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Germany’s talents are critical to the global effort of reducing catastrophic risks brought by artificial intelligence.

One Month to Study, Explain, and Try to Solve Superintelligence Alignment

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Managing Director at the Center on Long-Term Risk, where he leads work on reducing s-risks from powerful AI systems. Previously a researcher at CLR focusing on s-risk macrostrategy, he also led the community-building team and studied mathematics at the University of Cambridge and the University of Warwick.

Project manager at 1Day Sooner. Focused on biosecurity and policy.

Leads Lightcone Infrastructure, whose main product is LessWrong, a platform that has significantly shaped discussions on rationality, AGI risk, COVID-19, existential risk, and crypto compared with other similar communities.

Researcher, forecaster, and programmer based in Asunción, Paraguay, best known as a co-founder of Samotsvety. Which is one of the world's top superforecasting teams that won the CSET-Foretell competition by a substantial margin. He is co-founder and Head of Foresight at Sentinel, a nonprofit that publishes weekly early-warning briefs on global catastrophic risks by processing millions of news items and applying expert foresight. He previously worked at the Quantified Uncertainty Research Institute (QURI), where he built Metaforecast.org, an aggregator of prediction market data across platforms, and contributed to the Squiggle probabilistic programming language used for quantified uncertainty estimation. He runs Shapley Maximizers, an independent consultancy focused on estimation, evaluation, and impact auditing, and publishes a monthly forecasting newsletter with over 7,000 subscribers covering prediction markets and forecasting platforms. He has received funding from the Long-Term Future Fund for independent research on forecasting and optimal paths to improve the long-term future, and is active on the EA Forum, LessWrong, and the Alignment Forum under the handle NunoSempere.

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Investor @ Situational Awareness

Co-founded Mechanize and Epoch AI

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Independent writer and blogger based in NYC, best known for his Substack "Don't Worry About the Vase" (thezvi.substack.com). Which has over 32,000 subscribers and focuses primarily on AI developments, policy, and rationality. He holds a bachelor's degree in mathematics from Columbia University and was a professional Magic: The Gathering player, inducted into the Magic Hall of Fame in 2007. He previously co-founded and served as CEO of MetaMed, a medical research analysis firm, and has worked at Jane Street Capital. He is a board member of the Center for Applied Rationality and founded Balsa Research, a nonprofit policy think tank focused on evidence-based regulatory reform. His writing covers AI safety, AI policy, government regulation, economics, and strategic thinking, and he is a prominent figure in the rationalist community on LessWrong.

A regularly-updated guide on how to donate most effectively to the AI safety field, structured by donation amount and time available.

Lead of mech interp team at Google DeepMind

Co-Executive Director at MATS

Scott Alexander (Scott Alexander Siskind) is a psychiatrist and writer on the US West Coast best known for his blogs Slate Star Codex and its Substack successor Astral Codex Ten, where he writes long-form essays on reasoning, science, psychiatry, ethics, politics, and effective altruism. His blogs have become central venues for the rationalist and EA communities, and he has been widely cited and discussed in those circles. Clinically, he has practiced psychiatry at Lorien Psychiatry and previously at other US institutions, with a focus on treatment-resistant depression and related areas.

Loïc Watine is Director of EA Funds at the Centre for Effective Altruism, a role he took up in January 2026 after previously serving as Chief Research and Policy Officer at Innovations for Poverty Action.

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Open Philanthropy (now Coefficient Giving) identifies outstanding giving opportunities, makes grants, follows the results, and publishes findings.

The self-study section of AISafety.com curates courses, textbooks, and reading lists for independent learning in AI safety, covering both technical alignment and AI governance.

CEO \u0026 co-founder at FAR AI, a trustworthy AI non-profit. PhD AI UC Berkeley 2022; LTFF fund manager 2020-2022

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

COO of The AI Futures Project (the team that wrote AI 2027). All opinions expressed are my own, as are any grants.

CEO and co-founder of Apollo Research. An AI safety organization he co-founded in May 2023 that specializes in evaluating dangerous capabilities and deceptive behaviors in frontier AI models. He holds a PhD in Bayesian Machine Learning from the International Max-Planck Research School in Tübingen, as well as an M.Sc. in Machine Learning and dual B.Sc. degrees in Computer Science and Cognitive Science from the University of Tübingen. Prior to founding Apollo Research, he was a Research Fellow at Epoch AI (June 2022–April 2023), where he worked on AI forecasting. His research at Apollo focuses on scheming detection, AI control, and dangerous capability evaluations, and the organization collaborates with frontier labs including OpenAI and Anthropic as well as government bodies like the UK AI Security Institute. He serves as a mentor in the MATS program for AI safety researchers and became a Manifund regrantor in 2025. He was named to TIME's 100 Most Influential People in AI for 2025.

Executive and Research Director of the Center for AI Safety (CAIS). A nonprofit research organization based in San Francisco focused on reducing societal-scale risks from artificial intelligence. He received a B.S. from the University of Chicago in 2018 and a Ph.D. in Computer Science from UC Berkeley in 2022, advised by Dawn Song and Jacob Steinhardt. His research spans machine learning safety, robustness, out-of-distribution detection, and AI ethics. He is the primary author of the GELU activation function (2016), which is widely used in state-of-the-art models including BERT and GPT, and created the MMLU (Massive Multitask Language Understanding) benchmark (2020), one of the most widely used LLM evaluation benchmarks. He also co-developed the MATH benchmark, Humanity's Last Exam (HLE), and authored the 2024 textbook Introduction to AI Safety, Ethics, and Society. He serves as a safety advisor to xAI and Scale AI, both at nominal compensation, and has received early-career funding from EA-aligned organizations for his work on value learning and AI alignment benchmarks.

Independent AI safety researcher and philosopher. He grew up in Vietnam and New Zealand and studied computer science and philosophy at the University of Oxford (BA, 2017), then earned a master's degree in machine learning from the University of Cambridge (2018). He began a PhD in the philosophy of machine learning at Cambridge, examining parallels between AI development and human cognitive evolution, before leaving the program in 2021. He was a research engineer on the AGI safety team at DeepMind (2018-2020), with a prior internship at the Future of Humanity Institute at Oxford. From 2021 to November 2024 he worked as a research scientist on the governance team at OpenAI, focusing on forecasting AI capabilities and risks, before departing over concerns about the organization's direction. He is best known for the essay series "AGI Safety from First Principles" (2020), co-authoring "The Alignment Problem from a Deep Learning Perspective", and designing the widely-used AGI Safety Fundamentals curriculum. He is an active contributor to the AI Alignment Forum and LessWrong under the handle ricraz.

Co-founder and CEO of Manifund. An open and transparent charitable grant platform focused on AI safety and effective altruism causes. He previously co-founded Manifold Markets in 2021 alongside James and Stephen Grugett, a play-money prediction market platform that grew out of the EA and rationalist communities. Before founding Manifold, he worked as a Senior Software Engineer at Streamlit and as a software engineer at Google, and holds a degree from UC Berkeley. He stepped away from Manifold to launch Manifund, which operates a regranting program pairing domain experts with independent grant budgets to fund early-stage AI safety projects. Austin is active on LessWrong and the EA Forum, and also co-organizes Manifest, an annual forecasting conference held in the Bay Area.

Interpretability at Anthropic

A curated directory of AI safety podcasts, newsletters, YouTube channels, blogs, books, and forums maintained by AISafety.com. It helps newcomers and practitioners stay informed about rapid developments in the AI safety field.

Building Manifund, Ran EA @ Tufts, where I also studied math.

A directory within AISafety.com that consolidates free guidance calls from AI safety advisors, helping newcomers identify how to contribute most effectively to the field.

Researcher, writer, and co-founder of Arb Research, a consultancy that does empirical work, conceptual work, and forecasting across AI, policy, and science. He holds a PhD in AI from the University of Bristol, where his dissertation focused on tensorised probabilistic programming for approximate inference. He is a fellow at the Cosmos Institute, the Foresight Institute, and the Leverhulme Centre for the Future of Intelligence at Cambridge, and was a 2024 fellow at the International Strategy Forum. His research spans machine learning, metascience, epidemiology, and AI safety, and he has published in Science, PNAS, and NeurIPS. He co-authored The Scaling Era: An Oral History of AI, 2019-2025 with Dwarkesh Patel, published by Stripe Press in 2025. He received a grant from the Long-Term Future Fund for work on longtermist lessons from COVID, contributing to what he and Jan Kulveit called experimental longtermism. He blogs extensively at gleech.org and posts on EA Forum and LessWrong under the handle technicalities.

Currently: Grad student at MIT. Past: self-taught biology \u0026 neurosci @ Oxford, director @ Future Forum, math @ Berkeley.

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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.

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

The Long-Term Future Fund aims to positively influence the long-term trajectory of civilization by making grants that address global catastrophic risks.

ARC Evals

Program Manager @ Constellation

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AI Grantmaker at Longview and an AI DPhil Student at Oxford

Founder and president of the Mathematical Metaphysics Institute, working full-time on AI alignment since 2017 and helping with AI alignment grantmaking through Jaan Tallinn’s Survival and Flourishing Fund, after previously assisting with the EA Long-Term Future Fund.

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

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Member of Technical Staff at METR. A nonprofit that scientifically evaluates whether and when AI systems might pose catastrophic risks. He holds a BSc in Economics and Econometrics from the University of Bristol (2014–2018) and was a PhD student in Economics at New York University (2020–2023), where his research focused on forecasting and pandemic resilience. During his graduate studies he co-organized SHELTER Weekend (Oxford, August 2022), a strategic event aimed at building civilizational refuges to address existential risks, and received LTFF funding to pursue civilizational resilience projects stemming from that work. At METR he works on AI evaluation methods including time-horizon benchmarks and randomized controlled trials measuring AI's impact on developer productivity — his RCT of expert developers on major open-source projects showed that AI tools led to a 19% slowdown despite developers believing they were faster. He also co-authored research forecasting capability gains from post-training enhancements. Outside his primary work he is Founder/CEO of Qally's, a regrantor on Manifund, and was formerly one of the most profitable forecasters on Manifold Markets.

Leads 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.

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