Why does most AI investment never reach the P&L? What the 6% do differently

[00:00] The Olympian worker

RH: Today I'm joined by Keith Jacobs and Grant Baldwin, co-CEO and partners at geniant, an AI and experience consultancy, which is their latest company in partnership that goes back to before the dot-com boom. On the pod, Grant cites a study that finds that most AI investments in companies today, we filmed mid-2026, that most AI investments today show up nowhere on the P&L. So we dive into what it actually takes to make your AI investment ROI and be among the 5 to 6% of successful AI investments. And to no surprise, it's done by redesigning how the work gets done. We dive into their conviction that AI only creates value once you understand how people actually work, which is why they staff anthropologists and psychologists before any software gets built, and why tomorrow's workers will have to perform like Olympians. Once the busy work disappears from their day-to-day, they might have to step up and do even more and perform even greater, which not everyone might be willing to make that jump. We also get into the unique culture they fostered by having every employee own equity day one. We also go through a thought experiment of the future of software. If AI closes the gap between what you want from it and what you want to actually get done through what Keith calls context gardening, then software will stop being built for everyone and be built just for you, meaning every piece of software you interact with is custom to you. That sounds like an exciting future. Now, let's get to it.

RH: That struck me that you mentioned Keith earlier is that, and this is a good thing, right? With leveraging of the technology, we're going to be able to remove redundancies or administrative work that we don't like to do or that actually is a majority of some roles. However, that means that we have to be at a high performance state most of the time and that most roles will have to evolve or change. And then you call it, I think, the Olympian, you termed it with the Olympian worker. Can you shed some light on how you arrived at that conclusion? But then also as a leader, how are you seeing that translate with the teams that you're influencing with the AI implementations you're using, but then also with your own team? How do you balance that, so you're telling other leaders, here's what to look at, but then you're leading the other people, too, in your company.

[02:35] Performing at a higher level

KJ: Yeah, exactly. That's such a great topic. We could spend the whole podcast on this topic. But yeah, it's a thought experiment that is turning into reality as we at geniant are implementing AI-enabled solutions for our customers. We're automating away a lot of the busy work that our customers' employees do every day. So imagine the most boring job you had, probably years ago or maybe when you just entered the workforce. It was 90% kind of grunt work, boring tasks, and 10% of it you love. And then as your career grew, you loved more of the job and less of it was grunt work. And ideally, we would all like to be in this amazing flow state where all we're doing is the highest value work and we're performing at 99% of our capacity. But the reality is there are a lot of people in the workforce who will not be able to adjust to having less busy work and more high cognitive work. And I think of it like Olympians training for the Olympics. You know, there are only so many people who could run a 10 second 100 meter dash. And yet this automation of busy work that AI is going to create will require more people to perform at a higher level. So not just automating away work so that workforces are cut, but changing job descriptions and org charts. And it's a real change management challenge that's coming on the back of this AI work. And we, yeah, you're right. We at geniant have experienced that. And over the last few months, we've changed a lot of our own roles. For example, many of our designers, we now call design technologists. And they're doing design in new ways using AI to automate away some of the slower tasks and more boring parts of being a great designer. Some of our software developers are becoming forward deployed engineers and doing development faster and having to get deeper in business process and understanding our clients. So every company is going to have these changes that are driven by AI.

RH: You think it's requiring more of the person or the role is like, is AI pulling more of the person's humanity and intellect into it to perform at a higher level.

KJ: I think that is a beautiful way to put it and it depends on the person. The person who's optimistic and I think even not just, I mean certainly the tone of the leader will help here. But no, I think each person perceives their career differently. And a person who loves that top 30% of what they're doing is going to be thrilled by this. The person who just wants to show up work and just like the busy work and didn't have a lot of ambition for their career, I don't think this is going to end well for them. So it's bringing out the best in people who love what they do, and it's going to be tough for the people who don't.

RH: I think the cliche, the future job is entrepreneur. You have to have that drive and that tenacity to do it. Andrew DeBerry, I had him on the pod. He did some work with the DoD and Meta on the AI side. He said something that I really resonate with is that humans are beautifully inefficient. So I think we do have to balance that out, right? Like we do have to have like that, that sprint mode, but then being able to still hold on to our humanity when we're executing because we're not always going to be able to have the, I mean, throw a javelin 200 yards, 300 yards, or sprint or do the marathon. Like that's the performance, but then we do have to have that rest time and that on and off time to balance it. But with that, though, when you're working with teams and you're like, orchestrating execution and for them to get some of the benefits of this, Grant will dive into that in a second. But how do you frame for leaders in the change management side on how their organization will change, their team members will change, the mindsets will change, their perceptions of work will change.

[06:46] Building the right thing

KJ: Yeah, well, this is where we have a great advantage. And we've just loved this pivot that the world has done into AI because the reality is, is AI enables us to make software faster, but it does not yet enable us to, for sure, make the right software. So we might build things right, but we're not building the right things. And at geniant, we have full-time employees that are anthropologists and psychologists. And before our designers begin work, using AI to design new systems, we are doing qualitative research to understand the broken process and then fix that process. Imagine new ways to fix it. And out of that imagination comes deep understanding of what's needed for change management. And if all you did was start to design the new software without understanding the underlying motivations of the humans, then you might build an incredible piece of new software, but not understand how the job descriptions change, how the org chart needs to change, how processes need to change. And then the project isn't a success. And we do hear that a lot, that a huge percentage of AI projects aren't successful. And you have CEOs saying, I thought it was supposed to be amazing and we're not getting value out of it. This is why. It's the experience layer that is so important for doing AI work right.

RH: I hate to say it, but I do want to say that a lot of the firms that I hear online or you might be hit up all the time on LinkedIn as well, it's like, let me just build you a prototype. They always go straight to a prototype. Like, I don't need a prototype. Why are we going first to diving into understanding? So you're saying that let's dive into the experience layer, but from what I'm hearing, it starts with curiosity, like deep curiosity, knowing what's going on through with the client, right?

KJ: Absolutely. I love the word curiosity. Another word we use is deeply understanding, not just their business goals, although that is the number one most important thing, but the people that they are going to be using to achieve those business goals, now that we have AI, those people are going to do different things. And so what are the ideal, most efficient ways we can still use that human labor combined with AI automation.

RH: I think the stat, I mean, I don't know if this is a bland stat. And I hear it even in other major pods and just like in some of the threads is most, if not a majority of AI initiatives fail. Like it's like in the high 90s. So the failure rate is high. And like there's a lot of capital invested into it. Right. So you do have to be able to attribute ROI to it. Grant how like, I mean, you work with companies that like big, big names, Delta, Coca-Cola. And I'm pretty sure they had access to the other like big four consulting group. Like, what's the difference? Like, how do you make it? What's, I mean, if it's 95% failure, 96% failure, what's the 4%? What's the 3%? How do we get to 3% success rates? How do we like hit the nail on the mark?

[09:51] The 6% making real money

GB: I'm going to tell you how to make money with that little learning from the 5%. What that stat actually said was, that there was no measurable impact on the P&L. Nobody could prove that a dollar in created $5 out. And you talk to some of those leaders. I think McKinsey even just put out a recent study a few days ago. And they talked to 1,700 companies. And only 6% are making real money on AI. And 72% of that 6% were really successful because they admit. And so, you know, take that for what it's worth, that they redesigned how the work gets done. How the work gets done is the key part. And that has been a mantra of American business culture dating back over 100 years, right? You look at Henry Ford. You look at Carnegie and producing steel. These are not new concepts. They look at every aspect of how the work gets done and they define the goal. What is the business objective? And how do we work backwards from that business objective? It's the whole Elon Musk thing of I could have bought a Russian rocket for 65 million bucks. And everybody in America was saying how expensive it was going to be to build my own rocket from scratch. But actually 2% of the material cost of those rockets comes from that 65 million. So how do we deconstruct all that? And then how do we build the target operating model that is inclusive of AI is the thing that Keith and I have been lucky enough to be a part of. for the past 20 years, we happened to buy a company that was founded in 2003 that did all of the things, you know, the undercover boss, the Elon Musk sleeping on the factory floor, things that we are known for. And you reconstruct how the work gets done from a first principles perspective. And then you can wire in all these other complicated things that actually do work like AI.

[11:53] Paving the cow path

RH: Give me the framework. Like, if we look at like the industrial revolution, it was mass production. and it was like factory settings so that we were able to print thousands of widgets per hour via a process. Help us think, or even on the mental model, look at, how is work getting done now? Even if it's different per company, because every company is its own organism. You know, we have that, in my opinion, like humans, like we're about 98%, 97% the same. Skeletal structure, we've got a couple of hands, thank God, and we can walk. And that's the natural design. And then the 3% that makes us unique, our quirks, our heights, you know, where we're from, how much sunburn we are this week with the heat wave going through. But what is the theme that you're seeing and how work is getting done now versus how work was being done, let's say five years ago?

KJ: Yeah, this is a great question. And your question made me think of a workshop we just delivered for a really big insurance benefits broker. And we did a lot of research of their administrative staff as they were trying to run a regional P&L and, you know, they have two or three hundred customers and these customers are employers, like us, actually. And those employers are trying to keep their benefits costs as low as possible. So we go in and we're observing the broken process, not asking them what to build, but observing what isn't working and using those insights to create this new operating model. And as we talked to them, we found that their instinct once they learned about what was possible with AI was simply to take the three or four spreadsheets that they manually created to aggregate information to try to get wisdom out of that information and use AI to create those same spreadsheets. So we sometimes call this paving the cow path. Instead of thinking of the best place to place a sidewalk or a road, we just watch where the cows walked and put some concrete down there. It took effort to help them understand that in fact those spreadsheets in that entire process wasn't needed anymore. And that in reality, there would be a new way of work that would no longer require them to aggregate that data because the wisdom they were trying to get out of it could now be automatically analyzed by AI. And so AI could triage their next best action. A.I could decide what to do next and send them right on their way with a high value task. instead of having them spending four hours trying to figure out what the next next task should be. And so this is an example of, you know, if we had asked that client what they wanted, they would have said, automate our spreadsheets. But in reality, we needed to observe what they did. And then we could tell them, hey, there's this better way, which they could not have come up with on their own. And so we want to do that again and again, you know, in any client that will let us. And out of that comes this magic, you know, where you're just, amazed. Oh, my God, never thought about that. I don't have to do that work anymore. And yeah, that's what's been so exciting. It feels like the dot-com boom again for me. Back when we used to teach people, you know, I was a developer during the dot-com boom, but to teach people, hey, you don't have to use fax machines anymore. There's this thing called email. It's like we're back, we're back redefining the world again.

RH: Yeah, it's going to change the experience. But so it's jobs to be done and then also like how the current process might be broken. Do you face resistance? Like, what is that resistance like? Because we like to stay in a homeostasis. Like, we like not having change in life. And in careers, I don't want my job to change. Like some people might have. How do you navigate that resistance to the new operating system? Because that could potentially signal to some employees, hey, job loss.

[15:52] Scared to admit using AI

KJ: Yeah, yeah, exactly. I have been pleasantly surprised. We have faced very little resistance once we educate. So if we just come in, we would never, geniant would never do this because this is our culture. But if we were the type of company who just came in, hey, we're going to change the way you work and step back and, you know, we'll, we'll show you how it's going to be in a month or whatever. I don't, I think that would cause a lot of fear, uncertainty and doubt. Because we're engaging with the actual people in a qualitative research way, it's like evangelism. We can slowly, our interactions with them, show them that their job is going to be better than it is today. So we've had little resistance once we engage, but in the initial workshops that we do, almost every time, we hear some pretty surprising comments. We were in one of the largest pharmaceutical manufacturers doing an AI prioritization and strategy workshop to figure out where is the highest value, lowest cost stuff we could do. And we had a whole group, 20-something executives together. And two of them admitted, I don't even use AI at home because I'm scared that the company will know and they'll think I'm cheating. Now, I haven't met the CEO of this company yet, but I can guarantee you. The CEO would say, I would fire you if you don't use AI. Not if you do. But the fear of this tool and how it's perceived in the world, I mean, you have the investors of these foundation models who essentially used an apocalyptic pitch to try to raise money. And unfortunately the damage is done in society and we see it every day. Information workers who, like you said, they just think we're all going to get fired. Our jobs are going away. I mean, this is, this is techno pessimism, not techno optimism. And it was caused, you know, by those folks who said, oh, we're going to blow up the world with this technology. It is going to change everything, but I'm more optimistic about it.

[17:54] Air cover from the top

GB: I think there's one more beat to add to that, which is you have to have with these initiatives, the successful ones have a clear line of sight from somebody at the top of the organization that is clearing the path for them. There's so much fear and there's so much potential for change inside of these organizations with AI that if you leave the typical, in our case, mid-cap up to Fortune 50 companies, to their own devices, it's going to slow things down considerably. Everybody's going to have their pound of flesh. Somebody like technology is going to own AI, and it's going to work in the slow-moving, ordinary way that it has moved in the past, and you're not going to get the skunk works type velocity that you need to create the first jet fighter if you don't, you know, create a small team that reports directly to the leader.

KJ: Yeah, I totally, I'm glad you brought that up. I totally agree. We work with one of the top 20 banks in the world by size. And a marketing leader brought us in there to do a strategy and prioritization session to understand based on their future state processes, how AI could enable those processes. And as we started researching at the ground level of the teams, because that team hadn't had very good top-down direction, Each of the teams had created random tools with AI, some good, some not, many copies of each other. And then they were trying to integrate them and they weren't technical by nature. And so now we not only had a new future state to create, but we had a current state that had to be unwound. And I, as a CEO, I kind of didn't entirely blame them because I did love the energy. I mean, they were embracing it. I mean, they had a super top-down message: use more AI, but it's got to be more direct than that.

RH: Now, I'm glad that you brought both of those points up because it was with one of my clients, brand strategists for mid-cap, mid-cap companies which you serve. And we were just discussing like the adoption rates and how like the sentiment in the public right now, I thought it was like 20%, 30% positive. We did the research, it's like 9 to 11% positive view, Americans view on AI because of that, but through like the actual use case, like AI is good for society and good for the economy, but through your workshops and through education and through showing, I think people can change their minds and understand. I think it's one of the most important messages we can send out here in the pod is that just being a more educated about it, seeing what it can actually do and also changing the behaviors of what you're doing at work. Is this what you mean, by what you call behavior first operating model?

[20:49] A behavior first operating model

KJ: Yeah, I think it certainly generates optimism. So I do want to affirm that here in a second. But for us, behavior first operating model is the act of doing qualitative research to understand a process, not just creating a McKinsey-style swim lane diagram or asking a department what they want next. It's observing this broken process. and using that to inspire a new model that is different than what you would have gotten if you just did quantitative analysis. But out of that does come, this evangelism, this optimism, as people start to see themselves in the new operating model, their role, their careers, their jobs changing, their compensation increasing. You know, there is an upside to embracing what's positive about this. it's still a societal problem. It would be crazy to just say there's nothing, to dismiss it. We can't do that. I mean, there are massive societal impacts that we as a society need to work hard to avoid. But the per person in the business world, the per individual impact can't be understated. I mean, I think everyone will look back two years from now and love their jobs more today than they did in pre-AI era.

RH: I think you defined hope and at the same time defined fear. And I think I'll repeat it because it's important that I think a lot of the fear that's happening right now is because we can't see ourselves and what that future will look like and that creates fear and tension. But once you show them, here's the new model, what's possible. And then I can see myself in that new model. That's called hope. I see myself in the future. And that creates energy and then optimism towards the work. The deeper technically you get, like, I had a, I had a conversation at Napa, one of the theologians that helped write the Constitution of Anthropic. We had a very long conversation, about 20 minutes. It felt like five. At the end, at the end, I'm like, great conversation is like, that was great and terrifying. So I think that the more technical or the more philosophical, some people can end towards a, somewhat of a pessimistic view, a pessimistic approach. Because, I mean, some of the facts, the incentives to develop the LLMs or the large language models aren't there. They're the wrong incentives. There is misalignment in some cases. Why do you choose optimism? Why do you see the future and still hold on to optimism in the midst of the mockery?

[22:46] Staying optimistic about the risk

KJ: Yeah, I mean, I think Grant and I could both answer this one. And from my perspective, I'm optimistic because I get the luxury of every day in our work, seeing how one human at a time's life is changed by the work that we're doing. And it's not, let's be real. We're business consultants. So this idea that our mission should be, we change the world by X. I mean, I do think that we are making an impact. But when you see it in an individual, you see their face light up when they don't have to do four-hour-a-day task that was a beating and they just did it because they needed a salary. And now they, for the first time, they and their manager are freed to think, wow, what are my best strengths? What is my highest, best use or value? That's amazing. Like that a technology could do that in one human's life at a time times X billion people. I don't know how you can't be optimistic. Do we have to rein in all the things that are causing us fear at a societal level? Yes. But I would argue, let's not throw out the baby with the bathwater. There's just a ton of real value that can be created.

[24:45] 100 times more capable

RH: Yeah, because you see it now. You're seeing it now in time. Like we don't know what the end game or the end goal is and that future is not yet written. A lot of people are hypothesizing or fear mongering for the views. Except you're seeing it now execution by execution, team member by team member, what's actually happening, how they're changing and how that can actually spark, you know, hope for the future. Future pace me for a second. Where do you see this go? I know that this is a hard question to answer because it changes literally every week. I think, Grant, you're going through a program. It's one hour a day. Every day, probably something new comes through. But future pace me, what do you see in the horizon? And you can define that timeline yourself. It can be a year, 18 months, three years. What's on the horizon? But then what also are you hopeful for post horizon, like in the next four or five years?

GB: I think for me personally, I've learned more in the past couple of months than I have in the past decade. And we have the luxury in our business and Keith and I being around, you know, 150 IQ type people every day. So it's it's we're it's a luxury. I think for the average person, you know, the optimism I would say is back in the industrial revolution, And we, everything changed. Like we went from an agrarian society to an industrial society and everything changed. And at the same time, those people would be nervous and having the same weird sensation that maybe society does now. Or maybe they wouldn't. They'd just be, you know, enjoying their farming while they were doing it. In our society, we have the pleasure of being on a 24-hour news cycle. And I tend to be fairly apathetic towards everything I see there and take all that with a grain of salt and try to, you know, you know, read between the lines. Generally, I think, you know, compounding from 100 years ago through the dot-com days and into today, I have every reason to believe that my kids are going to live in an amazing society that I can't even comprehend, despite what the people in the news cycle want you to believe because they need to sell, you know, eyeballs. So I think for myself, again, I've seen my parabolic knowledge transformation and growth transformation and I think it's going to continue to be parabolic and it's you know in a few short months years maybe even days I'll be a hundred times more capable because I have my you know 100x brain next to me always working in the background doing the things that I need to do to be successful in the world.

[27:23] Closing the context gap

KJ: Yeah, that's beautiful. That's an awesome five to ten year vision and I just 100% agree. I'll answer in a one to two year answer. How about that?

RH: Let's do that.

KJ: So yeah, we do get this luxury of working with smart people and we get a lot of at-bats because we're hired to do AI strategy and transformation work and we are noticing an interesting trend. So this is a thought experiment. I'll give you a thought experiment. If you use AI today and you prompt to do something complicated for you in a single prompt, it's not surprisingly can't do it. So you keep giving the AI not knowledge in the form of additional prompts or you use retrieval augmented generation or a knowledge graph and you give AI more context. So you're closing the context gap and you get a better output from AI. So imagine a future where the context gap between what you want to receive from AI and what the AI can produce is zero. So it has full and complete knowledge of the problem space, industry, company, individual in such a way that it could, for example, in this thought experiment, in one prompt, generate an entire enterprise application because of all this knowledge behind the scenes that it has. If we came to that reality, and I think we are approaching it, I think we, as you know, this frontier of applied AI at geniant and many other people, of course, are on this journey, could achieve that end goal of that thought experiment. We could custom generate entire user experiences that were different for each individual. So today we buy Microsoft Word, and it works the same for each of us. We could maybe configure the toolbars or some things. Some designer had to do pattern recognition across billions of people who use that piece of software to design a single interface that would best work for everyone. What if every interface of software were custom to the individual forever? And as you changed, the interface changed, but it always worked for you. And what worked for Raul would not be the same thing that worked for Keith. And now we could on the fly generate experiences for each individual. It's this hyper-personalization that we were always promised, like in the 2000s and 2010s, that AI could enable this to happen. It would completely change experience design, our craft, from design the best experience that you can for 100 different people to something new. And our head of experience research calls this knowledge gardening, to garden this complete set of knowledge. Because if it's complete on day one, on day two, the knowledge has changed and it must be gardened. But if you could constantly maintain a somewhat complete set of knowledge to give to AI, you would get like frontier level results. And so it's more about understanding and collecting the knowledge. Let the foundation models, you know, companies improve their foundation model. Our job is to close the context gap. I think that's two years away. I think it's close.

[30:48] Who should own your data

RH: Do you see that with, I mean, this delves into data sovereignty, privacy, ownership. Where do you see that, like every person comes with their own? I know this is kind of archaic, but like just imagine each person come with their USB stick. Here's my algorithm. I'm going to plug it to the computer. Then everything gets custom to me. Is it like that? Like I bring my own data to the table. Then I tell you what you can access or where are you seeing? I mean, I agree with you. but this also begs the question of ownership, privacy.

KJ: Yeah, it's like you're in the room with us in this conversation because once you realize that it would be possible to do that, in this thought experiment, the very next thing you think is, what would they have to do to collect perfect knowledge? Ooh, that's creepy. They'd have to listen to every conversation. They'd have to, and like we're not going to voluntarily give that context up, especially to brands or companies that we don't trust. And so I think we need an opt-in approach where the person sees the value of the experience and decides to contribute knowledge as opposed to the not so opt-in approach that we had during the first years of building the foundation models. Oh, yeah. Where we're now learning, you know, did everyone really say yes to having their knowledge be included in the first versions of those open models? So yeah, it needs to be an opt-in based on the value the individual is getting as opposed to a theft model, which again, you know, we look, we, we ask ourselves like, you know, what really happened then and how do we feel about that? I mean, there's a huge cleanup, not just PR cleanup, real ethical cleanup effort that has to occur at a societal level. And yet, in individuals every day, we're seeing this, like, amazing experience like Grant described in his own life.

RH: It's also, like contributing to the internet, like this recording or this conversation recorded, will be transcribed, will be uploaded to the interwebs. So that's contributing our context or thought for the moment of today to whatever models will pick it up. I think there is that play, right? Is the behavior the opt-in, or is it like a disclosure form that everyone has to sign?

KJ: It's going to be a very, I mean, it's a wonderful question. If I said I had the answer, I would be lying.

RH: Yeah, no, it's good to churn on those or to think of those.

[33:19] Every employee owns equity

RH: To close out, I think one thing that struck me when I was reviewing your bio on the way that you structure the company is that, I mean, your goal was to, like, create wealth with not only the company, but also the family's working with you and that you're employing. And that this is the first-ever employee-owned company, which is, I think, is a, it's a cool approach because a lot of people are talking about UBI, what does this look like with AI doing stuff? But you're actually, we don't know that because no one knows the answer. I mean, at the point of this recording. But here's what we're going to do. We're going to do an employee-owned company. Why the inspiration to do that?

KJ: Yeah, yeah. I mean, this also could be a whole other podcast, but all of the companies that Grant and I have created, we chose from the very beginning that every employee would own equity in the business. So in other words, it would be an employee-owned and controlled company. And especially in our field, you know, consulting, professional services, most of those models are partner models, where very few people own equity in the company, and the individuals still get compensated well. So there isn't as much of an income gap as there might be, say, at Amazon, but there is a wealth gap. And ultimately, if we're talking about ways to improve the polarization that exists in society, and don't get me wrong, I'm very interested in the UBI discussions and I would love to see where that model heads. But we're not yet done with this late stage capitalism. We're going to create a lot of value and wealth in this fourth industrial revolution. And maybe the first step before we throw the model completely out is to make sure that everyone benefits from it. And not necessarily everyone equally, but in a way that each company makes sure that there is not a wealth gap and that every employee owns part of that business so that when that business contributes something to society, all of those families benefit. And so it's not an equitable distribution, but it is a significantly improved distribution, which I think can reduce the gap, the income and wealth gap that we have in the country can reduce this polarization that we're all feeling. It feels like the nation just can't agree on anything. I guess the passing of Dolly Parton, we could all agree she was pretty awesome. But, you know, other than that, everything seems to be polarized. And I think this is a huge cause. And so we're just trying to like be the change we want to see in the world, I guess. And so, yeah, starting with our first company, which we founded, you know, before the dot-com boom in the 90s, we made sure that every employee owned equity. And so when we had exits, people paid off cars or houses, as opposed to just getting a new job offer from the acquirer and seeing the big boss make a whole bunch of money. We made less money from it as a result. But boy, it helps you sleep at night. And now it turns out that this could be something that could really, really help, at least Western...

RH: I think it's a great model. Yeah, I think it's a great model, not only as a thought experiment, but also just to operate in the positive view of capitalism as well.

[36:38] Thinking like a CEO

GB: Yeah, for sure. One additional benefit has been Keith and our other business partners have always been great at teaching people how to be and think like a CEO. And we openly share all the information as owners of the business. And we talk to our employees as if they're the CEO of their business, right? You are the CEO. You have an opportunity to create meaningful change inside of this company and for our clients. So nothing gets overlooked. You know, everybody has the ownership of emptying the trash cans. And, you know, having a positive impact on our customers and thinking about things as investments and outcomes and allocation of capital and, you know, those sorts of concepts. And I think it prepares everybody to become CEO of the business someday, right? We often talk about how, when we're acquiring a business, some low-level person who empties the trash can can become, you know, Keith and run the business. So it's very empowering and it allows people to see themselves as a part of a community and a part of a team that are all rowing in the same direction. And I think that's wildly important when you're running, you know, in our case, what becomes very large businesses as we grow over time.

RH: And I think this model too, like a lot of the changes that we talked about earlier on this pod around the change of work behavior, how things are going to change dramatically with job roles, et cetera. Nothing, in my opinion, will prepare them better than the model that you're describing right now, this type of culture, a company making money for a profit that benefits the community, and then people have to employ their skill sets to the max that they can to help other people thrive. School ain't going to teach you that. School's going to change dramatically with that. So I think that what you guys are doing is good. I appreciate both of you being on the pod. For our listeners, where's the best place for people to get a hold of you or also read any insights or research or just know where you're at? Where are you online?

KJ: Yeah, yeah. Our website is geniant.com, G-E-N-I-A-N-T.com. We have perspectives there. You can contact us there. If you want to explore a project or even just an idea, learn more about our approach and our offerings. Yep, geniant.com.

RH: Wonderful.

GB: Best place for me is LinkedIn. I write a lot about these topics and, you know, happy to stay in touch and connect with anyone.

KJ: Absolutely. Grant is by far our most connected person on LinkedIn and our representative. So you can also find Grant Baldwin. I mean, you can find me too, but Grant is writing a lot more and is a lot more interesting out on LinkedIn, for sure.

RH: Before we close, Keith, when do you play the drums, before or after a strategy session with a client? Or during?

KJ: During. I am a fidgeter. So yeah, you will see me playing all the time. Now, percussion, I love it. Concert percussion, jazz percussion, these are my hobbies. And, you know, beating on an instrument is pretty stress relieving. So, yeah, I encourage everyone to pick up a musical instrument someday. If not now, then next year.

RH: I love it. I'll put those links in the show notes. Thank you again, guys.