Kendall Neukomm (00:00)
Hello everyone. Welcome to Whitley Penn Talks, where we give you valuable insights to help you make confident, informed decisions and move your business forward. My name is Kendall Neukomm and today we are back with “A Crude Bit of Humor.” Part two for those that were tuning in for our Part one episode focused on AI and emerging technologies in the energy space. We’re back. We have Jeff Chambers with us again, Buffie Campbell and Coby Nathanson. So hey everyone, welcome back.

Coby & Buffie (00:35)
Thanks for having us back. It’s like no time has passed at all. My goodness.

Jeff Chambers (00:35)
Howdy, thanks for having us again.

Kendall Neukomm (00:37)
Awesome, yeah. For those that are listening, we are wearing the same clothes. So we did not, we didn’t relocate. We’re not recording on another day. This is simply a part two to our original part one where we dove into AI. So if you didn’t tune into part one and you found us here, welcome. But if you want to listen to part one first, you can start there. And I’m sure that episode will be linked below.

Without further ado, we can go ahead and dive into the content that we have planned today, which kind of is a funny question to start us out. Open question for the group out of genuine curiosity. Do you all thank your AI chats when they give you an answer? So do you say thanks, Alexa, or thanks chat GPT, thanks, co-pilot? I’m just curious.

Coby & Buffie (01:26)
Go!

Jeff Chambers (01:27)
That’s very, very interesting.

I do actually and I think it’s just a function of my parents beating that into me as a child to be polite.

Kendall Neukomm (01:35)
I probably do too.

Coby & Buffie (01:38)
Yeah. 

Mine is more survival. Like I’m fairly confident that the robots are going to take over in my lifetime. And I want like when we’re filing in, they’re like that one. She was nice. I know that probably makes me seem like a weakling and I’ll be like the first to be culled, but that’s fine. I go out in the first round instead of like going through all the machinations of everything else. But no, please. Thank you.

So I’ve heard that it takes an enormous amount of energy every time you talk to AI. And Jeff, you may know this better than I do, but by saying thank you or please for that matter, like you’re forcing it to… Right? How many gallons did you say that was? What did you say? 

Jeff Chambers (02:22)
Yeah, you’d probably burn 15 gallons of water.

Kendall Neukomm (02:25)
At least.

Jeff Chambers (02:26)
Now I said 15, but it’s just a number.

Coby & Buffie (02:31)
But I know it uses an additional amount of energy and we can kind of look that up and add that in later on down the line. But I just don’t want to expel that type of energy for something so simple as Southern hospitality. Hold up. We worked it out.

Kendall Neukomm (02:42)
Right, I get caught between my own ethics of just being kind and saying thank you and then knowing is it wasteful? I don’t know, but.

Coby & Buffie (02:48)
Yeah. But TV Time Out, we’re in the energy industry, so I feel like it’s the right amount of self-serving to say thank you. You know, that’s good point. 

Jeff Chambers (02:57)
You should, yeah, totally.

Kendall Neukomm (03:02)
They balance out, yeah. Well, something like that. All right, well, good little icebreaker to get us started today for part two. So for our next question, Jeff, I’m gonna come to you. How do you see the timeline or delay between big ideas from large AI companies and the idea of being developed and released to the public coming into fruition and all of that? I know that Sam Altman specifically at OpenAI is very dedicated to idea and execution being closer together, but how do you see that playing out in your own role from your own perspective?

Jeff Chambers (03:31)
Mm-hmm.

Yeah, so it definitely has accelerated for us, right? Maybe some differences here. More than half of the company are software engineers. And so we have historically been able to sit in a conference room, have a discussion about something, and then 45 minutes later, ship code, right? That has not traditionally been true for a lot of organizations. But in today’s world of being able to vibe code, I fundamentally believe that English will be the programming language of the future and subject matter experts will become the best coders on the planet ⁓ within their industries in the not too distant future. With all of that, I think the time from idea to execution will just continue to shrink. I think that it’s definitely not getting longer. It’s only going to get quicker. And I actually think that it’s something that most companies should think of as part of their core DNA.

Kendall Neukomm (04:07)
Mm-hmm.

Jeff Chambers (04:25)
You know, in the software engineering world, we talk about feature acceleration, right? So how fast can we ship features? Can we make that faster over time? And I think that, in order to be a kind of tier one, highly competitive organization, you’ve got to be thinking about how quick can we get ideas to market? How fast can we iterate on those ideas? And the time from idea to execution is, I think, a key indicator of the maturity of an engineering organization.

Kendall Neukomm (04:55)
Yeah.

Coby & Buffie (04:56)
Do you find that there’s a lot of pushback from leadership when it comes to integrating AI into whatever the industry, whatever the space is? I know there probably is in the energy industry. And that may be age-related. I’m not 100 % sure. Maybe just a misunderstanding of what AI can actually do or what it actually is. But it feels like the companies that have really excelled where it comes to AI do have a younger leadership demographic and people that understand and grew up with technology a little bit more and it’s a little bit harder for especially like the energy industry just as a whole to push forward in that capacity. I feel like there’s a lot of pushback and maybe it has to do with the leadership aspect as opposed to just the industry.

Jeff Chambers (05:23)
Mm-hmm.

Yeah, so I’ve got a couple of thoughts on this, right? So one, I think that younger people are just more inclined to adopt technology for a variety of reasons. One of those is to have more free time than any of the rest of us. So they’ve been able to tinker around and mess around with this technology, you know, probably more minutes in a given week than most adults with families and full-time jobs and other responsibilities do over the course of a month or maybe even a quarter.

So they’re more adept at learning. They’re also at the stage of life where their entire life is learning, right? We talked about school earlier. And so if you’re in the mindset of continuously learning because you’re at a high school or college, you know, the idea of like learning new things outside of that isn’t that much of a stretch. Once you enter the workforce, you’re really focused on, you know, your job, if you have a family, your kids. And so carving out extra time to go teak around with a new idea is not usually the top five things you’re going to do when you have an extra free hour. So I think that’s one aspect of it. To answer the question about leadership, I think it’s kind of interesting. It has a lot to do with what role you are. So I’ve met a lot of CEOs that probably overestimate what AI can do for the organization today.

I think over long term, I don’t think they have overestimated, but I think there’s maybe some unrealistic expectations of what can be accomplished today. And it’s a function of them not having their hands on the tools ⁓ day to day. And what tends to happen is the CTO who is in the weeds on a daily basis has to deal with, you know, kind of the unrealistic vision of, you know, the CEO that says, why does this take two weeks? I spent 20 minutes last night with CHATGPT and I got something that was 80 % there. Like, why can this engineering team not do it faster? You have 12 people, right? And I think the reality is, and you know, there’s a saying that I throw on the office all the time that nine women in a month don’t make a baby. And so you can’t compress some of this work down into a smaller unit of time than it’s just actually going to take even with the tools.

So you’ve got to, you’ve got to balance kind of what the CEO’s vision is, and obviously they wanna be AI first, they wanna use technology, they wanna get as much done with as little resource as possible with the reality of where the technology is today. And it can be a huge leapfrog forward, but it’s not quite at the point where it’s gonna replace everything. So CTOs understand this, they have their hands on the tools on a daily basis. So they oftentimes are frustrated because they’re having to kind of push back on the narrative from the CEO, you know, that this should only take a few hours. In reality, it still takes a week or two. Now it might have taken two months prior to the technology, but you know, it just really depends on who you talk to within the organization. But the reality is it’s great, but it is not the silver bullet that everybody makes it out to be today.

Coby & Buffie (08:37)
Yeah, we all want now. Now, now, now, right? We’re all kind of at that mindset at this point.

Kendall Neukomm (08:37)
Right. Have to have. Yeah. But you also have to have a good framework of your technology to even be able to draw the conclusions that AI can from exactly like you said, Jeff, like what tools you’re working in. So not only having the tools, making sure the tools are connected and that they can talk to one another and that they can see into each other’s data sets and actually know what’s going on with your business at a given time.

Coby & Buffie (09:05)
I think that that part’s interesting. Jeff, I got to hear Clint talk at the Houston conference. And one of the questions that he took from the audience was… Well, hang on. For those of you that don’t know, Clint is the COO of Mineral Answers, correct? Okay. Wow. Sorry. It’s okay, because she got him wrong over text to I did, yeah. He was your CFO for about 30 seconds. I don’t think that’s right.

Jeff Chambers (09:10)
Mm-hmm. Yeah.

That’s right. Yep.

There you go. Well, gosh, we need one of those.

Coby & Buffie (09:31)
Well, you can contract one out with our CAAS group.

Jeff Chambers (09:34)
Yeah, there you go. Perfect. Yeah, see,

Kendall Neukomm (09:35)
Interim CFO Services.

Jeff Chambers (09:37)
Coby’s always selling. I love it.

Coby & Buffie (09:40)
I’m not a salesy person anyway. ⁓ Back to Clint. Back to Clint Barefoot, not Clint Eastwood. He took a great question from the audience about how operators are using ⁓ AI now at this point. He, I think he has a, correct me if I’m wrong, has a geo background.

And I think it’s interesting to kind of listen to his answer from that perspective. And I think of like how we use that in in geo with reservoir. What is it like? I think of like learning about seismic testing a thousand years ago. Right. And then how how that’s changed. thousand years ago. Listen, I’m going to make the young kids feel good. kind of thinking about it, shifting from that perspective. And I like the idea of thinking about it from the CEO saying, why does it take so long? That I think of just building out this data set of AI, it takes a long time to build out the specific questions and the prompts and the data mining to build up the foundation to go from zero to 60 overnight. That I personally don’t know how much time they’ve invested in downloading the stratigraphic columns, uploading it, if they’re comparing up against regulatory permitting sites, if we’re looking at sticks on a map to figure that sort of stuff out. I don’t think I really had a point, but I’m gonna pretend that I did and just say that I think it is very, very interesting that it’s doing this massive amount of work, yes, in a very short period of time to be able to get us to go zero to 60.

Jeff Chambers (11:07)
Yeah.

Now, one of my favorite like laws about time and work is Parkinson’s law, right? And Parkinson’s law states that work will swell in time complexity in proportion to the time allotted to complete it. So if you give yourself a week to do something, what do you typically do? Well, you’ll procrastinate and then you’ll cram and you’ll get it done in a week. But if you take that same task and you set your iPhone to 30 minutes and you say, I’ve got 30 minutes to accomplish this task, you can get it done in 30 minutes. Same thing can be true if you do it for 10. And so one of my favorite things to do when I really need to get focused because I’m being distracted by things, I will set my phone to like three minutes and I’m like, all right, for three minutes, I’m going to knock this out. And yes, maybe the work product is only 90 % is good, but for most things, 90% is good enough. And so you really can like play games with yourself with time to shrink work down, to actually just ship product faster.

And I think that if you go back to what we were talking about earlier with tooling, like I think a lot about, you know, we spend a lot of time around architecture patterns here at Mineral Answers because we want to be able to, again, affect kind of the ability to ship at speed and increase our development velocity. So being able to ship product features faster. And in order to do that, you need to have, you know, a really good set of processes, good testing frameworks, automated testing so you can ship things reliably and know that you’re not going to break things.

In our business, we’re moving large amounts of money for operators to vendors and royalty owners. So there is no room for error, right? We have to be 100 % accurate. But the tooling and framework that we have in place that allows us to do that and ship product at speed is super important. And we kind of took that same methodology to our AI journey when we started this a couple of years ago.

And we have what I feel like is a very good framework for, you know, leveraging the foundational models where you get just good general intelligence, but then being able to augment that with very specific models. And we have kind of a whole CMS layer, like content management system layer that sits on top of this now that allows us to, in plain English, go update articles, which that effectively updates the knowledge base of our AIs, both for the phone and for chat bots, to be able to learn faster.

But it’s really, you know, it comes back to what we talked about earlier in terms of having good tooling and good kind of interconnectivity between the tooling. You know, there’s a term for this now in AI with MCPs and basically an MCP server is basically the ability for you to expose kind of an API, if you will, for one set of tools to another and allows them to kind of communicate and share information between them seamlessly. Yeah, I’m a huge fan of spending the time upfront, getting the right tooling in place, being able to ship product faster for our customers.

Kendall Neukomm (14:09)
Yeah, definitely.

Coby & Buffie (14:10)
You had made a point at your on this at one of the Whitley Penn seminars and it was about kind of tying into what you just said about training that AI and making updates and changes to that AI. And I think it was something it was a quote from Elon Musk and it was that now AI is training AI rather than it learning from human sources. So can you like expound on that just a little bit more? I think that kind of blows my mind a little bit.

Jeff Chambers (14:34)
Yeah, for sure. Like it’s kind of meta, but in August, Elon put this tweet out that basically saying, Hey, like Grok has already used all the information that was available to train it, right? That was out there digitized. So books, transcripts, know, read it like all the websites that it could crawl. And so what was happening was in order for it to get smarter, it needs to go create synthetic data sets that it can use for like education and training itself. So it can have new sets of information to go off of. And so, you if you think about that, you have AI that’s generating content that’s gonna train AI, this generating content that’s gonna train AI. And so you kind of have this recursive like learning pattern that, you know, I don’t know exactly how I feel about that. ⁓ Just because, you you start to wonder, you know, if you think about cells and entropy and right, you have copies of cells and that copy is not as good. And this is why we age and have wrinkles. Right. And so does that happen with knowledge as AI generates content, you know, and it’s slightly off. Maybe it’s on, maybe it’s off. You know, I know that there was a, there was a discussion around, you know, can AI rewrite history? Right.

And so it’s an interesting question and I don’t know that it rewrites history, but clearly, you know, I think there’s a quote that I really like that says history is written by the victors, right? So the people that win get to say how it happened. And so ultimately if we think AI is gonna win, AI will ultimately have a say in like how it got here and what historical facts, ⁓ you know, were in the past that got us here.

Coby & Buffie (16:03)
Definitely. ⁓

See we just went full circle. And this is exactly why we have to be nice to them so that we know that we get to move on to the next level. She is, yeah. Well, let me ask, kind of furthering that, is it learning from social media? Because me and some friends have a little slogan, you know, if it’s on the internet three times, then it’s truth, right? So if you put it even in your social media and it gets repeated twice, it’s now the law of the land. So is AI learning from social media?

Jeff Chambers (16:44)
I would presume so, mean, Grok is trained largely on Twitter.

Coby & Buffie (16:48)
So that’s pretty terrifying.

Jeff Chambers (16:50)
Hahaha

I mean, everything that’s on Twitter is accurate, right?

Coby & Buffie (16:54)
Absolutely, And well, and I just think about all the things that people put out there just to troll other people on social media.

Jeff Chambers (16:59)
Yeah, like if you just think about, not to get too political or conspiracy theory, but just think of all the content that’s created by other nation states to create divisiveness in our state. Right. And I think they’re very good at it. Right. I read Twitter just for the entertainment value these days. And man, the stuff that I come across is, you know, it’s, kind of mind blowing, but it’s very effective at creating division in our country. And there’s definitely a class of a class of our global population that does not want us to come together and see how similar we actually are. And they’d much rather just keep us divided and fighting amongst one another over the dumbest things.

Coby & Buffie (17:32)
Right.

Well, and even that, it’s gone so quickly to the point where it’s very difficult to tell an AI video versus an actual video. And that’s kind of having a framework in AI, AI as well, which is also a little bit, as I said, terrifying. But how do we correct that side of it? Or can we?

Jeff Chambers (17:58)
What do mean by correct?

Coby & Buffie (17:59)
I think how do we make it to where we’re not just constantly getting AI making up videos and people creating videos that are AI based and that becomes the history of things.

Jeff Chambers (18:07)
Man, honestly, I think, I mean, our society, I think, is saying that it wants that nonsense, quite honestly. That’s why it continues to get created. Well, I’ll tell you another story. So in the early 2000s, Yahoo did a study, and this is a very interesting study about user research and its usefulness or not, but they did a study before they did a rebrand of their homepage, right? And so they asked everybody in the study, you know, would you rather, see actual news or do you want to see more articles about Britney Spears? And for those of you that are probably too young to know who Britney Spears is, go look her up. She used be a singer.

Coby & Buffie (18:42)
There’s a fascinating subreddit about her right now. But go back to the 90s. Do the 90s version of Britney. Yes.

Jeff Chambers (18:45)
Oh yeah, I imagine. I can only imagine. That’s right. Nineties Britney was better. So everybody said they’d rather see real news than articles about Britney Spears. But the reality is when Yahoo rebranded their site and put up real news and buried all of these social stuff about Britney Spears, the actual user behavior was completely inverse to what the statistical polls told him. Right? And so I guess I go back to the reason that stuff exists is because it’s being looked at. Right? The internet is still pretty largely driven by an advertising model. And so what gets attention gets revenue and what gets revenue gets more attention. And so to me, the fact that it exists at all and continues to grow in kind of scope and scale tells me that it must be what people want to see.

Coby & Buffie (19:41)
So, like, thinking through, because I’ve been hung up, not hung up in a bad way, but thinking through that bit of business about GROK has gone through and trolled everything, I do want to, I think, bring it back into a little bit more of the energy industry and how much more, like, owner regulatory reservoir data, listen, it’s an energy podcast. get it. I’m going to fake it.

Kendall Neukomm (20:08)
She’s crude.

Coby & Buffie (20:09)
I’m very nice, thank you. My Alexa will agree with me.

It is in a weird way kind of scary for me that A, data becomes a commodity and that was it, I think Clint had up at the seminar the other day, was it, the $200 million data licensing deal for? Yes. So data in and of itself becomes a commodity. Then what does that also mean in terms of how we will foresee operations moving that if we’re using AI in such a way that it just goes out and trolls all of it, like, let’s say, by way of example, let’s say it goes ahead and trolls the railroad commission website, right? It goes and it pulls all of the drilling permits, all of the saltwater disposal stuff, so that then, could I potentially then have like a regulatory AI bot that has, like, I’m sitting at my desk and I just type in, I want to drill an allocation well in Martin County, Texas. I think I want it to hit X formation, go and troll Railroad Commission and give me the optimum specs for me to put in with my drilling plan and then my optimum specs that will get my permits pushed through faster. That, is that, I don’t know. Is it possible? Is it possible? I think it is possible, but I don’t.

Jeff Chambers (21:23)
Mm-hmm.

Yeah, it’s

definitely possible. It’s just, has somebody gone and built that yet? No. You know, we have all the regulatory data, so.

Coby & Buffie (21:32)
Yeah. I would like to take a 2% royalty if anybody does. Thanks. Not greedy. It’s nothing. I’m like, I’m shark tank.

Jeff Chambers (21:37)
Yeah, perfect. Not at all. 2 % it’s nothing. ⁓ So we know it’s interesting because, you know, we have all the regulatory data. So the drilling permits, well spots, kind of the original thesis of Mineral Answers was to build kind of a very large knowledge graph around all of the assets within the energy industry.

And this was kind of how we started and then, you know, we evolved into getting into payments and that kind of took us down a different road for a short bit. But I do believe we will get back to kind of the roots of that. And I think the way that that, that world looks is much different than your typical dashboards and Spotfire and your BI tools for inspecting the data. think it will be very conversational. And so we’ve done some experimenting with that. Now we don’t go off and then file regulatory docs. One could, right?

Coby & Buffie (22:19)
Yes.

Jeff Chambers (22:25)
That’s not a far leap at all, but you certainly can have all the information at your disposal to just ask questions about and it becomes very conversational.

Coby & Buffie (22:34)
So then I think kind of coming off of that from like let’s talk about it from a revenue perspective.

Is it foreseeable? Is it possible that I will be able to have a dashboard or GUI that is more proactive with my system? And by that, mean, I’ve got like, let’s say an owner who’s in suspense and calls in. Will I then be able to have like this dashboard that gives me an alert that says, hey, they’ve been in suspense for 180 days. I’m making it up, but you’re getting to the point that you might have to be charged interest on it. Is it in the foreseeable future that we’ll be able to get a dashboard that is that intelligent, proactive, and communicative with us to help, I think we talked a little bit about it in part one, like this risk mitigation, is that something that we could see?

Jeff Chambers (23:22)
Yeah, absolutely. In fact, I’m smiling because I know that this is your podcast. It was a great question. You just teed me up. But ⁓ that’s literally what part of our owner relation product does for an operator. So we have a whole case management tool. So you can look up any BA in our system. You can see the current state of the world with them in terms of are they in suspense? If so, why? How long have they been paid? What’s their method of payment?

How much are they getting paid? So from a CS perspective, you’re answering questions because this owner calls in, like we or somebody on the operator staff can just look up a BA, see exactly everything they need to know about that BA. BA science for business associates, owner or vendor. And within the context of that dashboard, they see everything, right? And we’re continuously adding datasets to this dashboard to make it more useful and more interesting. And if you think about the process of, know, achievement, right?

So, when was the last time this person was contacted? What was the last known address? And so being able to solve real world problems for operators based on this data in an automated fashion, one that’s more proactive as opposed to reactive is exactly where this is all headed.

Coby & Buffie (24:30)
And I think that’s also fascinating with thinking about how, in part, using the data as a commodity. And I think of like a title opinion, right? I don’t think those are going to go anywhere anytime soon. I say that as a licensed attorney who maybe wants to have a job. But notice that if I’m an end user with mineral answers and I’m writing in to find out what’s going on with me being in suspense, if the operator have connected my title opinion into this data portal and let’s say it’s gone ahead and OCR’d all of that stuff, then I can get, I can actually get the portion of the title opinion that is relevant to my interest to identify what my requirement is and be more proactive on solving it. Not to necessarily, well, I think there would probably still be the land man aspect to it of whether I’m going to make a business risk to cure or wave the tunnel requirement.

But no, just, it’s like just thinking about that. It’s like there is, it’s so much, I feel like we mystify land and owner relations and mineral management a little bit, maybe for our own self-serving interest, which is okay. I’m owning it. It will be fine. But then like just making it readily available and not necessarily forcing it to be the data as a commodity like with that licensing agreement that we talked about, but how do we strike that middle ground so it’s still viable for owners for them to get their payments and then kind of this proprietary side for the operators.

Jeff Chambers (26:10)
Yeah, so I think our perspective on that is, know, we try to serve both sides, right? So we want to make the user experience for the owner as easy, seamless, and kind of self-service as possible, right? None of us love when we try to go look something up and we can’t find it, so then we have to call support.

Right. That’s a point of failure from my perspective. And then on the other side of the equation, we have the operator, right. And so if we can reduce the number of contacts that they have to staff up for because that question or inquiry was not able to be self-serviced, right. We’re driving contacts down for them. So we’re saving money. So the user experience on one end of the equation is getting better. You know, the costs for, you know, GNA are going down for an operator. So they’re happy about that.

And that’s the world that we sit in, right? So our job is to try to figure out, you know, how do we build products and services that accomplish both of those things? And that’s what we do every single day.

Coby & Buffie (27:06)
Buffy and I were texting a little bit about this yesterday. I think it’s kind of interesting. So let’s say I’ve got operator A and operator B in mineral answers. Both, like in this hypothetical, they’re both drilling in Howard County, all of that good stuff.

How do we ensure the confidentiality between those two different entities? So let’s say I’m an owner in both companies. I feel like there has to be some sort of wall between them and or are we eventually going to get to the point where that wall doesn’t exist anymore, that it’s just free for all.

Jeff Chambers (27:27)
Mm-hmm.

Yeah. So if you’re an owner in both, right, the confidentiality thing maybe is less. Now, if you’re an owner in one and not the other, you know, that that’s where I think that’s interesting. And that’s exactly why, like we, we had to take a different approach on how we built our AI. So instead of having like a general purpose agent that had all the knowledge across all of our operators, everything is segmented by operator. So we have some general knowledge kind of across the industry, but then for each specific operator, we have everything walled off to kind of their specific knowledge base and set of tools. And then on top of that, you know, there’s only so much information that we allow them to access and have answers to. We don’t give them access to the database. So everything again lives in the content management system.

We will eventually get access to the database so we can look up information on behalf of an owner, but then to your point, there are more security concerns around that. So how do you make sure that the person you’re talking to has access to that information? Are they that person? Do they have power of attorney to be asking those questions and get answers on behalf of that person? So those are the things from a security standpoint that we’re looking at and trying to figure out.

Like, how do we build basically a security agent that sits on top of this and without getting into too much of the weeds, you know, the multimodal kind of agent approach where you have multiple agents that do very specific things is kind of the direction that we’re going. Instead of just having one agent that does everything, we have a bunch of very, very highly specific agents that only do one or two tasks. And then they hand those jobs and requests off to other agents, which will complete tasks. But security and compliance around all of this confidential information is like obviously super top of mind. want to, you the way that I think about this, you’re always, when you’re building systems, you’re always balancing kind of usability and security and those two kind of things, you know, are, I don’t want to say diametrically opposed to one another, but they’re opposed to one another. And so, you know, if you’re going to make something more usable, you inherently decrease the security of it and vice versa.

Coby & Buffie (29:29)
Yeah.

No, that is really interesting. I think this probably overlaps with mineral management, especially when you see families calling in. They want to be all up in their cousins, sisters, business.

In my past experience, I’ve always said, for confidentiality reasons, I can only talk to you about your account. Would you want me sharing your information with your brother? Well, absolutely not. Same sort of thing. Just because you got beef doesn’t mean I have to answer your question. I don’t know, how are you seeing it, guess, from the mineral management side? 

In the future when it comes to AI capabilities on the mineral management side. I mean, we do run into that same issue with family members wanting information about other family members. And really our kind of response to that is it’s our job to make sure that everything is even. You have the decimal interest in the dollar amounts that are owed to you specifically. And if there’s an issue, we’re gonna take that up directly with that owner, right?

Jeff Chambers (31:15)
Mm-hmm.

Coby & Buffie (31:16)
But when it comes to dealing with like mineral answers and getting check detail and getting ⁓ payments of that nature, a lot of times we’re having to combine all that together, send it over to you as one request so that y’all can verify that stuff on your end on multiple levels. So, and that’s kind of one of the benefits of being the agent for multiple family members in that sense. So, not really AI related, but it’s how we are able to bypass that a little bit and then work directly with them.

Kendall Neukomm (31:53)
I almost think that this would be a part three. I think that some of these conversations and points that we’ve talked through today have been really interesting. I know that our listeners out there have been following along and listening in as we’re talking about where we think some of these capabilities are going in the future. I do agree. think we’re barely scratching the surface with what’s possible. I think that we’re really in just the infancy of where we could go and specifically to your point, Buffie, from episode one or part one of this episode that we’re breaking up. You mentioned that we are really just kind of starting out and I think that’s exactly where we are. So I appreciate this conversation so much. I do think that we will probably have a part three one day. I know, Jeff, that we’ll probably be checking in with you in a few months, maybe a bit to see.

Coby & Buffie (32:46)
We’re going to do an onsite in Austin for that one. It’ll be like fireside chat. Yes. I will say that I do love that you have all the Post-It notes behind you. It still gives me hope as a Post-It note type person that we are still going to be a little analog. Here we are. We’re still talking about AI. I love it.

Jeff Chambers (32:50)
Yes, please come up. We’d love to have you.

Kendall Neukomm (32:50)
Perfect, perfect. Hill country, ⁓ crude bit of humor goes on the road.

You’ve got to visualize your ideas, you know? ⁓

Jeff Chambers (33:07)
That’s right. That’s right.

Kendall Neukomm (33:10)
I love it. I love it. Well, thank you both all so much for being here. This is, this has been a really awesome conversation. So Jeff, Coby, Buffie, thank you all for the time. For those listening, if you enjoyed today’s episode, be sure to subscribe on YouTube, Spotify, Apple, or listen right on our website at WhitleyPenn.com slash podcast.

If you’re interested in receiving future episodes of this series straight to your inbox, check the link in the description to sign up for our email list. Again, thank you so much, Buffie, Coby, and Jeff. Jeff, we really appreciate it having you and look forward to the next.

Jeff Chambers (33:39)
Thank you.

Coby & Buffie (33:41)
Thank you.