Title: Greatstone.AI (STONE): The Distributed Keystone for Human-Centric Private Artificial Intelligence
Version: V1.0
Release Date: August 2025
Abstract
Greatstone.AI (STONE) is a fully private artificial intelligence ecosystem built on the Solana blockchain, designed to empower every user with a self-sovereign, privately-owned AI large language model (LLM) via blockchain and distributed computing technology. The project addresses core deficiencies in the current AI paradigm:
1. Breaking Public AI Monopolies: Prevents data control by centralized platforms and mitigates AI “black box” operations and inherent biases.
2. Achieving Knowledge Perpetuity: Enables the preservation and digital inheritance of an individual’s intellect, cognition, and wisdom through private AI models.
3. Distributed Compute Network: Leverages a global network of idle computational resources (smartphones, PCs, servers) to drastically reduce AI training costs, enabling truly decentralized resource utilization.
4. Anti-AI Risk Framework: Establishes a defense mechanism via private AI models against potential existential risks posed by misaligned public AI, ensuring AI technology remains subservient to human interests.
The STONE token is the core medium of exchange, incentive, and governance within the ecosystem, facilitating compute allocation, model training, and value transfer.
Table of Contents
1. Redefining AI: From Public Monopoly to Private Sovereignty
2. Technical Architecture: Realizing Distributed Compute and Private AI Models
3. STONE Tokenomics: Incentives, Deflation, and Value Accrual
4. Ecosystem Applications: Private AI Training, Knowledge Legacy, and Compute Markets
5. Governance and DAO: Fully Decentralized Community Governance
6. Partnerships and Compute Network Expansion Strategy
7. Risk Assessment and Anti-AI Catastrophe Mechanisms
8. Roadmap: The Three-Phase Realization of Private AI
9. Team and Advisors: Experts in AI, Blockchain, and Distributed Systems
10. Legal and Compliance: Data Sovereignty and Privacy Preservation
11.Conclusion: The Path to Perpetuity of Human Knowledge and Wisdom
12.Token Information
13. About Us
1. Redefining AI: From Public Monopoly to Private Sovereignty
1.1 Critical Flaws of Public AI
Current mainstream AI (e.g., GPT series) suffers from three fundamental issues:
● Data Monopoly and Privacy Leakage: User data is expropriated by platforms without fair compensation. Training data sources are uncontrolled, potentially containing false, biased, or malicious information (“poisoned data”).
● Algorithmic Black Box and Uncontrollability: The decision-making processes of public AI are opaque and can be manipulated for anti-human purposes (e.g., autonomous weapons, public opinion manipulation).
● Knowledge Atrophy and Centralization Risk: The wisdom, experience, and cognition of individuals cannot be effectively preserved or inherited. Upon an individual’s death, their intellectual corpus vanishes entirely.
1.2 The Vision of Private AI: A Digital Twin for Everyone
Greatstone.AI introduces the concept of a Private AI LLM:
● Full Personal Ownership: Model training data originates solely from the user (thoughts, cognitions, private data), stored locally or on encrypted distributed networks, accessible only via the user’s blockchain private key.
● Resistance to Public Data Pollution: Training data remains entirely confidential, avoiding contamination by public “poisoned data,” ensuring the purity and security of the AI.
● Knowledge Perpetuity and Legacy: Users can bequeath their private AI to descendants or designated heirs, enabling the permanent preservation of knowledge, values, and wisdom (e.g., a professor’s pedagogy, an engineer’s technical expertise, a parent’s educational philosophy).
1.3 Why Blockchain + Distributed Compute?
● Data Sovereignty: Blockchain technology guarantees complete user ownership and access rights to data, preventing platform tampering or appropriation.
● Compute Democratization: By aggregating global idle device compute power, AI training costs are slashed (to approximately 1/1000000th of centralized training) while avoiding compute monopolization.
● Censorship and Catastrophe Resistance: The distributed network has no single point of failure. Private AI remains operational even if parts of the network fail, preventing public AI “rebellion” or malicious control.
2. Technical Architecture: Realizing Distributed Compute and Private AI Models
2.1 Three-Layer Tech Stack
1.Blockchain Layer (Solana):
○ Handles compute transactions, model hash storage, and permission management.
○ Leverages Solana’s high TPS (65,000+) and low fees (~$0.00025) for high-frequency compute orchestration.
2. Compute Layer (Distributed Compute Network):
○ Aggregates GPU/CPU resources from global idle devices (phones, PCs, servers), matching compute demand via smart contracts.
○ Employs Federated Learning (FL): Raw data never leaves the local device; only model parameter updates are transmitted, ensuring privacy.
3. Storage Layer (Encrypted Distributed Storage):
○ User private AI models are encrypted and stored on local devices or networks like IPFS/Arweave, decryptable only by the user’s private key.
○ Model hashes are stored on-chain, ensuring immutability and proof of ownership.
2.2 Private AI Training Pipeline
1. Data Preprocessing: Raw data (text, audio, video) is cleaned and annotated on the user’s local device, generating an encrypted training set.
2. Distributed Training:
○ Training tasks are split into subtasks and allocated to idle compute nodes via smart contracts.
○ Nodes update model parameters using Federated Learning, while raw data always remains on the user’s device.
3. Model Generation and Storage:
○ The final model is encrypted and stored at a user-designated location (HDD, USB drive, private cloud), with its hash recorded on-chain.
4. Model Inference:
○ Users decrypt the model via private key signature for inference locally or in authorized environments, eliminating remote leakage risks.
2.3 Core Innovative Technologies
● Zero-Knowledge Machine Learning (zkML): Allows compute nodes to cryptographically prove correct task execution without revealing model details.
● Dynamic Compute Orchestration Algorithm: Intelligently allocates tasks based on device capability, network status, and energy consumption, maximizing training efficiency.
● Cross-Device Collaborative Training Protocol: Enables collaborative training across heterogeneous devices (phones, PCs, servers), overcoming single-device compute limitations.
3. STONE Tokenomics: Incentives, Deflation, and Value Accrual
3.1 Token Allocation and Release
● Total Initial Supply: 9,999,999,999 STONE (Fixed at genesis). Future issuance subject to community vote.
● Allocation:
○ Ecosystem Fund (47%): For tech development, compute subsidies, community incentives. Linear release over 5 years.
○ Team & Advisors (18%): 4-year linear unlock, 2-year cliff.
○ Compute Mining (20%): Rewards for compute contributors. Released dynamically based on task completion.
○ Private/Public Sale (10%): Fundraising. Linear unlock over 5 years.
○ Liquidity Reserve (5%): For DEX and CEX market making.
3.2 Token Utility and Value Accrual
1. Compute Payment: Users pay STONE to rent compute power for private AI training (costs ~90% less than centralized cloud services).
2. Compute Incentives: Nodes contributing compute resources earn STONE rewards, proportional to device capability and uptime.
3. Governance Staking: Staking STONE grants voting rights on ecosystem proposals (e.g., compute pricing, feature prioritization).
4. Model Trading: Users can license their private AI models to others, with revenue settled in STONE.
3.3 Deflationary and Potential Inflationary Mechanisms
● Transaction Fee Burn: 20% of all compute payment fees are permanently burned.
● Model Transaction Tax: A 5% tax on private model transactions; 3% burned, 2% allocated to the Ecosystem Fund.
● Staking Lock-up: Nodes must stake STONE to participate in high-value training tasks, reducing circulating supply.
● Community-Governed Issuance: STONE holders vote on potential future token issuance based on ecosystem compute demand and model training costs, ensuring sustainable growth.
4. Ecosystem Applications
4.1 Private AI Workshop
● Personalized Model Customization: Users upload personal data (journals, papers, lecture recordings) to train bespoke AI assistants.
● Expert Knowledge Repositories: Professionals (doctors, lawyers, engineers) build industry-specific AI advisors; knowledge can be inherited by descendants via private key transfer.
● Family Education Legacy: Parents train AI imbued with family values and educational philosophies, allowing children to interact and learn from it indefinitely.
4.2 Distributed Compute Marketplace
● Compute Leasing: Users can monetize idle phones, PCs by connecting them to the network (e.g., an RTX 4090 could earn 5-10 STONE daily).
● Corporate Compute Integration: Enterprises can contribute idle server capacity, generating additional revenue while supporting distributed AI.
4.3 Knowledge Inheritance and Digital Perpetuity
● Private AI Will: Users can set inheritance conditions for their private AI via smart contracts (e.g., automatic private key transfer to a designated heir upon death).
● Academic Legacy Preservation: Scientists, artists can preserve their life’s work within a private AI for future study and application.
4.4 Anti-AI Catastrophe Alliance
● Private AI Mutual Aid Network: Users can anonymously contribute spare compute and data to train public safety AI models monitoring public AI for anomalous behavior.
● Decentralized AI Ethics Committee: STONE holders vote on defining “dangerous” AI behavior and enact collective countermeasures.
5. Governance and DAO
● Tiered Governance Model:
1. Compute Node Council: Top 100 compute nodes by stake possess proposal rights.
2. STONE Staker Voting: All stakers can vote on proposals. Voting weight is proportional to stake amount and duration.
3. Expert Advisory Board: Provides counsel on AI ethics, law, and technology (non-voting).
● Key Governance Topics:
○ Compute pricing adjustments
○ New feature development prioritization
○ Ecosystem Fund allocation
○ Anti-AI catastrophe contingency plans
6. Partnerships and Compute Network Expansion
● Hardware Partners: Collaborate with OEMs (e.g., Xiaomi, Samsung, Dell, Lenovo) to pre-install compute sharing plugins.
● Academic Institutions: Partner with universities (e.g., Stanford, MIT) on federated learning R&D and preserve academic masters’ private AIs.
● Legal & Ethical Organizations: Work with IEEE, ETC to establish private AI ethics standards and inheritance frameworks.
7. Risk Assessment and Mitigation
7.1 Risk Analysis
● Technical Risk: Distributed training efficiency may lag behind centralized clusters.
● Mitigation: Enhance efficiency via dynamic task scheduling and heterogeneous computing optimizations.
● Regulatory Risk: Private AI may encounter data compliance and inheritance law complexities.
● Mitigation: Collaborate with regulators on compliant frameworks; support anonymized training.
● Public AI Backlash: Centralized AI platforms may attempt to suppress the distributed ecosystem.
● Mitigation: Build defensive moats through community consensus and cross-chain expansion.
7.2 Anti-AI Catastrophe Mechanisms
● AI Behavior Monitoring Network: Private AI nodes monitor public AI outputs in real-time, triggering alerts upon anomalies.
● Decentralized Kill Switch: If a public AI is deemed hazardous, STONE holders can vote to deny it compute resources and services.
8. Roadmap
The Greatstone.AI ecosystem is a vast and complex undertaking currently under active development. The date for Mainnet launch is not yet fixed and will be determined based on capital availability and its impact on development velocity. For the latest progress and announcements, please refer to Greatstone.AI’s official social channels.
● Phase 1 (2025 Q3-Q4):
○ Testnet launch with mobile device compute sharing.
○ Beta release of Private AI training tools.
● Phase 2 (2026):
○ Mainnet launch (contingent on development progress), targeting integration of 100k+ devices.
○ Introduction of knowledge inheritance smart contract templates.
● Phase 3 (2027+):
○ Achieve a 10 million+ node compute network.
○ Collaborate with 5+ universities to preserve academic masters’ AI models.
9. Team and Advisors
● Founders:A vision-driven individual with a deep belief in our shared human future, combining a passion for blockchain technology with idealistic values.
● Advisors:
○ Legal Advisor: We uphold the principle of open collaboration and are currently actively inviting and recruiting experts in digital inheritance and blockchain law, as well as ecosystem volunteers to join us.
○ Technical Advisor: Technical guidance is powered by our community of core developer volunteers within the ecosystem. We continuously welcome contributions and participation from top technical talents.
10. Legal and Compliance
● Data Sovereignty Declaration: All training data is owned solely by the user; the platform has zero access rights.
● Inheritance Compliance: Private AI inheritance adheres to relevant digital legacy regulations (e.g., EU GDPR right to inheritance).
● Compute Sharing Compliance: Nodes must comply with local electricity and internet regulations, avoiding illicit mining controversies.
11. Conclusion
Greatstone.AI leverages blockchain and distributed computing to realize fully sovereign private AI models, enabling everyone to preserve and perpetuate their intellect and cognition. The STONE token is not merely the economic lifeblood of the ecosystem but also a critical tool against public AI monopolies and potential catastrophes. We call upon AI developers, compute contributors, and knowledge creators to join this monumental endeavor in building a perpetual future for human knowledge and wisdom.
12.Token Information
Full Token Name：Greatstone.ai
Token Ticker：STONE
Blockchain Network：Solana (SPL)
Token address：
9Z4dcCsnnvh5BG3LxRRw3ZY1QuPEhGQfQaYWzkKZboVR
Creator：
9Z4dcCsnnvh5BG3LxRRw3ZY1QuPEhGQfQaYWzkKZboVR
Total Supply：9,999,999,999
Token Decimals：9
13. About Us
● Official Website: https://www.greatstone.ai
● Official Email: info@greatstone.ai
● Join Our Community:https://discord.gg/VfP5Vx4QaD
Twitter/X:htps://x.com/Greatstone_AI
● Community Donations & Support
We appreciate the support of our community! If you would like to contribute to the further development of the project, you can make a donation to the following addresses:
● Solana Address:
A9dqFqXuqw8ohNJANg6UhqvSorXq8nGAtZ7iTniUeQBf
● ETH/USDT(ERC-20) Address:
0x46a994a37Bc27774D1aE0cbc086d965dA01eacD2
Disclaimer: This whitepaper is a technical vision statement and does not constitute investment advice. The inheritance functionality of private AI must comply with local laws and regulations.