Trump Moves To Install Pro-Bitcoin Leader At The Federal Reserve

bitcoinistPublicado em 2026-03-05Última atualização em 2026-03-05

Resumo

President Trump has nominated pro-Bitcoin Kevin Warsh to lead the Federal Reserve, replacing Jerome Powell. The nomination has been sent to the Senate Banking Committee for review, though political opposition may slow the process. Warsh, a former Fed governor, has referred to Bitcoin as "new gold" and expressed openness toward it. Markets reacted positively, with Bitcoin surpassing $70,000. Warsh's background in both public service and private finance makes him appealing to some senators but raises concerns about the Fed's independence for others. His potential appointment is viewed as favorable for risk assets like Bitcoin, though confirmation faces hurdles from lawmakers seeking delays due to ongoing investigations.

US President Donald Trump formally sent the nomination of pro-Bitcoin Kevin Warsh to the US Senate on Wednesday, beginning a process that could replace Jerome Powell when his term ends in May.

Reports say the White House filed paperwork to seat Warsh as chair for a four-year term and as a governor for a longer term on the central bank’s board.

Nomination Sent To The Senate

According to multiple outlets, the nomination now moves to the Senate Banking Committee for review. The committee will decide whether to hold hearings and then whether to send the nomination to the full Senate for a confirmation vote.

The timing is uncertain. Some senators have already signaled they may slow the process until a separate Justice Department inquiry is resolved.

Bitcoin Proponent: Warsh’s Record And Views

Warsh served at the Fed in earlier years. Reports note he has talked openly about Bitcoin, calling it a kind of “new gold” for younger investors and saying it does not make him nervous.

Markets reacted quickly when the nomination was announced earlier: Bitcoin, at the time of writing, climbed past the $70,000 level, and some short positions were liquidated as traders digested the news.

BTCUSD now trading at $72,516. Chart: TradingView

Warsh’s background mixes public service and private finance. He was on the Fed’s board during turbulent times and later worked in the private sector and at a policy research center. That mix is part of what makes him attractive to some senators who favor lower rates, and worrisome to others who worry about the Fed’s independence.

How Markets Read The Move

Reports say traders see a Fed chair who favors rate cuts as friendly to risk assets. Bitcoin’s price moves reflected that view in the hours after the filing reached the Senate.

Some analysts cautioned that a faster shift in policy would depend on data, not headlines, and that inflation and global events complicate any easy return to lower borrowing costs.

Political Hurdles Ahead

Opposition is already forming. A Republican member of the Banking Committee has said he may block nominations until outside investigations are cleared, and leading Democrats have voiced concerns about Warsh’s alignment with the administration.

Those objections mean a smooth confirmation is far from certain, even with a friendly Senate majority.

Reports note the next formal steps are committee hearings, written questionnaires, and witness appearances. The committee could vote to advance Warsh, or it could stall the nomination.

If the committee approves him, the full Senate would then take up the matter. If hearings proceed, senators will ask about his views on inflation, interest rates, and the role of cryptocurrencies in financial stability.

Featured image from Unsplash, chart from TradingView

Perguntas relacionadas

QWho has President Trump nominated to potentially replace Jerome Powell as the Federal Reserve chair?

APresident Trump has nominated Kevin Warsh, who is described as pro-Bitcoin, to potentially replace Jerome Powell.

QWhat was the immediate market reaction to the announcement of Kevin Warsh's nomination?

AFollowing the announcement, Bitcoin's price climbed past $70,000, and some short positions were liquidated as traders reacted to the news.

QWhat is one reason Kevin Warsh's nomination is seen as attractive to some senators?

AHis background, which mixes public service and private finance, is attractive to some senators who favor lower interest rates.

QWhat is a major political hurdle that could delay or block Kevin Warsh's confirmation?

AA Republican member of the Banking Committee has signaled a potential block on nominations until a separate Justice Department inquiry is resolved, and leading Democrats have voiced concerns about his alignment with the administration.

QHow did Kevin Warsh previously describe Bitcoin, according to the article?

AHe described Bitcoin as a kind of 'new gold' for younger investors and stated that it does not make him nervous.

Leituras Relacionadas

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

Intel CEO Chen Liwu outlines a bold vision for the company's transformation in a podcast interview, targeting a "10x return in 5-10 years." He emphasizes a strategic shift beyond traditional process node scaling, focusing on advanced packaging (like EMIB), new materials (glass substrates, GaN, SiC, InP, synthetic diamond), and system-level integration. Key to this is Intel's foundry business, where he prioritizes building trust through superior yield, defect density, and cycle times. Chen sees strong CPU demand resurgence driven by agent AI and inference workloads. He details the Terafab collaboration with Elon Musk to address semiconductor infrastructure gaps and stresses the strategic importance of U.S.-based advanced manufacturing. Acknowledging Intel is still in the "crawl" phase of his crawl-walk-run framework, Chen believes the company's full potential—extending from its PC base into edge computing and physical AI—will become apparent by 2030-2032.

marsbitHá 1m

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

marsbitHá 1m

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbitHá 2m

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbitHá 2m

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbitHá 2h

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbitHá 2h

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbitHá 2h

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbitHá 2h

Alliance Co-founder's Letter to Entrepreneurs: Written at the Moment Cursor Sold for $600 Billion

Alliance Co-founder's Letter to Entrepreneurs: On Cursor's $60 Billion Sale Many aspiring founders see massive exits like Cursor's $60B sale and wonder why they can't achieve the same, often concluding opportunities are exhausted. But great companies aren't built in obvious, crowded spaces. Cursor, like Stripe, Figma, and Shopify before it, started with a non-consensus belief about the future. Before ChatGPT, they believed AI would transform knowledge work. They focused on a genuinely exciting domain, became their own customer, and obsessed over power users. Their journey involved years of "glass-chewing" effort before the market was ready. The pattern is consistent: identify a long-term technological shift, find a missed entry point, and execute for years before the trend becomes obvious. First-generation products (PayPal, Adobe, Amazon) prove a market exists. Second-generation winners (Stripe, Figma, Shopify) rebuild that market around new insights, technology, or changing customer behaviors. Founders must identify their phase in the cycle. Early entrants like Coinbase or Cursor focus on making new technology usable for power users. Later entrants find the "yin" to the established "yang"—the blind spots incumbents miss as they grow distant from individual users. The key is deep market immersion. Use every product in your space. Talk to users. Build an audience. Stop looking for ideas and start *seeing* them everywhere. Then, choose one. The idea must offer a 10x improvement or solve a "hair-on-fire" pain point—something severe enough that users are already crafting workarounds. When building, avoid feature bloat. Ask: why would someone switch? Great startups rarely force new behaviors; they improve familiar workflows with drastically lower friction (e.g., Cursor forked VS Code instead of creating a new editor). Distribution is the underestimated moat. Before product-market fit, achieve distribution-market fit. How do customers discover new tools? Founders like those at Airbnb, Stripe, and Cursor did unscalable, manual work to recruit early users. The final, unteachable ingredient is resilience. Cursor built for years pre-market, faced rejection, and persisted. So did Airbnb, Nvidia, and Rain (which launched post-FTX collapse). The lesson isn't that these founders were smarter, but that they stayed in the game long enough for their insights to compound. Framework: Spot technological cycles. Cultivate unique insight. Obsess over your market. Talk to customers. Find a hair-on-fire problem. Build the simplest wedge. Win your distribution channel. Above all, don't quit when it gets hard. Most people won't do these things consistently. The few who do build the next generation of great companies. Go build.

marsbitHá 2h