There are over 300 sign languages in active use worldwide, serving an estimated 70 million Deaf signers. Despite this, theoverwhelming majority of AI research on sign languages targets American Sign Language (ASL), with smaller but growingefforts on German, British, Chinese, and Indian Sign Language. Regional Indian variants and African sign languages remainalmost entirely unaddressed. This paper argues that closing this gap is not, as commonly framed, a question of building moredatasets in the same paradigm. We make three claims. First, the term "low-resource" means something structurally differentfor sign languages than for spoken languages — sign languages are not signed versions of spoken languages, they have nostandardised writing system, they are multimodal, and within-country dialectal variation is unusually high — and naivetransfer of low-resource spoken-NLP techniques produces misleading evaluations and brittle systems. Second, fourbottlenecks (data scarcity, inappropriate benchmarks, the limits of cross-language transfer, and structural exclusion of theDeaf community from research design) compound rather than substitute for each other; addressing only one will not produceusable systems. Third, a Deaf-led research agenda focused on Indian and African sign languages is both ethically necessary andscientifically productive — these contexts surface problems that ASL-centric research has been able to ignore. We proposespecific methodological commitments, six concrete research questions, and a strategic case for why Indian Sign Languageshould serve as a reference setting for the next phase of work.