Conditional Markets Are Beginning to Blossom

Introduction

Galaxy Research has covered prediction markets and their progeny, decision and impact markets, extensively over the last 18 months. We’ve looked at everything from decision markets’ applicability to decentralized autonomous organization (DAO) governance, to their potential as a cold-start mechanism for early-stage crypto projects, to the use of prediction and impact markets as hedging instruments, to Hyperliquid's HIP-4 outcome markets.

A throughline in our coverage was the idea that prediction markets served as a sort of “version zero” for event-driven markets and hedging instruments and that experimentation with the broader concept, via decision and impact markets, was blossoming in the background. At the time much of the space was simply an idea that lived in X posts and whitepapers, with some exceptions including MetaDAO. This has begun to change with a number of projects in beta and a new generation of experiments beginning to unfold.

The following covers where the sector currently stands, who is building in it, and the intellectual conversations and experiments the movement it created is heading toward.

Why Conditional Markets?

Previously, we framed impact and decision markets as two distinct concepts. Today it is best to treat them as subsets of a broader category, which we will call “conditional markets.” The two functionally hold the same market mechanic (i.e. conditional trades are placed in markets and only settle when a specific outcome occurs). The primary difference is that decision markets also settle an explicitly binding outcome (e.g. if price in the conditional market exceeds 5% from spot, then a management team adds a new protocol feature). Given the shared mechanic, and looming expansion in event-driven market types and hedging instruments, it makes most sense to bucket them together.

Definitions aside, conditional markets have distinct characteristics that differentiate them from prediction markets as event-driven markets and hedging tools. We covered this point in greater detail here and here, but a few key differentiators include:

• Transcending the binary, “all or nothing” nature of prediction markets.

• Allowing participants to trade real assets and their prices as opposed to synthetic probability-based ones.

• Emitting future events’ impacts on asset values as an informational byproduct as opposed to the probability of whether an event will occur.

These qualities, among others covered in the linked reports, set forth different possibilities that prediction markets fundamentally cannot offer from hedging, payout profile, and market signal perspectives.

What Is Happening with Conditional Markets?

Both decision and impact markets have progressed meaningfully since our last coverage. The sections below cover the state of each, the projects building at the market and infrastructure layers, and the experiments taking shape around them.

Decision Markets

The decision market design space expanded with the September launch of Umia, an ownership token-style launchpad for Ethereum Virtual Machine (EVM)-compatible chains. Previously, decision markets were mostly confined to Solana through MetaDAO, which trailblazed the concept and was the first implementation at scale.

Up to this point, decision markets have been binary: either the decision being traded passed or failed. Proposal drafters solicit feedback from the community, craft the proposal, and launch the market. This approach allowed decisions to be traded through markets, but the social element attached to the drafting process and the fixing of all proposal variables (e.g. increase monthly burn from $50,000 to $65,000 or convert 50% of the DAO treasury to a yield-bearing stablecoin) introduce friction. Traders must base their decision on a proposal's fixed parameters, even if they agree with it directionally but not precisely. (A trader might agree monthly spend should be increased but only to $60,000 or a that the DAO should put its treasury to work but no more than 30% of funds). This pushes the most optimal outcome to be socially filtered, possibly through repeated proposals, or never get priced and traded in the first place.

The graphic below highlights how these markets look from a high level.

With the launch of Umia, we now have multi-outcome decision markets where traders can make decisions on a proposal’s parameters (e.g. should we increase spend from $50,000 to $55,000 or $60,000 or some higher amount?). In this case, traders can directionally agree with the proposal, then express their view on its individual details. As a result, traders are pricing the parameters of a proposal, and the overall scope of the proposal is kept more general. By allowing traders to price individual parameters rather than a fixed proposal, this design can reduce the social element that binary pass/fail decision markets depend on, in order to converge on the “most optimal” outcome pre-launch. Rather than relying on drafters to socially filter the “right” version of a proposal before it becomes a market, multi-outcome markets let that filtering happen through price discovery. The practical result is that decisions can be priced more accurately and more efficiently. The market surfaces the specific parameter specification the community supports, rather than forcing judgement on whichever parameter happened to make it into the proposal and price whether the market agrees with it.

Rather than relying on drafters to socially filter the “right” version of a proposal before it becomes a market, multi-outcome markets let that filtering happen through price discovery.

The graphic below highlights how these markets look from a high level. They are nearly identical in nature to binary outcome decision markets but have multiple markets that can be traded.

One challenge with multi-outcome markets is that liquidity can become fragmented, as the number of tradable markets scales linearly with the number of possible outcomes a proposal carries. Rather than requiring traders to split capital across each market, Umia’s design allows a single deposit (either in stablecoins or DAO tokens) to be converted into a corresponding position across every outcome simultaneously (i.e. a single 5 USDC deposit can be used to trade each of the increase spend $5k, $10k, $15k, and no-op markets). This works because the markets are conditional on one another and only trades in the winning market ultimately settle. So, a trader’s capital can be present everywhere at once without ever being at risk of loss in more than one place. In effect, a given deposit covers the complete outcome space, letting the trader position across every possible proposal parameter without pre-committing to which one they believe will pass. This is what keeps multi-outcome markets liquid. Because one deposit is tradable across every market, adding more outcomes doesn’t divide capital, so depth doesn’t thin out as the number of markets scales.

Assets in Market-Governed Treasuries

There is now $50.5 million in total value managed by decision markets across 22 DAOs. This figure includes all stablecoins and tokens held by the treasuries (e.g. DAO tokens held on balance sheet, liquidity provider positions, and all other tokens held by the DAOs) of DAOs exclusively using decision markets to govern themselves. The MetaDAO universe commands the majority of assets in market-governed treasuries, occupying 21 of the 22 DAOs using decision markets and 78% of total assets in market-governed treasuries. While $50 million may sound small, MetaDAO’s market-governed DAO launchpad held its first raise just 11 months ago.

Only looking at the stablecoin portion of these DAOs’ balance sheets, there is $35.8 million in value managed by decision markets. This is an important figure to track because it represents the liquidatable value of these organizations in the event investors elect to claw back their funds. As is the case with cumulative assets in market-governed treasuries, the MetaDAO universe controls the majority of the stablecoin value governed by decision markets at 82%.

The Importance of Competition

Umia's launch introduces a facet of competition the decision market sector has not previously seen. This is important for the growth and proliferation of decision markets for two reasons: it helps scale assets in market-governed treasuries, and it expands the universe of chains decision market-based organizations can launch on.

Scaling assets in market-governed treasuries is arguably the sector’s most important key performance indicator (KPI), because the value of assets governed by decision markets determines how much is economically at stake in any given proposal, and that stake size is what can pull volume into the market. A proposal sitting on top of a small treasury or token market cap only has a small dollar impact if it passes, so traders have little incentive, or absolute inability, to commit meaningful capital to price it. As assets under market scales, the same category of decision carries a bigger value impact, which allows the market to support larger positions and draws in more capital and more sophisticated traders competing to price the outcome correctly. With multiple projects now able to launch decision market DAOs rather than relying on a single incumbent, the sector should also see more organizations running proposals through this mechanism at any given time, adding to aggregate volume even before any individual market grows larger.

Competition also expands the possible universe of chains these organizations can launch on. Previously, decision markets were effectively confined to Solana, because MetaDAO was the only project that had launched tokens most suitable for decision markets, a function of the "validity of objective function" problem we have outlined in the past. With Umia's launch, projects can now choose to launch decision market-based DAOs with valid tokens on either Solana or the EVM, which together account for roughly 92% of TVL by chain type, meaningfully widening the addressable market for this design.

Impact Markets

Impact markets have made real progress since our earlier coverage of the sector, with a few projects moving through the beta testing phase heading toward mainnet and new projects entering the arena at the market and infrastructure layers. Teams are taking notably different design approaches, and building at different layers of the stack, from standalone markets to the underlying infrastructure meant to support them. Soon, we should see tradable products launched on mainnet applications.

Markets need to be short duration and high velocity, closer to the rapid-fire cadence of prediction markets, enough to compensate for trader attention spans.

It is becoming clear that competition in this segment is set to expand over the next year. Understanding what is needed for individual projects to succeed as competition picks up clearly indicates what makes these markets useful in the first place, and it starts with traders. The information these markets produce is genuinely valuable, but it's a byproduct of a healthy market, not the product itself. What's being sold to traders is exposure, and exchanges have to be built around giving users a real reason to take, hold, and hedge risk, because that's what generates the volume on which the information depends. This means the approach to who wins needs to be trader-focused: liquidity has to be high enough for these markets to be cited with the same authority mainstream media now gives Polymarket and Kalshi odds, exposure needs to be potent, and leverage needs to be competitive with perps. Conditional markets also don't exist in isolation. Their relationship to underlying perpetual and prediction markets is central to their overall health, so hedging and arbitrage between them need to be practical and economical. Finally, markets need to be short duration and high velocity, closer to the rapid-fire cadence of prediction markets, enough to compensate for trader attention spans.

Market Layer

The following details several projects building impact markets.

Lightcone

Lightcone was the first team to publicly lay out the impact market product as it is known today, announcing the project in October 2025, with other teams following shortly after. Lightcone also coined the term “impact market,” effectively naming the category. The project is in beta testing and targeting a public launch before the end of the year.

Proof

Proof is taking a vertically integrated approach, building a full-stack exchange on its own infrastructure. The team has experience building and operating derivatives exchanges, with a strong trader focus and design decisions centered on supporting liquidity and attractive exposure to traders. The chart below highlights Proof’s vertically integrated design across its conditional market, perps, and prediction markets (it calls them “binaries”).

Proof was founded this year and has launched a testnet of its appchain alongside a v0 exchange, running a trading contest that onboarded roughly 500 traders to test the market design. The team is targeting a gated mainnet launch in the next one to two months (roughly November 2026). Capital efficiency and the relationship between primitives are central to Proof's design. Its margin framework supports leverage and netting across both perpetual and conditional markets. This serves three functions:

1. It gives traders more efficient exposure on individual positions;

2. It lets makers efficiently hedge conditional exposure against the underlying perp;

3. It allows for capital-efficient direct arbitrage between conditionals, perps, and prediction markets (this is important for pricing/market health and is a potential accelerant because the markets can connect flow from two previously disconnected market types).

Proof is also building its own perpetual exchange rather than layering conditionals on top of third-party infrastructure. This is necessary to allow margin netting between conditionals and the underlying. Because conditionals sit on top of this underlying exchange, its execution quality directly affects how much liquidity and pricing quality can be supported at the conditionals layer.

Proof’s broader view is that a well-executed exchange can capture arbitrage flow from prediction markets and perpetuals against the same underlying liquidity. This matters because shared liquidity can improve market depth, tighten pricing, and make the resulting exposure more attractive to traders. Proof also believes this integrated design can address some of the liquidity constraints that have historically limited event markets, potentially enabling deeper prediction-market exposure on non-sports/election market categories.

Proof’s rationale for vertically integrating these primitives is that markets do not exist in isolation. To make conditionals a compelling product, the surrounding components, underlying perpetual liquidity, margining, execution, settlement, and arbitrage pathways, need to work together as a unified system.

If Market

If Market uses a different design than Proof’s. Rather than creating an onchain solution, it offers exposure as a synthetic, cash-settled position. Users deposit stablecoins and positions settle in cash to the underlying asset’s spot price at market resolution. Nothing is tokenized or custodied through the entire flow. The team’s reasoning for this approach is twofold and runs along the lines of product demand and user experience (UX). There’s possibly less natural interest in holding tokenized versions of markets’ underlying assets, and a pure cash-settled design can create a simpler UX for a product meant to price event impact rather than custody assets.

Each market on the platform is organized around a shared event (e.g. “will the Fed cut in the next FOMC?”). Individual markets underneath represent different framings of that event (say, the price of Brent crude oil or gold or Microsoft shares if the event happens and if it doesn’t). Each market splits between YES and NO universes, and each universe carries its own full set of per-asset orderbooks, alongside one shared prediction market book used across each of that market’s assets. The prediction market serves two purposes. One, obviously, is pricing the odds of the event itself. The other is absorbing crystalized profit and loss (P&L) when traders close an impact market position before resolution, converting it to prediction market tokens they can sell immediately or hold till resolution. At resolution, positions in the losing universe return their committed margin in full.

Leverage on the platform went live in September. Positions use isolated margin per position, with position caps tiered by notional. Leverage is available up to 10x on select markets. Unlike perpetual-style designs, If Market has no funding rate. Instead, arbitrage keeps conditional prices anchored to spot, and that risk is managed through the position caps rather than by steering price toward the oracle. Liquidation marks use time-averaged book prices rather than single trades. Resolve prices come from outlier-filtered external sources and, where a market’s rules specify one, a short time-weighted window, which makes settlement harder to manipulate around anticipated resolution moments.

The product is still in beta, but the platform’s first trading competition ended Sept. 30 with more than 2,000 traders and over 6 million trades.

Infrastructure Layer

Aside from exchange and market development, impact markets have also begun seeing some movement at the infrastructure layer.

Branchpoint

Branchpoint is building Branchmap, a semantic namespace layer for “multiverse finance” (a term originally coined by Dave White, a research partner at VC firm Paradigm) in which impact markets are one supported application among several. Rather than building an exchange first, Branchmap is meant to function as shared infrastructure that gives events, predicates, and outcomes stable, machine-readable identities onchain. This allows prediction markets, DEXes, lending protocols, and any other onchain application to reference the same underlying event data and compose around it. For example, a single registered event, such as whether the Fed hikes rates at a given FOMC meeting, can be referenced simultaneously by a prediction market pricing the probability of a hike, a DEX listing BTC conditioned on that outcome, and a lending protocol accepting that conditioned BTC as collateral, all without each protocol needing to separately define or reconcile what the event is or how it resolves.

'Will the Fed hike at the next FOMC meeting?' can be referenced simultaneously by a prediction market pricing its probability, a DEX listing BTC conditioned on that outcome, and a lending protocol accepting that conditioned BTC as collateral, all without each protocol needing to separately define the event or its resolution.

The graphic below highlights how this namespace is structured. It offers an event context (e.g. a Fed rate decision) that contains a predicate (e.g. did the target rate increase?), which in turn produces named outcome entries for each possible result. The combination of outcomes selected across one or more predicates defines a “verse,” or the financial world in which conditional assets live, even though the verse itself is not a stored namespace entry.

The graphic below highlights how this namespace is structured. It offers an event context (e.g. a Fed rate decision) that contains a predicate (e.g. did the target rate increase?), which in turn produces named outcome entries for each possible result. The combination of outcomes selected across one or more predicates defines a “verse,” or the financial world in which conditional assets live, even though the verse itself is not a stored namespace entry.

The core idea is that prediction markets already function as a kind of numeraire for a given outcome: a binary contract that pays out $1 if an event resolves a certain way is economically equivalent to holding $1 in the universe where that outcome occurred. Branchmap builds upon this idea by letting any asset be split into versions conditioned on each possible outcome of an event, then recombine it once the event resolves. The graphic below shows this mechanic: a root asset splits into verse-conditioned copies for each outcome of a predicate, and once that predicate resolves, the surviving branch recombines back into the plain unconditioned asset.

This splitting mechanism creates a direct link between the probability a prediction market assigns to an outcome and the price of an asset conditional on that outcome, the same decomposition that impact markets are built to price. Branchpoint frames this linkage explicitly as one of its applications: the price of an asset’s claim in a given verse (its “branch price”) factors into a state price, supplied by the prediction market, and an in-verse value, the asset’s market-implied value inside that verse. The state price is the price of the prediction market’s token for that outcome, which approximates the market-implied probability when discounting is negligible. In practice, the conditional-asset market reveals the branch price, and dividing it by the state price recovers the in-verse value. The log of the ratio between an asset’s in-verse value across the “yes” and “no” branches of an event is, in Branchpoint’s framework, a measurable “impact” statistic (illustrated below). Because the namespace gives both sets of prices the same event, outcome, and settlement identities, impact markets can in principle be built on top of it rather than requiring bespoke infrastructure for each one.

Suppose the predicate is whether temperatures in Florida fall below 28°F for more than three straight hours in December, and the prediction market prices a freeze at 10%. If a claim on frozen concentrated orange juice (FCOJ) that pays only in the “yes” verse trades at $0.40 per pound, and the equivalent “no” verse claim trades at $1.80, dividing each branch price by its state price gives the in-verse values: $4.00 in the “yes” verse and $2.00 in the “no” verse. The market therefore expects OJ to be worth twice as much if a freeze occurs, and the impact is the natural log of that ratio: ln($4 / $2) ≈ 0.69. The two branch prices also sum to OJ’s unconditioned price of $2.20.

Branchpoint’s pilot deployment is expected to be a single-chain EVM namespace, initially controlled by the team, with a longer-term roadmap toward a decentralized, cross-chain namespace that other venues and protocols can claim and govern independently. As with other infrastructure-layer plays, Branchpoint's usefulness to the impact market segment specifically will depend on venues and market operators adopting Branchmap as the shared substrate for their events and conditional assets, rather than each building isolated event definitions and resolution logic of its own.

In the Background

The world is becoming more complex, and the downstream impacts of individual events, whether they be political, regulatory, technological, or environmental, are becoming more consequential for asset prices and company balance sheets. Some of the risks investors and businesses may want to more actively hedge today were more benign or absent in the past, or instruments to hedge certain risks or achieve certain payout profiles did not exist. The natural conclusion is that market participants and companies should have the ability to hedge against any event, not just the narrow set of risks traditional financial instruments were originally built around, and to achieve payout profiles tailored to their needs. Conditional markets are one expression of this idea, but they’re likely just one step along a broader path toward more flexible, event-based financial tooling.

The world is becoming more complex, and the downstream impacts of individual events, be they political, regulatory, technological, or environmental, are becoming more consequential for asset prices and balance sheets.

The primary hedging instrument onchain today, perpetual futures, also has some limitations that can push demand toward alternatives. Perps hold a linear payout profile (i.e. exposure scales directly with underlying price, independent of the level), which doesn’t fit every use case. Some risks are better expressed with a non-linear or event-contingent payout rather than constant 1:1 exposure. Perps also come with liquidation risk (see “10/10”), which not all traders are willing to take.

There are a few projects working on and greater thought going into the above ideas.

Options Protocols

Since Jan. 1, Derive's options business has grown meaningfully. Monthly options notional volume dipped from $1.47b in January to a low of $629m in February, then climbed steadily through the summer before accelerating sharply to $3.82b in September, roughly 2.6x January's level, with the trailing 30 days (Sept. 13–Oct. 12) running about 195% above the January pace. Options open interest followed a more uneven path, starting the year at $590m, sliding to a trough of $359m in late June, then rebuilding to a peak of $2.49b on Sept. 21 before settling at $1.66b by Oct. 12. That is up about 181% since Jan. 1, more than 4.6x off the June low, and roughly 34% below the September peak.

Onchain options have historically been kneecapped relative to their offchain counterparts due to funding requirements that made them structurally more expensive, particularly for sellers, who had to post 100% of margin upfront onchain rather than the smaller, dynamically margin-called positions available offchain. Nonetheless, the recent trajectory is a meaningful signal that onchain traders appear to be actively seeking alternative hedging vehicles beyond perps, which bodes well for adoption of the more exotic conditional and event-driven instruments covered elsewhere in this report.

Castle Tech

Castle Technologies is a New York-based startup founded this year. Its value proposition is to turn event risk that traditional insurance and derivatives markets don't price (e.g. tariffs, geopolitical disruption, or a specific piece of legislation) into structured, tradable hedges.

Castle's recent deal with a goat herding company in California is a useful illustration of what a hedge looks like when the risk is too narrow and idiosyncratic for any insurer to price, and there's no existing market to plug into.

Conditional markets are literally goated.

A lapsed wage exemption for California goat herders threatened to roughly triple or quadruple labor costs, to as much as $240,000 a year, at Western Grazers, an operator running roughly 4,000 goats for wildfire-fuel clearing. No insurance company writes a policy against a specific legislative outcome, and there was no options market, no perp, and no prediction market on this question before Castle got involved; it isn't the kind of event that generates natural two-sided retail interest on its own.

Castle's job was to build the pipeline for that exposure from the ground up by translating an ongoing legislative situation into a precise, self-certifiable settlement condition, then finding a counterparty willing to price and hold a risk no traditional underwriter would touch. Western Grazers entered into a swap agreement with Castle, structured so the company would receive a payout of roughly $500,000 if California's legislature failed to address the wage issue before an Oct. 1 deadline, and no payout if lawmakers fixed it. Castle laid off that risk through a back-to-back swap with quant trading firm Susquehanna, which priced and warehoused the exposure, with the trade ultimately routed through a Kalshi prediction market for liquidity and settlement.

A legislative change affecting California goat herders isn't the kind of event that generates natural two-sided retail interest on its own.

The result was a custom-underwritten policy executed through prediction-market exchange rails with one print, one counterparty, and one settlement condition drafted specifically for this trade rather than pulled off a shelf. Castle manufactured, end to end, a hedge that had no prior instrument, no prior market, and no insurer willing to offer it, and prediction market infrastructure was able to facilitate it all. This transaction signals demand for “hedge everything products.” Businesses increasingly face risks (regulatory, legislative, hyper-local) that sit outside what conventional insurance and derivatives markets are built to cover and are willing to pay a real premium for someone to structure a bespoke answer on demand.

Statebook

Statebook is more theoretical than a live product, but it offers a useful reframing of how event-based hedging instruments could be organized. Introduced in a working paper titled “Markets for All States of the World,” the idea is to design financial markets around a payoff profile (i.e. the exposure a given trader wants) first, then work backward to figure out which existing contracts, or combinations of contracts, can deliver the payoff, as opposed to starting from whatever instruments happen to already be listed and forcing traders to make do with them.

The math underpinning this idea echoes the state-price theory walked through in the Branchmap section earlier, but the two arrive at their state definitions from different places. Branchmap's state prices come from Arrow-Debreu-style partitions where a predicate (e.g. "did the Fed hike?") resolves via an oracle, and the resulting outcome directly defines a verse. No option chain is required for a verse to exist. Statebook's state prices come from a narrower but more specific model, Breeden-Litzenberger. Differentiating a European call option's price with respect to its strike recovers the market's implied price of the underlying finishing above (or within) any given level or range, turning an option curve into a ladder of boundary and interval claims. Branchmap uses its state definitions to give conditional assets a shared semantic identity, so onchain protocols can reference and compose around the same event. A statebook, by contrast, operationalizes its state definitions as an execution layer by treating ordinary options, boundary or threshold claims (e.g. "pays $1 if the index finishes above 100"), and interval claims (e.g. "pays $1 if the index finishes between 100 and 110") as different views into the same option-implied state-price data, so that a claim with no order book of its own, like a narrow interval bet, can still be filled by assembling it out of two more liquid boundary claims or a matched pair of options, with the statebook responsible for finding the cheapest available route and disclosing it as a firm, executable price.

This is, in effect, the same ambition driving conditional markets more broadly by building infrastructure that can hedge any precisely definable event or price outcome, not just the handful of payoffs that happen to have deep, purpose-built markets today. Statebook arrives at that goal through execution and routing rather than through the onchain semantic-namespace approach Branchmap takes, and it’s grounded overwhelmingly in TradFi and U.S. regulatory market structure rather than DeFi. Onchain venues such as Hyperliquid and Polymarket appear only as comparators rather than as the paper's primary subject. Still, the throughline is the same. Conditional markets, impact markets, and statebooks are all variations on the idea that markets should be organized around the states of the world traders want exposure to, rather than around whatever contracts happen to already exist. Notably, the statebook paper also points out that perpetual futures, the dominant onchain hedging instrument today, don't fit into this framework at all, because they have no fixed maturity and therefore no terminal payoff to define a state around, implying that perps and statebook-style claims are likely to serve different, complementary hedging needs rather than compete for the same one.

Conclusion

The design space for conditional markets is beginning to expand, as projects approach mainnet on the impact market front and multiple teams build on the concept of decision markets. What started as prediction markets serving as a “version zero” for event-driven finance has, over the last 18 months, created a Cambrian explosion of experimentation. Decision markets add binding governance outcomes to the mechanic. Impact markets isolate the effect of an event on an asset's price rather than the probability of the event itself. Now infrastructure and downstream concepts like Branchmap and statebook are pushing the underlying idea, that markets should be organized around the states of the world traders actually want exposure to, into new territory entirely. With Umia introducing competition to decision markets for the first time and multiple teams now building impact markets at both the market and infrastructure layers, competition across this stack should meaningfully pick up over the next year.

What started as prediction markets serving as a “version zero” for event-driven finance has, over the last 18 months, created a Cambrian explosion of experimentation.

That said, this is all still early. The education curve for traders and builders is steep, and every impact market project covered here is either in beta or, in the case of statebook, still a working paper rather than a live product. Liquidity for these new instrument types will need to be built up largely from scratch, demand for some of them may prove more niche than proponents expect, and the sector as a whole is still converging on what the best underlying designs actually look like, whether Proof's vertically integrated stack or If Market's cash-settled, non-tokenized approach. Decision markets, on the other hand, are further along, with DAOs actively using MetaDAO and Umia designs, and proliferation of decision-market-governed organizations is underway. The next year should offer a much clearer signal on how the open questions facing impact markets get resolved, and on how much further decision market adoption can scale from here.