Ramp CEO Eric Glyman: Using AI to Build “Self-Driving Money”

Contents

Introduction

Eric Glyman: Look, I think the most important thing, beyond just empathy, if you’re trying to make great products, you need to have great taste. You need people who are making to so deeply understand, really, the experience of building, the pain that people are going through, you know, what they’re actually—you know, the customer at the end is actually doing in order to run your business, that you understand it not just decently, but in some cases better than the customer has. And I think only then can you actually build products that are so well designed that they can actually automate the task. They can do it more efficiently. And I think part of why, as we’re releasing products, engineers are on the call with customers when we ship. They’re accountable for metrics, and how it ultimately performs. And you really won’t find people at Ramp who haven’t talked to customers with any level of recency. I think that’s just a core part of what makes great product cultures.

Ravi Gupta: Ravi Gupta: Joining us today is my friend Eric Glyman, co-founder and CEO of Ramp, which offers companies AI-powered financial tools to manage their spending and expense processes. Many companies rushed to add chat bots after the ChatGPT moment, but Eric has a different take. He doesn’t think the chat bots are the right form factor for everything. I mean, who wants to chat with their expense report? He thinks zero touch automation that works invisibly in the background can be much more valuable in many cases. In fact, he thinks a better analogy is to self-driving cars, or in this case, self-driving money. We’ll talk about Ramp’s approach to building an AI and how Eric sees the space evolving. Welcome to the show.

Building an AI agent in 2025

Ravi Gupta: Eric, we are so happy that you’re here. We—you know, the original invitation for this podcast, as you know, happened over dinner, and we’re so happy that you decided to do it with us. So we have tons of questions for you about the future of AI, and how it’s going to impact finance and businesses. But for the very few people that don’t know, do you mind just saying a few sentences on what Ramp is and what you all do?

Eric Glyman: For sure. I mean, look, we’re dedicated to making companies more profitable and operate more smoothly. The way you can think about Ramp is we built a command and control system for company finances. So from one place, you can issue cards, manage approvals, make payments of all kinds and even automate closing your books. And so for your finance teams, it means your operations are simpler. You can automate a lot of business processes, and it surfaces up data and intelligence on how your company can spend less. And so the upshot is the average company using Ramp is able to save about five percent per year on their expenses, which is pretty material.

Over the past five years we’ve been in business, this has added into billions of dollars of savings and the equivalent of thousands of years of labor that’s been saved. And so whether it’s large, publicly-traded companies like Shopify, Virgin Voyages, Boys and Girls Club in America, to 25,000 other businesses, it’s bringing benefit today. And a lot of what we’re trying to do is really answer the question of how do you make people more productive with their time and with their money?

Ravi Gupta: I love it. I love it. Well, one thing that I was surprised by, before we get into how Ramp is using AI, you and Karim—I saw you guys posted this the other day—you built an AI agent in 2015 at Paribus. I mean, this is way before it was cool. What did you build back then? And maybe tell us about that. And then we want to hear about what you’ve built now, and how it’s changed over the last nine years.

Eric Glyman: Sure. Yeah, for sure. So Paribus was a much—it was a very simple tool, and I think it is fair to call it an AI agent. Basically, go back to a decade ago. If you were buying something online, whether it’s at Amazon, Best Buy, whatever, all these stores would guarantee that if you bought some TV for $1,000, the next day it went on sale for $900, you could get the difference back if you asked them. These were price adjustment policies.

And so we built—it was an app that integrated with your email software, your Gmail, Yahoo, whatever you used, scanned your inbox for receipts, tracked the prices of what you bought, and if there was a price drop, you were eligible for something different, it generated an email to sound like you, sent it to the store. The store customer service responded and you know, you’d wake up to $100 back the next day. And so that was it. It was this agent that lived in your inbox and helped you save money.

And well, I guess I would say, like, a couple of things. I mean, first, I think many people are thinking about agentic AI and how is it going to change tools and this new use case. I would argue it’s actually been around for a long time. This was a decade ago. Millions of people use this in order to get price drop refunds from stores and retailers. But it was a very narrow use case. When I think about AI today in 2024, I think the sets of use cases where computers can be doing work on your behalf is far easier. It’s not just going to be these narrow surfaces like price adjustment guarantees—it’s really intensive and hard to do that—but more generalized reasoning, helping your finance team run more efficiently. Maybe not just asking for price refunds, but maybe even helping you negotiate and running more complex analyses. But I think this primitive of software that does things on your behalf has been here for a while, and I think is going to be growing pretty quickly.

Vision for Ramp AI

Sonya Huang: That’s a really good segue into what you’re building now at Ramp. I’d love to learn more about your vision for Ramp AI.

Eric Glyman: Yeah, for sure. I mean first, like, you know, forget even just Ramp AI. Like, we’re dedicated to this mission of helping people spend less money and spend less time. I think we’re very excited about AI because it’s a new set of tools that enables us to do this, but ultimately it comes down to where is the pain point? Are there processes in your business that result in your company spending more time than you need to, or are there areas in your business where you’re spending too much money?

And so when we think about where things are going to go over time, so much of running a finance team—and Ravi, you know this from your time as CFO at Instacart—there’s a lot of tedium and monotony. You know, you’re trying to grow your business, and yet when you look deep in your finance team, you’ll find people who are auditing expense reports by hand. They’re downloading spreadsheets in order to be able to tag and classify vendors. They’re rerunning analysis time and time again.

And what we’re trying to do at Ramp, our vision is how do you take these tedious and monotonous tasks and either through better design, integrated tools, automate this? And so the simple way to understand this is instead of needing two apps to buy one thing, your American Express and your Concur, you know, where people are going in and out and adding their receipts, what if it’s one tool? You tap your Ramp card in through zero-touch, where we will pull the receipt from the merchant from your email. Your expense report is not just easier to do, but it’s done for you. So there’s a lot of cases of zero-touch expenses. Better categorization. We sort of apply large language models to the transaction data itself as well as your general ledger, so we can autocomplete expenses for you, certainly faster, and for the vast majority of customers, more accurately. So your books are effectively doing themselves.

And over time, Ramp should be able to point to ways for your business to operate vastly more efficiently. Same sets of vendors, better prices, same business processes done automatically for you. And so there’s a lot in just the manifestations of how Ramp helps your finance team run more efficiently. There’s also ways we’re trying to experiment with even how should people interact with Ramp? Should it be something where you’re prompting Ramp to do these things, or should it be taking care of these things for you and doing work on your behalf? And so we can go either way, but that’s a loose framework of how we think about it.

Sonya Huang: And how do you decide what’s in scope? Like, anything that makes a finance team more efficient? Is that what’s in scope for you? Or what’s in scope and what’s out of scope?

Eric Glyman: Yeah, definitely. I mean, I think, Sonya, that’s probably the right way to put it. You know, it really is, can we create a product that should save time and money for our end customers? If so, I think it’s in scope. You know, today Ramp is largely used for payments out of a business. So think card expenses, bill payment expenses, business processes on top of that. But when you think about, you know, over time building towards self-driving money, you know, a finance department that’s improving itself, I think thinking about higher yield, more efficient collections to even more efficient recordkeeping, I think are all things that we’re thinking about over time. So I would argue there’s very little out of scope. And I would say in the same way, you know, we think about ourselves more as a productivity company than we do as just a money company. And so anything that makes the productivity of capital in your business go higher are things that we should be thinking about.

Zero-touch AI

Ravi Gupta: Eric, what is the intuition then on your side? You mentioned you’re thinking about how folks are actually going to use AI within Ramp, right? And you said them prompting Ramp versus things happening in the background on their behalf. How do you think it’s going to play out? I get that you guys are going to test it and you’re going to listen to customers, but where do you think it’s going to go?

Eric Glyman: So I mean, maybe just like a couple of notes, stepping back. You think about the first phase, what happened sort of after ChatGPT came out, suddenly there was a chatbot slapped on everything. And people say, “Finally, it’s your app and you can talk to it.” And I’ve personally never met anyone who says, “I just wish I could chat to my bank account, I wish I could chat with my expense report.” And so I would say I totally get the skepticism of the first set of people who find this really silly.

And I think that we’ve tried to classify internally when we think about the sets of user experiences, probably into two buckets, I mean, the first we think a lot about this paradigm of really zero-touch AI. And I would argue even Paribus was an early example of this. The interaction was actually no UX. You would sign up, you’d link your email and that was it. And you’d wake up the next day and it was done for you.

And there’s a whole category of experiences like this. You tap your card, your expense report is done for you, your accounting is done on your behalf. We’re suggesting memos for you to click one, two, three. And so we’re trying to predict and understand what are the user interaction inputs we may need. Can we get to a level of confidence where we can do this all the way, and can we expand the surface area of these?

And so there’s a whole set of things that happen, from the administration to the analyses to the operational closure of tasks that you can do. And so I think there’s a large category there. We can unpack and talk about the expansion surface area.

And there’s this next surface area we think—you know, internally, we think about it as agentic AI, where you’re going to want to effectively prompt some kind of outcome, maybe monitor what’s being done along the way, or once you get a level of trust, have that done for you.

So I’ll explain the use case. A few weeks ago, we launched right after the launch of GPT-4o, the new multimodal model from OpenAI, which had vision, audio and text understanding capabilities, we created a way for customers to just ask Ramp what it wanted done, and Ramp would show you how to do it and do that on your behalf. So you could say, “Ramp, you know, I’d like to issue a card with a budget of $50 that only can be used at Starbucks. Go.” And then effectively, the large language model could read everything that’s on your screen, everything you’re seeing, and would guide you through steps in order as to how to do this—we call this the tour guide. So you click here, enter in this text, and you could monitor this. And you can see with extreme accuracy. Again, we didn’t build into the interface, “click here.” This wasn’t a managed demo. The reasoning of large models is now capable to go and do that for you.

And I think there’s a whole class of services, and I would argue the majority of business is actually done this way. The interface that most people think about this today is, I think, as you’ve joked once before, Ravi, you know, you hire an analyst and you say, “Hey, I want this thing done. Go figure this out for me.” [laughs] And, you know, that’s your interface.

Ravi Gupta: And there have been forms of AI that have been around a long time, as I mentioned to you. Yeah.

Eric Glyman: Exactly. You know, you’ve had a large language model before, as an analyst. But no, I think that’s actually a good way of thinking about these things, right? I’d actually say it’s kind of strange that the status quo today is people are trained to learn how to use your app. Instead, I think it makes more sense to me that people should be thinking about how to run their business, how to sell to customers, how to find ROI, versus how to manage the intricacies of how your app is designed. And I think done right, you should be able to say, “This is what I’m trying to accomplish. Go get it done.” And in the early era you may monitor that, but over time, as you build trust in that process and the reasoning capabilities increase, you’ll see it go.

And I might draw an analogy even to the progression in self-driving cars. You know, when you think a decade ago around the hype of, you know, self driving, you would have people sitting in the driver’s seats ready to take over, you know, with a steering wheel. And now there are Waymos all around the Bay Area. And it’s strange when there’s someone in the seat, because the capabilities are actually more accurate. And I think there’s going to be sets of tasks over time that will get handed off. And I—you know, you can have an agent, its own driver, driving parts of your organization where there’s high fidelity. So I’ll pause there.

Sonya Huang: By the way, there’s so many jokes I could make about Ravi handing off work to others, but I’ll hold myself back.

Ravi Gupta: You know, some of us have been focused on ROI for longer than others. I’m not gonna apologize for that.

Sonya Huang: [laughs] Eric, if you had to guess, like, a lot of what you just described was almost more conversational. The way you kind of get an agent to do something, like, is the way you go—the way that Ravi would go talk to a teammate. Do you think that means that kind of software interfaces as we know them in terms of buttons and knobs, et cetera, kind of go away and fade into the background, or how do you think that all plays out? And what does an AI-first user experience look like?

AI-first user experience

Eric Glyman: First, yes. I do think so, Very much so. And I think that great design actually is about understanding, you know, the job to be done so well that you can reduce steps, you can make things easier, more intuitive and effortless to get things done. And, you know, I think balancing, you know, not only really the range of powerful tools that you can do against—and outcomes that you want to drive, to the simplicity of interfaces is always going to be attention.

But I would say we think about that quite a bit internally at Ramp. And so what I would say is I think you will want to be able to audit to understand functionally what are these powerful tools doing for you. But done right, yes, I think that it’s going to be more, in many cases, just prompting “Here’s the outcome I want to drive,” and the tool will go and drive it. Which just as a brief aside, like, for us, you know, I’ll put it this way. There’s over 25,000 businesses using Ramp. Some are run by sophisticated finance teams, you know, others are—they’re running small businesses. There’s lots of complexity and baggage that prevent them from focusing on the areas that really generate a lot of value from them. Meeting new clients, writing better pitches, investing in the parts of their business that generate return, building the next great product.

And so much time, I think, is stolen by people having to learn an amalgamation of tools, stitching together processes. And done right, the world should feel more frictionless, it should feel more smooth. And so I’d say, like, I think if we can’t accomplish that, I think it’s been a failure as an industry to deliver that for people.

Ravi Gupta: Well, one thing, Eric, that I’m curious about is you think about Ramp, it has a lot of pride in its culture, right? And, you know, it’s talked about externally. It’s something that you guys are known for: speed and quality and real customer obsession. How did you build the culture and engineering, in particular you and Karim, that sort of embraces wanting to implement AI and wanting to do it well? What did you guys do there? Because I think that there are places that are a bit more resistant to change and, you know, less embracing of a change like this.

Eric Glyman: Well, so a couple of things, I mean, I would go back to again, like, what is Ramp like? We’re not some AI-driven finance tool that we’re going to market. Do you want to adopt AI in your finance team? No. We exist to save you time and money. We lead with the benefit. We talk about the outcome that we’re trying to drive, and we put these in simple terms.

But I would say it’s actually not possible to use Ramp today without using AI. When people say that it’s so easy to submit expense reports, or that suddenly my books are getting closed faster, it’s often because AI is inserted at lots of different parts throughout the process. And so what I would say is I think one of the first things that we did to try to get at that was focus on what is the problem. We are trying to help businesses operate, you know, using less time, fewer hours and less capital.

And AI was a means to an end, but was not the end. So we didn’t want to have technology in search of a problem, but really focus on what is the problem. Then once you start to decompose the question of, like, what are all the areas that are wasting lots of time, it turns out a lot of time it’s process automation. And so I think it just became, like, the right tool for the job in so many different cases. And I think rather than trying to say, “Would you like to use this tool?” Instead you say, “Would you like to, you know, do fewer expense reports?”

And I think that the other thing too is in so many businesses—I know there are a lot of founders listening to this—I think abstract makers away from the problem directly. Which is to say, you know, you can go to a small startup with 10 people, and everyone’s talking to users. And then somehow you go to a company with 500 to 1,000 people, and you try to figure out who’s talked to a customer over the last week—hopefully all the salespeople. But you start talking in marketing and engineering, and people haven’t done it. And what I would argue, look, I think the most important thing beyond just empathy, if you’re trying to make great products, you need to have great taste. You need people who are making to so deeply understand really the experience of building, the pain that people are going through, you know, what they’re actually—you know, the customer at the end is actually doing in order to run your business, that you understand it not just decently, but in some cases better than the customer has. And I think only then can you actually build products that are so well designed that they can actually automate the tasks. They can do it more efficiently.

And I think part of why, you know, as we’re releasing products, engineers are on the call with customers when we ship. They’re accountable for metrics, and how it ultimately performs. And you really won’t find people at Ramp who haven’t talked to customers with any level of recency. And I think that’s just a core part of what makes great product cultures.

A step function change in ambition

Sonya Huang: Eric, since you’ve been building with AI for so long, do you think that, you know, the last year or two with these foundation models has been a kind of discontinuous step change in terms of your ambitions and what you’re building with AI, and in what way? Or do you think it’s kind of just, you know, gradually compounding more and more AI magic in the products?

Eric Glyman: I think it’s been a step function change, for sure, at least as a practitioner in the market, not a researcher. And I think it’s becoming increasingly obvious to us that this is the biggest shift to productivity certainly in my lifetime. And I think it has all sorts of ramifications for builders and people building software. When I think about, like, what are the sources of durability in moats in a lot of software businesses, sometimes it’s there’s more features, there’s more integrations, there’s lock in. You’ve been using my tool for 10 years and it’s really hard to take all your data out.

And I think now today, functionally you can have human-level reasoning, and in some cases superhuman level reasoning available through an API. I think it has profound ramifications for people building businesses. And I think it’s expressed not just in the ability to understand large sets of data and act on it, but it’s a wider variety. I think that there’s—I can say beyond even just the services we’re providing to Ramp, part of how we’ve grown so quickly is we have AI automation and outreach, or we have SDRs that are multiple times more productive than a competitor’s. It’s changed how we do customer service, it’s changed how we do copywriting. We can listen to 100,000 sales calls at once and ask “what did 100,000 people think?” It’s just things that weren’t possible even just a few years ago. And so I think it’s changed really rapidly. And I don’t think most people are really—I think people are experimenting in some cases with ChatGPT, which is great, but I think far too few people have actually started to incorporate into the crevices of how they’re actually working day to day and have felt it. But I think it should accelerate.

Ravi Gupta: When you think about that, Eric, you know, just that you’re talking about the rate of change and, you know, how people are kind of scratching the surface on this, what do you think the job of a forward-thinking, excellent finance leader looks like five years from now—pick the timeframe—you know, versus today? You know, how do they spend their time today? How do you think they will in the case that they incorporate Ramp, they lean into AI and they sort of maximize what this can do?

Eric Glyman: Well, I mean, first, I think it’s incumbent on anyone, whether it’s a finance leader, someone building tools, an engineer or designer, like, I think people should be thinking about really automating all the parts of the job maybe you don’t like, or maybe that are low value. Because I think there’s a whole class of problems in fact, in doing work that actually can be automated now. And I would—I think this is something, like, I’ve always internalized and appreciated from our conversations, Ravi, of, like, I think that great leaders are able to not just, like, create focus and urgency, but identify where there’s outsized returns to our time.

And I think what’s really true in a lot of finance organizations is yes, maybe the CFO has the time and focus on where is their value, but you look at actually the calendars and what’s happening in a lot of the rest of the team, it’s a lot of repetition. Okay, the month is closed. We’re going to spend the first six days—it’s eight in bad cases, tagging transactions, downloading spreadsheets, matching things into it. And that closes, it’s done, and finally, just in the last five days of the month, you’re able to do the real work, the reason you got into that.

I think people should be thinking now about how do I really take those tasks, which are rote intensive, and turn that process into a more automated process where I’d be thinking about. And I think if you’re doing this right, I really do believe, you know, in five years from now, you know, these work streams will tend to be more strategic, more insightful, more around where’s there creation—you know, value created in a business—and having people really obsess over that even more entirely. And I think to get there, it’s about how do you automate the processes, how do you design a more efficient system in the interim?

Sonya Huang: Eric, what do you think happens when we have a more efficient system? Like, is everyone just out on the golf course, or are we going to find new ways to work hard?

Eric Glyman: It’s—look, for me, I happen to think a lot of purpose in life is creation. I think people build tools, I think, you know, want to move things forward. And so look, don’t get me wrong, I’m sure people will find more time for leisure but, you know, I think of it—there was a really interesting—there was something that was making the rounds a few months ago—I need to find it. I think it was a set of statistics that the number of bookkeepers—there was a crisis in the U.S. how the number of bookkeepers had dropped by, I think over the last 10 to 20 years, like, a million less bookkeepers were employed, and people saying what was happening to all the bookkeepers? And it turned out if you looked at job descriptions for financial analysts, strategists, CFOs, that had grown by almost a million. And functionally there really wasn’t much of a change, but people were doing different things. Rather than tagging, tabulating, doing low-value tasks, people, I think, had moved to a higher level of abstraction in doing more valuable work for a business. And I think—I actually think a lot of that will happen.

I think there are certain levels of work that are uniquely human, that are uniquely high value. And frankly, too, I think in many cases, much more fulfilling. And I think that for those who are forward leaning, I think there’s going to be much more of that over time. And so, you know, that’s how I think about things, for sure. I mean, I think there’s also going to be strange things too, where there’s certain creative work that computers in some cases will do better. But, you know, I do think that part of what gives people purpose—or at least for me, excitement—like, has to do with creation. And I think that’s always going to be a very human thing.

Advice for founders and builders

Ravi Gupta: I mean, that’s as good of a segue as you can imagine for my next question, which is: Eric, you are a founder through and through, and you are someone that a lot of other founders probably look up to. What advice do you have for folks, for creators, for makers, for builders, given the moment we’re in, how do you meet it? What should you go and build? How do you approach this if you’re someone who wants to be a founder or a builder?

Eric Glyman: Well, first, I mean, part of being a founder and a builder, I think, is just about, like, running this very long-standing and continuous race. I think that great companies are built over many years and decades, and I hope Ramp is the last company that I ever work on. I want to be working on it for a long time. And I think there’s always these questions of what’s changing in the world, and how is that going to reset certain industries. And I think there’s a lot of opportunities, and we can talk about, like, the places to be spending time. But I actually think when you look at Ramp and part of what’s made it work has really been starting with what are the timeless truths that are not going to change whether it’s now or 10 years from now, or a hundred years from now.

I can’t imagine, you know, that 100 years from now people would say, you know, “Like, I wish—” to paraphrase Jeff Bezos—“like, I just wish you would have raised prices on us, Amazon,” or “I wish he would deliver these goods a little bit more slowly.” I think this is very much the case for Ramp. I think people want to, no matter what they’re creating, if you can create great work with less effort, less time, fewer dollars, I think that’s always going to be in style. And so I would say I would start first with being curious about people’s problems in the timeless, who are real customers that could serve, what are real businesses, and what are actual problems that they have now, and what are these problems that are not going to go away? And then I think you start to discover and uncover new technological shifts that can help you solve this in a new and unique, or in some cases, very disruptive way. And so I would say, like, focus on the timeless would be my top advice for this.

Ravi Gupta: Yeah, a friend of mine has this great quote which is, he’s like, “We try to be timeless rather than timely,” because the timely, it just—you know, it evaporates and it’s ephemeral. And I think that the way you all are building Ramp certainly fits with that.

Eric Glyman: Yeah, thank you.

Lightning round

Sonya Huang: Okay, we’re going to close it out with some rapid-fire questions. Maybe for starters, what is your favorite AI app?

Eric Glyman: Ooh.

Sonya Huang: Can’t say Ramp.

Eric Glyman: Oh my gosh. Well, to be honest with you, I’ve been really interested just from, like, a UX perspective and just like how it’s bent our thinking, Cognition Labs with Devin. I mean, really what they’re trying to do is build an AI engineer. And I know they’re working hard at that, but they took this agentic use case and had a few core innovations. They realized if you were going to hire an AI engineer to do work on your behalf, well, you would want it to have access to the tools that an engineer would have, and you would want to be able to understand what it’s doing.

And so rather than just as a prompt and you see what it does, Devin has a notebook, a planner, just like any engineer, and thinking through what is it going to do. It has a browser to search for things or to check stack overflow when it gets confused. It has shell access, and it’s connected to your tools. And so I think what’s most fascinating about it is as it does things, you can watch what Devin is doing, what it gets right and the mistakes that it makes. And so my favorite app right now is that because it’s taught me a new way to look and think about design, and how these tools may feel over the coming years.

Ravi Gupta: All right, so over the next part of the lightning round. Over the next five, ten years, other than finance, what other industries do you think are going to change the most? And by the way, I really like the self-driving money or self-driving finance term that you had earlier. That was—I hadn’t heard that before.

Eric Glyman: I hope people make this come to life. Look, for me, I really hope healthcare—and I really believe it will be. I mean, I think it’s already changing radically how diagnosis is done. I think a lot of whether sickness or wellness is taking large sets of data. It’s not just your annual checkup, but if you can have continuous measurement over many years and decades, that goes a long way. I hope it does, too. I think, you know, for many doctors, you know, it’s a glorified note-taking job. There’s very little in the way of diagnosis, listening to patients and having time to spend. And so I actually think, you know, that’s one that can actually return some of the humanity to the care of it. So I’m very hopeful there, for sure. I think, very obviously, design and creation. I think it’s no longer about what can you make and do you understand the tools in order to create, but can you make something that’s fundamentally interesting, that’s intuitive? And so I actually think that becomes very interesting, too. So those would be kind of the—healthcare and broader design ecosystems would be probably for me ones I think about.

Ravi Gupta: On the healthcare side, I totally agree with you, Eric, for what it’s worth, because you think about how crazy it is that companies have dashboards that we look at every hour for leading indicators on what’s going to happen, and for our health we go to an annual checkup that maybe we don’t even go to annually. Can you imagine if all you looked at was your company’s metrics once a year maybe just to see, “Well, I don’t know. How’d it go? Okay. I guess everything’s okay.” I mean, the idea that we don’t have a continuous look, that is, with the idea of it being, you know, know before you know the leading indicator side is crazy. And so I agree and share your optimism, and hopefully there’s more than just glucose monitors that we can have.

Eric Glyman: I hope so. I mean, wow. Totally. Like, if you could only get a look at how your business is doing, you know, every year or every six months, or it’s going really badly and we’re trying to figure it out, like, it’s a mess. Like, you want to find this stuff early and it’s possible.

Sonya Huang: What about within financial services? You know, you’ve built companies in both the consumer and the B2B side. Who else in financial services is doing interesting things with generative AI or, like, what are the big ideas and like kind of the intersection of finance and AI that you wish you had time to explore and build?

Eric Glyman: So I think there’s a couple maybe meta points that I would get at. I mean, first, one, I think just financial service has a long way to go, right? Like, I think about our competitors, folks like American Express and Chase and Citi, and all led by wonderful people, but their founders wore top hats, right? They’ve been around for a long time, and as the world went from no phones to flip phones to iPhones, a lot of these things never really changed. And there was a class of businesses that if you were a bank, you were allowed to move money and store money, and if you weren’t, well, you’re part of the rest of the world. And I think it’s been a big part of why it’s moved so slowly.

And what I would say is I think it’s really wonderful that now it’s not just banks that can do this, because I think you can finally introduce great technology in companies like Ramp where yes, we’re a fintech, but I think we’re a productivity company. I think we’re a company that’s interested in how does time intersect with the movement of money? And so whether it’s directly in financial services, or if it’s disruptors from adjacent who are going to encroach, I think it’s about time for the whole sector to get it even optimized. And so I know they wouldn’t call themselves this and they probably would blush and say, “No, we’re not as a fintech company,” but I think Apple right now is truly a fintech innovator, because they’re connecting the movement of money to identification of who people are, you know, of face ID in order to create less fraud in the system, reducing friction dramatically whether it’s offline or in person, and I think are increasingly going to be able to connect to outcomes at times. And so there’s a variety of folks, but I actually think a lot of disruptors won’t be from traditional finance companies would be my long-winded answer to you, Sonya. Sorry for—you’re giving me too much time. You’re just letting me run on.

Sonya Huang: That’s awesome.

Ravi Gupta: All right, last question, Eric. Who do you admire most in AI? And you’re not allowed to say Sonya.

Eric Glyman: Ravi, it’s gotta be—it has to be you, Ravi, of course.

Ravi Gupta: It’s been a great podcast. We thank you for your time.

Eric Glyman: Present company excluded.

Ravi Gupta: Present company excluded, yeah.

Eric Glyman: Look, I just think what Satya Nadella has done for the creation of the category, and how he’s partnered with, I think, the incredible team at OpenAI to go from what was primarily research to in production. And you know, I think in almost every aspect of computing today, I think, has been incredible. You know, he’s been an incredible mentor to me and the Ramp team in thinking about how can agentic AI be designed over time, I think he’s thinking, you know, in much larger cycles, not just weeks and years, but truly decades. And, you know, last, I think he’s person who’s been through a lot and I think has a lot of sincerity and kindness in how he operates. I haven’t read it. I think his book Hit Refresh is excellent. I think the way that he’s been able to partner deeply with all kinds of companies is pretty inspiring. And so present company excluded, I think Satya Nadella would be my pick.

Ravi Gupta: That’s a fantastic answer. And he is a very admirable person. Eric, thank you. This was awesome. We had a great time. We learned a lot. We learned a new expression in self-driving money, self-driving finance. And I think in all seriousness, the combination of innovation as well as applicability is very unique. So we are very, very, very happy that we get to be partnered with you guys.