Is Your AI Actually Worth What You're Spending? with Parker Conrad

Speaker A: Hi, I'm connie loyzas and this is alex gove. And this is strictly vc download.

Speaker B: Hi, and welcome back to Strictly VC Download. Today's guest is Parker Conrad, the founder and CEO of Rippling. When we last had Conrad on the Show Back in 2024, the company had just raised $200 million at a 13.4 billion doll, and Rippling was leading the return to office charge in San Francisco. A lot has happened since then, of course. The company has raised another $450 million at a $16.8 billion valuation. It's crossed a billion dollars in annualized revenue. And today, Conrad joins us to talk about Rippling's latest product launch, which is called Data Cloud. It's a project that's been roughly three years in the making, as you will hear him explain. We discuss what Data Cloud means for customers and why he believes that AI becomes dramatically more useful when it is paired with an organization's people data. We also get into how Rippling is managing its own AI spending and why the company is expanding into banking and financial services. We also talk a little bit about fundraising and the IPO market. And of course, we discuss the ongoing legal battle between Rippling and dealing with.

Speaker A: We always really enjoy talking to Conrad. We hope that you will enjoy the conversation as well, and we'll see you

Speaker B: back here next week.

Speaker A: Thank you for making time for us today. Okay, so I know we're going to be talking about a bunch of things. Let's start with your news about your data cloud. Maybe let's start with the core insight behind Data Cloud. What problem does it solve for your customers?

Speaker C: There's this sort of central understanding of your org that's really important and your org, both as it exists today, but also as it's been changing over time. And so we built this thing that is basically the modern data stack, but all in one system. And so Rippling for a long time has been building a lot of different systems in one where today, if you're trying to look at data about your business, you need to make like four separate big enterprise purchases. You need to buy a system that people traditionally called ETL in order to, like, move data over, and that's like 5tran and Airbyte and stuff like that. And then you need to have a system like Snowflake that is where you store the data and sort of does all the compute on top of it. And then oftentimes you have something like dbt, which is how you take it. And once it's in there, you kind of munge it and change it, you transform it is the term that people call to sort of get it to a point where you can really look at it in the right ways. And then you have like a bi tool, like tableau that sits on top of it where you can analyze the data. Uh, and what we did is we've combined like all four of those systems in one. We've been building this for about three years. It's been the biggest R and D initiative in the company. And the outcome of this is that Ripley is now, we think, the only system where you have all of this business context about your company, all of this data and AI on top of it in one place. And that combination is really powerful. Like there's some incredible stuff that you can do. So even, you know, Anthropic, obviously hugely successful company, but one of the sort of issues with Anthropic and their product is there's no data stack underneath Anthropic. And so they're constantly sipping data through a straw via like MCP connections or sort of whatever they can get in real time out of an API. And it's really inefficient because with AI, what we have found is that AI is useful to your company and in rough proportion to the sort of volume of data that it can access. And so you give it small amounts of data through a straw and it can do some pretty cool stuff. But man, if you pipe data into an LLM through a pipe that's a mile wide and a mile high, you know, it just, holy crap. The stuff it can do is really transformational and that's what we've seen.

Speaker A: I understand what you're saying about Claude. You are built on top of the LLMs yourself, like Claude and ChatGPT or maybe like, uh, a bunch of them,

Speaker C: um, uh, you know, underneath the hood, we're using a bunch of different models. So we've actually moved a lot of stuff from Anthropic to OpenAI recently. And then we.

Speaker A: That's interesting.

Speaker B: Why is that?

Speaker C: We found that 5.5 was both like better and more cost effective for what we were doing. But we're constantly assessing these things and we use different models in different places. So there are some types of things we might use CLAUDE for, some types of things we're using OpenAI for some types of things we're using open source models for. The balance between those is constantly shifting based on what we see as working the best or what we see as most cost effective. And that sort of stuff.

Speaker A: Sure. And Then I was going to ask about Workday's data cloud, which it rolled out in September. But I guess your point would be you still have to connect to Databricks and Salesforce and Snowflake.

Speaker C: Yeah, I think, like, what Rippling's done that's really unusual is built like the entire stack. We've got a bunch of what we call managed connectors. So if you're like, hey, I want to bring in my HubSpot data, or I want to bring in my anthropic usage Data, or my GitHub data, or my customer support data, you can just click it and install it. But then for the long tail of systems, you can go in and you can actually build with AI your own connectors, or we have people that will help you do it to connect up your other key business systems or even internal systems so that you can get all the data in. And even just like that, how do you, like, get the data and get it out and get it into the data warehouse? And so that's one piece of what we do. And then once you have it in, you can write full SQL inside of our transformations product and build pipelines, sort of adjust it to make it, get it to a point where you can present it very cleanly to users in a dashboard, or very importantly, where you can present it very cleanly, um, to an AI and all of those transformations. If you want to write SQL yourself, you can do that. But if you also, you know, want to just ask the AI to go and do it for you, you can do that too. We had recently had a compensation performance review cycle and I asked it to build me a dashboard showing comp adjustments and performance ratings from this review cycle. And, you know, it goes through and it looks at the distribution of ratings, the promotion rates by department, how much compa ratios, which is sort of compensation, like actuals versus targets shifted, sort of how people are sort of grouped by performance rating and potential ratings. And all of these things I can drill into, like, these are real dashboards. So I can be like, whoa, one point, you know, this is like pretty high. Then I can kick over here and see like the support ticket volume we're getting from Salesforce and like the scheduling that we've done. Where are we, like under or over capacity in our support Org and it goes through and it could tell me like, okay, you know, the enrollments team is severely understaffed. You know, travel's backlog is more than double the platform teams. It shows you the utilization rate by team, the regions that they're covered the overtime hours that each team is running. There's both data from Salesforce on ticket volume and a lot of people and scheduling data that you'd be looking at here to really understand this problem. And then I think probably like, the best example of this that I really like is trying to identify like, okay, I'm spending so much money on AI in engineering. So what this dashboard is doing is it's combining data from Claude that shows me, you know, how much individual engineers are spending. It's looking at performance rating data. So it's looking at like my top performers versus people that are performing at expectation versus low performers. And it's looking at GitHub data to see how many pull requests people are generating, how many lines of code they're generating. And those are not perfect signals on their own. But you look at all this together and then you look at things like how frequently are their peers sending them back to the drawing board on when they do a pull request review, when they do like, uh, a code review. And you sort of use that as a signal where you're like, man, well, if your peers are telling you go back and do this over all the time, maybe you're just generating a lot of slopes. And I can look at this by department, by team. And so this is the kind of stuff where I can be like, oh, man, I need to, you know, these people in red here, we're going to cut their limits. We're going to be like, you guys can't spend this money. You guys got to start doing something different. And so it's a great example of like, look, in order to do this, you have to figure out how to pull in GitHub data and join it with Claude code data and join it with performance management data and team and department data. And that's where you're kind of like a system like Rippling that really manages employee identity can do this. Like, that. This is something that, like, might be possible to do if you had a large data engineering team. But I did it, and this prompt ran for about 500 seconds and then produced this versus this is like weeks of work for a data team to try and put this together for an org.

Speaker A: I mean, I know we're here to talk about the product, but you were telling me exactly how you used it. What did you learn about how effectively Rippling is using its token budget? Since everyone's kind of interested in companies sort of blowing through their spend, or not blowing through their spend or how they should be thinking about this you

Speaker C: know, obviously this stuff is so powerful and incredibly useful. I think this sort of theory that I've developed is that there's like personal productivity and there's like organizational productivity and the two things are not the same. And so I think one of the things that we found is that there were a lot of people that were doing things that I think felt very productive to them, where there were employees that were doing things like, oh man, like, Claude is so helpful for me. You know, it sort of like analyzes my calendar and my email and puts together a plan for me with like, you know, all my meetings for the day and what I should understand. And like, it's really helpful. And that person was spending like at a run rate of $30,000 a year for this. And so on the one hand it was like, definitely helpful for them and improving their productivity. And on the other hand, the ROI on this was like meaningless, not great. They weren't solving more customer tickets or selling more customers. It was like, so the output was the same. Things were like a little more organized and efficient, but like this massive increase in cost to the organization. And so those are some of the things that we're looking at. And we didn't find anyone who was doing anything bad or wrong. You know, like no one was like, you know, building a video game on the side or something. But there were a lot of these things that, yes, felt incrementally sort of better, but like completely outweighed by the cost.

Speaker A: Do you have a system now where certain employees have access to a certain amount of tokens and.

Speaker C: Yeah, I mean, you know what, we went back and we really, um, restricted, put a lot of budgets in place in different areas and put more control over it and, and started to really develop, be a lot more opinionated around what types of spend we think is really high ROI and what types of spend we think is not. And so much of that, like, ties back to job and role and function and what are you doing with the tool? I think a lot of organizations are going to go through this experimental phase where then you need to sort of like dial it back a little bit or put more controls in place around what people use.

Speaker A: Does your software alert people when their token spend has been exhausted?

Speaker C: You can absolutely configure once you have all this data inside of ripplane, you can build workflows around it. So you can say, hey, when someone's monthly spend exceeds a certain limit, then do the following. It could be send an alert, it could be alert their manager. It could be Shut it off. You know, there's like. So one of the big advantages of the way that we've built the rippling data cloud is normally in data, you have this choice to make about whether you're storing data in a transactional database or an analytical database. The advantage of analytics databases is that you can do massive scale and analyze data very quickly so that when you need answers to questions that are just reading a massive volume of data, like we just did with building those dashboards, you can do that by. As soon as this value hits a certain level, you want the system to do something. You want it to notify someone, cut off spend, send them to go look at their, like, review the dashboard of what they've spent over what time frame. Being able to do both of those things in one system, it sounds really simple, but that's actually very unusual that rippling can do both of those, both that notification and the dashboard in one place.

Speaker A: It sounds like an easy sell. Can you just give me some sense of how much this costs or how you're charging customers?

Speaker C: So the way it works is there's a base SKU if you're using rippling AI and anyone who has access to rippling AI also has access to rippling data cloud. It's sort of one package sku. And basically there is a certain number of tokens that you get as part of that SKU, which I think is like, you know, around like 20 bucks a month. But you get access to AI and data and then if you go way above, there's some kind of usage threshold where you start incurring usage costs as well.

Speaker A: Got it. And how many of your customers are using ripple AI vs otherwise? And how many customers do you have altogether?

Speaker C: We started selling this in April, and in the first month we started selling it the last like 10 days of April, we added about 2 million of new revenue. And then in May we added a little over 5 million in revenue. And in June, so far we're at 2. We end up closing a lot of our business in kind of the last week of the month. So we actually expect June will close 6 or 7 million in revenue. So we're adding about 5 to 7 million in new revenue a month. You can see about 560 companies using it so far. Yeah, like a lot of other data on the pipeline, which sales Org has closed, how much revenue, different sales segments, their win rates, the revenue that they've closed. So you can see how like, you know, I can in just a couple minutes get an immediate view on Sort of like how that's going for us.

Speaker A: So 560 companies using it, and then how many users do you have in the aggregate?

Speaker C: Oh, like, how many total customers? Um, it's tens of thousands.

Speaker A: Okay. And I guess one obvious question is whether that revenue is costing you money at this point.

Speaker C: Yeah, yeah, for sure. So you're saying what we try on the AI stuff, we want to not lose money and ride the cost curve down over time. You know, we think as stuff gets cheaper, we'll be able to build in a profit margin. But, you know, our goal right now is getting people access to it. We're trying to keep it as inexpensive as we possibly can for customers to be able to get access.

Speaker A: And just out of curiosity, do you have some sense, based on the trends that you're seeing, of, uh, when you would become profitable?

Speaker C: I think the real answer is there are sort of historically two reasons why Rippling's not profitable. And one is, like, the outsized R and D spend that we have building something like data cloud, like, trying to recreate the modern data stack inside of Rippling, which, you know, we've been working on that. We were working on this for, like, three years. And it's like a large, expensive engineering team to do it. So that stuff is expensive. And the other companies in this space are barely technology companies. Most of them are spending less than 10% of their revenue each year on R and D. And so you have companies like public market competitors of ours, like, you know, Paylocity and Paycom, that are roughly similarly sized from a revenue perspective, spend about, you know, 8 or 9% of their revenue on R and D and Ripple, and spends like 45 to 50, I think, or like, something like, much higher than that. And so one thing is, we spend a lot on R and D, which we think is the right thing to do. And we're also, I think, as a result of that, we're growing much faster. And so Paylocity and Paycom are also growing at, like, less than 10% year over year. And Rippling, I think the most recent data point that we shared was that we had crossed a billion in revenue, and we were growing at just under 80% year over year. That's the other thing, which is that our growth is quite efficient, but it costs. You know, the faster you're growing as a SaaS company, because you need to spend upfront on sales and marketing to acquire customers. The faster you're growing, actually, the less profitable you are. And so it's very hard for companies that are growing at those rates to be cash flow positive. So we think in order to do this like growth has to slow a little bit. But there's some, you know, we think probably in the next two years we turn cash flow positive. The other way to do it is to slow way down on growth or cut back on the R and D investment. But we don't want to do that. We think that the business is doing really well and we want to keep investing in both sales, in both growth and in R and D. And so as long as we can continue to do that, we will. That's sort of why, although the business is quite healthy, it's not throwing off cash yet.

Speaker A: And I think the last time we talked it might've been two years ago now, but you were pulling people back into the office. How many employees do you have now?

Speaker C: It's over 5,000.

Speaker A: Okay. And that's holding steady?

Speaker C: Um, um, it's growing year over year. It's growing. I think we expect it will grow less next year in part as we get larger and you start to hit these scale efficiencies and also because of like, you know, everything that we are doing with AI internally. You know, there are definitely places where we're like, hey, we can pull the headcount flat next year. Even as you know, the volume of customers and revenue grows. We're not really like cutting. But uh, there are definitely places where we think we can hold things flat even as revenue grows a lot. And that, you know, that helps you get a lot more efficient.

Speaker A: Obviously the more bells and whistles your customers get, the greater value to them, the more money you make beyond charging customers more when their token usage exceeds whatever they've sort of committed to. Is there more of like a volume play here or does your way you charge customers change over time or. Not really. It's just like I had interviewed the um, CEO of Replit recently and we were talking a little bit about their margins, which are pretty attractive. I don't suppose there's any way you could talk a little bit about yours on that token usage?

Speaker C: Yeah, I don't, I think, you know, the thing about margins is they're going to change over time and.

Speaker B: Mhm.

Speaker C: We feel good about them. I can't, I'm not going to sort of disclose what they are particularly because it's kind of early. But we're definitely, we're not losing money. But also we're trying to make it as affordable as possible for customers to use this.

Speaker A: Parker also I was really interested to see that you released a banking product. And I mean, I have increasingly thought about rippling as also a rival to ramp. I mean, you're obviously competing with so many different customers. A little bit of systems integration, a little bit of banking, a little bit of this, a little bit of that. Do you see a world where companies don't need ramp, but they need you?

Speaker C: I mean, ramp's obviously doing incredibly well. But yeah, I mean, rippling and RAMP compete directly in this sort of spend vertical. So rippling has corporate cards, expense reimbursements, bill pay, banking, procurement. And so there are a set of customers. And that business is, you know, that we have is. Is smaller than ramp today, but it's growing very quickly and doing extremely well. And there are some advantages to sort of centralizing all of this, um, you know, with our banking product. One of them is just like, hey, if you're using rippling banking, and by the way, you don't have to use only rippling banking. You know, you can continue to use JP Morgan Chase or whatever for a lot of other stuff. But if you, like, are using us, at least for some of your banking, or you have an account with us, one of the things that that does is you can run instant payroll. Where, like today, when you run payroll, I don't know, you know, if you guys do this at TechCrunch. But one irritating thing is you've got to run your payroll several days ahead of when employees get paid. And the reason is you have to wait for, like, money to. And most of it is about getting money from the company and the payroll company being like, okay, we got it. The transaction didn't fail. It's not fraud. You know, we know that they're good for it. They don't have, like a negative balance in their checking account. It didn't bounce. And then you pay employees. And, you know, if you're using this for banking, we already know that we can do that. And so you can run payroll on a Wednesday morning and your employees get paid, like, really shortly thereafter, like sometimes maybe even within minutes, like, it shows up in their accounts. That's really powerful for a lot of companies. And you think about why that is. It's because. Well, but if you have to run payroll on a Monday and people get paid on a Friday, all kinds of stuff can happen in that timeframe. Like someone quits, you know, and you're like, crap, I already paid them. Someone else joins and you forgot to set them up in the system. You know, someone goes in and they. They change their bank account, or they change their health insurance enrollment and so their new deductions that they need, or they want to elect more money to be contributed into their commuter benefits this month. And like, it's too late because, like, everything's locked and loaded and money, we're already waiting on money to move. And so things are just so much easier or like, if you have hourly employees, like, you don't know what their hours are for the week yet. And you've got to, you know, either you've got to pay them much later or you've got to, you know, wait for that to finalize. And so in a world where you have banking and payroll, like tightly integrated and you can pay people instantly because of that, there's a lot of advantages of that to a lot of businesses. The data cloud stuff has all of this, like, incredibly sexy AI stuff that's very omo malt. But like, you know, the banking and payroll stuff is like, very powerful operationally for a lot of companies when you can combine those two.

Speaker B: I remember what you went through when

Speaker A: Silicon Valley bank went under.

Speaker C: Yes. And actually that was a big inspiration for this because like, right now, the way it happens is for every payroll company, other than rippling, if you're using this when you're moving money, the money spends. There's some period of time where it's left your account, but it's tied up with the payroll company and not in your employees accounts yet. And like with rippling, if you're using rippling banking, you can have that be in a bank account in your company's name where you know there is no window of time where you know it's not in your account.

Speaker A: So Ramp raised money at a $44 billion valuation. Your company raised money last year at a $16.8 billion valuation. And you mentioned they crossed, I think, a babillion ARR, maybe last fall. I think you crossed it in March. But people are very curious, of course, if you are raising money now, like what. How you're thinking about evaluation. I mean, you're a seasoned founder. You know, like the upside of a headline valuation. You also know what comes with those expectations. So, um, I'm just wondering if you could kind of talk us through this moment.

Speaker C: Yeah, I think it's been less than 18 months since we last raised. I think fundraising is usually like, pretty distracting. So I try and do it like when we need to as a company. So I expect that we will. We're not currently out fundraising right now. Within the next 12 months, I'm sure We will. Look, I feel really good about the business. I mean it's a crazy time obviously, but you know, rippling is now well over a billion in run rate revenue and growing at pretty extreme rates that you just, you know, you don't really see in the public markets. Obviously we're not the only private company that's growing at scale, but I think outside of foundation model companies, there are like five of them that are growing at these rates at scale. Over $1 billion in ARR and ramp is obviously another one. What I always tell people on the fundraising side is like I'm always the last to know what that's going to be. I do fundraising once every 18 to 24 months or so. I try and really not have an opinion about valuation when I go into that process because it's always, it's always bad if I do. Either my opinion on this is too high and unrealistic or too low. And you know, so my fundraising strategy has always been to go to investors and say, look, here's what's going on with the business. Here's all the data. You guys are pricing deals like this every day. You guys tell me what you think it should be. And you know, we're not, we're not trying to do a massive auction, but we're talking to a couple of people, then we kind of see where it nets out. That's the thing that's always worked for us.

Speaker A: Well, I guess to a related question, does it have to be private capital again, you know, anthropic OpenAI companies going public. Does all that activity change your calculus on timing?

Speaker C: I'm not religious one way or the other on capital structure here on like being public or private. And I know there, there are some people that really are religious on that topic. So I think we're going to make the right decision for the company. We are not like currently, not even like with a, uh, wink, wink, we're not going public right now. But you've always kind of got to look at private markets versus public markets and what makes the most sense, the weird thing right now about public markets is that you don't see for tech companies there isn't much growth in the public markets. And so the thing that's been sort of very uncertain for a long time is how would public markets think about valuation? For a company that is growing very quickly and really a company that's growing very quickly, that's growing very efficiently, but also as a result of that, like not profitable, there just aren't really examples of that in the public markets. And so it's like riskier. Like there are a lot of examples of that in the private markets, but the public markets have sort of become this retirement community for slow growth companies. You know, we would need to sort of understand how that would work for a company that is not this slow growth retirement community.

Speaker A: Uh, that's very funny. I haven't heard anybody.

Speaker C: And so that, that's kind of the dynamic and sort of how we think about it. And so over the next couple years, rippling is eventually going to start generating a lot of cash. And so that's one thing that changes. You know, the other thing that changes is like it could be that, you know, the way the public markets think about a rippling shaped business might change or it might be that we just develop more confidence in sort of how they would view it. Um, which, you know, there might be just other examples, you know, that you see in the public markets.

Speaker A: I was going to ask if there's a secondary market for rippling because obviously that's another reason why companies in some cases really don't need to go, you

Speaker B: know, public seemingly ever.

Speaker C: I mean, we've done tenders with every financing round that we've done for the last couple of years. I expect that that would be the case, that that'll be sort of a consistent practice. The big thing for us is that at some point, like a lot of companies, you have this big RSU thing, um, where you eventually want to either go public because of the RSU overhang or find some way, you know, it's kind of, you know, never, never guaranteed, but you eventually, you need to do something around RSUs because they eventually expire if you don't trigger them. And so that becomes either, usually either like a big capital raise in the private markets that's many billions of dollars or you know, an ipo.

Speaker A: Right. And thanks. I'd forgotten. I think we have talked in the past about the fact that you always do these tender offers run also, Parker, just as sort of like an industry participant.

Speaker B: Two quick questions.

Speaker A: One, are you an investor in OpenAI?

Speaker B: Me?

Speaker C: No. I'm a terrible investor. And uh, I do very little because my investment philosophy is buy high, sell low. And it's just never worked very well for me. And so I try not to do it.

Speaker A: That's very funny. Um, I was just wondering because you'd mentioned, I know you've got ties to YC and Gary Tan and so when you mentioned Kodaks, I just want to make sure that you don't have a biases there. But also I'm guessing a lot of founders turn to you for advice, if not for your investing advice, for your leadership of business, you know, fast scaling companies advice. And I'm wondering what you make of this sort of fast follow trend, which I don't think is very new. It's a little bit more public, but it's happening at such an accelerated rate and I don't know what I think about it. Um, fast follow up of course, meaning like these companies that are changing valuations within weeks because they announce something and then more investors pile in and suddenly the company's much more valuable. It seems insane to me, but a lot of what's happening right now seems insane to me.

Speaker C: Yeah, look, my view on this is usually like as an entrepreneur, if you can get inexpensive capital, it's probably a good thing to do and you don't have to spend it. So a lot of the stuff around the sort of judgment around people raising money at high valuations is usually about, oh, like it's crazy and you're going to spend all this money, you know, like, but you don't have to spend it. And so my advice is generally if you have uh, an opportunity, like do it and don't spend it and be very judicious about that. But there's nothing sort of like praiseworthy or sort of like inherently moral about raising money at lower valuations. Like you're just, you're giving up more of your company to investors. And I think generally that's not a great thing. Like, you know, it's probably better, all else being equal for your existing shareholders, for your employees, if you can raise the capital that you need and give up less ownership while doing it like that, that's a good thing. And a lot of the conversation around valuations, there are investors that are in a very literal sense invested in people believing that there's something, you know, maybe wrong or maybe damaging to the business about giving them less ownership for the capital that they're providing. I've just never bought into that. So I think raise the money, get the fundraising done. So do it at a fair valuation. But certainly if you get great terms, that's awesome. Like you should do that and treat it like the last money you're ever going to get and spend it very carefully.

Speaker A: That's great. I think too much about down rounds, I think from being in this industry for too long. I also wanted to ask, if you don't mind my asking, before I let you go deal, obviously very public spat Proceeding toward trial. Anything to say about that?

Speaker C: I think the whole thing is so crazy. It just seems very clear that, you know, the leadership of that company did exactly what we said they did. They were stealing from Rippling at Scale and paid someone off to send them not just general information about ripplane, but an active employee that they paid off to exfiltrate enormous amounts of data from ripplane. And you see it, there's public discovery of the payments that they made. And so this stuff takes a really long time. So it's winding its way through the court. And so we need to be patient on it. Uh, it's going to take a long time to sort itself out, but we feel very confident in the case.

Speaker A: What is the outcome that you think is just here?

Speaker C: You know, obviously there's. We believe there's enormous amount of economic damage to Rippling on this case. But also, I think ultimately what Alex, their CEO, did was stealing. Like he stole. Just stole from us. And yeah, I think, I think he belongs in prison and we don't control that piece. But, you know, yes, certainly my hope is that there are real consequences for this behavior. And if there aren't, I think that's a problem. You know, what are we saying about this industry? That it's, you know, are we saying, like, yeah, it's okay. You know, it's part of the playbook. This is your standard SaaS growth hack playbook is first you find a susceptible employee, you pay them money, you get them to steal the CRM data from your competitor, and then you use that to kind of growth hack your way to some large business. Like, come on. Like, that's not the way we want this industry to work.

Speaker A: It is really. It has been mind blowing to watch this sort of, you know, espionage case from afar. It's.

Speaker C: And look, I mean, calling it the spy thing, I mean, that always makes it like, entertaining. But I think it's like, you know, spies like, you could be like James Bond, right? Which is something that. That's how Alex pitched it to the spy. He's like, oh, you're going to be like James Bond. Which makes it sound like James Bond is like a good guy, right? But, like, ultimately what this was is it was just. Was just theft. It was just stealing.

Speaker A: Listen, I'm so happy to talk to you. Thank you so much for making all this time for us. We really, really appreciate it. I think that our audience, our, uh, readers and listeners are going to be very interested in this product and I would love to not let too much time elapse before we talk again.

Speaker C: Of course. Thanks a bunch, Connie.

Speaker D: The Strictly VC Download podcast is hosted by TechCrunch editor in chief Connor Hani Loizos, and me, Alex Gove, of Strictly VC. Strictly VC Download is produced by Maggie Nye with editing assistance by Teresa Lancansolo and Kel, thanks for listening. We'll see you back here next week.