Hello and welcome back to the Cognitive Revolution. Today I'm excited to welcome Wade Foster, co-founder and CEO of Zapier, back to the show. When I last spoke to Wade in September of 2024, some 400,000 customers had already used Zapier to delegate more than 100 million tasks to AI. And YC President Gary Tan was calling Zapier the AI-powered knowledge worker of the future. Since then, models have of course become dramatically more capable and Zapier has built out a full AI portfolio, including agents, chatbots, an MCP server, an SDK, and an AI guardrails product. And yet, somehow, white-collar work and the world as a whole have changed much less than I would have expected. With that in mind, I wanted to hear not only about what Zapier has built and how it's continued to evolve as a company, but what Wade and team have learned about how businesses across the economy understand and use today's AI tools.
At a high level, Wade believes that most people are now settling in to using a single daily driver, whether that's Claude Code, ChatGPT, Grokbot, or in Wade's case, Cursor, and that platforms like Zapier will need to adapt by making their tools available and effective in those environments. Practically, he observes that models still struggle with many business tasks, as illustrated by Astra setting a new high of just 40% success on Zapier's automation bench, which consists of roughly 600 knowledge work tasks across marketing, sales, HR, and operations. He also argues that many tasks that people are delegating to AI would be better done with deterministic code and that for a while longer at least there is therefore tremendous ROI to time invested in structuring and validating workflows.
We then go on to discuss what Zapier is doing to help people recognize exactly what AI might be able to do for them, starting with his own weekly automation, which Wade says they will soon productize for customers that reviews his activity across Gmail, Slack, the browser, Cursor, and more, and then proposes specific tools and workflows that he should be building. We also talk about how Zapier is implementing recursive self-improvement loops internally and how much value they're finding in running multiple different AIs on the same problem. Why Wade chose to put Zapier's chief people officer in charge of AI transformation but wouldn't necessarily recommend that strategy to other companies. How Zapier is moving toward public-by-default communications to make more and more context available to AIs. Why they still don't limit individuals' use of AI but have created dashboards to help employees better understand and manage their own usage. How Zapier, which holds a huge number of high-value user credentials, is thinking about security in the context of rapidly rising cybersecurity risks. And finally, how Wade thinks about co-authorship between humans and AIs, with the upshot being that he believes individuals should use AI to help improve their writing and shouldn't be afraid of being outpaced, but also that it's critical that people be prepared to explain and stand behind the work that they ship.
With that, I hope you enjoy this very grounded and highly practical conversation about making AI automation work for people outside the AI bubble with Wade Foster, co-founder and CEO of Zapier.
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Wade Foster, CEO of Zapier. Welcome back to the Cognitive Revolution.
Yeah, thanks for having me again, Nathan. Boy, what a difference a not super long period of time makes. I, you know, I can't believe it. Every time I have a returning guest, it's an opportunity to look back and look at what the state of AI was, models, outlooks, you know, what the possibilities were, what the capabilities were at the time. And suffice it to say, obviously, a lot has changed. We only have one hour today, so I'm going to try to discipline myself, not talk too much, and give you most of the air time. I would love to start off with just first of all an observation that like many ambitious software companies you guys at Zapier have been really prolific and I would say you've kind of built everything in the sense that you now have in addition to workflows which of course can call out to AIs you've got agents you've got chatbots you've got an MCP you've got an SDK you've got a guardrails product and probably more you know that I didn't even identify or mention. What have you learned by building all that stuff? Like what has taken off and resonated maybe better than you thought, what has been slower than you thought, and what's the sort of synthesis view that you have informed by these different product efforts and their relative successes?
Yeah. Well, I mean, shoot, I was on 18 months ago, and if I think back to that time, what I don't think we knew yet is what the shape of these AI experiences that were going to take off. Were we going to see, you know, AI infuse into like all the products that we already knew and love? Were we going to see these new products take up? Were we going to see new stuff out of the labs? That was going to be where AI was going to take off. And what would seem to have happened at least when we look at our own usage is that most folks seem to have adopted their own daily AI driver tool. Maybe this is Claude Code, maybe this is Cursor, maybe this is ChatGPT, you name it. But that's kind of where people do most of their work. And by and large, what that means is tools like Zapier MCP or any MCP server, tools that bring your context, bring your data into that person's daily driver. That feels like the way knowledge work is moving. This split in the market where there's some folks that are still trying to be like, 'Ah, we're going to force people to build agents on our own platform or we're going to bring them over here and that's kind of where things are going to get done.' And then you see like the Salesforces of the world that are like, 'We're headless, you can bring it wherever you want,' etc. And I definitely think that latter is the winning strategy. It just feels like that's what us as consumers of these tools want and that's where all the growth is. We kind of want our daily driver, we want to bring that context in, and we'll be able to manage it all in one place. Doesn't mean that there's not going to be tools that call out to all these third parties. I still think there's plenty of room for applications and tools that exist, but you kind of have to integrate with that person's core daily driver if you really want to be a part of their day-to-day workflow. And that feels like pretty big new learning for me in the last 18 months.
Yeah, that's interesting. So does that mean then like what's your daily driver? It sounds like you're kind of positioning Zapier not as being a daily driver, but more as being like an Uber tool for whichever daily driver you choose to use.
Yeah. So I mostly use Cursor every single day. We have an internal tool that a lot of our employees use as a sort of daily harness, but that also has an MCP associated with it. And so I have that pulled into Cursor. So it's using the data from that which has a virtual file system that acts as a context layer. It has automations that sit on top of it. It has apps that you can deploy. All the kind of things that you might expect from like a modern AI-capable tool. I just happen to use a lot of that stuff inside Cursor.
Very interesting. So okay, I had a conversation with Andrew Lee from Tasklet. He advanced the I thought provocative idea that in his mind only three kinds of software companies survive in the big picture and that kind of everyone is building the same thing which sometimes gets described as like the Mecca suit for models. So he described himself as kind of trying to earn a place in, you know, eventual winner circle of this like horizontal layer that sits on top of models, enables them greatly by like providing all these different tools and access points and guardrails and, you know, whatever else the case may be. And he thinks that there's like not a huge number of companies that win in that case, but that that layer, even if there's not too many companies ultimately in it, is super valuable because nobody wants to be beholden to just one model. They want to be overly locked into a single model provider. How would you compare and contrast your worldview against that summary?
Well, I definitely agree with that last statement. I think it is becoming more and more obvious that you don't want to be beholden to one model or one company's suite of models. We see this with our own Zapier's automation bench. We have a benchmark that measures all these models on automation tasks and last week Astra came out, GPT-6. It's the new state-of-the-art on that model. It performs about 40% of the tasks accurately which is the highest that there is. Now, it's more expensive than say something like Gemini 3.7, which does pretty good, but does it at a fraction of the cost. And you kind of have this curve that sort of exists where you're trying to figure out how much am I willing to pay for incremental, you know, performance on certain tasks. And as a result, I think any modern organization wants the ability to make those trade-offs to say these tasks, it's good at, you know, I can sort of pay this rate and get 100% of these types of tasks completed, but for this type of task, I need to maybe move to a state-of-the-art model to do well on it. So on and so forth. And so I do think that companies are going to look for the, you called it the Mecca harness or whatever, the sort of tool that allows them to swap in and out for different workflows of their choice. Which to that end I certainly believe that there's a lot of innovation yet to be had on the application layer but how those applications are used I think looks very different than the last decade. The last decade was SaaS and there's like all this sort of explosion of SaaS but now it feels like there's this almost explosion of headless tools that is happening and of course you have this new thing which is that your harness itself can build some of those tools and so the sort of build option is more readily available than it was in the past. Now, what's not as obvious to me is yes, it can build it, but should you have it build it? Because now you're accepting a certain amount of maintenance, a certain amount of reliability, a certain amount of uptime that you know may not actually be like the best thing for you in a given circumstance. And so, I actually think there's still a lot of innovation to happen at that application layer that we just haven't seen yet. I think everyone's kind of trying to figure out what that looks like. I think the model companies have a little bit of a head start, but they're going to struggle because they can't sell tokens from each other or from open source or all that sort of thing. And so it does feel like there needs to be a third party that helps you wrangle all the capabilities that are out there.
Obviously, Zapier came from a history of like very structured workflows because if you didn't fully encode what the workflow was supposed to do, there was no ghost in the machine back when you started to figure it out on the fly. Now I'd be curious to hear a little bit about like how would you describe the tasks of automation bench and like where are the models good, where are they not good, and then maybe you can describe how usage of Zapier is changing qualitatively like how often are people still doing box-by-box, you know, defining of workflows, how often are they prompting an AI which then turns their, you know, kind of thoughts into a structured workflow, how often is it happening through an MCP where it's the model deciding, you know, to even use Zapier given a range of options? And maybe there's even more there that I'm not intuiting.
You bet. Yeah. What automation bench measures is tasks that are kind of like the following. So, here's an example from our site. We just closed the Meridian Core platform deal. Market it as one and route it to the win notice in the right team per our routing policy. Confirm the account tier from the account hierarchy spreadsheet. Convert the currencies if needed and check for any open support escalations. So we have probably 600 tasks that are of that variety that describe like a normal knowledge work task across a variety of disciplines: marketing, sales, HR, operations, you name it, etc. So that's kind of what it's trying to measure. And as you can see, the models are getting better at it. But this is by no means a saturated benchmark yet. And what we are doing at Zapier is we are making sure that when you install Zapier alongside of your agent that you're actually getting better output on these benchmarks than you would if you were just using the models alone. And the reason you do that is you're teaching the model. What folks are doing is they're in their daily driver and they're saying, 'Hey, I want you to go build a workflow.' So they're calling Zapier MCP and it's going to say, 'Hey, I'm going to go build out that workflow.' And in some cases, I'm going to write code to actually complete that task so that it is deterministic, which means lower cost, better reliability, etc. And then I'm going to invoke an AI or build an agent for the parts that really require reasoning. And when we look across like even our own and customers' usage of agentic products, the vast majority of what people are using an agent for, 80% in fact, they probably should be using actually old-fashioned deterministic code. You really only want the AI to reason over the things that you need it to reason for. And so I still think there is a huge amount of work that is happening inside of these production workflows inside of a company that you shouldn't actually try and be delegating it to an AI. And we'll see how long that lasts. Obviously, AIs are getting better and better, but I struggle to think of a world where there are just certain jobs that like deterministic code is still going to be more reliable and cheap. And there are certain jobs that it cannot do. And for those, you need the ghost in the machine, you need the AI that can tackle those tools. And so, I think the right thing is you're trying to teach the agent how to go do that on its own. And so when you talk to it, it goes and builds those things in an optimized way instead of saying, 'Hey, I'm just going to build an agent that runs agentically every single time.' It has a better sense of which is the right tool for which job.
So in that one example that you gave, if I understood it correctly, first of all, it sounds like there's probably a ton of context that comes in the sort of test environment with that short prompt, right? It has to navigate, find the policy and parse that and all those other things kind of require additional information finding and understanding. Also struck me that that sounds like the kind of thing that only happens once so or, you know, you might acquire a handful of companies but you're not going to acquire like the volume of companies that one would traditionally associate with a zap. So like how are you seeing usage change or maybe what advice would you give if you're like, 'Okay, I used to think that for me to go to all the trouble to make a zap for something, I need to at least expect that zap to run, I don't know what, 500 times.'
That example is one, maybe I misspoke, but that example is one that happens every day, if not multiple times inside of a company. You close a deal. If you're any good organization, you're closing deals all day every day. There are so many workflows inside of a company that are kicking off all the time perpetually. And in some cases, they're happening at a rate that humans can't keep up with. If you're operating at the scale of like some of these companies that are excellent to consumers that are having purchases happen like many times a second, you have to use automation. You have no other choice. You cannot put humans in the loop for these tasks.
So, have you seen big shifts in terms of people moving away from blocking it out themselves and having AI do that? And have you also seen the scale threshold at which people start to think to use automation software come down substantially because the AI can do the setup?
Yeah, I think the big new opportunity is to have the AI do the build. Like, you know, it is able to sort of work through the logic much, much faster than a human and the idea of building a no-code, it feels antiquated to me. It's like the new no-code is code. What I think is we have learned is that humans still very much benefit from visualization of those workflows though. That visualization helps them verify is this thing doing what I intended it to do. It acts as documentation so you can share with other people and say, 'Hey, here's the thing I built, here's what I'm doing.' If you think of Zapier in the old school as like this thing that had a bunch of boxes and you're coming in and using that to configure it, by and large I don't think that is the way people are doing it now or even in the future. Instead they're talking to the agent and having the agent go make those edits for them. And then the other nice thing that is happening in the future is you're going to see that stuff that gets hardened into a deterministic workflow. But even when it fails, you can actually have then the agent go troubleshoot and fall back and say why did it fail? What happened here? And then it can use its reasoning to fix the workflow or to fix that instance of the workflow. And so you start to see that the agent almost take over the human role of building and maintaining it, but what is actually running is still a very deterministic workflow with AI interwoven in like the places that is most necessary.
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So when you go to the 40% or so success rate on automation bench, what can you bring that up to as you move from, you know, just asking Claude to do it to giving Claude Zapier and, you know, potentially iterating a little bit on initial failures? Like what kind of lift do you see and what sort of token savings do you see over time?
You're going to have to wait for automation bench v2 for that because that is exactly the right question is to see hey what happens when you give these models access to tools like Zapier, how much higher can you get those efficiency and or how much higher can you get the scores but also how much can you pull costs down, how much faster can they go because I think there's many dimensions when you give a model access to tools and capabilities and certain things like that where it's going to score better on these benchmarks at the end of the day and that's where I think the application layer has a lot of room to go run is to say what happens when you give access to the model to these extra things and the model's just going to get better at performing against a whole host of tasks. How do you put the model in position to be successful when something goes wrong and the model has to come in and like troubleshoot, debug? You know, there I'm sure that you've done who knows how many things over time to try to set the models up for, you know, within one generation of model haven't come back and like be able to fix the things that it got wrong the first time.
Yeah, I think there's two places that are interesting to talk about here. First is when you think about building these workflows. So the thing you want to do there is you want to make sure that the model can properly identify when it needs to be using AI versus where it should just be writing code. And there's many such examples where you don't want the agent to actually orchestrate the task. You just actually want it to run code that exists before. And so a lot of that is when the agent is building its plan. You want to make sure that the plan encodes like, hey, this is like what the optimized workflow looks like and it has a good plan to do that. The second place is then what happens when things break and how do you recover from that? And here we've done a fair amount of work just even our own support or trying to figure out how do we actually troubleshoot on behalf of customers and then how do we bake that troubleshooting back into the core product. And here a lot of it just boils down to one of the big learnings is we just have multiple agents run at it. And our current, we have this auto email program that's running right now. And for that it spins up five agents, independent agents that evaluate the troubleshooting situation. And we notice that when four of the five agents tend to agree, there's a pretty good chance that's actually the issue that is hitting into it. And so there's a lot of like data and measurement that just goes into that where you're just trying to honestly kill client and see like how do we actually try a different model, try a different prompting technique, how do we measure that stuff when we have humans that are editing like auditing the output. And so when it fails to and what those humans do is they basically either give a thumbs up approve or they give a thumbs up rejection and a reason why. And so those reasons why kick back in and help improve the overall system. And so you're just kind of going through this loop over and over again to continue just optimize your ability for that workflow to have a higher chance at getting the outcome that you want.
Is that loop and all the data that you've collected over time which I guess must be quite massive core to Zapier's defensibility these days? Like how do you think about what the, you know, what is the hill that you've climbed that will be hard for others to follow you up.
Yeah, I think that's a big part of what it boils down to. It's trying to identify what are things that are unique to you. What are the things that others can't replicate easily? And so certainly for us, we're really good at automation. We have tons of data on what it takes to do. We hook into everything. And so, how do we take that data to actually make our products better? And it has to be meaningfully better than somebody who doesn't have access to that could do. And I think this is where a lot of the incumbents have an advantage is if they're able to yield that to actually build a better product at the end of the day that the models alone can't do. And there's so much room for this because the models are they're great generally out of the box. But all of us even experienced this in our own just lives where you just you're hooking up to your Gmail inbox and now you start asking like hey tell me about help write an email and it's like it automatically does a better job of writing email because it sees how you write email and you've done very little in the way of trying to tune that workflow. It's just by hooking up your company's data, all of a sudden the models get better. And yeah, I think this using your own sort of company data to build an edge is a pretty spot-on technique these days.
So, does that look like a big retrieval problem for you? Certainly in my email, right? It's like I've got a lot of history and finding the right example to take inspiration from is probably especially if it's just using Gmail APIs and doing keyword, you know, kind of constructions that's probably just as hard if not harder than actually taking inspiration once you found the right documents to take inspiration from. In my personal context, I've tried to help it out by like exporting all that stuff, doing embeddings, various kinds of, you know, alternate search approaches so that hopefully the right content comes to the top more often. If I'm putting two and two together, right, it sounds like at Zapier you probably have like a database of like a zillion things that have gone wrong over time. And then to help inform the model of like how to fix for this particular situation, you got to dig in and find analogous situations. What does that look like? Is there an embedding model that would do a good job of that or have you had to kind of innovate at the retrieval stack layer in order to make that work well for such a use case as Zapier?
Yeah, coming back to the support example, a lot of it's just about taking the example that comes in, give it the old thumbs up, give it the thumbs down, provide a reason why, and just doing that over and over again. And so what that looks for our customers is just like giving them the same tools to do the same. A lot of this is not particularly fancy at the end of the day. It's just take the example, did you like it? Did you not like it? And just rinse, wash, and repeat.
Interesting. Okay. How do you think about competition in general? Like it sounds on the one hand we should I think all be worried about frontier model companies eating our lunch. Even me as a humble, you know, AI podcaster, I look at like NotebookLM and I'm like they're coming for me in my, you know, rather unlucrative niche. But you could, you know, say, well, those guys, they're only going to sell their own model, so they're kind of a different type of animal. We don't have to worry about them. There's like a variety of new tools that are coming online to try to be the Uber tool. I've done episodes with Composio, for example. ZeroXYZ is kind of out there. And then there's like, you know, kind of other daily driver or sort of agent builder type things. And then there's just kind of like other big incumbents, you know, you mentioned Salesforce and it's to some degree it's like maybe just big incumbents with lots of data, lots of resources kind of all end up colliding with each other. Like which of those kind of classes of competitor do you think are actually the ones that you need to be most concerned with?
You know, it's an interesting, we're in an interesting period for sure. I think to your point, everyone gives a lot of attention to the labs and tries to understand what are they doing. And I think that is important. You want to understand what are they going to be great at? What are they going to hill climb at? But I still remember PG's advice when we were going through YC where, you know, back in the day it wasn't hey what if Anthropic or what if OpenAI builds you, it was what would happen if Google built this, what would happen if Facebook built that. It was always like the question. And the thing that PG tried to instill in folks was you're not often competing directly with Google. You're not going toe-to-toe with Larry and Sergey. You're not going toe-to-toe with Zuck. Often times in the things that entrepreneurs are trying to build you're going toe-to-toe with a potential mid-level product director who's trying to get a promo, might be there for two years and then bounce, etc. And the reality is OpenAI, Anthropic, they're big tech now. These are not small tiny startups. Yes, they're obviously built capable of building incredible things and they're going to be best at the world at these foundation models. They're going to be incredible at that and they will have good products elsewhere, but they can't build everything. They just can't. And so that's where I think, you know, it gets really a wide open field. And I look around and it is a little confusing because you've got everyone that does seem to be building everything. There's a sort of sea of sameness out there that is a real challenge at the moment. While at the flip side, you go talk to the average user of AI tools and they're candidly not doing much. They might have used ChatGPT or Gemini. And so to me, I think most of us our competition isn't each other. It isn't the tools that you talked about. It's do people actually know what to do with these tools yet? They just haven't adopted anything at this point in time. And the real challenge is can you actually get your hooks in somebody whose only experience with AI is using Gemini in the default Google search and or they're using Microsoft Copilot at work. That's where most people are. And I think that can get so easily lost in the shuffle if you hang out on X all day because on X we're all just hyper aware of what model came out, what tool's gaining traction, who just raised a huge bunch of money and we're keenly aware of these micro differences between different products and most folks just don't know that. And I think the challenge we have is really making sure we're sort of keeping those two competing thoughts in our head which is yes we do have to be better at some dimension than like all of the sea of sameness and yet at the same time the opportunity is massive to just go
Educate the masses on how these tools can work, and these markets are enormous. The market for automation was orders of magnitude bigger than I ever thought it was when we started the company 15 years ago. It's probably a thousand times bigger than what we set out. And so there's plenty of room for us to go solve problems for customers who, candidly, they don't know about any of the competition you just rattled off. And so I think that's the biggest challenge for many companies today.
Hey, we'll continue our interview in a moment after a word from our sponsors. Today's episode is brought to you by Anthropic. By now, you know my story. Claude drafts my intro essays and I rewrite them. Not because the drafts are bad, but so I can stand behind everything I publish. Well, I have an important update. Claude Fable 5 is the first model to have me rethinking my rule. Today, I now think co-authorship, not sole ownership, should often be the goal. Where the model excels, rewriting its work can be more about vanity or a misplaced sense of duty than integrity. I feel it most in songwriting. I'm no lyricist, but I'm good with a song concept, and Fable writes some amazing verses. I give it feedback on its misses, and I push it to aim for higher inspiration, add layers of meaning, optimize syllable density, and above all, write a hit song. These days, I get compliments on just about every song we write together. Claude is the AI for problem solvers. It's the collaborator that understands your entire workflow and thinks with you, not for you. Whether you're debugging code at midnight, building a financial model, or strategizing your next business move, Claude extends your thinking to tackle the problems that matter. For problems worth solving, get started with Claude at claude.ai/tcr. That's claude.ai/TCR and check out Claude Pro, which includes access to all of the features mentioned in today's episode. Once more, that's claude.ai/TCR.
Yeah, certainly. I'm working a little bit on an episode where I'm just going to do things I've been wrong about and things I've been right about. And one of the things I've definitely been wrong about is I expected a lot more change to how things get done across the economy five years ago than we've actually seen. And if you, especially if you were to tell me then that we'd have Astro and that it would have been a roughly speaking smooth ramp to these capability levels and yet we still see, you know, revenue has exploded at the model companies obviously, but we don't see nearly as much change as I would have guessed. No, life is kind of the same. I kind of go about my day and I talk to my friends, I talk to my family and I watch what they do and yeah, their days are the same. And yeah, they use ChatGPT some to help with things here and meal planning or to help plan a vacation or to do a workout or something like that. Certainly not taking advantage of Astro-level model capabilities at all.
So, what have you learned then about what kind of help people need? You know, if you, I don't know, do you like talk about this when you're taking walks in your neighborhood? How do you, I'm interested in your method, but also even more so your takeaways. What is it that kind of gets people over the hump? How do you help them see a new way of working? Are there any patterns that, you know, seem generalizable or is it kind of idiosyncratic for every individual and small company?
There's definitely patterns, but there's a lot of the specifics matter a lot. And that's where it starts to feel idiosyncratic. And we've seen this for years. I remember for a long time, one of the hardest problems of Zapier, even till this day, has been helping people with recommendations. What actually should you use this stuff for? And if I personally sat down next to you and said, "Hey, just show me what you do every day," I could come up with half a dozen examples of things that would immediately be things you were like, "Yep, I want that. Yep, I want that. Yep, I want that." But then how do you actually bake that in? Like how do you give that experience of me or somebody who is knowledgeable about these areas sitting next to them and how do you bake that into the product? And this is where I get pretty excited about where AI I think can help cross that, I don't know, recommendations gap, use case gap, whatever you want to call it. Because if you're able to point the tools where you work, say, "Hey, I want you to go watch what I do every day, watch what I do in Gmail, watch what I do in Slack, watch what I do in my browser and watch what I do in my chat and just tell me what should I be doing different." I started doing this workflow at the beginning of the year and pretty much every week now I have new tools, new systems that start to automate bits and pieces of my job. And if you do that on a perpetual basis, you start to feel the difference after a month or two. You're like, "Wow, there's a lot that's kind of running for me now that I didn't have before." And I think even though many of the things that are built are things that I kind of knew I should be doing before, it's the specifics of where it's like I literally watched the thing that you did here and here and so I know exactly the tool that can get it done. It's that idiosyncratic part that makes it click in. And so I get pretty excited about how do you build, how do you give these models like awareness of just how people go about their day because I think there's tons of ideas that are just kind of trapped like latently lost inside of that that most of us just don't wake up and think about. We are creatures of habit and so we wake up and we go about our day the same way we did the day before and we don't think, oh, there might be a 5% better way of doing it or there might be like a 500% better way of doing this. It's just I have muscle memory. I know how to do it this way and I'm comfortable doing it this way so I'm going to keep doing it that way even if it's not the best. And so AI sort of has to be so good that it kind of knocks us out of our comfort zone and we're willing to say, you know what, I am going to go try it that other way because that sounds so much better.
So how have you set that up for yourself and how broadly deployed at Zapier is this sort of AI on your shoulder or screen record? I'm kind of expecting like a screen record.
Yeah, most people use Zapier MCP for this. So they just have all their tools hooked up into whatever harness of choice they use. Like I mentioned, we have our own internal harness, but it could be Claude Code, it could be Copilot, it could be ChatGPT Work, it could be Cursor, it could be anything, right? And they'll just have an automation that runs once a week and it just kind of collects all these signals across all the work they've done. It seems like the event streams and it can tell, it says, "Hey, I noticed you did this and this and here's a tool that I think you should go build." And so most people have something like that set up and then they just tell their agent, "Okay, great. I like that suggestion. Go build it." Or, "That suggestion's okay. What would make it great is if you made this tweak and do that. I really want you to go do that." And half the battle is just getting you to react to something. Even if the ideas aren't perfect, they only need to be like 50% good enough to get you to go, "Oh, I see where you're going for." And now that brainstorm process kicks off and you're able to run with it.
So, do I understand correctly that it literally just uses APIs to look at your digital history for the last period of time and then coalesces that together and comes up with ideas? Interesting. Is this something you think you'll productize for Zapier customers?
