Becker Private Equity & Business Podcast
DeepSeek and the Future of AI: A Nuanced Look at Cost Curves and Purpose-Built Models with Matt Wolf of RSM 2-2-25
Sun, 02 Feb 2025
In this episode, Matt Wolf, Health Care Senior Analyst and National Health Care Business Valuation Leader at RSM, provides a nuanced analysis of the DeepSeek AI model and the broader cost reduction trends in AI development.
Chapter 1: What insights does Matt Wolf share about the DeepSeek AI model?
Thanks, Chanel. So, you know, as of this recording, it's been an interesting news cycle with Deep Seek's launch of their R1 recursive learning model or related learning model. And what does this mean for the future of AI and export controls and, you know, mega scalar tech prices and all of this? And it's been a fascinating discourse to watch.
And I think there's a lot of nuance that has been missed in sort of the national discourse. financial news publications, and I thought I might just spend a few minutes talking about it, you know, from our perspective, my perspective, which is, again, I think a little more nuanced and based on the AI researchers that I've been reading and follow and study.
And I think the advent of this model is of the deep seek model is noteworthy only really because it came out of China. It's not surprising that a company was able to develop an AI model as cheaply as they did. However, the stated price of $6 million is not right. It was significantly more than that. But
Chapter 2: How has the cost of AI models changed over time?
We've seen with the development of AI models, similar to the development of other computer transistors, processors, memory, where the cost falls down.
precipitously you're able to scale more with less over time and this is a pretty significant factor and so you know this new model that came out that has everybody talking again about ai or the export controls working what does this mean for nvidia what does it mean for facebook you know it's important to note that yes it was developed more cheaply but it doesn't score as well as the leading models
Chapter 3: What is the significance of AI cost reduction trends?
And it's sort of falling on this expected cost curve of sort of cost reductions that we would expect to see really across all technology. So, you know, I think everybody can just maybe take a deep breath and realize that it's not necessarily the competitor that that is being touted as it's falling on the cost reduction curve.
Chapter 4: Why are purpose-built AI models important?
Now, what does this mean from a more, you know, I'm not going to talk about export controls, any of that, but from a business perspective, what I think, though, is really interesting is this cost curve, right? Because we've been, and the reason it's not being talked about now is, you know, these GPT, these large language models have been around only for a couple years, really, in the public's eye.
we don't have experience with the cost curve. We're used to the biggest, most expensive, highest performing model. And what the sort of realities of the economics of developing new AI models and the fact that over time you can more cheaply scale a new model that doesn't surpass the current benchmark, but can perform pretty well, what that means is we'll begin to see more purpose-built AI models.
As the costs come down, certainly it doesn't mean that Microsoft or other companies are silly for spending tens of billions of dollars, right? As the costs come down, they'll just spend more resources to do things even faster. That's what the leading companies in this sort of AI race will do. But for everybody else, it means that We expect to see more purpose-built models.
So instead of an AI, large language model, generative AI model that kind of is pretty good at doing a lot of things, we might begin to see, we expect to begin to see more models that are really good at doing a specific thing. you know, so maybe a specific or a set of specific tasks or functions for a healthcare provider or for a energy utility, right?
And so we'll begin to see more purpose-built models because as the cost of scaling models comes down, the economics of creating purpose-built models that, you know, can't help you code or help you you know, write a cover letter for a resume, but can help you optimize cooling and power usage in a data center, those will become more economically feasible. And I think that'll actually be very, um,
Disruptive isn't the right word, but transformative for those industries. We'll see these very specific purpose-built models. And that's the real thing to watch, I think, for most leaders, most investors is where are these newer purpose-built models coming into my space, into my industry or industries? And what does that mean for the future of my company's
um that'll be really interesting to watch we'll still see the hyperscalers spending an insane amount of money to build and develop these you know cutting edge models because there's just so much economic value add that those models can deliver that it almost doesn't matter how much money you you spend at it we're only limited by the actual technological limitations of the the chips that scale these models
That's one end of the spectrum or the bleeding edge of development. But again, for everybody else, what the DeepSeek R1 model demonstrated is companies will be able to develop models that are pretty good, not cutting edge, pretty good at a fraction of the cost. And at those economics, we can expect to see more specific purpose-driven models
AI models related to specific tasks or functions in given industries. And I think that will be very transformative in its own right.
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