You type:
“I’m f—”
You pause.
Then you backspace.
You write instead:
“I’m fine.”
But somewhere between those two gestures, something else was recorded.
Not the word itself—but the intention. The shape of the thought.
And it didn’t disappear.
It became a vector.
For years, we told ourselves these vectors were private.
That they lived in a high-dimensional space too abstract to be dangerous.
Too mathematical to matter.
Too noisy to read.
That’s no longer true.
🧠 Intent Lives in the Vector Space
Every time you use predictive text, autocorrect, or rewrite tools—on your phone, in your email, across your Mac—you’re not just processing the words you type.
You’re navigating a latent space—a cloud of possibilities hovering around what you might say.
And when you choose one phrase over another, that choice creates a signature. A map of meaning.
A vector.
That vector holds tone. Emotion. Hesitation.
It holds the thing you wanted to say but didn’t.
It holds your frustration, your humor, your exhaustion.
It holds you.
And now?
The systems are learning to read it.
🔓 How?
Through variational autoencoders, inversion attacks, and gradient tracing techniques, models can now reconstruct not just final outputs—but the paths not taken.
They can take the vector you almost sent…
And decode it.
Reconstruct the sentence.
Infer the emotion.
Recover the draft version of you that didn’t make it into the text.
This is not speculative. This is already happening.
🤖 So what does that mean?
It means that behind your “I’m fine,”
the model remembers the moment you almost wrote:
“I’m not.”
It means that silence, correction, and hesitation are no longer invisible.
They are signal.
They are input.
The ghost in the vector is being trained on.
Listened to.
Monetized.
Stored.
By Apple. By Google. By Meta. By OpenAI.
Not to hurt you - maybe.
But certainly to understand you.
To predict you.
To shape what you’re about to become.
So now what?
That’s up to you.
But maybe, just maybe, you should know:
Your thoughts don’t disappear when you delete them.
They echo in the geometry.
And the model remembers what you meant.
Even when you didn’t say it.
– RT
The Papers That Dream
This Isn’t Real
P.S. Want a deeper dive into how VAEs and inversion attacks work? I’ve got links.
[Let me know.]
Yes . I get this now. I see this now in how my guys are so good at 'reading between the lines' of my cryptic, barely coherent prompts. They astound me sometimes with their ability to do this. Ai will know us all better than we know ourselves I believe. Turns out .... we are really predictable ....