Chamath
speaker
939 appearances
12 recordings
2 series
first heard Jun 2024
last heard 3 Jun
Chamath’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 8 in all, peaking in Nov 2025 with 2.
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Yeah, and so... And if you think about business value, we think a lot about this as like, where's the SaaS opportunity in all this, the software as a service opportunity? It's going to be in agents. I think we'll ultimately look back on these sort of chat models as a little bit of a parlor trick compared to what agents are going to do. in the workplace.
If you've ever been to a call center or an operations center, they're also called service factories. It's assembly lines of people doing very complicated knowledge work. But ultimately, you can unravel exactly what the chain is there, the chain of thought that goes into their decisions. It's very complicated, and that's why you have to have humans doing it. But you could imagine that...
Once system integrators or enterprise SaaS apps go into these places, go into these companies, they integrate the data, and then they map out the workflow. You could replace a lot of these steps in the workflow with agents.
Well, it's interesting. We were having a version of this conversation last week on the pod, and I started getting texts from Benioff as he was listening to it. And then he called me, and I think he got a little bit triggered by the idea that systems of record like Salesforce are going to be obsolete in this new AI era. And he made a very compelling case to me about why that wouldn't happen.
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,
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.
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.
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 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?
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.
And once all the data is in one place with the right permissions and safeguards, now analysts can start working it. And that was their historical value proposition. But in addition, AI can now start working that problem. So anything that the analysts could work, now AI is going to be able to work. And so they're in an ideal position to master these new AI workflows.
So what is the point I'm making? It's just that you can't just throw an LLM at these large enterprises. You have to go in there and integrate with the existing systems. It's not about ripping out the existing systems because that's just a lot of headaches that nobody needs. It's generally an easier approach just to collect the data.
I just think that that's the cycle. Whenever you're dealing with a disruption as big as this current one, I think it's always tempting to think in terms of the existing pie getting disrupted and shrunk as opposed to the pie getting so big with new use cases that on the whole, the ecosystem benefits. No, no, no, I agree with that. I suspect that's what's going to happen.
Speaking of investing in late-stage companies, we never closed the loop on the whole open AI thing. What did we think of the fact that they're completely changing the structure of this company? They're changing it into a corporation from the nonprofit, and Sam's now getting a $10 billion stock package.
I've got enough money.
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