20VC: Nebius Co-Founder on AI Infrastructure Bubbles | The Real Impact of Open Source on OpenAI & Anthropic | How Price Elastic is Demand for Compute | Could Nebius Sell 10x More Compute If They Had It & more with Roman Chernin

25:19We see all these optimizations that happening that changes the price of the tokens in order of magnitude. So people so much speak about the cost of particular GPU, but if you do the right thing with the model, you can change the price like in the times. And this all should work together as a system, not just as a, again, if you speak about raw infrastructure, then you can manage only the price. But if you build the platform And if you provide the high level of the service to the customer, then you can extract much more economics, not only from the infrastructure, cost structure, right?

25:55Harry Stebbings:If we move to that second layer, then we move away slightly from capacity to GPU hours, the product itself, multi-tenant. What is the main question that you ask yourself within that segment? If in the first capacity, it's how much revenue concentration we have. What is the big question in that layer of value? What customer needs? You know, you speak with a lot of product founders, and this is the same. What customer needs at the end of the day, how customers evolve in their needs, where is the demand moving. So it's like we see all this transition from training to inference. We see transition from just using the models to building agents.

26:35And we see the transition from mostly AI labs consuming AI compute to enterprises coming in the game. And all the time, if we want to be relevant, we need to follow the changes. And this is the main question which we ask us in the product. What customer needs and what is Nebius, what is our value that we need to create? Because again, we are a small company. We cannot build everything. And we need to be very precise on what we can do better than others and where the value that we should focus on, given how customers are evolving.

27:07Harry Stebbings:What changes are you seeing in customer needs that you're not seeing discussed much in public? Everybody's talking about this moving from training to inference. I think it's just very 100 ,000 feet like view because this move means actually people build specific products. And in those products, they have their economics, they have their trajectory of growth. And it's not just like whatever. The same GPU is just used for other purposes. I think it brings the new requirements. You need to build your inference platform. You need to help your customers not only run inference, but where the model that the inference come from.

27:50Everybody is taking open source models and fine tune or RL them. So how do we help them? And then when they run them, they generate a lot of data. How do we help our customers? like when they already run their application, their inference to collect the data, to create it, and then use it to improve the model or the application that they run. So people like this flywheel analogy, like you run inference, you generate data, you can observe this data, then you can improve the model that you run and kind of continue improve the quality of the end product. So I think there are a lot of pieces, both on system level and both on AI magic level, if you want.

28:32And I think the most fascinating moment for me is that I think what we see is that barrier to build is going down. So we see more and more customers, like builders coming to the market that not necessarily AI researchers or not necessarily inference engineers. And the value that companies like Nebius can create is actually to lower the barrier to build AI-enabled products and AI-enabled applications that really work and incorporate, like hide from the developer all the complexity of infrastructure, some complexity of AI, like how you tune the model or how you optimize the inference. It's a lot like research heavy area as well.

29:16And just let people focus on their customers and use case. By the way, the same way like they do with closed ecosystems like Anthropics, OpenAI.

29:26Harry Stebbings:You mentioned the word differentiation. And one thing that I was discussing with my partner before that we had is a thing we have to discuss. And it's within these layers. But you've spoken extensively about product build out and the importance of building the product underneath capacity. When people look at you versus other NeoClouds, you know, we look at you versus a core weave. You both run GPUs. You both have NVIDIA relationships. You both have Matter as a customer. What's the difference? I don't like compare with others, but the principles we build are full stack. We call it full stack integration.

29:59And you can think about it like full stack down and full stack up. Full stack down is we are really deep in physical world. We build data centers. We build racks. and servers, we build the platform. And when you control these things downstream, you can move faster and you can squeeze more cost and provide more economically viable solutions for the customers. And then your vertical integration upstream is actually what we spoke about, like product and how can you follow the customer's needs and customer segments and not be limited by the small population of the people that just need infrastructure, but really serve kind of enterprises and product companies with like meet them where they need us.

30:47And this is like, I think what we're different. And then how it showing up, I would say is again, less concentration in the business, more diversified customer portfolio. We believe long-term better positioning for going to enterprises where we believe eventually a lot of demand will come from. Again, now most of our segment is working. It's AI natives working with AI natives. But we have a huge market of enterprises, existing companies, and someone needs to serve them. They will not buy raw compute. They will need platforms. They will need tools. They will need us to respect their legacy and being able to work with their more complex environment.

31:30They're not nimble. They have data to migrate. They have systems to integrate. and that's the big game and i think that for us it's kind of the main direction to move you

31:42Harry Stebbings:mentioned the third layer of the four pillared stack being managed inference for people that don't understand how do you think about this layer and how would you explain it to them yeah very simple you build your product on whatever call your where your wipe code i'm actually an open AI and a code. Okay, good enough. You built your great products with open AI, you cracked the use case, and you started growing, and you have amazing traction. The only problem may be you don't have enough margin, or you want to start applying more aggressively the data and tune the behavior of the model, and you cannot do it in the closed ecosystem.

32:24So you go to internet and you read there are a lot of great open source models that on the benchmarks are close to open AI and you think oh great it will be 10 times cheaper inference is cheaper I can tune those models I can apply my data and my product will be better my growth will accelerate so you go you you take the weights from hugging face you take some engine to run it like vllm sglang something and then it doesn't work because you need to really extract the value you expect. You need to do like optimizations. You need to deploy it in a proper way. You need not just one GPU tokens extraction or one host setting, but you are the large product.

33:09You run on hundreds of thousands of GPUs already. You need all the orchestration. You need the caching. You need the observability. Like your customers ask you, like, how does it work? And so on and so forth. And by the way, you had all of that on OpenAI because this is like the production service for you. You don't think about infrastructure when you work with OpenAI. Just subscribe for the plan you need and you pay for whatever. End result. And so that's where you need the product like TalkingFactory. TalkingFactory gives you the managed inference with the open source or specialized models. You can run existing open source, vanilla open source model, or you can tune the model and deploy your own like weights.

33:48And then we'll take care about all the rest. We'll apply all the optimization techniques, we'll manage the better economics for you. It will be reliable. You don't need to think about the next 100 GPUs where you will find them. It's like managed service.

34:04Harry Stebbings:With Token Factory, you run on 60 open source models. And you've said before about cutting inference cost per up to 70 % through optimization. Can I ask a dumb question, which is how do you actually make a token cheaper? Yeah. So it's not a magic again. You take the model, like some baseline model, and then you can optimize it for particular scenarios that you have. So you can actually, you can distill the model. You can make the same, like the smaller model that works with the same quality. You can do spec decoding, you can optimize caching and so on and so forth. So you take the model and out of this model, you actually build a system that in your particular case works with your requirements, with optimized economics.

34:50And by the way, one of the things that also I think important for customers to use managed platforms like Token Factory, the models are changing every week, every month. Like right today, Minimax 3 was released and there is Nematron Ultra that was announced. This happens every few weeks. And every time the new model released, it may work better on some benchmarks and maybe not on other benchmarks and so on. And you want to have flexibility. You want someone to support you on experimenting and actually adopting the new best models for your use case every time they come online. And then like the platforms like ours, abstract from you all the work that you need to do to actually change from one model to another to benchmark all of them and so on.

35:37You can be sure that you will be on the frontier every time something new is happening. It will be in the platform. You will be able to test it. If it works better for your use case, you will be able to switch and it all will be kind of smooth and transparent for you.

35:51Harry Stebbings:Does the pace of model development sustain? like you said that i would argue respectfully you said every couple of weeks i'd say every couple of days does that sustain in five years time are we seeing that level of iteration well i don't know i think that it's a good chances that we'll continue to see a lot of niche models show up and improved i don't know again i'm a believer that we're quite far from the wall and we will see a lot of models improvement happening. I think that what we also see is much more new modalities and specialized models coming in game. So we speak about this frontier LLMs, but there is entire world of life science models, robotics, world models, video models, image models.

36:41They all have their own use cases as well. And we see more and more small specialized models, particular use cases coming, like very much optimized. Just this morning, I spoke with a team here in Israel that develops cyber defense foundational model, like the model that optimized to build cyber defense agents. And again, they don't start from the scratch. They take some of the open source foundational model, but then they train it for the particular case optimized for the quality and the latency that needed in this like cyber defense use cases. And I think we'll continue to see it. We'll see a lot of specialized post-trained models that still need optimized inference and optimized like infrastructure around them to let customer use them.

37:31Harry Stebbings:On token costs and token usage, what are you seeing that you don't think other people are talking about enough? What has shocked you recently? Again, I think everybody is speaking the same thing, like how fast it's growing. When we see these trajectories of some companies like Anthropic and Coursor and Cognition and encoding, and now we start seeing in other verticals as well, some healthcare examples, financial use cases, I think. I think it's quite amazing. What's interesting is to see how non-EI startups are moving. I can give an example. We have the customer of Revolut. And when we started working with them, I think 99 % of their budget, inference budget, was in closed models, in OpenAI.

38:18And they started to crack some of the use cases. And some of them didn't work for them economically. So they practically couldn't replace the humans or cannot enhance the humans in the use cases they wanted to address. And they started moving to open source models. But it didn't move fast for them because they had to spend time on building the entire engine internally in the company. And first of all, they were focusing on evaluations. And I think this is something that people underestimate. How important to build kind of the foundation for improvements and experimentation engine when you understand as a company, as a team, what is good for you.

38:59Because again, like you close some use case, it works. But then you want to change the model. How do you know you don't ruin the quality? You need to have like metrics. You need to have a valve mechanism. You need to have the CICD process established for AI development. And I think that what we see, a lot of customers revolute. They have these foundational investments that need to do in the understanding of how to evolve the models, how to actually safely integrate them in their production processes. But when they solved these foundational problems, they start growing exponentially. And I wouldn't underestimate how fast those customers can grow when they build the system that let them ship fast.

39:47And ship fast means they know how to evolve, they know how to make decisions. And this is something that we see across a lot of customers. They have this, you can call it foundational investments, a cold start problem, how to start shipping. when they solve it, they start to grow exponentially. And they can use different models. They can build much more products inside the company and so on and so forth. And I think this is something that when you look from outside, you kind of, oh, they are not growing. They start small, they take time and so on. But this is if the company has a strong team, they build this foundation and then they start growing exponentially.

40:27And I think we'll see a lot of explosive growth in enterprises, in the digital, in the cloud native companies like Revolut, Shopify, Prosus, Booking.com. When they solve this cold start problem, they build the system how to ship and then they will grow like their AI adoption like crazy.

40:47Harry Stebbings:How much more do you think Revolut will pet you in three years time? I don't know. I don't know. I don't want to speak about that. No, but I can say that they, in total, I think they, like, they grow like this. We all see this AI companies reporting IRR growth, right? For them, it's not IRR. It's like their budget. But I think that the most advanced companies, their AI budget, and it's not like this fake, all this token maxing race. We see it, like, how they do it in the production workload. So they grow the same pace, like this AI native companies reporting. they are growing their AI consumption equal to their IRR.

41:27So the companies like Revolute, they're growing the same exponential trajectory.

41:32Harry Stebbings:I always push back on people who claim that open source would be a credible threat to the largest model providers because I said, listen, the biggest enterprises want reliability. They want security. And most of all, they want ease. They don't want to be tinkering around with all the architecture and shit beneath the surface. What you're telling me is you're able to be all of that to allow them to pipe away from those providers and have a cheaper, better experience because you take away the plumbing. Yes, but my point is closed models with open source models, it's not about like reliable or not reliable.

42:09Again, the work of the companies like Nebios to make possible, like as you say, not think about plumbing if you want to use alternative models. But I think it's about capabilities. I think that closed source models like frontier models are great and they will become even better and they will solve so many problems that we don't solve yet. And we have such a diversity of the use cases we want to solve that there will be market for the smartest models of the world, the fastest models of the world, the in-between models of the world, smart enough, but cheap enough. And you as a customer will be able to just pick the right source of token for each particular task.

42:53And back to the agentic layer point, maybe it's even one to be the customer task to choose which model to call now. It will be the engine that knows all the capabilities, like all the models underneath. needs. And then when you go to OpenAI and you do the research, you don't think in terms of how many loops you want it to make. You don't think in terms when it should go to LLM and when it should go to search. You don't think should it now call which prompt to call, right? It's happening. You give a task, there is an engine, the reasoning engine that decides how to run this task and you got the result.

43:35So I think that a lot of enterprise cases, a lot of these agentic tasks will be solved in the same way. When it's not you as a developer that focusing on customer need will need to kind of orchestrate all these tokens and models. And then we will need all the models, the smartest one for the most complex intelligence and the fast models that can do quick iterations. And again, we don't speak even about all the modalities and like what we'll need in the physical AI world and so on. So I think, again, my point, we will have enough of Pi for different models. And what we need to do as an infrastructure company is just help or extend we can to make developers comfortable how they use all these capabilities that models provides.

44:27Because as you rightly said, that it's not about model capabilities. It's not only about model capabilities. It's about not plumbing, getting them working, getting them optimized, getting them reliable.

44:40Harry Stebbings:When we look at the explosion of models and the specialization of models, like you said there, and how many will be built and the depth across different use cases, sadly, the one thing that is quite clear is that Europe does not have anywhere near the model build-out that we've seen both in the US and in China. How important do you think it is that nations have their own sovereign models? It's a good question, but looks like the world is divided. We may not like it. Having good enough foundational models available for the big parts of the world is important. And I think here in Europe, we should think how we have enough capabilities available here.

45:24We had a lot of conversations over the last couple of years about the sovereignty and so on, all this like so sovereign AI agenda. And I think it was too much concentrated around like megawatts and power rather than on what we have on the builders layer, right? Megawatts will come. I think that it's what we in Nebios always told is we will build infrastructure. The companies like us will build infrastructure if we have demand. And demand is coming from the builders. What we need to care about here is to have more great companies like Lovables, Black Forest Labs, Mr. Eyes of the world. And we have enough people that invest in research, have enough people that invest in the products.

46:09And then they will create enough of demand and there will be enough of flywheel again to have a good enough models if we need. So I think this is something that we should care about.

46:19Harry Stebbings:Where is the most interesting area to invest today? Okay, I'm giving you four options. Infrastructure, horizontal model, vertical model, application layer. I mean, we built infrastructure, so we are quite happy here. I think it's a good place to be in the current world, even though for some extent we are building kind of the easiest part. It's complex execution, but we kind of know what's needed and our customers help us to understand what's needed. I think the most amazing people in this industry are those who take a risk to go and build end user products, in my view. They actually drive the most of growth here.

47:00People who take a risk, like the real risk of building something people would need or not need. I think this is the most, the heroes of our like AI journey.

47:10Harry Stebbings:Speaking of heroes of AI journeys, before I do a show, I go and speak to, I'm very fortunate now, you mentioned earlier, I've interviewed some big people. I go and speak to some of those big people. A theme that did come up when I was speaking to them was the relationship with NVIDIA. And is a marriage a marriage if one has more power than the other? How do you think about the power dynamics in a relationship with NVIDIA when they have so much power? Who has power against NVIDIA? We look at this in a very simple manner. We just need to build what we build. We need to build our product. We need to tell our story.

47:46and then the rest will complement it. I think what is the most fascinating, NVIDIA is still, for a big extent, is an engineers-driven company. And I think the best thing you can do to get respect from NVIDIA, it's my read. They may have a different point of view. But if engineers in NVIDIA respect your engineers, you will have the right foundation for relations, let's say. We managed to prove again and again we know what we build and we have a strong engineering team and i think that they see it and they respect it and we have a lot of like engineers to engineers relations on physical like on a on a hardware level on the software layer on the inference platform layer and the better engineers and nvidia think about you the better relations and partnership i think it's enables and and again we may be maybe wrong thinking this way but that's what we see like we can do and we just focus on being reasonable and being kind of focused on the long-term value it sounds like fluffy everybody say it but do your fucking job at the end of the day right

48:59Harry Stebbings:i'm going to title this roman just do your fucking job no what else we can do i mean We are in such a race. We just can do the best to do our work better. Do your fucking job. I know it's funny. I like it. But like, what's the hardest part of just doing your fucking job today? Four dimensions. Build scale. Build product. Work with customers. It's actually like two dimensions we discussed, like scale and product. The third is customers. We are in the field business. We like to say that cloud is both sales business. When you sell, you sell the promise. And then you need to satisfy the customer. And working with the customers, covering the customers, having this strong customer-facing engineering team, FDE team, this is the third dimension.

49:46Go talk to your customers. Make sure that they know you, that you know them. This is the third dimension. And the fourth, the most boring but also the most exciting, is the capital. We are in the capital-intensive game, and we're competing with the most capitalized companies in the world.

50:04Harry Stebbings:If I gave you unlimited budget, what would you do differently? Build faster. That's very easy. Build what faster? Data centers and fulfill them with GPUs, like just build faster. Our Copics program this year is$2025 billion. Our competitors, hyperscalers, have 10 times, like eight times bigger. If I would have 10 times bigger capital, I would just build more data centers and fulfill them with GPUs faster and serve more customers. That's what we started with. Like, what would I do if I had like 10 times more supply? I would move faster. Gavin Baker said, I think quite intelligently, that permitting and regulation and the delayed build out of data centers has actually helped.

50:50Harry Stebbings:Because if I enabled you to build 10x the data centers today, it would actually create the glut. Yeah, it's actually a great question. And like our investors sometimes ask us, like, what is the main bottleneck? And the main bottleneck, again, it's everything. You need to look at this from the time span perspective. In the next six months, the capital cannot help. Six months is too short time. You have what you have, you need to deliver. Then in the next 12 months, you can accelerate something, but it's more like capacity constraints. And in the next 12 months, we can accelerate with the capital or with execution something.

51:30But then in 24 months, you definitely can unlock so many things. We are not building one data center. It's also important to understand. We are building the portfolio, the portfolio of capacity. The more execution power we have, the more capital we have, we can do the things in parallel. That's why we do how we do. We secure power and land, then we build data centers, then we fulfill them with GPUs. Every next stage requires more capital, but we do as much as possible in advance to make sure that when we will be in the next stage, we already have power secured. When we will have enough capital to deploy in GPUs, we will have data centers that are up and running.

52:09So it's like phases of investments. And again, the bottlenecks are different on the different time span perspective. So obviously, if you have more capital, you can move faster. Not in six months, but in whatever, 18, 24 months for sure.

52:24Harry Stebbings:Can I ask you, when you think about the data center build out there, we're seeing more and more public angst towards AI. Eric Schmidt's getting booed off stage, not because of the content, but because of the AI dimensions. And we're seeing public resentment towards data centers. I think 40 out of 100 now are not being built when they go through planning and approvals. how do you think about and reflect on that internally? This is the environment we need to work in. There are two sides of the thing. One is how we think pragmatically as a business. That's what I said. We think about it as a portfolio of the projects.

53:00We need to make sure that we are oversubscribed. And if one data center will be delayed, we will still deliver enough capacity to our customers. And most of the customers, they are not locked in one physical location. It's a cloud. We can build in different places and then bring the workloads where we have capacity. But this is the pragmatical side of the things. Then what we obviously see that communities and the local authorities require the companies like us to work closely with them and explain and show what we do and work with them on their concerns and address them. So this is the reality.

53:42You can compare it when Uber started growing and in many places there was the pushback, right? So, oh, what's happening is something new. It's moving too fast. We didn't expect it to move so fast and so on. And you go and work and you explain and it's just a part of your duty to engage and work with the new communities that become dependent on you. They have concerns and sometimes they just, they have concerns because they're not educated enough. Sometimes they have rational concerns that you can address and the same. Do your job. Do you think you've done a good job at it so far? We come from the place we always think that we didn't do enough.

54:25I think that we got quite a progress in the places where we started building. Historically, we had more experience in Europe. We now probably 70-75 % of the new capacity that we build midterm is in the US. So we build a lot of presence on the ground and like to communicate with those local communities in the US. And we try to do the best job here. We need to do better always, but we are moving.

54:53Harry Stebbings:Can you help me on another one? We laughed earlier when we said about space. Data centers on planet Earth is a very difficult logistical build out. Data centers in space? I love technology. I'm an optimist. Is that fucking nuts? I think everything we see is fucking nuts. My view is very simple. So many smart people now working to make it happen. So most likely, I may be less pessimistic that we'll see, I don't know what is there, we'll build more in space than on Earth in three years, maybe not in three years. My view, I'm humble enough to say that so many smart people are trying to solve this task and bring compute to the space, that why wouldn't I believe it will happen?

55:38I think there are a lot of challenges still, a lot of things to figure out. But if someone would say us that even three years ago that we will build like multi gigawatt data centers, it will be like large interconnected compute clusters, would you believe? I didn't think like that.

55:55Harry Stebbings:We are here. It's routine. I want to do a quick fire with you. So I say a short statement, you give me your immediate thoughts. What job does not exist today that you think will be very common in five years time? Space data centers technicians? No, probably it will be robots. So on a serious note, one thing that's obviously happening is we're democratizing what people called being developer, right? Now, each of us can be a developer. And like what I mean being developer is to convert the idea in some digital asset. I hope that this democratizing of building, letting each of us being builder, will open up so many opportunities that we even don't imagine yet when we will give like millions of new people, tens of millions of new people's ability just to convert their idea into something that works very easily.

56:51We will see a lot of new businesses and a lot of new ideas just coming in life and they will create a lot of new works that we don't even think exist so it's like second second orbital of all this kind of democratizing of the building also what is challenging and what will need to be changed and i think it's like as risk as an opportunity is how the education will change because now when everybody has access to intelligence what should people learn you definitely don't need them to learn the facts everything is available like all the knowledge is kind of available like how do you really like train people to think when they don't need to think so much how to teach people to to continuously change like many professions will be not stable how do you help people to find themselves in the changing environment think and learn the new concepts constantly.

57:52This is something that gives a lot of new opportunities but also creates a lot of risks.

57:59Harry Stebbings:You mentioned you have two teenage daughters. What do you advise them entering the workforce in the next 10 years? What do you advise them? What I literally tell them is I think two things will be needed. I don't know what will be needed, but I'm sure that two things will be needed. One is being able to communicate with the people with empathy, with empathic communications. So like understand humans, communicate with humans and be empathic. And the second is creativity. All the art, I hope that the art in a way will exist. So all the hard skills that I thought 10 years ago will be needed. I thought that the most important thing they need to learn is math and engineering.

58:40Now I'm far from this belief and I'm quite happy they match more in the soft skills than I was when I was a kid. Being able to communicate with humans, understand the humans, be empathic to the humans and have this creativity idea, like being able to try new things and be creative. I think this too, if you can help your kids to develop those, I think in 10 years they will be in demand.

59:06Harry Stebbings:There's a question of how do you teach creativity, but I completely agree with you. Finish this sentence. The biggest threat to Nebius is not competition, but dot, dot, dot. but consolidation yeah i think that the main thread for nebios as a business is the world will be too much consolidated we try to be diversified like we try to solve like problems of different customers and have different customers and different layers if you'll end up in the world where i don't know three five supermodels super companies super empires control the world then nebios or companies like nebios will be needed only to help them maybe serve their needs It's on physical layer.

59:46So I think that in general, the consolidation is our main. The more world democratized, the more world diversified, the more we need as a business.

59:57Harry Stebbings:Do you think that's likely? We're seeing the concentration of value to fewer and fewer players. We're seeing the opposite of diversification. I hope it will not happen. As a business, I think that it's better for us as humans as well. For you and me, the world remain quite diversified in different manners. And I'm optimistic here. I think that there are so many people that want to build something independently, let's say. Like there is a lot of people with the need to try things and build new things that it's organically creates this pressure and organically creates more diversified world. So hopefully those will remain.

1:00:38Harry Stebbings:Penultimate one. Leo Ashenbrenner is a famous investor right now, has a huge cult following. He recently disclosed a very large position for him, 5.3 % of the company. I think it's 15 % of his portfolio. How do you guys sit internally? Are you like, yeah, go Leo? No, yeah, like I would lie if I wouldn't say that we didn't notice it. Obviously, like everybody noticed it and like the stock jumped and it was a big news around. We take it as a justification of what we do. Then you got this justification, you say yourself, okay, those people still give you a credit that you will execute. I come back again and again to what we do is post-sale business.

1:01:25Every time we sign a deal, every time someone invests in us, they give us a credit and opportunity to deliver. But then go back to your job and deliver. And I think that we are in such a market where emotional market as well, that you should keep yourself down to the ground. Remember that all this growth, all these credits that customers give you, it's opportunity to deliver and go do your job.

1:01:54Harry Stebbings:You're such an Israeli. Americans would be like, yeah, go. You're like, no. I think I'm Russian in this way. Like Russians always know that you need to look very pragmatically. And, you know, Russians always with these faces always expect something will happen. You need to be ready. You need to be ready. So it's a really important part that comes from our CEO also and founder, Kadi. You wake up and it's a new customer, new day. You need to deliver. Nothing is guaranteed. Just you need to concentrate on the work. And I know how much effort the team is putting on things to work and how much depends on every day's dedication and how fast market is moving.

1:02:40And to stay relevant, you need to continue moving in the same pace or try to move in the same pace with the market. And I think that on a romantic note, I would say that we could celebrate a little bit more, but we just don't have time to use the opportunity. Actually, to say kudos to the team. I don't think we celebrate enough. And I think that, I think it's right we are not relaxed, but I think we could celebrate a little bit more. Just give the team like more respect and like how much is done. And it was not easy and it's still not easy and it will not be easy. But yeah, never stop. We cannot stop.

1:03:18Like it's like a shark. You are alive when you move, right? So this famous thing. So we have to move.

1:03:24Harry Stebbings:On that note, I cannot thank you enough for joining me. and for putting up with my very meandering questions. You've been fantastic, Roman. So really, huge thank you. Thank you, and too kind to me. But before we leave you today, you have the idea, but often with AI tools, you hit a wall. Well, Base44 is where that friction disappears, turning how you talk into how you build. Full stack web and mobile apps, sites, autonomous super agents, all built in minutes, not weekends spent on damn configuration. Base44 ships it all out of the box. The backend, the database, the authentication, and the hosting.

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