Exploring the Advancements and Challenges in AI Agent Deployment
John Nay, founder and Chief Executive Officer of Norm Ai, joins theCUBE's special presentation with NYSE Wired, focusing on the upcoming Artificial Intelligence Agent Conference 2025. Hosted by John Furrier, co-founder and co-Chief Executive Officer of SiliconANGLE Media, this insightful discussion covers the pivotal developments in AI infrastructure and the regulatory complexities faced by enterprises.
In this episode, Nay shares their expertise in regulatory AI infrastructure, particularly as it pertains to AI agent deployment in highly regulated sectors. The conversation, hosted by Furrier, delves into the evolving landscape of AI technology, compliance challenges, and the strategic initiatives underway at Norm Ai to address the pressing issues surrounding AI deployment. The discussion provides valuable insights for both technology and policy influencers.
Key takeaways from the discussion include the emphasis on the need for dynamic, real-time compliance frameworks that align with regulatory standards, as emphasized by Nay. Furthermore, the episode highlights how enterprises can leverage existing compliance structures to integrate AI technologies more effectively, offering a glimpse into the future of AI agent scalability and regulation. The conversation underscores the importance of bridging the gap between engineering, policy, and technology for sustainable AI innovation.
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Natan Linder, Tulip
Exploring the Advancements and Challenges in AI Agent Deployment
John Nay, founder and Chief Executive Officer of Norm Ai, joins theCUBE's special presentation with NYSE Wired, focusing on the upcoming Artificial Intelligence Agent Conference 2025. Hosted by John Furrier, co-founder and co-Chief Executive Officer of SiliconANGLE Media, this insightful discussion covers the pivotal developments in AI infrastructure and the regulatory complexities faced by enterprises.
In this episode, Nay shares their expertise in regulatory AI infrastructure, particularly as it pertains to AI agent deployment in highly regulated sectors. The conversation, hosted by Furrier, delves into the evolving landscape of AI technology, compliance challenges, and the strategic initiatives underway at Norm Ai to address the pressing issues surrounding AI deployment. The discussion provides valuable insights for both technology and policy influencers.
Key takeaways from the discussion include the emphasis on the need for dynamic, real-time compliance frameworks that align with regulatory standards, as emphasized by Nay. Furthermore, the episode highlights how enterprises can leverage existing compliance structures to integrate AI technologies more effectively, offering a glimpse into the future of AI agent scalability and regulation. The conversation underscores the importance of bridging the gap between engineering, policy, and technology for sustainable AI innovation.
play_circle_outlineTulip: Human-Centric Physical AI Converting Domain Expertise into Agent-Driven Frontline Workflows for Manufacturing, Warehouses, Labs
replyShare Clip
play_circle_outlineOriginated from MIT research; CEO Natan Linder founded Tulip.
replyShare Clip
play_circle_outlineEmpowering Frontline Engineers with No-Code Workflow Builders: Seamless Southbound Machine and Northbound ERP Integration
replyShare Clip
play_circle_outlineOptimistic Upskilling: Human Orchestrators and a GitHub-Like Library for Traceable, Regulated Agentic Autonomy in Manufacturing
>> Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen, and this May, theCUBE + NYSE Wired is going on the road not too far, midtown Manhattan, where we will be hosting at the AI Agent Summit with Simon Chan and his wonderful team. In advance of that, we're bringing some of the guests and headline speakers and awardees onto theCUBE + NYSE Wired to get a sneak peek at exactly what the story is behind some of these companies. Joining me now is Natan Linder, CEO and co-founder of Tulip. Welcome, Natan.
Natan Linder
>> Thank you for having me, Gemma. It's great to be here.
Gemma Allen
>> So your company carries an interesting marketing narrative. You want to keep AI human-centric-
Natan Linder
>> Yes....
Gemma Allen
>> at the factory level.
Natan Linder
>> Isn't that refreshing?
Gemma Allen
>> It's so refreshing. I have to say, I love this. I love the word human like I've never loved it before. Talk to me a little about exactly what it is you do and how you aim to achieve that.
Natan Linder
>> Yeah. So Tulip builds what we call frontline operation platforms. It's a set of tools, including AI agent workflows, application building tools to help companies who manufacture real products, transform the domain expertise, the knowledge of the people who actually build their product, design their production lines, run the machines, into digital workflows. And that is a really hard problem to do, because software is complex, expensive. Of course, AI and AI in the physical domain, changes all that.
Gemma Allen
>> For sure.
Natan Linder
>> These are engineers. These are not necessarily your software engineers, but they could be industrial process engineers, quality, operational excellence engineers, safety. All the folks that make a physical environment, whether it's a factory floor, a warehouse, a laboratory, where physical work happens, make that environment work. And they need access to AI, physical AI, not less than the engineer, the software engineer, the marketer, the trader down here, and that's what Tulip is focused on-
Gemma Allen
>> Wild....
Natan Linder
>> bringing that AI to those physical environments.
Gemma Allen
>> Fascinating. And your own story, I love the founder story.
Natan Linder
>> Sure.
Gemma Allen
>> You came to the US, studied at MIT, founded this company out of MIT. Am I correct?
Natan Linder
>> Yup. Yes.
Gemma Allen
>> And have been really working on this possibly from well before we all talked about LLMs and generative AI in our everyday lives.
Natan Linder
>> Absolutely. Actually, so I spent my entire career building complex hardware, software products. I grew up around wood shops, so I always loved the physical world. And when you work on designing and building mobile phones or robots, you end up in factory floors, and it was always fascinating how there's the best tools, the most digital pipelines when you design a product, but when you get to the production environments, it could be spreadsheets, papers, systems that are not connected. And that gap was on my mind for a long time, and that led to some of the research we've done at MIT and eventually what we took to build a new platform that treat those same engineers I mentioned before as first class constituents of the digital stack and kind of change that environment. So they're not left behind the, so to speak, knowledge work equation, which is a extremely arrogant term, because as soon as you use that term, knowledge work, and who's a knowledge worker and who's not, you know what happens? You define who is outside of that equation.
Gemma Allen
>> For sure.
Natan Linder
>> And those folks run production or work that is so critical for our economies.
Gemma Allen
>> Absolutely.
Natan Linder
>> And yeah, we have to ensure that the technology gets to them and they can continue to be productive.
Gemma Allen
>> So I think the market dynamics certainly see this vision, right? You guys have a valuation of 1.3 billion, which is usually impressive, so congrats. The term physical AI, what that means, we heard Jensen say it at GTC. I know you guys were there and we were there...
Natan Linder
>> Yup. We were there. Yeah. Yeah.
Gemma Allen
>> It has different resonance to different people, right? It's become somewhat of an umbrella term. People picture different things.
Natan Linder
>> Yes.
Gemma Allen
>> We saw robots cartwheeling around GTC, right? That might be one person's vision.
Natan Linder
>> So many robots, actually.
Gemma Allen
>> So many, right?
Natan Linder
>> And many of them were doing interesting things, like, I don't know, the tricks and some of them were carrying stuff, and that's certainly an incarnation of physical AI. And I think another very common one is the self-driving cars. I think it's really about what type of models and infrastructure is enabling what type of application. So of course, when you're thinking about training robots, that's very clear, like, why would AI be a very useful tool for that, or when you're trying to help a network of cars get from point A to B, right? But those are machines, robots and cars. And I think in the physical world, we'll still have humans.
Gemma Allen
>> For sure, running factory lines, right?
Natan Linder
>> Running, for example, or handling the loading dock or making sure some test in the lab is running. Or think about an airport or a hotel, those are also physical environments where people do work. So, I think having a focus on human-centric physical AI and using generative AI models that can produce text alongside visual models, and that's the work we've been kind of showing together with NVIDIA in GTC most recently, basically gives a closed feedback loop to the humans who are actually doing the work and their systems.
Gemma Allen
>> For sure.
Natan Linder
>> So you can plan work and then you can see what happened, and you can kind of tell the human in real time, like, "Hey, here's a deviation," or, "Over there, there's a security issue or a safety issue," for example, and that's really empowering, and...
Gemma Allen
>> And the humans essentially are behind these KPIs, right? Factories have always been output-driven above all else.
Natan Linder
>> Yes. If you follow sort of the religion of operation, which is defacto some variant of lean manufacturing, I'm sure you heard the term and the set of terms around Agile, Scrum, and all those terms.
Gemma Allen
>> Yeah, Six Sigma, .
Natan Linder
>> Six Sigma, there are so many variants.
Gemma Allen
>> Talk to me about the original use case for Tulip, right? Because if you think about it, there are so many contextual ways in which this is applicable now in the world of AI, right? AI is really changing what it means to define output.
Natan Linder
>> Yeah.
Gemma Allen
>> So what was your kind of moment where you thought, "Oh my God, we need to create this?"
Natan Linder
>> The main sort of moment was the people who have the best access to the work and therefore the knowledge are lacking those first class tools. So we started from providing no code and low code platform that allows you very quickly to, in a tool that feels like looks like PowerPoint, create real application without any requirement of software knowledge whatsoever. And operations like to make it effective for people who are actually running such complex environment, you need on one hand to understand what data is flowing from machines, that's southbound. Northbound, you need to talk to ERPs and other types of systems that run an enterprise. And to be able to help those people do real world application, for example, guiding people through a set of discrete steps, quality audits, training, management of production of what regulation dictates, like creating an electronic batch record if you're making a drug. And I can go on and on. All those things were locked up in legacy, complex, siloed B2B software platforms that kind of put humans and we have a narrow bandwidth in what is compared to computers. And instead of letting humans do what they're great at is like using their intuition to solve the real problem. We always used to say like, the best computer on your shop floor is your human.
Gemma Allen
>> For sure.
Natan Linder
>> And I think now with this kind of augmentation that we're all feeling, I'm sure like all of us, you're using whatever it is like ChatGPT or Claude on a daily...
Gemma Allen
>> And I just research you, Natan, .
Natan Linder
>> Yeah, on a daily basis. That augments your capabilities.
Gemma Allen
>> For sure.
Natan Linder
>> And by now, you're taking it not fully for granted because it's still new, but in a couple of years or even less, it's going to be, this is how we do things. Well, can you imagine how industry will be left behind if we don't provide that kind of functionality and ability to act on in a complex agentic world? And a lot of people talk about this from, "Oh my God, the robots are taking over and agents are going to run all the things," but this is not what's going to happen. What's going to happen is humans are going to become the orchestrators.
Gemma Allen
>> For sure. And also from a change management perspective, the documentation around regulation and sense checks is so hugely important because if you have folks prompting systems to implement changes, especially in agentic world where there's some level of autonomy at the agent end, the actual traceability side of that is huge, right?
Natan Linder
>> You're hitting on a critical point because you can't go through the day without five updates that puts you either in FOMO or fear and doubt, uncertainty with respect to AI and what's new and what you're doing or not doing. And that moves really fast and becomes the next model and the next thing people adopt. And that's great. And on the other hand, humans biologically evolves slowly and the laws and regulation we make are slow and you still need protections and you still need all those things. And to reconcile the speed of technology and that transformation, and people used to call this digital transformation, right?
Gemma Allen
>> Yeah.
Natan Linder
>> And that's another terrible term.
Gemma Allen
>> Many, many institutional folks still do, right?
Natan Linder
>> Yes.
Gemma Allen
>> There's a lot of legacy players that still use that term quite a bit.
Natan Linder
>> But it's meaningless.
Gemma Allen
>> Yeah, for sure.
Natan Linder
>> There's no transformation that is not digital.
Gemma Allen
>> For sure.
Natan Linder
>> And they're never over. So with the tool perspective, we think about it through the lens of continuous transformation. And so part of what we're doing is like helping those same humans, being able to, if one engineer can do a work controlling one line, if they can orchestrate agents, if they can get the data, if they can do it in an environment where they are trusted to use the tool and they trust the tool the same way, a software engineer would trust their Cursor driven IDE or Claude Code or whatever they're using, then that same engineer is augmented with superpowers and now they can run maybe three production lines. You know why that's critical? Because we don't have enough people in manufacturing in the United States or in Europe. We don't.
Gemma Allen
>> Sure, it's global problem. So talk to me, last question, a little bit about the vision here, especially from the perspective of scale. Do you envision a world where this is almost like a version of GitHub, what GitHub is for software engineers, for factory leaders? What is your view here? Is it one blueprint that iterates based on industry?
Natan Linder
>> Yeah. So first of all, we're already living the dream to a degree. So if you go to Tulip library, it has those blueprints and it's a community and there's thousands and thousand engineers that can actually now share best practices and all the way to context and agent. So content becomes context and it's shared and it's available. So that's a reality that's happening right now. I don't think those engineers were organized the same way software engineers. So this GitHub point you just made, we are thinking deeply on that point and I do think that will happen. But I think that the data and the horizontal knowledge that we're learning because Tulip is deployed in tier one pharma manufacturers and luxury good manufacturers and industrial manufacturers. And you're like, "What's going on here?" It's manufacturing is manufacturing is manufacturing. The first principles of production systems are not going to change. You're still going to want to manufacture on time on quality and so on. So building that into agenting tools and models eventually is certainly part of the vision, working hard to make it happen and doing it very, very close to customers because the tolerance for folks in operations to hype technology stories over like, how is this solving us a real problem that helps us get the result we need, whatever it may be from their perspective is critical. They're not tell us stories type people. It's like, "Show me how it works type people." So that's what we're doing and believe that we'll scale Tulip quite nicely the next few years alongside with our customers. So it's a great journey.
Gemma Allen
>> Well, love it. I love the fact that also there's an element of human control in the narrative, right? We're not roadkill. We are part of an iteration for our future. So it's a powerful message.
Natan Linder
>> We are not roadkill. And I think that it's a powerful message. I also believe that the flip side is very dangerous to think that we will just kind of a...
Gemma Allen
>> Which is becoming an increasingly looming reality though, right? On the other side, we hear if you follow headlines right now as a knowledge worker or blue white collar, there is certainly a lot of fear out there, right?
Natan Linder
>> Yeah. There's a lot of fear and I think we've heard over the past several years, keep hearing this word, I'm sure, here in this studio, how we need to upskill the workforce. And I think AI and especially human-centric physical AI like we're doing is phenomenal technology to help upscale those folks and we need them. We need them for our economy to stay competitive and it's almost as if operations manufacturing has a bad rap as an industry. And perhaps there's kind of a silver lining or some bright light here that not a lot of people still see, but I'm an optimist. And if we're able to pull the people who might not celebrating that and no stretch of imagination might, some job will be lost to AI in the knowledge work domain, the pure knowledge work domain, but they end up in a new knowledge work domain, which is operations, that's actually winning.
Gemma Allen
>> Yeah. So moving off on the value here. Well, to quote Jensen, "It's not about doing more with less. It's about doing more with more," right?
Natan Linder
>> It's about doing more with more. Absolutely.
Gemma Allen
>> Very interesting story. Look forward to seeing you in May. Thanks so much for coming on theCUBE, Natan.
Natan Linder
>> Yeah. Thank you for having me. It's been great. Thanks a lot.
Gemma Allen
>> I'm Gemma Allen at theCUBE Studio here at the New York Stock Exchange. This is part of our program with NYSE Wired covering the AI agent conference. Thanks so much for watching.
>> Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen, and this May, theCUBE + NYSE Wired is going on the road not too far, midtown Manhattan, where we will be hosting at the AI Agent Summit with Simon Chan and his wonderful team. In advance of that, we're bringing some of the guests and headline speakers and awardees onto theCUBE + NYSE Wired to get a sneak peek at exactly what the story is behind some of these companies. Joining me now is Natan Linder, CEO and co-founder of Tulip. Welcome, Natan.
Natan Linder
>> Thank you for having me, Gemma. It's great to be here.
Gemma Allen
>> So your company carries an interesting marketing narrative. You want to keep AI human-centric-
Natan Linder
>> Yes....
Gemma Allen
>> at the factory level.
Natan Linder
>> Isn't that refreshing?
Gemma Allen
>> It's so refreshing. I have to say, I love this. I love the word human like I've never loved it before. Talk to me a little about exactly what it is you do and how you aim to achieve that.
Natan Linder
>> Yeah. So Tulip builds what we call frontline operation platforms. It's a set of tools, including AI agent workflows, application building tools to help companies who manufacture real products, transform the domain expertise, the knowledge of the people who actually build their product, design their production lines, run the machines, into digital workflows. And that is a really hard problem to do, because software is complex, expensive. Of course, AI and AI in the physical domain, changes all that.
Gemma Allen
>> For sure.
Natan Linder
>> These are engineers. These are not necessarily your software engineers, but they could be industrial process engineers, quality, operational excellence engineers, safety. All the folks that make a physical environment, whether it's a factory floor, a warehouse, a laboratory, where physical work happens, make that environment work. And they need access to AI, physical AI, not less than the engineer, the software engineer, the marketer, the trader down here, and that's what Tulip is focused on-
Gemma Allen
>> Wild....
Natan Linder
>> bringing that AI to those physical environments.
Gemma Allen
>> Fascinating. And your own story, I love the founder story.
Natan Linder
>> Sure.
Gemma Allen
>> You came to the US, studied at MIT, founded this company out of MIT. Am I correct?
Natan Linder
>> Yup. Yes.
Gemma Allen
>> And have been really working on this possibly from well before we all talked about LLMs and generative AI in our everyday lives.
Natan Linder
>> Absolutely. Actually, so I spent my entire career building complex hardware, software products. I grew up around wood shops, so I always loved the physical world. And when you work on designing and building mobile phones or robots, you end up in factory floors, and it was always fascinating how there's the best tools, the most digital pipelines when you design a product, but when you get to the production environments, it could be spreadsheets, papers, systems that are not connected. And that gap was on my mind for a long time, and that led to some of the research we've done at MIT and eventually what we took to build a new platform that treat those same engineers I mentioned before as first class constituents of the digital stack and kind of change that environment. So they're not left behind the, so to speak, knowledge work equation, which is a extremely arrogant term, because as soon as you use that term, knowledge work, and who's a knowledge worker and who's not, you know what happens? You define who is outside of that equation.
Gemma Allen
>> For sure.
Natan Linder
>> And those folks run production or work that is so critical for our economies.
Gemma Allen
>> Absolutely.
Natan Linder
>> And yeah, we have to ensure that the technology gets to them and they can continue to be productive.
Gemma Allen
>> So I think the market dynamics certainly see this vision, right? You guys have a valuation of 1.3 billion, which is usually impressive, so congrats. The term physical AI, what that means, we heard Jensen say it at GTC. I know you guys were there and we were there...
Natan Linder
>> Yup. We were there. Yeah. Yeah.
Gemma Allen
>> It has different resonance to different people, right? It's become somewhat of an umbrella term. People picture different things.
Natan Linder
>> Yes.
Gemma Allen
>> We saw robots cartwheeling around GTC, right? That might be one person's vision.
Natan Linder
>> So many robots, actually.
Gemma Allen
>> So many, right?
Natan Linder
>> And many of them were doing interesting things, like, I don't know, the tricks and some of them were carrying stuff, and that's certainly an incarnation of physical AI. And I think another very common one is the self-driving cars. I think it's really about what type of models and infrastructure is enabling what type of application. So of course, when you're thinking about training robots, that's very clear, like, why would AI be a very useful tool for that, or when you're trying to help a network of cars get from point A to B, right? But those are machines, robots and cars. And I think in the physical world, we'll still have humans.
Gemma Allen
>> For sure, running factory lines, right?
Natan Linder
>> Running, for example, or handling the loading dock or making sure some test in the lab is running. Or think about an airport or a hotel, those are also physical environments where people do work. So, I think having a focus on human-centric physical AI and using generative AI models that can produce text alongside visual models, and that's the work we've been kind of showing together with NVIDIA in GTC most recently, basically gives a closed feedback loop to the humans who are actually doing the work and their systems.
Gemma Allen
>> For sure.
Natan Linder
>> So you can plan work and then you can see what happened, and you can kind of tell the human in real time, like, "Hey, here's a deviation," or, "Over there, there's a security issue or a safety issue," for example, and that's really empowering, and...
Gemma Allen
>> And the humans essentially are behind these KPIs, right? Factories have always been output-driven above all else.
Natan Linder
>> Yes. If you follow sort of the religion of operation, which is defacto some variant of lean manufacturing, I'm sure you heard the term and the set of terms around Agile, Scrum, and all those terms.
Gemma Allen
>> Yeah, Six Sigma, .
Natan Linder
>> Six Sigma, there are so many variants.
Gemma Allen
>> Talk to me about the original use case for Tulip, right? Because if you think about it, there are so many contextual ways in which this is applicable now in the world of AI, right? AI is really changing what it means to define output.
Natan Linder
>> Yeah.
Gemma Allen
>> So what was your kind of moment where you thought, "Oh my God, we need to create this?"
Natan Linder
>> The main sort of moment was the people who have the best access to the work and therefore the knowledge are lacking those first class tools. So we started from providing no code and low code platform that allows you very quickly to, in a tool that feels like looks like PowerPoint, create real application without any requirement of software knowledge whatsoever. And operations like to make it effective for people who are actually running such complex environment, you need on one hand to understand what data is flowing from machines, that's southbound. Northbound, you need to talk to ERPs and other types of systems that run an enterprise. And to be able to help those people do real world application, for example, guiding people through a set of discrete steps, quality audits, training, management of production of what regulation dictates, like creating an electronic batch record if you're making a drug. And I can go on and on. All those things were locked up in legacy, complex, siloed B2B software platforms that kind of put humans and we have a narrow bandwidth in what is compared to computers. And instead of letting humans do what they're great at is like using their intuition to solve the real problem. We always used to say like, the best computer on your shop floor is your human.
Gemma Allen
>> For sure.
Natan Linder
>> And I think now with this kind of augmentation that we're all feeling, I'm sure like all of us, you're using whatever it is like ChatGPT or Claude on a daily...
Gemma Allen
>> And I just research you, Natan, .
Natan Linder
>> Yeah, on a daily basis. That augments your capabilities.
Gemma Allen
>> For sure.
Natan Linder
>> And by now, you're taking it not fully for granted because it's still new, but in a couple of years or even less, it's going to be, this is how we do things. Well, can you imagine how industry will be left behind if we don't provide that kind of functionality and ability to act on in a complex agentic world? And a lot of people talk about this from, "Oh my God, the robots are taking over and agents are going to run all the things," but this is not what's going to happen. What's going to happen is humans are going to become the orchestrators.
Gemma Allen
>> For sure. And also from a change management perspective, the documentation around regulation and sense checks is so hugely important because if you have folks prompting systems to implement changes, especially in agentic world where there's some level of autonomy at the agent end, the actual traceability side of that is huge, right?
Natan Linder
>> You're hitting on a critical point because you can't go through the day without five updates that puts you either in FOMO or fear and doubt, uncertainty with respect to AI and what's new and what you're doing or not doing. And that moves really fast and becomes the next model and the next thing people adopt. And that's great. And on the other hand, humans biologically evolves slowly and the laws and regulation we make are slow and you still need protections and you still need all those things. And to reconcile the speed of technology and that transformation, and people used to call this digital transformation, right?
Gemma Allen
>> Yeah.
Natan Linder
>> And that's another terrible term.
Gemma Allen
>> Many, many institutional folks still do, right?
Natan Linder
>> Yes.
Gemma Allen
>> There's a lot of legacy players that still use that term quite a bit.
Natan Linder
>> But it's meaningless.
Gemma Allen
>> Yeah, for sure.
Natan Linder
>> There's no transformation that is not digital.
Gemma Allen
>> For sure.
Natan Linder
>> And they're never over. So with the tool perspective, we think about it through the lens of continuous transformation. And so part of what we're doing is like helping those same humans, being able to, if one engineer can do a work controlling one line, if they can orchestrate agents, if they can get the data, if they can do it in an environment where they are trusted to use the tool and they trust the tool the same way, a software engineer would trust their Cursor driven IDE or Claude Code or whatever they're using, then that same engineer is augmented with superpowers and now they can run maybe three production lines. You know why that's critical? Because we don't have enough people in manufacturing in the United States or in Europe. We don't.
Gemma Allen
>> Sure, it's global problem. So talk to me, last question, a little bit about the vision here, especially from the perspective of scale. Do you envision a world where this is almost like a version of GitHub, what GitHub is for software engineers, for factory leaders? What is your view here? Is it one blueprint that iterates based on industry?
Natan Linder
>> Yeah. So first of all, we're already living the dream to a degree. So if you go to Tulip library, it has those blueprints and it's a community and there's thousands and thousand engineers that can actually now share best practices and all the way to context and agent. So content becomes context and it's shared and it's available. So that's a reality that's happening right now. I don't think those engineers were organized the same way software engineers. So this GitHub point you just made, we are thinking deeply on that point and I do think that will happen. But I think that the data and the horizontal knowledge that we're learning because Tulip is deployed in tier one pharma manufacturers and luxury good manufacturers and industrial manufacturers. And you're like, "What's going on here?" It's manufacturing is manufacturing is manufacturing. The first principles of production systems are not going to change. You're still going to want to manufacture on time on quality and so on. So building that into agenting tools and models eventually is certainly part of the vision, working hard to make it happen and doing it very, very close to customers because the tolerance for folks in operations to hype technology stories over like, how is this solving us a real problem that helps us get the result we need, whatever it may be from their perspective is critical. They're not tell us stories type people. It's like, "Show me how it works type people." So that's what we're doing and believe that we'll scale Tulip quite nicely the next few years alongside with our customers. So it's a great journey.
Gemma Allen
>> Well, love it. I love the fact that also there's an element of human control in the narrative, right? We're not roadkill. We are part of an iteration for our future. So it's a powerful message.
Natan Linder
>> We are not roadkill. And I think that it's a powerful message. I also believe that the flip side is very dangerous to think that we will just kind of a...
Gemma Allen
>> Which is becoming an increasingly looming reality though, right? On the other side, we hear if you follow headlines right now as a knowledge worker or blue white collar, there is certainly a lot of fear out there, right?
Natan Linder
>> Yeah. There's a lot of fear and I think we've heard over the past several years, keep hearing this word, I'm sure, here in this studio, how we need to upskill the workforce. And I think AI and especially human-centric physical AI like we're doing is phenomenal technology to help upscale those folks and we need them. We need them for our economy to stay competitive and it's almost as if operations manufacturing has a bad rap as an industry. And perhaps there's kind of a silver lining or some bright light here that not a lot of people still see, but I'm an optimist. And if we're able to pull the people who might not celebrating that and no stretch of imagination might, some job will be lost to AI in the knowledge work domain, the pure knowledge work domain, but they end up in a new knowledge work domain, which is operations, that's actually winning.
Gemma Allen
>> Yeah. So moving off on the value here. Well, to quote Jensen, "It's not about doing more with less. It's about doing more with more," right?
Natan Linder
>> It's about doing more with more. Absolutely.
Gemma Allen
>> Very interesting story. Look forward to seeing you in May. Thanks so much for coming on theCUBE, Natan.
Natan Linder
>> Yeah. Thank you for having me. It's been great. Thanks a lot.
Gemma Allen
>> I'm Gemma Allen at theCUBE Studio here at the New York Stock Exchange. This is part of our program with NYSE Wired covering the AI agent conference. Thanks so much for watching.
Gemma Allen