The Accounting Podcast

There may be errors in spelling, grammar, and accuracy in this machine-generated transcript.

Blake Oliver: [00:00:04] So who is in charge of the IRS? Who has the legal or administrative authority? I mean, this all ties into that settlement that Trump got for himself, because who actually can sign that? I mean, if there's no confirmed IRS commissioner, is that even valid?

David Leary: [00:00:23] Coming to you weekly from the OnPay Recording Studio.

Blake Oliver: [00:00:30] Hey, everyone, and welcome back to the podcast. This is your weekly roundup of news in the accounting profession. I'm Blake Oliver.

David Leary: [00:00:37] And I'm David Leary.

Blake Oliver: [00:00:38] David, our two top stories are tied together this week. I've got a story about a controller at a SaaS company. Runs an entire finance team. Just him. He's the finance team using automation AI to do it all. They do.

David Leary: [00:00:57] 50 weeks ago we had a firm of one. And now you're having a finance team of one.

Blake Oliver: [00:01:02] They do 50 million a month in revenue through their marketplace. And this guy is doing it all by himself. I think it's a great case study. And you, David, have been digging into some recent studies about the capabilities of LMS like cloud and ChatGPT and how they perform in different accounting tasks, bookkeeping, and more advanced accounting milestones.

David Leary: [00:01:25] Let's just say.

Blake Oliver: [00:01:26] They're getting better and better, better and better. So we'll start with those two. I also have some follow up about the Trump Weaponization Fund and the IRS. Also former guest of the podcast, Christina Ho, who was a board member at the PCAOB, is starting an audit powered or an AI powered audit firm that's going to audit private companies and then maybe public companies as well. The big four are aware of this threat. Kpmg is going to Silicon Valley and partnering, possibly investing in AI companies that they view as a disruptive threat to their business model. We've got all that and more. But first, David, let's thank our sponsors.

David Leary: [00:02:12] Our sponsors. This week. We have on pay the value builder system and our cost seg. Are you tired of payroll headaches getting in the way of client experience? You want to deliver manual workflows, creating bottlenecks, compliance, nightmares, and endless support calls that go nowhere. There's a better way for your team and your clients on pay. Is the payroll partner that accountants and bookkeepers actually love? Why? Because it's easy to use, packed with value, and backed by support that actually supports you. Their team gets rave reviews for being fast, expert, and actually reachable when you need them on pay. Handles the heavy lifting. You get a dedicated onboarding support coordinator who sets up worker profiles and transfers year to date data from previous providers, all at no extra cost. They're seamless. Quickbooks and Xero integrations eliminate manual journal entries, and they support any type of business you serve. Farms, restaurants, nonprofits, you name it on page can handle the unique requirements without adding complexity. And on page pricing is simple to everything your clients expect, from multi-state filings to off cycle payroll is included. No hidden fees or surprises. To book a demo. Head over to The Accounting Podcast dot ProAdvisor that is Accounting Today dot promo forward slash OPAY, and I actually had to use on page support yesterday.

Blake Oliver: [00:03:26] How'd that go?

David Leary: [00:03:27] So I had my daughter come and work with us at the AME conference in Palm Springs, and I had a pair. So I paid her. And apparently, you know, now that she's kind of an adult, she went and created a new bank account in her own, but never changed the account numbers in on pace. So I sent her paycheck to her old closed bank account and it was painless. I got on chat with on pay and they just confirmed, do you want us to send it to the new numbers? I said yes, and they reprocessed her payroll and she texted me this morning she got paid, But yeah, it was super easy to do. They just they knew there was a problem. They just needed confirmation from me for the next step. It was great.

Blake Oliver: [00:04:03] I love hearing that. Thank you for being a big supporter of the show. All right. Let's talk about this controller at school. The company is called school. It's a SaaS business. They do online communities, educational communities, SKOOL. And the controller of school was on a webinar hosted by the Controllers Council recently. And he talked about how he runs the entire finance function for this startup by himself. No humans other than him, just AI agents. School handles over 5 million in monthly spend and nearly 50 million a month runs through its marketplace. The controller went on vacation for two weeks, and he came back and he was expecting a huge mountain of work He had 2000 transactions that had piled up while he was gone, but his automations had already coded, categorized, approved and synced almost all of them into their ERP. He had to review only 67 transactions by hand, and those were the tricky ones out of the 2000 that needed the human judgment. The entire cleanup after his two week vacation took him about 30 minutes, he said on the webinar. Yeah. Go ahead.

David Leary: [00:05:25] Can you repeat the numbers? So he had he had to manually fix 67 of 2000.

Blake Oliver: [00:05:29] 67 out of 2000 transactions. Are you like 3%? Is that right?

David Leary: [00:05:36] No, it's less than that. Right.

Blake Oliver: [00:05:38] Took him 30 minutes. How does he do it? He runs seven specialized AI agents, plus an admin agent that checks the work of the others and enforces the controls. And he basically describes that his job has changed from doing the work to reviewing the work. And so now he has time to do forecasting, cash management and strategy instead of processing transactions. So that is James Agius, A GIUS he's the financial controller at school. And that was on a controller's council webinar. And that may sound crazy, but I have heard about this. I have experienced just how much AI and automation is helping us in our accounting here at earmark, which we do ourselves still, because we want to be deep in the weeds on it, deep in the weeds, know what's going on. Um, and I'm curious how this fits with what you've been looking at, David, this week when it comes to how the AI models, the, the major ones collide and ChatGPT are performing with every new iteration. We've got some new data from digits.

David Leary: [00:06:52] Yeah.

Blake Oliver: [00:06:52] So ramp. Right?

David Leary: [00:06:54] Yeah. So take your example. That guy there, he's now at 96.6%, which is pretty believable, right? Because for the first time ever, the main models. So the main, when I say main models, this is going to be your off the shelf clod open, uh, open AI or ChatGPT, uh, Google Gemini. The, the models off the shelf are now beating according to digits human accountants. So digits has been comparing human outsourced accountants across 2000 transactions to get them categorized over time. And this is the first time ever all the base models are now beating human accountants, but the base models still are not anywhere near. Your guy is your guy was at 96.6%. The base models are like 79%, 79.4, 79.5, 79.9. Uh, clod. Opus 4.8 is now at 80.7%, and then digits itself is coming in at, according to their study is coming in at 97.8%. So just slightly higher than what? Uh, I forgot the guy's name. This one person.

Blake Oliver: [00:07:57] James at.

David Leary: [00:07:57] School finance team, right? Yeah.

Blake Oliver: [00:07:59] And so, so I just want to point out here on the chart, like I'm looking at this right now, you've got it on the screen. Human outsourced accountants are able to code correctly 79% of transactions in digit study correctly the first time. And they're saying that they can do close to 98%. But that like the other models, just without being wrapped in anything else, are able to do it at like between 79 and 80% also. So they're basically on, they're on par with, with humans.

David Leary: [00:08:31] Out of the box with nothing. And, and the conclusion maybe on this is that this is kind of a commodity. Now, the basic categorization, um, transaction classification is kind of a basic thing now that everybody's just going to get for free from these models.

Blake Oliver: [00:08:48] If you can plug it into your GL, then? Yeah. I don't see why it couldn't just do that.

David Leary: [00:08:53] But then you look at the example, the I forgot the finance guy's name that you just talked about. He, James was able to probably take things and because he's got a closed system, tweak it a little bit more. And that's how he's getting those higher percentages.

Blake Oliver: [00:09:08] Yeah. More context. Right. Yeah. Giving it source documents, giving it more information. It's all about the context. So you've also got data from ramp here. They also have been doing a study.

David Leary: [00:09:19] And we'll talk about this in a moment too. But ramp also is comparing. So they launched basically a thing called they're calling it stack. And think about it. It's like AI agents that plug into your current accounting stack at your firm and can talk to QuickBooks and other tools you use and do work accounting work for your firm. And they've measured that against the models itself. So ramps coming in at like 65.8%, it's different type of transactions they're doing versus just categorizations, but they're beating the model. So the the purpose built things. So ramp and digits are beating the models. But the models now are starting to beat humans for that kind of work.

Blake Oliver: [00:09:57] Did. So did ramp say like what the performance is versus the human performance on these.

David Leary: [00:10:02] Ramp never actually um says that they're they're not measuring what humans can do, but they're the tests are more, um, it's more complex, I think, than what digits is doing.

Blake Oliver: [00:10:14] Oh yeah.

David Leary: [00:10:15] So, so, so ramp instead of digits is taking like 2000 transactions across four businesses. And hey, let's categorize this. That's, that's their study. Bookkeeping work ramp is taking 237 accounting tasks across eight synthetic businesses and then grading it by real accountants when it's done. Yeah. So I the numbers are going to be lower because I think what the ramp study is doing is slightly more complex.

Blake Oliver: [00:10:43] Yes. Accounting accounting work, right. Financial close type work.

David Leary: [00:10:47] And I wish they compared it to the inhuman, because maybe the humans coming in at 60% still.

Blake Oliver: [00:10:52] Well, I think I think we saw this in some studies by anthropic recently. I don't have them top of mind, but I believe that they were getting close to around this amount, where it's like 50 to 60% able to complete these complex accounting tasks. And so ramps saying, we've got it now to 65%. This makes sense to me. That fits with what I'm familiar with. And you know, this is great for job security for accountants for now, because we need to be way higher than 65% in order for any of this to come close to automating our job, it really needs to be like almost 100%, which is where digits is going with its bookkeeping work. So, you know, just this, this totally makes sense if you've used AI, if you're using it to categorize transactions, it can do it now with extremely high reliability, thousands of transactions. You only have a few dozen to review. That's amazing. Do you even need a human bookkeeper for that basic categorization anymore? I think the answer is no. You don't need it.

David Leary: [00:11:53] It's possibly true. Yes, the job is changing. Or and or.

Blake Oliver: [00:11:58] Doing stuff like in bookkeeping, like matching transactions to a bank feed, importing transactions, finding missing ones, doing bank recs. I've got an AI agent in cloud Cowork that can reconcile my Xero file, and it can handle all of that. It'll import the missing transactions. It'll match them. It can suggest coding like this is all doable.

David Leary: [00:12:19] And I was able to catch up with the. Jeff Siebert, the founder and or co-founder and CEO of digits. Um, the 12 minute interview we can play here. And he kind of relates this at a high level. What he saw happen with code. Like what's happened is in the last 12 months, developers went from AI could write some code to AI, writing all the code. And he thinks we're about to tip that, like even more. Like not not the models are beating humans, but they still have room for improvement. They could do even better. What happens when they hit 90%, 95% out of the box? So let's press play on that little segment video here. And then after after that, I want to jump into and talk about more about what ramp launched with this stack, quote unquote.

Blake Oliver: [00:13:04] All right. So this is an interview you did with Digit's, uh, co-founder Jeff Siebert.

David Leary: [00:13:10] I'm going to press play. So all the AI models, the generic high level AI models, Gemini anthropic, OpenAI, you're saying now all can do accounting better than humans?

Jeff Seibert, CEO, Digits: [00:13:21] Yeah, this is pretty this is shocking. David. So first off, thank you so much for having me. Great to be here. Great to see you. Um, so yeah, we have been formally benchmarking all of the big third party AI models, the LLMs, over the past 18 months. And we just finished our latest rendition of it, our sort of fourth release of our white paper. And it's the most exciting one yet. It's actually fascinating. All of the major model providers. So OpenAI, Anthropic and Google Gemini have all for the first time beaten, real, outsourced human accountants at bookkeeping tasks. And this was a formal study on a large data set over 2000 transactions across many different businesses. And the difference is small. Today they beat the humans by about 1.6%. So it's relatively small, but it is the first time that has ever happened. And what we are seeing is just this constant march of progress where every few months the models get a percentage or two better.

David Leary: [00:14:18] So is each percentage like way harder to get to than the previous percentage? Like, is it is it exponential?

Jeff Seibert, CEO, Digits: [00:14:24] It is. It's exponential. And so to set the stage on this and where things stand. So all of the models right now are right about 80% accuracy on this data set. And so the way the benchmark works is we give you a transaction and we give you a chart of accounts, and we ask the model or the human to book that transaction into the chart of accounts. This data set has been previously reviewed by US based GAAP accountants. And so we have the actual answers. And we did this with all of the AI models. And we did this with outsourced human accountants. So we hired 12 outsourced accountants to perform the same study. And they came in at 79%. And you may think that feels low, but when you talk with firms that sort of offshore their bookkeeping, that is about the quality you get because you have accountants who understand general accounting principles, but they don't know anything about that business, its industry, its supply chain, its geography, its customer base, etc..

David Leary: [00:15:18] The judgment.

Jeff Seibert, CEO, Digits: [00:15:19] That accounts for the roughly 80% accuracy. And but there's some other interesting conclusions. So also all of the models are within three percentage points of each other. So you look at AI, it's already getting commoditized, at least when it comes to bookkeeping. There is no difference between OpenAI's GPT anthropic's Claude, Google's Gemini. They're literally all within a couple percent of each other and of the humans right now. And it really takes a dedicated system to surpass that.

David Leary: [00:15:49] So for perspective, then you're a dedicated system digits. Are you at the same percentage? Are you different? Obviously, you wouldn't be doing these studies if you weren't going to brag, right? So what's correct.

Jeff Seibert, CEO, Digits: [00:16:00] This is this is partially a marketing thing. Um, yeah. Digits is up at 98% accuracy. And so we are dramatically better. And the reason is we have literally spent six years building our own domain specific AI bookkeeping model architecture. It looks very, very different than an LLM. And so our models are predictive. They are deterministic. They can't hallucinate by their technical architecture. And it allows us to get extreme accuracy on bookkeeping tasks. And so the way to think about it is an outsourced human accountant is generic, right? It doesn't know your business digits mimics the knowledge of a dedicated accountant who you've worked with for a number of years, basically memorizes the history of your business and all of your financial relationships. And that's how digits does the accounting correctly.

David Leary: [00:16:46] Yeah, because it just you have access to the history.

Jeff Seibert, CEO, Digits: [00:16:49] Yep, exactly.

David Leary: [00:16:50] When you start looking at like your, the study that you've been doing and the task you're giving AI to do. Are there any workflows that you're just like, yeah, humans are still way better at that part of the workflow or that part of the accounting workflow.

Jeff Seibert, CEO, Digits: [00:17:02] Yeah. We look at it as the other 20% for the models, right? What are they getting wrong? And for digits, the other 2%, because we're up at 98% and that remaining 2% is complex accruals. And so that's right. It makes sense. The models don't really understand full accrual accounting rules, how to manage and manipulate those journal entries, etc.. And so that's been one of our major investments over the course of this year. And a few weeks ago, we launched fully automated schedules. So digits, we believe is the first ledger with native support for accrual schedules. So the AI identifies a transaction as the potential start of a fixed asset or a or prepaid expense or deferred revenue. And then the AI actually drafts the accrual schedule for you in the product and services it. And so you approve it or edit it, whatever you want. The ledger itself manages all the underlying journal entries. So you no longer need to manage recurring journals. You no longer need to roll forward your schedule. You no longer need a work paper sitting in Excel or Google Sheets or whatever it might be. You can see all that natively in the ledger. That's our effort to identify and start having the AI gain intelligence over the remaining set of transactions.

David Leary: [00:18:13] If all our listeners and firms go and look at your new benchmark report, what conclusion? If they look at it, do you think they're going to miss? Like, what's not obvious that's in there that maybe, you know, because you've been doing the study?

Jeff Seibert, CEO, Digits: [00:18:26] Well, no, not even the study. I want to bring in our experience on the software engineering side. So we're a technology company. It has changed our life over the past six months. Like I personally have not written code since December. And that is weird. My life is coding like my entire career.

David Leary: [00:18:43] You've been an engineer. You've written code.

Jeff Seibert, CEO, Digits: [00:18:45] Exactly like my passion is coding. I taught myself to program when I was 12. Okay. And I haven't written code since December because in December, Claude Opus 4.5 gained popularity and people realized it could write the code for you. The transition over Q1 of this year, we went from basically zero of our engineers using AI to 100% of our engineers using AI. It writes almost all of our code, and that's been a really traumatic, honestly, change for software engineers. And a lot of folks are emotional about it because it really changes the day to day of their role. I foresee that same journey happening in accounting. Like now that we have data showing the models are better at basic bookkeeping than a human, it's only going to accelerate from there. And so even if it's slow, but you could imagine over the next 6 to 12 months, substantially all of the month end. Close is automated. What does that make them? The profession. And to draw the parallel like we have not fired our software engineers. They are still critical, but the day to day has changed completely. Instead of them writing the code, they're guiding the agents, and I could see the same thing happening in the accounting side.

David Leary: [00:19:56] And then I think two months ago or so, you guys announced you have this outcome based pricing. So I can, if I'm and I think it's really actually smart because it makes a lot of risk so I can start using digits, but I'm not going to pay until it gets me 80% efficient or 80% automated. How does that how does that actually work, that model?

Jeff Seibert, CEO, Digits: [00:20:12] Yeah. So we have rolled this out for accounting firms that will commit at least 100 clients to digits. And the way it works is we work with you to you bring your clients on, we show you how effective digits is because we know in the core ledger, every transaction, the digits booked versus what your team had to edit. And so we talk about this concept of a zero touch transaction that means digits imported ingested the transaction from your clients, banks, cards, payroll, payment processor, whatever did the bookkeeping for it into the ledger. Did the review. Did the analysis, did the anomaly detection and drafted the report. And your team reviewed that report and closed the books without ever touching that transaction. For those transactions, we can say, hey, I think digits did the accounting for that. And so what we are guaranteeing with our outcome based pricing is digits will automate, will make 95% of your of the transactions for a given client, zero touch your team touches less than 5% of them. If we do that, you pay us. If we don't, digits is free for that client for that month.

David Leary: [00:21:17] I love it. I mean, I've been in this space a long time and I've seen lots of different SaaS apps and all their different billing models. And like, this is so smart.

Jeff Seibert, CEO, Digits: [00:21:25] Yeah, yeah, yeah. And, and just to be clear, the way the firms think about it is they are happy because if we do automate, then they're like, yes, we should pay you more than just the normal software cost because you've also taken all the tedium away. We can now repurpose that capacity to other clients or to higher value services for our existing clients.

David Leary: [00:21:45] So, so all the work's changed, right? Like you said, for engineers, it's changed like their day to day. It's completely changed. Now you're saying 4 or 5, ten years out from now, the work in accountant is doing, especially from a cost perspective, it's completely changed. So if I'm an accountant, what skills should I be developing then? Yeah. So I have it so I can be valuable five years from now.

Jeff Seibert, CEO, Digits: [00:22:04] So there's a couple things that to me are fundamentally foundationally human that the AI can never replace. And so to list a few. One is judgment. Like you really want the AI goes off in weird directions and that'll likely continue, right? You need someone with lived experience who is able to guide it and make those difficult judgment calls. And as you know, in accounting, there are so many judgment calls. And so it's not like everything is a black and white rule that the AI is going to follow. That is really important. And so folks that understand the space, the complexities and have the experience to make those calls. That is what you'll spend a lot of time doing. The second is trust. Business owners need an accountant they can trust, right? The AI will tell you anything you want, and it's so sycophantic. It'll tell you something. You'll catch it lying, you'll correct it, and then it'll just tell you the opposite and say, oh yeah, you were right. Right. You can never trust the AI. And so you want an accountant. You can trust and build that relationship. And then also the AI can never take accountability, right? It's never going to be liable for the numbers it gives you.

David Leary: [00:23:08] It says, I'm sorry all the time. It says, I'm sorry all the time. What do you mean?

Jeff Seibert, CEO, Digits: [00:23:12] It says, I'm sorry. It doesn't. Then learn from that. So. Right. Like, what are you going to do, sue your AI. And so that's the other aspect. And so you want accountants that sort of really surface those those traits. But more fundamentally, it's the soft skills. It's the relationship. And so you're still going to want an accountant who works with the business owner, guides their business using their judgment, builds that foundation of trust, and then ultimately stands behind the numbers they give them.

David Leary: [00:23:40] So in closing, a lot of, let's say our listener, just for example, I'm a 20 person accounting firm. What is the three technology decisions I should be making in the next 12 months for my firm to set me up for the future?

Jeff Seibert, CEO, Digits: [00:23:52] Yes. The key one is your system of record. I would focus first on which ledger you are choosing first to probably default your new clients onto, and then start working through migrating your existing clients, because that really serves as the foundation. And what we're seeing firms prioritize is one with a really powerful, strong, open API that then they can plug in other tools and an MCP server that's model context protocol, where you can plug in cloud or codex or so on, and do other stuff with AI on top of your tool. That's sort of the foundation. And then you need to start thinking about your practice management, because you will likely be in a world where instead of only managing teams of people, you will also be managing agents. And so how do you think about that as you run your day to day practice and who is doing what? And I think the more modern tools that allow you to balance work across humans and agents will become really, really interesting. And then beyond that, it's what new capabilities can you offer your clients? And so this goes back to the advisory front. And I know there's been so much talk about advisory for a decade plus, it never really panned out because folks spent so much time just getting the work done, the books closed that they didn't have time for advisory. I think this is the new birth of advisory because suddenly you will have the time. So then I would focus with your clients on what is most valuable to them, what would they really want from you, and what tools can you put in place to activate that new line of business for you and your team?

David Leary: [00:25:17] Thank you so much for coming on the show and clarifying like what's going on with digital in the study, right? And the difference between what the normal JL models are doing, which digits are doing and what firms could try to do on their own. It's super helpful. I appreciate you coming on the show, and hopefully we'll have you back soon.

Blake Oliver: [00:25:33] All right. Let's go ahead and thank our next couple of sponsors. And the first is value builder system. If you run an accounting firm, you've probably watched this happen. A client sells their business. They call their wealth advisor, their attorney, their consultant. And you only find out about the deal when the K-1 hits your desk. It's happening more than you think. 12% of business owner clients got a written offer to buy their company in the last year. When that offer lands, it kicks off a wave of high margin advisory work, tax planning, estate work, succession planning, quality of earnings engagements that often run into six figures. But to most of your clients, you're the tax pro, so the work goes elsewhere. Value builder is an exit readiness platform built for accounting firms. We give you the tools and certification to start the end game conversation with your business owner clients before someone else does. In a typical 2 or 3 partner firm, our data shows a hidden million in advisory fees sitting inside your existing client base to see how value builder helps you move from five figure compliance to six figure advisory engagements. Head over to The Accounting Podcast dot com slash value. That's The Accounting Podcast dot promo forward slash value.

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Blake Oliver: [00:27:57] Thanks to our sponsors. We really appreciate your support. Please help us out. Go support them. Visit those links. Welcome to our live stream viewers! Boring accountant. Nerd. Mommy, fluffy hammer bookkeeping. Christopher Perez, vertical golf. Great to have you with us. Thanks for chatting. Fluffy hammer says teach staff how to read the AI audit trail so they can follow the logic. If there is an issue, get away from making journal entries to fix things and move toward fixing what the AI understands. Christopher says, curious what the criteria is for inaccurate coding here and if the model is taking a planning angle. I think the way that they do these studies, correct me if I'm wrong, David, is they, they, they did them independently. And then they saw what, what the what had to be corrected for each the human versus the AI in the end after it went through review.

David Leary: [00:28:54] I think that's my understanding. And that's the way digits does their pricing too, is if you have to touch the transaction with your mouse afterwards, that would not be an accurate, uh, accurately recorded transaction.

Blake Oliver: [00:29:07] Vertical golf says, and this is in response to that story about the finance team of one. We used AI to build a series of month end close Python scripts, pulls data from source systems, and only comes in at the end if there are issues. Saves a full day of work. That's amazing. All right. I want to come back to ramp and what they're building with stack and AI agents. But I also want to talk about the pcob and the future of audit. So Christina Ho is a former board member of the public company Accounting Oversight Board, the PCAOB. She was a guest on our show and a vocal critic of the PCAOB and its approach to audit regulation when she was a board member there. She left, and she's now joined a company called oath, which is a newly licensed accounting firm that aims to automate 80% of its work by 2030 using AI. And it's a firm that's being built from scratch. It's a venture backed accounting firm that will audit that will offer audit and advisory services, but not tax services. So this is different from a lot of venture backed or private equity backed firms that are going after consulting, advisory Tax and not doing assurance. This one is specifically going to do it.

David Leary: [00:30:29] From day one. It's, it's AI based. It's they don't have old systems and all this like 30 years of baggage to, to turn a boat around is very hard from day one. They're good to go.

Blake Oliver: [00:30:39] They're under an alternative practice structure. So that means they have a CPA firm owned by CPAs that is linked to a CPA firm that does everything else other than the advisory or the assurance, and they got their CPA license. So they're going to start taking on private company audit clients immediately with the possibility of eventually expanding into public company audits. The company's goal is to become an independent verification platform for financial systems using AI and direct system integrations to change how audit work is performed. This was covered in The Wall Street Journal. They don't expect automation to ever reach 100%. That's according to the CEO, Lucas Ward. He emphasized that audit remains a human accountability function even if machines handle much of the underlying verification. Ward's vision is that over time, auditors are going to increasingly oversee systems producing financial information continuously instead of relying on periodic manual evidence gathering. How is this going to work in practice? Christina Ho said that oath plans to connect directly to clients accounting systems and document repositories to automatically extract, match and verify information. That could allow the audit to happen on a monthly cadence aligned with the client's financial close, which then reduces the need for sending over schedules and one time year end support packages that are common in traditional audits. Makes a lot of sense, and from a client's perspective, that would save them a lot of work. Having to export all that for the auditors.

David Leary: [00:32:22] Can you clarify something for me? So is this a software play where eventually other firms can be like, all right, I'm going to buy oath and purchase oath and use it in my firm to do audits. Or is this more like a bench play where they're going to be a closed system and they hope that oath helps them be so efficient. They're just going to get a lot of tons of audit work.

Blake Oliver: [00:32:40] To me, this feels like the latter. It's the latter. They are going to take the audit work, and they're going to do all their own internal systems to automate it, and they are going to be able to beat the big firms at the audit game. They're recruiting.

David Leary: [00:32:56] You had a story about that, right? That the the big the big firms are afraid of this.

Blake Oliver: [00:33:01] That's my next story. We'll get to this. Kpmg is worried they're going to Silicon Valley to see what they can do. Before we move on to that, I just want to point out this an interesting term the firm uses. They're recruiting what they call accounting engineers. It's a job that combines accounting expertise with computer science and systems engineering skills. This is no small deal. They raised $6.6 million in seed funding in December. The round was led by venture capital with participation from Chicago Ventures and M25. They're targeting a series A raise next year. So this is disruption coming to the audit market a direct competitor. Now let's talk about the big four and specifically KPMG. This was reported in the Financial Times by Stephen Foley. Kpmg's US leadership is making regular trips to Silicon Valley to meet AI startups, and their goal is to identify potential disruptors and either partner with them or invest in them before they grow into major competitive threats. Us CEO Tim Walsh said that this is due to the growing urgency inside the Big Four as AI reshapes professional services. The visits have been with major venture capital firms including Andreessen Horowitz, Bessemer, Emergence Capital and J. C two ventures. The whole management committee is now going to Silicon Valley every 5 or 6 weeks, and the goal is to educate their senior leadership about the pace of AI innovation and to find companies that KPMG might want to work with. More formally, they are open to alliances, technology partnerships or minority equity investments.

David Leary: [00:34:58] Or if these VCs in the Bay area, I think maybe we're having an AI bubble possibly happening. Here's a bunch of new customers to sell these shares to. On the next raise, in the next round, you get your exit earlier.

Blake Oliver: [00:35:13] All right, David, I've also got a story here about the accounting talent shortage. It has not gone away. Ai has not solved it yet. It's getting worse.

David Leary: [00:35:26] How is it getting worse?

Blake Oliver: [00:35:28] Okay, so a company called personify did a study. They've been doing this for a few years now, and this was reported in Accounting Today. It found a sharp jump in unfilled accounting and finance positions. So the average number of open roles per company it was 17 in 2026. This is 17 unfilled accounting of finance roles per company. That's up from 5 in 2025 and 2 in 2024. That's a huge jump from 5 to 17 in a single year. It's more than tripled. So we have a talent crunch. 84% of leaders this year said there's a talent shortage. This is finance and accounting leaders 84%. It was 87% last year, and it was 63% in 2020. So full eight out of ten. Finance and accounting leaders saying there's a talent shortage. 17 unfilled roles per company, up from five.

David Leary: [00:36:32] So are these unfilled roles because things are changing these companies and they're creating new positions. Or is this because we've had an exit of talent that just seems like like, what is that big jump there for? Because things are not too different than they were. You said that was how many years ago? That was 2025.

Blake Oliver: [00:36:48] Well, in 2025, it was just five unfilled roles per company. Now we're at 17 in accounting and finance. Why is this exactly? I mean, we have been talking about how accountants are getting older. Cpas in particular, have been aging out and retiring. So that totally makes sense, right? If we've been talking about how 75% of the profession is ready to retire for years now, well, now they're doing it.

David Leary: [00:37:13] Maybe it's happening. We're finally there.

Blake Oliver: [00:37:15] Yeah. And maybe they're like, uh, I don't want to learn all this new AI stuff. I might as well retire now, right?

David Leary: [00:37:20] So it makes.

Blake Oliver: [00:37:21] Sense. Um, 63% of leaders, so over half are saying they're using AI to ease pressure on hiring. That's up from 23% last year. So the AI use is skyrocketing as well. They're using 90/90 percent are using AI automation and outsourcing to reduce their current or future headcount needs. But it's not enough. There's still a ton of demand for accountants and some of the roles that are the most difficult to hire for. Senior accountant is the most difficult role to hire for. 43% of respondents said their that's their number one staff. Accountant is next, then tax accountant, then auditor, and it drops off really far after that down into the single digits. So it's basically senior accountants at 43% and staff accountants at 26%, and then tax accountants at 11%, Only 6%. So good time to be looking for a senior accountant role. We were talking a lot over the past few years about how, you know, staff accountants are deciding, uh, after a couple of years, I don't really want to keep going in this profession. So they're dropping out. So that's why it's hard to find seniors, right?

David Leary: [00:38:32] You are probably.

Blake Oliver: [00:38:33] Getting experienced people is difficult and they have opportunities elsewhere. They don't have to stick in accounting or finance. They can go do other things. Bennett Thrasher is an example that was cited in this Accounting Today article, um as an they were cited as an example of how firms are adapting. The chief growth officer, Michael Hoover, said that the firm moved talent acquisition out of HR and into the growth function. So recruiting is now as important as business development. And it makes sense if your business model is built on human labor, even when it's AI augmented, you actually you actually need more human like the human labor becomes more valuable because it's augmented.

David Leary: [00:39:14] Yeah. Like if they're short that senior, that next layer, right? A senior staff, those are the people that in theory would be managing this AI stuff. A lot of these AI agents. Yeah, it's going to make the demand even more for that senior experience level.

Blake Oliver: [00:39:28] And this all fits with that chart that we looked at a week or two ago that showed that accounting hiring for recent grads is, is flat, while many, many other white collar jobs is declining a lot by single or double digits. So they also say Bennett Thrasher says they haven't reduced entry level hiring. So good news for becoming an accountant and becoming a CPA. There's still a talent shortage. Ai is not going to make that go away, but you're going to have to learn how to use it. All right. David, do you want to talk about this ramp stack thing?

David Leary: [00:40:03] Yes. So ramp launched their AI platform and they're calling it stack. So like that's literally the the name of it. So don't it's confusing because.

Blake Oliver: [00:40:14] We talk about a tech stack.

David Leary: [00:40:15] A common word we use a lot on this show. So we're launching a product called stack. And basically what it does is you connect these AI AI agents to your QuickBooks, your stripe account, all, all the tools that your accounting firm, your clients use, right in your accounting department. And then you can give it plain text, things like, um, this client allocates revenue by location, not department split it across six different cost centers. So in theory, you can give common plain language commands to these agents. And they're going to go do all this work for you. And that's essentially what, what they've, they've rolled out. Um, it's, I have a couple thoughts on this. One thing is I always thought that Bill.com was going to head down this path of being the, the central operating system for a business because they bought all these small pieces and the only piece they didn't have was the GL, but it's starting to feel like ramps headed down this path of like, hey, we're going to be expense bill pay budgeting. Oh, all your AI agent workflows. Your canopy launched something similar last week or 2 or 3 weeks ago. And then if you look at the data we now have from, uh, the studies we just talked about, I'm starting to think that it's probably getting to the point where your firm should just pick a tool and just use it. You're going to get benefits. And so there's no point of comparing, okay, is it better to get these agents from this app here or get the canopy agents or the ramp agents or QuickBooks agents or zero agents? We're probably crossing a line where just pick one of them and use it, and you probably will see some benefit.

Blake Oliver: [00:41:47] We've got a video here from ramp. Let's go ahead and give it a watch and see what stack is. According to them.

Ramp Stack clip: [00:41:54] Every accounting firm is asking the same question how do I build AI into my process? Maybe you've tried a prompt for variance analysis or vibe coded a tool for reconciliation. It kind of works sometimes, but when security. Auditability and accuracy are not negotiable. These one off experiments break. That's why we built stack one secure place to orchestrate AI coworkers for accounting from day one. Stack handles real work end to end, like booking transactions, amortizing prepaids, and reconciliation. It connects directly with the ERPs and systems your clients already use. You can teach stack your ways of working for every client, and it learns over time, always giving you the final say before anything gets posted and every action it takes is fully recorded and auditable. The more you build, the lighter the work gets and the more clients you can take on. Stack. Ai built for accounting.

Blake Oliver: [00:43:04] So what this looks to me like is basically cloud work that they've built into ramp. You can do stuff that we've been doing in cloud work, fixed asset schedules, prepaids deferred revenue schedules, identifying duplicates, mis postings, pulling payroll reports, doing allocations, coding transactions, doing bank recs. This is really powerful. And I think here's why I think ramp has a big opportunity here. The the biggest challenge to getting AI agents to do this stuff right is lack of context. It doesn't have the agent doesn't have enough context, enough information about the business in order to do something well, the way a human does, because a human accountant working in a business gets familiar with its operations, gets familiar with all the different things going on in the business, and so has context that the agent doesn't. Ramp has a lot of context because of where it already sits in the business, which is at the spend level. So basically, every expense on a credit card or every bill that needs to get paid goes into ramp and it has access to the accounting system. If you want, you can give it access to emails to go out and fetch receipts, like you can give access to your Google workspace. Um, it has access to all sorts of like documentation around spend. So it knows a ton about your business already. And that's why this is interesting to me because everyone's trying to build AI agents into different apps, but I actually think that like the GL is not the best place for this to live necessarily. If the GL is like where the information goes after the fact, you want the agents at the, at the point of the transaction.

David Leary: [00:45:03] Doing.

Blake Oliver: [00:45:03] Right when the transaction happens. Yeah. And for a controller, you're really focused on spend. So there's like two places transactions happen, right? It's your sales and then your expenses. And so for an accountant, you want to, at that point of spend, it has all the documents, it has all the employees going in. It has access to all the people in your organization because they all need to spend, right? So it can go out and ask questions to all these people without you having to give them an access to the GL or like create an account in your ERP system. I mean, that's going to be a big problem with AI agents that are in GLS, right? Is you're going to have to what do I have to provision a NetSuite user for every single person that needs to ask a question? Answer a question.

David Leary: [00:45:44] I know that one day on the show, you just looked up, uh, Claude, and you sent a quick transaction to my QuickBooks for a dollar. Yeah. And it just comes in. It just says Blake did it. That's how it shows up in the audit trail. Like there's no like, this was done by a co-working tool or anything. The audit trails just don't show it. And I think this is where Trust is going to come in where people are going to be like, hey, I trust ramp or I trust a canopy, I trust a QuickBooks, I trust a zero versus the off the shelf model. So even the off the shelf models are now beating humans. But if the off the shelf models, I don't know if I just want it reading all my data or just connecting in. And maybe it doesn't really know how to post that correctly. It's just it's a risk you don't want. So you want that security and auditability. I don't want it just to say Blake did the transaction. I want it to be accountable. What did the transaction.

Blake Oliver: [00:46:36] And you're already trusting this like spend system like ramp with all your company spend, which is like has like so much critical accounting information, right? If you're already trusting with that, what's the issue with running it through AI? Now you're using AI in there. Like it's, it's a good natural place for it to live from a security standpoint.

David Leary: [00:46:54] It's a next step. Yeah. You're not, you're not taking another third party, giving it access to data like rampart has the data, if you already trust them. Yeah, of course, you just use their agents. It really makes a lot of sense. And I think that's the same for canopy. On the tech side, canopy has built all these agents, but you already have all your client data in canopy, so it makes sense.

Blake Oliver: [00:47:12] A lot of sense should live in the portal. Um, one more thing about ramp before we move on. And I think this might be related, but a few weeks ago they announced AI agents for for procurement. And I think it's really interesting what the procurement AI agents can do. It's full purchasing lifecycle. So the idea is that it can these agents can triage the employee purchase requests, can source vendors to fulfill the requests, can review contract terms and conduct compliance checks. So like, it can the agent can can facilitate all the way from the purchase request to purchase order to like or RFP request for proposal can analyze them and try to figure out which one's the best. I mean, this is such a natural place to include agents.

David Leary: [00:48:03] And the market loves this ramp. I don't know if you saw they just did a huge raise. They raised $750 million at a $44 billion valuation.

Blake Oliver: [00:48:11] That's wild.

David Leary: [00:48:12] And congrats to Laurent. It has to do with the position they're in. Right? If you think about they they're not used. They have 70,000 customers. They have over $1 billion billion dollars in annualized revenue. Um, they're working with 4500 CPA firms already, 92 of the top 100. And so they they already are starting to get their distribution model, their ducks in order for that. So the street obviously thinks ramp in this obviously ramp doesn't have a gel. But if they start doing a gel thing, this is this is why that on the street Intuit stocks down zero stocks down is that money's moving into things like ramp.

Blake Oliver: [00:48:50] David, you have a story here about KPMG and their dashboard to track how often their advisory employees use AI tools. This is a pretty funny story. I want to hear it.

David Leary: [00:48:59] I mean, it's the typical you rail on it like what you measure, right? And accounting firms are notoriously still stuck in this. How many billable hours? Right. We're going to count hours.

Blake Oliver: [00:49:10] Count your inputs you have.

David Leary: [00:49:11] If that's your mindset and you now have AI and you want your employees to use AI, you can't just say, I want you to use AI 75% of every working day. So essentially three days out of four days, you need to do something in AI.

Blake Oliver: [00:49:25] Is that what KPMG measurement?

David Leary: [00:49:27] I'm sorry.

Blake Oliver: [00:49:27] Is that what KPMG did?

David Leary: [00:49:29] Yeah, that's what KPMG did. So they rolled out an AI dashboard in late 2025 and started requiring the employees to use AI tools. And they're measuring if they get used.

Blake Oliver: [00:49:38] Like on a daily basis, like how much you're using them.

David Leary: [00:49:41] Exactly. So what's happened though, is the KPMG employees, even the ones that believe in AI, um, kind of mock this now. So they'll just have it make a drawing. Somebody says they'll have it summarize all the emails they already read, anything to make it look like they're using the AI. Oh.

Blake Oliver: [00:50:00] So that they're hitting their usage target.

David Leary: [00:50:02] Yes, because the accounting firm is just like when you count billable hours, you're not you're not actually measuring outputs, right. Or efficiencies. You're just counting how many hours did you jiggle your mouse or type into a prompt? It's really stupid that like we but that it just shows how ingrained the the billable hour mindset is in firms because this is just billable hour. You need to use it for this many hours a week, 30 hours or 40 hours a week. You have to use AI.

Blake Oliver: [00:50:32] So there's a term for this, David. It's called token maxing. And it's a problem in a lot of places where they've implemented policies like this and tracking tools like this. And Amazon just shut down their own internal leaderboard that tracked how much AI employees were using because staff were using AI just to climb the rankings, not to actually do work or solve problems.

David Leary: [00:50:58] And if you're bad at AI, you'll probably use even more tokens because you're going to do a lot more back and forth, right?

Blake Oliver: [00:51:03] Yep. Well, and now, you know, tokens are expensive, right? So you, you force your employees to like use tokens just to climb a leaderboard or to get a good performance review. And yeah, it's going to surge and your costs are going to go out of control and you're not going to see any results for it. It's pretty funny. It's but David, it is. I love the analogy of the billable hour because it's such a great example of that. Right? Token maxing. It's the same thing as hours maxing in a firm where they evaluate you primarily based on how many charge hours are on your timesheet. You're going to get employees who just max out their charge hours. Maybe they're lying about it, they're not being honest about it, or they're overworking themselves and burning themselves out. Right. There's all these unintended consequences, and you don't get the best results. It's measuring inputs instead of outputs.

David Leary: [00:51:54] But yeah, that's that's, that's the way to say it.

Blake Oliver: [00:51:56] But what's interesting is how quickly the tech companies are learning. They figured this out pretty fast, right? Because of the cost. Because there's a seat. That's the thing that's different is the tokens have a direct financial cost. So Amazon did this and now they're like paying up the wazoo for, you know, anthropic. Their anthropic bill is going through the roof. Right? But the cost of billable hours and charge hours and overworking your staff, that's not borne by the firm, that's borne by your people. And you only experience that when they get burned out and they quit, which is what happened over all these years, and which is why we don't have enough senior accountants. All right.

David Leary: [00:52:31] We wore them out. Yeah.

Blake Oliver: [00:52:32] So we were we've been talking about KPMG a lot. So I want to talk about follow up from the KPMG Australia story. If you recall, the CEO of KPMG Australia resigned, Andrew Yates resigned. This was because they basically took confidential information obtained in an audit of lend lease, and they used it to get other major audit engagements so not allowed to do. They had a whistleblower. They investigated, but they didn't investigate properly. They tried to cover it up. It sounds like. And so now the CEO has resigned. Well they are starting to lose some clients, it looks like. Australia's central bank said it probably won't renew KPMG's contract to run its whistleblower hotline. Oh that's funny. So KPMG did not did not. Kpmg Australia did not did not properly investigate a whistleblower claim internally. And so they're going to lose the contract to run Australia's central bank. Bank whistleblower hotline. That's that is that is like just deserts right there. You know what I mean? Like that's the opposite of irony. Um, well, I guess.

David Leary: [00:53:46] Because they mark it, you know, they're at the Academy Awards, they got the briefcase with the handcuffs. It's marketed as like, you can trust us. We're not going to drop the ball. But I think dropping the ball on the whistleblower line is kind of a big deal.

Blake Oliver: [00:53:58] A major punch. Major. A major pension fund called Rest, which manages over 105 billion Australian for over 2 million Australians, said it's concerned and digging. For more info. Kpmg is listed as one of his auditors and tax agents. Apparently KPMG is really tight with Australia's pension system, which is 4.5 trillion Australian. Uh, what do they call them? Buckaroos, something like that. The, uh. Yeah. So they continue to um, struggle there. And it's a, it's a lesson, it's a lesson to firms that, uh, you funny business is not going to not going to be rewarded. Okay, let's do a little more follow up. David. Um, about the Trump whistleblower. No, not whistleblower Weaponisation fund that $1.8 billion fund. The Trump administration said it will not move forward with the planned 1.8 billion fund tied to a settlement over leaks of Donald Trump's 2019 and 2020 tax information. The acting attorney general, Todd Blanche, announced the reversal during a congressional hearing, saying that the administration was dropping the fund. I think this was after a judge shut it down.

David Leary: [00:55:16] Well, that and I think you got a lot of Republican pushback. The Republicans were going to block funding for other Trump initiatives.

Blake Oliver: [00:55:22] Yes. Um, now, the thing that is still standing is the ban on tax related probes involving Trump, his family, and his companies. If those probes began before the settlement, and I want to clarify this because I think we were confused about it in the last episode. We weren't sure if this applied to just the previous ones or new ones. It only applies to tax years that have already been probes that were already underway or in progress.

David Leary: [00:55:50] Not a future pardon?

Blake Oliver: [00:55:51] No it's not. Okay. So, um, that of course is still very unusual because nobody has ever gotten a deal like that from the IRS in the history of, uh, US taxes, as far as I know. So that is done. The weaponization fund is done. And a little more follow up on tax. Uh, remember how we still don't have an IRS commissioner? Well, we don't have a confirmed IRS commissioner. And Treasury Secretary Scott Bessent was in front of the Senate Finance Committee, I think it was yesterday or the day before. And he acknowledged that he is, uh, well, he said that he is not the acting IRS commissioner because he was for a short time, because you're allowed to do that. But it expired. Right. But he did say he yes, he did say that he is performing the duties of the commissioner.

David Leary: [00:56:49] So so we do not have a true IRS commissioner whatsoever. We have an acting commissioner whose time expired, but he says, hey, I'm still kind of moonlighting and doing this on the side. And then we have this crazy position that was just created called the CEO.

Blake Oliver: [00:57:03] Ceo of the IRS. Yes. Which which apparently is not not performing the duties of the commissioner either. So who is in charge of the IRS? Who has the legal or administrative authority? I mean, this all ties into that settlement that Trump got for himself, because who actually can sign that if there's no acting? I mean, if there's no confirmed IRS commissioner, is that even valid?

David Leary: [00:57:36] I, I don't know. I'm shocked that this has gone on this long. Like they have not found a new commissioner yet.

Blake Oliver: [00:57:42] The Democrats on the commission, of course, were going after this settlement deal with this immunity essentially from from audits that Trump has given himself or the IRS has given Trump, which he's essentially given himself. Because Bessent works for Trump, Bessent is acting as the well, he's performing the duties of the IRS commissioner and made this deal, you know, and the Justice Department to write. It's like they made the deal. Trump made the deal with himself. So, um, Cortez Masto, who is a Democrat on the committee, he asked whether about 400,000 other taxpayers whose information was leaked by former IRS contractor Charles Littlejohn, would also receive the same immunity or protections that Democrats say Trump and his family received. Bessent did not give a direct answer. He said Treasury was represented by the Justice Department in the matter and cited ongoing litigation as the reason he could not provide more detail.

David Leary: [00:58:37] Who asked that? A reporter.

Blake Oliver: [00:58:39] Cortez Masto.

David Leary: [00:58:40] Congressman oh, Senator Catherine Cortez Masto. Okay. Got it.

Blake Oliver: [00:58:45] Oh, I'm sorry, I got I don't know, all the senators. My bad. Catherine Cortez Masto, thank you for correcting me on that. Um, I'm, you know, I need to brush up on my politics here. I like to stay out of it, but it.

David Leary: [00:58:56] Just I'm glad like it's the awareness is being raised that like, it wasn't just Trump's tax return that was leaked. We've had millions of people's tax returns.

Blake Oliver: [00:59:04] Hundreds of thousands, hundreds of.

David Leary: [00:59:05] Thousands, hundreds of thousands from the Little John leak. Yeah. But you could argue the IRS leaked millions to DHS and Ice as well. So there's millions of people's tax returns being leaked. You can't just make it okay for one guy and correct it for the one the president like. It needs to be corrected for everybody who's got leaked. It's not okay.

Blake Oliver: [00:59:28] David. I love doing this show with you. And thank you to all our livestream viewers who joined us. If you haven't checked us out on YouTube, go to The Accounting Podcast channel on YouTube. Subscribe. Hit that notification bell icon to get notified of when we go live. And you can join us and chat with us. You can, uh, tell us what you think. You can heckle us, um, all you like. Uh, you can see what we look like sometimes people are, like, really confused. They think that I sound like David, and David sounds like me when they see us.

David Leary: [00:59:55] Yeah, I get.

Blake Oliver: [00:59:55] A lot of life. Yes. Um, and get free CPE for listening. Whether or not you watch us on YouTube or you listen on the podcast, you can earn free continuing professional education credit. It's the best deal in CPE around. Get the earmark app, go to earmark.app in your web browser, or download the free app from the App Store. You can sign up for free. You can earn one free CPE per week for our show and many, many other fine accounting, tax and audit podcasts. It's a great deal, and if you want to get unlimited low, low price of $170 right now, that's going up July 1st to $200. So go ahead and subscribe. Lock in that price for this year. And, um, I think that's all I got. David. Anything else before we go?

David Leary: [01:00:41] The only thing is we have a piece of earmark news. We are rebranding earmark. Oh yeah. So the new logos, the rebranding. I think you put a blog post out last night at midnight. So you're going to see some changes where the ear with the check mark in the green, that color green is kind of going away. The check mark will still be around a little less ear, but we'll have the check mark in and a lot more modern branding. If you see the logo, you'll see.

Blake Oliver: [01:01:04] I hope that the response is more positive than everyone's response to the Spotify logo or the Spotify icon.

David Leary: [01:01:10] Spotify icon.

Blake Oliver: [01:01:12] All right David. See you around here next week. Bye, everyone.

David Leary: [01:01:14] I'll see you in Vegas.

Blake Oliver: [01:01:15] And we'll be at AICPA Engage. And we will be at scaling new heights. So come meet us there. We have a booth at AICPA Engage. I will be at New Heights, uh, doing a, doing a session with, um. No, I'll be at the relay booth at scaling new heights. So come hang out. Um, I'll have some copies of my book to give away and relay and sign and, um. Yeah. All right, everyone, talk to you later.