OASIS

OASIS – Community Prediction Market for Authenticity

Product Overview: OASIS is a platform that harnesses the wisdom of the crowd to determine content authenticity, structured as a prediction market. When a suspicious piece of content (a video, image, or claim) is submitted to OASIS, community members can place stakes (wagers) on whether it is authentic or fake. The market mechanism quantitatively aggregates the collective judgment – if most informed users bet that a video is a deepfake, the market price will reflect high probability of “fake”. Once the content’s truth is definitively resolved (e.g. via expert verification or passage of time), the correct predictors are rewarded and incorrect stakes are penalized. This creates an economic incentive for participants to analyze content rigorously and be first to spot the truth. In parallel, every content item and its eventual authenticity outcome become valuable training data. OASIS thus serves a dual purpose: a crowdsourced authenticity verdict and a pipeline of labeled examples (real vs fake content) to continuously train MirageTech’s AI models.

User Behavior & Incentives: OASIS introduces a new behavior: “betting on the truth.” While niche, it’s positioned to be early for one of the most controversial issues of the future, and is built on proven concepts from prediction markets and crowd-based moderation. Platforms like Kalshi and Polymarket have shown that distributed users will stake crypto on event outcomes, producing surprisingly accurate forecasts in some cases. Here, the outcome is the authenticity of content. The incentive loop is clear: if you’re skilled at spotting fake content (via knowledge of deepfake artifacts, reverse image searches, etc.), you can earn rewards by being right. This attracts skilled validators to OASIS. At the same time, those who might otherwise just comment opinions on a post now have a structured way to contribute to the truth-finding process, with skin in the game. This mechanism helps counter pure “vote brigading” or bias, unlike simple upvotes/downvotes, staking imposes a cost for being wrong, which encourages more careful, evidence-based participation.

Crucially, OASIS doesn’t rely on altruism alone; it taps into competitive and collaborative instincts. It’s similar to how Wikipedia relies on volunteer editors or how StackExchange rewards correct answers, but with a crypto-economic twist for higher-stakes issues. Over time, top performers on OASIS (those who frequently predict correctly) could themselves accumulate high reputation scores or earnings, motivating continued engagement. Meanwhile, casual users benefit from OASIS outputs without participating – e.g. seeing a content item’s “authenticity likelihood” as determined by the community, akin to a stock ticker of truth. Given that most people already consider source credibility before sharing content and want better verification tools, OASIS can integrate into user workflows by providing a quick crowd verdict that informs their decision to trust or share a piece of content.

Product-Market Fit: OASIS is an innovative solution at the intersection of social media, crowdsourcing, and Web3. Its product-market fit hinges on two factors: the magnitude of the deepfake/misinformation problem, and the limits of purely automated or top-down solutions. Both factors are evident in 2024–2025. Deepfakes and AI misinformation are no longer theoretical – they are expected to impact elections and public discourse (as 70–80% of respondents in multiple countries fear). At the same time, centralized content moderation is struggling; platforms alone cannot catch every fake or convince skeptics of their verdicts. OASIS offers a decentralized, transparent alternative: let the public help decide, and record the outcomes on an immutable ledger (ensuring results aren’t censored or altered).

There is precedent for community verification. Twitter’s “Community Notes” feature (formerly Birdwatch) invites users to add context to potentially misleading tweets, and has been effective at correcting narratives via consensus-based notes. OASIS takes this a step further by adding prediction and incentives, which can accelerate reaching consensus and attract a broader range of participants (including those motivated by profit as well as truth). Another analog is Kleros, a Web3 arbitration service where jurors stake tokens to adjudicate cases (including potentially disputes about content). Kleros demonstrated that token-driven crowd judgment can work for certain problems, though careful design is needed to prevent collusion and ensure fairness.

The TAM for OASIS can be viewed in two parts: (1) the content verification market (overlapping with what fact-checking organizations and social platforms spend on moderation), and (2) the AI training data market (since OASIS outputs data for model training). The latter is sizable – the global data annotation and labeling market is estimated around $1.9–2 billion in 2024, driven by demand for high-quality AI training datasets. If OASIS can supply verified authentic vs fake content data at scale, it could tap into those AI industry budgets. The former (verification/moderation spending) is harder to quantify but consider that big platforms invest heavily in trust & safety (Facebook’s content moderation budget was reportedly in the billions). OASIS could supplement some moderation functions, potentially saving costs and adding credibility (since a public market’s verdict may be seen as more impartial).

OASIS is well-positioned as a cutting-edge approach to a pressing problem. Its success will depend on network effects (needing enough skilled users to generate accurate signals) and on whether its predictions gain legitimacy in the eyes of the wider public and regulators. If it succeeds, it not only provides MirageTech a unique data stream and community, but could revolutionize how we collectively validate information in the Web3 era.