The GTMnow Podcast

Inside Reevo's $80M Bet to Kill the $10B Frankenstein Stack | David Zhu, Cofounder & CEO

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AI is rewriting go-to-market and most companies are still operating with a “Frankenstein stack.”

In this episode of GTMnow, we sit down with David, founder & CEO of Reevo, to unpack how AI-native companies will replace legacy GTM systems, why the future of sales teams looks radically different, and how AI agents are becoming the new operating layer for revenue teams.

David shares why Reevo stayed in stealth while building a vertically integrated AI revenue operating system, the problem of institutional knowledge loss in sales organizations, and why the old playbook for SaaS and GTM is breaking in 2026.

We also dive deep into:
- AI-native go-to-market strategy
- The death of the legacy SaaS tech stack
- Revenue operating systems explained
- AI agents for sales, marketing, and customer success
- How founders should think about AI adoption
- Building startups in the age of AI
- Why “failure is the cornerstone of innovation”
- AI-driven sales productivity and revenue efficiency
- Founder-led sales in 2026
- The future of CROs, CMOs, and RevOps
- AI copilots, Jarvis-style workflows, and GTM automation
- Scaling teams without scaling headcount

If you're a founder, CRO, CMO, GTM leader, SaaS operator, AI builder, or investor trying to understand where B2B software and go-to-market are headed next, this episode is for you.

Timestamps:
 00:00 Intro
 01:20 Why Reevo built in stealth
 04:50 Why AI can finally disrupt GTM tech stacks
 09:20 What Reevo actually does
 11:10 AI outcomes vs headcount growth
 15:00 How AI changes leadership & innovation
 19:00 Why legacy GTM teams are vulnerable
 23:00 The future of AI-native GTM organizations
 29:00 Running an AI-native company internally
 34:00 David’s favorite AI workflows & tools
 37:40 Advice for introverted founders & leaders

Host: Sophie Buonassisi, SVP Marketing at GTMnow
https://www.linkedin.com/in/sophiebuonassisi  

Guest: David Zhu, Co-founder and CEO at Reevo
https://www.linkedin.com/in/zhuventures/

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The GTMnow Podcast
The GTMnow Podcast is a weekly podcast featuring interviews with the top 1% GTM executives, VCs, and founders. Conversations reveal the unshared details behind how they have grown companies, and the go-to-market strategies responsible for shaping that growth.

Visit gtmnow.com for more episodes and other interesting content. 

SPEAKER_00

There's gonna be this tsunami wave of AI native companies that's gonna help your competitors to be 10x, 100x more efficient.

SPEAKER_01

What's kind of the pros and cons of building in stealth up to say a series A?

SPEAKER_00

Building stealth was I would say it's a tactic, less the strategy, and it's really just dependent on what is the intent of the problem that you're trying to solve. If you know something's gonna work, it's not innovation, it's repetition.

SPEAKER_01

David Zoo, co-founder and CEO of Revo.

SPEAKER_00

Revo stands for revenue evolution, and we are the first catter recruiter in revenue operating system, and what that means is that powers a company's entirety of go-to-market motion from marketing, sales, support, etc. And here at Revo, we're simply not in the business of doing repetition. And so this sort of framework applies to everything we do, the product we build, the AI techniques we adopt internally.

SPEAKER_01

What are things that they're still stuck on and believe that are just fundamentally different now in 2026? David, welcome to GTM Now.

SPEAKER_00

Thanks for having me.

SPEAKER_01

You bet. It's a pleasure to be sitting here with you today. And you actually came out of stealth in November of 2025 with$80 million raised, 70 million of which was a Series A co-led by CoSell Ventures and Kleiner Perkins. That's a significant announcement. And those are significant rounds to come out of stealth with. Was building in stealth up to that point part of your strategy?

SPEAKER_00

Well, I think the part of the strategy and the sort of intentionality up front was building a compound platform that essentially encapsulates close a dozen, what is nowadays known as point solution from the get-go. And so the challenge with that strategy is building for both the breadth, but also a level of depth that offers meaningful ROI to the customers when we do decide to come out of stealth. And so, you know, internally, our founding team, our co-founders, and early employees, we're just like, oh gosh, there's going to be so much, you know, heads down building without market validation, which is sort of the antithesis to what founders are being advised by, you know, consigliaries, right? Whether it's board members, founder friends, et cetera, which is like pick a niche thing, do fewer things really, really well, identify a pain point that you create a wedge for, and then you go and sell. And we just basically threw that out the door. And we're like, no, forget that. We understand that playbook. We understand the benefits of the playbook, but that strategy does not apply to us. And so it's actually really hard. But you know, to your point, coming out of stealth in November, late November, it was really a testament to our conviction in this approach of just building without much market validation. I mean, obviously we had design partners and early beta customers that helped us along the journey, but it was tough.

SPEAKER_01

And why build that way rather than go out for I'm thinking for founders listening to this? Because many now are kind of trying to walk that similar path, but it is a challenging one. What's kind of the pros and cons to doing or building in stealth up to say a series A or coming out earlier market validation?

SPEAKER_00

Aaron Ross Powell, yeah. So building in stealth was, you know, I would say it's a tactic, uh less a strategy. Um and it's really just dependent on what is the intent of the problem that you're trying to solve. And so for Revo, the problem we're trying to solve pertaining to go-to-market space was uh institutional knowledge loss at scale. Aaron Ross Powell And so we're like, okay, well, if we want to solve that problem, uh, what is a first principle way to solve it? What are the technology shift that we get to ride? What are the collection of the messy set of problems and pain points that saddle the existing companies and their go-to-market motion? And so we thought really from first principle, we're like, okay, the problem is institutional knowledge loss at scale. The cause of that was the existing sales and tech stack or t go-to-market tech stack that powers it. And what I mean by that is in go-to-market, you would have functions like sales, marketing, success, each of which need to procure a set of their specific tech stack to do their functional job to be done. But the challenge with that approach is that none of these tech stacks really talk to each other in the olden days. And so what ends up happening is um the best CROs and the best CMOs, they're being told by their peers, like, well, just procure yet another tool. Yeah, just add this tool. It'll just help. But we know nowadays, looking backwards, with the benefit of hindsight, that garbage in, garbage out is a thing, that if you truly want to leverage software to, you know, in the form of high precision insight, then you actually need to feed it with a maximum ground truth set of data. And in go to market, that means being able to capture all the input signals from your marketing teams, your sales teams, support team in one unified system versus a Frankenstack of stitched together system. And so because that was our approach, and because we knew that we needed to rebuild this whole thing from the ground up with AI at its core versus AI, you know, as engine, you know, powering a horse-drawn carriage, we needed to build it from the ground up the right way in a compound manner. And so that's why we took the sort of approach of being heads down, building a dozen different things, threaded together without really too much market validation. So advice to founders and others is like just really pick the strategy that or tactic that works for your that specific intent of what you're trying to solve.

SPEAKER_01

Completely makes sense. And that's fantastic advice. It's a huge problem, like you said. Teams are stitching together hundreds of tools oftentimes. So it is a big problem. It's over a$10 billion industry, the Frankenstein's DAC. So why now? Like, why is this the time? People have been trying to disrupt the consolidation movement for, you know, 20 years. Why now?

SPEAKER_00

I think there's like three things that really came together. And once again, we have the hindsight 2020 benefit of saying we were right. But at the time when we started a company a little bit under two years ago, it was really just sort of a hypothesis of where we saw that the sort of direction of innovation was going. And so the three things that we were betting on that can come together was one, we were betting that models or AI LMs are able to advance at a rate to capture context that is broader and more durable than humans can, right? Because the reason for that is because you know, humans uh in the absence of AI or effectively the source of truth.

SPEAKER_01

Yeah. Right.

SPEAKER_00

Very simple. And so number two is like the cost of the capture of set systems and interactions and contexts as some you know cool kids call it nowadays, uh, or the decision traces as you know even more cooler kids call it nowadays, the cost to capture that effectively has been commoditized. Right. And number three, we were betting on the sort of exponential uh improvement in model capability to evolve from just an AI agent that's like doing mundane work to being able to do actual complicated work. And some people call it, you know, going from synthesis to action, action to reasoning, reasoning to thinking and creating. But whatever that sort of like marketing terminology or growth set you want to call it, we're really betting on AI as a general intelligence layer to get better. And so those three things, turns out, were directionally correct. And so uh we get to ride the tailwinds of all three.

SPEAKER_01

I mean, hey, gotta ride it when it works out. That is fantastic. And it takes a lot. Those are not small things too to bet on, though like those are significant large bets.

SPEAKER_00

Yeah. I mean, bigger bets as well. But I I think within the space of go-to-market, those are the three that were most applicable. You know, I'll sort of take a detour real quick. I'm I'm a software engineer by training, and I always go back to that. And, you know, it it's been what, three months now since you know Kodaks and Gopez came out with their latest AI coding. And it's like, I actually can't remember the days before December.

SPEAKER_01

Yeah.

SPEAKER_00

It's kind of insane, right? You talk to any founder, CEO, technologist, uh, investor, it's like, oh my gosh, like 2026 is like a fundamentally different and you know, year for innovation. But let's take a trip down memory lane. Just, you know, I started my career 16 years ago. And back then, just to set some context for those on, you know, on the show, you know, this was back in the days when you had to like deploy code, build code manually. This is like before automation in the sort of CI CD space.

SPEAKER_01

It sounds archai archaic, but that was not that long ago.

SPEAKER_00

It's not that long ago. And it's actually kind of insane. I remember, you know, we would have these engineers called build engineers that effectively craft and make files of dependencies between one system to another. And we would create these like Java servlet uh artifacts and we would create jar files and war files, and we would manually deploy these from one on-prem system to another just to package it and then launch it. And this is all done manually. And the challenging part about that, Sophia, is whenever you have these build engineers leave the company, there's like a downtime for weeks, if not months, because that institution knowledge resided within software, within that specific software engineer. You don't have that problem anymore, obviously. Right. Because effectively nowadays we're trending towards a world where the software engineering agents are effectively the new steward of institutional knowledge of the code bases that power the companies. And so that really de-risks a company's business viability. But that's not true in go to market, unfortunately. Right. Right. Um, as companies scale, their top one, two reps who are always winning deals, hold that special sauce of knowledge in their head, the legacy systems that powers it. And so then begs a question of why. And so we're like, we don't know why that's the case. So let's go ahead and solve that and bridge the gap of institutional knowledge that we just observed in uh in software engineering to go to market as well.

SPEAKER_01

Mm-hmm. Incredible. And you're taking such a such a unique approach to it too, coming from having seen the problem solved on the engineering side to now doing so on the go-to-market side. For anyone unfamiliar with Revo, and I know we've we've talked around it a lot, but can you please just explain what Revo is, what you're solving for?

SPEAKER_00

Sure. Uh Revo stands for revenue evolution. And we are the first category creator in revenue operating system. And what that means is it's a platform vertically integrated that powers a company's entirety of go-to-market motion from marketing, sales, support, et cetera. And so we believe in a world where in a few years' time, a company would procure their favorite vendor of coding agents to be the software engineering department. They would procure the Harveys slash Lagoras of the world to be their GC department. Yeah. And in the similar breath, they would procure Revo to be their go-to-market department, empowering their existing marketers, sellers, success to become supervisors of Revo agents that provide outcomes to them. And so gone are the days where there's a linear association between headcount to outcome. Right? You want more code, you hire more engineers. Gone are those days, as we see. You want more revenue, you hire more AE and salespee folks. Gone are those days. We believe in a world where we can decouple that. And so you have a sales team of 10 that generate the output of with AI at their fingertips, with Rebel, Jarvis to their Tony Stark. Yeah. They want to generate the revenue as if it was hundreds in the Odin days. So that's what Rebo does.

SPEAKER_01

Amazing. Thank you for explaining that. And I think that's exactly what a lot of companies are striving to do right now, trying to tie outcomes to headcount. But what is that? How do you actually quantify that? A lot of teams are trying to become more efficient and thinking about it from a efficiency per headcount, or maybe it's an outcome per headcount still, but just different than revenue. How are you thinking about outcomes or how can other people think about outcomes in some form of measurable way right now?

SPEAKER_00

Yeah, I think it's still very nuanced per industry. Uh, and it really just sort of depends on if there is a universally defined definition of what success looks like. I don't know. Let's take like marketing or customer support, for example, in these sort of spaces where there is a clear definition of like job is done or not, like in support, you know, work the DoorDash. Scale it from 700 mil Series C to 75-ish bill post-IPO company in four years. And that was a very customer-centric, customer-focused, operational, excellent, sort of driven company. And the attention and focus to customers doing right by customers is very important. And so part of that was to be able to provide an amazing customer support experience, regardless of whether your order was like fulfilled correctly or not. And so, you know, I'm sure if many of us have ordered DoorDash before. And all too often. Yeah. And uh, you know, we hope every single delivery is magical and delightful, but the reality it's not. Right. And so in the cases where it's not, how do we do right by the customers? Well, there's a customer support uh SOP standard operating procedure that ultimately yields in whether something gets done or not. So for example, if I said my order was missing or incorrect and I go to customer support for DoorDash, regardless of whether AI, artificial, intelligence powered, or actual intelligence powered, humans, right? It's AI squared, regardless of either approach, as a customer, I don't care. What I do care is like, did DoorDash, the brand, yield me the outcome that I want? So if I'm Steph Curry and I'm saying I want a refund, you know, DoorDash should be smart enough to know that Steph Curry is a power user and don't ask any questions. Just give him the refund.

SPEAKER_01

Is that true? Is Steph Curry a DoorDash power user?

SPEAKER_00

Well, at the time when I was there, a lot of GoDonald Saiy Warriors were power users. I don't know about today. Yeah. It's been a while. Uh but that would be like the sort of like logic, the SOP that our support team had, which is like if you're a power user, if you're an MVP user of DoorDash, just do right by them. Yeah. Right. But similarly, if you are a frequent abuser of the credit system, the refund policy, then we're gonna take a little bit more of a detour for you and add a little bit more friction. And so in both cases, it's fairly deterministic in terms of what the right outcome should be per input user. And so in those cases, you know, the companies that are creating AI tooling or support or platforms for those use cases, they better align on the outcome.

SPEAKER_01

Right. Right.

SPEAKER_00

Because it's like so deterministic.

SPEAKER_01

Yeah.

SPEAKER_00

Um, but that's not true across every industry. And so I hope that we see a graduation over time where the vendor or the partner or the platform that's offering support to whatever company is procuring them gets closer, closer alignment on the business outcomes that m is most meaningful for their customers. And um, you know, in sales, for example, we would hope that Revo over time is able to deliver the outcome of a closed one and the renewal and upsell.

SPEAKER_01

Yeah.

SPEAKER_00

Right. But we're not there yet today. But that is the intent of what we're trying to trek towards. And so I guess a follow-up question that, you know, a lot of folks would have in their mind is like, well, what happens in between then? Right. What do we do?

SPEAKER_01

And how do we get there?

SPEAKER_00

And how do we get there? And so I think, you know, there's once again it's very nuanced, but I think teams are sort of progressing towards this seat-based pricing to hybrid, to more so on the consumption to ultimately outcome, which is directionally correct, but with a nuance that token eating does not necessitate alignment with the customer's outcome. That's sort of the caveat.

SPEAKER_01

Yeah, which feels like uh uh ground that we're still kind of brokering right now as people are just trying to get their hands wet and trying to get people using it.

SPEAKER_00

Yeah. I mean, even within engineering, we see it as well. We're very uh I I would like to think that Revo is like one of the most sort of forward in terms of applied AI in software engineering for our own innovation. And one thing we're very intentional about is like not goaling our engineers on token crunching. Yeah. Just because you crunch a lot of tokens doesn't mean that your vibe coded outcome is like the highest fidelity.

SPEAKER_01

How do you handle that at a leadership level? Yeah. How do you incentivize people to build, but also build intentionally for outcomes?

SPEAKER_00

Yeah. It's a tough one because there's no playbook that anyone can draw on, because we're in the land of innovation. And one thing I tell our teams is like, you know, innovation goes hand in hand with failure. If you know something's gonna work, it's not innovation, it's repetition. And here at Revo, we're simply not in the business of doing repetition. And so this sort of framework applies to everything we do, the product we build, the AI techniques we adopt internally. And so there's a lot of aspects of trial and learn and learn quickly. And the teams that learn the quickest are gonna come out on top. And so that's the sort of mantra I share with our ELT exec leadership team, as well as um all the sort of you know, builders that we have at Revo, which is try things, like adopt things. You know, don't get too nostalgic, uh, don't hold too much nostalgia to how things were done, especially in engineering. There's an aspect of like engineers love it's like, oh, I'm so proud of the software built. Yeah. I have that as well. I still remember a lot of the gnarly systems I built 10 years ago.

SPEAKER_01

Right. Near and dear to the heart always.

SPEAKER_00

Exactly. But like would I build it the same way today? No. But what I would take, and this is more of a direct answer to your question, is like I would think about the clarity of the spec and the clarity of the requirements that effectively still transcends time. That's the timeless piece. You know, one of like I remember 14 years ago, is like when I was a 22-year-old, 23-year-old, I remember um building this like new form of cash retrieval, C-A-C-H-E, cash on the software engineer side. And it was just like new algorithm that is like really gnarly. Yeah. Um, and I was super proud of the outcome. But in hindsight, you know, 14 years later, would I still write the code exactly how I read? No, I wouldn't. But what does still transfer is like if I can regurgitate the clarity of the requirements of that system and the benefit to the customer that it'll be able to deliver, I'd be able to take that, feed it into an opus or codecs or whatever is your favorite coding, you know, models nowadays, and it'll generate an output that's way better. And then I would take that, and five years later, I would do it again. And it'll be much better than today. And so I think that's the key, which is like, let's, you know, if we can incentivize our builders, regardless of whether in go-to-market, whether you're prodops, customer support, when you're adopting AI, when you're vibe coding, whatever is the sort of thing that's relevant to your function, think about the clarity of requirements. Think about the durability of the requirements, think about the durability and timelessness of the benefit to the customer. Because I think we're gonna get to a state where that same requirement, if we can feed it back into an ever-growing, you know, better model, then the outcome will always just be better anyway. And so then that's a very, very powerful flywheel. So it's not a one, you know, uh one trick pony instance in time. It's an ever-compounding set of better outcomes that rides the wave of the general intelligence models.

SPEAKER_01

I've heard you frame it as failure is the cornerstone of innovation, which I really love. And sounds like you're incentivizing your teams to think of experimentation, to never get comfortable, to continuously push those bounds. And one of the things that you spoke to was how nostalgic people can be about the code they wrote. Yeah. And I think the same thing applies to go to market of how we've structured teams for yourself, building something so disruptive. Like how are people receiving these conversations for a smart, you know, CRO or smart founder? What are things that they're still stuck on and believe that are just fundamentally different now in 2026?

SPEAKER_00

Yeah. I mean, with any technology shift, there is going to be, I think um, there's gonna be a J tr J-curve trough initially attributed to a combination of human sort of lack of interest to adopt because the known sort of inefficient ways of the past is better or more comforting in a sense than the sort of this like potentially disruptive way of the forward of the future. And so that's one. So human nature is, you know, an inhibitor to technology adoption from a inception perspective. Second is, you know, as technology gets better, the sort of track record of it just by definition isn't there. And so I think those two combined together is gonna be headwinds initially to every AI native company out there. But over time, once you sort of get past the point where, you know, the the sort of the lowest low of the J curve, then you start harvesting that outcome very, very, very rapidly. And so, you know, within go to market space, the you know, I'm we're seeing a spectrum of reaction from the market. But I always say universally, there's a lot of uh constant market fit today, and there's a lot of market pull for building. And what I mean by that is like, you know, the more experienced you are as a CRO and CMO, the more you've now come to realize the fragility of the legacy tech stack that you have, especially the larger the sales org or marketing org you lead. You know, the larger you are, you're probably managing. Managing a set of 60, 80, 100 different point solutions that you've either stood up over the course of 15 years or you've inherited from a predecessor since the role of a you know sales and marketing leadership turns over so quickly. And so then the question is like, oh my gosh, like do you continue doubling down on this sort of antiquated pattern of the past, knowing that there is gonna be this tsunami wave of AI native companies that's gonna help arm your competitors to be 10x, 100x more efficient? Are you still gonna be riding the sort of linear your relationship of like hire more AEs to get more revenue linearly? Or are you gonna say, let's be innovative and decouple that in the age of AI? And I think that's a state today that CROs are rethinking. You know, historically, and this is so funny because this is like as recent as just two years ago.

SPEAKER_01

Yeah.

SPEAKER_00

Uh and I say historically, but like two years ago, when we're when I was interviewing hundreds of CROs and uh go to market leaders, they're like, David, you're bat crazy for you know trying to do what you do. Like no CRO has been fired for selecting a legacy vendor, fill in the blank of whatever is the vendor you're using. And it's really interesting because at that time that's true, because that's all they've known.

SPEAKER_01

Right.

SPEAKER_00

But in the last 24 months, we're starting to see a shift where today, that same CRO, that same CMO, they're coming back and say, hmm, interesting. I might be fired if I stick with the legacy vendor and I don't know if they're a transformation. So that speaks to the former, which is the human nature and sort of resistance to change, but also speaks a combination of the fact that time has passed and trust in AI and ATO companies are starting to build, and trust equals consistency over time. You can't elongate time, nor can you compress time, but you can consistently do what you say you do as a company to build that trust over time, such that when time passes and human nature catches up to AI adoption and the AI platforms like Revo get to a state where it has enough track record, then magic can happen from adoption perspective.

SPEAKER_01

I love that saying around trust. And for CROs, CMOs who are feeling this pressure now to almost swing a 180 the opposite way and potentially move away from incumbents or just adopt more AI native platforms or platform in general. What does that shift look like? Because I think we're hearing a lot around the very AI native kind of built platforms that you can do a lot in. What does the transition state look like from where they are to there? Because I talk to a lot of CRO CMOs and a lot are starting to consolidate specific areas within their stack. And that's almost like, I don't know if that's the start or the end necessarily, but it's a stepping stone towards that. Like, what does the actual step look like for a CRR CMO to take that kind of walk? Is it going straight to a platform for, you know, all because it's a little scary to disrupt all the different disparate tech tools that you have under your belt, or is it consolidating a few? How should people be thinking about that? At GTM Fund, we invest at the very early stages of a startup's journey. And one critical and surprisingly complicated decision could be naming your startup. Founders often spend weeks chasing the perfect dot-com domain only to overpay or settle for a name that doesn't quite click and also potentially pivot names down the line. Before you spend a time securing a dot-com domain, I recommend checking out dot tech domains. Dot tech tells the world, your customers, your investors, anyone Googling you that you're building in technology. It's very simple. So if you're building a tech startup, this is a great option for you. You can secure your dot tech domain today from any registrar of your choice.

SPEAKER_00

Yeah, I I think the homogeneous answer is that over a few years into the future, there's gonna be one way to do it. But until then, there's gonna be different nuanced approaches depending on the type of company that you are. And the obviously for if you're a fortune, I don't know, like Fortune 20 or 200 or whatever, like you're gonna have a very complicated, deeply rooted system and process. And the change management on that entirety is gonna be very, very expensive.

SPEAKER_01

Yeah.

SPEAKER_00

And so yeah.

SPEAKER_01

That's a whole angle that I don't think it's talked about enough.

SPEAKER_00

Yeah. Stage management. Um and so then like how do you layer on in an accelerated manner an implementation, sort of accelerated implementation of a new AI native platform for that to power all of your marketing sales support? Answer is like, you probably can't. If I put myself in the shoes of the CEOs of fortune bracket, I don't see myself going doing a rip over place in a four-week manner. It's like, all right, let's yellow everything.

SPEAKER_01

Yeah.

SPEAKER_00

Right. Today I won't.

unknown

Yeah.

SPEAKER_00

Right. But in a few years, I probably will. Right. And then similarly, you know, for the down segment to that, depending on how you sort of carve down from enterprise or commercial enterprise, mid-market, SMB, founders, et cetera, there's the appetite to adopt is much greater. And I think it comes back to the simple sort of like frameworking equation of do you have more at risk to protect the value that you've built as a company? If so, then you're going to be more resistant, right? Yeah. Which is why enterprises by proxy is like can't move as fast as well, because they have this sort of collection of debt they've incurred, right? Customers, logos, contracts, tech debt, brand debt, marketing debt. And because of that debt, which one can say it's value protection, value maintenance, that is a lot of weight to adopt something new. It's a lot of weight for them to innovate in the age of AI. And so if you don't have that baggage as a company, as a, or if you don't have that baggage from a mentality perspective of somebody who physically has that sort of baggage, then you're more likely to say, you know what? I'm here to not just survive in the age of AI. I'm here to thrive. And if I'm here to thrive, I gotta go and rip off the band-aid and go through this transformation. Right. It's kind of like if you have, you know, cancer in your hand, you're you're gonna want to amputate it to save the rest of your body. You're not gonna be like, oh, well, let's just let it play out a little bit. It's like you're not gonna do that. Yeah, you're gonna lose an alarm. Exactly. And so I think in the go-to-market space today, I think you know what I'm seeing is that our customers, regardless of whether founders, multi-sales team orgs or mint market, regardless of whether in uh technology or none tech, which is actually most of our customers, the most AI forward leaders who understand that in a five-year time frame, that this is where it's gonna head, which is them running on a Revo that acts as their entire go-to-market department. Those who understand that, they're adopting it sooner because they also understand the benefit of compounding of knowledge, right? It's kind of like when you hire Revo as your AE, right, sales person. Yeah. You know, your human reps will have bad days, but Revo won't. Right. Every human rep you hire, you have to go through this thing in sales called enablement, which is a sort of like X number of weeks or months of training. So you know the collateral, you know the the talk track of how your best reps sell the company, you know the different pricing tactics that you levers you get to pull depending on the you know, prospect and deal and the opportunity. And it's a very laborious process that doesn't always yield a success at the end of the day. That's why you know there's a lot of churn in the sort of sales work. Well, but guess who doesn't have that? Right? AI agents. Yeah. And so if you have your best sales, your sellers um armed with a Revo, they graduate to become a mini CRO, supervising a set of effectively Revo seller agents that help them with the top of the funnel, the middle of the funnel, and the bottom of the funnel as well. And that's just such a more deterministic and predictable world to live in, right? It also elevates humans to do a lot more of the strategic creative thinking versus a mundane sort of paper pushing aspect. And then for sellers, sellers want to engage with in conversations with others.

SPEAKER_01

Yeah.

SPEAKER_00

Versus stuck between behind a you know, desktop.

SPEAKER_01

Oh, it's a dream to be an IC right now in a way, because everybody's always wanted to do strategy and suddenly everyone is a mini series. Exactly. Exactly.

SPEAKER_00

It's so powerful you can just pull up the app or your Apple Watch and you'd be like, hey, Revo, tell me XYZ. Hey, Revo, help me plan this thing. Help hey, Revo. Yeah, I have this idea, help me riff against it. Why is this not a good idea?

SPEAKER_01

Right.

SPEAKER_00

Everybody then is armed with, like I said, a Jarvis hither Tony Stark.

SPEAKER_01

I love it. And so you are enabling others to build this way. So naturally you are building yourself as an organization, Revo, like that. What are some of the biggest learnings you've had from running just an AI native org under one go-to-market roof, the way that you have as others try to pivot and transition to that?

SPEAKER_00

I think there are two major learnings, one related to the space and the customers we serve, and the second related to how we innovate. And uh maybe I'll start with the second because it's more relatable probably to all founders and entrepreneurs, which is what what I'm realizing that is that over the last 24 months, the sort of domain knowledge and experience of having been at several hypergrowth companies and building from sort of very small uh zero to one phase to a massive scale, a lot of those learnings are not applicable anymore. And that's kind of scary and exciting at the same time. It's scary because we're taught to accumulate experience and then counter match and apply it. And it's not applicable anymore because the how we, you know, what got us here won't get us there. And this every every single time there's a new transformation. And so the how we build is something that we're constantly discovering internally. And being able to teach our more, especially our more experienced leaders and experienced builders to say, don't lean so much on what got us here. Don't lean so much on the past knowledge of, you know, writing code or like whatnot. Like being able and very willing to throw away that legacy knowledge is like one of the hardest things. Um I'll give you a couple tactical sort of you know instances of that. You know, Revo, we're now past 100 employees, and um, most of which are still in engineering product design. So one of the sort of like knowledge of the past is like, well, let's bring on middle management, let's hire, you know, these sort of people leaders and let's like organize for success across these functional pods. That's a very legacy way of doing it. Right. You would have these like what Bezos used to teach at Amazon called two pizza teams, which by definition silos the rest of the company. Yeah. Right. And the reason why you would have these sort of micro teams, you know, even back at DoorDash, you would have these like small pockets of teams that sort of innovated quickly is because of the the drag on context transfer between humans was so high if you scaled it. It's the Dunbars wall.

SPEAKER_01

Yeah.

SPEAKER_00

But nowadays, if you built like that, you're gonna be a dinosaur as a technology team. How you're gonna need to evolve your engineering team and how we evolve our engineering team is basically look at every single builder as transferable, as transferable product leaders, effectively, across any job to be done to serve the customer. And so the need to focus on the customer, the elevation of that is much more of a stakeholder requirement. Whereas even five two years ago, that was a nice to have. That was a reason to promote. Now it's like a reason to even exist.

SPEAKER_01

Yeah.

SPEAKER_00

Uh, and it's very scary. And that's one example. Another tactical example is like what we're seeing from some of our uh hottest companies, as well as our founder friends, is that they're effectively elevating the best leads, the doers, the tech leads, the marketing leads, the sales leads, the finance leads to become mini CXOs of their company. So you have the best tech leads and software engineering become mini CTOs. You have effectively product managers become mini CEOs, your best AEs become CROs. And the level of agency that AI has, you know, is able to offer for the best builders is like tremendous. Right. And in the same breath, the sort of level of agency it takes away from the people managers is like insane as well. So that's like one. And then going back, popping the stack to the go-to-market side, how we sort of leverage AI and how we see you know transformation is the ability to apply first principle thinking in combination with independent thinking, those are very different. First principle and independent thinking is like very important. Uh and the reason for that is because like regardless whether you go on X or whatever social media, LinkedIn, you know, whatever the cool kids use nowadays, everybody will have something to say about everything.

SPEAKER_01

Always.

SPEAKER_00

Everybody. Yeah. Right? And it's like, okay, cool. Uh, how do you listen to it, but only take like 0.1% of it to what you do? Because if you listen in mass, well, if you don't listen, then you're like living, you know, like a hermit. Yeah. But if you listen in mass, then you become an echo chamber, which means that you can't really arbitrage on any sort of distinct value prop you're able to offer your customer. So those are sort of like two things that we're holding in our heads.

SPEAKER_01

Aaron Powell Very cool. And what about your personal AI usage beyond Revo specific? Are there any AI use cases that have been transformative for you personally as a co-founder and CEO?

SPEAKER_00

Beyond Revo. That sucks because Revo is probably one of my highest AI usage tools at the end.

SPEAKER_01

It can include Revo. Yeah.

SPEAKER_00

So Revo is actually um my favorite AI app to use. Uh very selfishly. And I'm not I'm not saying it's as a plug.

SPEAKER_01

Yeah.

SPEAKER_00

Um I'll just sort of share how I use Revo every day.

SPEAKER_01

Please.

SPEAKER_00

Right. So I'm both a hiring manager, okay. Um, a go-to-market, you know, founder led sales, and then also I'm an investor as well. Yeah. And so uh what's in common with all three of these? Pipeline tracking, yeah. Right? Conversations with humans, conversation across many, many, many different humans. So there's a lot of interaction. And one thing that Revo does for me is I basically get to act as a GP, a CEO, founder of Let Sales, and a hiring manager, three very distinct roles in one as part of my day, in addition to the rest of my day. Yeah. Right. And so then the question is like, how do you do that with Revo? It's like, well, I create pipelines and Revo's there as the Jarvis to the tone to my Tony Stark, right? And it'll refresh me on it's like, hey David, you're about to jump into a conversation with this candidate who you spoke with nine months ago, and this is like what they talked about, and this is what might be interesting to you. There might be, you know, as a GP, as an investor, I might have different touch points with portcodes six months out, right? It's you never say no, you always say just not now. Right. Right. And so it's like, well, you know, 18 months ago they were at traction X, 12 months ago, they were at traction Y, you know, six months ago traction Z. And today, you know, you could sort of then plot that graph. And um and then obviously within sales and go to market, our own GTM team uses Revo, right? And so I basically get to get to act as a mini CRO without having to wait for my WBR weekly business reviews, without having to wait for our RevOps to spin up, you know, cuts of forecast and reporting. Why? Because at any given point in time, I can just go and ask Revo. It's like, hey, what's my quarterly you know attainment? Right. Right? What's my tell me about my highest performing reps? Um what is the specific line that drives the highest close? What are the deals that we should have closed that we didn't? Right? Help me stay ahead of churn. These are things that I no longer have to wait for synchronous sort of weekly cadence feedback on or even monthly for some companies. Now I get it whenever I want. I get on my Uber, I get on my Waywalk. Yeah. Uh it's just intelligence on my fingertips. And I get to act on it because we're arming Revo to with a bunch of toolkits that the rest of the platform is able to offer from top of the funnel prospecting down to close one. And so just like very feels very empowering.

SPEAKER_01

Yeah.

SPEAKER_00

Um outside of Revo, I'm probably a heavy user of like deep research.

SPEAKER_01

Okay.

SPEAKER_00

Yeah. To plan different things, to just sort of like help me riff against ideas. Yeah. I have too many ideas, and if I grab humans to riff with me, then it's very distracting to the team. That's one thing I realized.

SPEAKER_01

Right.

SPEAKER_00

Um and so I I'm learning how to like not distract the team so much by grabbing random folks, like, hey, what are this idealists?

SPEAKER_01

Right.

SPEAKER_00

Because when it comes from the comes from CEO founders, like, do you want that implemented kind of thing? It's like, no, I just want it riff. And so like deep research, it allows me to sort of like riff back and forth and poke holes and an argument without distracting the rest of the team.

SPEAKER_01

Yeah, yeah. You can bake it a little bit and bring a little bit to them.

SPEAKER_00

Yeah. Yeah.

SPEAKER_01

Very cool. And last question, speaking to yourself as as a leader, you know, you are leading over an 100% organization. You're also investing, you're you're doing a lot, like we've talked about. And you're also an introvert. Any advice out there to other introverts that are leading or trying to be in leadership positions?

SPEAKER_00

Aaron Powell I think um one thing I realize over time is just like it's hard to change. So there is like what's intrinsic and what's idiosyncratic about people. Intrinsic are the things that, you know, is like innate to who you are, your values and et cetera. And the idiosyncratic piece are the things that are behavioral, you can learn over time. Um the introvert piece in media, you know, you can get a media coach and learn how to be presentable on podcasts, et cetera. And you'll get better over time. Like I'm clearly a work in progress and I'll get better over time.

SPEAKER_01

Hello, done.

SPEAKER_00

No way. And and a yeah, you know, intrinsic piece, don't try to change that. Like you are who you are. Yeah. And lean into it. There's so much power, and I forgot I heard this somewhere from somebody, I forgot who, but I would love to give attribute or credit and attribute it to whoever said this. There's so much power to be unique as a differentiator for your own brand and your own comp your company's brand. Um, if you stay true to who you are and do things the way that you know how and best and most comfortable, then by definition, you're very unique and you have an asymmetric advantage over anybody else because you are the best version of yourself. No one else can try to emulate you. So regardless whether you're introvert or extroverted, just be yourself. Those who matter don't mind, and those who mind don't matter.

SPEAKER_01

For anyone interested in following along your journey, Revo's journey, where can they find you?

SPEAKER_00

I'm probably in office six out of seven days in our Santa Clara or San Francisco office. Um I took locally stay off social media just so I can have clarity of mind. Um, but feel free to reach out to me, david at revo.ai, if you're an entrepreneur looking to exchange notes on how AI is affecting your space and where you see the trends going, or you're a go-to-market leader looking to embrace uh AI transformation for your org, and you really believe that in the future the best leaders and go-to-market are going to be procuring their AI avatars and AI conciliaries and co-pilots and the Jarvis sither, Tony Stark, and you want to learn a little bit more about Revo, that's where you can find me as well. But outside of work, I got two beautiful kids, six and eight, and uh they're going through a phase where they are very curious about building Legos and Minecraft and all that. So I like to jam with them once in a while, especially when they're building Legos, because I love tinkering with Legos as well.

SPEAKER_01

Yeah, so fun. And I love some of the marketing campaigns that Lego's doing. I know they just dropped the one with Messi and a bunch of other soccer players. They are just yeah, killing in on the marketing.

SPEAKER_00

Absolutely.

SPEAKER_01

Yeah.

SPEAKER_00

Lots to learn from.

SPEAKER_01

Very cool. Awesome. Well, thank you, David. Appreciate you coming on.

SPEAKER_00

Thank you. Thank you. Take care.