Group 1 - The core argument of the article highlights the ongoing debate and confusion surrounding the impact of artificial intelligence (AI) on employment, despite extensive research from various prestigious organizations [4][6]. - Numerous reports from organizations such as OECD, IMF, and McKinsey indicate a significant portion of jobs are at risk of automation due to AI, with estimates ranging from 0.4% to 67% of jobs being affected [3][4]. - The article identifies three main shortcomings in the quantitative assessments of AI's impact on employment: lack of comparability, limited scope of exposure measurement, and static nature of the studies [4][5]. Group 2 - The article discusses three operational challenges in quantifying AI's impact on employment, including the difficulty of isolating AI as an independent factor, the ambiguity in defining AI, and the unpredictability of future technological developments [6][7]. - It emphasizes that employment rates are influenced by multiple factors, making it challenging to attribute changes solely to AI [6]. - The dynamic nature of job markets complicates the assessment of AI's long-term effects, as many new jobs that may arise in the future are currently unknown [5][6]. Group 3 - The article stresses the limitations of data used in these studies, including potential biases driven by interests, challenges in obtaining accurate data, and the inherent unpredictability of human society [8]. - It points out that data may not always reflect objective reality and can be influenced by subjective factors [8]. - The article concludes that while data can provide insights, it cannot fully predict future outcomes due to the complexities and uncertainties of societal changes [8].