THIS GENERATION OF PEOPLE WILL NO LONGER CONSIDER HUMANOID ROBOTS TO BE NORMAL.
And that’s a much bigger deal than the dance.
Look at the adults.
Phones out.
Cameras up.
Everyone watching the machine because it still counts as an event.
Now look at the kid.
He stands next to it like it’s another person in the lobby.
That’s the split.
Adults still measure humanoids against science fiction.
Kids growing up in the same rooms may measure them against a speaker, a tablet, or anything else that was already there when they learned to walk.
Then the tape cuts the romance.
The unit leans toward the child.
A handler with a backpack grabs it by the torso and pulls it off.
Remote in the other hand.
The crowd keeps filming.
The robot resets and goes back to the routine.
Markets don’t flip when a humanoid can pose.
They flip when nobody in the room is holding a kill switch.
One generation asks why you’d put a robot in the house.
The next asks why you wouldn’t.
The shift isn’t only in the joints.
It’s in who still thinks this needs an audience.
AISA KARISHMA TUNE HANUMAN KAR DIYA
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🪔MAHABALI HANUMAN
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🪔MAHABALI HANUMAN
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👏MAHABALI HANUMAN
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JO RAM NE KALYUG TUMHARA NAAM KAR DIYA @grok
THE NEXT BIG AI SCALING LAW MAY NOT BE MORE PARAMETERS. IT MAY BE KNOWING WHEN TO THINK TWICE.
For years, making a model “deeper” basically meant building a bigger stack.
More layers.
More parameters.
More memory.
GPT-6 Astra points at a different direction.
Instead of passing through a computational block once and moving on, a looped transformer can send its internal state through the same weights again.
Same model.
More computation.
A simple question might need 4 loops.
A hard proof might need 32.
A difficult coding problem might deserve 64.
And none of that necessarily requires the model to write a longer chain of thought before answering.
That's the part I find much more interesting than another benchmark record.
We may be separating two things that used to scale together:
HOW BIG THE MODEL IS
and
HOW MUCH THINKING THE PROBLEM GETS.
Eventually the important question may not be:
“How many parameters does this model have?”
But:
“Did it know the answer was good enough, or was this token worth another trip around the loop?”