How Modern Accounting Software Improves Financial Strategy
Show Notes
TLDR: These show notes were optimized using Claude AI Haiku and are designed to make you listen to the full episode. We even asked Haiku to provide a Haiku summarizing the show notes for your reading pleasure.
Haiku:
Finance takes new shape,
AI-driven transformation,
Shaping the future.
Prepare to be captivated as hosts Paul Barnhurst and Glenn Hopper welcome Sasha Orloff, CEO of Puzzle, to uncover the technological advancements reshaping the world of finance and accounting. In this eye-opening episode, you'll discover:
How generative AI is seamlessly integrating into everyday software, automating tasks and delivering data-driven insights.
The rise of "AI agents" in 2024 - advanced systems designed to autonomously process complex problems and provide tailored solutions.
Sasha's visionary project, Puzzle, modernizing accounting software with real-time, flexible financial data processing.
The transformative potential of blockchain technology to enhance transparency and traceability of financial transactions.
The future of finance reimagined through AI and predictive analytics, empowering more precise forecasting and strategic decision-making
Sasha shares his insightful perspective:
"I'm building Puzzle, which is modern accounting software for growing companies."
"We may not yet be at a point where AI is going to take our jobs, but the future will be divided into two camps: people who use AI and those who don't. One of these groups is going to be left behind."
Don't miss this captivating conversation that explores the intersection of finance, technology, and AI. Gain invaluable insights to stay ahead of the curve and thrive in the rapidly evolving world of corporate finance.
Follow the Conversation:
Glenn Hopper (LinkedIn: https://www.linkedin.com/in/gbhopperiii)
Paul Barnhurst (LinkedIn: https://www.linkedin.com/in/thefpandaguy)
Sasha Orloff (LinkedIn: https://www.linkedin.com/in/sashaorloff)
Puzzle (LinkedIn: https://www.linkedin.com/company/puzzlefin, Website: https://puzzle.io)
Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.
Learn more at Qflow.ai/future-finance
In today's episode:
[01:52] Introduction
[03:30] The latest phase of the AI revolution
[04:49] Pros and cons of chatbots and the rise and evolution of AI agents
[09:15] Recent updates about AI
[12:51] Guest’s background
[14:00] Insights from guest’s career
[16:35] Introduction of Puzzle and future of general ledger in accounting
[25:10] How blockchain can be an effective tool for accounting
[30:22] How AI and predictive analytics can change finance in upcoming years
[34:32] Democratization of data science and various uses of machine learning
[39:05] Lighting Round Questions
Episode Transcript:
Paul Barnhurst: Welcome to the Future Finance show, where we talk about.
AI Bot: How they will come for your job. I for one, welcome our robot overlords.
Paul Barnhurst: Future Finance is brought to you by QFlow.ai the strategic finance platform solving the toughest part of planning and analysis B2B revenue-aligned sales, marketing, and finance seamlessly speed up decision-making and lock in accountability with QFlow.ai.
Glenn Hopper: Yet another podcast from Paul Barnhurst and Glenn Hopper. Man, these guys must really love to hear their own voices. Well, maybe. But also, we're very passionate about the evolving world of corporate finance and the tech that's driving it. Here at Future Finance, our aim is to focus on the technology and trends that are driving our profession, bringing you important updates and understanding around the technology that is going to shape not just finance and accounting, but every profession and industry in the world in the coming years. And you're going to hear me talk about generative AI a lot. And here's why. Unless you've been hiding under a mechanical loom for the past two years, you've probably heard a bit about these LLM chatbots like ChatGPT, Claude, LLaMa, Gemini, etc. These models have the potential to revolutionize the way we do, well, everything. They're more than clever chatbots, and with each technological iteration, we're seeing massive advancements in the technology from expanded use cases to emerging capabilities. And now we're seeing generative AI incorporated into existing software that we use every day. It's giving us a new way to interact with computer systems and software, a new way not just to automate our work, but to derive additional value from computer systems. These tools not only make it easier to analyze and report on data but can offer insights and recommendations based on our data. They are becoming resources that can provide insights and inputs.
Glen Hopper: We may not be 100% there yet, but it's coming and probably sooner than you expect. I'm not saying AGI is right around the corner. Maybe it is, but regardless, we don't need AGI to derive value from these tools. Let me explain why. The latest phase of the AI revolution has been driven by transformers. Um, a transformer, if you're unfamiliar, it's it's a powerful type of neural network that learns context and meaning from sequential data. It does this by tracking relationships between elements using a mechanism called attention, introduced in a 2017 paper called Attention is All You Need by a former Google researchers. Transformers have driven breakthroughs in machine learning and ignited the transformer AI revolution, which, let's say, started in earnest in around well in 2022. From that, we saw the emergence of LM powered chatbots like ChatGPT, and 2023 was clearly the year of generative AI or the year of Taylor Swift, depending on where you get your news. These chat bots are great at one shot answers, meaning they're designed to read your question and provide a response. Current chatbots. They're not great at reasoning. They just read the question and respond. And there are some. Sometimes there's errors around this problems like not being able to solve logic problems or hallucinating, which I'm sure we've all seen. It's where an LM generates a wrong answer and displays it with absolute confidence. Lms also aren't very good at math.
Glen Hopper: There have been some emergent capabilities around this, but as finance professionals, we're not at a point where we should trust LMS to even do something as simple as, say, build an amortization table. I will throw one note out there on this, though. Tools like ChatGPT Data Analyst, which actually writes Python code under the hood, that that's actually very good at these kinds of tasks, but that's all just foundational for the next step where we're going to see true advancements. This next step is in the realm of agents. If if 2023 was the year of generative AI, then 2024 is going to be the year of AI agents. I think there's a lot of confusion out there now about what agents are. Here's the best way to think of what an agent is and what it does. Like I said, LM chatbots read or hear your prompt and then they provide a response. They don't pause to think before they answer. They just start generating novel data. And they're not very good at solving problems that involve multiple steps. They require continued prompting directions and interaction to complete tasks. But an agent could read your prompt, no matter how complex, and then go work through the steps required to solve it. It possesses a level of autonomy to work through these problems internally, and then they output a response when completed. We haven't seen a completely successful iteration of these tools yet, but leading researchers and entrepreneurs are working on solving for this right now.
Glen Hopper: And in the interim, there are plenty off the shelf software tools that are out there right now, many of which can be designed with no or low code interfaces that allow users to create agentic workflows. So an Agentic workflow is doing what an agent does, but it requires more hand-holding and prompting. These Agentic workflows are capable of performing complex tasks. They string together multiple prompts and outputs to solve for these tasks, beyond what a typical chatbot could output in a single response. And when we start to see mass adoption of agentic workflows and ultimately true AI agents, well, that's going to be the moment when even the late adopters among us will have to take notice. About a year ago, I designed a framework for how to build an AI powered finance and accounting chatbot that this chatbot could perform tasks of, say, a junior financial analyst. I'll include a link to the framework in the show notes. Well. I'm talking about agents today because I finally realized how close they are to happening now. They may not yet be truly autonomous, but if we can design agentic workflows that complete the tasks that an agent would, these workflows are deployable and scalable and will give us the same result that a that an agent would. The Agentic workflow that I built was designed to analyze and report on a company's annual SEC report, known as the 10-K.
Glen Hopper: And I built this tool using Make.com. Uh, I used make perplexity and several custom assistants that I built on OpenAI's platform. And I did all of this with zero coding. There's other tools out there that allow users to create these workflows. I'm thinking of tools like Lang, Flow, Mind Studio, Zapier to some extent, and one called crew AI. Uh, crew AI does require some coding, but it gives builders even more flexibility to create these agentic workflows. If you aren't familiar with agents, my pro tip for the day is you need to learn about them right now. I'll post some links in the show notes to where you can learn more. Look, I know we're all busy, but if you're listening to this show, we know you're interested in the future of finance and in learning how you can integrate AI into your professional career. It is incumbent on all of us to not just pay attention to this tech but to learn how to use it. Because while we may not yet be at a point where AI is going to take our jobs, I do believe that the future will be divided into two camps one people who use AI to improve their lives and work tasks, and two those who don't. One of these groups is going to be left behind.
Paul Barnhurst: This week's news story is brought to you by your co-host, Paul Barnhurst, the Fpna guy. What some of the latest news out there about AI? Well, just recently, Claude AI, which is backed by anthropic, announced they released their new AI iOS app. And one of the things they mentioned. So I had to test it is it will access any of your photos on your phone and analyze them. So of course, what's the first thing I did? I uploaded a photo of myself. Let's see what I had to say. So I said. Here we go. Analyze this picture. That's it. And it shows. The image shows a close-up selfie of a man with a long reddish blonde beard, wearing glasses and a green baseball cap. He has a thoughtful, slightly concerned expression on his face. Pretty accurate. The man is wearing a black jacket with the Amplio logo on it, suggesting he make work for or be affiliated with that company. And then it tells me the the picture was taken indoors, probably at a home or workplace. Does a really good job. Then I said, let's have a little more fun. I asked, how old is the man? And it said based on the man's appearance in the photo, including the gray hairs in his beard are technology, some visible wrinkles and overall mature look. I would estimate he is likely in his late 40s to mid 50s.
Paul Barnhurst: Then it goes on to note how you can't tell the age from one picture. I'm 48. Nailed it. Next. I asked it if I was overweight. You probably all laugh at this one. It said. From what I can see in this close-up photo, the man does appear to be overweight or obese. Ouch. Based on the fullness in his face and neck area. However, it's important to note the weight assessments from facial features alone can be unreliable. Then I had a little more fun. I said based on the photo, do you believe the man is well educated? And it's like, I can't answer that there's not enough information. I said, do I appear to be healthy again, not enough information. So my new story is plot. I now has an iOS app, not Claude I yeah, Claude, I just like ChatGPT. It's kind of a fun one. Go ahead and upload some pictures, ask it to analyze them. Does a really good job. It did nail that. I'm overweight and it just about nailed my age. I was quite impressed. The technology just keeps getting better and better. So let us know in the comments. What is the technology that you love today? What is you? What are you seeing out there? What's your news story?
Sasha Orloff: Ever feel like your go-to.
Paul Barnhurst: Market teams and finance speak different languages. This misalignment is a breeding ground for failure, impairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. Meet Kubeflow Dot AI, the strategic finance platform purpose built to solve the toughest part of planning and analysis B to B revenue. Kubeflow quickly integrates key data from your go to market stack and accounting platform, then handles all the data prep and normalization. Under the hood, it automatically assembles your go to market stacks, make segmented scenario planning a breeze, and closes the planning loop, create airtight alignment, improve decision latency, and ensure accountability across the teams. Welcome to Future Finance. This is our inaugural episode. I am your co-host, Paul Barnhurst, so I'm super excited to have here with me Sasha Orlov and my co-host Glen Hopper. So a little bit about Sasha. He comes to us from San Francisco. He is the CEO of puzzle. We'll talk more about what that is in a minute. He hosts the finance podcast by turpentine, and he previously served as the founder and CEO of Lendup and Mission Lane. So Sasha, welcome to Future Finance.
Sasha Orloff: Thanks for having me. Excited to be here.
Paul Barnhurst: Yeah, we're really excited to have you. So why don't we start with you telling us a little bit about your background? Tell us about yourself.
Sasha Orloff: Yeah, well, my name is Sasha, as we just heard before, and I'm building puzzle, which is modern accounting software for growing companies. And, my sort of background was I've worked in, in fraud modeling, a lot of math inclined types of roles. So I worked in, in predictive modeling and fraud modeling. I worked in banking infrastructure, I worked in lending and payments, worked in venture capital for a little bit. And, uh, got excited about this idea of building a business where you could borrow money over a mobile phone. And so when I couldn't find the team to invest in as a venture capitalist, I got excited and and I joined a startup incubator called Y-combinator in the Bay area, built Lendup, ended up spitting out a division called Mission Lane. Um, each of those grew to a couple hundred million of revenue, um, and took some time off and, uh, decided to start something new.
Paul Barnhurst: How did you pick the exciting world of accounting as your next project?
Sasha Orloff: Well, I mean, I think as most of the listeners will, will know what is more exciting than accounting? Not many things. Um, but effectively, at the end of the day, I did what I got excited about was, was solving a problem. And the way that this dawned on me was, was sitting around this table as a CEO. Um, I would look at all my direct reports and everybody had beautiful software. They would open up their laptop and they could answer in real time everything except the finance team, and they would have a pad of paper and a pen, and they would write down all of these questions. And I was constantly frustrated and wondering, like, why can't you do this? And then you have one of those kind of moments where you're like, oh my God, it's not the finance team's fault, it's their software. Um, and we realized that we just as an industry, have a high tolerance of pain for mediocre software. And when that thing hits the mind of an entrepreneur, it's hard to ever let go of that excitement.
Paul Barnhurst: Love that. Glenn. You've written a book all about technology and corporate finance. What's your take? Is it mediocre software?
Glen Hopper: Yeah. Well, so I mean, this that's kind of a big struggle in, in my career and it's why. So first off the software you have isn't typically great. And then if you want to get something developed for a back office for finance and accounting, you're going to the back of the list. You're not getting, you know, moved up to the to the top because you're not, uh, generating revenue. So in my career, it led to a lot of, uh, citizen coding where you're trying to make up, you know, you're running, uh, an access database on your own laptop or something to try to get information and just. Yeah. So. Yeah. So yeah, no, I completely relate and I and I'm right there with you. What is more exciting than the world of accounting and just figuring out depreciation schedules and being sure you're following GAAP. And yeah, it's all it's riveting stuff. Yeah. We're gonna we do we're gonna run off our accounting listeners is what's going to happen. But if we're if we make too much fun.
Sasha Orloff: Bring it on. We got a, we got a whole big team ready to build, ready to build whatever. But I think you touched on something which is kind of hits to the point of, of why I get so excited about puzzle is when a, uh, somebody in accounting or finance needs to learn how to code to do their job. That's like a fault of the software of the industry. And that's a place where we think there's an opportunity that gets me excited.
Paul Barnhurst: Love that. Can you tell our audience, what do you see as the future of kind of the general ledger in accounting? I know puzzle is building a tool to help record all those transactions, but where do you think we're going here in maybe the next few years?
Sasha Orloff: Well, it's it's one of those weird industries where it has been decades, like 25 to almost 40 years since we've had a new, meaningful player in the space of a, of a general ledger. And if you were to rethink, how would I build this from the ground up today, with everything we know today in how software can be built and designed and all the technology advances, you just would build it differently. And so we did the hard work first of building the core general ledger from the ground up. Um, and we focused on two pieces that I thought were really important, uh, outcomes. One is to improve the trust and transparency of the software and the whole concept of accounting, really, aside from like, the foundation of data for all wonderful finance people to be able to model. And think about. Predicting the future is like we have to have a trusted, accurate source of like, what is the financial truth of a company? And today that's done through a lot of manual steps. And then the last piece, as a CEO, my favorite and most valuable and most impactful conversations are the debates that we have about what to do with the capital and the people, the resources of the company. And that is typically with a finance team. And right now, so much of the work is manual and rote and repetitive that you're spending so much time and resources just getting to the truth. You don't have enough time to talk about how do we actually improve the business, because we're spending all of our time just reporting on the business.
Glen Hopper: So I'm wondering, I'm thinking about, you know, double entry accounting is is double entry accounting goes back to Luca Pacioli. There's your, um, 1494. Oh my gosh. Yeah. It's someone who knows Luca Pacioli. Perfect. It's, um, the basics of accounting stay the same. But I'm wondering where you're going with this. Is it more like dimensions and tags or. I'm picturing it. Uh, you said, you know, capital and human resources. I'm thinking about, like, project accounting and cost accounting. Like, how did, like, what's the vision there? How does all this fit together?
Sasha Orloff: Yeah, I'll talk about it a bit at a high level. And then we can dive deeper into whatever parts. Um, make sense. But just to keep everybody on the audience that that might be less familiar with accounting. So your general ledger is like the backbone of accounting. It's it's where you actually are rendering your journal entries to generate your financial statements. Your. And so I think that's fairly well understood, the history of, of the accounting software today that we have online is really anchored around a fixed chart of accounts that have labels in it or your journal entries, and those render your sort of financial statements. But I think back to your point. If you thought about it a little bit more as like a horizontal data system in which you can have infinite tags on each money event. So let's just call them money events, because they could be transactions that actually happened. I bought a thing with cash. It can be things that are going to potentially happen in the future. Um, I like am anticipating this accrued expense that probably will happen or might not happen or like something that is likely to happen. I have an invoice, or I have a receivable or a or a payable that, that I'm going to put on the balance sheet and I'm gonna pay it off. Um, it's just a matter of when, like if your apple, you're going to wait 90 to 120 days because, well, you can but it still sits there.
Sasha Orloff: Okay. But if you were to think about what are the changes that you're going to make today, you would think about the architecture slightly differently so that you could have multiple views. So we started off with two things that were me. The most frustrating was you have to pick cash or accrual books. But that's kind of crazy because I want both. Like I need my crew books to see the financial health of a company, but I need cash books because I'm doing budgeting and forecasting. And like, you know, as much as I wish I can't make payroll on accruals, I got to do it with like, actual cash. People like getting paid. Second is when you think about how we think about building trust in an ecosystem, there's a lot of really exciting stuff to learn from, like the world of blockchain. And you could build a checksum, you could build an immutable ledger that's append only. So it gives you the flexibility to have as many different changes as you want. But you can never delete anything. Could you imagine how do you have trust in a system where you don't know who wrote in what it's like Excel? Like I can write something in Excel and then you can overwrite something in Excel, and who knows what that cell came from, where it came from, what was the source.
Sasha Orloff: And then also, you should have some enforcement mechanisms on accuracy around supporting documentation, around pulling transactions directly from the source, trying to avoid and tag manual or AI generated content that can't be supported with evidence. All of this stuff brings together something where you have both trust and visibility into a single system, and you're not limited. And then if you have all the underlying data and I'll be quiet, because I can talk about this for years and I love it. Like you can then do all this non accounting related stuff. What's variance analysis. What is spending? How am I seeing revenue by customers or by product or by account. You shouldn't have to have these limitations in your accounting software, just like you wouldn't have limitations on your pivots in Excel. Could you imagine? Excel is like you can only have one pivot. You have to choose it one time and you can't ever change it. You'd be like, well, like sometimes I want to see my revenue by customer, sometimes by product, sometimes by account, sometimes by geography, sometimes by department. But like, you can't really do that in your accounting software today, which is kind of crazy.
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Glen Hopper: I'm thinking about I mean, just my my CFO brain is going into overdrive here. I'm thinking about, uh, topside entries, you know, around an audit that you have to make. And the confusion around this is what the book said. But then we had to make this adjusting entry and just the and then multiple people making entries to the GL. And it's not having I mean, you can go into the admin and see who made the entry or whatever, but it's a it's it's work. It's not uh, it's not intuitive when you, when you make these entries. So that's this is fascinating to me.
Sasha Orloff: And it's pretty common across almost all other softwares where there's a level there's audit levels and there's transaction. You can like slice and dice, the history of things and version controls and manipulate. You're not stuck to like one view of your data. Like where else are you? Like if you're doing product analytics or whatever type of other website analytics, you can't just like, oh, you get this one view and this one view only like, how could I possibly make an effective decision about improving a sales funnel, or a conversion funnel, or product engagement or tour if I'm only allowed to have like one view of the data analytics, like that's crazy, that just doesn't exist. So we're trying to bring a lot more of that visibility, control and flexibility to people so they they can ask questions in multiple ways, not just the way that gap decides to do it. In this one way. You have to do that too. So we do that. But like you shouldn't be limited to one view.
Paul Barnhurst: I love that. The end statement, that's one I use a lot in Excel. Yeah. So you talked you've talked about a number of different things. And I think that event driven and the different approach to accounting and being able to look at a number of different things is really important. Excited to kind of see how that grows. But another area talked about is improving, you know, the transparency, the security, those type of things. So are you using blockchain with that or how do you think about blockchain? Because I've heard a lot of of that being a tool to use with accounting. Right. To be able to lock down those transactions and really have that history. So how do you think of that in all this?
Sasha Orloff: Yeah, I mean, there's a fairly simple similar approach from kind of blockchain to, um, sort of a data transformation like accounting. Accounting is effectively as a data transformation. Not not to dumb down. It's a lot of rules. It's really complicated. It's important. But at its core, if I think about it more like a modeler or a mathematician, there's a starting point and there's a set of transformations and there's an ending point, and blockchain builds the most trusted system that we've developed today to, in order to go from like, here is the origination of a coin or whatever sort of thing that you are tracking. And here's all the changes that have happened over time. In that case, it's a change of ownership. But at its core, like accounting is kind of the same thing. If we take a computer that you bought, like there was some transaction that generated into something that generated some treatment that generated into a slew of other components. And so in its simplest core, for cash, you would maybe just trace that computer to like, a computer expense. But obviously, if you're going to depreciate it as a fixed asset, it goes to the balance sheet and then it goes and then it gets depreciated sort of over a period of a couple years or so. All of that is just a series of events of moving this money event through a system around. You should be able to trace that whole thing from the end state, whether it's your fixed assets on your balance sheet or your computer, expense on your profit and loss statement, all of those sort of can trace all the way through.
Sasha Orloff: So if you brought that same level of transparency from the original source who did everything along the way, whether it's software or a human, whether it's been split, whether it's on your cash books, whether it's on a balance sheet when it transfers back over to expense, all of that is just a series of traceable events. So we brought that level of visibility and transparency to every single thing that happens in the system. And so that brings a level of like transparency, where you can not only just see and have a higher degree of trust of what your financial statements are today, you can trace it all the way back through the schedule down to the transaction. Everybody that touched on way. But something else that's really cool is you can also see the version of your financial statements at any point in time. So there's a state today. But you know, if you're looking back at 2023, let's say there's a state of what 2023 looks today. But there might have been a state that looked slightly different a couple of months ago because maybe you had to make some adjusting entries or, you know, something else came in that you have to modify, but you're now limited to like one view as opposed to like show me what this looked like and then show me the variance between these two things. Show me everything that just happened between these time periods. And so what that does is it just gives confidence again to like what happened, what changed, what's different? Did we expect it? And if not, how do we fix it?
Glen Hopper: I. So I feel like, um, if I'm an auditor, I love this because the the tracking and the control and understanding or if I'm a company preparing for audit and it's you're trying to have your your narrative of what happened here. And if an entry changed for some reason or something is off that it wasn't, you know, it looked one way in a K and certainly you don't you're not going to go back and change locks periods. But things things happen over time. And being able to have this trail seems like that would be. A very important piece of information for companies and for and for auditors as well.
Sasha Orloff: Yeah. We're excited. We're actually kicking off a few audits and that's part of our company goals for this year. So actually if any listeners are sitting on QuickBooks or Xero and like, oh my God, I got to prepare for my first audit. We would love to bring the power of 50 product managers, engineers, lots of former public auditors, CPAs, tax lawyers, tax experts, uh, to the table to help you pass your audit for the first time because we think this could be really powerful. Um, and the flip side is, again, as a CEO, you guys actually probably had to work in this and understand this. All I saw from the CEO was a bunch of people dressed in suits in our worst conference room for months, with a stressed out finance and accounting team going, oh my God, what are they going to ask for? What are they going to do? How are they going to do like, this is a stressful time for everybody. And like if we can bring the stress level down, then I think it has a better outcome. What we all want is we want accurate financial statements. We also want to review what are the controls and what are the risks in an area. So much of the audit fees are caught up in sampling and reviewing and doing stuff where software can help that, um, we think can help alleviate that a lot that we can spend more of the time. Just like back to my favorite conversation with the finance team. Let's talk about how we improve the business. Let's talk about how we improve controls. Let's talk about how we improve risk to the business. Let's spend less time worrying about is this accurate or not. We should be able to improve that with software. Um, so that's kind of our goals for 2024. What year are we in 2024. Yeah, yeah.
Paul Barnhurst: I want to kind of shift gears here a little bit. And Glenn and I have a couple AI questions. So I'll kick one of these off. And then I'll give you a couple here to ask Glenn. But how do you envision AI and predictive analytics changing finance. So not just accounting but the broader finance in the next, you know, 5 to 10 years?
Glen Hopper: I would add to that. I mean, especially with the with blockchain and with the traceability, you're adding a whole new level of data to to what's in the general ledger. So more data means more analytics and more and more, uh, everything that you can do around that. So I'm very curious, as you guys are building this out, how you, how you envision, you know, specific to what you're doing, but also maybe just the industry in general.
Sasha Orloff: It's a fun topic of conversation because it's hard to think about how much how fast AI is evolving and the capabilities get just mind blowing results. Every six six months, something like insane is happening, so it's a really fun time to be learning about and playing with this. So it's it's hard to know all of the things, but I'll share a couple things that I'm growing in. Confidence are going to be changing. So as new companies emerge like puzzle and there's others out there, actually the core underlying data architecture, it has to be built in a way that allows at least LMS in the version of, of AI to be able to read and understand it. So aside from just the hallucinations, just the retrieval of like, which revenue are we talking about, which burn are we talking about, which profit are we talking about? Um, if these things aren't structured and labeled the right way internally, then it's hard to retrieve it. It's just like, you know, putting together you know, one of these, like, things you buy on Amazon that comes with like random left hand drawn descriptions of like instructions on how to put together a table. And you're like, man, this is even worse than Ikea. Like, how could I possibly do this? These instructions are terrible. So if we don't set the foundation of like, rules and and visualization the right way, it's hard for AI to do anything really that impressive.
Sasha Orloff: So that's kind of the underlying architecture and database design. So things will be designed differently today. And I think that's going to be a challenge for a lot of the legacy companies, because it's hard to re-architect systems that already have lots of people on them. I think today there's a lot of opportunities to where I can help improve accuracy. So that's a place where we really focus because we're accounting. And so we sort of think about us. We're the data underlying your financial insights and modeling. And so we spend all of our time focusing on the accuracy and integrity of the data. And so when that happens, that makes everything like the garbage and garbage out. We're solving the garbage in sort of problems. Um, and then there's a lot of sort of helpful things where right now, a lot of anomaly detection is done with either a set of very discrete software rules. If this, then this, and or humans and you only have so much time in a month or in a time period to like do all of the potential scenarios. But where I can do is it can look at almost every type of change or anomaly in the system and highlight and say, hey, this is something that you should look at now. So I think that's the state of where we are today. A lot of in our experience, a lot of that financer FAA's job is like doing a lot of one ad hoc requests.
Sasha Orloff: And that is just about there's a specific question. This person knows how to ask that question. They know what they want, and the finance person has to now allocate this across all of their time and all the things they have to do, and then go like retrieve a custom thing. And so if we can either allow the retrieval of data or in a permissioned way, the sharing of certain data, like the CTO should know what their budget is and they should know how they're spending against that budget, and should need to ask for a bunch of analysis and have a 30 minute meeting. Um, along the way. I know that's just like one small example, but we can connect people to data in a way that like, you know, when you learn SQL, it's easier to like ask and get answers to data. And if you learn one of these or more modern data visualization tools, you can kind of play around a little bit more. Um, and I think AI is just going to remove the need to be technical, but still get the answers you want because you know what you want to ask for. You just don't know what the answer is. And it's kind of gatekept in a secret place that the finance team has access to.
Glen Hopper: Yeah, I love that because I've, um, you know, for years we've been talking about democratization of data. But to me, what AI does and generative AI specifically using LMS, it's democratization of data science. Now, you still have to know kind of you have to know the questions to ask. Just like in, uh, in finance and accounting, you have to know the difference between EBITDA and net income or what, you know, there's components that you have to understand. But in the if Python and SQL were a barrier to entry to that exclusive club of data science, those now get knocked down when you can interact with the data through natural language. That's kind of what I'm what I'm saying.
Sasha Orloff: Yeah. So just the easy application that we use in terms of sort of accounting to help make the core underlying value of data. And the example I like to give because it's fun and it happens all the time in a fast growing company full of engineers is, you know, when you buy something from Apple, you know, what you bought from Apple? Uh, and it might be a computer, but it might be a bunch of other things that aren't a computer. And if it's a computer and it's a, you know, these current apples that are, you know, three, four, $5,000, like you should you have to depreciate them. Um, so in order to know that you have to one provide the receipt. But, you know, you know, not everybody always does or you have to have context, uh, because the thing that holds up your books closing a lot of times is these bookkeepers and accountants on the other end, like, I don't know what this Apple thing is. And they got to trace down who the person is and ask them along the way. So if you could just let them respond to an email or a slack message and say, this is a computer, right? You don't have to know anything about accounting.
Sasha Orloff: You just have to know that I know what it is. It's a computer. I write this computer. Oh, this is a I we just hired Sasha. He's a developer, and he needed this, like, special computer that's, like, really expensive. And so he bought a computer. I know what it is. Llms are a really good job. Okay, well, let's now look at the chart of accounts. What should this be? Now let's look at the accounting policies. Oh, it looks like this qualifies for depreciation policy. Let's move it over to the fixed asset register. Let's automatically depreciate it according to the policy of like a three year depreciation policy, no terminal value or whatever. You know the company is for things and notes expense in every month. So just by this person saying this is a computer, we've now taken care of over three years of journal entries with the words this is a computer because of I, I think that's a really powerful sort of example. And it brings it to light in a really, really cool way. Fun way.
Glen Hopper: Yeah. And I think everyone right now, it's so, you know, machine learning has been used to, to great use from fraud detection to classification prediction. I mean, there's a million use cases for machine learning. Everyone's talking about generative AI because it's so accessible to the public, where trying to explain a random forest classifier to someone who's not in. It's like that's, you know, no one cares. But when you can type into a chatbot, you know, write a Limerick about depreciation or whatever, and it spits out something data scientists and people who've been using machine learning and even some, you know, sort of, uh, uh, hybrid finance people who've realized, oh, wow, I can do more, uh, with, with a Python program than I could in Excel. I know Paul May Paul may disagree with that, but.
Paul Barnhurst: No, I wouldn't.
Glen Hopper: Disagree. So it's funny that I feel like the machine learning and the AI applications that were pre generative AI and that are, you know, more tested and true at this point are almost getting overlooked. But would you say, you know, you mentioned kind of the limitations of where we are with generative AI. And I think they're being we're overcoming limitations every day. If it's hallucination we're addressing it through retrieval, augmented generation. And we're, you know, putting citations and whatever we're doing to address those. But is there anything like right now if I'm in finance and accounting, and I'm sure you guys are thinking about this as you develop out the product, but are there are there promising AI and machine learning applications that are specific for finance people that they could be looking at right now, today to, um, help them in their job to improve their forecasting and decision making.
Sasha Orloff: I have two answers. I have a biased answer called puzzle IO, and then I have a slightly, um, you know, less exciting but less biased answer as well, which is my my core underlying belief is one of the things that's been holding the entire industry back is the lack of an underlying data platform that's built for finance native to AI. And so if I think about what, a couple years from now looks like, and then I'll bring it back to today, um, we think that there is an unbundling opportunity for the ERP that is designed in the world of AI.
Paul Barnhurst: Thank you for sharing that one. We have totally enjoyed having you here. Honestly, I think we could probably talk for another hour, but I'm not sure the guests would want to listen that long. Well, we'll, uh, we'll go ahead and end it there, but we just want to thank you so much for joining us, Sasha, being on the first episode. And, you know, just last question. If anyone ever wants to get a hold of you or maybe learn more about you, what's the best way for them to do that?
Sasha Orloff: I am fairly active on LinkedIn and Twitter at Sasha Orlov, or I think just LinkedIn.com forward slash Sasha Orlov. That's the best way. Uh, and uh, yeah, we welcome feedback ideas. Uh, and, uh, hearing what you guys are up to.
Glen Hopper: Um, well, I want to say, uh, you've done, uh, what some may consider impossible. You've actually made an interview about accounting really exciting. So this has been this has been great.
Paul Barnhurst: I actually laughed while talking about accounting that that alone means you get a gold star.
Sasha Orloff: We're bringing fun back to accounting. Yeah. Perfect.
Glen Hopper: Um, so.
Glen Hopper: For our last segment, this is something we're trying out. We were, uh, Paul and I were, um, spitballing right before the show on what we wanted to do for our our, you know, different customer segment each time. So what we've done, we have a Bank of lightning round kind of personal questions. Um, and rather than have sort of canned questions every episode, we thought, well, let's put them into a database and we'll, we'll just generate, we'll pull one random, but then we added another layer to it. So we're going to give you three questions for each question. Before you hear them, you can either take one of our canned questions or the one that the LM is going to generate for you.
Sasha Orloff: Yes, I love it. That's what I was hoping for. Uh, who gets it?
Glen Hopper: So. And I think we'll try.
Sasha Orloff: Pete who has who has the harder question the more fun question.
Glen Hopper: So this week.
Glen Hopper: We're using Gemini. Um, and I don't know, I wonder if we should not expose that until after, but I'll go ahead and say we're using Gemini this week. We'll kind of roll through the LMS and see what kind of questions that they come up with. So, um, for, for question number one, would you rather take a random selection from Arcand questions or see what Gemini comes up with?
Glen Hopper: Oh, I.
Sasha Orloff: Mean, I feel like I have to go with Gemini. No offense, but your first episode. I don't want to insult you guys in your inaugural episode, but, like, let's go with Gemini.
Glen Hopper: Okay.
Glen Hopper: So let's see. Let me push the magic button here and say.
Glen Hopper: Okay. It's a all.
Glen Hopper: Right. So Gemini wants to know how do you unwind? How do you unwind or recharge after a long period of focused work?
Sasha Orloff: Uh, I mean, that's a good one. Um, I we'd have to ask Sasha from 15 years ago because I don't unwind. All I do is work, work, work, work, work. That's a long way. But if I were to have a time, I would say, um, my favorite thing if I had to do anything would be to go skiing. Of course, you can't really do that after a long days of work, because I live in the Bay area and be too long of a drive, I actually just, I if I don't have any other things, I take a bike ride or I walk around the block. Um, and I know it sounds really cheesy. Maybe this is just because I'm in San Francisco and that's kind of life. I actually, I take off my shoes and socks and I just stand in grass, like, at the park. Um, there's just something about having, like. Grass, but maybe a bit of of human and dog feces on your feet and standing there to really just like, kind of connect it just somehow it grounds me and I like it. And I'm that crazy person that people are like, who's, why is this person in the morning drinking or afternoon drinking coffee or tea, sitting, standing barefoot in a park with a bunch of stuff. But that's what I like to do.
Glen Hopper: I would add to that you could take the advice that Bruce Willis got in, um, Die Hard, where you take your shoes and socks off and you make fists in the grass and feces with your with your feet. I don't know if you remember from that movie, he wasn't in the grass, he was on the hotel carpet or whatever it was. But that may just be a new addition to help you relax even even more. And see, we didn't even need AI for that. We just needed the magic of an 80s of an 80s action movie.
Paul Barnhurst: All right, we're going to do, uh, do another question here. So do you want us to pick a random question or do you want AI to pick question number two?
Sasha Orloff: Pick a. Now let's now let's go human form.
Paul Barnhurst: All right. So we used a random number generator. And here is the first one we had for you. This one's pretty straightforward, but what do you do outside of work? And I think you kind of answered that in the first one.
Glen Hopper: Oh, see.
Paul Barnhurst: All you do is work. So I might just pick a different question. Give me a number between 1 and 8.
Sasha Orloff: Uh. Number seven. It's my old soccer jersey.
Paul Barnhurst: Do you? Okay. And you can't answer your company to this. Okay. Do you have a favorite finance software app that you like to use?
Glen Hopper: Do you have a favorite.
Sasha Orloff: Finance software app? Well, we're like the Switzerland of finance software. We have to work and play well with everybody out there. Um, but if I had to pick one, I would say one that I'm really excited about. Um, is runway comm.
Paul Barnhurst: I knew you were going to say that. I know, uh, CC really? Well, I know you guys work together.
Sasha Orloff: Yeah, it's it's it's so much a financial planning and analysis looks the same. And runway looks very different. Now, whether that's good or bad and whether or not finance wants to adopt it, you know, TBD. I wish them obviously a lot of success as I do a lot of other people, but that feels like one of the most different pieces of finance software that I have seen in a long, long time. And that gets me excited because I'm curious to see if it works, I love learning.
Paul Barnhurst: Love it. What do you think, Glenn? Should we give them one more? You want to do one? Let's go. We'll wrap up.
Glen Hopper: Let's go. All right. Yep.
Glen Hopper: Let's let's go.
Glen Hopper: One more here. Uh, so, so, uh, human question or AI question.
Sasha Orloff: Your question this time. Human.
Glen Hopper: All right, so what we've got for this one is what's the most surprising or unexpected outcome you've gotten from interacting with an LM?
Glen Hopper: What's the most surprising? Um.
Sasha Orloff: Let's see.
Paul Barnhurst: You are now out of time. I.
Sasha Orloff: One of the things that, um, I like to do, I guess we sort of talked to us before, but that I like to do is surprising as a theme is, um, taking sort of large sample data sets and just putting it in to sort of ChatGPT. So we did a survey, for example, we surveyed 150 CFOs about what bank they used and why they used it. And we got all sorts of crazy results, as you could imagine, of like large unstructured language. Like my CEO just said, I have to do this and but but I don't really want to. And I had to fight with this along the way. And we just fed it all into ChatGPT. And I said, I put it on one tab and I put what are the key themes in here? And then I did myself and was like, all right, let's go through and see what I can come up with along. And at the end, I sort of shared it along the way, and it found a couple like really interesting insights and themes that I missed, and I didn't pick up on it. And we're going to publish that, uh, hopefully soon. Um, and so we'll, we'll share that. But um, but yeah, but that, that was I think the most fun was like, it beat me uh, as it's now, uh, that's, you know, a little humbling, but, uh, it was fun. It was fun to see.
Paul Barnhurst: Thank you for sharing that one. We have totally enjoyed having you here. Honestly, I think we could probably talk for another hour, but I'm not sure the guests would want to listen that long. Well, we'll, uh, we'll go ahead and end it there, but we just want to thank you so much for joining us, Sasha, being on the first episode. Thanks for listening to the future finance show, and thanks to our sponsor, who Flow.i. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice and may your robot overlords be with you.
