The Future of Public Goods Funding: Outcome-Based Markets

Traditional funding is a black box. Did the public good actually get delivered? Grant committees can't track real-world impact, leading to misallocated capital and zero accountability.

• No verifiable proof of work completion or user adoption.
• Retroactive funding models like Optimism's RPGF are a step forward but still rely on subjective, after-the-fact voting.
• Creates a moral hazard where signaling effort is rewarded over producing results.

Smart contracts need objective truth. Oracles like Chainlink and Pyth are evolving beyond price feeds to verify any off-chain condition, creating a settlement layer for real-world agreements.

• Verifiable credentials and zero-knowledge proofs can attest to specific milestones (e.g., software commits, user attestations).
• Decentralized data streams turn subjective outcomes into objective, on-chain states.
• Enables conditional payment streams that auto-execute upon proof of delivery.

Funding should be a market, not a committee. Inspired by Prediction Markets (e.g., Polymarket) and Automated Market Makers, outcome markets allow continuous price discovery for the future success of a public good.

• Impact derivatives: Trade tokens representing "Project X will onboard 10k users."
• Liquidity pools aggregate capital from impact-seeking investors, not donors.
• Forkable templates from platforms like Hypercerts and Allo Protocol standardize the creation of outcome-based funding rounds.

Hypercerts are an ERC-1155 standard for representing claims of impact. They create a universal, tradable asset class for positive outcomes, enabling retroactive funding and impact markets.

• Composability: Enables secondary markets, fractionalization, and bundling of impact.
• Verifiability: On-chain attestations link funding to measurable results.
• Foundation: Powers platforms like Optimism's RetroPGF and Gitcoin Allo.

A minimalist, ZK-optimized protocol that runs trustless quadratic funding rounds on Ethereum L1. It proves that efficient public goods funding doesn't require a large L2 or committee.

• Minimal Trust: Uses MACI and zk-SNARKs for private voting and verifiable tallying.
• Cost-Effective: Batch processing keeps operational costs below $1k per round.
• Proven Model: Has facilitated over $2M in matched funding for grassroots projects.

Traditional grant-making suffers from high coordination costs, subjectivity, and misaligned incentives. Committees become bottlenecks, struggling to evaluate niche projects at scale.

• Slow Velocity: Months-long review cycles stifle innovation.
• Opacity: Decision-making is a black box, leading to disputes.
• Centralization: A small group holds disproportionate power over resource allocation.

Outcome-based markets align incentives by letting the crowd—not a committee—signal value. Funding follows proven impact, not promises.

• Retroactive Funding: Pay for results, not proposals (e.g., Optimism RetroPGF).
• Skin in the Game: Contributors stake on outcomes, creating a price for impact.
• Scalable Discovery: Harnesses the wisdom of the crowd to find undervalued public goods.

Pioneers outcome-based markets for data and AI. Its Compute-to-Data framework allows monetization of data without exposing the raw asset, creating a market for AI model training as a public good.

• Privacy-Preserving: Data stays private; only algorithms and results are exchanged.
• Monetizes Impact: Researchers can sell access to trained models or insights.
• Key Infrastructure: Enables decentralized science (DeSci) and AI data unions.

Platforms like Polymarket and Augur can be repurposed as high-resolution sentiment oracles for public goods. Markets can predict which research will be cited or which OSS library will get the most forks.

• Liquidity for Truth: Creates a financial stake in accurate forecasting.
• Continuous Evaluation: Real-time price signals replace periodic grant reviews.
• Schelling Point: Converges disparate opinions into a single, tradable metric of expected impact.

The entire system's integrity depends on the oracle (e.g., Chainlink, UMA) reporting the correct outcome. A corrupted oracle or a Sybil attack on its data providers can steal the entire funding pool by falsely claiming success. This is a single point of failure that scales with the total value locked.

• Attack Vector: Bribe or compromise oracle nodes.
• Consequence: 100% fund misallocation to malicious actors.
• Mitigation: Requires robust, decentralized oracle networks with high cryptoeconomic security.

When a measure becomes a target, it ceases to be a good measure. Projects will optimize for the easily measurable proxy (e.g., user count, transaction volume) rather than the intended, harder-to-quantify public good outcome (e.g., ecosystem health). This leads to value extraction, not creation.

• Example: Airdrop farming to inflate user metrics.
• Result: Capital flows to performative activity, not genuine utility.
• Challenge: Designing Sybil-resistant, multi-dimensional metrics is an unsolved problem.

Outcome markets require deep liquidity to function. Early markets will suffer from thin order books, leading to high volatility and manipulable prices for outcome shares. This creates a negative feedback loop: poor liquidity deters participants, which further reduces liquidity.

• Parallel: Similar to early Prediction Market failures (e.g., Augur v1).
• Risk: Market collapse before achieving network effects.
• Requirement: Needs liquidity bootstrapping mechanisms akin to Balancer/Curve pools or direct subsidization.

Outcome markets that tokenize real-world impact (e.g., carbon credits, R&D milestones) become de facto securities markets. This invites global regulatory scrutiny (SEC, MiCA) and creates a legal attack surface for all participants, from builders to liquidity providers.

• Exposure: Secondary liability for funders and platform.
• Precedent: Legal actions against The DAO and ongoing DeFi cases.
• Outcome: Potential for platform shutdown or geographic restrictions, defeating the 'global public good' premise.