Decentralized AI is not about "putting models on the chain." More accurately, it is a redesign of how data, models, compute, inference, evaluation, and revenue distribution are organized within AI systems.

Why Learn This

Today's large-model capabilities are primarily concentrated in the hands of a few platforms. They control the models, compute resources, data pipelines, inference gateways, pricing rules, and banning policies. For ordinary developers, this centralization provides significant convenience but also brings several practical problems:

• Models and inference services can change prices, rate-limit, or go offline at any time.
• Data sources, training processes, and model updates are difficult to verify externally.
• Users and contributors find it hard to prove their contributions to models, datasets, or application ecosystems.
• As AI Agents begin to perform tasks on behalf of users, platform-level control becomes a new trust bottleneck.

Decentralized AI is not about "centralization is inherently bad," but about identifying the stages where an open network can provide better verifiability, composability, and resistance to single-point control.

First Principles

What AI systems truly need to decentralize is not necessarily the model itself, but the control over critical resources and key decisions.

Whether model weights are open-source, whether inference nodes are distributed, whether data can be authorized for reuse, whether results are verifiable, and whether revenue can be settled—these are problems at different layers. A project can be valuable by opening up just one of these layers; however, if it cannot clearly state which layer it is opening, it risks remaining a mere narrative.

• Layer First, Then Judge Value: Data, models, compute, inference, evaluation, settlement, and governance should not be lumped into a single term.
• Openness Comes at an Engineering Cost: Distributed networks introduce latency, quality fluctuations, Sybil attacks, malicious nodes, and governance inefficiencies.
• The Chain is Better Suited as a Coordination Layer: Recording ownership, incentivizing contributions, settling tasks, and governing parameters are more realistic than directly hosting large-scale model training.

Knowledge Nodes

Model Market

A Model Market is a marketplace where model capabilities can be discovered, compared, called, and paid for like services. Its focus is not on "how many models there are," but on whether callers can know a model's capabilities, price, latency, version, license, context window, tool-calling abilities, and failure boundaries.

For AI x Web3 builders, the key issue in a Model Market is replaceability. If an Agent's task can be completed by multiple models, the system needs to move model selection from hard-coding to routing decisions: cheap models for classification and extraction, high-capability models for complex reasoning, local models for privacy content, and specialized models for code, images, or on-chain data.

A truly useful model market solves discovery, evaluation, routing, and settlement, not just listing models.

Related Topic

• Inference: Understand the latency, cost, and context constraints behind a single model call.
• Model Routing: How to choose the right model based on the task when multiple models are available.

Data Market

A Data Market discusses how data is authorized, priced, verified, and reused. The quality of an AI system depends heavily on data, but data markets are difficult to build because data is not a typical commodity: it can be copied, its quality is hard to judge in advance, authorization boundaries are complex, and privacy and copyright risks are high.

A more realistic data market usually doesn't just sell "raw data packages" but is designed around several specific objects:

• Datasets with verifiable sources.
• Training or fine-tuning data with authorization terms.
• Reproducible data processing pipelines.
• Auditable records of data contributions.
• Task-specific labeling, preference, or evaluation samples.

If a data market enters an on-chain scenario, on-chain records are better suited for storing authorizations, hashes, versions, contributions, and revenue distribution rather than directly storing large volumes of raw data.

Related Topic

• AI Privacy: Data markets must handle issues of privacy, authorization, and minimal disclosure.
• Filecoin Docs: Learn how decentralized storage supports data availability and storage proofs.

Compute Market

A Compute Market is a marketplace where GPUs, CPUs, storage, bandwidth, and inference services become purchasable resources. Its appeal is direct: when centralized cloud resources are expensive, queued, or restricted, open compute networks can provide new sources of supply.

However, the difficulties of compute markets are significant:

• Node performance is unstable; latency and throughput can fluctuate.
• GPU models, drivers, images, and network environments affect reproducibility.
• Task results need verification; otherwise, cheap compute might yield incorrect outputs.
• For privacy-sensitive tasks, remote nodes are not inherently trustworthy.
• Production systems require SLAs, which open networks often find harder to guarantee.

Therefore, compute markets are better suited to start with containerized inference, batch processing tasks, non-critical workloads, and repeat-verifiable tasks. High-value transactions, private data, or real-time systems require additional measures like TEE, ZK, redundant execution, sampling checks, or human auditing.

Related Topic

• Akash Network Docs: Learn the basics of how decentralized cloud and GPU markets work.
• Verifiable AI: When compute comes from untrusted nodes, additional verification of output is needed.

Inference Network

An Inference Network decomposes model inference into a network service: a user or Agent sends a request, the network selects a node for execution, results are returned, and billing, auditing, or proof is completed.

Its difference from a traditional API is that inference nodes do not necessarily belong to the same company. The system must handle node discovery, model versions, request routing, rate limiting, failure retries, result verification, and payment settlement. For developers, the most important thing is not just "whether an answer is returned," but also:

• Which model and which node the request was sent to.
• Whether model versions and parameters are traceable.
• Whether it can retry or switch nodes upon output failure.
• Whether user privacy data is exposed to untrusted nodes.
• Whether cost, latency, and quality are recorded.

If an Agent is to initiate on-chain actions based on inference results, the inference network also needs to clarify the liability boundaries between output and subsequent execution.

Model Routing

Model Routing involves making choices among multiple models, nodes, or services. It is an often underestimated layer in decentralized AI because an open network brings not just "one stronger model," but many sources of supply with varying capabilities, prices, and stability.

A practical routing strategy usually considers:

• Task type: Summarization, translation, code, on-chain analysis, mathematical reasoning, image generation.
• Risk level: Ordinary explanations can be automated; transaction decisions require higher reliability.
• Data sensitivity: Private data prioritizes local models or trusted execution environments.
• Cost budget: Low-value tasks should not call the most expensive models by default.
• Latency requirements: Interactive tasks and offline batch processing have different strategies.
• Evaluation feedback: Incorporating historical success rates, user feedback, and error types into routing.

The routing layer is essentially the scheduler of an AI application. It determines when the system pursues cost-efficiency, when it pursues reliability, and when it must refuse to execute.

Quality Benchmark

One of the biggest issues with open networks is inconsistent quality. A Quality Benchmark is used to answer: Is this model, node, or data source actually reliable?

Standard benchmarks only look at model scores on fixed datasets, but Decentralized AI requires looking at more dimensions:

• Whether results are stable.
• Whether nodes execute as agreed.
• Whether latency and failure rates are acceptable.
• Whether outputs can be reproduced or verified through sampling.
• Whether there is gaming, overfitting, or collusion.
• Whether evaluation tasks are close to real product scenarios.

For AI x Web3, benchmarks should not just serve leaderboards but also enter routing, pricing, reputation, and settlement. A node that performs well consistently can receive more traffic; a model with a high failure rate on a certain task should not be routed to high-risk scenarios.

Related Topic

• Trust and Reputation: Reputation systems can condense historical quality into usable selection signals.
• Evaluation: Understand how to design evaluations for model capabilities and product tasks.

Settlement

Settlement is the key for Decentralized AI to move from "open calling" to a "sustainable network." As long as resources come from multiple participants, it must be answered: who provided what, who used what, who should pay, how to refund failures, and how to handle disputes.

Settling AI tasks is more complex than standard APIs because result quality is often not binary. An inference might succeed but have poor quality, might timeout, might return a format error, or might be found unusable by downstream verification. Therefore, settlement design usually needs to include:

• Task quote and validity period.
• Request parameters and model version.
• Output hash or result summary.
• State machines for success, failure, timeout, and dispute.
• Rules for payment, escrow, refund, and arbitration.
• Task logs and evaluation evidence.

Related Topic

• Machine Payment: How Agents pay for APIs, data, and inference services.
• Settlement and Escrow: How task completion, acceptance, refund, and dispute handling are designed.

Position in AI x Web3

Decentralized AI is the most fundamental and often most sloganized set of topics in AI x Web3. It doesn't necessarily turn directly into a user product, but it will influence the infrastructure for Agents, payments, data, models, privacy, verification, and governance.

When evaluating a Decentralized AI project, you can consistently ask these questions:

• What does it decentralize: data, models, compute, inference, evaluation, settlement, or governance?
• What is the on-chain part responsible for: recording, payment, incentive, identity, governance, or verification?
• What exactly does it improve compared to centralized services: cost, availability, censorship resistance, verifiability, or contribution distribution?
• Is the quality fluctuation and governance cost brought by an open network worth it?

Minimal Practice

Design a minimal specification for an "open inference network," without necessarily implementing it.

Clearly specify:

• Which type of tasks are supported, e.g., contract summarization, governance proposal summary, or on-chain transaction explanation.
• Which roles are in the network: Requestor, Inference Node, Evaluator, Payer, Arbitrator.
• What fields are in the request: Model, input, budget, timeout, privacy level, output format.
• How nodes quote, how results are returned, and how they prove they completed the task.
• How results are accepted, how failures are refunded, and how disputes are handled.
• Which information goes on-chain, and which information only saves hashes or logs.

The goal of this exercise is not to draw a grandiose protocol but to break down "Decentralized AI" into discussable resources, roles, and state machines.

Extended Reading

• Akash Network Docs: Learn the basic structure of decentralized cloud, deployment, and GPU markets.
• Bittensor Docs: Understand how subnets, validators, miners, and incentive mechanisms organize AI service networks.
• Filecoin Docs: Learn about decentralized storage, data availability, and storage proofs.
• Oasis ROFL Docs: Learn how TEE applications provide the foundation for privacy and verifiable execution.
• RISC Zero: Learn how zkVM is used for verifiable computation.

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