Learn. Share. Evolve…

0:00:03 All right. This is super cool. This type of stuff only happens, I think, on MFM. But
0:00:07 here’s the deal. I have a friend named Wade Foster. He started a company called Zapier.
0:00:11 Zapier is a business that was bootstrapped to hundreds of millions in revenue, and it’s worth
0:00:17 $5 or $10 billion. And I had the CEO and founder on, Wade. I had him on MFM. And I said,
0:00:23 look, share your screen and show me how you’re using AI to save 10, 20, 30 hours a week.
0:00:28 It’s pretty amazing. He did it. And he broke down like three or four different ways that he
0:00:33 has automated parts of his life and his business. And it’s really, really cool because this
0:00:38 guy has an incredible perspective because he has such a large company, but because he helped
0:00:43 create this whole automation industry. So if you want to save a bunch of time, this stuff
0:00:47 is not complicated. I don’t know AI that well. And I’m going to implement a ton of the stuff
0:00:52 that I’ve just learned. And by the way, if you’re listening to this with audio only, you
0:00:56 might want to go to Spotify video or YouTube to watch this. It has a lot of visuals. You
0:01:00 can still get a ton of value on audio, but it’s just better, I think, on YouTube.
0:01:04 So give it a watch. Give it a listen. And let me know if you dug it. Talk soon.
0:01:09 I feel like I can rule the world. I know I could be what I want to.
0:01:13 I put my all in it like my days off. On the road, let’s travel.
0:01:20 So I’m thinking about how to introduce you. But basically, I met you in 2016 or 17, something
0:01:25 like that when you spoke at one of my events. And I asked you to speak at one of my events because
0:01:31 you founded and ran a company called Zapier. You know, you guys took off before AI was way
0:01:36 before it was ever a thing. But you basically, if I remember correctly, you only raised at the
0:01:42 time a million dollars, but you got it to nine figures in revenue and then eventually raise
0:01:48 a series A at something like a $5 billion valuation. Is that about those numbers about right?
0:01:55 Close. So the seed round was 1.2. And then the $5 billion number, that wasn’t, that was
0:01:58 all secondary. So none of that stuff went on to the balance sheet of the company.
0:02:01 How much did you guys raise? Was that public?
0:02:02 It wasn’t. We didn’t raise anything.
0:02:04 Well, you took money.
0:02:09 Early investors, early employees, like got some liquidity from it, but nothing came to the
0:02:10 company.
0:02:11 How big are you guys now?
0:02:15 What is the public number? I don’t know the public number. Nine figures is public, though.
0:02:21 You’re at nine figures in revenue. And dude, it’s one of my favorite companies. I never
0:02:25 thought in a million years I’d be fired up about such like a dorky product, like connecting
0:02:31 APIs. But basically my entire company and everyone I know, our entire companies run off of Zapier.
0:02:37 Well, it’s wild. Like the, so the first decade of existence, that dorky company thing, like
0:02:43 100% true. Like no one cares about workflow. Automation is like, like we think it’s cool.
0:02:46 And there’s like definitely people who do think it’s cool, but like the Silicon Valley
0:02:52 wisdom is, you know, raise a bunch of money, throw bodies at the problem, like blitz scale,
0:02:58 go like nuts. And so like people like say they, they cared about automation, but their actions
0:03:03 sort of like betrayed them in a lot of cases, certainly in tech. And so we’re kind of like
0:03:10 a out here doing our thing. Not a lot of people sort of in our space. Now you get into the AI
0:03:16 world and it’s sort of totally inverted where everyone cares about automation. Everyone cares
0:03:23 about AI. Uh, and so the market potential for us has just ballooned enormously. Um, but of course
0:03:29 there’s a lot more players in the space now. Yeah. At the time when I, like when you guys were just
0:03:34 getting going, it was basically you and maybe one other company doing this. Now there’s so many more.
0:03:39 Yeah. And you know, everybody has their angle, their niche, you know, vertical, this vertical,
0:03:44 that, um, you know, more dev centric, less dev centric. Like, yeah. So there’s, you know,
0:03:47 there’s just, everybody’s kind of trying to get a piece of this. What is, you know, going to be,
0:03:50 you know, a trillion dollar opportunity.
0:03:53 In 10 years, how big do you think your company will be in terms of revenue?
0:04:01 10 years. Uh, well, shoot, I think we should be well past, uh, a billion in ARR if we do our jobs,
0:04:01 right?
0:04:05 Dane. And you think you will still not have raised the money?
0:04:10 Uh, I don’t think we will.
0:04:14 So you’re going to, you’re going to be one of the largest, one of the larger ever
0:04:19 companies that only, I mean, I don’t know if you still say bootstrap, but you only raise,
0:04:23 um, you turn, uh, you turn a million dollars into something worth tens of billions of dollars.
0:04:25 I mean, that’s going to be one of the more efficient stories ever.
0:04:30 Yeah. You know, this is how they used to do it though. Um, you know, if you look at,
0:04:35 you know, Microsoft’s and how their, uh, fundraising trajectory went, if you look at Amazon and their
0:04:39 fundraising trajectory, now those companies obviously went public much earlier in their
0:04:42 life cycles, but you know, they didn’t raise huge gobs of money.
0:04:48 Yeah. How much did Google raise? I think they raised like, uh, low tens of millions of dollars.
0:04:52 Yeah. I think it was something like that. Google and Google was a lot like Google had a big round.
0:04:56 I think it was their A or their B that was huge. And people were like, Whoa, this is, this is nuts.
0:05:01 And nowadays, like, you know what the, the, the AI companies, the, the foundation model companies,
0:05:08 they’ll do like a billion dollar series. Hey, this is different. So I want to ask you about all of
0:05:16 that, but someone like I’ve noticed that I had better conversations with people while we are doing
0:05:22 stuff. Um, and that doing stuff is this thing where we basically have asked people like you.
0:05:26 So people who run big companies, how many, how many employees do you have?
0:05:29 700 and change.
0:05:34 Okay. So you have 700 employees and you’re trying to get everyone to use AI at your company. And you
0:05:38 guys are an AI company at this point. You were kind of an AI company a little bit for that was really
0:05:44 popular. The thing that I want to do that I’ve loved doing lately is I want you to share your screen
0:05:48 and show me how you’re using AI and really practical use cases. And you said, that sounds
0:05:52 good. And so while you’re showing the screen, I might ask you questions about like the background
0:05:58 of the company and things like that. So I asked you to make a list. You have a list. One of them
0:05:59 stuck out, right? You want to tell me what that was?
0:06:05 Well, so the first one, this is like a pretty basic, like thing that, uh, I use day to day,
0:06:11 which is like a instant dossier, um, creator. So, uh, you know, you can use it for all sorts
0:06:15 of things. Like it’s handy. Uh, it’s particularly handy when you’re sort of like out and about,
0:06:20 like you’re going to a dinner or you’re at an event and you got a list of names or you’re sitting
0:06:24 down and there’s name tags everywhere. And you’re like, okay, who are these people? Uh, and so
0:06:29 that use case, uh, you know, I usually just like feed Claude, uh, some details on the,
0:06:35 the person. And then, uh, Claude will just return like a quick little dossier that includes,
0:06:40 you know, public details about the person, but also like what’s going on. Like, are they a customer?
0:06:44 Is there any details in our HubSpot account? Like, is there anything that ZoomInfo can tell me,
0:06:48 uh, just to kind of just like get some quick hitters to be like, Hey, is there something I can sort of
0:06:53 talk to this person about? It’s sort of like, you know, if you ever watched Veep, uh, uh, Selena has
0:06:57 like, yeah, like has Gary in her ear, like, you know, just saying like these things. And so you
0:07:02 kind of get like your own little version of this. And so do you do a lot, you do a lot of these
0:07:05 dinners with customers and potential investors, things like that. A reasonable amount of them.
0:07:11 Uh, but the cool thing about this one is you can use it in all sorts of ways. So, you know, if you’re
0:07:15 just sitting in your home office and you’ve got a string of meetings coming up, you can do it for all
0:07:21 the people that you’re about to meet that day. If you’ve got, um, leads coming in on your website
0:07:26 and you want to go enrich those and send those over to your sales reps, you can do the same thing.
0:07:32 So like this process works in, is applicable in all sorts of different scenarios. And mostly
0:07:38 what you’re doing is you’re just sort of like amending how you utilize it based on the situation
0:07:38 you’re at.
0:07:44 Cutting your sales cycle in half sounds pretty impossible, but that’s exactly what Sandler
0:07:51 training did with HubSpot. They use Breeze, HubSpot’s AI tools to tailor every customer interaction
0:07:56 without losing their personal touch. And the results were incredible. Click-through rates
0:07:58 jumped 25%.
0:08:03 Qualified leads quadrupled, and people spent three times longer on their landing pages.
0:08:07 Go to HubSpot.com to see how Breeze can help your business grow.
0:08:11 So I can maybe do a screen share on this.
0:08:12 Yeah.
0:08:19 You know, Claude, I do a, the thing I love about Claude is Claude has, um, tools connected. And
0:08:26 so, uh, the Zapier MCP server is available here and you can see, uh, you know, if you click
0:08:30 into here, like you can connect all these different things, but you know, with Zapier, you have
0:08:35 access to a 8,000 different tools. And so I can just like turn on all sorts of different
0:08:41 tools to, to use within, um, Claude. So, uh, for example, you can see here, we’ve got eight
0:08:47 tools turned on, but basically these tools are a series of HubSpot, um, capabilities, like
0:08:52 finding contact information, finding company information, finding deal information, and
0:08:57 zoom info in points. So like finding again, contact information and finding company information.
0:09:03 And the other thing you can do inside of Claude is you can make projects. So I don’t know if
0:09:07 you’ve used projects before. So I’ve used it with ChatGPT. It’s like, I have a health project.
0:09:10 I have like a therapist project. I have like a business project.
0:09:16 There you go. So you can assign these tools like to, or you can give these projects like
0:09:22 certain system prompts to like help them do a certain task. And so within one of the projects
0:09:29 I have, it basically sort of coaches it on how to do like, uh, contact information for
0:09:34 me where I’m like, Hey, make these, these dossiers for myself. So, um, we can actually use the,
0:09:39 I actually made a quick one for us, which is this, my first million demo. And I have like
0:09:45 a, I got a buddy who’s going to be cool if I use them, uh, their, their name on this stuff.
0:09:51 So tell me more about Lars from social puppy.
0:09:54 And so what was in that file that you uploaded?
0:10:02 So I didn’t upload any files. Basically what this chat has access to is it’s got access to
0:10:09 the Zapier HubSpot account. And then it’s got access to our zoom info account. And then it knows
0:10:16 my like system instructions, which basically say, Hey, a step one is look up the contact
0:10:21 and company inside of HubSpot. Step two is look up information inside of zoom info. Step three is
0:10:27 go find anything on the web to, to go do this. Now you might’ve seen there were some errors popping up
0:10:34 here. This is one of the, the tricky things about, uh, MCP and Claude right now is that it’s still a
0:10:39 little buggy. Like MCP is technically like a, it’s been out for like maybe, I don’t know,
0:10:45 two or three, or their tool stuff has been out for maybe two or three months. And so it mostly works.
0:10:49 What does MPC stand for? And what is MPC?
0:10:57 Yeah. MCP is called model context protocol. So effectively what this is, is a way for agents
0:11:08 to talk to data. So, you know, in the old world, we would have APIs where, you know, you would say,
0:11:13 Hey, I want to talk to the Gmail API or want to talk to the HubSpot API or the Slack API. And these were
0:11:19 ways for like SAS companies to talk back and forth to each other. And so that protocol works really well
0:11:28 for the SAS tools with agents. They don’t exactly know how to utilize like all those API endpoints.
0:11:35 And so MCP is basically this layer that sits in the middle that helps them go find different tools that
0:11:42 they can go use and then, uh, take advantage of them. So unfortunately right here, you can see this
0:11:49 one is, uh, Oh, it, it worked. Um, but the cool thing you can see is it’s iterating over a bunch of different
0:11:54 endpoints. So it’s saying, Hey, this didn’t work. Um, let me try more director approach. Okay. That didn’t work.
0:12:00 So this is Claude trying to figure out how to go do this task. They’re like, Hey, I, you know, I’m not exactly
0:12:06 sure. I’ve got access to a bunch of different tools. Some of these things might work. Some of these things may not
0:12:12 work. And then at the end, it’ll spit out information on it. So it pulls up, you know, LinkedIn,
0:12:18 URL. You can see that Lars is a product manager at Zapier. Uh, he’s got, you know, he’s hanging out in
0:12:22 Vancouver. You can see all the different things that sort of, we know about them. And then social
0:12:29 puppy happens to be his side gig. Uh, it’s this way for people who own dogs to, to do meetups together.
0:12:34 Of course, he’s not paying for HubSpot. So, you know, they took a look at HubSpot and was like,
0:12:37 yeah, there’s no deal associated with this, um, for, for Lars.
0:12:42 Do you see what it says? It said colleagues describe him. Wait, what was that? Colleagues describe
0:12:45 him at, what is this? It has incredible drive and enthusiasm for the goals.
0:12:47 It’s having incredible drive and enthusiasm for the goals. Yep.
0:12:53 Okay. And he’s known for being fun and energetic. That’s cool. Okay.
0:12:57 And I would say that that’s, knowing Lars, I would say that’s accurate. Yeah.
0:13:04 So when you’re going to, uh, let’s say you have a work dinner, let’s say you’re, you’re flying
0:13:09 like, uh, to a different city and you want to meet with like 10 customers and just like host
0:13:11 like a cool hang with them to get to know them. Let’s say they’re big customers.
0:13:17 Would you just upload all six of the names and then just tell it like, make this in note
0:13:19 card format or, I mean, how would you do it?
0:13:24 Well, okay. So, uh, I want to show off something separate. So if I was doing that,
0:13:32 I would actually do this as an internal tool that we built now, this that I was just showing
0:13:37 off, this is kind of for like basic on demand stuff. Yeah. So you can see like it did a good
0:13:43 job, but you know, if I’m trying to show up to like a dinner and I know who’s going to be
0:13:50 there in advance, I want to come in well equipped. Like I don’t want to do this sort of like on the
0:13:56 fly sort of thing. I want to come in having done my research. So we built an internal tool. This is
0:14:00 a company brief generator. Now it uses Zapier interfaces. So this is like a pretty easy thing
0:14:06 to build. And then what it does is you put in a domain name. So you could put in Netflix or Shopify
0:14:12 Slack or whoever, you know, you’re meeting somebody from this company. And then what this does is it
0:14:20 goes and retrieves information from three sources. So one, it pulls from a web search. So it just goes
0:14:26 and finds like, what, what do we know about this company based on what’s on the internet to it does
0:14:33 a glean search. So we use this tool called glean internally, which is like, uh, an internal, like
0:14:38 Google, you can think of it. It’ll go search all your sort of like internal stuff.
0:14:45 Glean is basically, I’ve been trying to use it. It’s you just log in to everything and then it makes,
0:14:50 yeah. Okay. And so it’ll search like Slack and Google drive and you know, all the tools you give
0:14:57 it access to. So we’ll do a glean search. And then the third thing it will do is it will hit our actual
0:15:03 customer database. Well, a replica of it in Databricks and help understand like what
0:15:09 usage is going on inside the company. Then on the other side, it spits out a report.
0:15:14 And so I actually did Hampton before this call. Oh my God. Let me see.
0:15:18 All right. You want to see it? And who’s going to be using this? And at your company,
0:15:24 what roles are using this? Everybody uses this internally. So if you’re customer facing at all,
0:15:30 you will do this. So it doesn’t have like a crazy amount of information on, on Hampton,
0:15:37 but you can see here the company summary. So this is like public pulling a bunch of like public
0:15:45 data on it. Now I did some like research on it and it’s, it’s sort of correct. Like I’m betting
0:15:48 you’re spotting things here that you’re like, Oh, that’s not quite right.
0:15:51 I think the goal of having this, if, if you’re, if your intention is to connect with me at a dinner
0:15:54 or something like that, like you have ammo there.
0:15:59 Exactly. And that’s what I’m getting out of this. I’m not, you know, like, uh, like I looked up before,
0:16:04 like, it doesn’t look like Jordan is the CEO anymore. Right. So that’s a thing where I’d be like,
0:16:07 okay, you know, I probably don’t want to talk about that, but there’s, you know, this, Oh,
0:16:11 it’s a membership community. It’s in New York. Like, you know, here’s some things that I can,
0:16:15 you know, riff with you on, you know, it pulls up a bunch of information. Then you can see,
0:16:21 here’s our glean summary where it’s got, you know, more issues about like what’s going on with the
0:16:26 account and things like that. And so, you know, maybe there was a payment issue with you all at
0:16:32 some point in time. Yeah. And so I can be prepared where I’m like, if you come up to me and you’re
0:16:38 like, wait, like you really screwed me over in 2024. Like, uh, I can be like, yeah, Hey, I’m really
0:16:42 sorry about that. Here’s what we did to fix this. We did this, this, this, and this. And all of a
0:16:47 sudden you’re like, okay, Wade’s really on top of it. Like I feel better about, you know, Zapier as a,
0:16:52 as a vendor, uh, versus like, you know, if I didn’t have this and you were just like, Wade,
0:16:58 I’m still pissed about what happened in December in 2024. And I’m like, um, what happened? I don’t,
0:17:02 I don’t know what happened. And so now you’re like, man, he doesn’t even know what’s going on
0:17:08 in his own company. And so you have a tool like that. And then lastly, it didn’t actually pull much.
0:17:12 So I don’t know how much usage is going on here, but for companies that are really using Zapier,
0:17:18 the visualizations area pulls up a whole bunch of metadata about how their account is being used.
0:17:24 So I can see like, Oh, you know, they use Slack and HubSpot and Google, and they’re using AI or not
0:17:28 using AI in the account. Here are the users that are using it, all that sort of stuff.
0:17:29 Well, who built this?
0:17:34 I think it was like one of our, uh, data analysts built this tool.
0:17:35 On like Replay or something?
0:17:44 Uh, no, this is mostly just a Zap. So it hits, you know, a Zapier interface. So you put in a domain
0:17:51 name, then there’s a Zap that goes out and it hits those three areas. So it hits, uh, does a web search,
0:17:56 probably a step one. It does a glean search, a step two, and then it does an internal search on
0:18:01 our Databricks instance at step three. And then the output is an HTML file.
0:18:04 What would I Google to find the landing page that allows me to make this?
0:18:13 So we have, uh, a bunch of templates that you can check out. So zapier.com, uh, templates slash
0:18:19 use cases. This has like a bunch of places where you can go figure out how to do versions of this
0:18:25 for yourself. So like anything under lead management is going to give you like great examples for ways
0:18:29 to like build a version of this for yourself. And then the second place you can check out is we have
0:18:34 this, this project that’s product called agents, which is in beta, but it has its own templates,
0:18:39 which these are pretty slick as well too. So you can, you know, company research is the number one
0:18:44 thing here. So you can go build a version of this to, to help you with company research.
0:18:50 Have you seen, uh, on the office, they, um, they show like, uh, I guess Michael stole Dwight’s notes
0:18:54 on his customers, but he forgot how to like, he didn’t know how to like read them. And he’s like,
0:19:02 Oh, uh, I see you, uh, are Eric from a PA. Right. And, uh, you have a gay son, right? Uh,
0:19:08 like, well, they’re like weird. It’s like green is color coded. Green is color coded to mean this,
0:19:10 to mean this, which means stop. You’re just like, Oh man.
0:19:17 Hold up. Just a really quick break. I know you’re watching this and you’re thinking this
0:19:22 is a ton of information. Our producer Ari, she just listened to the entire episode and she wrote
0:19:27 out all the important stuff. So if you want to go and implement a lot of these workflows or whatever
0:19:31 you want to call them, she wrote them all out. And so you can use the QR code on the screen,
0:19:37 or there’s a link below in the description. You can click that and you should still watch and listen to
0:19:42 the rest of the episode. But the thing that she made, it’s a PDF and it has everything written
0:19:46 out. So you just click and link off to all the important, great stuff. All right, back to the
0:19:52 episode. Oh, what’s the second one? Cause you have a bunch here. Yeah. So, um, we could go to the agents
0:19:56 manning the inbox one, or there’s another even more basic one. Like how basic do you want to get?
0:20:03 You drive baby. I like I’m basic. I’m pretty, I, I, I’m a, I’m a, I’m a huge noob when it comes to this.
0:20:11 All right. So here’s one that I do all the time. All right. So what’s this use case? I, you know,
0:20:17 if you’re, if you run a company, you probably get like a Google doc, like of a business review or a
0:20:24 strategy memo or something like that shared with you all the time. And you’re trying to figure out like
0:20:30 what’s going on in this thing. And you know, the use case that I like to do is I like to just talk
0:20:35 to like, just go back and forth with chat GPT to try and better understand this. So I didn’t want
0:20:41 to share our own internal stuff. So I actually pulled the, uh, some of you might’ve seen this,
0:20:46 like Mr. Beast put out this, like, uh, leaked, had this leaked memo of like how they do their
0:20:51 production stuff. So I figured we could just try it for the viewer. Um, about a year ago, Mr. Beast,
0:20:57 the famous YouTuber, he has a production company. He has like a 50 or a hundred page document that
0:21:01 sort of breaks down how he wants his team to behave in different situations. So like,
0:21:05 yeah, for example, one famous thing is he was like, I want you to hire a consultant.
0:21:08 Consultants are amazing. They’ll like save you time, whatever. I want you to post this many
0:21:12 videos. So he like had the outline for the company processes and culture.
0:21:17 So here’s what I like to do. I like to give it to chat GPT, basically tell it what it is.
0:21:25 I like to say it’s a rough draft and then I want it to, uh, succinctly describe back to
0:21:32 me what is in the doc. So I mostly just don’t want it to, I don’t want it to like put its
0:21:36 opinion on it. So, you know, you’ll do that. And then, you know, it kind of describes back
0:21:43 to you like, okay, here’s what it is. You know, the core philosophy, what makes a YouTube video
0:21:49 go viral. You know, it kind of just goes through the doc a little bit, but it’s kind of, it’s kind
0:21:54 of generic. So I’m like, Hmm, like, I don’t, I don’t know that I love this. So then I might just
0:22:03 say like, Hey, be a hundred X, you know, more specific. And I stole that from this woman who’s
0:22:08 the runs product at whoop Hillary. Uh, I saw a YouTube video where she did this and I found that
0:22:14 to be like, okay, now you start to get like, all right, a lot of really good nitty gritty details
0:22:21 inside of this where you can see what’s going on. And all the while, you know, for Zapier internal
0:22:27 stuff, I, I’m able to now try and get a better sense of like, what is this team trying to tell me?
0:22:33 So when would you use this? So you’re using the Mr. Beast one as an example, but give me a realistic,
0:22:39 uh, Zapier, like, so you’re within your company, we’re trying to decide, you know, uh, maybe I’ll
0:22:46 give you an example. Like we’re trying to decide, should we launch a voice agent or not? Right.
0:22:51 Okay. Hypothetical thing. And some product team will say, Hey, I want to go make a case for it.
0:22:55 And they’ll write up a strategy memo to say, here’s why we should or shouldn’t do this stuff.
0:23:03 Now, if they’re like really good at making their case, they’ll have, you know, qualitative data to
0:23:08 support their evidence. They’ll have quantitative data to support their evidence. They’ll have
0:23:13 multiple options for like how to go tackle this. And, you know, we could go route a, we could go route
0:23:18 B, we could do, you know, a hacky version. Here’s what like the all in version looks like. And then
0:23:22 they’ll have their like strong recommendation for how to go do this stuff. Yeah.
0:23:30 That’s like what I great like strategy memo looks like to me, but not everyone like sort of comes in
0:23:35 and does that. And they don’t always tee up the information in the way that like I am best at
0:23:42 interpreting it. And so I will usually start by reading the raw document myself. So that way I like,
0:23:49 okay, I, I understand what the team is trying to say in their own words. Then I’ll upload it like this
0:23:54 to chat GPT and start going, okay, what does chat GPT think about this stuff to start to augment it and
0:24:00 get like a thought partner on what am I going to sort of try and what types of questions should I ask
0:24:05 the team? You know, what types of things am I trying to get out at the end of the day?
0:24:06 Got it. Okay.
0:24:12 So that’s how I end up using this. But the, the interesting question is like, you kind of ask it
0:24:17 a couple of questions like this. And so chat GPT starts to get a warmup and then
0:24:24 you ask it like the real questions, the 100 times more specific one. That’s a really good hack.
0:24:30 Another good hack that I use is I will tell chat GPT the problem I’m trying to solve and what I think
0:24:34 the prompt should be. And then I just say now write the prompt for me.
0:24:38 Totally. Yeah. Meta prompting. Right. You’re like, I need a prompt to do X.
0:24:45 Yeah. Yeah. And so this is like the, the, the way that I see like some of the, the people that I’m
0:24:50 like, Holy cow, you’re like really using chat GPT or like using these tools exceptionally well
0:24:57 is they don’t just like ask their question right away. They usually ask like these warmup questions
0:25:02 where they’re trying to like gather a bunch of contacts, get a bunch of specific information
0:25:10 going. And then once they sort of have enough of like things in memory for chat GPT, then they start
0:25:14 asking like the real things they want to know, which is like, where are my blind spots?
0:25:20 But the blind spot question is, uh, so let’s say that you’re using the voice AI. So you’re saying
0:25:24 where are Wade’s blind spots and deciding if this is a good idea? Is that what you’re saying?
0:25:30 Well, basically, you know, because I’m presenting it as my own idea, uh, it, and then when I ask,
0:25:33 where is the blind spots? Sorry, I’m scrolling really fast. I’m probably making people go nuts.
0:25:37 I understand what you’re saying. So where am I blind? You’re really saying the author of
0:25:39 Where’s the team’s, yeah. Where’s the team’s blind spots? I got it. Okay.
0:25:46 Yeah. And you know, as you read through this now, so like if I was, you know, uh, Mr. Beast
0:25:51 and I was going through the blind spots, you would say, Oh, okay. Interesting.
0:25:54 Role specific guidance. Yeah. There are different roles inside the team.
0:25:59 So should I add like, you know, training that’s specific to different roles or should
0:26:05 I not? You know, there’s no feedback system for creative feedback. Do I care about that
0:26:10 or not? And now when I asked this blind spot question, I’ll find that like, there’s usually
0:26:14 one or two things that chat GPT will catch where I’ll go, Oh yeah, that’s a good one.
0:26:19 And then there’ll be a bunch of things. We’ll be like, nah, I I’m good. Like that, that’s
0:26:23 not an actual blind spot. Like that’s not real. Like we’ll, we’ll be fine with that. And so
0:26:30 it’s like, I find this just like simple back and forth to be like good at just catching stuff.
0:26:35 Like it just catches like simple errors that are obvious. Um, it doesn’t do the work for
0:26:39 me. It doesn’t do the thinking for me, but it just, it augments it. It’s like just having
0:26:44 a thought partner there that can help you navigate any of the sort of tricky decisions
0:26:48 that you’re there. You’re talking to. What are some other good warmup questions?
0:26:54 You had, you had a good one, which was the meta prompt. The second thing that you can do is
0:27:03 just blab a bunch of context to it. So a lot of folks use things like a super whisper or whisper
0:27:09 flow. You talk to it. You talk to it. Yeah. I love that. So yeah, my co-founder goes
0:27:14 on like long walks with his dog and he will literally just talk back and forth,
0:27:21 back and forth, back and forth. And then he’ll take the transcript of that and then input that
0:27:28 and say, Hey, based on all of this, now I want you to go write a strategy memo on XYZ thing
0:27:31 versus saying, Hey, just write the strategy memo on this.
0:27:35 Can you actually walk, walk me through that? You know, I, I take, I’m like a history buff and
0:27:40 I like go for walks with AI with chat GPT and like, we’re just conversing about world
0:27:47 war two. Uh, and like, it’s so funny that like I, it’s great. Like I’m like, wait, so why
0:27:51 do they do this? And how did these people react to that? Like, that’s like, I have my, I have
0:27:56 like a historian who I have conversations with obviously using it this way is significantly
0:28:02 more productive. Can you walk me through, uh, his conversation with that? Like, is it on chat
0:28:06 chat GPT? And then does he say like at the end now transcribe all of this?
0:28:12 He, I see, I’m trying to think how he does it. So you, if you talk to chat GPT, it does give you the
0:28:17 transcript. And so I suspect he’ll just take the transcript out of it. I, maybe he, uh, he might
0:28:22 have it. He might actually just tell it like, Hey, give me the whole transcript of this conversation
0:28:26 in JSON format or something like that, because he’s an engineer and he likes stuff in JSON format and
0:28:33 he’ll be able to do something more sophisticated with it and then upload it back into the next
0:28:34 prompt.
0:28:41 Got it. Okay. This is awesome. So good ways to, um, warm up questions. So, uh, I like the meta
0:28:48 prompt. You like, um, be a hundred X more specific. Another one that I like to do that. I think people
0:28:48 don’t do a lot.
0:28:50 Describe it back to me.
0:28:55 Oh, that’s a good one. So you’ll say, describe it back to me. I like to say, before we get
0:29:01 into it, if there’s any questions that you think you need answered, so you have full context,
0:29:03 please ask them now.
0:29:08 Yep. I do that as well. That’s a hundred percent. A great one. Like I, I have a really long running
0:29:17 chat GPT project around like my personal health and wellness. And so there’s a whole bunch of stuff.
0:29:25 I don’t know in health and wellness space. And so I’ll just take exports of like, I have a workout
0:29:30 app that I have and I’ll just like upload the outputs of that. I have like a sleep app and I’ll
0:29:38 upload that. And I’m like, here’s a bunch of stuff data on me. You know, I want to know how to improve
0:29:44 my overall health and wellness, but tell me if I can give you more information that gives you better,
0:29:45 better suggestions for me.
0:29:50 Dude, this is awesome. So you basically like this whole episode, it’s turning into like a thing
0:29:56 where it’s like, how can you save me 20 or 30 hours per week? You’ve done a good job so far of saving
0:30:00 time. What are a few other ways that you’re using this stuff?
0:30:07 So Zapier Agents is another one that’s interesting to go give a try to. So most people, when they talk
0:30:14 about agents, they are usually talking about like chat agents, where you’re going back and forth with
0:30:20 chat GPT or you’re going back and forth with Claude. What’s different about Zapier Agents is these are
0:30:26 fully automated agents. Like these are things where you can say, hey, I want you to just go do this job
0:30:35 for me always. So for example, let’s make an agent that replies to your email. And actually, I don’t want it to
0:30:41 reply to my email because I’m a little scared that it might reply in a way that is not up to my
0:30:48 standards. So what I actually want you to do is make drafts for my emails. So I’m just going to do like
0:30:53 a really basic thing here, which is let me start from scratch here. So you can just do like new agent
0:30:57 agent here. And we’re just going to do a custom agent. We’re just going to like do like just kind
0:31:06 of blab into this box here. So what is this? This is Zapier. This is Zapier Agents. And so it has access
0:31:14 to all of Zapier’s tools. And it has access to Zapier’s trigger infrastructure, meaning these agents wake up
0:31:19 based on all the events that might happen in your world. So you get a new email, you get a new lead,
0:31:24 you get a new customer, you have a new project come in, like all the things that you can think about
0:31:30 that happen in all the SaaS tools you use in your software, you can use that to wake up your agent and have
0:31:36 the agent go do something. So in this case, we’re going to use Gmail anytime you get a new email to wake
0:31:43 this agent up, because then we want that agent to go reply to the email. So we’re going to just do some
0:31:47 a really basic prompt. This is not a prompt you would actually use, but I want to show off how this
0:31:58 works. So you would say, hey, anytime I get a new email, write a draft reply for me. So this is like
0:32:07 super basic. And we’re going to see what this thing goes and does. So you can see, you know,
0:32:13 building these agents takes a little bit of effort to do it. But you get this co pilot that starts to
0:32:19 like suggest, like, okay, we’re getting ready to go build this agent with you. I’m going to I’m going
0:32:25 to help you make this workflow. But I need some more details. So that question you were saying, Sam,
0:32:29 which is like, hey, ask me more quick questions to help me make this better. Like it’s baked into
0:32:34 the experience here. Because we know that like, when you say, hey, anytime you get a new email,
0:32:38 write a draft reply for me, if that’s what you’re going with, like, you’re probably not going to
0:32:43 actually have a great agent. Like you need to provide these agents with tons of details
0:32:49 to actually make it good. So, you know, it’s asking me like, what email service do you use? I’m like,
0:33:04 oh, I use Gmail for my email. Two, I want you to save it as a draft reply inside Gmail. And then reply
0:33:12 content, what should the draft reply include? You know, it should, you know, it’s like, hmm, I don’t
0:33:16 know, like, what should it include? Like, I get a lot of different types of emails. That’s interesting.
0:33:20 It’s like email filtering. Should this apply to all new emails, only emails from specific? So it
0:33:25 starts to ask you questions that start to make you think. Yeah. So maybe like, uh, only internal,
0:33:31 only my coworkers or something. Yeah. Well, so I was thinking, actually, I don’t want it to draft a
0:33:38 reply to every email. What I really want it to do is, uh, reply to everyone that is asking about a job
0:33:44 at Zapier. Uh, cause I get a lot of people that are emailing me and saying like, I’d love to work
0:33:50 at Zapier. Uh, this would be an amazing thing. Uh, it’s basically just like a, like a super smart
0:33:55 vague, like, um, uh, responder. Yeah. Like when I go on vacation. Yeah. Yeah. You used to have like
0:34:00 these autoresponders, but they were like, all those autoresponders were like pretty dumb, dude. And this is
0:34:05 great because like we get, you know, for MFM, we get dozens a day of people wanting to come on the
0:34:13 podcast and totally ruins my inbox. Yeah. All right. Okay. So now here we’re going, right? So
0:34:19 it’s starting to build the actual prompts for the agents. You know, when a new email is received in
0:34:23 Gmail, analyze the content. It’s Herman. If the sender is asking about a job or a career opportunity,
0:34:28 look for keywords like job, career, position, hiring, application, blah, blah, blah. So like we’re making
0:34:34 it, it’s actually making a really good prompt for ourselves. So now I start to go, ah, this is
0:34:39 interesting. So I can come in and edit this directly. So thank them for their interest in
0:34:44 Zapier. Yeah. I’d like that. Uh, direct them to the career page. Yep. That sounds great. Um,
0:34:49 mentioned that they can find current opera openings and apply directly through the career page. Yep.
0:34:55 I like that. Keep the tone welcoming and professional. Um, I like that. Okay. But you know,
0:35:03 what I really want it to do is, um, rub them the wrong way with a knob, like, uh, you know,
0:35:15 let’s see, I want them to be polite, but really brief. Cause I find that these agents like get really
0:35:19 wordy and that’s not how I write an email. Like I do really short emails where I’m like, Hey, thanks
0:35:24 for, thanks for checking us out. Did you check out the jobs page? You know, maybe I actually should
0:35:30 ask them maybe and say, thank them for their interest in Zapier and ask if they’ve already
0:35:37 applied for a job, something like that. Yeah. Um, if I was being really fancy, what I could do
0:35:43 is say, I want you to actually go look inside our applicant tracking system and go.
0:35:50 That’s crazy. If they’ve already replied. Um, so you could do something like that. Uh, say this is
0:35:56 a draft reply in Gmail. So you can, you know, review and send it manually. If the email is not job
0:36:00 related, take no action. Right. Uh, that’s really important. Like I do not, you know, I do not want
0:36:08 you writing draft emails for, I don’t know, a customer. So there you go. And I can be like, Hmm. All
0:36:14 right. So this feels pretty good here. I like this. So, you know, go test the agent and,
0:36:20 and turn it on. Uh, and so it’ll go in now and look at my own inbox and see, you know, Hey, do I have
0:36:26 anybody that is asking for a job in my inbox right now? And I actually cleaned out my inbox before
0:36:31 this. So it’s probably not going to find anything, which is why there was a problem testing this agent
0:36:36 here. How hard has it been to encourage, I guess, I mean, your company is very technical, but how hard
0:36:43 has it been to encourage your staff to really embrace this stuff? So for us, I think we have it better than
0:36:48 a marginal company because we’re an automation company. Like our employees nerd out over this
0:36:55 stuff. We have a company value that’s don’t be a robot, build the robot. So we’re literally trying
0:37:00 to teach people that automation is a core primitive. But even for us, there still is a learning curve.
0:37:06 Like, yeah, we employ a bunch of engineers, but you know, we have accountants on staff and HR folks on
0:37:14 staff and, you know, folks that, you know, probably haven’t by default been exposed to this stuff as
0:37:22 much. And so we really do make it our mission to help people help make this technology a lot more
0:37:27 accessible. So like you could see when we were building this agent, like we’re just assuming that
0:37:32 somebody is going to come in and do a bad prompt. Like we just know that because most people don’t come
0:37:37 in breaking the problem down step by step by step by step like this.
0:37:43 Do you have like a full, like you, you, you know, at 700 people, you almost need like a five person
0:37:47 team. And it’s like, all I’m going to, or, or do you just send them a bunch of YouTube channels? I
0:37:51 don’t know. Like, how do you train? Because the shit changes every three weeks.
0:38:02 We had a wake up call when ChatGPT launched because we were like, holy cow, our roadmap and how we are
0:38:09 operating the company, it needs to shift. Like it is, there’s so much more opportunity and candidly
0:38:15 threats to our business if we are not paying attention to this stuff. And so we did a handful of
0:38:24 things that has gotten our usage of AI from effectively zero to now just shy of a hundred
0:38:29 percent. The last time we, we did a stats on it, we were right around 90% of our employees using this
0:38:37 stuff daily. And so the three things were first, we called a code red and we did a hackathon where I
0:38:42 stopped the company for an entire week. And I said, I don’t care what job you’re in. You know,
0:38:48 if you’re in HR accounting or support or sales or engineering, we’re all going to press pause for
0:38:52 the whole week and we’re going to go build stuff with AI. You know, if you’re an engineer, maybe you’re
0:38:58 going to build a feature for our products. If you’re in recruiting, maybe you’re going to go see how you
0:39:06 could use ChatGPT to write job descriptions or, you know, how you could do basic research. You know,
0:39:15 this is 2023, I think. Right. So it’s pretty basic stuff back then. So that was really important for
0:39:20 people to just start to get familiar with the tooling. Then from there, at the end of the hackathon,
0:39:26 we did show and tell. So we said, Hey, everybody’s got to show off what they built. That does two
0:39:30 things. One, it promotes accountability. So people are actually going to take this stuff seriously
0:39:35 because they got to show it off to their teammates. Two, it also promotes knowledge sharing because
0:39:40 now you get to see how other people do this stuff. And I have found that that’s been the most impactful
0:39:45 for my own learning. It’s like, Oh, tell me what prompt you did there. Like, show me how you did that.
0:39:51 Because there’s just like a bunch of people out there that are like constantly experimenting and
0:39:56 nerding out on this stuff in ways that I just simply don’t have time on my hands to look like
0:40:02 try all this stuff. So I benefit a lot from just like watching how other people are using this stuff.
0:40:11 Then the second thing we did is we said, we’re going to go do these hackathons every so often. So about
0:40:16 every three to six months, we do it again. Now we don’t do it for a full week. Usually we just do a
0:40:22 day or two. But that gets people to keep coming back to the watering well to see what’s changed,
0:40:30 what’s new. And that helps again, people get like refresh their mental model of what these models are
0:40:33 capable of and what the tools are capable of. It’s so awesome, man. This is so awesome.
0:40:38 All right. So when my employees join Hampton, we have them do a whole bunch of onboarding stuff.
0:40:42 But the most important thing that they do is they go through this thing I made called Copy That.
0:40:45 Copy That is a thing that I made that teaches people how to write better. And the reason this
0:40:51 is important is because at work or even just in life, we communicate mostly via text right now,
0:40:57 whether we’re emailing, slacking, blogging, texting, whatever. Most of the ways that we’re
0:41:01 communicating is by the written word. And so I made this thing called Copy That that’s guaranteed
0:41:05 to make you write better. You can check it out, copythat.com.
0:41:09 I post every single person who leaves a review, whether it’s good or bad. I post it on the website
0:41:13 and you’re going to see a trend, which is that this is a very, very, very simple exercise,
0:41:16 something that’s so simple that they laugh at. They think, how is this going to actually impact
0:41:21 us and make us write better? But I promise you, it does. You got to try it at copythat.com.
0:41:24 I guarantee it’s going to change the way you write. Again, copythat.com.
0:41:27 Do you want to do one more or do you have one more or no?
0:41:33 I don’t, I can’t demo this one because it’s a lot more sophisticated to demo, but I want to show like
0:41:39 what great can actually look like. I think mostly what I’ve showed today is candidly like pretty
0:41:47 basic starter stuff. But this is where if you have a couple people embedded in all your functions inside
0:41:53 of a company, you can really start to use AI at a pretty impressive rate. So for example,
0:41:57 a couple of weeks back, like the internet went viral because there was this guy who
0:42:03 had been hired by a bunch of different YC companies. He was, he had like five or six jobs at once.
0:42:08 Tell that story. So basically, I think, I forget the guy’s name, but I started Mixpanel,
0:42:13 called him out, but he was like, just so you know, I just caught this one employee working for us.
0:42:18 And it turns out he’s had another job. And then like dozens of other companies were like,
0:42:25 dude, he worked for me too. Turns out he got called out. Then he did a podcast where he was like,
0:42:29 yeah, I’ve been working three to four to five jobs at any given point. I’ve been doing this for two to
0:42:36 three years. And everyone said the same thing, which was he passed the, uh, he was amazing. Like he,
0:42:42 I thought he was this amazing employee and he nailed the job interview and everyone was like,
0:42:45 how that, that, that was the most impressive part, which is how on earth did this guy
0:42:50 crush the interviews so well that he got these positions. So that’s the story.
0:42:57 Yeah. Yeah. And it turns out there’s a whole subreddit about this. There’s a subreddit called
0:43:03 like overemployed. It’s crazy. It’s crazy. It’s, it’s like, I mean, hats off to him. Like,
0:43:07 I think if these people worked that hard at like starting a business or like just even in their
0:43:12 core job, I think they would be incredibly successful. These folks obviously have some
0:43:18 unique skills. They just employ them in like, you know, nefarious ways. Yeah. Nefarious ways.
0:43:23 I’ve always thought like, I think there was, um, a book, uh, freaking Amits wrote a book where they
0:43:27 like look at drug dealers and like how much work they do. And they like, it turns out they only make
0:43:32 like $14 an hour for how much work. And like the conclusion was like, you guys are, you should do
0:43:35 normal jobs. You would make more money and you’re clearly very hardworking.
0:43:43 Totally. So what is this? This is a, this is a template that I think would have caught this guy.
0:43:50 It’s a candidate risk detector. So effectively what you do here is it hooks into things like Ashby,
0:44:00 Slack, Verifone, an IP API, and you run through applicants and then it tries to score their risk
0:44:09 on, is this applicant potentially fraudulent? And so effectively, you know, you get an applicant that
0:44:16 comes in, it takes the details of what came in from the applicant, and then it runs checks on the IP
0:44:22 address, phone numbers, and a whole bunch of other metadata. And then it compares them to other
0:44:28 applicants to try and spot mismatches or suspicious patterns or things like that.
0:44:34 10 years ago, this would have been like machine learning engineers that are like building this type
0:44:39 of stuff. They’d be like, you know, you, you really would have like, it would have really been
0:44:47 tough to go set this up. But this was built by Casey, who is on our talent team. She just,
0:44:50 just part of the talent team. Like that’s what she’s good at.
0:44:54 Is Casey like Frank Agnelli Jr. Where you remember the Catch Me If You Can movie where they like,
0:44:58 he gets, he’s like a Czech fraudster. And after 20 years, he gets caught. And the FBI is like,
0:45:03 hey, do you want a job catching other fraudster? So did Casey have like 10 jobs and you caught her?
0:45:06 No, she didn’t. But that would make a better story.
0:45:07 Yeah. If it, if we did.
0:45:09 Casey should have done more illegal stuff.
0:45:14 Yeah. And so you can see pretty much here, like how the, the, the process,
0:45:15 how the template actually works.
0:45:19 Do you, does the way your business work? So if you scroll up, it said,
0:45:24 can’t a kid, this is like an agent that someone made or do you guys call agent or templates?
0:45:28 Yeah, this is a, this is an eight, this is actually, I mean, you could call it an agent.
0:45:33 This is actually just a straight up workflow in this case, but it has AI as part of it.
0:45:38 So are you guys going to become a platform where people can sell their agents that they make?
0:45:41 I like that idea.
0:45:44 So yeah, understood.
0:45:49 Yeah. I mean, that, that’s a no brainer, you know, is a, that’s a no brainer.
0:45:50 So that would be awesome.
0:45:58 Yeah. And it would definitely like blow you guys up. I mean, very similarly to Microsoft and Shopify,
0:46:01 like the platform model, like is, is an amazing model.
0:46:02 Yeah.
0:46:06 And these are the types of templates you could sell, right? The thing, like the, you know,
0:46:10 thing I was showing before about the like email reply thing. It that’s, that’s super simple,
0:46:17 but this, this takes some effort. Like Casey, you know, she probably went, I don’t know. I’d have to
0:46:21 ask her, but I would guess this probably took her a couple days of just like trying to think through,
0:46:27 break it down into all the different steps, you know, find all the tools she needed to, to, to use.
0:46:35 But at the other end, you know, it saves our HR team, like a huge amount of headaches because now
0:46:40 we’re like not having to go spend time on these candidates that are, you know, maybe trying to
0:46:41 pull one over on us.
0:46:45 What, what do you call your industry automation, like platform automations?
0:46:51 So we think about this as like AI orchestration at the end of the day. It’s like AI orchestration,
0:46:57 workflow automation, like AI automation. This is the kind of stuff that like,
0:47:03 like the most sophisticated teams are, are doing right now is they’re building things like
0:47:08 these candidate risk detector for hiring teams. And they’re using that to like solve problems that
0:47:13 candidly they couldn’t solve before or like do work that they just couldn’t do before.
0:47:17 Who, who’s the biggest in your space? Are you the, are you guys the big guys?
0:47:24 There are, I mean, I, yeah, we are like, it’s us. It’s, um, you know, Microsoft’s got a product
0:47:27 that does stuff like this, you know, Workato that’s like,
0:47:32 you know, much, uh, has started like much, much, much more enterprise oriented though.
0:47:37 These days Zapier is like very enterprise oriented as well too. And then, uh, you know,
0:47:41 there’s a handful of like small startups that are doing stuff like this as well too.
0:47:46 Dude, this is awesome. I, um, do you remember what, so I used to have this trick and you’re
0:47:51 the first, uh, person who was a victim of this trick that I will admit that this was my trick.
0:47:58 So I had, so for years from the ages of like 24 to like 28, I would host this event called
0:48:04 hustle con where I would get people like you to come give a talk. And I would lie to everyone.
0:48:09 The speakers, I would be like, you know, you’re talking at three o’clock. You have to be there
0:48:16 at 10 AM for the, uh, mic check and all that stuff. And at conferences, there aren’t mic checks. The
0:48:21 mics, the mics work. It’s the same mic. It works fine. The reality was, is I wanted you to come
0:48:26 backstage to just hang out. I wanted to like, not necessarily hang out with me, but I wanted to see
0:48:30 Wade talk. I think it was like, I was in the room, you were in the room and then like the founder of
0:48:35 the athletic and I would just sit and listen to you talk. And, and just like, it was, it was very
0:48:43 inspiring. Um, that was, I think 16 or 17. I forget exactly when, and you were there. I didn’t even
0:48:48 have to lie to you. You were there from like eight to seven, two days in a row, just sitting on this
0:48:53 couch with me. And I don’t know if you were doing this, this, like on purpose, but I’m pretty sure
0:48:57 you were doing the same thing I was doing. And it was awesome because that, that I, that was the
0:49:01 biggest impact of probably on my business that I’ve ever had because, or my life, because I remember
0:49:07 being with you and the founder of WeWork, Miguel and Casey Neistat and all these ballers. And I was
0:49:12 like, Wade might be a little different, but most, all these guys, they’re not that much smarter
0:49:16 than me, but they’re like, I was like, they’re not a thousand times smarter than me, but there
0:49:21 are a thousand times more successful than me. Why, why does that gap exist? Uh, it’s because
0:49:26 they are fearful, but they do it anyway, or, you know, things like that. And it was very inspirational.
0:49:30 Well, I think that’s like, there’s this meme that goes around the internet of like, you can
0:49:36 just do things. Yeah. And I think that that is like, if I can sort of like go back to my former
0:49:41 self or just talk to the graduating class or whatever, it’s like, just do stuff. Like it’s
0:49:46 like, you know, I think most people are so scared that they’re going to have egg on their face. But
0:49:52 usually what happens is that when you fail, nobody even noticed, nobody even cares. Like that’s what
0:49:59 usually happens. And so if you mess up, who cares? Nobody saw it. Like try again. Um, and so there’s
0:50:05 just so much advantage to be had around just like trying stuff. Well, um, you did just do stuff.
0:50:11 You, you did the damn thing. So I appreciate you coming here and doing this. Um, uh, thank you for
0:50:15 being so gracious and making this happen. You bet. When I come back, we’ll actually have to do the MFM
0:50:21 thing and like jam on business ideas and stuff like that. Right after we get done hitting, uh, uh,
0:50:24 you know, off for this, we’ll, we’ll get you scheduled. We’ll do the second one, right?
0:50:29 I’m down. No, I’m, uh, we’re in. Um, all right. God bless. Thank you. That’s it. That’s the pod.
0:50:36 I feel like I can rule the world. I know I could be what I want to put my all in it. Like no day’s
0:50:43 all on the road. Let’s travel. Never looking back. All right, my friends, I have a new podcast for you
0:50:50 guys to check out. It’s called content is profit and it’s hosted by Luis and Fonzie Cameo. After years
0:50:54 of building content teams and frameworks for companies like Red Bull and Orange Theory Fitness,
0:51:00 Luis and Fonzie are on a mission to bridge the gap between content and revenue. In each episode,
0:51:03 you’re going to hear from top entrepreneurs and creators, and you’re going to hear them share
0:51:09 their secrets and strategies to turn their content into profit. So you can check out content is profit
0:51:11 wherever you get your podcasts.