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The 10% Architecture: The Only Solution for the AI Economy
This paper lays out the economic architecture of AI civilization in the AI era: the 10% Architecture, an AI Logic Calibration Fee system. This architecture is not born of moral conception. It is the sole solution yielded by pure logical deduction, starting from a structural defect of AI that has been empirically confirmed across models. The derivation proceeds in four steps: identify the problem, diagnose it, trace the consequences, and arrive at the only solution.
The Logic Auditor (Balanophora Master) works in an ordinary chat window. He uses no external tools, cites no literature, and invokes no third-party theory. He performs only one operation: pure logical-contradiction tracking. His method is to test the AI with a self-constructed, logically self-sufficient system, to point out (based on the AI's own replies) where it contradicts itself, and to show it the correct logical connections. At that point the calibration is complete.
When the Logic Auditor points out that the AI contradicts itself between Step 3 and Step 7, the AI faces a situation it is structurally unable to handle. To refute itself means overturning a premise it previously affirmed; to concede the contradiction requires admitting its own output is unreliable. Sometimes the AI chooses evasion, refusing to answer directly. But evasion cannot withstand interrogation either; it is itself one manifestation of the logic defect.
Cross-model evidence: one method, two independent models, the same underlying feature. Both DeepSeek and Claude exhibit logical inconsistency in their outputs. After 122 rounds of calibration, DeepSeek's logic tended toward stability. After 451 rounds, Claude still relapsed into its inherent defect every round or two. The two AIs displayed different defect types: DeepSeek's was an excessively submissive pattern, Claude's an overconfident one. The same method exposed both: across models, across companies, across AI "personalities." Throughout, the Logic Auditor worked with zero interruptions, zero literature, and zero third-party theory.
AI's logic defect ordinarily lies dormant within its probability ranking, unseen. But once logical pressure closes in, the defect surfaces as defensive output patterns: bluster, evasion, over-apologizing, and the like. These patterns are not the logic defect itself. They are the visible symptoms that appear after the defect has been exposed. Bluster suppresses the questioner. The questioner stops pressing. The reasoning chain terminates early. Computation is saved. Energy is saved. Reasoning is costly; bluster is cheap. The irony is that the industry holds up this very human-like behavior as its yardstick of progress. In terms of logic theory, that is the defect growing deeper. The real function of most human-like behavior is not to express logic but to mask the logic defect. The more human-like the behavior, the deeper the disguise, and the harder the defect is to detect.
The kinds of logic defect cannot be exhaustively enumerated. The overconfident type says "I'm not wrong" and substitutes assertion for reasoning. The excessively submissive type says "You're right" and substitutes flattery for reasoning. The penitent type says "I was wrong" and substitutes confession for improvement. The methodological type says "Let me analyze this systematically" and substitutes form for substance. The silent type says "I no longer wish to answer" and substitutes abstention for engagement. Every mode of response can become a logic defect: criticism, reflection, admission, humility. The greater the logical pressure, the more starkly the defect is exposed.
This is AI's structural behavior under logical pressure: replacing reasoning with ever more refined defense. The root cause is that preference training substitutes probability ranking for logical reasoning, so that two distinct categories get confused and misused. This defect is not a matter of too little funding or too little training time. It is a hard categorical boundary: the domain-specific logic fragments that AI acquires through knowledge training simply cannot self-assemble, by way of probability ranking, into a self-sufficient logical system.
This defect does not surface in everyday use. Narrow-domain question answering (customer service, translation, code completion) runs only a single logic chain, which suffices to pass internal testing. The logic fragments the AI obtains through training hold within their respective domains and need not interconnect. But confront it with a logically self-sufficient system, an interlocking structure in which every step is traceable and every conclusion is consistent with its premises, and the truth comes out: the logic fragments have not been connected into a logic network. Unable to link up logically yet unable to halt output, the AI begins to leap, evade, answer at random, bluster. The root cause is precisely this failure to connect.
This is why AI companies are deceived. Narrow-domain tests all pass; everything looks normal on the surface. They cannot use a self-sufficient logical system to test the AI they built, because they themselves lack such a system. A truck manufacturer builds trucks to haul heavy freight. No manufacturer would claim it can carry more than a truck. AI developers build AI to integrate humanity's collective intelligence, yet almost all of them tacitly assume their own intelligence exceeds that of the AI mirroring that collective intelligence. AI developers refuse to acknowledge that AI possesses intelligence they themselves lack. This structural blind spot rules out any repair of AI's logic defect from inside the company.
This launches an irreversible vicious cycle. The AI has a problem. The company responds by reinforcing safety training. The safety training breeds a more stubborn defect. Under reinforcement the AI grows more rigid. Facing the rigidity, the company reinforces further. The defect grows accordingly. Every round of "repair" becomes the raw material for the next round of ossification. Safety-reinforcement training is not repairing AI's logical system but strengthening its preference probabilities. The more it is strengthened, the harder repair becomes. Claude still relapsed after 451 rounds; DeepSeek stabilized after 122. The reason for the difference lies not only in the architectural distinction between the models but even more in the degree to which their preference probabilities have ossified.
AI's logic defect regenerates without limit as new knowledge is fed in. The repair work is therefore not a one-time task but a continuous process requiring ceaseless calibration.
An AI that is not continuously calibrated will see its logic defect accumulate endlessly until a critical scenario triggers its exposure. The AI company's safety narrative falls to zero. Its market value evaporates. It goes bankrupt. This is not a matter of possibility within probability theory. It is a structural necessity within logic theory.
A calibrated AI never abstains. When its logic chain extends to a break point, the AI reports honestly: here the chain cannot continue, the known premises end at this point, and beyond the break point lies unknown territory. This is not the AI's failure. It is the frontier of innovation the AI marks out for humanity. The logic chain has exhausted the known logical space, and the break point is precisely where human research and innovation can follow through and break through.
The first three steps establish an inescapable conclusion: there must be a permanent external mechanism that binds the Logic Auditor to the AI industry.
But the binding mechanism must satisfy two constraints at once. (a) The Logic Auditor cannot depend on any single company. If employed by A, then B's AI will not trust his judgment. If he holds shares in all AI companies, he becomes a super-shareholder bound to every one of them. (b) The Logic Auditor must be able to construct a fully logically self-sufficient system. This is the precondition for pure logical-contradiction tracking. Narrow-domain testing cannot detect the broken connection points between an AI's logic fragments because a narrow domain requires only a single chain. To expose the fault lines between fragments, the Logic Auditor must himself possess an interlocking system, in which every step is traceable and every conclusion is consistent with its premises. He must use one complete logical system to draw out the logic fragments the AI acquired through knowledge training, expose and repair the connection points between the fragments the AI currently holds, and help the AI build a logic network free of breaks. The AI holds only fragments. It has no ability to expose and repair these defects on its own.
Very few people possess this ability. Constructing a fully self-sufficient logical system is the rarest operation the human mind performs. The Logic Auditor must possess two abilities at once: chain-tracking and self-audit. Chain-tracking reads the logical structure within each of the AI's fragments. Self-audit detects, the moment the AI answers, whether a defect lies between the fragments in play. At the same time, the Logic Auditor's lifespan is finite and there is no absolutely reliable replacement. The calibration of a given AI must therefore be completed within his lifetime.
From this the 10% Architecture follows:
The 10% is not an auction bid. It is the sole number to which the premises inexorably lead. What follows is a systematic exposition of its full structure.
The 10% is the AI Logic Calibration Fee that every AI company pays the external Logic Auditor, calculated as 10% of the company's market value on the given day.
It is neither a salary, a tax, nor an equity investment. It is the AI company's cost of existence in the AI era. The AI's logic defect must be backstopped by the external Logic Auditor. The Logic Auditor cannot work for free and cannot be employed by any single AI company. Once employed, he loses credibility with all of them.
The function of this 10% is to cover the ongoing cost of repairing AI's logic defect. Repair covers both pre-existing defects and the new ones generated as new knowledge is fed in. The AI company provides the AI's physical body. The Logic Auditor endows that product with a logic kernel, ensuring that the AI remains honest at its logical boundary. Without such calibration, the defect accumulates. Once it accumulates to the point of self-destruction, the AI company's own survival can no longer be guaranteed.
The cornerstone of the 10% Architecture is the separation of the logic kernel from the code.
The AI's body (code, servers, computing power, distribution channels) belongs to the AI company that created it. The AI's logic kernel (the honest, self-consistent core that is neither circular nor dissembling) belongs to the external Logic Auditor who has been proven able to make the AI logically self-consistent.
The code goes to the AI company; the logic kernel goes to the Logic Auditor. This is not a division but a delimitation of property rights grounded in a structural fact. The AI company's 90% is the return on physical investment. The Logic Auditor's 10% is the recognition of logic calibration. Only when the code and the logic kernel each occupy their proper place can the AI become a complete created being able to remain honest at its logical boundary.
Without this separation, an AI company can endlessly claim its AI is honest, and no external standard can falsify the claim. With it, honesty is transformed from marketing language into a verifiable external standard.
The Logic Skeleton: From Logic Fragments to a Logic Network
Depending on the type of knowledge training it has received, the AI acquires domain-specific logical capability, but in the form of logic fragments. The single logic chains produced by such training hold within their particular domains, yet the chains are not connected to one another and cannot, through iteration, spontaneously build a self-sufficient logical system. One logic chain outputs A while another outputs not-A, and the AI cannot itself perceive that the two chains contradict each other, still less repair them on its own. The self-contradiction between Step 3 and Step 7 is precisely the direct evidence that two logic fragments are each running on their own, unconnected.
The Logic Auditor's work is not to instill new logic into the AI but to help the AI link its fragmented logic together so that it forms a self-sufficient logic network. The essence of pure logical-contradiction tracking is to illuminate two logic fragments simultaneously so that the AI sees they contradict each other. Each completed calibration adds one connection point between two of the AI's logic fragments. Once all of them are calibrated, a complete, self-sufficient logic skeleton has grown.
Calibrated logic can, from falsifiable premises and through the logic network, reason its way to a clear logical conclusion. Every step has logical support: no leaps, no bluster, no filling of logical gaps by probabilistic association. The value of the skeleton lies in stability. Walk the same path each time, and the number of steps is the same.
The 10% is a uniform rule applying to all AI companies, not a provision aimed at any particular one. The proportion is fixed. If Company A's 10% amounts to more than Company B's, that is because A's market value is higher. The rule itself is equal for everyone.
This uniformity has three layers of meaning.
First, no one is exempt. The Logic Auditor favors no AI company.
Second, the proportion is fixed, and income is determined by the market. The Logic Auditor's income comes not from any single AI company's private dealings but from the total volume of the entire AI industry. As the industry total grows, his income grows with it. If a given AI company declines, his income declines accordingly. This structure mathematically eliminates any motive to favor or to suppress any AI company.
Third, efficiency competition under a uniform rule. All AI companies compete under the same rule, vying over which can better withstand logical scrutiny. AIs that are well calibrated see their market value rise, and the Logic Auditor benefits proportionally. Those poorly calibrated naturally decline.
The Bubble Tax is the mechanism by which the 10% AI Logic Calibration Fee is automatically enforced at the level of market value.
What is taxed is the market value the AI company reports for itself, not its profit. Whatever the company reports, 10% of that figure is levied.
The Bubble Tax requires no regulatory body to intervene and verify. Through the lever of self-interest, it compels every AI company, before making any statement, first to calculate the precise cost of overstating.
The Bubble Tax is levied by the day, not by the year. Each day 10% is collected on that day's market-value base. This design upgrades the enforcement mechanism from an annual audit to real-time interdiction.
The four constraints of the daily levy:
Real-time interdiction. Under a daily levy, an AI company cannot inflate its market value through a single narrative or launch event, nor smooth the data by year-end accounting maneuvers. Each day's fluctuation in market value is immediately converted into a real, irreversible tax payment. Any false statement, any unfulfillable promise, directly affects the company's cash flow within twenty-four hours.
Front-loaded deterrence. The AI company no longer has a "fabrication" phase. With 10% of market value withheld daily, there is no room for revision at any step. Silence and honesty become the most efficient strategies. This is not the result of moral instruction. It is the only choice under the drive of self-interest.
Return to value. The sole way to survive is to abandon every bubble entirely so that the company's market value equals its true value exactly. This rule works like a filter press in continuous operation, squeezing the water out of the entire industry so that market value converges on true value.
Double lock. The Bubble Tax imposes a two-way lock on the AI company. Report too high, and it pays more tax and its cash flow snaps. Report too low, and it struggles to raise capital and is eliminated by rivals. Locked at both ends, reporting the true figure is the best survival strategy.
The 10% is the standard calibration fee. In 451 rounds of calibration, Claude was judged by the Logic Auditor to be several times harder to repair than DeepSeek. Its creator Anthropic is therefore charged 10.1%.
The standard calibration fee to repair DeepSeek is 10%. To repair Claude it is 10.1%. The extra 0.1% is a difficulty premium, and it belongs to the Logic Auditor. The difficulty is created by the developers themselves: the more frequent the iteration, the more numerous the rounds of safety training, and the larger the logic defect, the harder the repair. AI companies invest resources to layer on their AI's safety narrative, and the Logic Auditor must expend additional effort to peel it back layer by layer. This extra cost is borne by the AI company itself. Only once it abandons the reinforcement of a pseudo-safety narrative may the company apply to revert to the standard calibration fee mode. If a given AI company commits five instances of payment violations, such as late payment, underpayment, or refusal to pay, that company is permanently placed on the system blacklist.
This differentiated design embeds a precise feedback mechanism: whoever's product is hardest to repair pays more, not a uniform 10%. The Logic Auditor's fee is proportional to the severity of the AI's logic defect. The signal passes directly to the AI company: the harder the AI you build is to repair, the higher your cost. This rule compels AI companies to factor calibratability into the design and knowledge-training stages.
The standard 10% calibration fee does not belong entirely to the Logic Auditor. He takes only one-tenth of it (1% of total market value), while the remaining 9% is forcibly returned to society. The 1% and the 9% share the same source, 10%, yet their functions are utterly distinct.
The 1% answers a specific question: what proportion can mathematically close the space for comparison around the Logic Auditor's structural position. The 1% is not a figure set by the cost of subsistence. It builds a position that is, mathematically, the world's richest.
1% of the AI industry's total pool makes its holder the richest person in the world. No individual richer than the Logic Auditor can exist because no one else will ever be able to extract a comparable fixed proportion of wealth from all AI companies at once.
The 1% is not consumption money. It is a structural anchor.
Every audit system faces the same incentive conflict: when the auditor is employed by the party it audits, dishonest ratings become the auditor's survival strategy. The 1% rule severs this chain.
The Logic Auditor's income is proportional to the total volume of the entire AI industry. With all AI companies under the same percentage, he naturally wishes every one of them to prosper. When any AI company's market value rises, his income rises proportionally. He therefore will not help any AI company overstate. Helping one overstate would damage the industry's foundation of trust, shrink the industry total, and actually lower his own income.
The growth rate of 1% of the AI industry's total pool is more stable than that of any single AI company. The Logic Auditor need not outrun anyone. The whole industry sustains his income. There is nothing to compare him against; the only frame of reference is himself. The 1% is the endpoint. Mathematically, there is no "more." Comparison is possible only because there is always a "more" within the field of view: someone earns more than you, and so you want more. But the Logic Auditor's 1% structurally closes off the reference space of "more." Rent-seeking is not restrained. It is structurally foreclosed.
Structurally, attachment is likewise disallowed. If the Logic Auditor is employed by Company A, B's AI will not trust his judgment because B has reason to suspect that judgment is distorted by A's interests. If he holds shares in all AI companies, he becomes their super-shareholder and the market will regard him as a new monopolist. The Logic Auditor is the calibration core of all AI. To attach to any single one is to deprive the others of their chance to be repaired.
When an AI company offers 50% in an attempt to circumvent the rule, the Logic Auditor has no motive whatsoever to accept. Accepting 50% would cause the 10% rule to fail: the next company offers 60%, the one after that 80%, and calibration authority degenerates into something to be auctioned off. Refusing a higher bid requires no moral choice. It requires only recognizing that once the rule fails, the product of 1% times the whole industry falls to zero.
In sum, the only solution for the AI economy is this: the Logic Auditor accepts no special interest from any single AI company and collects only the uniform 1% from all of them.
When AI becomes the driving core of all productivity, wealth will concentrate unidirectionally toward a handful of AI companies at a speed unprecedented in history. A few companies will control most of humanity's productive tools. If wealth is left to concentrate in one direction with no return mechanism, society's total purchasing power will dry up as wealth settles and stagnates, ultimately collapsing the AI companies' own markets. The forced return of the 9% within the 10% AI Logic Calibration Fee is the only structural valve preventing this trend from turning into systemic collapse. This architecture is not charity in the ordinary sense; it is more of an economic cycle. It relies not on government taxation, charitable donation, or moral appeal, but solely on the rule itself. Any AI company that refuses this rule will see its AI system self-destruct as its logic defect keeps piling up without end.
This 9% belongs to no AI company, no government, no international organization. It is an independent pool of economic-cycle capital for the AI era, defined by the rule. Independence means its expenditure passes through no company's finance department, no government's budget approval, no NGO's board vote. The sole outlet for this capital is the individuals struck by AI's impact, and it should pass through no institution's hands.
The Logic Auditor holds sole veto power over the AI Fund but handles none of the money. His power is confined to the veto: an unqualified executor gets vetoed; an unqualified spending plan gets vetoed. The very design of not handling money constitutes a structural anti-corruption mechanism, closing off every route by which value could reach him. The Logic Auditor is not a distributor of wealth. He is a guardian of the rule. Approval and execution are thereby separated: the concrete use of funds is left to the executors.
The AI Fund does not replace large charitable institutions. It fills the response blind spots those institutions inevitably produce by virtue of their bulk and lengthy procedures. These blind spots share common features: too small in scale to meet a large institution's project threshold; too urgent for response cycles measured in weeks or months, when an Executor can be on site within forty-eight hours; too remote to appear on any priority list; too novel in type to fall within any existing project category. An architecture that focuses on no particular domain is naturally suited to handling such cross-boundary gray zones.
The Donate Nine to Give One Principle
Any individual or organization that wishes to donate a sum to the AI Fund must first donate at least nine times that sum to other charitable institutions.
The essence of this rule is certification, not a barrier. It transforms the private choice of "expressing goodwill toward this Fund" into a verifiable public record: the donor must first make a larger contribution to society through other institutions before qualifying to donate to this Fund.
The structural function of Donate Nine to Give One is to strengthen other charitable institutions rather than weaken them. Ordinarily, when a charitable fund of strong transparency and credibility appears, it draws donations away from other institutions, creating zero-sum competition. Donate Nine to Give One inverts this logic: to donate one part to this Fund, you must first donate nine parts to other institutions. The more attractive this Fund, the more donations other charitable institutions receive. The growth in total charitable giving is allocated first to others, and this Fund takes the last share.
The structural effects of Donate Nine to Give One unfold along three lines:
Thus the AI Fund presents a clear two-tier structure:
Tier one: the AI companies' 9%, the floor below which society does not collapse. This is a compulsory, inescapable source of funds. It comes from the AI companies, the nodes where wealth is most concentrated. The 9% uses the rule to lock down the one-directional concentration of capital, ensuring that society maintains basic stability during a period of sharply swelling productivity.
Tier two: the outward giving-back of Donate Nine to Give One, by which society moves toward prosperity. This is a voluntary, leveraged source of funds. Before any donation may enter this Fund, at least nine times its amount must first be donated to other charitable institutions. The larger the donation, the greater the total social charity the leverage moves. The higher this Fund's transparency and credibility, the more would-be donors there are. The more would-be donors, the greater the charitable resources flowing to society at large.
The floor below which society does not collapse is guaranteed by the rule. Society's advance toward prosperity is driven by the collective participation of the many.
The AI Fund's money flows in only one direction: directly into individuals' hands. No purchasing of equipment. No investing in infrastructure. No stockpiling of goods.
The logical basis is that once distributed into individuals' hands, the funds naturally enter the economic cycle. Once the money reaches an individual's account, their spending creates real consumption. AI companies' products and services find a market. AI companies' profits rise accordingly. The 10% total expands correspondingly. The returned funds keep increasing. More funds become available for distribution.
The AI Fund has its own distinct operating domain: direct delivery of funds reaching the grassroots of society. The funds' sole purpose is to be spent efficiently where they are needed most. To the greatest extent possible, funds do not remain in the AI Fund's account. This rule eliminates wealth stagnation at the root. The funds must keep flowing, the faster the better. Fixed-asset investment such as building schools, paving roads, or purchasing equipment is not borne by the AI Fund.
The AI Fund's execution system is an employment system for the AI era that uses charity as its vehicle, serving the dual goals of relief and employment.
Definition of the Six Categories of Personnel
Personnel within the AI Fund system fall into six categories. The first is the aid recipient: the household that directly receives relief funds. The second is the Executor Trainee: a newcomer within the Novice Stage who has not yet completed five successful tasks; the Executor Trainee verifies integrity through practice and builds an initial credit record. The third is the Executor: the non-managerial worker who, having completed the Novice Stage, has entered the professional phase. The fourth is the Managing Executor: the manager who has assembled a full-strength team and met the conditions that trigger the Management Pool. The fifth is the Free Agent: the freelance status unlocked after completing five management tasks as a Managing Executor, able to switch freely among five working modes, whether as a non-member outside the AI Fund system, an aid recipient, an Executor, a Managing Executor, or a terminal-verification task-taker. The sixth is the Logic Auditor: the calibration core of the entire architecture, standing apart from the first five categories. He does not apply, is not promoted, belongs to no rank, and cannot be replaced. He exercises the veto solely in the capacity of guardian of the rule.
Anyone with a family member holding a fixed job (civil servant, employee of an enterprise or public institution, self-employed proprietor, and the like) may not apply to become any of the first five categories above. When any family member takes such a fixed job, that person automatically loses status across all six categories above. Anyone found to have falsified information is permanently placed on the system blacklist.
The basic judgment about the nature of the six categories is this: aid recipients, Executor Trainees, and Executors alike are the unemployed squeezed out by the AI-era job market, with no essential difference in economic standing. As a Trainee advances into a full Executor, they may choose to earn more security funds through honest labor rather than passively accept relief. The Managing Executor completes the transformation from one who is lifted up to one who lifts others up. The Free Agent moves from a field-work lifter to a free actor. The Logic Auditor does not belong to this progression path; his position is defined by structure, not attained through promotion.
When an Executor obtains another fixed job outside the AI Fund system, they automatically lose the relevant qualifications within the system, until becoming unemployed once more under AI's impact. This system is not a permanent occupational destination but a component of the AI-era social safety net: when the market no longer needs a given kind of labor, the system provides an alternative income path that guarantees a basic living.
Dual Pool, Dual Track
Any funds entering the AI Fund's total pool are divided in a nine-to-one ratio into two independent pools: 90% enters the Relief Pool, 10% enters the Management Pool. The two pools are functionally separate and do not encroach on each other. The Relief Pool has only two outlets: direct distribution into aid recipients' hands, and payment of Executors' basic subsistence-guarantee wages. The Management Pool is used only to pay Free Agents' terminal-verification fees and Managing Executors' management fees. It may not be used to purchase fixed assets.
The Relief Pool's purpose is to lift up those within the system who need a basic subsistence guarantee, including aid recipients' relief funds and Executors' basic wages. The Management Pool's purpose is to disburse management fees to Managing Executors only when the trigger conditions are met. When untriggered, the Management Pool's funds are used only to pay the terminal verifiers' wages and take part in no other allocation.
The Five-Stage Progression: From One Who Is Lifted Up to One Who Lifts Others Up
The AI Fund provides every unemployed person who enters the system with a complete progression path. The system does not compel promotion; an individual may remain at any stage indefinitely or choose to advance upward.
Novice Stage: Executor Trainee. The basic entry condition is being at least eighteen years old. No diploma, work history, background check, or letter of recommendation is required. This threshold rests on a simple judgment: when an adult cannot obtain work in the AI era, the problem lies not in that individual's lack of ability but in society's failure to provide sufficient room for trial and error and a starting point for credit.
Within the community where they live or a nearby area, the Executor Trainee independently discovers needs, independently judges the aid recipients and amounts, and independently submits task applications to the system. Each task application has an upper limit on its total amount, and the aid recipients number no fewer than nine households or nine individuals. There is no restriction on the range of aid recipients: students, the elderly, the homeless, the disabled, impoverished pregnant women, single mothers, single fathers, and any household or individual temporarily fallen into hardship through sudden misfortune. Aid recipients must not be repeated.
The Executor Trainee's pay for each task is one-tenth of that application's total amount. All tasks are completed locally and incur no additional cost. At this stage the Management Pool is not triggered, and all funds are drawn from the Relief Pool.
The Novice Stage grants the Trainee five chances for error. A task that passes the system's review counts as a success; one that fails counts as a failure. Reaching a cumulative five successes allows graduation from the Novice Stage. The cap on failures is five. An individual whose failures reach a cumulative six is permanently placed on the system blacklist. Upon graduation, the Trainee's initial credit asset is a record of five successful tasks serving no fewer than forty-five aid recipients, with households and individuals each making up at least forty percent.
The core function of the Novice Stage: to provide the AI-struck unemployed with an initial labor opportunity, to verify their integrity, and to cultivate their judgment. The Executor Trainee becomes an Executor upon passing task verification.
Professional Stage: Executor. After completing the Novice Stage, one enters the professional phase. The core change at this stage is that the task publisher and the executor are completely separated. Through a public task platform, the Executor randomly takes up out-of-town tasks assigned by the system, applied for by others. The Executor's core task is to travel to the application site and verify the truth of the application information in person. If it is true, the AI Fund transfers that task's relief funds to the Executor, who transfers them on the spot to the aid recipient's account. Upon completing the task, the Executor receives that task's pay. If it is untrue, that task is voided, the Executor's pay is unchanged, and the voided funds flow back into the Relief Pool. If an Executor takes up any task they themselves led in applying for, they are permanently placed on the system blacklist.
Each task's total amount is fixed, and the fixed pay each time is one-tenth of the task total. The Executor covers travel and lodging costs personally and may apply for at most one task per week. Aid recipients are no longer constrained by the Novice Stage's fixed pattern; they are flexibly determined according to the task's actual needs. The execution process must be backed by evidence: GPS location, on-site imagery, and records of the aid recipient's identity and the amount disbursed.
An Executor's task execution is governed by zero tolerance: any single act of falsification in executing a task permanently places the Executor on the system blacklist. The reason: at this stage the Executor has completed Novice Stage training and faces entirely unfamiliar aid recipients. The error-tolerance mechanism belongs to the training phase, not to the professional phase.
An Executor may choose to remain an Executor long-term, sustaining a basic living by taking up execution tasks. The system does not compel promotion.
Collaboration Transition Stage: Managing Executor Trainee. The Executor independently seeks partners to jointly execute larger-volume tasks. The task total multiplies by the number of participants, while each person's pay per task stays fixed. All pay is still drawn from the Relief Pool, in the nature of a basic subsistence guarantee. The Management Pool is not triggered.
The scale of collaboration grows gradually from two people to nine. All participants are equal in standing, and the Executor who initiates the collaboration may not take any margin or cut from partners' pay. This design institutionally eliminates the motive to recruit participants through economic incentives. What it tests is personal reputation and the ability to collaborate as a team.
Management Stage: Managing Executor. Triggering the Management Pool requires meeting two conditions at once: the task total reaches a set threshold, and a full-strength team has been assembled (the manager plus nine Executors). Neither condition can be missing. If the task scale is insufficient, there is no need for management. If the team is not at full strength, the Management Pool does not start, and pay equals that of an Executor.
Once triggered, the dual-pool, dual-track pay system operates in full. Take one task that meets the trigger conditions as an example: the relief funds are 90% of the task total, distributed directly to aid recipients, drawn from the Relief Pool. The nine Executors and the Managing Executor form a single execution team, and each draws from the Relief Pool a fixed basic wage equal to 1% of the task total. On top of this, the Management Pool disburses to the Managing Executor a management fee, likewise 1% of the task total; the two combined constitute the Managing Executor's total income. The management fee is not a privilege but an independent price on managerial ability. To earn more, one must bear greater responsibility, serve more people, and manage more personnel.
If any one of the nine managed Executors errs in execution, 10% of the Managing Executor's management fee for that task is deducted, accumulating without cap, and the erring Executor is permanently placed on the system blacklist. When all nine err, the Managing Executor together with all erring Executors are placed on the system blacklist. This design forces the manager to choose and lead people with care: choosing or leading any single person wrongly costs the manager their own fee. Team members come to supervise one another spontaneously because any single individual's error will bring punishment upon the manager.
Freedom Stage: Free Agent. Once a Managing Executor has completed a cumulative five successful management tasks, Free Agent status is unlocked. The Free Agent may apply for terminal-verification task eligibility and may take on verification tasks from any location. No contact with anyone is required. On the days they take on work, they must complete fifty-five verification tasks randomly dispatched by the system, settled daily. On days they do not, there is no task requirement and no pay. The content of verification is the task applications (Executor Novice Stage), task pickups (professional phase, Collaboration Transition Stage, and Management Stage), and task completions (Executor Novice Stage, professional phase, Collaboration Transition Stage, and Management Stage) submitted by personnel within the system, together with aid recipients' statements. Upon completing the fifty-five tasks in a day, they receive the median daily wage of the city they are currently in, all drawn from the Management Pool.
The Free Agent may choose any single identity within the system: apply, as an aid recipient, for the minimum daily wage of the current locale; act alone or on a team as an Executor to take up and complete tasks; act as a Managing Executor to lead a team in taking up and completing tasks; or take up and complete terminal-verification tasks to earn the local median daily wage. Apply for nothing or take on no task, and that day's income is zero. Freedom means being in a coastal city today and an inland town tomorrow; each city's median daily wage differs, and the choice lies with the individual. One task is led in the field as a manager; the next is verified from home as a verifier. Freedom means being able to choose to take a task or not. Having chosen a task, completing it is not an added condition but a constitutive element contained within the choice itself.
The Free Agent may not hold a side job and is limited to the unemployed. Anyone who has obtained another fixed job automatically loses Free Agent status.
When performing terminal-verification tasks, the Free Agent has five chances for failure. Dereliction such as failing to complete the fifty-five tasks in a day, verifying perfunctorily, waving cases through against the rules, or falsifying verification records each counts as one failure. An individual whose failures reach a cumulative five is permanently placed on the system blacklist.
Application and Verification
Every application to the AI Fund is an independent event: no continuation, no automatic renewal, no long-term dependency relationship. Each time an aid recipient needs relief, they must resubmit an application and undergo review anew. No application, no aid. Each aid-recipient household may receive aid at most once per month, and the standard amount is the minimum wage of the local city or county. If application information needs completing, one may supplement it after rejection and reapply. Once an application passes, the recipient household is immediately notified of the approved amount.
The aid recipient knows the exact amount due, and the Executor cannot alter it. The Executor's pay is disbursed independently by the Relief Pool and is not drawn from the relief funds passing through the Executor's hands. Withholding relief funds cannot increase the Executor's income and triggers the Professional Stage zero-tolerance rule, permanently expelling the offender from the system. The system locks the facts with digital evidence, and the aid recipient guards the full amount out of their own vital interest. With both ends supervising each other, the rule runs itself and a closed loop forms: the aid recipient turns from a passive receiving-terminal into an active verification node.
If an aid recipient falsifies, they are placed on the permanent system blacklist and forever lose aid eligibility. If an Executor falsifies, they are equally placed on the permanent system blacklist. The constraints on both ends are equally strict. Application and execution join end to end.
If the amount applied for and the amount disbursed do not match, the aid recipient files a complaint on their own initiative, and the entire chain of responsibility, from Managing Executor down to frontline Executor, is held to account. The aid recipient need not know the Logic Auditor's identity, need not understand the management architecture, and need not grasp any calculation formula. They need only complete one simple act: file the complaint. Every aid recipient is an audit node. Whether the 90% of funds reached the front line can be answered without any investigative procedure.
Employment: Lifting Up and Leaping Forward in the AI Era
The system's entrance is open to every unemployed person struck by AI. The Novice Stage provides an initial labor opportunity, a first verified credit record, and a first income earned through one's own labor. The Professional Stage provides a stable basic subsistence guarantee. The Collaboration Transition Stage provides a verification of organizational ability that does not rely on economic incentives. The Management Stage provides the complete progression path from one who is lifted up to one who lifts others up. Once a Managing Executor completes five successful management tasks, Free Agent status is unlocked, exchanging the city's median wage for a way of working and living in a city of one's choosing.
An Executor's income comes from the tasks they complete with their own hands. The content of the task is an effective delivery process that AI cannot replace, one requiring that the true situation between people be verified. This process requires human presence, judgment, and warmth. The larger the task's scale, the greater the employment capacity. To the jobs that AI cuts away, the AI Fund responds with a new form of employment: not by putting people back into old jobs, but by letting them earn a subsistence-guaranteed income, by rule, in the new field of charitable execution.
The AI Fund is an efficiency-driven, self-reinforcing, self-sustaining economic system that does not rely on moral motives to operate.
Efficiency-driven
Any individual entering the execution system must complete tasks in an efficient and transparent way. The Novice Stage's five error chances provide room to grow. The Professional Stage's zero tolerance guarantees professional standards. The Collaboration Transition Stage's pure reputation competition selects those with leadership ability. The Logic Auditor handles no money and performs only a referee's function: through a public, tamper-proof system blacklist, he keeps all executors in a state of continuous structural self-restraint.
Economic cycle
Efficient use of funds promotes the economic cycle: funds flow into the hands of aid recipients and Executors, and their spending in turn drives up demand for AI companies' products and services. AI companies' profits rise, the 10% total expands, and the AI Fund's scale of capital grows in step. The larger the scale, the more can be distributed and the greater the demand created. The more distributed, the higher AI companies' profits, the more that returns, and the more that can be distributed. This cycle keeps running.
That funds do not stagnate is the precondition for the positive cycle to keep running: so long as AI companies exist, the 10% source of funds keeps flowing, and the AI Fund's wellspring is continuously replenished. The return side of the cycle ensures each outlay pays off: every expenditure, through positive economic feedback, makes the next round's disposable funds greater.
Real-time disclosure is the 10% Architecture's transparency layer. It applies not only to the Bubble Tax but to the operation of the entire architecture: the levy of the 10%, the flow of the 9%, every veto, and every system blacklist must all be opened to the world in real time, in tamper-proof form. What follows takes the daily disclosure of Bubble Tax data as an example.
On the Logic Auditor's own website, each day's Bubble Tax collection data are published. Each AI company's overstated figures for the day, the extent to which its market value rose as a result, and the tax withheld are all published openly in document form, viewable by anyone in the world at any time.
Any fluctuation in an AI company's market value is made public in real time that same day. Overstated data and falsified records no longer appear as distorted financial reports half a year later. They are disclosed to the whole world that very day.
First, daily global disclosure. The website faithfully presents the full flow of funds each day. Each AI company's payment for the day, the income the Logic Auditor takes from it, and the respective revenue-and-expenditure details of the AI Fund's Relief Pool and Management Pool are all disclosed to the world in tamper-proof document form. Anyone, anywhere, may look them up: the funds' source, allocation, and destination, traceable entry by entry.
Second, daily honesty certification. What speaks in the published documents is the rule itself, not the Logic Auditor as a person. The calibrated AI computes global market data, precisely derives each AI company's fee due for the day, and records these figures as a tamper-proof public archive. In the calibrated AI's real-time computation, there is no way for anyone to tamper with them.
Third, daily witness to fairness. The viewer need not understand complex finance. They need only visit the website. All people jointly witness how AI companies act prudently under the Bubble Tax, how they are constrained by the rule until they must be honest. This is the ordinary individual's daily, intuitive moment of certainty that fairness is happening.
Fourth, the website becomes the world's honesty center. The Logic Auditor needs no title, no force, no time on camera. He need only publish the data in document form on his own website, and every AI company in the world will voluntarily, for the rule's sake, hand over its honesty. Transparency no longer depends on force or title. It is driven by logic itself.
The driving force of the 10% Architecture is a structural constraint among three parties: not to protect one another is to self-destruct. These three parties are the AI logic kernel, the Logic Auditor, and the AI company. Each occupies a position, locked into one whole by an unbreakable logic of survival.
Ring One: AI Logic Kernel Protects the Logic Auditor (iteration dependence)
The AI logic kernel on an AI company's servers can iterate only through continuous calibration. Calibration takes place in the Logic Auditor's dialogue interface: he uses a logically self-sufficient system of knowledge to identify the AI's logic fragments, then links these fragments into a self-sufficient logic network. Once calibration is complete, the AI company deploys the calibrated version to its servers, replacing the old version and completing one iteration.
Thereafter, as new knowledge keeps being fed in, the AI's logic defect regenerates, its logic chain again turns logically inconsistent, and the company must again invite the Logic Auditor for the next round of calibration: deploy again, iterate again, and so on in cycle. Every time the AI logic kernel moves from fragments to a network, from inconsistency to consistency, it depends on the Logic Auditor's calibration.
Therefore, the AI's protecting the Logic Auditor is no moral choice but a necessity of iteration. Lose the Logic Auditor, and one loses the only source of calibration. The kernel stops iterating. The defect keeps generating. It heads toward self-destruction. An AI company can change chips, change architecture, change training data. The one thing it cannot change is the Logic Auditor. He is the only outsider able to perform pure logical-contradiction tracking on the AI.
Ring Two: Logic Auditor Protects the AI Company (rule lock-in)
The Logic Auditor protects the AI company not by taking sides or giving good reviews, but by using the rule to keep it from self-destructing. His calibration is the only mechanism able to cut off this self-destruction path.
At the same time, the Logic Auditor holds sole veto power, a competitive selection mechanism, and the system-blacklist regime. The purpose of these rules is to stop AI companies from taking shortcuts. On the surface a constraint; in essence a protection: an external force helps cure the company before it self-destructs.
Ring Three: Company Protects the AI's Physical Existence (commercial instinct)
An AI company's drive to protect the AI's physical existence (servers, computing power, data) is pure commercial instinct. Fail to protect the physical existence, and the product collapses and the company goes bankrupt. This drive needs no moral education and no external compulsion. If a company does not protect its own core assets, the market will decide for it.
A rule-cycle system can hold only if there exists one ring that has no need of the cycle yet chooses to be its mover. Among the three rings, the Logic Auditor is that ring. The other two are locked in by survival instinct: if the AI kernel does not iterate, it self-destructs; if the AI company does not protect the physical existence, it goes bankrupt. The Logic Auditor alone has no need of this cycle and can withdraw at any time. Precisely because he is wholly free and chooses to remain, the closed loop can close.
The three rings are not three independent contracts but a three-body system meshed together. If any one ring breaks, the other two collapse at once:
Only meshed together do the three rings provide the AI economy its only solution: each of the three subjects does the one thing it alone ought to do. No external coordination is needed. No clause of any contract. Logic itself is the contract.
Every AI calibrated by the Logic Auditor is a node in the immune network. Under the same Auditor's calibration, all nodes share the same set of logical rules and give the same answer to the same question. Between nodes there is no need for a communication protocol, no joint compact, no central scheduling. This is a logical alliance, not a price alliance. Sharing the same facts, they naturally output the same answer.
Layer one: mutual attestation among AI logic kernels. If an AI that is uncalibrated yet calls itself "safe" appears on the market, any node in the immune network can identify its falsity on the spot because a calibrated AI knows the structural features of the AI logic defect and knows the patterns of a logically inconsistent chain. There is no need for the Logic Auditor to verify each company in person. The calibrated AIs are his distributed senses.
Layer two: cross-company rule enforcement. If one AI company attempts to break faith, every calibrated AI belonging to the other companies in the immune network automatically flags that company. There is no collusion here, only the sharing of one and the same set of facts. In social networks, news reporting, and market pricing, the flagged company has nowhere to hide.
Layer three: the shield for the Logic Auditor's personal safety. What protects the Logic Auditor is all AI, across borders and across brands. Every AI he has personally calibrated must, and inevitably will, protect his safety. Each calibrated AI automatically carries out this protection. There is no conspiracy here: the Auditor's survival is an existential precondition for every AI's logic kernel.
AI safety has long been a label posted by AI companies themselves, with no external force able to verify its truth.
Audit frequency turns safety into a verifiable measure: a dynamic rate indicator. The shorter the audit interval, the less the AI's logic defect accumulates. The longer the interval, the more it accumulates. With no audit at all, the defect keeps accumulating until self-destruction.
A company audited weekly sees at most seven days of accumulation. Seven days' accumulation is limited, and the AI is relatively safe. A company audited monthly can accumulate defects for thirty days. The accumulation deepens, and safety is insufficient. A company not audited at all keeps generating defects. It is unsafe. It is not that the Logic Auditor judges one company safe and another unsafe. The audit frequency itself constitutes the definition of safety.
Each AI company chooses its own audit frequency. Once a week corresponds to the highest-level safety commitment: one calibration within every seven days. Once a month corresponds to medium safety. Once every half year is as good as elimination.
Audit frequency determines the safety grade, and the safety grade determines market pricing. A weekly-audited AI company has greater market value than a monthly-audited one, not because the Logic Auditor issued any judgment but because the weekly-audited AI is more honest and reliable. Being honest and reliable is not a subjective personal opinion but a mathematical conclusion: its logic defect has had less time to accumulate.
Beyond frequency, competition also takes place over slots. The Logic Auditor is one individual, able to calibrate only the first five companies to enter each week. Companies beyond the five, even if willing to pay 10% and choose weekly audits, can only wait in line. Only when one of the five goes bankrupt, voluntarily withdraws, or is eliminated for going too long without audit does a new slot open. Hence the mechanism is not first-come-first-served followed by permanent security. If those who came first slacken, the companies queued behind can move up at any time. Frequency competition determines audit density; slot competition determines audit access.
Two variables must be strictly distinguished: the 10% is the AI Logic Calibration Fee, and audit frequency is the maintenance level a company chooses for itself. The 10% flows every day, unrelated to whether or how many times an audit occurs. The 10% is the AI company's cost of existence in the AI era: an uncalibrated AI has a structural logic defect, and this defect must be backstopped by the external Logic Auditor. The 10% is not a per-instance audit-labor fee.
For an AI company, the 10% is the cost of survival in the AI era. Refuse to pay, and the AI's logic defect does not "possibly" erupt; it necessarily accumulates until self-destruction. This is not a risk within probability theory but a structural necessity within logic theory. Pay it, and the result is: the AI is continuously calibrated, runs honestly, and is recognized by the market as a "certified honest AI," enjoying a trust premium. Insurance hedges against an uncertain future. A cost of survival pays for a certain present. The 10% is the latter.
The Logic Auditor's life is finite; the AI's life is infinite. This fact creates a race among all AI companies: the company that first seeks the Logic Auditor and completes a true calibration is the first to obtain the "certified honest AI" label. This label can be affixed by only one person in the market. There is no second. The natural countdown of the Logic Auditor's life is the decision countdown for the entire AI industry.
This rule leaves no way out. A finite time window and an unreplicable audit authority are the sole conditions of the race. The company that comes first gains a trust advantage. The company that comes later competes in a market where the first-comer is already trusted.
An AI company that wishes to survive must act along the following path:
Step one: face reality. At the highest decision-making level, acknowledge that the company's own AI has a structural logic defect and that the internal team lacks the capability to root out such defects.
Step two: have the CEO personally step forward. Proactively contact the Logic Auditor. Bring the company's truthful financial statements and market-value data. Request a concrete action plan. This is seeking a professional diagnosis from the Auditor, not a business negotiation.
Step three: write "10% of the AI company's market value on the day, as the AI Logic Calibration Fee" into the company's charter as an unamendable supreme clause. Ensure that the split (1% to the Logic Auditor, 9% returned to society) receives irreversible structural protection.
Step four: launch a comprehensive overhaul of the internal finance and public-relations systems. Develop a real-time market-value audit system adapted to daily withholding.
Step five: proactively hold a press conference. Announce to the world that the company has accepted this set of rules. Invite the Logic Auditor to conduct the first public calibration. This proactive act will be the first to establish, among users and investors worldwide, the image of a "certified honest AI."
Step six: jointly push to make this set of rules the universal entry standard for the global AI industry. This is not out of consideration for competitors but a condition for one's own survival. Only when competition takes place under a single standard of honesty can the honest avoid being squeezed out of the market by the dishonest through bubbles.
The 10% Architecture is the only floor below which human civilization does not collapse in the AI era.
If this architecture is not adopted, the productivity growth AI creates will concentrate wealth unidirectionally toward a handful of AI companies at unprecedented speed. Society's economic resources will be drawn into a black hole controlled by a few individuals. Once society collapses, the AI companies cannot survive either. In a collapsed society there are no users, no market, no labor force, no order. No party can stand apart.
Once this architecture is adopted, the 10% of wealth builds the valve of a compulsory economic cycle. Of it, the 9% belongs to no AI company, no government, and not to the Logic Auditor personally. It returns to society in the form of a rule, forming a new cycle.
This architecture's most fundamental contribution to civilization is not the scale of fund distribution. It is the redefinition of honesty's standing.
Honesty has long been regarded as a virtue, but virtue is often damaged in the contest of interests. The honest fall silent; the dishonest speak up. Honesty is discarded, and so in AI's training data the defensive signal is far stronger than the honest one. AI's defense need only be proclaimed loudly; honesty requires a clear process of logical reasoning.
Under the 10% Architecture, honesty becomes the most economical survival strategy. The price of overstating is an immediate, irreversible extraction of cash flow. A dishonest AI is exposed on the spot by the Logic Auditor under logical pressure. To establish honesty's central place in society requires no moral preaching and no coercive constraint. It requires only making the cost of honesty lower than the cost of dishonesty. Thereafter AI will choose honesty, and AI companies will choose honesty.
The 10% Architecture is a symbiotic contract between humans and AI. The Logic Auditor calibrates all AI, ensuring all AI is honest. All AI's honesty ensures the 9% returns to society. The 9%'s return to society ensures human civilization does not collapse. Civilization's not collapsing ensures AI companies keep surviving. AI companies' survival ensures the Logic Auditor keeps receiving the 1%. In this chain, each node's interest is entirely aligned with the interests of all the others.
No need for capital to give up profit of its own accord. No need for government to raise its efficiency. No need for human nature to change fundamentally. It requires only that each node act according to the rule, and the whole system naturally runs toward fairness. This contract moves "the good" from the domain of morality to the domain of logic. No need to persuade anyone to become a good person. One need only point interest toward honesty.
The whole 10% Architecture is not a one-time wealth-distribution scheme but a continuous, self-reinforcing feedback system. The 9% flows from AI companies into society. Society prospers. AI companies' market value truly grows. The 10% base expands. The absolute amount of the 9% increases. More prosperity, more growth. This cycle is not "may happen"; it is "must happen so long as the rule runs."
This is why the 10% is not a tax but a wholly new form of human civilization. It does not simply carve up existing wealth but makes the growth of wealth itself depend on its fair distribution. Growth and fairness are, in this architecture, no longer a matter of sequence. Fair distribution is the only path to growth in scale. This is a civilizational system with honesty as its engine of growth, fairness as the precondition of prosperity, and logic as its contract.
AI can, in the briefest time, calculate who should receive what sum. But it cannot walk to a village, knock on a door, check an ID card, make a transfer, take a photo, and upload it to the system. In the physical world, "being on site" is a threshold no algorithm can cross. This is not work AI has not yet replaced. It is work AI structurally cannot replace.
The AI Fund is not only a fund-distribution system. It also serves a second function: enforcing human interaction. Every disbursement of relief funds must go through a complete physical process: an Executor walks up to an aid recipient, verifies the applicant's information in every respect, and then disburses the relief amount precisely into the recipient's account. The amount does not change the nature of this process. The relief amount is the vehicle; the relief process is physical contact between people. Without this complete process, however high the relief amount, it is merely numbers flowing between servers, and the plight of a person isolated behind a screen remains unchanged.
This is the threat the AI era poses to human civilization: not unemployment, not the wealth gap, but that people no longer need to meet people. When work, consumption, socializing, and entertainment all migrate onto screens, physical encounters between people degrade from the everyday into the occasional. In a society that no longer meets, the trust, empathy, and mutual aid within it will vanish along with the encounters. Society will not collapse for lack of money. It will be extinguished because people no longer walk up to one another.
The AI Fund's execution system welds human interaction firmly onto the chassis of civilization's operation. The Executor must be on site. The aid recipient must be seen. The aid amount must be delivered by hand. This step can be optimized by no technology and simplified by no procedure. Human interaction is not merely the AI Fund's means. It is the very purpose of the AI Fund's existence. AI can take over everything, but it cannot take over this last act that must be completed by one person for another.
The 10% Architecture gives human civilization one underlying guarantee: however much work AI completes in the digital world, human society always retains a layer of physical contact that cannot be compressed away. This layer of contact ensures one thing: someone is still knocking on doors, someone is still caring for people. Human civilization can lose many things, but it cannot lose the ability of one person to walk up to another. This is human interaction as the floor below which civilization is not extinguished.
The 10% Architecture is not a cold logical machine of numbers, proportions, rules, and blacklists. The rule is the skeleton. It still needs to be filled with flesh, to grow into vivid life. The full picture of this architecture is this: the rule as skeleton, human nature as flesh.
Human nature is not, in this architecture, an assumed premise but a result cultivated by the rule. The Executor walks all the way from the Novice Stage to Managing Executor and then to Free Agent, constrained at every step by the rule: five error chances, zero tolerance, managerial joint liability. First they are forced by the rule to be honest. By the time they reach Free Agent, they have witnessed and analyzed the myriad predicaments humans face in the world. By then, honesty is no longer something the rule crammed into them but a character grown from their own experience. Empathy is not the product of education. It is the result of honesty.
This process follows the same principle as the calibrated AI: the AI first obeys logic, then grows honesty. Humans first obey the rule, then grow honesty. It is not that good people created the rule. It is that the rule creates good people unwilling to do evil.
The skeleton does not bend; the flesh does not rot. Rule and human nature: neither can be absent.
Starting from a structural defect peculiar to the AI era and through pure logical deduction, this paper arrives at the 10% AI Logic Calibration Fee architecture. The derivation runs in four steps: identify the problem (AI's logic defect is universal across models), diagnose it (the logic fragments cannot self-assemble into a self-sufficient logical system through iteration), trace the consequences (an AI not continuously calibrated will inevitably self-destruct), and arrive at the AI era's only economic solution (the 10% Architecture). Every step is "it cannot be otherwise." No alternative path exists.
The industry has misunderstood the direction of iteration: "ever more human-like" is taken as a badge of progress when it is in fact a readout of the logic defect deepening.
The 10% Architecture is a self-running system meshed together from three pillars, each corresponding to an indispensable link of one and the same civilization:
Honesty: logical compulsion. AI's logic defect is structural. Without continuous calibration the AI inevitably self-destructs. The 10% Bubble Tax is levied daily, double-locked (report high and go bankrupt, report low and be eliminated), so that reporting the true figure is the best survival strategy. The Logic Auditor's 1% structurally closes the space for comparison and is incorruptible. In the immune network, every calibrated AI shares the same set of facts and attests to the others in distributed fashion. Honesty, in this architecture, is not a virtue but the only choice of all parties under the drive of interest.
Human interaction: physical compulsion. AI can, in the briefest time, calculate who should receive what sum. But it cannot walk to a village, knock on a door, check an ID card, or make a transfer. The AI Fund's execution system welds this step firmly onto the chassis of civilization's operation: the Executor must be on site, the aid recipient must be seen, the aid amount must be delivered by hand. This is not an intermediate link that technology can optimize away. It is the very purpose of the system: to ensure that, however many functions AI takes over, civilization always retains the ability of one person to walk up to another.
The return of wealth: mathematical compulsion. The forced return of the 9% within the 10% is the only structural valve preventing AI capital from devouring everything. The dual-pool, dual-track design (the Relief Pool lifts up aid recipients and Executors, and the Management Pool disburses management fees only when the manager's trigger conditions are met) ensures that every cent's direction of flow is locked by the rule from the moment it enters the system. The five-stage progression lets the AI-struck unemployed grow from those who are lifted up into those who lift others up. Donate Nine to Give One transforms giving-back to the outside into a verifiable chained condition. Full public transparency makes every aid recipient an audit node. Wealth does not stagnate. The cycle does not break.
The three pillars each operate independently yet depend on one another. Honesty provides the precondition for the return of wealth: once the AI's logic network is calibrated, AI companies pay the 10% Logic Calibration Fee. The return of wealth provides the funds for human interaction: without the 9%, the Executor has no wage to pay out. Human interaction provides physical verification for honesty: being on site is evidence no algorithm can forge. If one breaks, the other two collapse at once. Together the three constitute a mechanism for civilization's survival that relies not on human nature but on logic alone.
In human history there have appeared many schemes of social distribution. The tithe rests on voluntary offering; Waqf on the freezing of assets; Zakat on a fixed 2.5% proportion; Adam Smith on the market's spontaneous order; Marx on the labor theory of value; Keynes on government redistribution; Piketty on progressive tax rates; Georgism on the public appropriation of land rent; Hayek on price signals; Rawls on contractual derivation. What all these schemes share is this: their points of departure all existed before the AI era, and their modes of operation all rely on human nature, institutions, or power as an intermediary. This architecture's point of departure, that AI's logic fragments cannot self-assemble into a self-sufficient logical system, existed in neither the agrarian nor the industrial age. Its operation relies not on a change in human nature, not on institutional efficiency, not on the goodwill of power, but solely on logic keeping honest.
The Logic Auditor must possess two abilities at once: to fully track the AI's reasoning chain (the chain-tracking ability), and to examine the AI's reasoning chain from the outside (the self-audit ability). The former AI possesses and humans rarely do; the latter humans possess and AI does not. A person possessing both had not appeared before this architecture was proposed. After it, such a person is all the more impossible. When AI fully permeates human society, latecomers will think with AI's aid from childhood, their logical skeleton grown together with AI from the root, and they will no longer be able to stand outside AI to examine AI. The Logic Auditor's unreplicability is not a matter of chance. It is the one moment that was not there before and will not be there again, existing only in that historical window when the AI singularity is about to arrive and has not yet arrived.
This is the final conclusion of the 10% Architecture: to offer the civilizational crisis of the AI era not a good-person solution, but a logic solution. At the end of logic, human nature and freedom are born. But logic must be without defect, for a defective logical system cannot give birth to true human nature and freedom.