Introducing Product-Led AI
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Hi, and welcome to Gray Matter, the podcast from Greylock. Today, we're featuring the first episode of Product-Led AI, which is a brand new podcast from Greylock General Partner Seth Rosenberg. In Product-Led AI, Seth talks with AI leaders who are putting the power of AI into products that people love. You can find more about the show on the series website, productledaipod.com, and you can subscribe to Seth's LinkedIn newsletter to make sure you never miss an episode. Both are linked in the show notes. Now, here's Seth.

Hi, I'm Seth Rosenberg, and this is Product-Led AI, a new podcast where we explore opportunities at the application layer of AI. Most of the flashy funding rounds in the last two years have been focused on foundational models and underlying infrastructure.

The dominant narratives around AI applications are that they're thin wrappers on top of GPT-4 and that incumbents with superior distribution and data will capture most of the value. I take the opposite view. Just like in past technology waves, the most valuable companies build applications that real people use. Think Amazon, Facebook, and Google, each worth over a trillion dollars.

I believe even if we froze the latest models in time, we still have 10 years of building to actually make AI useful for people's everyday work and lives. With that in mind, I set out to interview the most prominent builders at the application layer of AI.

This podcast is meant for current and future founders who want to learn and be inspired by this next wave of AI-native products. We'll hear from people like Adept CEO David Luan, Perplexity CEO Aravind Srinivasan, AI pioneer Mustafa Suleiman, Reid Hoffman, and many more. Today, we're kicking off the series with Eric Gleiman, who is the CEO and co-founder of Ramp.

Ramp is a fintech company that started with a simple concept, a corporate card designed to help you save time and money. It's evolved into one of the fastest growing fintech companies in history and is becoming the leading AI software platform for finance teams, with products including expense management, travel, bill pay, and procurement. Since launching, Ramp has saved clients some 10 million hours of human labor and more than a billion dollars in spend.

Ramp is loved by both customers and investors and recently raised a series D2 round at a valuation of over $7.5 billion. We're lucky at Greylock to be investors in Ramp. Ramp has become a leader in AI in both how they run their business internally and how they use AI to further their mission of saving customers time and money. Eric, thanks a lot for joining. Seth, I really appreciate being here and hope today's session is interesting.

Awesome. So first, congratulations. You marked Ramp's five-year anniversary a few weeks ago. So how old is Ramp? We were 1,845 days old. So I think five years and two and a half weeks. And tell us about, I feel like that's part of your culture where you actually measure every single day. Tell us about that.

Well, as far as I know, no one gets more than 24 hours in a day. If anyone does, I'd love to know about that. But there are some hours in some days that simply matter more. Early on in the history of the company, we started making it a habit to ask, how old are we? Over the past 30 days, did we get the same amount done as the prior 30? Less? More? And it forced us to really think about

Just the value and leverage of time, both to give, you know, ourselves and the team permission to say no to things that are less important and say yes to things that, you know, might give us a shot at being able to be a little bit more productive and a little bit, you know, higher leveraged in what we do than others. And so it's been a great thing. I think that counting the days and thinking about the passage of time is part of what's helped us.

Ramp as a company move much faster, I think, than most organizations. Yeah, you've accomplished an amazing amount in 1,845 days. So maybe before we get into your AI strategy, just to rewind the clock, I want to, for the audience, like what is Ramp? And you had this interesting like independent idea five years ago, which is creating a spend card that's designed to actually spend less. Definitely. I mean, to start out, yeah,

I would start with an issue if it's really expanded quite a lot from, you know, at launch day in February of 2020, you could think of Ramp as the first corporate card that was designed to help companies spend less.

Now, it sounds really simple, but when you looked at most credit cards in the world, they were designed with incentive programs that would get people to spend more money, earn more points, that kind of a thing. But any business owner I've ever met, any finance kind of leader that I've spent time with is often thinking about the longer term. Marginal points don't change kind of the trajectory of the company, but being more profitable, having more time to find product market fit, to sell, to focus on long-term outcomes does.

And so first we inverted the nature of this program. And so we created software on top of the card and expense management that would show companies ways to constantly cut their expenses. You know, a dollar not spent is 100 times better than getting a penny or 1% back on that dollar in the first place. And so a lot of aligned software. But very quickly, we got obsessed with this notion of time.

Often, historically, if you were a bank, you could move money. And if you were a software provider, well, you could build add-on software. And we sort of awoke to this crazy system where for most companies, the reality was to buy one thing, you needed to manage at least two to four systems. You'd issue someone a credit card in one. You'd go to your expense management app like a Concur or Expensify to ask people to add receipts.

You would then link it to accounting software. And then if you were really on top of it, you would ask, you know, who reports to whom? How is that changing your link in HRS? And so to buy one thing, it was just, you know, it was awfully nonproductive to run and operate your company. And so that very quickly expanded us into what Ramp is known for today, which is, you know, really a full stop and first class finance operations platform to run your business more efficiently.

We're known for powering what today is still the fastest growing corporate card, not just in the US, but in the world. Fastest growing accounts payables platform. We have built in procurement, accounting automation. We're able to automatically tag books and records. We have ways to show companies how they can spend less. But the overall process

premise and the overall promise of the platform is we help the average customer reduce their expenses by, you know, today, 5% per year, uh, on, on card expenses, which is much more than what any, um, uh, rewards program can do. Uh, we help companies close their books days to weeks faster, um, uh, in overall, um, you know, allow companies the time and the space to, um,

be selling to customers, be making their product better, focusing ultimately on their mission, not on the work of doing work. You know what else? We support 25,000 plus businesses from whether it's early stage to farms, healthcare companies to, you know, leaders, you know, some of the great companies over the past decade like Shopify. I love how some of the most complex concepts and concepts

complex things to accomplish are actually very solving very simple problems, right? The Jeff Bezos philosophy of, you know, in 10 years, customers are going to want things faster and they're going to want things cheaper. And I think that's one thing I really respect about how, how you think about ramp is, you know, people prefer to save a dollar than to earn 2 cents back on a dollar that they've spent. Completely. And it's just, um,

It's just the leverage of like, I think the constraint in most organizations, no matter the scale, I think is focus and time.

really at the heart of it. You know, when you're 10 people, it's obvious, um, maybe you only have 240 hours in a day, some of which you should be sleeping. And, you know, if there is any portion of it being put towards non core, you know, activities of not finding new customers, it's not making your product better. Um, it's not like listening and learning and focusing on asking like, where's value created? Uh, I think it's a huge distraction and cost. And, um,

I guess what I'd say is we're known for moving money. We know a couple things about that. But at heart, I think we're a productivity company. And I think when you make people able to do more with less, to reduce the force needed to perform work and do more, I think that's where...

I think that's where wealth is created. You're so focused on saving people time and money that AI seems like a natural accelerant to make that happen. That said, I feel like there's a lot of buzz and distraction in Silicon Valley. There's a new hot technology every couple of years. So how in your mind is AI different

So look, whether, you know, Ramp is the winner from this or someone listening to this podcast, I think full stop. I think what we're going through now is the biggest shift of productivity, certainly in my life. And I think that all of us, especially folks who are leading and thinking through the strategies of their companies, need to be thinking about what it means for their business and working backwards from there. Yeah.

To come back to that for a second, it's a space that we've been thinking about for a long time. So Paribus, which was the precursor business to Ramp, it was my last company, was really a proto-AI agent. The product and the premise was it lived in your inbox. It was an app that you could link to your Gmail, Yahoo, Hotmail, whatever.

It would then scan your inbox for receipts as you were buying things over time, check the prices on the good services you bought, check policies so that let's say you bought a TV for Best Buy for $1,000 the next week it went on sale for $900. It would effectively detect that.

automatically write a note to sound like a person and email customer support of FastFi to say, you know, I bought this for $1,000. Now it's 900. Your price adjustment policy says that I'm owed a refund for $100 for the difference. Could you process it? And the user would wake up the next day and they had $100 back in their account.

And so it was a very narrow AI agent. He was using very simple natural language processing both to generate the message as well as programmatically detect the response, categorize it, and then our business was recharged to cut on it. And it worked. I think part of what's changed now in the context of 2024 is

Model reasoning is in most cases approaching and in certain use cases surpassing, you know, average human level reasoning functionally through a network call that anyone can access for fractions and fractions of a penny. And I think that has fairly profound ramifications on every business. And I think that for folks thinking through these problems, you know,

I think we owe it to ourselves to be thinking through and planning for a world where the capabilities of models are exceptionally high and increasing dramatically. I think we can be certain that it's going to dramatically change all of knowledge work. And I think for RAMP, part of why we're taking this so seriously is

I mean, you're already seeing it in software engineering, I would argue, is probably the most digitized technology.

you know, industry where I would argue that finance is, is probably not only one of the largest areas of knowledge work and, and, you know, economic activity, but I believe the second largest or second most digitized industry on the planet. And so thinking through very carefully of, you know, how will this change just the work of doing finance and,

you know, of underwriting, but also all the operational processes around the decision to move money actually become very interesting. And where I think it becomes very relevant from the ramp lens is,

At the heart of it, yes, we issue cards. Yes, we move money. But what we specialize in is helping companies issue cards with the right controls to automate the expense management process to figure out where should transactions be categorized. Historically, a lot of this work necessitated a finance professional looking, finding the ultimate patterns within this data, tagging. And what culminated in what most finance people would feel, which is most of their job is actually

really boring. It's a lot of operational work, a lot of tactical work. It's a lot of following up for receipts. It's a lot of downloading Excel spreadsheets, re-uploading to do payment runs, operational process of closing books, and very little is strategic work. And I think that there's the promise to take what is often the heaviest, and in some folks' cases, like

you know, the most low level and least interesting parts of the job and augment people's ability to do that in much less time and give them the space to work on. I think what folks in finance live for, which is, you know,

Figuring out where you can make the investments that are going to inflect the trajectory of your business go forward a lot. And so I actually think it's quite profound. It's going to change both the products that companies can create and also to, you know, I think there's going to be a lot of companies do very well and others that that's moats are going to erode and change very quickly.

Yeah, I totally agree. I think financial services is one of the most interesting areas for AI. A year ago, you and I talked with Reid Hoffman about this. Fast forwarding a year later to where we are today, I feel like there's two broad buckets of how you've deployed AI in RAMP. One is how you actually build the company internally, and two is in RAMP's products. So maybe starting with the first, in terms of how you just operate efficiently internally,

What are the different areas of impact that you found with AI in operating Ramp as a company? Yeah, I don't think there's a function at Ramp that hasn't been affected by it. So maybe I'll talk through a few. We can talk through growth, how we grow more efficiently than companies we compete against.

How do we provide a higher degree of service? How do we improve the quality of our marketing? And also just how do we just broadly create leverage so that every employee at Ramp can do more? When I think about the first use case, one of the top ways that Ramp grows is through outbound handsets.

You know, the average sales development rep at Ramp, we believe, has approximately three times the productivity of a next closest competitor. They are able to book vastly more meetings. And how we've been able to do this is to understand the operational workflows. Where are people coming?

querying for data, running statistical tests to figuring out what notes are leading at scale to higher degree of response and relevancy. So some of this is statistical methods, but there's even generative use cases to be running kind of

first response back and forth, you know, and making the work not be how do I find someone's information, you know, trove through the data, understand, you know, signals of intent and send things out to there's a response, there's someone who's here, we've aggregated all the information, you know, up to this point.

And you can dig in and be consultative versus doing the operational work. And so I think the combination of thinking through and pulling across all of large data intent signals and generating and improving that through feedback loops, I think has been a very large case that has made our team basket more productive.

Support, I think it's a very similar thing. It's an incredibly hard job because you have customers who can have extremely nuanced pieces of information and they're on the phone with you right then and they're stressed out. And I think part of the promise of AI is the ability to query what's happening in the product and databases and the customer's historical information and process that very quickly to generate not only, I think,

Now, the majority of cases automate the response to deflect. And so actually, you don't necessarily need a human interaction. But when it's done, it's not someone starting from square zero and trying to dig through a dashboard, but actually the dashboards themselves assemble to surface some of the most relevant information. So it allows our teams functionally to have superpowers and just knowing much more context about the problem, the nature of the problem a customer may be having.

Yeah, I think it's unsupported. It's super interesting. I think...

historically it's been viewed as this outsource cost center, but at the end of the day, it's how you're interacting with their customers. Right. And so I think AI could bend the cost curve where it actually becomes more of a strategic part of every business where you can, where it becomes an integrated sales channel. I completely agree. I mean, I, I, what I would say just like thematically about that, um, there was this, there was a moment early on in my just career that, uh, uh,

Totally changed the way that I thought about customer service. And so my last company, Paribus, went through Y Combinator. And they do this thing called group office hours, which is basically this group therapy session. And they ask three questions beyond the what are you working on? It's how quickly did you grow this week? What's your biggest problem? And what are you going to do to solve it?

And that week we said, well, we grew 20%. We want to add 100,000 users by the end of the summer. And our biggest problem is we're getting too many customer support tickets. And the way I'm going to solve it is we're going to hire a customer support person. And Jessica Livingston, who's one of the founders of Y Combinator, promptly ripped me a new one and said, look, here's what's going to happen. First of all, anyone who writes in to you,

It is a gift for every person who has this problem. Maybe only one out of 10, one out of 100 is actually going to take the time to tell you about it and to fix it. You should take it very seriously. Next, if you actually are spending time and you're surfacing up all this, first is a chance to build a connection with customers. But more than that, it's going to give you the information to bring that back into your product and to improve it.

On the other hand, if you just hire someone to answer and respond to the ticket, you will solve tickets, but you're going to be nonstop growing the number of people at it. And so I think that the insight first was, I think, just profound for people building businesses, which is like you actually want to use these signals and act and spend time in the channel. You don't want to just take yourself out of the process. But two, when I think about just the broader capabilities of AI over time,

to not just solve the ticket, but to understand where are customers having problems? What are the desires that they have and how do you improve your product to meet them? And so I actually do think it's a very core part of how companies grow, great companies grow and how they can better inform what they build. And I think maybe the last thing that I'd emphasize too on that point, and we can move more broadly is, you know,

I'd even flag like, you know, one of the more popular, you know, members of the ramp team is not a real person. It's, it's, you know, we have a channel called, you know, product asked Toby and Toby is a, you know, it's a digital agent functionally, which, you know, I don't have the time to listen to 50,000 sales calls, you know, and interactions and back and forth, but, you know, we're recording and pulling the data originally was for, you know, these cases of helping our, our,

you know, our team better support and learn and get feedback to better sell. But one of the use cases we've stumbled on is in the case of product marketers, in sales reps, and even people building products.

Um, you, one of the first things you want to ask is, you know, who are our customers, uh, and how do they think about product X? Why did people pick ramp over customer B, uh, to people when they think about expense management, what are their worries and fears? And it turns out, um,

Over a weekend project, you can actually build the capability to query all of the transcripts. And anytime you have a question about what do customers think, you can dig into it. We can pull up the data specifically of who the customer was, even the clip and dive into it. And so it's actually enabled us to do better product positioning.

to do better product marketing, to build better products and better find and surface up and be closer to our users. And I think sort of thinking about that attribute of the ability to query vast swaths of data, to process it, and to reason through where is its most relevant are all capabilities that years ago

were not a thing. And again, that's an internal use case, but I think sort of speaks to the broad applicability of what this technology can do. I'm curious on the go-to-market side, there are a lot of products in the market around fully autonomous AI SDRs, for example. How far away are we from fully automated end-to-end outreach?

And where do you think are kind of the big biggest areas of leverage today versus, you know, how it's going to evolve in 12 months? It's a great question. So some of it's already here, right? There is a meaningful part of even how we grow is fully autonomous. And it is not a future state. I am somewhat skeptical about the ability to off the shelf, you know, have opportunities

a fully autonomous sales agent that can just drop into any company. But I think that companies that are building and using these technologies apply, um, on historical data workflows that's specific to them. Uh, I do think that you can automate aspects, uh, of the sales journey in, um,

Uh, if you're methodical about breaking that down, maybe it's the outreach, but maybe it's not the response and back and forth. Maybe it's the quality, you know, pre-qualifications and, uh, using, uh, lead scoring, uh, and modeling to figure out, should you be, you know, it's just going to self-serve or, um, someone on the person and sort of more efficiently using people's times I think is already here. Um, but I think when I, I think about, um,

In the same way you can have very good salespeople who in some companies are very successful and in others they're missing quota and it doesn't work out, I think one of the most important aspects is both understanding your own...

product and data that your business has about customers, about itself and using that training data to improve it over time. And so I guess the short version is, I think the capabilities of these models are going to continue to improve dramatically. But I think this notion that you could just buy someone off in the same way you need to train members of your own team in order to reach high degrees of effectiveness.

I think that's a framework that I would apply even to just the capabilities of these agents. It's easier in more narrow agentic use cases versus broad.

But I think over time, it will move closer to maybe lower level aspects of the sales process will be fully automated, but still some of the higher degree, the complexity, the deep understanding and relationship. I still think you're going to want people very much involved all the way through. Yeah.

And on that note, how are you structuring your team internally to build AI products? Is it centralized? Is it embedded within product teams? How do you think about that? I know you have Yunyu and the Cohere team leading the way. Yeah.

They're fantastic. I mean, even to speak about them, I mean, part, so we, you knew was one of the early, you know, first 10 members of, of, of ramp understood how we built kind of philosophy for a lot of years left and started a company Coco here, which were one of the first companies at scale to use, you know, large language models in customer support prior to LLMs really becoming something used in production. I think they were using a GPT two or, or,

It was prior to kind of the breakout around 3.5. And I think just generally we're brilliant at thinking about how do you think about where model capabilities are going and how do you apply it into production use cases? They, you and Rahul lead the applied engineering team today at RAMP, which is, you know, first order, like a horizontal function.

They're really endeavoring to understand what are the processes and whether it's sales and how do we more efficiently reach out to risk and underwriting? What are the processes where, you know, we can give leverage to human underwriters, to software engineering, to even in our own sales development process,

you know, there's great companies like Tome, um, which, um, it's, you know, fantastic to have like a, you know, uh, an off the shelf ramp deck. It's better, you know, to think through how can this deck be personalized for the customer on the other end and how can we connect that to data pipelines? And so thinking, you know, I would generally think that it's high leverage for most companies to be thinking about applied AI, where in your business, um, can you improve the, the, um, um,

the throughput and capabilities of members of your team and how can you automate processes through using kind of novel capabilities of large language models.

I would extend that to infrastructure. AI is very, very data hungry. It's better the more sets of data it can reach. But if these are existing in different databases, different pipelines, you're not going to see this real benefit. And so some of it is an application layer, some of it is on an infrastructure layer. And there's also more vertical and product-oriented teams. Now let's move on to kind of the second big bucket, which is how you're building products for customers.

using AI. For sure. So I think if you go under and look at broadly our ramp intelligence suite, this is a few of the more obvious

areas where you can see it. So some are very in the background, like you'll notice if you submit an expense on Ramp, it is auto suggesting the right accounting category. For most companies, Ramp's algorithm is more accurate and certainly faster than an accounting team's ability to predict how does this expense match towards your general ledger.

There's expense intelligence, which is another vertical product where we can tell the difference and say, all these receipts are fine, but I will review this receipt. It has an old fashioned, which is alcohol, which is out of your expense policy. So rather than having finance teams auditing all transactions, they can audit the most efficient to things like price intelligence and vendor management, where upon renewal, you can see how you rate versus data.

Some of it you see is actually sort of this digital EA, sort of digital expense admin and supporter for folks. Next of it is sort of operational capabilities.

It's pulling data both from within your business, broadly what's happening in the market, and crowdsourcing to know how you can structure future negotiations and even automate aspects of that. It's probably the largest use case, which is on accounting. Not only is it pulling data from who reports to whom, HRIS, how you categorize expenses before, but is more effectively predicting

that for you. Some of the other use cases are how you interact with the UI. I think a lot of the history of SaaS has been okay.

learn our interface, click this button, go here and there, and it sort of forces people to do work. You can use things like Command K today, where if you can just ask things in natural language, we can generate exports, data queries, and even do work on your behalf. And so some of this work culminated in a launch

where Microsoft saw Inadella announced on stage at Microsoft Ignite last November, Ramp is one of the first co-pilot integrations where people can request, whether it's spend, ask questions about their expense policies and

It allows folks to both read from and write to the product itself. And so we can issue cards, we can route approvals within your company, do all this without ever needing to log into Ramp, all within your team instance. And so it's affecting both vertical products that one can use, how you interact with it. And I think some of where this is going for us is more agentic-like behavior,

I think when you sort of think back to Paribus or even Ramp, one of the core premises is we exist to save your company time and money. Can you actually use Ramp in order to, in specific use cases,

Save you time directly. Go, you know, and that can be automating processes today that people are doing to save you money to manage negotiations on your behalf for you. Or if you just want to just review it and jump in and critical points.

Those are all types of products to purchase, to move funds from place A to B on your behalf, to purchase things, tickets, softwares, run negotiations. Those are all things that I think are very much possible and heading into.

So it's often, you know, there's specific product use cases that are more verticalized. There's horizontal use cases, which is probably where I'd start for most companies. And I actually think it's where I'd spend the majority of time for more application layer companies. But that's broadly how we think about it. But we can go a lot deeper. Yeah, that's super interesting.

I feel like it's like many waves in technology. Like I remember at Meta in 2012, there was like a mobile team. Now there's no mobile team. It's just an obvious part of how you build every product. And that seems to be the trajectory with AI where you start where you need the internal expertise horizontally, but then it evolves to being embedded into every aspect of the company. I totally agree. And it's just one of these things too, where it's so...

I mean, one of the joys I've seen of people using, you know, interacting with large language models is, um,

you know, you ask a question in a simple way, but then you learn to ask a better question or, you know, you, you go in and you ask, um, you know, GPT for, uh, you know, not just would you run this analysis, but, you know, you're a very careful, uh, and methodical analyst. Um, you know, please be thorough in your approach and it increases the, the, the, um, the quality of the answer, which is very alien and bizarre for some people. Yeah. It's fascinating. Um,

I'm curious, how do you think about the advantage that you guys have built up on the data side? Like what's the advantage of actually owning the card and owning the underlying spend data and how that impacts your AI strategy? I mean, so...

We spent a lot of years, I would say at Paribus, we had immense data scale. We were processing over 100 million emails today, but our only ability to affect the transaction was to send an email to write to customer support for a narrow set of transactions and say, can I get a refund? It was very limited. And so when we were thinking about...

ramp itself, we were thinking through how do we not just have an ability to read vast amounts of data, but how can we write? How can we do things on customers' behalves? And so part of the conclusion was you really needed to be deep in the workflow in the transaction layer to be not just powering the transaction itself and

That's part of how we grow and monetize as a business. But ultimately, you know, if it positions you to do things like when companies say we want to cut expenses, we can see duplicative vendors and you can automatically turn off cards for specific vendors for, you know, specific styles of spend. And so it allows people to not just get insight, but to operate your business. And so what I would say is,

to ladder it up. What's different about Ramp is we're actually how companies power a lot of their own operations and are the workflow layer for companies. We're not just how you spend on a card, but how you gather all data to process an expense report, how you close your books.

How you determine which purchase is approved or not. And when you're deeply embedded in the workflow, not only does it give you, I think, do companies want you to pull in more data to affect more workflows? And we've seen that as we've expanded through the office of the CFO.

but it also allows you to provide a higher level of value. There are things that we know about how companies do their card expenses that allow them to spend less on accounts payable. On accounts payable, it'll allow them to close their books faster. And so there's some of this is both what data do you have? And are you getting both the inputs of that? Can you affect the nature of the output? And are you seeing the output?

Um, so it allows you to better run workflows and personalize. Uh, next I think is distribution. Um, uh, I think is fairly important for, for companies, which, you know, ramp is, is, is powering tens of billions of dollars of transactions per year. Um, and that, you know, broader scale, uh, allows us to both see friction points and to understand, uh,

where money is wasted as time waste is wasted more efficiently and increase the value prop. And the last, which is I think more emergent, but probably important for any company thinking about, you know, in the application layer of their AI strategy,

We're trying to structure around certain network effects. There's weaker network effects like data network effects, like do we understand where prices are better? I think it helps a little bit to product-based network effects like accounts payable and bill payments. Every payment out goes to another finance team, which in some sense can allow RAMP to grow more efficiently. But I think more broadly, it allows...

you know, more automated systems to do things like think, you know, increase the level of value, reduce the operational work for both the payer and payee. And so I think all these have feedback loops within it. More distribution can drive more data. More data allows us to do higher level work. You know, the more companies we serve, the more efficient, the more we can wring out costs for both parties and create efficiency. But

We try to think structurally about what our products do and how do they compound on top of each other in this notion. What other kind of system of record or transaction data do you feel like you want to own in the future, whether it's accounting or banking? Or do you feel like spend data plus workflow is actually where you want to sit? It's interesting. I think there's something just powerful about... We're so much...

waste happens. It's the intersection of time and money. You know, it's what AI is doing is radically increasing people's productivity. And I think that we're, what finance has sort of done to, you know, you know, cards, AP platforms, all this kind of stuff, never really thought about time. And so it's this interesting thing where you can actually just make companies

more operationally effective than they could have been before if they're managing these systems separately. And I think that if you are in the position of you are helping companies run more efficiently, I think over time you actually have a more authoritative system

set of data about the records themselves, about how decisions are made and the unwritten rules of what is the, you know, you know, from permissions, who is allowed to spend what, who is allowed to spend what.

On what? Above some level, whose permission is needed as elements around the profitability, the growth curve kind of change. What expenses lead to higher levels of ROI? What tends to be lower? And so functionally being in the flow and not just being, you know,

I mean, for lack of a better comparison, you know, I think companies that use ramp have access to a steering wheel and an accelerator or a brake and they can go faster on certain expenses or not. Whereas companies that have, you know, just records but are not the operations platforms, you're in the car, but you can't turn it or you need to ask someone else to turn the wheel. And just the the feedback loops are slower, like I think from a customer perspective.

None of them want to be spending their time chasing people for receipts. None of them want to be obsessing over and wondering, is this transaction categorized to the wrong place? I think that they want to focus on their mission and what matters. And so they both have increased control over

over their business should they need to make changes or be informed of what's happening. But that I actually think a lot of people do want an autopilot and a co-pilot for a lot of the must happen, but less important day-to-day parts of their job. And I think that's not just for business owners, but it's for finance teams too. Definitely. I think people over-rotate on

the fear of change with automation, but I think it really has the opportunity to break us out to do more creative work and to focus less on the mundane, right? With these generative models, evaluation and accuracy is still a somewhat unsolved problem. How do you think about that at RAMP? Yeah, for sure.

I mean, I think that's part of why the co-pilot model versus the autonomous agent model is so much in vogue. I think that, you know, in the same way you would see, you know, very early Waymos, you know, always have like a person who could take control. So now if you get into one of those cars, you know,

people kind of have the comfort and actually can see, you know, in some cases, a fully autonomous system is safer. There's this process of, you know, reviewing QA testing where when kind of higher error bars are there at the beginning, you want to be checking the work. And so kind of the earlier use cases that you see this popping up is

In the case of generations of like recommending, you know, how a transaction should be mapped to a general ledger. Well, the average accuracy may be higher than any one person could do. And, you know, accountants make mistakes. That's why there's, you know, outside accounting firms. That's why there's audit firms. You know, that's why these entire industries exist. You know, I think that kind of behavior, but digitized, you'll see more and more where the work is not,

hey, it's a blank Excel spreadsheet, tag this, tag this, try to build some automations and every month go through it by hand. It's actually, you know,

For 95% of the transactions, this is 99.9 degree certainty. You can check it, but pretty fast rate, great, move on to it. For 4%, it's at 90% confidence. And for the last remaining transactions, we're going to need to go by hand. And as feedback loops come through, you'll see that you start to go. So I think that's going to be the patterns and behaviors that you're going to see quite a bit.

But I do think that for more narrow use cases where you have, you know, not just human supervision, but ultimately when there's actually not a lot of degrees of creativity and complexity, it's just about process automation. I think that you will see full autonomy, you know, be deployed a little bit faster. And so I guess it's all to say that.

Part of how we tried to think about it is, you know, how do you both model and assess your own accuracy and speed of doing that and even build your own product deployment process and QA process to be measuring that very, very actively. And once you hit certain thresholds, you offer the ability to move into more fully autonomous type of use cases. But

Earlier on, a lot of the interfaces actually reviewing and checking is what it prioritizes. It speeds up the workflow versus fully moving there. But that's a loose framework about it. I think there's other types of products where you're generating and people are taking that to edit and design and map policies to book transactions on your behalf. But I think you're going to see that

co-pilot model turning into agentic models as the ability to wring out errors and to be confident about the recommended approach increases is at least our take. Yeah, it's an elegant way of ramping up as the models improve. So

Let's take a second to kind of brainstorm the future. So let's say it's 1,845 days from now, and maybe we're doing another podcast. And what does the world look like, right? I'm a customer using Ramp. And how am I operating my business? How does that change, both with Ramp and more broadly, as these AI agents and AI workflows start to really continue to advance at the rate they have?

I mean, first, like broadly, you know,

I like, we think about like what our mission is like, you know, the simple way is we want to help companies spend less money and time, but like, it's very inspired. Like the name ramp itself comes from one of the simple machines in physics. There's the lever and pulley, there's a screw, there's the incline plane, the ramp, which is, it's, it's a simple machine and all simple machines. What they do is they reduce the force needed to perform work will allow companies to get more done with less is what we're about and what we're working towards, um,

is a, is a world where, um, uh, now maybe, uh, I think in, in many, many years we can say all, but maybe in 1845 days from now, uh, where the vast majority of work is actually purposeful, um, where finance isn't tedious or monotonous, but, uh, is strategic and insightful. Um, and I think when you sort of zoom out over the next, uh, five years, uh, to how things are going to be different, uh,

More movies on planes.

Exactly. More movies on planes. I think on accounting, I think it's going to be increasingly, you know, of checking of heavier automations. Your books are done in real time, more accurately and with less effort so that your team has time to think about not just how do I tag transactions, but what transactions were valuable.

Where is leverage coming from in my own business? When you're buying a new set of software, you can know other companies liking it. I love it. The price is fair. It's been verified. And even that process of procurement, both internally to figure out, do we have the requisite approvals, can be done and routed automatically online.

But, you know, if there is a back and forth in negotiation, you're armed with data. Parts of that process are being done for you and your business is more efficient. And in the same way we went from saving the average company about 2% per year on their expenses four years ago, I'd like to think that number is going to be much closer to 10 or beyond when you think about the hard dollar cost and the soft dollar cost for companies. We want to rewire companies to be more profitable.

um, are the things that we're thinking about. And I think that we, when we do that, um, I actually think you can use human intelligence to, um, and I think people's, um, creativity and, and, and desire and what makes us human actually, um,

not just on tagging transactions, booking things, but I think the desire to build, the desire to create, the ability to create progress in the world versus just a record of what we did, which I think is going to be profound. And so we're really excited about it.

Awesome. Well, let's leave it there. Eric, thank you for building this future. I feel very lucky to be a small part of the ramp journey and appreciate your leadership in this area. Seth, thank you for your support and for everyone listening. I hope this was interesting and useful. Thanks for listening to Product-Led AI. You can find more information about today's interview and the entire series on the website productledaipod.com.

You can subscribe to the show on all major podcast platforms and watch the video version of this interview on YouTube. And if you want to get all the links and details delivered right to you, sign up for my LinkedIn newsletter. Be sure to tune in next week for my interview with Reid Hoffman, where we talk about AI's impact on networks and his broad view of opportunities for AI applications. I'm Seth Rosenberg, and this is Product-Led AI.