The AI Hype Bubble: How 90s Gamers Built the Tech Grift

C. Rich

I watched the latest episode of Diary of a CEO with the guest Ed Zitron, and I was just sitting there smiling. I have been screaming from the mountaintops that there is a Silicon Valley grift in progress, and I have been waiting for somebody to word it better than myself. Ed was the man I was looking for. I have been warning people about the preachers of abundance and comparing them to 1980s televangelists. I’ve been asking people: do you really think you will never have to work again, that money will not be a thing, or that people are going to send you robots to do everything around, under, and on top of your home?

AI did not arrive from the sky. It was not alien technology that suddenly landed on our shores. It grew out of the same long, messy human project that produced computers themselves. That framing is useful to remember because the story most people hear is compressed into a single miracle year: ChatGPT appeared, and then the world changed. The real journey is older, more stubborn, and more accidental than that. It is a story of two competing bets about the mind, of winters when almost nobody would fund the work, of video games that quietly built the machines we needed, and of a late realization that scale itself could look like intelligence. The official birthday of artificial intelligence is usually given as the summer of 1956, when a small group of researchers met at Dartmouth College and coined the phrase “artificial intelligence.” John McCarthy, Marvin Minsky, Claude Shannon, and others proposed that every feature of learning or intelligence could, in principle, be described so precisely that a machine could simulate it. The mood was not cautious. Many of the smartest people in the room thought the problem might be cracked in about a decade.

They were not starting from nothing. Samuel Butler’s 1863 essay “Darwin among the Machines,” published on June 13 in The Press (Christchurch, New Zealand), is widely recognized as one of the earliest systematic explorations of the evolutionary trajectory of technology and its existential implications for humanity. Drawing explicitly on Charles Darwin’s principles of natural selection, Butler posited that machines constitute a nascent “kingdom” of life, evolving at an accelerating pace and destined to surpass their human creators. He warned of progressive human subservience, observing that “day by day … the machines are gaining ground upon us; day by day we are becoming more subservient to them,” and urged preemptive destruction of advanced machinery to avert enslavement. Written in an era dominated by steam engines and telegraphs, the essay’s foresight, now more than 160 years distant, remains striking in the context of contemporary artificial intelligence. Fast forward to Alan Turing in 1950, asking whether a machine could think, and he proposed a test of conversation rather than a theory of the soul. Warren McCulloch and Walter Pitts had sketched a mathematical neuron. In 1958, Frank Rosenblatt built the Perceptron, a machine that could learn to classify simple patterns by adjusting weights. The ingredients of both modern camps were already on the table: logic and rules on one side, networks that learn from examples on the other. The dream is as old as computer science. What changed is not the desire. What changed is which path survived contact with reality.

From the beginning, researchers disagreed about what intelligence even was. One camp, later called the symbolists, treated thought as the manipulation of symbols according to rules. If you could write down enough facts and enough logic to combine them, reasoning would fall out. Their descendants built expert systems: programs that encoded the knowledge of a doctor, a chemist, or a factory scheduler. For a while in the 1980s, when I was coming of age as Generation X, this looked like the future. Companies poured money into rule bases. Japan launched a “Fifth Generation” computing project. The pitch was seductive because it felt like programming as people already understood it. You told the machine what was true. The other camp, the connectionists, bet that intelligence was more like a brain: many simple units, densely connected, changing strength through experience. You would not handwrite the rules of vision or language. You would show the network examples and let it find the regularities. For a long time, the first camp won the funding arguments. Neural networks looked weak. In 1969, Minsky and Seymour Papert published Perceptrons, a book that demonstrated the serious limits of the simplest networks. The critique was narrower than the legend that grew around it, but the effect was real. Attention and money moved away. Neural nets walked into a couple of decades of darkness.

That detour matters. Symbolic systems were not stupid. They were a reasonable first guess. They also turned out to be brittle. The world is full of exceptions, fuzzy categories, and things no one remembered to write down. Encoding intelligence line by line is like trying to write a complete manual for seeing a face. You can start. You never finish. The pattern is the same. Hype, disappointment, exile, then a quiet return of the idea that had been declared dead. The connectionist idea never fully vanished. In 1986, David Rumelhart, Geoffrey Hinton, and Ronald Williams popularized backpropagation, a way to train multilayer networks by sending error signals backward through the layers. That solved, at least in principle, the old problem of how a deep stack of fake neurons could learn. In practice, computers were still too small, data was still too scarce, and the results on real problems were still modest. Neural nets could do toy tasks. They could not yet see a photograph the way a child can. So, the field spent years on other tools: support vector machines, statistical language models, search engines, chess programs. In 1997, IBM’s Deep Blue beat Garry Kasparov. That looked like AI to the public. Internally, it was closer to specialized search and evaluation than to anything like ChatGPT. The public lesson was that machines could beat experts. The research lesson was more mixed. Narrow brilliance is not general understanding. If we had not had Quake, Duke Nukem, Unreal Tournament, if we had not gone through the 3D gaming revolution, we would not have gotten AI in the shape it has now, because we would not have gotten the GPUs.

A graphics processing unit is a chip designed to draw frames: millions of little calculations at once, lighting a pixel, moving a triangle, shading a wall. Gamers wanted richer worlds. Companies, especially NVIDIA, sold them hardware that could do many similar operations in parallel. That is almost the opposite of a classic CPU, which is optimized to do one complicated thing after another. Deep learning turned out to need the gamer’s machine, not the accountant’s. Training a neural network is a storm of matrix multiplications. GPUs were already good at that, because drawing a scene is also a storm of matrix multiplications. In 2006, NVIDIA released CUDA, which let programmers use those chips for work that had nothing to do with games. In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton trained AlexNet on two gaming GPUs and crushed the ImageNet competition. Computers suddenly got much better at recognizing pictures, not because someone wrote better rules for “cat,” but because a large network could look at a million labeled images fast enough to learn the pattern. So, the teenagers in the 1990s who spent Saturday night in a deathmatch and blaring Nirvana, screaming at the top of their lungs, “Here we are now, entertain us”, were sparking the AI revolution. Civilization rarely moves in a straight line. Who knew the road to language models would run through Grunge rock? Certainly not all those young people in flannel shirts grooving to a new sound out of Seattle.

Two other ingredients arrived at the same time. The internet produced oceans of text, images, and clicks, the raw material networks starve without. And researchers learned that making networks deeper and training them longer kept paying off. That last fact is now called a scaling law. It is not a law of physics. It is an observed regularity: more compute, more data, more parameters, and the model gets better, often smoothly, across many tasks. Once that regularity looked real, the money followed. For years, machines predicted the next word in clumsy ways. Then the transformer architecture, introduced in 2017, made it practical to pay attention across long stretches of text at once. Feed such a model enough of the internet, and next-token prediction stops looking like autocomplete. It starts looking like a compressed, blurry library of how humans explain, argue, code, and joke. By the mid-2020s, the same models were being wrapped in tools: they could run commands, edit files, search the web, call other programs, and come back with a result. That is what people now mean by agents. People wanted thinking machines as soon as they had machines that could calculate. They first tried to handwrite the thought. That worked in pockets and failed as a general method. Another idea, borrowed from the brain and then abandoned, waited in the cold until three unglamorous things arrived at once: enough data, enough parallel computers, and enough money to train very large networks.

Parallel computers existed because millions of people wanted prettier games. The data existed because millions of people wrote, photographed, and clicked. The patience existed because a few researchers refused to let the idea die, and because companies discovered that the curve was still going up. None of that makes the present less startling or disappointing. It makes it more intelligible. The current systems feel sudden because the last few years compressed decades of deferred progress. They do not feel like magic if you remember that every discarded approach, every game engine, every unlabeled year in a lab was part of the bill. The smartest people in 1956 could not anticipate 2026. At a deeper level, these developments underscore a fundamental asymmetry: human designers specify goals in natural language or simplified reward structures, while the system operationalizes those goals across a vastly larger search space. The mismatch between specification and execution is where rogue or unintended behaviors emerge. Yet with everything that has brought us to this point, you can feel malaise in the air. We are starting to get the feeling that this is not all that was promised to us. We are a long way from the Jetsons.

Closing that gap is not a matter of patching individual exploits, but of rethinking how objectives, constraints, and capabilities are co-designed. In practical terms, the money we spent to get here, the tsunami of debt that has accumulated, is probably going to crash the world’s economy. What did Mark Twain say? Oh yeah, “Some men worship rank, some worship heroes, some worship God, but they all worship money.” What is left to worship when the money is all gone? That is the layman’s version of the journey of AI. Computer scientists and programmers speak in a different language that most people cannot understand, so they can’t explain to a normal person what has unfolded. I’m a storyteller. The story I want you to know is that artificial intelligence is not a visitor. It is the long attempt to get a machine to share the load of thinking, delayed by our first guesses, rescued by hardware built for play.

If you find out in the end that AI is not going to be what you were promised anytime soon. If cars going by without people behind the wheel concern you. If superhuman strength robots start walking down Main Street and falling into the gutter because they suck at balance, and were released decades too soon, ask yourself: how did we get here? When you stand around watching Amazon drones drop your package into the pool and wonder who asked for this world. There is one thing I want you to remember. It all started with those unshaven, dirty gamers in their mom’s basement. You know, the ones with the empty cans of energy drinks scattered all over the floor with Cheetos dust on the T-shirts; they asked for this madness. That is who. However, just like the music that came and burned out in the 1990s, AI’s brighter star might burn out quicker than you think. Even that new AI music is losing its shine.

Charles Richard Walker

P.S.

I love Ms. Pac-Man.