Inside the Battle for Chips That Will Power Artificial Intelligence

26:26And so you can size this up because I know for like an NVIDIA GPU, and you can do it for different GPUs, I know how many operations per second each GPU can run. And I know how much these GPUs ballpark kind of cost. And so then you've got to assume like, well, okay, how many queries per day are you going to do? And you can come up with a number. And I mean, frankly, the number can be as big as you want. It depends on how many queries. But I think ATAM, at least in the multiple tens of billions of dollars, is not unreasonable, if not more. And just to level set, I mean, it gets to your Google question.

26:59Google does about 10 billion searches a day, give or take. I think a lot of people have been looking at that level as part of the end-all, be-all for where this could go. I'll be honest. I understand why people are, especially internet investors, are concerned that large language models and things like chat GPT can start to disrupt search. i'm not exactly sure that search is the right proxy personally it feels kind of limiting to me i mean you could imagine i've watched a little too much star trek i guess but i mean you could imagine you know you have like a virtual assist in the ceiling i'm calling out to it and you know it doesn't have to be just search on on my screen i i could have it in my car right i could have you know i call up american airlines that change my airline tickets and it's a chat box that the chat bot that's uh talking to me um so this could be very big and by the way i think to guess by the the one problem with this sort of calculation is kind of static.

27:50Like the cost is sort of an output rather than an input. I think to drive adoption, cost will come down. And we've already seen that. Like NVIDIA has a new product that's called Hopper, which is like two generations past those V100s that I was talking about, past the Volta generation. The cost per query to do this or the cost per training on Hopper is much lower than Volta because it's a much more efficient part. That's a good thing, though. So it's TAM accretive. It will drive adoption. NVIDIA actually has specific products specifically designed to do this kind of thing. And Hopper has specific blocks on it that actually help with the training and inference on these kind of large language models.

28:28And so I actually think over time as the efficiency gets better and better, you're going to drive adoption more and more. I think this is a big thing. And remember, we're still really early. ChatGPG only showed up in November. Yeah, it's crazy, isn't it? It's really early still. Well, just on that note, can you draw directly the connection between the software and the hardware here? Because I think at this point, probably everyone listening has tried chat GPT and you're used to seeing it as a sort of, you know, it's an interface on the internet and you type stuff into it and it spits something out.

29:01But like, where do the semiconductors actually come in when we're talking about crunching these enormous data sets? And what makes us, you kind of touched on this a little bit with nvidia but what makes a semiconductor better at doing ai versus more traditional computational processes yeah yeah you bet so to answer that second question i think ai is really much more around parallel processing and and in particular thing it's this kind of matrix map it's a single class of calculations that these things do very very efficiently and do very very well they do them much more efficiently than a cpu that performs a little more serially versus parallel.

29:42You just couldn't run this stuff on CPUs. Don't get me wrong. We've been talking about inference on large language models. There's all kinds of inference. Inference workloads range from very simplistic to very, very complex. Again, my cat recognition example was very simplistic. Something like this, or frankly, something like autonomous driving, that is an inference activity, but is a hugely computationally intense inference activity. And so there's still a lot of inference today that actually happens. In fact, most inference today actually happens on CPUs. But I'd say the types of things that you're trying to do are getting more and more complex and CPUs are getting less and less viable for that kind of math.

30:24And so that's kind of the difference between GPUs and other types of parallel offerings versus like a CPU. I should say, by the way, GPUs are not the only way to do this. Google, for example, has their own AI chips. They call them a TPU, tensor processing unit? One thing I really like about talking to Stacey, two things is, A, I think he comes up with better versions of our questions than we do. One thing about the question you just asked, he didn't actually ask. He's always like, all right, that's a good question, but let me actually reframe the question to get a better response. So I appreciate that.

30:57And he also anticipates, because I literally, like on my computer right now, I had Google cloud tensor processing units? Because that was my next question. And also in part because I think yesterday the information reported that Microsoft is also... So why don't you talk to us about that? These other... And what are they competing directly with? Yeah, and it actually gets into your software question. Yeah, yeah. Yeah, you bet. So Google... This is not new. Google has been doing their own chips for seven or eight years. It is not new. But they have what they call a TPU and they use it extensively for their own internal workloads.

31:30Absolutely. Amazon has their own chips. They have a training chip that's called, you know, kind of hysterically, it's called Trainium. They have an inference chip. It's called Interferentia. Microsoft apparently is working on their own. My feeling is every hyperscaler is working on their own chip, particularly for their own internal workloads. And that is an area, you know, we talked about NVIDIA software mode. Like Google doesn't need NVIDIA software mode. They're not running CUDA. They're just running TensorFlow and doing their thing. They don't need CUDA. Anything, however, that is facing an end customer, like an enterprise, like end customer, like on a public cloud, like a customer going to AWS and renting, you know, compute power, that tends to be GPUs because customers don't have Google's sophistication.

32:15They really do need the software ecosystem that's built around this. So for example, I can go to Google Cloud. I can actually rent a TPU instance. It can be done. Nobody really does it. and actually if you look how they're priced typically it's actually more expensive i've usually even than than have the way that google's pricing uh gpus on on on google cloud it's similar for amazon and others and so i do think that all the hyperscalers are working on their own and and there is a certain certainly a place for that especially for their own internal workloads anything that's facing a customer that that nvidia gpu ecosystem is really kind of so yeah this is so this is so actually these just to clarify because that point is really interesting that for like if again, Tracy and I want to launch OddLodge GPT, part of the issue would be not necessarily the hardware, the sort of the silicon, but actually that NVIDIA's software suite built around it would make it much easier for us to sort of start and use on NVIDIA for training our model.

33:19Yeah, yes, it would. And they built a lot. And it's funny, you can go listen to NVIDIA's announcements in their analyst days and things. And they're as much about software as they are about hardware. So not only have they continued to extend the basic CUDA ecosystem, they've layered all kinds of other application-specific things on top of it. So they've got what they call RAPIDS, which is for enterprise machine learning. They've got a library package called ISAX, which is for automation and robotics. They've got a package called CLARA, which is specifically for medical imaging and diagnostics.

33:50They've got something called CU Quantum, which is actually for quantum computer simulations. They've got something for drug discovery. So they're layering all these things on top, right? Depending on your application, they've got internal teams that are working on this. It's not just throwing the software out there. They've got people there that can actually like help you work and come along with it. They're doing other things easier. So they actually just launched a cloud service. And this is with Google and Oracle and Google and Microsoft, where you can almost, they'll do like a fully provisioned NVIDIA AI supercomputer in the cloud.

34:22So because they sell these AI servers and they can cost hundreds of thousands of dollars a piece. If you want now, you can just go to Oracle Cloud or Google Cloud or whatever. You can sort of rent a fully provisioned NVIDIA supercomputer sitting in the cloud that they'll all you got to do is access it right through a web browser. This was going to be super easy. This is going to be my next question, actually, because I take the point about software. But like, what do the AI supercomputers actually look like nowadays? Like, is there a physical thing in a giant data center somewhere? Oh, yeah. Are they mostly like cloud-based or what does this look like?

34:58Like walk us through the ecosystem. So NVIDIA sells something they call the DGX. It's a box. I mean, it's, I don't know, it's, what is it? Two feet, I don't know what the dimensions are. Two feet by two feet or something like that. It's got eight GPUs and two CPUs and a bunch of memory and a bunch of networking. They've got their own, like, you know, they bought a company called Mellanox a while back that did networking hardware. so it's got a bunch of proprietary network because that's but that's something else we haven't talked about it's not just enough to have the computer the compute these models are so big they don't fit on a single c gpu so you have to be able to network all this stuff together yeah right and so they've got networking in there and they have this this this box and then you can you can stack a whole bunch of boxes together like nvidia has their own internal supercomputer it's on the it's fairly high on the top 500 list they call it saline it's a bunch of these dgx servers that they make all just like stacked together effectively.

35:51And they sell for the older generation. Their prior generation was called Ampere and that box sold for$199 ,000. I don't believe they've released pricing on the Hopper version, but I know for the Hopper GPU, it costs two to three X what Ampere cost the prior generation. So this raises a separate question to me, which is, okay, there's the price and it exists and you could go to, you could theoretically go and use Google's tensor based cloud or is it available? Like, or, or is it, because I sort of get the impression that like for some of the technology that people want to use, it's not available at any price and that there is a actual scale.

36:31Is that real or not? It seems to be. So we're like, so their, their new generation, which is called hopper, which like I said, has characteristics of it that make it very attractive, especially for these kinds of like chat GPT, large language models is in tight supply. We're at the very beginning of that product cycle. They just launched it in the last couple of quarters. And so that ramp up takes time. And it does seem like they are seeing accelerated demand because of this kind of stuff. And so, yeah, I think supply is tight. We've heard stories about GPU shortages at Microsoft and the cloud vendors.

37:04I think there was a Bloomberg story the other day that said these things were selling for like$40 ,000 on eBay or something. I took a look at some of those listings. They looked a little shady to me. But yeah, it's tight. You have to remember these parts are very complicated. So the lead times to actually have more made, it takes a while. Wait, so just on this note, I joked about this in the intro, but, you know, could I buy like a Bitcoin mining facility and take all that computer processing power and like convert it into something that could be used for AI? Is that a possibility? You could. The Bitcoin stuff, at least a lot of the Bitcoin stuff was done that was with GPS.

37:41Those were still mostly gaming GPS. People were buying gaming GPUs and repurposing them for Bitcoin and Ethereum, mostly Ethereum mining. Yeah, they're not nearly as compute efficient as the data center parts. But I mean, in theory, yeah, you could get gaming GPUs if you could, but it would be prohibitive. And even now, most of that stuff's cleared out, I think, as Joe said. But the math is somewhat similar. I'd say for these kinds of models, though, again, like a Hopper, NVIDIA's new data center product, it has, they have something that they call a transformer engine. What it really does is it allows you to do the training at a slightly lower precision than, it lets you do it at an 8-bit floating point versus 16-bit.

38:22So it lets you get higher performance. And then there's another process. There's like a conversion process. Sometimes it has to go, when you go from training to inference, it's something called quantization. And with these transformer engines, you don't have to do that. So it increases the efficiency, which you wouldn't get by picking some random GPU. Where is Intel in this story? Well, so let's talk about the other competitive options that are out there. So we talked about some of the captive silicon and hyperscalers. That is there, and it is real, and they're all building their own, and they've been doing it forever, and it hasn't slowed anything down in the slightest because we're still early, and the opportunity is big.

38:56By the way, I will say, I don't worry, to lead with it, I don't worry so much about competition at this point because, think about it, NVIDIA's run rate in their data center business right now, it's something like$15 billion a year. That's where it is. It's growing, but that's where it is. So Jensen, NVIDIA's CEO, likes to throw out big numbers. And he threw out, I think he said, for Silicon and hardware, TAM in the data center. And he thought that their TAM over time was$300 billion. And it seemed kind of crazy, although I would say it's seeming a little less and less crazy every day. But if you thought the TAM was$300 billion or$100 billion or whatever, and they're running at$15 billion, there's tons of headroom.

39:35Competition doesn't really matter. And that's what we've seen. We've seen competition, but there's so much opportunity. Like, who cares, right? Versus, like, if you thought it was a$20 billion, Tam, like, they would have a problem, like, already today. So that's why I don't worry too much because I think the opportunity is still very, very large relative to where they're running the business today. In terms of other competitors, though, so you mentioned – let's talk about AMD first because AMD actually makes GPUs. They make data center GPUs. They don't sell very many of them. Their current product is something called the MI250.

40:08And they've sold de minimis, basically. And in fact, when the China sanctions were put on, and we've been talking about that, but the U.S. has stopped allowing high-end AI chips from being shipped to China. The MI250 AD's part was on the list, but it didn't affect them at all because they weren't selling any. So their sales were zero. They've got another product coming out at the following that's called the MI300. And people have been getting kind of excited about AMD. They've been sort of looking to play it as kind of like the poor man's NVIDIA. I'll be honest. I don't think it's the poor man's NVIDIA.

40:37Yeah. NVIDIA is doing, you know, close to$4 billion a quarter in data center revenues. I don't know that I see anything like that with the MI300. In fact, AMD, as far as I tell, has not even released any sort of specifications for what it looks like at this point. So, but that is an option. And some people would say there's maybe some truth to this is, you know, if you want an alternative, AMD will present an alternative. And if the opportunity is really that big, they'll get some. They'll probably get some if you have that. You have Intel. So Intel's got a few things. On their CPUs, their current version is called Sapphire Rapids.

41:11It has AI-specific accelerators for inference. Not so much maybe for this kind of stuff, but for general inference activities, they're trying to play up the capabilities of their CPU on that. Fine. And why are they doing that? It's because their accelerator roadmap isn't so good. So they have a GPU roadmap. The code name for it was Ponte Vecchio. and they've kind of gutted that roadmap. So the follow-on product was something called Rialto Bridge that they've since canceled. And one of the Ponteventio products recently, they just canceled. And Ponteventio originally was designed for the Aurora supercomputer and it was massively late.

41:49I mean, so like they took, how much was it? It was something like a$300 billion charge. I think it was at the end of 2021. It was either the end of 20 or end of 2021 where they basically, they gave it away. It was so late. So that's how late they were. They also have another product. They bought an Israeli AI company called Habana. And Habana has a product called Gaudi. It's not a GPU exactly, but it's like a specific accelerator technology. And Amazon bought some of them and they sell a little bit. But again, versus Intel's total revenues, it's de minimis. So they're not really there. There's also a bunch of startups.

42:27and the problem with most of the startups is their their their story tends to be something like you know we have a product that's 10 times as good as nvidia and the issue is with every generation nvidia has something that's 10 times as good as nvidia and they have the software ecosystem that goes with it by the way neither amd nor intel nor most of the startups have anything remotely resembling nvidia's software so that's another huge issue right that all of them are facing there's a few startups that have some niche success One of the ones that's probably gotten the most attention is called Cerebrus.

42:57And their whole thing, they make a chip. Imagine taking a 300-millimeter silicon wafer and inscribing a square on it. That's their chip. It's like one chip per wafer. And so you can put very large models onto these chips, and they've been deploying them for those kinds of things. But, again, the software becomes an issue, but they've had a little bit of success. There's some other names that you've got, Grok and some others, I think, that are still out there. And then there's a company called TensTorch, which is interesting, not because of so far what they're doing, because it's early, but it's run now by Jim Keller.

43:30And do you guys know who Jim Keller is? I do not. Jim Keller was sort of like a star chip designer. He designed Apple's first custom processor. He designed AMD's Zen and Epic Roadmaps that they've been taking a lot of share with. He was even at Tesla for a while and at Intel. And so he's now running 10-story. And they do, it's a RISC-V. RISC-V is another type of architecture. And they do an AI chip. So Jim is running that. So can I just ask, based on that, I mean, how, like, CapEx intensive is developing chips that are well-suited for AI versus other types of chips? And then secondly, like, where do the improvements come from?

44:12Or what are the, like, improvements focused on? Is it speed or like scale given the data sets involved and the parallel processes that you described? Yeah, so it's a few things. So in terms of CapEx intensive, these are mostly design companies, so they don't have a lot of CapEx. It's certainly R &D intensive. So maybe that's what you're getting. And NVIDIA spends many billions of dollars a year on R &D. NVIDIA has a little bit of advantage too because it's effectively the same architecture between data center and gaming. So they've got other volume effectively to sort of amortize some of those investments over.

44:46Although now, I mean, this year, I mean, data centers probably 60 % of NVIDIA's revenues now. So, I mean, NVIDIA is sort of the center of, data centers are the center of gravity for NVIDIA now. But it's very R &D intensive and probably getting more so. And you've got folks all up and down the value chain that are investing. You're both the silicon guys and the cloud guys and the customers and everything else. But I mean, that's kind of where we are. In terms of what you're looking for, so there's a few things. you're looking for performance and on training quite often that comes down to like time to train so i've got a model like some of these models i mean you could imagine could take weeks or months historically to train right and that's a problem like you you want it to be faster so if you can get that down you know to weeks or you know to days or hours that would be better so that's one thing clearly that they work on i don't want to do something notice yeah go ahead no finish your thought that I have it slightly.

45:39Oh, yeah. The other thing I was talking about, there's something around like, like scale out. So basically, remember, I said, you're connecting lots and lots of these chips together. So for example, if I if I increase the number of chips by 10x, does my trading time go back down by like a factor of 10? Or is it like by factor of two? So I do you would want like linear scaling, right? I want like, as I add resources, it scales linearly. So this is kind of getting was going to get into my next question, actually. And talk in another with someone else about certain like AI fantasy doom scenarios.

46:11But I'm not an AI architecture expert. I'm a Dunnoplasma action engineer. So I could just say you may want to get an AI. No, I know. But I am curious though, because I do think it relates to this question, which is that, okay, like with each one, like GPT-5, and they're going to like keep adding more knobs on the box, et cetera. And is your perception that this sort of quality of the output is growing exponentially? Or is it the kind of thing where it's like GPT-4, you know, there's a lot more knobs and they got a big jump from GPT-3. GPT-5 will be way more knobs. But like, is it going to be marginally better?

46:51Like, what is the sort of like, where are we in the sort of like, what is the shape of the output curve look like? And this sort of like cost of, you know, these chip developments in terms of getting there. I don't know, it's kind of - So there's a couple of things. So first of all, when you're talking about large language models, accuracy is sort of a nebulous term because it's not just accuracy. It's like it's also capability. Like what can it do? We can show what ChatGPT and GPT-4 can do. And also, like I think as you're going forward, you talk about the trajectories here. It's not just text, right?

47:22We're talking text to text, but there's also text to images. And if anybody played with like Dali, you know, it's generating images from a text prompt. And now we've got like video. What is it? mid was it mid midsummer is that what it's called mid journey yeah i can't mid journey yeah so it's it's creating like video prompts i mean so like the like text is just scrapped is just the tip of the iceberg i think in terms of what we're going to to need in in but they're never they're never going to get to where they could have three people having a conversation with voices why sound like tracy joe and stacy why no i'm just kidding no i'm just kidding it feels like Yeah, I mean, now, one of the dangers, clearly, and maybe this gets to capabilities.

48:06So one thing with chat GPT is it's very, very good. This is where I should worry about my job because it's very good about that. That's sounding like it knows what it's talking about, where maybe it doesn't. Maybe I should be worried about my job. You know, and accuracy, I think, is a big issue. But you have to remember. So but like on this accuracy question, like I assume, you know, like self-driving cars, Like when people were really hyped about them 10 years ago, they're like, oh, it's 95 % solid. We just have a little bit more and then it's solid. Yeah. And then 10 years later, it feels like they haven't made any progress on that final 5%.

48:40Yeah. I mean, these things are always a power lock. So this is my question. When we talk about accuracy or these things, like, are we at the point where like, is it going to be the kind of thing where it's like, yeah, GPT-5 will definitely be better than gbt4 but it will be like 96 of the way there well again let me separate out let me separate accuracy from from capability again so if there's an accuracy you have to remember like it the model has no idea what accurate even means it doesn't remember these things are not actually intelligent i know there's a lot of worry about like what they call like like like ai like artifacts with general intelligence right i don't think this is it this is predictive text yeah that's all the The model doesn't know if it's spewing bull crap or truth.

49:25It has no idea. It's just predicting the next word in the thing. And it's because of what it's trained on. So you need to add on maybe other kinds of things to ensure accuracy, maybe to put guardrails or things like that. You may need to very carefully, like more harsh, like your input, like data sets and things like that. I think that's a problem now. I think it'll get solved. There's enough data. But like, and this has already been an issue. You can take it like the other, like, I don't know if it's the converse of it or not, but things like deep fakes. People are deliberately trying to use AI to deceive.

49:55I mean, this is just human nature. This is why we have problems. But I think they can work through that. In terms of capabilities, though, I think it's really interesting to look at a response to a similar prompt between ChatGPT and GPT-4. And like what people are getting out of GPT-4, it's miles ahead of like some of the stuff that chat GPT, which was trained on GPT-3, the model, that what it was delivering in terms of nuance and color and everything else. I mean, and I think that's going to continue. I wouldn't be – and already – you're at the point where these things can already pass the Turing test.

50:32Oh, yeah. Right. It can be very difficult to know if it's if I'm putting the question of accuracy aside from it, it's very difficult to know for some of these things, if you didn't know any better, whether it was coming from a real person or not. And I think it's going to get like harder and harder to tell, like whether, you know, even if it's not, you know, quote unquote, really thinking, it's going to be hard for us to tell what's really going on. That is sort of like other interesting, you know, implications for what this might mean over the next five years or 10 years.

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52:48Brokered services by Open to the Public Investing, Inc. Member FINRA and SIPC. Advisory services by Public Advisors, LLC. SEC Registered Advisor. Generated assets is an interactive analysis tool. Output is for informational purposes only and is not an investment recommendation or advice. Complete disclosures available at public.com slash disclosures. Just going back to the stock prices, I mean, we mentioned the NVIDIA chart, which is up quite a lot, although it hasn't reached its peak back in 2021. The SOX index is recovering, but still below. And Intel, I mean, I won't even mention. But where are we in the semiconductor cycle?

53:27Because it feels like on the one hand, there's talk about excess capacity and orders starting to fall. But on the other hand, there is this real excitement about the future in the form of AI. Yes, yes. So semis in general were pretty lousy last year. They've had a very strong year-to-date performance and sectors up, you know, 22 % year-to-date, quite a bit above the overall market. And the reason is, to your point, we've been in a cycle. Numbers have been coming down. And we may have talked about this last time. I don't remember. But semiconductor investors, it turns out the best time to buy stocks in general is after numbers come down but before they hit bottoms.

54:06Like if you could buy them right before the last cut, if you could have perfect foresight, you never know when that is. But I mean numbers have come down a lot. So estimates, forward estimates for the industry peaked last June. And they are down over 30%, like 35 % since that point. And it's actually the largest negative earnings revision we've had probably since the financial crisis. And people are looking for playing the bottoming theme and that hopefully things get better in the second half. We get hopefully China reopening and you've got markets like, and this relates to Intel, like PCs and things where we've now corrected, we're back more on a pre-COVID run rate for PCs versus where we were.

54:48and the CPUs, which were massively overshipping at the peak, they're now undershipping. And so we're in that inventory flush part of the cycle. And so people have been sort of playing the space for that like second half recovery. Now, all that being said, if you look at the overall industry, if you look at numbers in the second half, they're actually above seasonal. So people are starting to bake in that cyclical recovery to the numbers. And if you look at inventories, just overall in the space, they are ludicrously high. I've actually never seen them this high before. So we've had some inventory correction, but we may have not.

55:20We may just be getting started there. And if you look at valuations, I think the sector's trading at something like a 30 % premium to the S &P 500, which is the largest premium we've had, again, probably since things normalized after the tech bubble or after the financial crisis, at least. So people have been playing this back up recovery. But, yeah, we better get it. As it relates to some of the individual stocks, like you mentioned, Intel. It's funny. You guys may not know this. I just upgraded Intel. Oh, how come? The title of the note was, we hate this call. I think it's the right one. And I meant that.

55:56That's a good, yeah. I desperately would like to stay under the program. It was, and it was not a, we like an Intel call. It was just, I think that they, that they're now under shipping in PCs by a wide margin. And I think for the first time in a while, the second half street numbers might actually be too low. So that's, it's not like a super compelling call, but I felt uncomfortable pushing there. Although they report earnings next week, I maybe can't do it myself. We'll see. NVIDIA, however, so it's clearly, you're right, it hasn't reached its prior peak from a stock price base. And the reason is the numbers have come down a lot.

56:28I mean, let's be honest. The gaming business was inflated significantly by crypto. Right. And so that's all come out, right? And then with data center, you had some impacts from China. China in general was weak. And then we had some of the export controls that they had to work their way around. So you had some issues there. Now, all of that being said, graphics cards in gaming, we talked about some of these inventory corrections. Graphics cards actually corrected the most and the most rapidly. So those have already hit bottom and they're growing again. And NVIDIA's got a product cycle there that they just kicked off.

57:00The new cards are called Lovelace. And they look really good, especially the high end. And they're starting to fill out the rest of the stack. So gaming's okay. And then in data center, again, this generative AI has really caught everybody's fancy. Yeah. And NVIDIA had a data center, and they're saying that they were at the beginning of a product cycle in data center. And they had an event a couple of weeks ago, their GCC event, where they actually basically directly said we're seeing upside from generative AI even now. So people have been buying NVIDIA on that thesis. And the last time the stock hit these peaks, at least in terms of valuation, the issue is we were at the peak of their product cycles, and numbers came down.

57:39This time, valuations kind of went back to where they were at those peaks, but were at the beginning of the product cycles. The numbers are probably going up, not down. So that's why. Stacey, I joked at the beginning that we could talk about this for three hours, and I'm sure we could. I'm sure we could. Because there's such a deep area. But that was a great overview of just like the state of competition, the state of play, and the economics of this. And a very good way for us to sort of enter talking about AI stuff more broadly. thank you so much for coming back on my pleasure anytime you guys want me here just let me know we'll have you back next week for Intel take care thanks Stacy bye bye

58:33I really like talking to Stacy He's really good at explaining complicated things. Yeah, I know he made a point of saying that he's not an AI expert, but I thought he did a pretty good job of explaining it. I do think the trajectory of how all this, I mean, this is such an obvious thing to say, but it's going to be really interesting to watch and how businesses adapt to this. And what's kind of fascinating to me is that we're already seeing that differentiation play out in the market with NVIDIA shares up quite a bit and Intel, which is seen as not as competitive in the space, down quite a bit.

59:09I was really interested in some of his points about software in particular. And so you think, OK, I hadn't realized that. Yeah. Yeah, like, I mean, I would, you know, like, sometimes I see, like, someone will post on Twitter, it's like, look at this cool thing NVIDIA just rolled out where they can make your face look like something else or whatever. But thinking about like, how important that is, in terms of like, okay, you and I want to start an AI company and do an idea for a large language model or something specific, we have a model to train, there's going to be a big advantage going with the company that has this huge wealth of like libraries and code bases and specific tools around specific industries, as opposed to it seems like where some of the other competitors are, or it's just much more technically challenging to even like use the chips if they exist, like Google's TPUs.

1:00:00Totally. The other thing that caught my attention, and I know these are very different spaces in many ways, but there's so much of the terminology and like that's very reminiscent of crypto. So just the idea of like an AI winter and a crypto winter. And you can see, I mean, you can see the pivot happening right now from like crypto people moving into AI. So that's going to be interesting to watch play out. Like how much of it is hype, the classic sort of Gartman hype cycle versus the real thing? But, you know, two things I would say, absolutely. You know, so two things I think would be interesting.

1:00:34It'd be interesting to go back to like past AI summers. Like what were some past periods in which people thought we made this breakthrough and then what happened? So that might be an interesting. And then the other thing is like, look, like, you know, in 2023, I have never actually like found a reason I've ever felt compelled to like need to use a blockchain for something. And I get use out of chat GPT on something like almost every day. And so, for example, we recently did an episode, you know, yeah, look, we'll do an episode now of a question at the end of like, oh, what is the difference? Like yesterday, you know, we recently did an episode on like lending.

1:01:11And so it's like, oh, what's the difference sort of structurally between the leveraged loan market and the private debt market? And I was like, this might be an interesting question for a chat GPT. And like I got this like very useful, clear answer from it that like I couldn't have gotten perhaps as easily from a Google search. So I do think like some of these hype cycles like are really useful, but like I am already in my daily life and very rudimentary reasons getting use out of this technology in a way that I cannot say for anything related to like Web3. No, that is very true. And, you know, the fact that this only came out a few months ago and everyone has been talking about it and experimenting with it kind of speaks for itself.

1:01:49Shall we leave it there? Let's leave it there. This has been another episode of the Odd Lots podcast. I'm Tracy Alloway. You can follow me on Twitter at Tracy Allaway. And I'm Joe Weisenthal. You can follow me on Twitter at The Stalwart. Follow our guest, Stacey Rasgen. He's at S. Rasgen. Follow our producers, Carmen Rodriguez at Carmen Armin and Dash O 'Bennett at Dashbot. And check out all of our podcasts at Bloomberg under the handle at podcasts. And for more OddLots content, go to Bloomberg.com slash OddLots. We blog, we post transcripts, we have a newsletter. and check out the OddLots Discord.

1:02:26People, listeners chatting 24-7 about all the things we talk about here. We even have an AI-specific room that's really fun. And semiconductor. And a semiconductor room. And so people chatting about these things. I even solicited some questions for today from that group. So it's really fun. I like hanging out in there. You should too. Go to discord.gg slash oddlots. Thanks for listening.

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