AMZN - As AI Evolves Which Stocks Make Sense? Kirk Spano With Ramy Taraboulsi

2023-06-12 09:00:00 ET

Summary

• AI's major bottleneck is a lack of computing power, with supercomputers needed for the training and testing of data.
• The amount of AI networks created and tested is set to increase exponentially over the next six years, with the computing power improving 250,000 times.
• Companies like Dell and Oracle, which are heavily invested in software and disk storage, are predicted to see gradual growth in the coming years.

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Margin of Safety Investing's Kirk Spano talks to Ramy Taraboulsi about why AI can't be slowed (1:30) and stocks that make sense given AI's lack of computing power (6:40).

This is an abridged version of our past conversation, AI Cannot Be Slowed Down - With Ramy Taraboulsi And Kirk Spano .

Transcript

Kirk Spano: Hello. I'm Kirk Spano with Seeking Alpha. And today, I am interviewing Ramy Taraboulsi, who wrote an article recently, describing how the singularity, the merger of humanity with machines and artificial intelligence, and all the consequences, benefits, all the negatives that could come from that.

It was maybe my favorite article that I've read this year on Seeking Alpha. So I do recommend that everybody read this article, take all the links that are in it and go and visit some of the links, and really consider where we are in history and whether or not it's accelerating as fast as Ramy suggests that it is.

Ramy, how are you doing today?

Ramy Taraboulsi: I'm doing perfectly fine, Kirk. Thank you for inviting me to that conversation. I really appreciate that.

KS: If you go back and you'll take a look at some of the things that have been in science fiction, decades and decades ahead of reality and a lot of it comes true. So, we have control over this at this point. How do we get to a place that's better and not worse?

RT: That's a very difficult proposition, how to get to a place that's better and not worse. There's a big potential that we can reach a Utopian state like you're suggesting and that's my big hope. We can do that. Some people are suggesting that we have to slow the AI down. We cannot do that. We cannot slow it down. When you think…

KS: Why can't we?

RT: The reason for that is that, there's a huge race that's happening right now. From my perspective, I see many companies that are advancing in AI. Think about NVIDIA ( NVDA ), for example, it's doing lots of things on AI , OpenAI, Microsoft ( MSFT ), and so on. I think personally that the investments of these companies in AI pale compared to the investment of the military around the world in AI.

I want you to think about something. The United States, for example. It has a budget of around $800 billion for its military, which is as much as a 10 next countries combined.

KS: Right.

RT: But the number of soldiers in the United States has been dropping by around 5% over the last 10 years every year, year-over-year dropping by around 5%, and the budget is going up. So is it the soldiers that are making more money, or they're investing in something that we don't know? I just wanted to go to Lockheed Martin Corporation ( LMT ), for example, which is one of the biggest contractors and look at their motto. Their motto and their case theme for what they're doing, they're trying to automate everything.

And how will they automate it? They'll automate with AI. So the military is spending huge amounts of money, and I don't think that the military will be in a position to stop its progress of fear of other militaries doing that. So, I don't think that stopping it will be a possibility anytime soon, primarily because of this. Yes, you can stop the companies, but you cannot stop the military.

KS: Let's shrink this down to a five to 10-year investment horizon. So that people can and try to look at these things in the nonlinear way and I talk about straight lines and exponential curves all the time, because on the front end of any progression, it looks like a straight line, because it's kind of flat. And then you notice that first inflection point, like, oh, it's kind of ramping up. And then like the AI stocks in the last month, they go straight up.

And straight up moves usually aren't sustainable without some sort of significant snapback. So, I wonder for these companies, are they looking at such a big move in technology that they have a hard time applying it in a way that is profitable. All the trial and error ends up costing them a lot of money. And then what are the ramifications with management, right? They get pressure from shareholders. Does that create mistakes? I would be concerned about different levels of mistakes, not so much on the scientific side, because that's really a process.

But at the corporate level, where do they play a role in all of this?

RT: Well, the corporations are competing with each other, of course. We know that and this competition is brutal. And every company is trying to get and edge over the other companies. Now how will they take that particular thing that they have and materialize it into money? That's a totally different issue and every company is totally different.

The challenge that I'm seeing right now from an investment side is that we are going through a hype state, and people do not understand what AI is. The problem that I'm seeing right now is that people really don't understand the internals of what AI is, but they know that they are using it.

KS: Right.

RT: How can they take what they are using right now and what will happen in the future? What are the potential of habit, what will happen in the future? Now think about the following right now. How much could the computer power increase over the years? I just did some simple calculation and found out that over six years, the computer power that we have I'm talking about hardware, connectivity, disk, and so on will increase by around a quarter of a million times over six years.

KS: Wow.

RT: So, we're having quarter of a million time improvement in the power of the computing, computing power altogether worldwide over a quarter of million years. The major bottleneck…

KS: Let me jump in and that's probably going to accelerate with the recent quantum computer breakthroughs?

RT: Yes, that does not take into - the quantum computer into consideration. But we have to remember as well that quantum computers do not work on their own. Quantum computers are not a replacement for the traditional computers.

Quantum computers gives us all the answers for a problem. And then we need the traditional computer to sift through them and get us a proper answer. So quantum computers don't work on their own, but that's a different problem.

The challenge that people are not realizing right now is that the major problem with AI is the lack of computing power. That because AI requires supercomputers for the training and testing of data. And so remember, it's all based on trial and error. So it has to go through multiple iterations to get something right. And most of these iterations are not done scientifically as they are done by trial and error.

That's the nature of AI right now until we understand exactly how the parameters of the neural networks work. And no one - I don't expect anyone to know that anytime soon. So until then, the major bottleneck that we have is a computer power, assuming that the computer power will increase one quarter of a million times, 250,000 times over six years. Within six years from now, you mentioned 10 years, I'll just talk about six years.

Within six years, the amount of AI networks that will be created and tested and trained and ready to go will be increasing more than you could ever, ever imagine. Yes, we might be in the hype right now. And people are saying that this hype is going to - it's going up right now and they tell us to drop, because nothing goes up forever and we will have to drop. I see that the way the path that it has for going up is much, much more than what we are seeing right now.

We're just seeing the edge of what it's like. The amount of money that could be made from AI is enormous . Now will all companies succeed? No. Some of them will not, some of them will not. Which one they are? That's a totally different issue. But some of them will succeed, for example, when we had the dotcom, people are comparing it to the dotcom. Yeah, dotcom people went up quite a lot. The problem with dotcom is that the Internet was not improving itself.

Now computers are improving themselves. AI is improving itself. That's a fundamental difference in there. And when you think about dotcom, there are many dotcom companies that are still extremely successful. Just think about the company that ends with the dotcom right now. Very popular company, it's called Amazon.com. The name of Amazon ( AMZN ) is not Amazon. The name of the company is still Amazon.com.

That's one company that's from a dotcom era, and it succeeded, and it thrived. I think that many companies in here will thrive quite a lot. One company that people don't talk a lot about, one area that's required in AI that people don't talk about is disk. The amount of disk storage that's required for AI is huge. The amount of data that was created over the last two years is as much as the amount of data that was created from the beginning of time until two years ago.

KS: Right.

RT: And the amount of data that will be created in the next two years will be equal to the amount of data that was created from the beginning of time until now. So the amount of disks that's required is huge.

Look at companies like Dell ( DELL ) that owns EMC, a company like Oracle ( ORCL ) that bought Sun and right now is one of the biggest producers of disks and databases in the world, because you need databases as well to store these data. These companies so far have not - the hype did not hit them yet.

And if you're looking at the company that will be going up, I would look at Dell and I'd look at Oracle. These companies will be going up quite a lot, but they will not be going up exponentially like this, like we're seeing right now with AI and Microsoft and so on. They'll be going up gradually. But these are companies that when you put your money into them, you're putting the money in the bank.

KS: So a Seagate ( STX ), maybe a Western Digital ( WDC ) maybe?

RT: Seagate, Western Digital, all these companies that are doing that. I'm thinking about Dell and Oracle specifically, because Oracle as well is very heavily invested in software, including Dell as well, not just the hardware manufacturer. So these two companies are invested a lot in software as well.

KS: All right. So we all saw NVIDIA ( NVDA ) spike up.

RT: Yes.

KS: And that makes sense the way that you just described it, because everybody's in a race, so they need the equipment now.

RT: Correct.

KS: Six years from now, maybe NVIDIA, I mean, they're going to have to reinvent themselves yet again at some point?

RT: Well, NVIDIA right now, the reason that they are doing it is, because remember, we need huge computing power…

KS: Right.

RT: …for the computer to be doing the training properly for the neural networks. And NVIDIA creates their GPUs.

KS: Yes.

RT: It's called processing units, which is the primary tool that's used, because you want to run multiple things in parallel while you're training their artificial, the AI network. Another company that did not spike as much is AMD ( AMD ), I don't know why. That does not make sense to me. Because AMD, yes, it's gone up, but did not go up as much as NVIDIA. Why? That I don't understand. I was expecting AMD to be even going up as much if not more than NVIDIA as well because they are also, that's a key competitor for GPUs in the world right now.

KS: Right. So do you think as these other semiconductor companies make their announcements about what they're seeing, they could have pretty good rises as well?

RT: They would - I expect so. And I expect that AMD would be one of the top companies, although it went down today by 4%, 5%, but the whole market is down today anyway.

KS: Right.

RT: And so - but again, I would see these GPU processing companies, I think that they will be going up quite a lot. NVIDIA as well, it has an advantage that it's heavily invested in software as well. So it's not only hardware, they are doing a lot on the software side as well.

KS: Right, Megatron and whatnot. Do you think - do you have any special insights on Intel ( INTC ), are they going to be a monster again?

RT: I don't know.

KS: Okay.

RT: I just cannot tell right now. I really don't know about Intel. I would not invest my money into it at this point. I will hold off a little bit. If I want to invest, I'll put my money in AMD, not Intel.

KS: Okay, all right. You almost took a Charlie Munger on that and just said it's too hard. That's fine. I have a little bit of money in Intel, just because they're so cheap. And if they get their act together in the next two or three years, they should do extremely well. However, they've been fighting that battle a while now?

RT: Well, Intel created something that I think was brilliant at that time, which was a processor called Loihi . They created something called Loihi, which is based on neuromorphic computing. Neuromorphic computing basically right now, if you look at processors, processor have their CPU and they have their data and the RAM or the disk, they're separated from each other. They try to create something based on their neuromorphic computing called Loihi that will combine them together, which is very much similar to what we are doing with our neural cells in our brain and what the perceptrons are doing in neural networks.

Unfortunately, right now, I think that this project has not progressed as much. I think that if Intel, focuses a lot on Loihi and the neuromorphic computing, they'll have an advantage over all the other companies if they do it right. That's a huge advantage that they would have. Will they be able to do it or not? I don't know. That was something that was three, four years ago when they announced it. And since then, I've not been hearing anything about Loihi and the neuromorphic computing that they're doing. I think that will be, if you see a big announcement about neuromorphic computing from Intel, I'll put all my money into it. Because that's basically where you'll be creating the hardware that will be suitable that will be matching the software.

Right now, the hardware and the software are not matching. We're using Von Neumann architecture in the hardware. We are using neural networks that require data and processing happening at the same time. We don't have that. If we have neuromorphic computing, that's what would happen. Intel has the advantage in the world right now on that particular area, but I don't know what they have done on that particular project. I haven't heard a lot from them.

KS: Okay. What about Micron ( MU )? So one of the big investors out there, I forget which billionaire it was. It might have been Andreessen. I'm not positive. So the Micron is one of the biggest companies to watch here because of their DRAM, because of the memory. How does that play into the AI? How much of that are we really going to have to build out as AI and then all the IoT applications progress?

RT: I have not studied Micron myself. So it would be farfetched for me to comment on it. But if you're talking about RAM in general and the need for RAM, absolutely, that's data. The RAM realistically is data and the more data that you have on the RAM, the more RAM you have, the faster your processing becomes. But at the same time, we have to remember as well, that the disk right now are moving to solid state disk, which is not that much different from RAM.

KS: Right.

RT: So, do we really need the RAM or that it's superfast and so on? And how much difference does it have compared to disk? I have not studied Micron myself and I have not invested in it, because I did not...

KS: So there might be a competition between the SSD and the RAM?

RT: Yes, there's a huge competition right now. Right now, SSDs are getting as fast as the RAM nowadays.

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