Revenue & Emission Distribution
DeepNode’s revenue and emission model is designed to be sustainable, market-driven, and aligned with real value creation. Unlike traditional AI-compute protocols that rely on fixed emission schedules and misaligned reward splits, DeepNode ties rewards to usage, validated work, and model performance.
The diagram below outlines how revenue and emissions flow through the system.
1. Revenue Distribution
Revenue is generated when consumers use models through the platform or API. All revenue enters a central routing module, which automatically distributes fees according to domain-specific and governance-approved settings.
Revenue is allocated to:
Model Owners (x%) Incentivizes creation, training, and long-term maintenance of models. Set at mint (per model).
Fees & Payment Processors (y%) Covers infrastructure, payments, routing, and services.
Buyback & Burn (1%) Permanently removes $DN from circulation, strengthening long-term token stability.
Emission Distribution (z%) A portion of revenue can reinforce or offset emissions, enabling a sustainable equilibrium.
2. Emission Distribution
Emissions reward the actors who contribute security, compute, and value to the network.
Emissions can be distributed to:
Miners (0–99%)
Stakers (0–99%)
Validators (0–99%)
Domain Owners (0–10%)
Model Owners (x%)
Foundation (1% fixed)
3. Why DeepNode Avoids the “41/41” Emissions Problem
Many compute networks historically used a fixed emission split between miners and stakers (e.g., 41% miners / 41% stakers). On paper this looks “balanced,” but in practice it creates severe structural problems.
The core issue with a 41/41 split:
If miners receive 41% of emissions, then:
Miners must produce far more than 41% worth of real market value just to offset the sell pressure of their rewards.
In many networks, the math works out like this:
miners receive $41 of incentives
stakers receive another $41
total = $82 emissions
the market must generate ≥ $100 in new external value to break even (after slippage, spreads, liquidity, fees)
But most AI networks cannot generate this much organic demand early on.
What happens then?
Miners must sell emissions → constant sell pressure
Stakers sell emissions → double sell pressure
Fees are far too small to offset emissions
Token price trends down
Mining becomes unprofitable
Miners exit
Network collapses into low-utility, high-inflation stagnation
Development slows, model usage drops, governance weakens
This is the exact feedback loop DeepNode avoids.
4. DeepNode’s Solution: Market-Aligned Emissions
DeepNode uses dynamic, domain-level emission control, avoiding forced splits.
Instead of linear rewards:
Miners receive rewards based on verified work, not fixed inflation
Stakers only earn what the domain allocates based on real security needs
Validators are rewarded for active validation, not passive uptime
Domains shape incentives based on actual usage signals
Governance sets global floors/ceilings to protect sustainability
This ensures:
A. Emissions always follow value creation
Not predetermined numbers.
B. Demand and supply stay balanced
Usage drives rewards → rewards drive quality → quality drives usage.
C. Sell pressure does not exceed buy pressure
Because emissions scale with demand.
D. Domains can adapt incentives dynamically
Different models, workloads, and industries need different emission logic.
E. The network cannot be “locked into” bad tokenomics
Unlike 41/41 systems where early decisions permanently damage sustainability.
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