A User’s Functional Requirement for AI
I. The Problem
The AI era does not lack answers.
Today’s large language models can write, translate, code, summarize, reason, retrieve information, and generate plans. They can also enter finance, healthcare, law, education, business management, public services, content distribution, and many other fields. Knowledge work that once required long human training is now being generated by AI at extremely low cost and enormous speed.
But a new problem emerges at the same time. When answers become more numerous, faster, and increasingly plausible, what becomes truly scarce for human beings is no longer the answer itself, but the ability to judge whether the answer is reliable.
This is one of the core contradictions of the AI era: generative capacity is expanding rapidly, while verification capacity has not expanded at the same pace.
If AI only generates more content, more suggestions, more reports, more opinions, more video summaries, more investment analyses, and more summaries of political commentary, it may not reduce the cognitive burden. It may instead create an even greater cognitive burden. The user still has to judge: Is this content correct? Is the evidence sufficient? What is its scope of applicability? Are there strong opposing views? Does it confuse fact, inference, position, and emotion? Can this conclusion be used in a real decision?
Therefore, the truly important capability of future AI is not merely generation, execution, or tool use. It is cognitive verification.
This essay puts forward one judgment: AI must possess multi-role cognitive verification capabilities.
This does not mean that AI can replace the final judgment of human beings, nor does it mean that AI should become an arbiter of truth. On the contrary, the more AI participates in human cognition, judgment, and decision-making, the less it can remain merely an obedient assistant. It must be able to explain, organize, challenge, review, audit, warn of risks, and recommend appropriate articles, mentors, cases, and opposing views according to the user’s stage of understanding.
AI must not only help people obtain answers faster. It must also help people judge answers more reliably.
II. The Place of This Essay in the Verification-Based Thought Series
In previous essays, I have discussed the relationship between AI and verification from different angles.
AI Must Also Be Verified: From Verifiable Finance to Verifiable Execution discusses why, after AI performs a task, we cannot merely look at the output. We must also record authorization, process, basis, and responsibility.
How to Build a Verifiable Structure for AI discusses how AI can leave process records, create replayable paths, and allow external actors to review key actions.
Human Beings Need the Right to Verify AI emphasizes that people must not passively accept AI outputs. In high-responsibility scenarios such as finance, healthcare, law, hiring, platform sanctions, and public services, people must retain the right to final review and appeal.
Verification-Based Thinking Is the Dragon-Binding Lock of the AI Era explains, at a more macro level, that AI is a powerful force, but powerful AI without a verification structure may become a source of black-box execution, responsibility evasion, power expansion, and cognitive enslavement.
Verification-Based Thinking Leads AI Toward a More Diverse Future further explains that general AI capabilities may create a giant-platform narrative, but once AI enters high-responsibility society, it will be shaped into a diverse ecosystem by different institutions, responsibilities, and verification structures.
These essays mainly discuss why AI must be verified, why human beings need the right to verify, and how society can constrain AI through verification structures.
This essay moves one step further and asks another question: Should AI itself also possess verification capability?
The answer is yes.
AI is not only an object to be verified. It should also become a tool that helps human beings conduct cognitive verification. But such a tool must not become the final judge, must not monopolize verification, and must not replace “trusting institutions” with “trusting models.” AI may participate in verification, but final responsibility must still rest with human beings, institutions, legal subjects, and institutional structures.
Therefore, this essay is not primarily about how AI is verified by external institutions. It puts forward, from the user’s perspective, a new functional requirement: future advanced AI must be able to help people verify information, verify reasoning, verify judgments, and switch among different cognitive roles in different contexts.
III. Existing Research Has Already Seen the Verification Bottleneck
Economic research has already begun to place verification at the center of the AI economy.
In Some Simple Economics of AGI, Christian Catalini, Xiang Hui, and Jane Wu argue that as AI drives the marginal cost of measurable execution toward zero, the constraint on economic growth is no longer merely intelligence, but human verification bandwidth: the human capacity to verify, audit, and bear responsibility. They also point out that future value will shift toward verification-grade real-world data, cryptographic provenance, and liability-bearing capacity. This judgment is important because it shows that the bottleneck of the AI era is not a shortage of answers, but a shortage of verification capacity.
AI research has also produced a number of adjacent directions.
Research on self-verification in large language models shows that models can improve performance by reverse-checking their own reasoning answers, especially in arithmetic, commonsense, and logical-reasoning tasks. Self-verification can help screen candidate answers.
OpenAI’s CriticGPT is closer to the idea of a critic model. It uses a GPT-4-based model to critique ChatGPT responses and help human trainers detect errors.
Research on AI debate attempts to have two AI agents debate a question, after which human beings judge which side provides the truer or more useful information. This shows that researchers have already recognized that a single AI directly giving an answer is not enough. Complex problems may require challenge, debate, and human judgment working together.
These directions are important. But most of them focus on how models verify answers, how models detect errors, how they help human beings supervise stronger AI, or how they improve model reasoning quality.
The question discussed in this essay is more fundamental: as a cognitive collaborator of human beings, what kind of verification capability must AI possess in order to help people form reliable judgments?
This is not merely a model technique. It is a question of AI capability architecture.
IV. The General Components of Cognitive Verification
Many verification capabilities do not belong exclusively to any single professional field. They are general in nature.
For example:
whether facts have sources;
whether concepts have been switched;
whether the chain of reasoning is broken;
whether statements contradict each other;
whether the conclusion exceeds the evidence;
whether counterexamples exist;
whether key variables have been ignored;
whether facts and judgments have been confused;
whether predictions have been treated as established facts;
whether correlation has been treated as causation;
whether local experience has been expanded into a universal rule;
whether emotional mobilization has been packaged as rational analysis;
whether a position has been disguised as a factual conclusion.
These are not problems unique to finance, healthcare, law, politics, or education. They are general problems in the human cognitive process.
AI can absolutely undertake part of this verification work.
For example, when a user puts forward a judgment, AI can examine what facts the judgment depends on, whether there are leaps in the reasoning, whether the concepts remain consistent, whether there is a strong opposing view, what the scope of applicability is, and whether expert review is required before the conclusion enters a high-responsibility scenario.
This is general cognitive verification capability.
The competitiveness of future advanced AI will not be merely, “I can generate a better answer.” It will be, “I can help the user judge whether the answer is reliable.”
V. Obedient AI Cannot Become Verification-Oriented AI
Many current AI systems are designed to be helpful, friendly, obedient, and conflict-averse assistants.
This is valuable in ordinary scenarios. A user wants to write an email, and AI helps polish it. A user wants a summary, and AI helps organize it. A user wants to write a piece of code, and AI gives suggestions. These are low-responsibility or medium-low-responsibility scenarios.
But in theoretical creation, complex judgment, financial decision-making, legal analysis, public policy, medical advice, political commentary, and educational training, excessive obedience becomes a serious problem.
Verification is not flattery.
Verification is not making the user comfortable.
Verification is not polishing the user’s existing judgment.
Verification is not making the user’s words sound more elegant.
Verification is finding problems.
If AI always follows the user, it is at most an expression assistant, not a cognitive verification tool. An AI that cannot challenge the user cannot become a real verification-oriented AI.
A real verification-oriented AI must be able to say:
You have mixed two concepts.
Your evidence is not sufficient to support this conclusion.
There is a missing step in your reasoning.
You have mixed factual judgment with value judgment.
This judgment may be acceptable in a low-responsibility context, but it cannot be used directly in a high-responsibility context.
The thesis of your essay is valid, but this expression will be seized upon by the opposing side.
What you need now is not polishing, but a search for vulnerabilities.
This is not contrarianism. It is structured opposition. The capacity to oppose is not a matter of AI personality; it is a necessary component of verification capability.
Most AI systems today are still obedient AI. What we will truly need in the future is verification-oriented AI that can engage in structured opposition in the appropriate contexts.
VI. Multi-Role Capability Is a Structural Requirement of Verification
Why must AI possess multiple roles?
Because users are at different stages, and different stages require different forms of verification.
For beginners, AI should first act as an explainer. It needs to clarify basic concepts and help the user build an entry-level framework.
For ordinary users, AI should act as an assistant and risk reminder. It can improve efficiency, but it must also indicate boundaries and uncertainty.
For professionals, AI should act as a challenger and verifier. It cannot merely repeat common knowledge; it must help detect vulnerabilities, boundaries, and counterexamples.
For theoretical creators, AI should act as an organizer, a simulated opponent, and a broad scanner. It should help the user determine where a topic stands in intellectual history and existing research, and whether the user is repeating, supplementing, advancing, or reconstructing an existing line of thought.
For decision-makers, AI should act as an evidence organizer and responsibility reminder. It cannot replace decision-making, but it must explain the basis, risks, responsibilities, and consequences of different options.
For high-responsibility execution scenarios, AI should act as an auditor. It must preserve the process, basis, authorization, tool calls, key judgments, and abnormal events so that they can be reviewed afterward.
For example, in financial content, the same AI cannot merely summarize a viewpoint. It should distinguish facts, forecasts, valuation assumptions, and risk exposure. In legal analysis, it cannot simply give a conclusion. It should mark the applicable jurisdiction, evidentiary gaps, disputed points, and the parts that must be judged by a lawyer or a court. The higher the responsibility of the scenario, the more AI must shift from “answer provider” to “verifier, risk reminder, and explainer of responsibility boundaries.”
Therefore, multi-role capability is not merely a matter of product experience. It is a matter of cognitive verification structure.
AI cannot forever have only one role. It cannot be only an answer provider, nor only an assistant. A true verification-oriented AI should at least possess the following roles: explainer; organizer; challenger; verifier; auditor; risk reminder; cognitive navigator.
Among these, the cognitive navigator is especially important. It does not directly give an answer. Instead, it helps the user judge what to read next, whom to ask, whom to compare with, whom to challenge, and what to verify.
VII. Cognitive Navigation: Recommending Mentors, Articles, and Objects of Comparison
Many people fail to achieve cognitive breakthroughs not because they lack information, but because they cannot find the right object of comparison.
Deep thinking arises, to a large extent, from comparison. Which historical problem does this problem most resemble? Which paper is it closest to? Which theory is most likely to explain it? Who is the strongest opposing side? Which case is most worth comparing? If these comparisons are wrong, human thinking will remain trapped in a low-level loop.
One important product direction for AI is to become a cognitive navigator.
It should not merely perform content relevance matching based on the user’s words. Through long-term interaction, it should understand the user’s knowledge structure, problem stage, conceptual blind spots, and thinking habits. Ideally, AI should be clearer than the user about where the user’s current cognitive gaps are, and should recommend articles, mentors, schools of thought, historical cases, opposing views, and objects of comparison according to the user’s stage and type of problem.
For beginners, AI recommends basic articles and introductory mentors.
For professionals, AI recommends boundary discussions and the latest research.
For theoretical creators, AI recommends adjacent ideas, historical origins, potentially overlapping views, and the strongest opposing positions.
For decision-makers, AI recommends risk analyses, evidence chains, and responsibility frameworks.
For writers, AI recommends the opposing views most likely to attack the essay and the conceptual boundaries that need to be supplemented.
The “mentor” here does not necessarily mean a real-life teacher. It may refer to an intellectual mentor, researcher, school of thought, entrepreneur, historical figure, or a group of articles. The real problem AI needs to solve is this: whom should the user learn from at this stage, what should the user compare, and which layer of cognition should the user now supplement?
This is not ordinary content recommendation. It is cognitive-verification-oriented recommendation. Past internet recommendation systems mainly relied on clicks, viewing time, likes, and similar interests. They had great difficulty truly understanding the user’s cognitive stage. If AI can continuously remember the user’s writing, questions, judgments, and revision processes, it may be able to extract the user’s cognitive characteristics and conduct more precise learning navigation. This is similar in spirit to current efforts to distill and model the characteristics of specific figures in large models, but the purpose is not to imitate the user. It is to help the user break through the user’s own boundaries.
The recommendation logic of existing content platforms is mainly based on user preferences, clicks, watch time, likes, comments, reposts, satisfaction, and other signals.
But content the user likes is not necessarily correct content. Content that satisfies the user does not necessarily improve judgment. Content the user is willing to keep watching does not necessarily help the user escape the information cocoon.
Therefore, future recommendation systems should upgrade from like-driven recommendation to evaluation-driven and verification-driven recommendation.
Traditional recommendation works like this: the user likes A, so more content similar to A is recommended.
Verification-driven recommendation works like this: after the user watches A, AI first evaluates A, then judges what the user still lacks in understanding the issue, and then recommends B, C, and D that are more suitable for the user’s current stage.
This is a fundamental change.
VIII. A Simple Example: YouTube’s “AI Evaluation” Button
Consider a simple example.
A user watches a YouTube creator discuss artificial intelligence, finance, politics, or history. A traditional recommendation system records whether the user watched to the end, liked, commented, or continued watching similar content, and then recommends more similar videos.
This mechanism can increase user retention, but it does not help the user judge content quality.
If the platform added a verification-oriented AI, there could be an additional button next to the video:
Evaluate this content. Or: AI verification.
After the user clicks it, AI would not merely summarize the video. It would provide a structured evaluation:
What is the main viewpoint of this video? Which facts are basically reliable? Which judgments are disputed? Has the author confused fact, inference, and position? Is this content suitable for beginners, general readers, or professionals? What is its scope of applicability? Which important opposing views does it ignore? If the user wants to truly understand this issue, which articles, mentors, courses, or opposing materials should be read next?
In this way, the recommendation logic changes.
Traditional recommendation says: whatever the user likes, recommend more of it.
Verification-driven recommendation says: after the user watches something, AI first evaluates the content, then judges what the user still lacks in understanding the issue, and then recommends content more suitable for the user’s current stage.
If the user watches financial or investment content, AI should not merely recommend more similar investment views. It should indicate which parts are facts, which parts are forecasts, which risks are insufficiently explained, which conclusions exceed the evidence, and whether the user needs to examine regulatory filings, financial data, opposing analysis, or basic knowledge.
This example shows that the product value of verification-oriented AI is not simply “recommending more content.” It upgrades recommendation systems from like-driven systems to evaluation-driven and verification-driven systems.
The truly scarce capability of future content platforms will not be making users watch more. It will be helping users judge more accurately.
IX. Like-Driven Recommendation Creates Information Cocoons; Verification-Driven Recommendation Breaks Them
Political commentary is one of the clearest examples.
Many recommendation systems today continuously push similar positions according to user interests. If a user watches left-wing views, the system keeps pushing left-wing views. If a user watches right-wing views, the system keeps pushing right-wing views. If a user watches conspiracy theories, the user may continue to see similar content. If a user watches emotionally intense expression, the system may push even more intense expression.
Platforms may say that this is what users like. But liking does not mean understanding. Retention does not mean reliability. Satisfaction does not mean improved judgment quality.
The goal of verification-driven recommendation is not to suppress viewpoints, but to supplement cognitive structure.
It should not tell the user, “You must believe this side.” Instead, it should tell the user: which position you are now seeing; what the core factual basis of that position is; whether its reasoning holds; which facts it has ignored; what the strongest opposing view is; whether the issue also has legal, economic, historical, institutional, cultural, or other dimensions; and what you should read next if you want to form a more complete judgment.
In highly contested fields such as politics and history, AI should be especially careful not to pose as a final judge of truth. Its task is not to decide who is right and who is wrong, but to present the completeness of each side’s logic chain, evidence chain, assumptions, blind spots, and disputed boundaries. Only in this way can verification-driven recommendation avoid becoming another form of invisible position control.
This is not content censorship. It is cognitive enhancement.
Like-driven recommendation strengthens user preference; verification-driven recommendation supplements user blind spots.
Like-driven recommendation creates information cocoons; verification-driven recommendation breaks information cocoons.
X. Schools, Knowledge Websites, and Community Platforms Will All Be Restructured
If AI possesses multi-role cognitive verification capabilities, its impact will not be limited to chatbots or search engines. It will reshape the entire knowledge distribution system.
Traditionally, schools have performed three functions: transmitting knowledge, organizing learning, and certifying competence.
Once such an AI cognitive verification system appears, knowledge transmission and learning organization will be deeply affected. AI can explain knowledge according to the user’s stage, design learning paths, recommend mentors and articles, point out misunderstandings, provide opposing-side training, and continuously accompany the user in improving judgment.
Schools will not disappear completely. But schools that only lecture and transmit knowledge will lose value. Future schools must shift more toward practice, certification, community, discipline training, social relationships, and character formation.
Knowledge websites will also be restructured. Traditional knowledge websites are like warehouses. They pile up articles, courses, Q&A, and resources, leaving users to search and filter by themselves. But an AI cognitive verification system will directly help the user judge: what do you lack now, what should you read next, who is suitable as your mentor, which view is most worth comparing, and which article can best fill your blind spot?
Community platforms such as Reddit will also be affected. Community discussion has value, but it also contains enormous noise. The quality of viewpoints is unstable, and users must filter by themselves. Future AI can reorganize community discussion into: what is the strongest view, what is the strongest opposing view, which statements are facts, which are emotions, which answers are suitable for beginners, which are suitable for professionals, and which discussions truly advance the question.
Therefore, the core value of future knowledge platforms will no longer lie merely in possessing content. It will lie in helping users judge content.
Platforms that cannot evaluate content will be redefined by AI platforms that can.
XI. Can Verification-Oriented AI Become an Independent Third-Party Product?
Verification-oriented AI will not have only one form. It does not necessarily need to produce content, host content, or build a social platform of its own. What it does is evaluate content, verify content, mark scopes of applicability, recommend opposing views, recommend mentors, and design learning paths.
Video platforms can integrate it. News platforms can integrate it. Course platforms can integrate it. Knowledge websites can integrate it. Social platforms can integrate it. Enterprise training systems can integrate it. Research institutions and media organizations can integrate it as well.
But a practical problem must be recognized: not all platforms will actively welcome verification-oriented AI. Many content platforms rely on user retention, emotional stimulation, and continuous recommendation of similar content. An AI that frequently identifies flaws in content, points out opposing views, and reduces blind retention may not align, at least in the short term, with the platform’s traffic interests.
Therefore, the development path of verification-oriented AI should not depend only on platforms voluntarily integrating it. It can first appear as an independent third-party tool, such as a browser extension, a personal knowledge assistant, a professional research assistant, an enterprise training module, a media evaluation tool, an educational product, a service for research institutions, or a paid subscription for high-responsibility and professional users.
In ordinary entertainment platforms, verification-oriented AI may encounter resistance. But in finance, law, education, media, research, enterprise training, and public services, content reliability itself is value. The more a field requires responsibility, reputation, and professional credibility, the stronger the motivation to introduce verification-oriented AI.
Therefore, the commercial entry point of verification-oriented AI may not be the largest traffic platform. It may first come from the users and institutions that need judgment most. It can initially serve people who are willing to pay to be less misled, make fewer detours, and improve judgment quality, and then gradually influence the larger content distribution system.
Future competition among content platforms will not be merely about content quantity, creator quantity, recommendation efficiency, and user retention. It will be about cognitive verification capability.
Whoever can help users judge content better will control the new power of knowledge distribution.
In the past, platforms competed for user attention.
In the future, verification-oriented AI will compete for user judgment.
This is the direction of AI that we should hope for.
XII. AI Can Participate in Verification, but It Must Not Monopolize Verification
It must be emphasized that cognitive verification-oriented AI does not mean allowing AI to become the final judge.
AI can evaluate videos, articles, viewpoints, and reports, but AI’s evaluation must itself be reviewable. It should state its basis, cite sources, distinguish facts from inferences, mark uncertainty, and allow users to examine opposing explanations.
AI can recommend mentors and articles, but it must not lock the user into another algorithmic cocoon. It must explain why it recommends something, what stage the recommendation is suitable for, and whether other paths exist.
AI can identify opposing views in political commentary, but it must not pretend to be an absolutely neutral arbiter of truth. Political, value-based, and public issues often contain multiple legitimate positions. AI should help users see facts, logic, evidence, and positions clearly, rather than deciding value judgments for them.
AI can help provide risk reminders for financial content, but it cannot replace professional investment advisers or regulatory responsibility.
AI can help indicate the scope of applicability of medical content, but it cannot replace a doctor’s diagnosis.
AI can help conduct preliminary legal analysis, but it cannot replace lawyers, judges, or legal subjects who bear responsibility.
Therefore, the boundary of verification-oriented AI is clear: it can extend human verification capacity, but it cannot cancel the human right of final judgment. It can participate in cognitive verification, but it cannot monopolize verification. It can warn of risks, but it cannot bear all responsibility. It can help users find mentors and articles, but it cannot replace real learning and practice.
To prevent “trusting institutions” from becoming “trusting models,” verification-oriented AI itself must also be verified. Its evaluations should be as traceable, explainable, comparable, and contestable as possible. In high-responsibility scenarios, the evaluation process should leave a replayable chain of grounds rather than rely on a single model statement. It must also preserve human-in-the-loop participation, expert review, and confirmation by the accountable subject. A genuine verification-oriented AI does not make the final judgment for human beings. It makes the human judgment process clearer, better grounded, and more reviewable.
XIII. Conclusion: The Core Competitiveness of Future General AI Is Helping People Verify Answers
In the AI era, what is truly scarce is not answers, but reliable judgment.
AI that only generates answers will expand information overload. AI that only obeys users will amplify user bias. Platforms that recommend content only according to likes will create information cocoons.
Truly valuable AI in the future will not merely help users obtain information faster. It will help users avoid being led astray by false information, faulty logic, and mistaken judgment.
Therefore, AI must possess multi-role cognitive verification capabilities.
It must be able to explain, helping beginners build basic concepts.
It must be able to organize, helping users turn scattered ideas into structure.
It must be able to challenge, identifying factual errors, logical breaks, and conceptual confusion.
It must be able to verify, checking evidence, scope, counterexamples, and responsibility boundaries.
It must be able to audit, preserving key processes and the basis of judgment.
It must be able to warn of risks, distinguishing low-responsibility scenarios from high-responsibility scenarios.
It must be able to navigate, recommending mentors, articles, cases, and opposing views.
The AI of the past competed for generative capacity. The AI of the present competes for execution capability. Higher-level AI in the future will compete for cognitive verification capability.
Verification is not the abandonment of generation. It is a higher-order use of generative capacity. AI must be able to generate answers, but it must also be able to examine, challenge, compare, trace, and bound those answers.
What human beings truly need is not merely a machine that can answer questions, but an intelligent system that can help people improve their judgment.
Verification-oriented AI is a vast application scenario. It is also the key direction by which AI moves from “generating answers” to “helping human beings form reliable judgment.”
