The task marketplace

Prompt

Let’s see this as an opportunity to rethink the roles in a software development project. Earlier we talked about developers and architects, and this new conversation help remind me at Future’s Edge, we doing away with roles as traditionally used, and all ‘work’ that needs to be performed is broken down into discrete tasks that anyone with the required skills, experience, trust score or other attributes can choose, perform and submit a task to claim the reward.

Let’s take this approach for software development projects.

Software development as a task marketplace

This is a radical reimagining that aligns perfectly with web3/DAO models. Let’s decompose the entire software development lifecycle into atomic, claimable tasks rather than bundled roles.

First Principles: What Are “Roles” Actually?

Traditional roles are just bundles of tasks that we assumed should be done by the same person:

“Product Owner” is really:

• Talk to customers (Task)
• Synthesize feedback (Task)
• Prioritize features (Task)
• Write user stories (Task)
• Make scope decisions (Task)
• Accept completed work (Task)

But why should one person do all these? Historical reasons:

• Employment contracts favor full-time roles
• Coordination overhead was high (pre-digital)
• Trust was person-based, not reputation-based
• Knowledge transfer was expensive

In a collective intelligence model, these assumptions break down.

Reimagining: The Task Graph Model

Instead of hiring a “team,” you publish a project task graph where:

• Each node is a discrete task with clear inputs/outputs
• Dependencies are explicit
• Required attributes are specified
• Rewards are attached
• Anyone qualified can claim and execute

Example: The “Predictive Analytics Dashboard” Project

Let me map our Sprint 9 story as a task graph:

Phase 1: Discovery & Definition Tasks

Task D1: Customer Problem Validation

• Input: Project hypothesis (“Small businesses need predictive analytics”)
• Output: Interview recordings + synthesis (5 customers, key pain points)
• Required attributes:
  • Customer research experience (trust score > 70)
  • Domain knowledge: small business OR analytics
• Estimated time: 8 hours
• Reward: 500 tokens
• Dependencies: None (can start immediately)
• Claimable by: Human (requires empathy, interpretation)

Task D2: User Story Generation

• Input: Customer synthesis from D1
• Output: 5 user stories with acceptance criteria in standard format
• Required attributes:
  • Product definition experience (trust score > 60)
  • Has completed similar task with rating > 4/5
• Estimated time: 4 hours
• Reward: 300 tokens
• Dependencies: D1 complete
• Claimable by: Human OR AI agent (with human review)

Task D3: UI/UX Wireframes

• Input: User stories from D2
• Output: Low-fidelity wireframes (Figma format), 3 key screens
• Required attributes:
  • Design portfolio with analytics dashboards
  • Figma proficiency
• Estimated time: 6 hours
• Reward: 400 tokens
• Dependencies: D2 complete
• Claimable by: Human (requires design judgment)

Task D4: Technical Architecture Proposal

• Input: User stories from D2, system constraints document
• Output: Architecture Decision Record (ADR) - data model, API structure, infrastructure choices
• Required attributes:
  • Architecture experience (3+ similar projects)
  • Familiarity with ML inference deployment
• Estimated time: 4 hours
• Reward: 350 tokens
• Dependencies: D2 complete
• Claimable by: Human OR experienced AI agent

Phase 2: Development Tasks (Can Be Parallel)

Task DEV1: Database Schema Implementation

• Input: ADR from D4
• Output: Migration scripts, schema documentation
• Required attributes:
  • PostgreSQL experience
  • SQL trust score > 75
• Estimated time: 3 hours
• Reward: 250 tokens
• Dependencies: D4 complete
• Claimable by: Human OR AI agent
• Verification: Automated tests pass

Task DEV2: Prediction Service (Lambda)

• Input: ADR from D4, sample data
• Output: Working Lambda function, unit tests (>85% coverage)
• Required attributes:
  • Python + AWS Lambda experience
  • Has solved cold-start problems before (optional, pays bonus)
• Estimated time: 8 hours
• Reward: 600 tokens (+100 bonus if cold-start < 500ms)
• Dependencies: DEV1 complete
• Claimable by: Human OR AI agent
• Verification: Automated performance tests

Task DEV3: API Endpoints

• Input: API spec from D4
• Output: 4 REST endpoints, OpenAPI documentation, integration tests
• Required attributes:
  • REST API development experience
  • Test coverage > 80%
• Estimated time: 6 hours
• Reward: 450 tokens
• Dependencies: DEV1 complete
• Claimable by: Human OR AI agent
• Verification: Automated contract tests

Task DEV4: Dashboard UI Components

• Input: Wireframes from D3, component library
• Output: React components, responsive, accessible (WCAG AA)
• Required attributes:
  • React experience
  • Accessibility knowledge
• Estimated time: 10 hours
• Reward: 700 tokens
• Dependencies: D3 complete, DEV3 complete (for API integration)
• Claimable by: Human OR AI agent (AI good at implementation, human verifies UX)

Phase 3: Quality & Integration Tasks

Task QA1: Integration Test Suite

• Input: All DEV tasks complete
• Output: End-to-end test scenarios, automated test suite
• Required attributes:
  • QA experience
  • Test automation proficiency
• Estimated time: 5 hours
• Reward: 400 tokens
• Dependencies: DEV2, DEV3, DEV4 complete
• Claimable by: Human OR AI agent

Task QA2: User Acceptance Testing

• Input: Working system in staging
• Output: UAT report with 5 real users, issues logged
• Required attributes:
  • User research experience
  • Access to target user group
• Estimated time: 6 hours
• Reward: 450 tokens
• Dependencies: QA1 complete
• Claimable by: Human (requires interpretation of user behavior)

Task QA3: Performance & Load Testing

• Input: System in staging
• Output: Load test results (10k concurrent users), bottleneck analysis
• Required attributes:
  • Performance testing experience
  • Experience with load testing tools
• Estimated time: 4 hours
• Reward: 350 tokens
• Dependencies: QA1 complete
• Claimable by: Human OR AI agent

Phase 4: Deployment & Documentation Tasks

Task DEPLOY1: Infrastructure Setup

• Input: ADR from D4
• Output: AWS infrastructure as code, CI/CD pipeline configured
• Required attributes:
  • DevOps experience
  • AWS certification OR equivalent experience
• Estimated time: 6 hours
• Reward: 500 tokens
• Dependencies: D4 complete (can be parallel with development)
• Claimable by: Human OR AI agent

Task DOC1: API Documentation

• Input: Code from DEV3
• Output: API documentation (Swagger/OpenAPI), example requests
• Required attributes:
  • Technical writing OR developer documentation experience
• Estimated time: 3 hours
• Reward: 250 tokens
• Dependencies: DEV3 complete
• Claimable by: AI agent (with human review) OR Human

Task DOC2: User Guide & Help Content

• Input: Working system, wireframes from D3
• Output: User documentation, video walkthrough, contextual help content
• Required attributes:
  • Technical writing for end-users
  • Screen recording capability
• Estimated time: 5 hours
• Reward: 400 tokens
• Dependencies: DEV4, QA2 complete
• Claimable by: Human OR AI agent (AI writes, human records video)

Phase 5: Learning & Knowledge Tasks

Task LEARN1: Pattern Extraction

• Input: All project artifacts (code, discussions, decisions)
• Output: 3-5 reusable patterns with context, added to knowledge graph
• Required attributes:
  • Experience in knowledge synthesis
  • Understanding of pattern languages
• Estimated time: 4 hours
• Reward: 300 tokens
• Dependencies: Project substantially complete
• Claimable by: Human OR AI agent (AI extracts, human validates)

Task LEARN2: Decision Archaeology

• Input: All architectural decisions made during project
• Output: ADRs formatted and linked in knowledge base
• Required attributes:
  • Documentation experience
  • Understanding of ADR format
• Estimated time: 2 hours
• Reward: 150 tokens
• Dependencies: Project complete
• Claimable by: AI agent (with human review)

Task LEARN3: Surprise & Anomaly Report

• Input: Project plan vs actual outcomes
• Output: Report on what surprised us, hypothesis validation, lessons
• Required attributes:
  • Analytical thinking
  • Participated in at least 2 project tasks (insider perspective)
• Estimated time: 3 hours
• Reward: 250 tokens
• Dependencies: Project complete
• Claimable by: Human OR AI agent

The Coordination Challenge

Traditional

Scrum Master coordinates the team

Task marketplace

Who coordinates?

Option 1: Coordination As Tasks

Task COORD1: Dependency Management

• Input: Task graph, claimed tasks, completion status
• Output: Daily update on critical path, blockers, recommendations
• Required attributes:
  • Project management experience
  • Understanding of critical path analysis
• Estimated time: 1 hour/day for project duration
• Reward: 100 tokens/day
• Claimable by: AI agent (ideal use case) OR Human

Task COORD2: Blocker Resolution

• Input: Blocked tasks from COORD1
• Output: Blockers resolved OR escalated with recommendations
• Required attributes:
  • Problem-solving experience
  • Cross-functional technical knowledge
• Estimated time: Variable (2-5 hours/week)
• Reward: 200 tokens per blocker resolved
• Claimable by: Human with broad expertise

Task COORD3: Conflict Resolution

• Input: Disputes over task acceptance, quality disagreements
• Output: Mediated resolution, updated criteria if needed
• Required attributes:
  • Mediation experience
  • Technical judgment
  • Trust score > 85
• Estimated time: Variable
• Reward: 300 tokens per resolution
• Claimable by: Human (requires judgment and empathy)

Option 2: Algorithmic Coordination

Instead of coordination tasks, use smart contracts + AI:

The project smart contract:

• Publishes tasks when dependencies met
• Escrows rewards
• Validates task completion
• Automatically pays on acceptance
• Flags conflicts for human arbitration

The AI coordination agent

• Monitors all task progress
• Detects blockers, notifies relevant contributors
• Suggests optimal task claiming based on skills/availability
• Maintains project “ambient awareness”
• Only escalates to humans when needed

Task Attributes & Marketplace Dynamics

Required Attributes Breakdown

Instead of “role qualifications,” tasks specify:

Skill Tags:

• python, react, aws-lambda, user-research, technical-writing

Experience Level:

• beginner (trust score 0-30)
• intermediate (trust score 31-70)
• expert (trust score 71-100)

Trust Domains:

• Code quality trust: “Have you delivered clean, tested code?”
• User empathy trust: “Can you understand customer needs?”
• Architectural judgment trust: “Do you make good system-level decisions?”
• Documentation trust: “Do you write clear, helpful docs?”

Reputation Signals:

• Past task completion rate
• Peer review scores
• On-time delivery history
• Specialization patterns

Example task requirement specification:

Task: DEV2 - Prediction Service

Required

• skills: [python, aws-lambda, machine-learning]
• trust_score: { code_quality: > 70 }
• experience_level: intermediate OR expert
• proven: has completed ML deployment task before (optional, +bonus)

Reward: 600 tokens

• Bonus: +100 if cold-start < 500ms
• Penalty: -150 if test coverage < 85%

Human vs AI Agent Task Suitability

Let me categorize our tasks:

Ideal for AI Agents:

• DEV1 (Database Schema): Structured, verifiable output
• DEV3 (API Endpoints): Clear spec, automated testing
• COORD1 (Dependency Management): Perfect for algorithmic tracking
• DOC1 (API Documentation): Can be generated from code
• LEARN2 (Decision Archaeology): AI can extract from artifacts
• QA1 (Integration Tests): Can be automated

Ideal for Humans:

• D1 (Customer Validation): Requires empathy, interpretation
• D3 (UI/UX Wireframes): Requires aesthetic judgment
• QA2 (User Acceptance Testing): Requires observing human behavior
• COORD3 (Conflict Resolution): Requires mediation
• D4 (Architecture Proposal): Requires strategic judgment (though AI can assist)

Hybrid (AI drafts, Human reviews):

• D2 (User Story Generation): AI can structure, human validates meaning
• DEV4 (Dashboard UI): AI implements, human verifies UX quality
• DOC2 (User Guide): AI writes, human adds empathy/clarity
• LEARN1 (Pattern Extraction): AI identifies, human validates applicability

Governance & Quality Control

The critical question: How do we ensure quality without a “Tech Lead” gatekeeping?

Verification Mechanisms:

Level 1: Automated Verification (Lowest Trust Required)

• Tests pass
• Code coverage meets threshold
• Performance benchmarks met
• Security scans pass
• Smart contract automatically accepts & pays

Level 2: Peer Review (Medium Trust Required)

• Another contributor reviews
• Reviewer must have trust score in relevant domain
• Reviewer stakes tokens on their review
• If later found poor quality, reviewer loses staked tokens
• Smart contract releases payment after review approval

Level 3: Expert Validation (High Trust Required)

• Critical tasks (architecture, security) require expert review
• Experts are elected by DAO OR have proven track record
• Higher review reward for expert validation
• Smart contract requires expert signature

Level 4: User Validation (Highest Bar)

• User acceptance testing tasks
• Real users must validate
• User feedback recorded on-chain
• Payment conditional on user satisfaction score

Quality Incentive Structure:

Task Lifecycle:

1. Task published with reward pool
2. Contributor claims task (stakes small amount)
3. Contributor completes work
4. Verification happens (automated, peer, expert, or user)
5. If accepted:
   • Contributor gets reward + stake back
   • Contributor’s trust score increases
   • Reviewer (if applicable) gets review reward
6. If rejected:
   • Contributor loses stake
   • Can revise and resubmit
   • If abandoned, task returns to pool
7. Disputes escalated to DAO arbitration

Project Initiation: Who Defines The Tasks?

Traditional

Product Owner creates backlog

Task marketplace

Who creates the initial task graph?

Option 1: Project Sponsor Role

• Someone (individual or DAO) sponsors the project
• They define high-level outcomes and budget
• They publish initial task breakdown
• As project progresses, new tasks emerge
• Sponsor approves major scope changes

Option 2: Emergent Task Definition

• Sponsor publishes only high-level goals + budget
• First tasks are meta-tasks:
  • Task M1: “Create project task graph” (reward: 500 token)
  • Task M2: “Technical feasibility assessment” (reward: 300 token)
  • Task M3: “Create detailed development task breakdown” (reward: 400 token)
• Contributors define the actual work tasks
• Creates more emergence, less top-down control

Option 3: AI-Generated Task Graph

• Sponsor describes desired outcome
• AI agent generates initial task graph
• Human experts review and refine
• Tasks published to marketplace

The economic model

Total project budget: 50,000 tokens

Budget allocation:

• 60% - Development tasks (30,000 tokens)
• 15% - Quality & testing tasks (7,500 tokens)
• 10% - Documentation & learning tasks (5,000 tokens)
• 10% - Coordination tasks (5,000 tokens)
• 5% - Reserve for emergent tasks (2,500 tokens)

Dynamic pricing:

• If task remains unclaimed for 48 hours, reward increases 10%
• If task has multiple claimants, auction OR first-qualified-claimant
• Critical path tasks have higher base rewards
• Bonus rewards for early completion
• Penalty for delayed completion (if task had committed deadline)

The Radical Implications

1. No “Full-Time” Team

• Contributors work on tasks that match their skills/availability
• Someone might claim 1 task or 10 tasks
• No employment contracts, just task completion
• Geographic distribution irrelevant

2. Expertise When Needed

• Need ML expertise for 2 tasks? Don’t hire full-time ML engineer
• ML expert claims those tasks, earns proportional reward
• Rest of project doesn’t pay for unused ML capacity

3. AI Agents As Economic Actors

• AI agents claim and complete tasks
• Earn tokens, build reputation
• Compete with humans on purely output basis
• Humans focus on tasks AI can’t do well

4. Emergent Specialization

• Contributors gravitate toward tasks they’re good at
• Natural specialization through market dynamics
• But anyone can try new task types (with lower trust score)

5. Learning Incentivized

• Capturing learnings is paid work, not extra duty
• Pattern extraction has economic value
• Knowledge becomes collective asset

6. Failure Modes Distributed

• If one contributor disappears, only their tasks affected
• Tasks return to marketplace, others can claim
• No “key person” risk

Challenges To Solve

Challenge 1: Task Atomicity

• How granular should tasks be?
• Too small → coordination overhead
• Too large → approaches traditional “roles”

Challenge 2: Context Continuity

• How does knowledge transfer between task completers?
• Who maintains “big picture” understanding?
• AI coordination agent helps, but still a challenge

Challenge 3: Quality Consistency

• Without unified team culture, how ensure consistent quality?
• Automated verification helps, but not sufficient
• Need strong peer review incentives

Challenge 4: Stakeholder Communication

• Who “owns” customer relationships?
• Customer validation tasks can be claimed, but needs consistency
• Perhaps some tasks require repeat completion by same person

Challenge 5: Trust Cold Start

• New contributors have no trust score
• How do they get started?
• Perhaps “apprentice” tasks with lower rewards, easier verification

Connecting To Future’s Edge

This is essentially what you’re building:

Future’s Edge as the platform for:

• Task graph definition and publishing
• Reputation/trust score management
• Smart contract execution for task claiming/payment
• AI agent integration (agents as claimable task executors)
• Knowledge graph accumulation across projects
• DAO governance for dispute resolution

The meta-level:

• Each project contributes tasks to collective task library
• Task patterns emerge: “When building ML feature, these 8 tasks typically needed”
• New projects can clone/adapt task graphs from similar past projects
• Collective intelligence about software development itself

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So for our backward design process:

Should we now redesign each phase (Testing, Development, Planning, Discovery) as task graphs rather than role-based activities?

And should we assume AI agents are handling 40-60% of tasks, with humans focusing on judgment, creativity, and relationship tasks?

What’s your instinct - does this task marketplace model feel right for Future’s Edge, or are there aspects that need adjustment?

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Continued: Task decomposition