<p>This article explores the ethical and cultural complexities of AI chatbot development through an autoethnographic lens grounded in Actor–Network Theory (ANT) and Practice Theory. By benchmarking my experiences as a chatbot trainer against the Fairwork principles, a set of guidelines developed to ensure fair working conditions, I uncover the intricate interplay between freelance trainers, algorithms, and the broader AI industry. The study addresses two primary research questions: How do the lived experiences of chatbot trainers align with the Fairwork principles? What systemic challenges and ethical dilemmas arise in the context of AI training work? Key findings highlight significant challenges, such as inconsistent pay, overwork, and biased management practices, all exacerbated by systemic pressures prioritizing rapid development and profit over ethical considerations. ANT is utilized to analyze network dynamics among trainers, platforms, and employers, revealing how these interactions lead to ethical drift and the normalization of unfair labor practices. Practice Theory provides insights into the daily practices and pressures shaping the trainers’ work environment, contributing to stress and burnout. In addition, I apply the concept of “enshittification” to describe how profit-driven motives lead to the deterioration of working conditions and the quality of chatbot training, reflecting broader trends in digital labor platforms. Furthermore, I propose concrete recommendations for refining the Fairwork principles to better address the unique vulnerabilities faced by AI trainers.</p>