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Electronics are getting more expensive—and the culprit has a lot to do with generative AI. Amid all the talk of AI taking over people’s jobs, automating people’s workflows, and doing people’s homework, we’re learning that actually scaling this technology is a lot harder, and more expensive, than it seems. On this week’s episode, the host of Galaxy Brain, Charlie Warzel, talks with his Atlantic colleagues Hana Kiros and Alex Reisner about why AI is such an engineering disaster, and how this memory shortage is affecting everything from MRI machines to smart fridges to kids’ access to technology in schools.
The following is a transcript of the episode:
Hana Kiros: This era of dirt-cheap consumer electronics seems like it’s at least temporarily over. I think that we might just enter sort of like a wartime era, like the way that during World War II we had like meatless Tuesdays or something. You know, people might just be rethinking their tech consumption.
[Music]
Charlie Warzel: I’m Charlie Warzel, and this is Galaxy Brain: a show where today, we’re going to talk about why electronics are getting more expensive, and what all of 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 look 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, computers, 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 Pei, the founder of the hardware company Nothing, summed it up 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. But the AI buildout—it might threaten all of that.
The AI-memory crisis, or as some people are calling it, the RAMpocalyse, 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 could 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. Hana Kiros is an assistant editor who has been writing about these price increases and 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.
[Music]
Warzel: Alex, Hana, thank you for coming on Galaxy Brain. Welcome.
Alex Reisner: Thanks for having us.
Warzel: So we are here to talk about the memory crisis—the RAMpocalypse, 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 origins here; very basic steps. Can you walk us all through how generative AI works? Like what are these—what do these large language models require to operate?
Reisner: 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.
Warzel: 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 these data centers work. Can you talk to me about the nuts and bolts of the chips, the storage, the intensity of actual, like, hardware resources in creating these models and training them?
Reisner: Yeah, so because the industry’s strategy has been to make the models bigger and bigger, they need more and more resources, right? Like, 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, you know, the previous 20 years.
Warzel: 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 notes that these companies “may be purchasing 70 percent of the world’s supply of high-end computer memory.” Now that’s a problem in its own right; we’re gonna get into that with Hana in a second. But this is also, as you argue the piece, an engineering disaster. You write that “the problem with generative AI, in the industry’s own jargon, is that it doesn’t scale.” What doesn’t scale here?
Reisner: Yeah; so I mean over the past 30 years we’ve seen other technologies rolled out, like major technologies. We now have kinds of devices that we didn’t have 30 years ago. We’ve got smartphones and 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.
Warzel: Why is it not polished? What about it isn’t polished?
Reisner: I think the simpler 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 start-ups, right? 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.
Warzel: So there’s this chart in your piece from Epoch AI, this organization that tracks the operating costs of major AI models. And they used several public AI models to show the exponentially increasing costs of serving more tokens. Tokens are the words that users type to chatbots; you know, the number of words in a response. And when you think about the enterprise-AI companies, or the enterprise companies that are using this AI stuff that are “token maxxing,” right? That are trying to use as much as possible. It seems to me like this is costing more and more and more money. Is there any way to like to solve for that?
Reisner: 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. Like, there’s subscription prices if you use these systems. There’s like, you know, $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. It’s—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 my understanding is that they’re nowhere near addressing the fundamental problem of exponentially scaling algorithms.
Warzel: So, Hana—Alex’s reporting here has established that these companies are building a technology. It’s incredibly resource intensive; it’s inefficient. Now we get into the second part of this, which is: What has this demand from AI companies done to chips and computer memory?
Kiros: 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 manufacturers would say, Okay, everyone’s gonna buy the new iPhone, and so I’ll give Apple a really cheap deal on this part. Because I know I’ll 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 a 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 to fulfill their orders. And they’re paying more for it. Because now, you know—one person that I was speaking to about Apple described them to me like an 8,000-pound gorilla in the supply chain. But now there’s an even heavier gorilla in 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.
Warzel: 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 electronic.
Kiros: Yeah; so the cheapest computer that Apple offers, the MacBook Neo, is now a hundred dollars more than it debuted at quite recently for the same device. The base iPad model now costs 30 percent 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 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 Macs when they go to college. And so these like hundred-dollar price increases, across all of these devices, around Christmastime and back-to-school season is when we really expect people to start to feel it.
Warzel: And Alex, you wrote in your reporting that hard drives that you bought just two years ago for $350 each are now, when you looked, 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?
Reisner: Yeah; I mean, I need those hard drives for my reporting. I bought a whole stack of them 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, you know, 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 shortage is gonna continue for years.
Warzel: This is exactly where I wanna go with this. Because I can imagine people are listening and they’re wondering, in some sense, kinda 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 Hana, 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?
Kiros: Well, reading about this, I was like: Wow, humans are so amazing. We can do really cool things. Because the fabs that they use 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 product. So these are just incredibly complicated manufacturing processes. And they’re only, like—most of the world’s memory chips are made by three companies.
And the lead time for making a new fab is like three to five years.
Warzel: Fab being shorthand for a fabrication facility, where these memory chips are made.
Kiros: And those efforts are in place, but also memory companies—they’re sort of cautious. Because let’s say demand disappears; you know, the AI boom is a bubble. Then they’ve spent billions of dollars building these fabs that now aren’t operating at full capacity. So the expectation is that, until 2030, we won’t have the capacity, like 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, you know, line go up. People get used to paying higher prices. And even when that extra manufacturing capacity comes online, I kind of find it hard to believe that Apple will say, like, Okay, and they’ll lower their prices. So prices just might be higher from now on.
Reisner: 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, at a 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, like, continually faster and cheaper. And that’s really not happening anymore—that kind of automatic progress that we came to expect.
Warzel: 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, you know, “training, feeding, more and more and more” paradigm?
Reisner: 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. So there are kind of grassroots efforts to create different types of AI.
If you look back historically, you know, AI was about trying to figure out how people use their own brains and translate that into code. Trying to, like, simulate human reasoning that way. And the kind of old approach that was used in the ’60s and ’70s was something that scaled better, for the most part. And there’s some people now that say we should go back to that method, or, you know, 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 just not that interested in changing course.
Warzel: So Hana, so 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?
Kiros: Yeah; so I think it’s already hitting certain parts of society harder than others. So I’ve spoken with IT managers for like public schools and hospitals. And I was 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 per student per device. And, you know, 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, you know, charge students more for their education. Like, 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 like order their lunch and also see all of their vitals and, you know, 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 you know, something that I could have bought a month ago for way cheaper, right? So maybe I hold on to my tech for longer. Maybe I decide to buy something used. Like after the Mac and iPad price increases debuted, there was a giant surge in looking at resale markets for those products. So, you know, I think that we might just enter sort of like a wartime era, like the way that during World War II we had like meatless Tuesdays or something. You know, people might just be rethinking their tech consumption. This era of dirt-cheap consumer electronics seems like it’s at least temporarily over, and I think that people’s habits will probably change.
Warzel: 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, right? Like, we’re not just talking about MacBooks, Chromebooks for kids, and iPads. And, you know, the MRI machine is a great example of this. Also, we’ve talked before offline about cars and things like that. You know—cars becoming basically just like computers on wheels, driving those prices up, changing, rippling, you know, 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, you know, 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’ve 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? Like, put a little more slack in the system in terms of the supply chain of all of that?
Reisner: I mean, if they were to solve the exponential-scaling problem, absolutely 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 fantasy. That’s a hypothetical scenario. There’s no evidence that that’s coming anytime soon.
Warzel: Part of the problem here is also outside of the models themselves needing more computing power. Our devices need more power too, right? Like, they are integrating AI technology and the features inside of them. Hana, is there a chance that our computers and phones will essentially have to, you know, dumb down in order to work if all this continues?
Kiros: When memory chips were dirt cheap, there was a big push to make everything smart. So like, 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. You know, I can imagine a “dumb renaissance,” basically, where we just—like, 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 increase is these ultra-cheap Androids that have razor-thin margins, that like the majority of people in like 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, like you access banking through your smartphone. Like aid 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.
Warzel: 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 18, versus I no longer can buy the phone I need to access a lot of very basic services, that we might take for granted here in America or in Europe or someplace like that.
Kiros: We’re kind of already seeing it. Global shipments of smartphones are down 11 percent, and that’s the lowest they’ve been since 2013. So 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.
Warzel: 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 then everything becomes harder to get?
Reisner: Yeah. You know, it’s hard for me to tell 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 were 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 that’s how these companies are making money. It’s just by pushing the technology into everything.
Warzel: Hana, 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 a consumer or actual, you know, person-on-the-ground backlash?
Kiros: Yeah; I think it already is. In the subreddits that I lurk in, people are like, you know: I can’t build a PC anymore, because of the quest to build a machine god. I think back-to-school season is when a lot of people will feel this, as they’re trying to buy like new tech for their kids going off to college. Or when 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 cure all disease and free us from labor? Like, maybe? But it just feels very amorphous. And I think that when it hits sort of the tangible digital stuff that helps run our lives like that? Yeah, you know, people aren’t gonna be crazy about that.
Warzel: I do think that that trade-off is a bit radicalizing to some people. Alex, have you seen or felt in your reporting anywhere this feeling of backlash?
Reisner: I haven’t talked to many people yet who are really aware that these price increases are 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 will do, versus what they actually—the benefits they actually see from using it.
Kiros: In the U.S., at least 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 a Mac on Facebook Marketplace, and the guy I bought it from was like, You’re getting such a good deal. You know, these prices are gonna increase. Like, I’m increasing my used-Mac prices. So I think that it’s annoying, and, you know, 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, you can get used electronics.
Companies may also be crafty. They might, you know, come up with smarter ways to allocate memory. Apple, really coolly, has this thing called unified memory that’s quite unique; that allows more tasks to be done with like less memory. So I feel like they’re—I don’t want to make it seem as if, like: Mac price goes up a hundred dollars; now my family can’t eat. I think that people will find ways to get a lot of the tech that they need. You know, 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.
Reisner: Yeah, although I watch 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.
Kiros: Yeah.
Reisner: 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 gonna cut off access to technology for a lot of people.
Warzel: 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 inefficiency of the hyperscalers as they build out and then, you know, how that may or may not affect whether these prices just keep going up. Whether, you know, the crunch on the memory industry continues to intensify.
Reisner: I think—I mean, the word bubble is used a lot. and I think if we are in a bubble and it does in fact pop, you know, 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 to 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.
Kiros: 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 pull to get the memory that they need at a price that’s sustainable. And so I think what we might see happen is Apple, Samsung, Dell, like Lenovo, 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, you know, We can’t even get memory companies to pick up the phone when we’re trying to fill orders. So I think we could get to a point where companies, I guess B tier and below—I think if we put like GoPro at B tier, 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 indie gaming consoles, I feel like that’s maybe a thing that doesn’t make sense. Or like, just anyone but the biggest dogs kind of losing out.
Warzel: 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, you know, looking to latch onto 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 wanna 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, then the demand is going to keep going up. This means the prices will keep going up. And so it feels like 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 feel really bad, or look really bad to consumers?
Kiros: I think it’s possible that the era of dirt-cheap computing that we’ve been in since the 1960s is like, you know, gone. And that was cool. And the transistor, you know, 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, and we design systems, architectures and software and hardware that is more efficient. And we get, you know, whatever the next transistor is, you know. 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 fridge used 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 we’ll have to adjust our expectations and the way we live our lives.
Warzel: Hana, Alex—thank you for coming on Galaxy Brain.
Reisner: Yeah. Thanks, Charlie.
Kiros: Awesome; yeah.
[Music]
Warzel: Thanks again to my guests, Alex Reisner and Hana Kiros. Before we go, though, a quick note from me. Talking to Alex and Hana, I’m 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 Doctorow of enshittification.
Enshittification is essentially the idea that tech platforms entice people with free or very useful and efficient services—and, over time, those services monetize, they get worse, and they exert more power over the people who’ve become locked into that 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 ecosystem 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 enCHIPification.
And it’s not clear how all of 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, may be 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-positioned 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’re 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 liked 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 at TheAtlantic.com/Listener. That’s TheAtlantic.com/Listener. Thanks so much, and I’ll see you on the internet.
This episode of Galaxy Brain was produced by Renee Klahr and engineered by Miguel Carrascal. Our theme is by Rob Smierciak. Hadley Robinson is our senior supervising producer. Claudine Ebeid is the executive producer of Atlantic audio, and Andrea Valdez is our managing editor.
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