How Finance Pros Can Fix Broken Reporting and End the Monday Morning Problem with Ian Wong

Welcome to the future finance show where we talk about The VP of data and VP of fans are like are you kidding me like where did I even start because there's hallucination like left, right, and center Number one and number two people think about vi coding and Frankly a lot AI slop now. There's strategic slom. There's analysis slom and we want to be the counterpoint to that, right? How do you think it picture yourself and up money morning meeting night? We talked about right picture yourself as a GM of the major business unit or C.O. Or would have you and your VP of finance or VP of operations is presenting you With here's how my business is doing. What do you expect? Well, you'd expect number one that it should be correct Like the numbers in there should be correct. Future finance is brought to you by Qflow dot AI. The strategic finance platform Solving the toughest part of planning and analysis B2B revenue align sells marketing and finance seamlessly Speed up decision making and lock in accountability with Qflow dot AI Welcome to future finance. I am Glenn Hopper along with my esteemed colleague Mr. Dr. Senator Paul Barnhurst. Yes, Senator. I'm running for Senate. Did you not know? Our guest today also with us is Ian Wong Ian is co-founder and CEO of summation Most recently Ian co-founded open door and served as CTO from inception to going public Before that he was squares first data scientist building the company's early fraud and risk systems He holds degrees in electrical engineering and statistics to about favorite topics from Stanford University one of my favorite universities Ian, welcome to the show. Thanks for having me on very excited. I love the square background because I worked for American Express So you know the fraud and all those type of things. I remember more than one Conversation. I think that's just a fascinating area. So totally. Yeah great area for data scientist, right? Like 100% yeah I mean classic and all fraud detection is one of the most typical applications of that Yeah, exactly right up a data scientist Sally machine learning all those things. So I Want to start with the question that kind of ties a little bit back to when you and I chatted Because I know you and I've had a couple conversations over the last year as you've been building your new project summation Which we'll get into but I loved you shared how when you're at open door You had an experience that you called the Monday morning problem And I really liked the way you framed it. So can you talk to our audience of what the Monday morning problem is and Why is this such a big problem for so many companies in your opinion the P&L the income statement the you know And actually a lot of the amount of P&L was literally printed and we all look at it Five lines into the P&L the question always comes up. Hey, why is this line red? Right, maybe it's revenue today. Maybe it's growth the next day. Whatever the case might be. Why is this line under budget? And that question used to drive me nuts because That would kick off two weeks worth of work between finance and data BI and bizzops And the issue is number one by the time they come back in two weeks The exact teams have forgotten what the question was embarrassingly, but more importantly, the insights no longer relevant And that was Monday morning when you'd ask a simple question about the business and we kick off these again What I think of as the expedition the armies of people go on to stitch data from you know data set number one data Where else number two and so on and so forth? That was Monday. That would be total P&L review Tuesday would be a pricing and inventory review sending happens One's day would be a marketing sales review the same thing happens And so it really felt like Sunday through Friday was this deja vu day after day where people were just constantly Asking questions not actually understanding how the business was doing and that was so frustrating that after a left open door I decided to do something about it. That's section. I mean anyone who's worked in FPNA or or BI That resonates with them because you got kind of your standard monthly reports that you do or daily reports weekly reports Whatever they are and you've got that and then you have all these ad hoc reports and they're never built in to the system So it takes all this work and it also ties into There's a big argument now around finding ROI around AI projects or anything analytics projects and The time to insight that Return that that getting quicker view into that it's hard to measure that to the satisfaction of a CFO where it's like Yeah, get it But what's the dollar value of it? It's well if you're asking a question on Monday morning and have to wait till the next Thursday to get the answer I mean ROI is the time that you lost in a number of days of being able to pull the right lever and act on it right yeah totally I think there are two aspects that I question it or that point you meet plan which is number one the speed up Right just the fact that you have all these people doing all these manual analyses. There's that element of just how much time can I see But more importantly, what's the depth that you can go into Right, what is a quality and the depth of the analytics and I think those are the two big issues when you know I think of the way that people do work as it relates to analytics and FPNA and data science We are in such an early era. I think in 10 years when we look back and they're like I can't I can't believe people used to do that way I can't believe that people used to have to click through to dashboards look at numbers see what's red And then figure out the next dashboard they should click into and repeat the same thing Or hey, that's literally how or it is done today And my hope is that over the next five years going to replace eyeballs on dashboards with agents and tokens Right doing that work for us so that we can actually not just automate the grunt work But actually get deeper better insights. You're preaching to the choir here I left here in that arguably and Very much so it could be said that for in 2025 you actually have the perfect background to be a founder especially in analytics and AI fields with electrical engineering statistics Stanford, but it's still There's a transition from a data scientist in the mindset you're in to Being an act of founder where I mean, I would suspect in your heart of heart It's like maybe your favorite thing to do is to sit down in front of a machine learning algorithm and our tuna model and And all that but what was me going from that really hands-on And I'm sure as a founder you still have a lot of this as well But what was the hardest part was there a notable transition going from data scientist to founder as a great question I think there are actually lots of things that are translatable. I think data science and finance are actually very similar in that sense And that you actually to be a great data scientist you have to empathize with the domain You can't lead with a technique you have to start with what does the business care about what's a customer Paul you're talking about how you wrote mx, you know, or you know, you worked with mx and thought about fraud detection Well, how do you design fraud detection system? We actually have to empathize not just with your risk operations team But you actually just think in terms of the fraudster and think about all the You know thread vectors that are coming at you and your job is back-and-day do classical feature interviewing to To check that So there are lots of things are translatable about data science and becoming a founder, which is empathy with the domain and the customer I say the hardest part honestly is a number of hats you have to wear as a founder Right, yes, you have to be a tentico co-founder like I did and you know, you have to manage data science and entering all that good stuff But I was also the GM of pricing and so I just oversee the P&L for the business And that was really hard and in one moment your context switching and like how do I be a functional leader for engineering and data science You know another moment your conduct switching like what's going on with the P&L? Like is it right in these areas? How do I course correct? And so I'd say the context switching and having to Get good suddenly in many different areas and not be um picky About what you got to get good at that that that is hard, but it's also what makes a job fine It's so switching costs right like just The it gets exhaustive for me you won't say this about yourself But I'll say it about me the hardest part and all that is oh wait, and this role actually have to care about people Well, you know, I would say actually one of the biggest founder lessons I learned is how do you be apathetic But also direct yeah, you know, I think a lot of that really challenging things about being leader especially You know when I kind of first managed in this was circa Early 2010s the Topic to Jor or the management term to Jor was authentic leadership or empathetic leadership And it's actually really easy to image mr. turper dot, you know to be abtication and not delegation And so there are lots of things in which how do you effectively manage teams on we can talk about that separately at some of the time But yeah, lots of management lots of their you know a couple things I uh I totally resonate with the context the switching costs So much higher now that I run my own business yes, I don't have a team to manage, but you know This calls a podcast next call is selling the next one is just somebody who needs to chat the next minute I'm you know trying to build a course and it's just constantly trying to carve out enough time That you're not switching so much that you get nothing done during the day Right because there is a real cost to that. I think AI can help with that some but and then the second when you mentioned fraud and fraudsters You know having made data science you'll appreciate this I worked and creepade at amics And we had one guy there that anytime they wanted to know the question of how the fraudsters would work Because he had to learn how to maximize Reward points and how to do everything legally the game the system Yeah, so you could question the ethics for sure But he always kept within the legal bounds and so anytime that a quick is like you do realize you round that promotion All you did was got a bunch of scammers and here's why or you did this and you realize that was terrible Go look at the data. Let me show you how much money you lost and he was just a master at basically You know, what is the equivalent of fraud in the sense of where are you losing money? It wasn't legally fraud But still taking advantage of the offering so it was really interesting. All right Let's stock summation here for a minute. I think uh believe you started summation almost 18 months ago I know you're in stealth for quite a while. I think we chatted for the first time was a bright eight nine months ago That's right And so why don't you share with our audience what summation is what you do Kind of why you started as give us a little bit of that founder story of summation Yeah, so I started the company because of that Monday morning problem and I became really motivated To ask one of enterprises actually had a summation layer To all their data and all their operations right a layer where I can actually interrogate and have a tactical feel Of how the business is doing my analogy is kind of like the f1 car right like you want to do the steer the business and run really fast But you feel How the business is moving and today Their operation and that feedback loop are so detached and that's what makes it really hard for leaders to operate So what do we do we are an AI platform that helps enterprise leaders transform on the complexity Across data and business into clarity and action and what customers use us for are they help use us to help them Automate reporting generate deep insights and more generally improve their decision making an operating cadences Whether that's a weekly operational deep dive to a monthly or quarterly business reviews These are all the different ways in which we can help our customers and the value that we really bring to our customers is that They're able to get this real-time tactical understanding of their business and find strategic growth opportunities I think it's so hard to actually understand how the business is doing and all the things we just talked about where it involves armies of people Weeks to figure out what's going on. We want to condense all that and give people really fast feedback cycles So right sparkly center on the summation website It says decision-grade AI platform for enterprise leaders and I think My first question when I saw that is like how is your deep research? I don't know if that's even what you refer to it as, but how is that different than what I could get from you know Chat GPT or Grock or any of the other research models out there. Yeah, that's a great question. One of our marquee features is what we call a deep dive And before talking about what a deep dive does let me give you an anecdote from the field I've been chatting a lot of CFOs, COOs and other Enterprise leaders One person I've been chatting with a lot is the VP of data and the VP of data and this is in more than one case Have told me that those CEOs of multi-billion dollar companies Are going to snowflake or data breaks or open or a redshift or any of these. There were analysis. They're taking some data Putting it into chat GPT and doing vibe analysis and after the output, you know And after this vibe analysis session they would take the output You know toss it over to the VP of data or VP of finance, but he can just check this because I think this is what we should be doing with our strategy And the VP of data and VP of finance are like are you kidding me? Like where do I even start because there's hallucination like left, right and center number one and number two People think about vibe coding and frankly a lot AI slop now. There's strategic slop There's analysis slop and we want to be the counterpoint to that, right? How do you think and picture yourself in that Monday morning meeting? I we talked about right picture yourself as a GM of a major business unit or CL or would have you and your VP of finance or VP of operations is presenting you With here's how my business is doing. What do you expect? Well, you'd expect number one that it should be correct like the numbers and there should be correct Number two, you expect it to be defensible, right? Which means if I poke at a number or if I poke At a line of reasoning that you can trace it through to the source. You're able to justify You're thinking your calculations, right? And number three, you expect a level of strategic depth Right, and you expect that to have a context of the business you expect that your VP or your GM is telling you something that's nontrivial Right, and so those are different elements that we really big in to our deep dot right number one is correct So every number is correct and we force lm not to spit out numbers, but actually spit out the way to get to the number and we actually do many checks before any numbers presented to our customer And every number that is shown is traceable back to the source And we actually so much of our work is actually this multi agent system to help our customers get there One more thing I'll mention is that we don't deliver answers. We deliver deliverables right as you think about a weekly business review It's not like an answer. It's not like an output from a chat that you want to see is actually a structured document Right, so we work with our customers to structure. Hey, here's what they mean by their weekly operational deep dot Here are the sections, here are topics. How do we break this down such as this multi agent system Can give you this decision-grade report back to the users. I It's so funny because I'm picturing sitting in that Monday morning meeting and If you're the If I had to find it or whatever and you're getting grilled on your numbers I could see in that meeting actually Wouldn't work for a huge company, but a small enough company where you have a a number of ledger entries that could fit into the context window of AI I could see in that meeting dumping the old GL or whole trial ballots or whatever into the cloud and saying help Please, what's going on here? And then you know, because it's that's kind of the dream and that's where I think a lot of people Went into problems like I've been testing plug for financial services 2.0 that I don't know if it's been officially released yet But it does amazing things with excel and you can tie it into data rooms And it has all these great features and outputs really nicely formatted excel workbooks But if you're going on the fly if you're not human in the review is not an inherent reason to trust that more than any other AI Other than it looks really professionally put together which that might be the fact that it looks so good Might be even more more of a reason to be duped by it so having that you know cross checking in against hallucinations I know we haven't eliminated them completely, but that is that's huge for finance You can be in gray area always say you know with sales and marketing because you can not on your analysis But if you're you know writing marketing copy or whatever Obviously there is a lot more gray area there, but with we can't be wrong. We can't be you know the whole probabilistic nature of Finance numbers mean Google can't go public and say oh sorry our AI gave the wrong number We really earned 4.3 billion last quarter. That's not gonna go over well. Yeah, right But the other thing to really hone in on too is there's a workflow Right, there is an existing process where you have to review your ledger for variances Maybe it's a monthly flux process that you go through so there's an existing process already and one of the challenges I see with most conversational analytics is that I don't it's not a conversational analytics problem I'm trying to put this flux analysis together These are the days that's these sort of questions and this is the format. I want to see the flux analysis in That's a workflow Right, and so what we do is that we deliver deliverable not just answers from a self-service conversational analytics Platform by the way, we do have a self-service conversational analytics part to our products And what we see actually from actual usage is out to be honest most users don't even know what to ask Right, it's kind of like in the bi world circa, you know, it's 2010s where Pablo and looker and all these things and you know, you have all these bi teams that make all these effectively what is now known as ontology And the idea is that hey, you can go in and configure your own dashboards like it's just a pivot table It turns out actually even despite all the how easy it is to pivot things in these bi tools people still don't Right and we're seeing the same thing with conversational analytics. There's a chat box You can literally ask at anything and get thing, you know, information or fingers but challenge that people don't ask However, there is a workflow. There was a meeting. There was a weekly business review. You need to present information in that meeting So that's what we really focus on ever feel like your go-to-market teams and finance speak different languages This misalignment is a breeding ground for failure In pairing the predictive power of forecasts and delaying decisions that drive efficient growth It's not for lack of trying but getting all the data in one place doesn't mean you've gotten everyone on the same page Meet qflow.ai the strategic finance platform Purpose built to solve the toughest part of planning and analysis be to be revenue QFO quickly integrates key data from your go-to-market stack and accounting platform Then handles all the data prep and normalization under the hood It automatically assembles your go-to-market stacks Make segmented scenario planning a breeze and closes the planning loop Create airtight alignment improve decision latency and ensure accountability across the team That makes a lot of sense because they're structured easier to structure that And to bring it back each time you have a workflow behind it. It's like I just did a webinar before we jumped on here You know, we've all seen vibe coding you talked about slop and all that now. We're seeing vibe working Microsoft's leaning if that just tell Excel what you want it to build and I told everybody The majority of you would get more benefit by getting better at excel and modeling than trying to use AI And when you talk, you know, it can sometimes look a little perfect We had one where built the moat from a format standpoint build a beautiful integrated three statement model Different assumptions have schedule did a really good job, but then it was out of balance So the balance sheet didn't balance and we asked it to fix it spend some time got a little closer and finally it came back and said It's only a 1.3 million variance. That's close enough. I'm not looking any further And we went I'm an intern and gave that to my boss. I just got fired. Sorry. I'm not looking any close. It's close enough You know, it just totally made us laugh like Wow, see when you talk a slop that was the example the other thing it did is it reversed the number formatting and made positive numbers negative with custom formatting And negative numbers positive, but only get it on like two lines in the entire model. You know long it took us to find that Yeah, oh my gosh. Well Going back to the, you know, operating canus is the reason why we focus on that a lot is companies have a sense of what good looks like Right people are already doing it. And so in many ways that defines evil Right, it's not like a Denoval You know deep research and you know, and by the way if you get deep research one of the issues that I see with a lot of again These platforms that you get a bunch of SQL queries now thrown at you. Well, now we're gonna check every SQL query Right like oh my gosh like, you know, he has to save me a bunch of time, but go to your point Checking everything in this Excel workbook checking every single you know thing. That's thrown at you That's really that's really hard. So a lot of what we focus on is Building an environment similar to our customers and again the the enemy is how work is done today the enemy is All these people are going on to all the status silos and manually slashing things together. How do we streamline all that? And going back to what you said about not knowing what to ask it and this when I talk to finance professionals all the time I say well, you still have the domain expertise of being a finance Person because if you don't know the difference between EBITDA and net income and operating income You know, you don't know the right questions to ask and I the same thing like on some levels the biggest barrier to entry Around data science was you had to be able to code to do it But now if you can vibe code your way into data science, but if you don't know if you don't know how a clustering algorithm works or you know Canier's neighbor I don't know whatever algorithm you're using then it might as well be a black box And it might as well be like what was the old dragon draw a data robot where it built amazing models But you put it in your own people's hands and they're using the completely wrong algorithms for their predictions So it is on one hand Yes, you have access to it, but it's also a lot of This is taking hallucination out of it, but it's a lot of power in the hands of someone who may not have a background If you know the right questions to ask or understand how they got the results even if you they see all the code or the sequel queries right there in front of them Yeah, so that last mile problem is a big one and that's does what we're looking solve Yeah, so I want to kind of get to an article you recently wrote called the query flood So you said about how it is coming and I think this really gets back to analytics and somehow we talked about the dashboards data And just you said with the query flood that's coming You mentioned it might take down our analytics infrastructure. So can you describe to our audience what the whole query flood is? Why it's such a concern? Why do you think it's going to be a a problem? Yeah, so I wrote that blog post because I've been looking at how our agents and summation work And they're issuing a ton of queries. So when we do a deep dive Intimation it can take minutes sometimes up to an hour to run And the reason why is we're spending up 20 30 agents and it's looking at every no concranny for business cross checking each other And one of these runs over the course of an hour it issued over 7,000 queries against our database and part of what we had to do is build an AI calculation engine that cannot accommodate all these parallel queries, but that's a lot of queries and reminding me of time when Back at square and I encourage um, you know the audience to read the blog post, but um Back when I was at square, you know, we used to the whole payment stock crazy enough was Runs one monolithic Ruby on Rails app and the dashboard was actually connected to the same database as That runs the transactions And so if anyone knows you shouldn't be running your analytics on the same operational database sester trend up payment system But we did because it's a startup. We were like 40 people at the time and the PM at square at the time was frustrated by how slow the dashboard Mooted so he just like rage health down command on and that just issued like 30 or 40 super expensive queries against the database and suddenly they'll site went down and people didn't understand my Payment stopped flowing and so you know, it was a fun post-morm and The experience of building this multi agent system kind of reminding of that where if Effectively the agents are the ones the art press and command are across every single dashboard in your business Right and think about that flood of queries are hitting our analyte system and then coming year right think about A world where humans are no longer issuing the queries, but the agents are issuing the queries And so the amount of analytics and amount of query that's going to be done I think it's going to be 10x maybe even 100x in the coming few years, right? I think we're already seeing that in software engineering at the amount of code as being written the same thing is going to happen to analytics And so my point is and by the way, I used to be the CTO and part my job is to keep my data warehouse and data platform bills down And I would get hard applications whenever I get a bill from you know data warehouses of the world Because it was like multi-million dollars growing like 50% year of a year Now you're telling me that agents are going to scale that number up by 10x 100x That's that's kind of scary. So my point is actually how we think about analytics and the whole data infrastructure stack needs to be just There needs to be fresh thinking Because the world's moving to a very different place and how we got to where we are in many ways is an Afterhang in overhang from how dube and how we think about what's going on and feel you know where we headed again We need to have some fresh thinking there Kind that's so funny. I was going to bring up a dube when you were talking through all that and then I was just picturing Everybody's data lake data warehouse data lake house whatever You know just together with a duct tape and bailing wire and what it would do to them to have you know 10x 100x more queries On this data. So yeah, that's a few people we gave hard palpitations to some of the CTOs the list isn't there and the data people are just like oh I can just see the system crashing Yep, yep, and the cost and the cost. Yeah, then there's the person who looks at the cost part go and wait I'm paying two million now you're killing me. It's gonna be 20 million That's right All right, and we've thrown you a lot of softballs I'm gonna now ask you the hardest question of 2025. This is the question. So I'm getting you to do my work for me here's what I'm hoping It's a question that I get every day and it is what we're gonna skip the ROI part of it just take ROI out of it But we are if you've seen the latest gardener hype cycle report so AI agents in particular are at the absolute peak of the hype cycle right now So there's all this fear there's fear around Missing out you're hearing all these tech bros say you know We're half our company is agents and we talk to them like their employees and then on the other side you've got People who aren't doing anything so they've kind of got a FOMO And then there's also fear of the technology because of everything we talked about with hallucinations but not known when you can trust it Not understanding how it works But since there's so much noise around the hype cycle right now like how would you advise Employees or companies in general to figure out where to apply it and maybe we make this specific degenerative AI But you can go broader if you want but where can we apply it AI in our company To get the most tangible benefit considering where technology is right now totally and it's a really great question Because like you said everyone's talking about AI. I was just chatting with Literally partners at some of the largest consulting firms think McKinsey of ECG and beans in the world and Their talks are clients will AI everyone's talking about AI, but No one's really doing AI and that's kind of where we're at all this hype and Honestly, by the way, I'm seeing start I'm starting to see the trough of the solution Right where people are like well, I don't trust honestly. It's actually affecting vendors like us where I don't trust AI Right because it's hyped it up to be you know this big thing and I try to actually be over my data and it doesn't work So you know, why would you guys work right and the point so well? That's exactly why we're here Anyway, but the point being like we are kind of in this weird moment in time What I would say and I'm sure everyone's sharing a similar advice. I think as a finance team we have to work both forward and backward Forward meaning go log on to activity like try it like I do think it's really valuable to understand The limits of the technology and where it can do over a ton of value So try that like try advanced prompting technique. Just get a subscription and try things out try chatroom tea Gemini Manus you name it like just just try them all the other part of it is working backwards though Right and what I mean by that is like what is a business process? Was it workflow like where do we think AI can help the most and classic like hey where are the bottlenecks in this process? Maybe it's financial operations and there are things around reconciliation that's really painful great can we apply there I've got to put it put together this weekly business review hopefully you will consider summation But if you want to do yourself fine like how do we what are the bottlenecks come that in that kind of workflow right? So the other part of it's like what are all the parts of my job that frankly sucks Because the super laborious Let's work backwards from that right? So I think it's a bit of Learning and going with the platforms to say get better and also doing an honest accounting of all the ways in which you think your job can be improved I love it You've disvalidated what I've been telling clients too. So thank you for that and Paul I'm going to do a shameless self-promotion here. You were talking about building workflows. I have a new course on LinkedIn learning Building finance automations with in aid in so all of our LinkedIn Viewers if you're on premium and do the LinkedIn learning you can you can check that out Look how fancy you are and other course I think we've covered so much on the the whole you know Hi disillusionment, but I just totally agree people just need to get in and start learning and trying and experimenting and you know That's why I tell so many people is you got to start somewhere start small learn and you know look at what others are doing Learn from experts in the space like Glenn and so many others out there that have done a lot of the work because the reality is yes There's hype but if you choose to just think it's all hype you're going to miss out Because I can guarantee you it can save you hours. There's no doubt no matter what your role is I don't care if you're in finance marketing sells anything if you work Any type of job it can save you time But you have to be willing to put in time and generally you have to know what you're doing so you can validate what it's giving you so many people think Like for a long time well AI killed Excel. No, well AI killed coding. No, it's a magnifier That's what I keep telling people if you don't know what you're doing. It's going to magnify that if you know what you're doing It's going to magnify that it's super exciting So I love what you shared there a lot of great advice all right. We're going to move into our personal questions kind of funny enough normally It always rates them one through twenty five This time it counted to six questions I gave it for the episode and gave me seven through 31 It didn't give me one through 25 it took me a couple minutes to figure out what the heck it did I'm like why did it start at seven and so instead of picking a number between one and 25 and Glenn does this the different way We'll let him go next There's two options is we can let the random number generator if I can speak here pick a number between seven and 31 Or you can pick a number between seven and 31 and I'll ask that question. I will let the Random number generator. How about all right and I got to set it up. I had it set. I was going to say it's one five You don't change it here. So you don't change your parameters there. Yeah, I knew all right. It gave me it gave me 10 So let's see what 10 is as squares first data scientist You expected complex fraud but found only 50 cases What was your most hilariously wrong assumption about the job? Yes I took a job at square because at the time I was a board graduate student That's time for it and I had just interned at Facebook and I realized oh my god. What am I doing in grad school? so I dropped out and Literally had two conversations one with Keith who was the CEO at the time and later on my co-founder open door And Jack Dorsey and then I got the job and there was no technical screening. I was actually a little sketched out So I didn't know what to expect out of the job and they were just like hey, we're a humans company There's gonna be fraud and just come in and build machine learning algorithms Like great, you know, that's what I study in school. That's what I'm great at I'm gonna go do it and like you mentioned Paul the first day I showed up I was a great. I'm here to build some models show me the fraud And the company was like a year and a half old. There was like 40 people. There was like no fraud There's like 50 cases of like suspected fraud So like what am I doing here? Like there's no Data science. There's no fraud and but that was such a blessing in disguise because That's the thing with startups. You kind of go in thinking you can do one thing But honestly, if you wear all these different hats So I started by building tools sort of a scops team and then eventually analytic infrastructure and eventually You know the ML side of the house, but um, honestly, I thought I was going to be doing one thing entering squared But I end up doing ten other different things and um that was awesome All right, you're up Glenn. Let's see what uh, the AI picks for in here. All right. So uh, yeah So I take the human completely out of the loop here We just turn it over if AI generated the questions and AI can We'll put hallucinate fraud in pick one. So uh, all right Actually, I really like this one. I think you're gonna like it too your twitter your twitter handle is at i hat from physics How would you explain your career? Wow, this is a weird one. How would you explain your career? Using only vector notation only AI could have come up with this question Oh my gosh. Yeah, I had by the way, I had to change it from i hat To in long underscore because people were just like not getting i hat. So I'm like very surprised I mean, it's a good thing that be auger You know, it's it off the call to twitter. So who knows how old that was versus x thus true Yeah, now that there's always AI stuff and this is maybe this is getting a little bit abstract But maybe a neurologist agree to sense for this giant universal algorithm that we're apart all part of So I'm just contributing my own little handy i hat Yeah, love and now we're gonna give you 10 minutes. Um, and there's a brilliant answer by the way I think you think you can use that on your personal statement for stanford Yeah, and now we're gonna give you 10 minutes to just talk about gradient descent and all the film Yeah, maybe it's a cyber account might might take a bit of a dip after We'll call anyone who wants to know what it is to go chat gpta instead of google it right that's right There you go This has been a great episode and they go by it so fast But I'm literally like grabbing my bag and walking out the door right after this So uh follow any any famous last words as we uh bit of do to in i hat Thanks for joining us and it was a real pleasure. Yeah, appreciate it. Thank you so much. Thanks Ian 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