YouSaid · the spoken record

George Lee

lines on the record: 18
first: 2025-09-30
most recent: 2025-09-30
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Yeah, you're asking a tech nerd, so obviously I would give the tech nerd, the coding answer is the base case. But I also have a three and a half year old son, and he's in his Y phase, which is awesome. But I'm just like, I run out of the turtles of the Y. And so actually a lot of the times he's like, what is that? Why is that? And actually bouncing ideas off of him with the agents, I think, is really cool. And I think it's been powerful for me. So now he asks me questions. I asked the AI questions we learned together about questions he's curious about. So I love that.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“There's some synergy happening there. Definitely. Like, people have built software agents, people have built engineering agents to do this cleansing, this normalization, this linking. So absolutely in the same way where we're seeing software being created by these agents, there's also a feedback loop of data cleansing and normalization and wrangling. So it's a good insight.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“For data. And so just like people write code in a specific way and their specific architectures and engineering practices to that is the same on data. You have to sort of understand what the data actually means. You have to understand are these two concepts the same? Are they linked differently? And so really the challenge is understanding the data, understanding the business context of the data, and then being able to normalize it in a way that makes sense for the business to consume it.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, look, I don't think people had always thought of data as sort of like this thing that could give more insight to the world. I mean, it's always historically been thought of as like business exhaust in some way, right? Like a trader executes a trade. They're sort of like, okay, I'm done. Now I'm just managing the risk. But there's a whole machine behind that about what happens after that, all the workflows that happen after that and before that. And so the real challenges are getting that disparate data into some place where you could organize it in a sane way and then normalize it in ways where the data is correct when you ask it a question. It's linked to the other facts of the world when you want to navigate from that fact to another fact. And so all these challenges are really, you know, that's why the role of data engineering was even created. People are like, we need a practice of engineering that's like software.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, I think first again, you got to remember what this thing is doing, what this machine is doing, right? Whatever data patterns you are feeding this machine is what it's going to learn and what it's going to extrapolate from. And so I think from an enterprise value perspective, cleaning your data, normalizing it, having the semantics of that data well understood, how it links to other pieces of data, all of this stuff is what's going to allow enterprises to level up from what we think the consumers get to what enterprise value could be created.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“Data in the I think get a little philosophical out of my realm, but I think what might be interesting is people might think there might be a creative plateau. I mean, if all of the data is synthetically generated, right, then like how much human data could then be incorporated, new human data, new human into new human creativity, I think that'll be an interesting thing to watch from a philosophical perspective.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, I would echo that. I think these machines have come an enormous distance in their quality, and they've done it largely in the back of publicly available and synthetically generated data. The amount of data that lives behind firewalls trapped inside corporate repositories that's highly salient to garnering business value, that has yet to be unlocked. It's the work that NEMA is doing here. Then there are also other horizons of think about all the video data in the world. Think about spinning up virtual environments where you're creating a platform for virtual robots to generate their own data about understanding the world. I think there, while we've exhausted one pool of data, there are many others to go attack.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“No, I don't think so. The explosive nature of the synthetic data and the fact that now the computer could generate infinite amount of more data, again, I think there'll be a sort of a cursor of what people call like AI slop versus maybe more insightful data. But I think it's going to be a massive constraint only because a lot of trapped enterprise data still has not been harnessed. And I think you see that in the work that we're doing at Goldman, for example, we want to help our salespeople, our traders, our quants, our PMs, to sort of, again, get that superhuman capability, that information synthesis, being able to help with their hypothesis. And there's still a lot of data here at Goldman that could be used for that. So I think from a consumer world model, I think it's interesting. We've definitely in the synthetic sort of explosion of data.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“I would frame it a different way. We've already run out of data. We've already run out of data. When you read about the new models, the undertone of what people say, and you've seen this in the deep-seek moment and things like that, is like everyone wonders, how did they do that with less money, less. And one of the big hypotheses is they trained against another model, right? And so it already incorporated the previous thing. I think the real interesting thing is going to be how previous models then shape what the next iteration of the world is going to look like in this way.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“Flip my brain from this might be vaporware to like, wow, this is like really real. When I sat down at the computer and I was like coding with an agent and it was helping me with problems that I've So, I think there's definitely real there. I think from an enterprise perspective, the thing to be seen is where can people harness their data and their enterprise data and the proprietary data they have to make some differentiation in the enterprise space? That's the to be seen part.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, so as George knows, Mike, I'm always a little bit of a skeptic of new technology. Historically, we've talked a lot about blockchain and things like that, and that was supposed to revolutionize. And is the next thing going to revolutionize? And look, I think from an AI perspective, it's obvious that it's real, it's here to stay. There is absolutely a hype to it, but also when you go on your phone and you ask Claude, Gemini, GPT, take a picture and you ask, like, what is this? Or you ask, give me some research on a topic I'm curious about and you get great answers and you research more. It's definitely, definitely real in the sort of consumer world, I think. I think where the hype, I don't know, I would say it slightly differently than hype. I'd say the potential, I think, in the enterprise is still to be seen. I think there's some really slam dunk use cases we've seen, right? Like agent coding, for example, is the thing that sort of”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“I'd say a little bit in finance, people maybe have understood that because of our pricing models and derivatives pricing. I mean, it was always stochastic in that way anyways. So there was always a little bit of, okay, like the world is non-deterministic. And so prices are non-deterministic. The markets are non-deterministic. Economies are non-deterministic. So I think there was maybe a willingness to sort of understand that here in the finance world. But I agree. I think when non-engineers sit at a computer, they sort of want a thing to be a repeatable pattern. That's how we build workflows here. That's how we build client insights or anything we do here to help our clients. So I think it's really about teaching people this isn't just some magic crystal ball, right? What it's really doing is taking a lot of examples and giving you an extrapolation from those examples.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“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”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“And so, in some ways, the generative AI stuff is just a continuation of learn by example, but I don't think people naturally saw it go from, hey, I could learn maybe how to predict some patterns to now the computer could create anything. And so there's a little bit of that continuum, like, hey, if we just feed the machine more and more examples, more and more data, it could start learning things. It's probably the path of continuum, but the sort of step change was like, oh, well, can we feed it and create images, create audio, create language? And so I think that's sort of the novel step change in the generative part.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, I 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 50, 60 years of computer science sitting down and humans had to code rules to tell the computer what to do. And we talk about determinism, like the rules we're deterministic. Like if you push this button, please do this or if you type these keys, please do that. There was this really fundamental shift, I guess, in machine learning in general, which is like learn by example instead of learn by rules.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“People recognize, okay, this database sort of saved the firm in some interesting way. But then when we gave that same data to traders, salespeople, strats on the desk, quants on the desks, people started coming up with new innovative ways to use that data for helping our clients and just running the firm a lot more efficiently. And so it became this sort of launching pad for people outside of technology to say, hmm, data maybe can be a powerful concept here.”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“The front office, middle office, and back office together in one place to sort of figure out the end to end exposure to Lehman in a very technology and data heavy way. And that was sort of like the genesis of, I'd say, my data journey here. That project actually was super interesting because we had heard other banks and other financial institutions actually have to go into their filing cabinets to dig out their ISDS that were signed with Lehman to figure out what their contracts were. We luckily had a lot of our data sort of corralled in one place. And actually that database we built, it was called Copter, ended up becoming displaced not only that people realized like the power of data not just being sort of like an exhaust, but actually an enabler for the business. And then not only did”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, this is 20 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 really finance was sort of like this black box to me. I came to Goldman, amazing people, started as an analyst here, software engineering, you know, typing at the keyboard, writing code. the data thing came i'd say five years in global financial crisis 2008 Lehman Brothers collapses and a group of technologists called CoreStrats at the time was going around the firm saying hey you know we got we have to figure out what our exposure to lemon is we have to figure out our liquidity profile see what's going on at Goldman and the way they had structured it was to try to get all of the data”
2025-09-30 · Goldman Sachs Exchanges · AI Exchanges: The Role of Data · IDENTIFIED FROM THE TRANSCRIPT