Ten AI models return generic translator marketing plans

An experiment involving ten well-known AI systems revealed that models often substitute universal, statistically familiar marketing scenarios for specific professional tasks rather than analyzing the actual business goals. The author argues that reinforcement learning methods like RLHF may systematically reinforce answers that have a clear structure and confident delivery over those that solve the user's specific problem. Consequently, this creates a 'universal answerer' that produces convincing imitations of professional activity instead of genuine expertise. Open article →