The article emphasizes the continued effectiveness of simple algorithms compared to more complex AI methods. It highlights that traditional algorithms, specifically those used in optimization and decision-making, often yield results that outperform advanced AI systems in practical applications. The author cites instances in various fields, such as finance and logistics, where these algorithms provide reliable, interpretable solutions without the unpredictability that can accompany AI. A key point is the dependence on data quality and the complexity of the problem at hand. Simple algorithms often require less data and are easier to tune. In contrast, AI models typically demand vast amounts of high-quality data for training and can be prone to overfitting or biases based on the input they receive. The article includes examples, such as the success of linear regression models in predicting stock prices, which, while basic, can be more effective than complex neural networks for certain tasks. The author also addresses the misconception that more sophisticated models are inherently better. He argues that while AI has its place, especially in areas involving large datasets and unstructured data, the simplicity and transparency of traditional algorithms can lead to better decision-making in many scenarios. The discussion points to a need for a balanced approach, integrating both simple and complex techniques depending on the specific requirements of the task.