How AI and Automation Is Bridging The Accountant Shortage With Mitchell Troyanovsky

Welcome to the Future Finance show where we talk about. I for one welcome our robot owner board.

We strongly believe that accounting, one of the most under resourced professions, especially today, what baseless is trying to leverage the absolute most cutting edge and techniques in AI to start to take a lot of these manual existing processes and be able to really have these digital assistants who can come in and start to take on parts of the world.

How would you describe the difference between a rule based system like you just alluded to versus what generative AI and what these LLMs are able to do?

I do think the most easy way to understand how intelligence really works is thinking of it like a person a little bit. One of the big unlocks of AI today is that you have things that can be relatively generalizably intelligent. I mean one thing we're very big on is.

Future Finance is brought to you by qflow AI, the strategic finance platform. Solving the toughest part of Planning and analysis. B2B Revenue align Cells marketing and finance seamlessly speed up decision making and lock in accountability. With qflow AI this week I have to get something off my chest Here at Future Finance, we're huge fans of AI and technology, but we're also big fans of Excel. And I'm tired of the BS and the hogwash that we're seeing on LinkedIn everywhere that AI has killed Excel. How many years have we heard Excel is dead? Here's the reality. Microsoft is investing as much in AI as any company in the world. They've already brought AI into Excel AI and generative AI will accelerate the ability to accomplish amazing things in Excel. So can everybody stop the clickbait and tell me Excel is dead, that AI's killed it? And can we stop the other extreme? We should use Excel for everything. No, we shouldn't. Excel is not a large scale database. Yes. Can you use it as a database? Can you do just about anything in it? Yes. Should you? No. And I think that's the balance we have to manage. AI is great. Generative AI is going to revolutionize so many areas and it will make a lot of things easier in the spreadsheet, easier in Excel, easier in Google sheets, easier in so many tools. And I'm super excited for the technology. But let's keep things in balance here. We have a tool set right when I build a house, I don't just use a hammer, don't just use a screwdriver, I use a set of tools. It's the same in finance. Spreadsheets are one of those tools. Generative AI is one of those tools. We need to make sure we're not on the extremes. We're not thinking AI, AI, AI, AI, Excel, Excel, Excel. We need to go beyond just Excel. I'm one of the biggest fans you'll find of Excel, but I recognize planning tools can also make a huge difference in our work, as can AI. My message is keep using generative AI, keep finding ways to be more efficient, but don't sleep on the technology that's been around for 40 years. Excel and spreadsheets for even longer. Use AI to become more efficient in spreadsheets, use other tools to help you to become better at your job, and really focus on being well rounded. Because as FPA professionals, having a set of tools you can use will make you a better FPA professional. And using the right tools, understanding technology allows you to focus on what is important as a finance professional. And what's important is that we're good at our job, that we're making a difference to the bottom line, that we're helping scale and grow and do things that at the end of the day, provide value for the business. And if that means using Excel to build that model, great. If that means using generative AI to help you do it, great. So that's really my monologue for today. What I wanted to talk about before we interview our guest and before Glenn shares the news story is I just had to get that off my chest. So I apologize for the rant, but I hope you understand the message. Excel's not dead. Spreadsheets aren't going anywhere. Neither is generative AI. Let's figure out how we could all be more productive together. And hopefully this show will help you be more productive by listening. Super excited for the future of finance and the role.

Jennifer and welcome back to Future Finance. I'm Glenn Hopper, the co host of the show with my esteemed colleague and partner, Mr. Paul Barnhurst. This week, I want to talk for a minute about the release of ChatGPT 4.0. Maybe it's because everyone is focused on when 5 might drop, but I get a sense that a lot of people thought this release was a bit of a nothing burger. But after spending around 30 hours working with 4.0 over the last week, I've got to say this release is a big deal. It's got multimodal integration, reasoning and contextual understanding that will blow your mind. It's like having a Swiss army knife, but instead of tools, it's packed with PhDs. In one case last week, I used the tool to create some fairly complex scenario analysis based on three years of historical financial information. ChatGPT created a baseline forecast using a sophisticated statistical technique called sarima, or Seasonal Autoregressive Integrated Moving Average. For those who aren't familiar, it did this in one shot. I then had IT create three different scenarios based on price volatility, government regulations and shifts in consumer demand. It created these variants, each with mild, moderate and severe versions, and kept them in memory, which allowed for easy comparison across the models. Then I had IT run several Monte Carlo simulations to stress test the scenarios. It performed this flawlessly, exhibiting not only better coding and more compute, but significantly better reasoning than any other model I've used. I've seen some of the early benchmarking and 4.0 is it's performing pretty well. It demonstrated high accuracy in math, particularly with chain of thought prompting where it matched or surpassed even Claude 3 Opus. In several cases it did better than Gemini 1.5 Pro in instruction following, and it's also looking better than Gemini at coding. These foundation models are getting so good so fast that I'm seeing now that Sam Altman knew what he was talking about. Been several months now I think when he said as AI systems get closer to AGI, we believe general purpose models will have the ability to handle a wider range of tasks with higher accuracy and efficiency compared to narrowly focused models. The versatility and adaptability of these advanced models will surpass the capabilities of specialized models, ultimately making them more beneficial across various domains. Do you guys remember Bloomberg GPT? Bloomberg leaned in quickly to generative AI and they built this tool over a year ago. It's a large language model specifically built to handle financial data and tasks. The project required a massive computational investment. It was something like 1.3 million hours of GPU time on Nvidia A1 hundreds. It was trained on a proprietary data set of more than like 350 billion tokens and it was augmented by a near equal amount from general data sets as well. The model is great at specialized financial tasks like sentiment analysis and named entity recognition, and it would probably outperform even the latest foundation models and specialized financial analysis. But I'm wondering how long can they maintain this lead? I think about what Alvin said about these massive and now multimodal models and wonder whether the high cost and complexity of building Bloomberg GPT or similar models is justified for broader use cases for really anyone beyond these tech giants. And that got me thinking about all these custom GPTs I built before GPT released the ability for users to create their own custom GPTs I was slaving away in front of a blinking cursor in Python trying to fine tune GPT 3.5 for finance specific tasks. And I was using RAG to link relevant texts. And it took a long time. But then these GPTs came along and anyone could do this without coding and in a fraction of the time. So I built a bunch of them. A whole army of finance and accounting chatbots available from my website. Robo, CFO, AI. But seeing where we are with GPT4O, I wonder if they're even needed. Don't get me wrong, there's still plenty of scenarios where specialized GPTs are the way to go. If you're dealing with proprietary or super niche information, it's not out there for everyone to see. That's when techniques like retrieval, augmented generation, or RAG and fine tuning and building GPTs all come into play. I'm thinking of things like company workflows, call center scripts, and maybe in working with specific data sets. But for professional specialization, I don't know. I mean, you can say these generative models are generalists, but if you're a generalist who knows more about every specialization than the specialists themselves, it's like hiring a chef who can also fix your car and do your taxes. So if you haven't tried 4.0 yet, I strongly encourage you to do so. Grab some financial statements and maybe the latest SEC filings from your favorite public company, load them into the tool and then ask it the questions you'd ask your financial analyst. And then maybe fire your financial analyst. I'm joking of course. It's not like GPT is going to be able to hold your feet while you do sit ups. Hi, I'm Glenn Hopper, co host of Future Finance with my esteemed colleague and broadcasting partner, Mr. Paul Barnhurst. Our guest on this week's show is Mitchell Troyanovsky. Mitchell is the co founder of Basis, an AI platform for accounting firms. Mitch, welcome to the show.

Thanks guys. Thanks for having me.

Yeah, we're thrilled to have you. So I'll kick it off with the first question here and I'm sure Glenn will pipe in in a minute and share some wisdom for all of us. So tell our audience about Basis. Maybe we'll start there. I know it's an AI platform for accounting firms, so tell us a little bit about Basis and why it's needed.

Yeah, absolutely. So, you know, we strongly believe that accounting is not only one of the most important professions in the US for like the functioning of the economy, but also one of the most under resourced professions, especially today. I mean, everyone always hears about, you know, less accountants going into the field. You know, coming outta school, everyone hears about all the retirees firms and you end up having this massive, massive shortage of accountants which already goes on top of an existing shortage of accounting work needed to account for all the complexity. So what basis is, is trying to leverage the absolute most cutting edge techniques in AI to start to take a lot of these manual existing processes and be able to really have, you know, these digital assistants who can come in and start to take on parts of the work.

Got it. So the, the idea is, I hear it here is to have a tool that can supplement the shortage of people in many ways. Is that kind of the way to think of basis to help augment the staff? Or how do I think of the two working together?

Yeah, absolutely. I mean, I think it will differ by workflow and also how good basis gets over time, how much of things they can do end to end versus where it needs to be working with existing accountants. But I think it definitely is a tool for accountants explicitly at the end of the day, the job that they can do the best, which is advise their clients, help their clients, help them make better decisions and really be that partner with them, rather than needing to be stuck in all the manual work needed to actually get to that actual real value add at the end of the day.

And that's a, that's a tall order. I mean, if you look at everything that happens in an accounting department or, or at an accounting firm, and I'm wondering when you set out, when you were putting everything on the whiteboard and you were designing, what basis does have you always seen it as? Ultimately the goal is to be an end to end assistant. And if so, or maybe you, you originally envisioned it as a smaller product but then realized that there were other areas you could move in. How did you, you know, how do you tackle something as big as providing AI assistance for accountants? And what did you start with and what's, what are you doing now and what's on your roadmap with it?

Yeah, absolutely. So we really think generally of basis as this like holistic intelligence that can take on, you know, sort of different workflows in the same way that if I were to hire outsourced help, let's say, you know, they can, I can go throw them at different workflows and they may not be amazing at all those workflows from day one. And of course they're different skill sets, but broadly they are sort of holistic in their ability. And we want basis to, you know, in the same way be this kind of holistic intelligence that you can take on different workflows. And so when you think about trying to build something like that, you almost have to. And it sounds sort of funny, but like, think about how you train accountants generally. And that normally starts with like debit and credits. And you do that before you're doing like full month closes, and you do that before you're doing, you know, full, let's say tax work or you know, like one day assurance work and things like that. And so we very much started in, you know, the kind of outsourced accounting world, the cast world, you know, goes by different names primarily because that was both the area that is fastest growing in accounting firms to the area that had, I think, probably the largest shortages. Also the area in which the business model is not hourly and so people can get much more pure margin. And then lastly, and most important from our perspective in terms of building this vision is that's where you do the debit and credits. That's where you're quite literally entering things into the ledger. And so if you start from the absolute foundation, it makes a lot more logical sense from an intelligence perspective to kind of build up the pyramid of skills that you would need to have that holistic intelligence.

Isn't that also probably where it's the most process oriented? Right. When you start with debits and credits because you're training something, the exceptions are always going to be the hardest to train, whether it's a human or a machine. Is that fair to say?

Yeah, I think that that is fair to say. But I would just add that one of the big unlocks of AI today, compared to everyone's heard about ML for a decade now, is that you have things that can be relatively generalizably intelligent rather than just being, hey, here's this one exact workflow. And this is the only thing you can do right? And you're automating this workflow. It's actually taking on workflows that might have many, you can call them edge cases, but it's actually, there's so many, they're all not even really edge cases. It's like that's just the work and being able to automate that, that requires real intelligence. And so even though debit and credits is very procedural, the reason you can't automate debit and credits with like a rule or with an if statement is because it isn't actually so, like, you know, binary. It's actually Very intelligent and kind of artful almost.

So I talk about this all the time and I think, you know, you mentioned machine learning that we've been using for over a decade now. And in finance it's been in, you know, fraud detection and you know, machine learning is, you know, from product recommendations to spam filters and all that. So it's, it's tested technology and people have been using that for years, I think for finance and accounting really until generative AI came along. And there aren't a whole lot of, you know, we're still early in it, so there's not a lot of broad adoption for it yet. But I think one thing that people have to get their head around is that I know a lot of people used RPA and robotic Process Automation for anyone who hasn't used that before. And it was, you know, RPA is very much rule based, very difficult to set up and it's very, you know, it's very picky on how things go. And with generative AI you're not just getting, you know, look here for this to find out what the sales tax is. You're, you're getting actual contextual understanding. And I'm for people who are not familiar with what's going on with generative AI. How would you describe the difference between a rule based system like you just alluded to versus what generative AI and what these LLMs are able to do?

Yeah, it's a great question. So I actually think one of the reasons it's hard for people to conceptualize this, I actually don't like the term generative AI personally because I think it makes a lot of people focus on the generation aspect of things. And that is obviously powerful and important, but it sort of hides the true power under that, which is this ability to, to reason over context and actually make decisions. And that is on its own the kind of true power of it. And in the same way that if I have an if statement, let's say, and I have five if statements and I run everything through those if statements, those if statements can triage between the various outcomes of that kind of list of rules. If someone has had to manage a thousand rules in a QuickBooks file, they're probably very familiar with this. And you end up having all the, all these exceptions and things, you know, don't work well. And the thing was capital versus not. There's no reasoning, there's no intelligence inherently. And it's like you'd look at that as somebody looking at the rules and you know, you as someone is intelligent. And so you can see that, oh, this is ridiculous. Of course you would never put, you know, a $5,000 Uber expense into a taxi ride because there's no way a taxi ride ever costs $5,000. But like, you had a rule that said if Uber, then do taxi, and so that the rule itself isn't smart. And I don't want to really anthropomorphize too much, but I do think the most easy way to understand how intelligence really works is thinking of it like a person a little bit, where if you work with a person, they may not do the if statement in the same perfect way, but they're going to actually understand it much better. And that allows you to have nuance. So that, I think, is that kind of big difference.

And you mentioned anthropomorphizing, and I know everyone says don't do that, but you and I have talked about this before. Example I always use is whenever you're, you're prompting these things, treat them as if they're a very bright but very green intern. And so when I talk to them, you know, give them very clear instructions and walk them through. And I'm, I'm wondering, you know, if as you fine tune and train, I mean, I know you're not building, you know, your own foundation models, but as you get the prompts right and get the workflows right and everything, I mean, how do you treat the technology and how do you sort of guide what you're, you're trying to make it do and what you're delivering to your customers?

Yeah, I mean, I think a lot of it goes into techniques like how you fine tune, you know, and making sure you're doing things very specific. I mean, one thing we're very big on is doing things extremely locally in the sense that, like, you know, the model and the workflow for one client is very client specific. You know, it's not being trained across clients, you know, for many reasons. But I think one of the big ones that people sometimes don't fully appreciate is efficacy. Right. Like you need to, you know, the way one transaction is handled for a client or the way one client does a close, or how all this works is different than it is for other clients. And you have all this variation both in, like, how accounting should be done, obviously across the chart of accounts, obviously across, like, you know, what the policies are. There's tons and tons of variation across clients. And if you're just thinking, oh, I'm going to train a model that knows that, like, Taxi equals Uber. Then you're sort of missing the forest from the trees because you're just looking at things at a very, very high level, not at the, not at like the client level, which is really like what's useful and what's important and then in terms of, you know, anthropomorphizing and how to think of it. I think the most important thing as people get more comfortable in working with these models is really trying to treat it more like that intern or maybe like a really open eyed new hire or something like that where they're really smart and they have an enormous amount of potential and they are extremely valuable. But it doesn't mean that they're always going to get every single little perfect thing right. And you need to work with them to give them feedback and iterate on how you work with them and build a rapport as to what things they can do and what things they can't do. You'll get that intern who will surprise you by how good they are at certain tasks that you didn't expect. And then there are other tasks where you're not there. And so you need to build out that rapport when you kind of integrate this into your workflows.

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Of tasks that happened today inside, let's say, you know, doing the outsourced accounting kind of work, you will start to have over the next 18 months for sure, AIs who are doing the wide majority of those. That doesn't mean they're now doing them fully autonomously or that they are, you know, in some ways replacing accountants, because they definitely are not. But what it means is you'll start having accountants much more their job, you know, will stop from going like, okay, I have to do this work and I'm going to start from scratch and do it to, okay, this work has been like done for me as a first draft. Let me now review it and let me understand. And then as people kind of get up leveled like you'll take, you'll have junior people who, you know, a larger percentage of their work rather than being this kind of monotonous manual work will start to be like, okay, we need to think about how the results of this impact the client. Let's like, you know, write some advice. Let's look into, you know, projections and start thinking about what sort of decisions will this client need to make based on this information. Things of that, things that are much more strategic, things that are and that really add a lot of value to the client. And so I think, you know, not only will people save a lot of time because you'll be able to have AIs come and do a lot of that like very base level work rather than doing from scratch. But also accountants will be able to start adding a ton more value to their clients because they'll have more capacity to do that kind of high value work, especially if the low level work that is normally blocking that starts to be done, you know, much faster and your closes start speeding up and you start having a lot of the, you know, transactions in at a much more timely fashion, et cetera.

Yeah, I think talking about replacing that low level work, one of the things that, you know, we talked earlier about the accounting shortage and that increases the need for products like basis. And I know, you know, nobody goes and gets a degree or a master's degree in accounting because they or finance because they're excited to go do data entry. But that is, that's kind of where everybody starts is at that low level. So I'm wondering, and also if fewer people are going into accounting right now, you know, it may be that the appeal of that sort of entry level work doesn't sound very great. So in some ways we're making the work more attractive that it's less of that mindless and more mindful work out of the gate. But the other, you know, the other side of that coin, I guess is it's a parent, an old paradigm that you have to have that level of experience to fundamentally understand it. So it's like you're, you're immediately. So say you're a fresh grad out of school. Instead of coming in and kind of learning the ropes, you're immediately going to be expected to add greater value. Do you think that's a new challenge for recent grads or do you think it makes the job more appealing to them? I don't know if that's something you guys have. That's a great question considered or talked about.

Yeah, no, it's a really, really good question. I mean, I think there's, in the new world, there will be lots more skill sets that we don't think about today that will start to become valuable and you'll have to learn coming in. So like, for example, how to work with these AIs is something that you don't, people don't even think about as like something you'll have to learn. But people will have to learn, you know, how to work with it, when to trust it, how to set up good tasks, like how to build rapport, you know, and that will be a skill in the same way that writing Excel formulas is a skill. And you know, do you learn to write Excel formulas when you first join the job or do you learn it like in school before? It kind of depends on the program and I think you'll have some similar dynamics there. I also think that there will be a lot of, you know, in the same way that the most junior employees, you know, 30 years ago would like literally read a bank statement and key in amount values. Today they don't have to do that. You don't need to do that like bottom level work to understand how accounting works and how the client works and things like that. If you are looking at outputs and helping review, you know, proposed journal entries and you're understanding like, how do the debit and credits, you know, fit in for like this specific balance sheet account? What happens normally, like, you still are learning a lot of that work, even if you may not be the one like actually keying it in. And I think you'll see a similar shift to what happened, you know, when you started having cloud based software and you didn't have to do as much like pure data entry.

It'll be interesting to watch. I, I do share Glenn's concern a little bit that there's something to be said when you've done a process. A lot of times you really start to learn it versus just reviewing it. And I sometimes wonder just AI in general, not just accounting, but are there people that are going to lose some of that? And I think everybody probably worried about that with the calculator and other things. And we'll figure it out at the end of the day. But it will be interesting to watch and you know, we'll probably, I'll probably be that old person complaining when I used to have to do it. Right. Although I was never an accountant, so I can't claim that one. I did process journal entries, but I have a finance degree, not an accounting degree. But it will be interesting to watch because I see both sides of that and I agree with you. We've always figured it out, we'll figure this out. But I do worry that there may be some stuff that's lost from going through that process, some experience that they may lose that we'll have to address.

Right. Like you didn't think of, like, you know, if, if, if initially your job was on manual spreadsheets to like do the math, you know, you would lose and suddenly you have Excel, you lose the like experience of literally adding up the numbers, but you gain the need to learn how to use Excel. And so I think it's, you'll have very similar dynamics.

There's a lot to be said to that There is trade offs. You learn in some new things, you might not pick up other things.

And I know we're very excited to get to the future finance bag of fun questions. But one, you know, kind of going on that while we're staying serious for a minute, I run into, and you know, I do a lot of speaking to finance and accounting groups and they're, it's interesting to watch reactions in the room when I show them a demo of what you can do with generative AI and show them sort of these agentic workflows where we can replace manual processes. And you know, it's maybe it's growing a little bit, but not much. There's a lot of, there's, there's fear of missing out and then there's fear of the technology itself and then there's fear of what happens to me when AI gets more and more powerful. You don't Even have to have AGI. You just need increasingly better, you know, incrementally better models than what we have now. So, so you probably encounter the same. I imagine if you're going into a client at an accounting firm, there are people who are scared or distrustful of using AI. So how do you assuage their fears and how do you talk to these people? Or what's the sort of positive? And maybe if there's still a negative now, but what is your interaction with people when they tell you, I'm scared to death, either that I'm going to lose my job because of AI or because I don't trust the output from it.

Yeah, absolutely. Maybe I'll start with the output and then I'll talk about the larger questions. So on the output, honestly, I think the most useful thing is here to get people to click. It is, you know, would you like if you could hire 1000 master like, like, you know, new hires, let's say, who you didn't have to manage who did work exactly the standardized way you had it, and you could do that effectively for no cost, would you trust the output for every single one of them? And the answer is obviously no. Right? You don't trust necessarily a new hire's output. You don't necessarily trust. If you go and outsource the workflow, the output of someone overseas, you may not trust your 20 years experienced tax partner's output. Depending on the workflow, you may have someone like review what happens. And so explaining that the reason you hire people in general is because people are really, really good at doing very intelligent tasks, but they make mistakes. And them making mistakes doesn't mean you don't hire people. And it just means you need to put in place systems to catch those mistakes to make sure that the most important workflows you have the most review is to make sure you set all those things up. And so I think broadly, once you kind of start to think of, you know, the new paradigm in AI, in that kind of mindset, that it's extremely valuable, but it will make mistakes and it will, right? There's, you know, if someone comes and says you hey, this thing will literally never make mistakes ever. That's just not possible. And you know, that's not the expectation I would want to put on somebody. So that's the thing for one is just like, you know, helping them understand that it is this thing that, you know, is almost closer to a human than, you know, a rule. But then on the larger thing, like, okay, if it's Going to make mistakes. How do I know when to trust it? And the key is it can't make really dumb mistakes and it can't make mistakes all the time. Right. It has to make mistakes a very small percentage of the time. And mistakes that are predictable. Right. And so there it's just more of like a proof is in the pudding. Like you have to, if you try it and it literally gets everything wrong, well, of course you're not going to use it. And you need to be able to hold to like certain benchmarks and you need to actually do the work. And if it doesn't, just like if you hired, you know, a bad employee, you would fire that employee and you would fire basis. And so I think in terms of convincing people that it works, they just have to try it. And, and we, and you know, we can do case studies and things like that to help with that. But you know, it's so new that at the end of the day it's tough to fully get it until, until you use it and see the outputs.

Thank you for that answer. We're going to move into our fun section. This is kind of the surprise section. So we use ChatGPT to generate 29 different questions to, to ask you. And some of these are getting to know you. Some of these might be future predictions, just different categories. We used it to ask. So we're going to use a random number generator for the first one. So give me a number between one. Actually, I'm just going to go ahead and randomly we'll let you pick on the next one. So give me a moment here. I'm on my random generator. You get question number 22. All right, here we go. You ready?

Let's do it.

What's your favorite finance related joke or meme?

Oh, I'm a big Matt Levine reader. I don't know if other people who listen or either you guys are big Matt Levine readers. My favorite running joke is that everything is securities fraud and that anytime any public company does anything wrong, even if it has nothing to do at all with the security, they'll think they'll get sued for securities fraud because they didn't disclose the like whatever random thing it.

Is, I've got to say with these questions. So we actually built a GPT that generates questions and we just were testing it out and we thought, well, we don't need to generate them live, we've got a bunch of them here. So we included them. But we last week or on the last episode used Gemini and tried to do it live. And the questions was a lot of duds. I'm not, you know, I don't know if where that is on the benchmark of being able to create interesting questions, but. So I was happier with what we got from ChatGPT and we may. In future episodes, we may. We may just do it live. But there were. There were a few duds in here. So there, There was some editing, but they, you know, we thought maybe since we're a future finance show, we should use technology to come up with these. So. All right, so let me see. Let me spin the wheel here and see what we. What we could go for our next question. Let's see. All right. If AI could take over one of your daily tasks, which one would you happily give up?

Oh, my God. I'm currently in charge of stocking the fridge of spin trips live.

We've got a bunch of them here, so we included them. But we last week or on the last episode used Gemini and tried to do it live. And the questions was a lot of duds. I'm not, you know, I don't know if where that is on the benchmark of being able to create interesting questions. But. So I was happier with what we got from ChatGPT. And we may, in future episodes, we may. We may just do it live. But there were. There were a few duds in here. So there, there.

And the boxes where the Spindrifts come in from Amazon are pretty heavy.

Was some editing, but they, you know, we thought maybe since we're a future finance show, we should use technology to come up with these. So. All right, so let me see. Let me spin the wheel here and see.

You know, they're very cheap and we drink a lot of them. So if I, you know, if we get one of those, like what we.

We could go for our next question. Let's see. All right, if AI could take over one of your daily tasks, which one would you happily give up?

Oh, my God. I'm currently in charge of stocking the fridge of spin trips. Tesla robots that come in and start unboxing boxes to put it in the fridge. That'd be nice.

I like it. When you get that, please put a photo. Send us a photo. We'll put it out there to let everybody know where your favorite.

Absolutely.

Or least favorite, I should say. All right, this time I'm just going to let you give me a number. I have 29 here on my list. Pick a number between one and 29. So this will be your.

And the boxes where the spindrifts come in from Amazon are pretty heavy. You know, they're very cheap and we drink a lot of them. So if I, you know, if we get one of those like Tesla robots to come in and start unboxing boxes to put it in the fridge, that'd be nice.

I like it. When you get that, please put a photo. Send us a photo. We'll put it out there to let everybody know your favorite turn to randomly.

Let's do 11.

11. All right, give me one second here. Let me go toward the top. Number 11. I like this one. If you had to explain blockchain to a 5 year old, how would you do it?

Are you a Web 3.0 guy, Mitch? Are you?

I'm well aware. I would tell them that it is like that board game that we always play called Monopoly and the Internet. And just imagine that every time we did a transaction, like I bought something for you or whatever, we like wrote that down on a piece of paper and then we always wrote that down. But this piece of paper was like really sacred stored as if it's like the Constitution, you know, in like, in like Washington D.C. and nobody can touch it and it always stays there. And just like Monopoly that the money's fake.

Wow. Wow. That was well done, sir.

I love it. We had Sacred the Constitution fake and Monopoly for blockchain. Well done. I think on that note, it's probably a good note for us to close here. Just kind of wrap up. We hope you had a little fun there at the end with our random questions. Of course, that we got to keep it, you know, we got to bring technology into everything we do. We're excited to see where basis goes. We wish you the best of luck because we know there's a shortage that's not going away in accounting. We know technology needs to help fill that gap. So, you know, we here at Future Finance are rooting for you and we'll just give you kind of a last word if someone wants to get a hold of you or anything you'd like to kind of share with our audience before we wrap up. It's all yours.

No, I appreciate it. Yeah, I mean, if, if you are, you know, run an accounting firm and you have a cast division or outsourced accounting division, and you want to hear a little bit about what we do. We're, you know, still small and growing, but, you know, please reach out to, you can find my email on the website or some of the contact info. And you know, we're constantly working with, with more and more firms especially those who are really forward looking about how technology is going to change the profession in the nature of this podcast, of course.

Perfect. And we'll put all that in the show notes too, so everybody will be able to reach out to you.

Thank you so much for joining us. We really enjoyed having you and love learning a little bit more about what you're doing.

Awesome. Thanks guys.

Thanks Mitch.

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