AI Doc
Thinking Patterns

Thinking Patterns

Mental models frontier engineers use to make AI and systems decisions

Short, dense essays and case studies from frontier practitioners — the mental models actively driving modern engineering decisions. AI-specific paradigms sit alongside general engineering philosophy that applies to AI work.

1972
David L. Parnas

Decomposing Systems into Modules

The original information-hiding paper. Module boundaries should hide design decisions that might change — not mirror execution flow. Every modern module system (OOP, Rust traits, Go interfaces, microservices) descends from this.

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1980
C.A.R. Hoare (Turing Award lecture)

The Emperor's Old Clothes

Hoare's Turing lecture. The 'billion-dollar mistake' (inventing null reference) + 'make it so simple there are obviously no deficiencies, not so complex there are no obvious deficiencies'. Shapes every modern language's Option/Result types.

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1986
Fred Brooks

No Silver Bullet

Essence vs accidental complexity. 'No single advance in technology or management will give a 10× improvement in a decade.' 40 years on, vindicated by LLM coding productivity data (~2× not 10×).

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2011
Rich Hickey (Clojure)

Simple Made Easy

The 60-min Strange Loop talk that gave engineers a vocabulary — simple vs easy, complect vs decomplect. 15 years later, still the default framework for architecture review arguments.

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2014
Chris Olah

Neural Networks, Manifolds, and Topology

The blog post that framed neural networks as geometric deformation of manifolds. Foundation of the 'embedding space / feature direction' vocabulary used in all modern LLM interpretability — and seed of Anthropic's mechanistic interpretability program.

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2017
Andrej Karpathy

Software 2.0

Reframes programming: neural network weights are source code. A 2017 prediction fully vindicated by the 2025 LLM era.

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2019
Rich Sutton

The Bitter Lesson

70 years of AI research in 800 words: general methods that leverage computation win. The North Star of modern AI engineering.

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2024
Case study · Musk / Kaplan / Chinchilla

First Principles in Engineering

First-principles thinking as a repeatable operation — not a mindset. Three cross-domain cases: SpaceX rocket economics (30× cost gap), Tesla cell-to-pack (55% cost cut), AI scaling laws (budget → calculable result).

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