The Reverse Information Paradox Follows You Home
This is a quick response to, and deliberate mirror of, Satya's "Reverse Information Paradox" piece. He framed the enterprise challenge. I think the more important version is personal.
In the age of intelligence, how should firms protect their core IP? That is the question Satya Nadella posed recently. Nobel Prize winning economist Kenneth Arrow famously described a paradox in the market for information: “Its value for the purchaser is not known until he has the information, but then he has in effect acquired it without cost.” In Arrow’s paradox, the seller risks giving away knowledge in order to sell it. AI, Nadella argued, creates the reverse problem: the buyer risks giving away knowledge just in order to use what they bought. He called it the Reverse Information Paradox, and urged every enterprise to own its learning loop.
He is right. But the sharpest version of this paradox is not playing out inside the enterprise tenant…
A person pays for intelligence twice as well. Once with money, and another with their personal information. To make an assistant useful you must feed it your questions, your plans, your drafts, your doubts. The better you want it to perform, the more of yourself you have to reveal.
Models learn from exhaust. A firm’s exhaust is prompts, tool traces, and corrections, distilled into institutional know-how. A person’s exhaust is more intimate. It is the messages you’ve sent, the questions you’ve asked, the album you replayed all week, the book you abandoned on page 40, the correction that says no, that is not me. Every correction is distilled into self-knowledge, the kind no advertiser could ever buy.
Over time the asymmetry skews further. The system learns what you love, what you avoid, how you decide, and what you will pay for. You learn almost nothing about what it has learned. Somewhere on someone else’s infrastructure sits the most complete portrait of you ever assembled, and you cannot read it, correct it, or take it with you.
Nadella writes that in consuming intelligence you are creating intelligence, and that what you create should belong to you. Nowhere is that truer than for a person. This is your particular intelligence in Hayek’s sense: the knowledge of time, place, and circumstance that no one else can hold. We have an older word for it. Taste.
Taste is a lifetime of experience compressed into a signal about what you will love next, and memory is the basis of identity. Whoever holds your memory holds a working draft of you.
The irony he flags lands harder here too. These models were trained on the public output of millions of people under fair use. Then the terms turn around: the provider reserves the right to learn from your private usage while granting you no equivalent right to the learning. When learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the people generating the signal.
We have already run this experiment once. In the social era, platforms modeled your taste in order to sell your attention. Your data created the value, and you got a feed. The AI era raises the stakes, because this time it is not your attention being modeled. It is you.
As Alex Karp put it: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it’s not being transferred to someone else.” Swap “technical customers” for “people” and the sentence holds. People just do not have a procurement department to negotiate it.
So the trust boundary Nadella prescribes for the enterprise has a personal equivalent, and we need it more.
One place where your memory, taste, corrections, and context accumulate and compound together. Legible to you. Editable by you. And a hard boundary across which nothing crosses, not even the exhaust, without consent. This is not privacy as abstinence. The point is not that the internet should know less about you. The point is that it should know you on your terms, and do things for you because of it.
That requires unbundling the stack: the agent you talk to, the model underneath, and the layer that holds who you are should not be one company’s black box. The model provides the intelligence. You should decide what it knows. And, importantly, none of this can feel like homework. It must be fun. Firms buy trust boundaries through procurement; people adopt what is fun. Whatever hands the learning loop back to individuals has to be expressive, delightful, and accessible to the average human, not just the tech-savvy among us, so that control becomes the byproduct of something you would use anyway rather than the pitch.
In the platform era, companies accumulated your data. In the AI era, they accumulate the learning of you. Protection has to evolve accordingly, from guarding information to guarding the mechanism through which you are learned. There are a few things every person should be able to expect:
Control: Your taste and memory is your private eval. It defines what you like and what you’re like, what “good” looks like for you, and no general benchmark can. Retain ownership of your memory, the record of you that you’ve nurtured, along with the right to grant or revoke access to any slice of it.
Capability: Keep a living model of yourself inside your own boundary, structured, legible, and correctable, something like a genome of the self that you author. Models should learn against your real life without absorbing it.
Choice: Decouple being known from any single assistant. Ask yourself: if the model you talk to every day were taken away tomorrow, would you still be known? The generalist model is rented. The veteran that knows you must remain yours.
Cost: Portable context lets you bring yourself to whichever model is best and most efficient for the task at hand, without sacrificing quality. There should be no loyalty tax on being understood.
Compound: Bring these together and you have what I think will change the internet: a continuous learning loop that belongs to you. Taste compounds over a lifetime. The understanding you invest in an agent should compound the value of your life, not the valuation of someone else’s model.
In other words, a person should be able to use intelligence without giving up themShelves. let’s see who spots that.