AI Agents Are Replacing Finance Workflows While Revenue Gets More Complex with Ali Hussain

Welcome to the future finance show where we talk about That is an area of where we believe the technology is good enough to fully take that over and end going into the next 12 months That is not a hypothetical that is an inevitable and the quality of the data the LMS the tooling that vendors like us have built Is starting to yield results where you can imagine that happening a good amount of the market in the decent future The harder part of agents is what I call the more intimate workflows like replacing what someone like a revenue accountant Or an FPN a lead or a strategic finance lead those that I think is going to take a little bit longer But that is also an area where agents are going to fully take over and so my threshold for agent is less about is it eating into ERP He spends or software spend or services spend that to me is AI automation Future finances brought to you by Qflow dot AI the strategic finance platform Solving the toughest part of planning and analysis Be to be revenue align cells marketing and finance seamlessly Speed up decision making and lock in accountability with Qflow dot AI Welcome to another episode of future finance Paul I've been back to back meetings all day. I don't even know where I am I'm going to be leaning heavily on you in this episode to really keep us on the rails and welcome our next guest and Let me know if I've just fade off and start rambling and well wait. That's what I do in every episode. This is what's changed Glen No great to see a Glenn excited to be here with you and this week we have Ollie who's on with us. Ollie welcome to the show. Thanks Glen. Hey Paul both of you. Thanks for having me I'm very excited to be here. I'm glad I get both versions of you today. Well, thank you. We're glad you're joining us I know we had hoped to record this a little earlier and had a delay it so I'm glad we could get our calendars back together and make this work So this will be a lot of fun 100% post-tax season always a better time to roll through a lot more Marlissers as well. All right, so a little background about Ollie and then we'll jump into things so Ollie is the CEO and co-founder of tabs the first AI native platform Automating the full contract to cash cycle for modern finance teams before founding cabs He served as COO at latch where he scaled the company from seed to IPO and Experience the inefficiencies of legacy accounts receivable systems firsthand His background spans product leadership at Google strategy consulting at BCG and public policy At tabs AI is modernizing the 125 trillion Was that all global being-to-be revenue market by replacing fragmented finance tools with intelligent AI agents the handle billing collections And revenue recognition under his leadership tabs a scaled rapidly Crossing one billion in annualized billing volume and serving over 300 customers Ollie is emerging as a leading voice in vertical AI and the future of finance and infrastructure So again welcome love the background love what you're doing at cabs and Really excited to get some of your thoughts today. I appreciate it. No, it's uh, I appreciate the bio and thanks for introducing us And I think the cool tip it is when even when we wrote that it was close to a billion I think and now we're close to five billion so when you just think about the velocity of how much money is moving in systems that are now leading on AI It's been amazing to see the scale in the market. It's exciting congratulations on closing in or getting that five billion Here's to 50 hundred 200 you know wherever it is right? That multi-taps brilliant number. I'm not even scratching the surface yet So talk a little bit about why you started tabs. I mean your background's consulting and operations not finance So why a finance company? It comes out of being a Humble observer of just different systems and one of the things that you mentioned earlier I'd been a COO before of a B2B business in which one of the things the COO gets to do is go Wide versus just deep and one of the Observations when I was kind of helping over-the-enterprise applications as I got to see what the CFO was buying an ERP the CRO was buying in sales tooling and the CTO was buying in product and engineering tooling and for me The fascination always was how much I saw a lot of the ERP and kind of the way you buy in service those products Kind of lag and this is well before AI and we're talking a decade ago and people were buying pre-earical in that suite and then on the other hand you had folks buying HubSpot and sales force and we started to see things like AWS and different cloud products come out And so part of my fixation was seeing a lot of the breath in different parts of the stack and always feeling like finance was being Short-changed in terms of innovation and product support that was coming into market And so that was a combination of seeing where I felt that in the thing on top of it and what's fascinating because like Office of CFO ERP spend is maybe the highest so it's not like Finance is underpaying for these systems If you look at overall finance and ERP spend it is more than the market caps of sales force and HubSpot combined annually and so that reconciliation of seeing other areas of enterprise apps advanced and seeing the ERP lag was an area Why I was hyper fixated on trying to build a company here specifically revenue then came to being the partner to the CFL and seeing how they were going about their ERP stack I noticed a very interesting trend in the business. I was at which is a lot of The use of a single ERP was unbundling and I saw that happened with AP and spend is we left net suites procurement module and AP module and went Products like Cooper and ramp and I obviously peer-owned benefits has always been in that bucket But we went from a world where you could not Move unless you went to work there ADP with products like rippling and gusto and A handful of like modern stack and so revenue just when I decided I wanted to go build a company bigger better faster Finance systems as the largest bucket of spend in the world and then on top of that I had this fixation with the revenue side being way behind the vendor management and employee management side of the ERP And that was kind of the the precipice for going and wanting to start taps So as really you were looking at a problem with kind of it within that ERP within the whole finance system Revenue was the area that kind of fascinated you in the sense of where you felt that's the area to focus Exactly had full fixation seeing that I saw what happened with the unbundling of my ERP And I saw modern platforms like rippling ramp and revenue was like it just doesn't make sense It's not like revenue is a second class workflow like it's the lifeblood of the business But there's no equivalent product tooling to what I saw an AP spend in payroll on the revenue side Got it makes a lot of sense So Ali we were talking before the show and it's completely unrelated to this podcast but I feel like Tabs is everywhere right now and this is just anecdotal but I just was a Tabs demo with the client and we're looking at a Tabs partnership through my firm again Not a sponsor if this was just happening through the natural ecosystem of business So I just need to bring that up no News conflict of interest here or whatever Money has exchange hands. Yeah, but because of that before we talked I've got a pretty good idea of what tabs does But I realized you know we get we gave it the once over in the intro But if you could for our listeners Maybe kind of walk us through what problem you're solving for businesses and in what tabs does it a maybe a little bit deeper level Than what we talked about in the in the intro so happy to do that So ultimately the palm we solve is we believe that at the end of the day if you're running Finance and accounting there's three kind of core operational layers as I mentioned earlier There's how do you hand it manager spend how do you manage your employees and really it's how do you manage your customers From a finance standpoint historically most companies manage their customer relationships and CRM whether that be HubSpot or Monday or Salesforce The problem with those tools is they're really good at kind of lead to close meaning how do I drive a lead how do I Demo them and drive them to a closed one deal, but they're not built or finance and accounting teams Which are really the performers around the obligation after an account is closed one meaning you've taken the customer They've actually decided to move forward and sign up for your product and so what tabs does is it effectively automates everything after the day of closed one And what that means is is we use an AI layer which is our commercial graph To read and structure all of your order forms or contracts whatever way you do business With your customer and that's like the key wedge of our products So before AI the reason people often say is why was revenue so left behind when Bill dot com came out and ram came out and Brex came out or zip came out and the reality is his revenue or that customer relationship Plenism, sure you've seen on these demos is a highly complex Commercial relationship and unlike just scanning a receipt or understanding payroll This is very complex and intimate and so AI starting in 2023 started to give us the technology To read and understand that and so historically you would have a finance team who'd have to go read contracts and type in the billing terms or the product or you would pay a third-party service provider to go in and duct tape into your Salesforce order and contract objects and try to pull that data that a sales rep typed in and it was never accurate and always missing things. And so our whole magic is we go in and we read these documents using AI. And from there, we structure and organize them within tabs. The primary use case for many of folks who use us is to then run all billing so that goes and creates invoices. If there's a usage component, we can compute the invoice. We handle all of the communication which accountings often refer to as "dunning" all the way to payment and cash app. And so we do that. On top of that, what makes us a revenue platform versus a billing platform is we, because we have all of that intimate kind of contracts, amendments, renewals, we can then understand all of the performance of those products from a 606 standpoint, because we're not just capturing billing terms. We can understand contract terms, we can understand different types of performance obligations, amendments, and then we can run your deferred revenue. And because we have that deep data within the system, we also know is it a flat fee product, is it a usage product, a tier product, a milestone based product, so we can appropriately allocate that revenue and do much more comprehensive, complex revrec than most systems. And then obviously all that data lives in the system, so we can do a lot of non-gap reporting like your cash forecasting, which is often the most missed metric people often ask like, what's your favorite part of the product? It's actually cash forecasting because we have all this amazing data. We can give you very clear view of how much you'll collect each month on a 13 week basis. And so those are the different parts of revenue that we help companies just scale with a lot leaner teams and avoiding to do a lot of manual work. I'm going to reference something we said off air again, because we were talking for a couple of minutes before the show, but we were always mentioning what, being an AI company right now in the LLMs, the frontier models are getting so much better that there's this. Well, why couldn't I just vibe code that myself? And I'm thinking about exactly what you're talking about. This wasn't an AI job. This is just some basic fractional finance work. I was doing company and we were going through and trying to use an LLM to find some contract, several hundred contracts, some had one specific line for cancellation in term. Anyway, it was when you're trying to chunk and vectorize and go through all the day, it was very hard to find that specific use case that we were looking for. And it was interesting because we thought it would be something that an LLM would have a really easy time doing, but just the way that the child and it was some more PDFs, some were like the image kind of anyway. So, I think it was a total nightmare of the situation and for you guys to solve that at scale, I always have a hard time not getting into making the sausage here and obviously a lot of that's proprietary. But this isn't something somebody's going to just go vibe code and I think it goes to my philosophy. I think LLMs are amazing tools, particularly for reporting. And the data is cleansed, structured and understood from multi-system, multi-contract, etc. Downstream, when I think about finance use cases, I was at a talk recently with the CFO of Anthropic, Chris Nan. They're using LLMs for amazing reporting, but they're still using traditional systems to run core accounting workflows. So, back to your point, like contracts, taking them over and putting them into operational parks of finance are very hard to do with just yourself out of the box with LLMs. And the reason is is by nature, LLMs are non-deterministic, but things like invoices and revrector are highly deterministic. I think there's a reason why a lot of them have been very effective in legal and areas of just understanding a simple clause and trying to get an alternative to that clause and those type of workflows. But something to get to a level of taking pricing out of a contract and producing an invoice, the problem with a public LLMs is you can do it three times and you'll get three different answers. And that's very difficult. It's still cool and you get to some insight very quickly. And so a lot of where we think the best finance systems are going to be highly defensible in the world of public LLMs is really tightly built context graphs, which use LLMs, but they also use other types of AI. And so if you look at our commercial graph, there's a lot of last mile machine learning that is required to get to consistency so that if you load those 200 contracts every time an invoice comes out or every time we build your deferred revenue from a gap standpoint, it is the same over and over again in line with the memory in the direction you've given on how to infer your contracts. We think that billing is a really bad use case of out of the box LMs, but once we have all of your data right we're happy to give it to you and the way you want to manipulate that to do reporting. It's an amazing use case. And so that's one of the ways many of our customers have been able to live the best of both worlds where they rely on tabs to get the context read contracts, build invoices do all their Rev Rec. And then we can give them their data to do a lot of their FPNA and a lot of their reporting in ways where they can pull data out of tabs and bill and in Gaston away that otherwise would have required a lot of tooling and a lot of human labor itself. You know, as I listen to you talk it. Reminds something you and I have said a few times Glenn. You got to have good data before you expect the LLM to give you good results. So extracting the data has to be yeah that's a huge part of it is pulling out the right data to feed these systems so that they can add any value. And to me there's like four layers of where the future is going. There's a data layer, which is the most important thing about it like that. The gasoline or the actual vitamins you put in your body like it's the most important not gasoline your body in your car, but like it's like allity of what you're feeling. And if that's broken everything down screen you're going to have a poor drive or your health is going to weaken or wherever may be. And so that context is the most important thing in the world of AI. Where hyper specialized tools that know the domain that can pull out contract data usage data. He fields from your CRM your data lake, etc. That is where you should always buy versus try to build the actual interfaces the software. And that's why in certain areas I'm just skeptical of all these AI tools that like are purely just a operational or software layer if they don't have a data mode that I do think is a race to the bottom but it is a necessary part because whether you are your accounting firm or your agent are working you still need systems of record. And so I'm going to show the work and show the audit trail and I have a hard time believing you're going to build something that's good enough for an auditor or third part of yourself. So I still think you should buy even though it's not as it's not going to be as expensive as it was in the past. And then the final piece to your guys this point the reporting part I do think gets more commoditized because that's so bespoke so long as you have good data coming from these systems. And I can bet when we're on this show together a year from now will be the specialized agents that are not humans that are also working on top. That is the other area there's the data part and then the last mile agent. I do believe those agents are going to get hyper specialized to these roles. And that will be another area that will be incredibly hard to build and you will just buy so everything in the middle gets commoditized but the data mode and the actual agents that start to look and feel like team members. There's fine tuning of. Agente work and context will get specialized enough where it will make sense to pay up for because of the quality of work they do and the specialty of accounting workflow they know how to do. Speaking of agents obviously we keep hearing about those it seems like yeah first was the LLM it was prompting you hear a lot of scripting but now we're hearing a ton on agents so how would you define an AI agent what do you think they're good at today what are their limitations and where are we going with them ever feel like you're going to market teams and finance speak different languages. This misalignment is a breeding ground for failure in pairing the predictive power of forecast and delaying decisions that drive efficient growth. It's not for lack of trying but getting all the data in one place doesn't mean you've gotten everyone on the same page. Meet Qflow dot AI the strategic finance platform purpose built to solve the toughest part of finding an analysis be to be revenue QFO quickly integrates key data from your go to market staff and accounting platform then handles all the data prep and normalization under the hood. It automatically assembles your go to market stats make segmented scenario planning of breeze and closes the planning loop create air title lineman improve decision latency and ensure accountability across the team. I think there's going to be a lot in for a little bit a lot of noise around agents where people will look at something cool that now I can do it be like oh that's an agent and I my bars much higher for that that is just a cool workflow or table stakes like automation and so to me there's a distinction between. things that move more automated, but are still requiring a human to work on top. That to me is AI automation. It's not necessarily a traffic. Agentec to me are end-to-end workflows. They're being fully done, independent of a human being involved. The human can give direction, but a human's not coming in and really touching the steering wheel. Getting from X to Y location, totally on their own, and starting to completely displace entire jobs that people do, whether it's a part of their job, all the way up to their entire job. And so, to me, I think the bar for an agent is very high. I think we are still in very decent days of who's actually accomplished end-to-end agentec outcomes. I think there's a lot of end-to-end AI automation, but an actual ability to say, "Hey, I'm not hiring anyone else in billing because now I get an agent from tabs, or I don't need a payroll admin anymore because I have an agent from deal. I think we're a little bit out from there, but we're not that far away." And so, to your question, Paul, where I think we're headed is in the coming 12 months, we're going to see a lot of lower-level jobs to be done, starting to get displaced. That doesn't mean necessarily you're cutting all your headcount, but you're able to continue to scale and do more with less. So, my prediction for the next 12 months, and a lot of where our roadmap is headed is, what are the entire jobs that can be done that are highly tactical? And Billings is a great example, like the amount of people that spend a good portion of their time each month building invoices, sending them, following up, sharing a PO number, submitting in the portal, like W9, trying to reconcile payments from a bank account. That is an area of where we believe the technology is good enough to fully take that over and to end going into the next 12 months. That is not a hypothetical. That is inevitable. And the quality of the data, the LMs, the tooling, then vendors like us have built, is starting to yield results where you can imagine that happening, a good amount of the market, in the decent future. The harder part of agents is what I call the more intimate workflows, like replacing what someone like a revenue accountant, or an FPNA lead, or a strategic finance lead those. That I think is going to take a little bit longer, but that is also an area where agents are going to fully take over. And so my threshold for agent is less about, is it eating into ERP spend or software spend or services spend? And that to me is AI automation, like if you're displacing modules and services from your ERP vendor or your third party admin, like that is like automation. But if you're starting to look at your finance team of 15 people and you're saying, look, I don't actually need to hire two more people in billing or in payroll or in revenue accounting or FPNA, that starts to actually, that's where I use the AOR, like where the agents have a right. Paul, I can't believe you asked about agents before I got a chance to. So I wanted to go for once because I didn't want your rant. No, I'm scared. I'm so aligned with you on people were calling everything an agent for a while. And if you, I think last time I checked agents were at the absolute peak of the Gardner hype cycle right now. And like what you were saying about using machine learning at various steps in there, I've fought this and Paul's heard my rant a million times. So I won't do it again. But when you call everything an agent, it lessens what a true agent is going to be. I eventually gave up that fight and I would build for clients an agentic workflow that had deterministic steps along the way. You sprinkle some AI where it makes sense. But otherwise, it's like a decision tree that it just goes in it. You know, it's a deterministic workflow and all that. But I mean, I'd be curious, like I know some of the coding apps. I don't remember which one on the benchmark had the longest running agent or whatever. It might have been carcer, but you know, they've got coding agents that will go out and work for like nine hours on something and then come doing truly agentic work as far as I can tell. But I do think it's important to really understand. And this goes back to your probabilistic versus deterministic approach. I'll come in earlier where an agent is still, if it doesn't have those decision gates on those guard rails, it may go off and do a task and do something completely different than it did before. So it's going to be interesting to see how it shakes out and then how we rein in the agents to keep them. I'm going to Nick Bostrom's, you know, like paperclip thought experiment right now. But it's wild times and in a lot of six months. 100%. And like that's why we're so fix it on this. I still think the most important part is the data context. That has to be perfect because if the agent is working on something that is imperfect, that a human would have been able to solve without a system, like we have a big problem. So the data parts huge, but to your point, there are decision rails, there are controls, there are areas that avoid hallucinations or things that could go and send the agent array. But also if you think about something like billing, you need preferences stored in memories, like this is the way we want you to communicate. This is the tone. Don't go escalate, donning to Chipotle. It's our top customer. We don't want to piss them off like turn off, donning like those type of controls are incredibly important. But they can be built. And I don't think you should build it yourself. But the way if you now have vendors like tabs that have fast one class engineering teams are building off data sets that are just yours, but off of hundreds, if not thousands of companies and getting data on hundreds of thousands of invoices being sent, you can actually pattern match to a pretty good system. Like if you think about cars now autonomously driving on complex highways and in cities, it's not that unfathomable that a billing agent can take you from a contract to cash without a human basically touching anything other than once in a lot of touching the steering wheel to make sure you approve of invoices or who can send the invoice or of a certain size or if you're going to accept credit card, etc. So that's the direction that I do think we are headed, but it's going to require really thoughtful specialists like us to get it right versus you going out and on a $50 million business trying to buy code something. I literally had a client, one of our customers that were about to go on board. Like they went from one system to another, not on the billing side, but on the ERP side and something got triggered and they sent 900 invoices twice to all of their customers on a completely different ERP. And just like those mistakes can be incredibly costly, just using a specialist tool and not spending your engineering time on it is incredibly valid. Yeah. And maybe this is 10-general to that or maybe I'm completely changing the subject now. I'll be curious to hear your response on this. You mentioned 606 and I'm thinking about sort of standard, you know, whatever SaaS contracts and all that. But as especially with AI being more and more a factor of what people are delivering, pricing models are changing. So there's, you know, it's usage-based, seat-based, it's value-based and all that. I don't know. I mean, I guess ultimately that will impact, you know, the kinds of agents you're building, but how are you, or maybe you already have it, but how are you handling increased complexity in contracts and being able to do this pricing? Because I know that's a big, especially around usage-based pricing rather than the seat pricing. 100%. The reason we've done so well in the last 12-18 months, I earlier, Paul asked why I started the company was because I believe revenue had been left behind for finance operators. And the second reason that I didn't get to this Paul is like, if you actually look at the 4.7 trillion accounting and finance economy, when you look at the jobs that are being gotten from a finance-composure standpoint, most are tied to the controlership and accounting, whether that at the corporate level doing things under the controller around billing and rev-rec. That is typically 60 to 70% of finance teams. But then if you look at the professional service side of a lot of these big top 100 firms, many of the people are an audit, revenue-related audit, revenue compliance, etc. So one is just a massive labor market. But then you're going on to something which is like the holy grill of what's going on right now, which is revenue is getting more complicated. Whether we acknowledge it or not, the prior conventional wisdom is you hire a mature finance team. They come in and they standardize your contracts. They once in a year align on pricing and packaging. And they create structure and controls. The last 12-18 months have thrown that out of like completely out, meaning with AI, revenue is getting more complicated. But no one actually knows the right answer to it other than we have to move away from flat fee and even seat-based pricing on the software side. But even on the non-sort software side, the way service providers, professional service firms, even hardware manufacturing companies are moving the way they sell products and the terms and all of that. And so our general mission is internally as a company is to make complex revenue simple at scale. And the way we handle it goes back to that alembler. Like we're agnostic to your pricing and packaging because we understand and structure it at the moment of contract science. We're not relying on a price book, any type of historical structure pricing or knowledge of your business model. the same way an accountant or a human reads a contract and then types in what their understanding is, the LLM and other deterministic machine learning is highly discerning of that and can support any type of business model. And so if someone asked me what's the most surprising thing about your business, 60 to 70% of our clients now have a usage component to it. And that's not just clients like cursoring together and others that are like AI companies. A lot of legacy platform companies are now adding some form of usage structure on top. And we're getting incredibly good at not only structuring it, but most companies then feed a usage feed into tabs. It doesn't have to be API. The LLM is really good at capturing unstructured CSV uploads as well from BI tools like Looker and Tableau. We do the math and we do the invoicing. But then most of my roadmap right now is going really deep on the revrex side because to your point when you add usage and all these complexity, doing your 606 on usage based products with tiers and minimums and pre-commitments and performance obligations, it is like a 7/11 freeze times 100 times over. And I mean like people are truly lost. How do you do SSP and revenue allocation at a time of usage and professional services at the same time? And these are all breakthrough macro chaotic moments that are really allowing us to go back and build products that are helping support finance teams going through immense change right now. The pricing landscape is a changing animal. It's why I'm glad my business is relatively simple on the pricing. All right, enough of the humor. But yeah, great answer there. And it's fascinating. We really interesting to watch over the next couple of years how this settles because yeah, if you're not careful, that pendulum will swing too far in complexity and pricing. But there's about usage and all these different things are great and getting the value based pricing. Holy grail at the same time, if it's overly complex, do people want value based pricing? And you never know what the number is going to be every month, right? So this will be fascinating to watch how it plays out. Yeah, you'll be Paul. My hot take right now is we're not actually seeing a lot of pure outcomes based pricing yet. Even though people say their value or even in usage, it's not pure risk based. Most people are really moving to some form of tier based pricing like and on top of that. But we're seeing a lot of upfront commitments too, especially folks who are moving and selling more AI products to the enterprise. They need some certainty on payment in term. And so I think as much as people are posturing really advanced, complicated usage, we're starting to pattern match a little bit. The problem just becomes the discounting and the things that it requires to get the deal done are highly sales, create, and what the customer is willing to pay. And that actually where a lot of the complexity still happens today in the market. So AI hasn't solved the relationship and all the things sales people do that you're like, I got to figure out now how to manage that. Sales maybe one engineering of resourcing, they're going out and hiring five more sales people. So I've yet to see that happen. Yeah, I know I'm with you. All right, so we're going to move into, we have a little section we like to do at the end. This is our AI section. So here's how it works. We feed an LLM and we use different ones depending on which week it is. I think this one won't claw, but I'm not sure. And so we feed it questions we had for the interview, your bio, your LinkedIn profile, until anything it can find on the web. And to come up with a mix of kind of personal fun and quirky questions. So we never know what we're going to get. We haven't even read the questions. Glenn and I each take a different approach. So my approach is you have two options. There's 25 questions here. We can stay with, you know, computer here and let the random number generator pick the number between one and 25. Or you can pick a number between one and 25 and we'll put a human in the loop. And then we'll ask that question. All right, I'm a big human in the loop guy still. I still think we need humans in the loop and we will for some time. So let's go, uh, let's go lucky 13 13. I'm not sure if we ever had that one. As a man, many. All right. Here we go. If AI agents were employees, what title and job description would you give the tabs agent? And what would its performance review look like after year one? Amazing. I am a good believer that agents must be named like employees, so humans and so I would pick whoever first name at tabs.com is still available. I don't think we'd yet have a Regina. So let's go Regina tabs.com. And the way I would assess Regina today is pretty similar to how I assess employees at tabs that put them on a one point one to four scale. With three to four being no one gets a force. I'd be very surprised if which is a top performer. I don't think yet Regina is at the level where they can be a three either, which is like overperforming. So I probably put the meta to I don't think there are one anymore. Last year one is typically someone who's on the lower end and struggling, but I think Regina is like probably a two. The only thing on top, I want to make sure Regina is not costing me more than an employee. So I'd also track their token utilization to make sure that the ROI in Regina is good enough so that it's not only better than employee, but I'm also getting some real ROI out of it. Great answer. Complete. I like it. I like it. Good right on the bar. I never thought about that one. So a hospital be all on the 13 there. So for my approach to these questions, I feel like I'm just throwing everything over to the AI overlords and instead of just putting a human in the loop at all or even just a random number generator, I ask it. What was, you know, you came up with the questions, which one of these do you like best should I ask? So I ran this. Let me guess question 13. I'm skinny. We have had that happen a couple of times lately. Just a weird glitch on the matrix or something. But now this is number this is number five. I almost wanted to do a human in the loop and call an audible here. This is okay question. The AI question is you worked on Capitol Hill before any of the tech roles. And this is the part I want to change. They say what's one thing about how DC actually works that you wish more tech founders understood? I want to change the question to be how on earth are we going to make our octogenary representatives understand the first thing about AI? So harder question, sure, but I'd love to hear your answer. This is not what I thought about other than I think the sooner agents get to also vote may start to create some changes. And so we may be not as far out from that than we realize. I don't know if it's I wouldn't be surprised at least in our lifetime we see some type of bill that passes that says an agent can vote in an election and that may be the biggest trigger of change. And I as crazy as that sounds, that's my hot take. Well, love the answer, love that you took the time to join us today. Oli, it's been a lot of fun chatting. It's a deep pleasure. Glenn Paul, I know you both both very busy. So thanks for him, ring me this afternoon. I'm excited for the next time. Okay, we appreciate you joining us. It's been a lot of fun and we'll look forward to the next time as well. Thanks for listening to the future finance show and thanks to our sponsor, Qflow.ai. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice. And may your robot overlords be with you.