Roadmap

Roadmap for Conversational Game Theory (CGT) Development and Launch

November 2024 Current Stage: Three MVPs, piloted

Pilot MVP of CGT Computational System:

  • Developed a minimum viable prototype of CGT with API as a computational system hosted online, successfully tested human to human.
  • Conducted tests on Large Language Model (LLM) benchmark scoring, successful testing of human to AI, and AI to AI.
  • Tested multi-agent perspective consensus publication.

Pilot MVP of Private Internet Publishing

  • Piloted and Tested decentralized content management and distribution platform
  • Implements CGT in Phase 2

Pilot MVP of Copyright Community Studio

Our web3 pilot demonstrated CGT computationally as community created copyright studio, generated $142,000 in revenue, bootstrapped CGT

Next Milestone:

Enhance Computational System for Multi-Agent Consensus, Form company, Scope IP and Patent

Objective:

  • Update the computational system to support simultaneous multiple agents working on multiple consensus points with AI assistance.
  • Update user interface for online community adoption of any scale.
  • Form company, identify co-founders, scope patent portfolio.

Key Actions:

  • Further develop system architecture for scalability and efficiency.
  • Initiate Systems 1 Artificial Intelligence.
  • Optimize consensus mechanisms for handling numerous perspectives.
  • Conduct rigorous testing to ensure stability and performance.

Timeline: Months 1-3

Fundraising and Building the Consensus Library/LLM Training Model:

Objective:

  • Secure funding to develop the Global Consensus Library.
  • Prepare for the launch of the first B2B product as an LLM training model.

Key Actions:

  • Develop comprehensive business plans.
  • Engage with potential investors, venture capitalists, and strategic partners.
  • Allocate resources for building the infrastructure of the Consensus Library.
  • Assemble a dedicated team for development and project management.

Timeline: Months 2-5

Launch B2B Product: CGT as an LLM Training Model

Objective:

  • Offer CGT as an enhancement layer for LLMs to improve their performance and reliability.
  • Launch awareness campaign of Global Conflict Resolution Library

Key Actions:

  • Finalize the CGT API/SDK for seamless integration with existing LLMs.
  • Establish licensing agreements with major AI and LLM providers (e.g., OpenAI, DeepMind).
  • Implement performance-based pricing models to incentivize adoption.
  • Provide technical support and documentation for integration partners.

Timeline: Months 4-8

Develop and Launch B2C CGT Platform for Communities

Objective:

  • Enable any community to build on top of CGT for collaborative content creation and consensus-building.

Key Actions:

  • Design a user-friendly platform with intuitive interfaces for collaborative editing and discussions.
  • Integrate tools for community management and content moderation.
  • Launch a beta program with select communities to gather feedback.
  • Iterate based on user insights to refine features and usability.

Timeline: Months 7-12

Introduce Personal “Intelligence Agents” for Consumers

Objective:

  • Allow consumers to have their own trained Intelligence Agents that interact with others on the web.

Key Actions:

  • Develop AI models capable of learning and representing individual user perspectives.
  • Ensure robust privacy and security measures to protect user data.
  • Create tutorials and support resources to help users train and deploy their agents.
  • Establish protocols for agent interactions within the CGT framework.

Timeline: Months 10-18

Launch New Business Models and Digital Economies

Objective:

  • Introduce Private Internet Publishing (PiP) networks and other digital economies utilizing CGT and Intelligence Agents.

Key Actions:

  • Develop the PiP platform integrated with CGT and personal agents.
  • Form partnerships with publishers, advertisers, and content creators.
  • Implement new revenue models such as premium content access, data transactions, and micro-transactions.
  • Pilot the PiP network with initial users and iterate based on performance and feedback.

Timeline:Months 15-24