E064 - AI or Not - Kumarsinh (Kumar) Jhala and Pamela Isom

Welcome to "AI or Not," the podcast where we explore the intersection of digital transformation and real-world wisdom, hosted by the accomplished Pamela Isom. With over 25 years of experience guiding leaders in corporate, public, and private sectors, Pamela, the CEO and Founder of IsAdvice & Consulting LLC, is a veteran in successfully navigating the complex realms of artificial intelligence, innovation, cyber issues, governance, data management, and ethical decision-making.

The grid is not just getting cleaner, it is getting faster, more digital, and far more complicated. As data centers ramp up, EVs plug in, and homes fill with smart devices, the distribution system is turning into the most active part of the electric power system. That means utilities cannot rely on old assumptions about one-way power flow or slow, predictable growth. We sit down with Kumar Jhala, principal energy system scientist at Argonne National Laboratory, to unpack what distribution system modeling really is and why simulation and analytics are now essential for grid modernization.

We walk through how flexible loads and grid-edge technologies are changing planning and operations, and why AI becomes so valuable once you have massive amounts of data and millions of devices behaving independently. We also get practical about infrastructure reality: new generation and transmission can take years, so operators need tools that help them manage reliability and resiliency right now, especially as large loads like data centers reshape local demand patterns.

Then we zoom into microgrids and distributed energy resources, from islanding during outages to providing grid services when connected. Finally, we tackle cybersecurity for a hyper-connected power system, including a cyber-physical approach that checks attacks against the laws of physics, plus the emerging challenge of securing AI itself. We close with a question worth sitting with: Are we working for AI, or is AI working for us?

Transcript

[00:00] Pamela Isom: This podcast is for informational purposes only.

[00:26] Personal views and opinions expressed by our podcast guests are their own and not legal advice,

[00:34] neither health, tax, nor professional nor official statements by their organizations.

[00:42] Guest views may not be those of the host.

[00:51] Hello and welcome to AI or not, the podcast where business leaders from around the globe share wisdom and insights that are needed right now to address issues and guide success in your artificial intelligence and that digital transformation journey.

[01:05] I am Pamela Isom and I'm the podcast host and we have this really cool and special guest with us today, Kamar Jhala. Kamar is a team lead distribution system, modeling, simulation and analytics.

[01:19] That's his area. He's a principal energy system scientist at Argonne National Laboratory.

[01:25] I'm so excited to have you with us today, Kamar. Welcome to AI Or Not.

[01:30] Kumarsinh (Kumar) Jhala: Thank you Pamela. Glad to be here.

[01:33] Pamela Isom: So yeah, this is going to be good here.

[01:36] So let's start out by having you tell me more about yourself, your career journey and how did you end up at the National Lab.

[01:45] Kumarsinh (Kumar) Jhala: Oh yeah. So I'm principal energy system scientist at Argonne National Laboratory in the suburbs of Chicago where I lead research on how AI, advanced modeling and cybersecurity can help modernize the electric grid.

[02:00] So my work focuses specifically on distribution systems, microgrid, flexible loads and grid edge technologies.

[02:08] So basically the capabilities that are making grid more digital,

[02:12] distributed and dynamic.

[02:15] I also help National Lab research efforts with DOE programs and industry partners to translate these ideas into tools that can support real world grid planning and operations. Right. And in terms of my journey, so I started with my undergraduate electronics and communication engineering back in India and early on I was drawn to wireless communication.

[02:40] So that was my original field in which I want trying to work on wireless communication, signal processing, and essentially how movement of information flows through networks. Right.

[02:52] And during my undergraduate years I had an opportunity to attend a summer school at Kansas State University.

[02:59] And that was the experience that changed how through research we can really make impact on the world.

[03:08] And I saw that research was not just about solving some technical problems using mathematics in isolation,

[03:15] but more about asking important questions and building solutions that could affect like real systems and real people. As I moved into graduate school, it became kind of increasingly fascinated by electric grid.

[03:29] Right. What drew me was that grid is one of the few systems where big data,

[03:36] physics, economics, human behavior,

[03:39] critical infrastructure, they all come together like you can think of any major science field and you will find some application of that in the grid or that relies on the grid.

[03:50] Right. Our economy is powered by the grid and that's where decisions have real consequences. Right.

[03:57] And that's how I start.

[03:59] As I moved through my master's and PhD, I slowly, slowly moved into the field of electric power system. Right.

[04:06] I worked on active consumer smart grid technologies.

[04:10] And later after completing my PhD, I wanted to continue that research. And Argonne National Laboratory was a perfect opportunity where I got so my work expanded into grid modernization and micro grids and cybersecurity.

[04:27] Then later I started learning more about AI and incorporating AI in our research.

[04:33] So like, how can we do AI enabled grid planning operation, AI enabled security.

[04:39] So for me, like this career has been about one central question, I would say is that how do we bring this intelligence into critical infrastructure that makes us more reliable,

[04:53] more resilient, more secure?

[04:56] How can we reduce the cost of electricity for consumers and how it could be useful for society?

[05:02] Pamela Isom: That's amazing.

[05:04] I'm glad that you shared those insights. I'm so glad to know you.

[05:08] Okay, so you're focused on distribution system modeling and I'd like to ask you to elaborate that more and tell me more about what is it and what is the impact considering the advancement of data centers and large loads.

[05:25] Like, what do you see as distribution system modeling? But first, tell us what is that?

[05:30] Kumarsinh (Kumar) Jhala: Yeah, so let's start with like what a distribution system is, right? So historically power was generated in large generators, right? You have seen large coal plants or large nuclear plants,

[05:43] and then you had large transmission lines that you see going across the country.

[05:48] And then finally there's a substation in your neighborhood through from where the electricity is delivered to your home.

[05:54] So the part of electricity infrastructure from that substation to your home,

[05:59] that essentially is the power distribution system.

[06:02] So in some sense that was the least technologically advanced component of the grid historically.

[06:10] And there was a very good reasons for reason for it. And it was that if something goes wrong in a generation,

[06:17] you might lose half of a state with electricity. Right. If you lose a transmission line, that impacts a significantly large amount of customers. But if you have a power outage in a distribution system, you are looking at maybe few tens of homes, right?

[06:33] So the impact was not that big. But what started changing in last decade or so is people started putting more and more generation in the system.

[06:45] And as the grid people started having smart devices in their home, they were connecting to Internet.

[06:53] There is a large amount of electrification of so many different types of loads that we are seeing. Right.

[06:59] More and more people are moving from gas stove to electric stoves. Lot of homes now move from gas heating to electric heating.

[07:09] Vehicles are becoming electric. So there is more and more new electric load coming in the system.

[07:14] These devices are smart. They're connecting to the system that created all these new market opportunities,

[07:20] right? Like smart thermostats, which are all controlled by a cloud somewhere that helps you manage your resources.

[07:28] And all those things became large enough that it would not only impact the distribution system, but also operation of the transmission system.

[07:39] So distribution system originally that was thought of like this is where the power is delivered and it's like a passive part of the grid now became very active.

[07:49] And that made us very important for us to understand how distribution system is modeled, how different solutions that we develop. Can we simulate those solutions on the our distribution system models and analyze its impact before we actually go and start implementing some of those things?

[08:09] Pamela Isom: Yeah, that makes sense. So when it comes to distribution system modeling and simulation and analytics, how is that helping with grid discovery?

[08:25] Kumarsinh (Kumar) Jhala: So the lot of new technologies in the grid in last decade specifically started on the distribution system side of things. Right now we have to model how customers behave, right? What are their preferences?

[08:39] And as the load grew, if your electricity demand increases,

[08:44] the traditional solution was you build new generation,

[08:47] you build new transmission line to carry that electricity from generation to customers, right? But that takes a lot of time. Like these, building new generation, building new transmission line sometimes take decades, right?

[08:59] And grid is moving much faster than that.

[09:02] So phenomenon that initially started as a problem that hey, customers are adding more generation in the system and power is not just flowing from generator to customers, but sometimes it's moving backwards.

[09:14] So customers are sometimes exporting power out of your home,

[09:18] right?

[09:19] That was originally seen as a problem, but then became a solution that hey, can we use these resources, these flexible resources? So in times where we don't have enough generation,

[09:29] can we reduce consumers energy consumption or draw the power from the batteries that people already have in their home to support the grid, right?

[09:39] And that's where a huge amount of discovery happens, right? New algorithms, how do you manage?

[09:45] Not so previously we were managing like hundreds of generations. Right now we are talking about thousands to millions of home devices that are somewhat controlled in a very distributed manner.

[09:58] So your thermostat is considered operated by consumers. Plus the algorithm that is inside your smart thermostat.

[10:06] Every device have their own algorithm that they are trying to figure out. Your vehicle is trying to decide when it's supposed to charge and when it's not supposed to charge and what are all of those things impact on the grid and can we utilize these resources to manage our grid more efficiently?

[10:23] And as we have more large load, specifically data centers. Right.

[10:28] Can we use customer resources to make sure that we have enough electricity supply all the time?

[10:36] And that's where I would say lot of new techniques have developed. And this is where AI became a very big part of things because now we are able to collect a large number of data.

[10:48] And once you have data, you can also use them very intelligently.

[10:52] Pamela Isom: I have,

[10:54] I moved from gas stovetop to electric stovetop. And like my whole house is predominantly electric.

[11:05] And I'm like being in the energy space, I'm constantly paying attention to what are the implications of an all electric utility supply.

[11:17] Kumarsinh (Kumar) Jhala: Exactly. And this is like for last 15 years, I think from 2005 to 2019,

[11:24] US electricity demand was almost flat. So it was growing at like 0.1% year over year.

[11:31] But since 2020, that has changed. The demand has grown to about like 1.7% over the years. And that's because of like data centers, AI, manufacturing, electrification we talked about. Right.

[11:46] So we are in new era of load growth. And the projections are that in next decade the total load in US will increase like 24% compared to what we have right now.

[11:57] So that requires significant amount of new infrastructure.

[12:01] And how do we manage the system while those new infrastructure comes into the system?

[12:08] Pamela Isom: Are you modeling what that new infrastructure should look like?

[12:13] Kumarsinh (Kumar) Jhala: So it's a very large problem. And I don't think one researcher or even one group can model the whole. Right. So when I say new infrastructure, it's building new transmission lines, building new generators.

[12:26] Right.

[12:27] And specifically, if you're talking about generators, people are looking at what are those new generation of generators?

[12:34] Sometimes we are bringing coal back, but sometimes we are also talking about small modular reactors. Right. And how these all generated, what are the different generator technologies that can actually help us build, build faster,

[12:47] cheaper,

[12:48] more reliable, cleaner. Right.

[12:50] And same on the transmission side.

[12:53] You have to decide where do I build transmission line? And it's not just a power problem. Right. Because you have to build actual lines that goes through people's communities, it goes through areas that is permitting.

[13:07] So it's not just electric sector, it's communities. They all get their feet input into how we build lines.

[13:15] There are geographical constraints, there might be mountains.

[13:18] So all those things are considered when we look at infrastructure, Right. On the distribution side, which is my side of field,

[13:25] we look at how can we input more smart controllers, how we can coordinate them,

[13:33] and how they all can work together to achieve an objective,

[13:38] which is to maintain reliability of the grid.

[13:42] So that's what we focus on.

[13:44] Pamela Isom: So tell me about micro grids. So how does distributed energy resources and microgrids fit into the equation? What's your perspective?

[13:54] Kumarsinh (Kumar) Jhala: Yeah, so microgrid is a very fascinating solution to reliability of the system, right. So originally,

[14:01] let's step back, right, what a microgrid is.

[14:04] So originally, if there is a transmission line failure, right?

[14:08] Or if your substation fails, all the customers which are downstream or that are drawing power from that substation will go out of power.

[14:17] So we thought, okay, now if people have more distributed energy resources and they have more generation on the customer side,

[14:25] that is enough power to power few homes. Right.

[14:29] So can we design a system where if needed in emergency situation, whether it's an extreme weather condition or something,

[14:37] we can keep lights on in a small part of the region where it's isolated, it's islanded, Right.

[14:43] You have enough generation to support a small community of people for a certain period of time. So that's through local generation, local storage.

[14:53] And initially their applications were for hospitals, for critical infrastructure defense installations, right.

[15:02] Where we want always wanted to make sure that power never goes off.

[15:06] And that has been a very successful solution for many applications. And now we see a lot of communities considering development of microgrids, many industries doing that on site.

[15:18] We have seen various ports, shipping ports, converting themselves into microgrids.

[15:24] And these microgrids now not only can connect from the grid, if something goes wrong with the grid, they can disconnect from the grid, keep power on, and then when they're connected to the grid, and because they have their own generation and storage, they can also support the grid when needed,

[15:40] they can sell services to the grid.

[15:43] So not only they are improving reliability and resiliency of their inside the microgrid, but they're also improving reliability of the grid in general.

[15:54] So that's a very important contributions that microgrid makes to the grid.

[15:59] Pamela Isom: And so do you model microgrids?

[16:03] Kumarsinh (Kumar) Jhala: Yeah, so we look at, okay, if you have a part of the distribution system that you want to convert into microgrid,

[16:10] you have to look at various timescales, right? So first we look at how much generation and how much storage you would need to support these customers for certain period of time.

[16:22] So you can design a microgrid that can sustain itself for six hours,

[16:26] 24 hours or longer, depending upon what your application is.

[16:30] And so we look at that,

[16:33] then we also can model it on a very small minute time. Resolution is like, okay, the moment the switches gets disconnected from the main grid, how does the power oscillate?

[16:45] Right. What happens to the frequency of the grid, voltage of the grid,

[16:49] how do we control that? So there is a role of grid forming and grid following inverters all these generations. So we look at, we also look at when these microgrids are connected to the grid, how it can provide services to the grid.

[17:03] Can microgrids sell power to the grid when it's needed? And not just microgrid selling power to the grid. Can grid rely on these microgrids when there is a need for these services?

[17:15] Can microgrids coordinated with the larger utility systems to where utility systems can ask a microgrid to provide a particular service, whether to improve voltage or provide certain amount of power or maintain frequency of the system?

[17:29] Pamela Isom: You know, if you're interested in that subject, because I'm doing the research on microgrid and we're in our last mile of wrapping up that research.

[17:36] And so some of the things that you're saying here resonates right in the sense that with these large loads,

[17:44] I feel like, and some of our research is starting to convey that the microgrids are going to start to handle, they're going to be there to offset some of the demand, but they also are going to need to start to support larger loads.

[17:58] Right. So that is one of the things that we just have to.

[18:03] We tend to minimize the scope of micro grids. And I think that we need to start to account for a greater demand.

[18:11] Kumarsinh (Kumar) Jhala: Yeah. And specifically these data centers as they are being designed,

[18:17] they are almost potentially a microgrids. Right. They have their own storage, they bring their own backup power generation.

[18:23] So they want to make sure that if there is a fault in the grid. Right. And that could happen for n number of reasons, whether it's a weather event, a tree falls on a line, they don't want to shut down their data center.

[18:35] So they have their own generation. So on a moment's notice they can disconnect from the grid and they can still maintain the power.

[18:42] So all of them are potentially a microgrid.

[18:45] Pamela Isom: Exactly. That's good insight there. So tell me about cybersecurity. What is your take on cybersecurity? The energy grid, power flow and the grid changes.

[19:00] Kumarsinh (Kumar) Jhala: Yeah. So cybersecurity is a very big area,

[19:03] specifically when you're talking about electric power system.

[19:06] And the reason is power system is a critical infrastructure and our national security depends on security of our power system. Right. When we talk about cybersecurity for electric power system, it's a very important issue specifically because power system is a critical infrastructure.

[19:24] All our Industries depend on it.

[19:27] And as historically we looked at cybersecurity and we solved the problem of cybersecurity by protecting all the communications of electric power system. So there was a dedicated communication channels, they were isolated from rest of the Internet.

[19:44] So essentially most of the systems were. The solution was to air gap the system. Right? But now that's not really possible because think about a given home. A home has maybe 20 devices now that can be controlled through your phone,

[20:01] that all depending on how you operate them, they change their power consumption.

[20:06] And all of them connected to Internet. So for example, your thermostat, right? It's connecting to your phone, is connecting to the cloud of the manufacturer of whoever manufacturer of the thermostat is, right.

[20:19] And is controlling your air conditioning. So it's changing its power output depending upon how you operate that.

[20:25] On top of that,

[20:27] as we start exploring use of these resources for flexibility in our system,

[20:32] then utilities want to connect to these systems, right?

[20:36] So there is all these algorithms out there and people talk about using smart charge management.

[20:42] So how can we control smart charging of electric vehicles? So we make sure that vehicles can charge during the off peak hours at night when there is no other electrical load.

[20:54] That means utility has to tell somehow to the electric vehicle when to charge, when not to charge. Right. Their mechanisms could be different, but that creates this communication link from that vehicle to utility control center.

[21:09] So cybersecurity becomes a very big challenge because now instead of protecting thousands of devices, now you are connecting to millions of devices and you cannot have a very expensive,

[21:23] impractical solution that limits functionality. Right.

[21:28] So on top of that, from our group side, we always try to look at cybersecurity from cyber physical perspective.

[21:35] So not only just monitoring data that's going in and out of the devices, but also how it impacts the grid.

[21:43] So if someone attacks group of devices,

[21:47] that will have impact on the electricity grid. We can monitor that,

[21:52] we can monitor how the power flows. There are laws of physics. So if a sensors don't follow the laws of physics, then you know that there is something wrong going on in the system and maybe something is compromised here.

[22:05] And we look at if there is a cyber attack, if certain penetration, certain number of devices are compromised,

[22:12] how can we mitigate that so that there is a minimal impact on the operation of the grid. On top of that there is a whole field of AI cybersecurity.

[22:21] Because AI is at the end, it's a software. So just like any other software, someone could hack an AI. So how do we make sure that the AI itself is not compromised.

[22:32] Is AI still doing what you train the AI model to do?

[22:36] Monitoring the AI as well. So we look at a lot of these different aspects of cybersecurity.

[22:42] Specifically when we talk about grid edge

[22:45] Pamela Isom: devices, do you see it evolving particularly in the day and time of AI agents or is it all the same?

[22:54] Kumarsinh (Kumar) Jhala: No, it evolves quite a bit. Right. Because as I said,

[22:58] we move from protecting large control centers to protecting large number of consumer owned device.

[23:05] Now these devices have AI in that. Right?

[23:08] Your adversaries are also using AI to attack your system. So the designing new AI based cyber attacks have gotten much easier. And we have seen from the example of Claude, right.

[23:22] The model was so good at finding cyber vulnerabilities that they decided not to make it public. But eventually that will get out. Right? Every technology is do so. That means your solution also has to be AI driven.

[23:35] Can you respond fast enough?

[23:38] And we need all the tools.

[23:40] Pamela Isom: Yeah,

[23:41] I agree with you on that.

[23:43] So tell me about lessons learned. So you're in this fascinating field and you're dealing with critical infrastructure.

[23:52] You're dealing with power flow and modeling the impacts of changes before oftentimes we deploy solutions. So your insights can be helpful.

[24:04] But what are some lessons learned that you could share with us regarding the modeling and the simulations?

[24:12] Kumarsinh (Kumar) Jhala: The lessons are, I would say first of all we learned a while back that okay, we cannot ignore distribution systems.

[24:21] Distribution system are very impactful to the operational rest of the grid and that's where the customers are. So your actually user experience is at the distribution system side of things.

[24:36] Second thing,

[24:37] we as a scientist, as an engineer, we can develop a lot of fancy technical solution which are very convoluted, sometimes very complicated. They work, but they're very complicated. Right, but we always have to look at who implements our solutions.

[24:56] These are utilities and system operators. Right.

[24:59] And they are not going to implement something that they don't understand because they need to understand where they can trust the solutions that we are providing.

[25:09] They need to know like so if you're developing a software that tells you,

[25:14] monitors the system and tells you what's going on in the system,

[25:18] or if there's an AI algorithm that we develop the system trusted where they cannot trust it.

[25:25] And that's a very big challenge and it's a very important challenge that we need to address that can we develop solutions that can actually be implemented?

[25:38] That system operator feels trust in using them.

[25:41] Because there is no shortage of solutions that were developed. They were technically very sound but did not get implemented or were not Used because system operators could not trust them.

[25:55] They did not understand how this works. They were just too complicated and people did not know how to use them.

[26:02] So the user experience is also very big part of it.

[26:07] Pamela Isom: So speaking of that user experience,

[26:13] like, do you have some thoughts on how to capture the user experience? Because what I'm thinking about is the researchers and the whole valley of death and how we're trying to overcome that and ensure that more products make it to the end users.

[26:31] And even if it's not for commercialization, we need the products to be implemented and really matter. Which is kind of what you're alluding to as well.

[26:39] So are there things we can do from a user experience perspective to help with that?

[26:44] Kumarsinh (Kumar) Jhala: Yeah, and the idea is once you reach a certain level of maturity for a particular technology,

[26:51] right.

[26:52] You need to work with system operators,

[26:55] utilities from beginning. Not like I develop the solution and then I take hey,

[27:02] would you like to use it? But more like,

[27:05] hey, we have this me develop tool that actually you can use that satisfy all your requirements,

[27:12] work with utilities, work with system operators, work with customers,

[27:18] and learn the lessons that you can from interacting with them. And not just work in lab environment. Right?

[27:27] Lab environment is very important to make sure there is a proof of concept to show that a particular technology works.

[27:34] But when whether a particular technology can be implemented that is based on users and you collecting that user feedback is very important.

[27:43] Pamela Isom: That's helpful because you know, I'm doing everything I can to help us get out of that lab and into the field so that the solutions will really matter. And you have kind of emphasized that a lot here.

[27:55] So I am pretty much to the place where we ask the final question,

[28:00] which is can you share a call to action or words of wisdom or both that you would like to leave with the listeners. And before that, I always give you space to if there's anything that we haven't talked about that you want to talk about,

[28:17] please feel free.

[28:19] Kumarsinh (Kumar) Jhala: So one thing, the age of AI is very important that as we start using these more and more AI tools that help us do things much faster,

[28:30] it's very important to use AI in a way that you learn and you develop the intuition that is required to develop expertise.

[28:37] Right.

[28:39] And that is very important, both from a new researcher as well as system operators that understand how AI makes decision. And also as we improve the speed of our software development or speed of our research,

[28:56] we also we develop that intuition that is required to develop deep understanding and expertise in the field. Right. And whatever your area of expertise is and that I believe is very important,

[29:09] specifically these days where we are working,

[29:14] lost, becoming an interface for AI or are we working for AI or is AI working for us? Right. That's a very important distinction that we need to make.

[29:22] Pamela Isom: And so you think that asking the question and contemplating are we working for AI or is AI working for us,

[29:30] you're thinking that that will help drive this intuition that you're referring to.

[29:36] Kumarsinh (Kumar) Jhala: Yeah. So like at least when I did my PhD, early days of my postdoc, there was no chatgpt or none of those things.

[29:45] Pamela Isom: Right.

[29:46] Kumarsinh (Kumar) Jhala: And when you work on equation, you are working on a problem for months over time.

[29:51] And when you spend so much time with the system, playing with it, right.

[29:56] You are constantly getting wrong answers and you are floating the field. You develop an intuition of how the system works.

[30:04] So for example, if you tell AI system operator that hey, what will happen if I put this amount of generation in this part of the system without running any models or any.

[30:17] They will be able to tell you, rough answer that, hey, this will impact this and this will cause this. So there is a mental model of the power system that you developed over time just by playing with the system.

[30:28] But now if you are using AI and if it's constantly giving you the right answer all the time, are you developing that mental model right of the.

[30:37] And that's developing those mental models are very important so that you develop this creativity comes when you are at the grid edge or edge of the technology,

[30:47] Right. So once you understand everything there is to know, then you also can see what is there that we don't know.

[30:56] And that's where as a researcher, as an engineer, we need to reach so that we know what is everything that is to know so we can find, hey, what are the missing pieces here?

[31:09] Pamela Isom: That's awesome. You know, when I listen to you talk, I know these are your parting words here, but when I listen to you talk, it makes me think about how you want to mature.

[31:18] So you're a principal, right.

[31:20] So if I'm a young researcher, just started my career or trying to grow,

[31:26] I would definitely take what you said into consideration because I see that as a level of maturity that we want to reach.

[31:34] Kumarsinh (Kumar) Jhala: Exactly. And we have seen this in other fields as well, Right. Specifically in law, right. We have heard this like AI can do work of paralegals, right?

[31:43] So people who are senior lawyers, they have this intuition and they are there. But if AI is doing work of all the junior associates,

[31:52] how do they going to develop enough expertise to become the senior lawyers of the future. Right.

[31:58] So that's critical part. We need to figure out that how do we make sure that we have the pipeline of talent and we train them in the right way in the age of AI so that they can also develop expertise,

[32:12] that they will develop their own new expertise. Of course, with the changing times,

[32:17] the expertise that people had who grew up without AI,

[32:22] we also want them to learn those aspects as well.

[32:25] Pamela Isom: Exactly. I really appreciate those insights. I want to thank you for joining the podcast, and so just thank you very much for your time.