icon-to-image#As someone who primarily works in Python, what first caught my attention about Rust is the PyO3 crate: a crate that allows accessing Rust code through Python with all the speed and memory benefits that entails while the Python end-user is none-the-wiser. My first exposure to pyo3 was the fast tokenizers in Hugging Face tokenizers, but many popular Python libraries now also use this pattern for speed, including orjson, pydantic, and my favorite polars. If agentic LLMs could now write both performant Rust code and leverage the pyo3 bridge, that would be extremely useful for myself.
Consider the energy crunch: Global data-center power demand will more than double by 2030, per the International Energy Agency, forcing upgrades to grids, water systems, and connectivity. China’s state grids are embarking on a 5 trillion yuan ($722 billion) expansion explicitly for AI and data centers that is equivalent to 4% of GDP, according to Moody’s. The Qatar Investment Authority has announced a project worth $20 billion (9% of the nation’s GDP), to develop AI data centers and computing infrastructure. And in Korea, despite AI-related spending only accounting for 0.4% of GDP, the country’s recently established sovereign wealth fund is almost exclusively targeted at high-tech industries including AI and chips, while planning to deploy a war chest worth 5.7% of GDP over the next five years.
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