Chamath

Which is? Well, first of all, I think AI models are predictive. I mean, at the end of the day, they're predicting the next set of texts and so forth. And when it comes to like your employee list or your customer list, You just want to have a source of truth. You don't want it to be 98% accurate. You just want it to be 100% accurate.

You want to know if the federal government asks you for the tax ID numbers of your employees, you just want to be able to give it to them. If Wall Street analysts ask you for your customer list and what the gap revenue is, you just want to be able to provide that. You don't want AI models figuring it out. So you're still going to need a system of record. Furthermore,

You want to know if the federal government asks you for the tax ID numbers of your employees, you just want to be able to give it to them. If Wall Street analysts ask you for your customer list and what the gap revenue is, you just want to be able to provide that. You don't want AI models figuring it out. So you're still going to need a system of record. Furthermore,

He made the point that you still need databases, you still need enterprise security if you're dealing with enterprises, you still need compliance, you still need sharing models. There's all these aspects, all these things that have been built on top of the database that SaaS companies have been doing for 25 years. And then the final point that I think is compelling is that

He made the point that you still need databases, you still need enterprise security if you're dealing with enterprises, you still need compliance, you still need sharing models. There's all these aspects, all these things that have been built on top of the database that SaaS companies have been doing for 25 years. And then the final point that I think is compelling is that

Enterprise customers don't want to DIY it, right? They don't want to have to figure out how to put this together. And you can't just hand them an LLM and say, here you go. There's a lot of work that is needed in order to make these models productive.

Enterprise customers don't want to DIY it, right? They don't want to have to figure out how to put this together. And you can't just hand them an LLM and say, here you go. There's a lot of work that is needed in order to make these models productive.

And so at a minimum, you're going to need system integrators and consultants to come in there, connect, hold on, just connect all the enterprise data to these models, map the workflows. You have to do that now.

And so at a minimum, you're going to need system integrators and consultants to come in there, connect, hold on, just connect all the enterprise data to these models, map the workflows. You have to do that now.

Well, by the way, he said he's willing to come on the pod and talk about this very issue. But just with you? No, no, no, no. He'll come on the pod and discuss whether AI makes SaaS obsolete. A lot of people are asking that question.

Well, by the way, he said he's willing to come on the pod and talk about this very issue. But just with you? No, no, no, no. He'll come on the pod and discuss whether AI makes SaaS obsolete. A lot of people are asking that question.

You could do that today. It's just that- That's an interesting point.

You could do that today. It's just that- That's an interesting point.

So let's wrap this up so we can get to the next thing. Yes, please.

So let's wrap this up so we can get to the next thing. Yes, please.

So look, I think that on the whole, I agree with Benioff here that there's more net new opportunity for AI companies, whether they be startups or you know, existing big companies like Salesforce that are trying to do AI, then there is disruption. I think there will be some disruption. It's very hard for us to see exactly what AI is going to look like in five or 10 years.

So look, I think that on the whole, I agree with Benioff here that there's more net new opportunity for AI companies, whether they be startups or you know, existing big companies like Salesforce that are trying to do AI, then there is disruption. I think there will be some disruption. It's very hard for us to see exactly what AI is going to look like in five or 10 years.

So I don't want to discount the possibility that there will be some disruption of existing players. But I think on the whole, there's more net new opportunity. For example, the most highly valued public software company right now in terms of ARR multiple is Palantir. And I think that's largely because the market perceives Palantir as having a big AI opportunity. What is Palantir's approach?

So I don't want to discount the possibility that there will be some disruption of existing players. But I think on the whole, there's more net new opportunity. For example, the most highly valued public software company right now in terms of ARR multiple is Palantir. And I think that's largely because the market perceives Palantir as having a big AI opportunity. What is Palantir's approach?

The first thing Palantir does when they go into a customer is they integrate with all of its systems. And they're dealing with the largest enterprises. They're dealing with the government, the Pentagon, Department of Defense. The first thing they do is go in, and integrate with all of these legacy systems. And they collect all of the data in one place. They call it creating a digital twin.