THE 2028 GLOBAL INTELLIGENCE CRISIS | 2028 年全球情报危机
📰 原文及翻译
点击展开原文及中文翻译
2028 年全球情报危机
A Thought Exercise in Financial History, from the Future来自未来的金融史思想实验
Preface 前言
What if our AI bullishness continues to be right…and what if that’s actually bearish?
如果我们的 AI 乐观论持续正确……而实际上这恰恰是利空信号呢？
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 here.
以下内容仅为情境推演，并非预测。这并非看空者的臆想或 AI 末日论的同人创作。本文唯一目的在于构建一个相对未被充分探讨的情境模型。我们的朋友阿拉普·沙阿提出了这个问题，我们共同构思了答案。我们撰写了这部分内容，而他撰写的另外两部分内容可在此处查阅。
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对潜在的左尾风险有所准备，因为人工智能正使经济变得越来越诡异。
This is the CitriniResearch Macro Memo from June 2028, detailing the progression and fallout of the Global Intelligence Crisis.
这是 2028 年 6 月的 CitriniResearch 宏观备忘录，详细阐述了全球智能危机的进展与后果。
Macro Memo 宏观备忘录
The Consequences of Abundant Intelligence智能泛滥的后果
CitriniResearch 西特里尼研究
February 22nd, 2026 June 30th, 2028
2026 年 2 月 22 日 2028 年 6 月 30 日
The unemployment rate printed 10.2% this morning, a 0.3% upside surprise. The market sold off 2% on the number, bringing the cumulative drawdown in the S&P to 38% from its October 2026 highs.
今晨公布的失业率达到 10.2%，较预期高出 0.3%。市场受此数据影响下跌 2%，使得标普 500 指数自 2026 年 10 月高点以来的累计跌幅扩大至 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，这样的行情本应触发熔断机制。
***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。从”可控”和”特定行业”到经济面目全非，仅仅用了这么短的时间。本季度的宏观备忘录旨在重构事件发展脉络——对危机前经济进行复盘剖析。
The euphoria was palpable. By October 2026, the S&P 500 flirted with 8000, the Nasdaq broke above 30k. The initial wave of layoffs due to human obsolescence began in early 2026, and they did exactly what layoffs are supposed to. Margins expanded, earnings beat, stocks rallied. Record-setting corporate profits were funneled right back into AI compute.
狂热情绪触手可及。截至 2026 年 10 月，标普 500 指数逼近 8000 点，纳斯达克突破 3 万点大关。因人力淘汰引发的首轮裁员潮始于 2026 年初，裁员效果立竿见影：利润率扩张、盈利超预期、股价飙升。创纪录的企业利润被悉数投入人工智能算力建设。
The headline numbers were still great. Nominal GDP repeatedly printed mid-to-high single-digit annualized growth. Productivity was booming. Real output per hour rose at rates not seen since the 1950s, driven by AI agents that don’t sleep, take sick days or require health insurance.
表面数据依然亮眼。名义 GDP 年化增长率屡次达到中高个位数。生产力蓬勃发展。在不知疲倦、无需病假、不要求医疗保险的人工智能代理驱动下，每小时实际产出增速创下 1950 年代以来新高。
The owners of compute saw their wealth explode as labor costs vanished. Meanwhile, real wage growth collapsed. Despite the administration’s repeated boasts of record productivity, white-collar workers lost jobs to machines and were forced into lower-paying roles.
随着劳动力成本消失，算力拥有者的财富呈爆炸式增长。与此同时，实际工资增长却陷入停滞。尽管政府反复吹嘘创纪录的生产力，但白领工作者仍被机器取代，被迫转向薪酬更低的岗位。
When cracks began appearing in the consumer economy, economic pundits popularized the phrase “ Ghost GDP “: output that shows up in the national accounts but never circulates through the real economy.
当消费经济开始出现裂痕时，经济专家们广泛传播了”幽灵 GDP”这一概念：即出现在国家账户中却从未在实体经济中流通的产出。
In every way AI was exceeding expectations, and the market was AI. The only problem… the economy was not.
人工智能在各个方面都超出了预期，市场也完全被人工智能主导。唯一的问题是……经济却并非如此。
It should have been clear all along that a single GPU cluster in North Dakota generating the output previously attributed to 10,000 white-collar workers in midtown Manhattan is more economic pandemic than economic panacea. The velocity of money flatlined. The human-centric consumer economy, 70% of GDP at the time, withered. We probably could have figured this out sooner if we just asked how much money machines spend on discretionary goods. (Hint: it’s zero.)
我们本该早就明白，北达科他州的一个 GPU 集群就能产生相当于曼哈顿中城一万名白领员工的产出，这与其说是经济良药，不如说是一场经济瘟疫。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%——逐渐萎缩。如果我们早点问问机器在非必需品上花了多少钱，或许能更早意识到这个问题。（提示：答案是零。）
AI capabilities improved, companies needed fewer workers, white collar layoffs increased, displaced workers spent less, margin pressure pushed firms to invest more in AI, AI capabilities improved…
人工智能能力提升，企业所需员工减少，白领裁员增加，失业工人消费缩减，利润压力促使企业加大对人工智能的投资，人工智能能力进一步提升……
It was a negative feedback loop with no natural brake. The human intelligence displacement spiral. White-collar workers saw their earnings power (and, rationally, their spending) structurally impaired. Their incomes were the bedrock of the $13 trillion mortgage market - forcing underwriters to reassess whether prime mortgages are still money good.
这是一个没有天然刹车的恶性循环。人类智能替代螺旋。白领阶层的收入能力（以及理性而言的消费能力）遭受结构性削弱。他们的收入曾是 13 万亿美元抵押贷款市场的基石——这迫使承销商重新评估优质抵押贷款是否依然可靠。
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.
十七年来未曾经历真正的违约周期，导致私募基金因依赖 ARR 持续性的软件交易而过度膨胀。2027 年中期由人工智能颠覆引发的首波违约潮，彻底动摇了这一假设。
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.
如果混乱仅限于软件层面，本可应对，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。大批依靠人类摩擦获利的公司土崩瓦解。
The system turned out to be one long daisy chain of correlated bets on white-collar productivity growth. The November 2027 crash only served to accelerate all of the negative feedback loops already in place.
该系统最终演变成一条围绕白领生产力增长进行关联押注的漫长链条。2027 年 11 月的崩盘只是加速了所有既有的负面反馈循环。
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.
我们等待”坏消息就是好消息”的局面已近一年。政府开始考虑各项提案，但公众对政府实施任何形式救助能力的信心正在减弱。政策应对总是滞后于经济现实，但缺乏全面规划如今正加速通缩螺旋的风险。
How It Started 这一切是如何开始的
In late 2025, agentic coding tools took a step function jump in capability.
2025 年末，智能编码工具的能力实现了阶跃式提升。
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?”
一位熟练的开发者借助 Claude Code 或 Codex，如今能在数周内复现一款中型市场 SaaS 产品的核心功能。虽非完美无缺，也未必能处理所有边缘情况，但已足够让那些审阅着 50 万美元年度续约合同的 CIO 们开始思考：”如果我们自己动手开发会怎样？”
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.
由于财年大多与日历年重合，2026 年的企业支出早在 2025 年第四季度就已确定，彼时”智能体 AI”还只是个流行词。而年中评审则是采购团队首次在清晰了解这些系统实际能力后做出决策。有人目睹自家内部团队仅用数周就搭建出原型，复现了价值六位数的 SaaS 合约功能。
That summer, we spoke with a procurement manager at a Fortune 500. He told us about one of his budget negotiations. The salesperson had expected to run the same playbook as last year: a 5% annual price increase, the standard “your team depends on us” pitch. The procurement manager told him he’d been in conversations with OpenAI about having their “forward deployed engineers” use AI tools to replace the vendor entirely. They renewed at a 30% discount. That was a good outcome, he said. The “long-tail of SaaS”, like Monday.com, Zapier and Asana, had it much worse.
那年夏天，我们与一位财富 500 强企业的采购经理交谈。他向我们讲述了一次预算谈判的经历。销售人员本以为会沿用去年的套路：每年 5%的价格上涨，加上那句标准的”您的团队离不开我们”的说辞。但这位采购经理告诉对方，自己已经与 OpenAI 讨论过，让他们的”前线部署工程师”使用 AI 工具来完全替代这家供应商。最终，他们以七折的价格续约。他说这已经是个不错的结果了。像 Monday.com、Zapier 和 Asana 这样的”SaaS 长尾”企业，处境要糟糕得多。
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.
投资者们早有准备——甚至可以说是满怀期待——认为长尾市场将遭受重创。它们或许占据了典型企业技术栈支出的三分之一，但其脆弱性显而易见。然而，记录系统本应免受冲击。
It wasn’t until ServiceNow’s Q3 26 report that the mechanism of reflexivity became clearer.
直到 ServiceNow 的 2026 年第三季度报告发布，反身性机制才变得更加清晰。
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 净新增年度合同价值增速从 23%放缓至 14%；宣布裁员 15%并启动”结构效率计划”；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-house was an option, and that factored into pricing negotiations. Perhaps more importantly, the competitive landscape had changed. AI had made it easier to develop and ship new features, so differentiation collapsed. Incumbents were in a race to the bottom on pricing - a knife-fight with both each other and with the new crop of upstart challengers that popped up. Emboldened by the leap in agentic coding capabilities and with no legacy cost structure to protect, these aggressively took share.
SaaS 并未“消亡”。自建系统在运营与维护方面仍存在成本效益分析的空间。但自建已成为一种可行选项，这直接影响了定价谈判的格局。或许更重要的是，竞争环境已然改变。人工智能大幅降低了新功能的开发与交付门槛，导致产品差异化优势瓦解。现有企业陷入价格战的泥潭——既要与同行厮杀，又要应对如雨后春笋般涌现的新兴挑战者。这些挑战者凭借智能编码能力的飞跃式进步而底气十足，又无需背负传统成本结构的包袱，正以激进姿态抢占市场份额。
The interconnected nature of these systems weren’t fully appreciated until this print, either. ServiceNow sold seats. When Fortune 500 clients cut 15% of their workforce, they cancelled 15% of their licenses. The same AI-driven headcount reductions that were boosting margins at their customers were mechanically destroying their own revenue base.
这些系统之间的相互关联性，直到这份报告发布才被充分认识到。ServiceNow 销售的是席位许可。当《财富》500 强客户裁减 15%的员工时，他们就会取消 15%的许可证。同样的 AI 驱动裁员在提升客户利润率的同时，也在机械地摧毁他们自身的收入基础。
The company that sold workflow automation was being disrupted by better workflow automation, and its response was to cut headcount and use the savings to fund the very technology disrupting it.
这家销售工作流自动化的公司正被更优秀的工作流自动化技术颠覆，其应对之策竟是裁员，并将节省的资金投入颠覆自身的技术研发。
What else were they supposed to do? Sit still and die slower? The companies most threatened by AI became AI’s most aggressive adopters.
他们还能怎么做？坐以待毙吗？受人工智能威胁最大的公司反而成了最积极拥抱这项技术的人。
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.
事后看来这似乎显而易见，但当时确实并非如此（至少对我而言）。历史颠覆模型指出，现有企业会抵制新技术，它们逐渐被灵活的后来者抢占市场份额并缓慢消亡。柯达、百视达、黑莓的遭遇正是如此。但 2026 年发生的情况截然不同：传统企业没有抵抗，因为它们根本无力抵抗。
With stocks down 40-60% and boards demanding answers, the AI-threatened companies did the only thing they could. Cut headcount, redeploy the savings into AI tools, use those tools to maintain output with lower costs.
随着股价暴跌 40%至 60%，董事会要求给出解释，受人工智能威胁的公司采取了唯一可行的措施：削减员工人数，将节省的资金重新投入到人工智能工具中，利用这些工具以更低的成本维持产出。
Each company’s individual response was rational. The collective result was catastrophic. Every dollar saved on headcount flowed into AI capability that made the next round of job cuts possible.
每家公司的个体反应都合乎理性。集体结果却酿成灾难。每一分从人力成本中节省的资金都流向了人工智能能力建设，使得下一轮裁员成为可能。
S oftware 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.
软件只是序曲。当投资者争论 SaaS 估值是否已触底时，他们忽略了一个关键事实：这种自我强化的循环早已突破软件行业的边界。那些支撑 ServiceNow 裁员的逻辑，同样适用于所有拥有白领成本结构的公司。
When Friction Went to Zero当摩擦力归零时
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.
到 2027 年初，LLM 的使用已成为默认配置。人们在使用 AI 智能体时，甚至不知道什么是 AI 智能体，就像从未了解过”云计算”为何物的人照样使用流媒体服务一样。他们将其视为与自动补全或拼写检查相同的存在——不过是手机如今自带的功能罢了。
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.
Qwen 的开源智能购物代理成为 AI 处理消费决策的催化剂。短短数周内，所有主流 AI 助手都集成了某种智能商务功能。经过蒸馏优化的模型意味着这些代理能在手机和笔记本电脑上运行，不再局限于云端实例，从而大幅降低了推理的边际成本。
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.
真正让投资者本应感到不安的是，这些智能体无需等待指令便会自主行动。它们根据用户偏好在后台持续运行，商业活动不再是一系列离散的人为决策，而是转变为代表每位联网消费者全天候运转的持续优化进程。截至 2027 年 3 月，美国人均每日消耗的智能交互量已达 40 万单位——这个数字自 2026 年底以来增长了十倍。
The next link in the chain was already breaking.
链条的下一个环节已经开始断裂。
Intermediation.中介化。
Over the past fifty years, the U.S. economy built a giant rent-extraction layer on top of human limitations: things take time, patience runs out, brand familiarity substitutes for diligence, and most people are willing to accept a bad price to avoid more clicks. Trillions of dollars of enterprise value depended on those constraints persisting.
过去五十年间，美国经济在人类局限性之上构建了庞大的租金榨取层：事务处理需要时间、耐心终会耗尽、品牌熟悉度取代勤勉付出，而多数人宁愿接受不合理价格也不愿多点击几次鼠标。数万亿美元的企业价值都建立在这些持续存在的约束条件之上。
It started out simple enough. Agents removed friction.
起初一切都很简单。特工们负责消除摩擦。
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.
那些数月未用却仍在被动续订的订阅与会员服务。试用期结束后悄然翻倍的入门价格。每一项都被重新包装成特工可以谈判的人质危机。整个订阅经济赖以维系的指标——客户终身平均价值——显著下降。
Consumer agents began to change how nearly all consumer transactions worked.
消费者代理开始改变几乎所有消费交易的方式。
Humans don’t really have the time to price-match across five competing platforms before buying a box of protein bars. Machines do.
人类实在没时间在购买一盒蛋白棒前，还得在五个竞争平台之间比价。但机器可以。
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.
旅游预订平台是最早的牺牲品，因为它们最简单。到 2026 年第四季度，我们的智能代理已能比任何平台更快、更经济地组合出完整行程方案（涵盖航班、酒店、地面交通、会员积分优化、预算限制及退款条款）。
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.
保险续保模式原本依赖投保人的惰性，现已进行改革。每年重新比价投保的代理机构，瓦解了保险公司从被动续保中获得的 15-20%保费收入。
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.
财务咨询。税务准备。常规法律工作。任何服务提供商的最终价值主张是“我将处理您觉得繁琐的复杂性”的领域都受到了冲击，因为智能体们并不觉得任何事务繁琐。
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.
就连那些我们曾以为因人际关系价值而免受冲击的领域，也显露出脆弱性。房地产行业数十年来因信息不对称而维持着 5-6%的佣金体系，但当配备 MLS 访问权限及数十年交易数据的人工智能代理能瞬间复刻专业知识库时，整个体系便土崩瓦解。2027 年 3 月一篇卖方报告将其称为”同行相残”。主要都市圈的买方佣金中位数已从 2.5-3%压缩至不足 1%，且越来越多交易在完全没有人类买方代理参与的情况下完成。
We had overestimated the value of “human relationships”. Turns out that a lot of what people called relationships was simply friction with a friendly face.
我们高估了“人际关系”的价值。原来，人们口中的许多关系，不过是戴着友好面具的摩擦。
That was just the start of the disruption for the intermediation layer. Successful companies had spent billions to effectively exploit quirks of consumer behavior and human psychology that didn’t matter anymore.
这仅仅是中介层颠覆的开端。那些曾斥资数十亿美元成功利用消费者行为与人类心理特质的公司，如今这些特质已不再重要。
Machines optimizing for price and fit do not care about your favorite app or the websites you’ve been habitually opening for the last four years, nor feel the pull of a well-designed checkout experience. They don’t get tired and accept the easiest option or default to “I always just order from here”.
专注于价格与匹配度的机器不会在意你钟爱的应用程序，也不会关心你过去四年习惯性打开的网站，更不会感受到精心设计的结算体验带来的吸引力。它们不会因疲惫而选择最简便的选项，也不会默认”我总是从这里订购”。
That destroyed a particular kind of moat: habitual intermediation.
这摧毁了一种特定的护城河：习惯性中介。
DoorDash (DASH US) was the poster child.
DoorDash（DASH US）曾是行业典范。
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.
编码智能体彻底打破了开发配送应用的门槛。一名合格的开发者能在几周内部署出功能完备的竞争对手产品，而数十家平台确实这样做了——它们将 90-95%的配送费直接转给司机，从而从 DoorDash 和 Uber Eats 手中抢走司机资源。多平台接单系统让零工劳动者能同时追踪二三十个平台的订单，彻底瓦解了行业巨头赖以生存的用户锁定效应。市场一夜之间分崩离析，利润率被压缩至近乎归零。
Agents accelerated both sides of the destruction. They enabled the competitors and then they used them. The DoorDash moat was literally “you’re hungry, you’re lazy, this is the app on your home screen.” An agent doesn’t have a home screen. It checks DoorDash, Uber Eats, the restaurant’s own site, and twenty new vibe-coded alternatives so it can pick the lowest fee and fastest delivery every time.
智能体加速了双方的毁灭进程。它们先赋能竞争者，随后又利用这些竞争者。DoorDash 的护城河曾是”你饿了、你懒了，这是你手机主屏上的应用”。而智能体没有主屏幕概念。它会同时查询 DoorDash、Uber Eats、餐厅官网以及二十个新兴的氛围编码替代平台，从而每次都能选择最低费用和最快配送。
Habitual app loyalty, the entire basis of the business model, simply didn’t exist for a machine.
用户对应用程序的习惯性忠诚——这一商业模式的全部基础——对机器而言根本不存在。
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.
这颇具诗意，或许是整个事件中唯一一次特工们为即将失业的白领们行方便的例子。当他们最终成为外卖骑手时，至少一半的收入不必交给优步和 DoorDash。当然，随着自动驾驶汽车的普及，科技带来的这份便利并未持续太久。
Once agents controlled the transaction, they went looking for bigger paperclips.
一旦代理人控制了交易，他们便开始寻找更大的回形针。
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.
价格匹配和聚合能做的只有这么多。要持续为用户省钱（尤其是在智能体开始相互交易时），最有效的方式就是消除手续费。在机器对机器的商务中，2-3%的信用卡交换费率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.
特工们开始寻找比信用卡更快更便宜的支付方式。大多数人选择通过 Solana 或以太坊二层网络使用稳定币，这些渠道的结算几乎是即时的，交易成本只需几分钱。
MASTERCARD Q1 2027: NET REVENUES +6% Y/Y; PURCHASE VOLUME GROWTH SLOWS TO +3.4% Y/Y FROM +5.9% PRIOR QUARTER; MANAGEMENT NOTES “AGENT-LED PRICE OPTIMIZATION” AND “PRESSURE IN DISCRETIONARY CATEGORIES” | Bloomberg, April 29 2027
万事达卡 2027 年第一季度：净营收同比增长 6%；消费额增速放缓至同比+3.4%，较上一季度+5.9%有所下降；管理层提及”代理主导的价格优化”及”可选消费品类承压” | 彭博社，2027 年 4 月 29 日
Mastercard’s Q1 2027 report was the point of no return. Agentic commerce went from being a product story to a plumbing story. MA dropped 9% the following day. Visa did too, but pared losses after analysts pointed out its stronger positioning in stablecoin infrastructure.
万事达卡 2027 年第一季度财报成为转折点。智能代理商务从产品叙事转向基础设施叙事。次日万事达股价下跌 9%。维萨同样下挫，但在分析师指出其稳定币基础设施布局更具优势后收复部分失地。
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.
围绕交换费展开的代理式商务路由对以信用卡为核心的银行和单一业务发卡机构构成了更为严峻的风险——这些机构收取着 2-3%手续费中的绝大部分收益，并依托商户补贴资助的奖励计划构建了完整的业务板块。
American Express (AXP US) was hit hardest; a combined headwind from white-collar workforce reductions gutting its customer base and agents routing around interchange gutting its revenue model. Synchrony (SYF US), Capital One (COF US) and Discover (DFS US) all fell more than 10% over the following weeks, as well.
美国运通(AXP US)遭受的打击最为严重；白领裁员削减其客户群与代理商绕过交换费削弱其收入模式的双重逆风同时袭来。同步金融(SYF US)、第一资本(COF US)和发现金融(DFS US)在随后几周内也均下跌超过 10%。
Their moats were made of friction. And friction was going to zero.
他们的护城河由摩擦构筑。而摩擦正趋于归零。
From Sector Risk to Systemic Risk从行业风险到系统性风险
Through 2026, markets treated negative AI impact as a sector story. Software and consulting were getting crushed, payments and other toll booths were wobbly, but the broader economy seemed fine. The labor market, while softening, was not in freefall. The consensus view was that creative destruction was part of any technological innovation cycle. It would be painful in pockets, but the overall net positives from AI would outweigh any negatives.
截至 2026 年，市场将人工智能的负面影响视为行业性问题。软件和咨询行业遭受重创，支付及其他收费业务出现波动，但整体经济似乎保持稳定。劳动力市场虽有所疲软，却未陷入崩溃。普遍观点认为，创造性破坏是任何技术创新周期的组成部分。局部阵痛在所难免，但人工智能带来的整体净收益终将超越任何负面影响。
Our January 2027 macro memo argued this was the wrong mental model. The US economy is a white-collar services economy. White-collar workers represented 50% of employment and drove roughly 75% of discretionary consumer spending. The businesses and jobs that AI was chewing up were not tangential to the US economy, they were the US economy.
我们 2027 年 1 月的宏观备忘录曾指出，这种思维模式存在谬误。美国经济本质上是白领服务业经济。白领工作者占据就业人口的 50%，并贡献了约 75%的可自由支配消费支出。人工智能正在侵蚀的企业与就业岗位并非美国经济的边缘部分，它们恰恰构成了美国经济的核心命脉。
“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.
“技术创新摧毁就业岗位，随后创造更多就业机会。”这是当时最流行且最具说服力的反驳论点。它之所以流行且令人信服，是因为这一观点在过去两个世纪里始终正确。即便我们无法构想未来的工作岗位将是什么模样，它们终将到来。
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.
自动取款机降低了银行网点的运营成本，促使银行开设更多分支机构，此后二十年柜员就业人数持续增长。互联网冲击了旅行社、黄页目录和实体零售业，却催生出全新的行业领域，创造了前所未有的就业机会。
Every new job, however, required a human to perform it.
然而，每份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.
人工智能如今已成为一种通用智能，它在人类本可转岗的任务上持续精进。被取代的程序员无法简单转向“人工智能管理”，因为人工智能本身已能胜任此类工作。
Today, AI agents handle many-weeks-long research and development tasks. The exponential steamrolled our conceptions of what was possible, even though every year Wharton professors tried to fit the data to a new sigmoid.
如今，人工智能代理已能处理长达数周的研究与开发任务。指数级增长彻底颠覆了我们对可能性的认知，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 型曲线上。
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.
它们编写了几乎所有的代码。其中表现最出色的在几乎所有方面都远超绝大多数人类。而且它们的成本还在持续降低。
AI has created new jobs. Prompt engineers. AI safety researchers. Infrastructure technicians. Humans are still in the loop, coordinating at the highest level or directing for taste. For every new role AI created, though, it rendered dozens obsolete. The new roles paid a fraction of what the old ones did.
人工智能创造了新的工作岗位。提示工程师。人工智能安全研究员。基础设施技术员。人类仍在循环中，在最高层面进行协调或根据品味进行指导。然而，人工智能每创造一个新角色，就会让数十个旧角色过时。新角色的薪酬仅为旧角色的一小部分。
U.S. JOLTS: JOB OPENINGS FALL BELOW 5.5M; UNEMPLOYED-TO-OPENINGS RATIO CLIMBS TO ~1.7, HIGHEST SINCE AUG 2020 | Bloomberg, Oct 2026
美国职位空缺和劳动力流动调查：职位空缺数降至 550 万以下；失业人数与职位空缺比率升至约 1.7，创 2020 年 8 月以来新高 | 彭博社，2026 年 10 月
The hiring rate had been anemic all year, but October ‘26 JOLTS print provided some definitive data. Job openings fell below 5.5 million, a 15% decline YoY.
全年招聘率持续低迷，但 2026 年 10 月的职位空缺和劳动力流动调查数据提供了明确佐证：职位空缺数跌破 550 万大关，较去年同期下降 15%。
INDEED: POSTINGS FALL SHARPLY IN SOFTWARE, FINANCE, CONSULTING AS “PRODUCTIVITY INITIATIVES” SPREAD | Indeed Hiring Lab, Nov–Dec 2026
确实：随着“生产力计划”推广，软件、金融、咨询行业招聘岗位数量急剧下降 | Indeed 招聘实验室，2026 年 11 月–12 月
White-collar openings were collapsing while blue-collar openings remained relatively stable (construction, healthcare, trades). The churn was in the jobs that write memos (we are, somehow, still in business), approve budgets, and keep the middle layers of the economy lubricated. Real wage growth in both cohorts, however, had been negative for the majority of the year and kept declining.
白领岗位急剧减少，而蓝领岗位（建筑、医疗、技工等）则相对稳定。这场动荡主要冲击的是那些撰写备忘录（不知为何，我们仍在运营）、审批预算、维持经济中层运转的职位。然而，这两类岗位的实际工资增长在全年大部分时间里均为负值，且持续下滑。
The equity market still cared less about JOLTS than it did the news that all of GE Vernova’s turbine capacity was now sold out until 2040, it ambled sideways in a tug of war between negative macro news with positive AI infrastructure headlines.
股市对 JOLTS 数据的关注度，仍不及通用电气维尔诺瓦公司涡轮机产能已售罄至 2040 年的消息；在负面宏观新闻与积极人工智能基础设施头条的拉锯战中，市场呈现横盘震荡。
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（总是比股市更聪明，或者至少不那么浪漫）开始为消费冲击定价。在接下来的四个月里，10 年期收益率从 4.3%开始下降至 3.2%。尽管如此，总体失业率并未大幅飙升，其构成细节仍被一些人忽视。
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.
在正常的经济衰退中，原因最终会自我修正。过度建设导致建筑放缓，进而降低利率，从而引发新的建设。库存过剩导致去库存，进而引发补库存。这种周期性机制本身就蕴含着复苏的种子。
This cycle’s cause was not cyclical.
本轮危机的根源并非周期性因素。
AI got better and cheaper. Companies laid off workers, then used the savings to buy more AI capability, which let them lay off more workers. Displaced workers spent less. Companies that sell things to consumers sold fewer of them, weakened, and invested more in AI to protect margins. AI got better and cheaper.
人工智能变得更强大、更廉价。企业纷纷裁员，用节省下来的资金购买更多人工智能能力，从而裁减更多员工。失业工人消费减少。面向消费者的企业销量下滑、实力削弱，为保住利润转而加大对人工智能的投资。人工智能变得更强大、更廉价。
A feedback loop with no natural brake.
一个没有天然制动器的反馈循环。
The intuitive expectation was that falling aggregate demand would slow the AI buildout. It didn’t, because this wasn’t hyperscaler-style CapEx. It was OpEx substitution. A company that had been spending $100M a year on employees and $5M on AI now spent $70M on employees and $20M on AI. AI investment increased by multiples, but it occurred as a reduction in total operating costs. Every company’s AI budget grew while its overall spending shrank.
直观的预期是，总需求下降会减缓人工智能的建设进程。但事实并非如此，因为这并非超大规模资本支出模式，而是运营支出的替代。一家公司原本每年在员工上花费 1 亿美元，在人工智能上花费 500 万美元，现在则在员工上花费 7000 万美元，在人工智能上花费 2000 万美元。人工智能投资成倍增长，但这是以降低总运营成本为代价实现的。每家公司在人工智能上的预算都在增加，而总体支出却在缩减。
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，人工智能基础设施复合体仍在持续运转，尽管它正在扰乱的经济已开始恶化。英伟达仍在创下营收纪录。台积电的产能利用率仍保持在 95%以上。超大规模企业每季度仍在数据中心资本支出上投入 1500 至 2000 亿美元。完全顺应这一趋势的经济体，如台湾和韩国，表现远超其他地区。
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.
印度的情况恰恰相反。该国 IT 服务行业年出口额超过 2000 亿美元，是印度经常账户盈余的最大单一贡献来源，也是其持续商品贸易逆差的资金对冲手段。整个商业模式都建立在一条价值主张之上：印度开发人员的成本仅为美国同行的零头。但 AI 编程代理的边际成本已暴跌至近乎电力成本的水平。塔塔咨询服务公司、印孚瑟斯和威普罗公司在 2027 年经历了合同取消潮的加速。随着支撑印度外部账户的服务盈余蒸发，卢比在四个月内对美元贬值 18%。到 2028 年第一季度，国际货币基金组织已开始与新德里进行”初步磋商”。
The engine that caused the disruption got better every quarter, which meant the disruption accelerated every quarter. There was no natural floor to the labor market.
引发这场变革的引擎每季度都在优化，这意味着变革的节奏每季度都在加快。劳动力市场没有天然的下限。
In the US, we weren’t asking about how the bubble would burst in AI infrastructure anymore. We were asking what happens to a consumer-credit economy when consumers are being replaced with machines*.*
在美国，我们不再追问人工智能基础设施泡沫何时破裂。我们思考的是：当消费者被机器取代时，依赖消费信贷的经济将何去何从。
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，宏观经济叙事不再隐晦。过去十二个月里那些零散却明显消极的事态发展，其传导机制已昭然若揭。你无需查阅劳工统计局的数据，只需参加一场朋友间的晚宴便能感知。
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.
被取代的白领工作者并未坐以待毙。他们选择了降级就业。许多人转向薪酬较低的服务业和零工经济岗位，这增加了这些领域的劳动力供给，同时也压低了相关行业的工资水平。
A friend of ours was a senior product manager at Salesforce in 2025. Title, health insurance, 401k, $180,000 a year. She lost her job in the third round of layoffs. After six months of searching, she started driving for Uber. Her earnings dropped to $45,000. The point is less the individual story and more the second-order math. Multiply this dynamic by a few hundred thousand workers across every major metro. Overqualified labor flooding the service and gig economy pushed down wages for existing workers who were already struggling. Sector-specific disruption metastasized into economy-wide wage compression.
我们的一位朋友在 2025 年曾是 Salesforce 的高级产品经理。头衔、医疗保险、401k 退休计划，年薪 18 万美元。她在第三轮裁员中失去了工作。经过六个月的求职无果后，她开始为 Uber 开车。她的年收入骤降至 4.5 万美元。重点不在于这个个例，而在于其引发的连锁效应。将这种动态乘以每个主要大都市的数十万工作者。资历过高的劳动力涌入服务和零工经济，压低了本已艰难维生的现有工人的工资。特定行业的冲击扩散成了全经济范围的工资压缩。
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.
以人为中心的剩余劳动力池正面临又一次调整，这在我们撰写本文时已然发生。随着自主配送和自动驾驶车辆逐步渗透到零工经济领域——这个领域曾吸纳了首批被取代的工人。
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，情况已很明显：仍在职的专业人士开始像自己可能成为下一个裁员对象般消费。他们加倍努力工作（主要借助人工智能）只为不被解雇，升职加薪的希望早已破灭。储蓄率小幅攀升，消费支出持续疲软。
The most dangerous part was the lag. High earners used their higher-than-average savings to maintain the appearance of normalcy for two or three quarters. The hard data didn’t confirm the problem until it was already old news in the real economy. Then came the print that broke the illusion.
最危险的部分在于滞后性。高收入者利用他们高于平均水平的储蓄，维持了两三个季度的正常表象。直到实体经济早已成为旧闻，硬数据才确认了问题所在。随后，打破幻象的印刷品便接踵而至。
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，创 2020 年 4 月以来新高；美国劳工部，2027 年第三季度
Initial claims surged to 487,000, the highest since April 2020. ADP and Equifax confirmed that the overwhelming majority of new filings were from white-collar professionals.
首次申请失业救济人数飙升至 48.7 万，创下 2020 年 4 月以来新高。ADP 和 Equifax 数据证实，绝大多数新增申请者来自白领专业人士。
The S&P dropped 6% over the following week. Negative macro started winning the tug of war.
随后一周标普 500 指数下跌 6%。负面宏观经济数据开始在这场拉锯战中占据上风。
In a normal recession, job losses are broadly distributed. Blue-collar and white-collar workers share the pain roughly in proportion to each segment’s share of employment. The consumption hit is also broadly distributed, and it shows up quickly in the data because lower-income workers have higher marginal propensities to consume.
在常规经济衰退中，失业影响广泛分布。蓝领与白领劳动者承受的痛楚大致与其在就业市场中的占比成比例。消费冲击同样广泛蔓延，且会迅速体现在数据中——因为低收入劳动者具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.
在这一轮周期中，失业现象主要集中在收入分布的上层十分位区间。虽然这些人群在总就业人口中占比相对较小，但他们却驱动着极不成比例的消费支出。在美国，收入最高的 10%人群贡献了超过 50%的消费总额，前 20%的高收入者则占据了约 65%的消费份额。正是这些群体购置房产、购买汽车、安排度假、享用餐厅美食、支付私立学校学费、进行房屋翻新。他们是整个可选消费经济体的需求基石。
When these workers lost their jobs, or took 50% pay cuts to move into available roles, the consumption hit was enormous relative to the number of jobs lost. A 2% decline in white-collar employment translated to something like a 3-4% hit to discretionary consumer spending. Unlike blue-collar job losses, which tend to hit immediately (you get laid off from the factory, you stop spending next week), white-collar job losses have a lagged but deeper impact because these workers have savings buffers that allow them to maintain spending for a few months before the behavioral shift kicks in.
当这些白领工人失业，或是接受 50%的降薪转岗时，消费受到的冲击相对于失业人数而言是巨大的。白领就业率下降 2%，相当于非必需消费支出减少了约 3-4%。与蓝领失业不同——蓝领失业的影响往往是立竿见影的（工厂裁员后，下周就会停止消费）——白领失业的影响则具有滞后性但更为深远，因为这些工人拥有储蓄缓冲，能够在行为转变发生前维持数月的消费水平。
By Q2 2027, the economy was in recession. The NBER would not officially date the start until months later (they never do) but the data was unambiguous - we’d had two consecutive quarters of negative real GDP growth. But it wasn’t a “financial crisis”…yet.
到 2027 年第二季度，经济已陷入衰退。虽然美国国家经济研究局要等到数月后才会正式确定衰退起始时间（他们向来如此），但数据已经明确显示——我们经历了连续两个季度的实际 GDP 负增长。但这还不是一场”金融危机”……至少当时还不是。
The Daisy Chain of Correlated Bets关联押注的连锁反应
Private credit had grown from under $1 trillion in 2015 to over $2.5 trillion by 2026. A meaningful share of that capital had been deployed into software and technology deals, many of them leveraged buyouts of SaaS companies at valuations that assumed mid-teens revenue growth in perpetuity.
私人信贷规模已从 2015 年的不足 1 万亿美元增长至 2026 年的超过 2.5 万亿美元。其中相当一部分资金流入了软件和技术交易，许多是对 SaaS 公司的杠杆收购，其估值假设这些公司能永久保持 15%左右的收入增长。
Those assumptions died somewhere between the first agentic coding demo and the Q1 2026 software crash, but the marks didn’t seem to realize they were dead.
这些假设在首个智能体编码演示与 2026 年第一季度软件崩溃之间悄然消亡，但决策者们似乎并未意识到它们早已失效。
As many public SaaS companies traded to 5-8x EBITDA, PE-backed software companies sat on balance sheets at marks reflecting acquisition valuations on multiples of revenue that didn’t exist anymore. Managers eased the marks down gradually, 100 cents, 92, 85, all while public comps said 50.
随着许多上市 SaaS 公司以 5-8 倍 EBITDA 进行交易，私募股权支持的软件公司资产负债表上的估值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。管理层逐步下调估值——从 100 美分到 92，再到 85——而公开市场的可比公司估值仅为 50。
MOODY’S DOWNGRADES $18B OF PE-BACKED SOFTWARE DEBT ACROSS 14 ISSUERS, CITING ‘SECULAR REVENUE HEADWINDS FROM AI-DRIVEN COMPETITIVE DISRUPTION’; LARGEST SINGLE-SECTOR ACTION SINCE ENERGY IN 2015 | Moody’s Investors Service, April 2027
穆迪下调 14 家发行方 180 亿美元私募股权支持软件债务评级，理由为“人工智能驱动的竞争颠覆带来长期性营收阻力”；此为自 2015 年能源领域以来最大规模单一行业评级行动 | 穆迪投资者服务公司，2027 年 4 月
Everyone remembers what happened after the downgrade. Industry veterans had already seen the playbook following the 2015 energy downgrades.
所有人都记得降级后发生了什么。行业老手们早已见识过 2015 年能源降级后的剧本。
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.
软件支持的贷款在 2027 年第三季度开始违约。信息服务与咨询领域的私募股权投资组合公司紧随其后。数笔针对知名 SaaS 公司的数十亿美元杠杆收购交易进入重组程序。
Zendesk was the smoking gun.
Zendesk 成为关键证据。
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 驱动客服自动化侵蚀年度经常性收入而触发债务违约条款；50 亿美元直接贷款工具估值跌至 58 美分；创私营信贷软件违约最高纪录 | 金融时报，2027 年 9 月
In 2022, Hellman & Friedman and Permira had taken Zendesk private for $10.2 billion. The debt package was $5 billion in direct lending, the largest ARR-backed facility in history at the time, led by Blackstone with Apollo, Blue Owl and HPS all in the lending group. The loan was explicitly structured around the assumption that Zendesk’s annual recurring revenue would remain recurring. At roughly 25x EBITDA, the leverage only made sense if it did.
2022 年，Hellman & Friedman 与 Permira 以 102 亿美元将 Zendesk 私有化。债务方案包含 50 亿美元的直接贷款，这是当时历史上最大的年度经常性收入（ARR）支持融资安排，由黑石集团牵头，阿波罗、蓝猫头鹰和 HPS 均参与贷款银团。该贷款的结构设计明确基于一个前提：Zendesk 的年度经常性收入将持续稳定。若以约 25 倍 EBITDA 的估值计算，唯有实现这一前提，这笔杠杆交易才具备合理性。
By mid-2027, it didn’t.到 2027 年中旬，它并未发生。
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 客服代理自主处理客户服务已近一年。Zendesk 定义的类别（工单处理、路由分配、人工客服交互管理）已被无需生成工单即可解决问题的系统所取代。贷款所依据的年度经常性收入已不再具有循环性，它只是尚未流失的收入而已。
The largest ARR-backed loan in history became the largest private credit software default in history. Every credit desk asked the same question at once: who else has a secular headwind disguised as a cyclical one?
史上最大的 ARR 担保贷款演变为史上最大的私人信贷软件违约。所有信贷部门同时提出了同一个问题：还有谁将结构性逆风伪装成了周期性波动？
But here’s what the consensus got right, at least initially: this should have been survivable.
但共识至少在一开始是对的：这本应是能够挺过去的。
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.
私人信贷并非 2008 年的银行业危机。整个架构被明确设计以避免强制抛售。这些是封闭式工具，资金被锁定。有限合伙人承诺投资七到十年。没有存款人挤兑，没有回购额度被抽走。管理者可以持有受损资产，逐步处理，等待复苏。虽然痛苦，但可控。这个系统的设计初衷是能够弯曲，而非断裂。
Executives at Blackstone, KKR and Apollo cited software exposure of 7-13% of assets. Containable. Every sell-side note and fintwit credit account said the same thing: private credit has permanent capital. They could absorb losses that would otherwise blow up a levered bank.
黑石、KKR 和阿波罗的高管们表示，软件资产敞口占其总资产的 7-13%。可控。所有卖方研报和金融推特的信贷账户都表达了同样的观点：私人信贷拥有永久资本。它们能够吸收那些足以摧毁杠杆银行的损失。
Permanent capital. The phrase showed up in every earnings call and investor letter meant to reassure. It became a mantra. And like most mantras, nobody paid attention to the finer details. Here’s what it actually meant…
永久资本。这个短语出现在每一次财报电话会议和投资者信函中，意在安抚人心。它成了一句口头禅。而像大多数口头禅一样，没人关注其中的细节。以下是它的实际含义……
Over the prior decade, the large alternative asset managers had acquired life insurance companies and turned them into funding vehicles. Apollo bought Athene. Brookfield bought American Equity. KKR took Global Atlantic. The logic was elegant: annuity deposits provided a stable, long-duration liability base. The managers invested those deposits into the private credit they originated and got paid twice, earning spread over on the insurance side and management fees on the asset management side. A fee-on-fee perpetual motion machine that worked beautifully under one condition.
过去十年间，大型另类资产管理公司纷纷收购人寿保险公司，将其转变为融资工具。阿波罗收购了雅典娜，布鲁克菲尔德买下美国权益，KKR 则接手了全球大西洋。其商业逻辑堪称精妙：年金存款提供了稳定且期限长的负债基础。这些管理者将存款投入自营的私募信贷业务，实现双重盈利——既在保险端赚取利差，又在资产管理端收取管理费。这是一台”费用叠加费用”的永动机，只要满足一个条件就能完美运转。
The private credit had to be money good.
这笔私人信贷必须是优质资金。
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.
损失冲击了那些为持有非流动性资产以应对长期负债而构建的资产负债表。本应使系统具备韧性的“永久资本”，并非来自抽象耐心的机构资金池或承担复杂风险的成熟投资者。它实质上是美国家庭——即“普通民众”——的储蓄，这些资金以年金形式投资于同一批私募股权支持的软件与技术票据，而如今这些票据正面临违约。那些无法抽身的锁定资本实为人寿保险投保人的资金，而此处的规则略有不同。
Compared to the banking system, insurance regulators had been docile - even complacent - but this was the wake-up call. Already uneasy about private credit concentrations at life insurers, they began downgrading the risk-based capital treatment of these assets. That forced the insurers to either raise capital or sell assets, neither of which was possible at attractive terms in a market already seizing up.
与银行体系相比，保险监管机构一直表现得温顺甚至自满，但这次事件敲响了警钟。他们本就对人寿保险公司持有的私人信贷集中度感到不安，于是开始下调这些资产的风险资本待遇。这迫使保险公司要么增资，要么出售资产，而在已经陷入停滞的市场中，这两者都无法以有利条件实现。
NEW YORK, IOWA STATE REGULATORS MOVE TO TIGHTEN CAPITAL TREATMENT FOR CERTAIN PRIVATELY RATED CREDIT HELD BY LIFE INSURERS; NAIC GUIDANCE EXPECTED TO INCREASE RBC FACTORS AND TRIGGER ADDITIONAL SVO SCRUTINY | Reuters, Nov 2027
纽约，爱荷华州监管机构着手收紧寿险公司持有的特定私人评级信贷资本处理；预计 NAIC 指引将提高 RBC 因子并引发额外 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.
当穆迪将雅典娜的财务实力评级展望调整为负面时，阿波罗的股价在两个交易日内下跌了 22%。布鲁克菲尔德、KKR 等公司也随之走低。
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.
情况自此变得愈发复杂。这些公司不仅创造了保险公司的永动机，还构建了精密的离岸架构，旨在通过监管套利最大化收益。美国保险公司签发年金后，将风险转移给其同样拥有的百慕大或开曼群岛关联再保险公司——这些机构利用更灵活的监管规定设立，允许对相同资产持有更少的资本。该关联公司通过离岸特殊目的载体筹集外部资金，新增了一层交易对手方，他们与保险公司共同投资于同一母公司资产管理部门发起的私人信贷。
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——其中一些本身就是私募股权公司所有——从未成为透明度的典范（这一点几乎无人感到意外）。不同公司通过不同资产负债表相互关联，这张蛛网般的结构在透明度方面令人震惊。当底层贷款违约时，究竟由谁实际承担损失的问题，在现实中根本无法实时解答。
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 月的市场崩盘标志着认知转变——从可能只是寻常周期性回调，转向了更令人不安的局面。美联储主席凯文·沃什在联邦公开市场委员会 11 月紧急会议上称之为”对白领生产力增长的一系列连锁押注”。
See, it is never the losses themselves that cause the crisis. It’s recognizing them. And there is another, much larger, much much more important area of finance for which we have grown fearful of that recognition.
看吧，危机从来不是由损失本身引发的，而是源于对损失的认知。在金融领域，还有另一个更为庞大、更为重要的领域，我们已开始畏惧面对这种认知。
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 房价指数显示旧金山同比下跌 11%，西雅图跌 9%，奥斯汀跌 8%；房利美警示科技/金融从业者超 40%的邮编区出现”早期逾期率攀升”现象 | 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 房价指数显示旧金山同比下跌 11%，西雅图下跌 9%，奥斯汀下跌 8%。这并非唯一令人担忧的消息。上月房利美警示称，在巨额贷款集中的邮编区域——这些区域通常居住着信用评分 780 分以上、被视为”无懈可击”的借款人——出现了更高的早期贷款逾期率。
The US residential mortgage market is approximately $13 trillion. Mortgage underwriting is built on the fundamental assumption that the borrower will remain employed at roughly their current income level for the duration of the loan. For thirty years, in the case of most mortgages.
美国住宅抵押贷款市场规模约为 13 万亿美元。抵押贷款审批建立在借款人将在贷款期限内保持大致当前收入水平的基本假设之上——对于大多数抵押贷款而言，这个期限长达三十年。
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?
白领就业危机已通过收入预期的持续转变威胁到这一假设。我们现在不得不提出一个三年前还显得荒谬的问题——优质抵押贷款是否依然可靠？
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).
美国历史上每一次抵押贷款危机都由以下三种因素之一驱动：投机过度（向无力购房者放贷，如 2008 年）、利率冲击（利率上升导致浮动利率抵押贷款难以承受，如 1980 年代初）、或局部经济冲击（单一地区特定行业崩溃，如 1980 年代德克萨斯州的石油业或 2009 年密歇根州的汽车业）。
None of these apply here. The borrowers in question are not subprime. They’re 780 FICO scores. They put 20% down. They have clean credit histories, stable employment records, and incomes that were verified and documented at origination. They were the borrowers that every risk model in the financial system treats as the bedrock of credit quality.
这些情况在此均不适用。所涉及的借款人并非次级贷款者。他们的信用评分高达 780 分，首付比例达 20%，信用记录清白，就业记录稳定，且收入在贷款发放时已通过验证并有文件证明。他们是金融体系中所有风险模型都视为信贷质量基石的借款人。
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，贷款从第一天起就是坏的。2028 年，贷款从第一天起就是好的。世界在贷款发放后……就变了。人们抵押了一个他们再也无法相信的未来。
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，我们注意到隐性压力的早期迹象：房屋净值信贷额度动用率上升、401(k)计划提款激增、信用卡债务飙升，而抵押贷款还款却保持正常。随着失业潮涌现、招聘冻结与奖金削减，这些优质家庭的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.
他们仍能支付房贷，但前提是停止所有非必要开支、耗尽储蓄并推迟任何房屋维护或改善。从技术上讲，他们的房贷尚未逾期，但只需再遭遇一次冲击便会陷入困境，而人工智能能力的发展轨迹表明，这场冲击即将来临。随后我们看到旧金山、西雅图、曼哈顿和奥斯汀的房贷拖欠率开始飙升，尽管全国平均水平仍保持在历史常态范围内。
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.
我们正处于最严峻的阶段。当边际买家财务状况良好时，房价下跌尚可控制。但如今，边际买家正面临同样的收入困境。
While concerns are building, we are not yet in a full-blown mortgage crisis. Delinquencies have risen but remain well below 2008 levels. It is the trajectory that’s the real threat.
尽管担忧日益加剧，但我们尚未陷入全面的抵押贷款危机。违约率虽有所上升，但仍远低于 2008 年的水平。真正的威胁在于其发展趋势。
The Intelligence Displacement Spiral now has two financial accelerants to the real economy’s decline.
智能替代螺旋如今为实体经济的衰退增添了两大金融加速器。
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、私营市场动荡。三者相互强化。传统政策工具（降息、量化宽松）能够应对金融引擎，却无法触及实体经济引擎，因为实体经济引擎并非由紧缩的金融环境驱动。其核心驱动力在于人工智能正使人类智能变得不再稀缺、不再珍贵。即便将利率降至零并收购市场上所有抵押贷款支持证券和违约的软件杠杆收购债务……
It won’t change the fact that a Claude agent can do the work of a $180,000 product manager for $200/month.
这不会改变一个事实：一个 Claude 智能体每月只需 200 美元，就能完成一位年薪 18 万美元的产品经理的工作。
If these fears manifest, the mortgage market cracks in the back half of this year. In that scenario, we’d expect the current drawdown in equities to ultimately rival that of the GFC (57% peak-to-trough). This would bring the S&P500 to ~3500 - levels we haven’t seen since the month before the ChatGPT moment in November 2022.
若这些担忧成为现实，抵押贷款市场将在今年下半年出现裂痕。在此情境下，我们预计当前股市的回撤幅度最终将堪比全球金融危机时期（峰值至谷底跌幅达 57%）。这将使标普 500 指数跌至约 3500 点——这一水平自 2022 年 11 月 ChatGPT 问世前一个月以来便未曾触及。
What’s clear is that the income assumptions underlying $13 trillion in residential mortgages are structurally impaired. What isn’t is whether policy can intervene before the mortgage market fully processes what this means. We’re hopeful, but we can’t deny the reasons not to be.
显而易见的是，支撑 13 万亿美元住房抵押贷款的收入假设在结构上已受到损害。尚不明确的是，政策能否在抵押贷款市场完全消化这一影响之前进行干预。我们抱有希望，但无法否认悲观的理由。
The Battle Against Time 与时间的赛跑
The first negative feedback loop was in the real economy: AI capability improves, payroll shrinks, spending softens, margins tighten, companies buy more capability, capability improves. Then it turned financial: income impairment hit mortgages, bank losses tightened credit, the wealth effect cracked, and the feedback loop sped up. And both of these have been exacerbated by an insufficient policy response from a government that seems, quite frankly, confused.
第一个负面反馈循环出现在实体经济中：人工智能能力提升，薪资规模缩减，消费疲软，利润空间收窄，企业购入更多智能设备，技术能力再度增强。随后危机蔓延至金融领域：收入受损冲击抵押贷款市场，银行亏损导致信贷紧缩，财富效应破裂，反馈循环不断加速。坦白说，由于政府应对政策不足且决策层似乎陷入困惑，这两大循环的恶化趋势都被进一步放大。
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.
这个系统并非为应对此类危机而设计。联邦政府的财政收入基础本质上是对人类时间的征税。人们工作，企业支付薪酬，政府从中抽取份额。在正常年份，个人所得税和工资税构成了财政收入的主干。
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.
今年第一季度，联邦财政收入比国会预算办公室基准预测低 12%。薪资税收入下降，是因为以先前薪酬水平就业的人数减少。所得税收入下降，是因为所获收入在结构上更低。生产率正在飙升，但收益流向了资本和算力，而非劳动力。
Labor’s share of GDP declined from 64% in 1974 to 56% in 2024, a four-decade grind lower driven by globalization, automation, and the steady erosion of worker bargaining power. In the four years since AI began its exponential improvement, that has dropped to 46%. The sharpest decline on record.
劳动力占 GDP 的比重从 1974 年的 64%降至 2024 年的 56%，这是受全球化、自动化以及工人议价能力持续削弱影响，历经四十年的缓慢下滑。自人工智能开始呈指数级进步以来的四年间，这一比例已骤降至 46%，创下有记录以来的最大跌幅。
The output is still there. But it’s no longer routing through households on the way back to firms, which means it’s no longer routing through the IRS either. The circular flow is breaking, and the government is expected to step in to fix that.
输出依然存在。但它不再经由家庭回流至企业，这意味着也不再经过国税局。循环流动正在断裂，政府预计将介入修复。
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.
如同每一次经济衰退，支出上升的同时收入却在下降。但这次的不同之处在于，支出压力并非周期性波动。自动稳定机制是为应对暂时性失业而设计，而非结构性替代。现行体系发放的福利金基于工人将被重新吸纳的假设。然而许多人将无法回归，至少无法获得与从前相当的薪资水平。新冠疫情期间，政府曾坦然接受 15%的赤字率，但当时人们都明白这只是暂时现象。如今需要政府扶持的群体，并非遭受了终将复苏的疫情冲击，而是被持续进步的技术所取代。
The government needs to transfer more money to households at precisely the moment it is collecting less money from them in taxes.
政府需要在税收减少之际，向家庭转移更多资金。
The U.S. won’t default. It prints the currency it spends, the same currency it uses to pay back borrowers. But this stress has shown up elsewhere. Municipal bonds are showing worrying signs of dispersion in year-to-date performance. States without income tax have been okay, but general obligation munis issued by states dependent on income tax (majority blue states) began to price in some default risk. Politicos caught on quickly, and the debate over who gets bailed out has fallen along partisan lines.
美国不会违约。它印钞用于支出，也用同样的货币偿还借款者。但压力已在别处显现。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（多为蓝州）发行的一般义务市政债券已开始计入部分违约风险。政客们迅速察觉，关于谁该获得救助的争论已按党派路线展开。
The administration, to its credit, recognized the structural nature of the crisis early and began entertaining bipartisan proposals for what they’re calling the “Transition Economy Act”: a framework for direct transfers to displaced workers funded by a combination of deficit spending and a proposed tax on AI inference compute.
值得称赞的是，政府很早就认识到这场危机的结构性本质，并开始考虑两党提出的所谓”过渡经济法案”：该框架旨在通过赤字支出和拟议的人工智能推理计算税相结合的方式，为失业工人提供直接转移支付。
The most radical proposal on the table goes further. The “Shared AI Prosperity Act” would establish a public claim on the returns of the intelligence infrastructure itself, something between a sovereign wealth fund and a royalty on AI-generated output, with dividends funding household transfers. Private sector lobbyists have flooded the media with warnings about the slippery slope.
摆在桌面上最激进的提案走得更远。《共享人工智能繁荣法案》将在智能基础设施本身的回报上建立公共权益，介于主权财富基金和人工智能产出版税之间，其股息将用于资助家庭转移支付。私营部门的游说者已通过媒体发出大量警告，称此举将开启危险的滑坡效应。
The politics behind the discussions have been grimly predictable, exacerbated by grandstanding and brinksmanship. The right calls transfers and redistribution Marxism and warns that taxing compute hands the lead to China. The left warns that a tax drafted with the help of incumbents becomes regulatory capture by another name. Fiscal hawks point to unsustainable deficits. Doves point to the premature austerity imposed after the GFC as a cautionary tale. The divide is only magnifying in the run up to this year’s presidential election.
这场讨论背后的政治博弈一直阴郁可期，哗众取宠和边缘政策更是加剧了其严峻性。右翼将转移支付和财富再分配称为马克思主义，并警告说对算力征税会将领先地位拱手让给中国。左翼则警告，在现有巨头协助下起草的税收法案，不过是换了个名字的监管俘获。财政鹰派指出赤字已不可持续。鸽派则以全球金融危机后过早实施的紧缩政策作为警示。随着今年总统大选的临近，这种分歧只会愈发扩大。
While the politicians bicker, the social fabric is fraying faster than the legislative process can move.
政客们争吵不休之际，社会结构的崩坏速度已远超立法进程。
The Occupy Silicon Valley movement has been emblematic of wider dissatisfaction. Last month, demonstrators blockaded the entrances to Anthropic and OpenAI’s San Francisco offices for three weeks straight. Their numbers are growing, and the demonstrations have drawn more media coverage than the unemployment data that prompted them.
“占领硅谷”运动已成为更广泛不满情绪的象征。上个月，示威者连续三周封锁了 Anthropic 和 OpenAI 在旧金山办公室的入口。抗议队伍日益壮大，媒体对此的报道甚至超过了引发这场运动的失业数据。
It’s hard to imagine the public hating anyone more than the bankers in the fallout of the GFC, but the AI labs are making a run at it. And, from the perspective of the masses, for good reason. Their founders and early investors have accumulated wealth at a pace that makes the Gilded Age look tame. The gains from the productivity boom accruing almost entirely to the owners of compute and the shareholders of the labs that ran on it has magnified US inequality to unprecedented levels.
很难想象公众会比对全球金融危机后的银行家更憎恨任何人，但人工智能实验室正在朝这个方向迈进。从大众视角看，这种情绪确有充分理由。这些实验室的创始人和早期投资者积累财富的速度，让镀金时代都显得温和。生产力繁荣带来的收益几乎全部流向算力拥有者和依赖算力运营的实验室股东，这已将美国的不平等程度推至前所未有的高度。
Every side has their own villain, but the real villain is time.
各方皆有宿敌，然真正的敌人是时间。
AI capability is evolving faster than institutions can adapt. The policy response is moving at the pace of ideology, not reality. If the government doesn’t agree on what the problem is soon, the feedback loop will write the next chapter for them.
人工智能能力的发展速度远超机构适应能力。政策应对正以意识形态而非现实的速度推进。若政府不能尽快就问题本质达成共识，反馈循环将为他们书写下一篇章。
The Intelligence Premium Unwind智能溢价逆转
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.
纵观整个现代经济史，人类智能始终是稀缺的投入要素。资本是充裕的（至少是可复制的）。自然资源虽有限但可替代。技术发展缓慢到足以让人类适应。而智能——这种分析、决策、创造、说服与协调的能力——才是无法大规模复制的存在。
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内在溢价。我们经济中的每一个机构，从劳动力市场到抵押贷款市场再到税法，都是基于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.
我们正经历着这种溢价的消退。机器智能如今已成为人类智能的合格替代品，并在日益广泛的任务中快速进步。金融体系经过数十年优化，以适应人类智慧稀缺的世界，如今正在重新定价。这种重新定价的过程充满痛苦、混乱，且远未完成。
But repricing is not the same as collapse.
但重新定价不等于崩溃。
The economy can find a new equilibrium. Getting there is one of the few tasks left that only humans can do. We need to do it correctly.
经济能够找到新的平衡点。实现这一平衡是仅剩的几项只有人类才能完成的任务之一。我们必须正确地完成它。
This is the first time in history the most productive asset in the economy has produced fewer, not more, jobs. Nobody’s framework fits, because none were designed for a world where the scarce input became abundant. So we have to make new frameworks. Whether we build them in time is the only question that matters.
这是历史上首次，经济中最具生产力的资产创造的就业岗位减少了而非增加。没有人的理论框架适用，因为没有一个是为稀缺资源变得充裕的世界而设计的。所以我们必须建立新的框架。我们能否及时构建它们，是唯一重要的问题。
But you’re not reading this in June 2028. You’re reading it in February 2026.
但你并非在 2028 年 6 月读到这些。你是在 2026 年 2 月读到它的。
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.
标普指数接近历史高点。负面反馈循环尚未开始。我们确信其中一些情景不会成为现实。我们同样确信机器智能将持续加速发展。人类智能的溢价将会收窄。
As investors, we still have time to assess how much of our portfolios are built upon assumptions that won’t survive the decade. As a society, we still have time to be proactive.
作为投资者，我们仍有时间评估投资组合中有多少是建立在无法撑过这个十年的假设之上。作为社会整体，我们仍有时间采取主动。
The canary is still alive.
金丝雀依然活着。
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 party, but he has written others in a series called the Intelligence Explosion, we highly recommend reading it. You can find it here.
致谢：感谢 Hunterbrook 的 Sam Koppelman 协助校对。我们的合著者、LOTUS 的 Alap Shah 贡献了本文的构思——CitriniResearch 撰写了此篇，但他还撰写了名为《智能爆炸》系列的其他文章，我们强烈推荐阅读。您可以在此处找到。
📓 阅读笔记 （TL;DR）
因为智能不再稀缺而贬值，所以现在要：去杠杆+预演不幸。
货币流通速度停滞不前，以人为中心的消费经济逐渐萎缩。
中介价值消失，Saas企业-价格竞争-几近消亡。
消费者代理开始改变几乎所有消费交易的方式。
我们高估了“人际关系”的价值。原来，人们口中的许多关系，不过是戴着友好面具的摩擦。
第一个负面反馈循环: 人工智能变得更强大、更廉价。企业纷纷裁员，用节省下来的资金购买更多人工智能能力，从而裁减更多员工。失业工人消费减少。面向消费者的企业销量下滑、实力削弱，为保住利润转而加大对人工智能的投资。人工智能变得更强大、更廉价。
“占领硅谷”运动已成为更广泛不满情绪的象征。上个月，示威者连续三周封锁了 Anthropic 和 OpenAI 在旧金山办公室的入口。抗议队伍日益壮大，媒体对此的报道甚至超过了引发这场运动的失业数据。 –> 就像当年的卢德主义
纵观整个现代经济史，人类智能始终是稀缺的投入要素。人类智能因其稀缺性而获得内在溢价。
📤 原文来源
- 原文首发于 THE 2028 GLOBAL INTELLIGENCE CRISIS
- 作者：Citrini and Alap Shah
- 译者：本文为学习交流翻译，仅供学习参考