Zapier wants to be the AI hub where all your agents connect

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I think it's really hard to be the CEO of a software company right now. What's your experience been?

I had another CEO friend say, ten out of ten fun and ten out of ten anxiety right now. I was like, Yeah, that that kind of resonates. It is not hard to find individuals that have, like, quote unquote, ten x their productivity with AI. It is much harder to go look around and find the companies that are ten x productive because of this. And so why is that? We have the technology, but we don't know how to build the organizations around it quite yet.

And so there's gonna have to be a lot of changes, not just with adopting the technology, but also rethinking how these organizations work from the ground up.

that's Wade Foster, the CEO of Zapier. Fifteen years ago, he co-founded Zapier on a simple idea to link thousands of apps that were never designed to talk to each other so that users could work seamlessly between them. Now, Zapier is doing that for AI agents, and that has changed everything for the company. I wanted to talk to Wade because he saw an opportunity in AI and scaled AI across his entire organization and totally redesigned his product, thinking about AI from the ground up.

I'm Rana El Kaliouby, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution. Hi Wade, welcome to Pioneers of AI.

Yeah, thanks for having me.

I'm so excited to have you on. So, um, I am a Zapier customer.

Thank you.

We use Zapier, yeah, to integrate all our different AI agents. We're gonna talk about that. But before we go there, I would love to hear the origin story of Zapier. Did you always know you wanted to be an entrepreneur?

I did not. I grew up in central Missouri, Jefferson City, it's the capital of the the the state. And uh, my exposure to jobs where you could either work for the state, you could be a doctor, a teacher, police officer, right? Like I my as a as a young kid, I'm like, man, the number of jobs that exist is very, very small. My worldview was was quite quite small. And so I didn't really get exposed to entrepreneurship until much later.

And what really kind of was the wake-up call for me was the tail end of university when the uh, financial crisis hit. And so while I was a good student, didn't matter, no one was hiring, no one was taking jobs. And I think that was when I realized, like, no one's waiting to give a handout to me. No one's wait, even if you do a good job, like you kind of have to make your own path.

And I was fortunate enough to find a local entrepreneur had a small software shop in Columbia, Missouri, and got hired as an intern there. And that's where I fell in love. I was like, Oh, the building software is really fun. And uh, started to have that itch where I'm like, man, I think I would really like to start my own company someday. So this is 2011, SaaS is on the rise. This is the year that Stripe launched, Twilio launched things like that.

And if you would go to the forums of these companies, you would see a very common type of customer request, and the customer request would always be, When will you integrate with X? And it was, yeah, it was followed by, you know, a bunch of customers saying, plus one, me too, yes, I want that. And eventually a product manager saying, Hey everybody, like, thank you for the feedback. Seems like an interesting idea. We'll take a look.

And if you've ever worked at one of these companies, you know that's code for, Yeah, probably not gonna happen. And so Brian sees this and is like, Hm, I think we can make sort of like a nice, simple UI, uh, so you don't have to know how to use the APIs and connect this stuff. My day job, I am working with a Marketo API doing email marketing, and I'm a bad engineer, and so I'm having a tough time.

So, anywho, we've got this local hackathon coming up, startup weekend. We go, we team up with Mike, we hold up into, like, a like a garage in the back of this building all weekend. And uh, came out and had a prototype. It worked, it demoed well, uh, so ended up winning the hackathon. And then, you know, after the weekend, it was like, Well, what do we want to do with this thing? We're in central Missouri. It's not like there's a lot of investors in the area that are just like jonesing to put dollars towards a bunch of just recently college kids.

Uh, and so we're like, okay, well, we want to keep working on this. We're having fun. So we said, we'll do it nights and weekends, kept our day jobs and just, you know, we'd meet up after work, we'd snack some, um, hi, five Chinese, which is the local grocery store, so just get some takeout and then we would just get to work.

at what point did you realize, Oh my god, we're onto something interesting here, we need to quit.

I had this gig at this local software company, which was doing like, um, some like natural language processing. In fact, it was a professor trying to, like, automatically grade student papers, which now seems quaint. It's like, ChatGPT could do that, but in 2011, it was like, th how, like, how how do you do this? And I remember trying to, like, sell this product and just it it like, even though the product was slick, well designed, good engineering, I just like, couldn't get anyone to buy this thing.

I just thought I was bad at my job. That was kind of, like, my recent history is like, man, this stuff is selling software is hard. Uh, then with Zapier, I remember the first customer we had, guy Andrew Warner. I'd emailed him, he ran a podcast, Mixer G at the time. And, uh, I'd I come across his name in a forum where he was looking for a PayPal high-rise integration. Just emailed him and said, Hey, you still looking for one of these?

And he said, No, but, you know, or did did you did you build a plugin for it or something? I said, Well, sort of. Um, you know, can hook up PayPal and High Rise. And then I did some snooping and found, like, what other tools he uses? Realized he used Woofu, AWeber and a few other things and said, you know, can hook that stuff up too. And he pretty much bit, hook, line and sinker was like, Oh, I need something to take my Woofu contacts and put them in my newsletter that runs on AWeber.

And so I was like, Okay, great, like, we do that. And I was like, Brian, Mike, build the Woofu AWeber thing, right? We got to go. So, uh, you know, give him access to the thing. And the next thing I remember is he's like, Wade, this looks cool, but I I can't get anything set up. Can you get on Skype and show me how to do it? I'm I'm dating myself with all these product names. I've used Skype.

Yeah, Yeah,

it's okay.

I I'm coaching him through setting all this stuff up. And, uh, every step of the way, like the product just barely works. Like it is rough. We've not been working on it, but for like a few weeks at this point in time, so it is not very good yet. But we get to the end, the zap is turned on, and we go to test it. So he fills out his form on Woofu, hits submit, and we go refresh the AWeber page and see, like, hey, is there an email address in there?

Sure enough, there's the email address. And his reaction is like, Oh, this is incredible, Wade. Like, how much money do I owe you? Like, this is gonna save me hours every single day. And that was when I knew I was like, Holy cow, like this product kind of stinks, like it barely works. And yet his reaction is like, how much do I owe you? Whereas in the past, I was working on this quote unquote slick software, the software that's like good, well designed, all this other stuff, and couldn't get a taker.

And so I was like, man, if we could just get this product to not totally stink, like, I think we're onto something. It was like early December that this happened, and, uh, by January, I'd quit the day job and was like, all right, let's go figure out how to, like, go faster on this thing.

I'll be right back with more of my conversation with Wade Foster. The companies winning right now aren't just building great AI. They're building the infrastructure to actually run a business on top of it, which is where Stripe comes in. Stripe is built for whatever comes next, with planetary scale financial infrastructure to help you power any business model. As a matter of fact, 86% of the Forbes AI fifty run on Stripe, along with industry leaders like Salesforce and Pepsi.

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So I want you to take us back to this moment in 2023, when basically, Zapier's relationship with AI changed. So ChatGPT four just launched and you called for an internal code red. What did you see about ChatGPT four? You saw both a concern and an opportunity, and what did you do with that?

yeah, you know, so we basically had noticed a couple things with GPT four. So first, it came out roughly six months, I think, after 3.5. Um, two, the capability improvement was obvious. Is just talking of GPT four was just a lot smarter than 3.5, and the cost had come down meaningfully. And so it was one of those things where it felt like, you know, what happens when 4.5 comes out? What happens when five comes out?

You know, does that happen six months from now? What happens if it happens four months from now? Are they gonna launch these things faster and faster? Are they gonna get better and better? Are they gonna get cheaper and cheaper? Even if one of those things is true, this is very impactful to the whole industry and to what we work on. If all three of those are true, then this is the most important technology that has maybe ever been invented.

Cer- certainly since the internet, certainly in my active career. And, you know, we weren't exactly sure what to do with that yet, but we were like, we need people to pay attention internally. And we never called the code red. We didn't even know what a code red. No no one even had any sort of like, oh, when when Wade calls a code red, here are the steps that you take. It was just like code red, and people are like, Okay, I hear that that's important.

What do we do? And so, you know, we were figuring out what we were what to do. And I remember teeing up like a handful of things that we were like, okay, this is what, like, I want to change in terms of how we operate. A lot of it was like, here's how the product needs to change, here's how operations needs to change, here's a list of stuff that's really important. When I look back at that time, though, I would say there was really one thing that stood out as being more effective than anything else.

We paused the company for an entire week and ran a hackathon. Didn't matter if you were in engineering or not.

That's awesome.

It's like everyone's gonna put your hand on a keyboard and you're gonna build something with AI. And at the end of that week, we went from about 10% of people using AI as part of their daily job to over 50%. Amazing. And as a result, like, more and more of the company started to build an intuition around what is this technology, what is it capable of, how might we build in tandem with the technology for our customers?

How can we use it ourselves to improve our own operations? And I think that just company pause where it was like, Hey, we need to just go do some sense making together. Um, I have since seen be, like, productive for almost any other company that does one of these. Like they they all cite the like hands-on learning time being way more impactful than, you know, the CEO memo or the AI committee or any of that kind of stuff.

So a few things came out of that hackathon, one of which I imagine is this new product strategy. So the reason why we use Zapier is, as I said, we use Claude code and we have a whole bunch of AI agents doing work on our behalf. But to really have these agents work for you, you need to connect them to your other tools. And so we wanted to connect Claude to our email and our calendar and Airtable and like Pitchbook and all these other platforms, and that led us to Zapier.

So you've become like the AI connector or the AI integration hub. How did that idea come about and what did it take to do that? And I would love for you to also explain what an MCP is, because that's basically how agents talk to each other.

Yeah, yeah. Uh, well, you know, I think some of this was a little bit fortuitous, right? You know, in 2011, we set out to just integrate stuff. We're gonna make integrations really easy for people. And so our integration story is, you know, fifteen years old. We've just gotten really good at integrating any tool that you have on the internet far better than anyone else. Pretty quickly, we started to notice, and as did many others, that these AI models are incredibly smart on, you know, a bunch of, like, textbook information.

You know, they're trained on the internet. And so, you know, if you want to ask it like, hey, how should I think about a marketing campaign? Or how should I think about, you know, this strategy? Or how should I think about building a product like this? It will give you a very generally good answer. But it kind of leaves you wanting. You're like, well, I don't want a generally good answer. I want something that's great for the environment that I operate in.

And so what a lot of folks started to notice is if you gave it that context, if you let it see your CRM data, if you let it listen to your like closed one and closed lost Gong calls, if you gave it access to your email, your Slack, like just all this stuff. And then started asking questions and then started building automations, you got very specific and very good answers. Stuff that you start to go, uh, that's as good as I'd get from, like, a good teammate internally, or maybe better in some cases, because it's able to read over all this stuff that most humans can't do and hold in context.

And so that was where it was like, oh, Zapier really has a role here because we can bring the integrations into these model capabilities and help you orchestrate the workflows, the agents, like however you want to sort of go build those out. And and so we sort of just doubled down on that. In the past, the integrations were all done through through APIs. Well, now there's this new thing, uh, MCP, you called it stands for model context protocol, but effectively it's just a way for agents to talk to other agents.

I think for most folks, it doesn't matter to know much more than that. Um, but the the nice thing you can do about it is it allows you to talk in natural language to an agent or to something else. And then behind the scenes, the AI knows how to convert that into structured requests, uh, aka an API call or something like that.

Maybe we can give an example here. So if I'm talking to my chief of staff, Blue, we call it Blue on Claude, and I'm like, Oh, like, can you add this new potential investment to the Air table? Here's the name of the founder, here's the website of the thingy, or or actually even better, go find, you know, details on the founder, go find the website, blah, blah, blah, and add it to the CRM, right? That then gets converted to a language that Airtable can understand.

And that's you guys, or that's the MCP, I guess.

exactly, basically, it takes this, like, um, unstructured request, which is like, Hey, do this thing and do it this way, et cetera, which when you're talking to another human, another human knows, like, Oh, I know exactly what you mean. But a computer computers need very structured requests to be able to to do those well. And so what the MCP does is sort of it takes that, you know, human language, and it converts it down into something that a computer knows how to do, but it allows it to talk to other agents, um, that are like not part of its core system.

It's kind of called tools is what a lot of people call this stuff. With the Zapier MCP, you can talk to all of your tools, you can talk to all your apps. So you can, you know, hey, pull out the, you know, five biggest customers we have out of the CRM, draft an email inviting them to this event. You can do that sort of like natural language type stuff, but you can also go a step further.

Like, that's where you are, like, talking to it. You can also say, hey, now every time this happens, I want you to build an agent that does this workflow for me. And it can go, oh, great, I'll make sure to set up an agent for you or a workflow that does that. So you don't always have to be reminding it, well, hey, it happened again, can you please do it again? Or it happened another time, can you please do it again?

It could just always be listening, um, and paying attention to the environment and then taking actions for you. And that's where I think, you know, a lot of folks started with AI as a chatbot. Now we're starting to get a lot of AIs like an assistant talking to these things. I think the next era is like, oh, AI truly taking action, delegating it to these agents, these bots, these workflows, et cetera. And that's what I'm really excited where I think Zapier can be like a huge unlock for, you know, individuals and companies.

Can you talk through the business model? What does it look like?

Yeah, I mean, for Zapier, it's pretty simple. It's a subscription with usage-based credits associated with it. I think a lot of this is pretty standard in the AI era. I think what's very different about these AI products is in the past, um, when you were working with software, you would have an interface where the human still has to do all the work. So I'm still the person in there, like writing the email, logging, doing the data entry, all that sort of stuff.

I think with these AI tools with things like Zapier, the value is it can do the work for you. And so it is closer to having this entity that does work on your behalf when anything pops up. And so it it sort of makes sense for it to be closer to like this credit-based, like style system where it's like, hey, every time, you know, this event happens, like, it's doing real work for me.

Is the right way to see Zapier as like the middle layer or the middleman? And if so, is this a vulnerable position to be on? And I'll give an example. I I did mention that we are using you guys, and in one particular instance, we wanted to integrate Pitchbook. We use Pitchbook a a lot, we're investors, right? We couldn't find their MCP on Zapier, so we asked Pitchbook and they were like, No, no, no, you can just like use our MCP directly.

And that made me wonder, like, what is the role that Zapier does, and how do you future proof yourself so that you're just not taken out of the equation?

Yeah, so I think there's a couple ways that folks should set up their AI stack. And I think getting this right is strategically important to companies. So right now, I see a lot of companies, organizations going all in on one AI stack. Maybe it's, hey, I'm I'm a Claude shop, or maybe it's I'm a ChatGPT code doc shop, or maybe I'm a Gemini shop, or maybe I'm a co-pilot shop, et cetera. Uh, the savvy companies are not doing this.

And the reason why is that the frontier is changing super duper fast. And so just because one of these labs has the best model at a particular job at any given moment in time does not mean that they will always have it. Like they're constantly leapfrogging each other, their models are good at different tasks, different capabilities. Plus, you've got open source not too far behind, which is almost nearly as good as these frontier labs at a fraction of the cost.

And so any savvy company is saying, okay, if, um, the the best capabilities are constantly changing, I need to build an AI stack that allows me to rotate the best tools for the best jobs. And so where we think Zapier is really valuable in building this stack is you want to hook all those connections into Zapier. And now anytime you want to swap in and out between these things, it's as simple as pointing your connections to a new agent and new different place and time.

And so that means you're not beholden to saying, a anytime you want to try a new setup, going and resetting all those connections up in a separate place. The second thing you really get out of it is you get a safe place to observe and govern all these agents. So if you're an organization, you probably have a lot of activity going on, and you want to be able to see what's going in and out of these tools and when and where.

And if you've got a bunch of fragmented MCP setups all over the place across your entire workflows force, you've got vulnerabilities hanging out all over the place that is eventually gonna bite you in the butt. The last thing that I think is really important for organizations, too, is you want to make sure that these things are running efficiently. And right now, deploying an agent is kind of hard. Uh, a lot of folks just have them running locally on their laptops.

It's like you can't ever shut your laptop lid anymore. Well, if you are able to deploy the workflow, deploy the agent to Zapier, there's two things you get. One, you get a place where you can host it online so you don't have to have a spare laptop running or anything like that. But two, the other thing that we're doing to help you is we're turning those workflows into deterministic workflows. Now, why is this important? Is if it's running agentically the full time, you're burning tokens left and right, and tokens are very expensive.

Whereas a workflow, it runs on code, and that code is reliable, it's low cost, and it's trustworthy. And so any place where you can be turning those agents into a deterministic workflow, you probably should. Like, it's really figuring out how do we be a AI first and build this sort of like agentic infrastructure that can run in an automated way and scale.

Yeah, and I want to move move us into that. In addition to seeing this opportunity on the product side for Zapier, like becoming the AI agent integration engine, if you like, or hub, you also saw an opportunity to reimagine how work is done within Zapier. And you have this framework or philosophy of moving from individual AI to institutional AI. So tell us more about that. Cause I feel a lot of organizations have individuals using AI, but it's not institutional.

It's a hot mess.

well, 100%. So, uh, yeah, I think we've all felt this, where you look inside your organizations and it is not hard to find individuals that have, like, quote unquote, ten x their productivity with AI. It is much harder to go look around and find the companies that are ten x productive because of this, where their revenue is like ten x, or their costs are like a tenth of the price or whatever. And so why is that?

Just because that person got ten x more, uh, improvement doesn't necessarily mean that ten x improvement flowed through the entire system and meant, okay, great, now we're generating ten x more customers. So if you really want to ten x the organization's productivity, I think you have to step back and say, okay, if my goal is to be generate ten x the number of of, uh, customers, what's the equation for my company that creates customers? If you want to be reductive about it, there's really two things that generate customers.

It's I've got a product, so it's the R and D department, like we've built something, we've made something, and then it's sales and marketing. It's like that we sold the product. And so, you know, those two things add together equal customer. And you know, when when you're a very, very, very small company, almost all the effort that the in company is putting 100% of it is about those two things. But as companies get bigger and bigger and bigger, there's all this coordination that kicks in.

And so there's all this other work that sort of happens inside of a company that's supporting, you know, generating, building products, that's selling and marketing, but it's not directly doing those things. And so when you think about, okay, if I really want to change the the equation here, how do I get ten x the productivity for a company? I think you've got to be hitting one of those three spots in the organization. How am I, like, ten xing the efficiency of my go-to-market engine?

How am I ten x-ing either the quality or the speed at which I'm generating new products, or how am I, um, making the coordination costs of delivering those things go down by a tenth so that I can redistribute the time and effort into those two buckets? And I don't see a lot of companies yet at that stage where they're sort of thinking through this in that same sort of, like, scientific way. I think a lot of it is a much more haphazard, where it's like, Hey, you individuals, we got you some AI tools, build some cool, crazy stuff that's gonna make us more productive.

And so I think we're in this stage where it's like, we have the technology, but we don't know how to build the organizations around it quite yet. And so there's gonna have to be a lot of changes that happen, not just with adopting the technology, but also rethinking how these organizations work from the ground up.

Don't go anywhere, I'll be right back. How are you approaching this internally? Like, what, uh, what changes have you made? Like, how are you upskilling people? What does your org chart look like?

There are places where it's changed, but I would say it still looks pretty similar to what it has in the past. I don't think it's gonna remain that way. But the three key AI transformation efforts that we have going on map to those areas pretty cleanly. So we have a software factory that's working on building the products of the future. And so that's, you know, instead of engineers, like sitting down and like writing the code, that's building the machinery that writes the code.

And engineers are now working on the factory and saying, like, Hey, how do we get this set up so these this code can run in a loop?

That's super cool. Hm.

We've got these go-to-market agents that are doing the same thing, you know, a lot of that is working at our, like, our small business funnel, where they never had sales deployed to help them in the first place, cause we couldn't afford to do it. But now with agents, you can put something in the loop there, and we're trying to figure out, okay, how does that change the go-to-market engine? And then we have, uh, this workflow for the whole company that I call internally, we call kind of like hive company type stuff, AI native or what whatever word we don't have really a great word for it yet.

But the idea is like, we want to make sure that all of our systems are legible to the AI and they can operate the company. And so that means, like, when you go into a meeting, uh, the agenda is crafted by the AI. When you're looking at action items, the action items are generated by the AI. Some of those action items are taken by the AI. So, like, you're doing a lot more of this work where in the past you'd have humans, like shuttling information from one department to the next and making sure this stakeholder is connected to that stakeholder and all that kind of stuff.

A lot of that now is like being encoded into these AI systems so that we can eliminate the human tasks that are around this, like operational coordination tasks that, uh, you know, companies have to face.

Give us an example of a task that was not automatable before, and now it's doable because of AI.

Let's talk meeting prep, you know? I'll give an example for me. Like, I, uh, take a lot of calls with customers, and the types of calls I take with customers are often pretty open-ended, pretty exploratory. Who knows if it's gonna lead to, like, a bigger deal or not? I'm just often trying to learn, trying to, like, figure some stuff out. Pre-AI, there was a couple ways I could prep for that call. One, I could go ask, like the account rep, like, Hey, could you give me a brief and, like, really get me up to speed on everything going on with this account?

If I ask that, I would be pulling the account team away from active deals, active prospects, things that could, like, actually put money on the board for them to sort of help me out with this, like, quote unquote, like, you know, more speculative thing. I'm asking, so they want to do a good job of it, so they'll put real time and effort into it. So that would one way I could do it. Two, I could just take it on and do it myself.

Okay, maybe I go do that same effort that they were doing. Maybe I put as much effort into it, probably don't, to be honest. With AI, you can put these agents to do really good prep for any of this stuff. And that's a example of a place where, like, a task that should have been done well before, just couldn't really be done well enough. Like, not really, but an AI can do it pretty dang good. And so all in all of a sudden, we've all got meeting prep agents for pretty much everything we do when nobody was really doing that before.

So you can see that in just about any profession, any place, like there's all sorts of places where tasks were simply just not being done. Because they were too hard, they were too expensive, they were not able to do it a high enough quality or high enough consistency. And it turns out with an agent, you're able to actually tackle that thing, and now the humans can do big bigger, better jobs on top of that. Yeah,

Yeah, I want to talk about the cost of AI, and you kind of alluded to it earlier. Everyone's token maxing right now, so AI at scale can become quite expensive. So we've seen companies like Microsoft cancel their cloud code licenses because of costs, and also other companies are spending about 7,500 per employee on tokens. How are you thinking about costs as you are kind of encouraging and leaning into everybody using AI at the organization?

yeah, uh, look, uh, at some point in time, any cost that a company spends has to translate into ROI. Like, that's how we think about any cost. Like, you know, none of us show up to our jobs and are allowed to spend more money than the company earns. So you do have to justify itself at a certain point. You know, I think with AI, a lot of companies in the last year said, Hey, we're in this experimental phase, and so we are going to be willing to overlook the ROI equation for the time being, because we need to go figure this stuff out.

We're gonna spend a lot of money. Some of this stuff is gonna work, some of this stuff isn't gonna work. And at the end of this whole exercise, we're gonna figure out how to be a little smarter about that. And I think that's what we're seeing right now is there was a lot of token maxing going on in the name of experimentation, in the name of figuring a lot of this stuff out. And, you know, it feels like we're kind of getting to the end of that era where companies are saying, Okay, we saw a lot of that stuff.

There was a lot of goodness here and there was a lot of waste. So on the waste side, like, how do we step back and think through a smarter way to go about this? And as a result, we'll see a whole host of solutions. Maybe employees get token budgets where it's like you're only limited as uh allowed to spend a certain amount. Maybe certain workflows are shifted from being agentic to deterministic. I think this is where Zapier plays.

It's like, hey, we can help you take some of these expensive workflows that maybe aren't generating a great ROI right now. But if you ran them at a half the cost or a tenth the cost, they actually would be something that you'd happy to keep on. So I think there's like a mix of like organizational solutions, technology solutions that can sort of help people figure out what of these things gets to continue on because it is justifying its spend versus what of these things needs to get a little tighter scrutiny.

Because it's just, you know, the the the meme is like, Hey, we're asking like Fable five to do this like basic task for us. And it's like, it just doesn't make sense. Like, you don't need that kind of power to do a basic task.

I think it's really hard to be the CEO of a software company right now because you're at a moment where a company's valuation could swing on a headline. And this whole idea of SaaS is dead and SaaS apocalypse uh moment in time. So what are your views on that? And we did have Aaron Levy, the CEO of Vox, on the show, and he talked about how he had to, like, jump back into founder mode. And what's your experience been?

Um, y- you know, it's interesting here. That's that was Aaron's response. I had another CEO friend say, ten out of ten fun and ten out of ten anxiety right now. Uh, and I kind of chuckled at that. I was like, Yeah, that that kind of resonates. Where is fun for founders is this is a period of intense creative disruption. Like you've been gifted this incredible technology, you get to rethink everything from first principle. And that's just like, so excited if you're like entrepreneurially oriented, like creative oriented, et cetera.

And so that's the part that is is just exceptionally fun and cool. Like, there's been times in Zapier's history where things have been much more stable, less chaotic, et cetera, but kind of more boring. It's like this time, like, you know, you can say a lot about this time period, but boring it is not. And so that's where you wake up pretty excited about it. Now, of course, there is a bunch of challenges. Like you do, you know, we we're fortunate enough that we don't have like a ton of outside investors.

And so we don't have the same scrutiny that, like, you know, a public company might have who's trying to figure out how to navigate this era. But we do have customers and we have, uh, employees that are like, you know, stakeholders, shareholders in some ways about what we're doing. And so, you know, there is a lot of work you have to do to sort of make sure that you're painting a clear picture of the future when you yourself are trying to figure out, like, what exactly is this gonna look like?

You kind of know the direction, but you don't know the final form factor yet. And so, like, that's a challenge. You've obviously got a massive like media storm around this stuff that has information just comes fast. And it's hard for the employee base to always discern fact from fiction. Like, not everything out there is factually correct. There's a lot of disinformation. There's a lot of people pitching their own agendas. And you're just trying to, like, really settle people and sort of reorient and say, Hey, here are the customers we're serving.

If we make sure to deliver the best services we can in this, this, and this dimension, that is gonna only lead to good things for us. And so those are the parts that, like, end up being, like, really challenging. Is the communications part of the job, the like, being able to rally people around a change. And then ultimately, when something new emerges or you find that your hypothesis is wrong, being willing to call it quickly and then get everybody to go, oh, not that way, more this way.

Like, uh, and then, you know, getting them to sort of keep doing that over and over again. Um, can it can be jarring, but I I do think that that is, uh, part of the fun too.

Ten out of ten fun, ten out of ten angst or anxiety, I think, encapsulates and summarizes pretty well everybody's experience with AI. Wade, thank you so much for joining us on the show. That was great.

Awesome, thank you for having me.

I really enjoyed my conversation with Wade, but what will stick with me is his theory of change around AI transformation for organizations and how he brought it back to basics or first principles. So he's basically saying, okay, any organization, you've got the people who are building the product, you've got the folks who are selling the product, and then you've got a whole bunch of support functions like HR and finance that are supporting the functions of an organization.

And I really like how he's reimagining each of these function areas. Instead of saying, okay, everybody, like, use AI now, he's doing it in a much more strategic, intentional, and organized way. Thank you so much for being here. I'll be back next week with a new episode. Pioneers of AI is a Wait What original. Our executive producer is Eve Trow. This episode was produced by Megan Tan. Video editing by Eric Purcell. Our senior talent executive is Stephanie Stern.

Mixing and mastering by Brian Pew. Original music by Ryan Holliday. You can join the conversation on LinkedIn, Instagram, TikTok, YouTube, and X. Just search for at Pioneers of AI.