Library
Books, papers, and thinkers that shaped how I think about intelligence, mathematics, and cognition. Not a reading list — an intellectual bibliography. These are the works I return to, argue with, and build on.
Canon
Books I return to. Each one changed something fundamental in how I see a problem that still matters.
Gödel, Escher, Bach: An Eternal Golden Braid
The most serious attempt ever written to explain consciousness through
self-reference and strange loops. Required reading before building any AI
system that claims to "understand" anything. Still unsurpassed.
The Emperor's New Mind
A physicist's argument that consciousness requires something beyond
computation. Wrong or right, it asks the question correctly —
and no serious response has fully disposed of it.
The Feynman Lectures on Physics
Not physics textbooks. A demonstration that deep understanding has a
distinct texture you can recognise and aim for. Clarity as a form of
rigor, not an alternative to it.
Probability Theory: The Logic of Science
Bayesian inference derived from first principles as an extension of
classical logic. The book that makes probability feel inevitable rather
than arbitrary. Foundation for how I think about uncertainty in AI systems.
Thinking, Fast and Slow
The rigorous empirical demolition of the rational agent model. Any AI
system that interacts with humans needs dual process theory as a
foundation. The gap between System 1 and System 2 is an engineering problem.
The Society of Mind
The original multi-agent system: intelligence as the emergent behavior
of many small, dumb processes with no central controller. Four decades
before transformers, Minsky got the architecture right.
The Structure of Scientific Revolutions
Required reading for anyone building AI systems who thinks they are
doing science. Most AI research is normal science; occasionally something
shifts the paradigm. Recognising which is which is a skill.
An Introduction to Information Theory
Shannon's framework, explained accessibly. Information is physical.
Entropy is not a metaphor. The mathematical foundation underneath
every loss function ever written.
Key Papers
Papers that changed how I think about specific problems. One sentence on why each one matters.
Agents
ReAct: Synergizing Reasoning and Acting in Language Models
Toolformer: Language Models Can Teach Themselves to Use Tools
A Survey on Large Language Model based Autonomous Agents
MemGPT: Towards LLMs as Operating Systems
Thinkers
Not a hall of fame. Honest notes on why each person's thinking changed how I see a specific problem.
Ramanujan
Mathematical truth can be intuited before it can be proved. The
counterexample to the idea that rigor and understanding are the same
thing. What he achieved is a standing challenge to every purely
computational view of cognition.
Leibniz
The original vision: reasoning as mechanical symbol manipulation.
The idea that intelligence could be formalised is older than computers
by three centuries. Leibniz was the first AI researcher.
Euclid
For the demonstration that an entire edifice of knowledge can be
derived from a small number of axioms. The model for what it means
to understand something from first principles.
Gauss
For the principle that mathematical elegance and empirical accuracy
are not in tension — they converge. The normal distribution
is both beautiful and everywhere.
Gödel
On the hard limits of formal systems. Every AI architecture that
claims to reason is implicitly navigating what Gödel showed cannot
be navigated from within. Understanding incompleteness is not optional.
von Neumann
Architecture as epistemology. How you structure computation shapes
what you can compute. The von Neumann bottleneck is not just an
engineering problem — it's a cognitive one.
Shannon
Information is physical. Entropy is not a metaphor. Every loss
function, every compression algorithm, every communication protocol
is a consequence of Shannon's 1948 paper.
Turing
The original definition. What computation means — and,
crucially, what it cannot mean. The halting problem is the oldest
alignment problem.
Feynman
For the principle that you don't understand something until you can
derive it from scratch. And for the demonstration that clarity is
a form of rigor, not an alternative to it.
Penrose
The most serious attempt to connect physics to consciousness, taken
seriously by someone who knows both. Wrong or right, The Emperor's
New Mind asks the question in a way that hasn't been answered.
Kahneman
The empirical demolition of the rational agent. Any AI system that
interacts with humans needs dual process theory as a foundation.
The gap between System 1 and System 2 is an engineering challenge.
Minsky
Intelligence as the emergent behavior of many small, stupid processes.
The original multi-agent system. Decades before transformers, Minsky
understood that mind might not have a center.
Hofstadter
Strange loops as the key to self-reference, consciousness, and meaning.
GEB remains the most honest book written about what it would
even mean for a formal system to understand itself.
Sri Aurobindo
On consciousness as ontologically primary, not emergent from matter.
A necessary counterweight to purely computational views of mind.
The question of whether intelligence requires consciousness is not settled.
Carl Sagan
For the cosmic perspective. Intelligence is rare in the universe —
we know of exactly one planet that has it. The stakes of getting AI
right are not just economic.