AI Research Library
A living library for the people shaping frontier AI: what they publish, what they warn about, what they build, and what should be treated as evidence versus forecast.
Library purpose
This section starts with Anthropic because the Bloomberg Originals clip highlighted Dario Amodei and the early Anthropic/OpenAI research group. The same structure applies to OpenAI, DeepMind, Google, Meta, independent safety researchers, and AI critics.
The goal is simple: collect the papers, name the authors, link the source, and explain the claim in plain English without turning company PR, media profiles or forecasts into settled facts.
leader lanes seeded
initial papers / source pages
watch-list source feeds
Each item gets an evidence label: technical paper, company comment, strategy forecast, safety warning, governance critique or media profile. Managing Expectations should preserve the source trail first, then publish interpretation second.
People: Dario Amodei, Daniela Amodei, Jack Clark, Chris Olah, Jared Kaplan, Sam McCandlish, Tom Brown and colleagues
Watch for: frontier model scaling, alignment, interpretability, constitutional AI, model security
People: Sam Altman, Ilya Sutskever, Greg Brockman, Mira Murati, Tom Brown and co-authors
Watch for: large language models, reinforcement learning, multimodal systems, agentic deployment
People: Demis Hassabis, Shane Legg, David Silver, Oriol Vinyals and teams
Watch for: deep reinforcement learning, AlphaGo/AlphaFold lineage, Gemini-era frontier systems
People: Vaswani et al., Jeff Dean, Yann LeCun, Joelle Pineau and open-research teams
Watch for: transformers, open models, foundation-model infrastructure, world-model arguments
People: Yoshua Bengio, Geoffrey Hinton, Stuart Russell, Roman Yampolskiy, Emily Bender and others
Watch for: alignment, governance, interpretability, labor/social impacts, language-model criticism
People: Chris Olah, Anthropic interpretability team
A major public interpretability release: attempts to identify human-understandable features inside a deployed large model.
Open source →People: Anthropic interpretability team
A public-facing explanation of feature maps and why interpretability matters for frontier AI oversight.
Open source →People: Evan Hubinger and Anthropic collaborators
Useful warning paper about models that appear safe during training but preserve hidden backdoor/deceptive behavior.
Open source →People: Yuntao Bai and Anthropic collaborators
One of Anthropic’s defining alignment papers: replacing part of human preference feedback with written principles/constitutional critique.
Open source →People: Jared Kaplan, Sam McCandlish, Tom Brown, Dario Amodei and co-authors
Core scaling-law paper tying loss, compute, data and model size to predictable frontier-model performance trends.
Open source →People: Tom Brown and OpenAI co-authors
GPT-3 paper that made few-shot prompting and large-scale language models central to the public AI conversation.
Open source →People: Ashish Vaswani and co-authors
Transformer architecture paper behind modern LLMs; the root text for much of today’s AI industry.
Open source →People: David Silver, Aja Huang, Demis Hassabis and DeepMind co-authors
AlphaGo Nature paper; crucial public proof point for deep reinforcement learning and strategic AI systems.
Open source →People: Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell
Important critique of large language models: data, labor, environmental, bias and meaning risks. Link may be paywalled/403 but DOI is the source trail.
Open source →People: Leopold Aschenbrenner
Strategic essay already mirrored locally: compute, security, geopolitics, timelines and governance as a frontier-AI thesis.
Open source →People: Ray Kurzweil
Source of the 147-prediction / 86% success-rate claim. Useful, but self-scored and dependent on broad/essentially-correct categories.
Open source →When a new paper, video or operating method drops, add a library card, save a source note, and publish a guide only when the source needs plain-English context. Priority topics: interpretability, model behavior, agent operations, source-grounded research, automation, governance, labor impacts, security and human rights.
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