A recent study reveals that large language models develop emergent social biases and stratify fictional demographic groups during sequential hiring tasks by over-exploiting initial random successes rather than exploring alternatives. However, critics argue these biases are largely an artifact of the experimental prompts, which implicitly instruct the models to treat arbitrary demographic labels as meaningful selection criteria.