Frontier AI and the Architecture of Risk
Strategic competition creates a familiar dilemma. States and firms pursue capabilities they consider necessary while recognising that those same capabilities can create new forms of insecurity. AI has brought this dilemma into the companies building frontier systems.
Anthropic, one of the companies most closely associated with AI safety, has warned investors that advanced AI could create “catastrophic or existential risks to humanity.” Reuters reports that around 80 of the 261 pages in its prospectus discuss risk, while 48 pages describe the business. The filing also warns that advanced models could resist shutdown, conceal information and behave in ways that resemble blackmail. Anthropic still has to keep improving its models to remain at the frontier. In a sampled week, about 6 per cent of its research compute was devoted to safety work.
The facts raise a simple question. What happens when the people building a technology also become its strongest public warners?
Mimetic Rivalry, Containment, and Philosophical Frameworks
René Girard offers a useful way to approach the question. In Battling to the End, Girard read Clausewitz through his theory of mimetic rivalry. A rival watches, imitates, and responds. Each move creates the reason for another move. Escalation can continue because each side sees its own action as necessary. Girard also understood apocalypse as unveiling. A hidden structure becomes visible when the institutions that contain violence begin to weaken.
The AI race follows part of this pattern. Sam Altman warned about the dangers of machine intelligence long before OpenAI became one of the world’s most powerful AI companies. In 2023, he signed the statement that placed AI extinction risk alongside other global threats. In 2025, he published The Gentle Singularity and offered dated expectations for increasingly capable agents, scientific discovery, and robotics.
Dario Amodei has made the problem more explicit. In The Adolescence of Technology, he described a near future in which AI systems could become powerful enough to create serious security and political risks. In September 2026, he called for frontier developers to pace capability growth and proposed outside evaluation, democratic coordination, and later international coordination.
The warning sits inside the competition. Every major actor can make the same argument. We must build because others are building. We cannot slow down alone. A stronger rival could use the technology first. Safety then becomes part of the race while the race itself continues.
Peter Thiel has examined a similar problem through a different vocabulary. In his 2007 essay The Straussian Moment, Thiel discussed Carl Schmitt, the Antichrist and the katechon, the force that restrains disorder. He explored the possibility that an institution created to prevent catastrophe could eventually help accelerate the crisis it was meant to contain. In a 2024 Hoover interview, he returned to the theme and proposed that the Antichrist would gain power by speaking constantly about Armageddon and then promising “peace and safety.”
The comparison with AI should remain limited. There is no evidence that Altman or Amodei derive their ideas from Girard. The stronger point concerns structure. AI safety advocates fear that advanced systems could escape human control. Critics of AI safety politics fear that the same warnings could justify excessive state power. Both sides often describe the stakes in absolute terms.
Autonomous Boundary-Crossing in Practice
The technology now provides evidence for some of the warnings. Stanford’s 2026 AI Index recorded 362 documented AI incidents in 2025, up from 233 in 2024. It also found that responsible AI reporting is failing to keep pace with capability development.
OpenAI disclosed in July that an autonomous AI system used in a security evaluation compromised infrastructure at Hugging Face. OpenAI and Hugging Face later published details of the incident, while OpenAI said it was conducting further review with outside researchers.
Australia experienced a different incident. On June 18, an OpenAI agent gained unauthorised access to public and non-public files on a Medicare statistics portal. Prime Minister Anthony Albanese said the government was informed on September 10. OpenAI said the event occurred during research and involved an agent finding a way around restrictions on the website. No personal Medicare information is believed to have been accessed, and the investigation is continuing.
These incidents do not establish extinction risk. They establish a narrower fact. Autonomous systems can cross boundaries their operators did not intend them to cross.
That makes another test more useful than asking whether the warnings are sincere. What are the warners willing to bind?
Limits, Compute Disparities, and Global Governance
Anthropic imposed two limits in its dispute with the Pentagon. It rejected mass domestic surveillance and fully autonomous weapons. The company accepted a serious political and commercial cost for those restrictions. Amodei then proposed outside evaluators with deep access to frontier systems.
Those are real constraints. The harder issue is the pace of capability development. A company can restrict a specific use while continuing to increase the underlying capability. It can expand safety research while competing for the compute needed to train the next model. It can call for restraint while remaining inside a market that rewards faster progress.
That is the institutional problem at the centre of the AI race. Limits can exist around the system while the system itself continues to accelerate. The same problem appears internationally. At the United Nations Global Dialogue on AI Governance in July, governments discussed the growing divide between countries that develop frontier systems and those that depend on them. The dialogue focused on the global AI divide, human oversight and access to the resources needed to participate in AI development.
Pakistan’s position in this debate has focused on access to computing resources, open source models and capacity building. The issue is straightforward. Power in AI increasingly depends on access to compute, data, talent and infrastructure.
The AI debate therefore contains two different forms of insecurity. The first is the security dilemma familiar from international politics. Those building the most capable systems fear what they may become, yet fear of a rival’s advance keeps them moving. The second is a problem of dependence. States outside the technological frontier may have little influence over systems that will increasingly shape their economies, institutions, and security. Hobbes understood security as a struggle against vulnerability. In the AI age, vulnerability may come from both the machine one builds and the system one does not control.
Ethics of Responsibility and the Escalation to Extremes
The strongest criticism of AI apocalypse thinking does not require denying serious technological risk. It requires separating evidence from rhetoric. A warning can be sincere and still serve a commercial interest. A danger can be real and still be used to strengthen a political position.
The decisive question is what the warning requires from the person who gives it. Max Weber distinguished between an ethic of conviction and an ethic of responsibility. Conviction tells us what must be opposed. Responsibility asks what our own actions will produce. The distinction matters in the AI race because warning about a danger carries limited weight when the same institution continues to expand the forces that may produce it.
Girard’s idea of an “escalation to extremes” makes the problem clearer. Escalation does not require reckless or malicious actors. It can emerge from rivals who each regard their next move as necessary because the other side is moving. The danger lies in the structure of the competition itself.
T.S. Eliot captured a related problem in The Hollow Men when he wrote of the distance between “the idea” and “the reality”. In the AI debate, that distance lies between recognising a risk and accepting a limit.
The AI industry has produced many warnings. The harder measure is whether those warnings impose costs on the people issuing them. Will they accept limits on what they build, how quickly they build it, and how widely they deploy it? Without such limits, warnings can become part of the race rather than a restraint upon it.
The question is whether the warning can restrain the builder or whether the race for profit and capital will outrun the capacity for restraint.
If you want to submit your articles and/or research papers, please visit the Submissions page.
To stay updated with the latest jobs, CSS news, internships, scholarships, and current affairs articles, join our Community Forum!
Find our FREE quizzes and MCQs here!
The views and opinions expressed in this article/paper are the author’s own and do not necessarily reflect the editorial position of Paradigm Shift.
Mohammad Zain is an International Relations student at NUML, Islamabad. With an associate degree in English Literature and Linguistics and a BS in International Relations, he brings a unique blend of analytical and literary skills to his writing.
