Emmanuel Ameisen is an interpretability researcher at Anthropic, where he looks inside LLMs like Claude to understand how they think. Before joining Anthropic, he spent years in applied ML at Stripe and Insight, and is the author of Building Machine Learning Powered Applications. His research has produced some of the most striking findings in the field: that models form general representations of concepts, plan many tokens ahead without stating those plans explicitly, and may exhibit something structurally similar to the distinction between conscious and unconscious processing in humans.
Why the “next-token predictor” framing undersells how models actually plan and think
Why anthropomorphizing AI can actually be the right approach
The smooth scaling curve behind what look like sudden capability jumps
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• Scaling Laws for Neural Language Models: https://arxiv.org/abs/2001.08361
• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens
• The Rise and Fall of Agent Civilizations: The whole OpenAI/Hugging Face story in plain English: https://www.dwarkesh.com/p/openai-huggingface
• The Utility of Interpretability — Emmanuel Ameisen, Anthropic: https://www.latent.space/p/circuit-tracing
• A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains: https://www.amazon.com/dp/0063286343