The Exchanges

Q Robin Hood, and others were, you know, um, that ECHOI structure as well, but I, I, I find it striking that in Europe it seems like a more and more common pattern. So it's just, it's kind of an interesting aside. Could you tell us a little bit about how Brex is starting to think about AI and some of, some of the innovations and approaches that you're taking there?

A There's three big areas for us. One is, um, the obvious, which is product. So how we can improve the product and, and, and make the experience of essentially Expense management better, right? And, and within product, there's two areas that we spend a lot of time. Um, accounting is one, and the other one is essentially expense assistant and expense management, basically what EAs would typically do. Um, second big bucket besides product is, uh, go to market and operations. So things that are very ops intensive internally, prospecting, you know, KYC, underwriting, compliance, uh, lots of use cases there. Uh, and the third broad bucket is, like, developer productivity. So how we can help engineers be more successful. Uh, and there, I don't think we've done anything particularly remarkable in this third one because, uh, you know, we're using the same tools that folks have used, co-pilot, and things like that. Uh, we're experimenting with new tools, but I would say buckets one and two is where we spend the majority of time so far.

AI assessment note: “There's three big areas for us. One is, um, the obvious, which is product.”

Q Maybe even one step back from that, like, at what point did you say, um, internally at Brex, like, I'm gonna get up to speed personally on this, or we're going to make this part of the product?

A Probably 18 months ago, I think right after ChatGPT launched, we, we started to just play with, um, you know, ChatGPT online and, and I had played with the GPT three, uh, through the APIs before ChatGPT was out. And, and obviously it was, it was impressive, but ChatGPT was that moment that everyone started to think, okay, what does that mean for my business now? The mental model that I don't think is particularly unique that we, we have is, you know, if we were to think about, you know, humans that are free, what would we do? Uh, it turns out there's a lot of work in expense management and accounting that people would automate, right? And the one that was really obvious to us early on is, you know, if we think about what is the best customer experience when it comes to expense management and corporate parts is essentially what executives have, which is there is no experience. You just swipe and it's done, right? There's an EA in the background that will figure out how to get a receipt for that, how to categorize his expense, how to get it approved, right? I prototyped something on the weekend with, you know, in, in Python with GPT-PT-Port five on, can we just get someone's calendar and use that context to generate a memo, categorize an expense, and potentially find a receipt, uh, on someone's email. Uh, and the results were like surprisingly good. Uh, and it took me, you know, …

AI assessment note: “Probably 18 months ago, I think right after ChatGPT launched”

Q That's really impressive velocity, especially in an area where, um, you know, I, like, from other fintechs to sort of traditional financial services companies, a lot of them feel like they cannot use AI because it is probabilistic and there's, like, risk and reliability issues. Like, how do you, how do you handle this or get something into production relatively quickly?

A So for us, um, that is sort of the holy grail of AI and fintech, I would say, is, like, how do you build this Degree of conviction that what you're, what you're suggesting is correct. Right. And, and, and, and maybe, maybe it's interesting to talk about accounting, which is one that people are fired if the results are wrong. But basically the way we thought of it is, is, um, is, is twofold. One is, um, how do we, uh, expose ambiguity to the user? Right. So instead of saying, Hey, let me just try to predict something and put it in front of you and say, Hey, Um, you know, this is the generate, this is what we generated, you know, good luck. We thought it would be a much better customer experience if instead of having, for example, like, you know, chatbots are particularly bad at this, where, you know, a chatbot gives you an answer. There's no affordance. Uh, you can't understand other potential options that are generated. You can't understand context. So a lot of it is just like building ambiguity into the UIs and into the flows. And for example, like our expense assistant, when we're generating a memo, Um, if we have really high conviction in a suggestion, we can go and say, hey, this is what we strongly believe is the answer. Uh, and we automatically apply it for you. If we're not that confident, we show suggestions in the bottom of like a field. And if we're not confident at a…

AI assessment note: “A lot of it is just like building ambiguity into the UIs and into the flows.”

Q If you were going to look at sort of outside of the product to, as you said, go to market and the operations intensive parts of the business, do you have like a rank ordering of like where you think it's going to have the most impact?

A In general, scaling marketing is, is the biggest one. And the way, the way I frame it internally to, to folks is like, you know, if you look at, again, the same framework applies everywhere, right? Which is just like, what would we do with infant humans? And back in the day, if I think about marketing 10 years or 15 years ago, Um, you, and you, and you were at like Salesforce and you were trying to close, you know, Coca-Cola or a really large enterprise customer. You literally had an account based marketing team where you literally have a PMM, a product marketing manager working alongside with a sales rep to market to that account, right? They were literally creating a pitch deck. They're creating materials. They're creating, they're going to Coca-Cola and meeting the executives of like very specific pitch of like the value that Salesforce can provide and so and so on. And, and really the way I think about it is like, there is a world now where you can generate, uh, effectively account based marketing for any accounts because the cost of doing that is, is marginal, right? So, um, the way we think about this is like, how can we prospect across accounts that never got the level of personalization, uh, and that level of care, uh, with a lot of these tools and, and, and really in a really specific way, Provide value to these accounts in ways that we couldn't provide before. So, you…

AI assessment note: “In general, scaling marketing is, is the biggest one.”

Q very commodity, um, and not very different from, like, one overworked SDR sending a spam campaign themselves, right? But what you're talking about requires, like, much more insight into, like, What is a demand signal, a value signal, like, specific data that Brexit itself has to collect because it understands that signal and wants to meet it now? And so I, I think, like, that should be much more powerful.

A A hundred percent. There's this book, like, Crossing the Chasm, that is, like, a kind of classic go-to-market starter book, and it talks about this idea of, like, what is the definition of a market, right? And, and a very important concept is the idea of customers that can reference each other. So, like, it's not, you're not really in the same market if this customer can't Go talk to this other person about your product and hear something, right? You know, like, do they know someone that uses it? And, and, and effectively, like, I think now there is this ability of creating, like, almost an infinite number of markets where effectively, like, you know, I want to create a market of, like, construction companies in Missouri that, you know, uh, are high spenders on card or, you know, they have complicated accounting needs that may leverage breaks. And, you know, you, you can essentially, like, Outscale your ability of doing this with humans in a way that you probably wouldn't be able to do before. Uh, and, and to your point on the, a lot of the tools doing that, um, the writing the email is the easiest part. That's actually not that hard. The part that is the hardest is aggregating all the data and essentially building this, um, this database of your whole team. Like who is every account in the market that could buy Brex potentially. And what do we know about them? And how do we en…

AI assessment note: “The part that is the hardest is aggregating all the data and essentially building”

Q enterprises. Um, that's not, like, an easy decision, right? So I'd love to hear more about how you made and implemented that decision, and then, you know, obviously the big enterprise piece is going well for you, but what, what learnings would you offer other companies and founders making decisions about not serving certain customer segments, um, and any, anything you would have done differently from the beginning or not?

A You know, it's that old saying, you know, you can be anything. You just can't be everything. And, and, and I think we, we had this, this moment at Brex where we were just growing across all segments. And we said, you know, we can do it all. We can serve small businesses. We can serve in market companies, enterprise startups, et cetera. And, and I think the reality is like, you know, I think focus is what gives meaning to your choices. And, and I think the fact that we were spreading ourselves across all these things meant that we were not deliberate about what we wanted to be world class at. And, and, and I think we, we did an okay job across all these segments, but that wasn't the goal. And, and I think we had this moment of saying, where do we want to be world class? And, and ultimately the way we made the decision is by saying, what is the customer focused thing to do? And, and for us, ironically, You know, it was going enterprise because our customers are going there. So, you know, for example, Scale AI, which I'm sure you all know, uh, you know, I met Alex when, you know, Alex was five employees in his office, and I went to physically deliver cars to his office, and then, you know, now we're five, six years later, and Alex is a massive company with, you know, a CFO and a global presence and with compliance needs and all of that. So, so for us, we had to scale with our cust…

AI assessment note: “ultimately the way we made the decision is by saying, what is the customer focused thing”