Seriously, Please Watch This Before You Declare n8n Dead

0:00 We have grown 10x in the past year. The week openly I launched HD kit was one of our fastest growth weeks ever. Even the frontier labs are using us. Many of our users using cloud code internally but still use for business critical use cases. Meet Yan Overhazer, the CEO and founder of Nadn which was just valued at $5.2 billion and crossed 100 million ARR. >> If you want to build something that's reliable, audible, and you can pass to your teammates, you really need NAN.

0:24 >> This tweet went viral from John Nennis. remember NADN went in irrelevant pretty quickly, huh? Do you need NADN anymore? >> There's a lot to unpack there. I think we have been called that probably a thousand times. I don't remember half of the things that that killed us over the years. >> You guys actually existed before Chad GPT. And you talked about in a podcast how you said you were scared a little bit. Nad was literally the hottest AI tool in the world in 2025. Now people are declaring you all dead.

1:00 Before we get into today's show, please take a second to check that you're subscribed on YouTube and following on Apple and Spotify podcasts. If you want access to all of my favorite AI tools, I've gotten them to give you an entire year of their paid plans. Check out bundle.ac.com for an entire year of Bolt.new, New air table, speechify, descript, magic patterns, linear, dovetail, arise, and mobin. And now into today's show.

1:32 Naden was the hottest tool in my newsletter last year. I wrote about it over 25 times. You can use it to learn the basics of AI like rag and fine-tuning. You can use it to automate your workflows. You can use it to create AI agents using its visual workflow builder. And then Claude Co-work came out. And then there was the rise of Claude Code. Today we have on Yan Oberhouser, the CEO and founder of NADN. We're going to get to the bottom of what can NADN do that Claude Code and Co-work can't. What is the role for NADN this year in 2026 and 2027? How should you think about using NADN versus these other tools? We'll also talk about how they build product at NADN, what their new latest metrics are, and much more.

2:22 Yan, thanks for being on the podcast. >> Thanks for having me. Excited to be here. >> So, NADN was literally the hottest AI tool in the world in 2025. Now, people are declaring you all dead. I looked at the Google search trends and there might be something behind it. If you look at these trends here, then this tweet went viral from John Nennis. Remember NAN? went irrelevant pretty quickly, huh? Do you need NAND anymore? >> I think there's a lot to unpack there. I think first probably important to call out that we I think we have been called that probably a thousand times. I don't remember half of the things that that killed us over the years. We're still here. So, I think that probably doesn't hold that too. Also quite exciting, quite interesting. I think if you check out half of the other tools out there and you type into Google Trends, I think you see very similar trajectories. I think it's just there obviously a hyped hype cycle where gets a bit overhyped and then it kind of goes into this kind of more normal framing again. I think that's also where we are with the most products there. But yeah, I think maybe first things to talk a bit more about cloud code and other ones. I think it's an amazing tool and really deserves to be out there. But I think the most important thing is like N and cloud code are very very different products. It's like and you need both in the end like cloud code is more like the genetic tool like it runs entropic models in your terminal and and at N you can see more as this kind of orchestration layer that really connects your kind of tools your your kind of LLMs your data sources and it kind of offers you this kind of visual canvas for for systems to really run reliably and securely. This is especially important for business critical use cases that where technical and nontechnical people really collaborate.

3:57 I think you I generally think it's also like more important than ever for for this like edit end is more important than ever for this especially business critical use cases where reliability security and audibility really matters where yeah we want to be 100% sure that you know what is running like you you can inspect what's running you can see what did run where you cannot just rely on it's working 95% of the times but you have to be 100% sure that it really works and I think this again where our mission canvas kind of really shines because it sees shows exactly what how it works, what it's done.

4:29 and this kind of u also when you work with other people where you kind of talk about like what is actually running like if you have something like a cloud code, it generates literally 10,000 lines of code that nobody can inspect and nobody knows if if if it's actually doing the right thing. Also do we see actually a lot of people that use it together like they very often prototype with claw code because again you can get something started very very fast but then actually kind of migrate it afterwards to something like nitn for the reasons that I already mentioned again especially for for the audability when you want to handle like large or complex data processing pipelines they want to be like able to kind of more precisely define what actually should happen and where also selfhostability really matter because like people care more and more about data privacy and security and also one important thing to be aware of actually multiple of the model companies are using Enaden as well and I think that also makes sense because in the end you want to use the right tool for the use case that actually using us for things like security or compliance use cases because that's again where we really shine and also if you look like in in our stats online you still see like crazy growth like we crossed 200,000 Gab stars recently we have one and a half million active diff users.

5:43 we have over 1,200 enterprise customers and I'm not talking about enterprises using Nen. I talking about 1,200 enterprise customers that using our enterprise solution out there. We have 300 ambassadors out there. There's literally one end community event worldwide every day. Like in September alone, we have over 50 events happening worldwide in a single month. we increased our revenues 10x over the last last year. I think the medium number of of enterprise instance users more than doubled over over the last months. So I definitely we still see a lot of growth. and I think one last thing to probably call out I think there's still two very critical things that kind of made me makes me very confident about the future of Enaden. One is the kind of model flexibility. It's like what people really care more and more about is kind of being able to switch models for the simple reason because new models appeal literally daily and all of them with different capabilities. All of them have different prices. People really value kind of this this flexibility of NET end to kind of really use the right model for your use case and we see generally kind of models and kind of intelligence being more and more commoditized and this again means that the value kind of shifts more in our direction. the other thing is also like when you want to kind of connect multiple models together again say hey maybe want to use for X use case I want an open AI model or for that use case I want to use entropic one or here I want to use an open source one. I used to think I had a retention problem. Turns out I had a messaging problem. I was sending the same onboarding emails to every new user whether they activated on day one or never logged in again. I had no idea who was slipping or why. Customer.io changed that.

7:20 Every message I send is now based on what users actually do in the product. Someone hits a key activation moment, they get nudged to the next one. Someone goes quiet, they get a different path entirely. Their AI agent makes it fast. I describe the campaign I want and it builds the full journey form. Triggers, timing, copy, even branching logic. And when I want to know how something is performing, I just ask the agent directly and it tells me what to do next. They also have an MCP server, which means AI tools like Claude can see directly what's happening in your customer.io workspace, your segments, your customer data, your attribution, all of it. So instead of explaining your business context every time you need help, Claude already knows it. Notion used customer.io to personalize their onboarding and hit nearly 50% open rate. Improved conversion by 6 to 7% with localized campaigns and pushed open rates up another 20% through AB testing. The idea is simple. Customer.io helps you deliver more impact from every message you send.

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8:58 Prompt-based experimentation makes what used to take days of developer time turn into minutes. Try promptbased experimentation on your own web apps. Visit chameleon.com/prompt to join the wait list. That's k a m e l e o n.com/prompt. And the last one is really this kind of the community and accessibility. Like I think NN we talked about that the hype cycle before. and obviously N started with the kind of very early adopters. We talk about this like 0.1% of of this people and now we kind of moving more into this kind of 99% who are like moving away from this early adopters but really the people that actually want to get things done that we kind of lower the entry barrier more and more. We just really make sure not just this technical we can only build red in it but literally anybody who can use a computer. This literally our our mission is defined to give everybody uses a computer and technical superpowers and that's what what we're thriving for these days. and again it just only feels like you're just getting started.

9:56 There's so much opportunity out there. So I'm really excited. >> Wow. So, I'm going to be paying attention when you show this to us in a little bit around privacy, security, auditility, reliability to really understand what those words mean because I feel like sometimes when we hear those words in abstract, it's hard to really understand what that means in the product. So, I'm going to be on the lookout for that. But, I want to dig in a little bit more on this detail around specifically the rise of Claude Co-work and Claude Code. I had on the founder of Lindy who I used Lindy personally for some of the similar things I would use NDN for like automating agentic workflows. He admitted Claude co-work had a really significant impact on their business. Did Claude co-work cla the rise of codeex have a noticeable impact on Naden's business?

10:43 >> In the end like we first going back to to to this things where we got killed like I think at some point open launched their their own agent builder. I think like H we had I think the best week ever is when we got called killed by them. So I mean it's like I think the nice thing about all of those opportunities is that people talk about it more and see more opportunity. >> So yeah that's that was quite exciting and we definitely I think every company these days like then people again hype things up and and they see a lot of usage of those products. I think what we're definitely seeing is where where the use cases change. I think a lot of the use cases in in the past is where people started to again use edit end for this a personal use case and they say hey again write me an email summary and what's changing more and more is again that people say hey this uses cases are great and I need them but where they really use N more is for those kind of business critical use cases and that's definitely probably the biggest shift we are seeing there >> so I was trying to find some numbers on NAND just in May you guys announced your strategic investment from SAP where you mentioned you had hit a $5.2 billion valuation. Startup riders earlier this year put you at a hundred million ARR.

11:58 You just had mentioned that you guys had 10xed your ARR over the last year. What can you tell us about your users and revenue now? Feel free to break any news. >> Honestly, we we're not showing much more numbers there. I I just say I can say we way across 100 million by now still growing strongly and we again the the user numbers already shared one and a half million users also growing strongly there as well but we're not sharing any additional numbers right now publicly >> I think you said 1,200 enterprise accounts >> exactly as well I think like the exciting thing is definitely kind of just seeing what kind of enterprises are dropping in it's like again talk about SAP again they're literally they're not just made investment that he talked about with with 5.2 billion valuation but they also they actually added NN to their to the product. So N will be available to literally any SAP customer out of the box they can use it without installing anything else without installing like setting up anything else without adding billing information anything else they can just use this out of the box. means like really this kind of makes and and this kind of AI layer inside of SAP where you can really kind of build your agent automations inside of the of the of the product also like customers like Mercedes which probably talk about how they're using end and how they see the company getting transformed through it. I think this is the the really exciting things like I think a lot of the the numbers people are sharing a lot of that is is a lot of hype but what actually what actually is the interesting thing when you actually see real usage of a product and especially really delivering real ROI because I think like people like in the past like revenue was great because like revenue was very close to actually also kind of real value but I think there's a lot of decoupling happened there and I think what our customers see very strongly is that like we really deliver a real ROI for them is like they're not just burning money, they actually see that getting something back there. I think why that is happening is I think another thing is maybe important to call out about NN is this kind of combination of AI with deterministic logic and human loop like I mentioned we talked about it before AI is amazing offers so many possibilities but at the same time it's it's not the solution for everything.

14:09 What you really want is kind of kind of link AI with the with deterministic logic because again it's much cheaper to use a simple if it's much faster and is 100% reliable and then you still need human in the loop. It's also something that demo you later because how it works in instead of end because for certain use cases you always want to make sure there is like a human still in the loop again. And I think that is probably the things people should log on look out on LinkedIn like not the the numbers that actually share but actually the real users behind it and the ROI it gets delivered for those customers.

14:42 >> So I asked Claude to go through my newsletter archive and it said I had written about NADM 25 times in the last 12 months which surprised even me. It also found that when I did my AI tool ranking a couple months back, we put it in the A tier above Zapier and make now my audience I've been spending a lot of time talking about PM operating systems in cloud code. So what they typically do is you know they would open cloud code connect some MCP servers and have a working agent in a couple minutes. What does NAN do for that person that they can't do themselves? Sure. Actually, I think it's probably the easiest simply show you what how it works. And this is the starting screen for all users.

15:27 right now, I think maybe the first important thing to call out there is like what you see here is our AI assistant, which the idea about it is to kind of I talked about before that the what what we're looking for is kind of really give everyone uses a computer technical superpowers to really kind of lower the entry barrier. And that's exactly what it's doing here. Like you can do the same thing you can do in a cloud code here. you can just describe literally what you want to get built and it's going to build it for you. but actually I I have something prepared but before I I show you how it got built and maybe show you the the outcome and to give you a better understanding how anything looks like and how it feels here you can see an AI assistant which actually works with your Google calendar and and your Google Gmail account. how generally end works is you get a as a trigger note something that starts a workflow in this case you have here an an an agent and you get a response and as well you have again different AI models it's important to call out here that here you have for example we use cloud set by default it actually does get used via our own gateway so you don't have to sign up for for separate accounts if you don't want to if you want to you can obviously still use your own one as Well, we even have a fallback model like we know this this language this this this providers are always very reliable. So it's always good to have a for in case they don't respond. and then we have a lot of different tools like in this case here list emails you can get a whole single email, you can write emails, get information from your calendar and so on. Here you can also see that here we have further actions like for example sending an email that is something I don't want the AI model to do by itself without asking me. So we have here this human like human the loop step but you can define literally like only execute that tool if you got approval before you can define how it should be approved and how the the output should be what it displays you then maybe give you a fast a demo like how it would work in this case you can use it externally or you can also test inside of nn so you can just say what is my next call and now I can also see this audibility piece you're not just seeing how the whole WordPress built. You can also see how it actually ex executes. So you can see here the agent gets did run. Then it kind of used a model. It called certain tools here.

17:51 It also got a memory. and also here you can go through literally each step what information got sent in and what information came out of it. And you can see here my next call is actually tomorrow at Thursday at 11:00 a.m. till 11:30. Then you can send off for example please send or please create another actually please create another another meeting with my VP of sales tomorrow at 300 p.m.

18:30 And now again you see again each node executing again if something goes wrong you would see it red then you can very easily debug and say hey I now fix that one thing again you can see u what's actually doing how it's executing and now you can see exactly that if you go over here you see now it's waiting here you can see it's it's it's not doing anything further and now it says I'm about to create a calendar event with with a certain information am I actually allowed to do it at now I can either cancel it or say add event if I say add event it goes in here actually creates the event and and it's done. I think that is generally how end works obviously like it looks now quite technical like and the not tech people just probably want to know how can I actually create that again that's where the AI system comes in and can show you how it was built and here you can for example see the prompt here can generate me a work agent for a person project you kind of see what it was supposed to be doing get get emails ary anen email search for events and so on. it kind of get prompted like similarly like you would be doing in claw code other solutions and then it starts thinking and asked some clarifying questions like which model should be used and and something else and then it actually thought for 8 minutes again very similar to cloud code and after 8 minutes it it kind of gave came back to you with the answer what it built and you can see here literally again that's the workflow we were just working at right now what you can also be doing again it gives you some information about the tools already used also again where used the approval step and then you can also kind of make additional adjustments and maybe that's something else to maybe also demo here where we maybe just say hey that's great but I for example very often want to schedule one-on ones with with with my people in the team. So I just tell it hey please now extended workflow make sure I can schedule this one-on-one meetings that should be 30 minutes long. You should add always a Google Google link. the gender should always be in the description and I should only pass like what's required and also like it should get the information from Google contacts and now you can see exactly that now it's going to keep on thinking for a little bit and then it's going to come back to us again in the meantime maybe just again go back up here how just to see actually like while it's thinking can still for example like interact with the workflow here you can still ask questions as well and kind keep on on working and it kind of keeps on working in the background and we can just ask further questions there and then we can anytime go back here see what it's doing trying to see while it's figuring things out in the end and I don't know it's probably going to take a few minutes I'm not sure enough time but at the end what you will see is that it adds additional tools to that workflow it will add again another tool that you can get contacts from from a Google Google from Google contacts and another additional tool to add actually specific calendar events for my one-on- ones. And I think that is again I think that shows the power of end especially like if you imagine now this one I can very easily hand over to some somebody else because like like even if if you're not technical you can still at least go in here and kind of very easily understand hey actually here is an agent you can see what is the system prompt that actually has been defined. You can see again what model it's used and you can see how they are defined. You can see here it actually creates a draft of an email. you can see what things get auto defined like all the things that is simply totally impossible in code.

22:13 because again it's just too much output or again if it's done 100% by AI you literally it's a black box. It can do the things for you but you have no reliability that the thing that worked yesterday is still going to be working tomorrow as well. That's again the reason why we are again great for anything that's where compliance is important or where you where anything is really business critical for you because you can really rely on on on those workflows because of the combination again because of AI but still deterministic logic as well and again the loop step and so on.

22:44 >> So I want to make sure I define for everybody watching those key terms we talked about. So starting with reliability, one element of reliability you just talked about and you showed us is hey Claude they barely have four nines of uptime right now. So you can put a back stop model if cla is not available use this other model. Is there any other component of reliability that people need to make sure they understand? >> like there's probably multiple ones like another one to call out is literally is running your own on your own infrastructure. So means like if you talk about you can literally like Nitn can run on your infrastructure. You can literally self host it. So if you want to make sure like it's close to your own data like there's no internet problems that can really cause any problems there. You can run it there.

23:31 you know like for example like all the all the tools like for example this get email tool or anything else is literally code we created that's literally tested and we we we we we maintain. So even if the API changes anything like that literally what all we have to do is we change the the kind of the code one time it gets changed for everybody. We know it's kind of this reliable reliable piece of tool that that that works for everybody and not something that literally any person worldwide has to reimplement every time in certain edge cases are forgotten like we can really think very deeply about the quality.

24:08 there on top of obviously do we have this kind of whole platform they not just again create workflows but you can also deploy them on so again like if something goes wrong you can just have retries you know where the data gets stored and so on I think there's a lot of things where it simply takes out a lot of the the wor the thoughts you normally have to to think about when you deploy something else or just get code written by another solution for you. M. So, another word we talked about was auditability. And I think the way I'm seeing it, although I'm curious if this is like technically accurate.

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27:18 5,000 plus students have graduated and they have 1,000 plus reviews. Use code Akash 550 to get $550 off your enrollment. When you build these things in Claude Code, the fundamental sort of unit, the source of truth is is code. It's a bunch of files that kind of live in some Python script, some random markdown. It's kind of like kind of like built on a brittle house >> versus here what we have I think and how I experience the product as a power user is the core is this workflow like editor >> and everything is thought of in terms of more stable components where each of these >> lines and nodes in the workflow is something that you guys really put your foot behind is stable. It is working and it's something that somebody can see versus just in code.

28:10 >> Exactly. Especially like this audibility piece. I think that again there there are multiple things to it like next to to what we can see what has been built again is the workflow itself. Again he can also see how did it run in the past like say hey like here are the last execution we for example went through. So you can see literally here step by step what how did it actually run again? It started here it called an agent. it caught those different tools again. You can see the information that went in and out of the product. and you kind of get so like you can so literally identify everything the AI really did for you. Again, with code you see the input and you see the output of the whole thing, but what really happened in there, you have no idea like why did it take certain decisions. For example, we could now add here for example between this the output of the agent and the output like an additional node that's for example the tendency logic and say hey if it outputs like again something that's smaller than 50 or larger than 50 do something very differently like you can be again 100% sure it's always doing this and you can then also after it's inspect why did it take a certain path because like that number actually got was the output then it went there and if it didn't go do a certain thing you know exactly again how you can debug it and change it and then you can literally rerun half of the workflow from that data point from that point in time and can then get it to the kind of state and the quality you actually require.

29:38 >> So another thing that you had mentioned that's important about nen is this idea of being able to pass it to somebody. So can you show us like what is the if I wanted to give this let's say I'm a manager and I want to give this to somebody on my team. You should also have this workflow. How would I do that? >> You can literally maybe there a few pieces there. You can literally share your workflow with other people and guess they just invite them there and then they can access it and kind of change it themselves. also few other important pieces you can literally have a version history. You can kind of see how it changed over time. again means that the person can actually go through and kind of see hey okay who made changes what went on there you can even have things like again publish certain verses where you kind of describe what changed between them similar to get another important one is actually where did it go you can even very easily export them so you can literally send another person an email a workflow via email if you want to and we also something that's not in this version yet here because I think it's an older version so I and update it is what we have is kind of review steps.

30:46 So you can actually say hey I I made this change please review that workflow see if it does the right thing for you and then you can approve it and only then it gets for example published >> okay what is making enterprises like the model companies themselves use it here we've shown the email and productivity assistant I guess for me I feel like I other products could also do this what are those use cases that nen is so uniquely equipped for >> certain ones are for example Again like the the more important reliability in security is obviously more it shines and we have definitely quite a few companies using it for use cases like like as a sore very often like security security orchestration meaning like don't know every time an email arrives with an attachment scan it then archive it if there's something is going wrong inform certain people or make something certain gets started or anything that lies is on like more sensitive information. It could be employment data or could be also be even things like on and offboarding employees where you don't want to like you really don't want to have anything go wrong because you don't want to have an half onboarded employee. You want you don't want to have even less than half offboarded employee either. We have people using us for for DevOps use cases. there's definitely like a wide variety I think in anything if you think about anything where you really want to kind of want to be 100% sure that that it really gets done and not just half done and so means more anything that is not a private use case. that is very often why people normally choose endn over other solutions. I feel like you guys have so much power that sometimes it's almost hard for people to figure out, at least when I talk to them about it, about like what is the specific thing that I should go do next. So, we've showed them the email and productivity assistant. We've given them a preview. This is amazing for security and compliance use cases.

32:45 If you're a product manager, you've built the email productivity assistant. What is the like second, third, fourth thing you should build in any? And I think this is probably more a question for the person is like think this probably been a very hard sound for like hours because you can do everything in the end like you want to do the thing that will be most impactful for you like if in the end you want people to think about like what's the thing I'm doing literally every day what is something where I'm using multiple applications where I'm very often copy pasting things between applications where I don't know maybe other get information from one place put it into like jbt or or claw and then do something with that. Create a PDF report. anything where where you think like, hey, I'm I feel like I'm wasting time. then at least you can get it started for your personal use cases. for kind of company wide use cases, I think that is very often again and then normally suggest people to start with something small like we we have a lot of companies that just want to start with something huge to say, hey, how can I totally transform X? And then they they spent literally building and building and building for for for weeks. And it's it's a very hard thing because at some point if you don't have experience yet you maybe think build things the very wrong way. And I think what what we have seen that sometimes workflows that literally just contain like an an agent a few other nodes actually very often the most impactful ones. And again not always do you need actually AI for everything. Again AI is one tool of very many. Like we have one company that literally used the NN to kind of do password resets of employees.

34:22 They have a lot of employees a lot of them for forget their passwords regularly and they were locked out for literally hours. They cut down the time like immensely and it saves them like literally multiple full-time employees a year in time saved. So like I think it's not about like the craziest one is really the one hey the where can I get started easily? what do you think is the most impactful one and then build build your way up from there especially this simple ones are a great kind of to kind of show off to other people say look what I built see what's possible I spend literally two hours on that and that's the impact you get other people involved and then the very interesting thing is like especially in or is like you the more you talk with other people that actually build you get other ideas as well like I built this yeah that's a very similar use case I have as well and you adopt it and on top we also have our our template library online.

35:14 There's over 10,000 workflows for people there. So you can just go in there say hey I'm using tool X Y and Z set or you can say hey I'm in sales and you get literally for each use case for each use case you l see hundreds of them you workflows and and agents that other people have built where you can either use them to get inspiration or l take them simply as they are at your own credentials and can get started there. Can you show us the template library and maybe give us some inside information about some of the popular ones and the ones that PMs should be looking out for?

35:49 >> Yeah, you see in the templates page mentioned before you can get started in saying, "Hey, I want to use Google Sheets." and then you see like a lot of you in this case, for example, 4,000 workflows that use Google Sheets. We can then say, "Hey, I want I'm in sales." Now you see all the sales use cases, I use a Google sheet. what we for example see very often is a lot of people use also edit in for web scraping just get information from web pages, get information from different data sources enrich it in in a certain way, save it in this case for example to Google sheet and then send that information to to Slack.

36:29 or we can go over to move everything here and go for example marketing maybe actually let's get in it ops. So maybe this one interesting ones yeah for example you have you can say the nice thing about end is again going back to the reliability piece like if something goes wrong you you can get informed in whatever way you want you can get informed via email you can inform this case is an example how you can get informed via telegram and then here we see like somebody used an end for monitoring here they for example track the SSL certificates on my server still up to date and then it kind of alerts them on on this this discord.

37:15 It sends it also to notion. So like there's a million use cases out there as you can see that you can use for for literally anything. you definitely see a lot of monitoring use cases in there as well again very often also in combination with AI because I think that's again where you want to react very fast where the reliability piece again matters where you can say hey again if by default just inform me and then maybe try to autofix it then use AI but you can really kind of very clearly define again the different steps it should be taken first and then you can make sure again again for example the inform informing piece always happens to 100% sent but this kind of autofixing part with the eye for example only happens later and again I think that's again one of the things where nit end really shines and I think I just advise anybody to just look through there and kind of get some inspiration of people what people are actually building with it >> yes a lot of scraping use cases I also have used nen a lot to teach AI like it's a really good place where you can very easily fine-tune a model you can very easily set up a rag system you can set up a vector database. So if you want to learn the fundamentals of AI, I think it's also really good for that outside of just workflows. So one of the areas we talked about was the ease of use. So can we see how the AI assistant has done on extending our personal productivity workflow?

38:39 Okay, here we see the prompt. Thought how I thought about it and then it said extend it here. It actually also ask a question in between. and now we can maybe go into the workflow. We can see on the side let me extend it actually a bit more. and now you can see how it extended it. So what it actually added now is for example this lookup. So means like it can now look up context on on Google. you also can see it it actually executed because what it's doing as part of the process already tries to kind of test the workflow for you and runs it. you can also see here this approval for oneonone. So if you want to book a one-on-one and you can also see here the same thing again says I'm about to book a 30-inut oneonone it will include a a Google link and do are you okay if it gets added to the Google calendar and only then again it it does that >> okay so it took five or six minutes it looks like but in the end you're going to get an updated workflow that actually makes sense that works with your tool >> exactly >> so your episode is coming right after Wade Foster, CEO and founder of Zapier.

39:48 So we have to ask the obligatory question Zapier versus NADN. What can NAND do that Zapier can't? >> I think like the the thing is like the great thing about Ned end is in like we focus from the very beginning about power and flexibility. I think what you really again when you when you get the most value out of it and is kind of really don't want to be kind of locked into this kind of automation where you kind of have something where you want to be sure like you build something today you think again I mentioned before you want to start with something simple and then kind of you want to kind of build on top of that and and and kind of build it up. I think that's again when shines very strongly because again we have this power and flexibility built in. We have you have things like code nodes where people can fall back to code any time. We have this very complex agents you can be building.

40:40 Again, it's not just basic agents. You can literally add your own memory. You can put out you can add outside passes to make sure the output is always a certain format. You can have again as I mentioned before, you can have different models. You can have fallback models. You have this human the loop steps. I think a lot of the things that I think when it becomes a little bit more complex valid than simple use cases again like wait is a great guy I I always enjoy talking to him I think safe is a great product but again as mentioned before there's always the right use case for the right product and I think like especially if it's about where you need power flexibility and a very deep integration to AI it's when ended end really really shines And again obviously things probably not worth mentioning but I think obviously if you want to self-host it and things like that that is where where obviously NN is I guess they say I guess cannot compete considering their their S solution there.

41:40 >> That's an important differentiator too. So I want to learn more about your guys growth because you have a pretty fascinating history. We talked about how many times people have called you obsolete. you guys actually existed before chat GPT nan has been around and I think that might have been one of the first times and you talked about in a podcast how you said you were scared a little bit but sprinkling AI on top would only give you 10 to 30% growth not the 10x growth that you guys have been seeing year after year since how did you make that call and can you break that down for us a little bit more what does it mean to sprinkle AI on top versus make it core Sprinkle eye on top is what I see is like somebody tells you add AI and you say hey here and add this AI button somewhere it does something with AI where I see again become but what we how we did is like really think about like how can we become part of the value chain how can you really kind of not just add AI to the product but really kind of make sure people actually build with NN. meaning like when when the idea was like how when people think about if I want to build an agent like they should be build want to build an agent with Naden. So we're not just so we actually seeing like you can actually get the the real value real value for people out there and I think like the thing is probably like when people get asked add AI to the product or build an AI agent or kind of spring like like we want to be the solution that kind of helps them to do that because that's where the real value lies and where you can again see a 10% or a 30% growth but literally like we like last year we grew grew literally 10xed exactly for that reason because we were part of the value chain and we kind of empowered people and we kind of provided real value for them.

43:26 >> I think a pretty crazy stat you guys released is 80% of workflows on NAN now use AI agents. Obviously that would have been zero at the beginning of 2023. So is that your 2022 users adopting AI? Is this a new crowd? If so, what happened to the old crowd? >> It's actually people adopting. It's like I mentioned before like our users are the tinkerers like the They're they're very interested in in in in in kind of checking out the latest tech and kind of really making their day-to-day more efficient. And obviously AI is the tool to do that. And all of them were super interested in actually adopting AI and kind of really making it part of of of their the daily usage. And I think that's the how we became so successful because we just made it super simple to and really get kind of people into this kind of to get people started with AI much faster and easier than with other solutions out there.

44:21 >> And how you've grown this thing is very different. Like the average company doing this type of growth, they would embrace per se pricing. They would have lead genen targets. You killed your lead gen target and you refuse per seat pricing. Why? >> The thing is like we in this very lucky situation that I can think very long term. Like I think that most AI companies out there that maybe grow their AR very strongly but at the same time they're just losing a lot of money.

44:46 So means like you have to kind of show a lot of growth to kind of get more money from investors. and they mean but what forces them to kind of really think very short term like how can I get more AR now to get more money tomorrow. and then the whole cycle repeats because of the way NN is built like we are actually sustainable like right now we we actually creating a profit. So it means like we don't rely on more invested money anytime soon or at all if you keep on doing what we're doing right now. Rather we can think about like what sets us up for success in the long term.

45:19 And I think right now like being able to kind of just capture a lot of the usage and not think about again how do I increase revenues as fast as possible but how do I really kind of create the best product out there and how can I create a lot of value for our customers and how can I get capture as much as possible of the of the opportunity out there. that's why it's obvious much easier decision for us to do something like that than for many other AI companies out there right now. So the lead genen and per seat pricing, those are pretty public for anybody who studied NN like me. What might be some other metrics you've deleted or a metric you're going to delete that people don't know about >> metrics we deleted? Maybe I can talk about an internal thing we we're focusing on very strongly is like one thing is obviously like what we want to achieve like in in certain goals regarding users and AI in the long term but the other thing is like how we want to kind of build the company how do we think edit end should function and we set this internal goal like we want to reach a billion users with less than a thousand employees mean like we don't like we we don't want to grow our headcount like we we obsely grow it because we have to. but it's the opposite of of what we're interested in.

46:36 We kind of we want to build this very efficient orc. The the idea behind it is very simple like organizations come to us to help them to kind of transform the organization to become more efficient to use AI better to automate more and so on. And if you're not kind of doing it internally and like you're literally hypocrites, like that's horrible. Like I hate nothing more. And especially I think we cannot do a great job. Like we can only help them transform if you actually transform internally as well and kind of had our own learned our own lessons know what worked, what didn't work, what was impactful, what wasn't impactful. and and for that reason again we we're not we're not talking about like how fast we have grown our headcount over the last years because there's literally nothing we think is is is a positive thing right now.

47:20 We actually doing the opposite where we kind of try to stay as lean as possible and really try to kind of build the organization of the future to again help our customers and our users do exactly the same thing as well. Maybe another thing to point out is like what what we're not what what we're not caring as much about is like obviously as as kind of this freely available product is obviously like you have obviously users that pay you and users that don't pay you. and we obviously could always kind of for example focus on how do we get people that currently don't pay us use our free fee version that self host us to our hosted solution where we actually earn revenues. there's nothing they're doing at all because it doesn't matter for us like all we care about is that they're using nit and we know it like we're generating value for them at some point like either or we work for an organization where they're going to want to pay us at some point in the future but again we don't care about if they're paying us right now or not all it matters for us is they're happy ended in users they keep on using us in the long term >> fascinating for somebody who was a VP of growth at Apollo.io IO where I was just focused on free to paid conversion free growth that's very interesting and very different for people who might not realize way to approach those free users.

48:32 >> You mentioned the employees and I actually wanted to talk about that. >> I think at some point you had said you want to be the first billion dollar company under 500 employees. I think your career page puts you at around 160 employees, but LinkedIn, it seems like a bunch of people are wanting to associate themselves with NADN and you have like over a thousand people listed working at NADN on LinkedIn. So, how many people really are working at NAN right now?

48:58 >> Currently, I think we around 370 people in in the ORC and we are hiring. We probably going to be around 500 by end of the year. you're right that the reason why the number is so inflated because we obviously have a lot of partners and agencies working with us and they don't very often appear as nen employees. also the other thing you mentioned is like our old talked about right now billion user less than with a thousand employees. Our old goal was a billion AR with less than 500 employees.

49:25 we changed it for two reasons. First we became like we did much more on the enterprise side of things and we realized hey as long as people as long as the the buying side is not done by AI we cannot do the selling side AI either like it's still a people game we need like people don't sign sign half a million dollar contract without talking to anybody so like it's going to stay very people heavy for a long time and the second thing was also that we realized like we mentioned before like what matters in the end is fast adoption and kind of talking about like the the the 1 billion AR was was not set because we said hey we want to make a lot of money. It was set to kind of as as kind of milestone of being like a big impactful organization. but at the same time it still confused people internally even externally saying hey like all it is is it's just about money and we kind of learned to be very clear saying hey it's not about money it's about about adoption. That's why kind of this this value switching away from AR to really actually users made much more sense for us. I have one more question about how you guys grow because it really is different from everyone else. Like let's say like Whisper like I think they're like a company that I put in your similar category like AI they're just taking off like crazy. So they launched their notetaker. Their entire focus was viral growth. They even bought like rickshaw ads in Delhi and things like that. You guys have p been perceived as much more quiet. Is that like a strategy? Is that your personality? What's going on there?

50:54 we actually I think it's probably also part of my personality. I'm not the person that has to be literally anywhere. I I'm more the introvert kind. So this kind of things don't come naturally to me and I'm not the person that really has to be anywhere on stage or so I think this probably I think there's definitely also a component about me there. But generally like as an or I think again we also a European organization. I think we are probably more the kind of underpromise overd deliver kind of people. So we don't really are in in the business of kind of really kind of buying any kind of viral growth there. Again like we think more long term again like we became not viral because we paid people to talk about us. We became viral last year because we had people being excited about it and getting a lot of value out of it. They wanted to share it. We had people that kind of created their business around Naden and then and kind of generated own money like money like that. And I mean this obviously kind of makes you even more interested in in actually talking about it because it's this nice win-win situation. It's like the more they talk about it, the more visible they are and the more visible they are, the more visible we are and then obviously get get more contracts. So it's this nice piece there as well. Again, we we had from the very beginning this very strong community focus. I talked already before about again having an event literally every day on the board somewhere about and and I think that is again this kind of more long-term thinking. We know hey like invest in a community is nothing that kind of gives you growth tomorrow or next week is again I think that takes many many years but once you have it and once it works it's amazing and also by by even by by now like most of the enterprise usage actually comes from our community people like they literally use enm privately they get a lot of value out of it they say hey I actually work for this huge org number x whatever and then I have also use case there so I can actually automate things there and then I do the just internal use case then they get other people excited. We have literally communities inside of large orcs that are like over a thousand people large big and that have their own internal community events inside of large organizations inside of the system integrators. I think that's just amazing. It's nothing you can again do very fast but I think this is kind of more long-term sustainable things that again works out very well for us.

53:07 >> Very European indeed. Although if you think about like a lovable or something they are more viral. So I think there is also a component to your personality and how you have grown this thing. So that's how you've grown this thing which is just amazing. I want to talk a little bit about product. You said 370 employees. How do you structure your product team? >> Right now we have we have like squads which are like between three and five engineers, one to two PMs and one designer.

53:37 we have a a VP of of of product. underneath we have a director and then we have underneath our PM teams. and then the engineers report to engineering leader again and designers to theirs and I think that works actually very well for us. We have this very small unit that can kind of work very fast and efficiently and kind of get things pushed out quite fast without a lot of the overhead. >> When did you hire your first head of product and how did you make that decision? We probably hired him around April 2021. So it was around the same time we raised our series A like then we had multiple engineers already.

54:20 I was obviously so totally involved in the product. but I also realized like it's important to have somebody who has the experience to lead a product or who can spend more time on it, who can go deep on the problems. and and I I met him and from the very beginning he like think he mentioned the past and it is a product he would love to have thought about himself like he's literally a productivity nerd. He has like a shortcut for literally anything on his computer. how he does everything from I don't know shopping to meetings all it is super efficient. He's probably one of the smartest people I know when it was just very very clear from the very beginning that he is the the right person to kind of lead a product company like NN on the product side of things.

55:05 >> So what did you hand him and what did you refuse to hand over? >> Originally I stayed very close like I was still very involved and also in the product side product development side of things. I literally still merged every PR so kind of really kind of still reviewed everything that kind of was happening there. but that obviously changed more and more over time. there again he kind of very early on kind of owned the road map. but again we we always talked about all the things were happening about literally every single feature in the very beginning and discuss things how we think they should work. and again as the product team grew more and more actually I I became for a lot of the things more more removed. I still have for example like for example check-ins with the design team. we had this this this case where after at some point they they built a feature. and they made certain decisions that honestly went against what I thought was the right direction to go in and then just realized actually I I probably have been too far away from the product at point in time. And then I said, hey, now actually I have to kind of go back there. I literally I we have like still to this day we have regular meetings every week with the design team where they kind of show me what things they're exploring, what things they're thinking about. I can give feedback there. They can hear me what what I'm thinking about things. I can get feedback very early on and it just gives me a better idea where everything is going and it just apart from that obviously like and and still my baby I just want to be very close to it and kind of understand what what's going on there and again just make sure it's it's always on the right level and then there's obvious certain things where it matters less and other things where it's worth to kind of really go a little bit deeper and understand how exactly you're thinking about things and and and what direction it's moving. So when a PM is talking to you these days at the CEO level, what are the ways they should be talking to you? What are the mistakes they might make?

57:03 >> It's like it has to be obviously on on the right level where it really matters in the sense of like object it comes that hence what they're coming to me about. Is it about a certain direction they want to be going in? I think that's that's exactly the right kind of level where it says like hey we think we we want to go in that direction. and we want to explore those things because then very early on I can give feedback I think it's right or we can obviously say very early on it's not the right direction and and can also give feedback there and we don't waste time and resources there but again everything is always happening very closely with David because like he's the product leader I I I I trust him literally unconditionally he's just a super smart guy but I think it's always good to kind of bounce his ideas off obviously where it's less helpful very often is like if it's probably just in the detail. I think like I I still like the design deals because again that's literally where I want to be in the detail because like I think that's is is important for me but if the last thing again any senior leader wants if they for you get involved for every small decision because most of them don't matter and I think like if it's about that hey should we put a button here or there or any kind of smaller things that don't really kind of make a substantial difference for the product experience or the opportunity I think that is probably not what you want to be talking with any single leader >> 100%. So I went and found an opening for Naden. I wanted to just see what are you guys looking for for your PM and a couple things stood out to me when I was looking at this. Obviously it's a very empowered job. You own the strategy and roadmap but where I wanted to go was what do you require? So some of the things you talked about here two to three years on platform. Well that's because it's a platform role. You talk a lot about technical depth. some of the words we were just defining earlier in this podcast, reliability and scalability tradeoffs, holding your own in an architecture discussion.

59:01 It seems like a very technical role. How technical do you have to be to be a successful PM at NAN or more broadly an AIPM these days? >> Like the experience we made is the more tech the better and honestly almost every role we hired. like also our designers are very technical. We also expect them to kind of kind of use cloud code create function they most get a we get a good understanding there. So like I think the more technical the better because you just get a I think you can have much more impact. You also kind of understand especially and recently our user base was quite technical. So it was even more important than now and I think also now where again we talk about pound flexibility.

59:44 The thing we never want to give up is this pound flexibility. People should never kind of feel locked in. I think like technical people are are great for that especially because they kind of really understand very deeply like what's possible like how much work is really something really to do there. Generally we always look for the kind of rising stars. I think that worked very well for us. alo we look like for really passionate builders like one of our values is literally like a culture like we are builders. Like we want people that kind of we often that are tinkerers like we love people that do like have home automation running at home and kind of get very excited about those things because again they are very close to to our users there and also people that kind of understand what it really means to kind of build something that scales and something that really is again reliable. again that's why we wanted people actually not just built a basic agent but actually understand hey you want to probably go deeper. You also want to think about evolations. I think around how does it scale? How do kind of improve it? how do we kind of make sure it's it's really reliable and all of those things. I think all of those if you find that this kind of treats in people which understand hey they're not just able to operate very high level but can also go deeper if they have to that is normally a very good sign for us and that are good PMS. so again, we we found some very amazing ones, but we also had had always a hard time finding the kind of more technical PMs out there. over the last years, >> I keep hearing this from AI CEOs, even less technical products than you, that they want these pretty technical PMs.

61:17 One thing I don't see on this list is eval. What do you think about evals? It's one of the hottest topics in product. Should PMs be owning the evals or at least defining, you know, the golden set? What is the role of evals for PMS and Aen? >> we actually have an own team. It's called the AI trust team which actually owns them. and they literally also have built an internal product around it as well. to to to actually make sure it was run the right way like especially like a a comp like a complex product like NN where we again where you want to test against and not just simply input output but we want to see in case for example and like have the right tools spin code and so on it becomes even more important actually not just get something that just again works for for for for half the people out there but we really need something very specific so again people have to understand very very deeply I think actually evils are I think it's kind people talk about them a lot but people don't really use them as much as they should because I think they're probably not very fun to create.

62:23 they're very hard to create. I think there's definitely still a lot of opportunity in that space as well and honestly also still a lot we could be doing much better at N. He also have obviously an EVA product in there as well like he can create your own EVAs in Nadn. think they're very valuable but honestly I would love if more people would actually ask for them and actually create more evals because I think that's again we talk about being able to kind of run things reliably evos are the way to actually ensure that especially if you want to switch for example a model or other things like that or for no matter whatever is a time time reason you want a faster through port or price or whatever you need to kind of just get very fast a good understanding if the performance is the same way or how we can change the prompts very easy to kind of make it maybe make it work for those models as well.

63:11 >> So a lot of people who are watching this podcast they're seeing this role and they're like I want this role. So if they want this role I have a two-part question. >> How do they get noticed by NAN? Like what are you looking for to interview people and then how are you interviewing people >> noticed? I say I'm I'm not directly involved at that level but I think like what's definitely always helpful if you just see people being active in the space maybe having built something like that already just just understanding what it actually means again especially again we talk about them having to be technical again like if we can literally very easily see that again by by having spilled something or just seeing how they kind of present things how they think through those things it's definitely very helpful and the Second part, sorry. The first thing was how they get noticed this >> and then how do they succeed in the interviews? Like there's so many different types of AIBM interviews these days. What are you guys running? How are you guys separating you know the pretenders who just use cloud code to ship some product on GitHub but they don't actually they aren't actually technical. How are you differentiating?

64:20 Okay, this is the PM in the interview process that could actually succeed here. We found that they kind of really kind of mentioned before that they really kind of able to kind of go deeper and not just it feels like they're repeating things they heard online without really understanding what it actually means. And I think this happens a lot like again you can by just asking the right questions. like we very lucky in the sense of that a lot of the the things that actually become problematic at at a certain point we actually also experience internally already. So we know what kind of questions we can ask and what kind of answers to expect there which actually kind of a person would answer if they actually thought about a problem more deeply versus the person that actually just repeats what what they heard and seems like it sounds good but actually there is nothing behind it.

65:14 I think like but but even then like I think with with the eye I think it definitely becomes like we also kind of have task for for for each role I think that becomes even more and more important there. you definitely seen in the past that a lot of task if they especially take home tasks it is yeah obviously they never know it was AI involved but other people involved like even prei we had cases where some other people simply helped out there. So like something like a live session where you just go through a problem and kind of try to kind of see like how would you work through the problem together actually tells us much more. We also had problems in the past that like you kind of can kind of obviously have people that kind of worked in the same space before and people that never heard about a problem. And I think it's very hard to to understand sometimes if if you have some a person already worked on the problem before and if they actually at the right quality did they just again are they actually at the right level or did they just again experience it before that's why they perform much better than people that never thought about it. And this is is just a very hard thing honestly very often you you have to still work with them for forum a month or two to really understand hey they really really meet the bar and I mentioned before like we having this goal reach a billion user less than a thousand employees like that obviously makes very clear talent density is really important if you have only a thousand people at at a certain scale you can only have the best ones and that kind of really ensuring that the quality is as high as possible obviously always means you kind of you have to always kind of keep the hiring bar high. But again, if something doesn't work out, I think it's the best for both sides if you also kind of set to let them go because I think it's obviously not going to be great for end, but it's also going to be bad for for the people actually to leave them in that role because they're not set up for success. Doesn't mean that they are bad in any way, but they're not the right fit for the role we need them in in the organization.

67:16 >> Wow, what a wide-ranging conversation. And we covered NAND's growth, NAND versus Cloud Code and Zapier. We showed you guys how to use NAND to create a personal productivity assistant and get other use cases from templates as well as how they grew. Yan, thank you for all of the alpha you dropped today. >> Thank you for having me. Really enjoyed the conversation. See you. >> All right. And if people want to find you online, where should they go?

67:41 >> Honestly, I'm only active on LinkedIn. I I left X and so only on LinkedIn. >> All right. Find him on LinkedIn. Obviously, go try NAND if you haven't. I have other videos that go deeper on NADN, which I will link right up here. And see you in the next episode. I hope you learned as much from today's episode as I did. If you can do one thing that's totally free that would help the show, it would be to check that you're following on Apple and Spotify podcasts.

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