The Paradox of Expertise in the AI Age

A crucial tension exists between automating systems to not require human intervention, like self-driving cars, and the need for human expertise when AI fails unexpectedly.

Key Points:

• Generative AI enables non-experts to achieve specialist-level work like programming, appearing to threaten the value of expertise.
• However, human strengths remain vital when AI falls short, presenting an inherent paradox between developing and requiring expertise.
• AI separates prediction from judgment, struggles adapting, increases decision complexity, and alters learning - amplifying ultimate need for human ingenuity.
• Examples across aviation, medicine, and driving reveal paradoxical risks of AI degrading expertise while making it more invaluable than ever when technology stumbles.
• Updating notions of intelligence in the AI age means valuing emotional aptitudes like ethics and hope alongside analytical skills.

In late 2018, protein folding researcher Mohammed AlQuraishi experienced an emotional rollercoaster. He was awestruck yet disheartened when DeepMind's AlphaFold outperformed human scientists at predicting protein structures. Despite appreciating this breakthrough, AlQuraishi couldn't escape a nagging feeling—was machine knowledge eclipsing human ingenuity? 

He observed a shift in what defined a revered scientist. Mastery of traditional discovery and "Eureka!" moments were being superseded by skills manipulating data to extract machine-generated insights. Expertise now required fluency in both core disciplines and AI techniques to uncover new knowledge.

This transformation prompts an uneasy question: What happens when developing expertise depends on AI that could simultaneously hinder its advancement? Aviation reveals these tensions between human mastery and automated systems.