AI’s chip glut may be hiding in plain sight

Monologue: The AI Data Center Overbuild

If hundreds of billions of dollars in AI hardware cannot run, the infrastructure boom may be building capacity before demand exists.

3 key takeaways

1Roughly $200–$300 billion in AI GPUs may be idle in warehouses or data centers without enough power.
2Ed Zitron argues hyperscalers such as Microsoft overstate how much AI capacity is actually operational.
3The resulting data-center overbuild could create a glut more severe than the dot-com-era fiber surplus.

Don't miss

Zitron’s estimate that $200–$300 billion in AI GPUs may be idle reframes the boom as a possible hardware glut.

The brief

Ed Zitron opens an investigation with The Guardian into where the AI boom’s chips actually are, finding hardware in warehouses and data centers that lack enough power to operate it.

The central tension is between the scale of AI infrastructure spending and the amount of computing capacity that is genuinely online, with Microsoft among the hyperscalers facing scrutiny.

Zitron estimates that roughly $200–$300 billion in AI GPUs may be sitting idle, turning a supposed capacity surge into a question about stranded hardware.

The warning extends beyond one company: a data-center overbuild could produce an infrastructure glut worse than the dot-com-era fiber surplus.

What was said on this episode

26 statements · 20 negative · 6 neutral

Ed Zitronon AI chipsNegative4:46
AI chips are primarily stored in warehouses or unpowered data centers.
“the answer is either in warehouses or unpowered data centers”
Listen at 4:46
Ed Zitronon Microsoft GPUs in serviceNegative4:55
Microsoft had 2.2 million GPUs in service, fewer than widely believed.
“Microsoft only had 2.2 million GPUs in service, far less than people believed”
Listen at 4:55
Ed Zitronon Microsoft GPUsNeutral5:04
Microsoft’s chips represented approximately 1.993 gigawatts of capacity.
“the total power of the chips was estimated at about 1.993 gigawatts of capacity”
Listen at 5:04
Ed Zitronon Microsoft GPUsNeutral5:09
The installed Microsoft GPUs cost approximately $50–60 billion.
“the overall cost of those GPUs installed somewhere in the region of $50 to $60 billion”
Listen at 5:09
Ed Zitronon AI GPUs and associated hardwareNegative5:45
Up to $106 billion of AI hardware is currently unpowered or warehoused.
“I estimate that as much as $106 billion worth of GPUs and associated hardware are now sitting either unpowered in data centers, or in complete data centers, or in warehouses”
Listen at 5:45
Ed Zitronon AI infrastructure companies’ construction in progressNeutral6:39
Named AI infrastructure companies held approximately $374 billion in construction in progress.
“Google, Meta, Oracle, Amazon, SpaceX and Tesla, neoclouds like CoreWeave and IREN, and co-location companies like Core Scientific and Applied Digital all have about $374 billion of construction in progress”
Listen at 6:39
Ed Zitronon Google construction in progressNeutral7:08
Google accounted for approximately $122 billion of construction in progress.
“A large chunk of that larger CIP number is Google's, which sits around $122 billion”
Listen at 7:08
Ed Zitronon AI construction in progressNegative8:00
Total AI construction in progress is estimated at $400–500 billion.
“the total construction and progress number is somewhere between $400 and $500 billion”
Listen at 8:00
Ed Zitronon Sold AI hardware and chipsNegative8:07
Approximately half of sold AI hardware is warehoused and will take over two years to deploy.
“approximately 50% of all AI hardware and chips that have been sold is being warehoused, and will take more than two years to fully ingest”
Listen at 8:07
Ed Zitronon Nvidia CorporationNeutral8:27
NVIDIA generated $200–300 billion from hardware sold 24–36 months ahead.
“I think NVIDIA has made somewhere between $200 and $300 billion in revenue on stuff that's been sold probably 24 to 36 months in advance”
Listen at 8:27
Ed Zitronon Anthropic and OpenAI AI compute purchasesNegative8:35
AI compute scarcity results from capacity being sold primarily to Anthropic and OpenAI.
“any scarcity around AI compute is a result of most of the capacity being sold immediately to Anthropic and OpenAI”
Listen at 8:35
Ed Zitronon Available AI capacityNegative9:09
Available AI capacity is substantially smaller than commonly believed.
“the available AI capacity is much, much, much smaller than we think”
Listen at 9:09
Ed Zitronon AI infrastructure companiesNegative9:49
If Microsoft struggles to bring AI capacity online, other companies likely do too.
“if they're failing, I think everyone is”
Listen at 9:49
Ed Zitronon AI capacity coming onlineNegative9:59
Actual AI capacity coming online is likely several gigawatts below expectations.
“very little actual capacity is coming online, likely gigawatts less than people believe”
Listen at 9:59
Ed Zitronon AI capacity allocationNegative10:03
Nearly all newly available AI capacity flows to Anthropic and OpenAI.
“almost all of it is flowing directly to Anthropic and OpenAI”
Listen at 10:03
Ed Zitronon Anthropic and OpenAI infrastructure backlogsNegative10:29
Anthropic and OpenAI account for 50–75% of major AI infrastructure backlogs.
“anywhere from half to 75% of those backlogs are directly from Anthropic and OpenAI”
Listen at 10:29
Ed Zitronon Anthropic and OpenAINegative10:34
Anthropic and OpenAI cannot actually pay for their contracted infrastructure backlogs.
“neither of whom can actually pay for them”
Listen at 10:34
Ed Zitronon OpenAI compute spending in Microsoft revenueNeutral11:08
OpenAI’s compute spending represented 70% of Microsoft’s AI revenue and 7% overall revenue in fiscal 2026.
“OpenAI's compute spend, which made up 70% of Microsoft's AI revenues in fiscal year 2026, and 7% of its overall revenues in that same fiscal year”
Listen at 11:08
Ed Zitronon AI capacity coming onlineNegative12:22
AI capacity is coming online at most at tens or hundreds of megawatts per quarter.
“the actual AI capacity is, at best, coming on at a rate of maybe tens or hundreds of megawatts a quarter”
Listen at 12:22
Ed Zitronon AI infrastructure situationNegative12:47
The AI infrastructure situation is substantially worse than the dot-com bubble.
“this is magnitudes worse than the dot-com bubble”
Listen at 12:47
Ed Zitronon Planned AI capacity versus demandNegative13:03
Planned AI capacity is 5–50 times greater than current demand.
“there's anywhere from 5 to 50 times the amount of capacity in planning than there is demand for it”
Listen at 13:03
Ed Zitronon AI infrastructureNegative13:15
AI infrastructure will not be used at the scale at which it is built.
“There is no situation where this is going to be used at the scale it's built”
Listen at 13:15
Ed Zitronon NVIDIA Vera Rubin and Feynman generationsNegative13:34
The AI industry may not progress beyond NVIDIA’s Vera Rubin or Feynman generations.
“I don't think we make it past Vera Rubin or Feynman”
Listen at 13:34
Ed Zitronon GPU economyNegative13:49
There will be no post-bubble GPU economy at the current construction scale.
“there is no post-bubble economy for GPUs, at least at the scale they're being built”
Listen at 13:49
Ed Zitronon AI inference economicsNegative13:54
AI inference economics resemble airlines with high fixed costs.
“the cost of serving inference is similar to an airline with high fixed costs”
Listen at 13:54
Ed Zitronon AI service operatorsNegative13:59
AI operators must purchase hundreds or thousands of GPUs regardless of realized demand.
“You as an operator must buy capacity, hundreds or thousands of GPUs, over a certain period of time, whether or not you actually have the demand”
Listen at 13:59

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