Gemini Just Beat Pokémon: 90s Kids, Let Me Explain

Once upon a time, in 1996, two cartridges changed the world.

Pokémon Red and Blue weren’t just games. They were childhoods on loop. A rite of passage. A masterclass in hoarding (just digital animals, thankfully). Beating them was about pride. About proving you could survive Viridian Forest without a map or your older cousin (this is before YouTube walkthroughs, fellas).

Fast forward to 2024: Google DeepMind’s Gemini just beat Pokémon Red. But this time, no kid. No Game Boy. Just (well, not just) AI, overlays, memory reads, chain-of-thought prompts, and a Reddit post that accidentally launched a PR cycle.

Everyone clapped. Twitter tech bros reposted it with 🔥 emojis. And for a second, it looked like we were on the cusp of AGI because Rattata followed orders.

But before we hand Gemini the Ash Ketchum hat, let’s slow down and unpack what really happened.

CLAUDE TRIED. GEMINI FINISHED. BUT DID IT ACTUALLY PLAY?

Anthropic’s Claude once took a shot at this. Same game, same dream. It didn’t quite make it past the early routes. The challenge wasn’t just about “playing” but surviving in a world where NPCs say “I like shorts!” and your rival somehow always has better Pokémon.

Then came Joel Z. A developer with a devout love for both AI and Game Freak nostalgia. He fed Gemini 1.5 a blend of real-time emulator data (mGBA), RAM reads, and a feedback-rich loop of planning, sub-agents, and goal setting.

The model wasn’t “looking” at the screen like a human. Instead, it saw the game like the Matrix. Raw data. HP bars. Coordinates. Objectives. Then it talked itself through what to do next. Sometimes it caught a Rattata. Other times it stared at a wall for 15 minutes.

Gemini didn’t learn the game. It project-managed it.

LET’S BREAK THIS DOWN: THIS WASN’T SOLO MODE

At a technical level, what Gemini did was impressive. But it wasn’t just Gemini sitting with a Game Boy and figuring it out.

Here's how the magic worked:

• Emulator: mGBA ran the game.
• RAM Access: Instead of guessing from pixels, the AI got internal game data. Like knowing your enemy’s health without guessing.
• Visual Overlays: Generated synthetic views of the game state, so the AI could “see” what’s happening.
• Chain-of-thought prompts: The model didn’t just react. It planned. Wrote out ideas. Then chose the best next move.
• Sub-agents: Like a team of interns inside Gemini. One decides where to go. Another figures out how to get there. A third double-checks if they’re stuck in a corner.

And yes, humans were involved. Not to hold its hand, but to occasionally tweak, restart, and refine prompt engineering. It’s like giving a Formula 1 car the best pit crew. The driver gets the glory, but it’s a team sport.

COOL TECH. BUT IS IT INTELLIGENCE?

Let’s get philosophical for a second.

Did Gemini actually understand Pokémon?

Nope.

It didn’t dream of becoming the very best, like no one ever was. It didn’t wonder why Nurse Joy looks the same in every town. It didn’t want to beat the game.

It just followed instructions. A glorified instruction follower, yes, but still that.

We’re in a strange era of AI where success often looks like intelligence but smells like scaffolding. Claude tried without scaffolding and struggled. Gemini, with scaffolding, succeeded. That’s not cheating, but it’s not emergent reasoning either.

It's the difference between a student solving a math problem and a student reading step-by-step hints off the board.

WHY THIS MATTERS (EVEN IF IT’S NOT AGI)

This isn’t about Pokémon. It’s about what happens when you add tools to language models. Like a Swiss Army Knife upgrade.

Large language models can do a lot. But hook them up to APIs, visual data, memory, and feedback loops? Suddenly, you get something much closer to a task-oriented agent. One that can set a goal, break it down, course-correct, and maybe beat Misty.

This matters because it’s not about this one game. It’s about:

• Personal assistants that can actually complete tasks over days
• AI agents managing workflows, not just writing emails
• The future of software not being apps but reasoning loops

We’re building less of an “AI brain” and more of an “AI operating system.” Think Windows 95, but instead of Solitaire, it can summarize PDFs, draft your resignation letter, and order Mani's Biryani.

MEANWHILE, IN THE LAND OF MEGACORP WARS

Zoom out, and this becomes a proxy war between Google’s DeepMind and Anthropic. Two labs, one dream: lead the multi-modal, reasoning-capable agent revolution.

It’s about mindshare.

When Gemini beats a childhood icon, it doesn’t just win Reddit karma. It wins perception. Meanwhile, open-source devs are watching closely, figuring out what to replicate and democratize.

This moment is a flex, but it’s also a breadcrumb in a much bigger trail toward consumer-facing AI agents. The kind that don’t just talk but act.

FROM PHILOSOPHY CLASS TO PRODUCT ROADMAPS

So here’s the real question:

Is this play? Is this understanding?

Or is it just mimicry wrapped in code?

From a humanities lens, this doesn’t qualify as intelligence. There’s no self, no desire, no meaning-making. Gemini didn’t learn to love Bulbasaur. It just knew he was a decent starter with good type advantages.

But from an engineering POV? This is gold. A repeatable pattern. Tool + model + memory + feedback = robust agent behavior.

From a business POV? It’s a use case disguised as a demo. And every use case inches us closer to monetizing the scaffolding itself.

OKAY, BUT WHAT NOW?

If you’re a policymaker or casual observer, don’t get distracted by the glitter.

Benchmarks like "beating Pokémon" are cute but not conclusive.

Here’s what actually matters:

• Long-horizon tasks across messy environments
• Models that can reason across modalities, not just regurgitate facts
• Agents that can recover from failure, not just execute perfectly tuned plans

The danger is benchmark inflation. Calling every clever orchestration a “breakthrough” muddies the waters for actual progress. It’s like giving someone a Nobel for assembling IKEA furniture with instructions. I wish they had those. I still wouldn't win any. AI has one up on me there.

TL;DR: AI ISN’T A KID PLAYING POKÉMON. IT’S A TEAM OF ENGINEERS RECREATING THE KID

But that’s still cool.

Because that team? They’re teaching the machine how to think in systems. And systems are what will shape the next decade of AI.

Gemini didn’t become a Pokémon master. But it did something far more telling:

It showed us what AI looks like when it stops pretending to be human, and starts working like a platform. And that's powerful.