Enrico Palmerino's Journey with Botkeeper and AI in Accounting

In this episode of Canopy’s Practice Success Podcast, Davis Bell, CEO of Canopy, interviews Botkeeper CEO Enrico Palmerino. They discuss Enrico’s journey from tackling accounting challenges as an entrepreneur to founding Botkeeper, a platform automating bookkeeping through AI.

Transcript

Davis Bell: 0:06

Hello and welcome to Canopy's Practice Success Podcast. I'm Davis Bell, the CEO of Canopy, and I will be sitting in as host for today's podcast. I, I'm excited. They don't usually let me do these things. They don't let me out in public often, so. Um, I don't know what happened, but, uh, glad, glad to be here. I'm super excited to sit down with Enrico Palmarino, who's the CEO of Botkeeper. And today we're going to chat about Botkeeper and what the accounting profession can learn from the world of tech and venture capital, as well as we see AI taking the profession. So welcome, Enrico. Great to be here with you.

Enrico Palmerino: 0:40

Thank you, Davis. Appreciate it, sir. Yeah, super

Davis Bell: 0:43

excited. So, um, I, I thought maybe, um, And I've got two questions. I think they're probably super related. One, I just want to know about your background prior to Bot Keeper. And my next question is what inspired you to create Bot Keeper? And I think those are actually very related. So maybe, you know, you just take them in whatever order you want.

Enrico Palmerino: 1:04

So, uh, background. Um, I was a quant major, uh, in college and while at Babson, uh, which is an entrepreneurial school. So, uh, everyone's always starting businesses there, which is a lot of fun. And I started a company that automated how you, uh, Analyze and design lighting systems. And eventually we became a big data aggregator in doing that and saw gaps in the marketplace for certain lighting products and then patented those and got into manufacturing. So now we were a software company and a manufacturer and we were doing that across the globe, which meant. For all of you accountants that are listening, we had the world's perfect storm of accounting challenges, deferred rev rack, complicated asset depreciation schedules, foreign currency conversions, multi entity, and of course, whip and bomb and, you know, inventory, you name it. So the business grew fast. Um, Our accounting couldn't keep up. It was a constant struggle. And so when that company got acquired, I was like, I'm going to solve, uh, I'm going to make a virtual finance department for businesses like me. did that with a husband and wife, uh, um, who had started smart books. So I teamed up with them and invested in, uh, their practice. It was like four or five, six, seven people, something like that at the time. Then a few years later we were, uh, you know, five plus million dollar CAS practice. Um, and that was just CAS. So that was like big at the time. We were fast growing and then we were struggling with all the same issues like Multiple apps that don't talk to each other, a G sheet to kind of keep track of who's working on what accounts and what apps they're using. You lose someone and became way harder to find and hire people like in a very short period of time, there's like the accountants, um, missing. And, uh, the idea of like, it was just like, God, it would be so amazing. Like there, every other industry has one platform with all the feature and functionality you need to do the job, but CAS. And so that would be great if you could have. Those features in a single system. And if it wasn't a single system, all the data would, you'd have all the data and all the data would tie out. And with all that data, um, you could build really powerful machine learning and AI. And if you could do that, you could automate away a lot of the processing, which would also help solve the, uh, you know, human shortage. And that was the, the impetus for, for Bachiever.

Davis Bell: 3:36

So to kind of summarize, you start this business in college that introduces you to the pain, um, of sort of dealing with accounting firms from the, from the entrepreneurial. sort of perspective. Then you went to go try to solve that by, by becoming a partner in a firm and then you discovered all the pains associated with, with running a firm. And so that, then you went and started, so it's your next business about, you know, the pains or experiences as a tech company or you just, you're going to stay put here.

Enrico Palmerino: 4:12

I have a couple ideas for solving pains with hiring, recruiting and all that cause of having like a high growth company. You know, a company like ours that wasn't. A huge challenge. Um, but there's plenty to do yet to come with bought deeper. So, uh, you know, maybe, maybe in a decade from now.

Davis Bell: 4:32

Well, I think, um, you know, I, I think my perspective is the products become great when they're being solved by people who've really understood the pain, uh, associated with the users of those products. And sometimes you can just go and figure that out. And sometimes you experience them, um, you know, in, in your own life, which Something that, that you did. So kind of honing in on bot keeper a little bit. You, you mentioned some of those problems. It sounded like, you know, disparate systems data that's not, you know, talking to, to each other. Um, the, the challenge of, uh, bringing new hires up to speed. Would you, would you consider those to be the key challenges or would you, are there others that, that you would mention that drove to kind of the, the founding.

Enrico Palmerino: 5:23

I, I think it, it, it all like in a weird way is entangled, right? So disparate systems and having a system switch between a half a dozen or a dozen apps to do bookkeeping for a single client times 20 clients. That's a whole lot of system switches on a given day or on a monthly basis. It's also hold a lot of security vulnerabilities. Um, that you're opening yourself up to like that much more potential for margin erosion as every one of those apps, you know, increases cost. Um, and then the, the data tie out and the siloed datasets becomes more and more of a major problem when you want to start leveraging AI. So. If you end up having a bunch of niche AI applications that don't talk to each other and require some sort of syncing of data, then you're wondering like, is the AI making good decisions based on bad data or is it making decisions on yesterday's data instead of today's data? And does the data actually tie out and therefore is the decision the right one to be made? We all encounter this on a daily basis when we go to like have a quarterly board meeting or executive meeting where like two departments are presenting different data numbers. But imagine like the complexity and the exacerbation when the speed of reaction is automated or AI is doing it. And then do you have visibility into what data set was used to make the conclusion? And you could just kind of see like this, this compounds. And then the other part of it is, um, stress. Cause like in a world where like you can't find and hire people, then the people you do hire, you want to make sure that you take care of them and that they really like what they're doing and they're not being like unnecessarily like burnt out or stressed out. And when you have a shortage of people, that means more work for the people you do have. And. I feel like accounting is just one of those things where like the second you come up for air, like to breathe at the end of the month, like you've got to start over in the next month and you're doing the next cycle. And if you lose a person or an employee, um, or someone takes vacation or sick time, it's like the whole world can come crashing down. And so the other aspect to like one of the big things we want to do with Blockkeeper was can we automate away as much of this like processing? That needs to be done or that could be done on a daily basis so that you could lose an employee and someone could cover the gap pretty easily because they have to only be trained or brought up to speed on like 20 percent of the transactions or the reconciliation happens automatically in the statements fetched and or the month end review. Like the AI is showing you what you need to touch or treat and address and instead of saving it all for the month end and Compressing the time frame to do the work. What if like you do that on a daily basis? There's no reason you shouldn't be able to sure and like all of that was just like more and more reason like Someone's got to figure this out

Davis Bell: 8:17

Yeah

Enrico Palmerino: 8:18

And the daunting part was you know as I started like look at I'm like I basic gotta build We have built one company that builds 10 companies worth of technology and features and functions. And now we're going to pull that off. But um, as we got into it, I think we just looked at it like, I always use Excel as like the example because 99 percent of Excel users use 1 percent of Excel. So I just need to figure out like of these functionalities, like what were the most critical and important ones. And then get rid of a lot of the noise. Um, and if I did that, you know, there is a chance that we could actually build 10 companies worth of tech, um, under one company umbrella.

Davis Bell: 9:00

Yeah. I mean, I, um, I think, you know, there's, there's sort of, you know, in software, there's, there's sort of two approaches, right? The first is you take this very narrow point solution approach, you know, you're going to do this very narrow thing. And that's really appealing. Um, initially because it's just much easier to get off the ground. It's easier to be billed. It's to build. It's easier to be good at one thing, but over time you then run in, you've created this problem for your customers, which is then they need to go buy a bunch of other things. And I think, you know, how you're, the way you're talking is very similar to the kind of approach we've taken here, which is. We're not just going to be that one thing. We're going to try to be kind of everything. And, you know, there's a trendy name for this in Silicon Valley currently, which is the compound startup, which whenever someone puts a new name on it, you know, they've invented it. Obviously you've been at this for a while. We have, um, but it's harder, right? It's see, I just call it the suite,

Enrico Palmerino: 9:56

which suite's been around for a long time. Exactly. Yeah. Meta suite has been around and Microsoft suite,

Davis Bell: 10:03

like, right.

Enrico Palmerino: 10:04

But suites stick, so, you know, you can round out the suite, has a longevity and lasting power. Whereas I think like the niche company, your ability to expand is difficult, and that's why most niches get acquired and put into a suite.

Davis Bell: 10:20

Yeah, I, I, um, you know, the, the, it's sort of like, you know, um, you, you, you, you live, I'm trying to remember the exact phrase, but it's sort of like you, You know, the phrase you either, um, die a hero or live long enough to become a villain, it's like you either die at point solution or live long enough to become platform. Right. So, so tell me, tell me just from like the, you know, entrepreneurial heroes journey, because then I want to, I want to talk a lot about AI, which, which you've already sort of mentioned. Um, what, what was the hardest thing you, you know, you set out, you, you bit off a lot, right? You're not just going to take a point solution approach. You're going to try to build this. this platform. Um, what, what was the, what was the hardest thing about doing it?

Enrico Palmerino: 11:08

I think the most difficult thing about the Bok, about Bokyem as a concept is you You had to build a plane while you were flying it. There was no, there was no such thing because AI requires data and the AI and the data we need to do automated accounting is private financial data. And it's exactly that it's private. There's no like mass data set somewhere of private company data. Um, and you can get like some public company data out there, um, but that doesn't help because like small businesses are structured very different. Their charts of accounts, everything's totally different than the, those on wall street. So we needed to acquire clients in order to acquire data sets, in order to build. And then we knew that like, we didn't want to be in the direct SMB market because like the economics and AI world, the winner is the, who has the most data and like, you can't get the most data if you're doing one to one sales. So we needed to find a way to get one to many, um, in the accounting channel. So this, like the difficult aspect was we had to go. Create enough value to have a business to get people to sign up and then using that to get the data to build dream product that we wanted to and then to like Go from building the dream product that we wanted to and having to like do it direct us and be So you get the proof points so that the you know world's laggard pessimistic tech adopters known as accountants would eventually be like trust the product enough to be able to buy it and And use it in their practices on their clients, which is a risky thing that Process and that complex complexity of you know, this tale of two cities and two worlds Took eight years like it took a lot of money. It took eight years of time But, you know, we finally got there and we launched infinite and now it's like, you know, I talked to our investors and I'm like, you've been at botkeeper almost arguably it's almost 10 years now, like next year we'll cross the 10 year mark. And they're like, do you still got it to keep going? And I'm like, yeah, you guys don't understand the last eight years or nine years where the prelude, like this is like infinite launch. This is chapter one, you know? We're, we're amped up and ready to go. And, and we see it too. And, you know, it took us eight years to, I think, acquire a couple hundred accounting firms. It took us, uh, three quarters to acquire more than that. Um, since, since then, so it's exciting.

Davis Bell: 13:47

I read this quote that was talking about, um, Ultimately, your only moat, competitive moat is obsession, um, because people who are obsessed with a problem are willing to go spend eight years, right? Like if it's someone who's just trying to build a little app and flip it to some big company and, you know, make some money. You know, they're just not going to do that, right? They're not going to spend eight years, um, suffer all, all the pain and, and build this slow build and you know, that you, you can build a generational company that way, but, um, you know, it's, it's a slow burn, right? And the, the platform approaches a slow burn too. Like it's just the nature of it. Um, but to your point, like. Once you've done it, you know, you, you can really camp out there for decades cause it's just super hard for anyone else to come along. And

Enrico Palmerino: 14:36

I mean, that's the moat, like, you know, people are like, well, what's stopping? Like, I'll tell you what's stopping. Eight years of development, um, a hundred and something million dollars and billions of transactions that we needed to, billions of transactions, tens of thousands of companies and hundreds of accounting firms of data and transactions to build what we built. And the unique thing about this is like when we first started, The only thing we were competing against in terms of like client and data acquisition was like old school human labor accounting, which meant we could charge three 99 a month per client to do this. And like, that was a third or a quarter of the cost of like the labor. Today, BotKeeper sells that same thing for 69 bucks a month. So, so anyone today that's like, Hey, I'm going to go do, I'm just going to follow BotKeeper's playbook, do the exact same thing. You'd hemorrhage so many more dollars than we did. Because you'd have to be, like, you'd have to start with human labor to process and get there and train the models. And you'd have to do it at such a loss. Like, you know, you know, you know, like we know we were barely breaking even or had like minimal gross margins and we were selling it at three 99 until the tech caught up, like if you had to come in and start selling it at 69 today and you were doing it by human labor. Like you just, you'd have to, you know, raise two or three times the amount of money that we did in order to pull it off and get to the same point. So it's a good place to be. It's just, it's a hard place to be. But like you said, I'm, I'm obsessed with it. I like, we, we love what we're doing. Uh, you know, we've got a great team, uh, that makes it a lot of fun to work with. And all of us, like, you know, we've kind of been working to the, for this thing for like so long and like see it come out is just like, yeah, it's fun. All right, now let's go.

Davis Bell: 16:38

Well, yeah, I mean it's it's a nice thing to be on the other side of, right? I mean eight years of kind of slugging it away and and you know slowly acquiring firms and then finally you get to this inflection point like it just there's there's there's nothing better. That's why they say it's a

Enrico Palmerino: 16:55

ten year overnight success.

Davis Bell: 16:57

Yeah Exactly. And yeah, I mean it really is true, right? I mean, it's it's you get these flash in the pan You You know, companies, but they're just exceedingly rare. And the minute you dig into it, it's just, you know, there was, there was that like decade of just sort of building and grinding and failure and iteration. Just kind of how it goes. Um, yeah, so I think they say like, it takes like

Enrico Palmerino: 17:22

on average 15 years for a company to get to, if you can get to a hundred million dollars, those who did it, it takes them like 15 years. And there's been like a handful in like the history of time, a handful of exceptions. Who have done in like eight, nine or 10.

Davis Bell: 17:38

Right.

Enrico Palmerino: 17:40

So

Davis Bell: 17:40

yeah, open, open AI is probably, you know, a more recent, uh, exception. And even Slack,

Enrico Palmerino: 17:45

Slack was one of them.

Davis Bell: 17:47

Right. Um, yeah, but there's not, there's just not that many of them, um, to your point. So you, you mentioned, you know, AI it's, it's, you know, impossible to have a conversation about either accounting or software and certainly accounting software without talking about AI. Um, So tell me, tell me first, you, you, um, you made a point with which I violently agree around AI and it's the ability to leverage AI. Relative to being a platform, um, versus a point solution. So talk, talk, talk to us a little bit about that.

Enrico Palmerino: 18:29

So, um, to me, I look at like the future of AI is where it's going to be very different than the AI we use today. Like the future of AI, a single prompt delivers task completion. Um, and, and that's not right now, like right now you're a prompt is completing like an aspect of a task and like an arguably like a tiny component, like if you consider bookkeeping as the task, like your prompt is completing one sub segment of like is actually completing a, an aspect of a sub segment of bookkeeping. So, and that, that works ish, because previously, like with an app stack, you have people playing traffic cops in terms of figuring out organization and timing and sequence, like you go into the app, you do the thing, you take the completed aspect of it, you move it into the next app, you do the next part of the equation, take it out and again and again. But in an AI world, you have all of these things that are going to be ideally idealistically happening automatically. But if they can't talk to each other and you see this all the time and like marketing agencies, and that's all they do marketing, they have a hard time getting the data actually tie out. If you've got all these disparate apps that aren't integrated with each other and there's no, I can't see that ever being fixed. There's 1600 apps in the zero and QBO marketplaces. The cost for one company to integrate with all 1600, not going to be the case. So you're going to have all these like siloed data sets, siloed apps, performing siloed tasks and functions that have no purview or context into anything else that's going on. And that just creates a world of hurt. Like, because you're not like the, basically the, the, the data traffic collides. Did the thing get done on time or did it get completed before like the other component was completed and I had the remaining data that it needed to complete its own task and what was used or was the context that goes into the completion of a task. And that's where I think like this, there's a reason why the biggest companies in the world that are the winning, the AI race are suites. Like Meta is acquiring companies that will round out its suite to give it the most context for its AI, whether it's Instagram or WhatsApp. Um, or, uh, uh, Oculus, like they're, they're trying to take all the components that you would need to create a very powerful AI platform. Same thing with Microsoft. Even like zoom is trying to expand its offering to get into it. But Microsoft was like, Hey, we already have most of the business suite offering and then video and, you know, met conferencing, all that, like is rounding it out. Google is another one. Open, look at what open is doing and how they're expanding. But you know, they're at the, the, the. For us, like, I think that's going to be one of the advantages we have down the road is having everything in a singular suite allows the AI to have context, which allows it to do like one of the new features we, um, have in beta right now that's coming out at the end of the year is insights. Where you can ask our AI, anything you want to know about your firm, how to be more efficient and it can look at all the data that's there and come up with that. Who's the top performer who could take on more clients? Like where are bottlenecks in your process? And it will. be able to see and deliver the answer to those, and in an ideal world, you could tell it to just, like, fix that. Right? And it would just go and fix, or do, like, go find me another six hours of time savings and make it happen. Um, and it can do it, and you can't do that without context. Um, and I think, you know, ultimately context is rounded out data sets, and Uh, and integrated data sets where the data actually ties out perfectly because if it doesn't and you have these AI tools that are producing an immense amount of data and ingesting an immense amount of data, you just start to have, like, we've all seen this where your database gets riddled with bad data and you end up having to, like, basically throw it out, like, or hit pause entirely and do a massive cleanup project and that cleanup project becomes near impossible when You know, gene sequencers and like in a totally unrelated space when they go off the wall, like someone creates a prompt, um, that has them sequencing, like out of order or putting it in the improper folder, they potentially create petabytes worth of data that ends up needing to be scrapped because it's and redone because it just can't be fit into the database in the right way. And there's no way to clean it up. Cause it's just too much data and it's easier to start over. So I think, uh, I think the world's really exciting for AI, but I think you're going to see this trend of those players who are niche apps trying to round out, um, and be more of a suite or more comprehensive or wholesome in their area. Um, and you're going to see probably more suites emerge in general in the, the accounting sector. And you'll see acquisitions. I think you'll, now that the markets are starting to open up again, you're going to see the players that are in there, like merging together to, you know, Create the suite or to round out the missing components.

Davis Bell: 23:59

Yeah, no, I, I mean, I think you very articulately explained how we think about it and how I think about it. I just, I, you know, typically a new innovation favors, um, The scrappy, you know, new, new entrant, right? Because the, you know, the, the, the, the person who's already out there shipping product just has a hard time innovating, taking advantage of it. But I think the nature of AI is such that its value comes from data at the end of the day. I mean, that, that is the fuel for AI, right? Like if you just plug AI into the software and there's just nothing there. It, you know, it like I sort of think about this framework is sort of like, you know, uh, data and action. So like, but the action has to be preceded by data, right? Like it, it doesn't know what action to take. The, the, the action will be based upon, you know, in our context, it's like, all right, well, what, what documents did they submit last tax season? Right. Well, therefore these are the documents they're going to need next season. And if it was blank, the AI is not going to know what, like, right. There's no pattern

Enrico Palmerino: 25:15

to recognize. Right. Yeah.

Davis Bell: 25:17

Yeah. So it, it, it, and I agree with you too, that, um, where we're at today, you know, it, it, to me, like one, one way to think about it, that, that resonates with me, I think about AI today as like a not super smart intern. So it's like, you know, if you've ever worked with an intern, you're like, Hey, can you go like, Google a list and like come back and they'll come back and you're like, well, that's not what I wanted. And you send them back and maybe the third time you're like, okay, finally, and you're, and you're sort of like, did I actually really save any time? I mean, I, I'm trying to help this intern out, you know, get some experience, but maybe I didn't save tons of time and maybe the first time you did. And, you know, so it's sort of nets out, but there's a big difference between that and just. You know, working with a seasoned career professional who you can just pick up, you know, slack them or text them or call them and be like, I need you to do this complex thing. And they just come back and it's done. And I think that we're in the dumb intern phase, but the rate of progression is such that I think, you know, and that's, this is what they call agentic AI, where it's like just doing it, you know, maybe at a prompt and maybe later, just not, it's just recognizing it needs to be done. It's hard to know exactly when that's going to come to fruition, but I think, yeah, I think we're, I think we're seeing the, the opportunity and the near term future very similarly.

Enrico Palmerino: 26:40

Yeah. And it's, um, to your point, the, the AI revolution, I think most of the benefit or value that we're going to see out of this is going to be the incumbents will become stronger, um, because they're the ones who have the vast data sets, the new players, to your point. One, haven't built their own AI. Like this is another thing too. I always point out most of these new AI companies, they're just leveraging open AI or another like existing AI model, which in general, all of those are also LLMs or generative AI, which doesn't bode well for accounting. Like you can do some narrative stuff, but you want, if you want task specific AI, like that just takes a whole lot of coding and you have to do that yourself. And if you want high accuracy accounting, you That's the only way to do it. Um, at least like that's what everyone always, that's what anyone says is the only way to do it today. Does that change? Maybe generative AI over time, um, can get better at it, but like how it's architected, it doesn't look like it's ever going to head in that direction. Cause it is general, um, and general doesn't bode well for specific, uh, accuracy that's based on like machine learning versus based on, um, inventive, uh, Synopsis of, of, of datasets. So yeah, I think the big players are definitely gonna have the advantage. The small players, it's a risky time to use like a brand new AI company because open AI made a change earlier this year that killed 1200 AI companies just because they got rid of one of their APIs. Um, Historically, you never had to worry about that. If you're using a brand new startup on the block, because They built their own software and you just had to worry how much runway they have. Now, if it's an AI brand new, they call them, uh, on the Silicon Valley, they call them rapper companies like you, you just, you're, you're wrapping someone else's AI with like a concept. And that has like, there's like no moat to that. I don't think, um,

Davis Bell: 28:45

yeah,

Enrico Palmerino: 28:45

but, uh, but it'll be interesting. I, you know, I think a lot's going to get done and there's so much unlocked potential that we just, we weren't leveraging and that companies were sitting on in their data. Um, and it's just going to take those incumbents to be willing to invest and explore and challenge the status quo.

Davis Bell: 29:04

Yeah. Well, I agree with you and maybe, maybe one last topic for conversation. We've been geeking out a little bit on the more, the software side of things. Bringing it back to accounting, I think, you know, my, um, my mental model for AI in accounting has been, um, the exosuit, and I first called this a cyborg, but we've got some nerds here on staff who corrected me, and, you know, if you think about the movie Avatar, the humans walk around in those, they're inside this robot, and it can pick up a 8, 000 pound boulder or whatever, move really fast, and, you know, You know, that's, that's sort of how I conceptualize AI. Um, because I mean, so much, I don't want to venture a guess, but an enormous amount of the, the actual human labor in a firm. Is menial, low value add, manual, just sort of, you know, pushing paper from one place to another. Parses. Yeah, and I think people don't like to do that, and it's a bad use of their time. Or they like it

Enrico Palmerino: 30:08

for all the reasons we wish they didn't, which is, it's easy, I don't have to think. Yeah, sure. Monday through Friday and just kind of like wake up and get paid.

Davis Bell: 30:19

Yeah. So I think that like most people get into the profession cause they're passionate about actually accounting and they want to think strategically and they want to help people strategically and then they just get mired down in the weeds of this stuff. So what, what would you agree? Disagree? Would you modify that? Like how, what's, what's your sort of model for it?

Enrico Palmerino: 30:41

No, I agree. I mean, for the longest time we, we used to, um, Uh, compare Bot Keeper to, uh, Iron Man. Tony Stark is nothing, really. I mean, he's a brilliant person. But like, he becomes a superhero when he has his suit. And we would see the A. I. s being a suit. And on its own, it can't do much. And like, even like the Iron Man suit, it requires Tony to command it, or direct it, or do stuff. And that is the difference between, arguably, like, A. I. actually being an intern, An intern, conceptually, like, while it might, uh, he or she might take, like, you know, directions from you to do stuff. The reality is, that person can also Go do things for you without you even asking because they recognize they're like self starting Take initiative. They recognize a problem. They attempt to solve and you we've all had those interns who are like Hey, by the way, I did this because I thought you might like it like, oh, that's cool Like we don't need it But like the self starting nature of and the fact that did is like really cool Ai can't do that right now. Ai is only going to do the things you specifically tell it to Every time you tell it to do something so until it can start to like problem solve for you without It is going to be an exoskeleton or, you know, the Iron Man suit that's just amplifying, uh, the intelligence and the use of our people. And arguably, it's like, kind of like the Iron Man suit. If you took You took, uh, your, your weakest, like, you know, engineer, like, you know, your weakest Tony Stark from a intelligence standpoint, you put them in the suit, he'll be able to do good stuff, like pick things up and fly. You take a brilliant person, you put them in the suit and they can accomplish like insane things. And I think we're going to see the same thing happen with AI. The smartest people out there, the best accountants are going to take accounting up notches and levels that like we've never thought possible. And those who are not. The, you know, strongest accountants, they're going to now be able to like be like the strongest accountants or be a really great senior accountant, thanks to AI, but they're not going to, they're still not going to play. It's not like, it's not like the all, uh, intensive, like plain feel leveler, uh, that some people think it is, there is varying degrees of, of skillset and use that, that amplify its outputs.

Davis Bell: 33:07

Yeah, no, I totally agree. Hey Enrico, this has been super fun. We'd love to do it again. Um, and just encourage everyone if they want to know more about Botkeeper, botkeeper. com super cool company, great products, great leadership, and I'm excited to see what, what you guys get up to next.

Enrico Palmerino: 33:26

Thank you so much, sir. I appreciate it.

Davis Bell: 33:28

Thanks Enrico.