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Lightcone Commons is a grantmaking platform operated by Lightcone Infrastructure that coordinates large-scale philanthropy in quarterly rounds, with a first round deadline of 23 August 2026 at 11:59pm Anywhere on Earth and grant recommendations announced around 23 October 2026.
It expects to distribute 15 to 25 million US dollars in the first round and comparable amounts every three months thereafter, with rolling applications between rounds. The platform's design solves a specific problem in AI safety funding: rather than applying separately to each funder, an applicant submits once and is seen by multiple funders operating from different worldviews. Significant participating funders emphasise AI alignment.
The Long Term Future Fund and the AI Risk Mitigation Fund each allocate roughly 2 million dollars and focus almost exclusively on shaping AI development in a positive direction, while Jaan Tallinn, the largest individual participant, directs the bulk of his funding toward work aimed at aligning, controlling or otherwise shaping superintelligent AI systems, with some giving extending to other ambitious causes.
Eligibility is unusually broad: applicants need not be 501(c)(3) organisations, and the platform can coordinate funding for for-profits, individuals, educational institutions, 501(c)(4)s and non-US entities. Practical obstacles exist for applicants based in certain countries including Russia and India.
The tradeoff to understand is that per-grant sizes are not published and funding decisions rest with individual funders, so the platform offers reach rather than a predictable award.
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Or search similar grants →According to the current listing, eligibility includes: Open to organisations and individuals doing work the participating funders judge highly cost-effective, across a deliberately wide range of worldviews. Applicants do not need 501(c)(3) status: the platform coordinates funding for for-profit entities, individuals, educational institutions, 501(c)(4) organisations and non-US entities. Practical funding obstacles exist for applicants based in certain countries, with Russia and India named specifically, so applicants in those jurisdictions should confirm feasibility before investing effort in an application. The first round closed on 23 August 2026 at 11:59pm Anywhere on Earth, with grant recommendations expected around 23 October 2026; subsequent rounds run every three months and applications are accepted on a rolling basis in between, so a missed round means a wait of roughly one quarter rather than a year. Per-grant amounts are not published, since each participating funder sets its own cheque size and makes its own decision - applicants should research which funders in the pool match their work and size the request to that funder rather than to the 15 to 25 million dollar round total. A single application is reviewed by multiple funders, which is the platform's principal advantage over approaching each separately. Confirm the full requirements in the official notice before applying.
The current listing shows lightcone Commons expects to distribute 15 to 25 million US dollars in its first round and similar amounts every three months thereafter, but it does not publish a per-grant size, because it is a coordination platform rather than a single grantmaker - each participating funder sets its own cheque size. The band used here, 10,000 dollars to 2,000,000 dollars, is inferred from the disclosed allocations of participating funders rather than stated by the platform: the Long Term Future Fund and the ARM Fund each allocate roughly 2 million dollars to the round and historically write grants from the low tens of thousands upward, while Jaan Tallinn directs the bulk of his funding through the platform and operates at the upper end. Applicants should treat these figures as indicative and size their request to the specific funder they expect to attract rather than to the round total. The structural point is that a single application is seen by multiple funders with different worldviews, so the platform's value is distribution rather than scale - one submission reaches capital that would otherwise require separate approaches. Verify award ceilings, matching requirements, and allowable costs in the official notice.
The published deadline was August 24, 2026, which has passed. Check the official notice for any future application windows before investing time in a proposal.
Lightcone Commons Quarterly Grant Rounds for AI Alignment and High-Impact Projects is funded by Lightcone Infrastructure, coordinating funders including Jaan Tallinn, the Long Term Future Fund and the AI Risk Mitigation (ARM) Fund. 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 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 VESRI Climate Modeling Challenge is a Schmidt Sciences call, run through the Virtual Earth System Research Institute, that funds research teams to make coupled climate models faster to improve, more reproducible and more accurate. The challenge will fund up to five teams with up to 2 million US dollars each over 24 months to implement and test new methods, explicitly including machine learning, improved representation of physical processes, and advanced calibration workflows. Expressions of intent were invited through 11 September 2026, with a full proposal stage to follow for shortlisted teams. The framing matters: VESRI is targeting the engineering bottleneck in climate modelling rather than climate science questions as such. Coupled model development cycles are slow because calibration is expensive and model updates are hard to reproduce, and the challenge asks teams to demonstrate methods that shorten that loop. Proposals that treat machine learning as an end in itself, rather than as a means to faster and more reproducible model iteration, are mismatched to the brief. VESRI already coordinates hundreds of climate and data scientists across nine projects, 17 countries and 65 research institutions, and Schmidt Sciences has granted 26 million dollars to researchers working on translating climate models into climate action, so this challenge extends an established portfolio. Applicants should expect to compete against teams with existing coupled-model infrastructure, and a proposal without access to a working coupled model to improve is at a structural disadvantage.
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