The Hidden Cost of Unverifiable Computational Science

DApps rely on centralized oracles (e.g., Chainlink, Pyth) for critical data, creating a single point of failure. This reintroduces the trust model blockchain was built to eliminate.

• Vulnerability: Manipulated price feeds can drain DeFi protocols.
• Cost: Oracle services extract rent for a function that should be native.

Validators and searchers exploit their position in the transaction supply chain to extract value, undermining fair execution. This is a tax on every user.

• Impact: Front-running, sandwich attacks, and ~$1B+ annual extracted value.
• Result: User trades execute at worse prices than the public mempool state promised.

Integrating AI agents (e.g., for trading, governance) without verifiable computation creates an unaccountable decision layer. You cannot prove the model's output is correct.

• Risk: Malicious logic or biased training data executes autonomously.
• Consequence: The blockchain becomes a ledger for decisions made in a trusted enclave, breaking the settlement guarantee.

Centralized data feeds like Chainlink rely on off-chain computation you cannot audit. This creates systemic risk for $10B+ in DeFi TVL.\n- Single Point of Failure: A compromised node can manipulate price feeds.\n- Opaque Logic: You cannot verify the computation behind the delivered data.

Projects like Risc Zero and Axiom execute complex logic off-chain and submit a cryptographic proof of correctness on-chain.\n- Verifiable State: Prove historical data or complex computations were processed correctly.\n- Composability: Outputs are trustless inputs for smart contracts, enabling new DeFi and gaming primitives.

Bridges and cross-chain apps rely on off-chain relayers and multisigs, creating $2B+ in bridge hack losses. Systems like LayerZero's Oracle/Relayer model introduce trusted assumptions.\n- Trusted Relayers: A small committee can censor or steal funds.\n- No Universal State: Each chain is an island with no shared security.

Succinct light clients (e.g., Succinct, Herodotus) and ZK bridges (e.g., Polygon zkBridge) verify chain state with cryptographic proofs, not social consensus.\n- Trustless Verification: A smart contract verifies a proof of the source chain's state.\n- Shared Security: Enables a unified, verifiable state across all connected chains.

Integrating AI models into on-chain apps requires trusting a centralized API. You pay for an output you cannot verify was generated correctly.\n- Unverifiable Outputs: No guarantee the promised model (e.g., GPT-4, Stable Diffusion) was used.\n- Centralized Rent Extraction: API providers act as unavoidable intermediaries.

Frameworks like EZKL and Giza generate ZK proofs for ML model inference. The on-chain contract verifies the proof, not the result.\n- Proven Model Execution: Cryptographic proof that a specific model ran on specific inputs.\n- On-Chain AI Agents: Enables autonomous, verifiable agents for prediction markets and dynamic NFTs.

An estimated 28 billion dollars is wasted annually on irreproducible preclinical research. The core failure is unverifiable computational pipelines where data provenance, code versions, and execution environments are opaque.

• Result: Peer review cannot validate the core computation.
• Impact: Slows discovery and erodes public trust in published results.

Projects like Risc Zero, Espresso Systems, and Celestia's Rollups provide a foundational layer for cryptographically verifiable computation. They generate zero-knowledge proofs (ZKPs) or fraud proofs that attest to the correct execution of any code.

• Guarantee: Any peer can cryptographically verify the result without re-running the entire analysis.
• Foundation: Enables trust-minimized collaboration and data markets.

Immutable data ledgers (e.g., using IPFS/Filecoin for storage with Ethereum or Celestia for consensus) create a permanent, tamper-proof record of the entire research artifact chain.

• Tracks: Raw data hashes, code commits, parameter sets, and final results.
• Enables: Full audit trails and automated reproducibility checks.

Protocols like DeSci Labs' VitaDAO and Molecule demonstrate how tokenized intellectual property (IP) and funding pools can align incentives. Verifiable compute makes these models scalable and fraud-resistant.

• Mechanism: Researchers stake reputation tokens; successful reproduction earns rewards.
• Outcome: Creates a crypto-economic layer for quality and truth-seeking.

Current ZK proving times and costs are prohibitive for large-scale simulation (e.g., climate modeling). The stack needs orders-of-magnitude improvement in prover efficiency to be practical for heavy science.

• Current State: Proving a complex model can cost ~$100s and take ~hours.
• Required State: Needs to approach the cost and speed of raw cloud compute.

The end-state is an ASO: a smart contract that holds funding, owns IP, commissions verifiable compute jobs on decentralized networks like Akash or Gensyn, and distributes rewards based on proven results. This automates the grant-to-discovery pipeline.

• Components: DAO governance, verifiable compute, on-chain IP.
• Vision: Removes human bias and administrative friction from funding science.