This podcast explores the regulation of AI chips in governing frontier AI development. It discusses challenges in regulating data and algorithms, the global supply chain in manufacturing AI chips, and the potential for government regulation of AI chip quantities. It also covers the assembly process and selling of AI chips, the dominance of US companies in the market, and the scale and cost of chip fabrication factories.
25:10
AI Summary
AI Chapters
Episode notes
auto_awesome
Podcast summary created with Snipd AI
Quick takeaways
Regulating AI chips allows governments to control frontier AI development and set rules for developers.
AI chips are vital for training advanced AI models, and their importance is reflected in the growing use of specialized chips in academia and industry.
Deep dives
Regulating AI Chips for Governance of Frontier AI Development
Regulating the use of AI chips can enable governments to govern frontier AI development and set rules for who can develop advanced AI models and under what regulations. While regulating data or algorithms is challenging, AI chips are physical objects that can be tracked and restricted by governments. The global supply chain for AI chips is dominated by a small number of companies and countries, giving governments the ability to enforce safety standards for importing AI chips. However, regulating AI chips may only temporarily regulate the development of potentially dangerous AI models, as the number and sophistication of chips needed to train AI models decreases over time. Well-resourced states could continue development without relying on cutting-edge AI chips, and changes in AI chip manufacturing could make export restrictions difficult to enforce.
Crucial Role of AI Chips in Frontier AI Development
AI specialized computer chips, such as Nvidia's A100 and Google's TPUV4, are crucial for frontier AI development. These specialized chips can perform vast numbers of operations required for training AI models, and they are much more efficient and cost-effective than using older chips or general-purpose chips. Frontier AI models are already trained using tens of thousands of AI chips, and trends indicate that more advanced AI will require even more computing power. The rapid advancement of AI capabilities is closely tied to increasing compute power, and training AI models with more compute tends to result in more advanced capabilities. The importance of computing power for AI is reflected in the growing use of AI chips in both academia and industry.
The Feasibility and Limitations of Compute Governance
Governments can likely regulate the use of AI chips due to their physical nature and the dominance of a few countries in the global supply chain. However, there are potential limitations to compute governance. As AI models become more efficient, the compute requirements for dangerous capabilities could be achieved with less cutting-edge hardware, making regulation more challenging. Well-resourced states could continue with advanced AI development using older AI chips, undermining strategies that rely on excluding them. Furthermore, drastic changes in AI chip manufacturing, such as the adoption of alternative hardware paradigms, may make export restrictions infeasible. Despite these limitations, compute governance can still be valuable in buying time and preparing for the proliferation of advanced AI capabilities. Governments have already taken major actions to regulate AI chips, such as restricting their export to China, and compute governance could also enable international cooperation and verification of AI agreements.
If governments could regulate the large-scale use of “AI chips,” that would likely enable governments to govern frontier AI development—to decide who does it and under what rules.
In this article, we will use the term “AI chips” to refer to cutting-edge, AI-specialized computer chips (such as NVIDIA’s A100 and H100 or Google’s TPUv4).
Frontier AI models like GPT-4 are already trained using tens of thousands of AI chips, and trends suggest that more advanced AI will require even more computing power.