Tom Siebel: That’s a really good question. And I mean you really asked a good question. And because there is a big opportunity to do this in enterprises that do not use C3 and do not intend to use C3, okay? But they want the unified view of their data. And so the honest answer is we have not figured out how to monetize that yet. We haven’t put a price on it yet, but there is potentially a very large market there. And it’s no place — it’s not in any of our operating plans yet, but it will be. But you — this is a very — it’s a non-obvious use of these open IR lips. This is not about Chat GPT a chat. It’s kind of cute, right? And someday, I think it will be useful. But that’s — we’re not doing chat here. We’re doing kind of the antithesis of chat. We’re using these large language models to basically crawl the enterprise. And so — but you asked a very good question. There is a monetization opportunity today that we haven’t figured out yet.
Operator: Thank you and one moment for our next. Our next question comes from the line of Michael Turits with KeyBanc. Your line is open. Please go ahead.
Michael Turits: Tom, just to continue on the product front and then I had a question or two for you, but on the product on generative today, I think it’s it makes a lot of sense to the combination and the story with DoD. I guess the question is you’re basically enterprise search that you’re talking about, which is the name of the product. But that’s not unless I’m wrong, was that a product you had before because that’s obviously a market unto itself, and there’s a lot of existing players in that market. So what’s the history of having developed that?
Tom Siebel: What is an example of an enterprise product in the market?
Gil Luria: Even like what Elastic does for a lot of people, right? So — and then how are you — what’s the sort of the innovation that allows you to interact that with other people’s generative AI models?
Tom Siebel: The innovation is the way that we have combined those core technologies in a new and novel approach for a non-obvious application, okay? So something that would define what’s patentable, okay? So we’re taking the enterprise search UI NLP degenerative AI, reinforcement learning and predictive analytics and combining those in a non-obvious way to solve a problem of how utility. Ed, why don’t you pick it up from there?
Ed Abbo: Yes. So as Tom said, basically, we’re using more modern techniques than, say, Elastic to do the interpretation of the questions using large language models and then the retrieval of information from across the enterprise information systems, documents, BI dashboards, et cetera. And so this is a, as Tom said, a novel approach to be able to search the entire corpus of an enterprise and leverages generative AI, leverages these large language models and all the all of the capability that we’ve developed over the past decade to be able to integrate and unified data across systems, sensor networks, images, tax, et cetera. So this is different than it’s new, and it’s much more effective than traditional approaches for indexing.
Michael Turits: Got it. Makes sense. And then just on Baker Hughes. So I think that we had in our model, I don’t know if it’s right or not, but I think we’re anticipating about another $270 million in the contracts? Maybe — I don’t know if you wanted to update that, but so do I just add $35 million to that? And does it change the amount you’re expecting each quarter? And what did you get this quarter from them?
