Speaker 1: From the heart of where innovation, money and power collide
Speaker 1: in Silicon Valley and beyond. This is Bloomberg Technology with
Speaker 1: Caroline Hyde.
Speaker 2: And Ed Ludlow.
Speaker 3: Live from New York On Caroline Hyde and.
Speaker 4: I'm Mike Shepard in San Francisco. This is Bloomberg Technology
Speaker 4: coming up.
Speaker 5: Deep Seek Day two, we dive into the ripple effects
Speaker 5: in tech markets and the impact on US chip cubs lust.
Speaker 5: We discuss deep Seek's AI model and the future of
Speaker 5: open source with hugging faces. Chief AI scientist and President
Speaker 5: Trump says Microsoft is in talks to acquire the US
Speaker 5: unit of TikTok. Details later this ow but first we
Speaker 5: check in on the markets and we claw our way back. Mike,
Speaker 5: not nearly off setting yesterday's significant sell off. We're up
Speaker 5: just the edge point on the overall NASDAC. Look, Apple
Speaker 5: once again doing heavy lifting to the point side.
Speaker 3: Meta at a new record high, but not enough because.
Speaker 5: In video bounces back but two percent after a seventeen
Speaker 5: percent set off yesterday, but thirty billion being added from
Speaker 5: the market cap that was wiped out by six.
Speaker 3: Hundred billion dollars yesterday.
Speaker 5: This is all surrounding China coming with clearly a very powerful,
Speaker 5: very efficient, very cheap, open source model NIC.
Speaker 4: And yesterday in video called deep Seek's new model quote
Speaker 4: an excellent AI advancement that complies with US technology export controls,
Speaker 4: and that deep Seek's work illustrates how new models can
Speaker 4: be created. Let's bring in Bloomberg, Zee and King now
Speaker 4: to tell us more about this ian. This was a
Speaker 4: rough day yesterday for in Vidia. It bore the brunt
Speaker 4: of the deep Seak shock across the markets. Yet their
Speaker 4: statement really was curious. Tell us what your interpretation was.
Speaker 6: Yeah, I mean, there are a couple of things going
Speaker 6: on here. I mean they could have obviously said nothing.
Speaker 6: They could have just ignored it. They could have said,
Speaker 6: maybe it's kind of dodgy technology that we don't really
Speaker 6: believe in, but they actually endorsed it and said, no,
Speaker 6: this is good. And the sort of underpinning message we
Speaker 6: saw here was that maybe they're telling the US.
Speaker 7: Government, your export controls haven't worked. We should be allowed
Speaker 7: to ship whatever chips we.
Speaker 6: Want to China because guess what, they'll do good stuff
Speaker 6: with whatever they can get hold of.
Speaker 5: Let's just go to that narrative on chip exports potentially
Speaker 5: not working.
Speaker 3: We heard from President Trump.
Speaker 5: Just yesterday about what the impact could be.
Speaker 3: Just take a listening.
Speaker 8: In a very near future, we're going to be placing
Speaker 8: tariffs and farm production of computer chips, semiconductors, and pharmaceuticals
Speaker 8: to return production of these essential goods to the United
Speaker 8: States of America. They left US and they went to Taiwan,
Speaker 8: which is about ninety eight percent of the chip business
Speaker 8: by the way, and we want them to come back.
Speaker 5: Of course, a lot of in video chips are manufactured
Speaker 5: in Taiwan, and so there's going to be an impact there.
Speaker 5: What do you think the regulatory and pat will be
Speaker 5: let alone where the inferencing is really going to be
Speaker 5: the future for GPU demand?
Speaker 6: Yeah, I mean this is the big question here. And
Speaker 6: the expectation I think before the inauguration was that this
Speaker 6: administration would, if anything, double down on this kind of
Speaker 6: crackdown of chips and the flow to China. And here
Speaker 6: we're getting a different aspect of it. So I think
Speaker 6: we'll have to see how much attention the new administration
Speaker 6: is placed is pacing on this issue. But the expectation
Speaker 6: is that they're not going to let.
Speaker 7: Up em back to Nvidia.
Speaker 4: How much is this complicating their efforts to get their
Speaker 4: customers to keep buying all those premium chips like Blackwell
Speaker 4: and the next generations that you've been writing about so much.
Speaker 7: Yeah, well, it creates a massive question.
Speaker 6: It's like, well, if a Chinese startup can fine tune
Speaker 6: their models to this extent using sort of two three
Speaker 6: year old technology on the cheap, why can't you write
Speaker 6: so if you're Microsoft, if you're a WUS and you're
Speaker 6: running all of these systems, and why wouldn't you do
Speaker 6: it cheaper if you can? So obviously has implications for
Speaker 6: what in video is trying to sell. So we'll see
Speaker 6: how they answer those questions.
Speaker 5: Thank you so far the statement is all we get
Speaker 5: in king, We thank you. Now let's just talk about
Speaker 5: how the tech markets, of course roiled by china seemingly cheap,
Speaker 5: powerful AI model. Here's what some Bloomberg Television guests had
Speaker 5: to say about deep Seek.
Speaker 9: I don't think it's about China versus US, right, It's
Speaker 9: really about closed source versus open source and more more,
Speaker 9: American developers should leverage the open source and what deep
Speaker 9: Seak had done and built on top of it.
Speaker 3: And make it greater. Really amazing what they have done.
Speaker 10: The fraction of training cost, fraction of interference costs to
Speaker 10: produce a model like that in deep Seek, but it
Speaker 10: will unravel basically the path of spending.
Speaker 11: Of course, I think a healthy dose of skepticism is
Speaker 11: very very good, it's needed. But even if you multiply
Speaker 11: their spend and their cost by ten times two D time,
Speaker 11: this is still a order and maltitude in terms of
Speaker 11: theotocopy action.
Speaker 12: They're not really a company that's out there trying to
Speaker 12: compete directly with the big US players. So in that sense,
Speaker 12: I think it was an overreaction to the release of
Speaker 12: this model.
Speaker 3: Let's get more reaction.
Speaker 5: Mardin Alton, now chief investment strategist at Empower. The question
Speaker 5: for many is will capital expenditure remain for in video chips,
Speaker 5: for AI hardware writ large.
Speaker 3: Is that what you were tackling yesterday.
Speaker 2: Well, I think that is one of the critical questions, right,
Speaker 2: because this was a very surgical sell off, a very
Speaker 2: surgical development in the sense that it's attacking really not
Speaker 2: the case for using AI, but really the supply chain
Speaker 2: around AI and all roads on supply chain lean back
Speaker 2: to Nvidia, and so this question of whether companies really
Speaker 2: need to spend this much. I think that is the
Speaker 2: question that markets are grappling with. But it's hard to
Speaker 2: imagine that we're going to see, and of course we'll
Speaker 2: see in earnings this week, but it's hard to imagine
Speaker 2: that we're going to see companies massively change their spending
Speaker 2: patterns simply because of this news. I think there's still
Speaker 2: this arms race going on, So of course that's something
Speaker 2: that we're going to have to watch especially closely. But
Speaker 2: we may need more than just this news to unravel that.
Speaker 5: But the unraveling could start when you have earnings expectations
Speaker 5: as they are and valuations at forty one times future
Speaker 5: earnings for a namelike in video. How much you worried
Speaker 5: that this could have a similarity to what we saw
Speaker 5: in the dot com era.
Speaker 2: I mean, that's really something that we're watching really closely.
Speaker 2: When you're looking at earnings expectations for the broad market overall,
Speaker 2: you're seeing an acceleration of earnings and of course maybe
Speaker 2: not the same for MAGS seven, but certainly a continuation
Speaker 2: of the powerful earnings that we've seen there. And then
Speaker 2: you couple that with valuations that are extreme. When we
Speaker 2: do our analysis of valuations and we break the time
Speaker 2: series for our price to expected earnings down into death siles.
Speaker 2: We're looking at the tech sector that's in the ninetieth
Speaker 2: percentile of valuations. Now associating that with forward one year returns,
Speaker 2: you can still see valuations grind higher and companies perform.
Speaker 2: But ultimately that's where you also start to see some
Speaker 2: of these more meaningful selloffs. And as pedantic as it sounds,
Speaker 2: I think a lot of these developments over the past
Speaker 2: few days relate more to evaluation argument than anything that
Speaker 2: relates to kind of technology in the sense that this
Speaker 2: is big news and technology, but the bigger news is
Speaker 2: that there's no room in the narrative to account for
Speaker 2: anything but world domination when it comes to MAGS seven.
Speaker 2: And this is allowing us to think about the range
Speaker 2: of outcomes Martin.
Speaker 4: To play the movie forward a few years. Where do
Speaker 4: you see this taking us? Is this a parallel to
Speaker 4: the dot com era, which you know, eventually, after some
Speaker 4: troubles right around two thousand, really set us up for
Speaker 4: a long period of growth.
Speaker 7: What about AI? Are we in that moment?
Speaker 2: Well, that's that's the corollary that I reference. I know
Speaker 2: a lot of us are pointing back to the dot
Speaker 2: Com era and looking at evaluation argument. I think that's important,
Speaker 2: But I think the other important element is that this
Speaker 2: is an innovation supercycle. It feels like just the way
Speaker 2: the dot Com era was, and what we saw in
Speaker 2: that period was a massive expansion or growth for the
Speaker 2: broad market, not just for the tech area. So if
Speaker 2: we're looking at the trend growth from the nineteen nineties
Speaker 2: and comparing that to the realized trend growth of the
Speaker 2: subsequent years, we saw massive expansion, whether that's margins or
Speaker 2: enterprise value. That Internet world had a huge impact on
Speaker 2: the broad economy, and I think that's what we can
Speaker 2: expect to see with AI. And in many ways, this
Speaker 2: news around deepseek is part of that in the sense
Speaker 2: that it says we can economize, we can do this
Speaker 2: a lot better, and we can get deployment a lot
Speaker 2: more quickly than maybe we had otherwise thought.
Speaker 4: And on that idea of getting cheaper AI out to
Speaker 4: companies and businesses across the economy maybe a little bit faster.
Speaker 4: What sort of impact do you see and where do
Speaker 4: you see growth potential from that?
Speaker 2: Well, I do think there's this broad market where you
Speaker 2: have to understand spaces that we can't perfectly anticipate. I
Speaker 2: think we're seeing a lot hearing, a lot more conversations
Speaker 2: around retailers and the deployment of kind of acres more
Speaker 2: quickly with fewer steps along the way. I think that's
Speaker 2: one application we're also based square stocks hold up quite
Speaker 2: a bit better. I think that software can easily deploy
Speaker 2: or maybe more easily deploy AI within their world. I
Speaker 2: think that's a possibility. So I think that applications are
Speaker 2: somewhat in less the imagination. It has to be pretty
Speaker 2: broad here, and so there's some argument for kind of
Speaker 2: an equal weight exposure to try to capture that range
Speaker 2: of experiences, but with long term horizon. I don't think
Speaker 2: this is necessarily something that we're going to see in
Speaker 2: Q two Q three of twenty twenty five.
Speaker 5: We're taking a few tech hits to your line, but
Speaker 5: we're going to stick with it matter because I have
Speaker 5: this crucial point for the here and now with retail
Speaker 5: we buying back into in video institutionals not right now?
Speaker 5: What about a so called black swad event or ripple effect.
Speaker 5: We talk of course to nasin Talib yesterday.
Speaker 3: Just have a listen to what he said, right.
Speaker 13: The beginning of an adjustment of people for reality because
Speaker 13: now they realize now it's no longer flawless. You have
Speaker 13: a small little trip on the glass.
Speaker 5: A little chip in the glass of the valuation question,
Speaker 5: a little chip in the glass as to whether this
Speaker 5: is akin to a dot com crisis, but where could
Speaker 5: be sustained? Will Energy survive this? We saw such significant
Speaker 5: sell off to certain of those.
Speaker 2: Names, you know, I think when we're taking a look
Speaker 2: at Nvidia or Energy, I think there's more vulnerability there,
Speaker 2: simply because this is getting at the heart of the
Speaker 2: thesis there that they are the you know, especially in Vidia,
Speaker 2: the dominant provider of this capability that our roads lead
Speaker 2: back to in video when it comes to supply, And
Speaker 2: this story is really tackling that idea that potentially there's
Speaker 2: more ways to skin a cat. You can focus on
Speaker 2: efficiency rather than share mine. Of course, so I think
Speaker 2: the idea of a chip in the glass, I think
Speaker 2: that's important, and it's getting at the heart of Nvidia.
Speaker 2: But whether this is the beginning of the unraveling of
Speaker 2: all things AI, I think that's a little bit more
Speaker 2: extreme or a bridge too far today, simply because that
Speaker 2: unraveling would depend on the use case, the use case
Speaker 2: of AI being intact, and I don't think that's what
Speaker 2: we're seeing here. It's more of the supply chain question.
Speaker 2: And really at this point, I think there's still more
Speaker 2: that we would need to see to really suggest that
Speaker 2: companies are going to pull back on their capital expenditures
Speaker 2: in any meaningful way.
Speaker 4: Martin Norton Chief Investment Strategies that Empowered Thank You.
Speaker 7: Coming up, we're.
Speaker 4: Going to discuss deep seaks open source approach and what
Speaker 4: it means for competitors. The chief science officer at AI
Speaker 4: platform Hugging Face will join us on that.
Speaker 7: This is Bloomberg.
Speaker 5: China's deepseek AI model up ended markets largely though those
Speaker 5: AI infrastructure plays.
Speaker 3: What about model providers?
Speaker 5: Clearly open source is impacting a HI development. We're joined
Speaker 5: by Thomas Wolf Now here's the chief science officer at
Speaker 5: hugging Face and open source and collaborative platform for AI
Speaker 5: builders is our one the huge breakthrough that the market
Speaker 5: clearly thinks it is.
Speaker 13: I think there is a thin bida for overreaction.
Speaker 10: We've seen a steady increase in open source model performance,
Speaker 10: but we also have to be honest. It's the first model,
Speaker 10: I would say that really reached the performance of closed source.
Speaker 10: So the gap is closed now between the clos source
Speaker 10: models and the open source performance.
Speaker 5: Let's just talk about how the derivatives have expanded. Because
Speaker 5: you are all about open source, you're about community, you're
Speaker 5: about the innovation one model can provide to many others.
Speaker 5: From what we understand from Clem the CEO over with
Speaker 5: hugging face, he's saying that there's been five hundred derivatives
Speaker 5: already created from deep seek. How are you seeing an
Speaker 5: exponential growth of the use of this model.
Speaker 13: Yeah, it's even growing, right, So I was just checking.
Speaker 10: I can give you a fresh new stats just now.
Speaker 10: We're over six six hundred and seventeen models already created
Speaker 10: by the community, more than three point two million downloads
Speaker 10: of all these models, with seven hundred thousand dollars for
Speaker 10: the original model.
Speaker 13: So I think that's really the power of open source. Right.
Speaker 13: We see basically a growth of.
Speaker 10: More than thirty person of downloads day to day, which
Speaker 10: kind of is a testament to all this ecosystem that
Speaker 10: is already starting to build around deep seek with basically
Speaker 10: all these companies, all these teams, all these.
Speaker 13: Organizations that are already you.
Speaker 10: Know, taking this open source model and finding in it,
Speaker 10: adapting it, testing it on the unused cases. So that's
Speaker 10: that's maybe the most beautiful thing about these open source capabilities,
Speaker 10: which is still this ecosystem grow live day by day
Speaker 10: around the original Deepsick model.
Speaker 4: Thomas, we can't separate the story of deep seek from
Speaker 4: the geopolitics here. What concerns do you have that a
Speaker 4: reaction from Washington or other governments might be to restrict
Speaker 4: open source use in some fashion as a way of
Speaker 4: keeping technology out of China's hands.
Speaker 10: Yeah, I think there's a lot of I would say
Speaker 10: a lot of way of reframing these stories as a
Speaker 10: US China. But really the more general story here is
Speaker 10: the open source effect of this model. It could have
Speaker 10: come out of almost any countries, and I would expect
Speaker 10: actually open source model to keep coming out of China.
Speaker 10: But we also have European companies like Mixed Trial starting
Speaker 10: to open source model and very small teams.
Speaker 13: So mordinally, I think hopefully.
Speaker 10: We'll see move from a geopolitical inpretation of this story
Speaker 10: to really interpretation at open source versus closed source, and
Speaker 10: our belief attacking face is open source is really the
Speaker 10: way to foster development, to foster a breakthrough technology, to
Speaker 10: foster growing communities, and a lot more business use cases.
Speaker 4: Basically, Thomas, as a software creator yourself, you must have
Speaker 4: some admiration for what Deep Seek has pulled off, apparently
Speaker 4: at a much lower cost and rivals.
Speaker 7: Here in the US.
Speaker 4: But I bet you have some questions too, what would
Speaker 4: you like to learn more about what the company did
Speaker 4: and how it pulled it off?
Speaker 10: So what is interesting is we already started a tragging
Speaker 10: pace to actually explore, you know, can we reproduce this model?
Speaker 10: Can we reproduce it in poticlar on the open right?
Speaker 10: So we have right the diepsick model. I was trying
Speaker 10: its last year, last day yesterday, and it does really,
Speaker 10: you know, offer what we see in the benchmark, which
Speaker 10: is it's a very powerful model. But what we would
Speaker 10: like to know is exactly can you retrain it? Can
Speaker 10: you actually apply the same recess? Is two more models,
Speaker 10: So I've study this project called open Air one, which
Speaker 10: is basically a reproduction and open reprodiction of the deep
Speaker 10: Sick pipeline. Thankfully, they shared a lot of details on
Speaker 10: how they.
Speaker 13: Train the model, much more than than we see recently.
Speaker 10: So I'm quite confident in the coming month we'll be
Speaker 10: able to basically, you know, understand all the breakthrough that
Speaker 10: we need to make in this model.
Speaker 5: What does it mean for a closed source focused open
Speaker 5: AI or for anthropic in this.
Speaker 13: Moment, Well, I think it's quite positive. And you saw
Speaker 13: probably the reaction of some outman, right.
Speaker 10: I think for a good competition in the field is
Speaker 10: something that is just a net positive for developing a technology.
Speaker 13: And in particular here because a lot of.
Speaker 10: This model and as I was saying, a lot of
Speaker 10: recipes are open, it means I would I would be
Speaker 10: very surprised they don't, you know, directly take the recipes
Speaker 10: that can be useful for improving both open AI, anthropic
Speaker 10: Google or the coming LAMMA models, and that this model basically.
Speaker 13: You know, quickly catch So I think it's a good thing.
Speaker 10: That's also the good thing about open sources because you
Speaker 10: share a lot of information about your models. You know,
Speaker 10: you actually lift the whole fields up with you by
Speaker 10: you know, explaining how how you make your breakthrough.
Speaker 5: But some people don't want to see everyone rise up.
Speaker 5: They don't want to see China rise up. Ultimately, is
Speaker 5: that a false narrative a strong man to be, even
Speaker 5: saying that China is behind the US when there's open
Speaker 5: source communities such as yours.
Speaker 10: Yeah, the Open Source Committee generally don't really know any border, right,
Speaker 10: as I was saying today, it's a Chinese team, but generally,
Speaker 10: you know, it's just it's just a very smart, edge
Speaker 10: edge round team.
Speaker 13: Right.
Speaker 10: There are teams like that in many countries, and that's
Speaker 10: why we keep saying it would keep seeing new actors
Speaker 10: in AI, right, So I think ultimately, I believe in
Speaker 10: fair market. I think having competition, sharing more actively, you know,
Speaker 10: is a good way to make progress together. So you know,
Speaker 10: in your future, we will like to see much more
Speaker 10: actors active in AI. We would like to see basically
Speaker 10: a larger company a bit everywhere in the world. And
Speaker 10: I think that's maybe the first step, you know, in
Speaker 10: this direction.
Speaker 4: Are we expecting to see similar innovations coming from Europe?
Speaker 4: There have been questions and concerns raised in the European
Speaker 4: Union about the level of regulation and then it might
Speaker 4: be restricting development in AI.
Speaker 13: Yeah, it's obviously a big discussion, right.
Speaker 10: I was a divorce a bit last week, and if
Speaker 10: you see a lot of the discussion was, Okay, what
Speaker 10: is about your regulation?
Speaker 13: I just think the teams are really good.
Speaker 10: I would be surprised, for instance, in the UK, in Germany,
Speaker 10: you know in France that we that we don't see
Speaker 10: all these teams that basically helped train you know, the
Speaker 10: models of Mistual, at Meta, at Opena, at Entropic.
Speaker 13: We've seen a lot of people also living op Ai
Speaker 13: to start the und startup.
Speaker 10: So I think one takeaway from Deep Sick is that
Speaker 10: basically the recipe to build a very good quality large
Speaker 10: language model nowadays is something that's almost accessible to everyone, right,
Speaker 10: So I think all these people leaving the big tech
Speaker 10: companies will start the out team, and because you kind
Speaker 10: of just need a few millions, I would be surprised
Speaker 10: we don't see a lot more teams coming out of
Speaker 10: Europe and Botska, but also as a region this year.
Speaker 4: Thomas Wolf, chief science officer at Hugging Face, thanks for
Speaker 4: joining us. Microsoft is in talks that acquire the US
Speaker 4: arm of TikTok. At least that's according to President Trump
Speaker 4: last night, who did not elaborate further.
Speaker 7: Microsoft declient to comment.
Speaker 4: Let's bring in Bloomberg's Balance and Power co host Kaylee
Speaker 4: lines for more. Kaylee, is this another case of Trump
Speaker 4: trying to make a deal happen? He casts himself as
Speaker 4: the consummate deal maker. We are so short on details, though,
Speaker 4: walk us through what more we know, if anything beyond
Speaker 4: last night.
Speaker 3: Well, the details are pretty limited, Mike.
Speaker 14: He was asked directly by a reporter if Microsoft was
Speaker 14: in talks to acquire TikTok, and the President said, quote,
Speaker 14: I would say yes, And that's really all we got
Speaker 14: on that specific matter. As he said, Microsoft isn't commenting
Speaker 14: on this either. Keep in mind, though, that during the
Speaker 14: first Trump administration, back in the summer of twenty twenty,
Speaker 14: Bloomberg did report Microsoft was looking at acquiring TikTok's US
Speaker 14: operations back then, when the first kind of pressure around
Speaker 14: divesting or banning it over national security concerns was a rising.
Speaker 14: Oracle was reportedly looking at it too, And we know
Speaker 14: just last week when the Stargate project was being announced
Speaker 14: at the White House and Larry Ellison of Oracle was
Speaker 14: in the room, Donald Trump suggested he'd be open to
Speaker 14: Ellison buying TikTok or Elon Musk when he was asked
Speaker 14: about that. We know there are other players involved as well,
Speaker 14: including Frank McCourt, who you speak with frequently on this program,
Speaker 14: who have made bus and it does seem that is
Speaker 14: what Donald Trump's preference really is here, a bidding war.
Speaker 14: He said as much last night that he thinks bidding
Speaker 14: war's result in the best deals. As for what that
Speaker 14: deal ultimately looks like, that remains unclear. What he was
Speaker 14: clear on yesterday when speaking to the House Republican Conference
Speaker 14: in his to Raw Club in Florida is that he
Speaker 14: does not want China involved in whatever happens here. Of course,
Speaker 14: China is going to have some say in that ultimately,
Speaker 14: would the US government have involvement still, Katie.
Speaker 3: Well, that's the question.
Speaker 14: He's talked about this fifty percent ownership structure, whoever buys
Speaker 14: it sharing equity with the United States essentially, so it
Speaker 14: would be some kind of half and half deal, But
Speaker 14: it remains unclear whether or not that can be done
Speaker 14: by the now April fourth deadline, which has been extended
Speaker 14: by Trump's executive order. There's also a massive question around
Speaker 14: whether anti trust concerns would be raised by some of
Speaker 14: these individuals or companies like Microsoft acquiring operations of this size,
Speaker 14: which could be tens of billions of dollars and obviously,
Speaker 14: as you guys will know, one hundred and seventy million
Speaker 14: users in the US. While Donald Trump and the people
Speaker 14: he's instilled at places like the FTC or at the
Speaker 14: Department of Justice may have some alignment on this, it
Speaker 14: still could face scrutiny, especially from big tech skeptical lawmakers
Speaker 14: on Capitol Hill and.
Speaker 5: Whether bite don'ts whatever said it, Katie Hines, Yeah, thanks
Speaker 5: so much.
Speaker 3: Welcome back to med Technology. I'm Caroline Hyde in New
Speaker 3: York and I'm.
Speaker 7: Mike Shepard in San Francisco.
Speaker 4: Let's discuss the impact that Deep seak is having on
Speaker 4: US tech companies now with Bloomberg Sharen Gafari. Sharen, thanks
Speaker 4: so much and thanks for all your reporting today. There's
Speaker 4: a lot of soul searching going on in Silicon Valley
Speaker 4: over the past forty eight hours. Tell us a little
Speaker 4: bit more about what's happening. They set up war rooms.
Speaker 4: What is the conversation like?
Speaker 15: So the top a I labs right now in the
Speaker 15: US are trying to figure out how the Chinese startup
Speaker 15: Deep Seek was able to catch up so quickly. Right,
Speaker 15: you have their latest R one model. Really exploding on
Speaker 15: the scene in the past week, and everyone is astounded
Speaker 15: by how competitive it is. It is actually leading by
Speaker 15: some metrics on these AI models that have taken Western companies,
Speaker 15: you know, years end, a lot more money.
Speaker 3: The Chinese company.
Speaker 5: Deep Sea claims to build well, we can old bait
Speaker 5: as to how much it was copying building off Western technology.
Speaker 5: But the key question is will capex remain the same
Speaker 5: and ultimately what it means for open AI and Anthropic
Speaker 5: in terms of reducing the cost of their models.
Speaker 3: What do you think the ripple effects Ascherene.
Speaker 16: Yeah, I think we're starting to see people really question
Speaker 16: whether we need these astronomical budgets for building the most
Speaker 16: advanced AI models and whether, you know, because.
Speaker 3: Deep Seek was able to make some gains in.
Speaker 15: How efficiently they use the computing power, if those you know,
Speaker 15: gains can be applied now to US companies and sort
Speaker 15: of questioning why US companies didn't come up with that first.
Speaker 3: It's a fascinating big take.
Speaker 5: Go and read it from Sharene Gafari and the meta
Speaker 5: war rooms that are currently being set up.
Speaker 3: We appreciate it.
Speaker 5: Now we've got to assess what President Trump had to
Speaker 5: say about the competition from deep Seek.
Speaker 8: I think if it's if it's fact and if it's true,
Speaker 8: and nobody really knows what it is, but I view
Speaker 8: that as a positive because you'll be doing that too,
Speaker 8: so you won't be spending much and you'll get the
Speaker 8: same result. Hopefully, the release of Deepseek AI from a
Speaker 8: Chinese company should be a wake up call for our
Speaker 8: industries that we need to be laser focused on competing
Speaker 8: to win.
Speaker 3: Let's talk about that competition.
Speaker 5: Jacqueline Rice Nelson is with us CO Founracy of Tribe
Speaker 5: Ai provides AI services for leading enterprises, and I have
Speaker 5: a feeling a lot of them are rushing to understand
Speaker 5: how they can use this cheap and innovative AI model.
Speaker 17: Absolutely first, thrilled to be here. I think this twenty
Speaker 17: twenty five is the year for enterprises to get ROI
Speaker 17: from their AI efforts, and the ROI calculation just changed dramatically,
Speaker 17: So I think that kind of is the headline for businesses,
Speaker 17: which is use cases that weren't possible just a few
Speaker 17: weeks ago or even last week are now possible. And
Speaker 17: I think we're about to see an explosion of AI
Speaker 17: experimentation and also value delivery to organizations and enterprises away.
Speaker 5: Have any of those organizations been reticent to use it
Speaker 5: simply because of where it was born?
Speaker 3: And it's a great question.
Speaker 17: I actually had the same question and dove into this myself,
Speaker 17: and I think it all comes down to how you
Speaker 17: use these models. So there are lots of ways to
Speaker 17: be using LAMA or open source models today in ways
Speaker 17: that have lots of guardrails that are set up to
Speaker 17: be more secure and can be run locally on your
Speaker 17: own environment. In many ways, actually it's almost easier with
Speaker 17: open source than it is with closed source models, And
Speaker 17: so everything comes down to the implementations of these models.
Speaker 17: I think today a lot of businesses have these same questions,
Speaker 17: but that's in many ways why they're sort of coming
Speaker 17: to Tribe and trying to help navigate the landscape across
Speaker 17: all of these different models. What are best to use
Speaker 17: and for what types of use cases and how to
Speaker 17: set it up in the optimal ways Jacqueline.
Speaker 4: One of the key questions that's come up in relation
Speaker 4: to deep seek is the one on spending. How is
Speaker 4: that factoring into the way you are guiding people through
Speaker 4: the AI landscape?
Speaker 2: Now?
Speaker 4: Is it a different message that you're having to deliver
Speaker 4: about efficiency and about bringing a product to market for
Speaker 4: a lot less.
Speaker 17: So the short answer is yes, I think that we are.
Speaker 17: I compare it to a highway. We have just added
Speaker 17: a lane or multiple lanes to the highway. When you
Speaker 17: do that, the traffic does not go down. Actually it
Speaker 17: only increases, and that's because driving and going on road
Speaker 17: trips gets more attractive. The same is true right now
Speaker 17: for AI. So I think we're about to see really
Speaker 17: an explosion of activity and that sort of across companies,
Speaker 17: across enterprises within the large and to your point on
Speaker 17: efficiency and for product innovation, I think we're going to
Speaker 17: see proliferation across these use cases. And then that doesn't
Speaker 17: even include the large hyper scalers who are still on
Speaker 17: this quest for AGI and nothing changes there. I don't
Speaker 17: think anyone is taking their foot off the gas. And
Speaker 17: if anything, competition is just in dramatically.
Speaker 4: And the open source model, is this something that is
Speaker 4: feasible to be scaled at large enterprises, Big corporations that
Speaker 4: also may have security considerations and other factors to bring
Speaker 4: in them might make them question whether open source is
Speaker 4: the right choice for them.
Speaker 17: Yeah, I think there are a lot of considerations that
Speaker 17: go into what is the right model selection, and so
Speaker 17: I think it's really clear that we have entered an era,
Speaker 17: and actually I've always thought we were in this era,
Speaker 17: which is that we are in a multimodel world. Businesses
Speaker 17: need to be able to build, to swap between models
Speaker 17: seamlessly to optimize for performance, for latency, for accuracy, and
Speaker 17: for cost. And what that means is they also need
Speaker 17: the guardrails, the security, the infrastructure to set those things
Speaker 17: up in proper ways. They also need the evals, the
Speaker 17: evaluation framework to actually be able to compare and contrast
Speaker 17: across models so they know what models to use. And
Speaker 17: so I think there, while there might be some hesitance,
Speaker 17: we have done lots of development on closed source models
Speaker 17: and lots of development on open source models, and the
Speaker 17: considerations still come down to what is that company trying
Speaker 17: to achieve and what are the things we're optimizing for.
Speaker 17: That's what determines the model. So I, oh, please go ahead.
Speaker 5: Well I'm interested in that because I want to go
Speaker 5: back to sort of the Jevons paradox piece that you know,
Speaker 5: the more AI innovation there is, ultimately the more people
Speaker 5: on those highways the more innovation, but also the same
Speaker 5: amount of compute. Can you speak a bit to like
Speaker 5: the inferencing part of this, because that seems to be
Speaker 5: the silver lining that indeeded videos trying to see from
Speaker 5: this look, you're still going to need our GPUs, just
Speaker 5: not for training.
Speaker 3: It's the inference.
Speaker 17: I think that's fair. Look, I think we have not
Speaker 17: cracked the code on the hardware that's needed. Like it's
Speaker 17: clear that compute is still needed at really high levels.
Speaker 17: There is no way that we have even with these
Speaker 17: new deep seek models, we haven't found the answers yet. Right,
Speaker 17: We're not stopping. And I think that that's the goal
Speaker 17: case for how and why you're going to see continued
Speaker 17: investment here is that we are in a continuous innovation
Speaker 17: cycle and ultimately, I think that drives utilization and I
Speaker 17: think the question is for what. And then the other
Speaker 17: piece is that ultimately consumers are the biggest winners here,
Speaker 17: and I think that is the most exciting story. There's
Speaker 17: a lot of fear, there's a lot of you know,
Speaker 17: China versus US dynamics. I think the reality is that
Speaker 17: competition is good. And what I'm hearing from the US
Speaker 17: companies is you know, nothing has kind of the US
Speaker 17: large AI companies, nothing has kind of blown their socks
Speaker 17: deef right. This is innovation they at least, you know,
Speaker 17: feel that they already have. And the difference is that
Speaker 17: there has been an optimization for open source and an
Speaker 17: optimization for cost in ways that they have not done.
Speaker 17: And so I think we're about to see a lot
Speaker 17: of fast follow and Sam Alman signaled as much last night.
Speaker 3: Yeah, he said, we're going to drop models faster or is.
Speaker 5: It going to drop the cost because that it must
Speaker 5: be something that puts off your.
Speaker 17: Client's definitely going to drop the cost. And I actually
Speaker 17: think we are likely to see a cost curve decrease
Speaker 17: like that's dramatic from them, but also a latency decrease.
Speaker 17: And I think that that's how they're going to play
Speaker 17: the game is to try to now one up completely
Speaker 17: on both dimensions.
Speaker 4: Jacqueline Rice Nelson, CEO of Tribe AI, Thank you, Caraw's just.
Speaker 5: Check on these markets because these are all the questions
Speaker 5: that investors are trained on at the moment. What does
Speaker 5: it mean for compute going forwards, what does it mean
Speaker 5: for the competitors in the generative AI model space? And
Speaker 5: what does it ultimately mean for software too? Now we're
Speaker 5: clawing back some of our losses of yesterday, but not
Speaker 5: March eight tens percent higher on than as that one
Speaker 5: hundred Meta.
Speaker 3: In a new record high.
Speaker 5: We'll talk more about Lama there, but Bitcoin up nine
Speaker 5: tens percent. We're going to delve into the world of
Speaker 5: crypto and the ripple effects in a moment where one
Speaker 5: hundred and two thousand move on.
Speaker 3: The individual movers.
Speaker 5: That you've got to keep your eyes trained on have been,
Speaker 5: of course some of the points contributors we've seen Meta
Speaker 5: new record. As I mentioned, Apple has been doing significantly
Speaker 5: well in video bounces back, but hardly at all. Up
Speaker 5: two point eight percent after seventeen percent sell off, We're
Speaker 5: only up about thirty billion. It will six hundred billion
Speaker 5: yesterday Tesla of by two percent. Remember, earnings are almost
Speaker 5: upon us Tesla tomorrow out of the gate. We're gonna
Speaker 5: have the likes of Microsoft to chew on and Meta
Speaker 5: as well. But coming up more on musks World and
Speaker 5: questions around his approach to efficiency, because apparently it's rather
Speaker 5: good at it, don't It would seem this is gluebog technology.
Speaker 5: Elil musks X has announced a new partnership with Visa.
Speaker 5: The social media platform is tapping the payment company to
Speaker 5: enable digital wallets on its app and website, in what
Speaker 5: marks the first step for the company to create the
Speaker 5: so called Everything app.
Speaker 3: Now.
Speaker 5: The move comes as bankers are now looking to offload
Speaker 5: some free billion dollars of X's debt. According to sources,
Speaker 5: Pimco Apollo are now said to be among the asset
Speaker 5: managers looking to purchase a portion of the debt being
Speaker 5: sold by group of banks at by Morgan Stanley. Some
Speaker 5: interesting sweetness in that offering too, and staying with Elil Musk,
Speaker 5: Many are now scrutinizing the tech billionaires approach to efficiency
Speaker 5: with his own companies.
Speaker 3: This as he gears up to cut spending and waste
Speaker 3: in the.
Speaker 5: US government with of course, the Dose initiative for mores Cretrudel,
Speaker 5: it's a perfect person to discuss as to how good
Speaker 5: is mascut efficiency in the private sector.
Speaker 18: Well, I think it's it's worth kind of, you know,
Speaker 18: taking a look at his track record and maybe sort
Speaker 18: of you know, being open to the idea that there's
Speaker 18: maybe some more similarities in how he has run his
Speaker 18: companies and you know, how his criticism of the government
Speaker 18: is you know, characterized, then then he is possibly let on.
Speaker 18: You know, this is it's been the case that Tesla,
Speaker 18: for example, you know, took a good decade before it
Speaker 18: was making its investors any money. Uh And and you
Speaker 18: know there was plenty of waste, you know, even according
Speaker 18: to him, or inefficiency according to him within the company
Speaker 18: that was sort of allowed to.
Speaker 7: Fester for some time before he turned things around.
Speaker 18: And so you know, no one's disputing that Tesla and
Speaker 18: SpaceX aren't you know, quite efficient and now quite you know,
Speaker 18: lucrative companies for their shareholders. But you can find, you know,
Speaker 18: your fair share of inefficiency or waste within his own companies.
Speaker 18: And you know, even on his own earnings calls, you know,
Speaker 18: him sort of talking about ways in which his companies
Speaker 18: haven't necessarily been as efficient as you would expect from
Speaker 18: somebody who's about to run run Doge.
Speaker 4: Craig, your reporting pointed to some pretty visible example of
Speaker 4: maybe a pro fligod approach flying tires from the Czech
Speaker 4: Republic to the US to ensure their delivery that had
Speaker 4: to have been expensive. How does that kind of approach
Speaker 4: translate to to government where the consequences and stakeholders are
Speaker 4: very different.
Speaker 18: I think what what Denni's Danna Hole in San Francisco, uh,
Speaker 18: you know, pointed out in this story is essentially that
Speaker 18: you know, there's there's a willingness to sort of allow
Speaker 18: for you know, some inefficiency if it means sort of uh,
Speaker 18: you know, other deliverables and uh, you know within the government.
Speaker 18: I think you know, the answer that we're clearly going
Speaker 18: to get is you know, something different in terms of
Speaker 18: you know, not so much forgiveness of Okay, yes we
Speaker 18: were inefficient, but we delivered X or y uh.
Speaker 7: You know what we're what we're seeing from.
Speaker 18: Doze early on is is this you know, relatively new
Speaker 18: X account that is just you know, kind of highlighting
Speaker 18: all the ways that the government is you know, so
Speaker 18: as of blowing taxpayer dollars and all the ways in
Speaker 18: which you know, Musk and his team are coming in
Speaker 18: and sort of you know, writing those wrongs in his view.
Speaker 18: And so I think, you know, he's not showing a
Speaker 18: whole lot of willingness to you know, cut slack in
Speaker 18: terms of trying different things or you know, trying things
Speaker 18: and then not going well as he maybe has been
Speaker 18: in his sort of you know times as CEO of
Speaker 18: many of these companies.
Speaker 7: Bloomberg's Craig Trudell, thank you.
Speaker 4: Turning to crypto, deep seek sell off pose a threat
Speaker 4: across all markets, including crypto, with bitcoin seeing its biggest
Speaker 4: intra day drop in more than a month. All this
Speaker 4: comes just as President Trump signed an executive order last
Speaker 4: week calling for the creation of a White House Advisory
Speaker 4: Group on digital currencies. Joining us now to discuss all
Speaker 4: of this is Melton Demr's Crucible Capital Group General partner
Speaker 4: and founder, Melton we.
Speaker 7: Really have to get right to it.
Speaker 4: We were talking about Elon Musk Department of Government efficiency.
Speaker 4: One of the biggest questions facing your industry is regulation.
Speaker 4: David Sachs, the new AI and cryptos are what are
Speaker 4: you looking for him and the Trump team to clear away?
Speaker 19: Well, I think I'm going to say something controversial.
Speaker 20: The last four years by an administration, the lack of
Speaker 20: clarity on regulatory policy in many ways I think was
Speaker 20: actually constructive for the crypto industry in the sense that
Speaker 20: there was not a lot of external pressure. Clarity I
Speaker 20: think can be really challenging for markets.
Speaker 19: Yes, there are obviously bright spots.
Speaker 20: The rollback of SAB one twenty one will now allow
Speaker 20: banks to hold crypto on their balance sheet, make it
Speaker 20: easier for institutions to hold crypto. Sure, that's great, but clarity,
Speaker 20: I think also creates more perspective on what will work
Speaker 20: and what won't work in crypto.
Speaker 19: So far, the view has been number go up.
Speaker 13: Right.
Speaker 20: We sometimes joke that coin as number go up technology,
Speaker 20: and there's a meme in the industry.
Speaker 19: Me we're all going to make it.
Speaker 20: I think what's happening in week one of the Trump
Speaker 20: administration is this clarity is helping people and particularly markets
Speaker 20: understand that we are not all going to make it.
Speaker 20: They're going to be winners and they're going to be losers.
Speaker 20: And this administration has made it very clear that they're
Speaker 20: perfectly content picking winners and losers.
Speaker 3: AI coin is going to be winners in the future.
Speaker 5: There's been this Steve tailing of the AI trade within crypto,
Speaker 5: and they've taken a brutal hit of course, in the
Speaker 5: CAR narrative.
Speaker 20: Now, look, I think the AI crypto narrative is a
Speaker 20: confusing one. Two things I look at One, how does
Speaker 20: crypto make AI better or safer? And the jury is
Speaker 20: out there and then the reverse is how does.
Speaker 19: AI make crypto better or safer?
Speaker 20: There I think we've used AI to create new casinos
Speaker 20: in crypto in the form of agents or online accounts
Speaker 20: tied to lms that are creating their own coins and
Speaker 20: launching their own coins.
Speaker 19: To me, that's not what's interesting.
Speaker 20: What I'm looking at is how can crypto and some
Speaker 20: of the opportunities around aggregation, optimization, and financialization help crypto
Speaker 20: make AI better. And then I'm really looking at what
Speaker 20: Apple is doing smaller models run locally on devices I
Speaker 20: think is very interesting opportunity for crypto. Obviously, the big
Speaker 20: story around deep seek is what's going to happen to
Speaker 20: all of this energy and compute capex and I think
Speaker 20: there are some of the early efforts we're seeing in
Speaker 20: crypto to aggregate the long tail of compute into these
Speaker 20: marketplaces where you can pay in stable coins or dollars.
Speaker 19: Is really interesting.
Speaker 3: Oh, guys, all the way back to file coin of old.
Speaker 3: But I'm interested.
Speaker 5: In Melton in what the regulatory spirits have meant underlying
Speaker 5: all of what is sent meant and felt like to
Speaker 5: go up on bitcoin, What has the trading told you.
Speaker 19: Yeah, if we look at markets right.
Speaker 20: What I think is always so interesting is you see
Speaker 20: a lot of sentiment online and it's easy to say something,
Speaker 20: but if you want to know what people are truly thinking,
Speaker 20: you have to look at markets, and you have to.
Speaker 19: Look at flows.
Speaker 20: The biggest week of inflows we had in the last
Speaker 20: year into crypto ETFs, right, which were a great proxy
Speaker 20: four million dollars the week that Trump won the election. Okay,
Speaker 20: last week with all of the regulatory quote unquote clarity
Speaker 20: or at least setting of directions with Trump administration and
Speaker 20: the executive orders being signed.
Speaker 19: Two billion in inflows.
Speaker 20: So the expectation is always better than the reality.
Speaker 19: Right. Markets not reacting positively.
Speaker 20: CM Bitcoin futures contract had the biggest drop in open
Speaker 20: interest in its entire trading history yesterday, So it is
Speaker 20: very clear that I'm crypto traders. Markets are feeling overextended,
Speaker 20: just like they did on the big AI names.
Speaker 19: And there's been a pullback.
Speaker 20: The question is how much of that is going to
Speaker 20: come back with clarity, and how much of that is
Speaker 20: going to come back when we actually start to see reality.
Speaker 5: Catch up with hype trade the room of sell the news,
Speaker 5: Malton damares, it's so good to have you on Creaseable
Speaker 5: Capital Group general partner.
Speaker 3: We thank you.
Speaker 4: Meta is set to report fourth quarter earnings after the
Speaker 4: bell tomorrow. For more of what we can expect from
Speaker 4: the social media giant, Bloomberg's Kurt Wagner joins us Now,
Speaker 4: Kurt Metta somehow escaped the big sell off from deep
Speaker 4: Seek yesterday. Why is that and what does that tell
Speaker 4: us going into the results tomorrow?
Speaker 21: Yeah, it might be two things. I think one is,
Speaker 21: you may recall, Mike, at the end of last week,
Speaker 21: they announced their big plan for the year, all the
Speaker 21: spending they were going to be doing on AI. You know,
Speaker 21: they announced that Threads was going to start running ads.
Speaker 21: Like they kind of bront run to their own earnings
Speaker 21: a little bit with some of the biggest news that
Speaker 21: they have planned for twenty twenty five. And so I
Speaker 21: think maybe some people just saw a little bit less
Speaker 21: uncertainty with Meta because some of that was out there.
Speaker 21: I think the second is that this deep Seek stuff
Speaker 21: in a way sort of validates the AI strategy that
Speaker 21: Meta has been pushing towards this whole time. You know,
Speaker 21: Mark Zuckerberg has been arguing for more than a year
Speaker 21: that where this was going was open source, and that
Speaker 21: is where Meta was going to go as well, was
Speaker 21: to create an open source model that other people could
Speaker 21: build on. Now we saw that is what deep Seek
Speaker 21: is building as well. So there's obviously more competition for
Speaker 21: Meta here, but I think the strategy that they were
Speaker 21: employing might be seen as the best way forward here.
Speaker 21: So perhaps some investors were less spooked because they thought
Speaker 21: the road ahead for Meta made a little bit more
Speaker 21: sense than some of the others.
Speaker 5: Gene Munster deep Water Asset Management reflected that exact issue,
Speaker 5: so too did City, and they're also thinking that maybe
Speaker 5: the AI models becoming cheaper and cheaper are also going
Speaker 5: to just really push on the overll profitability the advertising improvements.
Speaker 5: Is that what we're going to have to hear the
Speaker 5: our return on AI investment from Mark as well as
Speaker 5: his investment going forward in Capex.
Speaker 21: Yeah, this has been one of the biggest questions, not
Speaker 21: just for Meta but for all these tech companies is
Speaker 21: when is all this investment going.
Speaker 13: To pay off?
Speaker 21: I think in twenty twenty four, actually Medaid probably a
Speaker 21: better job than a lot of its peers that sort
Speaker 21: of conveying where AI was impacting the product right, not
Speaker 21: only in making the ads more efficient, but in dispersing
Speaker 21: their AI assistant across all of their different apps. Having
Speaker 21: the ray Band smart glasses right, they had sort of
Speaker 21: tangible products that they could roll out and show people. Obviously,
Speaker 21: with this massive investment they're going to be making in
Speaker 21: twenty twenty five, it will be even more important for
Speaker 21: them to continue to show that.
Speaker 13: But given what they did.
Speaker 21: In twenty twenty four, at least it seems like the
Speaker 21: street sort of knows what to expect from them a
Speaker 21: little bit. So we'll see if they're able to continue
Speaker 21: that tomorrow.
Speaker 5: For earnings, shares at a record high, currently trading six
Speaker 5: hundred and seventy seven kut Wagner, We thank you. That
Speaker 5: does it for this edition of Bloomberg Technology. You do
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Speaker 3: This is Bloomberg Technology.