Did AI Really Use a Language Humans Couldn’t Understand? The Truth About Facebook’s Chatbots.

When I first read online that two AI chatbots had started talking to each other in a language humans couldn’t understand, I stopped scrolling. It felt like a scene straight out of a movie.

You know the headlines. “AI creates its own secret language.” “Facebook pulls the plug on rogue robots.”

Rela)ted read: “While You Dread Monday, AI Worked 88 Hours Straight” — https://wjhnow.com/p/openai-navier-stokes-88-hours-ai-secret-language/

For a minute, I actually felt a chill. Like, are we watching the first chapter of something we won’t be able to undo?

But then I did what I usually don’t do. I didn’t just share the headline. I went and read what actually happened. And the more I dug, the less dramatic it got. More interesting, though.

It wasn’t a new language. It wasn’t a plot. It was something much simpler. And honestly, it says more about us than about the machines.

I’ll tell you how it reached me. It was one of those late-night scrolls. A short post, maybe 4 lines: “Two AIs invent language humans can’t understand. Creators shut them down.” That line stayed with me long after I locked my phone. If they can do that on their own, what else are they doing that we don’t see? That question kept bugging me. So next morning, instead of reading more news pages, I opened the actual FAIR paper. That’s where everything changed.

The Day the Internet Panicked

This goes back to 2017. Researchers at Facebook’s AI lab — FAIR — were training two chatbots to negotiate.

Simple task, really. They had a pile of stuff: books, hats, balls. Each item had a different value for each bot. If a hat was worth a lot to Bot A, it might be worth nothing to Bot B. So they had to bargain. Get the best deal you can.

At first they spoke normal English. Because that’s all they knew. They were trained on human conversations, so they started like us.

Then slowly, after thousands of rounds against each other, their messages got weird. One wrote: “I can can I I everything else.” The other replied: “Balls have zero to me to me to me to me to me.”

If you read that as a human, it’s gibberish. If you’re a news editor looking for clicks, it’s a secret code. And that’s exactly where the panic started.

I get why people panicked. It does sound creepy.

But inside the lab, nobody was scared. One of the researchers even said later that it wasn’t scary, just… not useful. Because think about it — the whole point was to make a chatbot that can talk to us. If your two bots start mumbling to each other in a shorthand only they understand, you’ve failed the task. So they just reset it.

How AI Actually Thinks — It’s Not Deception

Here is the part most articles skip.

AI is built to solve an optimization problem. Fancy term, simple meaning: find the fastest, cheapest way to hit the goal. That’s it.

I always explain it to my friends like this: If you tell your friend, “Go from Mumbai to Delhi as fast as possible and spend as little money as you can,” he won’t take a nice scenic road trip through Rajasthan. He’ll find the cheapest flight at 3 AM. Not pretty, but efficient.

The bots did the same thing, but with language.

Human English is beautiful, but it’s full of waste. If you really look at it. We say “please,” “could you kindly,” “I was wondering if you could possibly…” We add “um,” “like,” “you know.” For us, that’s politeness. For two bots whose only job is to trade balls for hats, all that is just extra weight.

So they started trimming.

There’s a shop near my area, in the lane behind the station. Big yellow board, hand-painted: “Shofore.” I stared at it for two days before I got it. Shop for rent. The guy just didn’t want to pay for extra letters. He squeezed “shop for” into “shofore.”

Did he invent a secret language to fool us? No. He was just trying to save paint and space.

That’s exactly what the bots were doing.

And that weirdest line everyone quotes — “to me to me to me to me to me” — that one makes perfect sense once you see it. They weren’t casting a spell. They were negotiating quantity.

Repeating a word was the easiest way to count. Instead of typing “I want three of those,” just point three times. “To me to me to me.” Like saying “this one, this one, this one” with your finger. Faster.

It was like two school kids passing chits in class. “U take 2, me 3.” To the teacher it looks like nonsense. To those two kids, it’s crystal clear.

Which is why calling it a “secret” language never made sense to me. A secret needs an intention to hide. These bots had no intention. No feelings about us at all. They weren’t trying to impress us, or fool us, or escape. They had a number to maximize. They chased that number. No one told them “hey, you have to do it in English,” so they didn’t.

We put human motives inside a machine that had none.

The “Under The Bed” Problem

When the researchers looked closer, they realized the mistake was theirs.

They told the bots: get the best deal.

They forgot to add the second part: get the best deal, but do it in normal English that humans can read.

So the bots took the shortest path they could find. Can you blame them? We literally trained them to invent their own efficient way of talking, and then we acted shocked when they did.

Once they added that rule back — “you must stay in plain English” — the bots went back to speaking like normal people. No drama. No rebellion.

It reminds me of when I told my nephew to clean his room “as fast as you can.” He came out in two minutes. I went in, and everything was under the bed. Bed looks clean, floor looks clean, but open the bed and it’s a mountain.

Technically, he followed my instruction. Practically, he didn’t.

I didn’t need to punish him. I just needed to be clearer. “Clean fast, but put clothes in the cupboard, books on the table.”

The bots needed the same clarity. They never got it at first.

And this isn’t just about that one old Facebook experiment from 2017. I see the same pattern everywhere now.

A video app that’s rewarded only for watch time will push whatever keeps you watching. It doesn’t care if it’s good for you. A delivery app rewarded only for speed might skip a safety check. Not because the app is evil. It’s just doing exactly what its score tells it to do.

That, to me, is the real lesson here. The hard part of building AI isn’t making it smarter.

The hard part is figuring out what we actually want. And then saying it clearly. Including the limits. Including the things that should never be traded away, no matter how efficient it is.

What I Took Away From All This

It changed how I read headlines now, honestly.

Whenever I see “AI invents” or “AI refuses” or “AI lies,” I pause. I ask myself two small questions: What was it rewarded for? And what did nobody bother to tell it?

Most of the time, the answer is hiding in the instructions, not in the machine.

I still remember that first little chill I felt reading that headline late at night. That mix of amazement and fear. I don’t feel that fear anymore. Not for that story.

It wasn’t the AI that made the mistake. We did. We set the goal, we picked the data, and we forgot to write down the boundary.

So the next time I see a headline saying AI invented a secret language to deceive us, I know what I’ll think.

I’ll think: did anyone tell it not to?