In 2023, the central issue driving the Screen Actors Guild-American Federation of Television and Radio Artists (SAG-AFTRA) and the Writers Guild of America (WGA) strikes against the Alliance of Motion Picture and Television Producers (AMPTP) was about workers' demand for regulatory frameworks against the impacts of AI on their livelihoods. It was about who gets to participate in shaping Hollywood's labor future. Workers won two key items: informed consent (meaning their voice and likeness cannot be used without consent) and compensation (for that use). This paper does double-duty. It takes a closer look at the significance of these historic wins by focusing on one entertainment industry arena: the growing yet understudied multi-billion-dollar audiobook industry. I also surface intersectional dimensions, as I center Latinidad by surfacing the experiences of U.S.-based Latina audiobook narrators in my analysis because these women bring multidimensional perspectives that are critical to detangling how power is operationalized in the digital economy. I engage with textual analysis, Plática interviews, and Culturally Intuitive Observation to track the incursion of AI into the audiobook narration labor force, highlight the key players in this space, unpack the role of the audiobook narrators' union, SAG-AFTRA, in this historic win, and surface if and where the union considers intersectional concerns—Latina experiences specifically—when fighting for AI guardrails. My findings reveal corporate double-speak as AI players in this arena have weaponize misinformation to train their AI without consent, a focused effort to unionize the audiobook space and develop narrator protections, they also indicate that information asymmetries and linguistic hierarchies present roadblocks to the development of equitable worker protections. This work contributes to our understanding worker organizing and resistance in digital labor by taking a closer look at the recent Hollywood strikes, labor wins, and how and why intersectional dimensions must be considered as we develop AI guardrails.