Poseidon Research is an independent AI safety laboratory conducting deep technical research in interpretability, control, and secure monitoring to make advanced AI systems transparent, trustworthy, and governable.
Poseidon Research is an independent AI safety laboratory conducting deep technical research in interpretability, control, and secure monitoring to make advanced AI systems transparent, trustworthy, and governable.
People– no linked people
Updated 04/02/26Funding Details
Updated 04/02/26- Annual Budget
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Org Details
Updated 04/02/26Poseidon Research is an independent AI safety laboratory and registered 501(c)(3) nonprofit dedicated to making advanced AI systems transparent, trustworthy, and governable. The organization conducts deep technical research across several interconnected areas: interpretability (understanding how AI models process information and make decisions), AI control systems (ensuring AI behavior remains aligned with human intentions), and secure monitoring (developing robust methods to oversee AI actions in real time). Based in Manhattan at the NYC Impact Hub, Poseidon Research represents part of the expanding landscape of AI safety organizations operating outside of traditional hubs like San Francisco and Cambridge. Their research portfolio also includes work on cryptography, steganography, and language model monitoring, reflecting a focus on the security and transparency dimensions of AI governance. The organization was noted at Effective Altruism Global NYC in late 2025 as an example of emerging AI safety research capacity in New York City. Poseidon Research actively seeks researchers who thrive in high-autonomy environments and value rigor, clarity, and open communication. They publish research updates and accept direct support through their website.
Theory of Change
Updated 04/02/26Poseidon Research believes that making AI systems interpretable, controllable, and subject to robust monitoring is essential to preventing catastrophic outcomes as AI capabilities advance. By conducting foundational technical research in these areas—particularly interpretability (understanding model internals), control (ensuring models do what humans intend), and secure monitoring (detecting deceptive or dangerous behaviors)—they aim to give humanity the tools to verify and maintain oversight of powerful AI systems. Their focus on cryptography and steganography for AI monitoring suggests a particular concern with detecting hidden or covert AI behaviors. The theory is that publishing and disseminating this research will raise the overall level of AI safety capability across the field.
Grants Received
Updated 04/02/26Projects– no linked projects
Updated 04/02/26Discussion
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