Becker Private Equity & Business Podcast
Robert Becker on DeepSeek and the AI Economy 1-28-25
Tue, 28 Jan 2025
In this episode, Scott Becker is joined by computer science graduate student Robert Becker to discuss DeepSeek’s groundbreaking advancements in AI efficiency, their implications for the AI economy, and the potential market corrections for NVIDIA.
Chapter 1: What is DeepSeek and why is it important?
And take a moment and introduce yourself.
Yeah, thank you so much for the introduction. I think bias is going to be one of the biggest questions here. But I'm Bobby, I'm your son, and I'm currently studying computer science at Tulane. And a lot of my research actually involves studying the historical networks that are embedded into large language models.
So like ChatGPT, DeepSeq, Cloud, there's a bunch of others of these chatbots people talk to, and they each have a different model of history. And that is one of my areas of research. So there are a lot of PhD level experts on AI, machine learning, large language models. But I do think I have a pretty good grasp on how they work and where everything's heading.
So I'm glad I got this opportunity to stand on my soapbox and sort of talk about where I think the AI economy is going and how DeepSeek is going to affect it.
Thank you. For people that aren't familiar, Bobby's a graduate student. He was an undergraduate in computer science philosophy, graduate degree now in computer science. Talk to us for a second. So what does DeepSeq mean? Why did it pull up in the video? Tell us a little bit about what DeepSeq is. We've all read a little bit about it the last couple of days.
We've seen more about some of these terms we had never heard of if you're not in the computer science world, like Javon's paradox or Javon's rule, whatever it is. But tell us what some of this means.
So DeepSeq had two main, well, there's a lot of different things that they did, but to simplify it, they had two main architecture improvements over the standard process of training and designing large language models in America. The first one was their pre-training process. And this is the bulk of the process of making a language model.
If you use ChatGPT, how it knows what different words are is because it was trained on a bunch of bunch of data. And this was considered in America kind of just a bottleneck of costs that people didn't really go after trying to make it more efficient. And that work actually came out a month ago.
And they found that they could train their models on around 120th the compute than we thought we could train models at the same strength. So that was a huge architecture improvement. And that's why there is a lot of worry about particularly NVIDIA on if their stock was overvalued because these big tough – So talk about that for a second because I think that's really informative.
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Chapter 2: How does DeepSeek improve AI efficiency?
Yes, I think you're outlining quite well. Now, I think it is important to note that in the long-term view, there is always efficiency being made on the hardware of computers, and there's always going to be need for more computes. So NVIDIA, they make amazing chips, so they're always going to be in business long-term. They're fine. But Will there be a short-term correction in the market?
Because in the short term, especially with, if you saw the Stargate announcement, Sam Altman, Larry Ellison, and the SoftBank guys saying they're going to invest $500 billion to build all these data factories, just all this very short-term people throwing money into it, that might not be as high. So there could be a correction there.
Fascinating, fascinating. And what does DeepSeek mean for the average company, like an average mid-market company or larger company? Is this going to make it easier for some of these companies to utilize some of these tools because it's not quite as expensive to do so?
Are there potential benefits to us to go with some of the negatives to the NVIDIAs and some of the players that have really made so much money and grown a bunch of market cap from this?
Well, so DeepSeek, obviously they're a company operating in China and there's some censorship issues with their model. But as within the bounds they're operating under, they seem to be as good faith and as committed to doing good research of a company as you can be. So one of the great things about DeepSeq is they did completely publish all of their research.
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Chapter 3: What does the market correction for NVIDIA mean?
So people at Meta yesterday, people at OpenAI, people at Anthropic, they're all completely dissecting every single thing that DeepSeq did to improve their models. And they're going to incorporate that into their next wave. So ultimately these means these models will be cheaper to deploy.
If you want it to have a software product that includes an LLM, that's going to be a lot cheaper to get a much more intelligent model. And then that also means that now the barrier to entry, if you're a startup who wants to have their own front, they call these, you know, the main models, the foundational frontier models, um,
The cost of barrier to entry has moved to $100 million of compute, obviously more for research and operating costs and this and that, but $100 million to $5 million. So we could see a lot more smaller players coming out with strong foundational models that don't have to rely on using OpenAI's API and stuff like that.
no and that's absolutely fascinating we've seen so much of that in the healthcare sector where originally certain robotics could only be bought by hospitals and health systems but then the cost was went down so surgery centers and other lower cost providers could utilize some of that technology too and it seems like and you've seen a lot of this in the fintech world where so much technology has gotten better and better it's more available to mid-market players and smaller players
And it sounds like the impact of this might be that more and more of this is available to midsize and smaller companies that just otherwise couldn't afford to play into level with this level of artificial intelligence tools and so forth. Is that part of the business hypotheses here or theory here?
Yeah, I think that that's definitely a possibility. So I think something that's important to note is companies like particularly OpenAI and Google, their focus over the past couple of years was trying to make the most intelligent model that they could possibly come up with and then turn it into a product.
And especially with OpenAI, about a couple of months ago, they showed a demo for O3 where they had a model think through and solve the most impressive, the most difficult math problems that math PhDs could come up with. And it solves a good percentage of that. But the compute cost of just solving those problems was like $500,000.
So that has been the main focus on the big players in the AI economy has been what is the most possible thing we can come up with? And then as a secondary focus for them, they're like, okay, how can you make this more efficient so people can use it? But I think definitely now that there is more work in making it efficient, that decentralizes it a little more.
That's a lot of people are talking about the difference between open source, which is what DeepSeek did, which is closed source, which is what OpenAI did in terms of not wanting to share all your research findings. And this is definitely a bigger win to the open source world of people publishing their research, their code, so it can be reproduced by everyone else with way less barrier to entry.
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Chapter 4: How will DeepSeek benefit average companies?
And I think one of the, you know, there's a lot of ways that, and we can talk about this a bit more if you want to, there's a lot of ways that I think Americans should be confident in our engineering ability and our science.
But one of the things where I think this makes us question our assumptions is in the American economy, there's been a lot more intermingling between the research and development side and the commercialization side. And obviously, in many cases, that's great. You know, for example, Chachi PT, that technology was around for a while before Chachi PT came out.
But it wasn't until they released that they realized this could be they could turn the research into a marketable product that people can use. But there is also and I've talked to people at Google who said this, there's increasing pressure where every single piece of research that people are doing has to be tied into some sort of product or some sort of path to commercialization.
But sometimes the pure research in development allows people to. make more foundational improvements in research and overall just lead to greater advancement. And that leads to, you know, that doesn't lead to, you know, Google publishing great papers might not directly be to the being more profitable, but it does improve their credibility and respect as a company.
So one other question, and we'll have you back on. I'd love to discuss this more in the next week or two, just more about both AI and also just what's going on here with DeepSeek and so forth.
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Chapter 5: What are the implications of DeepSeek's research for the AI industry?
So the next question is, would you still invest in NVIDIA at this point, or do you view it as a real question mark now as to where you invest in it when it's at a $3 trillion market cap versus $3.6 trillion a couple days ago? Do you invest in NVIDIA still or not right now?
Well, I am of the school of thought of don't be a stock picker. So I think whether NVIDIA will go up or down in the short term is an open question. I think... I'm of the school of thought that just have an index fund and just wait long term and be patient with the market.
But if you are, you know, if you are devoting some amount of money to do stock picking just for the fun and the as like I'd say more of a hobby than an actual financial strategy, I would say that.
i probably would not buy nvidia right at this moment you know i think that it's already skyrocketed to the number one stock in the world and you know it went down 10 and obviously people are freaking out about that but i think for the longer term view that's okay not every stock has to go in infinity and up and up and up um sometimes there are going to be corrections and i think there is a real risk of a short-term correction in nvidia
But again, I want to reiterate that long term, there's always going to be the need for better GPUs, better CPUs.
I want to thank you so much for joining us today on the Becker Private Equity and Business Podcast. Just a phenomenal guest. Thank you so much for joining us. You know, a great learning experience about NVIDIA. Thank you so much for joining us.
Thank you so much.
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