theCUBE + NYSE Wired: Marketing Leaders | Rajan Sheth, Hypergrowth

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Rajan Sheth

CMO

Together AI

play_circle_outline HyperGrowth advising AI companies on marketing and growth

play_circle_outline Shift in CMO role to focus on strategy and market presence

play_circle_outline Importance of finding unique levers for marketing success in AI industry

play_circle_outline Enhancing Marketing Strategies with AI-Driven Customer Engagement Optimization

play_circle_outline Leveraging domain expertise as a moat opportunity for startups

play_circle_outline The evolving role of marketers towards technical proficiency and problem-solving

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Rajan Sheth, Hypergrowth

Rajan Sheth

CMO Together AI

Rajan Sheth, general partner of HyperGrowth and chief marketing officer of Together AI, joins theCUBE at the CMO Leaders Summit to discuss the impact of Artificial Intelligence on marketing strategies. With extensive experience in AI, Sheth shares insights on driving user experience transformations across various business sectors. John Furrier of theCUBE leads the conversation, focusing on Sheth’s dual roles in shaping market accelerations for AI companies and overseeing growth dynamics at Together AI.

Key insights from the discussion highlight the eme... Read more

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What is the speaker's role at Together AI and what type of companies does HyperGrowth advise? add

What role have today's CMOs in AI taken on, and how has it evolved from traditional marketing functions? add

What are some strategies for a CMO to find long-term bets to invest in and scale effectively? add

What did she say about how businesses can optimize their customer relationships and marketing strategies using AI? add

What are some strategies for elevating researchers and founders within organizations to enhance their brand presence and create a sustainable competitive advantage? add

What problems can be solved through technical advancements in marketing that were previously considered too difficult or expensive to tackle, providing CMOs with new opportunities for problem-solving and transformation? add

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Rajan Sheth, Hypergrowth

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>> Hey, welcome to theCUBE here in Palo Alto, California. I'm John Furrier, host of theCUBE. We are here for the CMO Leaders Summit. We're talking to the top leaders in the industry around AI and how that's impacting the role of the CMO which actually drives all the user experience, of course. Business transformation is in all verticals, whether it's marketing, customer service, everything is being impacted on the user experience side. We're going to be covering it wall-to-wall for multiple days. Check out theCUBE.net and check out that site in partnership with the NYSC Wired Community, founded by Brian Baumann, and is an open community. So check it out. I've got Rajan Sheth here, who's the GP, general partner of HyperGrowth and CMO of Together AI. Rajan, thanks for coming on theCUBE. General partner of HyperGrowth... So you're doing two things. You got your hands as CMO, which is part of the program, but also you are investing, you've been involved in AI from the early days, a lot going on. So explain what you guys do on the HyperGrowth side, and your role at Together AI.

Rajan Sheth

>> Yeah, great. First of all, super excited to be here. Thanks for having me. So HyperGrowth, we advise AI companies across the stack. So I run an AI go-to-market accelerator as part of HyperGrowth, and we have companies across the stack, from infrastructure as well as agents, as well as applications, who we help on the marketing and the growth side. We partner with the founders and their marketing leaders to help them achieve hyper growth. And Together AI is part of our portfolio, along with some of the other companies like Deepgram, Composio and few application level companies. So my role at Together AI is I'm helping them as their CMO, building their entire team, market presence, growth, revenue side of it, partnering with the board, partnering with the executive team.

>> So there's two threads we're seeing in this market around CMOs. You're either kind of clean sheet of paper, reminds me of born-in-the-cloud days in the 2000s, in the mid-2000s when Amazon hit the scene, the folks who jumped on the cloud got all the benefits of not having to do the other stuff, provision the data center, and we've covered the cloud, since 15 years of theCUBE, and all those companies were startups and they were unknowns, and now they're huge brands, Dropbox, Airbnb, you name them all, they're there. And then the large enterprises came on. So that's one thread we're seeing in AI. And the other thread we're seeing in AI for CMO leaders is I've got all this stuff, I got a martech stack built 10 years ago, I've got inbound marketing, I got Salesforce, I have all this stuff, I got multiple databases. And so you're starting to see the technology kind of pop in both areas, and you're seeing examples like Glean who cracked the code on search, basically enterprise search. So okay, abstract away complexities. And then you got startups on this side like Cohere you used to work for. So there's really kind of two camps together. It's not like one versus the other. In the old days it was the cloud native, or born in the cloud, won everything. Of course we're born in the cloud when we started SiliconANGLE, we don't even have an IT department. I'm the admin, basically the IT admin on all emails. So that's a good thing. So now we're in this generation where you have the confluence of SaaS, cloud, and then now data, which has been an AI enterprise kind of thing, but it's stuck there. So again, what's your reaction to that? Because I think I see you doing a lot in camp one, but those camps have to sell into the enterprise. What's your take on all that?

Rajan Sheth

>> Yeah, I think the way I think about it is that there are two different areas. One is the value creators and the other one is value capture. In the last two years, what you have seen is a lot of value has been created by the infrastructure players, from the Coheres of the world, the open-source LLMs, companies who are building on top of open-source providing access to these application companies. Now in the last six months or so, now we are finally seeing the proliferation of adoption of these technologies into enterprises, and then it gives rise to a new stack of companies, and who are actually capturing the value at that level with enterprises, with new AI-native companies and businesses. So that's how I see two different camps. But at the same time, these two camps work so intertwined with each other, that in order for them to help these enterprises, they have to create the value and capture it. So that's how I see two different camps. Cohere is one infrastructure provider focused on the enterprise LLMs. Together AI is helping companies as their generative AI cloud, from everything from infrastructure to building applications. And then there are application companies like Glean that you mentioned who are actually providing productivity gains, accessing that internal data for enterprises, building those connectors. So that becomes an entire full stack, which actually now is driving a lot of value capture.

>> So as first of all, you wear two hats. One is on the HyperGrowth side as a general partner, obviously that's a startup side, getting in there, helping companies get some equity for that. Now on the CMO side, there's huge opportunities to use technology and data to one, be an unknown and springboard up to the top of the list, as well as existing enterprises can use AI. So what's your take on the opportunity for CMOs? Because there's a lot of business transformation. So there's two, you could either pivot, stop what you're doing and go in another direction, or change your trajectory of how you're doing marketing. So as a CMO that's watching or is out there, talk about your experiences as a CMO in your current role and how you're looking at it. Because again, you have a clean sheet of paper. You guys are looking at this kind of like born-in-the-AI-cloud if you will, or on-prem, because there's still a lot of on-prem activity. And then existing CMOs have all this legacy. What's the distinction for the folks that are on the enterprise side, how should they be thinking about the disruption opportunity? And then if you're going to be a startup, how do I get that escape velocity?

Rajan Sheth

>> Yeah, so there are two parts to it, right? One is how do you change the fundamental of how you approach growing an AI company? And the second is now how do you accelerate that using the tools and the technologies on the AI side? On the second one, I think the way the marketing organizations are being formed right now are going to be very different than a traditional SaaS or a cloud company that were before. For example, there are so many new AI-native tools right now. For example, you mentioned inbound and the marketing automation stack. Right now, I think workflow tools like Clay or Unifi are helping productivity gains between marketing and sales, which is you don't need now 10 to 15 SDRs or AEs. I think you can get to a couple of them and then you get the same level of productivity. Even on the marketing tech stack side, I think the team structure has changed overall. I don't have anyone who is now called a copywriter because now everything is done by AI. And production side of it, on the creative services side of it, I think there's going to be immense number of gains, especially as you think about scale. So those are on the tool side, how things are transformed, how the roles of marketing have changed. But if you think about how the fundamental role of a CMO or a leader has changed is the CMOs of AI companies are not those traditional typical B2B SaaS or typical SaaS CMOs. What they need to know right now is understand the product and the market really, really well. Because if you think about it, the B2B SaaS was very linear. You have a product, you find product market fit, you have sales and marketing grow, and then you repeat the cycle after the maturity. Now that all of this is things happening every couple of weeks or a couple of months, with newer innovation coming in, the newer technologies coming in. So understanding the marketing technology and being that strategist, partnering with the CEO and the board becomes really important for a CMO right now.

>> One of the things that you brought up there is this accelerated or compression of kind of cycle times. Speed wins. You're seeing speed become a big differentiator in the market for companies who could move fast-

Rajan Sheth

>> 100%.

>> ... and then scale up. So reduce the friction. Bill Tai was on theCUBE here on our last session. He had a great line about candor, reduce friction, repeat, and scale, hit that repeat. In that order, that's the order of operations. So getting back to the existing enterprise CMOs, if I'm a CMO out there, I used to provision a tech stack for marketing, martech. Now that would take time. That would be like a year. Get my leads, demand gen, understand journeys on the website. And now you've got tools like Perplexity, you have search, you have discoveries happening different in that organic base. Now this same team has to kind of change over. What do you look at when you see people, leaders out there in the CMO side or CXO side that recognize that, and what are they doing? Because again, they got the data. What's your view on that?

Rajan Sheth

>> I think a lot of them are starting to implement newer technologies as a wedge, right? They'll identify one or two new technologies that they would allow them to insert into that existing stack. And then right now I think there are multiple individual technology stacks who would solve part of your problem. But we haven't seen a disruption in AI, which is like a full-fledged platform that will replace your entire marketing automation stack, whether it's the Salesforces of the world, whether it's a HubSpots of the world that will replace that. So I haven't seen that come out yet. I know a couple of companies are building that. The platform is still being built. So right now you'll see a lot of Frankenstein systems right now when you have an existing stack and you try to insert some and -

>> Frankenstein meaning? Define what a Frankenstein system is for folks out there who doesn't heard that before because it's a great term.

Rajan Sheth

>> So Frankenstein basically means you are connecting n-number of technologies together to get that end output. Data flows between one system to other. You're building those integrations to get the outcome that you need and from the systems and unlock the data that you have. So right now there's no full platform that's coming up, it's being built, but I think you see the Salesforces of the world too are now starting to disrupt themselves with that.

>> Yeah, it's interesting. It's funny how you mentioned Frankenstein because there's always been this cliches, "Spaghetti code, Frankenstein," which is this big ugly monster, it's alive, but it's kind of dumb and awkward. And that's really kind of where we're coming into a lot of these efficiencies. Again, it's back to AI now, AI has cost... you can drive cost efficiencies, you mentioned that, in the cycles. But a lot of people say, "I don't really care about the money, I want to make money." So they optimize for revenue growth, and then there's like, "Okay, how do I sandbox?" We see three scenarios out there right now. "Okay, I want to drive cost reduction." That's easy. Go inside, reduce some of the BDRs or whatever the worst use case is. And then you have the, "Well, I want to do that, but I'm going to really optimized for productivity that drives top line revenue." And then you have the third area we're seeing, which is very narrow production window, meaning there's not a lot of room to get something into production. It could be a resilience bar, or it could be security, but there's tons of sandboxes. And that's sprawling. So you have sandbox sprawl. What's your reaction to that? Do you see the same kind of tracks?

Rajan Sheth

>> I see the tracks. I see less of number one. I don't think anyone right now is trying to reduce their cost. Everyone is going to grow their top line. They're willing to throw money at the problem if they can solve it. I think efficiency gains become a part of it, but unless there is a top line improvement, or top line gains with introducing some of these tools, I think it's very difficult to make a justification overall.

>> And the other thing I want to get your thoughts on, because I know you're on the side, I call the disruptor enabling side, which is a lot of the startups. It used to be you build a product, SaaS, put it in the cloud, you go to an enterprise or a customer base, if it's consumers a lot easier, they can just sign up and you get all kinds of growth hacks going on there. Now you have the enterprise, which was you target an enterprise, you meet their compliance, appsec review, you're in, and you scale up. You see things like Slack and whatnot, do all that. Now you're in an era where the enterprise themselves are building stacks. So now you're seeing a new dynamic where the startups have an opportunity to go to market into the enterprise. What's your take on that? Because now it's the stacks are coming together, so I don't need to jump on an IBM, or a Dell, or a big company, or Amazon to build on them to win. I can just integrate into their install base. I know it's very nuanced, but what's your go-to-market view on that? Because I see startups saying... When I was in New York, I mean a lot of the startups there, I mean the young guns under 30 who are, I call the young entrepreneurs, they're doing enterprise stuff. Why? Because the customers have needs, but they're also building. So you have this insertion stack kind of compatibility, I don't know what to call it, but the idea is it's not just, "We build it, do you want to buy it?" It's, "We have this. How do we integrate it," and what's your thoughts on that.

Rajan Sheth

>> Yeah, so I think that is most of the AI right now, especially because the AI tools right now cannot work independently within those enterprise systems. There are two parts to it. One is enterprises need more control. Enterprises need data security and privacy, but enterprises also have a lot of data that they need to unlock. So at the end of the day, your tools or your platforms that you're building integrate with AI, whether it's a codegen platform, whether it's an infrastructure platform, or a marketing tech platform, it's very difficult to work independently. You cannot just connect them and say that now it's going to help you immediately. So most of the AI that I'm seeing right now is implementation enterprises, is integrating with their existing stack or integrating with their existing data, or their existing work flow, so only then you can be productive right now.

>> Yeah, I think it's a huge opportunity and it's a new playbook by the way. So I'm curious to keep that thread going. We're going to still cover it in SiliconANGLE where that's an area we're digging into. So Rajan, I want to get your thoughts on just in general projects in AI for startups because the go-to market obviously has changed for the buyers that are going to buy this stuff. In marketing in particular, there's two camps I've heard people say, "The CMO is becoming less relevant," and on the other camp it's like, "No, the CMOs are becoming the most relevant. They're almost like the CIO kind of role because it's the customer piece." As you mentioned, a lot of people are going for the top line revenue growth. The CMO's job has always been, "Where are the leads," which basically means where's the sales? So what's your take on the new normal for CMOs? Because at the end of the day, things like earned media, owned media and paid media, which were traditional mechanisms, are all disrupted by the fact that the consumer experience has changed. Communities are building trust networks, NYC Wired, theCUBE Network, all this is kind of coming together. So the whole customer relationship has changed. Podcasts are hot. We saw that, people don't trust media. So again, everything around going to the market internally to the company, but then taking it to where the consumers are, the customers has changed.

Rajan Sheth

>> Yes, it has changed.

>> So email is now Slack, Slack's becoming instant message. You got memes. You're smiling, I can tell you love this.

Rajan Sheth

>> On your Slack, I'm going to make a comment. It's like I check my Slack more often than I check my emails. So there are emails I miss a lot. So I think you're 100% right. I think the role of CMO has changed. Rather than saying the CMOs of today in AI are not relevant, I would say this role of CMO has become so different. Now the CMOs have become an intersection between a COO who's partnering with the founders and the board on the strategy of how to go to market, versus just being running a marketing organization. Because if you can imagine all the products are so at the early stage that every time you would see, you cannot just market the product. You have to market more than the product. You have to create that brand. You have to create the market presence in very different ways, and you have to find those different unusual levers. The traditional paying ads no longer work in a sustainable way right now. So how do you find those angles? How do you find those one or two big long-term bets as a CMO that you want to invest in? Whether it is like you want to build an educational program, or whether you want to build a platform for developers, or whether you want to change your entire martech stack, you need to find two to three levers that will allow you to scale really well. And that's the role now of how CMO role has changed is it's no longer the same playbook, like, "I'm going to run paid ads, I'm going to throw an event, or I'm going to run some of the content." I think it's all changed now. I think it depends on the industry, it depends on the product, but it also depends on a lot more on unlocking the brand value of the people who are building this technology right now.

>> Yeah, it's interesting. I talked to a big time CMO, she's amazing, and she and I were talking, I won't mention the name because I don't want to help them, but I said, "Hey, how do you see it?" And she said, and I want to get your reaction to this, it's what you're kind bringing up, which is she said, "We've always been kind of slaves to the process and we use the word outcomes, but that's kind of been watered down and outcome has become a cliche, 'What's the outcome?'" What you're kind getting at is that what the end of the day they're trying to do is move products to customers, understand their customers. And so what she said was, "We will be more optimized for what the customer relationship might look like and how we can meet them and give them the appropriate message, content, whatever relationship that we want to have to engage business, and not get tied to the mechanisms. Whether it's we do email marketing and it's just rinse and repeat, paid ads, same thing. This is the changeover, this is the business transfer. Because AI is going to abstract the way a lot of that plumbing." Now you can have a martech stack and pave over it with great abstraction layer control plane, harmonization layer, whatever you call it. It's a name, but it's just the data plane. What's your take on her comments?

Rajan Sheth

>> I would say 100% percent right. Because I think all the things that you mentioned around the martech stack and the process, all are underneath, customers don't care about any of that. What customers care is like what is the benefit that I'm having using your platform, and what's the value that I'm creating? And then I think you think about there are two types of values. One is the value that you can provide immediately with the product that you have, and the second is the value that you would provide long-term to them. Because every week there is something new coming up, and the products are in very early stage. So now as a marketer, your job becomes very difficult because one, it's not just about selling and marketing what you have, but it's also selling the vision and the future of why you should be thinking about us overall. So I 100% agree. I think it's everything that we are doing to increase productivity on the marketing organization and the go-to-market side is hidden from the customers. It's something that we have to figure out. But at the end of the day, I think customers care about what is the value being delivered to them.

>> It's interesting, the final thing she said was, "At the end of the day, I want impact from my dollars, that's the ROI piece. But then following on the impact, not just saying, 'We did something, look at the analytics post-mortem, or whatever post-haste,' they go, 'Okay, we had an impact, let's just follow up and come back in.'" It's kind of like prompts, your prompts get better as you go into the AI. So you start to see a workflow around marketing changing. Do you see that, and how do you think about that as you look at these companies who can hit escape velocity faster with less people if they tune their formula properly?

Rajan Sheth

>> So we tune formula. Every company that I advise, including when I'm running marketing IT together, we tune the formula every other day. There is no consistent formula, there is no consistent thing.

>> The marketing-go-to-market formula?

Rajan Sheth

>> The marketing-go-to-market formula. For example, dare I mentioned DeepSeek one more time, but a couple of weeks ago that changed everything, right? Companies like Together AI, we are now getting more users using DeepSeek on our platform right now. So we had to change entire our marketing to go after DeepSeek. So you have to continuously tune to what the market is. I think there's no set formula. And that's why I 100% agree with what you said.

>> Yeah, it's interesting when you talk to some of the CEOs out there, when we mention to them, I say, "Hey, what's your biggest goal?" And it's not product sometimes, "Yeah, I want to make the stock price go up," if they're public, or drive sales for the customer. But when you dig deeper, it's like, "We want to leverage our resources to maximize our business model." And I'm like, "Okay, explain it." AI gives them an opportunity to one reconfigure, and I think the DeepSeek showed us was that the way they optimized under the constraints that they had with the chips and all that kind of story, is that they figured out a way to not hack but configure the system to do more and they were clever. So essentially turn on the cognition in your brain, you go marketing, "Hey, what are we trying to do? So when you see the data?" Getting data, that's a whole other workflow that might disrupt say CRM systems. What's your take on all that?

Rajan Sheth

>> Yeah, I think DeepSeek was a really good innovation. If you look at the... read the papers, you could actually see how they have transformed the existing architecture of training models, and now they're using reinforcement learning. So their code is really innovative. And that continues to happen. Two years ago we saw like Llama came out, right? Until then it was just closed-source models, and now the gap between open-source and closed-source has completely vanished. So you see continuous these Innovations. Now how do you use that innovation on the go-to-market site? So you have to constantly try to innovate how you are thinking about the market long term. And one of the thesis is, for Together AI, is that we are the platform for building with open-source. And with that thesis you can fit so many different things along the line, whether it is llama, whether it is DeepSeek, or whether it's any other AI innovation that the team is actually innovating. So now how do you use that, the research side of it, how do you use the market innovations, how do you use that to enable your customers? And that becomes the whole marketing for you then.

>> So you're saying research-led motion feeds in this iterative tuning of the marketing playbook?

Rajan Sheth

>> Exactly right. Because now your prospects are not just like... you're not just going after your prospects. You want to be where the AI conversations are happening. The AI conversations are happening right now in open-source. So you have to be talking about open-source. Two years ago the AI conversations were happening around AI doomsday narrative, but you have to figure out your narrative and be there. So it's more than your constituents and marketing has evolved. It's not just your prospect who's going to buy from you. It's like where the conversations are happening, whether it's in White House, whether it is in media, whether it is in specific AI forums, or whether it is in your community. So you have to build and you have to have that research angle to show your credibility and that becomes a really long-term moat on marketing, because no one can take that away from you.

>> So what you're saying is, if I hear you correctly, is you have to have a research organization, you have a media organization, and a data organization platform to be agile, responsive, and targeted, and on point?

Rajan Sheth

>> 100%, and you have to use that narrative, whether it's your one marketing organization, whether you have specific functions, but you have to use those narratives in-built as a long-term moat, because as I said, product can be disrupted in three months with the new innovations that are coming up. So now how do you as a marketers... what becomes challenging is how do you make long-term sustainable moats overall?

>> Yeah, that's awesome. And by the way, that's kind of how SiliconANGLE is built, right now we're seeing impact and service customers. On the more applied side, how does a company do that? Because the traditional research was bought by somebody, it's not in-house, media was bought, and earned was getting journalists to get articles and get ads out there, and owned was your website and your narrative and your conjecture as a company. As a CMO, how do you put that into practice? What are you guys doing? Give some examples of use cases because I believe that's the formula.

Rajan Sheth

>> Yeah, so I think some of this has changed. A lot of these companies now have research within their organization, especially if you look at the infrastructure side of it. Like Cohere has amazing researchers, like Aidan himself is the author of the transformers paper. At Together AI, I think you interviewed Tri Dao, anyone you ask in the research community, he's the inventor of FlashAttention and build those optimized kernels on top of NVIDIA chips. So people know these researchers really well in those communities. So now how do you elevate their brand, and how do you elevate their presence to command that brand overall becomes really important. Even on the application side for a particular vertical application, you would have founders who actually came from those vertical, for example, for legal type, there are founders who had lawyers before. And if you go in medical healthcare, there are founders who have been in that profession before. So now how do you elevate that, because you have a lot of in-house brand value right now that you need to leverage, and that becomes a more sustainable moat than, "Hey, I have this feature and I have this product overall."

>> So domain expertise is a moat opportunity for startups?

Rajan Sheth

>> 100%.

>> So the old classic VC model was, "Well, it's a feature, not a company." In this case, a small little tweak in a vertical could be a company, and scale and take over the category?

Rajan Sheth

>> Yes, 100%. Because I think if you have domain expertise in that area, and if you have technological advantage from a research perspective, and if you have the velocity of continuously producing new research, that is the goal that marketers are looking for right now.

>> And the speed. All right, so final question. I'll say we can do a whole other segment on open-source and DeepSeek, which I'd love to tap your brain on, and nice mention to Aidan over at Cohere, been on theCUBE many times, great guest, just a lot of talent coming in. It's clearly a new way. What is holding back the growth? Obviously DeepSeek and these I call DeepSeek-style execution on the back end and infrastructure side is going to create a renaissance and a tsunami of... a Cambrian explosion of apps. App developers are coming, you mentioned the vertical will be hot, enterprise is hot, that's where the money is. When apps start coming in, CMOs will be hiring developers, they don't have to have a whole team. So the ability to produce apps are going to come faster to the CMO. Right now we're seeing a lot of purgatory, POCs stuck right there, ready to break through. So we think we're in a breakthrough moment on this wave. What's your take on how to apply this? What are you seeing that's, from the developer standpoint, how do companies break through to get more into production?

Rajan Sheth

>> Yeah, so I think right now you see there are a few use cases that are so widely prominent. Every company is starting to now use coding assistance, from the GitHub copilers to the Codeians of the world, to Cursors of the world, and there are other applications like Gleam that you mentioned who was unlocking a lot of productivity. So as a marketer, some roles have changed completely. The marketing ops role has systematically, slowly it's going to get outdated. It's going to become like a go-to-market engineer, because now that person is actually building your entire stack in-house using these LLMs, and using this technology. So the role has changing. You need more technical bench in marketing itself overall to help you fuel that growth overall.

>> I love that engineering angle because the Glean example, and there's a few other companies that are doing the same thing, I mean, what they've done is used AI, and obviously the guys have pedigree from Google, Arvind Jain's awesome and the team there, but enterprise search was a big problem that couldn't be cracked. Schemas, unstructured data didn't work well together. Everyone who's been in that enterprise search category over the past two decades knows it's a slog, it's hard as hell to crack. Now that problem's now solved with AI. So the question to you is what problems can be solved that were what traditional big moonshot or huge transformation projects that were just either too hard to crack, too much cash to do, CMOs are now looking at an opportunity to go in and for the first time, tweak a problem, solve a problem. What's the problems that you see that are huge opportunities for CMO to move the needle big time.

Rajan Sheth

>> So I think there are two. You mentioned about some of these unlocking the data from enterprises. A lot of that has been unstructured data. If you think about what Glean's doing, what some of these LLMs are doing, is they bring unstructured data and give meaning to that. I think the big unlock that I see right now, especially for marketers and as well as on the go-to-market side, is unlocking the structured data, because there's so much data that's being captured from all the things on the marketing and the data warehouse side of it. Now how do you unlock systematically that structured data, use LLMs or use AI to help you drive more insights quickly, because no one has done that? We have traditional analytics and we have traditional tools to do that. But using LLMs now in your favor to help you arm with those insights becomes really, really valuable.

>> And that ties to your other point about being in the front lines, being faster, moving on data, retargeting, it's going to happen basically hyper-personalized, really fast.

Rajan Sheth

>> Exactly.

>> Hyper growth.

Rajan Sheth

>> Hyper growth. Yes, exactly, hyper growth. Everything is hyper growth right now, and that's going to unlock a lot of values from your existing data, your third-party platforms, as well as the consumer data that you can consume from third-party.

>> Rajan, thanks for coming on. I really appreciate it. At the last minute, give a quick plug for Together AI, what you guys are doing, why people should call you, what are the cool things you got going on?

Rajan Sheth

>> Yeah, so Together AI is a specialized generative AI cloud accelerating all the AI workloads from training foundational models, to fine-tuning existing open-source models, or running inference on like 200-plus open-source models like DeepSeek, Llama, Mistral, and all video, audio chat models, et cetera. So you get a full stack of platform from the high-performance Blackwell GPUs to a platform which allows you to build AI applications really fast and really efficient.

>> You can come in, sandbox, get things into production, test some stuff out, kind of a two-way door, go in, if you don't like it, you come back out. It's not a one-way door.

Rajan Sheth

>> And yeah, you keep the ownership of all your models and all your data using the platform.

>> All right, thanks for coming in. All right, we are here for the CMO leaders with theCUBE, and the NYSE Wire. I'm John Furrier, host of theCUBE. Thanks for watching.