What second by second phone data reveals about human attention with Byron Reeves

There's just a lot of individual variation. There's variation not only between people— that may be even overemphasized— but there's a lot of variance for any one of us over time. That next shopping trip might have a totally different story about, for that same person, why the strawberries got trashed. So it's the whole scene: media, human behavior, thoughts, emotions, and behaviors, and the media that we're interested in looking at in relation to them is just really complex. I like to say infinitely complex. So there's just a lot to know. And our job as researchers is to try to simplify that, but it just gets harder every day. Hello, fellow insight seekers. I'm your host, Molly, and welcome to The Curiosity Current. We're so glad to have you here. And I'm your host, Stephanie. We're here to dive into the fast-moving waters of market research, where curiosity isn't just encouraged, it's essential. Each episode, we'll explore what's shaping the world of consumer behavior, from fresh trends and new tech to the stories behind the data. From bold innovations to the human quirks that move markets, we'll explore how curiosity fuels smarter research and sharper insights. So whether you're deep into the data or just here for the fun of discovery, grab your life vest and join us as we ride the Curiosity Current. Today on The Curiosity Current, we are joined by Byron Reeves, chaired professor at Stanford University with appointments in communication, symbolic systems, and the Graduate School of Education. Byron has spent decades studying how humans respond to media and technology. His work has shaped the field of media psychology, and he's authored landmark books such as The Media Equation and Total Engagement that changed how people think about their relationships with screens and machines. Byron's research spans both academia and industry, from Microsoft Research to consulting for other global brands, and more recently through ventures exploring AI simulations and synthetic personas as tools for understanding human behavior. Today we're diving into one of the biggest tensions in modern research, the challenge of studying real humans in an increasingly complex digital world, and whether AI simulations might help us understand people in entirely new ways. Byron, welcome to the show. Thank you very much. I'm happy to be here chatting with you. Yes. Well, we are happy to have you. Just to jump right in with you, Byron, you spent much of your career studying how people relate to media, technology, and increasingly intelligent systems. Looking back, was there a moment where you realized that understanding human behavior was actually becoming harder or more complex rather than easier in this more connected world. Yeah, I noticed you said decades, spent decades. You made me tired thinking about how many decades that actually is. But when I started doing this, there were 3 television networks and maybe a newspaper that got delivered to my porch, and everything was all analog and it was pretty corralled and easy to get to. And so, I mean, that suggests— and that couldn't be less true right now. I mean, there's just an infinite amount of content to get to. So the recognition that this was a more complex world of media is certainly gradual, but they, when we went from analog to digital, let's say 25, 30 years ago, and everything started to mush together and there was a lot more chance for me to be in control and different companies combining forces and traditional media sources just kind of melting and So there was a 5, 10-year period where that was happening, but it's just been a gradual kind of drip torture or change in the media landscape that, that has made it just really complex. So it's really important. One other thing about that question is, is so the complexity of research is something that, that I understand we want to talk about, but it's really important to know why. Or let me congratulate you for having that be a first question, because when we talk about AI and the role of technology, The first thing that people think of is, oh, this is going to be faster and cheaper and it's going to be easier. I'm going to lose my job. You know, things are— this is just really a revolution here. And I think we forget that one of the main reasons for me anyway, not, not so much in the commercial products that are being in this going into this category, but one of the reasons that, that this interests me is research is hard to do. This is a really complex scene and maybe we can do it better, you know, with, with AI, not, not only faster and cheaper. Well, speaking of how research can be complicated, I think that in a lot of these pushes to go faster, utilizing AI, utilizing more technology, we sometimes forget how messy human behavior really is and how we're very nuanced and weird creatures. People say one thing, do another, and they sometimes don't even understand their true motivations for something. And from a previous podcast guest, we had Lori Darment on, and she talked about how she went on a shop-along with a customer, and she purchased organic strawberries, but non-organic broccoli. And she asked the shopper, "Why did you do that?" And she was a mom of young children, and she said, "Well, the strawberries are for my kids, and I think that it's really important to be cautious about what goes into them." But the broccoli's for my husband and he's kind of already grown up and cooked up the way that he is. And it doesn't really so much matter if he has organic or not. And the kids won't touch that broccoli. So I'm purchasing that for him, which was so interesting because on paper you wouldn't've gotten that insight because it's just very weird human behavior that she observed. So from your perspective, given all that context, what makes social research with humans particularly challenging today? Just the idiosyncrasy and the details and the complexity that you just told in your story. I mean, that is, but that's more in relation to what our interest is in terms of marketing. You know, you might be able to go into any one piece of that story you just told and simplify that a bit. But all those stories are relevant to the journey that that woman was having with the vegetables and the food choices. So it's complex behavior. There are many layers to it. When you map it onto media especially. So if you wanna know about food choices, you are biting off a complex piece of humanness right there already. So you have to have all those different stories kind of layered in there. And then you throw on this increasing complexity of the media itself, and then that makes the stories even more complex. And so it's just this, the idiosyncrasy is really an important word for me in thinking about how to do research and marketing and whatnot. There's just a lot of individual variation. There's variation not only between people, that may be even overemphasized, but there's a lot of variance for any one of us over time. You know, that next shopping trip might have a totally different story about why the strawberry, for that same person, why the strawberry's got treasured. So it's the whole scene, media, human behavior, thoughts, emotions, and behaviors and the media that are, that we're interested in looking at in relation to them is just really complex. I like to say infinitely complex. So there's just a lot to know. And our job as researchers is to try to simplify that, but it just gets harder every day. And I think that's also kind of fun though about it and what makes, what makes human beings so interesting to study. Yeah, of course. And it's, uh, that uniqueness and especially the specialness of any, any given instance. There's another bunch of answers to the question of complexity though, that are really important in my university work, just all over the university, thinking about doing academic and theoretical work on how people think and feel in relation to media. And these are challenges that are really significant, that get under, that we don't look at enough in the marketing context. I mean, in the marketing context, real humans or AI simulation, which is best? And one will win, or this is gonna be faster and cheaper. That's enough of a solution for me. But for me, one of my interests in these AI simulations is because we are having a crisis in research in a lot of domains that AI can help with. We're having a lot of issues with respect to reproducibility of findings. So, and this is human research is not necessarily the gold standard or the ground truth. And, you know, in evaluating AI, it's really hard to replicate human studies. If you do the same study twice, you're not going to get the same answer with humans. There's a generalizability problem. You have all these complex media stimuli that you have, like you have the, the advertisement for the organic strawberries that you mentioned. Well, that advertisement could be any of 10 million different versions. Do they all work the same? No, they don't. So how do you get all those special instances in there? We've got online fraud. That's really an important problem right now. We have a hard time getting to the samples of respondents that we'd like. We have a really hard time getting permission to ask really sensitive questions, which are increasingly important. You know, if it's about sex, drugs, or rock and roll, or finances, or relationships, or which are can be very important in terms of marketing, especially products that deal with issues that are— or products that are private and whatnot. So it's just all of these issues really make research a lot harder, hard to do. And the AI simulations have a chance of helping out in each of those areas, not just going faster and for less money and without those 3 jobs. Kind of in that same vein, It's, you can, a theme in this conversation already and, and generally on this podcast is that paradox of having more data than ever, but still struggling sometimes to explain, predict consumer behavior. I'm curious if you could talk a little bit about what do you think traditional research methods miss about the audiences that they seek to understand? Like even when they're done well, so not, not shoddy work, but you know, robust methods, like how are they not meeting the moment, would you say? They just miss the complexity. And it's, it's not like an intellectual critique or you forgot to take 3 classes in college, how to do this better. It's just really, really hard to do, to have research that represents that complexity. It, you know, how many hours did you spend on Instagram this week? Those kind of questions are almost useless right now, given the hugely fragmented nature of the way we were looking at all this content and seeing all the marketing messages within there. Just, it just, just very, very complex. So you have to bring in new methods that allow for the idiosyncrasy and the extreme fragmentation. And they, it just gets harder and harder, more data and more data, more complexity, more complexity. So that's the biggest problem I think that we have is representing the— trying to simplify in a way that allows people to take action on the research that they're doing, but still stays true to this almost inevitable complex answer that we don't often want to hear because I'm not sure what to tell the boss about, you know, how to spend our money if it's real complex. Right. I want to take a deep dive into the mention that you had specifically about Instagram and talk a little bit about your work as it relates to the human Screenome Project, which was a moment-by-moment analysis of digital behavior, which is so, so fascinating. What did your work reveal then about how different behavior— how does it show up versus how do people actually describe their screen habits? So it was, uh, well, first let me tell you what it is, the Screenome Project. What we did, this is pretty much unguided by a lot of great theory, but just, uh, this is maybe 7 or 8 years ago we started this.. But just this notion that we kind of thought people were on these smartphones and they weren't doing anything for very long before they did something else. So we developed technology, and this is a long story short, that allowed us to capture a screenshot of what people were doing on their computers or their smartphone every 5 seconds that the device was turned on, and then follow them for up to a year. So We take Molly's smartphone, we put some software on it. We talk to her a lot about privacy and sensitivity and permissions and whatnot. I've signed the forms. Don't worry, Byron, I signed the forms. Molly's so comfortable. Yes, she loves it. And then you don't have to do anything. We're just grabbing a screenshot every 5 seconds off your smartphone and we're compressing it and transmitting it to a server at Stanford University, and we collect a million of Molly's screens over the course of a year. And now we can look at your— and then do— we could do the same thing for Stephanie and, and hundreds, maybe not thousands, of other people. So that's what we were doing. And then what we did, and this is really a moment for a lot of us that were in the Screenome Project, is we— the first thing we did, our idea was we can get the computer to look at all this stuff for us. It can recognize, we can use machine learning and AI, we can get, but the first thing we did was actually make a movie of a day of Molly's smartphone use and then just play that movie, just watch the frames. And you play that movie and it is startling in how, first of all, how individual it is to you, how unrelated it is to the commercial pieces of media that are being presented to you. Like, Molly, like all the rest of the people in the world, is not looking at the whole news story, are not looking at whatever you do. I mean, it's very TikTok-ish even before we were enamored by that, or even before that actually took over as a format for it to be shown. So people were spending maybe 7 to 10 seconds doing any one thing before they did something else, They were joining together incredibly radically different content. You were tutoring your kid in math and then you were at a video conference for 10 or 20 seconds with coworkers, and then you were shopping online and just doing all these radically different things. And then plus some things that were really private in there as well. So you get this story of this narrative of what you're doing that is totally unique. So that, that was a watershed moment for us. So this notion that you can, that you could represent that with a general comment about how much time you spent with your smartphone or Instagram or anything else is just, just melted. It's just not possible anymore. And so one of the things that we've been doing in the research field this is highly relevant for thinking about market research is trying to recognize, first describe what it looks like, and then how do all these, how does all this content get joined and what is this big stew that's happening in media? And it's this, so that's a huge answer to this complexity problem. And it's a way to look at the complexity, kind of celebrate the complexity You know, your movie is different from my movie, but really take seriously what, how detailed that is and try to figure out what that means for how you do everything. You know, how you, how students at Stanford write a paper. I can tell you how they write a paper 'cause we've looked at their screens. They write it about a minute a time and then they'll do something else in between unrelated to writing the paper. So it's just this, you know, this real fragmentation and joining of kind of unrelated sequences. Maybe for good and bad. You know, I get to be, I get to be in charge of managing my own arousal and interest and engagement, but I'm also flying all over the place and maybe not getting as deep into some types of thinking as I should. Or so there are things to be worried about and maybe things that are interesting as well. But that's what's different. Part of what makes that so interesting too is that I don't think that any of us or many of us intend to be that fragmented, right? It is just the nature of the tech and the media landscape pretty much, right? The technology certainly encourages that, but I think the technology has found a way to really, if we worry about media, we'd say to take advantage of this kind of attentional interest. But I think humans are built to survey the world and about the units that we're working with now. And if I had to make any prediction, I think they'll be even shorter. Yeah, we have this notion, I mean, this is kind of an ecological, how to survive in the world a couple hundred thousand years ago that we don't need now, but we can't escape because we're just really built to look at novelty, to look at new things, to be concerned about what's over there and what am I missing? And I mean, you could even take any of the typical effects that we think about, fear of missing out in social media, as this constant need to be checking and to be surveying and is there something more important? Is there a more interesting mate, some food, a place to live? In the kind of cave person sense of surveying the world. So it's not just they've caused us to be that way, I think they've also figured out how we are built. That's such a good point. But like, then how do you, how do you think about that then in the context of like, I think as a parent of a 10-year-old, I'm watching all of this news and these, you know, uh, congressional hearings essentially right now around like tech out of schools because we have seen, right, that there's cognitive decline on the tests that we use anyway, right, for the first time, and that we're seeing this first generation of decline, and it is being tied, at least from what I hear from experts, directly to the tech. So it almost seems like it's tapping into something that is our nature, but it is doing so to a degree that is reshaping how we learn to a degree that is— I don't know. Does this worry you? Yeah, no, I think you're right. I totally support getting a tech out of the classroom because there's a natural engagement in what the tech is doing, which is a little bit different than saying, oh, they figured out how to change us in a terrible way that is— that hasn't yet. I'm not sure. I'm not sure I always buy that argument, but it, but I've— so early on when smartphones first came out, I never said anything about them in my classroom. And I've only in the last year or two said, No, you just got to get rid of them. You just got to close them down because it's, and me too, it's just too engaging, too enticing. And, you know, we want to do something different that we couldn't do if we were all online. We want to have something special happening here. So yeah, I'm all in favor of that. What's hard too is, at least for me, my son's a bit younger. My son is 9 months old and he sees me and my husband on our computers all day, every day, not because we necessarily necessarily are choosing this, but we both work from home, but we have, you know, challenging careers, and it requires us to be in front of our computers 8, 10, 12, 14 hours a day. And so he— we, we got him one of the, like, a very, like, old computer that we had that doesn't even work. And he sits— even now, he sits in his little chair, and he has a little table, and he smacks all over that computer. He knows already intuitively what it's for. And part of me is like, no, no, don't do that. But it's like, what else is he supposed to know? Mom and Dad stare at this all day, so I'm gonna stare at this all day. It's like how we used to do what our moms did, right? My mom's working in the kitchen, I need my play kitchen. It is that. Well, but you're both highlighting the nurture part of it. There's a nature part of that as well. Bright flashes of light fluorescent light from a screen get noticed in our world. And because we're human and you better notice things that are prominent in your environment because they may eat you. Yeah, sure. Yes. And so we're kind of built, it's kind of built in as well. So it's not only that it's happening, but don't put that computer in his room when he gets one of his own. No, maybe not. Maybe not allow that. In the living room. It doesn't work. It doesn't work. It's not on. It's just for him to smack buttons. But then he gets really bored of it because to your point, he comes and sits over by me and he sees the flashy faces and the Word documents going by and the Slack channels and he's like, how come my computer doesn't have that? And I was like, oh honey, no, you don't wanna be like me. Not yet. You have plenty of time to stare at a computer. It's not right now. That's great. I, I wanna pivot a little bit to talk about more what you touched on at the beginning of our conversation about AI. So let's talk about AI simulations and to our listeners, no, you don't necessarily have to know exactly what this means or all about it yet, because I sure don't know the context of this. So Byron, for someone hearing that phrase for the first time, what does it actually mean to you to simulate human response in market research? Yeah, so can we get a computer to connect to a computer that has cataloged the entire history of the world via the internet and everything that's been said in social media and everything that's been published in the med library and everywhere else on campus. So that huge volume of generations of knowledge, can we find a way to get a computer to jump into that space of information via these large language models and AI that everyone is talking about, and kind of journey around that space via the prompts that we give it and create a way to predict what people might do based on what they've done for, you know, 100 years as represented by information on the internet. Well, when you put it like that. Yeah. So we're trying to build a computer program or a computer instance or computerized instance of a human that simulates what a human might do based on all the information that we've gotten from this gigantic cataloging of human experience via the internet. And then we can ask questions about that model. We can ask that maybe don't have— that are not just looking up things that people have done in the past, but are actually developing some form of predictive extensions— you know, some people call it intelligence of some sort, but we're not simulating human brains. That's not what I'm most interested in, although there's a place on our campus where that's what people are using AI for. But can we make predictions about what someone might do next, you know, given prompts and materials that we can actually show them? So modeling humans to predict what they might do without having to actually find a human to talk to and find, construct a conversation that they're comfortable with, that my university's IRB, that the people who are interested in privacy and sensitive information and whatnot and human subjects and whatnot can live with, which is increasingly more stringent. So trying to get a model that predicts what people do, that's kind of run by a computer. I think it's important to understand the grand nature of these things. 'Cause maybe we say it in passing all the time, but to actually, sit in what that means, I think is important. Understandably in the field, there is curiosity, there's excitement about AI simulations, about synthetic personas, but there is also, I think we all know, a healthy amount of skepticism. I would love to get down to just some practical tacks here. What kinds of questions do you think that AI simulations are well suited to answer? Good question. And then the counterpart to that, I assume, will be which ones? Which ones can't they answer so well? You got it. Got it. Yep. Well, one thing to say is that they're going to be better at answering questions that the internet knew a lot about or knows a lot about. And that's not totally everything, but simpler questions, they'll be better at questions that get a lot of treatment in the internet. And I think this is, there's good news for market research in that because there's a lot of information, you know, social media, as well as, you know, gobs of millions of market research studies that can all be consolidated to allow a model to be constructed where you can predict this. So that's, I think, the most important thing to say. I think they'll be good. So simpler questions is an important thing to say as well. So there are complex answers to questions about why people behave the way, the way they do in the marketplace. That will be, so I'm gonna combine the pros and cons here. So the complex answers that if you're of this personality type and have this kind of environmental media experience and have this kind of educational category, but have been in these kinds of conference, just very complex answers to why people do what they do in the marketplace. The more complex that gets, the harder it's gonna be to create a model that's accurate. You know, imagine a model of an airplane. So we wanna talk about how do airplanes fly? Well, here, here's a model of an airplane and we, you know, we can see it has wings and the more things that it has, the harder it's gonna be for that model to actually be useful in explaining flight. So that I think the most important thing is the simple thing. Yeah, that, so let me start with that as a, I think an important first answer to that question. Here's something it can't do that is responsible for a lot of— you mentioned at the beginning of your question that some people are not terribly happy with this concept of synthetic personas or maybe are very critical of them. There's a couple reasons that have been put forward for those criticisms, but one is that the AI models are not simulating how humans work. They are not simulating the human brain and how the human brain experiences environmental information, emotional experience, relationships, desires, and whatnot. There, I'd mentioned there are people on my campus who are very interested in having a computer do that, because if we use the computer to do that, maybe we can actually model the complexity of the human brain in ways that have been really impossible heretofore. But I think for marketing, in the marketing context, we're much more interested in predicting. And I'm not sure how the human brain works, but I'm pretty sure if this cookie package is offered at this price versus this price, people would be more likely to buy it. Or there won't be any differences. So I'm pretty confident in that prediction, but I'm not really talking to you about the different regions of the human brain that were involved in processing rational versus emotional information or some complex kind of neuroscience explanation there. But the prediction is really pretty good, I would say. So prediction versus true understanding, but it also sounds like you're— and this was one of the questions we had and you've already answered it beautifully, but I was thinking, you know, in market research, prediction is good enough. But then there are these foundational fields like psychology where it's really about understanding. But I suspect that these labs are neuroscience or psychology-related labs, right? Yeah. Well, it's interesting. This prediction versus explanation dichotomy has really been, you know, has a long history in science. Yeah. And especially in technology. I mean, one example, I was director of an institute here at Stanford that was very was very active in the first technology related to speech recognition. So there was a group, this is, you know, several decades ago, that there was a group of linguists that thought the first thing we need to do is explain how human language works, how it develops, how people think about language, and theory, theory, theory. And when you're done with that, then come to me and we'll build some technology based on that theory. And then along come these computational linguists and said, you know, we might not need to know how, how language works. What we can do is just, we can try all possibilities of how, how this might be interpreted. Uh, you know, we've got computing power to spare. We've got these new machines. We'll just, you know, try a bunch of stuff and maybe we can match, you know, what was said with, with what it actually means. And they won. They sure did. Yeah, and I think that's happening in a lot of different areas. So it's unsatisfying, and I mean, the theory is really quite satisfying. It's really nice to be able to say why something happened. But you can also develop systems of prediction that add up, that you can build up into a theory or at least a concept of how how something's working. So yeah, prediction is, I would argue, is enough to really keep us interested in what AI simulations can do in marketing and other areas as well, like finding tumors in X-rays. Right, yeah. Figuring out how organizations work. The economists are really interested in AI simulations of people, a lot of them without too much interest in, well, why did you choose that versus that? It's that you did, and knowing that, I can then build a theory. So this is, it's the same, this is an important point, by the way. I think that these AI questions are the same across domains that, you know, where AI is being applied to thinking about humans. I wanna touch a bit on the adoption side. So you mentioned economists, you mentioned that there's other people that are looking at this and they're excited about it and they wanna implement it. And you've worked for major consumer brands for many years. When business leaders hear AI respondents, synthetic respondents, they can oftentimes get excited at the shiny new thing that's going to help them, the promise of better answers, faster, cheaper. But what questions before they dive in headfirst should they be asking either of themselves or of these providers? Yeah, good that they're leaning in to asking questions. So that's the first thing. I think, I think one first thing I'd say is that it's not AI or not AI, or AI or humans, is not a useful question. I would look into AI. I mean, I think there's— it's quite well demonstrated that there's something to look in here. So what that says though, that there's a huge amount of variance within AI category of what they could look into and the ways in which you would look into it. So this is not about using complicated software to do market research or, you know, with humans or talking to ChatGPT about, you know, which cookie package is better. This is not— it's not— but I mean, that might be a fun thing to do, to ask ChatGPT which of these packages is going to work best, but it's much more than that. There's a long list of best practices. So When you look into this, know that there are people that have thought about how it needs to be made available for people to use. How, if you're going to build AI simulations or AI personas, how many do you need in your research? What kinds of questions can they answer the best? How do you talk to them? How do you actually make individual models for that can be collected across different respondents?. So there's just a whole lot to know. So that's one thing, you'll need some help, and I think it's important. I can't imagine what it would be like to, I can actually imagine what it would be like to be a business person having to wake up and get 1,000 emails in your inbox about people selling you AI software that could actually turn your market research around and make you a fortune., but you've gotta kind of wade through that. But there are people that have been doing it. You know, I, I'm working with a group called Viewpoints AI that is, that is trying to do this, to build a comprehensive package that can, that can collect all this information, and make it easy for you to actually do these simulations and to, to catalog it and to, to, to use it in the company. So that's one thing is how, you know, how look into the detail, look into ways in which you can get some help on, doing this, what are the best practices and how can you get access to the best practices? I think the other thing I do, and I've talked to a lot of marketers who are buying this or thinking about how they should respond to this kind of software, but, and this may be true for market research even a little bit more than for some of the other domains, you know, like economics or organizational behavior or medicine or something. But the people who are going to use this software in market research organizations should be and are going to be and are already worried about their jobs. What of this technology is going— can be done that replaces me? And not all of it by any means. And not everyone's job is going to go away. And, you know, there are these famous sayings about, you know, AI, is it going to take your job? But somebody that knows how to use it might take your job. I like that kind of thing, but to be sensitive to that context. So you need to, as a business leader, you need to figure out how are you going to get that technology into your group and company in a way that makes everybody feel comfortable about exploring with it? Because that's what you want people to do. We don't know everything we need to know about how to use it. There needs to be a lot of experimentation and somebody's not gonna be a good experimenter if they're worried about their job. And it also kind of chases some people in marketing to, and here I'm being critical of a lot of my colleagues who have really interesting things to, critiques to say about these AI simulations, but it pushes people off to, I would say, kind of minutia critiques of, you know, well, there's sycophancy in AI and there's gender bias and the variance distributions for these kind of variables are different in AI than they are. Those are important things to look at, but I think they're that big relative to what's gonna happen in changing the way that we do research. And they're also things that once noticed, there are chances to repair. And I mean, the AI's never gonna be worse than it is today. It's just gonna be better. Tomorrow and next week. So there are a couple of comments about what I do. I really like that question. I think it's really important. You know, how would— as a business leader, how are you going to get this in here? How are you going to get experimentation done? To your point about how, you know, these tools are improving, what do you think becomes the most critical human function? And we can ground this in the research process, just so we're saying, you know, because I'm sure it's variable. But yeah, what remains human in this? Yeah. Great question. So there's this philosopher of science, pardon the professorial answer here, but this philosopher of science that was working on if science is the subjective process, what's left, you know, that's really human about science? And the answer in this one literature is the question. It is deciding what's important to try to know about. Is the most important thing. And he has this— this is a guy named Michael Polanyi, and he has this lot of really interesting literature. To become a connoisseur of scientific beauty or to become— have good intuitions about what in marketing are the most important questions to try to answer, that's going to be harder for AI to answer that, although they may get there at some point, but it's going to be harder to answer than it then, you know, create a sample of people that match all the people in Iowa and ask them, you know, who they want to vote for or whatever, or what they, what product they prefer. Whoever's doing that and making the calls and doing the interviewing and aggregating all the data and running all the stats, that's, that's going to, going to go by the wayside. But, but the, the being a connoisseur in the sense of tasting fine wine and knowing which one is better, this is something that's done. As an apprentice, as somebody that's participated and you become a connoisseur of excellence. Like a curator of questioning almost. Yeah. It's personal knowledge. It's not objective knowledge. It's totally mushed together with who we are as human beings. So that I think is one thing that connoisseurship is really quite important to stay on top of that because you will be known and You can develop a great profession and be known as somebody who's asking the best questions. Forget how you're gonna answer 'em for a minute, but just, you know, how are you gonna ask the questions? And I, I think the other thing I would say is that even with AI, you are going to have to answer the questions working with other people. And maybe you can ask ChatGPT how best to do that, but it'd be nice if you were also had some emotional intelligence to be able to work in a group with this kind of information. So I think in our classes, so if we have a, I teach a big course in media psychology and we have projects that we do, we do them all always in groups now. There's a tendency to think AI is, well, I can go to my couch and do my study. Right. And I don't have to talk to anybody. And that's not true. You've gotta talk about what's the question we need answered. What's the best thing to ask the personas that we create? What do we make of the results? Is it important? Is it actionable? How much money are we going to spend on that action? But doing that in a group because you've got to do that with different functions in your group. So connoisseur, work in groups, play nice with others. Yeah, I love that. So, Byron, for someone who's listening who feels excited but uncertain about where AI is taking the research industry, what is one mindset or core kind of truth or orientation you would encourage them to hold onto as this next chapter unfolds? Yeah, the larger answer maybe is lean in, don't run away. There are people that are kind of running away from, and there are a lot of places to go run away to that are not totally wrong. Like if you think that AI is going to destroy the universe in 10 years or 2 years, then maybe we should all be running. But to lean into experimentation and to changes of mindsets and to really, to experiment, to experiment. Get, in a practical sense, pilots for software have never been more important. Figure out a way to, and the companies that are building the software are more than happy to find a way for you to inexpensively take some of the AI slop that's being generated, whatever looks the best, whatever you can evaluate may have a good prospect here, and try it out. How valid is it on its face? Did it make you think of things that you didn't think of before? How consistent is it with information you've been collecting for the last couple decades? And just to lean into the experimentation. And kind of disconcerting, and it's a, you know, change, something that's a change. So it's, it can be hard in that sense, but the lean-in part I think is important. Yeah, that makes a lot of sense. Thank you so much for joining us today. I think one of the things that really struck me from this conversation is the reminder that in the age of AI and tech, that humans have always been astronomically complicated, and that adding more data about our behavior or what we do doesn't necessarily mean that that alone is gonna make us easier to understand. For sure. And I think another thing, quite honestly, that I am gonna be thinking about for the rest of today at least, is that this notion that prediction and understanding, they're really not antithetical, and that building enough prediction can get you in some cases to that theoretical space as well. And I really have not thought about that, and I think that's It's just terrifically interesting. So thanks for sharing. Thanks for your questions. Thank you so much for all of this wonderful perspective that you've shared with us today. And to everyone listening, thank you so much for being part of The Curiosity Current. We'll see you next time. 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