Invest Like the Best with Patrick O'Shaughnessy
00:15:36 · Gabe Stengel
[00:15:36] S1: The early days of Rogo was kind of this Roop Goldberg contraption where like you had 60 different model calls. A question comes in, you try to say what companies is Patrick asking about? Now what are their tickers? Now how do I feed those tickers into an API called a you know Bloomberg or fax it or some internal dataset? Now it comes back and I need to call a different model to pull it all together. And as the models get smarter, you kinda wanna be less prescriptive, less of a Rube Goldberg machine and just think about what are the simplest, best, highest quality tools. And the same way that if you have the world's smartest human being starting here tomorrow trying to be a great banker and investor, what are the tools that they would need that are not just intrinsic to being smart. Right? What are the data tools? What are the ways of going out and gathering information? What are the ways of auditing its own work? And And then what are the ways of presenting it and pushing it back into the systems that it needs? And so for us, we spend a lot of time thinking through what are all the data inputs that a great banker or a great investor would need to actually do their job. Then we spend a lot of time thinking about great. When you're doing that job, what are all the compliance and regulatory requirements to make sure that if someday some Delaware court judge makes AI inputs and research discoverable, you have actually done it all the right way such that you're not intermingling information. Cause the reality of AI investment judgments and AI banker outputs is that you're gonna be able to see the full lineage of how those things are created. And then we spend a lot of time benchmarking these models and creating different evals and data sets so we can always decide what is the most performant, what is the cheapest from a token perspective, what is the lowest latency, and route tasks to the appropriate type of models.
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?