What a 1982 Computer Teaches Us About 2036

“If I have seen further it is by standing on the shoulders of giants.”

Newton wrote that in 1675, echoing a centuries-old idea. It remains the simplest explanation for how progress happens: each generation inherits tools that make the next generation’s breakthroughs possible.

A few days ago, I walked through that idea in physical form.

At the Computer History Museum in Mountain View, you can move through the history of modern computing in about ninety minutes. Room-sized mainframes give way to microprocessors. Microprocessors give way to personal computers. Personal computers give way to smartphones, robots, and AI systems.

And then, behind glass in one corner, I saw the machine I learned to code on: the Sinclair ZX Spectrum.

I can still feel those rubber keys. I remember typing BASIC line by line, debugging by trial and error, waiting minutes for a program to load from a cassette tape and hoping it wouldn’t fail at the final block. What hooked me and what hooks almost everyone who has that moment is the realization that a few lines of text can make a machine do something.

THE BOY WITH THE CHESS WINNINGS

In 1984, an eight-year-old in North London bought the same computer with prize money from a chess match. He taught himself to program from books. One of his first projects was a Reversi program that could reliably beat his younger brother.

The boy’s name was Demis Hassabis (now Sir Demis Hassabis).

In 2024, he shared the Nobel Prize in Chemistry for AlphaFold, a system that predicted the structure of essentially every protein known to science.

The machine he started with had 48 kilobytes of memory. That’s not enough to hold a single image from this page.

A DESK THE SIZE OF A FOOTBALL FIELD

Think of a computer’s memory as a desk. Everything it is actively working on must fit on that desk. Everything else has to be fetched from somewhere slower.

The ZX Spectrum’s desk was roughly a single sheet of paper.

The phone in your pocket has about 8 gigabytes of memory. That’s roughly 175,000 times larger. If the Spectrum’s desk was a sheet of paper, yours is a football field. Across it run multiple cores, thousands of times faster, alongside graphics processors and dedicated AI hardware that simply did not exist in 1982.

Computing doesn’t progress linearly. It compounds.

Better chips enable better software. Better software creates new possibilities. Those possibilities justify building even better chips. Up close, it feels incremental. In hindsight, it feels inevitable.

MOVE 37 → PLATFORM 37

For decades, Go was considered out of reach for machines. The number of possible board positions exceeds the number of atoms in the observable universe, and strong play seemed to require something uncomfortably close to intuition. As late as 2014, many experts thought it would take at least another decade before an AI could beat a top professional.

In March 2016, AlphaGo defeated Lee Sedol, one of the greatest players of his generation, four games to one. In game two, it played a move that professional commentators initially assumed was a mistake. AlphaGo itself estimated that a human would choose it perhaps once in ten thousand times. The move won the game. It has since been memorialized as Move 37.

The system that played it was built by the same person who had once written a small

Reversi program to beat his younger brother. From that bedroom experiment to defeating a world champion: thirty-two years.

Google named their London headquarters Platform 37.

WHEN INTELLIGENCE DIFFUSES

That capability is no longer confined to a handful of labs.

In July this year, Moonshot AI released Kimi K3, an open-weight model with 2.8 trillion parameters. That’s trillions of adjustable values shaped by training. More importantly, it approaches leading proprietary systems on many coding and reasoning tasks, and the company has committed to publishing the full weights this month.

At the same time, models are moving onto personal devices.

A model that runs on your phone will not be the most powerful in the world. But most tasks do not require the most powerful model. They require something fast, private, and always available. And often they require something that works without a network, keeps data local, and responds instantly.

This is a familiar pattern. The mainframe did not disappear when the personal computer arrived. The cloud did not eliminate the personal computer.

Intelligence is following the same path. It is ceasing to be centralized.

From 48 kilobytes on a Spectrum to trillions of parameters in open models and now back down again into devices you carry.

THE NEXT EXHIBIT

Every generation builds the conditions for the next. Mainframe engineers made the microprocessor possible. Sinclair made computing affordable enough for a child. Those children became programmers. One of them helped map the structure of life itself.

None of them were finishing the story. They were building the staircase.

If the distance from a 48-kilobyte home computer to a supercomputer in your pocket took four decades, the distance from today’s models to what we will run in 2036 will likely be larger and arrive faster.

The only real question is whether you are planning for that world.

Somewhere tonight, someone is experimenting with a machine they do not fully understand yet. They are running a model locally, modifying it, pushing it slightly beyond what it was meant to do. It may be trivial. It may be a toy.

So was a Reversi program, once.

Every object in that museum was once a future being built by someone who could not see how far it would go.

The next exhibit is already in progress.

Until next time,

Ram

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Ram Srinivasan

MIT Alum | Author, The Conscious Machine | Global Future of Work and AI Adoption Leader published in Business Insider, Fortune, Harvard Business Review, MIT Executive Viewpoints and more.

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Ram Srinivasan currently serves as an Innovation Strategist and Transformation Leader, authoring groundbreaking works including "The Conscious Machine" and the upcoming "The Substrate Shift."

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