Hey everyone, welcome back to My Weird Prompts. I am Corn, and I am sitting here in our living room in Jerusalem. It is a beautiful February afternoon in twenty twenty-six, and the light is hitting the Old City walls just right. I am here with my brother, the man who probably has more tabs open about server architecture than anyone else on the planet, Herman Poppleberry.
Herman Poppleberry, reporting for duty. It is good to be here, Corn. I have been looking forward to this one because the world of infrastructure is finally having its moment in the absolute center of the spotlight. It used to be that only the deepest nerds cared about what was happening in a windowless building in Northern Virginia or Iowa, but now, it is the lead story on every financial and tech news site.
It really is. You know, for the longest time, the cloud was just this abstract concept for most people. It was where your photos lived or where your emails were stored. It was "someone else’s computer," and we didn't really care what that computer looked like. But lately, the physical reality of the cloud is becoming impossible to ignore. It is consuming massive amounts of power, it is changing the real estate market, and it is even restarting nuclear reactors. Today’s prompt from Daniel is about exactly that. He is curious about how data centers have changed in the era of artificial intelligence. Specifically, how the shift from traditional central processing units to massive clusters of graphics processing units has altered the very architecture of the buildings themselves.
It is a fascinating shift, Corn. Daniel mentioned looking at photos of data centers and thinking they look like low-rise cow sheds spread over huge swaths of land. And honestly, he is not wrong. From the outside, a data center is one of the most boring-looking buildings on earth. It is a windowless gray box. But what is happening inside those boxes right now is a radical departure from everything we have built over the last twenty years. We are moving from the era of the "digital library" to the era of the "intelligence factory."
Right, and Daniel’s question about whether new, artificial intelligence-first cloud companies have an advantage over the established giants is a great one. If you are building from scratch for GPUs in twenty twenty-six, are you in a better position than someone trying to retro-fit a facility built in two thousand ten?
The short answer is yes, but the long answer involves a complete rethinking of thermodynamics, electrical engineering, and networking. We should probably start by defining what the traditional data center was actually optimized for. Before the artificial intelligence boom—let's say, pre-twenty twenty-two—the primary goal of a data center was high availability for general-purpose compute. You had rows and rows of server racks, each filled with servers running CPUs. These were great for web hosting, databases, and running your average enterprise software. They were designed for "North-South" traffic—data coming in from the internet, being processed, and going back out to a user.
And those traditional setups were relatively predictable, right? I mean, you knew how much power a CPU-based server would draw, and you knew how much heat it would generate. It was manageable with standard air conditioning.
Exactly. In a traditional data center, a single rack might draw somewhere between five and ten kilowatts of power. You could cool that by just blowing cold air under a raised floor and through the racks. It was like cooling a room with a few high-end gaming PCs. The "hot aisle, cold aisle" configuration was the gold standard. But when you move to artificial intelligence workloads, specifically training large language models or running massive inference clusters, the power density explodes. We aren't talking about a small increase. We are talking about a total transformation. A single modern server rack today, filled with Nvidia Blackwell or the newer Rubin chips that are just starting to hit the floor, can draw one hundred, one hundred twenty, or even one hundred fifty kilowatts.
One hundred fifty kilowatts? Herman, I am trying to visualize that. That is like trying to run the power of an entire neighborhood through a space the size of a refrigerator. How do you even get that much electricity into a single rack without the wires just melting?
You hit the nail on the head. That is the first major architectural change: power delivery. In the old days, you could bring power in at standard voltages and distribute it across the floor. Now, we are seeing data centers bring medium-voltage power directly to the row or even the rack. We are seeing a shift from twelve-volt power delivery on the motherboard to forty-eight-volt or even higher to reduce resistive losses. If you don't do that, the "bus bars"—the big copper rails that carry electricity—would have to be as thick as your arm.
And then there is the heat. If you are pumping one hundred fifty kilowatts of power into a rack, almost all of that is coming out as heat. You can't just use a big fan for that, can you?
No way. Air is actually a terrible conductor of heat compared to liquid. We have reached the physical limit of air cooling. To cool a one hundred-kilowatt rack with air, you would need fans spinning so fast and moving so much volume that the noise would be deafening—like standing behind a jet engine—and the air would be moving at hurricane speeds. It is just not practical. This is why liquid cooling has gone from a niche enthusiast thing for overclocking gamers to an absolute requirement for modern artificial intelligence data centers.
So, what does that look like in practice? Are we talking about pipes running into every server?
Yes, exactly. There are a few main flavors. The "entry-level" for AI is rear-door heat exchangers. You basically replace the back door of the server rack with a giant radiator, like the one in your car, with chilled water running through it. The server fans blow hot air through that radiator, and the water carries the heat away. But for the really high-end stuff—the clusters training the next generation of models—we use direct-to-chip cooling. Cold plates are mounted directly onto the GPUs and the high-bandwidth memory. Liquid circulates through those plates, picks up the heat at the source, and carries it out to a cooling tower or a heat exchanger.
It is funny to think that the most advanced technology on the planet, these massive neural networks, are ultimately dependent on plumbing. We are basically building giant, high-tech water-cooled engines.
It really is plumbing. And that brings us back to Daniel’s question about the "greenfield" advantage. If you are building a new facility today—what we call a "greenfield" project—you are designing the floor to support the weight of these massive liquid-cooled racks. A liquid-cooled rack is significantly heavier than an air-cooled one because of the fluid, the manifolds, and the denser hardware. If you are an established provider like Amazon or Microsoft, you have hundreds of existing data centers that were built with raised floors designed for lighter loads and air cooling. Retrofitting those is a nightmare. You have to reinforce the concrete, rip up the floors to install heavy-duty piping, and often, you have to reduce the number of racks you can fit because the power and cooling infrastructure takes up so much more space.
So, the "AI-first" companies like CoreWeave or Lambda Labs, or even the specialized builds for X-A-I, they are building these "supercomputer-as-a-service" facilities from the ground up. They don't have to worry about legacy support.
Exactly. They can optimize every single inch. For example, they can build "slab-on-grade" floors instead of raised floors. They can design the entire building around a "liquid-to-liquid" cooling loop. They can even place the data center in a location where the outside air is cold enough to provide "free cooling" for the water loops most of the year. But beyond the cooling and the power, the second big shift is the "interconnect." This is something Daniel touched on when he mentioned the shift from CPUs to GPUs.
Right, in a traditional setup, servers are somewhat independent. If I am hosting a website, my server doesn't really care what the server next to it is doing. But AI is different.
It is fundamentally different. Training a large model is a "synchronous" process. You have thousands, sometimes tens of thousands of GPUs that all need to talk to each other constantly. They are essentially acting as one giant, distributed supercomputer. In a traditional data center, the "networking" was designed for that North-South traffic I mentioned—user to server. But in an AI cluster, the traffic is "East-West"—server to server.
And that traffic is massive, right?
It is staggering. We are talking about networking speeds of four hundred gigabits or eight hundred gigabits per second per link, and we are already moving toward one point six terabits. To make this work, you need specialized networking like Nvidia’s InfiniBand or the new Ultra Ethernet Consortium standards. And here is the architectural kicker: because the speed of light is a constant, the physical distance between these GPUs matters. If your cables are too long, the "latency"—the time it takes for data to travel—slows down the entire training process. This means you have to pack the racks as tightly as possible.
So, we have a paradox. We have these racks that are generating record-breaking amounts of heat, which suggests we should spread them out. But the networking requirements say we have to pack them together as tightly as possible for speed.
That is the "AI Infrastructure Tug-of-War." Physics wants them apart; logic wants them together. The only way to win that war is with extreme liquid cooling and incredibly dense power delivery. This is why the "cow shed" analogy is actually quite apt. These buildings are becoming highly specialized shells for a single, massive machine. We are seeing the rise of "megacampuses" where you might have a gigawatt of power—enough to power seven hundred fifty thousand homes—dedicated to a single site.
A gigawatt? That is a staggering number. I remember reading that Microsoft and OpenAI were talking about a project called "Stargate" that could cost a hundred billion dollars and require that kind of power. Is that where we are headed?
We are already there. In twenty twenty-five, we saw the first "gigawatt-scale" data center plans get approved. But finding a gigawatt of power on the existing grid is almost impossible. This is why we are seeing the "Nuclear Renaissance" in the data center world. Microsoft made headlines by helping to restart a reactor at Three Mile Island. Amazon bought a data center campus directly connected to a nuclear plant in Pennsylvania. Google is looking at Small Modular Reactors, or SMRs. These companies are becoming energy companies because the grid simply cannot keep up with the "AI-first" demand.
It feels like we are moving away from the "utility" phase of the cloud. It used to be like a water company—you turn on the tap, and the compute flows. Now, it feels more like a specialized industrial process.
That is a great way to put it. Daniel mentioned "elasticity." In the traditional cloud, elasticity was the killer feature. You could spin up a thousand virtual machines for an hour and then turn them off. But you can't really do that with a twenty-thousand-GPU cluster. Those clusters are so expensive—billions of dollars in hardware—that they need to be running at ninety-nine percent utilization twenty-four-seven to make the economics work. You don't "burst" into a Blackwell cluster; you reserve it months or years in advance. It is more like renting time on a particle accelerator than buying a utility.
Let’s talk about the VRAM issue Daniel mentioned. VRAM, or Video Random Access Memory—though in the data center we usually call it HBM, or High Bandwidth Memory—is the specialized memory that sits right next to the GPU. Why is that such a bottleneck for the architecture?
Because in AI, data movement is everything. The "weights" of the model—the billions of parameters that make it smart—have to be stored in that high-speed memory so the GPU can access them instantly. If the model is bigger than the memory on one GPU, you have to split it across multiple GPUs. This is where the "interconnect" we talked about becomes the lifeblood of the system. If your networking is slow, the GPUs sit idle waiting for data, which is like burning money.
And Daniel asked if established providers have to "re-architect for VRAM." Is that a physical building change or a hardware change?
It is both. On the hardware side, you are constantly cycling through generations. We went from the H-one-hundred with eighty gigabytes of HBM to the B-two-hundred with one hundred ninety-two gigabytes, and the upcoming Rubin chips will push that even further. But the "re-architecting" for the building comes down to the fact that these memory-heavy chips require more power and more cooling per square inch. A "legacy" data center might have the physical space for a thousand GPUs, but it might only have the power and cooling for a hundred. So you end up with a building that is ninety percent empty space because the "density" of the AI hardware has outpaced the "capacity" of the building.
That sounds incredibly inefficient. If I am a landlord of a traditional data center, I am looking at a lot of "dead" square footage.
Exactly. And that is why the specialized providers have the advantage. They don't build "empty" space. They build high-density cells. They might have ceilings that are thirty feet high to allow for massive overhead cooling pipes and power bus bars. They might not even have a "floor" in the traditional sense, just a structural grid to hold the racks.
I want to go back to the "cow shed" thing. Daniel mentioned they look like sheds in the middle of nowhere. Why are we seeing this shift toward rural locations? Is it just about the land being cheap?
Land is part of it, but the real drivers are "Power, Pipes, and Pints." You need a massive connection to the electrical grid—the "Power." You need high-capacity fiber optic lines—the "Pipes." And you need "Pints"—millions of gallons of water for cooling. In a crowded city like London or New York, you can't easily get a gigawatt of power or the water rights to cool a massive cluster. But in rural Iowa, or the plains of Texas, or the fjords of Norway, you can.
And as you mentioned earlier, for training a model, the "latency" to the end-user doesn't matter. If I am training "GPT-Six," it doesn't matter if the data takes thirty milliseconds to get to me. I just need the training to finish.
Precisely. This has led to a "decoupling" of the data center market. We now have "Training Centers" and "Inference Centers." The training centers are the giant "cow sheds" in the middle of nowhere. They are the factories. The inference centers—where the AI actually answers your questions in real-time—still need to be near the cities to keep the response time snappy. But even those inference centers are being forced to upgrade. Even "running" a large model is significantly more power-intensive than serving a webpage.
So, even the "edge" of the network is getting hotter and hungrier.
It is. We are seeing a "trickle-down" of liquid cooling. We are starting to see liquid-cooled racks appearing in "Colocation" facilities in downtown areas where they used to only have air cooling. It is a total transformation of the stack.
What about the "Sovereign AI" trend? I have been hearing that countries are now building their own national AI data centers. How does that fit into this architectural shift?
It is a huge driver. Countries like Saudi Arabia, the UAE, and even smaller nations in Europe are realizing that "compute" is a national resource, like oil or grain. They don't want their national data being processed in a "cow shed" in Virginia. So they are building their own. And because they are starting now, in twenty twenty-six, they are building "AI-first" from day one. They are skipping the whole CPU-era architecture and going straight to liquid-cooled, high-density GPU clusters. In a way, they are "leapfrogging" the old infrastructure, much like some countries skipped landlines and went straight to mobile phones.
That is a powerful analogy. So, to answer Daniel’s question directly: yes, the newcomers have a massive advantage in efficiency and density. But the incumbents—the Amazons and Googles—have something the newcomers don't: "Gravity."
Right. They have the existing data. If your company’s entire database is already in AWS, you are much more likely to use their AI tools, even if the underlying data center is a retrofitted older building, because moving petabytes of data to a new provider is expensive and slow. The "Big Three" are using their "Data Gravity" to buy themselves time while they frantically build new AI-specific zones.
It’s a race between "New Infrastructure" and "Old Data."
Exactly. And the scale of the investment is just mind-blowing. We are seeing capital expenditure numbers that look like the GDP of mid-sized countries. All to build these "factories of intelligence."
I wonder about the environmental impact, Herman. We talk about liquid cooling and nuclear power, but the sheer amount of water and electricity is a point of contention in a lot of these rural communities.
It is the biggest challenge the industry faces. In twenty twenty-four and twenty twenty-five, we saw several major projects get blocked by local communities worried about their water tables. This is why "Closed-Loop" liquid cooling is becoming the standard. Instead of evaporating water to cool the racks, you circulate the same water over and over, using massive fans to chill it—essentially a giant version of the radiator in your car. It is more expensive to build, but it uses almost no "consumptive" water.
It seems like every time we solve a physics problem, we run into a resource problem, and then an engineering problem.
That is the history of the data center. It is a constant battle against the second law of thermodynamics. Entropy always wins in the end, but we are getting very good at delaying it.
Let’s talk about the "VRAM" part of Daniel’s prompt one more time. He asked about "optimizing for VRAM." In twenty twenty-six, we are seeing things like "Unified Memory Architecture" and "C-X-L"—Compute Express Link. How does that change the physical layout?
That is a great technical deep-dive. Traditionally, the GPU memory was a silo. If the data wasn't on the chip, the chip couldn't use it. But with C-X-L and newer versions of NV-Link, we are starting to see "Memory Pooling." You can have a rack where the memory is somewhat "decoupled" from the processors. This allows for even more flexibility, but it requires even more complex cabling. We are seeing fiber optic cables being used inside the rack to connect chips because copper wires just can't carry enough data over those distances anymore.
Fiber optics inside the rack? That sounds incredibly delicate and expensive.
It is. We are moving toward "Silicon Photonics," where the light-based communication happens right on the chip package. This reduces heat and increases speed. It is another example of how the "architecture" isn't just the building; it is the "micro-architecture" of how the components talk to each other.
So, if Daniel were to walk into a state-of-the-art data center today, in February twenty twenty-six, what would he see that would look different from ten years ago?
First, he would notice the silence—or at least, a different kind of noise. Instead of the high-pitched whine of thousands of small fans, he would hear the low hum of massive pumps and the rush of liquid. Second, he wouldn't see "raised floors." He would be walking on solid concrete. Third, he would see thick, insulated pipes painted in bright colors—blue for cold, red for hot—running everywhere, looking more like a chemical plant than a computer room. And finally, he would see the racks themselves. They wouldn't be the thin, airy things of the past. They would be dense, heavy, "monolithic" blocks, glowing with the status lights of thousands of interconnected GPUs.
It sounds like a scene from a sci-fi movie. But it is the reality of how our emails are being drafted and our images are being generated.
It is the "physical body" of the AI. We spend so much time talking about the "mind"—the algorithms—but the body is this massive, thumping, liquid-cooled organism of silicon and copper.
I think we should talk about the practical takeaways for our listeners. If you are a developer or a business leader, why does this "plumbing" matter to you?
It matters because "Compute is the new Oil." In the twenty-tens, we lived in an era of "Compute Abundance." You could write inefficient code and just throw more cloud instances at it because they were cheap. In the twenty-twenties, we are in an era of "Compute Scarcity." The physical limits of these buildings mean that there is a finite amount of high-end AI compute available. If you can make your model ten percent more efficient, you aren't just saving money; you are potentially making it possible to run your product at all.
So, "Efficiency" is the new "Scale."
Exactly. Understanding the "VRAM" constraints Daniel mentioned is a competitive advantage. If you know how to "quantize" a model so it fits into the HBM of a single chip instead of needing two, you have just halved your infrastructure costs and reduced your latency. The "hardware-aware" developer is the most valuable person in tech right now.
And for the average person, it is a reminder that the "digital" world isn't weightless. Every time you ask an AI to summarize a meeting or generate a cat video, a pump somewhere in Iowa speeds up, a valve opens, and a few more watts of power are pulled from a nuclear reactor or a wind farm.
It is a very grounded way to look at the world. We are building a global brain, but that brain needs a massive metabolic system to keep it from overheating.
This has been such a great deep dive, Herman. I feel like I have a much better mental model of what is actually happening inside those "cow sheds" now. It is not just rows of computers; it is a massive, integrated organism.
It is the most complex machine humanity has ever built, and we are just getting started. The "Stargate" era is just beginning. By twenty thirty, these buildings might not even look like buildings. They might be integrated into the cooling systems of cities, providing heat for homes while they process the world’s data.
"Data Centers as District Heating." I love that. It turns the "waste" of the AI era into a resource.
That is the goal. Circularity. But we have a lot of plumbing to do before we get there.
Well, thank you, Daniel, for that prompt. It really forced us to look under the hood of the internet in twenty twenty-six. And thanks to all of you for listening. We have been doing this for over six hundred episodes now, and it is your curiosity that keeps us going.
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Alright, that is it for today. From our living room in Jerusalem, I am Corn.
And I am Herman Poppleberry.
Thanks for listening to My Weird Prompts. We will see you next time.
Goodbye, everyone! Keep your chips cool and your prompts weird!
