Classification
AI Systems Infrastructure
Overview
Compute refers to the hardware resources-such as CPUs (Central Processing Units), GPUs (Graphics Processing Units), high-performance computing (HPC) clusters, and Trusted Execution Environments (TEEs)-that are essential for running and scaling artificial intelligence (AI) systems. These resources determine the speed, efficiency, and feasibility of training and deploying advanced AI models. Compute availability can be a bottleneck for developing large-scale models and can also impact the accessibility of AI development across different regions and organizations. While compute is a foundational enabler for AI progress, it also raises concerns about concentration of power, environmental sustainability (due to high energy consumption), and the potential for misuse if powerful compute resources are used irresponsibly. A limitation is that access to state-of-the-art compute is often restricted to a few well-funded entities, potentially exacerbating global inequities in AI capabilities.
Governance Context
Governance of compute resources is addressed in several AI policy frameworks through obligations such as compute usage monitoring and export controls. For example, the U.S. Department of Commerce's Bureau of Industry and Security (BIS) imposes export controls on advanced GPUs and AI chips to certain countries to prevent misuse and proliferation. The EU AI Act introduces obligations for providers of high-risk AI systems, including requirements to document and assess the compute resources used, particularly for foundation models. Additionally, the OECD AI Principles recommend transparency in the development and deployment of AI systems, including disclosures about the compute infrastructure. Concrete obligations include: (1) mandatory reporting and documentation of compute usage for high-risk AI systems, and (2) enforcement of export controls and restrictions on access to advanced compute hardware. Controls such as regular auditing, user access management, and compliance with national security regulations are also becoming more prevalent, especially in contexts involving dual-use technologies.
Ethical & Societal Implications
The distribution and control of compute resources have significant ethical and societal implications. Concentration of compute in the hands of a few corporations or governments can exacerbate power imbalances and limit equitable access to AI benefits. High energy consumption of large compute clusters raises sustainability and environmental justice concerns. Inadequate governance may enable misuse, such as the development of harmful autonomous systems or mass surveillance tools. Conversely, overly restrictive controls may hinder beneficial research, especially in low-resource settings, impeding global progress and innovation. Ensuring fair, transparent, and sustainable access to compute is essential for fostering responsible and inclusive AI development.
Key Takeaways
Compute resources are foundational to AI model development and deployment.; Governance frameworks increasingly regulate access, documentation, and export of advanced compute.; Ethical risks include concentration of power, environmental impacts, and potential misuse.; Failure to manage compute can lead to security breaches or inequitable AI access.; Transparent and equitable compute governance is critical for responsible AI ecosystems.; Export controls and documentation requirements are concrete obligations in many jurisdictions.; Environmental sustainability must be considered in large-scale compute deployment.