Invest Like the Best with Patrick O'Shaughnessy
00:16:29 · Patrick O'Shaughnessy
[00:16:29] S0: Vanta automates compliance so your team can spend less time on security reviews and more time getting customers. Vanta cuts audit prep by 82% and gives you instant up to date proof of trust without all the manual work, so you can close deals faster and with less friction. Customers report a 526% return on investment, and more than 16,000 companies use Vanta, including Ramp, Harvey, and Snowflake. Get started with $1,000 off at vanta.com/invest. Ridgeline 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 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. How would you describe the state of the union today in your world in infrastructure? Like, obviously, it's changing really, really fast. There's new kinds of companies, but there do seem to be some layers or some categories. Yeah. How would you describe what it looks like today?
00:18:10 · Martin Casado
[00:18:10] S1: Yeah. So for me, the definition of infrastructure is like the differentiation is actually technical. Where I don't view SaaS or apps as that way. If you invest in a SaaS or an apps company, it's like a technical investment. It's like a business model innovation. So because for us as a technical innovation, it has to sit kind of in the traditional technical stack. So the traditional technical stack for infrastructure is compute, network, storage, and databases, right? And each one of those has a subcomponents and they all have buyers. We're seeing that being kind of reformed with AI a bit and so all of them are being disrupted but also now you have models which I would consider infrastructure. They look to me more like ASICs than they look like traditional software. I mean, like the capital needs, the competitive pressures on them. And then I think they're disrupting the entire application stack above them. Like, we don't even know if they will layer. I mean, maybe you could argue with Jeb, you can kinda put them in software, but traditionally, they're their own separate entity that you can't marry to software very well. And so I would say we're in kind of a point of maximum disruption, but all of the infrastructure supporting software, whether it's Vibe coded or not, or whether it's to build models or not, I mean, that doesn't change like the major components. And so I think we do have a bit of a traditional mindset of compute network storage databases. But we also throw up our hands and like, this is all changing so fast because for example, when you worried about dev tooling in the past, you're worried about ergonomics for a human. And now you don't really worry about that. And then actually a lot of the design decisions that we made as technologists were like, well a human's gonna be using this so it can't be too complicated. And then you would trade off things like safety or you trade off things like reliability. We don't really have to do that anymore because the models are doing it. So I
00:19:44 · Patrick O'Shaughnessy, Martin Casado
[00:19:44] S0: think this is kind of the biggest refresh of computer science infrastructure we've seen maybe since the internet, maybe forever. Would the shorthand for this be that each of those five portions of infrastructure are being refactored for agents? Is that overly simplifying? [00:19:58] S1: So I split it in two pieces. People are vibe coding a lot of stuff. There's more software being written ever before and that stuff has to run somewhere. That's not agents, that's just normal software. Maybe it's an agent that created it, maybe it's a human being created it but it needs databases and it needs compute. So that's one thing. It's like the product of agents. And then the other one is like we're building these new systems that have AI at the heart of them and then how does that change it? Those are the two. So just assume way more software being built and has to be run and deployed somewhere and then think that now we also have systems that actually create agents. [00:20:30] S0: It seems like each new wave of technology, there's a new kind of database that's needed. Are you seeing that across all the areas that you care about that because of AI, there's whole new entrants?
00:20:42 · Martin Casado
[00:20:42] S1: Yeah, I think so. I mean, I think a lot of infrastructure is getting a bit sleepy and now because agents are very sophisticated, that's no longer the case. Like so for example, let's take OLTP. What is OLTP? OLTP is a transaction database. I think the Oracles of the world. That's a pretty mature industry, you didn't see a lot of movement, maybe you saw like movement to the cloud or something like that but now we're seeing really interesting companies that are like, you know what, the hard thing about using a database from a programmer standpoint is databases always have their own languages and they've had their own languages for technical reasons and it's really really hard to have them work with normal programming languages. And one of the reasons is if you change that language, like anybody that knows databases wouldn't know how to use it and you'd have to train a whole version of programmers to use this new language. But now we're actually seeing databases come out, which are entirely new way of thinking about databases, they have way better properties. They don't really have to train anybody because the agents already know how to do it. I think these things are path dependent, like some sectors a new entrant is gonna come and change it. Like databases, OLTP may look entirely different in five years and some the incumbent may figure it out and be like, hey, listen, we're just gonna add a few things. Networking of course, which is this, did my PhD in networking so it's an area of special interest. Like that's been so sleepy and boring for the last fifteen years and now because these clusters that we're building are so large and they've got, like, such rigid performance constraints, we're seeing net new networking companies come out for the first time since Arista. You're seeing changes in the materials used to build them. You were seeing optical come up, so there's a lot of interesting stuff in networking. Compute, the whole notion of what are the abstractions to run an agent on? Is it like a container? Is it a VM? Is it a durable workflow? Something like temporal. That is changing. But I do think we're at a point of maximum disruption where many points of the infrastructure stack
00:22:31 · Patrick O'Shaughnessy
[00:22:31] S0: can be rebuilt. Can we spend a few minutes on models? Dropped S1 came out this Obviously, a beautiful view into the business itself. But I'm actually more interested because the infrastructure stuff in what you think about just the state of these things today. I'm interested in all the debates, closed versus open, the public frontier versus the private frontier that the companies keep for themselves, movement of talent, opportunities, things that don't exist that should, whatever's on your mind about these things and how you see them evolving, I'm fascinated to hear.
00:23:01 · Martin Casado
[00:23:01] S1: So I think the biggest question is is what happens if and when the market slows down? Because I think we've got three years of data of what happens in the existing very big fast growing market. And like what happens there is being on the frontier means you've got tremendous pricing power. Even if you're just an epsilon better than the next guy, you got tremendous pricing power. There's no demand problem. You've got basically infinite demand and so these frontier models that are the best tend to get 80% plus of the market dollar weighted and that's happened for three years. But you could argue the reason that's happening is because we've not saturated the market at all and people always gonna want the smartest thing. And so then under that view, there's nothing structural that's gonna change as long as the market's growing. And you could argue this is gonna go on for quite a while because... And what is the penetration? Point 1% or something like that? They say 3% of the enterprise, but actually from budget wise, it's like 1% or less. Right? And so there's just a lot to grow given how useful the technology is. The more interesting question to me is what happens if the market starts to saturate in pockets? So if that happens, things shift to open source. Will you lose pricing power on the frontier? One thing that we don't really do a good job of accounting for when we model out the numbers for these companies is what happens if you invest in a model and then you don't get the next model out really quickly? Let's assume that Anthropic stumbles and they don't release a model for six months. What is the lifespan do you think of the previous model?