The $ Trillion AI Bubble: How NVIDIA, OpenAI, and Big Tech Are Fueling a Hardware Crisis

Google confirmed that it’s going to build a $17 billion data center!
Don’t these companies have people who know how to manage their businesses properly?
Let me tell you something, but don’t tell anyone.
One of these GPUs, the HGX B200, is used in data centers. Its manufacturing cost is approximately $5,860.
That’s the manufacturing cost of just one unit!
How much does NVIDIA sell it to companies for? Approximately $34,620!
And this isn’t even the high-end model. It’s not the most powerful thing NVIDIA has. But this is the only GPU for which I was able to find the manufacturing cost.
When someone tells you that the artificial intelligence bubble is going to burst, what they usually imagine is that AI will disappear, vanish, leave the market, and never be part of your life again.
That’s what they have in mind. They think there will be no more development, no more progress, and no new technologies in this field.
But that’s not going to happen.
AI is not going to disappear.
The reality is that AI is one of humanity’s greatest achievements in the last thirty or forty years. Something like this isn't simply going to disappear.
AI itself is not the real bubble. Certain parts of the AI industry are the bubble.
Its infrastructure, such as data centers, is the issue, not the technology itself.
The rising costs of hardware and energy.
And without a doubt, one of the worst contributors to this bubble is NVIDIA. We’ll talk about that in a moment.
So, what’s going on with this infrastructure? Why is it causing problems for us or for AI companies?
Let’s talk about data centers.
A data center is where your request goes when you open ChatGPT on your phone and enter a prompt.
Your prompt travels to a data center.
The data center is the place where your prompt gets processed, where the AI performs its computations, and then sends a response back to you.
That’s what a data center does.
To build a data center with a power capacity of one gigawatt, which equals one billion watts, you need approximately $37 billion.
That’s the economy of an entire country!
So why is it so expensive? What’s going on?
Here’s an example of a data center. When you send a prompt from your phone, it arrives here, and one of these machines processes your request and sends the result back.
But massive companies like Google, OpenAI, and Anthropic don’t operate with just one room.
Sometimes, they literally build entire neighborhoods filled with these rooms and data centers.
And the problem with this massive infrastructure is that it isn’t covering its own costs.
In other words, the subscription fees people pay are not enough to cover the cost of these buildings and everything inside them.
They’re operating at a loss.
But what’s strange is that these companies continue throwing money into the business and burning through cash.
Without a doubt, the most expensive components in these data centers are the GPUs, the graphics processing units usually supplied by NVIDIA.
And NVIDIA has also contributed to rising RAM prices and, more recently, increasing graphics card prices.
Now, look at this rack. This is from NVIDIA’s own website.
The GB200 rack contains 72 Blackwell GPUs, 72 graphics processing units, and 36 processors.
The cost of just one of these racks is approximately $3 million.
Now, do you think a company would buy just two or three of these?
A massive company like Google, for example?
Actually, let me correct that.
Oracle and OpenAI purchased approximately 450,000 of these GPUs.
That translates to roughly 6,200 complete racks, costing around $18.6 billion.
So I’m a million percent certain that the most expensive part of today’s data centers is the hardware, particularly the graphics processing units supplied by NVIDIA.
But there’s something strange NVIDIA is doing.
Something that really makes you wonder:
What exactly is NVIDIA doing?
NVIDIA is essentially buying its own graphics cards!
Sounds strange, doesn’t it?
Let me give you an example.
NVIDIA invests in a company like OpenAI, the company behind ChatGPT.
Let’s say NVIDIA gives OpenAI $10 billion.
OpenAI then takes that $10 billion and spends, let’s say, $8 billion or $9 billion purchasing NVIDIA graphics cards or the server racks we saw earlier.
What NVIDIA is doing here is increasing its own market valuation.
Now, outside investors see NVIDIA’s sales figures, including the sales of its graphics cards and server racks.
They look at NVIDIA and think:
“NVIDIA is selling enormous amounts of hardware. This business is incredibly successful. Why shouldn’t we invest even more money in NVIDIA?”
What NVIDIA is doing is expanding its business using its own money, indirectly, through other AI companies.
But more importantly, NVIDIA’s stock price rises, and its market capitalization increases.
All of this happens within a closed financial circle involving NVIDIA and other AI companies.
And it makes you wonder:
If the money isn’t actually leaving this circle, then where exactly is the bubble?
Of course, this doesn’t prove that NVIDIA is manipulating the market.
But this situation could create serious supply problems.
If all the VRAM and Blackwell chips go to AI companies through NVIDIA, that means ordinary consumers, people like you and me, won’t be able to afford graphics cards or RAM.
In a way, you could say these companies are responsible for driving prices higher.
It almost looks as if they are deliberately creating a hardware bubble.
That’s a possibility.
Now, imagine that this bubble bursts.
Remember, we’re talking about the infrastructure bubble, not AI itself.
Would AI become more expensive, harder to access, or potentially disappear?
I believe the exact opposite would happen.
What would happen is that NVIDIA would suffer financially.
And I’m convinced of this because, if AI companies eventually realize that GPU prices are excessively inflated, they will reduce their orders from NVIDIA.
As demand decreases, NVIDIA’s growth will also slow down.
And the best possible outcome would be a collapse in GPU prices, potentially followed by a decline in RAM prices as well.
If GPU prices collapse, AI itself will become cheaper.
AI services will become more accessible to ordinary people.
Training AI models will also become less expensive because the hardware required to train them will cost less.
So whether this bubble bursts or not, remember that we are talking about an infrastructure and hardware pricing bubble, not the AI technology itself.
AI will still exist.
And if some people are worried that AI might disappear, there’s something called Hugging Face.
On Hugging Face, you can find open-weight and open-source AI models.
You can download these models and keep them for yourself until, hopefully, the hardware pricing bubble finally bursts.
Again, we’re talking about inflated hardware prices, not the disappearance of AI.
You could eventually purchase a very affordable graphics card and run these models locally on your own computer.
Now, why am I even talking about this?
Because whenever people see stock prices collapsing or signs that the bubble is beginning to deflate, they go into comment sections and say things like:
“Finally! AI is going to disappear!”
“Artificial intelligence is finished!”
“We’re finally going back to human programmers!”
Some people genuinely believe that.
They confuse the collapse of a financial bubble with the disappearance of AI technology from the market.
But that’s not how it works.
In fact, many people are hoping that this bubble of inflated hardware prices will finally burst.
And I’m one of them.
Why?
Let me show you something.
I remember buying my RTX 5080 a while ago for around $1,600, maybe $1,680.
Now its price has increased to somewhere between approximately $2,130 and $2,660.
Things like this convince me that the real bubble is artificially created.
It’s a bubble created by humans, and I believe it’s intentional.
Then you have the RTX Spark.
These devices NVIDIA launched, those laptops—or rather, those machines developed through NVIDIA’s partnership with Microsoft, using Spark processors and GPUs.
You might think:
“Okay, this should be an affordable computer.”
But even the models with the lowest RAM configurations, which I believe are 24 or 32 gigabytes, start at around $2,400 or more.
$2,400!
And the 128-gigabyte version costs approximately $5,860.
For a computer!
And what’s the maximum number of parameters in an AI model that you can run on it?
What, around 120 billion parameters?
Now, your next question is probably:
Will this bubble burst anytime soon?
Unfortunately, I don’t think so.
These companies are the ones keeping the bubble alive.
And for some reason—actually, not just some mysterious reason—the motivation is pretty obvious.
They want the bubble to keep growing.
Why?
So they can sell you a computer that costs as much as a Toyota Corolla.
So they can continue selling billions of dollars’ worth of graphics cards to data centers.
Do I hope this bubble bursts?
Absolutely.
Everyone wants that.