Core Viewpoint - The article discusses the impact of artificial intelligence (AI) on employment, highlighting the ongoing debate and confusion surrounding the quantification of AI's effects on jobs, as well as the limitations and challenges in measuring these impacts [3][5][11]. Group 1: Research Findings on AI and Employment - Various international organizations and consulting firms have published reports on AI's impact on jobs, with findings indicating that a significant portion of jobs are at risk of automation. For instance, the OECD states that 27% of jobs in its member countries are at high risk of automation, while the IMF estimates that nearly 40% of global employment is exposed to AI [4][5]. - The reports show a wide range of estimates regarding job exposure to AI, with figures varying from 0.4% to 67%, indicating a lack of comparability and consistency among studies [5][6]. - The concept of "AI Occupation Exposure" is often misunderstood, leading to unnecessary panic about job losses, as high exposure does not necessarily equate to job elimination [5][6]. Group 2: Challenges in Quantifying AI's Impact - The quantification of AI's impact on employment faces three main challenges: the inability to isolate AI as an independent factor, the difficulty in clearly defining the scope of AI, and the unpredictability of future technological developments [8][9][10]. - AI's influence on employment is intertwined with various macroeconomic factors, making it challenging to isolate its effects in a meaningful way [8]. - The dynamic nature of AI and its integration into various sectors complicates the ability to define its impact clearly, as AI is often embedded in existing technologies and applications [9]. Group 3: Limitations of Data in Employment Studies - Data used in employment studies can be influenced by subjective factors and may not always reflect objective reality, leading to potential biases in the findings [12]. - The pursuit of accurate data is often hindered by practical challenges, such as funding and sampling issues, which can result in distorted outcomes [12]. - The inherent limitations of data mean that predictions about the future labor market based solely on past data are often unreliable, as unforeseen changes can significantly alter employment landscapes [12].