The rapid growth of AI in the Web3 landscape makes it challenging to distinguish genuine innovation from hype. During ETHDenver, we explored 11 leading AI projects revolutionizing the space. Here’s an in-depth look at their visions, methodologies, and real-world applications.
Key Questions Addressed by These Projects:
Data
- Acquisition: How to source AI training data? (Grass)
- IP Protection: How to safeguard data source ownership? (Story Protocol)
- Alignment: How to verify unaltered datasets? (Space and Time)
Models
- Open Economics: How to incentivize decentralized platforms? (Bittensor, Sentient)
- Integrity: How to prove untampered model outputs? (Modulus Labs, Ora)
Infrastructure
- Unified Layer: How to integrate fragmented infrastructure? (Ritual)
Agents
- Autonomy: How to create composable, ownable AI agents? (Future Primitive, Olas, MyShell)
1. Grass: Decentralized Data for AI
Problem: High-quality training data is often restricted by gatekeepers.
Solution: Grass operates a global network of ~1M nodes scraping and structuring 1TB+ daily data.
Use Case: Active in 190 countries, enabling fair access to AI training datasets.
👉 Discover how Grass powers decentralized AI
2. Story Protocol: IP Management for AI
Problem: AI remixing lacks profitability and IP attribution.
Solution: On-chain "programmable IP" lets creators set rules for derivatives, automating revenue sharing.
Use Case: Custom licensing for regional, time-bound, or revocable content usage.
3. Space and Time: Verifiable Data for LLMs
Problem: Dataset tampering risks in LLM training.
Solution: GPU-accelerated ZK proofs for SQL/vector queries, ensuring data integrity.
Use Case: Auditable training datasets and real-time context retrieval for AI models.
4. Bittensor: Decentralized AI Marketplace
Problem: Centralized AI monopolies like OpenAI.
Solution: 32 subnetworks compete for TAO token rewards, incentivizing top-tier outputs.
Use Case: FileTAO (storage), Cortex TAO (OpenAI alternatives), and Fractal Research (text-to-video).
5. Sentient: Crowdsourced AGI Development
Problem: AGI risks under centralized control.
Solution: Token-incentivized, community-driven model training and composability.
Use Case: Trustless AGI development via open protocols.
6. Modulus Labs: ZK-Proofs for AI
Problem: Unverifiable AI outputs.
Solution: Custom ZK prover ("Remainder") with 180x lower cost vs. traditional methods.
Use Case: Upshot’s AI-driven asset valuations with on-chain proofs.
7. Ora: Scalable Chain-AI Oracle
Problem: High ZKML costs for large models.
Solution: OPML (linear-cost) for fraud-proofed AI inferences.
Use Case: Ethereum-based Stable Diffusion and LLaMA models.
8. Ritual: Decentralized AI Infrastructure
Problem: Centralized AI’s censorship risks.
Solution: Infernet oracles + sovereign chain with AI-native VM.
Use Case: Frenrug’s LLM for Friend.Tech trading; MyShell’s creator economy.
9. Olas: Autonomous Agent Protocol
Problem: Web2 agents lack ownership/composability.
Solution: Chain-managed, off-chain agents with OLAS token incentives.
Use Case: Olas Predict’s AI-driven prediction markets.
10. MyShell: AI App Creation Platform
Problem: Static creator economies.
Solution: No-code tools for AI apps (roleplay, learning tools).
Use Case: Custom AI companions, language tutors, and content generators.
11. Future Primitive: NFT-Based Agents
Problem: NFTs lack programmability.
Solution: ERC6551 turns NFTs into self-custodial, cross-chain agents.
Use Case: Autonomous asset management and multi-chain operations.
FAQ
Q1: How does Grass ensure data quality?
A: Nodes validate and structure scraped data, filtering irrelevant content.
Q2: Can Story Protocol prevent unauthorized AI remixing?
A: Yes, via on-chain licenses that enforce revenue-sharing rules.
Q3: Is Bittensor resistant to centralization?
A: Yes, subnets compete for rewards, and poor performers are removed.
Q4: What’s Modulus Labs’ cost-saving breakthrough?
A: Their ZK prover reduces overhead from 10,000x to 180x.
Q5: How does Olas incentivize agent operators?
A: OLAS tokens reward developers, stakers, and service owners.
Q6: Can MyShell apps monetize?
A: Yes, creators earn via token-gated features and usage fees.