The Accounting Podcast

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Blake Oliver: [00:00:04] An independent economic forecasting firm called Oxford Economics, has done some calculations, and they believe that the American economy may have entered recession territory. We might be in a recession. If you subtract the massive influx of capital that is flowing into the development of artificial intelligence.

David Leary: [00:00:28] Coming to you weekly from the OnPay Recording Studio.

Blake Oliver: [00:00:34] Hey, everyone, and welcome back to the Accounting Podcast, your weekly roundup of news in the profession. I'm Blake Oliver.

David Leary: [00:00:41] I'm David Leary and Blake. I've been sick and that's why we have this delay in our episode getting recorded. I've just been deadly sick. But before I got sick, Blake, I was in LA and we had to kill some time. We had a late flight out, and so I went with my sister and her husband, went down to Santa Monica. Have you been in Santa? You were from LA. You've been to Santa Monica?

Blake Oliver: [00:01:00] Oh, yeah. Like the promenade there?

David Leary: [00:01:02] Yeah, that outdoor shopping area, it's super high end stores. Guess what I ran into when I was there. And I'll add it to the video or the screen here. It's a new TurboTax store. And this is like these are expensive, expensive, probably some of the most expensive retail locations that are in California, right?

Blake Oliver: [00:01:21] Yeah.

David Leary: [00:01:22] How can you run a tech shop with this kind of rent? Like this is all I could think about. When I was there, it was like, these aren't it's not like the ridiculous margins that Patagonia is running or the Apple stores running. Like, how is that? How how is TurboTax doing this with a store, with these storefronts? Or is this just like in the same way they sponsored the Intuit Dome? This is just basically a billboard. It's not really actually to do the taxes in there. It's just a billboard for TurboTax.

Blake Oliver: [00:01:49] Well, and I mean, isn't the whole idea of TurboTax you're doing your taxes yourself on your computer at home. Like, I don't understand what is the what what do you well, what do you do when you go in.

David Leary: [00:01:59] The TurboTax live thing? And it's supposed to also. I think these stores are supposed to be like a little Apple App Store, a little that Capital One banking thing. Come in and have a coffee. It. But my thing is like they're doing in the most expensive real estate spots. It's not like, you know, you think about liberty tax. It's just like in the crappy shopping plazas you're driving by, right? It makes sense. You could probably run a tax shop that way, but who can afford? This is probably one of the most expensive retail locations in LA. I don't know how they can do this. It's crazy. Uh, so those of you that are listening, it is a beautiful storefront, all glass think the way you would have an Apple store and it says TurboTax, uh, Santa Monica opening soon. And it's this beautiful glass building. You see the, the red circle check mark, the TurboTax logo you used to, you're used to seeing, but they're beginning to bring the live exports to this retail location. I just don't know how the math works for this. This has to be a marketing expense. It's not actually they're not going to run each unit. They can't measure the ROI or the profit on one look, it's just too expensive.

Blake Oliver: [00:03:07] Well, David, um, maybe we'll get the answer to that at some point. Figure it out. In the meantime, let's go ahead and thank our sponsors of this episode and that sponsors, sponsors.

David Leary: [00:03:20] This week we have digits numeral on pay and cloud account and staffing.

Blake Oliver: [00:03:24] All right. I'll thank digits. Let's be honest, accounting software hasn't changed much in decades. The prices keep going up, but the software still expects you to do all the work. Digits is different. Digits is the world's first AI native general ledger with built in AI agents trained on your firm's standards across every client. In your book, they code transactions, prepare schedules, reconcile accounts, run quality checks, and even chase clients for open items. So your team moves out of prep and into reviews, advisory and the work that actually grows the firm. And because everything runs inside one platform, your ledger, reconciliations, schedules, reporting, bill pay, client collaboration, there's no more stitching six tools together just to close one client's books. Firms on digits are reporting a 70% gain in workflow efficiency, shipping annual cleanups in days instead of weeks, and running monthly bookkeeping in 1 to 3 hours per client accounting software that actually works for your firm. To see why hundreds of firms are making the switch to digits, head over to The Accounting Podcast dot com slash digits. That's Accounting Today dot io forward slash DIGI ts. Thanks, digits. What's new? David I am. I am going to the CCH connections conference this week in Orlando. This is my first time at CCH conference. I've been in accounting now for 20 years And, uh, I've, I've never been, I've always gone to, you know, conferences like, uh, Intuit connect and Zero con and scaling new heights. Those are, you know, cloud based, small firm conferences.

David Leary: [00:05:05] Because you're in the cast side always. That's right. That's your background.

Blake Oliver: [00:05:08] Yeah. So, um, you know, uh, CCH reached out Wolters Kluwer and said, hey, do you want to do a podcast at the conference? Do you want to come see what we're doing with AI? And so I've been diving into the new AI features that are coming. Uh, I'm looking forward to learning more and I'm going to do a live podcast episode, uh, there it'll be at the CCH Access Theater stage on Monday at 250. And we're going to talk about all the new AI stuff that is coming to, uh, their products, which is really interesting because we've been waiting for years for this. These are the products that the larger firms are using and they haven't had access the way that small firms have, because, you know.

David Leary: [00:05:55] This is more on the tax side.

Blake Oliver: [00:05:56] Tax side and audit side. Yeah. And I'm going to talk to Jason Marks. He's the CEO of Wolters Kluwer Tax and Accounting and Kathy Rowe. Um, and, uh, we're going to do a live episode there and, and broadcast that here on the show later. So, um, looking forward to that. So we got a lot to talk about. It's going to be a wide ranging episode this week, all sorts of different stuff. Um, one of the stories that really interested me the most from your list, David, is this one about unauthorized AI agents accessing banks. And the banks are kind of freaking out about this.

David Leary: [00:06:33] They're freaking out about it. And the big reason they're freaking out is they can't tell the difference between an AI agent. Let's say I use my muse for Facebook muse AI agent to do a banking task for me, or you try to use your cloud coworker to log into the bank and transfer money. It can't tell the difference between that, which arguably would be an authorized AI agent versus a rogue AI agent. So now the banks are panicking and they're just starting to shut everybody down.

Blake Oliver: [00:07:01] Yeah.

David Leary: [00:07:01] There's not going to let anybody access.

Blake Oliver: [00:07:03] Yeah, it makes sense, right? Because you can I mean, I've been doing this to some extent. Right. Um, like Claude will sometimes not log into a banking system or a bill pay system. There's like these internal safety checks that it does, but then sometimes it will. And so people are doing what I'm doing, right. Like I've got, um, Claude sometimes going into ramp in order to like add receipts to transactions, which it can't do via the MCP or pull statements from relay, for instance, and it will go in and it can log in as me. I, I get it in there and then it will do these tasks. Um, but it's a big concern because it could also as me in the website move money around. And so I have to like keep an eye on it.

David Leary: [00:07:56] Yeah, watch that second browser. What's happening.

Blake Oliver: [00:07:58] Exactly. But you know, um, we've got people all over the spectrum with AI and some people are playing fast and loose with it. So the, the, the risk of, of like these AI systems getting hacked via prompt injection and then going and moving money like this is going to happen. We are going to have fraud start to occur when a business owner or an individual tells the AI to go move money or to, to manage moving money around, and then it does something that they're not expecting. So I'm curious, like what, what, what, tell me more about this story with these agents accessing the banks. Like how did this come to your attention?

David Leary: [00:08:41] So this all the big banks, JP Morgan Chase, Bank of America, Citigroup, Morgan Stanley, Wells Fargo, all of them basically are now going to restrict third party. Ai agent just as a defensive measure because they don't know. They can't tell the difference. And ultimately, if they can't tell the difference, how are they ever going to be able to tell the difference unless there's going to have to be like, you're going to we've talked about this with GL, you almost need to have this layer, an orchestration layer in between, like it's going to have to go through like a Plaid, right? There's going to be one guy. All the agents have to go through Plaid to talk to a bank. It's going to be which, which sucks because that stifles innovation, but that's probably the only way they're going to be able to secure it. I mean, the banks can't have Wild West AI agents just connecting into accounts everywhere. And this makes me think about the whole self-driving cars. They have a goal to get you from point A to point B, and if it has to cross a double yellow line, it's going to do it. It's the same thing. Like I'm going to invoice somebody. They owe me the money and I'm going to tell an agent, go collect the money and it's going to go figure out how to get into somebody's bank account and take the money. I think that's the that will eventually happen. And so the banks have to shut this down because agents are going to go and try to do their goal, which is go get paid, right? Go get paid by this client and they're going to figure out how to do it.

Blake Oliver: [00:10:01] It's well, there there are a potential solutions here. You said to me before we started the show, you've got a story about how stripe is launching a Treasury agent. And so when you say Treasury agent, I'm imagining this is like an AI agent that can move money around. I hope securely.

David Leary: [00:10:25] That I don't have a lot by connecting to the banking services, but so stripe, I, we have to keep watching stripe because I feel like stripe is on a march to become an accounting operating system of the future. So step back and think about stripe. Historically, stripe at most small, most startups owns the profit and loss. The income line. Wine, the biggest line in the profit and loss. The income line is stripe. Right now stripe. Remember a few months back, stripe bought that token management company to manage the token spend at companies kind of getting in that game. Well soon token spend. If we believe Hector Garcia's theory, token spend is going to exceed payroll expense on your profit and loss. So now stripe has the two biggest lines in the profit and loss. They're involved in. Now they're getting into balance sheet workflows, paying bills, transferring balances, checking the balances. That's what they're announcing now. So they have these agents that will help people do that. And then they want to expand it into creating virtual cards, setting spending limits, that type of stuff. So stripe is slowly like pivoting into like this accounting OS. And I mean, arguably the gel is only going to be a teeny piece of that. Like that doesn't knock that out like nothing. And for, for a lot. Think about all these, you think about, uh, these startup banks like a mercury, right? They're targeting these startups and they're like, get your startup bank account. And then we're going to give you all these banking services slash accounting services. But who gets to startups first stripe? Because you've got you set up a startup. You usually need to start charging people money.

Blake Oliver: [00:12:01] Yeah, yeah, yeah.

David Leary: [00:12:02] So stripe's further ahead in this, uh, startup accounting ecosystem. And so they're just starting to provide all the same services. They don't provide the bank service, but they connect to the banks, right? This. And then on top of that stripe also kind of has that, like you just kind of keep the balance in stripe for a while. I mean, we do it, we keep a reserve balance in stripe now. Like it's, they're becoming a bank and, but they're also be becoming an accounting operating system. We have to keep watching them because they, they just keep adding these layers of accounting functionality. And, and just like I said, just look at the profit and loss. They own the two biggest lines on the profit loss.

Blake Oliver: [00:12:40] Now going back to this, um, question about AI agents accessing banks. Right. Let's just think about like why these traditional banks are having a problem. It's because they are they've never adapted to the, the modern, uh, need for multi-user access in many cases, unless you're on the, the business banking side of like a Wells Fargo or a B of a, you don't get as a business owner often you don't get like multiple user accounts with different permissions. And that's been a problem with accounting and bookkeeping forever, especially on the cast side, like I was on. Um, and, and often even like provisioning those user accounts with like different permissions is, is a huge pain. And so people just don't, and the business owner will have one login that they have to share with other people like me as their accountant. And it's fundamentally insecure because we don't have the different user permissions, right? It's even just getting a read only login is still a hassle. And so this is the same problem, you know, thinking about it, it's kind of the same problem we've been dealing with. They're now having this problem with AI agents because business owners are giving the full login to the agent and asking it to do work. But they were also doing that before with their human accounts, their accountants. Yes. And so the banks, because they never built out the multi-user kind of permissions, they now they're in this problem, right? They have this problem here. Yeah.

David Leary: [00:14:26] Because in theory, in theory we use relay. If we create a new relay user, uh, earmark agent@earmark.me or whatever, and we create an agent and we created login just for that agent at relay. We could say it only gets access to the statements for statement retrieval, and that's all it could do.

Blake Oliver: [00:14:47] Exactly. It can go in and it can pull transactions, but it can't pay any bills. Or let's say we want it to act as like an AP agent to do accounts payable. We could say it can stage bills and submit them for approval, but it can't pay them. That's the workaround. I mean, that's the solution, right? It's just to actually have multi-user access with permissions. And so an app like relay, a bank like relay is actually well positioned for this.

David Leary: [00:15:14] And yeah.

Blake Oliver: [00:15:15] You know, that's I've actually. That's what I've done. I haven't actually done that with.

David Leary: [00:15:20] Don't, don't tell me you're using your login and letting it go into relay. I'm going to freak out.

Blake Oliver: [00:15:25] Uh, well I've, I've watched it while it does it right and it can't log in without me, but, um, I've actually done this with zero where I've created an agent login. So it's, it's login just for the AI agent, just for clod and it has limited permissions, right? Just like a bookkeeper. So it can go in and and it can code transactions for me via Chrome and do reconciliation tasks. But it can't it can't do the work that I don't want it to be able to do. It's got restricted. Get into the.

David Leary: [00:15:54] Preferences and the admin features, and it's.

Blake Oliver: [00:15:56] Not the advisor level or admin level permissions. And so that's the solution I think is, you know, you want to give access to the bank for your AI agent to do work. It's really, we just have to think about this the same way. We have to think about the AI as just being like another human team member. And so that might mean giving it its own email address and giving it its own logins to the different systems. But of course, then you have.

David Leary: [00:16:20] To think about if you're, if you're at your accounting firm and you authorize or we've had this happen here, Blake, you essentially have God mode to all our systems. So if you don't, if you let AI use your login to our systems. Now, AI has God mode to all our systems. And yeah, you have to create. You have to separate this out. You need to have AI only have you have to treat it as a new employee. Spin it up its own credentials, give it the proper access, and then let it go to town. You just can't. You just can't have it. Use your login. Especially you, Blake. You can't have it. Whoever the highest level of permissions at your company, they should not be allowed to connect AI because that's going to give it God mode. Yeah.

Blake Oliver: [00:17:02] I want to keep talking about AI and and some better use cases actually for it. I don't think that, uh, you know, moving money around is the best use case for AI at this moment. I've got a story here, uh, from Zapier about a solo lawyer who uses AI to run his practice five times faster. And there's some great examples in this blog post, uh, that also would work well for solo accountants and CPAs. I want to get to that. But first, let's thank our next sponsor. And that is numeral. Let me ask you something. When you recommend a sales tax solution to a client, are you confident in the support they'll get? What about the pricing? What happens if something goes wrong? As accountants, you've all seen it clunky support, surprise invoices and nobody picking up the phone when there's a problem. Numeral is built differently. It's a modern sales tax platform built for accountants, and it supports registrations, filings, Nexus tracking exemption certificates, and even VAT and GST in over 70 countries, all with great support, simple, transparent pricing and no surprise invoices. Then there's the guarantee. If your client's sales tax isn't filed on time, numeral pays the penalties and interest for accounting firms looking for a true partner, numeral works alongside you to support your clients as they grow. Whether you're implementing it yourself or making a referral, numeral has you covered to check them out. Head over to The Accounting Podcast dot promo slash numeral, that's The Accounting Podcast dot promo forward slash NUMERAL.

Blake Oliver: [00:18:42] All right, so we've got small accounting firms, solo accountants using Claude ChatGPT cowork. Um, what's the what's the Microsoft one copilot all in. You know, so many different ways. And I wanted to highlight a few of these examples from the Zapier blog post. I think it's a great example. Um, so this was on Zapier blog and the attorney is named Damien Guzman. He's the founder of Oakland based FTE legal and he serves small businesses and fintechs. Um, and he has maintained a self-imposed ceiling of roughly 30 billable hours per week. And he has rebuilt his practice around Claude. And since April of this year, Zapier, MCP and the Zapier MCP is, uh, Zapier is like AI interface for all of its tools. So if you use Zapier, you know, they have connections to hundreds and hundreds of apps. And the Zapier MCP allows you to access those connections that they've already built through AI. So you can connect Clod to Zapier, MCP, and then it gives cloud access to all of those, uh, tools and all the things that you can do with them. And so Guzman has basically built his practice around these, these apps. And he has estimated that the practice now produces roughly five times as much output, completing equivalent work in 20% of the former time. And here are the examples in this article of the workflows that are really powerful. He has an hourly scan that has clod inspect gmail for actionable client requests, create tasks and Cleo, which is this practice management software and then begin work such as drafting NDAs, reviewing contracts, and marking up language.

Blake Oliver: [00:20:47] So what's happening is that the emails are coming in to his gmail from clients, and then he is Claude is automatically creating the tasks in his practice management software, and it begins doing the work. So it's basically processing those emails and getting started on things before he even looks at it. And he's saying that by the time he opens the request, roughly half the work is already complete. He's got some other automations that will replace Google Meet links with Zoom, create a 48 hour preparation buffer, deliver regulatory alerts, monitor dockets, and then, you know, remove party names from billing narratives. Big Four AI processes them to maintain confidentiality, and he's connecting. Cleo. Gmail, Google calendar, Zoom docs, drive sheets, Xero, and GitHub through the MCP rather than manually configured workflows. So Guzman has reduced the time it takes him to do the work by a factor of five. And so he is switching to flat rate pricing. Um, you know, because if he kept billing hourly, he'd have an 80% revenue decline. It's so funny to see the, you know, legal world facing the same challenge that we faced in, um, in client accounting services, you know, ten, 15, 20 years ago. So I wanted to highlight this because I think that this is the, probably the number one use case right now is, and this is something that you can set up now, and you can do it without like a ton of work.

Blake Oliver: [00:22:28] And it's basically using cloud clod to monitor your email inbox and to start processing requests right away. And, you know, good examples of that in accounting firms is like, um, uh, new client inquiries. So you get somebody interested in your services coming into your inbox and you've got cloud monitoring it. It can automatically with, say, scheduled tasks, you can set up a scheduled task to monitor your inbox. And then if it finds a new client inquiry can start doing the research on that new client can draft the email response and put it in your draft messages, and then it will. And then you can review it and send it. And that is like something that I've been doing. I have a, a scheduled task that's like an email sweep. And then it reads all of my emails and it automatically drafts replies based on the type of email it is. And I've basically trained it over time as to like what I need it to do. So if it's like scheduling, right, it can go into my Google calendar and it can suggest possible times and, and put those into the email along with a link to book. And I've taught it, I've trained it as to, you know, when those times are best where to find the times.

Blake Oliver: [00:23:53] Um, if it's somebody looking for like a new, uh, consulting project or a new social media type of engagement or somebody interested in appearing on the show, it intelligently can respond in my voice to, um, and it takes time, but you can do this and you basically just have to be in there telling it what to do. And then it learns like the scheduled task is basically like a saved prompt that it then refines over time. And I've got like operating rules and all that. So it's really powerful. Um, and I think that's kind of like the best way to get started is, is like using AI as an, as an admin, uh, processing emails. The only problem I'm having, David, is that, uh, as I've sort of built out this system, the way that it works better is that it has to gather more and more context about my preferences and what I want and what I need. And so it stores all that information in markdown files, which are basically just, you know, rich, kind of rich text files saved in, in, in my Google drive. And the challenge I'm having is that every time it loads this scheduled task, it's basically pushing a prompt. The prompt keeps getting longer and longer. And then the operating rules files that it has to load are getting bigger and bigger and bigger. And so the more scheduled tasks I have, the more tokens I'm spending.

David Leary: [00:25:26] And so the system it has to go and reteach itself from scratch what the rules are to reply to your emails.

Blake Oliver: [00:25:31] Yeah. It has to read all those rules and then apply them. And so the bigger and bigger the files get, the more and more tokens I'm spending. And so it's gotten really sophisticated. It's gotten really good. I mean, I've taught, you know, Claude, how to write in my style. I've taught it how to do all of these things that I, I was doing, but now I'm spending tokens in excess of the subscription. So I'm, I'm on the $100 a month plan, but I will regularly run out of tokens like halfway through the week and then have to spend. And I've got to figure out how to get this under control because I'm at $2,500 for October, so I might spend like $3,000 in tokens.

David Leary: [00:26:15] And many times you're spending that money to reply to a stupid email that we got from somebody else's agent. It it's kind of crazy. We're burning cash to reply to other AI agents.

Blake Oliver: [00:26:27] But if you think about it, it's like I've avoided hiring somebody to. Yeah, to do a job like for like the I, the number one use case for me is like all these inbound, like sales inquiries. So now I've got basically and outbound, right? So like, let's say we, we need to sell more ads on a particular show. I've got a scheduled task, which is, you know, it's basically the, the sales outbound BDR, uh, agent that I've created. And so it's running and drafting messages as me sending emails as me, uh, handling the, the, the responses that I get booking meetings. And that's allowed us to avoid hiring a person to do that job. And so I have to look at that spend and say, okay, well, is it worth it to spend $3,000 a month in tokens to avoid hiring a position that would cost, who knows, 70 $80,000 a year, perhaps, and have to manage that person. So that's kind of the ROI or cost benefit analysis that I'm doing in my head. But I guess what I'm saying is that it's I'm seeing personally now how token spend can just balloon out of control very easily. And it's hard for me as an individual to manage it. I can't even imagine trying to manage token spend across an organization with, you know, hundreds or thousands of people. And so that's where all of these orchestration layers, like you said, to manage this stuff are so important.

David Leary: [00:28:13] And this goes like with wrap last week, we talked about how ramp launched glass, right? It's going to be this like company wide AI orchestration layer to where it'll. Everyone your. It knows your company knowledge. Everybody uses it to make their AI requests, and it's going to use whatever AI model it needs to. So that way you have some control over the spend, right? Yes. And, and you have control over this like shared knowledge base of the company of what people, how you're responding instead of having it go retrain itself on your Google doc every single time. Yes. So that last week ramp launched that. Well, this week pilot, they have the Meridian product and they're launching a new product called Meridian Starter. And essentially, it's a shared AI workspace that's going to connect your cloud, ChatGPT directly to QuickBooks online and zero for your accounting stuff. So instead of you, everybody at your firm having their own connections with Claude ChatGPT into your goals, and you can't manage that. It's not manageable. The spend is not manageable. The knowledge sharing is not manageable across your thing. So since they've built this. Now what's interesting about this, I think about this more and more. This is what Intuit and Xero should have done or should be doing. They should be that trusted AI layer in between my company's GL and all this crazy AI Wild West stuff that's happening.

David Leary: [00:29:34] Be that connection before it gets to the GL. And no, instead, what if Zero Intuit been doing. They've been building stupid collection letter writing agents like. Think about it. It's so stupid. It really is like Intuit. Oh, it's going to create a. Want me to send a collection letter for you? Jax zero build. Jax does the same stupid stuff like this doesn't mean they're dead because of AI, right? These companies. But I do think they missed a future product opportunity here. There is a huge new product offering that could exist at companies that, uh, it's not really the company knowledge base, but it is kind of like a knowledge level, right? A trusted knowledge level or a trusted knowledge layer that orchestrates the AI talks to your GL and Intuit and Xero could have been that piece. They've probably missed the window. Now for that, it's wide open for somebody else to take, but it would have made sense. Intuitive. Xero have to just buy companies that do this, but it could have made a lot of sense for them to offer this and be that trusted layer instead of trying to be the AI. Yeah, like I could have used any stupid thing to write a collection letter, but what I need is I need a protective layer before data goes into my GL to make sure it's done safely and correctly.

Blake Oliver: [00:30:45] Yeah, well, the big challenge with like creating these orchestration layers when you're talking about doing repetitive work, is that the work for every client is a little bit different. So we're trying to the problem we're trying to solve is that we want standardization across a firm. So we want to optimize our token spend by creating a A a process essentially standardized prompts for how to do a particular task, such as a bank reconciliation, or such as like coding transactions or preparing a tax return. And then we want to have everyone in the firm using this system in the same way. But you're you're sort of like fighting. There are these these needs are at odds, right? Because the power of the AI agents is that they can do work that is very customized. Right? You tell it what you need. Here's the particular client, here's what needs to get done. And so as an individual, I can use a cloud or a coworker or a copilot or ChatGPT, and I can like create these outputs that I couldn't do before. And the AI can understand all the different subtleties of that client. But to do that at scale in a firm.

David Leary: [00:32:16] Yeah. So somebody else can handle that client when you're out. It's really hard.

Blake Oliver: [00:32:20] It's really hard. And to control spend in that way is really hard. And so like that is whoever can solve that is going to win just an enormous slice of the market for software, for accounting firms, because then we can control all of these agents at the firm level. And that's the problem right now. And this is why we haven't seen like workforce reductions in firms is because you can use these tools now, but they have to be guided by an individual who can be the human in the loop, review the work, oversee the work. So it helps me as a CPA working for my clients go way faster, but it cannot operate without me. There's very few tasks that are completely standard across a whole set of clients. And so like, that is why it's this really interesting technological shift because it's making individuals with experience so much more productive. But it's not eliminating any jobs. And so trying to figure out how to get like return on your investment as an accounting firm is challenging because how do we make more money in accounting firms? It's a, it's a, it's by reducing labor costs, right? Generally. But you can't do that. So then the only other way is, and this is the best way right now, if you want to try to like get a return or make money out of AI is to add more services, right? To do stuff for more clients that you couldn't do before. Sell more. And that's where like everyone's trying to focus now on, on doing advisory type services and stuff like that. Um, but that's hard because all of our really talented people are super busy.

David Leary: [00:34:21] So yeah.

David Leary: [00:34:23] Because it's literally managing an accounting firm, you're trying to do a cash practice, but nobody, none of your employees interact with each other. They just were working away heads down and they never communicated. Cross communicated best practices. They never shared domain knowledge clients. Once they moved to somebody else, it was like starting over from scratch. That's essentially what you're doing with agents, right? You're creating all these little miniature employees everywhere, but they don't have any context to the bigger company, all the clients or even that client itself. And there's a huge opportunity here for somebody to go and take this. And honestly, Xero and Intuit probably missed it.

Blake Oliver: [00:35:03] And if you want to give them that context, you can. But then the, uh, the agent has to go and read all that context, which is going to cost you a fortune in tokens.

David Leary: [00:35:11] Yeah.

Blake Oliver: [00:35:12] Yeah. Right. So, until we can actually train a model on all of our client data and actually have it, uh, what I would call remember everything about like the clients and the, and the firm, it's going to be really expensive because it's basically like, imagine, imagine if every time you sat down to do a task, David, if you're an AI agent, the way it works right now, the best parallel I can make is you are starting from scratch. It's like your memory is erased and you have to go and read everything about that client and the whole history that you have to gather all that context and then do the job. And then after you've done that job, your memory gets wiped again. And so the next time you come back, you have to read it all. And that's where the token spend is coming from.

David Leary: [00:36:03] And that's where the magic of like Facebook's meta or muse is at, right? I think muse keeps like a virtual server spun up that I'm accessing only it. So it just keeps storing all my stuff in its own server, right? That's why it's so delightful and helpful.

Blake Oliver: [00:36:18] Yes, but it's going to cost a fortune eventually. Run that because the more information it gathers about you, it has to keep rereading it over and over again that the difference between these, uh, AI systems and humans is that when we learn new things, we go to sleep at night and our brains process all that information and incorporate it into our mental model so that when you wake up, you remember all of this stuff, right? That's like the theory of how sleep works is that it's actually our, our model retraining every night. But these AI systems, they gather context and they store it, but they don't sleep and they don't, uh, they don't remember. And so that's why you can make these powerful systems, but they get more and more expensive to run. So somebody has to solve this problem or we're just not going to be able to use these systems to do repetitive work that requires a lot of context. And so that's why going back to this story about this attorney, right, who's using AI to process his inbox, I feel like that is one of the better cost effective time saving applications because it doesn't need that agent, doesn't need a ton of context in order to do that function.

David Leary: [00:37:38] Because in many cases, you can reply to an email based on the context clues in that email.

Blake Oliver: [00:37:43] Yes. Like it identifies. Here is what somebody is asking for. It's administrative often in nature, and it can go out and, and it can like do the prep of the email reply without having to go gather a ton of context. Read, spend a lot of tokens. And that's where we as accountants get lost is in our email inboxes. So if it can process that, like I think that is actually the best firm sort of wide way to, to automate things as opposed to trying to, you know, build like this way to. Well, and I guess like with a, with a system like a digits or these other, you know, AI based general ledgers, they can figure out how to do a lot of these manual tasks more effectively. You know, just like categorizing transactions, reconciling bank statements.

David Leary: [00:38:36] But the reality is what firm is 100% on one of these new AI native ledgers across their whole client base and firm. None. None. So nobody really can get these benefits yet.

Blake Oliver: [00:38:45] Yeah. But it's like, like you have to think about for repetitive work, what are the things that can be done basically the same way across every client every month. But then in order, like you said, in order to get the benefit of that, you really have to have all your clients in one system or it doesn't work. So like there's this, the standardization, again, it's the, it's the barrier. Just like with humans, if you've got all your clients on a bunch of different gels and they're all working in a bunch of different ways, just like with humans, you're going to spend if you have human team. Yeah. If you have a human team, when you onboard a new team member, you're going to spend a lot of time training them how to do everything because it's different for every client, different systems, all that stuff. The same problem occurs with the AI agents. So we can basically take the same lessons from the last 20 years and apply them to this new technology. In order to get the benefit, you have to standardize how your practice does work. And then you can automate this stuff across a bunch of clients. But if they're all different, it's going to be really, really challenging. Still, the the tech is not magical in that respect. Not yet anyway. And I think it's just going to be really expensive if it's not. So anyway.

David Leary: [00:40:01] And it's easy and it's easy to see a demo or investors VCs, they see a demo. Look it did this accounting. Yeah. And it's really easy for this one silo'd out instance. But now scale that to 100 clients all at once. It's almost impossible.

Blake Oliver: [00:40:16] That's why the demos are so impressive because they're not running at scale. So I, you know, and like, I think like, just in terms of like some basic stuff, like, for instance, just preparing a tax return, right? Gathering the info that you need from a client, communicating with that client via email or some sort of messaging system and gathering like source documents, creating the work paper, putting the numbers into the return, sending it to review. That's the sort of thing that actually could work really well and not be expensive from a token standpoint. And then from the audit standpoint, it's, you know, once the audit plan has been created, well, creating the audit plan could, could, Could be a great application of it. And then once it's been created, is, is like, uh, sort of like managing that whole process because there's a standard methodology that could be powerful, like doing the sampling, right? All of that. There's a standard methodology that you use across your clients. You can, you can use AI agents, but it all has to be like standardized. That's where the, the real value is from a firm, wide perspective. And then, and then you have individuals using AI to do the complex work, but guided by them. So it's sort of like two different, it's almost like two different things that we're talking about. We're saying AI, right? But the firm wide AI and the individual AI are completely different. We have to approach them in completely different ways.

Speaker 4: [00:41:57] So.

Blake Oliver: [00:41:59] David, before we go on, we should thank our next sponsor. Um, let's do the next two. Why don't we, uh, on page next, right?

Speaker 4: [00:42:09] Yeah.

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Blake Oliver: [00:44:26] Thanks to all of our livestream viewers who have joined us on this Sunday morning. I'm surprised how many we've got. Uh, great to see you here. We've got boring accountant. Uh, we've got, uh. Who else? Uh, our attorney. Tate. Great to see you. That's babafemi. That's Babafemi says the AI is magic, though sometimes.

Speaker 4: [00:44:51] Seem that way.

Blake Oliver: [00:44:52] Sometimes. Sometimes it doesn't quite work as we expected. Yes, says Happy Sunday, Kumar. Great to see you too. Thanks for joining us from India. Uh. All right, where do we go from here in the few moments that we have left?

Speaker 4: [00:45:10] I guess based.

David Leary: [00:45:10] On what we were just talking about then, do you think some of these big PE roll ups that are happening out there that they're in for a surprise. Example the latest. So platform accounting group, they're one of those, uh, industry roll ups. They're buying accounting firms. The bet is long term AI. We're going to be able to scale these AI firms and flip them or whatever. Right? So platform accounting group just closed a massive $355 million financing round led by Travel Capital Group. Um, they've already acquired 65 acquisitions since 2015 and are serving more than 50,000 clients across the U.S.. So I'm assuming the 355 million is just to buy 100 more firms, possibly, right? Or 50 firms or whatever it's going to cost to do this. But do you think like this bet that they're making, which is AI is going to make our firm so efficient. We're going to be able to do this and scale it. Like, is this a bet that maybe it's going to not pan out for a long time? Because we've just talked about this, this orchestration layer to really do this doesn't exist to scale.

Blake Oliver: [00:46:12] Well, I think the the challenge for small firms is embracing all this tech is challenging. Uh, how do you, how do you as a small firm owner.

Speaker 4: [00:46:28] Like a small firm? Yeah, a solo firm.

Blake Oliver: [00:46:30] Well, and the firms that they're acquiring, I understand, you know, like the smaller, the smaller ones, right? Like the, uh, a few partners or something like that. And you don't have like it expertise in house, right? It's just you. And so a group like platform accounting group, like it's literally in the name, they can provide the technology platform to all of their member firms, to all of their acquired firms, and then give them that productivity boost like that, that solo attorney that we talked about five times productivity boost because he's automated a lot of the like the routine work of like just task management emails like that takes up so much time. If you can build that layer and you can deploy it to those firms, then they're going to see a productivity boost. And it might not be five X, there might not be a, you know, 80% reduction in labor costs. But even if it's, you know, a little bit, right, when, when labor is, you know, 40, 50, maybe 60% of your costs in a firm, if you can, you know, reduce that by ten, 20%, you can see a real increase in margins, right? And the private equity firm only needs to get, you know, like a, you know what? 20% profit margin. And most of these firms are not as profitable as they could be. So it's like, help, help these firms raise prices, help them standardize on more modern technology, get productivity gains, uh, help them to, you know, fix their fees. If you can do that.

Speaker 4: [00:48:16] If you.

David Leary: [00:48:17] Just do that by using cloud technology, you're going to make a victory here.

Blake Oliver: [00:48:20] Exactly, exactly. So like, actually, that's the thing, David is like, you don't even really need as like a private equity roll up to even be doing that much with AI because you can still just get all the like, there's a lot of firms that never even got the benefit of, um, of standardizing on the cloud. And now the cloud tools are so robust and developed after all this time that it's a lot easier to, you know, roll them out and standardize them, you know, just, just getting all these firms on.

Speaker 4: [00:48:51] A practice management.

David Leary: [00:48:52] You don't have to invent anything new now.

Speaker 4: [00:48:54] Yeah, yeah.

Blake Oliver: [00:48:54] Like like a lot of small firms don't even have practice management software. They're just doing everything in.

Speaker 4: [00:49:00] Email.

Blake Oliver: [00:49:01] And like.

Speaker 4: [00:49:01] Excel and email.

Blake Oliver: [00:49:02] Right? And so yeah, I mean, and there's not a succession plan. So the private equity is like the only succession option for a lot of these small firms. And I guess the only risk is that they won't be able to keep the people right. If the people leave, if the staff leave, then who's going to serve the clients? But if they can like add in the AI stuff and avoid adding headcount or, you know, just replace the people that are leaving over time with more automation, I think it's a good bet. And obviously investors agree and are willing to fund these like massive roll ups. I think the thing that's interesting is the, the roll ups where like, I think it's this one is like that where the firms keep their local branding.

Speaker 4: [00:49:50] That's most of the time too.

David Leary: [00:49:51] Yeah. It's, it's, it's, it's, they get to keep your local relationships with your clients. Yeah.

Speaker 4: [00:49:57] Yeah.

Blake Oliver: [00:49:57] If you keep your firm brand, you know, you're not becoming, you're not rolling up into like a big firm, like with one brand. And, and the, the problem with that is like, as you do that, then you move up market and you have to shed your clients. But these, these types of private equity roll ups are really about just combining a bunch of small firms, standardizing them on the same technology stack, but they keep their branding and they keep their local presence, and they don't try to push up market with larger clients. And that's really fascinating. We'll see how that plays out.

David Leary: [00:50:32] Yeah. And then another raise that happened. I don't know if you saw gradient. They they just came out of stealth. They raised $4 million of pre-seed funding to run an AI native accounting firm. So focusing on US startups. So this is where actually these types of startups, they're really at the risk of somebody like a stripe, Right. Like stripes already owns. They're solving most of the profit and loss statement now, right? For a startup. I don't know what they're going to you know this is a software slash service. Play it sounds feels very bench like. I don't know if we'll see another $4 million at a you know AGL accounting as a service startup. We'll see what happens.

Blake Oliver: [00:51:16] Uh, so I have to talk about this story because it's in the, it's in the title of this episode. Uh, and I totally missed it. Totally forgot earlier. So let's hit it. This is about the US economy and how we're doing because it's sort of a tale of, I don't know if this is the right analogy. A Tale of Two cities. Um, we have the AI boom going on and we also seem to be like, it just feels from other, uh, other indicators seem to point to a slowdown.

David Leary: [00:51:54] Like the stock market's kind of great over here. But yeah, my bank account maybe isn't as great.

Speaker 4: [00:51:59] Yes. Yeah.

Blake Oliver: [00:52:01] And so, um, I lost the story here. Let me pull it right back up. So I spotted this in cfo.com and the headline is minus AI investments. The US Edges Toward a Recession. An independent economic forecasting firm called Oxford Economics has done some calculations, and they believe that the American economy may have entered recession territory. We might be in a recession. If you subtract the massive influx of capital that is flowing into the development of artificial intelligence. Michael Pierce, the US chief economist at Oxford Economics, said all the growth in the past couple of years has been in AI. Everything else has been falling. 2 to 3 years. So basically, if you subtract AI, if you don't consider the AI category, then we're essentially in a recession. Um, he delivered his remarks at the CFO Leadership Council's fall conference in Colorado last week, where, ironically, vendors are hawking a range of AI enabled products. So that's the the strange thing about this economy, right? We've got the stock market booming. We've got all this investment going into AI. But at the same time, we've had a slowdown in other sectors.

Blake Oliver: [00:53:21] And so what is going to happen when the AI bubble, if it is a bubble pops is then we'll be in a real recession is the concern. So Oxford Economics calculated that the US economy expanded about 2% to 2.5% over the first half of the year, and is likely to expand by something north of 3% in the third quarter. But you subtract all that and then we really subtract all the, you know, the AI stuff. And then we haven't really grown at all. And in fact, maybe a shrunk. So. That's, that's basically the, the crazy situation we're in, right. Is that if the AI investment doesn't pay off, if we don't start to see ROI, what is going to happen? And the thing is that it's, it's an interesting situation because you would think all the doom and gloom about AI has been about replacing jobs. And if it was doing that, I think we actually would be going more into a recession, right? Because we're a consumer driven economy. If we start to get all these layoffs, people are going to not be able to spend as much.

Speaker 4: [00:54:35] And people stop.

David Leary: [00:54:35] Getting mortgages, they stop mortgage, then homebuilding stops, construction stops, and then it all. Yeah.

Speaker 4: [00:54:41] Yes.

Blake Oliver: [00:54:42] But also, uh, and so, so that hasn't happened. So that's good. But then in order for all this AI investment in the economy to like pay off, there has to be some sort of like productivity boost, right? American productivity has to go up because of these tools. But like, how is that actually shaping out? Like we haven't yet seen it happen in just our sector, right? In accounting has, have we actually started to make more money for our firms as a result of AI yet?

David Leary: [00:55:20] Yeah. No. Companies like all my employees are saving four hours a week because they have less email to deal with now. And it's turned into this output at my company. We haven't seen that yet. You're not seeing.

Speaker 4: [00:55:29] That.

Blake Oliver: [00:55:30] I mean, I think like if the thing I've noticed is like personally is I am working, I'm able to work less because I've got the AI doing the manual tasks, but now I'm spending a lot more on tokens.

Speaker 4: [00:55:42] Yeah.

Blake Oliver: [00:55:42] Um, have I personally have we at earmark actually seen our like revenue increase as a result of our AI investments? Maybe a little bit right on like the sales outreach side, right? We created this, you know, uh.

Speaker 4: [00:56:00] Yeah, my.

Blake Oliver: [00:56:01] Agent.

David Leary: [00:56:02] Yeah, might have done one piece of reach out or got a lead or whatever you wanna call it.

Speaker 4: [00:56:05] Yeah.

Blake Oliver: [00:56:05] So, you know, and so like, it's increased our sponsorships of the podcast. But then if I put that in context of like the overall business, I mean, it's maybe delivered like a few percent right of increase.

Speaker 4: [00:56:19] That might have happened directly.

David Leary: [00:56:21] Even without it. Right.

Blake Oliver: [00:56:22] Could have happened anyway. So like, there's a, there's a lot that needs to happen in terms of productivity gains before we can say that AI is like a good investment. And so if if we don't see those productivity gains soon, you know, all these companies spending money on AI might cut back and they cut back and then the market sees that. And then, uh, the, these AI companies like their stock drops, right? The, the big technology companies. And then they start pulling back on investment. And that is what's been driving the economy, building all the data centers, all that.

Speaker 4: [00:57:04] Sort of thing.

David Leary: [00:57:04] We talked about that the boot makers.

Blake Oliver: [00:57:06] Right. So we have to in this economy, we have to see actual productivity gains, actual like ROI on AI meaningfully to justify these billions or trillions of dollars of investment. And the question is, will we see that soon enough?

David Leary: [00:57:26] I'm going to close out on a story that is just crazy. And you can think about it, you know, when you're flying on your flight to the conference or people can chew on this. So the Chicago mayor is proposing for the second time now a corporate head tax. So essentially the, uh, he's proposing a $25 per month tax on each employee at companies with more than 125 workers. With some exceptions for businesses in poorer South and West Side neighborhoods. As part of the fair share budget he unveiled on Wednesday, so the projected $25 per month per worker is expected to raise $101 million. To me, this is like the most craziest thing. Like if if you're going to hire the most employees in Chicago, shouldn't you like be rewarded or recognized or like it's like the opposite, like I'm going to hire a bunch of people in Chicago and I'm going to pay more taxes and I'm going to be punished for hiring people. It just doesn't make sense. Like, like this is really like, it's one of the most backwards taxes I could think of. You want to encourage companies to hire people.

Blake Oliver: [00:58:31] Wasn't wasn't it Chicago or Illinois that was also talking about taxing, uh, like AI agents as well.

Speaker 4: [00:58:38] Like so because this.

David Leary: [00:58:39] Is a disincentive to hire people.

Speaker 4: [00:58:41] Yeah, yeah.

Blake Oliver: [00:58:42] Yeah. Well, David, that's all the time we have for this week. Thanks everyone who joined us live. Check us out on YouTube. If you haven't seen us, we are the accounting podcast. Subscribe. Hit that notification bell icon and you will get notified when we are live and you can chat with us. Don't forget, you can earn free continuing professional education for listening to this episode, our back catalog and many other fine accounting and tax podcasts, go to earmark.app in your web browser, or get the free earmark app on the App Store. Create your free account earn one free CPE per week, subscribe for unlimited. You get unlimited CPE and access to subscriber only courses. It's just $200 a year. What a deal. David, I'll see you around when I'm back from CCH connections, and, uh.

David Leary: [00:59:31] I'll talk to you in a couple of days. Enjoy.

Blake Oliver: [00:59:33] See you soon. Bye, everyone.