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Tokenomics is often where good ideas go to die.
Too many protocols rely on emissions without demand, or “utility tokens” without real cash flow. FOMO was designed from the opposite direction: start with real AI usage, then build token mechanics around it.
This post explains how FOMO’s tokenomics work — using a concrete example — and why they are fundamentally different from speculative launchpads or inflation-driven incentive systems.
FOMO operates with two distinct token layers, each with a clear purpose.
$FLOCK is the network-wide asset of the FLock ecosystem. Its role is to:
Coordinate incentives across all deployments
Serve as the reward and governance token
Capture value at the protocol level
Crucially, $FLOCK is deflationary by design:
A portion of inference revenue across all models is used to buy back $FLOCK.
Emissions are fixed and routed only to productive deployments, not idle capital.
$FLOCK represents aggregate demand for AI intelligence across the network.
Each deployment launched through FOMO has its own Model Token (MT).
An MT represents:
A specific model
A specific deployment configuration
A specific inference economy
Each MT has:
A fixed supply (1,000,000 tokens)
A transparent allocation (sale, liquidity, incentives, operator, treasury)
Direct exposure to revenue-based buybacks and burns
In short:
$FLOCK captures network-level value; MT captures model-level value.
Let’s make this concrete with an example.
Alice is a Real Model Asset (RMA) Owner. She wants to deploy a high-quality model on the FLock API Platform.
She launches QwenALICE via a Real Model Asset Offering (RMO).
Total MT supply: 1,000,000 QwenALICE
40% sold in a fair launch
Users buy using $FLOCK
Funds are used for:
Protocol costs
Staking incentives
Liquidity provisioning
If the raise fails, everyone is refunded.
If it succeeds, the model goes live and starts serving inference.
Already, this is different from most launchpads:
No token without a real model
No model without real hosting
No liquidity without locked alignment
Once QwenALICE is live, users pay for inference in USDC or fiat equivalents, through our very own FLock API platform - it’s basically the same how API works in other platforms like OpenAI - we intend to keep fiction of onboarding extremely small.
From that point on, usage is the only thing that matters.
Each dollar of net inference revenue (after compute costs) is split deterministically:
30% → Buy back $FLOCK
30% → Treasury (operations, sustainability)
30% → Buy back and burn QwenALICE
10% → Paid to Alice as RMA yield
This means:
$FLOCK becomes scarcer as any model is used
QwenALICE becomes scarcer as this specific model is used
Alice earns real cash flow, not just tokens
There is no speculation required for value to accrue — inference alone is enough.
FOMO does use emissions — but very intentionally.
Each day, a fixed amount of $FLOCK is emitted.
These emissions are distributed only to models that are being used.
The allocation depends on a Deployment Score:
More revenue → higher score
Newer deployments → higher weight (via an age factor)
This achieves two things:
Rewards models that users actually want
Prevents emissions from being captured forever by incumbents
When QwenALICE receives its share of daily emissions:
10% goes to Alice (RMA bonus)
90% goes to stakers, weighted by gross usage
This is where Bob enters the picture.
Bob is a power user of QwenALICE.
During the RMO:
He buys QwenALICE tokens early
After launch:
He stakes them
He gets discounted inference
He receives $FLOCK rewards
Bob’s incentives stack:
Lower inference costs via staking
$FLOCK emissions proportional to his usage
Exposure to MT deflation as QwenALICE tokens are burned
Importantly, rewards are calculated using gross spend, not discounted spend.
So Bob is not penalized for using discounts:
He pays less
But still earns rewards as if he paid full price
This is a critical design choice — it aligns power users instead of punishing them.
Most token systems fail because:
Emissions grow faster than real demand
Rewards are disconnected from usage
Tokens exist without cash flow
FOMO avoids this by enforcing four constraints:
Fixed MT supply
No infinite minting.
Revenue-funded burns
$MT are burned via real usage.
Capped discounts
Inference never goes “free” at the protocol level.
Emission routing based on revenue
No usage = no rewards.
As a result:
Emissions bootstrap adoption,
but consumption ultimately dominates supply.
The deeper insight behind FOMO is that model deployments behave like franchises.
Alice is a franchise operator
Bob is both a customer and a stakeholder
The protocol is the franchisor
Early participants:
Accept risk
Receive upside via emissions and appreciation
Later participants:
Enjoy lower prices
Benefit from mature liquidity and stability
This is not possible in traditional cloud pricing.
It is only possible because tokens allow capital formation to subsidize usage.
FOMO introduces a new baseline for AI inference economics:
Models are priced competitively because users own part of the upside
Distribution is rewarded, not taxed
Incremental model upgrades must justify their cost
Over time, this removes artificial pricing power from centralized AI APIs and forces real efficiency and innovation.
FOMO’s tokenomics are not designed to “pump” tokens.
They are designed to do one thing well:
Route real AI usage into aligned economic outcomes.
If a model is used:
Tokens burn
Rewards flow
Operators earn
Users save money
If it isn’t:
Emissions fade
Tokens stagnate
Capital moves elsewhere
That is what honest tokenomics look like.
And that’s what FOMO is built to enforce.
Tokenomics is often where good ideas go to die.
Too many protocols rely on emissions without demand, or “utility tokens” without real cash flow. FOMO was designed from the opposite direction: start with real AI usage, then build token mechanics around it.
This post explains how FOMO’s tokenomics work — using a concrete example — and why they are fundamentally different from speculative launchpads or inflation-driven incentive systems.
FOMO operates with two distinct token layers, each with a clear purpose.
$FLOCK is the network-wide asset of the FLock ecosystem. Its role is to:
Coordinate incentives across all deployments
Serve as the reward and governance token
Capture value at the protocol level
Crucially, $FLOCK is deflationary by design:
A portion of inference revenue across all models is used to buy back $FLOCK.
Emissions are fixed and routed only to productive deployments, not idle capital.
$FLOCK represents aggregate demand for AI intelligence across the network.
Each deployment launched through FOMO has its own Model Token (MT).
An MT represents:
A specific model
A specific deployment configuration
A specific inference economy
Each MT has:
A fixed supply (1,000,000 tokens)
A transparent allocation (sale, liquidity, incentives, operator, treasury)
Direct exposure to revenue-based buybacks and burns
In short:
$FLOCK captures network-level value; MT captures model-level value.
Let’s make this concrete with an example.
Alice is a Real Model Asset (RMA) Owner. She wants to deploy a high-quality model on the FLock API Platform.
She launches QwenALICE via a Real Model Asset Offering (RMO).
Total MT supply: 1,000,000 QwenALICE
40% sold in a fair launch
Users buy using $FLOCK
Funds are used for:
Protocol costs
Staking incentives
Liquidity provisioning
If the raise fails, everyone is refunded.
If it succeeds, the model goes live and starts serving inference.
Already, this is different from most launchpads:
No token without a real model
No model without real hosting
No liquidity without locked alignment
Once QwenALICE is live, users pay for inference in USDC or fiat equivalents, through our very own FLock API platform - it’s basically the same how API works in other platforms like OpenAI - we intend to keep fiction of onboarding extremely small.
From that point on, usage is the only thing that matters.
Each dollar of net inference revenue (after compute costs) is split deterministically:
30% → Buy back $FLOCK
30% → Treasury (operations, sustainability)
30% → Buy back and burn QwenALICE
10% → Paid to Alice as RMA yield
This means:
$FLOCK becomes scarcer as any model is used
QwenALICE becomes scarcer as this specific model is used
Alice earns real cash flow, not just tokens
There is no speculation required for value to accrue — inference alone is enough.
FOMO does use emissions — but very intentionally.
Each day, a fixed amount of $FLOCK is emitted.
These emissions are distributed only to models that are being used.
The allocation depends on a Deployment Score:
More revenue → higher score
Newer deployments → higher weight (via an age factor)
This achieves two things:
Rewards models that users actually want
Prevents emissions from being captured forever by incumbents
When QwenALICE receives its share of daily emissions:
10% goes to Alice (RMA bonus)
90% goes to stakers, weighted by gross usage
This is where Bob enters the picture.
Bob is a power user of QwenALICE.
During the RMO:
He buys QwenALICE tokens early
After launch:
He stakes them
He gets discounted inference
He receives $FLOCK rewards
Bob’s incentives stack:
Lower inference costs via staking
$FLOCK emissions proportional to his usage
Exposure to MT deflation as QwenALICE tokens are burned
Importantly, rewards are calculated using gross spend, not discounted spend.
So Bob is not penalized for using discounts:
He pays less
But still earns rewards as if he paid full price
This is a critical design choice — it aligns power users instead of punishing them.
Most token systems fail because:
Emissions grow faster than real demand
Rewards are disconnected from usage
Tokens exist without cash flow
FOMO avoids this by enforcing four constraints:
Fixed MT supply
No infinite minting.
Revenue-funded burns
$MT are burned via real usage.
Capped discounts
Inference never goes “free” at the protocol level.
Emission routing based on revenue
No usage = no rewards.
As a result:
Emissions bootstrap adoption,
but consumption ultimately dominates supply.
The deeper insight behind FOMO is that model deployments behave like franchises.
Alice is a franchise operator
Bob is both a customer and a stakeholder
The protocol is the franchisor
Early participants:
Accept risk
Receive upside via emissions and appreciation
Later participants:
Enjoy lower prices
Benefit from mature liquidity and stability
This is not possible in traditional cloud pricing.
It is only possible because tokens allow capital formation to subsidize usage.
FOMO introduces a new baseline for AI inference economics:
Models are priced competitively because users own part of the upside
Distribution is rewarded, not taxed
Incremental model upgrades must justify their cost
Over time, this removes artificial pricing power from centralized AI APIs and forces real efficiency and innovation.
FOMO’s tokenomics are not designed to “pump” tokens.
They are designed to do one thing well:
Route real AI usage into aligned economic outcomes.
If a model is used:
Tokens burn
Rewards flow
Operators earn
Users save money
If it isn’t:
Emissions fade
Tokens stagnate
Capital moves elsewhere
That is what honest tokenomics look like.
And that’s what FOMO is built to enforce.
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