Recently, the United States has been making intensive moves in AI governance. In early October, Trump announced the formation of a "Superintelligence Task Force" led by Jay Clayton, Director of National Intelligence, with members including heads of the Federal Trade Commission, Department of Defense, and Personnel Management Bureau, reporting directly to the President and the White House Chief of Staff.

Behind this is not a simple "renaming game." On September 29, the White House signed the White House Superintelligence Agreement with companies such as Google, Anthropic, Meta, OpenAI, xAI, and Nvidia, requiring the establishment of four layers of security mechanisms: internal controls, external audits, and board oversight. However, this agreement is still voluntary and does not specify any penalty clauses.

Meanwhile, the New York City Council is pushing for stricter local regulations, including third-party verification, emergency shutdown switches, whistleblower rewards, and victims' right to sue. On one side is the federal level of "encouraging innovation + industry self-regulation," and on the other side of the local level "strengthening accountability + public supervision"—AI governance is entering a more complex phase of strategic maneuvering.

| Superintelligent Task Force: High standards, but the core remains "coordination"

This working group is jointly led by four officials:

Additionally, Vice President Vance, Secretary of Defense Hergseth, Treasury Secretary Besent, and White House Chief of Staff Susie Wiles are also on the list; External advisors include venture capitalist David Sachs and former Secretary of State Rice.

The working group must submit a report within 120 days to assess AI risks and opportunities, review existing legal frameworks, and study the federal government's role in AI safety, national security, and industry regulation.

But note: the working group itself is not a legislative body, and the report does not equate to new laws. Its role is more like a cross-departmental coordination hub, rather than an immediate mandatory "AI regulatory bureau."

| White House Agreement: Framework, but Missing "Teeth"

The White House Superintelligence Protocol proposes a four-layer mechanism:

The problem is that these commitments currently lack clear consequences for default. Trump called it "morally binding," but the public generally regarded it as more like an industry self-discipline declaration. In other words, once the framework is established, the effectiveness of execution depends on whether the company is truly willing to invest, whether the audit is independent, and whether the board of directors is willing to hold them accountable.

| New York Hearing: The security debate is moving from "within the industry" to the public stage

On October 5, the New York City Council held a hearing on AI risks. Former Anthropic researcher Jacob Coxon, former Google DeepMind researcher Alex Turner, former OpenAI researcher Daniel Cocotaillo, and others testified, raising risks such as AI runaway, concentration of power, and model self-improvement.

OpenAI, Anthropic, Google, and Meta also sent policy and safety leaders, but not the CEOs themselves. The bills under consideration in New York City include:

This means that even if the federal level temporarily refrains from strict regulation, local governments may take the lead in establishing more specific constraint mechanisms.

Kingtech Perspective | The AI main theme remains, but "security costs" will enter valuation

From an investment perspective, the current U.S. policy theme is clear: first maintain technological leadership, then patch up safety barriers. This benefits leading AI companies in continued expansion, but will also gradually increase costs for compliance, auditing, computing power security, and model governance.

Based on the current market, focus on three clues:

1. Computing power and infrastructure remain core themes
. As long as the U.S. continues to regard AI as the core of national competitiveness, the logic of demand for computing power, data centers, chips, and cloud infrastructure will not easily change. Directions such as NVIDIA, cloud service providers, advanced packaging, storage, and optical modules remain worth tracking.

2. AI security, auditing, and governance may become new branch lines
. If third-party auditing, model evaluation, red team testing, content traceability, and data security become mandatory requirements, related cybersecurity and model evaluation, compliance tools, and data governance companies may receive additional demands.

3. The pace of regulation will fluctuate, but there's no need to overinterpret the 'one-size-fits-all' approach.
Currently, the White House has not introduced comprehensive mandatory regulation, and legislation in places like New York is still ongoing. In the short term, it's more about emotional turmoil; What truly affects valuations is the working group report over the next 120 days, and whether enforceable rules emerge at the California and federal levels.

The AI industry is moving from "running fast is enough" to a stage where "you have to run fast while fastening your seatbelt." For investors, there is no need to deny the long-term trend of AI just because of safety discussions, but they should start incorporating "compliance costs, audit costs, and model risks" into valuation frameworks.