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 Zhu, founder & CEO of Reeva, 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 Reeva 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.

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.

Discussed in this episode:

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

Episode highlights

1:20 – Why Reevo built in stealth

4:50 – Why AI can finally disrupt GTM tech stacks

9:20 – 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

Key takeaways

1. AI will replace fragmented GTM tech stacks:
Reeva is betting that companies won’t keep stitching together dozens of sales, marketing, and support tools. Instead, AI-native “revenue operating systems” will unify GTM workflows and preserve institutional knowledge across teams.

2. Institutional knowledge is the biggest hidden problem in sales:
Top-performing reps often hold critical knowledge in their heads. When they leave, companies lose playbooks, deal intelligence, and customer context. David argues AI agents can become the permanent memory layer for go-to-market teams.

3. The future of GTM is outcomes, not headcount:
Traditional scaling meant hiring more SDRs, AEs, and support reps to grow revenue. AI changes that equation. Smaller teams equipped with AI copilots and agents can potentially generate the output of much larger organizations.

4. AI-native companies need a completely different operating model:
Old management structures and workflows are becoming obsolete. David explains why companies should empower builders with more ownership, reduce silos, and optimize for experimentation, fast learning, and adaptability instead of rigid process.

5. The companies that adopt AI early will compound advantages over time:
AI systems improve as they ingest more context, decisions, and customer interactions. Leaders who start adopting AI-native workflows now may build long-term competitive advantages through accumulated organizational intelligence and faster execution.

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GTM 190 Episode Transcript

00:00 – 00:07

David Zhu: There’s going to be this tsunami wave of AI native companies that’s going to help your competitors to be ten x 100 x more efficient.

00:07 – 00:11

Sophie Buonassisi: What’s kind of the pros and cons of building in stealth up to say, a series A.

00:11 – 00:21

David Zhu: Building in stealth was, I would say it’s a tactic, less a 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 going to work, it’s not innovation, it’s repetition.

00:21 – 00:24

Sophie Buonassisi: David Xu, co-founder and CEO of Revo.

00:24 – 00:47

David Zhu: Revo stands for Revenue Evolution, and we are the first category creator and revenue operating system. And what that means is that power is a companies entirety of go to market emotion from marketing, sales support, etc. and here we though were 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, what.

00:47 – 01:07

Sophie Buonassisi: are the things that they’re still stuck on and believe that are just fundamentally different now in 2026?

01:07 – 01:09

Sophie Buonassisi: David, welcome to GTM now.

01:09 – 01:10

David Zhu: Thanks for having me.

01:10 – 01:32

Sophie Buonassisi: You bet. It’s a pleasure to be sitting here with you today. And you actually came on to Stealth in November of 2025 with $80 million raised, 70 million of which was a series A co-led by Coastal 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?

01:32 – 01:56

David Zhu: Well, I think the part of the strategy and the sort of intentionality of front was building a compound platform that essentially encapsulates close to a dozen what is nowadays known as point solution from the gecko. 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.

01:56 – 02:30

David Zhu: When we do decide to come out of stealth and so, you know, internally, our founding team or co-founders and early employees or 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, conceal areas, whether it’s board members, founder friends, etc., 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.

02:30 – 02:52

David Zhu: And we just basically threw that out the door or 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.

02:52 – 02:58

David Zhu: I mean, obviously, we had design partners and early beta customers that helped us along the journey, but it was tough.

02:58 – 03:16

Sophie Buonassisi: 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. Yeah. What’s kind of the pros and cons to doing or building in stealth up to say, a series eight or coming out earlier? Absolutely.

03:16 – 03:36

David Zhu: Yeah. So so building stealth was you know, I would say it’s a tactic less a strategy. 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 institutional knowledge loss at scale. And so we’re like okay, well if we want to solve that problem what is a first principle way to solve it.

03:36 – 04:04

David Zhu: What are the technologies that we get to ride? What are the collection of the messy set of problems and pain points a saddle, the existing companies and their go to market motion. And so we thought really from first principle are like, okay, the problem is institutional knowledge loss at scale. The cause of that was the existing sales and tech stack, or go to market tech stack that powers it.

04:04 – 04:22

David Zhu: And what I mean by that is and 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 stocks really talk to each other in the olden days.

04:22 – 04:54

David Zhu: And so what ends up happening is, the best close and best close. They’re being told by their peers like, well, just procure yet another tool or I 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 the 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 and go to market.

04:54 – 05:18

David Zhu: That means being able to capture all the input signals from your marketing teams, your sales team, support team in one unified system versus a Franken stack 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.

05:18 – 05:38

David Zhu: 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 approach of being heads down, building a dozen different things, threaded together without really so much market validation. So of course, the founders and others like this just really take this strategy, that or tactic that works for you, that specific intent of we’re trying to solve.

05:38 – 06:01

Sophie Buonassisi: 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 $10 billion industry the Frankenstein stack. 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?

06:01 – 06:20

David Zhu: 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 hypotheses of where we saw the sort of direction of innovation was going. And so the three things that we were betting on that can come together.

06:20 – 06:46

David Zhu: It was one we were betting that models or AI looms are able to advance at a rate to capture context that is broader and more durable than humans can. All right. Because the reason for that is because, you know, humans, in the absence of AI or effectively the source of truth. Yeah, right. And so number two is like the cost of the capture of such systems and interactions.

06:46 – 07:10

David Zhu: And context is some, you know, cool kids call nowadays. Or the decision traces as you know, even more cooler kids calling nowadays. The cost to capture that effectively has been commoditized, right. And number three, we were betting on the sort of exponential, improvement in model capability to evolve from just an AI agent. That’s like doing mundane work.

07:10 – 07:34

David Zhu: Yeah, to be able to do actual complicated work. And some people call, you know, going from synthesis to action, action to reasoning, reasoning to thinking and creating, but whatever that sort of like marketing terminologies 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.

07:34 – 07:38

David Zhu: And so, we get to ride the tailwinds of all three.

07:38 – 07:50

Sophie Buonassisi: I mean, hey, gotta ride it when it works out. That is fantastic. And it takes a lot. Those are not small things to to bet on the way. Those are significant large bets. Yeah. I mean, there are other.

07:50 – 08:09

David Zhu: Bigger bets as well. But 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 a software engineer by training. And yeah, I always go back to that. And, you know, it’s been what, three months now since, you know, Codex and Opus came out with their latest AI coding.

08:09 – 08:33

David Zhu: And it’s like, I actually can’t remember the days before December. Yeah, it’s kind of saying, right. You talk to any founder, CEO, technologist, 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.

08:33 – 08:49

David Zhu: And back then, just to set some context for those on, you know, on on the show, you know, this is back in the days when you had to like, deploy code, build code manually. This is like before automation in this sort of Cicd space.

08:49 – 08:52

Sophie Buonassisi: It sounds archaic, but that was not that long ago.

08:52 – 09:16

David Zhu: It’s not that long ago. And it’s actually kind of saying, I remember, you know, we have these engineers called built engineers that effectively crafted makefiles of dependencies between one system to another. And we’ll create these like Java servlet, artifacts on create jar files and raw files. And we manually deploy these from one on prem system to another just to package it and then launch it and is all done manually.

09:16 – 09:45

David Zhu: And the challenging part about that, Sophia, is whenever you have these build engineers leave the company, there’s like downtime for weeks if not months, because that institutional 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 these software engineering agents are effectively the new steward of institutional knowledge of the code bases that power the companies.

09:45 – 10:07

David Zhu: And so that really risks a company’s business viability. But that’s not true. And go to market, unfortunately. Right, right. As companies scale their top one two reps court always winning deals, hold that special source of knowledge in their head the legacy systems of power. And so then begs the question of why. And so we’re like we don’t know why that’s the case.

10:07 – 10:15

David Zhu: So let’s go ahead and solve that and bridge the gap of institutional knowledge that we just observed in software engineering to go to market as well.

10:15 – 10:31

Sophie Buonassisi: 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 revenue. You know, we’ve we’ve talked around it a lot, but can you please just explain what revenue is, what you’re solving for?

10:31 – 10:32

Sophie Buonassisi: Yeah.

10:32 – 11:00

David Zhu: Sure. 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, etc. and so we believe in a world where in a few years time, a company will procure their favorite vendor of coding agents to be a software engineering department.

11:00 – 11:26

David Zhu: They will procure the Harveys liquor is of the world to be their GC department. Yeah. And in the similar breath, they would procure REvil to be their go to market department, empowering their existing marketers, sellers, success to become supervisors of REvil agents that provide outcomes to them. And so gone are the days where there is a linear association between a headcount outcome.

11:26 – 11:49

David Zhu: Right? You want more code, you hire more engineers. Garner those days as we see you want more revenue, you hire more A and sales B folks. Gone are those days. We believe in a world where we can decouple that. And so you have sales team of ten that generate the output of with AI at their fingertips with REvil Jarvis to their Tony Stark.

11:49 – 11:55

David Zhu: Yeah. They want to generate the revenue as if it was hundreds in the olden days. So that’s where we got this.

11:55 – 12:16

Sophie Buonassisi: Amazing. Thank you for 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.

12:16 – 12:21

Sophie Buonassisi: How are you thinking about outcomes, or how can other people think about outcomes in some form of measurable way right now?

12:21 – 12:51

David Zhu: Yeah, I think it’s still very nuanced per industry. And it really just sort of depends on if there is a universally defined definition of what success looks like under. 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 Singapore, you know, or the DoorDash sales scale up from 700 mil series C to 75 ish bill post IPO company in four years.

12:51 – 13:13

David Zhu: 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.

13:13 – 13:14

David Zhu: Yeah.

13:14 – 13:16

Sophie Buonassisi: Before and all too often.

13:16 – 13:49

David Zhu: Yeah. And, 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, SOP standard operating procedure that ultimately yields and whether something gets done or not. So, for example, if I say 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?

13:49 – 14:07

David Zhu: It’s I squared, regardless of either approach. As a customer, I don’t care. What I do care is like the DoorDash, the brand yield me the outcome that I want. So if I’m Steph Curry and I’m saying I want a refund, yeah, 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.

14:07 – 14:10

Sophie Buonassisi: Is that true? Is Steph Curry a DoorDash power user?

14:10 – 14:26

David Zhu: Or at the time when I was there, a lot of Golden State warriors or. Yeah, our users I don’t know about today. Yeah, it’s been a while, but that would be the the sort of like logic 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.

14:26 – 14:56

David Zhu: Yeah, right. But similarly, if you are a frequent abuser of the credit system, the refund policy, then we’re going to 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 outcomes 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.

14:56 – 14:57

Sophie Buonassisi: Right?

14:57 – 15:30

David Zhu: Right. Because it’s like so deterministic. Yeah. 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 is most meaningful for their customers. And, you know, in sales, for example, we would hope that Revo over time is able to deliver the outcome of a close one and the renewal.

15:30 – 15:45

David Zhu: Yeah. And upsell. Yeah. Right. But we’re not there yet today. But that is the intent of where we’re trying to track 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 the end result that we do and how do we get there and how do we get there?

15:45 – 16:07

David Zhu: 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 the nuance that token eating does not necessitate alignment with the customer’s Alco. That’s sort of the caveat.

16:08 – 16:16

Sophie Buonassisi: Yeah, which feels like, 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. Yeah. And token.

16:16 – 16:38

David Zhu: I mean, even within engineering, we see it as well. We’re very, I would like to think that retail is like one of the most sort of forward in terms of applied AI and software engineering for RL innovation. And one thing we’re very intentional about is like not going our engineers on token crunching. Yeah. Just because you crunch a lot of tokens doesn’t mean that you’re they’ve coded outcome is like the highest fidelity.

16:38 – 16:47

Sophie Buonassisi: How do you handle that at a leadership level? Yeah. How do you incentivize people to build. But also build intentionally for outcomes?

16:47 – 17:06

David Zhu: 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 going to work, it’s not innovation, it’s repetition. And here we go. We’re simply not in the business of doing repetition.

17:06 – 17:34

David Zhu: 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 aspect trial and learn. Learn quickly. And the teams that learn the quickest are going to come out on top. And so that’s the sort of mantra I share with our ELT leadership team, as well as 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.

17:34 – 17:47

David Zhu: That 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 we built. Yeah, I have that as well. I still remember a lot of the gnarly systems I built ten years ago.

17:47 – 17:50

Sophie Buonassisi: Not right near and dear to the heart. Always.

17:50 – 18:12

David Zhu: Exactly. But like what? I built 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 always 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 it’s like I remember 14 years ago is like when I was a 22 year old, 23 year old.

18:12 – 18:46

David Zhu: I remember, building this like new form of cache retrieval, cache and software and design. Yeah. And it was this, like new algorithm that it was, like, really gnarly. Yeah. And I was super proud of the outcome. But in hindsight, you know, 14 years later, what I still write the code exactly how I read know what it 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 we would deliver, I’d be able to take that, feed it into an opus or codex or whatever.

18:46 – 19:14

David Zhu: 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 would 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 and go to market, whether you’re a prot, ops, customer support when you’re adopting AI, when you’re vibe coding or whatever is the sort of thing that’s relevant to your function.

19:14 – 19:34

David Zhu: Think about the clarity of requirements. Think about the durability of the requirements. Think about the durability and timelessness of the benefits to the customer, because I think we’re going to get to a state where that same requirement, if we can feed it back into ever growing, you know, better model, then the outcome will always just be better anyway.

19:35 – 19:50

David Zhu: And so then that’s a very, very powerful flywheel. So it’s not a one, you know, one trick pony. Yeah. Instance in time. It’s an ever compelling set of better outcomes that rides the wave of the general intelligence models.

19:50 – 20:22

Sophie Buonassisi: I’ve heard you frame it as failures, 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 you spoke to is how nostalgic people can be about the code they wrote. Yeah. And I think the same thing applies to go to market about how we’ve structured teams for yourself, building something so disruptive, like how are people receiving these conversations for smart, you know, zero or smart founder?

20:22 – 20:28

Sophie Buonassisi: What are the things that they’re still stuck on and believe that are just fundamentally different now in 2026?

20:28 – 20:48

David Zhu: Yeah, I mean, with any technology shift, there is going to be I think there’s going to be a j J curve trough initially attributed to a combination of human sort of lack of interest to adopt. Yes. 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 future.

20:49 – 21:14

David Zhu: 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. Yeah. And so I think those two combined together is going to be headwinds initially to every AI native company out there.

21:14 – 21:41

David Zhu: But over time, once you sort of get past the point where, you know, the sort of the lowest level of the J curve, then you start harvesting that outcome very, very, very rapidly. And so, you know, we then go to market space. The you know, I’m we’re seeing a spectrum of reaction from the market. But I would say universally there’s a lot of cost a market fit today and there’s a lot of market pull for building.

21:41 – 22:03

David Zhu: And what I mean by that is like, you know, the more experience you are as a CFO 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 or you lead, you know, the larger you are, you’re probably managing a set of 60, 80, 100 different point solutions.

22:03 – 22:31

David Zhu: Yeah, you’ve either stood up over the course of 15 years or even heard it from a predecessor since the role of, you know, sales and marketing leadership turns over so quickly. And so then the questions like, oh my gosh, like, do you continue doubling down on this sort of antiquated pattern of the past, knowing that there is going to be the tsunami wave of I need a company that’s going to help arm your competitors to be ten x 100 x more efficient.

22:31 – 22:51

David Zhu: Are you still going to be writing this sort of linear? You, you relationship of like hire more a year to get more revenue linearly? Or are you going to say, let’s be innovative and decouple that in the age of AI? And I think that’s a state today that zeroes are rethinking, you know, historically. And this is so funny because this is like as recent as just two years ago.

22:51 – 23:15

David Zhu: Yeah. And I say historically, but like two years ago or when I was interviewing hundreds of crows and, go to market leaders, they’re like, dude, you’re bat crazy for, you know, trying to do what you do. Like no crows been fired for selecting a legacy vendor. Fill in the blank of whatever vendor you’re using. And it’s really interesting because at that time that’s true because that’s all they’ve known.

23:15 – 23:16

Sophie Buonassisi: Right?

23:16 – 23:41

David Zhu: But in the last 24 months, we’re starting to see a shift where today that same Chro, that same CMO, they’re coming back and say, interesting. I might be fired if I stick with the legacy vendor and I don’t know if they were 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 enable.

23:41 – 24:07

David Zhu: 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 we though get to a state where it’s has enough track record, then magic can happen.

24:07 – 24:08

David Zhu: From adoption perspective.

24:08 – 24:32

Sophie Buonassisi: I love that saying around trust. And for serious CMOs who are feeling this pressure now to almost single one had the opposite way and potentially move away from incumbents or just adopt more. I needed platforms or platform in general. What does that shift look like? Because I think we’re hearing a lot around the very AI native one built platforms that you can do a lot in.

24:32 – 24:54

Sophie Buonassisi: What is the transition state look like from where they are to there? Because I talked to a lot of sero CMOs and a lot are starting to consolidate specific areas with their stock, 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 what? Like what does the actual step look like for a CR or CMO to take that?

24:54 – 25:16

Sophie Buonassisi: Yeah, kind of walk. Is it going straight to a platform for, you know, all because it’s all 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? A GTM fund we invest at the very early stages of a startup. Three and one critical and surprisingly complicated decision could be naming your startup.

25:16 – 25:37

Sophie Buonassisi: Founders often spend weeks chasing the perfect dot.com domain, only to overpay or settle for name that doesn’t quite click, and also potentially pivot names down the line before you spend a ton securing a Com domain, I recommend checking out tech domains. Tech tells the world, your customers, your investors, anyone googling you that you’re building in technology, it’s very simple.

25:38 – 25:46

Sophie Buonassisi: So if you’re building a tech startup, this is a great option for you. You can secure your tech domain today from any registrar of your choice.

25:46 – 26:15

David Zhu: Yeah, I think the homogenous answer is that over a few years into the future, there’s going to be one way to do it. But until then, there’s going to 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 to 20 or 200 or whatever, like you’re going to have a very complicated, deeply rooted system and process, and the change management on that entire day is going to be very, very expensive.

26:15 – 26:17

David Zhu: Yeah. And so, yeah.

26:17 – 26:19

Sophie Buonassisi: That’s a whole angle that I don’t think that’s talked about enough.

26:19 – 26:43

David Zhu: Yeah. And so and so then like how do you layer on in an accelerated manner an implementation sort of accelerated indentation of a new AI native platform for that to power all of your marketing sales support answers like you probably can’t if I put myself in the shoes of the CEOs of fortune bracket, I don’t see myself going down a roof of a place in a four week manner is like, all right, let’s yolo everything.

26:43 – 27:10

David Zhu: Yeah, right. Today I want yeah, 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, etc., there’s the appetite to adopt is much greater. And I think it comes back to the simple sort of like framework equation of do you have more at risk to protect the value that you’ve built as a company?

27:10 – 27:35

David Zhu: 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 its value protection, value maintenance, that is a lot of weight to adopt something new.

27:35 – 27:53

David Zhu: 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 the 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.

27:53 – 28:09

David Zhu: I’m here to thrive. And if I’m here to thrive, I got to go and rip off the Band-Aid and go through this transformation. Right. It’s kind of like if you have, you know, cancer, your your hand, you’re going to want to amputate it. Yeah. Right. To say the rest of your body, you’re not going to be like, oh, well, let’s just let it play out a little bit.

28:09 – 28:44

David Zhu: It’s like, you’re not going to do that. Yeah. You know it’s the 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 they’re founders, multisite sales team orgs or mid-market, regardless of whether in technology or non 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 going to headed, which is them running on a rival that acts as their entire to go to market department.

28:45 – 29:10

David Zhu: 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 you’re a right sales person. Yeah. You know, your human reps will have bad days. Arrival won’t. Right? Every human rep you hire, you have to go through this thing and sales calling enablement, which is the sort of like X number of weeks or months of training.

29:10 – 29:29

David Zhu: So you know, the collateral, you know, the the torque 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.

29:29 – 29:57

David Zhu: That’s why, you know, there’s a lot of churn and the sort of sales org. Well, but guess who doesn’t have that right? AI agents. Yeah. And so if you have your best sales, your sellers, armed with the rival, 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 funnel and the bottom funnel as well.

29:57 – 30:20

David Zhu: And that’s is such a more deterministic, unpredictable 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 wanted engagement in conversations with others. Yeah. Versus stuck between behind a, you know, desktop.

30:20 – 30:26

Sophie Buonassisi: Oh it’s a dream to be an AI. See right now in a way because everybody’s always wanted to do strategy and suddenly everyone is I mean, you.

30:26 – 30:44

David Zhu: See exactly, exactly said it’s so powerful. You can pull up the app or your Apple Watch and be like, hey, rival, tell me XYZ a hey will help me plan this thing. Help! Hey we will. Yeah, I have this idea. Help me riff against it. Why is this not a good idea? Right. All right, everybody, then is armed with, like I said, a Jarvis to there Tony Stark.

30:44 – 31:06

Sophie Buonassisi: That love it. And so you are enabling others to build this way. So naturally you are building yourself as an organization. REvil 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 others try to pivot and transition to that.

31:07 – 31:42

David Zhu: I think there are two major learnings, one related to the space and the customer really serve, and the second related to how we innovate. And maybe I’ll start with the second because it’s more relatable prior to all founders and entrepreneurs, which is what I’m realizing that is that over the last 24 months, the sort of domain knowledge and experience of having been at several high growth companies and building from sort of very small, 0 to 1 phase to a massive scale, a lot of those learnings are not applicable anymore.

31:42 – 32:03

David Zhu: And that’s kind of scary and exciting at the same time. It’s scary because we’re taught to accumulate experience and then pattern, match and apply it. And it’s not applicable anymore because the how we, you know, what got us here, it won’t get us there. And every single time there’s a new transformation. And so the how we build is something that we’re constantly discovering internally.

32:03 – 32:26

David Zhu: And being able to teach our, 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 or not like being able and very willing to throw away that legacy knowledge is like one of the hardest things.

32:26 – 32:49

David Zhu: I’ll give you a couple tactical sort of, you know, instances of that, you know, Revo, we’re now pass 100 employees and, 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 our middle management. Let’s hire, you know, these sort of people leaders and yeah, let’s like organize for success across these functional pods.

32:49 – 33:12

David Zhu: 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 as because of the the drag on contacts transfer between humans was so high.

33:12 – 33:34

David Zhu: If you scaled it, it’s the Dunbar’s like cool. Yeah. But nowadays if you build like that, you’re going to be a dinosaur. As a technology team, how you’re going to need to evolve your engineering team, 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.

33:34 – 33:48

David Zhu: And so the need to focus on the customer at 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.

33:48 – 33:49

Sophie Buonassisi: Yeah.

33:49 – 34:13

David Zhu: And it’s very scary. And that’s one example. Another tech example is like all we’re seeing from some of our, hottest companies as well as our founder friends, is that they’re effectively elevating the best leads, the doers, a tech leads and marketing leads to sales, leads to finance, leads to become mini CXOs of their company. So you have the best tech leads and software engineer becoming a CTOs.

34:13 – 34:36

David Zhu: You have effectively product managers who become CEOs, your best aides become crows, 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, that sort of level of agency, it takes away from the people managers is like insane as well. So that’s like one.

34:36 – 35:03

David Zhu: 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 principles thinking in combination with independent thinking. Those are very different first ones. So an independent thinking is like very important. 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 some things to say about everything.

35:03 – 35:05

Sophie Buonassisi: Oh, is everybody right?

35:05 – 35:24

David Zhu: And it’s like, okay, cool. How do you listen to it but only take like 0.1% of it, you know, to to what you do? Because if you listen in mass or if you don’t listen, then you’re like living, you know, like a hermit. Yeah. But if you listen to mass, then you become an echo chamber, which means that you can’t really arbitrage out any sort of distinct value.

35:24 – 35:29

David Zhu: Probably you’re able to offer your customer. So those are sort of like two things that we’re holding in our heads.

35:29 – 35:40

Sophie Buonassisi: Very cool. And what about your personal AI usage beyond remote specific? Are there any I use cases that have been transformative for you personally as a co-founder and CEO? Yeah.

35:40 – 35:45

David Zhu: Beyond REvil, that sucks, because REvil is probably one of my highest AI usage tools.

35:46 – 35:47

Sophie Buonassisi: It can include Riva that.

35:47 – 36:07

David Zhu: Yes. So Riva is actually, my favorite AI app to use, very selfishly. And I’m not saying it’s as a plug. Yeah. I’ll just sort of share how I use REvil every day, please. Right. So I’m both a hiring manager. Okay. I go to market, you know, founder led sales, and then also, I’m an investor as well.

36:07 – 36:33

David Zhu: Yeah. And so, what’s in common with all three of these pipeline tracking, right. Conversations with humans, conversation across many, many, many different humans. So there’s a lot of interaction. And one thing that REvil does for me is I basically get to act as a GP, a CEO, founder, let’s sales and a hiring manager three very distinct roles and one as part of my day, in addition to the rest of my day.

36:33 – 36:50

David Zhu: Yeah, right. And so then questions like, how do you do that with retail? It’s like, well, I create pipelines and repos there as the Jarvis to talk to my Tony Stark. Right. And it’ll refresh visions 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.

36:50 – 37:07

David Zhu: And this is what might be interesting to you. There might be, you know, as a GPS investor, I might have different touch points with Porthos. Yeah. Six months out. All right. It’s you never say no. You go say just not now. Right? Right. And says like, well, you know, 18 months ago they were at traction ex 12 months ago, their attraction.

37:07 – 37:34

David Zhu: Why you know, six months ago trashes. And today, you know, you could sort of then plot that graph and and then obviously within sales and go to market, our own GTN team uses Revo. Right. And so I basically get I get to act as a, as a mini CRO, without having to wait for my weekly business reviews, without having to wait for our rev ops to spin up, you know, cuts of forecast and reporting.

37:34 – 37:59

David Zhu: Why? Because at any given point in time, I can just go and ask revolts like, hey, what’s my quarterly, you know, attainment, right? It was tell me about my highest performing reps. What is the specific line that drives the highest close? What are the deals that we should have closed? That we did it right. Help me stay ahead of churn these are things that I no longer have to wait for.

37:59 – 38:21

David Zhu: Synchronous sort of weekly cadence feedback, or even monthly for some companies. Now I get it whenever I want, I get on my Uber, I get on my way. Walk. Yeah, it’s just intelligence on my fingertips. And I get to act on it because we’re arming remote to with a bunch of toolkits, that the rest of the platform is able to offer from top of the funnel prospecting, get a close one.

38:22 – 38:29

David Zhu: And so, just like there feels very empowering. Yeah. Outside of Revo, I’m probably a heavy user of, like, deep research.

38:29 – 38:29

Sophie Buonassisi: Okay.

38:30 – 38:49

David Zhu: Yeah. To plan different things to, to 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, right. And so I’m learning how to, like, not distract the team. So much by grabbing random folks like, hey, what about this idea list?

38:49 – 39:04

David Zhu: Because when it comes first, the first layer. Yeah. When it comes to a CEO, founders like, do you want that implemented kind of thing? It’s like, no, I just want it riff feel like deep research allows me to sort of like riff back and forth and poke holes and the argument without distracting the rest of the team.

39:04 – 39:07

Sophie Buonassisi: Yeah, yeah, you can make it a little bit.

39:07 – 39:08

David Zhu: of a plan.

39:08 – 39:25

Sophie Buonassisi: Yeah, yeah. Very cool. And last question, speaking to yourself as a leader, you know, you are leading over and 100% organization. You’re also investing. 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?

39:25 – 39:50

David Zhu: I think, one thing I realize over time is just like it’s hard to change. So there’s like what’s intrinsic and what’s idiosyncratic about people, intrinsic are the things that, you know, is like in nature, who are your values and etc. and the idiosyncratic piece or the things I a behavioral you can learn over time, the introvert piece and media, you know, you can get a media coach and learn how to be presentable on podcasts and etc. and you’ll get better over time.

39:50 – 39:53

David Zhu: Like, I’m clearly a work in progress and I’ll get better over time.

39:53 – 39:55

Sophie Buonassisi: Oh, ten of ten no way.

39:55 – 40:14

David Zhu: And have yeah. You know, intrinsic peace. Don’t try to change that like you are who you are. Yeah. And lean into it. There is 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 attributed to whoever said this. There’s so much power to be unique as a differentiator for your own brand and your own company, your company’s brand.

40:15 – 40:34

David Zhu: If you stay true to who you are and do things the way that you know how and best the most comfortable, then by definition you’re very unique and you have a asymmetric edge 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 extrovert, it just be yourself.

40:34 – 40:36

David Zhu: Those who matter don’t mind and those who mind don’t matter.

40:36 – 40:40

Sophie Buonassisi: For anyone interested in following along your journey journey? Where can they find you?

40:40 – 41:17

David Zhu: Probably in office 6 or 7 days. Yeah, in Santa Clara or San Francisco office. I took this day off social media. And just so I can have clarity of mind, but feel free to reach out to me, David, at revel, I if you’re an entrepreneur or, looking to exchange notes on how AI is affecting your space and where you see the trends going, or your go to market leader looking to embrace, AI transformation for your org and you really believe that in the future, the best leaders and go to market are going to be picturing their AI avatars.

41:17 – 41:36

David Zhu: And I, Consiglio, AI’s and Copilots and the Jarvis that there are Tony Stark. And you want to learn a little bit more about revel. That’s where you can find me as well. But outside of work, I got two beautiful kids, six and eight. And, they’re going through a phase where they are very curious about building Legos and Minecraft and all that.

41:36 – 41:42

David Zhu: 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.

41:42 – 41:51

Sophie Buonassisi: Yeah. So fun. And I love some of the marketing campaigns that Legos do. I know they just dropped the one with Messi and a bunch of other soccer players. They are just yeah, killing it on the marketing.

41:51 – 41:53

David Zhu: Absolutely. Yeah. Lots of.

41:53 – 41:56

Sophie Buonassisi: Very cool. Awesome. Well thank you David coming on.

41:56 – 41:57

David Zhu: Thank you, thank you. Take care.

Sophie Buonassisi

Sophie Buonassisi is the SVP of Marketing at media company GTMnow and its venture firm, GTMfund. She oversees all aspects of media, marketing, and community engagement. Sophie leads the GTMnow editorial team, producing content exploring the behind the scenes on the go-to-market strategies responsible for companies’ growth. GTMnow highlights the strategies, along with the stories from the top 1% of GTM executives, VCs, and founders behind the strategies and companies.