Global Tech Selloff, DeepSeek Buzz

Published Jan 27, 2025, 5:43 PM

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Gene Munster, Managing Partner at Deepwater Asset Management, joins to discuss Chinese AI startup DeepSeek's new model raising questions about the dominance of US tech companies like Nvidia. Kim Forrest, Founder and CIO of Bokeh Capital Partners, discusses today's DeepSeek news and market impact. Josh Pantony, CEO of Boosted.ai, discusses DeepSeek news and the AI space.

Hosts: Paul Sweeney and Alix Steel

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Let's take it one step further. Gene Munster, managing partner, co founder for Loop Ventures, really one of the leading voices on all things technology really for the last twenty five thirty years. We appreciate getting a few minutes of his time. Gene, I'm just gonna speak for myself, but I probably speak for most of my radio and YouTube audience. I did not know what deep Sick was, deep Sik was before this morning, but now I'm learning. What should I be learning? What should I takeaway be today?

I think that the highest level this whole chapter is about. The first takeaway is that investors are nervous that this eighty five percent moving the NASDAC over the past couple of years is creating an environment where any sort of ripple in what that AI trade looks like is going to cause a significant impact on some of these valuations. So I think that's I think this is a temperature in terms of where the market is at as far as kind of the company itself is concerned deep seek. What's most important here is that they are advancing or presumably advancing the cost of training these models you've talked a lot about on the show today. And why that's important is there's a question about how much hardware is needed for that, and that obviously impacts the big hardware companies, and then separately, how does that accelerate the adoption? So there is the truth is always several layers below the surface, and I think when it comes to Dee Sep there are still some pretty significant unanswered questions, and I'll start with one of probably the biggest one is that this five to six million dollar training number that has the market upside down today, that was not their most recent model. They have an R model. It's unclear, you know, maybe that was twenty five fifty one hundred million dollars to train it. It probably was below what the most recent version of Opening Eyes model, which is anticipated or estimated to be about a five hundred million dollars in terms of training. So I think big picture here is that how we are delivering AI is evolving and there may be some pretty significant improvements in terms of the ability to do that at a lower cost.

So to that pointing, how much if we look at Envidia in particular, it's down about two hundred and eighty three points. What part of that is justified?

So I think the big picture here, I think that this is an overreaction. I think when you look at a company like Nvidia, it's a company we do own, and I think that this is an overreaction, and just specifically around that, if we can kind of zero in on this concept that these models are becoming more efficient, let's just take deep seek at face value that they've had some sort of improvement. Is that that improvement if you believe that the US tech companies are competent, that improvement is around some architectures that have been talked about for the last couple months. So those companies, the big tech companies, the companies that are behind all the announcements last week Meta increasing their CAPEX spend, and I think that that is all that is a belief that they still need this hardware. So when it comes back to AI and comes back to the Nvidia trade, is that I believe if they are reducing the costs to train these models that I actually can build. I think a credible case that that could increase the demand for some of this hardware if we are getting closer to general intelligence. If that potential is even closer, it's just going to be an insatiable arms race. It's going to continue. And so I understand why and video is down today. I think it's actually a healthy thing. But ultimately I think that their business is going to be just fine. We have to wait twenty one trading days before we hear from at least in VIDI on that, but I think it's going to be fine.

So, Gene, was it a coincidence that on Friday, Meta upsets capex forecast dramatically, which by a magnitude I've never seen, from fifty billion to sixty five billion, and then today we get this news on deep sick? Was Deep seek? Was that kind of I don't know, coincidence?

I think, what's I think that's just purely coincidence. But I do kind of go back to what we're just talking about a minute ago. Is I think it's important to note is that that announcement from Meta, if you assume a Meta is Meta is competent, and I believe that they are. That announcement came with the full knowledge of what deep Seek was doing, so it's new to most of us today. But for these companies who have been making these huge investments, the concept of what deep Seak had been trying to build has been They've had a model that's been out. This has been something that's been aware for the last couple of months, and so I think that is important. Is that I think again that this commentary from on Friday from Zuckerberg about the increased capex factors in some of these potential breakthroughs that we've seen with deep Seek. I want to stop short of saying it's definitively a breakthrough because a lot of unknowns around how good the models are and what the true costs are. But I think that the AI investment phase is still alive and well, this is still very early.

So if this could be an overreaction, we don't know fair enough. In Nvidia, obviously an overreaction that stocked. The way you look at it, does it make you think though a rating of forty one times estimated PE.

So from where my head's at and thinking about calendar twenty six at this point, and based on the calendar twenty six numbers, I think it's closer to like a twenty five, and I think that they can grow earnings more than that. This is where the basically the rubber hits the road, is that ultimately what happens in calendar twenty six, all this stuff we're talking about with deep seek today really impacts Ynvidia in twenty six. And if in fact this dramatically changes how the kind of hardware that people need less hardware, then I'm going to be wrong. But if this does create this accelerated arms race, then I think that they're going to grow faster. And so I consider a one peg on that twenty six earnings is actually attractive valuation relative to the rest of the group. So I'm comfortable with the evaluation.

Just a red headline crossing the Bloomberg criminals we speak, deep Seek says it is subject to large scale malicious attack. So that's a headline. We'll have more reporting on that going forward. Gene, just real quickly, we're going to hear from some of the other big tech companies, Microsoft, Meta, Tesla, how do you think they're going to address this issue?

It's obviously this is going to be front and center the topic, and I think they're probably going to address it by saying they still have plans to invest meaningfully more in counter twenty five over twenty four when it comes to Capex, and I think that that will play part of a for reassuring we're long ways away from that. We got two and a half trading days awave before we start to get some of that commentary before the mic gets turned over to the other side of the equation here.

All right, Gene, thank you so much for joining us. I know you're super busy today. Appreciate getting a few minutes of your time. Gen Munster, managing partner co founder Lupet Ventures, taking a little bit of a I guess a longer term view, in a more broader view of what this can mean for the indusuay.

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All right, let's get more on this text sell off here Kim Forrest, founder and CIO of Boca Capital Partners. She's also a former Chips person, like she did the stuff, so she knows the things.

Clearly.

The deep Seek news is upending most of the tech industry, right. You get the socks and DEXes down hard, you get all the hyperscalers are down hard, all the chip makers, all the power makers all down hard. Is this the reckoning or is this a by the dip moment?

Well, that is the question, isn't it. I would say it's a little bit of both. I think this is a good level set, re leveling, resetting our expectations. So the first thing is, we have to really see if this is true. Now, apparently the company is in deep Seek is an open source company. They have released how they've done this, but you know, replication is key, so we're gonna have to see if this actually works. That's thing number one, Like, don't get too upset about major developments until you know that they're real major developments, right, So that's thing one. Thing two is and I think this has been troubling me for a very long time, especially with like last week's Stargate announcement. It seems as if people have been expecting AI, which we don't know what the payoff is yet we have a pretty good idea it might help us out. But we're going to cover the earth essentially in data centers and use far more power than we create right now. So those two things are kind of troubling that people have been making investments in companies going to produce far more than they can produce now. And do you see what I'm saying here? We had these really big expectations, and I didn't think that they were going to come to happen in short or long term because of the well physical limitations of it.

So Kim, at this very early stage, I think I'm probably representative of most of our listeners and viewers had not heard of deep Seek before this morning. Sure, and so that's calling into question kind of what we think we understood about AI and its implication. It's not just for technology preference stock market in general. My only question that I think I have now that has any relevance is do I have to rethink my spending associated with AI.

It depends on how long your timeframe is. If it's the very short term, probably not you you know you'll get rewarded because well orders are in and all that kind of good stuff. And by short term I'm talking a year, but let's say three to five years maybe, And why is that? Well, if we can really train models more rapidly with less input, which is a good thing, right, where were we going to get all this electricity? That was like my biggest question. But anyhow, back to your question, I think you know this is part of being an investors understanding how long was I play? I'm keeping this, So that's part of the problem. We have two more problems that are not being clarified by deep Seek. One is, and remember I used to do this, so I'm way down the road from most investors in thinking about things. But as I use it, I'm not getting the results that I need, And by that I mean it's not right enough. The error rate that I get back from the questions that I'm asking are anywhere between five and maybe thirty percent, which that's huge. Like I can't just use AI. I have to babysit AI. So these are problems that aren't solved by anybody at this point. And then there is how are we actually going to use it? I think there was I think a Bloomberg report that was saying that probably two hundred thousand people could be laid off those lower level entry level people into finance and then that mid tier that are now you know, kind of like the AI for investment banking and equity research. But if we were that wrong, we wouldn't last right, Like, if you're five percent wrong in investment banking, your career is really short. So you know, these are problems that aren't being solved. So that's another issue for investors. How right How long is it going to be till these get right enough to replace a person?

That's such a great point because I was also reading an article that talked about the limitations so far that have been seen about Gee, what is it deep Seek? I want to say, geek see deep Seek was its lack of inability to discuss jijenping or to give real handswers on Tanam and square, just those topics that are near and dear to China's heart. And that's in part some of the issue that you're talking about. So who wins in this right now?

Who wins? Well, I think investors are losing in the short term. But if you like me think that AI is a path to productivity, and productivity always wins. I think you could be a winner. I think maybe companies like open Ai and Microsoft, you know, by dint of you know, investing in them, and then everybody else who's developing AI, they might be winners. Maybe the what is it Stargate? I don't know why. I can never remember. That's my break up here. I can ever remember that because it has nothing to do with stars or gates, that Stargate might be the loser in those people because we're not necessarily going to need to cover the earth in data centers.

Right, yep, all.

Right, Kim, thanks so much for joining us. Always appreciate getting your perspective of your technology background. Kim Farst, founder and chief investment officer of Book Capital Partners, joining us from Pittsburgh via the zoom Thing.

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Happy mindy, everybody, Alex Deal here alongside Paul Sweeny. This is Bloomberg Intelligence Radio. We are broadcasting to live for Interactive Brookers Studio right here in Midtown Manhattan. Also check us out on YouTube dot com. Or clearly have the tech sel off underway really concentrated in certain names the mag seven obviously, in Vidia, the chip stocks, although Apple's up, I should say, but in Vidia obviously getting pounded, the chip stocks getting pounded as well. So we wanted to continue the conversation about AI and you really need to rethink this investment thesis. Joining us now is Josh Pantoni. He's CEO boosted dot Ai Now. The company has helped dozens of investment managers who's AUM totals over one trillion dollars and how to implement machine learning in their portfolios. First off, Josh, have you seen this new geek seek? Geeks? Why do I keep saying geek seek? It's not geeks seek deep see see if you try deep seak? Have you talked to your clients about it? What do you think?

Yeah? So, I mean it came out I think last Monday. We've already ran a whole suite of benchmarks against it. Try that out over a bunch of things, and I'd say we're pretty familiar with it. I think it's impressive in a number of ways. It's impressive how how much power you get for the cost. I think the flip side is it's actually not quite something that we could even consider implementing it to our process. And I think there's a lot of clients that will reach the same conclusion after they start working with it. Why is that.

So?

A lot of how we help our clients is with things like trying to identify risk and trying to identify to build out these different workflows for different parts of the financial process. And a really big part of that is related to trust. You need to be able to give answers that you're very confident in. You need to give answers that you can sort of deeply into it where it's coming from. And I think one of the challenge of this model is that it has some very clear political biases right off the bat. So if I'm trying to use it to say understand what my portfolio exposure is to China's going to be a challenge to use it for things like that. Also, a lot of the things we're most excited about are things like being able to do computer use, being able to sort of teach the machine how to like interact with different apps and things on the computer, image recognition, trying to extract out charts and things like that, and a lot of those kind of capabilities that actually doesn't really have it and seem to be as sophisticated in so it can do very well in certain benchmarks, but when it actually comes to trying to apply it for at least most of the use cases that we're doing, it's not quite there yet.

What is there for you right now? Like what does work?

I think the most interesting thing for me is actually on the reasoning side. So of course, like one of the really big pushes Opening Eyes had right now is like with one and three, you've got these models coming in where in theory they can take a task, break it into subcomponents, and then start executing those components. And I think in that area it's actually quite good. I also think, you know, just from a cost basis, it's extremely impressive they've been able to do. There's been some controversy about exactly how much it costs to actually train the model. What I can confirm though, is the actual inference cost. The cost of running the model is extremely cheap compared to what you're getting with like oh one and oh three, and so just the fact is even possible to do that as a major technology breakthrough.

If nothing else does Deep Seek just highlight the cost issue and potentially the I don't know, the movement down in costs of implementing AI.

Yeah, the way I tend to think about it is the cheaper it is to train AI systems, the more advanced capabilities you can train.

In the stress you lost your audio Josh, you there, Yes, I am.

Here, still hear me. We're good?

Okay, perfect? Yeah. What the way I like to think about it is the cheaper it is to train these AI systems, Then the more advanced capabilities you can train, the more advanced capabilities you can train, the more use cases you unlock. The more use cases unlock, the more you actually see it accelerate. So I actually think this model will probably cause an acceleration in the capabilities of other models that are getting built as more folks start to adopt it. So for me, I actually see it as completely positive.

What outside of deep Seek and other models, like, have you worked with that you did?

Like yeah, so, I mean behind the scenes, we work with in thropic models we work with open AI models. We work with a bunch of fine tune models that we build in house ourselves. You know. I like to sort of describe it as an orchestra of different types of models. And one of the things we've sort of noticed is there's a lot of differences in capabilities. So something like the inthroduct models and thropic models tend to be better at very long context window where you try and handle like huge amounts of text, whereas something like the GPD form models tend to be a little bit better at like foreign language and just sort of the general verbiage it uses as it's giving outputs. So we use a whole bunch.

Where how are your clients and the asset management business, Josh using AI these days, in these early days.

Yeah, So the way I like to think about it is we give them the ability to create this sort of team of little AI workers where you teach them how to do some kind of task, and then they're going to do that task on a continuous basis. So let's say you to do something like write an investment thesis on a company, or let's say you had to do something like write an ESG report or Let's say you wanted to continuously monitor the world for any kinds of updates to something that might happen in the air space. These are all examples of sort of workflows you can teach the system and have the system start to automate.

Well, we really appreciate your time. It was so good to get that perspective. It's good to kind of get the user mindset in for this. Josh Josh Pantoni, CEO a boosted dot AI on deep Seek and sort of the pros and the hans there.

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