Cryptographic Verification: A Strategy for U.S. AI Export Leadership

The Problem: AI Trade Barriers

The ambitious U.S. strategy to export the full American AI technology stack has encountered a powerful and predictable countervailing force: the global rise of digital nationalism. The long-held assumption of a single, integrated global digital market has gradually been rendered obsolete. In its place, a fragmented landscape of “regulatory fiefdoms” has emerged, driven by a deep-seated desire among nations to assert control over their digital futures.

The global pursuit of “Sovereign AI” is a direct response to recent history. Supply chain disruptions and the clear strategic implications of technological dependence have alarmed governments worldwide. As a result, nations are launching multi-billion-dollar initiatives not just to participate in the AI economy, but to control their own AI infrastructure, data, and models. This has created a paradoxical environment for American technology providers. On one hand, the race for sovereign AI has generated unprecedented demand for U.S.-designed hardware, as allied nations rush to build domestic compute capacity. On the other hand, to ensure this new infrastructure is truly “sovereign,” these same nations are erecting complex regulatory barriers that govern data storage, model management, and content control, directly threatening the high-margin software and services that run on that hardware.

The core of the problem lies in a fundamental conflict of interests. The United States, motivated by legitimate national security and economic concerns, seeks maximum control and visibility over its exported technology. This is essential to prevent misuse, protect intellectual property, and ensure the technology cannot be turned against American interests.

Conversely, “sovereign buyers”—from highly regulated markets in the European Union to ambitious emerging powers in Asia and the Middle East—demand the exact opposite. They seek maximum sovereignty, which they define as complete control over their critical digital infrastructure and the data that flows through it. They are increasingly skeptical of relying on a technology stack that is ultimately beholden to the laws and strategic interests of another nation. This skepticism is ac tively fueled by competitors like China, which promotes a narrative of American untrustworthiness, warning of hidden “backdoors” and “kill switches” in U.S. technology. This narrative erodes the trust necessary for deep technological partnerships and makes the sale of an American-controlled stack more challenging.

This conflict manifests as a web of national and regional regulations that will increasingly pose barriers to entry in key global markets. The EU’s data localization rules, for example, could favor local European cloud providers if US providers cannot provide robust guarantees on where the data goes. Similar trends are arising in the Middle East and Asia. To win the infrastructure race, the United States must offer a solution that seamlessly and technically addresses these sovereignty concerns, moving beyond legal assurances to provide demonstrable proof.

The Solution: Trust Through Cryptographic Verification

In the current zero-trust geopolitical environment, policy assurances and contractual obligations are insufficient to overcome the sovereignty paradox. A sovereign buyer concerned about a “kill switch” or data access under the U.S. CLOUD Act will not be placated by a clause in a service-level agreement. One viable path to resolving this trust deficit is to build the American AI stack on a foundation of cryptographic verification. This approach moves the basis of trust from the identity of the operator to the provable mathematical properties of the system itself, offering sovereign buyers demonstrable proof of compliance and control.

The strategic challenge is to bridge the gap between the promise of security and the proof of it. The American technology stack must evolve from a model that says, “We promise not to access your data,” to one that can state, “We have cryptographic protections that make unauthorized access extremely difficult and detectable, and we can prove it to you on demand.” This shift from policy-based compliance to evidence-based assurance is the key to unlocking high-friction international markets.

This new paradigm of demonstrable proof is made possible by a confluence of mature technologies that, when integrated, provide a verifiable chain of trust from the silicon to the application.

Confidential Computing & Trusted Execution Environments (TEEs)

TEEs are secure, isolated areas within a main processor, such as those provided by Intel SGX/TDX or AMD SEV. They create protected memory enclaves where code and data are isolated during processing. Even an administrator with root privileges on the host operating system—or the cloud provider itself—cannot see or modify what is inside the TEE. This provides a powerful guarantee of data confidentiality and code integrity.

Hardware Roots of Trust (HRoT)

An HRoT, typically a Trusted Platform Module (TPM), is a dedicated microchip designed to pro vide secure hardware-based functions. It acts as the immutable foundation of trust for a computing platform. It can securely store cryptographic keys and, most importantly, can measure (cryptographically hash) the state of the system’s firmware and software during the boot process, creating a secure log of the platform’s configuration.

Remote Attestation

This is the protocol that makes the internal state of the system visible to an external party. A remote user (the sovereign buyer) can challenge the TEE or TPM. In response, the hardware provides a signed report containing the measurements of the software running on the system. This report is signed with a cryptographic key that is fused into the silicon and can be verified against the manufacturer’s public key. This allows the buyer to receive strong cryptographic evidence of the hardware and software configuration at boot time where their workload is running, confirming that it has not been tampered with and matches an approved configuration.

This fundamental security architecture is not just confined to CPUs; it is now a critical design pillar across the spectrum of modern AI accelerators. Recognizing that proprietary AI models and sensitive training datasets are immensely valuable assets, manufacturers like NVIDIA, AMD, Intel, and Google embed these hardware primitives directly into their GPUs and TPUs. These foundational technologies enable a range of specific, verifiable guarantees that directly address the concerns of sovereign buyers. These guarantees are not abstract principles but concrete deployment-ready capabilities that the American AI Technology Stack can provide to meet the demand for sovereignty from global customers.

• A. Data Residency: Is all data processing and storage confined within our nation’s jurisdiction?

• B. Confidentiality: Is citizen and business data encrypted and protected from all unauthorized access?

• C. Benchmarks: Has the AI model been validated against our national benchmarks for performance, safety, and compliance?

• D. Usage: Does the data center, including all model training activities, adhere to local regulations and compute usage restrictions?

Verifiable guarantees fundamentally alter the power dynamic between the technology provider and the sovereign customer. A U.S. company can build and operate the world’s most advanced AI infrastructure, while a foreign government or enterprise can use that infrastructure with strong cryptographic assurances that their data and workloads remain exclusively under their control.While customers must still trust the underlying hardware manufacturers and implementation, the reliance on trusting the provider’s legal entity or home jurisdiction is substantially reduced—the basis of trust shifts toward verifiable technical controls rather than policy commitments alone.

Furthermore, a platform architected around these principles can transform regulatory compliance from a manual, periodic, and costly audit process into a more automated, semi-continuous, and technically verifiable process. Instead of an auditor reviewing logs to verify data residency, the platform can generate cryptographic attestations that provide strong evidence that a workload ran exclusively on servers within a specific national border and in an isolated environment configured to restrict external network access. This allows U.S. firms to sell verification as a premium, high-margin service, turning trade barriers into a source of competitive advantage, especially in relation to the Chinese AI Technology Stack.

Deployment Configurations: A Framework for Global Markets

A successful global deployment strategy requires a nuanced understanding of the different ways the American AI stack can be configured, and what international trade barriers exist around each such configuration. The following three-axis model provides a framework for analyzing deployments:

Axis 1: Operator (The “Who”)

• O1: US Entity: The data center is built, owned, or operated by a U.S. corporation (e.g., AWS, Oracle) or a U.S. government entity.

• O2: Host Nation Entity: The data center is operated by a non-U.S. entity, such as a private French company, a German state-owned utility, or a sovereign wealth fund.

Axis 2: Jurisdiction (The “Where”)

• J1: US Homeland: The physical data center is located in the United States and is subject to U.S. law, with foreign customers accessing it remotely.

• J2: Partner Nation (Regulated): The physical data center is located within the borders of a partner nation (e.g., France, Germany, Japan) and is subject to its local laws, such as GDPR.

Axis 3: Technology Stack (The “What”)

• S1: US Model on US Hardware: The complete American stack, featuring a U.S.- developed AI model running on U.S.-designed hardware.

• S2: US Model on Foreign Hardware: A U.S. AI model running on hardware from a non-U.S. manufacturer.

• S3: Foreign Model on US Hardware: A foreign-developed AI model (e.g., France’s Mistral) running on U.S.-designed hardware.

• S4: Foreign Model on Foreign Hardware: A completely non-U.S. technology stack.

These axes define a spectrum of deployment scenarios, each with unique trust and regulatory implications. For example, an O1/J1/S1 deployment (AWS running a U.S. model in Virginia) is the default for many U.S. customers but is the most challenging to sell into high-sovereignty markets. Conversely, an O2/J2/S3 deployment (a German company running a French model on U.S. hardware in Frankfurt) presents a very different proposition that can be tailored to meet stringent EU requirements. The strategic goal should not be to force a single configuration on the world, but to sell a verifiable platform that enables a menu of secure, sovereign-respecting configurations. The product is not the stack; the product is verifiable trust.

Overcoming the Industry Coordination Problem

The primary obstacle to deploying a verifiably sovereign American AI Technology Stack is not a lack of technology, but a classic industry coordination problem. The capabilities for verification, based on technologies such as confidential computing and cryptographic attestation exist, yet they remain siloed within specific layers of the technology stack, making it difficult to deliver the integrated, end-to-end assurance that sovereign buyers demand. For example, a chip maker like Intel can attest to its hardware security properties, a cloud provider like Amazon can attest that a virtual machine is running on that chip, and an AI company can attest that its model is untampered with—but these attestations are not standardized or easily composable, leaving the customer unable to verify the entire process end-to-end through a single, unified chain of evidence.

This creates a ”chicken-and-egg” problem. While vital work on technical standards is underway in bodies like the Confidential Computing Consortium, Internet Engineering Task Force, and the Open Compute Project, the traditional process of industry-wide consensus and adoption takes decades. Crucially, the United States does not have decades. To win the current AI arms race and secure its technological leadership, it must solve this problem during this presidency. To effectively address the industry coordination problem, the Request for Proposal (RFP) for the American AI Technology Stack should be enhanced to cultivate products for digital trust. This can be achieved by embedding two core principles into the RFP requirements:

1) Mandate Integration with Independent Verification Technology Providers

The RFP should explicitly require that all submitted ”full-stack AI technology packages” are inte grated with a Third-Party Verification Technology Provider. These providers—which include the existing market of technical audit firms, privacy platforms, and cybersecurity companies—would be responsible for aggregating and presenting the cryptographic attestations that build customer trust in privacy, data residency, and system configuration. Their third-party independence is crucial for building impartial trust, as it avoids the inherent conflict of interest that arises when a technology provider, such as a hyperscaler, verifies its own systems.

Moreover, by making third-party verification a prerequisite for purchase, the financial burden of complying with international AI trade barriers shifts from American technology companies to the sovereign buyers themselves. This strategic shift not only alleviates the compliance costs but also cultivates a new, lucrative market for American firms specializing in verification technology.

2) Align Federal Incentives to Build the Verification Market

The RFP should directly link the federal financing tools mentioned in the Executive Order to the costs incurred by both the stack providers and their Verification Technology Provider partners. This approach addresses the current economic misalignment by funding the development and integration of the verification layer. By offering loans, guarantees, and other de-risking mechanisms, the government can accelerate the creation of a robust and competitive verification market.

A priority AI export package selected under these terms would help establish a de facto industry standard for verifiable sovereignty, positioning the American AI Technology Stack as not only the most powerful but also the most demonstrably trustworthy solution for sovereign buyers on the
global stage.