The AI Compute Crunch Is Here (and It's Affecting the Entire Economy)

The more pointed argument here isn't that compute is expensive — it's that the VC subsidy model that kept AI cheap for end users is structurally running out of road, meaning price normalization is coming whether the labs want it or not.

This story lands on top of a hardware supply picture that Modelwire has been tracking for weeks. The Verge's RAM shortage piece from April 18 already flagged that DRAM suppliers will cover only 60% of global demand by end-2027, with shortages potentially running to 2030. That's not a background condition — it's a direct ceiling on the infrastructure buildout this article describes. Meanwhile, Cerebras filing for IPO around the same time signals that specialized chip makers are betting the crunch creates a durable market, not a temporary one. And the Cursor fundraise at a $50B valuation, also from mid-April, shows capital still flowing aggressively toward AI tooling even as the underlying infrastructure costs that tooling depends on are rising. The tension between those two data points is the actual story: demand-side investment is accelerating while supply-side capacity is years behind.

Watch whether major cloud providers (AWS, Azure, Google Cloud) announce compute pricing increases or waitlist expansions for GPU capacity before Q3 2026 — that would confirm the subsidy model is unwinding in practice, not just in theory.