Beyond the Agentic AI hype with Superbo AI

Speaker A: Welcome back to the Serial Entrepreneur. I'm your host, Anna Wood. In today's episode, I'm joined by Dimitri, the founder and CEO of SuperBo AI. We discuss the importance of ignoring the hype and finding the real value in agentic AI. Why sorting data is the first step in ensuring success in automation and the hunger and drive that makes an entrepreneur. Let's Dig in podcast today. How are you doing?

Speaker B: Same. I'm, I'm fine. I'm, I'm perfect. Thanks, uh, so much for having me.

Speaker A: Yes, thank you for coming on. So we begin every episode with our signature icebreaker question, which is what's your favorite breakfast cereal and why?

Speaker B: Okay. When I used to live in the US My favorite one was the, um, the, the Fruit Loops. Fruit Loops, Yeah, with different colors. I like the, uh, the taste. But now I don't do cereal anymore. So the, the last thing I remember about Ceremonial was, you know, when I was in the U.S. but that's, that's the best I can do when it comes to cereal. I think they are too sugarly for me and I'm trying to remain fit for some reason. And this is something that you think when you're in my age, uh, but when you are on your early 20s, don't think about it.

Speaker A: I think a lot of people also tell us like, oh, we don't eat cereal anymore. But when I did, I'd always go for the really chocolatey or really sweet ones. So would you be able to introduce yourself and tell me a bit about your background?

Speaker B: My name is Dimitri. I'm the co founder of super bowl, but I've been around many more years before Super Bowl. Right. So, uh, my background. Well, I started as a professional musician. Yeah, I know it sounds very unorthodox, but I studied professional music and composition and, and then down the road I also had the idea how I'm going to bridge, uh, my musical talent to business. And the, the, the main reason was I was trying to make a living. You know, when you're in the music, you can create marvelous things, maybe things that you love, but. And only you, or maybe somebody will love only if they have the chance to, you know, if you have the chance to expose them in front of the proper audience. So very early I understood that I had to make a living. So, uh, I went to business, I graduated, and then I started working around business with media tech companies. Eventually, after some years, I had my own company in 2011, which was a mobile advertising company, uh, which I sold in 2019. And then I said, you know what? 20, 19, very young to retire. So uh, I probably have to do something with my time and my money. And I bootstrapped Superbo with my co founder. Here we are now, you know, five plus years we have Superbo. We're doing, uh, pretty well, pretty happy about it. That's my hand.

Speaker A: Great. Could you talk a bit more about Superbo? What do you do there?

Speaker B: All right, so we've been around. Superbo's been around since 2020. It was before the AI trend and before the AI revolution. So traditionally approaching things around machine learning and natural, um, language processing and understanding, nlu, nlp, all this kind of stuff. We always thought and we believed confidently that AI revolution would come. We didn't know when, but we knew it would come. Guess what? Two years down the road we were all introduced to OpenAI and the rest of the players. And so it was the, that was the tip off, right when everything started. And we had already build an asset critical enough to allowing us to start harnessing the available technology. Of course, since then many things happen and they keep happening on an exponential rate and we keep building. So we are asset builders today. Superbo is an agentic AI company by the full definition of agentic. The difference is that we just, we don't do A.I. agents, uh, as a standalone task force, but we do the whole agentic system which is, it's comprised from many different AI agents that communicate and talk with each other and they reason with each other and they observed and they plan and they design and they execute. So they are autonomous. Autonomous for the, for the workflows that you want them to embed, uh, to be embedded in. Right. And this is extraordinary because what we are achieving today, it is we get the work done in terms of, it's not just another, you know, fluff of AI agent as uh, a single task, uh, oriented tool. It's not a tool anymore what we do, it's, it's a brain that has the capability of executing all the intelligence within a workflow in diverse sectors, but also in diverse divisions inside an enterprise. Starting all the way from human resources, which sounds simple, it's not so simple, but it is simple all the way to customer support, procurement, marketing. It's been there, done that. Now it is also very important that we're one of those companies that along the way we were lucky enough or we had the, uh, capability to put some of those into production and Demos and beautiful PowerPoints have nothing to do with production. Right. So we have the production scale deployment when it comes to agency AI. So this is what we do and we keep building. We believe that now it's also worth mentioning that we are all in a pre inflection market. So, so it means that the agency AI, uh, adoption is not here yet. It will be. We also know that for sure once again, where content is coming, we don't know when. But my wildest guess would be in the next 18 to 24 months we're gonna have the first enterprises taking very seriously how they uh, deploy and they adopt agency AI. So this is what we do.

Speaker A: Great. So as you said, Agent Ki isn't being adopted on a large scale currently. So it still to some people sounds like an abstract concept. What does agentic AI currently mean in a business context?

Speaker B: Well, it has to do with the business. Agent AI in any context should mean one very precise thing. Agent AI is the squad of different agents communicating with each other, reasoning with each other. And they are goal oriented, not task oriented. So they have to achieve a goal. This is very important because when you build a business case, an uh, agentic workflow, whatever, you're doing it for a purpose, to reach a goal, to resolve something, to accelerate something, to make sure that something works better, faster, safer within the enterprise, Achieve AI sovereignty and make sure uh, you get that goal done. Now this is also very important because most of the enterprises out there, uh, they have no idea whatsoever A why they need agentic AI. B if they don't know, they cannot really see the value. So it is important first of all for the enterprises who are the buyers of that, uh, to understand, clarify what they're trying to solve and what is the value they're trying today. Because I can tell you from the available technology super bowl has today, we can bring value day one measurable value. I mean with a very solid return on investment. You pay X and you can have X multiple times back as a return because you are solving something that requires a lot of human capacity, effort, time, money. And don't take me wrong, we're not here to take people's jobs. On the contrary, we're here to make employees jobs a lot more productive, faster, safer and with uh, you know, and not so complicated. They have been wasting so much time in arguing, in fixing problems, in spending time, counterproductive. You get tired at some point. So instead of having this, you know, there is a hook in the market saying ah, uh, we're going to be replaced by AI. No you're not, you're not getting replaced by AI. What's going to happen though? Is that people who are going to use an enterprise, who are going to use a generic AI properly are going to replace you, not the technology itself.

Speaker A: Yes, definitely. I ah, think that's a sentiment that's been echoed a lot around the tech industry. So there's currently a gap. Yep, there's currently a gap between experimentation and actually operational AI. Where are companies getting stuck?

Speaker B: This is the one trillion dollar question. Okay, so this is the curse and uh, the blessing at the same time. Okay, let's go, let's you know, let's see from a helicopter view why enterprises have been choosing to experiment more with a proof of concept and pilot projects instead of going with full deployment in production. Again a, they have no idea why they need AI. All they know is that they need AI. Right. So it is more like a marketing stipulating economy saying that um, a, you know, you are the CEO of XYZ company, you have a CIO or CTO or maybe chief AI Officer or Data officer and you have no idea what to do with AI. All you need to know is that you need make some announcements saying that you're harnessing AI power and now uh, that you are AI powered, you have no idea why on earth you need that. So, and that creates the first level layer of noise. The second level of uh, layer of noise is that they invite multiple ventures and vendors for PoCs and at the end of the day they end up with 50, 60 different POCs and maybe a couple pilot projects and they consider these to be an experimental phase and they fail to understand how much money, interest, energy, time, effort, work they spend on that without the value being on the table. Because value will never come from POCs. It's totally different. So instead of going out there and you know, nailing down with, come up with a case, a use case or a workflow that you really need to solve, you really need to uh, boost in terms of production and accelerate. Instead of finding a vendor that has at least one agency AI in production, this is the best you can do. Find someone like Superbook that has agency AI introduction. Now SuperBo has done a little bit of exaggeration to tell you the truth. We have our own agent AI framework as well, which means that uh, we don't use any hyperscalers for that. So instead of going to Google Vertex and say we're going to build our own AI agents there, which is fine, which is okay. Yeah, we have our own framework because we always believe that the hyperscalers as uh, you know, they build horizontally. So eventually when you build an AI agent on their generative AI framework, eventually you're going to hit a wall. Eventually you're not going to be able to do a lot more of bespoke tailor making customization and it makes sense for them and that's why they are hyperscalers. So we never wanted to depend on that. We invested really lots of money, time and effort to achieve that. So we have our own framework building our own agentic AI systems and agents. Agents of course. So the gap is here. The gap is the, is a tick in the box that you need to make sure that you're still in the pre inflection market. The moment the gap will start diluting it means that you're entering the post inflection market.

Speaker A: What do you think are some of the best use cases of agent Ki that can actually provide that value to a company?

Speaker B: For sure. Use cases like a procurement, customer support, human resources, marketing, finance would be lowest can give fruit. They are today. They, you know, you have fantastic RPA systems out there or workflow systems and they do marvelous work but they, they are not AI. So if, if you really need, first of all you need to make sure that usually the gap in an enterprise, ah, the natural habitat of a gap is where AI can really come and provide value. Otherwise it's just noise. Right. So if you do your job with perfect RPA tool and you're happy, then you don't need agentic AI. Not yet. Right. But if you want to scale faster, if you're going to be, you know, part of the agentic AI era, if you want to boost your employees morale and productivity and also understand that they're, you know, they're not going to lose their job, then you need to come up with very specific parts of your workflow that would need an agentic AI as a node. Many people, they make a very common mistake. They say that you know, AI will be replacing workflows. No, no, no, no. A generic AI will be adding or replacing nodes in an existing workflow, not the whole workflow. You have fantastic software out there and companies that they do marvelous work when it comes to workflows and we're not competing with those guys, we are just complementary to them, maybe on top, it's adding value on top of them. So this is also one more part to be misinterpreted and misunderstood. And last but not least, they don't really, there is a very big attention deficit. People don't listen, they just read a lot of marketing jargon passwords, LinkedIn and they come and they say, oh, you know what, what we need is this. And um, out of courtesy, you remain silent because you don't know how to say to those guys that, ah, hey, you know what? They. This is not exactly what you need. This is what they have told you to say. This is what you need. So there is a way. There are so many sections and departments in an enterprise that could benefit from a generic AI today like human resources, procurement, finance, customer support, marketing. And yet who has the ownership? Now this is the question. In my humble perspective, there should be not one person. It would be one person per department. Who is the CMO Chief Marketing Officer. This is you. Okay? You're going to be responsible in knowing exactly what you want. And then you come to us, we are the experts and we can walk you through and tell you and recommend the same goes from the CFO for the finance and goes on. And then all of these can be probably for bigger enterprises. They can be uh, orchestrated by the uh, chief AI officer who can understand tech and he has a tech team or an AI team who can make, you know, evaluate your technology at the end of the day. But you know, when we talk to businesses, we don't sell technology because they don't understand technology. We sell the work. We sell what we can do for you and make your work simpler, faster, safer.

Speaker A: Yeah, great. So when it comes to deployment of these agents, what do you think tends to break first when companies introduce autonomous agents?

Speaker B: Security, Security, security. Boom. Yeah, it's been heavily underserved and undermined. Most of the companies. Let's, let's just see two steps back and see the reality. You have 95% of the Avengers out there are LLM rappers. And um, it feels simpler, faster for go to market. They can grab 20 logos by, by that maybe, you know, with 20k or 30k or whatever, they can deploy something very fast. But that does not really mean enterprise production with all the security and the sovereignty that is needed those days. And you know very well that um, enterprise sovereignty has been the talk of the town at least throughout 2026, especially in Europe. And uh, how we should not be dependent by non European companies and vice versa. You cannot get these from an LLM wrapper. This is not their job. You need to have all the layers of security and you also need to have the foundational layer, your abstraction layer. How are you going to deal with unstructured data? I can tell you something. Even the humongous enterprises, Fortune 500 companies in the US today, they have data structure deficit Problems, Uh, unstructured data, chaotic here and there, some on PDF, some on CSVs, some on ERPs, CRPs, whatever here and there. And then probably a couple of years ago they had a project to structure the data and they did it successfully and then again they let it uh, go down. And it's a vicious cycle. So you also need to have a solution on how you're going to handle the infrastructure data because this is the beginning of no project. So instead of thinking who's going to compete with me? I, uh, think your biggest competitor is a no project because they have this stumbling block which is unstructured data. And that's why in Superbo we uh, were very, you know, we've been, we still invest a lot of, on our abstraction layer to make sure that we structure the data. Um, which is we are making a favor to ourselves. Right. We structure the data in order to make sure that our agenda, AI systems can rely on those data and produce the best possible outcome.

Speaker A: Yeah. And I was going to ask what does good AI architecture look like inside of a scaling company? And the second part to that is if a company is building right now, what should be done from day one in order to be ready for autonomous

Speaker B: agents from an architectural perspective? Uh, you need to build layers. We always start in four layers in super bowl, and the cleaner the layer, the better it is. So you need your foundation layer, your abstraction layer, your knowledge layer, and uh, your agents. And all those should be independent from any LLM and from any cloud. It means that you should not rely to a model. Some models are better in reasoning, some others are better in graphics, some others in analytics. So for example, we, we are LLM agnostic. We use different LLMs whenever we want to achieve something different. That's number one. Number two, we're cloud agnostic. So we can deploy on any cloud or our cloud or the enterprise cloud, or we can even deploy on premise. So this is also something important. Architecturally speaking. You should not be locked in with a hyperscaler because let's, let's see the, let's see the reality. Some people, they say, okay, well I can get some of the pre, uh, built agents of, I'm not going to say a name but of X, Y, Z Hyperscaler weighs. Probably a, uh, leader in CRM. Yeah, okay. But these agents are perfect to make sure they work and they boost their existing core offering. How about your agents, the whole difference? I think the simplest way to put it with definitions of 60 years ago would be if you're going to Be renting your house or owning your house. So are you going to be renting your AI or owning your AI? What we are trying to solve here is that we offer enterprise or enterprise large enterprises to own their AI. And from a security standpoint, this is very important. So yes, architecture has to do with security. And so bridging your previous question with this question and uh, your next question was what would be the first thing to think for if you are building a new AI venture or if you are a potential buyer?

Speaker A: Mhm. Yeah. And how to like uh, how to be. If a company's building right now, what should be done from day one to be agent ready?

Speaker B: Okay, that's a question. All right. You need your data foundation. Okay? So you need to make sure you put some time and effort and invest in your data, invest in your data structure before rushing to get on another experimental poc or eventually uh, manage to give a uh, one year project to a uh, successful vendor like Super Boat. Hey, you know what, Take a step back and you have to tidy up your house, right? Your foundation needs to be there. Your foundation is your data. Otherwise you know, we in superb, we have solved that because we have the abstraction layer. But then again you need to make sure that your data, it's because you need to have your data structured. So take some time and you have to knit up your uh, house and your data and then whoever, you know, then it's easier to also choose your vendor and also something very important. Go back and see how people, what, what the, what the vendors and the AI vendors talk about and what they post about. Right? They post, you see, they post something. A couple of months ago and two months down the road forward they were gonna post something totally different just because it is uh, a better fit for the current trend and narrative. But you need to have an opinion and uh, you need to stay solid. Otherwise you know, you're just going with the trend and then you know, this is where, this is where things can really go south. So follow how, uh, you know, what the sea level says, what they post about. They're trying to, but I understand there's lots of noise. So yeah, so fix your data first and then go shopping.

Speaker A: So is there a way that companies can kind of try and move away from, I guess, the trends and really know what will provide value for them?

Speaker B: I think yeah, there is a way, a fix your data. If you have your data structured, you know exactly what's going wrong. You have an idea, you're not speculating, right. You know exactly what where and when is going wrong before us? You don't need super both for that, right? You can do it by yourself. So this is 50% of your success story. There is a 50%. Since you know where, when and what it is, your biggest uh, issue challenge or your biggest deficit in a specific department or division of your enterprise, then you know exactly what you need to ask from this particular vendor and say, hey, you know what? We see that we have a humongous deficit on our customer support. They pick up the phone, they're 24 7, they have no idea what to say. They cannot resolve. People are not happy. Your net promo scores, your nps. Our NPS is we need to fix that. We understand we have the data right? Or we're having a huge trouble onboarding new employees. So our human resources is having trouble with xyz. We need to fix that. So with specific use cases and they, they will also save you a lot of time back and forth. So when a potential prospect sits, sits on the table with us and you know, it's it, we and they have done their homework and they have structured the data and they know exactly what they want. This also enables us to unleash our firepower brain to upgrade the whole, not only the experience but, but to upgrade their own goals and help them see them. So it is a collaboration and they will never be out of the box or off the shelf. It's not gonna happen. Right. So you need to work, you know, your enterprise. So yes, fix your data. Then you know what on earth is going on.

Speaker A: Mhm. Definitely. So when it comes to industries that are quite regulated, if there's a company who's looking towards wanting automation and agentic agents, are there any considerations that they have to take before they deploy these systems?

Speaker B: Yeah, sure. Industries like BFSI or banking, healthcare, these are sensitive industries with very sensitive data. So first of all they need to make sure that their data remains sovereign. It means there's no leak to any large uh, language model hyperscaler out there. So you need to make sure the vendor you choose is capable and able to make sure that it will work with the same results in terms of outcome and quality without leaking your data into the model. This is very important. The other thing is that they, they need to make sure they have the security infrastructure in place by themselves and then allow the company to come and fit on top of that and make sure that again there's no leak, there is no um, when it could give you an example. It's if we get your data, your Name your last name, your tax number, your health file or your whatever, and we look it to the LLM for a fast answer just because we cannot anonymize it and mask it. Then you know that beforehand the LLM has your data for training purposes. You know that and you don't know. You don't, you, you really don't want that, right? So see what's happening right now with Universal Music and Suno, right? They're trying to find a way forward what's going to happen with the training data. And you know what? SUNO is making a fantastic work. I have lots of experience with suno. They are phenomenal in what they do and what they offer. Okay, now the next iteration of music business, for example, I'm taking music because this is maybe sound very familiar or the movie, uh, industry, right? So you have many companies like Runway or Midjourney, they have been training on movies, uh, data and actors and voices. Somehow this data got leaked out there, right? Nobody went out there to intentionally buy the data. There was no legal framework in place supporting that. Somehow your data one way or another leaks. Now imagine if you are an insurance company and your vendor leaks the data of your clients because he needs the uh, power he can get from the uh model from OpenAI or Cloud or whatever. Then even unintentionally your data is leaked. So you need to make sure that you can work with a vendor who can offer a sovereign solution to you. It sovereign solution means it is exactly the solution I described of what we offer. This is the only solution, architecturally speaking, that you need in order to make sure that you tick in the box when it comes to heavily regulated enterprises. Otherwise it's not safe to do so. It's not even safe to have a poc.

Speaker A: Yeah. So I think earlier on you mentioned that it's going to be within the next 12 to 18 months that you will really start seeing companies make the most of agent take. What do you think the next couple of years will actually look like for the companies that get it right?

Speaker B: The best question in the industry is this question. This question is. It requires a very simple answer for all of the companies to listen. So the trailblazers in terms of small medium enterprises or large enterprises who are going to finally adopt and put into production mature agentic AI systems, not just one AI agent, but a whole system of agents. Okay. And they put it successfully. It requires many steps to be successful besides choosing the right vendor. As I said, your data is very critical. Your data structure. The ones who do that are going to be the ones outperforming with a massive difference from the ones that don't. The rest of the companies are going to be dead in the water. They're not going to be efficient at all. They will not be able to compete with the rest of the companies in terms of pricing, in terms of production efficiency, in terms of speed, in terms of cost, the cost elements. So, uh, the difference is going to be in very simplistic terms. Trying to travel from New York to London on a boat compared to a commercial airliner. That would be the difference and it would be huge. And the more. Because you said in the next couple of years, okay, I'm sure this year we're going to see many more surprises. Uh, I'm not going to be surprised if uh, you know, very big ventures in the US achieve AGI probably by the end of the year or early 2027. So the available technology out there, it keeps becoming more powerful down the line, down the road. Right. So, but the rest of the industry and humanity is not absorbing that at the same pace, which is understood. Right. But the tech pace in terms of progress is multiple times faster than the adoption and the digestion pace of. Once this begins, then you're going to see, um, I'm sure we're going to see comments that we know today. They are probably leaders and they might be wiped out overnight.

Speaker A: So I think we've also spoken a lot about value and the value it can give to companies. And I think there's been a lot of talk about the time. Time is money and there's going to be the rise of the time economy. People are going to have more time on their hands. What do you think that looks like in practical terms for a founder that is running a business?

Speaker B: As uh, I said, if you want to save time, you need to focus on one. Getting one thing right. Okay?

Speaker A: Mhm.

Speaker B: Get your data right, structure your data. This is 50 plus percent of your success story. I can tell you. Everything else is just jargon and passwords and very nice fluffy glitter LinkedIn posts and videos and beautiful sales women doing whatever, um, whatever. If you don't have your data factored, you're dead, my friend. This is the end of the story. Do something else with your life. This is, this is time saving. This is cost saving. This is your insurance, a guarantee of, uh, achieving that in day one, very fast. I'm not saying it's easy, I'm saying it's absolutely necessary. You know, we have, I think this is the only year talking to Investors and to prospects that people have started understanding. A glimpse of understanding that. You know what they say? We're not ready for you guys. Oh, okay. Please elaborate on that. They say we don't have a structure yet. We understand the value that we can get, but it would be a total waste of time and money right now. Give us some time to structure our data. We have to tidy up things and then we're gonna circle back. And I appreciate that very, very much. Cause you're making your life easier and my work easier. You're making your value to be there day one. And then I can claim I gave you value one. So you helped me help you. Remember if you've seen the movie Jerry Maguire with Tom Cruise, he was, um, talking to Kuba Gooding Jr. He says, Help me help you, help me help you. This is exactly, this is exactly what we're in right now. Okay? If you want to help me, you have to help yourself by structuring your data. Just do it, do it. Stop any POCs and whatever experiments. Nothing. There's zero value in that. Okay. Structure your data and knock on my door.

Speaker A: Perfect. And I just kind of wanted to move away from the, um, agenda ki bit for a while and just say, as you previously founded another company and then went ahead and founded Superbo, what are the biggest lessons that you've learned from founding multiple companies?

Speaker B: The most important thing is that a, uh, perspective is something that you need to take seriously. The standpoint that you see things as an entrepreneur. First of all, I was blessed in my life not to come from an entrepreneurial family. So I used to be an employee. So I've been there, done that before I decided to become an entrepreneur. Now when I first decided to found a company, I thought that would be better because I'm not gonna have him. I have no boss, right?

Speaker A: Mhm.

Speaker B: So I can do and act at will, but that's not the way it goes down. Right. So you always have a boss. And the boss is not your investors. Actually they are your boss if you have investors, but your boss is your, this is the ecosystem. Right. The way that you speak to your clients. So over the years, I think now I'm very confident right now that, uh, clarity is very important when you talk to people. And clarity comes from very simple wording. If you use jargon, buzzwords, scientific, whatever, and you assume that your audience is there to understand and you are in a table with diverse audience, someone is from marketing, someone from tech, whatever, then you are losing your audience. Use baby language and helping People understand what you're trying to solve for them. This is number one. Number two, you, you have to respect your people, people who work to your venture. Okay. You know, startups, the most common thing is that, uh, you know, the startups struggle with finances and cash flow. It is important to make sure that you let your people understand that they can rely on you in terms of transparency and clarity. You're not there to, you know, they need to understand that you are the right person, that you're going to do your best in your capacity to make sure that you overcome problems like cash flow and finance and new projects and investors and stuff. This is very important to gain their trust because there are so many good reasons for someone to stop working for a startup and going to work for a big hyperscaler. But then again, we should not look for stability in times of great instability. And these are times of great instability. Right? You have plenty of wars going on. You have Ukraine, Russia, the Middle east, civil wars with proxies and stuff. And then you have technology, huge strides. You have to remain calm and maintain your psychology and your morale. And you know, it takes, I say guts to do that. If you, if you don't have the guts, don't do it. Something that, because you said about, uh, starting a company, I see that there is a very big tendency pushing and incentivizing people to start their own company. And I'm not in favor of that. Let me explain why. Many people, they have talents that can be really suppressed when they found the company themselves. Not everyone is fit to be an entrepreneur. Mhm. And should not. I think great people and great minds should often just be great minds and great people and don't do the dirty work. Being an entrepreneur in a startup has lots of dirty work. Okay. You need to do no sleep, lots of travel, no eating, different, very difficult family life, working, balance. Everything is dynamic, everything is fluid. You need to accept all those things. It's very hard to accept. So instead of incentivizing people to start their own company, I don't know why we incentivize them. Um, they could be great leaders for great big companies instead of incentivizing them to start their own company. For what? Do you think that an entrepreneur is better from an advisor? No. Do you think an entrepreneur is better from a C level who works for Google? No. It's just different. So entrepreneurs are different even, you know, even if I sell Superbo today for a billion dollars, I would be again an entrepreneur. I would continue to do my next venture. Not Because I need the money, because I need the suspense, right? Because I want to build and create something. So I want to be a constant builder of doing something. When you build something, you also need to be aware of all the pitfalls and the risky journeys. Has a lot, a lot of risk. So instead of incentivizing people to stop start up, uh, you know, their own venture, we should incentivize people to make sure they understand and clarify what is their biggest talent, not what they love most. But are you good in something? Are you very good in something? Okay, this is where you focus. You don't need to be an entrepreneur to do that. This is, uh, one of the poisons of our era. No, you don't need that. I'm not better than you, you're not better than me. We're just different. So, you know, we really need to rewire our brains the way we think. And I see that, you know, especially, especially in Europe, we have lots of. We incentivize people and there is, we incentivize people to start their own company. And, um, at the same time, in terms of startup community, Europe is 200 years behind the US so even from that perspective, it is a hike to do that. It's going to be hike, right. If you are in the US and you are in San Francisco, in Silicon Valley, maybe you can, you know, think about it in a more positive way. But Europe, I can tell you from first hands.

Speaker A: Great. And, um, my final question is, what's Next for SuperBo? AI, do you have any goals for the upcoming year?

Speaker B: Yeah, we do. Right now we're focused on keep building the, our asset. This is a never ending story. Okay? There's no. We will never reach a point that we say we feel confident with the asset now we can become complacent. No, you're not allowed to because you are in an ecosystem which is agency, AI and AI in general, which is an ecosystem that lives in a natural habitat that is called R and D. So all of the other gears, uh, previously and all of the other ecosystems used to have R and D on top, optionally. But here R and D is an option. R and D is actual the ecosystem. So we need to keep building the assets. So for sure, this is a constant dynamic goal for Superbo. Every single quarter, we keep building number two. I'm going to have to get, um, investment this year. We got an investment last year, but we need to, uh, run a second round and make sure we raise enough money to allow us to expand in new geographies and diverse geographies in new sectors and also keep building the asset on the architecture that we have. And I think our uh, people at Superbook have been doing a marvelous job. Our cto, our uh, chief product and innovations officer, uh, our uh, go to market Vice President, uh, our chief delivery officer, our chief commercial and operating officer. All these guys are, these are dream team. It would be the best team I could ever ask. And they have been really. This is, these are the building, these people are the building blocks of Superbo.

Speaker A: Mhm. Amazing. Well thank you so much for your time today Dmitri. It's been amazing learning more about agentic AI and about what superbody. Thank you.

Speaker B: Well thank you very much for having me.

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