The Future of Grantmaking Is Autonomous Funds Driven by Metrics

Retroactive funding (e.g., Optimism's RPGF) rewards past work but doesn't direct capital to future needs. It's a popularity contest with ~6-month feedback loops and subjective evaluation.

• Inefficient Allocation: Capital chases narratives, not provable impact.
• High Coordination Cost: Requires massive community voting and manual review.

Autonomous funds use smart contracts to disburse grants based on pre-defined, verifiable KPIs. Think continuous integration for funding.

• Example KPI: Grant $X for every 1,000 new active addresses a dApp onboards.
• Automated Execution: Funds release upon KPI verification, removing human bias.
• Composability: Metrics can be sourced from The Graph, Dune Analytics, or custom oracles.

Infrastructure like Superfluid for streaming finance and Allo Protocol for programmable pools creates the rails for autonomous funds. These are unstoppable, permissionless systems.

• Capital Efficiency: Funds are never idle; they stream continuously based on performance.
• Protocols as Examples: Gitcoin Grants Stack and Aragon are evolving into this model, moving beyond simple quadratic voting.

Any valuable metric will be gamed. Autonomous funds must be designed with cryptoeconomic security from day one.

• Defense Strategy: Use plurality of metrics, time-locked payouts, and sybil-resistant proofs (e.g., Worldcoin, BrightID).
• Iterative Design: Funds must be upgradeable to patch exploits, balancing immutability with adaptability.

Funding becomes a predictable, reliable public good for builders, akin to AWS credits for web2 startups. This attracts serious talent by de-risking development.

• Predictable Runway: Teams can model growth based on clear, achievable metrics.
• Ecosystem Flywheel: Successful projects attract more capital, measured by clear Total Value Secured (TVS) or fee generation.

A live prototype. Ocean Protocol automatically rewards data publishers and curators with OCEAN tokens based on consume volume and staked liquidity. It's a verifiable, on-chain KPI.

• Mechanism: veOCEAN holders allocate rewards to datasets, creating a market for data liquidity.
• Proof of Concept: Demonstrates that complex ecosystem value can be quantified and rewarded without committees.

DAO governance for grants is plagued by low participation and proposal spam, leading to capital misallocation.\n- Voter fatigue from reviewing hundreds of proposals.\n- High coordination costs for due diligence.\n- Susceptibility to lobbying over meritocratic outcomes.

Programs like Optimism's RetroPGF and Arbitrum's STIP shift funding to post-hoc rewards based on verifiable impact.\n- Pay for outputs, not promises: Fund what already demonstrated value.\n- Automate with on-chain data: Use metrics like TVL growth, user acquisition cost, or contract calls.\n- Align incentives: Builders focus on utility, not grant-writing.

Smart contracts that autonomously allocate capital based on pre-set KPIs, removing human committees. Inspired by Maker's Endgame and Compound's Grants.\n- Continuous funding streams based on real-time metrics.\n- Transparent and incorruptible rule-based execution.\n- Enables hyper-specialized funds (e.g., a fund for ZK-Rollup dev tools).

Not all value is easily measured on-chain (e.g., documentation, research). Pure automation risks missing critical public goods.\n- Sybil attacks on metric systems (see Gitcoin Grants history).\n- Short-termism bias towards easily measured KPIs.\n- Need for hybrid models: Combine automated filters with human curation.

On-chain metrics like wallet activity are trivial to fake. A protocol could inflate its own TVL or user count with a few scripted wallets, draining the fund.

• Key Risk: Sybil-resistant identity layers like Worldcoin or Gitcoin Passport are not yet universal.
• Key Risk: Automated systems lack human discernment to detect coordinated, low-value interactions designed to game KPIs.

Autonomous funds rely on oracles (e.g., Chainlink, Pyth) for off-chain metrics like GitHub commits or social sentiment. A compromised or manipulated data feed leads to misallocated capital.

• Key Risk: Centralized data providers become single points of failure and censorship.
• Key Risk: "Garbage in, gospel out" – the algorithm blindly trusts poisoned data.

Once a metric (e.g., developer commits, TVL growth) becomes a target, it ceases to be a good measure of true impact. Projects will optimize for the metric, not for genuine ecosystem value.

• Key Risk: Funds flow to projects skilled at metrics manipulation, not building.
• Key Risk: Creates perverse incentives that degrade long-term health for short-term KPI performance.

Algorithmic funds are backward-looking by design. They fund what has worked. The most groundbreaking, non-consensus ideas (e.g., early Ethereum, Uniswap) would have scored poorly on all standard metrics.

• Key Risk: Over-optimization for past patterns kills frontier innovation.
• Key Risk: The fund becomes a copycat capital engine, unable to identify paradigm shifts.

The ruleset (smart contracts, oracle choices, weightings) is set by governance. A malicious actor capturing the DAO (e.g., via token accumulation) can redirect the entire fund.

• Key Risk: Compound-style governance attacks become a direct theft vector.
• Key Risk: Slow, reactive governance cannot outpace a fast-moving exploit.

If the fund's native token is used for grants, a declining token price reduces grant size, discouraging builders, which further hurts the token—a reflexive doom loop.

• Key Risk: Mirror's Terra-Luna collapse dynamics applied to ecosystem funding.
• Key Risk: Creates volatility that makes long-term project planning impossible for grantees.