AI ComputeEmerging
85%conviction
AI infrastructure is experiencing a fundamental bottleneck migration from GPU allocation to power/interconnect capacity, creating systematic underpricing of energy-constrained compute.
Joins 4 signals · 4 substrate impacts
- The assumption that GPU allocation remains the primary bottleneck for frontier training faces mounting contradictory signals
- The assumption that interconnection queue times don't constrain datacenter siting faces mounting pressure from two contradictory signals
- Grid capacity assumption supporting three portfolio decisions faces mounting contradictions without offsetting support
- MISO demand baseline assumption faces pressure from 3.08σ spike to 121,514 MW
supports — Physical power and interconnect — not GPU supply — is becoming the binding constraint on AI-compute buildout
contradicts — GPU allocation remains the primary bottleneck for frontier training
contradicts — US grid can absorb datacenter load growth through 2027 without material curtailment
pressures — Cloud GPU on-demand pricing stays within +/-15% of current levels through 2026
What to watch next
Compute pricing will bifurcate into power-constrained premium tiers and power-available standard tiers, with energy-secured facilities commanding 30-50% premiums