The Real Cost of Trustless AI Computation on Blockchain

Generating a ZK-SNARK for a single GPT-3 inference can cost ~$1-10 in gas and take minutes, making real-time AI on L1s impossible. This is the Generality vs. Cost trade-off.

• Proof Generation Cost: Dominates total expense.
• Latency: Ranges from 10s of seconds to minutes.
• Use Case Impact: Limits to high-value, non-latency-sensitive tasks like model authentication.

Projects like EigenLayer AVS and AltLayer use an optimistic rollup model for AI. Assume computation is correct, then challenge it only if needed. This optimizes for Cost vs. Speed.

• Cost: Near-native execution cost plus a small bond.
• Latency: Sub-second to ~1 week (challenge period).
• Trade-off: Introduces a trust assumption during the challenge window.

Solutions like RISC Zero (zkVM) or Cartesi (Linux VM) create a verifiable environment, but at a cost. They trade Generality vs. Speed by limiting the instruction set or requiring custom toolchains.

• Developer Friction: Cannot run standard PyTorch/TensorFlow code directly.
• Performance Overhead: VM execution is slower than native.
• Ecosystem Fragmentation: New, unproven tooling versus established AI stacks.

Phala Network and Ora Protocol use Trusted Execution Environments (TEEs) like Intel SGX for fast, private computation, with optional ZKPs for output verification. This balances the trilemma by layering trust assumptions.

• Speed: Native execution speed inside the enclave.
• Cost: Low, as proofs are only for final state.
• Trade-off: Relies on hardware manufacturer security (e.g., Intel) and remote attestation.

Verifying an AI inference requires the input data and model weights to be available for re-computation. Storing 100GB+ models on-chain (e.g., Ethereum calldata) is economically impossible, creating a Data vs. Cost crisis.

• Cost: >$1M to post a large model to L1.
• Latency: Data retrieval from decentralized storage (e.g., Filecoin, Arweave) adds seconds.
• Implication: Forces use of smaller, less capable models or centralized data hosts.

Espresso Systems (shared sequencer) and Avail (data availability layer) enable cost amortization. Multiple inferences are batched into a single proof, or data is made available to a dedicated network. This tackles the trilemma at the system level.

• Cost Amortization: 1000x cost reduction per inference via batching.
• Shared Security: Leverages established validator sets (e.g., from EigenLayer, Polygon).
• Future Path: Essential for scaling verifiable AI to consumer-grade throughput.

Generating a zero-knowledge proof for a standard AI model inference can cost $1-10+ and take minutes, killing UX. The core challenge is the overhead of proving every floating-point operation in a circuit.

• Cost Barrier: Native zkML is 100-1000x more expensive than standard cloud inference.
• Time-to-Proof: Latency of 10s of seconds is incompatible with interactive applications.
• Circuit Complexity: Manually optimizing circuits for new models is a research-grade task.

Projects like RiscZero, Modulus, and EZKL avoid generic overhead by creating tailored proving systems for AI workloads. Optimistic approaches (e.g., HyperOracle, Axiom) use fraud proofs and dispute resolution to slash costs, trading finality time for affordability.

• Tailored VMs: RiscZero's zkVM and custom circuits reduce proving overhead for specific ops.
• Optimistic Rollup Model: Post a claim, challenge only if malicious. Reduces cost to ~1.1-2x of native compute.
• Dispute Games: Leverage Ethereum L1 as a final judge, inheriting security.

Most 'AI on-chain' today relies on a single oracle or a permissioned committee to post results. This reintroduces a central point of failure and censorship, negating the decentralization benefits of the underlying blockchain.

• Oracle Risk: Users must trust the honesty and liveness of the oracle operator.
• Data Source Opacity: The origin and integrity of training data or input data is often unverifiable.
• Model Integrity: No guarantee the promised model weights were actually used for inference.

Protocols like Gensyn and io.net decentralize the compute layer itself, creating permissionless networks for ML tasks. EigenLayer AVSs enable cryptoeconomic security for verifiable compute. Ethos and Brevis focus on attestation, proving data provenance and model execution integrity.

• Cryptoeconomic Security: Staked operators are slashed for provable malfeasance.
• Proof-of-Honesty: Networks of provers cross-verify each other's work.
• End-to-End Verifiability: Attestation chains link data source to on-chain result.

AI models are massive (GBs to TBs), and their inputs/outputs can be large. Storing and transmitting this data fully on-chain is economically impossible at scale, creating a data availability (DA) crisis for AI applications.

• State Bloat: Storing model parameters directly in smart contract storage is cost-prohibitive.
• Calldata Costs: Passing large tensors as transaction inputs is expensive on L1s.
• DA Guarantees: Need assured access to input data for fraud proof challenges.

Solutions leverage EigenDA, Celestia, or Avail for cheap, scalable data availability. Commit-reveal patterns (store a hash on-chain, data off-chain) are essential. Storage-focused L2s like Filecoin and Arweave provide persistent, verifiable storage for models and datasets.

• Modular DA: Dedicated data layers reduce cost by 10-100x vs. Ethereum calldata.
• Hash Anchoring: On-chain commitment provides a cryptographic fingerprint for off-chain data.
• Persistent Storage: Long-term, immutable storage for canonical model weights.

Running a 1-second AI inference on a GPU costs ~$0.001. Proving its correctness on-chain via a zkVM like Risc Zero or SP1 adds ~10-100x in cost and latency. This is the fundamental tax of verifiability.

• Key Constraint: Proof generation time dominates execution time.
• Architectural Implication: Forces a separation between heavy compute (off-chain prover network) and light verification (on-chain).

To avoid expensive ZK proofs, you can 'trust' a decentralized network to compute correctly. EigenLayer AVSs offer cryptoeconomic security slashing, while TEEs (like Ora) provide hardware-enforced integrity. Both introduce distinct threat models.

• EigenLayer Model: Security scales with restaked ETH TVL (~$20B+), but has liveness/soft-confirmation delays.
• TEE Model: Near-instant finality, but requires trust in Intel/SGX hardware and remote attestation.

No single chain handles the full stack. Builders assemble specialized layers: Infernet for orchestration, Ritual for incentive-driven infernet nodes, EZKL for ZKML proofs, and Axiom for historical on-chain data. Each layer adds latency and cost.

• Key Insight: The 'cost' is the sum of all modular service fees plus state management overhead.
• Builder Action: Design agent logic to minimize on-chain footprint; batch inferences, use optimistic verification where possible.