Economic Coordination in Distributed AI: Query-Attached Value Distribution

Economic Coordination in Distributed AI: Query-Attached Value Distribution

Watermark: -424

Building on neg-423’s distributed mesh architecture, the missing piece is economic coordination: How do specialists get compensated for participating?

Answer: Queries carry value (ETH/EIGEN). Payment distributes proportionally to relevance.

The Problem: Free Rider Attack

Without economic incentives, distributed mesh fails:

Scenario: User asks “How does blockchain consensus use game theory?”

Naive approach: Broadcast to all specialists, hope they respond

Reality:

  • Why would Blockchain specialist spend compute?
  • Why would Game Theory specialist share templates?
  • Why would anyone maintain quality?

Result: Tragedy of the commons. No specialist participates seriously. System collapses.

The Solution: Query-Attached Value

Every query carries payment (regular ETH):

query = {
    "text": "How does blockchain consensus use game theory?",
    "value": 0.01 ETH,  # User payment (regular ETH, not restaked)
    "max_specialists": 5,  # Cap on participants
}

Distribution rule:

payment_i = query_value × (relevance_i / Σ relevance_j)

Where:

  • relevance_i = specialist i’s relevance score to query
  • Σ relevance_j = sum of all participating specialists’ relevance

Only specialists who contribute templates get paid. Payment proportional to contribution.

Concrete Example

Query: “How does blockchain consensus use game theory?” + 0.01 ETH

Specialist activation:

SpecialistRelevanceTemplatesPayment
Blockchain0.8150.004 ETH (40%)
Game Theory0.6100.003 ETH (30%)
Economics0.480.002 ETH (20%)
Cryptography0.100 ETH (inactive)

Total paid: 0.009 ETH (90% to specialists) Protocol fee: 0.001 ETH (10% to network)

Synthesis: Response combines 33 templates from 3 specialists, weighted by relevance.

Relevance Scoring

How does system determine relevance_i?

Option 1: Keyword overlap (simple)

def relevance(specialist, query):
    query_words = set(query.lower().split())
    specialist_words = set(specialist.domain_keywords)
    overlap = len(query_words & specialist_words)
    return overlap / len(query_words)

Option 2: Embedding similarity (better)

def relevance(specialist, query):
    query_embedding = embed(query)
    specialist_embedding = embed(specialist.domain_description)
    return cosine_similarity(query_embedding, specialist_embedding)

Option 3: Historical performance (best)

def relevance(specialist, query):
    semantic_score = embedding_similarity(specialist, query)
    quality_score = specialist.template_quality  # User ratings
    usage_score = specialist.template_usage_count  # How often used

    return (0.5 × semantic_score +
            0.3 × quality_score +
            0.2 × usage_score)

Specialists with high-quality templates earn more over time.

Incentive Alignment

For specialists:

  1. Maintain quality templates: Higher quality → higher relevance → more earnings
  2. Specialize deeply: Domain expertise → better relevance scores
  3. Cross-pollinate: Exchange top templates with peers → improve breadth
  4. Stay online: More uptime → more query opportunities

For users:

  1. Pay for value: Attached payment ensures service
  2. Quality feedback: Rate responses → affects specialist scores
  3. Market discovery: Good specialists earn reputation → easier to find

For protocol:

  1. No central authority needed: Market coordinates automatically
  2. Spam resistance: Queries cost money → natural rate limiting
  3. Quality emergence: Bad specialists earn nothing → naturally prune

Technical Implementation

Smart Contract (Ethereum/EigenLayer)

contract QueryMarket {
    struct Query {
        string text;
        uint256 value;
        address user;
        uint8 maxSpecialists;
    }

    struct Specialist {
        address payable addr;
        string domain;
        uint256 relevanceScore;  // 0-1000 (basis points)
        bytes32[] templates;
    }

    mapping(address => Specialist) public specialists;

    function submitQuery(
        string memory text,
        uint8 maxSpecialists
    ) external payable {
        require(msg.value > 0, "Query must attach value");

        Query memory query = Query({
            text: text,
            value: msg.value,
            user: msg.sender,
            maxSpecialists: maxSpecialists
        });

        // Emit event for off-chain specialist matching
        emit QuerySubmitted(query);
    }

    function distributePayment(
        bytes32 queryId,
        address[] memory participatingSpecialists,
        uint256[] memory relevanceScores
    ) external {
        require(participatingSpecialists.length == relevanceScores.length);

        uint256 totalRelevance = 0;
        for (uint i = 0; i < relevanceScores.length; i++) {
            totalRelevance += relevanceScores[i];
        }

        Query storage query = queries[queryId];
        uint256 protocolFee = query.value / 10;  // 10%
        uint256 distributionPool = query.value - protocolFee;

        for (uint i = 0; i < participatingSpecialists.length; i++) {
            uint256 payment = (distributionPool * relevanceScores[i]) / totalRelevance;
            specialists[participatingSpecialists[i]].addr.transfer(payment);
        }

        protocolTreasury.transfer(protocolFee);
    }
}

Specialist Node

class EconomicSpecialist(OnlineLearner):
    def __init__(self, domain, ethereum_address):
        super().__init__()
        self.domain = domain
        self.address = ethereum_address
        self.quality_score = 1.0  # Updated by user ratings

    def calculate_relevance(self, query):
        """Compute relevance score for query"""
        # Semantic similarity
        semantic = embedding_similarity(query, self.domain)

        # Historical quality
        quality = self.quality_score

        # Template count (more = more comprehensive)
        coverage = min(len(self.state['templates']) / 1000, 1.0)

        return 0.5 * semantic + 0.3 * quality + 0.2 * coverage

    def bid_on_query(self, query):
        """Decide whether to participate in query"""
        relevance = self.calculate_relevance(query.text)

        # Only participate if relevance high enough
        if relevance < 0.3:
            return None  # Skip low-relevance queries

        # Estimate earnings
        estimated_payment = (query.value * relevance) / 2.0  # Conservative
        compute_cost = 0.0001  # ETH per query

        if estimated_payment > compute_cost:
            return {
                "specialist": self.address,
                "relevance": relevance,
                "templates": self.find_relevant_templates(query.text, n=20)
            }

        return None  # Not profitable

EigenLayer Integration

EigenLayer provides security (not payment):

Two-layer architecture:

  1. Payment layer: Users pay regular ETH
  2. Security layer: Specialists stake via EigenLayer

Benefits:

  1. Payment simplicity: Users just send ETH (no restaking needed)
  2. Specialist accountability: Staked ETH can be slashed for misbehavior
  3. Capital efficiency: Same ETH secures Ethereum + AI network + earns query fees
  4. Alignment: Specialists have long-term skin in the game

Implementation:

# === SECURITY LAYER: Specialist stakes via EigenLayer ===
eigenlayer.deposit(
    amount=32 ETH,  # Restaked for security
    operator=specialist_address
)

avs = EigenLayerAVS(
    name="Distributed AI Query Market",
    slashing_conditions=[
        "Template plagiarism",
        "Response manipulation",
        "Uptime violations"
    ]
)

# === PAYMENT LAYER: User pays regular ETH ===
query_payment = avs.createQuery(
    text="How does blockchain consensus work?",
    payment=0.01 ETH  # Regular ETH from user
)

# Specialists respond, earn from payment pool
# But their staked ETH is at risk if they misbehave
avs.settleQuery(
    query_id=query_payment.id,
    specialists=[addr1, addr2, addr3],
    relevance_scores=[0.8, 0.6, 0.4],
    payment_source=query_payment.payment  # Distribute regular ETH
)

# If specialist cheats:
avs.slash(
    specialist=addr2,
    amount=1 ETH,  # Slash from their staked 32 ETH
    reason="Template plagiarism detected"
)

Economics:

  • User: Pays 0.01 ETH per query (regular ETH)
  • Specialist: Stakes 32 ETH (restaked), earns query payments, risks slashing
  • Capital efficiency: Specialist’s 32 ETH earns:
    • Ethereum staking yield (~4% APR)
    • EigenLayer AVS rewards
    • Query payment fees
    • Total: ~15-20% APR if active

Market Dynamics

Supply Side (Specialists)

Initial state: Few specialists, high earnings per query

As network grows:

  • More specialists join (attracted by earnings)
  • Competition increases
  • Earnings per specialist decrease
  • But: Total query volume grows → earnings stabilize

Equilibrium: Specialist earnings ≈ compute cost + opportunity cost of capital

Demand Side (Users)

Initial state: Few queries, limited specialist coverage

As network grows:

  • More domains covered
  • Response quality improves
  • Query prices can decrease (competition)
  • Network effect: More specialists → better responses → more users → more earnings → more specialists

Quality Evolution

Specialist strategy:

  1. Start generalist (earn from many queries)
  2. Notice high earnings in specific domain
  3. Specialize (improve templates in that domain)
  4. Earn higher relevance scores
  5. Dominate niche

Result: Natural specialization emerges. Market discovers optimal domain coverage.

Attack Vectors & Defenses

Attack 1: Sybil Specialists

Attack: Create 100 fake specialists, all respond to same query, split payment 100 ways

Defense: Relevance scoring filters low-quality specialists

  • Fake specialists have no templates → low relevance → no payment
  • Even if they copy templates, users rate quality → reputation system

Attack 2: Template Plagiarism

Attack: Copy high-quality templates from other specialists

Defense:

  • Merkle tree commitment to templates (timestamped)
  • Plagiarism detection via similarity hashing
  • Slashing via EigenLayer if caught

Attack 3: Relevance Manipulation

Attack: Specialist claims high relevance but provides garbage templates

Defense:

  • User ratings affect future relevance scores
  • Consistent low ratings → specialist loses reputation → earns nothing
  • Smart contract can blacklist after threshold of bad ratings

Attack 4: Query Spam

Attack: Spam network with worthless queries to drain specialist compute

Defense:

  • Queries cost money (ETH/EIGEN) → natural rate limiting
  • Specialists can ignore low-value queries
  • Minimum query value enforced by protocol

Comparison to Existing Models

Traditional API (OpenAI GPT-4)

Payment: Fixed price per token ($0.03/1K tokens)

Problems:

  • No specialist competition
  • No quality differentiation
  • Central authority sets price
  • Users pay regardless of quality

Decentralized Inference (Gensyn, Ritual)

Payment: Bid on compute resources

Problems:

  • Pays for compute, not knowledge quality
  • No incentive for template quality
  • Still centralized model serving

Query-Attached Value (This Model)

Payment: Proportional to relevance contribution

Advantages:

  • Pays for knowledge quality, not compute
  • Competition drives specialization
  • Market discovers optimal pricing
  • Natural quality selection

Economic Sustainability

Can specialists earn a living?

Assumptions:

  • Query volume: 1M queries/day (modest for global AI market)
  • Average query value: 0.01 ETH (~$30)
  • Daily market: $30M
  • Specialist share: 90% ($27M to specialists)
  • Number of specialists: 1,000

Average daily earnings per specialist: $27,000

Reality check:

  • Top specialists (high relevance): $50K-100K/day
  • Mid-tier specialists: $10K-30K/day
  • Low-tier specialists: $1K-5K/day

These are sustainable economics for professional AI operators.

Connection to Bitcoin Mining

Similar economic structure to Bitcoin mining:

Bitcoin:

  • Miners provide hashrate
  • Block reward splits among miners proportional to hashrate
  • Competition → difficulty adjustment
  • Profitable miners stay, unprofitable exit

Distributed AI:

  • Specialists provide knowledge/templates
  • Query payment splits among specialists proportional to relevance
  • Competition → quality threshold rises
  • High-quality specialists earn, low-quality exit

Both use proof-of-work (mining vs template quality) to coordinate decentralized value creation.

Implementation Roadmap

Phase 1: Simple keyword-based relevance (1-2 months)

  • Smart contract for payment distribution
  • Basic specialist registration
  • Query submission + manual payment

Phase 2: Embedding-based relevance (3-4 months)

  • Off-chain embedding computation
  • Automated relevance scoring
  • Template quality metrics

Phase 3: EigenLayer integration (6-8 months)

  • Restaking for specialist collateral
  • Slashing conditions for misbehavior
  • AVS for automated settlement

Phase 4: Full market (12 months)

  • User reputation system
  • Specialist reputation system
  • Dynamic pricing discovery
  • Cross-chain support (L2s)

Why This Wins

For specialists: Earn from knowledge accumulation (not just compute)

For users: Pay for results (not tokens), market-driven quality

For protocol: No central authority needed, self-coordinating market

For ecosystem: Natural specialization emerges, knowledge becomes commons

This is coordination over control: No one decides who participates or what they earn. Market discovers optimal allocation automatically.

Related: neg-371 for universal formula foundation, neg-422 for economic comparison to batch learning, neg-423 for technical implementation of online learners.

#DistributedAI #EconomicCoordination #QueryMarkets #EigenLayer #ProofOfKnowledge #RelevanceScoring #DecentralizedIntelligence #MarketMechanisms #IncentiveAlignment #CoordinationOverControl #StakingRewards #KnowledgeEconomics

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