TL;DR
Hardware memory constraints, not capex, will be the bottleneck limiting AI compute deployment over the next two years despite massive infrastructure spending.
Key Points
- Current DRAM supply can only support ~15GW of AI infrastructure deployment, limiting capacity to ~30M agentic users consuming 1M tokens daily
- HBM DRAM manufacturing severely constrained by single-source EUV lithography equipment from ASML; new fabs take years to build
- Individual token consumption exploded 50x in 3 years among power users; 1B+ LLM users driving exponential demand growth
- Expect dynamic pricing models, reduced free tiers, and shift toward inference efficiency optimization as capacity constraints bite in 2026-2027
Why It Matters
Engineers and infrastructure teams need to understand that the real constraint on AI scaling isn't capital or willingness to spend—it's physical hardware supply chains. This will drive pricing pressure, force architectural innovations around memory efficiency, and reshape how AI services allocate compute during peak demand periods.
Source: martinalderson.com