Decentralised AI vs Centralised AI: Why On-Chain Infrastructure Matters

Yael

/Nov 4, 2025

Key Takeaways

• Centralised AI poses risks such as opaque governance and limited verifiability.

• Decentralised AI focuses on verifiable compute, open markets, and durable data layers.

• On-chain infrastructure enhances accountability, programmable safety, and efficient payments.

• Future AI systems will need to balance centralised strengths with decentralised trust foundations.

Artificial intelligence is rapidly consolidating around a few cloud providers, model labs, and data brokers. That centralisation has obvious strengths—speed, scale, and convenience—but it also concentrates risk: opaque governance, unilateral policy changes, biased access, and single points of failure. As 2025 begins, the question for builders and users is not “AI or crypto,” but how decentralised AI and on-chain infrastructure can make AI more open, verifiable, and resilient.

This article breaks down the core differences between centralised and decentralised AI, the key on-chain primitives that make decentralised AI viable, and how teams can build trustworthy AI systems that actually run in production.

Centralised AI: Power, But With Fragile Trust

The centralised AI stack is straightforward: proprietary models hosted in hyperscale clouds, closed datasets, and platform-defined safety and monetisation policies. This approach can ship quickly, but it also inherits platform risk.

Governance risk: Model policies and API access can change overnight, with little recourse or portability. Even the best-intentioned platform guidelines—like the OpenAI Model Spec—are ultimately enforced unilaterally by providers, not users or developers. See the OpenAI Model Spec for context on platform-defined behavior and enforcement.
Regulatory pressure: As the EU’s Artificial Intelligence Act phases in 2025–2026, developers will face new documentation, transparency, and risk management obligations. Centralised vendors can help, but they can also restrict access or features to reduce their own liability. Read the European Commission’s overview of the AI Act for timelines and obligations.
Concentration of infrastructure: GPU access, data pipelines, and deployment tooling are opaque and scarce. That enables speed, but also creates chokepoints, pricing power, and potential censorship.
Limited verifiability: Users and counterparties can’t easily verify what model ran, on which data, under which policy, or whether results were tampered with post-inference.

These are solvable problems—but they require a new trust foundation that centralised stacks struggle to provide on their own.

What “Decentralised AI” Actually Means

Decentralised AI isn’t about abandoning powerful models; it’s about re-architecting incentives and verifiability around four pillars:

Verifiable compute: Proving that specific computations happened correctly, via zero-knowledge proofs or optimistic verification games. See the a16z crypto primer on Proof of Inference and RISC Zero’s Bonsai for proving off-chain computation.
Open and permissionless markets for resources: Marketplaces that price GPU, storage, and bandwidth in real time, with on-chain settlement and slashing for misbehavior. Examples include Akash for decentralised compute, Bittensor for networked machine intelligence incentives, and Gensyn for training validation networks.
Durable, decentralised data layers: Permanent, content-addressed storage with cryptographic audit trails—critical for dataset provenance, model artefact distribution, and reproducibility. Explore Filecoin and Arweave.
Credibly neutral coordination: Smart contracts to orchestrate incentives, attestations, payments, and governance across participants without a central operator. Interoperability and settlement across chains use primitives like Chainlink CCIP and Cosmos IBC.

This stack doesn’t replace cloud entirely. It adds trust, portability, and market-driven incentives to the parts of AI where “just trust the platform” isn’t enough.

Why On-Chain Infrastructure Matters

Provenance and accountability: Hashing datasets, model weights, and prompts to immutable storage enables forensic audit trails and reproducibility. Pair this with content authenticity standards like C2PA and on-chain attestations via Ethereum Attestation Service to track who did what, when, and with which artefacts.
Verifiable inference and training: Zero-knowledge and optimistic verification can prove a model’s output or training step without exposing sensitive internals. This is early but advancing quickly; see a16z crypto on proof of inference and RISC Zero on zk-proving flows.
Programmable safety and access: Instead of hard-coding policy into closed APIs, on-chain policy registries and attestations can be referenced at runtime. This complements governance frameworks like the NIST AI Risk Management Framework that emphasize traceability and accountability.
Efficient payments and incentives: Microtransactions and streaming payments make sense for per-token inference, fine-tuning royalties, or data-provider revenue sharing. Ethereum’s Dencun upgrade made L2 fees dramatically cheaper by introducing blobs (EIP-4844), opening viable per-call economics for AI agents. Read the Ethereum Foundation’s Dencun mainnet post for the details.
Interoperable agent economies: AI agents must transact across chains, APIs, and identity domains. Cross-chain interoperability (Chainlink CCIP, Cosmos IBC) plus account abstraction (ERC-4337) enables delegated spending, session keys, and programmable budgets for agents. See the ERC-4337 standard for design patterns.

Design Patterns for Decentralised AI in 2025

Data provenance and licensing

Store dataset manifests and model cards on Arweave/Filecoin; anchor their content hashes on-chain.
Issue signed attestations (EAS) for data rights and usage constraints.
Combine with C2PA when dealing with media provenance across Web2 channels.

Verifiable inference

Start optimistic: publish outputs and verification transcripts; allow challenge windows with bonded stakes.
Where performance allows, add zk-proofs for key steps or compact models (zkML). Explore RISC Zero’s Bonsai and a16z crypto’s Proof of Inference resource for tradeoffs and circuits.

Decentralised compute backends

Route batch training or inference jobs to decentralised marketplaces like Akash; use slashing, reputation, and redundancy for reliability.
For model- or task-specific networks, consider incentive-layer designs inspired by Bittensor or emerging research networks like Gensyn.

Storage and artefact distribution

Use Filecoin or Arweave for weights, checkpoints, and prompts; gate access with encryption and decentralised key management (e.g., Lit Protocol) when IP protection is required.

Payments and economics

Adopt L2s benefitting from Dencun’s blob pricing for per-call inference and revenue sharing.
Implement streaming payments for long-running jobs via protocols like Superfluid.
Price risk via bonded collateral, slashing conditions, and insurance markets.

Cross-domain orchestration

Bridge calls and payments using Chainlink CCIP or IBC, depending on your stack.
Expose attestations, policy IDs, and service-level terms on-chain so counterparties can verify before paying.

What’s New and What’s Next

Regulation gets real: The EU AI Act’s obligations begin to phase in through 2025–2026, pushing more documentation, provenance, and risk management into production systems. See the European Commission’s AI Act page for scope, risk classes, and timing.
Safety institutes and standards: Governments are standing up institutions like the UK AI Safety Institute and expanding guidance in the US through the Executive Order on AI and NIST RMF, reinforcing the need for traceability and testing. Review the UK AI Safety Institute and the White House Executive Order.
Crypto x AI consolidation: Projects are experimenting with network effects and shared incentives, as seen in alliances across AI-token ecosystems covered by CoinDesk. Expect more standardisation around proof-of-compute and reputation.
Data availability and modular stacks: Specialized DA layers like Celestia are improving cost and throughput for off-chain data commitments, which matters for large AI artefacts and verification traces. See Celestia’s approach to modular data availability.

Challenges (and Practical Workarounds)

Latency and cost of proofs: Full zk-proofs for large models remain expensive. Use optimistic schemes with fraud proofs for hot paths; reserve zk for critical compliance checks or smaller circuits.
Data privacy: Encrypt artefacts; attach usage constraints via attestations; store only commitments on-chain. Lit Protocol and similar tools can enforce key access policies off-chain.
Model IP and leakage: Use gated distribution with provable fingerprints, watermarks, and on-chain license attestations; consider differential privacy for sensitive datasets.
MEV and agent safety: Agents executing on public mempools can be frontrun. Use private transaction relays, commit–reveal patterns, and session keys (ERC-4337) for protected execution.
Fragmentation: Standardize on schemas for artefact manifests, attestations, and service agreements; publish registries that agents can query on-chain.

A Short Build Guide for Teams

Pick an L2 with low blob fees post-Dencun and good tooling for data-heavy apps. Anchor artefact hashes on-chain; store artefacts on Filecoin/Arweave.
Implement attestations for datasets, model hashes, and policy IDs via EAS; expose them through your API.
Start optimistic verification for inference; add zk-proofs to high-value paths as circuits mature.
Use Akash or similar networks for overflow compute; bond providers; set slashing and redundancy.
Stream per-call payments on-chain, settle cross-chain with CCIP/IBC, and maintain agent budgets with ERC-4337 session keys.
Document risk controls in line with the NIST AI RMF and map them to on-chain evidence for audits.

Keys, Agents, and the Human-in-the-Loop

As AI agents start holding funds, signing transactions, and negotiating for compute, key management becomes the real attack surface. Even if your inference runs off-chain, settlement and control logic live on-chain—and must be guarded.

Hardware wallets are the practical anchor for human-in-the-loop governance:

Cold-sign critical upgrades, policy changes, and treasury movements.
Enforce spending limits for agents via account abstraction and session keys.
Maintain an auditable boundary between automated actions and human approvals.

If you’re adopting this architecture, OneKey is a strong choice for safeguarding developer and treasury keys. It is open-source, supports major EVM and Bitcoin networks, and pairs well with agent architectures that rely on reproducible, transparent signing flows. In a world where AI systems increasingly act on-chain, the simplest reliability win is to keep humans in control of the root keys—and to keep those keys in hardware.

Closing Thoughts

Decentralised AI is not an ideological stance; it’s a practical response to verifiability, provenance, and platform risk. On-chain infrastructure gives us the primitives—attestations, commitments, programmable incentives, and interoperable payments—to build AI systems we can actually trust.

Centralised platforms will remain vital. But the systems that scale sustainably through 2025 and beyond will combine their strengths with decentralised, verifiable foundations: provenance for what went into the model, proofs for what came out, and cryptographic guarantees about how everyone gets paid.

References and further reading: