Data centres support artificial intelligence (AI) development but place rapidly increasing demands on electricity and freshwater resources, with cooling representing a significant portion of their total energy consumption. Wastewater treatment plants (WWTPs) discharge large volumes of treated effluent with substantial cooling potential; however, their integration with data centre infrastructure has not been evaluated. Here we construct a global geodatabase of over 4775 data centres and 57,547 municipal WWTPs across 98 countries, integrating spatial analysis, engineering systems modelling, optimisation, and life-cycle assessment to quantify the benefits of combining treated water reuse with bidirectional thermal recovery. The analysis reveals a strong global spatial co-occurrence between data centres and WWTPs, enabling optimized national-scale pairings in which treated effluent is used for data centre cooling and the return heat is recovered to support sludge drying and anaerobic digestion. This symbiotic approach reduces greenhouse gas emissions by approximately 84 million tonnes of CO₂ equivalent annually, conserves approximately 1300 million m³ of freshwater, and provides net annual cost savings of approximately US＄95.4 billion. The greatest mitigation and water-saving potential lies in the United States, Japan, China, the Netherlands, and the United Kingdom. These findings establish data–water symbiosis as a readily scalable infrastructure solution that decouples AI from its carbon and water footprints. WWTPs are poised to evolve from disposal facilities into critical energy-coupling hubs, enabling efficient thermal and water exchange across urban systems and accelerating progress towards multiple Sustainable Development Goals.

The global intensification of harmful algal blooms severely compromises freshwater ecosystems, threatening biodiversity and critical ecosystem services through toxin exposure, hypoxia, and water quality degradation. Bloom formation involves a complex interplay of nutrient dynamics, hydrology, and microbial activity. Although subsurface processes—such as the release of sediment-bound nutrients and the germination of dormant cyanobacteria—are thought crucial to bloom initiation, these phenomena occur at fine spatiotemporal scales beyond the reach of conventional monitoring. As a result, the exact, rapidly evolving triggers of bloom emergence remain mostly unknown. Here we show meter-scale chlorophyll a (Chl-a) plumes rising from the sediment–water interface, triggered by heavy rainfall and directly seeding surface blooms. We captured these dynamics using a custom underwater drone that collected over 2.8 million data points at 5-m horizontal and 1-m vertical resolution. Algal blooms exhibit a clear vertical sequence: anomalous Chl-a levels first appear in deep benthic layers after rainfall-driven resuspension, then intensify simultaneously across near-bed depths, and finally reach the surface after a median lag of 0.8–1.5 days. These observations provide in situ evidence associating benthic algal seed stocks with surface bloom initiation, revealing that the origin and spatial heterogeneity of such events arise from rainfall-driven disturbances at the sediment–water interface. This robotic approach not only deciphers the subsurface origins of algal blooms but also empowers predictive modeling and adaptive management strategies, advancing global efforts to combat eutrophication amid escalating climate pressures and safeguard vital water resources.