Ethereum’s supply‑side roadmap, including The Merge and L2s, has advanced rapidly, but demand‑side growth has struggled to keep pace. Liquidity, builders, and users continue to leak to alt L1s which offer an integrated user experience that is often faster and cheaper. Despite the availability of fast, cheap L2 settlement, Solana has replaced Ethereum as the #1 network for DEX trades.
The Ethereum Foundation (EF) now aspires to play a more central coordination role in driving ecosystem growth. Yet two constraints remain:
Credible neutrality: Should the EF begin favouring specific applications or actors, it risks undermining the value proposition of Ethereum: a World Computer that cannot be biased against any of its users.
Resource constraints: The EF’s resources, mandate, and culture limit its ability to directly orchestrate or execute growth initiatives, in contrast to the Optimism Foundation’s approach to growing the Superchain ecosystem.
From 2022 to 2023, $2.7M ( 2%) of EF funding reached L2 security and cross-chain UX, meanwhile L2s spent only $3.5M and over ~$30B rested idle in DAO treasuries. The amount stolen from bridges reached $2.8B in 2024 (source) an implicit tax on Ethereum users.
Funding cross-chain UX and security benefits everyone, not just the funder; but without investment everyone suffers*.*
The result is a coordination vacuum: no entity is both empowered and sufficiently resourced to focus entirely on growth, UX, and adoption initiatives across the entire ecosystem (L1 + L2).
The Ethereum ecosystem has a track record of pioneering funding mechanisms, quadratic voting, retroactive public‑goods funding, deep funding. Conditional Funding Markets (CFMs) extend this lineage. They enable market participants to forecast the measurable impact of a funding decision and automatically allocate the grant based on those predictions.
CFMs are more accurate than alternatives. Our most recent CFM, deployed by Optimism Foundation, produced $22.5M in Superchain TVL; a stark contrast to the $2.5M produced by OP’s Grants Council’s selections.
Futarchy predicts the effect of a decision on a variable and selects the preferred outcome
**Conditional Funding Markets (CFMs)**, an implementation of Futarchy, are a type of prediction market. They leverage speculation to estimate the probability a funding decision will produce a desired effect before the decision is made. The resulting probability distribution of all possible allocations is used as an input to the funding allocation decision.
CFM deployers set a target metric, such as protocol revenue. For each proposal considered by the CFM, we produce a set of corresponding Conditional Tokens whose value corresponds to the target metric in a future where they do or do not receive funding, e.g., $0.25 = 25%.
Markets speculate on the value of these tokens, with each individual trader attempting to maximise their own payoff using the information available to them. As traders buy low and sell high, token prices come to reflect all available information about the most likely result of a funding decision.
Conditional Funding Markets have four major components:
| Component | Description |
|---|---|
| Prediction Markets | A CFM round is defined, which includes (i) budget and (ii) metric. |
Projects submit proposals to CFM that specify (1) the target metrics they expect to achieve, e.g., protocol revenue, and (2) a funding amount.
CFM creates a prediction market, an AMM pool with custom parameters, that invites forecasters to predict how much a project will improve on its proposed target metrics if it receives the requested funding amount. Forecasters trade Long or Short tokens to express their beliefs. Upon market creation, two tokens $(\textsf{Long}, \textsf{Short})$ are minted, which together can be redeemed for $1 of the liquidity supplied to the market. Hence, $\textsf{Long} + \textsf{Short} = \$1$, and $50 \ \textsf{Long} + 50 \ \textsf{Short} = \$100$.
Once the project produces actual metrics, we resolve the markets. If actual metrics are higher than the target metrics, Long tokens can be redeemed for a greater percentage of the underlying liquidity pool. If actual metrics are lower, Short tokens can be redeemed for a greater percentage.
E.g., for a front-end app targeting $1M in order flow generated over 12 months, we assume a range of $0 to $1M. If the project achieves $300K at resolution, the Short token will redeem for $\$0.70$ and the Long token will redeem for $\$0.30$.
This mechanism ensures that the Long/Short quote at any time reflects the market's collective assessment of how much the project can increase its metrics. | | Conditional Tokens | For each project, two prediction markets are created, each answering the same question but conditional on whether the project receives funding or not: • If the project receives funding, by how much will its metrics increase? • If the project doesn’t receive funding, by how much will its metrics increase?
Two pairs of tokens are created: $(\textsf{Long}{\text{yes}}, \textsf{Short}{\text{yes}})$and $(\textsf{Long}{\text{no}}, \textsf{Short}{\text{no}})$.
In the world where the project receives no funding, forecasters can purchase $\textsf{Long}_{\text{no}}$ tokens to increase their price if they believe the project is more likely to achieve its metrics than the token's current price suggests, e.g., $0.10 → $0.15
In the world where the project does receive funding, forecasters can buy $\textsf{Long}_{\text{yes}}$ tokens if they believe the project will perform better than markets are currently pricing the token.
Comparing yes/no token prices provides a market-estimated A/B test for funding.
Comparing the Long/Short quotes forecasted by both markets allows us to assess the ROI for each project.
Taking the example of order flow, $\textsf{Long}{\text{yes}}/\textsf{Short}{\text{yes}}$ would represent the order flow the project would generate over 12 months in the event it receives funding, and $\textsf{Long}{\text{no}}/\textsf{Short}{\text{no}}$ would represent the order flow the project would generate over 12 months in the event it doesn’t receive funding. | | Metrics Oracle | A prediction market is resolved when the metric is measured. Measurement is taken at a predetermined time after project submission, e.g. 12 months.
At this date, a request is sent to the Metrics Oracle to supply the value of the metric on-chain.
Taking the example of protocol revenue, we can measure protocol revenue attributable to a front-end app project over 12 months between the current date and a resolution time 12 months later.
Thus, traders are rewarded proportional to the accuracy of their predictions. | | Funding Mechanism | Whenever a decision rule, e.g., $\textsf{Long}{\text{yes}}/\textsf{Short}{\text{yes}} > \textsf{Long}{\text{no}}/\textsf{Short}{\text{no}}$ with some margin of error, the decision is taken to select one or more projects for funding.
At this point, the “no” market is canceled and all “no” tokens are made redeemable for their original price. Inversely, if the condition is not met, the “yes” market is canceled.
To even out market events and prevent manipulation, these values will be observed across a period of several days and averaged using a TWAP. |
CFMs are credibly neutral because they rely on markets, where accuracy, not political influence or privileged access, determines outcomes.