Invest Like the Best with Patrick O'Shaughnessy
00:13:51 · S0
[00:13:51] S0: Get started with $1,000 off at vanta.com/invest. Bridgeline is the first end to end system of record with embedded AI for investment management firms running portfolio accounting, reconciliation, reporting, trading, and compliance on one unified platform. Firms are moving off legacy technology and onto Ridgeline because of how far ahead Ridgeline's AI features are compared to anything else in investment management software. I've been hearing from a lot of investment managers about AI and they fall roughly into two camps, with some unsure where to even start and others convinced that they can build their own order management system over a weekend. The reality is that running an investment firm will always require governance, controls, and a single source of truth for your data. And no amount of AI enthusiasm changes that requirement. Ridgeline is built on exactly that foundation, which is why I believe that the firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform. If you're serious about your firm's AI strategy, Ridgeline should be part of that conversation, and you can request a demo at ridgeline.ai.
00:14:53 · Patrick O'Shaughnessy
[00:14:53] S3: Maybe it's a good time to talk about, I think, about the assembly line. Like, if Rogo puts out some really useful output, I wanna learn about each component part of the system that leads to that output. And I'm most especially interested in the data that you yourself have and use and buy and build and whatever and how you do that, you think about data, and then how you think about model. And these are both questions, one that I'm curious about your specific business for, but I also think that there will be some business like Rogo built in basically every vertical. And so I'm curious what can be abstracted to other professional services or other verticals that are interesting as well that will fall to AI progress. So talk us through, yeah, specifically like the data and model piece and how that's evolved over time.
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.