The Great AI Con: Paying for AI trained on Reddit and TikTok to help you with a BAS

Trent McLaren: Hello, everybody. Welcome back. We are kicking off our 10am session. We have one of my favourite people to watch, David Boyar, about to kick off this next session. David, how are you? Good to have you with us.

David Boyar: I'm great, mate. Thanks for having me. Congrats on putting this thing together. Congrats to the team at The Firm. Not an easy lift. You and I have organised these things before.

Trent McLaren: We have, and they are a ton of fun. I'm already feeling the buzz, and this is just going to be a day full of buzz. I'm pumped. We're so glad to have you with us to talk through all the fun things. Now, for everyone that was just in the last session, you haven't had much break, so we're going to keep this party rolling in the next one. For those that are just joining us, welcome to the AI in Practice Summit. We have two days of amazing speakers unpacking all the big AI things happening for accountants in public practice. Most of it is Australian-centric, but we do have some things that go US and UK, like Chad's last session. A lot of global stuff. Dave, your session is going to be, I guess, global focus, but it's primarily Aus, is that right?

David Boyar: Correct. Yeah, primarily. I mean, I love Aussie accountants, but I think the ideas remain the same, what I'm talking about. I'm going a bit future-focused on this one, right? And the good thing about future-focused stuff is, you know, people who paint themselves as futurists, they're the most unaccountable jobs in the world. So I'm going to put this out there. I believe what I'm saying today, but all this AI stuff moves so fast, it might all change. But I think today's going to be really important for strategic conversations for accountants all over the world.

Trent McLaren: Amazing. Love that.

David Boyar: I'm coming to you today from Chinchilla.

Trent McLaren: I love that.

David Boyar: Now, if you're in America, what is that? Chinchilla is in the middle of Queensland. This is the furthest from home I've ever been inside Australia, and I'm not going to be able to have the chat with my slides up, Trent, but if I can say a big hello to Michael Vella, coming from the mighty Footscray. He's got a double shot cap in Footscray. How modern of you. When I went to Footscray as a kid, we got some hot jam donuts after we'd hopefully pound Carlton. That game, they only kicked one goal, 10. I'm a massive Scrays fan. Good to see you, Michael.

Trent McLaren: My grandpa lived in Footscray, so I used to visit him all the time. I don't know if you knew that tip about me. Very familiar with the Footscray area, and my grandma was a Doggies... my other grandma. But geez, my one was a mad North Melbourne. The other one was a Doggie. So yeah, fun there.

Trent McLaren: Our superstar Dave Boyar is going to kick off the session: The Great AI Con: Paying for AI trained on Reddit and TikTok to help you with a BAS. Dave, I'm going to hand over to you and let you start sharing your slides, mate. It's over to you.

David Boyar: Thanks, mate. Thanks for the great intro. Somebody said no intro music. Trent McLaren is my intro music today. Thanks, Trent, for a great introduction.

David Boyar: The Great AI Con: Paying for AI trained on Reddit and TikTok to help you with the BAS. I jumped on LinkedIn when I saw this was advertised. I said, who writes these clickbaity topics? But in reality, it was a combination of me, the great team at Access and Trent that came up with it, because the reality is there is a little bit of a con going on inside access, and it is going to hit your profitability unlike anything you've ever seen coming before. And that's what we're going to talk about today, because for those who follow the stuff that I talk about, I'm very passionate about the profitability and wellbeing of accountants all over the world, but obviously particularly in Australia, which is something I know very, very well.

David Boyar: I'm going to start by talking about water. Why are we talking about water today, Dave? Well, I was flying over to Chinchilla early this morning. It was very green out here. There's been a huge dump of rain, and acres and acres, hectares and hectares, have gone from dry brown dirt to this beautiful green, lush... I won't quite say rainforest, but it is very beautiful flying out here right now. The reason we're talking about water is because I'm going to use it to make a comparison to what we're seeing with AI right now.

David Boyar: Water in Australia and most Western developed countries is largely free. It's out of a tap. It's pretty good quality. Sometimes you notice it tastes a little bit different depending on where we are. Sometimes it's a little bit treated, sometimes there's chemicals, sometimes there's not. Some people have filters in their houses. But water's largely free. For some reason, though, at some point over the last 20 years, we decided it would be cool to pay three bucks a bottle. And it's not the healthiest thing. It looks healthy, because it says it comes from natural spring water, and it's bottled at the source, and it's got magnesium and sulphate and all this stuff that's good for us, but it's also got a huge amount of microplastics. We're sitting there paying $3 a bottle.

David Boyar: In reality, right now, we're actually paying a lot more than that. Bottled water's like six, seven bucks a bottle if you're going to your local 7-Eleven and you're just a little bit thirsty, and you're not up for a Monster or a $1 cup of coffee. You want some water. Water's become expensive, and the reason water's become expensive is because the greatest con in modern history is how much we will pay for convenience. To give the allure that something is easy, we will pay a fortune. We pay people to bring up our kids, we pay people to mow our lawns. We don't mind paying for convenience, and we don't like working hard, earning a lot for ourselves, and then using some of that wealth that we've generated to just make life a little bit easier.

David Boyar: In this case, though, the ease is the difference between carrying a 600ml bottle of water and just buying it and throwing it out when you're done. Now, on the desk that I'm sitting at today, this has become popular. This is a big fat Yeti. This is like two, three litres or something. That's become trendy these days. But the price arbitrage that we pay on water is enormous, and the margins are absolutely massive. The interesting thing about water, though, is most of the cost that we're paying for is transport. What does that have to do with AI? Well, bear with me. We're going to tell a story. This is like a little bedtime story for everyone.

David Boyar: Let's have a look at how AI actually works right now. You've just heard about all the fantastic releases that came out. I was playing with Fable 5.2, which came out yesterday. I'll tell a little story about that later. Used up all my usage credits in about 30 minutes, which is why we're talking today about this topic. AI fundamentally predicts what comes next by reading everything you've given it every single time you ask. We think that we're going in there and we think that it's artificial intelligence. We think it's intelligence. So we think that when we put a document in there, it reads that document, remembers that document, and then when we ask it a question the next time, it says, oh yeah, I remember. That's why we're building all these big data centres, to be able to remember all the documents that we've put into it.

David Boyar: But in reality, it's actually reading everything every single time. Every time you ask a prompt, it reads everything, rereads your entire chat history, and then answers your question. It's derivative. That wasn't what I expected when I first started using it. I just thought, oh my God, this thing's amazing. It's remembering everything. It's not. It has no memory, but it's very fast at reading. Now, memory's coming. Apparently it's coming. But it ends up being very expensive, because if you think about your time, if you were reading that many documents, it would take a massive amount of time.

David Boyar: When we were putting this slide together, it made me think of these shows. I'm watching a show at the moment, War. I don't know if anyone's watching War on HBO. It's not as thought-provoking as I would have liked, but there's the classic scene. They're in a divorce case, and the big firm's trying to squash the little firm, so they send over hundreds of boxes of physical paper, and you watch the swarm of young legal clerks trying to read it all and sort it out. The AI is doing the exact same thing every single time. Now, admittedly, most AI now has, I don't know how to pronounce this, is it cache or cache? Cache sounds cool. It can cache the chat a little bit. But fundamentally, it's reading everything every single time.

David Boyar: This is why sometimes it loses context. This is what it does to try to keep its context. Rather than you asking the question, it has to remind itself, hang on, what are we talking about again? So it reads everything in your chat, it reads all the files that you've given it that are relevant, every single time. So whilst you're in the chat, you think you've only uploaded maybe 100 documents. By the time you get to the end of that, it could have read it 18,000 times. Now, if you translated that to human labour, that is extremely, extremely expensive human labour. But right now we're doing something interesting. It's a fixed cost; it doesn't cost much. It's like 20 bucks a month, depending on what plan you're on.

David Boyar: Because the reality is you're not paying for AI by the prompt, or even the question, every single time. You're paying for every single word it reads, every single word it writes back, and every single little bit of design that you put in your HTML documents that have become popular now. If anyone's noticed, when people are presenting, you can tell it's AI-generated, because people are presenting using these interactive HTML files instead of the corporate deck. I read a great article: the death of the corporate deck, the death of the slide, and the death of the PowerPoint.

David Boyar: I seem to find at Access, depending on which part of the business I'm talking to, different teams have different preferences for different things. If you come to the Access ChangeGPS webinars, you'll know we're big fans of PowerPoints that just list things out very clearly. But a lot of the output that we're getting now is that, and those consume a huge amount of tokens. So understanding the way the AI works is going to become really important for us, just like it's important for us to understand the way a graduate, an intermediate, a senior, even a partner who we're in business with, works as well, because that will help us estimate how long things should take and how much things are going to cost.

David Boyar: So, the bill that comes from AI, the cost that it's incurring, are actually... to what we're actually paying right now. Think about it: there's trillions of dollars being spent on AI infrastructure. In fact, I was getting a tour of Chinchilla before, and they're looking at building, it's in the news, a big data centre out here. And it's like billions and billions of dollars. Eventually somebody wants a return on that capital. And who's going to pay for that return on capital? Us, the customer. It has to be. It has to happen. It can't be subsidised this much forever, because it will literally break capitalism and break Western markets.

David Boyar: What is all of this? Well, it's actually a data tax. We, the customer, end up paying a data tax because of the way AI is set up to read everything every time, and the way that we're using AI, because we think that we have this unlimited amount, for most of the work that we're doing, that we can just keep using. It just keeps coming. We keep using, we keep coming. We're actually getting hooked on it. Every time we do that, we're consuming a massive amount of computing power and a massive amount of cost. And the cost, you read them in the news: it comes from electricity, construction and water to cool it all down. The data tax that we pay is because every time we ask anything, the AI is trying to work out, how much do I have to read to understand what on earth you are talking about?

David Boyar: About a year ago, the last Xerocon, which was in Brisbane, was that last year or the year before? I had a talk on how to prompt, and the prompts that were very popular back then were heavily structured. "You are a...", designed the job that the AI is doing. The output I want is, your objective is, your goal is, you should stop at, ignore this. It was this very, very thorough prompt. And because the AI has gotten so good, I think a lot of us have dropped away from bothering to put the effort into that very structured prompt. And actually, we started to see that the more structured we got, the worse the output was, because we're putting too many blinkers on.

David Boyar: But we've stopped doing that because it's getting better. And instead of us doing that and giving that narrow field, it's going wider to understand what we're talking about, and consuming more energy, more water. We're going to need more buildings to put more servers in to be able to do it. All of that cost comes out, and we pay it through a data tax. The reality is, though, that this should be pretty frustrating for accountants. Most of our job exists to produce clean, structured and critically verified information. Most of our outputs are extremely structured. The tax return is literally a form that the ATO prepares every year, or the IRS, or, I don't know what it's called, what's it called in the UK? His Majesty's... I wanted to say His Majesty's Secret Service.

Trent McLaren: HMRC.

David Boyar: HMRC. Thanks, Trent. Most of what we do is very, very structured. So this is actually a little bit mind-numbing for us. What do you mean you're reading everything every time? I've already done the transaction. I want you to do the transaction. Because we have structured data, in theory, we should be able to reduce the amount of context and the amount of thinking that the AI, or SI, depending on what you want to call it, should be able to do. Part of the reason that the AI has to go wide to understand what you're trying to reconcile, and why it could be wrong, is because it was trained heavily on the internet, particularly on Reddit. And when you go into Reddit at any point in time, you get some pretty crazy things. And this is the learning that the AI is coming from.

David Boyar: You know, and I think we made some of these up. "Is GST optional? Asking for a friend." "Just claim the dog as a dependant." You know, by the way, Trent, the Hawthorn Football Club that you go for became famous for claiming pets as members in their membership numbers. So this is not a joke. Things like this actually happen in data.

Trent McLaren: I would totally buy two memberships for my dogs as well. That's a great idea.

David Boyar: Yeah, it worked. And now Hawthorn gets to say they've got plus two members, but the dogs aren't going and watching the footy. Although you will probably spend money on them. I have a feeling that's the point; footy's become very commercial. You know, you have these people who aren't accountants: "this one trick to cut my tax to zero", "subscribe for more", I've got no idea what they're talking about. And then, of course, the tax talk threads on Twitter, on Instagram and on TikTok, which, by the way, some accountants are doing a fantastic job on, and really having an impact educating people.

David Boyar: I like this one: "my uncle says crypto's tax-free." AI has read all of this. This is what it's been trained on. And yeah, the models get better, and they become better at filtering it out, but you'll also find that they become more expensive as that goes on. You start to see, I'm talking a lot about capex, I'm talking a lot about cost, and I'm talking a lot about inputs, because the more inputs we have, the more distracted it gets, and the more this thing's going to cost.

David Boyar: Where I'm getting to with all of this, Trent, is that we've seen all of this before. I want to ask everyone to take their memories back to the 2012 to 2019 era of accounting software. I think it was the same in the UK. The US came a little bit later, when QuickBooks Online started and then this started to come around. But when our world started to change was when Xero invented the single ledger. And they were so focused on building ledgers, and if we used enough of them, we got free practice management, free tax. That was transformational during this period. The idea that you didn't have to pay for the only two critical things I actually need to run my business: unbelievable. And that our clients would pay, through their ledger fees and through their business accounts and the different packages and various things that they have.

David Boyar: And we followed the same story every single time, because in particular, Xero was so feature-light. It did a few things extremely well, but it was feature-light. Xero promoted the ecosystem strategy. ChangeGPS was one of the first ever practice tools to join the Xero ecosystem. Timothy Munro really saw where the tide was swimming and invested in that. And we had tools like Ignition and AccountKit, Fathom, all coming in. And almost all of us had some version of a free version at that time.

David Boyar: I'll also bet that many people in the chat, Trent, will remember having some founder saying at some point in time, hey, use this, promote it, and I'll give it to you for life, for free. And then at some point in time, everyone had to go back and say, actually, you can't have it lifetime for free. Although I am aware of one person who still has it free, one person I know that has it. I can't read the chat, but if you've still got any software for free from that era, maybe don't say it in the chat, because you don't want them to tap you on the shoulder. They probably don't even know; billing systems have changed so much, they can't track it anymore.

David Boyar: We went from free, and then we went to the freemium model. And the freemium model was what we started to understand. You got some cool features that you could play with, or you'd get a 14-day free trial, a 20-day, 30-day free trial, and if you wanted the things that actually moved the needle for you, you always had to pay. And then eventually you're where we're at now, where we're at full subscription model, and, to be honest, I think we're a little past full subscription. Not only are we paying full subscription, but we're seeing major changes to bundles, major changes to charging drivers: per seat, per output, different things are being included. The Xero bundles have been changed so much.

David Boyar: We're now in the era of price rises for all of the SaaS that we're paying for, and pretty much everyone does it. The whole revenue model of SaaS has gone full circle and it's sort of at its end. And that's part of the reason why we see the SaaSpocalypse on the share markets so much at the moment, because they're saying, well, the economics of that don't actually make sense. Because the idea was that SaaS was built once, and it's expensive; engineers are expensive. When interest rates were at 0%, it was very hard to get a good quality engineer for less than $200,000. That's changed now. Markets have stabilised a little bit more. But from there, we were able to...

David Boyar: Sorry, sorry, Tony's just coming in here. Yeah, come in, mate. No worries. You want to say hello to a bunch of accountants? That'd be cool. You're an accountant, aren't you? Oh, so I've got to get back to this. Yeah, cool. Sorry about that, everyone. Thanks, bud. He's the CFO of the business I'm at today.

David Boyar: So, what we had is a change in the way all these billing structures work. And you'll remember, if you were on Google, Dropbox went from free for everyone, and then they limited devices, and then you had to pay for more. Where we're at with AI right now is in that freemium-type space. You can go and use ChatGPT, you can go and use Gemini for free. But if you want to generate a meme image of your mates doing something stupid, they're going to say, no, you need to be in a paid tier to be able to do it. Or if you ask a question that's just too hard, it'll get a little bit through the chat and it'll say, sorry, you're gonna have to come back tomorrow. They're giving you a sample, but not as much as you'd like.

David Boyar: Yesterday, you heard in the update before, we got the new model from Claude, Fable 5.2, and I got an alert: hey, 5.2 is out. It uses usage credits faster than other models. We are giving you some usage credits for 14 days. It is the freemium pricing model. We've seen it before. Now, by the way, I used those credits in half an hour, not 14 days. There's not much else I've been able to use up in half an hour. So those things must be very expensive. And admittedly, they say it's for our most complex tasks, but in the list of the most complex tasks that Claude talks about, it's talking about financial account reconciliation. Because right now, the models that reconcile are roughly somewhere between 90 and 97% accurate.

David Boyar: The problem with 90 and 97% accurate is that the TPB doesn't care. You don't have a 3% margin for error. You need to do everything you can to try to get that to 100%. So we're at the freemium model now of AI. We're all getting hooked on it. We're all getting juiced up, and we're all using it heavily. And I'll have a guess, Trent, that most of the people here today are amongst the most heavy users of AI in the accounting industry. Why else would you be here, right? You're obviously very interested in the topic.

David Boyar: So where does the AI costing model go to? Is it going to go to the subscription model, like we saw before? Well, it can't, because that model is what caused some of our favourite software companies who are listed to have massive valuation changes. The one most relevant to our industry, Xero's down, I think, are they 70%? They clawed back a little bit. It's about down 70% from their peak. And fascinatingly, everyone's interested in this. I put a post up on LinkedIn about Xero's share price, and my comment was, eventually this gets too cheap and someone will buy it. 70,000 views on that. That's massive for me. That's the biggest thing I've ever done in my life. There's just massive interest in what happens to the revenue models and the pricing of the software that we use.

David Boyar: So here's the big change that accountants need to start getting. This is my futurist moment. AI is your next cost of goods sold. Right now, I'll bet you are tracking whatever AI you're using to your subscriptions account. That won't last. Very, very soon you will have an AI tokens cost of goods sold in your chart of accounts. And it'll be a variable cost, and it will need to be mapped above the line, and it will go into your margin. Right now, it's not in your chart, and it's definitely not tracked per client right now. It's actually very hard to track per client right now if you're getting into the token usage stage, if you've tapped out of what's in your limit and you're getting into token usage, which Claude is telling us the finance stuff, the very hard stuff that needs 100% accuracy, is going to be token-charged.

David Boyar: Well, we're going to have to put it into our margin. We're going to have to find a way to track the amount of tokens and the costs associated with those tokens per client, to understand client profitability. The last 15 years, we've debated whether timesheets are worth it or not. Good luck to the firm who lets staff run rampant with no token budget. Good luck. It's going to eat straight into your client margins, and we need to view these as direct input costs that impact particular outputs for clients.

David Boyar: And right now, nobody's optimising the AI the way you'd optimise your workflows. When we go to most things, it's, hey, here's how I optimised this workflow. This is the workflow I did. I bought this product from the app store, and it optimised this, and I got this fixed cost; per-seat pricing would be the most common one. But now these are all variable and they're uncapped. We have no idea how much a token actually costs. They don't tell us. We do know that roughly three-quarters of a token is a word, input or output. It's roughly that, but we don't actually know what it costs. What we can see, though, is when it's really busy, when the servers are really packed, we will get timed out. We could get timed out faster than when it's not. Does that sound familiar? Energy. That's how energy is priced. And we're starting to see more similarities with the way energy is priced than the way software has traditionally been priced.

David Boyar: The final point there, that nobody's optimising it, is twofold. At the same time, there is no AI that's really heavily optimised. There's a lot of startups receiving a lot of VC funding, trying to automate and eliminate the month end. But those startups are going to have input costs in the way of AI, and they're going to end up being passed on to you. So eventually we're going to have to optimise the way we work for it. And the second thing is we're going to have to train our staff. You can't just go into AI and say, hey, reconcile. Done. You can't do that, because that's going to drive up your costs, and that's going to eat the margin that you're going to have inside your job profitability.

David Boyar: So AI isn't the con. Paying it to work out what you already know is. And we've been conned into thinking that this thing is going to be as free and as cheap as it is forever. If the ambition of AI is to do work, then it needs to displace workers. That's got all sorts of questions for the world that we live in, and if ever you see me anywhere, let's have that conversation, because that's a big one. But if it's displacing workers, well, our wage bill is a hell of a lot more than our subscription bills. So how expensive is this stuff actually going to get? I don't know, but I know it's not going to be the couple of hundred bucks a month that we're paying now, because of all the arguments that I've made so far in this talk.

David Boyar: At the moment, we've got generalist AI. It knows the world; it learns because it read TikTok and Reddit and everything. It learns the context after you ask it. You ask it and then it works. And then you pay the data tax on every word. What we need to see coming, and what Evo is building for accountants, and actually it already has it, particularly if you're using our tax or ledger software, which about... I won't say the number, but I'm always surprised the number of accounting firms using the Access products is always greater than I thought it was, because I came from the Xero world. I've had to learn all of this, these Access customers. It's been a pleasure getting to meet them all in their practices. But it knows your practice, it understands your context before you ask, because it's got all of the data.

David Boyar: We're using JAX at the moment. JAX has a limit. I can't get everything that I want out of JAX. It doesn't let me question everything. And Xero has turned on the ecosystem and updated their terms of service. As an ecosystem partner of Xero, I'm forbidden from training any of my AI on any of Xero's data. I also have to pay for the privilege to be able to connect to Xero now, because Xero wants to be the firm that owns your practice. But the Access Group, if you're using Access Evo, and the more Access products you have, the more comes inside the hood, and we understand it already, which means the data tax becomes next to none.

David Boyar: Because the AI already has the data, it already knows, it doesn't need to pay for the connections that jump through all the APIs, and it actually can get more of the underlying data that you want to have for your clients, meaning you can get this done cheaper. The end result: profitability. Profitability per job and profitability for the practice. But you're gonna have to go back to your CA and CPA exams, and you're gonna have to open your cost accounting book, and you're going to have to re-familiarise yourself with unit allocations, because that's what we're getting to.

David Boyar: At that talk that I did at Xero about a year and a half ago, I spoke about it being just a grad. It's ambitious, it wants to know everything, it wants to learn anything, but it doesn't know anything. Well, now it's more like a temp. It knows a lot more because the AI models are better, but it forgets everything. You know the temps: hey, do it this way, and then they've done it wrong, and you have to go do it, and you have to tell them the same thing five times in a row. It's a little bit like that at the moment, and it's getting better. Definitely getting better every time, but we're still paying for it figuring things out. Whereas if we actually want our AI to help us, we need it to be a senior. That's what we really need. We don't need it to be a junior. We need it to be a senior that knows the context before you ask.

David Boyar: Interesting data from Anthropic came out this week, that the hiring practices of Anthropic have changed, that they are hiring fewer juniors and more seniors. Again, what does that mean for the future? What does it mean for the jobs my kids are going to have? I don't know. My kids are young enough; I hope this has all levelled out by the time they go into the job market. But if you enter the job market in the next five years, who knows? So we want to try to find specialist AI, where you're paying for the answer; you're not paying for the AI to figure it out.

David Boyar: The AI race is actually a data race. Meta has Facebook and Instagram data. It also has massive distribution; like 5 billion people or something crazy use it. Google has all of the search data, and it's got all the YouTube data. xAI has everything on Twitter, and it's also got Cursor, one of the most widely used coding platforms in the world. So that's where they're getting their data from. Now, we didn't have this in our preparation, and I don't know if anybody else is getting this, but I'm getting all these ads served to me on social media. There's a new type of business called data brokers. Has anyone seen this? Has anyone in the chat seen it, Trent? Because I can't see the chat.

David Boyar: There's a business called a data broker. And this person's job is to go find businesses who will sell customer emails, support tickets, interactions, workflows, operational guidelines, standard operating procedures, rules, policies. They are trying to buy that data set, and then the broker is buying it off you and then selling it to the big AI labs: Meta, Google, xAI, and we can put Anthropic and OpenAI on these as well. That blew me away, because as an accountant, I'm thinking, confidentiality requirements, I've got TFNs, I've got personal information, I've got so much stuff. I was so confronted when I saw this. And I started looking at the prices. They're paying between $205 million for particular types of data.

David Boyar: Why? Because they want to get more specific, and they can't get more specific unless they've got the specifics. So data brokers are this new business. Now, they're not as popular in Australia, because we have a lot of regulation, and yesterday Anthony Albanese announced even more regulation that we're going to have on AI, which I think will stifle some innovation. But I don't think you could do that in Australia because of our privacy laws. But if you're in the States, this market of data brokers is just crazy. There's just so much money being paid for the specifics. You're sitting on years of verified financial data. Don't sell it. Use it to inform your own specialised AI, so that in the future you can start to just meet the demand that you've got coming in, let alone actually think about reducing your labour costs over time.

David Boyar: There's a phrase that really triggers all of this. It's called RAG, retrieval-augmented generation. If you go nerdy into AI, if you hang out with nerdy AI people... I like to hang out with nerdy AI people, because I know what I know, I can read what I read on the internet, but I really want to understand what's going on here. RAG does the lift. It takes a pile of PDFs and emails. You'll ask it a question: what was GST on sales in July? And it reads everything. It just reads every document: emails, PDFs, Word, Excel, it reads everything. It might consume 50,000 tokens; this is illustrative. But if we had clean, structured ledger data, it would consume less, because our ledgers are well structured and well organised, and there's a history of how we've transacted in the past.

David Boyar: Which means that the specific AI consumes fewer tokens than the non-specific AI, which means, in theory, we'll be making more margin per job and per task, because this AI is cheaper. Specialist AI will be cheaper over time than generalist AI. We're seeing a lot of MCPs at the moment, really popular. I've been involved in many discussions internally at Access about whether certain products of ours should have MCPs or not. And I'll start: they're glorified, they're just glorified APIs, for those who are familiar with how APIs work. The thing about an API is you're sort of relying on somebody being benevolent and opening up their data for somebody else to share.

David Boyar: But as data becomes so important for updating models, what we're seeing is fewer and fewer endpoints are being put into the data that's available to get, and Xero protecting its data and limiting ecosystem partners from training on it is a really great example of it. We can stitch together a practice with MCPs, and we can think it's cool, and it is really cool when you do it, because you can natural-language question everything. But fundamentally, the plumbing is the same as what the API is. I don't know if anyone here has actually designed an MCP, but I've sat with our product team designing MCPs, and they sit there and they say, right, what data are we going to serve in the MCP? Exactly the same as the API conversation. What can you do in this? Because nobody wants to give you access to everything. They're going to put some guardrails on it to protect the IP and their businesses.

David Boyar: And this is sort of where, if you're DIYing, you can get stuck. You can connect your tools to AI through MCP, but it'll only allow you to see what the connector exposes, which is pretty good, but it's usually not everything. And different MCPs have different levels, and you can do different things in them, for those who are playing with it and trying it out. What we're trying to build with Access Evo is one environment, so it sees all the core data that you need: practice, payroll, jobs, tax data, because we've got our own tax software. There's no tax API in Australia, which is, I think, an ATO requirement. It's probably a good thing, to be honest, given how valuable that data is. Which means it can do more work across more data than it could if we were using a general system.

David Boyar: And I said at the start, Trent, that I applaud Access for coming up with a different vision. I think this is great, because here we're talking to some of the most innovative accountants, but the reality is, most accountants aren't sitting there connecting things together, building their own workflows, and building their own automations. That is just the truth of who the accountant personality type is. And the more AI can become off the shelf, use it, all the data's already here, the greater adoption will be, and the better the accounting industry will be able to respond to the massive excess demand that we have, triggered by increased regulation, challenges getting staff trained up and staff in. We know all the details around that.

David Boyar: So what does good look like? We want to log into our computer and we see something like this. This is what's already happened. The AI has done the work; this is what agents are doing, for those familiar with agents. You haven't typed anything. The AI is just coming to you and saying, here's what's going on. You get a call, there's a new inquiry, an ATO letter's been matched to a client, a draft's ready to go using ATO Smart Docs. You just want to log in and see, what do you need to do? I don't want to read anything. I don't want to look at dashboards anymore. We had a customer advisory board in Sydney recently, and a senior partner at a national firm said, I don't want to look at a dashboard anymore. I just want to be told what I need to do based on what's on the dashboard. I love that.

David Boyar: We want to break the loop, but not necessarily break the team. We want to break the loop of: we don't have capacity, our margins are under pressure, but we've got no time to change. People here clearly are investing time to change, investing in their education, but it's going to be really hard. It's impossible to keep up. Like Trent said at the start, just start, because you can't know everything, and there's always somebody who knows more. I really am starting to look down on people who are promoting themselves as AI experts, because they've just read a little bit more than you. But the reality is, the thing's getting so good that if you start in six months instead of today, you'll achieve more on your first day than the people who start today, because things are moving so fast.

David Boyar: And a great example of that, Clawdbot. And people started going crazy about that. What was that bot that came out? I forget what it was called. Yeah, it's like connect things in and build automations. It was sort of, I don't know, a connector on steroids. It sounded amazing, but I just thought, I don't have the brain to do this. And then Clawdbot came out, and I thought I could actually use this.

Trent McLaren: OpenClaw, I think you're talking about.

David Boyar: OpenClaw, thanks, Trent. My personality is not doing that; I'm not investing the time. So Clawdbot comes out and I don't have to set up anything. I don't need to think about anything. And Trent, you know what my first bot that I built was? Well, you should, because you responded to it when I posted it on LinkedIn. I built a bot that logged into my email and my kids' school account to give me alerts about everything that's going on with the kids. And it was so effective that for the first time ever, when I did drop-off, I knew that I had to go pick something up from lost property. So I have a better marriage because of this, mate.

David Boyar: And I think things like that, where AI is getting to where we don't have to bother about setup, I think that's going to be huge. So, the lesson for all of us: we have to start thinking about AI like labour. AI is coming for labour margin. That's their revenue model. So we need to see...

Trent McLaren: I lost my hat. I lost the hat when I was in Milan, in Italy, but I couldn't write or speak Italian, so I got it to find lost property on the website, write the email in Italian, and then managed to get my hat back, because it was able to converse and do all the things I needed to do.

David Boyar: Amazing, mate. It can help get you your hat back in a different country, and hopefully it's going to solve the massive margin and talent crisis in the accounting industry all at once.

Trent McLaren: Magic.

David Boyar: So this is really important. We need to start thinking like this. AI has a cost for every job, every output, every task. It's consuming a cost through token usage, and eventually you're going to be charged for it. Particularly if you're using the advanced models, we are already being charged on tokens. I saw a post from Chamath. He's a big thought leader in the States. He's got this big podcast; I think it's the most listened-to podcast in the States, the All-In Podcast. And one of his staff came to him with a problem, and Chamath just said, excuse me, just throw 500 bucks' worth of tokens on it and see if you can solve it. That's not a service business approach. That's just an extra 500 bucks taken off your margin, at least. No one in an accounting firm should ever say to a staff member, just throw 500 bucks at it.

David Boyar: What we need to get to is saying we need to price this. When we start doing quotes for clients, if we're going to start using AI, we need to work out the labour cost. We also need to start learning how we're going to estimate token cost. And I'd love to say the next wave of innovation in the accounting industry, if there's any founders listening: build a product that helps accountants measure token usage, based on specific jobs and outputs. Because a lot of people kept timesheets even if they were value pricing, because they wanted to understand the input costs. Reverse engineer that same process, but make it for the tokens.

David Boyar: And then finally, measure it. The way to measure it is margin per job, per person. Your best staff will maintain the revenue they're billing their clients, with a lower labour portion and a higher AI portion. But in theory, as the leader, what you want to see is the total cost for delivering that job starts to go down over time. This is gonna be really hard. This is a major change. When Ron Baker wrote the value pricing book, he never imagined this. That is the academic journal on value pricing for accountants. Accountants who lean into this very heavily and don't have controls around tokens will lose money. Guaranteed, unless we start getting the controls around it.

David Boyar: The good news is we've got the skills to do it. We know how to do unit pricing. We know how to build controls, we know how to build monitoring reports. So if you are building apps inside your firm, this is the first thing that you should be thinking about and building: how can I control the cost, and measure and monitor the cost per job, and the improvement in time and work quality per job inside my practice? Otherwise, you're just spending money doing something cool, probably getting lazy, probably getting dumber, and not actually getting any economic benefit for it. But who is getting economic benefit? The big labs and the AI companies.

David Boyar: We don't just want people who can use AI. We need people who can use AI profitably. And that is going to be the trend for the next five to 10 years in the accounting industry. And that's the question we should start asking when we're hiring people. Not just, oh yeah, you know, like 10 years ago, I can use the Office stack; oh yeah, I can use AI. I don't want "I can use AI". Show me exactly how you reduce the cost to produce a job. And that's going to be the highest paid employee moving forward.

David Boyar: So, three steps to stop paying the data tax. Put AI on your books: add it to your chart of accounts and try to start tracking it per client, just like we do per time. Start thinking about that. Step two, audit your AI like it's a client. What are your tokens paying for? Are they paying for thinking, or figuring out? And try to reduce the cost in your token usage, and start to develop some discipline in how people are prompting. Now it's good, because we're playing and it's free, we're in the freemium phase, but we need to start getting these skills very, very quickly. And the third one is using specialist AI as they start to come into the market, and Access Evo will be one of the leading ones for accountants in Australia. Does it already know your practice, or does it need onboarding every time?

David Boyar: We've also put together a data tax guide. I'll produce it at the end. But three things to commemorate: you're paying the data tax, it's a cost on every job, and your data is the advantage. Everyone wants to access your data. Your judgement of how you reconcile everything is what everybody wants to get. That's why the AI data brokers are paying millions of dollars to try to get it. They want to pay for your thinking so that they can do it. You have to think if you really want to do it, or if you want to keep that profit for yourself. I'm in the keep-the-profit-for-myself camp, personally. Why should they get it? AI is not magic, it's time back. If we're getting time back, we should start to see profit. That's what we need to start aiming at.

David Boyar: If you want to stop paying the data tax, I think this link works, Trent. Scan it, do the maths on your own practice. This is a little model that our team put together to try to work out how much we're actually... not paying, but how much we're actually spending on AI. We might be paying less, because we've got a really good bundle, 20 bucks, 200 bucks a month, whatever it is, but we've tried to have a crack at working out what the token cost should actually be. Have a scan of that, and get it into your practice, and start thinking about this now, so that we can start getting profit from AI rather than creating profit for the big AI labs. Now, Trent, how am I going for time, mate?

Trent McLaren: You're doing pretty well. I reckon you've got one to two minutes left before I kind of need to hard stop it.

David Boyar: That's all right, mate. We're heading up to it. Are there any questions in the chat, mate?

Trent McLaren: Lots of enthusiasm. If anyone does have a question for Dave, outside of your commentary... your commentary's been very appreciated, though, so keep that energy up.

David Boyar: Appreciate it.

Trent McLaren: But I don't see any specific questions, other than, again, just lots of good. I'll send you all the commentary after.

David Boyar: Does anyone disagree? Does anyone disagree with the idea that this thing is a unit cost and we need to start tracking unit costs?

Trent McLaren: No, I'm glad you're educating on it, because I chat to so many software vendors that are trying to figure out how they also charge for this stuff in the future. And you've kind of laid it out exactly here. The one idea I had, because there's always a million ideas to spin up, is actually trying to build a token usage monitor of sorts, something that you can just integrate, like an MCP, that can then almost do a timesheet, right? It's like a timesheet per client. Now we need to do a token sheet, or utilisation or usage or whatever, at a client level. And I think that would be really interesting when we get to that point, depending on what products you're using, because some products are already charging for usage and outcomes and so forth, which I think is the right way to do it.

Trent McLaren: But the reason they do that, to your point, is because they are trying to displace. You're paying someone X a month to do this; now you're going to let AI do it at half the cost, or whatever it is. And that is still better than what SaaS pricing is, right? Normally I pay $2.50 a month for a SaaS product. Now I'm paying $1,000 for an AI product, but technically you're thinking it's doing more than maybe what the person was doing before.

David Boyar: Correct. But if you're not measuring it, how would you know? And to your point, Trent, I'll tell you what I'd love to see. Do this for yourself first. And for anyone who is offering virtual CFO services: when your client works out they're spending a fortune on AI, who do you think they're going to go to, to work out if they're making profit on their AI?

Trent McLaren: You.

David Boyar: You could brand yourself to be the AI cost-proving accountant, and offer a service to help your clients understand their return on their AI investment.

Trent McLaren: Yeah.

David Boyar: Everyone would pay for that.

Trent McLaren: Yeah, agreed, agreed. I might send you all the chat so you can play it back through, mate. There's some really good discussion in here. People like flat rates. We all like flat rates, unfortunately. I think flat rates are...

David Boyar: It's gone, guys. Sorry, guys, it's gone. Not happening. It's just gone. And you might not like hearing it. There is no possible way that you'll be on a flat rate forever with AI. Just use your accounting brain. How can they spend trillions of dollars on these data centres, and you're going to pay 20 bucks a month?

Trent McLaren: Nope.

David Boyar: It doesn't make sense. It doesn't make sense. So the challenge for the accounting world, where all the energy needs to go, is how do we get our accounting firm to make sure we're passing these costs on to clients? This might be the end.

Trent McLaren: The question on that is, do you have a suggestion on how we should record and track the AI cost per job? Any thoughts on that at the moment?

David Boyar: Look, if you search for it, there are a few startups that are starting to try to do it. It's all very new. But the starting thing is, in your practice, do your staff work on specific jobs, or is it just one job, and everything for that client all gets in the one job? So it's going to start with making sure we're really structured. And the second thing is going to be just go to Claude, or whatever AI you're using, and say, hey, I want to try to understand... let's come up with a prompt, Trent, help me with this one. You'll go to prompting: I want to try to understand how many tokens I use per query type. What do you reckon? Is that a good prompt, Trent?

Trent McLaren: Yeah. Yep, yep. I actually have been, because, you know, I like to build. I've been building my own CRM, and within that building my own costs and caps to make sure we're not maxing out and spending more than X amount per day. And actually now the agent runs that I'm setting up on the CRM are showing, this is the job we did, and it cost me 0.02 cents or whatever. So you've got a really granular view of the run, the job, the costs, and you can change out models and all that kind of stuff. Dave Selleck's session tomorrow actually breaks down exactly how he does that within his own agentic operating system that he's built, which is pretty cool. Before I wrap, is there anything else you want to go through with everyone?

David Boyar: The reason why we're talking about this from Access is because it's specific AI; the cost is extremely minimal compared to what's coming. And if you really don't believe me on this: every single private equity firm in the country, in the world, who's heavily invested in tech, views that the only way to get out of the 60 to 70% reduction in enterprise value of their investments is to get their investments to start charging for outputs. Every major PE firm in the world is talking about it. So it's going to come. And you'll feel it first from Xero, because Xero is under the most pressure, you all know it. But that's where you're going to feel it first. And, you know, I might do a bit of work on this, Trent. I'm going to track back who Xero's major PE shareholders are, and I'm going to find out their views on where this is going to go.

Trent McLaren: Yeah.

David Boyar: And I think that'll start to validate this, mate. But if anyone wants to have a chat with me, as I said, I'm future-thinking this, which means I'm not accountable for what I'm saying right now. Hit me up on LinkedIn and let's have a chat, and let's talk through it, because the industry needs to prepare for this, mate.

Trent McLaren: Nigel had a final comment there, saying that "our staff are all using Copilot, so no tokens necessary. Hope that doesn't change anytime soon." Nigel, I'd love to believe it, but I think it will eventually, at some point. Maybe Microsoft will carry that burden a little bit longer. Maybe they'll develop their own models. I think if the whole industry starts to change in that direction, which it will, then even if they can find a way to make it happen with their own models, there's no reason they don't do it, because everyone else is already leading the charge on it. Therefore, it's better money for those providers as well. You might get away with it for a little while, maybe, but yeah, I think directionally we can all see it. That's also my belief as well. So, yeah.

David Boyar: Trent, we're a little bit early, but today's the 11th of November. A minute's silence begins in our southern states today, for those who have served and those who have fallen in the Australian military. Lest we forget. And if anyone has any family connected with their service... We're a little bit early today, so we won't do it right now, but a thank you to all of our armed service men and women who go out and protect Australia and look after the fantastic life that we've got here, mate.

Trent McLaren: I appreciate it. Thank you, Dave. Thanks for doing that. Malachi, I'm sorry, I'm not trying to jinx it. I'm just trying to go in with what's being said and what is probably going to happen.

David Boyar: You're an influencer, Trent, mate. Maybe you could influence lifestyle strategy.

Trent McLaren: Amazing. I love that. Very engaging. I think about all this stuff all the time. I think it's a great conversation; I haven't heard anyone bring it to the community. I think it's awesome. You can see that in the love you get in the reactions. Thanks for joining us, Dave. Thank you so much, mate. You're awesome, we appreciate it. Enjoy Chinchilla and we'll see you next time.

David Boyar: Thanks, mate. See ya.