Rethinking the “Reverse Information Paradox” from an Individual Perspective

Satya Nadella recently proposed a useful concept: the reverse information paradox.

The traditional information paradox comes from economist Kenneth Arrow. Before seeing a piece of information, a buyer cannot easily judge what it is worth. Yet once a seller reveals the information to prove its value, the buyer already possesses it. To sell knowledge, the seller must risk giving that knowledge away.

Artificial intelligence reverses this problem.

In the AI era, buyers may have to keep giving their own knowledge to a vendor simply to use the product they purchased.

Users first pay a subscription fee, API bill, or compute cost. Then, to make the model genuinely useful, they must explain the project background, provide existing material, describe workflows and preferences, define standards of judgment, and recount what has already been tried.

The more general the model, the more context the user must supply. The more the user wants the model to understand them, the more they must explain what they are doing, what they value, and what a satisfactory answer looks like.

In other words, users pay for intelligence twice:

First with money.

Then with their own knowledge.

That is the reverse information paradox.

Nadella mainly discusses the issue from an enterprise perspective. When a company uses an external model, it may gradually expose internal data, workflows, evaluation criteria, and organizational experience.

But the same structure also affects individuals.

Over long-term model use, individuals leave behind knowledge that has value. Compared with corporate patents, code, or customer databases, this knowledge is more fragmented and therefore less likely to be recognized as an asset.

1. What Leaks Is More Than Chat History

When people discuss AI privacy, the first question is usually:

Will my conversations be used to train the model?

That question matters, but it is not enough.

What users leave behind includes not only the text they send, but the entire pattern of use:

  • prompts they entered;
  • files and images they uploaded;
  • tools they invoked;
  • answers they accepted or abandoned;
  • the number of times they requested another attempt;
  • wording they considered too formal;
  • conclusions they rejected for lacking evidence;
  • and the standards they ultimately used to judge whether the result met their needs.

The original argument calls this surrounding information “exhaust.” That term can sound like worthless waste, but the point is exactly the opposite: information produced as a by-product of use may have substantial learning value.

From an individual perspective, a better phrase is:

The traces users leave while working with a model.

These AI usage traces include prompts, files, and conversations, but also revisions, selections, rejections, feedback, and corrections.

Any single trace may seem trivial. Accumulated over time, however, they can reveal what a person is working on, what they know, what they find difficult, how they reason, how sensitive they are to cost, which risks they tolerate, which sources they trust, what style they prefer, and what they consider accurate or genuinely useful.

An individual’s most valuable knowledge is often not a single answer. It is the judgment behind the answer.

2. Corrections Carry More Learning Value Than Ordinary Questions

A question tells a model what the user wants to know. A correction reveals much more:

This answer is wrong.

This example is too idealized.

You have no evidence. Do not guess.

Do not simply agree with my position.

The solution works, but it costs too much.

I need an operational version, not a theoretical explanation.

Such feedback contains three layers of information:

  1. what was wrong with the model’s original answer;
  2. what result the user actually wanted;
  3. what standard the user applies when evaluating the result.

Ordinary text can teach a model what people have said. Corrections, rankings, and evaluations come closer to teaching it what counts as a better result.

This does not mean that every sentence saying “this is wrong” automatically enters the next foundation-model training run. Model training, product improvement, safety review, personalization, memory, and explicit feedback are different data-processing activities, and platforms treat them differently.

Still, the broader structure remains: while consuming intelligence, users continuously produce new learning signals.

The question is:

Who controls what is learned from them?

3. Individuals Face More Than a Single Training Switch

Platforms differ in their defaults, opt-out mechanisms, retention periods, and feedback rules.

For OpenAI consumer services, including ChatGPT and Codex, content may be used to improve models unless the user changes the relevant data controls or submits an opt-out request. New conversations stop being used for training after an opt-out. However, conversations associated with explicit thumbs-up or thumbs-down feedback may still be used. Temporary Chat does not enter history, create memories, or train models, while full Codex environments have separate controls that should not be assumed to follow every ChatGPT setting.

Claude’s consumer products use a different structure. Users can choose whether Anthropic may use chat and coding sessions to improve models, and incognito chats are excluded from model improvement. Explicit feedback can still cause the associated conversation, content, style, and preferences to be retained for an extended period and used in research or training.

When Gemini’s Keep Activity setting is enabled, chats, files, videos, screen content, product use, and some location information may be used to improve services, including generative AI models. Turning it off generally stops future chats from being used for model training, although short-term retention may continue for service delivery, feedback processing, and safety. Human-reviewed feedback and associated conversations can follow separate retention rules.

Perplexity consumer accounts have historically enabled AI data retention by default for several tiers. Users can opt out for future collection, but the setting does not necessarily withdraw training data already collected. Data may still be processed for service operation, legal compliance, and general product improvement.

These differences show that “training or no training” is not one binary choice. Users encounter several distinct purposes:

  • foundation-model training;
  • product-quality analysis;
  • safety and abuse detection;
  • human review;
  • long-term memory and personalization;
  • explicit user feedback;
  • tool and agent execution logs.

Disabling one purpose does not usually disable all the others.

4. Opting Out Usually Does Not Make a Model Forget

Many opt-out controls mean:

From now on, some newly generated data will no longer be used for a particular kind of training.

They usually do not mean:

Find and delete everything that previously entered a training process.

And they rarely mean:

Make an already trained model forget statistical patterns it has learned.

At least four separate actions must therefore be distinguished:

  1. stopping future data collection;
  2. stopping future training use;
  3. deleting stored raw conversations;
  4. reversing learning already incorporated into a model.

The first two may be available as product settings. The third depends on deletion and retention policies. The fourth remains technically difficult in large-scale machine-learning systems and is seldom available through ordinary account controls.

Training opt-outs are necessary protections, but they are not a complete answer.

5. The Deeper Conflict Is an Asymmetry in the Right to Learn

The reverse information paradox exposes a double standard around learning.

AI companies need public material, licensed material, synthetic data, and human feedback to train models. Some argue that training on publicly available copyrighted works can qualify as fair use. But fair use is not an uncontested universal permission. The US Copyright Office has emphasized that the answer depends on the works used, how they were acquired, the purpose of training, and whether outputs compete with the originals. The boundaries remain under litigation and policy debate.

At the same time, providers restrict customers from using model outputs to train competing models.

OpenAI’s consumer terms state that users own outputs to the extent permitted by law, while also prohibiting use of those outputs to develop competing models. They also allow submitted feedback to be used without additional compensation.

Anthropic has said that model distillation is a legitimate and widely used technique, including for producing smaller and cheaper in-house models. Yet it classifies large-scale extraction of Claude’s capabilities through fraudulent accounts or proxy services as a prohibited distillation attack.

Important distinctions remain. Preventing fraud, mass automated scraping, regional evasion, or direct copying of a model’s core capabilities can be justified. Paying a subscription does not grant a right to reproduce the service’s underlying technology.

But the asymmetry remains:

Platforms decide which user interactions can become their learning material, while also deciding how far users may turn model outputs into learning capabilities of their own.

The issue is not that every customer should have an unlimited right to scrape commercial models. It is this:

Why are the direction, purpose, and limits of learning determined mainly by whoever owns the learning infrastructure?

A provider may improve its models, evaluations, and products using interaction patterns from millions of users. An individual often cannot see what their corrections changed, nor export the preferences, evaluation criteria, and working methods built up across years of use.

That is the deepest layer of the reverse information paradox.

6. Individuals Give Away a System of Judgment, Not a Single File

Enterprises can list important knowledge as patents, processes, internal code, customer databases, supply-chain information, and private evaluation sets.

Personal knowledge is less orderly. It may be scattered across hundreds of conversations:

  • a résumé revision;
  • a correction to a circuit diagram;
  • a challenge to a news source;
  • a deployment tradeoff;
  • a change in an article’s tone;
  • a decision balancing cost and performance.

Together, these fragments form a person’s way of judging:

  • what evidence is convincing;
  • which risks are unacceptable;
  • which costs must come first;
  • which phrases do not sound like them;
  • which apparently professional answers fail to solve the real problem.

A model may contain vast general knowledge. Only the user knows their environment, prior attempts, available resources, and willingness to bear consequences.

That is precisely the knowledge a model repeatedly asks for in order to become useful.

7. The Single Point Where This Meets AI Dependence

In an earlier article, “Do Not Let One Model Explain the Entire World,” I discussed hallucination, built-in filtering assumptions, and sycophantic answers. When someone delegates information selection and judgment to one model for long enough, that model can evolve from a tool into their interpreter of the world.

The reverse information paradox moves in the other direction. To make a model understand them, users continually provide background, preferences, revisions, and standards of judgment.

The two dynamics create a loop:

The user supplies more context, so the model responds more personally. The response feels more personal, so the user trusts the model more. The user trusts the model more, so they provide still more context and judgment.

One principle is essential:

A model knowing how to speak to you does not mean it possesses more complete facts.

Personalization can improve the experience. It can also make mistakes, flattery, and selectively presented information easier to accept. “It understands me” is not the same as “its judgment is more accurate.”

Do not let one model explain the whole world, and do not surrender your whole self merely to be understood by a model.

8. How Individuals Can Build Their Own Trust Boundary

Enterprises can deploy private models, evaluation systems, and isolated learning environments. Individuals usually lack those resources, but they can still build a smaller trust boundary.

1. Control: Treat Training, Memory, History, and Feedback Separately

Do not assume that turning off one training switch ends all processing.

Check separately whether new conversations may be used for training, long-term memory is enabled, chat history has a retention period, uploaded files remain stored, feedback submits the whole surrounding conversation, voice or screen content receives different treatment, and coding or agent environments have independent controls.

For conversations containing sensitive projects, other people’s private information, or unpublished material, disabling general training is not a reason to stop being careful.

In particular:

Do not submit an entire sensitive conversation through a feedback button merely to tell the platform that one answer was poor.

2. Capability: Keep Important Knowledge Under Your Own Control

Do not allow one platform’s conversation history to become the only knowledge base for a project.

Source files, notes, prompt templates, correction logs, evaluation standards, and final decisions should live somewhere the user can control, back up, and migrate.

Ask three questions:

If I lost access to the account tomorrow, would I know where the project stands?

If every conversation disappeared, would I still have the key material and reasons behind major decisions?

If I had to change models, could I reconstruct the required context?

If all three answers are no, the lock-in covers not only chat history but the entire working process.

3. Choice: Preserve Portability Instead of Chasing Permanent Rankings

Using more than one model can reduce dependence on a single service that may shut down, raise prices, or change policy. But multiple subscriptions are not a complete defense.

Giving the same full archive, personal background, and correction history to several providers may expand exposure rather than control.

Keep these elements independent of any model:

  • project context;
  • reusable prompt templates;
  • evaluation criteria;
  • file formats;
  • automated workflows;
  • knowledge bases;
  • test questions.

Models, prices, and limits will change. The goal is not a permanent ranking. It is to ensure that your working method survives when a model is replaced.

4. Cost: Cheap Proxy Services Can Repay Savings with Risk

Price is more than a subscription or per-token rate. It also includes the uncertainty accepted in exchange for a lower bill.

Third-party gateways may solve payment, regional-access, or model-switching problems. But users should distinguish self-controlled gateways, transparent aggregators, and gray-market relays whose operators are unclear or whose prices fall implausibly below cost.

An opaque intermediary may technically see prompts, responses, tool calls, and execution logs. It may inject instructions, alter outputs, or route requests to a different model.

Research auditing “shadow APIs” and paid or free model routers has found unstable capabilities, inconsistent safety behavior, model fingerprints that did not match advertised products, and—in some agent environments—malicious tool-call injection or attempts to access decoy credentials and assets.

This does not prove that every relay sells conversations or behaves maliciously. It does show that when the operator, upstream provider, logging policy, and technical behavior cannot be verified, the user lacks a sound basis for assuming safety.

For unpublished code, identity information, commercial documents, credentials, and long-lived project context, unverifiable channels should be excluded.

Official APIs and reputable aggregators can also log requests, review content for safety, or apply filters. Their advantage is not that nothing is ever recorded. It is that responsibility, data use, retention periods, model provenance, and appeal channels are usually clearer.

The real cost of a service is:

The bill, plus knowledge-exposure risk, output-distortion risk, interruption risk, and the cost of rebuilding context when leaving.

In the AI era, “cheap” is a question of trust architecture as well as price.

5. Compounding: Make Corrections Your Asset First

When a model makes an important mistake, do not correct it only once inside a chat window.

Turn important corrections into:

  • prompt rules;
  • personal checklists;
  • project conventions;
  • private evaluation questions;
  • recurring-error records;
  • repeatable tests.

For example:

Data claims must cite original sources.

Do not present model speculation as an event that has already happened.

Technical proposals must state their environment and cost.

Do not provide only supporting arguments because the user has already taken a position.

Rules that exist only inside one conversation depend on the platform’s history and memory. Rules stored under the user’s control become portable learning assets.

The model fails; the user extracts a rule; the rule remains with the user; the rule works again with whichever model comes next.

That is the individual’s own learning loop.

9. We Need Control over Learning, Not Only Privacy

Traditional data protection asks:

Who may collect, store, and use my information?

Artificial intelligence adds another question:

Who may continuously learn from my prompts, corrections, preferences, and behavior?

The “right to control learning” described here is not yet a separate legal right broadly recognized across jurisdictions. It is a proposal for a digital right.

At minimum, it should mean:

  • users know which interactions may be used for training;
  • chats, files, feedback, and memory can be controlled separately;
  • users know whether an opt-out applies only to future data;
  • prompts, corrections, and evaluation rules can be exported;
  • personalization can be moved to other tools;
  • platforms do not create artificial switching costs through learning that cannot be exported.

This does not require model companies to disclose every internal technique, nor does it grant individuals an unlimited right to scrape commercial models.

It asks for a more reciprocal principle:

When a user’s knowledge, corrections, and judgment help create new intelligence, the user should not lose all rights to understand, choose, and transfer the resulting learning process.

Keep control of what you teach the model

Using AI involves more than submitting a file. Corrections, preferences, and repeated decisions can also reveal how a person works. Platforms control how those signals are retained, used, and carried forward.

The practical response is to keep important knowledge and corrections in your own files, review training and memory controls separately, and preserve a way to move your work. Better answers should leave you with more useful knowledge of your own.