The Future of Finance for CFOs to Automate Accounting with AI-First General Ledgers - Sasha Orloff

Welcome to the Future Finance show where we talk about robots and how they.

Would come for your job. I for one, welcome our robot overlord. 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. 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, accounting is the foundation. Without proper accounting, you can't build the financial practices on top of it.

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. We focused on two pieces that I thought were really important outcomes. One is to improve the trust and transparency. And then the last piece are the debates that we have about what to do with the capital and the people, the resources of the company, 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. Blockchain builds the most trusted system that we've developed today.

Do you have a favorite finance software app?

One that I'm really excited about is.

Future Finance is brought to you by qflow AI, the strategic finance platform solving the toughest part of Planning and analysis. B2B Revenue align Sales, marketing and finance seamlessly speed up decision making and lock in accountability with qflow AI. 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 Orloff and my co host Glenn 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.

Thanks for having me. Excited to be here.

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.

Yeah, my name is Sasha, as we just heard before and I'm building Puzzle, which is modern accounting software for growing companies. And my, 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 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 I joined a. A startup incubator called Y Combinator in the Bay Area. Built Lendup, ended up spinning out a division called Mission Lane. Each of those grew to a couple hundred million of revenue and took some time off and decided to start something new.

How did you pick the exciting world of accounting as your next project?

Well, I mean, I think as most of the listeners will, 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, was solving a problem. And the way that this dawned on me was, was sitting table as a CEO, 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. 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.

Love that. Glenn, you've written a book all about technology and corporate finance. What's your take? Is it mediocre software?

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 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 generating revenue. So in my career, it led to a lot of citizen coding where you're trying to make up, you know, you're running an access database on your own laptop or something to try to get information and just been there. Yeah, so that. Yeah, so, yeah, no, I completely relate and I, and I'm, 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 gap and. Yeah, it's all it's riveting stuff. Yeah, we're gonna, we're gonna run off our accounting listeners is what's going to happen. But if we're, if we make too.

Much fun, bring it on. We gotta, we got a whole big team ready to build, ready to build, whatever. But I think you, you touched on something which is kind of hits to the point of why I get so excited about puzzles. 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.

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?

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 and 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 and 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.

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 1494. Today is, oh my gosh, yeah, someone who knows Luca Pacioli. Perfect. 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, 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?

Yeah, I'll talk about it a bit at a high level and then we can dive deep into whatever parts 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 here. 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 system in which you can have infinite tags on each money event, let's just call them money events because they could be transactions that actually happened. I bought a thing with cash. It could be things that are going to potentially happen in the future. 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 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. 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, 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, 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 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 do 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 or 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 GL 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's 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 jail 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.

Ever feel like your go to market teams and finance speak different languages? This misalignment is a breeding ground for failure in pairing the predictive power of forecast 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 qflow AI the strategic finance platform. Purpose built to solve the toughest part of planning and analysis. B2B Revenue QFollow 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 stats, makes segmented scenario planning a breeze, and closes the planning loop. Create airtight alignment, improve decision latency and ensure accountability across the team.

I'm thinking about I mean just my, my CFO brain is going into overdrive here. I'm thinking about top side 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, it not have. I mean you can go into the admin and see who made the entry or whatever, but it's a, it, it's work, it's not, it's not intuitive when you, when you make these entries. So that's, this is fascinating to me.

And it's pretty common across almost all other softwares where there's level, there's audit levels and there's transactional. 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, we get this one view in this one view only. Like, how can 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.

I love that the, the, the end statement, that's one I use a lot in Excel, you know. 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?

Yeah, I mean there's a fairly simple similar approach from kind of blockchain to sort of a data transformation like accounting. Accounting's effectively as a data transformation. Not, not to dumb down. It's 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's 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 into 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 of 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 loss statement, all of those sort of can trace all the way through. 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 sense, 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 and down to the transaction everybody that touched on way. But something else that's really cool is you could 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?

So I feel like 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, you're trying to have your, your narrative of what happened here and if, if an entry changed for some reason or something is off, that wasn't, you know, it looked one way in. Okay. And certainly you don't want, you're not going to go back and change locked 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.

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 Zero 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 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. 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 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 goals for 2020. What year are we in? 2024. Yeah.

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 five to 10.

Years 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 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.

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 like insane is happening. So it's a really fun time to be learning about and playing with us. So it's hard to know all of the things, but I'll share a couple of 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 LLMs in the version of AI to be able to read and understand it. So aside from just the hallucinations, just the retrieval of which revenue are we talking about, which burn are we talking about, which profit are we talking about? 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 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 can 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. 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 AI 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 garbage in, garbage out, we're solving the garbage in sort of problems. 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 discreet 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 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 the finance or FPA's job is like doing a lot of one ad hoc requests. 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 then 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, 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 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 sort of 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.

Yeah, I love that because I've, you know, for years we've been talking about democratization of data, but to me what AI does and generative AI specifically using LLMs, it's democratization of data science. Now you still have to know, you know, kind of, you have to know the questions to ask. Just like in, in finance and accounting you have to know the difference between EBITDA and Net Income or what. You know, there's components that you, you, you have to understand. But in the, 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.

Yeah. So just the, 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 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 it's these current apples that are three, four, $5,000, you're like, you should, you have to depreciate them. So in order to know that you, you have to one, provide the receipt. But you know, you know, 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 accounts on the other end. Like I don't know what this Apple thing is. And then you 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. You just have to know that I know what it is. It's a computer. I write this computer. Oh, this is a, I was just hired. Sasha, he's a developer and he needed this like special computer that's like really expensive. And so we bought a good. I know what it is. LLMs are 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 that's expensive 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 AI, I think that's a really powerful sort of example and it brings it to light in a really, really cool way. Fun way.

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. We're trying to explain a random forest classifier to someone who's not. And it's like, that's, you know, no one cares. But when you can type into a chat bot, 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 hybrid finance people who've realized, oh, wow, I can do more with a, with a Python program than I could in Excel. I know Paul May, Paul may disagree.

With that, but no, I wouldn't 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 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 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?

I have two answers. I have a biased answer called Puzzle IO, and then I have a slightly, you know, 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 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.

So for our last segment, this is something we're trying out. Paul and I were spitballing right before the show on what we wanted to do for our different customer segment each time. So what we've done, we have a bank of lightning round kind of personal questions. 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 LLM is going to generate for you.

Yes. I love it. That's what I was hoping for.

Who gets it?

So, and I think we'll try different chat GPT. Who has, who has the harder question? The more fun question.

So this week we're using Gemini, 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 LLMs and see what kind of questions that they come up with. So for, for question number one, would you rather take a random selection from our canned questions or see what Gemini comes up with?

Oh, I 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.

Okay, so let's see. Let me push the magic button here and say, okay, it's a. All right, so Gemini wants to know, how do you unwind? How do you unwind or recharge after a long period of focused work?

Oh, I mean, that's a good one. I, 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.

My.

Favorite thing, if I had to do anything would be to go skiing. Of course, you can't really do that after 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 anything, I take a bike ride or I walk around the block 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 there's just something about having, like, grass and maybe a bit of. Of human and dog feces on your feet and standing there to really just like, kind of connect. I 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? That's what I like to do.

I would add to that, you could take the advice that Bruce Willis got in Die Hard, where you take your shoes and socks off and you make fists in the grass and feces 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 more. And see, we didn't even need AI for that. We just needed the magic of an 80s action movie.

All right, we're going to do another question here. So do you want us to pick a random question, or do you want AI to pick question number two?

Pick a. Now, let's. Now, let's go human form.

All right, so we use the 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. All you do is work. So I might just pick a different question. Give me a number between one and eight.

Number seven. It's my old soccer jersey.

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?

Do I have a favorite finance software app? Well, we're like the Switzerland of finance software. We have to work and play well with everybody out there. But if I had to pick one, I would say one that I'm really excited about is Runway.com.

I knew you were gonna say that. I know seek you really well. I know you guys work together.

Yeah, it's. It's so. It's so much of financial planning, analysis looks the same, and Runway looks very different. Now, whether that's good or bad and whether or not finance you want 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, love It.

What do you think, Glenn? Should we give them one more? You want to do one more wrap up?

Let's go.

All right.

Yep, let's go. One more here. So. So human question or AI question?

Your question this time. Human.

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 LLM?

What's the most surprising? Let's see.

You are now out of time.

One of the things that I like to do, I guess we sort of talked about this before, but that I like to do is surprising as a theme, is 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, 50 CFOs about what bank they used and why they used it. And we got all sorts of crazy results, as you can 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 it. I had to fight with this along the way. And we just fed it all into chat gbt and I said, I put it on one tab and I put what is 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 when 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. And we're going to publish that hopefully soon and so we'll share that. But. But, yeah, but that, that was, I think the most fun, was like, it beat me as it's now that's, you know, a little humbling, but it was fun. It was fun to see.

Thank you for sharing that one. We totally enjoyed having 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.

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, 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.

You.