本文来自PRO会员通讯内容，文末关注「机器之心PRO会员」，查看更多专题解读。 引言： 近期在一份 OpenAI 泄露的备忘录里，Altman 提醒员工「公司不再是默认赢家，需要进入战时状态」，而 Anthropic 内部则预测 2026 年公司年化营收在 200 - 260 亿 美元之间。这样反差的业务预测指向了市场当前正在对齐的核心问题：2026 年头部大模型厂商押注的 AI，究竟能不能真正转化为可见的业务增长曲线？ 目录 01. 独立工作 8 小时，2026 年 AI 能力可以成为真实的业务增长？ 2026 年 AI 真能独立上班 8 小时？OpenAI 内部开始强调「进入战时状态、不再是默认赢家」？... 02 . 从 OpenAI、Anthropic 到阿里字节，2026 年头部玩家的 AI 战略路线有何同异？ 企业优先的 Anthropic，会在 OpenAI 之前盈利？Google、Meta 砸出的这轮 Capex，能换来多少「非泡沫」的 AI 增长？... 独立工作 8 小时，2026 年 AI 能力可以成为真实的业务增长？ 1、两个月前， Anthropic 的一位研究员 在其博客中推断 2 ...

Core Argument - A leading AI researcher argues against the prevalent "AI bubble" theory, stating that skepticism towards AI's exponential growth is a serious misinterpretation of technological trends, similar to the initial underestimation of the COVID-19 pandemic [1][2] Group 1: AI Performance and Trends - AI models are doubling their ability to autonomously complete complex tasks at an exponential rate, with the latest models capable of handling over two-hour software engineering tasks [2][7] - The METR study shows a clear exponential trend in AI's ability to perform software engineering tasks, with models like Sonnet 3.7 achieving a 50% success rate for one-hour tasks seven months ago [5] - New models, including Grok 4, Opus 4.1, and GPT-5, have surpassed previous trends and can now execute tasks exceeding two hours [7] Group 2: AI's Competitiveness Across Industries - The GDPval assessment by OpenAI evaluates AI performance across 44 professions in nine industries, showing that top AI models are "astonishingly close" to human performance and even challenge industry experts [9][10] - The latest GPT-5 model has demonstrated performance that is nearly on par with human experts, indicating significant advancements in AI capabilities [10][13] Group 3: Future Projections - Based on current exponential growth data, it would be "extremely surprising" if improvements in AI suddenly halted, with predictions suggesting that by mid-2026, models will be able to work autonomously for an entire workday (8 hours) [12][15] - By the end of 2026, at least one model is expected to reach human expert performance across various industries, and by the end of 2027, models will frequently surpass experts in many tasks [15]