OpenAI Announces 5 More Massive Data Centers Because They're Running Out of GPUs

OpenAI just announced they're building 5 more massive data centers because they keep running out of compute power. Each one needs enough electricity to run a small city, which definitely won't cause any problems with the power grid.

Wired reports that OpenAI is under "significant pressure" to meet demand, which is corporate speak for "our servers are on fire and users are pissed."

Building Data Centers in Random Places

They're building these monsters in Shackelford County, Texas; Doña Ana County, New Mexico; Lordstown, Ohio; and Milam County, Texas. Basically wherever they can get cheap land and the locals won't complain too much about the noise and power usage.

Texas makes sense because they have cheap electricity and don't give a shit about environmental regulations. Ohio is desperate for jobs after all the manufacturing left. New Mexico probably offered them massive tax breaks to build there.

The existing Abilene facility is already 1,100 acres and employs thousands of construction workers. That's the size of a small town, just to run AI models.

Leasing GPUs Because They Can't Afford to Buy Them

OpenAI is planning to lease GPUs instead of buying them because even they don't have $500 billion lying around. They're calling it "financial engineering" but it's really just "we need hardware but don't want to pay cash for it."

This makes sense when you realize that H100 GPUs cost $30,000 each and they need tens of thousands of them per data center. Leasing means they can upgrade to newer hardware without eating the depreciation costs when NVIDIA releases the next generation chips. CNBC reports that the massive scale of NVIDIA and OpenAI's data center plan raises questions about securing adequate power capacity.

NVIDIA's $100 billion deal with OpenAI also helps with the financing. Having NVIDIA as a partner gives banks confidence that the loans will get paid back, assuming nothing goes catastrophically wrong.

Running Out of Compute While Competitors Catch Up

OpenAI had to delay launching products outside the US because they literally don't have enough servers to handle current demand. Meanwhile Google, Anthropic, and Microsoft are all building their own massive data centers.

This is what happens when you promise AGI to everyone but haven't built the infrastructure to actually deliver it. OpenAI is burning through compute power faster than they can get new hardware online, which is why ChatGPT randomly shits the bed during peak usage. I learned this the hard way when our internal GPT-4 fine-tuning job got bumped three fucking times in one week because OpenAI needed the compute for their public API. Nothing like spending 2 days debugging why model convergence looks weird, checking your loss curves, tweaking hyperparameters, only to find out your entire training cluster got reassigned to serve ChatGPT requests. Great way to waste a week of work. Energy research institutes show data center electricity demand could consume 4.6% to 9.1% of total U.S. power by 2030. Deloitte analysis details the massive capital requirements, while Goldman Sachs research predicts even higher electricity demand from AI workloads.

Wired reports that they're targeting 7 gigawatts of capacity across all these facilities. That's enough power to run 5 million homes, just to answer people's questions about whether a hot dog is a sandwich. Visual Capitalist mapping shows U.S. data centers already consume 2-3% of the country's electricity and could double by 2030. MIT analysis warns about the sustainability crisis, while Nature journal studies document the climate impact of large-scale AI training. Carbon Brief research tracks the environmental costs of the AI boom.

What Could Go Wrong?

Building data centers that use as much power as entire cities has never been done before, so this should go great. Each facility needs not just massive amounts of electricity, but also cooling systems that won't shit the bed when Texas hits 115°F in summer. Institute for Energy Research warns that AI data center electricity demand could reach 20% of global electricity by 2030. IEEE Computer Society analysis details the cooling challenges at scale, while Data Center Dynamics reporting questions grid capacity. DOE high-performance computing research examines efficiency optimization strategies.

The Abilene facility needs fiber cable that could stretch to the moon and back, which sounds impressive until you realize that means thousands of potential failure points. When (not if) that network goes down, millions of ChatGPT users will be left hanging.

These data centers take 2-3 years to build, assuming no supply chain issues, construction delays, or permitting problems. So the facilities announced today won't be online until 2027-2028, by which time the AI landscape might look completely different.

Managing Multiple Partners Who Don't Talk to Each Other

OpenAI is working with Oracle on some facilities and SoftBank on others, which means coordinating between companies that have their own priorities and timelines. Oracle wants to sell cloud services, SoftBank wants financial returns, and OpenAI just wants the fucking data centers built on time.

Yahoo Finance reports that the partnerships will create over 25,000 jobs across the different sites. That sounds great until you realize it means 25,000 different people who need to coordinate on getting the power, cooling, networking, and hardware working together.

If any one of these partnerships hits regulatory problems or construction delays, it screws up the whole timeline. And in infrastructure projects this big, something always goes wrong.

The $500 Billion Bet That Scaling Will Continue Working

OpenAI is betting their entire future on the idea that throwing more compute power at AI problems will keep producing better results. Financial industry analysts think this approach could give them a competitive edge, assuming they can actually get these facilities online.

But what if algorithmic breakthroughs make all this infrastructure unnecessary? What if some smaller company figures out how to get GPT-4 level performance with 1/100th the compute? Then OpenAI just spent $500 billion on the world's most expensive paperweights.

Investopedia notes that these facilities won't be operational until 2027-2028. That's a long time in AI years. By then, we might have quantum computers, neuromorphic chips, or some other technology that makes massive GPU farms look primitive. Quantum computing progress could revolutionize AI training, while Intel's neuromorphic research shows alternative approaches. MIT's Computer Science and Artificial Intelligence Laboratory publishes breakthrough research that could obsolete current architectures, and Stanford's AI research demonstrates more efficient training methods.

OpenAI is trying to build data centers that have never existed before, and nobody knows if this shit will actually work. They're betting $500 billion on infrastructure problems that don't have proven solutions, which should end great.

Getting 50,000 GPUs to Work Together (Good Luck)

Getting tens of thousands of GPUs to cooperate without losing their minds is like trying to conduct an orchestra where every musician costs $30,000 and randomly decides to play a different song. One GPU dies, one network cable gets loose, or one cooling system hiccups, and suddenly your million-dollar training run crashes with a useless CUDA_ERROR_LAUNCH_FAILURE that tells you nothing about which of the 50,000 components actually broke. NVIDIA's DGX SuperPOD documentation outlines the complex orchestration required for multi-GPU training, while research from major cloud providers details the operational challenges of maintaining massive GPU clusters.

Ars Technica found out that most workloads are "reasoning tasks" where latency doesn't matter as much. But that's today's workloads. When real-time AI becomes critical, these data centers need to work perfectly all the time, which has never been done at this scale.

The latency between GPUs has to stay under a few microseconds or the whole training process turns into garbage. Try maintaining that when your facility is the size of a small town and every component is a potential failure point.

Supply Chain Reality Check

OpenAI admits their buildout is limited by GPU availability and supply chain problems. Translation: they're competing with everyone else for the same limited hardware, and throwing money at the problem doesn't magically create more chips.

NVIDIA can only make so many H100s, and they're already backordered for years. To their credit, the H100's 80GB HBM3 memory and 3.35TB/s bandwidth are genuinely impressive engineering achievements. OpenAI's $100 billion deal might guarantee them some GPUs, but it doesn't solve the fundamental problem that global chip production can't keep up with AI demand. TSMC Q2 2025 results show capacity constraints, while semiconductor industry analysis documents the supply-demand imbalance affecting AI hardware procurement.

Even basic shit like networking equipment and specialized cooling systems have months-long lead times. One supplier fucks up your delivery schedule, and your entire $20 billion data center sits there empty waiting for parts. I've seen production clusters sit dark for like three months because the wrong revision of Mellanox InfiniBand cards showed up - looked identical to the right ones, but couldn't handle the specified bandwidth without random packet drops. Took weeks to figure out why training jobs kept failing with mysterious network timeouts.

Each Data Center Will Use More Power Than a City

Every gigawatt of computing power these facilities need equals about 750,000 homes worth of electricity. Maybe 700,000? I've seen different numbers. That's just for the computing - you haven't even added cooling, networking, and all the other infrastructure that probably doubles the power usage.

OpenAI keeps talking about renewable energy, but renewable power isn't available 24/7. When the wind stops blowing and the sun goes down, these facilities still need massive amounts of electricity to keep running, which means backup power from the grid or enormous battery systems.

Good luck convincing Texas and New Mexico power grids to handle multiple gigawatt-scale facilities when they already struggle during heat waves. Rolling blackouts during summer are going to be fun when your AI training runs crash every time the AC turns on across the state.

Cooling Systems That Have Never Been Tested

Traditional cooling doesn't work when you're packing this much heat into a small space. They need liquid cooling systems that pump coolant directly to individual chips, which is great until one of those systems springs a leak and fries millions of dollars worth of hardware. I've watched a single coolant leak trigger cascade failures that took out an entire rack - first you get THERMAL_ALERT warnings, then GPU_TEMPERATURE_CRITICAL, then complete system shutdown as cards start hitting 95°C and throttling to protect themselves.

Immersion cooling sounds cool (pun intended) but it's never been done at this scale. You're basically putting server farms in giant fish tanks full of special fluid, then hoping nothing goes wrong with thousands of gallons of expensive coolant chemicals.

The facilities in Texas and New Mexico also have to deal with desert heat and water shortages. Cooling systems need massive amounts of water, but these regions are already fighting over water rights. When locals start choosing between drinking water and keeping ChatGPT running, guess what wins? Water resources research shows the sustainability challenges, while environmental impact studies document water usage concerns for large-scale data centers.

Networking That Needs to Be Perfect All the Time

The Abilene facility needs fiber cable that could stretch to the moon and back. That sounds impressive until you realize that's millions of potential failure points.

AI training moves petabytes of data constantly - model weights, training data, checkpoints, gradients. If any part of that network gets congested or fails, the whole training process slows down or crashes. And unlike Netflix buffering, you can't just pause and wait for it to catch up.

They need software-defined networking to route traffic dynamically, which means complex systems managing other complex systems. When something breaks (not if, when), figuring out which of the thousand components caused the problem becomes a nightmare.

Finding People Who Know How to Run This Shit

OpenAI needs to find thousands of people who understand AI workloads, massive-scale cooling, high-performance networking, and power systems that don't exist anywhere else. Good luck with that hiring process.

The construction workers building these facilities are just the beginning. Operating these facilities requires engineers and technicians who understand problems that literally didn't exist five years ago.

You can't just hire traditional data center people because traditional data centers don't run workloads like this. And AI engineers don't know shit about industrial cooling systems or gigawatt power distribution. Training people takes years, assuming you can find people smart enough to learn it.

Regulatory Battles Are Just Getting Started

Building facilities that use as much power as entire cities gets government attention fast. Environmental impact studies for projects this size take years and can kill the whole thing if local communities decide they don't want a power-hungry monster in their backyard.

Data security regulations add another layer of complexity when your facilities are handling training data for AI models that could be used for military or surveillance applications. Every agency from the EPA to the NSA probably wants a say in how these facilities operate.

Local permitting alone could add years to construction timelines. Try explaining to a county commissioner in rural Texas why they should approve a facility that uses more electricity than their entire county combined.

Technology Will Change Before These Are Built

These facilities won't be online until 2027-2028, which is forever in AI time. By then, we might have quantum computers, neuromorphic chips, or some other breakthrough that makes massive GPU farms look as outdated as using vacuum tubes for computing. Quantum computing research continues advancing rapidly, while Intel's neuromorphic computing initiatives represent alternative approaches to AI processing that could obsolete current GPU-based architectures.

OpenAI is designing for today's hardware and algorithms, but they're betting that the same approach will work for the next decade. What if algorithmic breakthroughs make training dramatically more efficient? What if new hardware architectures need completely different power and cooling requirements?

The facilities are trying to be "modular" so they can upgrade components, but you can't easily retrofit a cooling system designed for GPUs to handle quantum computers or whatever comes next.

This Could All Be a $500 Billion Mistake

If any of these technical challenges prove unsolvable, or if AI development takes a different direction, OpenAI will have the world's most expensive paperweights. Unlike cloud services where you can shut down servers when demand drops, these facilities represent massive fixed costs that don't go away.

Barrons thinks this approach might work, but plenty of massive infrastructure projects have failed spectacularly because the engineering challenges proved harder than expected.

The semiconductor industry is littered with companies that bet big on the wrong technology approach. Intel spent billions on Itanium processors that nobody wanted. OpenAI could be making the same mistake, just with data centers instead of chips.

But hey, at least when it all crashes and burns, they'll have some really impressive buildings to convert into the world's most expensive storage facilities.