Why AI Is Making Things More Expensive

Tired of rage-scrolling through AI Slop and Clickbait just to get a handle on today's news? Meet InPress, the algorithm-free news app that makes it fun and rewarding to stay informed. Track your media literacy and rediscover reality. Download today at inpress.app. That's i, n is in news nerd. Press. This era of dirt cheap consumer electronics seems like it's at least temporarily over. I think that we might just inter, sort of like a wartime era, like the way that Dring World were to. We had like, meetless Tuesdays or something. You know, people might just be rethinking their tech consumption. I'm Charlie Worsill and this is Galaxy Brain, a show where today we're going to talk about why electronics are getting more expensive and what all that has to do with AI. Now, if you've been in the market for any kind of consumer electronics recently, you've probably noticed that the price tags looked a little different this summer. Apple has raised prices on its MacBooks and iPads. Game consoles like the PS5, they cost more today than when they launched over five years ago. Samsung has raised the price of its Galaxy smartphones and Apple is expected to do the same with iPhones. All of this has analysts predicting that the primary smartphone market could decline by 14.8 percent in 2026, which would be a record drop. Supply chains and manufacturing are complicated, but in the case of these rising prices, the culprit is actually somewhat obvious. Everything is getting more expensive because we are in the middle of a memory crisis. Now, you've probably heard of RAM, which stands for random access memory. It's basically your device's short-term memory and there are different types of it. There's dynamic RAM, there's static RAM, but what you really need to know for this is that it is an essential component of TVs, computer, smartphones, gaming consoles, even many modern cars. And the price of these components is going up sharply. The prices of certain memory chips have tripled even quadrupled in the last trimester and the reason is the generative AI boom. AI companies are so hungry for memory that manufacturers can't keep up, meaning that there are production shortages and there's no real end in sight. Carl Pay, the founder of the hardware company Nothing, summed it up very succinctly earlier this year. The era of cheap silicon is over. Now, if this is actually the case, it would represent a fundamental shift in how we use electronics. iPhone and Xbox buyers, but also huge corporations, all of us. We have gotten used to purchasing electronics in similar ways, upgrading every few years when our machines get sluggish. Manufacturers build their technology with the expectation that the bulk of consumers will update to devices that are more powerful, more efficient, and have better memory. And this may sound like a luxury to you, but the point is that it isn't. The modern computing paradigm is one of uninterrupted progress. The AI buildout, it might threaten all of that. The AI memory crisis, or as some people are calling it the RAM apocalypse, is an emerging story. You may not be seeing or feeling it yet, but there are chances that you will very soon, because so many of the devices that we use every day are affected. Now, to better understand precisely why this is happening, and to chart the ways that the memory crisis just ripple through the economy and our lives, I've invited on two of my colleagues who've been reporting on elements of this story. Alex Reisner is a researcher, a programmer, and a staff writer here at The Atlantic, who investigates how artificial intelligence systems really work. Hanakiros is an assistant editor who's been writing about these price increases in the unexpected ways that they may be ushering in a new era of computing, and they both join me now to talk about it all. Alex Hanak, thank you for coming on Galaxy Brain. Welcome. Thanks for having us. So we are here to talk about the memory crisis, the RAM apocalypse, give it a name, whatever you want to call it, and specifically how this is causing a lot of things to get more expensive. But Alex, I want to start at the the origins here, very basic steps. Can you walk us all through how generative AI works? What are these, what did these large language models require to operate? Yeah, so large language models require a lot of language to operate. They're trained on millions, tens of millions of books, tens of millions of research papers, all the text that these companies can find they use to train these models. And basically, what they've discovered is that the more of that text they use to train the models and the more of that text the models store, meaning the larger the models are, the better they seem to work. So talk to me a little bit about the mechanics of that in terms of I think a lot of people are seeing news about data centers, how do these data centers work? Can you talk to me about the nuts and bolts of the chips, the storage, the intensity of actual hardware resources in creating these models and training them? Yeah, so because the model, because the industry strategy has been to make the models bigger and bigger, they need more and more resources. They require a particular kind of memory, this high speed, high bandwidth memory that can process just enormous amounts of data quickly, and they also need to increase the number of data centers dramatically. And so we're actually what I've seen from other people's research is that they're planning to increase data center capacity by eight times what it currently is, which is just a massive increase compared to the previous 20 years. So, okay, you have these data centers, you have these large language models, they're very resource intensive. You wrote a story about the AI engineering disaster for us recently that everyone should go and read. Your story knows that these companies, quote, "may be purchasing 70% of the world's supply of high-end computer memory." Now, that's a problem in its own right. We're going to get into that with Hana in a second, but this is also, as you are, you're the piece in engineering disaster. You write the problem with generative AI in the industry's own jargon is that it doesn't scale. What doesn't scale here? Yeah, so I mean, over the past 30 years, we've seen other technologies roll out like major technologies. We now have kinds of devices that we didn't have 30 years ago about smartphones, tablets. Generative AI is really unprecedented in that it is engineered in a way that it's like the polish hasn't been put on it in a way. It's kind of in the stage that usually products that are kind of still in testing would be in. Why is it not polished? What about it isn't polished? I think the simple thing to say is that it's really inefficient. And what I mean by that is two different things. So the first is it includes algorithms that just don't scale. And by scaling what we mean usually is scaling is what venture capitalists look for in startups, which is the ability to add new users and to grow really large without that costing a ton of money. And so they're looking for companies that can build products that for every additional user added, it actually costs less to support that user. And generative AI is the opposite. The more users are on the system and the more input they have, the more resources and time and money they consume. So that's the first thing. And then the second thing is, as I said before, the industry has decided that these models should be as big as possible that they're better and more capable when they're larger. And so they're just being given more input. They're made larger and larger. And so the combination of that with technology fundamentally that does not really get larger gracefully that consumes exponentially more resources is very bad. So there's this chart in your piece from Epoch AI, this organization that tracks the operating costs of major AI models. And they use several public AI models to show the exponentially increasing costs of serving more tokens. Tokens are the words that users type to chat bots, you know, the number of words in a response. And when you think about the enterprise AI companies, the enterprise companies that are using this AI stuff that are that are quote unquote token maxing, right? They're trying to use as much as possible. It seems to me like this is this is costing more and more and more money. Is there any way to to like to solve for that? I don't know. I think one of the biggest and most important mysteries of the AI industry right now is how much it actually costs to run these systems. There's subscription prices. If you use these systems, there's like $20 a month or something like that. We have no idea what it's actually costing these companies to run the systems. Is there a solution potentially? But the companies have been working on this for years already. They're well aware of what I'm saying in the piece. This is not news to anyone in the industry, but it's an extremely hard problem to solve. And they've made small progress here and there, but they are my understanding is that they're nowhere near addressing the fundamental problem of exponentially scaling algorithms. So, Hana, Alex's reporting here has established these companies are building a technology incredibly resource intensive, inefficient. Now we get into the second part of this, which is what is this demand from AI companies done to ships and computer memory? Yeah, so for a long time, a company like Apple could sort of twist arms and get the best deals possible for memory because they were the first and most important customer in line. So memory menu. manufacturers would say, okay, you know, everyone's going to buy, you know, the new iPhone and so I'll give Apple a really cheap deal on this part because I know I'll, you know, make a lot of money off of this. But now, the hyperscalers have come in and they're approaching memory companies and saying basically, we'll pay as much as you want for as much memory as you'll give us. They just are much more valuable customer than the consumer electronic companies that are building the products that sort of run our daily lives. So what's happened now is more of that manufacturing capacity is going to the hyperscalers and now only the biggest consumer tech companies are even able to get memory chips at the quantity that they need to fulfill their orders. And they're paying more for it because now, you know, there's one person that I was speaking to about Apple described them to me as like an 8,000 pound gorilla in the supply chain. But now there's an even heavier gorilla in the hyperscalers and they're setting the price and now the price is just higher for everyone. And as those build costs go up, you know, that's being passed on to the consumer and now the stuff that we buy is costing more too. Yeah, you wrote this great article for us in July calling this essentially an AI tax on a lot of consumer electronics, right? Describe to me what's happening. Let's use the MacBook as an example of what's happening to a specific very important consumer electronics. Yeah, so the cheapest computer that Apple offers the MacBook Neo is now $100 more than it debuted at quite recently for the same device. The base iPad model now costs 30% more. iPhone prices haven't increased, but the prediction is that the iPhone 18 Pro will cost $200 more than the previous model and some analysts that I've spoken to even expect the iPhone 17 to cost more even though it debuted, you know, last season for the same exact device. So what we're seeing is just, you know, Apple was sort of one of the last companies to be hit, but we see this across like Dell, Lenovo, every single gaming console costs more. Some indie gaming companies have shelved plans to make consoles because the margins don't make sense anymore because memory prices have pushed build costs up so much. This has been going on for a while, but the Apple price increases sort of raised people's eyebrows because the Apple ecosystem is really sticky. So like parents that have iPhones raise iPad babies and then they buy their iPad babies max when they go to college. And so these like $100 price increases across all of these devices like around Christmas time and back to school season is when we really expect people to start to feel it. And Alex, you wrote in your reporting that hard drives that you bought just two years ago for $300, $50 each are now when you look, they were $800, right? And that these prices are just out of control. What are you seeing across the industry when you look at it? Yeah, I mean, I need those hard drives for my reporting. I bought a whole stack of them and I two years ago and I'm now sitting on a small fortune in hard drives, which would be funny if I could sell them, but it's really bad because I may need more of them. Everything is going up in price. It's not a time to buy computers and unfortunately it seems like these companies, the manufacturers are not really very close to solving the problem. It seems like the short are just going to continue for years. This is exactly where I want to go with this because I can imagine people are listening and they're wondering in some sense kind of what the big deal is, right? Why these companies can't like get it together? Why can't we just make more chips? I'd love Hanna and then Alex, if you have something to add here, what in basic terms is the fabrication process like for this? Why can't they just make more chips? Well, reading about this, I was like, wow, humans are so amazing. We can do really cool things because the fabs that they used to make memory chips are like many orders of magnitude cleaner than like a hospital, clean room, a speck of dust can ruin millions of dollars worth of products. These are just incredibly complicated manufacturing processes and they're only like most of the world's chips are made by memory chips are made by three companies and the lead time for making a new fab is like three to five years. Fab being a shorthand for fabrication facility, which is where these memory chips are made. And those efforts are in place but also memory companies, they're sort of cautious because let's say demand disappears. The AI boom is a bubble then they've spent billions of dollars building these fabs that now are operating full capacity. So the expectation is that until 2030 we won't have the capacity and the manufacturing capacity needed to really drive costs down. And another thing that I've been thinking about is just that during COVID prices went up because the world stopped and supply chains were really disrupted and they've never really gone down. So there's kind of a tendency for like, you know, line go up like people get used to paying higher prices and like even when that extra manufacturing capacity comes online, I kind of find it hard to believe that Apple will say like, oh, okay, you know, and the lower their prices. So prices might might be higher from now on. It's also made worse by what people call the end of Moore's law. Moore's law was this kind of observation that was made in the 60s that computer hardware was getting significantly faster and cheaper, kind of pretty steady rate. And that was true from the 60s until about 10 to 15 years ago. And components have gotten so small at this point that they're having a really hard time shrinking them any further. We got used to computers getting always continually faster and cheaper and that's really not happening anymore. That kind of automatic progress that we came to expect. If you're trying to get news today, you'll find a lot of biased algorithms ragebait and AI noise. Think social media. That's where impress comes in. It's a social platform on top of factual journalism, not the other way around. Track your reading and earn points. It's like Strava for your brain. Plus, you can connect with other users in your city who share your genuine curiosity, ditch the echo chamber for reality with impress. Download it today for iOS or Android at impress.app. That's I and as a news nerd, press Alex, you quote one AI engineer who told you that the idea that one must rely on massive foundational models trained for millions of dollars by some big corporation in order to achieve success on hard tasks is a trap. What is the trap here? Do these AI models need to be built this way? Is there a way beyond the LLM training feeding more and more and more paradigm? A lot of people in the industry or some people in the industry, it's hard to know how many, but there's certainly a bunch of developers that are trying to build smaller models that scale better that take fewer resources. None of these models that I've seen so far are really replacements for large language models, but they are good at solving certain problems and some of them have been put into use already in places where companies are using other kinds of AI models. There are kind of grassroots efforts to create different types of AI. If you look back historically, AI was about trying to figure out how people use their own brains and translate that into code, trying to simulate human reasoning that way and those kind of old approach that was used in the 60s and 70s was something that scaled better for the most part. There's some people now that say we should go back to that method or combine that method with the current language model method. But I think there is so much momentum behind the chatbot products, essentially, that there is not much will within the industry to try to figure out how to do things differently. There's so much money coming in to support this approach, even though it's so bloated and inefficient that I think the industry is not that interested in changing course. Let's say we stay on the same trajectory here. Walk me through what this period looks like. How might this play out across different areas? Yeah, so I think it's already hitting certain parts of society harder than others. So I've spoken with IT managers for public schools and hospitals and speaking to a guy who works with a school district in Missouri and he was saying that basically they're starting this school year in the red because they are paying more for per student per device and they're getting less money from the department of education. So these places where you have to buy bulk tech and you can't just charge students more for their education. It's creating headaches for the school system. The school system they give kids when they enter middle school and when they enter high school, like Chromebooks and he mentioned that the school district is considering even just like maybe the kids don't need a laptop during middle school because we can't afford to do that for them. If this continues in health care, things like MRI machines use a massive amount of memory. In my reporting, I reached out to an MRI manufacturer and they were like, yeah, we might have to charge hospitals more for these machines. If these prices don't change, a health care consultant I spoke to said that the hospital system she works with, they were installing basically these iPads, these tablets in hospital rooms that would allow people to order their lunch and also see all of their vitals and their health charts. And they stopped the project because those tablets require memory and now it costs way more to buy them. And so in that way, we're already seeing the effects of the memory shortage. But there's also this idea of like, we've talked a bit about this AI austerity, the idea that I don't like paying more for something that I could have bought a month ago for way cheaper, right? So maybe I hold onto my tech for longer. Maybe I decide to buy something to use. Like after the Mac and iPad price increases, debuted, there was a giant surge in looking at resell markets for those products. So I think that we might just inter, sort of like a wartime era, like the way that Dring World War II, we had like meatless Tuesdays or something. People might just be rethinking their tech consumption. And I think that people's habits will probably change. I think when people think about the ripple effects, they don't often think of the ways that chips and memory is now just in everything. We're not just talking about MacBooks, Chromebooks for kids and iPads and the MRI machine is a great example of this. Also, we've talked before offline about cars and things like that, cars becoming basically just like computers on wheels driving those prices up, changing, rippling through the market, affecting used car sales, all that stuff. So it seems like it is something that is not just actually confined anymore to what we would traditionally call the consumer electronic space. But Alex, I want to go back for a second here to the inefficiencies of the language models. Because you reported and you alluded to earlier that some of these AI companies have found techniques for improving performance, but they've also not yielded significant gains. If it does improve, if they do find some ways to make some real significant gains in terms of the efficiency, do you think that that changes the resources, like the intensive amounts of resources that these demand put a little more slack in the system in terms of the supply chain of all of that? I mean, if they were to solve the exponential scaling problem, obviously it would change everything completely. I think AI would become a profitable business, but I think it's sort of, at this point, just a fast, that's a hypothetical scenario. There's no evidence that that's coming anytime soon. Part of the problem here is also, aside of the models themselves needing more computing power or devices need more power, too, right? They are integrating AI technology and the features inside of them. So, Hannah, is there a chance that our computers and phones will essentially have to dumb down in order to work if all this continues? When memory chips were dirt cheap, there was a big push to make everything smart. So you can have a smart fridge and a smart toaster, and it was just sort of a cheap way to justify charging more for your product. I can imagine a dumb renaissance, basically, where we just, you know, maybe your device doesn't need to be connected to the internet of things to be good, what's really being hurt by the memory? Price increases, these ultra cheap androids that have razor thin margins that, like, the majority of people in Africa, people in India use, some Chinese companies that make those Uber cheap smartphones, they've just gotten out of the business entirely because the margins don't make sense. But for a lot of people, you access banking through your smartphone, eight groups during famines, they identify who they reach through smartphones. So I think a lot of tech can be dumbed down, like I don't need a smart fridge. But I do worry about smartphones being people having to go from smartphones to dumb phones involuntarily because of price increases, because a lot of those ultra cheap android phones no longer make economic sense, given the memory crisis and the price increases we've seen. - I think it's a great point to note that this will play out differently in different areas of the world. For some people, it will be an issue of, I really want to upgrade because I like, you know, the new camera on the iPhone, whatever, 18, versus I no longer can buy the phone I need to access a lot of, you know, very basic services that we might take for granted here in America or in Europe or someplace like that. - We're kind of already seeing it, global shipments of smartphones are down 11%, and that's the lowest they've been since 2013. So like there is already like a historic dip in smartphone buying. So I guess, you know, it seems like the price increases are marginal, but people are like reacting to them. - There's a brutal irony here, I think, which is that these AI companies are spending unfathomable amounts of money, and they're trying to build what they believe is a super intelligence or just very, very powerful models. And yet this technology that they're building is, you know, theoretically driving up the price of the gadgets so that they may become both incredibly smart and less accessible to people. Like Alex, does this to you jeopardize the whole project that these labs are trying to infuse generative AI into everything, but the everything becomes harder to get? - Yeah, you know, it's hard for me to tell like what their plan really is. I think we would all like tools that help us do our work and make the world better. I'm not really sure what these companies are doing, right? Like they primarily want to make a profit in order to make a profit. They are advertising generative AI as something that is a useful tool. I think if you know where to look at the actual cost of AI, which we're really starting to see now in a very concrete way and compare that to the actual benefits of AI so far, it's a very strange ratio. Again, I think sort of unprecedented in the history of the tech industry. It's just a really weird project that I think is motivated in part by just the idea of artificial intelligence and wanting to build an artificial human, which is something that people have wanted to do for thousands of years. I think there's an almost religious need to just continue that project. And if getting funding for that means saying, hey, this is really useful. Everyone should have this. We're gonna put this in everything. I think, you know, that's how these companies are making money. That's just by pushing, pushing the technology into everything. If you're trying to get news today, you'll find a lot of biased algorithms, ragebait and AI noise. Thanks, social media. Plus, you can connect with other users in your city who share your genuine curiosity. Ditch the echo chamber for reality with in-press. Download it today for iOS or Android at in-press.app. That's I, and as in news nerd, press. - Hanna, AI companies are not exactly beloved by the general public right now. Do you think that this is going to lead, or have you seen already that this is leading to like a consumer or actual, you know, person on the ground backlash? - Yeah, I think it already is. In like the subreddits that I lurk in, people are like, you know, I can't build a PC anymore because, you know, of the quest to build a machine, God. I think back to school season is when a lot of people will feel this, you know, as they're trying to buy like new tech for their, for the kids going off to college. Or when, you know, the price increases hit the next iPhone and people are thinking about buying a new one. And I think part of why it feels weird is that when you talk to people about like, you know, will AI, you know, cure, cure all disease and free us from labor like maybe, but like it just, it just feels very amorphous. And I think that when it hits like sort of the tangible digital stuff that helps run and realize like that, you know, people aren't going to be crazy about that. - I do think that that trade-off is a bit radicalizing to some people. - Alex, have you seen or felt in your reporting anywhere that this feeling of backlash? - I haven't talked to many people yet who are really aware that this price increases or as bad as they are. I have talked to a lot of people who are just confused about why they're supposed to be using AI and the differences between what the companies say it can and what they'll do versus what they actually, the benefits they actually see from using it. - In the US, at least I don't, I think it will be, I don't think it's necessarily apocalyptic. I think a lot of people are crafty. Like I found out about the memory crisis in March because I bought it. I got a Mac on Facebook Marketplace and the guy I bought it from was like, you're getting such a good deal. You know, like these prices are gonna increase. Like I'm increasing my used Mac prices. So I think that it's annoying and it's painful. I think people sort of autopilot like, oh, I'll buy the next new thing. That might be disrupted for some people, but you can, even if used prices are going up, like you can get used electronics. Companies may also be crafty. They might, you know, come up with smarter ways to allocate memory. Apple really cool. We has this thing called unified memory. That's quite unique that like allows like more tasks to be done with like last memory. So I feel like they're, I don't want to make it seem as if like Mac price goes up $100. Like now my family can't eat. Like I think that people will find ways to get a lot of the tech that they need. I think a lot more people will probably buy used or just delay refreshing the tech and try to stretch their old tech out for longer. - Yeah, although I watched the price of used smartphones pretty carefully and I will say that that has been going up significantly in the last six months, which is something I've just never seen. And it's also important to note that like it is the most affordable devices that are being hit the hardest by this. I actually am not sure that certain people are going to be able to continue having a computer in the house, you know, when their current one doesn't work anymore or like the smartphone that they really want. I think that is really is going to cut off access to technology for a lot of people. - So this is a good segue into my next question, which is what do you both anticipate that the future looks like here? Like what are some possible visions for how all this plays out? And I'm talking about both the the inefficiency of the hyperscalers as they build out and then how that may or may not affect whether these prices just keep going up, whether the crunch on the memory industry continues to intensify. - I think, I mean, the word bubble is used a lot. And I think if we are in a bubble and as I was in fact, pop, who knows? I think the AI industry is in a very weird position. It's trying to sell a product that may not be profitable. It may not have the resources even continue selling that product its own costs may go up so much that these companies can't continue. The whole AI thing could in some sense fall apart. It's a little hard to imagine companies of that size really tanking, but we have seen that in the past. I don't know, it's very hard to predict what's gonna happen, but I think certainly everything can't continue going the way that it is. Like the path that we're on is not really sustainable. Something's gonna have to change. - I think something that sort of caught my attention is that GoPro told the federal government that the company was at risk of bankruptcy because of the memory crisis. And like GoPro is a name that I know. I guess they don't have enough poll to get the memory that they needed a price that's sustainable. And so I think what we might see happen is Apple, Samsung, Dell, like bigger companies that really buy it in bulk and have giant markets. Like they'll be able to get the memory they need to fill orders. They'll be paying more for it. So stuff will cost more, but they'll have what they need. Whereas smaller companies are saying we can't even get memory companies to pick up the phone when we're trying to fill orders. So I think you, you know, we could get to a point where companies, I guess I beat here and below I think it's we put like GoPro and beat here. They just can't make their products anymore. And then we end up having sort of like a leaner group of gadget makers. Like I think of like indie gaming consoles. I feel like that's maybe a thing that doesn't make sense. Or like just just anyone but the biggest dogs kind of losing out. That feels radicalizing to me again in this way that like if this actually starts to keep going in the way that it does, right? If trends continue, I think consumers, politicians who are looking to latch on to something watching these hyperscalers, these already huge massive companies driving other companies out of business, I think is a really bad look for these companies. I think it is something that could be just really galvanizing, really tangible for lots of people, especially if they watch companies go out of business simply because they can't get these memory companies to pick up the phone. But I want to end here, which is that reading all of your reporting, I'm struck by this possibility that we could really end up in a bit of a vicious cycle here. AI companies need to hoover up memory to run. If they succeed in scaling up and getting people to adopt the technology, the demand is going to keep going up. This means the prices will keep going up. And so it feels like if companies, if these companies win, we get trapped in this paradigm, right? And we, the consumers ultimately end up losing footing the bill for a lot of this. If this is a bubble and it bursts and the companies start to falter, then obviously we see prices go down. But that happens in tandem with what would be a serious financial crisis given the historic investment in AI. Is there a future here that doesn't really bad or look really bad to consumers? I think it's possible that the era of dirt cheap computing that we've been in since the 1960s is gone. And that was cool. And the transistor brought that about. And it's also like, I think the optimist take is that maybe this causes engineers to like get good. And there's like resource constraints in redesign like systems. Architecture is in software and hardware that is more efficient. And we get whatever the next transistor is. And we enter this era of like more efficient tech design. But I think that having a TV used to be a luxury, having a franchise to be a luxury like maybe-- I guess if we don't engineer our way out of this or if memory capacity never catches up with demand, we might just be paying more for electronics. And then we'll have to just our expectations in the way we live our lives. Hanna, Alex. Thank you for coming on Galaxy Brain. Thanks, Charlie. Awesome. Yeah. [MUSIC PLAYING] Thanks again to my guest, Alex Reisner, and Hanna Kuros. Before we go, though, a quick note from me. Talking to Alex and Hanna, I am struck by the ways that this memory crisis dynamic actually rhymes with a different issue in the tech world. This term that is coined by Cory Doctoro of Shitification. In Shitification is essentially the idea that tech platforms entice people with free or very useful in efficient services. And over time, those services monetize. They get worse. And they exert more power over the people who become locked in it at ecosystem. What's happening with memory is, in some ways, very similar. We've all gotten used to a style of computing that gets better and cheaper over time. In the case of smartphones, we now use them to replace all kinds of physical items in our lives from wallets to cameras to maps. We rely on them. And the companies who build these devices, they've locked us into their ecosystems over a long period of time. Apple, Android, you name it. But now, almost 20 years into the iPhone era, it's possible that we're starting to see the turn of the screw. Now, dependent on these products, they're becoming wildly expensive in order to help create the AI technology that is supposed to infuse these devices and make them even more powerful. You could call this in Shitification. And it's not quite clear how all this is going to play out. It seems plausible to me that the people who can will continue to pay, even if prices become astronomical. Those who can't, though, maybe left out, stuck with devices that aren't powerful enough, or easily repairable enough to work well. This would deepen the digital divide, which is a technological form of wealth inequality that's already very real across the globe. Now, it's possible, of course, that things could snap back. That demand will change slowly over time without a market crash or without a bubble bursting. But what seems really clear here is that the biggest best position technology companies and manufacturers in the world, they have this distinct advantage. They can hunker down, they can ride out this crisis. Others who can't afford to will not be so lucky. And so, regardless of what happens, it is clear that the AI boom is drastically reshaping the world in all kinds of unexpected ways. The hyperscalers and boosters argue that what they are building is an unalloyed good for humanity. But what is inarguable is that whatever it is they're actually building, it's coming at a genuine cost. Okay, that's it for us here. If you like to what you saw new episodes of Galaxy Brain Drop every Friday, you can subscribe on the Atlantic's YouTube channel or on Apple or Spotify or wherever it is that you get your podcasts. And if you want to support this work and the work of my fellow colleagues, you can subscribe to the publication. Theatelantic.com/listener. That's theatelantic.com/listener. Thanks so much, and I'll see you on the internet. [BLANK_AUDIO]