The Decentralized AI Landscape

As centralized AI becomes increasingly monopolized, the emerging decentralized AI (deAI) landscape offers a radically open alternative. Enabled by blockchain, decentralized compute, and a growing demand for transparency, this space is coalescing into a multi-layered ecosystem of infrastructure, intelligence, and incentives. In this post, we break down the decentralized AI ecosystem into clearly defined categories, explore the real-world utility of key players, explain how these parts interoperate, and articulate Lilypad’s role as a coordination layer for this emerging stack.

1. Physical Infrastructure: The GPU Backbone

These platforms form the compute base layer by renting out GPU resources from individuals or data centers, often using tokenized incentives.

• Akash: Offers a permissionless marketplace for general-purpose compute. Ideal for lightweight AI tasks or persistent inference endpoints.

• io.net: Aggregates idle enterprise-grade GPU resources. Supports heavy ML workloads like image generation and video processing.

• Aethir: Designed for gaming and real-time compute but increasingly focused on AI workloads.

• Hyperbolic: AI-specific DePIN network with focus on inference services and fine-tuning.

• Vast.ai: A fiat-based GPU marketplace with thousands of available rigs. Often used for Stable Diffusion and training.

• Golem: One of the earliest P2P compute networks; suitable for basic AI jobs and distributed simulations.

• Exabits, Spheron, Impossible Cloud: Differentiated on availability, performance pricing, and decentralized guarantees.

Interoperability: These GPU networks often plug into protocols like Lilypad, Gensyn, or Ritual to serve on-demand inference or training jobs. Lilypad coordinates job dispatch and payment, abstracting compute across providers.

2. Decentralized Cloud VMs: Stateless Infrastructure for AI Pipelines

This layer mimics services like AWS Lambda or Docker containers—but decentralized.

• Fluence: Offers WASM containers for stateless execution. Ideal for agent coordination, ephemeral inference, and middleware.

• Aleph.im: Focused on decentralized indexing and serverless hosting—useful for storing and calling AI model metadata.

• Swan Chain, Cartesi: Run secure off-chain compute, Cartesi through Linux-based VMs. Suitable for off-chain model evaluation or RL environments.

Interoperability: These platforms can host parts of AI workflows—such as metadata indexing or post-inference validation—and invoke Lilypad or OpenGradient for actual model execution.

3. AI Agents and Frameworks

The emergent UX of deAI is agentic: models that act autonomously, coordinate resources, and reason.

• Eliza, Morpheus, Virtuals: AI agents that autonomously run jobs, maintain memory, and interact with other agents or protocols.

• Naptha, Olas, Gaia, Theoriq, Recall: Frameworks for building and running these agents. Olas is the most robust, offering incentives, gossip coordination, and scheduling.

• Nevermined: Payments middleware for agents to settle costs autonomously.

Interoperability: Agents built on these frameworks submit jobs to inference platforms like Lilypad or Gaia, store state on IPFS or Arweave, and pay using smart contracts on chains like Optimism or Ethereum.

4. Data, Storage, and Databases

AI’s raw material is data. These platforms ensure it remains verifiable, accessible, and censorship-resistant.

• Data networks & datasets: Baselight.ai (structured datasets), Vana (user-owned data), Grass (web crawling), Openmesh (data commons).

• Storage: Filecoin (deep storage), Arweave (permaweb), IPFS (general-purpose distributed storage).

• Databases: Fireproof.storage, Space & Time (verifiable compute over indexed data).

Interoperability: Lilypad jobs often consume data from Vana or Openmesh, read/write artifacts to IPFS/Filecoin, and can validate lineage via Space & Time or Story Protocol.

5. Middleware & Service Layer

This category handles routing, orchestration, and economic logic in a composable deAI stack.

• SingularityNET: Offers a token-gated AI service registry. Hosts models and agents that can call each other.

• OpenGradient: Coordinating distributed training jobs, with tokenized rewards.

• Ritual: Programmable compute infrastructure for LLMs, often integrated with io.net.

Interoperability: These tools often wrap and route calls to infrastructure like Lilypad or Hyperbolic. For instance, Ritual jobs may be executed on Lilypad’s protocol but initiated through Ritual’s SDK.

6. AI Service Chains

Layer 1 or 2 chains optimized for AI use—either through VM design or native tokenomics.

• 0g: A modular AI operating system focused on scalable storage, data availability, and GPU scheduling. Ideal for high-throughput agent workflows.

• Near: Home to deAI projects and models. Supports contract-level model inference.

• OG Labs, Sahara AI, IoTeX: Building app-specific chains that integrate AI directly into their execution layers.

Interoperability: Lilypad can dispatch workloads or validate results using smart contracts on these chains. These chains also host front-end dApps that submit jobs.

7. Distributed Training Platforms

Instead of centralized clusters, these platforms coordinate training jobs across distributed nodes.

• Gensyn: The gold standard in this space. Coordinated LLM training with incentives for participants.

• Prime Intellect, Nous: Research-focused alternatives enabling RLHF, fine-tuning and collaborative model development.

Interoperability: Lilypad can route fine-tuning tasks to Gensyn or enable collaborative training pipelines across multiple datasets pulled from Vana or Filecoin.

8. Inference Platforms

These specialize in high-throughput, pay-per-use model serving.

• Hyperbolic: GPU inference network focusing on low-latency model runs.

• Gaia: Focused on LLMs and foundational inference for agents.

• Bittensor Subnets: Specialized for specific AI tasks, like vision or translation.

Interoperability: Lilypad is interoperable with Gaia, and in the future could connect to Bittensor subnets via bridging wrappers. Lilypad also offers its own marketplace with inference APIs.

9. Model Hosting Marketplaces

Places where models are deployed, discovered, and monetized.

• Bagel, Prime Intellect, Flock.io: Offer permissionless upload, API-based access, and in some cases, provenance.

• Bittensor Subnets: Some act as model hosts, rewarded via stake.

Interoperability: Models on Lilypad can be cross-posted to Bagel or exposed via Flock APIs. Model metadata can be stored on IPFS and referenced on Arweave.

10. Privacy & Security Layers

Key to enabling AI that respects ownership, user data, and safe execution.

• TEE: Phala, Nillion (confidential execution of inference jobs)

• ZK Proofs: Nexus (privacy-preserving inference verification)

• FHE: Zama, Gateway (encrypted model execution)

• Other Privacy: Lit Protocol (access control), Story Protocol (IP + provenance)

Interoperability: Lilypad can integrate ZK or TEE as plugins for sensitive jobs—e.g., confidential medical inference.

11. Reinforcement Learning Protocols

Still early, but these explore incentive-aligned training via RL.

• Newcoin: RL-based learning from social graph behavior.

• Cambrian Network: RL for distributed learning in autonomous agents.

Interoperability: Lilypad jobs can be used as reward signals or task environments within these RL frameworks.

12. IP & Provenance Tooling

Tracks who built what, how it’s used, and where value flows.

• Story Protocol: Royalty-enabled provenance across derivative works.

• EQTY Lab: Licensing and creator attribution.

Interoperability: Lilypad’s token rails and job graphs can integrate Story Protocol for remix royalties and IP lineage.

13. DeFAI: AI + DeFi Intersections

Finance rails designed specifically for AI workflows and autonomous agents.

• Glif, Parasail: DeFi rails for model monetization, agent staking, or inference loans.

Interoperability: Lilypad can use Glif as a settlement layer, or Parasail to underwrite compute requests.

14. AI Research & Commons

Think tanks, foundations, and data commons ensuring open AI development.

• Foresight Institute: Research funding for AGI safety and open science.

• RMIT Blockchain Innovation Hub, dbForest, CEL: Focused on building frameworks and commons for deAI.

Interoperability: Lilypad can support their agents, training models, or infrastructure. These orgs may also help shape governance.

Where Lilypad Fits

Lilypad is the decentralized execution and economic coordination layer for the AI ecosystem.

• For Model Creators: A frictionless way to deploy and monetize models

• For Compute Providers: Monetize idle GPUs via permissionless job participation

• For Developers: Plug into an on-chain model marketplace with API-based access

Key Differentiators:

• On-chain job routing, execution, escrow, and rewards

• Composability across data, models, compute, and agentic frameworks

• Modular and chain-agnostic, with EVM-based smart contracts

Lilypad acts as the “glue” of decentralized AI—connecting demand and supply across categories through a standardized protocol and token incentive layer.

Final Insight: deAI as a Parallel Chain

Decentralized AI is bigger than a category: it’s an ecosystem. Like DeFi redefined finance, deAI redefines intelligence, coordination, and value creation.

Lilypad’s role isn’t to compete with these players, but to make them usable, valuable, and coordinated.

The future is not closed AI APIs. The future is programmable, composable, community-owned intelligence. And Lilypad is building it.