David Arreaga is a materials scientist with a PhD from UT Dallas. He is the founder of Ares Materials, a company that pairs AI with complex chemical research to accelerate the development of new polymers.
Nate Wheeler is the host of the popular Manufacturing Insiders podcast. He also owns weCreate, a nationally recognized marketing agency that helps manufacturers grow, save money, and become more efficient.
In this episode of Manufacturing Insiders, David Arreaga explains how AI is breaking the decades-long development cycle for new materials. His team uses machine learning to guide chemical research, drastically reducing the time and guesswork involved in creating novel polymers. This approach allows them to design materials with specific properties to solve difficult manufacturing challenges.
He discusses the specific challenges of creating durable, flexible polymers for the next generation of consumer electronics. He also shares his firsthand experience in building a culture that successfully merges scientific experts with data scientists, a common hurdle for companies adopting AI. Read on to learn how this new R&D model is creating materials that solve real-world problems and the business strategy behind it.
Nate Wheeler (00:00.792)
Welcome to Manufacturing Insiders. Today I have a guest who is making exciting revolutions in the world of polymer development, pairing AI with complex scientific research to create polymer compositions that are changing the industry. David has a PhD from UT Dallas and has built a great team around his new company. Welcome to Manufacturing Insiders, David.
David Arreaga (00:33.312)
Thank you for having me.
Nate Wheeler (00:35.35)
I want to hear more about the applications of these polymers you’re developing and the problems you’re solving in the plastics space.
David Arreaga (00:49.454)
The main applications that started the company come from a need in the display industry. The industry is going through a slow evolution of form factors, moving from the typical flat device on your phone or tablet to more non-flat, dynamic form factors where you can adapt the shape of the display.
The problem is that we have been accustomed to using the same materials technology for the past 30 or 40 years. There has been little to no evolution in the materials we’re using; we’re still using PET, polyimides, or tack materials that have existed for decades. We created this company to change that and bring new materials technology that allows for new form factors, more durability, and adaptability to the new mechanical, electronic, and optical requirements.
Nate Wheeler (01:55.172)
Are we talking about a touch screen where you can interact with the technology, but also address the issue of a phone screen shattering when you drop it on an uneven surface? Could a polymer solve that problem?
David Arreaga (02:15.434)
Yes, precisely. We’re talking about the displays on your laptop, tablet, and phone. For phones, it’s not just about getting rid of glass because it breaks, but also about giving you the ability to expand your field of view. For example, if you’re on a plane watching a movie, going from a six-inch to a 12-inch diagonal screen gives you a much better experience.
The ability to fold out your display or roll out the screen is what we are looking to create with these materials. While we also have applications in semiconductors and consumer goods, displays are one of our main focuses right now.
Nate Wheeler (03:01.155)
That’s awesome. I’ve actually been talking about you a little bit. I recently interviewed someone else in the material development space who developed a 3D-printable solder for making highly conductive circuits with any 3D printer, which is revolutionary. There are some polymer compositions with conductive properties, but they are minimal compared to a copper wire.
I was talking with him about the challenges of embedding these electronics because if you’re melting something at a higher temperature than the plastic, you’ll have issues when you set it in there.
Nate Wheeler (03:47.51)
It would be very complementary to have a custom-developed polymer that could be 3D printed as the bed layer underneath this solder material. I just thought it was interesting how these conversations flow together.
David Arreaga (04:09.526)
Materials are the bedrock for all of the revolutionary technology we have seen over the last century. The evolution of materials allows for semiconductors, telecommunications, and even AI as we see it now with large-scale models. A fundamental breakthrough in materials enables all of those things. The problem is that materials take a long time to develop. It is a difficult scientific quest, and it can take decades to evolve from one material to another. That’s a big part of the challenge we are solving by integrating AI workflows into the materials development process.
One of the big conclusions we came to is that it’s no longer viable to just have a breakthrough in chemistry because these breakthroughs often have more optionality than the human mind can handle. I always like to say humans are pretty bad at making decisions. With two choices, you can quickly make up your mind. With three, you might make the right decision. But with five options, you’re more than likely not going to rank them correctly, and things can go south very quickly.
David Arreaga (05:26.794)
In science, you have that optionality and many ways to tackle a problem. We sometimes think of scientists as mighty entities that know it all and make the best decisions, but the reality is we’re all human, and we all have biases and preconceptions. We decided to use machine learning to guide that scientific instinct toward making the best decisions.
This narrows down the space in which scientists can still make mistakes, but it funnels them toward a more data-driven, ML-driven approach. This accelerates decision-making, making it more efficient and leading to faster development.
Nate Wheeler (06:04.108)
It seems like a strange pairing to have programmers and AI engineers on one side and scientists on the other. I’m not extremely knowledgeable about those personas, but I would imagine there are some major differences. How do you marry those two teams to create a good work environment and a productive collaboration to produce the chemistry you’re looking to accomplish?
David Arreaga (06:42.958)
It has certainly been a challenge. Chemists, particularly, have their own ways of doing things. When you tell them you’re going to use an algorithm, they look at you skeptically, as if you’re saying an algorithm knows everything they’ve studied for the last 10, 15, or 20 years. The reality is it takes time and a first-adopter mindset to do this, and not everyone is able or willing.
It’s a lot about company culture and how we select people for our team. As a startup with around 25 people, we have the privilege to forge a culture where people are willing, able, and eager to take advantage of these technologies. On the other side, you can’t just have a typical data scientist; you have to look for people with a scientific background who have also picked up data science. They need to understand the physical world and data science to bring the two together. Over the past few years, we have successfully merged the two cultures and gotten them to work together well.
Nate Wheeler (08:16.012)
This is a challenge every industry is seeing, including my own. Developers and programmers are worried that AI will take their jobs. On the other hand, I try to convince them that someone who understands development and can guide AI can hugely accelerate the process. You can use it for specific coding projects, if not the whole project, then for components of it.
Facilitating or developing that open-minded culture is something we’re all struggling with right now. I’d be interested to hear if you have any pointers or insights. Many manufacturers who might be listening should be integrating AI into their workflows, but they don’t know where to start or how to pair a department expert with an AI expert to identify problems and develop solutions. Any insights on how to build that would be interesting.
David Arreaga (09:30.317)
The bad news is there is no blueprint for this yet. Things are evolving so quickly that it takes the will of management at the forefront to have conviction about the destiny we’re all facing: AI being a part of our lives going forward. This is a force of nature that nobody’s going to stop. From my view, this starts at the very top. If there is no conviction that this is the route the company should go and that AI should be integrated into every corner of decision-making, it’s very hard for others in the organization to adopt it.
The key pointers for us have been instilling a culture of scientific quest. We are an R&D and engineering organization, but at our core, we are scientists. We maintain a mindset of always asking the tough questions and understanding that you need to separate the person from the idea to be able to criticize the idea. This has created a culture of pursuing truth, ability, and technical challenges.
That opens the door to the mindset that it’s not about me; it’s about solving the problem. If we can solve the problem faster with AI and machine learning, that opens up the conversation and makes people more eager to adopt the technology. But as I said, it takes the strongest will to convince the team, bring them to the table to talk about the challenges, and then instigate that culture throughout the company. You need to live and die for it.
Nate Wheeler (11:33.548)
I’ve experienced something very similar. As the leadership in my company, I’ve had a strong conviction that AI is the way of the future, and we need to figure out how to integrate it into all of our processes with an AI-first mindset. I’ve identified specific instances and told my development team that we need to look at AI as a solution. I get the sense that they humor me. They’ll say, “That didn’t work,” and I’m like, “Well, only you know the right question to ask next.” I’m getting shut down because I’m not a developer, but you know how you could be using this; you just don’t want to.
David Arreaga (12:19.502)
There is going to be a huge resistance, and as you say, it’s about asking the right question. I go back to my point about having the right mindset from the beginning and a company culture of always asking the tough questions. Even if you have the best large language model, if you don’t ask the right question or provide the right set of instructions—the right prompt—you’re not going to get the answer you’re looking for.
In our case, we were looking at a much more convoluted set of information: chemical reactions, chemical components, how they interact, and how they become a material. A lot of our effort has been focused on figuring out the important information to feed into the model so it can learn from the chemical reactions, rather than focusing on the model itself. Sometimes you don’t need an elegant AI solution with tons of GPUs; you just need to think about the right pieces of information to feed the computer to get the right answer. It’s about asking the right question and looking at the right metrics. It’s a bit of a cultural issue and also about targeting the right problems. You will always find resistance from people to adopt this, and it all starts from setting the example and going after well-defined goals.
Nate Wheeler (14:19.874)
That’s really good advice: clearly define the problem, take an AI-first mindset, and push it through your culture. What are the specific problems you are trying to solve right now? I’m interested to hear how far along you are in developing any given material.
David Arreaga (14:48.246)
From the AI perspective, we are solving for the fact that in the world of materials and chemistry, data is very expensive. You need to produce data to train models, and for each use case, you need to collect very specific data sets. Imagine you’re looking for a polymer with high abrasion resistance under certain temperatures and humidity levels. There is no data set you can buy or scrape from the web. You need to collect it yourself.
Creating that diversity of data allows you to more quickly design a new material for applications like AI or semiconductor packaging. This involves expanding the data set and identifying the right conditions and patterns. It still requires a lot of hands-on work in the lab, setting up experiments, but we see it as an investment in the future. As we build these data sets, the number of experiments we need to do will continue to narrow.
David Arreaga (16:32.422)
On the materials front, one of the main problems we are solving today relates to foldable phones like the Galaxy Fold. A major issue with those screens is the crease in the middle, a curvature that doesn’t look good. To be honest, that’s one of the main reasons Apple hasn’t launched a device with a foldable display yet. It’s essentially plastic that gets permanently deformed because it can’t withstand the stresses.
We have developed a set of materials that won’t suffer that deformation yet still have the desired flexibility and optical properties. We are essentially replacing old materials technology that the industry used because they had no other option. We are using AI-driven design to create materials with the right mechanical and optical properties to solve the problem. We’re very excited about our latest results, as we have built some prototypes and are now in the qualification process with major display manufacturers across Asia.
Nate Wheeler (17:38.806)
That’s awesome and super exciting. That could be the jumpstart for it. The technology you’re developing on the AI side to solve chemistry issues has almost infinite applications beyond polymers, like in pharmaceuticals or genetic engineering. There’s a lot you could address.
David Arreaga (18:06.35)
To be honest, we borrow a lot of inspiration from the pharmaceutical industry, which has been a pioneer in implementing AI for physical sciences. But as I alluded to earlier, each industry problem, whether in metallurgical applications or aerospace, will require some adaptation of the technology to use machine learning.
In fact, we started building our own platform because when we looked for solutions implementing what is called materials informatics—the implementation of AI and data analytics for materials development—we didn’t find anything of value from the perspective of having application-specific insight. That’s the expensive part. So, we decided to build our own from scratch in 2020. We’ve realized that while this platform won’t be applicable to every field in material science, it is looking very strong for all of polymer science.
David Arreaga (19:32.524)
How you formulate polymers in general, not just for displays but for a broad range of applications, can be generalized from one set of experiments to a completely different set of components without having seen them before. We were able to do that because we had the discipline to base our models on fundamental physics rather than just brute-forcing our way with a lot of data. You can learn patterns without seeing the reason behind them, or you can look at the fundamental physics that drive those patterns, learn from them, and generalize to a broader range of applications.
I believe that is what is driving the value in our company. That’s why we’ve been able to create a lot of data in the display space and then switch over to abrasion-resistant polymers and optical polymers for a completely different set of applications. Our models were able to recognize these new polymer materials and still predict properties and make accurate projections, even though they hadn’t seen them before.
Nate Wheeler (20:57.066)
That’s fascinating, creating a more flexible program. I see three potential business development scenarios for you. First, you formulate and develop a specific material, then sell it. Second, you develop a model that allows people to do their own research and develop their own polymers, which you could potentially sell. The third is providing consulting, where people come to you with a problem, and you use your model to develop the polymer. Which lane do you see yourself in?
David Arreaga (21:49.141)
I’m certainly biased as a material scientist at heart. The design and selling of materials to solve a specific application is where we have gravitated more towards throughout the life of the company. More recently, the pull from customers has been more towards them coming to us with a problem, and we help them solve it, but we also want to create the material that solves the problem. I believe there is a fundamental value we bring because other materials informatics companies fall short on the implementation side.
They will give you an insight, take your data, analyze it, create a model, and then send it back to you, leaving you with the problem of how to solve the physical, real-world issue. In our case, we have always been passionate about the model being a tool to solve a real problem. That’s why merging the two worlds—having the physical development and the AI in-house—is so important. At the end of the day, you can have a beautiful model with great features and metrics, but it doesn’t matter if you can’t walk into the lab, formulate the material you need, and pass real-world tests.
David Arreaga (23:36.222)
That’s why we want to stay between those two lanes. We have application-specific targets, with foldable phones and semiconductor packaging being key areas of interest for us. We also take on other interesting problems across the industry and solve them not just from an AI perspective but by providing the material solution that will actually tackle the problem. We don’t want to just provide consulting; that’s one line we probably won’t explore in the foreseeable future. We want to be the next world-class company that provides real material solutions, not just simulations.
Nate Wheeler (24:24.516)
That makes a lot of sense. In the two scenarios you’re willing to dip your toes in, you have the ability to retain some level of ownership over that material. If it becomes the next Plexiglas, that’s a big company.
David Arreaga (24:48.92)
Right. To differentiate them, there are cases where we will own the entire commercialization pipeline, from manufacturing to delivery to the end customer. There are also models where we design the solution and the process, and then we license the technology to someone who is much better at executing in that specific business area. Those are two very possible scenarios. The one scenario we don’t see ourselves in is just providing consulting services.
Nate Wheeler (25:21.156)
That makes a lot of sense. It’s fascinating. I’m going to switch lanes a little bit. You were born and raised in Mexico, is that right?
David Arreaga (25:35.2)
I grew up in Piedras Negras, right on the border of Eagle Pass, Texas.
Nate Wheeler (25:44.996)
I like to talk about geopolitical situations, tariffs, and so on. You’re an analytical, smart guy, and I can tell you think about these things. I’m curious to get your take on the relationship between Mexico and the U.S. and what opportunities exist there. I’ve been reading some interesting books about these geopolitical relationships, and I didn’t realize the degree of economic and manufacturing ties between the U.S. and Mexico. What are your broad thoughts?
David Arreaga (26:30.03)
When I talk to people who haven’t been to the border and they go for the first time, it’s always eye-opening to see the level of integration between the two countries. If you believe everything you see in the news, you probably wouldn’t imagine that kind of communication and interconnectivity. Before starting my PhD, I worked in the automotive industry in Mexico, and you can see how naturally integrated the two economies are.
Components for cars travel to Canada, are manufactured there, come back to the U.S., go back to Mexico, and then the final assembly happens somewhere in Michigan. I think a car crosses the border 30 times before it’s finally assembled. The automotive example is just one of many, including TVs, computers, and more. Mexico has become an extremely strategic partner to the U.S. That’s why when I hear talk about tariffs, I think about it differently.
David Arreaga (27:51.872)
While I see a fundamental value in bringing some degree of manufacturing back to the U.S. and I totally support that, I believe we should think more of a united North America—Mexico, the U.S., and Canada—as a strategy rather than just the U.S. itself. The competitors are China, India, and other places. We should think of the U.S., Mexico, and Canada as a united region with unique strengths, from manufacturing to addressing the population decline issue that Asia is going through.
Immigration is one of the biggest strengths of the U.S.; not having a population decline is a fantastic thing we have going for us. I think we should think about the strength of the region rather than just the U.S. itself. I grew up in Mexico and am a United States citizen, so I think about it as a region and see the benefits of a fully integrated economy. In fact, my entire AI and machine learning team is in Guadalajara, Mexico.
David Arreaga (29:19.926)
It’s super important that we recognize the value of the cultural affinity between the U.S., Mexico, and Canada. A lot of people may not see it, but there is a tremendous cultural affinity. There are many things we don’t have to worry about when Mexicans come to the U.S. or Canada, or vice versa, that you might not feel as comfortable with when going to India or Bangladesh because of cultural mismatches.
I do think we should really focus on the strength of the North American region, and Mexico is a critical part of it. Mexico is going through a very interesting time. The current president has been able to navigate the tensions with President Trump very well, and that has been highlighted across the world. I was talking to a diplomat from Canada the other day, and they were studying how the Mexican president managed that situation because she came out very strongly from that conflict. You can even hear Trump speaking well of her.
David Arreaga (30:38.648)
Mexico and the U.S. are going through a very interesting time in their relationship. Growing up on the border, it was always very clear to me how important that relationship was. I grew up crossing the border every weekend for shopping, and people came to the Mexico side for shopping as well. I think we should spend some time rethinking our strategy on the North American alliance.
Nate Wheeler (31:03.862)
I agree with you. In light of some things I’ve been reading, I didn’t realize the degree to which the aging population in the U.S. and Canada will impact the future. On the other side of the coin, Mexico’s demographics are actually getting a little younger. Mexico is positioned much better for the next two decades because of its younger population, which is a huge asset for a North American alliance.
I feel like a lot of the issues, at least on the surface, that Trump brings up are on the drug side of things. There’s not a lot you can do about it. Mexico could probably do a better job of keeping drugs out of the U.S., but they will find a way here one way or another. It will just affect the cost of those drugs. As long as the demand is there, the supply will be there. How much of an issue do you think the drug situation is?
Nate Wheeler (32:26.884)
It has a massive GDP effect on Mexico, accounting for 10 to 15% of its GDP, which is huge. In comparison, the U.S. auto industry accounts for 3 or 4% of our GDP. So, how much of an issue do you think it is, and how do you deal with it?
David Arreaga (32:37.445)
It is definitely a very significant issue. Safety, the state of law enforcement, and all those things are probably the biggest deterrents to economic growth in Mexico. The biggest issue is corruption and the permeation of cartels throughout the entire country. As you said, it’s very difficult to control the export of products as long as there is demand. But you also have to think about all the other factors in the supply chain.
Somebody is moving that money and laundering it from one place to another, and most likely, that’s happening within the U.S. because the money somehow magically gets close to Mexico. Guns and all the armament somehow flow to Mexico, and that’s one of the biggest challenges for Mexican law enforcement groups. They are facing an army that is sometimes better equipped than their own teams.
David Arreaga (34:20.662)
It is a very complex problem that has to be addressed on a united front rather than as a Mexico problem. It is a U.S.-Mexico-Canada problem because the demand is not on the Mexico side; the largest portion of the demand is on this side. Mexico should have control over its own law enforcement and safety, and they should be doing everything in their power to stop it, for sure. That’s unquestionable. But it should be thought of as a family problem, not just an individual problem.
Nate Wheeler (35:01.09)
Right, instead of pointing fingers and saying, “Your kid did it,” or “My kid did it.” That’s the feel you get from it. The other thing I didn’t realize is that the Mexican law enforcement problem is way more complex than it appears at face value. It isn’t as easy as just saying their law enforcement should be making sure these cartels don’t come to power and operate freely.
Because of Mexico’s geography and terrain, it’s naturally fragmented. The U.S. has assets like north-to-south rivers and flat plains for roadways, so everything is very accessible from a centralized government. In Mexico, these remote areas allow cartels to come to power because law enforcement doesn’t have access to them. It’s almost impossible to solve. I don’t even know what you do about it.
David Arreaga (36:05.821)
You can think of the case of El Salvador. Bukele comes in, starts putting all these people in jail, and then later figures out who should be there and who shouldn’t. Despite tons of violations taking place, all of a sudden, El Salvador is the safest country in this hemisphere. But when you look at the country, it has a small population and is a more concentrated area to control.
In Mexico, you have different regions from coast to coast, north to south, with geographic areas where it’s impossible to get into the mountains and have law enforcement. That is certainly a challenge. I don’t think it’s an insurmountable one if you’re able to tackle the problems from other sides, like cash flow. You have to think about the problem from a manufacturing perspective: the guns, supplies, and raw materials. Those are the areas where you can certainly clamp down and weaken these organizations. Again, the money moving around is where you need to focus.
I believe legalization is one way to solve this problem. I know it can be a controversial opinion, but I believe legalizing it is probably the most viable way to solve this from a safety and economic perspective. There will be other social aspects to address, but the social problem already exists. It’s not going to get worse or better because you legalize it; the social aspect of people consuming these substances is already present. You just need to create a legal framework so this doesn’t create subsequent problems across the border, in the U.S., and elsewhere.
Nate Wheeler (38:18.468)
That could be part of the solution. I’ve thought about that. The argument people make is that you’ll always have a black market because black market items will always be cheaper than regulated ones. I don’t know if that’s completely true, but there might be some global case studies. It’s a scary thought. With marijuana, sure, legalize it.
But cocaine, fentanyl—that’s a huge issue. How many more people will try it because it’s legal? That’s the question. It’s a complex issue. But I think you are positioned correctly in terms of what we need from U.S. manufacturing. We should be focusing on the highest value-add manufacturing we can possibly do instead of trying to take automotive work from Mexico. We should be leveraging our technology assets and university research departments to create manufacturing solutions that other countries can’t.
David Arreaga (39:34.915)
And you think about Mexico’s strengths on that side. I think Mexico is producing the highest number of engineers per capita. Over the last decade, the schools have grown, and many more people are getting prepared for manufacturing jobs from an engineering perspective. That is a huge value for the region. Again, thinking about this from a future perspective, the U.S., Mexico, and Canada should focus on higher value-add activities.
We should certainly look to other countries outside of China for some of the lower-value manufacturing. That is a strategic step that should be taken regardless. Primarily, we should be thinking about semiconductor infrastructure, robotics, and ways to make things more efficient in the U.S. Just bringing jobs back is a tough challenge. Who is going to do all of these jobs when we are at record lows in unemployment? If you bring a shoe factory back to the U.S., who’s going to work there? There aren’t enough people.
But if you have an automated plant for clothing manufacturing with a streamlined process, you can think about people doing maintenance on the robots and manufacturing lines, working with sensors, integration, and AI. That is a different story. It just takes time, but that is probably the best approach to tackle this challenge.
Nate Wheeler (41:16.674)
I think it’s the only approach. Like you said, who’s going to do the jobs? It’s not just a cultural problem where kids don’t want the jobs; there aren’t nearly as many young people as there were 20 years ago. The baby boomers, our largest generation, are almost out of the workforce. The next generation is smaller, and the following one is even smaller. So, automation and AI are going to make a big difference.
David Arreaga (41:46.584)
As I said, there are no people to do it.
Nate Wheeler (41:50.754)
Well, David, what you’re involved in is pretty exciting. I certainly hope you get some traction with the big phone and screen manufacturers. I think you’re going to be super successful, so congratulations on the business.
David Arreaga (42:11.918)
Thank you. I think you will be seeing things from us on the shelves in the next 24 months. I’m very excited about all of this.
Nate Wheeler (42:17.706)
That’s fantastic. I’d like to do a quick segment focused on what resources you could use right now, whether it’s investors, specific machinery, or technology. Is there anything you need?
David Arreaga (42:48.398)
Clean rooms. We’re about to expand our facility, so we’re looking for high-quality clean rooms, particularly now with all the supply chain disruption and cost changes for things coming from China. Having clean room access and building a new one can be challenging nowadays.
Investors. We’re growing and going through a Series D fundraise that is shaping up well, but we are open to conversations with investors interested in deep tech and using AI to solve fundamental physical problems. And of course, talented people. We are looking for material scientists who are curious and interested in implementing AI in materials development. Those are three things we are definitely interested in and would like people to reach out about.
Nate Wheeler (43:49.334)
Those are great. For the clean rooms, what class do you need to use? I’m trying to remember them all; there’s the ISO class and then a different classification for multiple levels.
David Arreaga (44:13.954)
It’s the first thing that came to mind because we’re going through that process right now. We need Class 1000 clean rooms for manufacturing polymers and facilities with UV filters. Up to a few years ago, it was all coming from China, so let’s see how things evolve in this new reality.
Nate Wheeler (44:40.49)
I have a couple of relationships in the UV space. I know two different companies with UV solutions. One of them uses 222-nanometer UV, and the interesting thing about that wavelength is you can use it constantly, even when people are present, but it still has the same disinfectant effect as a typical UV light.
That one is very interesting. The other uses a different wavelength but has some cool applications. If that’s something you’re looking for, I’d be happy to make an introduction. I don’t know if it has to be integrated with a clean room solution or if it’s a separate component.
David Arreaga (45:29.982)
We usually look at UV curing equipment, essentially UV LEDs, to convert the material from a liquid to a solid state. We try to keep external UV out of the lab with filters, and then we use UV LED lamps for the curing. Primarily, those come from either Germany or Japan nowadays, though there is a U.S.-based provider these days.
Nate Wheeler (46:02.04)
Okay, got it. Thanks for throwing those out. I’ll keep my eye out for anyone who can help with those needs. Thanks for joining today. Great talk.
David Arreaga (46:14.114)
Thank you, Nate. I appreciate the invitation.
Nate Wheeler (46:16.087)
Awesome.