招聘率的表现,看起来就像我们正处在一场经济衰退中。但失业率却是 4.6%。 这两件事怎么可能同时成立?因为企业停止招聘了,同时也停止裁员。 我来解释一下其中的机制。 在 2020 年之前,当一家公司需要 10% 的额外产出时,最直接的反应就是:招人。需要更多工作完成?那就多雇一些人。 而现在,当一家公司需要 10% 的额外产出时,第一反应变成了:AI 能不能搞定? 今年 9 月的一项调查显示,37% 的公司表示将在 2026 年底前用 AI 替代部分岗位。 但有一件事,市场几乎还没有真正计价。 企业并没有大规模裁员,因为它们根本不需要这么做。 AI 的转型,并不是通过裁员完成的,而是通过自然流失 + 招聘冻结。 有人离职了 → 不再补招 有人退休了 → 不再替换 团队需要扩张 → 给他们上 Copilot,而不是新人 失业率看起来依然健康,是因为裁员规模保持在低位。 但招聘率,才是在告诉你未来正在发生什么。 企业正在发现:用更少的人,也可以维持同样的产出。 每多一个招聘持续低迷的月份,就意味着:在没有新增员工的情况下,生产率继续复利式提升。 经济表现与企业招聘之间的那道鸿沟——那道鸿沟,就是 AI。 生产率的提升确实发生了,只是它没有流向新员工。 Forrester 预测:55% 与 AI 相关的裁员岗位,将被“悄然转移”到海外,以更低的薪资重新雇佣。工作岗位并没有消失,消失的是原本持有这些岗位的美国劳动者。 最先被冲击的是初级岗位。 斯坦福大学发现:在“高度易被 AI 替代”的入门级岗位中,就业已经出现下降。毕竟,既然 Claude 就能完成第一轮分析,为什么还要雇一个初级分析师? 这就是无就业增长的扩张(jobless growth)。 经济在扩张,GDP 在上升,企业利润屡创新高, 但招聘,却依然停留在衰退时期的水平。 劳动,并不是消失了——只是经济不再需要那么多人了。 The hiring rate is behaving like we’re in a recession. But unemployment is 4.6%. How can both be true simultaneously? Companies stopped hiring. They also stopped firing. Let me walk through the mechancs. Pre-2020, when a company needed 10% more output, they hired. That was the reflexive move. Need more work done? Bring on more people. Now when a company needs 10% more output, the first question is whether AI can handle it. 37% of companies say they’ll replace jobs with AI by end of 2026. That was a survey from September. But here’s what nobody’s pricing in. They’re not firing at scale because they don’t NEED to. The AI transition is happening through attrition and hiring freezes. Someone leaves. You don’t backfill. Someone retires. You don’t replace. A team needs to grow. You give them Copilot instead. The unemployment rate looks fine because layoffs stayed low. But the hiring rate tells you what’s coming. Companies are learning they can hold output steady with fewer people. Every month that hiring stays depressed is another month of compounding productivity gains without headcount growth. The gap between how the economy is performing and how much companies are hiring… that gap is AI. The productivity is going somewhere. It’s just not going to new workers. Forrester predicts 55% of AI-attributed layoffs will be quietly rehired offshore at lower salaries. The job doesn’t disappear. The American worker holding it does. Entry-level positions are getting crushed. Stanford found employment in AI-vulnerable entry-level roles already declined. Why hire a junior analyst when Claude can do the first pass? This is jobless growth. The economy expands. GDP rises. Corporate earnings hit records. And hiring stays frozen at recession levels. The labor just stopped needing as many people.
目录
观点原文
招聘率的表现,看起来就像我们正处在一场经济衰退中。但失业率却是 4.6%。
这两件事怎么可能同时成立?因为企业停止招聘了,同时也停止裁员。
我来解释一下其中的机制。
在 2020 年之前,当一家公司需要 10% 的额外产出时,最直接的反应就是:招人。需要更多工作完成?那就多雇一些人。
而现在,当一家公司需要 10% 的额外产出时,第一反应变成了:AI 能不能搞定?
今年 9 月的一项调查显示,37% 的公司表示将在 2026 年底前用 AI 替代部分岗位。
但有一件事,市场几乎还没有真正计价。
企业并没有大规模裁员,因为它们根本不需要这么做。
AI 的转型,并不是通过裁员完成的,而是通过自然流失 + 招聘冻结。
有人离职了 → 不再补招
有人退休了 → 不再替换
团队需要扩张 → 给他们上 Copilot,而不是新人
失业率看起来依然健康,是因为裁员规模保持在低位。
但招聘率,才是在告诉你未来正在发生什么。
企业正在发现:用更少的人,也可以维持同样的产出。
每多一个招聘持续低迷的月份,就意味着:在没有新增员工的情况下,生产率继续复利式提升。
经济表现与企业招聘之间的那道鸿沟——那道鸿沟,就是 AI。
生产率的提升确实发生了,只是它没有流向新员工。
Forrester 预测:55% 与 AI 相关的裁员岗位,将被“悄然转移”到海外,以更低的薪资重新雇佣。工作岗位并没有消失,消失的是原本持有这些岗位的美国劳动者。
最先被冲击的是初级岗位。
斯坦福大学发现:在“高度易被 AI 替代”的入门级岗位中,就业已经出现下降。毕竟,既然 Claude 就能完成第一轮分析,为什么还要雇一个初级分析师?
这就是无就业增长的扩张(jobless growth)。
经济在扩张,GDP 在上升,企业利润屡创新高,
但招聘,却依然停留在衰退时期的水平。
劳动,并不是消失了——只是经济不再需要那么多人了。
The hiring rate is behaving like we’re in a recession. But unemployment is 4.6%.
How can both be true simultaneously?
Companies stopped hiring. They also stopped firing.
Let me walk through the mechancs.
Pre-2020, when a company needed 10% more output, they hired. That was the reflexive move. Need more work done? Bring on more people.
Now when a company needs 10% more output, the first question is whether AI can handle it.
37% of companies say they’ll replace jobs with AI by end of 2026. That was a survey from September.
But here’s what nobody’s pricing in.
They’re not firing at scale because they don’t NEED to. The AI transition is happening through attrition and hiring freezes.
Someone leaves. You don’t backfill.
Someone retires. You don’t replace.
A team needs to grow. You give them Copilot instead.
The unemployment rate looks fine because layoffs stayed low.
But the hiring rate tells you what’s coming.
Companies are learning they can hold output steady with fewer people. Every month that hiring stays depressed is another month of compounding productivity gains without headcount growth.
The gap between how the economy is performing and how much companies are hiring… that gap is AI. The productivity is going somewhere. It’s just not going to new workers.
Forrester predicts 55% of AI-attributed layoffs will be quietly rehired offshore at lower salaries. The job doesn’t disappear. The American worker holding it does.
Entry-level positions are getting crushed. Stanford found employment in AI-vulnerable entry-level roles already declined. Why hire a junior analyst when Claude can do the first pass?
This is jobless growth. The economy expands. GDP rises. Corporate earnings hit records.
And hiring stays frozen at recession levels.
The labor just stopped needing as many people.
ABAB AI 解读
这段话不是在预测一次周期性衰退,而是在揭示一个更残酷、也更长期的结构变化:劳动需求函数本身被 AI 改写了。
我从宏观统计悖论 → 企业行为机制 → 劳动力分层 → 长期终局四个层级解读。
一、表面矛盾的本质:失业率是“存量指标”,招聘率是“流量指标”
失业率(4.6%):看的是“现在有多少人没有工作”
招聘率(接近衰退):看的是“企业是否在为未来配置人力”
📌 当企业既不裁员、也不招聘时,这两个指标可以同时成立。
这不是统计异常,而是一个非常清晰的信号:企业对“现有员工”满意,但对“新增员工”失去兴趣。
二、真正的机制变化:“10% 产出 = 招人”这个反射动作消失了
2020 年前的企业逻辑是线性的:需求 ↑ → 人力 ↑ → 成本 ↑
而现在变成了条件判断:需求 ↑ → AI 能不能覆盖? →
- 能 → 不招人
- 不能 → 极少量招人
📌 AI 成了新的“第一响应层”,而人类变成“兜底资源”。
这不是某个行业的变化,而是横向扩散到:
- 金融
- 咨询
- 软件
- 市场
- 数据分析
- 行政支持
三、为什么没有“大裁员”?因为最优路径是“不补招”,而不是“开除”
这是这段话最关键、也最容易被忽略的洞察。
企业发现:
- 裁员有:舆论成本、士气成本、法律风险
- 而自然流失 + 冻结招聘:安静、连续、几乎无摩擦
于是出现了新常态:
- 有人走 → 不补
- 有人退 → 不换
- 团队要效率 → 上 Copilot / Agent
📌 这是一种“看不见的裁员”。
宏观上:没有失业冲击,但就业机会持续消失
四、为什么“招聘率”比“裁员率”更重要?
因为招聘是企业对未来的真实下注。
当招聘长期低迷,意味着:企业认为未来增长不需要新增人力
生产率提升被视为:可持续、可复制、可继续深化
📌 这是“AI 生产率红利内生化”的标志。
每一个“没有招聘的月份”,都在放大一个事实:同样的 GDP,用更少的人完成。
五、生产率去哪了?它进了利润、而不是工资
这是政治与社会最敏感的部分。
- GDP 上升
- 企业利润创新高
- 股市表现强劲
但:
- 就业增长停滞
- 入门岗位萎缩
- 工资议价能力下降
📌 AI 把“新增产出”直接分配给资本,而不是劳动。
这就是为什么你会看到:经济看起来很好,但年轻人感觉越来越难。
六、Forrester 的预测揭示了更冷酷的一层
55% 的 AI 相关“裁员岗位”,会被低薪外包重新雇佣
这意味着:工作并未消失、工作地点与劳动者发生了转移
📌 这是“全球劳动力套利 + AI 自动化”的叠加效应。
结果是:高成本国家的中低端白领首当其冲、全球工资被向下拉平
七、为什么“入门级岗位”最先崩塌?
因为入门岗位的核心价值是:
- 做第一轮分析
- 处理初级信息
- 执行模板化任务
- 而这正是 AI 的最强项。
📌 当 Claude / Copilot 能做 70% 的初级工作时:
企业会选择:一个资深员工 + AI,而不是一个资深员工 + 一个新人
这直接切断了:学习路径、职业爬梯的第一阶
八、这不是衰退,这是“无就业增长的扩张”
这句话点中了历史性转折:
- Jobless Growth
- 经济扩张
- 利润增长
- 技术进步
但:
- 就业不扩张
- 人力需求收缩
📌 这在历史上极为罕见,因为以往的技术进步最终都会“创造更多岗位”。
AI 第一次让这个假设变得不再确定。
九、一句最高认知总结(核心)
失业率告诉你“现在还没崩”,招聘率告诉你“未来不需要你”。
AI 正在把经济从:“增长 = 更多工作”
变成:“增长 = 更少人 + 更多算力”
最后一句(非常现实)
如果你还在等:“下一轮招聘潮”、“等经济好了就有机会”
你可能在等待一个不会再以原形态出现的世界。
真正的问题已经不是:“会不会失业?”,而是:“在一个不再需要那么多人的经济中,你是否仍然不可替代?”
这,才是这段话真正想让你看到的未来。
相关观点
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- xAI 刚刚以约 2400 亿美元估值完成了 200 亿美元融资。 这轮融资的重要性,远不止标题看起来那么简单。 先看数字。 在 18 个月内,马斯克打造了一家市值接近 OpenAI 一半的公司,而其融资规模却只是 Sam Altman 所筹资金的一小部分。xAI 的累计融资约 320 亿美元;OpenAI 已融资 640 亿美元以上,并正在寻求 再融资 1000 亿美元,对应估值高达 8300 亿美元。这种效率差距,本身就说明了两家公司在运作方式上的根本不同。 战略投资者名单同样值得关注。 NVIDIA 和 Cisco 并不是“图个感觉”就投了 xAI。他们押注的是基础设施建设。当你的芯片供应商和网络设备巨头同时入股你的 AI 公司,这传递出的信号,显然不同于一轮普通的风投。 接下来,分析开始变得有意思。 xAI 公告中的 6 亿 MAU,是将 X 平台用户与 Grok 用户合并计算的结果。这是一种相当聪明的表述方式。独立数据显示,Grok 本身的月活用户大约在 3000 万到 6400 万之间,取决于数据来源。这依然是有意义的增长——在 Grok 3 发布后,用户增长了 436%。但 X + Grok 的合并指标,掩盖了真实的用户参与度究竟发生在哪里。 不过,比用户数量更有说服力的,是模型质量。 在近期的基准测试中,Grok 4 已经可以与 OpenAI 的顶级模型正面竞争。在 LMSYS Arena 排名中,Grok 4.1 Thinking 拿下了用户偏好榜首。在 GPQA Diamond 等科学推理基准测试中,Grok 与 Gemini 3 和 GPT-5 同台竞争。2023 年流行的“起步太晚”叙事,正迅速过时。 但从市场份额看,情况又是另一番景象。 ChatGPT 约占 68% 的流量; Gemini 约 18%,且增长迅猛; Grok 仅占 2%–3%(取决于统计方式); Claude 也在相近区间。 榜单清晰明了:ChatGPT 第一,Gemini 第二,其余玩家争夺剩余边角料。 OpenAI 拥有 8 亿周活用户,形成强大的反馈闭环,使模型迭代速度持续领先; Anthropic 掌控着企业级编程市场; Google 则控制着 30 亿台 Android 设备上的操作系统层。 xAI 2400 亿美元的估值与 OpenAI 5000 亿美元以上的估值差距,真实反映了市场地位的差异。 马斯克用这 200 亿美元买到的,是继续留在赛道上的时间,直到以下两种情况之一发生:要么 Grok 实现技术层面的全面对等,并充分发挥 X 的分发优势;要么市场充分碎片化,即便是第三或第四名,也依然是一个体量巨大的生意。 模型显示,他确实有时间。 算力仍在扩张,Colossus 已运行相当于 100 万张 H100 的算力规模,Grok 5 也正在训练中。问题已经不再是 xAI 能不能做出有竞争力的模型——这点他们已经证明了。真正的问题是:是否有人能在市场固化之前,撼动 ChatGPT 的“默认入口”地位。 历史提供了一些参考。 在搜索领域,Google 在技术上并非没有对手,却仍然主导了 20 年;但在社交网络中,格局翻转得更快——TikTok 在 3 年内就动摇了 Instagram 在短视频领域的统治。AI 助手,可能会走向这两种路径中的任何一种。 如果走“搜索路径”,OpenAI 胜出,其余玩家沦为细分市场;如果走“社交路径”,谁能打造最具吸引力的日常体验,谁就能捕获注意力,以及随之而来的所有价值。 这 200 亿美元的赌注,押的是 AI 更像社交,而不是搜索。也押的是:400M 的 X 用户规模、特斯拉生态整合,以及具备竞争力的模型,能让 xAI 成为相当一部分用户的“日常入口”。 这是一个严肃、完整的商业假设。你是否相信它,取决于一个核心判断:你认为 AI 工具,更像搜索引擎,还是更像信息流? xAI just closed $20B at roughly $240B valuation. This round matters more than the headline suggests. The math first. In 18 months, Musk built a company worth nearly half of OpenAI while raising a fraction of what Sam Altman has accumulated. xAI’s total capital sits around $32B. OpenAI has pulled in $64B+ and is hunting for another $100B that would value it at $830B. The efficiency gap tells you something about how these two companies operate. The strategic investor list deserves attention. NVIDIA and Cisco didn’t invest in xAI for the vibes. They’re betting on infrastructure buildout. When your chip supplier and networking giant both take equity positions in your AI company, that signals something different than another venture round. Here’s where the analysis gets interesting. The 600 million MAU figure in xAI’s announcement combines X platform users with Grok users. That’s a clever framing choice. Independent data shows Grok itself runs closer to 30-64 million monthly actives depending on the source. Still meaningful growth. Grok jumped 436% after Grok 3 launched. But the combined X+Grok metric obscures where engagement actually lives. The model quality story is more compelling than the user numbers anyway. On recent benchmarks, Grok 4 holds its own against OpenAI’s best. LMSYS arena rankings put Grok 4.1 Thinking at the top of user preference scores. On scientific reasoning benchmarks like GPQA Diamond, Grok competes with Gemini 3 and GPT-5. The “late to the game” narrative from 2023 looks increasingly dated. The market share picture tells a different story though. ChatGPT commands roughly 68% of traffic. Gemini sits around 18% and climbing fast. Grok holds somewhere between 2-3% depending on how you measure. Claude hovers near that range too. The leaderboard reads: ChatGPT, Gemini, then everyone else fighting for scraps. OpenAI has 800 million weekly users generating feedback loops that improve the model faster. Anthropic owns the enterprise coding market. Google controls the operating system layer on 3 billion Android devices. The gap between xAI’s $240B valuation and OpenAI’s $500B+ valuation reflects real differences in market position. What Elon is buying with this $20B is runway to stay in the race until one of two things happens: either Grok achieves technical parity and X’s distribution advantage kicks in, or the entire market fragments enough that being number three or four still represents a massive business. The model suggests he has time. Compute is scaling. Colossus runs 1 million H100 equivalents. Grok 5 is training now. The question isn’t whether xAI can build competitive models. They’ve proven they can. The question is whether anyone can dislodge ChatGPT’s default status before the market calcifies. History offers some guidance. Google dominated search for 20 years despite technically competent alternatives. But social networks flip faster. TikTok displaced Instagram’s grip on short video in three years. AI assistants might follow either pattern. If they follow the search pattern, OpenAI wins and everyone else becomes a niche player. If they follow the social pattern, whoever builds the most compelling daily experience captures attention and everything downstream from it. The $20B bet is that AI follows the social pattern. And that having 400 million X users, Tesla integration, and competitive models gives xAI a shot at being that daily experience for a meaningful segment of users. That’s a real thesis. Whether you believe it depends on whether you think AI tools are more like search engines or more like social feeds.Aakash Gupta · 2026/01/07
- 大多数创始人从未看清的一套隐藏数学: 风投(VC)要想继续生存,通常需要 3 倍基金回报。 一支 1 亿美元的基金,最终需要回收 3 亿美元。如果他们在你公司退出时持有 20% 股份,那么你至少要把公司卖到 15 亿美元,这一笔投资才能刚好帮他们完成目标。 而他们通常会投 30 个项目。其中大多数会归零。因此,少数赢家必须做到 10–50 倍回报,才能覆盖其他项目的亏损。 你以 3000 万美元退出、自己拿到 2500 万美元? 对 VC 来说,这笔投资只回了 0.6 倍本金。他们宁愿你去赌一个 5% 成功率、但可能卖到 10 亿美元的结果,也不愿意你以 40% 成功率卖 3000 万美元。 到了 B 轮,创始人通常只剩下不到 30% 的股份; 到了 C 轮,会降到 15–25%。 假设你以 5000 万美元退出,持股 20%, 在清算优先权之前,创始人能拿到 1000 万美元。 而一个自力更生(bootstrapped)的创始人,以 4000 万美元出售公司、持股 90%,最终能拿走 3600 万美元。 激励结构是完全相反的: * VC 优化的是投资组合整体回报 * 创始人优化的是个人最终结果 这两者,只有在 5 亿美元以上的退出时,才真正对齐。 在没有外部融资的情况下做到 300 万美元 ARR,通常意味着团队已经: * 找到了真正的产品市场匹配(PMF) * 建立了 VC 支持公司往往缺乏的成本纪律 * 这种运营能力会持续复利。 1000 万美元 ARR 的自力更生公司,学会的是如何印钞 1000 万美元 ARR 的 VC 支持公司,学会的是如何花钱 两条路都可能成功。真正的区别在于:这套数学,最终是为谁服务的。 The hidden math most founders never see: VCs need 3x fund returns to stay in business. A $100M fund needs to return $300M. If they own 20% of your company at exit, you need to sell for $1.5B just for them to hit target on that one bet. They’re making 30 bets. Most will zero out. So the winners need to return 10-50x to cover the losses. Your $30M exit where you keep $25M? Returns their capital 0.6x. They’d rather you swing for $1B with a 5% success rate than take the $30M with 40% odds. By Series B, founders typically own less than 30% of their company. By Series C, that drops to 15-25%. A $50M exit at 20% ownership nets founders $10M before liquidation preferences. The bootstrapped founder selling at $40M with 90% ownership walks with $36M. The incentive structures are pointed in opposite directions. VCs optimize for portfolio returns. Founders optimize for personal outcomes. These only align at $500M+ exits. Getting to $3M ARR without outside capital usually means the team has found product-market fit and maintained cost discipline that many VC-backed peers never develop. That operational muscle compounds. The $10M ARR bootstrapped company learned to print cash. The $10M ARR VC-backed company learned to spend it. Both paths can work. The difference is who the math works for.Aakash Gupta · 2026/01/04
- OpenAI 和 Jony Ive 能否搞定“环境计算(ambient computing)”? Humane AI Pin 售价 699 美元,现在已经彻底变砖。 Rabbit R1 以 199 美元上市,MKBHD 评价它“几乎没法评测”。 OpenAI 的 “IO” 设备将面临同样的物理限制,这些问题正是前辈们失败的原因: * 电池能量密度 * 本地大模型(LLM)推理延迟 一个令人不舒服的事实:你的手机已经做得更快。那么,OpenAI 真正押注的 65 亿美元到底是什么?是“上下文层(context layer)”。 设备本身几乎无关紧要。真正重要的是:持续监听、手写笔记的捕捉、以及对你周围环境的连续感知。他们不是在做一个小玩意,而是在构建一个上下文摄取管道,为模型提供它们此前从未拥有过的东西——规模化的、实时的人类行为数据。 Humane 和 Rabbit 问的是:“我们如何取代手机?” 而 OpenAI 问的是:“我们如何在手机之前,进入你生活的每一个瞬间?” 当你明白他们真正优化的目标时,“笔”的形态就说得通了: * 低社交摩擦 * 始终贴身 * 携带自然 * 看起来就像你本来就会放在口袋里的东西 真正棘手的技术难题依然存在: * 7 天续航意味着需要亚 500ms 的边缘侧推理 * 在如此小的设备上实现多模态融合,需要定制芯片 * 还需要在嘈杂环境中依然可靠的转录与说话人分离(diarization) Bee Computer 已经通过波束成形麦克风和专用转录管线做到了这一点。Limitless Pendant 也意识到:如果核心需求是记忆捕捉,其实不需要摄像头。 OpenAI 的优势不在于硬件创新,而在于:每一段被捕捉的对话,都会成为下一代模型的训练数据。每一个上下文窗口都在变长,每一个用户都在成为一个数据飞轮。 最终赢下环境计算的设备,不会是参数最强的那一个, 而是人们真的会忘记自己正在佩戴的那一个。 而收集到最多上下文的公司,将赢得下一场模型之战。 Can OpenAI and Jony Ive figure out ambient computing? Humane AI Pin cost $699. Now bricked. Rabbit R1 launched at $199. MKBHD called it “barely reviewable.” The OpenAI “IO” device faces the same physics problems that killed its predecessors: 1. Battery density. 2. On-device LLM latency. 3. The uncomfortable truth that your phone already does this faster. So what is OpenAI actually betting $6.5B on? The context layer. The device itself is almost irrelevant. What matters is the persistent listening, the handwritten note capture, the continuous awareness of your surroundings. They’re not building a gadget. They’re building a context ingestion pipeline that feeds their models what they’ve never had before. Real-time human behavior at scale. Humane and Rabbit asked “how do we replace the phone?” OpenAI is asking “how do we get inside every moment before the phone?” The pen form factor makes sense when you realize what they’re optimizing for. Low social friction. Always on body. Natural to carry. Looks like something you’d already have in your pocket. The hard technical problems remain. 7-day battery life requires sub-500ms edge inference. Multi-modal fusion on a device that size means custom silicon. And they need transcription plus diarization that works in noisy environments. Bee Computer already does this with beamforming mics and a dedicated transcription pipeline. The Limitless Pendant figured out that you don’t need cameras if memory capture is your primary use case. OpenAI’s advantage isn’t hardware innovation. It’s that every conversation captured becomes training data for the next model generation. Every context window gets longer. Every user becomes a data flywheel. The device that wins ambient computing won’t be the one with the best specs. It’ll be the one people actually forget they’re wearing. And the company that collects the most context wins the next model war.Aakash Gupta · 2026/01/03