In this episode, Sharran Srivatsaa’s four-column framework (Decide, Build, Check, Run) becomes the lens for where business owners should actually spend their time as AI absorbs more of the work. Serious Land Capital, having funded over $6.5 million in land deals at 41% operating margins, applies the model to argue that owners belong in the Decide and Check columns while AI and systems handle Build and Run. The framework maps directly onto Arthur Brooks’s split between fluid and crystallized intelligence, reframing which human capabilities actually appreciate in value.
Key Takeaways:
- The Decide-Build-Check-Run Framework Every business task fits one of four columns, and most owners exhaust themselves cycling through all four instead of delegating.
- Live in Decide and Check Owners keep the two columns AI cannot own, strategy and validation, and push Build and Run to AI and systems.
- Check Is the Expensive Column Validation commands the premium because knowing what right and good looks like cannot be scraped from the web or faked by AI.
- The Curse of Capability Naturally talented operators stall their own growth by engineering unnecessary complexity that traps them as the operator.
- Fluid Peaks, Crystallized Compounds Fluid intelligence (raw horsepower) peaks around the early 50s while crystallized wisdom keeps building, and AI now covers the fluid-heavy Build and Run work.
Listen to the full episode to map your own tasks to the four columns and stop spending your highest-value hours on work AI should already be doing.
(Podcast transcript below)
Welcome to the Get Serious podcast, where at Serious Land Capital we have successfully funded over six and a half million dollars worth of land deals with industry leading 41% operating margins. So today I wanted to go over this four column framework that I learned recently from Sharon Shravatsa, CEO of acquisition.com, associated with Alex and Layla Hormozi. if you aren’t familiar
with with them and also how this relates to the types of intelligences that we have as human beings and how they change over the course of our lifetime. So I’ll tie all of that together here. but this is a framework that has really been top of mind for me really since it came across across my desk and now
Like I can’t stop thinking about the the mental models here. So mm, jumping into it, Sean podcasted about this fairly recently and his whole topic was more under the umbrella of how there’s a curse of capability within a lot of businesses where a ton of entrepreneurs or business owners have a lot of natural talent and they’re good in a whole bunch of different areas and
They actually shoot themselves in the foot more by building in extra complexities that are not necessary for the ultimate success of the business. And you know, kind of keeps them in the loop as operators, given they understand how all the complexities work together.
and so then they end up kind of driving themselves in into the ground or not letting the the business flourish, you know, compared to what might fundamentally you know be its potential. And I’m certainly you know, f have have fallen within that camp. you know, it’s taken me many years to try to you know get out of that sense.
you know, be e even several years with i into the land business, which is, you know, a most successful venture to date here. there are just countless mistakes that that that I could go over and some I have on the podcast in the newsletter. some of which you know, w will be t you know, have have its moment in in the future to kind of do another postmort mortem on. But man, so many issues and yeah, complexities that that I’ve built into the business and
We we we could have been farther, much farther ahead than than we were. So we kind of succeeded in spite of a lot of deficiencies that I myself brought as primary operator for the business. whereas now I know I’ve commented a bit, you know, two young kids running around the home where it’s you know, just huge mental burden and and time sink to to say the least here, and where it’s like, yeah, from business perspective,
Everything just needs to be as simple as possible. like a bandwidth is it at all-time low here. So it’s like, okay, what can we do to dial in our systems to ensure we’re mu removing as much human decision making as possible while still seeing the business grow, especially in a you know more difficult market when you want to cut down overhead and so forth and not have a whole bunch of different tendrils going off in in in different directions that
might have limited utility. all of that comes into play here. And so with all that in mind, what was a framework that Sharon really focused on was this four column framework where he called it the decide, build, check, and run. so you can basically boil down any task, you know, actionable task within a business into one of those four columns.
And the punchline here is that according to Sharon, is that like most business owners are constantly running through all four of them themselves. Again, this is kind of the curse of capability. So you know, in in brief here, let’s start before any action is taken, right? You have to decide on what to do, taking into account the data in front of you, what your instincts are saying, what the data is saying, et cetera.
What is that next course of action? Is it going to take place now or sometime in the future? that’s one column. And we’ll expand on these going forward here. The build column is like, okay, once you’ve actually made a decision, what do you have to do? in order to accomplish that or or see that decision to fruition. Is that going to require a phone call?
At its most basic text message. Are you gonna have to build, I don’t know, an entire website? Are you gonna have to build an app? are you gonna have to build an entire I don’t know, factory or something for a physical product? like di different variations of what build could actually mean. next, once you’ve actually built something, you have to check it. Yeah, you have to validate. Does it actually work? Is it actually performing properly? like what what is your internal judgment based on your experience within the business?
or the collective data that you’ve collected, or yeah, of course, collected, with within the business telling you in relation to you know what what was actually built here. Does it again, does it work? is it doing what it’s supposed to, or do we have to go back and refine what was built? And after it’s been checked, then the last column is just to run it.
Okay, it if if this process was built properly, then in order to continue to accomplish the action that the decision was originally intended intended to complete, how long do we have to run this? Is this a permanent process? Do we have an automatic email going out? Are we, you know, producing so many physical widgets on a yearly basis? is this a one time thing?
or maybe we’re only running it for six months. you know, that is all all all the decision making that comes to the run portion and is really kind of a go, okay, what what can we automate from here? what still needs, you know, additional human decision making and so forth. so again, Sharon’s point is that most business owners, and subsequently their employees, because everything usually goes from the top down, is is just running through a constant cycle.
of just decide to build to check to run and over and over again. Or maybe, you know, some sometimes in between where it’s like, okay, decided on something, built, check, build, check, build, check, build, check, build, check, build. and then maybe run for a bit. And it’s like, nope, the run didn’t go well. So I gotta go to build to check and run again. maybe I have another fire coming up. I got to decide on a different course of action. Like we we all know
The cycles, right? Like again, a lot of entrepreneurs and business owners, you know, running around with with their hair on fire a lot of times and you know, not really making the income to justify the extra risk and time put into professional endeavors compared to what you might expect. so that that’s the process and the trap that a lot of folks run into. And again, I’ve dealt with this for
much of my professional career can can understand it thoroughly. And what Sharon was really pointing out is that from the business owner perspective, you know, first you have to be able to identify which tasks fit within those columns. like again, you it not not all of us are just, you know, running through each day and no and I’m in a decision making mode right now. now I’m in build mode and so forth. Like
It’s it’s all on kind of a spectrum where you’re just showing up and doing your day-to-day operations, but you don’t have like firm boundaries on what actually fill fits within that framework. So that’s the first step is like you know, actually understanding there is a framework to do this and you can actually map it out on you know e each task within within the business. And the goal.
and again, this is especially enhanced in in this age of super high powered AI tools. obviously I mention AI on effectively every piece of content we produce now, given its paramount importance for succeeding as a a p a business in today’s day and age for virtually any business. I really can’t think of a business where it wouldn’t be effective, or at least beneficial in you know some way.
for for standing out compared to to competitors. and so in in this modern age, and also just to help again allow the business to achieve its greatest heights, is that the business owner should be focused on the decide column and the check column. so this is very similar to what we’ve been going over the past few weeks.
in relation to like I’m kind of getting a into the AI side of things like being an AI driver versus an AI passenger. So going back to the decide piece, like this a business owner can never really escape from like, you know, no matter what, you know, CEO, chairman, whatever, the the ultimate decisions in terms of strategy, you know, maybe not tactics all the time, but certainly strategy.
Have to be coming from the folks with the most information and understanding of all the various pieces of data related to the business and, you know, incorporating their experience as well. Okay, what are the business leaders noticing from the data or their instincts and so forth, or what’s being said in meetings? And what is the decision that needs to be made in regard to
direction that that the business should be oriented to small decision big decision doesn’t matter like somebody has to make a call for where action is going to be taken so that that that that’s inescapable here but again like some of these things you know if it’s a it’s a and that like I’ve mentioned before if it’s a if it’s a reversible decision go ahead and make it quickly because you can always test it
Rapidly and figure out okay, is this even a pathway worth going on? You know, whereas like something that’s very irreversible, like something that could potentially put you out of business, or maybe it’s a key hire that you’re not sure is going to work out, or it’s gonna be you know, potential huge hit to the payroll, and you know, operational expenses if you end up hiring this person, or are they the right hire? I mean, again, you could fire, but.
You know, it’s not great for team morale either if if you’re not making the right hire. So, you know, bigger decisions like that, okay, you gotta spend more time on it. But ultimately, there’s no way to avoid having you know the the necessity to to make decisions. the key point here is that like as as much as I’m throwing in, hey, the humans have to make decisions, which is the case, certainly here,
And you know, what when I’m mentioning the AI piece as well, feel free to use AI as a thought partner. We do for a ton of decision making here. Like I’m not saying, yeah, do it’s it’s not it needs to be a a purely human endeavor to make decisions. like you can definitely incorporate as much data and you know thought partnership as you want, whether it’s technological or from from other human beings.
but ultimately, like again, that this is where you have to be the driver. Like, who is that who w where does the buck stop in the business? Like that person needs to ultimately make the call. Like the AI doesn’t have anything at stake. Humans do. So you know, unfortunately, a lot of folks are offloading their cognitive resources and their decision making capacity to AI and just like again, just consuming and thinking, okay, the AI knows best, I’m just gonna follow what it does. Like that.
is is the trap here. So there’s like always some some level of nuance in the directives that that I’m trying to elaborate on and the next piece though like the build this one should be the most obvious like for pretty much anything you know digital infrastructure here just set AI like they did this is where AI shines especially in these agentic you know
Coding applications, you know, the cloud codes, cloud co-works, you know, ChatGPT’s version, and and so forth, like the open clause setups, like all of that. you can just set these agents in AI to just go build, you know, replit, building web apps or mobile apps, what have you. So much more apparent for the digital infrastructure. Yeah, for physical, yeah.
Very likely you’re still gonna have to have some other human involvement in eventually when there’s more, you know, automated robots and so forth too. It’ll just be easier to offload like through a AI center and then go have your, you know, physical in infrastructure robots go and build what you need. bottom line though is is that this is not really the area where the business owner should be spending a bunch of their time in. Cause like again.
Pretty much anything from a digital infrastructure standpoint, AI is going to be able to do it better. or maybe not always better, but far more efficiently. and like on a cost per hour perspective, it’s just gonna be far less, especially if you know how to direct it from your correct decision making. and again, physical infrastructure is the o owner gonna be out there, I don’t know, building
Building actual physical buildings and so forth. No, like that’s gonna be, you know, hired out to somebody, somebody else or another firm there. So that should explain like, okay, what what what are we orienting and how are we going to tackle the build column? Next piece is the check. so this piece, again, that that this is where expertise and judgment come in. Like this is probably the most expensive task, and this is what
The th this is what earns you the the big dollars here. especially in this economy again, where people are just offloading decision making and just assuming anything AI is coming back is is actually gonna work. people who understand what to check for and how to check, like that skill is never gonna go or or ne and it never gonna be driven down to to zero. because there’s just
ultimately too much nuance. again, perhaps with some superintelligence or some ch general AI that has access to every single data point and on the level of human brains and so forth, maybe you you won’t need a a check from that standpoint. but we are still far off from that in in you know my read of of the situation here.
And again, like regardless of how intelligent AI and you know and capable it may become, like it doesn’t have the stakes that humans do. like again, the buck doesn’t stop with it, it doesn’t have desires, it doesn’t have you know money or a lifestyle, or just staying alive on the line here. So like the the incentives are always going to be exclusive to humans in regard to to that consideration.
and so that that checking is where the wisdom comes in. Again, the expertise, the understanding how to read the data, and knowing how to map that checking to the decision that was, you know, made on the front end. So again, checking is also where the owner is going to live, like the that validation piece. And to me that that this is like again, mapping exactly
What we’re doing. Actually, I’m gonna jump to the ru the run because that that one’s like more simple to explain. So yeah, when once you decide on something, you build it, you check it, make sure it’s working. And then it’s like, okay, like I mentioned earlier before, do we just automate this forever here? Again, AI or you know, certain human you know SOP processes and so forth. all of those are handling the run. Like that should be.
where your automations live, whether you have human decision making or AI handling virtually, virtually all of it. and a and a who who not how mentality. And the owner should not really be living there. So as an example, like when we’ve been building out this SLC chat co-work plugin, like, yeah, I had to decide, hey, this is the direction that I want to be taking the business in first again, because
It’s like I I need to simplify things. I need I have the tools in hand now that can more reliably go figure out comps, bring it back to me, give me a solid summary on whether something has legs or not. all of that is possible. But you know, what once I have that in mind, okay, AI, go and build this. And and that’s the fast part here. Like the this part again, like even a year ago, let alone five, 10 years ago.
way more money, way more time is needed for the build portion, just because it’s like extremely complex to build out the infrastructure. And you gotta have all these people involved with different skill sets in order to build it. Now I can just tell AI, hey, go do this. we already made the decision, we already spec’d it out, build it. that’s generally the fast part and like I I I can kind of shut off from there. The check part is where it gets difficult again. And this is usually like where I start procrastinating.
And I’m sure a lot of other entrepreneurs can relate here because it’s like, okay, yeah, now I have to actually go test it, figure out, hey, is this really working the way that I want it to be? no, this comp, like it’s not really appropriate, or it’s getting stuck in this particular part of the redfin map. Like it’s it’s it’s too wide of an aperture. I need to rewrite the directional
focus for where the plugin is actually going to decide where you know
The the correct distances, you know, radius-wise from the subject property in order to pull comps and so forth, like all of that has to be validated. And that’s the most time-consuming piece. But it’s also coming from all the knowledge that we’ve built up over the several years we’ve been in this business and the millions of dollars of deals that we’ve done and the thousands of deals that we have reviewed all across the country informing this process. Because again, anybody can go build something with AI, but
In order to validate it and check it, if you don’t know what right and good looks like, yeah, you can ask AI to like what what how should I be building this? Like your your world class, you know, land underwriter and so forth. But like that data doesn’t necessarily exist out on the web from where it it it’s pulling. And even if it does, it might not be exactly appropriate for the direction you want to take that particular product. It still might not be showing up in the true build version if you’re doing something totally new. So
That check process is where I’m living. And that that’s the expensive decision making. That that’s how we are able to price our product. because people pay for the validation, they pay for the data. They they want to know that it was built right by somebody who has done it well in the past with, you know, again, validated results. made millions of dollars on that. It’s very profitable. not a lot of people can say that. and then the run, the run piece again is like self-explanatory. Once you’ve done all those checks.
It’s already built out, now we can just run it. Yep, I can just throw properties at it and it and it’s being run again for the A AI handling that. so hopefully that gives you a good idea on how to consider all of this. the last piece I want to add on, again, you know, teased it in the opener here is how it relates to the type of intelligences that we have as human beings. so one of the most you know interesting and impactful books that I’ve read in the past several years.
and you know, suggested it to a number of other people. And they like it’s widely distributed. a lot of fo folks like have built a new mental model once they read it is that From Strength to Strength book by Arthur Brooks. I want to say it came out in 2022. and highly, highly encourage you to read. It’s it’s a quick read, yeah, easier to to read as well too. Like a very approachable author. Arthur has
a handful of other books too, especially when it comes to satisfaction and happiness in life and so forth too. Like I I really appreciate this guy’s viewpoints, but this particular book focused on as we age, there well, there there’s really he he’s he’s positioning intelligence is kind of like two different types of intelligence. One is is more fluid intelligence. So this is like just raw house hor horsepower problem solving
Capabilities, you know, able to be quick on your feet, so to speak, when it comes to again, just you know, raw, raw intelligence here. and that tends to be more dominant in our younger years. depending on the profession that we’re in, of course, too. And a little bit of nuance thrown in. The other type of intelligence is crystallized intelligence. So this is where you have your
wisdom built upon experience and being able to pattern match and understanding and again noticing when things are heading in the right direction versus not just built upon again your you know wealth of experience here but can come at the cost of just not having that you know again pure horsepower just speed of
you know, being able to take in, you know, tons of information and and come up with with the correct answer here. So the overall trend is like regardless of what profession you’re in, it it generally trends from, you know, higher fluid intelligence to eventually shifting over to more crystallized intelligence. And there’s no necessarily like limit on crystallized intelligence, I guess if you have like dementia or something, but you you you can always build up more or at least you know plateau within crystallized intelligence, whereas like
virtually e everybody. I in in fact like you consider a universal rule. Like everybody will peak with fluid intelligence. Some professions, like I don’t know, call call it your mathematicians, musical artists oftentimes are like, you know, quantitative, finance bros. for instance, like they tend to peak earlier. again, you think of all you know, raw horsepower having to go in, to to solve some really, you know,
Complex problems are like requiring a lot of creativity and new directions to to take you know raw raw in inputs into you could probably you know say like the the cutting edge AI researchers and so forth too that are creating like the newest models. So those that probably is you know very high fluid intelligence as well. Whereas the crystallized in intelligence
Or you know, some of those professions, like they they might take longer to, you know, reach that peak in fluid intelligence, like think historians, for instance, where it’s almost like more crystallized from the start. So they can peak way later because it just it relies so much on recall and pattern matching, versus having to, you know, work with raw inputs and creating creating kind of something out of nothing there. on average though,
You know, while there are differences in in the peaks, like you can roughly consider that most people will have peaked in their fluid intelligence by like, you know, early fifties, on average there. And then it’s just like kind of a downhill trend. and you know, the unfortunate reality is is that most folks either have too much ego or they’re not aware of this pattern. And so like they just keep trying to, you know, grind that you know, th think a lot of the achievers, a lot of the entrepreneurs, a lot of the folks listening to this, like we just
gotten where we are, just from like keep running into walls and you know, keep going and going and going until those walls get knocked down and then, you know, move on to the next e even stronger wall here. And like eventually when your fluid intelligence starts dissipating, is like that no longer works. So you can throw more and more work at it, but you’re not getting the same results that that you used to. And you you you’re not able to put together, hey, there’s like something
inherently biological that will no longer get you the the same results. And you know, again, a lot of folks by the time they hit that might be in kind of their peak of the career and most power. And so like a lot of folks won’t necessarily either have the power to have them, you know, step down or have the courage to tell them that. Or even if they do, that those folks might have too much ego to to step down anyway. So like again, you can probably think of a million examples about that but that’s just you know a pattern of of human life.
and the the goal of of the book was like okay to help to more gracefully work that transition from higher fluid intelligence to crystallized intelligence over time and being more of a mentor and a coach and so forth instead of kind of the star of the team. for for a lot of professions here. but you know, when we take this into the four column framework that Sharon brought up, as well too, is like the power of AI though, like.
That can handle a lot of fluid intelligence. and when we think about it, like the decide and the check piece, that’s really reliant more on the crystallized intelligence, especially the check piece, like the validation, the pattern matching, and so forth, the dis decision point. You could argue you know, a split between fluid intelligence or crystallized, maybe favoring certain fluid intelligence in in certain directions.
But like the build in the run portion, like when you can hand that off to AI and like think about, hey, when I’m just throwing something at coworker Claude Co., like it goes out and figures out, yeah, I’m gonna take all all this data and put together these unique solutions that might not have ever been come up in the, you know, history of humanity. like it can go solve that. It it has the fluid intelligence to go just throw horsepower. again, just
Tokens like crazy in order to figure out how to solve this thing. Is it always going to be right? No, not necessarily. But that that’s where the check comes in. and again, you can kind of see this, like mathematicians who tend to usually peek within their you know 20s and so forth. Like, take a look at all like some crazy mathematics that AI is help already helping solve with great accuracy. to the same degree, if not better, than you know, some of
the the best mathematicians in the entire world here. So like that gives you ni like some actual validation that there’s some truth to this. So it’s something that I you know want to continue to explore here and like I wouldn’t you know throw off the you know tendency toward you know crystallized intelligence being you know
Of what we trend to as humans, and we should probably still plan for that transition here, versus like you know keeping our head in the sand and just think we’re gonna be fluidly intelligent the rest of our lives here. But with these tools, I’m just I I think that curve can be blunted significantly more since we can live so much more in that decide and check framework going forward, where again crystallized intelligence is going to be.
of higher and higher value. So there’s a takeaway from all of this. It’s like keep building skills, keep building expertise, keep learning how to check. And again, this could be a whole other podcast, but like keep thinking critically here. Like the there’s so much research, especially with young people but adults too, just offloading everything to AI, not having patience to work on anything hard. as soon as there’s discomfort, like
hey, we’re we’re out of here. I just don’t want to offload this, not think about it. in order to decide and check properly, you have to be able to critically think. you have to understand what you’re looking at and know where where to push back. Like it’s the foundation of having good judgment. So that’s a very brief coda to an entire podcast worth, but I know this one’s already running long. So hope you all enjoyed this one. If you have a deal for us to look at seriousland dot capital
to submit. feel free to reach out about our SLC chat co-work plugin coming out shortly. Subscribe and share, everybody. Hope you have a good 4th of July if you’re in the US and listen to that prior to this week. Looking forward to next time. Take care now. Bye.
