 Hello everyone, welcome to theCUBE's live coverage here in Las Vegas for SAS Explore. Hashtag Explore SAS is on Twitter, check us out, we're there, theCUBE coverage. I'm John Furrier, Dave Vellante. It's hot outside, 117 degrees in the desert, Dave, but it's also cool in here with the AC, but it's hot with the announcements on the keynote. Keynote analysis, we had the CTO up there, Brian Harris, we had the head of Partners Analytics, Udu Slavo who was on there, partnerships all about the big news. SAS keynote was, I mean, they played, made fun of General AI as a hype cycle, but brought out the goods when it came to the demos. I thought it was an excellent keynote. I liked the practical vibe, right? You know, the wave is here, we acknowledge it, they see it, they understand it, and then said, we kind of get the reality of the marketplace right now. Short-terms hyped up, but long-term it's the real deal. CTO Brian Harris laid out the vision of SAS, and it's pretty good, it's got work bench for developers and data scientists, and the new app factory for developers and analysts and other business lines of users. Interesting, developers on both sides of the equation, so this is more proof to our thesis that the rise of the data developers here, that means that software development practices will emerge and be democratized, not just data science being democratized, Dave. We're seeing signs now that the development cycle, they have low code, no code, yes code. I like that play on words, and backing it up with products. So, you know, SAS Viva, that's their more productive AI analytics platform that they're pushing out, but it's a platform opportunity, and as they say, the quickest way from a billion points of data to a point of view is SAS. They have a point of view on AI and not bad. Yeah, they've been in AI for a long time. I mean, SAS is a company that's been around for a long, long time, and they span six decades. The company was founded in 1976, so I mean, you're talking about almost a 50-year-old company, but the reason why SAS is, first of all, it's an amazing American story. I mean, they're a private company. They've been threatening to do an IPO now for years, but they've remained private, and they're about a $3.2 billion company. There's not a ton of data out there on their financials, but they're obviously very successful, I mean, how many multi-billion-dollar software companies are around that have been around that long and have stayed independent, not let alone private, and they compete in a lot of different markets. I would say their primary market is that BI analytics market, which is probably a $30 billion market. It's probably going to grow to $50, $60 billion by the end of the decade, so they probably have, let's call that 10% of that market, so they got a lot of upset, but they also compete in MARTEC, they compete in artificial intelligence, they've done AI for a long, long time, and as we were talking, John, in the media room, they got really deep industry presence, so financial services, healthcare, they do a lot of fraud, they do a lot of KYC, working a lot of banking industries, a lot of manufacturing, they've got an IoT story, so that domain expertise will serve them well. Now, as you can imagine, a company that was founded in the 70s and sort of rose up in the 80s, no clouds, so they've had to shift to a cloud-native platform, what they call VIA, I don't know what percentage, sorry, VIA, I don't know what percentage of the, right, I call it VIA, you call it VIA. I don't know what percentage of the, no, it's VIA. No, I mean, it's VIA, it is VIA, I know it is, like a VIA above it, but I don't know what percentage of revenues it is, but it's the fastest growing part of their business, probably growing at 20, 25% per year, but like any software company that used to do sort of perpetual licenses, it's gone through that transition, it's doing so as a private company, so it's not on the 90-day shot clock, and I think, you know, the word is, they wanted to do an IPO in 2024, we'll see how the markets are, we'll see what the action is. Let's get into the keynote analysis, so the tagline for the conference is, code, collaborate, create, that's kind of bumper-stickered everywhere. The big news was the announcement of the Workbench product available in 2024. I couldn't get out, whether it's Q1 or Q4, end of year, I'll have to get the- No, it's early 2024. Okay, Q1, I think that was what I heard. So they got the Workbench for VIA, connects existing developments across multiple enterprises, each one get their own development environment, but that's a way to kind of create a kind of a sandbox opportunity for anyone who wants to innovate, so that to me is a huge deal for the data developer and for democratizing new applications. That's going to speed up a lot more backend wrangling and also connections and hard to connect enterprises. The other news was the App Factory, same thing on 2024, it was announced, not shipping, they got demos. This to me is where I think it's a magical product. The App Factory points to the trend we're seeing with things like, for other companies like Bedrock with AWS, these areas where you can bring the developer and bring things together in an environment to create AI native apps. And so I think with App Factory, you're going to see the explosion and a Cambrian explosion of apps that are going to have AI kind of embedded in it, either as a bolt on initially and then natively after the fact. So the combination of the Workbench, get the developers and the data scientists cranking out the connections and managing data under this idea of model management is another big point they made. That brings basically a platform opportunity across the enterprise and with AI really doing well in verticals, because in these vertical industries, the data is very valuable. So we've been waiting for this, how long we've talked on theCUBE? Over seven, eight years we've been saying that the verticals is where the specialism is, that's where the data is, that's where the unique workflows are. And we heard on the media Q and A after from Brian Harris, is that the best use of AI right now is where there's no ambiguity. So regulated markets like fintech, healthcare, this metric outcomes that you could actually get to, that's the low hanging fruit. And I think there's a lot of low hanging fruit around with SaaS. So the vertical specialism of the data, the value of that data, the value of the unique workflows plays into our power law analysis that we put out on theCUBE a couple of weeks ago. So models integrate with each other, developers cleaning up the back end, app factory for the front end apps. I got to say I'm pretty impressed with the platform. When I was heading over here, prepping for this an article found me, it was about Tesla's neural network and the way somebody described it was like, it's like chat GPT for self-driving cars. And I went, oh, that's a disaster waiting to happen. And so one of the big areas of emphasis for a company like SaaS is really to have that governed AI, that governed LLM. Now we've heard that a lot, but we haven't seen a lot of meat on the bone thus far. But these guys showed, they spent a lot of time, I would say at least half of the keynote was demoing. You know, real product. And so Brian Harris, you mentioned him, he had this sort of three-legged stool, synthetic data generation, digital twins, which I know you're not crazy about that term, but it has meaning, the digital representation of your business and LLMs, bringing those three together in a way to really lay the foundation for the future of data apps. I actually think they're, I'm actually more excited than they conveyed. I think they're underselling sort of that vision, but I think they're a conservative company from that standpoint. But the whole idea, you mentioned no code, low code and yes code. So they run the full gamut, they can run cloud, they can run on-prem, they're running at the edge. So they have a lot of optionality for customers. And John, you know, you and I and Rob Stretch, they created that power model of LLMs where you've got, on the vertical axis, you've got model size. So leave that to Amazon and Google Vertex and the big AI models, but that long tail is the model specificity and that's where SaaS is going to play within industry and within specific use cases. And they actually demonstrated, you know, some serious AI, some conversational AI, helping, and then the other piece that really resonated with me was Brian Harris was talking about creators and consumers. So the creators of code are going to use co-pilots, essentially, to develop code faster. And then the consumers as well are going to use AI to do things that they would normally have to go back to an expert for. I mean, I got to say, there's a lot in the keynote that got me excited. One was a little nuanced point. I don't know if you caught this, but there was a section on there where the VP of R&D came out. So I'm a technical jet and he showed that the no code will get you some product market fit, but you end up doing a rewrite anyway. So I found that, you know, sprinkling in the reality of coding, the coding reality of 2023 was a really notable point because you can get high on the AI, you can get drunk on AI all the excitement. But at the end of the day, the reality is you can get to validation, but you got to be mindful of the technical debt. And that's where the engineering and the coding comes in. I think that's a super important point to align with the customers saying, hey, it's a frothy market. Go out and experiment, but get in the reality. The other thing that's interesting about, that you mentioned about them sugar-coding that the more value is, I don't think it's in the company's best interest to drop everything. Brian in our one-on-one, we've grabbed them in the hallway after the media announcements briefing. You kind of see he's got more to say. We're going to see when he comes on the queue, but the point is, if you're a SaaS customer, you've been using the product, you're a loyal customer, you've been using the tools. It's almost waiting for that AI moment to come because AI is a tailwind and an accelerant for SaaS because even though they got some legacy and history with their customer base, it actually changes the game of the product capabilities which actually makes them a net winner in the game because you can take the existing stuff and abstract away the complexities with AI and some of these co-pilot-like tools augmenting the human. And again, the human is the value and that's where AI will shine. So I expect SaaS to light up their verticals with AI and then create an interconnect layer around the data. And they talk about deals with Snowflake. So again, interesting example of a big incumbent company doing extremely well with software, perfectly positioned for verticals AI data value. Just very interesting to see how they handle that. And I'm looking forward to digging in more because they could crack the code on this. So to your point on that technical debt, I think you're right. The reality is that AI might get you 75, 80% of the way there in terms of the code development. You know, it'll get you to MVP. It'll get you beyond that. But when you have to scale, that's where that last mile, that last 20% is going to be very, very difficult. And so the way I see it, John, is that AI is going to help get you to the 80% faster and it's going to free up time for the top developers to go focus on that last 20%. And that's where, if you have real AI, that these guys clearly do. They've been working on this forever. They've obviously come out of a university and so they've got a lot of really strong relationships. They're right at the heart in North Carolina. You're going down there soon. So your daughter's parents weekend. They're at the heart of that technical innovation on the mid-Atlantic. And so that has seeped into their product. Well, now I've got an excuse to go to North Carolina and visit Sass and get more briefings in person. So I can come visit my daughter at UNC. Now, the other thing I wanted to mention, so when you kind of hinted that the hallway grab with Brian Harris, he's got a lot more to say and he didn't want to lay it out too much. But what we were sort of pushing him on was this idea of people, places and things, kind of the Uber for the enterprises in real time where you've got a digital representation of your business, data coming in, action being taken in real time. More than just sort of metrics on top of a database, but actually the combination of synthetic data generation, digital twins and LLMs actually taking action for businesses. That's a big vision. They didn't lay that out, but they're laying the foundation to lay that out. I mean, how long have we been hearing over 10 years, Dave, we've been doing theCUBE. We've been hearing about democratization of data for a decade. It's finally here. And I think it's just more there too, the connecting the data sets, some notable quotes. I love the quote from Udu who said, taking computer science out of data science. That was a really notable tagline. Another notable announcement they made earlier in the year, a billion dollar investment in industries. That again validates our thesis of vertical specialization and the valuable workflows of the data. And then that's there. And then I noticed that the problems that they're solving, he mentioned a few of them, taking down the barriers to interact with data inside the enterprise, great problem to solve. The conversational AI was sprinkled in there. And another thing that was brought up, I think it's really important, we're going to hear about in the first segment, is making sure that data is good and that it's not being abused. So data ethics comes into play, Dave. So, you know, data for good, data for bad. I mean, if you look at what happened with Facebook and the elections and the social network, we saw how data was weaponized. Okay, so this very clear mandate in the public opinion right now that AI could be bad for people. I'm obviously not on that side of the camp. You know, I'm very bullish on AI. We drink AI for breakfast, lunch, and dinner in the cube these days. So I think it's going to be positive, but you got to have it at be explainable. And I think that's going to be a big theme out of SAS, a company that is conservative but practical, but not shy to lean into the trends. I mean, they're on all the marks, Dave. They're hitting all the right notes. Okay, they get the value of the data. They get the value of making sure it's going to be explainable. They get the value of democratization for users. And then they really get the developer angle. In fact, the persona of developers on both sides of the solutions. And that's really important that they actually recognize that developers. It used to be developers were like a third persona, a data science, business analyst, not anymore, Dave. Cloud native scale and with AI, you got it. So to me, I'm going to look at how well they execute on that vision, how they develop these verticals, and more importantly, can they attract an ecosystem? Well, and to that point, if they're going to see through that vision, because essentially they're a semantic layer to take all those disparate data elements and make them coherent. And if they're going to see through that vision, so you mentioned a partnership with Snowflake, I want to learn more about what's that all about. But I would say to see that vision through, they're also going to have to partner with other data types, data storage, like knowledge graphs, where you have, you know, the expressiveness, greater expressiveness, but the simplicity of query access, like of SQL. And so that ecosystem, and we see the ecosystem around here, a lot of industry stuff, a lot of optimization of analytics. And so we're definitely excited to see more there. And I hope they do an IPO. Our IPO is tomorrow. So that's going to hopefully open up the IPO, maybe not the floodgates, but at least open up the valves a little bit so we can get stuff flowing. They should be at a $3.2 billion, $3.5 billion company. They should have a $18 to $20 billion market cap in today's market, John. So that's a pretty sizable company to go from private to public after 47 years. Be pretty amazing story. I mean, you might want to stay private, thinking that much bank, you know, keep it private. You know, they do a SaaS championship golf tournament in the Preston Wood Country Club. So, you know, they got enough dough to throw around these beautiful corporate events. I mean, everyone loves to go to the golf events, Dave. Maybe we can get into the pro-am. Well, and their facility down there is it's actually quite remarkable. But I mean, I think they've resisted going public for a long, long time, but they're, you know, they're co-founder, Jim Goodnight, Dr. Goodnight, you know, probably wants to see this through, hand the reins over, you know, I think he's, I think he's in his 80s. And so he's, he's beyond the back nine. I mean, he's, you know, basically ready to, you know, hand over the, to the next generation. And I think- He's finishing up 18 as we speak right now? Yeah, I think, yeah, I think he's at the 19th hole, but I think- 19th hole for him. I think the goal is to have a generational company and to pass that on. Well, look, I got to tell you right now, all legacy aside, what they put forth in the keynote here is a vision and a roadmap with AI that makes sense. It's got a platform flair to it. It's obviously, they understand the developer market, they understand the data side of it, they understand kind of where the puck is going, if you will. And, you know, I, again, I think it's going to be killer. Yeah, John. So give us your last, last take, and then we got to, we got to wrap shortly. Again, I'll- I think they can automate the ability to stand up. We work benches and the app factory so that the faster they can develop app native develop applications, the more they can connect that enterprise data and abstract the way that complex mess that's the Byzantine data models inside an enterprise and bring that to work bench for developers and scientists and making an app factory if we can pump out native apps, that's a winning hand. I hope they can make progress there because I like the formula. Yeah, and to me, you know, if they are going to go public I love to learn more about their TAM expansion strategy. I think that definitely involves ecosystem. I think they're not going to be a data-based centric platform. That's not going to happen. They're going to be, you know, more analytic centric on top of those databases. So I want to see those partnerships and then expanding into, you know, it's going to be interesting to see what the objective is, John, with that app factory, whether or not they want to be essentially, you know, the app store for the enterprise, for data apps or they'll be a participant to really support those personas where they're strong, like the data science persona. Good job, Dave. Well, that's the keynote analysis. I'm John Furrier, Dave Vellante. You know, extracting the signal from the noise, you know, the vent coverage and continue to get whatever it takes to get that story go to silkenangle.com. We've got a great lineup coming up. We've got Brian Harris, Reggie Townsend, all the top executives, their customers, their partners, we'll have Microsoft coming on with Azure coming on as well to get that perspective of working with the hyperscalers. And stay tuned all day, wall-to-wall coverage. Stay with us and check us out on silkenangle.com and thecube.net where all the videos will be as soon as they're uploaded.