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Speaker 3: Hi, everyone, Welcome to the Bloomberg Business Week Weekend Podcast.
Speaker 3: A big focus for everyone this week. I gotta be honest.
Speaker 3: Since the IPO back in June, this has been a countdown.
Speaker 3: We are talking about SpaceX and its earnings. Investors got
Speaker 3: their first look at SpaceX's financials following its landmark IPO.
Speaker 3: For all the details on the results, head to the
Speaker 3: Bloomberg End at Bloomberg dot com. But Tim, like you
Speaker 3: kept saying it wasn't really about earnings because the company
Speaker 3: isn't profitable overall.
Speaker 1: Yeah, that always reminds me. Earnings mean you have earnings,
Speaker 1: and SpaceX didn't have any earnings. Bottom line For investors'
Speaker 1: top line, revenue surged staggering. Capex sent the stock tumbling
Speaker 1: in the release. That theme of the iwatering AI spend.
Speaker 1: It's something we dig into this hour with a noted
Speaker 1: critic of the AI build and spend.
Speaker 3: That tech critic he's also the publisher of Where's Your
Speaker 3: Ed At We're talking about ed Zitron. He's stopped by
Speaker 3: to dissect the hundreds of billions of dollars going into
Speaker 3: the AI capex movement and spend overall, and why the
Speaker 3: massive gap between data center spending and actual AI revenue
Speaker 3: is creating what Ed calls quote an unsustainable circular economy.
Speaker 3: He's kind of not alone in that thinking.
Speaker 1: No, he's definitely not, and I think what he says
Speaker 1: really resonates with an audience, and we see that when
Speaker 1: he comes on the program. AI's talked a lot about
Speaker 1: by us here at Bloomberg. You know that at this point,
Speaker 1: so too increasingly our prediction markets and the battles they
Speaker 1: are dealing with when it comes to their role in
Speaker 1: the financial world, and the growing legal challenges that are
Speaker 1: questioning them.
Speaker 3: That's right. We wanted to hear how calshe is keeping
Speaker 3: up with all the lawsuits filed against them. We do
Speaker 3: that with Bobby Denault, head of Enforcement and Legal Counsel
Speaker 3: at calshe needless to say, there's a lot.
Speaker 1: At stake and speaking of high stakes, and back to AI.
Speaker 1: Aaron Brown, Bloomberg opinion columnist and former chief risk manager
Speaker 1: at AQR Capital Management. That's Cliff Asns's fund weighs in
Speaker 1: on the AI hedge fund situational awareness and why is
Speaker 1: staggering four hundred and thirty nine percent first half return
Speaker 1: was a glaring warning sign about market mania.
Speaker 3: All of that to come this hour. We begin with
Speaker 3: legal battles surrounding prediction markets. The recent surge and volume
Speaker 3: across platforms like Calshie and Polymarket has made event based
Speaker 3: trading one of the fastest growing corners of finance.
Speaker 1: Yes, but also growing regulatory scrutiny of these markets. Just
Speaker 1: last week, New York State authorities sued call she for
Speaker 1: allegedly running an illegal on the un licensed gambling operation
Speaker 1: in the state, marking another legal hurdle for an industry
Speaker 1: that is one support from the Trump administration.
Speaker 3: We needed to learn more, and so for that we
Speaker 3: caught up with Robert Denault, head of Enforcement and legal
Speaker 3: counsel at Calshie.
Speaker 4: I mean, as both a New Yorker and a lawyer,
Speaker 4: I'm alarmed by the overreaching sentiment that's coming from the
Speaker 4: Attorney General's office. So calshe is a licensed federally regulated exchange.
Speaker 4: By her logic, any federally regulated exchange that's operating with
Speaker 4: a federal license and overseen by a federal regulator can
Speaker 4: suddenly be subject to the whims of state criminal enforcement
Speaker 4: if the Attorney General of a particular state wakes up
Speaker 4: and decides one day that these contracts actually come within
Speaker 4: New York state gambling law. That's not how any exchange
Speaker 4: in US history has ever operated, right, So we have
Speaker 4: we're here at Bloomberg. You guys talk about the New
Speaker 4: York Stock Exchange NASDAQ, other exchanges, all of them could
Speaker 4: be potentially affected by the breath and scope of the
Speaker 4: New York Attorney generals approach and legal theory in terms
Speaker 4: of how she believes she can regulate and bring to
Speaker 4: heal federally licensed exchanges here in New York State.
Speaker 3: Bobby, are you saying you're the exact same things as
Speaker 3: the New York Stock Exchange or the NASDAK markets. That
Speaker 3: you're saying you're the exact same thing apples to apples.
Speaker 4: Then what I am saying is that federal law dictates that.
Speaker 4: That is the case when a federal law like the
Speaker 4: Commodity Exchange Act exists and provides for a way for
Speaker 4: an entity to get licensed by something like the Commodity
Speaker 4: Futures Trading Commission or the Securities Exchange Commission, that licensure
Speaker 4: and that federal regulation is what governs that marketplace. Now,
Speaker 4: if states want to litigate with that regulator on a
Speaker 4: case by case basis about what types of contracts might
Speaker 4: implicate some state laws, that's one question, and that's some
Speaker 4: of the lawsuits we've seen over the last year on sports.
Speaker 4: But this is much more far reaching. This is claiming
Speaker 4: that that license means nothing, and if you don't hold
Speaker 4: a New York license, you're running a criminal operation and
Speaker 4: you need to be run out of the state. And
Speaker 4: I think it's important to ground this sort of in
Speaker 4: history of disruptive sort of new players in marketplaces. Right,
Speaker 4: call she's new, but this sort of licensed regime has
Speaker 4: existed for many decades. We've seen similar playbooks used against
Speaker 4: companies like Uber and Airbnb, where states try to throw
Speaker 4: their weight around and bring crazy cases to block what
Speaker 4: customers want. As a reasonable alternative, we think that that's
Speaker 4: a pretty similar playbook to what the Attorney General's following here.
Speaker 1: So why shouldn't cal she seek a license from the
Speaker 1: New York State Gaming Commission? Why wouldn't you do that?
Speaker 4: So it really goes to the way that the business operates.
Speaker 4: We are a federally licensed exchange that requires us to
Speaker 4: run open markets that are available nationwide. Our users set
Speaker 4: the price, traders set the price. We match traders in
Speaker 4: an open marketplace with one another. We are not on
Speaker 4: the other side of individuals trading. We don't run a
Speaker 4: casino where people can come in and drink and play
Speaker 4: card games. We don't run a sports book where we
Speaker 4: profit when people lose. What we do is run open
Speaker 4: marketplaces where users define the price point and that type
Speaker 4: of financial product, even if it touches on topics that
Speaker 4: is similar to a topic touched on by a sportsbook,
Speaker 4: the way that that product operates is typically what governs
Speaker 4: what regulations apply.
Speaker 3: So even though people keep coming at you and say gambling, gambling, gambling,
Speaker 3: that's why you're not gambling.
Speaker 4: So I think it's important to define exactly what they
Speaker 4: mean when they say gambling. To me, gambling is when
Speaker 4: you go up against the house. When you go to
Speaker 4: a place that controls whether you're going to win or lose,
Speaker 4: they're going to chase losers, They're going to maximize their
Speaker 4: ability to limit winners. They're not going to run like
Speaker 4: a true business. But where an exchange exists and individuals
Speaker 4: are setting price with one another, I think that it
Speaker 4: operates under a different regulatory framework. And it's not to
Speaker 4: say that a whole panoply of different topics can be
Speaker 4: touched on by different regulated products. So we see concerns
Speaker 4: in options trading or leverage trading, retail traders moving into
Speaker 4: markets that are traditionally regulated markets at the federal level,
Speaker 4: I think some of the same concerns exist for those markets.
Speaker 4: And if you read the lawsuit closely, a ton of
Speaker 4: the definitions and the language and the descriptions that Attorney
Speaker 4: General James's office uses could easily be transposed onto robinhood,
Speaker 4: crypto trading, derivatives trading, any sort of trading activity that
Speaker 4: the Attorney General suddenly decides poses a customer threat to
Speaker 4: individuals who want to participate in I mean.
Speaker 1: There is In April, she did sue coinbase in Gemini
Speaker 1: for running illegal gambling platforms. There were some like why
Speaker 1: do you think call she was signaled out in this
Speaker 1: typical or this this iteration of the lawsuit.
Speaker 4: You'd have to ask the Governor's office, why you know
Speaker 4: call She's was signal signaled out. I think the truth
Speaker 4: is there are a number of prediction markets that would
Speaker 4: be affected by this approach. There's a number of other
Speaker 4: exchanges that would be affected. There are number of like
Speaker 4: robin Hood's based in New York, right, novig Polymarket, They're
Speaker 4: all based in New York, none of whom have been
Speaker 4: subject to the Attorney General's focus. So I don't know
Speaker 4: if that's coming. I don't know if you'd have to
Speaker 4: ask her office, but certainly, you know, we're a bit
Speaker 4: alarmed that the Attorney General is seeing the scope of
Speaker 4: New York gambling law applying to potentially federal derivatives exchanges.
Speaker 3: But I do think about exchanges. Do you think about
Speaker 3: the composition of the bets that are on Like if
Speaker 3: we're going to use the gambling analogy that keeps getting
Speaker 3: kind of lobbied at you. I mean, if I think
Speaker 3: about exchange, there is incredible oversight, you know, and there's
Speaker 3: rules and so that there isn't you know, trades that
Speaker 3: are happening that are not legit or fair or right.
Speaker 3: You know, if if gambling is as you say, there
Speaker 3: is no house, right, it's just two parties figuring out something.
Speaker 3: Where is the oversight and concern about the composition of
Speaker 3: the bets being made and making sure that they are true?
Speaker 3: Because I think that's where you're get into gambling. If
Speaker 3: anybody can kind of put up a bet and another
Speaker 3: person can take the side, and who knows if those
Speaker 3: two sides didn't get together to figure out this bet,
Speaker 3: like you know what I'm saying, So that to me
Speaker 3: is where you get into the gambling. It's a little
Speaker 3: it feels a little loose.
Speaker 4: Sure, I don't think it's loose at all. So I
Speaker 4: think that when you're just to sort of zero in
Speaker 4: on some of the nuance that you just said, gambling
Speaker 4: is when there is the house. What you do on
Speaker 4: an exchange is where there is no house, there are
Speaker 4: two counterparties in a particular market agreeing on a price
Speaker 4: together and executing what we characterize as a swap, what
Speaker 4: the CEA qualifies as a swap. Those types of trades
Speaker 4: are heavily regulated under CFTC regulation. There are hundreds of regulations.
Speaker 3: You're saying all the trades that are happening on Kowshi
Speaker 3: will be heavily regulated and overset, And we understand the
Speaker 3: two sides.
Speaker 4: They are currently heavily regulated and being overseen. I meet
Speaker 4: with the CFTC's Enforcement Division. That's my line of work
Speaker 4: at Kalshi. I lead our exchange enforcement. I meet with
Speaker 4: their Enforcement division multiple times a week. But separately, they
Speaker 4: have other divisions division Market Oversight, other divisions that oversee
Speaker 4: the implementation of markets so that they are structurally fair
Speaker 4: for the participants operating in them, and that oversight requires
Speaker 4: Kalshi as an exchange, but all prediction market exchanges to
Speaker 4: work very closely with the CFTC on the products that
Speaker 4: they offer. They have to sell certify them to the CFTC,
Speaker 4: who can revoke approval for those products. And the CFTC
Speaker 4: recently came out with a two hundred and sixty seven
Speaker 4: page rule making that was specific toward prediction market contract
Speaker 4: So I think you know it isn't correct to say
Speaker 4: these aren't currently heavily regulated.
Speaker 1: We're speaking with Bobby Denald, he's head of Enforcement and
Speaker 1: Legal counsel at Kalshe He joins us here in the
Speaker 1: Bloomberg BusinessWeek studio, I want to shift gears and talk
Speaker 1: about some other recent news. It seems like you're really
Speaker 1: playing whack a mole at this point with these lawsuits.
Speaker 1: Like obviously it's keeping you busy. Also keeping you busy
Speaker 1: as enforcement and former Congressman George Santos agreed to pay
Speaker 1: more than thirty five thousand dollars to settle allegations that
Speaker 1: he manipulated a wager on your platform about whether he'd
Speaker 1: attend the twenty twenty six State of the Union. Is
Speaker 1: it your responsibility to police that. Are you the one
Speaker 1: who recognized that and flagged it?
Speaker 4: Yes, it is our responsibility to police that.
Speaker 1: Do you have the resources like this is one example,
Speaker 1: But do you have the resources to find every single
Speaker 1: one of them that allegedly has insider information?
Speaker 4: So I'd pause there and say, that's not our standard
Speaker 4: at the New York stylelock exchange, and that's not our
Speaker 4: standard in the securities and equities markets. What we expect
Speaker 4: our federally regulated exchanges to do is to have reasonable
Speaker 4: procedures to detect insider trading, market manipulation, etc. Calhi has
Speaker 4: very robust and in fact more robust than stock exchanges
Speaker 4: policing measures to prevent and detect insider trade. We do
Speaker 4: have twenty four to seven market surveillance. We use a
Speaker 4: surveillance vendor, but also in the coming days are going
Speaker 4: to announce an expansion of our surveillance systems and we
Speaker 4: police these markets both in real time and in retrospect,
Speaker 4: and we work with the CFTC to investigate specific markets
Speaker 4: and detect anomalous activity in those markets. We did detect
Speaker 4: the trading activity by mister Santos. We conducted an investigation
Speaker 4: that involved an interview with mister Santos, and we referred
Speaker 4: the entire matter and the evidence that we collected the CFTC,
Speaker 4: which allowed them to pursue enforcement. But separately, in the
Speaker 4: derivatives and commodities space, these exchanges, like ours, have a
Speaker 4: responsibility to also bring enforcement actions directly against the users
Speaker 4: who participate on them, and so we pursue direct exchange
Speaker 4: enforcement in areas where individuals come on and violate our
Speaker 4: CFTC approved exchange rules.
Speaker 3: And since that's happening, and how many investigations are you triggering,
Speaker 3: like on a daily basis, or a weekly.
Speaker 4: Basis, so we sort of estimate quarterly, and volumes upticked
Speaker 4: quite a bit, So the investigations have upticked quite a bit,
Speaker 4: but somewhere between one hundred and fifty and two hundred
Speaker 4: and fifty a quarter become material investigations. We make a
Speaker 4: number of referrals. I think year to date we've probably
Speaker 4: made about forty or fifty referrals to the CFTC. These
Speaker 4: matters take time, though the legal process. Everybody deserves due
Speaker 4: process rights, yep, So we afford people do process. We've
Speaker 4: settled or brought disciplinary actions in a number of cases.
Speaker 4: We're going to continue to do so. By the end
Speaker 4: of the year, you know, I'm sure you'll see a
Speaker 4: meaningful number of actions. I put for context, the SEC
Speaker 4: in the last year of the Biden administration bought thirty
Speaker 4: five insider trading actions, So you know, I think the
Speaker 4: expectation somewhere in the ballpark of that number.
Speaker 1: Do you think that there's some markets that are listed
Speaker 1: on KYSHI that are more susceptible to manipulation than others?
Speaker 4: I think, much like insider trading in the traditional securities
Speaker 4: and equities markets, there are certain paradigms that exist that
Speaker 4: create more likelihood for insider or manipulation.
Speaker 1: Risk, like mentioned markets.
Speaker 4: Perhaps you know, I think mention markets are a unique category.
Speaker 4: I don't know that they're necessarily more susceptible to insider risk,
Speaker 4: but I can see the context or argument for saying
Speaker 4: they maybe are more susceptible to manipulation where one person
Speaker 4: controls what word they say. On the counter of that,
Speaker 4: there's a small pool of people who could possibly be
Speaker 4: capable of manipulating that market. Because we're in exchange that
Speaker 4: collects user information, we follow KYC know your customer rules
Speaker 4: for every single individual trading on the exchange, we have
Speaker 4: a pretty good idea of who's taking positions in certain
Speaker 4: markets that allows us to police mention markets, just like
Speaker 4: we police all market.
Speaker 3: You're talking about Calsey public companies, right, I just are no.
Speaker 5: I mean, yeah, that's part of it.
Speaker 1: I mean, that's the new Just.
Speaker 3: Sixty seconds, I know, we've got to run it's interesting
Speaker 3: earnings updates, KPI forecasts, earnings I do, and I'm thinking, God,
Speaker 3: does this replace ultimately the earnings estimates that we follow?
Speaker 3: Is that the goal?
Speaker 4: Just quickly, I think there's two goals. I think the
Speaker 4: first goal is what we find is so many people
Speaker 4: are using Kalshi as a resource just to obtain information.
Speaker 4: Seventy five percent of people who visit the platform are
Speaker 4: there just to look to learn, They're not there to trade.
Speaker 4: And so this is a resource for people who might
Speaker 4: be on a trading desk, people who might be in
Speaker 4: a financial position where they want to analyze these really
Speaker 4: subtopic financial questions about a particular KPI. But you know,
Speaker 4: of course, we are seeing academic research that shows these
Speaker 4: are somehow and sometimes more accurate than our traditional metrics
Speaker 4: and financial markets, and so it's exciting to develop further
Speaker 4: in that Spaceically, we've come back and.
Speaker 3: Talk more about this, so I am fascinated about kind
Speaker 3: of where this goes and who will all be on it. Bobby,
Speaker 3: thank you so much.
Speaker 2: You're listening to the Bloomberg Business Weekdaily podcast. Catch us
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Speaker 3: This past week, we got further information that points to
Speaker 3: Microsoft generating most of its AI revenues and likely about
Speaker 3: seventy percent from one customer open AI. This is all
Speaker 3: according to new Microsoft company disclosures. Under an agreement between
Speaker 3: the two companies, Open Ai pays Microsoft our computing power,
Speaker 3: costs associated with building AI models, and a share of
Speaker 3: its revenue.
Speaker 1: Our next guest says that is worth watching here to
Speaker 1: pull apart big text catpecs obsession and why he believes
Speaker 1: this circular ecosystem is nearing a tipping point is ed Zitron.
Speaker 1: He's CEO of Easy Primary Research. He's the host of
Speaker 1: the Better Offline podcast And to note, we caught up
Speaker 1: with that before the news broke of Microsoft's AI sales.
Speaker 3: Where are we in terms of the AI narrative in
Speaker 3: your view and what's the reality?
Speaker 6: Well, I think investors have to ask a question right now,
Speaker 6: what am I getting into when I invest in Microsoft, Google,
Speaker 6: and Amazon. So UBS estimates that twenty seven percent of
Speaker 6: Google Clouds revenue this year will be open ai and anthropic,
Speaker 6: increasing to over forty eight percent next year. That is
Speaker 6: a remarkable amount of money. It's going to be over
Speaker 6: one hundred and twenty four billion dollars next year. Everyone
Speaker 6: is buying into these stocks because they believe all of
Speaker 6: that capex is going towards diverse and spread out AI demand,
Speaker 6: when in fact, what it's actually doing is helping create
Speaker 6: infrastructure for two unprofitable, unsustainable companies.
Speaker 1: So those the other one would be anthropic. Yes, you argue,
Speaker 1: so give us more data because you have the micro
Speaker 1: you're citing Microsoft, But what about AWL, Well.
Speaker 6: That was what I was saying. So Barclay's actually says
Speaker 6: that this year thirteen percent of AWS revenue will be
Speaker 6: both open air and anthropic, and next year will be
Speaker 6: eighteen percent. AWS much bigger business than Google Cloud. Now,
Speaker 6: just to be clear, when I was saying that twenty
Speaker 6: seven percent this year and forty eight percent next year
Speaker 6: for Google Cloud, I meant both anthropic and open al Okay,
Speaker 6: most people don't know that open ai is a large
Speaker 6: customer of Google Cloud. It's not a well, it's not
Speaker 6: a well known fact. But this was this was actually
Speaker 6: mentioned by you.
Speaker 1: Yes is Stephen jew So where would those companies be
Speaker 1: right now without anthropic and without open ai?
Speaker 6: Well, I have serious questions about that. So in counter
Speaker 6: year twenty twenty five, according to my own reporting about
Speaker 6: open aiy's numbers, sixty nine percent if the year over
Speaker 6: year growth of Microsoft Intelligent Cloud segment was actually from
Speaker 6: open Ai. Without that, it would have only grown eight
Speaker 6: percent year of view, which is barely beating inflation. And
Speaker 6: so everyone is being sold what I consider kind of
Speaker 6: a lie. It's honestly kind of a scandal.
Speaker 3: So this goes back to I feel like we have
Speaker 3: companies the circular financing the circularity of it all, and
Speaker 3: kind of creating demand for their products. So when does
Speaker 3: the party end? In your view?
Speaker 6: So with open Aiy's IPO, I think that could be
Speaker 6: one of the flashpoints. Remember, this company was meant to
Speaker 6: go public this year. They failed about a month or
Speaker 6: two ago, and now the New York Times has reported
Speaker 6: that they're considering they are delaying until twenty twenty seven.
Speaker 6: That's lethal for a number of people. But open Ai
Speaker 6: and Anthropic need continual flow of capital. They do not
Speaker 6: pay their bills out of existing cash flow, so when
Speaker 6: anything happens to that cash, I think that's the first
Speaker 6: kind of domino to four. But then again, there's also
Speaker 6: the overall problem of data center's just not getting built
Speaker 6: very fast, taking about twelve to thirty six months, depending
Speaker 6: on how small or larger data center is actually being
Speaker 6: built at And the problem is is that everyone believes
Speaker 6: that AI is coming out of cash flow, that AI
Speaker 6: is coming out of just this diverse revenue base, when
Speaker 6: it's really not. It's extremely narrow. The Information reported a
Speaker 6: few months ago that eighty nine percent of the largest
Speaker 6: AI companies, well, their revenue comes just from open AI
Speaker 6: and anthropic it's heavily centralized.
Speaker 3: Doesn't it have to be centralized to some extent? This
Speaker 3: is expensive to do or no in terms of data
Speaker 3: center build out and so on and so forth. And
Speaker 3: what's going to make AI generative?
Speaker 5: AI?
Speaker 3: The ability for it to be really, really good is
Speaker 3: having access to lots of information. So doesn't it have
Speaker 3: to be to some extent ed concentrated.
Speaker 6: Well, when I say concentration, I mean concentration of revenue
Speaker 6: in these two companies.
Speaker 3: No, I understand, but to make it good. So doesn't
Speaker 3: it make sense that those who are expose the most
Speaker 3: it's going to be concentrated to some extent.
Speaker 6: Well, I mean when we're talking about so Siteline Climates
Speaker 6: said that they saw back in February about one hundred
Speaker 6: and ninety gig what's worth of data center capacity being
Speaker 6: built in the next few years. It was built or
Speaker 6: under planning. Now, if you work that out.
Speaker 5: With a PUE.
Speaker 6: So just the efficiency rating of one point three, you're
Speaker 6: coming out to twelve million O megawatt over one point
Speaker 6: six trillion dollars of annual revenue needed to satiate those
Speaker 6: data centers. Having two customers is not going to do that.
Speaker 6: Even that, they're most spendy and rober and open Ai
Speaker 6: they can't afford anything they need venture Cappital, but they're
Speaker 6: only going to spend four hundred billion a year, and
Speaker 6: that's if they get that far, which I don't believe
Speaker 6: they will.
Speaker 3: How much do we know about their balance sheets?
Speaker 6: Really, really well, I from personal experience a great deal
Speaker 6: about open ayes because I reported their aud to do
Speaker 6: financials with the Financial Times right, and it's a company
Speaker 6: just burning cash. They lost twenty point nine billion dollars
Speaker 6: in twenty twenty five and things aren getting worse. And
Speaker 6: what's crazy as well, was over eight hundred million dollars
Speaker 6: of open Eyes revenue came from soft Bank for their
Speaker 6: Crystal Intelligence and yes that's really what it's called, their
Speaker 6: crystalli Intelligence program, which I can find no evidence of
Speaker 6: actually anything happening. And soft Bank a large shareholder of
Speaker 6: open Ai, with no board seats.
Speaker 3: Like you talk about for Google Cloud the exposure rate
Speaker 3: and you said forty eight percent next year. In terms
Speaker 3: of these two customers, I have to say that there
Speaker 3: are smart people running these companies. And normally you would
Speaker 3: say your exposure to just a handful of customers is
Speaker 3: not a great thing. Do you say that these companies
Speaker 3: that aren't doing their due diligence be it Alphabet or
Speaker 3: you know, pick your hyperscaler.
Speaker 6: I think they did the due diligence in the sense
Speaker 6: that they said, we are going to create our largest
Speaker 6: customers and we're going to own large parts of them,
Speaker 6: and on top of that, we're going to own all
Speaker 6: of their infrastructure. Google has a nice they have a
Speaker 6: nice thing going here. Yeah, by TPUs from well sorry,
Speaker 6: comm sells TPUs to Google. They are then sold to
Speaker 6: Anthropic and then rented back to Anthropic through Google, Google
Speaker 6: gets to double up on revenue. This sounds really good
Speaker 6: right up until you realize that Anthropic and open Ai
Speaker 6: are unsustainable. So what they may be. And the problem
Speaker 6: is with saying these are smart people is it immediately
Speaker 6: makes me think of Enron, the smartest guys in the room,
Speaker 6: not saying anything like that's happening.
Speaker 3: But I'm just saying you have a fiduciary responsibility, and
Speaker 3: you're right. You go back to Enron.
Speaker 6: And I think the point I'm making is with Google,
Speaker 6: they probably thought they would be more customers. I imagine
Speaker 6: with Azure and with Aws they thought would be more
Speaker 6: large players. But the problem with Anthropic and open Ai
Speaker 6: is they've raised two hundred and three hundred billion dollars
Speaker 6: of funding, but they've actually raised more because open Ai
Speaker 6: and Anthropic got all of their infrastructure built for them
Speaker 6: by Microsoft, Google and Amazon, they didn't have to pay
Speaker 6: I think in the samman Elon Musk trial one of
Speaker 6: the Microsoft executives said that they cost one hundred billion dollars.
Speaker 6: So call it like seventy eighty billion dollars infrastructure.
Speaker 3: Yeah.
Speaker 6: So the problem is is that nobody else can get
Speaker 6: as big as them. No one else can get that
Speaker 6: much compute, no one else could have fold that compute
Speaker 6: and have the chance to do the pre training runs necessary.
Speaker 6: Except now China is coming up behind them, and it's
Speaker 6: unclear how anyone really deals with any of the problems
Speaker 6: I've been listening for years, which is unsustainable, unprofitable, and
Speaker 6: also not really funding the ROI and II.
Speaker 1: Ed play this out for us because I think a
Speaker 1: lot of people think, okay, for there to be some
Speaker 1: sort of ROI on this one thing has to happen.
Speaker 1: And like the best case scenario for all this money
Speaker 1: being spent is that productivity increases, Fewer people are needed
Speaker 1: to do more things. There are some serious implications if
Speaker 1: that were to come true and to the labor for
Speaker 1: us and dario Amidae of Anthropic has talked about this
Speaker 1: in the past. Maybe he's talking his book. I don't know.
Speaker 1: The other side of this is, well, if that doesn't
Speaker 1: come true, then what does it mean for these stocks
Speaker 1: that have gained so much on hopes that they would
Speaker 1: be responsible for some of this productivity increase? Like how
Speaker 1: does the shoe drop?
Speaker 5: What happens?
Speaker 6: Well, the thing is if you think about what Amazon,
Speaker 6: Google and Microsoft had done and met to some extent,
Speaker 6: but they're not selling compute capacity yet. Is they have
Speaker 6: gone from being these cash heavy, these cash machines. They
Speaker 6: just spill out money, low cash burn high revenue, low
Speaker 6: assets into these bulbous GPU filled asset mongers who are
Speaker 6: just full of these semi built data centers. For two
Speaker 6: customers or three customers at best, so that they can
Speaker 6: do what rent them out, so that they can rent
Speaker 6: their models. And it isn't really clear what the plan
Speaker 6: is at this point. And the problem is for me
Speaker 6: to be right, it doesn't even have to go that badly.
Speaker 6: Open AI and Anthropic have to grow so large to
Speaker 6: be able to make all of this data center capacity good.
Speaker 6: I mean, Google's i think the UBS estimate was like
Speaker 6: seventy six billion dollars in twenty twenty seven of Google
Speaker 6: Clouds revenue will come from Anthropic. How's Anthropic going to
Speaker 6: afford that they burn ten billions of dollars? So it's
Speaker 6: not just that these companies are unprofitable and unsustainable, but
Speaker 6: they have to grow so very large to make AI
Speaker 6: pay off because otherwise they just isn't demand for compute
Speaker 6: at scale.
Speaker 1: Last time you're on with us, we got a really
Speaker 1: incredible response, to be honest, and a lot of people
Speaker 1: who weren't typical viewers or listeners of our show saw
Speaker 1: what you did and listened to what you did, and
Speaker 1: it really seemed like there's this what you're saying is
Speaker 1: resonating with a lot of people, like there's a It
Speaker 1: was almost like there's this anti AI fervor that that's
Speaker 1: out there, and I'm just curious why you think that is.
Speaker 6: So I'm not sure it is anti AI, don't get
Speaker 6: me wrong, but I think it's also anti financial shenanigans.
Speaker 6: I think everyone sees the circular financing. I think they
Speaker 6: see the Microsoft, Google and Amazon gets basically all of
Speaker 6: their AI revenues either through products they're pushing on their
Speaker 6: customers or indeed compute spend from anthropic and open AI.
Speaker 6: And the average person's existence right now is so expensive,
Speaker 6: so hard, so difficult. Getting a mortgage as a regular
Speaker 6: person is so difficult. But if you're standing up a
Speaker 6: theoretical data center in thirty six months full the Nvidia GPUs,
Speaker 6: the banks fall over themselves to give you the money.
Speaker 6: Or we've just raised what a nine percent bond, I mean,
Speaker 6: you can raise anything if you have a data center.
Speaker 6: And I think regular people can see that AI does
Speaker 6: not deliver what people promise. They can see the opulence
Speaker 6: of the people at the top of the AI industry,
Speaker 6: and they can also see that they're being lied to
Speaker 6: and being deliberately scared. On top of all of this
Speaker 6: egregious circular financing, So.
Speaker 3: You think people are actually lying.
Speaker 6: Like or do you think people specifically?
Speaker 3: I don't know, Like is it the companies at the hyperscalers,
Speaker 3: the CEOs, the bankers, like do you think you know?
Speaker 3: And to be fair, we really should reach out to everybody.
Speaker 3: But I mean, is that what you're saying that or
Speaker 3: do they or do they not really know?
Speaker 6: I think they're overstating things. I think lying would suggest
Speaker 6: the certain malice, what have you. I don't want to
Speaker 6: accuse anyone of but I believe that they are massively
Speaker 6: overstating what AI will do. You'll notice that AI people
Speaker 6: tend to speak in the future tense. They tend not
Speaker 6: to say, oh, well today it can It's always AI will,
Speaker 6: AI will oh we're going to get the singularity, Oh
Speaker 6: AI will do this and that. That's because when they
Speaker 6: talk about what's happening today, it's pretty mediocre outside of code.
Speaker 6: And on top of that, these things are horribly unsustainable
Speaker 6: and unprofitable. And on top of that, they've got these
Speaker 6: destructive data centers, these massive eye saws that poisoned black communities,
Speaker 6: these massive eye saws that need billions of dollars at
Speaker 6: a time when it's hard for a regular person to
Speaker 6: get a dime from the banks. So yeah, I think
Speaker 6: that there is beyond just the misleading, this general sense
Speaker 6: of unfairness that AI taps into. And on top of that,
Speaker 6: if this all goes pear shaped, these people are going
Speaker 6: to realize that there was an authority crisis happening, that
Speaker 6: so many people got beguiled by hyperscalar promises and what
Speaker 6: it ultimately is. And I'm quoting ed Elson of profgy
Speaker 6: markets here. Our media, I believe has a cult like
Speaker 6: worship of the wealthy, that they believe that whatever the
Speaker 6: wealthy says will come true. And in the past, with
Speaker 6: the tech industry, that's kind of come true, except it
Speaker 6: stopped really coming true about ten to eleven years ago
Speaker 6: and we exited the era of hypergrowth. And that's all
Speaker 6: AI is. AI is an attempt to restart hyper growth
Speaker 6: for hyperscalers who don't have a new Google Search, who
Speaker 6: don't have a new iPhone, and certainly do not have
Speaker 6: a next Amazon Web Services.
Speaker 3: You know, the conversation narrative is changing. Don't you think
Speaker 3: it will continue to change? And it might be uncomfortable
Speaker 3: in terms of how it plays out in financial markets.
Speaker 6: Yes, I think this conversation is only going to accelerate.
Speaker 6: Open Ai didn't cut prices because they found some mystical
Speaker 6: way of making from things cheaper. It makes something eighty
Speaker 6: percent cheaper. They saw the danger from China, and they
Speaker 6: saw the competition from Anthropic, and they said, well, we're
Speaker 6: allowed to burn billions of dollars, so why don't we
Speaker 6: just cut prices and then make it up in volume
Speaker 6: for an unprofitable product. I think the ROI conversation is
Speaker 6: only going to accelerate too, because we should have really
Speaker 6: had it years ago. We really should have had it immediately.
Speaker 6: But again, people believe everything the tech industry says, and
Speaker 6: they just thought, well, they wouldn't say this and be wrong,
Speaker 6: would they were?
Speaker 1: In your view, does Elon Musk and SpaceX fit into
Speaker 1: this conversation. I bring it up because we learned this
Speaker 1: afternoon that Elon Musk's net worth has fallen to six
Speaker 1: hundred and eighty four billion dollars, which, yes, is a
Speaker 1: lot of money. It has erase though the IPO gains
Speaker 1: from SpaceX. And you we haven't been with you. You've
Speaker 1: been on with us richest salary. Okay, you you haven't
Speaker 1: joined US since SpaceX iPod, but there's a data point
Speaker 1: there for at least in the short term reception to
Speaker 1: a public company that has pretty significant exposure with AI.
Speaker 6: Well, I think SpaceX is kind of the proof point
Speaker 6: you need. We have someone who can sink unlimited capital
Speaker 6: into this, who can hire anyone who can theoretically stand
Speaker 6: up as much capacity as possible, breaking multiple laws at
Speaker 6: the same time not getting the permits. And what did we.
Speaker 5: Get for it?
Speaker 6: We got groc and what grek. Well, it's a third, fourth,
Speaker 6: fifth tier LLM that really only some people use by
Speaker 6: accident when they turn on Twitter. So we have this
Speaker 6: thing where we've had our third anthropic and open AI.
Speaker 6: We've seen someone else try it. We've had what should
Speaker 6: be the proof point that AI is an industry that
Speaker 6: we can have many AI labs and oh, one thousand
Speaker 6: flowers will bloom. And what we have is manure. We
Speaker 6: have a company that loses billions of dollars to do what.
Speaker 6: I don't know.
Speaker 1: So should the US be in an arms race with
Speaker 1: China for AI?
Speaker 6: Oh? I think that the arms race with China in
Speaker 6: and of itself is a marketing ploy. What oh no,
Speaker 6: what's China going to do, make a cheaper and better LLM.
Speaker 6: Oh it already happened, Nothing happened, Nothing happened China.
Speaker 1: What about the security risks that these llms or these
Speaker 1: these some of these agents are exposing, those from open
Speaker 1: ai and those from Anthropic.
Speaker 6: Well, I think the biggest risk with open ai and
Speaker 6: Anthropics agents is they don't appear to do basic security practices.
Speaker 6: They don't appear to take care of how they're using
Speaker 6: their systems. Open Ai, say, I actually questioned this entire
Speaker 6: story that their agent ran autonomously for multiple days, burning
Speaker 6: what sounds like unlimited compute. Either this company has run
Speaker 6: so terribly that they were running up millions of dollars
Speaker 6: of bills to randomly do stuff, and also they don't
Speaker 6: watch what it's doing. Software does what it's told to do.
Speaker 6: We don't know the prompt, we don't know the training,
Speaker 6: and they're not releasing the model.
Speaker 1: But it doesn't change the fact that these agents reportedly
Speaker 1: found weaknesses in code that if not exposed or that
Speaker 1: could be vulnerable. Like what I'm saying is if there
Speaker 1: is an idea that if this gets into the wrong hands,
Speaker 1: then systems could break down, just very briefly.
Speaker 6: One thing, it's already in the wrong hands, open and anthropic.
Speaker 6: They've shown they do not have the responsibility to make
Speaker 6: security tools. They should not be making them. They don't
Speaker 6: know what they're doing. It's platantly obvious, and on top
Speaker 6: of it, it's the brute force to hacking agent. They
Speaker 6: shoved as much compute power into it as possible. You
Speaker 6: could also pay hackers to do that. It's illegal. Also,
Speaker 6: this all sounds illegal. I'm no lawyer, I'm no judge,
Speaker 6: but I don't know why they're allowed to do this. Yeah,
Speaker 6: these things are dangerous if they're allowed to be trained
Speaker 6: on cybersecurity measures and execute against them. We I just
Speaker 6: I find the whole thing repugnant because everyone is saying, oh,
Speaker 6: look at the scary LM versus looking at the companies
Speaker 6: that run out.
Speaker 3: We got to run twenty seconds, anything that would change
Speaker 3: your mind and make you say this is real, real quickly?
Speaker 6: Not really? No, Okay, thank you, thank you.
Speaker 3: Thank you, thank you. There's a lot of conversations this week,
Speaker 3: and it was great to get your input at thank
Speaker 3: you edit from his CEO Easy Primary Research. Right here
Speaker 3: in our studio.
Speaker 2: You're listening to the Bloomberg Business Weekdaily podcast. Catch us
Speaker 2: live weekday afternoons from two to five pm Eastern Listen
Speaker 2: on Apple CarPlay and Android Auto with the Bloomberg Business app,
Speaker 2: or watch us live on YouTube.
Speaker 3: Former Open AI researcher Leopold Oshenbrenner's fund, Situational Awareness, was
Speaker 3: riding high on a four hundred and thirty nine percent
Speaker 3: return until a brutal July tech selloff triggered massive margin calls.
Speaker 3: Ken Griffin came to the rescue, as he's wont to
Speaker 3: do with beaten down assets, and snapped up some of
Speaker 3: them at a discount.
Speaker 1: Citadel's flagship fund surged five percent in July after the
Speaker 1: firm bought most of Situational Awareness's public stocks at that discount,
Speaker 1: and the transaction helped boost year to day gains at
Speaker 1: Citadel to twelve percent. That's according to a person familiar
Speaker 1: with the matter.
Speaker 3: And then after all of that, and really to be
Speaker 3: quite fair, just a few days after his hedge fund
Speaker 3: came close to a collapse, Oshan Brenner is back in
Speaker 3: the game, plunking down four hundred million dollars on a
Speaker 3: privately held company, according to people familiar with the.
Speaker 1: Matter, Bloomberg opinions. Aaron Brown knows a thing or two
Speaker 1: about market risks. He's former chief risk manager at AQR
Speaker 1: Capital Management, the hedge fund of cliff Usness. Aaron argues
Speaker 1: that Situational Awareness's massive return was a quote warning, not
Speaker 1: a triumph eron.
Speaker 3: Nice to have you here with us, Welcome, Welcome. You
Speaker 3: do know a thing or two about risk. You write
Speaker 3: that math sets a speed limit on how fast a
Speaker 3: portfolio can compound. What is that math? Take us to
Speaker 3: Bell Labs and take us to nineteen fifty six.
Speaker 5: Thank you for having me, Carol. Yeah, So this is
Speaker 5: John Kelly from Bell Labs, a physicist, also a fighter
Speaker 5: pilot and a bridge player. Really fascinating guy. And he
Speaker 5: discovered that most people assume that taking more risk means
Speaker 5: you increase the possibility of very good and very bad outcomes.
Speaker 5: But what he discovered is there's a limit, and beyond that,
Speaker 5: all you do is increase the probability of very bad
Speaker 5: outcomes the Kelly point. And for those of you who
Speaker 5: are familiar with his work, probably in gambling context or
Speaker 5: investing context, the optim is half of the Kelly limit.
Speaker 5: You know, you go halfway to the cliff, and that's
Speaker 5: where you get your maximum growth four hundred and thirty
Speaker 5: nine percent under any reasonable economics, any analysis. You know,
Speaker 5: we only have partial information about situational awareness. We have
Speaker 5: a thirteen a half from April that doesn't have the
Speaker 5: shorts we have, you know, Wall Street Trader chatter, but
Speaker 5: any reasonable suggestion says four hundred and thirty nine percent. Men,
Speaker 5: they were well over the Kelly limit, meaning sooner or
Speaker 5: later you have this happened to you, you blow up,
Speaker 5: could be years, could be tomorrow.
Speaker 1: Aaron the columns about such a situational awareness, but it's
Speaker 1: also about the time period that we're in. Any reference,
Speaker 1: what's happening in South Korea and specifically with some of
Speaker 1: those celebrity tfs and the chip names there to you,
Speaker 1: does this illustrate sort of where we are and maybe
Speaker 1: a market cycle, a hype cycle. What does it tell you?
Speaker 5: Well, it's I'm in an Ama Bolt myself, and you
Speaker 5: know I have a some considerable investments in AI. Nothing
Speaker 5: we're talking about today, it would affect that. But having
Speaker 5: a long term vision that AI is going to be
Speaker 5: very big doesn't give you a reason to take unlimited risk.
Speaker 5: And whether we're talking about South Korean retail investors for
Speaker 5: that matter, in New York retail investors or the Situational
Speaker 5: Awareness Fund. You have to think about the long term.
Speaker 5: You have to think about do I survive long enough
Speaker 5: to collect on my debts?
Speaker 3: So I have a question for you, you know, investment folks, right,
Speaker 3: and companies often have you know, risk managers, and I
Speaker 3: understand you know, so so laughing, am I wrong? Is
Speaker 3: there not someone to say, Okay, you're in over your
Speaker 3: skis here, you know, Like, so what's your read on
Speaker 3: this this firm? I mean, it's still a ten billion
Speaker 3: dollar hedge fund, so it's not like it's collapsed. Was
Speaker 3: happy to take you know, but we understand that that's
Speaker 3: what they do. But I don't know, like, how do
Speaker 3: we kind of step back here in terms of internally
Speaker 3: what this company is doing or this hedge fund is doing.
Speaker 5: Okay, So Citadel has tremendous risk management as some of
Speaker 5: the best risk management on the planet, which is why
Speaker 5: they're in a position to do this kind of thing.
Speaker 5: I don't know anybody at Situational Awareness, but the fact
Speaker 5: that they had either four or seven I've seen different
Speaker 5: media reports total financial professionals makes me suspect and plus
Speaker 5: their investments make me suspect they did not have a
Speaker 5: risk manager, or did not pay attention to him, or
Speaker 5: did not have a qualified one, because there it just
Speaker 5: does not seem like a risk managed portfolio. And all
Speaker 5: of the public statements we've heard from them only mention
Speaker 5: expected return, you know, future outcomes. None of it mentions risks. So, yes,
Speaker 5: they should have had a better risk manager, and that
Speaker 5: ten billion is that's pretty misleading. First of all, I
Speaker 5: think that they're still carrying anthropic at five billion. I
Speaker 5: don't think they've marked it down at all from its peak,
Speaker 5: and it's certainly worth less than that. Second, you know,
Speaker 5: they started the year at one point five billion. We
Speaker 5: think they grew to well over you know, ten billion,
Speaker 5: and so I suspect most of the investors in essay
Speaker 5: on a dollar basis, have lost quite a bit of money.
Speaker 5: If you were in January, you know, if you were
Speaker 5: part of that one point five billion, you're still up
Speaker 5: I think thirty percent for the year Wall Street Journal reported,
Speaker 5: But most of the people got in closer to the
Speaker 5: peak and are probably well underwater. Today.
Speaker 1: We're speaking with Aaron Brown, columnists for Blooming Opinion, former
Speaker 1: chief risk manager at AQR Capital Management, also the author
Speaker 1: of Wrong Number, How to Extract Truth from a Blizzard
Speaker 1: of Quantitative Disinformation. I like that you brought up that
Speaker 1: private stake Anthropic because I don't want to give the
Speaker 1: whole column away, but you and I encourage everybody to
Speaker 1: go read it. I was just sending it around to
Speaker 1: some guests who've joined us in the past. You make
Speaker 1: the point that the anthropic investment, like it makes sense
Speaker 1: they still have that because they couldn't They couldn't take
Speaker 1: margin on that. They can't transfer shares of that, so
Speaker 1: it's like, at the end of the day, it's sort
Speaker 1: of the safest thing for them because they couldn't bet
Speaker 1: against it.
Speaker 5: Well, oh, well, they can't lever it. There are people
Speaker 5: who will lend you money against it, but they won't.
Speaker 5: They're not daily margins, so you're not getting the kind
Speaker 5: of leverage they out of public investments. And it's possible
Speaker 5: that they didn't lever it at all. I guess is
Speaker 5: they didn't. You know, if you have public stock, you're
Speaker 5: going to lever those. You don't have to go to
Speaker 5: your private But yes, a company like Situational Awareness, with
Speaker 5: their approach to the market they should be making private
Speaker 5: investments and not levering them.
Speaker 3: What about the banks that were lending the money, Like,
Speaker 3: what's the due diligence on that? And they were well
Speaker 3: known banks we keep citing like JP Morgan. I'm just
Speaker 3: curious how that typically works out.
Speaker 5: Well, it typically works out like this one. Did they
Speaker 5: get all their money better? Yeah?
Speaker 3: Okay, you know, but the due diligence ahead of it?
Speaker 3: Aaron like, do they just is there something that they
Speaker 3: look at ahead of it in terms of yeah.
Speaker 5: Yes, yes, they do that very carefully. And Archid Ghost
Speaker 5: was you know, a couple of years ago. That was
Speaker 5: the exception. That was where they all got burned because
Speaker 5: they went ahead over to their skis, as you put it,
Speaker 5: they they went, this is exactly how it is supposed
Speaker 5: to work. The banks always should do, okay. And what
Speaker 5: they were looking at is they were looking at the
Speaker 5: market for the stock they were. They knew Citadel was around,
Speaker 5: they knew there were other people around who would you know,
Speaker 5: be in a position to buy on a dip. And
Speaker 5: they quickly got out, you know, before before they got hurt.
Speaker 5: And and as they say, that's how it's supposed to work,
Speaker 5: and that's how it usually does work. That's why these
Speaker 5: companies are so big and rich.
Speaker 1: But you also make the point in piece that Citadel
Speaker 1: learned this lesson the hard way, like they're looked at
Speaker 1: right now, is coming and swooping in at this time,
Speaker 1: but post two thousand and eight they suffered some serious losses.
Speaker 5: And HR in two thousand and seven did as well. Yeah,
Speaker 5: so yes, risk management is a lot of unhappy experience,
Speaker 5: but learning from experience.
Speaker 1: So okay, So hindsight is twenty twenty if this if
Speaker 1: this portfolio, and again we don't have complete information, like
Speaker 1: you said, this is based around what has leaked in
Speaker 1: thirteen f's, but what would have been the right way
Speaker 1: to build positions in companies that you believe in that
Speaker 1: wouldn't have overexposed them on the downside?
Speaker 5: Well okay, so situational awareness. The investment thesis is that
Speaker 5: AI is going to be giantic is going to you know,
Speaker 5: take over the world, but it's going to be bigger
Speaker 5: than even most of the optimists think. But it has
Speaker 5: no thesis at least as twenty of the public statements
Speaker 5: about the path to getting there. So you have to
Speaker 5: think about that through and say, what are the scenarios
Speaker 5: where we're right but we don't get to keep our positions.
Speaker 5: It also has I think people are not aware of
Speaker 5: how complex its positions are. Again, this is looking at
Speaker 5: the thirteen F without the shorts, but we can see
Speaker 5: they're betting against a lot of these companies. They're picking
Speaker 5: and choosing, and so they've got longs and shorts, and
Speaker 5: they've got a lot of puts on. So they're betting
Speaker 5: certain segments will do well and others are going to
Speaker 5: get beaten out. So this is a very complex bet.
Speaker 5: So you have to think, Okay, what's the situation in
Speaker 5: which we're right, but what's the worst point between now
Speaker 5: and then? And can we survive it? It doesn't appear
Speaker 5: to me that they were asking that question, or they weren't,
Speaker 5: you know, taking it seriously enough.
Speaker 3: I want to wrap up with you know, you said
Speaker 3: earlier Erin that you are an eyeball and you have
Speaker 3: positions situational awareness to figure out is this kind of
Speaker 3: maybe a coal in the Canary mine when it comes
Speaker 3: to the AI trade and narrative? What would you say?
Speaker 5: It's not? No, No, I don't think so. I mean,
Speaker 5: you know, we had to pull back an AI, and
Speaker 5: you know, a lot of people got hurt, but really
Speaker 5: the only headlines disasters are the people who were over levered,
Speaker 5: either the EKF source situational awareness. Most of the investors
Speaker 5: are there for the long term. You know, you don't
Speaker 5: see a huge sell off. I don't see anybody changing
Speaker 5: their mind about AI. You had to expect. I mean,
Speaker 5: I mean, these stocks are extremely volatile, and the events
Speaker 5: of the summer have been you know, pretty much normal
Speaker 5: volativity for the sector. So if you were investing sensibly
Speaker 5: in AI, this summer was not an unpleasant experience for you.
Speaker 3: We're going to leave it on that note. We're so
Speaker 3: glad we could get you on. We read your column
Speaker 3: and thought it was super super smart and just a
Speaker 3: different take, and it was something we wanted to bring
Speaker 3: to our viewers and our listeners erin.
Speaker 1: Thank you so much.
Speaker 3: I hope you'll come back. Thank you well, Okay, good stuff.
Speaker 3: Aaron Brown, columnist for Bloomberg Opinion, former chief ust manager
Speaker 3: at AQR Capital Management, whose book Wrong Number.
Speaker 1: I'm glad he said he'd come back, because if he
Speaker 1: would have said I'm not going to come back.
Speaker 4: Yeah, I guess, I guess it wouldn't have been.
Speaker 5: It wouldn't have been.
Speaker 3: That's so fair.
Speaker 1: No, it's a good strategy.
Speaker 5: I like it.
Speaker 1: Now, we're going to hold it to it and we're
Speaker 1: going to get him on for our next column.
Speaker 3: It's a story like we're still trying to be.
Speaker 2: This is the Bloomberg Business Week Daily Podcast, available on Apple, Spotify,
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