On July 23, 2025, President Trump signed a sweeping Executive Order that prohibits federal agencies from procuring large language models (LLMs) that embed so-called diversity, equity, and inclusion (DEI) ideologies. This move marks a decisive shift in how the U.S. government defines trustworthy AI.
While controversial, the Executive Order is not an isolated action. It’s the first enforcement measure tied to the administration’s broader AI Action Plan, a national strategy based on three pillars:
- Accelerating AI innovation
- Building the infrastructure to support it
- Leading on international AI diplomacy and security
Together, these initiatives signal a new phase of AI policy that favors open competition, national security, and a distinctly American approach to technological dominance.
In this post, we break down what this new law actually says, how it fits into the country’s AI ambitions, and most importantly what it means in practice for vendors, developers, and AI teams looking to work with or around the U.S. government.
The Executive Order: Key Provisions
At the heart of this policy shift is the Executive Order titled “Preventing Woke AI in the Federal Government.” Its aim is explicit: to ensure that AI models used by federal agencies are not influenced by ideological agendas, particularly those associated with diversity, equity, and inclusion (DEI).
Target: Large Language Models (LLMs)
The order focuses specifically on LLMs, generative AI systems trained on massive datasets that produce natural language responses. These models, due to their scale and influence, are increasingly being used in government services, defense applications, and public-sector tools. The federal government is now drawing a clear line around what types of LLMs can and cannot be used.
Two Core Requirements for Government-Procured AI
To qualify for use in the federal government, LLMs must meet two foundational principles, referred to in the order as the “Unbiased AI Principles.”
1. Truth-Seeking
AI systems must be factually accurate, historically grounded, and scientifically objective. This includes acknowledging uncertainty when data is incomplete or contested. The emphasis is on outputs that reflect evidence, not ideology.
2. Ideological Neutrality
LLMs must avoid encoding or amplifying political, social, or cultural ideologies, especially those linked to DEI. Developers are barred from hardcoding these perspectives into the model’s behavior or responses unless explicitly requested by the user. The order argues that this is essential to ensure neutrality and user autonomy.
Implementation Framework and Vendor Obligations
The Executive Order includes a strict compliance structure that federal agencies and AI vendors will be expected to follow:
- OMB Guidance Timeline: Within 120 days, the Office of Management and Budget (OMB), in collaboration with other federal entities, will issue detailed guidance on how to apply the Unbiased AI Principles.
- Contractual Compliance: All new federal contracts involving LLMs must include clauses requiring adherence to these principles. Existing contracts may be amended where possible.
- Vendor Accountability: If a vendor fails to meet the requirements after a reasonable cure period, the costs of decommissioning the noncompliant model will be charged to the vendor, effectively raising the stakes for non-adherence.
The AI Action Plan: America’s Broader Strategy
To fully understand the significance of the “Preventing Woke AI” Executive Order, it’s essential to see it as part of a much broader effort: America’s AI Action Plan. This plan, introduced by President Trump at the start of his second term, outlines the U.S. government’s long-term strategy to dominate the global AI landscape not just through regulation, but through investment, infrastructure, and international influence.
The Executive Order serves as the legal and operational layer of this larger strategic vision. It’s not just about ethics or compliance, it’s about aligning AI development with America’s economic and geopolitical goals.
The plan is built on three strategic pillars:
Pillar 1: Accelerate AI Innovation
The first pillar emphasizes removing barriers to private-sector innovation while ensuring that AI applications drive real-world productivity. The goal is not just to build the most powerful models, but to unleash their value across the economy.
Key actions include:
- Eliminating red tape that slows AI deployment, particularly for high-impact commercial and industrial applications.
- Supporting open-source and open-weight models to encourage collaboration and reduce dependency on closed systems.
- Investing in foundational research, including interpretability, safety, robustness, and model evaluation.
- Building a thriving AI ecosystem that includes next-generation manufacturing, scientific research, and AI-enabled public services.
Pillar 2: Build AI Infrastructure
The second pillar recognizes that cutting-edge AI systems demand cutting-edge infrastructure, something the U.S. has neglected in recent decades, particularly when compared to China’s aggressive investment in grid capacity and chip manufacturing.
This pillar aims to reverse that trend by:
- Expanding the U.S. energy grid to support AI-related data center growth.
- Restoring domestic semiconductor manufacturing to reduce reliance on foreign chip supply chains.
- Building secure, government-grade data centers especially for military and intelligence use.
- Training a skilled AI infrastructure workforce, from engineers to cybersecurity professionals.
- Promoting secure-by-design technologies to harden critical systems against emerging threats.
Pillar 3: Lead in International AI Diplomacy and Security
The third pillar extends beyond domestic concerns, framing AI leadership as a matter of national and international security. The U.S. sees AI not only as a competitive advantage but also as a geopolitical lever to shape the rules of global AI governance.
Priorities under this pillar include:
- Exporting U.S.-aligned AI standards to allies and trade partners.
- Countering Chinese influence in multilateral organizations that set technical norms and ethical guidelines.
- Closing loopholes in export controls, particularly those related to high-performance chips and manufacturing equipment.
- Aligning security protocols across borders, especially for frontier model evaluation and biosecurity.
Together, these pillars illustrate how the U.S. is approaching AI not just as a tool, but as a national asset. The Executive Order is just one mechanism within this much larger framework, designed to enforce certain values domestically while advancing American influence abroad.
What This Means for AI Vendors and Developers
For AI vendors and technical leaders, the Executive Order marks a clear turning point: ideological neutrality is now a contractual requirement for doing business with the U.S. federal government. And while the scope of the law is currently limited to government procurement, its ripple effects are likely to influence broader industry norms.
Here’s what to expect and prepare for.
Model Behavior: Ideological Filters Are Now a Liability
If your LLM has been trained or tuned to promote representational diversity, moderate sensitive topics, or avoid politically charged outputs, that behavior could now be interpreted as a compliance risk in federal settings.
To qualify for government use, models must avoid shaping outputs through embedded ideological lenses. Developers will need to either:
- Remove default behavior that reflects DEI-aligned logic, or
- Make such behavior fully user-prompted, rather than system-driven.
This shift will likely reignite technical debates around reinforcement learning from human feedback (RLHF) and its use in aligning models with social norms, something that may now be considered a red flag in federal AI use cases.
Transparency Requirements Will Shape Documentation Practices
The order doesn’t require vendors to open-source their models, but it does require transparency around model behavior and design choices.
According to the implementation section, vendors must be prepared to disclose:
- System prompts
- Evaluation methodologies
- Model specifications or decision rationales
Crucially, the government signals a willingness to avoid demanding model weights or other proprietary IP “where practicable.” Still, this level of scrutiny will almost certainly increase the compliance burden on technical and legal teams, especially for vendors with generalized foundation models.
Contract Language Will Evolve With Real Consequences
Expect new clauses in government AI contracts that:
- Explicitly reference the Unbiased AI Principles
- Include cure periods for noncompliance
- Shift decommissioning costs to the vendor if violations aren’t resolved
This formalizes the stakes: model misbehavior won’t just be a PR risk, it will be a contractual liability.
Even if you’re not currently selling to the federal government, this sets a precedent that may be adopted by state governments or enterprise buyers looking to align with federal standards.
Private Models May Be Indirectly Affected
Although the order only applies to federal use cases, the technical architecture required to comply ideologically neutral prompts, transparent evaluations, detailed logging may become the default across deployments simply to streamline operations.
In other words: if your model needs to be “federal-ready,” it may no longer make business sense to maintain a separate version with different alignment logic. This creates indirect pressure to normalize “federalized” behavior across the broader market.
Vendors may respond by:
- Creating modular alignment layers that toggle based on deployment context
- Splitting product lines between public-facing models and compliance-optimized models
- Reframing safety and fairness in terms of factuality and user intent, rather than demographic sensitivity
Federal Procurement Will Set Precedents
Although the Executive Order is limited to federal agencies, its influence is likely to extend well beyond the public sector. In the world of technology adoption, procurement rules often become de facto industry standards, especially when they come from the largest buyer in the market.
A Strong Market Signal
The U.S. federal government is one of the world’s largest technology purchasers. When it introduces new requirements, especially around transparency, alignment, or model behavior, vendors pay attention.
Even companies that don’t currently serve government clients may feel pressure to align with the new standard to:
- Future-proof their models for public-sector opportunities
- Avoid costly model versioning
- Reassure large enterprise customers who may adopt similar principles in risk-averse industries (e.g., finance, insurance, healthcare)
In this sense, the order functions as both policy and market signal. Vendors will need to ask not just “what do the regulators want?” but “what will our clients now expect by default?”
Neutrality-by-Default Could Become a Baseline Expectation
The language of the Executive Order, particularly around truth-seeking and ideological neutrality, is prescriptive in spirit but flexible in execution. Vendors are allowed to take different approaches to compliance, but the outcome must be the same: AI systems that don’t embed partisan or ideological perspectives by default.
This could accelerate a broader trend toward “neutrality-by-default” models, where alignment is minimal unless specifically invoked by the user. In some cases, this might mean:
- Toning down safety layers designed to flag or deflect controversial queries
- Redesigning reward models used in RLHF to eliminate normative preferences
- Reconsidering how guardrails are applied across different domains (e.g., history, identity, or social behavior)
Over time, this may also shift how trust, bias, and safety are measured and defined, especially in U.S.-based evaluation benchmarks and tooling.
Selective Adoption Across Agencies
It’s also important to note that not all agencies will implement the Executive Order the same way. The policy includes language allowing agencies to interpret its applicability based on specific use cases and mission needs.
- Defense and Intelligence may adopt stricter implementations, prioritizing accuracy, secrecy, and neutrality in operational systems.
- Healthcare or Education departments might move more cautiously, balancing neutrality with ethical obligations around representation or harm prevention.
- Some legacy contracts or use cases may be exempt altogether.
This creates a fragmented compliance landscape where AI vendors will need to tailor offerings, or at least documentation, depending on the specific agency, domain, or contract.
Impacts on AI Governance, Safety, and Fairness
Beyond contracts and compliance, the Executive Order is poised to reshape how “trustworthy AI” is defined and enforced in the United States. It signals a departure from established norms that have, until now, prioritized fairness, inclusion, and harm reduction as pillars of responsible AI development.
Redefining "Trustworthy AI"
For years, the dominant frameworks guiding AI safety, both in the U.S. and internationally, have emphasized values like non-discrimination, fairness, and societal well-being. These principles have informed model alignment strategies, RLHF reward functions, and content moderation systems.
The new Executive Order shifts the emphasis to factuality and ideological neutrality. In doing so, it legally deprioritizes many of the fairness-oriented mechanisms that previously served as the foundation for trustworthy AI.
This creates a fundamental rebalancing:
- From: Avoiding representational harms, amplifying underrepresented voices, embedding social context.
- To: Preserving historical and scientific accuracy, minimizing subjective value judgments, deferring to user prompts rather than pre-programmed alignment.
For vendors, researchers, and policymakers, this signals a new axis of AI governance that prioritizes epistemic rigor over representational justice.
Conflicting Definitions Across Frameworks
This evolving definition of trustworthy AI now stands in sharp contrast with other major frameworks:
- NIST AI Risk Management Framework (U.S.) continues to highlight fairness, explainability, and bias mitigation as core values, raising questions about how federal agencies will reconcile NIST guidance with the Executive Order.
- The OECD Principles on AI emphasize inclusive growth and human-centric design, which may diverge from a neutrality-first approach.
- The EU AI Act imposes obligations around non-discrimination, risk categorization, and systemic bias, especially for high-risk systems.
As a result, U.S.-based vendors working globally may need to navigate competing expectations: neutrality and factuality for federal compliance, versus fairness and inclusion for international markets.
This fragmentation will likely intensify the need for customizable alignment strategies, jurisdiction-specific model behavior, and legal clarity around conflicting standards.
Ethical Trade-Offs and Open Questions
The shift toward ideological neutrality is framed by the administration as a safeguard against partisan manipulation, but it introduces complex ethical tensions:
- Censorship or correction? If models refuse to generate content that’s factually controversial or socially harmful, is that an ethical safety measure or a form of ideological bias?
- Who decides what is “neutral”? The line between neutrality and neglect can be thin, especially on topics involving race, gender, or power.
- What counts as factual? Many sociohistorical questions are inherently contested. Demanding objectivity in these areas risks oversimplification or selective interpretation.
- What about harm mitigation? By deprioritizing fairness, does this approach open the door to outputs that may be accurate but socially damaging?
These trade-offs won’t be resolved in a single policy document. But the Executive Order marks a clear inflection point: the U.S. federal government is recasting the ethical baseline for AI not as inclusion or caution, but as clarity, neutrality, and truth.
Final Thoughts
Whether you agree with the framing or not, its implications are profound. It challenges long-held assumptions about what makes AI “trustworthy,” introduces new standards for transparency and neutrality, and sets in motion compliance frameworks that will likely influence both public and private sector adoption.
For AI builders, investors, and leaders, the key challenge ahead is this: how do you balance innovation, compliance, and values, without compromising competitive edge or global relevance? That question will shape the next phase of AI development in the U.S. and beyond.
We’re happy to help you navigate these new regulations and evaluate what they mean for your AI strategy. Get in touch to explore how you can future-proof your models for this evolving landscape.
