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History of AI

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A 72-chapter book about how artificial intelligence actually came to be.

Most histories of AI tell you what algorithms were invented and when. This book tells you why those algorithms became possible — the math that had to be discovered first, the hardware that had to mature, the money that had to flow, the bottlenecks that broke, and the people who happened to be in the right room at the right moment.

The angle throughout is infrastructure-first: every era is shaped by what compute, data, and organizations could and could not do. Algorithms get the credit; infrastructure does the work.

Each chapter stands on its own. Read them in order for the full arc from Boole’s logic to today’s frontier models, dip into a single era, or jump straight to the technologies powering current systems — the connective tissue is in place either way.

Part 1 — The Mathematical Foundations (1840s–1940s)

Section titled “Part 1 — The Mathematical Foundations (1840s–1940s)”

Proving that human logic, reasoning, and probability can be formalized into mechanical algebra.

ChTitle
1The Laws of Thought
2The Universal Machine
3The Physical Bridge
4The Statistical Roots
5The Neural Abstraction

Part 2 — The Analog Dream & Digital Blank Slate (1940s–1950s)

Section titled “Part 2 — The Analog Dream & Digital Blank Slate (1940s–1950s)”

The transition from biology-inspired analog hardware to von Neumann digital architectures.

ChTitle
6The Cybernetics Movement
7The Analog Bottleneck
8The Stored Program
9The Memory Miracle
10The Imitation Game

Part 3 — The Birth of Symbolic AI & Early Optimism (1950s–1960s)

Section titled “Part 3 — The Birth of Symbolic AI & Early Optimism (1950s–1960s)”

The Dartmouth consensus, early search algorithms, and military funding.

ChTitle
11The Summer AI Named Itself
12Logic Theorist & GPS
13The List Processor
14The Perceptron
15The Gradient Descent Concept
16The Cold War Blank Check

Part 4 — The First Winter & The Shift to Knowledge (1970s–1980s)

Section titled “Part 4 — The First Winter & The Shift to Knowledge (1970s–1980s)”

The failure of early neural networks and the rise of hard-coded Expert Systems.

ChTitle
17The Perceptron’s Fall
18The Lighthill Devastation
19Rules, Experts, and the Knowledge Bottleneck
20Project MAC
21The Rule-Based Fortune
22The LISP Machine Bubble
23The Japanese Threat

Part 5 — The Mathematical Resurrection (1980s–1990s)

Section titled “Part 5 — The Mathematical Resurrection (1980s–1990s)”

The silent algorithmic breakthroughs that laid the foundation for modern Machine Learning.

ChTitle
24The Math That Waited for the Machine
25The Universal Approximation Theorem (1989)
26Bayesian Networks
27The Convolutional Breakthrough
28The Second AI Winter
29Support Vector Machines (SVMs)
30The Statistical Underground
31Reinforcement Learning Roots

Part 6 — The Rise of Data & Distributed Compute (1990s–2000s)

Section titled “Part 6 — The Rise of Data & Distributed Compute (1990s–2000s)”

The shift to empiricism, enabled by the internet and cluster computing.

ChTitle
32The DARPA SUR Program
33Deep Blue
34The Accidental Corpus
35Indexing the Mind
36The Multicore Wall
37Distributing the Compute
38The Human API
39The Vision Wall
40Data Becomes Infrastructure

Part 7 — The Deep Learning Revolution & GPU Coup (2010s)

Section titled “Part 7 — The Deep Learning Revolution & GPU Coup (2010s)”

The repurposing of graphics cards for massive parallel matrix multiplication.

ChTitle
41The Graphics Hack
42CUDA
43The ImageNet Smash
44The Latent Space
45Generative Adversarial Networks
46The Recurrent Bottleneck
47The Depths of Vision
48AlphaGo
49The Custom Silicon

Part 8 — The Transformer, Scale & Open Source (2017–2022)

Section titled “Part 8 — The Transformer, Scale & Open Source (2017–2022)”

Scaling laws, attention, and the democratization of AI through open weights.

ChTitle
50Attention Is All You Need
51The Open Source Distribution Layer
52Bidirectional Context
53The Dawn of Few-Shot Learning
54The Hub of Weights
55The Scaling Laws
56The Megacluster
57The Alignment Problem
58The Math of Noise

Part 9 — The Product Shock & Physical Limits (2022–Present)

Section titled “Part 9 — The Product Shock & Physical Limits (2022–Present)”

Consumer adoption, edge constraints, and AI transitioning to heavy industry.

ChTitle
59The Product Shock
60The Agent Turn
61The Physics of Scale
62Multimodal Convergence
63Inference Economics
64The Edge Compute Bottleneck
65The Open Weights Rebellion
66Benchmark Wars
67The Monopoly
68Data Labor and the Copyright Reckoning
69The Data Exhaustion Limit
70The Energy Grid Collision
71The Chip War
72The Infinite Datacenter

72 of 72 chapters published.