Speaker 1: Hello everyone, and welcome to Talk Nerdy. Today is Monday,
Speaker 1: August third, twenty twenty six, and I'm the host of
Speaker 1: the show Doctor Kara Santa Maria. And this is the
Speaker 1: last of three episode intros that I am pre recording
Speaker 1: before I leave for a trip to Australia and New
Speaker 1: Zealand with the other podcast that I work on, The
Speaker 1: Skeptics Guide to the Universe. I should be getting back today.
Speaker 1: Time travel is weird like that, isn't it. So next
Speaker 1: week I will make sure to thank any patrons that
Speaker 1: I missed in the meantime.
Speaker 2: And now I say we just dive into it. This
Speaker 2: is a fascinating episode. I had the opportunity to chat
Speaker 2: with doctor Rupica Rissom. She is a professor of Digital
Speaker 2: Humanities and Social Engage at Dartmouth, where her research explores
Speaker 2: how histories of race, empire, and technology shape the modern world.
Speaker 2: She's written several books, including New Digital Worlds, and was
Speaker 2: a co editor of Anti Racist Community Engagement and The
Speaker 2: Digital Black Atlantic. She's also the past president of the
Speaker 2: Association for Computers and the Humanities. But today we're going
Speaker 2: to be talking about her newest book. It's called Data Empire.
Speaker 2: The power of information to organize, control and dominate. So,
Speaker 2: without any further ado, here she is doctor Rupica. Rissom Well, Rupica,
Speaker 2: thank you so much for joining me today.
Speaker 3: Thank you so much. Care I'm excited to be here.
Speaker 2: I'm so pumped to talk about not just your new book,
Speaker 2: but also all of the work that you do and
Speaker 2: how it feeds into this that you have to tell,
Speaker 2: which I think is it's one of those interesting ideas
Speaker 2: that is unique in that we don't often, I guess,
Speaker 2: think about history and power structures having to do with data,
Speaker 2: especially historically. I think more and more people are being
Speaker 2: attuned to that now. But so it's like a unique
Speaker 2: way of telling this important insight but also incredibly ubiquitous
Speaker 2: and probably everybody goes duh once you start to talk
Speaker 2: about it, right.
Speaker 3: Absolutely, I feel like that's sort of the story of
Speaker 3: my life, which is I'm saying things that I feel
Speaker 3: like are very very obvious, and yeah, in conversation they're not,
Speaker 3: but once we start talking about it, all of a sudden,
Speaker 3: those dots get connected.
Speaker 4: Absolutely.
Speaker 2: So before we even get into the book, let's dig
Speaker 2: into sort of your academic positioning, because it's a very
Speaker 2: interesting multidisciplinary position. I guess, Wow, that wasn't redundant at all.
Speaker 2: So you are an associate professor of Digital Humanities and
Speaker 2: Social Engagement at Dartmouth. What department or are you like
Speaker 2: matrix between departments.
Speaker 3: I'm actually a full professor now as ten days ago.
Speaker 4: Stop it, I love it. Okay, I'm gonna update that
Speaker 4: for the intro. And I'm so congratulation.
Speaker 3: Thank you. I'm very excited. It's been a long time.
Speaker 3: And yes, I am this professor of this thing called
Speaker 3: Digital Humanities and Social Engagement. But I'm situated in two departments,
Speaker 3: the Film and Media Studies department and the Compared Literature Department.
Speaker 4: Ah interesting, Okay, I.
Speaker 3: Can make this way more complicated by explaining that I
Speaker 3: have a PhD in English, and I started out my
Speaker 3: career at Salem State University of Public University in Massachusetts,
Speaker 3: where I was in an English and a higher education
Speaker 3: and so I just go, I go with They'll take
Speaker 3: me really anywhere that wants to let me do my work.
Speaker 4: I love it.
Speaker 2: You know where I did my PhD Media Studies was
Speaker 2: in the psychology department.
Speaker 4: Oh nice, yeah, yeah.
Speaker 2: So it's I think a lot of these uh, these
Speaker 2: kind of boundaries are quite wiggily right.
Speaker 4: This is definitely a I don't know.
Speaker 2: I've had whole episodes that we dedicated to the problem
Speaker 2: non problem of human beings really needing to categorize things
Speaker 2: and put labels on things.
Speaker 3: Very fitting considering the topic of my book.
Speaker 4: So this is so interesting.
Speaker 2: So you've studied English, You're you're working in these two
Speaker 2: different departments that really have a lot of kind of
Speaker 2: culture and societal influence. You know, we're talking about historical
Speaker 2: but also contemporary ways that individuals I guess, uh, not
Speaker 2: even and just individuals, but whole systems exist within within
Speaker 2: culture and how that shifts and changes. So that has
Speaker 2: everything to do with race and gender and class and
Speaker 2: kind of country of origin, and also capitalism and socialism
Speaker 2: and different kind of financial structures and different governmental structures.
Speaker 2: Like how do you even know where to focus your attention?
Speaker 3: My interests have always been on the impact of colonialism
Speaker 3: on culture and on history, and it started with literature
Speaker 3: because when I was in college and read postcolonial literature,
Speaker 3: so literature from countries like India or Jamaica, for the
Speaker 3: first time, I felt like the writers put language to
Speaker 3: experiences that I had had that I did not want
Speaker 3: to talk about being an immigrant to the US myself
Speaker 3: as a young child and coming from a family who's
Speaker 3: originally from Kishmir. And so I was really interested because
Speaker 3: I thought, Okay, there's a language for talking about why
Speaker 3: it feels like some countries have more power over each
Speaker 3: other and how did I even get here? And so
Speaker 3: the point of entry for me was literature, but very
Speaker 3: very quickly I started becoming interested in history. I started
Speaker 3: becoming interested in digital culture and how we have these
Speaker 3: incredible tools for content creation available to us, whether it's
Speaker 3: to do it as individuals who want to tell our
Speaker 3: own stories or as academics who want to tell stories
Speaker 3: that haven't been told that come out of their research.
Speaker 3: And yet I always felt like, why aren't people actually
Speaker 3: using these tools to do that important thing? And that's
Speaker 3: why I got interested in the field of digital humanities.
Speaker 3: And it was at a moment in time in the
Speaker 3: late twenty tens where rather it was in the early
Speaker 3: twenty tens where the field was sort of starting to
Speaker 3: be a thing, and I thought, Okay, here's actually my
Speaker 3: moment to say, we have these powerful tools, let's actually
Speaker 3: use them to not just tell everybody who Shakespeare.
Speaker 2: Is right, such an such an interesting origin story. Do
Speaker 2: you have family still in Kashmir?
Speaker 3: Oh?
Speaker 2: Yeah, of course, So when you do you consider you know,
Speaker 2: I've I've had a lot of folks on the show
Speaker 2: when we talk about you know, intersectionality and identity and
Speaker 2: the I guess, the struggles and the difficulties, but also
Speaker 2: the beauty in you know, being bicultural or multi cultural.
Speaker 2: Do you consider yourself like a firmly bicultural individual at.
Speaker 3: The very least. So I was born in Wales.
Speaker 4: Oh, interesting, okay.
Speaker 3: And because my parents left Kashmir in the early seventies
Speaker 3: and they went to the UK, and they lived in London,
Speaker 3: outside of London, and eventually in Wales, where I was born.
Speaker 3: And then they decided after about four or five years
Speaker 3: there that they wanted something different from their lives, and
Speaker 3: we had some family in the US, and so we
Speaker 3: came here. And so I very much feel KASHMIRI I
Speaker 3: very much feel British, and I also feel American, and
Speaker 3: so it's very it's very strange. But you know, you
Speaker 3: can take the best parts of all of it and
Speaker 3: try to ignore the rest.
Speaker 2: Yeah, yeah, better advice, I have not heard. It's something
Speaker 2: we often talk about in so I work as a
Speaker 2: psychologist in a cancer center, and very often when we're
Speaker 2: talking about I don't know, mortality and religion and politics
Speaker 2: and all of these deeply existential things that people are
Speaker 2: grappling with today. That seems to be the consensus amongst
Speaker 2: the professionals and the patients, like is sort of take
Speaker 2: what serves you, take what you find joy in, and
Speaker 2: just leave the rest behind.
Speaker 3: It's really all we can do, especially when you consider
Speaker 3: so many of the pressures that we're dealing with right now,
Speaker 3: from particularly in the US, from a political system that
Speaker 3: feels very.
Speaker 2: Much in turmoil, to put it lightly, agree wholeheartedly. So
Speaker 2: it's so interesting this kind of very intersectional approach that
Speaker 2: you take to understanding you know, our contemporary I guess
Speaker 2: placement or situation context milieu against the historical background, especially
Speaker 2: with an eye towards colonialism, which of course encompasses all
Speaker 2: of those different features of misogyny and supremacy and racism
Speaker 2: and just power and othering and and yet this story
Speaker 2: not and yet but and also this story specifically is
Speaker 2: about like information, it's about data. So when we think
Speaker 2: about data, I think we often think about the Internet,
Speaker 2: or computers or modern tools that are used to store data,
Speaker 2: or even like DNA. But I think a lot of
Speaker 2: us forget that data really is just what recorded information.
Speaker 2: It's it's a it's a meta concept of seeing or
Speaker 2: organizing information and figuring out a way to transmit it.
Speaker 2: It doesn't it could have been quite crude in the past.
Speaker 3: Right, absolutely. And the way I think of it is
Speaker 3: that most things really just aren't that new, and even
Speaker 3: if they are, we got them somehow. And my interest
Speaker 3: has always been how far back can we trace idea
Speaker 3: a concept that is being positioned as being new or
Speaker 3: disruptive or unprecedented, which is my least favorite.
Speaker 4: Word, disruptive. Yeah, President good.
Speaker 3: So if we look as far back as we can
Speaker 3: based on what archaeologists have found so far, we get
Speaker 3: back forty three thousand years ago to the mountains at
Speaker 3: the border of Uswatini Mozambique in South Africa, the La
Speaker 3: Mambo Mountains, where archaeologists have found a baboon femur with
Speaker 3: these notches on it, and they've determined that they're not
Speaker 3: decorative and they're not accidental, and so someone was intentionally
Speaker 3: carving these little markers and they were tracking something, and
Speaker 3: it's so powerful just to think forty three thousand years
Speaker 3: ago someone thought, you know, I need to remember something
Speaker 3: i'm seeing or a phenomenon I'm observing, and there I'm
Speaker 3: going to carve these little notches into this bone to
Speaker 3: do that. And what else are we doing now, really
Speaker 3: other than carving notches and bones? Only now we have computers.
Speaker 2: Absolutely, even this concept of counting before we may have
Speaker 2: even developed a system for I've always been fascinated by
Speaker 2: the idea that mathematics is not really a language. We
Speaker 2: can talk about it like it's a language of organization,
Speaker 2: but very very simple. Mathematics is in some ways baked
Speaker 2: into the fabric of reality. And I think about that
Speaker 2: a lot when I think about our relationship to science,
Speaker 2: our relationship to the humanities, that a lot of what
Speaker 2: we do is just to try to use language or
Speaker 2: use tools to conceptualize things that we're just observing anyway.
Speaker 3: Absolutely, and that's where I start the story of data,
Speaker 3: which is that it's simply about creating a physical representation
Speaker 3: or a material representation of something that we think is important.
Speaker 3: And that, for me is actually why I get very
Speaker 3: frustrated when people try to claim that data is neutral
Speaker 3: and objective, because even from that moment forty three thousand
Speaker 3: years ago, there was an intention there. There is a decision,
Speaker 3: a decision to count and to track, and that's still happening.
Speaker 4: I just don't I've never understood the argument.
Speaker 2: I see it all the time working in the sciences
Speaker 2: and kind of working within this subculture of scientific skepticism,
Speaker 2: where I notice a lot of, like you said, claims
Speaker 2: that either scientific approaches or data can somehow be neutral
Speaker 2: or unbiased or you know, kind of like perfectly representative of.
Speaker 4: Existence that is out there.
Speaker 2: And it's like, I feel like just saying it's done
Speaker 2: by people should be enough, Right, Like people have minds
Speaker 2: and they make decisions about what they want to count
Speaker 2: and what they don't want to count, and they make
Speaker 2: decisions about why things are important and other things are
Speaker 2: less important. And you can't tease any bias, I mean,
Speaker 2: you can't not see bias inside of that. It doesn't
Speaker 2: necessarily mean it's bad or wrong, or there has to
Speaker 2: be a value judgment on it, although oftentimes there is one.
Speaker 2: But you can't you can't be quote pure in your
Speaker 2: approach if you're a person because by definition of being
Speaker 2: a person, you have a perspective.
Speaker 3: It's true, and I blame I blame the late seventeenth
Speaker 3: century scientists for this, and then in the eighteenth century
Speaker 3: scientists as well, because that's when we first see that
Speaker 3: word data showing up in the English language, and it
Speaker 3: starts in math, and it's used synonymously with the givens
Speaker 3: of a proof. We're doing geometrical proofs and we're saying, given,
Speaker 3: here's the fact, and that word eventually becomes used by
Speaker 3: people in royal societies for natural science as a way
Speaker 3: of talking about observed phenomena like a barometric measuring or
Speaker 3: where a star is in the sky, that they can
Speaker 3: write down and it's been plucked out of its natural
Speaker 3: context and put down on a piece of paper, and
Speaker 3: that it can be compared with other measurements, or it
Speaker 3: could be analyzed or calculated and then generalized now with
Speaker 3: the natural world, assuming that everyone has a shared vocabulary
Speaker 3: of how you're analyzing it, which isn't necessarily the case,
Speaker 3: depending on one's culture and the cultural context in which
Speaker 3: one's doing science. Other than that case, you know, so
Speaker 3: to the idea that you can just take something, write
Speaker 3: it down, compare it with something else and then make
Speaker 3: a generalized theory based on it is hiding a whole
Speaker 3: lot of human hands, Yeah, playing a role in.
Speaker 2: It, absolutely, and who are those human hands? And I
Speaker 2: think that's one of those. I mean, it's such a
Speaker 2: it's such a fascinating thing to start to try to
Speaker 2: really tease a part when you look when you try
Speaker 2: to empathize with the oppressor, for example, you try to
Speaker 2: empathize with the colonist, or even just with the individuals
Speaker 2: who were in power. Due to all of the different
Speaker 2: factors and features, you know, the cultural historical milieu, there
Speaker 2: is a privilege you know, we I don't use that
Speaker 2: word lightly, but there is a privilege that comes along
Speaker 2: with experiencing the world that you experience and being told
Speaker 2: from the time you're born that your view is somehow
Speaker 2: more important, more dominant, more clean, more reflective of reality
Speaker 2: that you probably just never questioned it that there were
Speaker 2: probably a lot of these, you know, seventeenth and eighteenth
Speaker 2: century thinkers that you were talking about, especially scientists, maybe
Speaker 2: less so the earlier philosophers, but also no, I shouldn't
Speaker 2: say that. Definitely the philosophers as well, but that just
Speaker 2: kind of we're like, well, I was endowed with this smarty,
Speaker 2: smart brain. Therefore what I'm saying is probably the most real.
Speaker 3: I wouldn't know. They never had that problem.
Speaker 4: Me neither.
Speaker 2: But you know, it's interesting to try to empu you know.
Speaker 2: I think when we talk about colonialism from the perspective
Speaker 2: of hindsight and knowledge, you know, what we know today,
Speaker 2: there's always conflict between the individuals who kind of see
Speaker 2: the havoc that has been reaked and the individuals. You know,
Speaker 2: it's a very like right wing perspective that we see
Speaker 2: often in the United States of like, well, I wasn't there,
Speaker 2: it's not my fault, and we're past that, and you know,
Speaker 2: it's all level playing field now, and you're like, Okay,
Speaker 2: that is definitely not true. But I think when you
Speaker 2: get beyond that conflict and you try to see the
Speaker 2: world as it was through the eyes of those who
Speaker 2: were in power and architects, I'm always curious how much
Speaker 2: they knew, you know, how much insight they had.
Speaker 4: I guess it's a good way to put it.
Speaker 2: Into what they were actually doing, and how much they
Speaker 2: thought that this was just the natural way of things existing.
Speaker 3: I think they absolutely did, and we think about the
Speaker 3: context of the eighteenth century, for example, in Europe, where
Speaker 3: a lot of this theorization of data is happening. These
Speaker 3: are the people who are in cultures that have gone
Speaker 3: out around the world and conquered other people and have
Speaker 3: felt that they absolutely had the right to it. So
Speaker 3: I'm not sure why I would also simultaneously be expecting
Speaker 3: them to have a critical eye on the production of science. Right.
Speaker 2: That's such a good point, and it's fascinating to think that,
Speaker 2: And that's why I'm always like very eked out.
Speaker 4: I don't know if you have the same experience, but.
Speaker 2: Among my peers and colleagues in circles that I respect
Speaker 2: and run in because I am very, very pro science,
Speaker 2: I always get incredibly eked out and kind of feel
Speaker 2: a defensiveness rise when I hear rhetoric about let's say,
Speaker 2: space exploration or about mineral rights or mining that feel
Speaker 2: so deeply colonial with just this unapologetic well, science for
Speaker 2: science sake, you know, exploring is our sort of natural proclivity,
Speaker 2: and it's a good thing that we should make sure
Speaker 2: that we're always promoting. Not that I think it's wrong
Speaker 2: to ask open ended questions, but I'm so worried about
Speaker 2: that reckless perspective, as if there is no potential downside
Speaker 2: to going places we've never been and just like fucking
Speaker 2: stuff up.
Speaker 3: One of the things we always talk about in my
Speaker 3: house is is it morally okay to colonize Mars? Yes? Exactly.
Speaker 4: It's a huge argument on my other podcast.
Speaker 3: Yeah, because the thing is, one could argue there are
Speaker 3: no people there, but then what about the bacteria?
Speaker 2: Yep, and that's what Yeah, that's always there. They're like,
Speaker 2: it's not the same as colonialism because there's no people there.
Speaker 2: And I'm like, yeah, but the colonists, at least the
Speaker 2: early ones who were like going west because they thought
Speaker 2: they might fall off the world if they didn't, they
Speaker 2: didn't expect there to be people there.
Speaker 3: Oh gosh, yeah, that's absolutely true, right. They thought it
Speaker 3: was just the empty land.
Speaker 2: Yeah, and then when people were in the way, that's
Speaker 2: how they saw them. They're like, look, gotta get rid
Speaker 2: of this complication that we've run into. And I'm not
Speaker 2: saying if we go to Mars we're gonna find people.
Speaker 2: We've been to Mars plenty of times with robots. We
Speaker 2: know there's no people, but.
Speaker 4: It's that perspective.
Speaker 2: That's that kind of very Western person, and I'm always
Speaker 2: just like, can we just listen to indigenous wisdom sometimes.
Speaker 4: Maybe once in a while.
Speaker 3: One of the things I was thinking about is that
Speaker 3: we talk about the most cutting edge large language models,
Speaker 3: we call them frontier models. Oh wow, right, it's the
Speaker 3: language is so baked in right there.
Speaker 2: Gosh you must it's I remember early on in my
Speaker 2: sort of identity development and then later in my career
Speaker 2: development and journalism. In psychology, there's that inflection point where
Speaker 2: you realize like, oh, patriarchy is everywhere, right, or like
Speaker 2: like white supremacy is everywhere. And you know, I had
Speaker 2: a professor who would always say, yeah, it's the water
Speaker 2: we swim in. It's why you don't even see it,
Speaker 2: because it's all around us all the time. I can't
Speaker 2: imagine that for you. Colonialism is the water use I mean,
Speaker 2: you must see it kind of manifesting everywhere in language,
Speaker 2: in even in math.
Speaker 3: It ruins everything I really want to enjoy project call Mary.
Speaker 3: Oh no, I really did. No. Actually I thought it
Speaker 3: was great, but I definitely at that moment of oh no,
Speaker 3: here we go again. Yeah, I still.
Speaker 2: A friend just told me I need to see it
Speaker 2: because I loved the Martian right, and I loved Andy Weir,
Speaker 2: and I loved the approach.
Speaker 4: But yeah, I was like, I have.
Speaker 2: A feeling I'm gonna have the same experience.
Speaker 3: It's really it's wonderful, though, don't please go see it,
Speaker 3: please sell.
Speaker 4: I think that's the thing too.
Speaker 2: We still have to live in the world we live in, right,
Speaker 2: And I think a lot of times there's a black
Speaker 2: and white way of thinking, and I can't imagine and
Speaker 2: tell me if this has been your experience as an
Speaker 2: academic who raises a lot of these uncomfortable conversations and truths.
Speaker 2: I imagine that you get trolls and haters and pushback
Speaker 2: from people who are very black and white in their thinking,
Speaker 2: who are kind of like, don't rain on our parade.
Speaker 2: Don't tell me all this bad stuff. Because I still
Speaker 2: want to be able to engage in the world that
Speaker 2: I engage in, and I still want you know, progress
Speaker 2: and capitalism and all the things that make my life easier.
Speaker 2: But like it's complicated, it's ambivalent. Both these things can
Speaker 2: be true at the same time, right.
Speaker 3: Absolutely, I am the ruiner of fun in many cases,
Speaker 3: but at the same time, this is the world that
Speaker 3: we live in, and the world that we live in
Speaker 3: is complicated, and I think one of my frustrations, whether
Speaker 3: it's coming from people who want to say racism doesn't
Speaker 3: exist or people who want to say I have developed
Speaker 3: this leftist ideological purity test that you must adhere to
Speaker 3: if you want to be respected, neither of those are
Speaker 3: really able to address the complexity of lived reality. And
Speaker 3: I don't say this to say I am all here
Speaker 3: to be a relativist about it. I think absolutely if
Speaker 3: you think that racism is not a problem, then we
Speaker 3: don't actually have very much to talk about it, right,
Speaker 3: because that is going to then influence this entire conversation.
Speaker 3: And I cannot take you very seriously unless you want
Speaker 3: to have a conversation about why you might be wrong, in
Speaker 3: which case right.
Speaker 2: But yeah, it's almost an a priori assumption that if
Speaker 2: that is the you know, sacred cow you hold on to,
Speaker 2: every conversation beyond that is in some ways moot or
Speaker 2: at least it's deeply biased, and you're probably not going
Speaker 2: to get anywhere. It's like it's like talking to a
Speaker 2: flat earth or about physics or geometry, Like there's just
Speaker 2: a certain point where it's like, there's no point of
Speaker 2: having this conversation.
Speaker 3: It's absolutely true. You know. I just worry so much
Speaker 3: though when I look at so just happening right now
Speaker 3: in the US, and I think about what's happening with
Speaker 3: NIH or you know, what's happened where they're going after
Speaker 3: the Smithsonian now, and just thinking about how on the
Speaker 3: one hand, sometimes I want to really dig in and
Speaker 3: I want to say, you know, we absolutely cannot have
Speaker 3: this conversation. And then on the other hand, you know,
Speaker 3: how do we otherwise try and convince people that they're
Speaker 3: about to make really, really big mistakes that are going
Speaker 3: to have material harm on people and their lives.
Speaker 2: Yeah, it's it's such a difficult place to be when
Speaker 2: individual reality.
Speaker 4: Is as polarized as it is.
Speaker 2: And yet I have to always stop and say to myself, like,
Speaker 2: this isn't new, is it? Like it feels new to
Speaker 2: us in America, but this concept globally is not new.
Speaker 2: People have been way more polarized, And yeah, I shouldn't
Speaker 2: say they got out without any suffering. There's been mass
Speaker 2: and horrific suffering because of it. But individuals have always persisted,
Speaker 2: and even forty five thousand years ago with the baboon bone,
Speaker 2: there was probably a lot of polarization amongst small, you know,
Speaker 2: hunter gatherer tribes.
Speaker 3: I'm sure there was, And I think back to really
Speaker 3: going to the moment where we start seeing humans settling
Speaker 3: down into more permanent living situations instead of nomadic life,
Speaker 3: and particularly in ancient Mesopotamia, as more and more people
Speaker 3: came to settle down because pretty good place to live,
Speaker 3: they how to start figuring out how do you actually
Speaker 3: deal with people who you're not related to and they're
Speaker 3: here and you all have to live together. And so
Speaker 3: one of the innovations in data is actually the development
Speaker 3: of tokens, which are simply just little counters made out
Speaker 3: of clay that people used to mark an obligation or
Speaker 3: a debt and to navigate that challenge of what happens
Speaker 3: when suddenly you can't inherently trust the people that you
Speaker 3: are living with because you are so different.
Speaker 2: So it's almost like it's funny because everything we talk
Speaker 2: about we talk about with a hindsight bias, as if
Speaker 2: everything was leading up to this point intentionally, but it
Speaker 2: wasn't right. We just happened to be at a point
Speaker 2: in history where we can not see the future, but
Speaker 2: we can see all the past, or at least as
Speaker 2: best as we can. And so you know, I say
Speaker 2: this with that caveat, but it's almost like an early
Speaker 2: tool for maybe money barter description, an early tool to
Speaker 2: allow for the structures that eventually will come into place,
Speaker 2: to talk about property rights, to talk about individuation, me
Speaker 2: and my stuff versus you and your stuff.
Speaker 3: Absolutely, and I think that's it always come back to
Speaker 3: trying to figure out to what extent is this a
Speaker 3: human thing, right, a desire to say this is mine
Speaker 3: and my families, and this is yours and your families,
Speaker 3: and I want that. How do I get that the
Speaker 3: most basic level, though, it starts with being about survival,
Speaker 3: and then what I think happens over time is that
Speaker 3: what it means to serve five changes, because I think
Speaker 3: I always think about one of the distinctions between what
Speaker 3: it means to be, say a Republican or a Democrat,
Speaker 3: or conservative or a liberal, is between I'm looking out
Speaker 3: for my people, for the Republican that's my blood family,
Speaker 3: and for the Democrat in some cases, in many cases,
Speaker 3: it's I'm looking out for people that people are my people,
Speaker 3: and this mentality between a really expansive notion of who
Speaker 3: counts as the people that you're going to make a
Speaker 3: political decision about representation to support. Is it all people
Speaker 3: or is it your perception of your blood family? And
Speaker 3: one makes very I think, one makes very different kinds
Speaker 3: of decisions if you're looking at those two different scales.
Speaker 3: And then I think, for example too, you know, I
Speaker 3: mean I vote for Democrats, not always a huge fan,
Speaker 3: but I mean better than the alternatives. And you know,
Speaker 3: I make decisions that either they're either going to have
Speaker 3: very little impact on my life individually or in some
Speaker 3: cases are probably gonna I'm na pay more taxes, but
Speaker 3: actually do not care, right, because I'm good, and I
Speaker 3: know that the alternative is potentially going to be so
Speaker 3: much more, so much worse for people who are more vulnerable.
Speaker 2: Right, kind of an understanding or at least a hope,
Speaker 2: because I don't know if we've ever been able to
Speaker 2: engage in this experiment fully, but a hope that if
Speaker 2: the most vulnerable among us at least have a basic
Speaker 2: level of safety and comfort, all of us will benefit
Speaker 2: from that. Versus, let's see how much me and mine
Speaker 2: can ensure my own safety and you know everybody else,
Speaker 2: you do you.
Speaker 3: Yeah, it is. And then and then the conversely, on say,
Speaker 3: the more right, the right wing side of the political spectrum,
Speaker 3: the most vulnerable person having rights becomes the threat to
Speaker 3: them and their family.
Speaker 2: Right exactly because it means, you know, because it's a
Speaker 2: zero sum game, and that means I have to take
Speaker 2: from what I worked so hard for and give to you,
Speaker 2: even though I look at you and I don't see
Speaker 2: you as working as hard as I am. And I
Speaker 2: think that is a very common perspective. And you know,
Speaker 2: while I don't think it is reflective of reality or
Speaker 2: you know, I don't see validity in it, I do
Speaker 2: always strive from a kind of.
Speaker 4: I don't know from.
Speaker 2: I always strive to at least try to understand it
Speaker 2: from that perspective, you know, instead of just discounting it
Speaker 2: whole cloth. I always want to understand why people think
Speaker 2: the way they think.
Speaker 3: I agree, And I think part of is also education
Speaker 3: and really understanding what the perspective you're opposed to is
Speaker 3: or what the person who the person you're whose existence
Speaker 3: you're opposed to really is. And I always think about
Speaker 3: my parents, who if you ask them, they will tell
Speaker 3: you they are Republicans. I'm actually in their house right now.
Speaker 3: Hopefully they're not listening. They will tell you they're Republicans.
Speaker 3: When you start drilling down, take away the label and
Speaker 3: you say to them, do you think we should have
Speaker 3: nationalized healthcare? Saying you know, they say, actually, yeah, we should, right, yeah,
Speaker 3: when you take all the indicators, all the like, I
Speaker 3: guess the terms that make it what it is.
Speaker 4: It's the same thing with my folks.
Speaker 2: It's fascinating, like we always come down to we want
Speaker 2: the same things, but we really disagree on how to
Speaker 2: get there.
Speaker 3: It's so true. And then I thought, you know, my
Speaker 3: father is saying last night, so and so is a leftist,
Speaker 3: And so I said, okay, let me play this game.
Speaker 3: Like okay, I was like, how do you feel about ice?
Speaker 3: How do you feel about billionaires? You sound a little
Speaker 3: bit like leftists right out.
Speaker 2: You know, I think that's such a perfect like those
Speaker 2: two examples that you just stated, like, you know, we
Speaker 2: can come back to our slice of time and look
Speaker 2: at ice and look at billionaires hoarding wealth and say,
Speaker 2: these are huge social problems that our system actually perpetuates
Speaker 2: and created. But there must be a version of that
Speaker 2: even in early civilization, right, the policing of those who
Speaker 2: were other and therefore a threat to whatever somebody's narrative is, safety, security, peace, prosperity,
Speaker 2: So the ice of ancient civilization. And then also billionaires
Speaker 2: like the person who was really adept at counting or
Speaker 2: figured out how to sort of game the token system
Speaker 2: early on, and everyone goes, m, that guy, he's real smart.
Speaker 2: Look at all the tokens he's got.
Speaker 3: That is exactly what happened.
Speaker 2: And instead of going look at all the tokens he's got,
Speaker 2: what a crook? Or like, oh, I don't trust that guy.
Speaker 2: He seems to always just be after our tokens. I
Speaker 2: think there has been a cultural I mean, and probably
Speaker 2: it's been up and down, but there have been many
Speaker 2: times in our cultural history where we're like, look at
Speaker 2: all those tokens, Maybe one day I could have tokens.
Speaker 3: That's the problem. The problem is everybody thinks they're gonna
Speaker 3: one day have tokens, and just in case, they make
Speaker 3: sure the taxes stay low because they don't want to
Speaker 3: give them away to anybody else.
Speaker 2: Right, And instead they're just keeping that one guy with
Speaker 2: the tokens, keeping all the tokens.
Speaker 3: So true. You know, in ancient Mesopotamia, after the town
Speaker 3: started becoming cities and the cities became city states, there
Speaker 3: actually were people. They were priests who assumed the position
Speaker 3: of the person who keeps track of all the tokens
Speaker 3: and then therefore controlled all the provisions or the wages.
Speaker 3: And there were there were upsides to it, which is
Speaker 3: someone was managing life, and therefore everyone didn't have to
Speaker 3: be subsistence farmers. You can become a potter, you could
Speaker 3: do metalwork. Sounds delightful, there's there over and over, right,
Speaker 3: we invest our trust in the person who has taken
Speaker 3: that administrative duty, and it there I don't it's hard
Speaker 3: to find examples where someone did that in a really
Speaker 3: great way, right, like, because you have to always ask why.
Speaker 2: I think it's the same thing, you know, the modern
Speaker 2: equivalent is like why people don't trust politicians, because there
Speaker 2: are people who get into politics because of a deep
Speaker 2: desire and a social justice kind of cause and orientation
Speaker 2: towards ensuring public safety and prosperity and serenity and you know,
Speaker 2: public good. And then we always have to ask the question,
Speaker 2: like does absolute power corrupt? Absolutely? Like did this person
Speaker 2: go into it because they were power hungry, or once
Speaker 2: they started to feel that power, did they realize. So
Speaker 2: you've got the early token counter who Maybe that person
Speaker 2: was in that role because they were good at counting,
Speaker 2: or maybe it was because they said I'm ordained by God,
Speaker 2: and everybody goes, I believe you.
Speaker 4: It's just like, oh, that's shisty.
Speaker 2: Or maybe that person got into it for any number
Speaker 2: of maybe they did see early on ooh the counter
Speaker 2: gets to pocket some of the tokens. But either way,
Speaker 2: even if it was like a kind of noble cause, right,
Speaker 2: like I'm just pretty good accounting and everybody says the
Speaker 2: counting person should probably count the tokens. After a while,
Speaker 2: the counting person goes, oh, nobody else can count. They
Speaker 2: won't notice a few of these missing.
Speaker 3: This is what I think about. Academic administrators and a
Speaker 3: hospital administrators think, well, so think about it, because you know,
Speaker 3: I feel like perhaps I will end up doing one
Speaker 3: of those jobs as an academic administrator someday, and then
Speaker 3: I think that, am I just going to become evil?
Speaker 4: Right? Exactly?
Speaker 2: Like does having that much you know, whatever you want
Speaker 2: to define it as. I was going to say resource
Speaker 2: or that much power and then it was like, oh, yeah,
Speaker 2: what are we talking about here? That much data, that
Speaker 2: much control over information, How does one do that without
Speaker 2: some amount of personal I mean, our president is like
Speaker 2: the most extreme example we have had in modern history.
Speaker 2: Like we all can tell that the reason he is
Speaker 2: the president is so he can have secondary gain, Like
Speaker 2: he only wants to be president to get richer and
Speaker 2: to get his name out there more and to be
Speaker 2: on buildings and line his pockets. Like he's using it
Speaker 2: as a conduit to that.
Speaker 3: Undoubtedly, I wonder sometimes I think about, how, okay, let's
Speaker 3: talk about President Obama. Who you know, there are things
Speaker 3: that he did that I feel like we're disappointing.
Speaker 2: So yeah, some area with some journalists, some stuff. You know,
Speaker 2: It's like, yeah, yeah, I could criticize him for sure.
Speaker 3: What I feel like he did really well is he
Speaker 3: surrounded himself with a smart people and b it doesn't
Speaker 3: seem like he surrounded himself with people are and tell
Speaker 3: him what he wanted to hear exactly.
Speaker 2: He surrounded himself with people who would challenge him. And
Speaker 2: I think that is so important. If the token counter
Speaker 2: early on said I'm going to count the tokens, but
Speaker 2: you guys can choose five other people from the community
Speaker 2: to check my books every day. That's a totally different system.
Speaker 2: Then I'm going to count all the tokens. Trust me
Speaker 2: and know you're not allowed to enter my office while
Speaker 2: I'm counting tokens.
Speaker 3: It's true. I think that's where it comes back to
Speaker 3: the twenty first century and what we see how much
Speaker 3: trust that governments around the world, and certainly the US
Speaker 3: government are putting in tech companies. Because let's talk about
Speaker 3: for example, we've been talking about Ice partnering with Palenteer
Speaker 3: to develop immigration os to find people to deport. We're
Speaker 3: in a pretty dangerous situation because governments want to do
Speaker 3: things that involve a lot of data. They don't have
Speaker 3: the capacity to actually do it themselves, so they contract
Speaker 3: with companies to do it. But in the US, particularly
Speaker 3: without something like GDPR, it's in an environment where there's
Speaker 3: very little in the way of regulation. And so essentially
Speaker 3: you have tech companies working with governments they have given
Speaker 3: them exactly what they want, which is room to do
Speaker 3: whatever they want, and governments who are saying, Okay, tell
Speaker 3: me how you're going to fix my problems. And so
Speaker 3: it's this little echo chamber in which where's the pushback and.
Speaker 4: There's the rails? Exactly where are the rails?
Speaker 2: Like the government says, hey, tech company, I want you
Speaker 2: to look at all this big data to tell us
Speaker 2: where the threats are. And the tech company goes, cool,
Speaker 2: give me a bunch of money for that, and also
Speaker 2: thanks for all the access. We're going to figure out
Speaker 2: how to monetize all this access on the back end too.
Speaker 4: What a good deal for us.
Speaker 2: And as you mentioned, nobody has the time to do
Speaker 2: it all themselves. So they're like, oh, we'll just develop
Speaker 2: an algorithm and now we have you know, llms and
Speaker 2: other AI approaches, we'll develop an algorithm using you know,
Speaker 2: the most cutting edge tech to just do it for us.
Speaker 2: But when you're using AI to develop that tech, you
Speaker 2: don't necessarily have access to the how Some things are
Speaker 2: a black box. So it's like, let's hope it all works,
Speaker 2: you know, and even if it does work the way
Speaker 2: they intend it to work, you know, it's what is
Speaker 2: that old adage that if you are not paying for
Speaker 2: the product, you are the product. Like we are, our
Speaker 2: own lives, our behaviors are, decision making is being traded.
Speaker 4: To market to like what's so depressing.
Speaker 3: It is. I don't know if I'm supposed to try
Speaker 3: and fix out that.
Speaker 2: But do you notice when it comes to this conversation
Speaker 2: about data, And I'm curious how you approach this in
Speaker 2: your work, you know, whether it's in the book or
Speaker 2: in your greater work. This movement away from like making stuff.
Speaker 2: And it's true, we still need stuff, but we've outsourced
Speaker 2: a lot of our stuff making to large, large countries
Speaker 2: with sometimes questionable regulation where individual human rights are really
Speaker 2: trampled upon, so that we can get cheaper and cheaper
Speaker 2: stuff because you know it's cheaper, the labor is cheap
Speaker 2: and or free, meaning human trafficking, slavery, all of those things.
Speaker 2: But a lot now of what is paying the bills
Speaker 2: for the government for these large tech companies is not stuff.
Speaker 4: They're trading their equity, their commodities.
Speaker 2: It's information, like we're not making anything anymore. So is
Speaker 2: it not like a bubble? Like is this not built
Speaker 2: on a house of cards.
Speaker 3: I'm completely convinced it is. And part of this is
Speaker 3: because when you hear there's recently a study that came
Speaker 3: out in January of twenty twenty five, and you ask
Speaker 3: CEOs if they thought that AI was going to help
Speaker 3: them reduce their labor force, as if that's a good
Speaker 3: thing they about it was around sixty percent or so
Speaker 3: said yeah, absolutely, this is going to lead to reduction aheadcount.
Speaker 3: And the study of CEOs from the same people doing
Speaker 3: doing came out last week said twenty one percent of
Speaker 3: them say that. I'm sure they think that's a bad thing.
Speaker 3: I think it sounds great because people having jobs, to
Speaker 3: me is a good thing. And also I do not
Speaker 3: trust the I don't trust that any technology should be
Speaker 3: making decisions about anything without humans being.
Speaker 2: Involved, right, at least having oversight or a check in
Speaker 2: a balance.
Speaker 3: Right.
Speaker 4: I'm happy to offload portions of my job.
Speaker 2: I cannot wait until my hospital has an AI that's
Speaker 2: good enough that I can do a therapy session with
Speaker 2: my patient and just press enter at the end and
Speaker 2: my note is written for me. Like that would be phenomenal,
Speaker 2: because that is a part of my job that I
Speaker 2: hate and I shouldn't be getting paid to do this,
Speaker 2: like grunt work that doesn't really serve anybody except the
Speaker 2: insurance commpanies.
Speaker 3: Yeah, I just like kind deal with my emails, Yeah exactly.
Speaker 2: Like there are things that it's like, yes, yes, yes, please.
Speaker 2: But I sometimes worry. I think about the way that
Speaker 2: we do ballad initiatives in California, like the way we
Speaker 2: vote where I love my state to death. But one
Speaker 2: of the things I think is very problematic is we
Speaker 2: sometimes will decouple the initiative from the way we're going
Speaker 2: to pay for the initial for you know, you have
Speaker 2: this ballad and we want all this cool stuff, and
Speaker 2: everybody goes yes, yes, someting seventeen or whatever, and then
Speaker 2: they go, Okay, we're gonna do this, this, this in
Speaker 2: order to pay for No, don't want that, And all
Speaker 2: of a sudden, we have all this stuff we said
Speaker 2: we're gonna do, and we don't have any funding for it.
Speaker 4: And I think about that with sure.
Speaker 2: An in a utopia, a tech ceo saying I'm going
Speaker 2: to cut my labor force in half and have the
Speaker 2: robots in the machines take all of this menial labor
Speaker 2: is great if we have universal basic income. You know,
Speaker 2: it's like, but you can't decouple those things.
Speaker 3: It's so true, And I think the hard part is
Speaker 3: that they keep talking as if one day AI generative
Speaker 3: AI superintelligence is going to free us from all of
Speaker 3: the things we don't actually want to do, and we're
Speaker 3: going to have these lives of leisure, And I wonder who's.
Speaker 4: Paying for that exactly, who's going to pay for it?
Speaker 3: Oh?
Speaker 4: Is that why you're amassing all this wealth? I don't
Speaker 4: think so.
Speaker 3: The other bubbly part about it is that the funding
Speaker 3: model is completely suspect. Because the enterprise, the enterprise license charges,
Speaker 3: the consumer subscription charges for any of these models Claude
Speaker 3: Mermanthropic or GBT for open AI, they're not actually covering
Speaker 3: the cost of running the models. But let's be even
Speaker 3: putting aside that there are an environmental costs with water
Speaker 3: and emissions and so forth. Those are all very real things.
Speaker 3: Even you put that aside. Their model is being supported
Speaker 3: by either mergers and acquisitions that are happening within Silicon
Speaker 3: Valley or vast amounts of venture capital, and all that
Speaker 3: funding is propping this up. And sometimes I just sometimes
Speaker 3: I look at my my stock I think it's great,
Speaker 3: And then sometimes more of the time I just think, so,
Speaker 3: what happens when finally the venture capitalists to say, you
Speaker 3: know what, we cannot infinitely prop up right a technology
Speaker 3: that can that actually has no return on investment.
Speaker 2: Mm hmm, yeah, the Great Depression happens, right, or the
Speaker 2: two thousand and eight financial crisis, Like your stock portfolio
Speaker 2: is in the toilet when that happens.
Speaker 4: Yeah, And that's.
Speaker 2: Like, that's a scary proposition for anybody who is and
Speaker 2: I say this myself being one of the anybody who
Speaker 2: is privileged enough to be concerned about that, you know,
Speaker 2: And it's.
Speaker 4: I love.
Speaker 2: The way that you directly tie these existential eschatological like
Speaker 2: really terrifying realities of this kind of precipice that a
Speaker 2: lot of us feel we are on in modern society,
Speaker 2: back to the earliest anthropologic evidence that we have of
Speaker 2: human beings existing around each other, and then carry it
Speaker 2: all the way through the recorded human history that we have.
Speaker 2: There are good examples though, right, Like there are examples
Speaker 2: where some cultures figured out how to utilize data, saw
Speaker 2: that data and information is power, and managed to at
Speaker 2: least maintain some level of I don't know, egalitarianism or
Speaker 2: is that just a myth.
Speaker 3: Absolutely there are examples of that, I think for back to,
Speaker 3: for example, the use of the KEEPU, which was the
Speaker 3: ink in word for a system of information management that
Speaker 3: involved tying knots and strings, which was used as early
Speaker 3: as around thirty five hundred BC or so, same time
Speaker 3: as the emergence of our civilizations of Mesodotamia, but in
Speaker 3: ancient Peru, where it's an interesting society archaeologically because there's
Speaker 3: actually no evidence of there having been any war. There's
Speaker 3: people who didn't have war, and there was also there's
Speaker 3: no ruler in the sense of there was a king,
Speaker 3: which was pretty standard for civilizations at that time to
Speaker 3: have a king. Certainly that there was hierarchical society, but
Speaker 3: it was not hierarchical in that same way that it
Speaker 3: wasn't say Mesopotamia the Near East, and so they were
Speaker 3: certainly someone was in charge of tying the knots in
Speaker 3: the strings, and they were sort of like mnemonic devices,
Speaker 3: so they feel the string and feel how it was
Speaker 3: tied or how how far was it spaced from another,
Speaker 3: not you know, to remember what the information was. But
Speaker 3: it was an example where it was, you truly just
Speaker 3: treated as a method of managing the scale of life
Speaker 3: without it having to become this kind of massive, you know,
Speaker 3: massively unequal society. And I think that's that's a really
Speaker 3: good example of that.
Speaker 2: Yeah, it's it's it almost like begs the sort of
Speaker 2: question of the chicken and the egg sort of is
Speaker 2: it that the tools themselves allowed for a intentionality that
Speaker 2: already existed, or is it that the tools created an intentionality?
Speaker 2: Like did this ancient inca society just have egalitarian values
Speaker 2: and did they develop these tools in order to have
Speaker 2: this sort of just I couldn't say completely neutral, but
Speaker 2: like likely neutral outcome? Or you know, the tokens, were
Speaker 2: the tokens there early on, the clay tokens and Mesopotamia?
Speaker 2: Were they there early on simply as a tool or
Speaker 2: were they actually you know, was it a feature not
Speaker 2: a bug that they became or existed as a means
Speaker 2: to divide power unequally.
Speaker 3: By completely unscientific speculation on this is that if it
Speaker 3: hadn't been the tokens, it would have been something else. Yep.
Speaker 2: That's how I feel about religion, if I'm being honest,
Speaker 2: Like it's not the really religion itself. I think a
Speaker 2: lot of people blame religion for a lot of things,
Speaker 2: and I'm like, no.
Speaker 4: That's just the way they did it. You know, those
Speaker 4: people were going to do that using some other method.
Speaker 3: Yeah, And I mean I'm not you know, trying to
Speaker 3: paint the ancient civilization Carol soupet Peru as like some
Speaker 3: kind of eutopian paradise. We actually don't know. We just
Speaker 3: know what we see there and what we don't see there.
Speaker 3: But at the same time, it's quite telling when you
Speaker 3: look to ancient Mesopotamia and you see over time, based
Speaker 3: on the you know, layers of archaeological research that people
Speaker 3: have done, is the movement of the tokens out of
Speaker 3: the homes into the granaries, you know, into the temples,
Speaker 3: and then the tablets with the cuneiform writing are in
Speaker 3: the temples. And so it's because it's very clear that
Speaker 3: the people in the temples of like assumed the control
Speaker 3: over this tool and then are using it, you know,
Speaker 3: I mean, perhaps let's be nice, like at first it's like, great,
Speaker 3: it's a tool. It can help me do a thing.
Speaker 3: But then you know, all of a sudden you are
Speaker 3: a warlord going after the next town and trying to
Speaker 3: take it over. And then think about you know, one
Speaker 3: of the recent studies that came out about about generative AI,
Speaker 3: which is that it was there's a study that says
Speaker 3: that it was making tiny changes in output it was
Speaker 3: generating on the topic of abortion rates, and that those
Speaker 3: tiny changes were actually changing the meaning. And if people
Speaker 3: aren't reading the output that they're asking for before they
Speaker 3: are using it somewhere, they may not actually realize that
Speaker 3: a small semantic change has actually changed the meaning of
Speaker 3: their sentence and the political meeting of their sentence. And
Speaker 3: so now we're in this weird situation where you know,
Speaker 3: again like there's this tool, you know, arguably not inherently bad.
Speaker 3: People are you using it? They're using it because you know,
Speaker 3: they want efficiency, they want optimization, they want to get
Speaker 3: ahead whatever it is they're using it for. But now
Speaker 3: it's sort of flipping in a way that the token couldn't,
Speaker 3: the tablet couldn't, in which the actual tool itself is
Speaker 3: changing the output. And that, to me is where we
Speaker 3: should be deeply worried.
Speaker 2: Yeah, there's this like potential for a runaway feedback loop.
Speaker 2: I mean, I think about climate change, and I think
Speaker 2: about all of these other sort of large systemic inflection
Speaker 2: points that human beings have reached where we see then
Speaker 2: massive population decline, where we see them huge you know,
Speaker 2: industrial revolution, right, huge increases and in danger to health
Speaker 2: and human safety, but also to all of the organisms
Speaker 2: on the planet. These financial systems, I sometimes worry, like, well,
Speaker 2: I don't even worry. I think I am somewhat cynical
Speaker 2: about the g being out of the bottle, Like, we
Speaker 2: can't recork this at this point, we just have to
Speaker 2: figure out how to manage it.
Speaker 3: I think that's absolutely right. I think we can think
Speaker 3: about it as something that we could iterate on and improve.
Speaker 3: I think the ship sailed. It was funny because I
Speaker 3: was writing this book. I realized after the fact that
Speaker 3: I had tried very, very hard to not have to
Speaker 3: not communicate that really this is just about capitalism, or
Speaker 3: really this is just about colonialism, because it's not. Certainly
Speaker 3: people aren't running a capitalist economy in an ancient sumer. Yeah.
Speaker 3: At the same time, it's a very interesting correlation that capitalism,
Speaker 3: transatlantic slave trade, colonialism, we're all happening at the same time,
Speaker 3: and it's hard to separate them out from each other,
Speaker 3: and so it's very kind afterwards, I was I think
Speaker 3: I was at the point at which I was doing
Speaker 3: my final final revisions, and I realized I had not
Speaker 3: used the word capitalism once because I was trying so hard. Yeah,
Speaker 3: not to just have this be oh like some like
Speaker 3: leftists like Gridigo just thinks that capitalism is bad like well, actually, yeah,
Speaker 3: I kind of do, but not trying to alienate the
Speaker 3: readers here.
Speaker 4: And it's so much deeper than that.
Speaker 2: Capitalism as we define it is a much more complex
Speaker 2: system and it has specific values and it has but
Speaker 2: this is like, this is the sort of the building blocks.
Speaker 2: These are the things that happened before capital and like
Speaker 2: the way that we're looking at it, it's even in
Speaker 2: socialist nations there were capitalists undertones, because where there is property,
Speaker 2: there's capitalism.
Speaker 3: One hundred percent. I mean, it's yeah, and it's not
Speaker 3: like this. I mean I have two examples, one from
Speaker 3: Solenist Russia and one from Mao's China, where it's not
Speaker 3: like things were going much better for the people living there.
Speaker 3: And so but it was very funny because I realized
Speaker 3: that I had taken such pains to not have this
Speaker 3: be reducible to capitalism. Is the problem that I didn't
Speaker 3: even have it in there, and then I had to
Speaker 3: go back fix that because I don't I'm not named
Speaker 3: part of the problem. But this at the same time,
Speaker 3: I think that this particular mode in the US of
Speaker 3: I guess some people call it late stage capitalism, but
Speaker 3: the deep lack of regulation, the you know, corporations or
Speaker 3: you have the rights of people, these are to me
Speaker 3: areas for improvement, and there's no reason we can't. I mean,
Speaker 3: I live in Salem, Massachusetts, and we're doing a universal
Speaker 3: basic income pilot.
Speaker 4: I think that's pretty cool.
Speaker 3: And you know, there's other cities, other towns around the
Speaker 3: country have found out that turns out, one way you
Speaker 3: can solve the problem of people being unhoused is providing
Speaker 3: them with housing.
Speaker 2: Yeah, and you've seen it in places as wildly different
Speaker 2: as like Salt Lake City, Utah, and Los Angeles, California. Yeah.
Speaker 3: And so if I think my worry is that the
Speaker 3: world feels so overwhelming right now that it's easy to
Speaker 3: feel like there's absolutely nothing that anyone could do about
Speaker 3: it because there's these big systems, right big tack capitalism.
Speaker 3: But that's not actually true. I just look at the Obviously,
Speaker 3: I live in like a little utopia in Sale, Massachusetts
Speaker 3: with our rainbow flag crosswalks and our universal basic GACO pilot.
Speaker 3: But I do think, you know, that's a community that
Speaker 3: said this is what we want to try and maybe
Speaker 3: it's gonna work, maybe it's not gonna work, but we're
Speaker 3: gonna try it. And that doesn't mean that everyone there agrees,
Speaker 3: because they're definitely people who think that this is completely
Speaker 3: ass backwards, but we are, on the whole have agreed
Speaker 3: to try to do something different. And I think it's
Speaker 3: that those kinds of changes on the local level, you know,
Speaker 3: in your community, what can you try and advocate for
Speaker 3: how do you talk to you know, how do you
Speaker 3: talk to your parent and say, like, are you really
Speaker 3: your pabucking? Because it doesn't sat me, you don't say
Speaker 3: that part, but be talking to them about specific policies
Speaker 3: or what do you really think? What do you agree with?
Speaker 3: What do you disagree with? You know, have you considered
Speaker 3: this perspective that maybe isn't your own? I think that's
Speaker 3: where that's where I find the hope.
Speaker 4: Yeah.
Speaker 2: Yeah, And it's so funny because a, you're very good
Speaker 2: at this, because any other podcaster would end the conversation
Speaker 2: right there, because you did such a beautiful job of
Speaker 2: introducing us to the concept of picking a few examples
Speaker 2: throughout history and then closing with this this beautiful message
Speaker 2: of hope. And yet I am not every other podcast,
Speaker 2: and I cannot help but like it's been boring into
Speaker 2: my brain the whole time we've been talking. You mentioned,
Speaker 2: you know, this approach where capitalism is underlying a lot
Speaker 2: of it, but you don't want it to be the
Speaker 2: end all and sort of the overall concept. We've talked
Speaker 2: about this sort of the intersectional nature of colonialism and
Speaker 2: of all of these kind of variables that underlie this
Speaker 2: long story of data and information and power. And yet
Speaker 2: I cannot help but keep going back in my mind
Speaker 2: to my interviews that have done his on the show
Speaker 2: before with Angela Sani and her work, both her books
Speaker 2: Inferior and Superior, Sure, but specifically her book The Patriarchs,
Speaker 2: about the rise of kind of the ruling male class
Speaker 2: and how it's situated back in these early attempts to
Speaker 2: record data and to pass on wealth. Right, this is yours,
Speaker 2: this is mine, who owns that piece? And while there
Speaker 2: have been matriline societies that she points to, we saw
Speaker 2: a huge shift into patrilineal property ownership and spreading, and
Speaker 2: I don't and we've have small examples of matriarchs, but honestly,
Speaker 2: I can't point to a single time in history where
Speaker 2: the dominant power structure was held by women, And I
Speaker 2: just cannot help, And it's my cynicism and my feminist
Speaker 2: ideals here, I.
Speaker 4: Just cannot help. But wonder what would all of this
Speaker 4: look like.
Speaker 2: If all the way at the beginning, that early decision
Speaker 2: making around power, however it came to be, was shifted
Speaker 2: in the other direction. Would women have just become men
Speaker 2: like or would everything we're talking about be wildly different.
Speaker 3: It's also great thought experiment because, of course, immediately I
Speaker 3: want to say Margaret Thatcher, but then one argues that
Speaker 3: we had Margaret Thatcher precisely because.
Speaker 2: Because of men, exactly because the only way to play
Speaker 2: with the boys is to become one of the boys,
Speaker 2: or even more of a boy than the rest of them. Yeah,
Speaker 2: and we see that a lot with women in positions
Speaker 2: of power in male dominant fields. Sure like the most
Speaker 2: violent and aggressive in their approach because they have to be.
Speaker 3: They absolutely do. And it always makes me think of how,
Speaker 3: you know, there are some women that one encounters in
Speaker 3: the workplace who are you know, senior to you, who
Speaker 3: they're either the kind who think I went through this
Speaker 3: horrible experience to get where I got to and therefore
Speaker 3: you must too, or the ones who think I went
Speaker 3: through it and therefore I want to make it better
Speaker 3: for you so you don't have to. And those are
Speaker 3: the people I wish would have been in power in
Speaker 3: ancient MESSI.
Speaker 2: Betaia say, say, those are the people I wish were
Speaker 2: in power now, and I just it's You're right. All
Speaker 2: it can be is a thought experiment, but I just
Speaker 2: can't help. It's the water, because the patriarchy is the
Speaker 2: water we swim in. I can't help but always want
Speaker 2: to point out that these conversations about power throughout human
Speaker 2: history also can't be separated from conversations about gender inequity
Speaker 2: because it's you know, it's like when we talk about
Speaker 2: violent crime and people are talking about like predictive value
Speaker 2: of violent crime in this area versus that area, and okay,
Speaker 2: is it the amount of guns?
Speaker 3: Is it?
Speaker 4: The amount of poverty? Is it, the amount of drugs?
Speaker 4: Is it the amount of whatever?
Speaker 2: The most predictive variable is maleness, but by and large,
Speaker 2: over anything else, it is so much more predictive, but
Speaker 2: we just leave it out of the formula because it's
Speaker 2: so obvious.
Speaker 3: It's true, and one of the things I really took
Speaker 3: a lot of care to try to do is to
Speaker 3: not have this book be yet another book of great men.
Speaker 3: And it's it's.
Speaker 2: Really hard because or men great.
Speaker 3: Yeah, exactly, it is a book. Ye, so many of
Speaker 3: them were men. You know. There is this one example
Speaker 3: that I'm thinking of that I didn't end up leaving
Speaker 3: in the book because there were just too there were
Speaker 3: too many examples. So a woman named tin Hannan who
Speaker 3: was essentially a leader of a group of Berbers around
Speaker 3: the beginning of the Common Era, and she this origin
Speaker 3: story is vague, but it's it comes out of the
Speaker 3: Berber legends of people who's still still you know, the
Speaker 3: culture community still exists, and in their legends she is
Speaker 3: sort of their great you know, matriarch. And she came
Speaker 3: kind of walking on a riding a horse and walking
Speaker 3: with her servant one day. She came from a family
Speaker 3: in the mountains and ended up you know, in the
Speaker 3: Suahelle and attracting groups of the nomadic people and doing so,
Speaker 3: you know, because of her knowledge of the earth, of
Speaker 3: her wisdom, her ability to figure out how to find water,
Speaker 3: and none of the none of the kinds of the
Speaker 3: other A lot of the other women I would encounter
Speaker 3: were always sort of mimicking the male model of gaining power. Yeah,
Speaker 3: and she wasn't right. It was a very very different approach.
Speaker 3: And I mean, I are one could argue, you know,
Speaker 3: it is sort of essentializing gender to say that the
Speaker 3: women who adopted a so called like sort of male
Speaker 3: model were doing that as opposed to you know, one
Speaker 3: can you know, be expressed female power in all kinds
Speaker 3: of ways. But this absolutely strike me to think that,
Speaker 3: you know, in an example like the one of Tin Hinnan,
Speaker 3: which is you know, happening outside of, say, the context
Speaker 3: of the Roman Empire, which is also happening at the time,
Speaker 3: somebody was saying things could be different, and that was
Speaker 3: a woman, right.
Speaker 4: I want you to write that book.
Speaker 2: Next project. I would love to learn more about her.
Speaker 2: She sounds absolutely fascinating. But first, everybody, you got to
Speaker 2: dig through this one because there's so much good stuff
Speaker 2: in here. We didn't even get We didn't even remotely,
Speaker 2: not that we didn't get to all of it, we
Speaker 2: barely got to any of it. But unfortunately we can't
Speaker 2: keep talking all all day long. But this has been
Speaker 2: such a fascinating chat. I would love to have you
Speaker 2: back on the show at some point because I just
Speaker 2: really enjoyed it and I learned so much.
Speaker 3: This is so much fun, and likewise, I would love
Speaker 3: to talk to you forever.
Speaker 4: Yay everybody.
Speaker 2: The book is Data Empire The Power of Information to Organize,
Speaker 2: control and Dominate by doctor Rupica Rissom and everybody listening.
Speaker 2: Thank you for coming back week after week. I'm really
Speaker 2: looking forward to the next time we all get together
Speaker 2: to talk nerdy