AI Supercycle Stack Economics

Stanford Engineering Value Chain & Energy Bottleneck Model
Scenario Presets:
Capital & Physical Inputs
$150B
12.5 GW
1.25
250k
5.0
$45B
6-Layer Stack Flow & Value Capture D3 Dynamic Flow
1. Energy Grid
2. Data Centers
3. Hardware/GPUs
4. Models
5. Middleware
6. Applications
Primary System Constraints
Primary Active Bottleneck
Energy & Power Distribution
Grid interconnection limits total cluster compute capabilities below invested hardware capacity.
Power Utilization
94.2%
Token Throughput
4.20T/s
Hardware ROIC
18.5%
App Layer ROIC
24.1%
Layer Returns Breakdown
Layer CapEx ($B) Margin % ROIC %
Canonical Model State Proof
Primary Bottleneck
Energy & Power Distribution
Energy Utilization
94.2%
Token Throughput (TPS)
4.2E+12
Hardware ROIC
18.5%
App Layer ROIC
24.1%
Model Layer Margin
12.4%

AI Stack Economics & Bottleneck Simulation

Read the explanation

The AI value chain distributes invested capital across six interconnected tiers, from physical power generation down to end applications. Power demand scales with deployed clusters and data center cooling efficiency, computed as clusters divided by one hundred times zero point one two times PUE. Selecting the energy bottleneck scenario cuts power capacity to four point five gigawatts, choking operational GPU factor and driving utilization to maximum. Constrained token throughput and layer ROIC update immediately, revealing how physical infrastructure limits dictate downstream application returns.

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