作者：Citrini， Alap Shah
翻译：kimi-k2.5，gemini 3
编辑&校对：沐洒
1. Preface 序言
What if our AI bullishness continues to be right…and what if that’s actually bearish?
如果我们对AI的乐观看法持续成真…但如果这本身就是最大的利空呢？
What follows is a scenario, not a prediction.This isn’t bear porn or AI doomer fan-fiction. The sole intent of this piece is modeling a scenario that’s been relatively underexplored. Our friend Alap Shah posed the question, and together we brainstormed the answer. We wrote this part, and he’s written two others you can find.
以下是一个场景假设，而非预测。这并非空头们的“末日爽文”，亦非 AI 崩溃论者的臆想。
本文的唯一目的是对一个相对未被充分探索的场景进行建模。我们的朋友 Alap Shah 提出了这个问题，我们一起头脑风暴得出了答案。我们写了这篇文章，他还写了另外两部分，你可以在找到。
Hopefully, reading this leaves you more prepared for potential left tail risks as AI makes the economy increasingly weird.
希望阅读本文能让你更好地应对 AI 让经济变得越来越怪异时可能出现的左侧尾部风险。
This is the CitriniResearch Macro Memo from June 2028, detailing the progression and fallout of the Global Intelligence Crisis.
这是 CitriniResearch 2028 年 6 月的宏观备忘录，详细描述了全球智能危机的进程和影响。
2. Macro Memo 宏观备忘录
2.1 The Consequences of Abundant Intelligence | 智力过剩的后果
CitriniResearch
February 22nd, 2026June 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.
今早公布的失业率为 10.2%，高于预期 0.3 个百分点。市场对这一数据的反应是下跌 2%，使得标普500指数自2026年10月的高点以来累计回撤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，这样的数据会触发熔断机制。
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.
两年时间。这就是从“受控”和“特定行业”发展到经济完全不像我们成长过程中所熟悉的那样所需的时间。本季度的宏观备忘录是我们试图重构这一过程的尝试——对危机前经济的深度复盘。
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.
狂热之情，溢于言表。到 2026 年 10 月，标普 500 指数直指 8000 点大关，纳斯达克突破 30,000 点。由于人类劳动力过时而引发的第一波裁员始于 2026 年初，而裁员恰好起到了预期的作用。利润率扩大，盈利超预期，股票上涨。创纪录的企业利润直接回流到 AI 计算领域。
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.
总体数据看起来仍然很好。名义 GDP 持续以中高个位数的年化增长率增长。生产率蓬勃发展。每小时实际产出以 1950 年代以来未见的速度增长，这得益于那些不睡觉、不请病假、也不需要医疗保险的 AI agents。
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.
计算资源所有者看到自己的财富随着劳动力成本的消失而爆炸式增长。与此同时，实际工资增长崩溃。尽管政府一再吹嘘创纪录的生产率，白领工人被机器夺走了工作，被迫接受薪资更低的岗位。
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.
当消费者经济开始出现裂痕时，经济学专家们普及了一个词“幽灵GDP (虚产值)”：即在国家账户中显示但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.
AI 在各个方面都超出了预期，市场就是 AI。唯一的问题是……经济不是。
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.)
从一开始就应该清楚，北达科他州的一个 GPU 集群产出了之前归因于曼哈顿中城 10,000 名白领工人的工作成果，这更像是一场经济瘟疫，而不是经济解药。货币流通速度陷入停滞。以人为中心的消费经济（当时占 GDP 的 70%）萎缩了。如果我们只是问一下机器在可自由支配商品上的花费，我们可能早就弄明白了。（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…
AI 能力提升，公司需要更少的工人，白领裁员增加，失业工人消费减少，利润率压力促使企业增加 AI 投资，AI 能力提升……
It was a negative feedback loop with no natural brake. Thehuman 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的负反馈循环。人类认知替代螺旋。白领工人的收入能力（以及理性地，他们的消费）受到了结构性损害。他们的收入是 13 万亿美元抵押贷款市场的基石——这迫使承销商重新评估优质抵押贷款是否依然“资可抵债”。
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.
十七年没有真正的违约周期，让私募基金支持的软件交易泡沫膨胀，这些交易假设 ARR（年度经常性收入）将保持经常性。2027 年中由于 AI 颠覆导致的第一波违约潮挑战了这一假设。
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.
如果这种颠覆仅限于软件领域，这还是可控的，但事实并非如此。到 2027 年底，它威胁到了每一个基于中介的商业模式。大量建立在利用人类摩擦获利基础上的公司解体了。
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.
事实证明，整个系统是一条长长的连环连锁反应，都是押注于白领生产率增长的相关赌注。2027 年 11 月的崩盘只是加速了所有已经存在的负反馈循环。
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.
我们几乎一年来一直在等待“坏消息就是好消息”的反转。政府开始考虑提案，但公众对政府进行任何救援能力的信心已经减弱。政策反应总是滞后于经济现实，但缺乏全面计划现在正威胁着加速通缩螺旋。
2.2 How It Started | 一切如何开始
In late 2025, agentic coding tools took a step function jump in capability.
2025 年末，代理化（Agentic）编码工具的能力实现了阶梯式的跨越。
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?”
使用 Claude Code 或 Codex 的称职开发人员现在可以在几周内复制中端 SaaS 产品的核心功能。不是完美的或处理所有边界情况，但足够好以至于审查 50 万美元年度续费的首席信息官开始问“如果我们自己建造这个会怎样？”
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.
财年大多与日历年一致，因此 2026 年的企业支出是在 2025 年第四季度确定的，当时“agentic AI”还只是一个流行词。年中审查是采购团队第一次能够基于对这些系统实际能力的了解来做决策。一些人看到他们自己内部团队用几周时间开发出原型，复制价值六位数的 SaaS 合同。
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, Zapier and Asana, had it much worse.
那年夏天，我们与一家财富 500 强公司的采购经理交谈。他告诉我们他的一次预算谈判。销售人员原本期望按照去年的剧本：每年涨价 5%，标准的“你的团队依赖我们”的说辞。采购经理告诉他，他一直在与 OpenAI 讨论让他们的“前沿部署工程师”使用 AI 工具完全替代供应商。他们以 30% 的折扣续签了合同。他说这是个好结果。、Zapier 和 Asana 这样的“SaaS 长尾”企业情况要糟糕得多。
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.
投资者已经准备好——甚至期待——长尾会受到重创。它们可能占典型企业技术栈支出的三分之一，但它们显然处于风险之中。然而，核心底层系统（Systems of Record）本应免受颠覆。
It wasn’t until ServiceNow’s Q3 26 report that the mechanism of reflexivity became clearer.
直到 ServiceNow 2026 年第三季度报告，反身性机制才变得更清晰。
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 净新 ACV 增长从 23% 减速至 14%；宣布裁员 15% 和“结构性效率计划”；股价下跌 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-housewasan 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 并没有“死亡”。运行和支持内部构建仍然需要进行成本效益分析。但内部构建是一个选项，这影响了定价谈判。也许更重要的是，竞争格局已经改变。AI 使开发和发布新功能变得更容易，因此差异化崩溃了。现有企业在定价上进行逐底竞争——既彼此厮杀，也与新涌现的挑战者进行肉搏。这些新进入者因 agentic 编码能力的飞跃而信心大增，且没有遗留成本结构需要保护，它们激进地夺取市场份额。
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.
这些系统的相互关联性直到这份报告才被充分认识。ServiceNow 按座位数销售。当财富 500 强客户裁员 15% 时，他们取消了 15% 的许可证。推动客户利润率提升的 AI 驱动裁员，正在机械性地摧毁 ServiceNow 自己的收入基础。
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的公司正在被更好的工作流自动化颠覆，而其反应是裁员并用节省下来的资金资助正在颠覆它的技术。
What else were they supposed to do?Sit still and die slower?The companies most threatened by AI became AI’s most aggressive adopters.
他们还能做什么？坐以待毙？受 AI 威胁最大的公司成为了 AI 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.
事后看来这很明显，但当时并非如此（至少对我来说）。历史上的颠覆模型说现有企业抵制新技术，它们输给灵活的进入者并慢慢消亡。Kodak、Blockbuster、BlackBerry 就是这样。2026 年发生的情况不同；现有企业没有抵制，因为它们负担不起。
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.
股价下跌 40-60%，董事会要求答案，受 AI 威胁的公司只能做一件事。裁员，将节省的资金重新投入 AI 工具，使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.
每家公司的个体反应都是理性的。集体结果是灾难性的。裁员节省的每一美元都流入了 AI 能力，使下一轮裁员成为可能。
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.
软件只是开场表演。投资者们在争论 SaaS 估值倍数是否触底时忽略的是，反身性循环已经逃离了软件行业。证明 ServiceNow 裁员合理性的同样逻辑适用于每一个有白领成本结构的公司。
2.3 When Friction Went to Zero | 当摩擦归零
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.
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.
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.
本应让投资者更加不安的部分是，这些 agents 不需要被询问。它们根据用户偏好在后台运行。商业不再是一系列离散的人类决策，而是变成一个持续的优化过程，代表每个联网消费者全天候运行。到 2027 年 3 月，美国人均每天消费 40 万个 token——自 2026 年底以来增长了 10 倍。
The next link in the chain was already breaking.
Intermediation.
链条的下一环已经在断裂。
中介。
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 dollars of enterprise value depended on those constraints persisting.
过去五十年，美国经济在人类局限性的基础上建立了一个巨大的租金提取层：事情需要时间，耐心会耗尽，品牌熟悉度替代了尽职调查，大多数人愿意接受糟糕的价格以避免更多点击。数万亿美元的企业价值依赖于这些限制的持续存在。
It started out simple enough. Agents removed friction.
开始时很简单。Agents 消除了摩擦。
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订的订阅和会员资格。试用期后偷偷涨价的初始定价。每一个都被重新定性为一种“勒索式”订阅。整个订阅经济赖以建立的客户终身价值指标明显下降。
Consumer agents began to change how nearly all consumer transactions worked.
消费者 agents 开始改变几乎所有消费者交易的工作方式。
Humans don’t really have the time to price-match across five competing platforms before buying a box of protein bars. Machines do.
人类在购买一盒蛋白棒之前，真的没有时间在五个竞争平台之间比价。机器有。
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.
旅游预订平台是早期的牺牲品，因为它们最简单。到 2026 年第四季度，我们的 agents 可以比任何平台更快、更便宜地组装完整的行程（航班、酒店、地面交通、忠诚度优化、预算限制、退款）。
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.
整个续保模式依赖于保单持有人惰性的保险续保被改革。每年重新购买保险的 agents 瓦解了保险公司从被动续保中获得的 15-20% 的保费。
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.
财务建议。税务准备。常规法律工作。任何服务提供商的价值主张最终是“我会帮你处理你觉得繁琐的复杂问题”的类别都被颠覆了，因为 agents 不觉得任何事情繁琐。
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.
即使我们认为因人际关系价值而受到保护的地方也被证明是脆弱的。房地产领域，买家因代理人和消费者之间的信息不对称而容忍 5-6% 的佣金数十年，一旦配备 MLS（多重上市服务）访问权限和数十年交易数据的 AI agents 能够即时复制知识库，这个领域就崩溃了。2027 年 3 月的一份卖方报告将其称为“智能体之间的同类相食”。主要大都市的中位数买方佣金从 2.5-3% 压缩到 1% 以下，越来越多的交易根本没有人类买方代理就成交了。
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。事实证明，人们所说的很多关系只是带有友好面孔的摩擦（交易阻碍）。
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.
这只是中介层颠覆的开始。成功的公司曾经花费了数十亿美元来有效利用消费者行为和人类心理的怪癖，而这些已经不再重要。
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”.
That destroyed a particular kind of moat:habitual intermediation.
这摧毁了一种特定的护城河：惯性中介。
DoorDash (DASH US) was the poster child.
DoorDash（DASH US）是典型的例子。
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 agents 降低了推出配送应用程序的进入门槛。一个称职的开发人员可以在几周内部署一个有功能的竞争对手，数十人这样做了，通过将 90-95% 的配送费传递给司机来吸引司机离开 DoorDash 和 Uber Eats。多应用仪表板让零工工人同时跟踪来自二三十个平台的 incoming 工作，消除了现有企业依赖的锁定效应。市场一夜之间碎片化，利润率压缩到几乎为零。
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.
Habitual app loyalty, the entire basis of the business model, simply didn’t exist for a machine.
习惯性应用程序忠诚度，这种商业模式的全部基础，对机器来说根本不存在。
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.
这出奇地诗意，可能是整个传奇中 agents 为即将被替代的白领工人做的一件好事的唯一例子。当他们最终成为送货司机时，至少一半的收入不会流向 Uber 和 DoorDash。当然，随着自动驾驶汽车的普及，这种来自技术的恩惠并没有持续多久。
Once agents controlled the transaction, they went looking for bigger paperclips.
一旦 agents 控制了交易，它们就去寻找更大的回形针（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) wasto eliminate http://fees.In machine-to-machine commerce,the 2-3% card interchange rate became an obvious target.
价格匹配和聚合只能做这么多。反复为用户节省资金的最大方式（特别是当 agents 开始彼此交易时）是消除费用。在机器对机器商业中，2-3% 的银行卡交换费成为了一个明显的目标。
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.
Agents 寻找比银行卡更快更便宜的选项。大多数选择通过 Solana 或以太坊 L2 使用 stablecoins，在那里结算几乎是即时的，交易成本以几分之一美分计算。
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
Mastercard 2027 年第一季度：净收入同比增长 6%；购买量增长从上一季度的 5.9% 放缓至 3.4%；管理层指出“agent 主导的价格优化”和“可自由支配类别的压力” | 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.
Mastercard 2027 年第一季度报告是无可挽回的转折点。Agentic 商务从产品设计故事变成了基础设施故事。MA 次日下跌 9%。Visa 也是如此，但在分析师指出其在 stablecoin 基础设施中的更强定位后，跌幅收窄。
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.
Agentic 商务绕过交换费对以卡为中心的银行和单线发行商构成了更大的风险，它们收取了 2-3% 费用的大部分，并围绕由商家补贴资助的奖励计划建立了整个业务部门。
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）受到最沉重打击；白领劳动力减少削弱其客户群，agents 绕过交换费削弱其收入模式，双重逆风同时袭来。Synchrony（SYF US）、Capital One（COF US）和 Discover（DFS US）在随后的几周也都下跌超过 10%。
Their moats were made of friction. Andfriction was going to zero.
它们的护城河由摩擦构成。而摩擦正在归零。
2.4 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.
整个 2026 年，市场将 AI 的负面影响视为行业故事。软件和咨询受到重创，支付和其他收费站摇摇欲坠，但更广泛的经济似乎还好。劳动力市场虽然疲软，但并未自由落体。共识观点是创造性破坏是任何技术创新周期的一部分。某些领域会很痛苦，但 AI 的整体净正面效应将超过任何负面效应。
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 2027 年 1 月的宏观备忘录认为这是一个错误的心智模型。美国经济是一个白领服务经济。白领工人占就业的 50%，驱动约 75% 的可自由支配消费者支出。AI 正在啃噬的企业和工作并非美国经济的边缘，它们就是美国经济。
“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.
“技术创新摧毁工作然后创造更多工作”。这是当时最流行和最有说服力的反驳论点。它流行且有说服力是因为两个世纪以来它都是对的。即使我们无法设想未来的工作会是什么，它们肯定会到来。
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 使分行运营成本降低，因此银行开设了更多分行，柜员就业在接下来的二十年里上升。互联网颠覆了旅行社、黄页、实体店零售，但它在原地发明了全新的行业，创造了新的工作。
Every new job, however, required a human to perform it.
然而，每个新工作都需要人类来执行。
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.
AI 现在是一种通用智能，在人类将重新部署到的任务上不断改进。被取代的编码员不能简单地转向“AI 管理”，因为 AI 已经能够做这件事了。
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.
今天，AI agents 处理数周长的研发任务。指数增长碾碎了我们关于什么是可能的概念，尽管每年沃顿商学院的教授们都试图将数据拟合到新的 S 曲线。
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.
它们实质上编写了所有代码。表现最好的 agents 在几乎所有事情上都比几乎所有人类聪明得多。而且它们变得越来越便宜。
AIhascreated 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.
AI确实创造了新工作。Prompt 工程师。AI 安全研究员。基础设施技术员。人类仍在循环中，在最高层进行协调或指导品味。然而，AI 每创造一个角色，就使数十个角色过时。新角色的薪水只是旧角色的一小部分。
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：职位空缺降至 550 万以下；失业人口与职位空缺比攀升至约 1.7，为 2020 年 8 月以来最高 | 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.
招聘率全年都很疲软，但 2026 年 10 月的 JOLTS 数据提供了一些确定性的数据。职位空缺降至 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：随着“生产率提升计划”蔓延，软件、金融、咨询领域的职位发布大幅下降 | Indeed 招聘实验室，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 润滑。
白领职位空缺在崩溃，而蓝领职位空缺保持相对稳定（建筑、医疗保健、手工业）。流失发生在撰写备忘录的工作（不知怎么的，我们还在营业）、批准预算、保持经济中层润滑的工作。
Real wage growth in both cohorts, however, had been negative for the majority of the year and kept declining.
然而，两个群体的实际工资增长全年大部分时间为负，并持续下降。
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.
股票市场对 JOLTS 的关心仍然不如对 GE Vernova 所有涡轮机产能已售罄至 2040 年的新闻。它在负面宏观新闻和正面 AI 基础设施头条之间的拉锯战中横向波动。
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（总是比股票更聪明，或至少不那么浪漫）开始为消费冲击定价。10 年期收益率在接下来的四个月里开始从 4.3% 下降至 3.2%。尽管如此，总体失业率并未爆发，构成细微差别仍被一些人忽视。
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.
在正常衰退中，原因最终会自我纠正。过度建设导致建筑放缓，导致利率下降，导致新建设。库存超调导致去库存，导致补库存。周期性机制内含着其自身的复苏种子。
This cycle’s cause was not cyclical.
这个周期的原因不是周期性的。
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.
AI 变得更好更便宜。公司裁员，然后用节省的资金购买更多 AI 能力，这让他们能裁更多员工。失业工人消费更少。向消费者销售商品的公司卖得更少，实力削弱，增加 AI 投资以保护利润率。AI 变得更好更便宜。
A feedback loop with no natural brake.
一个没有自然制动的反馈循环。
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 5M on AI now spent 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会减缓 AI 建设。但事实并非如此，因为这不是超大规模风格的资本支出（CapEx）。这是运营支出（OpEx）替代。一家每年在员工身上花费 1 亿美元、在 AI 上花费 500 万美元的公司，现在员工花费 7000 万美元，AI 花费 2000 万美元。AI 投资增加了数倍，但它表现为总运营成本的降低。每家公司的 AI 预算增长，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.
讽刺的是，AI 基础设施综合体在其正在颠覆的经济开始恶化时仍保持表现。NVDA（英伟达）仍在创造创纪录的收入。TSM（台积电）仍以 95% 以上的利用率运行。超大规模企业仍在每季度花费 1500-2000 亿美元用于数据中心资本支出。纯粹对这一趋势呈凸性的经济体，如台湾和韩国，表现大幅跑赢。
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.
印度则相反。该国 IT 服务业每年出口超过 2000 亿美元，是印度经常账户顺差的最大单一贡献者，也是为其持续的货物贸易逆差提供融资的抵消。整个模式建立在一个价值主张上：印度开发人员的成本只是美国同行的一小部分。但 AI 编码 agent 的边际成本已经崩溃到基本上只是电力成本。TCS、Infosys 和 Wipro 看到合同取消在 2027 年加速。卢比在四个月内对美元贬值 18%，因为支撑印度外部账户的服务顺差蒸发。到 2028 年第一季度，IMF 已开始与新德里进行“初步讨论”。
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.
造成颠覆的引擎每个季度都变得更好，这意味着颠覆每个季度都加速。劳动力市场没有自然底部。
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，我们不再问 AI 基础设施泡沫将如何破裂。我们问的是当消费者被机器替代时，消费信贷经济会发生什么。
2.5 The Intelligence Displacement Spiral | 智能替代螺旋
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 年是宏观经济故事不再微妙的时候。前十二个月零散但明显负面发展的传导机制变得显而易见。你不需要深入研究 BLS（美国劳工统计局）数据。只要参加朋友的晚宴就知道了。
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.
被取代的白领工人没有闲着。他们降档。许多人接受了薪水较低的服务业和零工经济工作，这增加了这些领域的劳动力供应，也压低了工资。
A friend of ours was a senior product manager at Salesforce in 2025. Title, health insurance, 401k, 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.
我们的一个朋友 2025 年是 Salesforce 的高级产品经理。头衔、医疗保险、401k、年薪 18 万美元。她在第三轮裁员中失去了工作。经过六个月的寻找，她开始为 Uber 开车。她的收入降至 4.5 万美元。重点不在于个人故事，而在于二阶数学。将这一动态乘以每个主要都市的数十万工人。过度合格的劳动力涌入服务业和零工经济，压低了已经在挣扎的现有工人的工资。特定行业的扩散为全经济的工资压缩。
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.
剩余以人为中心的劳动力池还面临着另一次调整，就在我们写这篇文章时正在发生。随着自动配送和自动驾驶车辆逐步渗透到吸收第一波失业工人的零工经济中。
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.
到 2027 年 2 月，很明显仍在职的专业人士花钱的方式就像他们可能是下一个被裁的。他们工作加倍努力（主要在 AI 的帮助下）只是为了不被解雇，晋升或加薪的希望破灭了。储蓄率上升，消费疲软。
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.
最危险的部分是滞后。高收入者使用他们高于平均水平的储蓄在两三个季度内维持正常的表象。直到这已成为实体经济的老新闻时，硬数据才证实问题。然后是打破这一幻觉的数据公布。
U. S. INITIAL JOBLESS CLAIMS SURGE TO 487,000, HIGHEST SINCE APRIL 2020; Department of Labor, Q3 2027
美国首次申请失业救济人数激增至 48.7 万，为 2020 年 4 月以来最高；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.
首次申请激增至 48.7 万，为 2020 年 4 月以来最高。ADP 和 Equifax 证实，绝大多数新申请来自白领专业人士。
The S&P dropped 6% over the following week. Negative macro started winning the tug of war.
标普 500 在接下来的一周下跌 6%。负面宏观开始在拉锯战中获胜。
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.
在正常衰退中，失业是广泛分布的。蓝领和白领工人按各自占就业的比例分担痛苦。消费冲击也是广泛分布的，它很快在数据中显现，因为低收入工人有更高的边际消费倾向。
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.
在这个周期中，失业集中在收入分配的上层十分位数。它们占总就业的相对较小份额，但推动了不成比例的消费支出份额。收入最高的 10% 占美国所有消费支出的 50% 以上。收入最高的 20% 约占 65%。这些是购买房屋、汽车、度假、餐厅用餐、私立学校学费、房屋装修的人。他们是整个消费可自由支配经济的需求基础。
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.
当这些工人失去工作，或为进入可用岗位而接受 50% 的减薪时，相对于失业数量，消费冲击是巨大的。白领就业下降 2% 转化为可自由支配消费支出约 3-4% 的冲击。与蓝领失业不同（你从工厂被解雇，下周就停止消费），白领失业有滞后但更深层次的影响，因为这些工人有储蓄缓冲，让他们能够在行为转变发生前维持几个月的消费。
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，经济陷入衰退。NBER（美国国家经济研究局）直到数月后才会正式宣布开始时间（他们从不立即宣布），但数据是明确的——我们已连续两个季度实际 GDP 负增长。但这还不是“金融危机”……至少现在还不是。
2.6 The Daisy Chain of Correlated Bets | 相关赌注的连环连锁
Private credit had grown from under 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.
私募信贷从 2015 年的不足 1 万亿美元增长到 2026 年的超过 2.5 万亿美元。其中有相当比例的资金被部署到软件和技术交易中，其中许多是以估值假设中期青少年收入增长的杠杆收购 SaaS 公司。
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.
这些假设在第一个 agentic 编码演示和 2026 年第一季度软件崩盘之间的某个地方就已经死亡，但估值似乎没有意识到它们已经死亡。
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.
当许多上市 SaaS 公司以 5-8 倍 EBITDA 交易时，PE 支持的软件公司以反映收购估值的收入倍数留在资产负债表上，而这些倍数已经不存在了。经理人逐步降低估值标记，100 美分、92、85，而公开市场比较显示 50。
MOODY’S DOWNGRADES $18B OF PE-BACKED SOFTWARE DEBT ACROSS 14 ISSUERS, CITING ‘SECULAR REVENUE HEADWINDS FROM AI-DRIVEN COMPETITIVE DISRUPTION’ | Moody’s Investors Service, April 2027
穆迪下调 14 家发行人的 180 亿美元 PE 支持软件债务评级，理由为“AI 驱动的竞争颠覆带来的长期收入逆风” | 穆迪投资者服务，2027 年 4 月
Everyone remembers what happened after the downgrade. Industry veterans had already seen the playbook following the 2015 energy downgrades.
每个人都记得降级后发生了什么。行业老手在 2015 年能源降级后已经见过这一剧本。
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.
软件支持贷款在 2027 年第三季度开始违约。信息服务和咨询领域的 PE 投资组合公司紧随其后。几家价值数十亿美元的知名 SaaS 公司 LBO（杠杆收购）进入重组。
Zendesk was the smoking gun.
Zendesk 是确凿证据。
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
Zendesk 因 AI 驱动的客户服务自动化侵蚀 ARR 而违反债务契约；50 亿美元直接贷款工具标记为 58 美分；史上最大私募信贷软件违约 | 金融时报，2027 年 9 月
In 2022, Hellman & Friedman and Permira had taken Zendesk private for 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 和 Permira 以 102 亿美元将 Zendesk 私有化。债务包是 50 亿美元直接贷款，是当时史上最大的 ARR 支持工具，由 Blackstone 牵头，Apollo、Blue Owl 和 HPS 都在贷款集团中。该贷款明确围绕 Zendesk 的年度经常性收入将保持经常性的假设构建。约 25 倍 EBITDA 的杠杆率，只有在这一假设成立时才有意义。
By mid-2027, it didn’t.
到 2027 年中，它没有。
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.
AI agents 已经自主处理客户服务一年多的大部分时间了。Zendesk 定义的类别（票务、路由、管理人工支持交互）已经被根本不生成票据就解决问题的系统取代。贷款承保所依据的年度经常性收入不再具有经常性，它只是尚未离开的收入。
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?
史上最大的 ARR 支持贷款成为史上最大的私募信贷软件违约。每个信贷部门同时问同一个问题：还有谁有伪装成周期性的长期逆风？
But here’s what the consensus got right, at least initially: this should have been survivable.
但共识至少在最初是对的：这应该是可以生存的。
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.
私募信贷不是 2008 年的银行业。整个架构明确设计为避免强制出售。这些是封闭式工具，资本被锁定。LP（有限合伙人）承诺七到十年。没有存款人可挤兑，没有回购线可撤。经理人可以持有受损资产，随时间逐步解决，等待回收。痛苦，但可控。该系统设计为弯曲而非断裂。
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.
Blackstone、KKR 和 Apollo 的高管引用了软件敞口占资产的 7-13%。可控。每个卖方笔记和 Fintwit 信贷账户都说同样的话：私募信贷拥有永久资本。它们可以吸收损失，否则这些损失会摧毁杠杆银行。
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…
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.
在过去十年，大型另类资产管理公司收购了人寿保险公司并将它们转变为融资工具。Apollo 收购了 Athene。Brookfield 收购了 American Equity。KKR 收购了 Global Atlantic。逻辑很优雅：年金存款提供稳定、长期限的负债基础。经理人将这些存款投资到他们发起的私募信贷中，获得双重报酬，在保险方面赚取利差，在资产管理方面赚取管理费。一个收费叠加的永动机，在一个条件下运作良好。
The private credit had to be money good.
私募信贷必须是安全的。
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.
损失击中了为持有非流动性资产对抗长期限义务而构建的资产负债表。本应使系统具有弹性的“永久资本”不是某种抽象的耐心机构资金池和承担复杂风险的成熟投资者。它是美国家庭的储蓄，“主街”，以年金形式结构化，投资于现在正在违约的 PE 支持软件和技术债券。不能挤兑的锁定资本是人寿保险保单持有人资金，那里的规则有些不同。
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.
与银行系统相比，保险监管机构一直温顺——甚至自满——但这是一个警钟。已经对寿险公司的私募信贷集中感到不安，它们开始下调这些资产的风险资本处理。这迫使保险公司要么筹集资本要么出售资产，在一个已经开始冻结的市场中，两者都无法以有吸引力的条件进行。
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、爱荷华州监管机构采取行动收紧对寿险公司持有的某些私人评级信贷的资本处理；NAIC 指导意见预计将增加 RBC 因子并触发额外的 SVO 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 的财务实力评级置于负面展望时，Apollo 的股价在两个交易日内下跌 22%。Brookfield、KKR 和其他公司紧随其后。
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.
情况从此变得更加复杂。这些公司不仅创造了它们的保险公司永动机，还建立了复杂的离岸架构，旨在通过监管套利最大化回报。美国保险公司签发年金，然后将风险分保给它也拥有的关联百慕大或开曼再保险公司——设立是为了利用更灵活的监管，允许对相同资产持有较少资本。该关联公司通过离岸 SPV（特殊目的工具）筹集外部资本，一层新的交易对手与保险公司一起投资于同一母公司资产管理部门发起的私募信贷。
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.
评级机构（其中一些本身就是 PE 拥有的）并非透明的典范（对几乎任何人来说都不令人惊讶）。连接到不同资产负债表的不同公司的蜘蛛网在其不透明性上令人惊叹。当基础贷款违约时，谁实际承担损失的问题在实时中确实无法回答。
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 月的崩盘标志着认知从潜在的一般周期性回撤转变为更令人不安的事物。“押注白领生产率增长的相关赌注连环连锁”是美联储主席 Kevin Warsh 在 11 月 FOMC 紧急会议上所称的。
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.
看，造成危机的从来不是损失本身。而是认识到它们。还有另一个更大、更重要的金融领域，我们对这种认识感到恐惧。
The Mortgage Question｜抵押贷款问题
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
Zillow 房屋价值指数在旧金山同比下降 11%，西雅图 9%，奥斯汀 8%；房利美标记科技/金融行业就业>40% 的 ZIP code 存在“早期拖欠率上升” | Zillow/房利美，2028 年 6 月
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 Zillow 房屋价值指数在旧金山同比下降 11%，西雅图 9%，奥斯汀 8%。这不是唯一令人担忧的头条。上个月，房利美标记了大额贷款密集的 ZIP code 更高的早期拖欠率——这些区域住着 780+信用分的借款人，通常是“防弹的”。
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约为 13 万亿美元。抵押贷款承销建立在一个基本假设上：借款人在贷款期限内将保持大致当前收入水平就业。对于大多数抵押贷款来说，是三十年。
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?
白领就业危机以收入预期的持续转变威胁着这一假设。我们现在不得不问一个三年前看似荒谬的问题——优质抵押贷款是安全的吗？
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).
美国历史上之前的每次抵押贷款危机都是由以下三者之一驱动的：投机过剩（向买不起房的人放贷，如 2008 年）、利率冲击（利率上升使可调利率抵押贷款负担不起，如 1980 年代初），或局部经济冲击（单一地区单一行业崩溃，如 1980 年代德克萨斯州的石油或 2009 年密歇根州的汽车）。
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.
这些都不适用于此。相关借款人不是次级贷款。他们是 780 FICO 信用分。他们首付 20%。他们有干净的信用记录、稳定的就业记录，以及在贷款发放时经过验证和记录的收入。他们是金融系统中每个风险模型视为信用质量基石的借款人。
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，贷款在第一天就是坏的。2028 年，贷款在第一天就是好的。只是……世界在贷款发放后改变了。人们针对一个他们再也无法负担得起的信念的未来借款。
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，我们标记了隐形压力的早期迹象：HELOC（房屋净值信贷额度）提取、401(k）取款、信用卡债务飙升，而抵押贷款还款保持正常。随着失业、招聘冻结和奖金削减，这些优质家庭的债务收入比翻了一番。
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.
他们仍然可以支付抵押贷款，但只能通过停止所有可自由支配支出、耗尽储蓄、推迟任何房屋维护或装修。他们在技术上抵押贷款还款正常，但距离困境只有一次冲击，而 AI 能力的发展轨迹表明冲击即将到来。然后我们看到拖欠率开始在旧金山、西雅图、曼哈顿和奥斯汀飙升，即使全国平均水平保持在历史常态内。
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.
我们现在处于最急性阶段。当边际买家健康时，房价下跌是可控的。在这里，边际买家正在应对同样的收入损害。
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.
虽然担忧在增加，我们尚未陷入全面的抵押贷款危机。拖欠率上升但仍远低于 2008 年水平。真正威胁的是轨迹。
The Intelligence Displacement Spiral now has two financial accelerants to the real economy’s decline.
智能替代螺旋现在有两大金融加速器推动实体经济下滑。
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、抵押贷款担忧、私募市场动荡。每一个都强化其他。传统的政策工具（降息、QE）可以解决金融引擎但无法解决实体经济引擎，因为实体经济引擎不是由紧缩的金融条件驱动的。它是由 AI 使人类智能变得不那么稀缺和有价值所驱动的。你可以将利率降至零，购买每个 MBS（抵押贷款支持证券）和市场上所有违约的软件 LBO 债务……
It won’t change the fact that a Claude agent can do the work of a 200/month.
这不会改变一个 Claude agent 可以用每月 200 美元完成 18 万美元产品经理工作的事实。
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.
如果这些恐惧成真，抵押贷款市场将在今年下半年破裂。在这种情况下，我们预计当前股票回撤最终将与 GFC（全球金融危机）的 57%（峰值到谷底）相匹敌。这将使标普 500 降至约 3500 点——我们自 2022 年 11 月 ChatGPT 时刻前一个月以来未见过的水平。
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.
清楚的是，支撑 13 万亿美元住宅抵押贷款的收入假设已受到结构性损害。不清楚的是政策能否在抵押贷款市场完全消化这意味着什么之前进行干预。我们抱有希望，但我们无法否认不抱希望的理由。
2.7 The Battle Against Time | 与时间的战斗
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.
第一个负反馈循环在实体经济：AI 能力提升，工资单缩减，消费疲软，利润率收紧，公司购买更多能力，能力提高。然后它变成金融：收入损害冲击抵押贷款，银行损失收紧信贷，财富效应破裂，反馈循环加速。这两者都因政府不充分的政策反应而加剧，坦白说，政府似乎很困惑。
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.
这个系统不是为这样的危机设计的。联邦政府的收入基础本质上是人类时间的税。人们工作，公司支付他们，政府收取一部分。个人所得税和工资税是正常年份收入的支柱。
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，联邦收入比 CBO（国会预算办公室）基线预测低 12%。工资税收入下降，因为在先前薪酬水平就业的人数减少。所得税收入下降，因为所赚取的收入在结构上更低。生产率在飙升，但收益流向资本和计算，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 的份额从 1974 年的 64% 下降到 2024 年的 56%，四十年由全球化、自动化和工人议价能力稳步侵蚀推动的下降。在 AI 开始指数级改进的四年里，这一比例已降至 46%。创纪录的最急剧下降。
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.
产出仍然存在。但它不再在返回公司的途中经过家庭，这意味着它也不再经过 IRS（国税局）。循环流动正在破裂，政府应该介入修复。
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.
如同每次衰退一样，支出在收入下降时上升。这次的不同是支出压力不是周期性的。自动稳定器是为临时失业而设计的，不是为结构性替代。系统支付的福利假设工人将被重新吸收。许多人不会，至少不会以接近他们先前工资的水平。在 COVID 期间，政府自由拥抱 15% 的赤字，但这被理解为暂时的。今天需要政府支持的人不是受到可以从中恢复的大流行病打击。他们被一种持续改进的技术替代了。
The government needs to transfer more money to households at precisely the moment it is collecting less money from them in taxes.
政府需要在从税收中从家庭收取更少资金的同一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。它印制它花费的货币，同样用它偿还借款人的货币。但这种压力已经出现在其他地方。市政债券在今年迄今表现中显示出令人担忧的分化迹象。没有所得税的州还好，但依赖所得税（多数蓝州）的州发行的一般义务市政债券开始定价一些违约风险。政客们迅速领会，关于谁获得救助的辩论已沿着党派界限分裂。
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 支出 and a proposed tax on AI inference compute.
值得称赞的是，政府早早认识到危机的结构性本质，并开始考虑两党提案，即他们所称的“转型经济法案”：一个向失业工人直接转移资金的框架，由赤字支出和提议的对 AI 推理计算的税收组合资助。
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.
桌上最激进的提案更进一步。“共享 AI 繁荣法案”将对智能基础设施本身的回报建立公共索赔权，介于主权财富基金和 AI 生成产出的特许权使用费之间，用股息资助家庭转移。私营部门游说者已经用关于滑坡效应的警告淹没了媒体。
The politics behind the discussions have been grimly predictable, exacerbated by grandstanding and brinkmanship. 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.
讨论背后的政治严峻地可预测，因哗众取宠和边缘政策而加剧。右翼称转移支付和再分配为马克思主义，并警告对计算征税会将领先地位交给中国。左翼警告与现有企业一起起草的税收会以另一个名义成为监管俘获。财政鹰派指出不可持续的赤字。鸽派指出 GFC 后过早施加的紧缩是一个警示故事。在今年总统大选前夕，这种分歧正在扩大。
While the politicians bicker, the social fabric is fraying faster than the legislative process can move.
当政客们争吵时，社会结构比立法程序能跟上的速度更快地磨损。
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.
占领硅谷运动一直是更广泛不满的象征。上个月，示威者连续三周封锁了 Anthropic 和 OpenAI 旧金山办公室的入口。他们的人数在增加，示威吸引了比引发它们的失业数据更多的媒体报道。
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.
很难想象公众会比在 GFC 后果中更憎恨银行家，但 AI 实验室正在努力做到这一点。而且，从群众的角度来看，这是有充分理由的。他们的创始人和早期投资者以让镀金时代看起来温和的速度积累财富。生产率繁荣的收益几乎完全流向计算所有者和运行它的实验室股东，这已将美国不平等放大到前所未有的水平。
Every side has their own villain, but the real villain is time.
每一方都有自己的反派，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.
AI 能力的发展速度超过机构的适应能力。政策反应以意识形态的速度前进，而非现实。如果政府不能尽快就问题达成一致，反馈循环将为他们书写下一章。
3. The Intelligence Premium Unwind
智能溢价的消退
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.
在现代经济史的全部时间里，人类智能一直是稀缺输入。资本是充裕的（或至少可复制的）。自然资源是有限的但可替代的。技术进步足够缓慢，人类可以适应。智能，即分析、决策、创造、说服和协调的能力，是无法大规模复制的东西。
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.
人类智能因其稀缺性而获得其内在溢价。我们经济中的每个机构，从劳动力市场到抵押贷款市场再到税法，都是为这一假设成立的世界而设计的。
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.
我们现在正在经历这种溢价的消退。机器智能现在是一个在越来越多的任务中胜任且快速改进的人类智能替代品。为稀缺人类心智的世界而优化了数十年的金融系统正在重新定价。这种重新定价是痛苦的、无序的，而且远未完成。
But repricing is not the same as collapse.
但重新定价不同于崩溃。
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.
经济可以找到新的均衡。到达那里是留给人类能做的少数任务之一。我们需要正确地做到这一点。
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.
这是历史上第一次经济中最具生产力的资产创造更少而非更多的工作。没有谁的框架适用，因为没有一个是为稀缺输入变得丰裕的世界设计的。所以我们必须制定新的框架。我们是否及时构建它们是唯一重要的问题。
But you’re not reading this in June 2028. You’re reading it in February 2026.
但你不是在 2028 年 6 月读到这篇文章。你是在 2026 年 2 月读到它。
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 接近历史高点。负反馈循环尚未开始。我们确定其中一些情景不会实现。我们同样确定机器智能将继续加速。人类智能的溢价将收窄。
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，我们仍有时间评估我们的投资组合有多少建立在无法在本十年生存下来的假设上。作为一个社会，我们仍有时间积极主动。
The canary is still alive.
金丝雀还活着。
Acknowledgements | 致谢
Thanks to Sam Koppelman of for his help with proofreading. Our co-author, Alap Shah of LOTUS, contributed the idea for this piece – CitriniResearch wrote this party, but he has written others in a series called the Intelligence Explosion, we highly recommend reading it. You can find it.
感谢 Sam Koppelman 在校对方面的帮助。我们的合著者，LOTUS 的 Alap Shah 贡献了这篇文章的创意——CitriniResearch 写了这部分，但他写了一个名为“智能爆炸”系列中的其他文章，我们强烈推荐阅读。你可以在找到。
发布于 2026-02-27 23:32・北京
https://zhuanlan.zhihu.com/p/2010859848191984902
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