Invest Like the Best with Patrick O'Shaughnessy
00:17:19 · Patrick O'Shaughnessy, Gabe Stengel
[00:17:19] S3: I'm sure your favorite question is how do you ultimately compete with Anthropic and OpenAI who view finance as a big... One of the few categories that like you see them talking about and thinking about. What can you do in the long run that's counter positioned against what they can or will do, do you think? [00:17:35] S1: I mean, you wanna build things that are perpendicular to what they wanna build. And sometimes you might build a chatbot because that helps you go to market faster but you should know there's a whole bunch of stuff underneath the surface that the labs are never gonna build that we need to build for finance or for any other vertical. And when you think about financial services and capital markets, how many businesses are there that generate more than 5 or $10,000,000,000 by just going deep into those workflows into the data sets and how work is done. There's a huge amount of TAM and spend on top of very messy specific problems. And all of finance is a collection of different niches with different data sets, different definitions of good, different regulatory requirements and we can get to $5,000,000,000 in revenue by going deep across those things and creating the systems, the systems of record that help manage them. That for anthropic would kinda be like stopping on the side of the road to pick up a penny because they're on the pathway of trying to go from a 100,000,000,000 in revenue to a trillion in revenue. And there's so much depth to these systems that actually need to be built beyond just intelligence.
00:17:35 · Gabe Stengel, Patrick O'Shaughnessy
[00:17:35] S1: I mean, you wanna build things that are perpendicular to what they wanna build. And sometimes you might build a chatbot because that helps you go to market faster but you should know there's a whole bunch of stuff underneath the surface that the labs are never gonna build that we need to build for finance or for any other vertical. And when you think about financial services and capital markets, how many businesses are there that generate more than 5 or $10,000,000,000 by just going deep into those workflows into the data sets and how work is done. There's a huge amount of TAM and spend on top of very messy specific problems. And all of finance is a collection of different niches with different data sets, different definitions of good, different regulatory requirements and we can get to $5,000,000,000 in revenue by going deep across those things and creating the systems, the systems of record that help manage them. That for anthropic would kinda be like stopping on the side of the road to pick up a penny because they're on the pathway of trying to go from a 100,000,000,000 in revenue to a trillion in revenue. And there's so much depth to these systems that actually need to be built beyond just intelligence. [00:18:40] S3: Is there a favorite example of that of some like pain in the ass thing that you've had to wire up last mile things? Yeah.
00:18:40 · Patrick O'Shaughnessy, Gabe Stengel
[00:18:40] S3: Is there a favorite example of that of some like pain in the ass thing that you've had to wire up last mile things? Yeah. [00:18:46] S1: I mean think about if you are ingesting MMPI because you are working on transactions or you're working on deals that have an effect on the market, the compliance requirements you have just in the auditability or what you can flag and how it feeds into the internal systems of an investment firm or a bank so that if you ever get audited or a regulator ever wants to see what you did, do you have all that plumbing in place that is pixel perfect? That's one example. Another example is if you actually wanna transact, say you are a big public company buying another big public company and you need to send data back and forth, you actually need some sort of data room. Something that is compliant, safe, and secure that coordinates. And ideally it's not just some static kinda Dropbox folder but something that's plugged into the way that you do work, your agents, your workflows. And I don't think Open Air and Thropic will ever wanna build a data room business. If you actually wanna be the exchange for all of high finance and all of capital markets, you not just need to own the intelligence, you need to own the transaction venue, the communication venue, the workflows, and all the data inputs that go into it. [00:19:51] S3: It's interesting. I asked about data and models, but actually it sounds like harness or infrastructure is probably the most important of the three.
00:19:51 · Patrick O'Shaughnessy, Gabe Stengel
[00:19:51] S3: It's interesting. I asked about data and models, but actually it sounds like harness or infrastructure is probably the most important of the three. [00:19:58] S1: Think about the fundamental difference between Cloud Code when it came out and Cloud Cowork versus OpenAI and ChatGPT. The models were actually fairly similar but the harness and the way that it was presented from Claude was far better and it just allowed the models to exercise more of their long running capabilities and that's why they had a run up in usage and a huge amount of expansion. And it just shows the way that you harness these models is so so important. And I think people under appreciate that intelligence, the reasons that humans are high agency and can do a lot is not just because we have high raw recall and IQ and knowledge, but there's all these different micro services in your brain. How do you put knowledge away? How do you retrieve it? How do you trigger things? Emotions are a way to trigger all these different micro services. That's why a great investor might have great judgment is because they have good spidey sense for when they see this sort of thing in the market. It actually triggers the recall from this event that informs a creative decision. All those kind of small microservices are things that need to be built out.
00:19:58 · Gabe Stengel, Patrick O'Shaughnessy
[00:19:58] S1: Think about the fundamental difference between Cloud Code when it came out and Cloud Cowork versus OpenAI and ChatGPT. The models were actually fairly similar but the harness and the way that it was presented from Claude was far better and it just allowed the models to exercise more of their long running capabilities and that's why they had a run up in usage and a huge amount of expansion. And it just shows the way that you harness these models is so so important. And I think people under appreciate that intelligence, the reasons that humans are high agency and can do a lot is not just because we have high raw recall and IQ and knowledge, but there's all these different micro services in your brain. How do you put knowledge away? How do you retrieve it? How do you trigger things? Emotions are a way to trigger all these different micro services. That's why a great investor might have great judgment is because they have good spidey sense for when they see this sort of thing in the market. It actually triggers the recall from this event that informs a creative decision. All those kind of small microservices are things that need to be built out. [00:20:59] S3: But if I force you to become an investor and your only goal is to invest in roguelike businesses, like one of these businesses that's, let's say, a vertical application that's wiring up the capabilities of AI to an industry, what features would you look for that would get you the most excited either in the industry or in the founder builder and their approach based on what you've learned?
00:21:20 · Gabe Stengel
[00:21:20] S1: A few things. One is the industry actually does have to have enough complexity and depth in the types of data, the types of systems of record that people use, the types of deployment models that you can spend a lot of effort solving those problems in order to have a wedge to solve everything else. Because if it's an industry that anyone can just walk into and sell the basic version of Chad GPT or co work or co pilot immediately and you don't have to solve all these weird integrations, you're not gonna have enough time to build all those things that are perpendicular to what you're doing. And so the industry itself needs to be adjacent enough to the core market. So that's one thing. The second thing I would look for is domain expertise from the team and the founders. And I don't have unique domain expertise but I had enough to get started and then I was so curious about finance and money and capital markets. And I grew up in New York and I was surrounded by people that all they could think about, all they could talk about was high finance and I was fascinated by it and I wanted to learn about it. And then we assembled a team that was uniquely passionate about it too. And so I have over a 100 people that have spent time within investment banks or within investment firms across the world's best institutions and so we can constantly take the models as they're released and harness them for finance. And our job is really to catch the change in the models and figure out how to apply it in these institutions. The final big thing I would look for is a business that's willing to constantly reinvent the core product and constantly willing to slash it to nothing. And I think anyone who their delivery method of their product is not something that they can fully cannibalize quickly like a terminal or like a very specific UX or interface is not going to be agile enough to constantly reinvent every six months when there's a step change.