Show Notes

Welcome to a special episode of our podcast! Today, we're thrilled to bring you an exciting Podswap. Stay tuned for an engaging conversation you won't want to miss. Here are the show notes from our previous episodes.

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 is 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/saor/)

• Puzzle (LinkedIn: https://www.linkedin.com/company/puzzlefin, Website: https://puzzle.io)

Future Finance is sponsored by Qflow.ai. Learn more at Qflow.ai/future-finance.

Financial Modeler’s Corner is sponsored by the Financial Modeling Institute. Learn more at https://fminstitute.com/podcast/.

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

Full Show Transcript

Paul Barnhurst: This week on Financial Modelers Corner, we're going to bring you a special bonus episode from another show that we really like. We're going to bring you an episode featuring Sasha Orloff, the CEO of Puzzle, a new AI-based accounting tool. The name of the podcast is Future Finance. The co-hosts are Glen Hopper, an AI expert, former CFO, and amazing host, along with Paul Barnhurst, who co-hosts with Glen Hopper. We hope you'll really enjoy the show and this special episode where we bring you some content about AI and technology because as financial modelers, the technology is changing quickly and I think learning about tech, listening to other shows will help us be better modelers. So I hope you enjoy this special bonus episode where we bring you something other than your typical episode, so hope you enjoy future finance.

Paul Barnhurst: Welcome to the Future Finance show, where we talk about.

Paul Robotic: 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.

Glenn 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. A transformer, if you're unfamiliar, 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 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 LLM-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 chatbots 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 are errors around these problems like not being able to solve logic problems or hallucinating, which I'm sure we've all seen. It's where an LLM generates a wrong answer and displays it with absolute confidence. LLMs also aren't very good at math.

Glenn Hopper: There have been some emergent capabilities around this, but as finance professionals, we're not at a point where we should trust LLMs 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's Data Analyst, which actually writes Python code under the hood, are 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 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, LLM 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 it outputs 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 this right now.

Glenn Hopper: And in the interim, there are plenty of 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 the 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 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 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.

Glenn 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. 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: 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.

Paul Barnhurst: This week's news story is brought to you by your co-host, Paul Barnhurst, the FP&A Guy. What are 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 have to say. So I said. Here we go. Analyze this picture. That's it. 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 works for or is 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? It said that based on the man's appearance in the photo, including the gray hairs in his beard are technology, some visible wrinkles, and an overall mature look.

Paul Barnhurst: I would estimate he is likely in his late 40s to mid-50s. Then it goes on to note how you can 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. Based on the fullness in his face and neck area. However, it's important to note that 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 news story is Claude AI now has an iOS app, ClaudeAI just like ChatGPT. It's kind of a fun one. Go ahead and upload some pictures, and 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 with the technology just keeps getting better and better. So let us know in the comments, what is the technology that you love today? What are you seeing out there? What's your news story? 

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Paul Barnhurst: Welcome back. Try two. This time we're recording this inaugural episode of Future Finance, so I'm super excited to have here with me Sasha Orloff 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 as I've worked 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 got excited about the 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 I joined a startup incubator called Y Combinator in the Bay area. Built Lendup, ended up spinning out a division called Mission Lane and each of those grew to a couple hundred million of revenue, and took some time off and, 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 know what is more exciting than accounting? Not many things. But effectively, at the end of the day, I did what I got excited about was solving a problem. And the way that this dawned on me 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?

Glenn Hopper: Yeah. Well, so I mean, that's kind of a big struggle 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 top because you're not generating revenue. So in my career, it led to a lot of citizen coding where you're trying to makeup, you know, you're running an access database on your own laptop or something to try to get information and just. No, I completely relate, 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 it's all riveting stuff. Yeah. Anyway, we're gonna run off our accounting listeners is what's going to happen. But if we make too much fun.

Sasha Orloff: Bring it on. We have a whole big team ready to build whatever. But I think you touched on something which is kind of hits to the point of, why I get so excited about Puzzle is, when 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: I love that. So can you tell our audience, what 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 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 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 and we focused on two pieces that I thought were really important 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 we actually improve the business because we're spending all of our time just reporting on the business.

Glenn Hopper: So I'm wondering, I'm thinking about, you know, double-entry accounting goes back to Luca Pacioli. There's 1494. Oh my gosh. Yeah. It's someone who knows Luca Pacioli. Perfect. It's 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? You said, 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. Make sense. But just to keep everybody in the audience, that might be less familiar with accounting. So your general ledger is like the backbone of accounting. It's where you actually are rendering your journal entries to generate your financial statements. So, I think that's fairly well understood. The history 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. I like 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 payable that I'm going to put on the balance sheet, I'm going to pay it off. Um, it's just a matter of when, like if you're 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 had to pick cash or accrual books. But that's kind of crazy because I want both. Like I need my accrual 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 couldn'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 the 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 you have trust in a system when 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. 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.

Sasha Orloff: 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. You can then do all this non-accounting-related stuff. What's variance analysis? What is spending? How am I seeing revenue by customers by product, or by gel 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, sometimes I want to see my revenue by customer, sometimes by-product, sometimes by account, sometimes by geography, sometimes by the department. But like, you can't really do that in your accounting software today, which is kind of crazy.

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Glenn Hopper: I'm thinking about it, just my CFO brain is going into overdrive here. I'm thinking about, top-side entries, 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 then multiple people making entries to the GL and it's not having. You can go into the admin and see who made the entry or whatever, but it's work. It's not intuitive when you make these entries. So, this is fascinating to me.

Sasha Orloff: And it's pretty common across almost all other software where there's a level there's audit levels and there's transactional. You can 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? if you're doing product analytics or whatever type of other website analytics, you can't just like, you get this one view and this one view only, 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 can ask questions in multiple ways, not just the way that GAAP 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 the end statement, that's what I use a lot in Excel. So you talked about a number of different things. And I think that event-driven and the different approaches 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 the transparency, the security, those types of things. So are you using blockchain with that or what do you think about blockchain? Because I've heard a lot of that being a tool to use with accounting. Right. To be able to lock down those transactions and really have that history. So what do you think of that in all this?

Sasha Orloff: Yeah, there's a fairly simple similar approach from kind of blockchain to, sort of a data transformation like accounting. Accounting is effective as a data transformation. Not to dumb it 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 in order to go from here is the origination of a coin or whatever sort of thing that you are tracking. And here are all the changes that have happened over time. In that case, it's a change of ownership. But at its core, 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 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 the 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 like 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?

Glenn Hopper: So I feel like, if I'm an auditor, I love this because of the tracking and the control and understanding, or if I'm a company preparing for an audit and it's you're trying to have your narrative of what happened here. And if an entry changed for some reason or something is off that wasn't, 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 happen over time and being able to have this trial seems like that would be a very important piece of information for companies and for auditors as well.

Sasha Orloff: 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, and tax experts, to the table to help you pass your audit for the first time because we think this could be really powerful. 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 the area. So much of the audit fees are caught up in sampling and reviewing and doing stuff where software can help that, 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. So that's kind of our goal for 2024. What year are we in 2024.

Paul Barnhurst: I want to kind of shift gears here a little bit. Glenn and I have a couple of 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 5 to 10 years?

Glenn Hopper: I would add to that. I mean, especially with the blockchain and with the traceability, you're adding a whole new level of data to what's in the general ledger. So more data means more analytics and more everything that you can do around that. So I'm very curious, as you guys are building this out, how you envision, 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 fast AI is evolving and the capabilities get just mind-blowing results. Every six months, something insane is happening, so it's a really fun time to be learning about and playing with this. So it's hard to know all of the things, but I'll share a couple of things that I'm growing in confidence and are going to be changing. So as new companies emerge like Puzzle and there are others out there, actually the core underlying data architecture, it has to be built in a way that allows at least LLMS in the version 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, putting together, one of these things you buy on Amazon that comes with random left-hand-drawn descriptions of instructions on how to put together a table. And you're like, man, this is even worse than IKEA. How could I possibly do this? These instructions are terrible. So if we don't set the foundation of like, rules 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 are a lot of opportunities where I can help improve accuracy. So that's a place where we really focus because we're accounting. 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 garbage and garbage out. We're solving the garbage sort of problem. 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, or humans and you only have so much time in a month or in a time period to do all of the potential scenarios. But what AI 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 FP&A's job is like doing a lot of 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 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, 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, along the way. I know that's just one small example, but we can connect people to data in a way that like, when you learn SQL, it's easier to ask and get answers to data. And if you learn one of these more modern data visualization tools, you can kind of play around a little bit more. 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.

Glenn Hopper: I love that because, for years we've been talking about democratization of data. But to me, what AI does, and generative AI specifically using LLMS, it's the democratization of data science. Now, you still have to know kind of you have to know the questions to ask. Just like in finance and accounting, you have to know the difference between EBITDA and net income. Or, there are components that you have to understand. But 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: 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? 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 these current apples that are three, four, five thousand dollars, you have to depreciate them. So in order to know that you have to provide the receipt. But not everybody always does or you have to have context 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 have 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 am writing this computer. We just hired Sasha. He's a developer, and he needed this, like, special computer that's, like, really expensive. And so he bought it. Peter, 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 three-year depreciation policy, with no terminal value or whatever. You know the company is for things and notes expenses 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 I think that's a really powerful sort of example. And it brings it to light in a really cool way. Fun way.

Glenn Hopper: Yeah. And I think everyone right now, it's so, machine learning has been used to great use from fraud detection to classification prediction. I mean, there are 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 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 sort of hybrid, finance people who've realized, oh, wow, I can do more with a Python program than I could in Excel. I know, Paul may disagree with that, but.

Paul Barnhurst: No, I wouldn't disagree.

Glenn Hopper: So it's funny that I feel like the machine learning and the AI applications that were pre-generative AI and that are more tested and true at this point are almost getting overlooked. But would you say, 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, 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 the product, but there are promising AI and machine learning applications that are specific for finance people that they could be looking at right now, today to 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 less exciting but less biased answer as well, which is 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 of years from now looks like, and then I'll bring it back to today, 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 guest would want to listen to that long. Well, 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, just one 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 Orloff, or I think just linkedin.com/in/sashaorloff. That's the best way. We welcome feedback, ideas, and hearing what you guys are up to.

Paul Barnhurst: Well, great. Well, we'll let you go. Thank you for joining us on Future Finance. On this show. We welcome our robot overlords, in case you were wondering. So thanks. Great. Thanks, Sasha. Bye bye. Thanks for listening to the Future Finance show and thanks to our sponsor QFlow.ai. 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.