Invest Like the Best with Patrick O'Shaughnessy
· Gokul Rajaram
And you've got to extrapolate to where can the capabilities of these agent building products go, and you've got to do something very, very different. So what that means is you've got to have durability, because ultimately as venture capitalists, or even as an entrepreneur, your time horizon can't be building something that lasts for one year. And that's the biggest challenge. It's not building an application, it's building an application that's durable, that basically will last a test of time. And I think there are a few things around durability. One, you need to have ownership of a scarce asset. A scarce asset could be a license of some kind, it could be a regulation of some kind where you have unique insight into it. Second, you might basically own a control point. A control point is a thing that controls how people interact with money or with data. Third, you want to maybe have hardware, which is hard to replace. Fourth, maybe you want to be part of an essential workflow. Fifth, you want to have network effects. You want to think about those things and figure out how after you take on that workflow, you can make it more durable. And finally, I think your ambition has to be to replace the entire system. In other words – increasingly what is going to happen, and I'm seeing this more and more, is every vertical has either a legacy, or somewhat new, what is called a system of record. Which is a system where most of the data is stored for that system. For example, in legal, there's a company called Filevine or another company called Clio. In sales, it's Salesforce. In healthcare, it's Epic. Now, for many years, these companies all had APIs that if you enter that industry, you could build an agent company on top of these APIs. In 2024, things changed. These companies started seeing that these agent companies, AI companies that are being built, they are starting to take on the functionality out of these companies and are treating them like a dumb database. So you started seeing last year that these companies are cutting off access to APIs. Slack has done it most publicly. Slack is owned by Salesforce. They cut off access to Glean, where Glean can no longer access Slack data. And the reason is they don't want Glean to build on top of them and then slowly suck out the value that Slack has.
· Gokul Rajaram
And I'm hearing from other verticals that they're doing one of three things. They're blocking access to APIs. They're offering their own agents for free, bundled. I think that is a great and effective strategy. Or they're charging these AI agent companies to access the data. Just to access data, the API was free. They're saying now it's $2 an API call. They're trying to make the model of these agent companies unviable. I think it's going to be very hard for an end customer to use multiple companies, where you have a system of record and then you have this agent that sometimes doesn't work with it properly. So the agent companies have no option but to also start building and offering a system of record. So every company I know is now trying to figure out how do I build the entire platform, and not just a system that does some workflows. I think last year, everyone was like, "Oh, we can do workflows. We can build what is called a system of action and live on top of the system of record." I don't think that's an option anymore.
· Patrick O'Shaughnessy
[00:00:00] S0: The Slack example is a good one of a last generation software company, which was very big and very successful. One of the most interesting investor questions, and I'm curious for your answer from the perspective of a builder and a technologist, is that the degree to which these horizontal model companies are going to destroy or be very bad for old software companies because over time it will be trivial to spin up your own Slack that has features that you want for your company and it's very reliable in all those same ways that Slack is, and therefore Slack's in a lot of trouble. How do you think about that question? Obviously, public markets seem to think software's in a lot of trouble. The multiples are really, really low. How much would you be worried if you ran a good, solid, but older software company today?
· Gokul Rajaram
[00:00:00] S1: There are two kinds of legacy companies. One is systems of records, and one is things that are priced based on outcomes. The software companies that should be the most worried right now, is where they are pricing the product based on utility. Zendesk is a good example. Literally, Zendesk prices seats, and each seat comes with utility. In other words, each seat corresponds to a customer service agent that takes a certain number of customer tickets. So, that company should be worried because I can have an AI agent sit right next to Zendesk and you can slowly siphon off. Instead of paying for 50 Zendesk seats, you can pay for 20, and I can have 30 AI agents sitting next to Zendesk. And that siphoning can happen over time. You don't have to have an all-in-one decision. It can be a two-way door decision. Those are the most endangered companies in my opinion. For these companies, you need to change your pricing model to be based on outcome, and you need to actually build the product to be based on outcome. It's easier said than done because literally you're going from a $20 or $30 per seat to maybe charging a buck, or $0.50 or $0.20 per ticket resolved and you don't know how that's going to turn out. So, you've got to change your pricing model and I think that's a very challenging thing. That's why I think many of them probably need to go private because they have to make this business model transformation in private. I think it's going to be hard for them to stay public. The companies that are less exposed are ones where the utility is not based on seats, but it's based on data that has been collected and captured over a period of time. The more timeless the data is, the more protected they are. Slack, for example, I would say might be in a little bit more precarious state because the data in Slack is not timeless. Half-life is very short. But if you have, ERP is a great example. Somebody uses NetSuite as an ERP. Now, I don't know how NetSuite actually charges but it doesn't matter how many seats you buy, the reality is it runs your whole business. And there is no compelling reason for someone to put their career at stake by ripping out NetSuite.
· Gokul Rajaram
I know over the last year there's been a lot of AI-enabled ERP businesses but there is no compelling reason to take NetSuite and say "I'm going to rip it out" because it is career limiting to suddenly take NetSuite out when you're a company running on NetSuite. So I think those companies are much more insulated, and I think obviously – you could argue that NetSuite has more time to build AI agents on top of it because they have the data. They can train the AI agent on top of it and bundle it. So essentially, I think the software public markets are not distinguishing between these two types of companies. Companies where the half-life data is low, and where you can actually have, you can literally take half of the value of this company and put it onto an AI company that sits next to it. Well, something like an ERP system, or even Salesforce for sales data and records, those are real customer records. It's going to be hard. So what are AI-native companies doing? The first thing you've got to do if you ever have to compete against them is you've got to spend a year or two first building a system that literally takes, migrates your Salesforce instance to your own company's platform. One of my companies is an AI-native company. They literally hired engineers in an Eastern European country for two years to build this migration thing, transition tool. So, you have to build a migration tool, because who's going to migrate it? You can just present your spanking new system, but this data is still there. Even for Square, for a small business, I remember, they had a point of sale. They wouldn't move to us even though it was cheaper because they had gift cards, customer data, loyalty data, payments data, all of that, even credit cards. So we had to build scripts and that took us months or years to build it for a simple POS. For something like Salesforce, you can't just say, "I'm a much better CRM." If you look at CRM, what does the CRM contain? It contains your customer record. Your customer support system contains what your customers are complaining about. And Jira or Atlassian contains what your product development team is building. Now, all of these things should be linked, because there is no linkage. You should be addressing the biggest complaints of your customers, which are in Zendesk.
· Gokul Rajaram, Patrick O'Shaughnessy
And those Zendesk customers, you should know where they came from, who bought them, who sold them. So all these three systems should be linked together, but they're all three different companies. So there are companies that are trying to unify these things, and it's a great value prop. But guess what? None of your customers is ever going to move unless you build a simple, seamless way to take the Salesforce data and move it to your instance, the data from Jira and move it to your instance, the Zendesk data move it to your instance. So literally, it's a two-year effort to build migration. Otherwise, you've got to get Accenture. [00:00:00] S0: How do you think about stickiness in this era, just as a general concept, when the friction for creators to build something that new is so low, you can do whatever you want really fast? How is anyone going to use anything for a long period of time? [00:00:00] S1: In the age of AI, stickiness, I think, comes from a few sources. One, you need to have network effects. So, DoorDash is sticky, not just because it has this beautiful app, but it's because, it's a network of restaurants, and dashers, and consumers. So you can't just attack one, you've got to go – [00:00:00] S0: You can't vibe code your way to those two.