“Every small business should run itself” | Lassie with a16z Transcript, AI Summary & Key Points

00:00 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. >> I never forgot what I saw the number one great adopter on Yelp spending 200 hours a month on paperwork.

00:18 >> 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. >> The battle between every startup and incumbent comes down to whe the startup gets the distribution before the incumbent gets the innovation.

00:34 >> 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 adopt it like very quick. They see us as someone they break in to like actually run the practice [music] for them." >> There was a great quote from Dr. Quan about you guys, which is that Lassie isn't replacing humans, but like freeing them from wearing so many hats.

00:53 >> It's not like, oh, AI is going to take the jobs. In many cases, you can't find somebody. >> How are you prioritizing what you build, who you sell to? Is there of the world where Lassie for dentists [music] makes lassie for physical therapists better? >> The end goal here is that >> so welcome and thank you for joining us. >> Thank you for inviting us.

01:15 Excited to be here. >> Maybe we'll start with the basics. So, Stein, this whole company started with a conversation with you and your own dentist, Dr. Quan. What did he tell you that made you decide to quit your tech job at Robin Hood and go process payments for him by hand? >> Yeah, I did not uh know that my American dream would look like this. Um it was uh it was interesting like I I um was at Robin Hood at the time.

01:38 Um and uh I came to this country to start a company. So after 6 years um I moved from Amsterdam to Silicon Valley like I was looking for a heart problem to solve. And then my doctor Dr. Quan uh 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?"

01:58 And I said, "Absolutely." Um, he walked me to the back and I never forgot what I saw there. Just like a small business owner that like 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 like build the patients.

02:16 So I'm like, "Wow, it's fascinating. This is like a couple of years ago." So I thought that this was a solved problem cuz in the 70s my mom worked in hospital and that's what she did. She brought backs of cash to the bank and then processed payments by hand. Uh but it wasn't a solved problem here. So that's uh why this piqued my interest >> though I have to ask when he gave you this offer were you like was it like that kind of reclined?

02:37 Could 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? >> Well uh up until now I don't have cavities so you know there was no drilling happening yet. Uh so you know knock on wood >> examination. >> Exactly. Exactly. Yeah. Um um no like he he uh took me aside like after that.

02:59 >> Okay. >> Um because he he knew that I was like looking for this like heart problem. I was roaming around. Um and um then like uh like after like that appointment he he showed me kind of like what was going on. Uh and then I thought maybe it's him, right? But that didn't really uh make sense to me because he was very like well rated. He used all these modern technologies.

03:17 So then um we started like talking to other doctors like because maybe this Dr. Guan was just like an anomaly but then you know I also like worked for a gastronologist in Scranton Pennsylvania and we saw the same there. I'm like wait you're doing this all by hand. Um so then we figured out wait there are like hundreds of thousands of these small businesses that literally like do this all by hand like that would be quite like a fascinating problem to solve.

03:41 We knew it was going to be a hard problem but uh we were kind of like looking for that. 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. >> Yeah. >> Um, how did you convince them to let you in and to kind of look through the the heart of the business and get into the financials?

04:04 >> Yeah, it's a little weird, right? It's like, hello, I work at Robin Hood on growth on the referral program. Uh, can I get a job like here? And by the way, Frederick worked at Superhuman on product. can we do the billing for you and take over the finances? Um I think that was the first sign that we were on to something big. Um because to our surprise all these doctors when we asked them so we we first talk to all of them like Dr.

04:25 Guam because everybody likes to talk about their problems. Um and uh when all these doctors started talking to us for hours we knew that okay this is a real problem they have. This is not some vitamin that you know maybe it's nice to solve that for them. Uh but this is this is something that keeps them up at night. it it makes them almost quit their job and say you know I got into this industry because of my passion and craft in this case like because I want to take care of patients.

04:49 Um so that was the first time that these people were not no chance that you can come work for me because you know like you don't have any experience running the finances uh what about like HIPPA 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. Quan said, "Just come and sit here night five.

05:11 You can do the job." Um Dr. Sha was in Scranton, Pennsylvania. Uh he sat us down behind the desk and said, you know, uh you can have access to anything you need to have access to or teacher like my son. Um so that was the first sign that like this was very broken uh and not a solved like problem. Uh and then I think that triggered like our intuition for okay we might be on to something because this is a real pain that uh people are desperately looking for a solution.

05:39 >> Yeah. And from a technical perspective. So Frederick you the business 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? I would say when we started the business, we were always obsessed with automating and and putting putting the business on autopilot.

06:05 So that hasn't really changed. Um back then the models weren't that good though. Uh especially uh especially reasoning models or didn't really exist in that form. Um but if you think about what it takes to automate any job, you're really looking at um getting context on the work, which you know in the case of a doctor office is basically you need to have access to all the historical data, the patient records, uh that sort of stuff.

06:30 Uh and then you need uh tools to do the work. Um you know, this is true if you're a human in the office or if or if you're an agent. Um and so we started building uh building the context layer and building the tools and it and it's just that the intelligence layer wasn't that intelligent. Uh but for the first uh for the first job it wasn't that necessary like the the most basic kind of uh you know automation didn't require that much reasoning.

06:56 Um but then as the models got really good we we had this kind of huge tailwind because we could uh we had all all this context already built uh all the tools already built and we could kind of you know as as the models got better just replace our intelligence uh and and the product would just get smarter over time. Um so in that way we we got a little lucky but I think the you know the the core vision hasn't really changed at all.

07:17 It was always about automating the work and not uh not building tools for for them that they would have to use. 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 that would be the case. Curious like how you think about that when you look at companies and maybe how it played into like the thesis around Lassie.

07:40 >> Yeah. Well, so I've given this whole presentation on the origin of software was basically take a filing cabinet and put it in it to a database >> and kind of pick the uh the the time equals zero moment for that with this company the Saber Systems because airlines would just keep reservations and filing cabinets. Saber Systems was a joint project between IBM and American Airlines.

08:01 That's why Saber is spelled with two A's, Saber. Um but then this kind of took wind everywhere else. So like there are HR filing cabinets and that became something like Peopleoft. There are legal filing cabinets and that became you know all of these Lexus Nexus products. There are accounting filing cabinets and that became Quickbooks and that became Netswuite.

08:19 So every that was the origin of software. So 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 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. uh starting with Saber and um but people still had to do the work.

08:40 So I 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 Peopleoft and then workday make HR efficient make 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 like 1950 as probably 2000 and instead of using filing cabinets that are you know that are guarded by Stein and Frederick you know making sure that nobody breaks into the HR files.

09:09 Now you have an IT department and a CISO to make sure nobody hacks into the IT files or the you know the HR filing cabinet. So nothing really got more efficient. I I'm somewhat exaggerating for effect here. But what you can now do with software is it can do 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.

09:33 So like if it's HR, let's do a background check, right? 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? Like imagine I'm a dentist and I see I have all these overdue invoices and I can look up look up in QuickBooks. What do I do? Well, I might want to call and say please pay me.

09:51 Like that's what the filing cabinet should be doing and not just giving you the information. So it just turns out that the work is orders of magnitude bigger than the storage of information that the work is done on. Um so that that's really the been the thesis and that you know you need the technology to catch up so it can actually do it. >> Mhm. >> Um because in 2023 it's like you know next word prediction wasn't really good at like going and uh which is basically what AI is.

10:18 Uh that was not good enough to go say I'm going to go run my practice or you know do background checks. I know I'm going to have statistical, you know, inference play out and that's how I'm going to 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.

10:38 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. And I know you and I have talked about this a bunch, >> but like Toast could have existed in 1985.

11:02 >> 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 cact 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 weight staff and you know having a little menu that showed up for the cook so they can you know make your

11:29 hamburger more quickly or something. Um 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. So fintech made the market much much bigger for software because of this bundling effect. Um, 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

12:01 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 at 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. actually the the best and funnest story of the the lassie um introduction or like the our announcement that we made together with you or your your announcement your your amazing video.

12:26 >> It was a great collaboration. >> Um so um my my first dentist hopefully he's listening to this podcast. His name is Ronald Sloop. Um it was my parents like first friend when they moved to Florida. I'm from Florida. Um he retired as a dentist. >> Was he a Dutch uh guy? He sounds very Dutch though. >> No, you know, Ashkanazi Jew from, you know, somewhere in Poland, Ukraine, whatever my family's from, too.

12:47 So, you know, uh, but, you know, he's probably 75, 80 years old right now. But 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. Yeah. >> 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.

13:06 He's like, oh my god, this is amazing. And I'm not talking this up because your like my dad called me about this because he saw the prosperity. 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. U just like you know playing golf or something in Southern California.

13:23 He moved there from Florida apparently. Um because it's just too hard to hire the person. So it's not like oh AI is going to 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.

13:44 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, wow, everybody would, you know, have somebody like, why isn't there a a Dutch receptionist at every dentist in America? >> Because, you know, there might be a guy that only speaks Dutch that shows up.

14:03 Yeah. Like, why not hire somebody who speaks Dutch? Well, because there's only a one in aundred chance that a Stein that only speaks Dutch shows up at their office. You're going to have to pay that person 40,000 euro. 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?

14:21 You know, stuff like that. So, it's just it's anyway, so that that's where the 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. >> Um, concentrically around that, you have like you know big on financial transactions that's even bigger.

14:39 That's why Visa has a very big market capital. >> White can exist or businesses. But then you go the the the concentric circle around that is just like you know it really is orders of magnitude bigger. >> Yeah. >> And we also see that so like the um the there about like 160,000 dental practices in the US alone and like they spend roughly $200,000 a year on like administrative costs.

15:03 And then the interesting part is that because we serve hundreds um 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 uh and it makes them like uh not like their job anymore. Um so I think that's that's very interesting because like these small business owners they just want to mainly spend time on their patients.

15:26 Um they don't really want to spend time on uh the administration part let alone like working with Betty and then in this case it's Wilder they can't find Betty. Um, so we combined 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 uh at least this part of the job.

15:48 Um so we come by and we say hey we actually have built this agent that can provide you already with tens of hours of labor um and ask your friends or the people in your study club like if this is real and then uh or they Google it and they see that this is real. >> [snorts] >> um they then adopt it like very quick. So it's it's super interesting to see.

16:06 And then uh indeed like on the why that's an interesting business because um uh this is indeed it comes out of the P&L uh on the uh on the on the labor budget. Um so like we are already charging five figures for kind of like this first agent that only does 30 hours uh of labor a month and there's 200 hours of labors to be done for Dr. Loop. Um and and that's like really interesting like to see that like they see us as um someone they break in to like actually run the practice for them which is also on the other hand

16:37 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. the loop 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's get deployed. Um like we can't do that.

16:54 So we needed to build an agent. That's why it took us years that like airs on the side of correctness because like if you take over a job uh reconciling all the insurance payments, interacting with the patient to kind of like bill um it needs to like work. Um so that was like technically like hard to do. Uh, 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.

17:20 It runs the business for Dr. Sloop, which makes it technically super interesting. >> Yeah, there was a a great quote from Dr. Quan 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.

17:42 I 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. >> They do. >> 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.

18:02 Like walk us through kind of that that journey. Yeah, I think um a big part of it was us actually spending the time in offices doing the work ourselves. Um I don't think we could have built uh built a product that works as well as it does if we if we didn't know how to do the job. >> Um I think another part we already kind of talked about it, but I think a huge difference uh between SMBs in general and and enterprises is that in SMBs there's nobody to to use the tools.

18:30 Like you can you can build a tool, but uh there's nobody sitting there that that's going to use it. And so uh >> loop at night. >> Yeah. >> Yeah. [laughter] Needs to go into the tool. >> We uh from the very beginning we we focused on um 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 uh we and we kind of automated away our own problems.

18:55 Um, and then at at some point, you know, we we got to uh a high enough level of automation that that we felt uh we felt comfortable handing it back back over to the the remainder back over to the office. And I think now um you know, we learn obviously when uh when we can't uh do something for for some reason, which is pretty rare, but but say say we we don't know how to do a certain case, we learn from what what the staff tells us.

19:19 Uh and we also think about I think um you know like we we want to get to like a sufficient level of automation across any product to uh 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 uh to 100 with one product and then do the next one. Um I think the uh we really think about the business more like as a whole like how much of the business can we uh uh can we automate and how much of the labor can we can we do with uh

19:48 with software. Um and as as soon as we can take over a job we take it over and then we move over to the next one. Um and then over time we'll just learn uh learn the long tale of cases. >> Yeah. >> For a business it also doesn't matter that much right like if um let's make Dr. Sloop famous in this podcast. Let's >> he's going to learn. >> Yeah. If Dr.

20:06 loop um said he can become a customer. So maybe we should talk about like someone [laughter] else that's still active. >> We could reactivate him. >> Yeah, we he might he might come out. That's how he sounded. >> Yeah. Yeah. [laughter] Dang. That's the series B story. We got Dr. Mac out of sto out of >> We had a dental shortage and now we don't [laughter] have come out at retirement.

20:26 >> We got 100 million more people in the states that get good uh dental care. um the um 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 like the way to see this because they live in a nightmare world where you have like to update 200 ledgers a week because that's how much patience 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.

20:48 If you instead need to do that like a handful of times rather than 200 times, >> it saves 10 20 hours. um if there are a handful of uh 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. Um I often compare it with like how we are served as tech companies. Um 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 and it is because there's great tools for us

21:17 that we can use. um uh are all these tools completely like running our finances autonomously yet or our payroll or HR? No. But they do save a tremendous like amount of time. So I think that's how we approach like this like built as well that we didn't want a human in the loop because we want software that skills and can be implemented quick. Um but it's fine if it does in this case for the first agent 98% of the work and then there's a sliver left.

21:41 And then the interesting thing like we have thousands of staffers that are basically giving us input on how to make that appeal that the agent currently cannot do. Uh we come from the consumer world, right? Like Frederick worked at Superhuman. Um I worked at Robin Hood. So we have a very high bar for shipping stuff. Um so we don't release it before it actually like okay this is this is good and it can be hands off and it like works and then these like staffers help us to kind of like um get it to like a even higher

22:08 percentage. >> 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.

22:36 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 to problem and to famously to 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.

22:54 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 partially because if if Comcast bought you um you you start Teo, Comcast buys you. Well, all of the comp all of the competitors to Comcast, they're like, "Well, we're going to not allow this to work." So, like uh you you have what what I call a control discount versus a control premium.

23:19 So, that's option one. Option two is they copy what you've done many years later. are much crappilier if that's a word because they have all the customers. Um or you know may maybe number three you do a licensing deal with them >> and uh 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

23:49 to build the fun feature on top. Um which I still stand by. I mean I like the vast and and this is why like maybe four years into trial pay I realized what I should build is this thing called stripe 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 had this other thing which in my

24:16 case was offerbased 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. Um, 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 that I say I have a great idea.

24:36 I'm going to do background checks for new employees as part of onboarding and I'm going to integrate with workday. Workday being the b the biggest HR information system. That's a great idea. Um however um 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

25:03 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. Um, so that excuse kind of goes away a little bit.

25:25 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 here's workday. It's a tech company. They already have software and they can add something to I I remember actually this is a cool story.

25:43 There was a company I think it was called X1. Microsoft Outlook had really bad search. >> And this company that was funded by Ideal Lab and all these VCs, you know what they did? It was like search for your Outlook email, which was so good, [clears throat] but it's like, you know who I think is going to do this eventually? I think Micro and again like that company unfortunately went to zero or actually Yahoo bought that long time ago.

26:05 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. Um >> uh 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.

26:30 And that's really exciting because now you don't have to worry about like oh shoot these guys are going to come in 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 uh 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

26:58 saying in many cases like oh you're a software company software is dead, software socks, software is zero. 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. Um, 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, Netswuite, but AI, it's like, you know, the AI, the pure play AI thing is software at its core.

27:24 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. >> Yeah. And we see exactly that is that um there isn't an incumbent >> that kind of like does this job or can do this job like quickly. >> Well, the incumbent was named Betty and she quit two weeks ago.

27:45 That's the incumbent. >> Yeah. Or or a billing agency. >> It's Dr. Sloop's old assistant. that's the incumbent >> or or version of that that like is somewhere overseas or in the states. Uh so that's indeed exactly like what you're competing with and that's what intrigued us so much about these small businesses because like there is no major player there.

28:07 >> Um 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. Um which is uh very defensible. how uh maybe we get to talk about it later. The the schle you have to do to like uh 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 because all these different systems have like a slightly

28:33 different definition of that. Uh which makes it also then harder to build because there is no incumbent. Um you need to kind of like stitch like a lot of things together but makes it extra defensible. Um 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 the AI version need access to uh 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

29:01 that you need to build agents that uh yeah you can't really build without actually doing the work. Um, so there's like years of work that you need to do to kind of like get that like going makes it very defensible. Like the the go to market side as well, right? Is this like you need to knock on millions of doors and say uh and the interesting thing we just talked about that is not necessarily that they're skeptical about AI because Dr.

29:22 Sloop is like, "Oh, I wish this was there." Is how do you get hold of Dr. Sloop? Right? Because Dr. Sloop um is not done at 7 p.m. There's an emergency patient that calls then he goes back into the office to treat that patient. uh 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.

29:43 So like for us but for any company selling to SMBs the interesting puzzle here that's why this is also uh 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. Um but that's that's the crux there that is like how do you get bit of busy nontechnical owners >> that adoption.

30:02 Well, I imagine the other part of the question for you, I'd love to hear your thoughts on this. Um, or I'm sure our audience would love to hear our thought 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? >> Yeah. >> Um, and how much of that can you automate?

30:21 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. Yeah, >> it would be amazing if it's just like download the lassie app like these these systems and processes are very manual >> and um 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 dist that like glass mile distribution

30:56 problem for Lassie? >> Yeah. Yeah. I think uh 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 um um our consumer backgrounds come in handy here because at Robin Hood or Superhum, you get the time window 48 hours.

31:21 If the thing doesn't work and you provide core product value, um you're out, right? And these doctors are very much the same. because not only are they 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. 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.

31:48 Um, and and that was like a lot of work and it's it's already to a point that it's self-s 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

32:18 information like are these the doctors that actually like work in your practice 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-s serve. Um, so I think that was a big part or is a big part of bringing this technology to people in Iowa.

32:37 Um, is that can you almost build a consumer-like uh on boarding like flow where a lot of the complexity is abstracted away and kind of like happens under the hood like the maybe not many people I think I'm very impressed by Robin. I'm a little biased because I work there. But like in order to set up an account for a user without a human in the loop, which is what Robin Hood pioneered because back in the days there was Charles CH swap and then hello I want to open an account.

33:02 Before that you had to go into the office. Uh but one of the many things that Robin Hood pioneered is that you could you can almost self-s 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 hood there to make that happen. I think we ran into a lot of these like similar like like situations.

33:20 is like how do you connect all these insurance portals um 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 these system of records so I think a big part of this was figuring that piece out yeah >> yeah 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

33:46 businesses that could use you guys >> how are you prior Prioritizing what you build, who you sell to. Is there, you think, a world where Lassie for dentists makes lassie for physical therapists better? >> The master plan. >> Yes. >> Are we at that part of the episode? >> Yes. >> Um, yeah. I think three steps like the end goal here is that uh every small business should run itself, right?

34:12 And the busy work is done by uh agents. uh we want to build agent for the business that then will interface with the personal agent of a consu 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.

34:38 Sloop 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 um so we're 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 temp um and and needs consumer-l like product right because what we discussed a big part of not this you get the AI to work 95% accurateish it needs to really work and the onboarding needs to be as simple as onboarding on Coinbase.

35:07 um or stripe. Um so like it will likely be another like doctor office type like um and then [sighs] 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 and write into. Um they all have customers in doctor offices they happen to be patients but it's interacting and transacting around payments.

35:31 You need to book appointments. So I think the end goal is if we served all the the doctor offices that we help all the small businesses across the world because we're just the best in you know building AI agents that uh salt of the earth people or people in Iowa and Paduka 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.

35:53 Um so that's the that's the end goal but I think again similar to superhuman and Robin Hood we work that 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. >> 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.

36:17 Robin Hood and Coinbase and Superhum 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 have you kind of had to unlearn or what do you think is new to being founders right now? >> There's a bunch of stuff that uh we're applying now that I I learned at Superhum.

36:43 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.

37:07 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 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.

37:33 We we have like set uh set points and checkpoints that we want to reach in certain time frames 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.

37:59 Um 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 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.

38:21 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?

38:41 I thought this would be covered. Uh so if you're just looking at like where does the time go, 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

39:06 to use uh which doesn't really save them a lot of time. And then neither of you I think were dental experts before you started the company. Although >> that's that's safe to say. >> You go twice a year. That's pretty good. I think for the average >> you're supposed to, right? >> Yeah, of course. >> I'm just doing my um [laughter] >> not a saving duty to say.

39:25 >> 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 of team members that you want to hire? >> Yeah. uh which is now mainly on our mind, right? Because like we found there a really great product market fit in a large market uh where you can go after the labor um that they can find.

39:47 [snorts] >> Um we we we currently have two takes on that like one not much has changed contrarian maybe 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 remained the same getting hold of doctor um slope um is just like the same exercises before you could do it in a more AI like native way.

40:13 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 pilness of a person. Uh I think that 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? Um building out a finance department uh do you think that's going to be completely different uh than before?

40:40 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.

41:04 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 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

41:33 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 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's that is the big interesting experiment that we are going to do over the coming years >> if it's so much easier if everybody

42:00 can hire Betty right? Just materialize a Betty. >> Yeah. >> Um 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 moes in the AI era in general >> we often talk about it with respect to software companies.

42:27 >> Y >> um so it's so easy to go replicate XYZ software. I did it on Replet 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. >> Um much much harder to say I'm going to go replicate Dr. Sloop's practice. >> Yeah. >> 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.

42:50 >> Yeah. >> And it's like uh if you know who Yogi Barra is, Yeah. you know, famous Yankees baseball player that said all these things that make no sense. >> And um but like >> quoted often though quoted often, right? And my one of my favorite ones, it's so crowded nobody goes here anymore. >> Yeah. >> Yeah. It doesn't make sense. Um 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?

43:16 Because theoretically that could erode the margin of a small business such that it's so crowded nobody goes here anymore. M um 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. >> Yeah, I think our our current take on that is that this assumes there's 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.

43:42 um 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 um 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.

44:00 Um because it's just really hard to find the good primary care doctor that is in your community, knows you, your family, um and takes takes care of you. So I think this we see as an opportunity to kind of like create like now there are 160,000 dentists or hundreds of thousands of dentists. What if uh America has half a million dentists um or can see kind of like uh twice as much patients maybe the same amount of dentists?

44:25 Plumbing like is the same and I think there's a lot of these like small businesses if they they wish they could you know bake more pies um 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. Um because then all of a sudden yeah you're going to see a better world.

44:45 I think that's that's currently our view which is super exciting um 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. Uh that was the main uh piece of like um a lot of people talked about that it's like wow an optimistic example um of like how this new technology like can be used.

45:04 uh because there's a lot of like what's going to happen to the world. Um [snorts] 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. Um uh depending on like who you are, of course, but uh I think that's so interesting about about this. Yeah.

45:24 >> Yeah. So you started the business in 2020 and that was like arguably pre-AII or pre what we think of as being this generative AI revolution uh which I I would call like the the BC80 divide of like November of 2023 when chat GBT launched right so um if you or I guess 2022 sorry uh November of 2022 when when chat GBT 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

45:58 years ago call it like what what are the hardest problems to solve like what is it when we talk about like software that does the job of labor what cannot be done right now what do you feel like we still need more technical advance to get there and sometimes it's like a 9010 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 [clears throat] of curious like from a technical lens What are the things where you would say

46:26 okay and it's not Alassie specific question it's just kind of more the technology and what it enables at large where does work still need to be done >> where it's just not quite good enough um 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 like you know what do you need and where are we on that curve if you had to estimate >> I think one thing that's interesting is that the models are trained on so much data and they're they're so large

46:53 and yet they actually don't really know how to do any of this work. Like they don't have the uh workflows encoded in any way. Um 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.

47:17 uh which uh to some extent humans would do the same but there's also a big amount of um just like human knowledge that is encoded in say these office managers and they just like know how to do this work uh that's weirdly not that accessible on the internet um >> I think we have a big advantage there because we have um 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 um but that's something we notice a lot I think actually when we started

47:48 using some of the, you know, later reasoning models, uh, 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 on all of this data. Um, but it it it turns out that they they don't know all the intricacies of most of these workflows. I think um, yeah, this is like less specific to Lassie, but I'm I'm personally kind of excited about uh smaller models that uh, you know, learn faster and can learn on less data.

48:12 I think uh that will be uh that will be really cool to see because I you know I think over time um we'll have intelligence kind of I think disseminated everywhere uh and uh you know like to the point where like you know like the the Pixar lamp that has its own personality like why why not I I I feel like I I want my intelligence to be to be everywhere.

48:32 Um and it feels like we're super far from that. Um 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. Uh but there there is something about um you know the models not being able to learn very uh very very fast. Uh and uh you know as it relates to us like we we put a lot of product work into how do you actually get input from uh you know from people uh about you know their preferences and like uh how they've done done the work uh in a way that we can

49:06 then kind of like useful input in a way that we can then use to actually make uh make our our agents smarter. Um and I think from a you know like product UX perspective I think there's a bunch of interesting stuff to to solve there as And maybe one kind of final question between technical and non-technical is uh I I've been thinking about this a lot how how the world changes when the marginal cost of arguing goes to zero, >> right?

49:28 So I was thinking about this because Sigma, 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.

49:48 And it's the same thing for like a lot of insurance. It's like, you know, they're it's like deny, deny, deny. They want to deny. Um, and like on the other side, it's like, you know, pretend that when my house burned down, I had a Picasso in there. Like both parties are trying to cheat each other. This is not new to the insurance agency or the insurance like industry and both sides, right?

50:06 >> 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 and then you can argue with me. Yeah, >> but like how does that change the business dynamic of things like insurance payments and collections? I mean, I'm sure you thought about this a lot >> because in many cases the counterparty that the dentist is dealing with is the insurance carrier.

50:29 >> Yeah. Right. >> A big part of the accounts receivables or or revenue comes from insurance companies, >> right? >> Um yeah, I think it's quite interesting because um it's pretty like well regulated. So um 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 Sikna that specifies if you give me you know this X-ray and you give me this narrative then we will cover that.

50:55 Um but for a human it's quite hard to like a to follow those like rules because there are like take dentistry there like 50 60 maybe 100 common billing codes. Um and it's quite a lot to like remember for Mildrat um or Betty the staffer. Um and in this case kind of like our agent like has like all the like uh documentation um it has access to the X-ray that is the relevant like one it it knows how to build like a treatment plan um and we'll then submit that with the insurance company and the insurance company like just

51:30 needs to follow the rules. Right. Right. >> Um and then they will like bait that out. Uh the other thing because indeed we we are very deep in this industry. Um that is interesting dynamic um it took me a while to understand this but um Sigma also has an incentive um uh to have good doctors in network that are happy with them um because every year they need to go um uh to Andre and say uh do you want to renew your dental plan with us and then reason is going to look at like are there good dentists like in the area that

52:00 are covered that Alex and Olivia can go to um and if the answer is no then because there's competition in this market they will go to like you know another insurance company um so like there is actually an incentive to keep Dr. loop and quan like in network um because like otherwise they will not renew the plan like with their employers. So like I think that it is a lot more uh 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

52:26 documentation to Frederick's point uh and we can talk about this for hours maybe another time but like we are also literally digitizing the file cabinet like um uh that's it's really interesting that a it's really hard to find these like files they're there uh and once we have them we know how kind of like to do this uh but all these payments you talked about a lot of doctors across America still get paid on paper, too.

52:48 So, like, um, they get $100,000 in checks like deposited on their desk. Um, and I did this, right? So, for Dr. Quan, I literally knock knock knock, open the door, 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 $100,000, like cash uh, 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.

53:11 And now the interesting thing, so even if we had the models, um, 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 do kind of like part of the job to keep the doctor office like running.

53:29 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 uh switch the direct deposits offer that as an option to a doctor. So if a doctor says I want to flip the switch um you need to do that the same as these itemized invoices you need to create a digital file format like for that.

53:49 Um and we are also writing that like till win. So you will see that like a lot of these small businesses I think the stats are 70% are still paid uh on paper and it's going through this massive like digitization revolution right now because federal inflection point that's regulatory. Um, 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 5 years ago like yeah the payments are still on paper and we are digitizing that in the

54:18 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. loop needs to also then spend 50 hours figuring out with Sigma and Delta and all the other insurance companies, how do I flip the switch?

54:36 It then comes into the bank account. 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. Um, [snorts] 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 uh which is also a hard product problem to solve but we

55:02 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 is super hard. Um and then in addition to that um how do you convert kind of like data that you need that's currently living in a file cabinet uh to like digital file format such that you can actually automate the work.

55:27 Yeah. To your uh earlier point actually about digital file cabinets not being that much more efficient. Part of the reason that uh 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, right?

55:44 like you actually need, you know, some tools to to use that uh use the PDF and it's it's preferable to just kind of, you know, work off of a sheet. Uh and so like there wasn't really much of an incentive to to digitize. Um now that's obviously different. >> So obviously there are a lot of dental practices that want or even desperately need products like Lassie, but they are distributed and they're all over the country and there are a lot of them to reach.

56:09 How do you think about reaching them? like how do you bring agents to kind of these mainstream American businesses? >> 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 stake dinners and then you sign a contract and then you have like 10 million in AR booked, right?

56:32 Um we literally need to go find like thousands, tens of thousands, hundreds of thousands of small businesses. Uh so it's a super interesting problem to solve because we've built this agent that's really good. Um and what we're doing right now is is literally mapping out like where are all these dentists in this case. Then after the the next small business type in the country, who's the owner?

56:52 Um what systems are they on? Like are there any intent signals that we can find, right? Like they're looking for a job because they they say it on Indeed. Uh and then get in touch with these like people uh with a message that resonates with them. Um and that cuts through the noise. I think that's especially with our um um customer type is very different, right?

57:13 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 going to buy my HR system. We can't really do that because Dr. Sloop is not in that database. He's often not on LinkedIn. Um so like this is a completely different playbook that we're developing here.

57:33 Um, and I think that's that's another very compelling kind of like thing that um we're figuring out basically what is the go to market playbook look like to adopt uh AI and like at many many businesses. Um so yeah that's it's quite an exciting opportunity and untapped. >> Thank you both so much for for coming to chat with us today. This was awesome.

57:53 We are very very excited for the future of Lassie. Uh, anyone who's listening who might be interested in in working with the Lassie team to build something generational here, um, check it out at lassie.ai. And you guys are very actively hiring from what I understand. >> Oh, yeah. >> Amazing. Great. Well, thank you guys again. >> Thank you so much. Thanks for hosting us. [music]