What AI Was, Is, and Is Becoming
Seventy years of nothing much, then everything at once. The machine that was supposed to think by 1975 learned to talk in 2022, and it is now in your kid’s pocket, your accountant’s software and your government’s war room. Time for a human take.
The seventy-year overnight success
Here is the first thing most people get wrong. Artificial intelligence did not show up in November 2022. It showed up in the summer of 1956, wearing a tie.
In August 1955, four researchers, John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon, wrote a funding proposal for “a 2 month, 10 man study of artificial intelligence” to be held at Dartmouth College the following summer. That document is where the phrase artificial intelligence comes from. It stated, with the confidence of men who had never met a deadline, that “a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”[5]
One summer. Ten guys. They were going to make a significant advance on machines that use language, form abstractions and improve themselves. To be fair to them, it was a very productive summer. It just turned out to be about seven decades long.
The predictions that followed have aged like milk in a hot car. In 1965, Herbert Simon, who would later win a Nobel Prize in economics, declared that “machines will be capable, within twenty years, of doing any work a man can do.” In 1970, Marvin Minsky told Life magazine that “in from three to eight years we will have a machine with the general intelligence of an average human being.”[6]
Three to eight years. From 1970. By that math, the average human being was supposed to be obsolete around the time Star Wars came out. Instead, we got the Pet Rock.
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1955–56
The name is born
Four researchers propose a two-month, ten-man summer study at Dartmouth. The phrase “artificial intelligence” comes from that proposal.[5] -
1965–70
The promises
Simon: any human work within twenty years. Minsky: a machine with average human intelligence in three to eight years.[6] -
1970s & 1980s
Two winters
The 1973 Lighthill report and the collapse of “expert systems” each dry up the money for years.[7]
This matters, and not just as a punchline. The pattern is not new. Brilliant researchers, thrilling demos, extravagant timelines, then a long stretch of nothing much. It is the oldest tradition in the field.
There were even names for the nothing-much stretches: the AI winters. The first arrived in the mid-1970s, after a British government review known as the Lighthill report concluded that AI had failed to deliver, and funding dried up on both sides of the Atlantic. The second came in the late 1980s, when the “expert systems” companies had spent fortunes on turned out to be expensive, brittle and about as flexible as a phone book.[7]
Twice already, the smartest people in the room told us the thinking machine was three to eight years away. Twice already, the money left the building. Anyone who tells you this time is different may well be right. But they should at least know the history, and most of the people selling it to you do not.
The wins did eventually come, just not the way the 1956 crowd imagined. In May 1997, IBM’s Deep Blue beat world chess champion Garry Kasparov.[8] In 2012, a neural network called AlexNet crushed the ImageNet image-recognition competition, and the modern “deep learning” era began in earnest.[9] In March 2016, DeepMind’s AlphaGo beat Lee Sedol, one of the best Go players alive, at a game people had said computers would not master for decades.[10]
Notice what those wins have in common. Chess. Image labelling. Go. Narrow, well-defined problems with clear rules and clear scores. Nobody’s job disappeared because a computer got good at Go. The machine was impressive. The world barely moved. That was the state of the art for most of the 2010s: very smart, very narrow, very far from your kitchen table. The line was still crawling.
Then somebody at Google wrote a paper with a title that sounds like a Beatles lyric.
In June 2017, eight Google researchers published “Attention Is All You Need,” introducing an architecture called the Transformer.[11] It was a new way for a machine to read a sequence of words and decide which of them matter to which other ones. It looked like a nice technical improvement for machine translation. It turned out to be the engine under every chatbot you have ever talked to. The T in GPT stands for Transformer.
OpenAI released ChatGPT to the public on November 30, 2022.[12] Within about two months it had an estimated 100 million monthly users, which analysts at UBS called the fastest adoption of any consumer application in history.[13]
The very confident autocomplete
Let us define the thing on your phone, because half the fear and all the hype depend on nobody doing that.
When people say “AI” today, they almost always mean a large language model: ChatGPT, Claude, Gemini, Copilot, the thing in your search engine that now answers before you finish asking.
Here is what a large language model actually is. A statistical system, trained on an enormous amount of text, that predicts which word is most likely to come next. Over and over, very fast, with a remarkable talent for sounding like a person. It is not looking things up. It is not reasoning the way you do. It has no idea whether what it just said is true. It has an idea of what sounds true, based on everything it has read.
That is the single most useful thing a regular person can know about AI, so let me put it in plainer words. The machine is a phenomenal pattern-matcher wearing a trench coat and pretending to be a librarian.
When the pattern in its training data lines up with reality, it is brilliant. Drafting an email. Summarizing a contract. Explaining your kid’s math homework four different ways until one lands.
When the pattern does not line up, it does not stop. It does not say “I don’t know.” It keeps going, fluently, confidently, and wrong. The industry calls this “hallucination,” which is a lovely word for “made it up.”
And this is not a bug a patch will fix next Tuesday. OpenAI itself published research last year explaining that hallucinations are a predictable result of how these systems are trained and scored. Models are rewarded for guessing rather than admitting uncertainty. Think of a student who leaves a multiple-choice question blank and gets zero, while the student who guesses might get lucky.[14] The machine has learned to guess. It is very good at it.
This is not another industrial revolution
You will hear the comparison constantly. “AI is the new electricity.” “It’s the industrial revolution of our time.” “It’s like when cars replaced horses.” I understand why people reach for those. They are comforting. We survived all of them. We even got weekends out of one.
But look at what each of those revolutions actually did.
Agriculture
Industry
The automobile
Electricity
Each one was enormous. Each one replaced one kind of thing we do: growing, making, moving, powering. And each one gave a generation or two the time to adapt, to retrain, to write the laws, to build the schools, to argue about it at length and eventually get bored of arguing.
Here is where I leave the history books and give you my own view. AI is not one of those. AI is what you get when you take all of them and point them at the one thing none of them touched: thinking. Not muscle, not motion, not power. Judgment, language, decision, memory, creativity.
The industrial revolution did not write your emails. The automobile did not tutor your daughter, diagnose your X-ray, draft your contract, write your competitor’s marketing, screen your resume and clone your voice for a scammer, all in the same afternoon, using the same tool. AI does all of that today.
And it does it inside every previous revolution at once. The farm’s sensors, the factory’s robots, the car’s cameras, the grid’s controls. It is not a new industry sitting beside the others. It is a new layer soaking through all of them.
And it is arriving in years, not generations. The industrial revolution gave families a century to figure out what happened to the weavers. This one gave a 23-year-old about eighteen months to figure out what happened to the job they trained for. More on that below, with numbers.
That is why the comparisons fail. Not because AI is more powerful than steam, though it may be. Because it is broader, faster, and aimed at the part of us we thought was ours.
There is no off switch
This is the part nobody likes to say out loud, so I will. Back to evidence for a moment.
AI is not just a product race between companies. It is a strategic race between countries, and the two biggest players have both said so in writing. China’s 2017 national plan set the goal of making China “the world’s primary AI innovation center” by 2030.[20] The United States has answered with export controls that block China from buying the most advanced AI chips,[21] with a national plan released in July 2025 whose actual title is Winning the AI Race,[22] and with the private spending spree described above. Nobody is being subtle.
In January 2025, a Chinese lab called DeepSeek released a model that matched the American frontier at a fraction of the cost, despite the chip restrictions, and rattled American markets. China has since ordered its state-funded data centres to use only domestic chips, and its newest models are being built for Chinese processors.
Analysts now describe two increasingly separate AI worlds, an American one and a Chinese one, with Europe, Japan, the Gulf states and India each trying to find a place between them.[23] Canada is in that middle group, whether we like it or not.
Think about what that does to the idea of slowing down. Every serious proposal to pause, to regulate hard, to take a breath, runs into the same wall. If we stop, they do not. If Washington slows down, Beijing does not. If Beijing slows down, Washington does not. If Ottawa or Brussels passes a strict law, the technology is built somewhere else and arrives anyway, through your phone, your bank, your kid’s apps.
Nobody is in charge of the whole line, and everyone on it believes falling behind is more dangerous than racing ahead. The people with the power to slow this down are precisely the people with the strongest reasons not to.