AI Data Center Infrastructure & Bubble Risk Simulator

Stress-test hyperscaler deployments under soaring GPU rack power densities (up to 120 kW/rack), 15-year utility power contracts, rapid 4-year hardware obsolescence, and sudden token demand shocks.

Hyperscale AI Financial & Physical Stress Matrix

Moderate Debt Stress
Total Initial CapEx
$16.0 B
$9.6B Debt / $6.4B Equity
Total Server Racks
5,882 Racks
Direct Liquid Cooling Req.
Annual Power Bill (PPA)
$361 M/yr
8,760 hrs @ 1.10 PUE
Net Stranded Asset Risk
-$5.4 B
Breakeven: Never (Default)
10-Year Free Cash Flow & Debt Paydown Bubble Shock Year marked
Rack Power Density vs. Cooling Envelope Extreme Liquid
Year Utilization Revenue ($M) Power PPA ($M) Opex + Maint ($M) Debt Service ($M) Net FCF ($M) Cumul. Balance ($M)
Vulnerable Hyperscale Trajectory: High leverage of 60% combined with a 2027 demand cliff will cause debt service to exceed post-crash HPC revenues, triggering balance sheet write-downs of $5.4B.

The Physical Paradox: 120 kW Racks

Traditional data centers run 5 to 10 kW per rack on air cooling. AI clusters packing Nvidia H100 and Blackwell servers generate 40 to 120 kW per rack. Standard chillers fail under this thermal load, requiring massive direct-to-chip liquid cooling loops and closed-circuit evaporative towers.

The Take-or-Pay Power Trap

Utilities demand 10-to-20 year take-or-pay power purchase agreements before expanding transmission or restarting nuclear units. If generative AI inference revenue slows, operators cannot simply turn off power without paying hefty contractual standby penalties.

Post-Crash Repurposing

Unlike empty fiber lines in the 2001 dot-com crash, depreciated GPUs can run molecular dynamics, weather simulation, and scientific rendering. However, rapid chip obsolescence means hardware loses 70% of peak resale value every 3.5 years.

Enjoy this tool? Build your own with Super