A Brief History of
Artificial
Intelligence

It starts with a teenager hiding in a library. Walter Pitts ran away from home at fifteen, taught himself logic from Principia Mathematica, and wrote to Bertrand Russell to point out errors in it. Russell wrote back. In 1943, together with the neurophysiologist Warren McCulloch, Pitts published a paper arguing that neurons were logic gates, and that a network of them could compute anything computable. Nobody built one. It was 1943; there were more urgent machines to build.

Seven years later, Alan Turing, fresh from breaking German ciphers, sat down and wrote the founding document of the whole field. He opened with a question and then immediately refused to answer it, proposing a game instead: if a machine can hold a conversation well enough that you cannot tell it from a person, does the difference matter? He predicted that by the year 2000, machines would fool an average interrogator about 30 percent of the time after five minutes of chat.

He was off by about two decades, and in the wrong direction. Nobody in 1950 guessed the real problem: not whether machines could fool us, but how much we would enjoy being fooled.

The machines of the 1960s could prove small theorems and move blocks around a virtual table. What they could not do was anything anyone would pay for. Machine translation, funded lavishly through the Cold War so computers could read Russian, produced output so bad that a 1966 government report concluded human translators were cheaper, faster and better. The money stopped overnight.

In 1969, Minsky and Papert published a mathematical takedown of the Perceptron, proving a single-layer network could not even learn XOR. The book was correct, narrowly. It was read broadly. Neural network research entered a coma that lasted fifteen years, which is a long time to be right too early, as Rosenblatt did not live to see: he died in a sailing accident in 1971.

Then came the coldest document in AI history. In 1973 the British government asked mathematician James Lighthill to review the field. His report was so devastating that the BBC staged a televised debate where the field's founders defended their life's work against it. They lost the room. Funding collapsed on both sides of the Atlantic. Researchers learned to stop saying "artificial intelligence" on grant applications, a survival trick the field would need again.

AI came back in the 1980s wearing a tie. The new idea was the expert system: interview a human expert, write down thousands of IF-THEN rules, sell the result to a corporation. It actually worked, briefly. XCON, which configured computer orders for Digital Equipment Corporation, was saving the company an estimated 40 million dollars a year by 1986. A whole profession appeared overnight, the "knowledge engineer", whose job was extracting rules from grumpy specialists one meeting at a time.

Japan announced the Fifth Generation Computer project and pledged 850 million dollars to leapfrog the West; the US and Britain, terrified, opened their wallets in response. An entire hardware industry sprang up selling machines optimised for Lisp, the language of AI.

By 1987 you could buy a desktop workstation that ran Lisp faster than a Lisp machine, at a tenth of the price. The specialised hardware market evaporated in a single year. Expert systems, meanwhile, turned out to be brilliant right up until reality changed, at which point a human had to rewrite the rules by hand, forever. The second winter arrived on schedule. "AI" became such a poisoned brand that researchers doing AI rebranded it as "machine learning", "informatics", or "pattern recognition", a linguistic witness-protection program that lasted twenty years.

In June 2012, Google connected 16,000 processor cores into a giant neural network, showed it ten million random YouTube frames with no labels at all, and checked what it had learned to recognise. The answer, delivered by the internet's own physics, was: cats. One neuron in the network had, entirely on its own, become a cat detector. The New York Times headline wrote itself.

The real earthquake came that autumn, from a far smaller machine. Geoffrey Hinton, one of the few researchers who had kept the neural network faith through two winters, entered the ImageNet image-recognition contest with two students and two gaming GPUs running in a bedroom-sized lab. Their network, AlexNet, did not just win; it nearly halved the error rate, 15.3 percent against 26.2 for the runner-up. In machine learning terms this was not winning a race, it was finishing a marathon while the field was still tying its shoes.

Within months, Hinton auctioned his three-person startup to the highest bidder. Google paid 44 million dollars for a company with no product, no revenue and no plan. It was the deal of the century. Every big tech company concluded the same thing simultaneously: buy every neural network researcher on Earth, whatever it costs.

That same month, in a Seoul hotel, something happened that people in the field still lower their voices to talk about. DeepMind's AlphaGo was playing Lee Sedol, one of the greatest Go players in history, in a game so intuitive that experts believed computers were a decade away from mastering it. In game two, move 37, AlphaGo placed a stone so alien that commentators assumed the operator had misclicked. The machine calculated a human would play it with probability 1 in 10,000. It won that game, and the match, 4 to 1.

Lee Sedol's single win, game four, came from move 78, a play so brilliant Koreans called it "the divine move", the machine's own blind spot found by the last human who will ever find one across a Go board. Lee retired from professional play three years later, saying that even if he became number one, there is an entity that cannot be defeated.

On 30 November 2022, OpenAI released a chat interface for its language model, mostly to gather feedback. Internally, expectations were modest. What happened instead had no precedent in the history of any product: one million users in five days, one hundred million in two months, the fastest adoption of any consumer application ever recorded. The before times ended on a Wednesday.

In March 2023, GPT-4 arrived and passed the bar exam. A public letter signed by tens of thousands, Elon Musk among them, called for a six-month pause on training bigger models. No one paused, least of all Musk, who founded his own AI lab about three months later. The whiplash between "this will kill us all" and "anyway, we raised ten billion" became the defining tone of the decade.

And in November 2023 the industry produced its finest soap opera. On a Friday, OpenAI's board fired Sam Altman without warning and without explaining why, not even to Microsoft, who found out minutes before the world did. By Monday, 700 of 770 OpenAI employees had signed a letter threatening to follow him to Microsoft. By Wednesday he was CEO again and the board was gone. Among the letter's signatories: the board member who had fired him, tweeting that he deeply regretted his participation. Five days, start to finish. The company building humanity's most consequential technology, and nobody, then or since, has fully explained what happened.

It is July 2026, and the argument has changed shape without ever resolving. In January, Sequoia published an essay titled "2026: This Is AGI", arguing that agents that work autonomously for hours already meet the bar. In the first six months of the year, American employers named AI as the reason for over 101,000 job cuts, double all of last year. Anthropic paid 1.5 billion dollars to settle with authors whose books trained its models, roughly 3,100 dollars per book, history's most expensive library fine. Prediction markets price the odds of the bubble bursting this year at about one in five, which is either reassuring or terrifying depending on your portfolio.

The agents write code now, browse the web, book the flights. In three weeks, on 2 August, the EU starts requiring AI-generated content to say what it is. Which brings up an awkward disclosure.

Every image on this page was generated by an AI. The code was written by one too, supervised by a human who mostly said "make the winter section colder". In 1950 that sentence was science fiction. In 2026 it is a Friday.