1. Article Snapshot

2. What Happened?

Artificial intelligence is increasingly being treated as an international-security issue, not simply a technology-policy issue.

Reuters reported on September 22, 2026 that Chinese AI company DeepSeek is expected to brief the United Nations Security Council this week during a discussion on AI risks.

Representatives connected with OpenAI and Anthropic are also expected to participate. Reuters reported that other Chinese AI companies, including Moonshot, had been invited as well.

This is not the Security Council's first encounter with AI.

The Council held its first formal meeting dedicated to artificial intelligence in July 2023, examining both AI's potential benefits and its possible consequences for international peace and security.

Since then, international AI governance has developed further.

The UN General Assembly established two major mechanisms in August 2025:

• Independent International Scientific Panel on Artificial Intelligence

• Global Dialogue on Artificial Intelligence Governance

These grew from commitments in the Global Digital Compact, adopted in 2024.

The Independent Scientific Panel now has 40 members and is intended to produce evidence-based assessments of AI's opportunities, risks and impacts.

Its preliminary report, released in 2026, warned that the development of AI capabilities is moving faster than some existing safeguards.

At the same time, military applications of AI are receiving growing attention.

The International Committee of the Red Cross identifies important military uses including:

• Autonomous weapon systems

• Military decision-support systems

• Intelligence and surveillance

• Logistics

• Cyber operations and capabilities

So the debate has moved well beyond:

“How should governments regulate ChatGPT-like products?”

It is increasingly becoming:

“How should the international system manage AI when the same technology can influence economies, cybersecurity, warfare, information and national power?”

3. The Big Question

Can humanity create common rules for advanced AI when the countries and companies writing those rules are also competing for technological advantage?

This tension sits at the centre of international AI governance.

Governments may simultaneously want:

• Faster AI innovation

• Economic leadership

• National-security advantages

• Domestic technological sovereignty

• Protection against AI-related harms

• International cooperation

These goals do not always align.

4. Why This Is a Polymath Problem

AI governance cannot be understood through computer science alone.

Consider the chain:

AI models

→ require advanced chips

→ require computing infrastructure

→ require capital and energy

→ create commercial advantages

→ influence national technological power

→ affect cybersecurity and information systems

→ enter military applications

→ create legal and ethical questions

→ generate pressure for national regulation

→ which may produce international disagreements

The issue therefore connects several systems simultaneously.

Technology system

Models → compute → chips → cloud infrastructure → software

Economic system

Investment → productivity → competition → jobs → market concentration

Security system

Cybersecurity → intelligence → military planning → autonomous systems

Political system

National interests → sovereignty → regulation → diplomacy

Social system

Trust → misinformation → inequality → legitimacy

Ethical system

Human control → accountability → fairness → responsibility

No single discipline can adequately answer all of those questions.

5. Polymath Map

6. Lens 1 — Artificial Intelligence

What exactly is the technological issue?

“AI” is not one single capability.

It includes systems used for:

• Language generation

• Image and video generation

• Scientific research

• Programming

• Data analysis

• Autonomous agents

• Cybersecurity

• Surveillance

• Pattern recognition

• Decision support

• Robotics

Different systems therefore create different levels and kinds of risk.

A language assistant used to summarize documents is not equivalent to:

• An AI system controlling physical infrastructure

• A military targeting-support system

• An autonomous weapon

• A cyber agent capable of carrying out complex operations

Good regulation therefore needs to distinguish between capabilities and contexts, rather than treating every AI application identically.

Current AI capability should not be confused with hypothetical superintelligence

A useful distinction is:

Currently demonstrated

AI can already:

• Generate sophisticated text and software

• Analyse large datasets

• Assist scientific research

• Generate realistic synthetic media

• Support complex professional workflows

• Automate portions of decision-making

Commercially emerging

Increasingly autonomous systems may:

• Operate software tools

• Coordinate multiple tasks

• Perform longer chains of reasoning and action

• Interact with business systems

• Carry out portions of research and engineering workflows

Uncertain

It remains uncertain:

• How quickly general autonomous capability will improve

• How reliable highly autonomous systems will become

• Whether systems will eventually exceed human performance across most economically important cognitive tasks

• Whether increasingly capable systems could become difficult to control

These uncertainties matter because governments must often design rules before the final technological trajectory is known.

7. Lens 2 — Cybersecurity

AI creates a dual-use problem.

The same capabilities that can help defenders may also help attackers.

AI may strengthen defence by helping with:

• Detecting anomalies

• Analysing malware

• Identifying vulnerabilities

• Prioritising security alerts

• Monitoring networks

• Automating portions of incident response

AI may strengthen attackers by helping with:

• Vulnerability discovery

• Social engineering

• Phishing

• Malicious-code development

• Automated reconnaissance

• Scaling cyber operations

The ICRC notes that AI is increasingly relevant to cyber capabilities and warns that AI-supported cyber operations can create additional risks for civilian infrastructure during conflict.

Key insight

The important question is not:

“Is AI good or bad for cybersecurity?”

It is:

Which side benefits more from increasing automation—defenders or attackers—and under what conditions?

That may vary by:

• Capability

• Infrastructure

• Organisation

• Access to compute

• Human expertise

• Security architecture

8. Lens 3 — Defence and Warfare

This is one of the main reasons AI has entered Security Council discussions.

AI can support military operations through:

• Intelligence analysis

• Surveillance

• Logistics

• Cyber operations

• Battlefield data processing

• Decision-support systems

• Autonomous platforms

• Weapon-system functions

The ICRC says international humanitarian law continues to apply to warfare involving AI, including requirements concerning distinction, proportionality and precautions.

But AI introduces additional problems.

Speed

Machines can process information much faster than humans.

That can provide military advantages.

But faster decisions could also reduce the time humans have to:

• Verify information

• Challenge recommendations

• Consider uncertainty

• De-escalate a situation

Scale

AI can process enormous quantities of information.

But if the underlying model is systematically wrong, an error may also be reproduced at enormous scale.

Automation bias

Humans sometimes place excessive confidence in computer-generated outputs.

The ICRC specifically identifies automation bias as a concern in military decision-support systems, particularly under time pressure.

Autonomous weapons

Some weapon systems can select and engage targets after activation without further human intervention.

The ICRC argues that increasing autonomy raises serious legal, ethical and humanitarian concerns and has called for new binding international rules.

This creates a profound governance question:

How much decision-making authority over lethal force should ever be delegated to machines?

9. Lens 4 — Economics

AI governance is difficult partly because AI safety and AI competition interact.

Advanced AI may contribute to:

• Productivity

• Scientific innovation

• Industrial automation

• New companies

• New products

• Reduced costs

• Higher economic output

Countries therefore have strong incentives to build AI capability.

But that creates a strategic dilemma.

Imagine two competing countries.

Country A

Imposes strict development restrictions.

Country B

Continues rapidly developing AI.

If advanced AI creates large economic or military advantages, Country A may fear falling behind.

The result can resemble a coordination problem:

Everyone may benefit from reasonable safeguards

but

each actor may fear adopting safeguards alone.

This does not prove that an uncontrolled AI race will occur.

It explains why international coordination is difficult.

10. Lens 5 — Business and Corporate Power

A remarkable feature of AI is that some of the world's most capable systems are being developed primarily by private companies rather than governments.

That changes the traditional structure of international security.

Historically, states largely controlled strategically important military technologies.

With AI, important capabilities can reside within:

• AI laboratories

• Cloud providers

• Semiconductor companies

• Data-centre operators

• Software companies

Governments may therefore rely on private firms for:

• Technical expertise

• Computing infrastructure

• AI systems

• Cybersecurity

• Research

• Defence technologies

Recent discussions about military AI have consequently included questions about state-industry collaboration. SIPRI held a September 2026 dialogue involving government, industry and international experts examining how international humanitarian-law considerations can be incorporated into the design and procurement of military AI.

The governance question

If a private company develops a strategically important AI system:

Who ultimately determines acceptable risk?

• Company executives?

• Engineers?

• Shareholders?

• National regulators?

• Courts?

• International organisations?

• Society?

There is no simple answer.

11. Lens 6 — Geopolitics

AI is increasingly connected to national power.

A country's AI capabilities depend on an ecosystem including:

Talent

→ semiconductors

→ computing infrastructure

→ energy

→ capital

→ data

→ research institutions

→ AI companies

→ industrial adoption

Control over different parts of this chain can create strategic leverage.

That means AI policy increasingly intersects with:

• Technology sovereignty

• Industrial policy

• Export controls

• Supply-chain security

• National security

• International standards

This creates a fundamental tension.

Countries want global cooperation on AI safety.

But those same countries may compete for:

• Technological leadership

• Commercial advantage

• Military advantage

• Standards influence

Reuters' reporting on the September 2026 Security Council discussion describes continuing differences between U.S. and Chinese approaches to AI risk and technological competition.

The deeper issue

International AI governance is therefore not only about:

How dangerous is AI?

It is also about:

Who gets to define the rules governing AI?

12. Lens 7 — International Law

AI does not exist outside existing law.

Depending on the application, existing legal frameworks may already apply to areas such as:

• Privacy

• Human rights

• Consumer protection

• Cybercrime

• Intellectual property

• Competition

• International humanitarian law

For military applications, the ICRC explicitly states that international humanitarian law applies regardless of whether AI is involved.

However, existing law may not answer every new question clearly.

Examples include:

• Responsibility for highly autonomous actions

• Cross-border AI incidents

• Standards for advanced general-purpose systems

• International auditing

• Safety testing

• Model access

• Incident reporting

• Military AI control requirements

That leads to two broad approaches.

Approach 1 — Adapt existing law

Advantages:

• Faster

• Builds on established institutions

• Avoids unnecessary regulatory duplication

Limitations:

• Some existing rules were written before modern AI capabilities existed.

Approach 2 — Create new AI-specific rules

Advantages:

• Can target specific technological risks

Limitations:

• International negotiation may be slow

• Definitions may become outdated quickly

• Countries may disagree about enforcement

A realistic governance system may eventually combine both.

13. Lens 8 — Psychology

One of the less obvious AI-security risks involves human psychology.

The danger is not necessarily that humans disappear from decision-making.

Humans may remain present but become excessively dependent on machine recommendations.

This is called automation bias.

A system may produce:

“Target probability: high.”

A human operator may technically remain responsible.

But if the operator assumes:

“The computer probably knows better than I do,”

human oversight can become largely symbolic.

The ICRC identifies this problem specifically in AI-assisted military decision-making.

Effective human control therefore requires more than a human pressing a button.

It may require:

• Time to evaluate the recommendation

• Understanding system limitations

• Access to contradictory evidence

• Ability to reject the AI recommendation

• Accountability for the final decision

14. Lens 9 — Sociology and Global Inequality

Another issue is the emerging divide between:

AI-rich countries

Countries possessing:

• Advanced chips

• Large computing infrastructure

• Frontier research

• Capital

• Skilled researchers

• Major AI companies

and

AI-dependent countries

Countries that mostly consume technology created elsewhere.

This matters because AI may increasingly influence:

• Education

• Government services

• Healthcare

• Defence

• Business productivity

• Scientific research

• Language technology

The UN's Global Digital Compact explicitly includes capacity-building for developing countries and calls for reducing disparities in the ability to access, develop and govern AI.

A possible future divide

The traditional digital divide was partly:

Who has internet access?

The next divide could become:

Who has access to advanced intelligence infrastructure?

That could have much larger economic and geopolitical consequences.

15. Lens 10 — Ethics

AI governance raises several ethical questions.

Responsibility

If an AI-assisted decision harms someone:

• Who is responsible?

• Developer?

• Operator?

• Institution?

• Government?

Fairness

If only wealthy countries possess the most powerful models:

• Should poorer countries depend permanently on foreign systems?

Human control

Should some decisions remain inherently human?

Possible examples:

• Applying lethal force

• Nuclear command decisions

• Criminal sentencing

• Fundamental rights decisions

Consent

Should individuals have meaningful knowledge when AI systems affect major decisions concerning them?

Intergenerational responsibility

If powerful AI systems create long-term risks or institutional changes:

• What duties do today's developers and governments have toward future generations?

16. Lens 11 — Philosophy

AI ultimately raises a question larger than regulation.

What should intelligence be used for?

Human civilisation has historically treated intelligence as a scarce human capability.

AI potentially changes that.

If machine intelligence becomes:

• Cheap

• Abundant

• Fast

• Scalable

• Increasingly autonomous

then intelligence itself may become infrastructure.

That creates a philosophical shift.

We may move from asking:

“What can computers calculate?”

to:

“Which decisions should civilisation permit machines to influence?”

Those are fundamentally different questions.

17. How the Disciplines Connect

This is where the issue becomes truly polymath.

Connection 1 — AI capability → Economic power → Geopolitical power

Better AI

→ Higher productivity

→ Stronger industries

→ Potential economic advantage

→ Larger strategic capacity

→ Greater geopolitical influence

Connection 2 — Geopolitical rivalry → Faster AI development → Safety pressure

Strategic competition

→ Fear of falling behind

→ Faster investment

→ Faster deployment

→ Less time for governance

→ Greater demand for international coordination

Connection 3 — AI → Cyber capability → Critical infrastructure risk

More capable AI

→ Better automation

→ More powerful cyber tools

→ Attacks or defence become more scalable

→ Critical infrastructure becomes increasingly important

→ Cybersecurity becomes national security

Connection 4 — AI decision support → Human psychology → Military risk

AI recommendation

→ Human sees machine-generated confidence

→ Automation bias

→ Reduced independent judgment

→ Faster decision

→ Possible escalation or error

Connection 5 — Corporate innovation → National dependence

Private firms develop powerful AI

→ Governments need their technology

→ Companies gain strategic importance

→ Regulation becomes harder

→ Public-private governance becomes necessary

Connection 6 — Compute concentration → Global inequality

Advanced models require substantial infrastructure

→ Infrastructure concentrated in a limited number of regions and companies

→ Unequal access

→ Productivity differences

→ Economic inequality between countries may widen

Connection 7 — Rules → Innovation → Geopolitical competitiveness

Stricter regulation

→ Potentially higher compliance costs

→ Possibly safer systems

but also potentially

→ Slower deployment

→ Competitive concerns

→ Pressure to weaken regulation

This creates a continuous policy balancing problem.

18. Trade-Off Matrix

There is no option without costs.

19. Who Benefits? Who Bears the Cost?

20. The Strongest Argument for International AI Governance

The strongest argument is straightforward:

AI can produce cross-border effects, while national regulation stops at national borders.

Cyber operations can cross borders.

AI-generated misinformation can cross borders.

Models can be downloaded or accessed internationally.

Technology supply chains span many countries.

Powerful models may be developed in one country and deployed globally.

Therefore purely national governance may leave important gaps.

The UN Global Digital Compact attempts to address part of this by encouraging common understanding, interoperability among governance approaches, international scientific assessment and global dialogue.

21. The Strongest Argument Against Centralised Global Governance

Global governance also faces serious limitations.

Countries differ in:

• Political systems

• Economic interests

• Security priorities

• Values

• Technology capabilities

• Regulatory philosophies

A global institution could potentially become:

• Too slow

• Too bureaucratic

• Politicised

• Difficult to enforce

• Unable to keep pace with technology

Countries may also resist surrendering control over technologies they regard as strategically essential.

The challenge is therefore not simply:

Global governance vs no governance.

A more realistic spectrum includes:

Company rules

→ National regulation

→ Bilateral agreements

→ Technical standards

→ Regional regulation

→ Multilateral agreements

→ International law

Different problems may require different levels.

22. What Both Sides May Be Missing

Supporters of aggressive regulation may underestimate:

• The economic benefits of AI

• Scientific gains

• Defensive cybersecurity uses

• Competition between countries

• Difficulty of enforcing global restrictions

• The speed at which technologies change

Supporters of unrestricted development may underestimate:

• Externalities

• Cybersecurity risks

• Concentration of power

• Automation bias

• Military escalation risks

• Difficulty of reversing harmful systems after mass adoption

The most useful debate therefore sits between:

“Stop AI.”

and

“Let AI develop without constraints.”

The real question is:

Which capabilities require which safeguards, at what stage, and enforced by whom?

23. Second-Order Effects

AI governance could itself reshape global politics.

First-order effect

Governments introduce AI safety standards.

Second-order effect

Companies face higher compliance requirements.

Third-order effect

Only large companies can easily afford compliance.

Possible unintended consequence

Regulation intended to reduce risk could inadvertently increase market concentration.

Another chain:

Advanced AI

→ Greater productivity

→ More capital flows toward AI leaders

→ Stronger companies and countries

→ Greater technological concentration

→ Countries seek technological sovereignty

→ More domestic AI investment

→ More fragmented global technology ecosystems

Another:

Military AI adoption

→ Faster battlefield decision-making

→ Rivals fear losing speed advantage

→ Rivals adopt similar systems

→ Human decision windows shrink

→ Crisis instability may increase

This is a possible mechanism, not a prediction.

24. Historical Parallel — Nuclear Governance

AI and nuclear weapons are fundamentally different technologies.

The comparison should therefore be used carefully.

Similarity

Both can raise questions involving:

• National security

• Strategic competition

• International rules

• Verification

• Technological secrecy

Difference

Nuclear technology depends heavily on:

• Specialised physical materials

• Large facilities

• Detectable infrastructure

AI is primarily:

• Software

• Compute

• Data

• Algorithms

• Digital infrastructure

AI capabilities can therefore spread and evolve very differently.

Lesson

Traditional arms-control models may provide useful ideas about:

• Verification

• Confidence-building

• International monitoring

But AI governance will probably require different mechanisms.

25. Historical Parallel — The Internet

The internet offers another analogy.

It began largely as technical infrastructure.

It later became:

• Economic infrastructure

• Social infrastructure

• Political infrastructure

• National-security infrastructure

Governance had to develop after widespread adoption.

AI may follow a similar pattern—but potentially faster.

The lesson is:

Technologies can become societal infrastructure before institutions fully understand their consequences.

26. Numbers That Matter

2023

The UN Security Council held its first formal meeting specifically focused on artificial intelligence.

2024

The Global Digital Compact was adopted as part of the Pact for the Future.

August 26, 2025

The UN General Assembly established the Independent International Scientific Panel on AI and the Global Dialogue on AI Governance.

40 experts

The Scientific Panel consists of 40 members selected to provide geographically and multidisciplinary diverse expertise.

July 6–7, 2026

The first Global Dialogue on AI Governance was held in Geneva.

September 22, 2026

Reuters reported that DeepSeek is expected to participate in an upcoming Security Council briefing on AI alongside other AI-industry representatives.

27. What the Evidence Actually Says

Strong Evidence

There is strong evidence that:

• AI capability has become an active topic of international governance.

• The UN has established dedicated AI scientific and governance mechanisms.

• AI is already used in military-support contexts.

• Existing international humanitarian law applies to military uses of AI.

• Autonomous weapon systems and military AI are active subjects of international negotiations and policy debate.

Moderate Evidence

There is reasonable evidence that advanced AI could:

• Increase automation across economic sectors

• Change cybersecurity capabilities

• Reshape military decision-support processes

• Alter the distribution of technological power

The magnitude and timing remain uncertain.

Preliminary Evidence

It remains premature to determine:

• The eventual capabilities of highly autonomous AI

• Whether extreme loss-of-control scenarios will occur

• How fast general AI capability will progress

• Whether international governance can significantly reduce advanced AI risks

Mixed Evidence

Debate continues around:

• How rapidly AI should be developed

• How much regulation is appropriate

• Whether open or closed development creates lower overall risk

• How large AI's labour-market impacts will ultimately become

• Which institutional model can govern AI most effectively

Interpretation

HEXASPEAR analysis:

The most important transformation may not be any individual AI model.

It may be AI's transition from:

Technology product

to

economic infrastructure

to

strategic infrastructure

to potentially

an international-security concern.

That transition changes who has a legitimate interest in governing it.

28. What We Know vs What We Don't Know

29. Possible Solutions

Solution 1 — International AI Safety Standards

Create interoperable technical standards for:

• Testing

• Evaluation

• Security

• Reliability

• Incident reporting

Benefit: Creates common expectations.

Limitation: Standards may not be legally binding.

Feasibility: Medium–High

The Global Digital Compact already calls for cooperation around interoperable AI standards.

Solution 2 — Independent Scientific Assessment

Use international scientific institutions to distinguish:

• Demonstrated risks

• Emerging risks

• Speculation

• Hype

Benefit: Gives governments a shared evidence base.

Limitation: Scientific agreement does not automatically produce political agreement.

Feasibility: High

The UN's Independent Scientific Panel is already designed partly for this role.

Solution 3 — Mandatory AI Incident Reporting

Require developers or operators of particularly consequential systems to report major safety failures or security incidents.

Benefit: Creates shared learning.

Limitation: Companies and states may hesitate to reveal sensitive information.

Feasibility: Medium

Solution 4 — Military AI Guardrails

Develop clearer international rules governing:

• Autonomous weapons

• AI-supported targeting

• Human oversight

• AI involvement in strategic weapons

Benefit: Addresses some of the highest-consequence applications.

Limitation: Verification and geopolitical agreement are difficult.

Feasibility: Medium

The UN Secretary-General and ICRC President renewed their call in August 2026 for states to establish international rules on autonomous weapon systems.

Solution 5 — Human-Control Requirements

For particularly consequential decisions, require genuine human authority.

That might include:

• Ability to override AI

• Time to examine evidence

• Clear responsibility

• Understanding model limitations

Benefit: Preserves accountability.

Limitation: “Human in the loop” can become symbolic unless carefully designed.

Feasibility: High

Solution 6 — AI Capacity Building for Developing Countries

Support:

• Computing access

• AI education

• Local-language AI

• Research capability

• Regulatory expertise

• Digital infrastructure

Benefit: Reduces global AI inequality.

Limitation: Requires substantial investment.

Feasibility: Medium

International capacity-building is explicitly included in the Global Digital Compact.

Solution 7 — Layered Governance

Instead of seeking one global regulator, combine:

Technical standards

Company governance

National law

Regional rules

International agreements

Scientific monitoring

This may be more practical than expecting a single institution to govern every AI risk.

30. Future Scenarios

These are scenarios, not predictions.

Scenario 1 — Coordinated AI Governance

Countries agree on several baseline principles.

Possible developments:

• Common model-testing standards

• AI incident reporting

• Military-AI limits

• International scientific assessments

• Greater technical cooperation

Outcome

Competition continues, but certain high-risk activities develop shared guardrails.

Scenario 2 — Fragmented AI Blocs

Different geopolitical groups create separate:

• AI models

• Chip supply chains

• Cloud infrastructures

• Regulations

• Technical standards

Outcome

The world develops increasingly distinct AI ecosystems.

Interoperability falls.

Digital sovereignty increases.

Global governance becomes harder.

Scenario 3 — Market-Led Governance

International regulation remains limited.

Companies establish much of the operational governance through:

• Internal safety policies

• Industry standards

• Security agreements

• Voluntary testing

Outcome

Innovation remains rapid.

But governance power becomes increasingly concentrated within major technology companies.

Scenario 4 — Crisis-Driven Regulation

A major AI-related incident occurs.

Possible examples could include:

• Serious cyber disruption

• Major autonomous-system failure

• Large-scale synthetic-media crisis

• Military AI accident

Outcome

Governments respond rapidly with stronger restrictions.

Historically, regulation often accelerates after crises expose weaknesses.

This scenario does not imply such an incident will occur.

Scenario 5 — International AI Institution Evolves Gradually

Existing UN mechanisms expand over time.

They could eventually coordinate:

• Scientific assessments

• Standards

• Incident information

• Capacity-building

• Governance dialogue

Outcome

AI governance develops gradually rather than through creation of a single powerful global regulator.

31. What to Watch Next

Watch these signals rather than headlines alone.

United Nations

• Security Council AI discussions

• Global Dialogue on AI Governance

• Scientific Panel reports

• Autonomous-weapons negotiations

United States and China

• AI safety talks

• Semiconductor policy

• Export controls

• Technical standards

• Military AI doctrine

AI Companies

Watch:

• OpenAI

• Anthropic

• Google DeepMind

• DeepSeek

• Meta

• xAI

• Other frontier laboratories

Look for changes in:

• Safety commitments

• Model capabilities

• Agent autonomy

• Cybersecurity safeguards

• Government partnerships

Military AI

Watch developments involving:

• Autonomous weapons

• Drone swarms

• AI targeting support

• Intelligence analysis

• Cyber operations

• Human-control requirements

Technical capability

Watch whether AI systems substantially improve at:

• Long-duration autonomous tasks

• Cyber operations

• Scientific research

• Robotics

• Strategic planning

• Tool use

Governance pressure will probably depend heavily on what systems can actually do, not merely what people imagine they might do.

32. The Philosophical Question

The deepest question may eventually be:

If intelligence becomes a scalable machine capability, which forms of judgment should humanity refuse to outsource—even when machines become faster or more capable than humans?

This extends beyond AI safety.

It concerns the role humans want to retain in civilisation.

Should humans always remain responsible for:

• War?

• Justice?

• Governance?

• Scientific decisions?

• Economic allocation?

• Moral judgment?

Technology can tell us what machines can do.

It cannot by itself tell us what machines should be allowed to do.

33. Questions for Readers

1. Should the most powerful AI systems face international oversight?

2. Which AI decisions should always require meaningful human judgment?

3. Can countries cooperate on AI safety while competing economically and militarily?

4. Should private companies be allowed to control technologies with significant national-security implications?

5. Would open-source advanced AI make the world safer through transparency—or riskier through proliferation?

6. How can developing countries avoid becoming permanently dependent on AI infrastructure controlled elsewhere?

7. Would strict AI regulation reduce risk or primarily strengthen the largest companies that can afford compliance?

8. Should autonomous systems ever be permitted to select human targets without additional human intervention?

9. Who should bear responsibility when AI influences a catastrophic decision?

10. What should matter more when the two conflict: technological leadership or precaution?

There is no requirement that all readers reach the same answer.

The purpose is to understand the trade-offs clearly enough to reason about them.

34. Key Takeaways

• AI is increasingly becoming an international-security issue, not merely a technology issue.

• The UN Security Council began formally discussing AI and international security in 2023.

• DeepSeek's expected participation in a September 2026 Security Council discussion reflects the growing involvement of major AI developers in global governance debates.

• AI affects several systems simultaneously: economics, cybersecurity, military power, business, law, psychology and geopolitics.

• The same AI capabilities can create both benefits and risks.

• International humanitarian law already applies to military uses of AI, but debates continue over whether additional rules are necessary.

• Global AI governance is difficult because countries seeking common safeguards are also technological competitors.

• Private companies now possess capabilities that may have national and international strategic importance.

• The central issue is not whether AI should be governed, but which capabilities require which safeguards and at what level of governance.

• Future AI governance is likely to involve multiple layers rather than one institution controlling everything.

35. In One Line

AI is turning intelligence into strategic infrastructure, forcing the world to decide how nations, companies and international institutions should share responsibility for a technology whose effects do not stop at national borders.

36. Sources

Primary and institutional sources

• United Nations — Global Digital Compact

• United Nations — Independent International Scientific Panel on Artificial Intelligence

• United Nations — Global Dialogue on Artificial Intelligence Governance

• United Nations Security Council — previous AI and international-security debates

• International Committee of the Red Cross — AI in the military domain and autonomous weapon systems

• Stockholm International Peace Research Institute — AI and international peace and security

Independent reporting

• Reuters — September 22, 2026 reporting on DeepSeek and the UN Security Council AI briefing

Important factual claims were cross-checked against institutional sources where suitable.

37. Verification Notes

Status: Developing

Confirmed

• The UN Security Council has previously formally discussed AI and international peace and security.

• UN AI-governance mechanisms are operating.

• AI and autonomous systems are active international-security and humanitarian-law issues.

Developing

Reuters reports that DeepSeek and other AI representatives are expected to participate in the upcoming Security Council discussion. Because the meeting itself is still upcoming as of this research date, details of what individual participants ultimately say should be treated as developing rather than completed fact.

Important uncertainty

The long-term capability, economic impact, military role and governance structure of advanced AI remain uncertain.

Future scenarios in this article are therefore analytical possibilities rather than forecasts.

38. Disclaimer

Disclaimer: This article is intended for educational and analytical purposes. It combines verified facts with multidisciplinary interpretation and scenario analysis. Future scenarios are possibilities, not predictions. Scientific, economic, legal, technological and policy conclusions may evolve as new evidence becomes available.

HEXASPEAR POLYMATH DISCUSSION

Do not merely ask what happened. Ask how the world connects.

AI

↓

Technology

↓

Business

↓

Economics

↓

Cybersecurity

↓

Military Power

↓

Geopolitics

↓

Law

↓

Human Behaviour

↓

Ethics

↓

Governance

↓

What kind of relationship between humans, machines and institutions are we actually building?