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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome the Money Stuff Podcast. You're a weekly

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Speaker 2: podcast where we talk about stuff related to money. I'm

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Speaker 2: Matt Levian and I write the Money Stuff column for

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Speaker 2: Bloomberg Good Pay.

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Speaker 1: And I'm Katie Greifeld, a reporter for Bloomberg News and

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Speaker 1: an anchor for Bloomberg Television.

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Speaker 2: What are you talking about today, Katie?

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Speaker 1: We're going to talk about open Aire, all the drama.

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Speaker 1: We're going to talk about chatbots and stock picking, and

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Speaker 1: then we're going to talk about bibles.

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Speaker 2: I'm gonna bell out right now.

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Speaker 1: Sam Altman open Ai. You know how open ai is

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Speaker 1: a nonprofit?

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Speaker 2: I don't well explain it to me.

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Speaker 1: No, we don't need to, because it's not anymore. It

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Speaker 1: won't be. According to people familiar with the matter, of course,

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Speaker 1: they're just discussing how to restructure to become a for

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Speaker 1: profit business officially and giving Sam Altman a seven percent

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Speaker 1: equity stake.

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Speaker 2: It's so good.

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Speaker 1: Yeah.

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Speaker 2: I read about this that if you just like didn't

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Speaker 2: know that it was a nonprofit and you just ignored

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Speaker 2: all of that stuff, you would sort of have a

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Speaker 2: better understanding of open Ai than if you did think

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Speaker 2: about it's nonprofit governance structure, because you know, they had

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Speaker 2: a nonprofit board whose job was to develop AI for

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Speaker 2: the benefit of humanity, and the nonprofit board decided that

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Speaker 2: the CEO of Sam Altman was apparently developing it not

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Speaker 2: for the benefit of humanity, and so they fired him,

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Speaker 2: and then everyone was like, never mind, there's no nonprofit board.

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Speaker 1: Well that's the thing. I feel like a lot of

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Speaker 1: people didn't know that open ai was a nonprofit until

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Speaker 1: they fired and then rehired Sam Ali.

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Speaker 3: Yeah.

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Speaker 2: Yeah, there was like a five day period where you

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Speaker 2: could be like, wow, open ai is really a weird

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Speaker 2: company and not just the hot tech startup. And then

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Speaker 2: they're like, nah, we're just kidding. We're a context out

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Speaker 2: And so now they're going to be a for profit

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Speaker 2: for real.

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Speaker 1: Yeah, taking the last step they're considering, according to people

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Speaker 1: familiar with the matter, becoming a public benefit corporation, tasked

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Speaker 1: with turning a profit but also helping society. But Matt,

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Speaker 1: are those two things at odds? Can you help society

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Speaker 1: and still make money? I don't know.

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Speaker 2: This podcast will try to find out. Look, you know

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Speaker 2: you can be a public benefit corporation. You know you're

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Speaker 2: trying to make a profit. But like you, the distinction

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Speaker 2: machine a public benefit corporation and a regular corporation is

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Speaker 2: pretty vague, right, because you can be just a regular

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Speaker 2: old for profit corporation and like also try to be good,

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Speaker 2: and the constraints on you as a public benefit corporation

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Speaker 2: are not that constraining, So you can be a public

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Speaker 2: benefit corporation and be kind of bad. But you know,

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Speaker 2: if you're going from being a nonprofit company to being

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Speaker 2: a for profit company, it probably does make sense to

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Speaker 2: stop along the way being a public benefit company because

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Speaker 2: it like sounds better, sounds like less of a stark transition.

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Speaker 1: It still has benefit.

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Speaker 2: Yeah, it's still for the benefit of the public. You ask,

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Speaker 2: somewhat sarcastically, can you do good while also making money?

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Speaker 2: And I think it's really interesting that, like the Silicon

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Speaker 2: Valley ethos is so strongly of the view that you

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Speaker 2: always read VCS saying Elon Musk has done more for

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Speaker 2: humanity in its for profit companies than you know, any philanthropist.

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Speaker 2: And you know, I think there's some truth to the

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Speaker 2: idea that like for profit development of technology can be

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Speaker 2: really good for the world and also make the people

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Speaker 2: who do it really rich, and it was always at odd.

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Speaker 2: It's with that ethos that open ai was a nonprofit

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Speaker 2: and always very awkward because you know it was like

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Speaker 2: founded by Silicon Valley, you know, sounded by literally Elon

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Speaker 2: Musk and like some PC types, you know, s Ham

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Speaker 2: Altman is you know kind of a he's like a

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Speaker 2: you know, bicombinator, like the VC guy, and you know

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Speaker 2: it's staffed by tech people, and all of those people,

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Speaker 2: I think, to some degree in their previous lives brought

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Speaker 2: into like the Silicon Valley ethos of like the way

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Speaker 2: to improve society is to to scalable low marginal cost

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Speaker 2: technology products that make a lot of money for their developers.

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Speaker 2: And there's this little blip of like at open Ai,

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Speaker 2: I was like, no, we're going to do it a

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Speaker 2: different way, and then they changed their months. So clearly

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Speaker 2: a lot of people at open ai think that they

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Speaker 2: can do good for humanity while also making a lot

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Speaker 2: of money. So the mission of open ai being this

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Speaker 2: technology in particular cannot be commercialized, This technology in particular

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Speaker 2: has to be done by a nonprofit, which was apparently

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Speaker 2: the theory for a while. It's always a little weird, right,

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Speaker 2: because like the product they've rolled out is sort of

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Speaker 2: this like consumer chat pottery. It's on a continuum with

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Speaker 2: a lot of, like you know, for profit technology products.

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Speaker 2: I think there's like the overlay of open ai believing

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Speaker 2: that AI could enslave humanity or red work obsolete, or

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Speaker 2: like usher in a new age of abundance where money

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Speaker 2: has no meaning. Yeah, if you really believe that your

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Speaker 2: product is going to create an age where money has

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Speaker 2: no meaning, then I suppose it makes sense to operate

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Speaker 2: it as a nonprofit. Yeah, but you know that market.

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Speaker 1: Well, the point before right where I mean Altman saying

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Speaker 1: publicly like I'm so scared of what we could be

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Speaker 1: creating is a great pitch for why you should give

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Speaker 1: me a lot of money. Like what we're working on

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Speaker 1: is so incredible that I am scared, right.

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Speaker 2: It makes it seem very important, and so it helps

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Speaker 2: them raise a lot of money. But eventually, you know,

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Speaker 2: those people who give them all the money want their

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Speaker 2: money back. Yeah.

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Speaker 1: I don't think like all the investors pouring so much

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Speaker 1: money into open ai, which is raising right now with

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Speaker 1: a one hundred and fifty billion dollars valuation, or necessarily

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Speaker 1: investing to help humanity, they're investing with the goal of

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Speaker 1: making a lot of money, I would imagine.

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Speaker 2: Yeah. And by the way, like even before this like

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Speaker 2: supposed change, open AI has this for profit subsidiary that

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Speaker 2: was already able to give to promise investors a big

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Speaker 2: return on their money. It was like a cap to

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Speaker 2: profit subsidiary, so you could only make one hundred times your.

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Speaker 1: Money up change.

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Speaker 2: So what is changing is I think two things. One

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Speaker 2: is like, like right now the sort of notional corporate

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Speaker 2: structure is that on top is a nonprofit and then

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Speaker 2: there's like the cap profit subsidiary under it, and you

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Speaker 2: can invest in the cap profit subsidiary and get profits.

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Speaker 2: But ultimately everything answers to the nonprofit board. The nonprofit

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Speaker 2: board has no fiduciary duties to investors. Their duty is

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Speaker 2: to humanity to develop AI in the best way. And

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Speaker 2: you know what happened is the old nonprofit board thought

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Speaker 2: that Sam all And wasn't doing that in some unspecified way,

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Speaker 2: and so they fired him, and then they all got

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Speaker 2: fired themselves. And so now there's a new nonprofit board

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Speaker 2: that is like a little bit more profit oriented. Yeah,

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Speaker 2: but it's still technically does an answer to the investors.

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Speaker 2: And I think the restructuring would mean that the board

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Speaker 2: is now more directly responsible to investors. And then the

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Speaker 2: other thing that's happening in this change besiudes, like the

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Speaker 2: for profit being the top company is sam Aldman is

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Speaker 2: supposedly getting seven percent of the company.

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Speaker 1: It's spend a lot of money. Theoretically.

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Speaker 2: I was thinking, if like Harvard University became a for

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Speaker 2: profit company tomorrow, would they give the president ten billion dollars.

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Speaker 2: It's like a really interesting you know, like typically founders

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Speaker 2: of hot tech companies get a lot of equity. Yeah,

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Speaker 2: because they retain a lot of equity, right, Like they

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Speaker 2: start with one hundred percent of the company and then

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Speaker 2: they raise money by selling portion of the company. But

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Speaker 2: like they started with a undred percent and they end

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Speaker 2: up with you know, seven or ten or twenty or

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Speaker 2: fifty percent. Sam Alban started with zero percent because it

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Speaker 2: was a nonprofit, and now they're retroactively being like, wow, okay, fine,

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Speaker 2: it was a regular startup, so you can't have zero

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Speaker 2: percent equity, so we're going to give you seven percent.

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Speaker 2: And so because he built this company from zero to

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Speaker 2: let's say, one hundred and fifty billion dollar valuation, even

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Speaker 2: though he did that without any equity stake. They're retroactively saying, well,

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Speaker 2: you did that, so you're a founder, so you get

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Speaker 2: ten billion of that value you created.

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Speaker 1: I like equation that went into How did they land

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Speaker 1: at seven percent?

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Speaker 2: Like?

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Speaker 1: Did they?

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Speaker 2: I'm very curious about the capital structure of this company generally, right,

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Speaker 2: because like this company is like you know, if you

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Speaker 2: if you just think of the for profit subsidiary, right,

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Speaker 2: like Microsoft owns quite a chunk of it, because they've

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Speaker 2: put a lot of money in at like lower evaluations

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Speaker 2: than the current one. And you know, there's some VC investors.

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Speaker 2: And then there are a lot of employees, not Sam alten,

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Speaker 2: but a lot of employees have gotten you know, chunky

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Speaker 2: stock grants of the years. They're not called stock grunts.

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Speaker 2: They're called something like performance something units, but they're.

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Speaker 1: Stock grunts, right, and benefit grants.

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Speaker 2: Yeah, there's stocks. And then the rest of the company

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Speaker 2: is owned by humanity, right, the rest of the company's

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Speaker 2: owned by the nonprofit. Right. No, and so the nonprofit

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Speaker 2: now is basically allocating money away from humanity to say,

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Speaker 2: Sam Altman, And how did they come up with that number?

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Speaker 2: I don't know. I think that like it has to

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Speaker 2: be a lot because first of all, he's a billionaire

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Speaker 2: front it's all their adventures. But it has to be

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Speaker 2: a lot because if you're just recasting open Ai as

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Speaker 2: a regular startup, then the founder CEO has to own

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Speaker 2: a lot of it because he has to be incentivized

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Speaker 2: in the right way, right. I Mean, one thing that's

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Speaker 2: happened in the open a story in the last year

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Speaker 2: or two is that you're constantly reading about Sam Altman

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Speaker 2: doing like other weird stuff. You know, he's like getting

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Speaker 2: involved in like data center deals and like power deals

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Speaker 2: and like other startups that use open ai technology, and

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Speaker 2: they all feel like confident of interest. And one explanation

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Speaker 2: of that is, Okay young zero percent of open Aif

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Speaker 2: she like can like make a little money on the

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Speaker 2: side by doing some sort of nuclear power deal, then

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Speaker 2: he'll do that. And so if you're an investor looking

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Speaker 2: to put money into open Ai at one hundred and

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Speaker 2: fifty billion dollar valuation, one thing you want is for

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Speaker 2: the CEO to be mostly working for open Ai. Yeah,

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Speaker 2: and you can imagine a lot of ways to try

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Speaker 2: to do that. But like the standard silicon of Valueay

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Speaker 2: is like he has most of his net worth in

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Speaker 2: open ai stock, so you have to give him enough

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Speaker 2: stock that most of his network is in open A stock.

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Speaker 2: And that's like, you know, come doing it.

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Speaker 1: She with Elon Musk parallels because you want the founder

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Speaker 1: CEO to be engaged and passionate and working on the company.

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Speaker 1: Elon Musk when it comes to Tesla, I mean that

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Speaker 1: was basically his argument that I want to focus on

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Speaker 1: the thing where I have the most at stake here,

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Speaker 1: and that's why I should own more of Tesla.

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Speaker 2: Yeah. Absolutely, I don't know who is asking for what here.

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Speaker 2: Like you could imagine I'm not saying this is true.

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Speaker 2: I don't think this is true. But you could imagine

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Speaker 2: Sam and be like, no, I'm good, I have two

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Speaker 2: billion dollars. I don't need more money from this company.

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Speaker 2: I'm in it for the benefit of humanity. You could

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Speaker 2: imagine the board of the investors saying, no, you need

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Speaker 2: to have a ligned incentives. You need to own seven

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Speaker 2: percent of the company. Here, take ten billion dollars so

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Speaker 2: that we know you're working for us. You can imagine that.

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Speaker 1: Some time twists my arm, all right.

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Speaker 2: Right, right, all right, And like, by the way, Elon

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Speaker 2: Musk will ask for the money, but he will also say,

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Speaker 2: I don't want it for the money. I just want it,

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Speaker 2: you know, to make sure that I'm building it in

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Speaker 2: the right place. So yeah, it's totally like Elon. It's

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Speaker 2: a little different because Elon has a lot of very

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Speaker 2: different ideas, but like have a lot of conflicts. But

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Speaker 2: you understand why they're different. Companies like Sam all been

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Speaker 2: when he's like raising money for ventures that build on

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Speaker 2: top of open Ai, it's like, well, let's do that

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Speaker 2: at open Ai. It's a little bit more like it

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Speaker 2: looked like he was using his position at open ai

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Speaker 2: to make money on the side because he couldn't make

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Speaker 2: money at open ai. And now it's like, wow, I

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Speaker 2: can make a lot of money open Ai, so do

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Speaker 2: that instead. But yes, the perils that Elon are interesting,

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Speaker 2: right because I said, you know, the notion that Elon

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Speaker 2: Musk has done a lot for humanity by running for

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Speaker 2: profit companies, It's like validates dispute to being a for

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Speaker 2: profit company. On the other hand, Elon Musk is also

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Speaker 2: suing open ai for becoming a for profit company.

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Speaker 1: That's so true.

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Speaker 2: This is it has one circumstance. He does not believe

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Speaker 2: you should be benefiting humanity and also making a profit.

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Speaker 1: It feels like a while since we've talked about Elon.

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Speaker 2: I wonder like if the resolution to all of this

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Speaker 2: is open a eye is now a for profit company.

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Speaker 2: Microsoft will own x percent of it, Sam Alton and

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Speaker 2: it humanity will ow you know, some single percentage.

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Speaker 1: Of Well give you a benefit.

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Speaker 2: We just give you on you know, an equity stake

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Speaker 2: for his his contributions. Why not get on side then

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Speaker 2: you get you on a line. Yeah, that's good. Although

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Speaker 2: he's got his own AI company, so I guess he

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Speaker 2: cant do that.

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Speaker 1: True? Was that XII chat thoughts? Shall we? Shall we

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Speaker 1: talk about Bridget.

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Speaker 2: Another AI transition.

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Speaker 1: Bridget Bridget bridge Wise.

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Speaker 2: So Bridgewise is this Israeli tech company, fintech company that

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Speaker 2: is launching a chatbot that will be your broker.

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Speaker 3: Yeah.

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Speaker 2: So it's this fascinating story because you read about it

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Speaker 2: and you're like, Okay, what does this chatbot do, like

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Speaker 2: gives your stock recommendations? Yeah? So, I mean I think

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Speaker 2: it's like you type of O. Can you tell me

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Speaker 2: like the top five stocks in like the industrial sector

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Speaker 2: that I should buy today? And it's like, yeah, well,

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Speaker 2: looking at the researcher, but like you can imagine just

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Speaker 2: being like, what stocks should I buy? And then Bridgett

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Speaker 2: says you should buy these five stocks, and you go

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Speaker 2: buy those five stocks, and then the next day you

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Speaker 2: come back and you're like, bridget they went down. What's

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Speaker 2: going on?

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Speaker 1: Yeah, Like, what information do you? What is the prompt?

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Speaker 1: Prompts matter so much with these chatbots. Like if I

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Speaker 1: said my name is Katie, I'm thirty one, I love horses,

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Speaker 1: what would you tell me to buy? Versus if I

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Speaker 1: said I'm Katie, you're up in New Jersey. That's it.

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Speaker 2: But those are bad prompts. The prompt that you want

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Speaker 2: is what stocks will go up the most?

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Speaker 1: Well, yeah, because you know you don't care.

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Speaker 2: But you're on the right track, because like this thing

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Speaker 2: is clearly a substitute for a broker, right. This is

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Speaker 2: like you call your broker, and your broker gets to

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Speaker 2: know you and learns about your risk tolerances and your

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Speaker 2: preferences and the themes that you're interested in. And then

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Speaker 2: your broker is like, well, given your risk tolerance and

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Speaker 2: you're interested in the theme of horses, you should buy

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Speaker 2: the horse, etf or whatever. Right, But that's true because

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Speaker 2: brokerage is a client service business. Where the product is

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Speaker 2: making the client happy with the service. But you can

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Speaker 2: imagine a different model where the product is like buying

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Speaker 2: stocks that go up. Yeah, right, I think that we

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Speaker 2: all understand that when you call your broker and you

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Speaker 2: say what stock should I either be like, what are

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Speaker 2: your risk tolerances? You should be in this CTF because

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Speaker 2: there's a good balance of what like they'll say something

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Speaker 2: other than like I know which stocks will go up

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Speaker 2: and these are them and you should buy them, because

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Speaker 2: like if they knewhich stocks would go up, yeah, they

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Speaker 2: would be doing something else.

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Speaker 1: Why can't I just ask chat ChiPT what stocks to buy?

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Speaker 2: You can apparently sometimes tell you I can't give you

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Speaker 2: stock recommendations.

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Speaker 1: Oh, you can get around it.

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Speaker 2: So chat GPT is like a general purpose chatbot, and

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Speaker 2: it can like read the internet and sort of say

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Speaker 2: what people will say, Like you can make like the

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Speaker 2: best guess that like what people would tell you if

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Speaker 2: you ask what stocks will go up? Bridget is trained

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Speaker 2: a little bit more on like financial information, and so

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Speaker 2: it might have a better idea what stocks will go up. Yeah,

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Speaker 2: maybe like a less good general purpose chatbot, but a

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Speaker 2: good picker of stocks. But I think the point is

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Speaker 2: like it's so interesting reading, Like you know, there's there's

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Speaker 2: a Bloomberg News story about it, and like you read

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Speaker 2: the story and it's like it's been tested a lot

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Speaker 2: to make sure it gives accurate recommendations.

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Speaker 1: Well, so it's been tested.

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Speaker 2: I don't think it means it's been back testing. I

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Speaker 2: think it means it's like if it says to you, oh,

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Speaker 2: JP Morgan Research that this stock is like x price target.

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Speaker 2: Those quotes are accurate, right, it's quoting information to you,

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Speaker 2: but it's not. I don't think back tested depict stacks

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Speaker 2: that will go up because if it were back tested

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Speaker 2: depict stacks like you go up and it was successful,

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Speaker 2: it would not be a chatbot.

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Speaker 1: For a brokerage form, that's true, it.

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Speaker 2: Would be Bridge.

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Speaker 1: It would be Renaissance, yeah, or you know, it could

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Speaker 1: just be the Bridget hedge fund. Yeah, and they can

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Speaker 1: make a lot more money. They should back test it, though,

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Speaker 1: I want to back test Bridget. I must chat GBT

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Speaker 1: to back test.

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Speaker 2: It's like in beta, but like we'll certainly be testing Bridget.

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Speaker 1: Yeah, this did remind me of a thing that you

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Speaker 1: wrote about already, But I was on vacation last week,

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Speaker 1: so we didn't talk about it on the pod, but

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Speaker 1: there is that chatbot ETF that just launched that's supposed

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Speaker 1: to mimic buffets Stanley Drackenmiller, David Tepper. And that's interesting.

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Speaker 1: I mean it's so it's basically.

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Speaker 2: Yeah, by the way, I haven't seen their back tests.

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Speaker 1: I would love to see their back tests as well.

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Speaker 1: But basically they have this investment committee that is built

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Speaker 1: on training the chatbot around investment ideas. It basically wants

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Speaker 1: them to mimic these guys, and he wants them to

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Speaker 1: learn their personalities and then emulate it. And that's crazy.

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Speaker 1: But it's kind of similar to what we're talking about

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Speaker 1: with Bridget. I mean, she's just like general financial information,

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Speaker 1: whereas this is like, this is what David Tepper would doing.

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Speaker 2: We should you know, like when you like want to

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Speaker 2: like change your airline flight, you like go to a chatbot,

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Speaker 2: and like the chatbot is like trying to replace the

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Speaker 2: human customer service but like someone must do it, and

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Speaker 2: like that's supposed to be been making the human customer

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Speaker 2: service agent, right, and like this I think is sort

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Speaker 2: of the same thing, right, It's like making the human

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Speaker 2: broker who like has the relationship with you and can

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Speaker 2: like give you stock tips, but like not with like

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Speaker 2: one hundred percent reliability that it will pick stocks that

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Speaker 2: go up, just like you know, serving the purpose of

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Speaker 2: a broker. The chat bot the like LLM ETF is

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Speaker 2: a little different, Like they're trying to pick stacks that

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Speaker 2: go up, Like you're not chatting with the chatbot, right

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Speaker 2: Like the person who runs that ETF is chatting with

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Speaker 2: the chatbot and asking what stocks will go up and

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Speaker 2: hoping that the chatbot gives them the right answer.

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Speaker 1: That's so funny.

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Speaker 3: I don't know.

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Speaker 2: I'm just the product with bridget is the relationship with

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Speaker 2: the customer, right, Like you want the customer to feel better,

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Speaker 2: to feel like they're getting the information they call for,

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Speaker 2: and that includes like stock recommendations. The product for the

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Speaker 2: ETF is just the returns stain of the ATF, right

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Speaker 2: Like the chatbot is all behind the scenes, and that

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Speaker 2: is putting a lot more emphasis on the ability of

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Speaker 2: the chatbot to actually pick stocks.

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Speaker 1: Also, the goal of the ETF isn't necessarily pickstocks that

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Speaker 1: will go up, because there's no guarantee that, Like you're right,

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Speaker 1: Warren Buffett or Stanley Drugon Miller will actually correctly identify

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Speaker 1: those stocks.

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Speaker 2: Okay, but two things. One, I think the ETF would

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Speaker 2: much rather the chatbot pick stocks that go up that

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Speaker 2: Warren Buffett would not have picked, than that it accurately

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Speaker 2: reflects Warren Buffett and pick stocks that go down. Right,

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Speaker 2: Like they don't actually care about Warren.

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Speaker 1: Buff You don't care about their tracking error.

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Speaker 2: The tracking air is entirely theoretical, right, because you can't

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Speaker 2: like find the port. I mean you can literally like

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Speaker 2: a Workshire Hathway. But this is like the stacks that

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Speaker 2: Warren Buffett would pick if you ask them, there's no

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Speaker 2: like visible tracking error.

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Speaker 3: Yeah.

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Speaker 2: But then the other thing is you're right that also

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Speaker 2: their goal is not really to pick stacks like you up.

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Speaker 2: Their goal is really to attract investors, right, And like

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Speaker 2: it's a good stick, right, it's a good pitch, Like well,

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Speaker 2: training to chat bought to be Warren Buffett and then

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Speaker 2: ask it what slacks? Like, that's clever. I like that.

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Speaker 2: I'd kick in money to that where they're not the stacks.

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Speaker 1: I can't wait to I mean it already launched. We're

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Speaker 1: talking about the Intelligent Livermore ETF, but it's only been

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Speaker 1: a couple of days. The ticker I believe is l

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Speaker 1: I v R. I'm so excited to see the uptake

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Speaker 1: of this. I want to call David Tepper and ask

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Speaker 1: how he feels about it, you know. Like again reading

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Speaker 1: the description of this, the firm is going to instruct

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Speaker 1: the lms to emulate the investors' personalities. That makes my Skinvid.

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Speaker 2: Tepper is not in the like prospectus, like the b

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Speaker 2: Regardic is like, yeah, David Tepper is on the list,

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Speaker 2: but like, I don't think you can really advertise formally

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Speaker 2: in writing. We're gonna like train an AI to be

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Speaker 2: David Tepper, Like David Tepper is out there like running

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Speaker 2: his own fund.

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Speaker 1: Well that's another, I mean good point against investing CTF.

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Speaker 1: You could just invest with David Tepper if you, you know,

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Speaker 1: meet the criteria.

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Speaker 2: Yeah, you can just buy Berser Hathaways.

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Speaker 1: Yeah exactly.

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Speaker 2: It's like this is a clever stack. Yeah, they're not

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Speaker 2: really meant to be David Tepper. By the way, the

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Speaker 2: ticker is l I V or whatever. Like they've filed

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Speaker 2: for several of them, and one of them is the

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Speaker 2: ticker is a I w B. And again they can't

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Speaker 2: put Warren Buffett's name. I know, in the perspectives they

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Speaker 2: can say a I w B.

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Speaker 3: I love it.

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Speaker 1: It's got to the good stuff about Bible washing.

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Speaker 2: So I feel for the US Curious and Exchange Commission.

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Speaker 1: You know, you're a sympathizer, I know.

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Speaker 2: And they have a hard job. And like one reason

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Speaker 2: they have a hard job is like they're there to

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Speaker 2: protect investors from like bad ideas, but kind of right,

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Speaker 2: like you know, so they want to do their goal.

438
00:21:20,880 --> 00:21:23,320
Speaker 2: That's not actually their mandate, right, their mandate is essentially

439
00:21:23,320 --> 00:21:27,359
Speaker 2: to enforce good disclosure. So like if you disclose a

440
00:21:27,400 --> 00:21:30,160
Speaker 2: really bad idea, then you can do that even if

441
00:21:30,520 --> 00:21:32,480
Speaker 2: it's a bad idea, even if the SEC doesn't.

442
00:21:32,240 --> 00:21:33,920
Speaker 1: Like it, as long as you're super upfront.

443
00:21:34,080 --> 00:21:36,919
Speaker 2: Yeah, and like there are limits on that. Many of

444
00:21:36,920 --> 00:21:39,520
Speaker 2: them discovered in crypto where you were like a lot

445
00:21:39,520 --> 00:21:41,160
Speaker 2: of people in crit are like this is a Ponzi scheme.

446
00:21:41,280 --> 00:21:43,440
Speaker 2: It's just like come on now. But so, like one

447
00:21:43,440 --> 00:21:47,040
Speaker 2: thing that happens is that the SEC is very interested

448
00:21:47,080 --> 00:21:50,160
Speaker 2: in climate change. Right in environmental investing, there's a lot

449
00:21:50,160 --> 00:21:53,400
Speaker 2: of focus on ESG investing, environmental, social and governance investing,

450
00:21:53,920 --> 00:21:56,040
Speaker 2: and there are a lot of people who think that

451
00:21:56,840 --> 00:22:00,920
Speaker 2: the firms that do that kind of investing are somehow

452
00:22:01,400 --> 00:22:06,000
Speaker 2: not doing it well. There's concerns about greenwashing where you

453
00:22:06,040 --> 00:22:08,600
Speaker 2: say you are an environmental investing firm, but then you

454
00:22:08,600 --> 00:22:11,840
Speaker 2: own some coal stocks, and like people get mad about that,

455
00:22:12,920 --> 00:22:15,280
Speaker 2: and the SEC gets mad about it too. The SEC

456
00:22:15,320 --> 00:22:19,120
Speaker 2: can't say things like if you say you're an ESG firm,

457
00:22:19,160 --> 00:22:22,120
Speaker 2: you can't own coal stocks because like who's to say

458
00:22:22,240 --> 00:22:23,840
Speaker 2: what is ESG not the SEC?

459
00:22:23,880 --> 00:22:26,560
Speaker 1: Also, maybe that coal company has really great governance.

460
00:22:26,640 --> 00:22:28,760
Speaker 2: No, but it's true, like these are fuzzy criteria, right,

461
00:22:28,880 --> 00:22:31,560
Speaker 2: so you could truly be like the best ESG investor

462
00:22:31,560 --> 00:22:33,000
Speaker 2: in the world and on a col stock. That's what

463
00:22:33,600 --> 00:22:37,639
Speaker 2: is an example everywhere, Like you know, oil companies, good governance,

464
00:22:37,680 --> 00:22:40,480
Speaker 2: oil companies that are improving their emissions so they're better

465
00:22:40,520 --> 00:22:42,399
Speaker 2: than you know, Like, there's all sorts of ways to

466
00:22:42,400 --> 00:22:44,000
Speaker 2: be an ESG investor, and a lot of people have

467
00:22:44,040 --> 00:22:47,479
Speaker 2: like genuine disagreements about what to prioritize and how to

468
00:22:47,600 --> 00:22:50,479
Speaker 2: choose between like owning the best oil company to encourage

469
00:22:50,480 --> 00:22:53,320
Speaker 2: oil companies to be better environmental citizens, or owning no

470
00:22:53,359 --> 00:22:55,840
Speaker 2: oil companies because they're all too you know, there's all

471
00:22:55,840 --> 00:22:58,320
Speaker 2: sorts of ways to do it, and the SEC doesn't

472
00:22:58,359 --> 00:23:02,000
Speaker 2: really substantively get to pick between them. But what it

473
00:23:02,040 --> 00:23:04,359
Speaker 2: does get to do is read your disclosures and make

474
00:23:04,400 --> 00:23:07,159
Speaker 2: sure they're accurate. And so it has broad cases against

475
00:23:07,160 --> 00:23:11,080
Speaker 2: like I think Bonie Mellon had a ESG fund where

476
00:23:11,080 --> 00:23:13,800
Speaker 2: they're like, you know, we have these ESG screening criteria

477
00:23:14,119 --> 00:23:16,720
Speaker 2: and we apply these criteria to each company before we

478
00:23:16,720 --> 00:23:19,240
Speaker 2: make an investment. And they went through their you know,

479
00:23:19,280 --> 00:23:22,400
Speaker 2: internal records and found that sometimes they made an investment

480
00:23:22,720 --> 00:23:25,480
Speaker 2: without getting a memo about the criteria, and they said, ahaha,

481
00:23:25,520 --> 00:23:28,399
Speaker 2: you're not actually doing your ESG investing, And so they

482
00:23:28,440 --> 00:23:30,720
Speaker 2: find them and they got in trouble, and like that's

483
00:23:31,080 --> 00:23:33,960
Speaker 2: the level of enforcement that the SEC can do, and

484
00:23:34,440 --> 00:23:37,879
Speaker 2: it's I think frustrating for them because they probably have

485
00:23:38,000 --> 00:23:41,120
Speaker 2: sub substantive ideas about who's doing a good job or not. Anyway,

486
00:23:41,480 --> 00:23:45,280
Speaker 2: this week, a company called Inspire Investing paid a fine

487
00:23:45,280 --> 00:23:49,800
Speaker 2: to the SEC because they advertised that they were applying

488
00:23:49,840 --> 00:23:53,359
Speaker 2: biblical principles to their investing and they were like doing

489
00:23:53,400 --> 00:23:56,680
Speaker 2: a rigorous screening to root out companies that had practices

490
00:23:56,720 --> 00:23:59,640
Speaker 2: they didn't like, and the SEC discovered that they weren't

491
00:23:59,680 --> 00:24:01,840
Speaker 2: doing as goes the screening as they wanted, and so they.

492
00:24:01,760 --> 00:24:04,719
Speaker 1: Find them some money three hundred thousand dollars to be exact.

493
00:24:04,800 --> 00:24:07,280
Speaker 1: I love this story. You called it Bible washing, and

494
00:24:07,400 --> 00:24:08,000
Speaker 1: I think that's the.

495
00:24:08,400 --> 00:24:11,200
Speaker 2: Grands Green question. But for biblical investing.

496
00:24:11,040 --> 00:24:13,000
Speaker 1: Yeah, I like this story and I like all the

497
00:24:13,720 --> 00:24:16,080
Speaker 1: I don't know the ESG parallels.

498
00:24:16,480 --> 00:24:18,760
Speaker 2: It's completely the same thing, right, I mean, it's this

499
00:24:18,960 --> 00:24:21,800
Speaker 2: sort of broad category of like people who are investing

500
00:24:22,240 --> 00:24:25,160
Speaker 2: for things other than financial returns. Right. They're telling investors,

501
00:24:25,240 --> 00:24:27,520
Speaker 2: we're doing your investing in some social way that you like.

502
00:24:27,880 --> 00:24:30,200
Speaker 2: A big chunk of that is ESG investing where people

503
00:24:30,200 --> 00:24:32,639
Speaker 2: care about climate change or whatever. But another chunk of

504
00:24:32,640 --> 00:24:36,840
Speaker 2: it is you know, conservative biblical principles investing where people

505
00:24:36,840 --> 00:24:39,400
Speaker 2: care about other things. And the SEC says, no matter

506
00:24:39,440 --> 00:24:42,960
Speaker 2: what you're doing, you have to disclose it accurately and

507
00:24:43,280 --> 00:24:44,960
Speaker 2: follow the procedures you say you're following.

508
00:24:45,040 --> 00:24:48,680
Speaker 1: Yeah, let's just read from this excerpt from Money Stuff.

509
00:24:48,720 --> 00:24:52,160
Speaker 1: So Inspire had the Inspire Impact score, which quote reflects

510
00:24:52,200 --> 00:24:57,439
Speaker 1: a rules based, scientifically rigorous methodology of faith based ESG analysis,

511
00:24:57,600 --> 00:25:01,000
Speaker 1: which is interesting. There's some tension between science and religion,

512
00:25:01,040 --> 00:25:03,760
Speaker 1: but in any case, this creates a level of consistency

513
00:25:03,800 --> 00:25:08,000
Speaker 1: and reliability of results necessary for making well informed, quantitatively sound,

514
00:25:08,080 --> 00:25:12,800
Speaker 1: biblically responsible investment decisions. And then the SEC checked the

515
00:25:12,840 --> 00:25:15,879
Speaker 1: donor lists. Caught a bit of a contradiction as you

516
00:25:15,960 --> 00:25:18,800
Speaker 1: lay out that certain companies that were excluded from Inspires

517
00:25:18,880 --> 00:25:23,000
Speaker 1: Investment Universe for donating to certain advocacy organizations are sponsoring

518
00:25:23,040 --> 00:25:27,200
Speaker 1: certain events that Inspired consider to be prohibited activities. At

519
00:25:27,200 --> 00:25:29,760
Speaker 1: the same time, multiple companies held within the inspired ETF

520
00:25:29,800 --> 00:25:33,720
Speaker 1: portfolios donated to organizations or sponsored events that were the

521
00:25:33,760 --> 00:25:36,920
Speaker 1: same or similar. So a three hundred thousand dollars fine.

522
00:25:37,600 --> 00:25:39,840
Speaker 1: These ETFs are still out there. We could buy and

523
00:25:39,880 --> 00:25:40,560
Speaker 1: sell them today.

524
00:25:41,200 --> 00:25:43,160
Speaker 2: And in fact, Inspire put out a press release about

525
00:25:43,160 --> 00:25:47,120
Speaker 2: this saying we are grateful to receive guidance from the

526
00:25:47,160 --> 00:25:50,879
Speaker 2: SEC on what it considers important regarding modern faith based

527
00:25:50,880 --> 00:25:54,960
Speaker 2: investment screening, and the SEC order takes no issue with

528
00:25:55,000 --> 00:25:59,280
Speaker 2: the conservative biblical values Inspire applies to screening categories like

529
00:25:59,480 --> 00:26:01,800
Speaker 2: they're going to keep doing this. They're just gonna, yeah,

530
00:26:01,800 --> 00:26:04,479
Speaker 2: they are, you know, Like what they advertise is like

531
00:26:04,680 --> 00:26:08,520
Speaker 2: all of our investments have no involvement in you know,

532
00:26:08,760 --> 00:26:12,520
Speaker 2: gearats organizations or abortion rights, and the SEC found that

533
00:26:12,560 --> 00:26:14,120
Speaker 2: some of them did, and so they're gonna like clean

534
00:26:14,200 --> 00:26:16,520
Speaker 2: up their act and be more accurate about screening out

535
00:26:16,560 --> 00:26:19,239
Speaker 2: companies that don't meet their criteria. But the SEC, as

536
00:26:19,280 --> 00:26:21,320
Speaker 2: they say, took no issue with their criteria. Right, if

537
00:26:21,320 --> 00:26:24,879
Speaker 2: you say you're going to exclude companies that contribute to

538
00:26:24,880 --> 00:26:27,199
Speaker 2: garage charities, then you just have to do that, and

539
00:26:27,240 --> 00:26:28,439
Speaker 2: then the SEC will bless it.

540
00:26:28,640 --> 00:26:31,000
Speaker 1: I almost said, hell, yeah, they're going to keep doing it.

541
00:26:31,119 --> 00:26:31,679
Speaker 3: Do you get it?

542
00:26:33,720 --> 00:26:37,080
Speaker 1: So one of these ETFs, bibl is the ticker, it's

543
00:26:37,080 --> 00:26:40,720
Speaker 1: this Inspire one hundred ETF. The CTF still exists. I

544
00:26:40,760 --> 00:26:43,960
Speaker 1: took a look at the performance it's doing. Okay, it's

545
00:26:44,040 --> 00:26:47,120
Speaker 1: up sixteen percent year to date on a total return basis.

546
00:26:47,240 --> 00:26:51,360
Speaker 1: By for comparison purposes is up twenty one percent. Its

547
00:26:51,440 --> 00:26:57,640
Speaker 1: top holdings are Caterpillar, Intuitive, Surgical, Progressive, et cetera. Its

548
00:26:57,640 --> 00:27:02,199
Speaker 1: top industry groups are reads, software, and healthcare. All you know,

549
00:27:02,720 --> 00:27:03,479
Speaker 1: holy stuff.

550
00:27:04,520 --> 00:27:08,440
Speaker 2: My point here is that neither the SEC nor I

551
00:27:08,480 --> 00:27:11,920
Speaker 2: can make any comment on whether this is holy stuff

552
00:27:12,000 --> 00:27:12,199
Speaker 2: or not.

553
00:27:13,080 --> 00:27:17,840
Speaker 1: So I do think the idea of principle based investing

554
00:27:17,920 --> 00:27:21,400
Speaker 1: is really interesting, and that's what this is that's what

555
00:27:21,920 --> 00:27:25,960
Speaker 1: ESG is, But why people do it is really interesting

556
00:27:26,000 --> 00:27:29,359
Speaker 1: to me. Like if you're investing along your values because

557
00:27:29,440 --> 00:27:31,680
Speaker 1: you don't want to invest in stuff that goes against

558
00:27:31,720 --> 00:27:34,520
Speaker 1: your value sure, or are you investing because you think

559
00:27:34,560 --> 00:27:38,600
Speaker 1: that your values will produce better returns. That's a really

560
00:27:38,600 --> 00:27:40,639
Speaker 1: interesting school of thought. Like if you believe that a

561
00:27:40,680 --> 00:27:46,680
Speaker 1: company is environmentally conscious and sound and will whether climate

562
00:27:46,760 --> 00:27:49,119
Speaker 1: change better than other companies, that could be a reason

563
00:27:49,440 --> 00:27:53,040
Speaker 1: that you invest Do you invest in this fund because

564
00:27:53,080 --> 00:27:58,439
Speaker 1: you believe that these biblically responsible companies are going to

565
00:27:58,480 --> 00:28:00,480
Speaker 1: do better than the one that aren't.

566
00:28:01,200 --> 00:28:03,520
Speaker 2: I haven't read enough of marketing literature to know, but

567
00:28:03,560 --> 00:28:07,320
Speaker 2: I think I think in ESG, I think asset managers

568
00:28:07,560 --> 00:28:11,520
Speaker 2: benefit a lot by kind of blurring that and sort

569
00:28:11,520 --> 00:28:14,600
Speaker 2: of hinting that you're getting both right, like you're getting

570
00:28:14,600 --> 00:28:17,520
Speaker 2: to live your values and you're also getting a higher return.

571
00:28:17,960 --> 00:28:21,239
Speaker 2: And I think like there's not really a contradiction between them,

572
00:28:21,240 --> 00:28:24,439
Speaker 2: because like you could easily believe that, like you have

573
00:28:24,560 --> 00:28:28,480
Speaker 2: these values, your values are right. Over time, other people

574
00:28:28,520 --> 00:28:30,680
Speaker 2: will come around to your values. And when other people

575
00:28:30,680 --> 00:28:33,720
Speaker 2: come around to your values, that will increase the stock

576
00:28:33,760 --> 00:28:36,200
Speaker 2: prices of companies that share those values, right, Like, that's

577
00:28:36,240 --> 00:28:38,480
Speaker 2: a fairly consistent thing to believe, and you could believe

578
00:28:38,520 --> 00:28:41,960
Speaker 2: that about, you know, environmental or biblical things. I don't

579
00:28:41,960 --> 00:28:45,080
Speaker 2: think you have to analyze it rigorously to say, like,

580
00:28:45,120 --> 00:28:47,360
Speaker 2: the companies that I believe in are also the companies

581
00:28:47,400 --> 00:28:49,360
Speaker 2: I'll go up because you could believe that, you know,

582
00:28:49,560 --> 00:28:53,560
Speaker 2: without either ESG or biblical principles. Yeah, you could be

583
00:28:53,680 --> 00:28:55,920
Speaker 2: like I really like Taco Bells. I think Taco bell

584
00:28:55,960 --> 00:28:56,720
Speaker 2: stock will go up.

585
00:28:57,240 --> 00:29:01,120
Speaker 1: So we're talking about BBL. They have amazing other tickers

586
00:29:01,120 --> 00:29:08,040
Speaker 1: as well, wwj D, what would John do? They also

587
00:29:08,080 --> 00:29:10,320
Speaker 1: have the ticker g l r.

588
00:29:10,520 --> 00:29:11,600
Speaker 2: Y walking away from.

589
00:29:11,520 --> 00:29:17,160
Speaker 1: The hie so so but yeah, no, the ticker game

590
00:29:17,200 --> 00:29:17,640
Speaker 1: is strong.

591
00:29:19,040 --> 00:29:20,520
Speaker 2: And that was the Money Stuff Podcast.

592
00:29:20,640 --> 00:29:22,640
Speaker 1: I'm Matt Levi and I'm Katie Greifeld.

593
00:29:23,040 --> 00:29:25,080
Speaker 2: You can find my work by subscribing to the Money

594
00:29:25,120 --> 00:29:27,600
Speaker 2: Stuff newsletter on Bloomberg dot com.

595
00:29:27,280 --> 00:29:29,760
Speaker 1: And you can find me on Bloomberg TV every day

596
00:29:29,840 --> 00:29:32,920
Speaker 1: on Open Interest between nine to eleven am Eastern.

597
00:29:33,160 --> 00:29:34,840
Speaker 2: We'd love to hear from you. You can send an

598
00:29:34,880 --> 00:29:38,080
Speaker 2: email to Moneypod at Bloomberg dot net, ask us a

599
00:29:38,160 --> 00:29:39,560
Speaker 2: question and we might answer it on air.

600
00:29:40,000 --> 00:29:42,200
Speaker 1: You can also Subscribe to our show wherever you're listening

601
00:29:42,240 --> 00:29:44,280
Speaker 1: right now, and leave us a review. It helps more

602
00:29:44,280 --> 00:29:45,160
Speaker 1: people find the show.

603
00:29:45,880 --> 00:29:48,680
Speaker 2: The Money Stuff podcast is produced by Anna Maserakus and

604
00:29:48,760 --> 00:29:49,840
Speaker 2: Moses ONEm Our.

605
00:29:49,880 --> 00:29:51,560
Speaker 1: Theme music was composed by Blake.

606
00:29:51,440 --> 00:29:53,640
Speaker 2: Maples, Brandon Frances Nanimus, our.

607
00:29:53,560 --> 00:29:56,840
Speaker 1: Executive producer and Stage Bauman is Bloomberg's head of Podcasts.

608
00:29:57,040 --> 00:29:59,360
Speaker 2: Thanks for listening to The Money Stuff Podcasts. We'll be

609
00:29:59,440 --> 00:30:00,880
Speaker 2: back next week with more stuff