EP 110: How Eric Glyman (CEO, Ramp) Runs One of The Fastest Growing Startups

Eric Glyman (CEO, Ramp) shared his operating playbook for leading one of the fastest-growing startups on his second appearance on the podcast. We also explored the unique ways Ramp leverages AI internally, strategies for startups to build moats in AI, and the concept of self-driving money. We concluded with one of the most profound and thoughtful discussions about hiring, motivations, and success I've ever had on the podcast. It was truly enjoyable to sit down with a seasoned portfolio founder. (00:00) Introduction and Episode Overview (01:03) Market Reflections and Ramp's Growth (04:01) Artificial Intelligence in Business (10:09) Ramp's AI Implementation: Toby the Slack Bot (16:49) AI's Impact on Business Models and Hiring (31:15) The Future of Organizational Structure (31:36) The Evolution of Bookkeeping (33:49) The Impact of Automation on Bookkeeping Jobs (35:31) Streamlining Expense Management with Ramp (37:00) The Concept of Self-Driving Money (38:09) AI Integration in Ramp's Services (39:38) Zero Touch AI and Customer Experience (40:54) Agentic AI and Practical Applications (49:15) Balancing Autonomy and Centralization in Organizations (56:09) Developing New Products at Ramp (01:04:01) Resource Allocation in Organizations (01:05:00) Creating Conditions for Success (01:08:37) Balancing Existing and New Products (01:11:02) Hiring for Curiosity (01:16:09) The Pursuit of Perfection (01:21:21) The Joy of the Process (01:24:37) Building a Timeless Product (01:33:15) Conclusion and Reflections Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA 🎥 Subscribe on YouTube: https://www.youtube.com/channel/UCugS0jD5IAdoqzjaNYzns7w?sub_confirmation=1 Follow on Socials 📸 Instagram - https://www.instagram.com/theloganbartlettshow 📱 X - https://twitter.com/loganbartshow 🎬 Clips on TikTok - https://www.tiktok.com/@theloganbartlettshow About the Show Logan Bartlett is a Software Investor at Redpoint Ventures - a Silicon Valley-based VC with $6B AUM and investments in Snowflake, DraftKings, Twilio, and Netflix. In each episode of The Logan Bartlett Show, we sit down with the people behind today’s most important startups and extract the tactics, lessons, and frameworks they’ve learned the hard way. Conversations span hiring to GTM, product, growth, fundraising and everything in between - collectively forming the ultimate playbook to make you a better CEO, investor or board member. Tap follow and enable notifications to stay ahead of the game.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:04Logan Bartlett?

  Welcome to the Logan Bartlett Show. On this episode, what you're going to hear is a conversation I have with co-founder and CEO of Ramp, Eric Kleiman. Eric and I touch on a number of topics, including artificial intelligence and how Ramp is using that, both internally for their own employees, as well as externally for their customers. We also have a long discussion about motivations. I feel lucky to be able to have the transparent relationship with them that I do, and for him to open up and share about what drives him and his motivations at Ramp. You'll hear that conversation here now. Eric, thanks for doing this.

• 0:38Speaker 01

  Great to hang again.

• 0:39Logan Bartlett?

  Good to see you. I guess it's probably been two years since we did the first episode of this. I don't know, we can probably look it up.

• 0:48Speaker 01

  Yeah, I think-

• 0:49Logan Bartlett?

  Seems like an easy thing to find out, probably something I should have known.

• 0:51Speaker 01

  We have the record. Times moved differently over the past few years.

• 0:54Logan Bartlett?

  Yeah.

• 0:54Speaker 01

  It feels about right.

• 0:56Logan Bartlett?

  Well, I went back and listened to it, and I think when we were talking, like, the market had started to turn, and there were some signs of softness in there. I remember at the time, we invested in Ramp. We did a big round in December of 21. It was sort- right? That was, the ramp was announced maybe in March or April-ish.

• 1:21Speaker 01

  I mean, you were with us, I mean, supporting us for far before, but I think first invested, I think in January of that year.

• 1:29Logan Bartlett?

  January of 21, yeah.

• 1:30Speaker 01

  Yeah, January of 21.

• 1:30Logan Bartlett?

  But I remember of all the companies, when we were sitting down, and we didn't know exactly how the public markets were going to play out, how the private markets were going to play out, how spend was going to play out within Ramp's business. I remember we were sitting there, and it was, you had built so much so quickly at that point in time, but we were also, in listening back and sort of preparing for this, it was definitely more uncertain of how the market was going to play out, and you know, what was going to be beneficial to Ramp versus not. But it's amazing how you guys have continued to execute over the last couple of years.

• 2:03Speaker 01

  It's, well, the support has been amazing. Yeah, I mean, look, the change in valuations for companies was dramatic, and the rise of interest rates is very costly for businesses, and- No doubt, like years ago when we were starting out, the fact that companies could just raise money easily and spend helped, but it turned out to be a huge accelerant for our business because our core value proposition of helping people spend less and for the vast number of people, like the free product that pays you to use it was really in vogue. It wasn't a line item to be eliminated like so many SaaS pieces of software. And we've been able to just keep up triple digit growth each year and just grow at times when most companies I think were really struggling.

• 2:45Speaker 01

  And so we're working through, but it's been a lot of fun, frankly, to have a product that went from a nice to have to a painkiller.

• 2:52Logan Bartlett?

  Yeah, it's, I mean, the fact that the value prop of spending less actually outpaced and resonated more than the venture pullback was actually an interesting thing to see. I don't know if I knew that in my heart of heart in June of 22 or whatever. I'm not sure I was fully appreciating the puts and takes of that. You guys, I think at this point are our biggest investment in the history of Red Point. And so I, you know, hopefully you didn't feel any of the stress from me, but maybe in the doldrums of 22, I was a little anxious about which way that was going to play out, but it's really impressive to see.

• 3:29Speaker 01

  It's, look, I think a little bit of stress is exactly what you want.

• 3:32Logan Bartlett?

  Yeah, that's right. I don't want to put it on you, though, is the problem.

• 3:36Speaker 01

  Yeah, yeah. Put it on us, makes us better.

• 3:38Logan Bartlett?

  Yeah, yeah. Well, so an in vogue thing to start with that, I know you've spent a lot of time thinking about this, artificial intelligence in general and how to operationalize that. And both, I guess there's stuff internally around it and how you can be more efficient with your employees and some of the stuff you can do. And then externally and what you can provide to your customers at this moment in time, as we sit here, June, July of 24. How do you think about AI and the value prop for a business like Ramp?

• 4:13Speaker 01

  A couple of things. I mean, first, I'll say what everyone is, I think, has heard and seen, but I think it's worth repeating because it's so profound and true. I think that AI is, in what's happened over the past two, represents the biggest shift to productivity, certainly in my, my lifetime. And I think, and when you have functionally, human level, and in certain cases, superhuman level reasoning, that is accessible through an API call that any business can go and access, I think you need to think about, how is this going to change the world and what does it mean for my business? And I think there's a lot of truisms people could rely on for the past 20, 30 years in businesses that are really going to shift. You know, if you were a software business, it was about how many features did you have? You know, how, how much are your customers locked into your data model?

• 4:58Speaker 01

  In a world where, you know, give a five, 10, two years, depending on who's right, but I don't like it's, it's outside of that. You have software engineering functionally, where, you know, I think what used to take months will take days, if not hours. It means you're, you're not defended based on the features that you have. Vendor lock-in is not going to be there. And, and so a lot of the typical moats that software businesses develop are going to erode. And I also think the model of, of selling people seats as a way to monetize, isn't going to be perfectly sloped to a reality when you actually don't need people to do a lot of jobs. And so you actually don't want to sell people more seats, you want to drive more outcomes. Unpacking it, a little bit, first we'll start with like

• 5:41Speaker 01

  Ramp's general premise, which is, we think we should get paid based on the outcomes we deliver to companies. Our founding value proposition has been, we are going to help you spend less money and time, and we are going to measure how much money and time we've saved your company. When we launched in February of 2020, we helped the average company save about 2% on their expenses. And we thought that was great. That was more than what cash back or rewards really could be for, for most businesses. Today, we help the average business save 5%. You know, between hard dollar cost savings and labor costs that companies don't need to spend anymore. And we think this is going to only go up and up and up. And so I think in a world where AI is not just assisting, but is actually driving outcomes for

• 6:23Speaker 01

  For companies, I think companies want to position themselves to be selling an outcome, selling a service like that. That's one.

• 6:30Logan Bartlett?

  Did you, um, did you think about selling seats ever in the early days? That was the, the model of Concur, right? With seat based pricing, I assume Expensify? I actually don't know. I know they've kind of moved since then. So was that ever a consideration? It was the status quo in the industry for some of the other people that you were looking at?

• 6:47Speaker 01

  It's, um, so first, I, I still think there's a place for selling seats. But the big thing, when you think about Ramp, well, very early on, I think in our very first board deck, we, we tried to really, we had no revenue and we wanted to, uh, on a first principles basis, reason through, what is our business model saying, telling us, how does that actually work? And if you look at most card businesses and fintech businesses, they're at their core consumption based businesses. The more you use, um, uh, the more ultimately, um, you know, we are able to monetize as a company. And we, if we do our job rates, the more value we're going to create for, for our customers. And so the, I'll, I'll spare you the details of the business equation,

• 7:31Speaker 01

  but at the core of it, it was if you want to make revenue, there's a few core variables. How much purchase volume are customers putting with you? What's your take rate on that volume? And what is the cost of funding it? And it turned out if you studied every single variable, there was only one thing that mattered, which is purchase volume. It turns out if you make more purchase volume, you make more revenue. If you make more purchase volume, you keep more interchange. If you make more purchase volume, you have a lower cost of funding at the bank. You're better at preventing fraud and losses. So there's one variable that drove the whole thing, which is how do you get people to want to use your product and spend with you? And so our whole view was, look, we want, you know, this is a business that fundamentally at scale gets much better and better. How do you get to scale as quickly as possible?

• 8:15Speaker 01

  And our hypothesis was, well, if you just make spending with us just simply better than spending anywhere else, rationally, people should want to spend more with you. I think how this gets expressed in, in real life is, you know, if you spend on Ramp today, um, you have very simple cash back and we show you ways to cut spend constantly. And so more spend that's put on Ramp, you're able to better benefit. You, you find that you actually are spending less over, over time. More spend you put through ramp, there's more expense reports you're able to automate, more accounting we're able to automate, more we're able to, you know, to show you ways to better benchmark and run your business better. And so all we simply try to do is a great, we have a simple model, let's get more purchase volume. And the way we're going to do this is create more and more customer value. So internally, all our product is think, our product team is

• 9:00Speaker 01

  thinking about is, you know, how much money and time are we saving customers? And how do we radically increase that? And if we do that, it should fairly directly drive the output variable we're trying to drive, which is purchase volume. And then over time, we should be able to expand margin, which is what's played out. It's been a big part of why as quickly as we've grown, our bottom line has grown dramatically faster over the past few years.

• 9:24Logan Bartlett?

  So you were set up well from the start that you were already focused on outcomes, and you didn't have sort of an orthogonal pricing mechanism that was at odds or potentially conflicting with the value prop. So I guess as you thought about AI, it was a first principles thing within the business, but we've reached this new vector of step function change, I think, in some of the reasoning. And so, once ChatGPT came out, and that moment was kind of unlocked with LLMs and all that, what did you do? How did you operationalize it within the business?

• 9:56Speaker 01

  So, a couple of sets of things. I mean, first, I think with anything, you want to develop taste about how, you know, a breakthrough ultimately works before you go and productize it. I think that the first wave of products that came out after the ChatGPT moment were, frankly, in hindsight, very bizarre, right? Like, I've never met a single customer who said, you know, my, I just would love to chat with my bank account. You know, I want to chat with my, my HR records, I want to chat with, like, it just is sort of crazy. In hindsight, people did this, but, you know, people were trying to, you know, slap on, you know, my company is the AI company of X, and just kind of went with it. And so, at the beginning, rather than going and

• 10:37Speaker 01

  trying to go and say, here's Ramp AI and all the ways that we can do this, we said, let's actually use this internally. Let's understand the ways that we can reach more

• 10:50Speaker 01

  But he's more efficiently, win more deals, learn faster from customers and improve processes. So really, for the first year, a lot of what we were investing in was this applied AI team, which is a horizontal team that is distributed out. Some of the most common use cases today that we use AI, and I think have been just transformative for us, is a very heavy, you know, a heavy portion of how we grow is functionally through an AI-based sales process. We're functionally, you know, the lead scoring, the outreach, the iteration on copy, you know, is fairly programmatically done. And we focus really the high-value work, people's time, on responding to customers who are interested.

• 11:33Speaker 01

  We spend, you know, every sales call between us and a customer, we record. Initially, it was for quality assurance and training and helping our reps do better. You know, but we look back and realize we had tens of thousands of sales calls that, you know, were digitally recorded, we had transcripts. And so internally, you know, about a year ago, we started building a, you know, there's an AI bot called Toby, which Toby, you know, no one on the team, no person has a time to listen to 50,000 sales calls, but a large language model does. And so it was a Slack bot we built in a weekend where you can ask, you know, Toby, why does, you know, do customers pick Ramp over Competitor X? Or an SDR can ask that kind of a question.

• 12:16Speaker 01

  Or what do customers think about X new feature? Or, you know, reporting or some need or something that we're researching and want to do. And Toby is able to go, frankly, within seconds, process all this information, query and pull out the right transcripts and show you what across, you know, hundreds to thousands in turn cases of all the transcripts, the summarized insights. And if you want to go deeper, you can go deep into this. And these are the types of things that are You know, have very little, you know, you could be selling widgets. It doesn't have to be spend management that helps companies become more efficient. It could be any nature of business. And we said, let's actually apply this. And you start to develop a taste of where large language models are well suited. It's a problem.

• 12:59Speaker 01

  I think they're much better at, you know, incredible at summarization. I think at times in generation. And as we developed taste for that, we started developing these hypotheses of where it would be deeply powerful in the ramp product. And happy to talk to you.

• 13:13Logan Bartlett?

  Yeah, no, I definitely, I definitely want to there. On the SDR point, and then on the Toby point as well, I guess. The, the SDR, I assume a lot of the tooling that you use is off the shelf stuff, be it outreach or Gong or whatever it is. But then I assume you've built a lot of things on top of that. Can you maybe talk about who was responsible for these things? How much, there's all these AI SDR companies that are coming out there. Do you think these are going to be commercialized products? And you guys are just at the early edges of this market right now. And so you home grew a lot of this stuff, but ultimately it's an opportunity for another company to do it.

• 13:53Speaker 01

  I think there are opportunities to build for sure. I think a lot of the, the state of the art comes with the ability to customize to your own business. And ensure that you're, you're, you're learning from lots of different signals and feedback loops that come into it. And so what I'd say is, I, I think like a relatively narrow AI SDR is, is going to be a tough sell, right? Sure, you can send a lot more emails and if you're not careful, create a lot of spam and, and test. But the most, the more interesting insights are actually based off of not just the customers that you win, the customers you lose. What, what happens after customers have been with you for six, nine months? And so if your data model only extends to, you know, here's what, you know, I'm seeing in my outbound data and response data.

• 14:39Speaker 01

  It's relatively narrow compared to what's happening. And so I think that companies that are deeply integrated down the stack should be able to do a lot. You get a backup. When we think about the problem generally, it's not just a, how do you outbound and respond to people quickly? But it's a question of, do you have a great customer data platform, do you have a great CDP to really understand? You know, from a graphic data, you know, what's the nature of these companies? What are intent signals? You know, that might be predictive in some sense of this is a company that may be in the market now or soon. Or the, the nature of the product that. We're selling. We don't want to sell somebody a product that isn't relevant to their needs.

• 15:18Speaker 01

  There might be details about what's happening in that company. Maybe they've raised funds. Maybe they have personnel changes. They've hired someone into a position. Maybe someone has gone over who used and loved Ramp before and we can see this. And so you need to think about structurally. Not just what are you sending, but what's all the data that upstream that's informing that? How do you get information and signals in constantly to make sure it's new and fresh? You don't want to, you know, act on list that are many years old. And then, once you're sending out what's happening, not just in the back and forth, what drives immediate conversion, but downstream. What do you learn from that? And then the interesting thing that I would argue ultimately makes these tools really, really profound.

• 16:00Speaker 01

  Is when you start to see not just, okay, a customer has taken, you know, a prospect has taken a meeting, they're in our sales funnel, but what happens? You know, over the course of a year. You know, how much value are you able to bring these customers? What are the ways that it's beneficial for the business? And do you start to bring that back? And so what I would say about, maybe like a larger abstracted points about AI, you know, AI in general. I think like one loose analogy to think about implementing AI in your business is, you know,

• 16:34Speaker 01

  you might think about, there are people with profound levels of intelligence, but maybe have lost,

• 16:41Speaker 01

  you know, interaction with their, you know, spinal cord or ability to use certain kind of limbs. And you can have people, you know, like Stephen Hawking, like amazing intelligence and can act as he's, you know, was very respected. People wanted to, you know, listen to and learn from, but there's a lot of limitations on his output. Based on, you know, his body, you know, and inability to act. What I'd say is like, if you have great intelligence, you want to think about what's, what, you know, do you have enough signals of inputs feeding back into it? And so it can process and act on a wide variety of things. But then do you have the ability to go and act on and get feedback on things? And so what I'd say about, you know, a lot of AI based tools. If they're very, very narrow and don't have the ability to start to build into, learn from,

• 17:27Speaker 01

  benefit and improve lots of different areas of how a business ultimately operates. Utility is far more limited. So I think point-based tools in certain clever areas can work, but I think it's especially important. And I think part of why I would say even people in your line of work in investing aren't so sure that AI will absolutely benefit startups. I think there's a lot of views that this might actually be beneficiary to the very, very large companies that have a huge amount of data. A lot of it isn't well-integrated.

• 18:05Speaker 01

  We can talk about how companies are approaching that to try to make sure data is stitched together in a way that you can act on it. But yeah, I think it's intelligence and action, I think, are the interesting paradigm to think through.

• 18:20Logan Bartlett?

  Before we hop off of AI internally, you referenced Toby. Can you describe what that is at maybe a finer level and how it came to be and how it gets used internally?

• 18:34Speaker 01

  Yeah, for sure. So there was a few points of major releases where we wanted to try to get

• 18:42Speaker 01

  exposure to how the latest APIs worked and how the new large models. And so I think it was created, I think, in the weeks following the release of GPT-4. And functionally, maybe to back up, for many, many years, because we're a hybrid company with salespeople everywhere, product folks that we wanted to have learn from customer calls, every call folks can always consent out, has a Gong recorder that joins at ramp. And so effectively, we were using that for sales training. We can see what percentage of the time is the ramp rep versus the customer speaking. It could give you a sense of, hey, we're talking too much. We're not listening. Let's help this salesperson improve and be more customer-centric.

• 19:27Speaker 01

  And feedback, but over time, we built this very large library of transcripts. And if you add in the video and audio, it's data, terabytes of data. Rahul, who's one of the leaders of the applied AI team, basically realized this could be a treasure trove of at scale, what is every customer basically telling you about why they're going to buy, why they're not, the problems that they have, the reactions over the course of the sales process. And this might be an incredible way at scale to understand what are customers actually thinking and feeling about Ramp. And so effectively, what he did, I think this was over a 12 hour sprint was the first version.

• 20:06Speaker 01

  Built this out, transformed all the audio data into transcripts, dumped it, I think it was at a size. I think we kept remnants of both into a red shift database, then built effectively a model to efficiently query. This was, I think, when calls to GPT-4 were still in order of magnitude more expensive. And so rather than having this large language model simultaneously act over, you know, call it 100,000 transcripts at once, you know, let's say you'd ask, why do people, you know, prefer Ramp over Expensify? Where do people prefer Expensify over Ramp? You could do a first round of querying to understand where is Expensify showing up in Ramp?

• 20:49Speaker 01

  You know, you take search terms and that sort of limits down the database in a set of objects you're going to query on to, let's say, who knows, a thousand, right? Something like that instead of 100,000. From there, you know, you feed in, you know, you target this as the output data. You overlay the question that you want to ask to it and, you know, it's spit out effectively a summary answer to your question.

• 21:18Logan Bartlett?

  And is that going into a large language model at that point? Like it's sitting in red shift, but how, when you get down to a thousand transcripts, is it then going into, are you feeding that in to get the answer back?

• 21:27Speaker 01

  Yeah, I mean, effectively it's a pointer where these are, you know, that's the, you know, part of the database that, you know, our input is going to be running over. And then we take the output of the models as well as some of the alternative outputs that were produced into our own database. And then part of that we put into a Slack bot. And so the way anyone at Ramp can interact with it is you can say, you know, hey, Toby. Go ahead and ask your question. Toby queries it a minute later, there is this output response. And if you want to go deeper, Toby can even say, in customer call X, Y, and Z, they said this. There were 500 relevant transcripts. And if you want to dig deeper and actually go and understand, or later on, reach back out to understand what was this customer saying, it's really, it's easy.

• 22:13Speaker 01

  But the interesting part about it is it actually makes like a very complex set of operations and questions that really only highly technical engineers could ask before, democratized. And so an SDR who has come up against, saying, hey, but we have Expensify, I can go and ask this a minute later, come back with all the reasons of why people prefer us over the competitor, and write better copy. A product marketer who has to think about a competitive page can go and take that and sort of think through what are people actually saying? So it's not just conjecture, or what do people say in the product marketing cave, but what are customers actually saying? And so, in some sense, it was actually a relatively simple thing to build,

• 22:57Speaker 01

  because we had large data sets and it was about building an analytics tool on top of it. But you can start to see and think about where this is going over time. If you have a great data model that's running, let's say, let's extend the SDR analogy, how we're reaching out and communicating with people at scale. There's one world in which SDR is writing a better response. There's another in which what we're learning over the course of customers is actually piping back into what we're ultimately sending and we're having kind of self-learning. There's pages that can be really improving based, not just when someone in product marketing thinks to ask the question, or someone running a conversion rate improvement is asking the question,

• 23:42Speaker 01

  but we're taking that and informing new experiments that we're able to drive. To even to, as we sort of think about ultimately building, part of how we build new products at Ramp is reasonably algorithmic. What's the spend that we could win or lose? How many more customers could we serve if we have it? And what's the most efficient way to build it? A lot of prioritizing comes down to, do you have a good sense of how many customers want it? How much they want it? And is this the only thing they're asking for, a broad set? And actually, you can start to prioritize. These features are coming up a lot in this segment that we're trying to win. We're hearing it now. Let's prioritize for the roadmap. There's research that can get done in order to kind of build that.

• 24:25Speaker 01

  And if you have great data models that stitch together, you can actually build more customer centric products much faster, I believe.

• 24:31Logan Bartlett?

  It's such an interesting example of AI and it's kind of unique in that you had a large data set that was aggregated in some way. And then you could layer on top reasoning and summarization, which seems to be the best AI use case today, at least within some confines. The generative stuff is is cool, but there's, you know, abstractions or different ambiguities that come with generative hallucinations or whatever it is. And so it's such a, it's such a perfect, I don't know, distillation of the possible today and hearing it in practice. I guess I'm curious, the democratization of this access and information. You mentioned a few different manifestations of it internally.

• 25:16Logan Bartlett?

  Is there something that's counterintuitive that's maybe come out of people having access to this in a way that wasn't possible in the past?

• 25:24Speaker 01

  There's things we've observed because there's both what we're building and also like, we're very interested. We love partnering and using lots of tools. And so we tend to get more pitches than most from like, you know, the new avant garde startups. What's the latest?

• 25:36Logan Bartlett?

  You're a good logo to have. Yeah. For startups.

• 25:38Speaker 01

  It's, we, I love working with people who are super high energy in building new things. And it's funny, I think right now there's a lot of noise being made in customer support. You know, as areas of this, is this going to be one of the areas that's automated? And I still believe people who really listen are taking to discernals and ultimately, you know, create a sense of trust and be there for customers is important. We'll talk about how many people need to be into it, but I actually think relationships get more important. I think there's certain parts of of of job functions that people thought it would be like the last to touch. And what's funny is actually one of the maybe hottest areas now, which I wouldn't have expected a, you know, a year or two ago, you know, of AI startups is sort of AI engineers.

• 26:21Speaker 01

  And when you think about it, I mean, it's such a very challenge. It's a really challenging job in order to be able to perform. But yet, when you, when you sort of flip and you think about what actually drives great AI products, it's about great input and output data. How much of this is digitized? And can you sort of start to see the effect? And when you sort of think about a lot of software engineering. You know, is fundamentally, it is fundamentally digitized.

• 26:43Logan Bartlett?

  Digital, it's deterministic, the inputs and outputs are measurable.

• 26:46Speaker 01

  Yeah, you can start to see, um, you know, standardization and how things are, are, are built. And I actually think that's been a part of the market where there's the most fascinating tooling. And so I also believe for, for, for what it's worth, like truly extraordinary, you know, engineers, I think become more and more valuable. And can act with even more levers than they could on their own. And so, uh, I'm not of the view, um, I think jobs are going to change. I, I don't, you know, I'm not of the view that like this stuff is, is, is existentially scary. I think humans fundamentally adapt. But I think there's been counterintuitive areas in, uh, where I think the meta and the most interesting tools have shown up.

• 27:25Speaker 01

  And I think that the other, which I think as a practitioner is obvious, but I think is worth stating for, for people who are building businesses. It's in my view, like, it's not just about, um, what is the functional use case? What is the AI tool do, do is, is being like the most important in tool selection? I actually think one of the most important things is that if, you know, again, if you have, if you're able to bring in digital intelligence into some aspect of your business. Having the ability, uh, to have well structured, organized data that is connected and interoperable enhances the ability for, for AI to add value to our business.

• 28:05Speaker 01

  And so, uh, I think that, um, you know, building applied AI and your team, nearing teams is super important. But I think building, uh, extraordinary data teams, being thoughtful about data models, uh, and then thinking about, um, really tuning your business to, uh, not just get insights, but to act on them becomes more important. And so I think it might make organizational design, uh, data structure design, uh, more important. And so I, I, I think it, it forces people to think about different areas in their business than maybe they may have assumed.

• 28:37Logan Bartlett?

  Have you guys changed your hiring at all on the engineering side with some of this tooling that's, that started to come out? Or is it still too early, uh, to, to see the impact at a business level?

• 28:48Speaker 01

  I mean, I, I, I would say it's. I think informed probably every area of hiring, at least in some set. Like I am, you know, engineering is one, but you know, look, I'll, I'll, I'll talk about a few others. Like I, when I think about, you know, design, I think one of the things that people inside the industry got a lot of notes and excitement about is I think we were, I think we were the first company to run a scaled out of home advertising campaign that was primarily powered by mid journey. And you think about some of those images that were out, you know, in a force of campaign.

• 29:31Speaker 01

  If you wanted to produce some of those single shots to get the effect, the lighting, the feel, the props and kind of the emotion of it, I think some of those shots would have cost into the tens, a thousands of dollars. You know, to, that would have been a multi hundred thousand dollar campaign to produce. It was done over the course of a day with as much as it cost to create a seat for some of that. And I think that, I think it's more important to find, you know, people with extraordinary taste. What, you know, can give you a sense of what is a striking image? What is going to connect with people emotionally? What should I produce that is interesting work? But the cost of production, I think of artistic and great work is probably as low as it ever been in human history.

• 30:12Speaker 01

  And so I, what I'd say is become more important in every hiring or hiring for any kind of job profile is, is this person fundamentally curious? Are they interested in how the world is changing?

• 30:25Speaker 01

  Or, you know, and how it's going to change your job and how they can do more and do their job differently. I think it's more important to find craftspeople. I think that as companies get bigger, I think something that everyone feels who's been a part of these organizations is, you know, the distance between crafts person and crafts person, people making things gets really large.

• 30:46Speaker 01

  And there's, you know, a lot of editing and things get diluted down to the point where it's, it's very few people actually working on the actual product. It's lots of people just making the organization and the bureaucracy work. And I think that companies are going to get a lot flatter. And so finding makers. People who want to learn, who are curious, who are interested in how does something fundamentally work? And how will it change your job, I think is more and more important. And so I don't think that's a, you know.

• 31:13Logan Bartlett?

  Unique engineering necessarily. And I guess one of the points that we were talking about before we went on, that I think is an interesting one as we segue from the AI internal topic is the role of bookkeepers over the last 10 years or so. Maybe, can you speak to that point and how bookkeeping has changed as an industry and the number of jobs?

• 31:36Speaker 01

  Oh, for sure. I mean, it's, I mean, first, just stepping back and thinking about it. Like, I mean, I think it was, I think double entry accounting was invented, I think, in the lead up, lead up to the Renaissance in Italy. And some people say actually the, you know, that was one of the precursors that generated a lot of wealth, which could drive to one of the greatest periods of creativity in human history. But like, I think the reason it exists is that humans make mistakes. You know, you have one set of how, you know, your finances are, but when you're interacting with lots of different parties, you know, it's hard to do it without,

• 32:17Speaker 01

  you know, it checks and balances and how you ultimately keep and do, do keep your own books. And that was, that's been the avant garde of how record keeping has been done. There's a lot of great things about it for 700 years, something like that. And it became very, very important, you know, call it 100 years ago, as companies started getting a lot bigger. It wasn't just regional banks. There was the creation of branch banking. You had national banks that could operate in far flung places. You had, you know, large industrials, the rise of Standard Oil, of General Motors, of Standard Chemical, these, these large companies.

• 33:00Speaker 01

  And suddenly people needed to be able to understand what was happening in these local branches. You know, how do we have multiple sets of records between what they're reporting, you know, audits against that. And so you needed to build not just a, you know, great systems you could scale like double entry accounting, but swaths of people in order to be able to measure, audit, report, and then act on this. Where I think it gets to your question is very interesting. I think it was an article in the Wall Street Journal, we'll try to dig it up, but I think it was something like, you know, it said this stat that looked wild on the face of it. It said, you know, there, a decade ago, there was 3 million, you know, bookkeepers, you know, professionally, or

• 33:44Speaker 01

  2 million bookkeepers professionally in America. Now it's, you know, it's a million less. And, you know, it looks like this is crisis until you realize that the number of, you know, financial advisors and analysts has gone way up, you know, if I, you know, call it a million people. And I think is indicative of what happens in progress, you know, when you have better tools and systems, it becomes a, you know, a system people move to more strategic or high level of abstraction is one meta point. I think the second meta point that's a little bit interesting is computers, you know, don't really make mistakes. Since people that program them make mistakes and you can modeling incorrectly,

• 34:28Speaker 01

  but fundamentally excellent at keeping of records. And I think in a world in which more, you know, transactions are digitized, it's easier to know with accuracy what ultimately happened. What I would say today for the reality of someone using, I think for the average business, the regular experience is, you know, to buy one thing, you need two tools. You need like a credit card and you need an expense management software and you take a digital transaction, you get an analog image of like a receipt or a printout. You then take a picture, you re-digitize it, you put it on some other app that then needs to determine, does this transaction match this transaction? And you port it. And so it's just very Kafkaesque. It's very clunky. You know, today, I think one of the original drivers of Ramp's product market fit was you

• 35:13Speaker 01

  don't need two tools to buy one thing. You know, you put, you tap your card, we check your expense policy in real time. You know, you get your receipt, we pull it from the merchant automatically or email, or we text you when the receipt's in your hand, you snap a photo and you're done. And so it sort of takes what used to take a month for you to get your receipt in on average on, you know, on these old tools to 30 seconds is the average on Ramp. So it's easier process. But as you look and how this is extended, you know, today, as you know, we, we issue corporate cards, bill payments, reimbursements, you know, we manage tens and tens of billions of dollars of purchases, and we stitch data from what's happening in our HRAS through what's happening in our HRIP. And where I

• 35:54Speaker 01

  think this all comes together is that for the average business using Ramp, they're dramatically more efficient. You don't need such large finance teams in order to be able to keep books and records, which is a good thing because it's not high value work. It's actually, I think, very painful parts of people's jobs. And instead, they're spending time on the interesting questions, which is like, where does return on investment come from? Where should companies be deploying more capital, not just, you know, doing lots of work in order to be able to measure what's happening? You know, they're not spending time on downloading a bunch of spreadsheets and running large pivot tables to figure out, okay, this transaction is UVR star 237, UVR star 123, all these say Uber. And so after an hour, you can finally say, this is what we spent on Uber. That's done for

• 36:34Speaker 01

  you. You know, how your books and records are kept are done for you. And I think that over time, there's been this like white whale in, I think, investing of this, this idea about a decade ago, people talked this, this notion of one day, there will be self-driving money. And companies and people will have their assets in the highest yielding security or asset at all times. They're not going to waste a dollar on things that they don't need. And they're going to have better insights on how to live a better financial life. And it didn't happen. But I think that as companies become increasingly digitized, they're connected together and you have a command and control system like Ramp. I think self-driving money is actually kind of a real thing. And in the same way, you know, a decade ago, people were excited about self-driving cars and, you know, it's finally here and people are

• 37:19Speaker 01

  just getting used to it. I think this, I think that's happening in the world of finance. And whether it's us or someone, it's going to happen. And we want to be the company that does that and brings it to people.

• 37:31Logan Bartlett?

  So as you guys have dog fooded, we spent a lot of time talking about the the internal processes that you've made better for AI.

• 37:39Speaker 01

  Yeah.

• 37:39Logan Bartlett?

  And then we also talked about the the outcome based pricing that you've been able to align with your customers. How has that now manifested in artificial intelligence for the end customer of Ramp? And how did you, I realize it was a first principles thing from the start of the business.

• 37:58Speaker 01

  Yeah.

• 37:58Logan Bartlett?

  But maybe, maybe take me through as ChatGPT happened and there was this proliferation of LLMs or it went mainstream, how you up leveled or thought about up leveling the the end customer experience with AI.

• 38:11Speaker 01

  For sure. So I mean, a couple of things, we were always interested in the field of it. Simply because there have been aspects of machine learning that have dictated how risk and underwriting have worked for decades. Like it's been in production for quite a while. And so very early on, some of our first hires came from Facebook AI Research, Google Brain, like first five people at the company kind of a thing. And so even back in 2019, our receipt matching was fundamentally, you know, you know, driven by machine learning by 2020, a lot of our underwriting was ML based. You can have human underwriting, but actually you could use data signals in order to inform how a company's finances may perform and how much limit we can extend.

• 38:56Speaker 01

  And so there's been aspects of it for a long time. What I would say is, you know, you know, it's certainly not today, but I don't think it has been for years possible to use Ramp without using AI in some way. The difference is historically, we haven't talked about it a whole lot.

• 39:11Logan Bartlett?

  Yeah.

• 39:12Speaker 01

  You know, you look at

• 39:12Logan Bartlett?

  You called it ML and not AI probably in the early days.

• 39:15Speaker 01

  Yeah, yeah. And well, even this, like, you know, we have lots of,

• 39:20Speaker 01

  you know, most of our businesses are traditional, like they're farms, you know, restaurants, hospitals, manufacturers. And if you look at reviews of Ramp, the most common thing you will see is it's really easy. You know, it's really, really simple. And if you look deeply, it's because there's

• 39:41Speaker 01

  AI driven in almost every process like you snap a photo of your receipt and it's zero touch AI. It just matches to the right thing. You go and you know you buy something at Costco and you get a text from ramp saying, you know, we have this suggested memo one, two and three, click one which feels right or fill in your own. You know, people pick one, two and three almost every time because we're better able to predict. We're better able to, you know, model and in tag transactions certainly more quickly, but also for the vast majority of customers more accurately. And so it's stitched in lots of different places. And so there's a lot of work around, you know, this category of products that we think internally about zero touch AI, where you don't need to call it that, but it just feels smoother, more intuitive,

• 40:26Speaker 01

  there's less friction in any part of the workflow than normally you think about. That's one. There's another class of more external pacing products that we're really interested in that, you know, we think about as agentic AI. And when you think about that, I think probably a use case that blew up on Twitter about a month ago was it was, you know, similarly development of Toby that followed the release of GPT-4. OpenAI released its GPT-4O multimodal model that you could take, you know, images, video, audio, you know, text and get the output of, of, you

• 41:11Speaker 01

  know, these large models. And the prompt was, you know, one of the top sets of questions that people will ask about ramp is hey, they know what they want to do. They know ramp can do it, but they're not sure how to do the thing. So an example could be, hey, I want to issue a card that only works at coffee shops, you know, and it's give it a limit of $50, but I couldn't figure out how to do that. Of course, you know, number one feedback, we want to make a product more intuitive, make it more easy for people to figure that out. But we said this actually might be a great application of the GPT-4O model. And so, you know, after, you know, a little bit of work, I think about a week of work from, you know, Alex and others in the

• 41:53Speaker 01

  team did an extraordinary job. What you can, what you can do is you can, you can say, hey, ramp, I'd like to issue a card with a limit of $50 just for coffee. Help me do this.

• 42:10Speaker 01

  What happens next is very interesting. What you see on the screen is ultimately fed into this model. And so it takes this text prompt inquiry and audits what's happening on the screen, as well as other screens that are possible to click through, and then shows you how to do it. And it says you can click down here into the cards model, hit new card, we're going to hit controls, you're going to say allowed merchants, coffee shops, it'll write this. And as it's doing it, it's showing you what it's doing. It's saying, I'm going to click over here. We're going to write this text in. And then it's going to click issue.

• 42:54Speaker 01

  And I think this is really interesting because it's one of the first cases where it's, as an agent, it's not just explaining how something is done. It's actually acting. It is doing things as an agent of yours, doing things, in this case, with you monitoring the outcomes. But it's very different than how companies' products historically have been done, where they're taking some action in the real world and it does it. Now, of course, I think the easy question is, that sounds great. Why show me the steps? Why don't you just do this? I think some of the answer is, these are alpha products. We're testing it. And we want people to kind of monitor and sit shotgun and see. In the same way for Waymo's before they were driving on their own.

• 43:38Speaker 01

  There was someone kind of there with a steering wheel just to see what's going on. We think that's a kind of a good paradigm to see how these go. But over time, you can kind of imagine, you can learn all the way a SaaS app works and all the tools and knobs and ways to configure it. Or you can say, here's what I want loosely. Can you go tool configure yourself? Can you go do this thing on my behalf? And tools should be more intuitive to be able to use directly. But also, I think tools are going to act on your behalf, not just in the tool itself, but outside of it, generally on the internet. And I think that Paribus is an interesting example where it was an app that wrote emails for you

• 44:19Speaker 01

  and chatted with people at other companies on your behalf. And I think those kind of use cases are really prominent. How do you get better deals on the software and sets of things that you know you're going to be buying? Can you perform certain analyses for me? And so when I, when I, I would say over time is the reasoning capabilities, these models increases dramatically. I think that just even the nature of what software can do for people is going to expand dramatically. And I think having a sense and developing taste of, of, of where things are great and working as expected really quickly. And where the running issues, I think is a really worthwhile thing.

• 45:01Logan Bartlett?

  As you think about that multimodal 4O use case, it sounds super interesting and cool.

• 45:07Speaker 01

  Yeah.

• 45:08Logan Bartlett?

  As these things get done internally and you empower people to do them, you're thinking about the, the incentives or like what success looks like. And I was just taking down as you were talking, like thinking of what success looks like. And one is that people actually want to operate that way. I think that would be the most idealistic one. People actually like, it's a good functional way of, of doing this and that's the most idealistic. Then there's two other ones that I've sort of, or three other ones, I guess. One is employee motivation in some ways that like you're empowering people to do cool things. And like whoever the engineer was that did that, I'm sure had a really fun time figuring this stuff out.

• 45:44Speaker 01

  Yeah.

• 45:45Logan Bartlett?

  Then there's sort of this like, I don't know the right terminology for it, but like some, like obligation in some ways that like you are a company at the forefront that has the ability to do these things. And so there's some like obligation that maybe you guys feel, I don't know, to continue to push the frontier of these. And then the other one is maybe the most cynical one is like PR and it shows really well. And it looks really cool in doing this. As you think about like those different components, are, are all of them some factors? Or does someone, you don't even think through these things and someone comes to you and you're like, hey, yeah, this seems like a good idea. And we should be at the forefront of continuing to push this stuff forward. And so let's do it.

• 46:30Logan Bartlett?

  Or does it not even bubble up to you and someone just goes and does it and it comes back and you're like, that's cool?

• 46:34Speaker 01

  There's a lot. I want to unpack all.

• 46:36Logan Bartlett?

  Yeah, yeah, yeah.

• 46:37Speaker 01

  So I mean, so first, there, me also like a note just about like company building.

• 46:47Speaker 01

  I think that there are a lot of organizations that are in the business of selling you products and saying, we want to sell more of that product. We make Tide Pods and we want to sell more Tide Pods. We're a credit card company. We want to sell more credit cards. We want to have more computers on more desks, that kind of a thing.

• 47:04Speaker 01

  Ramp started with a very different guiding mission.

• 47:08Speaker 01

  The way that we all measure ourselves is how much money and how much time have we saved our customers? It's not how many cards that we sell, it's not how many bill payments that we process, it's any of that stuff. It is what is the outcome that we're trying to drive? And when we do product mode maps, I think we're relatively hazy like on it. We say like, this is the future state that we want to drive. If we do this, you know, finances no longer be tedious, monotonous, but strategic and insightful. But how we get there, we actually give live leave like a lot of discretion to teams. And teams themselves are quite distributed. Some of the largest teams, like the spend management team at Ramp,

• 47:46Speaker 01

  you know, is on the order of a dozen people, which I think is very surprising for, for, for people when they, they know that Ramp is an 800 person organization. And so when I think you have a clear, we're trying to, to drive this, you're responsible for this area of, of the business and improving metrics on it, but how you get there is up to you. I think you, you inherently foster creativity and sometimes people look to and have the permission to take new paradigm shifts. And so it's not really me mandating, hey, we want to do cool. You can always feel it when there's someone with like too much centralized control, saying they're going to do this stuff like, oh, we're this AI this, or we're pivoting to big data or any of these large stuff like,

• 48:26Speaker 01

  I, you know, we, we hardly talk about this stuff. We really talk about like this tourism. We exist to save you time and money. Here's the products. Here's how they work. And I think it leaves a place for incredibly bright and curious and just like excited people to say like, I'm going to do this, but I'm going to radically improve the amount of time that we save, you know, and I'm going to implement AI as a tool. But it's not about like, what's the shift? It's more of what's the problems that people ultimately have and how do we go build, build against that?

• 48:52Logan Bartlett?

  One of the things that you did touch on there that I think is, Is an interesting one is is org design and you touched on this a little bit earlier as well, but there's this balance that exists between autonomy and empowerment of the individual and consistency and centralization of the experience and those two things are kind of at odds with one another at times and you know, if you run a dictatorship, the design, the output that someone sees from a, you know, company product or design or aesthetic is probably a lot easier to keep consistent, the font, the fonts and the colors and all that stuff than a decentralized organization that people are making a lot

• 49:36Logan Bartlett?

  of independent decisions and there's this tension between the two that exists. And so how do you think about like, what are the shared services that the organization has? Hey, we don't need, we're going to be an AWS shop and not a GCP shop and that's not something we're going to let individual people make their, their own decisions on versus, hey, we really do want you to solve the business outcome and how you get there is, you know, on, on you and where the tension or the, the balance exists between the two?

• 50:05Speaker 01

  It's, I think, an amazing set of questions and I'll maybe start, I mean, you highlighted, you know, AWS, Amazon Web Services versus GCP, Google Cloud Platform.

• 50:17Logan Bartlett?

  Yeah, or whatever it is.

• 50:18Speaker 01

  Yeah. I remember this debate when Ramp was on the order of 40 to 50 people, pretty early days and we were, we were trying to reason through, would we be a highly centralized, really deliberate, because we do have a point of view. We are opinionated on products should work a certain way, they exist to save you time and money. If you tightly couple certain products, they work better for certain people and you're a small company, you have very limited resources versus, you know, do you want to give lots of flexibility and sometimes you'll have, you know, some wasted effort, but you don't slow people down, people have a lot of discretion. And I would say probably the most extreme examples of this are probably the organizations

• 51:01Speaker 01

  you highlighted, Amazon on one end and Google on the other. Where Amazon, if you use and you explore all the tools and services available on AWS, it's like a zoo. Like there's, you know, multiple versions of tools that do the same thing. Competing teams where certain teams win and lose out. But, you know, for a long period, like really radical, lots of good stuff and a super thriving ecosystem. Google, I think is very famously the, kind of the opposite way. Where it's highly centralized decision-making. You know, as products come out, they tend to use the new norms, they sort of, you know,

• 51:45Speaker 01

  turn off products all the time. And there's these like mega organizations all working towards these one super products. And, you know, that's an extreme example of reasoning through which one do you want to be? Do you want to be Google? You know, with like one or two super large products, you're going to be Amazon where it's like, you know, it's more of a rainforest. Like crazy stuff going on in there.

• 52:03Logan Bartlett?

  It's the move fast and break things or the measure twice, cut once kind of dynamics.

• 52:07Speaker 01

  Yeah. And I'll tell you, like, after putting it in those terms and really thinking through it, you said, you know what, like, I think Amazon has got to be the way. Because it when, I think that one of the only, when you're a startup, like having really focused effort, high velocity, moving quickly, getting things out and learning often is the way. Cause we're wrong a lot, all the time. But if you can understand how customers react to it, you can improve and more accurately point the direction and vector of your product. You get a lot further in the right direction, a lot faster if you take these super large bets released in multi-month to multi-year cycles. And so we said, you know what?

• 52:51Speaker 01

  We are, we are at times going to have many different ways of solving the problems. It's going to create sometimes some clutter and we think that's a good thing. And we need antibodies and internal mechanisms that are, you know, doing occasional QA in order to be able to not stop. The production and the creation of new things, but to be cleaning and sort of sweeping the streets so often. And so when I think about what that means is like you actually start to build teams that are both. You have, you know, single threaded teams where you have people from the beginning and inception and customer research. You know, interviews of the products, to building alphas, to betas, to shipping these productions where it's the same teams.

• 53:29Speaker 01

  Really small groups, it may be a PM, you know, uh, you know, three to seven engineers, depending on the nature, it may be a designer. And then as it gets ready, then you later start to bring other people on and we have lots and lots of small teams. And so most of the organization is that. Then you also have certain horizontal orgs that you really build is how do you make sure that for any developer, I don't care what you're working on. It's easy to develop, you have standards sets of of infrastructure. And if you need new tools, we're going to make sure your experience of procuring, uh, working with and using that is really fast. Uh, and SLAs are incredibly important, uh, and you even review people differently. Like when you review, you know, products of how well do they do, you ask customers, you look at speed and velocity productions, you look at outputs.

• 54:12Speaker 01

  When we are reviewing folks in more infrastructure type organizations and more cross functional organizations. I actually care very little about what the manager thinks about a particular employee. I'm really interested in how the cross functional partners, that speed, they impact this ability to do their work, impact. That's probably the most important signal that you get. And so I'd even say it even goes down into like review design and thinking about performance where. I would say I wouldn't just pick one, you need a nature of both, but even, you know, assessing individual performance. Thinking about what are the SLAs, what are you measuring and try to optimize for becomes very important. And I, and what I would say is that where organizations get this wrong is they oversimplify and they say, you know, everyone needs to get reviewed by the, their managers and all their downward reports.

• 54:58Speaker 01

  And that works perfectly signed for some, but it's horrible for others and becomes very bureaucratic. Or you have certain practices that are really well set up for horizontal lunctions that don't, that don't deal with, you know, uh, single threaded teams. I think that's why like, you know, I had lots of friends who work at Google and they're like, I, I leave because I work on something that maybe if I'm lucky, like a year later ships. And that's fine for the horizontal people, but really tough for the vertical people who want to get things out. And so I would say no system is perfect. You know, I've heard of, some organizations that reorg every 18 months, just so you know, you have some period of time when you get the benefit of changing it. You know, I would rather not do that and be all one way or another, but sort of thinking about every individual function and how do

• 55:39Speaker 01

  you structure, what it does, how it can operate, the goals you set against, and even later on a performance basis, how you measure those people.

• 55:47Logan Bartlett?

  So when you set it up in that way, like maybe a new product initiative or something, you're going to roll out, you guys recently announced travel. And so does that get set up as its own independent entity and with a PM and a handful of engineers and designers? And then ultimately, does it roll back into some more centralized product organization when it reaches maturity? Is that the typical life cycle of it? Or does it stay independent and autonomous? Or does it depend?

• 56:13Speaker 01

  It's totally right. Travel is an awesome example of this and bill payments and others. But so first, what you said is exactly right. We started with a couple set of people, which is, hey, I think a few years ago, maybe 10% of all card transactions on ramp were bookings of flights, hotels, autos, travel, entertainment, that kind of a thing. And we could see very quickly, this is going up and up and up. You know, today, about 20% of all transactions on ramp are booking of flights and hotels and all those. And there was this customer

• 56:52Speaker 01

  need. People were saying, look, I would like to have more control over when, you know, an employee is booking a hotel and flight. I would love if your great card controls could actually dictate maybe a booking portal. So if people ultimately are booking, they see hotels that are in policy, they see flights that are in policy. When the receipt, you know, is the transaction is affected, the receipt is automatically pulled back. Right? And so there was, there was a clear customer need. It was building on top of additional product, all this kind of stuff. Now, we didn't go say spend management team go bill travel. What we said is we sort of took off a small team, we Functionally locked in a room for like a year. And we told the people, don't talk to them.

• 57:34Speaker 01

  They're just going to build travel and doing customer customer research to figure out what's the right form factor to build this. And it was exactly as you described. It was, I think initially predominantly engineers. I think some from product joined reasonably early on. And it was decisions in trying to do research. What were the customer problems reasoning through the right way to build? And then working with a, you know, an increasingly large set of first design partners, alpha customers and all that. Eventually we start seeing certain metrics get hit. We see you, you give it to a customer. They like the design, say, great, you like it so much, would you use it? You build the thing, they have access to it. They start booking with it and they don't just book once, they book twice, they book three times. You know, you start to see retention go through.

• 58:19Speaker 01

  And eventually you can start to get to this early set of product market fit type metrics where you can see this bucket is holding water. Customers are getting, you know, value off of it to the point where they don't want to use other tools. And effectively you allow like a young baby child product to start to grow large enough where it's ready to go off to school. It's ready to start to join all the organizations. And sort of I'd say is that, that team was predominantly interested in development around, you know, travel related products. They were absolutely beneficiaries of existing products that that exist. You know, DevOps, making standard tooling that makes it easy to create new tools on, on, on top of ramp.

• 59:01Speaker 01

  A BD team that can go and negotiate deals on their behalf as we're working and evaluating different infrastructure partners. Spend management, which can go and surface, you know, card data to know what's travel, what's not. A receipt automation team and accounting automation team. And so there's, you know, they're effectively building single threaded. How do I build a great travel booking experience? Or a great, you know, travel insights tool, whatever it is. And sort of thinking about what's the API interaction between this cluster of services and the other ecosystem. But what I think is so important is if you, if you put. You know, small orgs are building new products into the large matrix wide away.

• 59:45Speaker 01

  You know, it's very hard to get resources, you know, people would say, you know, I've got this large product, like, give me the engineer to go work on this and I have a large, you know, you know, portion of the business and, you know, things die still burst all the time. And I think even if you look at like early history, if you look at some of the breakthrough products that like Apple ever developed, if, if, you know, under the Scully era, it was all kind of the large teams and they were mandated to work on some new products and existing products and it was really challenging to do and, you know, for, I think largely for better, like Steve Jobs would go and put people in a different building and say, go talk to the, don't talk to these people, let them build that. And we try to model that on it. And so I think there's a lot around product market fit. I think that once you get to a certain scale, you start, the question is not how do you get a thousand people to love it, but how do

• 1:00:29Speaker 01

  you make sure this is stable, robust and the foundation is strong for 10,000, 100,000, a million people to use and love this. And so the nature of things change, the goals are not product market fit, they're more operational, SLA based, how well the service interacts with and reliable is it? And those can change when it's ready for it.

• 1:00:47Logan Bartlett?

  And does that change often the people that are leading it? Like you start with pirates and move to Navy over time or something? Like the people that like getting it from zero to, you know, whatever, almost one versus the people that like rounding out, do you find those to be different types of people internally?

• 1:01:02Speaker 01

  It depends. What I'd say is, is often yes, but like, I don't know, like, like the One Piece fans out there, like people want to be king of the pirates, like, you know, eventually you go from like this little ship to running larger organizations and there are some people who are that way, love it, understand a problem space so deeply and, and actually make that evolution. And there's other people who like, I just want to build the new cool stuff and, and, you know, work on that. And so what I'd say is like, actually, like, I think it's probably like a good meta point even just about like talent. Like sometimes I think interviews over optimize

• 1:01:40Speaker 01

  on, is someone on, on skills and how capable is someone at building something or doing some job? And like, I will never fight. With anybody about it, you should understand how how well is able someone to do the job. You should assess are they good engineer, they could designer. What does they do? They have functional expertise. But I think a lot of times what organizations leave out. And frankly, I learned, I think it was one of the positive things I learned was that Capital One is, you know. Everyone is the hero of their own story. Understanding people's motivations. What they want to do, who they see themselves at, where they want to be in in years. And not forgetting that and periodically referencing back.

• 1:02:23Speaker 01

  And checking in and saying, you know, hey, you wanted to, you wanted to start a company one day. You know, the zero to one problem is really, really important. I want to put you on zero to one problems and build that and then check in. Once they get to one, do they want to go from one to 10 and how are they feeling about it? And it's not just, I think in company and organization and building companies for the long term. You know, if it's true, all a company is this is a set of people. You know, making sure that people are working on problems and feeling like they're progressing against areas in their own life that they want to go against is. Is I think one of the most important things that you can do, and I think that. I think that.

• 1:03:02Speaker 01

  So many leaders across ramp are just excellent at this. I think that Kareem, Jeff, Diego. There's so many people through the organizations that are really trying to understand, not just functionally what they accountable to, but. But thinking about, okay, like, how are people ultimately doing? How are they feeling about it? And if they want to go deeper, they do it. But if not, like, putting them on types of problems that that feels like moves them closer to where they want in their career is. I actually think what makes what gives organizations longevity and I encourage people to do.

• 1:03:33Logan Bartlett?

  I want to move to hiring, but and that some of the things you look at there. But before we hop, I'd be curious. As you think about resource allocation within the organization, how much of if you were to split it into 100%? How much effort or headcount do you think gets dedicated to like? The meat and potatoes of ramp as a core products, the spend management, the card, all of that stuff versus. I guess new product initiatives and I don't know if there's a third bucket of moonshots or maybe that's the AI team that you guys have as well, but how do you sort of think about the resource split of what goes where?

• 1:04:13Speaker 01

  Yeah, we'll come back to the AI team because actually they work on a lot of optimization of existing processes.

• 1:04:18Logan Bartlett?

  I assume it's cross-functional probably at all.

• 1:04:20Speaker 01

  Yeah, we'll go into all that, but I think a couple of things. I mean, so first, I think, I mean, he's very in vogue, but he has some crazy kind of management recommendations, but like, I think something that I heard Jensen Huang at Nvidia say, which I think was really profound is, you know, he believes his role as CEO is to create the conditions by which people can do their lives work.

• 1:04:53Speaker 01

  And I think it's amazing, right? When you think about the journey of that company, you know, he describes it, one is, one of the important things to think through is, you know, first, how do you create the conditions? How do you find extraordinary people, create an environment in which people want to work, feel motivated, all this stuff that people, you know, obsess on, right? Maybe this is that whole, you know, do you have the right talents? Are they in seed? Do you have the right culture? All that, there's a lot that goes into that.

• 1:05:26Speaker 01

  And, you know, by which people can do their live work, I think is an important, you know, addition that I think is easy to really glaze over. You know, that company for a long time was making chips for video games and for crypto mining and multiple time he sort of gambled a company and he said, look, we could go from being like a, you know, a $5 billion company to a 10 to a 20 if we just go deeper in the video game industry. But he said, you know, I have hired people of such caliber that actually I need to be thinking about which work do I select and which do I deselect from the company? And I believe that the opportunity to build GPUs for AI or for biological research and genomic research is much more important. And so one of the important things that he

• 1:06:10Speaker 01

  Assesses his own effectiveness as a CEO is are you making sure that people aren't just doing work? But are they working on the right sets of things? And I think that, you know, that company and how deliberately they've they've taken large bets gambled and and I think time and time again done right. I think is a testament to his ability to do that. Do you select the right work? And so I think that that's a big part of it of a CEO's job. I think that the other is, you know, I'll say, I think we're 1,929 days old today.

• 1:06:44Speaker 01

  You know, it's less obvious now, but like I remember day zero when we had three people and I was 33% of the headcount. And when it's when there's just three people, it's obvious that all a company is is a collection of people. And so, you know, if you want to have any shot at building something great, you better find really great people. Get them into the company and then hopefully you can create the conditions for them to do great work. And so I think to actually back into the question you you really asked, like, I think. You still need to spend an enormous amount of time doing it. I think I do spend about roughly a third of my time, whether it's hiring. And probably a little more if you look at just trying to inspect what's happening in the business. You know, these really smart people, are they able to produce great things?

• 1:07:28Speaker 01

  Are people getting in each other's way? And are organizations working really efficiently? I think is a very large part of it. And I think it's important because one of my my I have the power to convene meetings. I have the power to change structures and organizations and I need to use that at times. And so I think there's a lot. And I have the ability to now more people are interested and responding to cold email from me than maybe a couple of years ago. And, you know, I can meet from and learn from a lot of people. And so I think like, you know, some of my job too is thinking about what are the things that only I can do? Or maybe do more easily and make sure I'm I'm putting my time on that. And so maybe it's finding extraordinary people, assessing structures, changing things out.

• 1:08:09Speaker 01

  And then, you know, maybe outside of this third, am I selecting some of the right work?

• 1:08:14Logan Bartlett?

  Interesting. And so that's you at a personal level. Organizationally, the, I guess, existing products versus new products. How do you think about that split or that ratio?

• 1:08:25Speaker 01

  Yeah, yeah, for sure. So I think it differs based off of job function, right? In some sense, if you're, you know, a sales or account management or customer success, you are working on existing products.

• 1:08:39Logan Bartlett?

  Sure.

• 1:08:40Speaker 01

  And that will, you know, I think, understanding customers and bringing in to maybe think about where the future is going is an important part, but you are definitely living in the today. I think for things like engineering and design, product, even aspects of like product marketing, brand, you want to be thinking about, you know, structures that exist over many years. And so there's some element of where is the world going? And how do I make sure I position myself as best as I can for where that's headed? And so I think these organizations tend to have much larger percentage, percentages of these orgs, working on the nature of those types of problems.

• 1:09:22Logan Bartlett?

  The new stuff.

• 1:09:23Speaker 01

  On the new stuff. And so at the beginning, I would say is like we, you know, before a company has product market fit, it's all the new stuff.

• 1:09:29Logan Bartlett?

  A hundred zero.

• 1:09:29Speaker 01

  It's a hundred zero, yeah. I still think today, if you look at, you know, still the majority of, you know, people related spend is in R and D and work. And I would say the majority of that is going into, whether it's new surface areas or sort of taking existing products to the next level. And I think that's great. And I think that's sort of been what people identify with at Ramp. It's just constantly improving. And that is one of the value propositions is it's not just, you know, maybe the best deal in business, the most high ROI, like it saves you time and money, it pays you to use it and it automates your accounting. But I think a feature of using Ramp, as you know, every, if you're really paying attention, you see it is every day.

• 1:10:13Speaker 01

  But you check back every, you know, month or so, and the tool just gets better. And I think that that is a quality of it. And so I think there is even this essential part of, you know, our culture and what people identify about our product is like, this is a tool that gets better and self improves. And so I think that's part of why I think that we, you know, put more effort than most at that.

• 1:10:39Logan Bartlett?

  You touched on one of the characteristics that you hire for is curiosity. Is that one of the principal things that you want to walk out of a meeting having assessed from an individual? Is that like the canonical trait that you think of a ramp employee having? Like where does curiosity fit on the Maslow's hierarchy of, you know, whatever characteristics of a ramp employee?

• 1:11:02Speaker 01

  Oh, I mean, I actually think it's very high and I don't think this is restricted to who's working on new stuff, right? Like I, at the end of the day, like back to the mission, save people money and time. If you want to save them money and time, well, every business is different and these are lots of different people. You need to understand where are people spending money? Why are they doing that? Where are they spending time? And so I would argue if you want to provide, you know, build better tools, but frankly, even offer good service, you need to be fundamentally curious, you know, about who is this person at the start of it. And so what I'd say is like, I actually don't think there's a single job at ramp in which, you know, curiosity isn't a, I think a quality that we look for.

• 1:11:46Speaker 01

  So I would say it's about as close to the top of my Maslow's pyramid.

• 1:11:50Logan Bartlett?

  How do you, how do you, how do you assess that out in an interview? Or like, what are the characteristics that you think, I'm sure everyone describes themselves as curious, but if you're sitting down to a conversation in a meeting, are there certain things that you look for in curious individuals?

• 1:12:07Speaker 01

  So there's different manifestations of it. Maybe like a meta point, just for people building companies, thinking about it and interviewing is, I think sometimes people try to do too much in single interviews where, you know, there's someone who's trying to cover all the bases and that's like tough, right? I think thinking about what is the overall, like how many interviews are you putting someone through? How do you make sure that every interview is very different? And you got a lot of companies, you know, people get relatively similar questions across interviews. People aren't that coordinated. And in a great interview process, people are getting very different experiences across different interviews. Maybe with me, I'm curious, you know, maybe I'm playing like, what's your motivation, you know? Today and I'm trying to understand kind of desires, your story, how did that meet?

• 1:12:52Speaker 01

  That might be one version of interview that I'd give. Maybe you talk with Kareem and he wants to see how you're doing under stress and maybe he'll like disagree with you about anything and see how you handle it to others is more can you do the job? Right. And so what I'd say is somewhere in there, there needs to be someone thinking about these different traits. I wouldn't try to like combine everything. I would try to have very rangy interviews where everything feels different. One of them should be about curiosity, not necessarily all would be, would be first. Second, when I think about curiosity, it can come in different ways. You know, I think it can, some of this is not just what people say, but what they've done, you know? Show me what you built. Why did you build that? What was the need that you were, you know, trying to solve against?

• 1:13:33Speaker 01

  Who inspired you in order to do this? And so people kind of, you know, demonstrating curiosity in their thought process. But even, you know, the, if they're a builder, do they have the curiosity to use new tools? To try things out in new ways for people who, let's say are less in a builder mode, but maybe organizing resources, you'd say, tell me about a project that,

• 1:13:56Speaker 01

  you know, you feel was particularly successful, like why were you working on it? What happened? What were you specifically responsible for for other people? Did it work? How'd you know it worked? You know, and then you ask a follow-up question after they kind of go through all that. Most people can't kind of give all the details and maybe focus on one of the great ones, you know? You know, can you can go through every aspect of that with a high degree of specificity? But then I think where you can sort of pull out curiosity of that question is great. What would you have done differently? What worked beyond kind of your expectation? And, you know, what went wrong? Like, how would you approach it? And if you see people kind of who they're not just thinking about it for the first time, but have actively wrestled with this, you know, even in the project they're most proud of,

• 1:14:41Speaker 01

  like there was something they could have gone better. I think is what you, when you start to see real curiosity emerge. And what I would say about, you know, I think great crafts, people, artisans, people doing, I think a lot of great foundational work, they're, they're never truly Satisfied with, you know, even on their best day, the best, finest work that they've produced, there's always something deeper that they could have done, something better, some level of improvement. And so it could have been the craft of selling, it could have been how they organize a product, it could have been what they built. But there's many different aspects, but I think a lot of it often resembles around, you know, what did they build? Why? What? Well, you know, I think another simple way is, you know,

• 1:15:28Speaker 01

  what are you reading? You know, tell me your, you know, your three favorite books or the last three books that you read. I'm not saying books are the only medium in a way to like learn stuff, but like it's, you'd be surprised in how many people don't read anything. And, you know, I think just sort of asking people just about like actual, not like hobbies, but like, you know, interest in where they've gone deep is, is good.

• 1:15:47Logan Bartlett?

  The, the, the point on the, the, everything can be better in some way. And the self flagellation or the really thinking about the optimizations. It's, it's such a good point. And I think if you look at some of the best artists across any medium, there's this high correlation with depression that exists for a lot of the artists. And it's this desire, I think a lot of people have to never be satisfied and to keep pushing. And it manifests itself in art and creativity in some ways that can be actually like psychologically problematic for people.

• 1:16:26Logan Bartlett?

  Because they look at what other, anyone else would have determined to be a great piece of art or a great album or whatever it is. Anyone else would have said it's done. And instead they say it's not good enough and keep pushing or keep iterating or keep working and don't release it. Past a point that is normal, right? In any way, shape or form. And not to say that, you know, the best entrepreneurs need to be depressed in some way, shape or form. But I think there's, I mean, if you look at some of the canonical examples of, of our time, I think Elon Musk has been pretty public about his, battles with, with mental health. And there's this desire for perfection or incremental,

• 1:17:06Logan Bartlett?

  incrementally approving on everything that I think some of the best employees have, hopefully not to the extent of mental health crises. But yeah, it's an interesting point.

• 1:17:16Speaker 01

  I mean, I think it's, I think it's real. I, I think that if you want to.

• 1:17:22Speaker 01

  Produce great work, create things that are really timeless. Like that's a very, you know, so few people are really, you know, beyond, you know, sets of friends and family and people around them are really remembered. And I think to, you know, build or create or work on anything that actually is transcendent, moves, you know, the, you know, societies, human race, people forward in some way. I think that that bar is so high.

• 1:17:49Speaker 01

  And I can understand, you know, for someone who wants to have that kind of an impact. And that kind of desire and it's so hard to know, will this work be timeless? Will it actually move forward? Like I can see why.

• 1:18:02Speaker 01

  You know, depression can be a really natural thing. And

• 1:18:06Speaker 01

  what I would say is like, I think it's both true that dissatisfaction is.

• 1:18:13Speaker 01

  I actually, like, I think there's something both deeply unhealthy about it, as you point out, where, you know, one way of looking at this, you know, as good as you've done, you'd always be better. And so, you know, there you go, you know, you should always feel a little bit bad about yourself. And I think that's real. And I think that you're right. I think there's very few entrepreneurs who, you know, I think people usually say like, Eric, you're pretty smiley and chipper guy. And like I am, but like, I think like, yeah, there are moments where like, you know, I think you can't help but feel this stuff. I think that the other

• 1:18:45Speaker 01

  way to think about it is how profoundly disappointing would it be though? If you truly made the best version that could ever be done in the history of the world of any particular thing, and there was no surpassing it. And that was it. And there was nothing beyond it.

• 1:19:04Speaker 01

  And in some sense, I think that would be more depressing. Yeah. And more profoundly sad. And like, and I think there's this broader set of points. I mean, maybe even the circle back to. How people think about, you know, various, like, I'm not a futurist. I don't know, you know, how things are surely going to play out. And I, you know, I'm more interested in how to help businesses be better today, how to help people live better lives today. And that excites me. But like, there's, there's one version of the AI world in which AI gets so good, there's no job that humans can do better. And would people, you know, live in some utopia where actually you can just hang out and never work. And like, you know, to me, that that feels very

• 1:19:48Speaker 01

  profoundly depressing. Because I actually think, you know, for me, part of, I think we're, we're joy and satisfaction and part of what makes life interesting comes from is the ability to work and produce something for other people. It's to improve your skills. It's that constant kind of pursuit. It's, yes, I've gotten better and maybe produce something at a new height, but like, there's still further to go. And I still want to keep climbing. And I still want to develop better tastes. And I still want to put out better products. And so, part of me thinks that even as tools and capabilities get better, I think that's part of what actually what makes

• 1:20:28Speaker 01

  us deeply human. It's the, it's the act of creation. It's the, you know, delivery of service. It's being there and tending to other people. And so, I don't think those ever, ever go away. But, you know, I would say for anyone who's like stuck in the riot and is, you know, worried if things are ever better, like, I think that's, that sort of line of thought has really been helpful, helpful to me and some others that I know.

• 1:20:55Logan Bartlett?

  It's such a profound point. And I think it's, I think it's fascinating that there's no hill you climb, at least I've yet to find it. And I've talked, I've sat here, I think this will be 110 episodes or something. And I sat with some of the people that I think have built technology in different ways. And I think one of the unifying themes throughout is there is no point in the journey that you're aiming towards, that you reach some fulfillment. That is like the canonical achievement. And the second you get to the top of one hill, you realize there's another hill to climb.

• 1:21:38Logan Bartlett?

  Or the, the beauty and the enjoyment truly comes from loving the process. And there's always going to be someone that built a bigger company, or there's a new room to be a part of. And I, I remember I always just, I wanted to be a general partner at a venture firm when I was, I don't know, whenever I figured out what venture was, 23, 24 years old.

• 1:22:02Speaker 01

  Yeah.

• 1:22:03Logan Bartlett?

  And I got there.

• 1:22:04Speaker 01

  You got there, yeah.

• 1:22:05Logan Bartlett?

  And I looked around and I was like, well, what, I'm, I'm here. Like I didn't, now what do I do? Right? And it's a funny thing that there's this nostalgia I have still for like the process of getting to that climb. And now there's new hills that I found to climb. But the achievement in and of itself wasn't the filling. It was actually the journey along the way that was the true enjoyment. I'm sure you feel that with your, your company, each incremental fundraise or an IPO. When that day comes, it'll be a nice checkpoint of celebration. But the, the actual inputs and the process is where the true enjoyment comes from.

• 1:22:47Logan Bartlett?

  And if you don't like that, then you're not going to find fulfillment, I don't think, at some end state.

• 1:22:53Speaker 01

  I, um, I mean, I think that's really profound and I think that's, that's really true. It's a, uh, I think it's both a, I, I think it's like an amazing, like divine blessing of like this hedonic adaptation. Like, you know, you finally get what you want and, you know, there's in some ways maybe no better feeling. And then, um, well, you know, it's a few days later and you're like, what's next?

• 1:23:17Logan Bartlett?

  It's insatiable.

• 1:23:18Speaker 01

  Yeah. And I think it's both, um, uh, in some ways it can be like really sad and deeply depressive. But also I, I think it's like one of the, uh, one of the great things, you know, it's a pursuit to do more.

• 1:23:31Logan Bartlett?

  And I think that's a good lesson or takeaway for anyone in any job that like. Like if you're not enjoying the inputs, and I know you guys focus more on inputs than outputs as a company.

• 1:23:42Speaker 01

  Yeah.

• 1:23:43Logan Bartlett?

  If you're not enjoying that, then that might mean you're in the long line of work, or you have the wrong perspective of what you're doing in some way, shape or form. And that might be a call to action to go do something else because there's not some end state that I found anyone that's particularly motivated gets to that they're like, okay, now I've made it. I can hang out or I feel totally fulfilled from this journey along the way.

• 1:24:09Speaker 01

  Yeah, for sure. It's, and it's interesting too. I mean, I think,

• 1:24:17Speaker 01

  I don't know, it's been fun learning from a lot of other entrepreneurs, but like I think one of the, you know, to me, kind of movies and documentaries that I like, learned quite a bit from and has informed just like even how we build at Ramp. But, you know, I think about like, you know, careers and in some ways like, I wouldn't go so deep, but like, I think it's worth, worth an hour of people's time. Like I've, I don't know if you ever talked to this, have you ever watched Jiro Dreams of Sushi?

• 1:24:46Logan Bartlett?

  No. Jiro.

• 1:24:48Speaker 01

  Jiro, J I R O. And I think it came out, you know, a decade, a decade and a half ago. And it's about,

• 1:25:00Speaker 01

  his name is Jiro Ono. I think he was the first sushi chef in the world to get three Michelin stars. And he started this, this restaurant that's in, you know, the, you know, the basement of a subway station. And it's a documentary about him, you know, in the years of what his life was about. And it's very fascinating. I think at the time he was in his eighties and still working extremely hard.

• 1:25:30Logan Bartlett?

  What year was this?

• 1:25:31Speaker 01

  I want to say this is 2010, 2011.

• 1:25:34Logan Bartlett?

  It was contemporaneous. It wasn't, I mean, it's, it looked back, but it was.

• 1:25:37Speaker 01

  It was about him.

• 1:25:38Logan Bartlett?

  Yeah.

• 1:25:38Speaker 01

  Yeah. It was about him and his process and, you know, running the restaurant. And it's not long. It's an hour, 15 minutes. And by the way, he's, he's still. Working. I think he's in his late 90s now and he is still, I mean, every night or most nights of the week, still showing up to work and trying to make things better. And there's, it's an amazing documentary. It's worth watching the whole thing, but there's this kind of amazing scene. I find with like a lot of lessons for building startups and frankly, working in anything, any kind of craft. And the narrator basically asks him this question, something to the effect of,

• 1:26:19Speaker 01

  you know, what does it take to make great sushi, you know, and to make great food? And he answers this in sort of this odd, but actually really obvious way. He says, well, you know, to make great food, you need to eat well. You need to eat and enjoy great fish, you know, and if you don't develop great taste and your sense of taste is lower than that of a customer, then how are you going to impress them? And then he goes on and he talks about like the guy he buys rice from and he's like, oh, Hiro Michi or whatever the guy's name is, you know, I only buy rice from him.

• 1:27:03Speaker 01

  He knows more about rice than anyone on the planet, you know, and he's going on and moves into this scene. And, you know, one, I think it's like this question of, I think it's great for craft people. Like if you don't actually like use the product, enjoy, understand the problems better, like how are you going to make something that deeply solves something in a new way? And I think that most people who like market products, build products, you know, talk, don't really talk to customers nearly enough and they should do that more. But I think that the other thing, you know, along the way, you know, is they talk about what he's trying to do. And he basically says, he's been making sushi since he was like 11 years old, something like that. And so, you know, now almost 90 years of every day showing up to do the same thing. And he talks about, he's still trying to climb this mountain and, you know, as high as you go,

• 1:27:46Speaker 01

  you're still never going to quite reach the peak, but you know, it's the pursuit of getting closer. And it's there. And I think it was one of those things, it was filmed in this interesting way where people were interested, not just in the quality of what was coming out, but who are these people that are developing it? And I think it's, I think it's great, but I think, you know, sometimes people look at him as he's such a stern, impressive guy, but like I think he's, I think there's a joy to what he's doing and you can see it clearly too.

• 1:28:11Logan Bartlett?

  Well, and it's to hear, and this sort of ties back to hiring people into an organization and on this curiosity point, to hear you talk about the history of double entry accounting.

• 1:28:21Speaker 01

  Yeah.

• 1:28:21Logan Bartlett?

  Or we did a blog post on the history of DIP rank and the implications that came for that for the fintech industry.

• 1:28:29Logan Bartlett?

  You can find craft or curiosity in literally anything like I double entry accounting. Might be one of the most arcane mundane topics, I think, out there. But like, there's a level of curiosity in in your craft and what you do that caused you to go back and actually learn about the derivative impacts of all of that. And I guess I've, I don't think I grew up. I certainly wasn't like genetically wired to be fascinated by the venture industry and technology. Like, I don't think I even knew venture was a thing until I was 23, 24.

• 1:29:02Speaker 01

  Yeah.

• 1:29:03Logan Bartlett?

  But like, I've enjoyed learning about the history and all the different people and the players and, you know, how all these things came to be. And so it really can be anything. It's really just finding leaning into and enjoying that. And I found that when you interview, and I'm sure you've seen the same thing. The people that can just go so deep in these things and talk about them forever are the people that persist within highly functioning organizations. And they take such a pride in the part of the iceberg that never actually gets seen. And if you peel it back a little bit, then they can keep going deeper and deeper.

• 1:29:44Logan Bartlett?

  But you know, to your earlier point about like the simplicity of ramp. There's a lot of underlying infrastructure that exists to expose something very simple at the, from a functional standpoint for the end customer.

• 1:29:59Speaker 01

  And it's totally right. Like, I, I think one articulation of like, really the fundamental problem that startups are going up against that I love was, I think, Justin Khan, one of the founders of Twitch and partner at Y Combinator years ago. He basically said, you know,

• 1:30:17Speaker 01

  startups biggest battle is people not giving a shit. And he's Totally right, right? Like when you think about kind of like your own life, like you got a lot of stresses. It's like want to generate like returns, you know, you have stuff going on in personal life. You're trying to get better, you're trying to find the next deal. There's all kinds of stuff going on and some random product marketing deal is like not high on your list of priorities. Right? And so you need to be both simple, interesting, direct and compelling enough that in a period of about five seconds, you know, I can somehow crack into like it is worth some remnant of your time to maybe look deeper into. And so I think it's both deeply important to remember that, um, you know, in the creation and sales, marketing of different products, you want to be very simple, very compelling, very clear.

• 1:31:01Speaker 01

  Uh, and I think for most people, uh, ramp is and it feels like, and, and, and, you know, I, I think this is, this is fair to say like, it's easy, it's intuitive, you know, and it's a, you know, spend management software that helps your business run better. Um, it saves you time and money. And for most people, that's all you, I think you need to think about. You don't need to think about how it works. You should just know that if you put this in your organization, it's true. You can expect that, um, if you're like the average company, your company will spend 5% less. We work hard at that and we're going to go and do that. Now, in order to create this simple product, it's easy to understand, easy to consume. You need a deep amount of interest in like,

• 1:31:44Speaker 01

  like what causes companies to spend time, what causes them to spend money? When money moves, how does it move? When it's accounted, how is it accounting for, where did it, you know? What is the history of accounting? Why are things done that way? Is it obvious that things should be built this way and should be building a car, not a faster horse? And so I think for the practitioner, there's no way, if you want to build something better, you have to go deep. And you have to do this. And so I think it's both keeping in one's mind. You want to be able to, um, articulate and craft and share this very simple version of the story. But both in how you build and also how you motivate people who are working to want

• 1:32:22Speaker 01

  to go so deep that you can find ways to, you know, to use the kind of, I think Elon Musk-ism, right? Like, you know, the best part is no part. Instead of building something with 20 parts, can you build it down to two, to one, to, you know, that, that, and keep it really simple. And if you don't really understand the function of why certain parts are there or were built in this way, how are you going to reduce it? And so I think that's part of the fun of building companies. You gotta think about both.

• 1:32:52Logan Bartlett?

  Well, Eric, thanks for doing this. Leave it here. That was very fun. We'll, we'll need to do round three at some point, some point soon.

• 1:33:01Speaker 01

  Thanks a lot, Logan. I appreciate your friendship and investing and supporting companies since the early days. And I hope this was interesting to listen to.

• 1:33:07Logan Bartlett?

  I listened. We, I enjoyed it. You know, I don't know if it's, I think people like it. So, thank you.

• 1:33:12Speaker 01

  Yeah, thanks.

• 1:33:15Logan Bartlett?

  Thank you for joining this episode of the Logan Bartlett Show with co-founder and CEO of Ramp, Eric Kleiman. If you enjoyed this episode, we really appreciate it if you subscribe on whatever platform you're listening to us on, as well as shared with anyone else you think might find it interesting. We'll see you back here soon on the next episode of the Logan Bartlett Show. Have a good weekend, everyone.