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- [00:00]
A
Yeah, it was, it was a gift. And somebody's like, oh, you got a beach house. You should definitely want to do some stargazing. Literally never used it. I tried looking through it one time and I looked at the instructions on how to, how to basically get it in focus. I'm like, I'll never do this. So much work to like calibrate the lenses and things.
- [00:22]
B
Hey everybody, welcome to another episode of the business launch podcast with your hosts, me, Roland Frazier and wonderful. Is that a, is that like a headphone thing? An earphone in your ear thing? Ryan Dice?
- [00:35]
A
Yeah, yeah, I'm not, I'm not my usual space. I'm, I'm, you know, I'm kind of low tech. I've got my, you know, airpod in doing both. All type, all types of audio, both sound and, you know, and the audio.
- [00:49]
B
So, yeah, I feel like I see a bed in the background and maybe like part of a peloton bike or is it a microphone thing or something like that? What is that?
- [00:58]
A
It's actually a telescope.
- [01:00]
B
Telescope. Nice. Because you're at the beach and like any, you know, average beach liver, you want to be able to zoom in on the flora and fauna that's going on out in the ocean. Right?
- [01:11]
A
Yeah, it was, it was a gift. Somebody's like, oh, you've got a beach house. You should definitely want to do some stargazing. Literally never used it. I tried looking through it one time and I looked at the instructions on how to, how to basically get it in focus. I'm like, I'll never do this. So much work to like calibrate the lens. But it looks good.
- [01:29]
B
But it looks good.
- [01:30]
A
Looks really cool. It's a conversation piece. So. Yeah, no, but it's been good. It's that time of year. Get to go down to the beach. Kids aren't in school, so we get to get to spend some time at the other house. So it's fun.
- [01:42]
B
I like it. Well, I'm kind of excited about what we're going to talk about today. We're going to talk about the Great Flattening. So the question is, what if the highest performing marketing team with quotes around it at your company was actually just one person? What if entire departments are about to pancake into individual super contributors? Welcome to the Great Flattening. So that's what we're going to talk about today. And the insight here is basically that AI is compressing entire departments into individual department of one kinds of roles. And so there's been a few companies that have that have kind of led the way to this that we'll talk about and then we'll go into a little bit of what's going on with it and, and chat about it. So the first one is WhatsApp. WhatsApp was the original pancake. We've decided WhatsApp was acquired by Facebook for just under $20 billion with only 55 employees processing 50 billion messages a day. So that's 345 million of value per employee. Now, generally, if we can get 2,300,000 per employee, we're feeling pretty good. Depending on like, in smaller businesses, if we can get up into the like 5, 6, 800,000 per employee, we're feeling really good. 345 million per employee is insane. I would be very happy with one employee at that rate. At that rate. So, I mean, the takeaway there is that like the small focus teams can create massive impact when they avoid what we're calling the coordination tax or the overhead of like meetings and coordinating and getting along and not getting along. The second case study would be midjourney. That is a creative department of one that, that's the art generation that does like graphics and stuff for ChatGPT. I think it's integrated in there now. But basically they reached 200 million in annual revenue with just 11 employees. Just 11 employees in 23. And then by 24 we're nearing 500 million with only 40 employees. So they created this giant global creative department that serves millions of people with only 40 people and without the huge creative structures that a lot of them had. And then the last one I want to talk about initially is Cursor. So if any of you guys are doing your own text to code development, Cursor is. It's an IDE is what they're calling them. But basically you type in what you want to have happen in plain English and it creates amazing code that's pretty daggone good. We're building apps with it. We're calling this the development pancake. So cursor hit 100 million in ARR annual recurring revenue in under one year with just 20 engineers. 20. And by 24 they were nearly 300 million in ARR. And AI is handling primarily most of the development and support. And each engineer represents several million dollars in revenue. So I want to talk a little bit and get your opinions, Ryan, on, like, why is it that traditional teams have a coordination like this hidden coordination tax? How is it that one person costs less than 10, but can actually deliver more? And, and what is the skill shift that's taking place to make this happen. So I'm going to kind of, I've thrown all that out there. Your thoughts?
- [05:12]
A
So just, just so I'm clear, this is a concept that you, you kind of introduced to me. The, the idea of the Great flattening. I never heard before. Is this your, is this, is this kind of a Rolling Fraser original?
- [05:24]
B
This is. So we got original with Pen. I like the Great Flattening. And then I'm with the kind of thinking of like flat as a pancake. And it really is, I guess I could say the panini vacation, but you know, because that's like a press down. But I like the pancake analogy. So.
- [05:38]
A
Yeah, okay. So the Great flattening TM thing.
- [05:42]
B
Yes, yes. But we're happy to share it with the world.
- [05:45]
A
Yeah. So the basic idea being that the days of not just large organizations, but specifically of deep organizations, so organizations where you have multiple layers of leadership, historically individual contributors reporting into managers, and then managers reporting into directors reporting into, you know, VPs and on and on and on. Right, right, exactly. And needing these multiple layers because you needed so many people. And historically you could kind of really only have 7 to 10 direct reports before it got unwieldy. And so that's why you needed all these different layers of management. If you were going to have a bunch of people, then you needed a bunch of managers just because you could only have so many people reporting to any one person. And so the idea being that, well, if because of AI and some of these other tools, one person can do the work of 10 now, these organizations can not just get smaller, but they can get much flatter. Yeah, that's, that's the basic. Just to make sure that I understand what, you know, what, what you're saying. Am I, am I generally understanding the concept? Okay, exactly. So I guess my question. Go ahead.
- [06:56]
B
I'm sorry.
- [06:57]
A
Well, I guess my question would be like in the examples that you, that you threw out there, they're all software companies, technology companies, like AI first companies. What about, you know, I mean, we've got companies in our portfolio that are accounting firms. Right. And companies like that, I mean, so are we seeing this across this, this flattening occur across like traditional service based companies where it's more, you know, human labor being thrown at stuff.
- [07:25]
B
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- [11:29]
A
Yeah, I think a great way to look at it is that everybody can understand whether you lead a team or not is if you think about a dinner party, kind of the maximum size that you can have at a dinner party before it ceases to feel like a singular dinner party is eight people.
- [11:43]
B
Right.
- [11:43]
A
If you've ever gone to a dinner and there's more than eight people, then it ceases to be like a singular dinner. What it really is, is a whole lot of people sitting in the same general area having a bunch of different conversations.
- [11:54]
B
Yeah. As long as you're big tables, like, it's exactly. It's like six is the number.
- [11:59]
A
Because six is perfect. Yeah. You're still breaking eight. Exactly. Eight is the max. Like eight, eight. It's going to require some. Some thoughtful coordination. Like you're going to need somebody who. It's frankly going to require some leadership. Right. It's going to require a host that's. That's going to be there to facilitate it. If you've got six, it's going to happen just automatically. Everybody's going to be there having the same type of conversation. And so that's why, you know, I would say, and that, that, that was, you know you mentioned Jeff Bezos and the two pizza rule. That's kind of what he said. Eight people is really about as big as a team can get before it kind of gets unwieldy. And we just see this across the board, you know, can you press that a little bit higher? Yes, if you have really, really, really amazing leaders. But even the military has found that about 10 is as high as you can go. Okay so for, for mere mortals 7, 8 is about the max that you could go. So I, I and, and the reason is again because of, of exactly what you're describing. You just simply a single human being cannot make their, the commander's intent known to, to more than that many people without a whole lot of loss being occurred. The telephone game. And so that's why we want to keep and as long as you can keep teams to eight or less, they're hyper efficient. The second they get beyond that and you've got managers reporting to managers and vice versa, that is when we get massive degradation.
- [13:24]
B
Yeah, and I think that's a, that's a really good point for us to mention too is that like even if you can't go to a department of one and we're going to talk a little bit about how we are going to department of one and helping people go to departments of one with digital marketer in the marketing department. But, but even if you can't go to one, if you could get down to that six or eight then you're going to be a way, way leaner in terms of cost but also way way leaner in terms of agile and ability to pivot and communicate and get things done than you are if you have to blossom up, you know to, or balloon up to all those others. So yeah, the we talked about, I'm going to use a couple of other examples and they will be like in marketing, in marketing you've got Jacob bank who was the CEO and founder of Relay. And I am going to get outside of software, but software. It's because AI and software is coming into everything that, that I feel like they're leading the way. But I am going to talk about some other companies including one of ours that's service based. But so he runs his entire marketing operation with just himself. The entire company has nine employees and he runs it with his marketing department with himself and 40 different AI agents. So he's replaced what would be a five person marketing team using these 40 agents and they handle everything from demo qualification to course personalization to progress tracking. And so they're qualifying the demo requests through HubSpot. They're making and accepting, they're making decisions about accepting and rejecting managing the entire community led growth program, courses, events, member onboarding, all of that without any extra marketing staff. And they he's done this and this is something we'll talk about when we talk about what DMs doing. But basically Jacob split the 40 agents into six departments effectively or core marketing functions. So social media which is content research, content creation, tracking and follow ups, blog and website blog content creation, promotion and tracking, email including the email newsletter and lifecycle marketing lead qualification including qualification research and then CRM updates. So he's got them doing, he's got a LinkedIn engagement coach, a blog post updater, competitor, pricing agent. And so these are all things that we would normally hire people for and pay them to do. And maybe they do a good job but they would definitely ask for raises, they definitely call in sick, they definitely not work efficiently 247 and all of that is eliminated here. And it seems like he and we are getting pretty good results from doing that. What's your thought on that? Like to people that say there's got to be human involvement in that for it to be efficient or effective versus we can effectively have an AI bot do it.
- [16:30]
A
I think you do need human involvement. I think you need a human who is running the robots, but you don't need to have one human for every robot. And I think that that is what a lot of people, myself included believed. Um, the, the comparison that I made early on and you could go back, gosh, not that many episodes ago, frankly you could probably go back, you know, a year ago and hear me use the, the comparison of AI frankly being like Photoshop is for designers. And there are still plenty of comparisons in so many ways in which the different AI tools do function like a Photoshop would for a designer. But it is so much more than that. Obviously what we are now finding, it can function and it can operate independently and it can just do whole entire. Not just to accelerate a task that a person would do, it can do an entire job that we would have previously hired people to do. The roles that you've described, we would have hired a separate social media person at one of our companies. It was on the open to hire list in Q1. It is no longer on the open hire list because what we found is that we had a social person that we could now centralize all of our social media efforts under this person. Because this one social person armed with AI and bots and good prompting and all these things now could do the work of three, four, five people. They can. Basically one person with these tools can do the work of a team. And I think that's the difference that we're talking about. So do you need a person? Yeah, I do, I do still think, and we may enter the agentic phase when you have agents that are able to completely do it. But I do still think, much like an assembly line, you still need people who are kind of running the machines, but yeah, coordinators.
- [18:24]
B
So I think it's moved more from like a, an executor of the thing to a coordinator of the automated executors of the thing. Right, right.
- [18:36]
A
And it's so much more efficient and it's happening now across all phases. Similarly, in one of our other businesses that is very, very service heavy. I mean, you're talking about client facing work. You know, they're interfacing one on one with clients. And we had three different opens to hire, believing that we were going to need to hire, you know, for this role, three additional people to interface with clients. Well, we just didn't have to do those because now each person who's interfacing with clients is, you know, about 200% more efficient because of these different AI tools. You know, they talk to a client and while they're talking to a client, this data that they're capturing is getting fed directly into AI. So when they're done with the call, the work is basically done. They don't then have to go and do additional work. I mean, it's just amazing how much faster and better everything has gotten. And so these roles, because we don't need as many people, that means that we don't need managers managing those people. So just by virtue of the fact of these teams being smaller, the organization.
- [19:41]
B
Itself is getting flatter and the coordination tax goes to zero because there is no coordination loss or leakage.
- [19:48]
A
Right.
- [19:48]
B
There's, there's.
- [19:49]
A
Which means the quality is getting better, right?
- [19:51]
B
Yeah, exactly. And, and the productivity I think as well, just from the standpoint of no, again, you know, no sick, no time off, no distractions. They say the average employee only really gets, you know, an hour to two hours a day of really effective work in an eight hour day. But the bots are getting, you know, that, that times their efficiency factor. So it's probably a 20 to 100 factor. Right. Which is kind of crazy.
- [20:14]
A
Yeah. And I'll tell you what we, what we need to figure out and we can, we can Pick up this, this later on if you want to talk about it. But this does impact compensation conversations a little bit. I don't know if you want to talk about that now.
- [20:26]
B
Let's talk about it. And remind me at the end of this, I think that we should talk about that. And it also, you know, the ethical question of, you know, can we replace humans with, you know, with AI and you know, is that good or bad or indifferent or should we. But I wanted to give a couple of non software examples. So one would be for us. In our tax prep business, we used to have an entire department of people that were taking the data that we would get from people that was uploaded to our portal and entering it into the software that prepares tax returns and coordinates and organizes. And that has all been eliminated with AI automated agents. So that basically we get the uploaded data from the client, it gets analyzed by the AI agents, they then determine the information that is still missing and then request that the information that has been delivered is transferred automatically into the software that sets everything up to be prepared in the right returns, identifies, scans prior returns and identifies the strategies that they're missing that they could be executing on that would save them, you know, more money in taxes, communicates that to the client. I mean it's, it's pretty crazy. That's just like. So I think data analysis, data entry, that's like no brainer. And then customer service as well. So we're using AI now to shrink departments like customer service on the front end where we had kind of robotic type processes that people had to do. I would argue that this actually makes the life of the worker far more fulfilling and interesting and robust than doing the same assembly line work over and over and over. But a good case study from the outside world is United H Vac Plumbing & Electric. And so the founder there, who I'm going to maybe mispronounce the name, but it's Becruz. And they took AI virtual assistants for customer service. And this is H Vac, which is heating, ventilation, air conditioning, right. So very, very traditional company service, you know, home services company. And the AI systems make learning personalized. And virtual assistants, those guys, those agents are helping the employees every step of the way. So chatbots and virtual assistants are handling customer calls. They're doing that outside business hours. Which again for us, having automated customer service in our tax business during tax season when we used to have to staff up with a bunch of temps who were just okay at their jobs, right. You know, like because they weren't full time Employees, and they didn't know, like, they didn't know the company. They're learning. It's like if you've. Back in the old days, there were answering services where, you know, people would answer the phone and they were basically useless because they couldn't do anything. They had no information or anything. But now the agents can tap in to answer questions about, like, you know, what's the status of my return? You know, that kind of stuff can all be answered 24, 7 at the convenience of the client. And if we have 10,000 people call in at the same time, they can all be handled. Whereas before, that would just stack and be delayed and we'd take 48 hours or 72 hours to get back to somebody, which is kind of not acceptable in today's age. So I think it's really cool to think about that. And so, like, in that H Vac business, they are providing basic advice about the H Vac and the problems that the customers that are prospects that are calling in or having, then they're scheduling the appointments and they are recognizing specific customer issues that are important to prioritize service requests, which I think is also really, really cool. That changes the need for a lot of people that used to be in those businesses. And so I think that's really, really cool. So I guess, like, one of the questions would be a dependency question that I'd like to ask Brian. So what happens when your department head is the entire department? Like, that's a pretty significant dependency and a risk that I think we would be thinking about. Right?
- [24:42]
A
Yeah, I mean, I think you've got to. You've got to consider. And it's funny because I was thinking about that as recently as this morning because I was having this conversation with one of our department heads and in this case, again, thinking about this department where we had these three different hires planned, and he was like, you know, good news. I don't think we're going to make these hires. I'm like, yeah, that is good news. But this department was going to go from two people to five people. And so, yay. Instead of needing five people, we only need two people. That is a cost savings. But what it also is is a dramatic risk increase. Because if one of those two people leave, we just lost 50% of that team. Right. Whereas if we had five, would you.
- [25:29]
B
Lose 50% of the productivity, though?
- [25:33]
A
It's a good question. Probably not. You probably wouldn't lose 50%, but you got to think you're losing. You got to think you're losing More than then. Then my guess is you're losing a lot, right? You're losing more than if you had five and they were still AI enabled. I do think that there's definitely more risk there. And definitely. And I say that to amplify it even more, if you're talking about a singular department head who is the department and that person leaves, well, then, yes, now you really are in trouble. Because if that person was the primary coordinator and they're the ones who know how the machine works and nobody else does, that's an issue. I mean, in Austin, Texas, where I live, a couple of years ago, we lost power. There was a major storm that came through, and so we lost power. And the main water treatment facility in the city, because it lost power, they needed to switch to a backup generator. Well, the problem is that the person who was working that night did not know how to flip the switch for this backup generator, which literally was created back in the 50s, right? There was a singular point of failure, and that person who worked didn't know how to do it because they didn't know how to do all the different switches to flip it. The entire water treatment facility lost power and we just, as a city did not have water for three days. Right? Because there was a singular point of failure because one person didn't know how the machine worked. Okay? I think that now that was really efficient because they only needed to staff one person. But that singular point of failure is really, really, really dangerous. And I think we saw this during COVID when we had absolutely optimized for efficiencies, and yet we had not optimized for redundancy. So I think you do need to be a little bit careful about pancaking too much. I think there's a point at allowing for a little bit of redundancy.
- [27:38]
B
So normally, like, traditionally we would handle dependency issues by cross training. And so I would advocate that a department of one is not smart. A department of two makes a lot of sense. So the department you were talking about, to have those two people and require them to cross train because they are coordinators, not doers. So if something happens to a coordinator, a coordinated group should continue to function because they don't need constant management. They're automated software, and we're really just coordinating and qcing the result to be sure that they're doing what they're supposed to be doing. And, you know, it's not like running rampant doing something stupid. Right? So if you've got two people in the department and we think about you Know, there's not that many departments that's still a fantastic, flattening right to pancake the, you know, the department down to just two people, ensure they're cross trained, they're both in coordinating functions, not executing functions. And then if one goes, maybe you'd have a 10 or 20% drop in productivity while you're finding a replacement or while they're out on disability or, you know, whatever, but you're really able to continue to go on and not suffer, you know, any more than you would with any kind of transition like that. What are your thoughts on that?
- [29:00]
A
Yeah, I mean, I think what, what this, what we may be ushering in is this may be the, the end of the quote unquote middle manager. And it may be kind of the emergence of the department leading, which is not a new concept you have in what is historically very large departments. You will have like, in sales organizations, this is fairly typical where you will have like a VP of sales and then you might have some sales managers, but then, you know, you might have somebody who is, you know, a sales lead. And so they're not managing people necessarily. They're, they're actively selling. But if the manager is out, they, they know enough, they're senior enough to where they can lead a sales meeting. They can give some basic feedback. They're essentially a player coach. Where I can absolutely see this going is we've got a lot more player coaches out there. There's no longer the need for the full time manager and you have kind of everybody is functioning in some sort of a player, coach, lead type role. And then there's just one person who's doing that lead. And it may be that it just rotates around who's doing that. And so now we got maximum redundancy.
- [30:06]
B
And what's interesting too, I think is the skill set changes because the skill set goes from high people skills and emotional intelligence to more technical skills. And that will be an interesting thing to observe. How does that work and how does that affect company culture too? Right.
- [30:23]
A
Lends itself really well to remote cultures as well. Right. Because now it is less about, you know, needing to build up those relationships because we, gosh darn it, we just have to be able to work together like, well, do you, you know, do you. Is that as essential? It, it does fundamentally, you know, change things. And, and I mean, I think there's societal implications to this as well. I mean, for so many people, work is also where they get a lot of their, you know, where a lot of the friendships happen. That implications that, that come from this. But I think that can, that can actually be healthy. You know, maybe you can be friends with people who aren't in your immediate department and so you know, you're not necessarily frankly like the CEO guy who's.
- [31:11]
B
Shacking up with his HR person from the Coldplay concert. Yeah, yeah. And, and maybe you have more time to develop actual friendships with people that you have other interests in outside work. So you know that that would be a very wonderful evolution. So this is kind of interesting. So this is the, what we'll call the pancake maker Flywheel. The so 38% of startups in 2024 were launched by solo founders without venture capital and solo led AI startups are reaching multimillion dollar revenues faster than SaaS companies. And I know that our friends that teach SaaS support SaaS and RSAs are all nervous about being replaced by agents. And this is to me a really interesting example. This is a guy named Banu Teja or Taeha who started Site GPT and so this is the like the product focused pancake and it's kind of meta because his thing helps other people pancake. But basically he's 24 years old. He created site GPT as a weekend project so not a lot of development in March of 23. It's an AI chatbot that allows you to create personalized chatbots trained on your website content. And so the idea came he said when he created a chatbot that was trained on software documentation to answer support queries, which is what Google's is it notebook LM does right that like really, really well. Although all the, the chats do. So he had basically he realized he could create a new product out of it and did. And so it's generating around $15,000 a month in monthly revenue with only him running the company. And it basically replaces what would traditionally require a customer service team, you know, across a whole bunch of websites. And, and he's done doing it all himself. And this is happening more and more and more. And so particularly as you see text to code, allow people to create jobs to be done, apps that can be created within a few hours, which we're doing across the board in our companies. I think you're going to see more and more of this like the, the one that does you take a picture of you in your hat like that or you know, you're out fishing or on the beach and then it makes you into a suited professional, you know, headshot those, those kinds of things. The one where you take A picture of the blueberry pancakes with syrup on your plate and it says, you know, all the nutritional stuff instead of having to enter all that. Those are all very, very successful multimillion dollar businesses that were created with, with not only a department of one, but a company of one one. And I think you're just going to see that increase. A couple others, Lumen Technologies. So technology company, but not software. Customer success, customer service process time from hours to 15 minutes. That was using Microsoft 365 Copilot. And they're going to save $50 million a year in time, which is crazy. Another one is l'. Oreal. So you know, again outside the tech space. But L' Oreal's ModiFace and Skin Consult AI effectively replaced in store beauty consultants with AI powered recommendation engines that allow one technologist, as opposed to a, would it be a cosmetologist to manage what previously required an army of trained beauty advisors across thousands of locations. That's pretty crazy. So you know like that's multilocational, free physical store, personal service product based that has been shrunk from thousands of people to one. That's pretty crazy, right?
- [34:56]
A
Yeah. Think about also the impact this is going to have on a lot of the overseas outsourcing type work. You know, there's been such a movement for that because that was such cheap labor. And now what you essentially have is free and infinite labor. I wonder how much that's going to impact the development that's occurring in some of these developing nations. Some of these businesses that have been built up to facilitate all that. I bet it's hurt. Yeah, it's a massive threat. Massive, massive threat.
- [35:30]
B
Think about places like China, the Philippines, Ethiopia, Colombia, you know, India, where that cheap labor has really, really been able to thrive and help people there evolve. I wonder what the next evolution is for them because they are clearly going to mostly be out of jobs in the next few years. Right. I mean certainly if you're service based.
- [35:55]
A
I mean if you're doing, if you're doing physical, you know, turning a wrench or you know, a screw or type thing, like you probably got time there. But even then the robots are coming so got to be care about. Like.
- [36:07]
B
Yeah, I mean robots can turn those wrenches way faster and of course they've been around but they haven't been smart. They've that now they're going to be smart. Right. I think you'll see actual minor surgeries and things like that starting to happen with machines too. Which is crazy. Crazy. But one thing that's really cool about this is if we think about like think about a marketing department and think about what our spend on the five people. Like you were going to go to five people. So what would that, what would the budget for that be?
- [36:36]
A
Half.
- [36:36]
B
Half million a year for that department or more?
- [36:38]
A
Yeah, more, more. I mean when you're talking about where marketing was, the average marketer was definitely getting more than six figures a year.
- [36:46]
B
Okay, so give me like five, that five person marketing budget department, fully built, built out. What's, what's our budget price?
- [36:54]
A
600, 750.
- [36:56]
B
Okay. And the person coordinating that would probably make what?
- [37:00]
A
Oh, I've got them factored into that. Yes. I mean the, the person coordinating that's definitely in the 150 to 175 range.
- [37:06]
B
So now think about this. If instead of paying somebody 150, we can hire a 450,000 person and still save 150, are we going to get a better marketing department result?
- [37:19]
A
Yeah, especially because you could put that same budget that you were going to spend into advertising. Right.
- [37:26]
B
So like think about the, the genius A player that you can get, you're replacing effectively 5B players or you know, 1A. They're not a because we can't afford them. Right. But now we can. That's my argument is like, like so if we had you know, two B players, two C players and a D player, now we basically get rid of all of those and we've got an A player and money for budget for ad spend. That's a game changer.
- [37:59]
A
It again, it happened, man. It happened in one of our companies. We had an open to hire for a head of marketing and ultimately what we decided mid hire was because we were looking at who we had and we didn't feel like we had the level of talent that we needed for the role. We said why would we settle? You know, instead we can just centralize, we can pancake, you know, more of this marketing under somebody who is really talented and just get them more resources of an AI perspective and what do you know that they can actually get more stuff done. It's been interesting how this has been sort of happening organically and we just didn't really give it a name. I don't think until you and I had this conversation. I don't even think I quite realized and put my finger on the fact that it was already happening and it's clearly happening to other companies as well.
- [38:50]
B
Yeah, that's why we're calling it the Great.
- [38:56]
A
And thinking about doing it intentionally also and there's clearly going to be implications in the employment market. I mean, as an employer, I'm like, yay. As a dad whose son's about to be a sophomore in college, who I'm wanting him to be able to go out there and, you know, maybe find employment, maybe he starts his own thing, I'm like, geez, this is, this is kind of scary. Like in terms of is there going to be anything, you know, for them?
- [39:24]
B
Well, you mentioned intentional. So let's talk about the pancaking cookbook. How do we cook up these pancaked companies? How do we go from department to individual? I think the first one would be we need a pancake test. Is this pancakeable? Right. Is this something that we can do? So, you know, from an assessment standpoint, I think we'd say, can this department's core output be produced by one exceptional person with AI support? And we could say, can this department's core output or can the core result we desire this department to deliver be accomplished and produced with one exceptional person and AI? So what are the natural pancakes? You know, the functions that are already carried by one star performer would be a good thing to say. You know, we've got rock stars in our company. We know who they are. You do too. Whoever's listening or watching. Right. Who are they and can they lead the way? They may need some skill, some upskilling or some cross skilling from what they're doing now that makes them a rock star to what will make them a rock star in this new. But if they're a rock star, to me it's once a rock star, generally always. And then what is the coordination tax that we're going to say? What are the hours that are spent in meetings and approvals and handoffs and miscommunications and that sort of thing. And then mapping the decision tree to think about how many people are touching each decision now and all that goes away. If we can pancake, I think that's a, a decent way to think about that test. What are your thoughts on that?
- [40:58]
A
I mean, it so, yeah, I mean, how many times do we think, oh, I wish I had another mat, you know, I wish I had another Sally. I wish, you know, I wish I could clone this person. Right? And, and, and what do we do? So we think that already, right? So I mean, if you're listening to this right now, think about that person. But at the same time, what is the perfect way more times than not to ruin that person? Make them a manager, right. Promote them I mean, it's called the, we get a term for it. It's called the Peter Principle. Right, Right. Talented people will be promoted to the level of their own incompetence. And why does this happen? The reason it happens is because we take these people who are talented in the role that they've been given and we remove them from that role and we give them a new job called management. A role that they're not trained for and are frequently ill suited for. And the classic example of this is sales. You take somebody who's a really great salesperson, you put them in a role of a sales manager. And you know, the old cliche is you, you trade a good salesperson for a bad sales sales leader. Typically, great sales leaders are not the most amazing salespeople. And typically the most amazing salespeople don't make the best sales leaders. Just like in sports, the best players rarely make the best coaches. The best coaches usually weren't the best, you know, the most of the most talented people. But what we're talking about here is now we actually can clone these people. Now we actually can make them a 10x version of themselves because we're not giving them a new role called manager. Right. We're just saying, hey, we essentially want you to have bionic arms. We want you to be kind of Neo in the Matrix where now you know kung fu in addition to all the other voodoo that you do so well. It's a way to multiply somebody's output without calling them manager. And that's something that before, I don't know that we've ever really been able to do.
- [42:55]
B
Yeah, it's an amplification of what they're able to accomplish of their rock stardom as opposed to a change of roles. Right. And that's a huge, huge thing, I think. So that's kind of the pancake test. And then how do we find that person that's going to run that department, that's going to effectively be that department, that's the super individual contributor. Right. Who is our rock star. So who are the people that already outperform their peers by 10 times? Right. And the skills that they're going to need, you're going to have to basically say they need to be strategic thinkers, not just brute force performers. Like the person that's working 18 hour days might not be the best choice for this. Like if they're a rock star just because they're out working everyone. We like the work ethic, but they need to be strategic, they need to be fluent. In AI or educatable and trainable and upskillable into it. And then they need to be able to execute autonomously because they're not going to be managed like a traditional mentorship manager. They, they'll still have whoever they report to, but, but it's definitely more hands off. So you want them to have kind of an ownership mindset test that, you know, that they can pass. So are they thinking like the owner of the department, maybe not the owner of the company, but are they owning the department and the things it's going to do? Are they self motivating? Are they. Because there's nobody to motivate them. Are they self? Executing? Are they okay not having social interaction with a bunch of people in their department to support them and praise them and you know, lift them up. Obviously we need to lift them up from above down, but those will all be things to think about which kind of goes into like can they be a cultural fit in a pancaked organization? Right.
- [44:50]
A
That.
- [44:50]
B
Because we don't want to put, you know, a square peg into a round pancake. So what are your thoughts on that?
- [44:56]
A
Yeah, I mean, I think it's, it's got to be somebody who is good enough at what they do, but not so good and proud and entrenched that they're the old dog unwilling to learn new tricks.
- [45:11]
B
Yeah.
- [45:11]
A
And I think what makes this a little bit hard is, is that can be a bit rare. Very often the person who, who is that 10x performer, they see themselves as being so good and as being so special that and as being so precious that it's like, just let me do my thing. And so to find somebody. And I think we've been fortunate in this area where the people that, that are those high performers, they've also been naturally attracted, you know, to AI And I do think you have those people as well, but you definitely are looking for both. And I think if you have somebody who is this high performer but they just absolutely refuse to, you know, to adopt this, then then probably as talented as they may be, that that's not your person.
- [45:56]
B
Yeah, I agree. So once you've identified that person, then we say, okay, how do we amplify them? So what's our AI amplification setup? And the first is going to be what tools are you going to give them? Because we're, we're basically thinking we want to use AI for analysis, to understand everything that exists now and that we want to make happen. How do we actually make it happen? So AI for execution and then how do we have our different AI agents coordinated with AI, Right. So how are they coordinated and talking to each other? And I think the way that we make that happen is we need to do a process map where we look at everything in the department that's going on now and we document every team process that AI is going to handle now. And then we're giving the AI agents the full authority within that defined box of each of the things that they're executing on. Does that make sense?
- [46:53]
A
Yeah, I mean, and this is really simple. If you, if you go through the process that, you know that, that we go take our clients through that, I talk about in, in, you know, the get scalable book of value engine mapping of business process mapping. That's exactly what you do. And not enough businesses do this, where you visually map each step and stage of your value creation, whether the, how you go about getting customers and clients, how you go about fulfilling and delivering. Once you have a customer client, how you go about, you know, actually creating, assembling the products and services that you make. Like every business has those three core value chains. If you can map them and visualize them now it becomes pretty easy to go through an audit and say, okay, which of these are aiable? That that 10x person touches?
- [47:37]
B
Yeah, I like that. And then we don't just do that, set it up and then assume everything's going to work out all right. We need safety nets. So that's going to be alerts, dashboards, override mechanisms, you know, some monitoring for quality control so that we do have a human looking at it. Because the AI won't know if it's off base for what we want other than the constraints that we build into it. So that's going to be an important part too. How do we build that safety net in? And I think that's it. It's alerts, dashboards and override. So then the last human phase and human touch.
- [48:15]
A
Right? Because I mean it's always the 10, 80, 10. Like what you described is that, that 10% of inputs, like let's identify and clarify that the awesomeness that is the person you're multiplying, that's the first 10%. AI is going to do 80% having that person who's there to do that final check, that last 10% of the check, but also the josing, you know, that, that, that kind of, let's spice it up a little bit. You know, if you think about a fancy restaurant, the chef creates the menu, the chef creates the recipe. Their, their cooks are are the ones making it, but they're, it's still going to go to the line and they're going to sprinkle some parsley on it and you know, wipe any spots off the dish before it gets sent out. And so that, that last 10%, I think is going to be important and they need to own that still.
- [49:03]
B
Yeah, I agree. And then I think the, like, how do we, how do we launch this, the communication I'm kind of interested in thinking about. How do you suggest the transition happen? Because I think like, we definitely soft pancake to start. We're going to run it parallel for 30 days to be sure that it works before we fully commit. How do we, how do we measure performance? You know, we have to set up KPIs to be sure that like, are they the same KPIs that we had departmentally before or did they change? Because some of those were human based and now we, they're machine based. So we, we need to modify them. And then how do we know that we were successful? And how do we communicate what's happening to the employees? And are they going to be redeployed? You know, are they going to be let go? Are they going to be upskilled? What, what happens there? How would you say running that transition process would be most efficient and effective?
- [50:02]
A
I can say how we've done it is, you know, because we've said, hey, everybody, good news. Instead of going in and, you know, hiring a bunch of new, new people, what we want to do is see if we can just make you all more efficient first. Because that's going to make the company, allow the company to be more efficient, which is going to create a lot more opportunity for everybody who's here. And so we don't want everybody just working harder. We want to make you all a lot more efficient. And so if we can give you the tools to do that, then that means we don't have to staff up as much, creates more margin, which ideally eventually means that everybody can make more money. And so that's how we rolled it out. Out initially and which was important because when we first talked about AI, and I do think that this is important, when you roll out AI to your team, you need to know that they all are assuming that they're going to get fired.
- [50:59]
B
Yeah.
- [50:59]
A
And I know because we had. Yep. We had people come around and say that. Yeah, yep. They're going to assume the worst. It's human nature. And what's the worst possible version of the story? That's the One they're going to tell. And so you, you have to inoculate against that and say, our goal is not to do this, to have an immediate reduction in force. Our goal is to do is to make everybody here as efficient as possible. Now look, if that's not your goal and your goal is in fact to have a reduction in force, I don't rec. I would never recommend lying. But just know that if you do roll this out to the team and your goal is to have a reduction in force, then I would look to get that data and make that decision, you know, quickly and take care of the people who you are going to, you know, release to the marketplace. Because that's going to be tough. That, that's going to be tough for them. They're going to know that it's coming and it's going to be a really, really, really painful process.
- [51:55]
B
And you mentioned at the beginning compensation. So let's talk about compensation and then let's finish with talking about what digital marketer has created and how that department of one agent organizational structure is something that we're putting out there in the market because we've done it ourselves.
- [52:17]
A
I think it's important to acknowledge that AI is different, but AI is still technology and so it's unique. It's unlike any technology that we've seen before. But I do believe that it's technology and that what it does is it is a multiplier of human performance. Right now, so far, it's not doing anything that humans don't do. Now we will get to the point of super intelligence where AI is truly doing things, thinking of things, calculations that humans could not have, not ever done. We're not there yet, is my understanding. And so still everything that AI is doing is building upon and is a multiplying force of what a human being would do. That's simply technology. That's the wheel, that's the hammer, that's the, that's the pulley, that's the assembly line. Every time technology has been introduced into the workforce, into the labor force, it has resulted in people having better, safer, higher paying jobs, period. I mean, the industrial revolution, while there were some, definitely some like painful, gross working conditions at different points in time, there is no doubt that the end result of that was a lot of people were pulled out of poverty. They ultimately did have safer working conditions and they did make more money. I believe that the same thing is going to happen, you know, with AI is that you're going to have people doing better jobs, more interesting jobs, more Way more fun, you know, interesting work. And I do believe that as these humans become a lot more efficient, employers are just going to have to pay more. We're going to have to pay more because the market is going to demand it. Like when the people who work for us, when instead of hiring five, we only need two, we're going to need to pay them more money. Just period. Now, we don't need to pay more money today. And right now, today we can't because it hasn't all caught up yet. Right. Like, we're not yet at a point where the revenue is there, but, like, that's going to come and the rest. Like, we're at this weird point right now where the gains are just starting to be made, but the output, you know, the gains are starting to be made on the productivity side, but the output hasn't been realized yet from a. From a revenue and profitability front. And it hasn't been realized at a large enough scale yet that it's impacted what everybody across the board is earning. But that is going to come, and I think it's going to come quickly. And what that's going to look like is for a while, layoffs. I think it's going to look like layoffs. I think it's going to look like. I think it's going to look like hiring freezes, but for those who have jobs, it's going to look like wage inflation.
- [55:12]
B
Yeah.
- [55:13]
A
Then what I think is going to happen is I think you're going to start to see these teams that got pancaked. I think you're going to see them grow again and deep pancake, but they're going to grow again and they're going to deep handcake looking a little bit different and being far more efficient. And so now they're going to get bigger again. But they're going to get bigger again with each and every person being 5, 10x more efficient and productive than they were before. And that will be the output, because that's kind of what it's always been. You know, a factory started out only needing a couple of people because they had this amazing assembly line. And then what do you know, they were super profitable. So now they have 10 assembly lines, and so they needed a bunch of assembly line workers, and so now they needed a floor manager, and so now they need it. And I think the same thing is going to happen here, but I do think it's going to take some time and I think the interim is going to be weird and painful.
- [56:13]
B
I agree. All right, let's finish by talking about what we are doing to help people, to help ourselves in the marketing side through Digital Marketer. You want to talk about that kind of product that we've created there?
- [56:26]
A
Yeah. I mean so at Digital Marketer and what we realized is that for years and years and years we've been preaching this concept of the full stack marketing team, right? That, that digital marketing, there, there is no such thing as the full stack marketer. Like there's no way for like one person to know everything that there is to know about marketing. It's just gotten too complicated, it's gotten way too specialized. And that there are these eight core, kind of critical core functions that you need within marketing, whether it is social content, email, you know, on and on and on, like all the different functions that you need within a marketing team that are, that are going to be there. And so what digital marketers focused on really for the previous decade is in training humans to do those functions. Well, the pivot that we made earlier this year is instead of training humans, let's train AI. And so now what we have the ability to do is to, because we've trained these AI agents is we can essentially say, hey, head of marketing here is essentially a fully staffed, and I'm throwing up the air quotes here, here's a fully staffed team of robots that is an expert marketing team that specializes in all the critical core functions. So here is a amazing world class social media marketer who knows every, who's an absolute social media rock star. And here's an amazing rockstar content marketer, you know, all the email marketer, like all the critical core functions that you need. And then what we do, we work with our, with, with the clients to intake everything about their business, train these agents on everything about the company's business. So now they know everything that there is to know about the, you know, the thing that they do. They don't yet know anything about the business that they're working for, but we input that and now all of a sudden what you have is essentially the perfect employee, in fact the perfect team that, that one head of marketing can run. And it's, it's what we've shifted over to, it's what you know, we're working with a handful of folks to, you know, to begin to test out. And we really do see it as not just the future of what digital marketer the company does, but we see it as the future of marketing teams as a whole. Whether you have a marketing team of one that got pancaked or if you have an amazing content marketer. So, I mean, we'll work with teams today and they're like, well, you know, I've got a content marketer. Do I need to fire them? It's like, no, you don't need to fire them, but like, give them this amazing marketing sidekick individual, Right? Exactly. Give them this amazing marketing sidekick. And not only do we need to. Should we train this agent on everything about your company, but let's also train them on everything that this person knows. So they're going to know everything that we know. They're going to know everything about your company. They're going to know everything this person knows. And so now if this person ever leaves, you still have the best marketing content marketer that your company's ever known. And so that's the idea. And yeah, this is where I kind of see basically all functional teams, certainly the ones that are doing internal functions going.
- [59:43]
B
I think you're right. So that's, I'd love to hear you guys thoughts on what we've talked about. This, this great flattening and pancaking. It's happening whether you realize it or not and it's gonna happen to you and your competition is going to be doing it. So if you don't, you're going to be left behind and you're going to have costs and talent that is not, not able to keep up. So it's not, I think, really a choice. It's something that, that is going to happen to you one way or the other. So definitely love to hear your feedback and thoughts on it everywhere. On social, obviously. Forward slash my name, forward slash Ryan's name and business lunch. So if you found this fun and interesting, share, let us know what you think and if you're interested in maybe being part of our beta program with the digital marketer stuff, Ryan, what would the best way for them to access that be?
- [60:34]
A
Yeah, I mean, hit me up on either, you know, Twitter, Instagram and maybe.
- [60:39]
B
Just go to digitalmarketer.com because we're gonna have something about it there.
- [60:43]
A
I'm guessing, right, it's because it's still in beta. There's nothing directly there on the website. So I would say if you want, if you kind of want to cut in line, DM me directly, I'll know. Tell me that you heard about it here and I'll make sure that you get to the right place. Just DM me beta on Twitter or Instagram. If you DM me beta, I'll, I'll make sure that you get to the right place.
- [61:11]
B
Fantastic.
- [61:12]
A
And by eye. Somebody on the team will see it.
- [61:14]
B
So your. Your AI will do it. I like it.
- [61:17]
A
Yeah.
- [61:17]
B
All right, guys. Thank you. We'll see you next time on Business Lounge. Hey, Roland Frazier here. If you're looking for a way to grow your business exponentially to get more customers and ultimately increase your wealth, there's no faster way to do it than to acquire other businesses that already have the customers, products, services, teams, and media that you want. If you want to double your sales, just acquire a company that has the same sales as yours. It sounds simple, but far too many people end up starting new businesses that fail and forget that they could skip all the hard stuff and just acquire one that already exists. There's a reason why private equity firms, family offices, big companies like Apple, Google, and some of the smartest entrepreneurs on the planet do not start new businesses from scratch. They acquire already successful businesses, and when they do it, they instantly increase their sales, their profits. If they want market share, they increase that they can get new products and services to offer, all instantly. Hey, look, 90% of new businesses fail. 90%. Why not acquire an already successful business and increase your chances of success by 900%? What most people don't realize is you can acquire highly profitable businesses with no money out of your own pocket in pretty much any country in the world, regardless of your credit, and without having to go find a bunch of investors or needing any experience. Look, I've been acquiring businesses for over 30 years now, and I cover the whole process in my EPIC Investing strategy training, and I want to give it to you 100% free. Just visit businesslunchpodcast.com epic to get your free access to my EPIC investing training right now, while it's available.