Code Red: How Zapier hit 50% AI adoption in one week, with Wade Foster

In moments like these, there's a lot of incumbents that have gotten fat and happy off of these big fat margins, and they're used to operating a different way, and that's what kills them. We're just like jumping in the deep end and saying, let's go figure out what this thing can do for us. We had never done a code red before. To me, all it meant was like, holy crap, this is important. We need to do some different stuff.

Hi, it's Greg AI True Serum, and I have an update. The podcast will no longer be called AI True Serum. It's gonna be called Supercompanies. So welcome to Supercompanies, my quest to understand how we build the most valuable companies in the age of AI. And you know, I've been fascinated with supercompanies cuz I've been mentioning along the way, cuz I really wanna understand how do we build organizations that create value, not just for shareholders and investors, but also for employees, for customers, and even the world at large, these will be the companies I think we're all gonna wanna either work for, lead, or invest in. So we're gonna be having still in-depth, honest conversations with leaders and AI experts. We're all gonna need to be super in the coming decade, and I want us to learn together how to do it. In this episode, we're talking with Wade Foster, CEO of Zapier. Zapier is a super company today and an automation super company. In terms of connecting other applications together for hundreds of thousands of customers to the effect of hundreds of billions of dollars of revenue and a $5 billion valuation. I've wanted to talk to Wade because for the last 3 years now, he's been leading the AI transformation at Zapier. From the moment GPT-4 launched, he realized it would pose threat and present great opportunity for his company. So how did he react? What has he done? How has he got his leadership and his employees on board? Why did he do a company-wide one-week hackathon? Why has he hired a head of AI now? And a bunch of other questions that Wade can help us answer. So here's Wade Foster, CEO of Zapier, an OG super company. Wade, great to see you again. How are you? I'm good.

How are you, Greg?

Good. Where are you joining us from today?

I am joining from my home in central Missouri.

Did you ever leave?

I lived in California for 10 years.

And then when you made a bunch of money and could afford real estate in California, you decided to leave?

I came back for family and started having little kiddos and aging parents and all that stuff.

A lot of us know Zapier, but we don't really know how You got to be a super company today. So just give us a little bit of the history and origin of the company.

Yeah, founded the company in 2011 with two good friends, Brian and Mike. We were working at this mortgage company in Columbia, Missouri. Brian and I played in a jazz and blues quartet and we'd pitch ideas back and forth. And one day Brian had spotted that if you would go to like the popular forums of these SaaS products at the time, think like Salesforce, QuickBooks, Evernote, Dropbox, there was a very common pattern. Which was, when will you build an integration with X? And there would always be plus one, me too, I want that as well. And then a product manager would chime in and say, hey, thanks for sharing the idea, we'll take a look. Which if you've been inside these companies, it's code for eh, probably not gonna happen. And so Brian saw that and said, hey, I think we could make a really simple tool that would make it easy for people to integrate these products without having to know how to use the APIs. And so that was the very original nugget of Zapier. Zapier.

And you're still working together, all three of you, and now it's hundreds of millions of dollars in revenue, right? Hundreds of employees, $5 billion valuation, fully remote.

Yeah. So all three of us still on the board, all still working very hard to make Zapier a success.

So listen, Wade Foster, this podcast is about super companies, meaning the companies in the future that will win or dominate their industries. My premise or hypothesis is that super companies are ones that leverage inference, leverage AI to the sort of maximum business value, better than their peers or competitors. And I just wanted to check in first of all to see if you agree. Will that be the defining feature of companies in the future?

I think that's a pretty darn good bet. If I was to try and attempt to take the counter position, it would probably be something like, in the future everyone is just so equally good at doing this that it ceases to be a competitive advantage. But I have a very tough time seeing how that is the case in the foreseeable future. Based on what I see on the playing field, there is a big disparity between the people who are being successful with it and the people who aren't right now.

[Speaker:Wade Foster] I'm glad you agree with the premise, 'cause that is the premise of the whole podcast. But you guys, I think, are the original super company. The more I learned about you guys, like I never knew, like a million and a half dollars raised and basically in total capital to get to where you've got. It's impressive. You were remote early. You didn't raise much money. You came out of YC. That's cool.

You're certainly proud. I think running a company can be pretty humbling though.

You are a super company. And so therefore I wanna spend the first few minutes talking about the past, and then I wanna talk about the present, and then I'd like to talk about the future, which is, of course, the future is how do you maintain that super company status over the next 10 years? As you said, how do you leverage inference better than others? Uh, cuz right now the playing field's very uneven, but I wanna go back to the past a little bit. Wade, what are the key operating principles that you've used personally and as a leadership team? When you look back at the past now 15, 16 years, what were the key ways in which you work that got you to the super company status today?

We have 5 values internally that we spend a lot of time advocating for. Zapier has been at its best when we are living those out thoroughly. So one is default to action. I think you see this amongst all successful companies. They're just very action-oriented. They see a problem, they try and fix it. One of the counterintuitive things that I ways I see a lot of folks fail is they often think that the best way to improve is to think hard about the problem and then take action. But often I have found that you take action in order to see what happens, and then you learn from that, and then you take action again. And that's how that learning loop kicks off. I think the second thing that's been really important for us is transparency. We've run this like big distributed team since the beginning, and we have this value of transparency, and this is really helpful for knowledge sharing, and it's enabled anyone new to the company to quickly search and find out how do we do this, why do we make this decision, things like that. It turns out that value is very fortuitous because agents, AI agents, massively benefit from that level of documentation being easily consumed and out there. And so we're starting to get even more tailwinds from investing in that transparency.

Is this showing up in all-hands meetings? Yeah. Is this show— is showing up in documents that you're letting people read that maybe other companies don't share?

Sure. All of our finances are public to everyone internal to the company. So we share the P&L, the balance sheet, the cash flow. Our CFO goes over them in detail once a month to explain, here's what's working, here's what's not. I'm pretty honest about, here are the three— here's some things I think we're doing great at, here's some things I think we got to be better at. And I try my best to also call out not just my own personal successes, but also places where I think I've made a mistake. And hey, we're going to make a course correction here. This is my fault. We're going to go fix it. And I try and encourage that throughout the entire company as well too, where I think one of the hardest things I see new Zappians when they join the company that they have to course correct to is getting used to working in public and getting used to sharing their work before they're ready. There's a personality type that, you know, is a bit more perfectionist and they'll think, oh, it's not ready to share with Wade yet. It's not ready to share. It's not ready for someone to see this thing yet.

Is your sense on that, Wade, that the high achievers are also the perfectionists, or that can you separate the high achieving from that perfectionism? You know what I mean? Because my experience is it's a lot of high achievers who don't want to show work until it's— they think it's really ready for prime time. Have you been able to separate those two kind of qualities?

The people that I've seen be the most successful are the ones that are the fastest learners, and usually the fastest learners are getting lots of feedback early and often. And so they're willing to put their work out there when it's rough and it still needs molding, and they're willing to go talk to smart other people in the company and say, here's a first pass. Here's the napkin math. Tell me what I'm missing. Tell me where I'm wrong. And that learning loop just fires so quickly. And so they're able to scale the mountain much faster than the person who goes off into the corner, shows up 2 weeks later, and they've never gotten a bit of feedback. And you're like, it's not bad, but like, where have you been?

They've been polishing on their own somewhere. Exactly.

Sometimes that works, but oftentimes you find that you've walked off in a direction and now you're not in the same direction as everybody else. That feedback loop and that iteration loop is so critical. That ties back into another value we have, which is growth through feedback. And I think this is about being a learning machine. Part of the longevity of Zapier at 14 years and part of what success we have had has been because we've tried to just keep learning from our mistakes and get better over time. And this takes some humility. It takes some willingness to say, hey, we don't get it right all the time. But with folks that are not successful, you often see them sticking on a thing where you're like, you're just wrong. You need to change your mind if you want to be successful. And so I think that's a big piece too.

And in that value or that sort of, in that way of working, do you try to differentiate between the low risk and the high risk decisions, or do you want to use that style or that operating principle all the time and just manage the risk?

There's the Bezos maxim of one-way doors and two-way doors. I think that's a pretty reasonable maxim. I think the challenge that most companies have is that many decisions become one-way doors, even when they're not really. And so that is the hardest thing is you just have to have the courage to just keep pushing and reminding people that things are two-way doors. I was in a meeting yesterday where the proposal was pretty good. There was probably like 2 or 3 things that I probably thought, yeah, if I was doing it, I don't know if I'd do it quite that way. Like it's probably not so good. But my feedback to the team was like, if the thing you just have right here, if this ships next week, I'll be doing somersaults. Figure out how to make this thing real. That's because I think the action is way more important than if I'm right or wrong about those 2 or 3 items that I wanted to nitpick on.

Is it just human nature to elevate things from a two-way door to a one-way door? Why do you think this happens?

What I've observed is as companies get bigger, especially of companies that are good learning environments, is when something goes wrong, they sit down and they say, okay, why'd this go wrong? And what can we do to fix it the next time? In any one instance, that is a very healthy practice. But what happens over time is this accumulation of rules that are there to minimize the risk of this mistake happening again. And so if you do that for a long period of time, the organization ceases to take chances, ceases to take risk. Everything starts to look like a one-way door, not a two-way door, because you've had all these situations that have learned behaviors over the team where you said, oh man, this bad thing happened, so let's not have that happen again. And so it's really hard, I think, organizationally to not do that exercise because it's a helpful exercise to go through that. And I think it takes usually some bold thinking internally for folks to say, you know what, We had this bad outcome, but it wasn't our fault. We're going to keep doing it the way we were doing it. And sometimes that's going to happen. And we're okay with that. We're willing to take the chance that we might have the same mistake again, because we think that there's also this upside if we keep doing it the way we're doing it. That is a difficult thing, I think, for humans to say, because all mistakes are bad. No, some mistakes are just cost of doing business. Like, you're going to make mistakes.

I often describe startups as one giant experiment. And inside the giant experiment, there's thousands or tens of thousands of small small experience. Some people are just not well suited to, to work in that kind of environment. Some people just don't like the idea of experimenting all the time. They want to experiment.

I know my job, right?

Okay, let's keep going. In terms of the past, other operating principles that you think are critical, have been critical to your success?

Yeah, we have this empathy over ego. I think this has been really important for the culture we've built, where we're trying to make sure that, again, it connects that learning culture. We want to empathize with our customers, we want to empathize with each other, We want to understand what that point of view is. If we let our ego get in the way of that, it really can prevent you from learning. Made Zapier be like a, a fairly like polite but rigorous company when we think about decision-making.

No prima donnas is how I think about that.

The culture tends to have organ rejection on that kind of thing.

That's good.

And then the last one is, has actually just been grown in importance for us is don't be a robot, build a robot. We're an automation company through and through by having that value. That's partly why we have been quicker than some others to jump on the AI train because we said, hey, there is an opportunity here to build a robot. Like we can use this tool as a leverage for our own internal operations, but we can also embed it into our products and give those capabilities to our customers as well too. We're just like jumping in the deep end and saying, let's go figure out what this thing can do for us.

And do you remember that moment, that aha or oh shit moment in terms of AI for you?

I can give you the progression. We thought it might be important a little before ChatGPT. So my two co-founders, Brian and Mike, had gone off, stepped out of their day-to-day jobs, started working as ICs in the company again, and we're like, we're going to go experiment with AI because we think this might be important. My job was to keep managing the existing business, and they had built this text bot where you could just text a phone number and ask it questions. And so like I'd ask, hey, do you have any ideas for my like wife's birthday? I got this weird like pain on my toe. What could that be? And you're basically just talking to GPT. Very simplistic precursor to ChatGPT. It was a cool experience. And so of course ChatGPT comes out later that year in November. That was exciting because we were like, oh yeah, this makes sense. But, you know, I don't know that it was the like flashing red light moment for us quite yet. Instead, our approach was we shared it in Slack in general. We said, hey, here's this really cool product. We like cool products, you like cool products, check it out. But again, very suggestive, very bottoms up. Then over the course of the next 6 months, a few things happened. One, we started to see more and more customers doing interesting things with AI inside of Zapier, things that in the past would have been impossible or very difficult. I remember seeing somebody build an invoice app for his spouse who was doing gardening. And that was the type of experiment that would've raised like a $5 million seed round 5 years ago. And I was like, holy crap. And so you're starting to feel this like momentum building where you're like, something's going on with this, right? Then GPT-4 launches in March of that year. That was the moment. And for us, the reason that was the like pull the lever code red moment was that it was only 6 months between 3.5 and 4. There was a huge capability improvement between 3.5 and 4, and the cost improvements were also massive. And so we just saw that and said, if you project that trend line forward another 6 months, this is the only technology that matters right now. And so we pulled the code red there.

By the way, what is code red? I feel like I should do a code something.

We had never done a code red before, so it was not this like known definition internally. To me, all it meant was like, holy crap, this is important. We need to do some different stuff.

Did you think it was risk or opportunity or both?

It was clearly both.

You saw both.

Totally.

Hence the code red.

Exactly. We sat down and just started to sketch out like, what are the things that need to change here? We looked at all the roadmaps and said, okay, instead of being very suggestive, we started to say, here are like 3 very specific places that we can embed AI into the product experience. To be better. We started to look at the operations of the business internally, and we started to say, okay, here are a handful of places that need to be rethought in the age of AI. Thing that really worked was to say, how do we actually just get the entire employee base to be much more aware of what this technology is and why it is important for our products and why is it important for them to use? And so we paused the company for a week and we did a hackathon with everybody. And that took us from around 10% of people using AI. After that week, it turned into just over 50%. So in one week, we got half the company basically addicted to this stuff.

I've never heard this before. This is a genius idea. Let's talk a little bit more about it. Pause the company, say more. What do you mean by pause the company?

Sure. So basically we said, hey, all these roadmaps you're working on, all the like marketing campaigns you're doing, all the stuff that you've got going on, Pause. And we want you to go build with AI. Now, Zapier has a long history of doing hackathons, so we know how Zapier-style hackathon works. And to do that, there was a few things that had to happen. First, you had to get your procurement team like dialed in, cuz you can't do a hackathon if you don't have tools. So my co-founder Brian did a bunch of like quick little Loom tutorials. You have to remember there wasn't like a lot of education on this stuff in 2023.

I remember it cuz that's why I pivoted my business to do AI education. He was just saying, here's how I did it.

Here's how you hit the API. Here's Here's how this works. Here's what's like, here's why it's like a normal API. Here's why it's not like a normal API. And just coaching people, especially developers on how to do this stuff. And so we told folks, just go build something. If you're in engineering, build the thing. If you're not in engineering, go play with the tools and figure out like how it could help you with a task that you have to do on a day-to-day basis. The other side of the hackathon, we always do a demo day. And the demo day part is always really helpful because one, it creates some accountability. You got to show off what you did. So you can't just like sandbag the week. But more so than the accountability, it's the learning. Because now everybody gets to see, oh, you did that? How did you do that? I didn't even think to do that. Why did you do it that way? And so that learning loop kicks off because now you're starting to learn from each other. I've talked to a number of companies who have been going through their own AI transformation, and one of the most common patterns with the successful ones is they do some form of hackathon. It just is a very simple way to get people excited about this stuff.

So let's bring it up to present day. I know you guys are pretty public about this. You hired a head of AI, which is interesting, right? Because you had an early start. You're a sort of an automation company, as you said, very software engineering, product-focused. So it feels like you were still frustrated with the lack of progress, or you thought you could do more in terms of workforce transformation, in terms of hiring your own internal kind of head of AI.

Our chief people officer took that head of AI transformation role. We held out on naming that role. Quite a while. And the reason why is I never felt like the way to solve this problem was to make it one person's job. This is a technology that everyone needs to learn how to do, just in the same way everyone's got to learn how to type, everyone's got to learn how to use email, everybody's got to learn how to use a document processor. You're going to have to learn how to wield AI in whatever way that looks like in your role. And I felt like if you named a person that all of a sudden people are like, oh, that's that person's job. So we held off on it. So all this stuff is like raising the floor internally. The next thing we observed was, okay, we've got the floor raised quite a bit. There's a mile-long list of ideas at this point in time of things we could go do to improve the product, to improve our internal operations. And they're not always the type of projects that a motivated individual could just show up and snap their fingers and be like, I've solved it. Ta-da! They often involved change management and org dynamics. And I wanted to raise the ceiling. I wanted to see this change more differently. And it turned out that Brandon, who was our Chief People Officer, his team, they had been some of the fastest to adopt these tools, and they were already running experiments in recruiting and performance management.

Wouldn't think of HR as you're like, I would not expect that to be happening necessarily in HR, but here inside of Zapier, it was happening.

And so I thought, huh, I really would just like to see what Brandon has done in HR happen more broadly across all these other areas. And because the problems we were having were not necessarily ones of technical expertise, it makes sense to put the person who has been doing that successfully in charge of those things. And so that's what led me to say, hey, Brandon, I want you to go figure this out for the whole company.

And does he get headcount to do that?

There is a central team inside of his crew that's like very small. That are kind of like AI engineers that can go in and help people out. But by and large, most of it is him going to different teams and figuring out who's the DRI, who's the person that's like really responsible for this stuff, and then bringing that group of people together to trade notes, share learnings, push on systemic gaps that are causing everybody to have problems on a certain problem internally, that kind of thing.

So what do you think he's really doing? Is it like accountability? Is he inspiring? Is he finding people that know they should be doing it, but don't want to do it? Or is he breaking through employee anxiety? What's he really solving for?

It's basically like corralling the troops around the high priority problems and making sure that those things happen.

You mean, where can we deploy AI internally into significant workflows and really drive efficiency or value?

Exactly. The types of things that weren't happening by default because it, it was too big for just a smart and powered person to go figure it out.

And do you think about AI, Wade, as an efficiency lever or a growth lever or both? And how do you toggle between the two when you think about your AI strategies and your product plans and so on?

Well, I think it's both, but I think the biggest gains we found have not necessarily been replacing existing tasks, but by doing things that we couldn't have done before. The narrative today in the market is, oh, hey, this is gonna one-to-one replace a person's job. Some of that definitely does happen unquestionably. The cooler things that I get more excited about are these problems that when we go look at the history of our business, there's certain problems where we just said, we just can't do it, that just doesn't work for us. And now we're able to go back and look at it again and say, maybe we could. We have a high-volume business, we're getting hundreds of thousands of people signing up for the product every month, and as a result, most of them don't ever talk to a human. Instead, it's only our enterprise prospects that actually get somebody there on the sales side, on the support side, that can sit down with them and help guide them through this experience. Most everybody gets nothing. What does it look like if you can actually put agents on that next segment of customers, the customers who are maybe not quite enterprise but like one layer down? Could you actually provide like a human AI-assisted version that could help those people? It turns out you can. What if you go to the next segment down? What if there's like a pure agentic experience and you could provide something good for that segment? Turns out you can. And so these are the places where it's just opening up lots of opportunity that it just didn't make economic sense in the past because you couldn't make it work. And so those are the types of things that I think are really exciting, is when you can use the technology to actually innovate on behalf of your customers, not just simply make the same old thing you've been selling before faster, better, cheaper.

So Brandon's job is maybe to go identify those things, maybe kick off the pilot or get the prototype working, and at some point the business units need to own it and he pulls away and goes on to the next one. Is that how you—

100%. This is not meant to be like we show up and build a bunch of stuff for you and then you don't have to think about it.

Do you have a head of AI in 2 or 3 years, do you think?

Maybe. I don't know. Hard to say.

Like, I think the transformation stuff is probably Is there a point where we're all so fluent with these tools and the kids coming outta college are, of course, AI native almost, the transformation's done? Or do you think these technologies continue to evolve so quickly and powerfully that this human transformation effort is ongoing for years?

As we think of it today, we probably don't have it in that same version. I think you have these moments in time where there's a big paradigm shift that requires a little more oomph where it's like, hey, we gotta all come together to rally this. And then once you get the momentum going, you can go back to, uh, more normal ways to look at it. That also assumes that there's not some other bigger, better change happening. Sure. Like, I think when you have these moments in time where it is such a jarring delta, you do need a group of people to come in and say, we're gonna be the stewards of helping all of us make this transition. So to the extent that exists, yeah, I think you probably need it.

Okay, let's look forward. What's going on? What's your sense of this moment and what's happening to anyone in the software business?

My co-founder Mike runs, uh, Arc Prize. And what Arc Prize is, it's a prize that sits on top of the Arc AGI benchmark. And so it tracks model improvements. And so if you go look at that benchmark, the models that launched in late last year, November of last year, they all showed massive improvements on these benchmarks. And benchmarks obviously are flawed in a lot of ways. They can be gamed, et cetera. But you also saw it in the people using the models, particularly developers who are using these tools all day, every day. They felt like, hey, there is something a lot different about these things. The agents are able to do harder tasks. They're able to operate for longer. Periods of time without checking back in with the human. As a result, a lot of these agentic experiences that folks have been trying to build in 2025 that were like sometimes working, sometimes not, all of a sudden it was like, ooh, these things work a lot more often. And so that to me was the big shift. And of course now everybody is trying to figure out what does that mean? What are the ideal product experiences of the future? And of course you've got the investor landscape that's trying to figure out like which companies survive and don't. And then you've gotta, whole media landscape that makes money off of clicks. So of course it set off a fury of pontification about that stuff.

It does seem like we have crossed another capabilities barrier, right?

I think unquestionably.

Are you worried about it for you, your company?

I'm pretty excited about it, honestly.

You're not worried that customers will figure this out themselves? Like, say more. Why are you so excited? You could peace out at this point. Shit, a lot of CEOs I know are like, hey, I think I'm gonna Head to the golf course or something, or the pickleball court, or I don't know.

If you were fancying yourself as like a tech founder, tech CEO, and you thought, hey, I'm never gonna have to innovate, you signed up for the wrong job. Your products have a shelf life. They always had a shelf life. And so you're always having to figure out like, what's the next innovation that needs to happen? And now I think the best CEOs are looking at their customer base, their distribution, the existing products that they've got, and they're saying, okay, How do I deliver even better experiences for my customers that take advantage of this technology? And what can I do that is differentiated from what these state-of-the-art models can do themselves? And the thing that makes it fun is that these things are really cool. Like, you can do some stuff that was just darn near impossible. And so I think that part is what makes this really exciting. I understand the anxiety and the fear. Shoot, I'm not immune to that. It's only natural that if you've built a business or built a career or built anything on the back of a particular way of working, now you might have to go figure out a different one. And so like, I understand where that comes from, but I think we're all far better for this technology existing than not.

I think we have to see, right? There's going to be a bunch of unintended consequences and costs, and there'll be a whole bunch of benefits too that we don't know today. I think we still have to see how that balance sheet shakes out.

When I look at my own personal life, I have experienced mostly positive gains. I've got a whole health system set up on these tools now where I'm tracking these things in a way that I couldn't. I've got family members who have had difficulties with the medical system who are now getting clearer answers to problems. Now they're getting better care. I don't see how that's not an unquestioned positive.

Back to software for a moment. Is it enough to say that we're gonna have— the shelf life is shorter and the differentiation is harder? Is that one way to just summarize what's gonna go on the next few years? For software companies?

I think that feels like a pretty good bet. It's definitely going to be different. It's definitely going to be different. I heard Marc Andreessen talking about this where they said if you go look back at all the people talking about the internet in like the '90s, or if you go look even further back talking about technology and other phases, everything folks said and thought was wrong in some way.

Most of the predictions are bullshit.

Yeah, exactly. It doesn't mean there's not predictions that are more right than others.

Let's think about Zapier in 10 more years and you're still a super company. That's our hope. Anything that you're doing differently now that you think you need to do differently in terms of those core operating principles or the way you work personally to make that happen?

Our operating principles are pretty durable. That don't be a robot, build the robot, can't hide from it. You have to lean in. You talked about like the competitive advantages, people who wield inference better than others. I believe that. I think there's a whole bunch of implications around how we build and operate teams. I was always a firm believer in small teams. Now I'm like, is it a team of one with a bunch of agents? Like, how does that impact how organizations are built and shaped? And I think there are some real implications there.

Let's make it real personal for a sec. What's on your to-do list?

Every hour of my day where I don't feel like I have like agents running or I'm like messing around with a product.

You're not being productive. Is that it?

Yeah, it feels like I'm missing out on a chance to learn. That is informing a lot of like how I think about what are the products that we need to build. What are the experiences customers are going to want to have.

I've got a couple of friends who are like, they literally have to force themselves to stop building. Mm-hmm. Or they find themselves like at midnight and they're like, I got to stop and take a break and walk outside and like recenter myself because they're just like, this is crazy.

There is a like addictive behavior to working with these agents. At the same time, if it replaces doom scrolling with building, I'm like, all right, sign me up.

Of course, these companies know how to make these things addictive.

They sure do.

That's Silicon Valley's business model. How are you going to measure leverage on inference? Do you think about it now as it's a certain amount of money each month or each year? It's going to be a big line item, right? On the expense side, I would imagine.

I think ultimately this is not any much different than the way we've done in the past. You know, if you're trying to measure the ROI, if anything, how fast do we answer our customer support tickets? What is the quality of those tickets? How much does it cost to deliver that quality? You have same set of metrics. That are telling you like, is my business working or not? And then on the inference side, that's just a, that's an input metric because you're trying to figure out, was it helping me do it faster? Is it helping me do it cheaper? Is it helping me improve the quality? And so you're comparing and contrasting that with other ways of working it. But ultimately the metric on the other side is still renewal rates and stuff. Yeah. All that kind of stuff. So I don't think the metrics, the measurement side changes too much. It's just the input side of that equation. Has different variables now.

And are you looking for executives now that understand that this is the key determinant of building a super company, which is to basically deploy inference in a more positive way?

I think this is a real challenge at the moment, is that you've got a current crop of executive leadership that grew up running these companies in a particular way. They learned a particular playbook, and in the past you often hired them because of what they knew. They knew these things, they knew these playbooks, they knew how to do this stuff. And now knowledge is not really the scarce thing. These models are way smarter than I am, but the skill in how to wield them and what that means for the organization, that, that's really interesting. I think it is pretty tough to find, you know, people who have the right combination of hands-on experience building themselves and also have the capacity to inspire, lead.

I always say that leaders are made in these moments, especially for, for young leaders that want to step up. This is just a great time.

Yep.

Okay, Wade, a couple things I wanted to get your take on. Revenue per employee, is that an important metric still? Was it ever for you, and is it going to be in the future?

We've always tracked it. I think it's a pretty useful heuristic to make sure you're not letting your spend get too out of control.

Yeah, and it seems like it still will be, right? Companies that leverage inference likely will have strong revenue per employee metrics, I would assume.

I think so too, yeah.

What about this idea SaaS is dead and everybody's going to build their own CRM or their own ERP or their own What's your take on that?

SaaS is not dead. It's definitely changing. Yeah, I don't think that most companies want to build, but they especially don't want to maintain software. You're going to have SaaS in the future. The business model of SaaS is probably going to be pretty different. There is definitely a lot of challenges with a seat-based model. There's a lot of challenges with the margin profile. There's a lot of things that I think are causing people to look at it a little differently.

I always think about it, Wade Foster, as the technology innovation is tough for some. The business model innovation is the killer for many, right? And it's the combination that brings down incumbents. The better product and the better business model, typically the more customer-friendly business model, that's where the incumbents just really start to scramble, right?

PGYC founder, he always says the best companies are like a cockroach, and that makes them unkillable. And I think in moments like these, there's a lot of incumbents that have gotten fat and happy off of like these big fat margins, and they're used to operating a different way. And that's what kills them more than, oh, the products stop being good. It's all these other things that it's like, we were just not unable to change our business model to meet the moment.

All right. The last one, the billion-dollar one-employee company. What's your take on that? Will we see one?

I think we probably will. I, I maybe softened my stance on this a little, but I do think that we're gonna see this. How durable it is, that's the part that's harder for me to suss out. I think you can get to a billion in ARR because the ramp rates on some of these things have been just so insane. But to keep it, you got to fend off everybody else who's going to say, wow, you can make a billion dollars with just one person? That's going to create a lot of good competition.

If it's that easy, we should all be doing it. Lots of competition. So no sense of punching out for you?

I'm ready to go.

Awesome. Love it. Thanks for joining us.

I love it. Thanks for having me, Greg.

So as I expected, Wade delivered on this promise of helping us understand what it takes to transform into a future super company. The 3 takeaways that I got, the first was brilliant idea, pause the company for a week and do an AI hackathon. We know that AI hackathons are particularly effective, but this idea of pausing for the whole week and letting everyone play with AI and then asking them to show their work was brilliant. And it raised AI proficiency levels from 10% to over 50%. In a single week. I also like the fact that Wade admitted he needed a head of AI, and 2 years into this transformation, 7 months ago, he asked his head of HR, one of the most innovative AI builders inside the company, to become the head of AI transformation in terms of AI's capabilities inside of Zapier. And then finally, it's obvious Wade's leading from the front. He's not one of those CEOs talking about using AI at the all-hands meeting. Wade is building and experimenting and prototyping with AI himself. In my opinion, in this moment, the head of AI for every company, Fortune 500 on down, is actually the CEO. You need to walk the walk here, and Wade's doing that. In the meantime, you can find me at gregshove.com where you can sign up for my newsletter of the same name, Supercompanies. And if you got a second right now, leave us a rating or review. It helps others find the podcast and validates me with my kids. Toxin.