If Not Lessons, Then What? (third-bit.com)

In a reflective piece on the impact of large language models (LLMs), the author shares their journey of transitioning from a hopeful retirement filled with personal tutorials to grappling with the reality of AI's capabilities. After three months of unemployment, they realize that potential learners are more inclined to seek quick, tailored responses from LLMs like ChatGPT and Claude, rather than diving into lengthy written tutorials. This shift highlights a significant transformation in how knowledge is shared and consumed in the AI/ML community. The author's experience underscores a crucial challenge for educators and content creators: while they may offer deeper insights, the efficiency and adaptability of LLMs present daunting competition. They acknowledge that their own explanations may be more accurate but take considerably longer to produce, raising questions about the value of traditional teaching methods in an era where LLMs can generate personalized content at remarkable speed. This reflection not only points to the evolving landscape of learning and knowledge dissemination but also invites a broader dialogue about the future roles of human educators in an AI-enhanced world.