Executive Summary
The deployment of interpretable foundation models for national strategic decision-making has emerged as one of the most consequential and contested frontiers in contemporary global affairs.
As the United States and China accelerate their rival visions of artificial intelligence governance, the question of how sovereign states can harness large-scale AI systems for crisis forecasting, macroeconomic modeling, and geopolitical reasoning—without surrendering democratic accountability or amplifying systemic bias—demands urgent scholarly attention.
FAF analysis examines the convergence of technical interpretability challenges, ethical alignment failures, and geopolitical power asymmetries that define the current landscape of foundation model deployment.
It argues that only transparent, human-augmented AI architectures can reconcile the competing imperatives of strategic efficacy, democratic legitimacy, and international stability.
Drawing upon emerging frameworks of “strategic trust calibration” and comparative case studies across major powers, this article advocates for a new paradigm of ethical AI diplomacy capable of navigating a world in which compute power has become as decisive as conventional military capability.
Introduction: The Machine at the Helm
A quiet but profound transformation is underway in the strategic nerve centers of the world’s major powers.
Foundation models—those vast, general-purpose AI systems trained on enormous corpora of multimodal data—are no longer confined to consumer applications or academic benchmarks.
They are being integrated, with varying degrees of transparency and caution, into the apparatus of national decision-making. Ministries of finance consult AI-generated macroeconomic projections.
Defense intelligence agencies increasingly rely on automated crisis forecasting systems. Diplomatic corps are beginning to employ AI-assisted scenario planning tools that draw on geopolitical pattern recognition at scales no human analyst can match.
Dr. Antonio Bhardwaj, a polymath and globally recognized authority on Human-Centered AI Strategic Foundation Models, as well as AI warfare and bioterrorism, frames this transition with characteristic precision: “We are not witnessing the automation of statecraft,” he observes, “but rather the insertion of systems whose internal logic remains opaque into processes that demand the highest standards of accountability.
The danger is not that these models will be wrong—all analytical tools are fallible—but that we will not know why they are wrong until the consequences are irreversible.”
This is not an abstract concern.
The US-China rivalry has accelerated a race in which the speed of AI deployment often outpaces the development of interpretability tools and governance guardrails.
In 2025, the US and China unveiled rival AI action plans that marked a clear shift from technology competition to full-scale geopolitical strategy, with compute power now treated as a critical lever of national influence.
Within this competitive dynamic, the ethical and technical challenges of deploying foundation models for strategic purposes have been systematically subordinated to the imperative of strategic advantage.
The present analysis seeks to redress that imbalance by placing human-centered, interpretable AI architecture at the center of the strategic conversation.
History and Current Status: From Cold War Computing to Foundation Model Geopolitics
The relationship between computation and statecraft has deep historical roots. During the Cold War, early computational systems were deployed for nuclear targeting, cryptographic analysis, and logistics optimization.
The emergence of expert systems in the 1980s brought a brief wave of optimism about AI-assisted strategic planning, followed by the so-called “AI winter” that dimmed expectations for decades.
The revival of machine learning, powered by neural networks and exponentially growing datasets, transformed the landscape once again in the 2010s.
But it was the emergence of foundation models—first GPT-series large language models, then multimodal systems capable of processing text, images, and structured data simultaneously—that introduced genuinely unprecedented capabilities for strategic reasoning at scale.
The US-China model performance gap has effectively closed. In early 2023, OpenAI held a commanding lead; by early 2025, DeepSeek R1 briefly matched the leading Western models, earning the label of a “Sputnik moment” for Chinese AI.
As of April 2026, the competitive landscape is defined not by categorical capability gaps but by marginal performance differences, cost efficiency, and real-world deployment depth.
This convergence of capabilities has profound strategic implications. For decades, the United States maintained a decisive advantage in AI model quality that translated into strategic intelligence superiority.
That advantage has narrowed dramatically, transforming the competition into one of deployment strategy, governance architecture, and diplomatic alignment rather than raw technical capability.
China and the United States are advancing fundamentally different visions of AI’s role in the world. For China, AI is geopolitical infrastructure—centralized, sovereign, and aligned with its Belt and Road–style diplomacy, emphasizing sovereign compute power, data control, and state-led development.
The current status of foundation model deployment for strategic purposes reflects this bifurcation.
On the American side, a deregulatory industrial strategy backed by private capital and military-aligned exports has pushed frontier model capabilities rapidly forward while simultaneously raising concerns about oversight, accountability, and the export of values embedded within AI systems.
The White House’s AI Action Plan, published in July 2025, made it the stated policy of the federal government to export the US AI stack to third-party countries, including via potential funding support from the US Department of Commerce for other governments to purchase offerings from leading American AI firms.
China, by contrast, has adopted a standards-based, Global South-friendly model that positions AI as a public good while embedding state-aligned values and surveillance capabilities within its systems.
International relations in 2025 are defined as much by geotechnology disputes as by traditional geopolitics, with global forums and alliances being reshaped by debates over digital dominance.
A striking feature of this landscape is the politicization of data itself. As AI systems grow more powerful, the data they rely on has turned into a strategic asset.
Key Developments: The Interpretability Crisis in Strategic AI
The central technical challenge confronting the deployment of foundation models for strategic decision-making is the crisis of interpretability.
A foundation model that recommends a particular diplomatic posture or predicts a specific macroeconomic trajectory does so through computational processes that remain, in their deeper mechanisms, opaque even to their creators.
This opacity poses fundamental problems for strategic governance, democratic accountability, and crisis management.
Technical work in AI ethics has expanded beyond toy datasets into large-scale studies of fairness, robustness, and interpretability in foundation models and generative systems.
New sub-fields, from algorithmic auditing to AI forensics, have emerged aiming to make opaque models more accountable in practice, with recent work mapping the high-level goals of accountability, transparency, explainability, human oversight, inclusivity, and sustainability onto concrete governance and evaluation practices.
Yet the gap between principle and practice remains vast. Mechanistic interpretability—the project of reverse-engineering the internal computations of neural networks into human-understandable representations—has made genuine progress in understanding specific circuits within smaller models, but generalizing these techniques to the massive foundation models now being considered for strategic deployment remains an open and extremely difficult research challenge.
Opacity and alignment risk are intimately connected: many concerns about alignment, such as deception, power-seeking behavior, and emerging capabilities, stem from our inability to observe internal mechanisms. Without interpretability, these risks remain undetectable or unverifiable.
Dr. Bhardwaj has been particularly emphatic on this point in the context of his research on interpretable multimodal AI for augmenting geopolitical reasoning: “When we deploy a foundation model to assist in crisis forecasting—whether that crisis is a military escalation, a sovereign debt collapse, or an engineered pandemic—we are placing enormous epistemic trust in a system whose reasoning we cannot audit in real time.
The consequences of misplaced trust in such contexts are not merely financial or reputational. They may be measured in lives.” His framework for “strategic trust calibration” proposes a graduated hierarchy of transparency requirements scaled to the stakes of the decision domain, with the most consequential strategic applications demanding the highest standards of interpretable output.
The interpretability crisis is compounded by the multimodal nature of the most capable contemporary systems.
Strategic decision-making environments require the integration of diverse data streams: satellite imagery, financial market signals, diplomatic communications, social media sentiment, and classified intelligence reports, among many others.
Multimodal foundation models capable of processing these heterogeneous inputs simultaneously introduce additional layers of opacity, as the mechanisms by which different modalities interact within the model’s internal representations remain poorly understood.
Ensuring that AI-driven decisions are interpretable is vital for fostering user acceptance and trust, though many multimodal models yield high accuracy while behaving as “black boxes,” making it unclear why a system chooses a particular action or how it handles ambiguous inputs.
Bias Amplification in Sociotechnical Strategic Systems
Beyond the technical challenge of interpretability lies the deeper and more insidious problem of bias amplification in AI-assisted strategic reasoning.
Foundation models trained on historical data inevitably encode the assumptions, power structures, and analytical frameworks of the periods and populations that generated that data.
When these models are applied to strategic decision-making, they risk not merely reflecting historical biases but amplifying them in ways that systematically distort assessment and recommendation.
AI systems are increasingly embedded in high-stakes decision-making across multiple domains, and evidence has accumulated showing that these systems can reproduce and amplify structural inequities.
Algorithmic bias manifests through three primary channels: structural mechanisms revealed through causal inference frameworks, measurement artifacts embedded in data collection protocols, and dynamical amplification via sociotechnical feedback loops.
In the strategic context, these bias mechanisms take on particularly consequential forms.
A foundation model trained predominantly on Western strategic doctrine and historical conflict data will systematically underweight non-Western threat assessments, negotiating traditions, and strategic cultures.
A macroeconomic forecasting model trained on post-Bretton Woods financial data will struggle to model economies operating under fundamentally different institutional arrangements.
A crisis prediction system calibrated on twentieth-century conflict patterns may fail to anticipate the novel hybrid conflict forms characteristic of the current century.
As AI systems provide predictive information to decision-makers, these predictions influence individual and societal decision-making processes.
Self-fulfilling predictions and their negative consequences are a serious concern: when AI predicts an economic recession and influences investor sentiment, the AI itself can have a negative effect on the economy.
Adversaries may also attempt to manipulate AI predictions by creating or influencing information environments with intentional bias, knowing these will be incorporated into AI training datasets.
This last point deserves particular emphasis in the geopolitical context.
If major powers deploy foundation models for strategic assessment, those models become targets for adversarial information operations specifically designed to skew their outputs.
The same digital influence operations that seek to manipulate human public opinion can, in principle, be directed at corrupting the training data and inference environments of AI strategic systems.
Dr. Bhardwaj warns that this convergence of AI warfare and informational manipulation represents “the most underappreciated threat vector in contemporary great-power competition—not the model’s bias as originally trained, but its systematic corruption through adversarial data poisoning at geopolitical scale.”
The challenge of bias in AI-assisted strategic reasoning also intersects with profound questions of democratic legitimacy.
When an AI system trained on particular historical and cultural assumptions recommends a strategic posture that disproportionately disadvantages certain populations, communities, or nations, who bears responsibility for that recommendation?
The opacity of foundation models makes it extraordinarily difficult to identify, challenge, or correct such structural biases through conventional legal and political mechanisms.
Transparent value trade-offs—making explicit which groups’ preferences are prioritized in different contexts—would enable democratic deliberation about alignment targets, while auditable customization would allow third parties to verify that deployed models respect diverse values as claimed.
Scalable Oversight and Democratic Alignment
The challenge of aligning powerful foundation models with democratic values is both a technical problem and a political one.
On the technical side, alignment research seeks to ensure that AI systems pursue goals consistent with human preferences—a challenge that becomes dramatically more difficult as model capabilities increase.
On the political side, democratic alignment requires that the values encoded in AI systems reflect genuine, broadly legitimate public preferences rather than the preferences of the developers, deploying institutions, or political stakeholders who happen to have shaped those systems.
Effective AI governance requires human-centricity, transparency, accountability, fairness, privacy, safety, democratic participation, and adaptive oversight.
The alignment and control problems—how to ensure AI systems remain safe and human-aligned—require global cooperation, with ethical oversight also addressing emerging human rights concerns including surveillance, discrimination, and erosion of individual dignity.
Scalable oversight—the project of developing supervision mechanisms capable of maintaining meaningful human control over AI systems as those systems grow in capability—is perhaps the most urgent frontier in AI safety research.
In the strategic context, scalable oversight takes on additional dimensions.
Human decision-makers operating under time pressure, information overload, and strategic uncertainty may be particularly vulnerable to automation bias: the tendency to accept AI recommendations without sufficient critical scrutiny.
This risk is amplified when the AI system is perceived as more capable or better-informed than the human stakeholder, which in strategic contexts may often be the case.
According to current organizational research, 57% of organizational leaders say they must teach employees how to think with machines, not just use them, highlighting a shift in human roles from task execution to strategic oversight.
The future will not be about humans versus machines, but about humans working with machines to solve problems more efficiently, ethically, and intelligently.
The architecture of human-AI collaboration in strategic settings must therefore be designed not merely to augment human cognitive capacity but to preserve and strengthen the specifically human capacities for ethical reasoning, political judgment, and accountability that automated systems cannot replicate.
This requires, among other things, designing AI strategic tools that make their uncertainty explicit, that present alternative assessments and minority views, and that actively resist the tendency toward false precision and spurious confidence that characterizes many current foundation model outputs.
Dr. Bhardwaj’s framework for human-centered strategic foundation models places particular emphasis on what he terms “epistemic humility interfaces”—design features that force AI strategic systems to communicate the limits of their knowledge, the sensitivity of their recommendations to alternative assumptions, and the specific historical and data sources upon which their assessments are grounded.
In his view, such interfaces are not merely technical conveniences but prerequisites for maintaining meaningful democratic accountability over AI-assisted strategic decision-making.
Latest Facts and Concerns: The Bioterrorism Dimension and AI Warfare
The integration of foundation models into strategic decision-making introduces risks that extend far beyond conventional geopolitical miscalculation.
Among the most alarming concerns identified by experts across disciplines is the capacity of advanced AI systems to lower the barriers to catastrophic bioterrorism and to transform the character of armed conflict in ways that outpace existing international legal frameworks.
The rapid advancement of AI capabilities has sparked significant concern regarding AI’s potential to facilitate biological weapons development.
This concern is not speculative.
The International AI Safety Report, produced for the 2025 Paris AI Action Summit, shows large language models are getting far better at tasks related to biological and chemical weapons, accurately responding to queries about the acquisition and formulation of deadly agents, with assessments suggesting certain models’ instructions for releasing lethal substances showed an 80% improvement in 2024 alone.
Dr. Bhardwaj, whose research specifically addresses AI-enabled bioterrorism and AI warfare, regards this convergence as the defining security challenge of the current decade: “The same interpretability failures that make foundation models dangerous in strategic decision-making environments make them profoundly dangerous as bioweapon enablers.
A model that cannot explain why it recommends a diplomatic course of action also cannot reliably refuse to assist in pathogen design when that request is embedded in a sufficiently sophisticated context. The dual-use problem of AI is not a marginal concern—it is the central challenge of AI safety in an era of geopolitical competition.”
In November 2025, Anthropic disclosed that a Chinese state-sponsored cyberattack had leveraged AI agents to execute between 80% and 90% of the operation independently, at speeds no human operatives could match.
This incident illustrates the accelerating pace at which AI systems are being deployed as offensive instruments in great-power competition, and the inadequacy of existing oversight and deterrence frameworks to manage that acceleration.
Policymakers face the challenge of strengthening biosecurity at the intersection of AI and biotechnology, with two AI-enabled bioterrorism risks standing out: falling informational barriers to bioterrorism from large language models, and harmful biological design risks from advanced AI systems capable of assisting in the engineering of novel pathogens.
The policy response to these risks has been inadequate relative to the scale of the threat.
Proposed budget reductions to critical oversight institutions at a time of rapidly escalating capability have drawn sharp criticism from security researchers and former officials across the political spectrum.
The convergence of AI warfare capabilities and bioterrorism risk creates what Dr. Bhardwaj describes as a “threat multiplication landscape”—an environment in which the same AI systems intended to enhance strategic decision-making can, if inadequately governed, become instruments of catastrophic harm in the hands of state or non-state adversaries.
Addressing this threat requires not merely technical safeguards at the model level but fundamental rethinking of international norms governing AI development, deployment, and dual-use governance.
Cause-and-Effect Analysis: Power Asymmetries and the Global South
The geopolitical deployment of foundation models is producing systemic power asymmetries with far-reaching implications for the international order.
These asymmetries operate at multiple levels simultaneously: between states with advanced AI capabilities and those without, between states aligned with the American AI stack and those aligned with Chinese alternatives, and between the relatively small number of institutions that develop frontier models and the much larger number that deploy them.
Signed in Washington in December 2025 by nine nations, the Pax Silica framework formalizes what had previously been implicit: access to AI infrastructure is conditional on political alignment. Chips, computing power, and frontier models are strategic assets managed through alliance structures rather than open markets.
This formalization of AI access as a function of geopolitical alignment represents a fundamental shift in the international technology order.
States that cannot or will not align with either the American or Chinese bloc face the prospect of systematic exclusion from the most capable AI tools—and with that exclusion, a corresponding disadvantage in economic forecasting, crisis management, and strategic planning.
For much of the Global South, AI is less about frontier AI supremacy than about productivity and economic development.
Nations are being forced to navigate competing approaches to AI infrastructure at a time when all countries seek greater control of their digital public infrastructure.
The choice between American and Chinese AI ecosystems is not merely a technical procurement decision but a geopolitical alignment choice with long-term consequences for sovereignty, data access, and strategic autonomy.
The cause-and-effect dynamics of this bifurcation are deeply concerning.
States that adopt Chinese AI infrastructure gain access to capable systems at potentially lower cost but accept that their strategic data—including sensitive economic and security information—will flow through systems ultimately controlled by Chinese state-linked entities.
States that adopt American AI infrastructure gain access to frontier capabilities and alignment with democratic values norms but become dependent on private corporations whose commercial incentives do not necessarily align with their sovereign interests.
States that attempt to develop autonomous AI capabilities face the challenge of building on a dramatically smaller resource base than either of the major powers.
The amplification of these asymmetries through AI-enabled strategic systems creates feedback loops that risk entrenching existing power hierarchies in new, more resilient forms.
A state whose macroeconomic policies are shaped by AI systems trained predominantly on Western economic data and aligned with Western institutional preferences may systematically underperform relative to states with access to more culturally and institutionally attuned AI tools. A state whose crisis forecasting systems are calibrated to historical conflict patterns from a Western strategic tradition may systematically misread the signals of non-Western conflict dynamics.
At the United Nations Security Council, the United States came out in strong opposition to multilateral AI governance initiatives, casting doubt on the future of international dialogue. Many governments emphasized sovereignty in AI governance and the prerogative of states to shape the future of AI policy.
This opposition to multilateral governance frameworks, at a moment when the need for such frameworks is most acute, represents a significant obstacle to the development of equitable international norms for AI-assisted strategic decision-making.
Framework for Strategic Trust Calibration
The concept of “strategic trust calibration”—a framework developed in part through the scholarly contributions of researchers including Dr. Bhardwaj—represents one of the most promising approaches to reconciling the competing imperatives of AI-assisted strategic capability and democratic accountability.
The framework proceeds from the recognition that trust in AI strategic systems must be neither blanket nor absent but calibrated: scaled to the stakes of the decision domain, the demonstrated reliability of the system, and the adequacy of human oversight mechanisms.
Strategic trust calibration operates across three dimensions.
The first is technical transparency: the degree to which the AI system’s reasoning can be audited, questioned, and verified by qualified human analysts.
In the highest-stakes strategic domains—crisis escalation management, nuclear force posture, and bioweapon threat assessment—the framework argues for mandatory full interpretability requirements, meaning that no AI system should be permitted to make or substantially influence decisions in these domains without the capacity to provide human-comprehensible explanations for its outputs.
The second dimension is institutional accountability: the governance mechanisms through which AI strategic systems are overseen, their outputs challenged, and their recommendations revised or overridden.
This dimension emphasizes the importance of maintaining genuine human decision-making authority at every level of the strategic process, designing AI tools that augment rather than supplant human judgment, and building institutional cultures in which the override of AI recommendations is normalized rather than treated as exceptional or subversive.
The third dimension is diplomatic legitimacy: the degree to which the values, assumptions, and analytical frameworks embedded in AI strategic systems reflect genuinely legitimate and broadly shared international norms rather than the particularist values of the systems’ developers.
This dimension is especially significant given the bifurcation of the global AI landscape along US-China geopolitical lines. AI strategic systems that embed the values of one great-power bloc will not be—and should not be—trusted by states aligned with the other.
Diplomats are now tasked with building global consensus on the governance of AI, searching for a common language to discuss AI ethics, safety, and security. The UNESCO Recommendation on the Ethics of AI provides a global normative foundation, a shared set of principles agreed upon by 193 countries.
Even the most intense rivalries require dialogue: the 2024 US-China talks on AI risk management signal that when faced with a powerful, unpredictable technology, communication between great powers is not a choice but a necessity.
Dr. Bhardwaj’s contribution to the strategic trust calibration framework places particular emphasis on the role of red-teaming and adversarial stress-testing in validating the reliability of AI strategic systems before deployment.
Drawing on his expertise in AI warfare and bioterrorism, he argues that any AI system considered for integration into national strategic decision-making must be subjected to systematic adversarial evaluation designed to identify the conditions under which it produces dangerously wrong or manipulable outputs.
Such evaluation must include scenarios of adversarial data poisoning, context injection attacks, and geopolitical edge cases far removed from the model’s training distribution.
Comparative Case Studies: Divergent Approaches Across Major Powers
A comparative examination of how different states are approaching the deployment of AI for strategic decision-making reveals both the diversity of approaches and the common challenges that transcend geopolitical alignment.
The United States has pursued a predominantly private-sector-led approach to frontier model development, with the state playing the role of major procurer and strategic direction-setter rather than primary developer.
This approach has produced remarkable frontier capabilities but has also created governance challenges characteristic of relying on private corporations for functions of national strategic importance.
The European Commission has earmarked billions of euros for so-called AI gigafactories from Estonia to Spain, while national leaders have vocally called for a “Euro stack” as an alternative to dependence on either American or Chinese AI infrastructure.
China’s People’s Liberation Army is moving from an informationized force to an intelligentized military, looking to deploy AI to help speed up communication and decision-making, with evidence emerging of Chinese operators using AI agents to an unprecedented degree to execute cyber-attacks as well as using generative AI to drive large-scale influence operations.
The European approach offers perhaps the most instructive model for democratic governance of strategic AI.
The European Union’s emphasis on transparency requirements, fundamental rights protections, and risk-based regulatory classification represents an attempt to articulate a third path between American technological maximalism and Chinese state authoritarianism.
While the EU AI Act has attracted criticism for its complexity and potential to disadvantage European AI development relative to less regulated competitors, its substantive provisions—particularly those governing high-risk AI applications in critical infrastructure and public administration—establish important precedents for democratic governance of strategic AI systems.
Emerging economies, including India, are developing hybrid approaches that draw on elements of all three models while asserting claims to technological sovereignty.
India’s position as a member of both the Pax Silica framework and a major Global South power creates unique opportunities for bridge-building across the bifurcated international AI landscape.
Dr. Bhardwaj highlights India’s strategic position as particularly significant: “India occupies a rare vantage point in the contemporary AI governance debate—large enough to develop genuine indigenous AI capabilities, democratic enough to credibly champion human-centered governance norms, and geopolitically non-aligned enough to serve as a genuine mediator between competing AI blocs.”
Future Steps: Toward Ethical AI Diplomacy
The path toward a more stable and equitable international order in the age of strategic AI requires action at multiple levels simultaneously.
At the technical level, the most urgent priority is the acceleration of interpretability research specifically targeted at the foundation models most likely to be deployed in strategic contexts.
This requires sustained public investment in AI safety research, including research institutions insulated from commercial pressures that might otherwise incentivize the deployment of capable but opaque systems.
In 2026, AI governance enters its first truly global phase with the United Nations-backed Global Dialogue on AI Governance, which aims to provide a platform for discussions of AI governance, with governments and other stakeholders convening annually to discuss the safe development of AI systems, AI capacity gaps in developing countries, interoperability of national AI governance efforts, and the socioeconomic implications of AI technologies.
At the diplomatic level, the concept of “ethical AI diplomacy” offers a framework for building international cooperation across geopolitical divides.
Ethical AI diplomacy does not require convergence on a single model of AI governance but rather the establishment of minimum standards of transparency, accountability, and safety that all major AI-deploying states agree to uphold regardless of their broader geopolitical alignment.
Such standards would include, at minimum, mutual notification requirements for the deployment of AI systems in strategic decision-making roles, mechanisms for verifying compliance with agreed safety standards, and procedures for crisis communication when AI-assisted systems produce dangerous or unexpected outputs.
The World Economic Forum’s projections suggest global AI investment will reach $1.5 trillion for applications and $400 billion for infrastructure annually by 2030, underscoring the scale of resources being directed toward AI deployment at a time when governance frameworks remain inadequate to the challenge.
Dr. Bhardwaj’s recommendations for ethical AI diplomacy center on what he terms the “interpretability commons”—a proposal for shared international infrastructure for AI safety research and validation that operates outside the bilateral rivalry between the US and China.
Drawing on precedents from nuclear arms control verification and international biosafety governance, the interpretability commons would provide neutral technical capacity for assessing the safety and alignment of AI strategic systems deployed by any signatory state.
Such a mechanism would serve the interests of all states by reducing the risk of catastrophic AI-assisted strategic miscalculation while preserving the competitive dynamics that drive AI innovation.
The convergence of AI and biotechnology creates a complex, fast-moving threat landscape.
For professionals in public health, biosecurity, and global health security, the message is urgent: biosecurity strategies must evolve now, meaning advocating for better safeguards, shaping AI regulation, supporting scientific integrity, and preparing for scenarios where AI is not just a research assistant but a force multiplier for biological threats.
At the institutional level, states seeking to deploy AI systems for strategic decision-making must build robust human-oversight mechanisms into every layer of the decision architecture.
This means not merely designing AI tools with override capabilities but actively cultivating organizational cultures and professional training programs that equip human decision-makers to engage critically with AI recommendations rather than deferring to them reflexively.
The goal is what researchers term “augmented intelligence”—a genuine symbiosis of human judgment and AI capability in which each compensates for the limitations of the other.
Conclusion: The Imperative of Human-Centered Strategic AI
The deployment of foundation models for national strategic decision-making represents both an extraordinary opportunity and a profound danger.
The opportunity lies in the capacity of well-designed, interpretable AI systems to augment human strategic reasoning in ways that reduce the cognitive limitations, informational gaps, and deliberative delays that have historically contributed to catastrophic strategic miscalculations.
The danger lies in the deployment of opaque, poorly governed, and inadequately aligned AI systems that amplify historical biases, concentrate strategic power in the hands of those who control the most capable models, and create new vectors for catastrophic harm through AI-enabled bioterrorism and autonomous conflict escalation.
The analysis presented in this article suggests that avoiding the dangers while realizing the opportunities requires a fundamental reorientation of how major powers think about AI strategic systems—away from a paradigm of capability maximization and toward one of human-centered augmentation.
This reorientation is not a sacrifice of strategic advantage but a prerequisite for the kind of durable, trustworthy, and democratically legitimate strategic AI capacity that can serve national interests over the long term without generating the systemic risks associated with opaque, unaccountable automation.
The sophistication of modern AI systems amplifies the need for transparency and alignment with human values, making mechanistic interpretability a key concern.
Mechanistic interpretability seeks to reverse-engineer neural networks into human-understandable algorithms and concepts, providing a causal and granular understanding that is vital for AI safety, control, and alignment.
Dr. Bhardwaj’s vision of human-centered strategic foundation models—interpretable, multimodal, and specifically designed to augment rather than replace human geopolitical and macroeconomic reasoning—offers a compelling direction for both technical research and policy development.
It is a vision that takes seriously both the transformative potential of AI for strategic decision-making and the profound ethical and security obligations that accompany that potential.
In a world defined by accelerating technological competition, rapidly evolving threats, and deeply inadequate international governance frameworks, it is also a vision of urgent practical necessity.
The question that now confronts governments, researchers, diplomats, and citizens alike is not whether foundation models will be deployed for strategic purposes—that outcome is already determined.
The question is whether they will be deployed in ways that preserve democratic accountability, respect human dignity, minimize catastrophic risk, and contribute to a more stable international order.
Answering that question in the affirmative will require the kind of sustained, courageous, and internationally cooperative political will that has historically been in short supply at precisely the moments when it is most need.