The Investment Banking Job Hasn’t Changed Since 1990. AI Is Changing It with Matan

The investment banker in 1990 is doing almost the same thing that the investment banker in 2023 was doing. And now all of a sudden you have this profound change that hasn't even happened yet, and everyone expects it to happen. So it's— Today our guest is Matan Feldman, the founder and CEO at Wall Street Prep, one of the most recognized financial training companies in the world. His responsibilities include business development, development of courses, and overseeing training programs.

He previously worked at JP Morgan, What does AI mean for kind of the investment industry and modeling?

It's almost like a Schrödinger's cat situation where at the same time nothing is really happening on the junior ranks and in senior ranks, while simultaneously there is some real existential questions around how does this industry change when AI does everything. I believe that Excel is a great canvas for learning. If you want to learn accounting, there's no better way than to put it on that canvas.

How does a modeler stand out as AI is doing more and more of that model build process?

You have to see the forest for the trees.

So one of the big challenges with Financial Modelers Corner is the world's premier modeling podcast. It is brought to you by Financial Modeling Institute, the world's leading financial modeling accreditation organization. Welcome to Financial Modelers Corner. I'm your host, Paul Barnhurst. This podcast, we talk all about the art and science of financial modeling with distinguished financial modelers from around the globe. The Financial Modelers Corner podcast is brought to you by the Financial Modeling Institute. FMI offers the most respected accreditations in financial modeling. That's why I completed the Advanced Financial Modeler. This week, I'm thrilled to welcome on the show Matan Feldman. Matan, welcome to the show.

Thanks, Paul. Nice to be here.

Yeah, really excited to have you. So a little bit about, uh, Matan's background, and then we'll jump into some questions here. He founded Wall Street Prep and he's their CEO. His responsibilities include business development, development of courses, and overseeing training programs. Matan has overseen training programs for clients including Morgan Stanley, Credit Suisse, FBR Capital Markets, JP Morgan, Wharton Business School, London Business School, Kellogg Booth, Stern, Cornell, and others. Prior to founding Wall Street Prep, he served in several capacities on Wall Street. First as an analyst in Chase Manhattan Bank's mergers and acquisitions group, and subsequently as an associate with JP Morgan's equity research group covering food and drug retail equities. So Mitan, again, welcome to the show. We start every episode with this story. Tell me that horror story. You worked in modeling. You have to have one.

Unfortunately, my modeling nightmare is sort of a recurring dream. So it happened over and over again in my time at Chase as an investment banker. What it looks like, it's almost always the same thing. It's 5 PM. This is again, this was back in 2000 and that plays a factor in this recurring nightmare. It's 5 PM. I've been working on a model for 5 hours. I forget to hit save. The model's gone. I end up going till 10 PM to do it again. I probably forget to hit save. I'm just going through that infinite death loop. Back then we did not have auto recover or anything like that. And so you are out of luck. And so that was a very common theme in that first year where just hours and hours of your life are just gone. So we'll leave it there.

How'd you break that unfortunate habit or that challenge? Because we've all been there. I've had that one as well, or at least any of us that are older have been there for sure.

Right, exactly. They, you break the habit by learning and hit Ctrl+S just repeatedly throughout the day, like just the tick, just Ctrl+S, Ctrl+S over and over again. But it actually goes to like, I think a bigger point, which helped me ultimately in my career, which is just how quickly the time flies and how locked in you get into building a model. I think like, to some extent, like that's either something that really turns you on or turns you off. That just the time flies and you forget to hit Ctrl+S or you forget to do the things you're supposed to do because you're just so locked in. And I think for me, I actually realized early on that I enjoyed living inside these models and the time went by much faster than maybe I would have liked to go.

So good lessons there. And it is interesting when you get into flow, it's amazing how fast time can go, whatever that is for you. But you get to that state where you're just enjoying something, you're just working, things are going well, and Yeah, it's easy to forget. Okay, I need to go eat, I need to hit Ctrl+Save, whatever it might be.

Correct.

I think everyone's dealt with that one. I think back to college more than once. But so how did you end up getting into training? I mean, you obviously built Wall Street Prep, you've built quite a business. So kind of what's the story of how that happened?

Like, very much a story of desperation, needing to get out of investment banking and trying to find a reasonable excuse to do it. So the reality for me was that I had spent about 4 years. Chase had been acquired or merged with JPMorgan. I had spent a couple of years in investment banking doing M&A and then in equity research covering food and drug retailers. And sort of the underlying technical work I actually found really, really interesting, which is ultimately what, why I started Wall Street Prep. But definitely the lifestyle and I think where I was in my career, it just felt like it was time to try something out on my own. So it wasn't like a eureka moment where all of a sudden I sort of said, okay, I've got to do Wall Street Prep. It was more like, What do I do to get out of here? So it was maybe I open a restaurant. You know, at the time I was covering on the food and drug retail side, I was covering supermarkets, drug retailers, you know, Whole Foods and Wild Oats back then. These were natural food grocers, you know, super hot and, and interesting. And I was like, oh, maybe I'll start one in Manhattan. You know, they're like just all kinds of ideas. I think scuba diving and, I mean, there was just a whole bunch of crazy ideas and this one was probably the least crazy at the time. You know, I think I'd ask myself like, what skill do I have that I can leverage and Didn't actually know how to scuba dive. Didn't really know much about, you know, running a grocery store, but I, better or for worse, financial modeling is a skill that I did think I had that could be parlayable into some sort of competitive advantage. And that is ultimately why I ended up starting Wall Street Prep. I didn't know at the time that I'd be, if I'd be any good at teaching it, but I did really enjoy the technical work that I was doing on the job and thought this would be an interesting avenue to try simply because at the time, again, this would've been 2004, so 22 years ago, where really access to this kind of training didn't exist outside of the bank. And my vision for it was very much making it available to folks outside of that corporate, that corporate veil. So it was a novel idea at the time that now anyone going online could get access to some of these lessons. But back then, that wasn't market.

No, I mean, training is much more available today, for better and worse, I think. But we'll get into that a little bit later. I love the honesty. You know, one of my biggest drawbacks— I consider going into consulting. I always thought investment banking would be interesting, but I always thought work-life balance— it's funny, I feel like as I've got older, it's almost in some ways got worse because I decided to start my business late. And you know, when you start a business, it's a lot of hours.

100%. Yeah, it's It's interesting. And even within that, so you think, okay, the work-life balance at the bank, you know, I was working 80, 90, sometimes even more hours per week. And as that scaled down to equity research, it, you know, that was, that scaled down to something like 60 or 70 hours a week. And you know, the catalyst, the thing that you're like, okay, I need, I need to do something different. Is in some respects about that work-life balance, although it wasn't actually front of mind for me. I was like, I, I sort of expected to work hard starting, starting the business, but there's something about the investment banking work-life balance where you're, because you're in a very regimented and sort of constant way for somebody else. For, for, for me, that was like, hey, I think I want to do something on my own. Um, so I never actually had a problem with the with the work. In fact, I, to your point, you end up starting a new, you know, you end up starting a company on your own. You end up working just as much and if not more. And in some respects, Wall Street Prep's been around for 22 years now. I'm sort of Benjamin Buttoning my time here where I'm working longer as the years go by. But it's an enjoyable process because it's something that you're doing on your own, at least for me. It's something that I'm doing at my own free will.

I understand, like you said, the own free will. I mean, I've really enjoyed doing my own business. It's just kind of funny because a lot of things I avoided is I didn't want— the hours found me either way.

Exactly. Exactly. Exactly.

You know, I want to focus most of the rest of our time on kind of two areas. Where I want to start is what does AI mean for kind of the investment industry and modeling? And then I'd love to get a few of your thoughts on the education space because Obviously AI is disrupting everything, but I think those are two areas you're thinking about pretty regularly. So let's start with what are you seeing around AI with investment banks?

Yeah, so, um, within investment banks, and, and this question has a different answer today than it did 6 months ago, and we'll have a different answer in 6 months. So just appreciating how quickly, um, things are evolving. But, you know, right now in investment banks, um, what's definitely starting to happen is the deployment of, um, tools, whether they're Copilot or ChatGPT or, um, or others, um, to help junior analysts with repetitive workflows. So, um, anything from document summarization to like, you know, modeling, you know, helping on modeling tasks. And again, these are not full end-to-end workflow, um, changes. That's not happening, uh, part, part, partly because just the technology is not there to make it possible. But, um, but, but certainly more and more banks have rolled out these tools, um, and people, and particularly in the junior ranks, are using them. Um, what's not yet happening is, for example, senior bank, uh, bankers, managing directors are not really using these tools in any meaningful way beyond the, the basic sort of prompting for like copy improvements and emails and things like that, that none of that sort of like real, you know, complex modeling, deck creation, and then for on the senior side, more real end-to-end workflows around communications are being rolled out. Like that is, we are, we are still in the early days of that. Um, and so, so that's, that's the state of affairs. And then I would say in parallel to that, what's happening is, um, decision makers are within the banks, whether it's, you know, chief investment officers, learning and development professionals, um, heads of, heads of divisions tasked with deploying these tools. Um, it's almost like there's like a a Schrödinger's cat situation where at the same time nothing is really happening on the, on the junior ranks and then senior ranks beyond sort of basic, you know, sort of productivity enhancements. Um, um, while simultaneously there is some real existential questions around, you know, like, how does this industry change when AI does everything. And so everything from the, the entire pyramid where, you know, analysts are on the bottom and you have sort of small, you know, associates, smaller amounts of associates than VPs, and then managing directors at the top, that, you know, that's being, um, questioned. Um, while at the same time, not even one analyst could be removed at this point. And, um, because of, because of these tools. So, um, that's really the weird state of where everything's at. So it's super in flux. And, but I will say lots, there's, it is the topic of conversation at banks by decision makers with a lot of, you know, a lot of like FOMO, like what else are people doing? We're sort of sitting at the intersection of that and hearing how all of it is Um, how all of it is coming together at banks and, and banks are, are coming to these, um, these rollouts at different speeds with different, different approaches. But ultimately there's just a lot of, there's a lot of consternation around, okay, where does this, where does this go and where does it lead to?

First, I have to commend you for a Schrödinger's cat reference. I haven't had one of those yet in almost 400 episodes, so well done.

Well, thanks. I'm, I'm, I'm not sure how, how well it works in this particular context, but Uh, but, but it does, but it does seem like two things are happening in parallel that are incompatible with one another. Um, that eventually I'm sure they'll find compatibility, but it does create a very interesting dynamic psychologically for everybody that's, that's dealing with this.

100% agree. I mean, right, there's people want to adopt it. They want to learn it. Then there's the fear. Well, what if I get really good now? I made all my job.

Am I going to lose it?

There's also the, okay, are they going to be hiring as many people? Is this a good career to go into? I think a lot of people in college are thinking, you know, their parents are thinking as they go in, oh, if they go into that career, are they going to be able to have a job in 4 years? And then like you mentioned, there's reality of right now it's not saving us a job, but we all see huge potential here for disruption. You know, how much we see, how far, we all have different opinions and everybody's trying to figure out to deploy that and what that means. So it's an incredible amount of change. Like you said, fear of missing out. I mean, all you have to go onto is LinkedIn for 5 minutes and you see the kind of fear of missing out and this promise of the whole world's going to change. And it's like, okay, step back and let's take a look at where we're at versus what we hear people say, which often are incongruent. I'm sure you see that a lot.

And actually, I, I've never seen it quite like this, right? So like, never in a— like, never in my career have I seen. I mean, actually taking a step back, in reality, the investment banking and, you know, our lens, I should take a step back. It's sort of our lens at this point. My background is investment banking and equity research, but really at Wall Street Prep at this point, our lens is a whole bunch of end markets. It's banking, it's asset management, private equity, hedge fund work, within sort of the investment management umbrella, FP&A, you know, just to a large extent. And so our lens is broadened, but really within, you know, I'll just kind of keep it focused on the investment banking lens, the, that work, the investment banking skillset has not changed all that much since Excel came about. And up until now. Where the conversations are really about a massive change in how, um, what the investment banking skill set looks like from the analyst all the way up to the management, uh, managing director. That's a completely new experience for people, um, really across now several generations, right? The investment banker in 1990 is doing almost the same thing that the investment banker, um, in 2023, uh, was doing. And again, caveats there notwithstanding, the skill set has not changed all, all that much. And now all of a sudden you have this profound, um, profound change that, um, that nobody— that hasn't even happened yet, and everyone expects it to happen. So it's— it is, um, the term I used, um, the other day, it's just— it's crazy-making for a lot of people that are trying to build, um, organizations and restructure their organizations, uh, and support this change without understanding where it lands. It's, it's, it's actually quite a— it's, it's a, it's somewhere on the spectrum of really challenging to impossible task to do right now.

Well, my background is in FP&A. I am also passionate about financial modeling. Like many financial modelers, I was self-taught. Then I discovered the Financial Modeling Institute, the organization that offers the Advanced Financial Modeler program. I am a proud holder of the AFM. Preparing for the AFM exam made me a better modeler. If you want to improve your modeling skills, I recommend the AFM program. Podcast listeners, save 15% on the AFM program. Just use code PODCAST. Yeah, right? I mean, you're trying to predict where we're going in an era where we've been changing and moving almost faster than any time in history as far as what can influence a job. I mean, you think what the models were doing, you know, on helping build stuff in Excel 6 months ago, 3 months ago, with 4.7 releasing, you know, they're even better and they continue to improve. But he said, okay, we haven't displaced jobs yet. They're not able to do everything. And so it's like, okay, what, what's the bet we make for 12 months, 18 months, 3 years, 5 years? Because that makes a huge difference on how successful you are as a firm.

And you've got to remember that the larger the firm, that kind of lead time, 12 months, 18 months, is also constrained by the fact that these are massive organizations with regulatory constraints and a whole— they can't just roll out, oh, you know, we were using ChatGPT, now 4.7 is out for Claude, so we're going to use that. Now we're going to roll this. Now we're going to move to that. Like, that, that doesn't work, uh, in, in large organizations. So, you know, there's all kinds of constraints that have nothing to do with AI that are now coming in contact with, like, the, the, this sort of new pace of change. And, and I think that that's really, really, um, really interesting and challenging for those that are tasked with, with how to manage it. And we see clients across the spectrum managing it in, in a variety of different, uh, different ways, all of them really stressed out about how to make this right.

I agree with you. I mean, big organizations, I mean, right? JP Morgan. I worked for American Express, and I was located next to a lot of the bank people that had to do CCAR and all those type of stuff. You can't just say, oh, I I used AI to do it and then next time I switched the model and, okay. And how much backtesting did you do?

Oh, a little bit. Right, exactly.

Yeah. That, that's only gonna get you in trouble. So what do you see? I wanted to ask one or two more questions here and then we'll kind of move into the education area. But what do you see as maybe the biggest opportunities as we move forward in this space? And then what's the biggest concern you have as we continue to roll out more and more AI?

So the biggest opportunities in the space, let me just sort of narrow the, narrow what that means. I think again, for, I think there are opportunities in every, and I think really the talking to the investment banks, the private equity kind of larger ones, what do you think is maybe the biggest there within the investment banking world? Um, I think that there are both opportunities on the, the demand side. And that's probably the big, that's the biggest unknown and that will take some time, but that is the, just the reality of, okay, banks are in the advisory and capital raising game, right? They help businesses acquire one another, sell businesses and raise capital. Well, in an AI, in an AI-driven economy, what What new advisory opportunities are unlocked for investment banks that become economically feasible that weren't economically feasible before? That's, that's an unknown right now, but I think it would be unreasonable to assume that the only thing that's happening is that there are supply side changes. The investment banking workflow becomes more efficient, but nothing happens in terms of the bank's ability to serve clients better. And all of a sudden more volume and more capacity is unlocked, right? To serve more clients. And that those clients themselves are able to be served because of all the benefits that AI creates for transparency around their own financial systems and their own ability to sort of present themselves and essentially become bankable, right? So the demand side, which is largely like unknown at the moment, because again, AI, the, the tools aren't there yet. To do what's to actually fulfill the promise of AI. But I do think that that's going to be a very, very important part of what happens to the investment banking industry. Those that are innovative are going to really look at the demand side of the equation and say, how else can we better serve our clients? And the same goes for capital raising, right? So there's advisory and capital raising. And, you know, the way that we raise capital today, whether it's through IPOs, bond issuances, loans, um, these are brittle and friction-filled ways to raise capital. And there are many, many ways that you can imagine raising capital that being, uh, augmented, improved, facilitated by AI. And so I think that that's probably question number one and an exciting sort of question, uh, the exciting question to ask about that industry. Again, it's, it's, it's different from private equity that, that has its own sort of, but I, I'll sort of linger on investment banking because obviously the flip side of that is what gets a lot of our attention and a lot of our focus, which is the supply side. What happens to the analyst? What happens to the modeler, right? We're on a modeling podcast. What happens to the modeler? And I think a lot of that is obviously more front and center because the tools are now starting to get good enough where modeling itself is much easier to do with an agent, with a, with a copilot. Um, sitting, sitting next to you. And so those questions are probably gonna come even sooner, which is, okay, um, what do, what is the role of an analyst now? Is an analyst really now an associate, um, that has to use a lot more judgment and essentially become a quarterback for the work that agents do, similar to what an associate currently does with analysts. Um, and, and I think that the, because, because we're on that road now for You know, where AI is getting good enough to do more and more of the sort of workflow of the investment banking job that you're going to have a lot of automation that changes the bank. And again, that pressure, that creates pressure to sort of flatten the pyramid, whereas the demand side puts pressure the opposite way or relieves that pressure, right? Because all of a sudden you need more—

It provides opportunities. It relieves some of that bottleneck. So it's like, how much do you flatten if we have more opportunity?

Exactly. Exactly, man.

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I guess the simplest sentence, and then I'll get into probably a little more detail on it. It's like you just, you have to see the forest for the trees. So one of the big challenges with modeling is that in that example I gave earlier on in the podcast where I'm, you know, I'm sitting there for 5 hours, you know, you know, deep in the modeling rabbit hole, you know, a very common experience of modeling for, for many of the listeners quite familiar with this is that you've got so much work to do to build the model. That you're, you're sort of like punting on the, I'm going to take a step back and then I'm going to look at what's actually happening here, right? So that's sort of that process of, I got a big job to do mechanically and then I can take a step back. So, um, what AI does almost instantly is accelerate the time, um, the, the time at which you can finally take that step back. In fact, there's, there's a point in the not too distant future where all you'll probably need— the role model will probably no longer be the right moniker for what this is. It's, um, and it'll be someone who, um, who has to evaluate these, these outputs. And so, um, right now those two skills are completely intertwined. And depending on how you like to work, you're stepping back, diving in, stepping back, diving in simultaneously. And the spreadsheet— and I'm, I'm probably preaching to the choir here— is just a uniquely wonderful way to, um, understand a business, to tell a story. I don't think that's ever going away in the sense that I don't think the spreadsheet ever goes away, but the role of a modeler, um, is— it's, it's almost, um, it— there will be a point where that as a skill set is, is no longer, um, is no longer the right way to look at that role, right? It, it has to be somebody who can look at models and evaluate models, um, and really understand the story that's being told. This is actually not only a, a real gap in most young and young modelers today. So young modelers today may know every function and every shortcut and sort of pride themselves on, on the speed at which they're building models, but there's an entirely different skill set which is going to be the key skill set. It's already a problem. I mean, so maybe taking a step back, one of the big challenges that, for example, our partners on the learning and development side that we see in training is that the modeling is sort of— there's, there's a lot of folks out there, and, you know, I count myself in that group, that just really love modeling, right? Just like are into that as a practice because it's, it's fun, it's a problem, it's a puzzle. Um, that, that, that is really— that historically has been half of the, half of the puzzle and half of the, of the skill set, because the other half is really understanding how a business runs. Uh, and frankly, I don't— I didn't understand how a business ran until I had to start a business. And so I was modeling for 4 years or whatever it was, and, um, didn't really understand how businesses function, right? Didn't really understand how to take a step back and, and understand the story around it. Today, again, part, part of the, you know, part of the occupational hazard here is I've been modeling less and running a business more for the last 23 years, but like the challenge to me about how does the business run and how do we think about how the business runs and using judgment around what is the story being told. I think that's still, that is not something, that's not something that's going to be relegated to AI. AI will certainly take a stab at telling you that story. But in the same way that every investor that you have, you know, Analysts, investment analysts coming to, you could take 10 analysts and they're all gonna come to different conclusions about whether Google is a buy or a sell, whether that's a good stock or not. And they all have access to the same information and they're all synthesizing it in different ways. And there's that, that human judgment element that with all the resources that are available, different analysts are coming at different conclusions. You're gonna have, that's gonna be the final layer of where AI doesn't exist, where right? Where, where human judgment comes in becomes really, really important. So long answer to your question, what's, you know, what is a great— how does a great modeler stand out? A great modeler at this point starts really leaning into what is happening, what is the story that's being told. I'm going to build a scenario analysis, I'm going to build a sensitivity analysis, that's great. Okay, when I've got— when I'm sensitizing revenue and margins and I'm getting all these different sort of outputs, Um, what's, what's under the hood of how is that generated? And does the rest of my model sort of— is the— is my model segmented in the right way to sort of reflect that? Is my model generating these outputs from a coherent story around investment and, and, um, and returns on those investments? And, um, I think that becomes super, super important. It also frees up the modeler, who I don't think will be called a modeler. I think that job will probably be a much more judgment and, um, and an investor, investment analyst type of, uh, title. But, uh, that frees you up to doing a lot of things that currently, uh, modelers can't do. Like, I just think about anything that investor needs to do right now— channel checks, interviews, you know, site visits. There's a whole bunch of things that, that you need to spend time on that you just don't have time to do.

Great point on that. Um, it will definitely free up time. I think you make a point of the value of judgment is going to be much higher. I want to do one or two questions before we kind of move on to the rapid fire and some of the standard questions I have here around the education side, because obviously you're thinking about that a lot as it's disrupting education space. So how are you thinking about AI in teaching modeling, in teaching people these skills that historically a lot of what the teaching was around is how to use Excel, how to, you know, obviously there's the thought leadership and the principles and everything, but historically it, you see a lot of modeling stuff focused on how to build in Excel. So how are you thinking about AI on the modeling side of education?

I believe strongly that in order to learn, well, take, taking a step back, I believe that Excel is a great canvas for learning, by the way, right? So as, as just a, as a, as a canvas for learning corporate finance and accounting, um, it's just, there's nothing, there's nothing like it, right? If you, if you want to learn accounting, there's no better way than to put it on that canvas and, right, and, and understand the flow of debits and credits in the context of a three-statement model, for example. There's just no no better way to, to do that. Um, so you have the— these, these modeling tools, and historically we have taught modeling, um, in a way that's different from how you actually build a model. You very rarely on the job start from a blank Excel spreadsheet and build a model, right? That's just not a— that's not a thing you typically do. You're reusing, you're repurposing, what have you. But from a learning perspective, It's incredible to start with a blank spreadsheet and learning from scratch. And, and, and I think to some extent that, that is, that doesn't change with AI, right? So there's learning and there's like the, the doing once you've learned the foundation. And I think those two things now we have to be very explicit about, um, as being distinct because once you have a strong foundation, you gotta use AI as much as possible. These are incredible tools. And you wanna leverage them as much as you possibly can. But as an, as an educator, as someone who's tasked with getting people up to a foundation around corporate finance, around, um, accounting, uh, around get— that foundation is ultimately the foundation for good judgment, right? I think everyone is sort of, I think we're all coalescing around the idea that judgment will become extremely important when all these technical skills start getting automated. How do you get to good judgment? Well, good judgment starts with a really solid foundation of these, these concepts. And I, I don't think there are shortcuts to that. So I think it, it really does start with the same kind of pedagogy, the same kind of approaches that we've used. What does change is then you now have to layer on top of that this whole world of AI productivity tools and how to interface with those tools. So the job hasn't gotten easier from a, from an education perspective. It's actually gotten harder. You, you need to both provide the foundation and you need to provide a, a real breakdown of how to use these tools constructively so that you, um, so that your own foundational muscles don't atrophy. Um, that's really, really hard. It's also hard because you're operating in an environment where, um, you know, there's, you, you still have the same number of days for training. You still have the same number of you, you still have the same time and, um, to do the work that needs to get done. So we're spending a lot of time leveraging AI tools actually to create personalized coaching to sort of leverage the tools on a pedagogical side, right? So whereas before you couldn't really have one-on-one training pedagogically, it's really, really important if you can get one-on-one tutoring around concepts you, you have, you struggle with. Well, that would be a huge unlock for an educator. Right. And, and now you have those tools. So AI is not only a new tool that you have to sort of support in terms of how you train people, but you can also use it as a pedagogical tool. And, and that, that's gonna be the way to, to, that's the way we're, we're using it to have our, have our cake and eat it too. You have students, new analysts, new professionals who need to learn a lot more than they did before, cuz you can't lose those foundations. But at the same time, we have tools that are enabling us to deliver that knowledge more efficiently, more effectively.

You make a great point about both sides, right? There's more learning that has to take place. You know, I've been talking a lot about this, just in— take FP&A as an example. You know, I talk a lot about that versus modeling. I'm like, you know, it's already a role that has a high degree of technical and a high degree of soft skills. Well, it's like many finance roles— financial modeling, investment banking, all those things are often some of the higher ones— and now you're asking everybody to learn a whole new tool. Which has its own technical learning curve in its own way. And it's really interesting to watch of, okay, how do you do that? How do you bring people up to speed and how much should you expect them to learn versus how much you go out and hire the consultant or the expert to set in that Agentic AI and a lot of things in place and they just need to learn how to prompt or use it in the workflow versus some places they're being expected to build it as well. And so it's fascinating to watch. I think you make a great point there of, hey, we have to learn these tools. I think everybody's trying to figure out what does that mean? And how deeply do people really need to learn 'em? Because it's gonna vary on depending on your role, the company, a lot of things. I don't think there's a clear answer of how far you need to go with, with the tools.

No, there's no clear answer. I, I will say the, the closest analogy, and it's gonna be a poor one because there's nothing like AI, but the closest analogy is like, you know, when I was starting out, um, the business, Google AdWords, uh, it was an, you know, an ad platform where you know, that was, that was a really important way that, you know, we, we raised awareness for our business. And I learned that, you know, that, that platform is fairly easy to learn, is fairly rudimentary. Now that platform is really complicated and you need like specialists to manage Google Ads and, and, and run all that. Um, but the foundations back then, um, that I built up actually created, um, a lot of leverage. Same with learning how, you know, I had to learn how to build my own website for the company. And so like those basic foundations, These tools will evolve, but like, there's nothing but good that comes from learning these tools in their rudimentary forms. You will build a foundation for how they're evolving, right? In, in the same way that today, if you use Opus 4.7, it's still not perfect, and you need to know how it's not perfect, in what ways it's not perfect, where the mistakes are happening, cuz they're not happening in the same way that like analysts make mistakes. They're, they're, they're happening in different ways. All of those, all, all of those things, even though we're, we're probably on the same page that, you know, these, these tools will, will get better, right?

That's not a, that's not a, yeah, I think anyone who says they won't, I don't know where, what they're thinking. They haven't been watching the last couple years.

Yeah. That's not like a, a hot take, you know, these, these things are gonna get better.

That's not a dangerous statement to make.

No. So that, that said, if you are, if you're, the more you're keeping up with them, the more you're aware of, of what's happening now, that, that provides its own foundation. For understanding where these things are going to go. Um, and you'll be, you'll be capable if you're— if you haven't used them, you're like, well, I'm just going to wait till they're like amazing and do full end-to-end work, you're going to miss the boat. Um, you got to be on top of what's happening now.

I agree. I think you make a great point. What the thought that came to mind is we often talk about, you know, first principles. There's a foundational level everybody has to learn. How deep you go is going to vary, but there is, like I said, there's a foundational level that needs to be in teaching and that we all need All right. So I've enjoyed this conversation. I'm going to move into some of our standard questions. We're going to have a little bit of fun here. We're going to do rapid fire in a minute, but before we do that, what's your favorite shortcut? Is it Ctrl+S?

I will tell you, I'm boring, you know, when it comes to modeling. I, I like, I like Ctrl+1 and just navigate around getting into my formats. I like, um, F5 for go to special.

Sure.

Um, these, you know, nothing, nothing too earth shattering, but I find I find those— I, I use those a lot more than others.

All right, so we'll go Ctrl+1, F5, and we'll throw in Ctrl+S for good times. Let's start good times.

Exactly, exactly.

All right, I'd love to know what's the most unique kind of fun thing you've created a model for or you've built in your personal life with a spreadsheet.

Okay, so this started out fun and ended not fun, um, uh, as most Excel endeavors for me go. So I was, I was into, uh, Wordle and I, I played it with my daughter and I was like, you know, and she was learning spreadsheets in her school on Google Sheets, not, not real spreadsheet, but Google Sheets.

And I was like, that's, that's gonna show up in his short. That's gonna be a line. We're gonna, yeah.

And I said, hey, let's create Wordle. I'm gonna create, I'm gonna show you that you can actually create Wordle in Excel. It's gonna be great. And, um, really, really hard. I ended up— I remember we were on a flight and I started it, um, you know, it was a 6-hour flight. I started, and like all Excel projects, first 80%, it's real, you know, feeling really good, everything's great. Then that last mile, oh my God. So 6 hours in, it's kind of working, I'm hacking some of the corner cases, and then I get— so we're like on vacation, I think we're in Florida or something, and now instead of Instead of like hanging out, I'm like, I'm like running out to finish this damn Wordle spreadsheet. And, um, and I think by the flight back I had gotten it just about wrapped up. It was still wasn't perfect. I never actually got it perfect. So that, that's the honest take.

I probably— how long ago was it? Could you use AI now to finish it?

Actually, it's a good question. I still have it like my, my sh— my, in my shame, it's sitting in some folder.

99% open up Opus 4.7, see if you can finish it. We'll publish it with the episode if you send it to me.

Actually, that's a, that's a great idea. I'm going to try that.

See if you do that. I'll put it on the website with the episode.

So fair enough.

We'll see what the final version looks like with AI. All right.

All right.

Yeah. I've been there where you're like, I'm just going to get the video finally.

Okay.

Close enough. You get to that point where you're like, I'm done.

Yep, exactly.

So rapid fire. Here's how it works. I have a list of about almost 15 questions.

Okay.

Your job is to give a quick answer. Take one side or the other. Yes or no. Or, you know, if there's multiple options, you pick one. No elaboration. Then at the end, you can take one or two that you, you know, you want to elaborate on because I recognize there's nuance to all these. So none of the classic it depends consultant answer. Okay, ready?

I'm a big 'it depends' guy, so this is going to be— this is going to be hard for me. But go ahead.

You know, Inshnor, you know, obviously he sponsors the show. First time I had him on, he couldn't do any of these. He had to add context to every single one. It's just not in his nature, so I understand.

Let's do it.

So let's see if you can do this. Circular references, yes or no?

Yes.

VBA?

No.

Lambdas in financial models?

No.

External workbook links?

No.

You said that easy. Yeah. Modelers, should they be keyboard warriors?

Yes.

All right. Should all models be print ready?

No.

Are merge cells ever acceptable?

No.

Should financial modelers learn Python in Excel?

No.

What about Power Query?

No.

Power BI?

No.

All right. Should every financial modeler be able to build a fully integrated three-statement model?

Yes.

Okay. Will Excel ever die?

On a long enough timeframe, we all die.

That's my answer.

But let's put it this way, not in our lifetime.

Okay. Fair enough. I had someone say, yes, just please don't let it be in my lifetime. And I laughed.

So, yeah.

All right. I'm pretty sure I know the answer to this one, but I'll ask it. Have you used AI to help you build a model?

Yes. Okay.

What financial statement is most important for modelers? Income statement, balance sheet, or cash flow statement?

It's the income statement and the cash flow statement. If I had to pick one, it's the income statement.

What's your favorite LLM?

Claude.

Okay. If you could pick one and only one for all of your models, Would it be sensitivity analysis or scenario analysis?

Sensitivity analysis. Okay.

Do you believe financial models are the number one corporate decision-making tool?

That's a yes.

Okay. We'll go with the yes. Then I don't have to ask the follow-ups. What's your lookup function of choice?

XLOOKUP.

All right. I could tell you struggled with a few of those. You were thinking for a while. So pick one or two you want to elaborate on.

And so a lot of these. The no. So for example, for, um, VBA and like Power Query, Power BI, like Python, these are all massive it depends projects. So like if you're in investment banking, the answer is no, right? Like almost certainly no in investment banking. If you were on the, like within a finance function, in an FP&A function, and you, and you are tasked with like a ton of like data analysis and you have to do that, it's definitely a yes. So that's why it depends. The problem is in investment banking, you have people that go crazy with some of this stuff and it actually gets in the way of what their job is. So a lot of these are, it depends. I would look at those from the investment banking context and it really is different in other contexts. I think, I mean, I'm trying to think like, what are the other ones that I was struggling with? I think corporate decision-making. So it's really, really difficult to look at a model and say, this is the number one corporate decision-making tool on strategy. So like, it's a great facilitator of strategy. It's hard. I'm now clouded by, I think I would have said yes on with no reservation up until 5 years ago. But I think as the years have gone by, there are things that a model cannot surface that, and there are more of those than I would have probably appreciated 5 years ago.

I say yes, but I'm a little more nuanced on that one than I probably, I used to be as well. My favorite answer I've got on that one all time was someone said no, politics. I'm like, how do you argue with that?

Exactly. So there are things, you know, politics is just one, but like, again, there's things that a model doesn't surface. So like return on investment, return on capital, it does surface. And that's an important strategic sort of decision-making tool. What it doesn't surface is obviously there's politics, but there's also external things like competitive dynamics, a whole bunch of things that obviously influence the model because they influence the return on investment. Then it's toward some of those decisions. But those are really, in many ways, exogenous to the, the model and have their own considerations and are actually really, really important corporate decision-making tools. So that's why, I mean, I did actually, I think I switched. I said yes at the end.

You did end up saying yes. You went back and forth. You left it at yes, but it's—

I'm just giving the counterpoint. I think it's ultimately so valuable that I brought it back to a yes, but there are lots of other considerations.

And I like how you clarified, right? Obviously, depending on your work, like certain type of work, you have to learn Power Query. You know, I say yes to that. I came from FP&A. He said no, you came from investment banking. A lot of that is colored by our experiences. So, and then there's some that you get very strong opinions like circular references and VBA, and again, colored by your experience. But it's always interesting to see because there's certain ones that you're like, I could go either way, I don't know that I care.

And there's others where it's like, that matters to Well, circular references is a great example of like, it's kind of a 50/50 call in banking. It's probably a no if you're just to look at the banking lens, but there's no project finance model, for example, that exists without circular references. I mean, it's like, it's an, it's a core feature of those models. So can't say no to those. And it, so it really is, goes back to my favorite line, depends. Unfortunately, that line doesn't work for your round.

Yeah, it doesn't make for great, great podcast if I let you say it depends 15 times in in a row.

No, no, sir, no.

All right, well, I know we're up against our time here, so last question. If our audience wants to learn more about you, potentially get in touch, you know, learn about the resources Wall Street Prep offers, what's the best way for them to do that?

Easiest way is to go on our website, wallstreetprep.com. You can also check out my LinkedIn page. That's it. Those are probably the two easiest ways.

Perfect. Well, thank you so much, Rakan, for joining me. A pleasure chatting with you. I love what you've built at Wall Street Prep. I know you got a lot of great programs out there and I've been fortunate enough to be instructor with the FP&A one. So keep up the great work.

Thanks, Paul. It was nice talking to you. Thank you.

Bye. Financial Modelers Corner was brought to you by the Financial Modeling Institute. This year I completed the Advanced Financial Modeler certification and it made me a better financial modeler. What are you waiting for? Visit FMI at www.fmiinstitute.com. Fm institute.com/podcast and use code podcast to save 15% when you enroll in one of the accreditations today.