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.
Last May, I wrote a blog post titled As an Experienced LLM User, I Actually Don’t Use Generative LLMs Often as a contrasting response to the hype around the rising popularity of agentic coding. In that post, I noted that while LLMs are most definitely not useless and they can answer simple coding questions faster than it would take for me to write it myself with sufficient accuracy, agents are a tougher sell: they are unpredictable, expensive, and the hype around it was wildly disproportionate given the results I had seen in personal usage. However, I concluded that I was open to agents if LLMs improved enough such that all my concerns were addressed and agents were more dependable.
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ProWritingAid VS Grammarly: Pricing Difference。关于这个话题，谷歌浏览器【最新下载地址】提供了深入分析
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