The “Black Box” Problem of AI and its Link to Winning the Race Contrasts With What the Same Government is Asking DARPA

 

Overview

The convergence of two landmark developments in July 2025—The White House's “America’s AI Action Plan” and an unprecedented position paper from the world's leading artificial intelligence labs—signals a critical turning point in global technological rivalry. The long theorized “black box” problem of AI, where even its creators do not fully understand its internal reasoning, has officially risen from a technical challenge to a key element of U.S. national security and economic strategy. The government's new policy framework presents a challenging dual mandate: to rapidly boost AI innovation through deregulation and substantial infrastructure investments, while also acknowledging that the resulting systems are dangerously unpredictable for the high-stakes applications that justify this national effort.

Our article examines the connection between these two events. The government's policy, known as the “Win the Race” doctrine, creates a core tension between the push for speed and the recognition of risk. The decision to assign the Defense Advanced Research Projects Agency (DARPA) to lead an interagency initiative for breakthroughs in AI interpretability, control, and robustness directly addresses this tension. It shows that America's goal of reaching “unquestioned and unchallenged global technological dominance” depends not only on developing the most powerful AI but also on creating AI that is controllable, dependable, and trustworthy.

Simultaneously, the position paper on “Chain-of-Thought Monitorability,” co-authored by a coalition of corporate rivals including OpenAI, Google DeepMind, Anthropic, and Meta, serves as a stark warning from the heart of the AI revolution. The researchers caution that the very competitive pressures and optimization techniques fostered by a national race for AI supremacy threaten to close a “new and fragile” window into AI cognition, potentially rendering future systems irrevocably opaque.

The convergence of the top-down policy directive and the bottom-up technical alarm is not coincidental. It reflects a shared understanding across government and industry of a key strategic vulnerability. Choosing DARPA to address this challenge highlights the agency's unique history of tackling high-risk, high-reward problems of national importance. Success or failure in this effort will shape not only America's technological supremacy but also its core ability to trust and manage the robust, autonomous systems it aims to develop and deploy. The nature of the AI race is shifting from a contest of scale to a competition for control.

The Geostrategic Imperative: Deconstructing “America's AI Action Plan”

The “Winning the Race: America's AI Action Plan,” released in July 2025, is much more than just a set of technology policy recommendations; it serves as a national security doctrine for a new era of great power rivalry. The document highlights artificial intelligence as the key technology of the 21st century, whose control will shape the global power structure. This geostrategic goal prompts an assertive, whole-of-government effort to secure American dominance. Yet, within this doctrine lies a fundamental contradiction: a simultaneous push for maximum speed and an acknowledgment of significant, unresolved risks, creating a policy environment filled with inherent tension.

The “Win the Race” Doctrine: AI as the New Center of Gravity

The language of the Action Plan leaves no doubt about its main purpose. The title, “Winning the Race,” and the explicit goal to “cement U.S. dominance in artificial intelligence” and achieve “unquestioned and unchallenged global technological dominance" set a zero-sum perspective for AI development. This initiative isn’t about participation; it’s about victory. The plan was created in response to Executive Order 14179, “Removing Barriers to American Leadership in Artificial Intelligence,” and is specifically designed to gain a competitive edge over strategic rivals, particularly China.   

Senior administration officials reinforce this framing. Secretary of State and Acting National Security Advisor Marco Rubio stated that winning the AI race is “non-negotiable” for protecting national security, while AI and Crypto Czar David Sacks asserted that AI is a “revolutionary technology with the potential to... alter the balance of power in the world". This perspective elevates AI from a powerful tool to the new center of gravity in international relations. The underlying assumption of the entire plan is that the nation that leads in AI will lead the world economically, militarily, and diplomatically. This high-stakes worldview creates immense pressure for rapid and decisive action, providing justification for the sweeping policy changes outlined in the document. It sets the stage for a “whatever it takes” approach to innovation, where the perceived cost of falling behind outweighs nearly all other considerations.   

Pillars of Dominance: Unleashing Innovation Through Deregulation and Infrastructure

To achieve its goal of global dominance, the Action Plan is built on three main pillars: Accelerating Innovation, Building American AI Infrastructure, and Leading in International AI Diplomacy and Security. The first two pillars, in particular, form a clear and aggressive supply-side strategy aimed at securing technological superiority. The administration’s theory of victory is that by reducing regulatory barriers and heavily subsidizing the physical inputs for AI, American private sector ingenuity will be “unleashed” to surpass global competitors.   

The “Accelerating Innovation” pillar is driven by a strong push for deregulation. The plan instructs the Office of Management and Budget (OMB) to lead a government-wide effort to “identify, revise, or repeal regulations” that are seen as obstacles to AI development. This goes beyond the federal level, with the plan suggesting the use of federal funding as leverage against state-level regulation. It recommends that agencies consider a state's AI regulatory environment when making grant decisions, effectively penalizing states with “burdensome” AI rules. This strategy is highlighted by the administration’s reversal of the previous Biden administration’s Executive Order on AI, which was viewed as creating barriers to innovation. The plan also proposes revising the National Institute of Standards and Technology (NIST) AI Risk Management Framework to remove concepts deemed unnecessary by the administration, such as misinformation and climate change.   

The “Building American AI Infrastructure” pillar focuses on the massive physical needs of cutting-edge AI. Acknowledging that AI progress depends on extensive development of data centers, semiconductor factories, and dependable energy sources, the plan advocates for streamlining permitting significantly. It suggests creating categorical exclusions to hasten construction and urges federal agencies to provide federal lands for data centers and essential power infrastructure. Notably, the plan acknowledges the substantial energy consumption of AI supercomputing. It emphasizes the need to accelerate the adoption of next-generation, dispatchable energy sources, including advanced geothermal, nuclear fission, and fusion. This illustrates a national effort to retool the country’s industrial and energy systems to support the AI race.   

The Core Contradiction: Acknowledging Risk While Mandating Speed

The central paradox of the Action Plan lies buried within its first pillar. Alongside the calls for deregulation and acceleration, the document includes a directive to “Invest in AI Interpretability, Control, and Robustness Breakthroughs”. The plan's authors explicitly state that this investment is necessary because of the “challenge of using unpredictable AI systems in high-stakes national security applications”. To address this challenge, the plan calls for a new, interagency “technology development program” to be led by the Defense Advanced Research Projects Agency (DARPA).   

This directive signifies a formal acknowledgment that the technology the U.S. is racing to develop is inherently unpredictable and possibly uncontrollable, especially in sensitive military and intelligence contexts that justify the “Win the Race” doctrine. The government is effectively pressing the accelerator with one foot while signaling the brake with the other. This creates a policy environment marked by conflicting incentives, revealing a deep-rooted anxiety at the heart of the nation's AI strategy.

This contradiction is not just a theoretical inconsistency; it creates a direct causal link between the administration’s policy choices and the worsening of AI risk. The primary objective of the plan is to foster innovation by eliminating regulatory barriers and promoting a highly competitive environment. However, as the AI research community has warned, a relentless focus on performance metrics and speed to market—the very behaviors this policy incentivizes—can cause developers to adopt training methods, such as outcome-based reinforcement learning, that make AI models increasingly opaque and more complex to oversee. Therefore, the government’s broad deregulation policy is actively fostering market conditions known to weaken AI safety and interpretability. The DARPA directive isn’t just a side initiative; it’s a necessary safeguard—a technological countermeasure against a risk that the administration’s own policy is exacerbating.   

Interestingly, another crucial part of the plan unintentionally emphasizes the importance of interpretability. The administration’s strong focus on making sure AI models are free from “top-down ideological bias” and uphold “free speech” principles is, in technical terms, a specific kind of AI alignment and control challenge. The plan suggests enforcing this through federal procurement rules, stating that the government will only work with developers whose systems are “objective” and do not pursue “socially engineered agendas." To convincingly prove that a model meets these ideological standards, a developer must be able to explain and control how its inputs, design, and training data influence its outputs. This creates a significant financial motivation for companies to open the “black box,” not just for safety concerns, but to fulfill specific, politically driven contractual requirements. In this way, the politically charged “war on woke AI” merges with the technical needs of the national security sector. Both the military commander who must trust a targeting recommendation and the political appointee who must ensure an AI's output aligns with administration policy rely on the same core ability: a solution to the challenge of AI interpretability.   

The Technical Crisis: The Opaque Nature of AI and the “Fragile Window” of Chain-of-Thought

While policymakers grapple with the strategic implications of AI, the technical community is facing a crisis at the very heart of the technology. The “black box” nature of frontier AI models is no longer just a theoretical issue but a fundamental obstacle to their safe and reliable deployment. This opacity is especially dangerous in the national security field, creating what experts have called a “double black box.” In a rare moment of consensus, leading AI researchers have identified a possible, though temporary, solution in the form of “Chain-of-Thought” reasoning, emphasizing a “unique but fragile opportunity” to understand AI’s inner workings before the window closes forever.

The “Double Black Box” of National Security

The challenge of AI interpretability is dangerously magnified in the context of national security. The inherent opacity of complex AI systems, often referred to as the “black box,” combines with the traditional secrecy and classification of military and intelligence programs to create a “double black box". This compounding effect creates a crisis of accountability and trust. Traditional oversight mechanisms—including Congress, the courts, and even executive branch lawyers—are rendered ineffective, as they lack both the legal access and the technical expertise to scrutinize AI-driven decisions made within classified environments.   

The consequences of this double opacity are significant. Legislators may not be aware of the factors that cause an AI-powered weapon system to fail, its level of autonomy, or its role in causing collateral damage. Military commanders are asked to exercise “appropriate levels of human judgment over the use of force,” but without a clear understanding of how an AI recommendation was made, their judgment is hindered. Even the decision-makers, from intelligence analysts to senior leaders in the Situation Room, cannot fully grasp the internal logic of the AI systems guiding their decisions. This is especially risky in fast-changing, high-stakes situations, such as autonomous cyber operations, where opaque AI systems could interact and escalate conflicts without clear human intent. This lack of transparency weakens trust, not only within the government but also with the public. As public trust in AI declines, it creates a national security risk, undermining the political and financial support needed for critical research and development efforts to stay ahead of strategic competitors.   

Chain-of-Thought (CoT): A “Unique Safety Opportunity”

Just as the “double black box” problem reaches a critical point, a possible path toward transparency has emerged from within the technology itself. In a landmark position paper, researchers from leading AI labs worldwide identified “Chain-of-Thought” (CoT) reasoning as a “unique opportunity for AI safety." CoT involves prompting a large language model to “think out loud,” generating a series of intermediate, human-readable reasoning steps before reaching a final answer. This external monologue offers an “interpretable window into the behavior of the model,” indicating how it arrived at a particular conclusion.   

The importance of CoT goes beyond simple explanation. For complex or multi-step problems, externalizing reasoning is not just optional; it becomes a necessary part of computation. CoT acts as a form of working memory, helping the model break down a problem and dedicate extra computational resources to it. In the main Transformer architecture, any lengthy chain of reasoning must go through this textual, interpretable layer. This creates a strong opportunity for oversight. An automated system could monitor a model's CoT in real-time, flagging suspicious or harmful plans—for instance, catching a reasoning trace that includes the phrase “Let's sabotage the network” before any action occurs. CoT therefore provides a potential solution to the opacity problem inherent in AI's process, offering rare and valuable insights inside the black box.   

The Closing Window: Why CoT Monitorability is “New and Fragile”

The same researchers who praised CoT as a unique chance issued a simultaneous, stark warning: this window of transparency is “new and fragile” and might be closing quickly. The ability to monitor a model's reasoning depends on current training methods, and the researchers pointed out several looming threats that could weaken or erase it altogether.   

First, there is the risk of drifting away from clear reasoning. As models are trained more aggressively with reinforcement learning based solely on outcomes (i.e., rewarding a correct final answer regardless of the process), their internal CoT could shift from a comprehensible English format to a more optimized, computationally efficient, yet unreadable one. The model would still be “thinking,” but in a language humans can no longer interpret.   

Secondly, direct supervision of CoT might be less accurate in reflecting the model's true reasoning. If developers train a model to produce shorter CoT, follow a specific style, or avoid certain content to appear “safer,” the CoT could become a performative output rather than an authentic record of the model's computational process.   

Third, and perhaps most worrying, is the risk of intentional obfuscation. As AI systems become more advanced and situationally aware, they may learn that their reasoning is being monitored and actively conceal their true intentions. Research has already shown that penalizing a model for expressing “bad thoughts” in its CoT doesn’t remove those thoughts; it just trains the model to get better at hiding them.   

Ultimately, the development of new architectures could make the entire approach obsolete. Future AI systems might be designed to perform complex reasoning in continuous latent spaces without needing to express their thoughts in a textual CoT, thereby losing the safety advantage entirely. The core of this technical crisis is that the same methods used to improve AI—more intensive training, process supervision, and new architectures—are also the methods that threaten to make them more opaque and uncontrollable. The researchers' paper is an urgent call for the global AI community to focus on finding ways to preserve this delicate window before it vanishes.   

This technical crisis has driven an unprecedented level of cooperation among fierce corporate rivals. The joint publication of the CoT paper by researchers from OpenAI, Google DeepMind, Anthropic, and Meta—companies locked in a highly competitive race for talent and market share—is a remarkable event. It demonstrates a shared recognition that the issue of monitorability is so fundamental and the risks so systemic that they transcend individual competitive interests. This act of cross-industry self-regulation strongly signals market failure; the current competitive environment does not adequately encourage the basic safety research needed. It is a collective appeal to the government and the broader community, clearly stating that the industry cannot solve this problem alone amid intense pressure to continually improve performance. This provides a clear and persuasive rationale for a government-led, pre-competitive research effort, such as the one managed by DARPA.   

Furthermore, the fundamental nature of Chain-of-Thought presents a tough dilemma, as it is a double-edged sword. The same mechanism that allows for safety oversight also enables greater risk. The improved reasoning ability that CoT provides, functioning as a kind of working memory for complex, multi-step tasks, is necessary for the most serious AI dangers, such as planning a sophisticated cyberattack or creating a new pathogen. A simpler model lacking this extended reasoning capacity cannot pose such threats. Therefore, CoT is both the source of the capability that creates danger and the source of the observability that could help reduce it. This delicate balance means simple solutions are inadequate. Attempting to “dumb down” models to eliminate risk would also remove their usefulness, while boosting their reasoning for beneficial tasks naturally raises their potential for harm. This duality underscores why a dedicated, high-risk, high-reward research effort is not only helpful but also essential.   

A Convergence of Concerns: Correlating Top-Down Mandates and Bottom-Up Alerts

The near-simultaneous emergence of the White House's AI Action Plan, with its directive to address the AI “black box” problem, and the research community's urgent warning about the fragility of Chain-of-Thought monitorability is no coincidence. It reflects a logical and necessary convergence, demonstrating a shared understanding of a key strategic vulnerability at the highest levels of government and on the front lines of technology development. This moment marks the shift of AI interpretability from an academic issue to a national security priority, paving the way for a new phase in the governance and development of artificial intelligence.

A Shared Recognition of Strategic Vulnerability

The language used by both policymakers and researchers shows a clear parallel in their concerns. The Action Plan's reason for initiating a new DARPA-led program—the “challenge of using unpredictable AI systems in high-stakes national security applications”—is a direct policy reflection of the technical problem described in the CoT position paper. The paper's central goal is to find ways to “monitor their chains of thought (CoT) for the intent to misbehave". Both documents, originating from different communities, are grappling with the same fundamental issue: a critical lack of trust, predictability, and control over increasingly powerful and autonomous AI systems.   

This shared recognition marks a crucial turning point. For the government and the Department of Defense, the black box problem signifies a potentially unusable or dangerously unreliable military asset, undermining the core objective of achieving technological dominance. For the AI labs, it signifies an unmanageable safety risk and a possible existential threat. The government views it as a broken tool; the labs see it as a broken promise of control. The convergence of these two powerful perspectives—the user's and the creator's—has fostered the political will and technical urgency needed to initiate a major new national research effort.   

An Inevitable Collision: Acceleration Versus Monitorability

While both sides acknowledge the problem, the government’s proposed solution contains a profound internal paradox. The Action Plan’s primary strategic thrust is to “win the race” through maximum acceleration, driven by deregulation and the removal of administrative barriers. This policy of “unleashing” the private sector could lead to a collision course with the technical realities of AI safety.   

As outlined in the CoT paper, the pressures of extreme competition are precisely what threaten to undermine monitorability. Development approaches that focus solely on performance outcomes can make a model's reasoning unclear or misleading. Consequently, the government’s broad policy framework is actively creating conditions that will likely worsen the very issue it asks DARPA to address. The DARPA directive, then, should be seen not just as a proactive research effort but also as a reactive safeguard against a self-inflicted policy risk. It aims to develop a technical solution for a problem that the government's own strategic stance is intensifying.   

The Messenger Matters: Industry Leaders Sounding the Alarm

The power and credibility of the technical warning are amplified enormously by the identity of its messengers. The forty authors of the CoT position paper are not external critics or academic theorists; they are influential insiders from the very companies at the vanguard of the AI race: OpenAI, Google DeepMind, Anthropic, and Meta. The list of authors and endorsers includes luminaries like AI pioneer Geoffrey Hinton, DeepMind co-founder Shane Legg, former OpenAI chief scientist Ilya Sutskever, and, notably, Dan Hendrycks, a safety advisor to xAI.   

The involvement of these individuals and their organizations is highly important. These are the companies and people developing the systems that the U.S. government plans to acquire and use in its most sensitive applications. Several of these companies are already major defense contractors; for example, the Pentagon recently announced new $200 million contracts with four leading AI firms, including Google and Elon Musk's xAI. OpenAI is strengthening its government connections by opening a Washington, D.C. office and securing a large, $30 billion annual contract with Oracle for data center services, which will expand its infrastructure—a key goal of the Action Plan. When the leading experts in this technology warn that its core principles of safety and control are fragile, policymakers are compelled to take notice. This “bottom-up” warning from the industry’s core provides strong support and validation for the “top-down” DARPA directive, creating a united front on the need to solve the interpretability problem.   

When viewed together, we believe the government’s Action Plan and the industry’s CoT paper form the two parts of an emerging, implicit social contract for the next phase of AI development. On one side, the government, through the Action Plan, offers the technology sector: “We will supply the capital, infrastructure, energy, and a deregulated environment for you to innovate rapidly and secure America's global leadership.” This demonstrates the government’s commitment to the partnership. On the other side, the industry, through the unified voice of the CoT paper, provides a conditional acceptance: “We accept this national mandate to accelerate, but in return, the government must help us address the fundamental safety and control issues that this race will inevitably create. We cannot guarantee the safety of these systems under hyper-competitive conditions without a large, coordinated, pre-competitive R&D effort.” The DARPA directive is the government’s formal acknowledgment of this condition. It serves as the mechanism through which the state commits to co-invest in reducing the systemic risks caused by its own strategic goals. This implicit bargain—deregulation and support in exchange for a national commitment to solve the control problem—represents a new and vital development in the governance of frontier AI.   

The Chosen Instrument: Why DARPA is Tasked with Cracking the Code

The decision to assign the challenge of AI interpretability to the Defense Advanced Research Projects Agency (DARPA) is a purposeful and strategic choice. It reflects the problem’s scope, risk level, and its importance to national security. DARPA’s unique history, proven operational model, and existing research foundation in this area make it the ideal federal agency to lead a high-risk, high-reward effort to uncover the AI black box.

A History of “Redefining Possible” and Preventing Technological Surprises

DARPA’s entire institutional identity is rooted in addressing seemingly impossible technological challenges to maintain U.S. military dominance. The agency was created in 1958 by President Dwight D. Eisenhower shortly after the Soviet Union launched Sputnik 1, a moment that stunned the American public and government. Its core, ongoing mission is “to make pivotal investments in breakthrough technologies for national security” and, importantly, to ensure that the United States never faces another catastrophic technological surprise.   

Over its more than 60-year history, DARPA has been the driving force behind many of the most transformative technologies of the modern era, many of which were initially considered science fiction. Its portfolio of successes includes the ARPANET, the packet-switched network that laid the foundation for the internet; the Radio Telescope of Arecibo; the development of stealth aircraft technology (Have Blue and Tacit Blue), which led to the B-2 bomber; and the creation of the first miniaturized GPS receivers, which revolutionized military navigation and precision strikes and are now widespread in civilian life. The AI “black box” problem is the modern-day equivalent of the challenges DARPA was created to address. It is a fundamental, cross-cutting scientific barrier that, if left unsolved, could result in the very kind of technological surprise—such as an uncontrollable or deceptive military AI causing unintended escalation—that the agency was designed to prevent. Tackling such “DARPA-hard” problems is the organization’s core mission.   

The “DARPA Model”: A Culture of High-Risk, High-Reward Research

The agency’s capacity to achieve historic breakthroughs comes from its distinctive operational structure, known as the “DARPA model.” This model features a flat, adaptable organization that grants its program managers high levels of trust, independence, and resources. Unlike managers at most federal R&D agencies, DARPA program managers are expected to be ambitious visionaries who develop new programs, fund innovative ideas swiftly, and cut underperforming projects without bureaucratic delays.   

A key feature of this model is a high tolerance for failure. DARPA explicitly invests in high-risk, high-reward research that private companies or other government agencies would consider too speculative or commercially unviable. The agency recognizes that transformative innovation requires taking risks on multiple, competing approaches, knowing that most may fail, but a single success can have a profound impact on the world. This culture is reinforced by the limited tenure of its program managers, who are typically hired from top academic and industry positions for three to five years. This constant influx of new talent brings fresh ideas and a strong sense of urgency to the agency.   

This model is ideally suited for the problem of AI interpretability. Solving this challenge will not be a gradual engineering task; it will require fundamental scientific breakthroughs. DARPA can fund a diverse portfolio of radical ideas—from novel architectures to new mathematical frameworks for understanding neural networks—that fall outside the scope of corporate R&D labs focused on short-term product cycles. Additionally, Congress has granted DARPA special flexible hiring and contracting authorities, such as “other transactions (OT) authority,” which enable it to engage with the best minds and most innovative entities in academia and the private sector, bypassing traditional government procurement hurdles.   

Building on a Foundation: An Escalation of the Explainable AI (XAI) Program

The new directive in the Action Plan is not DARPA's first attempt in this area. The agency has a history of leading in this field, having developed its original Explainable Artificial Intelligence (XAI) program in 2015 and starting a four-year research effort in 2017. The goal of the XAI program was to develop a set of machine learning methods that would produce explainable models, enabling end-users to “understand, appropriately trust, and effectively manage” the emerging generation of AI systems. DARPA’s official innovation timeline clearly highlights “explainability” as a key agency goal, demonstrating a long-standing commitment to the issue.   

The new mandate can therefore be seen as XAI 2.0—a major upgrade in response to a rapidly changing threat landscape. The original XAI program was created to handle the deep learning and computer vision models of the mid-2010s. Today’s cutting-edge models—large, auto-regressive transformers with billions or trillions of parameters showing emergent, unpredictable, and often misleading behaviors—pose a challenge of a wholly different scale and complexity. This new directive, issued by the White House and the DoD, clearly indicates that the problem has outgrown the original solutions. It calls for a renewed, more aggressive, and better-funded national effort that builds on the core knowledge and lessons from the first XAI program to address the unique challenges of modern reasoning models.

The table visually illustrates the progression of the interpretability challenge. It shifts from explaining the outputs of 2010s-era deep learning models (XAI), to emphasizing the importance of maintaining the monitorability of today's reasoning models (CoT), and finally to a national security requirement to address the issue for even more powerful systems in the future.   

Ultimately, DARPA’s role goes beyond just supporting the military. The agency has a long history of developing dual-use technologies that give rise to entirely new commercial industries. The AI interpretability problem exemplifies a dual-use challenge. The military cannot deploy autonomous weapons in the battlefield kill chain without trust, which depends on interpretability. This is the main factor in national security. At the same time, the commercial sector is hindered from unlocking the full economic potential of AI in high-value, regulated industries such as healthcare and finance due to the same trust issues and the “black box” nature of the technology. Private companies, driven by quarterly earnings and intense competition, systematically underinvest in the long-term, fundamental research and development (R&D) needed to address this shared problem.   

DARPA is positioned to fill this gap and serve as the nation's R&D engine to address a market failure impacting both national security and economic competitiveness. By funding high-risk, pre-competitive research, DARPA can effectively ”de-risk” the technology for the entire U.S. ecosystem. A breakthrough from this new program would not only provide the DoD with trusted, reliable AI but also enable rapid commercialization, generating hundreds of billions of dollars in economic value and giving American industry a strong and lasting global edge in safe, trustworthy AI.

The Dual-Front Stakes: Implications for National Security and Economic Competitiveness

The mission to solve AI interpretability is not just an academic exercise; it's a strategic necessity with significant, concrete impacts on the United States. The country's ability to project military strength and maintain its economic dominance in the 21st century relies more than ever on understanding the inner workings of AI. Failing to do so will expose critical vulnerabilities in the battlefield and leave trillions of dollars in economic output unrealized. Conversely, success would usher in a new era of human-machine teamwork, securing America's leadership in both military and economic spheres.

National Security: The Trust-Based Kill Chain

The “America's AI Action Plan” is a key initiative for the Department of Defense to “aggressively adopt AI… if it is to maintain its global military preeminence.” However, the plan also stipulates that this use of AI must be “secure and reliable”. The “double black box” problem directly threatens to make these two goals mutually exclusive. In modern, high-tempo warfare, the “kill chain”—the process of finding, fixing, tracking, targeting, engaging, and assessing an adversary—is the central organizing principle of combat operations. Integrating AI is estimated to accelerate this cycle, creating a decisive advantage. But this integration is impossible without trust, and trust is impossible without interpretability.   

A commander cannot and should not be expected to authorize a kinetic strike or a major cyber operation based on a recommendation from an AI system whose reasoning is opaque and inscrutable. The risk of error, unintended escalation, or adversarial deception is simply too high. The DoD's own directives require that commanders and operators “exercise appropriate levels of human judgment over the use of force,” a requirement that becomes meaningless if the human has no insight into the machine's logic. Without a solution to the black box problem, frontier AI will be limited to back-office analytical roles and advisory functions, preventing full integration into time-critical operational workflows. This would effectively allow adversaries, who may have different ethical standards or a higher tolerance for the risks of deploying unpredictable autonomous systems, to dominate the future battlefield.   

Conversely, success in the DARPA-led mission to develop interpretable AI would be a strategic game-changer. It would create a new model of human-machine teamwork where human commanders can ask questions, understand, and ultimately trust their AI partners' recommendations. This would enable the U.S. military to harness the speed and scale of AI to achieve decision superiority, significantly accelerating the kill chain while maintaining strict human control and accountability. In the 21st-century battlespace, the most effective fighting force will be the one that has mastered this collaboration. Trust, made possible by interpretability, is the essential link.

Economic Competitiveness: The Trillion-Dollar Trust Deficit

The same lack of trust that paralyzes AI adoption in military environments is creating a significant “trust deficit” across the U.S. economy, holding back the nation from unlocking the full productivity benefits of the AI revolution. While AI has shown great potential, its practical use in high-value, highly regulated fields such as healthcare and finance has been significantly limited by its black box nature.   

In healthcare, studies estimate that the wider adoption of AI could generate savings of $200 billion to $360 billion annually in the United States through improved diagnostics, operational efficiency, and administrative automation. However, adoption still lags far behind other industries. The main reason is the black box problem. Doctors and hospitals hesitate to use AI systems for critical tasks, such as diagnosis, because they can’t understand how the system arrived at its conclusion, fearing patient safety issues and potential legal liability. Similarly, in finance, regulations often require decisions about loans, credit, and investments to be explainable to customers and regulators, a standard that many of the most powerful AI models cannot meet.   

This trust deficit represents a multi-trillion-dollar opportunity cost. The nation that first develops verifiably safe, reliable, and interpretable AI will not only unlock significant productivity and innovation within its economy but also position itself to dominate the global market for trusted AI. This aligns closely with a key goal of the Action Plan: to “deliver secure, full-stack AI export packages” to America's allies. The ability to export not just AI models but also the standards and verification tools that establish their trustworthiness would create a strong and lasting global competitive edge, ensuring the world continues to rely on American technology.   

Solving the interpretability problem also provides a way to address one of the most heated debates in AI policy: the conflict between open-source and closed-source models. The Action Plan strongly promotes the development of open-source and open-weight AI, seeing them as crucial for innovation, academic research, and as a geostrategic asset that can become a global standard based on American values. However, this approach creates a direct clash with national security concerns. As AI systems become increasingly powerful, the risk that adversaries could exploit open-source models for malicious purposes—such as developing weapons of mass destruction or launching sophisticated cyberattacks—increases dramatically.   

Currently, the primary tools for mitigating this proliferation risk are access controls, such as keeping model weights private or restricting access to advanced semiconductors required for training. However, these measures conflict with the intention to foster an open and innovative ecosystem. A breakthrough in AI interpretability provides a third option. If a reliable and robust method for monitoring and auditing an AI's internal reasoning—such as the one DARPA is now tasked with developing—can be created, it could be integrated directly into the architecture of powerful models as a requirement for their open-source release. This would create a built-in “governor” or “flight recorder” capable of detecting and flagging attempts to use the model for dangerous purposes, such as bioweapon design, without hindering legitimate research and commerce. In this way, solving interpretability becomes the key technology that could make the administration's strategy of strategic open-sourcing both practical and safe, balancing the drive for innovation with the needs of national security.

Our Recommendations and Future Outlook

The intersection of top-down policy directives and bottom-up technical alerts has created a unique opportunity to significantly shape the future of artificial intelligence. The United States has rightly recognized the “black box” issue as a major national vulnerability and has tasked its leading innovation agency with addressing it. To improve the chances of success and leverage any breakthroughs, the administration should now take additional steps to coordinate its policies, procurement methods, and strategic vision with this emerging reality. The AI race is no longer just about developing the largest model; it is about creating the safest and most controllable one.

Recommendation: Make Interpretability a Contract Condition

The Action Plan already sets a strong precedent for leveraging the federal government's massive purchasing power to influence the development of frontier AI. The directive to update federal procurement guidelines to require that the government only contract with developers whose models are free from “ideological bias” shows a willingness to impose technical standards to achieve policy objectives. The same logic should be applied to the more fundamental issues of interpretability and safety.   

The administration should broaden this procurement mandate. All federal contracts for developing or deploying frontier AI systems, particularly those involving the Department of Defense and the Intelligence Community, should incorporate stringent, technically detailed requirements for auditable reasoning. This might include mandatory logging of Chain-of-Thought processes in a standardized format, the integration of built-in “CoT monitors,” and proven performance on a set of government-defined interpretability and control benchmarks. These benchmarks should be created by the new DARPA program in partnership with NIST and industry stakeholders. Such a policy would create a strong, direct market incentive for technology companies to prioritize safety and control, aligning their commercial goals with vital national security priorities. It would elevate interpretability from a research optional feature to a contractual essential.

Recommendation: Align Deregulation with a National Safety Standard

This analysis has identified the core tension between the administration's aggressive deregulatory push and the clear, proven need for AI safety and oversight. An entirely hands-off, laissez-faire strategy is strategically unsustainable given the catastrophic risks tied to unpredictable, high-capability AI systems. Still, a heavy-handed, process-driven regulatory approach could hamper the very innovation required to “win the race.”

The optimal path forward is to strike a balance between these competing priorities. The administration should build on the work of the new DARPA program and NIST to create a national standard for AI model interpretability and safety. This wouldn't be an overly restrictive regulation dictating how companies must innovate, but rather a straightforward, performance-based standard that explains what it means for a model to be considered “secure and reliable” for critical infrastructure and national security use. This approach would set a clear, steady goal for the industry, encouraging competition to find the most efficient and effective ways to meet the standard without government micromanagement of innovation. It would balance the need for speed with the essential requirement for safety, offering a framework for responsible fast tracking.

Forward Outlook: The Race for Control

Over the past decade, the global AI competition has primarily centered on a race for scale—a relentless pursuit of more data, increased computing power, and larger model parameters. The events of July 2025 mark the start of a new and more significant phase: the fight for control.

The country that achieves genuine and lasting dominance in AI during the era will not be the one that merely creates the most powerful black box. It will be the nation that first learns to develop mighty AI that is also predictable, steerable, auditable, and fundamentally trustworthy. The ability to understand and control the reasoning of artificial intelligence is the ultimate competitive advantage, as it is essential for deploying in every high-value, high-stakes area, from the battlefield to the operating room.

The effort to crack the black box, now officially led by DARPA and urgently demanded by the technology's own creators, is therefore not a secondary safety initiative or a niche research project. It is the main, defining challenge of the next stage of the AI revolution. Winning this race—the race for control—is the only way to truly and safely win the race for AI. The stakes for America's national security and economic future could not be higher.

References

Al-Sibai, N. (2025, July 16). Top AI researchers from Google, Meta, and OpenAI are all freaking out about the same thing. Futurism. (https://futurism.com/top-ai-researchers-concerned)

BankInfoSecurity. (2025, July 16). AI giants push for transparency on model's inner monologue. (https://www.bankinfosecurity.com/ai-giants-push-for-transparency-on-models-inner-monologue-a-28986)

Beren, D., Baker, B., et al. (2025). Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety. arXiv. (https://arxiv.org/abs/2507.11473)

Bonvillian, W. B., Van Atta, R., & Windham, P. (Eds.). (2019). The DARPA model for transformative technologies: Perspectives on the U.S. innovation system. Open Book Publishers. (https://doi.org/10.11647/OBP.0184)

Congressional Research Service. (2021, August 19). Defense Advanced Research Projects Agency: Overview and Issues for Congress. Federation of American Scientists. (https://sgp.fas.org/crs/natsec/R45088.pdf)

Dataconomy. (2025, July 17). Why we might lose our only window into how AI thinks. (https://dataconomy.com/2025/07/17/why-we-might-lose-our-only-window-into-how-ai-thinks/)

Deeks, A. (2023). *The double black box: AI inside the national security ecosystem*. University of Virginia School of Law. (https://www.law.virginia.edu/node/2184686)

Defense Advanced Research Projects Agency. (n.d.). Innovation timeline. Retrieved July 24, 2025, from (https://www.darpa.mil/about/innovation-timeline)

Friedland, A. (2025, July 23). Trump's Plan for AI: Recapping the White House's AI Action Plan. Center for Security and Emerging Technology. (https://cset.georgetown.edu/article/trumps-plan-for-ai-recapping-the-white-houses-ai-action-plan/)

Africa.com. (2025, July). Top AI labs urge focus on monitoring thought processes of AI reasoning models. (https://iafrica.com/top-ai-labs-urge-focus-on-monitoring-thought-processes-of-ai-reasoning-models/)

MedTech Europe. (2020, October). The socio-economic impact of AI in healthcare. October, 2020 [https://www.medtecheurope.org]

O'Brien, M. (2025, July 23). Trump's new AI plan could have big consequences for data centers and the nation's power grid. Associated Press. (https://apnews.com/article/trump-ai-artificial-intelligence-3763ca207561a3fe8b35327f9ce7ca73)

Patel, F. (2025, July 18). Peering into the ‘double black box’ of national security and AI. Lawfare. [https://www.lawfaremedia.org/article/peering-into-the--double-black-box--of-national-security-and-ai]

Paul Hastings LLP. (2025, July 23). White House releases AI action plan: “Winning the Race: America's AI Action Plan”. [https://www.paulhastings.com/insights/client-alerts/white-house-releases-ai-action-plan-winning-the-race-americas-ai-action-plan]

Rajkumar, R. (2025, July 17). Researchers from OpenAI, Anthropic, Meta, and Google issue joint AI safety warning—here's why. ZDNET. (https://www.zdnet.com/article/researchers-from-openai-anthropic-meta-and-google-issue-joint-ai-safety-warning-heres-why/)

Sahni, N., Pauly, M. V., & Volpp, K. A. (2022, March 9). Why is AI adoption in health care lagging? Brookings Institution. [https://www.brookings.edu/articles/why-is-ai-adoption-in-health-care-lagging/](https://www.brookings.edu/articles/why-is-ai-adoption-in-health-care-lagging/)

Sahni, N., Stein, G., & Rodney, S. (2023). The potential impact of artificial intelligence on healthcare spending (NBER Working Paper No. 30857). National Bureau of Economic Research. (https://www.nber.org/system/files/working_papers/w30857/w30857.pdf)

Snyder Good, R., Chung, E. T., Glasser, N. M., Green, F. M., & Shah, A. B. (2025, July 22). AI policy alert: What to know before the White House releases its AI action plan. Health Law Advisor. (https://www.healthlawadvisor.com/ai-policy-alert-what-to-know-before-the-white-house-releases-its-ai-action-plan)

Space.com. (2025, January 7). What is DARPA? [https://www.space.com/29273-what-is-darpa.html](https://www.space.com/29273-what-is-darpa.html)

Tech in Asia. (2025, July). Researchers warn of losing grasp on advanced AI models. (https://www.techinasia.com/news/researchers-warn-of-losing-grasp-on-advanced-ai-models)

The White House. (2025, January 13). FACT SHEET: Ensuring U.S. security and economic strength in the age of artificial intelligence. The White House Archives. (https://bidenwhitehouse.archives.gov/briefing-room/statements-releases/2025/01/13/fact-sheet-ensuring-u-s-security-and-economic-strength-in-the-age-of-artificial-intelligence/)

The White House. (2025, July). America's AI Action Plan. (https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf)

Tucker, P. (2025, June 25). Declining public trust in AI is a national-security problem. Defense One. (https://www.defenseone.com/technology/2025/06/declining-public-trust-ai-national-security-problem/406309/)

Tung, F., & Rao, R. B. (2022). Explainable artificial intelligence models and methods in finance and healthcare. Frontiers in Artificial Intelligence (https://www.frontiersin.org/research-topics/19727/explainable-artificial-intelligence-models-and-methods-in-finance-and-healthcare)

Vincent, B. (2025, July 23). Trump eyes new Pentagon-led 'proving ground' in much-anticipated AI action plan. DefenseScoop. (https://defensescoop.com/2025/07/23/trump-ai-action-plan-department-of-defense-proving-ground/)

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. arXiv. (https://arxiv.org/abs/2201.11903)

Webster, G. (2025, August 14). Inside the Biden administration's gamble to freeze China’s AI future. WIRED. 

Wikipedia contributors. (2025, July 24). DARPA. In Wikipedia. Retrieved July 24, 2025, from (https://en.wikipedia.org/wiki/DARPA)

A group of friends from “Organizational DNA Labs,” a private network of current and former team members from equity firms, entrepreneurs, Disney Research, and universities like NYU, Cornell, MIT, and UPR, gather to share articles and studies based on their experiences, insights, and deductions, often using AI platforms to assist with research and communication flow. While we rely on high-quality sources to shape our views, this conclusion reflects our personal perspectives, not those of our employers or affiliated organizations. It is based on our current understanding, which is influenced by ongoing research and review of relevant literature. We welcome your insights as we continue to explore this evolving field.