Inside Zapier’s Code Red: How CEO Wade Foster Hit Pause to Reinvent for AI

26:49They're chaining all these things along. So spot on that. Like that is where I think you you see the like most impressive results. And most people don't intuitively do that. Um, you know, the way in which I often try and explain it is to use examples, is to like, just step back and show how this can actually work. And, um, the one tool that I like to use is Zapier Agents. So Zapier Agents, the thing I like about it is when you go to prompt it for something you want to build, it will rewrite your prompt for you and show it to you. So the example I like to do is, hey, can you, I'd like you to reply to my email.

27:32And so, you know, most people, when they say like, oh, great, I can have an AI like reply to my email, they'll come in and say like, reply to my email. Like they don't sort of think through like, okay, if it's a recruiting email, I want you to reply this way. If it's a sales email, I want you to reply this way. If it's that in just like, you know, start breaking it down bit by bit by bit by bit by bit. So when you prompt the Zapier agent, it actually rewrites your prompt and it sets it up into like one, two, three, four, five steps. And so you start to read it and it sounds pretty human. You start to go, oh, interesting.

28:01Like it took that and turned it into this. And then when you read through what it turned it into, you start to realize, well, on step three, like that's not quite actually what I wanted to do. I wanted to do something more like this. And so it starts to train your brain to realize like, oh, I need to like guide it in this way. And, you know, before I was just giving it this like very generic step, but because it rewrote it for me, I realize I need to add steps five, six, seven, eight. And then you can you will often see that as folks learn this skill. It turns into like their zap titles will start to be like version 27, version 93, et cetera.

28:42So they start to like realize like, oh, wow, there's a whole optimization process that can happen here to really make this system like work really great for me versus just like reply to my email. Okay, we got to talk code red for a minute because clearly that I think is a seminal moment in your history and in the organization kind of as an inflection point. But I think it's also, it's a real, it's really great branding because I think a lot of organizations, they hear Code Red, they see how you describe it and they go, we need to do that. Or even maybe more pointedly, do we need to do that? Could you talk for a second about how you assessed as a CEO when it was time to call Code Red and how you advise other leaders to know when the right time to declare a Code Red is?

29:31Yeah. We've only done it Coderad once. And so it was in this moment and it was right after the GPT4 launch. So the reason we did it then was we were, my co-founders were messing around with GPT, I guess it would have been three before ChatGPT had launched. And so folks in the organization knew that like, hey, we were curious about this stuff. We were interested. But I think at the time it felt more like, oh, that's a weird founder thing. They're off kind of just exploring, doing what they do, right? But no one, it wasn't a serious discussion internally. ChatGPT comes out, that starts to change a little bit.

30:11We're like, hey, whoa, this stuff is pretty cool. And it has more widespread applicability than maybe we thought. So people are getting like more excited about this stuff. You're starting to see like a handful of individuals and teams like start to think through like, hmm, could we build a feature around this? Could we automate this process or the other? But I'd say still, you know, 90 plus percent of the org is like, you know, laser focused on my roadmap, my goals, my, you know, short term things that I got to go hit. But we have an offsite in February of, I guess it's 2024, where we get on stage.

30:46We put some customers on stage. They're doing cool stuff with AI. We're doing more things this way. And so more of the organization starts to get interested and curious about this. But we're still pretty organic about our push to go adopt this stuff. We're just more like, hey, this is a trend. We should be paying attention, da-da-da. And by organic, you just mean you're encouraging bottom-up experimentation. Yeah, very suggesting. But there's nothing top down. Okay, yeah. There's no mandates. There's no goals. Like, nobody's getting hired or fired because of this stuff. Not really. Like, but it's, you know, it's like subtle encouragement and pressure to like go experiment, try these things.

31:22GPT-4 comes out, I think, March of that year, though. And that was like a wake-up call because the improvement between 3.5 and 4 was enormous. And the release cycle between 3.5 and 4 was really short. And so we just looked at that and said, if we're going to have releases happening every six months and the improvement rate is going to be that, then how we're thinking about our roadmap, how we're thinking about our operations, like we're just totally miscalibrated on a lot of areas. And so we didn't have like all of the answers. We just knew a lot of stuff's got to change. and I remember a late night call with my co-founder saying like hey we we gotta we like we gotta do something different like we gotta chart a new course on this stuff because this is gonna be the this is the thing uh for the next decade and so we called the code red I don't think it was particularly popular at the moment um there was definitely folks in the organization who were like alarmist and you know sensational and like comments like that came up um but you know I think we did some things right.

32:30One, we ran an all-hands hackathon. So we said, hey, everybody, stop what you're doing. We'll put out a little bit of a couple of Loom training videos on how to use the open AI APIs, how to use various tools. We just gave people a week to just put their hands on the technology, stop what they were doing, and just build some familiarity with it to make it less of this fearful, scary thing and more of a like, oh, here's some awesome stuff it does. Here's some weird things it does. You know, at the time, like hallucinations were like really rampant inside these tools. And so folks are like, you know, you can have some fun over like, oh, it's like, clearly it's not going to take my job yet because it can't do this, that or the other.

33:08So that like familiarity with it made it a lot less scary and it made it a lot more just like, oh, here's another awesome tool in our toolbox. Now, granted, it's different because of its widespread applicability. And then we just kind of rinse, wash and repeated that like every, you know six months we do a hackathon we try and do show and tells at all hands and that like cadence i think just got every single time we do that we just see more use cases more ambition going up uh more folks sort of like jumping on the bandwagon and so what started off as like the founders were kind of off there like doing oddball stuff like it totally inverted now where it's like if you're not using ai like people are kind of like wait what like why so from From zero to 10, if zero is like not using AI at all, and 10 is like the most use of AI that you can imagine your organization could ever be capable.

34:00Where do you think you are on the journey right now? I mean, probably like a two. And I think we're doing better than most. I think if you talk to Moderna, which do very well, which is Logitech, which is very well, Bark, which I think it does very well. I think most founders of those organizations are with you we feel that we're at two right and and everybody else is like impressed like like we are with you like this is like really incredible what you're doing what do you think it is is it just because humans uh uh you know take some time to to change or what do you think is like the underlying thing that makes it complicated for people to adapt this this is back to people who were printing out their emails in the early days of the internet or what do you think is the the root cause of this?

34:48I think it's a lot easier to ship the tech than it is to change organizations. And this is one area where I have some envy for the companies that have started after ChatGPT, is that they got to just start with a clean slate. From the ground up, yeah. Yeah. And so, like, you often talk to founders there, and they're like, you know, Claude is writing, you know, 90 plus percent of my code. You're like, dang, that's incredible. Whereas, you know, we got a 14 year code base that like is not easily worked in by AI yet. Now we're working to change a lot of that stuff, but that's not something we can just snap our fingers and have happen.

35:24So I think there's this whole, just like, we got, you know, these, these organizations that are older than chat GPT, you have a whole bunch of stuff that have to get reinvented and rebuilt. And that just takes time and effort to work your way through. And, and there's creativity involved. Like it's not always obvious, like, Hey, if we shed this entire department and recalibrate it and retune it into this way, will that actually work? Like it, it might, but it's not a certainty. And so to like take that leap, like that's, that takes some conviction to get there. And so I just, I think it just takes a lot of time to like really get to, you know, like a 10.

36:05I don't know. I don't know of any organization that's at a 10. What I'm hearing too, a big part of this has changed management, right? I mean, part of, I think the reason you're a two out of 10 is because of the, you mentioned earlier, the depth of adoption varies greatly, right? You also mentioned that when you declared the code red, not everybody was happy about it. I'd love to talk for a second about how you think about organizations managing, you know, you mentioned in your memo, for example, I was just looking at it last night. Yeah, I'm reading verbatim here. Set a Q2 personal growth goal to use AI or CHGPT in at least one area of your daily work.

36:39If you're not sure how, talk with your manager. And if your manager isn't sure, they should talk with their manager and so on until the answer becomes clear. So I love that. And I totally agree. Find one way to use AI daily. Here's my question for you. How much patience do you give a human who needs to change before you say, you know what, I think the phrase Sam Altman has used is not gonna make it. There's some people who go, they're not gonna make it. Like they're not gonna make the transition. How much kind of encouragement, incentive, motivation do you provide before you say, you know what, we actually need to hire a different kind of individual?

37:16Yeah. Well, I think for us, the answer is, has empirically been about two years. Like we called the co-year two years ago. And where I'm at today is I can kind of tell, you know, if I talk to somebody and I can see like, hey, resistance is like the biggest thing. Like resistance, am I not going to make it? that that it's like hey I just we just can't work with that like that's not gonna be acceptable now people who are curious and want to do successful but maybe just haven't found the right use case or working in a part of the company that like doesn't lend itself well to these tools yet and they're just kind of banging their head against the wall still that I'm like tolerant of I'm like okay yeah we'll work our way through this like let's let's go this is not you know these things are, you know, as impressive as they are, they still are just tools at this moment in time.

38:10And so there is kind of like a line there between like resistance versus like interest, but struggling. And it's like, as long as you're willing to learn and put the effort in and try, like we're still willing to have you on the boat here at Zapier, so to speak. I don't know if you agree with this. I think at bats is a great metric. It's actually not like what your batting average is right now. But if you show up to a performance review in six months and you say, I haven't tried anything, that's a problem. If you say, here's the 20 things I've tried and they've all failed, I go, great. What's the next 20?

38:41Right? Yeah. I don't know if that's right. I actually like that framing. Okay. Okay. I think in a lot of organizations, the problem is inaction is permissible. Yes. And to say, I haven't done anything is perfectly acceptable. So you actually have to change the frame, but that is a really painful thing. I don't know if you have any reactions to that. I 100 % agree. I think if, you know, if we want to be like very reductionist about how you set culture inside of a company, it's what do you reward? What do you tolerate? And what do you not tolerate? And so to use this as an example, like inaction, we wouldn't tolerate at bats.

39:20We're like, Hey, that's good. And then what we reward is like actual successful attempts. And so you could sort of set up a pretty simple system there for how to enable managers to go manage against that. A follow-up on the culture stuff is for me is you have a pretty remote organization. You did this weight test, didn't you, where you were kind of like trying to see how many people could recognize it was you or AI. Yeah. So obviously AI has like, you know, a lot of use of AI has this tension between kind of like close-knit culture and organization, because you might just talk more to your chat to BT than your colleague.

39:59It has like a little bit bit of like this dehumanization, you know, if suddenly like your manager suddenly replies, most of the replies are done with AI. And at the same time, like you're a 12-year organization, like you've been through like a few cycles of people now and stuff like that. Talk to me a little bit about like, how do you think about AI remote and then culture and how to kind of like do that? You mentioned that you guys do off-site, so it sounds like maybe be one answer to that question. Are there other kind of ways that you try to kind of create like a coherent sense of togetherness?

40:34Yeah, I don't know that like AI actually does much for us on this dimension at the moment. Like I think the big way that we bring togetherness and camaraderie is one, the in-person gathering. So we do that for the whole company once a year. And then we have smaller groups that get together periodically. Two, we have things like show and tell and weekly demos and like, you know, all these sorts of things that give people a chance to like, you know, connect and give feedback and learn and share and, you know, cross-pollinate knowledge. We have, you know, a bunch of like off-topic channels at work.

41:11So these are all like prefixed with fun. And so there's like fun gardening and fun homeownership and fun whatever, where people can sort of, you know, get to know each other around, you know, shared interests. If there is one around ai it's like we do have a fun ai channel and that's where there's a lot of just like sharing cool stuff you know it ranges everything from news about ai to like wow did you see this demo that someone did internally or externally um that like just creates like a sense of like you know just oh the sense of possible around like what what we could be doing around these things and so all these just like little i don't know like cultural rituals or habits I do think help create, you know, a sense of togetherness, a sense of belonging, which is pretty critical if you're, you know, have a distributed team, which, you know, I'd say most of us have at least some part of our company are doing that these days.

42:06When you talk about culture and togetherness and belonging, I don't know if this is the right spectrum, but on the other end of the spectrum, I think about a phenomenon like burnout. And I think in remote organizations, you know, it's kind of, you're kind of always on, you know, in a way. How do you think about this is, and if you've listened much to our pod, you know, we get existential and practical. This is an existential question. Where should the benefits accrue? The benefits of, if you think about efficiency gains, productivity gains, if you take as a given, a lot of folks are burnt out.

42:38It stands to reason. They should actually, individual employees should probably accrue some of the benefits of the productivity gains. At the same time, an organization wants to drive, you know, everything to the bottom line. How do you think about where do we net out in five years from now? Are people actually legitimately working less? Are early stage startups much more profitable? You know, where do we net out in terms of where benefits accrue? I know so far capitalism feels pretty undefeated. So, you know. So, yeah, I think there's this belief in some circles that, oh, margins are just going to go through the roof because we're going to have a lot fewer people and, you know, a lot more is going to get done.

43:22But I think the reality is, like, as if margins get bigger and bigger and bigger, somebody, some competitor is going to see that and say, hmm, you know, the old Jeff Bezos quote, your margin is my opportunity. And they're going to step in and say, hey, we're going to want to go compete for some of that stuff. and you know i think that like net net it probably doesn't change that much like i think the types of work that ai can do for us obviously changes a lot of how we operate you know how much effort we're putting into work how many hours we're working the cultural norms around that like i don't know i think it i probably would bet it stays pretty similar would just be my guess um Because I think capitalism just sort of has a way of like evening this stuff out.

44:09Yeah. Yeah. I got the last question for you. Agents talking to agents. So obviously we've all learned how to use the autonomous agents now, like the EOMI, as you charge your perplexity or whatever. It seems that a lot of people believe that there'll be agents. So you'll have generalized agents and you'll have specialized agents, right? And at one point, there'll be kind of this hand over. And I know you guys done some work with MCP, which for people who don't know, is this one of these protocols that are kind of like being these languages that are being kind of explored, like how agents talk to agents or when one agent can kind of hand over to another resource.

44:48When you talk to a venture capitalist and you ask them, when do you start to see like real agent to agent kind of business kind of emerged? They say six to 18 months. But when we talk to people that actually do this every day, they're like, we don't even know what language they'll talk yet. Where are you on the whole spectrum of like agent to agent communication, how fast it will move, when we'll start to see stuff, what might be the first kind of use cases in that kind of like whole universe? Yeah. I mean, I think we can do agent-to-agent communication today. The problem that we see in practice is the reliability is really poor for any reasonably complex task.

45:31And I think the reason why is that you start to just chain along. You know, if a complex task has like five steps or 10 steps and every step is an agent handing off to another agent. There's a probability waiting there. Yeah. Yeah, it's like if the reliability is 90%, like, well, by the time you get to the end of that, like, it's way off in left field. And you're like, this is just not, it's just not good. And so like, that's the real challenge that I think, you know, we're facing. and i think what my sense is that the way in which we're going to get these much better is by having agents that are like you basically have to find a way to like scope the agent down to like a narrow enough set of tasks like narrow enough task to increase the reliability such that you can get closer to like an error rate that is tolerable for the organization's you know risk uh and so that that that takes away some of the magic at the end of the day where it's like well if we're really narrowing the agent down to be so so tiny like why aren't we not just writing like deterministic code here like we ought to just be stamping out like this very practical thing and so you know there's there's something in and around that that like we're gonna have to go solve um to to get this a lot stronger um to make these agents get these out But I think the end result is like we're going to see like a universe of agents that are very good, like very narrow sets of things.

46:57Wade, I have one last question, if you're willing. A number of our audience members, we kind of float, hey, you know, upcoming guests and say, if you've got suggestions for questions, let us know. One of the words from your memos that have come up a few times in audience questions is this idea of duplication. you mentioned in your memo, there's going to be duplication of effort. We're going to have duplication of experiments, et cetera, et cetera. Can you talk for a second about, I'm a huge, as like an innovation junkie, I'm a huge believer in parallel experimentation. So I get it, but talk for a second about how do you manage duplicate efforts and how do you organize knowing that duplication of effort is going to be a part of this process?

47:41At the beginning of this, you mostly don't manage it. You mostly are just managing the psychology of the team. It feels so wasteful to so many people internally that there would be multiple efforts. Why are we collaborating on this? Why are you doing this? I'm doing this. Why are you doing this? My thing should be the one to do it. That's the thing that is really painful for organizations. And so mostly you're just trying to let people know it's okay. It's okay. Well, how do you manage this? When somebody says, why are you, why are we doing this? What do you say? I mostly just say it's okay. It's okay.

48:17Like at some point in time, we are going to, you know, we're going to actually have a better sense of like, what is the path forward? And these things will consolidate. They will sort of come into one. But for right now, we actually don't want to get on that off ramp. Like we don't want to pour concrete around this particular solution because we don't know yet that this is the ideal one. and you see this like with some of the early ai products out there um if you go use them you can actually see where they took an off ramp and like their entire architecture of how the product works is kind of backwards now like i can think of a very popular product that we use internally where their asian architecture is just wrong like it's just wrong uh and like i kind of get it they sort of they were fast to market they they pushed a thing out you know if i was in their shoes like I might have made the exact same choice, but the reality is like, they're going to have to go rebuild all that now to take advantage of the way these tools work at this point in time.

49:16And so there's just a sense of just like letting people off the hook for like what they feel is like a failure. You know, that there would be so much disorganization that there would be like a thousand flowers blooming and saying like, we're okay. Burning may be the better analogy. Yeah, it could be burning too. It could be. I mean, what I'm hearing is you have conviction. You know, as an innovator, that some duplication of effort and parallel experimentation is absolutely necessary. And I think that's actually it's it's important to state that because I think that there is a number of people that don't know kind of call it the prior probabilities around innovation that don't know how it works.

49:55And if you think about managing it, like you're managing routine work, where it's deterministic, where you, you know, where you commission a single experiment to do a single thing. And I mean, one of my favorite examples is Steve Jobs' parallel commissioning experiments around what would become the iPhone. Yeah. You know, Tony Fidel, I don't know, most folks may not know this, but just for listeners who are kind of innovation history nerds like me, you go, okay, Tony Fidel was commissioned to make a click wheel version of the iPhone. because they didn't know at the time whether multi-touch would work.

50:27And so there were multiple teams in Apple building different iPhones, right? People think there's an iPhone team, right? You know, that one team with one approach. It's all that say what I'm hearing you say, Wade, is you as a founder and CEO know that duplication of effort is necessary to identify the best path forward. Therefore, you know, your job is to manage the psychology of individuals who don't know that. the first order thing for a leader who's listening to this show is you have to know as a leader duplication of effort is required then the question becomes how do i manage people's psychology who feel like this is wasted effort etc etc totally totally and then at some point in time you probably are going to have to deal with the the inverse pain which is now everyone's trained on this one way of working and now it's like oh we actually have some answers we do need to make some conviction vets and we have to figure out a way to like bring these approaches together and be a thing.

51:23That's a different moment in time and it requires you to shift gears again. And so you talked about change management earlier and it's like, God, that's like, it's like such a, once your organization gets to a certain size and scale, like a lot of that is the name of the game. Anything you want to say to tip your hat to anything regarding the future? We've got, we'll be announcing ZapConnects is opening up for registration here soon this week. and folks will, it happens in the fall. And if you want to see what a lot of the stuff we've got cooking around AI is coming, I would say come to that because we'll have some fun stuff dropping.

52:03That's super cool. That's awesome. Fantastic. Really, really, really enjoyed the conversation. Thank you so much for doing this. Yeah, and thanks for making the product. I'm a super use of it. So yeah, really appreciate it. I love it. Thanks for having me, guys. Thanks, Wade. Take care. Bye. Bye. Okay, so Henrik, as a super fan of the product, what stood out to you as a customer of Zapier? You know, the thing actually that mostly stood out with me is that he started his company around the same time as I started Bark and similar type size. And so it's kind of, it's always nice to just hear somebody kind of be at the same point in their organizational path.

52:51journey. I think, you know, a few things, let's stop there. For example, I am always impressed by people like Wade that we have on that is just pushing all this AI usage through the organization. And what stands out is that he says that he's a two out of a 10, right? And I think obviously, objectively, if you compare him to any other organization, he's probably a nine out of a 10. But I think people like him believe that there's so much opportunity for not just making it more efficient and reducing staff, but also just making the organization more resourceful and do more of this thing that he thinks that the organization deserves to do.

53:33And so I think the one thing is that thing, the kind of a permutation of that is something you said, which is just inaction is permissible in a lot of organizations. And I think a lot of founders, as you can hear on him, is just tired of that. And I don't think that that is going to stand for the next five years. I think leaders are just going to go, hey, if you don't even experiment about this, it's going to be really tough for you to have a long-term career. And I think that's just a universal kind of statement that people who basically still want to print their emails don't be Fred or whatever it is that...

54:09Right, don't be Fred, yeah, yeah. Then I think if you don't already take all the feedback from your customers and put it into a place where an agent can access it so that that data is available throughout the organization, that just seemed to be kind of like an absolute no-brainer for any organization at any size. have all feedback loops from the organization in a place where agents, chatbots, and thus the organization at large can get hold of it. And I think it was just a friendly reminder from him that they're doing it in secret benefits from it and everybody else should do the same. Yeah, yeah.

54:49And then, I mean, it's something that's basic. And I was kind of thinking about asking you about it because you're such a master of that. I do think that a lot of people listen to this podcast and who use AI a lot just can't get their head around why people are not using it more because it seems so useful when you start to use it. And I think it's just very conceptual how you do a lot of these things. And so just having very concrete example, I saw a LinkedIn post you posted the other day where you were talking about something that somebody from the forest service was doing. And so you seem to have all these forums where people are just showcases kind of like endless amount of like use cases And you go to Waits, kind of LinkedIn, and he had this kind of graph also with like a lot of, he used it for onboarding, he used it for offboarding, he used it for these, like, you know, just like 20 use cases.

55:37And so I, one takeaway is just like the simplicity of just constantly finding more avenues to show endless amount of use cases. And so the people who are not using as much can go like, hey, wait a minute, can you use it for that? And then go out and do it. Yeah. Yeah. I, um, I really resonate with your comments about practicality and his comments around, there's very little kind of simple approachable how to there's a, I think he said the refrain of hype is at a volume level 11. And I think similar to that hype, there's a distribution hype on the positive end doom on the other end. There's a ton of hype and doom, but there's not a lot of kind of humble, here's something really cool.

56:22Here's something really fun. And I think what differentiates Wade from, from many folks that I'm observing online is he's just being super helpful and super practical. We should link to several of the resources that he's provided in the show notes because he's got a great, I mean, you know, play by play breakdown of his hackathon week. He's got a great breakdown of the code red that they announced a year ago now. And And I think for any, he said two years, by the way, since Code Red, but I think it's only one year, right? I'm pretty sure. GPT-4 last year? I don't know. Yeah. GPT-4 had to be last year.

56:59I'm pretty sure it's March of 2023. And if they declared it Code Red, so he said two years, but I think it's one year. My thought is he's a year ahead of where most folks are. March 23. Yeah. Oh, 23. Okay, two years. There you go. Yeah, he was right. So March 23 is when GPT-4 came out. They announced their code red April of 2023, had their first AI hackathon April of 2023. We're filming July of 2025. I think most folks who are listening have yet to declare a code red, perhaps because they never thought of it, or perhaps they don't feel it's necessary yet. But you can kind of just start the clock from when do you declare this is so foundational and fundamental and important that we need to actually stop the gears, have folks spend a week experimenting, exploring, building.

57:52And then there's so much resources that he's provided. It doesn't have to be confusing. It doesn't have to be complicated. I mean, you could literally hit Renz repeat on a lot of his playbook, which he's graciously put out there. And one thing to add on that is that he said two things, and I think you brought one of them up, which is basically, when do you tell the team that this is a big deal, right? And using the code with us, the kind of like the moment. But I think what he then also says is like a lot of people felt internally, that was a bit alarmist, right? And I think a lot of people who don't call a code raid or something similar, it's probably like feeling the same pushback from the organization.

58:31they're like yeah like emails me mail like ai you know whatever um right the second thing that i think is fascinating is like this idea of a hold anxiety other his staff's anxiety on his shoulders like we will have many people try to do the same thing and that is fine you know we will have many of these projects that we will try and we accept a 90 kind of like uh success rate you know like even per chat. That is like something that a lot of organizations probably wouldn't do because, you know, like, hey, what do you say? Like the product only works 90 % of the time. So I think there's probably also like I think a leadership kind of component of it, which is like, hey, if I'm going to utilize AI and get all the benefits out of AI, I also am going to allow my team to kind of pass on some of this anxiety that they might have until me as a leader.

59:26because I'm willing to be a little bit alarmist. I'm willing to accept that I'm wasting resources, having many people do one thing and so forth. Yeah. I mean, he actually says quite eloquently in his post, we have determined that standing still is the only sure to fail approach. And so I think maybe a simple way to put this is there's a binary decision before you. do nothing or duplicate effort? Which will you choose to do? And I think for too many organizations, the perception of the pain of duplicating effort is leading them to the wrong conclusion, which is therefore let's do nothing because we don't know what to do.

1:00:11And the truth is, as with any other innovation, the only way you discover what's worth doing is by commissioning experiments, by duplicating effort in some areas in order to overcome that kind of inertia of the unknown. So Wade's a great example. This is super fun. You're probably a student of this because I would imagine this is not an AI thing. That's an innovation thing in general, right? Totally. 100%. And so what is the prevailing kind of wisdom or thesis of why organizations, I guess there's written many books like The Innovator's Dilemma and a lot of these different books on it. But if you were to kind of like just answer a leader's question now saying, hey, I don't understand why it's so difficult to get my organization to kind of like go from a zero to a five, you know, like in this journey.

1:00:58Where would you point them?

1:01:03I'd tell them to look in the mirror. Say more. we have you and i have had the privilege of speaking with so many amazing leaders who are so outspoken about their own experiments and their own behavior you know think about brad anderson sharing his screen before all hands meetings you think about diara busso sending loom videos to her team you think about kevin kelly's daily suno practice right on and on and on, right? A leader is saying, I'm trying to tell other people to dot, dot, dot is missing the point, right? Starting with yourself, you know, Greg shows third screen, right? And on and on and on the leaders whose organizations are transforming our leaders who have transformed themselves.

1:01:55Hey, man, it's a good piece of advice, professor. Well, uh, I hope people won't log off because of the moment of silence that it took me to actually think of the answer. No. Because sometimes the human, the old gray matter can't respond as quickly as GPT can. Can I, one of the most impressive degree shows I've ever been to was St. Martin's Lane in London where they have this kind of interactive degree show. And this student had made this kind of piece where she had recorded every time on any TV channel within a 24-hour kind of cycle where there was silence. And our thesis was that silence in today's world is when something really powerful is about to happen.

1:02:41Or has just happened. It's just after the car accident. It's just when somebody said something outrageous. It's just when something crazy is about to happen. And so I was kind of drilling in that moment. I'm like, oh, what's going to come? Delivered, sir. Well, let's leave it to the listeners. Maybe the secret code word should be either silence delivered or silence did not deliver. That's the code word. You get to choose. Let's do that. If you've enjoyed this episode, feel free to like, subscribe, share with a friend, perhaps share with a leader who needs to hear the message after the moment of silence.

1:03:21Thank you. Goodbye.