Too Much Computer: Ingrid Burrington discusses Hyperscale with Paris Marx

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All of these investors are jumping on that.
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And of course, even if they don't make money, the investors can still make uh make a significant profit when the company goes public, because for some reason these companies can be worth like crazy amounts of money on the stock market, even if they don't make any actual money.
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And of course, we're probably gonna see that happen very soon with like anthropic or open AI, unless the bottom falls out of the AI industry before they get to go public, right?
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It's it's just it makes no sense.
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And this week, before we get to my guest, who is actually sort of me, but the interviewer is the guest, sort of this time.
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If you remember from last time I uh released a book, this is what I like to do when a book comes out.
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Anyway, I'll talk about this in a second.
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Basically, the big news is that my book, Hyperscale, after months of telling you about it, is finally out.
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It officially came out Tuesday in the US, Canada, the UK, and I guess a number of other countries as well.
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And it's just kind of wild to think that this thing that I spent two years working on, researching, writing, editing, you know, all those pieces that go along with it, is finally out into the world now.
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You can read it, so many other people can read it, and that's both a bit daunting, but also exciting, right?
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Of course.
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It has to be exciting in some ways.
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No, believe me, it it really is.
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But you know what I'm gonna say.
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If you have not picked up a copy yet, you really should do so.
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I put a lot of work into this book.
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If you like my work, if you like this podcast, if you like my writing, I think you're really going to like my book.
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And it's about a topic that is just so topical right now.
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I could not have predicted two years ago that as soon as my book came out, data centers were going to be a massive issue that uh not only people across the United States, but in so many countries around the world were going to be talking about, discussing.
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And of course, you know, so many people pushing back on it, try to stop them being rolled out in their communities.
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The backlash is real.
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And my book, Hyperscale, explains many of the reasons why that is happening.
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And how we probably shouldn't have been surprised that when the tech industry started to try to build out a ton more data centers, that people were not going to be happy about it.
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Because we could see that already happening in places where they were building a lot of data centers already.
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So grab a copy of my book, Hyperscale.
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There will be a QR code here on the screen somewhere if you're on the video version.
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If you want to go get more information and buy a copy, you can obviously go to hyperscalebook.com where you can get more information.
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And of course, I would be remiss if I didn't also mention the fact that I'm gonna be in a ton of places over the next couple of months to talk about my book.
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And maybe you will want to come out to hear more about it.
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I know I mentioned this last week, but I'm gonna quickly tell you again that tomorrow, if you're listening to this episode, the day it comes out, I will be in Montreal.
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On Monday, October 26th, I will be in London, then in Bristol, November 3rd, I'll be in Boston, then Baltimore and Pittsburgh, Oakland and San Francisco, Los Angeles, then on November 13th, I'll be in Toronto, then Hamilton, Ottawa, Vancouver, Calgary, Edmonton, Waterloo, Ontario.
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And then on November 28th, I'll be over in Dublin, and I'll have a number of other dates in Europe to announce very soon.
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And certainly I'll let you know on the show as well if you're in Europe and you want to see me over there.
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Basically, that's all to say.
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I'm gonna be in a lot of places.
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You should come out and see me, you should get a copy of my book, and I would be more than happy to sign it for you if you do come to one of those events.
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So, with that said, I know I've gone on way too long about the fact that my book is out and you should buy it.
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Now we should pivot to actually talking about the book itself.
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And that is why I invited my friend Ingrid Burrington to basically come on the show, to take over hosting the show for a week, and to interview me about the book.
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If you've never heard of Ingrid or encountered her work, you're really missing out because she has been writing about internet infrastructure for over a decade and put a lot of this landscape on the map for American readers in particular when she did an 18-story online series for the Atlantic in 2015.
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That series certainly gave me a lot of insight into how this all works.
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She also wrote a book called Networks of New York, which was originally published in 2016, but was revised and republished this year.
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And you can still buy copies of it.
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I'll include a link in the show notes.
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She also hosts a cool and interesting podcast called Rick Corp that you know you should also check out.
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So when I was thinking of someone to come on the show to interview me about hyperscale, Ingrid seemed like the perfect person.
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She's someone who I was talking about early in the process of putting this book together.
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She's someone who is very knowledgeable about this whole space.
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And so I figured, of course, she should come on to talk to me about hyperscale.
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And she's going to have to come back, obviously, in the future to talk to us more about her work that she has been doing for quite some time and that I think that you'll we'll be very interested in.
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But for now, we're talking about hyperscale.
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I hope that you're really going to enjoy it.
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Obviously, I hope that you're going to pick up a copy this week.
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If you haven't already, thanks so much.
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Enjoy this week's conversation.
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Ingrid, welcome to Tech Won't Save Us.
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Thank you for having me.
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And thank you so much for writing this book.
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And it yeah, it means a lot that you you asked me to come do this.
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Absolutely.
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I I really appreciate that you accepted.
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And I also feel a bit self-conscious that the first time you're on the podcast is actually to interview me and not for me to interview you.
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But clearly that's something we're gonna have to change in the future.
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You know, we'll yeah, we'll we'll we'll come back to that.
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Absolutely.
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Get guests will know that Ingrid will be back next year to talk about your work and not just to talk about my work.
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I guess the there's sort of it always I was so silly asking a writer like, why'd you write a book?
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But a lot of the groundwork for for this book really started a couple of years ago with the Data Vampire series and really a lot of the work that the show has been doing for the last few years.
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And I'm curious about the decision to translate that into a book, you know, and the what what what a book does in the world that that a podcast doesn't do.
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Absolutely.
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Yeah, it certainly reaches, or I don't know, does it reach more people these days?
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I'm not even sure.
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Uh maybe it doesn't.
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Um, but I still felt that it was important to do it, right?
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And and maybe it's worth saying as well, you know, part of the reason that I wanted uh you to come and interview me about the book is because we had been talking about this, you know, and my ideas for the book and how it was going to look from like a very early stage.
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You know, you were someone who was very early to seeing, you know, my thinking about how I was putting this together and kind of getting some advice from you on that.
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Um, so you know, that's why I think that you're kind of the perfect person to come talk to me about it uh on the podcast.
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And so, yeah, the the decision to do the book is kind of like, well, you know, one, I was I was looking to do another book and I was looking for a subject, you know, that was part of it.
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Because of course I had done a book on transportation that was published back in 2022.
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And so, you know, by, you know, 23, 24, it was kind of like, okay, you know, what am I gonna do next?
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You know, what is the next thing that I really want to dig into?
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And at first I was thinking about doing something on the tech billionaires, you know, the Elon Musk's, the Jeff Bezos's, how they think about the world, that kind of a thing.
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But I just wasn't like, I don't know, I didn't have the motivation to like dig into that enough, I guess.
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Um, like it just wasn't interesting enough to me.
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And I felt like, you know, I had done so much work on Elon Musk in the past in particular, and I felt like I was starting to get a bit bored with Elon Musk at the same time that like unfortunately everybody was getting much more interested in him and turning against him.
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And I was like, I've been against this guy for so long, and now you're all catching up and I just don't care anymore.
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Not that I don't care, but you know, I was starting to see these stories about data centers in different parts of the world, you know, whether that was in Oregon or in Ireland or in Chile.
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And, you know, it seemed like this was starting to become an issue, right?
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As more of these were constructed and uh, you know, they were, they were making these demands on different communities that, you know, there was something here that it felt like wasn't getting the attention that it deserved and was also poised to become a bigger issue, especially as, you know, 23-24 is obviously uh after Chat GPT, and we're starting to get the uh, you know, the kind of early indications that the industry is in the process of at least planning to build out a lot more data centers in response to that.
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And so I start to feel that, okay, one, we're seeing the impacts that these data centers can have in communities where they're a lot built or or where they're not kind of properly located, maybe the resources aren't there to provide for them.
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And now if the industry is planning to build out so many more of them, then it feels like a lot more communities are going to be, you know, kind of bearing the brunt of this build out.
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Um, and you know, that that seems to be how it has played out, unfortunately.
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Yeah.
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Well, and I think um, in my opinion, about sort of what books do, um there's also like a temporal effect that they can have that's very different from a podcast, you know, like it's it's so much easier for a podcast or recorded interview audio work to kind of like fade into the RSS feed.
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And books have this.
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I my friend Max Neely Cohen has described books as time weapons because they extend like beyond you, right?
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Um they're a way of of making having that argument kind of exist against a kind of like longer trajectory.
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Uh, we'll talk a little bit more about some of the history stuff you get into in the book, but I'm curious about also sort of the way that you organize the book, which is not entirely chronological.
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And I think that was the right decision, but I think it would be nice to hear why you made that decision or how you thought through organizing the different sections of the book, the chapters.
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Of course.
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And and you know, just to pick up on your point around like the temporality of a book, you know, I will say the podcast did come back.
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It was resurrected on the RSS feed last month, as as listeners will know, and as you know well.
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Uh so that I guess that's a way to kind of keep it going, but you kind of have to republish it, right?
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Otherwise it does kind of fade into obscurity and fewer people know that it's there.
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But but even with the first book, you know, I remember when I was putting it together because at that time, you know, I had only really written articles and done podcasts and things like that.
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And it was much more immediate, right?
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Like if anything was going to take any time, it was maybe two or three months for a magazine article to come out, uh, not kind of the scale of time that a book took, right?
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Both to write it and then to go through the editing process, which is probably like a a year, you know, the editing and printing and all that kind of stuff until it actually comes out.
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So you finish the book, and then there's already this big time lag until it gets published.
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And, you know, I remember thinking like, is this even going to be relevant?
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Like when it comes out, uh, you know, will people have moved on from these issues?
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Uh, will it all be kind of outdated?
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And, you know, now we're in 2026 and that book came out in 2022, if I'm remembering correctly.
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Um, and it's kind of like, I don't know, I'm I'm sure like there have clearly been developments since then, but I also think that there's still a lot that remains relevant and I still have people bringing it up bring it up to me today and are still like recommending it to people and and things like that, right?
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And so that probably shouldn't have been surprising to me, but it was still surprising to me in some way because I was used to and and because like in this moment, it feels like everything is so uh so short, right?
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You know, the time frames are short, like, you know, everything has to be so immediate.
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And yeah, I guess it was kind of like maybe a learning process for me, so that I could see that things can have this longer time scale as well.
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Even though if I'm reading books that are super old, I would still be like, yeah, this is still super relevant, right?
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But yeah, right.
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Not not me.
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I can't, I can't have a lasting impact into the historical timeline, but all these other people, that's fine.
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Exactly.
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Exactly.
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I like I don't know if you found that with your with your work as well, but only all the time.
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Yeah.
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Um but yeah, I on the question of like of the book and and the layout, you know, it certainly felt like I don't even know how I would have really approached it chronologically.
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And I feel like if I would have started just with like a big dose of history, it probably would have been less interesting than, you know, kind of interweaving that history into different pieces of it.
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Part of the approach was also informed by how I did my first book, right?
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Where I was looking at different modes of transportation in different chapters and, you know, kind of digging into them individually, but also then trying to, you know, craft a larger narrative around what we were seeing, right?
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So dig into these specific cases or examples, but still have like a bigger story that is trying to be told around that.
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Um, and so then when I when I look to approach the data center question and the data center story, um, you know, I felt like that was still largely the right way to go, right?
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You know, give people a way into it, you know, by giving them some information on data centers, what data centers are, what goes into building data centers, a bit of history as to where data centers come from, and then dig into the specific issues around data centers that people are hearing about, right?
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Whether that's the electricity usage or the water usage or the climate impact or the wider supply chain.
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And I think that also makes it accessible to people where, you know, if they can't dig into the whole book right away or they're looking for something specific, they can maybe turn to that chapter and then get an idea of that aspect of what comes with data centers.
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And then, of course, through that, of course, you, you know, I'm I'm looking at those specific sections, but I also have this, you know, wider story that I'm trying to tell as well, right?
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About the scale of the build-out of computation, about how much computation is required and about why this feels fundamentally unsustainable and is driven by uh, you know, particular ideas, narratives, business goals by these tech billionaires that I think are very disconnected from the reality that most people face in their lives, but but also what would be best for people if we were thinking about building out technology in a way that actually served the public rather than serving these billionaires and their whims and their interests and things like that.
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So I guess that was like part of the motivation, but it also just seemed like that would be the most compelling way to structure it, um, at least, you know, as far as I could think to structure it.
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Yeah, I I I also feel like um you did a good job, I think, in the way that you organized it, in addressing a lot of the places where I think the most annoying bad faith arguments come up, right?
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Like because it's, you know, well, but is the water problem really that bad?
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Or like, you know, but if you compare it to this or that other thing and the real problem is the supply chain, it's like you actually you hit all those beats quite nicely and you shut down a lot of uh rather tedious uh debate that like I neither of us, I imagine, has much patience for.
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Yeah, I appreciate that, right?
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And and I was certainly paying attention to it like while I was putting it together.
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And there were certainly moments where I was like, okay, I need to address this, or maybe I need to like work this in in some way.
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Or like, you know, obviously when uh the controversy around uh Karen Howe's book, Empire of AI, and and her water figures kind of blew up, you know, as these kind of more pro data center people found something to pull out in her book in order to attack.
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It was like, okay, I need to go back and check my water figures and you know, make sure that all looks okay.
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But then also just seeing like how they were disingenuously using the water argument in order to try to like muddy the waters on the argument around data centers was also like, okay, this needs to be addressed too.
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Whether, you know, whether where they try to argue that, oh, data centers don't actually use very much water, you know, uh data centers aren't a problem because golf courses or agriculture use so much more.
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Uh, and I was like, okay, this needs to be brought in there as well, because by the time it comes out, people will have heard more of this, you know, argument or framing around data centers, and it will need to try to unpack that and explain why uh, you know, water use is still an issue, even if when we look at like, you know, kind of the 10,000-foot level uh and and try to abstract it, maybe it seems as though it's not as big of an issue, though, though even then, you know, the figures I saw suggest that data centers are still a top 10 water user in the United States as an industry, right?
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And so, you know, there is still there is still a problem there, even if you look at that, that kind of large scale.
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But the core of the problem is a local one, not you know, kind of an abstract national figure kind of one, right?
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Well, and it's it's kind of well, it's certainly not just a national one, right?
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Like as I as I love to joke with you, I am a hopeless provincial uh thinker, which is to say an American.
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And there is a way that the American uh narrative takes up a ton of oxygen uh in this in this conversation.
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But as you point out, like a lot of the things that are people are talking about and getting really rightfully upset about right now in the United States have been happening in other places already, right?
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The whole behind the meter thing was happening in Ireland well before it became a talking point of hyperscalers in the United States.
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Um, this actually just because you brought up sort of the scale thing makes me want to kind of jump to this question that I sent you about um about the problem of kind of scale in telling the stories of data centers, right?
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Because you have the kind of on-the-ground local conditions, and then you have this sort of like planet scale project of turning the world into a computer.
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And you have sort of those like mezzo-level things, right, at the level of like a supply chain or a place like Taiwan that becomes a central kind of choke point.
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Companies will often, as you point out, use these exceptional cases to sort of defend the whole of what they're doing, right?
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We have this like good hydropowered one, um, or their sort of manipulation of metrics to make things look like they're not as resource intensive as they are.
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And I'm curious about how you think about how those like how those tensions across scales have played out in the broader backlash.
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And I don't know, sort of for for better and worse, how that's kind of affecting resistance to the hyperscalers.
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Yeah, I think it's really interesting to think about that, right?
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And I feel like there's an interesting continuity between this book and my first book as well, where you know, the first book was also about the scale of the automotive system, right?
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And and why that was fundamentally unsustainable.
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And so when we were looking at tech solutions to um to transportation, if you didn't address the automobile and and the centrality of the automobile in the transportation system, like especially in uh places like North America, but not exclusively, then you were never going to clearly address the problem, right?
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Because the lack of sustainability or the kind of issues with the system largely came out of that aspect of it, right?
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Just the scale and the demands that come with expecting everyone to have their own automobile and things like that.
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And so, you know, then looking at what we're seeing with computation today, like obviously we have seen the internet expand to a massive degree.
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And, you know, almost everyone on the planet is online, though, though not everybody.
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The types of things that we do online have become more computationally demanding over time.
00:19:41.680 --> 00:19:49.839
Uh, you know, certainly there was this the discussion around streaming and how that uses more bandwidth and more data like 10 years ago or so.
00:19:50.079 --> 00:20:03.519
But now we're at this moment with generative AI where there is this significant push to really make us use very computationally intensive tools, often for things that don't really require those tools at all, right?
00:20:03.680 --> 00:20:13.440
And so there's a need for a significant amount of computation without necessarily having like the justification for why that is needed or why that is actually useful.
00:20:13.599 --> 00:20:19.839
Um, and so then when you like turn it to the infrastructural side of things, as you're saying, like it exists on many levels.
00:20:20.000 --> 00:20:30.400
And, you know, obviously I kind of quote you talking about this in the book, if I remember correctly, where I do think that there is like a difficulty in parsing it out to a certain degree, right?
00:20:30.480 --> 00:20:39.519
Like I think that the thing that motivates a lot of people who are opposed to data centers is seeing how this scale plays out in their backyard or in their communities or in their region, right?
00:20:39.599 --> 00:21:00.480
Uh, because of course you have uh people, you know, I remember talking to people in Michigan, for example, who were like, we're opposed to the data center near us, but we're also gonna go help the people in other communities who are opposed to the data center there, because we don't just want them to be moved down the road and that, you know, our kind of supposed neighbors or neighboring communities then have to deal with it when we kind of escaped it, right?
00:21:00.720 --> 00:21:10.799
And there is like this recognition that the demands are just too great, even on the local level in many cases, in order to justify the build out that we see happening.
00:21:11.039 --> 00:21:26.000
But then, like when we move to more of that global level, uh, you know, as you're talking about, and we think about kind of the broader project that these people are trying to bring into being, then even there you see that it is fundamentally unsustainable as well, right?
00:21:26.160 --> 00:21:46.000
So it's not just that the demands on local communities can be much too great, whether that is from uh the demands on the grid or the demands on the water system, or just the demands that come with locating this near communities that then have to kind of live with what it means to have a neighbor that is a massive data center and what can come along with that.
00:21:46.160 --> 00:21:59.759
But increasingly, as we see them build out more and more data centers, there's also the question of like, you know, the fossil fuels that are required in order to power this and how it is just kind of fundamentally disconnected from any notion of addressing climate change.
00:22:00.400 --> 00:22:09.200
Or having a planet that is livable for human beings into the future because of the amount of fossil fuels that they want to burn in order to do it.
00:22:09.440 --> 00:22:30.079
And so for me, then, you know, I guess the argument, if we think about the question of scale, is not like we need to eradicate all digital technology and completely get rid of it and go back to like a pre-digital time and go back to pencil and paper and all that kind of stuff, though I love my pencils and my paper and I don't want that to go away.
00:22:30.720 --> 00:23:01.119
But it's kind of like, what would it look like to have a computational system that exists at a scale that is sustainable and in line with kind of delivering technology that actually works for people rather than going along with kind of the ambitions and the fantasies of tech billionaires that has led us down this path of computation that exists at a scale that is completely out of sync with the planet, but also with, you know, uh a healthy society, I guess.
00:23:02.319 --> 00:23:02.720
Right.
00:23:03.359 --> 00:23:18.000
I've been thinking a lot about how how how the the kind of glimpses of that kind of relationship to computing to the extent it existed in sort of this initial era of personal computing was arguably an accident, you know.
00:23:18.160 --> 00:23:31.839
Um and you get you do a good job, I think, in the book of in your historically situating data centers and broader kind of computational technologies, acknowledging both the really like deep military foundations of this stuff, but also like business and industrial ones.
00:23:32.079 --> 00:23:40.480
And also you like the the Apple era sort of early pushing of this idea of compute, you know, the computer, the like bicycles for the mind thing, right?
00:23:40.880 --> 00:23:42.240
That was a marketing pitch, right?
00:23:42.480 --> 00:23:42.799
100%.
00:23:43.200 --> 00:23:46.880
And I was talking about this recently with uh Lane Nooney, who you should also have on your show.
00:23:46.960 --> 00:23:48.640
It's a I'd actually have you had them on the show.
00:23:48.799 --> 00:24:03.119
No, that I should recommend those those sort of liberatory seeds that sort of like Ted Nelson homebrew computer club type stuff, like that was not the mainstream experience of the personal computer.
00:24:03.359 --> 00:24:10.240
And and it's kind of that's always been the exception, not the standard by which this has occurred.
00:24:10.559 --> 00:24:20.160
Which isn't to say I think that it's impossible to make it a standard, so much as I think it's it's like it's the thing that you're kind of it's it's like we've never really done it.
00:24:20.559 --> 00:24:33.119
Um and part of the reason um is because to make any of this work at all, it's like it it there's this massive kind of financial like project of having to do it, right?
00:24:33.200 --> 00:24:45.519
The project of even making computation cheap is an incredibly kind of like you have to have a lot of other kind of revenue sources than like people self-actualizing with their technology or whatever.
00:24:45.839 --> 00:24:59.440
I wanted to use that kind of as a as a way to get into talking a little bit about the financialization that comes up a lot in the book, which I can I just pick up on what your point, what the point that you were making there before before we get into the financialization piece.
00:24:59.519 --> 00:25:01.039
Because I think it's really interesting, right?
00:25:01.200 --> 00:25:05.440
Because I think, you know, you're talking about kind of the early personal computer era.
00:25:06.000 --> 00:25:17.359
And I think in the book, you know, I bring up a bit, like obviously you're talking about Apple and things, but there was also a lot of opposition around kind of the rollout of computation in workplaces in that time.
00:25:17.519 --> 00:25:32.400
You know, uh obviously I've had Chris Carlson on the show who had the magazine Process World that, you know, was looking at uh workplace automation and, you know, how kind of the introduction of the computer was transforming things in like white-collar work, right?
00:25:32.480 --> 00:25:39.119
Uh, in in ways that I feel like that aspect of it has been rather taken out of the narrative of computation.
00:25:39.200 --> 00:25:42.559
Like we don't think about it or we don't talk about it nearly as much as we used to.
00:25:42.720 --> 00:26:03.039
But I even think a lot about like the early, or not the early 1990s, but just the 1990s in general, where you know, the internet is being privatized, but we also have this narrative, especially today, that, oh, if we could just go back to the early days of the web in the 90s, everything would be so great, everything would be so perfect, right?
00:26:03.200 --> 00:26:09.359
Um, and certainly that was a moment where there was less kind of commercialization of what was going on online.
00:26:09.599 --> 00:26:11.119
You know, there's no question about that.
00:26:11.200 --> 00:26:19.599
And and kind of the business models that take hold in the 2000s were not fully present by then, or like the infrastructures to enable them had not been fully built out.
00:26:19.839 --> 00:26:24.160
But I do think we forget that there was still platformization in that time.
00:26:24.319 --> 00:26:25.759
You know, that was still very much a thing.
00:26:25.839 --> 00:26:31.359
If you think of like AOL and things like that, these were services that already existed and that people were using.
00:26:31.519 --> 00:26:38.480
And for many of them, this was part of their experience of the web, even though there was this more kind of Wild West experience of it.
00:26:38.720 --> 00:26:45.119
But I feel like that also kind of negates the question of the materiality of the web in that moment as well, right?
00:26:45.279 --> 00:27:10.640
Where, and and you know, I think that this has been a continual part of our narratives around technology, whether it's looking at the Apple keynote and just seeing like the beautiful products on screen and never seeing what exists on the other side of that, is that even to enable what was happening in the 90s, you still had all this chip and hardware production that would have been largely occurring in in Asia by that time, but not exclusively, that was having impacts on communities, right?
00:27:10.880 --> 00:27:24.400
That very much goes against kind of the narrative, I guess, that we have of like the early internet in that moment and kind of the empowerment and stuff that came along with it, or or at least it complicates it to a certain degree that I feel like is often not taken into account.
00:27:24.480 --> 00:27:30.559
Uh, you know, especially when you hear about the more, you know, kind of libertarian or anarchist narratives of technology in the web.
00:27:30.640 --> 00:27:34.240
It feels like the production of the technology is so often left out of that.
00:27:34.319 --> 00:27:43.440
And the assumption is just that, you know, there are people somewhere who are going to have to provide this for us and they don't get kind of brought into the project and the vision of what that is.
00:27:43.599 --> 00:27:51.440
And then, of course, you know, those types of people would say, you know, if we're if we're hosting servers, we're just going to have our own servers and, you know, we're going to host them locally and all this kind of stuff.
00:27:51.599 --> 00:27:53.519
Those servers do still have to come from somewhere.
00:27:53.680 --> 00:27:58.000
But even in the 90s, you know, there is still, you know, all of this kind of hardware that does exist.
00:27:58.079 --> 00:28:00.720
There are still data centers that are being that are being set up.
00:28:00.880 --> 00:28:07.839
Like, you know, none of this stuff is so, so different from what we experience today in in aspects, in in ways it is.
00:28:07.920 --> 00:28:16.799
But I think that there's also a narrative that we miss out on where it's easy to look back with nostalgia and say, if we can just go back to that moment, things would be great.
00:28:16.960 --> 00:28:31.599
Because so often that kind of nostalgic picture leaves out a lot of what was actually happening and focuses on the parts of it that we want to uh, you know, kind of hold up or pay attention to or valorize to not deal with the complications of what existed then as well.
00:28:31.839 --> 00:28:55.680
And so that's why I think it's actually more, you know, if we're thinking about what something else looks like, it's actually more useful to actually think about what a project would be that would, you know, create something that is very different from what we have today and grapple with the kind of difficulties and nuances of the reality of digital computation or digital technology rather than assuming that it was that it was so much better at a certain time.
00:28:55.839 --> 00:28:56.079
Yeah.
00:28:56.559 --> 00:29:01.680
I think it's in like the big score, Mike Malone's uh history on Silicon Valley.
00:29:01.839 --> 00:29:12.720
He just very casually is like, yeah, like Apple computers were being built in with like piecework, you know, soldering being done by Filipino women in Redwood City houses.
00:29:12.799 --> 00:29:18.160
And it's it's not even the main point in the book, but it's this massive double take moment.
00:29:18.240 --> 00:29:28.880
Um that was the the like Santa Clara Center for Occupational Safety and Health uh did a lot of work in that period being like, hey, don't solder in your house.
00:29:29.200 --> 00:29:31.039
That's you're that's bad.
00:29:31.119 --> 00:29:31.920
That's unsafe.
00:29:32.000 --> 00:29:33.119
Uh please don't do that.
00:29:33.519 --> 00:29:34.240
Probably a bad idea.
00:29:34.400 --> 00:29:34.559
Yeah.
00:29:34.720 --> 00:29:39.680
I remember the first time I encountered that story was in Malcolm Harris's Palo Alto.
00:29:39.759 --> 00:29:42.240
And I was like, wait, this was a thing?
00:29:42.400 --> 00:29:42.880
What?
00:29:43.200 --> 00:29:44.079
Yeah, man.
00:29:44.720 --> 00:30:02.079
It's uh Yeah, I feel like the as someone who like Malcolm is from Silicon Valley, I I have a lot of uh it's I have a lot of frustrations at the way that that that is not treated as a geography and is instead sort of treated as an ideological condition.
00:30:02.319 --> 00:30:05.039
Because when you really look at the history, there's some really incredible stuff.
00:30:05.200 --> 00:30:08.640
Um speaking of history, I want to get back to talking about your book.
00:30:09.519 --> 00:30:21.839
Um yeah, I think you do a good job of situating some of the roles of financialization in both the historical kind of build-out of a lot of this stuff.
00:30:22.000 --> 00:30:28.079
I think um you do a really I really appreciated your discussion of ZERP as a part of the rise of cloud computing.
00:30:28.319 --> 00:30:36.000
I think that that in some ways the rise of something like an AWS is often treated as this sort of fate accomplished like, well, obviously it served this need, right?
00:30:36.079 --> 00:30:40.160
And it's and look, it is pretty good engineering.
00:30:40.240 --> 00:30:44.640
I'm not gonna like you, you don't, you can't kind of pretend that it isn't.
00:30:44.880 --> 00:30:54.559
And the the ability for there to be a market for AWS's services initially really had to do with there being kind of a glut of startups.
00:30:54.799 --> 00:31:00.480
What conditions even made it possible for there to be a bunch of companies that wanted to use AWS's services?
00:31:00.799 --> 00:31:01.519
Absolutely, right?
00:31:01.599 --> 00:31:05.599
You know, there's just so much money sloshing around and it's like it needs to go somewhere.
00:31:05.759 --> 00:31:11.599
Uh, and and so you have all these tech companies that are like lining up for the venture capital money, you know, they're lining up at the trough.
00:31:11.839 --> 00:31:19.920
Uh, you know, Mark Andreessen and all the boys are throwing out money at them, like the money is uh just unlimited, essentially.
00:31:20.079 --> 00:31:35.440
Um, and of course, you know, it's it's a very unique period, you know, kind of post-uh recession in particular, where the interest rates are just so low that it doesn't make sense to put your money into traditional investments because you're not going to get anything back on it.
00:31:35.519 --> 00:31:40.480
Um, and instead you should be throwing it at riskier bets and hoping that some of them pay off.
00:31:40.640 --> 00:32:01.839
Um, and because of the way that the tech industry works and because of the way that some of these companies can really just take off on a global scale and start reaping like massive profits because they can corner a market and then either through advertising or subscriptions or you know, whatever kind of mode that they choose, um, start making money hand over fist, that all of these investors are jumping on that.
00:32:01.920 --> 00:32:17.279
And of course, even if they don't make money, the investors can still make uh make a significant profit when the company goes public, because for some reason these companies can be worth like crazy amounts of money on the stock market, even if they don't make any actual money, you know?
00:32:17.440 --> 00:32:26.240
Um and of course, we're probably gonna see that happen very soon with like anthropic or open AI, unless the bottom falls out of the AI industry before they get to go public, right?
00:32:26.400 --> 00:32:28.240
It's it's just it makes no sense.
00:32:28.640 --> 00:32:28.880
Yeah.
00:32:28.960 --> 00:32:36.720
Well, and and there there were so many windows, there are so many windows to recoup even when something isn't successful, right?
00:32:36.799 --> 00:32:47.519
Like Sam Altman's first startup, Loop, was acquired by another company that his venture, the venture capital firm that had invested in it, had also invested in.
00:32:47.599 --> 00:32:55.279
And it it really, like when you kind of look at the details of it, like there was really it was really just an effort to kind of tidy up a mess, right?
00:32:55.359 --> 00:33:01.759
Like this company wasn't gonna actually succeed, and they needed to kind of recoup the costs, right?
00:33:01.839 --> 00:33:07.279
And like Sam walks away with a tidy profit, goes to Y Combinator, the rest is hagiography.
00:33:07.440 --> 00:33:08.559
Um totally.
00:33:08.799 --> 00:33:13.440
It also makes me think of like, what was it, Confinity and and the initial X, right?
00:33:13.599 --> 00:33:24.559
And they both also had, if I remember correctly, the same investor who was just eventually like, you're both doing online payments, you need to merge and like figure it out uh and and make some money that way, right?
00:33:24.960 --> 00:33:28.480
Of course, creating PayPal and Elon Musk being very mad that it wasn't called X.
00:33:28.640 --> 00:33:31.599
And, you know, that's a whole history we don't we don't need to get into.
00:33:31.759 --> 00:33:38.480
But yeah, I I think I think it's a really interesting moment because of course Amazon is is also like a pioneer of this model, right?
00:33:38.559 --> 00:33:41.680
Of uh don't go for the short-term profit.
00:33:41.759 --> 00:33:43.759
Don't try to turn a profit really quickly.
00:33:44.079 --> 00:33:51.359
Significantly expand your market share and what you're doing, and then you can make the money later and you know, the investors will get paid off then.
00:33:51.519 --> 00:33:56.319
But like let it, let's, let's go for domination first, and then the money will follow, right?
00:33:56.480 --> 00:34:00.799
And so Amazon moves into e-commerce, but also so many of these other areas.
00:34:00.960 --> 00:34:15.679
And part of that is, of course, cloud computing because it needs that for itself, but it also finds that there's a big opportunity to sell cloud computation, to basically centralize computation in one place, uh, you know, start to build out its own data centers.
00:34:15.760 --> 00:34:28.239
You know, initially it's kind of leasing data center capacity from other companies, but then the Amazons, Microsoft, Googles, they all move into building their own data centers over time, though they still do lease a lot of capacity even to this day.
00:34:28.480 --> 00:34:37.840
And the the you know, wholesale is like, listen, you startups, you have something else that you're trying to do, you don't want to have to deal with spinning up servers and whatnot.
00:34:38.000 --> 00:34:42.079
Buy the servers from us, buy the capacity from us, uh, then you don't need to worry about it.
00:34:42.159 --> 00:34:56.880
And you can just run your business and you can have it take off and you know, you can compete because if you are wasting time basically spinning up servers, then that's time that your competitors are improving their products and, you know, kind of beating you in the same market, right?
00:34:57.119 --> 00:35:10.320
And then I feel like on the kind of more institutional side, you know, the big companies that are already there, there is a is a slightly different cell where it's like, this is not your core competency, the, you know, taking care of this computation, taking care of these servers.
00:35:10.480 --> 00:35:14.079
So why should you be bothered with it when you can just pay us to do it for you?
00:35:14.239 --> 00:35:26.480
Um, and plus, you know, this also offloads some responsibility and some risk that you are taking on yourself because we can do more kind of cybersecurity stuff and whatnot that you can't do by having this centralized.
00:35:26.559 --> 00:35:43.760
And of course, you know, you major companies have already been under this wave of outsourcing for, you know, for a while now, uh, as you have been, you know, pushing call centers offshore and you know, trying to offload parts of your business that are not core to subcontractors and things like that.
00:35:43.920 --> 00:35:51.440
You know, even going back to Apple, how they uh, you know, were really a pioneer of moving their manufacturing over to Asia for their computers and things like that.
00:35:51.679 --> 00:35:53.039
Companies are already doing this.
00:35:53.199 --> 00:35:58.719
And an Amazon or later Microsoft and Google can come in and say, hey, we are doing this for computation.
00:35:58.880 --> 00:36:00.800
Just give this to us, pay us to do it.
00:36:00.960 --> 00:36:03.199
Now you don't need to be worried about it anymore.
00:36:03.280 --> 00:36:05.280
Uh, and there are a load of other benefits that come of that.
00:36:05.440 --> 00:36:08.159
And of course, the pitch initially is that you're going to save money.
00:36:08.239 --> 00:36:11.119
Uh, I think that that pitch becomes more elusive over time.
00:36:11.360 --> 00:36:13.360
But that's not because computation is so expensive.
00:36:13.760 --> 00:36:20.800
I would argue that is in part because you know, you get on the cloud, but then there are all these other services that the cloud providers start to sell you.
00:36:20.880 --> 00:36:23.840
And that is how your bill really starts to creep up, right?
00:36:24.000 --> 00:36:29.440
Uh, because you need all these AI tools to understand your data and what have you, right?
00:36:29.599 --> 00:36:31.679
So yeah, I do think it's an interesting moment.
00:36:31.920 --> 00:36:37.280
And the thing that really stands out to me, and part of the reason why I feel that that's an interesting thing to bring into the book, right?
00:36:37.440 --> 00:36:54.159
Talking about the history of the cloud and the and the history of computation is because I would argue, and and I think that this is an accurate argument, that there would be no generative AI today had that kind of centralization of computation not started two decades ago.
00:36:54.480 --> 00:37:08.000
Because without that amount of computation being held by a small number of companies, it would have been harder to train these models and make these uh AI products available in the way that the companies have done now.
00:37:08.480 --> 00:37:09.280
Um yeah.
00:37:09.920 --> 00:37:17.679
But you also wouldn't have had the same network or kind of world of companies, arguably.
00:37:17.920 --> 00:37:21.039
Like an Uber doesn't happen without an AWS, right?
00:37:21.199 --> 00:37:29.280
Like you like AW, as you point out, AWS kind of does this like long game, like lose a lot of money and then become the incumbent, and Uber has the same playbook.
00:37:29.360 --> 00:37:35.119
But like one of the reasons they were able to do that is because they could scale up quickly on platforms like AWS.
00:37:35.440 --> 00:37:38.639
Same with with the other kind of rideshare companies, same with Airbnb.
00:37:39.039 --> 00:37:40.480
Same with Palantir, man.
00:37:40.559 --> 00:38:01.280
Like they're there, yeah, all of these, like the the ability to make that leap is very much predicated on the availability of cheap compute that's someone else's problem while you know, simultaneously you know, lots of companies are getting money to go out there and fail fast from venture capital, right?
00:38:01.360 --> 00:38:10.000
Like it's it's like a lot of other incentives aligned in this very nice way that that made AWS make a lot more sense.
00:38:10.159 --> 00:38:22.239
Like when it started in 2006, I mean, I have this weirdly distinct memory of hearing like an NPR story that's like Amazon's gonna sell computer server something now, you know, and thinking like, oh, that's that's odd.
00:38:22.320 --> 00:38:23.760
I'm 20, I don't know.
00:38:24.000 --> 00:38:30.639
Um but yeah, by by 2008, nine, 10, like it makes a ton of sense, right?
00:38:31.119 --> 00:38:46.400
Another, another kind of financializing and or role of kind of marketizing that that you talk about that I wanted to make sure we talked a little bit about because it bothered me so much was was the use, oh, was the sort of like use of it in like greenwashing, right?
00:38:46.559 --> 00:38:57.280
The like problem solving environmental impacts of of industry through inventing a market through which to kind of buy and sell like culpability.
00:38:57.440 --> 00:39:01.599
Um please share with your readers what is a water replenishment market?
00:39:03.199 --> 00:39:06.079
Uh I wish we didn't even have to know, you know.
00:39:06.800 --> 00:39:08.000
I know, I know.
00:39:08.320 --> 00:39:10.079
It's it's so frustrating, right?
00:39:10.239 --> 00:39:28.559
Because and you know, people who have paid attention to uh, you know, climate fights and things like that for a while will not be unfamiliar with these things and how, you know, when we face a problem, there are this group of people that step in and say, you know what is actually the most efficient way to address this problem?
00:39:28.800 --> 00:39:39.440
It's not to just change how we act, but it is to introduce a new way for us to buy and sell financial products in order to address the problem.
00:39:39.599 --> 00:39:41.440
And then the market will respond, right?
00:39:41.519 --> 00:39:43.039
Then the market will do it.
00:39:43.199 --> 00:39:56.320
Uh that has worked out so well because as we all know, uh emissions have dropped precipitously since uh carbon markets have come into place, which is me being very sarcastic because that is not that is not how things are playing out.
00:39:56.559 --> 00:40:08.639
Um and it it's like even just as a quick side note, like in Canada we're in the process of totally dismantling like the climate legislation, the even shitty climate legislation that we had built up over the past decade.
00:40:08.800 --> 00:40:13.280
Um, and the argument is that those mechanisms were too harsh.
00:40:13.360 --> 00:40:19.599
And what we need to do is focus more on pricing uh because then the you know the companies will act to reduce their emissions that way.
00:40:19.760 --> 00:40:21.599
And it's like, dude, we have played this game.
00:40:21.760 --> 00:40:22.559
Like we have done this.
00:40:22.639 --> 00:40:24.079
This is not, this is not working.
00:40:24.239 --> 00:40:33.440
But but anyway, to get to the point, you know, if you look at these data centers, people will be familiar now about the fact that in some of the in some cases they're using a ton of water.
00:40:33.599 --> 00:40:36.559
Um, in other cases, they're using a load of fossil fuels.
00:40:36.719 --> 00:40:40.480
And increasingly the data centers are using more and more fossil fuels.
00:40:40.639 --> 00:40:45.039
But of course, they don't want to appear as though they are bad guys, right?
00:40:45.119 --> 00:40:47.840
Or at least they haven't wanted to up until recently.
00:40:48.000 --> 00:40:54.000
I feel like they're becoming, I feel like they care less and less about being held to account for at least the amount of fossil fuels that they use.
00:40:54.079 --> 00:40:57.519
Uh certainly they're trying to muddy the water on how much water that they use.
00:40:57.679 --> 00:41:00.400
But this is where these market mechanisms come in, right?
00:41:00.480 --> 00:41:07.679
And so a data center can use a load of fossil fuels, you know, a load of gas power or coal power or something like that.
00:41:08.000 --> 00:41:14.320
Or they can use just a ton of water in order to cool down the chips within the data center itself.
00:41:14.559 --> 00:41:23.920
And then if you look at the sustainability reports that the companies put out, it will look like they haven't used nearly as much fossil fuels or they haven't used nearly as much water.
00:41:24.159 --> 00:41:36.400
Because even though they're using the water and they're using the fossil fuels, they're then going into these markets and buying credits to be able to claim that actually they replenished a bunch of water somewhere else.
00:41:36.559 --> 00:41:47.840
And so that negates the water that they actually used in one place, or they bought a load of carbon credits that like protect a forest or help maintain a renewable energy project somewhere.
00:41:48.079 --> 00:41:56.320
And so that means that even though they burned all those fossil fuels to power their data center, that in practice, you know, they offset those fossil fuels.
00:41:56.400 --> 00:42:01.039
And so they didn't actually do it, which is just like there's so many problems with that, right?
00:42:01.199 --> 00:42:05.360
Because on the one hand, the water is still used, the fossil fuels are still burned.
00:42:05.519 --> 00:42:13.280
And then there's the question of like, you know, in many cases, especially when we look at carbon markets, okay, you bought these offsets or you bought these credits or what have you.
00:42:13.519 --> 00:42:19.760
Would those actions not have been taken anyway, regardless of whether the offset was purchased or something like that?
00:42:20.000 --> 00:42:34.000
Or is the degree of benefit from that, from purchasing that credit actually going to be maintained over time and actually be, you know, actually reflected in the way that you report these things in your sustainability reports or whatnot?
00:42:34.159 --> 00:42:40.880
Because what we see time again in carbon markets is that they like the claim that they are significantly offsetting and reducing emissions.
00:42:41.039 --> 00:42:55.920
Uh, with projects, you know, for example, if you look at the tree-based products, you know, the ones where they're supposedly protecting forests, that actually the benefit takes a long time to be realized because the tree doesn't just immediately pull the amount of carbon that you burned out of the air.
00:42:56.079 --> 00:42:57.440
It takes time for that to happen.
00:42:57.599 --> 00:43:09.440
And in many cases, the trees actually get either burned down by wildfires or get cut down within the time that it would have been needed to actually see that kind of benefit to the climate.
00:43:09.599 --> 00:43:12.000
So, yeah, anyway, I'm going on a bit too long about this.
00:43:12.159 --> 00:43:18.320
But it's all just like a complicated way of them trying to appear like they're not being nearly as bad as they actually are.
00:43:18.800 --> 00:43:42.159
And it's well, and I think it it comes back to that scale thing we were talking about earlier, because saying you've you've addressed, you know, all of the water use you did in Arizona because you you did something that was good for like a watershed in you know, Delaware, people still live in Arizona and still need to drink water, right?
00:43:42.239 --> 00:44:00.719
Like there is a and it feels also weirdly of a piece of that sort of like utilitarian long-termist thinking where it's like, well, if if only a billion people die, it's fine because like we're going to, you know, have tens of billions of people living in space or Something, right?
00:44:00.800 --> 00:44:11.519
Like that it's it's really a misunderstanding of like what timescale you're trying to live on and how you interface with and and relate to other human beings.
00:44:11.760 --> 00:44:12.400
Absolutely.
00:44:12.559 --> 00:44:13.119
Absolutely.
00:44:13.280 --> 00:44:16.960
And it it's just really frustrating to see the way that they get away with it, right?
00:44:17.039 --> 00:44:40.639
And you know, you're talking about water, but like on the carbon side of things, you know, I feel like a lot of people don't realize that they are spending a lot of money and expanding a lot of lobbying power to actually shape the way that carbon accounting rules are being written so that they can do these deceptive practices and not be held to account for the actual amount of emissions that they are emitting into the atmosphere, right?
00:44:40.800 --> 00:44:50.400
Especially as they have really thrown their climate targets uh to the side in order to pursue generative AI and the build-out of these massive data centers.
00:44:50.559 --> 00:44:54.639
I just think it's like a fundamental problem that they need to be held to account for.
00:44:54.800 --> 00:45:02.800
And they're getting away with it because, you know, again, we live under capitalism and this is the way that things are supposed to work under capitalism.
00:45:02.960 --> 00:45:11.440
You know, we're not allowed to really question what the capitalists say is the way that we're supposed to address these problems, even when they very clearly are not working.
00:45:11.599 --> 00:45:17.840
Um, and like, you know, we've passed 1.5 degrees now and emissions in the United States are are going up again.
00:45:18.000 --> 00:45:24.800
And plenty of countries are now saying that their climate targets are in jeopardy because there are so many uh gas-powered data centers being built out.
00:45:24.960 --> 00:45:38.960
Like it's just like I really just don't understand how we have allowed things to get to this point, uh, all because, you know, AI is generating a lot of money and investment right now, and all of our governments like need to get their piece of it or something.
00:45:39.440 --> 00:45:43.360
To me, it almost reads as though it has to generate a lot of money, right?
00:45:43.519 --> 00:45:53.920
Like you write a little bit about sort of the circular financing stuff and the the sort of so much has been put into making it succeed that it's it's not even that this is how it's supposed to go.
00:45:54.000 --> 00:45:59.920
It's like if it doesn't go this way, the capitalists don't know don't have another move, right?
00:46:00.159 --> 00:46:03.119
Or they do have a move, it's the state failing them out.
00:46:03.360 --> 00:46:19.599
Um and it is, I don't know, it's it's also an interesting moment because also you write a little bit about sort of these these efforts in the EU and other places towards trying to kind of like create a more like sovereign stack and like realizing like maybe, maybe depending on the superpower countries was a mistake.
00:46:19.840 --> 00:46:22.719
But that comes with its own sort of like messiness.
00:46:23.039 --> 00:46:31.760
It's not like a tidy sort of like, well, we'll we'll solve the problems of undemocratic uh capitalist computer by doing democratic state computer, right?
00:46:32.159 --> 00:46:40.480
Or or also just our own version of undemocratic, you know, like problematic computer that exists everywhere else.
00:46:40.639 --> 00:47:03.039
Like one of the really depressing things has just been to see how these conversations around like digital sovereignty and whatnot have been very effectively co-opted by by industry, right, in in many different parts of the world and certainly in the EU, where it has become, we don't necessarily want to use the American technology and certainly not the Chinese technology.
00:47:03.280 --> 00:47:12.480
We're still gonna have uh all the American companies still built build out a load of data centers for some reason that doesn't really fit with the narrative, but we're not gonna question that.
00:47:12.639 --> 00:47:22.159
But at the same time, you know, us becoming more sovereign and independent just means that we need to replicate everything that the American companies are doing on our own.
00:47:22.320 --> 00:47:24.960
So we need to build out a load of massive data centers.
00:47:25.039 --> 00:47:30.320
We need our own generative AI models, we need our own palantir so that we're not using their palantir.
00:47:30.480 --> 00:47:31.599
Is this really the move?
00:47:31.760 --> 00:47:43.280
Like, why can't we think on a much more fundamental level about the technologies we use and rely on and whether they're actually benefiting us and start to try to chart a different way?
00:47:43.519 --> 00:47:51.440
And I would argue that the reason that that doesn't happen is because the money is all tied up in this way of doing technology and digital technology.
00:47:51.599 --> 00:48:21.840
And until that is like fundamentally broken or, you know, that our governments are given permission once again to not just pursue the thing that means more money all the time, but can actually take a bit of a longer view and recognize sometimes, you know, you make investments in in something that don't provide an immediate return, but provide benefits in other ways that, you know, provide returns down the road in in other means, that, you know, kind of rethinking technology is just not something that is allowed to be on the agenda until that happens or something.
00:48:22.400 --> 00:48:43.119
I remember when when you at first kind of like, you know, did your uh some personal news share that you were doing another book, uh, the the working title of it was Too Much Compute, which at the time I definitely read as too much computer, which also like that's a great like 100% too much computer.
00:48:43.360 --> 00:48:59.119
It can be, or I've found that it can be really hard to get people to like actually get into a degrowth-y conversation or really think about like a moral economy of compute that like actually ask the question like, how much do you actually need?
00:48:59.840 --> 00:49:03.119
And what do we actually need it to do?
00:49:03.440 --> 00:49:08.960
And I think some of what makes people itchy about it is because there's a sense of like constraint.
00:49:09.119 --> 00:49:10.320
I don't like constraint, right?
00:49:10.400 --> 00:49:12.880
Nobody wants to be told that they can do less stuff.
00:49:13.039 --> 00:49:26.800
Um, but also like to the extent people like that that that sort of libertarian mythic, like mind, bicycle, computer dream exists, there's an assumption like, well, that means that I can do anything.
00:49:26.880 --> 00:49:30.239
So like, why would you say that I can do fewer things, right?
00:49:30.480 --> 00:49:48.000
I don't know if there's a real like answer to this question other than like it is a hard problem, but I think um in trying to make the case for like a more democratic approach to how computation and like computational resources are allocated on a societal level.
00:49:48.079 --> 00:49:57.519
Like, how do you think through, I guess, the case, the case for for that beyond sort of like more like beyond maybe also like a sort of shaming thing?
00:49:57.599 --> 00:50:02.320
Because I feel like that also just like has demonstrably not been particularly effective with most people.
00:50:02.719 --> 00:50:03.039
Totally.
00:50:03.119 --> 00:50:13.039
I I think we definitely need to think of like a carrot rather than a stick approach, you know, to trying to get people to come along with what an alternative is going to look like or what it could be.
00:50:13.199 --> 00:50:25.599
And I do think that, you know, it's not that there isn't an answer to it, but I think that the answer to why it can be difficult to get people to reconsider their technology use is a very complex and nuanced one, right?
00:50:25.760 --> 00:50:30.639
And I think it it depends in many cases on different motivations that that people have.
00:50:30.800 --> 00:50:39.280
You know, I think part of it is that we have been sold this idea for the past few decades that this is more convenient, that this is improving our lives.
00:50:39.440 --> 00:51:09.360
And even when it starts to turn against doing that, you know, even when, say, the Facebook platform turns from uh, you know, a platform where you're seeing what your friends are doing and you're sharing stuff with them to one where you're being fed a lot of ads and you're being fed a lot of posts just to keep you engaged, that it is hard to pull back because on the one hand, you have a social network that is still kind of tied up in this platform that can be hard to get rid of, especially if your own social connections have frayed.
00:51:09.599 --> 00:51:14.320
But also that there is kind of an appealing part of using the platform.
00:51:14.400 --> 00:51:17.599
Like there is kind of an, I would argue, an addictive part of it as well, right?
00:51:17.840 --> 00:51:21.360
Where there is something to seeing all of this engaging content.
00:51:21.440 --> 00:51:23.039
There's a reason that people watch it.
00:51:23.280 --> 00:51:30.079
You know, it's not just because they're like dumb or something like that, or, you know, they're they're being forced to buy a platform.
00:51:30.239 --> 00:51:41.440
It's because there it is uh kind of responding to things, I would argue, in our minds that that keeps us engaged, that keeps us coming back to it, that can make it difficult to fully get rid of and and drop, right?
00:51:41.599 --> 00:51:43.280
I wrote about this earlier this year.
00:51:43.440 --> 00:51:49.840
Like I used a ride healing platform for the first time, which, you know, I I had never done that before.
00:51:49.920 --> 00:51:52.239
Uh it wasn't Uber, it was Bolt actually.
00:51:52.480 --> 00:52:03.199
And I was kind of struck by just how quickly I became fearful of taxis, you know, against my own will, you know, because now I couldn't see exactly where it was at all times.
00:52:03.440 --> 00:52:21.039
And I could see how there was how the user interface worked in order to convince me that this was the way I had to do things and that, you know, I shouldn't be doing it in the way that I have been used to doing it and didn't have a problem, you know, kind of getting a taxi or getting a ride for decades, you know, up to this point.
00:52:21.199 --> 00:52:25.519
You know, there are benefits that people got from using technology in this way.
00:52:25.760 --> 00:52:34.719
Even as those benefits have eroded, it's hard to kind of pull back and change what you're doing, that they have been designed in such a way to keep people part of it.
00:52:34.880 --> 00:52:42.960
And there is also a network effect where if everyone else is doing it, why are you going to kind of drop out that that's difficult to do, but also to explain.
00:52:43.119 --> 00:52:47.280
But then I also just think that people are, in general, really busy.
00:52:47.440 --> 00:52:49.360
They have a lot of things on their plate.
00:52:49.599 --> 00:53:03.440
And you know, expecting them to do a lot of work around how they use technology is, I think, a big expectation for a lot of people when they have jobs to work, they have bills to pay, they have kids to take care of.
00:53:03.679 --> 00:53:08.159
You're asking them to do like a difficult thing and dedicate their time to this when they don't have a lot of time.
00:53:08.400 --> 00:53:15.760
And so I think that's part of the reason why it needs to be something that people can actually be brought along with, right?
00:53:16.079 --> 00:53:19.840
That there is like a kind of positive vision of what this can be.
00:53:20.079 --> 00:53:23.519
You know, I do think that we do see elements of that already today.
00:53:23.679 --> 00:53:27.599
And I do see that tech companies, I think, are even recognizing this.
00:53:27.679 --> 00:53:40.320
You know, if you look at like even a dating app like Tinder, they are promoting more in-person things because people are getting tired of just being online and just swiping on the app and they want to be, you know, in places with one another.
00:53:40.559 --> 00:53:47.039
And so, yeah, I think that there's a response there, but it's it's again still like a captured response to what is occurring.
00:53:47.119 --> 00:53:50.320
And so I don't have like the answer to how we get people off.
00:53:50.480 --> 00:53:54.559
I do think that that is like an ongoing project that's going to take a lot of time.
00:53:54.800 --> 00:54:02.639
But I also think that there's hope in this moment, especially when we do see that people are really pissed off about AI.
00:54:02.800 --> 00:54:09.920
I think people are also quite pissed off about social media and, you know, just kind of like how digital technology works more broadly.
00:54:10.079 --> 00:54:22.159
You know, I think that these movements against data centers and the fights against data centers have been really hopeful actually in a moment where there's not a lot to be hopeful about on the political level in many cases.
00:54:22.320 --> 00:54:25.760
And that's not just in the United States, but in many other countries as well.
00:54:25.920 --> 00:54:29.039
And that there is the inkling of something there.
00:54:29.280 --> 00:54:33.840
The question is, how does that anger get uh get used, right?
00:54:33.920 --> 00:54:47.760
And can it be kind of redirected into a positive vision of technology that works differently, that is more around a social project to improve people's lives rather than just how do we keep people looking at screens?
00:54:47.840 --> 00:54:49.440
How do we keep them looking at ads?
00:54:49.519 --> 00:54:55.280
Uh, you know, how do we sell them on the system that clearly isn't working for them, but that is making these companies a lot of money.
00:54:55.440 --> 00:55:01.199
And, you know, if I can say it, I feel like that is kind of what I want my next book to look into.
00:55:01.360 --> 00:55:08.079
And so when you when you talked about like too much computer, I was like, maybe that's what I pitched my next book as, and then it gets like retitled as something else.
00:55:08.320 --> 00:55:09.519
But yeah, I I don't know.
00:55:09.599 --> 00:55:10.639
I feel like we're in a moment.
00:55:10.719 --> 00:55:24.159
And I feel like, again, in the way that I felt a couple of years ago that data centers were going to take off, I do feel like there is a move toward pushing back on this digital technological infrastructure that's been created for us and that's not working for us.
00:55:24.320 --> 00:55:27.519
And this just the question is what does that look like?
00:55:27.760 --> 00:55:41.199
How do you bring people along and get people involved in that and make it again a more democratic thing that that people can be more engaged with and can actually be part of co-creating rather than just being kind of sold something else in the way that they were sold this system as well.
00:55:41.360 --> 00:55:42.800
And so yeah, I think I don't know.
00:55:42.880 --> 00:55:46.239
I think that in that sense we're we're in kind of an interesting time.
00:55:46.960 --> 00:56:02.400
And I and I yeah, I definitely agree with you that the rapid push to build out more data centers has really forced this conversation in uh kind wish it didn't have to have happened, but maybe it's like it's it's helpful that it's happening this way.
00:56:02.480 --> 00:56:21.679
I do, I I mean, and you talk a little bit about this in the book that like crypto in like the 2018-ish window did also start to really push this forward because it's a lot easier to persuade people that like crypto is useless because it is just like yo, you're turning gasoline into computer money.
00:56:21.840 --> 00:56:23.760
Like this this helps you.
00:56:24.079 --> 00:56:25.920
Hey, don't forget the ape pictures, you know.
00:56:27.039 --> 00:56:28.000
God, oh I know.
00:56:28.159 --> 00:56:29.599
Well, I mean, the apes are gone.
00:56:29.679 --> 00:56:32.719
So I mean rest in rest in peace the apes.
00:56:33.199 --> 00:57:01.119
But uh, but I think that AI, generative AI, and large language models, because of the novelty, the the market desperation to make it the next big thing, and and certainly the like, yeah, the the like wow factor of it um has made it a lot harder to say, like, oh, this compute is also maybe too much compute and doesn't really need to be happening the way that it's happening.
00:57:01.280 --> 00:57:11.599
Um yeah, it's it's I I have been joking to people for the last year, you know, like a decade ago, I was writing explainers that are like, this is what a data center is, right?
00:57:11.679 --> 00:57:16.559
And like this is where they are, this is like you should care about them.
00:57:16.639 --> 00:57:21.199
And now, you know, 10 years on, it's like everyone knows, everyone hates them.
00:57:21.840 --> 00:57:24.239
And that that's kind of rules.
00:57:24.320 --> 00:57:24.719
I don't know.
00:57:24.880 --> 00:57:26.559
I'm like actually pretty into that.
00:57:27.039 --> 00:57:27.360
Totally.
00:57:27.519 --> 00:57:29.679
I wonder if there's been traffic back to your old pieces.
00:57:29.760 --> 00:57:31.440
People are like, what are data centers?
00:57:31.519 --> 00:57:35.280
And they're like, ah, this person, Ingram Burrington, uh, they had it figured out.
00:57:35.920 --> 00:57:39.039
Well, I mean, most of it's paywall now, so I don't think that they're getting it together.
00:57:39.280 --> 00:57:40.159
The Atlantic, yeah.
00:57:43.360 --> 00:57:54.320
But yeah, I I'm I'm really excited to see how uh the book further kind of like pushes a lot of this like public discussion further.
00:57:54.480 --> 00:58:15.599
And I think um it it does a really good job, especially kind of as you're wrapping it up and framing this as like being not just about the data centers, but obviously the data centers are kind of where where some of the more abstract aspects of of these political debates become really, really tangible.
00:58:15.840 --> 00:58:20.000
And um yeah, uh congratulations again.
00:58:20.400 --> 00:58:20.800
Thank you.
00:58:20.960 --> 00:58:21.679
I appreciate it.
00:58:21.840 --> 00:58:26.960
And you know, obviously thank you so much for like coming on the podcast to chat with me about it.
00:58:27.119 --> 00:58:34.000
Because when I was thinking about people to talk to me, like you were the person who so obviously like I wanted to talk to about the book.
00:58:34.159 --> 00:58:39.840
Um, because again, I think that we're we're very like sympatico on how we think about these issues.
00:58:40.000 --> 00:58:43.840
Um and you know, certainly I'm interested in your perspective on this, on this as well.
00:58:44.000 --> 00:58:51.760
And of course, you know, we should say that your book uh, you know, was one of the books that I look to in in understanding these infrastructures has been reissued.
00:58:51.840 --> 00:58:55.840
And I know there's not many copies left, but people should try and and go and grab one.
00:58:56.320 --> 00:59:08.320
Yeah, I I'm debate, I think I may have to do a second printing because yeah, it's turns turns out people still want to know how the internet works, even though they already know what a data center is.
00:59:08.400 --> 00:59:09.679
So that's that's nice.
00:59:10.000 --> 00:59:10.639
How dare they?
00:59:10.719 --> 00:59:11.280
How dare they?
00:59:11.360 --> 00:59:11.679
Yeah.
00:59:12.159 --> 00:59:14.880
But we can put the link to that in the show notes as well so people can go.
00:59:15.360 --> 00:59:15.920
Yeah, appreciate it.
00:59:16.559 --> 00:59:19.920
Ingrid, just want to thank you again for coming on Tech Won's Save Us.
00:59:20.000 --> 00:59:23.440
And this certainly won't be the only time that Tech Won's Save Us listeners hear from you.
00:59:23.519 --> 00:59:25.280
We'll have to have you back on the show next year.
00:59:25.440 --> 00:59:26.000
Thanks so much.
00:59:26.079 --> 00:59:27.039
I really appreciate it.
00:59:27.280 --> 00:59:28.159
Well, yeah, thanks.
00:59:28.239 --> 00:59:30.079
This was this was a real treat.
00:59:30.800 --> 00:59:33.760
Thanks for joining us on this special episode of Tech Won's Save Us.
00:59:33.840 --> 00:59:40.639
Ingrid Burrington, who interviewed me in this episode, is the author of Networks of New York and host of the Rip Corp Podcast.
00:59:40.800 --> 00:59:45.440
Tech Won't Save Us is made in partnership with The Nation magazine and is hosted by me, Paris Marks.
00:59:45.599 --> 00:59:47.039
Production is by Kyla Hewson.
00:59:47.199 --> 00:59:52.079
Tech Won's Save Us relies on the support of listeners like you to keep providing critical perspectives on the tech industry.
00:59:52.239 --> 00:59:57.840
You can join hundreds of other supporters by going to patreon.com slash tech won't save us and making a pledge of your own.
00:59:58.000 --> 01:00:03.199
You can also pick up a copy of Hyperscale by going to hyperscalebook.com to get more information.
01:00:03.360 --> 01:00:05.840
Thanks for listening and make sure to come back next week.