SAS Innovate 2024 | John Boyd | SAS

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John Boyd

Vice President, Solutions Product Management

SAS

play_circle_outline Discussion on AI and production workloads

play_circle_outline Domain-specific IP in SAS solutions

play_circle_outline Distinction between design time and run time in AI

play_circle_outline Unlocking the Potential: Revolutionizing AI with Dynamic Run Time for Value Generation

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John Boyd | SAS

John Boyd

Vice President, Solutions Product Management SAS

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>> Welcome back everyone to theCUBE's live coverage here in Las Vegas for SAS Innovate 2024. I'm John Furrier, your host of theCUBE with my co-host, Dave Vellante, head of CUBE Research. Our next guest is John Boyd, Vice President Solutions Product Management at SAS. He's got the keys to the kingdom on the product management and he's got the solutions. Great . Thanks for coming on theCUBE.

John Boyd

>> Oh, thanks for having me. Great

>> to .So you get the product management, you get the solutions. So right now we're in a mode where people are experimenting with AI rolling out production workloads here at the keynote, Bryan Harris and the team showed a customer with a production workload with an AI stack that only had really Amazon as an external company and then a lot of SAS and then customer data and stuff, and gen of AI and outcome, not a lot of other things. So it's a real enterprise workload.

John Boyd

>> Yes.

>> Big time.

>> Correct, correct. And that's what we're seeing the Viya platform is great for that model governance, right? To get the design time of that model of what you want to do to get the business outcome. And then from a solutions perspective, we're taking a lot of that investment and trying to figure out how do we that runtime environment to adopt that model within our solutions. And whether it's coming from what Udo's organization going to do with his announcement or it's a customer is going to build on their own, et cetera, and build the business workflow around that to run the people's businesses.

>> So you are involved in the billion dollar investment, well, not you, your team and SAS, you work for SAS. You guys announced a billion dollars to be invested into the SAS teams to build AI. Can you share some of the results? What are we seeing? I mean, obviously, we saw, we were at Explore last year, only six months ago. Now we're seeing fruit on the tree there. Can you share information, data and color commentary?

John Boyd

>> Sure. I mean there's a lot that's gone in with that. So the first thing was getting the solutions on Viya , as you heard from Bryan this morning. So that was a big investment to get in there. And now you're seeing more and more innovation coming out. Last year we released SAS Health and we released audiences for CI360. In the upcoming months, we're going to have energy forecasting we released and SAS Clinical Acceleration Repository. And then towards the back half of the year, we're also looking at SAS marketing decisioning. So the intelligent decisioning that you would've saw at the opening session today and how that's going to evolve for marketers. And then obviously, all the GNI work that's happening along with Udo's work as well.

>> Can you talk about the domain-specific IP that you have there specifically? Help us unpack that and understand where SAS has unique IP that contributing to these solutions.

John Boyd

>> Yes. Well, it's an interesting thing, right? If you look at the market out there, you see a lot of people that are in the data and analytics space or people that are vendors in specific industries that are leveraging data and AI. And I think that's where our solutions make us unique in SAS because we bring both to the table. And why that's important is because when customers out there looking to simplify their ecosystem and reduce vendor costs, et cetera, we're one of the few options that can provide that both flavor for a build versus buy for them. And how we've done it is have that domain-specific IP that's gone deep within that industry. So we're solving for that industry problem. But as we've done that, customers have trusted us say, "Hey, what about this other business process that is an industry that's more horizontal related?" So it's created what I call woven product architecture between the vertical industry solutions we're doing and the horizontal business processes we're also getting into as well.

>> What are the salient aspects of that industry solution? I mean, there's data. Where's the data come from? There's the industry specific IP. What other components are there to make that package consumable?

John Boyd

>> Yeah, sure. So you have the data and one of the things I'm pushing for in our solutions is really reducing our time to value. So having that data in a format for our product, but looking at industry standards out there to hydrate that, so it makes it an easier lift for the customer, not have that ETL burden that a lot of people have. And then also, once you have that information, all the power of via analytics come into play, all the things you see from the platform, the capabilities, being able to deploy these models into the environment in conjunction with a business workflow is what solutions

>> is value.And you talked about the horizontal business process. How do you think about, or how are your customers thinking about them changing their processes as a result of AI and the GenAI awakening? Are you seeing a lot of movement there yet or expecting to see movement? And how would that affect your horizontal strategy?

John Boyd

>> Yeah, so I think there's a lot of interest. I think people are still trying to figure out how to get value. I mean, we can talk in theory about all the things you can do in generative AI, but when you're running a business, they want to know what's the bottom line and how much money is this going to save me or how much money it's going to make me? So from that perspective, we are looking at generative AI from that lens, not of just having generative AI for that purpose, but where we can put it in the most critical aspects of the business process to help them to be more profitable.

>> So right now it's very tactical?

>> Yes.

>> I mean, while we can opine about how things are going to change down in the future today, it's like show me the money.

John Boyd

>> Yes, correct. I mean, my philosophy with generative AI right now is you're seeing a lot of use cases in terms of querying data. That's your first step. And then the API, calling APIs based on that API orchestration to execute a business process. And I'm excited to see Bryan Harris's hot take and my hot take for generative AIs. Everyone's talking about prompts, but I think we're going to get to a point where it's prompt-less generative AI. Where it's working in the background and thinking about looking at the data and saying, "Hey, this is what you should be doing. Here are my recommendations. Which of course of actions do you want to take?" I'm trying to, from a product perspective, get ahead of the puck because I think that's where we're going to go, but we're setting up all the things to-I'm

>> really glad you brought that up because I was saying to Bryan, even though everyone's going to Gaga, including myself over the models, I said, the saved prompts is a really indicator where this is going. That is essentially the beginning of where you're going, where okay, when you get something that works, you save it and you use it. And then there'll be more of that. And what's going to happen is AI will figure out quickly what the real prompt is trying to ask based upon the market basket of prompts it went through. Almost like Dr. Goodnight talking about compiler versus interpreter. You're compiling all the LLM prompts and saying, hey, this is the ones that are going to be runtime ready. So LLM or foundation model ready-AI is going to come. That's like a compiler conversation or an interpreter. I mean, it makes things better. What's your reaction to that?

John Boyd

>> Yeah, yeah. I mean really everything about this is just learning on top of each other, and that's what we're all about with this, just building on top of it. And if you can have that where it's a prompt-less AI and where it's sitting in your business workflow where it's clearly telling you when something's successful and when something's not, you can learn from that to improve the recommendations going forward and thus improve your business operations exponentially as you move forward.

>> Talk about the couple variables we want to kick your thoughts on. So all the buzz has been, oh, training and inference. Okay, you got to train model. Sometimes you don't because already trained it's raw data, it's pure model. Inference gives the impression something's happening there, we're getting some logic and reasoning, and you say that going on. Then you got the prompt response, assume prompt-less, I agree with that. Assume that happens. If that happens, then you're going to have a lot of reasoning going on, and then a lot of reinforced learning happening on the sides. Okay, great. Now the question, okay, if I'm going to have all this reasoning, how do I change my application knowing that it's scoped end to end, to manage the best reasoning, best workload to performance hardware? How do I match what I need at any given time to be elastic-like? So, okay, I'm going to do a prompt that's prompt-less. That's a lot of IO in my mind. Then you say, well, I'm need to think about something. I might shift that workload prompt to a different cluster or a different system. This is going to require orchestrations and require scheduling. It sounds like an operating system to me.

John Boyd

>> Yeah. Yeah.

>> What's your reaction to that?

John Boyd

>> I mean, it is an interesting thought because I mean it is because basically trying to mimic human behavior and what we're thinking. So it is like operating, it's basically a human operating system or mimicking one, right? That's what we're getting to. Now, that's what excited about me with the solutions and the possibilities here, because one of the things that we have is solutions across these horizontals, across multiple disciplines. So to get the best answer, you're going to want something that connects something on for example, looking at your sales and how does that impact my cashflow downstream? And having an interconnected horizontal suite of solutions , is going to give our customers the best decision-making advantage possible because they're going to know the bottom line and how it ends.

>> And you're going to need data and you need performance. If you don't have the data, there's a lot of blind spots. Dave calls it Swiss cheese. There's holes in the what could be looked at, and that's really where the hallucinations come in that everyone's seeing on the mainstream. So the question is, how do you make all that data available? I mean, one, it's just naturally available. It's like, hey, I'm addressable. How do you guys see that with SAS? How do you guys think about, do you make it available? Is it in a self-contained system?

John Boyd

>> Yeah, I mean, from a data perspective, again, going back to reducing the time to value and hydrating with the big systems out there. Where all this data is, where are we running businesses on? We're running on ERPs, right? So connecting to those ERPs and getting that business information into our solutions, hydrating it, so we're taking their transactional information and putting into an enterprise analytic platform and gaining insights across our entire business.

>> What are you seeing in terms of adoption? And this is GenAI-specific data, but in the recent survey, about 1800 IT decision makers with our partner ETR. It was astounding. About 18% of the respondents said they're not doing GenAI. And so I went out and poking around a little bit and trying to find out why, and people said, "Well, first of all, it's moving too fast. It's really hard to predict. And we don't trust it." We're in a regulated industry, for example, and so we're going to sit back like the Bubba Gump shrimp, we're going to let the hurricane wash away, and then we're going to come in and double down on-

>> Forest Gump reference.

>> What?

>> That's a Forrest Gump reference. Nice.

>> Yeah, yeah, yeah, exactly. So right. You know what I'm talking about? That scene where they just wait out

>> for everybody to get washed out.. They monopolize the shrimp business and all the boats were parked.

>> They let everybody else make the mistakes. Those stats hold water. The other thing we see is that over 40% of the customers say they're stealing from other budgets to fund AI. It's not like the macro is just everybody's writing checks. There's a balance there with the two year T bill at 5%. Okay. Does that resonate with you or align with what you're seeing? Are there specific industries that are more aggressive or less aggressive because they're maybe not as regulated? What are you seeing?

John Boyd

>> Yeah, I think it does resonate. I think it's probably people going through the learning curve at GenAI. So everyone got excited, first of all, right? And wanted to test out, and they saw all the hallucinations upfront. And we've heard this from SAS before. It's like really, it's a conversational interface at this point in time. You still need other AI and business process to give you what the real answer is behind it. So when they realize that level of work is still needed to it, then they're starting to shy away a little bit, especially in regulated industries and things like that. I think that's where SAS solutions come into play, because we can do that for them, alleviate the burden and that lift for them so that they can be turnkey solutions that come in and get that information and the results that they want without having to invest all the intellectual manpower internally within their own company to go develop and build it themselves.

>> John, talk about the strategy of SAS, vis a vis other alternatives. The customers that you have, and they deal with everyone else too. You got a lot of ecosystem parties. I saw the big logos up on the stage on the screen. Some people want to have a horizontal play and some people have a vertical play depending on what you have. Some have built in open data platforms, some want to have industry specific solutions. So you have a real diverse approach to industries. And then complicate that with the third dimension, which is the domain specific models and intellectual property. So okay, all this data's very specific to my domain, but I want horizontally scalable everything. I want to integrate APIs with a competitor or potentially a partner. All this is going on. So the customers have to deal with multi-vendor, multi-environment, now multi-modal models. What makes SAS differentiated in that market? Because that's the current situation.

John Boyd

>> Yeah, correct. And I think it's the differentiation is the optionality that we provide to say, you can buy whatever you want, you can build whatever you want, and it doesn't have to be ours, right? We are not afraid to say we're boxing people out. We are going to stand on the value of our solutions as they stand. We're going to create an open ecosystems where other people can integrate and they can choose where they want to plug in things, where they want to use our things, et cetera, and we will win them on the value that we-So

>> you're saying to customers, you have optionality, we're going to give you optionality via choice?

>> Yes.

>> We think we've got a good system. And where do you say your winning strategy is when you say, but that being said, we're better here.Exactly.Fill in the blank. We're better because of blank.

John Boyd

>> Yeah. We'll give you optionality, but I'll tell you, having an enterprise analytic solution that goes across your business workflow is going to give you insights that none of these niche players can give you because they're only looking at their very specific parts of the business process. And two, we're going to come in a much more value because we're going to give you one vendor, one price point to work with for all these solutions going across, versus having to negotiate and manage all those ecosystems, different infrastructure and internal IT staff that you have to manage all that.

>> So your key is across multiple workflows, across multiple environments and potentially companies?

>> Yes.

>> Go ahead, please. Sorry.

John Boyd

>> No, I mean, giving all the optionality and flexibility meeting our customers where they're at and where they need us.

>> How do I consume these industry-specific models? I can subscribe to them. Is it a consumption model? How do you price that? How do I get started and what is it going to cost me?

John Boyd

>> Yeah, I mean even there, I think we're going to have optionality. Some people might want those models just by itself and not even use them with the solutions, go for it, right? But all those models coming out, I want to build a way to integrate those into the solutions so that you can pick and choose. So think about having a solution that, let's take a credit rating. It's taking a risk of default and putting a model on there. And having something in our solution that says, hey, here's a graph that looks like if you had subscribed to this model, here's the information and the outcomes you would get out of that. But for you to have this, you need to subscribe to this model and purchase it over here. So again, even within the solution itself, giving the optionality to our customers whether they want that specific model, whether they want to build their own model and plug it in and get that functionality and the solution with their own development or getting it somewhere else.

>> John, it be great to have you on theCUBE because you're touching product management and solutions, which the cloud and AI is very Lego block-like, but now you've got end-to-end, put that all together, a lot of opportunities. I want to ask you about what's next, the future of SAS, because you talk about time to value.

>> Yes.

>> Okay. But there's also, you got the design time versus time, and then you've got the generative AI piece behind it. But what is the difference between design time and run time, and what's the distinction between those two factors?

John Boyd

>> Yeah, sure. So the design time is really getting the models trained up in terms of getting them functional and getting the outcome that you want. That takes a lot of horsepower. That's perfect for Viya and what that provides there. But once you have that, you don't need that same horsepower to execute it on a transactional basis to get the outcomes that you want. And that's what I'm talking about time. Solutions are really run time in nature. They are delivering business outcomes once the model's already produced and you have the AI capabilities that you need, so embedding them with that workflow, let's reduce that cost in terms of the infrastructure that you need, et cetera. So it lets us go downstream in the market and also pass down some of that savings to our customers.

>> Not to bring up run time again, but when you think about generative AI, it's generating, it's not static world that's pre-programmed. It's generating. It's at run time. Again, Dave, back to the operating system. At run time, things got to happen. They got to load. That's going to require new software. That's the key to success, right?

John Boyd

>> Yeah, correct. And there's a lot of pieces that are happening that you can see, and we're looking at all of it with SAS, right? We have to design run time to value generative AI, taking models, embedding in the solutions, and that's my role here, putting all these pieces together and delivering

>> .Final question real quick. What's the vibe like inside SAS right now? We got a billion dollars saying, hey, that's the North Star. I'm sure the founders are like, okay, we're all in. That's saying we're burning, we're going to transform right now. We heard that last event we were at. What's the vibe inside the folks in SAS right now and

John Boyd

>> customers?Yeah, I mean, I think they're excited. I mean, look around here and all the things that we're cranking out, and I mean, I thought the opening session was amazing. You see the passion and the people up there demoing Workbench and things like that, et cetera, and the capabilities that they're able to do in such a short time. We were only here, I think back in October, a few months ago, and all the things we're announcing now that weren't announced even a few months ago. Here we are.

>> It's nice to see fruit on the tree, right? Coming off that harvest.

>> Yes.

>> Not yet harvest. It's still blooming.

>> Yes, yes.

>> Too many metaphors. Sizzle on the steak, fruit on the tree. Next year when we're here talking next, what are we going to be talking about? What do you anticipate is going to be the top story and what you'll be talking about?

John Boyd

>> Yeah. I think you're going to be seeing a lot more solutions. Again, looking at the design time versus run time. So us going further down market with App Factory, you're going to see us getting more traction with the models that we're going to release and having those embedded within our solutions. And then also, keep on reducing that time to value. Looking at all those industry standards out there so that we can hydrate that end and get our customers up and running as fast as possible.

>> I mean, it's a great opportunity to accelerate the application process, the value, utility, governments, citizens, big enterprise, customers.

>> Yes.

>> Thanks for coming on.Yeah.

>> John

>> Boyd, Vice President Solutions Product Management, SAS. I'm John Furrier with theCUBE, with Dave Vellante, my co-host, will be right back with more coverage of theCUBE, the leader in high-tech coverage.

John Boyd | SAS

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>> Welcome back everyone to theCUBE's live coverage here in Las Vegas for SAS Innovate 2024. I'm John Furrier, your host of theCUBE with my co-host, Dave Vellante, head of CUBE Research. Our next guest is John Boyd, Vice President Solutions Product Management at SAS. He's got the keys to the kingdom on the product management and he's got the solutions. Great . Thanks for coming on theCUBE.

John Boyd

>> Oh, thanks for having me. Great

>> to .So you get the product management, you get the solutions. So right now we're in a mode where people are experimenting with AI rolling out production workloads here at the keynote, Bryan Harris and the team showed a customer with a production workload with an AI stack that only had really Amazon as an external company and then a lot of SAS and then customer data and stuff, and gen of AI and outcome, not a lot of other things. So it's a real enterprise workload.

John Boyd

>> Yes.

>> Big time.

>> Correct, correct. And that's what we're seeing the Viya platform is great for that model governance, right? To get the design time of that model of what you want to do to get the business outcome. And then from a solutions perspective, we're taking a lot of that investment and trying to figure out how do we that runtime environment to adopt that model within our solutions. And whether it's coming from what Udo's organization going to do with his announcement or it's a customer is going to build on their own, et cetera, and build the business workflow around that to run the people's businesses.

>> So you are involved in the billion dollar investment, well, not you, your team and SAS, you work for SAS. You guys announced a billion dollars to be invested into the SAS teams to build AI. Can you share some of the results? What are we seeing? I mean, obviously, we saw, we were at Explore last year, only six months ago. Now we're seeing fruit on the tree there. Can you share information, data and color commentary?

John Boyd

>> Sure. I mean there's a lot that's gone in with that. So the first thing was getting the solutions on Viya , as you heard from Bryan this morning. So that was a big investment to get in there. And now you're seeing more and more innovation coming out. Last year we released SAS Health and we released audiences for CI360. In the upcoming months, we're going to have energy forecasting we released and SAS Clinical Acceleration Repository. And then towards the back half of the year, we're also looking at SAS marketing decisioning. So the intelligent decisioning that you would've saw at the opening session today and how that's going to evolve for marketers. And then obviously, all the GNI work that's happening along with Udo's work as well.

>> Can you talk about the domain-specific IP that you have there specifically? Help us unpack that and understand where SAS has unique IP that contributing to these solutions.

John Boyd

>> Yes. Well, it's an interesting thing, right? If you look at the market out there, you see a lot of people that are in the data and analytics space or people that are vendors in specific industries that are leveraging data and AI. And I think that's where our solutions make us unique in SAS because we bring both to the table. And why that's important is because when customers out there looking to simplify their ecosystem and reduce vendor costs, et cetera, we're one of the few options that can provide that both flavor for a build versus buy for them. And how we've done it is have that domain-specific IP that's gone deep within that industry. So we're solving for that industry problem. But as we've done that, customers have trusted us say, "Hey, what about this other business process that is an industry that's more horizontal related?" So it's created what I call woven product architecture between the vertical industry solutions we're doing and the horizontal business processes we're also getting into as well.

>> What are the salient aspects of that industry solution? I mean, there's data. Where's the data come from? There's the industry specific IP. What other components are there to make that package consumable?

John Boyd

>> Yeah, sure. So you have the data and one of the things I'm pushing for in our solutions is really reducing our time to value. So having that data in a format for our product, but looking at industry standards out there to hydrate that, so it makes it an easier lift for the customer, not have that ETL burden that a lot of people have. And then also, once you have that information, all the power of via analytics come into play, all the things you see from the platform, the capabilities, being able to deploy these models into the environment in conjunction with a business workflow is what solutions

>> is value.And you talked about the horizontal business process. How do you think about, or how are your customers thinking about them changing their processes as a result of AI and the GenAI awakening? Are you seeing a lot of movement there yet or expecting to see movement? And how would that affect your horizontal strategy?

John Boyd

>> Yeah, so I think there's a lot of interest. I think people are still trying to figure out how to get value. I mean, we can talk in theory about all the things you can do in generative AI, but when you're running a business, they want to know what's the bottom line and how much money is this going to save me or how much money it's going to make me? So from that perspective, we are looking at generative AI from that lens, not of just having generative AI for that purpose, but where we can put it in the most critical aspects of the business process to help them to be more profitable.

>> So right now it's very tactical?

>> Yes.

>> I mean, while we can opine about how things are going to change down in the future today, it's like show me the money.

John Boyd

>> Yes, correct. I mean, my philosophy with generative AI right now is you're seeing a lot of use cases in terms of querying data. That's your first step. And then the API, calling APIs based on that API orchestration to execute a business process. And I'm excited to see Bryan Harris's hot take and my hot take for generative AIs. Everyone's talking about prompts, but I think we're going to get to a point where it's prompt-less generative AI. Where it's working in the background and thinking about looking at the data and saying, "Hey, this is what you should be doing. Here are my recommendations. Which of course of actions do you want to take?" I'm trying to, from a product perspective, get ahead of the puck because I think that's where we're going to go, but we're setting up all the things to-I'm

>> really glad you brought that up because I was saying to Bryan, even though everyone's going to Gaga, including myself over the models, I said, the saved prompts is a really indicator where this is going. That is essentially the beginning of where you're going, where okay, when you get something that works, you save it and you use it. And then there'll be more of that. And what's going to happen is AI will figure out quickly what the real prompt is trying to ask based upon the market basket of prompts it went through. Almost like Dr. Goodnight talking about compiler versus interpreter. You're compiling all the LLM prompts and saying, hey, this is the ones that are going to be runtime ready. So LLM or foundation model ready-AI is going to come. That's like a compiler conversation or an interpreter. I mean, it makes things better. What's your reaction to that?

John Boyd

>> Yeah, yeah. I mean really everything about this is just learning on top of each other, and that's what we're all about with this, just building on top of it. And if you can have that where it's a prompt-less AI and where it's sitting in your business workflow where it's clearly telling you when something's successful and when something's not, you can learn from that to improve the recommendations going forward and thus improve your business operations exponentially as you move forward.

>> Talk about the couple variables we want to kick your thoughts on. So all the buzz has been, oh, training and inference. Okay, you got to train model. Sometimes you don't because already trained it's raw data, it's pure model. Inference gives the impression something's happening there, we're getting some logic and reasoning, and you say that going on. Then you got the prompt response, assume prompt-less, I agree with that. Assume that happens. If that happens, then you're going to have a lot of reasoning going on, and then a lot of reinforced learning happening on the sides. Okay, great. Now the question, okay, if I'm going to have all this reasoning, how do I change my application knowing that it's scoped end to end, to manage the best reasoning, best workload to performance hardware? How do I match what I need at any given time to be elastic-like? So, okay, I'm going to do a prompt that's prompt-less. That's a lot of IO in my mind. Then you say, well, I'm need to think about something. I might shift that workload prompt to a different cluster or a different system. This is going to require orchestrations and require scheduling. It sounds like an operating system to me.

John Boyd

>> Yeah. Yeah.

>> What's your reaction to that?

John Boyd

>> I mean, it is an interesting thought because I mean it is because basically trying to mimic human behavior and what we're thinking. So it is like operating, it's basically a human operating system or mimicking one, right? That's what we're getting to. Now, that's what excited about me with the solutions and the possibilities here, because one of the things that we have is solutions across these horizontals, across multiple disciplines. So to get the best answer, you're going to want something that connects something on for example, looking at your sales and how does that impact my cashflow downstream? And having an interconnected horizontal suite of solutions , is going to give our customers the best decision-making advantage possible because they're going to know the bottom line and how it ends.

>> And you're going to need data and you need performance. If you don't have the data, there's a lot of blind spots. Dave calls it Swiss cheese. There's holes in the what could be looked at, and that's really where the hallucinations come in that everyone's seeing on the mainstream. So the question is, how do you make all that data available? I mean, one, it's just naturally available. It's like, hey, I'm addressable. How do you guys see that with SAS? How do you guys think about, do you make it available? Is it in a self-contained system?

John Boyd

>> Yeah, I mean, from a data perspective, again, going back to reducing the time to value and hydrating with the big systems out there. Where all this data is, where are we running businesses on? We're running on ERPs, right? So connecting to those ERPs and getting that business information into our solutions, hydrating it, so we're taking their transactional information and putting into an enterprise analytic platform and gaining insights across our entire business.

>> What are you seeing in terms of adoption? And this is GenAI-specific data, but in the recent survey, about 1800 IT decision makers with our partner ETR. It was astounding. About 18% of the respondents said they're not doing GenAI. And so I went out and poking around a little bit and trying to find out why, and people said, "Well, first of all, it's moving too fast. It's really hard to predict. And we don't trust it." We're in a regulated industry, for example, and so we're going to sit back like the Bubba Gump shrimp, we're going to let the hurricane wash away, and then we're going to come in and double down on-

>> Forest Gump reference.

>> What?

>> That's a Forrest Gump reference. Nice.

>> Yeah, yeah, yeah, exactly. So right. You know what I'm talking about? That scene where they just wait out

>> for everybody to get washed out.. They monopolize the shrimp business and all the boats were parked.

>> They let everybody else make the mistakes. Those stats hold water. The other thing we see is that over 40% of the customers say they're stealing from other budgets to fund AI. It's not like the macro is just everybody's writing checks. There's a balance there with the two year T bill at 5%. Okay. Does that resonate with you or align with what you're seeing? Are there specific industries that are more aggressive or less aggressive because they're maybe not as regulated? What are you seeing?

John Boyd

>> Yeah, I think it does resonate. I think it's probably people going through the learning curve at GenAI. So everyone got excited, first of all, right? And wanted to test out, and they saw all the hallucinations upfront. And we've heard this from SAS before. It's like really, it's a conversational interface at this point in time. You still need other AI and business process to give you what the real answer is behind it. So when they realize that level of work is still needed to it, then they're starting to shy away a little bit, especially in regulated industries and things like that. I think that's where SAS solutions come into play, because we can do that for them, alleviate the burden and that lift for them so that they can be turnkey solutions that come in and get that information and the results that they want without having to invest all the intellectual manpower internally within their own company to go develop and build it themselves.

>> John, talk about the strategy of SAS, vis a vis other alternatives. The customers that you have, and they deal with everyone else too. You got a lot of ecosystem parties. I saw the big logos up on the stage on the screen. Some people want to have a horizontal play and some people have a vertical play depending on what you have. Some have built in open data platforms, some want to have industry specific solutions. So you have a real diverse approach to industries. And then complicate that with the third dimension, which is the domain specific models and intellectual property. So okay, all this data's very specific to my domain, but I want horizontally scalable everything. I want to integrate APIs with a competitor or potentially a partner. All this is going on. So the customers have to deal with multi-vendor, multi-environment, now multi-modal models. What makes SAS differentiated in that market? Because that's the current situation.

John Boyd

>> Yeah, correct. And I think it's the differentiation is the optionality that we provide to say, you can buy whatever you want, you can build whatever you want, and it doesn't have to be ours, right? We are not afraid to say we're boxing people out. We are going to stand on the value of our solutions as they stand. We're going to create an open ecosystems where other people can integrate and they can choose where they want to plug in things, where they want to use our things, et cetera, and we will win them on the value that we-So

>> you're saying to customers, you have optionality, we're going to give you optionality via choice?

>> Yes.

>> We think we've got a good system. And where do you say your winning strategy is when you say, but that being said, we're better here.Exactly.Fill in the blank. We're better because of blank.

John Boyd

>> Yeah. We'll give you optionality, but I'll tell you, having an enterprise analytic solution that goes across your business workflow is going to give you insights that none of these niche players can give you because they're only looking at their very specific parts of the business process. And two, we're going to come in a much more value because we're going to give you one vendor, one price point to work with for all these solutions going across, versus having to negotiate and manage all those ecosystems, different infrastructure and internal IT staff that you have to manage all that.

>> So your key is across multiple workflows, across multiple environments and potentially companies?

>> Yes.

>> Go ahead, please. Sorry.

John Boyd

>> No, I mean, giving all the optionality and flexibility meeting our customers where they're at and where they need us.

>> How do I consume these industry-specific models? I can subscribe to them. Is it a consumption model? How do you price that? How do I get started and what is it going to cost me?

John Boyd

>> Yeah, I mean even there, I think we're going to have optionality. Some people might want those models just by itself and not even use them with the solutions, go for it, right? But all those models coming out, I want to build a way to integrate those into the solutions so that you can pick and choose. So think about having a solution that, let's take a credit rating. It's taking a risk of default and putting a model on there. And having something in our solution that says, hey, here's a graph that looks like if you had subscribed to this model, here's the information and the outcomes you would get out of that. But for you to have this, you need to subscribe to this model and purchase it over here. So again, even within the solution itself, giving the optionality to our customers whether they want that specific model, whether they want to build their own model and plug it in and get that functionality and the solution with their own development or getting it somewhere else.

>> John, it be great to have you on theCUBE because you're touching product management and solutions, which the cloud and AI is very Lego block-like, but now you've got end-to-end, put that all together, a lot of opportunities. I want to ask you about what's next, the future of SAS, because you talk about time to value.

>> Yes.

>> Okay. But there's also, you got the design time versus time, and then you've got the generative AI piece behind it. But what is the difference between design time and run time, and what's the distinction between those two factors?

John Boyd

>> Yeah, sure. So the design time is really getting the models trained up in terms of getting them functional and getting the outcome that you want. That takes a lot of horsepower. That's perfect for Viya and what that provides there. But once you have that, you don't need that same horsepower to execute it on a transactional basis to get the outcomes that you want. And that's what I'm talking about time. Solutions are really run time in nature. They are delivering business outcomes once the model's already produced and you have the AI capabilities that you need, so embedding them with that workflow, let's reduce that cost in terms of the infrastructure that you need, et cetera. So it lets us go downstream in the market and also pass down some of that savings to our customers.

>> Not to bring up run time again, but when you think about generative AI, it's generating, it's not static world that's pre-programmed. It's generating. It's at run time. Again, Dave, back to the operating system. At run time, things got to happen. They got to load. That's going to require new software. That's the key to success, right?

John Boyd

>> Yeah, correct. And there's a lot of pieces that are happening that you can see, and we're looking at all of it with SAS, right? We have to design run time to value generative AI, taking models, embedding in the solutions, and that's my role here, putting all these pieces together and delivering

>> .Final question real quick. What's the vibe like inside SAS right now? We got a billion dollars saying, hey, that's the North Star. I'm sure the founders are like, okay, we're all in. That's saying we're burning, we're going to transform right now. We heard that last event we were at. What's the vibe inside the folks in SAS right now and

John Boyd

>> customers?Yeah, I mean, I think they're excited. I mean, look around here and all the things that we're cranking out, and I mean, I thought the opening session was amazing. You see the passion and the people up there demoing Workbench and things like that, et cetera, and the capabilities that they're able to do in such a short time. We were only here, I think back in October, a few months ago, and all the things we're announcing now that weren't announced even a few months ago. Here we are.

>> It's nice to see fruit on the tree, right? Coming off that harvest.

>> Yes.

>> Not yet harvest. It's still blooming.

>> Yes, yes.

>> Too many metaphors. Sizzle on the steak, fruit on the tree. Next year when we're here talking next, what are we going to be talking about? What do you anticipate is going to be the top story and what you'll be talking about?

John Boyd

>> Yeah. I think you're going to be seeing a lot more solutions. Again, looking at the design time versus run time. So us going further down market with App Factory, you're going to see us getting more traction with the models that we're going to release and having those embedded within our solutions. And then also, keep on reducing that time to value. Looking at all those industry standards out there so that we can hydrate that end and get our customers up and running as fast as possible.

>> I mean, it's a great opportunity to accelerate the application process, the value, utility, governments, citizens, big enterprise, customers.

>> Yes.

>> Thanks for coming on.Yeah.

>> John

>> Boyd, Vice President Solutions Product Management, SAS. I'm John Furrier with theCUBE, with Dave Vellante, my co-host, will be right back with more coverage of theCUBE, the leader in high-tech coverage.