Cosmos Grants
Fast funding for early prototypes of AI that strengthens human autonomy and truth-seeking.
How it works
Selected builders receive up to $10,000 and 90 days to build a working prototype, plus a place in a community at the frontier of AI and philosophy. Each round begins from a conviction about what AI should strengthen:
AI for Truth-Seeking
Will AI sharpen our thinking and become a partner in seeking truth — or will it quietly control what we’re allowed to see?
We believe AI should empower open inquiry.
That’s why Cosmos Institute and FIRE are funding early-stage projects that advance truth-seeking.
Areas we're keen to back include: technology that supports the open contestation of ideas and human judgment in knowledge production.
Grant pool
$1 million (cash + Prime Intellect compute credits)
Typical award
$1k-10k fast grants (larger amounts considered for exceptional proposals)
Community access:
Connect with an exceptional network of builders and thinkers at the AI × philosophy frontier
Duration
90 day period for the development of a working prototype
Application window
Round 3 to open in late June 2026.
Cohort 2
Cosmos write up of some project highlights.
AI and Human Autonomy
Whether through writing that allowed us to externalize memory or the printing press that relieved us of the drudgery of copying, technology has expanded what we can do and become.
AI is the first technology to go further by tempting us to offload decisions about what to do and how to live well. This faculty, autonomy, is the most at risk and the most in need of preserving.
Areas we're keen to back include: reducing excessive deference to AI systems, and systems that support our capacity for self-formation.
Typical award
$1k-10k fast grants (larger amounts considered for exceptional proposals)
Community access
Connect with a vetted network of builders and thinkers at the AI × philosophy frontier—advisors, mentors, and past grantees
Duration
90 day period for the development of a working prototype
Application window
Round 1 to open in late June 2026.
AI-accelerated Scholarship with IHS
Accelerate Your Research with AI. Expand the Frontiers of Freedom.
Academia is at a turning point. While AI challenges traditional methods of learning, its potential to deepen scholarship, broaden audiences, and speed the pace of discovery is enormous.
We're excited to support people pursuing research in philosophy and the humanities, or work that helps refine the role of judgment in scholarship.
Typical award
Expenses for AI tools / credits needed for research and experimentation (typically <$5k).
Community access:
Connect with a vetted network of academics experimenting with AI tools for accelerating their research like Seth Lazar and Kevin Vallier.
Who is eligible?
Faculty, graduate students, and academic researchers working on projects in the humanities and social science disciplines as well as philosophy, public policy, and law are eligible to apply for funding.
Past Grantee Projects
We've highlighted a few projects to spark ideas, and show prototypes connecting to tangible outcomes. You can also see all projects and grantees at the link below.
About this project
What was built?
Priori surfaces the silent decisions hidden in every AI answer (e.g. what tone to take, what to emphasize, which values to assume), and lay them out as adjustable controls beside the response. As AI systems grow more capable of making decisions on our behalf, users are increasingly asked to trust lengthy outputs whose underlying choices are difficult to identify and costly to challenge, creating the conditions for deference. Priori makes meaningful oversight practical, enabling humans to exercise their judgment, contest AI defaults that do not reflect their values, and stay involved in decisions that would otherwise remain hidden.
What outcomes were there?
Steven presented his project at an AI and Truth-seeking symposium Cosmos hosted with FIRE. He's now collaborating with fellow grantee Cathy Fang on a research study on Priori's effects on autonomy, and has recently accepted a Cosmos Fellowship to build out a research agenda around steerable, faithful, and complementary AI.
About this project
What was built?
MoSaIC, an assessment tool that combines AI-powered conversation analysis with human expert evaluation to capture the social dynamics of learning. It maps interaction patterns like questions, challenges, and shared discoveries to reveal how "social tinkering" fuels curiosity and resilience. These are dynamics that solitary AI tutoring can end up flattening.
What outcomes were there?
Caitlin wrote up her broader thesis in a Substack post with Cosmos. Her work on MoSaIC formed the basis of empirical research toward understanding different qualities of human and AI interaction in learning, published in conferences including ISLS CSCL (paper) and IEEE FIE (paper). These projects formed part of her PhD at the MIT Media Lab.
About this project
What was built?
Thought Refractor lets you paste in any text and turns it into multiple visual representations instead of a summary. These include step-by-step breakdowns, comparison frames, tables, flowcharts, concept maps, and timelines. Each surfaces a different kind of structure hidden in the writing. Moving between views lets you see the shape of an argument from multiple angles rather than following it in a single linear path.
What outcomes were there?
Cheng-Wei has founded a startup, Wondering, that uses Thought Refractor's conversion from text to visual learning forms. He's expanding on his ambitions in building learning tools for learning, after leaving NotebookLM.
About this project
What was built?
TLM-1 is a language model that explicitly accounts for time, jointly learning to predict both a document's contents and the year it was written. Trained on American English from 1990 to 2019, it can surface long-arc trends in language and detect when words shift meaning over time—like the word "cell" moving from biology toward technology.
What outcomes were there?
Brandon's resulting paper was accepted to the ACL 2026 Main Conference. He also presented his project to AI researchers and policy experts at an event Cosmos hosted with FIRE on AI and Knowledge Production.
About this project
What was built?
A set up that showed how AI safety training can fail when models learn the letter of the law rather than the spirit of the desired behavior. The project highlighted how models can be manipulated through chain-of-thought forgery, where users mimic the model’s internal reasoning style to increase the chance they comply with harmful requests.
What outcomes were there?
Jasmine won OpenAI’s red teaming contest and planning to continue work on this kind of persona/prompt-injection research.
About this project
What was built?
Campus is a project-based learning tool that expands human agency by adapting complex projects to your skill level. The tool prompts users for info, flipping standard human-AI interactions, and helps develop learners' abilities for self-directed growth.
What outcomes were there?
Kasey documented his learnings building Campus (later Whole Earth AI), and has since started a new company called Primitive focused on coordinated intelligence.
