CoreWeave Reveals: Physical Limits Are Slowing AI Hardware Delivery to a Crawl

The AI gold rush has a supply chain problem. CoreWeave, a major player in GPU cloud infrastructure, points a finger at a stubborn bottleneck: the physical world itself.

Silicon's Hard Stop

It's not just about software or algorithms anymore. The real barrier to the next leap in artificial intelligence might be the tangible, unyielding limits of manufacturing and physics. Chip fabrication, cooling systems, and raw material logistics are hitting walls that code alone can't solve.

Demand Outpaces Atoms

The insatiable hunger for more powerful AI models collides with the slow, expensive reality of building the hardware to run them. Every hyperscaler and startup is chasing the same finite pool of advanced components, creating a scramble that looks less like innovation and more like a commodities squeeze—just with fancier packaging and more zeroes on the invoice.

The result? Project delays, inflated costs, and a looming question over whether the AI industry's ambitions have finally outpaced the factory floor's ability to deliver.

For investors betting billions on AI's exponential growth, it's a sobering reminder: sometimes the biggest disruptor isn't a new algorithm, but an old-fashioned shortage. The market can price in hype indefinitely, but it still has to wait for the physical shipment to arrive.

CoreWeave believes physical limits is what is slowing AI hardware delivery

“The primary constraint is a physical bottleneck associated with getting the most performant compute into the hands of the most cutting edge players,” Michael said.

Michael added that this pressure reaches far beyond cloud firms and chip suppliers. Michael shared details from a recent conversation with a mining company executive, who he did not name, where the discussion focused on materials needed to build infrastructure. He said the mining executive explained that the strain goes two levels deeper into the supply chain, reaching raw metals and copper used to support AI systems.

Michael said the mining executive made a direct request for cooperation across industries to increase output. “We need to work together as a group,” the executive told him. Michael said similar comments from leaders inside AI companies are often criticized as proof of a circular economy. “If I say that in the AI space, I get accused of being in a circular economy,” he said. “So that’s all I’ll say on the circular economy is, like, you do that by working together.”

Concerns from critics have also focused on risk tied to debt and customer exposure. Some warn that if CoreWeave fails to refinance obligations or loses a major client, lenders could release large volumes of used GPUs into secondary markets, hurting prices and causing disruption. Michael addressed that scenario by pointing to demand trends instead. He said the growth he sees is fast and aggressive, not fragile.

CoreWeave faces stock swings as demand from big tech stays intense

Michael said CoreWeave sits at the center of rising demand because of its focus on parallel computing, which supports modern AI workloads. He said requests from large technology firms continue without pause.

Since its IPO, CoreWeave’s stock has been wobbly, but Michael only pointed out that shares now trade NEAR $90, compared with an IPO price of $40.

Michael also addressed past reliance on Microsoft, which once accounted for 85% of company revenue, saying that exposure has dropped after active diversification. He claims that no single customer now makes up more than 30% of the backlog, urging investors to ignore short delays that draw heavy reactions, such as a data center opening pushed back by a week, which he said triggered “bedlam” from short term observers.

Michael described the current phase as a macro super cycle driven by a MOVE from sequential to parallel computing that opens access to far greater compute capacity.

Michael said the challenges slowing delivery remain tied to policy limits, physical infrastructure, and energy access. “The reasons that you have challenges in delivering that compute is because of policy, because of physical infrastructure, because of energy,” he said. “You do that by working together.”

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