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How One Hackathon Took Zapier’s AI Usage From 10% to 97% | CEO of Zapier

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0:00 | 39:13

Wade Foster is the CEO of Zapier, a company that sits between 7,000+ apps and runs millions of automations every single day. That gives him a front-row seat to how companies are actually adopting AI, not just talking about it.

In this episode, Wade breaks down the exact decisions he made at Zapier to go from 10% AI usage to 97% company-wide, why agents and workflows are not the same thing, and what most leaders are getting completely wrong about AI fluency.

What you'll learn:

  • The difference between agents and workflows (and when to use which)
  • What triggered Zapier's internal "Code Red" after GPT-4 launched
  • The one-week hackathon that took AI adoption from 10% to 50% overnight
  • The AI fluency rubric Zapier built: Unacceptable, Acceptable, Adaptive, Transformative
  • Why leaders who aren't using AI are the biggest bottleneck in their companies
  • How to measure AI ROI: floor raisers vs ceiling raisers
  • How AI now handles 50% of Zapier's customer support tickets
  • Wade's personal "advisory council" of AI sub-agents he uses for every major decision
  • Why building a company today is 10x cheaper but distribution is 10x harder
  • The truth about fundraising: you're selling your company, not raising money
  • How Zapier stayed profitable by only hiring when it hurt


Guest: Wade Foster, CEO of Zapier
LinkedIn: https://www.linkedin.com/in/wadefoster/
Company - Zapier: https://zapier.com

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


Episode highlights

0:00 - Intro

1:13 - The Seinfeld Quote & Kanye Text story

3:16 - Workflows vs. Agents: What's the difference?

6:09 - Zapier's Code Red moment

8:55 - The hackathon that moved AI adoption from 10% to 50%

12:06 - Making AI fluency a hiring requirement

13:47 - Building the AI fluency rubric

16:40 - Why leaders are the biggest AI bottleneck

18:12 - Revenue impact of going AI-first

22:09 - Would Wade build Zapier differently today?

23:20 - Is Zapier's moat at risk from agents?

24:59 - Staying profitable with minimal capital

28:37 - The riskiest contrarian bet that paid off

31:52 - Wade's 3 personal AI workflows

36:53 - Favorite books for founders


GTMnow is the media brand of GTMfund, sharing go-to-market insights from working with hundreds of portfolio companies backed by 350+ of the best GTM executives. 

Subscribe for weekly episodes with the operators, founders, and investors behind the fastest-growing software companies.

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_01

In 2023, you called a company-wide code red at Zapier. And after that moment, the I adoption five Zapier actually went from 10% to 50% in a single week.

SPEAKER_00

We looked at that and said, holy cow, we need to rethink our roadmap. And candidly, this could be a threat as well if we don't react to it.

SPEAKER_01

Zapier sits between over 7,000 apps, has millions of automations running every single day. There's actually quite a distinction between workflows and agent.

SPEAKER_00

I actually think leaders are one of the biggest reasons like companies are held back. Because it's because they haven't used it. So they don't know what is good look like, what is bad look like. A workflow is deterministic. It behaves the same way every time. An agent is something you give a goal and you give it instructions on how to complete that goal. But it has its own logic, its own reasoning, its own way about going about it.

SPEAKER_01

Just in a moment to kick us off where you saw a workflow, somebody don't thought. Holy shit, this is the future of companies you're gonna operate. Wade, welcome to GTM Now. Yeah, thanks for having me, Sophie. Wade, Zapier sits between, you know, over 7,000 apps, has millions of automations running every single day. So that means that you actually see what people try to automate, what gets you know left behind after a couple of weeks and how people are actually successfully building. So what's been a moment to kick us off where you saw workflows somebody built and thought, holy shit, this is the future of how companies are going to operate, or at least gives you a perspective of how they're gonna operate.

SPEAKER_00

We started the company in 2011 focused on integrations specifically. And we quickly realized that folks wanted more than integrations. They did want workflows. But when we launched workflows in 2016, you know, it wasn't exactly obvious all the different ways that people would use it. And I remember one of our very early power users of that feature, this guy in Australia, he messaged me and was like, Zapier is incredible. I had built an entire business and like application on top of it. And I was like, Oh, that's interesting. Tell me more. And it turns out he had built basically like gag apps. So one was called Seinfeld quote and the other was called Kanye text. And effectively what you could do was you would put in like a friend's phone number and then he had hooked it up to Stripe and Twilio. And basically you could buy like a smaller, medium, or large package. And depending on what you bought, it would basically spam your friends with either Seinfeld quotes or Kanye texts. Uh, and it was like all built on top of Zapier. I was just like, holy cow! Like I had not anticipated people building like full-on applications or like businesses on top of the platform in that way. And so that was a very early eye-opener. It basically was like, oh, we're just thinking way too little about what this thing could become. How early was that in the journey? So that would have been around when we launched workflows. So that would have been around 2016 or so, which was like five years in. You know, it took us a while. Like integrations was like such the focus for the first, you know, couple of years. You know, probably about two or three years in, we're like, we gotta go figure this workflow thing out. And, you know, it took a year for us to get that, you know, uh product ready and shipped. Mm-hmm.

SPEAKER_01

And I mean, ZPR's evolved tremendously since then, too. And now the world also looks very different. Everybody's talking about agents, of course, and everybody wants to build agents, but there's actually a quite a distinction between workflows and agents. So I'd love for you to actually walk us through what that distinction is, you know, a traditional workflow, workflow that uses AI, maybe a true agent, and if I'm missing anything else on there too.

SPEAKER_00

Yeah. Certainly, like everyone uses agents to call the superset for for everything. I tend to think of agents in the difference between agents and workflows on a spectrum. A workflow is deterministic, it behaves the same way every time. It's like a computer program. You say, hey, I want it to do this, and then I want it to do this, and then I want it to do this, and I want it to do this. And then you're like, great. Then the computer will go execute it the same way every single time. And so you get like reliability, you get costs, you get all these sorts of things. And so that's what a workflow actually looks like. An agent to me is different. An agent is something you give a goal and you give it instructions on how to complete that goal. And then maybe you give it access to data or tools or something like that. And then every time you know the agent has the opportunity to go complete that goal, it goes and figures out how to do it. But it has its own logic, its own reasoning, its own way about going about it. It might be different than the way you would go about it. And as a result, you can give it a wider variety of tasks because you don't need to know all the possible ways that it needs to behave to go solve the problem. But you're also trading off against reliability, cost, things like that too. And so to me, agents' workflows, they sit on a spectrum. And you might want one for a particular job and you might want a different one for a different type of job.

SPEAKER_01

Completely makes sense. And what does that evolution mean like over time, almost the stages of evolution, since you've seen them all to get to this point where we are now?

SPEAKER_00

Well, I think obviously like agents is blowing up in the age of AI. Like there is so much more use cases that have been unlocked because of the ability to work with AI. And so that part of the puzzle is where all the momentum is today. That said, what is interesting is that there is also been a renaissance of workflows as well. And I think the reason why is because a lot of folks maybe had like a mental block or had just sort of said, Oh, I can't solve, like this is not for me. Like I'm not an engineer, I'm not tech laugh. I don't know how to go solve this problem. And so when you go sit down and, you know, talk to people now, they have this appreciation that AI exists. And so their mind's like, oh, what problem can I solve? And they start saying, Can you automate this? Can you automate that? Can you automate this? And honestly, like nine times out of 10, no AI required. It's something that they could have been doing all along, but they just didn't realize it that that was something that was available to them. So in a lot of ways, I think AI has been this like catalyst for all of us to be more generative with our ideas and say, oh, there are like and get more excited about the automation potential. And the irony is, of course, much of that stuff we we could have been doing all along.

SPEAKER_01

Yeah, that that's super interesting. A really good point because, like you said, it's been there all along. We just might not necessarily have had the creative inspiration to actually go there. And now that we are being pushed across that kind of chasm towards AI because of the advent of AI agents, in 2023, you called a company mind code red at Zapier. And for anyone unfamiliar, code red just means that you drop everything and the entire company shifts focus to one urgent priority. And I believe after that moment, AI adoption inside Zapier actually went from 10% to 50% in a single week. What triggered that decision to call a code red?

SPEAKER_00

The key insight that we had was we had seen the ChatGPT launch in the fall of 2022. You know, I think a lot of people that was like, whoa, moment. For us, it was like, this is cool, but it didn't yet like cause us to like monumentally change our behavior. Um, instead, we were, you know, we shared the product launch in our Slack channel in general. It was like, hey, check out ChatGPT. This is a cool product. We like cool products, you probably like cool products. Check it out. Some of the ideas in this might be useful in our own products. So if you know you're a product manager, an engineer, and like you're trying to think of like cool, interesting like problems you can solve and automation on behalf of our customers. This is another tool in your toolbox. Like, you know, go go think about using it. And that was kind of it. Then over the course of you know the next like four or five months, there was more areas where we just started to bump into users, partners, doing interesting things with it, where that sort of like organic, you know, more casual adoption, we started to just get like a little more forceful, a little more forceful, a little more forceful here or there. And that all culminated though with the GPT-4 launch, which happened in the spring of 2023. The reason that set off the code red for us was that one, it was only six months between 3.5 and four. And four was a vastly superior model to 3.5, and the costs kept going down. And so we looked at that and said, holy cow, if this represents any sort of trend for the future, we need to rethink our roadmap, we need to rethink our internal operations. There is a monumental opportunity for the companies that jump on this, and candidly, this could be a threat as well, if we don't like react to it. And so I remember a Thursday night call with my co-founders where we were just like trying to figure out like, what are we gonna do? What are we gonna do? What are we gonna do? And ultimately we called the exec team in at like 7 a.m. the next morning and we're like, hey, here's here's what's on our mind. Like, this is a code red moment. We need to go figure out what that means. Now, ultimately, we had never called a code red internally, so we didn't actually have a concept of like, well, behaviorally, what does that mean? And so we had to go figure out what exactly do we mean by code red? Like it sounds serious, but like, you know, beyond just sort of everyone like throwing their hands up in the air and being like, ah, let's figure it out. Ah, yeah, like what is this practically? And we did a bunch of things in the like days and weeks following that. But there was one thing in particular that like really stood out as a very effective technique. And that was the following week, the following Monday, we kicked off a company-wide hackathon. So not just engineering, but you know, marketing and sales and HR and finance and all these functions. And we said, hey, we want you to go build with AI. And, you know, for engineers, that probably meant building features with, you know, the open AI APIs for, you know, our sales team or a marketing team that might be just using Chat GPT and like trying to have it do research for you or do tasks for you or things like this. What we saw was over the course of that week, yeah, it's like a lot of people just like got a lot of experience using these tools. And we did a show and tell at the end of it, a demo day where people showed off. There's a bunch of learnings and all that sort of stuff. And the culmination of that was that pre that hackathon week, there was probably just under 10% of folks using AI like daily uh for their work. And after it was just north of 50%. So almost, you know, 40% of the company, you know, went from very little to no usage of the technology to, you know, now using it pr pretty frequently. And so that was probably the single most effective thing that we did to jumpstart acceleration of how we think about AI inside of Zapier.

SPEAKER_01

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SPEAKER_00

So since that time, I have talked to probably hundreds of companies, founders, CEOs, executives. And the singular most effective tactic that I've heard is some concepts of a hackathon, builder sessions, luncheon builds, like workshops. Like you hear that repeated over and over and over again amongst the companies that are seeing the most success. It makes sense to me. Like you see a very different mentality between the people who are using the technology versus the people who are pontificating on the technology. The folks pontificating about it, there's a lot more fear-mongering. There's a lot more sensationalism. The people who are actually putting their hands on it, like they have a just a like a richer understanding of where this is a powerful technology and how it can help them. They understand the limitations and what it is not capable of, where their role is. And like the thing I end up just coaching people on is like AI is a it's a tactile thing where it's like you benefit so much from using this stuff. And so I I I recommend it wholeheartedly. And I think that is not just like I wouldn't say Zapier is in of one here. This is like an in of you know 100 plus where I've seen now that this is a very effective technique.

SPEAKER_01

Yeah, that I mean, that's fantastic. And we've definitely seen some portfolio companies leaning to that approach. I know for myself, I've just got a network of folks and we host hackathons just separately from our companies, just amongst us. We if we share similar use cases and things like that. It's just so, so valuable to be able to focus on it and learn from others. Now, in I believe it was 2025, correct me if I'm wrong, Wade, but you actually made AI fluency a requirement for each new hire. So you took it a step further and built a rubric unacceptable, acceptable, adoptive, transformative. Tell me about this rubric.

SPEAKER_00

You know, I think we had just seen a couple things. We'd invested so much in just providing opportunities for folks to learn and have space to kind of figure out how to use the technology. And as we'd seen that, we definitely were starting to see that the folks adopting it the most were almost like these super contributors inside the company. Like their productivity was sky high. They were able to do things that they couldn't do before. And they were providing leverage for themselves, for their team, for the company in all sorts of interesting ways. Also, we had seen, you know, adoption of AI go from 50% post that first hackathon to now, I mean, I think by the time we announced that, we were like at 97% or something like that. So basically everyone in the company was using it uh regularly. And so the thinking there was like, hey, if you're not using this stuff coming into the company, like you're just gonna be behind inside of the company like Zapir. So at this point in time, this is kind of just a job requirement to be successful inside of a company like Zapier. The second thing was it was an important signal for our employees, but also to the market, where it was, you know, we we want to be known as the type of company that is going to invest in this technology and invest in like the future of work. And so we wanted our employees to know that, like, hey, we are gonna be on the cutting edge. And so to the extent you're fearful about your skills continuing to be modern, continuing to be relevant, like you don't have to worry about Zapier being a place that's gonna support that. We are 100% gonna stand behind you and support you in like making this transition to our to this sort of new AI world. And just as importantly, we wanted people out there in the world who were maybe in companies where that wasn't true, but they were excited about working this way, maybe doing this like in their spare time to say, hey, come come work at us. Like we want you here. Like this is gonna be awesome for you. So we said about that as well. I think the biggest challenge was we had to just define what the heck does AI fluencing mean. Um, you know, I think a lot of companies at this time were struggling with this where they wanted more AI adopt. In fact, I'd say companies even today still are struggling with this. Like they want more AI adoption inside the company, but they don't have a good way to talk about it. Like, what does it practically mean? What's the rubric, you know, at the end of the day to say, is this person good or bad? And so we just set about like a very simple exercise, which was we went to leaders in every function, we went to the power user in every function and just said, Hey, what are the things that you feel like are baseline level skills for people working with AI in your function? So if you're an engineer, you need to be able to do this. If you're a designer, you need to be able to do that. If you're a marketer, you need to be able to do this, so on and so forth. And, you know, we said, okay, what's sort of like the basic things? Like what would be the type of stuff you kind of just expect people are doing these days? What would be the type of thing that we go, like, oh, you're kind of ahead of the curve? And then what would be something that you'd be like, holy cow, you figured that out? Like, we haven't figured that out yet. Something that would just like really surprise you. And so we just made like a simple rubric and said, hey, this is kind of the, you know, not acceptable, acceptable, you know, adaptive and transformative. And we put that out there and said, Hey, this is what it looks like. You know, I think alongside that, we said, hey, we fully expect this rubric to change. Um, it certainly has a year in because the technology moves very, very quickly. So, you know, it is an important exercise to do this, but it's also just as important to recognize that the slope here is like more important than any, you know, dot along the way because of how fast the technology is moving.

SPEAKER_01

Definitely. And I would echo you that it's something people are constantly struggling with right now. We get requests all the time from leaders just around how do you actually evaluate AI usage? And the the methodology people are using just is skewed so heavily. And there's no right or wrong per se, but there's so many different forms out there. This is probably one of the most standardized forms that I've seen. If a sales leader, let's just say, wants to implement this rubric, what does that actually look like? Like, do they have a rubric for each scale? Is it function specific or uh tactically, how does this rubric work?

SPEAKER_00

So, in effect, we've created these based on uh it's like any other rubric, like if you were trying to hire for, you know, like a content marketer, it's like, well, you probably want to see that they're good at writing. You probably want to see that they're like have good ideas, you probably want to see that they, you know, have some understanding of like search or like headline writing. Uh like so you you kind of know like, okay, these are sort of like the skills that are required to be good at this job. You've probably worked with people before where you're like, oh, this is the best of the best. This is like pretty good, this is like not very good, and you just create a rubric out of it. It's not any different for AI, like at the end of the day. This you you do the same thing. I think where I see folks really struggle with is you mentioned like leaders struggling with this. I actually think leaders are one of the biggest reasons like companies are held back because a lot of these leaders are not putting their hands on the keyboard and not using the technology. And so they're asking, how do I create a rubric for this? And the reason they can't do it is because they haven't used it. So they don't know what is good look like, what is bad look like. And, you know, that is where I find like so many leaders are in a bad position themselves because they're running a dated playbook. They sort of grew up in this era where they had once upon a time their skills were fresh, modern, best of the best. But now they've sort of like, you know, grown into this management job and they've kind of lost touch with their craft. And the craft is changing. Like how they do the work is actually changing. And those folks have not gone back and stayed relevant on this. I grew up with a bunch of folks in the medical field and in my family. And if you're a doctor or a pharmacist or a nurse or any of these people, you're always doing continuing education. Like you always have to go back and do these things because there's, you know, maybe there's a new like uh technique for surgery or there's like a new medicine that comes out that sort of impacts these things. To me, these are like, you know, folks in the medical field who never did their continuing education. And it's like, well, of course you don't know how to like stay fresh on AI because you're not putting in the work yourself. And I like I would just caution if you're a leader in one of these positions, like you got to go figure out how to rectify that because you're not gonna be able to lead your team effectively if you are not yourself being a user of this technology. And it doesn't mean that you have to be like the world's foremost expert at this stuff. You just gotta like start and play around with it and you'll get better, just like anybody else.

SPEAKER_01

Yeah, a good lesson uh just get your hands dirty on AI for everyone. And Wade, if you're open to sharing, what has been the actual revenue impact of going AI first in Zapier?

SPEAKER_00

I think there's a couple areas that we see the most. Like one, we've pushed it into our product in a so many different areas. And so it's not just like that our operations are different, it's that we are building AI capabilities for our customers. And so when you look at the growth curve of our users who are using our AI capabilities versus the ones who are not, it's like a night-day difference. Uh, you know, those like those cohorts are growing just vastly, vastly faster than than than the other cohorts. And I think it just speaks to the power of the technology. It's like this is a really powerful way to work. Um, internally, there's sort of two ways that I think about this. One is easier to have an R to measure the ROI. One is much harder. So the whole like 100% of people are using AI daily, I think of this as like a floor raiser. This is, you know, let a thousand flowers bloom. Everyone is using it as like a personal tool to help them with their day-to-day. If you were to ask me, if you're asked our CFO, if you're asking any of these people, like what ROI is there on that, it would be I would be hard-pressed to tell you. I know that there is because I see it in my own work, I see it in other people's work. But if you ask me to measure it in a very concrete way, I just don't even, I'm not even sure how I would like go about that problem. And so there is just like a leap of faith there where I'm like, I know this is working, but it's hard for me to sort of point to like the ROI impact of that. Then you have like your ceiling razor project. These are places where you can look at a very concrete business result and say, like, this is working. So for example, in our customer support team, some 50% of our customers' requests are now being fielded by AI. And those folks get one much faster response times, but they also, for those set of questions, cite higher customer satisfaction as well. Too. And so that's one of those things where you can very like concretely measure, hey, this is working. It's better than the way that it was working before. And I think that's the thing when people ask, like, how do you go about measuring AI productivity? The answer is like, we you don't really measure AI productivity. You measure your business outcomes. And so generally that's like, are you improving like sales throughput or conversion rates, or are you, you know, decreasing the cost to serve in some way, somehow, like all the sort of KPIs that you probably measure today, those are the things that you should be seeing improvement on if you really are deploying the technology effectively. And so if you're trying to look for how do I actually go impact the business, you probably have to start with those KPIs and work backwards and say, you know, what are the bottlenecks that are causing us to not be like more ambitious with these goals and then figure out, okay, can AI like meaningfully change the fundamental unit economics of how that works in a particular way?

SPEAKER_01

I love that advice, Wade. Is it really as futile to just do AI for the sake of AI, which right now feels like there's a lot of pressure for a lot of people? But at the end of the day, if it's not driving business outcomes, what's the point?

SPEAKER_00

I would say the point is that there is like a little bit of play that has to happen first. Like it's really hard with something new to just wake up and go, okay, if you cannot show like monumental business results, I will not let you touch this, you know. Yeah. And the reality is like there is like a learning curve. And so your first few things you probably try, they probably don't change the color of the sky for the business. But That learning that's put into that, it will end up paying off, you know, by the time you get to something that actually can have a much larger impact. And so I think that's where, like, in this moment in time, companies do have to probably be like a little tolerant of like play and experimentation. Some might call it waste, but to me, I call it investment.

SPEAKER_01

I love it. Flip it on its head. And plus there's the technology kind of curve too, where, you know, it might not be at the point where some leaders feel like it makes sense to implement fully, but if you're not playing with it now, then that learning curve just gets harder as it goes. Like the people that were experimenting early, that adopted early, like yourself, have such an advantage with that compounding knowledge than those that are jumping in later. So you took Zapier through quite a transition and really embraced AI. If you were starting Zapier, say today in an AI native world, would you build anything differently?

SPEAKER_00

I mean, we started the company almost 15 years ago. So it is such a different world. I think there's two sort of like general observations I have. One, the cost to build stuff is so much cheaper now with these tools. Like you can be way more ambitious in what you build, the speed at which you build it. And so unquestionably, there would be like different approaches we would take just in building like the first capabilities. The flip side of that is marketing, distribution, attention, I think is probably 10 times as hard, maybe even more than 10 times as hard. And I suspect that that is where we would have to do things much, much differently. It's hard to like think through exactly what that would be at this moment in time because like all the channels we benefit from, like we basically popularized and did those. And so that's like the key question to ask.

SPEAKER_01

Yeah, that is. And and it'll be interesting to see how people build now as it is far more saturated. Are you concerned at all around Zapier's moat just as agents become more prevalent?

SPEAKER_00

I think every company is probably asking themselves the moat question and sort of rethinking from first principles like what those could be. Yeah, I think the ultimately, like, you're trying to figure out how can you counterposition against other companies? How can you do things that they can't do or won't do? And I do think that the best companies probably do have some like keen observations around how that works. But I do think oftentimes motes reveal themselves rather than are discovered over time. And so I think the best companies are just ferociously listening to their customers, shipping crazy fast. And along the way, they sort of discover, oh, wow, like there, we we have an edge because of this thing we did. And maybe didn't totally appreciate it when they did it. Certainly that was like when we built our original modes, like that's how we found like we found them. It wasn't that we set out to say, ah, this is our moat. This is what we're doing. It's just like, well, this is how we're gonna get customers, these are the important things. And it turned out those things happen to have characteristics of a moat. Maybe there are some founders out there that are like much more savvy and can like identify them in advance. But I find that it's like anytime you hear people pontifica at these companies, it's so easy to connect the dots looking backwards. But I doubt it actually played out that way looking forward.

SPEAKER_01

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SPEAKER_00

Well, I think it's a lot easier than it used to be. I think a lot of companies could have done it before too, that just chose not to. Now I think it's just more popular. I think, you know, especially right now, a lot of founders went through the sort of like zero interest rate period. They hired big teams, their companies ballooned bigger than they probably should have been. They experienced all the pain and headache of overhiring and you know, coordination costs, like before the company was ready for that stuff. Then of course you saw the market drawdown and they just like lived how painful that was. And, you know, it kind of came out the other side and said, you know what? I think I shipped faster with a smaller team. I had more fun with a smaller team. I could grow revenue faster with a smaller team. And, you know, well, it used to be somewhat contrarian to say, hey, we're gonna like keep headcount like low, we're gonna only hire when it hurts. Like a lot of that contrarian advice has now actually become more in vogue and saying, like, hey, we actually can do this. And so as a result, you're seeing a lot more companies building this way. And I think AI does help because you don't need as much engineers, you don't need as much other things to like go ship and deliver, you know, first, second, third versions of your product. So yeah, I think my advice to folks would be just like, well, one, just lean into that. Like go use these like capabilities that you have for yourself. I think the second thing that I see a lot of folks get tripped up on is they don't really think maybe for themselves. The way I've always thought about it is fundraising is is selling your company effectively. Like that's what it is. Yeah. But a lot of times that's not how it gets like positioned to founders by VCs and other folks. You know, it's raising money, not selling your company. It's like, no, you're selling your company. Now, in a lot of case, you're hoping by selling a part of your company you can make the company bigger than what it is. But if that is the case, you should be able to step back and have a clear hypothesis around that. And so the exercise we always went through, you know, when we had opportunities to raise more money is we stepped back and we paid attention to like the underlying metrics of the business. And we said, okay, what is actually the bottleneck that is preventing us from growing? Like if we had, you know, another million dollars, another$10 million, what thing would we invest in? How would we deploy that money to help us grow faster? If the answer was money isn't the bottleneck, something else is the bottleneck, we just decided we're not going to raise more money. Why? Like, we don't want to sell more of our company to solve a problem we don't have. And I think a lot of founders don't go through that exercise. I think they basically listen to Groot Think, you know, hey, the best companies raise money. Don't you want to have a war chest? Don't, you know, wouldn't it be nice to have work with, you know, this fancy brand name, things like that. Which look, those might be useful. Those might be helpful things, but you gotta step back and understand like, do you actually need those? Is that the thing you need right now? Or is that just something that like helps you feel better, helps you sleep better at night, et cetera? You know, I don't fault anyone for operating one way or the other. I mean, we've raised money ourselves. I think it is a valuable tool in the tool chest, but it becomes the de facto path so often. And that's where I see people get tripped up and they overraise their companies, get, you know, ballooned, they lose control. And even if they don't lose control, like the outcome for them personally is never gonna be as good as it could have been had they sort of grown in a way that was more reflective of what the opportunity inside that company looked like in the first place.

SPEAKER_01

Yeah, I hear you and definitely see the same thing. It's surprising at you how many conversations that we have regularly with founders around is this the right like vehicle for raising money for you? Because sometimes it's not. And it's got to be that transparent conversation and decision where it has to make sense. Like you said, it has to be so intentional. So maybe it is the profitability side, or maybe it's something else. But I'm curious, Wade, if there was, you know, a company building decision that you made that felt maybe risky or wrong at the time, but turned out to be one of the best decisions that you could have made for Zapier.

SPEAKER_00

Yeah, I mean, the fundraising one was definitely one that I think worked very well in our favor. I think another contrarian decision we made that worked really well in our favor was building a distributed team in 2012. That was something that was not a popular choice at the time. I remember, you know, people around the company and you know, when we were doing like folks asking about it. And yeah, I remember one particular comment was like, no important company has been built this way. And so I remember like hearing stuff like that. But yeah, we felt like we knew something maybe before other people had figured it out. It's like, well, no company has been built this way because the tools weren't available to build it that way, but they are now, and so it's just a matter of time before companies can be built this way. And as we sort of went about it, we realized that, you know, we could get access to like top-tier talent in tier two, tier three cities for a fraction of the rates, which was huge for a startup that, you know, was effectively like near bootstrapped to be able to tap into that talent. You know, those folks, in a lot of ways, were better than, you know, sort of your average Silicon Valley like engineer uh at the end of the day. I'm sure there is like elite Silicon Valley engineers that maybe could go to toe-to-toe or or beat these folks, but you know, the number one in a particular city can be better than most others. And so that that was like a great talent arbitrage opportunity for us. And then we learned how to like build a good culture, like, you know, operate the company, like so many things that you know, I think we had to learn just kind of on our own. And it ended up, I think, paying off in spades for us uh along the way.

SPEAKER_01

I'm seeing a common pattern here. You're zagging while others are zing, and it's clearly working out in your favor.

SPEAKER_00

I I do think that's like a pretty reasonable kind of coming back to the moats category. Like a good way to find alpha is to do the opposite of what conventional wisdom says. It doesn't always work, like you got to be right too. Like that's the hard part. You know, there's definitely some places where we, you know, probably had that contrarian streak and we were just wrong. Uh and that obviously doesn't help you out. But if you sort of pick the right dimension to do the opposite of what everyone's doing, and it turns out you're right, you're gonna get a lot of upside from that.

SPEAKER_01

Brilliant. And it's 2026 right now. What are you leaning into in terms of go-to-market motion as you look towards this next stage of growth?

SPEAKER_00

Well, I mean, speaking of things that like we were contrarian on and were perhaps wrong, is you know, we are are spending a lot uh investing in our enterprise motion. You know, Zapier was product-led growth, like self-serve business for the longest time. And we we definitely resisted that, that sort of expansion up market along the way. And you know, I think early 2020s, we realized, oh, we're just wrong here. Like that there's we have a product that is incredibly valuable in these organizations. There are feature gaps that we need to go invest in. And if we do so, like these folks are gonna benefit massively from what we've built. They already are. They're just like, you know, these sort of edges that need to be sanded off and, you know, to work a little better in these their particular unique cases. And so we went about building a lot of those capabilities. Nowadays, Zapir is a full-blown enterprise platform for automation. And so a lot of the work we're doing on the GTM side is to mirror that, is to make sure that, you know, folks in these companies are aware that Zapir can do uh a lot more than I think they probably historically might think it can. Uh, so that's a big effort for us.

SPEAKER_01

Completely makes sense. That's fantastic. Very, very exciting times ahead. And what kind of personal AI flows are you using? Like what are the three AI workflows you personally use the most every single week?

SPEAKER_00

So I have uh like an AI chief of staff that I built out inside of, I use Cursor for this. And it uses Cursor and the Zapier MCP, which uh allows me to hook in all my tools. So I have, you know, Gmail, calendar, Slack, my to-do list, which is on Coda, granola, which is what I use for meeting notes. Also, all this context that, you know, sort of is around me. And, you know, there's a whole bunch of workflows I use around this. One, I have it generate a morning brief for me every day that sort of preps me for the day. So I know like who I'm meeting with, what are the key topics, coaching for like what I should be trying to do inside those calls. So, you know, if it's like a sales call, it's like, hey, here's the things you need to listen for, pay attention to. If it's a you know, a customer, like here are the questions you need to ask them about, the usage that they already have on the platform, any friction that they've already had with the platform, stuff like that. And then I also do an end-to-day recap with it where it sucks in all the granola notes for the day, it pulls out key action items, it starts to actually do the the action items to the extent it can. Some of them it can complete entirely on its own, some of them it does partial, some of it it can't quite do yet. And then I have it prompt me for like, hey, how did the day go? And so then I go augment it. I say, hey, here what you know, today was a great day or today was a bad day, and you know, here are the like two or three things that we need to make sure to keep track of and follow up on from the day. And so that workflow is like really keeps me organized on a day-to-day basis. The second thing that I do is that I love is I have uh, you know, inside this like AI chief of staff, I've got all of our company context. So it has the company strategy, the product strategy, our ideal customer profile. It's got my own like personal 360, like the things I'm working on. It's got my personal goals and and so much more inside this. It's like a like a second brain or a shared brain. And one of the skills I built out for it is I call it an advisory council. And so it has a bunch of personas that are subagents. So it has like a the ruthless CFO, the wartime operator, the contrary and board member. But for every decision that I face, I often will, you know, think about the decision on my own, but I also ask the advisory council to consult with me. And so, based on the decision that it is, it will also generate subagents that have a persona attached to them. So if I'm asking them about uh like a candidate where, you know, I'm reviewing a candidate that we want to go hire, it will spin up probably like a recruiter persona. It'll spin up like an expert in that domain, it'll spin up like, you know, two or three of these personas, and then the the advisory council will go like critique this, and then it will come back and it will provide there's seven members on the advisory council. So each of them will provide their personal individual suggestion. And then there's like a, I don't know, like a chairman of the council who is like, here is the like, you know, the sort of vote that I would advise you on. And I use this for like so many decisions, and it helps like sharpen my thinking because you just get this like rubber duck style thing that it sees patterns that maybe you're not seeing. It like catches things that you didn't catch. It's just super powerful. Uh and I find myself often sharing the output of this with folks on the team where I'm like, you know, you ship me this to review for my approval. Here is like the the advisory council's feedback. Like, have you thought about these things? And yeah, sometimes they'll be like, yes, we have here, here, here, and here. And other times we'll be like, oh, that's interesting. I haven't thought of that. So that one is like super interesting. You know, I know everyone says, like, don't vibe code your CRM. And I definitely would advise like, don't vibe code your CRM. I did break the rules. I call it a CEO CRM. It sits on top of our CRM, though. It sits on top of database HubSpot. Yeah. So it's it's really more of like a view on top of the CRM, and then it has a handful of very specific workflows that I use. So yeah, I find that the like the UI of our CRM was like, it's just more, it was just have more features than I actually needed. And I wanted like two or three capabilities that were very focused on the jobs that I use the CRM for over and over and over and over again. And so that's what I built on top of it. So I I could go on, but these are like, you know, three things that I'm using a lot right now.

SPEAKER_01

Those are fantastic, fantastic. I'm sure people will be starting to build out their own personal kind of advisory committees also. And how do you learn about AI? Where do you go for sources of learning?

SPEAKER_00

I think a couple places. One, you know, inside Zapier is like just this vibrant experimental bed. So there's like a bunch of people like playing around and doing stuff inside of here. So I learn from our team a lot where they're like, I'm trying this, I'm trying that, etc. So that's a big source of learning. X, like a lot of folks are sharing like their own workflows, tips and tricks on X all the time. And so I'm just paying attention to like what's going on there, learning about the products, trying all the products, things like that. We live in like a really lucky time where a bunch of the like smartest AI researchers are like very actively going on podcasts and things like that too. So it's like a handful of podcasters out there that are like continually getting like fantastic guests. And so I'm often like listening to those, trying to understand like what's happening at the cutting edge, things like that as well, too. So, you know, just building a disparate source of resources and you know, trying to catch like as much things through that as possible.

SPEAKER_01

Very cool. And what about less digital? Do you have any favorite books that have been impactful for your career?

SPEAKER_00

A few of my favorites that I've sort of read over the years, How to Win Friends and Influence People is like fantastic like book. Uh, it sounds a lot more scammy than it actually is since in a very practical way. Yeah. You know, when we are first getting started with Zapier, it was right around the launch of things like the Lean Startup, Four Steps to Epiphany, things like that. And so learned a lot about just, you know, how to think about experiments and prototyping and, you know, how to get like your early customers and product market fit and things like that from books like those. As we scaled up the company, Hard Thing About Hard Things, High Growth Handbook, like these tools were just like very helpful at like, yeah, just like building and growing a company and doing a thing that I haven't done before. So I don't know, those are those are a handful that come to mind.

SPEAKER_01

Very cool. Great reading list. Wade, this has been fantastic. Really appreciate you joining, sharing all your AI knowledge. We are happy to have your customers of GTM Fund and uh excited for what 2026 has in store for you.

SPEAKER_00

Awesome. Thanks for having me, Sophie.

SPEAKER_01

Thank you, Wade.