Felix Research's Origins & AI for Finance

Interviewing CEO & Founder, Ben Jaletzke  

Ben and I head over to a Battersea pub for an informal interview about the origins of Felix Research, the mission driving it and the industrial pain points that inform Ben’s approach to product building. We are joined by James (CCO & co-founder) to discuss AI for enterprise use, as well as the past and future of the workforce.  

Sav: Of course I already know this, but tell me how the idea came to you. Give us the origin story. Discovery. Pain points. Perceived opportunity. 

Ben: Well, you can read all about it in my new book, Felix One! 

Sav: "Rags to Riches" for the low, low price of five ninety-nine, ninety-nine! 

Ben: But in seriousness, I mean, I don't think there was that... eureka moment. 

We get comfortable in their chairs, having opted for stools outside but under the glow of space heaters – crucial for a brisk November evening in London. 

Intro & Background

I'd thought about various ideas before, but I don't think there was ever a moment where I was like, “That's The Thing”. It was more that while I was working elsewhere, I was playing around with a bunch of ideas in little elements that either annoyed me or that I thought I had a better approach to. So I starting programming. 

I wait expectantly.

Ben: Well I was just sort of playing around with thoughts and one of them was how do you make it easier for people in the team to share files and links and data with each other? Rather than doing it in an email or a teams chat or a WhatsApp chat, having one sort of centralised space where every time you share a thing, you do it there. That could have just been a plugin or whatever, but it was one my initial frustrations.  

Sav: Okay we'll certainly get back to that but before we do, at the time what were you working on?  

Ben: Like, job wise or? 

Sav: Yeah, like give us a bit of personal background but also some context in terms of where you were encountering these pain points. 

Ben: Yeah, I mean having spent some years in industry [institutional finance], as much as the efficient markets people want to say that there's some theoretically perfect information and yada yada, no one has even a modicum of genuine information. Like, if you wanted to break down any investment decision or possible investment decision into like its “prime factors”, - 

Sav: - Nice. 

Ben: - then there's too many of them to consider. So we just never have the actual data for it. That's broadly why Felix Research exists.

For example, if you really want to get into what inflation’s going to be like next year, there's too much data to do that. It's a very simple thing at a high level - you can look and see it’s at 2% right now and think that there's going to be a supply demand. So the assumption is that it's going to be slightly higher or lower and then you shift that 2% by some decimal place. But if you tried to do the real math, you would need an infinite amount of data to practically do that, because every single payment and every single action and every single choice would flow into that. 

So when I was doing the fundamentals, it became clear that we have a chance here to actually set up a very clean research infrastructure, from scratch, for a firm without - I don't know what you want to call it - like corporate debt, almost. 

Sav: And by that you mean figurative debt? Like an unpleasant residual? 

Ben: Yeah, I mean the overhang of bad behaviours and practices. Like, if you're a company subscribed to certain databases, then those are the databases you use to inform decision-making. If you have Bloomberg, you have a lot of data and you're sort of lucky in that sense, but most people either don't have it or can't use all of it.  And that led me to the practical question of how can we actually do this? Because, again, practically speaking, I can't download 500 terabytes of information and then search it. I don't even know how to search it. 

You have your one investment bank that sends you investment reports, so you use those, plus of course, other information. But that's sort of the only primary source you have in that sense. So you miss out on 99.99999999... essentially 100% of the information in the market if you're only considering that single source. So the question that I wanted to answer, from an investment perspective - which made me want to do the startup – was: How can we get closer to not missing out on the 100%? The only way to do that is programmatically. To have something that can search millions of data points or millions of ideas at the speed of core cycles. 

Sav: Can I push you to elaborate on “the missed 100%”? Get into that a bit more. 

Ben: So if you have your one advisor database and we assume you don't have any other database, from a numbers perspective, you have let's say 10 thousand documents. But there's 50 million documents out there. So you have a dismally small fraction of the total information. If you break it down by industry or sector or company type, the numbers change, but the ratios probably only get worse. Because if I'm only searching, let's say, the Goldman Sachs database, they have a lot of information, but they don't have any macro information outside of what's in their report. So if I didn't have a macro database, then in terms of making any kind of macroeconomic assumptions, I would have zero data, effectively, except whatever I read in a given report.  

So now if I add a macro database to my set, I have much more information. But now I have another problem: how do I combine that information sensibly? I don't want to be reading an article and then having to go to my macro browser, then to looking up the time series, understanding the data, and then going back to my project workspace - that takes too long. It's unwieldy.  

If you look at the European Central Bank, for example, they have so many small data points. They're all reliable, but you need a consolidated version of that.  So all that’s to say, you need to expand how much data you could have as your input, you need that amount of data to be manageable to work with and you achieve this by having a system that can look through whatever you want it to look through. 

Sav: So if we drew a straight line, narratively, from the link sharing exploration stuff to Felix One, what would it look like? 

Ben: The link sharing process, in relation to Felix One, was me trying to take a first step. There's this sort of philosophical ideal of what research data should look like, which is to be able to search all data from any source in any fidelity, perfectly and instantly. That's essentially sort of like quantum computing for data, if you will. Like, ideally you search infinite mutations and get the result you want, yes, but we can't do that. That doesn't work. It's not feasible. What we can do at least - with the link sharing thing, for example, is if we have a file, I can make sure that you also have that file or you can see that file. So we're not yet cutting out the noise, but we're at least making a tiny fraction of working with some amount of data more manageable. So Felix One is a first step into extending a researcher's dataset and also making it easier to work with the data. 

Workflows & Enterprise Finance

Sav: Talk to me a bit about workflows. We spoke earlier this year on the blog about technology alone being an insufficient strategy; how do developers close the gap between the software and the human end user - financial researcher or otherwise? 

Ben: Within the finance industry there is nothing except for your take on something; essentially it’s your IP that separates you from another firm. There is functionally no difference between two boutique investment banks in that there’s nothing that one bank generally has that another bank can't also get - that goes for industry knowledge too. You can always hire the person with whatever expertise you’re looking for. In that way it’s kind of a people-driven business. Therefore, if you leave a big firm where you became very good at a Thing, you're going to leave it to do that Thing for someone else or for yourself. But the inverse means that even if you're smaller, you can be highly effective.  

If you were making Citadel, like, 2B a year, then you can usually do that exact same thing one-for-one in a different context. Oftentimes, you even take your old team with you after a while. So that's not only Citadel losing your revenue, but they're losing potential investors who might go with that team, which is why that kind of thing is strictly regulated.  

Sav: Sounds fairly zero-sum-y. 

Ben: Yeah the vibe is very zero-sum but the situation isn’t entirely. It's largely the attitude people have that creates the zero-sum game. And you know, that's partly a function of it being an industry where the thing you're producing is also the thing that you get paid.  

Sav: Sure. 

Ben: It's not like car manufacturing, where you make a lot of cars and then some percentage of that after cost and after supply chain might be a bonus of like your performance or whatever. It's like if you make more money, you get more money. Therefore, people are very much all in that zero-sum mindset. Like "I would like all of the money if I can". And that's reasonable.  

But, going back to the idea for Felix One and the idea of the Super-Empowered Professional,  I think part of the problem is that some of the ideas that I'd like to pursue are ones that are actually quite complex in that they require both immense man-power and considerable compute. Some issues and ideas are conceptually very easy to understand, but the Doing It is a different matter. Sometimes a million connections have to be made for something to work the way it's supposed to, as seamlessly as it's supposed to. But then that’s sort of what I hope the future work of Felix Research to be, more broadly once we’re able to do so. 

So, the link sharing thing would have involved the huge headache of connecting every single thing that someone could possibly use to chat and figuring out how to do that securely and quickly.  

Sav: Sounds as unwieldy as it would be helpful! 

Ben: Yeah, I mean, I think helpfulness has to be the Ultimate Good for our purposes - it's no good if you design a thing that makes people's lives easier but you’ve actually ended up adding steps to the workflow. 

Sav: Definitely. 

Ben: With some footnotes to that, of course. Ultimately, the trade-off has to make sense.  

Sav: Yes. How many steps are you adding versus how much better is it? 

Ben: If you're adding five workflow steps, but each step actually adds value, then maybe it's okay to add the five steps. 

Sav: And I guess we're in interesting times for AI-powered workplace software in that people are rushing to the table with “Productivity Tools” that neither eradicate the administrative bloat meaningfully, nor create massively valuable output. Hello! James has just joined us. 

 James Enters. 

 James: Hello! Please continue. 

James pulls up a stool and casts a glance at the recording setup - a very artfully/ precariously balanced phone atop a coffee cup, if memory serves.

Sav: I was going to say, on this issue of creating diminishing returns, where you're catering to a pain point, but actually making the process more complicated. You're seeing this emerging issue wherein companies are implementing these AI pilots that are glorified wrappers and putting their staff through training. That's nonsense.  

James: Yep to then use a tool that isn't very agile, doesn't reflect the reality of their workflow and ends up gathering dust a year later. 

Sav: So it seems that you're uniquely positioned because the solution you're creating was a pain point to an end user within industry - yourself. Rather than a reverse engineered attempt to see whatever businesses will pay for. 

Ben: I mean the overarching mission has been to build a simple website that makes mundane parts of the workflow easier. There have been so many fleeting instances of like “If I could just improve this one tiny thing, I would find Product much nicer to use”. 

So there are lots of these opportunities for little tweaks, where something is really good but there’s a gap to be bridged as far as real life usability. Like, I just need to adjust these things for my personal workflow - now, my personal workflow is by no means the best workflow, but I found that by making these tweaks, I inadvertently started learning more about these tools and what it takes to develop one. 

There's also a kind of connectivity to the learning. An example is another orbiting idea that I placed on the back-burner because it was never intended to be a standalone product. It was a multi-format text editor, where instead of having four different editors for four different text formats, we just have one page where I can, at the click of a button, switch between HTML, markdown, rich text and other formats and get an output ready to paste into website code.  

Sav: And why wasn't it ever intended to be or developed into a standalone product? 

Ben: I wanted it to exist differently. The principles of centralisation and interoperability are attractive, evidently, but people don't pay for text editors. It's the kind of thing that you would bundle it in some subscription where you get it as an add-on for some extra pounds per month or something. 

James: Fair. Realistic. I suppose a benefit of having familiarity with the landscape you’re trying to sell within. 

Ben: Yeah, but at the same time, it was another step in the direction of like, okay, what can I use of the available tools? And then tweak them slightly or put them together a bit like Lego and put them together in a slightly different way to make them nicer for myself. And that’s how we get to the early stages of Felix One. After those practical functional questions came the kind of... self-belief part. 

On Personal Process

I was catching up with some friends from my old job and one of them said to me, “Oh, I never thought you'd stay in finance long. I always thought you'd do a startup at some point”. And he kind of gave me the impetus to consider, “Maybe I should give it a try”. And the more I kept talking to people, the more I got these enthusiastic responses – I found it really encouraging. Honestly, Rebecca had a huge role in getting me out of my head and out there doing validation. You know, I don't like to talk ideas because I see all the reasons why they're not good enough ideas, or why not yet. But she was like, “Just talk to people. You have to talk to people about it to get feedback on what could make it better.” 

Sav: Amazing.  

Ben: And so that's how I started.  

Sav: And so to be clear, you were in the mode of “I would benefit from this” purely.  

Ben: I was in the mode of "I would benefit from it and there's probably a way to turn that into larger benefit for others". The original idea for Felix One was called Parsley and it was just the idea of parsing data. I called it DocParse, which, you know - very Me Naming A Product. And Rebecca looked at it and was like, “Oh, you can call it Parsley, like the leaf”. 

 Sav: Love that. 

James: That's so good.

Ben: Right? I was like, "Fuck, that's a really good name". So the first time I went to one of the Gathr events as a founder was for Parsley. And then it kind of, you know, quickly snowballed from there. Obviously you and I started talking and I started to work on it more with the explicit aim of making it a product how can we make this a product?  

I never liked the idea of doing something that's just a little bit useful, because that doesn't feel as satisfying if it's a plugin. You also probably wont like using it as much. Its unmemorable. I didn't like that. Then I would have rather taken a standard job; I also didn’t want to start a company for the sake of starting a company. And then it just sort of took shape.

As I started working on it in April ish, I was more like, okay, so actually the link sharing thing and the centralised editing things are kind of connected in that if we can make it easier for you to share files and we could make the files more readily available to edit, what we're essentially doing is reducing the workflow to its core component of Words In A File. You don't care about the formatting anymore. You care about the information contained in it and the relevant context. The parsing function is obviously just an extracting exercise. But that eventually morphed into the idea of well how can we build a full interface in which you can, you know, read PDFs easily?  

No one likes whatever tool they use for PDFs. Yeah, like I will give Apple's preview a sort of shout out here, not that I'm in a position to shout out to Apple, but there's a lot of annoyance in the modern workflow itself and there's a dearth of good products that are made specifically for the financial industry. And that’s always slightly baffled me because it's the largest and most important - well, important in that it's one of the most significant - industries in the world. 

But its an issue of How. How is it that video editors and photographers and doctors and academics and industrial designers, everyone has their own domain-specific software and for finance its like, “Ah, you just use Excel.” Fuck off?

Sav: Is that, I mean, within the sector, is that almost a point of pride? You know, people who actually enjoy using a kind of no nonsense, no fuss, no frills, bells or whistles kind of thing? 

Ben: So this is the other thing. It’s a mixture. It is a big point of pride for people in finance to become good at using Excel and Bloomberg and those sorts of tools that all require quite a learning curve. 

Sav: Understandably. 

Ben: And I get it, because once you become good at it, it is very powerful. But more and more, no doubt also because of AI and everything, you see people actually becoming far worse at it nowadays. I was talking to a friend the other night. He was struggling to find anyone who could build a really good financial model. 

James: Why? 

Ben: Just because you're learning it less and the people who learned it as stringently are becoming more senior, so they're not the ones training the people anymore.  

With Excel, there's always been this issue which is that you have to justify whatever you're doing as a junior to your senior, who's the actual stakeholder. Excel and PowerPoint are easy in that they’re tangible for the older generation to use - even if the senior doesn't model himself anymore, he can still go into an Excel spreadsheet and know how to navigate it. If you click F2 on a cell, you see the calculation - it's there in that cell, in front of your eyes. And the entire thing, start to finish, is auditable, which you don't get in the same way if you're in a web application oftentimes. You can learn all the rules and you can work with it, but it's not auditable in the same way. And the problem is, the only other option so far for finance has essentially been Python programming or C++, sort of like what you see in quantitative terms for building algorithmic models. And they are way faster and way better in a lot of ways, but the problem is, you can't manipulate them as easily. You can't take Python code and just be like, “What if we replace this entire table with the Q3 table?” It's static code that requires a greater level of learning that language properly, learning how to use it, learning the tools required to display images and dashboards, and so on. 

AI & The Modern Workflow

Now we're at a stage where, because of AI, you can still give someone a spreadsheet, but you can have a thing that works in the spreadsheet alongside them. And I do think as the industry changes and as everything adapts to AI, there'll be a big adjustment in finance. There's already a growing movement in some private firms to stop hiring juniors. A senior who knows what they're doing financially and from an investment perspective has much less need for an analyst to build them a full model because he can still build enough of it to not need the analyst to do all the number crunching.  

So what we need to do is to become part of that more modern workflow and make it such that, I guess in a slightly romantic way, the people who work in finance are people who are really good at finance. But you don't also have to be really good at Excel modelling specifically to be good at finance.  

Just like if you went back 60 years and saw people doing industrial and architectural design, you would have offices with like 50 or 100 people doing Blueprint drawings and calculations on paper, essentially. And now you have your CAD software that is orders of magnitude more effective. 

James: A guy with a CNC and CAD software can make what would have taken a team of 100 people to design. 

Ben: Yeah. I still feel like Excel and probably things like Bloomberg are the only real innovations that have happened in terms of how you do finance. Everything else has been a philosophical adjustment of private and public markets or emerging markets. But the sort of, the workflow itself has never really caught up. And it's probably because until the introduction of AI, there hasn't been a need really to change anything it out the workflow. 

Sav: So regarding the advent of AI, what do you think? I mean, obviously the disruption is already happening, but what do you see as the effect of AI on finance?  

Ben: Well, so there are different ways to approach that. One is obviously from an investing perspective, which isn't really the point of this right now, but it's shifted what you have to focus on. Because you now have to, for example, look at how much more resilient a business is to either integrating with or existing in spite of AI, which from an investing perspective changes the game a bit.  

Why does Docusign, for example, have 10,000 employees? There's a good chance that as AI models get better and better, a lot of those roles become redundant. And the same thing in things like marketing. So all of a sudden when you're doing company analysis, you have to take a different approach. But what it's also meant is that other industries are seeing the effects of the benefits of simple AI, like summarising a dot com, or email apps that reply and manage your calendar. We're getting closer to the point of being able to use AI for fundamental analysis. And it's, you know, novel in the sense that for a long time there have been people building ML models (pre-LLM) that try to analyse the tone of, for example, the Fed Board of Governors meeting - “were they sounding positive or negative?” For years there’ve been these investigations, not that they've ever been particularly accurate, but they're better than having to read a thousand tweets. 

Sav: Indeed. I guess for some of us that's our hobby. 

Ben: Quite. But if you do it 10 times a day for 10 companies, it becomes something else entirely. 

But I think there's good and bad ways to use AI and it's important to be wary of it. But equally, I think if you are too wary of it and you have competitors starting to use it properly, then you're going to lose out. Like, if you're still using antiquated workflows that are also much more painful from a quality of life perspective for the people using them, then you're much less attractive as a place to work as well.  

But most important is speed of analysis. There's a lot of events that you have to react to quite quickly in finance and usually whatever happens, there's a lot of source data and a lot of calculation that goes into those reactions. If, for example, there's a regulatory announcement by a government party, there’s usually a great many implications to that. And if you do read over those regulatory documents, the way that they're written is deliberately difficult to understand - it makes them annoying to read. So just having a thing that simplifies without reducing the depth of context available will become a game changer for the industry. 

Sav: And you said there are good ways of using AI and bad ways of using AI and you know, you don't want to lose an advantage that your competitor has, but you want to be wielding it properly. Why don't you just tell me a bit about what that means practically, not as a industry commentator, but as a business owner. To close out. 

Ben: For the most part, I think the bad ways to use AI are to rely on the outputs as factual. I mean, there's plenty of proof that AI models lie almost as much as they don't. Well, actually that's not true.  

*All laugh* 

It's like 80% true, but that's a 20% error rate, which is unacceptable by most standards, let alone professional standards. In finance, if you look at a model and there a 20% chance of what you see being wrong, then it's entirely useless. The problem with AI is a lot of the time you don't necessarily know which 20% is wrong. It's not like the first 80% are really good and then at the end it drops off. It can be sprinkled here and there. So that is the bad way to use it.  

The good way to use it is to employ it in ways such that it has less of a chance to make a mistake. Build auditability into your workflow. Essentially, if you ask it to “summarise a document” or to “extract the table A”, it's much easier to check. You just want to have the information to hand for reference - if you're extracting the table, you can visually see both results next to each other. You can even do a little Excel true/ false thing just to highlight if all the cells match. That's a good way to use AI because it takes you more time to do that than it takes the AI. It's sort of a speed enhancement, not a replacement for thinking. And I think that's the core issue - if you use it to free up valuable time and activities, it's good. If you use it to replace your thinking, then at least for now, it's bad. 

James: The benefits of keeping a human in the loop! 

Sav: The mission is Clarity at the speed of Thought for a reason!