AI Exchanges: Will falling costs drive new opportunities?
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Welcome to Goldman Sachs Exchanges. I'm Alison Nathan. This year, we've decided to look closer at the rise of AI and everything it could mean for companies, investors, and economies. So we're bringing you this series of special podcast episodes we're calling AI Exchanges, which I'll be hosting alongside my colleague, George Lee. George is the co head of the Goldman Sachs Global Institute. He's the former CIO of Goldman Sachs. And before that, he was co chairman of the Global Technology, Media, and Telecommunications Group in our investment banking business. George, thanks for joining me for th
Oh, thank you, Allison. It's great to be doing this with you. Look forward to it.
I am super excited about this. The goal here is to help listeners understand the impacts that AI is having today and the ways it could change in the years ahead, the implications, as I just said, for businesses and economies. And, George, there's really no better time to be having this conversation because, of course, we've recently seen some really fundamental questions about the AI build out ripple throughout the markets quite powerfully. We all have observed the volatility we've seen in the markets this week. It's been well reported that a China based AI company called DeepSeek has rolled o
First of all, Allison, it has been great to have this running discussion with Jim, and it's in such a momentous and dynamic time as you've discussed. So it makes for really fruitful and interesting dialogue. I would say though that to your point precisely, I would say much to the contrary, I think this answers some of Jim's principal concerns, not all of them, but some of them. 2 of his understandable and well founded concerns were the eye watering capital cost of building this pretraining infrastructure, which you referenced, and then his belief that the technology would never really get chea
But the question is, lots of companies have been spending lots of money. Do you think that spending is essentially not going to be useful spending?
Oh, no. I've again, to the contrary. First of all, this doesn't mark the end. And perhaps it marks the beginning of even more pretraining activity by more people who can afford to embark on this at lower capital cost. And so people talk about Jevons paradox as the price of something declines, abundance tends to increase. Moreover, I think one important part of this debate is DeepSeq has performed some really interesting engineering hacks to address pretraining costs. These models have already shifted towards much more dense and abundant computing at inference time, at test time. And so that it
Right. And more cost effective use cases. And, essentially, could we see the applications and the adoption of them speed up?
Absolutely. And, you know, Jim cites in our dialogue a few applications where the technology is useful for us, and yet it remains prohibitively expensive relative to human capital. And this actually, I think, changes that equation. And again, this will embrace new use cases we can't even really imagine today, which will be fun and interesting. And again, this is part of a repeated history of the way that technology happens. This feels like a very discontinuous moment because the sharpness of the client in potential capital costs and token costs. But if you scope back out and you view it in the
That's really interesting. Let's bring Kim Posnet into this conversation. Kim is the global co head of investment banking in our global banking and markets business and the former global head of the technology, media, and telecommunications group.
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