How to Raise Your Agent Episode 7 | Tom Redman, "The Data Doc" & President, Data Quality Solutions

Speaker A: It is a little simplistic to say AI is data. AI is also people. And the people dimensions of AI, people talk about them at a high level, but people talk about transformation as it's sort of like, unplug this and plug that in. And it's just not that simple. I mean, the only important places where transformation occurs is between the ears, and it has to happen between the years for large, large numbers of people. And so the data is getting ignored, the people are getting ignored in sort of deference. And by the way, the technology is amazing, but that's not where the work that we need to do lies.

Speaker B: Welcome to Smarter Markets, a weekly podcast featuring the icons and entrepreneurs of technology, commodities and finance, ranting on the inadequacies of our systems and riffing on ideas for how to solve them. Together, we examine the Are we facing a crisis of information or a crisis of trust? And will building smarter markets be the antidote? This episode is brought to you in part by ABAX Technologies. AI agents are fundamentally changing how people and organizations interact with computers and the Internet. A race is underway to establish the path that will govern the nature of these interactions. Abax Technologies introduces ABAX Labs as its center for engaging with the developer community through the release of open source software to promote a path that empowers users to maintain control of their identity. Data and Agents in Digital Spaces ABAX Labs first release is the Agents Library, a subset of ABAX's ID software development kit that has been tuned for use with AI agents. Download agents on GitHub today at, uh, GitHub.com abaxlabs that's GitHub.com abaxlabs.

Speaker C: Welcome back to how to Raise youe Agent on Smarter Markets. I'm Dave Greely, chief Economist at ABEX Technologies. Our guest today is Tom Redman. The Data Document. We'll be discussing how, in his view, AI is data, how much of our data is not fit for purpose, and how the solution begins with keeping the focus on the customer and getting people and their AI agents to see themselves as part of a system in which they are both the data creators and customers. Hello, Tom. Welcome to Smarter Markets.

Speaker A: Thanks for having me, Dave. Looking forward to our discussion.

Speaker C: Oh, me too, me too. Thanks so much for coming. On our podcast for our listeners, you're known as the Data Doc. You've spent your career helping people and companies manage and improve the quality of their data. And I just wanted to ask you, as we get started, what got you interested in working with data as your career?

Speaker A: Yeah, I mean, That's a good question. I was trained as a statistician. My first job was at Bell Labs, right. One of the things that I was able to do was pull on some threads and things that interested me. And one of them turned out to be this thing called data quality. And so in the late 80s and early 90s, we set up a little lab. It's a group of guys to explore data quality. And I feel so lucky to have gotten into it, first of all, so early. And with Bell, uh, labs and AT&T, which was really exploring and pushing the envelope at things. And I mean, it's just turned out to be this fascinating career, like, with gross problems. By that I mean large and ugly, and then real subtleties like, what is data? What is information? How are they different? Does it matter kind of thing?

Speaker C: That's a while. And to work at Bell Labs, which had such history and reputation and, um, when you got into it, uh, it makes a lot of sense. Being a statistician, you're trying to work with data, you're finding problems in it that I'm sure were frustrating, throwing off results or making them untrustworthy. What are some of the common data problems? I think most people who don't work with data might think, oh, well, things might be missing, like missing observations or something was put into the spreadsheet wrong. But is it broader than that? Like, how do you think about data quality?

Speaker A: Look, here's the way I look at it. Uh, sort of the quality of anything is around fitness for use, right? Somebody, we refer to them, we call them a customer in quality, right? Needs the data to do something. And it could be get you your sweater that you just ordered, or it could be that you want to make some decision or plan where. I have a lot of the discussion now on AI, and I divide the main issues into three broad categories. The first is the obvious one is the data, right? Are, uh, the values correct? Is there missing data? And so forth. And the second broad one is, is this the right data? And so start with a problem, and the question is, do we have the right data to address the problem at hand? And then the third is it presented in the right way? So, like, we're all familiar with barcodes, right? They contain some simple data, but they're set up for optical scanners, not the human eye. And anyway, so practically every problem I've run into, there's always a question of is the data good enough and is the data relevant? And then sometimes is it presented in the right way? And. And then it Gets more involved from there.

Speaker C: And you mentioned AI, which of course is so data intensive. How would you describe the data problem when it comes to AI?

Speaker A: So David, do you know what the economist word for 2025 was?

Speaker C: I do not. I do not. What was it?

Speaker B: Slop.

Speaker A: This is the most important data problem that AI has. And, and not just with data, but it's the most important problem it has. Too much of what comes out is slop and it is causing people, at best a lot of rework. It is causing productivity gains to go down and it's angering a lot of people. It is hard to see how a technology succeeds when it angers so many of its customers and so much so that it sort of becomes a cultural zeitgeist in slop. It's going to take a lot to recover from that.

Speaker C: And the problem with sloth to delve into a little bit more. It's we're able to generate so much data now through these LLMs, but then being able to assess their quality or even just to take in that amount of information as a human being is really overwhelming. How do you think we begin to deal with that?

Speaker A: So from my perspective, everything in the quality space begins and ends with customer, right? And so you using an LLM, and so maybe you're using the LLM to complete a report that your boss needs or some other manager needs or whatever. And it's really, really easy to uh, generate a 30 page report. But is that fit for purpose? Is that what's needed? And so in quality, everything begins with understanding who the customer is, what they want, and then tailoring, in this case our data, our answers, our predictions, our uh, reports, whatever it is to meet those customer needs. By the way, at uh, Bell Labs we had a saying that was a kiloton of analysis is worth an ounce of insight, by the way. Do not confuse the two. If what you're really being asked is to provide an insight and instead you come with 30 pages of stuff, even if it's true, it is certainly not helpful.

Speaker C: Never put the burden of doing the thinking on uh, the customer provide them the insight. And I wanted to ask you, as many of us are the find ourselves using AI, we're in the customer seat when it comes to data. And many people who aren't statisticians or they haven't been necessarily worked with data before, but the ability to use LLMs lets them m work with data perhaps in a way that they weren't able to when it was use a spreadsheet or some sort of coding language. How do we think about AI as that sort of data problem now that we've got a lot of people in the driver's seat that weren't in the driver's seat before?

Speaker A: So I've been field testing this little expression the last seven or eight months and it just goes, AI is data, right? And the logic behind this is for the vast majority of people, they're not going to worry about a frontier model or a foundation model. They're not going to be developing an application and their company, they may be coding some simple stuff, uh, up. But for most people, the thing that they get to worry about is they get to worry about the data as presented to their customer, the outputs and then the creating leverage by putting good stuff into whatever they're using. So be that an agent or a machine learning model or an LLM. And there's too much focus, in my view, around the technology and not near enough around the data, particularly when that's what the vast majority of people can work on. Uh, right now I want to be a little careful. I mean it is a little simplistic to say AI is data, AI is also people. And the people dimensions of AI, people talk about them at a high level, but people talk about transformation as it's sort of like unplug this and plug that in. And it's just not that simple. I mean the only important places where transformation occurs is between the years, and it has to happen between the years for large, large numbers of people. And so the data is getting ignored, the people are getting ignored in sort of deference. And by the way, the technology is amazing, but that's not where the work that we need to do lies. The one last point, and that is if we're going to make the data fit for AI, a, uh, large number of people are going to have to get in and contribute.

Speaker C: And what advice would you have for people who are using AI for the first time? Learning how to use it, but they've never really worked with data before. Is there any advice or insights you could give them to avoid common pitfalls?

Speaker A: So let's start with a 16 year old who has a language arts assignment, right? They're supposed to write an essay on, you know, whatever they're doing in class and say, pop the copy and paste into your favorite LLM. I have a, uh, I. When somebody in our family worked for Google. So I always say Gemini, Pop the question in and out comes the essay. Copy and paste the essay, Turn it into your teacher. Now what's the Worst possible result for the student. Well, the worst possible result is the dreaded seeing me because the teacher knows that you're not capable of doing this level of work and so obviously you get it done and so forth. It's an unbelievable attractor to a 16 year old. Right. But the thing to do is think about the teacher and if they're going to recognize you can't do that level of work, well then put in the prompt, hey, I'm in 10th grade, I'm a C plus student, I want to get a B, B minus, maybe a B on this assignment. Make sure you misspell a couple of words, have some grammar errors now by the way. So that's what the smart 16 year old is, is doing and it's said differently. Well, they're thinking through the customer and then there's it, okay, here's what the customer needs, here's what they expect. How do I, what do I do in crafting my prom or whatever it may be to meet their needs? Okay. Now by the way, I've chosen this example because there's a lot to object to in it. Right. This is like, right, well, is this what we really want to teach 16 year olds to be manipulative like this? And, and, but they're going to figure it out anyway. And so that's the advice is think through where the answer is going. If you're just doing it for yourself, that's one thing. If the next person in line is your boss or a teacher or something like that, then you have to think about it a little bit differently.

Speaker C: And um, that advice of focusing on the customer, focusing on the goal is very different than the way a lot of businesses are currently using it. Right. What's often coming up now is, well, I just wanted to do everything we're already doing, but I wanted to do it cheaper with fewer people or I'm a manager and now I can just do everything myself. I can code up the website, I can write the reports. I don't have to deal with the frictions, some might say the necessary frictions of interacting with other human beings and the pushback and the tension that that creates as you work on something. How are you thinking about that piece and what you're seeing with businesses? Is there too much focus on the cost cutting aspect of it and not enough in transforming how the business can serve its customers?

Speaker A: Oh my goodness, I'm so glad you asked that question. Right. Think about it. If five years down the road, all we got out of AI is that people cut A bunch of heads. Wouldn't that just be an awful result? Right? I mean nobody really understands the potential in, in these technologies. Nobody understands what problems they can, can actually help us address. It's fascinating to me that, that Demis Hasibus and so they won the Nobel Prize for unlocking protein unfolding. And it's going to take time. But think about the economic and the health benefits that that is going to unlock right now. I mean most of us aren't him, but companies by and large have the ability to think through where are those likely markets and how can we attack them. I want to build on this. I'm also testing this theory that I think too much AI is being done in the metaphorical factory. It's being done by line managers. And let's face it, line managers have a tough job. They got to keep production up. All kinds of things happen. They don't get the supplies in they need. People don't show up to work right. All kinds of things happen. And so it is their goal to get things under control and keep them under control. And it is a hard, hard job. Well, AI uh is the complete opposite of that. It is to get things out of control, to get them to a new level, to think about things in a new way. And we're asking managers and you know, good, honest, hardworking humans to do things that's not in their sweet spot. I think companies into particularly when they're thinking beyond, well, how am I going to cut two heads? They need a lab. Okay. And maybe it's not like a great research lab like Bell Labs or something like that, but a place where people can think a little bit longer about the potential for how this technology can change the business and bring a little bit more creativity, a little bit more inventiveness to the company. One last thing. I mean I'm fully aware that the last thing you want is an ivory tower lab. Uh, okay. And a lot of people criticize Bell Labs for being an ivory tower. And maybe there were parts of it that were. But it was not that way with me. Day in and day out I was working to better understand and address problems that were facing the company. And maybe not like getting the shipment out, but what mattered in just slightly longer term. And it sort of one of the geniuses of Bell Labs was research forward looking work. A series of steps that got things from pure invention into practice. I personally think companies and to buy those that ask me to be building a unit like that, I mean, by the way, also, you know, all kinds of Little questions come up and so like how is this chatbot really going to work? How are customers really going to, going to react to it? Is the fact that we've increased capacity and, and now we can serve a hundred people at a time rather than 12? Is that a trade off? Is that a reasonable trade off? We're not speaking to a human being. I mean nobody knows the answer to that. And so you gotta try it. And the place to try things like that is in a lab.

Speaker C: Such a great point in that we keep talking about how AI is gonna transform businesses, but that involves kind of breaking old processes and creating new ones and requires an incredible amount of thought and also experimentation and failure without killing off your business as it is. Is that a lot of what was happening in Bell Labs is trying to understand how to, how to bring some of these into the business without breaking it.

Speaker A: So in Bell Labs, but also on the business side as well, right. So I mean that was viewed as an end to end process. Now most inventions don't turn out to be transformational. You build a better circuit pack and you double the capacity the network while you're just plugging in a pack, that's different. But when you get into how people behave, that's where things get hard and interesting. I've, I've been through, uh, advised on or one way or another, been connected on 14 things that I involve that I include as transformation. And in some respects that's 13 more than any human being should have to go through. Transformation is hard, right? Every question about everything about how you run your business, about how you think about your job, about how you relate to others, gets thrown into the mix. And there's no way of knowing how those answers are going to come out. And so it takes a lot of courage. Now the other side of this is lots of transformations fail and business leaders should not subject their employees and their company to it unless they are willing to get in and succeed or fail as a team. It is just not. That is just bad practice to do otherwise.

Speaker C: And I wanted to bring it back to data in that you've got businesses that are starting to integrate AI. But going back to your earlier comments about data needs to be essentially fit for purpose. Many large businesses, large organizations are sitting on incredible quantities of proprietary data. Might be in filing cabinets, might be on notebooks and desk drawers, may have made its way into the digital world, but maybe it's in a spreadsheet, maybe it's in an email database, a CRM, whatever. Are you seeing businesses grapple with how they need to change their data or repackage it or whatever. The proper word would be to actually make it amenable for this use with AI.

Speaker A: That's an insightful question. Let me tell you what I'm seeing. So, up until three years ago, um, most people in businesses, they viewed data quality and data generally as a nice to have. Right. Sort of over there and subordinate to tech. And just as long as it's not on fire, then we're not going to deal with it. Uh, by the way, there's been some exceptions, and we'll talk about those in a minute. But in the last two or three years, I found that every person and every company, at least those that will talk to me, right? So not everybody wants to talk to me. I get that. But at least those that talk to me, they get the joke that data matters, okay? They get the joke that quality is really, really important. And they very much suspect that their stuff is not fit for use. They're more like deer frozen in the headlight not knowing what to do. Okay? And it is not as simple as you go. Um, Dave, you take it, right? There's sort of this wish that we have to deal with this. I hope somebody else. Somebody else I want to emphasize, else will deal with it. And what I'm really trying to do is help them break through that. Until somebody steps up, you're toast. All right? And fortunately, by the way, over the course of my career, plenty of people have stepped up and we've tackled some pretty important problems inside companies, but not at the scale that's demanded. Now, I've called those people who stepped up provocateurs. And companies need lots and lots of provocateurs who on their own initiative decide, okay, well, we're trying to do something over in the marketing space. They're a marketing person. Our data is not fit. Uh, we've got to step up and take what actions, and we need to do that. And so I again, I mean, I see a lot of people knowing this needs to be done and sort of, yeah, I hope he or she will do it, not me.

Speaker C: You're kind of making me think, too. I think people that haven't worked with data as part of their job, there's almost an assumption that it's just kind of there and it's free and it's available. And whether you're a statistician, my background is as an economist, you kind of realize that by the time you get to actually doing the statistical work, you've already done 90% of your work, preparing for that moment. It's a little bit like when you're painting a room when 90% of the effort is prepping the walls and actually putting the paint on is the last 10%. Do you think there's uh, a, almost a, uh, kind of like an educational aspect of people realizing like, oh, there's a lot more to this than just typing in a prompt and getting back exactly what you wanted.

Speaker A: I think the education is necessary and on a broad scale and not just, okay, you gotta do this, but very, very specific on what you need to do. I also think that there's a little bit of courage needed. Now I'm not the only one who's been saying data matters, right? There's a great paper by Google researchers called everybody wants to do the model work, Nobody wants to do the data work. And, and there's a great quote. I think it's from a guy named James Beckter at OpenAI. The IT in AI is data, right? So this message that everybody is ignoring, right? I mean there's sort of like, I guess there's a knowing doing gap, right? And it is not sufficient to just train people. This is up, up and down and people have to see what's in it for them personally, right? Now if you go into data, chances are it's a career ender because the problems are so diverse and so many and, and one person's just not going to get that. One other thing I find is like, it is observably true that everyone in every company plays two roles. They are a data creator in the sense that stuff they do, others use, right? And they are a data customer and that they depend on stuff created by others to do their work. Now everybody, it is observably true that everybody serves in those two roles, but when you point it out to them, well, did you know you were a data customer? Right. Usual reaction is, you know, I never thought about this, right? And so data creator, you know, I never thought about it. So it's, it's like the training depends on the switch have gone off and people seeing that. And by the way, every time I talk I'm like, nobody doesn't get that, right? It's a pretty simple concept and lots and lots of people have gotten it. But what's not happened is the sort of thing in organizations that get one person doing it and then two and then 10 and then 100 and so forth, there's sort of cascade that's going to go through an organization and, and I don't Know, I spend all of my research time, like thinking through how we're going to get that aspect to go. I mean, and if we don't get that aspect to go, then AI is not going anywhere. And all this talk of this is the essential transformation.

Speaker C: It's a simple statement, but it's a very powerful one to think in terms of being the consumer, the customer and the creator of data. It changes your perspective and lets you understand that you're kind of sitting in a flow of information. And if you're generating too much analysis, not enough insight, you're making it harder for the next customer down the line and creating bottlenecks in the system. Now, one of the issues we'll also have is that it's not just humans in that system. Now we're introducing AI agents. And I wanted to ask, how is the use of AI agents changing how we need to be managing our data and its quality?

Speaker A: So I don't think it changes anything, anything substantial. It doesn't change the customer creator thing. It doesn't change getting the management right, attacking it in the right way, but what it does is it brings an incredible new level of urgency, right? So an awful lot when you dig down and ask people like about their jobs, right, they're in this thing and I, we call it the human in the loop. And data quality, I sometimes call it the hidden data fact. But people are doing these jobs to accommodate bad data just as part of their job, right? So like a typical salesperson's job has two parts. They their salesperson and then they're dealing with the data so that they can do the sales job. And so if we take those humans out of the loop and the agents cannot recognize and deal with the data problems, then those errors are going to cascade downstream. Usually the longer it is until you catch something, the poorer the result is. And so it is a very, very risky thing to do. So, uh, sometimes I like to use this notion of there's sort of a factor of 10. If you can deal with an issue here, and it cost a bone, right? A euro, a pound, a dollar, whatever it is, and instead you deal with it downstream, you it's going to cost 10, right? So sort of do the math. You only have to screw up one in 10 things as an agent for the math to completely destroy the productivity gain that everybody's hoping for.

Speaker C: I really like that emphasis you put in the example of the salesperson, because I think we don't think through enough. All the jobs people are doing that are necessary to do their Jobs, but it's not really the, it's not where the profitability piece the focus is. So everybody wants to think of the salesperson as closing the deal, but not thinking about all the data work that's done that's necessary to get to the point where you can close the deal. And if you can't handle that part, you can't just jump to the end.

Speaker A: I mean, just think about it. Companies hire these high price skilled with connection salespeople and then a third or a half of their job is dealing with data issues. You know, no wonder salespeople change jobs so fast.

Speaker C: You brought up the idea that right now you can spin up a 30 page white paper in minutes. And certainly with agents that's only increasing exponentially. It's never been easier to generate paperwork. And from that mindset of we're both the customers and the creators, how do you think about managing that potential for information overload on the humans in the system so that we're not passing along too much information or the machines aren't passing along too much information now that they can talk back?

Speaker A: So look, I mean, we'll have to sort through how it works out for machines. I think that's an unknown. But I suspect a lot of things in quality, you pass the data on and it's not going to a human being, it's going to the next application or whatever it is. And so I'm not expecting that with agents that's going to change substantially. I'm sorry, say that you know, they're going to make some decisions, but we've always passed stuff along and I mean, and I know we're repeating it over and over again, that's okay. But it's a really important part and that's who's the customer, what do they need? Right. And if somebody says, I need four pieces of data, uh, and you give them 30 pages and those four pieces are buried in there somehow, well, you really haven't met the need.

Speaker C: And do you think about ways to use AI more with that customer focus? Right. Because I think when it first started it was, look how much I can generate content, I can do all these things that I hate doing and get away from being at the keyboard all the time, but then instead being the focus more on the customer, the consumer? How do I distill all the information that's out there, down to the insights, down to the things that are actually digestible to my brain?

Speaker A: It's really important to understand when it comes to the customer, AI is at an extreme Disadvantage. Okay, so if you're here, you're in, your use of AI is here, and here's the data. You can bring everything that's ever been put into some model and all your company stuff via rag and the steps and rules of your process. You're sort of an agent kind of thing. Uh, well, like people have spent a long time thinking about that and worrying about that. What, what they haven't spent the time thinking about your customer. And your customer is very, very specific to you. Right? AI doesn't know about that. The only way it's going to find out about that is if you tell it. And so, uh, I don't know, maybe it'll turn out that machines are really good at saying, well, that's not what I really wanted kind of thing, and feeding it back. But this whole area of the customer has been way, way understudied and way, way underappreciated.

Speaker C: I wanted to come back to something I've heard you say in the past, which is that it's easy to confuse a technology problem with a data problem. I love that saying. And can you give an example of that? And how do you tell the difference between the two?

Speaker A: The classic example is systems don't talk. And so what's going on is somebody's trying to reconcile this system with customer data and that system with customer data in it, and they're blaming the systems. What we're really going on is over here, you know, in marketing, a customer is viewed as a prospect, right? And over here in finance, the customer is viewed as the entity responsible for paying the bill. Right? Now, those are not completely at cross purposes to one another. But blaming the system because the data definitions are wrong, you can't solve that problem with a system. You have to get into the data and figure out the commonality and figure out the resolution.

Speaker C: And there's been a move to push more towards, like, these large pools of data that are available. When you bring up that example, is the answer there to go to a larger, more flexible pool of data, or is it just to understand that you're going to have different sets of data for different needs and different purposes?

Speaker A: I like what you said. Second, I think that most of the questions that we ask are pretty darn simple. And even the most complicated ones like protein unfolding, well, you don't need all the data in the Internet to do that. You need specific data about protein unfolding to answer it. And, um, so the way I'd like to see it work is we start with customers, right? What is it they need? Okay, what data do we need to answer that question? And that's the way we should start whether we're using an AI or anything for that matter. Right? Start with customers, work your way back. It's, it'd be the same in one manufacturing a product. Well, what do customers need? Well, how do we build that into the product? And so the direction should go like this as opposed to, okay, well, we found this data. It could be useful for something. We'll throw it into a model and, and then let people have at it. And by the way, we'll have frat boy confidence in our answers and whatever happens, happens. And what's happened so far is slop.

Speaker C: There was one last question I wanted to ask you and before I do, thank you so much for this conversation. It's been great. But I know that you've worked with many different companies across a very wide range of industries. I feel like some of this we probably already discussed. But what I wanted to ask you was like, what is the big thing that you've learned about how the most common data problems tend to arise and the best way for a business to approach fixing them?

Speaker A: Yeah, that's a good question to answer to end on. So with, uh, respect to common things, you remember your Tolstoy. I'm sure all good families are the same and all dysfunctional ones are dysfunctional in their own unique ways. What I've seen over and over again is that companies try to address data quality by making mistakes in one part of the organization and cleaning them up in another.

Speaker C: Right.

Speaker A: And they're not recognizing the customer creator stuff and management through ignorance or laziness or, or whatever is allowing this to persist. Now the underlying like, well, why is that? How are you going to get in front of it? That is an awful lot of the art. And what I try to m to bring to companies so far, I see two things as necessary. I mean, first, you have to admit you have a problem. Okay. That can be hard to do. Many companies, at the very least the like wobegone effect is, you know, all our data is above average. Right. Kind of thing. Or in this company, you have to be perfect. Our data is perfect. When the data is not perfect here, people die. So you have to be able to admit that you're not perfect. And the other thing you need, I found you need is a person willing to take a specific problem on. Okay. And, and because otherwise we continue with this finger pointing stuff. Now once somebody takes on a problem and gets traction and shows a pathway for how you solve those problems. Not sort of at a high level kind of thing, but in this company then you have a chance for things to go further and faster. And at some point it's helpful if management says, yeah, we gotta, we gotta provide some support, we gotta get some policy in place, we gotta get common metrics and does that kind of thing. But the flywheel always starts with a uh, combination of pair of a problem and a person willing to take it on. And usually that person, uh, sometimes they're senior level, but more commonly they're in the middle.

Speaker C: Thanks again to Tom Redmond, the Data Doc we hope you enjoyed the episode. We'll be back next week with another episode of how to Raise youe Agent. We hope you'll join us.

Speaker B: This episode is brought to you in part by Abax Technologies. AI agents are fundamentally changing how people and organizations interact with computers and the Internet. A race is, um, underway to establish the path that will govern the nature of these interactions. ABAX Technologies introduces ABAX Labs as its center um, for engaging with the developer community through the release of open source software to promote a path that empowers users to maintain control of their identity, data and agents in digital spaces. Abax Labs first release is the Agents Library, a subset of ABAX's ID software development kit that has been tuned for use with AI agents. Download agents on GitHub today at, uh GitHub.com abaxlabs that's GitHub.com. That concludes this week's episode of Smarter Markets by abax. For episode transcripts and additional episode information, including research, editorial and video content, please visit SmarterMarkets Media. Please help more people discover the podcast by leaving a review on Apple Podcast, Spotify, YouTube, or your favorite podcast platform. Smarter Markets is presented for informational and entertainment purposes only. The information presented on Smarter Markets should not be construed as investment advice. Always consult a licensed investment professional before making investment decisions. The views and opinions expressed on, um, Smarter Markets are those of the participants and do not necessarily reflect those of the show's hosts or producer. Smarter Markets, its hosts, guests, Inc. Employees, and producer Abex Technologies shall not be held liable for losses resulting from investment decisions based on informational viewpoints presented on Smarter Markets. Thank you for listening and please join us again next week.