Invest Like the Best with Patrick O'Shaughnessy
· 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.
· Gokul Rajaram
[00:00:00] S1: Exactly. And so network effects. The second example of stickiness is when you have financial or money moving through you. I think that's another way to be sticky. Many of the systems of records, for example, Toast, have payments going through them. And I think that really is interesting because you can't just start building the point of sale. You also have to have money flowing through it. And I think if you look at the banks, banks are a good example. Once you have something like Mercury, as a business bank, your money flowing through it is hard to then switch because you have regulations and other stuff embedded. So I like things that are a combination of financial services and software because of that. The third stickiness is from hardware. You can actually have hardware. Toast is a good example where Toast gives you hardware for free. But if you try to return the hardware, you have to pay them. But either case, the hardware is there. And somebody can't just build software. They also have to take hardware and put it into the thing and rip out the Toast hardware. The fourth one is access to a unique asset. I was thinking about a good example. And I came up with the example of Sierra, which I think the unique asset is Bret Taylor. I mean, they have full control of Bret, who's one of the best salespeople, chairman of OpenAI. He can make a call to any company, any country, and they'll take his call. Well, you can't really outsell Bret. There's alpha in that. You need one of these four or five things, which are basically indicators of durability. The half-life of software today is so short that unless you have one of these things that make it durable – Hamilton Helmer has this thing called 7 Powers. So you've got to have a few of those seven powers that basically are embedded in the business model from day one.
· Patrick O'Shaughnessy
[00:00:00] S0: You've been so lucky to work for some of the most well-known CEOs and founders of this modern era. I'd love the chance to ask you a little bit about each of them and what you learned from them. And then more generally, just things you've learned about what great leaders do to run companies. But maybe going all the way back to Google, and starting with Larry and Sergey, what did you learn from watching them operate and lead?