U.S. Senate Housing Bill Links Affordability Reforms With CBDC Restrictions

TheNewsCrypto2026-03-03 tarihinde yayınlandı2026-03-03 tarihinde güncellendi

Özet

U.S. Senate lawmakers have introduced a provision in a housing-focused bill, the 21st Century ROAD to Housing Act, that prohibits the Federal Reserve from issuing a retail central bank digital currency (CBDC) accessible to the public until at least 2031. The measure requires the Fed to obtain congressional authorization before launching a digital dollar, though it may continue researching the technology. This unexpected addition to housing legislation has sparked debate over the timeline and links CBDC policy to broader discussions on affordability. The ban reflects congressional concerns over privacy, financial stability, and the potential impact of a CBDC on the traditional banking system.

A move by US Senate Democrats and Republicans to pass a wide-ranging housing bill. They added a new section that prohibits the Federal Reserve from launching a retail central bank digital currency, or CBDC. The section of the bill would prevent the Fed from launching a digital version of the US dollar, or a digital dollar, until at least 2031 if it is accessible to the public.

The section prohibiting the Fed from launching a CBDC was added to the 21st Century ROAD to Housing Act. This is a bill focused on housing legislation but that touches on financial technology policy. The addition of the CBDC prohibition to a housing bill was a surprise to some. As the past debates have focused on CBDC policy in finance legislation.

According to the provision, the Federal Reserve would need permission from Congress to issue a CBDC to consumers. The reason for the requirement, based on the rationale provided by the advocates, was that it would prevent the Fed from issuing digital currency without permission. The ban does not stop the Fed from continuing to research and experiment with digital currency concepts.

The focus of the ban is on public issuance and use of digital currency by individuals or businesses. The lawmakers have set a date in the future to sunset the ban. This created a debate about whether it is enough time to allow for technological advances in the field. The inclusion of the bill in the housing legislation may require lawmakers to consider digital currency in the context of housing affordability.

Policy and Market Implications of the CBDC Ban

The CBDC ban demonstrates the continued congressional interest in central bank digital currencies and issues of privacy, surveillance, and financial stability. Opponents of CBDCs claim that Fed-issued CBDCs might interfere with the traditional banking system or undermine existing privacy protections for consumers.

This measure might have implications for the way financial tech companies and digital asset platforms plan their future development strategies. Some analysts suggest that a formal ban through to 2031 indicates a more cautious legislative approach to CBDCs. Financial markets that track this type of regulation might reassess the prospects for future U.S. digital currency policies.

The housing bill still has to pass through committee stages before becoming law. Congressional leaders from all parties must balance competing policy interests, which are part of a broader budget debate. Supporters of the CBDC ban are hoping that the inclusion of this measure in this bill will give it a greater chance of being considered.

Highlighted Crypto News:

Hong Kong and Shanghai Authorities Integrate Cargo Data on Blockchain

TagsbillCBDCFederal ReserveFederalReserveU.SU.S SenateUS Senate

İlgili Sorular

QWhat is the main purpose of the U.S. Senate housing bill regarding CBDCs?

AThe bill prohibits the Federal Reserve from launching a retail central bank digital currency (CBDC) accessible to the public until at least 2031, requiring Congressional permission for any such issuance.

QWhy was the addition of the CBDC prohibition to a housing bill considered surprising?

AIt was surprising because past debates about CBDC policy have typically occurred within financial legislation, not housing-focused bills.

QDoes the bill completely stop the Federal Reserve from working on digital currency concepts?

ANo, the ban does not prevent the Fed from continuing to research and experiment with digital currency concepts; it only prohibits public issuance to consumers.

QWhat are some concerns that opponents of CBDCs have raised according to the article?

AOpponents claim that Fed-issued CBDCs might interfere with the traditional banking system and undermine existing privacy protections for consumers.

QWhat must happen before this housing bill with the CBDC provision becomes law?

AThe bill still has to pass through committee stages, and Congressional leaders must balance competing policy interests as part of a broader budget debate.

İlgili Okumalar

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.

marsbit5 dk önce

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

marsbit5 dk önce

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.

marsbit2 saat önce

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

marsbit2 saat önce

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.

marsbit2 saat önce

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

marsbit2 saat önce

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.

marsbit2 saat önce

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

marsbit2 saat önce

İşlemler

Spot
Futures
活动图片