Why Games?
My original experience with games was the arcade.
The reason was simple: we didn’t have a Famicom at home, and my grandmother was a gaming fanatic.
As a kid, every time we went shopping at the department store, my grandmother would take me to the game corner without fail. Sometimes she’d let me look through the periscope she was peering into, and I got hooked on the game where you fire torpedoes at warships.
My grandmother, born in the Taisho era, was the only gaming enthusiast in our family. My dad’s idea of games was mahjong at best, and my mom was a workaholic to the core.
I wasn’t in a position where I could play games freely. The most I got was going to the neighbor’s house to play Famicom. Otherwise, I had no choice but to make my own games on our home PC.
By the time I was finishing second grade, I could write a decent range of programs in BASIC. But it wasn’t until third grade that I realized programming and making games were fundamentally different things.
Hopscotch, kick-the-can, tag — those kinds of primitive “games” kept me obsessed every day. Children’s play is remarkably clever and refined, and every game was genuinely thrilling.
But whenever I tried to create a completely new game with original rules, I first had to explain those rules, and then somehow enforce them. In other words, children’s play requires a consensus on the rules before anything else — and games with complex rules were simply impossible for most kids to actually play.
What was revolutionary about computer games was that children and adults alike could play games of the same complexity. The enforcement mechanism — the thing that made you follow the rules — had been automated by the program.
Take table tennis or tennis: you can’t play without knowing the rules first. But with PONG, you could just spin the dial randomly and have fun. Knowing the rules was optional.
In our industry, we call the act of randomly mashing the joystick without knowing the rules “lever-gacha.” Even in that completely rule-ignorant state, given a little time, you can understand the rules and improve. There are two kinds of beings that can do this: children, and AI.
When DeepMind — later acquired by Google — announced Deep Q-Network, not many people were excited about it. I remember the reaction on sites like Hatenabookmark being quite cold. I was enthusiastic, but most people dismissed it as “pointless nonsense.” And that’s why none of you won a Nobel Prize. (Neither did I, to be fair.)
Deep Q-Network, in a sense, evolved naturally and directly into reinforcement learning AIs like AlphaGo and AlphaZero, and ultimately became AlphaFold, which brought a major breakthrough in chemistry — and the development team received the Nobel Prize in Chemistry. I believe it was the first time that people whose profession was computer programming won a Nobel Prize.
As someone who had been in the game industry, I understood it well. If you can learn through lever-gacha, that is essentially real intelligence. Because virtually everything humans learn — children included — is learned through a kind of lever-gacha.
In the 1990s, the game industry was poor but dazzling. I saw a particular potential in computer games.
One was the computer game as a device for “thought experiments” or “experiential thinking.” SpaceWar!, the very first computer game born from gravity simulations, existed as an extension of thought experimentation. You could say it was the origin of simulators.
Early games started in space because screens were black. And there’s no doubt that the golden age of space opera — Star Trek, Star Wars — was still in full swing, and the excitement of Apollo reaching the moon had not yet faded.
Star Wars was such a massive hit that space-themed content became widely accepted. More than that, there was a growing hunger to somehow experience “something Star Wars-like” for yourself.
If Star Wars hadn’t existed, I probably wouldn’t have thrown myself into computers with such intensity.
Star Wars contained two things that would become indispensable to my life. One was the experience of space itself — the aerial combat aboard X-Wings, the trench run across the surface of the Death Star. The other was droids.
Among all the droids in Star Wars, the lead mech R2-D2 is genuinely strange.
For one thing, it’s unclear whether it even has a face. R2-D2 boldly discards the “facial symmetry” that vertebrates — humans, fish, cats, dogs — and even arthropods like insects all possess.
And yet, rather than looking frightening to a child, it somehow looks adorable.
On top of that, R2-D2 never speaks a word of human language, and yet comes across as calmer and more intelligent than any other character in the film.
Having never seen a character like that before, I was completely captivated by R2-D2 as a child. The white-and-blue color scheme was part of it too. I don’t know exactly why, but Doraemon is also white and blue. So for me, white and blue have always been the colors of AI.
Building artificial intelligence was too difficult for a first-grader, so my programming journey began with an interest in space.
To deepen my understanding of pseudo-3D techniques, the masterpiece Space Harrier was essential.
Around that time, for some reason my dad decided to buy a Famicom(NES) game as a gift when we were visiting relatives. He bought some obscure title at Takeyaya in Okachimachi — and it turned out to be something extraordinary.
It was a game that reproduced the gameplay of Space Harrier on the vastly less powerful 8-bit Famicom. I wouldn’t learn until about 30 years later that an acquaintance had secretly developed it. But by comparing Space Harrier and Attack Animal Gakuen, my understanding deepened considerably.
Even a game that looks simple now taught me things: that scaling is the core technique, and that the way objects scale must not follow 0.5× → 1× → 1.5×, but rather something close to doubling — 0.1× → 0.2× → 0.4× → 0.8×.
By comparing these two games, back when I hadn’t even learned fractions yet, I came to intuitively understand something called “perspective projection” from a 3D programming textbook:
For me, games were textbooks. Games were teachers.
With just this one small insight — this one simple calculation — you could express the sensation of three-dimensional space.
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This can be reproduced with a very simple program.
Back then, cameras were very expensive and taking photos wasn’t so easy — especially for an elementary school kid. Today I would probably line up some caramel boxes at equal intervals, take a photo, measure the distances on screen, and develop the intuition that way. But in those days, all I had was games.
If I hadn’t had this moment before adolescence, I would never have later installed vectors and matrices as a kind of second language in my own mind.
After grasping this, my head became extraordinarily clear. Until then, every concept had swirled together in a muddy fog. But once I began moving back and forth between three-dimensional space and a two-dimensional plane, my imagination expanded to a literally different dimension.
The educational power of games should never be underestimated.
Take Oregon Trail, for example. Every American of a certain generation has played it. It’s a game about the westward expansion, through which you can learn American history.
Practically mandatory in schools from the 1980s through the 1990s, it taught players about the hardships of the American frontier and the importance of budget allocation.
When I went independent in 2003 and founded my first company, I spent countless hours obsessively playing “The Conveni” and SimCity before we even opened our doors.
“The Conveni” is literally a game about managing a convenience store. SimCity is about running a city. What they share is the management of investment and debt, income and expenditure.
I decided I would never hire an employee until I had fully mastered “The Conveni.” My business instincts were sharpened in places like this — seemingly unrelated and trivial. The feeling of unease when looking at numbers, the excitement of reading a cash flow statement — I cultivated all of that here.
But thinking about it more carefully, almost no business simulation games actually include a cash flow statement. Maybe the people making them have never seen one.
If someone made a game where you manage a business while looking at a real cash flow statement, you could dramatically reduce the number of executives who run their companies into the ground. Venture capitalists should require applicants to submit their score from that kind of game. Maybe I should just build it myself — I’m a manager with a quarter-century of experience and a game creator, after all.
The educational potential of games has attracted serious attention in many quarters.
Carnage Heart, for example, was considered a promising teaching tool for programming.
Though from my perspective as a programmer, it was simultaneously too difficult in some ways and too cumbersome in others.
Age of Empires taught me the most fundamental lessons for navigating adult society: selection and concentration of resources, how to deploy military force, Lanchester’s strategy, alliances and negotiation. Age of Empires is a long-running series that still releases new installments.
That was what games meant to me. I became obsessed because they demanded real thinking. The rush of outsmarting an opponent and pulling off a comeback — nothing else comes close.
Games of that era had powerful educational value, and they worked just as well as entertainment for adults.
And yet today, the meaning of “making a game” has completely changed.
Despite how dramatically generative AI has advanced, games have hardly benefited at all.
The only sliver that’s benefited is the narrow domain of “game development” itself — and even there, the conversation is mired in a rather trivial debate: should in-game content really rely on generative AI, or is it better to have human artists craft everything by hand?
I will say this plainly: right now, there is not a single game in the world that has been built to genuinely unleash the full potential of generative AI.
AI Art Impostor was a masterpiece that caught the wave of generative AI early (from the development side, however, it was cost-inefficient). In the early days of LLMs, there was AI Dungeon — but that too remained an unfinished game in many respects.
Today, if you seriously try to incorporate generative AI into a game, three walls stand in your way.
The first is the API wall. API usage fees are still high. To let players enjoy a game freely, you’d need to pass those API costs onto users — and no one wants a game that complicated.
The second is the hardware wall. If you want to build a fully local AI game with nothing in the cloud, you’d need something like an RTX 5090 as a baseline. That means a PC costing close to one million yen(almost $7,000 ) — and the market for that is tiny. Only a small group of enthusiasts would ever play it.
The third is the budget wall. If a game might incur unlimited API fees, or requires a one-million-yen PC to play, no company will fund it. Even venture capitalists would tell you to build a gacha game or a hyper-casual game instead. And VCs are complete amateurs when it comes to games. They have no interest whatsoever in the cultural or educational significance of what games can be.
So the conclusion is this: right now, the only people who can build a game that fully leverages AI are amateurs. Building it professionally is impossible.
If I were still an active executive running a company of a hundred employees, I wouldn’t make — couldn’t make — such a reckless bet.
But fortunately, I’m alone now. There’s no one else I need to feed.
And unlike before, the assets a game requires can, in principle, all be generated. What’s more, for someone unemployed, I have an extraordinary environment at my disposal: forty GPU servers provided free of charge by Sakura Internet, the AI supercomputer “Tsugino-suke”(A100 80GBx8), the Memeplex machine(A6000x2) donated by Dospara (Thirdwave), and various other GPU resources lent or donated by a range of companies — all mine to use freely.
So this year, I’m going to take on a challenge that no one else is doing — something that might be completely pointless to the world, but that I alone believe has meaning, and that perhaps only I can do.
I’m going back into game development.
Not for money.
Purely as research into how to use this technology.
Now is the time to pursue that research — and there is no more ideal subject for it than games.
