Hey everyone, welcome back to My Weird Prompts. I am Corn, and I am sitting here in our living room in Jerusalem with my brother, the man who probably has more browser tabs open than there are stars in the observable universe. It is a rainy February afternoon here, and the hum of the space heater is almost being drowned out by the sound of Herman’s mechanical keyboard.
That is a fair assessment. Herman Poppleberry here, and honestly, those tabs are all necessary research. I have been tracking the spot prices for semiconductors since six in the morning, and let me tell you, the charts look like a mountain range that only goes up. It is good to be back, even if the news I am bringing is a bit grim for the wallet.
So, we have a bit of a situation in the house. Our housemate Daniel has been going through what I can only describe as a home server odyssey. He thought he was in the clear after a motherboard failure last month—he actually managed to find a replacement board for his older Epyc system—but then he hit a wall that I think a lot of people are hitting right now. He was looking to replace some memory that got fried in the power surge, and he found that the market has essentially turned into a post-apocalyptic wasteland.
It is being called the RAMpocalypse, or RAMgeddon, depending on which corner of the internet you frequent. And Daniel is not exaggerating. The price jumps we are seeing in early twenty twenty-six are absolutely unprecedented in the history of consumer electronics. We are talking about thirty-two gigabyte kits of D-D-R-five that used to be one hundred thirty dollars now pushing four hundred dollars or more. If you are looking for high-density E-C-C modules for a server like Daniel’s, you might as well be trading in precious metals.
It is wild. I remember when RAM was the cheap part of the build. You would just throw in an extra stick like it was nothing. It was the "free space" on the bingo card of PC building. But Daniel sent us this prompt because he found a statistic that is almost hard to believe. Apparently, OpenAI alone is consuming an estimated forty percent of the global dynamic random access memory supply.
That is the figure currently circulating in the Q-one industry reports. It sounds like a typo, right? How can one single entity, even one as massive as OpenAI, gobble up nearly half of the entire planet's production of memory? But when you look at the architecture of the clusters they are building right now—specifically the phase five expansion of the Stargate project—the math starts to make a terrifying kind of sense.
Well, that is what I want to dig into today. I want to understand how the supply chain gets this brittle, what is actually happening in those data centers that requires that much silicon, and whether there is any light at the end of the tunnel for people like Daniel who just want to run a home server without taking out a second mortgage.
It is a deep rabbit hole, Corn. We are looking at a fundamental shift in how silicon is allocated globally. It is not just a temporary shortage like we saw during the pandemic; it is a structural realignment of the entire industry. The factories haven't stopped working; they've just stopped working for us.
Okay, so let's start with that forty percent number. That is the one that really sticks in my throat. We are talking about a global market that supplies every laptop, every smartphone, every gaming console, and every server on earth. How does one company, even one running the world's most famous large language models, take up forty percent of that?
You have to look at the scale of the clusters they are building for training and inference. We aren't talking about a few racks of servers in a rented cage anymore. We are talking about projects like the Stargate supercomputer, which is a joint venture between Microsoft and OpenAI. By some estimates, that single project aims to house over a million G-P-Us. When you train a model with trillions of parameters—like the rumored G-P-T-five or whatever they are calling the latest iteration—you aren't just limited by the processing power of the graphics processing units. You are limited by the memory wall.
The memory wall. Explain that for the non-engineers.
Think of the G-P-U as a world-class chef who can chop vegetables at lightning speed. But the chef only has a tiny cutting board. If the ingredients—the data—aren't sitting right there on the board, the chef has to stop, walk to the pantry, and come back. That walk to the pantry is the latency of going to a hard drive or even standard system RAM. To keep the chef working at full speed, you need a massive cutting board. In the AI world, that cutting board is H-B-M, or High Bandwidth Memory.
Right, because if the data isn't in the memory, the processor is just sitting there idling, waiting for the network to catch up. And at the scale of a hundred-billion-dollar supercomputer, an idling processor is losing you thousands of dollars a second.
Exactly. And in these high-end AI servers, they aren't using the kind of RAM you and I buy for our desktops. They are using something called H-B-M-three-E right now, and they are already moving toward H-B-M-four. But here is the kicker: the production lines that make H-B-M are the same ones that make the standard D-D-R-four and D-D-R-five memory that goes into Daniel's home server.
Ah, so it is a zero-sum game. If Samsung or S-K Hynix decides to dedicate a factory line to H-B-M for OpenAI's next big cluster, that is a line that is no longer making the sticks of RAM we see on the shelves at the local computer store.
Precisely. And it is actually worse than a one-to-one trade. The yield for H-B-M is much lower. It is a more complex manufacturing process where you are stacking memory dies on top of each other and connecting them with thousands of tiny vertical wires called Through-Silicon Vias. Because the stack is so tall and complex, if one layer is bad, the whole stack is often trash. It takes about twice as many wafer starts to get the same amount of usable memory in H-B-M format compared to standard D-D-R-five. So, when OpenAI says they need enough memory to power a cluster of a million G-P-Us, they are effectively pre-ordering the entire capacity of these fabrication plants for months or years in advance.
So, it isn't just that they are buying the finished product. They are essentially renting the factories and telling the manufacturers to ignore everyone else.
In some cases, they are literally providing the capital to build the factories. We have seen these massive multi-billion dollar deals where the big AI players basically say, "Here is five billion dollars upfront. Build the capacity, and we get first dibs on everything that comes off the line for the next three years." For the manufacturers like Micron or S-K Hynix, it is a no-brainer. Why sell a thirty-two gigabyte kit to a consumer for a small margin when you can sell the equivalent amount of silicon to an enterprise customer for ten times the price as part of an H-B-M stack?
It feels like the consumer is being priced out of the silicon age. I mean, if this trend continues, does the concept of a home computer even stay viable? Or do we all just become thin-client users because the hardware is too expensive to own?
That is the dark side of this, Corn. We have talked about the democratization of technology for decades—the idea that a kid in their bedroom could have the same computing power as a university lab from ten years ago. But the RAMpocalypse is a massive step in the opposite direction. It is a centralization of resources. If forty percent of the supply is going to one company, that means everyone else, from Dell and H-P to the small-time enthusiasts like Daniel, is fighting over the remaining sixty percent. And that sixty percent has to cover every smartphone, every car, every smart fridge, and every laptop on the planet.
And I imagine the other big players like Google, Meta, and Microsoft aren't exactly sitting on their hands. If OpenAI is taking forty percent, what are the others taking?
That is the thing. If you add up the top five AI players—OpenAI, Google, Meta, Amazon, and Microsoft—you are looking at a situation where more than seventy-five percent of the global high-end memory supply is being funneled into these massive training silos. This is why Daniel is seeing those four hundred dollar price tags. The supply for the consumer market has been squeezed to a fraction of what it was just two years ago. We are living on the leftovers of the AI giants.
It reminds me of the graphics card shortage during the crypto mining boom around twenty twenty-one, but this feels different. Crypto was a speculative bubble. This AI demand feels like it's built into the core of how the global economy is trying to function now. It doesn't feel like it's just going to pop and go away.
You are right. Crypto was about hashing for rewards—it was an external incentive. AI is about the foundational infrastructure of the next era of computing. The companies buying this RAM aren't just trying to make a quick buck; they are trying to build the "God Models" of the twenty-first century. They see this as a winner-take-all race. If you have the most memory and the most compute, you have the best model. If you have the best model, you own the market for everything from medical diagnosis to automated coding. So, they are willing to pay almost any price to secure that supply. If the price of RAM triples, they don't care. They just add another zero to the debt facility.
Let's talk about the manufacturers for a second. You mentioned Samsung, S-K Hynix, and Micron. These are the "Big Three" that control over ninety percent of the world's D-RAM production. Are they just laughing all the way to the bank? Or are they struggling to keep up with the technical demands too?
It is a bit of both. Their profits have reached record highs—S-K Hynix recently reported its most profitable quarter in history. But they are also under immense pressure. The technical requirements for H-B-M-three-E are insane. We are talking about stacking twelve or sixteen layers of memory with micron-level precision. The heat management alone is a nightmare. I was reading an analyst report recently where the chief operating officer of Dell, Jeff Clarke, mentioned that they have never witnessed cost escalation at this pace. When even a giant like Dell, who buys millions of units, is complaining about the price of components, you know the manufacturers are in a very powerful, but very stressed, position.
But surely they are trying to build more factories? If the demand is this high, the logical economic response is to increase supply.
They are trying, but you can't just snap your fingers and build a semiconductor fab. It takes three to five years and tens of billions of dollars. We have the new fabs coming online in Ohio and Arizona, but those won't reach full capacity until late twenty twenty-six or twenty twenty-seven. And there is another bottleneck: the machines that make the chips. Companies like A-S-M-L, which make the extreme ultraviolet lithography machines, have their own multi-year backlogs. So even if Samsung wants to build a new line today, they might have to wait until twenty twenty-eight just to get the equipment to put in it.
So we are in this perfect storm where demand has jumped by an order of magnitude, but supply is physically constrained by the laws of construction and high-precision engineering. It’s like trying to widen a highway while the number of cars is doubling every week.
Exactly. And there is a second-order effect that people don't often think about. When manufacturers shift their focus to the high-end, high-margin AI memory, they stop innovating on the lower-end consumer stuff. Why spend research and development money on making D-D-R-four cheaper or more efficient when you can spend it on making H-B-M-four faster for a customer who will buy every single chip you make? We are seeing a stagnation in the technology that regular people use, even as the price of that technology goes through the roof.
That is a really depressing thought. It is like the luxury car market taking over so much of the steel supply that even a basic bicycle becomes unaffordable and stops getting better.
It is a very apt analogy. And it is not just RAM. Daniel mentioned N-A-N-D flash memory too. The same thing is happening there. Large language models need massive amounts of fast storage—petabytes of it—to feed the data into the memory. So, solid state drive prices are also climbing. The entire storage and memory ecosystem is being sucked into the AI gravity well. We are seeing enterprise S-S-Ds being sold for five times their twenty twenty-four prices.
Okay, so let's look at the numbers Daniel mentioned. He saw a two hundred to four hundred percent price increase. Is that consistent across the board? Or is it specific to certain types of memory?
It is most severe in the high-capacity kits. If you are looking for sixteen gigabytes of standard D-D-R-five, you might only see a sixty or seventy percent increase. But once you move into the thirty-two, sixty-four, or one hundred twenty-eight gigabyte range—the kind of stuff Daniel needs for his server—the prices go exponential. This is because the high-density chips required for those kits are exactly what the data centers want. They are competing for the same physical silicon dies.
So Daniel's home server, which probably needs a lot of RAM for virtualization or running local containers, is the exact use case that is getting hit the hardest. He’s essentially competing with Sam Altman for the same piece of silicon.
Precisely. If you are a gamer and you can get by with sixteen gigabytes, you are annoyed. But if you are a professional or a home lab enthusiast who needs sixty-four gigabytes to run your environment, you are looking at a thousand-dollar bill just for memory. That is more than the cost of the rest of the server combined in many cases. It has turned the economics of home computing upside down.
I want to go back to the OpenAI thing for a second. Forty percent. If they are consuming forty percent of the world's supply, what does that actually look like in terms of hardware? Are we talking about millions of sticks of RAM arriving at their data centers every week?
It is more about the integrated modules. Think about the Nvidia Blackwell B-two-hundred chips. Each one of those units has one hundred ninety-two gigabytes of H-B-M-three-E physically bonded to the processor. When OpenAI orders hundreds of thousands of these units for a single cluster, they are effectively taking millions of gigabytes of the highest-quality memory off the market in one fell swoop. And it is not just the training. It is the inference. Every time someone asks an AI a question, that model has to be loaded into memory somewhere. As the user base grows into the hundreds of millions, the amount of RAM needed just to keep the service running becomes astronomical.
So it is a double whammy. You need the RAM to build the model, which takes months, and then you need even more RAM to let people use it in real-time.
And as the models get bigger, the RAM requirements grow. We went from models that could fit on a single high-end card to models that require hundreds of cards working in parallel just to generate one sentence. The appetite is insatiable. There is no "enough" in the current AI arms race.
It makes me wonder about the efficiency of these models. Are we just throwing silicon at a problem that could be solved with better software? Are we being lazy because we assume the hardware will always be there?
That is the trillion-dollar question. There is a lot of research into model compression, quantization, and more efficient architectures like Mamba or Liquid Neural Networks. But right now, the brute force approach—scaling laws—is what is winning the race. And as long as brute force is the path to the most capable AI, companies will keep buying every scrap of memory they can find. They would rather spend ten billion dollars on RAM than risk falling six months behind a competitor.
You know, we have done a lot of episodes over the years. I think back to some of our earlier discussions about the cloud and how everything was moving to these centralized data centers. It felt like a convenience then. Now it feels like a resource war.
It really does. It is a struggle for the fundamental building blocks of computation. And the consumer is currently losing that war because we don't have the collective bargaining power of a Microsoft or a Google. We are just individuals looking at a Newegg listing and crying.
So what is the play for someone like Daniel? He is sitting there with a server that won't boot because of a memory mismatch, and he is looking at a four hundred dollar replacement cost. Does he wait? Or is this the new normal?
This is the toughest part. Usually, in the semiconductor industry, things are cyclical. We have a shortage, prices go up, manufacturers build more capacity, we have an oversupply, and prices crash. We have seen this cycle dozens of times since the nineteen seventies. But the AI demand might be what economists call a structural break.
Meaning the old rules don't apply anymore?
Meaning the demand curve has shifted so far to the right that the traditional cycle can't keep up. If the demand for AI compute continues to grow at its current pace, we might not see a return to those one hundred thirty dollar kits for a very long time, if ever. We might be entering an era where high-performance local hardware is a luxury item, like a fine watch or a sports car.
That is a grim outlook, Herman. You are saying there is no light at the end of the tunnel?
Well, there is some hope. First, as newer types of memory like H-B-M-four come online in twenty twenty-seven, some of the pressure might ease off the older D-D-R-five lines. If the AI giants move to the latest and greatest, they might leave some crumbs for the rest of us.
"Crumbs." That is a great way to describe the consumer market right now. We’re the birds waiting under the table at the silicon banquet.
It is. But also, there is the possibility of a plateau in model size. If we hit a point of diminishing returns where making a model ten times bigger doesn't make it ten times better, the insane scramble for more RAM might slow down. But looking at the roadmap for the next eighteen months, I don't see that happening yet. The "Stargate" project is just the beginning.
What about the secondary market? If new RAM is too expensive, should people be looking at used parts? Or is that market also picked clean?
The used market is also being affected. When new prices go up, used prices follow. Plus, you have to be careful. A lot of the used enterprise RAM coming onto the market has been run at high temperatures in data centers twenty-four-seven for years. It is a bit of a gamble. But for a home server like Daniel’s, looking for "pulled" modules from decommissioned servers might be the only viable path right now. You just have to be prepared for a higher failure rate.
It is funny, we always think of technology as getting cheaper and better over time. This is one of the few instances where the opposite is happening. My computer from three years ago might actually be worth more today than when I bought it, just because of the RAM and storage inside.
It is a weirdly appreciating asset right now. If you have a sixty-four gigabyte kit of D-D-R-four or D-D-R-five sitting in a drawer, you are basically sitting on a small gold bar. I’ve seen people on Reddit trading RAM for high-end graphics cards or even car parts.
I should go check our spare parts box in the hallway. We might be sitting on a gold mine.
Don't get too excited, Corn. Most of our stuff is probably D-D-R-three, which is ancient history at this point. It’s like having a collection of very nice V-H-S tapes.
Fair point. But let's look at the broader implications. If RAM stays this expensive, how does it change the software we use? Do developers start getting more disciplined about memory usage again?
You would hope so. For the last decade, we have been very lazy with memory. We have these massive web-based applications—looking at you, Electron apps—that eat up gigabytes of RAM just to show a chat window. If RAM stays at four hundred dollars a kit, companies might actually have to hire engineers who know how to optimize code. We might see a return to the efficiency of the late nineties era, out of pure necessity. We might see the "bloatware" era finally come to an end because the hardware can no longer subsidize the bad code.
That would be a silver lining. I am tired of my browser using eight gigabytes of RAM just to show me a few news articles.
Exactly. But it also means that new, innovative software that requires a lot of memory might never get off the ground because the hardware requirements are too high for the average user. It could stifle innovation in areas like local AI—where you run the model on your own machine for privacy—or high-end gaming and complex simulations. If only the top one percent can afford the hardware, the market for that software shrinks.
That is the part that worries me the most. We are building these massive central brains in the cloud, but we are making the individual's computer weaker in the process. It is a shift from personal computing back to a sort of mainframe-and-terminal model. We’re losing our digital autonomy.
It is very much like the nineteen sixties and seventies. The "Big Iron" is in the data center, and we just get to peek at it through a browser window. The dream of the "Personal Supercomputer" is taking a serious hit right now.
So, for Daniel and our listeners who are facing this RAMpocalypse, what is the practical advice? If you absolutely need the RAM now to get a system running, do you just bite the bullet?
If it is for work or a mission-critical project, you might have to. But if it is a hobby project, my advice would be to look for alternatives. Can you optimize your current setup? Can you use "swap space" on a fast N-V-Me drive to compensate, even if it is slower? Or can you look at refurbished enterprise gear that uses older, slightly more available memory standards like D-D-R-four? It won't be as fast, but it will be a lot cheaper.
And what about the timing? Is there any window in the next year where things might dip?
Some analysts are pointing to the end of twenty twenty-six as a potential easing point, as several new fabrication plants in the United States and Europe are scheduled to come online. But that is a long time to wait if your server is down today.
Yeah, that doesn't help Daniel get his Home Assistant and media server running this weekend.
No, it doesn't. One thing I will say is to keep an eye on the smaller manufacturers. Sometimes they have inventory that hasn't been snatched up by the big players yet. And avoid the high-performance "gaming" brands if you don't need the flashy lights and heat sinks. Plain old "green P-C-B" sticks from companies like Crucial or Samsung are sometimes significantly cheaper because they aren't marketed to the same demographic.
That is a good tip. Function over form, especially when form costs an extra two hundred dollars.
Exactly. And hey, if you are listening to this and you have managed to find a reliable source for affordable memory in this climate, let us know through the contact form on our website. We are all in this together, trying to survive the RAMgeddon.
Yeah, myweirdprompts.com is the place to go. We would love to hear your survival stories or even your "I can't believe I paid this much" stories.
It is a wild time to be a nerd, Corn. We are seeing history happen in the most literal, silicon-based way possible. We are watching the physical limits of our digital ambition.
It really is. I mean, forty percent of the global supply for one company. It is a staggering statistic. It speaks to the incredible ambition of these AI projects, but also to the fragility of our global supply chains. We have built a world where a breakthrough in software in San Francisco can make it impossible for a guy in Jerusalem to fix his home server.
The butterfly effect of the twenty-first century. A transformer model flaps its wings in a data center, and your bank account for computer parts disappears.
Well put. I think we have covered the what and the why. The "when" is still a bit up in the air, but at least we understand the forces at play. It is not just corporate greed; it is a fundamental shift in what our civilization values in terms of computation. We’ve decided that "knowing everything" is more important than "everyone having a computer."
And as long as the value of AI continues to skyrocket, the price of the silicon that powers it will follow. It’s the new oil, Corn. And we’re just trying to fill up our tanks.
It is a sobering thought. But I think it is important for people to understand that they aren't crazy. When you see those prices, it is not your imagination. The world really has changed.
It has. And we are just trying to navigate it one stick of RAM at a time.
Well, Herman, thanks for diving into the data on this one. I feel a lot more informed, even if my wallet feels a bit lighter just thinking about it.
Always a pleasure, Corn. I will keep an eye on the spot prices and let you know if I see any movement. I’ve got a script running that pings the markets every five minutes.
Of course you do. And to everyone listening, thanks for joining us on this deep dive into the RAMpocalypse. If you have been enjoying the show, we would really appreciate it if you could leave us a review on your podcast app or on Spotify. It genuinely helps other people find the show and helps us keep doing this.
It really does. We love seeing your feedback and hearing your thoughts on these prompts. It makes the research worth it.
Absolutely. This has been My Weird Prompts. You can find all our past episodes, including our deep dives into the semiconductor industry from last year, at myweirdprompts.com. We are also on Spotify and wherever you get your podcasts.
Thanks to Daniel for sending in this prompt and sparking such a great discussion. We will see you all in the next episode.
Take care, everyone. And good luck with your hardware builds. You are going to need it.
Bye for now.
Goodbye.
