“Every small business should run itself” | Lassie with a16z

Alex Rampell and Olivia Moore speak with Lassie cofounders Steijn Pelle and Frédéric Renken about bringing AI to one of the most overlooked parts of the economy: small businesses. Inspired by time spent working inside dental practices, Pelle and Renken set out to automate the administrative work that keeps healthcare providers away from patients. They discuss how AI agents are changing billing, insurance claims, patient payments, and other operational workflows, allowing practices to spend less time on paperwork and more time delivering care. The conversation explores AI agents, software that performs work rather than simply storing information, onboarding AI into real-world businesses, and why healthcare administration offers one of the biggest opportunities for automation. Along the way, they discuss product design, go-to-market strategy, and what it takes to build AI systems that operate reliably in complex business environments. Timestamps: 00:00 - Intro 01:12 - Story Behind Lassie 03:46 - Embedding in the Customer's Office Before Building a Product 05:39 - How the Product Build Has Changed with Better Models 07:23 - Software Never Did the Work but AI Finally Does 17:24 - 98% Automation: How Lassie Got Agents to Actually Run a Practice 22:08 - Startup vs Incumbent in the AI Era 33:35 - The Master Plan: From Dentists to Every Small Business 39:27 - What It Takes to Hire & Build When You're Selling to Main Street 55:59 - How Do You Reach Hundreds of Thousands of Small Businesses? Resources: Follow Steijn Pelle on X: https://x.com/steijnpelle Follow Frédéric Renken on X: https://x.com/fredericrenken Follow Alex Rampell on X: https://x.com/arampell Follow Olivia Moore on X: https://x.com/omooretweets Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Alex Rampell?

  AI is overhyped in Silicon Valley, but underhyped in Iowa. I would actually argue, software just kind of took things that were stored in paper format, and then they made them available first on-prem via green screen computers. But people still had to do the work.

• 0:13Speaker 01

  I never forgot what I saw. The number one rated doctor on Yelp, spending 200 hours a month on paperwork.

• 0:19Speaker 02

  The models are trained on so much data, and they're so large. And yet, they actually don't really know how to do any of this work. Initially, we were actually the humans in the loop. We kind of automated away our own problems.

• 0:30Alex Rampell?

  The battle between every startup and incumbent comes down to whether the startup gets the distribution before the incumbent gets the innovation.

• 0:35Speaker 01

  We come by and we say, hey, we actually have built this agent that can provide you already with tens of hours of labor. They then adopted like very quick. They see us as someone they break in to like actually run the practice for them.

• 0:46Olivia Moore?

  There was a great quote from Dr. Kwan about you guys, which is that Lassie isn't replacing humans, but like freeing them from wearing so many hats.

• 0:54Alex Rampell?

  It's not like, oh, AI is going to take the jobs. In many cases, you can't find someone.

• 0:58Olivia Moore?

  How are you prioritizing what you build, who you sell to? Is there a world where Lassie for dentists makes Lassie for physical therapists better?

• 1:08Speaker 01

  The end goal here is that-

• 1:10Erik Torenberg?

  AI isn't just changing how software is built. It's changing what software does. For decades, software mostly stored information. Today, AI can increasingly perform the work itself. In this episode, Alex Rampell and Olivia Moore speak with Lassie co-founder Stein Pella and Frederick Rankin about building AI agents that automate administrative work for healthcare practices, from insurance billing to patient payments. They discuss why small businesses may be one of AI's biggest opportunities, and what it takes to build software that operates autonomously, and why the future of enterprise software may be measured less by features and more by the amount of work it can take off people's plates.

• 1:56Olivia Moore?

  So welcome and thank you for joining us.

• 1:58Speaker 01

  Thank you for inviting us.

• 1:59Speaker 02

  Thank you.

• 2:00Olivia Moore?

  Maybe we'll start with the basics. So Stein, this whole company started with a conversation with you and your own dentist, Dr. Kwan. What did he tell you that made you decide to quit your tech job at Robinhood and go process payments for him by hand?

• 2:13Speaker 01

  Yeah, I did not know that my American dream would look like this. It was interesting. Like, I was at Robinhood at the time, and I came to this country to start the company. So after six years, I moved from Amsterdam to Silicon Valley, like I was looking for a heart problem to solve. And then my doctor, Dr. Kwan, I was a patient there, so I saw him twice a year, as you do with a dentist, knew that I was looking for a heart problem. And he said, do you want to see how I run my business? And I said, absolutely. And he walked me to the back, and I never forgot what I saw there. A small business owner that is the number one rated doctor on Yelp, spending 200 hours a month on paperwork and busy work. So submitting claims by hand, and he had to stick around himself because he couldn't find people to build the patients. So I'm like, wow, it's fascinating. This is a couple of years ago, so I thought that this was a solved problem because in

• 2:58Speaker 01

  the 70s, my mom worked in hospital, and that's what she did. She brought back soft cash to the bank and then processed payments by hand. But it wasn't a solved problem here. So that's why this piqued my interest.

• 3:08Alex Rampell?

  Although I have to ask, when he gave you this offer, were you like, was it that kind of reclined? Could he actually process your answer as yes versus no if your mouth was open and drills were in your mouth? Or how did that go down?

• 3:19Speaker 01

  Well, up until now, I don't have cavities. So, you know, there was no drilling happening yet. So, you know, no convo-

• 3:24Alex Rampell?

  Examination.

• 3:25Speaker 01

  Exactly. Yeah, exactly. Yeah. No, he took me aside after that.

• 3:29Alex Rampell?

  Okay.

• 3:29Speaker 01

  Because he knew that I was like looking for this heart problem and was roaming around. And then after that appointment, he showed me kind of like what was going on. And then I thought, maybe it's him, right? But it didn't really make sense to me because he was very well rated. He used all these modern technologies. So then we started talking to other doctors, like because maybe this Dr. Kwan was just an anomaly. But then I also worked for a gastroenterologist in Scranton, Pennsylvania. We saw the same there. I'm like, wait, you're doing this all by hand? So then we figured out, wait, there are like hundreds of thousands of these small businesses that literally do this all by hand. That would be quite a fascinating problem to solve. We knew it was going to be a hard problem, but we were kind of looking for that.

• 4:09Olivia Moore?

  Yeah. The Lassie story is so unique to me because you both spent months, if not years, before kind of fully releasing the product, like literally in the office of the customers.

• 4:19Speaker 01

  Yeah.

• 4:20Olivia Moore?

  How did you convince them to let you in and to kind of look through the heart of the business and get into the financials?

• 4:26Speaker 01

  Yeah, it's a little weird, right? It's like, hello, I work at Robinhood on growth on the referral program. Can I get a job here? And by the way, Frederick worked at Superhuman on product. Can we do the billing for you and take over the finances? I think that was the first sign that we were onto something big. Because to our surprise, all these doctors, when we asked them, so we first talked to all of them, like Dr. Kwan, because everybody likes to talk about their problems. And when all these doctors started talking to us for hours, we knew that, again, this is a real problem they have. This is not some vitamin that maybe it's nice to solve that for them, but this is something that keeps them up at night. It makes them almost quit their job and say, I got into this industry because of my passion and craft, in this case, because I want to take care of patients.

• 5:08Speaker 01

  So that was the first sign that these people were not, no chance that you can come work for me because you don't have any experience running the finances. What about HIPAA and security reasons that you have access to all this information? So I think the first sign to us that these people said yes, Dr. Kwan said, just come and sit here night five. You can do the job. Dr. Shah was in Scranton, Pennsylvania. He sat us down behind the desk and said, you can have access to anything you need to have access to, or teach you like my son. So that was the first sign that this was very broken and not a solved problem. And then I think that triggered our intuition for, okay, we might be on something because this is a real pain, that people are desperately looking for a solution.

• 5:50Olivia Moore?

  And from a technical perspective, so Frederick, this is started in 2020, and so much was different then in terms of what was even possible to build. Like, how has your product building process changed over time? How is what you thought possible then different from what you think is possible now?

• 6:07Speaker 02

  I would say when we started the business, we were always obsessed with automating and putting the business on autopilot. So that hasn't really changed. Back then, the models weren't that good, though. Especially reasoning models didn't really exist in that form. But if you think about what it takes to automate any job, you're really looking at getting context on the work, which in the case of a doctor office is basically, you need to have access to all the historical data, the patient records, that sort of stuff. And then you need tools to do the work. You know, this is true if you're a human in the office or if you're an agent. And so we started building the context layer and building the tools. And it's just that the intelligence layer wasn't that intelligent. But for the first job, it wasn't that necessary.

• 6:50Speaker 02

  Like, the most basic kind of automation didn't require that much reasoning. But then as the models got really good, we had this kind of huge tailwind because we had all this context already built, all the tools already built, and we could kind of, as the models got better, just replace our intelligence, and the product would just get smarter over time. So in that way, we got a little lucky, but I think the core vision hasn't really changed at all. It was always about automating the work and not building tools for them that they would have to use.

• 7:15Olivia Moore?

  I feel like as the models have improved, we are more and more seeing software do the job of labor, which Alex, I would say you were the first to argue and famously argue that would be the case. Curious how you think about that when you look at companies and maybe how it played into like the thesis around Lassie.

• 7:32Alex Rampell?

  Yeah, well, so I've given this whole presentation on the origin of software was basically take a filing cabinet and put it into a database and kind of pick the time equals zero moment for that with this company, Sabre Systems. Because airlines would just keep reservations in filing cabinets. Sabre Systems was a joint project between IBM and American Airlines. That's why Sabre is spelled with two As, Sabre. But then this kind of took wind everywhere else. So like there were HR filing cabinets, and that became something like PeopleSoft. There are legal filing cabinets, and that became all of these LexisNexis products. There are accounting filing cabinets, and that became QuickBooks, and that became NetSuite. So that was the origin of software. So software just kind of took things that were stored in paper format, and then they

• 8:16Alex Rampell?

  made them available first on-prem via green screen computers. Because that was a lot more efficient to book an airline ticket and change it if you didn't have to use an eraser anymore, starting with Sabre. But people still had to do the work. So I would actually argue that the world didn't get that much more efficient with software, because all that software did was like take HR, like did PeopleSoft and then Workday make HR departments more efficient? Like, I don't think so, because the same number of people worked in HR for the exact same size company in 1950 as probably 2000. And instead of using filing cabinets that are guarded by Stein and Frederick, you know, making sure that nobody breaks into the HR files, now you have an IT department and

• 8:58Alex Rampell?

  a CISO to make sure nobody hacks into the IT files or the HR filing cabinet. So nothing really got more efficient. I know I'm somewhat exaggerating for effect here, but what you can now do with software is it can edit the filing cabinet, right? It's no longer just the dumb storage. It's actually like the smart implementation of changes against those things. So if it's HR, let's do a background check, right? Or let's do an onboarding, or let's explain the benefits to this person. If it's accounting, what do you do with the financial statements? Imagine I'm a dentist and I see I have all these overdue invoices, and I can look up in QuickBooks. What do I do? Well, I might want to call and say, please pay me. Like, that's what the filing cabinet should be doing, not just giving you the information.

• 9:39Alex Rampell?

  So it just turns out that the work is orders of magnitude bigger than the storage of information that the work is done on. So that's been the thesis and you need the technology to catch up so it can actually do it. Because in 2023, it's like, you know, next word prediction wasn't really good at like going in, which is basically what AI is. That was not good enough to go say, I'm gonna go run my practice or do background checks. I know I'm gonna have statistical inference play out and that's how I'm gonna do a background check and make sure that Frederick didn't commit any crimes before I go hire him for my company. Like, no. But now things have gotten good enough and that just massively expands the market size.

• 10:20Alex Rampell?

  And if you think about fintech, fintech massively expanded the size of many non-financial markets because now you could, you could bundle in financial products with non-financial products. And what I mean by that is like, my favorite example of this is Toast. I know you and I have talked about this a bunch.

• 10:37Speaker 01

  Plenty.

• 10:38Alex Rampell?

  But like Toast could have existed in 1985. Like, you know, everybody had an IBM PC. They worked pretty well. Microsoft DOS worked pretty well. Why didn't, why wasn't there a restaurant software company in 1985? Well, number one, it was too hard to use. But then number two is you have this CAC to LTV issue. Because could you get a big restaurant that grosses $5 million a year to spend $100,000 on an MS DOS software product for keeping reservations and paying waitstaff? And, you know, having a little menu that showed up for the cook so they can, you know, make your hamburger more quickly or something? Nobody would pay $100,000 for that. But if you bundle in payment processing, you're effectively charging $100,000 for that, right? Because maybe you get a 2% vig.

• 11:22Alex Rampell?

  So fintech made the market much, much bigger for software because of this bundling effect. And that pales in comparison to now software doing the job of labor. Because it's like, yeah, fintech made it a little bit bigger. But now, instead of just being a dumb pipe for data or a dumb storage of data, and instead of just like charging incrementally more by bundling in, you know, financial processing, now we can do work and we can charge for work. And we can charge for work in a way that is cheaper than humans, better than humans. But I think both of those sell the opportunity short because in many cases, you can't even find a human. The best and funnest story of the Lassie introduction, or like the, our announcement that we made together with you, or your announcement, your amazing video.

• 12:07Speaker 01

  It was a great collaboration.

• 12:08Alex Rampell?

  So my first dentist, hopefully he's listening to this podcast, but his name is Ronald Sloop. It was my parents' like first friend when they moved to Florida. I'm from Florida. He retired as a dentist.

• 12:21Speaker 01

  Was he a Dutch guy? Sounds very Dutch though.

• 12:23Alex Rampell?

  No, you know, Austrian, Nazi Jew. Summer in Holland, Ukraine, wherever my family's from too. So, you know, but, you know, he's probably 75, 80 years old right now. But he, part of why he retired was he lost his like, you know, key woman that did the books and everything else. He's like, I can't deal with this anymore. I quit. And then he sold his practice to his like junior practitioner and now he's out of the dentistry business. So he saw this announcement, he's like, oh my God, this is amazing. And I'm not talking this up because you're, he's like, my dad called me about this. But he saw the press release, wow, I talked to Dr. Sloop about this. And he said that if this had been around, he wouldn't have retired. Like, this is why he's now like doing nothing with his life. Just like, you know, playing golf or something in Southern California. He moved there from Florida apparently. Because it's just too hard to hire the person.

• 13:08Alex Rampell?

  So it's not like, oh, AI is gonna take the jobs. In many cases, you can't find somebody. This is the part that people don't realize. Or you can't find somebody, but there's like a, imagine that there is something that every human on earth would pay a dollar for, but the cost of manufacturing that thing is $100. You just have a market failure. And I kind of call this everything to the right of the supply, demand equilibrium point on like an Econ 101 graph. So it's like, well, everybody would, you know, have somebody, like, why isn't there a Dutch receptionist at every dentist in America? Because, you know, there might be a guy that only speaks Dutch that shows up. Like, why not hire somebody who speaks Dutch? Well, because there's only a 1 in the 100 chance that a stein that only speaks Dutch shows up at their office. You're gonna have to pay that person 40,000 euro.

• 13:53Alex Rampell?

  Like, it just doesn't make sense. But if it were free, or if it cost a dollar, then every dental receptionist would have a Dutch counterpart, right? You know, stuff like that. So it's just, it's, anyway, so that, that's where the market just expands massively once you throw in labor. Because it's like you have this tiny, tiny market for software, which by the way, is not that tiny. It's like a trillion dollars. Concentrically around that, you have like, you know, vig on financial transactions. That's even bigger. That's why Visa has a very big market capital.

• 14:22Speaker 01

  Why Toast can exist or.

• 14:23Alex Rampell?

  Why Toast can exist. But then you go, the concentric circle around that is just like, you know, it really is orders of magnitude bigger.

• 14:32Speaker 01

  And we also see that. So like the, there are about like 160,000 dental practices in the US alone. And like, they spend roughly $200,000 a year on like administrative costs. And then the interesting part is that because we serve hundreds already, like, they can't find people. So like what Alex like shared is we come across that literally every day. That like, it is the doctor themselves with their Harvard degree that sits there till like midnight. And it makes them like, not like their job anymore. So I think that's, that's very interesting because like, these small business owners, they just wanna mainly spend time on their patients. They don't really wanna spend time on the administration part, let alone like working with Betty. And then in this case, it's wilder, they can't find Betty.

• 15:15Speaker 01

  So we combine, I think a lot of people are also surprised that, but isn't there a lot of skepticism? It's like, no, these people are in real pain. And they are, to Alex's point, about to quit or just like they hate their job, at least this part of the job. So we combine, we say, hey, we actually have built this agent that can provide you already with tens of hours of labor. And ask your friends or the people in your study club, like if this is real. And then, or they Google it and they see that this is real. They then adopt it like very quick. So it's, it's super interesting to see. And then, indeed, like on the, why that's an interesting business? Because this is indeed, it comes out of the P and L on the, on the, on the labor budget.

• 15:57Speaker 01

  So like, we are already charging five figures for kind of like this first agent that only does 30 hours of labor a month. And there's 200 hours of labors to be done for Dr. Sloop. And, and that's like really interesting, like to see that like they see us as someone they break in to like actually run the practice for them, which is also on the other hand, complicated to do. Because if all of a sudden the requirement for software becomes, hey, this is not a tool that I give Dr. Sloop, and then Dr. Sloop is still on the line. In fact, one could argue a lot of AI companies are still like that. There is a human in the loop that ultimately the software engineers decide what to get deployed. Like, we can't do that. So we needed to build an agent, that's why it took us years, that like errors on the side of correctness.

• 16:41Speaker 01

  Because like, if you take over a job reconciling all the insurance payments, interacting with the patient to kind of like bill, it needs to like work. So that was like technically like hard to do, which also makes this super interesting from a technology perspective. Because you all of a sudden need to build autonomous systems that run on its own and don't have a human in the loop. It runs the business for Dr. Sloop, which makes it technically super interesting.

• 17:04Olivia Moore?

  Yeah. There was a great quote from Dr. Kwan about you guys, which is that Lassie isn't replacing humans, but like freeing them from wearing so many hats. And your launch video had a clip of him talking about how he can actually coach his kids soccer teams now and go to his, their games, which is amazing. There's a gap between wanting that and being willing to adopt AI and actually, you know, having it run payments in a practice and in fact, doing it so well that most of your growth is word of mouth. So it's dentists recommending it to other dentists.

• 17:36Speaker 01

  They do.

• 17:37Olivia Moore?

  How did you approach the technical build process for that? What was it like getting the product to? I think you guys are at 98% automation. Like, walk us through kind of that, that journey.

• 17:48Speaker 02

  Yeah, I think a big part of it was us actually spending the time in offices doing the work ourselves. I don't think we could have built, built a product that works as well as it does if we, if we didn't know how to do the job.

• 18:02Speaker 02

  I think another part, we already kind of talked about it, but I think a huge difference between SMBs in general and enterprises is that in SMBs, there's nobody to, to use the tools. Like, you can, you can build a tool, but there's nobody sitting there that's gonna use it. And so.

• 18:17Speaker 01

  Dr. Sloop at night.

• 18:19Olivia Moore?

  Yeah.

• 18:20Speaker 01

  Needs to go into the tool.

• 18:21Speaker 02

  We, from the very beginning, we, we focused on, you know, initially, we were actually the, the humans in the loop. So we, we kind of took over all of the work and we were like, you know, we'll, we'll just do this work for you. And we, and we kind of automated away our own problems. And then at, at some point, you know, we, we got to a high enough level of automation that, that we felt, we felt comfortable handing it back, back over to the, the remainder back over to the office. And I think now, you know, we learned, obviously, when, when we can't do something for, for some reason, which is pretty rare, but, but say, we, we don't know how to do a certain case. We learn from what, what the staff tells us. And we also think about, I think, you know, like, we, we wanna get to like a sufficient level of automation

• 19:06Speaker 02

  across any product to, before we sell it. So, you know, for us, I think that's like 95 plus, say, but not necessarily 100. Like, I don't think we're going to wait until we get into a, to 100 with one product and then do the next one. I think the, we really think about the business more like as a whole. Like, how much of the business can we, can we automate? And how much of the labor can we, can we do with, with software? And as soon as we can take over a job, we take it over and then we move over to the next one. And then over time, we'll just learn, learn the long tail of cases.

• 19:38Olivia Moore?

  Yeah.

• 19:39Speaker 01

  For a business, it also doesn't matter that much, right? Like if, let's make Dr. Sloop famous in this podcast.

• 19:46Alex Rampell?

  He's gonna love it.

• 19:46Speaker 01

  Yeah. If Dr. Sloop said he can become a customer, so maybe we should talk about like someone else.

• 19:52Olivia Moore?

  We could reactivate him.

• 19:54Alex Rampell?

  He might, he might come back. That's how excited he sounded.

• 19:57Speaker 01

  Yeah, yeah. Dang, that's the series B story. We've got Dr. back out of, Sloop out of.

• 20:02Alex Rampell?

  We had a dental shortage, now we don't. And all the dentists have come out of retirement.

• 20:06Speaker 01

  We've got 100 million more people in the States that get good dental care.

• 20:11Speaker 01

  The, it's fine for like a business owner, if there is a tiny sliver left of claims that need to be touched every week, right? So the way to see this, because they live in a nightmare world where you have like to update 200 letters a week. Because that's how much patients you see. And then you have to like go to an insurance portal, update like a system of record check against the bank account 200 times a week. If you instead need to do that like a handful of times rather than 200 times, it saves 10, 20 hours. If there are a handful of claims that you need to file yourself, but the majority kind of like is on autopilot, makes it tremendously more easy to run a business. I often compare it with like how we are served as tech companies. It's the third company I'm building and it is a lot easier and with a lot smaller team than we used to do kind of like 10, 15 years ago.

• 20:56Speaker 01

  And it is because there's a great tools for us that we can use.

• 21:01Speaker 01

  Are all these tools completely like running our finances autonomously yet or our payroll or HR? No. But they do save a tremendous amount of time. So I think that's how we approached like this, like built as well that we didn't want a human in the loop. Because we want software that scales and can be implemented quick. But it's fine if it does in this case for the first agent, 98% of the work and the nurses left. And the interesting thing that we have thousands of staffers that are basically giving us input on how to make that appeal that the agent currently cannot do. We come from the consumer world, right? Like Frederick worked at Superhuman, I worked at Robinhood. So we have a very high bar for shipping stuff. So we don't release it before it actually like, okay, this is, this is good.

• 21:41Speaker 01

  It can be hands off and like works. And then these staffers help us to kind of like get it to like an even higher percentage.

• 21:49Olivia Moore?

  Yeah. From an implementation and onboarding perspective, you guys integrate with existing practice management systems for the most part. Versus kind of making them switch a bunch of software to adopt Lassie. And Alex, you have written and talked a lot about kind of startups getting distribution before an incumbent can innovate. Curious your thoughts on like that in the AI era and then would also love to hear from you guys, how you thought about which path to take there?

• 22:16Alex Rampell?

  Yeah, I mean, I had this epiphany when I was building my company, which is holy crap, like there really aren't. If you build something, I call this the TiVo problem and TiVo famously, TiVo and Replay TV both invented the digital video recorder. So you could pause online television, which is an amazing innovation, but a terrible company. Because you really have very few outcomes that are good. You either end up selling to one of the, the big guys like a Comcast or Time Warner Cable, but they're not going to pay you that much. And partially because if Comcast bought you, you start TiVo, Comcast buys you. Well, all of the, all of the competitors to Comcast, they're like, well, we're gonna not allow this to work.

• 22:56Alex Rampell?

  So like, you, you have what I call a control discount versus a control premium. So that's option one. Option two is they copy what you've done many years later, much crappily, that's a word, because they have all the customers. Or you know, maybe number three, you do a licensing deal with them and they take all the economics because they have all the customers. So that hence, you know, my, my recognition was like, the thing that a lot of startups should do is they should do the boring thing. Like they should build like the raw pipes, the raw, like just own the customer and then you get to build the fun feature on top. Which I still stand by. I mean, I like the vast, and this is why like maybe four years into trial pay, I realized what I should build is this thing called Stripe.

• 23:40Alex Rampell?

  And this was not like revisionist history because Stripe had five people. It's like, wow, we should, we should do boring payment processing, which is a commodity business. Because if we do that, then we own the customer chronologically, you get them first, it's a very, very boring thing. But then we have this other thing, which in my case was offer based payments, which was very lucrative. But you can only do that if you control the pipe, just in the same way that you can only build the digital video recorder if you have digital video to record. So how does this change with AI? It changes with AI because in many cases, these are non categories, like there is no incumbent. So for most categories, like imagine I say, I have a great idea, I'm going to do background checks for new employees as part of onboarding.

• 24:21Alex Rampell?

  And I'm going to integrate with Workday, Workday being the biggest HR information system. That's a great idea. However, it's such a great idea that it's a very obviously great idea that this thing called Workday might copy. And they own all the customers and that's where it's like, you know, battle between startup and incumbent. It's like the incumbent might win there because their ability to add things, and I feel like that is actually magnified in the AI era. Because you can have, like, why are big companies not good at replicating small companies? There are lots of different reasons, but the ones that, one of the reasons is they hire very bad engineers and they have lots of process. But now AI kind of makes a bad engineer into like a pretty good engineer, you know, kind of. So that excuse kind of goes away a little bit.

• 25:05Alex Rampell?

  But this is the cool thing about a lot of the AI software companies or the AI that does the work. Like who is the giant ass incumbent of dental software? You and I know the answer on this, but it's not, it's not the same thing as it's like, ooh, here's Workday, it's a tech company. They already have software and they can add something to, I remember actually, this is a cool story. There was a company, I think it was called X1, Microsoft Outlook had really bad search. And this company that was funded by Idea Lab and all these VCs, you know what they did? It was like search for your Outlook email, which was so good. But it's like, you know, who I think is going to do this eventually? I think, and again, like that company unfortunately went to zero. Or actually Yahoo bought that a long time ago.

• 25:46Alex Rampell?

  So I kind of think the same rules apply, you know, the battle between every startup and incumbent comes down to whether the startup gets the distribution before the incumbent gets the innovation. But with many changes, one change is the incumbent can get the innovation much more quickly.

• 26:01Alex Rampell?

  But the other is that there are a lot of categories where there never was an incumbent software company. Because the only job to be done was like actual human labor. And that's really exciting because now you don't have to worry about like, oh shoot, these guys are going to come and eat my lunch. Like who? Right? Like who does, like there are a lot of industries that just don't have an incumbent software solution. For the industries that do have an incumbent software solution, yeah, the risk is very high. That they will start releasing AI features and it's a really interesting way that the market is playing out right now. Because if you look at the public markets, the public markets are saying in many cases like, oh, you're a software company, software is dead, software sucks, software is zero.

• 26:44Alex Rampell?

  Oh, you're an AI, it's the opposite of what VCs are saying. It's like, oh my God, you do AI stuff? But AI is software, software is AI, like the two are the same. But like, I don't think everybody's come to that realization yet. So like, if you're doing, you know, Workday but AI, if you're doing, you know, NetSuite but AI, it's like, you know, the AI, the pure play AI thing is software at its core. And the, the pure play software thing, it's like they're, they're smoking crack or not showing up to work if they're not working on implementing AI features. Because that's what their customers are demanding of them.

• 27:16Speaker 01

  Yeah, and we see exactly that is that there isn't an incumbent that kind of like does this job or can do this job like quickly.

• 27:24Alex Rampell?

  Well, the incumbent was named Betty and she quit two weeks ago. That's the incumbent.

• 27:28Speaker 01

  Or a billing agency.

• 27:29Alex Rampell?

  Right. It's Dr. Slope's old assistant. That's the incumbent.

• 27:32Speaker 01

  Or a sucky version of that, that like is somewhere overseas or in the States. So that's indeed exactly like what you're competing with. And that's what intrigued us so much about the small businesses. Because like, there is no major player there. And then if you show up with software that they have never seen before, which is now possible, they will adopt it and you can grow. Which is very defensible now, maybe we get to talk about them later. The, the schlep you have to do to like, do the actual labor and talk to all these like systems. And you need to build an ontology to make sure that everybody in this whole ecosystem is on the same page about an insurance claim and the patient payment.

• 28:11Speaker 01

  Because all these different systems have like a slightly different definition of that, which makes it also then harder to build because there is no incumbent. You need to kind of like stitch like a lot of things together, but makes it extra defensible. Because like, what we had to do was like, okay, first figure out kind of like all these read and write integrations between all these systems that Betty, the AI version, I need access to. Then you need to figure out like, okay, what is the data model that can be used across like all these like systems? And then on top of that, you need to build agents that, yeah, you can't really build without actually doing the work. So there's like, here's a work that you need to do to kind of like get that like going, makes it very defensible. Like the, the go to market site as well, right?

• 28:55Speaker 01

  Is this like, you need to knock on millions of doors and say, and the interesting thing we just talked about that is not necessarily that are skeptical about AI. Because Dr. Slope is like, oh, I wish this was there, is how do you get hold of Dr. Slope, right? Because Dr. Slope is not done at 7:00 PM, there's an emergency patient that calls, then he goes back into the office to teach that patient dinner with the kids. And then opens the computer and then oops, the supplies need to be ordered because Betty left, so I need to do this myself right now. So like for us, but for any company selling to SMBs, the interesting puzzle here, that's why this is also a super interesting go to market work. Because like, how do you adopt and spread AI in small businesses is a super interesting puzzle to solve.

• 29:37Speaker 01

  But that's, that's the crux there. It is like, how do you get a bit of busy, non-technical owners that adopt AI?

• 29:43Alex Rampell?

  Well, I imagine the other part of the question for you, I'd love to hear your thoughts on this. I'm sure our audience would love to hear our thoughts on this, but this is not like you download the AI app from the app store and then you're all done. Right? Like, how do you actually do the onboarding?

• 30:00Alex Rampell?

  And how much of that can you automate? Because that's part of what makes a business that is selling these things work or not work, right? Because if you have to, you know, send your own Betty to every single office in the country, that's really hard. It would be amazing if it's just like, download the Lassie app from the app store and then you're all done. But like, there's a lot of like these, these systems and processes are very manual. And I kind of think like AI is overhyped in Silicon Valley, but underhyped in Iowa. And like, there are a lot of people in Iowa, like how do you solve that district, that like last mile distribution problem for Lassie?

• 30:38Speaker 01

  Yeah. Yeah, I think it's a super interesting problem. Assume you can knock on all these doors and get them to try it, right? Then like, how do you get this adopted? Because the same argument still stands, they're very busy, they are not technical. So like, good luck setting up AI in their like business. And I think that's also why our consumer backgrounds come in handy here. Because at Robinhood or at Superhuman, you get the time window, 48 hours. If the thing doesn't work and you provide core product value, you're out, right? And these doctors are very much the same. It's not only already hard to reach and hard to work with, but if it doesn't work in a couple of like months from now, like you're out of the door. Um, it really needs to be plugged in and then do the job.

• 31:19Speaker 01

  So I think a lot of the work went into not only building this agent, but also how do you set people up on that agent such that almost all the friction is gone? Um, and, and that was like a lot of work and it's, it's already to a point that it's self-serve almost. Um, so there are a few more things like left, but it's literally that the doctor in Iowa, let's not use Loop again. Um, like says, yes, I want this. Um, they then uh go to almost like a Stripe-like checkout or a Repling-like onboarding like flow, where they hook up the bank account of the practice in the product. They link the system of record, they link all the insurance portals that claims come in from. Um, it confirms business informations, like are these the doctors that actually like work in your practice?

• 32:03Speaker 01

  Um, and then under the hood kind of like configures like things. There's like one or two pieces left, but I think we're like months out until you basically like have an agent that you can almost like set up like self-serve. Um, so I think that was a big part or is a big part of bringing this technology to people in Iowa. Um, is that can you almost build a consumer-like uh onboarding like flow? Where a lot of the complexities abstracted away and kind of like happens under the hood like the, we could maybe not a many people, I think I'm very impressed by Robinhood, I'm a little biased because I worked there. But like in order to set up an account for a user without a human in the loop, which is what Robinhood pioneered, because back in days there was Charles Swapp and then hello, I want to open an account. Before that, you had to go into the office.

• 32:46Speaker 01

  Uh, but one of the many things that Robinhood pioneered is that you could, you can almost self-serve your way onto an account and KYC is getting done, your bank is getting linked. Uh, and a lot of product work went under the hoods there to make that happen. I think we ran into a lot of these like similar like, like situations. It's like, how do you connect all these insurance portals? And, and kick off like the, the uh, the process such that the digital claims are coming in like reliably? How do you reliably link a bank account, all the system of records? So I think a big part of this was figuring that piece out. Yeah.

• 33:16Olivia Moore?

  I guess to that point, like there's more you can build and are building for dental practices. Then there's all these other types of healthcare practices that could use Lassie. And then there's like the broader universe of small businesses that could use you guys. How are you prioritizing what you build, who you sell to? Is there, you think, a world where Lassie for dentists makes Lassie for physical therapists better?

• 33:41Speaker 01

  The master plan.

• 33:42Olivia Moore?

  Yes.

• 33:43Speaker 01

  Are we in that part of the episode?

• 33:45Olivia Moore?

  Yes.

• 33:46Speaker 01

  Um, yeah, I think three steps, like the end goal here is that uh every small business should run itself, right? And the busy work is done by uh agents. Uh, we want to build an agent for the business that then will interface with the personal agent of a consumer, consumer highly likely. That then will interface with an agent at the insurance company or other parties that the business needs to interface and interact like with. Uh, but step one is to Alexis's point, like there are 160,000 dental practices in the US alone. $200,000 in labor that Dr. Slope and others can't find. So like um, serving that market first, you're looking at a $1 billion in like a recurring revenue as a market.

• 34:26Speaker 01

  Um, so that's like step one and then uh likely like we will pick another doctor office type. Um, that like is not well-served and has a big TAM. Um, and, and needs consumer-like products, right? Because what we discussed, a big part of not this, you get the AI to work 95% accurate-ish. It needs to really work and the onboarding needs to be as simple as onboarding on Coinbase. Um, or Stripe. Um, so like it will likely be another like doctor office type like, um, and then uh, the last uh part there is I think uh we've then trained AI agents uh to like run the small business. And all small businesses at an abstract level, like have a system of record they need to read or write into.

• 35:07Speaker 01

  Um, they all have customers in doctor offices, they happen to be patients, but it's interacting and transacting around payments. You need to book appointments. So I think the end goal is if we served all the doctor offices, that we help all the small businesses across the world. I guess we're just the best in, you know, building AI agents that uh salt of the earth people or people in Iowa and Petuka, Kentucky, and hopefully in Amsterdam like down the line, where I'm from, um, and Germany, Hamburg, where Frederick is from can, can start like using as well. Um, so that's the, that's the end goal. But I think again, similar to Superhuman or Robinhood, we worked at like laser focus on getting one thing really, really right. Um, and then scale it like from there. Yeah. But the end goal is to help them all.

• 35:49Olivia Moore?

  Amazing. I love that as a master plan. It's a good one, a big one. Um, you both have been part of scaling many important companies in the past. Robinhood and Coinbase and Superhuman among others. And building a company in 2026 is like a whole brave new world. It's so different than ever before. What are like the biggest things you've carried over? You mentioned some of them already and then what, what have you kind of had to unlearn? Or what do you think is new to being founders right now?

• 36:20Speaker 02

  There's a bunch of stuff that uh we're applying now that I, I learned at, at Superhuman. Um, I think for one, uh focusing on uh the right ICP and being really strict about who you onboard, uh to basically guarantee that uh they're going to have a great experience. Um, I think in our case, it's uh particularly important because if we onboard the wrong practice and say, we can't actually automate that much of their work, then now we're kind of stuck with this customer that uh, you know, we, we claimed we're going to automate a bunch of labor for them. We can't do it. Are we going to do it? Are we going to offboard them? Uh, it's, it's particularly painful, maybe more painful than uh than in a kind of old world product. Um, so there's that. There's also, uh, we talked a bunch about the onboarding already, but uh we,

• 37:01Speaker 02

  we've really obsessed about uh getting to uh the core product value really quickly. Um, and almost like our onboarding is, is a little bit like a uh, you know, it's kind of like a story or a playbook or like a movie. We, we have like set uh set points and checkpoints that we want to reach in certain timeframes and we make, uh, we make sure it happens every time. Um, and uh, you know, and we measure that of course. Um, I think one thing that's, that's really different in particular about product building uh from uh from back then to now is that uh, if you think about the products that you built uh in the past, I think it was much more about kind of functionality or like the ability for the user to do something.

• 37:42Speaker 02

  And now uh we think really only about uh like what, what kind of labor can we automate? And what uh, or like work can we do and uh where can we save time? So if you're thinking about say uh like patient billing as an example, um, I think previously you would have built uh, you know, the ability to send a statement and the ability to receive a patient payment. Um, but if you're looking at where does the actual work of patient billing go today? Uh, it's really like figuring out is the money or is the, is the statement that we're going to send the patient the correct amount? And or like, once the statement is sent, um, you know, the patient calls and asks about like, why do I owe this amount? Uh, do I really have to pay this? I thought this would be covered. Uh, so if you're just looking at like, where does the time go?

• 38:26Speaker 02

  It actually goes in like oftentimes the customer communication or, or some other kind of more like, you know, fuzzy, fuzzy part of the work. Uh, and, and when we're thinking about like shipping, say a product like that, we're really thinking about, okay, once we, you know, once we deliver patient billing, the office should not have to spend any more time on patient billing, which is super different to giving them a tool that they then need to use, uh, which doesn't really save them a lot of time.

• 38:50Olivia Moore?

  And then neither of you, I think, were dental experts before you started the company, although

• 38:55Speaker 01

  That's safe to say.

• 38:56Olivia Moore?

  You go twice a year. That's pretty good, I think, for the average patient. You're supposed to, right? Yeah, of course.

• 39:01Speaker 01

  I'm just doing my um, not a civic duty, yeah, I was about to say.

• 39:06Olivia Moore?

  Um, when you're, well, maybe tell us like, how big is the team now? When you're hiring, are you looking for expertise in dental? What, what are the kind of characteristics of team, of team members that you want to hire?

• 39:17Speaker 01

  Yeah, uh, which is not mainly on our mind, right? Because like we found this really great product market fit in a large market, uh, where you can go after the labor um that they can find.

• 39:29Speaker 01

  We, we currently have two takes and that like one, not much has changed, contrary in maybe you take here. You still need people with steep slope, um, that like are very ambitious and, and driven. Uh, and have skills that are just like top five percentile, either in engineering or in selling, right? Um, and I think that remains the same. Getting hold of doctor um slope um is just like the same exercise as before. You could do it in a more AI-like native way, uh, but I think the skills are the same and you're, uh, so I think that has not changed. Maybe the only thing is that the AI piltness of a person. Uh, I think we see a pretty clear, a clear division between uh across the board, like um, do you believe that, you know, the way you code will change completely as a result of building a company in this era?

• 40:14Speaker 01

  Um, building out a finance department, uh, do you think that's going to be completely different uh than before? So in that way, we, we interview specifically for, for that. Uh, because we want to build a 2026 version of a, of a, of a big organization, right? Uh, where like we ship twice as much than others, we move like four times as like fast because uh, we should not only like have AI adopted in these businesses, but the big puzzle for us is how do you build a team? Um, that kind of like um, also incorporates AI in all these like functions. So I think that, yeah, on one hand, nothing has changed, um, because yeah, the bar is still the bar, right? The, I used to be track and field runner. Um, almost became a professional like track and field runner, took a different path in life.

• 40:58Speaker 01

  Um, but my friends went on to the Olympics. Uh, and um, yeah, Olympic like uh running is still the same, right? You need to train twice a day. You like, like push it to the edge. And that's not given like to everyone mentally and physically. So like that, I think, has not changed in company building. I think what you can do and the output you can generate is just like four or five times. But that's the big experiment that we're doing together, right? It's fun to see, okay, how quick can we get to all the dentists in America and build a really good product? Um, such that just 200 hours gone, like how quick can we then bring it to another vertical and then help all small businesses? Um, I think that uh, that, that is the big interesting experiment that we are going to do over the coming years.

• 41:40Speaker 02

  If it's so much easier, if everybody can hire a Betty.

• 41:46Alex Rampell?

  Right, just materialize a betty.

• 41:47Speaker 01

  Yeah.

• 41:48Alex Rampell?

  How does that change? I mean, like, it could actually work out where, like, small businesses, it's much easier to start one and run one, but then actually, paradoxically, it's much harder to be one. Because you do have, if you think about moats in the AI era in general, we often talk about it with respect to software companies.

• 42:07Speaker 01

  Yep.

• 42:08Alex Rampell?

  So it's so easy to go replicate XYZ software. I did it on Replit, or I did it on Lovable, or I did it on Claude. Like, you hear this right, you know, left and right all the time.

• 42:19Alex Rampell?

  Much, much harder than this, I'm gonna go replicate Dr. Sleep's practice.

• 42:22Speaker 01

  Yeah.

• 42:22Alex Rampell?

  But one of the things that makes a small business somewhat defensible is actually, it is an accumulation of people that are required to deliver the end product.

• 42:30Speaker 01

  Yeah.

• 42:31Alex Rampell?

  And it's like, if you know who Yogi Berra is?

• 42:34Speaker 01

  Yeah.

• 42:34Alex Rampell?

  You know, famous Yankees baseball player that said all these things that make no sense. And, but like-

• 42:39Speaker 01

  Quoted often, though.

• 42:40Alex Rampell?

  Quoted often, right? And one of my favorite ones, it's so crowded, nobody goes here anymore.

• 42:44Speaker 01

  Yeah.

• 42:46Alex Rampell?

  It doesn't make sense. But I guess my question is, how do you think small business changes? If it's easier to run a small business and start one? Because theoretically, that could erode the margin of a small business such that it's so crowded, nobody goes here anymore? And like, this one moat that exists of just materializing people to deliver a product? It's now so much easier, but therefore, it's actually harder.

• 43:12Speaker 01

  Yeah. I think our current take on that is that this assumes there is a cap on kind of like demand. And if you just look at the dental practice, but the same applies to try to find a good plumber. There's just like twice as much demand that currently can be supplied. And I think this is a great opportunity for, you know, everybody to have great dental care and go twice a year. And I think the same can be said about primary care doctors. I grew up in the Netherlands. I had a very different primary care experience than like most people in America here. Because it's just really hard to find a good primary care doctor that is in your community, knows you, your family, and takes takes care of you. So I think this we see as an opportunity to kind of like, create like,

• 43:55Speaker 01

  now there are 160,000 dentists, or hundreds of thousands of dentists. What if, uh, America has half a million dentists? Or can see kind of like, twice as much patients, maybe the same amount of dentists. Plumbing like is the same. I think there's a lot of these like small businesses, if they wish they could, you know, bake more pies, but they're constrained on labor, basically. And I think what this unlocks is that people have twice as much time for the craft. And that is an exciting future. Because then all of a sudden, yeah, you're gonna see a better world. I think that's that's currently your view, which is super exciting about this technology. I think that's also, to your point, like when we launched and we finally told the world, hey, this is what we've been up to. That was the main piece of like, a lot of people talked about that.

• 44:40Speaker 01

  It's like, wow, an optimistic example of like, how this new technology like can be used. Because there's a lot of, like, what's going to happen to the world? But yeah, nobody really can be against cleaning up busy work for small business owners that should be baking pies, or polishing nails, or cleaning teeth, or drilling. Depending on like who you are, of course. But I think that's so interesting about about this.

• 45:04Alex Rampell?

  Yeah.

• 45:04Speaker 01

  Yeah.

• 45:05Alex Rampell?

  So you started the business in 2020. And that was like arguably pre-AI, or pre what we think of as being this generative AI revolution, which I would call like the B C eighty divide of like November of 2023 when ChatGPT launched, right? So if you, or I guess 2022, sorry. November of 2022, when ChatGPT launched public. What is, what are the kind of remaining? So, you know, then you had reasoning models. You have all of these things that have kind of built on top of the original revolution of, you know, four years ago, call it. Like, what, what are the hardest problems to solve? Like, what is it? And we talk about like, software that does the job of labor. What cannot be done right now?

• 45:47Alex Rampell?

  What do you feel like we still need more technical advance to get there? And sometimes it's like a ninety, ten thing, where it's like, you know, the last 10% is really, really hard, but you can't be a feature complete solution until you've done that? So I'm just kind of curious, like, from a technical lens, what are the things where you would say, okay? And it's not a Lassie specific question, it's just kind of more the technology and what are the enables writ large? Where does work still need to be done? Where it's just not quite good enough? And then what do you think the curve of that looks like? When, so it's not, you know, it's like a question of AGI for small business.

• 46:23Speaker 01

  Yeah, yeah.

• 46:24Alex Rampell?

  You know, what do you need? And where are we on that curve if you had to estimate?

• 46:29Speaker 02

  I think one thing that's interesting is that the models are trained on so much data and they're so large. And yet, they actually don't really know how to do any of this work. Like, they don't have the workflows encoded in any way. So, for example, we're working on a product now where we, we have to like, collect all of these like, basically SOPs on like, and documents about like, how are you supposed to bill insurance claims to certain payers and all this kind of stuff? Which, to some extent, humans would do the same. But there's also a big amount of, just like, human knowledge that is encoded in, say, these office managers. And they just like know how to do this work. That's weirdly not that accessible on the internet.

• 47:13Alex Rampell?

  Yeah.

• 47:14Speaker 02

  I think we have a big advantage there because we have all of this like historical data out of their ERPs that we can look at and kind of infer, you know, some of these workflows from. But that's something we notice a lot. I think actually, when we first started using some of the, you know, later reasoning models, we kind of assumed like, oh, they probably just know how to do this work. Because like, why would they not, right? Like, they're trained, they're trained on all of this data. But it turns out that they, they don't know all the intricacies of most of these workflows. I think, yeah, this is less specific to Lassie, but I'm, I'm personally kind of excited about smaller models that, you know, learn faster and can learn on less data.

• 47:54Speaker 02

  I think that will be, that will be really cool to see because I, you know, I think over time, we'll have intelligence kind of, I think, disseminated everywhere. And, you know, like, to the point where like, you know, like the Pixar lamp that has its own personality, like, why not? I feel like I want my intelligence to be, to be everywhere. And it feels like we're super far from that. So I'm, you know, curious about, curious about that. I don't, I don't know if it'll benefit us necessarily that that much. But there, there is something about, you know, the models not being able to learn very, very fast. And, you know, as it relates to us, like, we, we put a lot of product work into,

• 48:35Speaker 02

  how do you actually get input from, you know, from people about, you know, their preferences? And like, how they've done, done the work in a way that we can then, kind of like useful input, in a way that we can then use to actually make, make our, our agents smarter. And I think from a, you know, like product UX perspective, I think there's a bunch of interesting stuff to dissolve there as well.

• 48:58Alex Rampell?

  And maybe one kind of final question between technical and non-technical is, I've been thinking about this a lot, how, how the world changes when the marginal cost of arguing goes to zero? Right? So I was thinking about this because Cigna, who I have for my health insurance, will only send paper checks. I was like, oh, I must have missed the whole like online enrollment. Nope, nope, they don't have one. Why don't they have one? Well, they're kind of hoping that like you might lose the check, you might not deposit it. It's just like this kind of like intentional delay. And it's the same thing for like a lot of insurance. It's like, you know, there, it's like deny, deny, deny. They want to deny. And like on the other side, it's like, ooh, you know, pretend that when my house burned down, I had a Picasso in there. Like, both parties are trying to cheat each other.

• 49:41Alex Rampell?

  This is not new to the insurance agents, or the insurance like industry on both sides, right? But now that everybody has this like superpowered thing that costs effectively nothing to go argue in perpetuity. It's like, I can argue with you?

• 49:54Speaker 01

  Yeah.

• 49:54Alex Rampell?

  And then you can argue with me.

• 49:56Speaker 01

  Yeah.

• 49:56Alex Rampell?

  But like, how does that change the business dynamic of things like insurance, payments, and collections? I mean, I'm sure you've thought about this a lot.

• 50:04Speaker 01

  Yeah.

• 50:04Alex Rampell?

  Because in many cases, the counterparty that the dentist is dealing with

• 50:07Speaker 01

  Yeah.

• 50:07Alex Rampell?

  is the insurance carrier.

• 50:09Speaker 01

  Yeah.

• 50:09Alex Rampell?

  Right?

• 50:10Speaker 01

  A big part of the cost receivables or revenue comes from insurance companies.

• 50:14Alex Rampell?

  Right.

• 50:15Speaker 01

  Yeah, I think it's quite interesting because it's pretty like well-regulated. So- If a dentist does a crown, like, and you provide them with the correct narrative, Frederick was already talking about it, there is just a document at Cigna that specifies, if you give me, you know, this x-ray, you give me this narrative, then we will cover that. But for a human, it's quite hard to like, A, to follow those like rules, because there are like, take dentistry, there are like fifty, sixty, maybe hundred common billing codes. And it's quite a lot to like, remember for Mildred or Betty the staffer. And in this case, kind of like, our agent like, has like, all the like, documentation.

• 51:01Speaker 01

  It has access to the x-ray that is the relevant like one. It knows how to build like a treatment plan, and will then submit that with the insurance company. And the insurance company like, just needs to follow the rules, right?

• 51:12Speaker 02

  Right.

• 51:12Speaker 01

  And then they will like, bait it out. The other thing, because indeed, we are very deep in this industry, that is interesting dynamic, it took me a while to understand this, but Cigna also has an incentive to have good doctors in network that are happy with them. Because every year, they need to go to Andreessen and say, "Do you want to renew your dental plan with us?" And then Andreessen is gonna look at like, are there good dentists like, in the area that are covered, that Alex and Olivia can go to? And if the answer is no, then, because there's competition in this market, they will go to like, you know, another insurance company. So like, there is actually incentive to keep doctors' loop and quan like, in network.

• 51:54Speaker 01

  Because like, otherwise, they will not renew the plan like, with their employers. So like, I think that it is a lot more, for us, it's quite interesting, because why I also think these agents are so well-equipped to do this work, because there's very clear documentation. Now, to Frederick's point, and we can talk about this for hours, maybe another time, but like, we are also literally digitizing the file cabinet. Like, that's, it's really interesting that, A, it's really hard to find these like files, they're there. And once we have them, we know how kind of like, to do this. But all these payments you talked about, a lot of doctors across America still get paid on paper too. So like, they get hundred thousand dollars in checks, like deposited on their desk. And I did this, right? So for Dr. Kwan, I literally knock, knock, knock, open the door,

• 52:37Speaker 01

  I sit on my bar stool, and there's the mailman in this case, that gives me a stack. And I kid you not, I open all these envelopes, deposit hundred thousand dollars like, cash, in the bank account of the doctor, and then I have to go to work on all these itemized invoices that are attached to the checks.

• 52:52Speaker 02

  Yeah.

• 52:52Speaker 01

  And now the interesting thing, so even if we had the models, you couldn't really do this up until a couple of years ago, because that was the status quo. So the file cabinet was literally the file cabinet. In the file cabinet, like, were the checks and the itemized invoices that you need to kind of like, part of the job to keep the doctor office like, running. And then the federal government stepped in, and they said, "This has to stop." Right? You cannot do paper checks anymore for much longer. So then they mandated this industry to switch to direct deposits, offer it as an option to doctors. If a doctor says, "I want to flip the switch," you need to do that. The same is these itemized invoices, you need to create a digital file format like, for that. And we are also writing that like, till when?

• 53:34Speaker 01

  So you will see that like, a lot of these small businesses, I think the stats are seventy percent are still paid on paper. And it's going through this massive like, digitization revolution right now, because federal inflection point that's regulatory. So I think that, combined with these models being so good, makes this like, super interesting. Because even if you have the models, or had the models five years ago, like, yeah, the payments are still on paper. And we are digitizing that in the meantime, which is also what we talked about earlier. Like, it is not one click and then kaboom, because otherwise, the whole country would be on digital payments, right? Like, why, why would kind of like, Dr. Sloop not do this? Well, Dr. Sloop needs to also then spend fifty hours figuring out with Cigna and Delta and all the other insurance companies, "How do I flip the switch?" It then comes into the bank account.

• 54:20Speaker 01

  Do you want the staffer to have access to that bank account? Likely not, because the rent payments aren't there, like, the other data that you don't want your staff to see. So that's why the status quo is the way it is. And then we built this like, massive engine that basically, hey, give us the business info that we need, the tax ID number and some other nonsense, and then we will go do that conversion to digital payments, which is also a hard product problem to solve, but we have kind of like, also productized like that. So, yeah, it's super interesting to think about what other industries like, still have that. Because that's, I think, even a more interesting mode, where it's like, okay, you can apply these models, bring them to Main Street, it's super hard. And then in addition to that, how do you convert kind of like, data that you need, that's currently living in a file cabinet,

• 55:05Speaker 01

  to like, digital file formats, such that you can actually automate the work?

• 55:08Speaker 02

  Yeah. To your earlier point, actually, about digital file cabinets not being that much more efficient, part of the reason that all of these payments are still on paper is that the staff basically prefers doing the work by hand on paper, right? Because if you just digitize everything, now you have a PDF instead of like, a paper in front of you.

• 55:25Speaker 01

  Right.

• 55:26Speaker 02

  Like, you actually need, you know, some tools to use that, use the PDF. And it's preferable to just kind of, you know, work off of a sheet. And so like, there wasn't really much of an incentive to digitize. Now, that's obviously different.

• 55:39Olivia Moore?

  So obviously, there are a lot of dental practices that want or even desperately need products like Lassi, but they are distributed, and they're all over the country, and there are a lot of them to reach. How do you think about reaching them? Like, how do you bring agents to kind of these mainstream American businesses?

• 55:57Speaker 01

  Yeah, this is a very different playbook than where currently, I think, the cutting edge is. It's like, you have these models good enough, and apply them in enterprises, and you do like, a few steak dinners, and then you sign a contract, and then you have like, ten million in ARR booked, right? We literally need to go find like, thousands, tens of thousands, hundreds of thousands of small businesses. So it's a super interesting problem to solve, because we've built this agent that's really good. And what we're doing right now is literally mapping out, like, where are all these dentists? In this case, then, after the next small business type in the country? Who's the owner? What systems are they on? Like, are there any intense signals that we can find, right? Like, they're looking for a job, because they say that on Indeed.

• 56:43Speaker 01

  And then get in touch with these like, people, with a message that resonates with them, and that cuts to the noise. I think that's, especially with our customer type, is very different, right? Because if you were to sell to like, me, you like, find me, you know, in clay, and you enrich it with some Apollo data, and then you look me up on LinkedIn, then you know, okay, this is the guy that's gonna buy my HR system. We can't really do that, because Dr. Sloop is not in that database. He's often not on LinkedIn. So like, this is a completely different playbook that we're developing here. And I think that's, that's another very compelling kind of like, thing, that we're figuring out, basically, what is the go-to-market playbook look like to adopt AI in like, many, many businesses?

• 57:28Speaker 01

  So yeah, that's quite an exciting opportunity, and untapped.

• 57:31Olivia Moore?

  Thank you both so much for, for coming to chat with us today. This was awesome. We are very, very excited for the future of Lassi. Anyone who's listening, who might be interested in, in working with the Lassi team to build something generational here, check it out at lassi.ai. And you guys are very actively hiring, from what I understand.

• 57:51Speaker 01

  Oh, yeah.

• 57:52Olivia Moore?

  Amazing. Great. Well, thank you guys again.

• 57:55Speaker 02

  Thank you so much.

• 57:56Speaker 01

  Thanks for hosting us.

• 57:59Erik Torenberg?

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or a review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X, A16Z, and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode.

• 58:25Erik Torenberg?

  As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z company. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com/disclosures.