How the NNSA RFI Aligns With and Compares to the Genesis Executive Order & the OSTP RFI

by ChatGPT-5.1

The NNSA Request for Information (RFI)—Transformational AI Capabilities for National Security—explicitly positions itself as an implementation instrument for the Genesis Executive Order (EO) issued on 24 November 2025. The EO mandates the creation of an integrated AI platform leveraging federal scientific datasets to train foundation models, create AI agents, and accelerate research and hypothesis testing. The NNSA RFI states upfront that it is issued “in furtherance of the Genesis Mission” and describes the EO’s scientific foundation-model agenda almost verbatim.

In parallel, the RFI echoes and operationalizes themes from the OSTP RFI on the Federal Science Enterprise, especially around:

• AI for science acceleration

• Public-private collaboration models

• Data governance, FAIR principles, and infrastructure modernization

• Use-inspired research and translation into applications

Although the OSTP RFI is not explicitly quoted in the files, the alignment is clear: both seek stakeholder input on data, model development, governance, and partnership frameworks to modernize the U.S. scientific innovation ecosystem.

Below is a structured comparison showing how the NNSA RFI interacts with both policy frameworks.

1.1 Alignment with the Genesis Executive Order

a. Purpose: Building a federal AI infrastructure for scientific discovery

Genesis EO goal:
Create “an integrated Artificial Intelligence (AI) platform to harness federal scientific datasets to train scientific foundation models” and develop AI agents to test hypotheses and automate research workflows.
The RFI repeats this explicitly and extends it to nuclear security, weapons design, and nonproliferation monitoring.

NNSA-specific amplification:
The EO does not specify weapons or deterrence timelines; the RFI does. The RFI frames AI acceleration as necessary for “accelerating nuclear weapons development timelines” and strengthening national security. This goes beyond the EO’s general science goals and moves into high-consequence national security applications.

b. Implementation Mechanism: Partnerships & Market Capabilities

Genesis EO assigns DOE responsibility for implementation; the RFI is DOE/NNSA’s first operational step.
The DOE press release emphasizes that the RFI demonstrates “swift action” (one week after the EO) and shows NNSA’s commitment to “deploying and rapidly advancing AI” in line with the EO’s mandate.

Where the EO sets the vision, the RFI provides:

• Specific technical areas (classified AI, self-improving models, data curation, HPC integration)

• Partnership models (consortia, public-private co-design)

• IP frameworks and data rights considerations

• Security vetting requirements (RTES program)

These details operationalize the EO.

c. Data Strategy: FAIR, interoperable, AI-ready

The EO focuses on unlocking federal scientific data to accelerate model training; the NNSA RFI mirrors this with more technical specificity. It requests:

• FAIR repositories for classified environments

• Data desensitization, anonymization, and preprocessing methods

• Requirements for structuring physics/engineering datasets

This is consistent with Genesis’ data governance emphasis, but tailored to nuclear and classified contexts.

d. AI Model Development Priorities:

Genesis EO: Scientific foundation models and AI agents for discovery.
NNSA RFI: Extends these to:

• High-consequence engineering

• Nuclear deterrence design cycles

• Nonproliferation monitoring

• Integration with classified HPC systems

In effect, the RFI is creating a parallel national security version of Genesis: same platform architecture, different mission domain.

1.2 Alignment with the OSTP RFI (OSTP: Public-Private Models, Data, Translation)

The OSTP RFI (per the text in your separate OSTP questions) emphasizes:

• Stronger public-private collaboration

• Technology transfer

• Regional innovation ecosystems

• Data governance

• AI for accelerating scientific research

The NNSA RFI operationalizes many of these themes, but with more restrictive guardrails:

a. Public–private collaboration

The RFI seeks partnerships with:

• AI developers

• Cloud/secure infrastructure providers

• Universities

• Think tanks

• Investors

This directly mirrors the OSTP’s goal of mobilizing the private sector for use-inspired research.

But the NNSA version adds:

• Classified facility requirements

• National security vetting

• Proprietary IP constraints

• RTES restrictions on foreign influence

This is much stricter than OSTP’s general science environment.

b. Data governance and infrastructure

The OSTP RFI calls for FAIR data, standardization, and infrastructure modernization.
The NNSA RFI mirrors this, requiring:

• FAIR repositories within classified architectures

• Need-to-know controls

• Privacy-preserving training methods

Thus, NNSA is translating OSTP’s open-science frameworks into closed science for national security.

c. Translation of scientific breakthroughs into applications

The OSTP RFI discusses accelerating the path from lab to market.
The NNSA RFI reframes this as:

• Accelerating nuclear weapons development

• Enhancing nonproliferation missions

• Developing “self-improving models” for high-consequence design

This resembles the same innovation pipeline, but directed toward deterrence rather than commercial markets.

d. Governance models

OSTP: modern, equitable, innovation-friendly frameworks.

NNSA: governance frameworks that ensure:

• Compliance with DOE mandates

• Classified environment requirements

• Technology security (RTES)

• Severe restrictions on participation if foreign influence risks arise

This tension is significant: OSTP promotes openness, NNSA demands controlled access.

2. What the Scientific Community & Scholarly Publishers Should Be Mindful Of

Despite the national security framing, this RFI contains multiple elements highly consequential for universities, research organizations, and scholarly publishers.

Here are the main points that require attention.

2.1 Expansion of Foundation Models Built on Federal Scientific Data

Genesis EO establishes federal scientific datasets as training sources for foundation models. The NNSA RFI reinforces this and includes classified science, physics, and engineering data.

Implications:

• Scientific datasets curated by federally funded universities may be drawn into Genesis-related model training.

• Publishers hosting large volumes of scientific data may be asked for structured access pathways.

• Once integrated into a national platform, scientific data could be used in multiple mission domains—including military ones—raising ethical and attribution issues.

2.2 Pressure to Standardize Data for AI-readiness

The RFI’s data curation section is clear: NNSA expects FAIR, structured, AI-ready datasets for training advanced models.
This has implications for:

• Repository architectures

• Metadata standards

• Licensing formats

• Data quality expectations

• Accessibility and interoperability requirements

Scientific publishers may face new demand to supply data formats compatible with Genesis systems.

2.3 IP Rights and Data Rights Frameworks Could Shift

The RFI asks for input on new models for:

• Invention rights

• Data rights

• Patent rights

to support “AI dominance for agile deterrence.”
This is notable: DOE is asking whether standard DOE IP frameworks need to be loosened or redesigned.

For publishers, this may mean:

• Requests to relax data-use restrictions for federal AI training

• New expectations for bulk access and machine-readable licenses

• Greater governmental claims over data shared via consortium participation

2.4 National Security Vetting (RTES Requirements)

Appendix I sets severe limitations on participation:

• No involvement by “entities of concern”

• Mandatory disclosure of foreign affiliations, investments, partnerships, and IP transfers

• Obligations to report changes in governance, ownership, or foreign ties within 15 days

• Government right to terminate involvement unilaterally

This is an extremely strict version of research security protocols that will affect:

• Global research collaborations

• Publishers with multinational ownership or operations

• Editorial boards with foreign affiliations

• Institutions depending on international students and researchers

2.5 Potential Impact on Open Science Norms

Genesis promotes unlocking federal data for AI, but NNSA’s version introduces:

• Classified environments

• Restricted datasets

• Controlled computing enclaves

• Zero tolerance for foreign influence

• Governance models designed around secrecy rather than openness

This represents a divergence between:

• Open science (OSTP)

• Closed national security science (NNSA)

Scientific stakeholders must prepare for this bifurcation.

3. How the Scientific Community & Scholarly Publishers Should Respond

Below is a strategic response framework tailored to universities, scientific societies, and publishers.

3.1 Engage Proactively — But Establish Clear Boundaries

Respond constructively to the RFI and the OSTP consultation, emphasizing:

• Expertise in data governance and FAIR data stewardship

• The ability to support discovery workflows

• Curation quality, metadata schemas, and reproducibility frameworks

But draw limits around:

• Extractive or unilateral IP arrangements

• Uses inconsistent with scientific ethics

• Lack of attribution or provenance guarantees

• Any attempt to require unbounded access to proprietary scientific content

3.2 Advocate for Strong Attribution, Usage Transparency, and Data-Rights Safeguards

Given the RFI’s openness to revising DOE data rights and patent rights, publishers should argue for:

• Mandatory attribution of any training dataset origins

• Clear governance on downstream usage of publisher content

• No “functionally derivative” outputs that reproduce copyrighted material

• Restrictions on re-use of scientific content for classified weapons applications without explicit agreements

Genesis risks creating a quasi-compulsory licensing regime for federal AI projects unless stakeholders push back.

3.3 Protect Open Science Norms in the Face of National Security Pressures

Publishers and universities should emphasize:

• The need for a dual-track model: open-science AI and classified/scientific AI must remain separate

• Preservation of cross-border scientific collaboration

• Balance between research security and academic freedom

Without such advocacy, research security rules (RTES) may become a template for broader scientific governance.

3.4 Address Risks in Written Responses

Key Risks to Highlight:

1. Risk of data appropriation:
   Genesis could create expectations that all scientific data—public or proprietary—should be available for government AI training.

2. Risk to international collaboration:
   RTES criteria would make many multinational research programs impossible.

3. Risk of erosion of copyright and licensing norms:
   Requests for revised IP frameworks may be used to justify compulsory licensing.

4. Risk of dual-use drift:
   Scientific research could be repurposed for weapons development without transparency or consent.

5. Risk to academic freedom and scientific neutrality:
   The RFI heavily ties scientific AI research to nuclear deterrence priorities.

3.5 Identify Benefits — but Frame Them Carefully

The RFI does create opportunities:

• New funding streams for AI-for-science infrastructure

• Partnerships with national labs

• Modernization of data repositories

• Increased demand for standardized metadata, APIs, and high-quality scientific content

• Enhanced visibility for publishers leading in data-ready pipelines

These should be framed as conditional benefits, dependent on:

• Respect for IP

• Clear licensing terms

• Ethical guardrails

• Non-militarization of civilian data without consent

3.6 Propose Concrete Recommendations to the Agencies

For DOE/NNSA (RFI response):

• Require tiered access to scientific data rather than blanket rights.

• Guarantee dataset provenance tracking within models.

• Adopt federated training approaches to avoid content exfiltration.

• Provide clear separation between scientific and weapons-design AI pipelines.

• Specify use-purpose limitations for publisher content.

For OSTP (Genesis-wide governance):

• Create a federal Scientific Data Licensing Framework for AI.

• Mandate model disclosure of data sources used in federal foundation models.

• Protect international collaboration under research security rules.

• Fund university and publisher data-modernization efforts to meet FAIR/AI-ready standards.

Conclusion

The NNSA RFI is the first concrete operationalization of the Genesis Executive Order, and while it aligns closely with the EO and shares its themes with the OSTP RFI, it introduces significantly more stringent national security constraints. These constraints—especially RTES foreign influence rules, new IP frameworks, classified FAIR data requirements, and explicit weaponization contexts—require careful attention from the scientific community and scholarly publishers.

Publishers and universities must engage, but they must also set boundaries, ensuring:

• IP rights are respected

• Scientific data is not appropriated

• International scientific norms remain intact

• The dual-use risks of AI are acknowledged and mitigated

• Ethical and mission-aligned uses of scientific content are preserved

The research is clear: the future is not “AI everywhere.” The future is “AI integrated through evidence, rights, and human expertise.”

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