Tool-use is already a bottleneck in AI reasoning models, and it will become even more problematic as the underlying models become faster. The reason is that the sandbox, infrastructure, and execution environment needed for the tools (e.g., running code, search, database and file access) can be a very resource-intensive tax on top of the costs of running the AI model. For example, Hierarchical Decoupled Policy Optimization (HDPO), a technique developed by Alibaba, trains LLMs to avoid excessive tool-use. Another interesting example is Code World Model (CWM) by Meta, which uses a world model to predict code results without spinning up execution environments. This helps speed up the training of LLMs on coding tasks. Moving forward, as inference becomes faster and cheaper, I expect the other elements to catch up, such as faster containers and execution environments for AI tools.