Training advanced AI models requires large investments in computational resources, or compute. Yet, as hardware innovation reduces the price of compute and algorithmic advances make its use more efficient, the cost of training an AI model to a given performance falls over time - a concept we describe as increasing compute efficiency. We find that while an access effect increases the number of actors who can train models to a given performance over time, a performance effect simultaneously increases the performance available to each actor. This potentially enables large compute investors to pioneer new capabilities, maintaining a performance advantage even as capabilities diffuse. Since large compute investors tend to develop new capabilities first, it will be particularly important that they share information about their AI models, evaluate them for emerging risks, and, more generally, make responsible development and release decisions. Further, as compute efficiency increases, governments will need to prepare for a world where dangerous AI capabilities are widely available - for instance, by developing defenses against harmful AI models or by actively intervening in the diffusion of particularly dangerous capabilities.

翻译：训练先进的人工智能模型需要大量的计算资源投入。然而，随着硬件创新降低计算成本，算法进步提高计算使用效率，将AI模型训练至既定性能水平的成本随时间推移而下降——我们将这一概念称为计算效率提升。研究发现，虽然接入效应使得更多主体能够随时间推移训练出既定性能水平的模型，但性能效应同时提升了每个主体可获得的模型性能。这使大型计算投资者能够率先开拓新能力，即使当能力扩散时仍能保持性能优势。由于大型计算投资者往往最先开发新能力，他们尤需共享AI模型信息、评估新兴风险，并总体上做出负责任的开发与发布决策。此外，随着计算效率提升，政府需为危险AI能力广泛普及的世界做好准备——例如，通过开发防御有害AI模型的手段，或主动干预特别危险能力的扩散。