The History of AI:
From 1943 to Now

The mathematical and philosophical groundwork for artificial intelligence is laid by a small group of mathematicians, logicians, and neuroscientists. The field does not yet have a name.

• 1943McCulloch and Pitts publish the first mathematical model of a neuron — the conceptual ancestor of every neural network that followed.
• 1950Alan Turing publishes "Computing Machinery and Intelligence" and proposes the Turing Test — can a machine think?
• 1956The Dartmouth Conference coins the term "Artificial Intelligence." John McCarthy, Marvin Minsky, Claude Shannon, and others gather for a summer to define a new field.

Enormous optimism. Early programs solve algebra problems, speak English, play checkers. Researchers make bold predictions — machines will surpass human intelligence within 20 years. Government funding flows freely. The promises vastly exceed what the technology can actually deliver.

• 1957Frank Rosenblatt builds the Perceptron — an early neural network that learns to classify images. The New York Times predicts machines will walk, talk, and think.
• 1965ELIZA, the first chatbot, created by Joseph Weizenbaum at MIT. Many users believe it understands them. Weizenbaum is disturbed by this.
• 1966ALPAC report concludes machine translation is going nowhere. Early warning that AI's self-promotion was ahead of its capability.

Funding dries up. The Lighthill Report in the UK concludes AI has failed to deliver on its promises. DARPA cuts funding. The field contracts severely. Researchers who stay are considered eccentric.

• 1973The Lighthill Report concludes AI research has disappointed — machines could handle toy problems but not the complexity of the real world.
• 1974US and UK governments dramatically cut AI research funding. Labs close. Researchers move to other fields.

A new approach revives the field: instead of general intelligence, build narrow systems that encode expert human knowledge in specific domains. Corporations invest heavily. Japan launches a major national AI initiative. Commercial AI briefly appears to have arrived.

• 1980XCON, an expert system for configuring DEC computers, saves the company $40M/year. Corporate AI investment surges.
• 1981Japan's Ministry of Trade launches the Fifth Generation Computer project — a $850M national AI program meant to leapfrog the US.
• 1985The AI industry reaches $1 billion in annual revenue. Companies like Symbolics, Lisp Machines, and IntelliCorp attract major investment.

Expert system hardware becomes obsolete overnight. The Lisp machine market collapses. DARPA again cuts funding. Japan's Fifth Generation project quietly fails. The second winter is deeper and more disillusioning than the first because so much more money had been committed.

• 1987The market for specialized AI hardware collapses — $500M industry gone in a year as desktop computers outperform it at a fraction of the cost.
• 1991Japan's Fifth Generation project ends without achieving its goals. The US Strategic Computing Initiative winds down.
• 1993DARPA cuts AI funding again. The term "AI" becomes toxic in funding circles — researchers rebrand their work as "machine learning" or "knowledge engineering."

A fundamental change in approach: instead of trying to encode human knowledge as rules, let systems learn patterns from data. The internet provides vast training data for the first time. Computing power grows. Progress is steady but unsexy — happening in research labs, not headlines.

• 1997IBM's Deep Blue defeats chess world champion Garry Kasparov. The first time a computer beats a human champion at a major intellectual game.
• 1998Yann LeCun develops convolutional neural networks for handwriting recognition — the direct ancestor of modern image AI.
• 2002Roomba launched — practical AI in a consumer product. AI begins quietly entering daily life without the label.
• 2006Geoffrey Hinton publishes work on deep belief networks — beginning the quiet revival of neural networks that would transform everything.

A genuine paradigm break. Deep neural networks trained on massive datasets and powerful GPUs begin outperforming every prior approach — not incrementally, but dramatically. Image recognition, speech, translation — one by one, systems exceed human performance. The research community is stunned. Investment follows.

• 2012AlexNet wins the ImageNet competition by a shocking margin — 26% error rate vs 16% for the neural network. The deep learning era begins.
• 2014Google acquires DeepMind for £400M. The AI talent acquisition race begins in earnest.
• 2016AlphaGo defeats Go world champion Lee Sedol. Go was believed to be a decade away from computer mastery. The field recalibrates its expectations upward.
• 2017Google Brain publishes "Attention Is All You Need" — introducing the Transformer architecture that underlies every major AI system today.

The Transformer architecture enables a new approach: train enormous models on vast amounts of text, and emergent capabilities appear that nobody explicitly programmed. Language models begin to write, reason, translate, and summarize at unprecedented quality. The scale of computation — and the infrastructure required to support it — grows by orders of magnitude.

• 2018Google releases BERT — a large language model that dramatically improves search and translation. AI begins powering products used by billions.
• 2020OpenAI releases GPT-3 — 175 billion parameters. Researchers are startled by its ability to write coherent prose, answer questions, and complete code.
• 2021DeepMind's AlphaFold 2 solves the protein folding problem — a 50-year scientific challenge. Immediate implications for drug discovery and disease research.
• 2022DALL-E 2, Stable Diffusion, and Midjourney bring AI image generation to the public. For the first time, millions of non-technical people interact directly with generative AI.

ChatGPT reaches 100 million users in two months — the fastest consumer product adoption in history. Generative AI enters every industry simultaneously. Investment reaches unprecedented levels. The infrastructure arms race begins: data centers, power grids, water systems, and semiconductor supply chains are pushed to their limits. The public conversation about AI — its benefits, costs, governance, and risks — becomes unavoidable.

• 2022ChatGPT launched November 30. 1 million users in 5 days, 100 million in 2 months. The public AI era begins.
• 2023Microsoft invests $10B in OpenAI. Google rushes Bard to market. The AI investment arms race goes into overdrive. Kenyan workers who trained ChatGPT's safety filters speak publicly about their conditions.
• 2024Frontier models — GPT-4, Claude 3, Gemini Ultra — demonstrate reasoning, coding, and analysis capabilities that begin affecting professional labor markets. Data center construction announcements multiply. Community opposition begins organizing.
• 2025AI attributed to 54,836 job losses tracked in the US alone. Community moratorium efforts grow from 8 to 78 active campaigns. Sanders/AOC introduce the AI Data Center Moratorium Act. The questions of who owns AI, who bears its costs, and who governs its development move from research papers to town halls.