The article explores the concept of the "bitter lesson," which suggests that systems trained on large amounts of data tend to outperform human-designed methods. It discusses the potential limitations of this lesson in certain contexts and emphasizes the importance of understanding when traditional approaches may still be beneficial. The author argues for a balanced view that recognizes both the power of data-driven models and the value of human expertise.