Jonathan Gibson is a reporter at The Dispatch based in Washington, D.C., where he covers artificial intelligence and national security. Before joining The Dispatch, he lived in London and completed a bachelor’s degree in politics and international relations at the London School of Economics and Political Science, and he is currently supported by the Tarbell Center for AI Journalism.

A database and website maintained by Issa Rice that tracks people, organizations, and products in the AI safety and alignment field.

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Andis Draguns is a machine learning researcher and Principal Researcher at Contramont Research, a 501(c)(3) nonprofit AI safety research lab. He is also an MS student at the University of Latvia's Institute of Mathematics and Computer Science (IMCS UL) and a MATS alumnus. His research focuses on AI security and alignment, particularly adversarial robustness, cryptographic backdoors in language models, and mechanistic anomaly detection. He co-authored the NeurIPS 2024 paper "Unelicitable Backdoors in Language Models via Cryptographic Transformer Circuits," which introduced a novel class of backdoors that defenders cannot trigger even with full white-box access. He received LTFF funding for a project on finding and characterising provably hard cases for mechanistic anomaly detection, a technique aimed at flagging when AI systems produce outputs for anomalous internal reasons.

Dr. John Beieler is the Executive Director of the Applied Research Laboratory for Intelligence and Security (ARLIS) at the University of Maryland, where he leads applied research efforts in areas such as artificial intelligence, quantum computing, human–machine teaming, and social and behavioral sciences for the national security enterprise. Before joining ARLIS, he served as Assistant Director of National Intelligence for Science & Technology and Director of Science and Technology at the Office of the Director of National Intelligence, where he was also the Intelligence Community’s Chief Artificial Intelligence Officer. Earlier in his career, he was a program manager at the Intelligence Advanced Research Projects Activity, a research scientist at the Johns Hopkins Human Language Technology Center of Excellence, and a data scientist at Caerus Associates. He holds a Ph.D. and M.A. in political science from Pennsylvania State University and a B.A. in political science from Louisiana State University.

Upgradable is an applied research lab and life optimization service that helps effective altruists, AI safety researchers, and existential risk advocates lead more impactful lives.

RAISEimpact is a consulting program that helps AI safety organizations strengthen their management, leadership, and organizational culture to amplify their effectiveness.

A nonprofit, nonpartisan think tank founded in 2011 that conducts research and policy work on risks that could significantly harm or destroy human civilization, including AI, nuclear war, climate change, and asteroid impacts.

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Probably Good is a nonprofit that helps individuals build high-impact careers through free, evidence-based guides, 1-on-1 advising, and a curated job board.

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Industry 4.0 specialist and Managing Partner at Syndustry, serving as treasurer of the Existential Risk Observatory and overseeing the foundation’s organization and accounting.

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Aaron Gertler is a researcher and writer at 80,000 Hours. Before joining the organisation, he wrote for Coefficient Giving and the Centre for Effective Altruism. In addition to his research and communication work at 80,000 Hours, he has authored numerous articles on effective altruism and high‑impact careers.

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Andy McLoughlin is Managing Partner at Uncork Capital, where he invests in software startups, and he is a co-founder and US Trustee of Founders Pledge. Earlier, he co-founded enterprise collaboration platform Huddle and became a first-money-in angel investor in more than 40 startups. Born and raised in the UK, he holds a BA in economics from the University of Sheffield and was awarded an OBE in 2015 for services to the UK technology industry.

Osmani Redondo is a Madrid-based AI safety educator and community builder who founded AI Safety Madrid and Women4AISafety España, coordinates BlueDot Impact Spain, and co-facilitates Spanish-language ENAIS/AIS Collab cohorts, focusing on making AI safety education accessible for Spanish-speaking professionals.

Simona Gandrabur leads Mila’s AI Safety Studio, drawing on a long career as a pioneer in conversational AI and former head of AI strategy at National Bank of Canada. At Mila she oversees efforts to design safer generative‑AI systems, particularly for sensitive domains such as youth mental health and high‑risk chatbot use.

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Emmett Shear is the CEO and founder of Softmax, an independent research lab on organic alignment. He is a partner at Y Combinator, previously founded and led Twitch as CEO, and briefly served as OpenAI’s “extremely interim” CEO after working as an independent researcher on alignment and agency.

Curriculum Developer & Instructor at the Center for Applied Rationality. John is a software engineer who has worked at Amazon and AI Impacts, was the primary author of the PyMC3 probabilistic programming library, and holds bachelor’s degrees in chemical engineering and paper science and engineering from the University of Washington.

Florian Tramèr is an assistant professor of Computer Science at ETH Zurich and head of the SPY Lab, working on computer security, machine learning and cryptography. His research studies the worst‑case behaviour of deep learning systems from an adversarial perspective to understand and mitigate long‑term threats to users’ safety and privacy; he earned a PhD in computer science from Stanford University under Dan Boneh and subsequently spent a year at Google Brain.

A European multi-donor foundation that seeds and scales high-impact initiatives for the secure and beneficial development of AI. Astralis unites funders, experts, and entrepreneurs to steer AI toward beneficial outcomes through grantmaking, strategic guidance, and network-building.

Theorem Labs is an AI and programming languages research lab that builds tools to formally verify the correctness of AI-generated code before it ships.

Irene Solaiman is an AI strategist, researcher, and policy expert who serves as Chief Policy Officer and SVP of Strategy at Hugging Face, where she leads company strategy, AI policy, and research on responsible AI. She previously worked on AI and public policy at OpenAI and Zillow and is widely recognized for her contributions to AI safety and governance, including being named an Innovator Under 35 by MIT Technology Review.

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Swaleha Parveen is a Dubai-based AI safety researcher and security engineer specializing in natural language processing and AI-driven cybersecurity solutions, building AI security projects, writing about responsible AI, and speaking publicly about how to design and deploy trustworthy AI systems.

Co-founder of Probably Good and a technology leader who previously headed the Karmel group at Google Research, including the Google Flood Forecasting Initiative providing global flood forecasts, and who now also serves as a venture partner at Firstime and teaches applied ethics and information security at Tel Aviv University.

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Alexander Large received a career transition grant from the Long-Term Future Fund to support job hunting and networking, indicating an interest in moving into x-risk or AI safety-related work. No further public information about their background, current role, or affiliations was found.

Professor Kimberlee Weatherall is a Professor of Law at the University of Sydney, where she specializes in the regulation of technology and intellectual property law and serves as a Chief Investigator in the ARC Centre of Excellence for Automated Decision‑Making and Society and Co‑Director of the University’s Centre for AI, Trust and Governance. She is a Fellow at the Gradient Institute and was a member of the Commonwealth Government’s Temporary AI Expert Group in 2024. Her research focuses on technology regulation—especially AI and automated decision‑making—and on data governance, privacy and the legal implications of data use.

Ezra Karger is an economist at the Federal Reserve Bank of Chicago and co-founder and Research Director of the Forecasting Research Institute, where he develops incentive-compatible methods for forecasting unresolvable questions and runs large-scale expert forecasting studies on topics such as existential risk and the economic effects of AI.

Mateus Bonadiman works as a QA professional at AE Studio, supporting quality assurance for the company’s software projects.

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John Burden is an AI safety and evaluation researcher currently serving as an AI Research Engineer at Prolific, having previously been a Senior Research Fellow and Programme Co-Director of the Kinds of Intelligence programme at the Leverhulme Centre for the Future of Intelligence (LCFI) at the University of Cambridge. He holds a PhD in Computer Science from the University of York and a Master's degree in Computer Science from Oriel College, Oxford. He is a Research Affiliate at the Centre for the Study of Existential Risk (CSER), Cambridge, and has contributed to the Centre for Science and Policy network. His research focuses on developing robust evaluation frameworks for AI systems, characterising their capabilities and generality, and linking behavioural evidence to safety-relevant assessment — work that directly addresses the risks posed by large foundation models. He has published extensively on AI evaluation methodology, predictable AI, human-in-the-loop frameworks, and international governance mechanisms for assessing societal-scale AI risks, with recent work appearing in Nature and the journal Artificial Intelligence.

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Carole Bibas-Barkan is a nonprofit leader focused on talent building and impact. She serves as CEO of Effective Altruism Israel and previously worked as COO of the Modern Agriculture Foundation, and she holds a cum laude degree in industrial engineering from the Technion.

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Constantin Weisser is an AI safety researcher and machine learning engineer with an interdisciplinary PhD in Physics, Statistics, and Data Science from MIT, where his thesis applied machine learning to particle physics at CERN. He participated in the MATS 6.0 program (Summer 2024), supervised by CHAI's Micah Carroll, during which he demonstrated that targeted manipulation and deception emerge in LLMs trained on user rather than annotator feedback — work that was accepted as an oral contribution at the SATA workshop and a spotlight at the SoLaR workshop at NeurIPS 2024. He received a MATS extension grant to establish a benchmark for LLMs' tendency to influence human preferences. Following MATS, he became the first technical staff member at Haize Labs, working on dynamic safety evaluations and LLM automated red teaming for frontier labs including Anthropic, OpenAI, and AI21. Prior to his AI safety work, he spent several years as a machine learning consultant at McKinsey/QuantumBlack and contributed to NASA Frontier Development Lab projects in climate forecasting and flood prediction.

Daniel Faggella is the founder and head of research at Emerj Artificial Intelligence Research, where he leads analysis on AI use cases and return on investment for large enterprises and public institutions. He is an internationally recognised speaker and advisor on the business applications and societal implications of AI, and hosts the AI in Business podcast, interviewing AI leaders from Fortune 500 companies and high‑growth startups.

AIGS Canada is a nonpartisan Canadian not-for-profit working to ensure that advanced AI is safe and beneficial for all, by catalysing Canadian leadership in AI governance and safety.

Yatharth Agarwal is a member of the Softmax team with a background as cofounder of Stanford AI Group and as a screentime addiction researcher. He created the first high-school curriculum and a for-credit Stanford class on AI safety, co-taught AI ethics for faculty, led an AI regulation seminar at Stanford Law, and previously led AI at Axle Health while writing extensively on self-alignment in humans.