THE 2028 GLOBAL INTELLIGENCE CRISIS
打开互动全文版（逐段中英对照 + 图/公式 + 论文问答）→Citrini 研究提供主题股票投资和全球宏观交易的见解——跨资产、横向思维。我们的承诺：你永远不必问“交易是什么？”订阅即表示您同意发布者的服务条款和隐私政策、Substack 的使用条款，并确认其信息收集通知和隐私政策。
Citrini Research provides insights on thematic equity investing and global macro trading—with cross-asset, lateral thinking. Our promise: you’ll never have to ask “what’s the trade?” By subscribing, you agree the publisher's Terms of Service and Privacy Policy, Substack's Terms of Use, and acknowledge its Information Collection Notice and Privacy Policy.
Citrini Research 提供主题股票投资和全球宏观交易的见解——跨资产、横向思维。我们的承诺：你永远不必问“交易是什么？”。
Citrini Research provides insights on thematic equity investing and global macro trading—with cross-asset, lateral thinking. Our promise: you’ll never have to ask “what’s the trade?”
订阅即表示您同意发布者的服务条款和隐私政策、Substack 的使用条款，并确认其信息收集声明和隐私政策。
By subscribing, you agree the publisher's Terms of Service and Privacy Policy, Substack's Terms of Use, and acknowledge its Information Collection Notice and Privacy Policy.
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如果我们对 AI 的乐观预期继续被证实……而实际上这可能是悲观信号呢？
_What if our AI bullishness continues to be right...and what if that’s actually bearish?_
以下是一个情景，而非预测。这不是悲观色情片或 AI 末日同人小说。本文的唯一目的是建模一个相对未被充分探索的情景。我们的朋友 Alap Shah 提出了这个问题，我们一起集思广益得出了答案。我们写了这一部分，他写了另外两部分，你可以在这里找到。
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 here.
希望阅读本文能让您更好地为潜在的左尾风险做好准备，因为 AI 正使经济变得越来越奇怪。
Hopefully, reading this leaves you more prepared for potential left tail risks as AI makes the economy increasingly weird.
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展和后果。
This is the CitriniResearch Macro Memo from June 2028, detailing the progression and fallout of the Global Intelligence Crisis.
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_~~2026 年 2 月 22 日~~ 2028 年 6 月 30 日_
_~~February 22nd, 2026~~ June 30th, 2028_
今早公布的失业率为 10.2%，比预期高出 0.3 个百分点。市场因此下跌 2%，标普 500 指数自 2026 年 10 月高点以来的累计跌幅达到 38%。
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.
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。
Traders have grown numb. Six months ago, a print like this would have triggered a circuit breaker.
_两年。_ 从“可控”和“行业特定”到如今的经济面貌与任何人成长时所熟悉的经济截然不同，只用了两年时间。本季度的宏观备忘录试图重构这一过程——对危机前经济的事后剖析。
_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.
当时的狂热显而易见。到 2026 年 10 月，标普 500 指数逼近 8000 点，纳斯达克突破 30000 点。因人类被淘汰而引发的第一波裁员始于 2026 年初，其效果正如预期：利润率扩大，盈利超预期，股市上涨。创纪录的企业利润被重新投入 AI 算力。
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.
宏观数据依然亮眼。名义 GDP 多次录得中高个位数的年化增长。生产率飙升。实际每小时产出增长率达到 1950 年代以来从未见过的水平，这得益于不睡觉、不请病假、不需要医疗保险的 AI 智能体。
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.
算力拥有者的财富随着劳动力成本的消失而暴涨。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，白领工人却被机器取代，被迫从事收入更低的工作。
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.
当消费经济开始出现裂痕时，经济评论员们推广了“_幽灵 GDP_”这一说法：指那些出现在国民账户中但从未在实体经济中流通的产出。
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.
_AI 在各方面都超出预期，而市场就是 AI。_ 唯一的问题是……经济并非如此。
_In every way AI was exceeding expectations, and the market was AI._ The only problem…the economy was not.
其实从一开始就应该清楚，北达科他州的一个 GPU 集群产生的产出，相当于曼哈顿中城 10000 名白领工人的产出，这更像是经济疫情而非经济良方。货币流通速度停滞不前。以人为中心的消费经济（当时占 GDP 的 70%）萎缩了。如果我们当初问问机器在非必需品上花多少钱，也许就能更早意识到这一点。（提示：答案是零。）
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.)
AI 能力提升，公司需要的工人减少，白领裁员增加，失业工人支出减少，利润率压力促使企业更多投资 AI，AI 能力进一步提升……
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…
这是一个没有天然刹车的负反馈循环。_人类智能替代螺旋_。白领工人的收入能力（以及理性地，他们的支出）受到结构性损害。他们的收入是 13 万亿美元抵押贷款市场的基石——迫使承销商重新评估优质抵押贷款是否仍然可靠。
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.
十七年没有真正的违约周期，使得私募市场充斥着 PE 支持的软件交易，这些交易假设 ARR（年度经常性收入）将持续。2027 年中由 AI 颠覆引发的第一波违约浪潮挑战了这一假设。
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.
如果颠覆仅限于软件行业，这本来是可以管理的，但事实并非如此。到 2027 年底，它威胁到了所有基于中介的商业模式。大量建立在为人类摩擦收费基础上的公司土崩瓦解。
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.
整个系统原来是一连串相互关联的、押注白领生产率增长的链条。2027 年 11 月的崩盘只是加速了所有已经存在的负反馈循环。
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.
近一年来，我们一直在等待“坏消息就是好消息”。政府开始考虑各种方案，但公众对政府实施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。
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.
2025 年底，智能体式编码工具的能力出现了阶跃式提升。
In late 2025, agentic coding tools took a step function jump in capability.
一名熟练的开发者使用 Claude Code 或 Codex，现在可以在数周内复制出一个中端市场 SaaS 产品的核心功能。虽然不完美，也未处理所有边缘情况，但足以让负责审核 50 万美元年度续约合同的 CIO 开始问：“我们自己建一个怎么样？”
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?”
财年大多与日历年对齐，因此 2026 年的企业支出已在 2025 年第四季度确定，当时“智能体式 AI”还只是个流行词。年中审查是采购团队首次在能够看到这些系统实际能力的情况下做出决策。一些人目睹自己内部团队在数周内就搭建出原型，复制了价值六位数的 SaaS 合同。
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.
那年夏天，我们与一家财富 500 强企业的采购经理交谈。他告诉我们一次预算谈判的经历。销售代表原本打算沿用去年的策略：每年提价 5%，以及标准的“你的团队依赖我们”的说辞。采购经理告诉他，自己一直在与 OpenAI 沟通，考虑让他们的“前向部署工程师”使用 AI 工具完全取代该供应商。最终他们以七折的价格续约。他说，这已经是个好结果了。而“SaaS 长尾”公司，如 Monday.com、Zapier 和 Asana，情况要糟糕得多。
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.
投资者已经准备好——甚至预期——长尾公司会受到重创。它们可能占典型企业技术栈支出的三分之一，但显然暴露在风险中。然而，记录系统本应不受颠覆影响。
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.
直到 ServiceNow 的 2026 年第三季度报告发布，反身性的机制才变得更加清晰。
It wasn’t until ServiceNow’s Q3 26 report that the mechanism of reflexivity became clearer.
SaaS 并没有“死亡”。运行和支持内部构建仍然需要成本效益分析。但内部构建确实成了一个选项，这影响了定价谈判。或许更重要的是，竞争格局已经改变。AI 使得开发和发布新功能更加容易，因此差异化消失了。现有企业陷入了一场价格战——既与彼此竞争，也与突然涌现的新兴挑战者竞争。这些挑战者因智能体式编码能力的飞跃而备受鼓舞，且没有遗留成本结构需要保护，因此 aggressively 地抢夺市场份额。
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.
这些系统的相互关联性直到这份报告发布才被充分认识。ServiceNow 按席位销售。当财富 500 强客户裁员 15%时，他们取消了 15%的许可证。同样的 AI 驱动裁员在提升客户利润率的同时，也在机械地摧毁 ServiceNow 自己的收入基础。
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.
这家销售工作流自动化的公司，正被更好的工作流自动化所颠覆，而它的应对措施是裁员，并将节省下来的资金投入到颠覆它的技术中。
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.
他们还能做什么呢？坐以待毙，死得更慢？那些最受 AI 威胁的公司，反而成了 AI 最积极的采用者。
What else were they supposed to do? _Sit still and die slower? The companies most threatened by AI became AI’s most aggressive adopters._
事后看来这显而易见，但在当时并非如此（至少对我来说）。历史上的颠覆模式是，现有企业抵制新技术，将份额拱手让给灵活的进入者，然后慢慢消亡。柯达、百视达、黑莓都是如此。但 2026 年发生的情况不同；现有企业没有抵制，因为它们承受不起。
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.
股价下跌 40-60%，董事会要求给出答案，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到 AI 工具中，利用这些工具以更低的成本维持产出。
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.
每家公司的个体应对都是理性的，但集体结果却是灾难性的。每一美元的人力成本节省都流入了 AI 能力，而这又使得下一轮裁员成为可能。
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.
软件只是开胃菜。当投资者还在争论 SaaS 估值倍数是否已触底时，他们忽略了反身性循环已经逃出了软件行业。适用于 ServiceNow 裁员的逻辑，同样适用于每一家拥有白领成本结构的公司。
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.
到 2027 年初，使用大型语言模型已成为常态。人们在使用 AI 智能体时甚至不知道什么是 AI 智能体，就像那些从未学过“云计算”的人使用流媒体服务一样。他们对此的看法与自动补全或拼写检查无异——不过是手机现在能做的事情罢了。
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 的开源智能体购物助手是 AI 处理消费者决策的催化剂。几周内，每个主要 AI 助手都集成了某种智能体商务功能。蒸馏模型使得这些智能体可以在手机和笔记本电脑上运行，而不仅仅是云端实例，显著降低了推理的边际成本。
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.
本应让投资者更加不安的是，这些智能体不会等待被询问。它们根据用户的偏好后台运行。商业不再是一系列离散的人类决策，而变成了一个持续优化的过程，全天候为每个联网消费者运行。到 2027 年 3 月，美国普通个人每天消耗 40 万 token——自 2026 年底以来增长了 10 倍。
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.
链条中的下一个环节已经断裂。
The next link in the chain was already breaking.
过去五十年，美国经济在人类局限之上建立了一个巨大的租金抽取层：事情需要时间，耐心会耗尽，品牌熟悉度替代了勤勉，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约束的持续存在。
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.
起初事情很简单。智能体消除了摩擦。
It started out simple enough. Agents removed friction.
那些尽管数月未使用却仍被动续费的订阅和会员。那些在试用期后偷偷翻倍的 introductory pricing。每一个都被重新定义为智能体可以谈判的人质局面。整个订阅经济所依赖的指标——平均客户生命周期价值——显著下降。
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.
消费者智能体开始改变几乎所有消费者交易的运作方式。
Consumer agents began to change how nearly all consumer transactions worked.
人类真的没有时间在购买一盒蛋白棒之前跨五个竞争平台比价。机器可以。
Humans don’t really have the time to price-match across five competing platforms before buying a box of protein bars. Machines do.
旅行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，我们的智能体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、预算约束、退款）。
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.
保险续保，其整个续保模式依赖于投保人的惯性，被改革了。每年重新比较你保险覆盖范围的智能体瓦解了保险公司从被动续保中赚取的 15-20%的保费。
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.
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“我将处理你觉得繁琐的复杂性”的类别都被颠覆了，因为智能体觉得没有什么繁琐的。
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.
甚至那些我们认为因人际关系价值而受到保护的领域也被证明是脆弱的。房地产，几十年来买家因代理与消费者之间的信息不对称而容忍 5-6%的佣金，一旦配备 MLS 访问权限和数十年交易数据的 AI 智能体能够瞬间复制知识库，就崩溃了。2027 年 3 月的一份卖方报告将其称为“智能体对智能体的暴力”。主要都市区的买方佣金中位数已从 2.5-3%压缩至 1%以下，且越来越多的交易在买方完全没有人类代理的情况下完成。
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.
我们高估了“人际关系”的价值。原来人们称之为关系的很多东西，不过是带有友好面孔的摩擦。
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.
这只是中介层颠覆的开始。成功的公司花费数十亿来有效利用消费者行为和人类心理的怪癖，而这些现在都不再重要了。
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.
优化价格和适配性的机器不在乎你最喜欢的应用或你过去四年习惯性打开的网站，也不受精心设计的结账体验的吸引。它们不会疲倦而接受最简单的选项，或默认“我总是从这里订购”。
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”.
这摧毁了一种特定的护城河：习惯性中介。
That destroyed a particular kind of moat: habitual intermediation.
编码智能体已经降低了推出配送应用的准入门槛。一个称职的开发者可以在几周内部署一个功能性的竞争对手，并且有几十个这样做，通过将 90-95%的配送费转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可以同时跟踪来自二三十个平台的工作，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。
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.
智能体加速了破坏的两面。它们使竞争对手成为可能，然后它们又使用这些竞争对手。DoorDash 的护城河实际上是“你饿了，你懒，这是你主屏幕上的应用。”智能体没有主屏幕。它会检查 DoorDash、Uber Eats、餐厅自己的网站以及二十个新的 vibe-coded 替代品，以便每次选择最低费用和最快配送。
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.
习惯性应用忠诚度，整个商业模式的基础，对机器来说根本不存在。
Habitual app loyalty, the entire basis of the business model, simply didn’t exist for a machine.
这颇具讽刺意味，或许是整个故事中智能体为即将被取代的白领工人做的好事的唯一例子。当他们最终成为配送司机时，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，这种来自技术的好处并没有持续多久。
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.
一旦智能体控制了交易，它们就开始寻找更大的回形针。
Once agents controlled the transaction, they went looking for bigger paperclips.
比价和聚合只能做这么多。为用户反复省钱的最大方式（尤其是当智能体开始相互交易时）是消除费用。在机器对机器的商业中，2-3%的卡交换费率成为了一个明显的目标。
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._
智能体开始寻找比卡更快、更便宜的选项。大多数选择使用通过 Solana 或以太坊 L2 的稳定币，在那里结算几乎是即时的，交易成本以几分之一美分衡量。
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.
万事达卡 2027 年第一季度的报告是转折点。智能体商务从产品故事变成了管道故事。次日 MA 下跌 9%。Visa 也下跌，但在分析师指出其在稳定币基础设施中更强的定位后收窄了跌幅。
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.
智能体商务绕过交换费对以卡为中心的银行和单一发卡机构构成了更大的风险，这些机构收取了 2-3%费用的大部分，并围绕由商户补贴资助的奖励计划建立了整个业务板块。
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美国运通（AXP US）受到的打击最大；白领劳动力减少削弱了其客户群，而智能体绕过交换费则削弱了其收入模式，两者构成了双重逆风。Synchrony（SYF US）、Capital One（COF US）和 Discover（DFS US）在接下来的几周内也都下跌了超过 10%。
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.
它们的护城河是由摩擦构成的。而摩擦正在归零。
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.
Their moats were made of friction. And friction was going to zero.
直到 2026 年，市场将 AI 的负面影响视为一个行业故事。软件和咨询业遭受重创，支付和其他收费业务摇摇欲坠，但更广泛的经济似乎还好。劳动力市场虽然疲软，但并未崩盘。共识观点是，创造性破坏是任何技术创新周期的一部分。它会在局部造成痛苦，但 AI 带来的整体净收益将超过任何负面影响。
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.
我们 2027 年 1 月的宏观备忘录认为这是错误的思维模型。美国经济是白领服务业经济。白领工人占就业的 50%，并驱动了大约 75%的可自由支配消费支出。AI 正在吞噬的企业和工作并非与美国经济无关，它们就是美国经济本身。
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.
“技术创新摧毁就业，然后创造更多就业。”这是当时最流行也最有说服力的反驳观点。它之所以流行且有说服力，是因为它在两个世纪里都是正确的。即使我们无法构想未来的工作是什么，它们也一定会出现。
“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.
自动取款机使分行运营成本降低，因此银行开设了更多分行，柜员就业人数在接下来的二十年里反而上升了。互联网颠覆了旅行社、黄页、实体零售，但它在其位置上创造了全新的行业，催生了新的就业岗位。
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.
然而，每一个新工作都需要人类来执行。
Every new job, however, required a human to perform it.
AI 现在是一种通用智能，它在人类可能重新部署的任务上不断进步。被取代的编码员不能简单地转向“AI 管理”，因为 AI 已经能够胜任那项工作。
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，AI 智能体处理长达数周的研究和开发任务。指数级增长碾压了我们对可能性的认知，尽管每年沃顿商学院的教授都试图将数据拟合到一条新的 S 形曲线上。
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.
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它们编写了几乎所有的代码。其中性能最高的在几乎所有事情上都比几乎所有人类聪明得多。而且它们还在不断变得更便宜。
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.
AI 确实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。人类仍然在循环中，进行最高级别的协调或根据品味进行指导。然而，AI 每创造一个新角色，就会让几十个旧角色过时。新角色的薪酬只有旧角色的一小部分。
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.
全年招聘率一直低迷，但 2026 年 10 月的 JOLTS 数据提供了一些确凿的数据。职位空缺降至 550 万以下，同比下降 15%。
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.
白领职位空缺正在崩溃，而蓝领职位空缺（建筑、医疗、技工）保持相对稳定。流失的是那些撰写备忘录（不知何故，我们仍在营业）、批准预算以及维持经济中层运转的工作。然而，两个群体的实际工资增长在全年大部分时间里都为负，并且持续下降。
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.
股票市场对 JOLTS 的关注仍然不如 GE Vernova 所有涡轮机产能已售罄至 2040 年的消息，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盘整理。
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.
然而，债券市场（总是比股票更聪明，或者至少不那么浪漫）开始对消费冲击进行定价。10 年期收益率在接下来的四个月里从 4.3%开始下降至 3.2%。不过，总体失业率并未飙升，构成上的细微差别仍未被一些人察觉。
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.
在正常的衰退中，原因最终会自我修正。过度建设导致建设放缓，进而导致利率下降，进而引发新的建设。库存过剩导致去库存，进而导致补库存。周期性机制本身包含了复苏的种子。
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.
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AI 变得更好、更便宜。公司裁员，然后用节省下来的钱购买更多 AI 能力，这又让它们能够裁掉更多员工。被取代的工人减少支出。向消费者销售商品的公司销量减少，实力减弱，并更多地投资于 AI 以保护利润率。AI 变得更好、更便宜。
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.
直觉上的预期是，总需求下降会减缓 AI 的建设。但事实并非如此，因为这不是超大规模资本支出式的资本支出。这是运营支出替代。一家原本每年在员工上花费 1 亿美元、在 AI 上花费 500 万美元的公司，现在在员工上花费 7000 万美元，在 AI 上花费 2000 万美元。AI 投资增加了数倍，但这是在总运营成本减少的情况下发生的。每家公司的 AI 预算都在增长，而其总体支出却在缩减。
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.
具有讽刺意味的是，AI 基础设施综合体在其所颠覆的经济开始恶化时仍在表现良好。NVDA 仍在创下收入纪录。TSM 的利用率仍超过 95%。超大规模企业每季度仍在数据中心资本支出上花费 1500-2000 亿美元。纯粹顺应这一趋势的经济体，如台湾和韩国，表现远超其他地区。
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.
印度则相反。该国的 IT 服务部门每年出口超过 2000 亿美元，是印度经常账户盈余的最大单一贡献者，也是为其持续的商品贸易逆差提供融资的抵消项。整个模式建立在一个价值主张上：印度开发者的成本仅为美国同行的零头。但 AI 编码智能体的边际成本已经降至基本等于电费。TCS、Infosys 和 Wipro 的合同取消在 2027 年加速。随着支撑印度外部账户的服务业盈余蒸发，卢比在四个月内对美元贬值了 18%。到 2028 年第一季度，IMF 已开始与新德里进行“初步讨论”。
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.
造成颠覆的引擎每个季度都在变得更好，这意味着颠覆每个季度都在加速。劳动力市场没有自然底部。
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.
在美国，我们不再问 AI 基础设施泡沫将如何破裂。我们在问，当消费者被机器取代时，一个消费信贷经济会发生什么。
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 _._
2027 年，宏观经济的故事不再微妙。过去十二个月里那些零散但明显负面的事件的传导机制变得显而易见。你不需要查看劳工统计局的数据。只需参加一个朋友的晚宴。
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.
被替代的白领工人并没有坐以待毙。他们降级就业。许多人接受了低薪的服务业和零工经济工作，这增加了这些领域的劳动力供给，也压缩了那里的工资。
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.
我们的一位朋友在 2025 年是 Salesforce 的高级产品经理。有头衔、健康保险、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，她开始为优步开车。她的收入降到了 4.5 万美元。重点不在于个人故事，而在于二阶数学。将这种动态乘以每个主要都市区的几十万工人。资质过高的劳动力涌入服务业和零工经济，压低了原本已经挣扎的现有工人的工资。特定行业的颠覆演变成了整个经济的工资压缩。
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.
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剩余以人为中心的岗位池还有一次调整在前，就在我们写这篇文章时正在发生。随着自主配送和自动驾驶车辆逐步渗透到吸收了第一批被替代工人的零工经济中。
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.
到 2027 年 2 月，很明显，仍在就业的专业人士的消费行为就像他们可能是下一个被裁的人。他们加倍努力工作（大多借助 AI 的帮助）只是为了不被解雇，晋升或加薪的希望已经消失。储蓄率上升，支出放缓。
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.
最危险的部分是滞后。高收入者利用他们高于平均水平的储蓄，维持了两到三个季度的正常表象。硬数据直到问题在实体经济中已是旧闻时才确认。然后，打破幻想的报告出现了。
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.
首次申请失业救济人数飙升至 48.7 万，为 2020 年 4 月以来最高。ADP 和 Equifax 确认，新申请中绝大多数来自白领专业人士。
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.
标普 500 指数在接下来的一周下跌了 6%。负面宏观因素开始赢得拉锯战。
The S&P dropped 6% over the following week. Negative macro started winning the tug of war.
在正常的经济衰退中，失业是广泛分布的。蓝领和白领工人大致按各自在就业中的份额分担痛苦。消费冲击也是广泛分布的，并且由于低收入工人的边际消费倾向更高，数据中会很快显现。
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.
在这个周期中，失业集中在收入分布的最高十分位。他们在总就业中占比较小，但驱动着不成比例的消费支出。美国收入最高的 10%的人占所有消费支出的 50%以上。收入最高的 20%的人约占 65%。这些人是购买房屋、汽车、度假、餐厅用餐、私立学校学费、房屋装修的人。他们是整个消费可选经济的需求基础。
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.
当这些工人失业，或为了进入可用岗位而接受 50%的减薪时，相对于失业人数，消费冲击是巨大的。白领就业下降 2%转化为可选消费支出下降约 3-4%。与蓝领失业不同（通常立即产生影响：你从工厂被解雇，下周就停止消费），白领失业的影响滞后但更深，因为这些工人有储蓄缓冲，可以在行为转变开始前维持几个月的支出。
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.
到 2027 年第二季度，经济陷入衰退。美国全国经济研究所（NBER）要到几个月后才会正式确定衰退开始日期（他们总是如此），但数据是明确的——我们已经连续两个季度实际 GDP 负增长。但这还不是一场“金融危机”……至少目前还不是。
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.
私人信贷从 2015 年的不到 1 万亿美元增长到 2026 年的超过 2.5 万亿美元。其中相当一部分资本被部署到软件和科技交易中，许多是对 SaaS 公司的杠杆收购，其估值假设了永续的十几%收入增长。
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.
这些假设在第一个智能体式编码演示和 2026 年第一季度软件崩盘之间的某个时刻就死了，但估值标记似乎没有意识到它们已经死了。
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.
当许多上市 SaaS 公司以 5-8 倍 EBITDA 交易时，PE 支持的软件公司资产负债表上的估值标记仍反映着基于收入倍数的收购估值，而这些倍数已不复存在。管理者们逐步下调标记，从 100 美分到 92、85，而公开可比公司显示为 50。
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.
每个人都记得降级后发生了什么。行业资深人士在 2015 年能源降级后就已经看到了这个剧本。
Everyone remembers what happened after the downgrade. Industry veterans had already seen the playbook following the 2015 energy downgrades.
软件支持的贷款在 2027 年第三季度开始违约。PE 投资组合中的信息服务和咨询公司紧随其后。几家知名 SaaS 公司的数十亿美元 LBO 进入了重组。
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.
2022 年，Hellman & Friedman 和 Permira 以 102 亿美元将 Zendesk 私有化。债务包是 50 亿美元的直接贷款，这是当时历史上最大的 ARR 支持融资，由 Blackstone 牵头，Apollo、Blue Owl 和 HPS 都在贷款集团中。该贷款明确基于 Zendesk 的年度经常性收入将保持经常性的假设。以大约 25 倍 EBITDA 计算，只有在这种假设成立时杠杆才有意义。
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.
AI 智能体已经自主处理客户服务近一年了。Zendesk 定义的类别（工单、路由、管理人工支持交互）已经被无需生成工单就能解决问题的系统所取代。贷款所依据的年化经常性收入不再是经常性的，它只是尚未离开的收入。
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.
历史上最大的 ARR 支持贷款变成了历史上最大的私人信贷软件违约。每个信贷台同时问同一个问题：还有谁面临着伪装成周期性的长期逆风？
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?
但共识最初正确的一点是：这应该是可以生存下来的。
But here’s what the consensus got right, at least initially: this should have been survivable.
私人信贷不是 2008 年的银行业。整个架构明确设计为避免强制抛售。这些是封闭式工具，资本被锁定。LP 承诺了七到十年。没有存款人可挤兑，没有回购线可抽回。管理者可以持有受损资产，逐步处理，等待复苏。痛苦但可控。这个系统本应弯曲而非断裂。
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.
Blackstone、KKR 和 Apollo 的高管表示软件敞口占资产的 7-13%。可控。每份卖方报告和金融推特信贷账户都说同样的话：私人信贷拥有永久资本。它们可以吸收那些本会摧毁杠杆银行的损失。
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.
“永久资本。”这个词出现在每份旨在安抚人心的财报电话会议和投资者信中。它成了一句口头禅。像大多数口头禅一样，没人关注细节。以下是它的实际含义……
_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…
在过去十年中，大型另类资产管理公司收购了人寿保险公司并将其转变为融资工具。Apollo 收购了 Athene。Brookfield 收购了 American Equity。KKR 收购了 Global Atlantic。逻辑很优雅：年金存款提供了稳定、长期的负债基础。管理者将这些存款投资于他们发起的私人信贷，并赚取两次费用：在保险端赚取利差，在资产管理端赚取管理费。一个费用叠加费用的永动机，在一个条件下运行完美。
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.
私人信贷必须是“钱好的”。
_The private credit had to be money good._
损失冲击了为持有非流动资产以匹配长期负债而构建的资产负债表。本应使系统具有韧性的“永久资本”并非某种抽象的耐心机构资金和承担复杂风险的复杂投资者。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同一批 PE 支持的软件和技术票据。无法逃离的锁定资本是人寿保险保单持有人的钱，而那里的规则有点不同。
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.
与银行系统相比，保险监管机构一直温顺——甚至自满——但这是警钟。已经对寿险公司私人信贷集中度感到不安的他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，而在已经冻结的市场中，这两者都无法以有吸引力的条件实现。
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.
当穆迪将 Athene 的财务实力评级列入负面观察名单时，Apollo 的股票在两个交易日内下跌了 22%。Brookfield、KKR 和其他公司紧随其后。
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.
从那时起情况变得更加复杂。这些公司不仅创造了他们的保险永动机，还建立了一个精密的离岸架构，旨在通过监管套利最大化回报。美国保险公司签发年金，然后将风险分给其拥有的关联百慕大或开曼再保险公司——这些公司利用更灵活的监管，允许对相同资产持有更少资本。该关联公司通过离岸 SPV 筹集外部资本，这是一层新的交易对手，与保险公司一起投资于由同一母公司资产管理部门发起的私人信贷。
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.
评级机构——其中一些本身由 PE 拥有——并非透明度的典范（这几乎让所有人都不惊讶）。不同公司关联不同资产负债表的蜘蛛网在透明度上令人震惊。当基础贷款违约时，谁真正承担损失的问题在实时中确实无法回答。
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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东西。美联储主席 Kevin Warsh 在 FOMC 紧急 11 月会议上称之为“关于白领生产力增长的关联赌注的连锁反应”。
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.
看，从来不是损失本身导致危机。而是认识到损失。而在金融领域还有另一个更大、更重要的领域，我们对这种认识感到恐惧。
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.
本月，Zillow 房屋价值指数在旧金山同比下降 11%，西雅图下降 9%，奥斯汀下降 8%。这并非唯一令人担忧的头条新闻。上个月，房利美指出来自高额贷款集中邮编区的早期逾期率上升——这些地区居住着信用评分 780 以上的借款人，通常“坚不可摧”。
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.
美国住宅抵押贷款市场约为 13 万亿美元。抵押贷款承销基于一个基本假设：借款人在贷款期间将大致保持当前收入水平。对于大多数抵押贷款，这是三十年。
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”.
白领就业危机以收入预期的持续转变威胁了这一假设。我们现在必须问一个三年前似乎荒谬的问题——优质抵押贷款是“钱好的”吗？
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.
美国历史上每一次抵押贷款危机都由以下三个因素之一驱动：投机过度（如 2008 年贷款给买不起房的人）、利率冲击（如 1980 年代初利率上升使可调利率抵押贷款变得不可负担）、或局部经济冲击（如 1980 年代得克萨斯州石油行业或 2009 年密歇根州汽车行业单一行业在单一地区崩溃）。
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?_
这些都不适用于当前情况。涉及的借款人不是次贷。他们的 FICO 分数是 780。他们支付了 20%的首付。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都视为信用质量基石的借款人。
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).
2008 年，贷款从第一天起就是坏的。2028 年，贷款从第一天起是好的。只是在贷款发放后世界……改变了。人们借入了一个他们再也负担不起相信的未来。
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.
2027 年，我们标记了隐形压力的早期迹象：HELOC 提款、401(k)取款和信用卡债务激增，而抵押贷款支付仍保持正常。随着失业、招聘冻结和奖金削减，这些优质家庭看到他们的债务收入比翻倍。
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.
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、耗尽储蓄和推迟任何房屋维护或改善。他们在技术上仍处于抵押贷款正常状态，但距离困境仅差一次冲击，而 AI 能力的轨迹表明那次冲击即将到来。然后我们看到旧金山、西雅图、曼哈顿和奥斯汀的逾期率开始飙升，即使全国平均水平仍处于历史正常范围内。
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我们现在处于最严重的阶段。当边际买家健康时，房价下跌是可控的。但在这里，边际买家正面临同样的收入受损。
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.
尽管担忧在积累，我们尚未陷入全面的抵押贷款危机。逾期率有所上升，但仍远低于 2008 年水平。真正的威胁在于轨迹。
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.
智能替代螺旋现在有两个金融加速器加剧实体经济的衰退。
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.
劳动力替代、抵押贷款担忧、私人市场动荡。每一个都强化另一个。传统的政策工具包（降息、量化宽松）可以解决金融引擎，但无法解决实体经济引擎，因为实体经济引擎并非由紧缩的金融条件驱动。它是由 AI 使人类智能变得不那么稀缺和更有价值驱动的。你可以将利率降至零，购买所有 MBS 和所有违约的软件 LBO 债务……
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.
但这不会改变一个事实：一个 Claude 智能体可以以每月 200 美元的成本完成年薪 18 万美元的产品经理的工作。
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如果这些担忧成为现实，抵押贷款市场将在今年下半年破裂。在这种情况下，我们预计当前股市的回撤最终将与全球金融危机相媲美（峰值到谷底 57%）。这将使标普 500 指数跌至约 3500 点——自 2022 年 11 月 ChatGPT 时刻前一个月以来未见过的水平。
The Intelligence Displacement Spiral now has two financial accelerants to the real economy’s decline.
明确的是，支撑 13 万亿美元住宅抵押贷款的收入假设在结构上受损。不确定的是，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，但我们不能否认不乐观的理由。
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…
It won’t change the fact that a Claude agent can do the work of a $180,000 product manager for $200/month.
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.
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.
第一个负反馈循环出现在实体经济中：AI 能力提升，工资单缩水，消费疲软，利润空间收窄，企业购买更多能力，能力进一步提升。随后转向金融领域：收入受损冲击抵押贷款，银行亏损收紧信贷，财富效应破裂，反馈循环加速。而这两者都因政府政策回应不足而加剧——坦率地说，政府似乎不知所措。
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.
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这个系统并非为应对这样的危机而设计。联邦政府的收入基础本质上是对人类时间征税。人们工作，公司支付报酬，政府从中抽取一部分。个人所得税和工资税是正常年份财政收入的主干。
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.
今年第一季度，联邦财政收入比国会预算办公室基线预测低 12%。工资税收入下降，因为就业人数减少且薪酬水平不如从前。所得税收入下降，因为所赚取的收入在结构上更低。生产率飙升，但收益流向了资本和算力，而非劳动力。
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.
劳动收入占 GDP 的比重从 1974 年的 64%下降到 2024 年的 56%，这是由全球化、自动化和工人议价能力持续削弱驱动的四十年下行趋势。在 AI 开始指数级改进的四年里，这一比例降至 46%。这是有记录以来最急剧的下降。
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.
产出仍然存在。但它不再通过家庭回流到企业，这意味着它也不再通过国税局流转。循环流动正在断裂，而政府被期望介入修复这一问题。
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.
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与每次经济衰退一样，支出增加的同时收入减少。但这次的不同之处在于，支出压力并非周期性。自动稳定器是为暂时性失业设计的，而非结构性替代。该系统支付的福利假设工人将被重新吸纳。但许多人不会，至少不会以接近之前工资的水平。在新冠疫情期间，政府欣然接受 15%的赤字，但人们理解这是暂时的。如今需要政府支持的人并非遭受了一场他们会恢复的疫情。他们被一种持续改进的技术所取代。
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.
政府需要在从家庭征收更少税收的同一时刻，向家庭转移更多资金。
The government needs to transfer more money to households at precisely the moment it is collecting less money from them in taxes.
美国不会违约。它印刷自己支出的货币，也是用于偿还借款人的货币。但这种压力已在其他地方显现。市政债券在年初至今的表现中显示出令人担忧的分化迹象。没有所得税的州情况尚可，但依赖所得税的州（多数为蓝州）发行的一般责任市政债券开始计入一些违约风险。政客们迅速察觉，关于谁应被救助的争论已沿党派路线展开。
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.
值得肯定的是，政府早期就认识到危机的结构性，并开始考虑两党提案，即所谓的“转型经济法案”：一个通过赤字支出和拟议的 AI 推理算力税相结合来资助的直接转移支付框架，用于补偿失业工人。
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.
讨论中最激进的提案更进一步。“共享 AI 繁荣法案”将建立对智能基础设施本身回报的公共索取权，介于主权财富基金和 AI 生成产出版税之间，其股息用于资助家庭转移支付。私营部门游说者用关于滑坡谬误的警告淹没了媒体。
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.
讨论背后的政治格局令人沮丧地可预测，并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，并警告对算力征税会将领先地位拱手让给中国。左翼警告说，在现有企业帮助下起草的税收不过是另一种形式的监管俘获。财政鹰派指出不可持续的赤字。鸽派则以后金融危机时代过早实施的紧缩政策作为警示。在今年的总统选举前夕，分歧只会进一步放大。
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.
在政客们争吵不休的同时，社会结构正以比立法进程更快的速度瓦解。
While the politicians bicker, the social fabric is fraying faster than the legislative process can move.
“占领硅谷”运动体现了广泛的不满情绪。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。他们的人数在增长，示威活动吸引了比引发它们的失业数据更多的媒体报道。
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.
很难想象公众会像在后金融危机时代憎恨银行家那样憎恨任何人，但 AI 实验室正在迎头赶上。而且，从大众的角度看，这有充分理由。它们的创始人和早期投资者积累财富的速度让镀金时代相形见绌。生产率繁荣的收益几乎全部归于算力所有者和运行算力的实验室股东，这已将美国的不平等放大到前所未有的水平。
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.
每一方都有自己的反派，但真正的反派是时间。
Every side has their own villain, but the real villain is time.
AI 能力进化的速度超过了制度适应的速度。政策回应以意识形态的步伐前进，而非现实。如果政府不能尽快就问题所在达成一致，反馈循环将替他们书写下一章。
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.
在整个现代经济史中，人类智能一直是稀缺的投入要素。资本是充裕的（至少是可复制的）。自然资源是有限的但可替代的。技术进步缓慢到人类能够适应。智能——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。
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.
人类智能因其稀缺性而获得固有的溢价。我们经济中的每一个制度，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。
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.
我们现在正在经历这种溢价的消退。机器智能现在已成为人类智能在越来越多任务上的合格且快速改进的替代品。金融体系经过数十年的优化，适应了人类思维稀缺的世界，正在重新定价。这种重新定价是痛苦的、无序的，且远未完成。
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.
但重新定价不等于崩溃。
But repricing is not the same as collapse.
经济可以找到新的均衡。达到这一均衡是仅剩的只有人类才能完成的任务之一。我们需要正确地做到这一点。
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.
这是历史上第一次，经济中最高效的资产产生的就业岗位减少而非增加。没有人的框架适用，因为没有一个框架是为稀缺投入变得充裕的世界而设计的。因此我们必须创造新的框架。我们能否及时构建它们，是唯一重要的问题。
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.
但你不是在 2028 年 6 月读这篇文章。你是在 2026 年 2 月读它。
_But you’re not reading this in June 2028. You’re reading it in February 2026._
标普指数接近历史高点。负反馈循环尚未开始。我们确信其中一些情景不会发生。我们同样确信机器智能将继续加速。人类智能的溢价将会收窄。
_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._
作为投资者，我们仍有时间评估我们的投资组合中有多少是建立在无法持续到本十年末的假设之上的。作为一个社会，我们仍有时间采取主动。
_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._
致谢：感谢 Hunterbrook 的 Sam Koppelman 帮助校对。我们的合著者，LOTUS 的 Alap Shah，为本文贡献了想法——Citrini Research 撰写了这一部分，但他还撰写了系列文章“智能爆炸”中的另外两部分，我们强烈推荐阅读。您可以在此处找到。
_Acknowledgements: Thanks to Sam Koppelman of_Hunterbrook_for his help with proofreading. Our co-author, Alap Shah of LOTUS, contributed the idea for this piece - CitriniResearch wrote this part, but he has written two other parts in a series called the Intelligence Explosion, we highly recommend reading it. You can find it here._
Citrini Research 是一个读者支持的出版物。要接收新文章并支持我的工作，请考虑成为免费或付费订阅者。
Citrini Research is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
Alap Shah 的客座文章，乐观的 AI 现实主义者。公开市场投资者和商业建设者。LOTUS Investment Management、Littlebird.ai、Thistle、Sentieo 的联合创始人。订阅 Alap
[](https://substack.com/profile/4042478-double-down-capital)[](https://substack.com/profile/5895295-panos-papadopoulos)[](https://substack.com/profile/2086489-ben-brostoff)[](https://substack.com/profile/6453085-alexander-green)[](https://substack.com/profile/1375117-saurabh-singh)
[](https://substack.com/@alapshah1?utm_source=byline)A guest post by Alap Shah Optimistic AI Realist. Public mkts investor and biz builder. Cofounder: LOTUS Investment Management, Littlebird.ai, Thistle, SentieoSubscribe to Alap
[](https://substack.com/profile/4716836-brant-hammer?utm_source=comment)
发人深省，但令人无比沮丧。
Thought provoking but infinitely depressing.
少数几个没人征求过意见的人，正在引领并鼓吹建造一种没人要求、甚至没人需要的技术。结果呢？少数人获得无限财富，而成百上千万人辛苦建立的生活在他们眼前化为乌有。
A few people no one asked anything of are leading and cheerleading the building of a technology that no one asked for and no one even needed. The result? A few gain infinite wealth while the lives hundreds of millions worked hard to build evaporate before their eyes.
净收益？绝无可能。一个失业率高达 20-30%的社会是糟糕的居住地。一个人们靠转移支付整天无所事事、没有目标的社会，则更糟。
Net benefit? Not a chance. A society with 20-30% unemployment is a terrible place to live. A society where people are paid transfers to sit around all day with free time and no purpose, even worse.
人类需要 AI，这完全没有逻辑解释或理由。总有一天，大多数人会意识到这一点。他们的反应不会好。内乱很可能会升级为热战。
There was zero logical explanation or reason why humanity needed AI. At some point the majority will realize that. They're not going to respond well. The civil unrest will likely turn hot.
如果 AI 像核武器一样被对待，实行不扩散，世界会美好得多。这永远不会发生，但人可以梦想。
The world would be a much better place if AI was treated the same as nuclear weapons, non-proliferation. It will never happen, but one can dream.
这是我读过的最发人深省的文章之一。干得好，伙计们！
[](https://substack.com/profile/22465991-jarek-lewis?utm_source=comment)
This is one of the most thought provoking pieces I have ever read. Great work guys!