Manifund

Joel Becker

Member of Technical Staff at METR; CEO at Qally's

Neel Nanda

Lead of mech interp team at Google DeepMind

Ryan Kidd

CEO & Co-Founder, MATS Research

Gavin Leech

Cofounder Arb Research, fellow at Cosmos, LCFI and Foresight.

Ethan Josean Perez

I lead the adversarial robustness team at Anthropic, where I’m hoping to reduce existential risks from AI systems. I helped to develop Retrieval-Augmented Generation (RAG), a widely used approach for augmenting large language models with other sources of information. I also helped to demonstrate that state-of-the-art AI safety training techniques do not ensure safety against sleeper agents. I received a best paper award at ICML 2024 for my work showing that debating with more persuasive LLMs leads to more truthful answers. I received my PhD from NYU under the supervision of Kyunghyun Cho and Douwe Kiela and funded by NSF and Open Philanthropy. Previously, I’ve spent time at DeepMind, Facebook AI Research, Montreal Institute for Learning Algorithms, and Google. I was also named one of Forbes’s 30 Under 30 in AI.

Alexandra Bates

Program Manager @ Constellation

Richard Ngo

AI safety and governance researcher

Marcus Abramovitch

Effective altruist, earning to give running a crypto fund. Very concerned with animal welfare and longtermism and their intersection. Ex-poker player and chemisty PhD student.

Lisa Thiergart

Director at SL5 Task Force, prev. Research Lead at MIRI Technical Governance Team

Marius Hobbhahn

I'm the CEO of Apollo Research. An evaluations research organization focused on frontier AI safety risks like scheming. Previously, I did a PhD in ML and worked as a Fellow for Epoch

Thomas Larsen

I work at the AI Futures Project, most recently on AI 2027.

Tamay Besiroglu

I co-founded Mechanize and Epoch AI

Lauren Mangla

Hi, I’m Lauren! I'm the COO of The AI Futures Project (the team that wrote AI 2027). All opinions expressed are my own, as are any grants.

Adam Gleave

I am no longer active on this platform -- please do not e-mail me about grant applications as I will not be able to respond.

Isaak Freeman

Currently: Grad student at MIT. Past: self-taught biology & neurosci @ Oxford, director @ Future Forum, math @ Berkeley.

Roy Rinberg

I'm a CS PhD student at Harvard, and I work on privacy and security in machine learning.

Falcon Fund

Nuño Sempere

Researcher & forecaster

Ted Suzman

ARC Evals

Aidan O'Gara

I'm an AI Grantmaker at Longview and an AI DPhil Student at Oxford

Rachel Weinberg

Building this site! Ran EA @ Tufts, where I also studied math.

Austin Chen

I work on Manifund!

Thomas Larsen

Evan Hubinger

AGI safety Research Scientist at Anthropic. Previously Research Fellow at Machine Intelligence Research Institute.

arenal

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Dan Hendrycks

Executive Director of the Center for AI Safety

Tristan Hume

Interpretability at Anthropic

Leopold Aschenbrenner

Investor @ Situational Awareness

Keri Warr

especially excited to fund any frontier AI Security efforts

Zvi Mowshowitz

Yeah, that one.

Renan Araujo

Field-building and research to make AI safer

Holly Elmore

Not using Manifund anymore due to Manifund hiring FTX fraud felon Caroline Ellison

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Yang Gao

🐵

Shubhashis Roy Dipta

🐶

Amartya kumar

🦑

Sheldon Thom

🐢

Giuseppe Canale

CTO and co-founder, Silicon Psyche Labs. I measure the behaviour of language models from their output and fine-tune them. CISSP, author of the Cybersecurity Psychology Framework.

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Armaan Sandhu

🌸

Oleg Atayev

🍎

Sean Maxwell

🌳

Michele Joseph

🐠

Abdellatif Sadeq

Attila Tóth

🐳

Naman Garg

🌳

Amelia Lubelska

🦁

Abdullah Hamdan

🥥

Aryan Padarthi

Piotr Zaborszczyk

Ex-programmer interested in AI x-risks mitigation. Experienced in leading small orgs.

🐬

Tom David

Brian McCallion

Independent researcher working on foundational models of large language model behaviour and alignment. My work focuses on mechanistic explanations of hallucination, coherence, and failure modes using systems theory, information theory, and learning dynamics. I am currently developing a boundary-mediated framework for understanding inference and learning in LLMs, with an emphasis on testable predictions and alignment-relevant design patterns.