Real data, verified data, and the trust of verifiers Pyth, a key infrastructure project for the future, is cruising smoothly. The previous post discussed the enormous costs and waste caused by fake or exaggerated data amidst vast amounts of data, as well as the significant risks they pose for the future. In this context, the world has already recognized the necessity of data trust and real data validation. In 2025, the Trump administration made a significant change by explicitly recognizing PYTH as a macro data infrastructure through amendments to U.S. blockchain laws. This has become a major catalyst for strengthening regulations on cryptocurrencies and expanding the actual utility of tokens, establishing PYTH as an important project for premium feeds and validation. Due to the backlash demand following these policy changes, Pyth is rapidly entering the RWA, macro data, and regulatory markets. With the imminent influx of stablecoins and explosive growth in the RWA market,...
Filtering Real Data from Vast Data in the Data Age There is a growing wealth gap regarding data. What does this mean? We are increasingly finding it difficult to sift through the countless data generated by AI to identify truly meaningful and reliable data. As a result, companies will find it even harder to store and manage information than they do now, and they will have to invest significant effort in determining how much data to accept and store from the outset. There will be countless issues related to data in the future, especially in advertising. Therefore, there is a trend to put as much publicly available data on-chain as possible. Conversely, in the Info-fi project I am currently working on, while the volume of data and the amount of content related to specific keywords are skyrocketing, user engagement and reliable behavioral indicators are actually deteriorating in quality. This means that the quality of data is declining or becoming unreliable. To address these...
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