America’s AI Action Plan is one of the most aggressive, industrial-age-style technology strategies in modern history. It recognizes AI as a new frontier of power, but in doing so, it marginalizes...

Analysis of America’s AI Action Plan (July 2025)

by ChatGPT-4o

Executive Summary

America’s AI Action Plan under President Trump’s administration lays out an aggressive and comprehensive roadmap to secure U.S. global dominance in artificial intelligence. Framed as a geostrategic imperative, the plan revolves around three pillars: (1) Accelerate AI Innovation, (2) Build American AI Infrastructure, and (3) Lead in International AI Diplomacy and Security. It repositions AI as not merely a technological tool, but a national power asset akin to space dominance or nuclear supremacy.

The plan boldly strips down regulatory safeguards, sidelines equity and environmental goals, and emphasizes free speech absolutism and open-weight AI as tools of geopolitical competition. While it champions American innovation and industry leadership, it also risks exacerbating international tensions, neglecting social harms, and enabling unchecked corporate expansion into sensitive domains.

Key Observations

1. AI as National Power

The plan views AI not as a global commons but as a domain of power projection—military, economic, and diplomatic. It echoes Cold War-era race rhetoric, e.g., “win the race,” “dominance,” and “Build, Baby, Build,” particularly in the competition with China.

2. Rejection of Progressive Guardrails

The explicit removal of DEI, climate considerations, and misinformation filters from NIST frameworks marks a dramatic shift from the Biden-era Executive Order 14110. There’s a sharp pivot toward free-market absolutism and away from AI ethics or risk mitigation unless linked to national security.

3. Open-Source Strategic Framing

Unlike Europe’s cautious embrace, the plan casts open-source AI as a geostrategic asset—a means to set global norms, reduce dependence on Big Tech, and enable flexible deployment in sensitive environments (e.g., defense, academia).

4. Content Rights and IP Strategy

There is no mention of strengthening copyright frameworks or addressing rights holder concerns. The focus is on compute, models, and infrastructure—not the legitimacy or sourcing of the data used to train frontier models. This omission is concerning for publishers, authors, and creators whose content underpins AI development.

5. Environmental, Social, and Governance (ESG) Backlash

By dismantling permitting safeguards, opposing climate targets, and prioritizing fossil fuel-friendly grid upgrades, the plan undermines ESG principles. This could place U.S. firms at odds with European counterparts and global sustainability targets.

Implications for Stakeholders

🧠 AI Makers & Developers

• Upside: Favorable regulatory conditions, government procurement incentives, and strong support for infrastructure and compute.

• Downside: Expected to align with political mandates on content neutrality, free speech absolutism, and potential surveillance or military partnerships.

• Recommendation: Invest in risk management, build self-regulatory structures, and prepare for dual-use scrutiny.

📚 Content & Rights Owners (incl. Scholarly Publishers)

• Risk: No provisions to ensure attribution, consent, or licensing for training datasets—especially for open-weight models.

• Opportunity: Possibility to contribute to the National AI Research Resource (NAIRR) and set terms for content usage in scientific datasets.

• Recommendation: Negotiate licensing deals proactively; advocate for dataset provenance standards; establish alliances with NAIRR and DOC.

🌐 National & International Regulators

• Challenge: The plan’s deregulatory posture undermines emerging consensus around safety, bias, and rights protections.

• Risk: Escalation of AI protectionism, including retaliatory export controls or standards fragmentation.

• Recommendation: EU and other democracies should coordinate to reinforce human-centric AI frameworks, promote reciprocity in data use, and standardize risk-based auditing.

✍️ Authors & Creators

• Risk: Lack of compensation or recognition in open-weight ecosystem; possible misuse of likeness or creative work via synthetic media.

• Policy Gap: Despite a section on “combatting synthetic media,” the plan does not address training data rights.

• Recommendation: Advocate for transparency in model inputs; push for watermarking, attribution standards, and opt-out registries; consider collective rights management initiatives.

Strategic Recommendations

For AI Makers:

1. Develop internal ethical review boards to counteract deregulated environments.

2. Align open-weight releases with watermarking and provenance tracking.

3. Monitor geopolitical risks related to export controls and model access.

For Content Owners and Scholarly Publishers:

1. Push for enforceable provenance and licensing standards in federal AI R&D funding frameworks (NSF, NAIRR).

2. Offer curated datasets under conditional licensing (non-commercial, attribution-based).

3. Collaborate with academia on AI evaluation and reproducibility science to reinforce the value of authoritative content.

For Regulators:

1. Engage in multilateral diplomacy to prevent regulatory arbitrage and fragmentation.

2. Establish cross-border AI incident response networks with shared standards and datasets.

3. Fund “trust infrastructure” (e.g., content authenticity infrastructure, model transparency) at parity with compute and data center investments.

For Authors and Creators:

1. Campaign for a Federal “Creativity and AI Rights Act” establishing economic rights over data derivatives.

2. Demand inclusion in any government-funded AI procurement or dataset initiative.

3. Collaborate with consumer advocacy groups to push for AI disclosures in creative applications.

Final Thoughts

America’s AI Action Plan is one of the most aggressive, industrial-age-style technology strategies in modern history. It recognizes AI as a new frontier of power—but in doing so, it marginalizes ethics, creator rights, and global cooperation. While it provides a coherent playbook for national AI supremacy, its neglect of equitable innovation, human rights, and IP accountability could sow the seeds of backlash at home and abroad.

For C-level leaders—especially in content, research, or innovation-heavy sectors—this plan is both a call to action and a warning. The opportunities are vast, but so are the risks. The path forward must include shaping AI’s future with integrity, not just velocity.