Hugging Face Calls on US Government to Prioritize Open-Source Development in AI Action Plan
Hugging Face has emphasized the importance of open-source development in its statement to the Office of Science and Technology Policy (OSTP), urging the US government to prioritize open-source development in its forthcoming AI Action Plan. The company believes that thoughtful policy can support innovation while ensuring AI development remains competitive and aligned with American values.
Hugging Face proposes an AI Action Plan centered on three interconnected pillars: strengthening open-source AI ecosystems, efficient and reliable adoption of AI, and promoting security and standards.
Strengthening Open-Source AI Ecosystems
Hugging Face stresses the importance of technical innovation stemming from diverse actors across institutions and support for infrastructure, such as the National AI Research Resource (NAIRR), and investment in open science and data. The company argues that this infrastructure allows for the additive effect of contributions and accelerates robust innovation.
Efficient and Reliable Adoption of AI
Hugging Face believes that spreading the benefits of AI technology requires actors across sectors to shape its development. The company argues that more efficient, modular, and robust AI models require research and infrastructural investments to enable the broadest possible participation and innovation, enabling diffusion of technology across the US economy.
Promoting Security and Standards
Hugging Face highlights the need to promote security and standards, suggesting that decades of practices in open-source software cybersecurity, information security, and standards can inform safer AI technology. The company advocates for promoting traceability, disclosure, and interoperability standards to foster a more resilient and robust technology ecosystem.
Open-Source is Key for AI Advancement in the US (and Beyond)
Hugging Face underlines that modern AI is built on decades of open research, with commercial giants relying heavily on open-source contributions. Recent breakthroughs, such as OLMO-2 and Olympic-Coder, demonstrate that open research remains a promising path to developing systems that match the performance of commercial models and can often surpass them, especially in terms of efficiency and performance in specific domains.
Practical Factors Driving Commercial Adoption of Open-Source AI
Hugging Face identifies several practical factors driving the commercial adoption of open models:
- Cost efficiency: Developing AI models from scratch requires significant investment, so leveraging open foundations reduces R&D expenses.
- Customization: Organizations can adapt and deploy models specifically tailored to their use cases, rather than relying on one-size-fits-all solutions.
- Reduced vendor lock-in: Open models give companies greater control over their technology stack and independence from single providers.
- Open models have caught up to and, in certain cases, surpassed the capabilities of closed, proprietary systems.
Hugging Face’s Policy Recommendations to Support Open-Source AI in the US
To support the development and adoption of open AI systems, Hugging Face offers the following policy recommendations:
- Enhance research infrastructure: Fully implement and expand the National AI Research Resource (NAIRR) pilot.
- Allocate public computing resources for open-source: The public should have ways to participate via public AI infrastructure.
- Enable access to data for developing open systems: Create sustainable data ecosystems through targeted policies that address the decreasing data commons.
- Develop open datasets: Invest in the creation, curation, and maintenance of robust, representative datasets that can support the next generation of AI research and applications.
- Strengthen rights-respecting data access frameworks: Establish clear guidelines for data usage, including standardised protocols for anonymisation, consent management, and usage tracking.
- Invest in stakeholder-driven innovation: Create and support programs that enable organizations across diverse sectors (healthcare, manufacturing, education) to develop customised AI systems for their specific needs.
- Strengthen centres of excellence: Expand NIST’s role as a convener for AI experts across academia, industry, and government to share lessons and develop best practices.
Prioritizing Efficient and Reliable AI Adoption
Hugging Face highlights that smaller companies and startups face significant barriers to AI adoption due to high costs and limited resources. According to IDC, global AI spending will reach $632 billion in 2028, but these costs remain prohibitive for many small organizations.
For organizations adopting open-source AI tools, it brings financial returns. 51% of surveyed companies currently utilizing open-source AI tools report positive ROI, compared to just 41% of those not using open-source.
However, energy scarcity presents a growing concern, with the International Energy Agency projecting that data centers’ electricity consumption could double from 2022 levels to 1,000 TWh by 2026, equivalent to Japan’s entire electricity demand.
Ensuring broad AI accessibility requires both hardware optimisations and scalable software frameworks. A range of organizations are developing models tailored to their specific needs, and US leadership in efficiency-focused AI development presents a strategic advantage. The DOE’s AI for Energy initiative further supports research into energy-efficient AI, facilitating wider adoption without excessive computational demands.
With its letter to the OSTP, Hugging Face advocates for an AI Action Plan centered on open-source principles. By taking decisive action, the US can secure its leadership, drive innovation, enhance security, and ensure the widespread benefits of AI are realized across society and the economy.
FAQs
Q: What is the main argument made by Hugging Face in its statement to the OSTP?
A: Hugging Face emphasizes the importance of open-source development in its statement to the OSTP, urging the US government to prioritize open-source development in its forthcoming AI Action Plan.
Q: What are the three interconnected pillars of Hugging Face’s AI Action Plan?
A: The three pillars are strengthening open-source AI ecosystems, efficient and reliable adoption of AI, and promoting security and standards.
Q: What are the practical factors driving the commercial adoption of open models?
A: The practical factors include cost efficiency, customization, reduced vendor lock-in, and open models having caught up to and, in certain cases, surpassed the capabilities of closed, proprietary systems.
Q: What are some of the policy recommendations made by Hugging Face to support open-source AI in the US?
A: The policy recommendations include enhancing research infrastructure, allocating public computing resources for open-source, enabling access to data for developing open systems, developing open datasets, and strengthening rights-respecting data access frameworks, among others.
