Yuliya Grinberg PhD on Human & Machine

Yuliya Grinberg is a digital anthropologist and qualitative researcher with a PhD in Sociocultural Anthropology from Columbia University. She is currently Research and Insights Manager at Mastercard. Her book, Ethnography of an Interface: Self-Tracking, Quantified Self, and the Work of Digital Connections, was published by Cambridge University Press in 2025.

A note for readers: Yuliya has offered a 20% discount on Ethnography of an Interface for anyone coming from this interview. Use code GRINBER24 at checkout here.

So I know—I think you know this, right? You’ve listened to interviews before. I start them all with this same question, which I borrowed from a friend of mine, who’s also a neighbor, who helps people tell their story. And I borrow it because it’s really big and beautiful. But because it’s big and beautiful, I kind of over-explain it the way that I’m doing now. And so before I ask it, I want you to know that you’re in total control. And you can answer or not answer any way that you want to. And it’s impossible to make a mistake. And the question is, where do you come from?

So thank you for that question. I love this question. First of all, as an anthropologist, I appreciate that this is a question really about context, right? First and foremost, at least how I read it.

And then, on some level, I feel like there are so many ways to answer this question. We can really be here all day. It’s a really loaded and big question, which is exciting. So I’ll answer it.

And so, in a couple of ways—you know, in a geographic sense, I’m from Russia originally. But I also moved to New York, Queens first, in the early 1990s with my family. So I think that sense of that experience has really shaped my worldview as well.

I’ve also lived for many, many years afterwards on Brighton Beach, which is kind of the Russian diaspora community, especially on the East Coast. So that’s shaped me as well.

I’m also a product of my experiences, I would say more broadly, kind of zooming out a little bit, right, is immigration has been a really pivotal experience in my life. It has really shaped how I thought, how I think, how I kind of even comport myself, how I relate to others.

There’s something about moving to a completely different country with a different cultural code in middle school that upends your reality in the way that it does.

And maybe without it, I sometimes think I might not have been as interested in culture as I am now professionally. I do wonder if that experience really kind of set me on a new professional course without me knowing it, even way back then.

And as an immigrant kid, kind of taking interest in culture really, for me at that time, has become a little bit of a survival mechanism at first, and now it’s become a professional habit.

And so, also, I would say I’m a product of my family. I’m very much my grandmother’s granddaughter, in the sense that she was a very pivotal figure in my life personally, but she was also kind of the ultimate matriarch in our family.

So she really played a really kind of key role in how I look at the world as well. Yeah, and professionally, from all over the place—from advertising to anthropology to marketing research. Yeah, out of many different chairs.

Oh, that’s interesting. I’m curious that you said middle school. What can you tell—can you tell a story about what that was like for you to sort of move to a whole new country?

I was 11. I was turning 11 when I moved. And moved with a lot of, you know, ideas about what a different world and different country would look like.

I don’t think any of us were prepared at that time to really imagine immigration or the U.S. kind of really large in our imaginations in Russia when I was growing up, but really without any clarity of what it would look like.

My sense of the U.S. was really taken from things like 90210, the show. I quickly learned when I moved to gentrified Bushwick that was not the U.S. of my experience, and just the expectations of struggle.

One story I like to tell my kids about what it was like is stepping off the airplane, which was actually a really kind of exciting experience for myself and my twin sister. I have a twin sister. We had never been on an international flight prior to that. We were really excited about that as a trip.

I don’t think we really fully comprehended that we were permanently leaving or that we weren’t going to really understand much of anything in that world.

I remember stepping off the plane and thinking, wow, JFK is just so noisy. And I realized I didn’t understand a single word. That really was kind of just like a visceral shock, of just that difference.

And that’s something you kind of experience with your body. You can’t really intellectualize it. We talked a little bit about it, of course, with our family, but you experience that as a very physical phenomenon.

I think I remember that—how it felt in my body to be all of a sudden in this really, really radical new place. And I had to figure out how to orient myself. I had to find my feet in it.

And did you say that you didn’t understand what anybody was saying? Is that what you’re describing?

Exactly. I didn’t speak any English, aside from maybe introducing myself with my name.

What did that feel like?

It felt really confusing. I think it was just overstimulating.

And I’ve had many experiences like that since because I’ve traveled, I’ve studied abroad, I’ve traveled to different countries, I’m really interested in studying people in different settings.

So I’ve found that that kind of physicality that you confront—all your antennas all of a sudden up—the things you take for granted in your everyday world, everything is input. So in some ways, it’s overstimulating.

You don’t really have that kind of first-order, second-order hierarchy of what things mean. They all mean everything at the same time. Equal importance.

It’s funny. I was an adult. I remember I traveled to Egypt and I was in Cairo. I remember being in Cairo on a street corner. And I had the realization that I had no idea what anybody was saying. Yeah, I loved it. I think it felt very quiet all of a sudden.

Very quiet, exactly. Yeah, yeah, yeah. I spent a lot of that first year in my head, observing.

It’s also a technique, I guess, in some ways—practicing for the future—listening, observing, comparing. I never really experienced myself as an outsider prior to that experience, and that was an interesting change of perspective.

Do you have a recollection of kind of what you wanted to be like when you grew up?

I was thinking about this question because I noticed, yes, that others—and I really reflected—I was not a kid really that had a strong sense of “I really want to be X, Y, Z.”

I think I had a sense of what I should become. Maybe my parents’ perspective, my grandmother’s perspective. Maybe I should be a lawyer. Maybe I should be a doctor.

Those were not things that appealed. Fortunately, I figured that out pretty early. That wasn’t going to be my set of expertise.

I didn’t spend too much time pursuing that path. But I didn’t really have a really clear idea of what I wanted to be. But I did know a few things. I knew the kinds of things I cared for, or the kinds of information I craved.

For instance, I grew up in a very musical and artistic family. My uncle is a conductor in an orchestra. My mom was a music teacher in Russia. All my grandparents were in music theory or music school in one way or another.

So music was a big part of my early childhood—going to classical concerts, especially visiting museums. That was really important.

But I will say personally, I never connected to aesthetics or sound on its own. It never really clicked for me why that was important or why that was valuable.

Maybe it was because I didn’t have the same talent. What I always wanted to know was about the people who made that music—what world did they live in.

If we visited a museum, I was always curious about the narration, about the context in which some of that was made. Those were the kinds of things that always appealed to me, I remember.

So it was kind of—I read a lot of biographies as a kid, I think, like early on. I didn’t know exactly why, but I really wanted to know kind of the behind-the-scenes world behind the public face, like public persona, or just the sound, like just aesthetic quality.

So I think that that’s something that I felt early on.

And then the second thing I would say is I really didn’t want to settle on anything. It was so hard for me to choose a major or to, you know, be very narrowly focused on one particular career, and explain that kind of zigzag professional life I’ve had as well.

You know, my happy place was always like doing several things at the same time. So in school, I majored in business, but I also wanted to study art history, and I also wanted to study, you know, languages, and social sciences were really interesting.

Professionally, I’ve also kind of done so many different things.

And I used to think of it as being indecisive. But, you know, as I’ve become an academic, I’ve learned a better word: interdisciplinary.

So I think that interdisciplinarity was also always kind of an instinct. It felt insufficient to me to just narrowly focus. Like, I remember studying marketing, and I just couldn’t focus on it as its own thing. It felt really myopic.

So I always wanted to kind of have an almost, like, contrasting view from a different discipline. And yeah, so those are the two things I think that really—I wanted to do as a young person. And I think those are the kinds of instincts that, you know, in my professional life.

You know, I’ve switched from brand strategy to academia, and now I’m more formally in private sector research. So I think that’s always been more interesting to me. Yeah, yeah.

So to catch us up, where are you now, and what are you doing for work?

Yeah, so now I am doing—I’m a manager of research and insights at MasterCard. I’ve been here for a little over a year, which is exciting. And in this role, you know, I think I get to do a little bit of the kinds of things I’ve described.

I work with a lot of different teams internally, many product teams, and some of the more specific research questions they might have, and helping them kind of conceptualize research and find the right partners for that work.

I’m also doing a lot of thought leadership more broadly for the organization on various topics. For instance, AI—how do people feel about AI right now—is one piece of work we’re doing.

So, you know, prior to that, I was also doing kind of UX research and private sector research.

And I started off—this maybe kind of loops back to where I started. I started off in advertising as a brand strategist. But that took me into kind of a more academic direction.

I was fortunate enough to work with anthropologists on staff at the time when I was coming up. And I really took—it really seemed like a really fantastic perspective.

You know, I was a little bit tired of sitting in on focus groups and reading the same syndicated research. I kind of wanted—like, I really was really kind of attracted to the anthropological perspective.

So that took me to pursue anthropology as a field of study. And I earned my PhD. And since then, I’ve done a lot of different research on the academic side, as well as on the private sector side.

Yeah. What was your—what was the encounter that you had with anthropology that inspired you?

So, you know, I remember very clearly, I was fortunate to work with Tim Malafite. I don’t know if you know him.

Yeah. I know the name.

Yeah, he’s now at Fordham. He teaches kind of at the intersection of business and anthropology at Fordham. But at that time, he was working at BBDO as an onsite anthropologist.

And we were working together. He was doing ethnographic work for Campbell’s, which was a brand that I was on.

And really trying to understand how people—you know, Campbell’s is a brand that was always interested in cooking, but promoted at the marketing level often pitched as a quick and easy solution to an irritating problem, which is cooking, especially for busy parents.

And at the time, the work we did with Tim kind of zoomed out and encouraged the company to think about how people think of cooking more positively.

You know, if we do think of cooking in terms of speed and convenience, how do we also talk about it in the same language that people talk about it?

So we—I remember, you know, of course, as a strategist, I was part of different types of research initiatives.

But on the ethnographic front, we went to visit people in their homes and spent some time cooking meals with them, seeing how they put meals together, shopping with them for some of those meals, and hearing them talk about what role cooking played in their families and their everyday life.

How they related to that experience, where they wanted to kind of indulge and take more time, where they wanted to save time, and just that language—I thought it was really amazing to have a broader context.

And I thought, I want to do that more, make that a more permanent picture of my work. So I took that leap, eventually.

Yeah. You’ve been at MasterCard for about a year. And you talked about thought leadership. And I guess my question is, what is the story you tell about anthropology, and how does anthropology work? What’s the value of being an anthropologist in an organization like MasterCard that’s trying to make decisions?

You know, and I think it’s an ongoing conversation. I feel like, you know, I’m not the only one facing this challenge. But of course, you know, anthropology—qualitative research more generally—I think is always up against quantitative methods.

Now, more recently, AI methods. And, you know, there’s always a kind of push and pull. You know, I like to think of it as, you know, sometimes I think there’s been a lot of reflection in the industry. What anthropologists ask ourselves is, what do we have to offer to the business world?

It used to be kind of an obvious answer. We offer context. We offer the perspective of a human being, right? We offer an opportunity to have a conversation, kind of report on that encounter, as a complement to kind of more narrow, maybe numbers-driven analysis.

I think AI, in some ways, has really made that distinction a little fuzzier at times, because a lot of AI instruments appear to do the very same thing, but at scale, right?

So the question might be, who needs an anthropologist, really?

I think that’s a question that I ask myself. How do we articulate the value of what we have to offer?

And, you know, I always go back to what anthropology—or maybe qualitative disciplines more generally—offer. On some level, I feel like what I’m about to say is often seen as a kind of shortcoming of qualitative research, but I see that as a kind of benefit, which is that it’s subjective in nature, by definition. There’s no going around it, right?

Of course, not only is it an exercise that’s much smaller in scale—you speak to five people, 25 if you’re lucky, right? It’s a small number of people—and the researcher and that person’s interpretation of that encounter is always front and center.

That’s what’s often leveled as a kind of critique: why should we trust this research? It’s only a matter of that one person’s opinion, right? Or maybe the opinion of these 25 people, or five people, what have you.

But I think that’s the value—that the subjective nature of this research is so visible in qualitative methods, right?

In quantitative methods, it also exists. Especially in AI, it exists for sure. We just don’t always ask or see it in the same way.

And I would say it’s this reminder, right, of the subjective nature of all of our work. We need to keep it front and center, whether we’re doing qualitative work with small-scale groups, or quantitative work, and now AI work, right?

I don’t like to use necessarily the word bias, because it makes it sound like we can fix the problem. As soon as we isolate the issue, we can remove it. There’s always a subjective aspect to this analysis.

Whose information are we accessing? How was it coded? How was the process? Who did that kind of analysis? Those are always issues we need to be asking.

And I think anthropologists and researchers can continue to bring that kind of questioning spirit. You know, I hope I can bring that to MasterCard as well.

Yeah. What do you love about the work? Like, where’s the joy in it for you?

That’s a good question. You know, I’ve sat, as I said, on many different chairs, right? And I’ve found joy in different aspects of it.

And then yet I returned to the private sector. Prior to only a couple of years ago, I spent primarily my time teaching. I was a professor of marketing and of anthropology at a local liberal arts university.

And I really thought that was going to be—after I finished my PhD—the full-time trajectory of my career. Of course, I did ongoing research. I took on some consulting work, but I primarily worked as an academic for a number of years.

And as exciting as I found that work, I really realized that what I love to do is the research. You know, as an academic, completing my program—that was the exciting part of my work. I got to really dig deep into a topic.

I’ve published a book, which I’m really proud of, called Ethnography of an Interface.

And, you know, even as I transitioned out of my advertising career into this period of academic work, it was really with the purpose of diving very deeply into a topic and thinking about how can I really understand that thoroughly?

You know, the business world doesn’t always allow us that level of thoroughness. And as a teacher, I was excited to share these perspectives with students, but I found I really missed being an active participant in the output.

I find it exciting. I find it interesting to be kind of at the forefront of what people are thinking about, how people are thinking about those things—not just on the consumer side, but also on the business side, right? How is the business world evolving?

So I wanted to return, yeah, return to my roots a little bit. Yeah.

Yeah. As somebody who, like—I came up, you know, my first job was at, like, a consultancy. Everything I learned about any of this stuff happened in a company doing work in the private sector, as you say.

And then I kind of sort of educated myself about all this other stuff. And so I always have a little bit of imposter syndrome talking to academically trained anthropologists.

But I’m wondering, what’s your sense of the difference? What’s it like being—what’s your experience of the difference between being an anthropologist in academia versus being in the private sector and working with corporate clients?

You know, first of all, just a comment about being an imposter: don’t worry, we all feel like imposters all the time.

Thank you.

You know, the more I learn, the more I realize how much there really is to learn. So it’s always this exercise of never knowing enough. So I guess there’s the joy of constantly learning as well.

The difference I see between the academic world and the private sector work most immediately—and I hate to use this word, it’s so cliché in some ways—is impact.

And I don’t necessarily just mean, you know, the business KPIs or something like that, but just that you can see your work reflected, and either taken up or not, in very immediate ways, in ways that academia doesn’t.

You know, the time scale is so protracted, and the impact sometimes is limited. Of course, there’s the impact of the classroom, but it’s a different kind of conversation.

And I think you don’t necessarily always get to follow the impact in the same way as you do in your professional life.

I think being closer to the proximity between the work I’m doing and its effect—or lack thereof sometimes—on the company I’m working with is more immediate. I find that rewarding.

It’s almost a little bit more—I wouldn’t say instant gratification—but at least there is that kind of, yeah, there’s more visibility in that sense for me, you know, how what I’m doing shapes an organization’s activities.

You mentioned that you, in response to the question about what you love, you talked about the doing. So I guess I want to follow up—what do you love about the research? What do you love about the doing?

Yeah, such a good question.

You know, thank you actually for giving me the opportunity to think about it. Sometimes we just do, do, do, and not always take the time to reflect.

I like learning new things all the time. I find that super exciting.

You know, in some ways, the speed with which business moves requires you to learn at a faster clip than maybe academia. Maybe not necessarily the same level of depth, but definitely a bigger breadth in a shorter amount of time. And I find that exciting.

I think that’s really stimulating and energizing, and it makes me feel more connected somehow to the things that are going on around me.

I did have a period of time where I spent a number of years teaching, and you start to feel kind of disconnected a little bit. You experience the work through the eyes of your students, but otherwise feel a little bit on the outside.

So I find it exciting to have a little bit of a closer seat.

And, you know, in a very real sense, more resources to do that research. Academia can make it challenging to do the research. A lot of the onus is always on the researcher to find ways not just to connect the threads, but to execute that work.

And the private sector makes it much more accessible. So there’s more of it, and I love that.

And I want to talk about the book because I feel like—yeah—so talk, tell us a little bit about the book that you did. Yeah. So let me show you right here. My third baby—I have two babies—my third one, which took almost as long as my actual children to bring into the world.

So the book—when I was conceptualizing the book, I kind of started broad. I was trying to get a sense, as I kind of talked about landing in JFK and finding yourself amidst the noise and all of a sudden not really being able to tell what’s what—what’s important, what’s not important, what’s familiar, what’s not familiar.

I think when I started doing my research initially, it was a time in the early 2010s when there was a lot of intense conversation about data.

The same way we talk about AI in some ways—that was kind of the hype cycle—datafication of everything, especially personal data. So for me, the key questions were: what does that mean for how we think about ourselves as people? What does that mean for being human?

There was a lot of enthusiasm around what companies could achieve with personal data, both in the business sense, but also how much they could help people learn about themselves.

There was this kind of euphoria about a future where we’ll know everything there is to know about everything, including ourselves. We’ll no longer, in some ways, be mysterious to ourselves with the help of this new technology. And it was just coming up.

So I think in the beginning, it was that moment of a lot of stimuli, again—kind of taking that in and trying to find where can I stand? Where do I find my feet in this?

Really trying to get a grasp of what’s happening academically with regard to this dialogue, what’s happening in the popular discourse around this type of conversation.

And what I found was, obviously, on the business side, lots of hype, lots of enthusiasm, lots of really breathless predictions—similar things we’re seeing about AI right now.

On the academic side, a lot of fear and a lot of judgment around the kind of companies that produce this data, their intentions, and also a kind of sense that the researcher knows best. There was a sense that there’s something fundamental that technologists don’t understand about the way the data functions or how it impacts people, and it’s kind of the work of the researcher to reveal that, right, or to make that clear.

And I really respect a lot of this academic discourse. I think there’s a lot of important work that’s come out of thinking about the impact on our privacy, thinking about the role social media plays in how we consider ourselves as people. A lot of these academic conversations have come to the fore now.

We talk about it more openly in general ways.

So I was thinking, where do I situate myself as an academic, as a researcher, in this discourse? There’s so much of it.

And I found that I was really interested in this kind of behind-the-scenes again. As I was saying earlier, I wanted to understand, as a child, the life of the artist behind the music or behind the work. I really wanted to understand the creators of these tools, and how their lives—their professional, perhaps, in many ways, expectations and necessities—shape what we encounter as consumers.

So less how we process this information as consumers of data, but how this data comes to be in the form that it does.

And what are their questions and concerns and issues and ambitions? How do they all shape what we can then, in a sense, see about ourselves using this data—or what we don’t see?

So kind of this broader social and political context of the companies that make these tools—that’s kind of the thrust of this book.

Yeah. The title is amazing, right? Ethnography of an Interface.

And so can you tell us a little bit about the research that you did? And I feel like there must be so much—I’m curious about the degree to which that research feels like it’s echoing now in how AI is coming into being and the discourse around it.

Yeah, definitely. Very much so. I think that’s, again, such a good question. So the interface in this book, and the title especially, is kind of—obviously—a play on words.

On the one hand, the interface is how we, you know, computer interfaces—how we interact with really complex and obscure computer functions, right? That’s a computer term.

And how we often talk about interfaces as this technology that made it possible for non-specialists to interact with computer programs that otherwise would have been inaccessible. You don’t have to write in code. You don’t have to study obscure programming languages. We can just recognize icons and click on buttons, and that makes that world accessible to us.

So in some ways, it’s a reference to that kind of interface.

And in my work, I was thinking about how I would access complex corporate dynamics. Through which interface would I be able to mediate that interaction?

So I started to follow along and take part in this group called the Quantified Self, which was kind of up and coming again during the time that I was doing research.

As both language, as an expression, Quantified Self was on the rise, but it also was an actual group of people—ostensibly people who were really enthusiastic about personal data and using all kinds of gadgets, sometimes digital methods, sometimes not, in thinking about themselves.

But in my experience, I also found that it attracted a lot of technologists, tech makers, startup founders, for different reasons.

So that encounter made it possible for me to access some of the business priorities and challenges. That exposure to people who were part of that group became my interface, right?

And then I also use it in a different sense, academically—or maybe more practically.

We think about the interface as a technology that facilitates access, but we don’t often think about it as a technology that inhibits access.

For instance, there are certain things the interface allows us to see, and then there are certain things that the interface does not allow us to see.

We don’t see how this data is cleaned on the back end. We don’t see the work processes or the decisions that go into the aesthetics that we are served with online. We don’t see a lot of those decisions. We don’t see the people who are necessarily involved in that.

So in that sense, I also wanted to think about using Quantified Self as a kind of entry point, to a certain degree, into some of the discussion—some of the things that we don’t see.

In the same way that the Quantified Self, as a group or more broadly, technologies present a kind of public face, what’s the context and what are the decisions in the background, and also the challenges?

What have you learned about the Quantified Self community, or the motivations? What was driving that for the people who were participating?

I think it’s multiple, right? And I think that’s one of the reasons it really became part of a cultural conversation for a period of time. I think a lot of people wrote and commented quite eloquently in different ways, and it brought people together for a variety of reasons.

For instance, patient advocates, or folks who found themselves kind of on the margins of healthcare practices or experiences, really wanted to kind of turn to themselves and to their own experiences to understand, record, and report some of the things that are going on in response to maybe some of the challenges they were experiencing with healthcare, with the healthcare space.

I found, especially being involved with the group on the East Coast primarily, as I said, it attracted a lot of people who were interested in understanding—it was constituted as a kind of—I’m thinking about how to explain it.

I think the popular idea of Quantified Self as a community that attracted a lot of data enthusiasts didn’t make it easy to see that it also actually attracted a lot of people who were interested in observing that kind of community.

So a lot of participants were, in fact, in some ways, saw themselves as kind of participant observers, as kind of anthropologists attending these kinds of meetings—just by, you know, consumer needs or trying to understand people’s relationship to technology.

While all the while, it actually attracted a lot of people who were creating that technology and producing that discourse in a way that was kind of a consumer group in some ways that was created from within the industry.

That then, you know, different kind of, let’s say, industry mechanisms also allowed people to separate themselves and say, hey, it’s not that we’re creating this community by participating in it, by presenting on the part of it, by talking quite a bit about it.

It allowed people to kind of point to it as though it’s already an existing consumer segment on the rise, kind of as an early sign. You know, in those days it was seen sometimes as an early sign that there’s this bigger consumer response.

But it was really kind of created in some ways—I hate to say manufactured—but in some ways, you know, developed by the very entrepreneurs, startup developers, technologists who wanted to see that enthusiasm out in the world.

So it was a kind of co-creation. Yeah.

And I mean, it’s so fascinating, sort of like dizzying, to hear you just describing all the different forces at work. And I remember that time, of course, and to the degree that it’s sort of just speculative, right?

I mean, people wanted stories about the power of big data, right? We were sort of—that was the bubble that we were in—we believe in data’s the new oil, right? So everybody’s going to be obsessed with any way people are doing things with data.

Exactly.

And it was exciting or, you know, useful to have a community to point to: hey, look, it exists out there. And that became part of a narrative, right? You can tell to your investors and the boardroom, to your colleagues.

In some ways, it kind of became a mechanism that, in some small way, really moved the industry forward.

And so now, I mean, you know, what is that—was 2010? You said you were doing your research 2010s to 20, like mid-20.

So it’s 15 years later now. It’s funny, I’m thinking of that guy, Brian Johnson—is that his name? That guy? Like, is he the sort of the apex of the guy who’s trying to live forever? Like, is that his name, Brian? Do you know who I’m talking about? The guy who’s like a health longevity?

Yeah, like, there are lots of—you know—there’s this kind of Quantified Self biohackers. There are all kinds of life hackers. I think there are different communities that were kind of adjacent to each other and sometimes spoke past each other, sometimes spoke to each other.

A lot of it is obviously shaped by kind of this broader Silicon Valley culture.

I think it’s super interesting, the shift between computer programmers in the 80s and 90s as the most slovenly, you know, the least focused on their self roles, you know, well-being, to now the most focused, the most optimized. So, you know, that may be a topic for a completely different—

That’s, I think that’s a lovely observation. Yeah. What do you make of that?

Yeah, I would have to think about it, you know, why that happened.

Exactly. Right. Like, I think, in some ways, the narrative of data shaped this discourse. First of all, it legitimized programming as not just as a peripheral occupation of really obscure eccentrics, but as a kind of the central driving force of our economy, right?

So it became—you kind of re-packaged that person then, right, as the leader. And in some ways, probably people at the leading edge of this took that on very seriously, right? They took on an almost entirely different identity and brand.

Part of it—and, you know, that’s something I do reflect on in the book a little bit—is the discourse of data itself.

You know, there’s that language—you talked about data as oil—but there are so many metaphors, especially in those early days, around data. And, you know, the quantity of the metaphors alone is dizzying.

And in some ways, it’s paradoxical, because the language at the time was, we can connect all these data streams, but the language that was used to describe it was sometimes so contrasting, it made it difficult to imagine how does data as gas and data as oil and data as water, right, fit together—on the metaphorical, linguistic level.

But the language of data as this liquid metaphor was always really interesting to me.

And there is a little bit of a kind of purifying aspect to it, right? Like water, almost in a religious sense, it has a purifying quality.

And when you think about data in this liquid sense—data as water, as oceans, as lakes—again, kind of rhetorically, it has this cleansing quality.

If you apply regimes of data, you know, cleansing to yourself, you yourself become purified of bad habits, right, bad practices, become a cleaner individual.

So there is that—maybe, you know, in some ways, that also shapes our idea of who the entrepreneurs, the tech entrepreneurs, are right now: the most cleansed by data.

Yeah, yeah. It’s amazing. It’s amazing. I love the thought. Yeah.

So what is it—so it’s about this intersection of man and machine, broadly speaking, if we like that, and now we’re in a different, maybe, phase or stage of the same discourse, right, about man and machine.

What do you, based on the time you’ve spent, what do you see now in terms of AI? And I don’t know—what are the kind of conversations that you’re having within MasterCard about what does this mean for how do we listen to people? Do we use synthetic research?

Yeah, you know, I would say I would speak first as kind of as an individual outside of my connections, or, you know, corporate connections—just as an observer, as an academic—and how I connect that a little bit to my work.

You know, so as I said, two kind of things that were really important to me in my research were: how do people—what are the decisions that go into the types of data that we interact with, right?

How do people think about data themselves on the inside? How do these professionals think and relate to data, not always as a subjective instrument that delivers clarity.

Really, you know, there’s a kind of sense that data are messy, that they’re complex, and as political and social as they are technical, right?

To bring—to note: it’s technical, but it’s also a social exercise. And it’s a political exercise as well.

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And I think, in reality, you know, if we even follow business news, we can see that the politics—the people on the ground—are very aware of those dynamics, even if they only speak in the language of objectivity and clarity.

And I would say it’s interesting now to see the new hype, which is AI, the new darling. If everything had to be data—you know, everything had to be kind of, there had to be the prefix “data” to everything we did—now AI seems to have taken on that same primacy everywhere, not just in the business world, right, in academia as well.

You know, teachers have to articulate how they are or are not using AI, their positions in relationship to AI, how they’re helping students understand AI.

So the way connected to what I’m observing now is an equal kind of lack of, I would say, public awareness or public discourse around how messy those data sets are as well.

And again, I don’t say biased, because bias makes it sound like you can remove it. I say messy by default.

You know, it’s not a secret—a lot of the data sets are kind of black boxes because there are proprietary algorithms that companies possess.

But also more broadly, I think the sense of scale that AI has has overtaken the need to interrogate how these data sets come to be produced, what decisions went into shaping the algorithmic methods that are used to process it.

You know, I think a lot of times there’s an intuition that, well, the whole of the internet was scraped to deliver me a ChatGPT answer, even though we don’t really know exactly where that answer came from.

That sense of scale, I think, has ushered AI into a space that data once occupied—this kind of flawless space, where the scale of big data made it seem like you could eliminate subjective nuances.

So I would really love to see us come to a place where we equally interrogate the data sets we interact with vis-à-vis different platforms.

And I think already, as the dust is starting to settle, you start to see people in the research community asking more specific questions—not just where we can use AI, but how do we understand the output, really?

How do we relate to what we interact with? How do we think about why we’re seeing this particular output on the screen and then another one?

So I hope that’s going to be the next phase of our AI interactions.

What do we call that? Is there a name for that level of reflection? Is that just reflection? Or what is that?

Oh, I don’t know. Let’s think about it.

That’s a good idea, too. Yeah. How would we—yeah—what would be the label? How would we call that?

So what’s your experience with AI? I guess the second part of that earlier question is what do we do, or what’s your experience with AI and research and synthetic research, and how do you talk or think about it with regards to work and your own practice?

And I think, on some level, it’s a practical question. In some ways, it’s not a question of either/or, right?

I think even a few years ago, we were asking ourselves, will we use it? Should we use it? I think those questions are basically rendered irrelevant right now because it’s entered every nook and cranny of our experiences.

I think the more relevant question now is how should we use it? And I think that’s the question we’re asking ourselves every day at work.

We’re experimenting with different approaches. We’re experimenting with synthetic research, of course. We’re experimenting with using AI for different inputs across our research process.

I think right now it’s a very experimental moment, but that’s probably the exciting part, where we actually get to ask how, right?

Not just bluntly accepting what is, but interrogating and developing a point of view.

And maybe again, I’m thinking—going back to my sense of when I stepped off that plane—you know, now is the moment of us trying to figure out: what are people actually saying? What’s important in this conversation? What does it all mean?

I think that’s kind of, in some ways, an exciting moment. And yeah, nobody has the crystal ball.

I really wish I could have a very clear perspective on what will happen. And in some ways, I’m nostalgic for the good old days of just straight-up human conversation.

And I think there will be a space for that again. In some ways, I see there’s more—a little bit of suspicion around AI.

You know, participants—are they using AI? Are they not using AI? I think there are companies trying to figure out how do you evaluate that.

Will that mean a return, on some level, to a human—human, perhaps? So I hope so. Yeah.

What other impacts do you see on qualitative, face-to-face kind of research as a result of the availability of AI across the research process?

Yeah. So, you know, on the one hand—and I want to be optimistic about it a little bit—I cringe a bit when I hear people say things like, “We can now deliver 10,000 consumer interviews in the span of a half an hour.”

And I sometimes ask myself, when am I ever going to be in a position where I need the answers so immediately, so rapidly? Something has gone terribly wrong if we’re doing it so last-minute.

I had not heard that. I love that so much—that there’s something problematic about being so fast.

Yes.

You know, it’s offered as a solution, but then I’m asking myself, well, what is the problem? Is it that they forgot to do research in their business practice?

So in that sense, I think we’re kind of in this euphoric moment again, where people are trying to put this label everywhere, and it’s not clear exactly where it will stick.

Although some things are starting to fall off the board, which I’m happy to see. I think the speed of it is important, but the fact that it has to be solving for this particular problem—research as a really condensed, short-term activity.

There’s a place for that, but I don’t know that we’ll necessarily see research being such an afterthought that it’s just brought in in such a last-minute way.

And maybe people are using research more. In fact, from what I see on my end, when people are leaning into synthetic research, it’s often in moments when there wouldn’t be any research introduced at all.

Maybe for ideation or brainstorming around the table. Some of that can help discipline the thinking a little bit, whereas there wouldn’t have been an opportunity to go out and do any type of research at all.

So in some ways, it’s almost leading to more research, ironically.

You know, where I see an interesting tension—and this is not my idea; smarter people than me have thought of this—is in the ethnographic practitioner community.

I’m part of EPIC, the community of ethnographic practitioners. You’ve been part of that space. And just recently, in Helsinki this year, I was one of the co-moderators of a panel on AI in research—what does that mean? How are practitioners using it?

And I was really struck by something.

Two of the presenters—Eric Gray and Kevin Gotchevar, researchers at Nissan—were experimenting quite heavily with synthetic research and what utility it might have in their work.

And one thing they said really, really rang true to me, and I continue to come back to that sentiment. One of the accusations that’s often leveled at qualitative conversations is: how can we trust what the consumer has said?

People say, well, how can we trust AI? You can say the same of people. How can we trust what a person has said?

People make things up. They get nervous. They try to perform. In some ways, there is that quality of invention that’s inevitable in research.

But again, the point they made was this: whenever you see a contradiction, or a consumer says something, or a person you’re speaking to in a research context says something that feels unusual, or contradictory, or just new—that’s an opportunity to probe further.

Hey, tell me more about this.

And that can often be the space of real discovery. With AI, it’s less clear that hallucinations or AI imagination lead to the next “tell me more” in the same way. It doesn’t necessarily open up to the same level of revelation as a human contradiction.

So I think navigating these two—when do we want to lean into some of the quirks of human engagement—yeah. Where will that lead to actually bigger insights? And then where—where is the opportunity for synthetic research, right?

I mean, I feel like I haven’t read deeply on this, but I encounter the studies that are out there. What you said—yeah—I feel like what I’ve heard people describe is that it’s really good at the center, but it’s really bad at the fringe. You know what I mean? And so it’s really great for validation, but not so good for discovery.

Yeah. And it’s especially good when you already have—when the person interacting with this data, and we often hear, you know, when we share synthetic results from our team with our teams, they often say, “That really confirmed my hunch,” or “That’s really how we were thinking of it as well.”

And that becomes then an extra boost. Then, yeah, the person is able to, in a sense, validate the research or say, “Something is off here. It doesn’t make sense. Maybe I’m going to interrogate my own assumptions a little more.”

Yeah.

But that requires the person to have a set of expertise, not just to rely on synthetic outputs. You know, that really still requires the person to be really actively involved in evaluating.

Yeah.

I have a puppy eating a Christmas ornament.

Oh no.

Last question, which I think is—I heard—which is building on what we just talked about, but I also feel like at the beginning of the conversation about AI, there was a moment where you really acknowledged—because I think there’s a natural kind of defiance that we have as a qualitative practitioner or a research person—that of course no computer is going to replace us, like what we do is so special. But you were really honest about it.

And I had this experience myself where it’s like, you know, for a lot of uses, it completely does the same thing that I do as a person in the work that I do.

And it was sort of—I mean, uncanny might not be the right word—but it was definitely disorienting to realize that was the case, right?

Humbling.

Yeah, humbling for sure. And so I guess—so building on that idea that, wow, this stuff really does do things that we currently get paid for now.

Two parts. First is: what is the value of qualitative to begin with? Why is it so important to begin with—what you do, what you know, as an anthropologist? What is it? What’s the proper role of qualitative, face-to-face qualitative? Why is it so important generally?

And then especially in the age when all this synthetic stuff is there—in 30 minutes you can have perfectly good synthetic data delivered.

Yeah. I mean, these are questions that I’m asking myself all the time, that I’m being asked a lot of the time. In many ways, I’m still formulating the answer. We’re kind of building the plane as it turns out, as we’re flying in it.

Because I think what’s interesting in the current moment is that we’re being asked to shape-shift a little bit as researchers. We’re really being asked to articulate our value even more strongly.

And I go back to anthropological expertise and the value and primacy of context. For me, that becomes even more important.

Because in many ways, I think we can get really great responses online. I ask a question, I get a clean response—almost too clean sometimes.

But what we’re lacking is: what’s the context in which that response was made? Who is that person? Again, to go back to that original sense—who’s that person that made that comment? What’s that person’s world?

I was listening to a podcast by Zadie Smith, the novelist, and she said something really beautiful. She said, “Each person is a world.”

And I think we really lose that—the world that’s within each person—when we rely too heavily on generalized, pressed-together, summarized data points.

We really lose full sight of the idiosyncrasies of each person. And the fact that each person is bigger than this one question we’re asking.

What’s the world in which they live? And what’s the world that’s inside each person? So I think there’s still real value in zeroing in like that. Sometimes that can give you much more depth, even if it doesn’t give you breadth for your research questions. So I hope there’s still a place for that.

What do we lose when we lose context? We lose perspective. We really lose perspective.

Anthropology, for me, the biggest value that I’ve taken is meaning. What does it mean? In the anthropological sense, there are no universals. What does this thing mean to different people?

Whether we’re talking about hamburgers or soups, what does that particular object—when we work as researchers for brands, for products—what is this experience? What is this tool? What is this product?

What does this mean to a specific group of people, a set of individuals, rather than as an object in and of itself out there in the world?

It’s too generic then. It loses its usefulness in that cultural sense. It loses its interest. So I think to keep things interesting, we still need culture. And we still need to understand it.

Beautiful. Well, that’s a beautiful way of ending. I want to thank you again for joining me and accepting my invitation.

Yes. Thank you so much for inviting me. It was really a pleasure.

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