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Speaker 2: Let's take it one step further. Gene Munster, managing partner,
Speaker 2: co founder for Loop Ventures, really one of the leading
Speaker 2: voices on all things technology really for the last twenty
Speaker 2: five thirty years. We appreciate getting a few minutes of
Speaker 2: his time. Gene, I'm just gonna speak for myself, but
Speaker 2: I probably speak for most of my radio and YouTube audience.
Speaker 2: I did not know what deep Sick was, deep Sik
Speaker 2: was before this morning, but now I'm learning. What should
Speaker 2: I be learning? What should I takeaway be today?
Speaker 3: I think that the highest level this whole chapter is about.
Speaker 3: The first takeaway is that investors are nervous that this
Speaker 3: eighty five percent moving the NASDAC over the past couple
Speaker 3: of years is creating an environment where any sort of
Speaker 3: ripple in what that AI trade looks like is going
Speaker 3: to cause a significant impact on some of these valuations.
Speaker 3: So I think that's I think this is a temperature
Speaker 3: in terms of where the market is at as far
Speaker 3: as kind of the company itself is concerned deep seek.
Speaker 3: What's most important here is that they are advancing or
Speaker 3: presumably advancing the cost of training these models you've talked
Speaker 3: a lot about on the show today. And why that's
Speaker 3: important is there's a question about how much hardware is
Speaker 3: needed for that, and that obviously impacts the big hardware companies,
Speaker 3: and then separately, how does that accelerate the adoption? So
Speaker 3: there is the truth is always several layers below the surface,
Speaker 3: and I think when it comes to Dee Sep there
Speaker 3: are still some pretty significant unanswered questions, and I'll start
Speaker 3: with one of probably the biggest one is that this
Speaker 3: five to six million dollar training number that has the
Speaker 3: market upside down today, that was not their most recent model.
Speaker 3: They have an R model. It's unclear, you know, maybe
Speaker 3: that was twenty five fifty one hundred million dollars to
Speaker 3: train it. It probably was below what the most recent
Speaker 3: version of Opening Eyes model, which is anticipated or estimated
Speaker 3: to be about a five hundred million dollars in terms
Speaker 3: of training. So I think big picture here is that
Speaker 3: how we are delivering AI is evolving and there may
Speaker 3: be some pretty significant improvements in terms of the ability
Speaker 3: to do that at a lower cost.
Speaker 4: So to that pointing, how much if we look at
Speaker 4: Envidia in particular, it's down about two hundred and eighty
Speaker 4: three points. What part of that is justified?
Speaker 3: So I think the big picture here, I think that
Speaker 3: this is an overreaction. I think when you look at
Speaker 3: a company like Nvidia, it's a company we do own,
Speaker 3: and I think that this is an overreaction, and just
Speaker 3: specifically around that, if we can kind of zero in
Speaker 3: on this concept that these models are becoming more efficient,
Speaker 3: let's just take deep seek at face value that they've
Speaker 3: had some sort of improvement. Is that that improvement if
Speaker 3: you believe that the US tech companies are competent, that
Speaker 3: improvement is around some architectures that have been talked about
Speaker 3: for the last couple months. So those companies, the big
Speaker 3: tech companies, the companies that are behind all the announcements
Speaker 3: last week Meta increasing their CAPEX spend, and I think
Speaker 3: that that is all that is a belief that they
Speaker 3: still need this hardware. So when it comes back to
Speaker 3: AI and comes back to the Nvidia trade, is that
Speaker 3: I believe if they are reducing the costs to train
Speaker 3: these models that I actually can build. I think a
Speaker 3: credible case that that could increase the demand for some
Speaker 3: of this hardware if we are getting closer to general intelligence.
Speaker 3: If that potential is even closer, it's just going to
Speaker 3: be an insatiable arms race. It's going to continue. And
Speaker 3: so I understand why and video is down today. I
Speaker 3: think it's actually a healthy thing. But ultimately I think
Speaker 3: that their business is going to be just fine. We
Speaker 3: have to wait twenty one trading days before we hear
Speaker 3: from at least in VIDI on that, but I think
Speaker 3: it's going to be fine.
Speaker 2: So, Gene, was it a coincidence that on Friday, Meta
Speaker 2: upsets capex forecast dramatically, which by a magnitude I've never seen,
Speaker 2: from fifty billion to sixty five billion, and then today
Speaker 2: we get this news on deep sick? Was Deep seek?
Speaker 2: Was that kind of I don't know, coincidence?
Speaker 3: I think, what's I think that's just purely coincidence. But
Speaker 3: I do kind of go back to what we're just
Speaker 3: talking about a minute ago. Is I think it's important
Speaker 3: to note is that that announcement from Meta, if you
Speaker 3: assume a Meta is Meta is competent, and I believe
Speaker 3: that they are. That announcement came with the full knowledge
Speaker 3: of what deep Seek was doing, so it's new to
Speaker 3: most of us today. But for these companies who have
Speaker 3: been making these huge investments, the concept of what deep
Speaker 3: Seak had been trying to build has been They've had
Speaker 3: a model that's been out. This has been something that's
Speaker 3: been aware for the last couple of months, and so
Speaker 3: I think that is important. Is that I think again
Speaker 3: that this commentary from on Friday from Zuckerberg about the
Speaker 3: increased capex factors in some of these potential breakthroughs that
Speaker 3: we've seen with deep Seek. I want to stop short
Speaker 3: of saying it's definitively a breakthrough because a lot of
Speaker 3: unknowns around how good the models are and what the
Speaker 3: true costs are. But I think that the AI investment
Speaker 3: phase is still alive and well, this is still very early.
Speaker 4: So if this could be an overreaction, we don't know
Speaker 4: fair enough. In Nvidia, obviously an overreaction that stocked. The
Speaker 4: way you look at it, does it make you think
Speaker 4: though a rating of forty one times estimated PE.
Speaker 3: So from where my head's at and thinking about calendar
Speaker 3: twenty six at this point, and based on the calendar
Speaker 3: twenty six numbers, I think it's closer to like a
Speaker 3: twenty five, and I think that they can grow earnings
Speaker 3: more than that. This is where the basically the rubber
Speaker 3: hits the road, is that ultimately what happens in calendar
Speaker 3: twenty six, all this stuff we're talking about with deep
Speaker 3: seek today really impacts Ynvidia in twenty six. And if
Speaker 3: in fact this dramatically changes how the kind of hardware
Speaker 3: that people need less hardware, then I'm going to be wrong.
Speaker 3: But if this does create this accelerated arms race, then
Speaker 3: I think that they're going to grow faster. And so
Speaker 3: I consider a one peg on that twenty six earnings
Speaker 3: is actually attractive valuation relative to the rest of the group.
Speaker 3: So I'm comfortable with the evaluation.
Speaker 2: Just a red headline crossing the Bloomberg criminals we speak,
Speaker 2: deep Seek says it is subject to large scale malicious attack.
Speaker 2: So that's a headline. We'll have more reporting on that
Speaker 2: going forward. Gene, just real quickly, we're going to hear
Speaker 2: from some of the other big tech companies, Microsoft, Meta, Tesla,
Speaker 2: how do you think they're going to address this issue?
Speaker 3: It's obviously this is going to be front and center
Speaker 3: the topic, and I think they're probably going to address
Speaker 3: it by saying they still have plans to invest meaningfully
Speaker 3: more in counter twenty five over twenty four when it
Speaker 3: comes to Capex, and I think that that will play
Speaker 3: part of a for reassuring we're long ways away from that.
Speaker 3: We got two and a half trading days awave before
Speaker 3: we start to get some of that commentary before the
Speaker 3: mic gets turned over to the other side of the
Speaker 3: equation here.
Speaker 2: All right, Gene, thank you so much for joining us.
Speaker 2: I know you're super busy today. Appreciate getting a few
Speaker 2: minutes of your time. Gen Munster, managing partner co founder
Speaker 2: Lupet Ventures, taking a little bit of a I guess
Speaker 2: a longer term view, in a more broader view of
Speaker 2: what this can mean for the indusuay.
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Speaker 4: All right, let's get more on this text sell off
Speaker 4: here Kim Forrest, founder and CIO of Boca Capital Partners.
Speaker 4: She's also a former Chips person, like she did the stuff,
Speaker 4: so she knows the things.
Speaker 5: Clearly.
Speaker 4: The deep Seek news is upending most of the tech industry, right.
Speaker 4: You get the socks and DEXes down hard, you get
Speaker 4: all the hyperscalers are down hard, all the chip makers,
Speaker 4: all the power makers all down hard. Is this the
Speaker 4: reckoning or is this a by the dip moment?
Speaker 5: Well, that is the question, isn't it. I would say
Speaker 5: it's a little bit of both. I think this is
Speaker 5: a good level set, re leveling, resetting our expectations. So
Speaker 5: the first thing is, we have to really see if
Speaker 5: this is true. Now, apparently the company is in deep
Speaker 5: Seek is an open source company. They have released how
Speaker 5: they've done this, but you know, replication is key, so
Speaker 5: we're gonna have to see if this actually works. That's
Speaker 5: thing number one, Like, don't get too upset about major
Speaker 5: developments until you know that they're real major developments, right,
Speaker 5: So that's thing one. Thing two is and I think
Speaker 5: this has been troubling me for a very long time,
Speaker 5: especially with like last week's Stargate announcement. It seems as
Speaker 5: if people have been expecting AI, which we don't know
Speaker 5: what the payoff is yet we have a pretty good
Speaker 5: idea it might help us out. But we're going to
Speaker 5: cover the earth essentially in data centers and use far
Speaker 5: more power than we create right now. So those two
Speaker 5: things are kind of troubling that people have been making
Speaker 5: investments in companies going to produce far more than they
Speaker 5: can produce now. And do you see what I'm saying here?
Speaker 5: We had these really big expectations, and I didn't think
Speaker 5: that they were going to come to happen in short
Speaker 5: or long term because of the well physical limitations of it.
Speaker 2: So Kim, at this very early stage, I think I'm
Speaker 2: probably representative of most of our listeners and viewers had
Speaker 2: not heard of deep Seek before this morning. Sure, and
Speaker 2: so that's calling into question kind of what we think
Speaker 2: we understood about AI and its implication. It's not just
Speaker 2: for technology preference stock market in general. My only question
Speaker 2: that I think I have now that has any relevance
Speaker 2: is do I have to rethink my spending associated with AI.
Speaker 5: It depends on how long your timeframe is. If it's
Speaker 5: the very short term, probably not you you know you'll
Speaker 5: get rewarded because well orders are in and all that
Speaker 5: kind of good stuff. And by short term I'm talking
Speaker 5: a year, but let's say three to five years maybe,
Speaker 5: And why is that? Well, if we can really train
Speaker 5: models more rapidly with less input, which is a good thing, right,
Speaker 5: where were we going to get all this electricity? That
Speaker 5: was like my biggest question. But anyhow, back to your question,
Speaker 5: I think you know this is part of being an
Speaker 5: investors understanding how long was I play? I'm keeping this,
Speaker 5: So that's part of the problem. We have two more
Speaker 5: problems that are not being clarified by deep Seek. One is,
Speaker 5: and remember I used to do this, so I'm way
Speaker 5: down the road from most investors in thinking about things.
Speaker 5: But as I use it, I'm not getting the results
Speaker 5: that I need, And by that I mean it's not
Speaker 5: right enough. The error rate that I get back from
Speaker 5: the questions that I'm asking are anywhere between five and
Speaker 5: maybe thirty percent, which that's huge. Like I can't just
Speaker 5: use AI. I have to babysit AI. So these are
Speaker 5: problems that aren't solved by anybody at this point. And
Speaker 5: then there is how are we actually going to use it?
Speaker 5: I think there was I think a Bloomberg report that
Speaker 5: was saying that probably two hundred thousand people could be
Speaker 5: laid off those lower level entry level people into finance
Speaker 5: and then that mid tier that are now you know,
Speaker 5: kind of like the AI for investment banking and equity research.
Speaker 5: But if we were that wrong, we wouldn't last right, Like,
Speaker 5: if you're five percent wrong in investment banking, your career
Speaker 5: is really short. So you know, these are problems that
Speaker 5: aren't being solved. So that's another issue for investors. How
Speaker 5: right How long is it going to be till these
Speaker 5: get right enough to replace a person?
Speaker 4: That's such a great point because I was also reading
Speaker 4: an article that talked about the limitations so far that
Speaker 4: have been seen about Gee, what is it deep Seek?
Speaker 4: I want to say, geek see deep Seek was its
Speaker 4: lack of inability to discuss jijenping or to give real
Speaker 4: handswers on Tanam and square, just those topics that are
Speaker 4: near and dear to China's heart. And that's in part
Speaker 4: some of the issue that you're talking about. So who
Speaker 4: wins in this right now?
Speaker 5: Who wins? Well, I think investors are losing in the
Speaker 5: short term. But if you like me think that AI
Speaker 5: is a path to productivity, and productivity always wins. I
Speaker 5: think you could be a winner. I think maybe companies
Speaker 5: like open Ai and Microsoft, you know, by dint of
Speaker 5: you know, investing in them, and then everybody else who's
Speaker 5: developing AI, they might be winners. Maybe the what is
Speaker 5: it Stargate? I don't know why. I can never remember.
Speaker 5: That's my break up here. I can ever remember that
Speaker 5: because it has nothing to do with stars or gates,
Speaker 5: that Stargate might be the loser in those people because
Speaker 5: we're not necessarily going to need to cover the earth
Speaker 5: in data centers.
Speaker 4: Right, yep, all.
Speaker 2: Right, Kim, thanks so much for joining us. Always appreciate
Speaker 2: getting your perspective of your technology background. Kim Farst, founder
Speaker 2: and chief investment officer of Book Capital Partners, joining us
Speaker 2: from Pittsburgh via the zoom Thing.
Speaker 1: Listening to the Bloomberg Intelligence Podcast. Catch us live weekdays
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Speaker 4: Happy mindy, everybody, Alex Deal here alongside Paul Sweeny. This
Speaker 4: is Bloomberg Intelligence Radio. We are broadcasting to live for
Speaker 4: Interactive Brookers Studio right here in Midtown Manhattan. Also check
Speaker 4: us out on YouTube dot com. Or clearly have the
Speaker 4: tech sel off underway really concentrated in certain names the
Speaker 4: mag seven obviously, in Vidia, the chip stocks, although Apple's up,
Speaker 4: I should say, but in Vidia obviously getting pounded, the
Speaker 4: chip stocks getting pounded as well. So we wanted to
Speaker 4: continue the conversation about AI and you really need to
Speaker 4: rethink this investment thesis. Joining us now is Josh Pantoni.
Speaker 4: He's CEO boosted dot Ai Now. The company has helped
Speaker 4: dozens of investment managers who's AUM totals over one trillion
Speaker 4: dollars and how to implement machine learning in their portfolios.
Speaker 4: First off, Josh, have you seen this new geek seek? Geeks?
Speaker 4: Why do I keep saying geek seek? It's not geeks
Speaker 4: seek deep see see if you try deep seak? Have
Speaker 4: you talked to your clients about it? What do you think?
Speaker 6: Yeah? So, I mean it came out I think last Monday.
Speaker 6: We've already ran a whole suite of benchmarks against it.
Speaker 6: Try that out over a bunch of things, and I'd
Speaker 6: say we're pretty familiar with it. I think it's impressive
Speaker 6: in a number of ways. It's impressive how how much
Speaker 6: power you get for the cost. I think the flip
Speaker 6: side is it's actually not quite something that we could
Speaker 6: even consider implementing it to our process. And I think
Speaker 6: there's a lot of clients that will reach the same
Speaker 6: conclusion after they start working with it. Why is that.
Speaker 5: So?
Speaker 6: A lot of how we help our clients is with
Speaker 6: things like trying to identify risk and trying to identify
Speaker 6: to build out these different workflows for different parts of
Speaker 6: the financial process. And a really big part of that
Speaker 6: is related to trust. You need to be able to
Speaker 6: give answers that you're very confident in. You need to
Speaker 6: give answers that you can sort of deeply into it
Speaker 6: where it's coming from. And I think one of the
Speaker 6: challenge of this model is that it has some very
Speaker 6: clear political biases right off the bat. So if I'm
Speaker 6: trying to use it to say understand what my portfolio
Speaker 6: exposure is to China's going to be a challenge to
Speaker 6: use it for things like that. Also, a lot of
Speaker 6: the things we're most excited about are things like being
Speaker 6: able to do computer use, being able to sort of
Speaker 6: teach the machine how to like interact with different apps
Speaker 6: and things on the computer, image recognition, trying to extract
Speaker 6: out charts and things like that, and a lot of
Speaker 6: those kind of capabilities that actually doesn't really have it
Speaker 6: and seem to be as sophisticated in so it can
Speaker 6: do very well in certain benchmarks, but when it actually
Speaker 6: comes to trying to apply it for at least most
Speaker 6: of the use cases that we're doing, it's not quite
Speaker 6: there yet.
Speaker 4: What is there for you right now? Like what does work?
Speaker 6: I think the most interesting thing for me is actually
Speaker 6: on the reasoning side. So of course, like one of
Speaker 6: the really big pushes Opening Eyes had right now is
Speaker 6: like with one and three, you've got these models coming
Speaker 6: in where in theory they can take a task, break
Speaker 6: it into subcomponents, and then start executing those components. And
Speaker 6: I think in that area it's actually quite good. I
Speaker 6: also think, you know, just from a cost basis, it's
Speaker 6: extremely impressive they've been able to do. There's been some
Speaker 6: controversy about exactly how much it costs to actually train
Speaker 6: the model. What I can confirm though, is the actual
Speaker 6: inference cost. The cost of running the model is extremely
Speaker 6: cheap compared to what you're getting with like oh one
Speaker 6: and oh three, and so just the fact is even
Speaker 6: possible to do that as a major technology breakthrough.
Speaker 2: If nothing else does Deep Seek just highlight the cost
Speaker 2: issue and potentially the I don't know, the movement down
Speaker 2: in costs of implementing AI.
Speaker 6: Yeah, the way I tend to think about it is
Speaker 6: the cheaper it is to train AI systems, the more
Speaker 6: advanced capabilities you can train.
Speaker 4: In the stress you lost your audio Josh, you there, Yes,
Speaker 4: I am.
Speaker 5: Here, still hear me. We're good?
Speaker 6: Okay, perfect? Yeah. What the way I like to think
Speaker 6: about it is the cheaper it is to train these
Speaker 6: AI systems, Then the more advanced capabilities you can train,
Speaker 6: the more advanced capabilities you can train, the more use
Speaker 6: cases you unlock. The more use cases unlock, the more
Speaker 6: you actually see it accelerate. So I actually think this
Speaker 6: model will probably cause an acceleration in the capabilities of
Speaker 6: other models that are getting built as more folks start
Speaker 6: to adopt it. So for me, I actually see it
Speaker 6: as completely positive.
Speaker 4: What outside of deep Seek and other models, like, have
Speaker 4: you worked with that you did?
Speaker 6: Like yeah, so, I mean behind the scenes, we work
Speaker 6: with in thropic models we work with open AI models.
Speaker 6: We work with a bunch of fine tune models that
Speaker 6: we build in house ourselves. You know. I like to
Speaker 6: sort of describe it as an orchestra of different types
Speaker 6: of models. And one of the things we've sort of
Speaker 6: noticed is there's a lot of differences in capabilities. So
Speaker 6: something like the inthroduct models and thropic models tend to
Speaker 6: be better at very long context window where you try
Speaker 6: and handle like huge amounts of text, whereas something like
Speaker 6: the GPD form models tend to be a little bit
Speaker 6: better at like foreign language and just sort of the
Speaker 6: general verbiage it uses as it's giving outputs. So we
Speaker 6: use a whole bunch.
Speaker 2: Where how are your clients and the asset management business,
Speaker 2: Josh using AI these days, in these early days.
Speaker 6: Yeah, So the way I like to think about it
Speaker 6: is we give them the ability to create this sort
Speaker 6: of team of little AI workers where you teach them
Speaker 6: how to do some kind of task, and then they're
Speaker 6: going to do that task on a continuous basis. So
Speaker 6: let's say you to do something like write an investment
Speaker 6: thesis on a company, or let's say you had to
Speaker 6: do something like write an ESG report or Let's say
Speaker 6: you wanted to continuously monitor the world for any kinds
Speaker 6: of updates to something that might happen in the air space.
Speaker 6: These are all examples of sort of workflows you can
Speaker 6: teach the system and have the system start to automate.
Speaker 4: Well, we really appreciate your time. It was so good
Speaker 4: to get that perspective. It's good to kind of get
Speaker 4: the user mindset in for this. Josh Josh Pantoni, CEO
Speaker 4: a boosted dot AI on deep Seek and sort of
Speaker 4: the pros and the hans there.
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