Climate Change AI Innovation Grants for Machine Learning and Climate Action

Climate Change AI Innovation Grants for Machine Learning and Climate Action is sponsored by Climate Change AI (fiscally sponsored by Future Earth; supported by Quadrature Climate Foundation, Google DeepMind, Global Methane Hub). This program supports catalytic projects using AI and machine learning for climate action, funding research and deployment challenges in climate change mitigation, adaptation, and climate science.

The AI Safety Policy Entrepreneurship Fellowship is FAS's route for turning technical and domain expertise into frontier AI safety policy that actually moves. It is a part-time hybrid programme running 30 September 2026 to 28 February 2027, with applications due 7 September 2026, and it pays a USD 5,000 stipend plus up to USD 1,000 as a merit award. Fellows commit roughly five hours a week to developing a policy memo on a specific AI safety challenge, attend training sessions, join an in-person retreat in California from 4 to 7 November 2026, and present at a capstone event in Washington DC during the week of 22 February 2027. The design target is explicit and unusual: it recruits early- to mid-career professionals who have limited direct public policy experience but deep expertise elsewhere - technical AI research, academia, think tanks, civil society, industry, law, cybersecurity and national security - and teaches them the mechanics of getting an idea adopted. Selection weighs clear understanding of AI governance challenges, concrete implementation-oriented solutions rather than broad principles, awareness of which stakeholders must be moved, and a credible commitment to translating expertise into policy outcomes. Because the stipend is modest and the time commitment part-time, this is designed to sit alongside an existing job rather than replace one.

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.

Foresight's AI for Science and Safety Nodes RFP is a small-cheque, high-variance programme aimed at work that conventional funders will not touch, and it is one of the few AI safety calls verifiably open with a future deadline of 31 October 2026. The framing joins two problems: ensuring a safe transition to highly capable AI, and using AI to unlock scientific breakthroughs. Proposals sit in one of three layers. Local compute covers community-owned and controlled computational infrastructure - an explicitly decentralist counterweight to frontier-lab concentration. Coordination and accountability covers human-AI interaction, supercollaboration and AI risk assessment mechanisms. AI-first science covers automated nanotechnology, whole-brain emulation and frontier biotechnology. The single most important screening criterion is stated plainly: Foresight wants projects where AI is the primary engine of progress, not projects where AI is, in its words, tapped onto an existing research program - so an established lab adding a machine learning component to ongoing work is the archetype of what gets rejected. Evaluation weights alignment with a focus area, impact on AI existential risk reduction, feasibility within short AGI timelines, execution capability, and high-risk high-reward potential. Two conditions are non-negotiable: all outputs must be open-sourced, and applicants must commit to active participation at Foresight's San Francisco or Berlin nodes, which makes this poorly suited to a purely remote team. Application is a single Airtable form with a roughly three-month review.