The Top Scaling Lessons from Zapier's AI Transformation with Wade Foster, Co-Founder & CEO of Zapier

Today we're talking to Wade Foster, co-founder and CEO of Zapier, the sponsor of today's episode. This one is all about how top companies are actually making AI scale within their organizations. You're listening to Joel Beasley, Modern CTO.

I'm particularly excited about this conversation because I've been a customer happier north of 10 years. So when, when your team reached out, I was like, yeah, this is going to be fun. Can you tell me the origin story of how you started this company?

Sure. So, gosh, it's 2011, so almost 15 years ago now. And, you know, my co-founder and I, we were working at a mortgage company and you know, on the side we were playing in a like blues and jazz quartet and we were doing a bunch of like hacking side projects, like building WordPress sites and plugins and that kind of thing. Anything just to make a buck. And you know, we kind of just did what, you know, couple of nerds would do, which is we would text each other cool startup ideas all the time. Be like, what if we made a product like this? What if we built a company around this? All this sorts of stuff. And look, most of 'em were just kind of, just that. It was just like, hey, this is like an interesting idea. And then one day Brian had texted me and he said, hey, I noticed that if you go to the forums of most of the like early SaaS products, so think like Dropbox and Evernote and Zendesk and Salesforce and QuickBooks Online, and they all had these public help forums, and if you looked at them, there was a very common type of question, which was, when will you integrate with X? And you would go read those threads and they all again followed the same pattern. It would be, me too, plus one, I can't believe you don't have this yet. And they'd go on and on and eventually a product manager would chime in and say, hey, thanks for the feedback everyone, we'll take a look at this. And if you've ever worked in one of these companies, you know that's code for, not gonna happen. And so, uh, Brian texted me this, uh, in not so many words and was like, hey, I think we could make a like no-code version of this that abstracts away APIs and allows these folks to set up their own integrations between these tools. And so at the mortgage company, my day job is in email marketing. At the time, I'm in charge of setting up Marketo. And they've got this old school SOAP WSDL API. The documentation is in a PDF. I'm having a bad time. I'm not a particularly good developer. And so I hear this idea and I'm like, "Oh, if this existed, I would just be using that product and not whatever the heck it is I'm trying to do over here." And so that was like the nugget of the idea. And we are in Columbia, Missouri. There's a local startup weekend coming up, so we go to the Startup Weekend. We team up with our third co-founder, Mike. We build the first prototype. We have a lot of fun building it. And when the weekend's done, we're like, well, that was fun. Should we keep working on this thing? And, uh, we decided, ha, yeah, let's go give it a shot. And, um, you know, we're in, we're in Central Missouri, so you, you don't go raise a bunch of money, you just build stuff. And so we keep our day jobs. Mike's still in school and nights and weekends we just kind of set out like trying to build the, the beginnings of what is now Zapier.

Oh, that's amazing. Did the name happen at the time of the competition or later?

So it, it happened in like 5 minutes. You know, we knew that the company was gonna be all about integrating APIs, and so I thought, well, it'll be pretty cool if we could fit API in the name. Of the company. And then -ier is a common suffix in English. So we're like, okay, what could go with APIER? And almost instantly we thought, ah, we have it, Snapier. And so we go by the domain, we think we've got it like sorted. And then after the weekend, you know, we're like, okay, we gotta go do things properly. And so we go out and start to like search the space and see if, hey, is there anyone kind of in our world that's doing stuff like this? Turns out there's like a quasi-competitor, sort of, maybe that had like some snappy stuff going on. And we were like, uh, let's just steer clear of that. We need to change the name. And, uh, the next obvious choice was Zapier, which is one letter shorter. So even better.

Yeah. It's like flip that S around, you know, there you go. Now, was your first integration the Marketo stuff that you were working on or was surprisingly not?

But it was email. So we will— let's see, the first thing we did was we had PayPal, we had Highrise, which was a CRM from 37signals. Yeah. So that's a throwback. There was Twilio. Got to text people. And what was the other one? Oh, Twitter. Twitter was like all the rage at that time. So those are the first 4 that we, we sort of added in.

That is brilliant. And then you made it so that they could integrate it themselves into their own stuff.

Yeah, about a year in, we built out our developer platform that allowed people to start building their own integrations. But yeah, for the first year it was just us cranking away. I think we built the first 50 ourselves in like, I don't know, 6 months, something like that.

And, and were you remote or in person? How did that go?

Well, so it's a side project at first. So there definitely is no office. We're mostly just, you know, talking on Gchat at the time. And then our old boss at the mortgage company would occasionally let us come into the, to the office at nights and use it. So we would meet up there. Then we moved to the Bay Area about 6 months in when we got accepted to YC, and then The 3 of us lived in a 2-bedroom apartment. You do the math on that. Not exactly glamorous. Um, and then after, Mike moved back to Missouri to be with his then girlfriend, now wife, as she was wrapping up law school. And that was kind of the first, like, inkling of, okay, how are we gonna go build this company? Mike's in Missouri, we're in California. We gotta go, like, we had to hire somebody to help us with customer support. It was like, are we gonna try and hire someone in the Bay Area or do we care? and, um, the advice we got at the time, we had no experience hiring folks, was, well, you should just hire people you've worked with in the past that you know and trust, and that'll de-risk some things for you, um, given that you're woefully inexperienced at doing this on your own. And turns out most of the people we know are not in the Bay Area. So we hire a couple people, kind of one's in St. Louis, one's in Columbia, one's in Chicago. And that kind of sets us down this like distributed remote path. And gosh, as best we could tell, it seemed to be working. Customer count was up, you know, team was healthy, uh, revenue was up, products were shipping. I, you know, there was nothing we looked at and said, oh, this model won't work. Um, it like, at least for us, it felt like it was working. So we were like, let's keep doing this 'cause nobody else is doing it. And if we can do it well, that'll give us an advantage.

Now, at what point did you raise any money?

We raised a small amount at Demo Day after YC, about, um, well, I say a small amount, it seemed like, uh, uh, it seemed like a large amount at the time to someone who has not seen that amount of money, but it was $1.3 million. Um, nowadays that feels quaint. Um, yeah, but, uh, that was the last money we ever raised.

So you just raised once at Demo Day.

That's it.

And then from that you were able to push this thing into a profitable scenario and scale it up to a $5 billion valuation.

That's what we did.

That's awesome. Are you still working there today?

I am.

You still CEO position? Is that what it is?

That's what it is.

Are you loving it? Are you still loving it? Are you st— it's hard to stay, look, it's hard to stay excited. You can make a bunch of money as I've met some people who make a ton of money doing their dream and they're miserable. Are you able to stay excited?

I'll tell you what has been really reinvigorating. I, I felt like at the tail end of the last decade, it, it started, it was starting to get a little like, I don't know, it was like everybody's product roadmap, ours included, was stuff like Command K and like dark mode, which like, okay, cool, I guess. But now this age of AI, it sort of feels like this just unbounded opportunity has been unlocked. And that has like just reinvigorated me and the company in just like so many unique ways because it feels like a lot of the stuff we had wanted to do, but like just couldn't quite get it working now feels like, oh my gosh, we can do this. And, you know, a thousand other ideas.

That is true. That's actually a really good way to phrase that. Everyone was working on dark mode and then AI came out and we're like, oh, stop, stop playing around, guys. Really is.

It's like that. I mean. Nowadays you just like, you know, Claude Code, give me dark mode, you're done.

Give me dark mode and it will, it will give me dark mode in one style sheet.

Yeah. It's like, it's not much to it.

When did Agents of Scale, your podcast that you host, when did that start up?

Let's see, we did the first episode, I guess it's probably been 6 months ago now, give or take. Nice.

And then what are you talking about there? What's the whole angle of that podcast?

So, The angle is we, we tend to have leaders, executives come on and share what is working for them at changing how their company operates in the age of AI. It's a mix of folks, some technical, some not technical. We talk about like actual practical stuff, but we talk a lot about culture too. Um, I think a lot of the battle that companies are facing is cultural. It's like, hey, we have to like unlearn all these bad habits, or, oh, it's not even bad habits. It's just like these were the habits that worked in the last decade. And now in the age of AI, they have become bad habits without people actually realizing, oh, that, that actually is the old way of doing things. And so it's fun to just hear how different people are tackling that problem and how they're getting people to sort of shift and what, what tips and tricks are working, what tips and tricks are not working.

And you've been running it for 6 months. What's the one conversation that you remember the most?

Um, gosh, I— the one— I, I've gotten to talk to like a ton of interesting people, um, you know, folks from, I don't know, HubSpot, Klaviyo, Gamma, uh, Newfront, um, and many others. Um, I, I think the one that I was maybe the most inspired by was the, the founder of Gamma. Um, that product is just on a tear. For those who don't know, it's, it's AI-generated slide decks. But even that, I feel like I'm underselling it. They would do like so much better. But just like hearing how they went from, you know, basically like kind of puttering along in sort of the pre-AI era to like just going all in on this like AI-generated way of building and all the lessons they've learned scaling up to I mean, tens of millions of users. Um, it was, it's just pretty fun to see sort of like a company that has like kind of figured out a lot of things from first principles.

Hmm. So they do, they generate slide decks.

Yeah. I mean, that's sort of like the simplistic version, but I think what's more exciting about it is these slide decks can be dynamic. So, you know, if you think about like the old way of doing this, you might have generated like a slide template in Google Slides. And you know, if you had a meeting with like a sales prospect coming up, you would come in and you'd like paste in their logo, paste in like something relevant to them. And you know, that would be like what customization would look like. Um, what Gamma can do is you can take that dynamic version, but you can you know, dial it up to 11, where instead of just like swapping out the logo, you swap out like entire value props, entire vision, you know, positioning, entire chunks. And so you can start to prompt and say, hey, you know, based on what you learn about the program, like change the visuals, change the like reference logos, change the case study that you slot in, like find the one that's closest based on our bank. And like all that type of stuff gets to be really powerful. And I've seen folks actually hook it up with Zapier where they'll say, hey, you know, take a lead as it comes through, like, you know, maybe get a lead from Facebook or Google or something like that, funnel it through Zapier, hit these like custom prompts, and then land it in this like Gamma template that just goes and generates this like entirely custom deck, uh, for that customer. Uh, and so, you know, that, that's where I'm like, man, just calling it an AI slide deck, it sort of feels like it sells it short.

Well, I've got to play with it. This is me learning about it for the first time right now. So, I've got to check it out because it sounds pretty interesting. And yeah, that's what we would do when we were pitching our leadership software platform. We would just go and flip out the logo and make it as personalized as we could, but it was—

You couldn't do much, right? You were just like—

It wasn't a whole lot. Yeah. Interesting. Well, I'll take a look at that. What other conversations have you had that are particularly interesting to you?

Well, I, I think the, uh, the most common learning that is shared across these companies that I see from the ones that are actually driving AI change in their organizations tends to share a couple traits. One, usually the founder CEO is preaching this stuff, but also walking the walk. So they're out there saying, hey, we need to go work this way, but they're also got their hands on the keyboards, they're doing the stuff themselves. The second thing is they generally have their like procurement process reasonably dialed in. One of the biggest friction points like these companies face as they get to scale is that folks want to use it, but they haven't actually bought tools for people, so they can't even get started because there's no— there's nothing for them to go use or play with. And so they're all stuck using like GitHub, you know, Copilot, uh, instead of like the most cutting-edge stuff. And then the third thing, and this is like the one that I think is the absolute most common, is they're all running hackathons. And they're running hackathons for not just engineering, but sales and marketing and finance and everybody in the org. And I think what folks are finding when they do that, we've certainly seen this internally at Zapier, is that when people put their hands on the keyboard, the fear starts to fade into the background and the excitement starts to be more visceral where they're like, whoa, I can do this now. And they start to see like, I actually can go solve problems that I couldn't solve before. But they also know what it's good for. They also know its limitations. They know that it's not perfect. And I don't know, it just becomes like a much more like energetic and pragmatic, like cultural shift that happens. Whereas, you know, when folks are not doing any of those, you sort of hear it, it like, it's almost like the mainstream starts to like drown out the, the actual boots on the ground experience for folks.

Yeah. In my experience, there was two types of people. There were the people, or this is to your point of the CEOs being on board with it. There was two types of executives I'd run into. I'd run into the ones that put their hands on the keyboard for 5 minutes and was like, this is all hype. They couldn't get it to do whatever they wanted it to do, and they were just like, this is way— it'll take years. And then there's the people who push through that moment and either learned from a friend how to do it, saw someone do it, monkey see monkey do, or just push through and was persistent enough to ask it to how to best use it. And then they got hooked. And so it's really just people are like, uh, dismissive or addicts. Rarely do I meet anyone in the middle.

It is kind of wild. Like, it does sort of feel like once you once you get it, it, it, it, I mean, addicts is a good word. Like I think it is addictive, but, but honestly, I think it's kind of like the best kind of addictive. Like in my mind, it's replaced my doom scrolling habit with prompting. And I'm like, that actually sounds like a win to me. Like I'm, I'm great with this kind of addiction.

It is a win. I use it in my personal relationship when I want to communicate things to my wife. I'm like, tell me the best way to communicate it to her.

Yep.

And, and it gives it to me and I'm like, all right, there we go. She'll send me text messages sometimes that are so sweet. I'll respond back and I'll say, thanks, Grok.

Oh, that's amazing. Now you just got to turn it around and have it critique your stuff.

I know.

That's when it gets brutal, man. The funny thing I found is, you know, so most people have this experience where the AI just like agrees with them on everything and sort of like tells them they're amazing, especially if you're just using kind of like vanilla ChatGPT or something like that. And the tip I always have to share with people is like, you know, when you build kind of like your shared brain or your, like your, your personal brain or your context, you have to like use that, like the AI's like natural inclination to want to help you to, to your benefit. And so you basically have to like instruct it to be like, you are a coach and the best way you can help me is to be hypercritical of my stuff. And if you do that, like you start to get some like really powerful coaching out the other side of these tools.

It's an amazing human psychology thing when I can tell that it's doing that and I say, no, no, no, I want to see the other side of this and it'll just flip instantly and it's like, thank you.

Yep. It's like that, that, that hurt, but you know, all right, I'll go fix it.

The conversation coming up a little bit, changing gears here, uh, is SaaS dead? Vibe coding, everything that's going to be happening in the future. Uh, I know you had a little bit of a conversation about, I think with Dharmesh on this. Well, give me the, give me the TL;DR on that convo.

Well, you know, I think Dharmesh had shared that absolutely not. Like, you know, they're— I think HubSpot's a, I don't know how many thousands of employees they have, and they're a software company through and through and through. And the number of SaaS tools that they have replaced with a vibe coding tool was zero. So, you know, I think that Obviously, like, I think it's a little more nuanced than that, but by and large, my take is that if you can buy software, you probably should. Like, if there's software available that solves your problem, it's generally just way cheaper to buy it and let somebody else maintain it and like build all the workflows around it and all that sort of stuff. So you're, you're probably better off doing that. But I think what a lot of us are increasingly realizing is that there are as many places where we'd like software to tackle a problem for ourselves, but there's nothing to buy. And these tools are increasingly very good at helping you bridge that gap. Then there's probably like a third category, which is stuff that's like kind of in the middle. You know, I think there was a lot of like SaaS that kind of blossomed in the last, I don't know, decade or so that, you know, was kind of like these like tiny little micro SaaS, like these little gap filler things. Those I think are what folks start to feel like, do I really need to go spend another $50 a month on that or $100 a month on that? It really does only one tiny little thing for myself. That stuff I'm like, you know, when people say is SaaS dead, I can start to go like, yeah, I can see that. But you know, your CRM, your ERP, your like financial tools that, require like proper governance, you know, regulatory issues, compliance, like deterministic stuff. Good freaking luck, like vibe coding your way to a solution on that.

Vibe code a QuickBooks? You're getting audited, bro.

I know. It's like, let's see what the IRS has to say about, um, that.

No, it's interesting though, 'cause you've been out there talking to all these people. I'm out there talking to all these people and we're coming to the same conclusions, which I think is fantastic. Those are the same sort of the way I looked at it was utility, which I think is what you were talking about with like maybe just a small data transfer app you're paying for. And it's like, well, this is really basic. It does one single function type deal. We might be able to play with that. Um, but I think, you know, it's easy to overreact and see the one-shot apps and just imagine SaaS is going to be dead. I think what's more likely to happen is what's always happened, and that's just margin compression. Mm-hmm. So you'll just get other startups that'll make something. Now they— with less resources, they can make something just as good. And then a lot of it I see is like stuff going forward, as you mentioned. So less about getting rid of all the stuff you're currently entangled in to run your business and more about, hey, we need to do that. Is this something we can build an app for with AI? Like, is it faster to put 2 engineers on it with some AI tools, or is it better to go hunt and go through procurement or whatever it is? So yeah, I think, I think maybe there might be some velocity slowdown of new SaaS. I could be wrong. This could be the mail thing all over again. Well, I think you're, I think you're right.

Like, I think we were already seeing this with the market correction that hit in 2023 where CFOs were hardcore hammering on seat usage. And, you know, a lot of these like SaaS providers had you know, huge number of seats deployed and charging exorbitant rates. And so, you know, CFOs were already like laser-focused on, hey, we're going to go scrutinize that spend and we don't think that that spend is like right-sized. Then you get into a world of AI where companies are just not growing headcount as fast. And so you don't see seats expand as much. And so, you know, to me, it's not that like for SaaS to quote unquote have a problem, it doesn't have to be replaced. It just has to have like be compressed on the edges where it's like, oh, there's not as many seats anymore. Okay, well, it's not growing as fast anymore. You know, people are a little scrutinizing the price tags a little bit more than they were. So, okay, you're not going to be able to command as much pricing and power. Software is still very useful, still very valuable. You don't want to go build it on your own. It's just you've got a threat of a different alternative that, you know, maybe wasn't as prevalent before. And so on the margins, I do think that's kind of what we're feeling with SaaS right now.

You have instituted in May of 2025, 100% of new hires must be AI fluent. How do you do that?

I look, I'm not going to say we're perfect at this, but what we practically did was we went function by function, and we just tried to understand how are like the best people in those functions deploying AI, what are the use cases and what are the benefits they're getting? And so then when we would go design, you know, hiring tests, we would just try and screen for those things and see, hey, are these people already doing some of this stuff or have they figured out something more novel? And you know, we have, we have like a, a 4, like the, there it's on like a, a, a scale of like 4 things. There's like unacceptable, acceptable, adoptive, and transformative. And you know, it kind of comes back to like the thing you were saying before where there's like sort of 2 camps of AI users. Yeah, there's a lot of people when you go like say, hey, show me what you're doing with AI, you can kind of sniff it out pretty quick where they're like, okay, I'm, ask ChatGPT to rewrite this memo or summarize this thing. You're like, okay, well, you're not doing nothing, but there's like a whole other world that you can go explore. And so you're mostly just trying to find people that are like hyper curious and, you know, have started to push the limits on these things. So I think that's the approach we generally take. We gotta go update that stuff like, you know, every 6 months or so because of how fast the industry moves and how much the use cases are evolving really quickly. you know, the thing we released in, uh, mid last year is directionally correct, but dated at this point in time. You have to like update it for, um, you know, the, the, the new capabilities that have come out.

So let me repeat to make sure I got this right. You went function by function within the company and looked at how some of your best performing people were using AI, and then you took some of that knowledge and somehow put it into the screening for when you're hiring people. To break them into 4 levels, and those levels were unacceptable, acceptable. What were the other 2? Uh, adoptive and transformative. Okay. And so that's you ranking what you think the candidate, where their AI maturity is. You got it. Okay. Ooh. And that's so smart because it's by function. Oh, that's, that's actually pretty cool.

I mean, it makes sense. Like the, you know, the types of use cases a marketer has are different than the types of use cases that someone in finance has, and it's different than the types of use cases that somebody in engineering has. And yes, there's overlap, like you're, you know, generally you want like a deep sense of curiosity and play. Like that is something that is sort of ubiquitous across like all the best users of AI. But the way they go deploy the tools are different. And you can kind of see, you know, you can start to see they all sort of, there's like some shared thinking bits, but like there's novel things based on their own domain expertise they bring to the equation.

I got a question and we can, we can leave this part out if you want to. It's just for me. This is a just for me moment. Um, when it's unacceptable, do you tell them that that's where their AI maturity is so that they can then go make a better choice in life? Or do you just kind of like—

we, we give, we do give feedback to folks who get farther along in our interview process. Um, So, but I don't think that like we're giving just like straight up unacceptable— "Get outta here.

Get outta here.

Go." It's not, you know, we're not your like high school math teacher with a big red pen and a, you know, an F at the top.

Isn't that so hard though? 'Cause you can create so much hate for your brand if you start like being too honest with people too soon.

You know, like funny enough, like This is where AI can help. It's like, let somebody down. I need you to be direct, but, and that's where Joel gives me it. Thanks, Grok. Yeah. I need some help right now.

Oh, this is fun. Tell me about this code. Well, first of all, let's take a break for just 2 minutes. You've, how many kids do you have?

I got 2 kids. What are their ages? They're 3 and 5.

What has being a father taught you about leading people?

Honestly, I think a lot of communication. Like, you realize— gosh, how do I say this without being condescending? I think when you speak to a 3-year-old, you realize that you can't just say, "Go get ready for bed." You have to say, I need you to go upstairs. I need you to take your clothes upstairs. I need you to go to the potty. I need you to go wash your hands. I need you to get your toothbrush out. I need you to brush your teeth. I need you to put your jammies on. Like, you know, like, go to bed. That's what an adult, like, sort of hears. But like, for a 3-year-old, they don't have, like, the concept of any of these things. Now, of course, in the workplace, No one is a 3-year-old, but oftentimes I've been working in this business for almost 15 years. And so I can do the Zapier equivalent of, hey, go get ready for bed. And for someone who's brand new to the company, I might as well have said, you know, an entirely different language. And so you realize, oh, if I'm going to get the best out of people, I need to get really good about how I explain like instructions to these things. Turns out that's not only pretty useful for humans, that's also very useful for AI too. Yes. So these like communication skills are like, yeah, I, I sort of knew even pre-kids, you sort of know that this stuff is important. Um, but raising kids is like just an up, like it's just a daily reminder of how crisply you have to communicate, you know, what, what you want. And if you want to get a good outcome, Well, it's only going to be as good as the information you give them.

That is so good. Yes, exactly. And it's fascinating how you connected the, the delivering communication in the correct size chunks with both children and AI, because I have that. That's the art with working with AI. You can, you and I can easily give it way too much information and have it not have a quality result. It's knowing the exact— it's an art right now Uh, and I, I would say that I'm, uh, unconsciously competent in doing it, but giving it just the right amount to get what I need for that specific situation. Totally.

And the more you play with it, you start to get a sense of like, oh, it's going to need this bit of detail. It's going to need this bit of detail and it's going to need this bit of detail on it. And like the order in which I give it to it and sort of, you're, you're almost like priming the pump where it's like, I've got this, I've got this task that I'm like, I know I'm leading up to it. But I'm not gonna just go ask it to do the task first. I'm gonna like, I'm gonna prime it with this and prime it with this and prime it with this and prime it with this. And then right when you sort of think you've got it all, you're like, okay, now that you've collected all this stuff, here's what I want you to go do. And yes, you just get way better outcomes than saying go to bed.

Tell me about this code red at Zapier. It sounds very intense. What was that?

So we'd never had a code red before, but, you know, this would've been in spring of, I guess, '23. So, you know, we had seen the ChatGPT moment and we were pretty excited about the ChatGPT launch, but that was like kind of just a point in time. And so, you know, we didn't really mobilize the company with like a, like a, like a, differing set of urgency and energy at that moment in time. Rather, we were just like, "Hey, check this product out." We shared it in general in Slack and we're like, "We like cool products, you like cool products, like check out a cool product." Like that was kind of the vibes. And it was very much just, this might be interesting for your roadmap. So if it is, take a look, maybe you'll wanna change it. So very suggestive, very organic, et cetera. And, you know, over the course of the next 6 months, you know, we started like applying pressure, you know, softly, fairly gently in a handful of places in the organization where we felt like AI was gonna have a bigger impact. But again, not a lot of like mandates. The thing that caused us to call the code red was the GPT-4 launch. The reason why is it happened roughly 6 months after ChatGPT came out. And the delta between 3.5 and 4, both in terms of model quality, costs, was pretty big. And so now you've got 2 data points. It's not exactly a trend line yet, but it didn't take a genius to look out and go, if any of this continues in any way, this has big implications on, you know, how we work, the products we offer, um, Everything. And so we felt like the company sort of needed to like meet that moment and we just borrowed from, you know, medical terminology and just said, hey, code red, we're going on this stuff. Company, we didn't, we didn't even have, we didn't even define code red internally up until that point. So it definitely was jarring for the company because, um, A lot of folks were like, what does this even mean? It sounds intense. Uh, and you know, we're like, yeah, it's meant to be intense. Um, we, we need to wake up because I think this is the type of thing that is worth, you know, playing that card. Like, I think as a CEO, you have to be very careful because if you pull that lever all the time, people learn to ignore it. But if you pull it in the moments that matter, it's jarring. Yes. But it's jarring in the way that like, people are like, okay, this one matters. I think the most impactful thing we did right after we called that code red, we did a bunch of stuff, but the one that really mattered was the hackathon. Like, we stopped the company for a week. We had everybody put their hands on a keyboard and start building with this stuff. And we went from about, yeah, just under 10% of people who were like using AI weekly in their job to over 50% in one week. And that was like easily like the singular most important thing that like lurched the company ahead. And I think, you know, in spring of 2023, we were probably already one of the more sophisticated companies that were like using AI. Just because of doing the hackathon. It took, you know, I think another year for us to get that to, you know, 100% of folks using AI regularly. But that was a— yeah, I mean, it's just a big moment to like reorient the company around what was coming. That's all.

Do you have a favorite model provider or do you— for different functions, do you like different ones?

I use all sorts of stuff for different things. I like to have them critique each other. You know, I do think I've been using Opus 4.6 recently, launched last week. It's like a faster 0.5 or 0.45. 0.45 was pretty good. The GPT-5.3 is really good for like hard like tasks and it can think really, really long. So that's like a pretty impactful model. You know, I use I use Gemini like for Nano Banana. I use it to—

Nano Banana is pretty good. I know, it's pretty fun.

I use it to, I have one use case where anytime I go to a restaurant, I always talk to Gemini. I don't know if it's actually better, but I'm just like, Google has Google Reviews, so I just sort of feel like it should be better at that. I don't know, I try to keep all our AIs happy. Like I want all the models to be on good terms with me. You know, just depending on how things roll.

Sometimes you just talk to them and say nice things to them.

Yep. And I have, like I said, I like switch back and forth and have them critique each other's work. And like, I assign the same tasks to multiple of ones of them. And I just kind of like to get the differences, you know.

That is awesome. I'm curious, what are, like, you get to see how people are using these Zaps across the entire organization, all these industries. Tell me about what people are doing in relation to hooking AI workloads up to it. Are you seeing anything interesting? Are they using it for like human-in-the-loop stuff? Tell me about that.

Yeah, I think the most practical usage we see is for these workflows that require, like high reliability. And so one of the nice things about Zapier is like it's a deterministic workflow builder, works the same way every single time. And the nice thing about adding AI to that is you can get pretty high reliability out of a task. And so what that might look like is, hey, I've got a lead coming through the site. I want to go hit an enrichment API, or when I have it go do deep research, and then I want to go land it in the CRM. Um, pretty simple workflow. In the past, you just wouldn't do the AI step. You just take, you know, first name, last name, email, company, URL, plop it in the CRM. But now you can go do this AI enrichment step and just get a lot more interesting context for it. You could plug it into the Gamma use case we were talking about before. Ah, go generate a like custom slide deck for these things. And so there's these like hybrid deterministic like AI workflows that work really well in a setup like this. And you know, I think they're pretty powerful compared to like a purely agentic use case that, you know, can create like pretty magnificent demos and can work okay some of the time, but some of the time it just doesn't and you're not exactly sure why. You know, these tools do have a little bit of a black box feel to them and it, you know, you have to set up evals and all these systems to like really eke out those gains. And I think a lot of workflows internally, that's just not something you're gonna wanna go do. Like it makes sense for like, you know, if you're building a product and you need something production grade, but for your marketing workflows, your sales workflows, things like that, you kind of want to blur between the lines a little.

Does, does Zapier have any like consultative part of the business where they actually help businesses build these workflows?

Yeah, we have started, uh, an AI transformation program where we'll come in and work with, you know, either the whole company or a particular function in your company, and we'll help modernize the whole thing. Uh, and so you know, a lot of that of course we're using with Zapier, but you know, we'll help you with a wide variety of tools. We integrate with 8,000 different tools. We integrate with all the major model providers. And so, you know, we can, you know, sort of help answer questions and advise on, you know, maybe the best way to sort of modernize your stack as well too. Uh, and you know, do, do some lightweight support for those tools also along the way. Uh, and I think that's like been pretty helpful for a lot of these companies because, um, you know, especially folks that either functions or teams that are just not like super well-versed in this. A lot of people just don't know where to start. You know, they kind of have used ChatGPT. They know how to like summarize an email and, you know, write a cool poem or something like that. But that's pretty different from, you know, a production, you know, AI workflow.

If you are spending your free time Using AI to write cool poems. Please ask your AI for help. Social life things to do. No, I'm kidding. I'm kidding. I mean, you've probably seen this too.

Like, it's like, it's kind of what's going on.

I might do that actually. I might do that for my wife. Just have— maybe I'll do that this afternoon. I'll email it to you, let you know how it went, Wade Foster. All right, two things I want to make sure we touch on. They're very close to my heart. Uh, before we wrap up here, the first one is, can you tell me the correct way to say the name of the company? Zapier makes you happier. Okay. Thank God. Do you have any idea how many arguments I've had with my British friends that it's not Zapier? Oh my gosh. I finally have an official clip. I can clip this and I'm sending it to all my British friends.

You know, I, I would, uh, I'd have debates with our old CMO about this and The way I justified it to myself is that the naming confusion just was free word of mouth.

Yeah.

Well, it's a zap. You're right.

Your little actions are little zaps, like zap, zap, zap, like quick, like little zaps.

Yeah, you don't zap things.

You don't zap things, you zap things. I've had that conversation more times in my life than I would like to admit.

My own family has been like, could you not afford the other P? Like, come on. Fun.

No, it's, it's efficient. It's efficient. I love API, dude. I would have never have thought that the API, right?

We were more clever about it. If you go find our very first logo, the API was in gray and the Z and the E and the R was in orange. And so we're a little, little too, too cute for school, I think.

Hey, your revenue though, that's all that's like, that's what matters. You're making an impact in the marketplace. Uh, and the next thing as we wrap up, I love this idea that I heard you have, and I want you to tell me about it, about you think people should automate themselves out of a job because we share this view together, but I rarely run into people who articulate it. And so I want to hear your version of it.

Well, you know, I think there's this fear amongst the workplace where, you know, oh, if I automate myself out of a job, then I will just be let go. I'll become redundant. I certainly would never do this. And I know of no other CEO or leader where a person came to me and said, I have automated my entire job, whose reaction is not, holy crap, show me how. And I have 9 other projects that I'm ready to go turn you loose on. Like, that is such— like, if you've legitimately figured out how to do that, you are— you have instantly become one of the most valuable employees in the company because you know how to do something that is just very, very rare. Uh, and so I, I think the people that are like thinking in systems and are figuring out how to like break these down, yeah, you're, you're, you, you have one of the most valuable like traits and you should not be nervous about doing that. You should, you know, run full steam ahead. Um, and, and using more automation.

I love it. It's, it's so hard and it is so rare, like as a founder to see that if, if somebody came to me and did that, I'd be like, well, let's adjust your comp plan and let's get you on some new projects because we want you to be, we want you to love this place. We're not going to say let's go home. Yeah.

Well, I guess, I guess that's it. You know, like, uh, have a good life.

Yeah. But I get it though, because like early on in your career, I was there. See, the reason why I have so much empathy for this is when I was very, very early in my career, I was, I could, I can understand how that could be. I could, and I can understand that feeling of, of, you know, let's be safe and let's, let's inject myself, uh, into this role and versus fully automated out. So I get it and I have sympathy for it. And I think that's why as an employer now, I just, tell me if you've come up with anything better, but I just say it out loud a lot. Just so that people hear me remind them, because like you could say it once in their onboarding, but like if you don't build it into the culture of it being something that's said frequently, it doesn't have the same impact.

We have a value at Zapier called "Don't be a robot, build the robot." And so I think, um, we've just attracted people who are like more predisposed to this mindset, uh, probably in part because of the value, in part because of the products we build. Um, Doesn't mean that we don't have like folks that get a little fearful on this, that, or the other, but, uh, I, I, I think on the margins we probably have less of this challenge than maybe your typical America, you know, corporation.

Yeah. Well, I mean, it's at the root of what you guys do. You automate mundane things. You make it easy to do that.

Yeah. Yeah. Like everyone here wants to take a machete to boring work, so.

I like it. Uh, where could people learn more about the pod? How can they subscribe to your podcast?

Well, check out Agents of Scale on any of your preferred podcast platforms, and then you can follow me on socials, Wade Foster on X, on LinkedIn. Check out Zapier if you haven't. Check it out.

I'm a customer. They've been awesome for over a decade. We did it, Wade. We made a podcast. How do you feel?

This is great. Super fun, Joel.

Before you go, check out a little bit of Wade Foster's podcast, Agents of Scale. Here's the first few minutes of their episode with John from Gamma. Listen to the full episode wherever you get your podcasts.

Hey folks, welcome to Agents of Scale. It's a show where we are exploring how the world's most forward-thinking leaders are operationalizing AI across their companies. I'm your host, Wade Foster, co-founder, CEO of Zapier. And today I'm joined by John. He's a co-founder and head of product at Gamma. If you haven't checked out Gamma, Gamma is reinventing the slide deck and they're doing it with AI. Who doesn't want an AI to make all their slide decks? Sounds awesome. Um, and, uh, they've had some really impressive growth, scaled to 50 million users in just a year. But there is a big old story, uh, behind that. Uh, not exactly an overnight success as I think a lot of folks like to portray it. But before we talk about Gamma, John, I want you to tell me about PlateMate. What's the story around PlateMate?

Uh, well, happy to be here. Thanks for Thanks for hosting me. And I love that we're, we're going back in my history. This is great. So PlateMate is a project that I actually worked on in college way before any of the generative AI would've made it actually good or easy. But the basic concept, you could say it was my first entrepreneurial idea, but I just did it in, in college as a project, was what if you could just take a picture of your food, your lunch, your dinner, whatever, and have an app analyze it and tell you you had this much protein, this many carbs, this many calories, and add it all up instead of people having to like count this up for themselves or whatever it might be. Great concept, but I would say before its time in terms of technology. But it was a neat one to try to build. At the time, AI just could not solve this problem. We were so far— this is probably like, gosh, 15 years ago now. AI was just not there to be able to do this. And so we actually used human intelligence. We used Amazon Mechanical Turk. If listeners don't know what Mechanical Turk is, Turk is, is this platform where you could basically pay people by the minute, maybe even by the second, to complete tasks for you online for like, I don't know, probably below minimum wage. And so you would just put tasks up there and the task would be like, label a box, draw a box around all the foods you see on this plate. And another person's task would be identify what's in box number 3, and they'd be like, I don't know, I think that's mashed potatoes or something. And then person number 3's job would be look at the mashed potatoes and say, "Okay, match this to this USDA food category so that we can actually find out how many calories or carbs are in there." And then our app would stitch it all together. It was a cool concept. This was when the wisdom of crowds was sort of like this big thing. I had like dreams of building a mobile app. Unfortunately, it has never got beyond a research project because it never actually got to be cost-feasible at the time. I think we came to the point where it would cost maybe, you know, like a buck per meal to have someone like label all your food for you. So you're talking 3 meals a day, 30 days a month, like $100 a month subscription just to break even on somebody telling you what's in your food. I think there could be some medical applications for it or whatever, but hard to see it really taking off as a business. So unfortunately I had to put it back on the shelf, but I'm psyched to see that now there are companies doing this with AI at way lower cost.

Yeah, I mean, these products exist. Like if if you were to build it today, like how quick do you think you could build a version of this today compared to 15 years ago?

Oh my God, you could live code it so fast. I really think.

Yeah. Like a weekend project, right?

There's actually no magic there now. It's like the vision models just do it. I think, you know, the hard part is like tuning the accuracy and making sure it's right. Yeah. And getting people to trust it. I also had this whole other idea of like, what if it was a social network? What if we all posted our food pictures, which people have been doing since time immemorial. Reel on Instagram and Twitter or whatever. But then I would, I would label what's in your lunch and you would label what's in mine, like for fun. I think, I still think there's an idea there. I never figured out the formula for it, but yeah, just the version where AI does it. If I coded an afternoon, it'd be fun.

Yeah, I've heard it's still actually pretty tricky because it's hard to see like what are the ingredients in this. Like you need to have like, I don't know, you have to have like a quarter there for like, you know, to, to scale, for scale. And then, uh, yeah, you just like don't know how much How much butter did the person actually put in this thing or not? Like, it's hard to tell. But you know, it's, it's all about what your baseline is.

So the thing we did in that paper that I thought was really interesting was we actually compared it to professional nutritionists. And also to self-reporting of people on their own eating. And it turns out just averaging together a bunch of these Mechanical Turk people did at least as well as the professional nutritionists. And often better than people self-reporting. The nutritionists, because it was just one person working off the same limited whereas if you averaged 5 or 10 strangers, you actually could get a more calibrated average and better than self-reporting, because it turns out that even when we know how much butter we put in, we lie to ourselves about it. And so an impartial stranger without the information often does better than you yourself with the information. And so considering that, it might still work.

Yeah, I've heard that, like, when people ask, like, why is restaurant, like, you know, food so much better than the homemade one? And it's like, well, you're, you're paying me the cook, so you don't have to see how much butter I actually put in this thing.

I love that. Please don't tell me. I couldn't possibly know. Then you make cookies at home and you're like, dear Lord, that's how you do this?

It's like, yeah, so that's, that's, that's why it tastes so good. Um, I love it. Uh, what do you like, what do you think you learned about building products from PlateMate? You know, since that time you've had a pretty like, uh, uh, epic career across, you know, Microsoft and Optimal Myslie and, you know, now Google, like, have worked on some pretty impressive products. Like, what groundwork did you sort of pick up on working on PlateMate?

Gosh, that's a good question. I mean, the first thing is, like, work on a universal problem that lots of people have.

Thank you so much for listening, and if you found this episode useful, please share it with a friend or a colleague who you think would get value from it. And if you have topics that you'd like to hear discussed on the podcast, either add me on LinkedIn or send me an email, joel@moderncto.io. Every time I get an email or LinkedIn message, it absolutely makes my day and inspires me to keep going.