Token Utility ($LAM) - Action Model

LAM is Action Fuel: Unlike speculative tokens, LAM has real utility—every click, type, and navigation performed by AI agents consumes $LAM. This isn’t a currency; it’s the gasoline that powers the automation economy.

The $LAM Token: Powering the Action Economy

More Than a Token—It’s Ownership

LAM represents a fundamental shift in how AI value is created and distributed. When Big Tech trains on your data to build their AI, they keep 100% of the profits while you get nothing. When Action Model’s AI performs actions, the value flows back to the community through $LAM.

Action Fuel

Every agent click, type, scroll, upload, or form submit consumes $LAM as fuel

DAO Governance

Decentralized Autonomous Organization - Action Model is the Uprising

Marketplace

Earn from your workflows and agents in the creator economy

Train the LAM

Contributors help train the Large Action Model and earn rewards

Action Credits

Partners and devs preload $LAM to fund fleets of agents

Referrals & Quests

Build your network through referrals, affiliates, and quest rewards

How $LAM Powers Every Action

The Action Loop

The Action Loop - How Each AI Decision Consumes $LAM

Environment Input

The LAM receives the current state: screenshot, DOM, user goal, and action history

Tree Search

The AI searches the Action Tree to find the optimal next action

Action Decision

The LAM determines the precise GUI action to execute

$LAM Consumption

This single action consumes $LAM tokens based on the current pricing model

Action Execution

The action is performed (click, type, navigate, etc.)

Loop Continues

Process repeats until the goal is achieved or time expires

Every Action Loop = Billable Action: Each loop through this process is one action that consumes $LAM. A complex workflow might require 100+ actions, creating continuous token demand.

The Action Loop - How Each AI Decision Consumes $LAM

$LAM Token Overview

LAM is Action Fuel: Every GUI action executed by a LAM agent consumes $LAM.

Dynamic Pricing Model

Action Price is fixed by LAM version (e.g., LAM-1 = $0.01/action), while the number of tokens per action floats with the token price.

Token Distribution

Each consumed action distributes tokens: Creator Distribution (33%), Burn (34%), Ecosystem (33%).

Buy Pressure Engine

B2C + B2B + Partners create continuous buy pressure.
Subscriptions and API usage require $LAM purchases to fund actions

Deflationary Dynamics

Because a share of every action is burned/locked, usage reduces supply, increasing token value

Token Economics Model

Fixed USD Pricing, Floating Token Cost

Price Stability for Businesses: The USD cost per action is fixed by LAM version (e.g., LAM-1 = $0.01/action), while the number of tokens consumed adjusts with market price. This gives businesses predictable usage limits and costs while allowing token value to appreciate.

Pricing Formula

For LAM version v with USD price per action pv:Tokens Required Per Action:

τ(v,t) = pv / Pt

Where:

τ(v,t) = Tokens required per action at time t
pv = USD price per action for version v
Pt = Market price of $LAM in USD at time t

Token Distribution Per Action

Where Every $LAM Goes

Token Distribution - Creating Value Through Every Action

When an action consumes tokens, they are distributed as follows:

33% to Creators

Marketplace RewardsGoes to workflow creators based on their Creator Epoch when their automations are used

34% Burned

Deflationary MechanismPermanently removed from circulation, creating scarcity with every action

33% Ecosystem

Platform GrowthPowers ecosystem development, operations, and community initiatives

Distribution Mathematics

Distribution	Percentage	Formula	Purpose
💡 Creator	Up to 33%	`(≤0.33) × τ` tokens	Rewards workflow creators based on Creator Epoch
🔥 Burn	34%	`0.34 × τ` tokens	Permanently removed from circulation, creating scarcity
🌱 Ecosystem	33%	`0.33 × τ` tokens	Powers ecosystem growth, development, and operations

Creator Epoch Rewards: The actual creator percentage varies by when workflows are published. See Creator Epoch table for specific reward rates.

Example Distribution

Action Size	Tokens Consumed	💡 To Creator	🔥 Burned	🌱 Ecosystem
Small Task	10 tokens	3.3 tokens	3.4 tokens	3.3 tokens
Medium Task	100 tokens	33 tokens	34 tokens	33 tokens
Large Task	1,000 tokens	330 tokens	340 tokens	330 tokens
Enterprise	10,000 tokens	3,300 tokens	3,400 tokens	3,300 tokens

The B2B Buyback Loop

How Business Demand Drives Token Value

Business Subscribes

Enterprise pays $1,000-2,000/month per AI agent

Platform Buys $LAM

Subscription revenue used to purchase $LAM from market to fund Action Loop fuel

Agents Consume Tokens

AI agents perform actions, consuming $LAM as fuel

Tokens Distributed

Up to 33% to creators, 34% burned, 33% to ecosystem

Supply Decreases

Burning reduces total supply permanently

Value Appreciates

Decreased supply + continued demand = price appreciation

The Virtuous Cycle

Self-Reinforcing Economics: Each step in the cycle strengthens the next, creating exponential growth. More usage leads to more burning, which increases scarcity, driving up value, attracting more creators, who build better automations, which attracts more businesses.

Token Utility Ecosystem

Multiple Demand Drivers

Direct API Usage

Developers and Partners

Pre-load $LAM for API calls
Each API action consumes tokens
Usage-based pricing model
No subscription required

Consumption Rate

Average workflow: 50-200 actions
Complex automation: 500+ actions
Daily agent operations: 10,000+ actions

Subscription Discount Model

Pay with $LAM, Save 10%

Businesses can pay subscriptions directly in $LAM tokens for a 10% discount:

Example: $1,000 monthly subscription

Pay in USD: $1,000
Pay in $LAM:$ 900 worth of tokens

This incentivizes businesses to hold and use $LAM, creating additional buy pressure.

Discount Formula

Tokens Required = S × (1 - d) / Pt

Where:

S = Subscription amount in USD
d = Discount rate (0.10 for 10%)
Pt = Current token price

Deflationary Dynamics

Why $LAM Becomes More Valuable Over Time

Continuous Burning

34% of Every Action

Millions of daily actions
Permanent supply reduction
Accelerates with adoption
Cannot be reversed

Growing Demand

Multiple Demand Sources

B2B subscriptions
API usage
Marketplace activity
Speculation and holding

Real-World Usage Examples

Understanding Token Consumption

Simple Email Automation

Social Media Management

Enterprise Data Processing

Formal Tokenomics Model

Mathematical Framework

Pricing Model (Versioned “Price Per Action”)

Let pᵥ be the USD price per action for model version v (e.g., LAM-1 has p₁ = $0.10 per action)
Let Pₜ be the market price of $LAM in USD at time t
Tokens required per action for version v at time t is:

Formula:

τᵥ,ₜ = pᵥ / Pₜ

Fractional tokens are supported to meter actions precisely.

Examples (LAM-1):

If Pₜ = $0.01, then τ₁,ₜ = 0.10 / 0.01 = 10 tokens/action
If Pₜ = $0.10, then τ₁,ₜ = 1 token/action
If Pₜ = $1.00, then τ₁,ₜ = 0.1 token/action

USD cost per action is stable for developers; tokens per action adjusts automatically to market price.

Token Fuel Distribution (Per Action)

Let the distribution shares be:

Creator share μ = 0.33
Burn share β = 0.34
Ecosystem share ε = 0.33

For one action that consumes τ tokens:

Creator tokens/action = μ · τ    (if a Marketplace workflow was used)
Burned tokens/action = β · τ
Ecosystem tokens/action = ε · τ

USD Equivalents Per Action:

USD to creator/action = μ · pᵥ

USD burned/action = β · pᵥ

USD to ecosystem/action = ε · pᵥ

For LAM-1 (p₁ = $0.10): $0.033** goes to the creator (if used), **$ 0.034 is burned, and $0.033 to the ecosystem per action, independent of token price.

No-Marketplace case: If a run doesn’t use a Marketplace workflow, the creator share can be (a) retained in an Ecosystem Rewards pool or (b) redirected to Ecosystem. The DAO should ratify the default.

The Buyback Loop (Why Usage Creates Demand)

B2B subscriptions (e.g., $1k–$ 2k per agent/month) and API partners include usage allowances measured in actions
To deliver those actions, operators must acquire $LAM (buy on the market or hold inventory)
When actions run, tokens flow through the Fuel distribution above (with 34% burned)

Paying Subscriptions in $LAM (10% Discount)

Paying a $1,000 subscription in$ LAM yields a 10% discount: you spend tokens equivalent to $900 at the time of payment
Those tokens may be escrowed and released into the Fuel distribution as actions are consumed (implementation detail—DAO parameter)

Tokens needed to pay a subscription S in USD with a discount d:

Formula:

Tokens = S(1 - d) / Pₜ

Advanced Tokenomics Calculations

Per-Action Accounting (LAM-v)

Tokens per action: τᵥ,ₜ = pᵥ / Pₜ
Burn: Bₐcₜ = βτᵥ,ₜ
Creator: Mₐcₜ = μτᵥ,ₜ · 𝟙marketplace
Ecosystem: Eₐcₜ = ετᵥ,ₜ

Network-Level, Period t (e.g., monthly)

Let Aₜ be actions executed in t. Then:

Tokens needed    Dₜ = τᵥ,ₜ · Aₜ
Creator tokens   Mₜ = μ · Dₜ · Pr(marketplace use)
Burned tokens    Bₜ = β · Dₜ
Ecosystem tokens Eₜ = ε · Dₜ
USD spent on actions Uₜ = pᵥ · Aₜ    (price-invariant)

Supply Dynamics

Let Sₜ be circulating supply at start of t. Let Eₜ be new emissions (training rewards, grants) approved by DAO.

Supply Evolution:

Sₜ₊₁ = Sₜ + Eₜ - Bₜ

Conditions:

Deflationary condition: Bₜ > Eₜ
Neutral: Bₜ = Eₜ
Inflationary: Bₜ < Eₜ

Break-even emissions (deflation-neutral):

Formula:

E*ₜ = β · τᵥ,ₜ · Aₜ

“Half-Life” Under Constant Burn and No Emissions (Illustrative)

If actions are steady and Eₜ = 0, supply declines linearly. Time to reduce supply by half:

Half-Life Formula:

T₁/₂ = S₀ / (2 · Bₜ) = S₀ / (2 · β · τᵥ,ₜ · Aₜ)

Token Training Distribution

Rewarding Data Contributors

Future Enhancement: A portion of consumed $LAM may be distributed to trainers who provided the action branches/trees used in executed workflows.

How It Works

When users train the LAM by demonstrating actions (e.g., creating an AWS EC2 instance), they contribute to the Action Tree. When workflows use these trained paths, contributors earn rewards.Example Flow:

1,000 users train “Create EC2 Instance” action
Enterprise workflow uses this action path
Training distribution portion flows to those 1,000 trainers
Rewards proportional to contribution quality

Incentive Alignment

Benefits:

Rewards quality training data
Incentivizes rare/complex actions
Creates passive income for trainers
Improves model accuracy

Distribution Formula:

Trainer Reward = (Quality Score × Usage Count) / Total Contributors

Action Model API (Developers)

Building with the Large Action Model

Unlike LLMs that output text, the LAM outputs actions. Each API call returns the next GUI action to execute, not generated content.

Fundamental Differences

Aspect	OpenAI API (LLM)	Action Model API (LAM)
Input	Text prompt	Environment state + goal
Output	Generated text	Next action to execute
Pricing Unit	Input/output tokens	Actions performed
Use Case	Content generation	Task automation
Billing	Per token	Per action

Long-Term Economic Benefits

Why $LAM Creates Sustainable Value

Price-Stable UX

Predictable Costs for BusinessCustomers think in actions and USD, not token volatility. The floating token model ensures:

Fixed USD cost per action
No surprise bills from price swings
Easy budgeting for enterprises
Seamless B2B adoption

Automatic Market Clearing

Counter-Cyclical DemandAs $LAM price changes, token consumption auto-adjusts:

Price rises → Fewer tokens needed per action
Price falls → More tokens needed per action
USD revenue remains stable
Natural price discovery mechanism

Permanent Scarcity Creation

Usage = ValueEvery action permanently removes supply:

34% burn rate compounds over time
Millions of daily actions = massive burns
Supply decreases while demand grows
Mathematical path to appreciation

Creator Alignment

Earn Real Value, Not SpeculationCreators receive cash-equivalent rewards:

33% of action value in any market
Income tied to usage, not price
Incentivizes quality over hype
Sustainable creator economy

Why $LAM is Different

Comparing Token Models

Feature	$LAM	Traditional Tokens	Meme Coins
Utility	Powers every AI action	Often minimal	None
Demand Driver	B2B usage + API calls	Speculation	Hype only
Supply Mechanism	Deflationary (burning)	Usually inflationary	Fixed/Inflationary
Revenue Model	Real business revenue	Token trading only	None
Value Backing	Automation demand	Project promises	Community sentiment

Getting Started with $LAM

Earn Tokens

Start training the LAM and earning your first tokens

Use Tokens

Deploy AI agents that consume $LAM to automate tasks

Create Workflows

Build automations and earn from every usage

$LAM isn’t just a token. It’s the fuel for the automation revolution. Every action builds value. Every burn increases scarcity. Every holder owns the future.

The Action Model

Actionist.ai

Marketplace

The Large Action Model (LAM)

Tokenomics

​The $LAM Token: Powering the Action Economy

​More Than a Token—It’s Ownership

Action Fuel

DAO Governance

Marketplace

Train the LAM

Action Credits

Referrals & Quests

​How $LAM Powers Every Action

​The Action Loop

​$LAM Token Overview

Dynamic Pricing Model

Token Distribution

Buy Pressure Engine

Deflationary Dynamics

​Token Economics Model

​Fixed USD Pricing, Floating Token Cost

​Pricing Formula

​Token Distribution Per Action

​Where Every $LAM Goes

33% to Creators

34% Burned

33% Ecosystem

​Distribution Mathematics

​Example Distribution

​The B2B Buyback Loop

​How Business Demand Drives Token Value

​The Virtuous Cycle

​Token Utility Ecosystem

​Multiple Demand Drivers

​Direct API Usage

​Subscription Discount Model

​Pay with $LAM, Save 10%

​Discount Formula

​Deflationary Dynamics

​Why $LAM Becomes More Valuable Over Time

Continuous Burning

Growing Demand

​Real-World Usage Examples

​Understanding Token Consumption

​Formal Tokenomics Model

​Mathematical Framework

​Pricing Model (Versioned “Price Per Action”)

​Examples (LAM-1):

​Token Fuel Distribution (Per Action)

​USD Equivalents Per Action:

​The Buyback Loop (Why Usage Creates Demand)

​Paying Subscriptions in $LAM (10% Discount)

​Advanced Tokenomics Calculations

​Per-Action Accounting (LAM-v)

​Network-Level, Period t (e.g., monthly)

​Supply Dynamics

​“Half-Life” Under Constant Burn and No Emissions (Illustrative)

​Token Training Distribution

​Rewarding Data Contributors

How It Works

Incentive Alignment

​Action Model API (Developers)

​Building with the Large Action Model

​Fundamental Differences

​Long-Term Economic Benefits

​Why $LAM Creates Sustainable Value

Price-Stable UX

Automatic Market Clearing

Permanent Scarcity Creation

Creator Alignment

​Why $LAM is Different

​Comparing Token Models

​Getting Started with $LAM

Earn Tokens

Use Tokens

Create Workflows

The $LAM Token: Powering the Action Economy

More Than a Token—It’s Ownership

How $LAM Powers Every Action

The Action Loop

$LAM Token Overview

Token Economics Model

Fixed USD Pricing, Floating Token Cost

Pricing Formula

Token Distribution Per Action

Where Every $LAM Goes

Distribution Mathematics

Example Distribution

The B2B Buyback Loop

How Business Demand Drives Token Value

The Virtuous Cycle

Token Utility Ecosystem

Multiple Demand Drivers

Direct API Usage

Subscription Discount Model

Pay with $LAM, Save 10%

Discount Formula

Deflationary Dynamics

Why $LAM Becomes More Valuable Over Time

Real-World Usage Examples

Understanding Token Consumption

Formal Tokenomics Model

Mathematical Framework

Pricing Model (Versioned “Price Per Action”)

Examples (LAM-1):

Token Fuel Distribution (Per Action)

USD Equivalents Per Action:

The Buyback Loop (Why Usage Creates Demand)

Paying Subscriptions in $LAM (10% Discount)

Advanced Tokenomics Calculations

Per-Action Accounting (LAM-v)

Network-Level, Period t (e.g., monthly)

Supply Dynamics

“Half-Life” Under Constant Burn and No Emissions (Illustrative)

Token Training Distribution

Rewarding Data Contributors

Action Model API (Developers)

Building with the Large Action Model

Fundamental Differences

Long-Term Economic Benefits

Why $LAM Creates Sustainable Value

Why $LAM is Different

Comparing Token Models

Getting Started with $LAM