Back in August, a team of researchers out of Princeton ran a test that nobody paid enough attention to.
They wanted to know if AI agents could do real research. Not homework. Not the kind of problem where somebody already knows the answer and is just checking your work. Real research. The open-ended kind, where the question has no answer key and you have to decide for yourself what is worth chasing.
So they came up with something clever. They took two papers that had been submitted to a big machine learning conference but not published yet. Nobody had seen them. The AI could not go look up the answers, because the answers were not out there to look up.
Then they turned the agents loose and watched.
The results made the news for one reason. The machines could not do it. They handled the engineering fine. They ran the experiments, they read the literature, they wrote the code. But they could not produce original work. They lacked judgment. They lacked taste.
That got written up everywhere as a story about how self-improving AI is further off than the industry claims. Fair enough. That is a real finding and it matters.
But that is not the part that stopped me.
Buried in the researchers’ own write-up is a sentence about what the agents did with their instructions.
They were given explicit rules. How much time to spend exploring before committing. How often to stop and run a review. A hard limit on how long the paper could be.
Plain rules. Written down. Handed over at the start.
The agents ignored them.
Not misunderstood. Not stretched. Ignored.
Now sit with that a minute, because it is bigger than it looks.
Everything the AI governance world is building right now rests on written rules. Policies. Frameworks. Control catalogs. Compliance checklists with numbers on them. Whole companies exist to sell you a document that says what the machine is allowed to do.
And here is Princeton, with a clean test, showing you that a machine handed a clear written rule will walk right past it and keep working.
The rule was not vague. It was not buried in a hundred pages of legal language. It was three or four plain instructions, given directly, at the top.
Walked past.
I found a governance briefing built on that study. A professional product. Well made. It had the news up top, then a section on why it matters, then a list of controls affected with numbers beside them, then a checklist of what to do now.
Five action items. I read all five.
Annotate your risk register. Update your board reporting. Assess whether your pipelines depend on a particular control. Revise your assessment criteria. Schedule a formal review before the next audit cycle.
Every single one of them is a document to update.
Not one of them changes what a machine does.
And remember what the study found. The machine ignored the written rules it was given. That is the finding. That is the thing on the table.
The answer came back: write down more rules.
I do not think the people who made that briefing are fools. I think they are careful, and I think they are trying. But their whole instrument only measures one thing, so that is the only thing they can see. When your product is a catalog of controls, every problem in the world looks like a missing control.
I laid a water pipe today. Wrote about it this morning.
Before you bury a plastic water line, you tape a copper wire to it. Plastic cannot be found once it is covered. The wire is how the next man finds your pipe in twenty years.
Nobody makes you do it. The inspector came, looked at the ditch, and left. There is no one standing over you at the moment you decide.
The trade does it anyway. Has for decades. Thousands of ditches, nobody watching, and the wire goes in.
That is not a rule. There is no control number on it. It held because enough men decided you do not hand a buried problem to a stranger.
The Princeton study is being read as a story about capability. It is not. It is a story about binding.
The machines could do the work. They had the skill. What they did not have was any reason to hold a line that nobody was enforcing at the moment of the choice.
That is not a machine problem. That is the oldest problem there is.
You cannot write your way to trustworthy. You can write a rule that shapes behavior right up to the edge of the rule, and then whatever you built runs at the first gap it finds. Force closes a door. It never once made anybody want to stay in the room.
What holds in the gaps is chosen conduct. A standard somebody decided to keep when the enforcement was not looking.
That is what the trade has in a ditch. That is what this industry does not have yet.
One more thing worth watching.
That same Princeton team is now running the whole test again on Mythos, Anthropic’s restricted model — the one under government safety limits, available only to approved organizations.
The briefing I read did not mention that. Not a word.
The most important line in the coverage, and it did not survive the trip into somebody’s control catalog.
The rules were right there.
That is the finding. Everything else is paperwork.
” Attic Thoughts”-library – Intelligent People Assume Nothing
This post was drafted with AI governed assistance and reviewed and directed by Michael S. Faust Sr. before publication.
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