Monologue: Blame AI For Making Everything More Expensive

Summary

Podcast Summary: Better Offline — "Monologue: Blame AI For Making Everything More Expensive"

Host: Ed Zitron
Release Date: July 10, 2026
Podcast Network: Cool Zone Media & iHeartPodcasts

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Episode Overview

This episode features a blistering solo monologue by host Ed Zitron, who systematically dissects how the accelerating boom in AI infrastructure—namely, the building of massive data centers and the speculative rush to buy high-bandwidth memory (HBM) and GPUs—is driving up costs across the entire tech sector and, by proxy, everyday consumer electronics. Zitron lays the blame squarely at the feet of a handful of powerful tech figures and companies, arguing the current AI bubble is an avoidable and destructive spectacle motivated by profit and visionless ambition rather than real user demand or social good.

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Key Discussion Points & Insights

1. AI Infrastructure Spending: Driven by Speculation, Not Real Demand

• Zitron opens by challenging the assumption that booming sales figures for Nvidia, Broadcom, and RAM companies reflect genuine industry demand.
• Instead, he frames the AI build-out as “speculative purchasing based on the Vegas of projections” (00:53), with little revenue generated to justify the massive investments.

2. How AI Drives Up RAM and GPU Prices

• AI GPUs require a specialized, scarce, and increasingly costly type of memory: high-bandwidth memory (HBM).
• Only three companies—SK Hynix, Micron, and Samsung—can manufacture HBM at scale, making it what Zitron dubs a “triopoly” (01:46).
• Because demand is artificially inflated by speculative AI investment, these memory producers can “charge effectively whatever they want” (02:17).
• The AI boom constricts the supply for regular RAM (the kind used in PCs and smartphones), which further drives up prices for all electronics and everyday tech.

3. Absurd Quantities of RAM Consumed, and the Downstream Cost Explosion

• Zitron describes the staggering RAM requirements of each modern data center rack:
  • A “GB300 rack” contains about 20.7 terabytes of HBM and 17 terabytes of the RAM found in mobile devices (03:30–04:00).
  • One “gigawatt” data center requires thousands of such racks—nearly $2 billion of HBM alone (04:20).
• As the price of HBM rises, so does all related infrastructure spending, feeding a cycle where AI development makes everything from smartphones to HVAC systems more expensive.

4. Bubble Dynamics: Overproduction and Grisly Write-Offs Ahead

• The numbers are “astonishing”—from 600 million gigabytes of HBM sold in 2022 to a projected “7 trillion or more” in 2026 (05:15).
• Zitron warns that this overproduction “means they're going to have years worth of this crap just either sitting around or being attached to Nvidia GPUs they can't sell to anyone” (05:35).
• When capital expenditure (CapEx) retracts, memory makers and hyperscalers will face massive write-offs similar to the post-pandemic supply chain crash.

5. No Meaningful AI Business Model Yet

• Leading AI hyperscalers are not revealing revenues from AI products, suggesting profits are “pretty pissed poor” (06:31), and that genuine user or customer demand may not exist.

6. Vicious Feedback Loop and Market-Wide Inflation

• “The longer that hyperscalers sustain this base, the larger the return needs to be. And at this point, none of them have disclosed their actual AI revenues…” (06:45)
• Pulling back CapEx would expose the speculative nature of the bubble, but charging ahead means further negative cash flows and continued inflation for all tech goods.

7. Final Attribution of Blame

• Zitron squarely blames major tech leaders for the crisis, calling out Sam Altman, Dario Amodei, Sundar Pichai, Andy Jassy, and Mark Zuckerberg for their choices:
  • “They all fucking suck. They could have done literally anything else. They could have built housing, they could have fixed their own products. But they chose to do this because they have no vision. These men are losers. I despise them for what they’ve done and I hate them for what they’ve done to the computer.” (08:01)

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Notable Quotes & Memorable Moments

• On the core problem:

  “The actual demand for AI, the actual revenue generated by this industry is kind of pathetic. And honestly, we don't really have any reason to build these data centers. Nobody has trouble accessing these services. I mean, it's just being done because they've got nothing else to do.”
  — Ed Zitron [00:55]

• On the memory manufacturers’ market power:

  “It's a triopoly, I guess you'd call it… their memory companies can charge effectively whatever they want.”
  — Ed Zitron [01:46]

• On Nvidia’s relationship with HBM prices:

  “Nvidia is one of the largest, if not the largest HBM buyer in the world… This makes it actually a victim of this same inflation because memory is getting more expensive across the board, even for high bandwidth memory.”
  — Ed Zitron [02:46]

• On the size and cost of modern data centers:

  “A gigawatt data center has about 4,933 of these racks working out to about $1.94 billion in high bandwidth memory alone for a single data center.”
  — Ed Zitron [04:20]

• On the future reckoning:

  “Somebody is going to be left with a bag. By which I mean somebody is going to either have too much high bandwidth memory or gear that they can't sell with it put on the side that won't be useful or really sellable to someone else.”
  — Ed Zitron [05:53]

• A razor-sharp indictment:

  “These men are losers. I despise them for what they've done and I hate them for what they've done to the computer.”
  — Ed Zitron [08:01]

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Timestamps for Important Segments

• AI Speculation and Lack of Real Demand – [00:53]–[01:30]
• RAM Market Cartel and Price Mechanics – [01:30]–[03:30]
• Data Center RAM Scale and Cost – [03:30]–[04:20]
• Overproduction and Projected Write-Offs – [05:15]–[06:00]
• Vicious Cycle & Market-Wide Inflation – [06:00]–[07:30]
• Final Blame and Closing Rant – [07:40]–[08:30]

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Summary Flow and Takeaways

In his trademark direct and sardonic style, Ed Zitron exposes how the unchecked speculative spending on AI infrastructure—with no meaningful business model or real demand—has unleashed a feedback loop that’s making tech and everything with a computer chip more expensive for everyone. He dissects the market mechanics, calls out industry cartels and key tech leaders by name, and predicts a reckoning: oversupply, massive losses, and years of inflated costs, all perpetrated for the sake of an AI bubble he considers “entirely avoidable and horribly destructive.”

If you want to understand why your gadgets are pricier (and who to blame besides “AI progress”), this episode delivers a scathing, informed answer.

Transcript

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Call Zone Media. Hello and welcome to this week's Better Offline monologue. I'm your host Ed Zitron.

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Better Offline

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and today I'm talking about the great parasite of AI and how it's making everything more expensive. And to be clear, before we go any further, what you're seeing in these remarkable sales from Nvidia and all the RAM companies and Broadcom and all them, isn't a measure of industry demand, but of speculative purchasing based on the Vegas of projections. Because the actual demand for AI, the actual revenue generated by this industry is kind of pathetic. And honestly, we don't really have any reason to build these data centers. Nobody has trouble accessing these services. I mean, it's just being done because they've got nothing else to do. And I know that sounds kind of flippant, but really is what's happening. To make matters worse, the more the bubble inflates, the more expensive everything gets. The more data centers that get funded, the more chips that are ordered, the more resources this takes and the more the RAM companies can crank up the prices and fuck over the customer. You see, AI GPUs use a special kind of memory, high bandwidth memory that takes up more of the limited amount of fab space where sk, Hynix, Micron and Samsung build their ram. These are also the only three companies that really can build hbm. It's a triopoly, I guess you'd call it. And because Nvidia, Broadcom and other chip manufacturers quite literally can't build the GPUs that hyperscalers crave, without them, their memory companies can charge effectively whatever they want. AI GPUs have become a massive amount of guaranteed revenue as the AI bubble inflates, leaving less fab space for the regular ram, creating a supply shortage or put another way, a reason for the memory companies to bump up the prices aggressively. And yeah, they're allowed to do that. There's nobody to stop them. It's not like we have regulatory authorities who would bother. I should also add that Nvidia is one of the largest, if not the largest HBM buyer in the world. 55 to 65% of all high bandwidth memory sales go to Nvidia. And this makes it actually a victim of this same inflation because memory is getting more expensive across the board, even for high bandwidth memory. Goldman Sachs reports that high bandwidth memory is on course to increase by as much as 90% year over year in 2027. A linear increase in for every single Nvidia GPU and every single new one. That one of the four hyperscalers oafs insists on pre ordering only serves to inflate a bubble for effectively no reason. They're not making a profit. Shit, they won't even talk about revenues. Maybe they're not really making that much outside of OpenAI and anthropics compute Like I've been saying, but good lord, good lord. Good golly Miss Molly, do these data centers take up a shit ton of ram? Analysts estimate that by the time that Nvidia ships its first next generation Vera Rubin GPUs, it'll cost $18.40 per gigabyte of high bandwidth memory sold 288 gigabytes at a time. In fact, let me give you some scale as to how much RAM a data center actually needs, because it's an astonishing amount. The current generation GB300 racks, the vast 72 GPU servers that fill out modern data centers and they have a CPU for every two GPUs, have about 20.7 terabytes of high bandwidth memory and 17 terabytes of LPDDR5X RAM. And by the way, that's the RAM that you find in smartphones and mobile devices, which is also contributing to the supply chain shortages and driving up the price. Anyway, a gigawatt data center has about 4,933 of these racks working out to about $1.94 billion in high bandwidth memory alone for a single data center. A number that's certain to increase across the board even for current generation gear. Because the price of high bandwidth memory is going up. I haven't even included the many separate computers and different machines inside the data center that require RAM too. Entire racks of power management gear that themselves require RAM or H Vac systems alone. They're effectively servers unto themselves. And every data center is basically a giant warehouse of different RAM dependent devices and machines, each controlled by other RAM dependent machines or all to do one thing. AI that's yet to Prove it even has a meaningful business model or one that, I don't know, makes more money than it loses. And that high bandwidth memory, by the way, is also not particularly useful for anything else, at least not at the scale it's being built. Meaning that once there's a noted CapEx pullback, somebody is going to be left with a bag. By which I mean somebody is going to either have too much high bandwidth memory or gear that they can't sell with it put on the side that won't be useful or really sellable to someone else. And I must be clear how much more of this they are selling than usual. Back in 2022 they sold about 600 million gigabytes of high bandwidth memory. So sounds like a lot until you realize they're expecting to sell 7 trillion or more of it in 2026. That's, that's a problem. That's a problem because the more of it they sell, the more expensive it gets. But that's they're going to have years worth of this crap just either sitting around or being attached to Nvidia GPUs that can't sell to anyone. And the longer it takes for capex to retract, the more expensive everything becomes. As a result, the more GPUs that get sold, the more capacity that gets put towards high bandwidth memory. And the more that Micron, sk, Hynix and Samsung can charge for it, which makes it more expensive to buy AI GPUs, which increases the amount that hyperscalers are spending on AI capex for effectively the same amount of gear. The longer that hyperscalers sustain this base, the larger the return needs to be. And at this point none of them have disclosed their actual AI revenues, which heavily suggests that there's yet to be even a single dollar of profit. And the actual quarterly revenues are pretty pissed poor. Yet the more they commit, the more committed they have to be. Pulling back at this point will prove to the markets that all of the hyperscalers have committed far too much to too much capacity. Yet not pulling back means that hyperscalers will continue to turn their free cash flows negative in pursuit of some indeterminate goal. It's a vicious cycle, made worse by the fact that every spin of the capex wheel increases the price of just about every consumer electronic in the creating a market wide inflation for what amounts to a speculative asset bubble and will leave them sitting with a bunch of GPUs they don't really need now and certainly won't in the future. What great works are these data centers in search of what possible achievement can you point to at this time that remotely justifies the current expenditures, let alone the trillion plus more planned for the future? This is the vicious cycle of the AI bubble. The more data centers they build, the more expensive it becomes to build them. Because of the cartel of memory manufacturers that adjust the prices up at basically what it whatever rate they want to, and hyperscalers that have no idea what to do other than spending more money. RAM is in effectively everything and thus everything is going to get more expensive from here. Samsung, SK, Hynix and Micron have committed more than $2 trillion to building out more capacity, even though this bubble has been inflated based on building high bandwidth memory that is being built at a scale beyond any reasonable level of industry demand. While that capacity, because you can build other things in the same RAM capacity, could theoretically be used for something else. The question is how they intend to pay for it. How many of these actual commitments they've made with their own money, and indeed how many of the commitments made to them by hyperscalers by Nvidia can be canceled? And I expect we see a larger, more grotesque version of the massive write offs and losses across memory manufacturers that we saw when the supply chain Crisis ended in 2023, after the supply chain crisis that started in 2021. And when the time comes, know who to blame. Sam Altman, Dario Amadei, Sundar Pichai, Andy Jesse and Mark Zuckerberg. The men who created this entirely avoidable and horribly destructive situation. They all fucking suck. They could have done literally anything else. They could have built housing, they could have fixed their own products. But they chose to do this because they have no vision. These men are losers. I despise them for what they've done and I hate them for what they've done to the computer. Thanks for listening. We'll be back next week with a special memory focused episode with Steve Burke of Gamers Nexus. And I'll be back of course with another monologue. Cheers, my dears.

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