软银孙正义回应 AI 泡沫论:这是对 AI 的侮辱,人工智能仍处于发展初期
软银创始人兼首席执行官孙正义在 6 月 24 日的年度股东大会上表示,人工智能仍处于发展初期,任何声称行业出现泡沫的言论都是“对人工智能的侮辱”。他将软银比作“下金蛋的鹅”,抱怨公司市值(约 37 万亿日元)与资产总额(约 74 万亿日元)之间的巨大落差。孙正义同时宣布软银已开始量产机器人,并推动旗下 Arm 从芯片设计者向芯片提供者转型。
软银创始人兼首席执行官孙正义在 6 月 24 日的年度股东大会上表示,人工智能仍处于发展初期,任何声称行业出现泡沫的言论都是“对人工智能的侮辱”。他将软银比作“下金蛋的鹅”,抱怨公司市值(约 37 万亿日元)与资产总额(约 74 万亿日元)之间的巨大落差。孙正义同时宣布软银已开始量产机器人,并推动旗下 Arm 从芯片设计者向芯片提供者转型。
维基百科联合创始人吉米·威尔士表示,尚不信任AI直接参与词条编辑,因为模型“幻觉”问题依然非常严重。不过AI可用来提醒编辑社区关注小众新闻。他指出,各AI平台依赖维基百科内容导致AI机器人访问量上升,人类用户访问量下降8%,并呼吁AI公司承担服务器费用,已开始封锁不守规矩的平台。
Oracle在截至5月31日的财年裁员21000人,员工总数降至141,000人,降幅12.9%。公司称AI技术的采用导致劳动力缩减,同时重组成本达18亿美元,同比增长481%。Oracle计划2026年通过债务和股权筹集450至500亿美元,扩建Oracle Cloud Infrastructure,服务OpenAI、xAI、AMD、Nvidia、Meta等客户。公司债务超1200亿美元。分析人士指出裁员有助于改善现金流,但Oracle也承认大规模裁员可能带来生产力下降、人才短缺和员工士气受损等风险。
推荐理由:Oracle裁员2.1万人并大举借债押注AI,这既是AI替代人力的鲜活案例,也暴露了云巨头激进投资背后的财务风险,做投资和战略的该看看。
昆仑万维旗下短剧业务DramaWave内容负责人张薇在6月22日上海电视节白玉兰论坛上分享海外微短剧市场洞察。DramaWave采取付费和免费双轨运营,海外月活用户超亿,年度经常性收入达6亿美金。AI技术极大提升了内容产能——男频玄幻类AI短剧填补了真人拍摄成本高、难度大的空白,平台目前一个月可上线200余部AI剧。张薇指出,AI作为工具代替的是制作环节而非故事内核,99%的观众只关心故事是否好看。合作模式包括成品剧上传、合拍等,平台持续欢迎好剧本。
科幻作家兼科技记者 Cory Doctorow 在新书中主张,戳破 AI 泡沫需要从其根本入手,而非仅仅批判过度炒作。该书分析了 AI 行业的结构性根源,并探讨了泡沫破灭后的替代路径。
马斯克和 http://Z.ai 对中国人工智能的发展速度确实存在分歧。
@jietang @teortaxesTex On benchmarks, yes, but as measured by true usefulness even Q1 would be very impressive. Anthropic has rightly focused on maximizing useful intelligence, which does not show up in benchmarks, but definitely shows up in revenue.
中央广播电视总台中国之声报道,近期多地消费者反映KTV中原版MV被AI生成视频替换,画面与歌曲意境完全不符,例如温婉情歌高潮处出现大猩猩持枪扫射。KTV工作人员称,多数因MV无版权或歌曲本身无MV,曲库服务方打包设备时替换为AI生成内容,商家无法控制。律师指出,该做法不能规避MV版权费,反可能侵犯放映权及保护作品完整权,额外收取版权费也不合规。消费者可向消费者权益保护单位及KTV运营主管部门投诉。
截至2026年6月,豆包大模型日均tokens调用量达180万亿,较发布时增长超1500倍。字节跳动CEO梁汝波将年度关键词定为“勇攀高峰”,核心是聚焦提升大模型能力,火山引擎PaaS转为基础业务,对MaaS投入长期坚定。视频生成模型Seedance新版本将发布3D白膜预演功能,系业内率先。Seedance年化收入已达20亿美元(约135.74亿元),单月超10亿元,绝大多数收入来自企业客户。
GLM 5.2 vs DeepSeek V4:OpenRouter上token份额 与大多数开源权重发布相比,GLM 5.2的采用速度非常快。
Astonishing difference between US and Chinese AI capex, even after taking into account differences in PPP
生成式AI让房地产经纪人一键虚拟装修房源照片,导致租户看房时发现现实与图片严重不符。纽约租户Joyce看到带有壁炉、厨房翻新的曼哈顿工作室,实地却发现没有壁炉,炉灶缺旋钮。另一位租户Madison在StreetEasy上看到大量AI增强图片,家具细节明显不真实。佛罗里达州经纪人Bee使用ChatGPT和Stuccco、BoxBrownie等工具为客户演示装修方案,但认为用AI制造虚假房源会引发诉讼。纽约州已立法要求广告披露AI使用,加州《修改图像法》进一步要求房产广告披露AI修改的图像。租户表示连房源描述也显AI痕迹,充斥着“迷人”“温馨”等套话。
这似乎愚蠢得令人难以置信,但显然SaaS空头的真实信念是“所有软件都是0,因为Claude可以一次性生成这些应用” 这种说法简直是惊人的短视。
咨询公司贝恩使用 vibecoding 构建收购目标软件的 AI 复制品,帮助潜在买家评估其技术是否容易被复制。2023 年由专门工程团队开始,现已扩展至数百个由普通顾问构建的粗略原型。该做法已影响实际交易:一位私募股权投资者称,贝恩的 vibecoded 分析平台复制品直接促使其放弃竞标。贝恩全球私募业务负责人 Rebecca Burack 表示,vibecoding 如同从 2D 看到 3D,用以了解软件公司的实际能力及代码是否是真正的竞争护城河。
Counterpoint Research报告显示,支持生成式AI的机型2026年将占全球智能手机出货量的45%,高于2025年的36%,2027年预计达52%。受内存供应危机影响,2026年全球智能手机出货量预计同比锐减13.9%至10.8亿部,创历史新低。内存成本大幅上涨使低价手机市场收缩,高端市场扩大。运行AI模型需额外DRAM,AI手机批发价目前难以降至400美元以下。长期看,内存压力缓解和端侧模型效率提升将推动生成式AI下放至更亲民的价位。
2026年世界杯小组赛,佛得角2:2战平乌拉圭,此前0:0逼平西班牙。赛前12家大模型全部预测乌拉圭胜,0家预测平局或佛得角胜,命中率0%。佛得角连续两场让AI预测模型翻车:AI依赖历史战绩、世界排名、球员身价等量化指标,而佛得角的团队执行力、防守纪律和反击效率难以被数字描述,成为系统性的预测盲区。
市场将 Adobe 视为一家走向末路的传统软件公司。然而实际数据显示,它是 GenAI 崛起最大的受益者之一。事实上,它是当今最盈利且增长最快的五大 AI 公司之一,而在一个盈利稀少的行业中。
@PeterDiamandis AI probably exceeds the sum of all human intelligence in 4 or 5 years
dot-com bubble vs. a possible AI bubble. From the famous "Dean of Valuation", Professor Aswath Damodaran, of NYU Stern School of Business, “And that’s the real big difference between the dot-com boom and bust and the AI boom. We don’t know whether there’ll be a bust. History suggests there will be a bust. The dot-com boom and bust had no huge capital expenditure in that cycle. In fact, there was very little traditional CapEx, or even R&D, driving it. People started apps. They basically started going on it. This has been the biggest infrastructure run-up I think I’ve ever seen in business. You can go back and compare it to the automobile business 100 years ago. The amount of money that’s being put into AI CapEx is immense, which means that when the correction comes, the pain will be more intense. And herein lies the second problem. The dot-com boom and bust was almost entirely equity-funded. You think, so what? Well, when the bust came, those shareholders lost 60%, 70%, 80%, or 90% of their money. You felt sorry for them, but the loss was restricted to the shareholders. The problem with the AI CapEx boom is that not only is it immense, but a big chunk of it is funded with debt, and the debt is coming from private capital rather than banks. There’s a very real chance that if there’s a correction and companies start having problems, that problem is going to show up as distress and default, and that really doesn’t stay restricted. It spills over into the rest of society. I’m not saying it’s going to be 2008, but 2008 is an example of what happens when lenders overreach, when they lend money at too low a rate, and the correction comes. The pain spills over. So that is my concern with this big market illusion: the potential societal cost of having to deal with debt coming due that you’re unable to pay. It’s much more painful than your share price dropping 90% and you feeling the pain." ---- From "Excess Returns" YouTube channel, (link in comment)
UC Berkeley分析美国一所大型公立研究型大学超50万份成绩发现,自ChatGPT推出以来,写作和编程任务多的课程A等级比例跃升13个百分点,平均GPA上升0.12分。成绩涨幅主要来自家庭作业而非考试,口头展示类作业成绩未受影响,表明AI替代学生完成作业而非改善学习。研究建议重新设计作业格式。OpenAI CEO Sam Altman警告若不调整教育体系,批判性思维可能萎缩。挪威已在小学全面禁用AI。
25岁创业者徐子文(Ziwen Xu)启动自制《GTA6》项目GT-Caliber,使用Anthropic旗下Claude Max 20x大模型,配合多款AI智能体循环运行,实时收集社区需求并自动生成代码。开发首周先后从Godot引擎迁移至虚幻引擎,最终选定Unity。九天进展包括路人NPC自主行走、道路交通、武器系统及仿Instagram游戏手机,第七日生成洛杉矶风格居民区。项目开源托管于GitHub,无发行商支持。R星官方已公布《GTA6》预购6月25日开启,11月19日登陆PS5、Xbox Series X|S。
Levie now uses Salesforce 5x more than at any point before. The Box CEO @levie connected Salesforce's MCP server to Claude Code. Now he runs customer and market intelligence queries he would never have bothered pulling up by hand. The agent removes the friction. The underlying system gets queried more, not replaced. If you hold $CRM, the agentic era is an engagement tailwind, not a disruption risk - gated on whether the data platform handles the query load. Why incumbents gain from agents: https://podcastalpha.substack.com/p/aaron-levie-on-ai-agents-for-cios Source: CXOTalk - https://www.youtube.com/watch?v=ylBDQHk3BhI
“Growth, when it’s accompanied by huge amounts of reinvestment and substandard gross margins, might not just be neutral to value, but actually be value destructive.” Aswath Damodaran joined Kai Wu on The Intangible Economy to explain why the biggest stories in markets still have to become businesses. They discuss: ☑️ His detailed valuation of SpaceX and how it compares to the current market valuation ☑️ Why total addressable market can be a trap for investors ☑️ Why AI’s unit economics may matter more than the size of the opportunity ☑️ How growth can destroy value when it requires massive reinvestment ☑️ Why AI is turning parts of big tech from asset-light businesses into infrastructure companies ☑️ Why great companies can still be bad investments at the wrong price
dot-com bubble vs. a possible AI bubble. From the famous "Dean of Valuation", Professor Aswath Damodaran, of NYU Stern School of Business, “And that’s the real big difference between the dot-com boom and bust and the AI boom. We don’t know whether there’ll be a bust. History suggests there will be a bust. The dot-com boom and bust had no huge capital expenditure in that cycle. In fact, there was very little traditional CapEx, or even R&D, driving it. People started apps. They basically started going on it. This has been the biggest infrastructure run-up I think I’ve ever seen in business. You can go back and compare it to the automobile business 100 years ago. The amount of money that’s being put into AI CapEx is immense, which means that when the correction comes, the pain will be more intense. And herein lies the second problem. The dot-com boom and bust was almost entirely equity-funded. You think, so what? Well, when the bust came, those shareholders lost 60%, 70%, 80%, or 90% of their money. You felt sorry for them, but the loss was restricted to the shareholders. The problem with the AI CapEx boom is that not only is it immense, but a big chunk of it is funded with debt, and the debt is coming from private capital rather than banks. There’s a very real chance that if there’s a correction and companies start having problems, that problem is going to show up as distress and default, and that really doesn’t stay restricted. It spills over into the rest of society. I’m not saying it’s going to be 2008, but 2008 is an example of what happens when lenders overreach, when they lend money at too low a rate, and the correction comes. The pain spills over. So that is my concern with this big market illusion: the potential societal cost of having to deal with debt coming due that you’re unable to pay. It’s much more painful than your share price dropping 90% and you feeling the pain." ---- From "Excess Returns" YouTube channel, (link in comment)
Take-Two 在 2025 年 4 月裁撤了整个 AI 研究团队,该团队长期从事传统 AI 研究而非生成式 AI。前负责人卢克·迪肯博士接受采访时表示,生成式 AI 当前热度被严重夸大,泡沫一旦破裂,外界可能连同传统 AI 研究一并抛弃。他认为业界有道德责任妥善管理生成式 AI,但完全拒绝也不是正确答案。迪肯警告,公众缺乏细致判断来区分传统 AI 与生成式 AI,大语言模型已进入幻灭低谷期。
纽约大学金融学教授达摩达兰6月20日警告,AI行业泡沫破裂冲击可能比2000年互联网泡沫更严重。AI需巨额资金建设数据中心等实体基础设施,且大量投资靠举债,市场调整时损失将向社会扩散。他怀疑AI商业模式能否实现规模效应,因每次使用均消耗更多算力,成本类似Spotify按次付费而非Netflix分摊模式。DeepSeek等中国竞争对手可能继续压低价格,当前利润率已不高。即便最乐观情景成真,若AI直接取代完整岗位,一半白领可能失业,社会代价极其惊人。
纽约大学金融教授Aswath Damodaran在播客中警告,AI行业若发生回调,冲击可能比2000年互联网泡沫破裂更痛苦。他指出AI需巨额物理基础设施投资且多依赖债务融资,损失将波及社会。Damodaran质疑AI商业模式能否规模化,因AI每次使用都消耗算力,规模经济弱于Netflix、更像Spotify;中国Deepseek等对手带来价格侵蚀,利润率本已很低。他还警告,若AI实现替代整个岗位的愿景,将导致一半白领失业,带来巨大社会成本。科技巨头因重注AI进入不熟悉的资本密集型领域,苹果的谨慎态度在他看来更明智。
挪威宣布自8月下旬新学期起,小学1-7年级(6-13岁)全面禁止使用生成式AI工具。首相约纳斯·加尔·斯特勒称,学校首要任务是培养阅读、写作和数学能力,无批判使用AI会跳过关键学习步骤。他指出自2015年起学生整体学习成果下滑,主因是智能手机、电子屏幕和算法。初中阶段(14-16岁)可在教师监督下谨慎使用AI。政府还计划立法要求学校提供实体教材,推动更多纸质教科书回归课堂。
dot-com bubble vs. a possible AI bubble. From the famous "Dean of Valuation", Professor Aswath Damodaran, of NYU Stern School of Business, “And that’s the real big difference between the dot-com boom and bust and the AI boom. We don’t know whether there’ll be a bust. History suggests there will be a bust. The dot-com boom and bust had no huge capital expenditure in that cycle. In fact, there was very little traditional CapEx, or even R&D, driving it. People started apps. They basically started going on it. This has been the biggest infrastructure run-up I think I’ve ever seen in business. You can go back and compare it to the automobile business 100 years ago. The amount of money that’s being put into AI CapEx is immense, which means that when the correction comes, the pain will be more intense. And herein lies the second problem. The dot-com boom and bust was almost entirely equity-funded. You think, so what? Well, when the bust came, those shareholders lost 60%, 70%, 80%, or 90% of their money. You felt sorry for them, but the loss was restricted to the shareholders. The problem with the AI CapEx boom is that not only is it immense, but a big chunk of it is funded with debt, and the debt is coming from private capital rather than banks. There’s a very real chance that if there’s a correction and companies start having problems, that problem is going to show up as distress and default, and that really doesn’t stay restricted. It spills over into the rest of society. I’m not saying it’s going to be 2008, but 2008 is an example of what happens when lenders overreach, when they lend money at too low a rate, and the correction comes. The pain spills over. So that is my concern with this big market illusion: the potential societal cost of having to deal with debt coming due that you’re unable to pay. It’s much more painful than your share price dropping 90% and you feeling the pain." ---- From "Excess Returns" YouTube channel, (link in comment)
智能的一种定义是样本效率,但近年AI进步主要靠扩充数据分布和增加算力。强化学习本质是合成数据生成——投入大量算力通过验证器筛选“好”数据,再训练模型预测正确输出。这一过程需要每个领域和技能的海量人类专家示例,数据行业年收入已达数十亿美元。近日Epoch报告,开源模型仅落后前沿闭源模型4个月,原因在于数据可从公开API蒸馏,而超参数等不易复制。人类一生接触约2亿token,前沿模型训练在数十到数百T token之间,相差近百万倍——机器人、自动驾驶等领域同样存在巨大效率差距。
推荐理由:Dwarkesh 把 AI 的‘笨’归因于数据效率远低于人类,计算虽简但直指要害,他给出的一个逆向洞察是开源模型四个月追上闭源,正是数据驱动进步的最好证据。
LLM在作为医疗助手时,评估与部署场景下的性能存在显著差距——Bean等人(2025)发现准确率相差61个百分点。这一差距并非源于基准测试设计缺陷,而是因为评估协议中嵌入的隐含假设在部署时不再成立。研究提出将假设分为任务假设和结果假设两类,用以诊断差距成因并明确弥合路径:需将假设显式化、检验哪些假设在部署中成立,并据此更新评估协议。