Laura Fu: Hello, everybody, and welcome to this week's episode of the State of the AI Union. My special guest today is Janis Zech, who is CEO and cofounder of Weflow. Janis, would you like to introduce yourself and also Weflow? Tell us a little bit about what you guys do.

Janis Zech: Yeah. Thanks for having me, Laura. Excited to be talking about this very important topic. Yeah. I'm one of the cofounders of Weflow. We are like Gong, but for fifty percent the price. We do that for mostly, you know, growth companies, so scale ups, and we're particularly good for Salesforce customers that have custom data setups. So we go from activity capture conversation intelligence, pipeline intelligence, forecasting analytics. That's essentially the entire platform offering we have today with four products. You can basically buy each product standalone or combine them as bundles. There's obviously a lot of AI use cases we'll talk about that, you know, touch that specific product. Before this, you know, I started my first company when I was twenty one. Didn't work out. You know, I wanted to stop studying. My mom said, look. Please get a real job. Then I started my second company, which I ran for eight and a half years to three hundred fifty people, seventy five million in ARR. I sold it. It was actually listed in Frankfurt. I'm German. And, yeah, I had, like, eight and a half years of experience there and then started Venture Studio, started another company. We sold that to Personio. It's a company here in Europe, like a Rippling in Germany. And, yeah, and then started Weflow to essentially help go to market teams, revenue teams to be more efficient and effective in what they do.

Laura Fu: That's awesome. So, I mean, you started a series of companies and you've landed here at Weflow, which is a lot about, like you said, helping go to market organizations. And now we're in the era of AI. And one thing that we said we're going to talk about is — well, what is AI doing in the go to market world? Lots of people are talking about it, that's for sure. And the deployment may be catching up. Tell us more about what you're seeing.

Janis Zech: Yeah. I mean, so maybe for context, so I've probably now talked to around like four fifty teams in companies between a hundred and, you know, ten thousand people. And we talk a lot with revenue operations teams, sales operations, business operation teams, and then, obviously, also with the sales and customer success. So this is typically, you know, the companies we service. We are very deep in the Salesforce ecosystem, so, you know, the point of view I have on this is maybe a bit biased, and I would describe it a bit like this. Right? Like, if you open LinkedIn today, you see all these fancy workflows in n8n or, right, like, these charts where basically everybody tells you, well, you know, like, agents basically replace humans. Right? And I think, to be honest, that's not what we've seen. I think there are some examples where this is actually happening to a certain extent. And the best example there is a company called Intercom with a product called Fin, where they automate the customer support requests. Yeah? But I would say the majority of products and tooling today are very much an enabler of the human. Right? So it basically automates nitty gritty workflows instead of replacing the humans. And I think this is what we see currently. The reality we also see is that the hype cycle is very high and the deployment is still nascent. And what I mean with that is, like, obviously, if you're in tech, right, you probably see and read the most posts by people that are in tech, and they are probably the most advanced. And so for example, we work with companies like Ritual and Abridge and, like, some really amazing go to market teams. And, you know, they obviously deploy AI on various different use cases we'll talk about. But it's not true that there's no SDRs anymore. There's no AEs anymore. There's no content people anymore. They all leverage AI in different facets. And I think the game is changing. So if you did outbound ten years ago, right, that game has completely changed and AI plays a role in that. But yeah. And then you go into the enterprise, deployment very nascent. You go outside of tech in terms of industries, deployment also very nascent. You go into different geographies, like, for example, Europe, a lot of fear around data sovereignty, where do I have the data, where is it stored, and so on and so on. Right? So I think tremendous potential yet deployment still lagging. And I think it would be great to just talk about what's actually possible today in various different use cases and then how the future might look like, to give a take on that as well.

Laura Fu: Yeah. And tell me more about, you know, deployment being nascent here. Do you think that that is an infrastructure thing, like because the infrastructure makes it difficult to deploy — is that a result of it? Or do you think it is the drive of, like, actually getting it done? Or do you think it's the possibilities of what it can do that kind of make it lie behind?

Janis Zech: Yeah. Great question. I mean, to be honest, I think it's all of it, actually. Right? So I think in the enterprise, right, like, typically, like, a sales cycle might be one to two years. Right? Like, it's actually just, like, two years that we are in this cycle. So, obviously, right, like, the deployment cycle there is maybe not there yet at scale. Right? But then, you know, the other piece is that we just started building these new experiences, and some of those products are still very early. I mean, I think everyone has heard of AI SDRs, and it sounds so great. Right? Like, you just basically feed the AI, and it books your meetings and you drown in meetings and actually pipe gen is not an issue anymore. Right? Like, I mean, who wouldn't love that? Right? That's a great value proposition. I would buy that all day long. The reality, though, when most companies tested those solutions was they were not delivering on the promise. They were not yet, you know, actually — like, they were mostly burning your TAM, and you paid a lot for them. Right? Because they basically price it as a human labor budget. So I think it's a great example where, like, basically, they were automating the cadences sequences we typically use with not a lot more context data. And so I think it was actually a product problem. Like, the product didn't live up to the value proposition. Everybody buys the value proposition. But if the product doesn't deliver repeatedly on the value proposition, it's just not yet a real, you know, repeatable product. And so I think this, you know, deployment, for different reasons lagging. And then there's also product not yet being at the realm of what is actually possible and what will be possible. So let's talk about what's possible today. You talk with lots of teams and you do a lot of deployments as well. So tell us what you think is possible today and then what's possible in the next two, three years maybe.

Laura Fu: Yeah. And tell me more about what's possible today and then what's possible in the next two, three years maybe.

Janis Zech: Yeah. So, I mean, obviously, I don't have all the answers. Right? Like, but here's what we currently see. Right? So, like, obviously, right, like, on the content creation side, top of funnel, a lot of AI being used, right, like, to create content. I'm not an expert in this. I use this a lot. Right? Like, we do a lot of content marketing. It definitely speeds things up, but I wouldn't say, like, okay, this is exactly how it works. Right? So I can't really speak to that. I think one area that is fundamentally changing is the whole outbound game, which I think is really changing towards a combination of three main things. You basically marry the, let's say, signal data — so the intent data, the context data, the scalability of cadences, multichannel cadences, different type of cadences — and the ability of LLMs to personalize that experience at scale. So I think that is happening with companies like Clay, Common Room, right, that marry these different areas together. And so some call it outbound, some call it signal based outbound. But it is the idea that if you're in the universe of your TAM, that you have better data to know where to focus your time of SDRs, BDRs, go to market engineers. I think that is a fundamental shift. It's not entirely just the AI shift. And I think this only works if you have strong context data. So if you're a new company and you don't have any context data, right, like, you might type into job changes or, like, some signals you get, but everybody gets that. Right? So, obviously, if you have production databases that have signals or you have website signals, those are a lot stronger. Right? So your first party signals. It has always been the case. And sophisticated companies have done that actually ten years ago, but now it's a lot easier to do. And so the efficiency per SDR, XDR, or, you know, like, the automation and the level of automation you can actually run in terms of also personalizing those emails and so on. Right? Like, that has fundamentally changed. But at the same time, there's also saturation in all these channels. So, right, like, the game, to a certain extent, gets easier, but then the saturation of these channels, because everybody can do it, makes it again harder because suddenly a lot of people are jumping on that. But specifically, this is essentially an automation workflow builder that has an LLM step in it that you feed with data. And that is something that is certainly happening today. Then I think — right? Like, if you're in ops or, like, you know, c-level position, VP position, you get a lot of probably cold emails these days. And so I think you can probably relate to what I'm just saying is, like, yes. I mean, these are probably sometimes very personalized, very well written first emails that are AI generated, but still because there's so many, you might not really — they might not really work. Right? So but I think this is certainly an area that is very interesting. And, again, not just because of AI, but because of the signal data. And then the signal data is sometimes enriched with AI. Right? So there's, like, research agents that basically feed the context data. But the more valuable signal data is actually just a cookie on your website that tracks, you know, and the opt in and then the anonymization and things like that. Right? That, again, were technologies that maybe the cookie was there ten years ago, maybe a lot harder to operationalize. De-anonymization wasn't really as far as it's now. So I think these are some fundamental changes on the top of funnel side. And I think these are great. Right? Like, they're driving performance. But I think it's not yet all AI, but then the workflow builders provide you with a lot of ability to further customize. The inbound intake SDRs, they use that to book the meetings in a more efficient way. And then I think it's always in context of your TAM and your go to market motion. Right? So geo and then industry and also deal size, how aggressive you can deploy those tactics. Right? If you basically sell to non-tech companies that actually don't really get spammed as much, and, you know, they actually don't understand what's going on, then this is obviously very interesting. Right? And gives you complete new capabilities that weren't possible before. If you sell to an audience that knows exactly what's going on, it might not really have as much of an impact.

Laura Fu: Agree. Yeah. What else are you seeing that's, like, possible today?

Janis Zech: Yeah. So, I mean, so this is basically the top of funnel — so like content, then all things like allbound. Then when you come into the deal, let's say from first meeting towards close and then expansion renewal, I think the fundamental thing that people have been doing is — right? Like, I think the fundamental challenge has always been centered around, like, the typical AE spends thirty, thirty five percent on just selling. And the rest is preparing meetings, administrative work, preparing meetings, following up on meetings, feeding the CRM, updating CRM. And I think this is basically where we play with Weflow, right? The idea is how do you enable 10x AEs or CSMs or AMs by automating all those nitty gritty workflows to automate data capture so that you actually combine activity data that is automated, conversation intelligence data, CRM data, web data, and you take that to provide insights to inform deal pipeline forecast management processes as well as coaching and performance management processes. When you then go deeper into the specifics there, right, I mean, there's a variety of different use cases that are centered around — like, the CRM typically is a structured system of record where often a lot of context data is missing. So you have to start basically getting the context data. Very similar actually to the top of funnel, but in the middle of funnel or bottom of funnel. And the context data there is — I think the fundamental question people have to ask is like, when you look at a deal, can you tell whether it actually is healthy or it's derailed? And so then the question is, okay. How do you do that? And, typically, you do that by understanding, you know, the activity velocity on the opportunity level. Right? Like, if I send ten emails, how much do I get back? It's by understanding the contact of the buying committee. Right? Like, understanding the velocity of communication around the buying committee, who is involved, do you have access to power — very much centered around, like, the typical sales methodology, but automated. And then it's very much centered around, like, your core signals, push count, right, like time in stage, progression. And that data often lives in video conferencing tools or field meetings. So AI note takers, they're spreading everywhere. They become the norm. I think a lot more people get acquainted to those because now Teams, Meet, Zoom, they all have their in-house built capabilities, but they obviously don't really use the data to then update Salesforce fields or HubSpot fields, right? Or to automate the email follow-up or to provide AI coaching or to use the data to then understand what good looks like and enable the teams. Again, that's something that has been around. Gong started that trend, right? And there's a lot more companies today. But I think what's different now is that you can deploy completely new experiences that take the data and then let AI do the coaching. Right? So there's no manual call review anymore. The AI automates that and scores every call against your methodologies and enables you to basically see over time, you know, how your sales methodology is operationalized or, you know, the meeting notes are automatically captured and summarized or the fields are automatically — like, MEDDIC fields or SPICE fields are automatically updated or the follow-up email is in your draft folder and you just review it and send it or you send it at some point automatically. Right? So these are all things that I think weren't really possible before. And then if you take that a bit further, obviously, there's, like, role plays, right, you could do, which is very interesting to train, not on, like, live data or, like, live prospects, but, like, you know — and then this all goes into the deal, the pipeline, the forecast engine, and there's a lot more to do there. But let me stop here in case of questions — I feel like I've been rambling for a long time. I'm sorry.

Laura Fu: No, I love it. Actually, Janis, everything that you mentioned — what I'm hearing is that right now, today, what you're seeing a lot of is AI helping individuals, individual contributors, go to market teams do their job faster and more efficiently. Right? However, you mentioned on a number of occasions that, you know, a lot of this — like, we were already doing it. It's just that we can do it in a better way now. And what I'm wondering is, have you seen anything that is materially different in terms of — okay, for example, you know, selling, what do we used to do? You know, make a couple of cold calls and then set up a meeting that would do the discovery, that kind of thing. Like, is there something that's fundamentally different that AI is actually helping us do, or are they just helping us do what we were already doing just faster?

Janis Zech: Yeah. It's a great question. I think it's for sure productivity gains. Right? Like, and then I think the other piece is being a lot better at what you do. Right? I think that's the — like, being more effective on how you do things and what you do. So I think both is true. And the way I would describe it is on average, we see a lot of meeting data. On average, different industries, different years, but reps have five to seven meetings a week. That's actually not a lot if you think about it. So I think the economic impact we're talking about here is — I mean, you have ten million salespeople globally. So if everybody would do the double amount of meetings because they don't have to do all the other pieces, this would be a huge efficiency booster. And so from a management perspective, typically your P&L looks like — I mean, in tech companies, forty percent of cost is in R&D, forty, fifty, sixty percent is in sales and marketing. And so I think your capacity model fundamentally changes. And it all starts with going from a gut based — I think this is how it works — to a data driven approach, which actually has nothing to do with AI. We've been doing this for ten years and some companies do it a lot better and some companies still don't do it. And I think it's surprising to see how many don't do it very well. And so you want to make sure that you can actually trust your metrics, trust the visibility, trust your reporting. And this is fundamentally a data capture problem, which has been around for a long time. But with AI, you can capture and map a lot better against your custom data structure. And I think that sometimes it's not overemphasized enough. Because the ripple effects of that is you start measuring more accurately and then you make better strategic decisions around what works, what doesn't work. And then you can basically start optimizing on a more holistic level — sales efficiencies, the general quota attainment and things like that. But is everything different now? I think right now, I wouldn't say that. And I think then when you break it down, right, like in high velocity sales, maybe it's all different, but then you had product led for a long time where people came in and they just bought the software and you didn't need anyone to talk to them. Right. And that's the trend. Right. So I think you either have use cases like Intercom that are very narrow and you have a clear way of basically — the resolution would cost me twenty bucks. And if the AI does the resolution, it costs me one buck. That is a good investment. I do that. That is a very, very narrow and specific thing. But if you think about the average AE, CSM, AM — it's not as easy to do that. Because they're doing a hundred things. They have a hundred different use cases.

Laura Fu: Exactly. And the AI is like — if you basically say you replace everything, you basically replace the workflows.

Janis Zech: And I think that's basically what's happening right now. We are building that. Other companies are building that. But we're basically replacing all these nitty gritty workflows that most people hate so that they can focus on the more impactful things. I think that's actually why I love building this so much because this has the potential to really help a lot of folks have better work lives. And then I think then at some point, what will happen next in the next wave is the typical ratio of, like, an AE to SE, onboarding specialist, SDR — that is quite bloated. There's actually a great talk from Sapphire Ventures on this at SaaStr, like, two years ago, where they basically looked at the data of their entire portfolio and other portfolios. And what they saw is that the ratio was bloating up. So you had kind of an organizational bloat. And so I think that AI — at least that's what we are building towards — is you'll basically start orchestrating specific agents that sit on top of the workflow automations that then completely change the game for even certain roles. And I think the one role specifically I have in mind is — if you think of the support case, in many cases, you have situations in meetings where the AEs are there, but then there's an SE joining because they are the more technical or they do the demos. I think those roles will converge. And you'll have an agent that basically supports you throughout the call answering technical questions because they can hook into context data. And the same is true with meeting prep. Everybody does the same thing for the meeting. You get basically briefed instead of you clicking on thousands of different things. You get the brief, you know exactly what happened in the past, you have all the web data that you typically look at, right? So you augment again those workflows. And will they completely — so that the humans can focus on what the humans are good at, like building a relationship and doing the more complicated things that are still required. And then I think the question is do the buyers wanna talk to an agent when they buy ten, fifty, or five million dollar deals, or do they want to talk to somebody on the other side? Right? And I think, like, right, like, there's still a long way to go until that completely changes. And I think that's a big unknown — like, how fast will that change? And I think to a certain extent, it's already happening with product led, and there's, like, large deals being signed product led. Right? Like, but then, you know, like, product led was in vogue very much for a very long time. And then at the same time, you see all the product led companies we know investing in sales teams and, you know, product led sales and, you know, all this. Right? So I think as soon as you go mid-market enterprise, like, that plays a big role. Is that a fundamental shift? I think it is a fundamental shift, but it's more incremental than a lot of LinkedIn influencers try to make it, I think.

Laura Fu: Yeah. I like what you said actually about the roles converging because right now, you know, we have to be specialists in all our areas. And maybe, you know, with AI, it's just giving us more breadth in terms of what we can cover in our roles. And so the capacity model, you know, fundamentally changing to support a much lower ratio of like SDRs, AEs, or SEs to AEs — that kind of thing. That sounds like a very good future to look forward to in terms of how sales is gonna go.

Janis Zech: Yeah. I mean, I think if you think about that, right, like, it's okay if you have one market and, like, the market has unlimited TAM. But if you have then international markets and you always need a minimum of x people to actually make it work, it becomes very bloated very quickly. And then you have — right? Like, the other big problem in go to market teams is obviously, like, the fast turnaround of talent. People don't stick around very long. And so if you have that and you combine that, then, like, the ramp time to basically productivity model just doesn't add up. And, like, the fundamental problems we're talking about here is, like, CAC payback is, I don't know, thirty two months. Quota attainment, according to rep reviews, at below fifty percent. So almost sixty percent of the teams don't hit that quota. Right? And so I think we're, to a certain extent, a bit in a crisis on the go to market front where some of the old models don't work. And then the last few years, right, there was this idea — oh, the AEs can also do prospecting. And now everybody's like, wait a second. That's actually not really creating the result I wanted. And I think it's right. So is the AE closer to an SE? And should that — right? Like, if you have an AE that can't do a demo, well, that's a fundamental problem, actually. I think you have to deeply ask yourself, what kind of roles do you actually have? What are the skill sets of the roles? And how do you enable that skill set to be rounded and covering a variety of different things? And I think the skill set of a go to market engineer and calling on the outbound side is the thing to do. Either you do mass and scale or you go more human, which is calling. And I think that has a huge renaissance right now, at least what we are hearing. Then on the AE side, of course, they should build pipeline, but then can they do that better than a specialized person that's doing cold calls? Like, you know, maybe not, like, twelve hours a day, but, like, six hours a day, which is very intense if you do it every day. I don't know. I mean, my assumption would be that, like, I would bet on that person doing that six hours a day. And their specialization makes sense because the AEs, if you are in the mid-market enterprise — and I think the high velocity, that's probably gonna go away. But, like, the mid-market enterprise where you actually go through more complicated sales scenarios and you need to multithread and you need to have multiple stakeholders and break into new areas, that I think requires strong mid-market enterprise AEs that are very good at that and are very good at the product side, very good at the sales side of things. So I think the kind of SE-AE role converging to me sounds a lot more natural. And then maybe the AE roles also converging into the expansion model. Right? Like, that's something we're seeing. How do you drive NRR, GRR? So, I mean, again, right, like, all this said, this has been around for like twenty four months, the technology. We're just getting started. And I think this is the exciting piece that — I mean, yes, everybody says now you can vibe code your platform. That's just not true. Right? Like, good software needs to be built, and then you need the customers to give feedback. And it's the same with AI, and they'll give you feedback, and you need to basically find these killer use cases. And I think some of them are clearer than others. But there's a lot being built right now, and I think it's gonna be a quite exciting future in go to market because, yeah, I think the tooling side will only get better and better and better. And, yeah, I think that's also helping everybody involved in the process, I hope.

Laura Fu: Right. Janis, what are some of the most interesting use cases that you have deployed so far?

Janis Zech: I mean, look, I think, you know, like, my co-founder and I were, like, most amazed about was when we, like, first started building our, you know, conversation intelligence tool. We didn't have auto language detection, so Philipp and I, we talked in German. And the transcript was completely — I mean, it was just not readable.

Laura Fu: Messed up. Okay.

Janis Zech: Completely messed up. And we had an AI summary, and the AI summary was amazing. And right? Like, you see this and you're like, oh my god. This is insane. Like, this is really insane because this basically means LLMs are between languages. Right? So to me, this was like — it has nothing to do with this very specific, like, hey, this is how we make you better use case. It's more like for me, this was like a wow effect where like, oh, my God, this is just amazing. Right? But outside of that, I think, you know, obviously, the killer use cases is like taking unstructured data, structuring it, and, you know, just basically bringing different — what we call sensitive context data together so that you have a system of truth, a system of intelligence, and a system of action. Right? And whether the action is then taken by an agent or by a human is a decision that you need to make. But I think these are things that we're excited about and we've deeply ingrained into the Weflow platform.

Laura Fu: Yeah. And tell us how come you can be fifty percent cheaper than Gong?

Janis Zech: I mean, look. I think it's fairly simple. Right? Like, when Gong started, transcription costs were very expensive, and then they became the market leader. I mean, they had three hundred million ARR. The last valuation was seven billion. So they need to grow fast and to do an IPO and basically justify the valuation. I think everybody knows Gong is an expensive product, and I think you can build this a lot cheaper today. And cheaper doesn't mean worse. Right? So we started by building one of the most user friendly interfaces you can imagine. Right? So I think this is our hook. Right? Because it's easy to prove. Right? Like, you know Gong pricing. You look at our pricing. Well, it's true. Right? But what is a lot harder to argue is, like, integrate with Salesforce via API so that the time to value is — like, you can basically log in with your Salesforce account. You get your Salesforce status bidirectionally synced. We basically read all standard custom objects, respect all your permissions, validation rules. Right? So, you know, it's super flexible. You have tons of templates. I mean, we work very often with RevOps teams. Right? So they gave a lot of feedback around, like, how can I customize the experience for our users? And those are things that are a lot more complicated to explain, but people see it in the demos. Right? And then they compare, right, like Gong, us, maybe another tool, and they can try it. Right? And then, you know, make a decision and make the best decision. Look. Gong is a great company. No question about it. To build something like Gong, I have the highest admiration for. But the reality is, like, you know, there's a lot of innovation you can drive with a focused team. And, I mean, it's not my first company. So I think what I like about this very much is the founder-product market fit is very high. Right? Like, I've always run product. I've run go to market teams. I saw a lot of inefficiencies, and I feel like this is the type of product I really love building. I use it every day. So it helps to make the product better, and it's now used by hundreds of customers globally. We're obviously super happy about receiving all that feedback. And in the end, it's like you really wanna become amazing at product. Right? That's like my deep passion. And then also amazing at go to market. Right? We do a lot of content for the RevOps audience, and we try to lead with value. Right? Like, what is it that the audience wants? Right? There's a trend towards consolidation, but then you're in a renewal cycle with your forecasting solution. And the CFO just blocks you because you can't have two forecasting solutions. But, hey, you can start with activity capture and conversation intelligence and then grow into the forecasting solution for maybe ten bucks more instead of another hundred, two hundred, or like a hundred fifty dollars per user per month. Right? So these things, right, like, that's what the audience wants. So we basically price and package against the audience so that they basically get what they're looking for. And then I think typically good things come out. So a lot of companies say they listen to their customers, but then you have to act that way. And acting that way, meaning you maybe take your TCV and ACV a bit down, but you're better suited for your audience. And I think that is something that I truly believe in. As long as you lead with value, something good will come back to you. And I think you can see this on our LinkedIn profile. We do a lot of content there that tries to be tactical, useful, not this like, oh, here's an SEO article. We might do this at some point, but that's like — people don't really wanna read that. Right? Like, so I think it's just a deep notion of, like, how do we create value to the audiences? And I think that's very much what drives us.

Laura Fu: So, I mean, I think the efficiency — what you're saying is the efficiency of just being an AI native company is just far outweighs, you know, kind of any kind of legacy, you know, brand name or maybe even like capabilities that people spent a long time building. So this is the era. It's the era to be building new companies in.

Janis Zech: Yeah. Absolutely. I mean, I love it. I think there's a lot to do. And obviously, the transformation to AI allows for completely different experiences that can be deployed. So I think it's a great era. And at the same time, you still have to ask yourself, right, like, is it a nice to have? Is it a must have? You know? How important are you in the stack? And I think yeah. I mean, we lived through this ourselves. We started with something which was nice to have, and now I think where we are is, like, pretty much every company needs it. And whether they need us, that's a different question. Right? Like, all these tools have pros and cons. Go out, talk to a bunch and compare, and then, you know, you'll find the best solution that fits your needs and your requirements.

Laura Fu: Yeah. Awesome. Okay, Janis, thank you so much. We had a really interesting conversation and talked a lot about, you know, what's going on in the go to market world right now with AI, what's possible. Any last things that you wanna share with the audience before we wrap up here?

Janis Zech: No. I mean, first and foremost, thanks for having me. If anyone is interested in our cheat sheets or so, I mean, you can go to get weflow dot com slash revops. There's twenty five cheat sheets on all different topics as well as a podcast. We also launched a community called RevOps Chat, a Slack community with over thirteen hundred RevOps folks that exchange and share ideas, help each other. So, yeah, these are just some things that, you know, when I talk about value, I hope that they drive a bit of value in the community and same with our podcast and stuff like that. Right? So I think in the end, it's great that