Part 1 – Introducing the Problem
The Silent Revolution of Decentralized Prediction Markets: Navigating the Future of Information and Financial Forecasting
Part 1 – Uncovering the Informational Bottleneck in Crypto
In a space obsessed with decentralization, market efficiency, and financial innovation, one critical mechanism remains largely unexplored and underutilized: decentralized prediction markets. Despite their conceptual alignment with crypto's core principles—permissionless access, censorship resistance, and incentive-driven truth discovery—these markets remain overshadowed by DeFi, NFTs, and even meme coins. The result? A glaring bottleneck in how reliable information is surfaced and priced in crypto ecosystems.
Historically, prediction markets have been theorized as powerful tools for aggregating collective intelligence. In the early internet era, projects like the Iowa Electronic Markets proved that even small pools of informed participants could outperform pollsters and analysts. But in crypto, despite open networks and programmable money, modern equivalents struggle to gain traction.
The current problem centers around liquidity fragmentation, oracle reliance, and regulatory ambiguity. Most decentralized prediction platforms suffer from shallow liquidity that fails to sustain meaningful trading volumes. Without liquidity, outcomes can be manipulated or left unresolved due to unbacked or unchallenged positions. Add to this the dependency on centralized oracles or subjective arbitrators—undermining foundational guarantees like censorship-resistance—and the markets become brittle and prone to trust failures.
Furthermore, even with technical foundations in place, these markets face cultural and economic inertia. A protocol can incentivize liquidity providers for an AMM, but steering economic actors toward event-based risk-taking with asymmetric information? That's a harder sell. This challenge mirrors early-stage struggles seen in decentralized exchange models like SushiSwap, which only gained traction after innovating around incentives and governance models. Our previous analysis on SushiSwap's DeFi criticisms highlights similar structural misalignments between purpose and participation.
Instead of acting as universal mechanisms for resolving subjective claims—ranging from DAO governance outcomes to Layer 2 protocol launches—today's decentralized prediction markets are largely siloed, low-volume experiments. The infrastructure is capable, but the coordination and incentive layers are brittle.
Through this series, we'll explore the roots of this systemic inefficiency, dissect existing protocols attempting to address it, and assess whether prediction markets can evolve from academic vision to core crypto meta-layer. But to fix the product-market fit of truth prediction in crypto, we'll first need to dissect where the architectures fail—and why these failures persist, despite decentralized infrastructure being, in theory, ideal for the task.
Part 2 – Exploring Potential Solutions
Decentralized Prediction Markets: Technological And Cryptographic Solutions Exploring the Frontier
At the core of decentralized prediction markets lies the challenge of ensuring truthful data aggregation while avoiding manipulation and collusion. A range of emerging technologies is being explored to address this, each with unique advantages and critical limitations.
1. On-Chain Oracle Systems and Their Trust Assumptions
While oracles like Chainlink have become a default solution for feeding external data into smart contracts, the reliance on off-chain data providers introduces potential centralization bottlenecks. Decentralized Oracle Networks (DONs) attempt to mitigate this via multisignature consensus across data providers, but sybil resistance and economic attack vectors remain. The core issue is epistemic—markets trust the data source over the wisdom of the crowd, inadvertently reverting to authority-based validation.
2. Truth-By-Consensus: Higher-Order Schelling Points
Projects such as Augur and Omen implement incentive-based consensus systems relying on participants voting on the "truth" with financial stake. Vitalik Buterin has articulated this as using Schelling points as coordination devices. But these systems are susceptible to lazy consensus (participants following the majority) or griefing attacks, where large stakeholders have disproportionate influence. Moreover, in low-liquidity markets, participants can manipulate outcome resolution through misinformation campaigns.
3. Zero-Knowledge and Privacy-Preserving Mechanisms
ZK-SNARKs and other privacy-preserving proofs introduce a compelling way to validate participation without revealing user identity or strategy. This helps protect voters in contentious or high-stakes markets. However, integrating ZK tech into already complex economic systems introduces computational overhead and UX friction, which can dissuade casual participants—hindering necessary market depth.
4. Token-Curated Registries and Reputation Systems
Some have advocated for combining prediction markets with token-curated reputation systems, where oracle nodes and participants are systematically rewarded or penalized based on past accuracy. While this aligns long-term incentives, governance capture and initial bootstrapping present significant challenges. Newer projects attempting this have yet to scale effectively, often facing the same issues seen in DAOs—a topic explored in The Untold Story of Blockchain-Based Decentralized Autonomous Organizations and Their Role in Shaping Future Governance Models.
5. Cross-Market Arbitrage and Meta-Markets
Cross-market arbitrage, where players capitalize on inconsistencies between different markets on the same topic, offers a decentralized incentive to surface truth. However, this mechanism assumes efficient market actors and liquidity, which are not guaranteed. Meta-markets that aggregate confidence scores from multiple sub-markets try to address this but are currently toyed with only in experimental form.
Each of these paths offers technical promise but brings design trade-offs, particularly in balancing anonymity, participation incentives, and resilience to manipulation. In Part 3, we’ll dive into how these concepts are (or aren’t) working when deployed in the wild and what lessons early implementations can offer the broader ecosystem.
Part 3 – Real-World Implementations
Real-World Implementations of Decentralized Prediction Markets: Case Studies and Technical Friction
While the theoretical benefits of decentralized prediction markets—censorship resistance, trustless outcome resolution, and distributed liquidity—are compelling, translating these into functioning platforms has been anything but seamless.
Augur was one of the earliest attempts at a truly decentralized prediction market. Built on Ethereum, its v1 faced significant friction. Gas fees, especially during periods of Ethereum congestion, made market creation and participation prohibitively expensive. Users often found themselves paying more to create or resolve markets than they could profit. Augur v2 attempted to mitigate this via a switch to DAI and an overhaul in the user interface. However, outcomes still relied on a dispute-heavy oracle system, introducing delays and UX bottlenecks. Additionally, Augur never resolved the cold-start liquidity issue, resulting in numerous orphaned or illiquid markets.
Polymarket, built on Polygon, attempted a different route—partial centralization to meet regulatory thresholds. With fast transaction speeds and low fees, user experience dramatically improved, but at the cost of full decentralization. Notably, Polymarket faced regulatory intervention that led to the shutdown of some markets. Their use of on-chain conditional tokens for information markets is technically elegant, but it also locks users into relying on specific data feeds—raising questions about oracle manipulation risks in thin markets.
Gnosis Prediction Markets were integrated within a broader DAO framework, but failed to capture user mindshare. Wallet UX issues and high technical onboarding requirements limited accessibility. Moreover, the GNO token’s broader governance roles created unclear distinctions between prediction users and platform stakeholders, muddying incentives.
Smaller entrants like Omen attempted modularity by utilizing Reality.eth oracles and enabling permissionless market creation. Yet, the fragmentation of liquidity across too many markets without aggregation harmed discoverability and price accuracy. A lack of incentive mechanisms for resolvers also led to inaccurate final results in low-value markets.
Interfacing with DeFi infrastructure remains another bottleneck. Integration bridges with platforms like SushiSwap— which has its own liquidity routing and governance intricacies (explored in https://bestdapps.com/blogs/news/sushiswap-governance-empowering-community-voices-in-defi)—are underutilized due to technical incompatibility or low cross-pollination incentives.
Cross-chain implementations remain largely theoretical. Projects advertising interoperability haven’t solved the oracle coordination problem at scale. Even solutions leveraging optimistic rollups struggle with latency in outcome finalization.
Despite these challenges, these real-world experiments offer critical insights about on-chain game theory, incentive alignment, and the delicate balance between decentralization and usability. These lessons will shape how this primitive evolves.
Part 4 – Future Evolution & Long-Term Implications
Future Evolution of Decentralized Prediction Markets: Scalability, Integration, and Emerging Challenges
The evolution of decentralized prediction markets hinges on breakthroughs in scalability, interoperability, and composability with other DeFi and Web3 primitives. Most current implementations suffer from limitations in throughput and high friction in liquidity provisioning. Future systems will likely embrace layer-2 rollups and zero-knowledge proofs to offload computational processing while preserving validity, unlocking greater market resolution speeds and cost-efficient participation.
We’re already seeing research into commitment schemes that enable off-chain oracles to generate privacy-preserving event outcomes. When combined with on-chain verification through zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs), these systems could support sensitive or controversial event categories, breaking from the current constraints of publicly verifiable events only. The long tail of information markets—including regulatory enforcement or whistleblower disclosures—may finally become economically viable to host without exposing users to direct retaliation.
Cross-chain compatibility is another inflection point. With decentralized markets dependent on diverse asset types, the ability to interoperate with tokens and data across L1 and L2 ecosystems is becoming critical. Protocols like BitTorrent Chain have laid groundwork for cross-chain infrastructure that may soon support the composability of markets across chains without fragmented liquidity. However, the user experience around cross-chain interactions remains technically fragile and increases attack surface complexity.
Decentralized autonomous liquidity aggregation could further transform incentive dynamics. Rather than siloed liquidity pools, markets may soon source liquidity from aggregator networks composed of LPs participating across multiple event types, optimized by bonding curves or AI-driven risk weighting. This evolution, while capital-efficient, introduces systemic risk vectors—especially if market resolution oracles are manipulated or collude.
Expect friction around integrating prediction markets into compliant, regulated frameworks. As prediction markets push into domains like insurance, corporate forecasting, or even climate derivatives, friction between pseudonymous infrastructure and legal accountability will intensify. Projects leaning into privacy tech to mitigate surveillance concerns may clash with emerging compliance overlays, prompting diverging usability paths between permissionless and regulated forks of the same base protocols.
Additionally, composability with NFT-gated access models, DAO-managed liquidity vaults, or soulbound token identities may redefine how markets segment participants and align incentives. We’re already seeing experimentation with NFT-bound access rights or DAO-controlled market curation, echoing patterns in ecosystems like SushiSwap that extend beyond token trades to include community-powered infrastructure—as detailed in the SushiSwap governance models.
As advancements accelerate, emergent behaviors in decentralized prediction markets will increasingly blur boundaries between collective intelligence, speculation, and quant-driven governance. The next phase of development will force infrastructure to adapt governance processes—sovereign, distributed, and adversarial-resistant.
Part 5 – Governance & Decentralization Challenges
Navigating Governance and Decentralization Risks in Prediction Markets
The decentralization of prediction markets promises censorship resistance, community governance, and trustless outcomes—but the path to meaningful decentralization isn’t clear-cut. As with many on-chain ecosystems, managing decentralized governance for prediction markets involves a high-stakes trade-off between resilience, coordination, and attack resistance.
One prevalent governance model involves Decentralized Autonomous Organizations (DAOs), where token holders vote on key protocol parameters. While this introduces a participative structure, it also opens the door to plutocratic control. Token distribution imbalances can lead to cartelization, where whales dominate voting outcomes, turning "decentralized governance" into oligarchic rule. We’ve already seen this dynamic play out across several DeFi protocols, with SushiSwap’s experience being a case study in community governance tug-of-war (https://bestdapps.com/blogs/news/sushiswap-governance-empowering-community-voices-in-defi).
Governance attacks represent another existential threat. If not properly designed, a DAO can be captured through a Sybil attack, rushed quorum manipulation, or governance bribery schemes, particularly with liquidity-voting mechanisms or vote delegation enabled. These vectors can irreversibly compromise market integrity, enabling malicious parties to steer oracles, reward mechanisms, or dispute processes to serve private profit motives. The cost of recovery from such failure is often prohibitively high.
Centralized prediction platforms avoid these pitfalls at the expense of economic censorship resistance. They are liable to regulatory capture, can selectively gate market creation, and operate with opaque decision-making structures. Moreover, their trusted oracles can serve as both points of corruption and attack.
Hybrid models attempt to strike balances—using centralized interfaces for UX, but decentralized backends for settlement mechanics. However, this approach risks reintroducing trust bottlenecks while providing only a superficial layer of decentralization.
Localized jurisdictional pressures further complicate the decentralization spectrum. Even fully on-chain platforms must mitigate risks around contributor doxxing and DAO legal exposure if the governance remains reliant on a core team. This tension becomes particularly pronounced when governance needs to respond dynamically to regulatory demands while appearing credibly neutral to users.
In the context of prediction markets specifically, decentralized dispute resolution layers—such as staking-based arbitration—require aligned incentive design and long-term mechanism resilience. Otherwise, they risk becoming prime targets for fork wars or dishonest majority outcomes.
As we explore the infrastructural bottlenecks and engineering friction that outline the upper bounds of decentralized prediction markets, the next focus will be on scalability constraints and protocol-layer trade-offs impeding adoption at mass scale.
Part 6 – Scalability & Engineering Trade-Offs
Scaling the Unscalable: Architectural and Engineering Trade-Offs in Decentralized Prediction Markets
Building decentralized prediction markets that are secure, fast, and scalable is a multidimensional engineering challenge that forces hard trade-offs among blockchain’s most fundamental attributes: decentralization, security, and throughput. Termed the blockchain trilemma, this tension is particularly pronounced for prediction markets where real-time data ingestion and fluid position settlements are essential.
Ethereum’s dominance in dApp development belies severe scaling bottlenecks. On L1, gas-intensive smart contracts required to manage conditional payouts, market resolution, and oracle data feed integrations result in prohibitive fees and latency. This undermines usability, especially when markets require frequent state transitions or micro-transactions. Layer-2 solutions like Arbitrum and Optimism address throughput but introduce sequencer centralization risks, which contradict the ethos of censorship-resistant market platforms.
Scaling horizontally through sharding—as attempted by platforms like Zilliqa—offers higher transaction throughput but can increase latency for state synchronization across shards, which is problematic in markets where resolution events affect many contracts simultaneously. For deeper insight into Zilliqa’s approach and challenges, explore https://bestdapps.com/blogs/news/a-deepdive-into-zilliqa.
Alternative architectures such as directed acyclic graphs (DAGs) aim to bypass traditional block confirmation mechanisms altogether. While these models (e.g., Hedera Hashgraph) claim theoretical scalability, their consensus algorithms often rely on permissioned or semi-centralized validator sets, raising questions around governance resistance and potential gatekeeping.
Consensus mechanisms themselves introduce platform-specific strengths and liabilities. Proof-of-Work chains offer robust security, but low TPS makes them nonviable for prediction use cases with time-sensitive outcomes. Proof-of-Stake enables faster confirmations and parallelization but remains susceptible to validator collusion in cases of oracle manipulation—an especially salient risk in adversarial market environments.
Cross-chain execution via interoperability layers (e.g., BTTC and Cosmos IBC) promises scale without platform lock-in but introduces latency due to the need for state finality on origin chains before activity on target chains. Commanding liquidity across chains also remains an unsolved issue, amplifying the risk of fragmented market depth. For more, refer to https://bestdapps.com/blogs/news/bttc-vs-rivals-a-blockchain-showdown.
Even oracle integration is a scalability chokepoint. Pulling off-chain data for resolution at volume—whether through Chainlink, UMA, or custom decentralized oracles—introduces latency, economic attack vectors, and dependency on external availability. Without robust, decentralized consensus for truth, prediction markets cannot maintain integrity at scale.
Part 7 will shift focus from technical constraints to the regulatory and compliance landmines facing decentralized prediction markets.
Part 7 – Regulatory & Compliance Risks
Navigating the Legal Minefield: Regulation & Compliance Risks of Decentralized Prediction Markets
Decentralized prediction markets operate in a legal vacuum riddled with pitfalls. The fundamental issue lies in their core structure—permissionless smart contracts that facilitate real-time event wagering across jurisdictions without centralized control. While inherently aligned with Web3’s ethos of censorship resistance, this structural design invites intense regulatory scrutiny from entities tasked with overseeing gambling, financial markets, and illicit activity prevention.
In the United States, the legal positioning of prediction markets intersects painfully with the Commodity Futures Trading Commission (CFTC)’s authority over derivatives trading. Contracts that resemble binary options on future events can be deemed "event contracts," potentially subjecting platforms to registration and compliance mandates under the Commodity Exchange Act. This interpretation has already led to enforcement actions targeting even university-led research projects, demonstrating regulators’ strict stance regardless of decentralization levels.
European jurisdictions, while fragmented, are increasingly aligning around MiCA (Markets in Crypto-Assets Regulation), which does not cleanly address use cases like decentralized markets for political or sports outcomes. Instead, these could fall under national-level gambling laws. Germany, for instance, enforces strict licensing requirements under Glücksspielstaatsvertrag, which could classify even non-custodial front-ends as illegal if they facilitate bet-like activity accessible to local users.
Asian policy environments are similarly diverse. Japan’s Financial Services Agency (FSA) maintains a conservative approach where unauthorized financial derivatives are criminal acts. In contrast, jurisdictions like Singapore take a more regulatory sandbox-based route, although decentralized infrastructures make KYC and AML enforcement practically impossible. The result is a regulatory patchwork where one protocol can unwittingly violate multiple national laws through simple user interaction.
The precedent set by previous DeFi protocols under fire only intensifies the landscape for prediction markets. Projects enabling token wrapping or perpetuals have already seen executives arrested, domain seizures executed, and smart contracts forcibly deplatformed—raising pressing questions about the survivability of entirely autonomous front-ends. In light of such vulnerability, some projects have explored full decentralization via DAOs to escape centralized liability, following paths similar to what was explored in https://bestdapps.com/blogs/news/the-untold-story-of-blockchain-based-decentralized-autonomous-organizations-and-their-role-in-shaping-future-governance-models. However, whether this offers meaningful protection remains legally untested.
With opaque legality, cross-border usage, and high AML sensitivity, decentralized prediction markets may be approaching the same regulatory crucible once endured by early DEXs. In Part 8, we will examine the broader financial consequences this technology could impose, from capital market disruption to systemic liquidity shifts.
Part 8 – Economic & Financial Implications
Economic and Financial Implications of Decentralized Prediction Markets: Winners, Losers, and System-Wide Disruptions
Decentralized prediction markets (DPMs) challenge the traditional financial architecture by introducing a permissionless and trust-minimized mechanism for forecasting and information monetization. Their economic impact is less about fringe speculation and more about how they reconfigure access, pricing, and incentives around event-based capital flows.
For institutional investors, DPMs offer early access to sentiment data that can be more predictive—and less manipulated—than traditional analyst consensus. If liquidity deepens and regulatory ambiguity fades, hedge funds may begin integrating market outcomes into trading algorithms as supplemental signals, particularly for macroeconomic or geopolitical events. However, legal uncertainties leave institutions exposed to regulatory risk if they engage too early, stalling adoption despite an appetite for alpha.
Retail traders and degens may thrive initially in thin markets, exploiting early inefficiencies with asymmetric information. Yet as protocols mature and liquidity floods in, edge opportunities will decline, cementing advantages for those with data science tooling and access to oracles. Front-running and oracle manipulation, particularly in smaller markets, remain unresolved vulnerabilities that could tarnish long-term viability.
Developers stand to benefit significantly. Protocol creators may monetize via native tokens or fees embedded into smart contract settlement layers. The composability of DPMs also makes them valuable DeFi primitives—particularly for use-cases like insurance, DAO governance, or synthetic assets. That said, outcomes markets add latency and potential reorg risk to settlement paths, especially when controversial topics intersect with chain consensus disputes.
On the macroeconomic front, DPMs could distort market integrity if outcomes influence rather than merely predict events. For example, a heavily capitalized market betting on a specific court ruling or election result could raise concerns of predictive manipulation. Similarly, the feedback loop between prediction markets and RWAs (real-world assets) might drive volatility previously filtered by centralized editorial gatekeepers.
This interplay with RWAs could catalyze new opportunities and threats, echoing concerns raised in the-future-of-tokenized-real-world-assets-bridging-tradition-and-blockchain-technology, especially when market signals begin dictating flows across tokenized infrastructure, commodities, or energy contracts.
As DPMs become embedded infrastructure within DeFi portfolios, the risk isn’t their failure—it’s their potential to work too well, substituting traditional price discovery with speculative consensus. The efficient market hypothesis wasn’t built for frictionless, distributed speculation with near-zero barriers to entry and anonymous participation.
This economic reconfiguration sets the stage for more fundamental questions about collective information, trust, and public epistemology. These themes demand examination not just through a financial lens, but through a societal and philosophical one as well.
Part 9 – Social & Philosophical Implications
The Economic Disruption of Decentralized Prediction Markets: Opportunities and Liability Zones
At their core, decentralized prediction markets (DPMs) are a new paradigm in capital allocation and risk hedging, potentially threatening traditional financial models and introducing layered complexity into speculative instruments. Their emergence doesn’t merely streamline information; it directly incentivizes insights and penalizes misinformation—an inversion of the status quo in speculative markets.
For institutional players, DPMs pose both a threat and an arbitrage window. On one hand, they risk disintermediation, especially entities reliant on opaque pricing mechanisms like bookmakers, insurance firms, and traditional exchanges. On the other, institutions with deep liquidity and data access may employ DPMs to hedge against geopolitical, economic, or even regulatory shifts with superior granularity. However, the challenge lies in DPM transparency; open prediction trends may leak alpha, eroding the edge of large liquidity providers.
For independent traders and quantitative funds, DPMs introduce an econometric battlefield not bound by financial correlation alone. The connectivity between event markets enables complex strategy crafting. Traders could, for instance, simultaneously wager on election outcomes, legislative decisions, and token governance votes, creating decentralized macro structures. This is reminiscent of the relationship between DAO snapshots and DeFi incentive behavior—as explored in https://bestdapps.com/blogs/news/sushiswap-governance-empowering-community-voices-in-defi, token-holder behavior often informs broader DApp trends.
Developers, particularly those constructing oracle bridges and reputation layers, find themselves building the rails for these markets. Protocol engineers optimizing for censorship resistance or on-chain privacy could see increased relevance, especially if real-world consequences become tied to DPM outcomes. The challenge will be balancing incentives with ethical restrictions—when markets open for sensitive topics, the question isn't just what can be predicted but what should be.
A looming hazard lies in the unknown behavioral feedback loops. If corporate boards, activist investors, or even political actors begin leveraging DPM prices to guide decisions—or worse, destabilize them—these markets risk catalyzing reflexive cycles. Liquidity-backed misinformation, where actors fund one side to distort truth perception, could be weaponized with as much impact as financial derivatives once were.
This dual nature—where value discovery and manipulation potential coexist—poses an existential risk to ecosystems that blindly adopt prediction markets without governance structure and civic foresight.
This sets the stage for the next exploration: how decentralized forecasting mechanisms affect not only economies, but collective intuition and civic responsibility.
Part 10 – Final Conclusions & Future Outlook
The Inevitable Fork: Will Decentralized Prediction Markets Transform or Decay?
Decentralized prediction markets have charted a curious course—emerging from niche experimental platforms to platforms capable of aggregating crowd-sourced intelligence with blockchain-backed incentives. Across this series, we dissected the vital components that drive these protocols: incentive design, resolution oracles, liquidity mechanics, governance coordination, and their sociopolitical ramifications. The insight is clear: while the architecture exists for revolutionary change, the ecosystem still teeters between two stark futures.
In the best-case scenario, decentralized prediction markets evolve into a key layer of decentralized finance and governance. As communities become increasingly reliant on accurate forecasting for both economic hedging and decision-making, protocols that reward accurate information without centralized arbiters could become central to betting, governance, and insurance primitives. When governed well and integrated into broader DeFi ecosystems—similar to how SushiSwap has catalyzed composability in decentralized exchanges—these platforms may become indispensable. (See: https://bestdapps.com/blogs/news/sushiswap-vision-innovations-and-future-roadmap)
However, the other edge of the knife is sharp. Prediction markets suffer from well-documented issues: low liquidity, oracle dependency, sybil vulnerability, and regulatory chilling effects. Without resolving governance bottlenecks, aligning incentives between market creators and traders, and scaling protocol throughput, these systems risk becoming abstract tools for ideologues and crypto hobbyists—not robust infrastructure. Worse, if manipulation becomes commonplace due to low economic security, they could turn into misinformation engines with financialized disinformation dynamics.
Unresolved questions remain. Who builds and secures oracles at scale, immune to off-chain collusion? Can incentive mechanisms be tuned to avoid plutocratic market capture without stifling liquidity? And how does anonymity, especially in permissionless systems, balance against regulatory compliance and civil risk?
Mainstream adoption still hinges on abstracting away protocol complexity and regulatory ambiguity. UX remains clunky, liquidity fragmented, and onboarding intellectually demanding. To challenge centralized forecasting or insurance incumbents—let alone elections or journalism—markets must become embedded into everyday interfaces without sacrificing decentralization.
The pieces are on the board. Whether they come together as the consensus engine for global truth or collapse into another cryptographic curiosity may depend on a convergence of oracle innovation, protocol-level UX, governance legitimacy, and sociopolitical relevance.
So the final question stands: will decentralized prediction markets become the keystone application of blockchain—resonant and irreversible—or are we watching the slow burn of another elegant but impractical crypto experiment?
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