半年战绩数亿:特朗普家族成“加密总统”最大赢家

比推Pubblicato 2025-07-22Pubblicato ultima volta 2025-07-22

自2025年1月20日唐纳德·特朗普(Donald Trump)二主白宫以来,曾以房地产和传统媒体巨头示人的川普家族,通过一系列大胆的数字资产投资,巧妙地将其政治品牌与新兴的加密经济深度融合,不仅刷新了自身的财富版图,也为政治人物如何利用数字资产积累财富和影响力提供了新的样本。

以下为这半年以来,特朗普与加密行业相关的关键事件梳理:

TRUMPH1.jpg

川普家族在加密领域的布局,自2025年1月上任后便呈现出清晰的时间线和多元化的投资组合:

  • 1月20日,就任前后meme币的诞生: 总统夫妇率先在Solana区块链上推出了TRUMP和MELANIA代币。这些代币不仅是政治支持的象征,更成为其家族数字资产布局的开端。

  • 1月21日,对Ross Ulbricht的赦免: 这一举动被视为川普政府向加密货币社群释放积极信号,尤其受到加密自由意志主义运动的欢迎。

  • 3月6日/7日,战略布局与行业对话: 川普政府通过行政命令设立国家战略比特币储备,并举办白宫加密峰会,邀请行业领袖和政策制定者共同探讨加密货币的未来,展现了其对数字资产的高度重视。

  • 3月25日,稳定币USD1的推出: 川普家族支持的World Liberty Financial(WLFI)推出美元稳定币USD1。该稳定币首先在以太坊和币安智能链上流通,并计划在未来应用于大规模的机构投资。

  • 5月29日,高调的TRUMP晚宴: 川普在纽约举办的TRUMP晚宴,进一步巩固了$TRUMP代币在其支持者中的地位,并为早期投资者提供了独特的回报。

  • 7月18日,《GENIUS法案》的签署: 川普签署了这项两党合作的法案,为稳定币制定了监管和消费者保护规定,旨在鼓励创新并为加密货币市场创建新的结构。这标志着加密货币在美国的监管框架正逐步完善。

  • 7月21日,DJT宣布累积巨额比特币: 川普媒体与科技集团宣布已累积约20亿美元的比特币及相关资产,其中比特币将占据川普媒体流动性总资产的三分之一,凸显了比特币在其战略投资中的核心地位。

根据《福布斯》的计算,唐纳德·川普的净资产已飙升至56亿美元,其中加密货币持有量贡献了超过10亿美元,成为其家族财富增长的关键驱动力。

具体来看,川普家族的加密货币资产组合收益包括:

  • NFTs: 川普早期的NFT交易卡销售带来了约700万美元的收入,税后净收益约为400万美元。

  • World Liberty Financial (WLFI) 代币: 估计为川普家族带来了约3.9亿美元的收益(税后约2.46亿美元)。WLFI推出的USD1稳定币市值已达22亿美元,预计每年可产生约1亿美元的利息收入,其中川普个人在此业务中的权益可能高达5900万美元。

  • $TRUMP迷因币收益: 通过交易费和与美元挂钩的加密货币,川普家族获得了约3.15亿美元的收入。此外,其持有的$TRUMP储备中,已解锁部分目前价值约4.27亿美元,未来还有92%的代币将陆续解锁,这预示着巨大的潜在收益。

过去六个月,美国政治与加密市场的边界正在被重新定义。白宫的新主人以令人惊讶的速度,完成了一场个人财富与行业发展的双重实验,不过,这种模式在带来巨大经济利益的同时,也引发了对政治人物利用影响力进行个人利益变现的深思。无论争议如何,川普无疑正在将加密货币推向主流,并在全球范围内重新定义政治与财富的关系。

作者:Mary Liu


Twitter:https://twitter.com/BitpushNewsCN

比推 TG 交流群:https://t.me/BitPushCommunity

比推 TG 订阅: https://t.me/bitpush

说明: 比推所有文章只代表作者观点,不构成投资建议

Letture associate

A Chip Company Releases AIDC Energy Storage Certification Standards. Why NVIDIA? Computing Power Reshapes Power Supply Logic. Who's in the Lead and Who's Left Out?

NVIDIA has released a "Battery Energy Storage System Self-Certification Guide," setting strict technical standards for energy storage systems specifically for AI data centers (AIDC). The guide focuses solely on certifying the Power Conversion System (PCS), not the batteries, with 10 mandatory performance metrics and 12 validation tests requiring real-world and simulation comparisons. Key requirements include rapid dynamic response to AI workloads, high-frequency system telemetry, and detailed electromagnetic transient models. The move is driven by the extreme and fluctuating power demands of next-generation AI hardware. Modern AIDCs require energy storage systems to act as intelligent, controllable grid assets, not just passive backup, to manage instantaneous, massive power load shifts that traditional UPS systems cannot handle. This redefines the competitive landscape for energy storage providers, shifting focus from capacity and cost to advanced control capabilities and system integration. While the market potential is significant—with forecasts of hundreds of GWh in new demand by 2030—the certification creates a high barrier to entry. It requires proven PCS delivery volumes and credible plans for rapid capacity scaling, favoring established, well-resourced players. Early movers like Fluence (partnering with Siemens) and several Chinese companies have secured projects ahead of the standard, but new entrants must now navigate this rigorous, costly, and time-intensive certification process to compete in the AIDC energy storage market.

marsbit12 min fa

A Chip Company Releases AIDC Energy Storage Certification Standards. Why NVIDIA? Computing Power Reshapes Power Supply Logic. Who's in the Lead and Who's Left Out?

marsbit12 min fa

After Missing the 20x, I've Found a 'Dumb' Method for AI Investing

**Missing the 20x Opportunity: A Simple 'Dumb' Approach to AI Investing** The AI boom, driving NVIDIA's revenue from $60B to $216B in two years, creates immense investment pressure. However, like the internet bubble of 2000, the largest AI opportunities likely lie ahead, perhaps after a correction. Instead of rushing in now or waiting paralyzed for a crash, the author proposes a third way: building a "knowledge warehouse" by systematically mapping the AI industry to be ready when opportunities arise. The core of the strategy is understanding AI's four-layer value chain: 1. **Compute Infrastructure (The "Engine"):** This foundational layer, where all money eventually flows, includes: a) **Chip Design:** NVIDIA's dominance via its CUDA ecosystem, b) **Chip Manufacturing/Packaging/Memory:** TSMC's near-monopoly in advanced manufacturing and SK Hynix's lead in High Bandwidth Memory (HBM), c) **Optical Interconnects:** Essential for large-scale AI clusters (e.g., Lumentum, Coherent), d) **Cooling & Power:** Critical for high-density AI data centers (e.g., Vertiv), e) **Servers/Data Centers & Cloud Platforms:** The physical and virtual wholesale providers. 2. **Models & Tools (The "OS"):** The competitive layer of foundation models (OpenAI, Anthropic, Google, Meta, xAI), now generating real revenue. A key shift is the center of gravity moving from **Training** models to **Inference** (running models), which demands different chip characteristics and could challenge NVIDIA's monopoly. 3. **Middleware & Platform ("The Glue"):** Connects models and applications (e.g., Scale AI, Hugging Face). This layer could explode if applications take off. 4. **Vertical Applications ("The Cash Register"):** Where AI meets end-users (e.g., enterprise AI, coding tools, medical AI, robotics). A critical cross-cutting constraint is **Energy**, as AI's massive power consumption drives investment in nuclear and other energy infrastructure. The author identifies four key questions for further research: 1) How will the shift from Training to Inference reshape the competitive landscape? 2) With tech giants spending over $600B on capex, where is the ROI from AI applications? 3) What are the under-the-radar opportunities in the "second" and "third" circles of the value chain (e.g., cooling, specialty foundries)? 4) How will geopolitics (e.g., U.S.-China chip restrictions) bifurcate the supply chain? The conclusion is that missed opportunities stem from insufficient research, not slow timing. By methodically studying each layer—its business models, competition, and valuations—investors can build the "killer intuition" needed to act decisively when the market presents its chance.

marsbit32 min fa

After Missing the 20x, I've Found a 'Dumb' Method for AI Investing

marsbit32 min fa

Trading

Spot
Futures
活动图片