2万字血书！一封来自2028年的宏观备忘录，全球危机已然降临身边...我们所熟悉的社会体系即将迎来翻天覆地的改变...

本文是对Citrini最新文章的全文精译，

推荐所有人阅读和思考....

Macro Memo  宏观备忘录

The Consequences of Abundant Intelligence

人工智能泛滥的后果

February 22nd, 2026 June 30th, 2028

2026 年 2 月 22 日 2028 年 6 月 30 日

The unemployment rate printed 10.2% this morning, a 0.3% upside surprise. The market sold off 2% on the number, bringing the cumulative drawdown in the S&P to 38% from its October 2026 highs.

今晨公布的失业率录得 10.2%，较预期高出 0.3 个百分点。受此数据影响市场下跌 2%，标普 500 指数较 2026 年 10 月峰值累计回撤幅度达 38%。

Traders have grown numb. Six months ago, a print like this would have triggered a circuit breaker.

交易员们已经麻木了。六个月前，这样的数据发布足以触发熔断机制。

Two years. That’s all it took to get from “contained” and “sector-specific” to an economy that no longer resembles the one any of us grew up in. This quarter’s macro memo is our attempt to reconstruct the sequence - a post-mortem on the pre-crisis economy.

两年。这就是从“得到控制”和“行业特定”演变为一个与我们成长环境中任何经济体都迥然相异的经济形态所需的全部时间。本季度宏观备忘录旨在重构这一演变轨迹——对危机前经济形态的病理剖析。

The euphoria was palpable. By October 2026, the S&P 500 flirted with 8000, the Nasdaq broke above 30k. The initial wave of layoffs due to human obsolescence began in early 2026, and they did exactly what layoffs are supposed to. Margins expanded, earnings beat, stocks rallied. Record-setting corporate profits were funneled right back into AI compute.

市场狂热态势昭然若揭。截至 2026 年 10 月，标普 500 指数触及 8000 点关口，纳斯达克指数突破 30000 点大关。首轮因人力淘汰引发的裁员潮始于 2026 年初，裁员效应精准显现：企业利润率扩张，盈利数据超预期，股价强势攀升。创纪录的企业利润被持续注入 AI 算力建设。

The headline numbers were still great. Nominal GDP repeatedly printed mid-to-high single-digit annualized growth. Productivity was booming. Real output per hour rose at rates not seen since the 1950s, driven by AI agents that don’t sleep, take sick days or require health insurance.

宏观数据依然亮眼。名义 GDP 同比增速持续维持在 5%-9%区间，生产力呈爆发式增长。在无需休眠、不请病假且无需健康保险的 AI 智能体驱动下，每小时实际产出增速创下 1950 年代以来新高。

The owners of compute saw their wealth explode as labor costs vanished. Meanwhile, real wage growth collapsed. Despite the administration’s repeated boasts of record productivity, white-collar workers lost jobs to machines and were forced into lower-paying roles.

计算资源的拥有者眼睁睁看着财富急剧膨胀，而劳动力成本却消失无踪。与此同时，实际工资增长全面崩盘。尽管政府反复吹嘘生产力屡创新高，但白领工作者仍被机器取代，被迫转入薪酬更低的岗位。

When cracks began appearing in the consumer economy, economic pundits popularized the phrase “Ghost GDP“: output that shows up in the national accounts but never circulates through the real economy.

当消费经济开始出现裂痕时，经济评论家们广泛传播"幽灵 GDP"这一概念：即显现在国民经济账户中，却从未在实体经济中流通的产出。

In every way AI was exceeding expectations, and the market was AI. The only problem…the economy was not.

人工智能在各方面都超越了预期，而市场本身也已成为人工智能的战场。唯一的问题是...经济本身却并非如此。

It should have been clear all along that a single GPU cluster in North Dakota generating the output previously attributed to 10,000 white-collar workers in midtown Manhattan is more economic pandemic than economic panacea. The velocity of money flatlined. The human-centric consumer economy, 70% of GDP at the time, withered. We probably could have figured this out sooner if we just asked how much money machines spend on discretionary goods. (Hint: it’s zero.)

我们本该早就清醒认识到：北达科他州的一个 GPU 集群，其产出能抵过曼哈顿中城万名白领员工的工作成果，这更像一场经济瘟疫而非经济良方。货币流通速度陷入停滞。占当时 GDP 70%、以人类为中心的消费经济日渐萎缩。如果我们早些追问机器会在非必需品上花费多少钱，或许就能更早看清这个困局。（提示：答案是零。）

AI capabilities improved, companies needed fewer workers, white collar layoffs increased, displaced workers spent less, margin pressure pushed firms to invest more in AI, AI capabilities improved…

人工智能能力提升，企业所需员工减少，白领裁员数量增加，失业人群消费收缩，利润压力迫使企业加码人工智能投资，人工智能能力进一步提升……

It was a negative feedback loop with no natural brake. The human intelligence displacement spiral. White-collar workers saw their earnings power (and, rationally, their spending) structurally impaired. Their incomes were the bedrock of the $13 trillion mortgage market - forcing underwriters to reassess whether prime mortgages are still money good.

这是一个没有自然制动机制的恶性循环。人类智力替代螺旋就此形成。白领阶层的创收能力（以及理性层面的消费能力）遭受结构性削弱。他们的收入曾是 13 万亿美元抵押贷款市场的基石——这一变化迫使承销机构重新评估优质抵押贷款是否依然具备偿付保障。

Seventeen years without a real default cycle had left privates bloated with PE-backed software deals that assumed ARR would remain recurring. The first wave of defaults due to AI disruption in mid-2027 challenged that assumption.

长达十七年未经历实质性违约周期，导致私募市场充斥着大量基于"年经常性收入将持续产生"假设的软件行业私募股权投资。2027 年中旬，首波由人工智能颠覆引发的违约潮冲击了这一根本假设。

This would have been manageable if the disruption remained contained to software, but it didn’t. By the end of 2027, it threatened every business model predicated on intermediation. Swaths of companies built on monetizing friction for humans disintegrated.

若冲击仅局限于软件行业尚可应对，但事实并非如此。截至 2027 年底，所有建立在中介模式之上的商业模式都面临威胁。大批通过解决人类交易摩擦盈利的企业土崩瓦解。

The system turned out to be one long daisy chain of correlated bets on white-collar productivity growth. The November 2027 crash only served to accelerate all of the negative feedback loops already in place.

该系统最终演变成一条长长的"菊花链"——由一系列相互关联的白领生产力增长押注构成。2027 年 11 月的市场崩盘，只不过加速了所有既存负面反馈循环的运转。

We’ve been waiting for “bad news is good news” for almost a year now. The government is starting to consider proposals, but public faith in the ability of the government to stage any sort of rescue has dwindled. Policy response has always lagged economic reality, but lack of a comprehensive plan is now threatening to accelerate a deflationary spiral.

近一年来，我们始终期盼着"坏消息即是好消息"的市场逻辑。政府已开始审议各类提案，但公众对政府实施任何形式救助能力的信心正在消退。政策应对总是滞后于经济现实，而全面规划的缺失正加剧通缩螺旋加速的风险。

一、

How It Started 

危机肇始

In late 2025, agentic coding tools took a step function jump in capability.

2025 年末，智能编程工具实现了阶跃式能力突破。

A competent developer working with Claude Code or Codex could now replicate the core functionality of a mid-market SaaS product in weeks. Not perfectly or with every edge case handled, but well enough that the CIO reviewing a $500k annual renewal started asking the question “what if we just built this ourselves?”

如今，一位熟练的开发者借助 Claude Code 或 Codex，几周内就能复现出中端市场 SaaS 产品的核心功能。虽非尽善尽美，边缘场景未必全部覆盖，但其完成度足以让审阅 50 万美元年度续费的 CIO 开始思考："我们何不自力更生？"

Fiscal years mostly line up with calendar years, so 2026 enterprise spend had been set in Q4 2025, when “agentic AI” was still a buzzword. The mid-year review was the first time procurement teams were making decisions with visibility into what these systems could actually do. Some watched their own internal teams spin up prototypes replicating six-figure SaaS contracts in weeks.

由于财年大多与自然年重合，2026 年的企业预算早在 2025 年第四季度就已确定，彼时"智能体 AI"仍是流行词汇。年中评审成为采购团队首次在切实洞悉这些系统实际效能后作出决策的关键节点。某些团队目睹内部人员仅用数周便搭建出可替代六位数 SaaS 合约的雏形系统。

That summer, we spoke with a procurement manager at a Fortune 500. He told us about one of his budget negotiations. The salesperson had expected to run the same playbook as last year: a 5% annual price increase, the standard “your team depends on us” pitch. The procurement manager told him he’d been in conversations with OpenAI about having their “forward deployed engineers” use AI tools to replace the vendor entirely. They renewed at a 30% discount. That was a good outcome, he said. The “long-tail of SaaS”, like Monday.com, Zapier and Asana, had it much worse.

那年夏天，我们与一位《财富》500 强企业的采购经理交谈。他向我们讲述了一次预算谈判的经历。销售人员原想沿用去年的套路：年度价格上涨 5%，配上标准的"贵团队离不开我们"话术。这位采购经理告诉对方，自己已与 OpenAI 商谈过，准备让他们的"前沿部署工程师"运用人工智能工具完全取代供应商。最终他们以七折价格续约。他表示这已是不错的结果——像 Monday.com、Zapier 和 Asana 这类"SaaS 长尾应用"处境要严峻得多。

Investors were prepared - expectant, even - that the long tail would be hit hard. They may have made up a third of spending for the typical enterprise stack, but they were obviously exposed. The systems of record, however, were supposed to be safe from disruption.

投资者早有准备，甚至预期到长尾市场将遭受重创。尽管这些企业可能占据了典型企业技术栈支出的三分之一，但其风险敞口显而易见。然而，记录型系统本应免于冲击。

It wasn’t until ServiceNow’s Q3 26 report that the mechanism of reflexivity became clearer.

直到 ServiceNow 发布 2026 年第三季度报告后，这种反身性机制才逐渐清晰起来。

SERVICENOW NET NEW ACV GROWTH DECELERATES TO 14% FROM 23%; ANNOUNCES 15% WORKFORCE REDUCTION AND ‘STRUCTURAL EFFICIENCY PROGRAM’; SHARES FALL 18% | Bloomberg, October 2026

SERVICENOW 新增年度合同价值增速从 23%放缓至 14%；宣布裁员 15%并启动"结构性效率计划"；股价暴跌 18% | 彭博社，2026 年 10 月

SaaS wasn’t “dead”. There was still a cost-benefit-analysis to running and supporting in-house builds. But in-house was an option, and that factored into pricing negotiations. Perhaps more importantly, the competitive landscape had changed. AI had made it easier to develop and ship new features, so differentiation collapsed. Incumbents were in a race to the bottom on pricing - a knife-fight with both each other and with the new crop of upstart challengers that popped up. Emboldened by the leap in agentic coding capabilities and with no legacy cost structure to protect, these aggressively took share.

SaaS 并未"消亡"。企业自建系统在运营和维护方面仍需进行成本效益分析。但自建选项的存在影响了定价谈判。更重要的是，竞争格局已然改变：人工智能降低了新功能的开发与部署门槛，导致产品差异性急剧缩小。现有厂商陷入价格战泥潭——既与同行厮杀，又要应对雨后春笋般涌现的初创挑战者。这些新兴企业凭借智能编码能力的飞跃，以毫无历史成本负担的优势激进抢占市场份额。

The interconnected nature of these systems weren’t fully appreciated until this print, either. ServiceNow sold seats. When Fortune 500 clients cut 15% of their workforce, they cancelled 15% of their licenses. The same AI-driven headcount reductions that were boosting margins at their customers were mechanically destroying their own revenue base.

直到这份报告发布，人们才真正认识到这些系统之间相互依存的特性。ServiceNow 出售软件席位。当《财富》500 强客户裁减 15%的员工时，他们便取消了 15%的软件许可。那些曾帮助客户提升利润率的人工智能驱动型裁员措施，正机械地摧毁着软件供应商自身的收入基础。

The company that sold workflow automation was being disrupted by better workflow automation, and its response was to cut headcount and use the savings to fund the very technology disrupting it.

这家销售工作流自动化软件的企业，正被更先进的工作流自动化技术所颠覆，而它的应对之策竟是裁员，并将节省下的资金投入到颠覆自身的技术研发中。

What else were they supposed to do? Sit still and die slower? The companies most threatened by AI became AI’s most aggressive adopters.

他们还能怎么做？坐以待毙、缓慢消亡吗？受人工智能威胁最严重的企业，恰恰成为了这项技术最激进的采用者。

This sounds obvious in hindsight, but it really wasn’t at the time (at least to me). The historical disruption model said incumbents resist new technology, they lose share to nimble entrants and die slowly. That’s what happened to Kodak, to Blockbuster, to BlackBerry. What happened in 2026 was different; the incumbents didn’t resist because they couldn’t afford to.

事后回想起来这似乎显而易见，但当时（至少对我而言）却并非如此。历史上的颠覆模式表明，行业巨头往往抵制新技术，最终被灵活的后来者蚕食市场份额并逐渐消亡。柯达、百视达和黑莓的遭遇皆是如此。但 2026 年发生的情况却截然不同：老牌企业没有选择抵抗，因为他们根本无力承担抵抗的代价。

With stocks down 40-60% and boards demanding answers, the AI-threatened companies did the only thing they could. Cut headcount, redeploy the savings into AI tools, use those tools to maintain output with lower costs.

随着股票暴跌 40%-60%且董事会不断追责，这些受到人工智能威胁的企业只能采取唯一可行的措施：裁员，将节省的资金重新配置到 AI 工具，并利用这些工具以更低的成本维持产出。

Each company’s individual response was rational. The collective result was catastrophic. Every dollar saved on headcount flowed into AI capability that made the next round of job cuts possible.

每家公司的个体决策都合乎理性。但集体行动的结果却酿成灾难。每一笔人力成本节省的资金都流向 AI 能力建设，而这又为下一轮裁员创造了条件。

Software was only the opening act. What investors missed while they debated whether SaaS multiples had bottomed was that the reflexive loop had already escaped the software sector. The same logic that justified ServiceNow cutting headcount applied to every company with a white-collar cost structure.

软件仅是序章。就在投资者争论 SaaS 市盈率是否触底时，他们忽略了一个关键事实：这种自我强化的反馈循环早已突破软件行业的边界。那些被用来证明 ServiceNow 裁员合理性的逻辑，同样适用于任何拥有白领成本结构的公司。

二、

When Friction Went to Zero

当摩擦系数归零

By early 2027, LLM usage had become default. People were using AI agents who didn’t even know what an AI agent was, in the same way people who never learned what “cloud computing” was used streaming services. They thought of it the same way they thought of autocomplete or spell-check - a thing their phone just did now.

截至 2027 年初，LLM 应用已成为默认配置。人们在使用甚至不理解"AI 智能体"为何物的智能代理，正如从未了解"云计算"概念的用户仍能流畅使用流媒体服务。公众将其与输入联想或拼写检查等同视之——不过是手机新增的基础功能。

Qwen’s open-source agentic shopper was the catalyst for AI handling consumer decisions. Within weeks, every major AI assistant had integrated some agentic commerce feature. Distilled models meant these agents could run on phones and laptops, not just cloud instances, reducing the marginal cost of inference significantly.

通义千问的开源购物智能体成为 AI 接管消费决策的催化剂。短短数周内，所有主流 AI 助手都集成了某种形式的智能商务功能。经过蒸馏优化的模型意味着这些代理可在手机和笔记本电脑本地运行，无需依赖云端实例，这使推理的边际成本实现了断崖式下降。

The part that should have unsettled investors more than it did was that these agents didn’t wait to be asked. They ran in the background according to the user’s preferences. Commerce stopped being a series of discrete human decisions and became a continuous optimization process, running 24/7 on behalf of every connected consumer. By March 2027, the median individual in the United States was consuming 400,000 tokens per day - 10x since the end of 2026.

本应让投资者更为不安的是，这些智能代理并非被动响应指令，而是依据用户偏好在后台持续自主运行。商业活动不再由人类进行离散决策，而是演变为代表每位联网消费者的全天候持续优化进程。截至 2027 年 3 月，美国个人日均代币消耗量已达 40 万枚——较 2026 年末激增十倍。

The next link in the chain was already breaking.

产业链的下一环已在断裂。

Intermediation.  中介化。

Over the past fifty years, the U.S. economy built a giant rent-extraction layer on top of human limitations: things take time, patience runs out, brand familiarity substitutes for diligence, and most people are willing to accept a bad price to avoid more clicks. Trillions of dollars of enterprise value depended on those constraints persisting.

过去五十年间，美国经济在人类局限性之上构建了庞大的租金榨取层：事务处理需要时间、耐心终会耗尽、品牌熟悉度取代勤勉考察，大多数人宁愿接受不合理报价也不愿多点几下鼠标。数万亿美元的企业价值都建立在这些持续存在的约束条件之上。

It started out simple enough. Agents removed friction.

起初一切足够简单。中介消除了交易摩擦。

Subscriptions and memberships that passively renewed despite months of disuse. Introductory pricing that sneakily doubled after the trial period. Each one was rebranded as a hostage situation that agents could negotiate. The average customer lifetime value, the metric the entire subscription economy was built on, distinctly declined.

尽管数月未使用仍被动续期的订阅服务与会员资格。试用期结束后价格悄然翻倍的入门优惠。每一次消费行为都被重新定义为可供特工谈判的人质事件。支撑整个订阅经济体系的平均客户生命周期价值，明显呈现下滑态势。

Consumer agents began to change how nearly all consumer transactions worked.

消费者智能代理正开始改变几乎所有消费交易的运作方式。

Humans don’t really have the time to price-match across five competing platforms before buying a box of protein bars. Machines do.

人类确实没有时间在购买一盒蛋白棒前，逐一对五家竞争平台进行比价。但机器可以做到。

Travel booking platforms were an early casualty, because they were the simplest. By Q4 2026, our agents could assemble a complete itinerary (flights, hotels, ground transport, loyalty optimization, budget constraints, refunds) faster and cheaper than any platform.

旅游预订平台成为早期牺牲品，因其商业模式最为简单。截至 2026 年第四季度，我们的智能代理已能比任何平台更快速、更经济地完成全套行程规划（包含航班、酒店、地面交通、会员权益优化、预算控制及退改政策）。

Insurance renewals, where the entire renewal model depended on policyholder inertia, were reformed. Agents that re-shop your coverage annually dismantled the 15-20% of premiums that insurers earned from passive renewals.

保险续保业务——其整个续保模式依赖投保人的惰性——遭到彻底改革。每年重新核保的智能代理打破了保险公司依靠被动续保获取 15%-20%保费收入的盈利模式。

Financial advice. Tax prep. Routine legal work. Any category where the service provider’s value proposition was ultimately “I will navigate complexity that you find tedious” was disrupted, as the agents found nothing tedious.

理财咨询、税务筹划、常规法律事务——任何以"为您处理繁琐复杂事务"为核心价值的服务领域均被颠覆，因为智能代理从不知繁琐为何物。

Even places we thought insulated by the value of human relationships proved fragile. Real estate, where buyers had tolerated 5-6% commissions for decades because of information asymmetry between agent and consumer, crumbled once AI agents equipped with MLS access and decades of transaction data could replicate the knowledge base instantly. A sell-side piece from March 2027 titled it “agent on agent violence”. The median buy-side commission in major metros had compressed from 2.5-3% to under 1%, and a growing share of transactions were closing with no human agent on the buy side at all.

即便是我们认为受人际关系价值庇护的领域也显露出脆弱性。房地产行业数十年来因经纪人与消费者间的信息不对称而维持着 5-6%的佣金比例，如今当接入 MLS 系统并掌握数十年交易数据的 AI 智能体能够瞬时复制全部知识库时，这个体系便土崩瓦解。2027 年 3 月一篇卖方报告将其称为"智能体间的自相残杀"。主要都市区的买方佣金中位数已从 2.5-3%压缩至不足 1%，且越来越多的交易在完全没有人类经纪人介入的情况下完成。

We had overestimated the value of “human relationships”. Turns out that a lot of what people called relationships was simply friction with a friendly face.

我们高估了"人际关系"的价值。原来，许多被称作关系的东西，不过是戴着友好面具的摩擦。

That was just the start of the disruption for the intermediation layer. Successful companies had spent billions to effectively exploit quirks of consumer behavior and human psychology that didn’t matter anymore.

这仅仅是中间层颠覆的开端。那些曾经成功的企业耗资数十亿美元，只为有效利用消费者行为与人类心理的某些特质——而这些特质如今已不再重要。

Machines optimizing for price and fit do not care about your favorite app or the websites you’ve been habitually opening for the last four years, nor feel the pull of a well-designed checkout experience. They don’t get tired and accept the easiest option or default to “I always just order from here”.

专注于价格与匹配度的机器算法，不会在意你最喜爱的应用，也不会关注你过去四年习惯性访问的网站，更不会感受到精心设计的结算体验带来的吸引力。它们从不会感到疲惫而选择最简便的选项，也不会默认"我总是从这家下单"。

That destroyed a particular kind of moat: habitual intermediation.

这摧毁了一种特殊的护城河：习惯性中介。

DoorDash (DASH US) was the poster child.

DoorDash（DASH US）正是这一现象的典型代表。

Coding agents had collapsed the barrier to entry for launching a delivery app. A competent developer could deploy a functional competitor in weeks, and dozens did, enticing drivers away from DoorDash and Uber Eats by passing 90-95% of the delivery fee through to the driver. Multi-app dashboards let gig workers track incoming jobs from twenty or thirty platforms at once, eliminating the lock-in that the incumbents depended on. The market fragmented overnight and margins compressed to nearly nothing.

编码代理迅速降低了推出配送应用的门槛。一名合格开发者数周内便能部署功能完备的竞争者应用，而数十家新兴平台正是这样做的——它们将 90%-95%的配送费直接让利给司机，从而从 DoorDash 和 Uber Eats 手中争夺劳动力。多平台集成面板让零工工作者能同时追踪二三十个平台的订单流，彻底瓦解了既有平台赖以生存的用户锁定效应。市场一夜之间碎片化，利润率被压缩至近乎归零。

Agents accelerated both sides of the destruction. They enabled the competitors and then they used them. The DoorDash moat was literally “you’re hungry, you’re lazy, this is the app on your home screen.” An agent doesn’t have a home screen. It checks DoorDash, Uber Eats, the restaurant’s own site, and twenty new vibe-coded alternatives so it can pick the lowest fee and fastest delivery every time.

智能代理从供需两端同时加速了这场颠覆。它们既赋能新兴竞争者，又反过来利用这些竞争渠道。DoorDash 曾经的护城河本质上是"当你饥饿又懒散时，主页屏幕上只会出现这个应用"。但代理程序没有固定主页——它每次都会同步查询 DoorDash、Uber Eats、餐厅官网以及二十个新兴的情绪化编码平台，只为筛选出最低费用与最快配送方案。

Habitual app loyalty, the entire basis of the business model, simply didn’t exist for a machine.

对于机器而言，基于使用习惯的应用程序忠诚度根本不存在，而这恰恰是整个商业模式赖以建立的根基。

This was oddly poetic, as perhaps the only example in this entire saga of agents doing a favor for the soon-to-be-displaced white collar workers. When they ended up as delivery drivers, at least half their earnings weren’t going to Uber and DoorDash. Of course, this favor from technology didn’t last for long as autonomous vehicles proliferated.

这过程带有一种奇特的诗意，这或许是整个事件中唯一能体现智能体为即将被取代的白领群体行方便的案例。当这些前白领最终成为外卖骑手时，他们至少能保留超过一半的收入，而不必被优步和 DoorDash 平台抽成。当然，科技带来的这种"恩惠"并未持续太久——随着自动驾驶车辆的普及，连这份工作也消失了。

Once agents controlled the transaction, they went looking for bigger paperclips.

当智能体掌控交易后，它们便开始追逐规模更大的"回形针"（注：引申为更高阶的优化目标）。

There was only so much price-matching and aggregating to do. The biggest way to repeatedly save the user money (especially when agents started transacting among themselves) was to eliminate fees. In machine-to-machine commerce, the 2-3% card interchange rate became an obvious target.

比价与聚合服务终究存在上限。要持续为用户节省开支（尤其当智能体开始相互交易时），最有效的方式就是彻底消除手续费。在机器对机器的商业场景中，2-3%的信用卡交换费自然成为首要优化目标。

Agents went looking for faster and cheaper options than cards. Most settled on using stablecoins via Solana or Ethereum L2s, where settlement was near-instant and the transaction cost was measured in fractions of a penny.

智能体开始寻找比信用卡更快捷廉价的支付方案。多数最终选择通过 Solana 或以太坊二层网络使用稳定币结算，其清算速度近乎瞬时，单笔交易成本仅需几分钱。

MASTERCARD Q1 2027: NET REVENUES +6% Y/Y; PURCHASE VOLUME GROWTH SLOWS TO +3.4% Y/Y FROM +5.9% PRIOR QUARTER; MANAGEMENT NOTES “AGENT-LED PRICE OPTIMIZATION” AND “PRESSURE IN DISCRETIONARY CATEGORIES” | Bloomberg, April 29 2027

万事达卡 2027 年第一季度：净营收同比增长 6%；消费额增速从上季度的同比+5.9%放缓至+3.4%；管理层提及"智能体主导的价格优化"及"非必需品类别承压" | 彭博社，2027 年 4 月 29 日

Mastercard’s Q1 2027 report was the point of no return. Agentic commerce went from being a product story to a plumbing story. MA dropped 9% the following day. Visa did too, but pared losses after analysts pointed out its stronger positioning in stablecoin infrastructure.

万事达卡 2027 年第一季财报成为转折点。智能体主导的商业从产品叙事演变为基础设施叙事。次日股价下跌 9%。维萨卡同步走低，但在分析师指出其在稳定币基础设施领域更具优势后收复部分跌幅。

Agentic commerce routing around interchange posed a far greater risk to card-focused banks and mono-line issuers, who collected the majority of that 2-3% fee and had built entire business segments around rewards programs funded by the merchant subsidy.

智能体商业通过绕开交换费通道，对以银行卡业务为核心的银行及单一发卡机构构成更严峻威胁——这些机构不仅收取 2-3%费率中的绝大部分，更依托商户补贴支撑的积分体系构建了完整的业务板块。

American Express (AXP US) was hit hardest; a combined headwind from white-collar workforce reductions gutting its customer base and agents routing around interchange gutting its revenue model. Synchrony (SYF US), Capital One (COF US) and Discover (DFS US) all fell more than 10% over the following weeks, as well.

美国运通（AXP US）受创最重：白领裁员潮侵蚀其客户基础，与智能体绕开交换费侵蚀其营收模式的双重打击形成叠加效应。同步金融（SYF US）、第一资本金融（COF US）和发现金融（DFS US）在随后数周亦全部下跌超 10%。

Their moats were made of friction. And friction was going to zero.

他们的护城河由摩擦构筑。而摩擦正归零而去。From Sector Risk to Systemic Risk

从行业风险到系统性风险

Through 2026, markets treated negative AI impact as a sector story. Software and consulting were getting crushed, payments and other toll booths were wobbly, but the broader economy seemed fine. The labor market, while softening, was not in freefall. The consensus view was that creative destruction was part of any technological innovation cycle. It would be painful in pockets, but the overall net positives from AI would outweigh any negatives.

截至 2026 年，市场将人工智能的负面影响视为行业性事件。软件和咨询行业遭受重创，支付及其他收费业务领域出现波动，但整体经济似乎表现良好。劳动力市场虽然有所疲软，但并未陷入自由落体式下跌。普遍观点认为创造性破坏是任何技术创新周期的组成部分。虽然局部领域会经历阵痛，但人工智能带来的整体净收益将超越任何负面影响。

Our January 2027 macro memo argued this was the wrong mental model. The US economy is a white-collar services economy. White-collar workers represented 50% of employment and drove roughly 75% of discretionary consumer spending. The businesses and jobs that AI was chewing up were not tangential to the US economy, they were the US economy.

我们在 2027 年 1 月的宏观经济备忘录中指出，这种认知模式存在谬误。美国经济本质上是白领服务业经济。白领雇员占就业人口的 50%，并推动着约 75%的可自由支配消费支出。人工智能正在侵蚀的企业和就业岗位并非美国经济的边缘组成部分——它们本身就是美国经济的核心。

“Technological innovation destroys jobs and then creates even more”. This was the most popular and convincing counter-argument at the time. It was popular and convincing because it’d been right for two centuries. Even if we couldn’t conceive of what the future jobs would be, they would surely arrive.

"技术创新在摧毁就业的同时，总会创造更多岗位"——这曾是当时最流行且最具说服力的反方论点。其所以令人信服，是因为过去两个世纪的历史经验始终验证着这个规律。即便我们无法构想未来的工作岗位形态，它们终将如期而至。

ATMs made branches cheaper to operate so banks opened more of them and teller employment rose for the next twenty years. The internet disrupted travel agencies, the Yellow Pages, brick-and-mortar retail, but it invented entirely new industries in their place that conjured new jobs.

ATM 的普及降低了银行网点的运营成本，促使银行开设更多分支机构，柜员就业人数随之增长了二十年。互联网冲击了旅行社、黄页目录和实体零售业，但催生了全新的行业领域，从而创造了新的工作岗位。

Every new job, however, required a human to perform it.

然而，每个新岗位仍需人类亲手执行。

AI is now a general intelligence that improves at the very tasks humans would redeploy to. Displaced coders cannot simply move to “AI management” because AI is already capable of that.

如今，人工智能已成为通用型智能体，在人类可能转型的领域持续进化。被替代的程序员无法简单转向“AI 管理”岗位，因为人工智能已能自主执行这类工作。

Today, AI agents handle many-weeks-long research and development tasks. The exponential steamrolled our conceptions of what was possible, even though every year Wharton professors tried to fit the data to a new sigmoid.

当前，AI 智能体已能处理长达数周的研发任务。指数级增长的技术突破不断颠覆我们对可能性的认知——尽管每年沃顿商学院的教授们都在尝试用新的 S 型曲线拟合数据。

They write essentially all code. The highest performing of them are substantially smarter than almost all humans at almost all things. And they keep getting cheaper.

几乎所有代码都由它们书写。在绝大多数事务上，表现最优异的它们远胜于近乎所有人类。且它们的成本仍在持续降低。

AI has created new jobs. Prompt engineers. AI safety researchers. Infrastructure technicians. Humans are still in the loop, coordinating at the highest level or directing for taste. For every new role AI created, though, it rendered dozens obsolete. The new roles paid a fraction of what the old ones did.

人工智能创造了新的工作岗位。提示工程师。AI 安全研究员。基础设施技术员。人类依然身处决策环路，从事最高层面的协调工作或把控品位导向。然而，人工智能每创造一个新岗位，就会导致数十个传统岗位消失。这些新岗位的薪酬仅为旧岗位的零头。

U.S. JOLTS: JOB OPENINGS FALL BELOW 5.5M; UNEMPLOYED-TO-OPENINGS RATIO CLIMBS TO ~1.7, HIGHEST SINCE AUG 2020 | Bloomberg, Oct 2026

美国 JOLTS 数据：职位空缺降至 550 万以下；失业与空缺比升至约 1.7，创 2020 年 8 月以来新高 | 彭博社，2026 年 10 月

The hiring rate had been anemic all year, but October ‘26 JOLTS print provided some definitive data. Job openings fell below 5.5 million, a 15% decline YoY.

全年招聘率持续疲软，但 2026 年 10 月 JOLTS 数据提供了明确信号。职位空缺数降至 550 万以下，同比下降 15%。

INDEED: POSTINGS FALL SHARPLY IN SOFTWARE, FINANCE, CONSULTING AS “PRODUCTIVITY INITIATIVES” SPREAD | Indeed Hiring Lab, Nov–Dec 2026

INDEED 招聘实验室：随着"生产力提升计划"推广，软件、金融、咨询领域岗位发布量锐减 | 2026 年 11-12 月

White-collar openings were collapsing while blue-collar openings remained relatively stable (construction, healthcare, trades). The churn was in the jobs that write memos (we are, somehow, still in business), approve budgets, and keep the middle layers of the economy lubricated. Real wage growth in both cohorts, however, had been negative for the majority of the year and kept declining.

白领岗位持续崩塌，而蓝领岗位（建筑、医疗、技工类）保持相对稳定。这场震荡冲击的是那些撰写备忘录（不知何故我们仍在运转）、审批预算、维系经济中间润滑层的职位。但值得关注的是，这两个群体的实际工资增长全年多数时间为负值且持续下行。

The equity market still cared less about JOLTS than it did the news that all of GE Vernova’s turbine capacity was now sold out until 2040, it ambled sideways in a tug of war between negative macro news with positive AI infrastructure headlines.

股市依然对 JOLTS 数据不太在意，反而更关注通用电气维尔诺瓦所有涡轮机产能已售罄至 2040 年的消息，在负面宏观新闻与人工智能基础设施利好消息的拉锯中震荡盘整。

The bond market (always smarter than equities, or at least less romantic) began pricing the consumption hit, however. The 10-year yield began a descent from 4.3% to 3.2% over the following four months. Still, the headline unemployment rate did not blow out, the composition nuance was still lost on some.

然而债券市场（总比股市更敏锐，或至少更不耽于幻想）已开始为消费冲击定价。随后的四个月里，十年期国债收益率从 4.3%开启下行通道，最终跌至 3.2%。不过，官方失业率并未大幅飙升，其结构性变化细节仍被部分市场参与者忽视。

In a normal recession, the cause eventually self-corrects. Overbuilding leads to a construction slowdown, which leads to lower rates, which leads to new construction. Inventory overshoot leads to destocking, which leads to restocking. The cyclical mechanism contains within it its own seeds of recovery.

在常规衰退中，诱因往往能自我修正。过度建设引发施工放缓，进而带动利率走低，继而催生新建项目。库存过剩导致去库存阶段，随后又步入补库存周期。这种循环机制内部本就孕育着复苏的种子。

This cycle’s cause was not cyclical.

但本轮周期的根源并非周期性波动。

AI got better and cheaper. Companies laid off workers, then used the savings to buy more AI capability, which let them lay off more workers. Displaced workers spent less. Companies that sell things to consumers sold fewer of them, weakened, and invested more in AI to protect margins. AI got better and cheaper.

人工智能变得更好且更便宜。企业裁减员工，随后将节省下的资金用于购置更多人工智能能力，这又使得它们能够进一步裁减员工。失业群体减少了消费。面向消费者的企业销量下滑、实力削弱，为保护利润率而加大人工智能投资。人工智能变得更好且更便宜。

A feedback loop with no natural brake.

这是一个没有天然制动装置的反馈循环。

The intuitive expectation was that falling aggregate demand would slow the AI buildout. It didn’t, because this wasn’t hyperscaler-style CapEx. It was OpEx substitution. A company that had been spending $100M a year on employees and $5M on AI now spent $70M on employees and $20M on AI. AI investment increased by multiples, but it occurred as a reduction in total operating costs. Every company’s AI budget grew while its overall spending shrank.

直觉预期是：总需求下降将减缓人工智能建设进程。但这并未发生，因为此次建设并非超大规模资本性支出模式，而是运营性支出的替代。一家原本每年在员工上支出 1 亿美元、在人工智能上投入 500 万美元的企业，如今将 7000 万美元用于员工薪酬，2000 万美元投向人工智能。人工智能投资成倍增长，却以总运营成本降低的形式实现。每家公司的 AI 预算都在膨胀，而其总体支出却在萎缩。

The irony of this was that the AI infrastructure complex kept performing even as the economy it was disrupting began deteriorating. NVDA was still posting record revenues. TSM was still running at 95%+ utilization. The hyperscalers were still spending $150-200 billion per quarter on data center capex. Economies that were purely convex to this trend, like Taiwan and Korea, outperformed massively.

颇具讽刺意味的是，在受其冲击的经济体开始恶化之际，人工智能基础设施复合体却持续高歌猛进。英伟达依然录得创纪录营收，台积电产能利用率仍维持在 95%以上，超大规模企业每季度在数据中心资本支出上仍投入 1500-2000 亿美元。与此趋势高度同构的经济体——如中国台湾与韩国——则实现了远超大盘的强劲表现。

India was the inverse. The country’s IT services sector exported over $200 billion annually, the single largest contributor to India’s current account surplus and the offset that financed its persistent goods trade deficit. The entire model was built on one value proposition: Indian developers cost a fraction of their American counterparts. But the marginal cost of an AI coding agent had collapsed to, essentially, the cost of electricity. TCS, Infosys and Wipro saw contract cancellations accelerate through 2027. The rupee fell 18% against the dollar in four months as the services surplus that had anchored India’s external accounts evaporated. By Q1 2028, the IMF had begun “preliminary discussions” with New Delhi.

印度则恰恰相反。该国信息技术服务行业年出口额超过 2000 亿美元，不仅是印度经常账户盈余的最大贡献者，更是抵消其持续商品贸易逆差的支柱。整个商业模式建立在单一价值主张之上：印度开发人员的成本仅为美国同行的零头。但人工智能编码代理的边际成本已暴跌至近乎仅剩电力成本的水平。塔塔咨询服务、印孚瑟斯和威普罗公司在 2027 年遭遇合同取消潮加速蔓延。随着支撑印度外部账户的服务贸易盈余蒸发，卢比在四个月内对美元贬值 18%。截至 2028 年第一季度，国际货币基金组织已开始与新德里方面进行"初步磋商"。

The engine that caused the disruption got better every quarter, which meant the disruption accelerated every quarter. There was no natural floor to the labor market.

引发这场变革的引擎每季度都在进化，这意味着变革的加速度每季度都在提升。劳动力市场已然失去了天然底线。

In the US, we weren’t asking about how the bubble would burst in AI infrastructure anymore. We were asking what happens to a consumer-credit economy when consumers are being replaced with machines.

在美国，我们不再追问人工智能基础设施泡沫将如何破裂。我们开始思考：当消费者被机器取代时，以消费信贷为核心的经济体系将何去何从。

三、

The Intelligence Displacement Spiral

人工智能代际螺旋

2027 was when the macroeconomic story stopped being subtle. The transmission mechanism from the previous twelve months of disjointed but clearly negative developments became obvious. You didn’t need to go into the BLS data. Just attend a dinner party with friends.

2027 年是宏观经济叙事不再隐晦的一年。此前十二个月里种种不连贯但明显负面的事态演变，其传导机制已昭然若揭。你无需查阅劳工统计局的数据，只需参加一次朋友间的晚宴便能体会。

Displaced white-collar workers did not sit idle. They downshifted. Many took lower-paying service sector and gig economy jobs, which increased labor supply in those segments and compressed wages there too.

失业的白领们并未坐以待毙。他们主动降维转型——大批人投身薪资较低的服务业与零工经济领域，这股劳动力供给的涌入进一步压低了相关行业的薪酬水平。

A friend of ours was a senior product manager at Salesforce in 2025. Title, health insurance, 401k, $180,000 a year. She lost her job in the third round of layoffs. After six months of searching, she started driving for Uber. Her earnings dropped to $45,000. The point is less the individual story and more the second-order math. Multiply this dynamic by a few hundred thousand workers across every major metro. Overqualified labor flooding the service and gig economy pushed down wages for existing workers who were already struggling. Sector-specific disruption metastasized into economy-wide wage compression.

2025 年，我们的一位朋友还在 Salesforce 担任高级产品经理，拥有体面的头衔、健康保险、401k 养老金账户，年薪 18 万美元。她在第三轮裁员中失业，经历六个月的求职无果后，开始为 Uber 开网约车，年收入骤降至 4.5 万美元。重点不在于个人遭遇，而在于背后连锁反应的数学逻辑——将这种状况乘以全美各大都市数十万劳动者的基数，资质过盛的劳动力涌入服务业和零工经济，压低了本已艰难度日的在职者薪资。特定行业的结构性冲击，最终演变为全经济领域的薪资通缩。

The pool of remaining human-centric had another correction ahead of it, happening while we write this. As autonomous delivery and self-driving vehicles work their way through the gig economy that absorbed the first wave of displaced workers.

以人为核心的劳动力市场正面临新一轮调整，我们撰写本文时调整已然发生。随着自动驾驶配送和无人驾驶车辆逐渐渗透曾经吸纳首批失业者的零工经济领域，传统就业空间正持续收缩。

By February 2027, it was clear that still employed professionals were spending like they might be next. They were working twice as hard (mostly with the help of AI) just to not get fired, hopes of promotion or raises were gone. Savings rates ticked higher and spending softened.

截至 2027 年 2 月，仍在岗的专业人士明显表现出"末日消费"心态——他们以双倍强度工作（主要借助人工智能辅助）只为保住职位，升职加薪的希望早已破灭。国民储蓄率悄然攀升，消费支出持续疲软。

The most dangerous part was the lag. High earners used their higher-than-average savings to maintain the appearance of normalcy for two or three quarters. The hard data didn’t confirm the problem until it was already old news in the real economy. Then came the print that broke the illusion.

最危险的隐患在于滞后效应。高收入群体动用远超平均水平的储蓄，硬生生维持了两到三个季度的体面假象。当实体经济早已感知寒意时，宏观数据才迟迟印证衰退。最终，一份报告彻底击碎了幻象。

U.S. INITIAL JOBLESS CLAIMS SURGE TO 487,000, HIGHEST SINCE APRIL 2020; Department of Labor, Q3 2027

美国当周初请失业金人数飙升至 48.7 万，创 2020 年 4 月以来新高；美国劳工部，2027 年第三季度

Initial claims surged to 487,000, the highest since April 2020. ADP and Equifax confirmed that the overwhelming majority of new filings were from white-collar professionals.

首次申请失业救济人数激增至 48.7 万，创下自 2020 年 4 月以来的最高纪录。ADP 和 Equifax 证实，绝大多数新申请者来自白领专业人士。

The S&P dropped 6% over the following week. Negative macro started winning the tug of war.

标准普尔指数在接下来的一周内下跌了 6%。负面宏观经济因素开始在这场拉锯战中占据上风。

In a normal recession, job losses are broadly distributed. Blue-collar and white-collar workers share the pain roughly in proportion to each segment’s share of employment. The consumption hit is also broadly distributed, and it shows up quickly in the data because lower-income workers have higher marginal propensities to consume.

在典型的经济衰退中，失业是广泛分布的。蓝领和白领劳动者承受的冲击大致与其在就业市场中的占比相符。消费萎缩同样呈现普遍性，由于低收入劳动者拥有更高的边际消费倾向，这种影响会迅速体现在数据中。

In this cycle, the job losses have been concentrated in the upper deciles of the income distribution. They are a relatively small share of total employment, but they drive a wildly disproportionate share of consumer spending. The top 10% of earners account for more than 50% of all consumer spending in the United States. The top 20% account for roughly 65%. These are the people who buy the houses, the cars, the vacations, the restaurant meals, the private school tuition, the home renovations. They are the demand base for the entire consumer discretionary economy.

但本轮周期中，失业潮集中在收入分配的前 10%阶层。虽然他们在总就业人口中占比较小，却驱动着极不成比例的消费份额。在美国，收入最高的 10%群体贡献了超过 50%的消费总额，前 20%群体则约占 65%。正是这些人群购置房产、汽车、度假产品、餐厅消费、私立学校学费以及住宅装修——他们构成了整个可选消费经济的需求基石。

When these workers lost their jobs, or took 50% pay cuts to move into available roles, the consumption hit was enormous relative to the number of jobs lost. A 2% decline in white-collar employment translated to something like a 3-4% hit to discretionary consumer spending. Unlike blue-collar job losses, which tend to hit immediately (you get laid off from the factory, you stop spending next week), white-collar job losses have a lagged but deeper impact because these workers have savings buffers that allow them to maintain spending for a few months before the behavioral shift kicks in.

当这些白领工作者失业，或为转岗接受 50%的降薪时，消费受到的冲击与失业人数完全不成比例。白领就业岗位减少 2%，就可能转化为约 3-4%的可支配消费支出萎缩。与通常立竿见影的蓝领失业影响不同（工厂裁员后下周就会缩减开支），白领失业的冲击具有滞后性却更为深远——他们拥有储蓄缓冲垫，足以维持数个月的消费水平，而后才会真正改变消费行为。

By Q2 2027, the economy was in recession. The NBER would not officially date the start until months later (they never do) but the data was unambiguous - we’d had two consecutive quarters of negative real GDP growth. But it wasn’t a “financial crisis”…yet.

到 2027 年第二季度，经济已陷入衰退。尽管美国国家经济研究局数月后才正式确认衰退起始时间（他们历来如此），但数据确凿无疑——我国已连续两个季度出现实际 GDP 负增长。然而这还算不上"金融危机"...至少暂时不是。

四、

The Daisy Chain of Correlated Bets

关联押注的连锁反应链

Private credit had grown from under $1 trillion in 2015 to over $2.5 trillion by 2026. A meaningful share of that capital had been deployed into software and technology deals, many of them leveraged buyouts of SaaS companies at valuations that assumed mid-teens revenue growth in perpetuity.

私人信贷规模已从 2015 年的不足 1 万亿美元扩张至 2026 年的逾 2.5 万亿美元。其中相当比例的资本流入了软件和科技交易，大量资金用于杠杆收购 SaaS 公司——这些收购估值建立在年化收入持续保持 15%左右增长的假设之上。

Those assumptions died somewhere between the first agentic coding demo and the Q1 2026 software crash, but the marks didn’t seem to realize they were dead.

这些假设在首个自主编码演示系统问世与 2026 年第一季度软件业崩盘之间已然破灭，但市场参与者似乎尚未意识到其失效。

As many public SaaS companies traded to 5-8x EBITDA, PE-backed software companies sat on balance sheets at marks reflecting acquisition valuations on multiples of revenue that didn’t exist anymore. Managers eased the marks down gradually, 100 cents, 92, 85, all while public comps said 50.

随着众多上市 SaaS 公司的估值跌至息税折旧摊销前利润的 5 至 8 倍，私募股权持有的软件企业在资产负债表上的估值仍停留在以收入倍数为基准的收购定价水平——而这种倍数体系已在公开市场不复存在。基金管理人逐步调降账面价值，从 100 美分到 92、85 美分缓缓下探，而公开市场可比公司的估值已腰斩至 50 美分水平。

MOODY’S DOWNGRADES $18B OF PE-BACKED SOFTWARE DEBT ACROSS 14 ISSUERS, CITING ‘SECULAR REVENUE HEADWINDS FROM AI-DRIVEN COMPETITIVE DISRUPTION’; LARGEST SINGLE-SECTOR ACTION SINCE ENERGY IN 2015 | Moody’s Investors Service, April 2027

穆迪下调 14 家发行人总计 180 亿美元私募股权软件债评级，指"人工智能驱动的竞争颠覆引发结构性营收阻力"；此为 2015 年能源行业降级以来最大单行业评级行动 | 穆迪投资者服务公司，2027 年 4 月

Everyone remembers what happened after the downgrade. Industry veterans had already seen the playbook following the 2015 energy downgrades.

市场至今清晰记得降级后的连锁反应。行业资深从业者早已见证过 2015 年能源行业降级后的标准剧本。

Software-backed loans began defaulting in Q3 2027. PE portfolio companies in information services and consulting followed. Several multi-billion dollar LBOs of well-known SaaS companies entered restructuring.

软件抵押贷款于 2027 年第三季度开始出现违约。信息服务与咨询领域的私募股权投资组合公司紧随其后。多宗针对知名 SaaS 企业、规模达数十亿美元的杠杆收购案相继进入重组程序。

Zendesk was the smoking gun.

证据确凿，Zendesk 是导火索。

ZENDESK MISSES DEBT COVENANTS AS AI-DRIVEN CUSTOMER SERVICE AUTOMATION ERODES ARR; $5B DIRECT LENDING FACILITY MARKED TO 58 CENTS; LARGEST PRIVATE CREDIT SOFTWARE DEFAULT ON RECORD | Financial Times, September 2027

AI 驱动的客服自动化侵蚀其年经常性收入，Zendesk 债务条款违约；50 亿美元直接贷款估值跌至 58 美分，创私募信贷软件违约规模之最｜《金融时报》，2027 年 9 月

In 2022, Hellman & Friedman and Permira had taken Zendesk private for $10.2 billion. The debt package was $5 billion in direct lending, the largest ARR-backed facility in history at the time, led by Blackstone with Apollo, Blue Owl and HPS all in the lending group. The loan was explicitly structured around the assumption that Zendesk’s annual recurring revenue would remain recurring. At roughly 25x EBITDA, the leverage only made sense if it did.

2022 年，Hellman & Friedman 与 Permira 以 102 亿美元将 Zendesk 私有化。债务方案包含由黑石领投、阿波罗、蓝猫头鹰及 HPS 共同参与的 50 亿美元直接贷款，这是当时史上规模最大的年经常性收入担保融资。该贷款方案明确基于 Zendesk 年经常性收入将持续产生的假设构建——以约 25 倍 EBITDA 的杠杆水平，唯有实现该假设，举债才具合理性。

By mid-2027, it didn’t.  至 2027 年中，该假设已然破灭。

AI agents had been handling customer service autonomously for the better part of a year. The category Zendesk had defined (ticketing, routing, managing human support interactions) was already replaced by systems that resolved issues without generating a ticket at all. The Annualized Recurring Revenue the loan was underwritten against was no longer recurring, it was just revenue that hadn’t left yet.

人工智能代理自主处理客户服务已近一年。Zendesk 所定义的类别（票务、路由、管理人工支持交互）早已被无需生成工单就能解决问题的系统所取代。贷款所依据的年度经常性收入已不再具有循环性，仅仅是尚未流失的收入而已。

The largest ARR-backed loan in history became the largest private credit software default in history. Every credit desk asked the same question at once: who else has a secular headwind disguised as a cyclical one?

史上最大规模的 ARR 担保贷款，成为了史上最大规模的私募信贷软件违约。所有信贷部门同时提出了同一个问题：还有谁将结构性逆风伪装成了周期性波动？

But here’s what the consensus got right, at least initially: this should have been survivable.

但至少在一开始，市场共识的这一点是正确的：这本应是可承受的。

Private credit is not 2008 banking. The whole architecture was explicitly designed to avoid forced selling. These are closed-end vehicles with locked-up capital. LPs committed for seven to ten years. There are no depositors to run, no repo lines to pull. The managers could sit on impaired assets, work them out over time, and wait for recoveries. Painful, but manageable. The system was such that it was supposed to bend, not break.

私募信贷并非 2008 年的银行业。其整体架构被明确设计为避免强制抛售。这些是资本锁定的封闭式工具。有限合伙人承诺了七至十年的投资期限。没有存款人挤兑，没有回购额度抽离。基金管理人完全可以持有受损资产，通过时间逐步处理，等待价值修复。过程固然痛苦，但本应可控。这套体系的韧性在于可弯曲而非断裂。

Executives at Blackstone, KKR and Apollo cited software exposure of 7-13% of assets. Containable. Every sell-side note and fintwit credit account said the same thing: private credit has permanent capital. They could absorb losses that would otherwise blow up a levered bank.

黑石、KKR 和阿波罗的高管提及，其软件相关资产敞口占整体资产的 7-13%，风险可控。所有卖方研报和金融社交媒体的信贷账户分析都传递着相同观点：私募信贷拥有永久资本。这类资本能够消化可能摧毁杠杆银行的损失。

Permanent capital. The phrase showed up in every earnings call and investor letter meant to reassure. It became a mantra. And like most mantras, nobody paid attention to the finer details. Here’s what it actually meant…

永久资本——这个反复出现在财报电话会和致投资者信中的词汇本意为提振信心，却逐渐沦为陈词滥调。如同多数口号般，无人深究其精微内涵。其真实含义实则是……

Over the prior decade, the large alternative asset managers had acquired life insurance companies and turned them into funding vehicles. Apollo bought Athene. Brookfield bought American Equity. KKR took Global Atlantic. The logic was elegant: annuity deposits provided a stable, long-duration liability base. The managers invested those deposits into the private credit they originated and got paid twice, earning spread over on the insurance side and management fees on the asset management side. A fee-on-fee perpetual motion machine that worked beautifully under one condition.

过去十年间，大型另类资产管理公司通过收购寿险公司将其转化为融资工具。阿波罗收购雅典娜保险，布鲁克菲尔德收入美国权益人寿，KKR 则将全球大西洋保险纳入麾下。其商业逻辑堪称精妙：年金存款提供了稳定且久期较长的负债基础。管理人将这些存款投入自主创设的私募信贷资产，实现双重盈利——既赚取保险端的利差收益，又获得资产管理端的管理费收入。这种"费上叠费"的永动模式在特定条件下运转完美。

The private credit had to be money good.

这笔私募信贷必须确保资金安全。

The losses hit balance sheets built to hold illiquid assets against long-duration obligations. The “permanent capital” that was supposed to make the system resilient was not some abstract pool of patient institutional money and sophisticated investors taking sophisticated risk. It was the savings of American households, “Main Street”, structured as annuities invested in the same PE-backed software and technology paper that was now defaulting. The locked-up capital that couldn’t run was life insurance policyholder money, and the rules are a bit different there.

亏损冲击了为持有非流动性资产以应对长期负债而构建的资产负债表。本应使系统具备韧性的"永久资本"，并非某种抽象且有耐心的机构资金池，也非承担复杂风险的成熟投资者；而是美国家庭的储蓄，即"普通民众"的资金，这些资金以年金形式投资于同一批如今违约的私募股权支持的软件和科技类票据。无法撤离的锁定期资本正是人寿保险保单持有人的资金，而相关规则在此略有不同。

Compared to the banking system, insurance regulators had been docile - even complacent - but this was the wake-up call. Already uneasy about private credit concentrations at life insurers, they began downgrading the risk-based capital treatment of these assets. That forced the insurers to either raise capital or sell assets, neither of which was possible at attractive terms in a market already seizing up.

相较于银行体系，保险监管机构此前一直较为温和——甚至可以说是自满——但这次事件敲响了警钟。本就对人寿保险公司私募信贷集中度感到不安的监管机构，开始下调这些资产的风险资本处理标准。这迫使保险公司要么增资，要么出售资产，而在已陷入停滞的市场中，两者均难以以理想条件实现。

NEW YORK, IOWA STATE REGULATORS MOVE TO TIGHTEN CAPITAL TREATMENT FOR CERTAIN PRIVATELY RATED CREDIT HELD BY LIFE INSURERS; NAIC GUIDANCE EXPECTED TO INCREASE RBC FACTORS AND TRIGGER ADDITIONAL SVO SCRUTINY | Reuters, Nov 2027

纽约及爱荷华州监管机构着手收紧对人寿保险公司持有部分私募评级信贷的资本处理标准；美国保险监督管理协会预计将提高风险资本系数并触发证券估值办公室的额外审查 | 路透社，2027 年 11 月

When Moody’s put Athene’s financial strength rating on negative outlook, Apollo’s stock dropped 22% in two sessions. Brookfield, KKR, and the others followed.

当穆迪将 Athene 的财务实力评级展望调整为负面时，Apollo 的股价在两个交易日内下跌了 22%。Brookfield、KKR 及其他公司亦随之下挫。

It only got more complex from there. These firms hadn’t just created their insurer perpetual motion machine, they’d built an elaborate offshore architecture designed to maximize returns through regulatory arbitrage.The US insurer wrote the annuity, then ceded the risk to an affiliated Bermuda or Cayman reinsurer it also owned - set up to take advantage of more flexible regulation that permitted holding less capital against the same assets. That affiliate raised outside capital through offshore SPVs, a new layer of counterparties who invested alongside insurers into private credit originated by the same parent’s asset management arm.

此后局面愈发复杂。这些公司不仅构建了保险业的永动机模型，更打造出精密的离岸架构体系，旨在通过监管套利实现收益最大化。美国保险公司签发年金保单后，将风险转移给其同样控股的百慕大或开曼群岛关联再保险公司——这些机构利用更灵活的监管规则，得以对相同资产持有更少的资本金。这些关联公司通过离岸特殊目的载体筹集外部资金，形成新的交易对手层，与保险公司共同投资于同一母公司资产管理部门发起的私人信贷资产。

The ratings agencies, some of which were themselves PE-owned, had not been paragons of transparency (surprising to virtually) no one. The spider web of different firms linked to different balance sheets was stunning in its opacity. When the underlying loans defaulted, the question of who actually bore the loss was genuinely unanswerable in real time.

部分信用评级机构自身就由私募股权控股，其透明度表现从来不是行业典范（这对几乎所有人来说都不意外）。相互关联的不同企业构成盘根错节的资产负债表网络，其不透明程度令人震惊。当底层贷款发生违约时，实际损失承担方的问题在当下根本无法厘清。

The November 2027 crash marked the transition of perception from a potentially garden-variety cyclical drawdown to something much more uncomfortable. “A daisy chain of correlated bets on white collar productivity growth” was what Fed Chair Kevin Warsh called it during the FOMC’s emergency November meeting.

2027 年 11 月的市场崩盘标志着市场认知的转变：人们意识到这并非寻常的周期性回调，而是更为严峻的危机。美联储主席凯文·沃什在联邦公开市场委员会紧急会议上，将其描述为“对白领生产力增长的一连串关联性押注”。

See, it is never the losses themselves that cause the crisis. It’s recognizing them. And there is another, much larger, much much more important area of finance for which we have grown fearful of that recognition.

危机的根源从来不是损失本身，而是对损失的确认。当前金融领域存在着另一个更庞大、更关键的风险敞口，我们正日益畏惧对其进行确认。

五、

The Mortgage Question 

抵押贷款风险预警

ZILLOW HOME VALUE INDEX FALLS 11% YOY IN SAN FRANCISCO, 9% IN SEATTLE, 8% IN AUSTIN; FANNIE MAE FLAGS ‘ELEVATED EARLY-STAGE DELINQUENCIES’ IN ZIP CODES WITH >40% TECH/FINANCE EMPLOYMENT | Zillow / Fannie Mae, June 2028

2028 年 6 月数据显示：ZILLOW 房价指数同比跌幅——旧金山 11%，西雅图 9%，奥斯汀 8%；房利美警示科技/金融从业者占比超 40%的邮编区出现“早期逾期率攀升”现象 | 数据来源：Zillow / 房利美

This month the Zillow Home Value Index fell 11% year-over-year in San Francisco, 9% in Seattle and 8% in Austin. This hasn’t been the only worrying headline. Last month, Fannie Mae flagged higher early-stage delinquency from jumbo-heavy ZIP codes - areas that are populated by 780+ credit score borrowers and typically “bulletproof”.

本月旧金山 Zillow 房屋价值指数同比下跌 11%，西雅图下跌 9%，奥斯汀下跌 8%。这并非唯一令人担忧的消息。上个月房利美警示，在巨型贷款密集的邮编区域（居民信用评分普遍 780 分以上、通常被视为"防弹级"优质借款人）出现了更高的早期违约率。

The US residential mortgage market is approximately $13 trillion. Mortgage underwriting is built on the fundamental assumption that the borrower will remain employed at roughly their current income level for the duration of the loan. For thirty years, in the case of most mortgages.

美国住宅抵押贷款市场规模约 13 万亿美元。抵押贷款核发的根本前提是假设借款人在整个贷款期间（多数为三十年）能维持当前收入水平的稳定就业。

The white-collar employment crisis has threatened this assumption with a sustained shift in income expectations. We now have to ask a question that seemed absurd just 3 years ago - are prime mortgages money good?

白领就业危机已通过持续的收入预期转变动摇了这一基本假设。我们不得不提出一个三年前看似荒谬的问题——优质抵押贷款还是可靠资产吗？

Every prior mortgage crisis in US history has been driven by one of three things: speculative excess (lending to people who couldn’t afford the homes, as in 2008), interest rate shocks (rising rates making adjustable-rate mortgages unaffordable, as in the early 1980s), or localized economic shocks (a single industry collapsing in a single region, like oil in Texas in the 1980s or auto in Michigan in 2009).

美国历史上的每一场抵押贷款危机均由以下三种原因之一引发：投机过度（向无力承担住房贷款的人放贷，如 2008 年），利率冲击（利率上升使浮动利率抵押贷款难以负担，如 20 世纪 80 年代初），或局部经济冲击（单一地区特定行业的崩溃，如 80 年代得克萨斯州的石油业或 2009 年密歇根州的汽车业）。

None of these apply here. The borrowers in question are not subprime. They’re 780 FICO scores. They put 20% down. They have clean credit histories, stable employment records, and incomes that were verified and documented at origination. They were the borrowers that every risk model in the financial system treats as the bedrock of credit quality.

当前情况与上述因素皆不相关。所涉借款人并非次级贷款者。他们拥有 780 分的 FICO 信用评分，支付了 20%的首付款，信用记录清白，工作经历稳定，且在贷款发放时已验证并记录了收入状况。这些借款人是金融体系中所有风险模型视作信贷质量基石的优质客户。

In 2008, the loans were bad on day one. In 2028, the loans were good on day one. The world just…changed after the loans were written. People borrowed against a future they can no longer afford to believe in.

2008 年的危机中，贷款在发放首日即存在隐患。而 2028 年的危机中，贷款在发放初期是稳健的。只是在贷款合约签订后，世界格局发生了剧变。人们基于对未来生活的预期进行借贷，而如今这种预期已变得遥不可及。

In 2027, we flagged early signs of invisible stress: HELOC draws, 401(k) withdrawals, and credit card debt spiking while mortgage payments remained current. As jobs were lost, hiring was frozen and bonuses cut, these prime households saw their debt-to-income ratios double.

2027 年，我们捕捉到了隐性压力的早期征兆：房屋净值信贷额度提用激增、401(k)计划提前支取、信用卡债务飙升，而按揭还款却依然正常。随着失业潮涌现、招聘冻结与奖金削减，这些优质家庭的负债收入比已然翻倍。

They could still make the mortgage payment, but only by stopping all discretionary spending, draining savings, and deferring any home maintenance or improvement. They were technically current on their mortgage, but just one more shock away from distress, and the trajectory of AI capabilities suggested that shock is coming. Then we saw delinquencies begin to spike in San Francisco, Seattle, Manhattan and Austin, even as the national average stayed within historical norms.

他们仍能勉强偿还月供，却不得不全面停止非必要消费、耗尽储蓄储备，并推迟所有房屋维护与升级计划。从技术层面看，其按揭还款尚未违约，但距离财务危机仅一步之遥——而人工智能的发展轨迹预示着这场冲击即将来临。此后我们观察到，尽管全国平均违约率仍处历史常态区间，旧金山、西雅图、曼哈顿与奥斯汀等地的信贷拖欠率已开始急剧攀升。

We’re now in the most acute stage. Falling home prices are manageable when the marginal buyer is healthy. Here, the marginal buyer is dealing with the same income impairment.

我们正处于最严峻的阶段。当边际买家财务状况健康时，房价下跌尚可控制。而当前的边际买家正承受着相同的收入下滑压力。

While concerns are building, we are not yet in a full-blown mortgage crisis. Delinquencies have risen but remain well below 2008 levels. It is the trajectory that’s the real threat.

尽管忧虑情绪不断累积，但我们尚未陷入全面的抵押贷款危机。拖欠率虽有所上升，但仍远低于 2008 年水平。真正构成威胁的是其恶化轨迹。

The Intelligence Displacement Spiral now has two financial accelerants to the real economy’s decline.

智能替代螺旋如今获得了两个助推器，正加速实体经济下行。

Labor displacement, mortgage concerns, private market turmoil. Each reinforces the other. And the traditional policy toolkit (rate cuts, QE) can address the financial engine but cannot address the real economy engine, because the real economy engine is not driven by tight financial conditions. It’s driven by AI making human intelligence less scarce and less valuable. You can cut rates to zero and buy every MBS and all the defaulted software LBO debt in the market…

劳动力替代、抵押贷款隐患、私募市场动荡——三者相互强化。传统政策工具（降息、量化宽松）或许能缓解金融引擎的压力，却无法启动实体经济引擎，因为驱动实体经济的并非紧缩的金融环境，而是人工智能正在使人类智能不再稀缺且持续贬值。即使将利率降至零、收购市场上所有 MBS 和违约的软件杠杆收购债务……

It won’t change the fact that a Claude agent can do the work of a $180,000 product manager for $200/month.

这无法改变一个事实：一个 Claude 智能体可以完成年薪 18 万美元产品经理的工作，而月成本仅需 200 美元。

If these fears manifest, the mortgage market cracks in the back half of this year. In that scenario, we’d expect the current drawdown in equities to ultimately rival that of the GFC (57% peak-to-trough). This would bring the S&P500 to ~3500 - levels we haven’t seen since the month before the ChatGPT moment in November 2022.

若这些担忧成为现实，抵押贷款市场将在今年下半年出现裂痕。在此情境下，我们预计当前股市的回撤幅度最终将堪比 2008 年金融危机时期（峰值至谷底跌幅达 57%）。这将使标普 500 指数跌至 3500 点附近——这个水平自 2022 年 11 月 ChatGPT 问世前一个月以来就未曾出现过。

What’s clear is that the income assumptions underlying $13 trillion in residential mortgages are structurally impaired. What isn’t is whether policy can intervene before the mortgage market fully processes what this means. We’re hopeful, but we can’t deny the reasons not to be.

显而易见的是，支撑着 13 万亿美元住宅抵押贷款的收益预期已出现结构性损伤。尚不明确的是，政策干预能否赶在抵押贷款市场完全消化这一影响之前展开。我们怀有希望，但也不得不正视那些令人担忧的缘由。

六、

The Battle Against Time 

与时间的赛跑

The first negative feedback loop was in the real economy: AI capability improves, payroll shrinks, spending softens, margins tighten, companies buy more capability, capability improves. Then it turned financial: income impairment hit mortgages, bank losses tightened credit, the wealth effect cracked, and the feedback loop sped up. And both of these have been exacerbated by an insufficient policy response from a government that seems, quite frankly, confused.

第一个负面反馈循环出现在实体经济中：人工智能能力提升，薪资支出缩减，消费疲软，利润率收窄，企业购入更多智能设备，技术能力进一步增强。随后这种循环蔓延至金融领域：收入受损冲击抵押贷款市场，银行亏损收紧信贷闸门，财富效应出现裂痕，反馈循环持续加速。更雪上加霜的是，政府应对政策始终未能到位——坦率地说，决策层似乎已陷入混乱。

The system wasn’t designed for a crisis like this. The federal government’s revenue base is essentially a tax on human time. People work, firms pay them, the government takes a cut. Individual income and payroll taxes are the spine of receipts in normal years.

现行体系从未为应对此类危机而设计。联邦政府的财政收入本质上是对人类劳动时间的征税。民众工作，企业支付薪酬，政府从中抽成。在正常年份，个人所得税与薪资税构成财政收入的主干命脉。

Through Q1 of this year, federal receipts were running 12% below CBO baseline projections. Payroll receipts are falling because fewer people are employed at prior compensation levels. Income tax receipts are falling because the incomes being earned are structurally lower. Productivity is surging, but the gains are flowing to capital and compute, not labor.

截至今年第一季度，联邦财政收入较国会预算办公室基准预测低 12%。工资税收入下降，是因为以原有薪酬水平就业的人数减少。所得税收入下降，是因为结构性收入水平整体降低。生产率正大幅提升，但收益正流向资本与算力而非劳动力。

Labor’s share of GDP declined from 64% in 1974 to 56% in 2024, a four-decade grind lower driven by globalization, automation, and the steady erosion of worker bargaining power. In the four years since AI began its exponential improvement, that has dropped to 46%. The sharpest decline on record.

劳动力占 GDP 比重已从 1974 年的 64%降至 2024 年的 56%，全球化、自动化及工人议价能力持续削弱推动这一比例经历了四十年的缓慢下滑。自人工智能开始指数级发展至今的四年间，该比例已骤降至 46%，创下有记录以来的最大降幅。

The output is still there. But it’s no longer routing through households on the way back to firms, which means it’s no longer routing through the IRS either. The circular flow is breaking, and the government is expected to step in to fix that.

经济产出依然存在，但不再经由家庭部门回流至企业，这意味着也不再通过国税局体系循环。经济循环流动正在断裂，政府被预期将介入修复这一机制。

As in every downturn, outlays rise just as receipts fall. The difference this time is that the spending pressure is not cyclical. Automatic stabilizers were built for temporary job losses, not structural displacement. The system is paying benefits that assume workers will be reabsorbed. Many will not, at least not at anything like their prior wage. During COVID, the government freely embraced 15% deficits, but it was understood to be temporary. The people who need government support today were not hit by a pandemic they’ll recover from. They were replaced by a technology that continues to improve.

如同历次经济下行期，支出上升恰逢收入下降。此次不同之处在于开支压力并非周期性波动。自动稳定机制是为应对暂时性失业而设计，而非结构性岗位替代。现行体系发放的补助金建立在工人将重返岗位的假设之上。然而许多人将无法回归，至少无法获得与过往相当的薪资水平。新冠疫情期间政府曾坦然接受 15%的赤字率，但当时共识是阶段性举措。如今需要政府援助的人群并非遭受可复原的疫情冲击，而是被持续演进的技术所取代。

The government needs to transfer more money to households at precisely the moment it is collecting less money from them in taxes.

政府正需要在税收减少的时刻向家庭转移更多资金。

The U.S. won’t default. It prints the currency it spends, the same currency it uses to pay back borrowers. But this stress has shown up elsewhere. Municipal bonds are showing worrying signs of dispersion in year-to-date performance. States without income tax have been okay, but general obligation munis issued by states dependent on income tax (majority blue states) began to price in some default risk. Politicos caught on quickly, and the debate over who gets bailed out has fallen along partisan lines.

美国不会违约。它印钞支付开支，也用同样的货币偿还借款。但压力已在其他领域显现。市政债券在年初至今的表现中呈现出令人担忧的分化迹象。无所得税的各州情况尚可，但依赖所得税的州（多数为蓝州）发行的一般责任市政债已开始体现部分违约风险。政界迅速察觉，关于救助对象的争论已沿党派分歧展开。

The administration, to its credit, recognized the structural nature of the crisis early and began entertaining bipartisan proposals for what they’re calling the “Transition Economy Act”: a framework for direct transfers to displaced workers funded by a combination of deficit spending and a proposed tax on AI inference compute.

值得肯定的是，政府较早认识到危机的结构性本质，并开始考虑两党提出的《转型经济法案》提案：该框架主张通过赤字支出与拟议的人工智能推理算力税相结合的方式，为失业工人提供直接转移支付。

The most radical proposal on the table goes further. The “Shared AI Prosperity Act” would establish a public claim on the returns of the intelligence infrastructure itself, something between a sovereign wealth fund and a royalty on AI-generated output, with dividends funding household transfers. Private sector lobbyists have flooded the media with warnings about the slippery slope.

摆在桌面上最激进的提案走得更远。《共享人工智能繁荣法案》将为智能基础设施本身的收益设立公共权益，这种制度介于主权财富基金与人工智能产出版税之间，其股息将用于资助家庭转移支付。私营部门的游说团体已铺天盖地通过媒体发出警告，称这将导致不可逆转的滑坡效应。

The politics behind the discussions have been grimly predictable, exacerbated by grandstanding and brinksmanship. The right calls transfers and redistribution Marxism and warns that taxing compute hands the lead to China. The left warns that a tax drafted with the help of incumbents becomes regulatory capture by another name. Fiscal hawks point to unsustainable deficits. Doves point to the premature austerity imposed after the GFC as a cautionary tale. The divide is only magnifying in the run up to this year’s presidential election.

这场讨论背后的政治角力阴郁得如预期般上演，作秀政治和边缘政策更使其雪上加霜。右翼指责转移支付和财富再分配是马克思主义，并警告对算力征税将把领先地位拱手让给中国。左翼则警示在既得利益者协助下起草的税收法案终将沦为变相的监管俘获。财政鹰派指出赤字已不可持续，鸽派则以全球金融危机后实施过早紧缩政策的历史教训作为警钟。随着今年总统大选的临近，这种分歧正在持续扩大。

While the politicians bicker, the social fabric is fraying faster than the legislative process can move.

当政客们争执不休时，社会结构的瓦解速度已远超立法进程所能追赶的步伐。

The Occupy Silicon Valley movement has been emblematic of wider dissatisfaction. Last month, demonstrators blockaded the entrances to Anthropic and OpenAI’s San Francisco offices for three weeks straight. Their numbers are growing, and the demonstrations have drawn more media coverage than the unemployment data that prompted them.

"占领硅谷"运动已成为了更广泛不满情绪的象征。上月示威者连续三周封锁了 Anthropic 与 OpenAI 旧金山办公室的入口。抗议队伍不断壮大，其引发的媒体报道甚至超过了触发这场运动的失业数据。

It’s hard to imagine the public hating anyone more than the bankers in the fallout of the GFC, but the AI labs are making a run at it. And, from the perspective of the masses, for good reason. Their founders and early investors have accumulated wealth at a pace that makes the Gilded Age look tame. The gains from the productivity boom accruing almost entirely to the owners of compute and the shareholders of the labs that ran on it has magnified US inequality to unprecedented levels.

很难想象公众对任何群体的憎恶会超过金融危机后的银行家，但人工智能实验室正在朝着这个方向迈进。从大众视角来看，这种情绪确有缘由——其创始人与早期投资者的财富积累速度，令镀金时代都显得黯然失色。生产力爆发带来的收益几乎全数流入算力所有者及依赖算力运行的实验室股东口袋，将美国不平等推至前所未有的程度。

Every side has their own villain, but the real villain is time.

各方都有自己认定的反派，但真正的反派是时间。

AI capability is evolving faster than institutions can adapt. The policy response is moving at the pace of ideology, not reality. If the government doesn’t agree on what the problem is soon, the feedback loop will write the next chapter for them.

人工智能能力的进化速度远超体制的适应能力。政策应对仍停留在意识形态层面的讨论，未能跟上现实发展。若政府不能尽快就问题本质达成共识，这场不断自我强化的循环将替他们书写下一章。

七、

The Intelligence Premium Unwind

智能溢价的消失

For the entirety of modern economic history, human intelligence has been the scarce input. Capital was abundant (or at least, replicable). Natural resources were finite but substitutable. Technology improved slowly enough that humans could adapt. Intelligence, the ability to analyze, decide, create, persuade, and coordinate, was the thing that could not be replicated at scale.

贯穿整个现代经济史，人类智慧始终是稀缺要素。资本曾充裕无虞（至少具备可复制性），自然资源虽有限但存在替代方案，技术演进缓慢到足以让人类适应调整。唯有智慧——这种分析决策、创造发明、说服协调的能力——始终无法被规模化复制。

Human intelligence derived its inherent premium from its scarcity. Every institution in our economy, from the labor market to the mortgage market to the tax code, was designed for a world in which that assumption held.

人类智慧因其稀缺性而自带天然溢价。从劳动力市场到抵押贷款市场再到税法体系，我们经济中的每个制度设计都建立在"智慧永恒稀缺"的前提之上。

We are now experiencing the unwind of that premium. Machine intelligence is now a competent and rapidly improving substitute for human intelligence across a growing range of tasks. The financial system, optimized over decades for a world of scarce human minds, is repricing. That repricing is painful, disorderly, and far from complete.

如今我们正见证这种溢价的消解。在日益扩大的任务范围内，机器智能已成为人类智慧合格且快速进化的替代品。数十年来围绕"人类智慧稀缺"范式优化的金融体系正在重新定价——这个过程充满阵痛、杂乱无序，且远未终结。

But repricing is not the same as collapse.

但价格重估不等于崩溃。

The economy can find a new equilibrium. Getting there is one of the few tasks left that only humans can do. We need to do it correctly.

经济可以找到新的平衡点。实现这一目标，是当前仅有人类才能完成的少数任务之一。我们必须将其妥善完成。

This is the first time in history the most productive asset in the economy has produced fewer, not more, jobs. Nobody’s framework fits, because none were designed for a world where the scarce input became abundant. So we have to make new frameworks. Whether we build them in time is the only question that matters.

这是历史上首次出现经济中最具生产力的资产导致就业岗位减少而非增加。现有理论框架皆不适用，因为所有理论都建立在稀缺资源的基础上，而非为资源丰沛的世界设计。因此，我们必须创建新的理论框架。能否及时构建这些框架，是当前唯一关键的问题。

But you’re not reading this in June 2028. You’re reading it in February 2026.

但你读到这段文字时并非 2028 年 6 月——此刻正是 2026 年 2 月。

The S&P is near all-time highs. The negative feedback loops have not begun. We are certain some of these scenarios won’t materialize. We’re equally certain that machine intelligence will continue to accelerate. The premium on human intelligence will narrow.

标普 500 指数正接近历史高点。负反馈循环尚未启动。我们确信其中部分场景将不会发生。我们同样确信机器智能将持续加速发展。人类智力的溢价空间将收窄。

As investors, we still have time to assess how much of our portfolios are built upon assumptions that won’t survive the decade. As a society, we still have time to be proactive.

作为投资者，我们仍有时间评估投资组合中有多少建立在无法延续十年的假设之上。作为社会整体，我们仍有时间主动应对。

The canary is still alive.

金丝雀依然存活（截止目前）。

源自--地平线全球策略