gin up pretty quickly.

451
00:25:58,880 --> 00:26:02,679
Speaker 4: And so the really low long term investment comes from

452
00:26:02,960 --> 00:26:06,639
Speaker 4: the state and has always done, and then it's commercialized,

453
00:26:07,320 --> 00:26:09,360
Speaker 4: you know, And I think that that sort of has

454
00:26:09,440 --> 00:26:13,080
Speaker 4: worked well, except that you get to the point where

455
00:26:12,520 --> 00:26:16,040
Speaker 4: the venture capitalists who are commercializing are saying, well, we

456
00:26:16,040 --> 00:26:18,160
Speaker 4: shouldn't have to pay any taxes to fund the state

457
00:26:18,280 --> 00:26:21,720
Speaker 4: that develops these technologies. They also benefit, as they said,

458
00:26:21,840 --> 00:26:28,560
Speaker 4: enormously from laws that they lobbied for in the late seventies.

459
00:26:28,640 --> 00:26:33,720
Speaker 4: I believe changes to ARISA, which allowed pension funds to

460
00:26:33,800 --> 00:26:37,520
Speaker 4: venture to invest in venture capital, basically didn't exist before.

461
00:26:37,800 --> 00:26:40,800
Speaker 4: And at that same period they were lobbying for changes

462
00:26:40,840 --> 00:26:41,920
Speaker 4: to the capital gains taxes.

463
00:26:42,040 --> 00:26:45,879
Speaker 2: Well, you have the carried interest loophole exact continues to persist.

464
00:26:47,000 --> 00:26:50,040
Speaker 2: I'm drawing a blank on the author's name. There's a

465
00:26:50,119 --> 00:26:55,680
Speaker 2: book Americana four hundred Years of Technological Innovation that makes

466
00:26:55,720 --> 00:26:59,159
Speaker 2: the argument you're making. Go back to the telegraph funded

467
00:26:59,200 --> 00:27:04,080
Speaker 2: by Congress, go to railroad. Like every major technological innovation

468
00:27:05,080 --> 00:27:09,199
Speaker 2: or most major innovations got seeded with the government, and

469
00:27:09,240 --> 00:27:14,080
Speaker 2: then eventually the private sector takes over. And what has

470
00:27:14,200 --> 00:27:18,439
Speaker 2: changed in recent years is that public private partnership seems

471
00:27:18,480 --> 00:27:19,640
Speaker 2: to have broken.

472
00:27:20,359 --> 00:27:20,600
Speaker 3: Yeah.

473
00:27:20,640 --> 00:27:22,359
Speaker 4: Actually so, the book I really like on this is

474
00:27:22,400 --> 00:27:24,200
Speaker 4: Margaret O'Mara's book, The Code Who Does?

475
00:27:24,400 --> 00:27:28,960
Speaker 3: She does a great history of Silicon Valley, and yeah.

476
00:27:28,800 --> 00:27:35,080
Speaker 4: I think the the understanding that there was a quidber

477
00:27:35,240 --> 00:27:42,200
Speaker 4: quo has sort of fallen away. So always the private

478
00:27:42,200 --> 00:27:46,040
Speaker 4: sector has commercialized this technology. But if we have an

479
00:27:46,119 --> 00:27:50,600
Speaker 4: unwillingness to sort of pay any taxes, if we have

480
00:27:50,640 --> 00:27:55,080
Speaker 4: an unwillingness to invest in government capacity to invest in

481
00:27:55,200 --> 00:27:57,879
Speaker 4: universities where so much of this stuff is developed, you know,

482
00:27:57,960 --> 00:28:02,160
Speaker 4: you take Mark Andresen. He he got his start because

483
00:28:02,200 --> 00:28:05,919
Speaker 4: he was happy or sorry, lucky enough to be a

484
00:28:06,000 --> 00:28:08,080
Speaker 4: student at the University of Illinois at the time where

485
00:28:08,119 --> 00:28:11,160
Speaker 4: they had a special grant to look at the beginnings

486
00:28:11,200 --> 00:28:14,239
Speaker 4: of the internet. He worked on a team there that

487
00:28:14,320 --> 00:28:18,399
Speaker 4: developed a prototype internet browser, and then he went into

488
00:28:18,440 --> 00:28:20,680
Speaker 4: the private sector and they let him build one from

489
00:28:20,680 --> 00:28:22,760
Speaker 4: the private sector, and that was Netscape, and that's how

490
00:28:22,800 --> 00:28:24,879
Speaker 4: he made his fortune. So he was sort of in

491
00:28:24,920 --> 00:28:27,320
Speaker 4: the right place at the right time to take advantage

492
00:28:27,560 --> 00:28:32,120
Speaker 4: of public investment in this kind of thing. And yet

493
00:28:32,200 --> 00:28:34,080
Speaker 4: this is the kind of thing that we're seeing that

494
00:28:34,119 --> 00:28:36,080
Speaker 4: these leading venture capitalists want to shut down.

495
00:28:36,800 --> 00:28:42,800
Speaker 2: Really interesting, since we've been talking about books, you've criticized Abundance,

496
00:28:42,920 --> 00:28:47,000
Speaker 2: which is by Derek Thompson and as recline as the

497
00:28:47,040 --> 00:28:50,560
Speaker 2: whole concept of abundance is sort of a sexy way

498
00:28:50,560 --> 00:28:54,360
Speaker 2: to make excuses for techno solutions to tell us a

499
00:28:54,400 --> 00:28:55,120
Speaker 2: little bit about that.

500
00:28:55,480 --> 00:28:58,320
Speaker 4: Yeah, so this is something I get into a lot

501
00:28:58,320 --> 00:29:00,880
Speaker 4: of conversations with people these days because I think there

502
00:29:00,920 --> 00:29:04,160
Speaker 4: are some elements of the original sort of abundance agenda

503
00:29:04,200 --> 00:29:06,720
Speaker 4: that are very appealing to people in terms of, for example,

504
00:29:06,760 --> 00:29:10,400
Speaker 4: increasing housing capacity. And I do think that that is

505
00:29:10,440 --> 00:29:12,920
Speaker 4: something that needs to happen and has to be done

506
00:29:12,920 --> 00:29:16,760
Speaker 4: in the right way. But if you look at who

507
00:29:16,840 --> 00:29:20,280
Speaker 4: is funding the abundance movement, they have conferences, et cetera.

508
00:29:20,440 --> 00:29:23,520
Speaker 4: It is Andreesan Horowitz and other people from Silicon Valley,

509
00:29:24,080 --> 00:29:30,080
Speaker 4: and it seems to be this attempt to essentially put

510
00:29:30,520 --> 00:29:36,160
Speaker 4: a happier face on the deregulatory project that Silicon Valley

511
00:29:36,200 --> 00:29:38,680
Speaker 4: is looking for, to sort of make it seem kinder, gentler,

512
00:29:38,720 --> 00:29:42,240
Speaker 4: and more progressive, because the abundance movement is sort of

513
00:29:42,280 --> 00:29:45,120
Speaker 4: in a nutshell, is supposed to be, well, we shouldn't

514
00:29:45,120 --> 00:29:48,040
Speaker 4: have artificial scarcity, we should build more of what we

515
00:29:48,120 --> 00:29:50,640
Speaker 4: want to do, that we should take away some of

516
00:29:50,640 --> 00:29:52,960
Speaker 4: the roadblocks that are getting in our own way. And

517
00:29:53,000 --> 00:29:54,480
Speaker 4: when you say it like that, it's sort of hard

518
00:29:54,520 --> 00:29:55,840
Speaker 4: to disagree with, Well.

519
00:29:55,720 --> 00:30:00,760
Speaker 2: That works for housing, you have nimbiism with housing, but

520
00:30:00,880 --> 00:30:03,479
Speaker 2: when you take that away, it also means you're going

521
00:30:03,560 --> 00:30:08,000
Speaker 2: to end up with perhaps high rises or multi family

522
00:30:08,080 --> 00:30:11,080
Speaker 2: units in a suburban area that some people don't want

523
00:30:11,120 --> 00:30:13,880
Speaker 2: in their neighborhood. There's always a series of trade offs

524
00:30:13,880 --> 00:30:16,520
Speaker 2: with people who are already there versus people want to

525
00:30:16,560 --> 00:30:21,400
Speaker 2: get there. What is the specific problem with abundance as

526
00:30:21,440 --> 00:30:26,640
Speaker 2: a philosophy towards building more of what we want as

527
00:30:26,680 --> 00:30:27,920
Speaker 2: a society.

528
00:30:28,200 --> 00:30:30,920
Speaker 4: Because it's who gets to decide what more of what

529
00:30:30,960 --> 00:30:33,320
Speaker 4: we want is. And if you look at who's funding

530
00:30:33,320 --> 00:30:37,040
Speaker 4: the abundance agenda, it is the billionaires and the tech elite.

531
00:30:37,400 --> 00:30:40,280
Speaker 4: And these are people who have really shown that they

532
00:30:40,320 --> 00:30:43,520
Speaker 4: are quite willing to run roughshod over regulations that are

533
00:30:43,520 --> 00:30:46,479
Speaker 4: there to protect the public from harm if that enables

534
00:30:46,480 --> 00:30:50,240
Speaker 4: them to profit. And so I am just skeptical that

535
00:30:50,320 --> 00:30:53,720
Speaker 4: a movement that is funded by these people is really

536
00:30:53,960 --> 00:30:57,000
Speaker 4: going to be prioritizing the kinds of projects that would

537
00:30:57,040 --> 00:31:00,640
Speaker 4: benefit the economically precarious. I think it's more likely that

538
00:31:00,640 --> 00:31:05,640
Speaker 4: there'll be benefiting themselves and will lose protections for people

539
00:31:05,680 --> 00:31:08,040
Speaker 4: with less voice that are currently in place.

540
00:31:08,600 --> 00:31:12,920
Speaker 2: So what sort of overhyped products do you think best

541
00:31:13,240 --> 00:31:18,240
Speaker 2: explain the problems with this approach, like what are these

542
00:31:18,280 --> 00:31:23,240
Speaker 2: companies putting out that either is a result of regulatory

543
00:31:23,280 --> 00:31:26,480
Speaker 2: capture or just don't do what they promise, Because you

544
00:31:26,520 --> 00:31:29,640
Speaker 2: would think that in the world of venture either your

545
00:31:29,680 --> 00:31:34,040
Speaker 2: product finds an audience, it finds a customer base, or

546
00:31:34,080 --> 00:31:36,600
Speaker 2: it doesn't and fails and that goes out of business.

547
00:31:37,240 --> 00:31:39,719
Speaker 4: Yeah, so that's sort of the perverted part of this

548
00:31:39,960 --> 00:31:42,560
Speaker 4: is that that market logic, like you know, survival of

549
00:31:42,560 --> 00:31:47,480
Speaker 4: the fittest, because of all the subsidies that benefit venture capital,

550
00:31:47,840 --> 00:31:49,800
Speaker 4: that doesn't really apply that logic anymore.

551
00:31:49,920 --> 00:31:51,800
Speaker 3: So, you know, give us an example Crypto.

552
00:31:52,560 --> 00:31:57,560
Speaker 4: Crypto should have died many times already, particularly it should

553
00:31:57,600 --> 00:31:59,920
Speaker 4: have died in twenty twenty two when we had the

554
00:32:00,160 --> 00:32:06,400
Speaker 4: crypto winter. At that time, particularly Andresen Horowitz. Crypto had

555
00:32:06,400 --> 00:32:09,600
Speaker 4: this huge war chest of funds that they had raised,

556
00:32:09,880 --> 00:32:12,480
Speaker 4: and they stopped investing in crypto startups at that point

557
00:32:12,520 --> 00:32:15,440
Speaker 4: because you know, everything was maribund. But what they started

558
00:32:15,520 --> 00:32:22,400
Speaker 4: using that money for was lobbying, political spending, and they

559
00:32:22,440 --> 00:32:26,400
Speaker 4: really worked very hard on members of Congress to essentially

560
00:32:26,520 --> 00:32:30,640
Speaker 4: create laws that would allow the crypto industry to keep

561
00:32:30,680 --> 00:32:33,920
Speaker 4: doing what they're doing, which was not allowed under the

562
00:32:34,240 --> 00:32:37,200
Speaker 4: securities laws as they were, so the whole business model

563
00:32:37,200 --> 00:32:41,520
Speaker 4: was regulatory arbitrage. They wanted laws that would sort of

564
00:32:41,520 --> 00:32:47,000
Speaker 4: give a patina of legitimacy and hopefully encourage institutional investment,

565
00:32:47,120 --> 00:32:52,239
Speaker 4: attract more money to the space, but not actually make

566
00:32:52,280 --> 00:32:56,360
Speaker 4: them have to. For example, like coinbase combines the functions

567
00:32:56,360 --> 00:32:59,120
Speaker 4: of a broker dealer and in exchange, that's not allowed

568
00:32:59,160 --> 00:32:59,920
Speaker 4: in securities.

569
00:33:00,040 --> 00:33:01,680
Speaker 3: You can see why. Therese all kinds of conflicts of

570
00:33:01,680 --> 00:33:02,880
Speaker 3: interests that coright.

571
00:33:02,520 --> 00:33:05,440
Speaker 2: Either you're in exchange or a brokerage firm, not both.

572
00:33:05,440 --> 00:33:07,840
Speaker 4: But in crypto you're both, right, And so if you

573
00:33:07,880 --> 00:33:11,760
Speaker 4: applied the securities laws to crypto, they would have to

574
00:33:11,760 --> 00:33:16,160
Speaker 4: disaggregate and basically would probably destroy their business model. So

575
00:33:16,280 --> 00:33:18,200
Speaker 4: what they wanted was a law that said, no, it's

576
00:33:18,240 --> 00:33:20,400
Speaker 4: fine crypto special you do both.

577
00:33:21,160 --> 00:33:23,240
Speaker 3: And so that really.

578
00:33:24,920 --> 00:33:28,960
Speaker 4: An industry that should have failed, is you know, again

579
00:33:29,520 --> 00:33:33,400
Speaker 4: rising being propped up all through this sort of aggressive

580
00:33:33,560 --> 00:33:38,840
Speaker 4: political spending. And I mean I've talked to people in

581
00:33:38,880 --> 00:33:41,480
Speaker 4: Congress off the record who have said that they've only

582
00:33:41,560 --> 00:33:44,280
Speaker 4: voted for these laws because they're afraid that if they

583
00:33:44,640 --> 00:33:49,400
Speaker 4: don't that crypto industries will target them.

584
00:33:49,440 --> 00:33:53,120
Speaker 2: What other products do you think are overhyped and fail

585
00:33:53,600 --> 00:33:55,360
Speaker 2: to satisfy their markets?

586
00:33:55,920 --> 00:33:57,840
Speaker 4: Well, right now, the obvious answer is a lot of

587
00:33:57,840 --> 00:34:02,920
Speaker 4: the AI products, the anything sort of. It's hard when

588
00:34:02,960 --> 00:34:05,080
Speaker 4: you talk about AI because it's such an umbrella term

589
00:34:05,120 --> 00:34:06,320
Speaker 4: for so many different things.

590
00:34:06,440 --> 00:34:09,800
Speaker 2: Right, I have perplexity on my phone. It does a

591
00:34:09,840 --> 00:34:12,600
Speaker 2: better job with search than Google. Does I get better

592
00:34:13,120 --> 00:34:17,920
Speaker 2: more comprehensive answers? What's wrong with AI?

593
00:34:18,920 --> 00:34:21,120
Speaker 4: Well, let me disaggregate it first, because there's plenty of

594
00:34:21,200 --> 00:34:23,719
Speaker 4: AI that there's nothing wrong with. Right, So, AI is

595
00:34:23,760 --> 00:34:25,800
Speaker 4: not intelligent in any way, shape or form. It's a

596
00:34:25,800 --> 00:34:28,440
Speaker 4: market that's a marketing term. What is it is is

597
00:34:28,560 --> 00:34:31,799
Speaker 4: it's an applied statistical engine. You have an algorithm that

598
00:34:32,040 --> 00:34:36,080
Speaker 4: looks for patterns in data and then acts accordingly. And

599
00:34:36,560 --> 00:34:38,960
Speaker 4: that kind of technology has been around for a long time.

600
00:34:39,000 --> 00:34:41,200
Speaker 4: It does like, for example, it's great for fraud detection

601
00:34:41,400 --> 00:34:44,200
Speaker 4: in a bank, for credit card transactions for example. So

602
00:34:44,200 --> 00:34:46,359
Speaker 4: that that's you know, that's that's an a plus use

603
00:34:46,400 --> 00:34:50,759
Speaker 4: of AI. But the last few years, everybody has been

604
00:34:51,080 --> 00:34:56,080
Speaker 4: pouring everything they've got into these LLM based tools, these

605
00:34:56,160 --> 00:34:58,000
Speaker 4: large language made model based tools.

606
00:34:58,080 --> 00:35:00,120
Speaker 3: So these are tools that can.

607
00:35:02,120 --> 00:35:05,759
Speaker 4: You know, old AI tools would just sort of classify something,

608
00:35:05,800 --> 00:35:08,920
Speaker 4: put something in a group, or predict something. But now

609
00:35:08,960 --> 00:35:14,120
Speaker 4: we have these tools that generate content, particularly text.

610
00:35:13,880 --> 00:35:17,120
Speaker 3: But also you know, video, music, et cetera.

611
00:35:17,680 --> 00:35:22,439
Speaker 4: And there are so many problems with this technology because

612
00:35:22,440 --> 00:35:26,960
Speaker 4: it's being sold as technology that can replace humans, right,

613
00:35:27,000 --> 00:35:31,719
Speaker 4: that can Basically it's worth throwing trillions of dollars into

614
00:35:31,719 --> 00:35:34,319
Speaker 4: this because of the productivity gains that we'll get by

615
00:35:34,400 --> 00:35:37,200
Speaker 4: firing all the humans. Essentially is the story they're telling.

616
00:35:38,920 --> 00:35:40,560
Speaker 4: First of all, that would be great.

617
00:35:40,680 --> 00:35:44,000
Speaker 2: Right, that's a problem in and of itself. The way

618
00:35:44,160 --> 00:35:51,480
Speaker 2: I have heard it described that's a little less catastrophic,

619
00:35:51,680 --> 00:35:55,480
Speaker 2: is this is going to make everybody more efficient, more productive.

620
00:35:55,840 --> 00:35:59,439
Speaker 2: It'll make companies more profitable, and we'll all be able

621
00:35:59,480 --> 00:36:03,600
Speaker 2: to do more with our existing stayoff than having to

622
00:36:03,680 --> 00:36:05,840
Speaker 2: go out and hire hundreds of more people.

623
00:36:06,320 --> 00:36:08,720
Speaker 3: But that is not true, sadly, that's the pitch line.

624
00:36:08,800 --> 00:36:12,880
Speaker 4: Right, So, these these tools make a lot of mistakes,

625
00:36:13,920 --> 00:36:17,000
Speaker 4: you know, even the very best ones make mistakes.

626
00:36:17,480 --> 00:36:19,360
Speaker 2: We've seen a lot of attorneys. You and I are

627
00:36:19,400 --> 00:36:21,799
Speaker 2: both attorneys. A lot of judges have been calling out

628
00:36:21,840 --> 00:36:26,440
Speaker 2: attorneys who theoretically are supposed to be doing this on

629
00:36:26,480 --> 00:36:29,560
Speaker 2: their own and instead are outsourcing it to AI and

630
00:36:29,600 --> 00:36:33,560
Speaker 2: all of its hallucinations and citing cases that don't exist.

631
00:36:34,600 --> 00:36:37,680
Speaker 2: The assumption is that's going to get better eventually, but

632
00:36:37,800 --> 00:36:38,240
Speaker 2: it won't.

633
00:36:38,360 --> 00:36:40,280
Speaker 3: So this is this is the problem, but it won't.

634
00:36:40,360 --> 00:36:41,000
Speaker 3: But it won't.

635
00:36:41,400 --> 00:36:45,880
Speaker 4: So these things are statistical engines, right, They they can't

636
00:36:46,080 --> 00:36:49,799
Speaker 4: check for accuracy because they don't understand accuracy as a concept. Right,

637
00:36:49,960 --> 00:36:55,240
Speaker 4: there's no reasoning it's it's literally the most statistically most

638
00:36:55,320 --> 00:36:57,600
Speaker 4: likely word after the last word I gave you is

639
00:36:57,640 --> 00:37:02,920
Speaker 4: this word. There is no way to make that care

640
00:37:03,480 --> 00:37:06,160
Speaker 4: about accuracy because it's it's not a it's not a

641
00:37:06,160 --> 00:37:11,160
Speaker 4: thinking machine. And I think there's increasing acceptance that these

642
00:37:11,440 --> 00:37:13,800
Speaker 4: models have hit a wall and they are as accurate

643
00:37:13,840 --> 00:37:14,799
Speaker 4: as they are going to get.

644
00:37:15,000 --> 00:37:20,000
Speaker 2: Really, yeah, that's kind of fascinating. My concern was, at

645
00:37:20,080 --> 00:37:23,360
Speaker 2: least on the legal side, Hey, you have this existing

646
00:37:23,440 --> 00:37:26,920
Speaker 2: body of work and all this research and brief writing

647
00:37:26,920 --> 00:37:29,880
Speaker 2: and arguments that exist as of now. If you're going

648
00:37:29,920 --> 00:37:31,800
Speaker 2: to replace people from doing that, are you're going to

649
00:37:31,960 --> 00:37:35,600
Speaker 2: freeze the state of legal knowledge at twenty twenty six

650
00:37:35,960 --> 00:37:38,799
Speaker 2: and five or ten years from now. If you don't

651
00:37:38,840 --> 00:37:41,640
Speaker 2: have people writing these briefs. You don't have people writing

652
00:37:41,719 --> 00:37:46,839
Speaker 2: these decisions. How can AI respond to what's taking place

653
00:37:46,840 --> 00:37:49,360
Speaker 2: over the past ten years if we don't have the

654
00:37:49,440 --> 00:37:51,440
Speaker 2: humans actually doing the grunt work.

655
00:37:51,960 --> 00:37:54,239
Speaker 4: Yeah, I mean there's a there's I mean, I think

656
00:37:54,440 --> 00:37:57,040
Speaker 4: those kinds of concerns have been expressed very much in

657
00:37:57,080 --> 00:37:58,120
Speaker 4: the cultural context.

658
00:37:58,200 --> 00:38:02,040
Speaker 3: You know, if we disincentivize.

659
00:38:01,200 --> 00:38:04,520
Speaker 4: Creators from making new music and new aret or is

660
00:38:04,560 --> 00:38:04,840
Speaker 4: this it?

661
00:38:05,000 --> 00:38:08,000
Speaker 3: Are we stuck with with what we've got with something

662
00:38:08,120 --> 00:38:08,720
Speaker 3: like the law.

663
00:38:08,960 --> 00:38:11,520
Speaker 4: One of the challenges is that you know, these large

664
00:38:11,560 --> 00:38:15,200
Speaker 4: language models, they don't get updated on a day to

665
00:38:15,280 --> 00:38:17,319
Speaker 4: day basis. You know, there's there's sort of a stop point,

666
00:38:17,360 --> 00:38:21,160
Speaker 4: and then they don't know well, they don't know anything

667
00:38:21,160 --> 00:38:24,279
Speaker 4: that they don't have the data from after a certain date,

668
00:38:24,360 --> 00:38:26,880
Speaker 4: So that that's a limitation. But the thing I worry

669
00:38:26,920 --> 00:38:32,239
Speaker 4: most about with the law is that you have to

670
00:38:32,400 --> 00:38:35,480
Speaker 4: be able to spot the hallucinations or you're going to

671
00:38:35,480 --> 00:38:37,680
Speaker 4: get yourself in very big trouble. And I think this

672
00:38:37,760 --> 00:38:40,520
Speaker 4: is true for a lot of different feels in and

673
00:38:40,560 --> 00:38:44,440
Speaker 4: this is again just to digress a little, why the

674
00:38:44,480 --> 00:38:48,719
Speaker 4: profitability narrative is not true right because the only place

675
00:38:48,719 --> 00:38:51,600
Speaker 4: where you can just put this content out and just

676
00:38:51,719 --> 00:38:54,799
Speaker 4: leave it there is in very low stakes places right

677
00:38:54,880 --> 00:38:57,880
Speaker 4: where it doesn't matter if you get something wrong. But

678
00:38:58,600 --> 00:39:01,200
Speaker 4: even you know, things that you wouldn't think are such

679
00:39:01,239 --> 00:39:02,919
Speaker 4: a big deal have proved to be quite high stake.

680
00:39:03,000 --> 00:39:06,440
Speaker 4: So Air Canada had a chat bot that told a

681
00:39:07,239 --> 00:39:11,240
Speaker 4: customer that if they wanted to apply for a bereavement

682
00:39:11,280 --> 00:39:14,440
Speaker 4: discount for a flight, they could do that after their

683
00:39:14,440 --> 00:39:17,440
Speaker 4: flight was done. Now that's not Air Canada's policy. You

684
00:39:17,480 --> 00:39:19,920
Speaker 4: had to do it in advance. And so this customer

685
00:39:20,080 --> 00:39:22,799
Speaker 4: tried to get their refund after the fact, and Air

686
00:39:22,840 --> 00:39:25,160
Speaker 4: Canada said, well, the chat buck got it wrong, too bad,

687
00:39:25,200 --> 00:39:26,920
Speaker 4: So sad for you, and.

688
00:39:26,960 --> 00:39:30,200
Speaker 2: It's your chat bought you own well responsible for it exactly,

689
00:39:30,280 --> 00:39:32,560
Speaker 2: yet not my mistake, your mistake exactly.

690
00:39:32,640 --> 00:39:35,560
Speaker 4: And so even in these sort of reasonably low stakes

691
00:39:35,600 --> 00:39:39,919
Speaker 4: customer service interactions, there's reason to be really worried about inaccuracy.

692
00:39:40,400 --> 00:39:43,320
Speaker 4: Now you start dialing up to things, to medical advice,

693
00:39:43,800 --> 00:39:48,439
Speaker 4: legal advice, you know, it's just you can't rely on them.

694
00:39:49,120 --> 00:39:51,520
Speaker 4: And I worry that we're putting people in a very

695
00:39:51,560 --> 00:39:55,280
Speaker 4: difficult position because it's a lot easier to get something

696
00:39:55,400 --> 00:39:57,719
Speaker 4: right when you write it yourself than it is to

697
00:39:57,760 --> 00:40:00,920
Speaker 4: find mistakes in something so some one else is put together.

698
00:40:01,280 --> 00:40:03,919
Speaker 2: So let me push back a little bit, because I've

699
00:40:03,920 --> 00:40:11,160
Speaker 2: been watching the AI reading medical scans, and at some

700
00:40:11,320 --> 00:40:15,239
Speaker 2: point last year, when maybe it was two years ago,

701
00:40:15,600 --> 00:40:23,520
Speaker 2: the technology theoretically past the accuracy rate of humans, fewer

702
00:40:23,560 --> 00:40:29,399
Speaker 2: false positives, more identifying missed negatives that should have been

703
00:40:29,440 --> 00:40:34,680
Speaker 2: positive than people. Is that not accurate or where are

704
00:40:34,719 --> 00:40:37,279
Speaker 2: we with the medical application of that?

705
00:40:37,520 --> 00:40:39,200
Speaker 4: So this is why I think it's so important to

706
00:40:39,200 --> 00:40:42,400
Speaker 4: disaggregate the different kinds of AI, because that is not

707
00:40:42,680 --> 00:40:46,080
Speaker 4: sort of LLLM based AI. And as I said, some

708
00:40:46,120 --> 00:40:47,759
Speaker 4: of those tools are great. I can't weigh in on

709
00:40:47,880 --> 00:40:50,480
Speaker 4: medical imaging and things like that, so it may very

710
00:40:50,520 --> 00:40:53,319
Speaker 4: well be the case. What I'm talking about is, you

711
00:40:53,320 --> 00:40:57,919
Speaker 4: know what, if you've got you know, a doctor coming

712
00:40:57,960 --> 00:41:02,200
Speaker 4: up with instructions for a care plan for their patients

713
00:41:02,920 --> 00:41:05,799
Speaker 4: and they let the AI do it, right, if there's

714
00:41:05,800 --> 00:41:08,120
Speaker 4: a mistake in there, they're much less likely to catch it.

715
00:41:08,160 --> 00:41:11,279
Speaker 4: If the AI, because you know, you know how things go,

716
00:41:11,600 --> 00:41:13,719
Speaker 4: you'll be expected to look at more of these because

717
00:41:13,760 --> 00:41:17,480
Speaker 4: you're not generating them yourself, right, And it's always easier

718
00:41:17,920 --> 00:41:21,040
Speaker 4: to get things right when you do it yourself than

719
00:41:21,080 --> 00:41:23,480
Speaker 4: when you're reviewing someone else. I mean, when we were lawyers,

720
00:41:23,520 --> 00:41:25,000
Speaker 4: we used to That's why you want to have the

721
00:41:25,040 --> 00:41:26,919
Speaker 4: pen on contracts. You want to you want to hide

722
00:41:26,920 --> 00:41:29,399
Speaker 4: things from the other side. And now it's now it's

723
00:41:29,400 --> 00:41:32,880
Speaker 4: the AI hiding stuff from you. And I worry that,

724
00:41:33,320 --> 00:41:36,239
Speaker 4: especially with younger lawyers coming up through the ranks who

725
00:41:36,280 --> 00:41:39,280
Speaker 4: are encouraged to rely on these tools from the beginning,

726
00:41:39,320 --> 00:41:44,000
Speaker 4: who won't actually develop the skills because you don't learn

727
00:41:44,080 --> 00:41:47,400
Speaker 4: well when you sort of don't process it yourself. So

728
00:41:47,440 --> 00:41:50,759
Speaker 4: if you're spent your whole career using AI, you're not

729
00:41:50,800 --> 00:41:53,520
Speaker 4: going to be able to spot the problems.

730
00:41:53,000 --> 00:41:54,879
Speaker 3: In the AI and not can have the skill set.

731
00:41:55,200 --> 00:41:57,799
Speaker 4: No, And so then I'm worried about, you know, those

732
00:41:57,840 --> 00:42:01,440
Speaker 4: young lawyers getting sued from malpractice because they missed something

733
00:42:01,440 --> 00:42:03,680
Speaker 4: that the AI generated, but they were never even given

734
00:42:03,680 --> 00:42:06,440
Speaker 4: the opportunity to learn how to spot it themselves.

735
00:42:06,239 --> 00:42:10,560
Speaker 2: It's a problem with the wrongs on the ladder being removed,

736
00:42:10,680 --> 00:42:16,359
Speaker 2: especially we see that now manifesting itself. The unemployment rate

737
00:42:16,400 --> 00:42:19,080
Speaker 2: of the under thirty is about double what it is

738
00:42:19,239 --> 00:42:22,080
Speaker 2: for the national unemployment rate. And I can't help but

739
00:42:22,160 --> 00:42:26,440
Speaker 2: wonder how much of that is somehow related to the

740
00:42:26,480 --> 00:42:29,800
Speaker 2: proliferation of AI tools for white collar job.

741
00:42:30,719 --> 00:42:33,920
Speaker 4: I think, you know, Corey Doctor, who does a lot

742
00:42:33,920 --> 00:42:35,520
Speaker 4: of work in the tech space, has a great quote

743
00:42:35,520 --> 00:42:37,440
Speaker 4: on this that I'm going to butcher a little, not

744
00:42:37,520 --> 00:42:39,040
Speaker 4: say it quite as well as he does it, but

745
00:42:39,120 --> 00:42:43,279
Speaker 4: he said, the AI can't do your job, but the

746
00:42:43,360 --> 00:42:46,480
Speaker 4: AI salesman can convince your boss to replace.

747
00:42:46,120 --> 00:42:48,280
Speaker 3: You with AI that can't do your job.

748
00:42:49,040 --> 00:42:52,520
Speaker 4: Right, So it's I think you're right that there is

749
00:42:53,760 --> 00:42:57,239
Speaker 4: at this moment, you know. I mean, it's also hard

750
00:42:57,280 --> 00:42:59,440
Speaker 4: to say how much of this is a AI washing

751
00:42:59,719 --> 00:43:04,080
Speaker 4: as a post to real AI displacement. Right the economy's

752
00:43:04,120 --> 00:43:05,759
Speaker 4: not in a great place right now. People don't want

753
00:43:05,800 --> 00:43:08,680
Speaker 4: to hire anyway. It looks a lot better if you say, well,

754
00:43:08,680 --> 00:43:11,160
Speaker 4: we're not hiring because we're replacing them with AI, than

755
00:43:11,280 --> 00:43:13,800
Speaker 4: just we're having a rough time we're not hiring.

756
00:43:13,920 --> 00:43:16,840
Speaker 2: AI washing is a phrase I haven't heard used in

757
00:43:16,880 --> 00:43:20,080
Speaker 2: modern parlance yet, but it certainly makes a whole lot

758
00:43:20,080 --> 00:43:22,160
Speaker 2: of sense. The line I heard, and I don't know

759
00:43:22,200 --> 00:43:24,480
Speaker 2: where I'm stealing this from, is you're not going to

760
00:43:24,560 --> 00:43:28,319
Speaker 2: be replaced by AI. You're going to be replaced by

761
00:43:28,360 --> 00:43:32,040
Speaker 2: somebody with a greater facility working with AI than you have,

762
00:43:32,440 --> 00:43:35,879
Speaker 2: and it sort of creates a self fulfilling arms race

763
00:43:36,000 --> 00:43:39,959
Speaker 2: to make sure you learn how to use that tool.

764
00:43:40,160 --> 00:43:43,160
Speaker 2: Otherwise you're at risk for being replaced by somebody who

765
00:43:43,560 --> 00:43:44,640
Speaker 2: knows how to use that tool.

766
00:43:44,800 --> 00:43:47,359
Speaker 4: I've heard that too, But I don't think these tools

767
00:43:47,360 --> 00:43:49,200
Speaker 4: are that hard to use, right, I mean, that's a

768
00:43:49,239 --> 00:43:51,319
Speaker 4: failure on the part of the AI companies if they're

769
00:43:51,320 --> 00:43:54,160
Speaker 4: so hard to use, right, It wasn't hard to use Google.

770
00:43:53,920 --> 00:43:59,960
Speaker 2: Search Perplexity and even chat GPT is absolutely easiest part

771
00:44:00,080 --> 00:44:03,200
Speaker 2: I use, don't. I don't find them difficult. Sometimes you

772
00:44:03,239 --> 00:44:08,320
Speaker 2: have to keep changing the prompts to get an improved answer.

773
00:44:08,880 --> 00:44:11,000
Speaker 2: Like if you just ask a question and walk away,

774
00:44:11,480 --> 00:44:15,279
Speaker 2: well then you're getting what everybody gets. But if I

775
00:44:15,320 --> 00:44:20,440
Speaker 2: don't really buy into the prompt engineer job title, but

776
00:44:21,120 --> 00:44:23,560
Speaker 2: a little exposure is the more you ask it and

777
00:44:23,600 --> 00:44:26,239
Speaker 2: the more you vary it, you get a variety of

778
00:44:26,280 --> 00:44:29,560
Speaker 2: answers and eventually you come up with something, Oh that's

779
00:44:29,600 --> 00:44:31,920
Speaker 2: interesting and different. Let me take a look at that.

780
00:44:32,080 --> 00:44:34,040
Speaker 4: So, I mean I have strong feelings about this as

781
00:44:34,040 --> 00:44:37,720
Speaker 4: an educator, because if these tools are worth their salt.

782
00:44:38,520 --> 00:44:40,400
Speaker 4: It shouldn't take our students long to figure out how

783
00:44:40,440 --> 00:44:42,680
Speaker 4: to use them, right, right, So why are we bringing

784
00:44:42,719 --> 00:44:45,760
Speaker 4: them into education where what they really need to learn

785
00:44:45,880 --> 00:44:48,600
Speaker 4: is how to spot hallucinations, how to think critically, so

786
00:44:48,680 --> 00:44:50,640
Speaker 4: that if they are going to use these tools later,

787
00:44:50,719 --> 00:44:52,680
Speaker 4: they can use them to the best of their abilities.

788
00:44:53,280 --> 00:44:54,640
Speaker 3: This whole arms race.

789
00:44:54,480 --> 00:44:56,680
Speaker 4: Sense of well, they need to use them in school

790
00:44:56,719 --> 00:44:58,879
Speaker 4: so they don't get left behind. I'm like, it didn't

791
00:44:58,880 --> 00:45:00,520
Speaker 4: take learn long to learn Google.

792
00:45:00,640 --> 00:45:01,320
Speaker 3: They'll be fine.

793
00:45:01,719 --> 00:45:05,640
Speaker 2: You've been pretty critical of things like crypto and stable coin.

794
00:45:05,760 --> 00:45:08,880
Speaker 2: We're going to get to those in a moment. I

795
00:45:08,920 --> 00:45:13,720
Speaker 2: want to talk about some other things you've discussed. You've

796
00:45:13,840 --> 00:45:20,319
Speaker 2: brought up the whole idea of technology as a branding exercise,

797
00:45:20,800 --> 00:45:28,520
Speaker 2: phrases like democratizing finance, disruptive technology, banking, the on banks.

798
00:45:29,040 --> 00:45:32,319
Speaker 2: You've described these as just you know, marketing and not

799
00:45:32,400 --> 00:45:35,800
Speaker 2: really accomplishing anything. Tell us a little bit about those

800
00:45:35,920 --> 00:45:37,480
Speaker 2: and give us some examples.

801
00:45:38,360 --> 00:45:40,600
Speaker 4: Sure, I mean, I think at the heart of all

802
00:45:40,680 --> 00:45:44,480
Speaker 4: this is innovation speak in innovation worship, right. We alluded

803
00:45:44,520 --> 00:45:49,320
Speaker 4: to that earlier, This sense that anything that is innovative

804
00:45:49,440 --> 00:45:53,120
Speaker 4: is inherently good and must therefore be permitted at all costs,

805
00:45:54,160 --> 00:45:56,600
Speaker 4: and that is sort of the font of a lot

806
00:45:56,600 --> 00:45:59,880
Speaker 4: of the rhetoric and narrative that we get out of

807
00:46:00,080 --> 00:46:04,960
Speaker 4: Silicon Valley that ultimately is there to attract funding, yes,

808
00:46:05,200 --> 00:46:09,920
Speaker 4: but also to procure legal treatment that facilitates what they

809
00:46:09,960 --> 00:46:13,360
Speaker 4: want to do. It actually creates offen an unleveled legal

810
00:46:13,400 --> 00:46:16,080
Speaker 4: playing field where you have the incumbents who have to

811
00:46:16,120 --> 00:46:19,040
Speaker 4: comply with all the laws, and then the disruptors, as

812
00:46:19,040 --> 00:46:23,040
Speaker 4: you say, who don't have to comply with all the

813
00:46:23,120 --> 00:46:25,839
Speaker 4: laws and can succeed on that basis even if their

814
00:46:25,880 --> 00:46:29,160
Speaker 4: product isn't superior in the way we would typically expect

815
00:46:29,520 --> 00:46:34,239
Speaker 4: a disruptor's product to be. So yeah, I mean, disruptive

816
00:46:34,360 --> 00:46:40,360
Speaker 4: innovation goes back to Clayton Christiansen and the innovator's dilemma,

817
00:46:40,880 --> 00:46:44,319
Speaker 4: This sense that if you stay still and just make

818
00:46:44,360 --> 00:46:47,920
Speaker 4: good products, you'll be out competed by someone who is

819
00:46:47,960 --> 00:46:52,719
Speaker 4: trying to do things a little differently. But you know,

820
00:46:53,160 --> 00:46:57,640
Speaker 4: there's no real formula that you can take away from

821
00:46:57,680 --> 00:47:00,800
Speaker 4: that as to what disruptive is in the of the beholder.

822
00:47:00,920 --> 00:47:03,040
Speaker 2: So let me push back on that a little bit.

823
00:47:04,160 --> 00:47:06,480
Speaker 2: And all my VC friends, I could just hear their

824
00:47:06,560 --> 00:47:11,560
Speaker 2: voices in my head, and the pushback is, Look, most

825
00:47:11,600 --> 00:47:15,960
Speaker 2: new companies fail, most new technologies crash and burn, most

826
00:47:16,080 --> 00:47:19,040
Speaker 2: new ideas never make it. And even the best of

827
00:47:19,080 --> 00:47:22,600
Speaker 2: the best vcs, they'll make one hundred investments for that

828
00:47:22,719 --> 00:47:25,839
Speaker 2: one moonshot that works out, and most of the other

829
00:47:25,920 --> 00:47:31,520
Speaker 2: ninety nine are at best break even but mostly losers.

830
00:47:31,560 --> 00:47:35,120
Speaker 2: How could you say this is true? Oh? And real

831
00:47:35,160 --> 00:47:42,000
Speaker 2: innovation often finds itself in between the regulatory regime because

832
00:47:42,120 --> 00:47:46,560
Speaker 2: the technology that's being created was never anticipated by the

833
00:47:46,640 --> 00:47:48,560
Speaker 2: regulators or anybody else.

834
00:47:49,239 --> 00:47:52,600
Speaker 3: Fair pushback a lot of points that I would quibble with.

835
00:47:52,600 --> 00:47:56,479
Speaker 3: There something that's fair. Quibble away, quibble away. All right, So.

836
00:47:57,920 --> 00:48:00,760
Speaker 4: There's this idea that the law is a bat innovation

837
00:48:00,840 --> 00:48:02,920
Speaker 4: because law is old and innovation is new, and the

838
00:48:03,000 --> 00:48:08,320
Speaker 4: law couldn't possibly have contemplated the innovation. The story about

839
00:48:08,320 --> 00:48:11,239
Speaker 4: the innovation is what makes it new. Right, most of

840
00:48:11,239 --> 00:48:14,719
Speaker 4: the things that we're seeing in the fintech space, they're

841
00:48:14,719 --> 00:48:16,439
Speaker 4: not that new. Right, As I said, you know, we've

842
00:48:16,440 --> 00:48:18,759
Speaker 4: got fintech lending has a lot of the things that

843
00:48:18,800 --> 00:48:23,400
Speaker 4: we didn't like about payday lending. Right, why shouldn't the

844
00:48:23,440 --> 00:48:28,400
Speaker 4: laws from payday lending apply crypto basically. I mean the

845
00:48:28,400 --> 00:48:30,840
Speaker 4: crypto markets for all the world look like the stocks

846
00:48:30,880 --> 00:48:33,440
Speaker 4: and bonds and the unregulated markets of the nineteen twenties.

847
00:48:33,480 --> 00:48:34,520
Speaker 3: We saw how that ended.

848
00:48:34,880 --> 00:48:37,520
Speaker 4: They ended in such a spectacular crash that we ended

849
00:48:37,600 --> 00:48:40,840
Speaker 4: up with the securities laws. Why shouldn't they apply? What's

850
00:48:40,880 --> 00:48:45,520
Speaker 4: so different? Right, So, this construction of novelty is something

851
00:48:45,560 --> 00:48:50,800
Speaker 4: that is done intentionally as a narrative. Now, I fully

852
00:48:50,800 --> 00:48:53,799
Speaker 4: appreciate that we need the optimists in this world who

853
00:48:53,840 --> 00:48:54,960
Speaker 4: are going to try new things.

854
00:48:55,000 --> 00:48:57,800
Speaker 3: And I say that very early on in the book.

855
00:48:58,080 --> 00:49:02,280
Speaker 4: The people who these stories are you because they attract

856
00:49:02,320 --> 00:49:04,400
Speaker 4: funding two new things. So I'm not saying we should

857
00:49:04,440 --> 00:49:07,560
Speaker 4: do away with it completely. My argument is that the

858
00:49:08,040 --> 00:49:14,000
Speaker 4: yin and yang, the balance between the optimists and the realists,

859
00:49:14,440 --> 00:49:17,040
Speaker 4: is badly out of whack because we give so much

860
00:49:17,080 --> 00:49:21,439
Speaker 4: deference to the stories about innovation, about disruption, about how

861
00:49:21,480 --> 00:49:25,319
Speaker 4: technology can solve problems that have been with us for centuries.

862
00:49:25,560 --> 00:49:30,200
Speaker 4: We can magically get rid of intermediaries now with blockchain technology, apparently.

863
00:49:30,120 --> 00:49:33,680
Speaker 2: Well, that was one of the story narratives, was this

864
00:49:33,800 --> 00:49:38,799
Speaker 2: intermediation until it no longer was the story. But let's

865
00:49:38,800 --> 00:49:42,239
Speaker 2: talk about some specific companies that you've mentioned, that you've

866
00:49:42,280 --> 00:49:45,640
Speaker 2: written about, and I want to get your sense on it.

867
00:49:45,719 --> 00:49:51,799
Speaker 2: And the oldest one was PayPal to this day, and

868
00:49:51,840 --> 00:49:54,640
Speaker 2: I was a PayPal user back in the nineteen nineties,

869
00:49:55,000 --> 00:49:58,319
Speaker 2: with eBay and those sort of things. To this day,

870
00:49:58,400 --> 00:50:01,719
Speaker 2: I don't understand what they did was any different than

871
00:50:01,760 --> 00:50:05,440
Speaker 2: a credit card, other than being a bit of middleware

872
00:50:06,640 --> 00:50:11,279
Speaker 2: that eventually became a rentier. Why not just use a

873
00:50:11,280 --> 00:50:14,279
Speaker 2: credit card? Why do I need PayPal between me and

874
00:50:14,400 --> 00:50:15,920
Speaker 2: Amazon or me and eBay.

875
00:50:16,520 --> 00:50:20,120
Speaker 4: So this is really an interesting story, and I learned

876
00:50:20,160 --> 00:50:22,280
Speaker 4: a whole lot about this in research for this book

877
00:50:22,520 --> 00:50:26,040
Speaker 4: by reading Max Chafkin's book The Contrarian about Peter Tiel

878
00:50:26,120 --> 00:50:29,360
Speaker 4: and the start of the beginning of PayPal. And in fact,

879
00:50:29,760 --> 00:50:33,319
Speaker 4: the idea for PayPal came from the same place that

880
00:50:33,400 --> 00:50:35,320
Speaker 4: the idea for crypto has come from, which is this

881
00:50:36,160 --> 00:50:40,560
Speaker 4: techno libertarian idea of we don't like regulation, we don't

882
00:50:40,640 --> 00:50:43,760
Speaker 4: like central banks, we would like to have private money,

883
00:50:44,239 --> 00:50:48,360
Speaker 4: and we would like technology to help us have private money.

884
00:50:48,760 --> 00:50:51,160
Speaker 4: And PayPal wasn't the only one of these kinds of

885
00:50:51,160 --> 00:50:53,760
Speaker 4: startups back in the early dot com bubble.

886
00:50:53,880 --> 00:50:55,879
Speaker 3: So PayPal, I.

887
00:50:55,760 --> 00:50:59,080
Speaker 4: Think succeeded because it sort of lucked into this deal

888
00:50:59,080 --> 00:51:01,319
Speaker 4: with eBay, as you said, right, it sort of had

889
00:51:01,360 --> 00:51:04,640
Speaker 4: no distinguishing features as far as I can tell, that

890
00:51:04,719 --> 00:51:07,440
Speaker 4: made it any superior to the beans is and the

891
00:51:07,440 --> 00:51:13,400
Speaker 4: fluses of this world. It lucked into this deal with eBay, so.

892
00:51:13,280 --> 00:51:19,480
Speaker 2: And eventually they buys them to solve their I guess

893
00:51:19,600 --> 00:51:22,640
Speaker 2: credit card management problem. I don't really understand. I still,

894
00:51:23,000 --> 00:51:25,040
Speaker 2: you know, twenty or twenty five years later, I still

895
00:51:25,040 --> 00:51:27,680
Speaker 2: don't understand why they were necessary.

896
00:51:28,320 --> 00:51:29,160
Speaker 3: I think. Yeah.

897
00:51:29,200 --> 00:51:32,239
Speaker 4: I mean, my knowledge of this comes primarily from reading

898
00:51:32,280 --> 00:51:35,319
Speaker 4: Max Chafkin's book, which I highly recommend. But that's that's

899
00:51:35,400 --> 00:51:39,120
Speaker 4: my understanding too. And so you know, they are a

900
00:51:39,200 --> 00:51:45,400
Speaker 4: payments technology. I too struggle to sort of understand what

901
00:51:45,440 --> 00:51:50,520
Speaker 4: they offer that a credit card doesn't in many ways.

902
00:51:51,680 --> 00:51:53,640
Speaker 3: One thing they are, though.

903
00:51:53,560 --> 00:51:57,400
Speaker 4: Is they are sort of the or regulatory arbitrage story

904
00:51:57,400 --> 00:51:59,520
Speaker 4: in fintech, right, So you know I've said so much

905
00:51:59,520 --> 00:52:01,839
Speaker 4: of fintech is actually about arbitrage in the law rather

906
00:52:01,840 --> 00:52:07,160
Speaker 4: than technological superiority. PayPal from the beginning was flaunting quite

907
00:52:07,200 --> 00:52:10,640
Speaker 4: aggressively the banking laws because only banks are allowed to

908
00:52:10,680 --> 00:52:15,560
Speaker 4: accept deposits, and people were keeping money in their PayPal

909
00:52:15,600 --> 00:52:17,760
Speaker 4: wallets and for all the world, that looks like keeping

910
00:52:17,760 --> 00:52:21,120
Speaker 4: a deposit. Peter Tiel from the beginning was very aggressive

911
00:52:21,480 --> 00:52:24,759
Speaker 4: on the lobbying to make sure that that was not

912
00:52:24,920 --> 00:52:28,279
Speaker 4: considered deposit taking. Early on there were multiple states that

913
00:52:28,320 --> 00:52:30,680
Speaker 4: were investigating it because they thought it was the unflawful

914
00:52:30,719 --> 00:52:35,320
Speaker 4: taking of deposits. He lobbied heavily in Congress, and lobbied

915
00:52:35,320 --> 00:52:40,960
Speaker 4: heavily at the FDIC, and ultimately, you know, that worked.

916
00:52:41,080 --> 00:52:43,480
Speaker 4: And so I think that has sort of been the prototype,

917
00:52:43,520 --> 00:52:48,400
Speaker 4: that blit scaling prototype. I think people perhaps underestimate the

918
00:52:48,480 --> 00:52:51,600
Speaker 4: degree to which blit scaling is really about playing it

919
00:52:51,600 --> 00:52:54,640
Speaker 4: on an unlegal, unleveled legal playing field.

920
00:52:55,120 --> 00:52:57,920
Speaker 2: Let's talk about stable coins. What sort of value do

921
00:52:58,040 --> 00:52:58,719
Speaker 2: they provide?

922
00:52:59,239 --> 00:53:03,640
Speaker 4: Again, unless you are trying to do illicit transactions or gamble,

923
00:53:03,880 --> 00:53:05,240
Speaker 4: not a whole lot, right.

924
00:53:05,160 --> 00:53:07,520
Speaker 2: So, well, stable coin is worth a dollar, and it

925
00:53:07,600 --> 00:53:12,240
Speaker 2: promises to always be worth a dollar. Don't we have dollars?

926
00:53:12,280 --> 00:53:13,600
Speaker 2: Why do I need a stable coin?

927
00:53:14,040 --> 00:53:17,680
Speaker 4: Well, you need a stable coin often to do illicit payments.

928
00:53:18,400 --> 00:53:21,719
Speaker 4: So if you want, you know, if you're they're very popular,

929
00:53:21,880 --> 00:53:25,759
Speaker 4: for example, with all kinds of dark cartels, and they're

930
00:53:25,800 --> 00:53:26,920
Speaker 4: good for sanctions of Asian.

931
00:53:28,239 --> 00:53:30,120
Speaker 3: They're also very good if.

932
00:53:30,360 --> 00:53:32,160
Speaker 4: You want to gamble in crypto and you want to

933
00:53:32,239 --> 00:53:34,080
Speaker 4: use it as sort of a cash management tool in

934
00:53:34,160 --> 00:53:36,840
Speaker 4: between crypto investments, kind of like a money market mutual

935
00:53:36,880 --> 00:53:39,720
Speaker 4: fund in your brokerage account for parking funds in between

936
00:53:39,800 --> 00:53:45,640
Speaker 4: crypto gambling. But they've really never had any utility in

937
00:53:45,680 --> 00:53:48,799
Speaker 4: any big way as a legal payments mechanism.

938
00:53:48,960 --> 00:53:52,400
Speaker 2: All right, So what about you mentioned the blockchain. I

939
00:53:52,719 --> 00:53:56,440
Speaker 2: keep reading that blockchain is going to allow us to

940
00:53:56,520 --> 00:54:00,439
Speaker 2: use smart contracts and have things happen automatically that now

941
00:54:00,520 --> 00:54:03,520
Speaker 2: have to be manually. What's the problem with blockchain?

942
00:54:04,280 --> 00:54:07,759
Speaker 4: Well, first of all, smart contracts can work without a blockchain.

943
00:54:08,040 --> 00:54:11,200
Speaker 4: Smart contracts pre date blockchains. They can run on all

944
00:54:11,320 --> 00:54:13,960
Speaker 4: kinds of databases. So if you want that kind of

945
00:54:14,000 --> 00:54:16,800
Speaker 4: functionality and it has pros and cons, and I've written

946
00:54:16,840 --> 00:54:20,239
Speaker 4: about this aton, you can have that without a blockchain.

947
00:54:20,760 --> 00:54:23,359
Speaker 4: The reason why you don't want to have it on

948
00:54:23,400 --> 00:54:25,719
Speaker 4: a blockchain, and this is something that does not get

949
00:54:25,760 --> 00:54:28,759
Speaker 4: anywhere near the attention it needs, is that there's all

950
00:54:28,840 --> 00:54:32,080
Speaker 4: kinds of operational risks associated with the blockchains themselves. So

951
00:54:32,080 --> 00:54:38,000
Speaker 4: blockchains are software, they are maintained by in the case

952
00:54:38,040 --> 00:54:40,799
Speaker 4: of the Bitcoin blockchain, just a few individuals. In the

953
00:54:40,840 --> 00:54:44,239
Speaker 4: case of the Ethereum blockchain, it's the Ethereum Foundation. They're

954
00:54:44,239 --> 00:54:49,560
Speaker 4: not regulated at all. They have no obligation to invest

955
00:54:49,600 --> 00:54:54,320
Speaker 4: in cybersecurity, to invest in getting their blockchains up and

956
00:54:54,400 --> 00:54:58,000
Speaker 4: running again. Should something go wrong, You're just you're really

957
00:54:58,040 --> 00:55:02,160
Speaker 4: sort of as I sometimes say, yolo ing operational risk

958
00:55:02,280 --> 00:55:06,520
Speaker 4: with regards to these blockchains. And so if you want

959
00:55:06,560 --> 00:55:10,840
Speaker 4: smart chain, so sorry smart contract functionality, don't use a blockchain.

960
00:55:11,120 --> 00:55:11,319
Speaker 1: Huh.

961
00:55:11,640 --> 00:55:15,040
Speaker 2: Coming up, we continue our conversation with Professor Hillary Allen

962
00:55:15,640 --> 00:55:20,959
Speaker 2: discussing her new book, Fintech Dystopia, a summer beach read

963
00:55:21,160 --> 00:55:26,000
Speaker 2: about Silicon Valley and how it's ruining fits. I'm Bury Ridults.

964
00:55:26,040 --> 00:55:43,479
Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm

965
00:55:43,480 --> 00:55:47,640
Speaker 2: Barry Riddults. You're listening to Masters in Business on Bloomberg Radio.

966
00:55:47,920 --> 00:55:51,040
Speaker 2: My extra special guest this week is Hillary Allen. She

967
00:55:51,200 --> 00:55:56,200
Speaker 2: teaches at the American University Washington College of Law in Washington,

968
00:55:56,320 --> 00:56:00,960
Speaker 2: d C. Where she specializes in regulation of financial and

969
00:56:01,160 --> 00:56:05,600
Speaker 2: technology laws. So we mentioned stable coin, we've mentioned blockchain.

970
00:56:06,120 --> 00:56:09,960
Speaker 2: Is there any value in any of the crypto coins?

971
00:56:10,400 --> 00:56:15,600
Speaker 2: Be it bitcoin or ethereum. I know, we can't actually

972
00:56:15,680 --> 00:56:19,040
Speaker 2: describe the last one hundred coins that are out there

973
00:56:19,080 --> 00:56:23,799
Speaker 2: on the radio will violate George Colin's seven words you

974
00:56:23,800 --> 00:56:28,040
Speaker 2: can't say on TV or radio. But there's an outside

975
00:56:28,080 --> 00:56:32,759
Speaker 2: of the you know, ebu, doge coins and everything below that.

976
00:56:33,480 --> 00:56:38,360
Speaker 2: What's the value of the first five or so cryptocurrencies?

977
00:56:38,480 --> 00:56:42,080
Speaker 2: Is there anything worthwhile to these or is this just

978
00:56:42,160 --> 00:56:44,320
Speaker 2: a solution in search of a problem.

979
00:56:44,400 --> 00:56:47,120
Speaker 3: It's a solution in search of a problem. I mean essentially.

980
00:56:47,200 --> 00:56:50,719
Speaker 4: Even so, Bitcoin often is seen as the most credible

981
00:56:50,920 --> 00:56:52,960
Speaker 4: of these because it's been around the longest and has

982
00:56:53,160 --> 00:56:53,960
Speaker 4: large Bitcoin and.

983
00:56:53,920 --> 00:56:56,360
Speaker 2: Eth those are the two I hear about the most.

984
00:56:57,719 --> 00:57:00,440
Speaker 4: But both of them are essentially ponzi's in the sense

985
00:57:00,480 --> 00:57:04,120
Speaker 4: that there's nothing backing them. The only reason they have

986
00:57:04,239 --> 00:57:07,120
Speaker 4: value is because someone else might buy them from you.

987
00:57:07,880 --> 00:57:10,200
Speaker 4: If they choose not to, it could go to zero.

988
00:57:10,239 --> 00:57:12,200
Speaker 4: And actually, someone put it to me this way. It's

989
00:57:12,239 --> 00:57:13,719
Speaker 4: not that they could go to zero. They could go

990
00:57:13,760 --> 00:57:15,719
Speaker 4: to less than zero because they don't even have any

991
00:57:15,719 --> 00:57:19,840
Speaker 4: assets that could be used to administer a winding up right,

992
00:57:19,960 --> 00:57:21,760
Speaker 4: and and that's expensive.

993
00:57:22,480 --> 00:57:24,160
Speaker 3: You know you're gonna get the lawyers.

994
00:57:23,920 --> 00:57:25,760
Speaker 2: In the courts that everybody involved that tho, Well, you're

995
00:57:25,800 --> 00:57:28,480
Speaker 2: not suggesting that if you own bitcoin you may have

996
00:57:28,520 --> 00:57:31,480
Speaker 2: a liability down the road. Is that? Is that the implication?

997
00:57:31,880 --> 00:57:35,400
Speaker 4: No, I'm just saying that if someone was trying to

998
00:57:35,680 --> 00:57:38,040
Speaker 4: work out the end of one of these things, there

999
00:57:38,080 --> 00:57:41,400
Speaker 4: wouldn't even be you know, office furniture you could sell

1000
00:57:41,480 --> 00:57:42,440
Speaker 4: to pay the lawyers.

1001
00:57:42,880 --> 00:57:50,000
Speaker 2: Okay, you you've written about startups like farrahos I remember

1002
00:57:50,200 --> 00:57:54,840
Speaker 2: juico tell us a little bit about those two, and

1003
00:57:55,000 --> 00:57:58,480
Speaker 2: was that just, you know, one of these products that

1004
00:57:58,600 --> 00:58:01,280
Speaker 2: just didn't work out? What what's the problem with that

1005
00:58:02,040 --> 00:58:06,400
Speaker 2: technology solution to our juicing problems?

1006
00:58:07,080 --> 00:58:09,360
Speaker 4: So Juiceara is just my favorite metaphor for all of this.

1007
00:58:09,520 --> 00:58:11,760
Speaker 4: So for those of you who are unfamiliar with the

1008
00:58:12,080 --> 00:58:16,120
Speaker 4: gift that is Juicero. So basically, this was a machine

1009
00:58:16,120 --> 00:58:17,560
Speaker 4: that cost hundreds of dollars.

1010
00:58:17,600 --> 00:58:18,840
Speaker 3: It was Wi Fi enabled.

1011
00:58:18,960 --> 00:58:21,720
Speaker 2: Well, roll back the guy and you described this in

1012
00:58:21,760 --> 00:58:25,160
Speaker 2: the book. The guy who invented this previously had set

1013
00:58:25,200 --> 00:58:30,720
Speaker 2: off a fairly successful was it a juicing chain of

1014
00:58:30,760 --> 00:58:34,880
Speaker 2: companies that got bought, and so he had some credibility

1015
00:58:34,880 --> 00:58:38,240
Speaker 2: in the space. And now I'm not going to run restaurants.

1016
00:58:38,320 --> 00:58:42,040
Speaker 2: I'm going to create a technology that people can juice

1017
00:58:42,040 --> 00:58:42,439
Speaker 2: at home.

1018
00:58:42,960 --> 00:58:45,320
Speaker 3: It was venture funded. They put a lot of money into.

1019
00:58:45,160 --> 00:58:48,640
Speaker 2: This, one hundred plus million dollars and these these.

1020
00:58:48,960 --> 00:58:50,920
Speaker 3: What it did was it squeezed these juice patches.

1021
00:58:51,440 --> 00:58:54,560
Speaker 4: The problem was that people could just squeeze the juice

1022
00:58:54,600 --> 00:58:56,880
Speaker 4: patches with their bare hands and get all the juice.

1023
00:58:57,040 --> 00:59:00,520
Speaker 2: There was a notorious Bloomberg article of about out this,

1024
00:59:01,800 --> 00:59:06,840
Speaker 2: but it raises the question did the company already squeeze

1025
00:59:06,880 --> 00:59:10,360
Speaker 2: the juice and put in these pouches? Why didn't they, Like,

1026
00:59:10,440 --> 00:59:13,400
Speaker 2: why wasn't this set up so that you can actually

1027
00:59:13,400 --> 00:59:17,320
Speaker 2: put fresh fruit? Like doesn't it defeat the purpose if

1028
00:59:17,360 --> 00:59:20,560
Speaker 2: you're buying pouches? Or was the whole idea of the

1029
00:59:20,720 --> 00:59:21,880
Speaker 2: razor blade model?

1030
00:59:22,360 --> 00:59:24,480
Speaker 4: So, I mean, the reason why I love this as

1031
00:59:24,520 --> 00:59:28,720
Speaker 4: a metaphor is it really gets at this techno solutionism,

1032
00:59:28,760 --> 00:59:31,120
Speaker 4: which is one of the concepts that I'm really coming

1033
00:59:31,160 --> 00:59:35,280
Speaker 4: for in this book. And techno solutionism is this idea

1034
00:59:35,320 --> 00:59:38,320
Speaker 4: that everything in our world can be reduced into a

1035
00:59:38,320 --> 00:59:41,680
Speaker 4: technology problem, and that the only reason we haven't solved

1036
00:59:41,720 --> 00:59:44,440
Speaker 4: certain things is because we haven't spent enough time and

1037
00:59:44,480 --> 00:59:48,360
Speaker 4: money on developing the technology. And what that does is

1038
00:59:48,480 --> 00:59:52,720
Speaker 4: it sort of flattens some problems into it gets rid

1039
00:59:52,760 --> 00:59:56,640
Speaker 4: of the human messiness. It flattens problems, It ignores domain expertise.

1040
00:59:56,680 --> 00:59:59,200
Speaker 4: People who've been working in particular fields for a long

1041
00:59:59,240 --> 01:00:01,840
Speaker 4: time and know a lot of non tech stuff. It

1042
01:00:01,920 --> 01:00:06,600
Speaker 4: sort of dismisses their expertise. And sadly, you know, there's

1043
01:00:06,640 --> 01:00:09,560
Speaker 4: just this magic associated with technology at this point. And

1044
01:00:09,880 --> 01:00:11,960
Speaker 4: as I said, I'm not anti technology. A lot of

1045
01:00:12,000 --> 01:00:15,200
Speaker 4: it's great, but it doesn't deserve the level of sort

1046
01:00:15,200 --> 01:00:17,800
Speaker 4: of magical deference that we give it. It can't solve all

1047
01:00:17,800 --> 01:00:20,880
Speaker 4: our problems. And when we get into this mindset where

1048
01:00:20,920 --> 01:00:23,600
Speaker 4: we think that if we throw enough money at technology,

1049
01:00:23,600 --> 01:00:25,560
Speaker 4: it can solve anything and it will always be the

1050
01:00:25,560 --> 01:00:30,520
Speaker 4: best solutions, we end up squeezing pouches with a machine

1051
01:00:30,520 --> 01:00:31,560
Speaker 4: that we could squeeze.

1052
01:00:31,200 --> 01:00:31,920
Speaker 3: With our bare hands.

1053
01:00:31,920 --> 01:00:34,200
Speaker 4: And a joke that I try and make in the

1054
01:00:34,200 --> 01:00:37,480
Speaker 4: book is like with AI, we may be better off

1055
01:00:37,480 --> 01:00:39,000
Speaker 4: squeezing things with our bare minds.

1056
01:00:40,000 --> 01:00:43,760
Speaker 2: So one more company I have to ask about Farahose.

1057
01:00:44,840 --> 01:00:47,560
Speaker 2: I love the book Bad Blood, what really went into

1058
01:00:47,640 --> 01:00:53,160
Speaker 2: details about how corrosive and co opting, co opting the

1059
01:00:53,240 --> 01:00:57,120
Speaker 2: company itself was for everybody around it, including the attorneys

1060
01:00:57,160 --> 01:01:01,480
Speaker 2: and all sorts of other bad actors. Why wasn't thereno

1061
01:01:01,560 --> 01:01:04,680
Speaker 2: It's just an idea that didn't work. That you can't

1062
01:01:05,200 --> 01:01:07,400
Speaker 2: if you want to draw a blood from a vein,

1063
01:01:07,800 --> 01:01:10,280
Speaker 2: you have to draw a blood from a vein. You

1064
01:01:10,320 --> 01:01:14,120
Speaker 2: can't just prick your fingertip and think that's going to

1065
01:01:14,200 --> 01:01:16,280
Speaker 2: be the same as venal draws.

1066
01:01:16,640 --> 01:01:18,920
Speaker 4: Well. So that's the thing with this techno solutionism. It

1067
01:01:18,960 --> 01:01:22,320
Speaker 4: presumes that everything is a tech problem waiting to be solved.

1068
01:01:22,760 --> 01:01:25,920
Speaker 4: It doesn't even countenance the possibility that there may not

1069
01:01:26,200 --> 01:01:29,240
Speaker 4: be a technological solution for what you want to do,

1070
01:01:29,280 --> 01:01:32,360
Speaker 4: that the technology you want may not be able to

1071
01:01:32,480 --> 01:01:35,120
Speaker 4: do the thing you wanted to do. And when you

1072
01:01:35,280 --> 01:01:39,440
Speaker 4: have that sort of collective sense, though, I think we

1073
01:01:39,520 --> 01:01:42,600
Speaker 4: have now that if we throw enough money at any technology,

1074
01:01:42,640 --> 01:01:45,120
Speaker 4: it can solve any problem we give it. You can

1075
01:01:45,160 --> 01:01:48,440
Speaker 4: see how people get so susceptible to being sort of

1076
01:01:48,520 --> 01:01:54,040
Speaker 4: drawn into the stories that outright con people like Elizabeth

1077
01:01:54,040 --> 01:01:57,080
Speaker 4: Holmes might be telling, but also the stories that we're

1078
01:01:57,080 --> 01:01:58,400
Speaker 4: being told about.

1079
01:01:58,720 --> 01:01:59,560
Speaker 3: You know about.

1080
01:01:59,280 --> 01:02:02,880
Speaker 4: AI right now, about crypto. You know, the more you

1081
01:02:02,960 --> 01:02:08,400
Speaker 4: know about these technologies, the less impressive they seem, and

1082
01:02:08,640 --> 01:02:13,520
Speaker 4: the more clearly it becomes illuminated that they just can't

1083
01:02:13,600 --> 01:02:15,680
Speaker 4: do a lot of the things that they're going to do.

1084
01:02:16,040 --> 01:02:18,640
Speaker 4: But that's so counter to how we typically talk about

1085
01:02:18,680 --> 01:02:21,440
Speaker 4: technologies that it sort of it feels a bit weird

1086
01:02:21,480 --> 01:02:25,120
Speaker 4: to talk like that, and you sort of you're going

1087
01:02:25,160 --> 01:02:27,840
Speaker 4: against societal norms in a way. And so one of

1088
01:02:27,840 --> 01:02:30,160
Speaker 4: the things that I really wanted to do with this

1089
01:02:30,600 --> 01:02:34,880
Speaker 4: is to start making it easier to talk about these

1090
01:02:34,880 --> 01:02:38,040
Speaker 4: things critically, to be not such an outlier to express

1091
01:02:38,080 --> 01:02:40,920
Speaker 4: your frustrations. And I think we're actually having a moment

1092
01:02:41,040 --> 01:02:44,600
Speaker 4: like that about AI because so many people really hate it.

1093
01:02:45,440 --> 01:02:51,120
Speaker 2: Really, so you use the phrase techno solutionism, and Fairhos

1094
01:02:51,160 --> 01:02:54,439
Speaker 2: is really the poster child for that. Because as you're

1095
01:02:54,480 --> 01:02:59,360
Speaker 2: describing a lot of these things, I am recalling the story,

1096
01:03:00,160 --> 01:03:04,240
Speaker 2: especially what you're referring to with domain expertise. She had

1097
01:03:04,280 --> 01:03:10,000
Speaker 2: no medical or medical device training. None of the vcs

1098
01:03:10,120 --> 01:03:16,640
Speaker 2: who put money into Fairnose were healthcare, biotech medical devices

1099
01:03:17,240 --> 01:03:21,360
Speaker 2: like they all passed. Eventually, she hired a number of

1100
01:03:21,400 --> 01:03:25,640
Speaker 2: people to try and with some background, but they seem

1101
01:03:25,720 --> 01:03:28,920
Speaker 2: to turn over pretty quickly because now you can't do

1102
01:03:29,000 --> 01:03:32,080
Speaker 2: that what you just pricking the skin, You're getting all

1103
01:03:32,120 --> 01:03:36,040
Speaker 2: the interstitial tissue and fluids, and you're corrupting the sample

1104
01:03:36,080 --> 01:03:38,680
Speaker 2: that you want to test for something you have. The

1105
01:03:39,240 --> 01:03:43,320
Speaker 2: reason we draw from the vein is very medically specific,

1106
01:03:44,320 --> 01:03:50,400
Speaker 2: and yet it attracted Henry Kissinger and the all sorts

1107
01:03:50,440 --> 01:03:54,280
Speaker 2: of big law firms and everybody plowed in. She's the

1108
01:03:54,280 --> 01:04:00,160
Speaker 2: next Stieve Jobs, the youngest self made female billionaire. Is

1109
01:04:00,400 --> 01:04:04,240
Speaker 2: about us, that we're just so susceptible to buying into

1110
01:04:04,280 --> 01:04:07,520
Speaker 2: these narrative twels that turn out to be nonsense.

1111
01:04:08,520 --> 01:04:10,600
Speaker 4: So, I mean, part of it is that we're humans,

1112
01:04:11,160 --> 01:04:17,479
Speaker 4: and humans have often sort of been snowed by things

1113
01:04:17,480 --> 01:04:19,920
Speaker 4: that are flashy and shiny and exciting. I mean, that's

1114
01:04:20,000 --> 01:04:23,960
Speaker 4: just very much the human condition. Some of the stuff

1115
01:04:23,960 --> 01:04:26,520
Speaker 4: I talk about in the book that I really enjoyed

1116
01:04:26,640 --> 01:04:30,640
Speaker 4: working on was the cognitive psychology aspects of it.

1117
01:04:30,720 --> 01:04:31,520
Speaker 3: You know, sort of.

1118
01:04:32,920 --> 01:04:40,160
Speaker 4: When we hear certain stories, it's very difficult to budge ourselves.

1119
01:04:39,680 --> 01:04:40,800
Speaker 3: And be contrariant.

1120
01:04:40,800 --> 01:04:43,080
Speaker 4: And as I was saying earlier, so you sort of

1121
01:04:43,200 --> 01:04:48,280
Speaker 4: need a collective tipping point where people start to question

1122
01:04:48,360 --> 01:04:50,880
Speaker 4: it so you don't feel like an outlier or the

1123
01:04:50,960 --> 01:04:53,680
Speaker 4: norm when you start to question these things, and so

1124
01:04:54,120 --> 01:04:55,840
Speaker 4: I think there's a role for media here. I think

1125
01:04:55,840 --> 01:04:59,200
Speaker 4: there's a role for education. Unfortunately, the people who benefit

1126
01:04:59,240 --> 01:05:02,240
Speaker 4: from technically also know this and have a very big

1127
01:05:02,280 --> 01:05:05,080
Speaker 4: media presence and invest a lot in education. So it's

1128
01:05:05,400 --> 01:05:09,560
Speaker 4: an uphill battle to start talking about these things differently.

1129
01:05:10,560 --> 01:05:15,760
Speaker 4: But you know, ultimately we are all human and it's

1130
01:05:15,880 --> 01:05:19,160
Speaker 4: nicer to believe that something will succeed than that it

1131
01:05:19,200 --> 01:05:22,000
Speaker 4: will fail. I mean, you might not think I'd be

1132
01:05:22,080 --> 01:05:23,440
Speaker 4: much fun at cocktail parties.

1133
01:05:23,240 --> 01:05:23,800
Speaker 3: Although I am.

1134
01:05:24,600 --> 01:05:28,360
Speaker 2: And the book is available for free at Fintech Dystopia

1135
01:05:29,040 --> 01:05:33,400
Speaker 2: dot com dot com. Let's jump to our final questions,

1136
01:05:33,480 --> 01:05:36,880
Speaker 2: our favorite questions we ask all of our guests, starting

1137
01:05:36,920 --> 01:05:41,040
Speaker 2: with tell us about your mentors who helped steer your career.

1138
01:05:42,520 --> 01:05:46,040
Speaker 4: So my first mentors probably my first law firm partner

1139
01:05:46,160 --> 01:05:53,160
Speaker 4: boss in Australia, Stephen Kevinnagh, and I had thought I

1140
01:05:53,280 --> 01:05:56,800
Speaker 4: was going to be an ip lawyer, but we had

1141
01:05:56,800 --> 01:05:59,720
Speaker 4: a rotation system and we ended up and I ended

1142
01:05:59,800 --> 01:06:04,520
Speaker 4: up in his financial services practice and he was just

1143
01:06:04,520 --> 01:06:06,200
Speaker 4: a wonderful person to work for. It was a time

1144
01:06:06,240 --> 01:06:08,840
Speaker 4: when the law had just changed in Australia and he

1145
01:06:08,920 --> 01:06:13,160
Speaker 4: really was willing to hear what I had to say

1146
01:06:13,240 --> 01:06:17,840
Speaker 4: about this new law, and so it was just I

1147
01:06:17,920 --> 01:06:20,880
Speaker 4: just felt very invested in and that was lovely. And

1148
01:06:20,920 --> 01:06:25,560
Speaker 4: then I think as an academic Patricia McCoy, who I adore,

1149
01:06:26,400 --> 01:06:29,040
Speaker 4: sort of, I have had a very non traditional path

1150
01:06:29,040 --> 01:06:31,720
Speaker 4: to academia. I had more practice experience than is usually

1151
01:06:31,720 --> 01:06:33,800
Speaker 4: the case. I had fewer of the bells and whistles

1152
01:06:33,800 --> 01:06:37,560
Speaker 4: credentials that people usually have. And again, she just saw

1153
01:06:37,840 --> 01:06:43,200
Speaker 4: in me someone who was really passionate about preventing financial crises,

1154
01:06:43,280 --> 01:06:46,920
Speaker 4: about sort of systemic risk, and sort of was willing

1155
01:06:46,960 --> 01:06:49,720
Speaker 4: to look through the fact that I wasn't as polished

1156
01:06:49,720 --> 01:06:52,240
Speaker 4: as most of the other people trying to enter academia

1157
01:06:52,320 --> 01:06:54,360
Speaker 4: and support me, and I was very grateful for that.

1158
01:06:54,840 --> 01:06:58,120
Speaker 2: We've talked about a run of different books. What are

1159
01:06:58,120 --> 01:07:00,479
Speaker 2: some of your favorites? What are you reading now?

1160
01:07:01,280 --> 01:07:03,600
Speaker 4: Oh, I was an English lip major, so I have

1161
01:07:03,640 --> 01:07:08,320
Speaker 4: a many favorites. I'm very into the dystopian track, so

1162
01:07:08,440 --> 01:07:11,920
Speaker 4: Handmaid's Tale nineteen eighty four. I just finished the Parable

1163
01:07:11,920 --> 01:07:14,320
Speaker 4: of the Sewer in that Vein, which was parable of

1164
01:07:14,360 --> 01:07:20,120
Speaker 4: the Parable of the Sewer Octavia Butler. I also have

1165
01:07:20,200 --> 01:07:22,800
Speaker 4: always had a soft spot for really good children's literature,

1166
01:07:22,920 --> 01:07:27,320
Speaker 4: So Philip Pullman's Dark Materials trilogy is one of my favorites.

1167
01:07:27,480 --> 01:07:32,200
Speaker 4: And right now I'm reading with my kids Catherine Rundell's

1168
01:07:32,240 --> 01:07:37,040
Speaker 4: books Impossible Creatures and The Poison King, and it's just

1169
01:07:37,080 --> 01:07:40,400
Speaker 4: they're just so good. And then work wise, I've just

1170
01:07:40,480 --> 01:07:43,800
Speaker 4: started Jacob Silverman's Gilded Rage, which is very much on

1171
01:07:43,880 --> 01:07:46,120
Speaker 4: point for the conversation we're having Gilded Rage.

1172
01:07:46,280 --> 01:07:49,800
Speaker 2: You know, we talked about a few crypto related books.

1173
01:07:49,840 --> 01:07:55,200
Speaker 2: Did you see Zeke's number go up? It really is

1174
01:07:55,480 --> 01:08:00,880
Speaker 2: just an astonishing, astonishing work. What sort of advice would

1175
01:08:00,880 --> 01:08:03,880
Speaker 2: you give to a recent college grad interested in a

1176
01:08:03,960 --> 01:08:10,280
Speaker 2: career on whether it was law, financial technology, regulation, what's

1177
01:08:10,280 --> 01:08:11,480
Speaker 2: your advice to those people?

1178
01:08:11,800 --> 01:08:14,080
Speaker 4: It's a really hard time for them. And I talk

1179
01:08:14,200 --> 01:08:16,439
Speaker 4: to my students a lot about the careers, and you know,

1180
01:08:16,520 --> 01:08:18,479
Speaker 4: things are the ground is shifting under our feet, and

1181
01:08:18,479 --> 01:08:21,400
Speaker 4: in this time of uncertainty, really it's really hard to

1182
01:08:21,439 --> 01:08:25,839
Speaker 4: figure out what to do. So I would recommend investing

1183
01:08:25,840 --> 01:08:28,559
Speaker 4: in the fundamentals, and I think it's hard to do

1184
01:08:28,680 --> 01:08:31,920
Speaker 4: when AI is being pushed, but becoming a good communicator,

1185
01:08:32,240 --> 01:08:35,479
Speaker 4: learning how to write and speak to people clearly will

1186
01:08:35,520 --> 01:08:36,120
Speaker 4: never I.

1187
01:08:36,080 --> 01:08:39,160
Speaker 3: Think, go out of fashion. And investing in relationships.

1188
01:08:39,160 --> 01:08:41,479
Speaker 4: Again, we're in this time where everything is sort of

1189
01:08:41,479 --> 01:08:43,599
Speaker 4: becoming technologized and atomized, et cetera.

1190
01:08:43,720 --> 01:08:46,559
Speaker 3: But in my career, having good.

1191
01:08:46,439 --> 01:08:48,840
Speaker 4: Relationships with people, and I'm pretty sure you'll agree with

1192
01:08:48,880 --> 01:08:52,120
Speaker 4: this has been one of the most successful things that

1193
01:08:52,160 --> 01:08:55,439
Speaker 4: has helped me along the way. And so just investing

1194
01:08:55,439 --> 01:08:59,360
Speaker 4: in personal relationships I think is always good advice.

1195
01:09:00,000 --> 01:09:05,440
Speaker 2: Final question, what do you know about the world of fintech? Investing?

1196
01:09:06,080 --> 01:09:09,760
Speaker 2: Regulation today might have been useful twenty twenty five years ago.

1197
01:09:12,200 --> 01:09:16,439
Speaker 4: Honestly, I'm not sure that there's much because the world

1198
01:09:16,479 --> 01:09:19,479
Speaker 4: was very different twenty to twenty five years ago. You know,

1199
01:09:19,560 --> 01:09:25,040
Speaker 4: I always just invested in in index funds basically, and

1200
01:09:25,600 --> 01:09:29,519
Speaker 4: you know, and that worked out frankly great, really well.

1201
01:09:29,640 --> 01:09:33,880
Speaker 4: The challenge is, and I study financial crises, the challenge

1202
01:09:33,960 --> 01:09:37,480
Speaker 4: is that when things go horribly wrong, everything is correlated.

1203
01:09:37,960 --> 01:09:39,040
Speaker 3: Everything is correlated.

1204
01:09:39,080 --> 01:09:41,559
Speaker 2: All correlations go to one in a crisis, for sure.

1205
01:09:41,680 --> 01:09:45,480
Speaker 3: And I think we're on the brink of a crisis.

1206
01:09:45,920 --> 01:09:49,800
Speaker 2: When you say on the brink, days, weeks, months, years.

1207
01:09:49,800 --> 01:09:50,160
Speaker 3: Ah well.

1208
01:09:50,240 --> 01:09:52,920
Speaker 4: John Maynard Kaine said that the markets can stay irrational

1209
01:09:52,960 --> 01:09:54,880
Speaker 4: longer than you, and I can stay solvent, so I

1210
01:09:54,920 --> 01:09:56,400
Speaker 4: will never put a time.

1211
01:09:56,280 --> 01:09:56,840
Speaker 3: Frame on it.

1212
01:09:57,040 --> 01:10:00,559
Speaker 4: But I you know, all warning indicators are flat read

1213
01:10:00,600 --> 01:10:02,479
Speaker 4: at the same time as we are pulling back old

1214
01:10:02,520 --> 01:10:05,439
Speaker 4: regulatory apparatus, So I think it's safe to say we're

1215
01:10:05,520 --> 01:10:06,800
Speaker 4: on the brink of a crisis.

1216
01:10:06,880 --> 01:10:11,480
Speaker 2: How could that ever go wrong? How could regulation unleaches

1217
01:10:12,120 --> 01:10:14,880
Speaker 2: the animal spirits? As long as we're talking about canes,

1218
01:10:16,560 --> 01:10:22,400
Speaker 2: it's all good. Perhaps not, perhaps not? Hillary, Thank you

1219
01:10:22,479 --> 01:10:25,280
Speaker 2: so much for being so generous with your time. We

1220
01:10:25,400 --> 01:10:28,840
Speaker 2: have been speaking with Hillary Allen, professor of law at

1221
01:10:28,840 --> 01:10:33,680
Speaker 2: American University Washington College in DC and author of the

1222
01:10:33,800 --> 01:10:39,800
Speaker 2: book available for free online, Fintech Dystopia, a Summer beach

1223
01:10:39,880 --> 01:10:44,320
Speaker 2: read about how Silicon Valley is ruining things. If you

1224
01:10:44,520 --> 01:10:47,840
Speaker 2: enjoy this conversation, well check out any of the six

1225
01:10:48,000 --> 01:10:52,719
Speaker 2: hundred previous discussions we've had over the past twelve years.

1226
01:10:53,040 --> 01:10:57,560
Speaker 2: You could find those at iTunes, Spotify, YouTube, Bloomberg, or

1227
01:10:57,560 --> 01:11:02,280
Speaker 2: wherever you find your favorite podcast. I would be remiss

1228
01:11:02,280 --> 01:11:04,479
Speaker 2: if I didn't thank our crack staff that helps put

1229
01:11:04,520 --> 01:11:10,840
Speaker 2: these conversations together each week. Alexis Noriega is my video producer.

1230
01:11:10,960 --> 01:11:16,760
Speaker 2: Sean Russo is my researcher Anna Luke is my podcast producer.

1231
01:11:17,280 --> 01:11:21,320
Speaker 2: I'm Barry Ritolts. You've been listening to Masters in Business

1232
01:11:21,840 --> 01:11:23,600
Speaker 2: on Bloomberg Radio.