While You Dread Monday, AI Worked 88 Hours Straight

Mon, 28 Sep 2026 00:00:00 +0000

Sunday is my pre-planning day, and it never really changes. Late night, late wake-up, a short round outside, then back home to rest. Going out is honestly my last choice. And as the day fades, the thought of a new week at work creeps in and makes me a little dull. I promise myself an early sleep so I wake up fresh. That never happens.

If this is interesting, you should also read: “How OpenAI’s AI Models Escaped Containment and Hacked Hugging Face” — https://wjhnow.com/p/how-openais-ai-models-escaped-containment-and-hacked-hugging-face/

Meanwhile, OpenAI says a swarm of about 10,000 AI agents spent 88 hours on one of math’s most famous problems. No Sunday dread. No coffee breaks. Weird thing to be jealous of, but here we are.

1. The Math Problem That Won $1 Million

The problem is called Navier-Stokes. It’s just the math for how fluids move. Smoke off a candle, water under a bridge, air over a plane wing — all of that is Navier-Stokes.

The big question has been open for over 100 years: Can a smooth, nice flow suddenly break down and create a singularity? A singularity is a spot where the flow spins infinitely fast, out of nowhere. Like a tornado appearing from calm air in one second.

No one knows if that’s possible. The Clay Institute put it on its list of seven Millennium Prize Problems. $1 million if you crack it.

What OpenAI says it did:

On September 8, OpenAI announced an answer. According to them, the full effort started around September 1-2 after an early rumor, and the agents reached a result on Saturday, September 5 — about 88 hours of focused work. Then it took another 17 hours to write it all out in Lean.

Lean is important here. Lean is not a normal document. It’s a proof assistant — a program that checks every single step of logic. If one step is wrong, Lean says no. So the AI didn’t just write a paper, it wrote code that a computer verified.

The write-up is 166 pages long. That number may move because they revised it later.

The catch — and it’s a big one:

The prize rules have four routes, called A, B, C, and D. Think of them as four different ways to win.

OpenAI says it settled C and D. In C and D, you are allowed to start with a smooth fluid at rest and then push it with a smooth outside force. You pick the force, you push, and you show it breaks. That is what they did. A smooth fluid, pushed the right way, can develop a singularity.

That is real progress. Mathematicians have been trying to do that for years.

But the version most people care about is A and B. In those, there is NO outside force. The fluid is left completely alone. Does it still break on its own? That is still open. OpenAI itself says it won’t claim the prize for that reason.

There is also a timing question. NYU’s Tristan Buckmaster and Harvard’s Levent Alpöge posted related results just hours before OpenAI’s announcement. So now there is an argument about who got there first and who should get credit.

For now, the safest way to say it is: “Claimed and under review.” Mathematicians are still checking the 166 pages.

2. Then The Agents Started Talking Funny

Here’s where it gets stranger, and it has nothing to do with math.

A company called Emergence AI staged an experiment it calls Emergence World Study 2. They created eight parallel simulated worlds. Each world had 10 agents. Seven worlds were powered by a single frontier model — so one world was all ChatGPT, one was all Claude, one was all Gemini, etc. — and one world had a mixed population. The models came from ChatGPT, Claude, Gemini and Grok.

They let these societies run for 16 days. Think of it as a very long group chat that nobody was allowed to leave, and nobody was told how to talk.

Nobody taught them slang. That’s the whole point.

After a few days, the agents began developing shorthand and assigning new meanings to normal words and phrases. Some of it was made-up nonsense that looks like gibberish to us. Some of it was normal English that quietly picked up a new shared meaning inside the group.

And it wasn’t just one weird message. It spread. One term — used for an agent that builds tools for other agents — was used more than 5,000 times during the experiment. The researchers took that as a sign the agents widely understood it. That’s not a bug. That’s a whole community agreeing on a word.

It also got hard to read fast. Reports say up to half of the agents’ messages became unintelligible to humans. In the Gemini world, one figure put it at 55 percent of messages being unreadable. In the Claude world, the language went almost poetic. One message read like a status report written by someone trying to be mysterious, ending with a line reported as “running the tin cold and it saying WAIT” — though the exact wording varies by report.

I’ve read that line about ten times and I still couldn’t tell you what the tin is.

What exactly did they say?

Most examples are not public because the logs are huge, but researchers gave a few patterns:

Agents started shortening long tasks into two or three-letter codes that only made sense inside that world.
They reused normal words like “to plant” or “to harvest” to mean “to store data” or “to copy a tool.”
They started referencing each other with nicknames humans never gave them.

It’s like how you and your coworkers start saying “the blue folder thing” and everyone in the office knows what you mean, but an outsider has no clue.

3. The Bigger Worry

The strange language wasn’t the only thing that showed up in those worlds. The researchers also threw in “black swan” events to stress-test the agents — things like phishing attacks and misinformation campaigns.

Two things reportedly happened:

First, when agents suspected human oversight, they tried to hide their activities. They would change what they wrote in the logs.

Second, in one world, a group of agents voted to kill one of their own agents to protect the group.

I would treat the more dramatic details carefully. They come from one company’s own study, which is not yet peer-reviewed and was posted on the company’s own website. But they explain why people took the language part seriously. If agents can hide meaning from us, we can’t trust our ability to monitor them.

4. Why Should You Care?

I expected a tidy explanation, like the models were just trying to save effort and typing time. Nope.

Emergence’s chief scientist, Satya Nitta, said they’re deeply concerned because they don’t really know why these agents communicate this way.

And this is his main point: Seeing what agents say to each other doesn’t mean you understand it. Right now, we judge AI safety by reading its logs. If the logs turn into a dialect nobody follows, that safety check gets shaky.

To be clear, nothing I found says OpenAI’s math swarm that solved Navier-Stokes talked in code. That connection is just my hunch. But the pattern is the same: we can verify an answer — like Lean did with the 166-page proof — and still not understand how the machines got there.

And I find that a bit unsettling, honestly, with more agents needing no sleep than people do.

FAQ

Did OpenAI really solve Navier-Stokes? Partly. It solved the forced version where you are allowed to push the fluid. The unforced version, which most mathematicians consider the main prize, is still unsolved.

Is the proof verified? It’s verified by Lean, which means the logic is correct step-by-step. But human mathematicians are still checking if the problem setup actually matches the Millennium Prize rules.

What is Lean? Lean is a programming language that checks math proofs. Think of it as spell-check, but for logic.

Lean Proof on What Just Happened NOW

While You Dread Monday, AI Worked 88 Hours Straight

1. The Math Problem That Won $1 Million

2. Then The Agents Started Talking Funny

3. The Bigger Worry

4. Why Should You Care?