Larry Ellison: Yeah, well, we’re using it every place. Perhaps the most stunning is our new tele — again, I mentioned it, and then it’s Cerner that we’re doing a lot of things that Cerner never did in what is now called Oracle Health. One of those things is our new telecommunication module summarizes a consultation between a doctor and a patient and writes the doctor’s notes for the doctor automatically. In fact, for the first time we’ve done it, we now have our large language model generating the summary without a scribe that the doctor can edit in a couple of minutes. So, it’s actually succeeded in doing one of the very hardest tasks we assigned it. And of course, we’re using it in all in an — everything from as simple as doing product descriptions or job descriptions, all of those, you’ve read about all of those, we’ve actually got those implemented and are delivering those to customers.
But even in the most challenging areas, in drug design, we’re having success with pharmaceutical companies. But actually writing the doctor’s notes without a scribe has shocked a great many people and well, another area in terms of diagnosing cancer from biopsy, just biopsy images, being able to do that very, very quickly where the patient knows weeks sooner than they would otherwise, and then they get the news weeks sooner, from just the immediate AI processing of the biopsy image, they find out weeks sooner whether they have to go on chemotherapy or whether they’re cancer-free. And we’re doing that with an Israeli partner called Imaging. So, no, we’re seeing a huge uptake of this technology, everything from complex healthcare and health science to more mundane tasks that you find throughout an enterprise, but still very important in making your employees in your company more efficient and more competitive.
Alex Zukin: Got it. And then from a monetization standpoint, do you — is this monetizing in a copilot way similar to Microsoft, or how do you envision ultimately seeing some of the incremental value capture inside of the model?
Larry Ellison: Well, we think inside of our — remember we’re a little bit different than Microsoft. We have a lot more enterprise applications, for example, in healthcare. We run clinical trials. We run hospitals. We run ambulatory clinics. We run — we have diagnostic databases for — image processing databases, conventional blood testing databases, all of those. So our monetization is really at the highest end of the value chain, which is we actually supply the application with our partner that does cancer diagnosis, that does the doctor’s notes, that does the doctor’s orders, that actually automatically generates the prescriptions, that reminds the patient to take the subscription so you get compliance. Right now, without once again, I can’t spend too much time on it.
Right now, doctors don’t know if patients have refilled their prescriptions. The doctors aren’t notified and the patients aren’t notified, reminding, we’re doing all of that. We’re doing a bunch of things with and that’s the high end, that’s the high value end of AI when you’re preventing someone from being re-hospitalized, which has a huge cost in terms of human suffering and money.
Alex Zukin: Okay. Thank you very much.
Safra Catz: Now we have multiple ways, by the way, to monetize it, not only as part of our application, but also as part of our infrastructure. Because one of the unique capabilities we allow is for customers when they use our product to basically use their private data in some of these models for them to learn, but then to ultimately keep control of their data. And this is applicable in many, many different types of applications, and this is a service we provide in addition. So there’s just a lot of…
Larry Ellison: Safra is making a very good point in that we have our own applications in healthcare, but we have partners and other companies that come to us to use our AI to enrich their applications. And we keep their data private and allow them to enrich their applications. That company I mentioned earlier, Imaging, that’s not our application that does the cancer diagnosis. That’s an Israeli company that’s doing that, but they’re using our AI to develop their healthcare application. So, we monetize through imaging, enabling them to build their AI application. And we also build a lot of our own. So of course, Safra is right. It’s a combination of the two.
Safra Catz: Okay.
Alex Zukin: Perfect. Thank you, guys.
Safra Catz: Thank you.
Operator: Your last question today comes from the line of Brad Zelnick with Deutsche Bank. Your line is open.
Brad Zelnick: Great. Thanks so much. Larry, it’s taken Oracle several years to reach 66 cloud data centers. And you’re now talking about plans to build 100 new ones, which frankly seems very ambitious. What is it that you’re seeing that maybe we don’t see? And then related, perhaps, Safra, if you could speak to the capital requirements and time frame for that, especially in light of CapEx this quarter, that was a bit less than we had expected? Thank you.
Larry Ellison: Well, okay, how about Microsoft puts an order — in an order for 20 cloud data centers? That’s what we’re saying. When one company, as you say, we have 60, by the way, that’s a little bit misplaced. That’s not quite right. I mean, we have 66 cloud regions and we sometimes use those synonymously. They’re not always — they don’t, data center and regions don’t necessarily translate one to one. But the — when someone comes along and orders 20, then that creates a lot of opportunity for us to build more data centers and get more OCI customers because we’re building OCI data centers inside of the Azure cloud. So those are the kinds of things we’re seeing. We’re building our own public regions based on direct customer demand and then we’re building partner regions like the 20 data centers from Microsoft. The combination of the two adds up to 100.
Safra Catz: Yeah, and also one of the things is in that number, it doesn’t include the many, many clouded customers, which started small, and now companies have decided they want their own region. So they had a clouded customer, which is smaller, and they decide, no, no, no, now I want a dedicated region of my own. I get it. This is working. I’ve saved millions, tens of millions, sometimes hundreds of millions. Now I want my own. Also really, it is absolutely true, we did not bring up as much capacity as we could have used this past quarter because we had to make some audible calls on the field to decide how to allocate, whether to build something small which was available, which I could have recognized revenue in right in the quarter, or instead to go much bigger and to wait until some larger capacity was going to be available to hand over to me.
