WEBVTT 00:00.171 --> 00:06.020 [SPEAKER_00]: Amode, Altman, all of the AI CEOs, they do want to create the impression that a great disruption is coming. 00:06.060 --> 00:09.766 [SPEAKER_00]: That makes it easier for them to sell more automation software. 00:10.607 --> 00:15.155 [SPEAKER_00]: It's a product in the array of products that they're selling is enterprise AI automation. 00:32.130 --> 00:35.092 [SPEAKER_01]: Hello and welcome to TechOneSafe Usmanated Partnership with the Nation Magazine. 00:35.112 --> 00:37.754 [SPEAKER_01]: I'm your host Paris Marks and this week my guest is Brian Merchant. 00:38.154 --> 00:48.381 [SPEAKER_01]: Brian is the author of Blood in the Machine and also writes a newsletter on the same name where he's recently been publishing a series of essays articles under the name AI killed my job. 00:48.981 --> 01:01.390 [SPEAKER_01]: Now, you might also remember Brian from a podcast we were doing together earlier this year called System Crash, or just from hearing him on the show before we were actually doing that show in the past number of years. 01:01.930 --> 01:11.817 [SPEAKER_01]: But since Brian has been talking to so many workers about the impacts that generative AI and the rollout of those tools in various companies in many different sectors, 01:12.217 --> 01:17.583 [SPEAKER_01]: are having on their professions, on their work, on basically the sectors that they work in. 01:18.084 --> 01:33.040 [SPEAKER_01]: I figured it was a good moment to have him back on the show, so we can discuss, you know, not just the effects that gendered AI is having on work, but also this broader narrative of AI that we've been seeing over the past number of years, these questions that many people are posing now about. 01:33.560 --> 01:40.022 [SPEAKER_01]: the state of the AI bubble and the AI hype that we have been experiencing and whether we're finally starting to see it deflate. 01:40.282 --> 01:45.243 [SPEAKER_01]: You know, I think it's still very much an open question and we'll have to see where that goes. 01:45.663 --> 01:56.605 [SPEAKER_01]: But also to try to understand, you know, what the longer term consequences of generative AI might be even if this bubble does eventually burst that doesn't mean the technology is going to disappear. 01:56.925 --> 01:59.706 [SPEAKER_01]: There's still kind of remnants of the metaverse out there. 02:00.046 --> 02:03.187 [SPEAKER_01]: Of course cryptocurrency has turned into a political force 02:03.707 --> 02:06.549 [SPEAKER_01]: So what might happen with generative AI in the future? 02:06.829 --> 02:10.452 [SPEAKER_01]: So I think there are a lot of interesting discussions that we have in this show. 02:10.532 --> 02:18.418 [SPEAKER_01]: And of course, you know, you'll hear that we get on pretty well when we're talking together because we've known one another for years and we also host the show together for quite some time. 02:18.878 --> 02:31.467 [SPEAKER_01]: So I have a little doubt that you're going to enjoy this episode with me and Brian, where we dig into these issues that we've both been paying so much attention to writing so much about over the past number of years, but also to look specifically at the work that he has been doing recently. 02:31.807 --> 02:37.309 [SPEAKER_01]: So if you do enjoy this episode, make sure to share the show on social media or with any friends or colleagues who you think would learn from it. 02:37.669 --> 02:40.630 [SPEAKER_01]: If you want to support the work that goes into making tech won't save us every single week. 02:40.650 --> 02:43.191 [SPEAKER_01]: So I can keep having these critical in-depth conversations. 02:43.211 --> 02:48.273 [SPEAKER_01]: They help you better understand the technologies that pervade our lives and the tech industry that pushes them on us. 02:48.653 --> 02:58.657 [SPEAKER_01]: You can join supporters like Christopher from Stockholm, Lavenia, in Geneva, Switzerland and Steve from Indy by going to patreon.com slash tech won't save us where you can support the show as well. 02:59.037 --> 03:00.798 [SPEAKER_01]: Thanks so much and enjoy this week's conversation. 03:01.899 --> 03:03.539 [SPEAKER_01]: Brian, welcome back to Tech Won't Save Us. 03:03.940 --> 03:06.920 [SPEAKER_00]: I have to say, it's just, it's such an honor to be here. 03:06.940 --> 03:07.861 [SPEAKER_00]: I'm just such a fan. 03:07.881 --> 03:12.202 [SPEAKER_00]: I'm listening to the podcast for so long. 03:12.362 --> 03:21.484 [SPEAKER_00]: I just hope being one day that I would, you know, get this invite to join the show and spend some time with you, my favorite podcast host, Paris Marks. 03:21.784 --> 03:29.266 [SPEAKER_01]: I appreciate all this high praise coming from you, a person who I don't know very well, coming onto the show like this. 03:29.866 --> 03:37.210 [SPEAKER_00]: Right, practically strangers who didn't spend the most of a year talking weekly for hours at a time. 03:37.511 --> 03:39.872 [SPEAKER_01]: Yeah, I was just talking to myself as we don't know. 03:39.912 --> 03:40.052 [SPEAKER_01]: Right. 03:40.532 --> 03:42.273 [SPEAKER_01]: This was an AI filter on my voice. 03:42.634 --> 03:43.174 [SPEAKER_00]: That's right. 03:43.654 --> 03:46.016 [SPEAKER_00]: It's has never been proven or disproven. 03:46.676 --> 03:51.159 [SPEAKER_00]: So whoever it was in the tech one say was community that put that theory forward. 03:51.319 --> 03:54.601 [SPEAKER_00]: I just say, you haven't been proven wrong definitively. 03:55.001 --> 03:55.741 [SPEAKER_00]: I might not exist. 03:58.924 --> 04:02.908 [SPEAKER_00]: Oh, it's good to talk again, Paris, it's good to... Don't you miss this? 04:03.389 --> 04:04.911 [SPEAKER_01]: Oh my god, absolutely. 04:05.351 --> 04:07.634 [SPEAKER_01]: You know what, I do miss like, for what? 04:08.455 --> 04:13.520 [SPEAKER_01]: Nine months, eight months or something, we were like chatting every single week and every now and then I'm like, man. 04:14.221 --> 04:34.957 [SPEAKER_00]: haven't heard from Brian in a little while wonder how he's doing I know it was a nice way to it was a nice way to process all that all the shit that was going on unfortunately was also a lot of work for these things it was my first time podcasting so now I know it goes into the sausage a little bit 04:35.157 --> 04:37.180 [SPEAKER_01]: more respect for the podcast or community. 04:37.520 --> 04:38.702 [SPEAKER_00]: Yeah, more respect. 04:38.742 --> 04:44.529 [SPEAKER_00]: And I'm sure there's some overlap between, you know, tech won't save us in system crash or maybe like a one hundred percent overlap. 04:44.609 --> 04:52.620 [SPEAKER_00]: So to all those who've been asking us if we're going to come back or say we missed the show, just say late with thank you so much for 04:53.285 --> 04:55.106 [SPEAKER_00]: for the kind words and for reaching out. 04:55.427 --> 04:59.150 [SPEAKER_00]: And we still don't know. 04:59.170 --> 05:05.795 [SPEAKER_00]: It was honestly, it was just like we both have so much going on and Paris is what you're halfway through the right in a book or how far you almost done. 05:06.356 --> 05:08.197 [SPEAKER_01]: But a bit past a quarter, I guess. 05:08.297 --> 05:10.519 [SPEAKER_01]: Yeah, yeah, slowly chipping away. 05:10.719 --> 05:12.100 [SPEAKER_01]: The first quarter's the hardest quarter. 05:12.400 --> 05:13.541 [SPEAKER_00]: Yeah, and then the ball's rolling. 05:13.581 --> 05:15.803 [SPEAKER_00]: It's like, got momentum going downhill. 05:16.003 --> 05:16.304 [SPEAKER_01]: Totally. 05:16.324 --> 05:18.846 [SPEAKER_01]: I like we were saying before, you know, we got on the pod. 05:18.866 --> 05:20.367 [SPEAKER_01]: Like I'm feeling good about 05:20.932 --> 05:22.752 [SPEAKER_01]: the momentum and where things are going. 05:22.832 --> 05:24.613 [SPEAKER_01]: Hopefully people are gonna like the book. 05:24.773 --> 05:25.853 [SPEAKER_00]: That's no small thing. 05:26.253 --> 05:29.453 [SPEAKER_00]: Anyone out there who's tried to write a book, it can be very daunting. 05:29.473 --> 05:36.135 [SPEAKER_00]: You're just like waiting through a swamp of words and it's hard to curl them into any meaningful sort of shape or direction. 05:36.375 --> 05:40.795 [SPEAKER_00]: And so, I am not kidding when I say that first quarter is probably the hardest quarter. 05:41.015 --> 05:49.937 [SPEAKER_01]: Even just getting into it like entirely was just trying to get myself in the head space to be able to start writing it was daunting in itself and took me weeks. 05:50.317 --> 05:53.221 [SPEAKER_00]: And now's when I should probably say that I am ghost writing the whole thing. 05:55.944 --> 05:58.147 [SPEAKER_00]: It's really Paris on the podcast, but it's right. 05:58.187 --> 05:59.388 [SPEAKER_01]: We weren't supposed to tell anybody. 05:59.408 --> 06:00.770 [SPEAKER_00]: But it's really me writing the books. 06:01.030 --> 06:02.332 [SPEAKER_00]: So I'm writing Chris books. 06:02.432 --> 06:03.954 [SPEAKER_00]: He's being me on the podcast. 06:04.234 --> 06:06.177 [SPEAKER_01]: I'm getting to pretend to be stressed out. 06:06.197 --> 06:06.577 [SPEAKER_01]: Yeah. 06:09.381 --> 06:12.064 [SPEAKER_01]: But no, it's like you're saying it's great to have you back on the show. 06:12.124 --> 06:14.348 [SPEAKER_01]: You know now that we're we're not doing system crash. 06:14.728 --> 06:25.083 [SPEAKER_01]: I'm sure you'll be making more regular appearances back on this show as we talk about what you're up to and you know these big issues that we're both talking about now that you know we're not talking about every single week. 06:25.944 --> 06:29.188 [SPEAKER_00]: Yeah, if my schedule permits it, I'm pretty busy these days. 06:29.208 --> 06:30.150 [SPEAKER_01]: Sorry. 06:30.170 --> 06:41.045 [SPEAKER_01]: I know you're in really high demand and you know, just a little old tech won't say us might not be able to catch someone like Brian Merchant too often, but we appreciate when we can get your time. 06:41.305 --> 06:46.109 [SPEAKER_00]: Of course, now always, always a pleasure to be here. 06:46.309 --> 06:50.152 [SPEAKER_01]: But you have been doing a ton of writing and reporting. 06:50.232 --> 06:55.555 [SPEAKER_01]: And we're doing it while we were doing system crashes well on AI and the broader effects of these things. 06:55.716 --> 06:59.699 [SPEAKER_01]: And you have this great series that you have been writing called AI killed my job. 06:59.839 --> 07:01.880 [SPEAKER_01]: And so I wanted to talk to you more about that. 07:01.940 --> 07:10.747 [SPEAKER_01]: But if we're talking about AI, I think the big thing that we have to start with is obviously all of this discussion that we've been having about an AI bubble for the past little while. 07:11.347 --> 07:20.912 [SPEAKER_01]: I think it's pretty well established that these AI companies are overvalued that they are making claims about their products that are not really supported by what the products are actually doing. 07:21.312 --> 07:30.797 [SPEAKER_01]: And it feels like in the past few weeks we have reached this point where it's kind of been like okay to acknowledge that there is an AI bubble and to question whether 07:31.257 --> 07:33.878 [SPEAKER_01]: that bubble is finally going to burst in the near future. 07:33.918 --> 07:36.378 [SPEAKER_01]: You know, we've seen some difficulties in the stock market. 07:36.719 --> 07:40.480 [SPEAKER_01]: Different companies have been pulling back on certain initiatives and things like that. 07:40.880 --> 07:45.081 [SPEAKER_01]: Obviously, we saw Sam Altman come out and basically acknowledge that AI is in a bubble. 07:45.461 --> 07:56.404 [SPEAKER_01]: So I wonder what your vibe is at the moment and how you're feeling about where this kind of AI market, where this AI bubble, where the AI hype is in this moment. 07:56.638 --> 08:04.982 [SPEAKER_00]: There is a lot going on and I think there are a few major developments that have sort of changed the conversation, perhaps permanently. 08:05.723 --> 08:16.889 [SPEAKER_00]: Number one is that when GPT-V came out, which was this long-awaited mega-hyped product from OpenAI, that was supposed to be sort of like 08:17.709 --> 08:23.270 [SPEAKER_00]: the next incarnation of almost AGI artificial general intelligence. 08:23.290 --> 08:26.631 [SPEAKER_00]: It was supposed to be this amazing transcendent moment. 08:26.771 --> 08:31.612 [SPEAKER_01]: Yes, supposed to be this like massively that Sam Altman has been talking about for like months and months and months and months, right? 08:31.632 --> 08:42.895 [SPEAKER_00]: Months and months and months for two years almost because, you know, it was three when when a chat GPT first came out and kind of made its first splash print in short order. 08:43.455 --> 08:47.016 [SPEAKER_00]: They went into GPT four and then 08:47.636 --> 09:07.160 [SPEAKER_00]: sort of everybody was kind of in the AI community was like, okay, well GPT five is going to be the one because from three to four was a pretty noticeable or the performance was much better of the models and it just kind of like felt more like an actual artificial intelligence in terms of a product and interactivity and all that. 09:07.920 --> 09:13.441 [SPEAKER_00]: And then five just you could kind of now in hindsight, it's pretty clear that it was the question constantly haunted them. 09:13.461 --> 09:14.061 [SPEAKER_00]: Like is this 09:15.021 --> 09:31.987 [SPEAKER_00]: going to be enough and it seemed like the answer was always no so as they would iterate and like release new models they started to get into you know the point five's and then like the letters and then four oh and then Orion or whatever and it was a very incremental progress. 09:32.887 --> 09:48.232 [SPEAKER_00]: And that sort of complicated matters we can see now because if you release an incremental product update and then say, this is the next coming of AGI, then people are bound to be disappointed, which is exactly what happened. 09:48.652 --> 09:56.815 [SPEAKER_00]: So my sense is that Altman and his C-suite at OpenAI were kind of like just like, 09:57.581 --> 09:59.923 [SPEAKER_00]: Well, we got to pull the trigger sometime. 10:00.403 --> 10:04.205 [SPEAKER_00]: It's only gonna look worse if we wait another year or whatever. 10:04.806 --> 10:10.769 [SPEAKER_00]: And it was like kind of like this safe distance from when they secured their last round of mega funding from soft bank. 10:11.870 --> 10:15.292 [SPEAKER_00]: And it was just like, okay, maybe we can just release it now and maybe we can get away with it. 10:15.452 --> 10:16.553 [SPEAKER_00]: And they couldn't, right? 10:16.633 --> 10:20.816 [SPEAKER_00]: It was like users had already sort of baked in a host of assumptions. 10:21.276 --> 10:26.960 [SPEAKER_00]: Other users were quite sort of addicted already to the previous iteration of the product. 10:27.800 --> 10:34.405 [SPEAKER_00]: And it was sort of just on the terms that OpenAI set for itself, a failure. 10:34.465 --> 10:36.146 [SPEAKER_00]: And that's what I think is important. 10:36.466 --> 10:39.689 [SPEAKER_00]: Because some people are saying, oh, like this is, it's amazing. 10:39.729 --> 10:42.130 [SPEAKER_00]: And the critics are, you know, being too harsh or whatever. 10:42.170 --> 10:46.814 [SPEAKER_00]: But I'm judging this by the terms that OpenAI set out for itself. 10:47.074 --> 10:52.578 [SPEAKER_00]: And you can look back at Sam Altman's comments himself that he published on this blog, just in February, where it's like, 10:53.278 --> 10:55.519 [SPEAKER_00]: We're getting close to AGI, right? 10:55.619 --> 10:56.900 [SPEAKER_00]: Like it's in the air. 10:57.000 --> 10:58.941 [SPEAKER_00]: Like it's going to be with very close. 10:59.001 --> 11:02.803 [SPEAKER_00]: And then what happens after the launch six months later of GP D five. 11:03.523 --> 11:06.605 [SPEAKER_00]: Suddenly, AGI is not really a useful term anymore. 11:06.825 --> 11:09.626 [SPEAKER_00]: It's not a super useful term to quote Sam Altman. 11:09.966 --> 11:10.907 [SPEAKER_00]: I was like, are you 11:11.167 --> 11:40.873 [SPEAKER_00]: getting kidding me because you have been banging this drum and maybe it's not super useful for you right now because you're going to be criticized about it but it has been incredibly useful to you as a fundraising tool as something to tout as you go to Microsoft or go to you know perspective enterprise clients and say AGI is around the corner we're building it give us ten billion dollars or you know invest in this next round or or you know bias suite of GPT for business or whatever 11:41.453 --> 11:47.317 [SPEAKER_00]: And so that disappointment, I think, finally solidified. 11:47.337 --> 11:53.701 [SPEAKER_00]: The fact that the level of improvement is not going to continue. 11:53.881 --> 12:03.067 [SPEAKER_00]: I mean, we can debate the actual sort of benchmarks or how well that the model did in this context or what it's good at. 12:03.127 --> 12:06.950 [SPEAKER_00]: But the bottom line is that like, critics, you know, folks like, 12:07.370 --> 12:17.516 [SPEAKER_00]: Gary Marcus most notably probably have been talking about how sort of that just scaling which is just feeding more and more data into into the systems into the models. 12:17.536 --> 12:26.962 [SPEAKER_00]: It had hit a limit and now it's pretty clear that he was right about that and that means that whatever else happens it doesn't mean that like you know AI isn't going to 12:27.542 --> 12:29.124 [SPEAKER_00]: be able to do interesting things or different. 12:29.144 --> 12:47.902 [SPEAKER_00]: But it means this model where you're just getting more and more and more data getting the LLMs to terrain on more and more data and then to produce output based on just more and more and more that ethos has sort of reached its limits and there's going to have to be new interjections of symbolic reasoning or different configurations. 12:47.922 --> 12:49.263 [SPEAKER_00]: There's going to have to be something else. 12:49.824 --> 12:50.304 [SPEAKER_00]: And so 12:51.465 --> 13:00.809 [SPEAKER_00]: That I think has permitted the business press, the tech press to sort of take stock of what's actually happened on the ground so far. 13:01.329 --> 13:10.212 [SPEAKER_00]: And I think it's also worth noting that into this sort of environment came this study from MIT that showed that ninety five percent of businesses that have adopted AI 13:11.753 --> 13:30.035 [SPEAKER_00]: have essentially struggled to do so and had to have not showed major gains and so you have like well the business cases if he the model sort of improvement is if he has slowed down and the future all of a sudden seems very uncertain because as listeners of this pod know that like 13:30.315 --> 13:36.881 [SPEAKER_00]: This is an incredibly capital intensive technology where it's not just like oopsie, this didn't work. 13:36.921 --> 13:37.762 [SPEAKER_00]: Let's try something else. 13:37.822 --> 13:48.731 [SPEAKER_00]: It's like you have already sort of baked in massive contracts with data centers with cloud compute providers chip purchases from Nvidia where like 13:49.311 --> 13:51.453 [SPEAKER_00]: It really, really matters. 13:51.473 --> 13:53.795 [SPEAKER_00]: Because again, it was all predicated on scale. 13:53.855 --> 13:55.096 [SPEAKER_00]: It was all predicated on scale. 13:55.536 --> 13:59.480 [SPEAKER_00]: And so you have to take a hard look at the throughlines. 13:59.540 --> 14:15.713 [SPEAKER_00]: And so now we're at this sort of cloudy moment where it's like, oh, wait a minute, meta, which was just like a month ago, or even weeks ago, like paying a hundred million dollars signing bonuses to get AI researchers away from open AI is going like, 14:15.853 --> 14:20.394 [SPEAKER_00]: actually maybe we're going to pause our super intelligence team that they're calling it. 14:20.474 --> 14:22.855 [SPEAKER_00]: Yeah, we need to reorganize in this moment. 14:23.235 --> 14:24.556 [SPEAKER_01]: We need to reorganize. 14:24.756 --> 14:27.677 [SPEAKER_01]: I think that what you're saying is so important to understand, right? 14:27.717 --> 14:36.180 [SPEAKER_01]: Because the biggest assertion around generative AI for so long has been like it's on this exponential curve, like so many of these other like tech products, right? 14:36.480 --> 14:37.300 [SPEAKER_01]: And so if you have 14:37.760 --> 14:40.581 [SPEAKER_01]: GPT-five come out and it's not showing that. 14:40.841 --> 14:55.185 [SPEAKER_01]: Then all of a sudden, like, the whole thing that this whole boom, that this whole market, that this whole, like, supposed business and business venture is built on is being called into question because these products are not actually getting so much more powerful on this exponential curve. 14:55.645 --> 15:07.109 [SPEAKER_01]: It's like, okay, you had this moment where it came up, but now it looks like we're on the S curve that we've seen with AI for so long where you're going to have this advancement and it goes up, but then it platoes again for a long time. 15:07.569 --> 15:17.574 [SPEAKER_01]: until you, you know, maybe ten years down the road or something, there's this next development or this next kind of series of research that results in this next level of advancement. 15:18.014 --> 15:27.558 [SPEAKER_01]: And then on the narrative side of things, it's like, like you were saying, you know, you have this MIT study, you have just the general things that these companies have been saying, the way that GPT five comes into all this. 15:27.938 --> 15:36.462 [SPEAKER_01]: But then you also have these increasing like stories from employees talking more about how AI is not making them more productive, is not making things better. 15:36.982 --> 15:56.453 [SPEAKER_01]: And I think the big thing like the past couple months has really been like just a growing wave of these stories about the mental health consequences, about people committing suicide after having talked to chatbots and gotten really concerning series of dialogue from them, where they're basically egging on their suicidal ideation. 15:56.473 --> 16:02.477 [SPEAKER_01]: And there's even this story about this guy who's like, high up in open AI, who is apparently kind of 16:03.137 --> 16:20.287 [SPEAKER_01]: having mental health consequences as a result of this, I don't know the best way to describe it, but it feels like kind of the stories about the health and human consequences of this technology are just growing so rapidly that more and more people are like, what is going on here? 16:21.347 --> 16:37.773 [SPEAKER_00]: One thing that has always been sort of unique about the AI boom is that it has sort of required all this forward motion all these promises of AGI and things like that to sort of overtake any critical backlash or introspect because it's always been there. 16:38.093 --> 16:46.857 [SPEAKER_00]: From the beginning, this is a technology that has never been sort of a majority of people surveyed, for instance, have never said, I love this technology. 16:47.397 --> 16:54.902 [SPEAKER_00]: From the beginning and up until recently, you know, Pew has done polling, tech equity has done polling of California. 16:55.282 --> 17:03.248 [SPEAKER_00]: There's lots of polling and time and time again, you find that consumer sentiment and worker sentiment is more negative than positive. 17:03.288 --> 17:04.589 [SPEAKER_00]: People are more concerned 17:05.149 --> 17:33.066 [SPEAKER_00]: then excited by significant margins over AI and they have been and that's always been that even some industry insiders have have pointed out like been kind of the risk of touting this technology as so powerful right like the doom hype was I think taken as a tactic because it was working for a while but now as you're saying we might see some of that sort of backlash come because it used to be like well at this technology is so powerful that like it's going to take over the world at least it'll help me sort of 17:33.326 --> 17:48.937 [SPEAKER_00]: replace my workers or at least it'll be sort of addictive to users so we all better invest in it and then if that pitch can't bear any fruit if it turns out like well actually it's just like a semi successful automation technology 17:49.257 --> 18:14.042 [SPEAKER_00]: that is only useful and if you key contacts and with a lot of oversight and work and we have to hire other people to make sure that the AI works and we have to pay the then all of a sudden that whole calculus is thrown out of whack and that those criticisms can then sort of shine through and take up more of the space and I think you're right I think we'll start seeing that happen more and and sort of you know those those very real and 18:14.482 --> 18:35.078 [SPEAKER_00]: And that's also not to say that those criticisms haven't been more developed and become sharper over the years as we have more data to point to, more kids whose lives have been ruined by AI addiction, more educators just completely exasperated by the way that AI has sort of taken a wrecking ball to the classroom. 18:35.499 --> 18:38.441 [SPEAKER_00]: And then, yeah, what we can talk about, which is labor, 18:39.962 --> 18:47.004 [SPEAKER_01]: Before we pivot to a labor question, can I just ask you one final thing on the say I bubble and then we'll get into your series in the work that you've been doing on that? 18:47.384 --> 18:50.805 [SPEAKER_01]: I wonder how you feel about the state of that bubble at the moment. 18:50.825 --> 18:54.926 [SPEAKER_01]: Because for me, I feel like there's certainly questions in this moment, right? 18:54.986 --> 19:07.350 [SPEAKER_01]: Questions that are being asked much more publicly, there's clear evidence of the vulnerabilities and the problems and the kind of the lies that this market, the valuation of this technology was built on. 19:08.205 --> 19:13.347 [SPEAKER_01]: I think there's still energy incentive to try to keep this bubble inflated, not just from investors. 19:14.047 --> 19:23.671 [SPEAKER_01]: But for me, I always look at how the technology has kind of become this geopolitical football where you have all these countries trying to pretend that they are going to be leaders on AI too. 19:24.331 --> 19:42.743 [SPEAKER_01]: And I feel like even if these vulnerabilities are becoming clearer, these issues with the narrative that the valuations were built on, I still think it's entirely possible that the bubble remains inflated, at least to a certain degree, because of that kind of geopolitical aspect, the aspect that is beyond the business case. 19:43.223 --> 19:46.085 [SPEAKER_01]: But I wonder how you kind of feel about where that stands at the moment. 19:46.425 --> 19:46.745 [SPEAKER_00]: Yeah. 19:47.747 --> 19:59.771 [SPEAKER_00]: This is always kind of been in my sense as well that I think for a number of reasons AI is essentially at this point, you know, too big to fail. 19:59.951 --> 20:12.655 [SPEAKER_00]: And I, you know, I've actually, I've had some really, really good arguments about this with folks like Ed Zitron who, you know, can also persuasively make the case that there's a house of cards quality, especially to a lot of the companies. 20:13.035 --> 20:21.644 [SPEAKER_00]: and that once things start going south and investors pull out a lot of what the AI companies are doing is unsustainable. 20:21.724 --> 20:24.127 [SPEAKER_00]: And I think that can be persuasive too. 20:24.627 --> 20:29.812 [SPEAKER_00]: But we've already seen precisely what you gestured towards, which is that 20:30.533 --> 20:40.076 [SPEAKER_00]: we're already in this new kind of era where there is a new sort of formation of Silicon Valley and the federal government in the U.S. 20:40.116 --> 20:57.483 [SPEAKER_00]: especially this new sort of Silicon State here that is still much more maybe insulated from a lot of like sort of you know the market activity and that the state can do a lot to prop up a company as we're seeing right like we're seeing like 20:58.463 --> 21:02.825 [SPEAKER_00]: The state is taking an actual stake in intel for instance. 21:02.905 --> 21:16.592 [SPEAKER_00]: It's making weird deals to exempt Nvidia from export controls and it has close relationships with a lot of the AI companies and their architects and their executives. 21:16.813 --> 21:21.115 [SPEAKER_01]: I will say it was interesting to see the exemption that Nvidia got. 21:21.795 --> 21:37.449 [SPEAKER_01]: and then like Howard Lutnik basically turned around and made some comment on like how they were gonna have to treat China differently or whatnot and China like immediately was like yeah we're discouraging anyone buying these chips at all even if they're now available even if they're for sale yeah yeah 21:38.470 --> 22:04.080 [SPEAKER_00]: which is to say that you know I think so that's on one layer like the state like whether that whether it's through direct contracts whether it's through actually taking a stake in account which is I mean we're that it that's interesting that's not really something that I necessarily would put on my bingo card seeing like Trump want a ten percent stake in Intel or anything else but now you know I we have to understand like how interested it the state is in AI 22:04.960 --> 22:05.460 [SPEAKER_00]: This is a point. 22:05.480 --> 22:07.762 [SPEAKER_00]: I think we made on system crash, but I'll make it again here. 22:08.282 --> 22:21.771 [SPEAKER_00]: And that is just like, everybody should be asking themselves why it is that the one technology for all intents and purposes, the one non-overly military technology that the Trump administration is interested in is AI. 22:21.991 --> 22:33.159 [SPEAKER_00]: He's cutting subsidies for clean tech, gutting, electric vehicle supports, pulling the rug out from under health sciences and investments in vaccines and things like that. 22:33.759 --> 22:35.080 [SPEAKER_00]: And yet, here we are, like, AI. 22:35.120 --> 22:36.141 [SPEAKER_00]: We're pro AI. 22:36.762 --> 22:44.489 [SPEAKER_00]: And that's because it's so well suited to be a technology of control, of domination, of surveillance. 22:45.009 --> 22:50.214 [SPEAKER_00]: It is, it can produce shitty propaganda that the White House can put on its Twitter feed. 22:50.834 --> 23:06.944 [SPEAKER_00]: It can, you know, be pumped into government agencies in hopes that it can do the jobs of fired public servants and all the while sort of concentrating control under a fewer number of officials and sort of allies. 23:07.804 --> 23:32.179 [SPEAKER_00]: It is, and it can be a tool, you know, at least as the way that Silicon Valley has pitched it, for military might, for on the geopolitical stage, as you said, whether it's to conduct sort of hacking that malware attacks or, you know, help guide more conventional weapons or do target selection, as we've seen the IDF do in Gaza. 23:32.199 --> 23:32.239 [SPEAKER_00]: So, 23:33.800 --> 23:40.083 [SPEAKER_00]: The state at least right now is is also quite invested in AI. 23:40.183 --> 23:42.704 [SPEAKER_00]: The American state is so that's one factor. 23:42.844 --> 23:54.709 [SPEAKER_00]: The second factor is like whether or not this is comparable to the dot com boom of twenty thirty years ago or AI is in any way shape or form the next internet or anything. 23:54.829 --> 24:01.672 [SPEAKER_00]: I do not think that it is of course, but that's what the industry is treating it as that's like this is their idea. 24:02.072 --> 24:03.454 [SPEAKER_01]: Everything is the next internet. 24:03.554 --> 24:06.838 [SPEAKER_01]: Crypto's the next internet with three AI's the next internet. 24:07.179 --> 24:18.113 [SPEAKER_00]: But more than crypto, more than the metaverse, more than NFT, there has been a convergence on this and investment in this idea that has kind of made it the only game in town. 24:18.774 --> 24:23.596 [SPEAKER_00]: And that's not to say they can't, you know, scatter to the wind and pivot away or try to afterwards. 24:24.137 --> 24:25.717 [SPEAKER_00]: But that's number two. 24:25.777 --> 24:31.000 [SPEAKER_00]: Silicon Valley, I think, is two sort of invested in this idea and propping it up. 24:31.080 --> 24:37.743 [SPEAKER_00]: So I think we'll see some interesting things happen if and when that bubble bubble or rather when that bubble does start to burst. 24:37.803 --> 24:44.226 [SPEAKER_00]: I think it will burst what happens next will be the interesting thing, whether there will be government intervention. 24:44.906 --> 24:48.969 [SPEAKER_00]: or how the companies will react and the scale of that bursting. 24:49.430 --> 25:08.485 [SPEAKER_00]: And number three, and I think the dark horse factor here is just that it's such an alluring idea for the clients of this technology to have a tool like AI that can automate and labor and surveil the smaller workforces that remain in theory. 25:09.145 --> 25:32.687 [SPEAKER_00]: It's a much more appealing pitch than like then crypto was where you know you if you're Whatever Walmart you're looking at crypto and going like how does this I don't care you know how does this affect me, but you know there's it's that there's a reason why like almost every organization has been like how do we do AI like how do we get AI every like CFO in the world has been like a ready 25:33.007 --> 25:34.267 [SPEAKER_00]: bring on the AI. 25:34.327 --> 25:36.508 [SPEAKER_00]: Let's let's cover labor costs here. 25:37.008 --> 25:41.569 [SPEAKER_00]: So I think those three factors are going to make this uniquely sort of resistant to a bubble. 25:41.689 --> 25:47.351 [SPEAKER_00]: It also might make it all the more cataclysmic, even when that bubble goes full burst. 25:47.911 --> 25:49.551 [SPEAKER_01]: No, I think you've put that so well. 25:49.672 --> 25:52.252 [SPEAKER_01]: And I think it pivots us really well to start talking about labor. 25:52.572 --> 25:58.754 [SPEAKER_01]: But there's one thing I want to tell you before we start talking about your labor reporting and what you've been hearing from workers and what you're seeing. 25:59.314 --> 26:05.616 [SPEAKER_01]: You know, obviously on system crash we used to talk a lot about what's going on in Canada and you know Canada has a new AI minister. 26:05.636 --> 26:09.577 [SPEAKER_00]: I'm sorry remind me what is that like a city in Europe? 26:09.637 --> 26:19.159 [SPEAKER_01]: Yeah, that's your fifty-first date don't you remember like oh That's right not Washington DC it's a Canada 26:20.671 --> 26:24.053 [SPEAKER_01]: But so the AI minister gave an interview the other day. 26:24.173 --> 26:29.175 [SPEAKER_01]: And he was like, there's this bill from the old parliament that I needed to understand. 26:29.595 --> 26:31.996 [SPEAKER_01]: And so I ran it through Google Gemini. 26:32.116 --> 26:36.938 [SPEAKER_01]: And I had Gemini make a fifteen minute podcast for me about the bill to explain it to me. 26:37.019 --> 26:39.500 [SPEAKER_01]: And I listened to it on the car on the way to work. 26:39.600 --> 26:40.520 [SPEAKER_01]: And it was fantastic. 26:40.560 --> 26:43.041 [SPEAKER_01]: Let me pull it up and let you listen to it. 26:43.101 --> 26:43.622 [SPEAKER_01]: It was great. 26:43.662 --> 26:47.223 [SPEAKER_01]: And I was like, man, tech policy in Canada is so fucking screwed. 26:47.463 --> 26:48.744 [SPEAKER_01]: If this is what we have. 26:51.486 --> 26:52.587 [SPEAKER_01]: It's bad up here, man. 26:52.667 --> 26:53.148 [SPEAKER_01]: It's bad. 26:53.808 --> 26:56.110 [SPEAKER_01]: Not as bad as down there, I know of it. 26:56.411 --> 26:56.951 [SPEAKER_01]: It's not good. 26:57.312 --> 27:02.176 [SPEAKER_00]: I think that's explicitly like there's a Bloomberg profile of such an edela, I think. 27:02.296 --> 27:05.659 [SPEAKER_00]: And that's what he said that he would download books into it. 27:05.919 --> 27:09.463 [SPEAKER_00]: In the chat box that he could then talk to and ask questions about. 27:10.103 --> 27:12.705 [SPEAKER_00]: It's like it's so deranged. 27:12.745 --> 27:15.267 [SPEAKER_00]: What even is that piece of information anymore? 27:15.427 --> 27:25.754 [SPEAKER_00]: It's already been regulated in process through the massive human knowledge into something that could probably not even be discernible as the book anymore. 27:26.895 --> 27:27.995 [SPEAKER_00]: You're just talking with nothing. 27:28.075 --> 27:31.517 [SPEAKER_00]: It's just like someone might as well be just like blowing hot air on your face. 27:31.797 --> 27:34.217 [SPEAKER_00]: Like it's just so ridiculous. 27:34.457 --> 27:45.881 [SPEAKER_00]: But yes, you know, the UK Canada, like, I continue to be astonished by the openness and the eagerness that, you know, states around the world have, you know, not all. 27:46.041 --> 27:46.361 [SPEAKER_00]: Not all. 27:46.381 --> 27:46.942 [SPEAKER_00]: There's plenty. 27:46.962 --> 27:51.223 [SPEAKER_00]: There are some good, you know, exceptions who have basically said fuck off. 27:51.543 --> 28:01.047 [SPEAKER_00]: And so, but yeah, it's Canada screwed, UK screwed, we're obviously screwed, but man, the Anglophone world, we're all massive. 28:01.468 --> 28:06.570 [SPEAKER_01]: But yeah, let's pivot and talk about your labor series because, you know, I think you set us up really well to get into it. 28:06.610 --> 28:14.734 [SPEAKER_01]: You know, you have this series AI killed my job where a bunch of people have been sending in their stories about how they're seeing AI affect their professions, their workplaces, 28:15.334 --> 28:25.056 [SPEAKER_01]: What the effects of that are, of course, you publish two pieces, you know, kind of directly telling those stories so far and you know, you have more kind of in the pipeline as you're going through more of these things. 28:25.436 --> 28:33.718 [SPEAKER_01]: And for me, it's been really fascinating to read through that and to see the types of things that people are saying about their workplaces, about their work. 28:33.758 --> 28:37.339 [SPEAKER_01]: Of course, you know, the ones that you've published so far on tech workers and on translators. 28:37.819 --> 28:49.162 [SPEAKER_01]: But even then, I think there are so many things that just feel so much more broadly applicable, potentially, of the things that they are talking about, even going from some of the things that you were just saying. 28:49.662 --> 28:53.663 [SPEAKER_01]: So I guess to start, like, how did you decide to do this series? 28:53.943 --> 29:02.986 [SPEAKER_01]: And was there anything that you were surprised about when you started to get these stories coming in from people telling you about what was happening in their work lives? 29:03.498 --> 29:21.853 [SPEAKER_00]: Yeah, so I know I'm also just adding note to say that I think that the talk of the AI bubble and that some of the mythology and the more sort of you pie in the skyness of the AGI conversation sort of beginning to evaporate kind of allows us in a lot of ways to 29:23.374 --> 29:41.319 [SPEAKER_00]: see generative AI and the generative AI tools being sold by these companies for what they are, which is either socially mediated kind of entertainment products like chat bots that people talk to or its enterprise workplace or personal automation. 29:41.479 --> 29:45.400 [SPEAKER_00]: It's sort of, you know, souped up productivity software. 29:45.881 --> 29:46.241 [SPEAKER_00]: And so 29:47.221 --> 29:51.707 [SPEAKER_00]: You know, with that in mind, I mean, that's kind of how I've always approached generative AI as you know. 29:52.008 --> 30:03.203 [SPEAKER_00]: That's, I think, to me, the most useful way to look at a technology like this that is being sold as something that is going to disrupt the workplace or transform work or, you know, 30:04.103 --> 30:08.167 [SPEAKER_00]: Beget a jobs apocalypse in the words of some of these AICOs. 30:08.227 --> 30:17.994 [SPEAKER_00]: It's just like look at history and look at all the times when similar pronouncements have been made and other technologies have been sort of entered into working life. 30:18.335 --> 30:26.281 [SPEAKER_00]: And so the best way to do that is just look at the material conditions on the ground and who's doing the introduction and the adoption and how it's changing. 30:27.742 --> 30:33.447 [SPEAKER_00]: And I killed my job series came about because I spent a lot of time talking to workers. 30:33.507 --> 30:35.669 [SPEAKER_00]: And I have since the beginning. 30:36.309 --> 30:43.935 [SPEAKER_00]: And part of that is just because, you know, that's just sort of like where my beat has naturally been talking to before it was AI. 30:44.095 --> 30:53.102 [SPEAKER_00]: I was talking to Uber drivers, talking to lift drivers and Amazon workers, and trying to understand what was happening on the ground, on the other end of 30:54.063 --> 31:01.626 [SPEAKER_00]: Jeff Bezos is, or Travis Kalanax pronouncements about the transformation of this, you know, of the workplace or the future of work or whatever. 31:02.367 --> 31:11.310 [SPEAKER_00]: And it seemed especially acute to me that during the AI boom, so few people were really just going right to the workers. 31:11.370 --> 31:12.391 [SPEAKER_00]: So like, okay, great. 31:12.491 --> 31:14.992 [SPEAKER_00]: Dario Amode from Anthropics says that 31:15.652 --> 31:17.912 [SPEAKER_00]: whatever, ten percent of all jobs are going to be gone. 31:17.952 --> 31:20.353 [SPEAKER_00]: Maybe half of all young collegiate. 31:20.373 --> 31:21.353 [SPEAKER_00]: So okay, great. 31:21.393 --> 31:22.993 [SPEAKER_00]: He's a CEO selling a product. 31:23.033 --> 31:25.554 [SPEAKER_00]: So what's actually, what's actually happening? 31:25.574 --> 31:30.675 [SPEAKER_00]: Like where is the technology actually like hitting the pavement? 31:31.135 --> 31:42.197 [SPEAKER_00]: And that's usually you can find that out by talking to the people that these tools have been thrust upon or that are using them voluntarily or that are parts of organizations. 31:42.517 --> 31:47.726 [SPEAKER_01]: And when you say, when you talk about the tools there, do you mean the AI products or do you mean the executives? 31:47.766 --> 31:52.554 [SPEAKER_01]: Sorry, I couldn't help myself. 31:52.674 --> 31:55.339 [SPEAKER_00]: I'm both the tools in their own way. 31:56.295 --> 32:12.108 [SPEAKER_00]: Yeah, I'm finally reading why we fear AI by Hagen Blix and Ingo Board Glimmer and an important point that they make is that these companies and the AI salesmen are all just being motivated by the same 32:12.728 --> 32:35.537 [SPEAKER_00]: capitalist forces that are animating the whole the whole to do so in a sense they have to say that you know like our product is gonna put even more people out of fork than yours and and then it becomes a sort of arms race to see who can scare people more but but I digress another point that I want to make without digressing too much before we talk about the workers is that 32:36.217 --> 32:49.871 [SPEAKER_00]: Talking about AI and labor replacement or a jobs crisis is so fraught because on the one hand, amode, altmen, all of the AI CEOs, they do want to create the impression that a great disruption is coming. 32:49.911 --> 32:51.172 [SPEAKER_00]: That makes it easier for them to sell. 32:51.512 --> 32:53.293 [SPEAKER_00]: more automation software, right? 32:53.414 --> 33:00.299 [SPEAKER_00]: Like, it's a product in the array of products that they're selling is enterprise, AI automation, productivity software. 33:01.119 --> 33:07.984 [SPEAKER_00]: And I think there is a tendency even on the left to sort of like push back, completely saying like, 33:08.865 --> 33:10.846 [SPEAKER_00]: This stuff is bullshit. 33:11.246 --> 33:11.966 [SPEAKER_00]: It sucks. 33:12.886 --> 33:17.968 [SPEAKER_00]: And giving that idea any credence is just like playing into these corporate narratives. 33:18.628 --> 33:29.831 [SPEAKER_00]: And then some of like, I think some of the better critiques like from Aaron Beninoff, who's great, looking at sort of the sort of middleing results of like sort of previous sort of in industrial, you know, 33:30.611 --> 33:34.275 [SPEAKER_00]: mask scale job scares and saying like that just it isn't born out. 33:34.875 --> 33:38.239 [SPEAKER_00]: I do want to caution against minimizing too much. 33:38.719 --> 33:45.906 [SPEAKER_00]: So and I think that's part of this project is that I think in no way are we going to see anything like a mask scale jobs apocalypse. 33:46.787 --> 33:47.708 [SPEAKER_00]: It's not going to happen. 33:48.148 --> 33:52.410 [SPEAKER_00]: The AI is not suited to do enough jobs. 33:52.631 --> 33:54.131 [SPEAKER_00]: It requires too much oversight. 33:54.171 --> 33:55.052 [SPEAKER_00]: It's too expensive. 33:55.432 --> 34:03.397 [SPEAKER_00]: But that said, there are still a lot of use cases where a management can either use it as a tool, use it as leverage. 34:03.977 --> 34:11.642 [SPEAKER_00]: to emissarate, or yes, sometimes even replace workers or freelancers, especially where there are workers and more precarious conditions. 34:12.162 --> 34:22.529 [SPEAKER_00]: And so I do think we want to be careful about swinging the pendulum too far the other way because I've been talking to probably hundreds of workers at this point. 34:22.769 --> 34:29.353 [SPEAKER_00]: And that's not like, you know, obviously a meaningful sample size if you're looking at the global economy or the American economy. 34:29.773 --> 34:30.394 [SPEAKER_00]: But it's enough 34:31.114 --> 34:44.531 [SPEAKER_00]: For me to get a sense of what I think is happening in particularly sort of vulnerable industries where executives can use AI maliciously or aggressively to cut costs. 34:44.972 --> 34:47.435 [SPEAKER_00]: And in a lot of cases it can still be very pernicious. 34:48.095 --> 35:10.180 [SPEAKER_00]: in the way that it is used to sort of reshape a job or to take away parts of a job that people think are meaningful and replace it with button pressing or where your job used to actually be to translate the text, for example, now because some, you know, person in middle management was susceptible to a pitch from some tech company. 35:10.680 --> 35:17.642 [SPEAKER_00]: Now the part of translation is outsource to a machine, but you still need a human to go over the output and correct it 35:18.243 --> 35:34.884 [SPEAKER_00]: And sometimes I heard over and over in my survey of translators, that job was just as time consuming sometimes, but it's just far, like you're not actually doing the translation, which is considering meaning and considering context and place and person. 35:35.685 --> 35:52.559 [SPEAKER_00]: painting a picture of a game or a piece of art or prose and then translating that instead you're taking the automated output and trying to see if it lines up because somebody somewhere on the supply chain got convinced that that's more effective and it can save the firm a few bucks. 35:52.859 --> 35:58.084 [SPEAKER_00]: So there are a lot of impacts like that that are still rolling out and that I'm hearing about. 35:58.164 --> 35:59.965 [SPEAKER_00]: So yeah, I really just wanted to 36:00.926 --> 36:01.966 [SPEAKER_00]: here from the workers. 36:02.046 --> 36:09.028 [SPEAKER_00]: I guess that's a long-winded way of saying that like, I want to hear all this corporate Silicon Valley AI speak. 36:09.589 --> 36:12.649 [SPEAKER_00]: How's it playing out on the ground by the people who have to deal with this stuff every day? 36:13.210 --> 36:17.691 [SPEAKER_00]: And yeah, I decided to sort of separate it by industry for now. 36:17.911 --> 36:20.172 [SPEAKER_00]: I might do other things as I move along. 36:20.712 --> 36:23.553 [SPEAKER_00]: I started with tech workers because they're in a very interesting place. 36:23.633 --> 36:24.913 [SPEAKER_00]: It's one of the more, you know, obviously, 36:25.473 --> 36:30.637 [SPEAKER_00]: management at a lot of tech companies is the most sort of gung-home and aggressive about deploying AI. 36:31.057 --> 36:45.308 [SPEAKER_00]: And then so it becomes interesting to see the disparity between an AI-loving executive and a senior software engineer who really knows what they're talking about and is just going like, I can't believe we have to use this stuff or, you know? 36:45.748 --> 36:48.851 [SPEAKER_00]: And so there's a lot of great stories that came from that one. 36:48.871 --> 36:53.114 [SPEAKER_00]: A few where it was, you know, we think that the AI boom sort of 36:53.694 --> 37:02.122 [SPEAKER_00]: convinced our executives to close down a department or to fire me as part of a layoffs as part of an AI for strategy or something. 37:02.762 --> 37:06.225 [SPEAKER_00]: But more often than not, it was like, I work for Google. 37:06.285 --> 37:13.112 [SPEAKER_00]: I've worked here for a long time and they're automating the AI generated coding process and they're just 37:13.652 --> 37:15.593 [SPEAKER_00]: just injecting it directly into our code base. 37:15.633 --> 37:17.534 [SPEAKER_00]: And I think that's a disaster waiting to happen. 37:17.834 --> 37:19.154 [SPEAKER_00]: That was a really interesting one. 37:19.174 --> 37:20.555 [SPEAKER_00]: You know, stories like that. 37:20.915 --> 37:39.062 [SPEAKER_01]: I completely hear where you're coming from with that kind of distinction between what jobs are being destroyed and kind of what jobs are being transformed and often transformed in a way that is degrading them right, making them more precarious, lowering the pay, making the work more frustrating, I guess, maybe in order to have to deal with, like, 37:39.362 --> 37:50.419 [SPEAKER_01]: I think that these are important things to understand and when the executives are just coming out and talking about productivity and replacing jobs, like the actual material consequences of that can be abstracted, right? 37:50.640 --> 37:54.366 [SPEAKER_01]: But when you go in and actually talk to the workers, you can see what is happening there. 37:54.806 --> 38:19.499 [SPEAKER_01]: And my view has always been in part informed by like the last AI wave I've talked about this in the past that the actual job destruction is often minimal and is often focused on particular tasks that's my view and what we see much more of is the use and kind of the weaponization of these technologies by bosses management and executives, you know, things that you've of course written plenty about 38:19.839 --> 38:31.529 [SPEAKER_01]: through your career in order to try to change the work to make it so that workers have less power so that they're being paid less so that they have fewer abilities to really intervene in the work process. 38:31.649 --> 38:42.898 [SPEAKER_01]: And I feel like this was something that really came out in some of those stories that the translators and the tech workers were talking about where it really felt to me like some of them were saying like you know in the translators case that 38:43.599 --> 39:00.031 [SPEAKER_01]: with open AI and with LOMs, the quality of the translation has not actually gotten significantly better, but it seemed like more like the hype of the past couple years provided a justification for a lot of these companies to 39:01.212 --> 39:15.008 [SPEAKER_01]: adopt and roll out these tools and change the profession of a translator in a way that wouldn't have been justifiable in the past, but because of the hype it was now okay to do it even though the quality wasn't there. 39:15.469 --> 39:18.472 [SPEAKER_01]: And that to me seemed like a really significant kind of 39:19.133 --> 39:21.535 [SPEAKER_01]: bit of detail to come out of these things that you were talking about. 39:21.555 --> 39:33.843 [SPEAKER_01]: So I wonder how you reflect on that piece of things after and the way that executives in particular have been able to take advantage of this after talking to the workers about how they have seen and actually play out in their professions. 39:34.473 --> 39:36.194 [SPEAKER_00]: That's absolutely correct. 39:37.034 --> 39:48.641 [SPEAKER_00]: As I put in a previous piece, in one that actually helped spur this project and one that I spoke to, laid off, do a lingo contractor after that company pivoted to AI. 39:49.061 --> 39:54.324 [SPEAKER_00]: And this was the same time that sort of like the doge clearings of houses was that in full effect. 39:54.384 --> 39:59.707 [SPEAKER_00]: So I wrote a piece that argued that sort of the real AI jobs crisis 40:00.407 --> 40:08.931 [SPEAKER_00]: is sort of the cultural logic that it allows executives to embrace and to, you know, impart onto their organizations. 40:09.492 --> 40:17.055 [SPEAKER_00]: But it's less that, you know, AI can actually do any one of those jobs of the civil servants that have gotten laid off. 40:17.135 --> 40:22.278 [SPEAKER_00]: It's just, it provides sort of like the window dressing, the cover, the idea, like the futurity. 40:22.858 --> 40:36.412 [SPEAKER_00]: necessary to at least sort of gesture towards this concept of replacement, that there's going to be the same level of functionality even after these people are gone when it's just really what management wanted to do anyways. 40:36.432 --> 40:42.498 [SPEAKER_00]: And I think there are some cases, the dual Lego cases interesting because I think this is just one of those guys it really seems like he's just 40:43.058 --> 40:49.340 [SPEAKER_00]: really does like either believes in the hype or maybe he'd been itching to get rid of all of his contractors for years anyways. 40:49.801 --> 40:51.281 [SPEAKER_00]: I mean, we can't know. 40:51.361 --> 40:59.224 [SPEAKER_00]: He certainly seems very credulous about the capacities of AI, at least he did until everybody sort of revolted and started pushing back. 40:59.764 --> 41:01.005 [SPEAKER_00]: But I think that's a lot of it. 41:01.425 --> 41:03.466 [SPEAKER_00]: But there is this layer. 41:03.546 --> 41:09.348 [SPEAKER_00]: So like I also don't want to minimize the experience of the people that have said like, my work is gone. 41:09.888 --> 41:10.088 [SPEAKER_00]: Right? 41:10.228 --> 41:12.009 [SPEAKER_00]: Like, my work is gone. 41:12.069 --> 41:12.769 [SPEAKER_00]: It's dried up. 41:12.909 --> 41:26.952 [SPEAKER_00]: Like, that cultural cover allowed my boss to select the good enough option, which is sort of, you know, auto-generated code in the case of the tech workers that, you know, in some cases can be good enough. 41:26.992 --> 41:34.414 [SPEAKER_00]: Again, if you have somebody, maybe you can hire somebody who's less expensive to sort of spot check that output, or in the case of translators. 41:34.454 --> 41:38.395 [SPEAKER_00]: Maybe it's just like, you know what, consumers of this particular, like, 41:38.975 --> 41:57.262 [SPEAKER_00]: Japanese video game that we're putting out maybe they don't need a good translation or maybe this is good enough they can still get the gist and they'll still you know and so it facilitates like those trade-offs and it does sort of again provide cover to management to make these decisions and because like ultimately you just got it like it's AI 41:58.542 --> 42:01.624 [SPEAKER_00]: in these contexts is an automation technology. 42:02.264 --> 42:06.367 [SPEAKER_00]: At the end of the day, it's just like, it's what management chooses to do with it. 42:06.467 --> 42:16.633 [SPEAKER_00]: And usually that is just, again, yeah, squeeze, surveil, control, or replace tasks or jobs that management think it can. 42:16.693 --> 42:22.537 [SPEAKER_00]: So it's going to be deployed in those same contexts that automation technologies always have been 42:22.757 --> 42:30.543 [SPEAKER_00]: there's nothing particularly mystical or, you know, be fuddling about it when you actually get down into the details. 42:31.544 --> 42:40.331 [SPEAKER_00]: As such, it still stands to be a pretty potent force because it's been imbued with these properties, right? 42:40.351 --> 42:43.093 [SPEAKER_00]: Because logic has become powerful enough. 42:43.393 --> 42:47.697 [SPEAKER_00]: And I think that we go back to what we're talking about at the top about the bubble 42:48.537 --> 43:06.930 [SPEAKER_00]: One really interesting thing to see will be whether or not that sort of wipes away some of the eagerness to use this as an automation technique is it going to like oh like actually we were over zealous on this maybe we maybe it can't do everything that we we were sold on it being able to do and now we have to change tack or there's 43:07.831 --> 43:12.352 [SPEAKER_00]: potential, you know, fork in the road where it's like, well, we've sunk all these costs anyways. 43:13.032 --> 43:27.796 [SPEAKER_00]: Everybody's just going to have to deal with subpar output, subpar cultural products, subpar customer service experiences and AI is going to win the day because it's a little bit cheaper and we've already bought the enterprise contracts. 43:28.257 --> 43:51.157 [SPEAKER_01]: Yeah or in the case of a country like the UK apparently they're looking to just buy an open AI subscription for like the whole government or something like it's a wild but I think that's really interesting right because as I was reading like the translation piece in particular I was also thinking about how I have seen this being used in like other parts of the world as well like I spoke at an event in Amsterdam earlier this year where 43:51.777 --> 44:12.645 [SPEAKER_01]: they were using basically AI like live translation of speakers and I was like this is weird like who knows what that thing is like claiming that speakers are saying when they're on the stage but again like it's in place of where in the past you would have someone actually like doing that right or maybe you just wouldn't have it at all I don't know 44:13.145 --> 44:25.449 [SPEAKER_01]: And then I was speaking to some publishers who operate outside the English language and they were like frequently using chatGBT for like correspondence to English speakers and stuff like that. 44:25.869 --> 44:30.310 [SPEAKER_01]: I don't know, I like gave them a bit of shit for using chatGBT, but I also like 44:30.630 --> 44:37.313 [SPEAKER_01]: Kind of understood it like, you know, if English is not your first language, that makes it a lot easier to potentially converse with people outside of that. 44:37.373 --> 44:47.859 [SPEAKER_01]: And so like, I feel like I've been picking up on a lot of how I'm seeing people using these tools and normalizing these tools and, you know, not super comfortable with it, obviously, but 44:48.599 --> 44:53.541 [SPEAKER_01]: You know, sometimes I feel like it's a bit more prevalent than I expected it to be. 44:54.041 --> 45:01.284 [SPEAKER_01]: And that makes me wonder like what kind of the, you know, say post bubble burst kind of use cases of this technology are going to be. 45:01.304 --> 45:02.905 [SPEAKER_00]: I mean, it is pretty pervasive. 45:03.105 --> 45:07.726 [SPEAKER_00]: It's being used by hundreds of millions of people every week. 45:07.806 --> 45:09.447 [SPEAKER_00]: There's a pretty big user base. 45:09.907 --> 45:11.908 [SPEAKER_00]: I mean, which is going to open up. 45:12.088 --> 45:33.801 [SPEAKER_00]: Another can of worms because a lot of those use cases are extremely unhealthy and concerning I think like was it Harvard Business Review did a survey of like the most common AI uses and right at the top was like therapist people were you know treating it as an AI therapist and you know that's just like just like red flags just came tumbling out 45:34.521 --> 45:35.942 [SPEAKER_00]: go the sky for me on that one. 45:36.402 --> 45:38.643 [SPEAKER_00]: But yeah, it is terrifying. 45:38.703 --> 45:47.908 [SPEAKER_00]: So it is and and look like I think that you don't have to deny that there are some genuinely like interesting contexts and use cases. 45:48.028 --> 45:58.054 [SPEAKER_00]: I remember when like you had computer vision that could you could like take a picture of like a road sign in a foreign country and then it could translate that and you know that was something that you just 45:58.454 --> 46:03.379 [SPEAKER_00]: You know, you could try to ask somebody what it meant, but in a lot of times, you know, maybe you can't find somebody who speaks the same language. 46:03.399 --> 46:16.473 [SPEAKER_00]: So there are, like, utilities, where people are, are finding East, well, there's also people just like following into the trap because it's so useful or so, it's so much easier for them to do this than, I mean, famously, homework, right? 46:16.493 --> 46:19.597 [SPEAKER_00]: It's so much easier to just, like, have chat GBT generate. 46:20.217 --> 46:22.558 [SPEAKER_00]: answers for you, then to actually do it. 46:22.678 --> 46:32.984 [SPEAKER_01]: Oh, I definitely know people who turn to it to answer like any number of questions instead of just having to think about it themselves or even turn to Google as maybe they would have done in the past and now it's just catchy BT instead, right? 46:33.124 --> 46:37.086 [SPEAKER_00]: Yeah, I mean, absolutely my my I think some of those use cases. 46:38.106 --> 46:39.928 [SPEAKER_00]: will be like, I think filtered out. 46:40.008 --> 46:44.612 [SPEAKER_00]: Some of the mass automation stuff will eventually be filtered out. 46:44.632 --> 47:03.370 [SPEAKER_00]: I mean, I think in other cases, we, I think we were going to be stuck with hard questions about like what we're going to fight for and what we're going to because there's that famous line about how AI and technology were supposed to like automate doing, you know, the dirty work, doing laundry and dishes. 47:04.050 --> 47:06.671 [SPEAKER_00]: and giving us time to do art and music. 47:06.771 --> 47:13.895 [SPEAKER_00]: And instead, it's automating art and music and forcing us to spend more of our time working, doing the grunt work. 47:14.195 --> 47:24.180 [SPEAKER_00]: And the impact on creative industries is something that's just going to have to be negotiated against, fought against, same with, I think, translation. 47:24.240 --> 47:26.701 [SPEAKER_00]: Like, do we value translators? 47:27.142 --> 47:27.742 [SPEAKER_00]: I think we do. 47:27.962 --> 47:28.242 [SPEAKER_00]: I do. 47:28.322 --> 47:31.224 [SPEAKER_00]: Like, doing this piece really has underlined my 47:32.224 --> 47:34.627 [SPEAKER_00]: my sense of the importance of this work. 47:34.647 --> 47:46.960 [SPEAKER_00]: And in this sense, I'm really grateful for having done this piece and really thinking about, how many translated works of books have I read over the years? 47:46.980 --> 47:50.423 [SPEAKER_00]: How many translated documents 47:51.024 --> 48:10.312 [SPEAKER_00]: a lot of them now recognizing sort of like the art and the labor and the toil that goes into that process and really you know spending time with folks who and their stories who love that who love the act and the art of taking something that somebody else said 48:10.852 --> 48:20.137 [SPEAKER_00]: thinking about it, contextualizing it, and then making it accessible to a whole other culture, creating an intermediary between cultures. 48:20.557 --> 48:21.878 [SPEAKER_00]: This stuff is so important. 48:21.958 --> 48:29.142 [SPEAKER_00]: I mean, it probably wouldn't make the top ten list of things most people are concerned about in AI or automation. 48:29.242 --> 48:35.486 [SPEAKER_00]: But now, the prospect of automating that process, 48:36.372 --> 48:57.639 [SPEAKER_00]: feels incredibly sad to me, you know, instead of actually, you know, humans putting cultures in touch with one another, negotiating those meanings together, discussing them and sort of ensuring that things are as best accounted for, the details, the nuances, the color, the, you know, you name it, it's all intact. 48:58.099 --> 48:59.460 [SPEAKER_00]: You know, I feel like something really 49:00.140 --> 49:22.534 [SPEAKER_00]: stands to be lost if we just automate these processes and it's like in one side out the other and we just have these like tubes of content production that are going each way and I want to take this opportunity to shout some of the groups that are that are kind of standing up and trying to try and to fight against this and I would love to spend more time talking about them. 49:22.694 --> 49:27.337 [SPEAKER_00]: There's a group with a name after my own heart translators against the machine 49:27.957 --> 49:41.677 [SPEAKER_00]: They're a group that sort of gathering stories and data about what it's like to work in translation right now in order to sort of build solidarity and to fight the encroachment of tech companies into their their professions because they 49:42.859 --> 49:44.819 [SPEAKER_00]: is really important, I think what they do. 49:45.160 --> 50:03.384 [SPEAKER_00]: And it is one of these areas that I think Silicon Valley companies do stand to sort of grind away, you know, whether just for a few extra enterprise automation contracts or just as sort of like a thoughtless by product of this rush to build and and release these products. 50:03.904 --> 50:15.991 [SPEAKER_00]: And there's also, if you're a translator who's worried about or interested in organizing around the impacts of AI, the National Riders Union has a translators organizing committee. 50:16.451 --> 50:23.375 [SPEAKER_00]: And you should check them out at nwu.org slash chapters slash t-o-c. 50:23.475 --> 50:27.978 [SPEAKER_00]: So there are folks who are out there doing some stuff about this. 50:28.298 --> 50:31.100 [SPEAKER_00]: And I think it's a space, like I said, 50:31.640 --> 50:32.940 [SPEAKER_00]: That it's not going to go away. 50:32.960 --> 50:46.645 [SPEAKER_00]: If the AI bubble bursts, there are still going to be these automation products that are widely available and in use and you're still going to have executives and clients who like who still will want to use them. 50:47.365 --> 51:03.248 [SPEAKER_00]: And it's going to have to be, it's going to be a fight, as it is with, I think, you know, people in the arts professions, illustrators, copywriters, graphic designers, screenwriters, who are the, you know, who won the first round of their fight, you know, and there's going to be many more. 51:03.588 --> 51:14.390 [SPEAKER_00]: And finally, I would shout the work of Lucille Danelov, who's a translator, who's written a lot about games localization, and has a website called lockedinloaded.net. 51:15.330 --> 51:31.088 [SPEAKER_01]: that you should you should check out if you're a translator interested in this stuff awesome yeah well what those in the show notes so people can more easily find them and said having to remember what you were what you were saying there but I think that's fantastic that that you laid those out and I just wanted to pick up on a couple things that you were saying right like 51:31.789 --> 51:36.070 [SPEAKER_01]: You know, we think about the ways that these technologies are rolling out for language and translation as well. 51:36.551 --> 51:53.296 [SPEAKER_01]: It immediately brought to mind what I heard from Maori speakers and people who advocate for that indigenous language in New Zealand and have their worry about, you know, how AI will continue to hamper efforts to kind of, you know, renew, restore and live in that language and keep kind of the 51:53.796 --> 52:03.769 [SPEAKER_01]: all their pronunciations and things alive as it just becomes jumbled and treated as this translation of English and be related more to English rather than what it previously was. 52:04.210 --> 52:10.077 [SPEAKER_01]: But also, as someone who is from like an officially bilingual country, I think having that kind of 52:10.598 --> 52:23.803 [SPEAKER_01]: back and forth that proper translation and the understanding of the context between the two languages, even if you are a monolingual person and are trying to engage with the whole of French and English culture in Canada. 52:24.603 --> 52:29.965 [SPEAKER_01]: I believe there were a couple people in the article who were from Canada who were talking about things like this. 52:30.085 --> 52:37.868 [SPEAKER_01]: I think that there's such a huge loss if instead of having these translators who can translate that context, who can actually 52:38.628 --> 52:49.637 [SPEAKER_01]: have the meaning there instead of just replacing words is going to be such a loss for a country that still has kind of linguistic divides and identity issues around language and things like that. 52:50.158 --> 52:54.441 [SPEAKER_01]: I think it potentially harm some of those kind of like national unity questions there as well. 52:54.821 --> 53:00.286 [SPEAKER_01]: These kind of bigger issues that we talk about and obviously you guys in the states have English and Spanish. 53:00.306 --> 53:04.950 [SPEAKER_01]: It's a bit different up here where it's officially bilingual on the government level and things like that. 53:05.610 --> 53:11.912 [SPEAKER_01]: And, you know, just to close off our conversation, I wanted to ask you, you know, you have been talking to so many of these workers. 53:12.052 --> 53:16.373 [SPEAKER_01]: You have been writing about learning about speaking to workers for so long about this. 53:16.453 --> 53:23.355 [SPEAKER_01]: But I wonder after doing this project, AI killed my job after hearing so many stories from people about this latest wave. 53:23.375 --> 53:29.596 [SPEAKER_01]: You know, has this changed how you assess the impact of AI on work after doing this for so long? 53:29.636 --> 53:32.817 [SPEAKER_01]: Like, what has been kind of the main takeaways that you've had from this experience? 53:33.188 --> 53:44.132 [SPEAKER_00]: You know, I wrote a year and maybe even a year and a half ago, like before I was really even fully doing the newsletter, and I would just kind of like jot out some thoughts on it occasionally. 53:44.272 --> 53:51.955 [SPEAKER_00]: I wrote a post, and it was called Understanding the Real Impacts of AI on Jobs, or something like that. 53:51.975 --> 53:55.796 [SPEAKER_00]: And I was just randomly going over it again, because it popped up. 53:55.896 --> 54:00.478 [SPEAKER_00]: And when I was looking for going through my archives, looking for something to link to in a recent piece. 54:01.638 --> 54:09.405 [SPEAKER_00]: Pretty much everything that I predicted would happen has more or less been born out so far. 54:09.886 --> 54:22.657 [SPEAKER_00]: The fact that it's really going to be a question of management using AI as a tool to sort of cut labor costs when possible to sort of 54:23.437 --> 54:45.131 [SPEAKER_00]: to concentrate their power or gain control and an organization to use as leverage, which we've seen happening to some extent for sure, where would less than, and I also, you know, I didn't think from the beginning that we were going to see a jobs apocalypse either, or that it would be sort of this mass unemployment event. 54:45.411 --> 54:46.632 [SPEAKER_00]: And you know, no, I think, 54:48.753 --> 55:01.925 [SPEAKER_00]: I've been a little surprised by or at least a year or two ago, I would have been surprised at sort of like the pervasiveness at how many corners that companies have been determined to just ram AI into. 55:02.025 --> 55:06.989 [SPEAKER_00]: Just you know, part of that's just like FOMO that like everybody's saying, AI, AI is the buzzword. 55:07.029 --> 55:13.095 [SPEAKER_00]: Like if I don't, if I'm a middle manager and I don't find a way to like have some kind of an AI program, my boss is going to think I'm stupid. 55:13.175 --> 55:14.296 [SPEAKER_00]: So I better get it in there. 55:14.816 --> 55:24.301 [SPEAKER_00]: I've been a little surprised by the extent to which, like, in education, that a lot of the teachers have been adopting AI and certain contacts. 55:24.321 --> 55:32.525 [SPEAKER_00]: So, like, I feel like there's case by case instances where I'm a little bit surprised by a certain use case and even when it seems like. 55:33.385 --> 55:36.226 [SPEAKER_00]: It's obvious that this isn't a great idea. 55:36.526 --> 55:47.349 [SPEAKER_00]: And as that MIT study found ninety five percent of the time, it's just like not not going to generate any real savings or advantages for your firm or your institution. 55:48.150 --> 55:51.851 [SPEAKER_00]: So yeah, the the zealousness may be a little bit. 55:52.151 --> 55:55.372 [SPEAKER_00]: So I should also say that the project is ongoing. 55:55.452 --> 55:59.413 [SPEAKER_00]: We have at least four more installments to do. 55:59.433 --> 56:00.353 [SPEAKER_00]: And so if you 56:02.286 --> 56:04.651 [SPEAKER_00]: Dear listener of Tech Won't Save Us. 56:05.722 --> 56:10.125 [SPEAKER_00]: have had AI kill your job in any way. 56:10.225 --> 56:14.888 [SPEAKER_00]: And I should say, I'm a little, I've still to the same people in about, because AI killed my job. 56:14.928 --> 56:22.053 [SPEAKER_00]: It's supposed to be, you know, AI has, like, made as change, transform, made unpleasant, immiscerated. 56:22.173 --> 56:30.798 [SPEAKER_00]: In whatever way, blanket, it's not, I don't need to give the impression that AI is an autonomous force that's killing jobs, that's the antithesis of everything I'm about. 56:30.818 --> 56:33.920 [SPEAKER_00]: It's supposed to sort of help explode that myth. 56:34.080 --> 56:35.101 [SPEAKER_00]: AI is a sentient thing. 56:35.281 --> 56:38.624 [SPEAKER_01]: It wouldn't be as catchy if all the additional context was in there, you know. 56:38.704 --> 56:39.365 [SPEAKER_00]: I tried. 56:39.385 --> 56:47.812 [SPEAKER_00]: I was like, you know, the AI that my boss bought from an enterprise AI company killed my, so it just wasn't flying. 56:47.952 --> 56:55.459 [SPEAKER_00]: So, so if AI has killed your job or you know somebody who's dealing with AI in the workplace or who has seen their work, 56:56.039 --> 56:56.619 [SPEAKER_00]: fall off. 56:57.000 --> 56:59.461 [SPEAKER_00]: It's AI killed my job at pm.me. 56:59.481 --> 57:02.062 [SPEAKER_00]: It's a proton male account. 57:02.562 --> 57:08.145 [SPEAKER_00]: I'm particularly interested in the next two installments are going to be health care workers. 57:08.665 --> 57:11.206 [SPEAKER_00]: So if you're a nurse, if you're working as a 57:13.067 --> 57:18.691 [SPEAKER_00]: therapist, if you're working in a hospital in admin, if you're a healthcare worker, I would love to hear from you. 57:19.251 --> 57:36.803 [SPEAKER_00]: And secondly, I'm looking at illustrators and graphic designers, artists, people whose work has been impacted by the rise of services like mid-Journey or or or Bali, which is not just chat, JPT, but the the image generation side. 57:37.203 --> 57:40.787 [SPEAKER_00]: So, but everybody, we're there's going to be more installments after that. 57:40.827 --> 57:42.789 [SPEAKER_00]: Those are just likely to be the next two. 57:42.989 --> 57:44.971 [SPEAKER_00]: So, I would love it if you have one to share. 57:45.331 --> 57:47.353 [SPEAKER_00]: We'll throw that in the show notes too, or Peris will. 57:47.553 --> 57:50.196 [SPEAKER_00]: I've been longer and control throwing things in that show. 57:51.057 --> 57:51.557 [SPEAKER_01]: We'll see. 57:51.757 --> 57:53.219 [SPEAKER_01]: Maybe I'll put it in the show notes. 57:55.441 --> 57:56.201 [SPEAKER_01]: That's great, Brian. 57:56.321 --> 57:58.362 [SPEAKER_01]: I think it's so important that you're doing this. 57:58.402 --> 58:07.186 [SPEAKER_01]: I think it's shed such important light on this facet of what we've been seeing with this AI hype over the past few years and really kind of grounds it for us, right? 58:07.226 --> 58:18.592 [SPEAKER_01]: So that we can actually feel the tangible effects that this is having, which as you say is not always destroying a job, but can still have massive terrible repercussions for people's lives, people's work, 58:19.152 --> 58:20.793 [SPEAKER_01]: How people are living in this world, right? 58:21.093 --> 58:27.795 [SPEAKER_01]: And often, it doesn't get nearly attention that it deserves, especially when we see these statements from these executives, absolutely everywhere. 58:27.855 --> 58:29.015 [SPEAKER_01]: So I think it's a great series. 58:29.335 --> 58:36.358 [SPEAKER_01]: And I've really been enjoying it so far and can't wait to hear and read the next installments, kind of installments. 58:36.398 --> 58:36.718 [SPEAKER_01]: Thank you. 58:37.999 --> 58:53.630 [SPEAKER_00]: Well, it's certainly been really eye-opening to me to talk to all these workers and hear their stories hear your stories and I'm so grateful to all of the workers and translators tech workers and everybody's still to come who has submitted stories answered my questions over email and you know started 58:54.711 --> 58:58.073 [SPEAKER_00]: bringing to light these issues in the reality of AI on the ground. 58:58.113 --> 59:00.995 [SPEAKER_00]: So thank you to everybody who's participated so far. 59:01.415 --> 59:01.896 [SPEAKER_01]: Absolutely. 59:01.996 --> 59:04.237 [SPEAKER_01]: And Brian, great to speak to you as always. 59:04.297 --> 59:11.062 [SPEAKER_01]: Thanks for coming back on Tech won't save us after such a long period where you weren't here, but that was of course because we were talking every week somewhere else. 59:11.082 --> 59:12.362 [SPEAKER_00]: We were doing our own show. 59:12.983 --> 59:14.864 [SPEAKER_00]: Oh, Paris, it's always good to be here. 59:14.944 --> 59:16.345 [SPEAKER_00]: You know that always a pleasure. 59:16.365 --> 59:19.227 [SPEAKER_00]: Oh, I'm sure I'll see you again before long. 59:22.477 --> 59:25.980 [SPEAKER_01]: Ryan Merchant is the author of Blood in the Machine and writes a newsletter of the same name. 59:26.321 --> 59:30.264 [SPEAKER_01]: Tech Won't Save Us is made in partnership with the nation magazine and is hosted by many Paris Marks. 59:30.525 --> 59:31.886 [SPEAKER_01]: Production is by Kyla Hucen. 59:32.206 --> 59:36.810 [SPEAKER_01]: Tech Won't Save Us relies on the support of listeners like you to keep providing critical perspectives on the tech industry. 59:37.211 --> 59:42.376 [SPEAKER_01]: 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. 59:42.756 --> 59:44.618 [SPEAKER_01]: Thanks for listening, make sure to come back next week.