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Abstract Recent advances in AI culminate a shift in science and engineering away from strong reliance on algorithmic and symbolic knowledge towards new data-driven approaches. How does the emerging intelligent data-centric world impact research on real-time and embedded computing? We argue for two effects: (1) new challenges in embedded system contexts, and (2) new opportunities for community expansion beyond the embedded domain. First,on the embedded system side, the shifting nature of computing towardsdata-centricityaffects the types of bottlenecks that arise. At training time, the bottlenecks are generallydata-related. Embedded computing relies onscarcesensor data modalities, unlike those commonly addressed in mainstream AI, necessitating solutions forefficient learningfrom scarce sensor data. At inference time, the bottlenecks areresource-related, calling forimproved resource economyandnovel scheduling policies. Further ahead, the convergence of AI around large language models (LLMs) introduces additionalmodel-relatedchallenges in embedded contexts. Second,on the domain expansion side, we argue that community expertise in handling resource bottlenecks is becoming increasingly relevant to a new domain: thecloudenvironment, driven by AI needs. The paper discusses the novel research directions that arise in the data-centric world of AI, covering data-, resource-, and model-related challenges in embedded systems as well as new opportunities in the cloud domain.