- AI engineering is emerging as the mainstream successor to web development, with agents disrupting both how software is built and what it achieves.
- Agents represent a new software paradigm, making a vast amount of previously uneconomical automation now viable, leading to a significant increase in total software production.
- Engineers are crucial in this evolving landscape, building the application layer atop commoditized AI models, which drives demand for software engineers and fosters innovation.
The New Application Layer - Malte Ubl, CTO Vercel
- AI engineering is positioned as the legitimate successor to web development, shaping the next decade of software development.
- The next generation of engineers will be molded in the AI world, making them adept at this new discipline.
- Agents make previously uneconomical automation viable, enabling a massive expansion of software that "should exist."
- Practical agent archetypes include 24/7 customer service/support, automating research phases for human decision-making, and surfacing existing information within an organization.
- Building agents to eliminate "boring work" (toil) significantly improves job satisfaction for human teams.
- Infrastructure development must adapt, prioritizing APIs and CLIs over UIs, as agents become the primary users of software platforms.
- Agent deployment requires sandboxes with rapid startup times and a fundamental architectural separation between the agent harness and where generated code executes.
- The future likely involves commoditized AI models, empowering engineers at the application layer to create business value and drive innovation.
agent — A new kind of software capable of automating tasks that were previously not economically viable using traditional methods.
sandbox — An isolated computing environment used to execute untrusted code or processes safely, common for running AI agents.
SaaS — Software as a Service; a software licensing and delivery model in which software is licensed on a subscription basis and is centrally hosted.
deflection rate — In customer support, the percentage of customer inquiries that are resolved by automated systems (like agents) without requiring human intervention.
agent harness — The framework or system that orchestrates an AI agent's actions, connecting it to tools and managing its execution flow.
paradigm shift — A fundamental change in approach or underlying assumptions, especially regarding a scientific theory or philosophy.
commoditizing — The process by which a product or service becomes indistinguishable from others and is primarily valued based on price, like a commodity.
First speaker draws on over 25 years of software engineering experience. From his time at Google and now VERSEL, he will explore what it means to build infrastructure and applications in a world where agents are both the builders and users of software. Please join me in welcoming to the stage the CTO of VERSEL, Malta Ubel. I've been hacking on the thing called chat SDK which is a way to hook your agents to whatever slack, telegram, WhatsApp, chat up, you like, and I've been working on just bash which is a bash interpreter written in TypeScript that gives you something like a sandbox with Zin nano seconds startup time for your agents because they love bash. One thing I wanted to mention is that the reason why I'm so excited to be here is that I used to run a little conference in Berlin called JSConf. And I feel that once in my life I had completely impeccable timing because it was the summer of 2019 and we decided after 10 years it was enough and we went out with a bang. And the reason why this was such a great timing was that there wouldn't have been a conference one year later because of COVID. But also when we decided that we would step away we were hoping that someone else would take the reins and again that did not happen because of COVID. So it's now been more than half a decade and I'm very excited the things are finally starting up again. But it was also clear that it wasn't going to be like a web development conference that would really bring the tech community and Europe back together in 2026. In many ways I think AI engineering is the legitimate successor to web development as a really mainstream discipline of engineering that will shape the next decade of software development as software engineering itself faces an unprecedented disruption. So you definitely the right place today and it's more important that ever to come together as a community and reflect on both our profession as software engineers and AI engineers. And that's because we're facing a disruption of both how we built, which is with AI and what we built, which is AI and agents. And of course disruption can sometimes lead to anxiety. In fact I really actually very often get asked, hey Malta, is there still a place for engineers in the future? And what about that next generation of engineers? And I couldn't be more convinced of the answers, yes. I often give this example of like envision me doing a TikTok video. They mentioned in the intro to have 25 years of experience, which is actually substantial under statement. And so I would not be good at the TikTok video. I should not be recording TikTok videos because I didn't grow up at this. And in a very similar way, the next generation of junior engineers are going to be so much better at this discipline because they get molded in the AI world, just like all of you are. But it's not only the kids that are going to be all right. Well I'll be fine and this is why. One of our main see this is that agents are a new kind of software. Because there was always all this stuff we wanted to automate. But not all of it was economically viable to do with traditional software. But it is with agents. And what that means is there will be just so much more software in the future. It does me with a Venn diagram. Maybe the circle should be better because the circle represents all software that should exist. Imagine all software that should exist. The problem was that we couldn't write all of it because it was too expensive using traditional methods. You can envision all these things. If all this statements, you have all this knowledge about the business, you have to figure it all out, you have to hard code into the application. So much of the software you just would never write because it was obviously too expensive. But now with agents, that part of software becomes economically viable. I can build it now with much effort. And that means that with AI agents, essentially all the software, maybe not all of it, we'll find more in the future. But that circle gets filled out. All of this stuff that should be automated is automated. There's going to be so much more software out there. In a similar line, more and more companies, when they ask that question, whether they should buy some software like a SaaS product or make some software themselves, they're answering that with the make site. Over in Silicon Valley where I live today, we are talking a lot about the SaaS complot galips. I think that's what it's called. People make their own stuff and they don't buy the SaaS software anymore. Actually think the SaaS companies will be all right. Don't worry about them. But as engineers of self, more companies making more software, again, leads to us having more work, even if it's faster. And in fact, the way I've been kind of framed with this for a while is that we are speed running what's really an experiment in economics of how elastic the software market is. The seed is being, but the cheaper it is to make software, the more software we're going to make. And as a consequence, what's actually happening is that demand for software engineers is going up. Now we don't know, there's going to be an S-curve. But there's no signs of us reaching the S-curve. In fact, because we're getting better at agents, et cetera, there's so much leeway in the future, I think we'll be all right. So as AI engineers, it's our job to build that next application layer. And of course, what that actually means is building agents. I want to spend some time talking about archetypes of agents that I'm seeing actually being built today, actually being effective, actually something you can do today without having to make major changes. I think we're all a little bit drunk on the coding agents because they're so great. They work so well. And it seems so obvious that you can translate that to all other domains. And sometimes these things don't go so well. But the thing is that we don't really have to be doing the most advanced agents. You could possibly imagine that there's just so much low-hanging fruit to be done, where you can really, really help companies save them millions or billions of dollars without actually making these massive changes of processes that in practice will always take a long time, fail often, et cetera. So this is what I'm actually seeing in the wild. The first part, when you think about what agents you can think build is people think, well, agents that rings a bell, have a team of support agents. Maybe I can automate part of that. And that's also where the first generation of what we call agent as a service. You can make that acronym in your head. Startups are shipping, right? Like the CRS and decogons of this world. But more generally, I think it's worth asking yourself, in your business, is there a job where it would be quite transformative if that we went from a 9-5 thing because people need to sleep. And I can actually do a 24-7 because agents don't need to sleep. And I think there's many places for that. The next one is probably actually even more important, a call-a-compress-story search. Because every business has a certain type of business process that in a very abstract fashion, heads the following shape. There's some business event and you have to do some research and then you make a human decision, right? And you can just build an agent that does the research phase automatically and that's all you do. That's all your ship, right? And the important part is why this is such an easy thing to ship is because the process is still the same. There's still that business event. There's another research. And there's a human decision. The research goes faster. And maybe it goes from something that took a human 30 minutes. Now they can do the same thing in five minutes. And if you run that process 100,000 times a year, you just save the company whole lot of money, but you didn't increase the risk profile and you didn't have to change the process. At first, we actually have at least two agents of this shape. When you go to Versailles-Com and you hit the contact sales button, that message actually goes to an agent, right? And I hear about 75% of the time that agents say, well, actually, they just want to support and end it over to the support team. But then in the other case, it will go, oh, that's interesting. Let me check out their LinkedIn. Let me Google the company. Let me figure out how large they are. Let me route it to the right person, right? And then there's a human eventually taking a look at it makes sense, but that obviously was something that took maybe a person 15 minutes before and now they don't have to do it anymore. And another example is the same process. If you sent us a abuse report, again, there's an agent taking a look. It's that website abusive. What should we do, right? Still obviously the decision in the end should be done by the extra professional, but they don't have to do all this research themselves anymore. Next is what I think is probably the most magical thing that you can do in any company today. It's just to surface information that already exists. It's extremely common that there's information somewhere in the company, right? But for all intents and purposes, you cannot practically use it. Take for example, everyone, you all engineers, you have issue trackers, right? So is it up to date? From me now, all the time, right? Could it be up to date? Does the information exist? Did you slack it? Did you have a granola recording that technically contains the information that could update your issue tracker? Yes, right? Like probably yes. And so you can build an agent that does this for your company, right? Whenever you have a manager saying, well, give me a list of updates, right? Why don't they already have the updates? Why doesn't an agent have already kind of done their research already? So again, this just makes it takes advantage of existing information, which is so powerful. And finally, for the last big category, there's a magical question that you can do to figure out agents you should build in your company, which is to ask folks, what do you hate most about your job? And actually have a case study about this in Ed for Cell. So we actually did build our own in-house support agent. And it has what's called a 90% deflection rate. It's a 90% of the time. It just helps the person in real time, rather than going down somewhere else. And what happened? This job satisfaction rate on our support team exploded. Why? Because they no longer have to do the boring stuff, right? Oh, my credit card did not reject it. Blah blah blah. Now they get to actually go and figure out actually interesting cases, actually help people who really need help rather than doing all the toil, right? So that's, I think, eliminating boring work is a very noble mission that we should all strive to do for the companies that we work for. Cool. So clearly that new application layer are agents, but we also have to shift. We have to consider another shift that the software itself is going to be used by agents now, right? And I work in software development, developer tools, etc. And I think we're kind of ahead of the game here, speech running, that transformation. What I will share though, is that on our own web properties, humans are actually now on the minority. So in the last seven days, we have not shared this before, over 60% of page use on Versailles Comm where AI agents. In a similar way, we're seeing the way you use a platform going from people clicking around in the dashboard to use it's shifting to our API and CILI. So whenever I now have someone proposing a feature to me and they show me like a UI, I'm like, guys, what's the CILI? Like how do I automate this? How does an agent use this? And though, you know, UI is now something that's so cheap. The other thing that we're observing is that kind of the relationship changes between software development, software development and infrastructure, right? If I didn't write the code myself, I also don't have maybe as strong feelings about how that stuff runs in production, right? And so for a company like us, it's really important that we shift how we deploy infrastructure to a model where most of the software was written by agents and has to just run. And people like expected to run just like they prompted the agents to do the work. And finally, and nobody here obviously surprised about this, the applications themselves are agent though. And that requires us to have different infrastructure available, right? Everyone's now shipping sandboxes. I think it's almost a meme. I was mentioning earlier that I created the same culture as SPASH. And I'm really interested in kind of this innovation of how you can give an agent a computer maybe without giving them a computer. There's lots of interesting stuff there in the market. And I'm sure this conference is going to have lots of stuff there as well. And then also more broadly, again, it was mentioned here for a while. We're marching hat on into security nightmare. It almost feels like a little bit like 1999 where really everything can be hacked, right? And we just didn't know how to make something secure. I think we'll have a root awakening. But what that really means is that we have to be open-minded for how to change things. I will give one example. We think almost all currently popular agent harnesses have fundamentally the wrong architecture. And that is that they combine where the harness runs with where the code that it generates runs. Right? As of actually yesterday, I did see that anthropic, this agrees with that thesis because they on the new agent product. They do have that separation. And it's really, really key. And that's really just also a point that these are all solve over problems. But my message to this is that we are still in the very early innings. And we have to be prepared to be open-minded about paradigm shift happening in the future. We just have the paradigm shift of agents being these very general sandbox-using things. In the future, we will see more of those paradigm shifts. Cool. Last point I want to make is that this new application layer that we're building can thrive in the opinion of the models. Because sometimes model X is better, sometimes model Y is better. But we are as AI engineers building a stable layer on top. And one of the very interesting consequences is that we don't have to work at a model lab to drive AI innovation. In fact, and I think this is almost like a narrative violation, Europe is the leader in AI engineering innovation. Our own AI SDK, which we sell makes, it takes now over 10 million dollars a week. And it's that by Las Gramell who lives in Berlin. Right? It's working on this. Then there's obviously Pi, the coding agent, made in Austria. You'll be hearing from Mario about it tomorrow. And of course, probably some of you have heard of it. There's a little thing called OpenClaw and Peter will be on stage here in an hour. And so it appears to be that Europe against all odds is taking actually a leadership role in AI engineering. But we also have to be realistic, right? Like Europe isn't going to play a major role on the model side. But I don't think it needs to. In fact, I do see kind of two big futures ahead of us. One is where the big model labs win. In that world, AI will stay very expensive. All the value of all that cool agent's tech will accrue to that company. And we won't really be engineers anymore, right? It will be like four deploy engineers, whoever, the winner is if it's open AI and thrumping Google. But I don't think that's very likely. And I think what's actually going on is that the opposite is happening. The model companies are commoditizing. Claude is amazing. Codex is amazing. Google will catch up. And importantly, I'll give them props now. Because I think Google's playing an amazing role here because they have the cheapest infrastructure on the cost side. And so in that commoditized world, they will always decide to make a cheaper. Right? And that will keep the price for where it should be, which is very low. And that's the outcome that we want. Because in that world, we, the engineers are the powerful ones. Our agents are the one that actually create the business value. And it's the application layer where the real innovation happens. This is where open clog gets invented. And that's where the next paradigm of AI engineering gets discovered. And that's really all I wanted to leave you with today. Thank you very much.
TL;DR
- AI engineering is emerging as the mainstream successor to web development, with agents disrupting both how software is built and what it achieves.
- Agents represent a new software paradigm, making a vast amount of previously uneconomical automation now viable, leading to a significant increase in total software production.
- Engineers are crucial in this evolving landscape, building the application layer atop commoditized AI models, which drives demand for software engineers and fosters innovation.
Takeaways
- AI engineering is positioned as the legitimate successor to web development, shaping the next decade of software development.
- The next generation of engineers will be molded in the AI world, making them adept at this new discipline.
- Agents make previously uneconomical automation viable, enabling a massive expansion of software that "should exist."
- Practical agent archetypes include 24/7 customer service/support, automating research phases for human decision-making, and surfacing existing information within an organization.
- Building agents to eliminate "boring work" (toil) significantly improves job satisfaction for human teams.
- Infrastructure development must adapt, prioritizing APIs and CLIs over UIs, as agents become the primary users of software platforms.
- Agent deployment requires sandboxes with rapid startup times and a fundamental architectural separation between the agent harness and where generated code executes.
- The future likely involves commoditized AI models, empowering engineers at the application layer to create business value and drive innovation.
Vocabulary
agent — A new kind of software capable of automating tasks that were previously not economically viable using traditional methods.
sandbox — An isolated computing environment used to execute untrusted code or processes safely, common for running AI agents.
SaaS — Software as a Service; a software licensing and delivery model in which software is licensed on a subscription basis and is centrally hosted.
deflection rate — In customer support, the percentage of customer inquiries that are resolved by automated systems (like agents) without requiring human intervention.
agent harness — The framework or system that orchestrates an AI agent's actions, connecting it to tools and managing its execution flow.
paradigm shift — A fundamental change in approach or underlying assumptions, especially regarding a scientific theory or philosophy.
commoditizing — The process by which a product or service becomes indistinguishable from others and is primarily valued based on price, like a commodity.
Transcript
First speaker draws on over 25 years of software engineering experience. From his time at Google and now VERSEL, he will explore what it means to build infrastructure and applications in a world where agents are both the builders and users of software. Please join me in welcoming to the stage the CTO of VERSEL, Malta Ubel. I've been hacking on the thing called chat SDK which is a way to hook your agents to whatever slack, telegram, WhatsApp, chat up, you like, and I've been working on just bash which is a bash interpreter written in TypeScript that gives you something like a sandbox with Zin nano seconds startup time for your agents because they love bash. One thing I wanted to mention is that the reason why I'm so excited to be here is that I used to run a little conference in Berlin called JSConf. And I feel that once in my life I had completely impeccable timing because it was the summer of 2019 and we decided after 10 years it was enough and we went out with a bang. And the reason why this was such a great timing was that there wouldn't have been a conference one year later because of COVID. But also when we decided that we would step away we were hoping that someone else would take the reins and again that did not happen because of COVID. So it's now been more than half a decade and I'm very excited the things are finally starting up again. But it was also clear that it wasn't going to be like a web development conference that would really bring the tech community and Europe back together in 2026. In many ways I think AI engineering is the legitimate successor to web development as a really mainstream discipline of engineering that will shape the next decade of software development as software engineering itself faces an unprecedented disruption. So you definitely the right place today and it's more important that ever to come together as a community and reflect on both our profession as software engineers and AI engineers. And that's because we're facing a disruption of both how we built, which is with AI and what we built, which is AI and agents. And of course disruption can sometimes lead to anxiety. In fact I really actually very often get asked, hey Malta, is there still a place for engineers in the future? And what about that next generation of engineers? And I couldn't be more convinced of the answers, yes. I often give this example of like envision me doing a TikTok video. They mentioned in the intro to have 25 years of experience, which is actually substantial under statement. And so I would not be good at the TikTok video. I should not be recording TikTok videos because I didn't grow up at this. And in a very similar way, the next generation of junior engineers are going to be so much better at this discipline because they get molded in the AI world, just like all of you are. But it's not only the kids that are going to be all right. Well I'll be fine and this is why. One of our main see this is that agents are a new kind of software. Because there was always all this stuff we wanted to automate. But not all of it was economically viable to do with traditional software. But it is with agents. And what that means is there will be just so much more software in the future. It does me with a Venn diagram. Maybe the circle should be better because the circle represents all software that should exist. Imagine all software that should exist. The problem was that we couldn't write all of it because it was too expensive using traditional methods. You can envision all these things. If all this statements, you have all this knowledge about the business, you have to figure it all out, you have to hard code into the application. So much of the software you just would never write because it was obviously too expensive. But now with agents, that part of software becomes economically viable. I can build it now with much effort. And that means that with AI agents, essentially all the software, maybe not all of it, we'll find more in the future. But that circle gets filled out. All of this stuff that should be automated is automated. There's going to be so much more software out there. In a similar line, more and more companies, when they ask that question, whether they should buy some software like a SaaS product or make some software themselves, they're answering that with the make site. Over in Silicon Valley where I live today, we are talking a lot about the SaaS complot galips. I think that's what it's called. People make their own stuff and they don't buy the SaaS software anymore. Actually think the SaaS companies will be all right. Don't worry about them. But as engineers of self, more companies making more software, again, leads to us having more work, even if it's faster. And in fact, the way I've been kind of framed with this for a while is that we are speed running what's really an experiment in economics of how elastic the software market is. The seed is being, but the cheaper it is to make software, the more software we're going to make. And as a consequence, what's actually happening is that demand for software engineers is going up. Now we don't know, there's going to be an S-curve. But there's no signs of us reaching the S-curve. In fact, because we're getting better at agents, et cetera, there's so much leeway in the future, I think we'll be all right. So as AI engineers, it's our job to build that next application layer. And of course, what that actually means is building agents. I want to spend some time talking about archetypes of agents that I'm seeing actually being built today, actually being effective, actually something you can do today without having to make major changes. I think we're all a little bit drunk on the coding agents because they're so great. They work so well. And it seems so obvious that you can translate that to all other domains. And sometimes these things don't go so well. But the thing is that we don't really have to be doing the most advanced agents. You could possibly imagine that there's just so much low-hanging fruit to be done, where you can really, really help companies save them millions or billions of dollars without actually making these massive changes of processes that in practice will always take a long time, fail often, et cetera. So this is what I'm actually seeing in the wild. The first part, when you think about what agents you can think build is people think, well, agents that rings a bell, have a team of support agents. Maybe I can automate part of that. And that's also where the first generation of what we call agent as a service. You can make that acronym in your head. Startups are shipping, right? Like the CRS and decogons of this world. But more generally, I think it's worth asking yourself, in your business, is there a job where it would be quite transformative if that we went from a 9-5 thing because people need to sleep. And I can actually do a 24-7 because agents don't need to sleep. And I think there's many places for that. The next one is probably actually even more important, a call-a-compress-story search. Because every business has a certain type of business process that in a very abstract fashion, heads the following shape. There's some business event and you have to do some research and then you make a human decision, right? And you can just build an agent that does the research phase automatically and that's all you do. That's all your ship, right? And the important part is why this is such an easy thing to ship is because the process is still the same. There's still that business event. There's another research. And there's a human decision. The research goes faster. And maybe it goes from something that took a human 30 minutes. Now they can do the same thing in five minutes. And if you run that process 100,000 times a year, you just save the company whole lot of money, but you didn't increase the risk profile and you didn't have to change the process. At first, we actually have at least two agents of this shape. When you go to Versailles-Com and you hit the contact sales button, that message actually goes to an agent, right? And I hear about 75% of the time that agents say, well, actually, they just want to support and end it over to the support team. But then in the other case, it will go, oh, that's interesting. Let me check out their LinkedIn. Let me Google the company. Let me figure out how large they are. Let me route it to the right person, right? And then there's a human eventually taking a look at it makes sense, but that obviously was something that took maybe a person 15 minutes before and now they don't have to do it anymore. And another example is the same process. If you sent us a abuse report, again, there's an agent taking a look. It's that website abusive. What should we do, right? Still obviously the decision in the end should be done by the extra professional, but they don't have to do all this research themselves anymore. Next is what I think is probably the most magical thing that you can do in any company today. It's just to surface information that already exists. It's extremely common that there's information somewhere in the company, right? But for all intents and purposes, you cannot practically use it. Take for example, everyone, you all engineers, you have issue trackers, right? So is it up to date? From me now, all the time, right? Could it be up to date? Does the information exist? Did you slack it? Did you have a granola recording that technically contains the information that could update your issue tracker? Yes, right? Like probably yes. And so you can build an agent that does this for your company, right? Whenever you have a manager saying, well, give me a list of updates, right? Why don't they already have the updates? Why doesn't an agent have already kind of done their research already? So again, this just makes it takes advantage of existing information, which is so powerful. And finally, for the last big category, there's a magical question that you can do to figure out agents you should build in your company, which is to ask folks, what do you hate most about your job? And actually have a case study about this in Ed for Cell. So we actually did build our own in-house support agent. And it has what's called a 90% deflection rate. It's a 90% of the time. It just helps the person in real time, rather than going down somewhere else. And what happened? This job satisfaction rate on our support team exploded. Why? Because they no longer have to do the boring stuff, right? Oh, my credit card did not reject it. Blah blah blah. Now they get to actually go and figure out actually interesting cases, actually help people who really need help rather than doing all the toil, right? So that's, I think, eliminating boring work is a very noble mission that we should all strive to do for the companies that we work for. Cool. So clearly that new application layer are agents, but we also have to shift. We have to consider another shift that the software itself is going to be used by agents now, right? And I work in software development, developer tools, etc. And I think we're kind of ahead of the game here, speech running, that transformation. What I will share though, is that on our own web properties, humans are actually now on the minority. So in the last seven days, we have not shared this before, over 60% of page use on Versailles Comm where AI agents. In a similar way, we're seeing the way you use a platform going from people clicking around in the dashboard to use it's shifting to our API and CILI. So whenever I now have someone proposing a feature to me and they show me like a UI, I'm like, guys, what's the CILI? Like how do I automate this? How does an agent use this? And though, you know, UI is now something that's so cheap. The other thing that we're observing is that kind of the relationship changes between software development, software development and infrastructure, right? If I didn't write the code myself, I also don't have maybe as strong feelings about how that stuff runs in production, right? And so for a company like us, it's really important that we shift how we deploy infrastructure to a model where most of the software was written by agents and has to just run. And people like expected to run just like they prompted the agents to do the work. And finally, and nobody here obviously surprised about this, the applications themselves are agent though. And that requires us to have different infrastructure available, right? Everyone's now shipping sandboxes. I think it's almost a meme. I was mentioning earlier that I created the same culture as SPASH. And I'm really interested in kind of this innovation of how you can give an agent a computer maybe without giving them a computer. There's lots of interesting stuff there in the market. And I'm sure this conference is going to have lots of stuff there as well. And then also more broadly, again, it was mentioned here for a while. We're marching hat on into security nightmare. It almost feels like a little bit like 1999 where really everything can be hacked, right? And we just didn't know how to make something secure. I think we'll have a root awakening. But what that really means is that we have to be open-minded for how to change things. I will give one example. We think almost all currently popular agent harnesses have fundamentally the wrong architecture. And that is that they combine where the harness runs with where the code that it generates runs. Right? As of actually yesterday, I did see that anthropic, this agrees with that thesis because they on the new agent product. They do have that separation. And it's really, really key. And that's really just also a point that these are all solve over problems. But my message to this is that we are still in the very early innings. And we have to be prepared to be open-minded about paradigm shift happening in the future. We just have the paradigm shift of agents being these very general sandbox-using things. In the future, we will see more of those paradigm shifts. Cool. Last point I want to make is that this new application layer that we're building can thrive in the opinion of the models. Because sometimes model X is better, sometimes model Y is better. But we are as AI engineers building a stable layer on top. And one of the very interesting consequences is that we don't have to work at a model lab to drive AI innovation. In fact, and I think this is almost like a narrative violation, Europe is the leader in AI engineering innovation. Our own AI SDK, which we sell makes, it takes now over 10 million dollars a week. And it's that by Las Gramell who lives in Berlin. Right? It's working on this. Then there's obviously Pi, the coding agent, made in Austria. You'll be hearing from Mario about it tomorrow. And of course, probably some of you have heard of it. There's a little thing called OpenClaw and Peter will be on stage here in an hour. And so it appears to be that Europe against all odds is taking actually a leadership role in AI engineering. But we also have to be realistic, right? Like Europe isn't going to play a major role on the model side. But I don't think it needs to. In fact, I do see kind of two big futures ahead of us. One is where the big model labs win. In that world, AI will stay very expensive. All the value of all that cool agent's tech will accrue to that company. And we won't really be engineers anymore, right? It will be like four deploy engineers, whoever, the winner is if it's open AI and thrumping Google. But I don't think that's very likely. And I think what's actually going on is that the opposite is happening. The model companies are commoditizing. Claude is amazing. Codex is amazing. Google will catch up. And importantly, I'll give them props now. Because I think Google's playing an amazing role here because they have the cheapest infrastructure on the cost side. And so in that commoditized world, they will always decide to make a cheaper. Right? And that will keep the price for where it should be, which is very low. And that's the outcome that we want. Because in that world, we, the engineers are the powerful ones. Our agents are the one that actually create the business value. And it's the application layer where the real innovation happens. This is where open clog gets invented. And that's where the next paradigm of AI engineering gets discovered. And that's really all I wanted to leave you with today. Thank you very much.