AI Exchanges: The Role of Data

Welcome to Goldman Sachs Exchanges. I'm Alison Nathan, and I'm here with George Lee, the co head of the Goldman Sachs Global Institute. Together, we're co hosting a series of episodes exploring the rise of AI and everything it could mean for companies, investors, and economies. George, good to see you again.

Great to see you, Allison. Good to be here.

So, George, we've had several conversations about how AI is shaping the economic and business landscape. But today, we want to actually get a little bit more under the hood and talk about the technology itself and, in particular, the role that data will play enabling or possibly stalling its progress. We have a terrific guest to dive into these issues with us, Nima Raphael, the chief data officer and head of data engineering here at Goldman Sachs. Nima, welcome to AI Exchanges.

Thanks for having me. Excited to be here.

So before we dig into the topic at hand, first, just tell us a little bit about how you got here, your career journey, and what your current role here at Goldman Sachs entails.

Yeah. This is twenty plus years for me at Goldman. I started right out of college, studied computer science. And during that, I sort of realized that I wanted to apply technology to a domain that I had not known before. And so, finance was sort of like this black box to me. I came to Goldman and met amazing people. Started as an analyst here, software engineering, you know, typing at the keyboard, writing code. And then, really the data thing came, I'd say, five years in global financial crisis two thousand eight, Lehman Brothers collapses, and a group of technologists called CorStrats at the

That's great. And so, in your role as Chief Data Officer now, you oversee all of that. Other things you've done in your career have been involved with what we used to call in the dim dark past machine learning. The point is, AI has been around. We've used it at the firm. It's been broadly proliferated. But the rise of generative AI has garnered so much attention. Is it fundamentally different than the journey we've been on or is it just an extension of the continuum of good old fashioned AI?

Yeah, think I'd say a little bit of both because it feels like some sort of step change function from the historical you know, I always talk about the first fifty, sixty years of computer science sitting down and humans have to code rules to tell the computer what to do. And we talk about determinism, like the rules were deterministic, if you push this button, please do this. Or if you type these keys, please do that. And so there was this really fundamental shift, I guess, in machine learning in general, which is like learn by example instead of learn by rules. And so, in some ways, the gener

And you illustrated something I think is very fundamental in terms of company culture in this shift, which is we're used to deterministic computing. For a given input, the outputs are correct, repeatable, and traceable. We're no longer in that sphere. As you pointed out, these are probabilistic machines. Something emerges from it that you can't trace and is often right, but not always. Talk about the mindset difference inside an organization of getting business users in particular to be comfortable with that.