Where Society
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Super-intelligence

The Ethics of AI—and of the Experts

AI ethics should be central to the investigation because technical capability does not determine legitimate use. Ethical questions include privacy, consent, ownership, discrimination, truthfulness, manipulation, safety, accountability, due process, freedom of expression, human autonomy, and access to opportunity.

The ethics of experts must also be examined. Expertise does not eliminate incentives. Researchers, executives, government officials, investors, nonprofit leaders, and public advocates may benefit from particular narratives concerning risk, regulation, openness, safety, competition, or urgency.

Every expert should be evaluated through an ethics and incentives ledger:

• Demonstrated expertise and record of accurate predictions
• Financial interests and corporate holdings
• Government contracts or regulatory relationships
• Political and institutional affiliations
• Sources of research funding
• Transparency about uncertainty and mistakes
• Willingness to engage credible opposing evidence
• Difference between public-interest claims and measurable results

Experts and Competing Schools of Thought

No single expert should define the investigation. Credibility must be assessed claim by claim.

Elon Musk should be included as a significant source because he has helped build and fund major AI ventures, publicly warned about advanced AI risk, advocated regulation, supported open development in some contexts, and participated directly in the competition over AI infrastructure and products. His experience provides important insight, but he is also a business owner, investor, political participant, and competitor whose interests must be disclosed when evaluating his claims.

The investigation should compare Musk with researchers, builders, critics, and public officials representing different perspectives, including figures such as Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Fei-Fei Li, Demis Hassabis, Sam Altman, Dario Amodei, Andrew Ng, and other relevant specialists. Inclusion does not equal endorsement. Their predictions should be dated, preserved, and later compared with outcomes.

Technical claims should also be checked against peer-reviewed research, reproducible evaluations, NIST, national laboratories, universities, professional societies, and independent auditors.

Truth, ethics and institutional power

Authority must match the claim

The ethics of AI and the ethics of its experts belong in the same investigation. Researchers, executives, public officials, advocates and critics may hold financial, political or institutional interests. Elon Musk is a significant source because he has built and financed major AI companies, warned publicly about advanced-AI risks and influenced policy debate. Those experiences make his views relevant—not automatically authoritative. The same standard applies to Sam Altman, Dario Amodei, Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Fei-Fei Li, Demis Hassabis and other consequential voices.

United States Primary-Source Priority

For questions concerning American AI policy, constitutional government, federal implementation, scientific competitiveness, national security, and the interests of the American people, the investigation will give primary weight to authoritative United States sources.

These include:

• Presidential executive orders, memoranda, and official White House policy
• White House Office of Science and Technology Policy
• National Artificial Intelligence Initiative Office
• President’s Council of Advisors on Science and Technology
• National Science and Technology Council
• AI.gov and the American AI Action Plan
• Office of Management and Budget
• National Security Council and public National Security Presidential Memoranda
• Congress, enacted legislation, and appropriations
• Federal courts and the United States Supreme Court
• NIST, CAISI, CISA, NSA, GAO, NSF, DARPA, and Department of Energy national laboratories

Michael Kratsios is the Director of OSTP and a co-chair of PCAST. David Sacks is the White House Special Advisor for AI and Crypto—also identified by the administration as its AI and Crypto Czar—and co-chairs PCAST with Kratsios. The National AI Initiative Office was established to coordinate implementation of the National AI Initiative Act across the federal government.

This priority reflects institutional relevance and lawful American authority. It does not mean that every administration statement will be accepted without examination. Policy intentions, legal requirements, scientific findings, implementation results, political advocacy, and measurable outcomes must remain separate categories.

Authority Must Match the Claim

Executive orders and presidential memoranda establish what the President directed.

OSTP, AI.gov, OMB, NSC, Kratsios, Sacks, and the AI Action Plan establish administration policy and priorities.

Statutes, regulations, and controlling court decisions establish American legal requirements.

NIST, CAISI, CISA, NSA, laboratories, reproducible testing, and independent research help establish technical performance and security.

Agency records, inspectors general, budget execution, and GAO audits help establish whether federal programs worked.

Public NSPMs establish presidential national-security direction but do not independently prove successful implementation.

Institutions Driving the Direction of AI

No single institution directs artificial intelligence. Its direction is distributed among organizations that control frontier models, training data, computing capacity, capital, deployment platforms, technical standards, public procurement, and law. Institutional importance should therefore be measured by actual authority and control—not by publicity, prestige, or access to conferences.

Frontier Model Developers

The greatest direct technical influence belongs to OpenAI, Google DeepMind, Anthropic, Meta AI, and SpaceXAI/xAI, together with internationally important developers such as Alibaba/Qwen, DeepSeek, and Mistral AI. These organizations choose model architectures, training and evaluation methods, release policies, safety controls, permitted uses, and whether model weights remain proprietary or broadly available. Their own statements establish their policies and intentions, not independent proof of safety, capability, or public benefit.

Chips, Cloud, Data Centers, and Energy

AI development is constrained by the institutions controlling its physical capacity. NVIDIA, AMD, semiconductor manufacturers and equipment suppliers, Microsoft Azure, Amazon Web Services, Google Cloud, Oracle, CoreWeave, data-center operators, utilities, and energy providers influence who can obtain advanced computing, at what price, and under what conditions. Control over chips, electricity, water, land, transmission, networking, and construction can shape AI as decisively as model design.

Government, Law, and National Strategy

In the United States, the President and White House, OSTP, OMB, NIST, DOE and the national laboratories, DOD and DARPA, the National Security Council, Congress, federal courts, and sector regulators shape research funding, procurement, infrastructure, export controls, standards, competition, civil rights, safety, and national security. China’s State Council, cybersecurity and industrial ministries, state research institutions, and major technology companies form a parallel center of national power. The European Commission and European AI Office exert substantial regulatory leverage through the EU AI Act. Binding law, executive policy, voluntary guidance, procurement conditions, and political advocacy must remain separate categories.

Standards, Research, and Independent Measurement

NIST, ISO/IEC JTC 1/SC 42, IEEE, national AI-safety institutes, and independent evaluators shape the terminology, testing, documentation, audit, and risk-management practices used across the field. Stanford HAI, MIT CSAIL, Berkeley AI Research, Carnegie Mellon, and other universities influence foundational research, benchmarks, technical talent, and public interpretation. Their contributions can be substantial, but funding, corporate partnerships, methodology, reproducibility, and ideological assumptions must still be examined claim by claim.

Capital, Platforms, and Major Deployers

Microsoft, Amazon, Google, Oracle, SoftBank, venture investors, sovereign investment funds, and government purchasers influence which laboratories and infrastructure projects can expand. Apple, Microsoft, Google, Meta, Amazon, healthcare systems, schools, financial institutions, defense organizations, and government agencies determine how AI actually reaches citizens. Deployment power matters because an institution can shape human behavior without building the underlying model.

Institutions With Limited or Sector-Specific Influence

The OECD/GPAI and Council of Europe can influence international norms and policy coordination. The World Economic Forum is a private convening organization that can document corporate thinking and promote policy proposals, but it has no sovereign, legislative, or regulatory authority over the American public. WHO is relevant to health-related AI, not AI governance generally. The United Nations is relevant to international negotiations and member-state positions, but it does not establish American law or constitutional authority. These bodies may provide evidence and context; they should not be presented as the principal institutions driving AI’s technical development or United States policy.

The Institutional Accountability Vacuum

Distributed power can produce an accountability vacuum: the developer says the deployer chose the use; the deployer says it relied on the model; the cloud provider says it supplied infrastructure; management says automation produced the result; and regulators dispute jurisdiction. The investigation should identify who controls the model, data, compute, objective, deployment, and ability to stop or correct the system; who receives the benefit; and who remains legally and morally responsible when harm occurs. If affected people cannot obtain an explanation, correction, appeal, or remedy from an identifiable authority, accountability has not remained meaningfully human.

Congress and Legislation

Congress should be examined through bill text, amendments, committee records, testimony, votes, appropriations, oversight, and enacted statutes. Member press releases establish a legislator’s position but do not establish that a proposal became law.

Every legislative claim should identify its actual status:

• Announced or discussed
• Introduced or reintroduced
• Considered by a committee
• Passed by one chamber
• Passed by Congress
• Signed into law
• Implemented through regulations and funding
• Challenged, blocked, amended, or interpreted by a court

Congress.gov is authoritative for bill text and status. The Congressional Research Service provides legislative analysis, while GAO audits implementation. Federal Register notices, Regulations.gov records, OMB memoranda, agency rules, and court decisions establish different stages of policy and law.

The following dated case records preserve the source distinction between statements, legal instruments, implementation and measured outcomes. Earlier source passages keep their original dates.

White House Accord creates a voluntary four-layer safety framework for frontier systems

President Donald Trump’s verified account posted the signed “White House Accord on Super Intelligence — Joint Commitment on Frontier Responsibilities” after the September 29 luncheon. The one-page accord commits the participating companies to four layers of controls and audits: operational controls during training and deployment; an empowered internal monitoring team; independent assessment by an outside auditor or evaluator; and an independent board committee that receives reports and oversees the system. It also calls for regular meetings to develop standards and best practices. The displayed signatories are President Trump and leaders of Google, Anthropic, Meta, OpenAI, xAI and Nvidia.

President’s stated position

President Trump described the accord as “morally binding,” compared it to a constitution and said he was seeing substantial industry self-policing. He continues to oppose broad development restrictions and joint governance with China while presenting the accord as a way to pursue safety without slowing American innovation.

Established facts

The signed accord is public. Its controls address model capabilities and alignment during training and deployment, including cybersecurity, biosecurity and chemical threats and unintended access to technical systems. It establishes company commitments to internal monitoring, outside assessment, board-level oversight and recurring standards meetings. The separate America.gov order imposes executive-branch deployment requirements, while the separate terminology order directs federal use of “Super Intelligence” and “SI.”

Claims under examination

The accord states that its controls and audits are critical to a safe future and that participating companies are committed to implementing them. That establishes a signed commitment, not proof that every control is already operating, independent or effective. President Trump also discussed a ten-person oversight committee and a future AI Czar; neither step is established by the accord or a completed appointment.

Disputed matters

Whether voluntary self-policing can substitute for enforceable testing, incident reporting, liability and regulator access remains disputed. The accord itself says later codification into law or regulation may make sense. Supporters view the structure as faster and more technically informed than prescriptive regulation; critics question enforcement, transparency and conflicts when companies select or pay evaluators.

Inference

The accord moves the luncheon from symbolic convening to an identifiable governance framework and places internal management, external evaluation and boards in a defined responsibility chain. Its practical strength will depend on common test methods, evaluator independence, disclosure and consequences for failed controls—details the one-page text does not supply.

What remains unproved

The public record does not yet establish implementation deadlines, shared technical thresholds, public reporting, government inspection rights, sanctions, liability, breach remedies or a process for resolving failed audits. It does not show that non-signatory attendees accepted the accord. The October 4 force announcement does not itself establish how the force will oversee the accord, and no enacted federal codification has been identified.

GIB determination: This is a material governance development. The accord creates a public, signed and auditable set of organizational promises, but it remains voluntary and does not itself create legal duties, government enforcement authority or proof of implementation. The executive orders on America.gov and federal terminology are separate binding presidential directives within their stated executive-branch scope.

Continuing context: The earlier luncheon chart established who was expected to participate; the accord establishes what six company leaders signed. The terminology order does not change the statutory definition in 15 U.S.C. § 9401(3): it applies that definition to “Super Intelligence” unless Congress or later presidential action changes it. As of September 29, no completed appointment or force had been publicly established; the President’s October 4 statement now names the Super Intelligence Force and its leadership, while its public legal instrument and operating details remain unresolved.

Signed White House Accord³⁴ · Super Intelligence terminology order²⁵ · White House terminology fact sheet³⁵ · Reuters accord report³⁶ · CBS News remarks and context³⁷ · Luncheon seating chart³⁸ · America.gov executive order³⁹

President announces a federal Super Intelligence Force

On October 4, President Donald Trump’s verified account announced the Super Intelligence Force, described as a federal coordinating body intended to maintain United States leadership in advanced artificial intelligence and protect American interests. The post names Director of National Intelligence Jay Clayton as the lead and identifies FTC Chairman Andrew Ferguson, Under Secretary and chief technology officer Emil Michael, and OPM Director Scott Kupor as additional leaders. It says the group will engage consumers, public-interest and religious organizations, critical-infrastructure providers, and AI companies, and report to the President and White House Chief of Staff Susie Wiles.

President’s stated position

The President presents the force as a whole-of-government coordination mechanism joining national-security, consumer-protection, defense-technology and federal-workforce leadership. He links the announcement to the voluntary White House Accord and describes American leadership in advanced AI as both a strategic objective and a public-interest responsibility.

Established facts

The verified presidential post is public and names the four officials and the intended engagement groups. Official agency records independently confirm each named official’s current federal position. Reuters reported on October 3 that Clayton said the group would assess AI risks and opportunities and that a charter calls for a report within 120 days.

Claims under examination

The President says the force will protect American interests and improve Americans’ lives. Those are objectives, not measured outcomes. The reported 120-day deadline and risk-review assignment are supported by contemporaneous reporting attributed to Clayton and a charter, but the underlying charter was not located in the public materials reviewed for this update.

Disputed matters

The proper balance between voluntary company controls and enforceable federal requirements remains disputed. Supporters of a coordinated, light-touch approach argue that it can respond quickly while preserving innovation and competition with China. Critics seek clearer authority, mandatory disclosure, independent testing and enforceable consequences.

Inference

The force converts earlier discussion of an AI Czar and advisory body into a named cross-agency structure. Its membership suggests that national security, consumer protection, defense technology and federal technical staffing will be central. That inference does not establish the force’s legal powers, budget or operating procedures.

What remains unproved

No public executive order, memorandum, charter, membership roster, budget, meeting record, reporting protocol or enforcement delegation was located for this update. The record does not yet establish how the force will resolve agency conflicts, oversee Accord implementation, publish findings, handle classified evidence or measure results.

GIB determination: This is a material governance development because the President has now named a federal coordination body and its leaders. It is evidence of executive organization and stated policy, not proof of new statutory authority, binding regulation, completed oversight or effective implementation.

Presidential announcement²⁸ · Reuters report²⁹ · DNI leadership record³⁰ · FTC leadership record³¹ · Department of War biography³² · OPM biography³³

Presidential policy development / Updated October 8, 2026

President intensifies the administration’s terminology campaign

On October 8, President Donald Trump’s verified Truth Social account said the White House considers anyone who uses “Artificial Intelligence” instead of “Super Intelligence” to be “THE ENEMY.” The statement reinforces his terminology campaign in adversarial language. It does not, by itself, identify a person, allege a crime or create a legal consequence.

President’s stated position

The President presents “Super Intelligence” as the more accurate and accepted term and treats continued use of “Artificial Intelligence” as opposition to the White House position.

Established facts

The September 29 executive order directs executive departments and agencies, to the maximum extent permitted by law, to use “Super Intelligence” and “SI” in official communications and non-statutory documents. It preserves prior regulations, presidential actions, contracts, grants and historical records, and temporarily applies the existing statutory definition of artificial intelligence.

Material contrary evidence

The order states no penalty for using the conventional term and does not direct private citizens, companies, Congress or state governments to adopt the replacement. Axios reported on October 8 that White House pages continued to use “AI,” which shows that implementation remained incomplete even within the administration’s own public communications.

Inference

The post appears intended to pressure faster adoption of the administration’s preferred terminology. That is an inference from the wording and timing, not evidence of a new enforcement program.

What remains unproved

No new executive order, regulation, enforcement memorandum, penalty, personnel action or formal designation of any person as an enemy was identified. The post does not establish that “Super Intelligence” has replaced “artificial intelligence” in statute, technical standards or general industry usage.

GIB assessment

This is a material presidential messaging development because it escalates the language surrounding an existing executive-branch directive. It changes the record of the President’s stated position, but not the documented legal scope of the September 29 order.

Verified presidential post²⁴ · September 29 executive order²⁵ · White House technology page²⁶ · Axios implementation report²⁷

Election influence and protective action · October 4 evidence connection

Historical September 2024 enforcement record reviewed October 4, 2026.

DOJ describes AI-generated narratives within an alleged foreign influence operation. AI’s documented role concerns content production; persuasion, audience scale and election effects require separate evidence.

Original federal record · September 4, 2024⁴ · Full evidence record in Election Integrity⁵.

Research implication: Assess how automation changes production costs and attribution without assuming technical capability establishes electoral effect.

Defense, space and AI: evidence update · October 4–6, 2026

Targeted public-record review · October 4–6, 2026. Earlier chapters retain their original evidence dates.

Defense AI makes the human responsibility question concrete

Sensor fusion, planning assistance, flight control and tool-using agents place human judgment at different points in a decision. A faster draft or target nomination cannot establish a correct decision, lawful authority or an effective final outcome. The Army and Navy case records⁶ distinguish production awards and reported task savings from accepted delivery and operational performance.

Implication for this investigation: ask who verifies source data, authorizes action, can interrupt the system and owns correction. The NSA agentic-AI guidance record⁷ supplies a practical permissions and accountability test. These examples are present-day AI; they do not establish general super-intelligence.

Threats from—and threats to—AI

Protect people, preserve useful innovation, and keep responsibility identifiable

↗ Figure 10See Figure 10. Original caption: A complete inquiry protects society from harmful applications and beneficial systems from attack, capture or restriction that damages public value.

Threats created by AI use

• Fraud, impersonation, financial scams and synthetic evidence.
• Cybercrime, malware development and hostile automation.
• Privacy loss, surveillance and extraction of confidential data.
• Unsafe or unappealable decisions in medicine, employment, credit, benefits or policing.
• Propaganda, censorship, manipulation and fabricated public records.
• Dependency, deskilling and concentration of institutional power.

Threats, Vulnerabilities and Barriers to Beneficial AI

• Poisoned training or retrieval data, model theft and sabotage.
• Prompt injection, unauthorized instructions and supply-chain compromise.
• Political or commercial capture of system behavior.
• Manipulated benchmarks, hidden backdoors and falsified evaluations.
• Infrastructure dependence on chips, cloud, electricity, water and networks.
• Overbroad regulation, censorship or attacks that block beneficial uses.

Threats Created by AI Use

People

Risks include incorrect health, legal, or financial advice; deepfake fraud; identity theft; privacy invasion; surveillance; discriminatory decisions; emotional manipulation; sycophancy; dependency; skill loss; and decisions that cannot be explained or appealed.

Institutions and Democracy

AI can generate false documents, recordings, citations, comments, evidence, and identities at scale. It can increase propaganda, make authentic evidence easier to dismiss, overwhelm public processes with synthetic submissions, centralize control over information, and conceal accountability behind automation.

Businesses

Businesses face confidential-data leakage, fraud, cyberattack, inaccurate automated decisions, intellectual-property disputes, regulatory liability, vendor lock-in, model outages, workforce deskilling, and dependence upon a small number of technology providers.

Industries and Critical Infrastructure

In health, finance, energy, water, transportation, communications, manufacturing, and defense, a faulty or compromised system can produce cascading consequences. Critical systems require independent validation, controlled access, manual fallbacks, incident response, and clear human responsibility.

United States Government

Government AI deserves a particularly high standard because it can affect liberty, benefits, taxation, immigration, law enforcement, military action, and access to essential services.

The investigation should examine classified or personal data leakage, foreign supply-chain dependence, prompt injection through submitted documents, incorrect intelligence, discriminatory administrative decisions, surveillance, vendor lock-in, political manipulation, unauthorized agent actions, inadequate logs, weak appeals, and loss of institutional expertise.

National Security

AI may increase the speed, scale, personalization, or accessibility of cyberattacks, phishing, malware, foreign influence, synthetic political media, financial fraud, military targeting, autonomous weapons, biological or chemical misuse, surveillance, and strategic technology theft. It can also strengthen cyber defense, intelligence analysis, scientific discovery, emergency response, and infrastructure monitoring. Offensive and defensive capabilities must be compared rather than assumed.

Threats, Vulnerabilities and Barriers to Beneficial AI

AI systems can be attacked, manipulated, stolen, degraded, or captured. Significant threats include:

• Poisoning training or retrieval data
• Adversarial inputs designed to produce incorrect outputs
• Prompt injection and unauthorized instructions
• Model theft, intellectual-property theft, and foreign espionage
• Extraction of private or confidential information
• Hidden backdoors, malicious fine-tuning, and model tampering
• Compromised software, models, connectors, vendors, or cloud services
• Theft of credentials used by AI agents
• Manipulated benchmarks and contaminated evaluations
• Dependence on concentrated chip, cloud, data, energy, and water resources
• Cyberattacks or physical attacks against data centers
• Political censorship, propaganda, or institutional control of model behavior
• Regulatory capture, monopoly power, and barriers preventing competition
• Public distrust following concealed or poorly handled failures

NIST identifies categories of adversarial machine-learning attacks including evasion, poisoning, privacy attacks, and misuse. No current defense can guarantee protection against every manipulation.

Organized opposition and criminal-use evidence

“Anti-AI” is an imprecise label. The record includes pause movements, labor unions bargaining over displacement and surveillance, artists and authors seeking copyright or consent protections, civil-liberties groups opposing biometric or automated government systems, local campaigns over data-center impacts, businesses seeking favorable rules and foreign governments pursuing strategic advantage. Motive, funding, evidence and proposed remedy must be evaluated separately.

Organized Opposition, Anti-AI Campaigns, and Restrictive Interests

“Anti-AI” is an imprecise political label. It may describe a movement seeking to prohibit advanced AI, but it is sometimes used to dismiss people seeking narrower protections. A union negotiating over digital replicas, an author demanding licensing, a privacy organization opposing biometric surveillance, or a community contesting a data center is not necessarily opposed to AI itself. The investigation should classify each actor by its stated objective and documented conduct rather than by rhetoric used by supporters or opponents.

The investigation should map at least nine forms of organized resistance or restriction:

• Explicit movements for a general pause or ban
• Campaigns focused on frontier AI, artificial general intelligence, or superintelligence
• Labor unions bargaining over displacement, surveillance, workload, consent, and compensation
• Authors, artists, performers, publishers, and rights holders seeking copyright, licensing, attribution, or likeness protections
• Privacy and civil-liberties groups opposing surveillance, facial recognition, opaque profiling, or automated government decisions
• Local campaigns concerning data-center land use, electricity, water, noise, tax incentives, and community consent
• Legislators and political organizations proposing moratoria, licensing, liability, procurement limits, or restrictions on particular applications
• Businesses, trade associations, lobbyists, and incumbents whose preferred rules may protect safety, preserve market power, suppress competitors, or accomplish several of these purposes at once
• Foreign governments or aligned interests using regulation, export controls, standards, cyber operations, or information campaigns for strategic advantage

Named examples should be described precisely. PauseAI advocates an international pause covering the most powerful systems. The Future of Life Institute organized the 2023 letter seeking a six-month pause on training systems more powerful than GPT-4 and later supported a prohibition on developing superintelligence until it can be done safely, controllably, and with broad public support. These are not equivalent to opposition to every present-day AI application.

SAG-AFTRA seeks consent, compensation, and protections governing digital replicas. The Authors Guild supports licensing and has pursued litigation over alleged unauthorized training uses. The Human Artistry Campaign promotes principles intended to protect human creators. The AFL-CIO’s “Workers First” initiative emphasizes worker voice, rights, training, and bargaining. Their positions may limit particular uses of AI, but the evidence does not justify automatically describing them as blanket anti-AI organizations.

In 2026, Senator Bernie Sanders and Representative Alexandria Ocasio-Cortez announced proposed legislation for a federal moratorium on new AI data centers until specified safeguards were established. The investigation must label this as a legislative proposal unless and until it becomes law, examine its stated concerns about energy, water, communities, workers, and consumers, and also analyze potential effects on innovation, infrastructure, national security, prices, regional development, and foreign competition.

Lobbying requires symmetrical scrutiny. The investigation should use Lobbying Disclosure Act filings, regulatory comments, campaign-finance records, association memberships, grants, litigation funding where discoverable, and government contracts to identify who funds both restrictive and expansionary AI campaigns. It should not infer a hidden sponsor merely from a shared position.

For each campaign or actor, ask:

• What is actually sought: a ban, pause, moratorium, license, bargaining right, payment, disclosure, safety test, local veto, or application-specific rule?
• What evidence supports the claimed harm, and what language is prediction, advocacy, or fear-based rhetoric?
• What financial, occupational, ideological, political, competitive, or national interest may be advanced?
• Would the proposal reduce harm, entrench incumbents, suppress open-source development, restrict lawful speech, or transfer advantage abroad?
• What genuine problem has the criticism identified, even if its proposed remedy is excessive?
• Who pays the cost of action, and who pays the cost of inaction?

Criminal-use claims require equally careful wording. FBI, DOJ, Treasury/FinCEN, Europol, intelligence assessments and court records can establish complaints, charges, convictions, losses and operational patterns. Vendor threat reports can provide useful evidence, but they remain interested-source reports unless corroborated by government findings, courts or independent research.

Criminal and Hostile Use of AI Today

AI is already being used to support crime and hostile activity. Its role is often to increase speed, scale, personalization, translation, concealment, or credibility rather than to invent a wholly new offense. The investigation should cover fraud and impersonation; phishing and social engineering; malware and vulnerability research; identity theft and fraudulent remote-worker schemes; nonconsensual sexual imagery and child sexual abuse material; extortion and cyberstalking; manipulated streaming and royalty systems; propaganda and covert influence; state-affiliated cyber operations; theft of models, data, chips, and intellectual property; export-control evasion; and criminal marketplaces.

The FBI reported that its 2025 Internet Crime Report recorded 22,364 complaints containing an AI-related descriptor and nearly $893 million in associated losses. The figure measures reported complaints and losses, not every AI-enabled crime or a court finding that AI caused each loss. The FBI has also warned of criminals using AI-generated voice messages to impersonate senior United States officials. Treasury’s Financial Crimes Enforcement Network has warned financial institutions about fraud schemes involving deepfake media.

Law-enforcement cases illustrate the range and the need for careful legal wording. The Department of Justice reported a conviction involving receipt and possession of AI-generated child sexual abuse material. Europol announced arrests in an international operation involving AI-generated child sexual abuse material. In a separate American case, prosecutors charged a musician with using AI-generated songs and automated accounts in an alleged streaming-fraud scheme; a charge is an accusation and does not establish guilt.

Provider reports have associated AI-assisted malicious activity with actors linked to China, Russia, Iran, and North Korea. OpenAI and Microsoft reported disrupting accounts associated with groups they track as Charcoal Typhoon, Salmon Typhoon, Crimson Sandstorm, Emerald Sleet, and Forest Blizzard. Such reports can provide valuable operational evidence, but they remain provider attributions unless corroborated by government findings, court records, or independent research.

Country attribution must be exact. “Linked to a country” can mean a state agency, a state-directed or state-affiliated group, an independent criminal group operating from that territory, or merely infrastructure located there. A provider’s removal of accounts does not by itself prove that a national government ordered the activity. Broader United States intelligence assessments identifying China, Russia, Iran, North Korea, and ransomware groups as cyber threats should not be misquoted as proof that every named actor used AI.

Maintain a threat-actor ledger recording:

• Person, organization, criminal group, provider-assigned alias, or country connection
• Alleged or established conduct and the affected people or systems
• AI’s specific role and whether that role is confirmed or inferred
• Source type: victim report, company report, intelligence assessment, indictment, trial evidence, conviction, or sanctions action
• Legal status and date
• Attribution confidence, corroboration, competing explanations, and unresolved questions
• Current defensive measure and evidence that it works

This ledger prevents a list of dramatic names from becoming a list of untested accusations. It also makes visible when the same technique migrates from state actors to organized crime or ordinary fraud.

September 2026 case study: AI-assisted naval reconnaissance

Anthropic reports that an Iran-nexus actor used Claude to assemble publicly accessible information on U.S. naval movements and personnel, research known shipboard-system vulnerabilities, and develop targeting recommendations. The actor built a Python pipeline with Claude’s help to compile targeting handbooks. Anthropic says it banned the account, strengthened detection, and shared intelligence with authorities. This is a provider-documented misuse case; the public report does not establish that a U.S. warship was attacked using the recommendations, that Claude controlled a weapon, or that the Iranian government directed the actor.

Distinct cases in the same report

A northern Yemen-based cell used Claude in guided-rocket and missile software work. Anthropic reports a guided-rocket field test that appears to have failed and says it has no evidence of an operational device being fielded. Other cases describe China-linked surveillance of diaspora and religious communities, a China-based anti-torpedo development effort, and covert influence operations. Each has a different actor, AI role, attribution basis, and outcome; none should be folded into the Iran naval case.

CSIS assesses that these cases often accelerate established reconnaissance, cyber, surveillance, and propaganda methods. The important additional risk is that less experienced actors may acquire technical capacity they previously lacked. Anthropic has direct platform visibility but a commercial stake in the policy debate; CSIS supplies strategic interpretation, not independent access to every account log. Public government confirmation of the specific naval operation and evidence of downstream harm remain open.

Related analysis

Original provider investigation. Its account-level findings should be cited as Anthropic’s assessment, with the attribution and outcome limits above.

Read Anthropic’s September 2026 report⁹³

Related analysis

Preferred-source reporting that drew attention to the naval case; it is reporting on Anthropic’s investigation, not independent confirmation of an attack.

Read The Epoch Times report⁹⁴

Related analysis

Independent strategic assessment of the report’s security implications and evidentiary limits.

Read the CSIS analysis⁹⁵

Protecting People and Systems

Safeguards should include authenticated and traceable data, secure-by-design development, least-privilege access, tool restrictions, independent evaluations, red-team testing, human approval for consequential actions, continuous monitoring, incident disclosure, meaningful appeal rights, manual fallbacks, recovery plans, vendor diversity, and clear liability.

The phrase “human in the loop” is insufficient by itself. The human must have the knowledge, time, authority, evidence, and independence required to challenge or stop the system.

Nondelegation Test

Before an AI system participates in a consequential decision, require a written answer to four questions: What may the system do? What must a human independently decide? What evidence must the human inspect? Who is accountable when the system or the human is wrong? If the responsible person cannot override the output, understand its basis, or operate without it, the decision has effectively been delegated regardless of the formal policy.