From Human Regulation to Verifiable Regulation

I. Why Does Regulation Exist?

Most people believe that regulation exists to prevent fraud, control risk, and correct market failure. These explanations are not wrong, but they remain at the surface.

The deeper reason regulation exists is that many critical facts cannot be independently verified. When facts cannot be verified, people must rely on trust. When trust is insufficient, regulation becomes necessary. In this sense, regulation is fundamentally a response to the unverifiable.

For centuries, whether in banking, securities markets, or insurance, the cost of regulation has not primarily come from rules themselves. It has come from information asymmetry. Critical facts are held by a small number of institutions, while most market participants cannot independently verify them. Society therefore developed auditing, rating, disclosure, supervision, and enforcement as compensating institutional mechanisms.

Regulation uses organizational cost to compensate for verification cost.

With the emergence of the Public Credit Root, Verifiable Finance, and artificial intelligence, humanity is now seeing another possibility: a gradual shift from regulation based on human inspection to regulation based on factual verification.

This may become a new governance revolution brought by AI and cryptocurrency. This essay is one possible application of Verifiable Finance in the AI + Crypto era.

II. The Proportional Relationship Between Verification and Regulatory Intensity

Verification and regulation are not opposites. They are, in many respects, substitutes.

The more complete verification becomes, the lower the need for regulation. The harder verification becomes, the higher the need for regulation.

At one extreme, if a system is completely unverifiable, it requires something close to one hundred percent regulation. When verification is impossible, people can only rely on trust. This is the foundation on which traditional credit and brand value have long existed.

In the past, reputation, brand, licensing, and institutional endorsement mattered because users could not directly verify critical facts. When a bank said it was safe, users could only trust the bank. When an exchange said it held sufficient reserves, users could only trust the exchange. When a company said it was financially healthy, investors could only trust financial statements and auditors.

But once critical facts can be continuously verified, the structure of credit changes. People no longer need to blindly trust institutions; they can trust verification results.

When verification is impossible, trust becomes necessary. When verification is available, trust becomes optional. Verification is therefore not merely a supplement to trust. It is an upgrade of trust.

If a system can achieve sufficient automated verification, its need for regulation approaches zero. The Bitcoin system is the clearest example of this logic.

The need for regulation is proportional to the degree of unverifiability.

Regulation is not the goal. It is a substitute mechanism when verification is insufficient.

Graduated from the Department of Automatic Control, Beijing University of Technology. Subsequently, he conducted research at the Institute of Industrial Economics, Chinese Academy of Social Sciences. In 1991, he left his position and founded Yuxing Tech. Yuxing became the first privately-owned high-tech enterprise from Mainland China to be listed on the Hong Kong Growth Enterprise Market in 2000. He sold Yuxing Tech in 2015, and in 2019, he participated in buying Yuxing back. Since then, he has remained a major shareholder and serves as an advisor. He is a legendary figure in China’s industrial sector.

Characteristics

Meticulous in his approach, with deep research and practical experience in product design

and market operations. He also has research and accumulated knowledge in

macroeconomics, monetary theory, capital operations, management theory, and

investment theory.

He began studying cryptocurrencies in 2017 and got involved in investments. The

Chainless System White Paper and the series of articles on the Chainless website are the

results of his years of learning, understanding, practical involvement, research outcomes,

and product expertise.

III. What Bitcoin Has Proved

Bitcoin’s greatest innovation was not simply the creation of a digital asset.

It created an open, long-running, globally verifiable, and extremely difficult-to-tamper-with Public Credit Root.

In this system, anyone can verify the ledger, anyone can verify transactions, and anyone can verify the monetary supply.

Satoshi Nakamoto designed a mechanism that relies on verification rather than trust. More than a decade of operation has shown that when critical facts can be continuously verified, dependence on centralized regulation can decline significantly.

Bitcoin has not eliminated regulation.

But it has proved, for the first time, that verification can replace part of regulation.

This is a major breakthrough in the history of finance.

IV. The Lesson of Bitcoin’s Autonomous Mechanism

Verification can solve problems of truthfulness. It cannot solve problems of governance.

Even if all facts can be verified, people still need to answer a different set of questions: How should the system be upgraded? How should rules be changed? How should disputes be resolved? How should interests be coordinated? These questions go beyond verification.

In these matters, Bitcoin has mainly relied on a technical coordination mechanism. Many people call it the “Bitcoin community,” but the word “community” is too broad. More precisely, it is a technical coordination mechanism.

The problems exposed by Bitcoin and Ethereum suggest that loose communities may not be enough in the future. Technical coordination mechanisms may need to evolve into Technical Coordination Institutions.

Such institutions are neither traditional companies nor state agencies. They are a form of Digital Autonomous Organization. Their purpose is to ensure the system’s own security, stability, upgrade path, and long-term development.

In other words, the goal of a Coordination Institution is the maximization of system interests.

This is the fundamental difference between a Coordination Institution and a regulatory authority.

V. How AI + Crypto Changes Regulatory Governance

Unlike Coordination Institutions, regulatory authorities aim to maximize social interests. They are responsible for safeguarding the public interest, legal boundaries, and social stability.

The two cannot replace each other, and neither should attempt to eliminate the other. The future task of regulation is not to take over autonomous organizations, but to establish boundaries for them. Laws governing crypto-assets have begun to appear, but regulation of autonomous mechanisms, Coordination Institutions, and Digital Autonomous Organizations remains far from mature.

Such autonomous institutions will require regulators to summarize the operating experience of cryptocurrencies and, much as company law formalized the governance of corporations, create institutional recognition and boundary constraints for Digital Autonomous Organizations.

With the emergence of AI, regulation has gained new tools for factual judgment. If Crypto solves the problem of factual verification, AI solves the problems of verification efficiency and governance efficiency.

Traditional regulation relies on reports, inspections, approvals, and penalties. This model is inefficient and inherently delayed.

Future regulation will increasingly rely on real-time verification, real-time auditing, real-time communication, and real-time consultation. AI can undertake a large amount of foundational work, including anomaly detection, data analysis, knowledge-base maintenance, rule retrieval, and risk alerts.

Regulatory personnel can then focus on rule interpretation, liability determination, boundary judgment, and formal decisions. Regulation will shift from ex post punishment to process-based consultation, and from black-box decision-making to transparent communication.

Regulators will no longer simply sit in offices waiting for documents. They will participate directly in regulatory communities. AI will provide immediate responses and organize issues; when necessary, regulatory authorities will issue formal replies.

In the traditional regulatory system, a large share of resources is spent discovering and obtaining facts. In a Verifiable Finance system, the recording, verification, and preliminary judgment of facts will increasingly be completed automatically by the system.

As a result, the role of regulators will gradually shift from “finding facts” to “interpreting facts,” and from “obtaining information” to “maintaining boundaries.”

The most important task of future regulators will no longer be to find facts, but to define which facts must be verified and which facts should not be verified.

This represents an entirely new regulatory model.

VI. A 23-Person Global Regulatory Model

Regulatory institutions are highly complex. Some regulate commodities, others regulate securities, and others regulate banks. Each regulatory object is different, and each regulatory method is different. Yet their front-line regulatory functions share certain similarities. If we abstract the front office of direct project regulation into a model, the regulatory organization of the future may look very different from that of today.

With the support of a Public Credit Root and AI, the organizational structure of regulatory agencies will change fundamentally. Most factual verification work will be completed automatically by the system. Regulators will no longer need large auditing teams or inspection teams.

Because cryptocurrencies and AI systems operate twenty-four hours a day, regulatory agencies must adapt to this condition as well.

Under the logic of Verifiable Finance, a typical global front-line regulatory model may require only about twenty-three people.

Group One: Verification System and AI Support, five people. This group maintains the verification system, continuously improves AI capabilities, and provides technical and data support to the other groups.

Group Two: AI Audit, three people. This group reviews issues discovered by AI and directs the deployment of “AI employees” to handle routine audit tasks.

Group Three: Coordination and Communication, three people. This group supervises and guides continuous communication between AI and market participants. Such communication takes place in real time through regulatory communities.

Group Four: Liability Handling, three people. This group provides final review of AI-generated proposals on dispute resolution, liability determination, and penalty recommendations.

Group Five: Rules and Strategy, three people. This group is responsible for rule upgrades, institutional design, and long-term planning.

Group Six: Rotation and Backup, four to six people. Since the regulated systems operate twenty-four hours a day, a rotation mechanism is necessary. Front-line leaders should also participate in rotations, so that the regulatory organization does not once again become a hierarchy detached from the front line.

This model may seem exaggerated, but its logic is straightforward. Fact acquisition has been automated. Preliminary judgment is completed by AI. The primary duties of human regulators are to supervise AI work, interpret rules, coordinate interests, and maintain boundaries.

Of course, moving from idea to implementation will take time. Yet from the perspective of AI’s exponential growth, the number of people used in this model may not be small.

Because regulatory agencies themselves do not face market competition, once established, their staffing tends to expand into pyramid-shaped bureaucracies. Someone must therefore first articulate an ideal model and use it as a constraint condition for future institutional design.

This regulatory model rests on three foundations: automation, transparency, and real-time communication with regulated entities.

Under the assumption that AI functions as a tool rather than an independent legal actor, AI has no institutional incentive to deliberately distort outcomes for organizational benefit. Continuous communication gives regulated entities sufficient rights of appeal. Public communication mechanisms and the presence of AI also constrain the power of regulators themselves.

This would significantly reduce the frequency and cost of going to court.

The design draws on the core Bitcoin principles of openness, fairness, and equality. It does not constrain only the regulated. It also constrains the regulators.

VII. Verifiable Regulation Is Suited to Intelligent Regulation

Industrial-era regulation was built on trust. Regulation in the AI and Verifiable Finance era will be built on facts.

Crypto makes facts verifiable. AI makes verification low-cost. Coordination Institutions solve internal governance problems for projects. Regulatory authorities safeguard the public interest. Together, these four elements form a new governance system for the future.

The shift from human regulation to verifiable regulation does not mean the disappearance of regulation. It means the upgrading of regulation. Regulation will no longer center on finding facts, but on interpreting rules, coordinating interests, and maintaining boundaries.

This may look like a distant goal. But when one considers how rapidly AI is destroying or reshaping many once-powerful white-collar industries, it becomes clear that regulatory departments may not be exempt.

From a software implementation perspective, Verifiable Regulation is not necessarily more complex than many industries currently being transformed by AI. On the contrary, regulatory agencies possess rule-making and enforcement authority, and regulated entities must comply with regulatory requirements. This transformation may therefore arrive faster than many imagine.

AI is naturally suited to handling rules, texts, data, anomalies, and processes. Verifiable Regulation may be one of the most suitable applications for AI.

This may ultimately prove to be the most profound contribution of the convergence between AI and Verifiable Finance to modern governance.