Thomas Siebel: And that’s quite easier to buy rather than saying 10, 20, 30, 40, 50, I think one deal we did was $0.5 billion if I’m not mistaken, okay, pretty well it was $300 million plus a couple of things. We’re saying, hey, it’s a $0.5 million, if you like it, keep it, okay? And so after they pay their $0.5 million if it goes that way there is no RPO.
Juho Parkkinen: That’s right.
Mike Cikos: Got it, got it. And maybe just one more if I could and apologies to be taking all the time here, but I did just want to circle up, I know that you guys are talking about the C3 Generative AI pilots being $250,000, 12 weeks and the remaining product lines, I believe, and correct me if I’m wrong, but you have typically about six months for those pilots, can you help us think through what — is it just a ton of value on these Gen AI pilots is so much quicker than you think that these customers can convert that much faster?
Thomas Siebel: It is quicker Mike. In one case, we might have to add or load all the data, model supply chain, and build machine learning models that fit the — that fit the scale of the enterprise of a Cargill, which is roughly $100 billion business or the United States Air Force, which is a pretty big business, okay? With generative AI, we don’t have to do any of that, okay? We just load their data into a deep learning model and it kind of takes the learnings from those data, stores it in a vector store and we’re kind of — we’re the Masters of the Universe at aggregating structured data, non-structured data, sensor data, enterprise data, images, what have you into a unified federated image, we have 14 years of that, we’re really good at that, so that’s easy, and then with all the mappings are worked out by one deep learning model, they are stored in a vector data store and then the — so we don’t have these huge data science projects that we have at all these other organizations.
So, yes, the time to value is faster, the implementation effort is easier, and it’s technically, honestly, it’s an order of magnitude easier problem.
Mike Cikos: Awesome. Thank you very much, guys, I appreciate it.
Thomas Siebel: And there is nobody who doesn’t want to talk about it.
Mike Cikos: Great to hear. Thank you, guys.
Operator: Thank you. One moment for questions. Our next question comes from Kingsley Crane with Canaccord Genuity. You may proceed.
Kingsley Crane: H. Thanks for taking the question, and congrats on the result. It sounds like your plan is to invest more in lead-gen branding market awareness, customer success, you’ve mentioned that you have more than 140 qualified leads in Gen AI, so it seems like you’ve done tremendously well in generating leads. So as we think about the incremental change to the profit guidance are you balancing investments between customer success and pilot conversion without lead-gen and brand awareness?
Thomas Siebel: I’m sorry, what was the — how we’re balancing between customer success and lead-gen? Okay. A lot of this is branding and lead-gen, Kingsley, is what we’re looking at. Kind of like we used to do in 2021 when we establish the brand for enterprise AI that worked out pretty well and we’re going out to plant a flag on this Generative AI market and we’re going to — we’re first to market, how many companies out there, have 28 enterprise Generative AI solutions in the world. I know how many, exactly one, and we’re going to communicate that, we’re going to make it available. So that’s what the bulk of it is. At the same time, if we have a customer in any one of these markets where we need to really get resources to make them successful with their pilot, you can be sure, we’re going to make them successful with their project. And as we get down the learning curve will get increasingly efficient at it and gross margins go up.
Kingsley Crane: Okay, thanks, Tom. That makes a lot of sense. And so if I could ask one more, hoping you gain some clarity on the 28th domain-specific Gen AI solution, so for example, if you’re an oil and gas customer you’re building a solution in sales and this is ultimately linked into Salesforce is that requiring three separate apps or how would that be consumed and priced?
Thomas Siebel: That will be in one, basically, it’s price per CPU. I mean, that looks like it’s going to be on a judgment basis whether it’s a discrete projects or whether it’s a — whether the union out them is one generative AI application, whereas as you have described it, the union of them is one generative AI application, it will be a $0.25 million to bring it live in 12 weeks, and after that pay $0.35 per VCPU hour or VGPU hour.
Kingsley Crane: Okay, very helpful. Keep up the good work. Thank you.
Thomas Siebel: And as it relates to when it gets to runtime pricing, it doesn’t really matter whether it’s one application or whether it’s three, it’s going to be the same amount of run time.
Kingsley Crane: Thank you.
Operator: Thank you. One moment for questions. Our next question comes from Pinjalim Bora at JPMorgan. You may proceed.
Noah Herman: Hey, guys, this is Noah on for Pinjalim, thanks for taking our questions. So on the semi-pilots that are active at the moment, if we exclude the pilots that have been extended one or two months, is there any way to parse out how many of these pilots are under production licenses? And I have a quick follow-up.
Thomas Siebel: I think — thanks, Noah, for the question. So I think at this point, the way we are looking at this that there were 73 pilot deals that we’ve been doing, 70 are either converted or in the process of the pilot or we are negotiating a production license on those. I think the meaningful amount or a meaningful message you should take from this that out of 73 pilots, we only have three notes. So we have a pretty — we feel very comfortable and very bullish about how that pilot program is currently progressing.
Noah Herman: Understood. And then maybe just a double click on the gross margins, I know you commented that with the transition to function.
