（来源：经济日报） 转自：经济日报 租车自驾、租相机记录美景、租无人机航拍……这个国庆假期，"以租代买"的消费模式在年轻游客中悄 然兴起。 再如，更关注产品实际使用价值。年轻群体更看重即时体验与性价比，不愿为低频使用的高价商品买 单。广告词这样说，"不在乎天长地久，只在乎曾经拥有"。而现在，"以租代买"让用户将重点从"拥 有"转向"使用"。 正是看到这一趋势，众多平台与商家积极布局租赁业务，配套推出保险、维保与物流服务，进一步降低 了商品使用门槛。"以租代买"甚至反过来推动企业改变盈利模式与产品策略。 信用体系的完善也为租赁经济提供了支撑。第三方征信机构纷纷入场，逐步建立起适应新租赁场景的信 用规则。以信用代替押金，成为新租赁经济最显著的特征之一。 当然，"租经济"也面临挑战。随着可租赁品类与场景不断拓展，平台方需要在放宽信用门槛与控制履约 风险、坏账率之间找到平衡。"残值管理"也是行业共同难题——当产品因老化或过时而无法继续出租 时，是该回收利用还是折价出售？目前市场上尚未形成一套公认的、标准化的残值评估体系。 随着假日消费场景不断细分，租赁正从一种"临时选择"逐渐发展为"长期习惯"。伴随人工智能、物联网 ...

Core Viewpoint - Indonesia has introduced a regulation aimed at assisting small and medium-sized enterprises (SMEs) and cooperatives in obtaining mining licenses without undergoing a bidding process [1] Group 1: Regulatory Changes - The new regulation is based on a law passed in February that grants priority access to certain mining areas for smaller companies, including business units of religious organizations [1] - Previously, Indonesia prioritized state-owned enterprises in resource allocation [1] - Only SMEs and cooperatives that meet specific criteria will be eligible for priority access [1] Group 2: Industry Context - Indonesia is the world's largest producer of nickel and exporter of thermal coal, with abundant deposits of tin, copper, and bauxite [1] - The country is also working on extracting rare earth elements from by-products generated during nickel and tin processing [1]

前言 昨天在知乎看到一个问题：如果老板在国庆放假前一天，突然给你加一大堆「优先级高」的工作，你会如何应对？ 不得不说，这种担心并非空穴来风，节前临时给咱打工人加活，让咱过不好节日的破事，很多打工人都经历过。 回复一句好的，然后整个假期肝在项目上，对我们个人来说不合适，对公司来说也未必是好选择。 当然，最好的选择是跟随一个靠谱的上司。先预祝大家都能过一个不用碰电脑的国庆假期。 不可能三角 分享一个我眼中的工作不可能三角： 关于优先级的话题，我之前写过的「重要紧急」四象限的讨论。但如果在此基础上，叠加一个三维空间的资源轴。 我能力有限，画不出来，大家可以想象一下。 重要+紧急→大量投入资源 平胸而论，再人性化的公司，也会有各种各样临时冒出来需要救火的事情。 尤其是好公司的打工人要有一个意识，得让好公司活下去，如果企业文化好员工福利好的公司没人救火最后死了，咱可能就不得不去一些日常压榨打工人 的公司。但这不意味着我们单个打工人要扛住所有压力，毁掉自己的假期。 举个常见的例子，给重要客户的解决方案在国庆前一天发现出了问题。客户气急败坏要解决方案，这时候无论咋样，都要给客户有个交代。 这个时候，就需要投入大量资源。 老 ...

Core Viewpoint - The article discusses the hidden energy costs associated with polite language in AI interactions, highlighting a global resource allocation dilemma as AI usage increases [6][8]. Group 1: Energy Consumption and AI - Each polite request in AI interactions, such as using "please" or "thank you," significantly increases energy consumption, with a single token processing requiring 0.0003 kWh [9][12]. - ChatGPT processes approximately 200 million requests daily, leading to an estimated annual energy consumption of 415 billion kWh for global data centers, enough to power Japan for 18 days [9][12]. - 40% of this energy is used for cooling systems, raising concerns about the environmental impact of AI technologies [9][14]. Group 2: Environmental Impact and AI Development - The article critiques claims from tech giants like Google and Microsoft that downplay the environmental impact of AI, arguing that the cumulative effect of billions of polite requests creates a significant ecological burden [11][12]. - In Virginia, data centers consume more electricity than the entire state's residential usage, causing local ecological damage, such as increased water temperatures leading to fish deaths [13][14]. Group 3: Solutions and User Behavior - Tech companies are exploring different strategies to mitigate energy consumption, such as OpenAI's $500 billion investment in new data centers and Meta's reduction of energy use in AI models [15][18]. - Research indicates that if users stopped using polite language, AI energy consumption could decrease by 18%, suggesting that user behavior plays a crucial role in energy efficiency [17][18]. - Innovations like "de-politeness" plugins and AI that anticipates user intent could further reduce unnecessary energy use in AI interactions [17][18].

Core Viewpoint - The article emphasizes the importance of not wasting time on problems that are likely to resolve themselves due to technological advancements or the passage of time, advocating for a focus on enduring challenges that require active solutions [5][6][12]. Group 1: AI Product Development Insights - AI products face two types of challenges: "transitional problems" that will be resolved with the next model update and "eternal dilemmas" that will persist regardless of AI advancements [5]. - Granola's approach to initially ignore a significant limitation in their product and instead focus on enhancing the quality of notes exemplifies the wisdom of prioritizing long-term value over immediate fixes [5][6]. Group 2: Broader Life Applications - The article draws parallels between the concept of not fixating on transient issues and common life scenarios, such as parental anxiety over children's development or workplace conflicts that may resolve themselves over time [7][9]. - It highlights the importance of recognizing which problems will naturally dissipate, thereby allowing individuals to allocate their resources more effectively [11]. Group 3: Strategic Resource Allocation - The article introduces a framework for resource allocation based on three perspectives: spatial awareness of the problem's context, temporal understanding of its evolution, and probabilistic assessment of its likelihood to disappear [11]. - It suggests categorizing problems into three classes: those solvable by technological progress, those that will resolve with time, and those that require active intervention [14][15]. Group 4: Actionable Guidelines - A three-step filtering method is proposed: classify problems by their likelihood of resolution, focus resources on critical issues, and conduct regular reviews to identify which problems have become irrelevant [14][15]. - The overarching message is that in a rapidly changing environment, sometimes the best action is to refrain from acting on issues that will resolve themselves, thus allowing focus on defining future challenges [15].

Group 1 - The core message emphasizes the importance of not wasting time on problems that are likely to resolve themselves due to technological advancements or the passage of time [2][12][38] - AI products face two types of challenges: "transitional problems" that will be automatically resolved with the next model update, and "eternal challenges" that will persist regardless of AI advancements [3][4] - The case of Granola illustrates the strategy of focusing on improving product quality rather than fixating on a temporary limitation, which was later resolved with the release of GPT-4 [5][9][10] Group 2 - The article discusses the broader implications of resource allocation, highlighting the need for a comprehensive perspective that includes spatial, temporal, and probabilistic views [27][29][30] - It suggests that recognizing which problems will disappear over time is a crucial skill in the rapidly evolving AI landscape [37][38] - The proposed "three-step filtering method" for problem management includes categorizing issues, focusing resources on core challenges, and regularly reviewing which problems have resolved themselves [42][43][44]