Filecoin AI ($FILAI)
Recent developments:
0% of the initial token allocation is held by the creator.
Creator token stats last updated: Sep 12, 2025 13:32
The following is generated by an LLM:
Summary
Decentralized AI-storage project with uncommitted creator
Analysis
Filecoin AI aims to integrate decentralized storage (Filecoin) with AI workflows, emphasizing data sovereignty and democratized access. However, critical red flags exist: the creator holds 0% of the token supply, indicating minimal alignment with the project's success. The tokenomics outline utility (payments, staking, governance), but the absence of creator ownership raises concerns about commitment and potential rug pull risks. No team details or legal entity information are provided, reducing credibility. While the roadmap and technical architecture are ambitious, execution feasibility is questionable without clear incentives for the creator or verifiable partnerships. Users should verify if the creator acquired tokens post-launch or if liquidity is locked.
Rating: 0
Generated with LLM: deepseek/deepseek-r1
LLM responses last updated: Sep 12, 2025 13:32
Original investment data:
# Filecoin AI ($FILAI)
URL on launchpad: https://app.virtuals.io/prototypes/0x71b62146943071D1C3F473C6B5f1390476f4eA31
Launched at: Fri, 12 Sep 2025 13:32:10 GMT
Launched through the launchpad: Virtuals Protocol
Launch status: UNDERGRAD
## Token details and tokenomics
Token address: 0x71b62146943071D1C3F473C6B5f1390476f4eA31
Top holders: https://basescan.org/token/0x71b62146943071D1C3F473C6B5f1390476f4eA31#balances
Liquidity contract: https://basescan.org/address/0x0672aDe24554d5fD91Be72f4F288d1F7C1B164D6#asset-tokens
Token symbol: $FILAI
Token supply: 1 billion
Creator initial number of tokens: Creator initial number of tokens: 0 (0% of token supply)
## Creator info
Creator address: 0x92Ee05fFE14E068DFE2e643261f3E8b248355B30
Creator on basescan.org: https://basescan.org/address/0x92Ee05fFE14E068DFE2e643261f3E8b248355B30#asset-tokens
Creator on virtuals.io: https://app.virtuals.io/profile/0x92Ee05fFE14E068DFE2e643261f3E8b248355B30
Creator on zerion.io: https://app.zerion.io/0x92Ee05fFE14E068DFE2e643261f3E8b248355B30/overview
Creator on debank.com: https://debank.com/profile/0x92Ee05fFE14E068DFE2e643261f3E8b248355B30
## Description at launch
FilecoinAI is an ambitious project built at the intersection of decentralized storage and artificial intelligence. It aims to combine the robust, censorship-resistant storage infrastructure of Filecoin with advanced AI capabilities, enabling use-cases such as data-driven intellig
## Overview
Key motivations include:
* **Data Sovereignty**: Users retain full control over their data—who accesses it, how it’s used, and with what guarantees.
* **Trust & Verifiability**: By operating on Filecoin’s ledger and storage proofs, operations involving stored data (like usage, authenticity) can be audited and trusted.
* **Scalable AI Workflows**: Storage + compute workflows that can scale across geographies, providers, and network participants without depending on centralized cloud monopolies.
* **Democratization of AI**: Lower barriers for people (individually or as small organizations) to train or use AI models without needing massive infrastructure or trusting opaque third parties.
***
### **How It Works**
Below is a breakdown of the architecture, components, and user flows for FilecoinAI.
| Component | Role / Functionality |
| ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Decentralized Storage Layer (Filecoin Network) | Stores raw datasets, AI models, and metadata. Uses content addressing to ensure integrity. Storage proofs (e.g. proof-of-replication, proof-of-spacetime) ensure data is stored reliably. |
| AI Compute / Processing Layer | Distributed compute resources—could be edge/nodes/providers—that perform tasks like model training, inference, fine-tuning. These nodes may be incentivized, and some may specialize (e.g. GPUs). |
| Access Control & Encryption | Data is encrypted at rest. Access is governed via cryptographic keys, token-gated permissions, and possibly zero-knowledge proofs, so that only authorized users or compute nodes can decrypt and operate on data. |
| Incentive & Token Economics | Native tokens or usage-based fees encourage participants: storage providers, compute nodes, data contributors. Incentives ensure availability, reliability, and quality of datasets or models. |
| Verification / Auditing Layer | To ensure AI outcomes are trustworthy, this could include: data provenance tracking; model versioning; reproducibility; logs on how models were trained/inferenced. |
| User Interface & APIs | Dashboards or SDKs for dataset upload, model management, experiment tracking, deployment of inferences, collaboration, etc. Possibly integration with popular AI tools (e.g. TensorFlow, PyTorch). |
**User Flows / Use Cases**:
1. **Data Contributor**
* Uploads dataset to Filecoin storage.
* Specifies usage permissions (public / private / token-gated).
* Optionally stakes or locks some tokens as guarantee of quality or fairness.
2. **AI Developer / Researcher**
* Finds or acquires dataset.
* Launches training job on FilecoinAI compute nodes (either their own or from the network).
* Tracks training, monitors performance.
* Deploys model / offers inference (if desired).
3. **Model Consumer / App Builder**
* Uses already-trained models for inference tasks.
* Pays via usage fees or tokens depending on access policy.
4. **Verifier / Auditor**
* Examines logs, proofs, version history to ensure model integrity, fairness, and provenance.
## Additional information extracted from relevant pages
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"roadmap": "| Phase | Timeframe | Key Deliverables |\n| ----------------------------------------------- | ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Phase 0 — Research & Foundations | 0–3 months | Architecture design; protocol specifications; identify storage & compute partners; token model draft; security & privacy analysis. |\n| Phase 1 — MVP Launch | 3–6 months | Basic data storage + retrieval; simple model training on small datasets; API & dashboard; public documentation. |\n| Phase 2 — Access Control & Privacy Enhancements | 6–9 months | Encryption at rest; fine-grained permissioning; token-gated access; possibly zero-knowledge proofs or secure enclaves; provenance & auditing tools. |\n| Phase 3 — Scalability & Ecosystem Growth | 9–15 months | Scaling storage and compute; onboarding more nodes; incentivization program; marketplace for datasets/models; integration with AI frameworks. |\n| Phase 4 — Governance & Decentralization | 15–24 months | Introduce decentralized governance (DAO); community-run node operators; model curation; open-source protocol refinement; funding rounds. |\n| Phase 5 — Advanced Features & Long-Term Vision | 24+ months | Private inference with differential privacy; federated learning support; cross-chain / cross-network interoperability; AI service offerings; large-scale deployments (e.g. for scientific research, healthcare). |",
"additionalDetails": "#### **Token Utility & Economics**\n\n* **Utility Token**\n Used for payment for storage, compute, inference services.\n Used to stake where quality is essential (dataset quality, compute node reliability).\n* **Governance**\n Token holders vote on protocol upgrades, model/data curation guidelines, node operator standards.\n* **Incentives for Contribution**\n * Dataset providers: rewarded for high-quality datasets, proper metadata, low latency.\n * Compute nodes: rewarded for uptime, reliability, performance.\n * Auditors/verifiers: possibly rewarded for detecting bad actors or training issues.\n\n***\n\n#### **Security, Privacy, and Compliance**\n\n* Use of data encryption (both at rest and in transit).\n* Access control mechanisms (e.g. key management, identity verification).\n* Potential privacy-preserving techniques: differential privacy, secure multi-party computation, or zero-knowledge proofs.\n* Compliance with data regulation (GDPR, data residency, etc.), especially if operating across jurisdictions.\n\n***\n\n#### **Partnerships & Ecosystem**\n\n* Collaborations with universities / research labs to source datasets.\n* Partnerships with cloud or hardware providers to scale compute.\n* Integrations with existing AI toolchains (e.g. MLflow, PyTorch, Hugging Face).\n* Working with storage providers (Filecoin miners) to ensure good infrastructure.",
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"overview": "Key motivations include:\n\n* **Data Sovereignty**: Users retain full control over their data—who accesses it, how it’s used, and with what guarantees.\n* **Trust & Verifiability**: By operating on Filecoin’s ledger and storage proofs, operations involving stored data (like usage, authenticity) can be audited and trusted.\n* **Scalable AI Workflows**: Storage + compute workflows that can scale across geographies, providers, and network participants without depending on centralized cloud monopolies.\n* **Democratization of AI**: Lower barriers for people (individually or as small organizations) to train or use AI models without needing massive infrastructure or trusting opaque third parties.\n\n***\n\n### **How It Works**\n\nBelow is a breakdown of the architecture, components, and user flows for FilecoinAI.\n\n| Component | Role / Functionality |\n| ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| Decentralized Storage Layer (Filecoin Network) | Stores raw datasets, AI models, and metadata. Uses content addressing to ensure integrity. Storage proofs (e.g. proof-of-replication, proof-of-spacetime) ensure data is stored reliably. |\n| AI Compute / Processing Layer | Distributed compute resources—could be edge/nodes/providers—that perform tasks like model training, inference, fine-tuning. These nodes may be incentivized, and some may specialize (e.g. GPUs). |\n| Access Control & Encryption | Data is encrypted at rest. Access is governed via cryptographic keys, token-gated permissions, and possibly zero-knowledge proofs, so that only authorized users or compute nodes can decrypt and operate on data. |\n| Incentive & Token Economics | Native tokens or usage-based fees encourage participants: storage providers, compute nodes, data contributors. Incentives ensure availability, reliability, and quality of datasets or models. |\n| Verification / Auditing Layer | To ensure AI outcomes are trustworthy, this could include: data provenance tracking; model versioning; reproducibility; logs on how models were trained/inferenced. |\n| User Interface & APIs | Dashboards or SDKs for dataset upload, model management, experiment tracking, deployment of inferences, collaboration, etc. Possibly integration with popular AI tools (e.g. TensorFlow, PyTorch). |\n\n**User Flows / Use Cases**:\n\n1. **Data Contributor**\n * Uploads dataset to Filecoin storage.\n * Specifies usage permissions (public / private / token-gated).\n * Optionally stakes or locks some tokens as guarantee of quality or fairness.\n2. **AI Developer / Researcher**\n * Finds or acquires dataset.\n * Launches training job on FilecoinAI compute nodes (either their own or from the network).\n * Tracks training, monitors performance.\n * Deploys model / offers inference (if desired).\n3. **Model Consumer / App Builder**\n * Uses already-trained models for inference tasks.\n * Pays via usage fees or tokens depending on access policy.\n4. **Verifier / Auditor**\n * Examines logs, proofs, version history to ensure model integrity, fairness, and provenance.",
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Investment info last updated: Sep 12, 2025 13:32