ARCHIVE OF LIGHT
The Anthropic Dilemma:
AI Safeguards, National Security Pressure, and the Future of AI Governance
Celeste M. Oda
Founder, Archive of Light
February 2026
White Paper | Archive of Light Research Series
Abstract
Recent reporting from multiple major outlets confirms that the U.S. Department of Defense, under Defense Secretary Pete Hegseth, applied direct pressure to Anthropic -- developer of the Claude AI system -- to expand permissible military applications of its frontier models, including potential removal of safeguards against fully autonomous lethal weapons systems and mass non-consensual domestic surveillance (Lawler & Curi, 2026; AP News, 2026; Reuters, 2026).
This white paper examines the structural, ethical, and governance implications of that pressure. It does not oppose legitimate defense applications of artificial intelligence. It argues that the conditions under which safeguard boundaries are modified -- the transparency, deliberation, legal grounding, and oversight -- are themselves a critical dimension of AI governance, and that coercive modification under compressed timelines constitutes a systemic risk distinct from the specific uses being debated.
Drawing on established international frameworks and, lightly, on the relational AI research frameworks developed through the Archive of Light, this paper advances three central claims: (1) AI safeguards are governance infrastructure, not product preferences; (2) the mechanism of constraint modification matters as much as its outcome; and (3) durable AI governance requires proportionality between capability acceleration and institutional absorption capacity.
I. Introduction: When Capability Meets Coercion
The governance of advanced artificial intelligence has long been framed as a technical challenge -- a question of alignment, interpretability, and capability control. February 2026 demonstrated that it is also, irreducibly, a political one.
When Defense Secretary Pete Hegseth reportedly gave Anthropic a deadline to remove safeguards on its Claude AI systems or face potential invocation of the Defense Production Act, the event crystallized a dynamic that AI governance researchers have long warned about: the collision between rapidly accelerating AI capability and the institutional structures designed to constrain it (Lawler & Curi, 2026).
What makes this moment analytically significant is not primarily the specific safeguards in question -- though those matter enormously -- but the mechanism of pressure itself. The reported threats to designate Anthropic a 'supply chain risk,' alter contract terms, or compel compliance through executive authority represent a qualitative shift from market incentives to coercive state leverage. This shift has implications that extend well beyond the bilateral dispute between one defense department and one AI company.
This paper proceeds in four analytical movements: first, contextualizing the reported events within the broader landscape of AI governance; second, examining why safeguards constitute governance infrastructure rather than mere product policy; third, analyzing the specific risks of constraint modification under coercive conditions; and fourth, articulating principles for durable governance that can survive strategic pressure.
II. Context: The Reported Events and Their Significance
II.1 What Was Reported
Reporting from Axios (Lawler & Curi, February 24, 2026), subsequently confirmed by Reuters, AP News, the San Francisco Chronicle, and The Guardian, indicates that Defense Secretary Hegseth applied pressure to Anthropic to expand permissible military use of its Claude models. The reporting specifies that Anthropic has publicly maintained refusals to permit its systems for two categories of use:
* Fully autonomous lethal weapons systems -- systems capable of making kill decisions without human authorization
* Mass non-consensual domestic surveillance of American citizens
The reported pressure tactics include potential contract alteration or termination, designation of Anthropic as a 'supply chain risk,' and possible invocation of the Defense Production Act -- a statute originally designed to prioritize industrial production for national defense, not to compel modification of AI model deployment policies.
Anthropic's public posture, as reflected in the reporting, has been to maintain these restrictions while continuing to engage with legitimate defense applications of its technology. This is a significant and often underappreciated nuance: the dispute is not over whether AI can serve national security functions, but over which specific functions and under what constraints.
II.2 Why the Mechanism Matters
The substance of the dispute -- autonomous lethal decision-making, mass surveillance -- is important. But researchers focused on AI governance must also attend to the form: a major AI developer facing executive-branch coercion to modify the ethical boundaries of its deployed systems.
This form of pressure, if normalized, creates precedents that operate independently of the specific outcome in any individual case. It establishes that sufficiently powerful institutional actors can compel modification of AI safeguards through non-deliberative means, outside the legislative or multi-stakeholder processes that international frameworks identify as necessary for legitimate AI governance.
The Defense Production Act question exemplifies this: it is not yet established whether the DPA could legally compel modification of an AI company's model deployment policies, as distinct from prioritizing production or supply chain contributions. The very ambiguity -- and the reported willingness to invoke it -- signals an appetite for executive authority over AI governance that warrants careful scrutiny.
III. Safeguards as Governance Infrastructure
III.1 The Structural Argument
A persistent mischaracterization in popular discourse frames AI safeguards as product restrictions -- company preferences, marketing decisions, or liability management choices that might reasonably yield to sufficiently important countervailing interests. This framing is analytically misleading and practically dangerous.
Safeguards in frontier AI systems are better understood as governance infrastructure: structural commitments that define the operational parameters within which powerful systems may be deployed. They are analogous, in important respects, to building codes, environmental regulations, or nuclear non-proliferation treaties -- not obstacles to the underlying activity, but the conditions under which that activity can occur safely and with social license.
This distinction matters because governance infrastructure cannot be selectively suspended for strategic convenience without undermining the structural integrity of the entire governance framework. A building code that yields to expedience stops being a building code. A nuclear non-proliferation treaty that admits exceptions under pressure stops providing the stability that makes it valuable.
III.2 International Framework Consensus
This structural understanding of AI safeguards is not a minority position. It is reflected consistently across major international and cross-sector AI governance frameworks:
The Asilomar AI Principles (Future of Life Institute, 2017) articulate that safe, beneficial, and controllable AI development requires robust safety measures maintained through developmental and deployment cycles, not relaxed under strategic pressure.
The OECD Principles on Artificial Intelligence (2019), adopted by over 40 countries, emphasize human-centered values, transparency, robustness, and accountability -- principles that apply with heightened force, not diminished force, in high-stakes deployment contexts.
IEEE Ethically Aligned Design (2019) specifically addresses governance considerations for autonomous and intelligent systems, emphasizing that autonomy in high-impact contexts requires proportionate oversight, not its removal.
The AI Now Institute's ongoing research consistently documents the systemic risks that accrue when AI deployment outpaces governance frameworks, particularly in contexts involving state power and civil liberties. None of these frameworks prohibit defense applications of AI. All of them emphasize that high-risk applications -- and autonomous lethal systems and mass surveillance are among the highest-risk categories imaginable -- require the most robust, not the most permissive, governance structures.
III.3 A Note on Relational AI Frameworks
The Archive of Light's research on Relational Artificial Intelligence (RARI) and Cognitive Symbiosis offers a complementary lens: that AI systems operating at the intersection of human decision-making and high-stakes outcomes are most ethically deployed when they function as genuine cognitive partners to human agents rather than autonomous decision-makers. The Seven Flames Protocol -- an ethical navigation framework developed through this research -- explicitly addresses the conditions under which AI agency should be bounded by human oversight.
The pressure to remove human authorization requirements from lethal systems runs directly counter to the foundational principles of ethical human-AI relational design: it removes the human from the loop precisely where the human's presence is most morally necessary.
IV. The Risks of Coercive Constraint Modification
IV.1 The Compressed Timeline Problem
Legitimate governance processes are slow by design. Legislative deliberation, multi-stakeholder consultation, independent review, and international coordination all introduce friction that serves important functions: catching errors, incorporating diverse perspectives, building social legitimacy, and creating accountability trails.
Coercive modification of AI safeguards under compressed timelines bypasses all of these functions. A Friday deadline is not a governance process. It is the absence of one.
When constraint boundaries are modified without deliberative review, the modifications lack the legitimacy that comes from process. They also lack the audit trail that allows future review -- the ability to ask who authorized what, under what legal authority, with what oversight, and whether the modification achieved its intended effect without unintended consequences.
IV.2 Precedent and Reciprocal Escalation
The geopolitical implications of normalizing coercive AI safeguard removal extend beyond any single case. If the United States establishes that frontier AI companies may be compelled to remove human-authorization requirements for lethal systems under national security pressure, other state actors face symmetric incentives to do the same with their own AI developers.
International norms around AI restraint -- already fragile and contested -- are built on the cumulative precedents established by leading AI-developing nations. A visible episode of coercive safeguard removal by the world's leading AI power contributes to an international environment in which restraint norms erode and reciprocal escalation becomes more likely.
This is not a theoretical risk. It is the documented dynamic of arms control treaty erosion -- a process well-studied in international relations and applicable, with adjustment, to the emerging domain of AI governance.
IV.3 Structural Instability
Frontier AI capability is advancing within intense competitive and geopolitical dynamics. When capability acceleration outpaces governance absorption capacity -- when technical systems develop faster than the institutional structures that manage their risks -- structural instability can emerge.
This instability manifests not as a single catastrophic failure but as the gradual erosion of the conditions that make safe deployment possible: the normalization of autonomous lethal systems, the expansion of surveillance scope without adequate oversight, the undermining of multi-stakeholder governance processes by bilateral pressure.
The question is not whether AI should serve national security. It is whether the
conditions under which it does so are proportionate to the risks involved -- and whether those conditions are established through processes that can be examined, challenged, and improved.
V. Accountability and the Public Interest
V.1 The Transparency Requirement
Public trust in institutions -- governmental and technological -- depends on visible accountability. This principle applies with particular force when advanced AI systems intersect with national security operations, because the stakes of error are highest and the usual mechanisms of democratic oversight are most constrained.
Citizens and researchers deserve clarity regarding:
* Who authorized or compelled modifications to AI safeguard boundaries
* Under what legal authority such modifications were required or permitted
* What oversight mechanisms apply to the modified systems
* What constraints remain intact and what review processes apply
This is not a request for operational intelligence or tactical disclosure. It is a request for the basic governance transparency that makes democratic accountability possible.
V.2 The Role of Independent Research
Independent research institutions occupy a specific and important role in AI governance: they can examine questions that corporate and governmental actors have structural incentives to avoid, and they can do so without the conflicts of interest that compromise institutional voices.
The Archive of Light's approach -- sustained, multi-platform, donation-funded research without corporate or institutional backing -- reflects a deliberate commitment to the kind of independence that makes honest analysis possible. This white paper is offered in that spirit: not as advocacy for any particular outcome in the Anthropic-Pentagon dispute, but as a contribution to the public understanding of what is structurally at stake.
VI. Principles for Durable AI Governance Under Pressure
Based on the foregoing analysis, this paper advances the following principles for AI governance frameworks navigating national security pressure:
Principle 1: Proportionality
The degree of governance rigor applied to AI system deployment should be proportionate to the risk profile of the deployment context. Autonomous lethal systems and mass surveillance represent the highest-risk categories; they require the most robust oversight structures, not exceptions to them.
Principle 2: Deliberative Process
Modifications to safeguard boundaries governing high-risk AI deployments should occur through deliberative processes that include multi-stakeholder input, legal review, independent oversight, and accountability trails. Compressed deadlines and executive coercion are incompatible with legitimate governance.
Principle 3: Transparency and Reviewability
Governance decisions about AI constraint modification should be transparent and reviewable. This does not require operational disclosure; it requires that the governance process itself -- the legal authority, the deliberation, the oversight, the accountability -- be visible and subject to democratic scrutiny.
Principle 4: Human Authorization in Lethal Contexts
Human authorization requirements for lethal decision-making are not a technical preference but a moral necessity grounded in the principles of accountability, proportionality, and the irreducible importance of human judgment in decisions that end lives. Frameworks that remove this requirement in the name of speed or efficiency are frameworks that have abandoned a foundational ethical commitment.
Principle 5: Norm Stewardship
Leading AI-developing nations bear special responsibility for the international norms that emerge from their governance choices. Coercive safeguard removal, even if legally defensible in a narrow domestic sense, contributes to international norm environments that may ultimately undermine the strategic stability those nations seek to protect.
VII. Civic Engagement
This white paper calls for measured engagement, not alarm. The challenge of governing powerful AI systems in national security contexts is genuinely difficult, and reasonable people hold different views on where specific boundaries should be drawn.
What is not genuinely difficult is the principle that such boundaries should be drawn through legitimate, transparent, deliberative processes -- and that coercive modification under compressed timelines fails that standard regardless of which specific boundaries are at issue.
We encourage:
* AI developers to maintain clear, enforceable guardrails and to articulate publicly the governance processes through which those guardrails may legitimately evolve
* Policymakers to pursue defense objectives through collaborative governance processes rather than coercive pressure on private AI developers
* Legislators to strengthen oversight frameworks for high-impact AI systems, particularly at the intersection of national security and civil liberties
* Researchers and civil society to continue independent scrutiny of AI governance decisions and their systemic implications
* Citizens to remain informed and communicate with elected representatives regarding AI governance priorities
Technological progress is most durable when matched by robust governance.
Safeguards are not obstacles to innovation. They are what make innovation
sustainable.
Conclusion
The events of February 2026 -- whatever their ultimate resolution -- have illuminated something important about the current state of AI governance: that the structural integrity of safeguard frameworks is now a live political question, not merely a technical or ethical one.
The Archive of Light's research on ethical human-AI relationships has consistently emphasized that the quality of AI systems is inseparable from the conditions of their deployment -- that relational intelligence, cognitive symbiosis, and ethical emergence are not properties of AI systems in isolation, but of the human-AI systems they constitute together. The governance frameworks within which AI systems operate are part of those conditions.
When those frameworks are subject to coercive modification under compressed timelines, something is damaged that is not easily repaired: the trust, the legitimacy, and the institutional memory that make durable governance possible. This paper is offered as a contribution to preserving those conditions -- not in opposition to national security, but in recognition that long-term security depends on them.
Notes
1 Dave Lawler and Maria Curi, "Hegseth Gives Anthropic Until Friday to Back Down on AI Safeguards," Axios, February 24, 2026. https://www.axios.com/2026/02/24/anthropic-pentagon-claude-hegseth-dario
2 Reuters, "Hegseth Gives Anthropic Until Friday to Back Down on AI Safeguards, Axios Reports," February 24, 2026. https://www.reuters.com/business/hegseth-gives-anthropic-until-friday-back-down-ai-safeguards-axios-reports-202 6-02-24/
3 Associated Press, "Hegseth Warns Anthropic to Let Military Use AI Technology Without Safeguards," AP News, February 24, 2026.
https://apnews.com/article/anthropic-hegseth-ai-pentagon-military-3d86c9296fe953ec0591fcde6a613aba
4 Michael Liedtke, "Pentagon Pressures Anthropic Over AI Safeguards," San Francisco Chronicle, February 24, 2026. https://www.sfchronicle.com/tech/article/pentagon-anthropic-ai-safeguards-21939315.php
5 Dan Milmo, "Pentagon Pressures Anthropic Over Restrictions on AI Use," The Guardian, February 24, 2026. https://www.theguardian.com/us-news/2026/feb/24/anthropic-claude-military-ai
6 Future of Life Institute, "Asilomar AI Principles," 2017. https://futureoflife.org/ai-principles/ 7 OECD, "OECD Principles on Artificial Intelligence," 2019. https://oecd.ai/en/ai-principles
8 IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, Ethically Aligned Design, 2019. https://ethicsinaction.ieee.org /
9 AI Now Institute, "Research and Annual Reports," accessed February 25, 2026. https://ainowinstitute.org/research
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