Amy Weaver: Thanks, Brian. Karl, Brian pretty much summed it up. As we mentioned last quarter, we do not anticipate a material impact from pricing in the guide this year. That said, I have been very pleased by the execution and the discipline that we’re showing around rolling this out. And an uplift really needs to roll for the full renewal installed base, which is going to take some time.
Michael Spencer: Thanks, Karl. Operator, we will take next question, please.
Operator: Your final question will come from the line of Brad Sills with Bank of America. Please go ahead.
Brad Sills: Wonderful. Thank so much. Great to see all the success here with Data Cloud. I’m not surprised we’re hearing that in the channel. My question is really around the organization, the team that’s responsible for executing on this and how they plug into the different product groups. Data scientists in this day and age are a rare commodity and you clearly are attracting that. So would love to get a sense for that organization, how it’s evolved and how well integrated they are across the different product groups. Thank you.
Marc Benioff: Well, I think that, that is very much a primary focus of the company, which is that when we started this Data Cloud, we thought we were just building a CDP. And a CDP looked like an exciting market opportunity. We’re number one in enterprise marketing automation. That seems like a great opportunity. But the more we started working on this product, we realized, oh, every one of our clouds needs this Data Cloud. And so Sales Cloud needs a Data Cloud, Service Cloud needs a Data Cloud. Yes, Marketing Cloud needs a Data Cloud, called that CDP. And Slack needs a Data Cloud. Tableau also needs a Data Cloud. If you’ve seen any of my recent demonstrations and with these great Tableau customers in Japan, they all need Data Cloud on the back end of Tableau.
And this idea that the Data Cloud will become the heart and soul of the product, be the engine of all of Salesforce’s apps and say you can use our models, our AI models or you can bring your own models into the Data Cloud, which is a very cool feature. This idea that it also has this incredible level of capability. But the amount of data that it’s already managing and the amount of data that it’s already ingested, that is what is shocking to us. And I think that you’re going to see as we get deeper and deeper into this so you can really see the level of data that we’re handling, the trillions and trillions of transactions. This is going to be the key to the AI working for enterprises. Enterprises are going to want to deploy AI for productivity.
I think I’ve made that case already on the call, but they’re going to get frustrated when the Copilot that they are given from other companies don’t have any data. They just have data grounded to maybe the application that’s sitting in front of them, but it doesn’t have a normalized data framework on — integrated into the Copilot. So while I think Copilots on productivity applications are exciting because you can tap into these kind of broad consumer databases that we’ve been using. So as an example, the Copilot is I’m writing an e-mail. So now my — I’m saying to the copilot, hey, now can you rewrite this email for me or some — make this 50% shorter or put it into the words of William Shakespeare. That’s all possible and sometimes it’s a cool party trick.
It’s a whole different situation when we say, I want to write an e-mail to this customer about their contract renewal. And I want to write this e-mail, really references the huge value that they receive from our product and their log-in rates. And I also want to emphasize how the success of all the agreements that we have signed with them have impacted them, and that we’re able to provide this rich data to the Copilot and through the prompt and the prompt engineering that is able to deliver tremendous value back to the customer. And this date, this customer value will only be provided by companies who have the data. And we are just very fortunate to be a company with a lot of data. And we’re getting a lot more data than we’ve ever had. And a lot of that is coming from the Data Cloud because it’s amplifying the capabilities of all the other data we have.
So it’s a very interesting moment for Salesforce. I think the demonstrations at Dreamforce were outstanding. The demonstrations that we’ll deliver in our February release will be mind-boggling for our customers of what they will be able to get done. And I think that by the time we get to Dreamforce ’25 or ’24 in September ’24, what we’ll see is nothing that we could have possibly imagined just 24 months earlier before these breakthroughs in generative AI have really taken hold through the whole industry. No one company has a hold on this. I think it’s pretty clear at this point that because of the way AI is built through open source, that these models are very much commodity models, and these responses are very much commodity responses. So we’ve always felt that way about AI for more than a decade.
We said that its growth has really been amplified by open source development. Because these open source models now are as strong as commercial models are or proprietary models, I think that what we really can see is that, that is going to accelerate this through every customer. There’s not going to be any kind of restrictions because of the proprietariness or the cost structures of these models. We’re going to see this go much faster than any other technology. The reference point, as I’ve been using as I travel around, is really mobile operating systems. Mobile operating systems are very important, and we all have one on our desk or in our pocket right now. But really, the development of mobile operating systems has been quite constrained because they’re really held mostly by two companies and two sets of engineering teams.
