Speaker 1: Bloomberg Audio Studios, podcasts.
Speaker 2: Radio news man Deep Singh joins us, Right now, did
Speaker 2: you know what deep seek was before twelve or eighteen
Speaker 2: hours ago?
Speaker 3: I did, yes, And so what did you know about
Speaker 3: them two days ago?
Speaker 1: That it is a competitor in the LM space. They've
Speaker 1: been training their own model and it's used mostly in
Speaker 1: the East Asian region.
Speaker 2: Okay, what I learned, thank you zero head for this
Speaker 2: from Morgan Brown at dropbox is basically they've said, we're
Speaker 2: not going to be as perfect as pristine as the
Speaker 2: others out to thirty two decimal points. We're just going
Speaker 2: to get it done out to point eight decimal points.
Speaker 2: Is that what this is all about is they're not
Speaker 2: going to be perfect.
Speaker 1: I think there's more to it than just you know,
Speaker 1: using the floating point fewer floating point operations than open
Speaker 1: AI and Entropic and others. And in this case, they
Speaker 1: basically focused on hardware efficiency, using the hardware in the
Speaker 1: most efficient fashion. They had the benefit of all these
Speaker 1: lms being out there. They use meta Llama as a
Speaker 1: reference point. It's another open source model, and they figured
Speaker 1: out a way to do this most efficiently than everyone else.
Speaker 1: I mean, everyone is focused on scale right now, they
Speaker 1: focused on hardware efficiency.
Speaker 3: Call the Lama meta.
Speaker 2: It's Facebook is the grandson of the original Lama on
Speaker 2: Saturday Night.
Speaker 4: There you go, right, very good man, Deep You've got
Speaker 4: an analyst Bloomberg Intelligence in Hong Kong, Robert Lee. I'm
Speaker 4: reading his research literally as we speak. And for folks
Speaker 4: on the Bloomberg terminal, go to big and check out
Speaker 4: if you want to need to figure out what's going
Speaker 4: on with Deep seek and what China's doing with AI.
Speaker 4: Bloomerg Intelligence has got the data and the research there.
Speaker 4: Can China support a real competitive AI environment? It seems
Speaker 4: like they're government is more restricting some of the technology there.
Speaker 1: Well, so now we know the export controls probably weren't
Speaker 1: as effective as they were supposed to be here, you know,
Speaker 1: in terms of restricting the access to Nvidia latest chips,
Speaker 1: and some people are saying they did use you know,
Speaker 1: the latest h one hundred chips, albeit they were not
Speaker 1: of the same scale as were available to Open AI
Speaker 1: and the others. But look, we don't know the extent
Speaker 1: of the hardware that was used for training. All I
Speaker 1: can say, you know, based on this the fact that
Speaker 1: they have a comparable model in performance to the others.
Speaker 1: It shows that they clearly have a combination of algorithms
Speaker 1: and compute to compete with the others.
Speaker 2: Most of the financials are the revenue growth models that
Speaker 2: you and b I have. Of all these fancy AI people,
Speaker 2: are they now at risk?
Speaker 3: Absolutely?
Speaker 1: I mean, look at open ais oh one pro model
Speaker 1: they're charging two hundred dollars per month because it's the
Speaker 1: best model they have to offer. The fact that you
Speaker 1: have an open source model that is comparable to the
Speaker 1: model that Opening Eyes chargeable.
Speaker 3: You're saying it's comparable.
Speaker 1: I mean it is, yeah, I mean look at the
Speaker 1: benchmarks and that's why you know all these models have
Speaker 1: common benchmarks. This is within one to two percentage points
Speaker 1: of that benchmark.
Speaker 3: I know you don't do buy hole cell, but are
Speaker 3: we going to sell here? Are we going to see
Speaker 3: Wall Street put a cell on an video? I think
Speaker 3: predict that.
Speaker 1: I mean right now, the fact that Meta raised their
Speaker 1: Capex on Friday to me earning season is where we
Speaker 1: will find out what all these companies end up doing.
Speaker 1: So you know, all this has happened in the last
Speaker 1: forty eight hours, where Meta raised their CAPEX Deep Sea
Speaker 1: came out it suddenly everyone is going crazy.
Speaker 2: Paul the President lined up with the son of Japan
Speaker 2: to do a ginormous tech deal as well.
Speaker 3: You okay, there, you look at you.
Speaker 4: I'm doing social Here are you doing social words? Doing
Speaker 4: that out the social I'm saying, take a look at that. So, Mandy,
Speaker 4: what do we do here? As we think about AI's
Speaker 4: obviously from technology perspective, it has been the story for
Speaker 4: the last two years at least. How do you think
Speaker 4: about it now? Has anything changed in the last twenty
Speaker 4: four hours for you?
Speaker 1: A lot of software companies will feel, you know, they
Speaker 1: can do their own AI now and not having to
Speaker 1: rely on the big hyperscalers because the narrative was if
Speaker 1: you can't spend upfront on Capex, then you don't have
Speaker 1: a play. And it comes to the foundational model there
Speaker 1: and so suddenly everyone feels empowered that they can do
Speaker 1: their own AI with this development, I.
Speaker 4: See Nvidio, Broadcom, some of these big chip makers that
Speaker 4: have had such a huge run on the IP side,
Speaker 4: they're down ten to eleven twelve percent here today? Is
Speaker 4: that realistic? Is that does that seem reasonable to you?
Speaker 1: I mean with semi companies we know the risk is
Speaker 1: always that cyclical element. And if we are calling that
Speaker 1: this is the the top of the cycle in terms
Speaker 1: of semi demand and estimates aren't going up anymore, then
Speaker 1: you know you will see this sort of stock reaction.
Speaker 1: But I go back to my point about Meta raising
Speaker 1: their capex projects target at it one hundred billion.
Speaker 5: Of that, we haven't talked to you about that fifty
Speaker 5: billion they were Meta Cappex was twenty five billion in
Speaker 5: twenty nineteen, then it was fifty billion for this year.
Speaker 4: Now they're saying to go on a sixty five I
Speaker 4: feel like it when I look at those numbers, it
Speaker 4: feels like drunken sale or time. What Did they have
Speaker 4: a real strategy behind that spending or are they just
Speaker 4: saying we need to be in this game, We need
Speaker 4: to spend whatever we need to spend, typical Meta type
Speaker 4: of spending.
Speaker 1: Yeah, and I think there is that aspect where you
Speaker 1: want to be that front end player when it comes
Speaker 1: to the lllms. But in the case of Meta, the challenge,
Speaker 1: in addition to the fact that you know deep sea
Speaker 1: model is out there, is they don't have a cloud business.
Speaker 1: You look at Microsoft, you look at Amazon, you look
Speaker 1: at Google, they don't they have a cloud business to
Speaker 1: monetize those GPUs. Even if let's say deep Sea came
Speaker 1: up with a better model, Microsoft can use that capacity
Speaker 1: for inferencing on the cloud.
Speaker 3: They can generate cloud revenue. Mandy, what are the meetings
Speaker 3: like in Silicon Valley? Now?
Speaker 2: I mean, these guys never get up before nine am,
Speaker 2: But today they're gonna get up at six am their time.
Speaker 2: They're gonna be rock at nine am surveillance time. What
Speaker 2: are the meetings gonna be like at Google? What's the
Speaker 2: meeting gonna be like for Zuckerberg?
Speaker 1: Oh? So I think again, Given these companies are reporting
Speaker 1: earnings next week right now, they have to figure out
Speaker 1: what is it that they relate to the investors in
Speaker 1: terms of the scaling laws. Like up until now, the
Speaker 1: biggest debate was will the scaling laws hold in twenty
Speaker 1: twenty five? And based on this development, we don't talk
Speaker 1: about scaling laws.
Speaker 3: Do they replicate what deep seek is doing?
Speaker 1: Absolutely? I think they will use some of the innovations
Speaker 1: that deep Seek has showed in their paper, and given
Speaker 1: they have open source it. My bed is everyone cares
Speaker 1: about hardware efficiency, even if Microsoft is spending eighty billion
Speaker 1: on AI Capex. They want to use that cap ex efficiency.
Speaker 2: The smartest thing I've heard of from the technologist Paul Sweeney,
Speaker 2: your Ted talk on this right, your Ted talk.
Speaker 3: You almost wore a tie to your Ted thing. Exactly.
Speaker 3: They're drunken sailors.
Speaker 2: I mean, that's the smartest thing they've heard. Do you
Speaker 2: see capex responsibility?
Speaker 3: No? I think.
Speaker 1: I mean to my mind, all this Capex can be used,
Speaker 1: you know, for the future, and it's quite fungible. It's
Speaker 1: not like laying a fiber network and oh it's gonna
Speaker 1: go waste. Compute is always used in some form. Now, yes,
Speaker 1: they may have overpaid for the GPUs. You could argue
Speaker 1: was it worth paying thirty thousand dollars for a GPU?
Speaker 1: Probably not, but still this compute is still useful. Man.
Speaker 2: Deep single those folks. We're gonna go around equities, bond's
Speaker 2: currencies come out of these again. We're off four percent.
Speaker 3: No, sit down, you're not done yet. You know where
Speaker 3: you're going.
Speaker 4: Two segments you should.
Speaker 3: See as an englishakfast on Monday. It's like this huge
Speaker 3: kind of a thing.
Speaker 2: The bond market yields in big time, which you'd expect
Speaker 2: here the screening yield four point one eight percent in
Speaker 2: nine basis points, the tenure yield in eleven basis points.
Speaker 2: First thing, I looked at the real yield crushes in
Speaker 2: ten basis points from a two point two zero to
Speaker 2: two point one zero some of the angst there in
Speaker 2: the market.
Speaker 3: And what I look at, and it's not a good
Speaker 3: number yet.
Speaker 2: With the Vicks twenty one point five four, not thirty,
Speaker 2: but from fifteen to twenty one.
Speaker 4: That gets your attention, poll Man, Deep is a concern here.
Speaker 4: The deep Seak product could be as good an AI
Speaker 4: solution as what some of the Western companies are providing,
Speaker 4: but at a lower cost. Is that the bottom line here?
Speaker 1: Yeah? I mean look at the app stores that deep
Speaker 1: Seak is the top appro right now here in the US,
Speaker 1: and everyone is using it multitude chat.
Speaker 3: No, I don't mean ititer up, but this is important.
Speaker 2: Lesa Matao's not using it, Tom Keene is not using it.
Speaker 2: An adult like you or Joe Wisenthal who's knee deep
Speaker 2: into this stuff.
Speaker 3: Good morning, Joe, Joey. Joe's not up yet, Okay, Joe
Speaker 3: Wisenthal is going to bring up deep seek and bring up.
Speaker 2: Chet GPT of one of the different managed levels. Can
Speaker 2: you tell the difference?
Speaker 1: Look, I mean there is an element of personalization that
Speaker 1: these apps pro wide when it comes to asking the
Speaker 1: type of questions you're asking. But if you have a
Speaker 1: generic query, deep seek is going to give you an
Speaker 1: answer that's comparable to Chat GPT.
Speaker 4: I'm looking Tom, tell me you're on deep Seek Now.
Speaker 4: I google the top app number one, deep Seek, number
Speaker 4: two Chat GPT, and number three Paramount plus for land
Speaker 4: Man and for Yellowstone. That's what I'm talking about.
Speaker 2: That we had a land Man weekd at home. I said,
Speaker 2: Sweeney's liked Deep into.
Speaker 4: The number seven.
Speaker 3: Fox Sports get one more in here, one more in here,
Speaker 3: because man, Deep's coming back at eight.
Speaker 4: What do you need? What do you think you're gonna
Speaker 4: hear from some of these technology companies that next week
Speaker 4: as they talk about China, as they talk about this,
Speaker 4: what is a deep Seek thing?
Speaker 3: Yeah?
Speaker 1: I mean the whole aspect around CAPEX and scaling laws
Speaker 1: and what to do with the latest GPUs. Are you
Speaker 1: spending more on training or inferencing? Everything is on the
Speaker 1: table in terms of hardware efficiency.
Speaker 2: I have no idea what inferences is a clinic for
Speaker 2: the man deep sing of Bloomberg intelligence