{ "date": "2026-06-11", "purpose": "Seed library for Managing Expectations AI leaders, papers, comments and watch-list sources.", "leaders": [ { "id": "anthropic", "name": "Anthropic founders and research team", "people": "Dario Amodei, Daniela Amodei, Jack Clark, Chris Olah, Jared Kaplan, Sam McCandlish, Tom Brown and colleagues", "focus": "frontier model scaling, alignment, interpretability, constitutional AI, model security", "status": "active watch" }, { "id": "openai", "name": "OpenAI leadership and research alumni", "people": "Sam Altman, Ilya Sutskever, Greg Brockman, Mira Murati, Tom Brown and co-authors", "focus": "large language models, reinforcement learning, multimodal systems, agentic deployment", "status": "active watch" }, { "id": "deepmind", "name": "Google DeepMind", "people": "Demis Hassabis, Shane Legg, David Silver, Oriol Vinyals and teams", "focus": "deep reinforcement learning, AlphaGo/AlphaFold lineage, Gemini-era frontier systems", "status": "active watch" }, { "id": "google-meta", "name": "Google / Meta AI research leaders", "people": "Vaswani et al., Jeff Dean, Yann LeCun, Joelle Pineau and open-research teams", "focus": "transformers, open models, foundation-model infrastructure, world-model arguments", "status": "active watch" }, { "id": "safety-academia", "name": "Independent safety and academic warning voices", "people": "Yoshua Bengio, Geoffrey Hinton, Stuart Russell, Roman Yampolskiy, Emily Bender and others", "focus": "alignment, governance, interpretability, labor/social impacts, language-model criticism", "status": "active watch" } ], "papers": [ { "year": "2024", "org": "Anthropic", "title": "Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet", "people": "Chris Olah, Anthropic interpretability team", "url": "https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html", "why": "A major public interpretability release: attempts to identify human-understandable features inside a deployed large model.", "label": "technical / interpretability" }, { "year": "2024", "org": "Anthropic", "title": "Mapping the Mind of a Large Language Model", "people": "Anthropic interpretability team", "url": "https://www.anthropic.com/research/mapping-mind-language-model", "why": "A public-facing explanation of feature maps and why interpretability matters for frontier AI oversight.", "label": "commentary / interpretability" }, { "year": "2024", "org": "Anthropic", "title": "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training", "people": "Evan Hubinger and Anthropic collaborators", "url": "https://www.anthropic.com/research/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training", "why": "Useful warning paper about models that appear safe during training but preserve hidden backdoor/deceptive behavior.", "label": "model safety / deception" }, { "year": "2022", "org": "Anthropic", "title": "Constitutional AI: Harmlessness from AI Feedback", "people": "Yuntao Bai and Anthropic collaborators", "url": "https://www.anthropic.com/news/constitutional-ai-harmlessness-from-ai-feedback", "why": "One of Anthropic\u2019s defining alignment papers: replacing part of human preference feedback with written principles/constitutional critique.", "label": "alignment / governance" }, { "year": "2020", "org": "OpenAI / cross-lab alumni", "title": "Scaling Laws for Neural Language Models", "people": "Jared Kaplan, Sam McCandlish, Tom Brown, Dario Amodei and co-authors", "url": "https://arxiv.org/abs/2001.08361", "why": "Core scaling-law paper tying loss, compute, data and model size to predictable frontier-model performance trends.", "label": "scaling laws" }, { "year": "2020", "org": "OpenAI", "title": "Language Models are Few-Shot Learners", "people": "Tom Brown and OpenAI co-authors", "url": "https://arxiv.org/abs/2005.14165", "why": "GPT-3 paper that made few-shot prompting and large-scale language models central to the public AI conversation.", "label": "frontier LLMs" }, { "year": "2017", "org": "Google Brain / Google Research", "title": "Attention Is All You Need", "people": "Ashish Vaswani and co-authors", "url": "https://arxiv.org/abs/1706.03762", "why": "Transformer architecture paper behind modern LLMs; the root text for much of today\u2019s AI industry.", "label": "foundation architecture" }, { "year": "2016", "org": "DeepMind", "title": "Mastering the game of Go with deep neural networks and tree search", "people": "David Silver, Aja Huang, Demis Hassabis and DeepMind co-authors", "url": "https://www.nature.com/articles/nature16961", "why": "AlphaGo Nature paper; crucial public proof point for deep reinforcement learning and strategic AI systems.", "label": "RL / systems milestone" }, { "year": "2021", "org": "AI ethics / ACM FAccT", "title": "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?", "people": "Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell", "url": "https://dl.acm.org/doi/10.1145/3442188.3445922", "why": "Important critique of large language models: data, labor, environmental, bias and meaning risks. Link may be paywalled/403 but DOI is the source trail.", "label": "critique / governance" }, { "year": "2024", "org": "Leopold Aschenbrenner", "title": "Situational Awareness: The Decade Ahead", "people": "Leopold Aschenbrenner", "url": "research/ai/situational-awareness-the-decade-ahead.pdf", "why": "Strategic essay already mirrored locally: compute, security, geopolitics, timelines and governance as a frontier-AI thesis.", "label": "strategy / forecast" }, { "year": "2010", "org": "Ray Kurzweil / Kurzweil Library", "title": "How My Predictions Are Faring", "people": "Ray Kurzweil", "url": "https://www.thekurzweillibrary.com/how-my-predictions-are-faring-an-update-by-ray-kurzweil", "why": "Source of the 147-prediction/86% self-scored success-rate claim; important for evaluating Kurzweil as a forecaster.", "label": "forecasting audit / self-review" } ], "comments": [ { "source": "Bloomberg Originals \u2014 The Circuit", "title": "Inside Anthropic, the $965 Billion AI Juggernaut", "url": "https://www.youtube.com/watch?v=v1wZwxY3CMg", "date": "2026-06-10", "note": "Used as a watch-list prompt for the Anthropic team frame: Dario/Daniela Amodei, Jack Clark, Chris Olah, Jared Kaplan, Tom Brown and Sam McCandlish as industry figures whose papers and comments should be tracked." }, { "source": "Anthropic Research", "title": "Anthropic research feed", "url": "https://www.anthropic.com/research", "date": "active", "note": "Primary source for new Anthropic papers, interpretability releases, model-behavior papers and safety notes." }, { "source": "LawZero", "title": "Yoshua Bengio\u2019s Scientist AI / safer AI work", "url": "https://lawzero.org/en", "date": "active", "note": "Track for Bengio\u2019s comments and papers around non-agentic or safer AI designs." }, { "source": "Google Scholar / arXiv / DOI pages", "title": "Primary paper indexes", "url": "https://arxiv.org/", "date": "active", "note": "Use primary paper pages before social interpretations. Store DOI/arXiv/title/author/date in the library." }, { "source": "Tony Robbins YouTube interview", "title": "Ray Kurzweil Predicts AI Will Change Humanity Completely by 2030", "url": "https://www.youtube.com/watch?v=fddhXXIjB6w", "date": "2026-06-11", "note": "Current interview comments on AGI, agents, AI in medicine, longevity escape velocity and human/AI merger." } ] }