Profile 7 min read

Lessons from David Chalmers

David Chalmers defined consciousness’s “hard problem”: why brain activity generates subjective experience. With Andy Clark, he proposed the extended mind thesis, reframing cognition through external tools while asking how virtual reality and AI reshape reality.
Profile 9 min read

Lessons from Stephen Wolfram

Stephen Wolfram built Mathematica and Wolfram|Alpha as a physicist and computer scientist. His claim that simple computational rules underlie the universe connects irreducibility and fundamental physics with questions about human cognition and artificial intelligence.
Profile 9 min read

Lessons from Joscha Bach

Joscha Bach is a cognitive scientist and AI researcher building computational models of the mind. He treats consciousness as a virtual simulation rather than a physical property, using that premise to connect intelligence, society, and human experience.
Profile 9 min read

Lessons from Juergen Schmidhuber

Jürgen Schmidhuber co-invented the Long Short-Term Memory network, solving vanishing gradients and enabling machine translation. His wider work links algorithmic information theory, Gödel machines, artificial curiosity, and intelligence as a stage of physical evolution.
Profile 10 min read

Lessons from Buck Shlegeris

Buck Shlegeris, CEO of Redwood Research, developed AI control for safely operating highly capable models that may be deceptive. His applied alignment work examines the tradeoff between safety and usefulness and paths for engineers entering technical research.
Profile 8 min read

Lessons from Trenton Bricken

Trenton Bricken is an Anthropic researcher reverse-engineering large language models. With dictionary learning and sparse autoencoders, he turns compressed networks into readable features to directly trace model logic, apply biological analogies, and automate alignment.
Profile 9 min read

Lessons from Sholto Douglas

Sholto Douglas is an Anthropic researcher, formerly at Google DeepMind, working on inference scaling and reinforcement learning with language models. His focus asks how test-time compute shapes reasoning, agent autonomy, and the prospect of automated AI research.
Profile 10 min read

Lessons from Neel Nanda

Neel Nanda leads a mechanistic interpretability team at Google DeepMind after working at Anthropic. Through grokking, induction heads, and Othello-GPT, he reverse-engineers neural networks to improve how researchers read internals, test safety, and conduct experiments.

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