Zapier is doing hundreds of millions in ARR, has 800 employees, and has more AI agents than people. That ratio isn’t an accident.
Wade Foster, CEO and Co-founder of Zapier, built one of the most capital-efficient software companies in history on less than $1 million in venture funding. When GPT-4 launched in March 2023, he called a company-wide “code red” (a term he’d never used before) and shut the company down for a week-long hackathon. What happened next reshaped how Zapier hires, operates, prices its product, and thinks about the future of software.
In this episode of Founded & Funded, Karan Mehandru sits down with Wade to unpack:
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- Why ChatGPT didn’t trigger urgency at Zapier, but GPT-4 did — and the specific signal Wade used to make that call
- How Zapier went from 10% AI tool adoption to 90%+ across the company
- The pricing overhaul that simplified Zapier’s model around task-based usage, and why agents made seat-based pricing structurally broken
- Why Zapier’s head of HR became the Chief People and AI Transformation Officer, and what that reveals about who actually leads change inside organizations
- The “build first, run always” framework Wade uses for deploying AI agents safely inside enterprise workflows
For founders and operators navigating their own AI transformation, this is a practical, unfiltered look at what it actually takes from a CEO who’s in the middle of it.
Listen on Spotify, Apple, and Amazon | Watch on YouTube.
You can also read Karan’s tactical advice for founders here.
This transcript was automatically generated and edited for clarity.
Karan: It’s always special for me to chat with my good friend Wade here. I never miss an opportunity to try and get his pearls of wisdom, and Wade and I go back a long ways. I first met Wade when he came out of Y Combinator. It’s been just a joy to watch the company scale. I’m lucky enough to be an investor, but more lucky to be Wade’s friend. So, thank you Wade for everything. One of the things I’ve always respected about Wade above many of the things that he has is he’s always been a first principles thinker. And I think this market requires first principle thinking more than any other market that I’ve ever seen in my career.
So maybe we’ll start there, but before we go into the first question, Wade, maybe you can give a little bit of context on yourself, where Zapier is today, how big you guys are, to the extent that you can share, how many human employees you have. I guess at some point I’m going to have to ask how many agent employees you have as well, but any color and context on you and Zapier would be super helpful.
Wade: Yeah, so founded the company in 2011, so it’s been coming up on 15 years now that we’ve been working on this started as integrations. That was the key thing we were trying to do is help non-technical folks build integrations between all the SaaS products they were using, and quickly realized that as valuable as integrations were. Workflow was where a lot of the value was really lying is, if you could build these deterministic workflows, you could actually automate real work. And so, in a lot of ways the zaps of the time were kind of like these mini agents. They were this deterministic workflow that would do work for you. Now, they don’t have all the power that you know an AI agent has these days, but at the time, that was the best way to do automation. Fast forward to today, the business is doing well, several hundred million in ARR. It’s got 800 employees working here, fully distributed, far more agents than there are people. I think that’s going to continue to just grow and grow and grow. But we’re still hiring people too.
I do think that there’s still, like humans still are valuable too. I think they’re both pretty uniquely useful. I think that kind of catches folks up to where we’re at today. We’ve had to do a lot of work to retool the company for the AI era. Both from a product perspective, but also an operational perspective. And you know, it’s still very much a work in progress.
Karan: One of the things you didn’t mention, which I think some people know, but some people don’t, is that not only are you in hundreds of millions of revenue, which makes you special. I think the other part that makes Zapier special is that you’ve done that with less than $1 million of venture capital ever raised in the history of the company. I think that puts you in like very rarefied air. So congrats on all that.
So let’s go back to like when ChatGPT came out, and I remember you and I had a conversation around the time and you knew immediately that this was something that was worth paying attention to That was October 2022 That was Walk us through that moment when it came out, what that represented for Zapier, how you felt, what your first few actions were, and then how did that sort of permeate across So walk us back there.
Wade: Sure. We had been dabbling with AI a little bit before the ChatGPT launch. My two co-founders, Brian and Mike, had moved into IC roles in the company and they were like, “Hey, this AI thing looks like it’s going to be important enough that we should go figure out what this means ourselves.”
I was like, okay I’ll go manage the entire company. You always go figure out what the next part of the company needs to become.
you know, that was like two months before ChatGPT. And one of the things they built was a a text bot. And so it was a phone number that you could text and ask questions. And, you know, I would ask it things like, “Hey, what do you think I should get my wife for her birthday?” Or like, “i’ve got this weird toe pain what’s going on with that?” And it would answer. And if this sounds familiar, it’s because well that’s what happened two months later is ChatGPT launches. And that’s the kind of things you could do with ChatGPT. W hen ChatGPT launched, it was impressive, but because we were already experiencing the, like the texting thing, it felt like, okay, this is cool. And we didn’t actually, at the ChatGPT launch, respond with much ferocity. Instead, we were very just kinda like suggestive to the company. So, you know, post in general in Slack and say, here’s a cool product. I like cool products, you like cool products, maybe you like this too. And that was very much the vibe at the time. We encouraged a lot of product managers and engineers to be thinking about how you should be injecting these capabilities into our product.
And, we launched a handful of things over the coming months. Probably the most interesting was you could insert a ChatGPT step inside of a Zap. But again, that was all fairly organic by watching our customers respond to them, things like that. The real tide shift for us was the GPT-4 launch, and that happened about six months later in March of 2023. And the reason that was a big tide shift was we saw that the improvements over 3.5 and 4 were so large. The cost decrease was so large and then it only took six months. And so we started to just say like, well play that forward the next six months. What will we see then? And if it’s even a fraction of the improvements that we saw in these six months, that is massively disruptive to the entire ecosystem.
And so we called the code red right then, and we said, Hey, we need to rethink our entire product roadmap. We need to rethink how we operate the company. And, at the time, we’d never even used this like code red language internally. So people were like, okay, Wade, you’ve got our attention. What the heck does this even mean? What do we do about a code red? And truth be told, I was like, I don’t know, it’s code red, let’s figure it out. And we did a bunch of things at the time, but the thing that was most impactful was we stopped the company for an entire week and called a hackathon and said, “Hey, everybody’s going to go build with this. Not just engineers, but marketing and sales and support and finance. We want you to just go play around with the tools.” And so we worked on making sure we got procurement lined up so people had access to ChatGPT. We made sure that there was a couple Loom demo videos of here’s how to call the API, here’s the types of things you can do, here’s how it works. Like a normal API, here’s how it’s like maybe a little different from an API. We made sure we had like our, data privacy and all our compliance I’s dotted and t’s crossed. So no one was doing anything like willy-nilly. But we mostly just turned people loose.
And what the result of that was pre that week we had roughly 10% of the employee base was using AI with some frequency. After that week it was over 50%. We had massively changed the companies understanding of what is happening in this moment? And then from that point on, we just rinse, wash and repeated. Every four to six months we do another hackathon just to keep people’s mental models up to date on this stuff. And, we still do that to this day. As things advance, as things improve, we’re constantly trying to get the team to be on the cutting edge, both in like how they use the tools, but also what does that mean in terms of how we build our own products.
Karan: You already had hundreds of employees at the time. You knew this was significant and it needed to be addressed. It could be a challenge, could be an opportunity. Don’t know exactly what, and you were throwing a bunch of stuff at the wall. I remember a statement that you made to me back when we had our regular catch up and you said, “yeah, a lot of the people inside the company feel really uncomfortable because they feel like we’re throwing a whole bunch of stuff at the wall without really having a clear strategy.” And I remember you said to the team, which I still remember. Is that, “I know this feels uncomfortable, but trust me the alternative is worse.” You know, it’s a lot of times when you’re trying to make this big change inside a company, there’s, it’s not just the making the change. It’s managing the psychology and the emotions and all of that stuff and getting past that. So maybe just talk to us a little bit about this because some of our companies are at scale, and they are seeing this sort of threat or opportunity, whatever you want to call it. And it does require a step function change, not an incremental or linear change. So how did you get the team corralled with the sense of urgency that you wanted and not have people think we’re chasing our tail, but in fact trying to find the right way forward?
Wade: It took some time. I’d say know, maybe six months to a year of just beating the message into folks’ heads But also like a lot of show and tell, a lot of hackathons, a lot of just like builder opportunities. And some folks didn’t make it like some folks left the company of their own choice. Some people we asked to leave the company. But I think the mindset that I had just locked into was basically what you repeated is like, Hey, this might be uncomfortable, but the alternative is worse. And the way I saw it for these folks is this is going to be the defining technology probably of my career and probably of everyone in the company’s career. And if you are in a position and in a company that is going to help you and encourage you to yield these tools for maximum benefit, there is no better thing for your career than that. I get that you might be comfortable in the way you’re working. I get that may be scary because you see all these headlines about how AI’s taking jobs or how, you know, it’s going to, kill companies or all these sorts of things. And even if you believe that to be true, the best thing for you can do, if you’re going to remain in tech, and this is going to be your career path, the best thing you can do is learn how to use these things.
And so I am going to make sure that Zapier is the type of embracing these tools maximally. So anytime, I would bump up against friction inside the company where, a manager, a leader would be like, “Hey, there’s a lot of change going on right now. I’m not sure that people can. I’m not sure they’re ready for this. I think they’re pretty stressed.” I’d be like, “yep, I’m sure that’s true. I guarantee they will be more stressed if we do not do this.” And so I just kept coming back to that push and saying I know it stinks. I know it stinks. I know it stinks, but trust me, it’s going to be worse if we don’t do this. You know, In some ways it’s a lot like exercising. No one wants to get up and go running every single morning. Some people do. I personally don’t. But the reality is, if you do it, it is going to be much better for you in the long haul.
Karan: It’s interesting because Eoghan, at Intercom wrote an essay. Intercom by the way, is customer service SaaS company. Was troubled. It grew really fast and then basically hit a wall. Then ChatGPT came out and customer service became the first application that was outcome driven versus user seat driven. And he had to reinvent. He came back being the CEO. He reinvented the company, launched this product called Fin. So, in some ways it was a existential moment for the company. And one of the things he says in his essay is, “I was willing to destroy the past to build the future.”
And I wonder if that, the way Zapier kind of reoriented the whole company around AI was a little bit of where you just had the mentality of: I’m willing to burn the boats to make sure that we are relevant in this new world that we’re going into. ’cause it’s that significant, or was it shy of that?
Wade: You, I think it was. I think one thing Eoghan calls out in that post he shared is that, he says “partly this was easier for me because we had no choice.” And he actually shows his growth rate. Like you can just see what is the company.
just see what was going
Karan: went down and then it went back up.
Wade: And I do think that in some ways that’s probably a gift to him where it’s like, well look what choice do we have, we’re a dying company anyway? And so it probably made it easier. Think the thing that is maybe scarier is there’s a lot of companies out there where your numbers look okay. They’re not great. You’re not, they’re philanthropic going from $1 to $14 billion in a year or whatever. But like you’re doing okay and it’s like, yeah, you wish you were growing. Okay, maybe I missed the quarter, but I still got to 90% of my goal. I think that’s a really tough position to be in because you can very easily convince yourself that, Hey, we’re actually okay. We don’t have to have this sense of urgency. We don’t maybe need to make that tough call. And, I think for those people in those positions, you have to have more conviction. You have to push harder. You’re going to have an employee base that is going to have more pushback. Because the reality is when they say hey “it looks okay to me.” They might not be wrong. It’s like it’s harder to convince that, where Eoghan can literally just look at the scoreboard and go,
Karan: yeah.
Wade: It’s not that it’s okay.
Karan: Status quo is not
working. Yeah.
Wade: you know, I do think for companies in that like middle ground, like right now is a particularly important time for CEOs to show leadership and say, Hey, let’s get ahead of this and let’s really make sure that we will be able to meet the moment.
Karan: All right. So that’s great. And so you went
through this moment, you realized you did start doing these hackathons. Some people were part of the solution. Some people were part of the problem and the people that were part of the problem exited the company either by your behest or by them choosing to leave. How has that evolved into your hiring practices now? And as you think about now, you’ve on the other side of it AI is such a big component of it. I think you’ve put a post out there that you have more AI agents than humans today. And I know Brandon, who’s your Chief People Officer, is now the Chief People and AI Transformation Officer. So talk to us about how all of that led to the changes that you made operationally, tactically, and related to hiring and recruiting.
Wade: You know, I think there’s a handful of things we’ve been doing. First. We were really focused on initially raising the floor, let a thousand flowers bloom. We wanted everyone in the company to be working with and using these tools. And we’re somewhat agnostic to how. We buy technology from all the labs. We buy all the applications that are doing these things. Like we’re very promiscuous with our tool usage. Because if someone finds something that’s interesting and enables them to do something, we want to empower that productivity. We want to like cross pollinate those learnings. And we also want to be inspired by like these other products and go oh, they figured out a novel like interaction pattern or way to use the data in a unique way. So like, how do we bring that into our own technology? So we’re very, we are very promiscuous in terms of pulling stuff in. And that I think cross pollinated just like a lot of ideas going on inside the company. Now, one of the limiting factors there is that approach doesn’t necessarily raise the ceiling. You get a lot of stuff, but you get a lot of things that one person can do on their own. Hey, I can go make this happen. And so one of the next bottlenecks we observed was we have these, like bigger bets we want to go take, but it needs like coordination. An example of this, like in engineering might be, okay we’re generating a lot of code right now, which is great, but now the bottleneck is code review. And so we have to go reinvent our entire like code review process. So that an agent is actually doing all the code review. And then how do we get it to where our time to ship is like happening in minutes like for code review to be improved versus like days. Which was what was happening before. And that requires you to rebuild the entire system from the ground up. And it’s not just one engineer that can show up and generate more code. It’s like your whole engineering org has to say, we’re going to go do this a little bit differently. And you can rinse, wash, and repeat those types of problems across various functions in the company.
And so we felt like, okay, now we need somebody who is very senior who can help us look across all these functions and drive a lot of the change management required. And it turned out, in our case at least, our HR team, funny enough, had been the function that was doing the best at this. They were racing ahead and doing a lot of these things. Brandon is really good at change management and so I just said, “Hey Brandon, can you help all of these other functions figure out how to take a swing at some of these more ambitious projects?” We weren’t struggling with the technology side of the house. We were just struggling with, Hey let’s be bold and let’s try some of these harder things.
And so that’s been the progression for us is going from let’s get everybody just using the tools bottoms up adoption to now let’s systematically find the big levers in the company that we can generate massive ROI and go work on those on a sort of project by project basis.
Karan: When I said in the beginning that you’re a first principles thinker, that there’s constant examples of that. And the one most recently was the one that you just mentioned, which is the fact that your chief AI transformation officer inside the company is in HR And usually companies have this preconceived notions that innovation comes from either product or some other group in the company.
But in your case like, look, some person in the company is amazing at change management. They’ve embraced it more than anybody else and they’re driving their group to perform at a level that you want every group to perform. So that’s one of the things I really liked hearing and also just respect about you, so that’s awesome.
Wade: I think one of the interesting things about that with AI is that there’s this weird thing that I’ve observed, which is that if you are an expert at a particular domain, you often have these like preconceived notions about how something is to be done. You can find somebody who maybe doesn’t do anything in that domain but is like really excited about using AI and they will come in and they will achieve things that you’re like, why did you do it that way? I didn’t. , It wouldn’t even dawn on you to do it that way because you’re so used to doing this in a very comfortable way. And I think this is what’s exciting about this technology wave is it really is tearing all the gates down. And so now you’re just seeing this huge influx of people who are achieving things that just felt like it wasn’t in their skillset or wasn’t in the range of outcomes for them now. And I think it’s exciting to be able to see people just even inside Zapier, like seeing how many people are able to deploy code or how many people are able to contribute to this area where they had interesting ideas, but for one reason or another they just weren’t able to jump into the fray. But now it’s just so much easier with these tools.
Karan: Yeah. By the way, you mentioned code review as one of those problems that had to be re-engineered. Just outta curiosity, how much of the existing code of Zapier had to be refactored or rewritten? If you had to ballpark a number. And I asked this because this was literally a conversation I had with one of one of my other founders where we talked about that the, we’re going to have to refactor our code to be written for agents as opposed to for humans.
Wade: Yeah. I don’t know the exact percentage, but this is a very real problem. We’ve got a legacy monolith that’s been around since 2011. And it’s like that is the area where there’s the most challenge for, ironically it’s hard for a human, but it’s also hard for an agent to get in there and work on those things. Whereas like greenfield stuff, very easy for an agent to jump in and go, Hey, i’m going to go build you a thing. And so yeah, there’s a fair amount of work in those areas to make sure the code base is easily able to be worked in by an agent.
Karan: Alright, so let’s move to, one of the other trends that’s happening, and it’s not necessarily a trend, it’s the reality, which is, not only is the code and the product need to be reinvented, but so does the business model for a lot of these companies. And so we’re sort of moving from workflow that was sold to departments on a user-based pricing model to now outcomes that is delivered by agents with consumption or outcome-based pricing. I know pricing at Zapier has been a it’s the most interesting story because I remember your first pricing matrix, which was the Fibonacci series. And I’m curious to hear, did you try to apply the Fibonacci series again to your outcome-based pricing?
Or like,
Wade: again.
No.
Karan: but, okay, walk us, what, how did you figure out the whole business model and has that changed and evolved since you’ve incorporated AI or have you stuck to user based pricing at this
Wade: We’ve almost always had a some form of like usage-based mechanism inside of Zapier. And so early on we, we metered by counts of zaps and counts of tasks. But I think the thing that we had to go refactor was — we did a big price change in 2024 — was the way we had rolled out our usage-based pricing was very confusing. It was confusing to humans. And if it’s confusing to humans, it’s probably going to be confusing to an agent. But in particular, the problem was that some plans, you could have usage-based pricing. Some you had to move between subscription tiers. It was somewhat arbitrary, like why some plans worked the ways that they did. And as a result you could just see in our customer feedback that people were just feeling like Zapier was nicking and dimming them and just not treating them very right. And so in 2024, we basically went in and refactored all of this stuff, and we said, okay, every plan is going to have a subscription tier based on tasks alone. And so, you could buy a bucket of tasks and if you went over that subscription amount, you could upgrade to the next subscription rate or you could go pay as you go. And now the pay as you go rate will be slightly higher than the subscription rate because we want to incentivize people to commit to us. But you can still do it. We can provide that flexibility in case you’re just need a handful of more tasks — not ready for the full bundle to come along. And so we made that big shift. And as a result, we just saw a lot more people making use of the usage-based option. And so now that’s something we’ve just leaned into all across the entire portfolio is to say, Hey, how do we make sure that anytime we’re launching something that we can boil it back to this usage-based approach versus continuing to bolt on like a feature here that costs an add-on or a new product there that costs another add-on. And it just continues to confuse the whole thing.
I don’t wanna put Zapier up as like on a pedestal say we’ve done this part particularly well, but I do think that when you think about how software will be consumed in the future. It’s very likely that you’re just going to see more and more software is used by an agent and not by a human. And if that is the future, it also seems pretty likely that the agent is going to choose what software to use. It is going to choose what software to buy. And now how it buys it. Maybe they’re just buying by like spending tokens. Right? Now we defacto give these agents a budget by allowing them to spend tokens even though we’re not giving them like a physical wallet. And yeah, I do think we’re going to increasingly be in this world where an agent is going to sort of just go out and figure out, okay, how is, what is the best way for me to go solve this problem. And they’re going to have usage-based pricing makes sense for that
Now. I guess it’s possible you could see seat based pricing remain for like certain products, but increasingly, that’s going to be a problematic approach, I think. And when you hear a lot of the, there’s been a lot of talk about the SaaSpocolypse think there’s a lot of reasons for that. But the one that resonates the most with me is how much AI eliminates the need for seats. And so the SaaS is still useful. It’s just that you just don’t need the same number of seats that you needed in the past, that’s where those business models get disrupted.
Karan: I actually just wrote an article on LinkedIn about my own view on this stuff with the SaaSpocolypse and user based pricing. And I think you’re right in that before a lot of SaaS was selling workflow to humans that were naturally delineated because of the functional roles they were doing in the functions that they belong to. Agents don’t respect those boundaries like we did in a human world. And so I think this is where the whole importance of the whole context graph comes into play for the future of AI productivity software, because AI can go across boundaries. You don’t have to have a product for sales and someone for marketing, one for customer success because the humans aren’t, you know, it’s like the agents can go across those boundaries. And you’ve always lived in the glue logic between these functional roles. Because Zapier was always about connecting these roles. And so I’m curious to hear if this conversation around these context graphs that power AI agent outcomes that go across boundaries, is a threat to the value prop of Zapier, which was doing that anyway, but still selling to humans in those departments. Or is that an accelerant?
Wade: I think it can be both. Is the an is the honest answer. If your sort of view of Zapier is like, Hey, this is a dumb pipe that pushes data from one app to another, then, yeah, that part is increasingly getting commoditized. It’s much, much easier to do these things. If the view of the future is, hey, something has to reliably, run these workflows, govern them appropriately, make sure that it’s deployed in a way that enterprise feels comfortable, those are the areas where I think it’s a huge opportunity for Zapier because we have a long history of doing this stuff very well. And if our product roadmap can innovate fast enough to make this stuff interoperable, governed, etc., we’re better positioned than any other incumbent or startup to go tackle these things. And, I think this is. True for most people right now is there’s aspects of your business that don’t look so great when you look forward. But then there’s probably aspects of your business that are like very well positioned to take advantage of these trends. And so you have to figure out how do you take some of the things that you’ve built that are valuable and repackage them up to, ride this new wave.
Karan: Got it. Actually, that, that brings me to a question that’s asked in the chat about when you mentioned governance and guardrails. So the question is how do you think about the uX of guardrails for agents? So the more conservative enterprise customers selling into regulated verticals and things like that can also trust agents with critical work.
Wade: Yeah. The thing we’ve observed, the pattern that works the best is, when you are building a workflow, this is where it’s really great to work with an agent that’s non-deterministic, probabilistic because it has the flexibility to go back and forth with you to figure out like what is the workflow that you like. And then once you get it to complete a task, you have this like magic aha moment. You’re like, holy crap, that worked. This is increadible. Then you start to go okay, I want to do this over and over again now. At that moment in time, that’s where you want the agent to go build something that’s deterministic. You want to say, okay, we figured out how to do this thing now I want you to repeat it every the same way, every single time. You know, maybe that’s writing code, maybe that’s deploying a zap. Whatever it is, you want it to operate in a very structured way. I almost think about it as this like creation moment where there’s a lot of back and forth and you have the like, probabilistic moment and that’s like a really powerful building mechanism. And then there’s the operating workflow that’s like the repeated thing in the background and that’s where enterprises really want things to work the same way all the time.
That sort of build first run dynamic is the thing I think people are learning how to go do.
Karan: The other question here is: you recently launched functions, and AI is really good at writing code. How have you seen AI change the way that teams think about building workflows and integrations?
Wade: Yeah, I think it comes down to the thing I just described. When you think about the workflows of the future, they’re very likely going to be a coding agent that’s going out and building these things. And it is hooking into the tools that you use. At Zapier we use Zapier MCP and the SDK ton to help build these tools. And so like just this morning I was building out a hiring workflow where I had figured out how to — we do these bar raiser exercises when we’re hiring and my workflow today,
Karan: Describe the bar
raise
Wade: Basically, my role in it is I still approve every hire that goes through, and I mostly just go review in Ashby as our, our applicant tracking system. And I go look at all the scorecards. And I’m mostly auditing the process and just being like, have we done a good job of holding a high standard and a high bar or do I see us falling prey to like common patterns where, a common pattern might be everyone, there’s like soft yeses across the board. And so no one’s like sticking their neck out to say, this candidate is not good enough. Or, they’re missing certain like dimensions where maybe they’ve screened them really well for like technical competency, but they haven’t really done a good job at a value screen.
There’s missing data there. Or maybe the references are very rosy and you can tell they didn’t ask like the critical questions. And these are the things I’m usually auditing to say like have we actually done a good job to really understand is this person a fit. So one of the tools that I recently built to help me with this is I have a hiring council skill that spins up a bunch of subagents. Those subagents go independently, review the scorecards, come back with their own assessment on is this candidate going to raise the bar? And each of those subagents pretends to be a different be a different persona. So one might be like a hiring expert, one might be a domain expert, one might be a CEO, one might be a like thrifty CFO one might be a wartime operator like, you know, a bunch of these different personas to say does this candidate truly do it?
And so the way I was doing this workflow is I would go into Ashby, I’d copy and paste all the data out of it. I’d come into Cursor, I’d invoke the War Council. When I get the feedback back, I’d go over to Slack or go paste it into a Google Doc and then I would share the Google Docs. So it’s just this cumbersome, like multi-step little process. And so one of the things I was doing with the Zapier SDK was I was just like, Hey, so what I need you to do is go hit the Ashby API suck down the scorecards when I give you a link, run it through the War Council. Take the output, generate the Google Doc. Go find the Slack channel or go inspect the hiring team that’s on the panel. Make sure the doc gets shares with them, and then go find the hiring panel inside of Slack and post this to it whenever a candidate comes through. All that workflow was built in code, but each step where we’re, fetching from Ashby, where we’re creating the Google Doc, when we’re posting to Slack, all of that stuff is using my tokens through the Zapier SDK. And so that to me is where I see the world is changing, is a lot of these workflows are going to be built in code by an agent that has access to a whole bunch of tools, whether it’s like an SDK or an MCP or an API, and those tools are like well governed by the organization to make sure that the agent only has access to the things that it should have access to. That people aren’t like copying, pasting tokens into these agents left and right, willy nilly. Which you might be scared to learn happens all the time right now.
So I do think there’s a lot of power that’s coming, but there also is a lot of best practices that are going to have to be built to do this. Otherwise we’re going to see some pretty, pretty scary stuff in the news, i imagine, at some point in time.
Karan: So let’s talk a little bit about you. You touched on something that kind of spurred a thought in my mind, which is that the future doesn’t affect everybody in a very evenly distributed way and nor will AI. And it’ll be absorbed and consumed and either disrupt verticals and various different sort of velocity rates. And the same is true for companies and groups within companies as well. So you mentioned, you created this process and a way of thinking where every group inside of Zapier is embracing AI, but I’m sure not all groups embraced it equally. It wasn’t as successful in certain groups versus others. And so maybe A) just describe if it worked really well in certain groups what caused it? And if it didn’t in some cases, then what caused that? So that would be super helpful.
Wade: I would boil this down to two buckets for why it maybe worked or didn’t. And the two buckets would be one, there are some domains that aI just worked better in earlier on, and so things maybe took off faster there. This would be like your engineering teams that are working particularly in like new areas. These are places where it’s so to quickly see the power of these tools and you have been able to see it for a while. Then the second thing that I think also very much influenced the teams that did well is when they had leaders that also were very hands-on with the technology. And so there you tended to just more experimentation happening and you also saw the standards be higher. Like these managers just were able to like, hold people to a higher bar because they themselves saw what was possible when people used these tools. And those were the two traits that I saw of the teams that tended to move the quickest is they had those traits inherent to them.
Karan: It’s interesting. I’m observing it even across our own portfolio. There’s probably some resonance between your comment and the fact that a lot of the founders are coming back and running these companies because this transformation is more about product as opposed to go to market in a lot of ways. And so I wonder if, maybe it, it causes us to revisit how we think about leaders inside the organization and do they have to be the best at leveraging product within their domains and then rise them to be the leaders of those groups as opposed to great people managers. So anyway, it’s an open question, but I wonder if there’s of view there. You guys have always operated on your own rule book, and so I’m curious have you changed the way now that you think about promoting leaders and hiring leaders in those groups because of the comment that you just made?
Wade: There’s definitely like a learning curve or that we are figuring out about what the best leaders look like in the future. One thing that we’ve realized is that definitely like high agency, action oriented, invention oriented people tend to do like very well. Their value is so much more accentuated. This is where the like hired gun management tends to struggle. They’re used to coming in and saying, Hey, something has worked really well, or it has worked well enough, like a founder has figured out a thing. Now my job is to take it from, some success to like lots and lots of success. And, a lot of these companies were in a moment where the job is not to go from success to more and more success. The goal is to rebuild the thing from the ground up. The old thing doesn’t work quite the same way anymore. Now we’ve got to do it totally different.
That just flexes at a like different muscle. That’s very different from the things in the past. I can just share like a thing we were talking about yesterday when we were doing a talent review is, we were noticing that there were some leaders that we have in the organization that have historically been very strong. But we’re increasingly going huh, it feels like it stalled out a little. And the realization was there was a group of people who were very good critics and they were very comfortable speaking their mind around these things. And those folks in companies in the past have tended to be very helpful because they call BS on stuff, and so they help shut things down. They help like reorient to things that are more all that sort of stuff. But one of the failing traits of that personality is that they aren’t as inventive. They’re like what should we do? What is the thing we have to go build?
Increasingly what we’re realizing is that AI is a very good critic. All of a sudden that task, that job of being like the BS detector, is increasingly getting delegated to these like council skills and things like that, that are able to say, Hey, you don’t have clear DRIs. You don’t have this, and this. Your operational plan is weak on this dimension. You need to go fix that. Then you’re like okay, how should I go fix it. That’s where like these high agency creative get stuff done people are like increasingly very valuable. And yeah, it’s just like little things like this that you just observe by using the tools more and just like trying to figure out what’s, what is valuable, what is not.
I’m sure that like over time, even some of what I just said will change. Like I, I’m noticing, there’s this like whole thing, six months ago where it was like the agent it’s not as creative as me. And like I’m finding for more and more tasks, I just ask the agent like, what would you do? And it’s pretty dang good. And so, you know, is creativity like an edge any more? For certain things, sure. But there’s a lot of jobs where you’re just like, I just need like the normal answer here. Can you just give me the normal answer so we can get going?
Karan: Yeah. Yeah.
It may not be, it may not be creativity, but I think judgment is still as important, if not more important, given that you can create a lot of things. But at some point in I think you need judgment on top.
Wade: Yep. Yep. AI’s getting pretty good at judgment too
Karan: It’s, I don’t know. I’m counting the days until it starts investing in people in companies, and I can go sail in the sunset as well.
Wade: Yeah, it’s odd like it’s, you just have these moments the more you use it where all the sudden, you start to realize this task that I’ve done for a long time I don’t know that i’m any better at this than the tool is.
So
Karan: yeah,
Wade: I
guess I’m just going to let the tool do it now.
Karan: What do, so what do you think in that sort of world? Now we talking about the future. There’s. So much, narrative right now and, as buffet said, ” in the short term the markets are voting machines in the long term, they’re weighing machines,” and so we might be in the weighing scale part of SaaS, and we’re still in the voting side of AI, although we’re getting pretty close to the weighing side as well. What do you think people are underestimating? What do you think people are overestimating in all this narrative? There’s the doom and gloom scenario. There’s also like just the greatest time in history to create jobs and tools and software and produce outcomes and help society. I don’t want to get to motherhood and apple pie, but as you think about and read all this stuff and from your purview of building workflow software to make people more productive what do you think we’re over weighing and what do we think, what do you think we’re under weighing?
Wade: I think it’s very, it’s human nature. It’s easier for us to assess loss than it is to imagine I think societally right now, like you see the headlines filled of things that AI is And so I think it’s much easier for us to go, oh my, this is what is dissapearing. But the reality is there’s just as much that’s being created, if not far more, The demand for code and software and all that stuff is going through the roof. insert something, somehting, Jevons paradox here. And I think we’re just in this magical moment where we are massively underestimating the incredible things that are about to be built.
Yes, aI is increasingly taking more and more tasks away, but oftentimes those tasks were like not all that interesting and valuable in the first place, and they’re freeing us up to go do bigger, more ambitious things. And so I don’t know. That’s how I would answer. I I think we’re probably a little too scared about what’s dissapearing and not excited enough about what’s coming.
Karan: I totally agree with you. If you, obviously Zapier is at scale and you’re trying to change engines and change things while you’re in flight, but we have obviously a lot of companies that are very early in our portfolio just getting off the ground, sometimes don’t even have a product released yet. So if you were rebuilding Zapier from the ground up today with everything that is around you: tools, technologies. You are now 15 years older and wiser what would you do differently if you were to build Zapier in an AI native world as in an AI enhanced world?
Wade: Gosh it’s so much different, right? Building is easier than ever. So that’s the exciting part. Like code is not expensive anymore. It’s much more trivial to go build these things. I think the bottleneck that I think most startups are running into is attention …distribution. It’s like, how do you actually get anyone to notice you or care about what you’re doing. And that is just like really hard these days. You’ve got a lot of saturated channels. You’ve got channels that are decaying. You’ve got new channels that are not obvious, like how they work yet. Obviously like you can go viral for a minute, but like that attention comes and goes,
Karan: Yeah. And you guys,
and I know Zapier did so well with organic content marketing in the early years to get that attention, has that evolved as well for you all?
Wade: 100 Percent.
Yeah. It still is like a really important channel for us, but increasingly we’re paying attention to what do these agents recommend? What do you, how do you find, how do we show up in ChatGPT and in Claude and, inside the and all that sort stuff. We want to make sure that they think zapper is a good solution for the various problems that are being asked of it.
Karan: Yeah. interesting. I think one of the other things we’ve noticed is how, I wrote this in my LinkedIn article, which is the land and expand funnel in AI is inverted. Which is in SaaS world, you could land and that took a bunch of time, effort, money, resources, people, and then expansion was a little bit easier. Today, to your point, it’s easy to build product, it’s easy to sell product because a lot of people have experimental budgets, but we don’t know if those deals are going to renew. And so in some ways the expansion is the land. How do you feel about that statement? Do you feel like attention is obviously an issue. And, but I think durability is probably another issue that a lot of firms, companies are dealing with, which is you get a few logos but they don’t renew because people are trying everything.
Wade: Yeah, everyone’s trying everything. They’re very promiscuous. They’re happy to try pilots. They’re happy to switch to something else. I think another thing that’s very unique is that a lot of the enterprises that are buying this stuff, they don’t know how to use it yet. And so you do have like big heavy services and the rise of the FDE, you know, it has become more and more important because they want the outcomes, but they’re not exactly sure even how to use the software. They’re like what what exactly is this? And so there’s a lot more education you have to do to land these customers. And so it is a just much more involved upfront sale. Versus in the past.
Karan: The SS have flipped on SaaS. It’s now services as software.
Wade: Yeah. It’s just a very different world. Like when we launched, we were so focused on how do we make the product so dang easy? You don’t have to talk to a person. And now I believe you can do that with AI products. But there’s also so much that the customer doesn’t know that it feels like to get the best outcome, you do benefit a lot having having a guide show how.
Karan: yeah, that’s totally true. Are you, so speaking of outcomes, are you planning, that was a question as well. Are you planning to move to an outcome-based pricing model or are you going to stick with usage and consumption?
Wade: Yeah, we’re sticking with usage for the time being. We’re a pretty horizontal product used across a lot of different areas. We don’t have any intents to move to an outcome based model. I don’t, I’m not sure it makes sense for Zapier, but we have an open mind on pretty much everything. think that’s the, maybe the meta learning is that pretty much anything we felt was a settled issue in the past we’re coming back to and saying maybe it’s not so settled anymore.
Karan: Yeah. One thing I’ve observed, at least in my career is that and speaking as a sales and marketing hat on, I’ve, I feel like 50% of your customers might prefer an outcome-based model, but a hundred percent of them want the choice. And so it might be the most the most I can offer as far as insight, and I think it’s worked in a couple of cases with folks.
But anyhow I know we’re out of time. Thank you, Wade. It’s always a joy and a pleasure to talk to you. If I spent all my waking hours listening to you, I’d be a lot wiser than I am today. Thank you for being so gracious with your time.