Hugging Face Advocates for Open-Source AI in White House Plan
In a climate where minimal AI regulation is increasingly favored, Hugging Face is presenting a different perspective to the Trump administration: that open-source and collaborative AI development could be America’s most significant competitive edge. The AI platform company, hosting over 1.5 million public models, has put forth its recommendations for the White House AI Action Plan, positing that recent breakthroughs in open-source models display capabilities that match or even surpass those of closed commercial systems, at a fraction of the cost.
The company’s official submission highlights key achievements like OlympicCoder, demonstrating superior performance on complex coding tasks using only 7 billion parameters, and AI2’s fully open OLMo 2 models, rivaling the performance of OpenAI’s o1-mini.
This submission is part of a larger effort by the Trump administration to gather information for its upcoming AI Action Plan. Executive Order 14179, titled “Removing Barriers to American Leadership in Artificial Intelligence,” mandated this plan and was issued in January; it signaled a shift from the more regulation-focused approach of the Biden administration, emphasizing U.S. competitiveness and reducing development barriers.
Hugging Face’s submission contrasts sharply with those of commercial AI leaders like OpenAI, which has been lobbying for light-touch regulation while warning about China’s increasing AI capabilities. OpenAI’s proposal advocates for “a voluntary partnership between the federal government and the private sector” rather than state laws that, in their view, are “overly burdensome.”
How Open Source Could Power America’s AI Advantage
Hugging Face centers its recommendations on three interconnected pillars to democratize AI technology, arguing this enhances America’s competitive standing. Their submission stated, “The most advanced AI systems to date all stand on a strong foundation of open research and open source software — which shows the critical value of continued support for openness in sustaining further progress.”
The company’s first pillar supports strengthening open and open-source AI ecosystems through investments in research infrastructure, like the National AI Research Resource (NAIRR), and ensuring broad access to trusted datasets. This approach contrasts with OpenAI’s emphasis on copyright exemptions, which would enable proprietary models to train on copyrighted material without explicit permission. Hugging Face noted that “Investment in systems that can freely be re-used and adapted has also been shown to have a strong economic impact multiplying effect, driving a significant percentage of countries’ GDP,” highlighting how open approaches boost economic growth.
Smaller, Faster, Better: Democratizing the AI Revolution
Hugging Face’s second pillar concentrates on addressing resource constraints faced by smaller organizations that can’t afford the computational demands of large-scale models. By advocating for more efficient, specialized models that can operate on fewer resources, Hugging Face argues that the U.S. can broaden participation in the AI ecosystem. The submission explains, “Smaller models that may even be used on edge devices, techniques to reduce computational requirements at inference, and efforts to facilitate mid-scale training for organizations with modest to moderate computational resources all support the development of models that meet the specific needs of their use context.”
Addressing security, an administration focus, Hugging Face makes the counterintuitive case that open and transparent AI systems may be more secure for critical applications. The company suggests that “fully transparent models providing access to their training data and procedures can support the most extensive safety certifications,” while “open-weight models that can be run in air-gapped environments can be a critical component in managing information risks.”
Big Tech vs. Little Tech: The Emerging Policy Battle
Hugging Face’s stance underscores the industry’s growing policy divisions. While companies like OpenAI and Google push for faster regulatory processes and reduced government oversight, venture capital firm Andreessen Horowitz (a16z) has proposed a middle ground. They argue for federal leadership to prevent a patchwork of state regulations, focusing regulation on specific harms rather than model development. A16z wrote in their submission, “Little Tech has an important role to play in strengthening America’s ability to compete in AI in the future, just as it has been a driving force of American technological innovation historically,” echoing Hugging Face’s democratization arguments. Google’s submission focused on infrastructure investments, emphasizing “surging energy needs” for AI deployment, a practical concern shared across industry positions.
Between Innovation and Access: The Race to Influence American AI
As the administration navigates competing visions for American AI leadership, the core tension between commercial advancement and democratic access persists. OpenAI prioritizes speed and competitive advantage through a centralized approach, while Hugging Face argues that distributed, open development can deliver similar results while spreading benefits more broadly. Economic and security arguments will likely determine which vision prevails.
If administration officials embrace Hugging Face’s assertion that “a robust AI strategy must leverage open and collaborative development to best drive performance, adoption, and security,” open-source could find a prominent role in national strategy. However, if concerns regarding China’s AI capabilities dominate, OpenAI’s push for minimal oversight might win out.
Ultimately, the AI Action Plan will shape American technological development for years to come. As Hugging Face’s submission concludes, open and proprietary systems can play complementary roles, suggesting that the most effective policy might leverage the unique strengths of each approach, rather than choosing between them. The critical question is whether American AI leadership benefits the few or fosters innovation for the many.
