Group 1 - The rapid advancement of AI technology, exemplified by the release of GPT-5, contrasts with the slow adaptation in real-world applications, highlighting significant barriers to practical implementation [2][3][5] - The quality of official economic data in the U.S. is deteriorating, which raises concerns about the reliability of metrics that influence critical economic decisions such as interest rates and social security [5][9] - The Consumer Price Index (CPI) data collection methods have become increasingly reliant on estimations, with 35% of the data being approximated rather than directly collected, indicating potential inaccuracies in economic reporting [6][8][9] Group 2 - The recent non-farm payroll (NFP) report showed a disappointing job growth of only 73,000 positions in July, with significant downward revisions for previous months, marking one of the worst performances since the pandemic [9][10] - The Bureau of Labor Statistics (BLS) continues to use outdated methods for data collection, which introduces various uncontrollable factors that can skew results, such as response rates and the accuracy of reporting by businesses [11] - There is a growing trend in the U.K. towards automated data collection for CPI, utilizing supermarket scanner data to enhance accuracy and coverage, suggesting a potential path forward for improving economic data collection methods [15]