Building the Universal AI Automation Layer ft n8n CEO Jan Oberhauser

What is the stack that you see people using with N8N actually quite diverse.

I think it actually probably doesn't make sense because the nice thing about that system like N is you can actually connect everything to anything. 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. Exactly. 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 need the best one? If it's going to be one of it's going to be a million different ones, 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.

Today we're joined by Jan oberhauser, founder and CEO of N8N, who is one of the most remarkable growth stories in the automation era. Quadrupling revenue in eight months after six years of steady building. What makes this breakthrough particularly compelling isn't just the explosive numbers, but the strategic pivot that made it possible. Transforming from a workflow automation tool into an orchestration layer for AI powered applications. Jan discusses the counterintuitive marketing strategy that abandoned lead generation to focus on community adoption, the delicate balance between serving free users and enterprise customers, and why he believes horizontal platforms will ultimately win over vertical AI applications. He also shared how their open source ethos informs, how they prioritize what to build and how to package it, and the importance of fair code usage in commercializing their technology. Yann's vision for N8N is to become the Excel of AI, the default tool people think of when building anything AI related. He reveals why empowering builders at the bottom of the market may be the key to capturing enterprise customers at the top, and why the future belongs to those who can connect everything to anything in an increasingly fragmented AI ecosystem. Enjoy the show. So Jan, hello and welcome. Thank you for joining us. And we've been in business together for a long time, I think as far back as 2020. So the early days of N8N. So we're grateful for you being with us and sharing some of the perspectives. I thought maybe a good question to kick off with is just to share a little bit about the last eight months of really N8N's history. It's been incredible run. You added I think four times the revenue in the last eight months that it took the first five or six years to really achieve in the company. So the business is really ripping. What happened? What changed?

First, thanks for welcoming me here. Great to be here. To answer your question, what happened? I think it comes down to things that we actually did quite a few years ago. Actually almost two by now. And I think there's been two things. One was obviously our focus on AI. It's like when this whole AI wave started we kind of honestly were worried a little bit. The beginning we were not sure what does it really mean for us. And we knew it's probably going to be mean. One of two things. Either it's going to be a huge opportunity or actually the demise of the company. You obviously want to be very sure it's going to be one and not the other. Then we kind of looked at the market. It's like what are other companies doing? And what the most companies did is they kind of added nice AI features to the application. And we kind of realized that this probably not the kind of thing that make sure we're going to stay around long term. What is really important is to kind of become actually part of the value chain. So what we did is exactly that is kind of. We not just added AI features, we actually allowed people to build AI powered applications with nl. There's obviously nothing that the user base or people kind of realize overnight like what NN has become back then. This obviously takes some time to kind of catch on. And that's where also the second piece comes in. Historically we kind of focused on mainly two things internally at N and N especially kind of in the marketing department they always had like this dual focus on obviously kind of creating leads but also kind of making sure like the inbound that kind of we get more adoption. This kind of always caused a problem in the sense of we're always very good at adoption but at the same time leads normally fall short. So what happened is that that the marketing department and put everything from adoption in leads just to make sure that we make that goal as well. And that was obviously problematic because all the long term planning went away. They couldn't really have the impact it could have had. We realized hey, you 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 which is really focusing on this kind of top of the funnel. That's what we did. We kind of removed the lead goal. We actually exchanged it with an adoption of large organizations. Just went all in the community, making sure as many people as possible are using Net N. And it kind of also is another thing that you can decide and you make the change, but it's. Nothing happens overnight. You actually have to fight. You have a lot of trust in that. And you have to kind of really double down and kind of wait because these things kind of take some time to mature. And that's actually the thing that actually happened. Like it matured, it kind of really doubled down. We kind of empowered our community more. We created more events, created more content. 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 the market finally realized that we have become this AI tool. But also the community, especially around on YouTube and what people created more and more content. Obviously more content people create. The more other people want to create content, the better it gets. It gets ranked. And then everything kind of started to explode.

I had an amazing story at our board meeting a couple of weeks ago, Yann that you have Yan 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?

No, 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 was a lot of the time, honestly right now I'm. I always love that, to be honest. But now I also feel like destroying more than actually helpful. So I kind of create more like MVPs or something like that. I play around rather than actually adding production code on planes or trains anymore, which is sad.

On dodgy wi fi maybe Jan, 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 timeframe?

I think what always stayed true is for sure the kind of focus on the community. Like actually the first people, like in the first week in April, a few people started and one of the first people that started was at efrel because I always knew, like if you want to make sure that LL becomes meaningful and kind of becomes the company like I wanted to build, I really have to kind of make sure kind of I invest in the community very early on. I think that kind of always Stayed true. I think another thing is probably something around values. We always, especially I always have to try to be as honest and upfront about things as possible. You also see that for example in our license you probably have seen like we never call ourselves open source because we don't have an OC approved open source license. So what does it mean? Means our source code is available. Everybody can use it totally for free. They cannot even use it in production. It's innovative. It's somebody home privately or somebody in large organization, everybody can use it. What is however different in our license is that people cannot commercialize our code. It means nobody can just take our code. And often I can now host it version of N for example or kind of create XYZ automator in the product. Why is it important? Because I saw a lot of organizations in the past, like open source organizations kind of changing license and people obviously got very angry and they really hated that. And what I thought was quite interesting is people didn't hate it because of the license that the companies chose. People were mainly angry because people like this company changed the rules and that's never nice. And so but I thought hey, 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 building NN 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 can be. I want to make sure all the other people can get paid as well. Because I actually think it's in the interest of everybody. So that is why I chose a dice and I think that's we always had stayed very true around that again. Now getting to the second part of your question is like what changed? I think it's definitely like around like what, what, what we started at is like I said as mentioned before, like we started as this kind of automation tool. Honestly I never have thought that we would AI ever. It was like never even crossed my mind that we would go into the AI space. But I think in the end it's like sometimes you see opportunities out there and you just have to take them. And also I think it makes a difference between building a big sustainable company that really matters and probably building kind of a start at it at some point just as he goes out of business heavily.

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 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?

I think for the very beginning, it was very important for me just to kind of include people. For example, the. In the beginning, we didn't have any telemetry data at all. We didn't collect any telemetry, which was always fine at the very beginning. But obviously as a company grows, you kind of literally need data to kind of improve the product very fast. Again, this kind of makes a difference between a company that survives or one that doesn't. So we had to add talent, read data. Another thing we had to add was at some point we obviously added paid features. And every time we made a bigger change, we kind of posted it to the community forum first. We kind of like we shared it with them, what we wanted to do, gave them the reason why we wanted to do it, and then kind of listen to their feedback. I think that is just kind of taking them with you when you're doing something. I think that's a very important thing. And another thing is definitely kind of trying to kind of empower them actually. Like, one of the earliest edited employees was Ricardo. Ricardo, he is this amazing guy working in Florida and he was one of the first contributors to end it in. He discovered a product and product hunt and he started to kind of 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 then he learned and he was excited and created more notes. And at some point he created like 50 or 60 notes, like integrations for N. Then obviously as soon as I had the first Monday banker, I hired him. But I think it still very important to kind of show people that you care and take them with you and, and kind of show how, how much you value them. I think I really hope people, the community really knows how much value them. We try to give back a lot. I think that's generally, I think this kind of. Even though we're not open source, we have this open source ethos where you kind of really give a lot first. And I think you normally get so much more back than you actually receive. We see the same thing again also with our community today, where we Just say, 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. I think that that worked out very well. That's something they're still doing to this day.

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, and the beginning was definitely easier. Like, in the beginning, like, I didn't really worry about monetization at all. So I just wanted to make sure I built the best product. And I just built things that people really wanted so I could. Literally from the very beginning, we had a community forum where people could add feature requests and people could upvote the ones they wanted to. Wanted to have the most. 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 build it very much. And that works for quite a while. At some point you have to be a bit more opinionated where you not just kind of building 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, again, that's. That's sustainable. That again, especially also something what honestly is also kind of large organizations use as well, and also just generally where you see the market going. And I think that's what. What we're doing right now. We try to find a good mix 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.

You mentioned earlier, toward the start of the conversation, that you had this 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 Nadin, or I think the word you used was the demise of the demise of Nadin. Can you take us back to that moment? Like, what did you see 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 N8 in so well with respect to these AI tailwinds?

One thing I actually saw is I saw it was the funding announcement of Pinecone, where so I think they Graced like a hundred million dollar round or something like that. I also saw that they kind of raised around a year before, at some point before and I was wondering like what changed? It's like why? Why are companies suddenly care so much about them? And I realized again, it was exactly that. It's like what they were in the past is they were directed database. What they've become, they became deep database for AI. That's not. Hey, that's where arena said we have to do something very similar with the N as well. That's exactly what we did. What we also did on this in the past and what the most of our competitors did is they kind of created an OpenAI node. A node they kind of connected via HTTP, get requests to OpenAI and you could do quite some nice things with it. And you can still do it up to this point in time. But what we have realized is that this is honestly not enough. Like you can do quite nice use cases, but the really powerful things are not possible. You really have to be able to kind of create proper agents like change auto prompts, add tools, add a vector database and output parsers and all of these things. And that is when we then kind of build out our advanced AI functionality where we allowed people to do the things in N in this kind of low code, no code way that was previously only possible. Writing Python scripts, we kind of drastically reduced the entry barrier for people to build things and also reduce the speed because this stuff has historically also been quite finicky, honestly. Like you connected A with B and it didn't work and it's taking away all of those pain points and takes care about it underneath the hood and you just click a few times, add an agent, add a model, add a memory and so on and it's working magically. 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, you know, 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 about with open source is it's like it's kind of, it's driving very rapid innovation in the end, like because you have like this kind of huge army of people that really, really care very deeply, they're very smart and very often have a lot of time and they kind of do things that organizations can very often not do because again they have very different incentives or have very different timelines or have simply that's the work. And I think the nice thing about Open source is like you can do whatever you want and you can explore things that maybe wouldn't even make sense in a company setting. So I actually think like open source is like really this amazing thing that kind of opens up the world to many different possibilities in the end. Like some of them biddens and a lot of open source projects never make it to anything. Obviously that's the ones nobody talks about but some of them they kind of hit something and that those are the kind of the mindset really kind of matter and kind of really shape kind of very often the whole industry in the end.

Maybe a follow up question to that Yan. 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.

Definitely very, very much. I think like first, I'm not sure like first if open source is that very often it's the cheapest thing. I think this is kind of a misconception. It's actually very often not true. I think you get a lot of things like being able to kind of self host, knowing where data is stored and what's happening with that. I think there's a lot of value there. But very often is actually the cheapest thing. Very often is actually the more expensive thing because you have hardware that's probably idle for 99% of the time and this actually kind of turns out to be more expensive. So actually I'm not sure if that's actually true. But the second part I think is true. I think people care about it more and more. Like we actually work now with a few organizations 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 did care about the data privacy and data security angle. And I think that's also what we are always historically seeing with nn. Very often it's like people want to kind of self host and want to know where the data stored and admit themselves hosting obviously don't mean they run it on the computer underneath the desk. They still run it on the cloud, but obviously in their own private cloud.

Yeah, one I think noticeable shift over the last couple of years, something that of course nad I'm sure has been a beneficiary of is just improvements in the communication protocols between different systems and different models. 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 and where you think they might evolve?

I think that if you kind of standardize in the end you kind of accelerate things. I think that's very important. Even interestingly if the standard is maybe not perfect, I think it still adds so much value. And I think if MCP is going to be the one you're going to use in a few years, I have no idea, but I think it's great to have like a starting point and kind of, you kind of can build on top in the end. This MCP is like the HTTP of the AI of AI workflows. I think that's really amazing. I think it's kind of it will enable, it already enables now things like agent to agent and it kind of, it's a kind of building blocks you actually need for this kind of more powerful use cases like for example you had marketplaces or like plug and play automations. And I think NN can act as the kind of the orchestration layer between this kind of diverse MCP agents and tools. And also obviously N is also the best kind of platform to build also tools that we can access with MCP as well.

Actually on that, Jan, we haven't really done the straightforward 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 in AI I think and.

It'S by now probably the easiest to use most powerful way to build AI agents right now. I think it allows you to kind of again build things you previously never even thought you were able you could build. I think the nice thing about you can go very fast from a first idea you have to a first prototype and then bring that prototype into production.

What is the stack that you see people using with innate N actually quite diverse.

The nice thing about that system like N8N is you can literally connect everything to anything. 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. Except it's just the thing that makes anything great because honestly 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 of it's going to be a million different ones, it's going to be a small one, big one, I have no idea. But that's the great thing about NN 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.

Any interesting observations on what's been trending positive or negative in the N8N universe.

But people definitely use quite a lot. It's honestly still like all the kind of Google tools. Google is definitely still I think the one the kind of application you can use everybody like people use a private in large organizations. I think that's an amazing thing. That's why we definitely see that's probably one of the strongest ones. We definitely also see a lot of this kind of communication platforms. If it's a Slack or Telegram or anything like that. It also kind of makes sense especially in this kind of AI world because you obviously need like an interface how you kind of interact with the agency built 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 N you can connect literally anything. So it's not just kind of applications. Also kind of low one low level things as well. And also honestly very often also internal tools as well.

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 Inadian user base? Has it changed as you've gotten more and more AI adoption?

It's quite interesting like when actually our user base started to explode like since the last eight months. You are really wondering 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 bad people. It's just like the kind of people that are successful with NITN and it didn't which was quite surprising. People that are quite either already quite technical or people that really like have a use case and they care very deeply about what they're building and are just willing to put in their work like maybe they're not as technical when they kind of enter, but they have a problem they really want to solve. Actually, quite a few users that actually started to learn to code because of N. And why do they do that? Because they can realize, hey, I can do that much like if I'm technical, but I cannot code, but cannot code. But again, this can literally go anywhere. If I can go and kind of this whole new world opens up. That is actually 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. I think our, our community is really amazing. It's just the ideas they're having, how driven they are and how much they also want to help each other out. I think that's just great.

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? I mean, you've lived it over the last eight months.

Yeah, I think that definitely changed things. I think the main thing is like the past, it was easier to plan and know where you're going. And right now you're always living in this constant, I wouldn't call it fear, but kind of constant state of uncertainty where the world is moving. You have to be really willing to kind of like you have to find that you have to strike a very good balance between not missing anything important, but at the same time not jump on anything that's coming up your way. And I think that is definitely much harder, it feels like, than it was in the past.

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, you know, in different parts of the company, how do you sort of assess the sort of durability of that and make sure you're investing behind the right things?

This actually probably talks about something I think is generally quite important. So definitely harder. In kind of this open source world we are having as we have, like obviously we have some use cases that generate revenues, like with enterprises and we have a lot of people that, that use the product for free and we never generate revenues with them. But honestly, none if it's more important than the other one because like one is, obviously it's going to help us directly, but the other one is the thing that drives all of it. So it means like you have to strike a very good balance between both of them. And honestly, normally I'M leaning much more heavily into kind of. For example, I guess you obviously asked about signals, but I think it's important. Point is like I normally lean much more heavily in kind of giving away more for free because again everything we give away for free kind of makes the whole product better for literally everybody and drives more adoption and also drives more revenues in the long term versus everything we built for the enterprise organizations obviously just kind of helps a subset of the users. I think it's still important to do both and I think you have to listen to both very closely. But also I think it's around timing. In the end it's like right now I think it's really 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 capture the market generally and literally capture like the smallest builder out there. Because we already, what we already see is that through the smallest business we get into the largest organizations out there. What I think is impossible is if you now focus very heavily on enterprise only and I think you can never go then downwards again. I think nobody, like nobody went from enterprise to kind of owning a space. And I think that is again also Google started the same way as well that maybe own quite a lot. The same with Microsoft as well. But you have to start in the right direction there.

You know, right now we're seeing this explosion of people building with AI capabilities. Over the last few years we've seen an explosion in the AI capabilities themselves. And so the question is, is that technological foundation, the AI capabilities themselves, do you think that that is 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?

I think probably looking at GPT5, I think it definitely feels like it's slowing down. This kind of makes sense. I think it's not very often what happens is like there's more low hanging fruits in the beginning than the later point in time and you can again you can grow more compute power and more data for quite a while but at some point you kind of max out there and at some point it just becomes too much. So I think definitely kind of peace country slowing down. However, just think that's more or less 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 and some of them are still very early and I think they're not at the right stage. But I think as some of them were coming to this kind of to the right stage, I think we're going to see probably another acceleration.

I mean, maybe building on that. 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?

I think one thing is, I think it probably talks a little bit to the kind of role as a developer. 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. Now with tools like N and all of the wipe coding tools that's changing and 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. And I think that is really amazing. And honestly, it also plays exactly what ended in was always about it always. It was always about empowering people. And this I think it's great. I think literally every developer is candid is kind of going into the role of empowering other people to build the things they actually want and need. Yes, developers, obviously I still have the vibe coders and other people like that. They're not going to build everything. They're still going to roll for the classical engineer. But I really love that kind of world where people are empowered and the people that have the problems are the best equipped to kind of solve them themselves. And I think that's kind of a world where we are going and I think that's. 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 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 some of the more horizontal tools coming out of the labs and the like.

Yeah, I think like vertical is obviously amazing. If you had like one very specific use case that do this one thing perfectly and they can do it much better than every horizontal tool. Obviously that's exactly what happened with SaaS. Like the SaaS world, like they have a, like a Vertical application for literally everything. That is again why we had the need for something like N. Because you have to kind of bring it all of them together. Again, I think there's also a limit, but also probably happens more and more in AI right now where people build this kind of very vertical tools as well. But as this happening there again, the complexity increases again and you either need kind of another orchestration tool or they kind of need a horizontal tool that kind of built everything for you again. So either kind of, again, you bring all those tools together or use a more horizontal tool to kind of build this thing in this one tool rather than kind of a million different ones. Again, I think that's again why we obviously feels like in a very good position there. Because again, I think no matter which way it's going to turn out, I think we are very well positioned for this world.

If everything goes right, what will N8N be in five or 10 years? What role will N8N play in the world?

The idea is always like for very many different reasons. I was compared N to Excel. They always thought about, hey, if people 15 years ago they heard spreadsheets, they thought about Excel. And if people in a few years think about, hey, I have to build AI, I have to do anything with AI. The only thing that should come to mind is N. So I see it more as kind of this 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. I think that is where I think and it's going to naturally evolve. And I think we are very well positioned as we already kind of starting to be like the default building tool already.

So, Jan, you know, you build a remote first company obviously centered in Europe, but. 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.

Like obviously we started in Europe and the whole team was based here with what we always was quite interesting. Like, even though we were based in Europe, like, our user base was always very global. 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 us like a very, very big need in the US that's why we also right now expanded into the US and actually just right now opening our office in New York. And that's also why we're hiring quite a lot. It's like especially obviously in the US but Literally like also guess we could say worldwide, literally everything from engineers to people in support and especially a lot of people in the go to market Org. I think it's also the kind of the thing that really gets me excited is like I think not many European organizations kind of have the possibility to really build something really global, something that really matters. I think N and N is probably one of the arcs. I think that has quite a lot of potential. It's nice to see that we are now finally kind of going that direction and kind of also kind of capture the US and take that market over as well.

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. I think one just from your reflections of the last six years, what's maybe one of the hardest truths you've learned about Jan as a founder CEO?

One thing is probably I really don't like to say no. There's so many different things out there and I think that is, I think very often it turned out to be okay like to kind of do a lot of stuff in parallel. Sometimes in hindsight this actually didn't turn out that well. Interestingly I think that most things very luckily turned out to be quite well. So I'm very lucky there. But I think it also kind of turned out very differently and think for the most companies actually turns out very differently.

We want you to keep taking those big swings.

Yeah.

What is one must read or must watch piece of content in AI blog.

Book show like I still love her. That's just a movie. I think it's just. Yeah, I think it's just this, this thing is that I think what I think especially amazing about it is like a few years ago it was sci fi and it's now suddenly this thing that is just 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 in AI that you would recommend or that really interested you?

I think I just very recently and probably very late I came there but I started to use granola. I think it's such an amazing tool to. It's so great, so simple to use, does such an amazing job. So I think that would probably be the one that comes to mind right now.

What AI application or application category do you think is most likely to break out in the next six to 12 months?

I think it's kind of probably more this kind of AI powered internal tooling. 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. We also see that already at n quite a lot. That's obviously why I'm also excited about it. Yeah.

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 look forward to the future.

Thank you very much. Thanks for having me.

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