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The Activate AI: Economic Opportunity Challenge is a $10 million global initiative by data. org and Zoom Cares supporting innovative applications of artificial intelligence that empower people, organizations, and communities to unlock inclusive economic growth.
The challenge distributes funding through anchor grants to national and global leaders in AI for impact, plus regional and community-based grants supporting on-the-ground changemakers. At least five winning projects receive $115,000 each in grant funding plus in-kind technical and capacity-building support to scale AI for social impact.
The challenge targets three priority areas: (1) AI workforce development and organizational capacity building, (2) creating pathways to future-friendly jobs leveraging data and AI, and (3) building climate-resilient communities for a green economy. Announced at Zoomtopia 2025, the program is part of data.
org's broader portfolio of challenges that have previously distributed millions in funding for data science and AI social impact projects. The challenge accepts applications from for-profit and nonprofit organizations globally.
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Or search similar grants →According to the current listing, eligibility includes: For-profit and nonprofit organizations globally are eligible. Projects must serve a charitable purpose and deliver public benefit. International applicants welcome. Projects should address AI workforce development, future job creation through AI, or climate-resilient community building. Preference for solutions that combine data, AI, and social good for inclusive economic growth. Confirm the full requirements in the official notice before applying.
The current listing shows $10 million total over three years. At least five winning projects receive $115,000 each in grant funding plus in-kind technical and capacity-building support. Anchor grants go to national and global leaders in AI for impact, plus regional and community-based grants for on-the-ground changemakers. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Data.org and Zoom Cares Activate AI Economic Opportunity Challenge for AI Social Impact is funded by data.org and Zoom Cares. Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
CIFAR and the Canadian AI Safety Institute fund Catalyst Project proposals addressing sociotechnical considerations in AI safety. The program supports interdisciplinary research in machine learning applications to science and society, with recent funded projects spanning misinformation combat, trustworthy language models, democratic alignment of AI systems, Indigenous AI governance, and real-world safety in autonomous systems. Designed to catalyze new research areas and collaborations at the intersection of social sciences, humanities, and AI safety.
The Climate Change AI Innovation Grants program supports projects that address research and deployment challenges in climate change mitigation, adaptation, and climate science by leveraging AI and machine learning, while also creating publicly available datasets and tools to catalyze further work. The program enables key partnerships that accelerate the research-to-deployment cycle, creating synergies between academic researchers, nonprofits, startups and other companies, and governmental or intergovernmental organizations. Funded by the Quadrature Climate Foundation, Schmidt Futures, and Google DeepMind, with Future Earth serving as fiscal sponsor, this is one of the few dedicated grant programs specifically targeting the intersection of AI/ML and climate change. Projects typically involve climate modeling, weather prediction, emissions monitoring, energy optimization, biodiversity monitoring, and other environmental applications of machine learning. The 2026 competition opens with a full proposal deadline of September 15, 2026. The program has grown steadily since its inception, funding 23 projects to date across diverse climate domains and geographies.
Climate Change AI Innovation Grants provide seed funding for research, deployment, and the creation of datasets and tools that address climate change mitigation, adaptation, and climate science using AI and machine learning. Supported areas include power and energy systems, agriculture and food, climate science and modeling, weather prediction, ecosystems and biodiversity monitoring, remote sensing, disaster management, carbon capture, oceans, forests, and transportation. Projects are expected to produce publicly available datasets and tools.
On July 22, 2026, NSF announced $83M in Integrated Data Systems & Services (IDSS) awards — national-scale data infrastructure that sits underneath AI-for-science alongside NAIRR. Category I awards run $10M–$30M over five years; Category II transitions regional pilots to national operations. The FY26 cohort is set and the July 28 deadline just closed. Here is who won, how the two categories and the one-proposal rule work, and how to build a competitive submission for the annual cycle that reopens next July.
Read articleNSF just put $83 million into six national-scale data systems through its Integrated Data Systems and Services program — the plumbing that makes AI-for-science actually work. Here is who won, how the three-category structure (national-scale, transition, planning) really functions, why the Category III planning grant is the on-ramp most teams should be aiming at, and how to build readiness before the next fourth-Tuesday-in-July deadline.
Read articleOn July 22, 2026, NSF announced $83 million in Integrated Data Systems and Services awards and launched a companion program, Unlocking Dataset Value for AI-Enabled Scientific Discovery, offering up to $100 million in $2M-$5M awards. Together they fund the least glamorous and most decisive part of the AI-for-science stack: the data. Here is what each program funds, who won the first round, how the two fit the Genesis Mission, and the concrete strategy for research teams that missed the first cohort.
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