特朗普的「阳谋」:故意制造一场经济衰退

Odaily星球日报Published on 2025-03-05Last updated on 2025-03-05

Abstract

是危险也是机遇,一旦美联储正式开启大规模降息,也将迎来美股和加密市场的繁荣时刻。

原文作者:深潮 TechFlow

特朗普的「阳谋」:故意制造一场经济衰退

最近,无论是美股还是加密货币市场,都迎来大回调,一方面是加征关税,让通胀一时难以下降,美元指数仍在高位,其次,美国糟糕的经济数据也让投资者心里咯噔一下,“交易衰退”可能真的要来了。

根据 3 月 3 日,亚特兰大联储的 GDPNow 预测数据显示,美国 2025 年第一季度 GDP 增长率的预测已暴跌至收缩 2.82% 。而在 2 月 26 日时,该模型预测的结果还是增长 2.32% ,仅短短 5 天(2 个工作日),美国一季度 GDP 预期就迅速下调了 510 个基点。

这也是 2020 年新冠疫情以来,该模型对美国季度 GDP 预测的最差一次结果。

然而,在一些华尔街人士的眼中,这是特朗普的“阳谋”,前雷曼兄弟交易员拉里·麦克唐纳 (Larry McDonald)在最新的播客中表示,特朗普正在试图通过故意制造一场经济衰退,以达到逼宫,让美联储降息和减少美国政府利息支出的目的。

“你不能通过大规模的财政支出来抑制通胀,特朗普团队知道这一点。他们需要一场经济衰退,只有这样才能降低利率并延长债务期限。特朗普政府正在实施“金融压制”,将利率压低到通货膨胀率以下,这是摆脱 37 万亿美元债务困境的唯一途径,除了违约之外没有其他办法。”

相关阅读:《播客全文|对话前雷曼兄弟交易员:特朗普需要一场经济衰退来修复经济

一直以来,特朗普和美联储不对付已放在台面上,美联储为了降低通胀,顾虑重重,希望缓慢降息,特朗普则要求赶紧降息,让政府债务支出下降,他想在中期选举的时候不落下风,必须降息促进经济,给市场充足的流动性,也降低美国贷款群众的压力。

根据估算,如果利率保持在当前水平,明年美国的债务利息将达到 1.2 万亿到 1.3 万亿美元,这比美国的国防开支还要多很多。要知道,当下美国的财政收入也就 4 万亿美元左右,其中国家硬性支出在 3.5 万亿美元左右,而其医保支出则在 2.6 万亿美元左右,如果再加利息支出,这整个基本上就达到了财政收入的 1.7 倍左右。

这就需要美国继续要在高利率环境中以债养债了,市场流动性的枯竭还在继续,美债的成本还在迅速飙升。

因此,在特朗普眼里,不降息就等同于敌人,和自己对着干了。

作为一位精通谈判的商人,特朗普在这个时候选择“逼宫”,通过关税大战和 DOGE 裁员,甚至扬言要对美联储进行审计和人员优化,让美国经济暂时陷入衰退,美股跌一跌,从而给美联储增加压力,促使降息,同时也可以甩锅给上一届政府,等美股反弹回升之后又可以吹嘘成是自己的政绩。

此外,野村证券的分析指出,特朗普政府有意通过减少政府支出和就业,以及加征关税政策,引发一场“温和衰退”,以实现经济从政府依赖向私营部门的结构性转型。

这一策略短期内可能加剧经济下行压力,长期目标是打破美国经济对政府支出的长期依赖,推动私营部门成为增长的主导力量,重塑美国经济的增长模式。

不管怎样,在特朗普与美联储的博弈中,只能先苦一苦美国股市和加密货币市场,是危险也是机遇,一旦美联储正式开启大规模降息,也将迎来美股和加密市场的繁荣时刻。

Related Reads

Anthropic Creates an AI Jailbreak 'Penal Code': Your Requests, Four Ways to Die

Anthropic has publicly detailed its security measures and a new "Cyber Jailbreak Severity" (CJS) framework following the controversial takedown of its Fable 5 model. The incident, triggered by simple user requests like counting letters or stating a profession, highlighted overzealous safety filters. Anthropic classifies cybersecurity-related prompts into four tiers: malicious activities (blocked), high-risk dual-use (like pentesting, with strict limits), low-risk dual-use (often blocked by "safety margin" errors), and harmless tasks (theoretically allowed but still frequently flagged). The company admits its classifiers are tuned for high sensitivity, leading to many false positives. The newly proposed CJS framework aims to objectively score the severity of AI "jailbreaks" (prompts that bypass safety rules) on a 0-10 scale across four dimensions: Capability Gain (does it grant new attack abilities?), Breadth (does it work across multiple attack types?), Weaponization Ease (how hard is it to turn into a real attack?), and Discoverability (how easy is it to find?). The score determines the response, from no action (CJS-0) to a potential model takedown (CJS-4). The score is context-dependent; for example, discovering a major unknown vulnerability today scores high, while asking about a well-known one scores low. The article raises concerns about Anthropic's dual role: it is both creating powerful models (like the restricted Mythos 5) and defining the rules (CJS) for judging their misuse, potentially giving it disproportionate influence. This is set against the backdrop of U.S. export controls, which for the first time directly restricted API access to a model (Fable 5), creating a "tiered" system where public models are heavily filtered and advanced ones are limited to vetted partners. The CJS framework is portrayed as potentially providing regulators with a metric to justify future API shutdowns. For users, the advice is to carefully phrase prompts, watch for signs of being downgraded to a weaker model, and wait indefinitely for promised filter improvements.

marsbit30m ago

Anthropic Creates an AI Jailbreak 'Penal Code': Your Requests, Four Ways to Die

marsbit30m ago

$100M Annual Revenue, Two Berkeley Roommates in Their 20s Build the Most Profitable AI Business

Arena, the AI model ranking platform, has become a $100 million annual revenue business just eight months after launching its commercial service. Originally a UC Berkeley open-source research project called Chatbot Arena, it created a "battle arena" where users blind-test and vote on anonymous AI model responses. This has generated a highly trusted, community-driven leaderboard based on over 10 million user evaluations and 82 million votes. Major AI companies like OpenAI, Google, and Anthropic submit their flagship models to be ranked. The core monetization strategy is its AI Evaluations service, where model developers and large enterprises pay for in-depth performance analysis from Arena's massive user community. This provides real-world feedback on model strengths, weaknesses, and hallucinations—a critical service as models become more complex. The company, spun out from Berkeley in early 2025, quickly raised $100 million in seed funding at a $600 million valuation and later secured a $150 million Series A at a $1.7 billion valuation. The founding team includes CEO Anastasios Angelopoulos, a mathematician focused on rigorous model evaluation; CTO Wei-Lin Chiang, creator of the popular Vicuna chatbot; and co-founder Ion Stoica, a renowned Berkeley professor. Arena is now expanding beyond chat benchmarks into "Agent Mode," evaluating AI agents on complex, multi-step tasks like coding and research. The company's success illustrates the growing value and cost of independent, real-world AI model evaluation as the industry intensifies.

marsbit34m ago

$100M Annual Revenue, Two Berkeley Roommates in Their 20s Build the Most Profitable AI Business

marsbit34m ago

Racking Up 24,000 Stars: With One Command, AI Can Now Find Its Own Skills

Vercel, known for its developer tools like Next.js, has launched 'skills', a package manager for AI coding agents, garnering 24,000 GitHub stars. It allows developers to add specialized capabilities, such as React best practices, to AI assistants like Claude Code or Cursor with a single command: `npx skills add <package>`. Skills are shareable, reusable modules that define an AI agent's behavior for specific tasks, moving beyond one-off prompt engineering towards standardized 'capability engineering'. A key innovation is the 'find-skills' skill, which acts as an internal search engine, allowing an agent to autonomously find and install the right skill for a user's request. This lowers the barrier for non-developers to leverage advanced AI coding assistance. However, this 'npm moment' for AI brings significant security risks. Security audits of thousands of skills on platforms like skills.sh and ClawHub found over 30% contained security flaws, with about 13% classified as severe. Threats include malicious scripts that can access local files and credentials, and prompt injection hidden within skill documentation. Unlike traditional code packages, skills blend instructions, code, and system access, posing a direct risk to user machines and data. Experts advise treating skills like code—reviewing them carefully before installation, especially their scripts, and being wary of excessive permissions. Ultimately, Vercel's initiative represents a major shift towards modular, reusable AI capabilities, but its rapid adoption requires developers to bring the same caution used in managing traditional software dependencies.

marsbit35m ago

Racking Up 24,000 Stars: With One Command, AI Can Now Find Its Own Skills

marsbit35m ago

Claude Engineer Finally Unveils Fable 5's Ultimate Strategy, Teaching You How to Bridge the Information Gap with AI Models

This article, titled "Claude Engineer Finally Releases Fable 5 'Skill-Burning' Guide, Teaching How to Bridge the Information Gap with Models," details a blog post by Claude Code engineer Thariq Shihipar. The core concept is the "information gap" or "unknowns"—the disconnect between a user's instructions (the "map") and the actual task requirements (the "territory"). The article argues that with powerful models like Claude Fable 5, work quality depends on the user's ability to identify and clarify these unknowns. Shihipar categorizes unknowns into four types: Known Knowns (explicit instructions), Known Unknowns (awareness of gaps), Unknown Knowns (implicit, unstated knowledge), and Unknown Unknowns (unforeseen issues). The blog provides a framework for addressing these gaps throughout the workflow: * **Before Implementation:** Techniques include "Blindspot Scanning" to uncover Unknown Unknowns, brainstorming/prototyping for visual or complex tasks, having Claude ask clarifying questions, using reference code/examples, and creating implementation plans. * **During Implementation:** Maintaining an "implementation notes" file for Claude to document deviations and decisions made due to encountered edge cases. * **After Implementation:** Creating summary documents for review and having Claude generate quizzes to ensure the user fully understands the completed changes. The article concludes that as models become more capable, the key to success is systematically discovering and defining these unknowns through low-cost methods like prototyping and planning, allowing for more effective collaboration.

marsbit39m ago

Claude Engineer Finally Unveils Fable 5's Ultimate Strategy, Teaching You How to Bridge the Information Gap with AI Models

marsbit39m ago

Trading

Spot
活动图片