In 1995, the internet was a nascent technology characterized by slow connections and limited trust among users, leading to polarized opinions about its future. Optimists envisioned a digital revolution that would transform commerce and social interactions, while pessimists dismissed it as a passing trend. Today, a similar divide exists surrounding the impact of Artificial Intelligence (AI), with one side predicting mass unemployment and the other forecasting job creation and widespread automation. Both perspectives may hold elements of truth, as history shows that the influence of new technologies is complex and varies significantly by industry. The article highlights the "employment paradox" of automation, particularly in healthcare, using the example of radiology. Predictions that AI would replace radiologists have not come to fruition; instead, the field has seen growth in job opportunities and income. The phenomenon can be explained by Jevons Paradox, which suggests that improvements in efficiency can lead to increased overall demand. As AI enhances productivity in radiology, more patients seek scans, thereby driving employment growth rather than decline. However, experts caution that job displacement is more likely in roles characterized by repetitive tasks that are easily automated, rather than in complex professions like radiology. Examining historical data from the textile, iron & steel, and motor vehicle industries offers further insight into the relationship between automation, productivity, and employment. In sectors where demand was initially low but surged due to automation, such as textiles, employment grew until saturation was reached, leading to eventual declines. In contrast, industries like motor vehicle manufacturing, where demand remains unmet, continue to see stable or increasing employment levels. The software industry presents a unique challenge, as the potential for automation could lead to a saturation point in app development, raising questions about the future demand for software engineers and the nature of technological constraints. Overall, the article emphasizes the importance of understanding industry-specific dynamics to navigate the complexities of AI's impact on jobs and the economy.