The Growing Compute Shortage

Summary

At the semiconductor level, TSMC’s advanced-node capacity—particularly N3, which underpins much of the AI accelerator ecosystem—is approaching full utilization through at least 2027.4 Advanced fabs take years to construct, and the EUV lithography machines required to equip them have similarly extended production timelines. Memory has emerged as another major bottleneck. High-bandwidth memory (HBM), a specialized type of dynamic random-access memory (DRAM) essential for AI accelerators, remains structurally undersupplied as demand from hyperscalers exceeds existing production capacity. But AI systems also rely on large amounts of ordinary DRAM, and as manufacturers shift capacity toward HBM, the broader tightening of the memory market has driven spot DRAM prices up approximately 8x since early 2025.5 Importantly, this dynamic not only impacts AI infrastructure providers. Industries competing for similar memory supply, including smartphones and consumer electronics, are already seeing upward pressure on component costs. Even if silicon constraints ease, power infrastructure remains a critical limiting factor. Forecasts for global datacenter electricity demand now imply 200-300 gigawatts of data center power demand globally by 2030.6 Hyperscalers are pursuing gigawatt-scale campuses while simultaneously racing to secure turbines, transformers, switchgear, and grid connectivity. GE Vernova and Siemens Energy have both said they are nearly sold out of gas turbines through 2029.7 Transformer lead times have stretched to multiple years, and utilities and regional grids are increasingly struggling to respond to the rising demand from AI datacenters. Scarcity Is Repricing Assets As a result, the market is beginning to reprice anything perceived as a bottleneck to the AI buildout. One of the clearest examples is bitcoin miners. For years, many crypto mining assets were viewed as structurally impaired following the collapse in crypto valuations and increasing competition withi...