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- [00:00]
A
Welcome to the proven podcast where I don't care what you think, only what you can prove. Normally I go into a long list of who the person is and what's going on. Sure. I could talk about Mark exiting out of HubSpot and the fact that he's run a fund and the fact that he's a professor at Harvard and the fact that he's been rigorous and broken down the science of scaling in such a way that I've already changed how I operate my businesses after I got off the call with him. But that's not the exciting part of this episode. This episode is absolutely my favorite of the year because he breaks down not only where AI is going to be for the next 30 minutes, but where it's going to be in the next 30 days and more so the next 30 years. How it's a fundamental change of how we run our orgs, how it changes leadership and how it changes scaling across the board. This is one of those episodes where you stop, take a breath and then re listen to it. That show starts now. All right, everybody, welcome back to the show. Mark, I'm excited to have you on, man.
- [00:51]
B
Thanks, Charles. Great to be able to jam with you here.
- [00:55]
A
So for the four or five people on this planet that don't actually know who you are, which is wild to me, could you give a little bit of heads up? Who are you? What have you done?
- [01:02]
B
Oh, come on. There's a lot of people that don't. Yeah, I guess at the foundation, I'm a tech entrepreneur. I've just found very like variant ways to express it. So in the first part of my career, probably about 15 years, I started three companies, three or four. The last one is the one I'm most known for, HubSpot. I was the fourth employee there, joined as the first salesperson and then was the founding CRO through the IPO over a nine year period that ended back in like 2013 in the goal to take a minor break. I was fortunately recruited to join the faculty full time at Harvard Business School to teach sales to the MBA program, build and teach the sales program, which I still do 13 years later. So that's been a blast and has been quite instructive to some of the things that we're going to talk about here and the pattern recognition that comes from that post. And then eight years ago was approached by a gentleman at Bessemer Venture Partners to start the first VC firm run and backed by the best sales and marketing leaders in tech, which we have done. It's called Stage 2 Capital. We have four funds that we have raised and deployed across 150 startups. And that also has been quite instructive to what we're about to talk about today.
- [02:22]
A
Yeah, so there's a lot there. There's HubSpot, there's Harvard, there's stage four. There's a bunch of stuff you're doing that's moved around. You and I have started at kind of the same start line, but then you outran me within about three seconds. So thank you for that.
- [02:34]
B
Oh, come on, Dan. Just different expressions. Yeah, and actually, I forgot, Charles, which partially we're talking about today too, is I've written two books. One, the Sales Acceleration Formula, 12 years ago after HubSpot, and more recently published the Science of scaling, which we can, you know, weave in today. Um, we'll talk a little bit about it. And I'm, I've donated the proceeds to all, to both of them to different causes. The most recent one with signs of scaling 100% is donated to mental health. So we might be able to take a, A little tangent on why there during our discussion.
- [03:04]
A
Let's go right into that tangent if you want to. Let's.
- [03:06]
B
Let's talk about why you do it. We'll do it toward the end. It's all good. Yeah, yeah.
- [03:10]
A
All right. So everyone talks about the scaling and how important this is and all that, but most people are terrified of AI right now, and they don't understand that artificial. It doesn't mean artificial intelligence. It means always incorrect at the moment. But that's going to change. And the, you know, 12 to 36 months. How do you scale and leverage AI versus just being terrified of it? That's going to eat you alive.
- [03:32]
B
Oh, yeah, everyone's talking about that right now. I have to do a lot of thinking about that. It's a part of my job. It's also a part of my just natural curiosity. I love tech and the way it can change society for better and for worse. You know, lean into the better and mitigate the worse. And that's part of what we're trying to do. Let me give you a construction. Probably it's hard to know how long of a period this construct is. I think, like when we had the shot heard around the world in November of 2022 with the release of ChatGPT, the version that went viral. I mean, at the time, if you remember, people were predicting, like, no humans would have jobs in 18 months, you know, like, so I think, like, historically, so far, I feel like Silicon Valley has been probably a little too aggressive on their prediction on timing. Okay, so we have that backdrop. This is going to take longer than the techies are predicted, but it's going fast. I mean every quarter it's like it's crazy to think back three months ago where we were and where we are today. And so let me give you sort of like a longer term view of four potential phases that are specific to go to market but can be extracted to other functions. Okay. So phase one is really the elimination of all work besides humans talking to humans. So you still have human sellers talking to human buyers, but a lot of the other work goes away. That's pretty much measured by selling time. Selling time is an industry standard that measures the percentage of a seller's week that is spent with a buyer or prospect. And over the last few decades, best in class orgs have gotten that to like 25 or 30%, which sounds embarrassing and low and it just kind of shows the upside that we have. And I do think even this year some best in class adopters of AI in the sales org can get that to 75% and that's going to be a profound improvement in productivity. Okay, so we'll circle back to that because that's the shorter term opportunity that I think you're asking about. Charles. Phase two. I do think at some point we will have AI agent sellers. We're starting to see some signs of that in the tech. I think it will start with like what we see today in product led growth, very transactional, maybe SMB, simple sales. I don't think it'll ever get there in like the big million dollar deals. I think you'll, you'll have, you'll still have humans involved. But I think we'll start to see a trend in that in phase two. Phase three will be we'll have AI agent buyers. You know, if you're doing a new ERP assessment for a large global organization, I just think AI today can do a much deeper and more accurate assessment of the needs of the org than 100 person global committee and can assess vendors without being contaminated by steak dinners and golf outings and you know, Taylor Swift tickets, whatever. And so, so I think we'll, we'll have that in the, in the mix later on. And then finally, if you want to talk Star Trek, I think the phase four is the functional boundaries within an org blur. I think if you extract the organizational design back to first principles. The reason why we have a finance department and a marketing department, a product department, an engineering Department, a sales department is because of human limitation. You know, you don't see a lot of people who study finance and then go code and vice versa. And so we build these departments around our, our, you know, what we've studied and the experience we've built up, which has advantages but also disadvantages. Those, those boundaries create inefficiency. You know, finance would love to be closer to revenue, product would love to be closer to customer support. You know, like there's, there's alignment. I think in phase four, those will blur and you'll start to see organizations that almost operate more like GMs of business units with that are very agent enabled, very cross functional. Um, and so like whatever we can talk about. Yeah, but let me circle back Charles, to your short term question of like, you know, what, what does it look like in like the next year? And you know, I, I show up to a lot of board meetings and people are like, hey, good news, like we are so AI enabled in our sales org. Like we are so AI advanced. And I'm like, prove it to me dude. Like how are we measuring this? And so that's what I'm kind of been working with the ecosystem on is how do you measure in 2026 how AI enabled your sales team is? And my working hypothesis now that I have a decent amount of conviction on is the two input measures are one selling time, which we talked about. So first off, measure selling time, right? So like use AI to like see how often your reps are either in meetings with people with a calendar integration or on zooms with people with the zoom integration. What, how are we going to do it? And try to get that to 75%. And then the other one is your rep to manager ratio, which historically at least in tech has been seven to one. And that varies if inside or outside, whatever. But like today's AI can coach reps better than humans, most human managers. And so you can push that to 15 to 1 if you've adequately enabled AI as a deal support and deal coacher. Okay. So that the combination of selling time going from 25 to 75% and the combination of the rep to manager ratio going from 7 to 1 to 15 to 1, I think at least will double productivity per rep. So if you had Marty and J, you know, Jane producing 250k a quarter for the last three years, they'll be producing 500k at least, which would be profile. Now that's like pie in the sky. I'm happy to dig in, Charles, to very specific use cases and how it works, but I'll let you take it from there.
- [09:52]
A
Yeah, so there's a lot of different things there. I like how the silos or the orgs are starting to blur. So marketing, sales, and then you've got tech and you've got accounting. And I still love that HR still has its own silo on its own, and no one still wants to talk to hr. Makes me happy. That's just me ripping on HR from there. When we get into the AI side, there's a lot of people who are freaking out about not only the future, but what's happening right now. And before we even jumped on the call and started recording, we talked about how what's happening right now isn't what life's going to look like politically or economically in the next 30 years. And there's a. Via this idea of just, can we survive through the next garbage that we're dealing with right now? The influx of how things are changing right now, politically or economically, how do you see that also playing into it? Because everyone's talking so much about AI right now, not understanding that we have huge ways of how we operate across the world, from data centers to politics to power to all those other things are changing pretty intensely. So how do we survive the next four to five years?
- [10:54]
B
Yeah, let's go there, Charles. I rarely get to on podcasts, but, dude, you're. You're a guy that we can do this with. And I got like, we. We have to take off. I gotta take. I should probably take the vest off because I'm. When I'm about to do this clip. I am not Marker Berge, managing director at Stage two, professor at Harvard.
- [11:15]
A
Yeah.
- [11:15]
B
I am Marco Bears, United States citizen and earthling. Okay, so this is just me, guys. All right? And if you try to twist it, shame on you folks.
- [11:24]
A
Right?
- [11:25]
B
So, yeah, I mean, it's crazy how much effort, time, capital is going into building AI and not thinking through and how society can adapt.
- [11:42]
A
Correct.
- [11:43]
B
And that's troubling. And that's part of why I'm donating the proceeds of my book to mental health. Because I do think that all of us in tech need to do more to balance that. Don't just build, but help society adapt. We can't delegate this to Washington or academia, not because they're not qualified, but because they're just not close enough to it to really understand it to the level that you need to look ahead and help society adapt. And with each technical revolution, let's, like we most recently lived through the Internet one, we do come out the other size as a species evolved, but it doesn't come without its scars.
- [12:29]
A
Right.
- [12:29]
B
And you know, we, we are exper. We're still experiencing them from the Internet waves, specifically in social media. And they're going to be worse if they aren't mitigated in AI.
- [12:40]
A
Yeah. When we go, when we talk about social media, like, oh, we're going to be more and more connected. And then if you look at the patterns over the last couple years, people who want to do early exits, which is a nice way of saying, you know, offering themselves radically increase because loneliness increase and isolation increase, bullying increase and all of these things because we're being fed dopamine hits that are coded very specifically based off human behavior to make us react a certain way. The positive for that, for the companies are we're glued to these little screens because if I would have told you, because I'm old enough, I'm 48, if I would have told you when I was 15, 16 years old that I'm going to be staring at a screen for 10 hours a day, I'm like, you're out of your mind. Go outside. The graphics are better. But as someone who was a Microsoft trainer, I got to see it up front. I'm like, this is fundamentally changing how human beings exist, how we interact with each other, how we interact, how we make money, how we connect with human beings with each other. AI is just the amplifier of that. Where I used to say alcohol is an amplifier. If you're a jerk before you drink and I give you a bunch of alcohol, you're going to be a bigger jerk. If you're a goofball and I give you a bunch of alcohol, same thing. Same thing with money. AI and tech is that force multiplier as well, where it just takes off and it scales it. And I do agree with you, how it's evolving us at a rate that I don't believe we as a species can keep up with fast enough without some help.
- [13:55]
B
Yeah. And I think, like, if you want to go there, and this is an area where we can talk later more tactically about the principles of the book on like, how to scale revenue, which I'm one of the most well read, I think, in the world, if I could say that humbly, and I can speak to you in depth there, what I'm about to speak to you, I'm not certainly at that level. I'm not an economist. I'm not like a politician. But I am a tech entrepreneur. I think I'M decent at vision and I've also have been curious about this for many years because I don't want to bring a tech into the world that harms society. So I think it's important that we think about this. My personal opinion is you have to zoom way out to like almost a multi thousand year view. I do think this movement around AI is not comparable to the transition to the Internet. It's comparable to when we went from a nomadic species to an agricultural one. It's comparable to when we went from feudism to democracy. And if you look at the deep histories there, there was a lot of skepticism as to whether that could even be had. And there were multiple generations of massive pain in the transition. If you look at the history of moving from nomadic to agricultural, there was tremendous skepticism. It was like, where are you gonna find enough animals to eat if you just stay in the same place? And how are you gonna protect yourself from like attack? If you stay in the same place, everyone's gonna know where you are. And they tried it and everybody went back to nomadic because not only did those two things happen, everyone was dying of disease. Cause as a species, we'd never lived in close quarters with animals for so long that we didn't adapt. So people gave up on agricultural for three generations, but after living through a hybrid, they eventually figured out and got there. Now you look at, you know, feudism, we got kings and queens with like walls and we were protected. Now someone like Adam Smith comes around with the wealth of nations and is like, hey, if you make people free, if you give them free will, everyone will be better off. It's a thing called capitalism. And they're like, what are you talking about? You're going to let the peasants decide? You're going to like, you're going to create like mass hysterical craziness. And yeah, United States was this wonderful experiment. Was it easy? No. In 1840 we were killing each other in a civil war. Do you think people thought like democracy was working? Right. So like when you go through these massive ships, if not mitigated with massive intelligence, you go through generations of pain. Yes. And the pain that is coming upon us is we have a very difficult situation. And I'll speak specific to United States government and some of this can be, you know, applied to other government entities. I believe in our long term mission, which is human rights and freedom. I believe in that. And I think we have been the world leader on that mission for centuries. So I want to see us continue to drive that mission. And it is Questionable in the short recent years how well we've done at that. But like, that mission is going to be tricky because our version of democracy and capitalism, our economy is likely not compatible with AI. A post AI world, our version of capitalism is like 2% GDP growth, 4% unemployment, start work at 22, end at 65, work 8 hours a day, 5 hours a week, 2 weeks vacation. Which by the way, why is it that way? It seems all why is that? And that's not compatible, I don't think, with a post AI era. Now, on one hand, and this is the first risk we have as a country is the first instinct is, well, stop it and slow it down, turn it off. And that's actually being discussed. That'd be awesome. But we're also in the midst of a military war too, because it's almost like nuclear weapons are being developed because whoever builds the best AI can also build the best AI army and take everyone else over. So if we stop it or over regulate it, we significantly run the risk that other entities are become better at AI and will take us over. And those entities may not believe in human rights and freedom. Correct. So we have to go as fast as possible, but at the same time, we have to adapt our society and economy to the realities of post AI. And that's really tricky. It's almost like a higher form of entrepreneurship where you're almost like building the plane while you're flying it. Now, the last thing I thought I'll leave you with Charles in this massive zoom out and listen to your thoughts, is this issue becomes perhaps slightly easier to conceptualize a positive outcome when you really think about what the goal of our economy is. Because the entire discussion is around GDP growth and employment and no one is talking about surprisingly rigorous metrics called the happiness index. Because I just wonder if, like, are we really trying to be like economically accelerated and employed or are we trying to be happy? And I'm not suggesting socialism or anything like that. What I'm suggesting is like, we aren't by this metric, we aren't happy. We've fallen to 22 in the list of countries and we're going down. And like most of America is like working at the manufacturing plant in Nebraska during the day and driving Uber at night just to afford rent. Like, we're just not happy. And when you look at this, like, what AI Just like, AI can do the things we talked about a second ago on sales and totally, it can do that in our lives. Like, we can have, we can work two days a week five hours a day so that we don't have to outsource our kids and our parents anymore and have time for hobbies that actually lead to happiness. We can have a better quality of life because of that. God forbid we don't have a job. We don't have to be on food stamps, living in a brick house. We can actually live in a nice condo with free health care and shop at Whole Foods. And we can have a lower tax rate. It could go down to 5%. With a smaller government, less socialism, we can have it all be more free. And I just think, like, if we can reframe this around the pursuit of happiness and health and not employment and gdp, things start to make sense.
- [21:28]
A
Yeah. So there's very little of what you just said that I disagree with. When I've discussed AI and they say, how big is this culture? I'm like, this is the equivalent of the discovery of fire. I'm like, it's going to fundamentally change everything. Everything. And everyone's worried about the current tidal wave that's hitting them, not seeing that there's six or seven after that one that are coming. And you've got to learn to read it. If I sat down and I told you in the late 90s, hey, there's this new thing, it's the Internet, it's going to take over the world. It's going to change how we do business. And if you're like, nope, I'm not going to adapt to the Internet. That's not what's going to happen. I'm never going to get on one of these screens. I'm not bringing the Internet in my house. You're sol. You cannot stop it. It is coming. It is a tidal wave. It's a situation is you're going to have to ride this wave and adapt to it or you're just going to get. You're gone. It's over. It is a fundamental change in the human species. So I agree with that completely. Regrettably, when you talk about if we're going after employment and GDP versus happiness, statistically and just if we walk around, I'm around individuals who make more money than I know what to do with. These are very successful entrepreneurs. Most of the people are billionaires. I would say the majority of my people who are clients at least don't have bad days. They have days where they don't want to have any more days. And that's why I would love to. The mental health side of this is most of the guys are miserable because to dumb it down to the audience. Think about walking into a bar and you ask the bartender for a glass of chocolate milk and he says, here's a glass of orange juice. She's like, no, I'm sorry, chocolate milk. Okay, here's two gallons of orange juice. No, no, no glass of chocolate milk. Here's 300 gallons of orange juice. You're never going to be happy because all you wanted was your chocolate milk. But most of us have no idea what that chocolate milk is that's going to be make us happy. And on top of that, we're in a society that says, no, you, you don't get to decide what chocolate milk you want. I need you to get out the zero dark hundred, work all day, go to sleep, you know, be around your family for 27 seconds and then repeat that for the next 40 years. That's going to create problems and there's going to be growing pains across the board. But I agree with you, there is this idea that we have to look at it as it's inevitable. We have this inevitability of AI. How do we start going towards where we think we want to go versus where other people want to go? Because some people, to your point, the Uber drivers and the people working full time shifts, they're like, I'd love to be happy right now. I'd love to be driving somewhere and heading towards that, but my car is on fire and there's a lion in the backseat trying to eat me. Can we get rid of it? Can we put the pin in the grenade first? So I get there's this conceptual idea of I want to be happy and let's change it. And this is how I sleep and red light therapy and you're seeing the glasses and the type of food and going to eat at whole paycheck and all of that. I get that. But there's so much of society, even outside the United States, that is just trying to put the pin back into a live grenade. And that's going to create growing pains. And it is what it is. So I think it's a long term play above and beyond even what's happening on the very micro political right now, which I will leave alone on this podcast because I don't discuss those things on a recorded line. I think they're outside of what's happening right now. The long term play is no matter what, we are going to have some serious growing pains in society that you knew back in the early 2000s. It doesn't exist anymore. Just like society that I grew up with in the late 80s, early 90s. That's gone too. You know that nostalgia is nice, but that's not where we're going. We've invented planes. We know how to fly. No one's taken a boat made out of wood across the ocean anymore. We're in a different world. So I agree with all of that. I want to get back to what you are known for so you can put the jacket back on and we can talk about the logos and all of that. To play fair, there are things that you're really well known for and that you're really well read on. What are the things that you. That drives you is what I'm curious about. What is the passionate thing that you're like, God, I wish more people do this about economics, about scaling, about. These are the questions that when you get those couple students at Hobbit and they come over to you and they want to ask you questions, and you're like, these are the questions that I love the most. I wish I could talk about this more because this is what they're missing. This is what isn't in the books. When we talk about scaling and we talk about AI and we talk about the economics of it all, what are the ones you're like, God, guys, forget everything I teach publicly. These are the conversation. I wish you guys knew this.
- [25:33]
B
Yeah. Okay, so let's get back to, like, today and let's get back to, like, you will change something in your business after this podcast, like, within the hour. Okay, so it's really about the scaling, like, and turning. Scaling of revenue from this. Like, lick your finger, put it in the air, do it how OpenAI or Google did it to actually a science. And this is, you know, this work has been something I've been working on for 10 years. I never intend to honestly write a book. I literally just, like, go out when people ask me to do speeches just like you, Charles. I speak about what I'm seeing, and I do that. I probably create a new speech twice or two or three times a year and usually launch it in, like, a big venue like south by. Or Saster or whatever. This particular work dated back to a speech at saster in 2019. And again, I've done. I did 20 speeches. 20 big new speeches before, 20 big new speeches after. This one happened to just go massively viral. And people asked me to keep writing. I keep speaking about it, and eventually, like, Stanford's like, can you write this up right? And it really was a reflection on, like, having been in the Boardroom. For so many of these companies, watching some go IPO and many go bankrupt, what was the common theme? And the most common theme was the strategic decision on when they decided to scale revenue, which in a B2B context means ad salespeople and a B2C context means 5 extra marketing budget and how fast they decided to do it. And it's actually been absurd how haphazard it is. It's crazy how many classes we have in college on how to account for and accrue your revenue and next to zero classes on how to scale it with the same rigorous frameworks. And that's really what this journey has been about, is to bring those frameworks so that people can calculate using their own data, when they should scale and how fast. Okay, so let's extract that back to a couple principles. Oftentimes when I start this out with an audience and class, whatever, I'll just be like, all right, well when are you ready to scale? And a common answer is when you have product market fit. Which I like. Product market fit. I would credit Eric Rees with the lean startup at the beginning of the century. And it really evolved us as entrepreneurs from building products haphazardly in a lab to co building products hand in hand with customers in an agile way. Beautiful, beautiful change. To product development. And out of that came this word, product market fit, which is cool. But when I ask 50 people to define product market fit, I get 50 different answers, which does not do well for a rigorous framework we're trying to make a critical decision around. Right? So. And in fact, most of those answers are either when I have a hundred customers, when I have 500,000 in revenue, or when I have a thousand inbound leads, all of which I think are wrong. You can define however you want. But my follow up question to each of those answers is like, what if you have those things but half the customers churn and don't like your product? Do you have product market fit? Well, no, but the founder will say, I'll just adapt the product to their needs. And I'll be like, okay, well how will you know when you got it? And it's like when they don't churn and I'm like, exactly right. Product market fit is not about selling people, getting revenue, closing customers. It's about the customers realizing the value you promised. And that is best quantified by retention. Whether you're a software company and they come up on an annual renewal and renew, or you're a sweatshirt company and they buy the fall edition and then they buy the winter edition. That's when you know you have product market fit. And so, like, we can unpack that more. You know, the book goes into, like, how do you create a leading indicator around that and, like, codify the whole thing, just like you would an income statement or cash flow. But that's really the first goal of that decision, is a more rigorous definition of product market fit, rooted in customer value creation and retention.
- [30:07]
A
So if you've got one, and just to get the people out there, let's say you've got a product that's. You're driving in thousands of new subscriptions every month, whatever, and you're crushing it. And your churn rate ON that is 90 days. And you're like, hey, I'm known for this. When you're having these conversations with board of directors and you're having these conversations with founders and owners, what are the first steps you do? Because I want to make this rigorous and I want to make this scientific, because, again, this is. It's proven. This is what works. You do this, you do this, you do this. Churn rate's huge in most of the industries, for almost everything. It's huge. There's very few people have cracked that code. And the people who have cracked that code are seeing some crumbles in that foundation as well, because the market changes and what people want changes. So there's always this adaptivity to it. When someone's in, it's like, hey, I'm doing a great job getting people in the door. The top of my funnel is full. We're converting, we're closing them, and then we're spitting out for whatever reason. Even if they have phenomenal service, even if they're. They're doing A, B, C, D, E. How do you reset there where you're like, oh, crap, I'm losing people. We have spent all this. The LTV on these individuals is just. It's collapsing. What are some of the things that you've seen that could be kind of. Again, back to rigorous, because I love that word that you can go through and mark that out.
- [31:17]
B
The first one is instrument it so we understand. The second one is like, diagnose and fix. Okay, so the instrumentation is. Is critical here because oftentimes this. This measurement of retention is a lagging indicator. You know, you just said it, Charles. Like, I've got these subscriptions coming in, and, like, 90 days later, 180 days later, they're not renewing. Like, so you can't make a bunch of fixes and then sit around for 90 days and wait to see what happens. Right? So that's the first step is we have to bring that measurement back to a leading indicator and I call that the lead indicator of retention. Okay. And as a rigorous framing, I suggest that you define it as P percent of customers do E event every T time. Okay? So now I've honed this in on the decision of three variables, P, E and T. Okay? Now let me just like try to bring that to life to conceptualize a bit. For slack, it was 80% of customers send 2,000 team messages every month. That's beautiful, right? So like imagine Stuart, the founder of BA of Slack being like, you know, there's five engineers in the room, they're about to put the first version of the product out and he's like, our first goal is to get to a million in revenue. Like you can see how the organization would react. Hire a bunch of salespeople, try to increase ACVs, whatever. Or our first North Star metric is to get 80% of our users to send 2000 team messages every month. Totally different set of actions. Really close Microsoft on post sale activity. User happiness. Like what bugs are preventing that from happening, right? So that's the first step. All right, so like for Hubspots it was 80% of customers use five or more features in the platform every month. Okay? So like we can, we can talk about how you make the decisions on those. But point number one is take that long term metric of retention and bring it back to something that you can measure one month after a customer signs up. So we know if we're fixing it. Okay, the second part is now you can diagnose why that's not happening. And so you have a couple options there. Like let's just say half the people are churning, half the people aren't hitting that lead indicator of attention.
- [33:43]
A
Right?
- [33:43]
B
Well that means half of them are. So oftentimes you can like look at them from a Persona or segment perspective and you can be like, oh my gosh, everybody who has more than 50 employees is hidden it. And everybody under 50 employees is not. Well, great, let's now only acquire people with more than 50 employees, right? So like that's a very simple example, but like that's just really a refinement of what we call the ICP or ideal customer profile. So sometimes you can do it in that way as long as that, that icp, that segment that's, that's doing well. If that's big enough for you to scale through your growth aspirations for the next three Years, you're good because you'll be able to scale and hit those goals for three years. And that will also give you time to open up the other segments where it's not working, to run experiments, to tweak, to adapt so you can increase that TAM and grow even further. Okay, so that's one. Other ones are just like, you know, sometimes there's product gaps, you got to do that, sometimes there's onboarding opportunities. The biggest one where the most common one, where people look last is how it's being sold, if you're selling because like part of it's like what we already talked about, which is who are you selling to? And part of it is like, how do you sell it? Are you be, are you lying about what it takes to set it up? Are you lying about the extreme value they will get? Are you including it or whoever's needed to set it up in the presale conversation? Like, I don't have to do any of these to get the contract, which is the problem. When we set the goal as the contract, I sti skip these hard steps that will ultimately prevent the user from seeing the value. Right. So those are that, that would be. It is. Number one, you've got to extract the long term retention goal back to a lead indicator retention that can measure it in the first week or month of a customer's experience with you. And number two, using that, you need to diagnose the situation and make some changes to the icp, the onboarding, the product or the sales process.
- [35:57]
A
So what I like about this is it's very rigorous and we changed our goal from the beginning. We changed the target and then moved it back into. And that'll obviously affect the target in the long run. It's kind of like the conversation of we have right now where trains are trying to become more like planes and that's it's a wrong thing to do. People who want to get there fast are willing to deal with discomfort and not have tables. People who want to be on a train want to have that first class experience. They don't want to be corralled, they don't want to do that. So when you see the Amtraks and you see that the trains across the company or across the country trying to be more like planes, I'm like, you're going to lose, you're more expensive, you're slower, you're never going to make it. Let's redesign this. And because the first class experience, I get a table and I get to sit, I have a Lay down chair and I get food. And this is amazing. And I have this very nice person bring me amazing food. And if I'm on Swiss Air, I get move and pick ice cream, which makes me very heavy and doing all of that. But if I'm on a train and I'm on Amtrak, you're trying to make it more like a plane. Stop it.
- [36:52]
B
Stop it. Boston to New York, Amtrak. Make the WI fi work in.
- [36:57]
A
Ken, please, please, a decent food.
- [37:00]
B
That's the whole differentiation.
- [37:02]
A
I will take your stupid train if you don't make it like a plane. If I wanted to be on a plane, it's not that complicated. So understanding what your clients really want, that changes retention. Have fun Amtrak. You could use all of this in export media and we'll just yell at you even more. Stop trying to be planes. So where are some companies that you see this mistake all the time where they're trying to be planes? Where you, again, for that example, where you see, you know, AI or software companies or the people who are, you know, the biggest mistake for any of these industries, like, God, guys, just stop, stop doing this and have some tangible action, right?
- [37:38]
B
Oh, it's a really good question, especially now because we are in this like Clay Christensen's innovator's dilemma moment where we've had, we've. We're on the brink of a massive technology shift, which Clay's work has shown that the new startups actually win most of the time relative to the incumbents because of what he calls an innovator's dilemma. And so, you know, we can probably conceptualize this best by looking back 20 years to the rise of the Internet and what the innovators dilemmas were then. Now keep in mind, in, you know, the B2B software layer where I spend most of my time, the number one CRM in 1998 was Siebel. The number one HR platform was PeopleSoft, and the number one IT software platform was BMC. If you look at the market share capture in the last 20 years in those categories, their capture is abysmal. Compared to Salesforce and HubSpot on the CRM stage, compared to Workday in the HR Days stage, and compared to ServiceNow in the IT Management stage. All cloud first startups, right? Why is that? What were the innovators dilemmas? These can often be technology shifts, business model shifts, distribution shifts, whatever. All three of those were drivers, first one, pricing. You know, all three of those incumbents, it was like, here's a floppy disk, give me $4 million right Salesforce was like, here's a login, pay me 500 bucks a month.
- [39:25]
A
Right?
- [39:26]
B
They couldn't copy it. It would have killed their stock price. Everyone would have left. Right in. In innovators dilemma number two, the entire sales team was like good looking outside sales reps. Salesforce and Workday were distributed through MQLs, passed to an inside rep. It was, it was too politically disruptive to do that. Okay. Number three, they had to rebuild the floppy disk product to a cloud product. Like McKinsey was literally in there being like, it's going to cost you a billion dollars to rebuild this. We just interviewed 500 CTROs in 1998. None of them said they would ever put their data in the cloud. No one's going to buy this thing. Innovator's dilemma. The rest is history. So the question now is what are the innovators dilemmas that today's no name AI native startups that are happening at Calte and MIT and Harvard today will take over the category a hundred percent. And I can talk about what some of those are, but to your point, Charles, if you are listening to customers exactly on what they want, you will be iterative and fall into the incumbent's world.
- [40:44]
A
Absolutely. You'll be.
- [40:45]
B
Customers are terrible. Yeah, customers are terrible visionaries. That is your job. Yes, listen to customers. That's the near term value. Like show them the vision, evangelize it, whatever. But to your point, like you, you have to be close to customers, but you can't let them dictate your vision and business strategy completely. You have to be a visionary, especially in this moment.
- [41:07]
A
So here's the question I have on this one because I run into this all the time where people come in, they're like, hey, can you help me scale? There is conversations where I'm like, yes, but not you. In other words, you're so big over here and you're doing this that if I make these changes, I won't make it out of your boardroom alive. Your staff will lynch me. It's just, it's just, it's not going to happen. To make these scaling changes that you need to do an example of this is Citrix. That the conversation in that world, I was like, guys, it's over. Give the best parachute you can to your staff, do the best thing you can to them. It's over. You're too big of a ship to do it to turn on a dime. There's a lot of people who, when they look at the science of scaling and they Were like, okay, I've read the book and I'm going to do this and it's going to be amazing. You're like, yeah, that, that's adorable. That would work if you're a pigeon, not if you're a triceratops, Just, it's just new. How do you deal with that in that environment when people come in and say, hey, you know, I'd love to be able to do this. I read your book, I understand what you're saying, this all makes sense. But I'm a cruise ship trying to do a 180 in a puddle. What are you doing?
- [42:11]
B
Great, great context. Because like, it's a good and just to give people the context here. During the time between HubSpot and stage two, I spent five years as a senior advisor at BCG doing about a different engagement every quarter with companies that were doing north of 10 billion in revenue global helping them with this situation. And I've also like, you know, done a lot of tangential work around it, speeches, et cetera. And so this particular work, the science of scaling, is not just for brand new startup, build new product today. It is also the way that a $10 billion company will bring a new product to market or take their existing product to a new market. Either way you follow the same steps. Okay, now the implementation strategy is kind of what Charles is digging into here, which is it's not advisable to completely blow up the org and just try to push this into the core org. Sometimes you have to do it if you're just dead in six months. And you have to like, you know, the street just needs a big answer quick, but that's like hopefully a Hail Mary. You never have to deal with what instead you should be doing is running small experiments separate from the core Org, as separate as possible. And this is right outta Clay's work. And also a guy named Michael Tushman in the Ambidextrous organization, if you want to check out his work, where you're running these experiments to almost essentially disrupt yourself. Okay, so like the problem, like we talk about Citrix for a second. The problem with a lot of these large incumbents is when they're thinking about how to redefine themselves in the AI era, they do what I call an inside out strategy, which is like, okay, what do we have as leverageable assets that we can bring to the AI opportunity? That's what causes you to get disrupted. Because the right answer is you have to like remove everything we have and just say, okay, what if we were Starting a company from scratch, clean slate. What would we build knowing what we know about the problem, the way this problem will be solved in a very AI mature area era? That's the answer. Now it's like, how do we go through the difficult work of having this to get over there as fast as possible? And usually what that entails is let's take, you know, 20 engineers and you know, 10 go to market people, support sales, marketing, whatever, and like open up an office as far away from headquarters as possible. Yeah, maybe we even acquire a small startup to do this and like, let's give them some sandbox. Maybe like we're struggling in the southeast. Let's take the southeast away from the core business and give it to this little group and see if they can create the vision that we just did. Brand new product, new distribution, new pricing model, whatever, and see what they can do. The board's not going to be all up. The board's going to be psyched about that. It's like, it's like 0.01% of your spend and 0.01% of your revenue. It doesn't matter. But then let's be clear in the principles of the science of scaling. We just talked through a couple of them of the milestones they have to achieve and like, hopefully they succeed and then we can give them the Northeast and then the Northwest and then the Southwest and then we can give them Asia. You know what I mean? That's a little aggressive. But you get the point is you're essentially cannibalizing yourself systematically and you're giving that team a shot to go after the right answer as opposed to being distracted by the competencies of the current legacy org.
- [45:47]
A
Right. You're having this self cannibalization versus before the market does it to you. It's the idea of, hey, you might be the best hockey players in the world and you show up to the rink, but if that rink is now a pool, all of those hockey players are done. It's over. So you have to be able to pivot and do it beforehand because again, using situation as an example, sorry, y' all got eaten alive and you're incumbent. You couldn't turn that 180. So having these smaller ways to do this and pivot around is super huge in order to do that. Have you seen any ways when you have that innovation? Because this is what people are going to turn around going, well, I've done it like this for 20 years. And we just started this conversation with the fact that the world that you grew up in does not exist anymore. It's gone. Do you have ways when you've sat down with these people who have done it this way and they're going to have to innovate, which is tough for people, but sorry, it's now a swimming pool, it's not an ice rink anymore. Deal with it. What are some specific scientific or back to our term of this podcast, rigorous ways. Because when I think about this, most businesses, it reminds me of the movie. Probably going to be too old for this reference, but there was a movie called the Last Starfighter and they were sitting there spinning out and at the very end it's like we're about to smash into this planet. What do we do? And this thing smashes in front of his eyes like we die. Most people are. Most of these people are like, that's where you're going. You don't have this. How do you innovate? How do you teach your squad that, okay, we've got to innovate through this? Is it through acquisition or what are the ways that you normally do it?
- [47:13]
B
Yeah, any of them can occur. I think in this AI world when I'm talking to incumbents, I do prefer acquisition because to your point, I do think that one of the biggest innovators dilemmas for this cloud to AI shift is not necessarily going to be in the product offering or the pricing model. Those will play a part, but they're going to be how the actual company runs. Right. Like kind of the stuff we were talking about in the beginning with phase one, two, three and four getting to that GM model, if that actually comes to life. Your current staffing is not set up around that.
- [48:00]
A
Correct.
- [48:01]
B
And so like if that, if we have a significant evolution, rapid evolution, and it's happening in how a company is structured and runs and that changes the optimal hiring profile and skillset of an engineer and a seller. It's gonna be a lot easier for a brand new company to have that answer and run in that way and pass those advantages onto the customer than it is for Citrix to retrain everyone or reorg the company around it. Right. So I think the org design and skill capabilities internally is the biggest innovator's dilemma. And because of that. But I do think you have to solve this through acquisition to acquire that talent. Nucleus from the beginning, keep them separate, let them run at the right answer and systematically cannibalize yourself. And the other transition to that is being very aggressive on your corp dev team and your venture arm, probably triple your budget. There Triple the staffing. That increases the amount of ears and eyes you have on the ground of what innovations are happening in your category. It allows you to make investments, make acquisitions, to basically codify this. So that's kind of my move with the big companies right now.
- [49:25]
A
So one of the things that people are talking about within their orgs is personnel and leadership. That because the entire ballgame has changed. And just to stay with the example, it's no longer an ice rink, it's now a swimming pool. Leadership and how we interact on human behavior is going to evolve as well. The way that we interacted, we led back in the early 90s is very different how we do it now. I'm very blessed to be around some individuals who are operators and they talk about how their leadership had to change from when they were downrange in Vietnam versus when they were in Iraq versus what we have now. Those things are changing. We're implementing. One of the notorious ones is this concept of extreme ownership and using decentralized command. And it's worked very well for a very long time. AI is disrupting that as well. What are you seeing from the human behavior side and from the leadership side when you're building these org charts which are now completely different and we're no longer siloed except hr, you go stay over there. Hr. These other silos that are connecting and they're doing this. How are you seeing leadership styles change as well? And then how do you leverage that in scaling?
- [50:26]
B
Yeah, I mean I think there's like one, you're reversing the specialization movement. So we can look at that both in the R and D org and the GTM org. In 1992 in the R and D Org there was one role and it was engineer or like computer programmer, whatever. And now it's product manager, front end engineer, back end engineer, designer, data analyst, data scientist. Like it was, it was the right thing for that time. But we're starting to see Trends where like PMs are being asked to do more of the deployment. Right. Because of the, the advancements of like in simplification of product development. So, so that has a pretty big implication on the org as well as the leadership is, you know, leaders are really, you know, hiring more like founder types, athletes as opposed to specialists. Right. And you see the same thing in, in the go to market arena. Like we had one role in 1992 and it was salesperson. Here's a phone book, here's a territory book. Some meetings sell some customers and renew them. Right. And like fast forward 25 years, we have SDRs and AES and AMs and CSMs and SES, like super specialized. I think we're going back. I think we're going because of AI. Like that specialization has some disadvantages, starting with a terrible buyer experience who gets passed from one person to the right. So, like, we're going back because the advantages don't offset the disadvantages in a post AI era. And that has a massive implication on the leadership structure that you don't have these siloed departments that need to be aligned. You instead are measuring almost like many business owners. You're managing many business owners, which has an implication on your hiring model, your ramp model, how you hold them accountable.
- [52:28]
A
I think the concept of going back, we started with this idea that, hey, we went from nomadic and then non nomadic and then back again. This going back, circling back to that. And that brings me to. Because you are connected to Hobbit. There's this idea that we have right now that college is garbage and no one should do it. And it's a dead institute and the time has passed and it's not something valuable anymore. And most of the people I meet who are young, they're like, I'm never going to college. Why would I waste that money? That's just stupid. Sure, that's where I was going. Yeah. Where are you in that. That ball game where it's like, should we do this at Ella?
- [53:01]
B
There's a lot of, like, it's just so frigging expensive.
- [53:06]
A
Yeah.
- [53:07]
B
And I have some colleagues. Stig, for example, is doing some great work on this at Harvard, where I do think, like, I hate to say it, but some of these, like, lower tiered colleges are just as expensive and just don't have the career promise that it's gonna make sense. So, like that, that it's just very. I'd rather see you do other stuff than do that now. Like, you know, I'll stand behind today's, you know, Harvard, mit, Stanford, like that. Especially if you're coming out of like, you know, not much. Like, it is such a transformational experience. I think it's worth it. Even though it's expensive. And you'll be shocked. Like, the actual real cost to people. The amount of grants and scholarship that is given is extraordinary. And that's not really talked about as much. So just like. So we have the facts there. But yeah, I mean, if we zoom out and in the same way that during this discussion we've blown up and reinvented, you know, government, economy and Sales teams. Let's go at it with education as well, while we're at it. Yeah, dude, seriously, you're 18 and you go through four years of school and that's it. And then you're done. And in school, you're learning how to be like, CEO of a company as a 21 year old. That's stupid. Like, literally. And you come up with these stupid, like, you're, you, you and you. Like, you studied like 17th century Eastern Europe history and spent $400,000. Like, it's crazy.
- [54:41]
A
Makes no sense.
- [54:42]
B
I mean, this needs to be. You graduate high school, you go to one year of school to prepare you for the next eight years of your life, basically an individual contributor. Then you go back to school for a year to prepare for the next eight years of your life, which is basically managing people. Then you go back to school for a year to prepare for the next eight years of life, which is basically running companies. Then you go back to school for one more year, for the next eight years, which is the final quartile of your career, which might be boards, investor, like chairmans, like, philanthropic, whatever. Like it needs to be reinvented.
- [55:17]
A
I agree. I think it goes back to the reinventing. I don't know if it's the one every eight years things, but, you know, my, you know, my undergrad is at fau, which is this tiny little college, which FAU does not stand for Florida Atlantic University. It stands for Find Another university. And it was garbage when I was there. And sorry. So that when I lectured at Yale and I saw the difference, I was like, this is a completely different experience. It's the difference between, hey, here's Taco Bell versus here's the Four Seasons. I was like, oh, my God, this is not the same. Why are they now being charged the same? So I agree. So thank you for letting me ask that. The next one I want to ask is there's a list of books, because I believe books have been the best things I've ever invested in other than my health. You've mentioned a bunch of people for some of them who will never get into the Yales and never get into the Harvards and won't be able to sit with you if they're going, Jesus. Clearly this is a different way of thinking that operates differently and that's producing results differently and innovating differently. And are there certain books that obviously, other than your own, that you need to. That you would say, listen, go read these now, this is the core stuff. You'll get the same. It's a goodwill hunting idea. I got the same education for a library card. What are the ones that you're like, please go read these as fast as you can.
- [56:29]
B
Yeah, I know that answer cold for I need to learn how to sell. Like I'm a founder. I need to learn how to sell. I would like to perhaps. There's always going to be sell in my career and my go to is 120-year-old book. Dale Carnegie's how to Win Friends and Influence People is phenomenal. I love Neil Rackham's Spin Selling and I love the work of Sandler, which has been codified in a book. And I love the work recently of Jocko at Winning by Design. I think his most recent one is revenue architecture. So if you're deep in the go to market front, that's what I like as a founder. We teach the principles of Lean Startup with Eric Rees at Harvard. So I think that's an important like routine. Everyone loves Ben Horace's book the Hard Things about. I forget the name of it and I think that's good. I think I just like I'm trying to think of other influential ones for me, like I'm a pretty spiritual person too and like I'm a deacon at my Christian church. But I also aggressively read, you know, Islam and Judah, Jewish works of Jewish, Buddhism, Hindu. I love Hindu. And so if you go down that route, I love Autobiography of a Yogi. He's pretty predictive of what's going to happen in this AI era, actually, which I think is a cool work. It's actually the reason I was turned on to it was I was reading the biography of Steve Jobs and there was one book he read every year of the last 20 years of his life and it was the autobiography Yogi. So that's how I stumbled across it. But I think just to like zoom out of your question, Charles, I think it's just in terms of like if I can zoom that out to be how do you educate yourself? It's just that that is dramatically changing. I just think like first off, carve out 5% of your week for personal learning. Like a lot of people just get caught up in it. Make sure you schedule that and you can just like start. I I don't actually usually start with a book. I usually start with a question and problem. And five, 10 years ago I used to go out on social media and read the blog articles about it and sometimes that led to a book. These days I start in an LLM and triple down in I like Perplexity because I think the advice is sourced well so I can click through to the actual sources and check their validity. And that sometimes leads to a reading of a book. But it's really that mechanism that I think is power. If we have zoomed out your question to how to stay educated.
- [59:20]
A
Right. I also think starting with the question versus the book is a huge pivot that most people don't do. It's like, okay, what is the question I'm trying to answer? Yeah, sure, I could read everything about anatomy, but if I'm just trying to figure out how to fix a sinus infection, why don't you start with the actual thing and then kind of pull
- [59:35]
B
up and you see, even if, like what to your point, like, I want to be a great founder, how do I learn about being a good founder? Like, just prompt that and start reading. That might lead to a book, but it probably, it might Lead to like 12 articles that may actually in, in aggregate be more valuable to your development.
- [59:51]
A
So, so we've, we've gone through the process of how to educate yourself where the world's changing. The idea that, you know, everything's about to change and that how to scale. The last one I want to go into is funding. Right. A lot of people get into like, how do we fund? How do we, how do we grow into that? When we're standing in front of VCs or we're running into certain capital companies. I know you're connected to this. When people run into that. What are the things that you wish people would know? Especially as cause you see this over at stage four, you know, one of the things that you're running into and that you're like, guys, this is a different ballgame. When you come into this world, when you try to come into this and we're doing acquisitions or funding or where you're going down with this, what are some things you wish people would know more about before they approach you? Over at stage four, the biggest thing
- [60:32]
B
is like in the same way that you only focus your efforts and sales on qualified accounts and don't waste time on unqualified accounts, it's the same thing you have to do with funding sources both from the category as well as the individual firm. You would be shocked how often I have to talk entrepreneurs out of taking venture capital because they will make way more money faster if they don't like you. Just more wealth is created outside of venture capital backstories. Just, just get that straight. Bootstrapped family businesses, private equity backed. So like you just because VC says no doesn't mean it's a bad idea and you shouldn't do it and you won't make a ton of money. Okay, like let's just like conceptualize this for a second. We built HubSpot. Halligan was our co founder, CEO. I think he owned 5% of the company when we went public. We went public at a billion dollars. It took nine years. That's a $50 million payday. Great. Okay. I've talked to an entrepreneur last week that is building a feature within the go to market stack. I forget what it did. I think it like helped you uncover the intent of a buyer. Okay. I'm like dude, this is cool. Don't raise venture capital. If you raise venture capital, our asset class invests in companies that have the potential to exit at north of a billion dollars. If you raise venture capital, they're going to force you to 100x your product footprint to match HubSpot and Salesforce, which is going to be tremendous risk and take 10 years. Your chances of pulling that off are low. And if you pull it off you're going to be so diluted. Let's say you pull it off and you exit a billion just like Halingen did and made the $50 million payday. He made a lot more with the journey after, believe me. But, but great, great payday. And what you should do is just build the product that you're telling me about, go from 1 million to 3 million to 10 million over the next three years, sell it to Salesforce or HubSpot for 120 million and you own 90% of the company and you made $100 million in three years.
- [62:41]
A
Correct. Versus maybe. Yeah, it's a different ballgame.
- [62:45]
B
Yeah. So it's like. And yeah, you can pull together our friends and family million dollar round to get you going, pay yourself a little, get some engineers, whatever. So the first point is like it's all in the mindset of like qualifying your investor. That's the asset class. What type of capital is right for my opportunity? Let's say you are good for venture. Let's say you are. I talked to a buddy yesterday, he's building out like a, like you know, I think it's like a financing marketplace type thing. Okay. That's a venture backed deal that could exit multi billion dollars. You have to find the VCs that invest at that stage in that, that type of company. Like people. I get notes all the time like hey Mark, I'm building a pharmaceutical lab in Japan. That's really cool. That's probably gonna be like. If that pulls off, it's a mo. That's not what we do, right? We, like, we. We have a huge endowment that gave us like hundreds of millions of dollars. The reason we got the money was because we said that we invest in B2B software companies all over the world. Everything from pre seed just starting to build the product up to Series a. We deploy $4 million for between 8 and 15% ownership. If I was like, oh, hey, we just invested, you know, $2 million for 1% of a company, they'd be. They don't even care what the company is. They'd be like, what are you doing?
- [64:20]
A
Right?
- [64:21]
B
If I invest in a pharmaceutical lab in Japan from the most brilliant pharmaceutical person in the. They'd be like, dude, what are you doing? That's not what you told us you were gonna do. So when you're. When you're. Even if you are venture backed, make sure that you are not the first deal that that firm has invested in at that stage in that category.
- [64:43]
A
No, I love all of that. There's so many radical technical things that you're giving people. Very rigorous, very proven. There are gonna be people who wanna just spend time with you and track you down and connect with you and read everything out of your brain that just. It is what it is. I'm one of them. So if we want to track you down, because again, it's a little different, I have access to you differently. If people want to track you down and they want to connect with you and they want to learn more, how do they find you?
- [65:09]
B
Yeah, I'm Most active on LinkedIn. Just go there. I've been there for a long time and I am playing around with Instagram and TikTok more. So it's probably easier to get my attention there. I'm just trying to make sure that, you know, the young, talented techies in college, I don't know where they are. I'm trying to, like, just stay relevant there. So I do some fun videos there that I think are educational as well. But any of those work, I do my best to keep up with those things and I post there frequently. And if I could. Charles, just before we wrap, you know, I just want to mention two things. One, we just scratched the surface on the work of science of scale. And I think that the audience got a good view on some of the ways I view the world. What we talked about with the lead indicator attention, essentially, that's chapter two of 27 chapters. Just so you know. You know These are all in, like, the rigorousness of answering, when should we scale and how fast? So if you do want to check out the book, that'd be great. And as I mentioned, all the proceeds are donated to mental health for two reasons. The second reason, the first reason we discuss, which is it's just crazy how much energy is being put into building AI but not understanding the societal impact. I think all of us in tech need to do a better job of diversifying our time and energy with also the societal impact. And this is my little thing for now, is the donate version of the book and the work around that, and I'll do more as time unfolds. And the second reason is because mental health has been a big part of my life. It's crazy that when we, when we interview a candidate who is a cancer survivor, we elevate our perception of that person. But if we find out that the candidate had a massive struggle with mental health five years ago, we may have concerns about hiring them because the stigma still exists, even though both are genetic and out of their control and curable and all that kind of stuff. And so I have been a caretaker so directly to loved ones. I've also been a patient, and I'm lucky to have the credentials that society values on my resume so I can be transparent about that situation where many have to suffer in silence. And so that's the other reason, you know, why I am donating the proceeds. And I appreciate all the support from the community. And Charles, thank you for the platform for me to be able to say that on.
- [67:41]
A
Yeah, absolutely. I think people don't understand that therapy is the gift you give to yourself. And there is a difference between depression and depletion. So one of the things that I had to work through in all my stuff was sitting down and being told, hey, you're not depressed right now. You're just depleted. You're spent. You're an adrenal fatigue. You've been running from something. So there is that ball game. So if for those of you who are listening and I've been coaching and doing this for 20 something years, there's not a single one of my clients that has ever come to me when they're like, hey, I want to make $100 million that we don't immediately end up at some sort of mental health conversation, because the people who are getting at that 50 to $100 million ball game, most of them, again, they don't have bad days. They want to have days where they don't want to have any more days and having that conversation and sitting down with them and really embracing that. And they're saying, yeah, we've been there before, we get it. And if you're out there, I'm not a therapist in any way, shape or form, but make that call. It's important. Track down the person, have the conversation, start going. That ball game. It's much more common than you know. And therapy is absolutely the gift that you give yourself. And I would recommend it to everybody. Other than the book, you know, go track down this book, because I need to go track it down and read your book now, because I said, God dang it. So, Mark, I appreciate it more than I could possibly tell you. This is a conversation that I could go for probably another five or six days, and I'm sure the audience is going to want me to bring you back and have more conversations. Thank you so much for jumping on and sharing your time. I appreciate it more than I can tell you.
- [69:11]
B
Thanks, Charles. Appreciate it.
- [69:13]
A
Thanks for tuning in to the podcast. Mark talked about companies that were the best in the world at what they did. Like the best hockey team on the ice. But then the ice turned into a pool. All that skill stopped mattering back in 1998. Siebel led the way in CRM, PeopleSoft led HR, and BMC led IT software. None of them saw what was coming. Salesforce Workday and ServiceNow took over because they built something different from day one instead of trying to protect what they already had. That's the real risk with AI right now. It's not about keeping up. It's about knowing when the game itself changes. See you in the next one.