n8n CEO Jan Oberhauser on Building the Universal AI Automation Layer

[00:00] What is the stack that you see people using within Aden? [00:03] actually quite diverse. I think it's actually [00:07] Obviously makes sense because the nice thing about that system like NIS, you can actually connect everything to anything. [00:13] It means like there is probably not kind of this default stack people are using. Like they use literally any LLM. They use any kind of memory or vector store or any kind of applications. And it's just the thing that makes anything great because I don't know what LLM is going to win the race in the end. Who's going to be the best one? If it's going to be one, if it's going to be a million different ones, if it's going to be a small one, a big one, I have no idea. But that's the great thing about LLM is like we don't have to care because the nice thing is you can use whatever is best for your use case. And I think that is what makes it so powerful. [00:43] *music* [00:58] Today we're joined by Jan Oberhauser. [01:02] Founder and CEO of N8N. [01:04] who is one of the most remarkable growth stories in the automation era. [01:07] quadrupling revenue in eight months after six years of steady building. [01:12] What makes this breakthrough particularly compelling isn't just the explosive numbers. [01:16] by the strategic pivot. [01:17] that made it possible. [01:18] transforming from a workflow automation tool into an orchestration layer for AI-powered applications. [01:25] Jan discusses the counterintuitive marking strategy. [01:28] that abandon lead generation to focus on community adoption.

[01:31] The delicate balance between serving free users and enterprise customers. [01:35] and why he believes horizontal platforms will ultimately win over vertical AI applications. [01:41] He also shared how their open source ethos informs how they prioritize what to build and how to package it. [01:47] and the importance of fair code usage in commercializing their technology. [01:51] Jan's vision for N8N is to become the Excel of AI. [01:55] the default tool people think of when building anything AI related. [01:59] He reveals why empowering builders at the bottom of the market [02:02] may be the key to capturing enterprise customers at the top. [02:05] and why the future belongs to those who can connect everything to anything. [02:08] in an increasingly fragmented AI ecosystem. [02:11] Enjoy the show. [02:12] so jan hello and welcome uh thank you for joining us um we've been in business together for a long time i think as far back as 2020 so the early days of n8n um so we're grateful for you you know being with us and sharing some of the perspectives [02:26] I thought maybe a good question to kick off with is just to share a little bit about the last eight months of really NHN's history. It's been an incredible run. You added... [02:35] I think four times the revenue in the last eight months. And it took the first five or six years to really achieve in the company. So the business is really ripping. What happened? What changed? [02:44] First, thanks for welcoming me here. Great to be here. To answer your question, what happened? [02:50] I think it's... [02:51] And it comes down to things that we actually did quite a few years ago, actually almost two by now. [02:58] And I think there's been two things. One was obviously our focus on AI.

[03:02] is like [03:04] when this whole AI wave started, [03:07] We kind of, honestly, we were worried a little bit at the beginning. We weren't sure what does it really mean for us. [03:13] And we knew it's probably going to be one of two things. Either it's going to be a huge opportunity or actually the demise of the company. [03:19] You obviously want to be very sure it's going to be one and not the other. [03:22] Then we kind of looked at the market, it's like, what are other companies doing? [03:27] And what the most companies did is they kind of [03:29] added nice AI features to the application. [03:32] And we kind of realized that it's probably not the kind of thing that [03:35] make sure we're going to stay around long term. What is really important is to kind of [03:39] become actually part of the value chain. [03:41] So what we did is exactly that. We not just added AI features. We actually allowed people to build AI-powered applications with NNM. [03:49] It's obviously nothing that [03:51] the user base or people kind of realize overnight, like what NLN has become back then. And it obviously takes some time to kind of catch on. [03:59] And this is where also the second piece comes in. [04:02] historically we kind of [04:04] focused on mainly two things internally at NNN. [04:07] especially in the marketing department, they always had this dual focus. [04:11] and if they kind of creating leads, [04:13] but also kind of make sure that they're inbound, that we get more adoption. [04:18] This kind of always caused a problem in the sense of, [04:21] We were always very good at adoption, but at the same time, leads normally fall short. [04:26] So what happened is that [04:27] that the marketing department put everything from adoption in leads just to make sure that we can make that go as well.

[04:34] And that was a big problematic because all the long-term planning, [04:38] went away like that. They couldn't really have the impact they could have had. [04:41] And you realize, hey, [04:42] We have this amazing community. We have this amazing bottom-up adoption. So instead of actually kind of forcing something that's not really true to us, we just really focus on what was already working. This is really... [04:51] focusing on this kind of top of the funnel. That's what we did. We kind of removed the... [04:57] The lead goal, we actually exchange it with the adoption of large organizations, which went all in the community, making sure as many people as possible [05:05] are using NNN. [05:07] And they're kind of... [05:08] Also, it's another thing that you can decide and you make the change, but it's nothing that kind of happens overnight. You actually have to fight. You have a lot of trust in that and you have to kind of really [05:17] double down and kind of wait because these things kind of take some time to mature. [05:22] And that's actually the thing that actually happened. Like it matured, we kind of doubled down, we kind of empowered our community more. [05:28] You've created more events, you've created more content. [05:31] And that is then in the end, this thing that materialized, especially in the beginning and the end of last year in December is where [05:38] the market finally realized that we have become this AI tool but also connected to the community especially around on YouTube where people created more and more content obviously more [05:47] content people create, the more other people want to create content, the better it gets ranked. And then everything kind of started to explode. [05:54] I had an amazing story at our board meeting a couple of weeks ago, Jan, that you have Jan focus time on long flights, and you actually coded the first version of that AI nodes product flying back from San Francisco. Is that true?

[06:04] Actually, I created quite a few things on planes in the past. That thing I didn't create on planes or trains. I think I always love the time. Honestly, right now, I always love that to be honest. [06:16] But now I also feel like destroying more than actually helpful. So I kind of create more like a MVP or something that I play around rather than actually adding production codes on planes or trains anymore, which is sad. [06:27] On dodgy Wi-Fi. Yeah. Maybe. And if you can take us back to 2019, I think it was just you sort of founder entrepreneur when you went out and raised your pre-seed round. What was kind of the aspects, the origin story that really stands out to you? What has really stayed the same from day one of N8N? What has really changed over that time frame? [06:45] I think what always stayed true is for sure the kind of [06:48] focus on a community like [06:50] the first people [06:52] Like in the first week in April, a few people started. [06:58] And one of the first people that started was at FRL, because I always knew like, [07:02] if we want to make sure that Enlil becomes meaningful and kind of becomes the company I wanted to build, [07:08] I really have to kind of make sure kind of, [07:10] I invest in the community very early on. [07:13] I think that it kind of always stays true. [07:16] I think another thing is probably something around values. [07:20] Actually, I was at the view. [07:21] try to be as honest and upfront about things as possible. You also see that, for example, in our license. [07:28] You probably have seen like [07:29] We never call ourselves open source. [07:32] because we don't have an OSI-approved open-source license.

[07:35] What does it mean? It means our source code is available. Everybody can use it totally for free. [07:40] They can't even use it in production. No matter if it's somebody home privately or somebody in a large organization, everybody can use it. [07:49] which is however different in our license, [07:51] is that people cannot commercialize our code. It means nobody can just take our code [07:56] and often I can now a hosted version of NNN, for example, or kind of create XYZ automator in the product. [08:03] Why is it important? [08:04] Because... [08:05] I saw a lot of organizations in the past [08:08] like open source organizations kind of changing license [08:12] And people obviously got very angry and they really hated that. [08:16] And [08:16] But what was quite interesting is people [08:18] I didn't hate it. [08:19] because of the license that the company chose, people were mainly angry because people like this company changed the rules and that's never nice. [08:26] And so I thought, hey, [08:28] I think it's a very reasonable thing. So I'm just very honest and upfront about it from the very beginning. I said, hey, I'm not, [08:34] building NNN and giving it away for free because I'm a good person. Like I actually want to build a business around it. I want to make sure I can get paid. I want to make sure... [08:42] Or the other people can get paid as well because they actually think it's in the interest of everybody. [08:46] So that is why I chose the Dyson thing. We always had stayed [08:49] very true around that. [08:52] Again, now getting to the second part of your question is like, [08:54] What's changed? [08:56] I think it's definitely like... [08:57] Avantiq [08:58] what we started at is like I said as mentioned before like we started as this kind of [09:03] Automation tool [09:05] Honestly, I never have thought that we would

[09:07] AI ever. It's like, it's never even crossed my mind that we would go into the AI space. [09:13] But I think in the end, it's like, [09:15] Sometimes you see opportunities out there and you just have to, [09:17] take them and also I think it makes a difference between building a big sustainable company that really matters and probably building a kind of a startup at some point and [09:27] Just because all the business said he [09:29] Jan, you've done a masterful job on the community building aspect of N8N. And you mentioned the values, which I'm sure were really critical to... [09:38] earning the trust of your community. Can you also talk a bit about the tactics? It's kind of the constant push and pull of trying to kind of shape or curate the community while also really trying to listen and, you know, and see where they want to go. How tactically, how have you managed the community over the years? [09:56] I think for the very beginning, it was very important for me just to kind of include people. For example, in the beginning, we didn't have any telemetry data at all. We didn't collect any telemetry data. [10:08] It's also always fine in the very beginning, but obviously as the company grows, you kind of initially need data to kind of improve the product very fast. Again, it's going to make the difference between a company that survives or one that doesn't. [10:17] So we had a talent credator. [10:20] Another thing we had to add was [10:22] At some point, we obviously added paid features. And every time we made a bigger change, we kind of [10:27] kind of posted it to the community forum first you kind of [10:30] We shared with them what we wanted to do, gave them the reason why we wanted to do it. [10:35] And then kind of listen to their feedback.

[10:38] I think that is just kind of [10:39] taken them with you when you're doing something. I think that's a very important thing. [10:44] Another thing is definitely kind of trying to kind of [10:46] empower them, [10:48] actually like one of the earliest immigrant employees, [10:51] was Ricardo. Ricardo, see, he is this [10:54] amazing guy working in Florida. And he was the first, one of the first contributors to enter then. [11:01] the Discovered Product in the Product Hunt, [11:03] this started to kind of [11:04] create one note. Then he created two notes, created three notes. And every time he contributed something, I said, hey, that's great. Thank you very much. And then I kind of gave feedback. I improved things and he learned and he was excited and created more notes. [11:16] You get some point. [11:17] He created like 50 or 60 nodes, like integrations for NNN. [11:21] and [11:22] Then obviously, as soon as I had the first Monday banker, I hired him. [11:25] But I think it's very important to kind of [11:27] show people that you care and take them with you and kind of show how [11:32] how much you value them. [11:33] I think [11:34] I really hope the community really knows how much value that we try to give back a lot. [11:39] I think that's certainly, I think that's kind of, [11:41] Even though we're not open source, we have to say this. Open source ethos where you kind of really... [11:46] give a lot first. And I think you normally get so much more back than you actually receive. [11:50] We see the same thing again also with our community today where we just say, [11:54] like we empower you and then they create all of this amazing content no matter if it's on YouTube on LinkedIn they create tutorials nothing that worked out very well it's something they're still doing to this day [12:05] Jan, has there been a shift in, I mean, given the scale of the community today, right, it's hundreds of thousands of members in size. Has there been a change in how you try and surface the right information or the right ideas and how you choose which to prioritize coming from that community base? Yeah, in the beginning, it was definitely easier.

[12:23] Thank you. [12:24] In the beginning, I didn't really worry about monetary station at all. So I just wanted to make sure I built the best product and [12:31] I just built things that people really wanted. So I could literally... [12:34] At the very beginning, we had a community forum where people could add feature requests and people could upwatch the ones they wanted to have the most. [12:42] That is literally kind of how we chose very early on what to build. Unless this was something totally out of whack, we normally build it because we knew people really wanted it very much. [12:52] And that works [12:53] but actually for quite a while. [12:55] At some point, you have to [12:58] if you'd have been more opinionated. [12:59] but you're not just kind of building... [13:01] what the community wants, but we actually have to kind of think about like where the market is going, also how you kind of build something. [13:08] Again, that's sustainable, that again, especially also something that obviously is also kind of large organizations used as well. [13:14] and also just anywhere you see the market going. [13:16] And I think that's what we're doing right now. We're trying to find a good mix [13:20] between what are people really asking for and what do we think we have to build to be successful in the long term, both because like something like AI, but also again, what, for example, enterprise organizations need. [13:31] You mentioned earlier toward the start of the conversation that you had this [13:35] sort of make or break moment in the early days of AI, where you realized that AI was either going to be the future of N8N, or I think the word you used was the demise, the demise of N8N. Can you take us back to that moment? Like, what did you see? [13:50] that made you realize how important an inflection point in the trajectory of the company this is going to be? And then what did you do to figure out how to position in and in so well with respect to these AI tailwinds?

[14:04] One thing I actually saw is I... [14:07] So it was the funding announcement of Pinecone. So I think they raised like a $100 million round or something like that. They also saw that they kind of raised around a year before, at some point before. And I was wondering like, what changed? [14:21] Why... [14:22] why a company certainly cares so much about them. [14:26] And then I realized, again, it was exactly that, it's like, [14:29] What they were in the past is they were the vector database. [14:31] What did become, did became Deep Database for AI. [14:35] That's where I realized we have to do something very similar with the end and end as well. [14:39] That's exactly what we did. What we also did in the past and what the most of our competitors did is they kind of created... [14:46] and open the eye note. [14:48] a node, a kind of connected via HTTP, that requests to OpenAI. [14:53] And he could do quite some nice things with it. He can still do it up to this point in time. [14:58] What we have realized is that this is honestly not enough. Like you can do quite nice these cases, but the really powerful things are not possible. You really have to be able to kind of create proper agents. [15:09] change auto prompts, add tools, add a vector database and output parsers and all of the things. And that is [15:18] Then we then kind of built out our advanced AI functional routine where we allowed people to do the things in and at the end in this kind of... [15:26] low-code, low-code, where it was. [15:28] We were the only positive writing Python scripts. So we kind of drastically reduced the entry barrier for people to build things and also reduced the speed because...

[15:37] The stuff has historically also been quite finicky, honestly, like connected A with B and it didn't work and it's taking away all of those pain points and it takes care about it underneath the hood. And you just click a few times, [15:49] and an h and add a model, [15:51] add the memory and so on and it's working magically. [15:54] Yeah, and talking about companies that share, I guess, an open source ethos across AI, obviously the role of open source, I think, is changing across the industry at large. We've seen, obviously, a lot of headlines coming out of Meta, out of Mistral, OpenAI released their GPT open source version, et cetera. What are just some perspectives you can share on how you perceive the state of open source in AI more generally? And maybe how do you see its role changing in the future? I think the great thing, we always had the bug with open source, is, [16:22] It's like, [16:24] It's kind of... It's driving... [16:26] very rapid innovation in the end like because [16:29] You have like this kind of huge army of people. [16:32] that really, really care very deeply, [16:35] They're very smart and very often have a lot of time. [16:38] And they kind of do things that, [16:40] organizations, [16:41] can be of not doing because again they [16:44] They have very different incentives or very different. [16:47] timelines. I have [16:49] Yeah. [16:50] I think the nice thing about Opsos is like, [16:54] you can do whatever you want and you can explore things that maybe wouldn't even make sense in a company setting. [16:59] So I actually think like open source is like really this amazing thing that kind of opens up the world to very many different possibilities. And in the end, like some of them bins and that a lot of open source projects never make it to anything. Obviously, this is the ones nobody talks about.

[17:12] But some of them, they kind of hit something. [17:15] And those are the kinds of the minds that really kind of matter and kind of really shape kind of very often the whole industry in the end. [17:21] Maybe a follow-up question to that, Jan. I mean, do you see a shift in the role of open source? I mean, do you see that companies, I mean, today might be using some of these open source technologies to save money, to save costs, but maybe actually having more control inside of organizations over the performance of models might be a value proposition that evolves in the future? [17:38] Definitely very, very much. [17:40] I think like, [17:42] First, I'm not sure if open sources very often is the cheapest thing. I think this is kind of misconception. It's actually very often not true. [17:50] I think you get a lot of things like being able to kind of self-host, knowing where data start and what's happening. I think there's a lot of value there. [17:58] But very often is actually the cheapest thing. Very often is actually the more expensive thing because you have hardware [18:03] This is probably idler for 99% of the time. And this actually kind of turns out to be more expensive. [18:08] So actually, I'm not sure if that's actually true. [18:11] But the second part, I think, is true, and people care about it more and more. [18:15] like the extreme [18:16] work now with a few organizations. [18:18] And that's actually the main reason why they wanted to use open source. It's not because it's free. It's because they actually care about the data privacy and data security angle. [18:25] And I think that's also what we are always historically seeing within it, and very often, [18:29] people want to kind of self-host and want to know [18:32] where the data start [18:34] And with Salesforce, obviously, don't mean they run it on the computer, the desk. They still run it on the cloud, but obviously in their own private cloud. [18:42] Yeah, and one, I think, noticeable shift over the last couple of years, something that, of course, NAN, I'm sure, has been a beneficiary of is just improvements in the communication protocols between different systems and different models.

[18:52] And obviously, MCP is a high-profile candidate that has really driven the industry forwards. Can you talk a little bit about that, kind of how you see the changing role of some of these protocols in the future? [19:02] and where you think they might evolve. [19:03] I think that if you kind of standardize, you kind of, in the end, you kind of, [19:06] accelerate things. I think that's very important. [19:09] Even interestingly, if the standard is maybe not perfect, I think it's still... [19:13] adds so much value. And I think if MCP is going to be the one we're going to use in a few years, I have no idea. But I think it's great to have like a starting point that you kind of can build on top. [19:24] In the end, this MCP is like, [19:25] the HTTP of AI workflows. I think that's really amazing. [19:30] It will in neighbor [19:32] It already enables now things like agent to agent, and it's a kind of building blocks you actually need for this kind of more powerful... [19:40] use cases like, for example, marketplaces or like, [19:44] plug-and-play automations, and I think NNN can act as the kind of orchestration layer between this kind of service MCPs. [19:51] agents and tools and obviously NLN's also the best [19:54] kind of platform to build also tools that you can access with MCP as well. [20:00] Actually, on that, Jan, we haven't really done the straightforward [20:04] What is N8N? So for somebody listening who has a passing knowledge of N8N but wasn't sure exactly what it is, what is N8N and when should people think about it when building NAI? [20:17] I think, and it's by now probably the [20:20] the easiest [20:21] the most powerful way to build AI agents right now.

[20:25] I think it allows you to kind of [20:27] again, build things you previously never even thought you were able to. [20:31] you could build. I think the nice thing that body can go very fast, [20:36] from a first idea you have, [20:38] to a first prototype and then bring that prototype into production. [20:42] What is the stack that you see people using within Aden? [20:46] actually quite diverse. The nice thing about that system like NIT NIS, you can actually connect everything to anything. There is probably not kind of this default stack people are using, like, [20:56] They use literally any LLM. They use any kind of memory or vector store or any kind of applications. Exactly. It's just the thing that makes anything great because... [21:04] Like, honestly, like, [21:05] I don't know what LLM is going to win the race. In the end, who's going to be the best one? If it's going to be one, if it's going to be a million different ones, if it's going to be a small one, [21:13] Big one, I have no idea. [21:14] But that's the great thing about NNN is like, we don't have to care because the nice thing is you can use whatever is best for your use case. [21:20] And I think that is what makes it so powerful. [21:23] Any interesting observations on what's been trending, positive or negative, in the N8N universe? What people definitely use quite a lot is honestly still like all the kind of Google tools. Google is definitely still... [21:37] I think the kind of application you can use, like people use a private in large organizations. I think that's an amazing thing. [21:45] So we definitely see this probably one of the [21:47] the strongest ones. We definitely also see a lot of this kind of communication platforms if it's a [21:51] Slack or Telegram or anything like that. [21:54] It's also going to make sense, especially in this kind of AI world, because you obviously need an interface, how you kind of interact with the agency built.

[22:02] And I think that is probably a few to call out. And apart from that, obviously, also things like just databases, any kind of, again, the nice thing about edit and you can connect. [22:10] literally anything. So it's not just kind of applications, also kind of low level things as well. And also, honestly, very often also internal tools as well. [22:18] One of the nice things about N8N is you struck a good balance between the sort of flexibility, customization, control that a developer might want with the ease of use that somebody who's less technical might want. Has your user base changed, kind of the complexion of the N8N user base? Has it changed as you've gotten more and more AI adoption? Yeah. [22:42] It's quite interesting. [22:44] When actually our user base started to explode since the last eight months, [22:49] You are really wondering... [22:51] if [22:52] if those people are really going to be the most successful, like if the quality actually dropped. And quality doesn't mean obviously it's just bad people. It's just like the kind of people that are successful within it. [23:01] And they didn't, which was quite surprising. Like a million people [23:05] that are quite either already quite technical [23:09] or people that [23:10] really like have a use case and they care very deeply about what they're building and they're just willing to put in their work. [23:15] Like maybe they're not as technical when they kind of enter [23:19] but they have a problem they really want to solve. We have actually quite a few users that actually started to learn to code [23:24] because I've ended in. [23:26] And why do they do that? Because they can realize, hey, I can do that much. [23:30] Like if I'm technical, but I cannot code, but again, it can literally...

[23:35] go anywhere if I can go and kind of this whole new world opens up. [23:39] That is actually cool. [23:40] It didn't change that much. And I think that's really amazing. There's so many technical people out there and people are really interested in our community. [23:48] It's really amazing. It's just the ideas they're having. [23:50] how driven they are and how much they also want to help each other out. I think that's just great. [23:56] Jan, do you think with the proliferation of adoption of many of these different AI technologies, the pressures are different on being a founder in 2025? [24:05] I mean, you've lived it over the last eight months. [24:08] Yeah, I think that... [24:10] it definitely changed things. I think the main thing is like, [24:13] the past [24:14] It was easier to plan and know where you're going. [24:17] And right now you're always living in this [24:19] constant [24:22] I wouldn't call it fear, but kind of [24:25] constant state of uncertainty where the world is moving. [24:28] You have to be really willing to kind of [24:31] You have to strike a very good balance between [24:35] "Not missing anything important?" [24:37] At the same time, not jump on anything that's coming up your way. I think that is definitely much harder, it feels like, than it was in the past. [24:44] Given that, how do you think the signals that you get as a CEO have changed in terms of how you weight them? I mean, even things like, you know, the usage you see across the business or the revenue being generated in different parts of the company. [24:56] How do you sort of assess the sort of durability of that and make sure you're investing behind the right things? [25:01] This actually probably talks about something I think is generally quite important. It's definitely harder

[25:08] in kind of this open source world we are having because we have like [25:12] Obviously, we have some use cases... [25:15] that generate revenues, [25:16] like with enterprises, [25:18] And we have a lot of people that use the product for free. [25:21] And we never tillage revenues with them. [25:24] But honestly, none of them is more important than the other one. [25:27] Because like one is obviously it's going to help us directly. But the other one is the thing that drives all of it. [25:33] To me, it's like, [25:34] You have to strike a very good balance between both of them. [25:38] And honestly, normally I'm leaning much more heavily into kind of, for example, like I [25:42] You obviously asked about signals, but I think it's important. It's like I normally lean much more heavily and kind of giving away more for free. [25:50] Because again, everything we give away for free, [25:53] kind of makes the whole product better for literally everybody. [25:55] and drives more adoption and also drives more revenues in the long term, versus everything we build for enterprise organizations. [26:01] on the task kind of [26:03] tops a subset of the users, [26:05] I think it's still important to both, and I think you have to listen to both very closely. [26:10] But also I think it's around timing in the end. It's like right now, I think it's really... [26:14] about capturing the markets. And we have to listen, what do people really want to build? And I think what is important is to really [26:21] capture the market generally and literally capture like the smallest builder out there [26:26] Because what we already see is that through the smallest buildings, we get into the largest organizations out there. [26:32] What I think is impossible is if you now focus very heavily on enterprise only, [26:37] Um, [26:38] And I think you can never go then downwards again. I think nobody, like nobody went from enterprise

[26:43] to kind of... [26:45] owning a space. And I think that is again, also Google started the same way as well. They maybe own quite a lot, the same with Microsoft as well, but you have to start in the right direction there. [26:55] You know, right now we're seeing this explosion of people building with AI capabilities. [27:01] Over the last few years, we've seen an explosion in the AI capabilities themselves. [27:07] Yeah. [27:07] And so the question is, is that technological foundation, the AI capabilities themselves, [27:12] Do you think that that is... [27:14] still innovating at the same rate it has over the last few years? Or are we finally asymptoting in terms of the capabilities coming out of foundation models or out of the open source world? Do you have a point of view on that? [27:26] I think probably looking at GPT-5, I think it definitely feels like it's slowing down. [27:32] This. [27:33] kind of makes sense. I think it's not very often what happens is like this [27:37] There's more low hanging flutes in the beginning than the later point in time and you can again [27:42] You can... [27:43] more compute power and more data for quite a while, but at some point you kind of mix out there and [27:49] and at some point it just becomes too much. So I think it's definitely kind of [27:52] Please cancel slowing down. However, just think that small is a kind of a temporary thing because there's so much money in the market right now where people explore a lot of different things. [28:02] And some of them are still very early, and I think they're not at the right stage yet, but I think as some of them were coming through this kind of [28:09] to divide stage, I think you're going to see probably another acceleration. [28:12] I mean, I'm maybe building on that.

[28:14] to Pat's question. I mean, we're seeing more code being written by machines, right? And more end-to-end agent-to-agent automation. How does that change the positioning of N8N? How do you think about areas you'll invest maybe given that future? [28:26] I think one thing is, I think it's probably... [28:29] talks a little bit to the kind of role as a developer. [28:32] Like in the past, a developer was somebody that kind of built something for you. It's like they said, hey, I need X and they built X for you. [28:40] Now with tools like [28:42] and at all of the vibe coding tools, [28:44] That's changing. You see developers more as the people that kind of generate, kind of create the guardrails for you, the people that empower you to build the things you actually want and need. [28:55] And I think that is really amazing. And honestly, it's also placed exactly... [28:59] what Ended In was always about, it was always about empowering people. And I think it's great. I think literally every developer is... [29:06] candidates is kind of going into the role of empowering other people [29:09] to build the things they actually want and need. [29:12] Yes, developer obviously I still have [29:14] The Vibe coders and other people, they're not going to build everything. They're still going to roll for the classical engine here. But I really love that kind of world where people are empowered and [29:25] The people that have the problems are the best equipped to kind of solve them themselves. [29:29] And I think that's kind of where we're going. And I think that the engineers and developers are definitely a very important part for that. I mean, Jan, something I think we'd love to know your opinion on. I'm sure a lot of people listening are struggling between building, you know, verticalized applications that solve a very acute use case versus kind of having broader platform visions, right, for their companies. NAN is the horizontal of horizontal tools, right, in many ways. How do you see the positioning changing given some of those dynamics of some of these very vertical applications and then,

[29:55] some of the more horizontal tools coming out of the labs and the like. Yeah, I think like Verdict was obviously amazing. If you had like one very specific use case, [30:03] that do this one thing perfectly and they can do it much better than every horizontal tool. [30:09] in the outside of the opposite, that's exactly what happened with SARS. [30:13] In the SaaS world, they have a vertical application for literally everything. [30:18] That is again why we had the need for something like NNN, because you have to kind of make it all of them together again. [30:23] I think there's also a little bit [30:25] But also probably happens more and more in the AI right now where people build this kind of very vertical tools as well. But as this is happening, the complexity increases again. [30:35] And you either need kind of another orchestration tool [30:38] Or you need a horizontal tool that kind of built everything for you again. So either kind of, again... [30:43] bring honest tools together or use a more horizontal tool to kind of, um, [30:47] and build this thing in this one tool rather than kind of a million different ones. [30:51] Again, I think that's again why... [30:53] it feels like in a very good position there because again, [30:57] I think no matter which way it's going to turn out, I think we are in a very well position for the world. [31:02] If everything goes right. [31:05] what will N8N be in five or 10 years? What role will N8N play in the world? [31:11] The idea is always like for very many different reasons that I was compared in it into Excel. [31:16] They always thought about, hey, if people 15 years ago, they heard spreadsheet, they thought about Excel. [31:22] And if, if, [31:22] People in a few years [31:24] think about, hey, I have to grab

[31:26] AI, you have to do anything with AI. The only thing that should come to mind is NNN. [31:31] So he's more of that kind of this... [31:32] It's kind of default orchestration layer, like what is kind of platform from everything to building to deploying and where you find your agents, anything like that. And I think that is where I think it's going to naturally evolve. I think we are very well positioned as we already kind of started to be like a default building tool already. So Jan, you know, you build a remote first company, obviously centered in Europe, but with global ambitions. Just talk a little bit about, you know, thinking about going into the US and kind of building the team and scaling the organization over the last year. [32:02] started in Europe and the whole team was based here. [32:05] which was always quite interesting, like, based in Europe, [32:09] like our user base was always very global. [32:11] Like for quite a long time, Europe and the US had the same size, even though we honestly didn't provide our US customers the best experience. It definitely shows there's like a very, very big need in the US. [32:21] That's why we also right now expand into 3S. And we actually test right now opening our office in New York. [32:27] And this also why we're hiring quite a lot, especially in the US, but literally also [32:33] I guess I can say worldwide. [32:35] Literally everything from engineers to people in support and especially a lot of people in the go-to-market org. [32:42] I think it's also kind of the thing that gets me excited. It's like, I think, [32:46] Not many European organizations kind of have the possibility to really build something really global, something that really matters. [32:53] I think Ended End is probably one of the orgs. I think that has quite a lot of potential. It's nice to see that we are

[32:58] Now finally kind of going the direction and kind of also kind of capture the US and taking that market over as well. [33:04] And maybe to wrap, we love to ask some kind of rapid fire questions just to get your take on a couple of key themes. [33:12] Just through your reflections of the last six years, what's maybe one of the hardest truths you've learned about Jan as a founder CEO? [33:19] One thing is probably... [33:21] I really don't like to say no. [33:24] There's so many different things out there and I think that is [33:27] I think very often it turned out to be okay, like to kind of do a lot of stuff in parallel. [33:32] standards in hindsight is actually didn't turn out [33:34] That well? [33:36] Interestingly, I think that most things very luckily turned out [33:39] to be quite well so I'm very lucky there but I think also it has turned out very differently and I think for the most companies it actually turns out very differently [33:46] We want you to keep taking those big swings. Yeah. What is one must read or must watch? [33:53] piece of content in AI. [33:55] blog, book, show. [33:57] I still love her. It's just a movie. I think it's just... Yeah, I think it's just this thing is where... I think what I think is especially amazing about it is... [34:05] Like a few years ago, it was sci-fi. And it's now starting to think that it's... [34:10] That's around the corner. That's why I just love it. I think that's also a great movie. Is there a tool or a product, Jan, you've been playing with an AI that you would recommend or that really interested you? [34:21] I think I just very recently and probably very late that I came there, but I started to use Granola. I think it's just an amazing tool. It's so great. So simply use, just such an amazing job.

[34:30] So I think that would probably be the one that comes to mind right now. [34:33] What AI application or application category do you think is most likely to break out in the next 6 to 12 months? [34:41] I think it's kind of probably more just kind of [34:44] AI-powered internal tooling, [34:47] I think there's obviously like this external part, I think it's a little bit hard. You have to be very careful there, but internally you can take much more risks. [34:53] We also see that already in it quite a lot. That's obviously why I'm also excited about it. [34:59] Needless to say, we appreciate you sharing some of your story, looking into your crystal ball for us and kind of sharing some of the perspectives over the last six years. It's been a privilege for Sequoia to be a part of it. So thank you for having us on the journey. We live forward to the future. [35:12] Thank you very much. Thanks for having me. [35:13] *music* [35:37] Thank you.