# Problems ### The Age of Data Confidence Disruption, Why Proof Is Needed We are now witnessing a collapse in trust at the peak of technological progress. Although AI produces content, algorithms judge users, and Web3 advocates distributed rewards, there is no proof system for the only question that should exist above all of those technologies: "Is this data real?" According to Statista, the global digital advertising market is expected to grow to about $700 billion by 2025. However, more than half of it is wasted on abnormal traffic such as bot traffic, duplicate switching, and automatic clicking. Advertising has become more efficient, but the authenticity of the data has collapsed. In a market where "how much the number came out" takes precedence over "who participated," trust has no longer become an economic indicator but a loss factor. ### Structural Limitations of Web2 – Closed Wall of Trust Web2 marketing has become technically sophisticated, but it has retreated from a trust perspective. Large platforms such as Google, Meta, and TikTok monopolize data, and advertisers are trapped in a structure where they have no choice but to believe the figures provided by the platform. The number of clicks, exposures, and conversion rates inside the platform are not verified externally. Advertisers do not know whether the actual participation is by users or automated bots. As a result, advertising costs increase, data is distorted, and even AI algorithms learn incorrect data. Eventually, the Web2 ecosystem is losing competitiveness in a vicious cycle of rising costs → falling trust → data opacity. The more data you have, the more difficult it is to reach the 'real user'. ### Web3's contradiction – decentralized, but failed to prove it Web3 promised 'decentralization' and 'transparency', but the problem of real data trust has intensified. Most Web3 campaigns today have short-term reward-oriented structures such as Zealy, Galxe, and Gleam. They create a fast inflow, but have an unstable structure that deviates by more than 90% after the campaign is over. Only the on-chain data remains, and off-chain activities such as Twitter, Discord, and Telegram are cut off. As a result, the project cannot prove the overall conversion rate and ROI, and a large portion of the marketing budget is consumed by bots and duplicate wallets. In addition, Web3 is still a influencer (KOL) dependent structure. Trust between the project and KOL is easily destroyed because the performance metrics are unclear and there is no way to verify the contribution data. Eventually, Web3 distributed, but the proof failed. ### AI's Paradox - Technology of Efficiency Breaks Trust AI has penetrated every step of marketing. In content creation, target prediction, Fraud detection, CTR analysis, etc., AI has become It has become a symbol of "efficiency," but the data on which it is based is already contaminated. As of 2024, approximately 38% of the major commercial AI learning data consists of noise, duplication, and false events. As a result, the prediction accuracy of AI models is declining by 3-5% every year, and above all, there is no way to prove the origin of the data learned by AI. In the end, AI has been reduced to "a technology that quickly spreads contaminated data" rather than "a technology that increases accuracy." For AI to be the true trust base of the industry, a Verified Dataset is essential. However, there is no Proof Layer in the current industry to guarantee this. ### Industry-wide Pain Point — Vacancies in trust This issue is not limited to advertising or Web3 marketing. The project (advertiser) cannot prove the ROI of the campaign, KOL cannot prove its contribution, and users leave with only short-term rewards. AI companies learn contaminated data and lose the trust of analysis results. Even in actual statistics, about 70% of the $1M airdrop campaigns participated in duplicate in the same wallet group. More than 80% of the rewards were paid to fake users, and advertisers could not secure real-user-based data. The root of the problem is clear. There is no Data Trust Layer to verify and guarantee the trust of the data. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://web3-growth-agent.gitbook.io/wga-whitepaper-ver-1.5_en/.-market-analysis-and-core-problems/problems.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.