Robert Becker on DeepSeek and the AI Economy 1-28-25
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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.
not heard it said like that 1 20th of the cost or computer power to get to the same place in terms of advanced sort of learning or ai learning and what that means there's been all this investment this is now i'm starting to understand this was all this investment in super fast chips and all this structural engineering power and grids that need to be built for all that all that power computing power but if you do the same thing with you know d batteries versus super special batteries it makes the cost structure a lot less and so a lot of this investment in grids and everything else in all this infrastructure and then the video super super chips may not be as necessary as people thought. Is that sort of the business situation that's going on or the discussion?
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Chapters
5 chapters
1
What is DeepSeek and why is it important?
0:58–4:20
2
How does DeepSeek improve AI efficiency?
4:20–5:59
3
What does the market correction for NVIDIA mean?
5:59–9:54
4
How will DeepSeek benefit average companies?
9:54–11:29
5
What are the implications of DeepSeek's research for the AI industry?
11:29–13:14
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