The Dual Scarcity: Compute & Energy
The Energy Bottleneck Compute is capped by Energy. In the physical world, a GPU without power is just expensive scrap metal. As global data center electricity consumption doubles by 2030, the true scarcity—and the true pricing power—lies not just in the hardware, but in the electrons.
1. AI compute demand is exploding far beyond current infrastructure
Global data-centre electricity consumption is projected to nearly double by 2030, reaching ~945 TWh, with AI-optimized data centres expected to grow 4× from 2024 levels. This creates a structural imbalance:
AI workloads grow exponentially
Compute infrastructure (GPU supply, racks, energy availability) grows linearly.
The Energy Bottleneck: While GPU supply grows linearly, power grid capacity is inelastic. Access to stable, scalable power is becoming the primary constraint for AI scaling.
This gap creates persistent scarcity, and scarcity leads to high, volatile pricing in GPU rental, cloud instances, and co-location markets.
2. Capital demand is surging, financing remains slow and inefficient
Data-centre investment needs keep accelerating:
U.S. data-centre financing reached $30B in 2024, projected to exceed $60B in 2025.
Global AI infrastructure capex from hyperscalers is forecast to hit ~$490B by 2026 and potentially >$2.8T by 2029.
Yet, financing mechanisms have not evolved:
Deals are bespoke, opaque, and slow.
Providers often rely on traditional credit, collateral-heavy lending, or expensive equity cycles.
On-chain capital cannot participate because compute revenue lacks standardization or verifiable yield paths.
The OpEx Drain: In traditional models, ~40% of operational expenditure is electricity bills paid to external utilities. This is massive value leaking out of the AI ecosystem.
The result is a market where capital wants yield, compute providers need capital, but there is no transparent, programmable bridge between the two.
3. Compute revenues exist — are trapped off-chain
AI compute already generates stable, recurring revenue from:
Model training
Inference jobs
Enterprise GPU rentals
Cloud resale and batch workloads
Energy Arbitrage & Grid Services
These revenues are:
Non-standardized
Not tokenized
Not auditable in real time
Not distributed on programmable rails
So despite the sector’s massive demand and predictable cash flows, there is no unified financial layer that turns these revenues into investable, yield-bearing on-chain assets.
4. Users cannot access compute-backed yield — only speculation
Although AI is the fastest-growing market today, most on-chain users face only two options:
Speculative AI tokens, or
Emissions-based yields unrelated to real activity.
Users do not have access to:
Real compute cash flows
Real revenue-sharing
Transparent utilisation data
Standardised staking products tied to physical output
the 'Senior Debt' of AI—the stable yields backed by energy payments
The market lacks a mechanism that lets everyday users, DAOs, or treasuries earn yield from real AI compute, the same way institutions earn yield from T-bills or credit pools.
5. Why this matters
The global AI infrastructure economy is measurable, revenue-generating, and growing faster than any digital asset sector — yet none of this value flows on-chain today.
This is the gap RAX aims to fill: RAX aims to build the Sovereign Financial Layer for AI. By assetizing the full stack—from the GPU to the energy—we capture the industry's biggest expense (Energy) and transform it into the ecosystem's most stable yield, creating a self-sustaining internal economy.
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