Why Proof-of-Inference is the New Proof-of-Work

Proof-of-Work's security is a function of energy burn rate, creating a ~$30B/year externality. This model is inherently zero-sum, leading to centralization around cheap power and ASIC oligopolies.\n- Inelastic Security: Hashpower is a commodity, not a service.\n- No Intrinsic Value: The work itself secures nothing but its own ledger.

Proof-of-Inference monetizes useful computation—like AI model inference or scientific simulation—as the cost of consensus. Validators earn fees for providing a verifiable service, not just burning electricity.\n- Dual-Sided Revenue: Fees from both blockchain security and the inference marketplace.\n- Sybil Resistance: The cost to attack is the cost to provide the service at scale.

Networks like Ritual, Gensyn, and Akash are not L1s competing for TVL; they are verifiable compute layers. Their security derives from the economic value of their output, making them crypto-economic coprocessors to Ethereum and Solana.\n- Modular Stack: Specialized execution + generalized settlement.\n- Intent-Centric: Users submit a task, the network guarantees verifiable completion.

The attack surface shifts from 51% hashpower to adversarial machine learning. Attackers must now corrupt model outputs at scale, requiring deep ML expertise instead of just capital for hardware. This creates a higher cognitive barrier to attack.\n- Security via Complexity: Exploiting the system requires understanding the service.\n- Continuous Auditing: Inference results are constantly verified by the network.

Tokens in a PoI network are staked against real-world economic activity, not just inflation. The Total Value Secured (TVS) metric expands to include the value of all inferences processed, creating a direct link between utility and security spend.\n- Value-Aligned Security: More useful work → Higher fees → More stakers → Stronger security.\n- Deflationary Pressure: Service fees can be burned, unlike perpetual PoW issuance.

Just as Bitcoin followed Moore's Law, Proof-of-Inference rides the AI exponential. Global demand for verifiable AI compute will dwarf the market for pure hashpower. Networks that anchor security to this demand will capture the next trillion-dollar crypto primitive.\n- Market Pull: The world needs trustless AI, not just trustless money.\n- Existential Hedge: This is how blockchains remain relevant in an AI-centric world.

On-chain AI agents require trust in centralized API providers like OpenAI. Proof-of-Inference provides cryptographic proof that a specific model was executed correctly, enabling verifiable on-chain AI.

• Enables Autonomous Agents: Smart contracts can now trustlessly trigger and verify AI outputs.
• Creates a Permissionless Marketplace: Any GPU provider can sell provable compute, breaking cloud oligopolies.
• Foundation for DePIN AI: Turns idle GPUs into a decentralized inference network, akin to Filecoin for storage.

Ritual is building an inference layer where models are hosted and executed in a trust-minimized environment. It's the base infrastructure for the PoInf economy.

• Infernet Nodes: A decentralized network that executes models and generates cryptographic proofs (ZK or TEE-based).
• Model Marketplace: A permissionless repository for fine-tuned, monetizable AI models.
• Native Integration: SDKs for smart contracts to easily request and consume verified inference.

EigenLayer's restaking model provides the cryptoeconomic security for Proof-of-Inference networks. Actively Validated Services (AVSs) like Ritual or io.net bootstrap security from Ethereum.

• Pooled Security: Inference networks tap into $15B+ in restaked ETH instead of bootstrapping a new token.
• Slashing for Liveness: Guarantees nodes perform inference honestly or face stake loss.
• Fast Bootstrapping: New AI protocols launch with battle-tested security from day one.

Proof-of-Inference is the new Proof-of-Work. Instead of burning energy for hash puzzles, the network burns compute for useful AI work, creating a tangible economic flywheel.

• Useful Work: Compute is spent generating valuable inference, not random hashes.
• Global GPU Liquidity: Unlocks a trillion-dollar hardware market for on-chain settlement.
• Native Crypto Primitive: Inference becomes a verifiable, tradeable commodity like bandwidth (Livepeer) or storage (Arweave).

Verifying an AI inference is computationally intensive, often rivaling the cost of the original task. This creates a paradox where the proof-of-work becomes the work itself.

• Verification Cost: Can be ~10-50% of the original inference cost, destroying economic viability.
• Latency Blowback: Adding a ZK-proof or optimistic fraud-proof can increase latency from ~100ms to 2+ seconds, breaking real-time use cases.
• Centralization Pressure: Only large validators can afford the hardware for fast verification, leading to a <10 entity oligopoly.

Every new model architecture (Llama, Claude, Stable Diffusion) requires a new, audited verifier circuit. The ecosystem risks fracturing before it unifies.

• Circuit Development Lag: New SOTA models launch every 3-6 months, but secure verifier circuits take 6-12 months to develop and audit.
• Liquidity Splintering: Compute markets fragment by model, preventing the formation of a universal liquidity pool akin to Ethereum's EVM.
• Vendor Lock-In: Projects like EigenLayer AVS or io.net risk becoming walled gardens if they only support a narrow set of verified models.

Proof-of-Inference proves a computation was done correctly, not that the input data was valid or the output is useful. This recreates blockchain's oracle problem for AI.

• Garbage In, Gospel Out: A proven inference on manipulated or biased data is worthless but cryptographically verifiable.
• Output Subjectivity: For creative or subjective tasks (e.g., content moderation), consensus on correctness is impossible, requiring trusted committees.
• Data Provenance Gap: Without a proof-of-data-origin, systems like Chainlink Functions or Brevis co-processors become mandatory, adding layers and points of failure.

AWS, Google Cloud, and Azure can undercut any decentralized network on price for standard models, confining Proof-of-Inference to niche, censored, or proprietary model use cases.

• Economies of Scale: Centralized clouds operate at ~60% gross margin; decentralized networks struggle to compete on pure inference cost for Llama-4.
• Commoditization Ceiling: Only valuable for politically sensitive or custom-fine-tuned models where centralized providers refuse service.
• The Akash Network Precedent: Despite $200M+ in compute orders, decentralized GPU marketplaces remain a <1% sliver of the total cloud market.