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Clear filters to view everything →John Wentworth is an independent AI alignment researcher best known for developing the Natural Abstraction Hypothesis, which proposes that a wide variety of cognitive systems tend to converge on similar high-level abstractions due to the low-dimensional structure of physical information at large distances. He holds a BS in Computational and Applied Mathematics from Harvey Mudd College and has been working as an independent researcher since approximately 2018, previously having worked as a software engineer and data scientist. His research agenda centers on formalizing abstraction and agency, with work spanning questions such as what makes certain concepts like 'trees' or 'cars' natural objects of thought, why biological organisms are modular, and how such modularity may carry over to machine learning systems. He publishes extensively on LessWrong and the Alignment Forum, where he has accumulated over 400 posts and more than 60,000 karma, and he has authored eight sequences. He attended the MIRI Summer Fellows Program in 2019 and has served as a mentor in the SERI MATS (ML Alignment Theory Scholars) program, working with scholars on natural abstraction and related research directions. He has received substantial research funding from the Long-Term Future Fund and the Survival and Flourishing Fund totaling over $700,000 to support his independent alignment work.
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Scott Viteri is a CS PhD candidate at Stanford University's Center for Automated Reasoning, admitted in Autumn 2019 and advised by Prof. Clark Barrett. He holds a B.S. in Computer Science and Electrical Engineering from MIT (2018), and before starting his PhD he worked on interactive theorem proving at CMU with Simon DeDeo, publishing research on abduction in mathematics in the journal Cognition. His research focus has evolved from formal verification and programming languages to AI alignment, driven by his view that advanced AI poses a substantial existential risk. His core work involves training language models to produce causally grounded chain-of-thought reasoning via reinforcement learning, as demonstrated in his 2024 paper "Markovian Transformers for Informative Language Modeling" (arXiv 2404.18988), which achieved large gains on QA benchmarks. He has also received a grant from the Long-Term Future Fund to research a novel method for training prosociality into large language models, and Open Philanthropy recommended a grant of $153,820 to Stanford University to support his and Barrett's AI alignment research.
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6 months salary. Turn intuitions, like goals, wanting, abilities, into concepts applicable to computational systems
Jed McCaleb is the founder of the Astera Institute and serves as Co‑Founder and CEO of its Neuro & AGI program, where he is directing a large, long‑term philanthropic commitment to neuroscience‑informed AGI research. A software engineer and serial entrepreneur, he previously co‑founded Ripple and the Stellar Development Foundation, created the eDonkey network and the Mt. Gox bitcoin exchange, and later founded the space company Vast, where he is founder and board chair.
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Laurence D. (Larry) Fink is Co‑Chair of the Board of Trustees of the World Economic Forum and Chairman and Chief Executive Officer of BlackRock, the global investment and technology solutions firm he co‑founded in 1988.
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A model-agnostic benchmark for detecting deceptive reasoning in LLMs through behavioral fingerprints — no weight access required.
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Aya Abdelsalam Ismail is co-founder and chief science officer of Guide Labs. Previously she was a senior machine learning scientist at Prescient Design in Genentech, and her research focuses on making neural networks more interpretable. She earned a PhD in computer science from the University of Maryland and has published over a dozen papers at top machine learning conferences such as NeurIPS and ICLR.
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Seth Lazar is a professor in the Johns Hopkins University School of Government and Policy and a leading scholar in the moral and political philosophy of artificial intelligence. He leads the Machine Intelligence and Normative Theory (MINT) Lab, which works on AI safety, governance, and resilience, and previously served as a professor of philosophy at the Australian National University. He holds a D.Phil., M.Phil., and B.A. (Hons) from the University of Oxford, and his research is supported by funders including the Templeton World Charity Foundation, the Centre for Security and Emerging Technology, the Survival and Flourishing Fund, AI2050, Google, OpenAI, and the Australian Research Council.
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Gaia Marcus is Director of the Ada Lovelace Institute. She previously held senior roles across the UK Civil Service, including Deputy Director (Advanced Analytics and Local Capabilities) in the Spatial Data Unit at the Department for Levelling Up, Housing and Communities, Deputy Director for the Integrated Data Service at the Office for National Statistics, Head of Engagement for Civil Service Reform at the Cabinet Office and Head of National Data Strategy at the Department for Digital, Culture, Media and Sport. In the non-profit sector she has led data strategy and participatory approaches to research and innovation at organisations such as Parkinson’s UK, Centrepoint and the RSA, and has served as a trustee of Samaritans.
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Starting funds and moving costs for a DPhil project in AI that addresses safety concerns in ML algorithms and positions
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Simon Skade is an independent AI alignment researcher based in Germany. He studied computer science at the Technical University of Munich and began self-studying machine learning and AI safety through the rationalist and effective altruism communities. He conducted mostly non-prosaic alignment research from February 2022 through August 2025, during which time he won $10,000 in the Eliciting Latent Knowledge (ELK) contest and participated in MLAB (ML Alignment Bootcamp) and SERI MATS cohorts 3.0 and 3.1. His research focused on ontology identification and an interdisciplinary approach to understanding minds — drawing on linguistics, psychology, and neuroscience — with the goal of creating more understandable and better-targeted AI systems. He received funding from the Long-Term Future Fund for independent study to deepen his understanding of the alignment problem. More recently, he has turned his attention toward advocacy for international coordination to more safely navigate the AI transition.
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Identifying and auditing reasoning circuits in LLMs within Algoverse 2026 using Sparse Autoencoders (SAEs).
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This grant will support Naoya Okamoto upskill in AI Safety research. Naoya will take the Mathematics of Machine Learning course offered by the University of Illinois at Urbana-Champaign.
Top-up funding for a 3-month new hire trial to help me connect, expand and enable the AGI gov/safety community in Canada
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Charlotte Monico is Chief Executive Officer of Founders Pledge. A long-time member of the organization, she previously served for around six years as Chief Operating Officer and has worked in close partnership with founder David Goldberg since 2019, bringing strong strategic and operational leadership as the organization scales.
Pranav Pant is a software and quantitative developer at Graviton Research Capital and an IndiaAI Fellow of the Government of India, with a B.Tech in Computer Science and Engineering from IIT Jodhpur and research experience in deep learning and multimodal AI.
Founder of CEEALAR (formerly the EA Hotel). He has a background in astrophysics and Earth system modelling and previously ran a 3D-printing/open-source hardware business, which he pursued with an eye toward supporting effective altruism.
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Additional funding for AI strategy PhD at Oxford / FHI
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Yoshua Bengio is Co-President and Scientific Director of LawZero and a full professor of computer science at Université de Montréal. A pioneer of deep learning and one of the world’s most-cited scientists, he received the 2018 ACM A.M. Turing Award, founded Mila – Quebec AI Institute, and now focuses his research and public work on mitigating catastrophic risks from advanced AI, including leading LawZero’s Scientist AI approach to safe-by-design systems.
