{
  "text": "更多时候,人民需要的是足够强,同时便宜、稳定、能部署,并且不会因为地区限制、产品分层或者政策变化突然断供的模型。\nHello大家好,我是Jacky。最近Anthropic,也就是Claude背后的公司,被曝在澳洲寻找大规模数据中心。\n它已经和澳大利亚政府签了AI的合作文件。据媒体报道,Anthropic还在接触大约1.4吉瓦级别的数据中心容量。\n这个新闻其实放在日新月异的AI圈很容易被掩埋\n但我觉得它真正有意义\n是把AI竞争从模型榜单拉回了物理世界\nAnthropic是一个最会写代码写文章做推理的AI公司\n但它下一个最关心的东西\n开始变成了哪里有地哪里有电\n哪里能接上数据中心\n过去我们总是在问谁的模型更加聪明\n谁的推理能力更强\n但随着AI的发展\n中美都发现\n如果AI最后变成一种\n可以被大规模部署\n持续调用\n按token计价的能力\n那竞争的范围就远不止是模型本身\n而是谁能把这种能力\n以低成本稳定大规模的供给出去\n那如果把模型\nAPI和智能体\n看成是AI公司卖出去的产品\n那背后的生产函数很清晰\n芯片是机器\n数据中心是厂房\n算法是工艺\n数据是配方\n而电力就是持续消耗的生产资料\n虚拟一个模型\n本质上是在把电\n芯片时间\n数据和算法工艺\n压缩成一组模型权重\n那么这个模型每推理一次\n本质上就是在把电和算力\n持续换成一个又一个token\n所以Anthropic去找数据中心\n找电力\n这件事它一点都不奇怪\n我们之前太习惯\n把AI想象成云上的魔法了\n好像模型只要足够聪明\n它就能无限复制\n无限扩张\n但现实根本不是这个样子\nIEA的报告里有个很直观的数字\n全球数据中心用电到2030年预计会翻倍\n到大约945泰瓦时\n美国能源部LBNL的报道也提到过\n美国数据中心在2023年\n已经消耗了美国大约4.4%的总用电量\n到了2028年可能会上升至6.7%到12%\n你再看Answerpick自己今年2月份的公开承诺\n他说愿意承担自家数据中心带来的消费者定价上涨\n还愿意支付电网升级的成本\n那以前的软件公司扩张用户多了\n无非就是服务器带宽云成本上去一点\n而现在一个前沿模型公司的扩张\n可能会直接改变一个市一个州\n甚至一个电网区域的复合结构\n那如果你在审视中美AI发展的时候\n带上这个视角\n那中美AI的差异就不只是谁的模型能力更强了\n而是谁更能把模型能力做成长期低成本稳定可用的供给\n先来说美国\n美国在AI领域确实是领跑者\nOpenAI\nAnswerpick\n谷歌这些公司仍然在定义最前沿必源模型的节奏\n更重要的是\n美国它拥有一整套前沿AI的产业配套\n英伟达\nAMD这样的芯片公司\nAWS\nGoogle Cloud这样的云平台\n包括成熟的开发者生态\n以及愿意持续砸大钱的资本市场\nStanford AI Index 2026 里也能看到\n美国在有影响力模型的数量和私人 AI 投资上仍然非常领先\n2025 年美国机构产生了59个有影响力的模型\n中国是35个\n私人 AI 投资这一块美国也明显是高得多\n所以如果你只是问最强的闭源模型是谁做出来的\n那我觉得在很长一段时间里\n这个答案大概率还是美国\n不过这条路线的代价也日渐凸显\n伯克利Lab今年的并网电力数据\n很能说明这个问题\n到2025年底\n美国有超过2060吉瓦的发电和储能项目\n在排队接入电网\n注意啊\n这不是已经能用的电\n而是还在排队等着接入电网的项目\n而在过去\n2000年到2020年申请进入电网的容量\n你到了2025年真正商业运营的只有13%\n美国的基础建设效率可见一斑\n真的是太慢了\n这个就是美国AI的现实处境\n它不是没有模型\n也不是完全见不到电力\n而是前沿AI的负载太大\n增长太快\n开始和美国分散的电网体系\n漫长的审批周期\n地方电价争议\n包括输电瓶颈发生冲突\n美国的AI公司很快可以出到钱\n可以买到芯片\n但基础设施的建设速度\n不会像模型版本一样\n几个月就迭代一次\n而是按照年甚至几年为单位的\n而中国的AI路线\n我认为很可能不会复制美国这条路\n中国的AI方面当然有巨大的差距\n尤其是高端GPU\nHBM\n先进制程\n云上高端算力的供给\n这些都是硬约束\n但中国模型这两年的进步\n有一个非常明显的特点\n如果美国的顶级大模型是100分\n那中国厂商在不断的\n把85分90分的开源模型能力\n做得明显更便宜\n更开放\n更容易部署\n还是斯坦福AI Index 2025\n有一个观点我觉得很值得看\n中美顶级模型的能力差距\n已经明显缩小了\n2025年以来\n中美模型多次交换领先位置\n他甚至提到了\n截至2026年3月\n美国顶级模型\n相对中国顶级模型的领先\n大约只有2.7%\n当然这个2.7%\n不是说中国模型\n已经全面追上美国了\n那不同榜单侧的东西也不一样\n但它至少说明了一件事\n就是中美的模型能力\n已经不是三年前那种一眼看不到边的差距了\n那如果你再看开放权重模型\nArtificial Analyst今年6月的文章里\nGLM 5.2在它的Intelligent Index上\n成为领先的开放权重模型之一\n并且在一些企业任务评测里\n它已经进入了高能力低成本的区间\n这个例子能说明一个方向\n就是中国模型不只是追赶\n它是在把接近前沿模型的能力\n用更开放更低成本的方式来释放出来\n而这条路线比很多人想象的重要\n因为市场并不总是只买最强的\n很多企业开发者出海场景\n其实不需要世界第一聪明的模型\n前段时间你但凡狠狠调过\n非GPT模型的人应该会有同感\n那个token的消耗真的可怕\n成本真的很高\n更多时候人们需要的是足够强\n同时便宜稳定能部署\n并且不会因为地区限制\n产品分层或者政策变化\n突然断供的模型\n这个就是中国开放模型的长期价值\n这不是低端替代\n而是中国模型更能够把AI能力去产业化和规模化\n而一旦你把AI看成工业化的供给\n那电力和基础设施就变成了中国这边非常重要的一张牌\n国家能源局2024年的数据很夸张\n2025年中国可再生能源新增装机4.52亿千瓦\n占全国电力新增装机的83%\n其中太阳能新增3.18亿千瓦\n风力新增1.2亿千瓦\n到2025年底\n中国可再生能源的装机会达到23.4亿千瓦\n大约占全国总装机的60%\n所以能感知到中国在能源\n所以能感知到,中國在能源和基礎設施上的動員能力非常的強。\n那隨著模型能力越來越強,訓練新模型肯定是仍然重要的。\n但推理過程的成本會變成AI發展的關鍵變量。\n因为只要AI真正进入企业办公教育等等越来越多的场景\n那AI公司未来最大的账单很可能不是训练一个模型要花多少钱\n而是每天几十亿几百亿次的调用单位成本能不能压下来\nToken听起来抽象\n但它就是AI能力被调用被计费被规模化交付的最小单位\n未来AI如果真的变成了基础设施\n那谁能把每一次Token的成本压下来\n谁就能让AI覆盖更多场景\n这也是为什么我会觉得\n未来中美AI很可能不是一个\nB元打败开元或者开元打败B元的故事\n更可能是两套市场长期并存\n最顶级的B元模型\n继续服务于科研复杂推理和最前沿的agent\n它们会很贵\n会分层\n会有访问门槛\n同时也会继续代表模型能力的天花板\n但更大的市场\n可能是企业内部的流程\n自由化的部署\n本地化的应用\n一些相对低成本的智能体\n大规模的推理和调用\n这里要的它不是世界第一聪明\n而是够强稳定便宜可控\nClaude最大的问题就在这里\n它不是不强\n但如果一个模型公司依赖的是最强模型\n可以一直卖出稀缺溢价\n它就会不断的被重新定价\n被OpenAI的下一代闭源模型重新定价\n被DeepSeek这样便宜量大的开源模型重新定价\n被中国一批又一批开源模型重新定价\n你最强当然是有很大价值的\n但如果别人用便宜很多的价格\n做到你八九成的能力\n还能私有部署\n还不受你这套访问规则的约束\n那很多真实的生产场景\n就会开始重新算账了\n那这个账一算\nAI竞争就已经从互联网逻辑\n进入了能源工业的逻辑\n而在能源工业的逻辑里\n最重要的是产能成本和供应稳定性\n所以Claude去欧洲找电这件事\n表面看是一个AI公司\n要扩数据中心\n但往深里看\n它是在提醒我们\n模型并不是浮在云上的魔法\nAI公司卖的是智能\n但它每天消耗的是电\n抢的是芯片\n租的是实实在在的厂房\n所以我认为中美的AI竞争\n不能只看谁的模型最聪明\n还要结合访问限制\n单位Token的价格\n还有要观察\n应用市场是不是真的愿意为最强模型\n付出高昂代价\n我认为从0到1的创新\n和从1到10的落地\n这两条路的竞争\n也许会一直持续下去\nOK\n那本期视频的主要内容就这么多了\n大家伙日常在用哪个模型最多\n也可以打在评论或者弹幕上\n我们分享一下\n如果喜欢本期视频的话\n请到点赞朋友订阅我的话\n我是HYK\n我们下期视频见\n祝你好运\n拜拜",
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      "text": "更多时候,人民需要的是足够强,同时便宜、稳定、能部署,并且不会因为地区限制、产品分层或者政策变化突然断供的模型。",
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      "text_original": "更多时候,人民需要的是足够强,同时便宜、稳定、能部署,并且不会因为地区限制、产品分层或者政策变化突然断工的模型。",
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      "text": "Hello大家好,我是Jacky。最近Anthropic,也就是Claude背后的公司,被曝在澳洲寻找大规模数据中心。",
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      "text_original": "Hello大家好,我是Jacky。最近Answerpik,也就是Cloud背后的公司,被曝在澳洲寻找大规模数据中心。",
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      "text": "它已经和澳大利亚政府签了AI的合作文件。据媒体报道,Anthropic还在接触大约1.4吉瓦级别的数据中心容量。",
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      "text_original": "它已经和澳大利亚政府签了AI的合作文件。据媒体报道,Answerpik还在接触大约1.4级瓦级别的数据中心容量。",
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      "text": "这个新闻其实放在日新月异的AI圈很容易被掩埋",
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      "text_original": "这个新闻其实放在日新月异的AI全很容易被掩埋",
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      "text": "但我觉得它真正有意义",
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      "text_original": "但我觉得它真正有一次地方",
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      "text": "是把AI竞争从模型榜单拉回了物理世界",
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      "text": "Anthropic是一个最会写代码写文章做推理的AI公司",
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      "start": 39.74,
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      "text": "但它下一个最关心的东西",
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      "text": "开始变成了哪里有地哪里有电",
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      "text": "过去我们总是在问谁的模型更加聪明",
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      "start": 49.02,
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      "text": "谁的推理能力更强",
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      "start": 50.38,
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      "text": "但随着AI的发展",
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      "text": "中美都发现",
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      "end": 55.16,
      "text": "如果AI最后变成一种",
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      "text": "可以被大规模部署",
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      "start": 56.6,
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      "text": "持续调用",
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    {
      "start": 57.74,
      "end": 59.32,
      "text": "按token计价的能力",
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      "end": 62.88,
      "text": "那竞争的范围就远不止是模型本身",
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    {
      "start": 62.88,
      "end": 64.92,
      "text": "而是谁能把这种能力",
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    {
      "start": 64.92,
      "end": 68.06,
      "text": "以低成本稳定大规模的供给出去",
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    {
      "start": 68.06,
      "end": 69.48,
      "text": "那如果把模型",
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    {
      "start": 69.48,
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      "text": "API和智能体",
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      "start": 70.86,
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      "text": "看成是AI公司卖出去的产品",
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      "start": 73.28,
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      "text": "那背后的生产函数很清晰",
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    {
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      "text": "芯片是机器",
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      "start": 76.64,
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      "text": "数据中心是厂房",
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      "start": 78.32,
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      "text": "算法是工艺",
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      "start": 79.48,
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      "text": "数据是配方",
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      "text": "而电力就是持续消耗的生产资料",
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      "start": 84.08,
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      "text": "虚拟一个模型",
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      "text": "本质上是在把电",
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      "start": 86.7,
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      "text": "芯片时间",
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      "start": 87.74,
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      "text": "数据和算法工艺",
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    {
      "start": 89.28,
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      "text": "压缩成一组模型权重",
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      "start": 91.5,
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      "text": "那么这个模型每推理一次",
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      "start": 93.46,
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      "text": "本质上就是在把电和算力",
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      "text": "持续换成一个又一个token",
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      "start": 97.82,
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      "text": "所以Anthropic去找数据中心",
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      "text_original": "所以Answerpik去找数据中心",
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      "text": "找电力",
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      "start": 100.78,
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      "text": "这件事它一点都不奇怪",
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      "start": 102.2,
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      "text": "我们之前太习惯",
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    {
      "start": 103.7,
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      "text": "把AI想象成云上的魔法了",
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      "start": 106.2,
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      "text": "好像模型只要足够聪明",
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      "start": 108.0,
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      "text": "它就能无限复制",
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    {
      "start": 109.36,
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      "text": "无限扩张",
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    {
      "start": 110.12,
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      "text": "但现实根本不是这个样子",
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      "start": 112.4,
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      "text": "IEA的报告里有个很直观的数字",
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      "text": "全球数据中心用电到2030年预计会翻倍",
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      "start": 118.56,
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      "text": "到大约945泰瓦时",
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    {
      "start": 121.4,
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      "text": "美国能源部LBNL的报道也提到过",
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      "text": "美国数据中心在2023年",
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      "start": 127.14,
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      "text": "已经消耗了美国大约4.4%的总用电量",
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      "text": "到了2028年可能会上升至6.7%到12%",
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      "text": "你再看Answerpick自己今年2月份的公开承诺",
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      "start": 139.16,
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      "text": "他说愿意承担自家数据中心带来的消费者定价上涨",
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      "text": "还愿意支付电网升级的成本",
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      "start": 145.96,
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      "text": "那以前的软件公司扩张用户多了",
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      "start": 148.82,
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      "text": "无非就是服务器带宽云成本上去一点",
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      "text": "而现在一个前沿模型公司的扩张",
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    {
      "start": 154.86,
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      "text": "可能会直接改变一个市一个州",
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      "start": 157.38,
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      "text": "甚至一个电网区域的复合结构",
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      "start": 159.84,
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      "text": "那如果你在审视中美AI发展的时候",
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      "start": 162.6,
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      "text": "带上这个视角",
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      "text": "那中美AI的差异就不只是谁的模型能力更强了",
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      "start": 168.0,
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      "text": "而是谁更能把模型能力做成长期低成本稳定可用的供给",
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      "start": 173.28,
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      "text": "先来说美国",
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      "text": "美国在AI领域确实是领跑者",
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      "text": "OpenAI",
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      "text": "谷歌这些公司仍然在定义最前沿必源模型的节奏",
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      "text": "更重要的是",
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      "text": "美国它拥有一整套前沿AI的产业配套",
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      "text": "英伟达",
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      "text": "AMD这样的芯片公司",
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      "text": "AWS",
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      "text": "Google Cloud这样的云平台",
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      "text": "包括成熟的开发者生态",
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      "start": 194.2,
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      "text": "以及愿意持续砸大钱的资本市场",
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      "text": "Stanford AI Index 2026 里也能看到",
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      "text": "美国在有影响力模型的数量和私人 AI 投资上仍然非常领先",
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      "start": 204.8,
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      "text": "2025 年美国机构产生了59个有影响力的模型",
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      "start": 208.84,
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      "text": "中国是35个",
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      "start": 210.34,
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      "text": "私人 AI 投资这一块美国也明显是高得多",
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      "start": 213.54,
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      "text": "所以如果你只是问最强的闭源模型是谁做出来的",
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      "text_original": "所以如果你只是问最强的必源模型是谁做出来的",
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      "start": 217.1,
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      "text": "那我觉得在很长一段时间里",
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      "start": 219.1,
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      "text": "这个答案大概率还是美国",
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      "start": 221.12,
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      "text": "不过这条路线的代价也日渐凸显",
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    },
    {
      "start": 223.58,
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      "text": "伯克利Lab今年的并网电力数据",
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      "text_original": "伯克利Lab今年的并广对立数据",
      "correction_applied": true,
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    {
      "start": 227.02,
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      "text": "很能说明这个问题",
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    {
      "start": 228.06,
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      "text": "到2025年底",
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      "start": 229.52,
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      "text": "美国有超过2060吉瓦的发电和储能项目",
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      "text_original": "美国有超过2060几瓦的发电和储能项目",
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      "start": 234.72,
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      "text": "在排队接入电网",
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      "start": 235.84,
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      "text": "注意啊",
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      "start": 236.74,
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      "text": "这不是已经能用的电",
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      "start": 238.18,
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      "text": "而是还在排队等着接入电网的项目",
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      "start": 240.96,
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      "text": "而在过去",
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    {
      "start": 241.68,
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      "text": "2000年到2020年申请进入电网的容量",
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      "text_original": "2000年到2020年申请进入对立的容量",
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      "text": "你到了2025年真正商业运营的只有13%",
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      "start": 249.5,
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      "text": "美国的基础建设效率可见一斑",
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    {
      "start": 252.82,
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      "text": "真的是太慢了",
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      "start": 254.1,
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      "text": "这个就是美国AI的现实处境",
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      "start": 256.64,
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      "text": "它不是没有模型",
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    {
      "start": 258.1,
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      "text": "也不是完全见不到电力",
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      "text_original": "也不是完全见不动电力",
      "correction_applied": true,
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    {
      "start": 260.2,
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      "text": "而是前沿AI的负载太大",
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    {
      "start": 262.82,
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      "text": "增长太快",
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    {
      "start": 263.8,
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      "text": "开始和美国分散的电网体系",
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    {
      "start": 266.44,
      "end": 267.9,
      "text": "漫长的审批周期",
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      "start": 267.9,
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      "text": "地方电价争议",
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      "start": 269.28,
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      "text": "包括输电瓶颈发生冲突",
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      "start": 271.14,
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      "text": "美国的AI公司很快可以出到钱",
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      "start": 273.6,
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      "text": "可以买到芯片",
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      "start": 274.68,
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      "text": "但基础设施的建设速度",
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      "start": 276.42,
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      "text": "不会像模型版本一样",
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    {
      "start": 278.44,
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      "text": "几个月就迭代一次",
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      "text_original": "几个月就结在一次",
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      "start": 279.8,
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      "text": "而是按照年甚至几年为单位的",
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      "start": 282.34,
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      "text": "而中国的AI路线",
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      "start": 284.62,
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      "text": "我认为很可能不会复制美国这条路",
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      "start": 286.82,
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      "text": "中国的AI方面当然有巨大的差距",
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      "start": 289.2,
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      "text": "尤其是高端GPU",
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      "start": 290.9,
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      "text": "HBM",
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      "start": 291.56,
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      "text": "先进制程",
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    {
      "start": 292.4,
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      "text": "云上高端算力的供给",
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      "text_original": "云上高端酸力的供给",
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    {
      "start": 294.0,
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      "text": "这些都是硬约束",
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      "text_original": "这些都是应约数",
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      "text": "但中国模型这两年的进步",
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      "start": 297.78,
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      "text": "有一个非常明显的特点",
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    {
      "start": 298.94,
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      "text": "如果美国的顶级大模型是100分",
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      "text_original": "如果美国的顶级BN模型是100分",
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    {
      "start": 302.42,
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      "text": "那中国厂商在不断的",
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    {
      "start": 304.82,
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      "text": "把85分90分的开源模型能力",
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    {
      "start": 307.78,
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      "text": "做得明显更便宜",
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      "text_original": "做的明显更便宜",
      "correction_applied": true,
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    {
      "start": 309.88,
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      "text": "更开放",
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    {
      "start": 310.62,
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      "text": "更容易部署",
      "chunk": 0,
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    {
      "start": 311.64,
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      "text": "还是斯坦福AI Index 2025",
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      "text_original": "还是斯坦福AI Index 2026",
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      "start": 313.78,
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      "text": "有一个观点我觉得很值得看",
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      "start": 315.44,
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      "text": "中美顶级模型的能力差距",
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    {
      "start": 317.72,
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      "text": "已经明显缩小了",
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    {
      "start": 319.06,
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      "text": "2025年以来",
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      "start": 320.22,
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      "text": "中美模型多次交换领先位置",
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      "start": 322.56,
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      "text": "他甚至提到了",
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    {
      "start": 323.88,
      "end": 325.64,
      "text": "截至2026年3月",
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    {
      "start": 325.64,
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      "text": "美国顶级模型",
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      "start": 327.44,
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      "text": "相对中国顶级模型的领先",
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      "start": 329.5,
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      "text": "大约只有2.7%",
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      "start": 331.7,
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      "text": "当然这个2.7%",
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      "start": 333.28,
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      "text": "不是说中国模型",
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      "start": 334.38,
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      "text": "已经全面追上美国了",
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      "start": 336.0,
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      "text": "那不同榜单侧的东西也不一样",
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      "start": 337.98,
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      "text": "但它至少说明了一件事",
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      "start": 339.48,
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      "text": "就是中美的模型能力",
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      "start": 341.72,
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      "text": "已经不是三年前那种一眼看不到边的差距了",
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      "text": "那如果你再看开放权重模型",
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    {
      "start": 347.54,
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      "text": "Artificial Analyst今年6月的文章里",
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      "start": 350.44,
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      "text": "GLM 5.2在它的Intelligent Index上",
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      "start": 353.36,
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      "text": "成为领先的开放权重模型之一",
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      "text": "并且在一些企业任务评测里",
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      "text": "它已经进入了高能力低成本的区间",
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      "text": "这个例子能说明一个方向",
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      "text": "就是中国模型不只是追赶",
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      "text": "它是在把接近前沿模型的能力",
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      "text": "用更开放更低成本的方式来释放出来",
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      "text": "而这条路线比很多人想象的重要",
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      "text": "因为市场并不总是只买最强的",
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      "text_original": "因为市场并不总是直卖最强的",
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      "text": "很多企业开发者出海场景",
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      "text_original": "很多企业开发者出这场景",
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      "text": "其实不需要世界第一聪明的模型",
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      "start": 384.0,
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      "text": "前段时间你但凡狠狠调过",
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      "text_original": "前段时间你但凡狠狠登过",
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      "text": "非GPT模型的人应该会有同感",
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      "text_original": "非波5模型的人应该会有同感",
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      "text": "那个token的消耗真的可怕",
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      "text": "成本真的很高",
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      "text": "更多时候人们需要的是足够强",
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      "start": 395.06,
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      "start": 397.4,
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      "text": "并且不会因为地区限制",
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      "start": 399.38,
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      "text": "产品分层或者政策变化",
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      "start": 401.26,
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      "text": "突然断供的模型",
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      "text_original": "突然断工的模型",
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      "text": "这个就是中国开放模型的长期价值",
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      "text": "这不是低端替代",
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      "start": 407.06,
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      "text": "而一旦你把AI看成工业化的供给",
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      "text": "那电力和基础设施就变成了中国这边非常重要的一张牌",
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      "text": "国家能源局2024年的数据很夸张",
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      "text_original": "国家能源局2025年的数据很夸张",
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      "text": "2025年中国可再生能源新增装机4.52亿千瓦",
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      "text": "占全国电力新增装机的83%",
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      "text": "其中太阳能新增3.18亿千瓦",
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      "text": "风力新增1.2亿千瓦",
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      "start": 436.88,
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      "text": "到2025年底",
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      "text": "中国可再生能源的装机会达到23.4亿千瓦",
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      "text": "大约占全国总装机的60%",
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      "text": "所以能感知到中国在能源",
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      "start": 447.733,
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      "text": "所以能感知到,中國在能源和基礎設施上的動員能力非常的強。",
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      "text": "那隨著模型能力越來越強,訓練新模型肯定是仍然重要的。",
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      "text": "但推理過程的成本會變成AI發展的關鍵變量。",
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      "text": "因为只要AI真正进入企业办公教育等等越来越多的场景",
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      "text": "那AI公司未来最大的账单很可能不是训练一个模型要花多少钱",
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      "start": 469.073,
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      "text": "而是每天几十亿几百亿次的调用单位成本能不能压下来",
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      "text_original": "而是每天几十亿几百亿丝的调用单位成本能不能压下来",
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      "text": "Token听起来抽象",
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      "start": 475.253,
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      "text": "但它就是AI能力被调用被计费被规模化交付的最小单位",
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      "start": 480.333,
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      "text": "未来AI如果真的变成了基础设施",
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      "text": "那谁能把每一次Token的成本压下来",
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      "text": "谁就能让AI覆盖更多场景",
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      "text": "这也是为什么我会觉得",
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      "text": "未来中美AI很可能不是一个",
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    {
      "start": 493.533,
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      "text": "B元打败开元或者开元打败B元的故事",
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      "start": 497.233,
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      "text": "更可能是两套市场长期并存",
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      "text": "最顶级的B元模型",
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    {
      "start": 501.593,
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      "text": "继续服务于科研复杂推理和最前沿的agent",
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      "text_original": "继续服于科研复杂推理和最前沿的agent",
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      "text": "它们会很贵",
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      "start": 506.293,
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      "text": "会分层",
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      "start": 507.373,
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      "text": "会有访问门槛",
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      "start": 508.453,
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      "text": "同时也会继续代表模型能力的天花板",
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      "text": "但更大的市场",
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      "text": "可能是企业内部的流程",
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      "text": "本地化的应用",
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      "start": 516.353,
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      "text": "一些相对低成本的智能体",
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      "text": "大规模的推理和调用",
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      "text": "这里要的它不是世界第一聪明",
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      "text": "而是够强稳定便宜可控",
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      "text": "Claude最大的问题就在这里",
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      "text_original": "Cloud最大的问题就在这里",
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      "text": "它不是不强",
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      "text": "但如果一个模型公司依赖的是最强模型",
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      "text": "可以一直卖出稀缺溢价",
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      "start": 536.513,
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      "text_original": "被OpenAI的下一代币源模型重新定价",
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      "start": 539.773,
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      "start": 543.493,
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      "text": "你最强当然是有很大价值的",
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      "text": "但如果别人用便宜很多的价格",
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      "start": 550.993,
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      "text": "做到你八九成的能力",
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      "text": "还能私有部署",
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      "start": 553.453,
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      "text": "还不受你这套访问规则的约束",
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      "text": "那很多真实的生产场景",
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      "text": "就会开始重新算账了",
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      "text": "那这个账一算",
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      "text": "AI竞争就已经从互联网逻辑",
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      "text": "进入了能源工业的逻辑",
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      "text": "最重要的是产能成本和供应稳定性",
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      "text": "所以Claude去欧洲找电这件事",
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      "text": "表面看是一个AI公司",
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      "text": "要扩数据中心",
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      "text_original": "但往身里看",
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      "text": "它是在提醒我们",
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      "text": "模型并不是浮在云上的魔法",
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      "text": "AI公司卖的是智能",
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      "text": "但它每天消耗的是电",
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      "text": "抢的是芯片",
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      "text": "OK",
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      "text": "请到点赞朋友订阅我的话",
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      "text": "祝你好运",
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