Byron 0:00 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 treasured. So it's it 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.

Molly 0:39 Hello, fellow insight seekers. I'm your host, Molly, and welcome to the Curiosity Current. We're so glad to have you here.

Stephanie 0:47 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.

Molly 0:56 Each episode, we'll explore what's shaping the world of consumer behavior from fresh trends and new tech

Stephanie 1:02 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.

Molly 1:13 So whether you're deep into the data or just here for the fun of discovery,

Stephanie 1:17 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.

Molly 1:36 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.

Stephanie 1:53 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.

Molly 2:09 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.

Stephanie 2:22 Byron, welcome to the show.

Byron 2:25 Thank you very much. I'm happy to be here chatting with you. Yes.

Stephanie 2:28 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? ght 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?

Byron 2:53 Yeah. I noticed you said decades, spent decades. You made me tired doing thinking about how many decades that actually is. But when I started doing this, there were three 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 suggest and that couldn't be less true right now. I mean, there's just an infinite amount of content to to get to. So the recognition that that this was a more complex world of media is certainly gradual, but they when we went from analog to digital, let's say twenty five, thirty 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, uh, kind of melting. And so there was a five, ten year period where that was happening, but it's just been a gradual kind of drip torture or change in in the media landscape that that has made it just really complex. So it it's really important. One other thing about that question is is so the complexity of research is something that that I understand we wanna 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 gonna be faster and cheaper, and it's gonna be easier. I'm gonna lose my job. You know, things are this is just really a 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 it disinterests 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. 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 it disinterests 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.

Molly 4:53 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 Laurie Darmont on, and she talked about how she went on a shop along with a customer, and she purchased organic strawberries but nonorganic broccoli. And she asked the shopper, why did you do that? And she was a mom of of young children, and she said, well, the strawberries are for my kids, and I 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 have 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? 't touch that broccoli. So I'm purchasing that for him, which was so interesting because on paper, you wouldn't have 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?

Byron 6:13 Just the idiosyncrasy and the details and the complexity that you just told in your story. I mean, that 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 that woman was having with with the vegetables and the and the food choices. So so it's it's complex behavior. It's there are many layers to it. When you map it on to media, especially, so if you if you if you wanna know about food choices, you are biting off a complex piece of humanness right right there already. So you have to have all that all those different stories kind of lay 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 I mean, idiosyncrasy is really an important word for me in in thinking about what how 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 the strawberry for that same person, why the strawberries got treasured. So it's it 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.

Molly 7:58 And I think that's also kind of fun, though, about it and what makes what makes human beings so interesting to study. 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.

Byron 8:05 Yeah. Of course. And it it's, uh, that uniqueness and a special 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 all over the university, thinking about doing academic and theoretical work on on 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, it's real humans or AI simulation, which is best, and one one will win. Or it 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 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 in, you know, in in evaluating AI. It's really hard to replicate human studies. You if you do the same study twice, you're not gonna 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 10,000,000 different versions. Are 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 of respondents that we 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 and the AI simulations have a chance of helping out in each of those areas, not just going faster and for less money and what without those three jobs.

Stephanie 10:28 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? ict 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?

Byron 11:00 They just miss the complexity. And and it's it's not like an intellectual critique or you forgot to take three classes in college how to do this better. It's just really, really hard to do to to have research that represents, uh, uh, that 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 that allow for the idiosyncrasy and the extreme fragmentation and 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 they're doing, but still stays true to this almost inevitable complex answer that we don't often wanna hear because I'm not sure what to tell the boss about, you know, how to spend our money Right. If it's if it's real complex.

Stephanie 12:13 Right. I wanna take a

Molly 12:14 deep dive into the mention that you had specifically about Instagram and 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 that how does it show up versus how do people actually describe their screen habits?

Byron 12:38 So it it was, uh, well, first, let me tell you what it is, the the the Screenome project. What we did this is pretty much unguided by a lot of great theory, but just, uh, this is maybe seven or eight years ago we started this. But just this this notion that we kinda 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 of what people were doing on their computers or their smartphone every five 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.

Molly 13:28 I've signed the forms. Don't worry, Byron. I signed

Stephanie 13:29 the forms. Molly's well comfortable. Yes. She loves it. n 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.

Molly 13:28 I've signed the forms. Don't worry, Byron. I signed

Stephanie 13:29 the forms. Molly's well comfortable. Yes. She loves it.

Byron 13:33 And then you don't have to do anything. We're just grabbing a a a screenshot every five seconds off your smartphone, and we're compressing it and 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 ScreenOM project, is we the first thing we our ideas, we can get the computer to look at all this stuff for us. You know? It it can recognize. We can use machine learning and AI. We can but the first thing we did was actually make a movie of a day of Molly's smartphone use and and then just play that movie. To 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, that are not looking at whatever you do. I mean, it's it's very TikTok ish even before, uh, we were enamored by that or or even before that actually took over as a format for it to be shown. So people were spending maybe seven to ten seconds doing any one thing before they did something else. They were joining together incredibly radically different content. You you you were tutoring your kid in math, and then you were at at a video conference for ten or twenty seconds, uh, of 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, you know, in there as well. So you get this story of, uh, this 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 a general comment about how much time you spent with your smartphone or Instagram or 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 in the research field, you know, this is highly relevant to for thinking about market research is is is trying to recognize first, describe what it looks like. And then, you know, how do how do all these how does all this content get joined, and what is this big stew of you know, that's happening in media? And and it's this so that that's a huge answer to this complexity problem, and it's it's a way to look at the complexity, uh, kinda celebrate the complexity and, 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. it's this so that that's a huge answer to this complexity problem, and it's it's a way to look at the complexity, uh, kinda celebrate the complexity and, 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.

Molly 16:34 You

Byron 16:34 know, how you how students at Stanford write a paper. I can tell you how they write a paper because we've looked at their screens. They write it about a minute a time, and then they'll do something else in 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, uh, but I'm also, you know, flying all over the place and maybe not getting as deep into some types of thinking as I should or so so there there's things to be worried about and maybe things that are interesting as well, but that's what's different.

Stephanie 17:16 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 the tech and the media landscape pretty much.

Byron 17:33 Right? The technology certainly encourages that, but I think the technology has found a way to really if we worry about, maybe we'd say to take advantage of this kind of attentional interest. But I think humans are built to survey the world and about this 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 we have this this notion. I mean, this is kind of an ecological how to survive in the world a mill or a couple 100,000 years ago that's that we don't need now, but we can't escape it because we're just really built to to look at novelty, to look at new things, to be concerned about what's over there and what am I missing. And all I mean, you could even take, you know, what in any of the the typical effects that we think about, fear of missing out on in social media as this this constant need to be checking and to be surveying and to is there something more important? Is there a more interesting mate, some food, a place to live? You know, in in the in the kind of cave person sense of of surveying the world. But so it's it's not just they've caused us to be that way. I think they've also figured out how we are built. cking and to be surveying and to is there something more important? Is there a more interesting mate, some food, a place to live? You know, in in the in the kind of cave person sense of of surveying the world. But so it's it's not just they've caused us to be that way. I think they've also figured out how we are built.

Stephanie 18:45 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 Yeah. We have seen, right, that there's cognitive decline on the test 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 in you know, from what I hear from experts directly to the text. So it almost seems like it's it's tapping into something that is our nature, but it is doing so to a degree that is, like, reshaping what it you know, how we learn to a degree that is like, I don't know. Like, does the learning?

Byron 19:34 Yeah. No. I think you're I think you're right. I'm I totally support getting a tech out of the classroom because there's a natural engagement in in what the tech is doing, which is a little bit different than saying, oh, they figured out how to change this in a terrible way that is, you know,

Stephanie 19:51 that happened. Yeah. So

Byron 19:52 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. We're you just gotta get rid of them. You just gotta close them down because it's, uh, and me too, it's just too too engaging, too enticing, and, you know, we wanna do something different that we couldn't couldn't do if we were all online. We wanna have something special happening here. So, uh, yeah, I'm all in favor of that.

Molly 20:23 What's hard too is, at least for me, my son's a bit younger. My son is nine months old, and he sees me and my husband on our computers all day every day, not because we 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 eight, ten, twelve, fourteen 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.

Stephanie 21:13 How we used to do what our moms did. Right? My mom's cooking in the kitchen. I need my play kitchen. It is that thing. t 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. How we used to do what our moms did. Right? My mom's cooking in the kitchen. I need my play kitchen. It is that thing.

Byron 21:20 But you're both highlighting the a 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, you know, that are prominent in your environment because it they may eat you.

Molly 21:37 Yeah. Sure. Yes. Yeah.

Byron 21:39 And so so we're we're kinda built it's kinda built in as well. So it's not only that, you know, that it's happening, but don't put that computer in his room when he gets one of his own.

Molly 21:51 No. No. This computer that.

Byron 21:53 In the living room

Molly 21:54 room room. 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 Oh, yeah. 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 of 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?

Byron 22:46 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 on campus. So that huge volume of, you know, generations of knowledge. Can we find a way to get a computer to jump into that space of information and 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, a hundred years as represented by information on the on the Internet.

Molly 23:38 Well, when you put it like that

Byron 23:40 Yeah. So we're trying to build a computer program or a computer instance or computerized instance

Stephanie 23:48 Right. e might do based on what they've done for, you know, a hundred years as represented by information on the on the Internet.

Molly 23:38 Well, when you put it like that

Byron 23:40 Yeah. So we're trying to build a computer program or a computer instance or computerized instance

Stephanie 23:48 Right.

Byron 23:48 Of of a human that that simulates what a human might do based on all the information that we've gotten from this gigantic cataloging of 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 in the past, but are actually developing some form of predictive extensions. You know, some people call it intelligence of some sort, but I'm we're not simulating human brains. That's not what what I'm I'm most interested in, although there's a place on our campus where people that's what people are using AI for. But can we make predictions about what someone might do next, you know, given prompts and 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 that my university's IRB, that the, uh, the people who are interested in privacy and sensitive information and whatnot and human subjects and whatnot can live with, which is increasingly, you know, more stringent. So I'm trying to get a model of that predicts what people do that's that's kind of run by a computer.

Molly 25:06 I think it's important to understand the grand nature of these things. Because maybe we say it in passing all the time, but to actually sit in what that means, I think, is important.

Stephanie 25:16 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 tax here. What kinds of questions do you think that AI simulations are well suited to answer?

Byron 25:37 Good question. And then the the counterpart to that, I said, will be which one

Stephanie 25:42 which ones can't they have as well? Yeah. You got it. Good. Yep. love to get down to just some practical tax here. What kinds of questions do you think that AI simulations are well suited to answer?

Byron 25:37 Good question. And then the the counterpart to that, I said, will be which one

Stephanie 25:42 which ones can't they have as well? Yeah. You got it. Good. Yep.

Byron 25:46 Well, one thing to say is that they're gonna 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 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 they way the way they do in the marketplace. That 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 ex 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 that it has, the harder it's gonna be the for that model to actually be useful in explaining flight. So that I think the most thing most important thing is the simple thing. Yeah. That so let me let me start with that as the the, I think, an important first answer to that that question. Here here's something you can't do that is responsible for a lot of you you mentioned at the beginning of your question that, you know, some people are not terribly happy with this concept of synthetic personas or maybe are very critical of. And there's a couple reasons why those are 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, you know, 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 that have been really impossible heretofore. But I think for marketing, in the marketing context, we're much more interested in predicting. And and and I'm not sure how the human brain works, but I'm pretty sure if it's 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 is really pretty good, I would say. fferences. 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 is really pretty good, I would say.

Stephanie 29:06 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?

Byron 29:28 Yeah. Well, it's interesting. This prediction versus explanation, uh, 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.

Stephanie 30:37 They sure did.

Byron 30:38 Yeah. So they won. And I think that's happening in a lot of 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 it's also you 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 is, I would argue, is is is enough to really keep us interested in in what AI simulations can do in in marketing and other areas as well, uh, like, you know, finding tumors and and x rays. Right.

Stephanie 31:21 Yeah.

Byron 31:22 Figuring out how organizations work. The economists are really interested in in AI simulations of of people. What 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 the these AI questions are the same across domains that, you know, where AI is being applied to thinking about humans. uch 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 the these AI questions are the same across domains that, you know, where AI is being applied to thinking about humans.

Molly 31:55 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? any 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?

Byron 32:29 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 what that says though, that there's a huge amount of variance within this 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 CHET g p t about, you know, which cookie package is is better. This is not it's not but, I mean, that might be a fun thing to do to ask CHET g p t which of these packages, but it is good is gonna 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 so that's one thing. You're you you'll need some help, and it's I think it's important. It it's there's just I can't imagine what it would be like to I I can't actually imagine what it would be like to be a a business person having to wake up and get, you know, a thousand emails in your inbox about people selling you AI software that could actually turn turn your market research around and make you a fortune. But you've gotta kinda wade through that. But there are people that have been doing it. And I've 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, uh, doing this. What are the best practices and how the 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 or thinking about how how they should respond to this kind of software, but and this may be true for market research even a little bit more than from 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 gonna go away. And, uh, you know, there are these famous sayings about, you know, AI is gonna take your job, but somebody that knows how to use it might take your job. Uh, you know, I I like that kind of thing, but 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 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 all 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 worried about their job. And it also kind of chases some people in marketing too. And here I'm being critical of one of my colleagues who have really interesting things to critiques to say about these AI simulations, but it it pushes people off to, I would say, kind of minutiae critiques of, you know, well, there's sycophancy in, you know, AI, and there's there's gender bias. And, you know, 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, you know, what's gonna happen, you know, in in changing the way that we do research. And there are also things that once noticed, there are chances to repair. And, I mean, the AI is never gonna be worse than it is today. It's just gonna be better tomorrow and next week. So there a couple of comments about what I do. I I really like that question. I think it's really important. You know, how would as a business leader, how are you gonna get this in here? How are you gonna get experimentation done? never gonna be worse than it is today. It's just gonna be better tomorrow and next week. So there a couple of comments about what I do. I I really like that question. I think it's really important. You know, how would as a business leader, how are you gonna get this in here? How are you gonna get experimentation done?

Stephanie 37:10 But 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 what remains human in this?

Byron 37:28 Yeah. Great question. So there's this philosopher of science pardon that professorial answer here, but this philosopher of science that was working on if science is this objective 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 he has this this 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 gonna be harder for AI to answer that, although it may get there at some point, but it's gonna be harder to answer than it than, you know, create a sample of people that match all the people in Iowa and ask them, you know, who they wanna vote for or whatever or, uh, or what they what product they prefer. Whoever's doing that and making the calls and doing the interviewing and and aggregating all the data and running all the stats, that's that's gonna gonna go by the wayside. But but the, uh, the being a connoisseur in the sense of tasting fine wine and knowing which one is better, it is just something that's done as an apprentice, as as somebody that's participated and and you become a connoisseur of excellence and

Stephanie 38:54 Like a curator of

Byron 38:56 Yeah.

Stephanie 38:56 Yeah. Yeah. What's happening almost. Yeah. tasting fine wine and knowing which one is better, it is just something that's done as an apprentice, as as somebody that's participated and and you become a connoisseur of excellence and Like a curator of

Byron 38:56 Yeah.

Stephanie 38:56 Yeah. Yeah. What's happening almost. Yeah.

Byron 38:58 It's it's personal knowledge. It's not objective knowledge. It's totally mushed together with who we are as human beings. So that's that I think is one thing that that that that connoisseurship is really, uh, 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 them 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're going to have to answer the questions working with other people. And maybe you can ask to TTP how best to do that, but it'd be nice if you were also had some, you know, emotional intelligence to be able to work in a group with this kind of information. So, you know, I think in in in our classes, so I if 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. You know, there's a tendency to think AI as well. I can go to my couch and do my study. Right. And, you know, I don't have to talk to anybody. And that's not true. You've got to talk about what's the question that we need answered, what's the best thing to ask the personas that we create, what do we make of the of the results, You know, is it important? Is it actionable? How much money are we gonna spend, you know, on on that action? But doing that in a group because you've gotta do that with different functions in your group. So connoisseur, work in groups, play nice. Oh, my.

Stephanie 40:32 Yeah. I love that. So, Byron, for someone who's listening who feels, you know, 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 on to as this next chapter unfolds? ps, play nice. Oh, my. Yeah. I love that. So, Byron, for someone who's listening who feels, you know, 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 on to as this next chapter unfolds?

Byron 40:49 Yeah. The 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 run away too that are not totally wrong. I like if you think that AI is going to destroy the universe in ten years or two years, then then maybe we should all be running. But but to lean into experimentation and to change the mindsets and to really to experiment to experiment. Get in in a practical sense, pilots for software have never been more important. Figure out a way to and 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's flop that's been generated. Whatever looks the best, you know, whatever can you you can evaluate may have a good prospect here and try it out. How does what how how valid is is it on its face? Uh, 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, you know, couple decades? And just to lean into the experimentation. And it's, uh, kinda disconcerting, and it's a, you know, change something that's a change. So it's a it can be hard in that sense, but the lean in part, I think, is important.

Stephanie 42:12 Yeah. That makes a lot of sense.

Molly 42:14 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.

Stephanie 42:35 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 just terrifically interesting. So thanks for sharing.

Byron 43:00 Thanks for your questions.

Molly 43:02 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.

Stephanie 43:12 The Curiosity Current is brought to you by AYTM. To find out how AYTM helps brands connect with consumers and bring insights to life, visit aytm.com. And to make sure you never miss an episode, subscribe to the Curiosity Current on Apple, Spotify, YouTube, or wherever you get your podcasts. Thanks for joining us, and we'll see you next time.