如果卡玛拉·哈里斯选择加里·根斯勒担任美国证券交易委员会主席,将会发生什么

币界网Pubblicato 2024-08-21Pubblicato ultima volta 2024-08-21

币界网报道:

未经证实的报道称,如果副总统卡玛拉·哈里斯赢得11月的总统选举,她可能会任命美国证券交易委员会主席加里·金斯勒为财政部长。引用的消息来源是参议院高级职员和共和党消息人士。

根据《华盛顿报道》的初步报道,几名参议院高级职员表示,卡玛拉·哈里斯正在考虑让Gensler担任未来政府的财政部职位。这与著名共和党人过去的警告是一致的,特别是众议员汤姆·埃默(明尼苏达州共和党人),他建议不要采取这样的行动。

明尼苏达州众议员Tom Emmer此前曾警告称,卡玛拉·哈里斯可能会选择Gensler,或者在最坏的情况下,选择参议员伊丽莎白·沃伦担任她的财政部长。他说,这样的举动“对经济来说将是一场灾难”。

他一直在各地提起诉讼,但都输了。那个时代已经过去了。加里·詹斯勒需要继续前进。他在政府的职业生涯应该结束了。众议员Emmer

鉴于Gary Gensler在美国证券交易委员会的严格监督历史,他在卡玛拉·哈里斯政府下担任财政部长的潜在候选资格可能会对加密监管产生重大影响。他的提名可能会导致针对加密实体的更严格的政策和执法措施,从而可能改变监管格局。

卡玛拉选择担任美国证券交易委员会主席将给她带来麻烦

据消息人士透露,如果卡玛拉·哈里斯击败特朗普,共和党参议院高级工作人员预计“共和党统一反对”詹斯勒,但他很可能会得到民主党的压倒性支持。

实际上,加密货币行业可能面临更严格的审查和合规要求,这可能会阻碍该行业的创新和扩张,但也可能导致更大的主流采用,如果,也许只有在制定更明确的法规的情况下。

政治谣言并没有就此结束。还有报道称,Gensler可能会辞去美国证券交易委员会主席一职。这将使拜登政府能够在11月大选前任命一位新主席。

民主党全国委员会周日发布了2024年党纲。这份概述民主党在下一次选举前未来政治抱负的文件没有提到加密货币。

美国财政部长负责监督该国的经济政策,管理政府资金,并监管金融机构,特别是那些处理加密货币的金融机构。该职位还包括打击金融犯罪和在国际金融事务中代表美国。

拜登任命的Gensler表示强烈反对加密监管。众议院颁布FIT21法案后,他强烈反对该法案。

Mark Cuban等知名人士甚至表示,Gensler与加密货币相关的行动可能会产生深远的政治影响,威胁到拜登总统退出竞选时的连任机会。现在卡玛拉·哈里斯已经填补了这些空缺。

Letture associate

Gensyn AI: Don't Let AI Repeat the Mistakes of the Internet

In recent months, the rapid growth of the AI industry has attracted significant talent from the crypto sector. A persistent question among researchers intersecting both fields is whether blockchain can become a foundational part of AI infrastructure. While many previous AI and Crypto projects focused on application layers (like AI Agents, on-chain reasoning, data markets, and compute rentals), few achieved viable commercial models. Gensyn differentiates itself by targeting the most critical and expensive layer of AI: model training. Gensyn aims to organize globally distributed GPU resources into an open AI training network. Developers can submit training tasks, nodes provide computational power, and the network verifies results while distributing incentives. The core issue addressed is not decentralization for its own sake, but the increasing centralization of compute power among tech giants. In the era of large models, access to GPUs (like the H100) has become a decisive bottleneck, dictating the pace of AI development. Major AI companies are heavily dependent on large cloud providers for compute resources. Gensyn's approach is significant for several reasons: 1) It operates at the core infrastructure layer (model training), the most resource-intensive and technically demanding part of the AI value chain. 2) It proposes a more open, collaborative model for compute, potentially increasing resource utilization by dynamically pooling idle GPUs, similar to early cloud computing logic. 3) Its technical moat lies in solving complex challenges like verifying training results, ensuring node honesty, and maintaining reliability in a distributed environment—making it more of a deep-tech infrastructure company. 4) It targets a validated, high-growth market with genuine demand, rather than pursuing blockchain integration without purpose. Ultimately, the boundaries between Crypto and AI are blurring. AI requires global resource coordination, incentive mechanisms, and collaborative systems—areas where crypto-native solutions excel. Gensyn represents a step toward making advanced training capabilities more accessible and collaborative, moving beyond a niche controlled by a few giants. If successful, it could evolve into a fundamental piece of AI infrastructure, where the most enduring value in the AI era is often created.

marsbit11 h fa

Gensyn AI: Don't Let AI Repeat the Mistakes of the Internet

marsbit11 h fa

Why is China's AI Developing So Fast? The Answer Lies Inside the Labs

A US researcher's visit to China's top AI labs reveals distinct cultural and organizational factors driving China's rapid AI development. While talent, data, and compute are similar to the West, Chinese labs excel through a pragmatic, execution-focused culture: less emphasis on individual stardom and conceptual debate, and more on teamwork, engineering optimization, and mastering the full tech stack. A key advantage is the integration of young students and researchers who approach model-building with fresh perspectives and low ego, prioritizing collective progress over personal credit. This contrasts with the US culture of self-promotion and "star scientist" narratives. Chinese labs also exhibit a strong "build, don't buy" mentality, preferring to develop core capabilities—like data pipelines and environments—in-house rather than relying on external services. The ecosystem feels more collaborative than tribal, with mutual respect among labs. While government support exists, its scale is unclear, and technical decisions appear driven by labs, not state mandates. Chinese companies across sectors, from platforms to consumer tech, are building their own foundational models to control their tech destiny, reflecting a broader cultural drive for technological sovereignty. Demand for AI is emerging, with spending patterns potentially mirroring cloud infrastructure more than traditional SaaS. Despite challenges like a less mature data industry and GPU shortages, Chinese labs are propelled by vast talent, rapid iteration, and deep integration with the open-source community. The competition is evolving beyond a pure model race into a contest of organizational execution, developer ecosystems, and industrial pragmatism.

marsbit12 h fa

Why is China's AI Developing So Fast? The Answer Lies Inside the Labs

marsbit12 h fa

3 Years, 5 Times: The Rebirth of a Century-Old Glass Factory

Corning, a 175-year-old glass company, is experiencing a dramatic revival as a key player in AI infrastructure, driven by surging demand for high-performance optical fiber in data centers. AI data centers require vastly more fiber than traditional ones—5 to 10 times as much per rack—to handle high-speed data transmission between GPUs. This structural demand shift, coupled with supply constraints from the lengthy expansion cycle for fiber preforms, has created a significant supply-demand gap. Nvidia has invested in Corning, along with Lumentum and Coherent, in a $4.5 billion total commitment to secure the optical supply chain for AI. Corning's competitive edge lies in its expertise in producing ultra-low-loss, high-density, and bend-resistant specialty fiber, which is critical for 800G+ and future 1.6T data rates. Its deep involvement in co-packaged optics (CPO) with partners like Nvidia further solidifies its position. While not the largest fiber manufacturer globally, Corning's revenue from enterprise/data center clients now exceeds 40% of its optical communications sales, and it has secured multi-year supply agreements with major hyperscalers including Meta and Nvidia. Financially, Corning's optical communications revenue has surged, doubling from $1.3 billion in 2023 to over $3 billion in 2025. Its stock price has risen nearly 6-fold since late 2023. Key future catalysts include the rollout of Nvidia's CPO products and the scale of undisclosed customer agreements. However, risks include high current valuations and potential disruption from next-generation technologies like hollow-core fiber. The company's long-term bet on light over electricity, maintained even through the telecom bubble crash, is now being validated by the AI boom.

marsbit13 h fa

3 Years, 5 Times: The Rebirth of a Century-Old Glass Factory

marsbit13 h fa

Trading

Spot
Futures
活动图片