Case Study: Piloting Conditional Funding Markets for Unichain Growth

Conditional Funding Markets (CFMs) represent a new way to allocate resources for decentralized communities. Instead of relying solely on token-holder voting or committees, CFMs use markets to forecast impact, and direct funds. This approach can bring credible neutrality and efficiency to grantmaking: surfacing collective intelligence to determine which initiatives can create the most value. In 2024, the Uniswap Foundation awarded a grant to Butter to pilot CFMs for capital allocation. Our goal: to explore how new InfoFi tools like CFMs can make grants funding more effective, transparent, and scalable.

Overview

In July 2025, the Uniswap Foundation ran its first CFM in partnership with Butter. The market, CFM 1, ran from July 7–11, 2025 and focused on growing Unichain’s lending protocol TVL, with two primary goals:

1. Drive measurable growth for Unichain via lending protocol TVL.
2. Test CFMs as a governance mechanism, evaluating their ability to allocate capital credibly and neutrally.

Four lending protocols—Compound, Euler, Morpho, and Venus— participated in the week-long market. Each team submitted a public application that specified how much TVL they expected to generate if they were given $100k, as well as how much TVL they expected to generate if they were not awarded funding.

Forecasters were then able to trade in each of the project’s markets and drive the price towards a TVL number that they believed the project would actually achieve, depending on whether the project was funded or not. The project with the greatest difference between its `funded` and `not funded` would receive the $100k grant.

Results

Over five days, 29 forecasters traded more than $70k in volume, and the market resolved cleanly, awarding the $100k grant to Morpho. Over the following 30 days, Morpho deployed this capital proportionally to their most recent rewards campaign. In turn, as of market resolution, Morpho exceeded its growth projections, generating a $207M 30-day trailing average TVL: 11.8% higher than expected.

Key learnings

While the pilot was successful, it also surfaced important challenges:

• Insider participation had more influence than anticipated, raising questions about credible neutrality
• Forecasting complexity (TVL projections) limited participation and confidence among forecasters
• CFMs introduce new user behaviors that require education and simple ux to reduce barriers to broad engagement

Together, these findings show that CFMs can work, but only under carefully designed conditions. Future implementations must solve for complexity, while strengthening guardrails against insider bias.

Next steps

This first CFM provided us with an exciting proof of concept. Looking ahead, the Uniswap Foundation will:

• Collaborate with Butter to design markets that mitigate insider influence
• Scope future grants more narrowly to sharpen incentives and evaluation criteria
• Continue experimenting with CFMs to explore their role in decentralized governance

Decentralized communities need unbiased, efficient ways to allocate capital. With additional funding set aside for this work, the UF remains committed to testing the conditions under which CFMs can become a reliable governance tool, and, more broadly, to advancing research in autonomous decision-making mechanisms.