Inside Ramp, the $32B Company Where AI Agents Run Everything | Geoff Charles

If you're not using cloud code this year, no matter what your role is, you're probably underperforming compared to others on the company. PMs often pride themselves on like the spec, the perfect spec. They have to understand that it's actually AI that's reading the spec now versus engineers. 50% of RAM's code is built by AI. And that's 50% up from 30% in December. It'll probably be 80% by March. — And this is not just like a front-end prototype, right? — This is the real product, back end, front end, and I have a PR and I can just submit it to the engineer team. PMs are shipping tons using Inspect. So are designers, so are operators, so are like account managers and sales people are also getting activated. My job is to automate my job and our all our jobs is to automate our jobs. All right, everyone. My guest today is Jeff uh CPO of RAMP and RAMP is one of the fastest growing companies ever and probably the most AI native company that I know outside of the big labs. So last year uh Jeff and the team shipped over 500 features and hit over a billion dollars in revenue all with around 25 pfms. So yeah, really excited to talk to Jeff today and uh welcome Jeff. — Super excited to be here. Thanks for having me Peter. — Awesome man. So um you know I've worked at a lot of big tech companies but like can you give us a quick overview of how ramp ships features like from idea to launch? Yeah, I I'll skip the basics and just jump into the fact that it's a crazy time right now and uh the way that we are building has always been around velocity and the way that you move fast is by leveraging tools and AI is just an incredible accelerant to um everything that we do and you know I hope during this call that you know I'll be able to share a few of the ways that we've leveraged AI to accelerate um to inspire folks and and help amplify then the learnings I also expect that a lot of the things that we're going to talk about today are going to be outdated, you know, even by the time that you even share this uh this recording. So, I'm excited for it. But yeah, I mean the product development process uh you know hasn't dramatically changed in terms of root principles, right? It's about understanding customer painoint about identifying the right solution about uh building the solution and then testing and iterating and I think um AI just lowered the cost of each of these sections uh dramatically. you know the cost of code is basically down to almost zero apart from the tokens and so uh PMs just need to uh be actually writing the specs for um the agents rather than the engineers themselves and I think that's a complete shift in terms of um how we go about it. — Yeah. So basically the PMs will make the product first pretty much by themselves right or make the prototype at least and like get some validation before doing anything else. — Yeah. Yeah, I mean we you know PMs often pride themselves on like the spec, the perfect spec and um they have to understand that it's actually AI that's reading the spec now versus engineers. And so — um the spec itself is is basically the output of a prompt and then the output of the spec is the product. So um at the end of the day it's just prompt to product back to prompt back to product. Um and yeah, we are essentially um collaborating on an actual product itself and a prototype. Um I would even call it a prototype. it's actually a working product rather than um the actual spec itself. — Yeah, I always suspect that engineers don't read my specs carefully. So like I always try to keep my specs to like less than two pages to begin with because you know no one wants to read the but like yeah the AI agent will actually thoroughly read re read it. So that's a good thing. Okay. So before we even get to the spec though like first you have to like you said you have to understand the customer understand problem and like how do you how do you guys work with AI to figure out what to build or what the customer painoint is? — Yeah. So the advantage that we have is that you know we have 50,000 plus customers on ramp and growing super fast. We have over a million end users and so that gives us a ton of signal. We also have a ton of people on sales on support on account management. And so those are all touch points um that we can leverage to understand kind of what the problems are and what opportunities are and what we should be focusing on. The the question is around like how do you actually sift through all this noise and that's where like a large language model is fantastic. So the first thing we invested in is um what we call voice of the customer and uh typically was you know it was a person that we hired that tried to you know do all this work uh themselves. Now, it's basically an agent and that agent is essentially able to sift through all our Gong recordings, all our Salesforce notes, um all inapp surveys, all support tickets, all inapp chats, um any email that is being sent to um to account managers and essentially gather all that context as well as our Snowflake database and our analytics and help answer any question that product managers have around their persona, the pain points, their workflows and the gaps of their products. Um, so happy to jump into that, but that's a huge thing that we've invested in.

— Yeah. Do you want to uh are you able to give a live demo of that or like show us how that works? — Let me share one version of that. So, — yeah, — this is our voice versus the customer tool and um and you know, as you can see, you know, you can ask any question for on this on this bot and this bot will literally go through any type of question. So, you know, for this demo, I asked, you know, what's feedback that we people have on our procurement product, right? And uh you can see that the sources. So, do you want me to look through support tickets, chat logs, sales research, feature requests, etc.? I said, "Okay, let's just go through support ticket and chat logs. " It literally went through 90 days of support tickets and chat logs and identified the actual key uh topics that we needed to focus on as well as links to the underlying uh assets for me to double click into. So, you know, purchase order management, approval, flow routing, uh chat, you know, uh chat understanding with ramp assist, exports, currency constraints. I mean this is like this was done in you know from 38 to 40 so about eight minutes um and something that would have taken eight days for a human to actually do across the entire volume. — Yeah I mean this is basically like kind of prioritizes your road map for you like has number of support tickets and and everything. — It at least helps you identify with a ton of context the problems that your customers are facing with and enables you to go deeper. So then you can you know it's essentially a conversation right? Imagine you have this like full-blown analyst. How do you continue prompting the analyst to go deeper? So now it's like, okay, I want to go super deep on this specific problem case. Bring customer quotes. Bring me some like log rocket sessions. Bring me um uh you know customer IDs that I can go and research to. Create a email that I can go that I can use, draft in my Gmail account for me to actually automatically send to set customer to book meetings on my behalf. All these things are basically prompts and this agent actually has all the connectivity to be able to do these things. And I love how the user interface is just like a Slack channel or like I guess you can DM the agent too if you want. — Yeah, 100%. We've seen like Slack being a great place to actually host these things because that's essentially what you would do with a human, right? You would Slack your product operator or Slack a team channel being like go do these things. So, it's a very natural way of of uh of doing work and personalizing these agents as essentially your co-workers. — Oh, wow. Okay. — This episode is brought to you by Granola. If you're in back-to-back meetings, you know how much work it is to take notes live and clean them up afterwards. That's why I love Granola, the best AI meeting notes app in the market. Here's how I use it. Granola automatically takes notes during a meeting, and I can add my own notes, too. After the meeting ends, I use a Granola recipe to extract clear takeaways and next steps in the exact format that I want. Then, I can just share her notes directly in Slack with my colleagues or even get Granola to share her notes automatically. Honestly, of all the AI apps that I use, Granola is the one that saves me the most time. Try it now at granola. ai/peter and use the code Peter to sign up and get three months free. That's granola. ai/pater. Now, back to our episode. — So, that's a qualitative piece that you show me. What about the metrics and the data piece? Like, how do you like you just give people access to like pull data themselves or how do you Yeah.

— Yeah. So, so there's uh you know the space is moving very quickly. So, six months ago, you know, we we launched uh our own bot for data analysis. Um I'll give a quick quick view of this and this is now outdated. Um and I'll tell you why. — Um so we launched uh what we call ramp research. So it's funny like before you would ask a data analyst or you would try to do it yourself with you know looker. Hex is getting pretty good at like creating your own prompts etc. But it was still, you know, fairly a lot of work to like get an answer to a question, right? And now it's like, — hey, I have a question. Give me the answer. Um, so uh now we have ramp research that essentially I mean the use cases are insane and actually by making it easier for people to ask questions about data, you actually increase the number of people who actually ask questions about data and you actually become more dataentric uh as a company. So you know what's an example? Um, you know, let's say that you have an automated email campaign and you want to understand the performance. What's the open rate of automated emails that customers sent? Boom. — You know, ramp research understands our entire database and understands all the schemas. It understands what you're trying to do and automatically like generates the actual interpretation of set results. And this is, you know, this is used, you know, so much and this was in two minutes, right? Um, by literally everyone. So sales people trying to find, you know, what are our customers in Milwaukee? um support people trying to figure out like the common use cases of XYZ product uh marketers trying to figure out the performances of their campaigns etc. But I shared that this was outdated um in the sense that we now basically have moved to uh Snowflake CLI plus cloud plus skills. Um so essentially we we've now moved to using you know cloud code. Um we have our own database of skills that we've developed. So um we have a data anal an analyst skill that essentially fully understands our database and understands how we go about approaching a data analytics problem and the best practices of that. And then we essentially can now prompt cloud to say hey um you know bu you know build me a full report of the performance of you know our procurement product and identify you know the top reasons why people um opt in the top uh blockers in our funnel um and you know draft with me you know 10 different growth ideas that we could be running and and Claude will actually generate you know a full HTML report fully baked into our data that is directly actionable. — Yeah. Okay. So it's not just Q& A anymore. is actually doing work for you, right? That's why it's better than the thing. — Yeah. I mean, at the end of the day, like it's funny like you you know, you ask a question, but you have a goal, right? So sometimes you ask the question and you get the result, but uh you should just tell AI what your goal is. And you'll actually be surprised at the uh questions that the AI can actually ask themselves to get to the goal. — And um have you given the entire company access to clock or is it like just engineers or everybody can you use it? — Anyone can use it, right? Um and in fact like you know we'll get into this around like how you actually become more AIdriven as a company but um if you're not using clot code um this year um no matter what your role is um you're probably underperforming compared to others on the company and so it's certainly not um a product for engineers um it is absolutely a product for builders and you know we talk a lot about cloud code right now like you know opus 4546 like big launches and a big movement in the last like three months I mean you just saw the anthropic $30 billion raise, but I I expect the tools to continue evolving. Like, you know, by the time we meet next um you know, the next 90 days, it might actually be completely different, right? Um and so um uh it's less about like forcing people to use one tool. It's about um giving people full access to any tool they want to share openly what where people are using and then get people to adopt to get to the aha moment. And then um but yeah, we don't want to be dogmatic, but we want to like radically empower and also have like full visibility on what people are doing. — Yeah, let's talk about later, man. Because I I think so many companies still don't get this. They're like, oh, you know, like what's the cost of this? What's the ROI of this? Why should I give some salesperson this thing? Just don't get it, man. We'll talk about later. Yeah. So, let's keep going. Let's keep going down the product development process. So now you have all this crazy uh feedback coming and the quant I mean people talk about product sense like it's some mythical thing but I think it's just like how much product feedback are you getting like how much are you like embedded in like the feedback loops and like the data every single day and then you kind of just develop that as a second nature kind of thing you know so but after you have that like how do you actually um you know you mentioned that you don't actually read specs anymore like how do you define the solution you just make the product right off the or — yeah so there's the in there's the problem identification then there's like the actual like writing of requirements um and we have you know our own clot skills for that right so you know claude has full access to our notion um notion has the full context and all our personas um and all the research we've done that's all automatically transcribed um and aggregated and then we have skills in in cloud that is

like your product spec skill um and we've prompt we've designed it so that um it's a conversation based approach. So just like you have maybe like a manager or a peer reviewer on your spec uh claude will actually like interact with you and ask you clarifying questions. So for example, you know what's the main goal here? What are the main here are the trade-offs like should we trade out this or that? Like have you thought about this? Like what is the intersection with that? It has all the context uh about what we're trying to do because it has all the other projects that we're building and all that is is in notion. And so it helps you basically refine and and get to an end state. But yes, we don't really talk about the spec itself. That's just a step in the process. We talk about the actual uh product. And so happy to share a little bit of like how fast we move in terms of prototypes and and show you kind of what we're talking about here. — Yeah. Please show us the skill and you know everything else. Yeah. — Yeah. So uh let's go through like you know an example of this skill um and then we'll talk about the actual uh how we build. So this is an example of like you know clot skill like you know folks should be pretty well verssed in this in this world. Yeah. So you know product shaping um defining like the role right push for simplicity surface trade-offs surface questions um you know key definition of the problem um you know like looking up like uh you know all the data that we have access to um do the actual research uh look at different competitors customer evidences it has links to all these different things um synthesize the completion help me shape this question so you know present the synthesis ask 20 questions around you know, the forcing decisions, um, all the different principles that we have, um, and then like related skills. So, like this is like a skill that you that we load up. And this was actually just built by one of my PMs. Um, other PMs actually have their own skills. We're trying to figure out like how we actually get to like one strong skill as part of like the the evaluation process. But this is one of the examples I want to share. — So, you basically just have to be like, hey, I want to build like some expense tracking feature and this thing actually drives the conversation, right? you actually drive the conversation with you. Yeah, — exactly. Okay. — And then let's go into like the build.

So so you know 50% of RAM's code is built um by Yeah. Um and and that's 50% up from 30% uh in December. It'll probably be 80% by March. And um you know, it's not inconceivable for it to be like 90 to 100%. — Yeah. Like we've hit coding uh escape velocity and and it's a brave new world out there. um we the question becomes like how do you make it easy for a non-builder uh to engage with you know code because it's obviously pretty intimidating. So uh we invested a lot in building um our own uh visual on top of uh you know any large language model um and to radically accelerate like how builders can build and how even like PMS can build. I mean if you uh if you have like infinite coders at your disposal you know you are actually the bottleneck and you actually need to like um start moving faster. So I'll give you an example. Let's say that you actually want to uh you have a lot of feedback from customers saying hey I need more visibility on um what needs my attention um and I need to understand kind of what's overdue uh what's on track and like what's upcoming. So I need to understand like my accounts payable cash flow. Okay.

— Yeah. So all I need to do is I will go in and say, "Okay, please build me a report on top of this table that has four metrics. Uh my overdue paint, my overdue bills, my upcoming bills, uh 0 to 30 days, 30 to 90 days, and the total amount outstanding that we'll need to pay. " Okay, so this is obviously a shitty spec. This is just for demo purposes but the but inspect will go and it will actually implement this product. Um and it will understand the task it needs to do. It'll actually plan it'll understand the codebase. Um you've already directed it exactly to where you actually need um and it also has access to our design component library. Right? So I don't need to teach it to design, you know, what a metric should look like, what a module click should look like. It has all these components baked in. And so it's actually able to just reuse a lot of our existing code to build this thing. And I actually did this yesterday for just this demo purposes. So — yeah, — you know, here here's like where it gets to. See if this is working. Boom. — Wow. — So now you have on top of the bills table your entire uh your entire metrics. uh what's past due, what's coming up, and the total to pay. And this took five minutes. — And this is not just like a front-end prototype, right? This is like the real product or — this is the real product, back end, front end. Um and I mean, a lot of this is front-end code though because I didn't need to create more endpoints like we already have all these endpoints, but inspect is able to do uh both front end and back end. Dude, that's because I I've been using like, you know, Google AI Studio and stuff just to make prototypes, but that that's just like pure front-end code. It's not you can't actually push through prod. But, uh, sounds like — and it doesn't have context on your codebase. It doesn't have you need to you need it doesn't look the same as your product. Um, uh, no, I mean here I can literally now I can, uh, I can go in and, you know, I have a PR and I can just submit it to the engineer team and we have an automatic PR review processes where, you know, a double- digit percentage of our PRs are automatically approved. So PMS are shipping um tons uh using inspect and so are designers so are operators so are like you know some extent like account managers and and sales people are al also getting activated um on this piece. So it's just a massive accelerant and the number one users are also engineers um engineers using Inspect and and here's the other crazy thing about um this technology that I want to share. Um, — yeah. — So, you know, often times you you have feedback. So, we we love feedback. We obsess over like customer feedback. We have, you know, tons of Slack channels where people are just constantly posting things, right? — Mhm. — It's very overwhelming once you have, you know, the number of customers that we have. Um, you know, this is an example of a uh a UX channel. Um, and you know, basic thing, right? Hey, like treasury is a product should probably be case sensitive, right? — Yeah. um add inspect in the web repro change the following sentence PR merged like this is just one and this is a very easy thing — but like anything any question that you have say you're an engineer say where do I get started there's an escalation a problem like anytime there's an escalation on ramp uh you know AI takes a first step it understands exactly what happened it understands where in the codebase it creates the actual PR And uh oftentimes it ships it. Same thing with uh with support tickets. Anytime there's a support ticket that comes in where someone is confused uh we have inspect basically run through that and and recommend changes and have the PR up and ready for like the PM or the product operator or even the engineer to review and ship it like that. The speed at which we can move with these some of these things is like radical. And this is — kind of have the AI give the first pass. Yeah. — First pass everything. Yep. — Let me push back on a little bit. If everyone in the company is shipping these PRs all day, how are you going to keep the product cohesive or like keep the quality bar high? — A lot of the PRs themselves are like quality of life improvements. Um — uh we also, you know, within Inspect, we have an understanding of like complexity and so we do have a process by which we review things based on like the sheer amount of complexity that it has. Um and it does route to the right person based on you know whether this is like a big change on the product side or on the engineering side etc. Um but we haven't yet gone to a big problem. We also have a pretty robust release process. So once the PR is merged like we will like slowly roll it out and um before any like major changes happen um on the product that goes to the rest of our customers we have an automated process by which you know I get involved or the directors of product get involved as well. — Okay. So, so you have the typical like um first like everyone in the company plays away with it and then you know if nothing breaks you get them beta users to play with it and then you loyal to all users. — Yeah. Exactly. So we have we have doc fooding alpha is like your your customers that are um you know as part of your research group. Beta is anyone that opted into the beta tier. We have about you know 10% of our customer base that's in the beta tier. So you can launch very quickly to beta and then you track uh analytics on that. And then to go from beta to Ga we basically uh you know require for large announcements like not really like this you know naming convention or anything like that. Um for any large feature uh that is material to the customer we basically have a review process that's fully automated. So because everything is you know in our databases we have another bot you know uh ramp releases that uh creates a ramp release report. It it pulls all the information of the context. It pulls uh you know a preview of the actual product that we can use. Um it pulls from our snowflake databases the impact that this feature has had. Um it pulls from uh you know any slack channel a summary of all the work that was done. Um and it basically like you know synthesizes all the things that uh it also can create it can do work. So to do release you basically need you know a help center article well that it gets written automatically. you probably need like a internal enablement of like what this feature is, how do you use it, why do we build it, it writes that automatically. You can also post in Slack. Um, so yeah, that's a little bit of like how we speed up that process. — And when you review these like larger features, are you reviewing the actual product or you reviewing because you know like a lot of companies just like that the PM writes some sort of document, right? And then they go through like multiple rounds of reviews and then and then you approve it and then they finally go build a product. But like I don't think that's how it works at RAM, right? — Yeah. I mean the question is like what is my role in all of this now, right? And and I think you know in the past my role was well you know I'm the best at like the craft or understanding what customers want you know understanding the data and that's no longer true like you have a super intelligent like platform that you can leverage. So yes, like I I will try my best to um look at all the customer feedback, make sure that this is actually meeting the customer feedback. I will look at the metrics and call on like this is not good enough or this is not big enough. That's something that is fairly subjective. I will go into the product and test it out and play with it and like really just hone in on on like what's working, what's not working. But I think the higher level job for leaders now is — based on your feedback, what broke down in the process, — right? So if you caught a poor user experience, — Mhm. — what broke down, what prompt failed, what skill failed, what design system failed, because giving feedback to the person and so that they can just fix it, that's that's a — that's a one-time band-aid, right? What you want to do is you want to figure out within the process what broke down and fix that process so that next time you never have that feedback again. Like a classic example for me is like I've asked I I've told the team 10 times the call to action — needs to be above the fold. — That's like you want to you want to just you know six years of of AB testing you want to increase conversion. It's a big button that's above the fold. That's it. And and I' I've said that like 10 times or maybe a hundred times but now it's part of our design uh create process which is a fully automated uh process in and of itself. And so before it gets to me uh you know within our Figma prototypes uh th those core concepts are actually fully integrated. — Okay. Got it. Okay. So you don't have to yeah say the same thing over again. You can provide maybe like higher level feedback or something. — Yeah. — My job is to automate my job. That's and our all our jobs is to automate our jobs. Um — uh we can talk about what happens next, but yeah. — And how about the how about that cuz another thing that sucks up a lot of time is like this annual planning process of like oh I spent like a month to figure out what we're going to build for the year or like for the next three years, right? Like h how do you guys manage that process or is there even like a like how far do you guys look on the stuff? — Uh honestly 3 months. — Okay. We we can only predict within 3 months now. And by the way, like within 3 months, you can do what you can do in 3 years now. So like 3 months is actually a really long time. — Yeah. — Um you know, planning for me is um I think there's actually like three main objectives to to planning. One is actually aligning on strategy, which is much more important. Like what problems are you focused on? — What are problems are you not focused on? And which customer segments are you going after? And how are you thinking that we're going to win longterm? like what is the end state for this thing? So, it's about trade-offs and I think like the conversation should really be about trade-offs. The second thing that planning is good for is just having some level of commitment from the teams, right? Um some level of accountability. And the third is um to have some baseline for sales to know what's coming — um for them when they when they talk to a customer and the customer asks okay like this is great but you know um I I have a lot more needs when it comes to our international exposure and the sales team needs some basic assets and so that's that's the third kind of pillar and that also is like fairly automated. So once the team kind of does you know their backlog and their plan uh in notion we have an automatic like uh process that creates uh you know one pagers and then it creates slides and content for the sales organization within our own uh branding guidelines. Um and then the sales team can essentially just like look at a higher level view of our road map to be able to sell effectively against it. — Wow. Okay. And then you have all these like vision and like you know how we're going to win stuff. Obviously AI can read it and if something changes you can just ask AI to update it. Is that is that how it works? I mean what I ask AI to do is to synthesize information like a lot of leadership time is about helicoptering between the nitty-gritty problems and then the higher level like strategy and roadmap and and making sure that every level of the organization understands information at the bottom and information at the top like how you communicate to the CEO and the board is very different than how you communicate to the director is very different to how you communicate with the teams and that LLMs are incredibly good at because and so like the translation layer, right, when I'm at an all hands meeting versus when I'm at a team in a boardroom. Very different. And so I waste a lot less time on those things. Got it. Okay, great. Let's get to the key question then. I mean, you just mentioned that your job is to automate your job. I'm sure all your PMs feel the same way. And so what's

going to happen to the PM function? Do you think — do you think it's game over or like uh — Yeah. What's going on? You know, it's funny like um I was surprised by like once you automate code like the a lot of people concluded that PMs are it's like over for PMs and I and I was I thought to myself it's over for the engineer for most engineers. So maybe it's like a lot of engineers who are like I'm going to be a PM now because — uh the engineering function is is um has changed a lot. Now obviously there's a ton of value for engineers because um I think an engineer now is managing hundreds of thousands of agents um and they can actually scale their impact. — Um but let's go back to the PM role like a lot of what a lot there's a lot of bad PMs out there or badly trained PMs. I think that the way we've trained PMs in the past has been really bad and we've trained them on stakeholder management. prioritization. We've trained them on communication. We've trained them on frameworks. And those are all outdated because code is free. And so like all that matters now is are we going the right direction? How fast can we go? And how do we remove bottl necks build a system by which like we can accelerate and and to do that I think PMs need to really rethink their skills. So like a lot of PMs join product management because it's a safe job. You know they might not be good enough at the engineering task. design task but like they're really good at you know the the consultant. I'm an ex-conultant. Like that's why I joined the function. Like I understand that the customer I can communicate to engineers and I can like I can really I can I can somewhat facilitate decision- making. The downside is that if you're a riskadverse PM, you're not going to change your way. So I still see, you know, very high performing PMs who are who don't get it, who haven't yet adopted these core skills, who haven't changed the way that they're working because it's worked for them so far in their career. They've been successful because of it. That is the biggest danger that I'm seeing. So um uh I think that the role of the PM is going to shift and I think it's going to shift in two directions. Uh PMs are going to become much more builders, right? Because code is free. So just like I showed like uh a product, right, that I that I basically built in in five minutes. Um it's going to require then like the iteration from the product very quickly. And so I think the craft and the building is going to be like really essential versus the spec. like you no longer have to write the spec anymore. You need to actually like be in the product itself. — Now an engineer, a great product engineer can do that and a great product designer can do that. The other path for uh product is um is the business side. So you know what engineers are and designers often lack is an understanding of the context in which the business operates and what actually matters and um and how we're going to win long term. So they're really good at maybe they're really good at building really good products and so give them that and then the product team should be focused on like okay but now that we have this really good product like how are we competing how are we positioning how are we distributing how are we monetizing how are we actually using this to win and drive enterprise value and and I think that you know even looking at open anthropic like the the it's a it's a decision of strategy — um they have different strategies and that's actually where the PM should be really focused is the underlying way that we're going to win and playing that GM mindset. Uh because they're you're going to have a ton of builders that can build great products that can iterate on customer feedback that have all the context. You've built that system. So now focus on like what actually no one can do which is to make sure that the product you're building is going to have insane amount of value in the market and insane amount of money for your business. — And like a lot of PMs are just like stuck like you mentioned they're stuck in like cross functional alignment meetings all day like back toback. So like and I think it's like a company culture kind of thing too, right? Like do you do you make sure your pre PMs actually have time to build or is it sort of like do they have to get alignment from 10 people to ship anything? No, it doesn't seem like that that's the case. Yeah. — No, I mean we we've designed the organization so that we do not have committees and signoffs. Um you just need to prove that you've added value and then you can go for the races. I will say that like it's it's actually really important for PMs to carve out time to uh build. And I say this not just PMs, but like managers. — Um I think that it's a really tough time to be a manager right now because you're managing a team whose skill set needs to change and you might not actually have that skill set. So I I think that right now like going back to IC mode is paramount. — Um and I've done this for myself where I say like, "Hey guys, like I'm going to be in way less meetings. I'm going to be way less one-on- ones and I'm going to be like I'm just going to be adopting AI tools. I'm going to be building and vibe coding and understanding what's working, what's not working so that I can be become more educated because it's just this is just the beginning. I mean the sheer amount of changes that happened over the even the last like 3 months is is profound and I think um if you're stuck in meetings you're not going to you're not going to be effective. So definitely creating space for work and honestly, you know, that's also where nights and weekends come in, which is like this is the year that like you need to really prioritize learning and growth because no one's going to do that for you. Um so yeah, uh it's uh it's going to be a wild ride — and if doing the old way like your company's going to die basically, right? If it's do the waterfall and all this kind of stuff, it's not going to survive. Yeah. Let's skip to uh talking about um companies that are watching this. They want to become AI native like ramp like how you guys operate like how do they go about like uh doing like you know building systems and that kind of stuff. Yeah.

— So there isn't like one right way but I'll share kind of what we've done. Um — um and we've kind of like built a framework around this. So um we think about like being AI proficient in like multiple levels. Okay. the bottom level is like people who sometimes use chat GBT, right? We'll call them like the L0. Okay. The level one is like people who've built their custom GBTs, maybe they've built a notion agent, maybe they've built uh they've used like cloud code to like do some of these things, etc. Level two is people who are actually like fairly proficient. They have been able to build an app that um that automates part of their job. uh they have been able to commit uh code or feedback to other people's work. And then level three is like the fundamental like systems builders. Okay. And our job is to get everyone in the organization up the ladder. And the way we do that is as follows. The people who are still in L0, they will most likely not be at the company because the fact is like you can you can tell them as much as possible. If you're not a self-starter and you don't have that growth mindset, like it's going to be very hard to train you out. So, so um that's L0. The L1's need to get L2s, L2s into L3s. And L3 is like basically like influence and the rest of the organization. And the way we do that is um we have uh a lot of public channels around people sharing uh what they've built. We've made it really easy for anyone to adopt these things. So we've removed any constraints around access, around tokens, around budgets. We've uh we have like uh the setup of those tools are are extremely well done. So you have access to all the different MCPs, skills. We even have like an internal repository of skills that people are deploying to. You can pull from those. Um and then we have you know a lot of culture around you know in all hands around like showcasing non-builders doing things you know our finance team building their own treasury management system our legal team you know doing contract reviews our marketing teams automating like website creation um to get people inspired and then we have office hours that people can join to uh to ask any questions to get set up. We have um designated experts that people can just ping and like their entire job is to get to evangelize, to get you set up, to get you comfortable, to get you going. Um those are like some of the principles there. And then we and then the other piece is just like you know hiring and performance management. So on the hiring front, we now have an absolute requirement for anyone who joins the company to be uh somewhat proficient for these tools. Um there's just absolutely no excuses. And in the

interview process, we'll have basically a dedicated session for this where like they will either I mean for the product team, I literally have a session where you you're going to build a product like you're going to show me a product that you've built and you tell me exactly how you built it, how it works like it is a full-blown prototype. — Um and then we also track usage of AI across the company. So uh you know we have uh we vibe coded it this this product even within the team where we can see everyone in the company and their full usage of tokens across notion AI chat GPT uh cloud code cloud coworker um our inspect tools are or any of the internal apps and we can see kind of like who is actually pushing the bar to amplify and who's not and who we need to contribute on. Do you worry about like this cost stuff running out of control or like the ROI is so clear that there's no just give everyone access, let them do it. Yeah. — I mean, I I haven't done the ROI around like if you let's say you have a person who's has $100,000 salary, how many tokens should this person use? Um, and there's debates right now around you know uh — uh productivity versus just like noise and you don't actually need these things. I think right now we need to invest the budget for people to discover and if we are not as efficient in that spend that's okay that's our competitive advantage that's why we raised money that's why we have a pretty good war chest — but um I can safely say that you know we pay our employees a lot of money and it and the token consumption per employee is not even close to double digits and I think it I think it's not unreasonable to think that it should be higher than your salary because like if you have agents that are able to do 10 times more work than you, then why would you not pay them twice as much as you? — And so I think that's like the way that we should be really framing it. Um but yeah, I would say like we're not really worried about costs. Um we're worried we're mainly worried around we have like the next x months or x years where um AI has not yet fully one-shotted a ramp platform um and we need to use that to our competitive advantage to move as fast as possible. — Yeah. And I feel like a lot of the internal tools that you show me are also really great for ramp customers, right? You can just like, you know, make that available for ramp customers. — 100%. — Okay. La last question, man. So, so if I'm a you PM or builder, like uh how should I think about my career DD these days? Like um you know like the old climb the l ladder to VP or whatever like is that still going to work or how should I think about being employable still?

still? — I would say um I think that the where you should be optimizing is not management. It is being the best builder in the world. I would say that management is probably dead. Um, there's always going to be value in someone giving you feedback and coaching and and being your advocate and being a team leader, but now is not the time to build that skill set. Now is the time to like be very proficient in this new technology and to um radically improve the the way that you use it. Um, and so I would say for for all the PMs out there, um, you know, get really embedded in these tools. And that's why engineers are so good at at you know understanding what AI is capable of because they live and breathe it like that they're that's the first knowledge work that has been you know mostly automated with with coding agents but it's coming for everyone else I mean it's going to come from PMs for designers it's going to come from for any white collar uh job and so I would say just get very proficient um and using these tools and um ultimately like the your career is about impact and right now the impact that you can have is to um you ship great products faster and move more metrics for customers into the business. And so um you know create a lot of space to learn these things um and have the beginner's mindset, the humility to understand that the way you're doing things uh is not the best way. Um and I think my job as a leader is just to get people to get to that aha moment. And even my brightest PMs, I had to sit down with them and say like we're going to go through this workflow together. what what have you done today and I will show you a new way of doing it and once you get that aha moment that like red pill — there's no coming back like you were like I oh I get it now and it'll also make you a better builder because your the software you're building if you're in B2B and even in B2C — it is going to look radically different than what exists today I mean fundamentally software is dead it's all going to be like co-workers and if you haven't used co-workers in your own job you don't understand how like that actually might look like your product a lot more than you think, right? So, — yeah, you don't have to process it. — That's exactly right. Like the ramp itself is going to look much more like a finest coworker than it does, you know, tables and charts and workflows. — Yeah. I find like um it's all like, you know, I've been using open call. It's all like CLIs and like, you know, there's like no one wants to touch buttons anymore. It's just like let me talk to my co-orker and let you know, get him to do stuff for me. That's basically it. Yeah. Cool. and and how do you build one of those great co-workers? Domain level expertise is another one. Like I think that in the past it was like I'm going to talk to customers. I'm going to kind of understand the requirements and kind of build a product for them to do their job, right? — But if you're doing the job of your customers so that they can do other things, you need to actually be an expert or you need to build a system by which you can ingest that expertise. Right? So accounting like you can build an accounting workflow where they have to go and code things. But if you're actually going to code on behalf of the accountant, you need to deeply understand the philosophy or be able to extract that knowledge like how do you download, you know, CPA and all the best practices and actually bake that into your product. So it's a very different way of thinking where fundamentally like a login in your product in the future um I think is going to be a failure, right? And I think that's also how we think about it. We track the amount of time you spend in ramp and how we can actually reduce that time as much as possible which is by the way the opposite of how many PMs are trained. the the Facebook and Netflix, right? The fangs of the world that are mainly advertising businesses like it is the opposite and I think um there's going to be reckoning for sure, but also a very exciting time. I mean, you know, I think it's very scary and a lot of people are alarmist and everyone should be paranoid, — but man, it's an amazing time to be builder right now and especially a product manager where you have taste and vision. The time it takes to go from your taste and vision to a product is shorter than ever. And I think it's a really really exciting time to be a builder here. — Yeah. And I think uh another theme you mentioned is just like setting up systems to dedicate all the work to a AI, right? So you can focus on stuff they actually enjoy doing. Like that's a key part of it. So yeah. All right, Jeeoff. Well, I mean, thanks for being inspiration, man. Like I I think uh hopefully well hopefully every company can learn how to operate like rap. Yeah. — We're just getting started. There's also a lot of things that, you know, we're not doing well that other companies are doing super well. I think, you know, part of me going on this talk is not to share that we've that we have all of it figured out. Most of the things that you saw here are things that we built in the last months. — Um, so excited to keep the conversation going, excited to continue learning and uh, you know, really a privilege to to be here today. Thanks a lot for having me. — Yeah. SF.