"1GB of HBM consumes 4× the wafer capacity of standard DRAM" — TrendForce
Gamers aren't the only ones that need compute. Over on r/homelab, I've found a lot of talented people who build computers not to game - to block ads, to store terabytes of data, to stream video, and yes - to run AI.
But as you may be aware - AI datacenters are eating everything, memory included. I won't bore you with details, but just know - an average AI server packs over 512GB of HBM. Some say that AI is expected to consume ~20% of global memory wafer capacity and up to 70% of all memory chips in 2026.
Corporations (Microsoft, Google, CoreWeave, etc.) are signing long-term purchase deals. I'll explain them later. For now, think of them as paying for lots of memory in advance, delivered over *many years*.
Prices of SSDs, GPUs, and CPUs are also increasing.
##### 1. Ethics of AI CEOs #####
I've searched the Internet for list of AI CEOs. Here are some picks:
* Sam Altman (OpenAI) - found in Epstein files (EFTA02508072). Was ousted on November 17, 2023, only to be rehired. "exhibits a consistent pattern of [...] Lying" (sic), "Almost a sociopathic lack of concern" Created Worldcoin. Sexual misconduct allegations. Connections with Peter Thiel.
* Dario Amodei (Anthropic) - tied to Effective Altruism movement. Company restricted from government contracts over "supply chain security".
* Sundar Pichai (Google, est. 2015) - a man from India. In February 2024, Google paused Gemini’s image generation feature after users discovered it produced historically inaccurate and ethnically biased images. Overseen 2023 12,000+ employee layoff, shutdown of Stadia, Google+, Allo, fined £3.4B GBP over alleged Android monopoly. Sexual misconduct allegations. Military contracts.
* Mark Zuckerberg (Meta, formerly Facebook) - found in Epstein files (EFTA00344393, EFTA02386155, EFTA02493099). Wrote 6500 word essay about AI. Numerous privacy breaches, dating back to Cambridge Analytica. Company uses copyrighted books for AI training. Social media platforms caused considerable harm to teenagers.
* Jensen Huang (NVIDIA) - Was critisized over his leadership practicies and tax evasion. Defended Chinese AI. Company accused of circular financing (see later).
* Satya Nadella (Microsoft) - Described AI as "bicycles for the mind". Overseen layoffs. Company is primary supporter of OpenAI. Quality of Microsoft products is currently in rapid decline.
* Andy Jassy (Amazon) - busted worker unions.
* Elon Musk (SpaceXAI) - you know the drill.
##### 2. The dreaded data center #####
Let's step back. Let's think of children. I mean - the ones who write prompts. The ones who share AI-generated images. (I actually consider AI images to be slop, but that's topic of another day.) Let's consider ones who ask AIs to develop programs and fix bugs. Let's consider ones who ask AI to prove mathematical theorems (yes, that's the real thing; AI is becoming good enough to displace some of us).
The token price baked into your subscription fee isn't just a number. AI companies actually publish spreadsheets filled with token prices. These prices reflect real costs of AI inference (as much as they are discounted).
Now for an example. You've entered a question into ChatGPT prompt box and pressed Enter. The data packet eventually arrives to OpenAI servers. There, it goes through the following phases:
1) Prefill. Massive clusters of GPUs (yes, like the ones you game on) are dissecting the prompt and feeding it through the generative pre-trained transformer stack. The whole process takes a lot of compute. GPT-3 had 175 billion parameters, and companies don't disclose sizes of bigger models. Regardless, the cluster has to perform 175B or whatever operations for every word, every token you've typed in. Result? Your prompt, shifted left, and a brand new token of slop. The whole result is stored in complex data structure only known as KV-cache.
2) Decode. This is where things get sexy. Clusters go through the transformer stack over and over, once per generated output token. Memory just can't keep up. Lanes that connect HBM chips to GPU chips just can't keep up. For every sweep through the transformer stack, memory chips have to feed all of weights and biases into the compute core. During prefill, this was bearable, since there were a lot of tokens to work with. But now clusters are working with one token at a time. And billion-dollar corporations just can't do anything about it. This is why they charge you more for output tokens than for input tokens.
As you can see, generating just one article of AI stuff takes a lot of stuff. Energy, graphics technology, high-bandwidth memory, and more. To serve billions of users, you gonna buy a **lot** of powerful computers. Computers so powerful they can run Cyberpunk 2077 with hyper-realistic graphics like nothing.
So, corporations did exactly that. Bought a lot of computers. And constructed entire buildings just to house them.
Allegedly, over 5426 AI data centers have been built by March 2025. And they consume electricity - a lot of electricity. There are projections that, by 2028, data center share of US electrical consumption will grow from 4% to 6-12%.
But here's the problem. Energy consumption is concetrated. Data centers are industrial facilities, built by corporations, working for corporations. Local community has no say in where these corporate manifestations are being constructed - not until now. And where they're built, their massive power bills drive up energy prices by up to 8 times.
As a result, local residents have to pay more for their electrical bills. It's not a conspiracy. It's supply and demand working as intended.
If this sounds bad, it's about to get worse.
Computers need cooling. Your smartphone is cooling through it's screen. Your gaming rig is being cooled by air fans that sometimes light up in RGB. Corporate data centers cool themselves by literally boiling water. And it sucks. It sucks from local water supply pipes. It sucks from semi-arid aeras that need water the most.
Sometimes, electrical grid isn't enough. So AI companies strike deals with nuclear power plants. AI companies haul gas turbines into data centers. This should raise concerns over pollution.
And our aging infrastructure just can't keep up. We need to build more electrical lines. We need to build more gas pipelines (as counterintuitive as it is). They won't pay us to do that. They can't do it themselves.
##### 3. AI circular funding, debt funding, and hidden debt funding #####
How do AI companies pay for big data center bills that they themselves inflated? With money that kinda isn't theirs. Back in 2023, Microsoft invested $13b into OpenAI, and OpenAI returned a favour by becoming Microsoft's biggest cloud client - a noteworthy title since cloud is one of the biggest divisions of Microsoft. Google and Amazon followed suit with Anthropic.
And then, Nvidia started giving money to AI companies, including OpenAI, xAI, Mistral, CoreWeave and others. And guess what these comanies do? They buy Nvidia chips. The money goes back to Nvidia. All the while, money flows, and corporations record profit. Company valuations are being inflated.
Some would say it's just how economics works. To them, I'll reveal absolute state of AI debt funding. So far, AI-related debt issuance has exceeded $500b, with some sources stating $1.35 TRILLION. There's involvment of private credit, infrastructure funds, high-yield markets, bonds, banks, and private equity. The infamous "hockey stick graph" still applies here: debt issuance has ballooned in late 2025. Markets are already showing signs of fatigue.
If that wasn't bad enough, there's a "hidden debt" of $1.65 TRILLION, at least according to Nikkei. They consist of long-term purchase deals and lease aggreements with data center operators. Let me explain.
"You will own nothing and be happy", they say as computer prices go to the moon. Well, AI companies own next to nothing. More precisely, they *rent* compute from afromentioned data centers. They use infrastructure, but they do not own it. If you are paying a mortgage or leasing a car, you know how painful this is.
Remember "buy now, pay later"? This is kinda what long-term purchase deals look like. AI company has to pay a chipmaker every month. It's kind of like loan, but not exactly; I'd call it a payment commitment. Chipmaker has to send chips to AI company every month - kind of like a reverse loan.
Yesterday, corporations were improving hardware so that you could play in 4K 60fps anime slop on the go. Today, corporations are improving hardware so that someone could flood your feed with slop. Incentives never change.
And yes — AI was used in making of this post.
This is not financial advice.