AI for America's Small Businesses | Lassie

Summary

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The a16z Show – "AI for America's Small Businesses | Lassie"

Date: July 30, 2026
Host: Andreessen Horowitz
Guests:

• Lassie Co-Founders: Stein Pella & Frederick Rankin
• a16z Partners: Alex Rampell, Olivia Moore
• Additional Host/Interviewer: (D/E; not specifically named but referenced in the transcript)

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Episode Overview

In this episode, the a16z Show dives deep into how generative AI is transforming America’s small businesses—focusing especially on healthcare practices like dentistry. The conversation features Lassie, a startup building AI agents to automate time-consuming administrative tasks (billing, payments, claims) that weigh down independent practices. The hosts and Lassie’s founders discuss why AI’s biggest opportunity may lie outside of Silicon Valley hype—in places like “Main Street” Iowa, where overworked small business owners desperately need help, but few solutions have existed before. The episode offers technical insights, business strategy, and reflections on what “autonomous software” really means for the future of work.

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Key Discussion Points and Insights

1. The Problem: Small Businesses Are Drowning in Admin Work

• Many small healthcare practices still process payments and insurance claims by hand ([02:13]-[03:08]).
• Example: Dr. Kwan, a top-rated dentist, was spending 200 hours per month on paperwork—echoed nationwide.
  • “A small business owner that is the number one rated doctor on Yelp, spending 200 hours a month on paperwork and busy work.” — Stein ([02:21])
• Lassie’s founders firsthand experienced the pain by literally working in doctors' offices to understand the administrative burden.

2. The AI Approach: Beyond Tooling to True "Labor Automation"

• Traditional software just digitized paperwork but left the labor to humans.
• New thesis: AI can now do the job, not just store the data ([07:32]-[12:07]).
  • “Software just kind of took things that were stored in paper format and then they made them available … But people still had to do the work.” — Alex ([00:00])
• Lassie built a “context and tools” layer in their product so that as AI reasoning improved, they could swap in newer models, making the agent increasingly capable ([06:07]-[07:15]).

3. Market Opportunity: SMBs and Jobs Only Humans Once Did

• Most small businesses can’t find or afford administrative labor (“Betty,” the archetypal loyal office manager, has retired/become impossible to replace):
  • “The incumbent was named Betty, and she quit two weeks ago.” — Alex ([27:24])
• Market size: 160,000 dental practices in the US, $200,000/year per practice in admin overhead ([14:32]). The need is enormous.
• AI’s promise isn’t just replacement or cost savings—it’s unlocking new supply (e.g., preventing retirements, letting doctors focus on patients):
  • “If this had been around, he wouldn’t have retired.” — Alex, on Dr. Sloop ([12:17])
• Most growth is word of mouth because the pain is acute and solutions are rapidly adopted when proven ([17:36]).

4. Technical and Product Building Journey

• Lassie founders started by manually handling admin work to learn the job ([17:48]).
  • “Initially, we were actually the humans in the loop. We kind of automated away our own problems.” — Frederick ([18:22])
• Rigorous focus on automating to 95-98%+, not waiting for 100% perfection: most of the “nightmare” is eliminated if software takes care of 95% ([19:39]).
• Autonomy and correctness are critical—when software does the job, it must really work.

5. Strategic Go-To-Market & Distribution

• For Lassie, integration with existing practice management systems (not try to replace everything at once).
• AI changes traditional startup strategy: in this category, incumbents often don’t exist (or are just labor agencies/individuals).
  • “With many changes. One change is the incumbent can get the innovation much more quickly. 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 actual human labor.” — Alex ([22:16])
• Distribution challenges: reaching busy, non-technical small business owners who are often not on traditional SaaS buying platforms ([29:44], [55:57]).
• Building self-serve, consumer-like onboarding is crucial, inspired by experiences at Robinhood and Superhuman ([30:38]).

6. Vision for the Future

• Lassie's long-term goal: Every small business runs itself, with specialists spending time on their craft rather than admin ([33:47]).
• Sequence: perfect for dentists, then expand to other healthcare practices, eventually all SMBs.
  • “The end goal here is that every small business should run itself right. And the busy work is done by agents.” — Stein ([33:47])
• AI adoption means small businesses can grow, serve more people—eliminating the labor bottleneck is a societal good ([43:12]).

7. What’s Technically Hard (and What’s Next)

• Models are large but don't “know” real-world workflows; SOPs and process knowledge doesn’t live on the public internet ([46:29]).
• Need for rich feedback loops, data integration, and even regulatory tailwinds (digitizing check payments, itemized records) ([50:09]-[55:08]).
• Even with great models, much of the US still operates on paper—AI needs simultaneously to handle digitization and workflow ([55:08]).

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Notable Quotes & Memorable Moments (with timestamps)

• AI in Silicon Valley vs. Iowa:
  • “AI is overhyped in Silicon Valley, but underhyped in Iowa.” — Alex ([00:00])
• The Unseen Cost of Admin:
  • “A small business owner that is the number one rated doctor on Yelp, spending 200 hours a month on paperwork and busy work.” — Stein ([02:21])
• On What Software Has (and Hasn’t) Changed:
  • “Did PeopleSoft and then Workday make HR departments more efficient? I don't think so.” — Alex ([07:32])
• Labor Shortage as AI Market Opportunity:
  • “It’s not like, oh, AI is going to take the jobs. In many cases, you can't find someone.” — Alex ([00:54])
• The Human Angle:
  • “Lassie isn't replacing humans, but freeing them from wearing so many hats.” — Dr. Kwan quote, as shared by host ([17:04])
• Standards for Automation:
  • “We really think about the business more as a whole. How much of the business can we automate and how much of the labor can we do with software?" — Frederick ([18:22])
• On Defensibility/Distribution:
  • "The incumbent was named Betty, and she quit two weeks ago.” — Alex ([27:24])
  • "It’s Dr. Sloop’s old assistant. That’s the incumbent.” — Alex ([27:30])
• Vision for Small Business Utopia:
  • “The end goal here is that every small business should run itself right. And the busy work is done by agents.” — Stein ([33:47])
• Workflows Are Not Just in the Data:
  • “The models are trained on so much data…yet they actually don't really know how to do any of this work.”— Frederick ([46:29])
• Onboarding Excellence:
  • “At Robinhood or Superhuman, you get the time window, 48 hours. If the thing doesn’t work and you provide core product value, you’re out.” — Stein ([30:38])

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Important Segments & Timestamps

• Personal story of Lassie’s founding ([02:00]-[04:09])
• Software's evolution from data storage to labor automation ([07:32]-[12:22])
• Technical hurdles and journey from "human-in-the-loop" to full automation ([17:48]-[21:49])
• Distribution & go-to-market in AI era ([22:16]-[29:44])
• Challenges of onboarding non-tech small business owners ([29:44]-[33:16])
• Lassie’s master plan & future of SMB automation ([33:41]-[35:49])
• Hiring and “AI built-ness” of team members ([39:17]-[41:41])
• What current AI still can’t do (workflows, local knowledge) ([46:29]-[48:58])
• Paper vs. digital in healthcare admin; regulatory tailwinds ([50:09]-[55:39])
• Strategy for national reach: how Lassie finds small business customers ([55:57]-[57:31])

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Takeaways

• AI for small businesses is more about meeting desperate, unmet needs than automating already-efficient organizations.
• True automation must be nearly hands-off, which demands deep workflow understanding, technical rigor, great onboarding, and (often overlooked) regulatory and practical integrations.
• Unlike prior software waves, AI’s largest upside is where there are no real software incumbents.
• The ultimate goal is to let skilled professionals get back to their craft—and perhaps even grow the number of businesses and care providers, not reduce them.

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For those interested in the promise of AI “future of work,” particularly for the under-served backbone of the economy—America’s small businesses—this episode offers a guiding vision, technical deep dive, and honest look at the immense practical challenges (and opportunities) ahead.

Transcript

[A]

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.

[B]

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

[C]

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.

[A]

The battle between every startup and a covenant. It comes down whether the startup gets the distribution before the incumbent hits the innovation.

[B]

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 actually run the practice for them.

[D]

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.

[A]

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

[D]

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

[B]

The end goal here is that AI

[E]

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 Rampel and Olivia Moore speak with LASSI co founders 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.

[D]

So welcome and thank you for joining us.

[B]

Thank you for inviting us.

[A]

Excited for you to be here.

[D]

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

[B]

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 a 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. Quan, 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. 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 the 70s, my mom worked in hospital, and that's what she did. She brought bags of cash to the bank and then processed payments by hand. But it was the solved problem here. So that's why this piqued my interest.

[A]

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 the drills were in your mouth? Or how did that go down?

[B]

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

[E]

Exactly.

[B]

Yeah. Yeah, exactly. Yeah. No. He took me aside after that. Okay. Because he knew that I was, like, looking for this heart problem. I was roaming around. And then after that appointment, he showed me again 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 Scrimpton, Pennsylvania, and 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 there was going to be a hard problem, but we were kind of looking for that.

[D]

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. 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.

[B]

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, okay, 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. So that was the first time 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 nine to five, you can do the job. Dr. Shah was in Scranton, Pennsylvania. He set us down behind the desk and said, you can have access to anything you need to have access to or a teacher like my son. So that was the first sign that this was very bro and not a solved problem. And then I think that triggered our intuition for, okay, we might be onto something, because this is a real pain, that people are desperately looking for a solution.

[D]

And from a technical perspective. So, Frederic, 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?

[C]

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. 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.

[D]

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.

[A]

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 pick the time equals zero moment for that with this company, the 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 called with two A's Sabre. But then this kind of took wind everywhere else. So like there are 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 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 a CISO to make sure nobody hacks into the IT files or the HR filing cabinet. So nothing really got more efficient. 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. 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 going to go run my practice or do background checks. I know I'm going to have statistical 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. And if you think about fintech, fintech massively expanded the size of many non financial markets because now 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, 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 Cactail LTV issue because could you get a big restaurant that grosses $5 million a year to spend a hundred thousand dollars on an Ms. DOS software product for keeping reservations and paying wa, 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 a hundred thousand dollars for that. But if you bundle in payment processing, you're effectively charging a hundred thousand dollars for that.

[B]

Right?

[A]

Because maybe you get a 2% vig. 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. Actually, the best and funnest story of the last seeing introduction or like the. Our announcement that we made together with you, or your. Your announcement, your. Your amazing video.

[B]

It was a great collaboration.

[A]

So my. My first dentist, hopefully he's listening to this podcast. 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.

[B]

Was he a Dutch guy? Sounds very Dutch, though.

[A]

Yeah, you know, Ashkenazi Jew. Yeah, yeah, you know, somewhere in Poland, Ukraine, whatever. My family's from too. So, you know. But he's probably 75, 80 years old right now. But part of why he retired was he lost his 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 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 he's like, my dad called me about this because 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. This is why he's now doing nothing with his life of 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. 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 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 a hundred dollars. You just have a market failure. And I kind of call this everything to the right of the supply, demand, equilibrium point, like an econ 101 graph. So it's like, well, everybod, 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. Yeah, like, why not hire somebody who speaks Dutch? Well, because there's only a one in a hundred chance that a stein that only speaks Dutch shows up at their office. You're going to have to pay that person €40,000. Like, it just doesn't make sense. But if it were free or if it cost a dollar, then every dental receptionist was. Would have a Dutch counterpart. Right? You know, stuff like that. So it's just, it's. Anyway, so that, yeah, 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, big on financial transactions. That's even bigger. That's why Visa has a very big market cap.

[B]

White toast can exist, or why toast can exist.

[A]

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

[B]

And we also see that. So, like, 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 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, like, not like their job anymore. 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. They don't really want to 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. So we come by and I think a lot of people are also surprised. 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 if we come By. And 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 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 labor budget. So we are already charging five figures for kind of like this first agent that only does 30 hours hours of labor a month. And there's 200 hours of labors to be done for Dr. Sloop. And that's really interesting to see that they see us as someone. They break in to 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. But to get deployed, like, we can do that. So we needed to build an agent. That's why it took us years. That, like, errs on the side of correctness. 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 are technically super interesting.

[D]

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 their games, which is amazing. There's a gap between wanting that and being willing to adopt AI and actually 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 dentist recommending it to other dentists?

[C]

They do.

[D]

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.

[C]

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 a product that works as well as it does if we. If we didn't know how to do the job. 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 use the tools. Like, you can build a tool, but there's nobody sitting there that's going to use it.

[B]

For sloop at night needs to go into the tool.

[C]

From the very beginning, we focused on initially, we were actually the humans in the loop. So we kind of took over all of the work and we were like, we'll just do this work for you. And we kind of automated away our own problems. And then at some point, we got to a high enough level of automation that we felt comfortable handing it back over to the remainder, back over to the office. And I think now we learn, obviously, when we can't do something for some reason, which is pretty rare, but say we don't know how to do a certain case, we learn from what the staff tells us. And we also think about. But I think we want to get to a sufficient level of automation across any product before we sell it. So for us, I think that's like 95 plus, say, but not necessarily 100. I don't think we're going to wait until we get to 100 with one product and then do the next one. I think we really think about the business more as a whole. How much of the business can we automate and how much of the labor can we do with software? And as soon as we can take over a job job, we take it over. And then we move over to the next one. And then over time, we'll just learn the long tail of cases.

[A]

Yeah.

[B]

For a business, it also doesn't matter that much. Right. Like if. Let's make Dr. Sloop famous in this podcast he's gonna love.

[A]

Yeah.

[B]

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

[D]

We could reactivate him.

[B]

Yeah, we will reactivate.

[A]

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

[C]

Yeah.

[E]

Yeah.

[B]

Dang. That's the series B story. We got Dr. Back out of stoop. Out of.

[A]

He had a dental shortest and now we don't.

[C]

Yeah, Exact.

[A]

All these dentists have come out of retirement.

[B]

You got 100 million more people in the states that get good dental care. It's fine for 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 to update 200 ledgers a week because that's how much patients you see. And then you have to go to an insurance portal, update 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 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. And it is because there's great tools for us that we can use. Are all these tools completely 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 approach this build 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 then there's a sliver left. And then the interesting thing, 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 Frederic 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 good and it can be hands off and it works. And then these staffers help us to kind of get it to like an even higher percentage.

[D]

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.

[A]

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 can pause online television, which is an amazing innovation, but a terrible company is you really have very few outcomes that are good. You either end up selling to one of the big guys like a Comcast or a Time Warner cable, but they're not going to pay you that much, partially because if Comcast bought you, you start TiVo, Comcast buys you. Well, all of the competitors to Comcast, they're like, well, we're going to not allow this to work. So like 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, er, that's a word, because they have all the customers. Or 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 my recognition was the thing that a lot of startups should do is they should do the boring thing. They should build the raw pipes. The raw just own the customer and then you get to build the fun feature. On top of 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. And this was not like revisionist history because Stripe had five people. It's like, wow, 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 could only do that if you control the price 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 that I say I have a great idea, 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 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 and. 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. 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 Idealab 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 Micro. And again, like that company unfortunately went to zero. Or actually Yahoo bought that a long time ago. 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. 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 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 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 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. 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 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.

[B]

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.

[A]

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

[B]

That's the incumbent or a billing agency.

[A]

It's Dr. Sloop's old assistant. That's the incumbent.

[B]

Or a stocky version of that. That like 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 these 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 it later. The schlep you have to do to do the actual labor and talk to all these 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 a patient payment. Because all these different systems have 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 it makes it extra defensible because 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 need access to. Then you need to figure out like, okay, what is the data model that can be used across all these systems? And then on top of that, you need to build agents that you can't really build without actually doing the work. 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 site as well. Right. Is just 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. Sloop is like, oh, I wish this was. There is. How do you get hold of Dr. Sloop? Right? Because Dr. Sloop is not done at 7pm there's an emergency patient that calls. Then he goes back into the office office to teach that patient dinner with 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 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. But that's the crux there. It is like, how do you get bit of. Of busy, non technical owners that adopt AI?

[A]

Well, I imagine the other part of the question for you, I'd love to hear your thoughts on this or 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 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 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 there's a lot of these systems and processes are very manual. And I kind of think AI is overhyped in Silicon Valley but underhyped in Iowa and there are a lot of people in Iowa. How do you solve that distribution, that like last mile distribution problem for Lassie?

[B]

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 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 business. And I think that's also why our consumer backgrounds come in handy here. Because at Robinhood or 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. Not only are they hard to reach and hard to work with, but if it doesn't work in a couple of months from now, you're out of the door. 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. And that was Like a lot of work. And it's already to a point that it's self serve almost. So there are a few more things like left. But it's literally that the doctor in Iowa, let's not use loop again, says, yes, I want this. They then go to almost like a stripe like checkout or a replaying 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. It confirms business information like are these detectors that actually work in your practice? And then under the hood kind of like configures things. There's like one or two pieces left, but I think we're like months out until you basically have an agent that you can almost set up like self serve. So I think that was a big part or is a big part of bringing this technology to people in Iowa is that can you almost build a consumer like onboarding, like Flow, where a lot of the complexity is abstracted away and kind of like happens under the hood like the. Because maybe not many people. I think I'm very impressed by Robinhood. 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 Robinhood pioneered. Because back in the days there was Charles Swap and then, hello, I want to open an account. Before that you had to go into the office. But one of the many things that Robinhood pioneered is that you can almost self serve your way onto an account. And KYC is getting done, your bank is getting linked and a lot of product work went under the hood there to make that happen. I think we ran into a lot of these similar situations. How do you connect all these insurance portals and kick off like the process such that the digital claims are coming in 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.

[D]

I guess to that point 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 Lassi. And then there's 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 lassi for dentists makes Lassi for physical therapists better?

[B]

The master plan?

[D]

Yes.

[B]

Part of the episode.

[E]

Yes.

[B]

Yeah. I think three steps, like the end goal here is that every small Business should run itself right. And the busy work is done by agents. We want to build agent for the business that then will interface with the personal agent of a consumer. Highly likely that then we'll interface with an agent, agent at the insurance company or other parties that the business needs to interface and interact like with. But step one is to Alexis point like there are 160,000 dental practices in the US alone, $200,000 in labor that Dr. Sloop and others can't find. So like serving that market first, you're looking at a $1 billion in recurring revenue as a market. So that's like step one. And then likely we will pick another doctor office type that is not well served and has a big TAM and needs consumer like product. Right. Because what we discussed a big part of not this, you get the AI to work 95% accurate. Ish. That needs to really work. And the onboarding needs to be as simple as onboarding on Coinbase or Stripe. So it will likely be another Docker office type. And then the last part there is, I think we've then trained AI agents to run a small business. And all small businesses at an abstract level have a system of record they need to read and write into. They all have customers in Dr. 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 because we're just the best in building AI agents that salt of the earth people or people in Iowa and Paducah, Kentucky and hopefully in Amsterdam down the line where I'm from. And Journey Germany, Hamburg, where Frederick is from, can start using as well. So that's the end goal. But I think again similar to Superhuman and Robinhood, we work that laser focus on getting one thing really, really right and then scale it from there. But the end goal is to help them all.

[D]

Amazing. I love that as a master plan. It's a good one, a big one. 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 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?

[C]

There's a bunch of stuff that we're applying now that I learned At Superhuman. I think for one, focusing on the right ICP and being really strict about who you onboard to basically guarantee that they're going to have a great experience. I think in our case it's 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 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? It's particularly painful, maybe more painful than in a kind of old world product. So there's that. We talked a bunch about the onboarding already, but we've really obsessed about getting to the core product value really quickly. And almost like our onboarding is a little bit like a, you know, it's kind of like a story or a playbook or like a movie. We have like set points and checkpoints that we want to reach in certain time frames and we make sure it happens every time and we measure that. Of course. I think one thing that's really different in particular, particular about product building from back then to now is that if you think about the product that you built in the past, I think it was much more about kind of functionality or the ability for the user to do something. And now we think really only about what kind of labor can we automate or work can we do and where can we save time. So if you're thinking about say patient billing as an example, I think think previously you would have built the ability to send a statement and the ability to receive a patient payment. But if you're looking at where does the actual work of patient billing go today, it's really like figuring out is the money or is the statement that we're going to send the patient the correct amount? Or once the statement is sent, the patient calls and asks about, why do I owe this amount? Do I really have to pay this? I thought this would be covered. So if you're just looking at where does the time go, it actually goes in oftentimes the customer communication or some other kind of more fuzzy part of the work. And when we're thinking about shipping, say a product like that, we're really thinking about, okay, 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, which doesn't really save them a lot of time.

[D]

And then neither of you, I think, were Dental experts before you started the company.

[B]

Although that's safe to say you go twice a year.

[D]

That's pretty good, I think for the average patient.

[A]

Supposed to, right? Yeah, of course.

[B]

I'm just doing my. Another civic duty.

[D]

When you're 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 have higher.

[B]

Yeah. Which is not mainly on our mind. Right. Because like we found this really great product market to fit in a large market where you can go after the labor that they can find. We currently have two takes on that. Like one, not much has changed. Contrarian. Maybe you take here. You still need people with steep slope that like are very ambitious and driven and have skills that are just like top 5 percentile either in engineering or in selling. Right. And I think that remained the same. Getting hold of Dr. Slope is just like the same exercise as before. You could do it in a more AI like native way. But I think the skills are the same. So I think that has not changed. Maybe the only thing is that the AI builtness of a person, I think we see a pretty clear division between. Across the board. Like, do you believe that the way you code will change completely as a result of building a company in this era, building out a finance department, do you think that's going to be completely different than before? So in that way, we interview specifically for that because we want to build a 2026 version of a big organization, right. Where we ship twice as much than others, we move four times as fast. Because we should not only have AI adopted in these businesses, but the big puzzle for us is how do you build a team that kind of also incorporates AI in all these lab functions? So I think that, yeah, on one end nothing has changed because yeah, the bar is still the bar. I used to be track and field runner, almost became a professional. Like track and field runner took a different path in life, but my friends went on to the Olympics and yeah, Olympic running is still the same. Right. You need to train twice a day. You push it to the edge. And that's not given to everyone mentally and physically. So 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 such that just 200 hours gone. Like, how quick can we then bring it to another vertical and then help all small businesses? I think that is the big, interesting experiment that we are going to do over the coming years.

[A]

If it's so much easier if everybody can hire Betty. Right. Just materialize a Betty, 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. Yep. So it's so easy to go replicate X, Y, Z 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. Much, much harder to say, I'm going to go replicate Dr. Sloop's practice.

[B]

Yeah.

[A]

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. And it's like if you know who Yogi Berra is, you know, famous Yankees baseball player that said all these things that make no sense.

[C]

Sense.

[A]

And.

[B]

But, like, quoted often, though.

[A]

Quoted often.

[C]

Right.

[A]

And my. One of my favorite ones, it's so crowded, nobody goes here anymore.

[C]

Yeah.

[A]

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, people to deliver a product, it's now so much easier, but therefore, it's actually harder.

[B]

Yeah. I think 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. There's just like twice as much demand that currently can be supplied. And I think this is a great opportunity for 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 care of you. So I think this we see as an opportunity to kind of like create like now there are 160,000 dentists, hundreds of thousands of dentists. What if America has half a million dentists or can see kind of like twice as much patients? Maybe the same amount of dentists, Plumbing is the same. I think there's a lot of these small businesses, if they wish they could 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 you gonna see a better world. I think 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. It's like, wow, an optimistic example of how this new technology can be used because there's a lot of 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. Yeah, yeah.

[A]

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, I would call like the, the BC 80 divide of like November of 2023 when ChatGPT launched. Right. So if you. Or I guess 2022, sorry, November of 2022 when, when ChatGPT launched public, what, 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. What do you feel like? We still need more technical advance to get there. And sometimes it's like a, 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 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 it 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. You know, what do you need and where are we on that curve? If you had to estimate?

[C]

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 have to collect all of these, like, basically SOPs and documents about 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 human knowledge that is encoded in, say, these office managers, and they just know how to do this work that's weirdly not that accessible on the Internet. I think we have a big advantage there because we have all of this historical data out of their ERPs that we can look at and kind of infer some of these workflows from. But that's something we notice a lot. I think, actually, when we first started using some of the later reasoning models, we kind of assumed like, oh, they probably just know how to do this work, because why would they not? They're trained on all of this data. But it turns out that they don't know all the intricacies of most of these workflows. I think, um, 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. 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 everywhere and it feels like we're super far from that. So I'm curious about that. I don't know if it'll benefit us necessarily that much, but there is something about the models not being able to learn very fast. And as it relates to us, we put a lot of product work into how do you actually get input from people about their preferences and how they've learned, 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 our agents smarter. And I think from a product UX perspective, I think there's a bunch of interesting stuff to solve there as well.

[A]

And maybe one kind of final question between technical and non technical is I've been thinking about this a lot. How the world changes when the marginal cost of arguing goes to zero. Right. 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. And it's the same thing for like a lot of insurance. It's like, you know, 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. This is not new to the insurance agents or the insurance industry on both sides. Right. But now that everybody has this like super powered 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. 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 because in many cases the counterparty that the dentist is dealing with is the insurance carrier.

[B]

Yeah, right. A big part of the accounts receivables or revenue comes from insurance companies. Um, 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 human, it's quite hard to like a, to follow those like, rules because there's, there are like, take Dentistry, there are like 50, 60, maybe 100 common billing codes. And it's quite a lot to like remember for Mildred or Betty Dystopher. And in this case kind of like our agent like has like all the like documentation. 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? Right. And then they will, like bait it out 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 Andreessen is going to 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 an incentive to keep doctors Loop and Kwan, like, in network because like, otherwise they will not renew the plan like with my 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 Frederik's point. And we can talk about this for hours, maybe another time. But like, we are also literally digitizing the Falcon. Like that's. It's really interesting that it's really hard to find these 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 a hundred thousand dollars in checks deposited on their desk. And I did this, right? So for Dr. Kwan, 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 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. 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 do 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 that as an option to a doctor so if a doctor says, I want to flip the switch, you need to do that the same as these itemized invoices. You need to create a digital file format like for that. And we are also writing that like till wind. So you will see that like a lot of these small businesses, I think the stats are 70% are still paid on paper. And it's going through this massive digitization revolution right now because federal inflection point, that's regulatory. So I think that combined with these models being so good makes this super interesting. Because even if you have the models or had the models five years ago. Yeah. The payments are still on paper. And we are digitizing that in the meantime, which is also what we talked about earlier. It is not one click and then kaboom. Because otherwise the whole country would be on digital payments. Right. Why would countries of like Dr. Sloop not do this? Well, Dr. Sloop needs to also then spend 50 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. 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 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, is 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 to like digital file formats such that you can actually automate

[C]

your to work 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. Because if you just digitize everything now you have a PDF instead of a paper in front of you, you actually need some tools to use the PDF and it's preferable to just kind of work off of a sheet. And so there wasn't really much of an incentive to digitize. Now that's obviously different.

[D]

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?

[B]

Yeah, this is a very different playbook than where currently, I think the cutting edge is, 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 10 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 business type in the country, who's the owner? 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 say they don't. Indeed. And then get in touch with these, like, people with a message that resonates with them and that cuts through 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 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. So this is a completely different playbook that we're developing here. And I think that's another very compelling kind of thing that we're figuring out. Basically, what does the go to market playbook look like to adopt AI and many, many businesses? So, yeah, that's quite an exciting opportunity. Opportunity and untapped.

[D]

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 Lassie. Anyone who's listening who might be interested in. In working with the Lassie team to build something generational here, Check it out at Lassie AI. And you guys are very actively hiring from what I understand.

[B]

Oh, yeah.

[D]

Amazing. Great. Well, thank you guys again.

[C]

Thank you so much. Much.

[B]

Thanks for hosting us.

[E]

Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like Comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X16Z and subscribe to our substack@a16z substack.com thanks again for listening listening, and I'll see you in the next episode. 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 code. 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.