Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Bloomberg Tech is a
Speaker 1: live from coast to coast with Carolline Hide in New
Speaker 1: York and Eva Low in San Francisco.
Speaker 2: This is Bloomberg Tech coming up. Meta agrees to buy
Speaker 2: AMD chips and data center gear and a deal worth
Speaker 2: tens of billions of dollars.
Speaker 3: Plus AI whiplash in markets, It's erupts again as investors
Speaker 3: navigate the disruptive power of agentic tools and unveil by
Speaker 3: Anthropic and Warner Brothers.
Speaker 2: Discovery is considering a new takeover bid from paramounts Guidearce,
Speaker 2: with its board set to review the proposal and respond.
Speaker 3: First, we check in on these markets that bounce back
Speaker 3: after yesterday's sell off again, Anthropic Front and Center, the
Speaker 3: latest AI tools that have been battering software and now
Speaker 3: adding some fuel to them. We'll get into that story
Speaker 3: a little bit later, but at the moment, that's that
Speaker 3: one hundred up, my nine tensven percent reprieve. Remember also
Speaker 3: consumer confidence from a macro perspective coming in better than anticipated.
Speaker 3: I give a bitcoin, though does nothing better than anticipated with.
Speaker 4: This particular asset.
Speaker 3: We're currently at sixty three, nine hundred and ninety three.
Speaker 3: The pressure still there, D But what have you got?
Speaker 2: Let's get to our top story. Shares of AMD pushing higher.
Speaker 2: Meta flat had been slightly softer. At one point in
Speaker 2: the session, AMD was up about nine percent and on
Speaker 2: track for its best day since November. This is a
Speaker 2: six gigawa deal. Metas agreed to buy AMD chips and
Speaker 2: data center supplies in a deal worth double digit billions
Speaker 2: per gigawat. This is the social media giant looks to
Speaker 2: prioritize it's AI ambitions. Here to break it down, Bloomberg's
Speaker 2: Ian King on the chip side, Riley Griffin on the
Speaker 2: Meta side, around the table, Ian, I'm going to start
Speaker 2: with you the basics of the deal. Six gigawatts over
Speaker 2: time AMD accelerators, but also other gear. It's a big
Speaker 2: deal for them.
Speaker 5: Yeah, it's a real endorsement from one of the biggest
Speaker 5: buyers of this kind of equipment. Obviously, they're playing catch
Speaker 5: up with the in Video who had their own deal
Speaker 5: with Meta last week. So this is a real strong
Speaker 5: affirmation and it also includes a share warrants as well
Speaker 5: that are going to Meta.
Speaker 3: Riley, this is all about metacompute. Talk us through just
Speaker 3: how large the scale is for demand for this sort
Speaker 3: of infstructure right now.
Speaker 6: It's insatiable demand from Meta. Mark Zuckerberg last month announced
Speaker 6: Meta Compute, with ambitions to get hundreds of gigawatts to
Speaker 6: fuel its data centers and ultimately reach that super intelligence
Speaker 6: score where AI can outpace human intelligence. And this is
Speaker 6: just the latest in a frenzy of deals. Again, we're
Speaker 6: anticipating one hundred and thirty five billion in capex this year,
Speaker 6: but now we know the spending won't stop.
Speaker 2: The warrants part is very interesting. It's very similar to
Speaker 2: what AMD did with open Ai. There are operational and
Speaker 2: financial milestones in both directions in order for Meta to
Speaker 2: potentially become a very big shareholder AMD. Could you explain
Speaker 2: that in.
Speaker 5: Yeah, the way to look at it, Lisas he said, Look,
Speaker 5: this basically ties as closer to Meta. They get these
Speaker 5: shares if we're in a very good position. I mean,
Speaker 5: the last chalm I believe is about six hundred dollars there.
Speaker 5: You know yesterday they closed at less than two hundred,
Speaker 5: so we've got a room to go over before that
Speaker 5: becomes a reality. But you know, her point was that
Speaker 5: this kind of locks us together and tightens our relationship.
Speaker 2: It was really interesting to read through the materials in
Speaker 2: our reporting. Riley, like AMD has played this card before.
Speaker 2: The emphasis is on inference. So m I four fifty
Speaker 2: generation of accelerator. The engineering teams will work together, but
Speaker 2: the inference part. Is there anything unique about this different
Speaker 2: to what Meta is doing with nvidio because we had
Speaker 2: a very similar deal very recently.
Speaker 6: It's a really good question. Not only is it similar
Speaker 6: to what Meta is doing with in a Video, but
Speaker 6: Meta also has its own internal pipeline of custom chips
Speaker 6: that it's building for AI purposes. What we heard yesterday
Speaker 6: is that they see different applications, different workloads here being
Speaker 6: supported by all three of those verticals, and so they're
Speaker 6: trying to diversify as they pursue this massive scale in
Speaker 6: terms of compute.
Speaker 3: In take us back to in video because we've got
Speaker 3: their earnings coming up tomorrow. And how much does this
Speaker 3: show that there is anxiety that companies are diversifying looking
Speaker 3: at MD, looking their own in house chips, or actually
Speaker 3: does it still not a concerned in video is still dominant?
Speaker 1: Yeah.
Speaker 5: I mean at this point all we can do is say,
Speaker 5: look at the numbers, and the numbers assuming in video
Speaker 5: comes in and executes on what Wall Street thinks it's
Speaker 5: going to do. The numbers say there's no anxiety here,
Speaker 5: or boats are floating. Everybody is benefiting from the same
Speaker 5: massive spending in equal measure. I guess what would change
Speaker 5: that would be if in Vidia does not hit those targets,
Speaker 5: does not exceed those targets, that I think would change
Speaker 5: the conversation considerably.
Speaker 3: And Riley, the conversation is nuanced when it comes to
Speaker 3: capital expenditure. Many would say, yes, a lot of it
Speaker 3: is about the chips, it is about the day centers,
Speaker 3: but it's also about the power. Can we understand really
Speaker 3: how much Meta is having to focus in on where
Speaker 3: they can spend on GPUs or their own homegrown chips,
Speaker 3: and where they do it globally as well.
Speaker 6: So we asked yesterday where they thought they would deploy
Speaker 6: these chips, and they couldn't specify which data centers. We
Speaker 6: know that some of their biggest projects are targeting five
Speaker 6: gigawatts right, but they need to get certain regulatory approvals
Speaker 6: and they need energy companies to be able to deliver
Speaker 6: there on the ground. So as for the merging of
Speaker 6: the energy and the compute here time will tell as
Speaker 6: we home in on where those chips will actually go.
Speaker 2: It's probably time for a bit of a reality check in,
Speaker 2: and I'm hoping that you'll provide it for us. Like
Speaker 2: in any given quarter, right now, AMD is going to
Speaker 2: do ten billion dollars of revenue all told, every single
Speaker 2: segment in Vidia's data center business? Is it more than
Speaker 2: fifty billion dollars a quarter? Yes, that includes networking. But
Speaker 2: they're now one and the same, right, you can't have
Speaker 2: chips without the other. How do we know if this
Speaker 2: is evidence that MD's catching up.
Speaker 5: I mean again, you have to go back to the percentages,
Speaker 5: the growth percentages. At the moment, both of them would say,
Speaker 5: you know, this isn't about competition. This is about there's
Speaker 5: so much demand a company like Meta, which makes his
Speaker 5: own chips, is buying from both of us. That would
Speaker 5: be the answer. But ultimately, when the market slows down,
Speaker 5: and you can be the judge of when that happens,
Speaker 5: then we'll see the market share shift, because then it'll
Speaker 5: really matter.
Speaker 2: Meta is not a hyperscaler, but it operates its own
Speaker 2: data centers at hyper scale, if you know what I mean.
Speaker 2: I'm thinking back to when Mark Zackobog was sat next
Speaker 2: to the president six hundred billion dollars over the next
Speaker 2: few years, and then he's also talked about this idea
Speaker 2: that they might misspend two hundred billion dollars here or there,
Speaker 2: but that's no bad thing. He's front loading purchases. Just
Speaker 2: explain his thesis a bit.
Speaker 6: Yeah, His strategy is just that he's used the language
Speaker 6: of front loading capacity. They're going to try to get
Speaker 6: as much as they can gobble it up while there's
Speaker 6: still availability. And this, he says, could be applied not
Speaker 6: just for AI purposes, but for that core social media business,
Speaker 6: which still drives more than ninety eight percent of their revenue.
Speaker 6: Right so they see applications across the board and they're
Speaker 6: not worried about getting too much in the interim.
Speaker 3: In why do is AM do you have to keep
Speaker 3: giving it shares away?
Speaker 5: Again, we asked Lisa about this and she said that
Speaker 5: this is Lisa to the CEO, and she said, this
Speaker 5: is different than the open air idea. The open air
Speaker 5: ideal is a slightly different arrangement. That is a company
Speaker 5: obviously that is seeking liquidity. Meta clearly does not need liquidity.
Speaker 5: This is just a way of showing Meta's commitment to
Speaker 5: what they're doing. You've got to remember that, you know,
Speaker 5: AMD is only a couple of generations into being any
Speaker 5: type of presence at all in this market. So if
Speaker 5: a big buyer of this stuff comes along and says, oh,
Speaker 5: we like your gear, oh guess what, we also like
Speaker 5: you as a company, like your prospects, and that is
Speaker 5: somehow a bigger validation than just a straight purchase agreement.
Speaker 5: And that really was what they were pushing as an idea. Look,
Speaker 5: this is how closely we are tied.
Speaker 4: Together, beneffecting from the upside.
Speaker 3: Bloomberg's in King and Riley Griffin, thank you so much
Speaker 3: for joining.
Speaker 4: Us on that roundtable.
Speaker 3: Meanwhile, coming up software earnings, they take center stage two.
Speaker 4: What to expect as AI fears enter the picture. This
Speaker 4: is Bloomberg Tech.
Speaker 2: Shares of IBM had the worst day in more than
Speaker 2: twenty five years Monday, after Mthropic announced that its clawed
Speaker 2: code could help to modernize the dated coding language Cobol,
Speaker 2: largely run on IBM computers. This is in Fropit continues
Speaker 2: to unveil new AI tools for its cowork agent software
Speaker 2: across human resources, investment, banking, and design. Bloomberg's Brady Foyd
Speaker 2: joins us with the latest Cobol was invented in nineteen
Speaker 2: fifty nine. There are two hundred billion lines of OBO
Speaker 2: called the underpin financial systems, banking payments around the world.
Speaker 2: And basically anthropics said it would be really useful to
Speaker 2: use Claude to help maybe investigate looking to change that,
Speaker 2: and then one of the most story technology companies in
Speaker 2: history had its worst day in twenty five years. Take
Speaker 2: it from that.
Speaker 7: You've got to feel powerful if you're anthropic right now,
Speaker 7: right if you just say the name of a product
Speaker 7: the company associated with that project tanks. I mean, IBM
Speaker 7: has been talking about using AI for cobal modernization and
Speaker 7: it seems successfully offering that tool for a couple of
Speaker 7: years now. And yeah, markets are really jumpy, right, I mean,
Speaker 7: the potential that Claude or another AI tool can disrupt
Speaker 7: the leaders in a given software category is really frightening
Speaker 7: investors right now. And it's just it's an incredibly jumpy market.
Speaker 3: Ibmci A VP Rob Thomas was pushing back against this
Speaker 3: writing in a blog, and you quote him in your
Speaker 3: story that the value of IBM mainframe delivers has nothing
Speaker 3: to do with cobol is trying to talk about the
Speaker 3: platform more broadly brody, but what you say is so brilliant.
Speaker 3: How powerful anthropic must feel. Also to the upside, because look,
Speaker 3: they do a partnership with Intuit today and that fuels
Speaker 3: into it shares a little bit on the upside, so
Speaker 3: it can be make or break in either direction.
Speaker 7: It fuels into it a bit on the upside. But
Speaker 7: the company's still down what forty percent this year. I
Speaker 7: don't know if it's quite that dramatic, but it's a
Speaker 7: scary time to be an application software vendor, right. I
Speaker 7: mean the most extreme idea that companies are just going
Speaker 7: to vibe code their own solutions. I don't think anybody
Speaker 7: really believes that, But the idea that software vendors lose
Speaker 7: the kind of pricing leverage they've enjoyed for so long
Speaker 7: because of that potential disruption. It's really something that markets
Speaker 7: are struggling to grapple with right now, and it's going
Speaker 7: to be a really interesting earning cycle.
Speaker 2: It's basically pitching itself now as a platform. Right. So
Speaker 2: historically Claude is focused on code and making engineering more efficient,
Speaker 2: and like if you're an into it customer, you're aready
Speaker 2: used of into its platforms and data. You just have
Speaker 2: an agent inside of it. If you're a small business
Speaker 2: or or a consumer. It's the same with DocuSign. We're
Speaker 2: seeing all these names move. How is it making these
Speaker 2: companies better, Brodie, you cover this beat inside and out.
Speaker 7: Well, it's forcing them to really try to drive adoption
Speaker 7: and use among customers. Like we all know that a
Speaker 7: lot of the AI height was a bit premature in
Speaker 7: terms of how mature these tools were actually and how
Speaker 7: ready they were to be used in the enterprise. And
Speaker 7: it's really holding toes to the fire that hey, it's
Speaker 7: time to make sure your customers are actually using your
Speaker 7: AI tools or else the market's going to sell your
Speaker 7: stock and act like you are going to go poof
Speaker 7: in a couple of years.
Speaker 3: Plombo's Brodie Ford always a perfect way with words.
Speaker 4: We thank you.
Speaker 3: Let's get more on the wider AI impact on tech
Speaker 3: and software with Clearbridge Investments senior research analysts for Software
Speaker 3: for IT services. Anyway, Fresh Henry the perfect person. I
Speaker 3: go back to in many ways what Brady was just saying.
Speaker 3: We're questioning just how good the tools are. How have
Speaker 3: we seen a sudden shift in how brilliant these AI
Speaker 3: agents are.
Speaker 4: In the last few months we have.
Speaker 8: We're seeing this sellof and software in particularly SaaS because
Speaker 8: so much is changing so rapidly, and these tools are
Speaker 8: evolving and tremendously. There's probably a pretty big disparity between
Speaker 8: some of the capabilities of the tools and what's happening
Speaker 8: and likely to happen, probably even intermediate term in the marketplace,
Speaker 8: particularly the enterprise marketplace. But investors shoot first and ask
Speaker 8: questions at some very distant dates. So we're seeing a
Speaker 8: pretty dramatic compression in terminal multiples of many of the names.
Speaker 3: At what point are they going to become discerning? Because
Speaker 3: at the moment, it's very hard for a CEO, for
Speaker 3: Aarvin krishnover IBM, for whether it's service now, Bill McDermott,
Speaker 3: to disprove that they're not going to be impacted, that's right.
Speaker 3: So how do they prove a negative? How do you
Speaker 3: start to see people willing to catch falling knife and
Speaker 3: be like, oh, these are platform companies are not going
Speaker 3: to be disrupted.
Speaker 8: Or maybe they will be sure sure, It's so hard
Speaker 8: to tell the near term reaction to any kind of
Speaker 8: event it's been asymmetrically skewed to the downside. I think
Speaker 8: what the vendors really have to do is show that
Speaker 8: AI is additive to the business, to beat and raise
Speaker 8: and show that AI isn't just off setting into clients
Speaker 8: on the core business, but it's actually adding to the business,
Speaker 8: and that could really change the narrative. We actually saw
Speaker 8: that with the Snowflakes of the World two years ago,
Speaker 8: with MAMO d B last year into this year. But
Speaker 8: that's typically what's required for investors to realize it's not
Speaker 8: just a zero sum game.
Speaker 2: On the Bloomberg terminal, there are let's say a dozen
Speaker 2: reports of single name stocks that are moving because of
Speaker 2: the association with Anthropic, who held an event this morning.
Speaker 2: You studied the software sector right very closely. Any evidence
Speaker 2: those names are moving because people genuinely believe there's a
Speaker 2: fundamental change to how they do business, or is it
Speaker 2: just simply name association At this point.
Speaker 8: I think it has to do with the idea that
Speaker 8: if Anthropic sees the need to partner with these companies,
Speaker 8: and Thropics sees that they provide something that Anthropic doesn't,
Speaker 8: and they understand these are incumbent vendors with installed customer
Speaker 8: bases and loyalty and processes and things like that.
Speaker 4: It doesn't mean.
Speaker 8: There isn't future disintermediation disintermediation risk, we all know that,
Speaker 8: but I think it's a signal to investors at these
Speaker 8: companies are viable and important to the ecosystem, and investors
Speaker 8: have not been assuming that.
Speaker 4: Over the prior few weeks.
Speaker 2: Hiary, there was a research report, a bearish research report
Speaker 2: from a little known outfit called Catrini that basically outlined
Speaker 2: various risks to different sectors from AI. One of the
Speaker 2: co authors joined Bloomberg Television. I just want to play
Speaker 2: you some of that conversation.
Speaker 9: I thought there was going to be a small reaction.
Speaker 9: It was definitely larger than we expected. But I think
Speaker 9: it's not that surprising when you take a step back
Speaker 9: and consider kind of where the markets are in the US.
Speaker 9: You know, the AI trade has been going on for
Speaker 9: three and a half years. It's been more or less
Speaker 9: a straight line up, and essentially like everyone is max
Speaker 9: long today and so there really aren't many incremental buyers left,
Speaker 9: and so it on the one hand, you know, spookspeople
Speaker 9: when you do consider what is negative about this, But
Speaker 9: I think specifically the market right now is trying to
Speaker 9: digest this idea that AI has gotten a lot more
Speaker 9: powerful in the last six months.
Speaker 2: You know, in that he's kind of saying, well, this
Speaker 2: is kind of the function of the market of the
Speaker 2: last three years, but still poses the central question, which
Speaker 2: is is AI rendering existing software obsolete or is it
Speaker 2: making it better? Just your reaction to that research report
Speaker 2: and the co author's thoughts there.
Speaker 4: Sure, I think it's both.
Speaker 8: I think AI represents represents a risk and it also
Speaker 8: represents an opportunity for many of the incombent vendors. We
Speaker 8: could take sas separately, but we've been talking internally about
Speaker 8: what I call the great catchdown to sas. Every other sector,
Speaker 8: every other part of software selling down to SAS on
Speaker 8: disintermediation risks. But there are reasons why a lot of
Speaker 8: subsectors should be relatively more defensible, and we could talk
Speaker 8: about those a.
Speaker 3: Few like, Okay, let's talk about what's just spent defensible
Speaker 3: Because there's this drip feed of AI dooomerism, shall we say,
Speaker 3: in these various reports and blog posts coming from the
Speaker 3: CEO of Anthropic himself to over at Szuctrini. We've also
Speaker 3: had Harvard coming out with their piece that we're seeing
Speaker 3: it being incredibly effective when it comes to financial analysis
Speaker 3: and portfolio decision making. The Harvard led study is talking
Speaker 3: about how trading mutual fund training decisions. Seventy one percent
Speaker 3: it predicted right the model was trained over a five
Speaker 3: year window. But they're also talking about maybe the outperformance,
Speaker 3: but is what it misses? So from your perspective, where's
Speaker 3: the outperformance going to come from? Some of the companies
Speaker 3: that can withstand this, that can show that they're really
Speaker 3: effective while using the AI tools.
Speaker 8: Sure Sure, I think of security as an example of that.
Speaker 8: Anthropic introduced a vulnerability management tool, not even management, just
Speaker 8: a scanner for vulnerabilities and code that's produced on the platform,
Speaker 8: and that's a build time phenomenon. Most security vendors are
Speaker 8: run time, but they bring a lot to bear that
Speaker 8: we could talk about from a technological perspective that it's
Speaker 8: going to.
Speaker 4: Be hard for an l ELAM or an agent to have.
Speaker 8: They have the physical infrastructure, they cover all the enforcement points.
Speaker 8: It's partly a physical phenomenon, not just a digital phenomenon
Speaker 8: per se. But on top of that, when you think
Speaker 8: about it. I don't know how entities, and especially regulated
Speaker 8: entities can have the fox guarding the henhouse when it
Speaker 8: comes to security, and kind of the same existed in
Speaker 8: cloud as well. Because AI creates so many vulnerabilities, so
Speaker 8: many new vulnerabilities that we've never had, that it's a
Speaker 8: major issue when you're looking to what's becoming the greatest
Speaker 8: attack surface to be the solution to the problem. Similarly,
Speaker 8: the data platform vendors are really the platforms for the
Speaker 8: next generation of AI applications. We're seeing them build on
Speaker 8: top of the snowflakes and Mango dbs, even potentially the
Speaker 8: Oracles to some degree data dog that's slightly different, but
Speaker 8: what customers are looking for is higher levels of automation
Speaker 8: to make their lives easier when there's a tsunami.
Speaker 4: Of data coming at them.
Speaker 8: These infrastructures and oftentimes are quite different. The data models
Speaker 8: are different from llms, the infrastructure on which they run
Speaker 8: are far more efficient than what lmms do and seem
Speaker 8: to be able to do. And so I believe the
Speaker 8: greatest source of automation is going to come from these
Speaker 8: incumbent vendors. I should have mentioned data bricks of course,
Speaker 8: in which Clearbridge has a private investment, and so solving
Speaker 8: and headaches for customers in the face of a ton
Speaker 8: of complexity is going to be a Paramount Imports.
Speaker 2: Hillary Fresh clear Bridge Investments, thank you very much. Now
Speaker 2: coming up on the program, Paramount is said to have
Speaker 2: raised its offer for Warner Brothers More on the Drama
Speaker 2: to buy one of Hollywood's most story brands, the snackt
Speaker 2: This is Bloomberg Tech.
Speaker 4: Time now for Talking Tech and First Up.
Speaker 3: Athropic has accused Deep Seat, Minimax and Moonshot of distilling.
Speaker 4: Its clawed models to bolster their.
Speaker 3: Own AI capabilities, adding to concerns in the US about
Speaker 3: Chinese firms improperly gaining an edge.
Speaker 4: Representatives for the three firms didn't respond to requests for comment.
Speaker 3: Sticking with Anthropic look, sources say the company will let
Speaker 3: some current and former employees sell their shares at evaluation
Speaker 3: for about three hundred and fifty billion.
Speaker 4: Dollars, the level reached during a recent fund raise.
Speaker 3: Anthropic has lined up five to six billion dollars from
Speaker 3: outside investors, though the amount and details have not yet
Speaker 3: been finalized, and Canada Well It's summoned open AI executives
Speaker 3: after it was revealed the company debate but ultimately didn't
Speaker 3: refer a chat GBT user to the police. That person
Speaker 3: became the sole suspect in one of Canada's worst ever
Speaker 3: mass shootings. Opening I bound the use of months before
Speaker 3: the attack, but said it found no credible or imminent
Speaker 3: threat ed.
Speaker 2: Okay, let's take a look at shares Warner Brothers, Discoveries, Paramount,
Speaker 2: and Netflix and get the latest.
Speaker 10: Paramount has raised.
Speaker 2: Its offer for Warner Brothers from its thirty dollars a
Speaker 2: share all cash proposal. That's according to Bloomberg Reporting. Now
Speaker 2: Warner Brothers Discovery and its board is reviewing that revised proposal.
Speaker 2: Bloomberg's managing editor for Media and Entertainment leading the screen
Speaker 2: Time team because sure, we actually don't have the specifics.
Speaker 2: I don't think yet of how improved this offer is.
Speaker 2: But it's on the desk of Warner Brothers Discovery, It's
Speaker 2: leaders and its board. Take it from that.
Speaker 11: Yeah, I mean, you see both sides taking this more
Speaker 11: seriously and being quiet. I mean there's been a kind
Speaker 11: of a progression with these talks, where back when the
Speaker 11: first bidding war was happening, you had Warner Brothers reviewing
Speaker 11: offers from Comcast Netflix in Paramount, it was very quiet, right,
Speaker 11: people didn't say a lot because everyone thought they were
Speaker 11: in it. Then Warner Brothers picked Netflix, and I got
Speaker 11: really loud, Paramount accusing Warner brother of doing things that
Speaker 11: weren't right. Netflix and Warner Brothers often firing back. And
Speaker 11: now it's gone quiet again, which to me is clearly
Speaker 11: a sign that the board is seriously reviewing the Paramount offer.
Speaker 11: If Paramount has increased it five a dollar to a share,
Speaker 11: which we assume they have, it seems hard to believe
Speaker 11: that they would outright reject it as they have the
Speaker 11: recent ones, which means that they would have to go
Speaker 11: to Netflix and sort of say, what do you got
Speaker 11: for me?
Speaker 3: Well, exactly, we'll get a drip feed of what it
Speaker 3: is exactly that they've got. But if it is markedly better,
Speaker 3: do we expect Netflix to respond in kind?
Speaker 11: You know, it's a tough question to answered. Netflix has
Speaker 11: communicated to some of its shareholders. I've heard from people
Speaker 11: at Netflix that they feel they have the balance sheet
Speaker 11: to go up.
Speaker 9: Right.
Speaker 11: We're talking about a growing business with a really significant
Speaker 11: market cap. Even if this stalk's taken a little bit
Speaker 11: of a beating during these negotiations. So relative to paramount
Speaker 11: and even relative to the Ellison family, Netflix should have
Speaker 11: the money to go there. It's a question of whether
Speaker 11: they want to. You know, their stock is down. I've
Speaker 11: lost track at this point. It's more than thirty percent
Speaker 11: since these negotiations started, and they may decide that there's
Speaker 11: a number that is too high for them. We just
Speaker 11: don't know what that threshold is.
Speaker 3: It continues to evolve and we know that you always
Speaker 3: have the story, but mostly Beka, sure, thank you so much.
Speaker 3: I'm coming up more on Meta and AMD's multi billion
Speaker 3: dollar deal as the race to power AI intensifies and
Speaker 3: AMD up coming off of those previous highs.
Speaker 2: Yep, and then Meta flat as a pancake. Much more
Speaker 2: to come. It is halftime. This is Bloomberg Tech. Welcome
Speaker 2: back to Bloomberg Tech. We're continuing to track the impact
Speaker 2: of AI on legacy software. Essentially lots of names actually
Speaker 2: moving to the upside.
Speaker 10: The story is.
Speaker 2: An anthropic and basically taking its clawed AI agent and
Speaker 2: linking it in a platform for everything from HR investment
Speaker 2: banking documents, and that is pushing a lot of names higher.
Speaker 2: IBM is up four percent, but remember yesterday Monday it
Speaker 2: fell by the most since two thousand when it said
Speaker 2: that Claude could be used basically to improve archive change
Speaker 2: update cobol coding bases, a program that was invented in
Speaker 2: nineteen fifty nine. Otherwise, our top story is a deal
Speaker 2: between AMD and Meta six gigawatts of capacity over the
Speaker 2: balance of the decade, largely focused in the first instance
Speaker 2: on AMD's latest accelerator and the server that is based
Speaker 2: on mi I four fifty, but this is basically sixty
Speaker 2: billion dollars of potential revenue for AMD over that time period.
Speaker 2: The stock up more than seven percent. MEATA has traded flat,
Speaker 2: but I guess that it's hard to gauge if this
Speaker 2: makes any different Semara at all given its existing spending commitments.
Speaker 4: Karrac Well, someone's been writing on just that.
Speaker 3: We meg Intelligence senior analyst Manday saying says that Meta's
Speaker 3: six giga what IMD deal actually lowest chip spending is
Speaker 3: a share of capex and secure supply tailored to AI inference.
Speaker 4: Also right, so the stock.
Speaker 3: Link structure could give Meta added leverage against Nvidia, Mande
Speaker 3: saying joints us now, why does any leverage against Innvideo
Speaker 3: It struck a deal last week with them. Is this
Speaker 3: more like we've got other options?
Speaker 9: Yeah?
Speaker 12: And look, I mean the fact that they are planning
Speaker 12: to use Nvidia's CPUs, to me, that was a sign that,
Speaker 12: you know, if you had to get the GPU capacity
Speaker 12: from Nvidia, you pretty much had to use everything they
Speaker 12: had to offer. With this deal, I think AMD sort
Speaker 12: of comes across as the more desperate partner in terms of,
Speaker 12: you know, giving Meta their stock warrants and making sure
Speaker 12: that you know, they are at Meta, which is going
Speaker 12: to spend one hundred and thirty five billion dollars. Out
Speaker 12: of that, half of that will be on chips approximately,
Speaker 12: So think of it this way. They currently are about
Speaker 12: a ten to twelve billion dollar rund rate. This could
Speaker 12: potentially double, you know, every year in terms of just
Speaker 12: by adding one customer at the scale. Right, you know
Speaker 12: they plan to buy the chips from AMD.
Speaker 2: You know the way that that AMD puts say right,
Speaker 2: single double digit sorry, billions of dollars per gigawa, but
Speaker 2: the gigawatts need to get built, right, There are operational milestones.
Speaker 2: Every time I go and cee AMD or we speak
Speaker 2: to Lisasus she puts a lot of emphasis on inference,
Speaker 2: you know, m I for fifty in particular, and what
Speaker 2: those systems can do in the inference phase. Do you
Speaker 2: in this deal see a point of differentiation for AMD
Speaker 2: if they do go after the inference piece.
Speaker 12: I do, and simply because all these companies are trying
Speaker 12: to emulate what Google has been doing with TPUs. And
Speaker 12: remember TPUs are used for both training and inferencing, not
Speaker 12: just for Gemini but also for Entropics. So the fact
Speaker 12: that you know they have signed this deal and are
Speaker 12: willing to, you know, go from being a merchant silicon
Speaker 12: provider to a custom silicon provider for Meta, and Meta's
Speaker 12: workloads are more skew towards you know video and you
Speaker 12: know the family of apps they have similar to Alphabet.
Speaker 12: To me, this is a sign that's what Meta fields
Speaker 12: they can accomplish, you know with the AMD chips. Yes,
Speaker 12: they will continue to use in video for training, but
Speaker 12: for inferencing, Meta's workloads are different from you know, any
Speaker 12: other provider when it comes to the chat boards or
Speaker 12: anything else that they're doing.
Speaker 2: You know, AMD was it pains to point out they'll
Speaker 2: kind of co engineer the roadmap to future server designs
Speaker 2: starting m I four fifteen and go beyond that man
Speaker 2: deep seeing a Boomberg intelligence with the React Thank you
Speaker 2: so much. Now coming up on the program, Rayna Pope
Speaker 2: from matt X joins us to discuss his chip startup's
Speaker 2: latest fundraise as it aims to take on in Nvidia
Speaker 2: that's coming up next.
Speaker 10: This is Bloomberg Tech. AI startup Matt Eggs.
Speaker 2: Has raised more than five hundred million dollars to build
Speaker 2: hardware to compete with Nvidia. The chip company was founded
Speaker 2: by two alumni of Google semiconductive business and its aim
Speaker 2: is to make a product specifically designed to run large
Speaker 2: language models. Co founder and CEO Ryana Pope joins us, Now,
Speaker 2: I think we should get into the specifics, but my goodness,
Speaker 2: five hundred million dollars is quite a large series B.
Speaker 2: What do you need that level of capital for? And
Speaker 2: what does it reflect?
Speaker 13: Yeah, so, I mean I would say I'm very happy
Speaker 13: to be here and thank you one of the Really
Speaker 13: what it reflects is, on the one hand, very strong
Speaker 13: confidence from some of our lead investors in the product.
Speaker 13: This is from Jane Streets and situational awareness have have
Speaker 13: led our around very strong on Jane Street sides. They're
Speaker 13: absolute technical experts. They understand the kind of product we're doing.
Speaker 13: And then situational awareness. That's Leopold ashen Renner's fund. He
Speaker 13: wrote the book on AGI brand. He really understands where
Speaker 13: this old space is going, what we like, what we
Speaker 13: are looking to do with with this product and with
Speaker 13: this money. Firstly, I would say the demand for l
Speaker 13: M computer is just insatiable. All of the frontier labs
Speaker 13: are looking at where this space is going, and they're
Speaker 13: all concerned I'm going to run out of silicon.
Speaker 4: I won't be able to solve all the demand I've got.
Speaker 13: So our goal overall has been to make the highest
Speaker 13: throughput per per square milorde of silicon that any product.
Speaker 2: Right, this is about computational density, that's right. That's right.
Speaker 2: And so you guys are basically saying through in terms
Speaker 2: of flops per minimeter squared, like, this is something you
Speaker 2: can own. What was the breakthrough?
Speaker 14: You know?
Speaker 2: What is it that you're so good at to achieve this?
Speaker 13: Yeah, So there's really a combination of two things. If
Speaker 13: you look at the products in the market. Previously, there's
Speaker 13: been the HBM based family, which is in video Google, Amazon,
Speaker 13: and then there's been the s RAM based family and
Speaker 13: you are we are both actually uniquely so it's kind
Speaker 13: of taking two good ideas and putting them together. It
Speaker 13: is possible to do both very high throughput as you
Speaker 13: get from HBM, but also very low latency as you
Speaker 13: get from ASTRAAM, and do that in the same product.
Speaker 13: What it gives you is actually a product that is
Speaker 13: better than any other product in the market at through put.
Speaker 13: This is exactly the flop Spisco milimeter that you described,
Speaker 13: while also matching some of the best like the Cerebras
Speaker 13: and the groc at latency.
Speaker 3: Let's talk about how quickly people can start deploying this miner,
Speaker 3: because what your.
Speaker 4: Aim is to complete the final.
Speaker 3: Design this year, you hope to start manufacturing shipping even
Speaker 3: in twenty twenty seven. Who do you need to partner
Speaker 3: with on that? How do you expect to be manufacturing
Speaker 3: him in the.
Speaker 4: US or abroad?
Speaker 2: Yeah?
Speaker 13: So, I mean there's a few big parts of the
Speaker 13: supply chain, and this is common for US as well
Speaker 13: as many other semiconductor firms in this space. Really, you
Speaker 13: need logic wafers, memory wafers, which is HBM, and then
Speaker 13: you need rack buildouts, and so those are the big
Speaker 13: parts of.
Speaker 4: Our supply chain.
Speaker 13: TSMC is well recognized as a the best provider of
Speaker 13: logic wafers, and then the memory wafers. There's the big
Speaker 13: three which are Skhiinix, Samsung, and Micron, and then there's
Speaker 13: a whole range of providers across the rack and manufacturing side.
Speaker 13: One of the big things that if you want to
Speaker 13: manufacturing very large volumes we hear about these you know,
Speaker 13: multi gig or what deals that are coming out. These
Speaker 13: require you know, billions of dollars of manufacturing and then
Speaker 13: actually setting up like hundreds of millions of dollars put
Speaker 13: into setting up supply chains in advance of delivering that.
Speaker 13: And so that's a big part of what we're excited.
Speaker 4: To be able to do.
Speaker 11: Now.
Speaker 3: You left Google in twenty twenty two and the goal
Speaker 3: was creating a better chit from scratch, Ryanan, But have
Speaker 3: you been impressed by.
Speaker 4: The leaps at TPU has taken. It seems to have
Speaker 4: impressed the market.
Speaker 3: What is it that you felt wasn't at Google for
Speaker 3: you that you now can bild better?
Speaker 13: Yeah, So I think what is really required is if
Speaker 13: you want to absolutely nail the LLM workload, you have
Speaker 13: to be willing to break compatibility with previous chips. And
Speaker 13: so one of the strong guarantees you see all of
Speaker 13: the existing players providing is you can take a program
Speaker 13: that was written on my previous generation ship or my
Speaker 13: generation of chips five years ago, and it will run
Speaker 13: on my next generation chip. And so a lot of
Speaker 13: what that means is there are constraints on my chip
Speaker 13: has to support all of the previous number formats I supported.
Speaker 13: It has to support all of the different programming model
Speaker 13: the way I communicate between cores on the chip, but
Speaker 13: all of those have to be the same as each other.
Speaker 13: We felt that it would be necessary, like if you
Speaker 13: really want to just absolutely nail this workload without regards
Speaker 13: for backwards compatibility or other workloads or anything like that,
Speaker 13: you need to something of a blank slate design is required.
Speaker 13: For us, this means very large matrices, very low precision support,
Speaker 13: and then in fact an ability to split your very
Speaker 13: large cystolic.
Speaker 2: Ray into small pieces. You name checked Grok, I think
Speaker 2: with some admiration a Sesson Ago. I mean like when
Speaker 2: in video acquired Grock Jensen. Wong's view is that they
Speaker 2: were struggling to find their place in the world. In
Speaker 2: markere forswere Sarah Brasse you named it as well, filed
Speaker 2: confidentially for IPO yesterday. Why might you succeed where CROC
Speaker 2: had to go to Nvidia And I guess they're working
Speaker 2: on something you know, and the public markets you know
Speaker 2: are needed for capital going forward.
Speaker 13: Yeah, so I would say this is like historically the
Speaker 13: market has been won by the HBM place based players,
Speaker 13: that's the Google Amazon in VideA, and not by Grock
Speaker 13: and SERIBRUSSTRAM only chips are very good for latency, but
Speaker 13: when you want to run very long context models you
Speaker 13: run out of memory capacity.
Speaker 4: SRAM is too small.
Speaker 13: It's fast, but too small. Really, the hybrid of doing
Speaker 13: weights INSTRAM so you get the low latency as well
Speaker 13: as having the HBM for for very long context support
Speaker 13: is we believe that's what enables the low latency without
Speaker 13: all of the compromises that you would get otherwise.
Speaker 3: Minor Pope, thank you so much for joining us today,
Speaker 3: Matt x CEO, Look we gotta stit with funding. News
Speaker 3: Basis startup making AI tools for account unting, has raised
Speaker 3: one hundred million dollars and an over one million dollar valuation.
Speaker 4: This, of course is investors we're putting.
Speaker 3: Wary of disruption from agentic AI here with us is
Speaker 3: Matt Hart based as CEO. I go to the point,
Speaker 3: how is BASIS different from anthropics cowork plug in for
Speaker 3: an accounting business.
Speaker 15: Yeah, well, first of all, great to be here, thanks
Speaker 15: for having me, Caroline. Look, we have an excellent relationship
Speaker 15: with Open Eye and with Anthropic, and I think Cowork
Speaker 15: cha attribut the whole range of tools that they have
Speaker 15: are absolutely fantastic. I think though you can only sort
Speaker 15: of serve so many masters, and when it comes to
Speaker 15: domain specific work specifically in our situation accounting, there's a
Speaker 15: need to build with that domain specificity in mind. I
Speaker 15: think comes down to a couple of things. First of all,
Speaker 15: you want domain specific capabilities. So, for instance, we announced
Speaker 15: today the first example of a long running agent completing
Speaker 15: an entire business tax return workbook. That's something that you
Speaker 15: can't really do in any other AI tool that exists
Speaker 15: out there. You need domain specific accuracy. So we are
Speaker 15: able to guarantee to the firms that we work with
Speaker 15: that the I will meet the requisite level of accuracy
Speaker 15: for it to be used in a real manner and
Speaker 15: we can sort of guarantee that performance. You need demain
Speaker 15: specific user experience so that it's fluid and it works
Speaker 15: well for people who are experts in their field as
Speaker 15: they go about doing their work. You can't just have
Speaker 15: a chat pot or you know, the variety of other
Speaker 15: sort of basic user experiences that you need need to
Speaker 15: build enterprise sort of grade features. You know, collaboration, audit trails,
Speaker 15: you know, audibility etc.
Speaker 16: Things of this nature.
Speaker 15: And finally you need demain specific deployment approaches. It's going
Speaker 15: to you know, the capacity of a lot of these
Speaker 15: AI tools to be extremely useful in workflows probably far
Speaker 15: exceeds the adoption today and there's going to be a
Speaker 15: great challenge over the next decade of figuring out how
Speaker 15: to get AI into all the places it needs to
Speaker 15: get into. And so we think for all those reasons,
Speaker 15: being demained specific is very important. I think that's pretty
Speaker 15: evident to this.
Speaker 3: Stat And so is that one hundred million to ensure
Speaker 3: the adoption curve is where you need it to be.
Speaker 3: Where does that money get deployed first and.
Speaker 15: Foremost, Yeah, it's a combination of things. I think for
Speaker 15: us first and foremers who are always focused on building
Speaker 15: the most capable and the most accurate AI for accounting,
Speaker 15: So we are growing our engineering and mL teams dramatically.
Speaker 15: We're a fully New York based company, and we essentially
Speaker 15: want to be the home for applied AI work in
Speaker 15: New York City, so that's always number one priority. But
Speaker 15: the other aspect of it is we are now starting
Speaker 15: to serve accounting firms across all of their practices CAS
Speaker 15: and core accounting being the initial segment, but now also
Speaker 15: tax audit, broader advisory, and over time. Figuring out how
Speaker 15: we can get AI into all these various places is
Speaker 15: extremely important. Demand has exceeded our capacity to serve it,
Speaker 15: and so we felt it was important to bring additional
Speaker 15: capital to make that puzsible.
Speaker 2: So now I'm going through my taxes right now, and
Speaker 2: through the poor too of my accountant, who I won't name.
Speaker 4: They do a good job.
Speaker 2: I upload all of the documents W two, ten ninety nine,
Speaker 2: ten ninety eight whatever, but there is an element of
Speaker 2: observability because I still have to go in and double
Speaker 2: check all of the automated part of it. I know
Speaker 2: that's not the same, but the case study you gave
Speaker 2: of a business tax filing start to finish. The observability
Speaker 2: piece must be critical if you're handling that for businesses
Speaker 2: of scale.
Speaker 15: Yeah, absolutely, good question. You know, we think that the
Speaker 15: ability for an accountant to understand what's happening at a
Speaker 15: granular level as the AI is helping them get work
Speaker 15: done is extremely important. That goes back to a little
Speaker 15: bit about what Caroline was referring to in terms of
Speaker 15: the you know, what makes us different than generic tools.
Speaker 15: Having demain specific user experiences and demain specific auditibility functionality
Speaker 15: is extremely important. And then we also think that accounting
Speaker 15: firms have a very important role to play at the
Speaker 15: end of the day. Where people like yourself and what
Speaker 15: businesses around America probably want is a human that they
Speaker 15: have a relationship with, that they trust to do this
Speaker 15: in chreatly important work. And so having the AI collaborate
Speaker 15: with humans to make that possible in an even better
Speaker 15: fashion maybe will improve the experience that you have on
Speaker 15: your taxes in future years as firms figure out how
Speaker 15: to bring these two things together is extremely important.
Speaker 2: So here in the central question is is it an
Speaker 2: aid to an existing job or does it displace an
Speaker 2: existing role in accounting going forward, we just have thirty seconds' not.
Speaker 15: A thirty second question, but I'll give you my best
Speaker 15: answer on it. So I think, you know, if you
Speaker 15: look at what's happened with software engineering over the course
Speaker 15: of the last year, and you can look at this
Speaker 15: internal into basis, pretty much today, no engineer at the
Speaker 15: company should be writing any meaningful amount of code, and
Speaker 15: yet our engineering team, our machine learning team is as
Speaker 15: busy as its ever been. We're hiring as aggressively as
Speaker 15: we possibly can be in those areas, and that's because
Speaker 15: there's so much additional engineering work that we want to
Speaker 15: be able to do. That when you free up time
Speaker 15: to focus on more things, you can do more ambitious things.
Speaker 15: You can build things you never thought you'd want, you
Speaker 15: never thought you'd be able to do. And the same
Speaker 15: thing is true for accounting. There's tons of accounting work
Speaker 15: in the world that doesn't get done today. The Pentagon
Speaker 15: just failed its eighth consecutive audit. You know, companies are
Speaker 15: misstating financials because they don't have enough accounting resources. Beyond that,
Speaker 15: you know the accounting. You know, if you go to
Speaker 15: any hospital system in America, they can't tell you how
Speaker 15: much it costs them to provide a simple procedure like
Speaker 15: a knee surgery. These are all things that accounting can
Speaker 15: make possible. It is sort of the fundamental way that
Speaker 15: we understand economic activity that goes on in and around
Speaker 15: our organizations, and so we think there's a huge opportunity
Speaker 15: to do more accounting work. Most accounting firms the opportunity
Speaker 15: as well, and need the capacity to be able to
Speaker 15: do that. And so from our perspective, this is going
Speaker 15: to allow firms and the accountants at those firms to
Speaker 15: take on even more work and get things more done
Speaker 15: and get more things done in the same way that
Speaker 15: sort of software engineering has been able to take off
Speaker 15: over the course of the last twelve months.
Speaker 2: Matthew Hart, CEO and co founder of Basis. That was
Speaker 2: a pretty good, pretty good summary. Actually a little more
Speaker 2: than a pay seconds, so we'll.
Speaker 4: Give you it. Thank you very much.
Speaker 2: Now, coming up, we've got to look forward to what
Speaker 2: to expect from President Trump's State of the Union address,
Speaker 2: which is later tonight.
Speaker 4: We have the preview.
Speaker 2: This is Bloomberg Tech. All eyes are on President Trump's
Speaker 2: State of the Union speech tonight. This only days after
Speaker 2: the Supreme Court decided to strike down his tariff policies.
Speaker 2: Bloomberg TV's Washington correspondent Tyler Kendall joins us, what do
Speaker 2: we need to expect?
Speaker 4: Via Hey Edwar.
Speaker 14: President Trump is expected to tout his economic policies that
Speaker 14: are already enacted, but also push ahead some policy proposals
Speaker 14: that he would like to see enacted related to affordability,
Speaker 14: as the White House really plays defense on the issue
Speaker 14: according to recent polling. In fact, the Wall Street Journal
Speaker 14: is now reporting that President Trump is set to announce
Speaker 14: a negotiated commitment from big tech companies to pay more
Speaker 14: when it comes to electricity costs related to AI data centers,
Speaker 14: in a bid to remove some of that burden from
Speaker 14: US consumers.
Speaker 4: I was here at the White.
Speaker 14: House just last month speaking to the US Energy Secretary,
Speaker 14: Christopher Wright, as the administration urged the nation's largest power
Speaker 14: grid to hold an emergency power auction specifically for these
Speaker 14: big tech companies. So perhaps we'll get some firmer details
Speaker 14: on that tonight, because, as you well know, this has
Speaker 14: been a White House trying to thread the needle between
Speaker 14: bolstering artificial intelligence but also trying to quell some of
Speaker 14: those concerns from Americans. When it comes to jobs, and
Speaker 14: of course the cost of living related to electricity, because
Speaker 14: that is going to be the number one issue as
Speaker 14: we head into the MA in terms of a new
Speaker 14: poll out this week from Ipsos finds that fifty seven
Speaker 14: percent of US adults disapprove of the President's handling of
Speaker 14: the economy. Front and centered, as you mentioned, is going
Speaker 14: to be that tariff policy, and sitting there in front
Speaker 14: of the presidents tonight is going to be those Supreme
Speaker 14: Court justices that struck down those IPA teriffs.
Speaker 3: Last week, Mex Tyler Kendall with a rundown, thank you
Speaker 3: very much.
Speaker 4: And look, the state of the Union.
Speaker 3: Is also the target for bets on prediction markets like Cawshi,
Speaker 3: like poly markets. Look a market that was once a
Speaker 3: fringe obsession of economists.
Speaker 4: And election longks.
Speaker 3: Now traders are wagering on just about everything. Critics call
Speaker 3: it unregulated gambling, and its today's big take deep dive
Speaker 3: and please Save Blumberg contributor to Chris Beams here to
Speaker 3: talk us through it.
Speaker 4: It is a wonderful deep dive.
Speaker 3: And just as we think about the state of the Union,
Speaker 3: how has the idea of regulating this changed in the years.
Speaker 16: So currently the status quo is that prediction markets are
Speaker 16: regulated by the CFTC, and the reason is that the
Speaker 16: technically offer what are called event contracts, and event contracts
Speaker 16: are derivatives, and so the CFTC argues that falls under
Speaker 16: their purview. Now, there's a lot of people who disagree
Speaker 16: with the CFTC state gambling regulators. A lot of casinos
Speaker 16: and other members of the gambling industry argue, yes, that
Speaker 16: prediction markets should actually count as gambling and therefore be
Speaker 16: regulated by the states.
Speaker 2: How good are prediction markets at predicting the future.
Speaker 16: So it depends what kind of market you're talking about.
Speaker 16: A lot of scholars have looked at political markets and
Speaker 16: prediction markets to predict elections and have overall found that
Speaker 16: prediction markets are more accurate than polling, and certainly more
Speaker 16: accurate than you or me or any individual trying to
Speaker 16: prognosticate about elections. What's interesting, though, is that those markets
Speaker 16: become less accurate the smaller they are. So, you know,
Speaker 16: a presidential election market could be quite accurate, but then
Speaker 16: once you get down to more you know, state and
Speaker 16: local races, when there's less liquidity, that's going to be
Speaker 16: less accurate.
Speaker 4: I mean some of the bets that we see your extraordinary.
Speaker 3: I mean, I don't know how much liquidity there is
Speaker 3: on whether Jesus Christ is going to return in the
Speaker 3: next couple of years, but that's literally something you can
Speaker 3: go and place a wager or at least a prediction
Speaker 3: on using these markets.
Speaker 4: How have the companies themselves, CALCI.
Speaker 3: And Polymarket navigated what has been them thrust into really
Speaker 3: now success but also backlash at the same time and
Speaker 3: worries about insider trading in.
Speaker 16: Thus, Yeah, the companies have taken different approaches. CALSHI has
Speaker 16: really tried to position itself as the adult in the room.
Speaker 16: They emphasize the internal rules that they have around insider trading,
Speaker 16: around market manipulation. They have whole teams and software algorithms
Speaker 16: designed to detect trading patterns that might set off red flags,
Speaker 16: which they then report to regulators. Polymarket is in a
Speaker 16: slightly different position because so much of their trading happens
Speaker 16: overseas and is not regulated by the CFTC, and they
Speaker 16: also just haven't talked about it as much. So I'd
Speaker 16: say CALSHI, particularly because it was the earlier entrant in
Speaker 16: the US, has emphasized cooperating with regulators, trying to be
Speaker 16: above board about the markets that they provide.
Speaker 2: It's the latest Bloomberg, a big take, and it's a
Speaker 2: must read. Bloomberg contributes its crispy thank you very much.
Speaker 2: That does it for this edition of Bloomberg Tech. It's
Speaker 2: not as if there's a shortage of things to come.
Speaker 2: This week State of the Union and then in video
Speaker 2: on Wednesday, Oh.
Speaker 3: And earnings after the Bell today as well. Look, just
Speaker 3: don't forget to check out our podcast. You can find
Speaker 3: it on the terminal as well as online on Apple
Speaker 3: or Spotify.
Speaker 4: On iHeart. The tech news keeps coming as as a disruption.
Speaker 4: This is Bloomberg Tech